transactions variable creation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "import calendar\n",
    "import timeit\n",
    "import datetime as dt\n",
    "import re\n",
    "from math import exp\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "start_time = datetime.datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96753, 18)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('card transactions.csv')\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recnum</th>\n",
       "      <th>Cardnum</th>\n",
       "      <th>Date</th>\n",
       "      <th>Merchnum</th>\n",
       "      <th>Merch description</th>\n",
       "      <th>Merch state</th>\n",
       "      <th>Merch zip</th>\n",
       "      <th>Transtype</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Fraud</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "      <th>Unnamed: 14</th>\n",
       "      <th>Unnamed: 15</th>\n",
       "      <th>Unnamed: 16</th>\n",
       "      <th>Unnamed: 17</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5142190439</td>\n",
       "      <td>1/1/10</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEX SHP 12/23/09 AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5142183973</td>\n",
       "      <td>1/1/10</td>\n",
       "      <td>61003026333</td>\n",
       "      <td>SERVICE MERCHANDISE #81</td>\n",
       "      <td>MA</td>\n",
       "      <td>1803.0</td>\n",
       "      <td>P</td>\n",
       "      <td>31.42</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5142131721</td>\n",
       "      <td>1/1/10</td>\n",
       "      <td>4503082993600</td>\n",
       "      <td>OFFICE DEPOT #191</td>\n",
       "      <td>MD</td>\n",
       "      <td>20706.0</td>\n",
       "      <td>P</td>\n",
       "      <td>178.49</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5142148452</td>\n",
       "      <td>1/1/10</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEX SHP 12/28/09 AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5142190439</td>\n",
       "      <td>1/1/10</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEX SHP 12/23/09 AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Recnum     Cardnum    Date       Merchnum        Merch description  \\\n",
       "0       1  5142190439  1/1/10  5509006296254   FEDEX SHP 12/23/09 AB#   \n",
       "1       2  5142183973  1/1/10    61003026333  SERVICE MERCHANDISE #81   \n",
       "2       3  5142131721  1/1/10  4503082993600        OFFICE DEPOT #191   \n",
       "3       4  5142148452  1/1/10  5509006296254   FEDEX SHP 12/28/09 AB#   \n",
       "4       5  5142190439  1/1/10  5509006296254   FEDEX SHP 12/23/09 AB#   \n",
       "\n",
       "  Merch state  Merch zip Transtype  Amount  Fraud  Unnamed: 10  Unnamed: 11  \\\n",
       "0          TN    38118.0         P    3.62      0          NaN          NaN   \n",
       "1          MA     1803.0         P   31.42      0          NaN          NaN   \n",
       "2          MD    20706.0         P  178.49      0          NaN          NaN   \n",
       "3          TN    38118.0         P    3.62      0          NaN          NaN   \n",
       "4          TN    38118.0         P    3.62      0          NaN          NaN   \n",
       "\n",
       "   Unnamed: 12  Unnamed: 13  Unnamed: 14  Unnamed: 15  Unnamed: 16  \\\n",
       "0          NaN          NaN          NaN          NaN          NaN   \n",
       "1          NaN          NaN          NaN          NaN          NaN   \n",
       "2          NaN          NaN          NaN          NaN          NaN   \n",
       "3          NaN          NaN          NaN          NaN          NaN   \n",
       "4          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "   Unnamed: 17  \n",
       "0          NaN  \n",
       "1          NaN  \n",
       "2          NaN  \n",
       "3          NaN  \n",
       "4          NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 96753 entries, 0 to 96752\n",
      "Data columns (total 10 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   Recnum             96753 non-null  int64         \n",
      " 1   Cardnum            96753 non-null  int64         \n",
      " 2   Date               96753 non-null  datetime64[ns]\n",
      " 3   Merchnum           93378 non-null  object        \n",
      " 4   Merch description  96753 non-null  object        \n",
      " 5   Merch state        95558 non-null  object        \n",
      " 6   Merch zip          92097 non-null  float64       \n",
      " 7   Transtype          96753 non-null  object        \n",
      " 8   Amount             96753 non-null  float64       \n",
      " 9   Fraud              96753 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(2), int64(3), object(4)\n",
      "memory usage: 7.4+ MB\n"
     ]
    }
   ],
   "source": [
    "data.dropna(how='all', axis=1, inplace=True)\n",
    "data['Date'] = pd.to_datetime(data['Date'])\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96397, 10)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[data['Transtype'] == 'P']\n",
    "data = data[data['Amount'] <= 3000000]\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Recnum                  0\n",
       "Cardnum                 0\n",
       "Date                    0\n",
       "Merchnum             3198\n",
       "Merch description       0\n",
       "Merch state          1020\n",
       "Merch zip            4300\n",
       "Transtype               0\n",
       "Amount                  0\n",
       "Fraud                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_orig = data.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean and impute merchnum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Merchnum'] = data['Merchnum'].replace({'0':np.nan})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3251"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Merchnum'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "merchdes_merchnum = {}\n",
    "for index, merchdes in data[data['Merch description'].notnull()][data['Merchnum'].notnull()]['Merch description'].items():\n",
    "    if pd.isnull(merchdes) == True:\n",
    "        continue\n",
    "    elif merchdes not in merchdes_merchnum:\n",
    "        merchdes_merchnum[merchdes] = data.loc[index, 'Merchnum']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill in by mapping with Merch description\n",
    "data['Merchnum'] = data['Merchnum'].fillna(data['Merch description'].map(merchdes_merchnum))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2094"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Merchnum'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# assign unknown for adjustments transactions\n",
    "data['Merchnum'] = data['Merchnum'].mask(data['Merch description'] == 'RETAIL CREDIT ADJUSTMENT', 'unknown')\n",
    "data['Merchnum'] = data['Merchnum'].mask(data['Merch description'] == 'RETAIL DEBIT ADJUSTMENT', 'unknown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1403"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Merchnum'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['MONTGOMERY COLLEGE-PHONE', 'PACKAGE PLACE  THE',\n",
       "       'CUBIX CORPORATION', 'SIGNAL GRAPHICS PRINTING',\n",
       "       'C & M OFFICE EQUIPMENT', \"TOMMY'S TRAILERS\",\n",
       "       'Z WORLD/RABBIT SEMICONDUC', 'IMPAC/TRI-COUNTY/FREED',\n",
       "       'REPROGRPHC TECHNLGIES INC', 'STP SPECIALITY TECH',\n",
       "       'VANGARD INTERNAITONAL', 'BLACKWELL SCIENCE', 'CDN ISOTOPES INC',\n",
       "       'INTERACTIVE SOFTWARE S', 'H R WILLIAMS MILL SUPP',\n",
       "       'ELSEVIER SCIENCE BV', 'COLORADO GARDEN SHOW',\n",
       "       'PEARSON EDUCATION CANADA', 'PONTOTOC AREA VO-TECH',\n",
       "       'NATIONAL BAG COMPANY'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data.Merchnum.isna(), 'Merch description'].unique()[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "508"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1403 NULL Merchnums with 508 unique Descriptions\n",
    "data.loc[data.Merchnum.isna(), 'Merch description'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create new Merchnums using the description field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding new merchnums\n",
    "# each new unique merchnum will be max(merchnum) + 1\n",
    "merchnum_create = {}\n",
    "max_merchnum = pd.to_numeric(data.Merchnum, errors='coerce').max()\n",
    "for merch_desc in data.loc[data.Merchnum.isna(), 'Merch description'].unique():\n",
    "  merchnum_create[merch_desc] = str(int(max_merchnum + 1))\n",
    "  max_merchnum += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill in by mapping with Merch description (newly created merchnums)\n",
    "data['Merchnum'] = data['Merchnum'].fillna(data['Merch description'].map(merchnum_create))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recnum 0\n",
      "Cardnum 0\n",
      "Date 0\n",
      "Merchnum 0\n",
      "Merch description 0\n",
      "Merch state 1020\n",
      "Merch zip 4300\n",
      "Transtype 0\n",
      "Amount 0\n",
      "Fraud 0\n"
     ]
    }
   ],
   "source": [
    "for i in data.columns:\n",
    "    print(i, data[i].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean and impute State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1020"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Merch state'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.2600e+02, 9.2900e+02, 1.4000e+03, 6.5132e+04, 8.6899e+04,\n",
       "       2.3080e+04, 6.0528e+04, 9.3400e+02, 9.0200e+02, 7.3800e+02,\n",
       "       9.0805e+04, 7.6302e+04, 9.0000e+00, 9.1400e+02, 6.0000e+00,\n",
       "       9.5461e+04, 5.0823e+04, 2.0000e+00, 4.8700e+04, 6.8000e+02,\n",
       "       1.0000e+00, 6.8100e+02, 6.2300e+02, 7.2600e+02, 9.3600e+02,\n",
       "       1.2108e+04, 7.9100e+02, 9.0700e+02, 9.2200e+02, 9.2000e+02,\n",
       "       3.0000e+00, 8.0100e+02, 8.0000e+00, 3.1040e+04, 3.8117e+04,\n",
       "       4.1160e+04])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data['Merch state'].isnull()) & (data['Merch zip'].notnull())]['Merch zip'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dict for mapping\n",
    "zip_state = {}\n",
    "for index, zip5 in data[data['Merch zip'].notnull()]['Merch zip'].items():\n",
    "    if zip5 not in zip_state:\n",
    "        zip_state[zip5] = data.loc[index, 'Merch state']\n",
    "        \n",
    "zip_state['00926'] = 'PR'\n",
    "zip_state['00929'] = 'PR'\n",
    "zip_state['00934'] = 'PR'\n",
    "zip_state['00902'] = 'PR'\n",
    "zip_state['00738'] = 'PR'\n",
    "zip_state['90805'] = 'CA'\n",
    "zip_state['76302'] = 'TX'\n",
    "zip_state['00914'] = 'PR'\n",
    "zip_state['95461'] = 'CA'\n",
    "zip_state['00680'] = 'PR'\n",
    "zip_state['00623'] = 'PR'\n",
    "zip_state['00726'] = 'PR'\n",
    "zip_state['00936'] = 'PR'\n",
    "zip_state['12108'] = 'NY'\n",
    "zip_state['00791'] = 'PR'\n",
    "zip_state['00907'] = 'PR'\n",
    "zip_state['00922'] = 'PR'\n",
    "zip_state['00920'] = 'PR'\n",
    "zip_state['00801'] = 'VI'\n",
    "zip_state['31040'] = 'GA'\n",
    "zip_state['41160'] = 'KY'\n",
    "zip_state['00681'] = 'PR'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "merchnum_state = {}\n",
    "for index, merchnum in data[data['Merchnum'].notnull()]['Merchnum'].items():\n",
    "    if merchnum not in merchnum_state :\n",
    "        merchnum_state [merchnum] = data.loc[index, 'Merch state']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "merchdes_state = {}\n",
    "for index, merchdes in data[data['Merch description'].notnull()]['Merch description'].items():\n",
    "    if merchdes not in merchdes_state :\n",
    "        merchdes_state [merchdes] = data.loc[index, 'Merch state']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill in by mapping with zip, merchnum and merch description\n",
    "data['Merch state'] = data['Merch state'].fillna(data['Merch zip'].map(zip_state))\n",
    "data['Merch state'] = data['Merch state'].fillna(data['Merchnum'].map(merchnum_state))\n",
    "data['Merch state'] = data['Merch state'].fillna(data['Merch description'].map(merchdes_state))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# assign unknown for adjustments transactions\n",
    "data['Merch state'] = data['Merch state'].mask(data['Merch description'] == 'RETAIL CREDIT ADJUSTMENT', 'unknown')\n",
    "data['Merch state'] = data['Merch state'].mask(data['Merch description'] == 'RETAIL DEBIT ADJUSTMENT', 'unknown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "346"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Merch state'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change non-US states\n",
    "# might actually be useful cus fraud could be foreign transactions\n",
    "# maybe put a 'foreign' tag or just leave them as is\n",
    "\n",
    "states = [\"AL\", \"AK\", \"AZ\", \"AR\", \"CA\", \"CO\", \"CT\", \"DC\", \"DE\", \"FL\", \"GA\", \n",
    "          \"HI\", \"ID\", \"IL\", \"IN\", \"IA\", \"KS\", \"KY\", \"LA\", \"ME\", \"MD\", \n",
    "          \"MA\", \"MI\", \"MN\", \"MS\", \"MO\", \"MT\", \"NE\", \"NV\", \"NH\", \"NJ\", \n",
    "          \"NM\", \"NY\", \"NC\", \"ND\", \"OH\", \"OK\", \"OR\", \"PA\", \"RI\", \"SC\", \n",
    "          \"SD\", \"TN\", \"TX\", \"UT\", \"VT\", \"VA\", \"WA\", \"WV\", \"WI\", \"WY\", \n",
    "          'VI', 'PR', np.nan, 'unknown']\n",
    "\n",
    "for index, state in data['Merch state'].items():\n",
    "    if state not in states:\n",
    "        data.loc[index, 'Merch state'] = 'foreign'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Merch state'].fillna('unknown',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Merch state'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean and impute zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4300"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Merch zip'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "merchnum_zip = {}\n",
    "for index, merchnum in data[data['Merchnum'].notnull()]['Merchnum'].items():\n",
    "    if merchnum not in merchnum_zip :\n",
    "        merchnum_zip [merchnum] = data.loc[index, 'Merch zip']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "merchdes_zip = {}\n",
    "for index, merchdes in data[data['Merch description'].notnull()]['Merch description'].items():\n",
    "    if merchdes not in merchdes_zip :\n",
    "        merchdes_zip [merchdes] = data.loc[index, 'Merch zip']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill in by mapping with merchnum and merch description\n",
    "data['Merch zip'] = data['Merch zip'].fillna(data['Merchnum'].map(merchnum_zip))\n",
    "data['Merch zip'] = data['Merch zip'].fillna(data['Merch description'].map(merchdes_zip))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2658"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Merch zip'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# assign unknown for adjustments transactions\n",
    "data['Merch zip'] = data['Merch zip'].mask(data['Merch zip'] == 'RETAIL CREDIT ADJUSTMENT', 'unknown')\n",
    "data['Merch zip'] = data['Merch zip'].mask(data['Merch zip'] == 'RETAIL DEBIT ADJUSTMENT', 'unknown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2658"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Merch zip'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recnum</th>\n",
       "      <th>Cardnum</th>\n",
       "      <th>Date</th>\n",
       "      <th>Merchnum</th>\n",
       "      <th>Merch description</th>\n",
       "      <th>Merch state</th>\n",
       "      <th>Merch zip</th>\n",
       "      <th>Transtype</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>5142204384</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>5000006000095</td>\n",
       "      <td>IBM INTERNET  01000025</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>20.15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>5142146340</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>5000006000095</td>\n",
       "      <td>IBM INTERNET  01000025</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>23.90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56</td>\n",
       "      <td>5142260984</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>5000006000095</td>\n",
       "      <td>IBM INTERNET  01000025</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>19.95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>5142204384</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>5000006000095</td>\n",
       "      <td>IBM INTERNET  01000025</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>20.15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>5142204384</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>5000006000095</td>\n",
       "      <td>IBM INTERNET  01000025</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>20.15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61</td>\n",
       "      <td>5142204384</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>5000006000095</td>\n",
       "      <td>IBM INTERNET  01000025</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>20.15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>62</td>\n",
       "      <td>5142204384</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>5000006000095</td>\n",
       "      <td>IBM INTERNET  01000025</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>20.15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>63</td>\n",
       "      <td>5142253356</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>5000006000095</td>\n",
       "      <td>IBM INTERNET  01000025</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>27.41</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>65</td>\n",
       "      <td>5142204384</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>5000006000095</td>\n",
       "      <td>IBM INTERNET  01000025</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>20.15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>66</td>\n",
       "      <td>5142204384</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>5000006000095</td>\n",
       "      <td>IBM INTERNET  01000025</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>20.15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>67</td>\n",
       "      <td>5142260984</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>5000006000095</td>\n",
       "      <td>IBM INTERNET  01000025</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>19.95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>69</td>\n",
       "      <td>5142260984</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>5000006000095</td>\n",
       "      <td>IBM INTERNET  01000025</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>37.51</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>70</td>\n",
       "      <td>5142260984</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>5000006000095</td>\n",
       "      <td>IBM INTERNET  01000025</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>19.95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>72</td>\n",
       "      <td>5142204384</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>5000006000095</td>\n",
       "      <td>IBM INTERNET  01000025</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>28.13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73</td>\n",
       "      <td>5142204384</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>5000006000095</td>\n",
       "      <td>IBM INTERNET  01000025</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>20.15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>76</td>\n",
       "      <td>5142253356</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>5000006000095</td>\n",
       "      <td>IBM INTERNET  01000025</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>12.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>5142204384</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>5000006000095</td>\n",
       "      <td>IBM INTERNET  01000025</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>20.15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>5142149994</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>5000006000095</td>\n",
       "      <td>IBM INTERNET  01000025</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>101.40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>80</td>\n",
       "      <td>5142153201</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>5000006000095</td>\n",
       "      <td>IBM INTERNET  01000025</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>19.95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>88</td>\n",
       "      <td>5142255416</td>\n",
       "      <td>2010-01-03</td>\n",
       "      <td>8053478940091</td>\n",
       "      <td>MCGHEE &amp; COMPANY INC</td>\n",
       "      <td>WV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>55.80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>5142257356</td>\n",
       "      <td>2010-01-03</td>\n",
       "      <td>2000049710067</td>\n",
       "      <td>LASER ACCESS  42760017</td>\n",
       "      <td>GA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>1940.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>219</td>\n",
       "      <td>5142172995</td>\n",
       "      <td>2010-01-03</td>\n",
       "      <td>6700046420068</td>\n",
       "      <td>MARYS GIFTS   41480013</td>\n",
       "      <td>IL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>17.97</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>231</td>\n",
       "      <td>5142221571</td>\n",
       "      <td>2010-01-03</td>\n",
       "      <td>6005030600003</td>\n",
       "      <td>FORMA SCIENTIFIC</td>\n",
       "      <td>OH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>72.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>259</td>\n",
       "      <td>5142171582</td>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>4900000004673</td>\n",
       "      <td>WALGREEN      00004179</td>\n",
       "      <td>IL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>21.28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>263</td>\n",
       "      <td>5142257575</td>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>unknown</td>\n",
       "      <td>RETAIL DEBIT ADJUSTMENT</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>320.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>273</td>\n",
       "      <td>5142124791</td>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>unknown</td>\n",
       "      <td>RETAIL DEBIT ADJUSTMENT</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>970.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>294</td>\n",
       "      <td>5142171582</td>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>4900000004673</td>\n",
       "      <td>WALGREEN      00004179</td>\n",
       "      <td>IL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>6.76</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>380</td>\n",
       "      <td>5142183904</td>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>1700000096481</td>\n",
       "      <td>AMES DEPT STOR 0021436</td>\n",
       "      <td>MA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>401</td>\n",
       "      <td>5142276099</td>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>unknown</td>\n",
       "      <td>RETAIL DEBIT ADJUSTMENT</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>82.59</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>417</td>\n",
       "      <td>5142173617</td>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>6100020004006</td>\n",
       "      <td>USGPO SUPT DOCS-SUB/PU</td>\n",
       "      <td>DC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>98.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>477</td>\n",
       "      <td>5142267793</td>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>unknown</td>\n",
       "      <td>RETAIL DEBIT ADJUSTMENT</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>17.59</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>483</td>\n",
       "      <td>5142257356</td>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>2000049710067</td>\n",
       "      <td>LASER ACCESS  42760017</td>\n",
       "      <td>GA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>600.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>488</td>\n",
       "      <td>5142267793</td>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>unknown</td>\n",
       "      <td>RETAIL CREDIT ADJUSTMENT</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>19.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>516</td>\n",
       "      <td>5142267793</td>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>unknown</td>\n",
       "      <td>RETAIL CREDIT ADJUSTMENT</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>17.59</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>545</td>\n",
       "      <td>5142193730</td>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>unknown</td>\n",
       "      <td>RETAIL CREDIT ADJUSTMENT</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>105.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>558</td>\n",
       "      <td>5142234471</td>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>unknown</td>\n",
       "      <td>RETAIL CREDIT ADJUSTMENT</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>1149.97</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>738</td>\n",
       "      <td>5142267793</td>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>unknown</td>\n",
       "      <td>RETAIL CREDIT ADJUSTMENT</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>18.64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>740</td>\n",
       "      <td>5142267793</td>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>unknown</td>\n",
       "      <td>RETAIL CREDIT ADJUSTMENT</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>17.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>745</td>\n",
       "      <td>5142267793</td>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>unknown</td>\n",
       "      <td>RETAIL CREDIT ADJUSTMENT</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>17.59</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>759</td>\n",
       "      <td>5142267793</td>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>unknown</td>\n",
       "      <td>RETAIL CREDIT ADJUSTMENT</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>24.68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>818</td>\n",
       "      <td>5142230669</td>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>9996060597906</td>\n",
       "      <td>TOMMY'S TRAILERS</td>\n",
       "      <td>OK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>48.97</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>853</td>\n",
       "      <td>5142174305</td>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>unknown</td>\n",
       "      <td>RETAIL CREDIT ADJUSTMENT</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>379.42</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>869</td>\n",
       "      <td>5142205500</td>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>5000006000095</td>\n",
       "      <td>IBM INTERNET  01000025</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>14.09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>906</th>\n",
       "      <td>907</td>\n",
       "      <td>5142288897</td>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>9996060597908</td>\n",
       "      <td>IMPAC/TRI-COUNTY/FREED</td>\n",
       "      <td>MD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>467.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>932</td>\n",
       "      <td>5142214551</td>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>6176269</td>\n",
       "      <td>MUNKSGAARDS FORLAG</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>1790.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1040</th>\n",
       "      <td>1041</td>\n",
       "      <td>5142230181</td>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>9996060597910</td>\n",
       "      <td>STP SPECIALITY TECH</td>\n",
       "      <td>foreign</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>486.18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>1083</td>\n",
       "      <td>5142132574</td>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>9900020008506</td>\n",
       "      <td>UNICOR FED PRISON IND</td>\n",
       "      <td>KY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>1090.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1162</th>\n",
       "      <td>1163</td>\n",
       "      <td>5142179159</td>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>674906173338</td>\n",
       "      <td>G B SCIENTIFIC</td>\n",
       "      <td>CA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>849.89</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1174</th>\n",
       "      <td>1175</td>\n",
       "      <td>5142139011</td>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>7300020006306</td>\n",
       "      <td>GENERAL SERVICES ADMIN</td>\n",
       "      <td>MD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>609.67</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>1196</td>\n",
       "      <td>5142260689</td>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>679960185332</td>\n",
       "      <td>BUSINESS WIRE</td>\n",
       "      <td>CA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>532.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Recnum     Cardnum       Date       Merchnum         Merch description  \\\n",
       "51        52  5142204384 2010-01-02  5000006000095    IBM INTERNET  01000025   \n",
       "54        55  5142146340 2010-01-02  5000006000095    IBM INTERNET  01000025   \n",
       "55        56  5142260984 2010-01-02  5000006000095    IBM INTERNET  01000025   \n",
       "58        59  5142204384 2010-01-02  5000006000095    IBM INTERNET  01000025   \n",
       "59        60  5142204384 2010-01-02  5000006000095    IBM INTERNET  01000025   \n",
       "60        61  5142204384 2010-01-02  5000006000095    IBM INTERNET  01000025   \n",
       "61        62  5142204384 2010-01-02  5000006000095    IBM INTERNET  01000025   \n",
       "62        63  5142253356 2010-01-02  5000006000095    IBM INTERNET  01000025   \n",
       "64        65  5142204384 2010-01-02  5000006000095    IBM INTERNET  01000025   \n",
       "65        66  5142204384 2010-01-02  5000006000095    IBM INTERNET  01000025   \n",
       "66        67  5142260984 2010-01-02  5000006000095    IBM INTERNET  01000025   \n",
       "68        69  5142260984 2010-01-02  5000006000095    IBM INTERNET  01000025   \n",
       "69        70  5142260984 2010-01-02  5000006000095    IBM INTERNET  01000025   \n",
       "71        72  5142204384 2010-01-02  5000006000095    IBM INTERNET  01000025   \n",
       "72        73  5142204384 2010-01-02  5000006000095    IBM INTERNET  01000025   \n",
       "75        76  5142253356 2010-01-02  5000006000095    IBM INTERNET  01000025   \n",
       "77        78  5142204384 2010-01-02  5000006000095    IBM INTERNET  01000025   \n",
       "78        79  5142149994 2010-01-02  5000006000095    IBM INTERNET  01000025   \n",
       "79        80  5142153201 2010-01-02  5000006000095    IBM INTERNET  01000025   \n",
       "87        88  5142255416 2010-01-03  8053478940091      MCGHEE & COMPANY INC   \n",
       "199      200  5142257356 2010-01-03  2000049710067    LASER ACCESS  42760017   \n",
       "218      219  5142172995 2010-01-03  6700046420068    MARYS GIFTS   41480013   \n",
       "230      231  5142221571 2010-01-03  6005030600003          FORMA SCIENTIFIC   \n",
       "258      259  5142171582 2010-01-04  4900000004673    WALGREEN      00004179   \n",
       "262      263  5142257575 2010-01-04        unknown   RETAIL DEBIT ADJUSTMENT   \n",
       "272      273  5142124791 2010-01-04        unknown   RETAIL DEBIT ADJUSTMENT   \n",
       "293      294  5142171582 2010-01-04  4900000004673    WALGREEN      00004179   \n",
       "379      380  5142183904 2010-01-04  1700000096481    AMES DEPT STOR 0021436   \n",
       "400      401  5142276099 2010-01-04        unknown   RETAIL DEBIT ADJUSTMENT   \n",
       "416      417  5142173617 2010-01-04  6100020004006    USGPO SUPT DOCS-SUB/PU   \n",
       "476      477  5142267793 2010-01-05        unknown   RETAIL DEBIT ADJUSTMENT   \n",
       "482      483  5142257356 2010-01-05  2000049710067    LASER ACCESS  42760017   \n",
       "487      488  5142267793 2010-01-05        unknown  RETAIL CREDIT ADJUSTMENT   \n",
       "515      516  5142267793 2010-01-05        unknown  RETAIL CREDIT ADJUSTMENT   \n",
       "544      545  5142193730 2010-01-05        unknown  RETAIL CREDIT ADJUSTMENT   \n",
       "557      558  5142234471 2010-01-05        unknown  RETAIL CREDIT ADJUSTMENT   \n",
       "737      738  5142267793 2010-01-05        unknown  RETAIL CREDIT ADJUSTMENT   \n",
       "739      740  5142267793 2010-01-05        unknown  RETAIL CREDIT ADJUSTMENT   \n",
       "744      745  5142267793 2010-01-05        unknown  RETAIL CREDIT ADJUSTMENT   \n",
       "758      759  5142267793 2010-01-05        unknown  RETAIL CREDIT ADJUSTMENT   \n",
       "817      818  5142230669 2010-01-06  9996060597906          TOMMY'S TRAILERS   \n",
       "852      853  5142174305 2010-01-06        unknown  RETAIL CREDIT ADJUSTMENT   \n",
       "868      869  5142205500 2010-01-06  5000006000095    IBM INTERNET  01000025   \n",
       "906      907  5142288897 2010-01-06  9996060597908    IMPAC/TRI-COUNTY/FREED   \n",
       "931      932  5142214551 2010-01-06        6176269        MUNKSGAARDS FORLAG   \n",
       "1040    1041  5142230181 2010-01-06  9996060597910       STP SPECIALITY TECH   \n",
       "1082    1083  5142132574 2010-01-06  9900020008506     UNICOR FED PRISON IND   \n",
       "1162    1163  5142179159 2010-01-07   674906173338            G B SCIENTIFIC   \n",
       "1174    1175  5142139011 2010-01-07  7300020006306    GENERAL SERVICES ADMIN   \n",
       "1195    1196  5142260689 2010-01-07   679960185332             BUSINESS WIRE   \n",
       "\n",
       "     Merch state  Merch zip Transtype   Amount  Fraud  \n",
       "51            NY        NaN         P    20.15      0  \n",
       "54            NY        NaN         P    23.90      0  \n",
       "55            NY        NaN         P    19.95      0  \n",
       "58            NY        NaN         P    20.15      0  \n",
       "59            NY        NaN         P    20.15      0  \n",
       "60            NY        NaN         P    20.15      0  \n",
       "61            NY        NaN         P    20.15      0  \n",
       "62            NY        NaN         P    27.41      0  \n",
       "64            NY        NaN         P    20.15      0  \n",
       "65            NY        NaN         P    20.15      0  \n",
       "66            NY        NaN         P    19.95      0  \n",
       "68            NY        NaN         P    37.51      0  \n",
       "69            NY        NaN         P    19.95      0  \n",
       "71            NY        NaN         P    28.13      0  \n",
       "72            NY        NaN         P    20.15      0  \n",
       "75            NY        NaN         P    12.50      0  \n",
       "77            NY        NaN         P    20.15      0  \n",
       "78            NY        NaN         P   101.40      0  \n",
       "79            NY        NaN         P    19.95      0  \n",
       "87            WV        NaN         P    55.80      0  \n",
       "199           GA        NaN         P  1940.00      0  \n",
       "218           IL        NaN         P    17.97      0  \n",
       "230           OH        NaN         P    72.00      0  \n",
       "258           IL        NaN         P    21.28      0  \n",
       "262      unknown        NaN         P   320.00      0  \n",
       "272      unknown        NaN         P   970.00      0  \n",
       "293           IL        NaN         P     6.76      0  \n",
       "379           MA        NaN         P    13.00      0  \n",
       "400      unknown        NaN         P    82.59      0  \n",
       "416           DC        NaN         P    98.00      0  \n",
       "476      unknown        NaN         P    17.59      0  \n",
       "482           GA        NaN         P   600.00      0  \n",
       "487      unknown        NaN         P    19.69      0  \n",
       "515      unknown        NaN         P    17.59      0  \n",
       "544      unknown        NaN         P   105.00      0  \n",
       "557      unknown        NaN         P  1149.97      0  \n",
       "737      unknown        NaN         P    18.64      0  \n",
       "739      unknown        NaN         P    17.00      0  \n",
       "744      unknown        NaN         P    17.59      0  \n",
       "758      unknown        NaN         P    24.68      0  \n",
       "817           OK        NaN         P    48.97      0  \n",
       "852      unknown        NaN         P   379.42      0  \n",
       "868           NY        NaN         P    14.09      0  \n",
       "906           MD        NaN         P   467.58      0  \n",
       "931      unknown        NaN         P  1790.00      0  \n",
       "1040     foreign        NaN         P   486.18      0  \n",
       "1082          KY        NaN         P  1090.00      0  \n",
       "1162          CA        NaN         P   849.89      0  \n",
       "1174          MD        NaN         P   609.67      0  \n",
       "1195          CA        NaN         P   532.00      0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = data[data['Merch zip'].isna()]\n",
    "temp.head(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New imputation logic added"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'TN': 38118.0, 'MA': 1803.0, 'MD': 20706.0, 'OH': 45429.0, 'GA': '31040', 'IL': 60061.0, 'TX': '76302', 'WA': 98101.0, 'WI': 53052.0, 'NJ': 8701.0, 'FL': 32514.0, 'NY': '12108', 'CA': '90805', 'MS': 39201.0, 'VA': 20190.0, 'MO': 63026.0, 'PA': 17201.0, 'NC': 27560.0, 'WV': nan, 'CT': 6513.0, 'KS': 66202.0, 'OR': 97015.0, 'SC': 29407.0, 'MI': 48080.0, 'MN': 55802.0, 'KY': '41160', 'ME': 4032.0, 'CO': 80134.0, 'DC': 20001.0, 'AK': 99501.0, 'IA': 50309.0, 'NH': 3087.0, 'MT': 59601.0, 'OK': 74145.0, 'unknown': nan, 'NV': 89103.0, 'RI': 2882.0, 'NE': 69160.0, 'AZ': 85225.0, 'NM': 87114.0, 'UT': 84112.0, 'ID': 83814.0, 'SD': 57049.0, 'AL': 36601.0, 'DE': 19713.0, 'foreign': nan, 'LA': 70433.0, 'IN': 46229.0, 'VT': 5146.0, 'ND': 20740.0, 'AR': 72221.0, 'HI': 96813.0, 'WY': 82609.0, 'PR': '00926', 'VI': '00801'}\n",
      "1150\n"
     ]
    }
   ],
   "source": [
    "# my imputation logic\n",
    "# not the best\n",
    "\n",
    "state_zip = {}\n",
    "for index, zip5 in data[data['Merch state'].notnull()]['Merch state'].items():\n",
    "    if zip5 not in state_zip:\n",
    "        state_zip[zip5] = data.loc[index, 'Merch zip']\n",
    "\n",
    "state_zip['PR']='00926'\n",
    "\n",
    "state_zip['CA']='90805'\n",
    "\n",
    "state_zip['TX']='76302'\n",
    "\n",
    "state_zip['NY']='12108'\n",
    "\n",
    "state_zip['VI']='00801'\n",
    "\n",
    "state_zip['GA']='31040'\n",
    "\n",
    "state_zip['KY']='41160'\n",
    "\n",
    "\n",
    "print(state_zip)\n",
    "data['Merch zip'] = data['Merch zip'].fillna(data['Merch state'].map(state_zip))\n",
    "print(data['Merch zip'].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Merch zip'].fillna('unknown', inplace=True)\n",
    "data['Merch zip'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('transactions_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 96397 entries, 0 to 96752\n",
      "Data columns (total 10 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   Recnum             96397 non-null  int64         \n",
      " 1   Cardnum            96397 non-null  int64         \n",
      " 2   Date               96397 non-null  datetime64[ns]\n",
      " 3   Merchnum           96397 non-null  object        \n",
      " 4   Merch description  96397 non-null  object        \n",
      " 5   Merch state        96397 non-null  object        \n",
      " 6   Merch zip          96397 non-null  object        \n",
      " 7   Transtype          96397 non-null  object        \n",
      " 8   Amount             96397 non-null  float64       \n",
      " 9   Fraud              96397 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(1), int64(3), object(5)\n",
      "memory usage: 10.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Target encoded variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## to be safe, check the data type of dates first\n",
    "df.Date = pd.to_datetime(df.Date)\n",
    "df.Date.dtypes\n",
    "## all good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "## find the day of the week\n",
    "df['Dow'] = df.Date.apply(lambda x: calendar.day_name[x.weekday()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "## we want to not use the oot for target encoding variables\n",
    "train_test = df[df.Date < '2010-11-01']\n",
    "c = 4; nmid = 20; y_avg = train_test['Fraud'].mean()\n",
    "y_dow = train_test.groupby('Dow')['Fraud'].mean()\n",
    "num = train_test.groupby('Dow').size()\n",
    "y_dow_smooth = y_avg + (y_dow - y_avg)/(1 + np.exp(-(num - nmid)/c))\n",
    "df['Dow_Risk'] = df.Dow.map(y_dow_smooth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fraud</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dow</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>0.008711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>0.007127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>0.009788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>0.018626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>0.025994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>0.010095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>0.009630</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Fraud\n",
       "Dow                \n",
       "Monday     0.008711\n",
       "Tuesday    0.007127\n",
       "Wednesday  0.009788\n",
       "Thursday   0.018626\n",
       "Friday     0.025994\n",
       "Saturday   0.010095\n",
       "Sunday     0.009630"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_dow=y_dow.reset_index()\n",
    "cats=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "y_dow['Dow']=pd.Categorical(y_dow['Dow'], categories=cats, ordered=True)\n",
    "y_dow=y_dow.sort_values('Dow')\n",
    "y_dow=y_dow.set_index('Dow')\n",
    "y_dow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('ggplot')\n",
    "fig, ax = plt.subplots(figsize=(20,10))\n",
    "plt.bar(data = y_dow, \n",
    "        x = y_dow.index, \n",
    "        height = 'Fraud',\n",
    "        color = 'darkblue'\n",
    "        )\n",
    "#ax.set_ylim(bottom = 0.013)\n",
    "ax.axhline(y = y_avg, ls = '--', lw = 2, label=\"Average Fraud Population\")\n",
    "\n",
    "for i, v in enumerate(y_dow.index):\n",
    "    ax.text(v,y_dow.loc[v,'Fraud']+0.0001,round(y_dow.loc[v,'Fraud'],5),horizontalalignment='center',fontsize=15)\n",
    "\n",
    "plt.legend(['Average Fraud Population'], fontsize=15)\n",
    "plt.xlabel(\"Day of Week\",fontsize=20)\n",
    "plt.ylabel(\"Percentage\",fontsize=20)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.title(\"Fraud Proportion of Each Weekday\", fontsize=30)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Month of Transaction (New variable created)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the month of each transaction\n",
    "df['Month'] = df.Date.apply(lambda x: calendar.month_abbr[x.month])\n",
    "df['Month'].value_counts()\n",
    "\n",
    "train_test = df[df.Date < '2010-11-01']\n",
    "c = 4; nmid = 20; y_avg = train_test['Fraud'].mean()\n",
    "y_month = train_test.groupby('Month')['Fraud'].mean()\n",
    "num = train_test.groupby('Month').size()\n",
    "y_month_smooth = y_avg + (y_mth - y_avg)/(1 + np.exp(-(num - nmid)/c))\n",
    "df['Month_Risk'] = df.Month.map(y_mth_smooth)\n",
    "y_month=y_month.reset_index()\n",
    "cats=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct']\n",
    "y_month['Month']=pd.Categorical(y_month['Month'], categories=cats, ordered=True)\n",
    "y_month=y_month.sort_values('Month')\n",
    "y_month=y_month.set_index('Month')\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "fig, ax = plt.subplots(figsize=(20,10))\n",
    "plt.bar(data=y_month, \n",
    "        x=y_month.index, \n",
    "        height='Fraud',\n",
    "        color='darkblue'\n",
    "        )\n",
    "\n",
    "ax.axhline(y=y_avg, ls='--', lw=2, label=\"Average Fraud Population\")\n",
    "\n",
    "for i, v in enumerate(y_month.index):\n",
    "    ax.text(v, y_month.loc[v, 'Fraud'] + 0.0001, round(y_month.loc[v, 'Fraud'], 5), horizontalalignment='center', fontsize=15)\n",
    "\n",
    "plt.legend(['Average Fraud Population'], fontsize=15)\n",
    "plt.xlabel(\"Month\", fontsize=20)\n",
    "plt.ylabel(\"Percentage\", fontsize=20)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.title(\"Fraud Proportion of Each Month\", fontsize=30)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recnum</th>\n",
       "      <th>Cardnum</th>\n",
       "      <th>Date</th>\n",
       "      <th>Merchnum</th>\n",
       "      <th>Merch description</th>\n",
       "      <th>Merch state</th>\n",
       "      <th>Merch zip</th>\n",
       "      <th>Transtype</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Fraud</th>\n",
       "      <th>Dow</th>\n",
       "      <th>Dow_Risk</th>\n",
       "      <th>Month</th>\n",
       "      <th>Month_Risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5142190439</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEX SHP 12/23/09 AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>0.025994</td>\n",
       "      <td>January</td>\n",
       "      <td>0.003235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5142183973</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>61003026333</td>\n",
       "      <td>SERVICE MERCHANDISE #81</td>\n",
       "      <td>MA</td>\n",
       "      <td>1803.0</td>\n",
       "      <td>P</td>\n",
       "      <td>31.42</td>\n",
       "      <td>0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>0.025994</td>\n",
       "      <td>January</td>\n",
       "      <td>0.003235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5142131721</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>4503082993600</td>\n",
       "      <td>OFFICE DEPOT #191</td>\n",
       "      <td>MD</td>\n",
       "      <td>20706.0</td>\n",
       "      <td>P</td>\n",
       "      <td>178.49</td>\n",
       "      <td>0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>0.025994</td>\n",
       "      <td>January</td>\n",
       "      <td>0.003235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5142148452</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEX SHP 12/28/09 AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>0.025994</td>\n",
       "      <td>January</td>\n",
       "      <td>0.003235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5142190439</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEX SHP 12/23/09 AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>0.025994</td>\n",
       "      <td>January</td>\n",
       "      <td>0.003235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Recnum     Cardnum       Date       Merchnum        Merch description  \\\n",
       "0       1  5142190439 2010-01-01  5509006296254   FEDEX SHP 12/23/09 AB#   \n",
       "1       2  5142183973 2010-01-01    61003026333  SERVICE MERCHANDISE #81   \n",
       "2       3  5142131721 2010-01-01  4503082993600        OFFICE DEPOT #191   \n",
       "3       4  5142148452 2010-01-01  5509006296254   FEDEX SHP 12/28/09 AB#   \n",
       "4       5  5142190439 2010-01-01  5509006296254   FEDEX SHP 12/23/09 AB#   \n",
       "\n",
       "  Merch state Merch zip Transtype  Amount  Fraud     Dow  Dow_Risk    Month  \\\n",
       "0          TN   38118.0         P    3.62      0  Friday  0.025994  January   \n",
       "1          MA    1803.0         P   31.42      0  Friday  0.025994  January   \n",
       "2          MD   20706.0         P  178.49      0  Friday  0.025994  January   \n",
       "3          TN   38118.0         P    3.62      0  Friday  0.025994  January   \n",
       "4          TN   38118.0         P    3.62      0  Friday  0.025994  January   \n",
       "\n",
       "   Month_Risk  \n",
       "0    0.003235  \n",
       "1    0.003235  \n",
       "2    0.003235  \n",
       "3    0.003235  \n",
       "4    0.003235  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# statistical smoothing\n",
    "c = 4\n",
    "nmid = 20\n",
    "y_avg = train_test['Fraud'].mean()\n",
    "y_state = train_test.groupby('Merch state')['Fraud'].mean()\n",
    "num = train_test.groupby('Merch state').size()\n",
    "y_state_smooth = y_avg + (y_state - y_avg)/(1 + np.exp(-(num-nmid)/c))\n",
    "df['state_risk'] = df['Merch state'].map(y_state_smooth)\n",
    "top15_states = pd.DataFrame(y_state.sort_values(ascending=False).head(15))\n",
    "plt.style.use('ggplot')\n",
    "fig, ax = plt.subplots(figsize=(20,10))\n",
    "plt.bar(data=top15_states, x=top15_states.index, height='Fraud', color='darkblue')\n",
    "\n",
    "plt.title('Top 15 Fraud Merchant State with Highest Fraud Percentage', fontsize=30)\n",
    "\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.xlabel('Merchant State',fontsize=25, labelpad=20)\n",
    "plt.ylabel('Fraud Percentage',fontsize=25, labelpad=20)\n",
    "\n",
    "ax.axhline(y=y_avg, lw = 4, ls='--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# statistical smoothing\n",
    "c = 4\n",
    "nmid = 20\n",
    "y_avg = train_test['Fraud'].mean()\n",
    "y_cardnum = train_test.groupby('Cardnum')['Fraud'].mean()\n",
    "num = train_test.groupby('Cardnum').size()\n",
    "y_cardnum_smooth = y_avg + (y_cardnum - y_avg)/(1 + np.exp(-(num-nmid)/c))\n",
    "top15_cardnum = pd.DataFrame(y_cardnum\\\n",
    "                             .sort_values(ascending=False).head(15))\n",
    "plt.style.use('ggplot')\n",
    "fig, ax = plt.subplots(figsize=(20,10))\n",
    "plt.bar(data=top15_cardnum, x=top15_cardnum.index.astype(str), height='Fraud', color='darkblue')\n",
    "plt.title('Top 15 Card Numbers with Highest Fraud Percentage', fontsize=30)\n",
    "\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.xlabel('Card Number',fontsize=25, labelpad=20)\n",
    "plt.ylabel('Fraud Percentage',fontsize=25, labelpad=20)\n",
    "plt.xticks(rotation = 80)\n",
    "\n",
    "ax.axhline(y=y_avg, lw = 4, ls='--')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fraud</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Merchnum</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>450730006NOT0</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6006333528866</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4503738417400</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600660007477</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19908503337</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7000330100777</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8834000695423</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7593860080752</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92891948003</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6070095870009</th>\n",
       "      <td>0.931034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938909877224</th>\n",
       "      <td>0.780488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8292309000040</th>\n",
       "      <td>0.689655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6929</th>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6005030600003</th>\n",
       "      <td>0.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3831009006589</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Fraud\n",
       "Merchnum               \n",
       "450730006NOT0  1.000000\n",
       "6006333528866  1.000000\n",
       "4503738417400  1.000000\n",
       "600660007477   1.000000\n",
       "19908503337    1.000000\n",
       "7000330100777  1.000000\n",
       "8834000695423  1.000000\n",
       "7593860080752  1.000000\n",
       "92891948003    1.000000\n",
       "6070095870009  0.931034\n",
       "938909877224   0.780488\n",
       "8292309000040  0.689655\n",
       "6929           0.666667\n",
       "6005030600003  0.615385\n",
       "3831009006589  0.500000"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistical smoothing\n",
    "c = 4\n",
    "nmid = 20\n",
    "y_avg = train_test['Fraud'].mean()\n",
    "y_merchnum = train_test.groupby('Merchnum')['Fraud'].mean()\n",
    "num = train_test.groupby('Merchnum').size()\n",
    "y_merchnum_smooth = y_avg + (y_merchnum - y_avg)/(1 + np.exp(-(num-nmid)/c))\n",
    "\n",
    "# comment this out so we don't include this variable because it overfits\n",
    "# data['merchnum_risk'] = data['Merchnum'].map(y_merchnum_smooth)\n",
    "top15_merchnum = pd.DataFrame(y_merchnum\\\n",
    "                             .sort_values(ascending=False).head(15))\n",
    "top15_merchnum.head(20)\n",
    "\n",
    "# plt.style.use('ggplot')\n",
    "# fig, ax = plt.subplots(figsize=(20,10))\n",
    "# plt.bar(data=top15_merchnum, x=top15_merchnum.index.astype(str), height='Fraud', color='darkblue')\n",
    "# plt.title('Top 15 Merchant Numbers with Highest Fraud Percentage', fontsize=30)\n",
    "\n",
    "# plt.xticks(fontsize=20)\n",
    "# plt.yticks(fontsize=20)\n",
    "# plt.xlabel('Merchant Number',fontsize=25, labelpad=20)\n",
    "# plt.ylabel('Fraud Percentage',fontsize=25, labelpad=20)\n",
    "# plt.xticks(rotation = 80)\n",
    "\n",
    "# ax.axhline(y=y_avg, lw = 4, ls='--')\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1. Data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 96397 entries, 0 to 96752\n",
      "Data columns (total 15 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   Recnum             96397 non-null  int64         \n",
      " 1   Cardnum            96397 non-null  object        \n",
      " 2   Date               96397 non-null  datetime64[ns]\n",
      " 3   Merchnum           96397 non-null  object        \n",
      " 4   Merch description  96397 non-null  object        \n",
      " 5   Merch state        96397 non-null  object        \n",
      " 6   Merch zip          96397 non-null  object        \n",
      " 7   Transtype          96397 non-null  object        \n",
      " 8   Amount             96397 non-null  float64       \n",
      " 9   Fraud              96397 non-null  int64         \n",
      " 10  Dow                96397 non-null  object        \n",
      " 11  Dow_Risk           96397 non-null  float64       \n",
      " 12  Month              96397 non-null  object        \n",
      " 13  Month_Risk         83970 non-null  float64       \n",
      " 14  state_risk         96397 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(4), int64(2), object(8)\n",
      "memory usage: 13.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df['Cardnum'] = df['Cardnum'].apply(str)\n",
    "df['Merchnum'] = df['Merchnum'].apply(str)\n",
    "df['Merch zip'] = df['Merch zip'].apply(str)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "### add leading 0 to zips\n",
    "### note: there are some zips that are state abbrv. as we imputed them ealier, so pandas read the column as str\n",
    "\n",
    "def leading_0(x):\n",
    "    \n",
    "    if '.0' in x:\n",
    "        x = x[:-2]\n",
    "        if len(x) == 5:\n",
    "            return x\n",
    "        else: \n",
    "            return '0'*(5-len(x)) + x\n",
    "    else:\n",
    "        return '0'*(5-len(x)) + x\n",
    "\n",
    "# df['Merch zip'] = df['Merch zip'].apply(leading_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "### delete white spaces in merch description\n",
    "df['Merch description'] = df['Merch description'].str.replace(r'\\s', '')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create entities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 96397 entries, 0 to 96752\n",
      "Data columns (total 15 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   Recnum             96397 non-null  int64         \n",
      " 1   Cardnum            96397 non-null  object        \n",
      " 2   Date               96397 non-null  datetime64[ns]\n",
      " 3   Merchnum           96397 non-null  object        \n",
      " 4   Merch description  96397 non-null  object        \n",
      " 5   Merch state        96397 non-null  object        \n",
      " 6   Merch zip          96397 non-null  object        \n",
      " 7   Transtype          96397 non-null  object        \n",
      " 8   Amount             96397 non-null  float64       \n",
      " 9   Fraud              96397 non-null  int64         \n",
      " 10  Dow                96397 non-null  object        \n",
      " 11  Dow_Risk           96397 non-null  float64       \n",
      " 12  Month              96397 non-null  object        \n",
      " 13  Month_Risk         83970 non-null  float64       \n",
      " 14  state_risk         96397 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(4), int64(2), object(8)\n",
      "memory usage: 13.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['card_merch'] = df['Cardnum'] + df['Merchnum']\n",
    "df['card_zip'] = df['Cardnum'] + df['Merch zip']\n",
    "df['card_state'] = df['Cardnum'] + df['Merch state']\n",
    "df['merch_zip'] = df['Merchnum'] + df['Merch zip']\n",
    "df['merch_state'] = df['Merchnum'] + df['Merch state']\n",
    "df['state_des'] = df['Merch state'] + df['Merch description']\n",
    "\n",
    "# these next entity take a long time to calculate the variables for, and I don't know why\n",
    "# df['state_zip'] = df['Merch state'] + df['Merch zip']\n",
    "\n",
    "df['zip3'] = df['Merch zip'].str[:3]\n",
    "df['card_zip3'] = df.Cardnum + df['zip3']\n",
    "# df['merchnum_zip'] = df.Merchnum + df['Merch zip']\n",
    "# df['merchnum_zip3'] = df.Merchnum + df['zip3']\n",
    "df['Card_Merchdesc'] = df['Cardnum'] + df['Merch description']\n",
    "df['Card_dow'] = df['Cardnum'] + df['Dow']\n",
    "df['Merchnum_desc'] = df['Merchnum'] + df['Merch description']\n",
    "df['Merchnum_dow'] = df['Merchnum'] + df['Dow']\n",
    "# df['Merchdesc_State'] = df['Merch description'] + df['Merch state']\n",
    "# df['Merchdesc_Zip'] = df['Merch description'] + df['Merch zip']\n",
    "df['Merchdesc_dow'] = df['Merch description'] + df['Dow']\n",
    "df['Card_Merchnum_desc'] = df['Cardnum'] + df['Merchnum'] + df['Merch description']\n",
    "# df['Card_Merchnum_State'] = df['Cardnum'] + df['Merchnum'] + df['Merch state']\n",
    "df['Card_Merchnum_Zip'] = df['Cardnum'] + df['Merchnum'] + df['Merch zip']\n",
    "# df['Card_Merchdesc_State'] = df['Cardnum'] + df['Merch description'] + df['Merch state']\n",
    "df['Card_Merchdesc_Zip'] = df['Cardnum'] + df['Merch description'] + df['Merch zip']\n",
    "df['Merchnum_desc_State'] = df['Merchnum'] + df['Merch description'] + df['Merch state']\n",
    "# df['Merchnum_desc_Zip'] = df['Merchnum'] + df['Merch description'] + df['Merch zip']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Recnum', 'Cardnum', 'Date', 'Merchnum', 'Merch description',\n",
       "       'Merch state', 'Merch zip', 'Transtype', 'Amount', 'Fraud', 'Dow',\n",
       "       'Dow_Risk', 'Month', 'Month_Risk', 'state_risk', 'card_merch',\n",
       "       'card_zip', 'card_state', 'merch_zip', 'merch_state', 'state_des',\n",
       "       'zip3', 'card_zip3', 'Card_Merchdesc', 'Card_dow', 'Merchnum_desc',\n",
       "       'Merchnum_dow', 'Merchdesc_dow', 'Card_Merchnum_desc',\n",
       "       'Card_Merchnum_Zip', 'Card_Merchdesc_Zip', 'Merchnum_desc_State'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "entities = list(df.iloc[:, np.r_[1, 3, 12:len(df.columns)]].columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cardnum',\n",
       " 'Merchnum',\n",
       " 'Month',\n",
       " 'Month_Risk',\n",
       " 'state_risk',\n",
       " 'card_merch',\n",
       " 'card_zip',\n",
       " 'card_state',\n",
       " 'merch_zip',\n",
       " 'merch_state',\n",
       " 'state_des',\n",
       " 'zip3',\n",
       " 'card_zip3',\n",
       " 'Card_Merchdesc',\n",
       " 'Card_dow',\n",
       " 'Merchnum_desc',\n",
       " 'Merchnum_dow',\n",
       " 'Merchdesc_dow',\n",
       " 'Card_Merchnum_desc',\n",
       " 'Card_Merchnum_Zip',\n",
       " 'Card_Merchdesc_Zip',\n",
       " 'Merchnum_desc_State']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Date = pd.to_datetime(df.Date)\n",
    "df1 = df.copy()\n",
    "final = df.copy()\n",
    "df1['check_date'] = df1.Date\n",
    "df1['check_record'] = df1.Recnum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make the Benford's law top 40 tables and variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    26603\n",
       "3    18670\n",
       "2    16178\n",
       "4     8278\n",
       "5     6955\n",
       "6     6017\n",
       "7     5027\n",
       "8     4534\n",
       "9     4135\n",
       "Name: first_digit, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# another way to get the first digit\n",
    "bf = data.copy()\n",
    "bf['amount_100'] = (bf['Amount'] * 100).astype(str)\n",
    "bf['first_digit'] = bf['amount_100'].str[0]\n",
    "bf['first_digit'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recnum</th>\n",
       "      <th>Cardnum</th>\n",
       "      <th>Date</th>\n",
       "      <th>Merchnum</th>\n",
       "      <th>Merch description</th>\n",
       "      <th>Merch state</th>\n",
       "      <th>Merch zip</th>\n",
       "      <th>Transtype</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Fraud</th>\n",
       "      <th>amount_100</th>\n",
       "      <th>first_digit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5142190439</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEX SHP 12/23/09 AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5142148452</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEX SHP 12/28/09 AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5142190439</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEX SHP 12/23/09 AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>5142149874</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEX SHP 12/22/09 AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.67</td>\n",
       "      <td>0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>5142189277</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEX SHP 12/28/09 AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Recnum     Cardnum       Date       Merchnum       Merch description  \\\n",
       "0       1  5142190439 2010-01-01  5509006296254  FEDEX SHP 12/23/09 AB#   \n",
       "3       4  5142148452 2010-01-01  5509006296254  FEDEX SHP 12/28/09 AB#   \n",
       "4       5  5142190439 2010-01-01  5509006296254  FEDEX SHP 12/23/09 AB#   \n",
       "5       6  5142149874 2010-01-01  5509006296254  FEDEX SHP 12/22/09 AB#   \n",
       "6       7  5142189277 2010-01-01  5509006296254  FEDEX SHP 12/28/09 AB#   \n",
       "\n",
       "  Merch state Merch zip Transtype  Amount  Fraud amount_100 first_digit  \n",
       "0          TN   38118.0         P    3.62      0      362.0           3  \n",
       "3          TN   38118.0         P    3.62      0      362.0           3  \n",
       "4          TN   38118.0         P    3.62      0      362.0           3  \n",
       "5          TN   38118.0         P    3.67      0      367.0           3  \n",
       "6          TN   38118.0         P    3.62      0      362.0           3  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropfedex = bf[bf['Merch description'].str.contains('FEDEX')]\n",
    "dropfedex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 3, 4, 5, 6, 9, 10, 11, 12, 15]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "droplist = dropfedex.index.tolist()\n",
    "droplist[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[96246, 96291, 96292, 96319, 96397, 96415, 96426, 96433, 96459, 96727]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "droplist[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11775"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(droplist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recnum</th>\n",
       "      <th>Cardnum</th>\n",
       "      <th>Date</th>\n",
       "      <th>Merchnum</th>\n",
       "      <th>Merch description</th>\n",
       "      <th>Merch state</th>\n",
       "      <th>Merch zip</th>\n",
       "      <th>Transtype</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Fraud</th>\n",
       "      <th>amount_100</th>\n",
       "      <th>first_digit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5142190439</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEX SHP 12/23/09 AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5142183973</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>61003026333</td>\n",
       "      <td>SERVICE MERCHANDISE #81</td>\n",
       "      <td>MA</td>\n",
       "      <td>1803.0</td>\n",
       "      <td>P</td>\n",
       "      <td>31.42</td>\n",
       "      <td>0</td>\n",
       "      <td>3142.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5142131721</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>4503082993600</td>\n",
       "      <td>OFFICE DEPOT #191</td>\n",
       "      <td>MD</td>\n",
       "      <td>20706.0</td>\n",
       "      <td>P</td>\n",
       "      <td>178.49</td>\n",
       "      <td>0</td>\n",
       "      <td>17849.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5142148452</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEX SHP 12/28/09 AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5142190439</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEX SHP 12/23/09 AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Recnum     Cardnum       Date       Merchnum        Merch description  \\\n",
       "0       1  5142190439 2010-01-01  5509006296254   FEDEX SHP 12/23/09 AB#   \n",
       "1       2  5142183973 2010-01-01    61003026333  SERVICE MERCHANDISE #81   \n",
       "2       3  5142131721 2010-01-01  4503082993600        OFFICE DEPOT #191   \n",
       "3       4  5142148452 2010-01-01  5509006296254   FEDEX SHP 12/28/09 AB#   \n",
       "4       5  5142190439 2010-01-01  5509006296254   FEDEX SHP 12/23/09 AB#   \n",
       "\n",
       "  Merch state Merch zip Transtype  Amount  Fraud amount_100 first_digit  \n",
       "0          TN   38118.0         P    3.62      0      362.0           3  \n",
       "1          MA    1803.0         P   31.42      0     3142.0           3  \n",
       "2          MD   20706.0         P  178.49      0    17849.0           1  \n",
       "3          TN   38118.0         P    3.62      0      362.0           3  \n",
       "4          TN   38118.0         P    3.62      0      362.0           3  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96397, 12)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(84622, 12)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bf1 = bf.drop(droplist)\n",
    "bf1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# datefilter = datetime.datetime(2010,11,1)\n",
    "# bf1 = bf1[bf1['Date'] < datefilter]\n",
    "# bf1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recnum</th>\n",
       "      <th>Cardnum</th>\n",
       "      <th>Date</th>\n",
       "      <th>Merchnum</th>\n",
       "      <th>Merch description</th>\n",
       "      <th>Merch state</th>\n",
       "      <th>Merch zip</th>\n",
       "      <th>Transtype</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Fraud</th>\n",
       "      <th>amount_100</th>\n",
       "      <th>first_digit</th>\n",
       "      <th>bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5142183973</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>61003026333</td>\n",
       "      <td>SERVICE MERCHANDISE #81</td>\n",
       "      <td>MA</td>\n",
       "      <td>1803.0</td>\n",
       "      <td>P</td>\n",
       "      <td>31.42</td>\n",
       "      <td>0</td>\n",
       "      <td>3142.0</td>\n",
       "      <td>3</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5142131721</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>4503082993600</td>\n",
       "      <td>OFFICE DEPOT #191</td>\n",
       "      <td>MD</td>\n",
       "      <td>20706.0</td>\n",
       "      <td>P</td>\n",
       "      <td>178.49</td>\n",
       "      <td>0</td>\n",
       "      <td>17849.0</td>\n",
       "      <td>1</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>5142191182</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>6098208200062</td>\n",
       "      <td>MIAMI COMPUTER SUPPLY</td>\n",
       "      <td>OH</td>\n",
       "      <td>45429.0</td>\n",
       "      <td>P</td>\n",
       "      <td>230.32</td>\n",
       "      <td>0</td>\n",
       "      <td>23032.0</td>\n",
       "      <td>2</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>5142258629</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>602608969534</td>\n",
       "      <td>FISHER SCI ATL</td>\n",
       "      <td>GA</td>\n",
       "      <td>30091.0</td>\n",
       "      <td>P</td>\n",
       "      <td>62.11</td>\n",
       "      <td>0</td>\n",
       "      <td>6211.0</td>\n",
       "      <td>6</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>5142124791</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5725000466504</td>\n",
       "      <td>CDW*GOVERNMENT INC</td>\n",
       "      <td>IL</td>\n",
       "      <td>60061.0</td>\n",
       "      <td>P</td>\n",
       "      <td>106.89</td>\n",
       "      <td>0</td>\n",
       "      <td>10689.0</td>\n",
       "      <td>1</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Recnum     Cardnum       Date       Merchnum        Merch description  \\\n",
       "1        2  5142183973 2010-01-01    61003026333  SERVICE MERCHANDISE #81   \n",
       "2        3  5142131721 2010-01-01  4503082993600        OFFICE DEPOT #191   \n",
       "7        8  5142191182 2010-01-01  6098208200062    MIAMI COMPUTER SUPPLY   \n",
       "8        9  5142258629 2010-01-01   602608969534           FISHER SCI ATL   \n",
       "13      14  5142124791 2010-01-01  5725000466504       CDW*GOVERNMENT INC   \n",
       "\n",
       "   Merch state Merch zip Transtype  Amount  Fraud amount_100 first_digit   bin  \n",
       "1           MA    1803.0         P   31.42      0     3142.0           3  high  \n",
       "2           MD   20706.0         P  178.49      0    17849.0           1   low  \n",
       "7           OH   45429.0         P  230.32      0    23032.0           2   low  \n",
       "8           GA   30091.0         P   62.11      0     6211.0           6  high  \n",
       "13          IL   60061.0         P  106.89      0    10689.0           1   low  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bf1['bin']= bf1['first_digit'].apply(lambda x: \"low\" if x == \"1\" else (\"low\" if x == \"2\" else \"high\"))\n",
    "bf1.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    25697\n",
       "2    15827\n",
       "3    10297\n",
       "4     7686\n",
       "5     6749\n",
       "6     5469\n",
       "7     4699\n",
       "8     4152\n",
       "9     4046\n",
       "Name: first_digit, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bf1['first_digit'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Merch zip</th>\n",
       "      <th>bin</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>high</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>low</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>high</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>low</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>high</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7092</th>\n",
       "      <td>76302</td>\n",
       "      <td>low</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7093</th>\n",
       "      <td>90805</td>\n",
       "      <td>high</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7094</th>\n",
       "      <td>90805</td>\n",
       "      <td>low</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7095</th>\n",
       "      <td>unknown</td>\n",
       "      <td>high</td>\n",
       "      <td>688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7096</th>\n",
       "      <td>unknown</td>\n",
       "      <td>low</td>\n",
       "      <td>462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7097 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Merch zip   bin  count\n",
       "0          1.0  high      6\n",
       "1          1.0   low      5\n",
       "2          2.0  high      7\n",
       "3          2.0   low      9\n",
       "4          3.0  high     14\n",
       "...        ...   ...    ...\n",
       "7092     76302   low     14\n",
       "7093     90805  high     76\n",
       "7094     90805   low     41\n",
       "7095   unknown  high    688\n",
       "7096   unknown   low    462\n",
       "\n",
       "[7097 rows x 3 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculating n_low and n_high\n",
    "zip_bf =  bf1.groupby(['Merch zip','bin']).agg({'bin': ['count']}).reset_index()\n",
    "zip_bf.columns=['Merch zip', 'bin', 'count']\n",
    "zip_bf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Merch zip</th>\n",
       "      <th>n_high</th>\n",
       "      <th>n_low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4568</th>\n",
       "      <td>31040</td>\n",
       "      <td>44.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4569</th>\n",
       "      <td>41160</td>\n",
       "      <td>59.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4570</th>\n",
       "      <td>76302</td>\n",
       "      <td>22.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4571</th>\n",
       "      <td>90805</td>\n",
       "      <td>76.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4572</th>\n",
       "      <td>unknown</td>\n",
       "      <td>688.0</td>\n",
       "      <td>462.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4573 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Merch zip  n_high  n_low\n",
       "0          1.0     6.0    5.0\n",
       "1          2.0     7.0    9.0\n",
       "2          3.0    14.0    4.0\n",
       "3          5.0     1.0    1.0\n",
       "4          6.0     5.0    2.0\n",
       "...        ...     ...    ...\n",
       "4568     31040    44.0   49.0\n",
       "4569     41160    59.0   67.0\n",
       "4570     76302    22.0   14.0\n",
       "4571     90805    76.0   41.0\n",
       "4572   unknown   688.0  462.0\n",
       "\n",
       "[4573 rows x 3 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if either n_low or n_high is zero, set it to 1\n",
    "zip_bf = zip_bf.fillna(1)\n",
    "zip_bf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Merch zip</th>\n",
       "      <th>n_high</th>\n",
       "      <th>n_low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>5.0</td>\n",
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       "      <th>4568</th>\n",
       "      <td>31040</td>\n",
       "      <td>44.0</td>\n",
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       "    <tr>\n",
       "      <th>4569</th>\n",
       "      <td>41160</td>\n",
       "      <td>59.0</td>\n",
       "      <td>67.0</td>\n",
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       "    <tr>\n",
       "      <th>4570</th>\n",
       "      <td>76302</td>\n",
       "      <td>22.0</td>\n",
       "      <td>14.0</td>\n",
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       "    <tr>\n",
       "      <th>4571</th>\n",
       "      <td>90805</td>\n",
       "      <td>76.0</td>\n",
       "      <td>41.0</td>\n",
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       "    <tr>\n",
       "      <th>4572</th>\n",
       "      <td>unknown</td>\n",
       "      <td>688.0</td>\n",
       "      <td>462.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4573 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Merch zip  n_high  n_low\n",
       "0          1.0     6.0    5.0\n",
       "1          2.0     7.0    9.0\n",
       "2          3.0    14.0    4.0\n",
       "3          5.0     NaN    1.0\n",
       "4          6.0     5.0    2.0\n",
       "...        ...     ...    ...\n",
       "4568     31040    44.0   49.0\n",
       "4569     41160    59.0   67.0\n",
       "4570     76302    22.0   14.0\n",
       "4571     90805    76.0   41.0\n",
       "4572   unknown   688.0  462.0\n",
       "\n",
       "[4573 rows x 3 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zip_bf =  zip_bf.pivot_table(index='Merch zip',columns='bin',values='count',aggfunc='sum').reset_index()\n",
    "zip_bf.columns=['Merch zip', 'n_high', 'n_low']\n",
    "zip_bf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cardnum</th>\n",
       "      <th>bin</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>high</td>\n",
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       "      <th>2</th>\n",
       "      <td>5142110313</td>\n",
       "      <td>high</td>\n",
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       "      <th>3</th>\n",
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       "      <td>low</td>\n",
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       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3128</th>\n",
       "      <td>5142310598</td>\n",
       "      <td>low</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3129</th>\n",
       "      <td>5142310768</td>\n",
       "      <td>high</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3130</th>\n",
       "      <td>5142310768</td>\n",
       "      <td>low</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3131</th>\n",
       "      <td>5142847398</td>\n",
       "      <td>high</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3132</th>\n",
       "      <td>5142847398</td>\n",
       "      <td>low</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3133 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Cardnum   bin  count\n",
       "0     5142110002   low      1\n",
       "1     5142110081  high      4\n",
       "2     5142110313  high      1\n",
       "3     5142110313   low      2\n",
       "4     5142110402  high      8\n",
       "...          ...   ...    ...\n",
       "3128  5142310598   low      2\n",
       "3129  5142310768  high      2\n",
       "3130  5142310768   low      2\n",
       "3131  5142847398  high     35\n",
       "3132  5142847398   low     10\n",
       "\n",
       "[3133 rows x 3 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculating n_low and n_high\n",
    "card_bf =  bf1.groupby(['Cardnum','bin']).agg({'bin': ['count']}).reset_index()\n",
    "card_bf.columns=['Cardnum', 'bin', 'count']\n",
    "card_bf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
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       "      <th>1635</th>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1636</th>\n",
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       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>1637</th>\n",
       "      <td>5142310598</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>1638</th>\n",
       "      <td>5142310768</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>1639</th>\n",
       "      <td>5142847398</td>\n",
       "      <td>35.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1640 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Cardnum  n_high  n_low\n",
       "0     5142110002     NaN    1.0\n",
       "1     5142110081     4.0    NaN\n",
       "2     5142110313     1.0    2.0\n",
       "3     5142110402     8.0    3.0\n",
       "4     5142110434     NaN    1.0\n",
       "...          ...     ...    ...\n",
       "1635  5142310397     1.0    NaN\n",
       "1636  5142310525     3.0    1.0\n",
       "1637  5142310598     NaN    2.0\n",
       "1638  5142310768     2.0    2.0\n",
       "1639  5142847398    35.0   10.0\n",
       "\n",
       "[1640 rows x 3 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "card_bf =  card_bf.pivot_table(index='Cardnum',columns='bin',values='count',aggfunc='sum').reset_index()\n",
    "card_bf.columns=['Cardnum', 'n_high', 'n_low']\n",
    "card_bf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
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       "      <th>1637</th>\n",
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       "    <tr>\n",
       "      <th>1638</th>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>1639</th>\n",
       "      <td>5142847398</td>\n",
       "      <td>35.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1640 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Cardnum  n_high  n_low\n",
       "0     5142110002     1.0    1.0\n",
       "1     5142110081     4.0    1.0\n",
       "2     5142110313     1.0    2.0\n",
       "3     5142110402     8.0    3.0\n",
       "4     5142110434     1.0    1.0\n",
       "...          ...     ...    ...\n",
       "1635  5142310397     1.0    1.0\n",
       "1636  5142310525     3.0    1.0\n",
       "1637  5142310598     1.0    2.0\n",
       "1638  5142310768     2.0    2.0\n",
       "1639  5142847398    35.0   10.0\n",
       "\n",
       "[1640 rows x 3 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if either n_low or n_high is zero, set it to 1\n",
    "card_bf = card_bf.fillna(1)\n",
    "card_bf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calclating R, 1/R, U, n, t U_smoothed\n",
    "c=3\n",
    "n_mid=15\n",
    "card_bf['R'] = (1.096 * card_bf['n_low']/card_bf['n_high'])\n",
    "card_bf['1/R'] = (1/card_bf['R'])\n",
    "card_bf['U'] = list(map(lambda x, y : max(x,y),card_bf['R'],card_bf['1/R']))\n",
    "card_bf['n'] = card_bf['n_high'] + card_bf['n_low']\n",
    "card_bf['t'] = ((card_bf['n']-n_mid)/c)\n",
    "card_bf['U_smoothed']= list(map(lambda x, y : (1 + (x-1)/(1+exp(-y))),card_bf['U'],card_bf['t']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Cardnum</th>\n",
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       "      <th>R</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>5142253356</td>\n",
       "      <td>5.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>13.371200</td>\n",
       "      <td>0.074788</td>\n",
       "      <td>13.371200</td>\n",
       "      <td>66.0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>13.371199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5142299705</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>9.133333</td>\n",
       "      <td>0.109489</td>\n",
       "      <td>9.133333</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>9.027976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5142197563</td>\n",
       "      <td>134.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.122687</td>\n",
       "      <td>8.150852</td>\n",
       "      <td>8.150852</td>\n",
       "      <td>149.0</td>\n",
       "      <td>44.666667</td>\n",
       "      <td>8.150852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5142194617</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.166061</td>\n",
       "      <td>6.021898</td>\n",
       "      <td>6.021898</td>\n",
       "      <td>38.0</td>\n",
       "      <td>7.666667</td>\n",
       "      <td>6.019548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5142288241</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.084308</td>\n",
       "      <td>11.861314</td>\n",
       "      <td>11.861314</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>5.533836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5142239140</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.845333</td>\n",
       "      <td>0.171077</td>\n",
       "      <td>5.845333</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>4.834555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5142144931</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.219200</td>\n",
       "      <td>4.562044</td>\n",
       "      <td>4.562044</td>\n",
       "      <td>36.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.558799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5142192606</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.124000</td>\n",
       "      <td>0.140371</td>\n",
       "      <td>7.124000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.062000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5142204384</td>\n",
       "      <td>54.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>4.038963</td>\n",
       "      <td>0.247588</td>\n",
       "      <td>4.038963</td>\n",
       "      <td>253.0</td>\n",
       "      <td>79.333333</td>\n",
       "      <td>4.038963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5142284940</td>\n",
       "      <td>6.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.836000</td>\n",
       "      <td>0.260688</td>\n",
       "      <td>3.836000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.784991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5142189113</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.274000</td>\n",
       "      <td>3.649635</td>\n",
       "      <td>3.649635</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.631901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5142225308</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.257882</td>\n",
       "      <td>3.877737</td>\n",
       "      <td>3.877737</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.534703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5142116864</td>\n",
       "      <td>18.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>3.531556</td>\n",
       "      <td>0.283161</td>\n",
       "      <td>3.531556</td>\n",
       "      <td>76.0</td>\n",
       "      <td>20.333333</td>\n",
       "      <td>3.531556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5142293257</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.168615</td>\n",
       "      <td>5.930657</td>\n",
       "      <td>5.930657</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.465328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5142173286</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.168615</td>\n",
       "      <td>5.930657</td>\n",
       "      <td>5.930657</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.465328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5142246929</td>\n",
       "      <td>25.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>3.463360</td>\n",
       "      <td>0.288737</td>\n",
       "      <td>3.463360</td>\n",
       "      <td>104.0</td>\n",
       "      <td>29.666667</td>\n",
       "      <td>3.463360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5142224699</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.306880</td>\n",
       "      <td>3.258603</td>\n",
       "      <td>3.258603</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>3.250816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5142847398</td>\n",
       "      <td>35.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.313143</td>\n",
       "      <td>3.193431</td>\n",
       "      <td>3.193431</td>\n",
       "      <td>45.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.193331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5142273608</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.313143</td>\n",
       "      <td>3.193431</td>\n",
       "      <td>3.193431</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.153979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5142147267</td>\n",
       "      <td>76.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.317263</td>\n",
       "      <td>3.151958</td>\n",
       "      <td>3.151958</td>\n",
       "      <td>98.0</td>\n",
       "      <td>27.666667</td>\n",
       "      <td>3.151958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5142224769</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.288000</td>\n",
       "      <td>0.304136</td>\n",
       "      <td>3.288000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>2.924507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5142242241</td>\n",
       "      <td>51.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.343843</td>\n",
       "      <td>2.908303</td>\n",
       "      <td>2.908303</td>\n",
       "      <td>67.0</td>\n",
       "      <td>17.333333</td>\n",
       "      <td>2.908303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>5142260984</td>\n",
       "      <td>101.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>2.875644</td>\n",
       "      <td>0.347748</td>\n",
       "      <td>2.875644</td>\n",
       "      <td>366.0</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>2.875644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5142113192</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.182667</td>\n",
       "      <td>5.474453</td>\n",
       "      <td>5.474453</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>2.867770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5142191416</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.818286</td>\n",
       "      <td>0.354826</td>\n",
       "      <td>2.818286</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>2.755655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5142194228</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.028000</td>\n",
       "      <td>0.165893</td>\n",
       "      <td>6.028000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-0.666667</td>\n",
       "      <td>2.705717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5142308889</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.028000</td>\n",
       "      <td>0.165893</td>\n",
       "      <td>6.028000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-0.666667</td>\n",
       "      <td>2.705717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5142212038</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.384000</td>\n",
       "      <td>0.228102</td>\n",
       "      <td>4.384000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.692000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5142195887</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.384000</td>\n",
       "      <td>0.228102</td>\n",
       "      <td>4.384000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.692000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5142225184</td>\n",
       "      <td>11.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.690182</td>\n",
       "      <td>0.371722</td>\n",
       "      <td>2.690182</td>\n",
       "      <td>38.0</td>\n",
       "      <td>7.666667</td>\n",
       "      <td>2.689391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>5142257356</td>\n",
       "      <td>58.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2.683310</td>\n",
       "      <td>0.372674</td>\n",
       "      <td>2.683310</td>\n",
       "      <td>200.0</td>\n",
       "      <td>61.666667</td>\n",
       "      <td>2.683310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>5142216493</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.068800</td>\n",
       "      <td>0.325860</td>\n",
       "      <td>3.068800</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>2.637231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5142239106</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.381217</td>\n",
       "      <td>2.623175</td>\n",
       "      <td>2.623175</td>\n",
       "      <td>31.0</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>2.615376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5142144593</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.313143</td>\n",
       "      <td>3.193431</td>\n",
       "      <td>3.193431</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.603526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>5142126842</td>\n",
       "      <td>16.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2.603000</td>\n",
       "      <td>0.384172</td>\n",
       "      <td>2.603000</td>\n",
       "      <td>54.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>2.602996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5142117315</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.383600</td>\n",
       "      <td>2.606882</td>\n",
       "      <td>2.606882</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.577980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5142218798</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.557333</td>\n",
       "      <td>0.391032</td>\n",
       "      <td>2.557333</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.546910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>5142180432</td>\n",
       "      <td>25.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2.542720</td>\n",
       "      <td>0.393280</td>\n",
       "      <td>2.542720</td>\n",
       "      <td>83.0</td>\n",
       "      <td>22.666667</td>\n",
       "      <td>2.542720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>5142264155</td>\n",
       "      <td>12.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.466000</td>\n",
       "      <td>0.405515</td>\n",
       "      <td>2.466000</td>\n",
       "      <td>39.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.465508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5142294614</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.365333</td>\n",
       "      <td>2.737226</td>\n",
       "      <td>2.737226</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>2.461235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Cardnum  n_high  n_low          R        1/R          U      n  \\\n",
       "0   5142253356     5.0   61.0  13.371200   0.074788  13.371200   66.0   \n",
       "1   5142299705     3.0   25.0   9.133333   0.109489   9.133333   28.0   \n",
       "2   5142197563   134.0   15.0   0.122687   8.150852   8.150852  149.0   \n",
       "3   5142194617    33.0    5.0   0.166061   6.021898   6.021898   38.0   \n",
       "4   5142288241    13.0    1.0   0.084308  11.861314  11.861314   14.0   \n",
       "5   5142239140     3.0   16.0   5.845333   0.171077   5.845333   19.0   \n",
       "6   5142144931    30.0    6.0   0.219200   4.562044   4.562044   36.0   \n",
       "7   5142192606     2.0   13.0   7.124000   0.140371   7.124000   15.0   \n",
       "8   5142204384    54.0  199.0   4.038963   0.247588   4.038963  253.0   \n",
       "9   5142284940     6.0   21.0   3.836000   0.260688   3.836000   27.0   \n",
       "10  5142189113    24.0    6.0   0.274000   3.649635   3.649635   30.0   \n",
       "11  5142225308    17.0    4.0   0.257882   3.877737   3.877737   21.0   \n",
       "12  5142116864    18.0   58.0   3.531556   0.283161   3.531556   76.0   \n",
       "13  5142293257    13.0    2.0   0.168615   5.930657   5.930657   15.0   \n",
       "14  5142173286    13.0    2.0   0.168615   5.930657   5.930657   15.0   \n",
       "15  5142246929    25.0   79.0   3.463360   0.288737   3.463360  104.0   \n",
       "16  5142224699    25.0    7.0   0.306880   3.258603   3.258603   32.0   \n",
       "17  5142847398    35.0   10.0   0.313143   3.193431   3.193431   45.0   \n",
       "18  5142273608    21.0    6.0   0.313143   3.193431   3.193431   27.0   \n",
       "19  5142147267    76.0   22.0   0.317263   3.151958   3.151958   98.0   \n",
       "20  5142224769     5.0   15.0   3.288000   0.304136   3.288000   20.0   \n",
       "21  5142242241    51.0   16.0   0.343843   2.908303   2.908303   67.0   \n",
       "22  5142260984   101.0  265.0   2.875644   0.347748   2.875644  366.0   \n",
       "23  5142113192    12.0    2.0   0.182667   5.474453   5.474453   14.0   \n",
       "24  5142191416     7.0   18.0   2.818286   0.354826   2.818286   25.0   \n",
       "25  5142194228     2.0   11.0   6.028000   0.165893   6.028000   13.0   \n",
       "26  5142308889     2.0   11.0   6.028000   0.165893   6.028000   13.0   \n",
       "27  5142212038     3.0   12.0   4.384000   0.228102   4.384000   15.0   \n",
       "28  5142195887     3.0   12.0   4.384000   0.228102   4.384000   15.0   \n",
       "29  5142225184    11.0   27.0   2.690182   0.371722   2.690182   38.0   \n",
       "30  5142257356    58.0  142.0   2.683310   0.372674   2.683310  200.0   \n",
       "31  5142216493     5.0   14.0   3.068800   0.325860   3.068800   19.0   \n",
       "32  5142239106    23.0    8.0   0.381217   2.623175   2.623175   31.0   \n",
       "33  5142144593    14.0    4.0   0.313143   3.193431   3.193431   18.0   \n",
       "34  5142126842    16.0   38.0   2.603000   0.384172   2.603000   54.0   \n",
       "35  5142117315    20.0    7.0   0.383600   2.606882   2.606882   27.0   \n",
       "36  5142218798     9.0   21.0   2.557333   0.391032   2.557333   30.0   \n",
       "37  5142180432    25.0   58.0   2.542720   0.393280   2.542720   83.0   \n",
       "38  5142264155    12.0   27.0   2.466000   0.405515   2.466000   39.0   \n",
       "39  5142294614    15.0    5.0   0.365333   2.737226   2.737226   20.0   \n",
       "\n",
       "             t  U_smoothed  \n",
       "0    17.000000   13.371199  \n",
       "1     4.333333    9.027976  \n",
       "2    44.666667    8.150852  \n",
       "3     7.666667    6.019548  \n",
       "4    -0.333333    5.533836  \n",
       "5     1.333333    4.834555  \n",
       "6     7.000000    4.558799  \n",
       "7     0.000000    4.062000  \n",
       "8    79.333333    4.038963  \n",
       "9     4.000000    3.784991  \n",
       "10    5.000000    3.631901  \n",
       "11    2.000000    3.534703  \n",
       "12   20.333333    3.531556  \n",
       "13    0.000000    3.465328  \n",
       "14    0.000000    3.465328  \n",
       "15   29.666667    3.463360  \n",
       "16    5.666667    3.250816  \n",
       "17   10.000000    3.193331  \n",
       "18    4.000000    3.153979  \n",
       "19   27.666667    3.151958  \n",
       "20    1.666667    2.924507  \n",
       "21   17.333333    2.908303  \n",
       "22  117.000000    2.875644  \n",
       "23   -0.333333    2.867770  \n",
       "24    3.333333    2.755655  \n",
       "25   -0.666667    2.705717  \n",
       "26   -0.666667    2.705717  \n",
       "27    0.000000    2.692000  \n",
       "28    0.000000    2.692000  \n",
       "29    7.666667    2.689391  \n",
       "30   61.666667    2.683310  \n",
       "31    1.333333    2.637231  \n",
       "32    5.333333    2.615376  \n",
       "33    1.000000    2.603526  \n",
       "34   13.000000    2.602996  \n",
       "35    4.000000    2.577980  \n",
       "36    5.000000    2.546910  \n",
       "37   22.666667    2.542720  \n",
       "38    8.000000    2.465508  \n",
       "39    1.666667    2.461235  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top40_card_bf = card_bf.sort_values(['U_smoothed'], ascending = False).head(40).reset_index(drop = True)\n",
    "top40_card_bf.head(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Merchnum</th>\n",
       "      <th>n_high</th>\n",
       "      <th>n_low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>003100006NOT6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>004740006ABC6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>005590006PNB6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>014430619  14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>014938913  51</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Merchnum  n_high  n_low\n",
       "0  003100006NOT6     1.0    NaN\n",
       "1  004740006ABC6     NaN    1.0\n",
       "2  005590006PNB6     1.0    NaN\n",
       "3  014430619  14     NaN    1.0\n",
       "4  014938913  51     1.0    NaN"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculating n_low and n_high\n",
    "merch_bf =  bf1.groupby(['Merchnum','bin']).agg({'bin': ['count']}).reset_index()\n",
    "merch_bf.columns=['Merchnum', 'bin', 'count']\n",
    "merch_bf =  merch_bf.pivot_table(index='Merchnum',columns='bin',values='count',aggfunc='sum').reset_index()\n",
    "merch_bf.columns=['Merchnum', 'n_high', 'n_low']\n",
    "merch_bf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Merchnum</th>\n",
       "      <th>n_high</th>\n",
       "      <th>n_low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>003100006NOT6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>004740006ABC6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>005590006PNB6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>014430619  14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>014938913  51</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13586</th>\n",
       "      <td>DU49038320006</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13587</th>\n",
       "      <td>JCPENNE9 CO</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13588</th>\n",
       "      <td>PENNE9 CO #05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13589</th>\n",
       "      <td>PENNE9 CO #68</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13590</th>\n",
       "      <td>unknown</td>\n",
       "      <td>417.0</td>\n",
       "      <td>274.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13591 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Merchnum  n_high  n_low\n",
       "0      003100006NOT6     1.0    1.0\n",
       "1      004740006ABC6     1.0    1.0\n",
       "2      005590006PNB6     1.0    1.0\n",
       "3      014430619  14     1.0    1.0\n",
       "4      014938913  51     1.0    1.0\n",
       "...              ...     ...    ...\n",
       "13586  DU49038320006     1.0    1.0\n",
       "13587    JCPENNE9 CO     2.0    1.0\n",
       "13588  PENNE9 CO #05     1.0    1.0\n",
       "13589  PENNE9 CO #68     1.0    1.0\n",
       "13590        unknown   417.0  274.0\n",
       "\n",
       "[13591 rows x 3 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if either n_low or n_high is zero, set it to 1\n",
    "merch_bf = merch_bf.fillna(1)\n",
    "merch_bf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calclating R, 1/R, U, n, t U_smoothed\n",
    "merch_bf['R'] = (1.096 * merch_bf['n_low']/merch_bf['n_high'])\n",
    "merch_bf['1/R'] = (1/merch_bf['R'])\n",
    "merch_bf['U'] = list(map(lambda x, y : max(x,y),merch_bf['R'],merch_bf['1/R']))\n",
    "merch_bf['n'] = merch_bf['n_high'] + merch_bf['n_low']\n",
    "merch_bf['t'] = ((merch_bf['n']-n_mid)/c)\n",
    "merch_bf['U_smoothed']= list(map(lambda x, y : (1 + (x-1)/(1+exp(-y))),merch_bf['U'],merch_bf['t']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Merchnum</th>\n",
       "      <th>n_high</th>\n",
       "      <th>n_low</th>\n",
       "      <th>R</th>\n",
       "      <th>1/R</th>\n",
       "      <th>U</th>\n",
       "      <th>n</th>\n",
       "      <th>t</th>\n",
       "      <th>U_smoothed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>991808369338</td>\n",
       "      <td>181.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006055</td>\n",
       "      <td>165.145985</td>\n",
       "      <td>165.145985</td>\n",
       "      <td>182.0</td>\n",
       "      <td>55.666667</td>\n",
       "      <td>165.145985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8078200641472</td>\n",
       "      <td>1.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>64.664000</td>\n",
       "      <td>0.015465</td>\n",
       "      <td>64.664000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>64.663981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>308904389335</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.020679</td>\n",
       "      <td>48.357664</td>\n",
       "      <td>48.357664</td>\n",
       "      <td>54.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>48.357557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3523000628102</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>37.264000</td>\n",
       "      <td>0.026836</td>\n",
       "      <td>37.264000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>37.217908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>808998385332</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.030444</td>\n",
       "      <td>32.846715</td>\n",
       "      <td>32.846715</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7.333333</td>\n",
       "      <td>32.825921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>55158027</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.592000</td>\n",
       "      <td>0.033793</td>\n",
       "      <td>29.592000</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>29.221627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8916500620062</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.035355</td>\n",
       "      <td>28.284672</td>\n",
       "      <td>28.284672</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>28.190609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3910694900001</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>27.400000</td>\n",
       "      <td>0.036496</td>\n",
       "      <td>27.400000</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>26.741995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>881145544</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.304000</td>\n",
       "      <td>0.038017</td>\n",
       "      <td>26.304000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>25.432399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8889817332</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.304000</td>\n",
       "      <td>0.038017</td>\n",
       "      <td>26.304000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>25.432399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5600900060992</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.040593</td>\n",
       "      <td>24.635036</td>\n",
       "      <td>24.635036</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>24.328875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6844000608436</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>25.208000</td>\n",
       "      <td>0.039670</td>\n",
       "      <td>25.208000</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>24.059914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>92891948003</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.045667</td>\n",
       "      <td>21.897810</td>\n",
       "      <td>21.897810</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>21.177981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5803301245621</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.016000</td>\n",
       "      <td>0.043448</td>\n",
       "      <td>23.016000</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>21.069793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3433000017263</td>\n",
       "      <td>3.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>19.362667</td>\n",
       "      <td>0.051646</td>\n",
       "      <td>19.362667</td>\n",
       "      <td>56.0</td>\n",
       "      <td>13.666667</td>\n",
       "      <td>19.362645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>467615916337</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.049818</td>\n",
       "      <td>20.072993</td>\n",
       "      <td>20.072993</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>18.833836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>817004638227</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.824000</td>\n",
       "      <td>0.048022</td>\n",
       "      <td>20.824000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>17.674579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2376700063599</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>16.440000</td>\n",
       "      <td>0.060827</td>\n",
       "      <td>16.440000</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>16.386771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>993620816222</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.057684</td>\n",
       "      <td>17.335766</td>\n",
       "      <td>17.335766</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>14.740518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>993620810220</td>\n",
       "      <td>76.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.072105</td>\n",
       "      <td>13.868613</td>\n",
       "      <td>13.868613</td>\n",
       "      <td>81.0</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>13.868613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>465614140337</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.060889</td>\n",
       "      <td>16.423358</td>\n",
       "      <td>16.423358</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>13.205914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>8999000079657</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.060889</td>\n",
       "      <td>16.423358</td>\n",
       "      <td>16.423358</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>13.205914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8317600900099</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>13.152000</td>\n",
       "      <td>0.076034</td>\n",
       "      <td>13.152000</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>12.849118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5000006000095</td>\n",
       "      <td>23.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>12.056000</td>\n",
       "      <td>0.082946</td>\n",
       "      <td>12.056000</td>\n",
       "      <td>276.0</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>12.056000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>9420966064460</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.064471</td>\n",
       "      <td>15.510949</td>\n",
       "      <td>15.510949</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.608354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5186264200136</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.064471</td>\n",
       "      <td>15.510949</td>\n",
       "      <td>15.510949</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.608354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>600000201284</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.087680</td>\n",
       "      <td>11.405109</td>\n",
       "      <td>11.405109</td>\n",
       "      <td>54.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>11.405086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5600000060302</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.068500</td>\n",
       "      <td>14.598540</td>\n",
       "      <td>14.598540</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>9.985322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>7080606900600</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.068500</td>\n",
       "      <td>14.598540</td>\n",
       "      <td>14.598540</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>9.985322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>6070095870009</td>\n",
       "      <td>3.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>9.498667</td>\n",
       "      <td>0.105278</td>\n",
       "      <td>9.498667</td>\n",
       "      <td>29.0</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>9.419493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>999960264339</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.117429</td>\n",
       "      <td>8.515815</td>\n",
       "      <td>8.515815</td>\n",
       "      <td>31.0</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>8.479703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>555400670006</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.073067</td>\n",
       "      <td>13.686131</td>\n",
       "      <td>13.686131</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>8.390562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>881894855</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.073067</td>\n",
       "      <td>13.686131</td>\n",
       "      <td>13.686131</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>8.390562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1960400470068</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.402667</td>\n",
       "      <td>0.119010</td>\n",
       "      <td>8.402667</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>8.218159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>993620559229</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.127442</td>\n",
       "      <td>7.846715</td>\n",
       "      <td>7.846715</td>\n",
       "      <td>48.0</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>7.846601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2586000448258</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.078286</td>\n",
       "      <td>12.773723</td>\n",
       "      <td>12.773723</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.886861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>8100544800098</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.078286</td>\n",
       "      <td>12.773723</td>\n",
       "      <td>12.773723</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.886861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>604901367333</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.078286</td>\n",
       "      <td>12.773723</td>\n",
       "      <td>12.773723</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.886861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>6000330043193</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.248000</td>\n",
       "      <td>0.070185</td>\n",
       "      <td>14.248000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>6.530110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>6005300190068</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.248000</td>\n",
       "      <td>0.070185</td>\n",
       "      <td>14.248000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>6.530110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Merchnum  n_high  n_low          R         1/R           U      n  \\\n",
       "0    991808369338   181.0    1.0   0.006055  165.145985  165.145985  182.0   \n",
       "1   8078200641472     1.0   59.0  64.664000    0.015465   64.664000   60.0   \n",
       "2    308904389335    53.0    1.0   0.020679   48.357664   48.357664   54.0   \n",
       "3   3523000628102     1.0   34.0  37.264000    0.026836   37.264000   35.0   \n",
       "4    808998385332    36.0    1.0   0.030444   32.846715   32.846715   37.0   \n",
       "5        55158027     1.0   27.0  29.592000    0.033793   29.592000   28.0   \n",
       "6   8916500620062    31.0    1.0   0.035355   28.284672   28.284672   32.0   \n",
       "7   3910694900001     1.0   25.0  27.400000    0.036496   27.400000   26.0   \n",
       "8       881145544     1.0   24.0  26.304000    0.038017   26.304000   25.0   \n",
       "9      8889817332     1.0   24.0  26.304000    0.038017   26.304000   25.0   \n",
       "10  5600900060992    27.0    1.0   0.040593   24.635036   24.635036   28.0   \n",
       "11  6844000608436     1.0   23.0  25.208000    0.039670   25.208000   24.0   \n",
       "12    92891948003    24.0    1.0   0.045667   21.897810   21.897810   25.0   \n",
       "13  5803301245621     1.0   21.0  23.016000    0.043448   23.016000   22.0   \n",
       "14  3433000017263     3.0   53.0  19.362667    0.051646   19.362667   56.0   \n",
       "15   467615916337    22.0    1.0   0.049818   20.072993   20.072993   23.0   \n",
       "16   817004638227     1.0   19.0  20.824000    0.048022   20.824000   20.0   \n",
       "17  2376700063599     2.0   30.0  16.440000    0.060827   16.440000   32.0   \n",
       "18   993620816222    19.0    1.0   0.057684   17.335766   17.335766   20.0   \n",
       "19   993620810220    76.0    5.0   0.072105   13.868613   13.868613   81.0   \n",
       "20   465614140337    18.0    1.0   0.060889   16.423358   16.423358   19.0   \n",
       "21  8999000079657    18.0    1.0   0.060889   16.423358   16.423358   19.0   \n",
       "22  8317600900099     2.0   24.0  13.152000    0.076034   13.152000   26.0   \n",
       "23  5000006000095    23.0  253.0  12.056000    0.082946   12.056000  276.0   \n",
       "24  9420966064460    17.0    1.0   0.064471   15.510949   15.510949   18.0   \n",
       "25  5186264200136    17.0    1.0   0.064471   15.510949   15.510949   18.0   \n",
       "26   600000201284    50.0    4.0   0.087680   11.405109   11.405109   54.0   \n",
       "27  5600000060302    16.0    1.0   0.068500   14.598540   14.598540   17.0   \n",
       "28  7080606900600    16.0    1.0   0.068500   14.598540   14.598540   17.0   \n",
       "29  6070095870009     3.0   26.0   9.498667    0.105278    9.498667   29.0   \n",
       "30   999960264339    28.0    3.0   0.117429    8.515815    8.515815   31.0   \n",
       "31   555400670006    15.0    1.0   0.073067   13.686131   13.686131   16.0   \n",
       "32      881894855    15.0    1.0   0.073067   13.686131   13.686131   16.0   \n",
       "33  1960400470068     3.0   23.0   8.402667    0.119010    8.402667   26.0   \n",
       "34   993620559229    43.0    5.0   0.127442    7.846715    7.846715   48.0   \n",
       "35  2586000448258    14.0    1.0   0.078286   12.773723   12.773723   15.0   \n",
       "36  8100544800098    14.0    1.0   0.078286   12.773723   12.773723   15.0   \n",
       "37   604901367333    14.0    1.0   0.078286   12.773723   12.773723   15.0   \n",
       "38  6000330043193     1.0   13.0  14.248000    0.070185   14.248000   14.0   \n",
       "39  6005300190068     1.0   13.0  14.248000    0.070185   14.248000   14.0   \n",
       "\n",
       "            t  U_smoothed  \n",
       "0   55.666667  165.145985  \n",
       "1   15.000000   64.663981  \n",
       "2   13.000000   48.357557  \n",
       "3    6.666667   37.217908  \n",
       "4    7.333333   32.825921  \n",
       "5    4.333333   29.221627  \n",
       "6    5.666667   28.190609  \n",
       "7    3.666667   26.741995  \n",
       "8    3.333333   25.432399  \n",
       "9    3.333333   25.432399  \n",
       "10   4.333333   24.328875  \n",
       "11   3.000000   24.059914  \n",
       "12   3.333333   21.177981  \n",
       "13   2.333333   21.069793  \n",
       "14  13.666667   19.362645  \n",
       "15   2.666667   18.833836  \n",
       "16   1.666667   17.674579  \n",
       "17   5.666667   16.386771  \n",
       "18   1.666667   14.740518  \n",
       "19  22.000000   13.868613  \n",
       "20   1.333333   13.205914  \n",
       "21   1.333333   13.205914  \n",
       "22   3.666667   12.849118  \n",
       "23  87.000000   12.056000  \n",
       "24   1.000000   11.608354  \n",
       "25   1.000000   11.608354  \n",
       "26  13.000000   11.405086  \n",
       "27   0.666667    9.985322  \n",
       "28   0.666667    9.985322  \n",
       "29   4.666667    9.419493  \n",
       "30   5.333333    8.479703  \n",
       "31   0.333333    8.390562  \n",
       "32   0.333333    8.390562  \n",
       "33   3.666667    8.218159  \n",
       "34  11.000000    7.846601  \n",
       "35   0.000000    6.886861  \n",
       "36   0.000000    6.886861  \n",
       "37   0.000000    6.886861  \n",
       "38  -0.333333    6.530110  \n",
       "39  -0.333333    6.530110  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top40_merch_bf = merch_bf.sort_values(['U_smoothed'], ascending = False).head(40).reset_index(drop = True)\n",
    "top40_merch_bf.head(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calclating R, 1/R, U, n, t U_smoothed\n",
    "c=3\n",
    "n_mid=15\n",
    "zip_bf['R'] = (1.096 * zip_bf['n_low']/zip_bf['n_high'])\n",
    "zip_bf['1/R'] = (1/zip_bf['R'])\n",
    "zip_bf['U'] = list(map(lambda x, y : max(x,y),zip_bf['R'],zip_bf['1/R']))\n",
    "zip_bf['n'] = zip_bf['n_high'] + zip_bf['n_low']\n",
    "zip_bf['t'] = ((zip_bf['n']-n_mid)/c)\n",
    "zip_bf['U_smoothed']= list(map(lambda x, y : (1 + (x-1)/(1+exp(-y))),zip_bf['U'],zip_bf['t']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Merch zip</th>\n",
       "      <th>n_high</th>\n",
       "      <th>n_low</th>\n",
       "      <th>R</th>\n",
       "      <th>1/R</th>\n",
       "      <th>U</th>\n",
       "      <th>n</th>\n",
       "      <th>t</th>\n",
       "      <th>U_smoothed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55435.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30.688000</td>\n",
       "      <td>0.032586</td>\n",
       "      <td>30.688000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>30.411428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7054.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>13.700000</td>\n",
       "      <td>0.072993</td>\n",
       "      <td>13.700000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>13.471575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77023.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.060889</td>\n",
       "      <td>16.423358</td>\n",
       "      <td>16.423358</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>13.205914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>91372.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>13.152000</td>\n",
       "      <td>0.076034</td>\n",
       "      <td>13.152000</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>12.849118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75230.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.084308</td>\n",
       "      <td>11.861314</td>\n",
       "      <td>11.861314</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>11.720619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16258.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.064471</td>\n",
       "      <td>15.510949</td>\n",
       "      <td>15.510949</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.608354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2881.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.064471</td>\n",
       "      <td>15.510949</td>\n",
       "      <td>15.510949</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.608354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7645.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.064471</td>\n",
       "      <td>15.510949</td>\n",
       "      <td>15.510949</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.608354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>22091.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>11.508000</td>\n",
       "      <td>0.086896</td>\n",
       "      <td>11.508000</td>\n",
       "      <td>46.0</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>11.507658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20015.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>10.960000</td>\n",
       "      <td>0.091241</td>\n",
       "      <td>10.960000</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.935373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>22033.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.096706</td>\n",
       "      <td>10.340633</td>\n",
       "      <td>10.340633</td>\n",
       "      <td>74.0</td>\n",
       "      <td>19.666667</td>\n",
       "      <td>10.340633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1020.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>9.133333</td>\n",
       "      <td>0.109489</td>\n",
       "      <td>9.133333</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>9.027976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>39466.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.073067</td>\n",
       "      <td>13.686131</td>\n",
       "      <td>13.686131</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>8.390562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>92649.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.402667</td>\n",
       "      <td>0.119010</td>\n",
       "      <td>8.402667</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>8.218159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>64133.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7.306667</td>\n",
       "      <td>0.136861</td>\n",
       "      <td>7.306667</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>6.896928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>79764.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.248000</td>\n",
       "      <td>0.070185</td>\n",
       "      <td>14.248000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>6.530110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>59860.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.248000</td>\n",
       "      <td>0.070185</td>\n",
       "      <td>14.248000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>6.530110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>12108</td>\n",
       "      <td>44.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>6.526182</td>\n",
       "      <td>0.153229</td>\n",
       "      <td>6.526182</td>\n",
       "      <td>306.0</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>6.526182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>21140.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6.356800</td>\n",
       "      <td>0.157312</td>\n",
       "      <td>6.356800</td>\n",
       "      <td>34.0</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>6.347303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>48184.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.160390</td>\n",
       "      <td>6.234793</td>\n",
       "      <td>6.234793</td>\n",
       "      <td>47.0</td>\n",
       "      <td>10.666667</td>\n",
       "      <td>6.234671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>46229.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.084308</td>\n",
       "      <td>11.861314</td>\n",
       "      <td>11.861314</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>5.533836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>29634.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.152000</td>\n",
       "      <td>0.076034</td>\n",
       "      <td>13.152000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-0.666667</td>\n",
       "      <td>5.122489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>14625.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.152000</td>\n",
       "      <td>0.076034</td>\n",
       "      <td>13.152000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-0.666667</td>\n",
       "      <td>5.122489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>95616.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.146133</td>\n",
       "      <td>6.843066</td>\n",
       "      <td>6.843066</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>4.860843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>41001.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4.566667</td>\n",
       "      <td>0.218978</td>\n",
       "      <td>4.566667</td>\n",
       "      <td>31.0</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>4.549530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>22310.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.228333</td>\n",
       "      <td>4.379562</td>\n",
       "      <td>4.379562</td>\n",
       "      <td>29.0</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>4.348078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>15222.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.231846</td>\n",
       "      <td>4.313205</td>\n",
       "      <td>4.313205</td>\n",
       "      <td>126.0</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>4.313205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>75050.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.156571</td>\n",
       "      <td>6.386861</td>\n",
       "      <td>6.386861</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>4.138225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>20057.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.156571</td>\n",
       "      <td>6.386861</td>\n",
       "      <td>6.386861</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>4.138225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>95030.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.124000</td>\n",
       "      <td>0.140371</td>\n",
       "      <td>7.124000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.062000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>18041.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.384000</td>\n",
       "      <td>0.228102</td>\n",
       "      <td>4.384000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>3.846387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>11701.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.384000</td>\n",
       "      <td>0.228102</td>\n",
       "      <td>4.384000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>3.846387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>31210.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.243556</td>\n",
       "      <td>4.105839</td>\n",
       "      <td>4.105839</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>3.831284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>6611.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.243556</td>\n",
       "      <td>4.105839</td>\n",
       "      <td>4.105839</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>3.831284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>66212.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.243556</td>\n",
       "      <td>4.105839</td>\n",
       "      <td>4.105839</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>3.831284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>20250.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>3.786182</td>\n",
       "      <td>0.264118</td>\n",
       "      <td>3.786182</td>\n",
       "      <td>49.0</td>\n",
       "      <td>11.333333</td>\n",
       "      <td>3.786148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>10305.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.265697</td>\n",
       "      <td>3.763686</td>\n",
       "      <td>3.763686</td>\n",
       "      <td>41.0</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>3.763210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>19428.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.114667</td>\n",
       "      <td>0.195516</td>\n",
       "      <td>5.114667</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>3.718792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>22151.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.653333</td>\n",
       "      <td>0.273723</td>\n",
       "      <td>3.653333</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>3.587201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>46032.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.601143</td>\n",
       "      <td>0.277690</td>\n",
       "      <td>3.601143</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.583734</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Merch zip  n_high  n_low          R        1/R          U      n  \\\n",
       "0    55435.0     1.0   28.0  30.688000   0.032586  30.688000   29.0   \n",
       "1     7054.0     2.0   25.0  13.700000   0.072993  13.700000   27.0   \n",
       "2    77023.0    18.0    1.0   0.060889  16.423358  16.423358   19.0   \n",
       "3    91372.0     2.0   24.0  13.152000   0.076034  13.152000   26.0   \n",
       "4    75230.0    26.0    2.0   0.084308  11.861314  11.861314   28.0   \n",
       "5    16258.0    17.0    1.0   0.064471  15.510949  15.510949   18.0   \n",
       "6     2881.0    17.0    1.0   0.064471  15.510949  15.510949   18.0   \n",
       "7     7645.0    17.0    1.0   0.064471  15.510949  15.510949   18.0   \n",
       "8    22091.0     4.0   42.0  11.508000   0.086896  11.508000   46.0   \n",
       "9    20015.0     3.0   30.0  10.960000   0.091241  10.960000   33.0   \n",
       "10   22033.0    68.0    6.0   0.096706  10.340633  10.340633   74.0   \n",
       "11    1020.0     3.0   25.0   9.133333   0.109489   9.133333   28.0   \n",
       "12   39466.0    15.0    1.0   0.073067  13.686131  13.686131   16.0   \n",
       "13   92649.0     3.0   23.0   8.402667   0.119010   8.402667   26.0   \n",
       "14   64133.0     3.0   20.0   7.306667   0.136861   7.306667   23.0   \n",
       "15   79764.0     1.0   13.0  14.248000   0.070185  14.248000   14.0   \n",
       "16   59860.0     1.0   13.0  14.248000   0.070185  14.248000   14.0   \n",
       "17     12108    44.0  262.0   6.526182   0.153229   6.526182  306.0   \n",
       "18   21140.0     5.0   29.0   6.356800   0.157312   6.356800   34.0   \n",
       "19   48184.0    41.0    6.0   0.160390   6.234793   6.234793   47.0   \n",
       "20   46229.0    13.0    1.0   0.084308  11.861314  11.861314   14.0   \n",
       "21   29634.0     1.0   12.0  13.152000   0.076034  13.152000   13.0   \n",
       "22   14625.0     1.0   12.0  13.152000   0.076034  13.152000   13.0   \n",
       "23   95616.0    15.0    2.0   0.146133   6.843066   6.843066   17.0   \n",
       "24   41001.0     6.0   25.0   4.566667   0.218978   4.566667   31.0   \n",
       "25   22310.0    24.0    5.0   0.228333   4.379562   4.379562   29.0   \n",
       "26   15222.0   104.0   22.0   0.231846   4.313205   4.313205  126.0   \n",
       "27   75050.0    14.0    2.0   0.156571   6.386861   6.386861   16.0   \n",
       "28   20057.0    14.0    2.0   0.156571   6.386861   6.386861   16.0   \n",
       "29   95030.0     2.0   13.0   7.124000   0.140371   7.124000   15.0   \n",
       "30   18041.0     4.0   16.0   4.384000   0.228102   4.384000   20.0   \n",
       "31   11701.0     4.0   16.0   4.384000   0.228102   4.384000   20.0   \n",
       "32   31210.0    18.0    4.0   0.243556   4.105839   4.105839   22.0   \n",
       "33    6611.0    18.0    4.0   0.243556   4.105839   4.105839   22.0   \n",
       "34   66212.0    18.0    4.0   0.243556   4.105839   4.105839   22.0   \n",
       "35   20250.0    11.0   38.0   3.786182   0.264118   3.786182   49.0   \n",
       "36   10305.0    33.0    8.0   0.265697   3.763686   3.763686   41.0   \n",
       "37   19428.0     3.0   14.0   5.114667   0.195516   5.114667   17.0   \n",
       "38   22151.0     6.0   20.0   3.653333   0.273723   3.653333   26.0   \n",
       "39   46032.0     7.0   23.0   3.601143   0.277690   3.601143   30.0   \n",
       "\n",
       "            t  U_smoothed  \n",
       "0    4.666667   30.411428  \n",
       "1    4.000000   13.471575  \n",
       "2    1.333333   13.205914  \n",
       "3    3.666667   12.849118  \n",
       "4    4.333333   11.720619  \n",
       "5    1.000000   11.608354  \n",
       "6    1.000000   11.608354  \n",
       "7    1.000000   11.608354  \n",
       "8   10.333333   11.507658  \n",
       "9    6.000000   10.935373  \n",
       "10  19.666667   10.340633  \n",
       "11   4.333333    9.027976  \n",
       "12   0.333333    8.390562  \n",
       "13   3.666667    8.218159  \n",
       "14   2.666667    6.896928  \n",
       "15  -0.333333    6.530110  \n",
       "16  -0.333333    6.530110  \n",
       "17  97.000000    6.526182  \n",
       "18   6.333333    6.347303  \n",
       "19  10.666667    6.234671  \n",
       "20  -0.333333    5.533836  \n",
       "21  -0.666667    5.122489  \n",
       "22  -0.666667    5.122489  \n",
       "23   0.666667    4.860843  \n",
       "24   5.333333    4.549530  \n",
       "25   4.666667    4.348078  \n",
       "26  37.000000    4.313205  \n",
       "27   0.333333    4.138225  \n",
       "28   0.333333    4.138225  \n",
       "29   0.000000    4.062000  \n",
       "30   1.666667    3.846387  \n",
       "31   1.666667    3.846387  \n",
       "32   2.333333    3.831284  \n",
       "33   2.333333    3.831284  \n",
       "34   2.333333    3.831284  \n",
       "35  11.333333    3.786148  \n",
       "36   8.666667    3.763210  \n",
       "37   0.666667    3.718792  \n",
       "38   3.666667    3.587201  \n",
       "39   5.000000    3.583734  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top40_zip_bf = zip_bf.sort_values(['U_smoothed'], ascending = False).head(40).reset_index(drop = True)\n",
    "top40_zip_bf.head(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here are the tables for the Benford's law. They would be useful for a forensic analysis\n",
    "top40_card_bf.to_csv('Benford top cards.csv')\n",
    "top40_merch_bf.to_csv('Benford top merchs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "top40_zip_bf.to_csv('Benford top zips.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1640 entries, 0 to 1639\n",
      "Data columns (total 9 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Cardnum     1640 non-null   object \n",
      " 1   n_high      1640 non-null   float64\n",
      " 2   n_low       1640 non-null   float64\n",
      " 3   R           1640 non-null   float64\n",
      " 4   1/R         1640 non-null   float64\n",
      " 5   U           1640 non-null   float64\n",
      " 6   n           1640 non-null   float64\n",
      " 7   t           1640 non-null   float64\n",
      " 8   U_smoothed  1640 non-null   float64\n",
      "dtypes: float64(8), object(1)\n",
      "memory usage: 115.4+ KB\n"
     ]
    }
   ],
   "source": [
    "card_bf['Cardnum'] = card_bf['Cardnum'].apply(str)\n",
    "merch_bf['Merchnum'] = merch_bf['Merchnum'].apply(str)\n",
    "card_bf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4573 entries, 0 to 4572\n",
      "Data columns (total 9 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Merch zip   4573 non-null   object \n",
      " 1   n_high      4573 non-null   float64\n",
      " 2   n_low       4573 non-null   float64\n",
      " 3   R           4573 non-null   float64\n",
      " 4   1/R         4573 non-null   float64\n",
      " 5   U           4573 non-null   float64\n",
      " 6   n           4573 non-null   float64\n",
      " 7   t           4573 non-null   float64\n",
      " 8   U_smoothed  4573 non-null   float64\n",
      "dtypes: float64(8), object(1)\n",
      "memory usage: 321.7+ KB\n"
     ]
    }
   ],
   "source": [
    "zip_bf['Merch zip'] = zip_bf['Merch zip'].apply(str)\n",
    "zip_bf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "card_bf.set_index('Cardnum',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Cardnum</th>\n",
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       "  <tbody>\n",
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       "      <th>5142253356</th>\n",
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       "    <tr>\n",
       "      <th>5142299705</th>\n",
       "      <td>9.027976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5142197563</th>\n",
       "      <td>8.150852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5142194617</th>\n",
       "      <td>6.019548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5142288241</th>\n",
       "      <td>5.533836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5142239140</th>\n",
       "      <td>4.834555</td>\n",
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       "    <tr>\n",
       "      <th>5142144931</th>\n",
       "      <td>4.558799</td>\n",
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       "    <tr>\n",
       "      <th>5142192606</th>\n",
       "      <td>4.062000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5142204384</th>\n",
       "      <td>4.038963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5142284940</th>\n",
       "      <td>3.784991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            U_smoothed\n",
       "Cardnum               \n",
       "5142253356   13.371199\n",
       "5142299705    9.027976\n",
       "5142197563    8.150852\n",
       "5142194617    6.019548\n",
       "5142288241    5.533836\n",
       "5142239140    4.834555\n",
       "5142144931    4.558799\n",
       "5142192606    4.062000\n",
       "5142204384    4.038963\n",
       "5142284940    3.784991"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "card_Ustar = pd.DataFrame(card_bf['U_smoothed'])\n",
    "card_Ustar.sort_values(['U_smoothed'], ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "merch_bf.set_index('Merchnum',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>U_smoothed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Merchnum</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>991808369338</th>\n",
       "      <td>165.145985</td>\n",
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       "    <tr>\n",
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       "      <td>64.663981</td>\n",
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       "    <tr>\n",
       "      <th>308904389335</th>\n",
       "      <td>48.357557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3523000628102</th>\n",
       "      <td>37.217908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808998385332</th>\n",
       "      <td>32.825921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55158027</th>\n",
       "      <td>29.221627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8916500620062</th>\n",
       "      <td>28.190609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3910694900001</th>\n",
       "      <td>26.741995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881145544</th>\n",
       "      <td>25.432399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8889817332</th>\n",
       "      <td>25.432399</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               U_smoothed\n",
       "Merchnum                 \n",
       "991808369338   165.145985\n",
       "8078200641472   64.663981\n",
       "308904389335    48.357557\n",
       "3523000628102   37.217908\n",
       "808998385332    32.825921\n",
       "55158027        29.221627\n",
       "8916500620062   28.190609\n",
       "3910694900001   26.741995\n",
       "881145544       25.432399\n",
       "8889817332      25.432399"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merch_Ustar = pd.DataFrame(merch_bf['U_smoothed'])\n",
    "merch_Ustar.sort_values(['U_smoothed'], ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "zip_bf.set_index('Merch zip',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>U_smoothed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Merch zip</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55435.0</th>\n",
       "      <td>30.411428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7054.0</th>\n",
       "      <td>13.471575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77023.0</th>\n",
       "      <td>13.205914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91372.0</th>\n",
       "      <td>12.849118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75230.0</th>\n",
       "      <td>11.720619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16258.0</th>\n",
       "      <td>11.608354</td>\n",
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       "    <tr>\n",
       "      <th>2881.0</th>\n",
       "      <td>11.608354</td>\n",
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       "    <tr>\n",
       "      <th>7645.0</th>\n",
       "      <td>11.608354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22091.0</th>\n",
       "      <td>11.507658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20015.0</th>\n",
       "      <td>10.935373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           U_smoothed\n",
       "Merch zip            \n",
       "55435.0     30.411428\n",
       "7054.0      13.471575\n",
       "77023.0     13.205914\n",
       "91372.0     12.849118\n",
       "75230.0     11.720619\n",
       "16258.0     11.608354\n",
       "2881.0      11.608354\n",
       "7645.0      11.608354\n",
       "22091.0     11.507658\n",
       "20015.0     10.935373"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zip_Ustar = pd.DataFrame(zip_bf['U_smoothed'])\n",
    "zip_Ustar.sort_values(['U_smoothed'], ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "final = final.merge(card_Ustar, how = 'left',left_on='Cardnum',right_on=card_Ustar.index)\n",
    "final = final.rename(columns={'U_smoothed':'U*_cardnum'})\n",
    "final = final.merge(merch_Ustar, how = 'left',left_on='Merchnum',right_on=merch_Ustar.index)\n",
    "final = final.rename(columns={'U_smoothed':'U*_merchnum'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "final = final.merge(zip_Ustar, how = 'left',left_on='Merch zip',right_on=zip_Ustar.index)\n",
    "final = final.rename(columns={'U_smoothed':'U*_zip'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recnum</th>\n",
       "      <th>Cardnum</th>\n",
       "      <th>Date</th>\n",
       "      <th>Merchnum</th>\n",
       "      <th>Merch description</th>\n",
       "      <th>Merch state</th>\n",
       "      <th>Merch zip</th>\n",
       "      <th>Transtype</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Fraud</th>\n",
       "      <th>...</th>\n",
       "      <th>Merchnum_desc</th>\n",
       "      <th>Merchnum_dow</th>\n",
       "      <th>Merchdesc_dow</th>\n",
       "      <th>Card_Merchnum_desc</th>\n",
       "      <th>Card_Merchnum_Zip</th>\n",
       "      <th>Card_Merchdesc_Zip</th>\n",
       "      <th>Merchnum_desc_State</th>\n",
       "      <th>U*_cardnum</th>\n",
       "      <th>U*_merchnum</th>\n",
       "      <th>U*_zip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5142190439</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEXSHP12/23/09AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5509006296254FEDEXSHP12/23/09AB#</td>\n",
       "      <td>5509006296254Friday</td>\n",
       "      <td>FEDEXSHP12/23/09AB#Friday</td>\n",
       "      <td>51421904395509006296254FEDEXSHP12/23/09AB#</td>\n",
       "      <td>5142190439550900629625438118.0</td>\n",
       "      <td>5142190439FEDEXSHP12/23/09AB#38118.0</td>\n",
       "      <td>5509006296254FEDEXSHP12/23/09AB#TN</td>\n",
       "      <td>2.178008</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.944592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5142183973</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>61003026333</td>\n",
       "      <td>SERVICEMERCHANDISE#81</td>\n",
       "      <td>MA</td>\n",
       "      <td>1803.0</td>\n",
       "      <td>P</td>\n",
       "      <td>31.42</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>61003026333SERVICEMERCHANDISE#81</td>\n",
       "      <td>61003026333Friday</td>\n",
       "      <td>SERVICEMERCHANDISE#81Friday</td>\n",
       "      <td>514218397361003026333SERVICEMERCHANDISE#81</td>\n",
       "      <td>5142183973610030263331803.0</td>\n",
       "      <td>5142183973SERVICEMERCHANDISE#811803.0</td>\n",
       "      <td>61003026333SERVICEMERCHANDISE#81MA</td>\n",
       "      <td>1.604857</td>\n",
       "      <td>1.001244</td>\n",
       "      <td>1.155442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5142131721</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>4503082993600</td>\n",
       "      <td>OFFICEDEPOT#191</td>\n",
       "      <td>MD</td>\n",
       "      <td>20706.0</td>\n",
       "      <td>P</td>\n",
       "      <td>178.49</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4503082993600OFFICEDEPOT#191</td>\n",
       "      <td>4503082993600Friday</td>\n",
       "      <td>OFFICEDEPOT#191Friday</td>\n",
       "      <td>51421317214503082993600OFFICEDEPOT#191</td>\n",
       "      <td>5142131721450308299360020706.0</td>\n",
       "      <td>5142131721OFFICEDEPOT#19120706.0</td>\n",
       "      <td>4503082993600OFFICEDEPOT#191MD</td>\n",
       "      <td>2.368143</td>\n",
       "      <td>1.025818</td>\n",
       "      <td>1.009474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5142148452</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEXSHP12/28/09AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5509006296254FEDEXSHP12/28/09AB#</td>\n",
       "      <td>5509006296254Friday</td>\n",
       "      <td>FEDEXSHP12/28/09AB#Friday</td>\n",
       "      <td>51421484525509006296254FEDEXSHP12/28/09AB#</td>\n",
       "      <td>5142148452550900629625438118.0</td>\n",
       "      <td>5142148452FEDEXSHP12/28/09AB#38118.0</td>\n",
       "      <td>5509006296254FEDEXSHP12/28/09AB#TN</td>\n",
       "      <td>1.044105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.944592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5142190439</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEXSHP12/23/09AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5509006296254FEDEXSHP12/23/09AB#</td>\n",
       "      <td>5509006296254Friday</td>\n",
       "      <td>FEDEXSHP12/23/09AB#Friday</td>\n",
       "      <td>51421904395509006296254FEDEXSHP12/23/09AB#</td>\n",
       "      <td>5142190439550900629625438118.0</td>\n",
       "      <td>5142190439FEDEXSHP12/23/09AB#38118.0</td>\n",
       "      <td>5509006296254FEDEXSHP12/23/09AB#TN</td>\n",
       "      <td>2.178008</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.944592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96392</th>\n",
       "      <td>96749</td>\n",
       "      <td>5142276053</td>\n",
       "      <td>2010-12-31</td>\n",
       "      <td>3500000006160</td>\n",
       "      <td>BESTBUY00001610</td>\n",
       "      <td>KY</td>\n",
       "      <td>41042.0</td>\n",
       "      <td>P</td>\n",
       "      <td>84.79</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3500000006160BESTBUY00001610</td>\n",
       "      <td>3500000006160Friday</td>\n",
       "      <td>BESTBUY00001610Friday</td>\n",
       "      <td>51422760533500000006160BESTBUY00001610</td>\n",
       "      <td>5142276053350000000616041042.0</td>\n",
       "      <td>5142276053BESTBUY0000161041042.0</td>\n",
       "      <td>3500000006160BESTBUY00001610KY</td>\n",
       "      <td>1.002393</td>\n",
       "      <td>1.001244</td>\n",
       "      <td>1.586823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96393</th>\n",
       "      <td>96750</td>\n",
       "      <td>5142225701</td>\n",
       "      <td>2010-12-31</td>\n",
       "      <td>8090710030950</td>\n",
       "      <td>MARKUSOFFICESUPPLIES</td>\n",
       "      <td>OH</td>\n",
       "      <td>45248.0</td>\n",
       "      <td>P</td>\n",
       "      <td>118.75</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8090710030950MARKUSOFFICESUPPLIES</td>\n",
       "      <td>8090710030950Friday</td>\n",
       "      <td>MARKUSOFFICESUPPLIESFriday</td>\n",
       "      <td>51422257018090710030950MARKUSOFFICESUPPLIES</td>\n",
       "      <td>5142225701809071003095045248.0</td>\n",
       "      <td>5142225701MARKUSOFFICESUPPLIES45248.0</td>\n",
       "      <td>8090710030950MARKUSOFFICESUPPLIESOH</td>\n",
       "      <td>1.137948</td>\n",
       "      <td>1.288057</td>\n",
       "      <td>1.488667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96394</th>\n",
       "      <td>96751</td>\n",
       "      <td>5142226486</td>\n",
       "      <td>2010-12-31</td>\n",
       "      <td>4503057341100</td>\n",
       "      <td>TECHPAC,INC</td>\n",
       "      <td>OH</td>\n",
       "      <td>45150.0</td>\n",
       "      <td>P</td>\n",
       "      <td>363.56</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4503057341100TECHPAC,INC</td>\n",
       "      <td>4503057341100Friday</td>\n",
       "      <td>TECHPAC,INCFriday</td>\n",
       "      <td>51422264864503057341100TECHPAC,INC</td>\n",
       "      <td>5142226486450305734110045150.0</td>\n",
       "      <td>5142226486TECHPAC,INC45150.0</td>\n",
       "      <td>4503057341100TECHPAC,INCOH</td>\n",
       "      <td>1.201338</td>\n",
       "      <td>1.106055</td>\n",
       "      <td>1.106055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96395</th>\n",
       "      <td>96752</td>\n",
       "      <td>5142244619</td>\n",
       "      <td>2010-12-31</td>\n",
       "      <td>8834000695412</td>\n",
       "      <td>BUY.COM</td>\n",
       "      <td>CA</td>\n",
       "      <td>92656.0</td>\n",
       "      <td>P</td>\n",
       "      <td>2202.03</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8834000695412BUY.COM</td>\n",
       "      <td>8834000695412Friday</td>\n",
       "      <td>BUY.COMFriday</td>\n",
       "      <td>51422446198834000695412BUY.COM</td>\n",
       "      <td>5142244619883400069541292656.0</td>\n",
       "      <td>5142244619BUY.COM92656.0</td>\n",
       "      <td>8834000695412BUY.COMCA</td>\n",
       "      <td>1.499767</td>\n",
       "      <td>1.029441</td>\n",
       "      <td>1.041781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96396</th>\n",
       "      <td>96753</td>\n",
       "      <td>5142243247</td>\n",
       "      <td>2010-12-31</td>\n",
       "      <td>9108347680006</td>\n",
       "      <td>STAPLESNATIONAL#471</td>\n",
       "      <td>NJ</td>\n",
       "      <td>7606.0</td>\n",
       "      <td>P</td>\n",
       "      <td>554.64</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9108347680006STAPLESNATIONAL#471</td>\n",
       "      <td>9108347680006Friday</td>\n",
       "      <td>STAPLESNATIONAL#471Friday</td>\n",
       "      <td>51422432479108347680006STAPLESNATIONAL#471</td>\n",
       "      <td>514224324791083476800067606.0</td>\n",
       "      <td>5142243247STAPLESNATIONAL#4717606.0</td>\n",
       "      <td>9108347680006STAPLESNATIONAL#471NJ</td>\n",
       "      <td>1.233441</td>\n",
       "      <td>1.113448</td>\n",
       "      <td>1.039926</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96397 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Recnum     Cardnum       Date       Merchnum      Merch description  \\\n",
       "0           1  5142190439 2010-01-01  5509006296254    FEDEXSHP12/23/09AB#   \n",
       "1           2  5142183973 2010-01-01    61003026333  SERVICEMERCHANDISE#81   \n",
       "2           3  5142131721 2010-01-01  4503082993600        OFFICEDEPOT#191   \n",
       "3           4  5142148452 2010-01-01  5509006296254    FEDEXSHP12/28/09AB#   \n",
       "4           5  5142190439 2010-01-01  5509006296254    FEDEXSHP12/23/09AB#   \n",
       "...       ...         ...        ...            ...                    ...   \n",
       "96392   96749  5142276053 2010-12-31  3500000006160        BESTBUY00001610   \n",
       "96393   96750  5142225701 2010-12-31  8090710030950   MARKUSOFFICESUPPLIES   \n",
       "96394   96751  5142226486 2010-12-31  4503057341100            TECHPAC,INC   \n",
       "96395   96752  5142244619 2010-12-31  8834000695412                BUY.COM   \n",
       "96396   96753  5142243247 2010-12-31  9108347680006    STAPLESNATIONAL#471   \n",
       "\n",
       "      Merch state Merch zip Transtype   Amount  Fraud  ...  \\\n",
       "0              TN   38118.0         P     3.62      0  ...   \n",
       "1              MA    1803.0         P    31.42      0  ...   \n",
       "2              MD   20706.0         P   178.49      0  ...   \n",
       "3              TN   38118.0         P     3.62      0  ...   \n",
       "4              TN   38118.0         P     3.62      0  ...   \n",
       "...           ...       ...       ...      ...    ...  ...   \n",
       "96392          KY   41042.0         P    84.79      0  ...   \n",
       "96393          OH   45248.0         P   118.75      0  ...   \n",
       "96394          OH   45150.0         P   363.56      0  ...   \n",
       "96395          CA   92656.0         P  2202.03      0  ...   \n",
       "96396          NJ    7606.0         P   554.64      0  ...   \n",
       "\n",
       "                           Merchnum_desc         Merchnum_dow  \\\n",
       "0       5509006296254FEDEXSHP12/23/09AB#  5509006296254Friday   \n",
       "1       61003026333SERVICEMERCHANDISE#81    61003026333Friday   \n",
       "2           4503082993600OFFICEDEPOT#191  4503082993600Friday   \n",
       "3       5509006296254FEDEXSHP12/28/09AB#  5509006296254Friday   \n",
       "4       5509006296254FEDEXSHP12/23/09AB#  5509006296254Friday   \n",
       "...                                  ...                  ...   \n",
       "96392       3500000006160BESTBUY00001610  3500000006160Friday   \n",
       "96393  8090710030950MARKUSOFFICESUPPLIES  8090710030950Friday   \n",
       "96394           4503057341100TECHPAC,INC  4503057341100Friday   \n",
       "96395               8834000695412BUY.COM  8834000695412Friday   \n",
       "96396   9108347680006STAPLESNATIONAL#471  9108347680006Friday   \n",
       "\n",
       "                     Merchdesc_dow  \\\n",
       "0        FEDEXSHP12/23/09AB#Friday   \n",
       "1      SERVICEMERCHANDISE#81Friday   \n",
       "2            OFFICEDEPOT#191Friday   \n",
       "3        FEDEXSHP12/28/09AB#Friday   \n",
       "4        FEDEXSHP12/23/09AB#Friday   \n",
       "...                            ...   \n",
       "96392        BESTBUY00001610Friday   \n",
       "96393   MARKUSOFFICESUPPLIESFriday   \n",
       "96394            TECHPAC,INCFriday   \n",
       "96395                BUY.COMFriday   \n",
       "96396    STAPLESNATIONAL#471Friday   \n",
       "\n",
       "                                Card_Merchnum_desc  \\\n",
       "0       51421904395509006296254FEDEXSHP12/23/09AB#   \n",
       "1       514218397361003026333SERVICEMERCHANDISE#81   \n",
       "2           51421317214503082993600OFFICEDEPOT#191   \n",
       "3       51421484525509006296254FEDEXSHP12/28/09AB#   \n",
       "4       51421904395509006296254FEDEXSHP12/23/09AB#   \n",
       "...                                            ...   \n",
       "96392       51422760533500000006160BESTBUY00001610   \n",
       "96393  51422257018090710030950MARKUSOFFICESUPPLIES   \n",
       "96394           51422264864503057341100TECHPAC,INC   \n",
       "96395               51422446198834000695412BUY.COM   \n",
       "96396   51422432479108347680006STAPLESNATIONAL#471   \n",
       "\n",
       "                    Card_Merchnum_Zip                     Card_Merchdesc_Zip  \\\n",
       "0      5142190439550900629625438118.0   5142190439FEDEXSHP12/23/09AB#38118.0   \n",
       "1         5142183973610030263331803.0  5142183973SERVICEMERCHANDISE#811803.0   \n",
       "2      5142131721450308299360020706.0       5142131721OFFICEDEPOT#19120706.0   \n",
       "3      5142148452550900629625438118.0   5142148452FEDEXSHP12/28/09AB#38118.0   \n",
       "4      5142190439550900629625438118.0   5142190439FEDEXSHP12/23/09AB#38118.0   \n",
       "...                               ...                                    ...   \n",
       "96392  5142276053350000000616041042.0       5142276053BESTBUY0000161041042.0   \n",
       "96393  5142225701809071003095045248.0  5142225701MARKUSOFFICESUPPLIES45248.0   \n",
       "96394  5142226486450305734110045150.0           5142226486TECHPAC,INC45150.0   \n",
       "96395  5142244619883400069541292656.0               5142244619BUY.COM92656.0   \n",
       "96396   514224324791083476800067606.0    5142243247STAPLESNATIONAL#4717606.0   \n",
       "\n",
       "                       Merchnum_desc_State U*_cardnum U*_merchnum    U*_zip  \n",
       "0       5509006296254FEDEXSHP12/23/09AB#TN   2.178008         NaN  2.944592  \n",
       "1       61003026333SERVICEMERCHANDISE#81MA   1.604857    1.001244  1.155442  \n",
       "2           4503082993600OFFICEDEPOT#191MD   2.368143    1.025818  1.009474  \n",
       "3       5509006296254FEDEXSHP12/28/09AB#TN   1.044105         NaN  2.944592  \n",
       "4       5509006296254FEDEXSHP12/23/09AB#TN   2.178008         NaN  2.944592  \n",
       "...                                    ...        ...         ...       ...  \n",
       "96392       3500000006160BESTBUY00001610KY   1.002393    1.001244  1.586823  \n",
       "96393  8090710030950MARKUSOFFICESUPPLIESOH   1.137948    1.288057  1.488667  \n",
       "96394           4503057341100TECHPAC,INCOH   1.201338    1.106055  1.106055  \n",
       "96395               8834000695412BUY.COMCA   1.499767    1.029441  1.041781  \n",
       "96396   9108347680006STAPLESNATIONAL#471NJ   1.233441    1.113448  1.039926  \n",
       "\n",
       "[96397 rows x 35 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final['U*_cardnum'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11775"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final['U*_merchnum'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final['U*_zip'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "final['U*_cardnum'].fillna(1,inplace=True)\n",
    "final['U*_merchnum'].fillna(1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final['U*_cardnum'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final['U*_merchnum'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(96397, 35)\n",
      "(96397, 32)\n"
     ]
    }
   ],
   "source": [
    "print(final.shape)\n",
    "final.drop(columns=['U*_cardnum','U*_merchnum', 'U*_zip'],inplace=True)\n",
    "print(final.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recnum</th>\n",
       "      <th>Cardnum</th>\n",
       "      <th>Date</th>\n",
       "      <th>Merchnum</th>\n",
       "      <th>Merch description</th>\n",
       "      <th>Merch state</th>\n",
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       "      <th>Transtype</th>\n",
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       "      <th>card_zip3</th>\n",
       "      <th>Card_Merchdesc</th>\n",
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       "      <th>Card_Merchnum_Zip</th>\n",
       "      <th>Card_Merchdesc_Zip</th>\n",
       "      <th>Merchnum_desc_State</th>\n",
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       "      <td>5509006296254FEDEXSHP12/23/09AB#TN</td>\n",
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       "      <th>1</th>\n",
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       "      <td>2010-01-01</td>\n",
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       "      <td>MA</td>\n",
       "      <td>1803.0</td>\n",
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       "      <td>31.42</td>\n",
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       "      <td>5142183973180</td>\n",
       "      <td>5142183973SERVICEMERCHANDISE#81</td>\n",
       "      <td>5142183973Friday</td>\n",
       "      <td>61003026333SERVICEMERCHANDISE#81</td>\n",
       "      <td>61003026333Friday</td>\n",
       "      <td>SERVICEMERCHANDISE#81Friday</td>\n",
       "      <td>514218397361003026333SERVICEMERCHANDISE#81</td>\n",
       "      <td>5142183973610030263331803.0</td>\n",
       "      <td>5142183973SERVICEMERCHANDISE#811803.0</td>\n",
       "      <td>61003026333SERVICEMERCHANDISE#81MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5142131721</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>4503082993600</td>\n",
       "      <td>OFFICEDEPOT#191</td>\n",
       "      <td>MD</td>\n",
       "      <td>20706.0</td>\n",
       "      <td>P</td>\n",
       "      <td>178.49</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5142131721207</td>\n",
       "      <td>5142131721OFFICEDEPOT#191</td>\n",
       "      <td>5142131721Friday</td>\n",
       "      <td>4503082993600OFFICEDEPOT#191</td>\n",
       "      <td>4503082993600Friday</td>\n",
       "      <td>OFFICEDEPOT#191Friday</td>\n",
       "      <td>51421317214503082993600OFFICEDEPOT#191</td>\n",
       "      <td>5142131721450308299360020706.0</td>\n",
       "      <td>5142131721OFFICEDEPOT#19120706.0</td>\n",
       "      <td>4503082993600OFFICEDEPOT#191MD</td>\n",
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       "      <td>5142148452</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEXSHP12/28/09AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5142148452381</td>\n",
       "      <td>5142148452FEDEXSHP12/28/09AB#</td>\n",
       "      <td>5142148452Friday</td>\n",
       "      <td>5509006296254FEDEXSHP12/28/09AB#</td>\n",
       "      <td>5509006296254Friday</td>\n",
       "      <td>FEDEXSHP12/28/09AB#Friday</td>\n",
       "      <td>51421484525509006296254FEDEXSHP12/28/09AB#</td>\n",
       "      <td>5142148452550900629625438118.0</td>\n",
       "      <td>5142148452FEDEXSHP12/28/09AB#38118.0</td>\n",
       "      <td>5509006296254FEDEXSHP12/28/09AB#TN</td>\n",
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       "      <td>5142190439381</td>\n",
       "      <td>5142190439FEDEXSHP12/23/09AB#</td>\n",
       "      <td>5142190439Friday</td>\n",
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       "      <th>96392</th>\n",
       "      <td>96749</td>\n",
       "      <td>5142276053</td>\n",
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       "      <td>3500000006160</td>\n",
       "      <td>BESTBUY00001610</td>\n",
       "      <td>KY</td>\n",
       "      <td>41042.0</td>\n",
       "      <td>P</td>\n",
       "      <td>84.79</td>\n",
       "      <td>0</td>\n",
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       "      <td>5142276053410</td>\n",
       "      <td>5142276053BESTBUY00001610</td>\n",
       "      <td>5142276053Friday</td>\n",
       "      <td>3500000006160BESTBUY00001610</td>\n",
       "      <td>3500000006160Friday</td>\n",
       "      <td>BESTBUY00001610Friday</td>\n",
       "      <td>51422760533500000006160BESTBUY00001610</td>\n",
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       "      <td>5142276053BESTBUY0000161041042.0</td>\n",
       "      <td>3500000006160BESTBUY00001610KY</td>\n",
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       "      <th>96393</th>\n",
       "      <td>96750</td>\n",
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       "      <td>8090710030950</td>\n",
       "      <td>MARKUSOFFICESUPPLIES</td>\n",
       "      <td>OH</td>\n",
       "      <td>45248.0</td>\n",
       "      <td>P</td>\n",
       "      <td>118.75</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5142225701452</td>\n",
       "      <td>5142225701MARKUSOFFICESUPPLIES</td>\n",
       "      <td>5142225701Friday</td>\n",
       "      <td>8090710030950MARKUSOFFICESUPPLIES</td>\n",
       "      <td>8090710030950Friday</td>\n",
       "      <td>MARKUSOFFICESUPPLIESFriday</td>\n",
       "      <td>51422257018090710030950MARKUSOFFICESUPPLIES</td>\n",
       "      <td>5142225701809071003095045248.0</td>\n",
       "      <td>5142225701MARKUSOFFICESUPPLIES45248.0</td>\n",
       "      <td>8090710030950MARKUSOFFICESUPPLIESOH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96394</th>\n",
       "      <td>96751</td>\n",
       "      <td>5142226486</td>\n",
       "      <td>2010-12-31</td>\n",
       "      <td>4503057341100</td>\n",
       "      <td>TECHPAC,INC</td>\n",
       "      <td>OH</td>\n",
       "      <td>45150.0</td>\n",
       "      <td>P</td>\n",
       "      <td>363.56</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5142226486451</td>\n",
       "      <td>5142226486TECHPAC,INC</td>\n",
       "      <td>5142226486Friday</td>\n",
       "      <td>4503057341100TECHPAC,INC</td>\n",
       "      <td>4503057341100Friday</td>\n",
       "      <td>TECHPAC,INCFriday</td>\n",
       "      <td>51422264864503057341100TECHPAC,INC</td>\n",
       "      <td>5142226486450305734110045150.0</td>\n",
       "      <td>5142226486TECHPAC,INC45150.0</td>\n",
       "      <td>4503057341100TECHPAC,INCOH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96395</th>\n",
       "      <td>96752</td>\n",
       "      <td>5142244619</td>\n",
       "      <td>2010-12-31</td>\n",
       "      <td>8834000695412</td>\n",
       "      <td>BUY.COM</td>\n",
       "      <td>CA</td>\n",
       "      <td>92656.0</td>\n",
       "      <td>P</td>\n",
       "      <td>2202.03</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5142244619926</td>\n",
       "      <td>5142244619BUY.COM</td>\n",
       "      <td>5142244619Friday</td>\n",
       "      <td>8834000695412BUY.COM</td>\n",
       "      <td>8834000695412Friday</td>\n",
       "      <td>BUY.COMFriday</td>\n",
       "      <td>51422446198834000695412BUY.COM</td>\n",
       "      <td>5142244619883400069541292656.0</td>\n",
       "      <td>5142244619BUY.COM92656.0</td>\n",
       "      <td>8834000695412BUY.COMCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96396</th>\n",
       "      <td>96753</td>\n",
       "      <td>5142243247</td>\n",
       "      <td>2010-12-31</td>\n",
       "      <td>9108347680006</td>\n",
       "      <td>STAPLESNATIONAL#471</td>\n",
       "      <td>NJ</td>\n",
       "      <td>7606.0</td>\n",
       "      <td>P</td>\n",
       "      <td>554.64</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5142243247760</td>\n",
       "      <td>5142243247STAPLESNATIONAL#471</td>\n",
       "      <td>5142243247Friday</td>\n",
       "      <td>9108347680006STAPLESNATIONAL#471</td>\n",
       "      <td>9108347680006Friday</td>\n",
       "      <td>STAPLESNATIONAL#471Friday</td>\n",
       "      <td>51422432479108347680006STAPLESNATIONAL#471</td>\n",
       "      <td>514224324791083476800067606.0</td>\n",
       "      <td>5142243247STAPLESNATIONAL#4717606.0</td>\n",
       "      <td>9108347680006STAPLESNATIONAL#471NJ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96397 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Recnum     Cardnum       Date       Merchnum      Merch description  \\\n",
       "0           1  5142190439 2010-01-01  5509006296254    FEDEXSHP12/23/09AB#   \n",
       "1           2  5142183973 2010-01-01    61003026333  SERVICEMERCHANDISE#81   \n",
       "2           3  5142131721 2010-01-01  4503082993600        OFFICEDEPOT#191   \n",
       "3           4  5142148452 2010-01-01  5509006296254    FEDEXSHP12/28/09AB#   \n",
       "4           5  5142190439 2010-01-01  5509006296254    FEDEXSHP12/23/09AB#   \n",
       "...       ...         ...        ...            ...                    ...   \n",
       "96392   96749  5142276053 2010-12-31  3500000006160        BESTBUY00001610   \n",
       "96393   96750  5142225701 2010-12-31  8090710030950   MARKUSOFFICESUPPLIES   \n",
       "96394   96751  5142226486 2010-12-31  4503057341100            TECHPAC,INC   \n",
       "96395   96752  5142244619 2010-12-31  8834000695412                BUY.COM   \n",
       "96396   96753  5142243247 2010-12-31  9108347680006    STAPLESNATIONAL#471   \n",
       "\n",
       "      Merch state Merch zip Transtype   Amount  Fraud  ...      card_zip3  \\\n",
       "0              TN   38118.0         P     3.62      0  ...  5142190439381   \n",
       "1              MA    1803.0         P    31.42      0  ...  5142183973180   \n",
       "2              MD   20706.0         P   178.49      0  ...  5142131721207   \n",
       "3              TN   38118.0         P     3.62      0  ...  5142148452381   \n",
       "4              TN   38118.0         P     3.62      0  ...  5142190439381   \n",
       "...           ...       ...       ...      ...    ...  ...            ...   \n",
       "96392          KY   41042.0         P    84.79      0  ...  5142276053410   \n",
       "96393          OH   45248.0         P   118.75      0  ...  5142225701452   \n",
       "96394          OH   45150.0         P   363.56      0  ...  5142226486451   \n",
       "96395          CA   92656.0         P  2202.03      0  ...  5142244619926   \n",
       "96396          NJ    7606.0         P   554.64      0  ...  5142243247760   \n",
       "\n",
       "                        Card_Merchdesc          Card_dow  \\\n",
       "0        5142190439FEDEXSHP12/23/09AB#  5142190439Friday   \n",
       "1      5142183973SERVICEMERCHANDISE#81  5142183973Friday   \n",
       "2            5142131721OFFICEDEPOT#191  5142131721Friday   \n",
       "3        5142148452FEDEXSHP12/28/09AB#  5142148452Friday   \n",
       "4        5142190439FEDEXSHP12/23/09AB#  5142190439Friday   \n",
       "...                                ...               ...   \n",
       "96392        5142276053BESTBUY00001610  5142276053Friday   \n",
       "96393   5142225701MARKUSOFFICESUPPLIES  5142225701Friday   \n",
       "96394            5142226486TECHPAC,INC  5142226486Friday   \n",
       "96395                5142244619BUY.COM  5142244619Friday   \n",
       "96396    5142243247STAPLESNATIONAL#471  5142243247Friday   \n",
       "\n",
       "                           Merchnum_desc         Merchnum_dow  \\\n",
       "0       5509006296254FEDEXSHP12/23/09AB#  5509006296254Friday   \n",
       "1       61003026333SERVICEMERCHANDISE#81    61003026333Friday   \n",
       "2           4503082993600OFFICEDEPOT#191  4503082993600Friday   \n",
       "3       5509006296254FEDEXSHP12/28/09AB#  5509006296254Friday   \n",
       "4       5509006296254FEDEXSHP12/23/09AB#  5509006296254Friday   \n",
       "...                                  ...                  ...   \n",
       "96392       3500000006160BESTBUY00001610  3500000006160Friday   \n",
       "96393  8090710030950MARKUSOFFICESUPPLIES  8090710030950Friday   \n",
       "96394           4503057341100TECHPAC,INC  4503057341100Friday   \n",
       "96395               8834000695412BUY.COM  8834000695412Friday   \n",
       "96396   9108347680006STAPLESNATIONAL#471  9108347680006Friday   \n",
       "\n",
       "                     Merchdesc_dow  \\\n",
       "0        FEDEXSHP12/23/09AB#Friday   \n",
       "1      SERVICEMERCHANDISE#81Friday   \n",
       "2            OFFICEDEPOT#191Friday   \n",
       "3        FEDEXSHP12/28/09AB#Friday   \n",
       "4        FEDEXSHP12/23/09AB#Friday   \n",
       "...                            ...   \n",
       "96392        BESTBUY00001610Friday   \n",
       "96393   MARKUSOFFICESUPPLIESFriday   \n",
       "96394            TECHPAC,INCFriday   \n",
       "96395                BUY.COMFriday   \n",
       "96396    STAPLESNATIONAL#471Friday   \n",
       "\n",
       "                                Card_Merchnum_desc  \\\n",
       "0       51421904395509006296254FEDEXSHP12/23/09AB#   \n",
       "1       514218397361003026333SERVICEMERCHANDISE#81   \n",
       "2           51421317214503082993600OFFICEDEPOT#191   \n",
       "3       51421484525509006296254FEDEXSHP12/28/09AB#   \n",
       "4       51421904395509006296254FEDEXSHP12/23/09AB#   \n",
       "...                                            ...   \n",
       "96392       51422760533500000006160BESTBUY00001610   \n",
       "96393  51422257018090710030950MARKUSOFFICESUPPLIES   \n",
       "96394           51422264864503057341100TECHPAC,INC   \n",
       "96395               51422446198834000695412BUY.COM   \n",
       "96396   51422432479108347680006STAPLESNATIONAL#471   \n",
       "\n",
       "                    Card_Merchnum_Zip                     Card_Merchdesc_Zip  \\\n",
       "0      5142190439550900629625438118.0   5142190439FEDEXSHP12/23/09AB#38118.0   \n",
       "1         5142183973610030263331803.0  5142183973SERVICEMERCHANDISE#811803.0   \n",
       "2      5142131721450308299360020706.0       5142131721OFFICEDEPOT#19120706.0   \n",
       "3      5142148452550900629625438118.0   5142148452FEDEXSHP12/28/09AB#38118.0   \n",
       "4      5142190439550900629625438118.0   5142190439FEDEXSHP12/23/09AB#38118.0   \n",
       "...                               ...                                    ...   \n",
       "96392  5142276053350000000616041042.0       5142276053BESTBUY0000161041042.0   \n",
       "96393  5142225701809071003095045248.0  5142225701MARKUSOFFICESUPPLIES45248.0   \n",
       "96394  5142226486450305734110045150.0           5142226486TECHPAC,INC45150.0   \n",
       "96395  5142244619883400069541292656.0               5142244619BUY.COM92656.0   \n",
       "96396   514224324791083476800067606.0    5142243247STAPLESNATIONAL#4717606.0   \n",
       "\n",
       "                       Merchnum_desc_State  \n",
       "0       5509006296254FEDEXSHP12/23/09AB#TN  \n",
       "1       61003026333SERVICEMERCHANDISE#81MA  \n",
       "2           4503082993600OFFICEDEPOT#191MD  \n",
       "3       5509006296254FEDEXSHP12/28/09AB#TN  \n",
       "4       5509006296254FEDEXSHP12/23/09AB#TN  \n",
       "...                                    ...  \n",
       "96392       3500000006160BESTBUY00001610KY  \n",
       "96393  8090710030950MARKUSOFFICESUPPLIESOH  \n",
       "96394           4503057341100TECHPAC,INCOH  \n",
       "96395               8834000695412BUY.COMCA  \n",
       "96396   9108347680006STAPLESNATIONAL#471NJ  \n",
       "\n",
       "[96397 rows x 32 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cardnum',\n",
       " 'Merchnum',\n",
       " 'Month',\n",
       " 'Month_Risk',\n",
       " 'state_risk',\n",
       " 'card_merch',\n",
       " 'card_zip',\n",
       " 'card_state',\n",
       " 'merch_zip',\n",
       " 'merch_state',\n",
       " 'state_des',\n",
       " 'zip3',\n",
       " 'card_zip3',\n",
       " 'Card_Merchdesc',\n",
       " 'Card_dow',\n",
       " 'Merchnum_desc',\n",
       " 'Merchnum_dow',\n",
       " 'Merchdesc_dow',\n",
       " 'Card_Merchnum_desc',\n",
       " 'Card_Merchnum_Zip',\n",
       " 'Card_Merchdesc_Zip',\n",
       " 'Merchnum_desc_State']"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cardnum',\n",
       " 'Merchnum',\n",
       " 'Month',\n",
       " 'Month_Risk',\n",
       " 'card_merch',\n",
       " 'card_zip',\n",
       " 'card_state',\n",
       " 'merch_zip',\n",
       " 'merch_state',\n",
       " 'state_des',\n",
       " 'card_zip3',\n",
       " 'Card_Merchdesc',\n",
       " 'Card_dow',\n",
       " 'Merchnum_desc',\n",
       " 'Merchnum_dow',\n",
       " 'Merchdesc_dow',\n",
       " 'Card_Merchnum_desc',\n",
       " 'Card_Merchnum_Zip',\n",
       " 'Card_Merchdesc_Zip',\n",
       " 'Merchnum_desc_State']"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If you want, remove some entities that take a long time\n",
    "# these take a long time and don't add much\n",
    "entities.remove('state_risk')\n",
    "entities.remove('zip3')\n",
    "entities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cardnum',\n",
       " 'Merchnum',\n",
       " 'card_merch',\n",
       " 'card_zip',\n",
       " 'card_state',\n",
       " 'merch_zip',\n",
       " 'merch_state',\n",
       " 'state_des',\n",
       " 'card_zip3',\n",
       " 'Card_Merchdesc',\n",
       " 'Card_dow',\n",
       " 'Merchnum_desc',\n",
       " 'Merchnum_dow',\n",
       " 'Merchdesc_dow',\n",
       " 'Card_Merchnum_desc',\n",
       " 'Card_Merchnum_Zip',\n",
       " 'Card_Merchdesc_Zip',\n",
       " 'Merchnum_desc_State']"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing my custom variables\n",
    "# these take a long time and don't add much\n",
    "entities.remove('Month')\n",
    "entities.remove('Month_Risk')\n",
    "entities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "final.shape\n",
    "numstart = len(final.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Cardnum_day_since ---> Done\n",
      "# Day-since variables is  1\n",
      "Cardnum_count_0 ---> Done\n",
      "Cardnum amount variables over past 0 ---> Done\n",
      "Cardnum_count_1 ---> Done\n",
      "Cardnum amount variables over past 1 ---> Done\n",
      "Cardnum_count_3 ---> Done\n",
      "Cardnum amount variables over past 3 ---> Done\n",
      "Cardnum_count_7 ---> Done\n",
      "Cardnum amount variables over past 7 ---> Done\n",
      "Cardnum_count_14 ---> Done\n",
      "Cardnum amount variables over past 14 ---> Done\n",
      "Cardnum_count_30 ---> Done\n",
      "Cardnum amount variables over past 30 ---> Done\n",
      "Cardnum_count_60 ---> Done\n",
      "Cardnum amount variables over past 60 ---> Done\n",
      "# Frequency & Amount variables is  64\n",
      "Merchnum Run time for the this entity ----------------- 4.889977334000001s\n",
      "\n",
      "Merchnum_day_since ---> Done\n",
      "# Day-since variables is  65\n",
      "Merchnum_count_0 ---> Done\n",
      "Merchnum amount variables over past 0 ---> Done\n",
      "Merchnum_count_1 ---> Done\n",
      "Merchnum amount variables over past 1 ---> Done\n",
      "Merchnum_count_3 ---> Done\n",
      "Merchnum amount variables over past 3 ---> Done\n",
      "Merchnum_count_7 ---> Done\n",
      "Merchnum amount variables over past 7 ---> Done\n",
      "Merchnum_count_14 ---> Done\n",
      "Merchnum amount variables over past 14 ---> Done\n",
      "Merchnum_count_30 ---> Done\n",
      "Merchnum amount variables over past 30 ---> Done\n",
      "Merchnum_count_60 ---> Done\n",
      "Merchnum amount variables over past 60 ---> Done\n",
      "# Frequency & Amount variables is  128\n",
      "card_merch Run time for the this entity ----------------- 34.025245625s\n",
      "\n",
      "card_merch_day_since ---> Done\n",
      "# Day-since variables is  129\n",
      "card_merch_count_0 ---> Done\n",
      "card_merch amount variables over past 0 ---> Done\n",
      "card_merch_count_1 ---> Done\n",
      "card_merch amount variables over past 1 ---> Done\n",
      "card_merch_count_3 ---> Done\n",
      "card_merch amount variables over past 3 ---> Done\n",
      "card_merch_count_7 ---> Done\n",
      "card_merch amount variables over past 7 ---> Done\n",
      "card_merch_count_14 ---> Done\n",
      "card_merch amount variables over past 14 ---> Done\n",
      "card_merch_count_30 ---> Done\n",
      "card_merch amount variables over past 30 ---> Done\n",
      "card_merch_count_60 ---> Done\n",
      "card_merch amount variables over past 60 ---> Done\n",
      "# Frequency & Amount variables is  192\n",
      "card_zip Run time for the this entity ----------------- 1.661040290999999s\n",
      "\n",
      "card_zip_day_since ---> Done\n",
      "# Day-since variables is  193\n",
      "card_zip_count_0 ---> Done\n",
      "card_zip amount variables over past 0 ---> Done\n",
      "card_zip_count_1 ---> Done\n",
      "card_zip amount variables over past 1 ---> Done\n",
      "card_zip_count_3 ---> Done\n",
      "card_zip amount variables over past 3 ---> Done\n",
      "card_zip_count_7 ---> Done\n",
      "card_zip amount variables over past 7 ---> Done\n",
      "card_zip_count_14 ---> Done\n",
      "card_zip amount variables over past 14 ---> Done\n",
      "card_zip_count_30 ---> Done\n",
      "card_zip amount variables over past 30 ---> Done\n",
      "card_zip_count_60 ---> Done\n",
      "card_zip amount variables over past 60 ---> Done\n",
      "# Frequency & Amount variables is  256\n",
      "card_state Run time for the this entity ----------------- 1.886241959000003s\n",
      "\n",
      "card_state_day_since ---> Done\n",
      "# Day-since variables is  257\n",
      "card_state_count_0 ---> Done\n",
      "card_state amount variables over past 0 ---> Done\n",
      "card_state_count_1 ---> Done\n",
      "card_state amount variables over past 1 ---> Done\n",
      "card_state_count_3 ---> Done\n",
      "card_state amount variables over past 3 ---> Done\n",
      "card_state_count_7 ---> Done\n",
      "card_state amount variables over past 7 ---> Done\n",
      "card_state_count_14 ---> Done\n",
      "card_state amount variables over past 14 ---> Done\n",
      "card_state_count_30 ---> Done\n",
      "card_state amount variables over past 30 ---> Done\n",
      "card_state_count_60 ---> Done\n",
      "card_state amount variables over past 60 ---> Done\n",
      "# Frequency & Amount variables is  320\n",
      "merch_zip Run time for the this entity ----------------- 2.188245875s\n",
      "\n",
      "merch_zip_day_since ---> Done\n",
      "# Day-since variables is  321\n",
      "merch_zip_count_0 ---> Done\n",
      "merch_zip amount variables over past 0 ---> Done\n",
      "merch_zip_count_1 ---> Done\n",
      "merch_zip amount variables over past 1 ---> Done\n",
      "merch_zip_count_3 ---> Done\n",
      "merch_zip amount variables over past 3 ---> Done\n",
      "merch_zip_count_7 ---> Done\n",
      "merch_zip amount variables over past 7 ---> Done\n",
      "merch_zip_count_14 ---> Done\n",
      "merch_zip amount variables over past 14 ---> Done\n",
      "merch_zip_count_30 ---> Done\n",
      "merch_zip amount variables over past 30 ---> Done\n",
      "merch_zip_count_60 ---> Done\n",
      "merch_zip amount variables over past 60 ---> Done\n",
      "# Frequency & Amount variables is  384\n",
      "merch_state Run time for the this entity ----------------- 32.95245154100001s\n",
      "\n",
      "merch_state_day_since ---> Done\n",
      "# Day-since variables is  385\n",
      "merch_state_count_0 ---> Done\n",
      "merch_state amount variables over past 0 ---> Done\n",
      "merch_state_count_1 ---> Done\n",
      "merch_state amount variables over past 1 ---> Done\n",
      "merch_state_count_3 ---> Done\n",
      "merch_state amount variables over past 3 ---> Done\n",
      "merch_state_count_7 ---> Done\n",
      "merch_state amount variables over past 7 ---> Done\n",
      "merch_state_count_14 ---> Done\n",
      "merch_state amount variables over past 14 ---> Done\n",
      "merch_state_count_30 ---> Done\n",
      "merch_state amount variables over past 30 ---> Done\n",
      "merch_state_count_60 ---> Done\n",
      "merch_state amount variables over past 60 ---> Done\n",
      "# Frequency & Amount variables is  448\n",
      "state_des Run time for the this entity ----------------- 34.023255459s\n",
      "\n",
      "state_des_day_since ---> Done\n",
      "# Day-since variables is  449\n",
      "state_des_count_0 ---> Done\n",
      "state_des amount variables over past 0 ---> Done\n",
      "state_des_count_1 ---> Done\n",
      "state_des amount variables over past 1 ---> Done\n",
      "state_des_count_3 ---> Done\n",
      "state_des amount variables over past 3 ---> Done\n",
      "state_des_count_7 ---> Done\n",
      "state_des amount variables over past 7 ---> Done\n",
      "state_des_count_14 ---> Done\n",
      "state_des amount variables over past 14 ---> Done\n",
      "state_des_count_30 ---> Done\n",
      "state_des amount variables over past 30 ---> Done\n",
      "state_des_count_60 ---> Done\n",
      "state_des amount variables over past 60 ---> Done\n",
      "# Frequency & Amount variables is  512\n",
      "card_zip3 Run time for the this entity ----------------- 4.609189666999995s\n",
      "\n",
      "card_zip3_day_since ---> Done\n",
      "# Day-since variables is  513\n",
      "card_zip3_count_0 ---> Done\n",
      "card_zip3 amount variables over past 0 ---> Done\n",
      "card_zip3_count_1 ---> Done\n",
      "card_zip3 amount variables over past 1 ---> Done\n",
      "card_zip3_count_3 ---> Done\n",
      "card_zip3 amount variables over past 3 ---> Done\n",
      "card_zip3_count_7 ---> Done\n",
      "card_zip3 amount variables over past 7 ---> Done\n",
      "card_zip3_count_14 ---> Done\n",
      "card_zip3 amount variables over past 14 ---> Done\n",
      "card_zip3_count_30 ---> Done\n",
      "card_zip3 amount variables over past 30 ---> Done\n",
      "card_zip3_count_60 ---> Done\n",
      "card_zip3 amount variables over past 60 ---> Done\n",
      "# Frequency & Amount variables is  576\n",
      "Card_Merchdesc Run time for the this entity ----------------- 1.964401874999993s\n",
      "\n",
      "Card_Merchdesc_day_since ---> Done\n",
      "# Day-since variables is  577\n",
      "Card_Merchdesc_count_0 ---> Done\n",
      "Card_Merchdesc amount variables over past 0 ---> Done\n",
      "Card_Merchdesc_count_1 ---> Done\n",
      "Card_Merchdesc amount variables over past 1 ---> Done\n",
      "Card_Merchdesc_count_3 ---> Done\n",
      "Card_Merchdesc amount variables over past 3 ---> Done\n",
      "Card_Merchdesc_count_7 ---> Done\n",
      "Card_Merchdesc amount variables over past 7 ---> Done\n",
      "Card_Merchdesc_count_14 ---> Done\n",
      "Card_Merchdesc amount variables over past 14 ---> Done\n",
      "Card_Merchdesc_count_30 ---> Done\n",
      "Card_Merchdesc amount variables over past 30 ---> Done\n",
      "Card_Merchdesc_count_60 ---> Done\n",
      "Card_Merchdesc amount variables over past 60 ---> Done\n",
      "# Frequency & Amount variables is  640\n",
      "Card_dow Run time for the this entity ----------------- 1.0719121670000078s\n",
      "\n",
      "Card_dow_day_since ---> Done\n",
      "# Day-since variables is  641\n",
      "Card_dow_count_0 ---> Done\n",
      "Card_dow amount variables over past 0 ---> Done\n",
      "Card_dow_count_1 ---> Done\n",
      "Card_dow amount variables over past 1 ---> Done\n",
      "Card_dow_count_3 ---> Done\n",
      "Card_dow amount variables over past 3 ---> Done\n",
      "Card_dow_count_7 ---> Done\n",
      "Card_dow amount variables over past 7 ---> Done\n",
      "Card_dow_count_14 ---> Done\n",
      "Card_dow amount variables over past 14 ---> Done\n",
      "Card_dow_count_30 ---> Done\n",
      "Card_dow amount variables over past 30 ---> Done\n",
      "Card_dow_count_60 ---> Done\n",
      "Card_dow amount variables over past 60 ---> Done\n",
      "# Frequency & Amount variables is  704\n",
      "Merchnum_desc Run time for the this entity ----------------- 1.566243416000006s\n",
      "\n",
      "Merchnum_desc_day_since ---> Done\n",
      "# Day-since variables is  705\n",
      "Merchnum_desc_count_0 ---> Done\n",
      "Merchnum_desc amount variables over past 0 ---> Done\n",
      "Merchnum_desc_count_1 ---> Done\n",
      "Merchnum_desc amount variables over past 1 ---> Done\n",
      "Merchnum_desc_count_3 ---> Done\n",
      "Merchnum_desc amount variables over past 3 ---> Done\n",
      "Merchnum_desc_count_7 ---> Done\n",
      "Merchnum_desc amount variables over past 7 ---> Done\n",
      "Merchnum_desc_count_14 ---> Done\n",
      "Merchnum_desc amount variables over past 14 ---> Done\n",
      "Merchnum_desc_count_30 ---> Done\n",
      "Merchnum_desc amount variables over past 30 ---> Done\n",
      "Merchnum_desc_count_60 ---> Done\n",
      "Merchnum_desc amount variables over past 60 ---> Done\n",
      "# Frequency & Amount variables is  768\n",
      "Merchnum_dow Run time for the this entity ----------------- 4.009386583000008s\n",
      "\n",
      "Merchnum_dow_day_since ---> Done\n",
      "# Day-since variables is  769\n",
      "Merchnum_dow_count_0 ---> Done\n",
      "Merchnum_dow amount variables over past 0 ---> Done\n",
      "Merchnum_dow_count_1 ---> Done\n",
      "Merchnum_dow amount variables over past 1 ---> Done\n",
      "Merchnum_dow_count_3 ---> Done\n",
      "Merchnum_dow amount variables over past 3 ---> Done\n",
      "Merchnum_dow_count_7 ---> Done\n",
      "Merchnum_dow amount variables over past 7 ---> Done\n",
      "Merchnum_dow_count_14 ---> Done\n",
      "Merchnum_dow amount variables over past 14 ---> Done\n",
      "Merchnum_dow_count_30 ---> Done\n",
      "Merchnum_dow amount variables over past 30 ---> Done\n",
      "Merchnum_dow_count_60 ---> Done\n",
      "Merchnum_dow amount variables over past 60 ---> Done\n",
      "# Frequency & Amount variables is  832\n",
      "Merchdesc_dow Run time for the this entity ----------------- 5.192437458000001s\n",
      "\n",
      "Merchdesc_dow_day_since ---> Done\n",
      "# Day-since variables is  833\n",
      "Merchdesc_dow_count_0 ---> Done\n",
      "Merchdesc_dow amount variables over past 0 ---> Done\n",
      "Merchdesc_dow_count_1 ---> Done\n",
      "Merchdesc_dow amount variables over past 1 ---> Done\n",
      "Merchdesc_dow_count_3 ---> Done\n",
      "Merchdesc_dow amount variables over past 3 ---> Done\n",
      "Merchdesc_dow_count_7 ---> Done\n",
      "Merchdesc_dow amount variables over past 7 ---> Done\n",
      "Merchdesc_dow_count_14 ---> Done\n",
      "Merchdesc_dow amount variables over past 14 ---> Done\n",
      "Merchdesc_dow_count_30 ---> Done\n",
      "Merchdesc_dow amount variables over past 30 ---> Done\n",
      "Merchdesc_dow_count_60 ---> Done\n",
      "Merchdesc_dow amount variables over past 60 ---> Done\n",
      "# Frequency & Amount variables is  896\n",
      "Card_Merchnum_desc Run time for the this entity ----------------- 1.7312168329999906s\n",
      "\n",
      "Card_Merchnum_desc_day_since ---> Done\n",
      "# Day-since variables is  897\n",
      "Card_Merchnum_desc_count_0 ---> Done\n",
      "Card_Merchnum_desc amount variables over past 0 ---> Done\n",
      "Card_Merchnum_desc_count_1 ---> Done\n",
      "Card_Merchnum_desc amount variables over past 1 ---> Done\n",
      "Card_Merchnum_desc_count_3 ---> Done\n",
      "Card_Merchnum_desc amount variables over past 3 ---> Done\n",
      "Card_Merchnum_desc_count_7 ---> Done\n",
      "Card_Merchnum_desc amount variables over past 7 ---> Done\n",
      "Card_Merchnum_desc_count_14 ---> Done\n",
      "Card_Merchnum_desc amount variables over past 14 ---> Done\n",
      "Card_Merchnum_desc_count_30 ---> Done\n",
      "Card_Merchnum_desc amount variables over past 30 ---> Done\n",
      "Card_Merchnum_desc_count_60 ---> Done\n",
      "Card_Merchnum_desc amount variables over past 60 ---> Done\n",
      "# Frequency & Amount variables is  960\n",
      "Card_Merchnum_Zip Run time for the this entity ----------------- 1.0461267080000027s\n",
      "\n",
      "Card_Merchnum_Zip_day_since ---> Done\n",
      "# Day-since variables is  961\n",
      "Card_Merchnum_Zip_count_0 ---> Done\n",
      "Card_Merchnum_Zip amount variables over past 0 ---> Done\n",
      "Card_Merchnum_Zip_count_1 ---> Done\n",
      "Card_Merchnum_Zip amount variables over past 1 ---> Done\n",
      "Card_Merchnum_Zip_count_3 ---> Done\n",
      "Card_Merchnum_Zip amount variables over past 3 ---> Done\n",
      "Card_Merchnum_Zip_count_7 ---> Done\n",
      "Card_Merchnum_Zip amount variables over past 7 ---> Done\n",
      "Card_Merchnum_Zip_count_14 ---> Done\n",
      "Card_Merchnum_Zip amount variables over past 14 ---> Done\n",
      "Card_Merchnum_Zip_count_30 ---> Done\n",
      "Card_Merchnum_Zip amount variables over past 30 ---> Done\n",
      "Card_Merchnum_Zip_count_60 ---> Done\n",
      "Card_Merchnum_Zip amount variables over past 60 ---> Done\n",
      "# Frequency & Amount variables is  1024\n",
      "Card_Merchdesc_Zip Run time for the this entity ----------------- 1.649755749999997s\n",
      "\n",
      "Card_Merchdesc_Zip_day_since ---> Done\n",
      "# Day-since variables is  1025\n",
      "Card_Merchdesc_Zip_count_0 ---> Done\n",
      "Card_Merchdesc_Zip amount variables over past 0 ---> Done\n",
      "Card_Merchdesc_Zip_count_1 ---> Done\n",
      "Card_Merchdesc_Zip amount variables over past 1 ---> Done\n",
      "Card_Merchdesc_Zip_count_3 ---> Done\n",
      "Card_Merchdesc_Zip amount variables over past 3 ---> Done\n",
      "Card_Merchdesc_Zip_count_7 ---> Done\n",
      "Card_Merchdesc_Zip amount variables over past 7 ---> Done\n",
      "Card_Merchdesc_Zip_count_14 ---> Done\n",
      "Card_Merchdesc_Zip amount variables over past 14 ---> Done\n",
      "Card_Merchdesc_Zip_count_30 ---> Done\n",
      "Card_Merchdesc_Zip amount variables over past 30 ---> Done\n",
      "Card_Merchdesc_Zip_count_60 ---> Done\n",
      "Card_Merchdesc_Zip amount variables over past 60 ---> Done\n",
      "# Frequency & Amount variables is  1088\n",
      "Merchnum_desc_State Run time for the this entity ----------------- 1.060368416000017s\n",
      "\n",
      "Merchnum_desc_State_day_since ---> Done\n",
      "# Day-since variables is  1089\n",
      "Merchnum_desc_State_count_0 ---> Done\n",
      "Merchnum_desc_State amount variables over past 0 ---> Done\n",
      "Merchnum_desc_State_count_1 ---> Done\n",
      "Merchnum_desc_State amount variables over past 1 ---> Done\n",
      "Merchnum_desc_State_count_3 ---> Done\n",
      "Merchnum_desc_State amount variables over past 3 ---> Done\n",
      "Merchnum_desc_State_count_7 ---> Done\n",
      "Merchnum_desc_State amount variables over past 7 ---> Done\n",
      "Merchnum_desc_State_count_14 ---> Done\n",
      "Merchnum_desc_State amount variables over past 14 ---> Done\n",
      "Merchnum_desc_State_count_30 ---> Done\n",
      "Merchnum_desc_State amount variables over past 30 ---> Done\n",
      "Merchnum_desc_State_count_60 ---> Done\n",
      "Merchnum_desc_State amount variables over past 60 ---> Done\n",
      "# Frequency & Amount variables is  1152\n",
      "Total run time: 2.3256242618166665mins\n",
      "CPU times: user 1min 52s, sys: 31.3 s, total: 2min 23s\n",
      "Wall time: 2min 19s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "start = timeit.default_timer()\n",
    "for entity in entities:    \n",
    "    try: print(entity,'Run time for the this entity ----------------- {}s'.format(timeit.default_timer() - st))\n",
    "    except: print('')\n",
    "    st = timeit.default_timer()    \n",
    "\n",
    "# Day-since variables:   \n",
    "    df_l = df1[['Recnum', 'Date', entity]]\n",
    "    df_r = df1[['check_record', 'check_date', entity, 'Amount']]    \n",
    "    temp = pd.merge(df_l, df_r, left_on = entity, right_on = entity)   \n",
    "    temp1 = temp[temp.Recnum > temp.check_record][['Recnum','Date','check_date']]\\\n",
    "                                                   .groupby('Recnum')[['Date', 'check_date']].last()\n",
    "    mapper = (temp1.Date - temp1.check_date).dt.days\n",
    "    final[entity + '_day_since'] = final.Recnum.map(mapper)\n",
    "    final[entity + '_day_since'].fillna((final.Date - pd.to_datetime('2006-01-01')).dt.days, inplace = True)\n",
    "    print('\\n' + entity + '_day_since ---> Done')  \n",
    "    daysince = len(final.columns) - numstart\n",
    "    print('# Day-since variables is ', daysince)\n",
    "    \n",
    "# Frequency & Amount variables:  \n",
    "\n",
    "##  Velocity\n",
    "\n",
    "    for time in [0,1,3,7,14,30,60]:       \n",
    "        temp2 = temp[(temp.check_date >= (temp.Date - dt.timedelta(time))) &\\\n",
    "                       (temp.Recnum >= temp.check_record)][['Recnum', entity, 'Amount']]       \n",
    "        col_name = entity + '_count_' + str(time)    \n",
    "        mapper2 = temp2.groupby('Recnum')[entity].count()      \n",
    "        final[col_name] = final.Recnum.map(mapper2)        \n",
    "        print(col_name + ' ---> Done')       \n",
    "        final[entity + '_avg_' + str(time)] = final.Recnum.map(temp2.groupby('Recnum')['Amount'].mean())\n",
    "        final[entity + '_max_' + str(time)] = final.Recnum.map(temp2.groupby('Recnum')['Amount'].max())\n",
    "        final[entity + '_med_' + str(time)] = final.Recnum.map(temp2.groupby('Recnum')['Amount'].median())\n",
    "        final[entity + '_total_' + str(time)] = final.Recnum.map(temp2.groupby('Recnum')['Amount'].sum())\n",
    "        final[entity + '_actual/avg_' + str(time)] = final['Amount'] / final[entity + '_avg_' + str(time)]\n",
    "        final[entity + '_actual/max_' + str(time)] = final['Amount'] / final[entity + '_max_' + str(time)]\n",
    "        final[entity + '_actual/med_' + str(time)] = final['Amount'] / final[entity + '_med_' + str(time)]\n",
    "        final[entity + '_actual/toal_' + str(time)] = final['Amount'] / final[entity + '_total_' + str(time)]       \n",
    "        print(entity + ' amount variables over past ' + str(time) + ' ---> Done')\n",
    "    print('# Frequency & Amount variables is ', len(final.columns) - numstart)\n",
    "\n",
    "    del df_l\n",
    "    del df_r\n",
    "    del temp\n",
    "    del temp1\n",
    "    del temp2\n",
    "    del mapper2  \n",
    "\n",
    "print('Total run time: {}mins'.format((timeit.default_timer() - start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(96397, 1184)\n",
      "# new variables is  1152\n"
     ]
    }
   ],
   "source": [
    "print(final.shape)\n",
    "print('# new variables is ',len(final.columns) - numstart)\n",
    "numstart = len(final.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run time: 0.22058900000001813s\n",
      "CPU times: user 189 ms, sys: 32.6 ms, total: 222 ms\n",
      "Wall time: 221 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "start = timeit.default_timer()\n",
    "\n",
    "## Relative Velocity\n",
    "for ent in entities:\n",
    "    for d in ['0', '1']:\n",
    "        for dd in ['7', '14', '30', '60']:\n",
    "            final[ent + '_count_' + d + '_by_' + dd] =\\\n",
    "            final[ent + '_count_' + d]/(final[ent + '_count_' + dd])/float(dd)\n",
    "            final[ent + '_total_amount_'+d+'_by_' + dd]=\\\n",
    "            final[ent +'_total_'+d]/(final[ent+'_total_'+dd])/float(dd)\n",
    "            \n",
    "print('run time: {}s'.format(timeit.default_timer() - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96397, 1472)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(96397, 1472)\n",
      "# new variables is  288\n"
     ]
    }
   ],
   "source": [
    "print(final.shape)\n",
    "print('# new variables is ',len(final.columns) - numstart)\n",
    "numstart = len(final.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run time: 0.11195449999999596s\n"
     ]
    }
   ],
   "source": [
    "## Velocity-density\n",
    "start = timeit.default_timer()\n",
    "for ent in entities:\n",
    "    for d in ['0', '1']:\n",
    "        for dd in ['7', '14', '30', '60']:\n",
    "            final[ent + '_vdratio_' + d +'by' + dd] =\\\n",
    "            final[ent + '_count_' + d + '_by_' + dd]/(final[ent + '_day_since']+1)\n",
    "            \n",
    "print('run time: {}s'.format(timeit.default_timer() - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96397, 1616)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(96397, 1616)\n",
      "# new variables is  144\n"
     ]
    }
   ],
   "source": [
    "print(final.shape)\n",
    "print('# new variables is ',len(final.columns) - numstart)\n",
    "numstart = len(final.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run time for entity Cardnum in field Merchnum ---> Done\n",
      "Run time for entity Cardnum in field card_merch ---> Done\n",
      "Run time for entity Cardnum in field card_zip ---> Done\n",
      "Run time for entity Cardnum in field card_state ---> Done\n",
      "Run time for entity Cardnum in field merch_zip ---> Done\n",
      "Run time for entity Cardnum in field merch_state ---> Done\n",
      "Run time for entity Cardnum in field state_des ---> Done\n",
      "Run time for entity Cardnum in field card_zip3 ---> Done\n",
      "Run time for entity Cardnum in field Card_Merchdesc ---> Done\n",
      "Run time for entity Cardnum in field Card_dow ---> Done\n",
      "Run time for entity Cardnum in field Merchnum_desc ---> Done\n",
      "Run time for entity Cardnum in field Merchnum_dow ---> Done\n",
      "Run time for entity Cardnum in field Merchdesc_dow ---> Done\n",
      "Run time for entity Cardnum in field Card_Merchnum_desc ---> Done\n",
      "Run time for entity Cardnum in field Card_Merchnum_Zip ---> Done\n",
      "Run time for entity Cardnum in field Card_Merchdesc_Zip ---> Done\n",
      "Run time for entity Cardnum in field Merchnum_desc_State ---> Done\n",
      "Run time for entity Merchnum in field Cardnum ---> Done\n",
      "Run time for entity Merchnum in field card_merch ---> Done\n",
      "Run time for entity Merchnum in field card_zip ---> Done\n",
      "Run time for entity Merchnum in field card_state ---> Done\n",
      "Run time for entity Merchnum in field merch_zip ---> Done\n",
      "Run time for entity Merchnum in field merch_state ---> Done\n",
      "Run time for entity Merchnum in field state_des ---> Done\n",
      "Run time for entity Merchnum in field card_zip3 ---> Done\n",
      "Run time for entity Merchnum in field Card_Merchdesc ---> Done\n",
      "Run time for entity Merchnum in field Card_dow ---> Done\n",
      "Run time for entity Merchnum in field Merchnum_desc ---> Done\n",
      "Run time for entity Merchnum in field Merchnum_dow ---> Done\n",
      "Run time for entity Merchnum in field Merchdesc_dow ---> Done\n",
      "Run time for entity Merchnum in field Card_Merchnum_desc ---> Done\n",
      "Run time for entity Merchnum in field Card_Merchnum_Zip ---> Done\n",
      "Run time for entity Merchnum in field Card_Merchdesc_Zip ---> Done\n",
      "Run time for entity Merchnum in field Merchnum_desc_State ---> Done\n",
      "Run time for entity card_merch in field Cardnum ---> Done\n",
      "Run time for entity card_merch in field Merchnum ---> Done\n",
      "Run time for entity card_merch in field card_zip ---> Done\n",
      "Run time for entity card_merch in field card_state ---> Done\n",
      "Run time for entity card_merch in field merch_zip ---> Done\n",
      "Run time for entity card_merch in field merch_state ---> Done\n",
      "Run time for entity card_merch in field state_des ---> Done\n",
      "Run time for entity card_merch in field card_zip3 ---> Done\n",
      "Run time for entity card_merch in field Card_Merchdesc ---> Done\n",
      "Run time for entity card_merch in field Card_dow ---> Done\n",
      "Run time for entity card_merch in field Merchnum_desc ---> Done\n",
      "Run time for entity card_merch in field Merchnum_dow ---> Done\n",
      "Run time for entity card_merch in field Merchdesc_dow ---> Done\n",
      "Run time for entity card_merch in field Card_Merchnum_desc ---> Done\n",
      "Run time for entity card_merch in field Card_Merchnum_Zip ---> Done\n",
      "Run time for entity card_merch in field Card_Merchdesc_Zip ---> Done\n",
      "Run time for entity card_merch in field Merchnum_desc_State ---> Done\n",
      "Run time for entity card_zip in field Cardnum ---> Done\n",
      "Run time for entity card_zip in field Merchnum ---> Done\n",
      "Run time for entity card_zip in field card_merch ---> Done\n",
      "Run time for entity card_zip in field card_state ---> Done\n",
      "Run time for entity card_zip in field merch_zip ---> Done\n",
      "Run time for entity card_zip in field merch_state ---> Done\n",
      "Run time for entity card_zip in field state_des ---> Done\n",
      "Run time for entity card_zip in field card_zip3 ---> Done\n",
      "Run time for entity card_zip in field Card_Merchdesc ---> Done\n",
      "Run time for entity card_zip in field Card_dow ---> Done\n",
      "Run time for entity card_zip in field Merchnum_desc ---> Done\n",
      "Run time for entity card_zip in field Merchnum_dow ---> Done\n",
      "Run time for entity card_zip in field Merchdesc_dow ---> Done\n",
      "Run time for entity card_zip in field Card_Merchnum_desc ---> Done\n",
      "Run time for entity card_zip in field Card_Merchnum_Zip ---> Done\n",
      "Run time for entity card_zip in field Card_Merchdesc_Zip ---> Done\n",
      "Run time for entity card_zip in field Merchnum_desc_State ---> Done\n",
      "Run time for entity card_state in field Cardnum ---> Done\n",
      "Run time for entity card_state in field Merchnum ---> Done\n",
      "Run time for entity card_state in field card_merch ---> Done\n",
      "Run time for entity card_state in field card_zip ---> Done\n",
      "Run time for entity card_state in field merch_zip ---> Done\n",
      "Run time for entity card_state in field merch_state ---> Done\n",
      "Run time for entity card_state in field state_des ---> Done\n",
      "Run time for entity card_state in field card_zip3 ---> Done\n",
      "Run time for entity card_state in field Card_Merchdesc ---> Done\n",
      "Run time for entity card_state in field Card_dow ---> Done\n",
      "Run time for entity card_state in field Merchnum_desc ---> Done\n",
      "Run time for entity card_state in field Merchnum_dow ---> Done\n",
      "Run time for entity card_state in field Merchdesc_dow ---> Done\n",
      "Run time for entity card_state in field Card_Merchnum_desc ---> Done\n",
      "Run time for entity card_state in field Card_Merchnum_Zip ---> Done\n",
      "Run time for entity card_state in field Card_Merchdesc_Zip ---> Done\n",
      "Run time for entity card_state in field Merchnum_desc_State ---> Done\n",
      "Run time for entity merch_zip in field Cardnum ---> Done\n",
      "Run time for entity merch_zip in field Merchnum ---> Done\n",
      "Run time for entity merch_zip in field card_merch ---> Done\n",
      "Run time for entity merch_zip in field card_zip ---> Done\n",
      "Run time for entity merch_zip in field card_state ---> Done\n",
      "Run time for entity merch_zip in field merch_state ---> Done\n",
      "Run time for entity merch_zip in field state_des ---> Done\n",
      "Run time for entity merch_zip in field card_zip3 ---> Done\n",
      "Run time for entity merch_zip in field Card_Merchdesc ---> Done\n",
      "Run time for entity merch_zip in field Card_dow ---> Done\n",
      "Run time for entity merch_zip in field Merchnum_desc ---> Done\n",
      "Run time for entity merch_zip in field Merchnum_dow ---> Done\n",
      "Run time for entity merch_zip in field Merchdesc_dow ---> Done\n",
      "Run time for entity merch_zip in field Card_Merchnum_desc ---> Done\n",
      "Run time for entity merch_zip in field Card_Merchnum_Zip ---> Done\n",
      "Run time for entity merch_zip in field Card_Merchdesc_Zip ---> Done\n",
      "Run time for entity merch_zip in field Merchnum_desc_State ---> Done\n",
      "Run time for entity merch_state in field Cardnum ---> Done\n",
      "Run time for entity merch_state in field Merchnum ---> Done\n",
      "Run time for entity merch_state in field card_merch ---> Done\n",
      "Run time for entity merch_state in field card_zip ---> Done\n",
      "Run time for entity merch_state in field card_state ---> Done\n",
      "Run time for entity merch_state in field merch_zip ---> Done\n",
      "Run time for entity merch_state in field state_des ---> Done\n",
      "Run time for entity merch_state in field card_zip3 ---> Done\n",
      "Run time for entity merch_state in field Card_Merchdesc ---> Done\n",
      "Run time for entity merch_state in field Card_dow ---> Done\n",
      "Run time for entity merch_state in field Merchnum_desc ---> Done\n",
      "Run time for entity merch_state in field Merchnum_dow ---> Done\n",
      "Run time for entity merch_state in field Merchdesc_dow ---> Done\n",
      "Run time for entity merch_state in field Card_Merchnum_desc ---> Done\n",
      "Run time for entity merch_state in field Card_Merchnum_Zip ---> Done\n",
      "Run time for entity merch_state in field Card_Merchdesc_Zip ---> Done\n",
      "Run time for entity merch_state in field Merchnum_desc_State ---> Done\n",
      "Run time for entity state_des in field Cardnum ---> Done\n",
      "Run time for entity state_des in field Merchnum ---> Done\n",
      "Run time for entity state_des in field card_merch ---> Done\n",
      "Run time for entity state_des in field card_zip ---> Done\n",
      "Run time for entity state_des in field card_state ---> Done\n",
      "Run time for entity state_des in field merch_zip ---> Done\n",
      "Run time for entity state_des in field merch_state ---> Done\n",
      "Run time for entity state_des in field card_zip3 ---> Done\n",
      "Run time for entity state_des in field Card_Merchdesc ---> Done\n",
      "Run time for entity state_des in field Card_dow ---> Done\n",
      "Run time for entity state_des in field Merchnum_desc ---> Done\n",
      "Run time for entity state_des in field Merchnum_dow ---> Done\n",
      "Run time for entity state_des in field Merchdesc_dow ---> Done\n",
      "Run time for entity state_des in field Card_Merchnum_desc ---> Done\n",
      "Run time for entity state_des in field Card_Merchnum_Zip ---> Done\n",
      "Run time for entity state_des in field Card_Merchdesc_Zip ---> Done\n",
      "Run time for entity state_des in field Merchnum_desc_State ---> Done\n",
      "Run time for entity card_zip3 in field Cardnum ---> Done\n",
      "Run time for entity card_zip3 in field Merchnum ---> Done\n",
      "Run time for entity card_zip3 in field card_merch ---> Done\n",
      "Run time for entity card_zip3 in field card_zip ---> Done\n",
      "Run time for entity card_zip3 in field card_state ---> Done\n",
      "Run time for entity card_zip3 in field merch_zip ---> Done\n",
      "Run time for entity card_zip3 in field merch_state ---> Done\n",
      "Run time for entity card_zip3 in field state_des ---> Done\n",
      "Run time for entity card_zip3 in field Card_Merchdesc ---> Done\n",
      "Run time for entity card_zip3 in field Card_dow ---> Done\n",
      "Run time for entity card_zip3 in field Merchnum_desc ---> Done\n",
      "Run time for entity card_zip3 in field Merchnum_dow ---> Done\n",
      "Run time for entity card_zip3 in field Merchdesc_dow ---> Done\n",
      "Run time for entity card_zip3 in field Card_Merchnum_desc ---> Done\n",
      "Run time for entity card_zip3 in field Card_Merchnum_Zip ---> Done\n",
      "Run time for entity card_zip3 in field Card_Merchdesc_Zip ---> Done\n",
      "Run time for entity card_zip3 in field Merchnum_desc_State ---> Done\n",
      "Run time for entity Card_Merchdesc in field Cardnum ---> Done\n",
      "Run time for entity Card_Merchdesc in field Merchnum ---> Done\n",
      "Run time for entity Card_Merchdesc in field card_merch ---> Done\n",
      "Run time for entity Card_Merchdesc in field card_zip ---> Done\n",
      "Run time for entity Card_Merchdesc in field card_state ---> Done\n",
      "Run time for entity Card_Merchdesc in field merch_zip ---> Done\n",
      "Run time for entity Card_Merchdesc in field merch_state ---> Done\n",
      "Run time for entity Card_Merchdesc in field state_des ---> Done\n",
      "Run time for entity Card_Merchdesc in field card_zip3 ---> Done\n",
      "Run time for entity Card_Merchdesc in field Card_dow ---> Done\n",
      "Run time for entity Card_Merchdesc in field Merchnum_desc ---> Done\n",
      "Run time for entity Card_Merchdesc in field Merchnum_dow ---> Done\n",
      "Run time for entity Card_Merchdesc in field Merchdesc_dow ---> Done\n",
      "Run time for entity Card_Merchdesc in field Card_Merchnum_desc ---> Done\n",
      "Run time for entity Card_Merchdesc in field Card_Merchnum_Zip ---> Done\n",
      "Run time for entity Card_Merchdesc in field Card_Merchdesc_Zip ---> Done\n",
      "Run time for entity Card_Merchdesc in field Merchnum_desc_State ---> Done\n",
      "Run time for entity Card_dow in field Cardnum ---> Done\n",
      "Run time for entity Card_dow in field Merchnum ---> Done\n",
      "Run time for entity Card_dow in field card_merch ---> Done\n",
      "Run time for entity Card_dow in field card_zip ---> Done\n",
      "Run time for entity Card_dow in field card_state ---> Done\n",
      "Run time for entity Card_dow in field merch_zip ---> Done\n",
      "Run time for entity Card_dow in field merch_state ---> Done\n",
      "Run time for entity Card_dow in field state_des ---> Done\n",
      "Run time for entity Card_dow in field card_zip3 ---> Done\n",
      "Run time for entity Card_dow in field Card_Merchdesc ---> Done\n",
      "Run time for entity Card_dow in field Merchnum_desc ---> Done\n",
      "Run time for entity Card_dow in field Merchnum_dow ---> Done\n",
      "Run time for entity Card_dow in field Merchdesc_dow ---> Done\n",
      "Run time for entity Card_dow in field Card_Merchnum_desc ---> Done\n",
      "Run time for entity Card_dow in field Card_Merchnum_Zip ---> Done\n",
      "Run time for entity Card_dow in field Card_Merchdesc_Zip ---> Done\n",
      "Run time for entity Card_dow in field Merchnum_desc_State ---> Done\n",
      "Run time for entity Merchnum_desc in field Cardnum ---> Done\n",
      "Run time for entity Merchnum_desc in field Merchnum ---> Done\n",
      "Run time for entity Merchnum_desc in field card_merch ---> Done\n",
      "Run time for entity Merchnum_desc in field card_zip ---> Done\n",
      "Run time for entity Merchnum_desc in field card_state ---> Done\n",
      "Run time for entity Merchnum_desc in field merch_zip ---> Done\n",
      "Run time for entity Merchnum_desc in field merch_state ---> Done\n",
      "Run time for entity Merchnum_desc in field state_des ---> Done\n",
      "Run time for entity Merchnum_desc in field card_zip3 ---> Done\n",
      "Run time for entity Merchnum_desc in field Card_Merchdesc ---> Done\n",
      "Run time for entity Merchnum_desc in field Card_dow ---> Done\n",
      "Run time for entity Merchnum_desc in field Merchnum_dow ---> Done\n",
      "Run time for entity Merchnum_desc in field Merchdesc_dow ---> Done\n",
      "Run time for entity Merchnum_desc in field Card_Merchnum_desc ---> Done\n",
      "Run time for entity Merchnum_desc in field Card_Merchnum_Zip ---> Done\n",
      "Run time for entity Merchnum_desc in field Card_Merchdesc_Zip ---> Done\n",
      "Run time for entity Merchnum_desc in field Merchnum_desc_State ---> Done\n",
      "Run time for entity Merchnum_dow in field Cardnum ---> Done\n",
      "Run time for entity Merchnum_dow in field Merchnum ---> Done\n",
      "Run time for entity Merchnum_dow in field card_merch ---> Done\n",
      "Run time for entity Merchnum_dow in field card_zip ---> Done\n",
      "Run time for entity Merchnum_dow in field card_state ---> Done\n",
      "Run time for entity Merchnum_dow in field merch_zip ---> Done\n",
      "Run time for entity Merchnum_dow in field merch_state ---> Done\n",
      "Run time for entity Merchnum_dow in field state_des ---> Done\n",
      "Run time for entity Merchnum_dow in field card_zip3 ---> Done\n",
      "Run time for entity Merchnum_dow in field Card_Merchdesc ---> Done\n",
      "Run time for entity Merchnum_dow in field Card_dow ---> Done\n",
      "Run time for entity Merchnum_dow in field Merchnum_desc ---> Done\n",
      "Run time for entity Merchnum_dow in field Merchdesc_dow ---> Done\n",
      "Run time for entity Merchnum_dow in field Card_Merchnum_desc ---> Done\n",
      "Run time for entity Merchnum_dow in field Card_Merchnum_Zip ---> Done\n",
      "Run time for entity Merchnum_dow in field Card_Merchdesc_Zip ---> Done\n",
      "Run time for entity Merchnum_dow in field Merchnum_desc_State ---> Done\n",
      "Run time for entity Merchdesc_dow in field Cardnum ---> Done\n",
      "Run time for entity Merchdesc_dow in field Merchnum ---> Done\n",
      "Run time for entity Merchdesc_dow in field card_merch ---> Done\n",
      "Run time for entity Merchdesc_dow in field card_zip ---> Done\n",
      "Run time for entity Merchdesc_dow in field card_state ---> Done\n",
      "Run time for entity Merchdesc_dow in field merch_zip ---> Done\n",
      "Run time for entity Merchdesc_dow in field merch_state ---> Done\n",
      "Run time for entity Merchdesc_dow in field state_des ---> Done\n",
      "Run time for entity Merchdesc_dow in field card_zip3 ---> Done\n",
      "Run time for entity Merchdesc_dow in field Card_Merchdesc ---> Done\n",
      "Run time for entity Merchdesc_dow in field Card_dow ---> Done\n",
      "Run time for entity Merchdesc_dow in field Merchnum_desc ---> Done\n",
      "Run time for entity Merchdesc_dow in field Merchnum_dow ---> Done\n",
      "Run time for entity Merchdesc_dow in field Card_Merchnum_desc ---> Done\n",
      "Run time for entity Merchdesc_dow in field Card_Merchnum_Zip ---> Done\n",
      "Run time for entity Merchdesc_dow in field Card_Merchdesc_Zip ---> Done\n",
      "Run time for entity Merchdesc_dow in field Merchnum_desc_State ---> Done\n",
      "Run time for entity Card_Merchnum_desc in field Cardnum ---> Done\n",
      "Run time for entity Card_Merchnum_desc in field Merchnum ---> Done\n",
      "Run time for entity Card_Merchnum_desc in field card_merch ---> Done\n",
      "Run time for entity Card_Merchnum_desc in field card_zip ---> Done\n",
      "Run time for entity Card_Merchnum_desc in field card_state ---> Done\n",
      "Run time for entity Card_Merchnum_desc in field merch_zip ---> Done\n",
      "Run time for entity Card_Merchnum_desc in field merch_state ---> Done\n",
      "Run time for entity Card_Merchnum_desc in field state_des ---> Done\n",
      "Run time for entity Card_Merchnum_desc in field card_zip3 ---> Done\n",
      "Run time for entity Card_Merchnum_desc in field Card_Merchdesc ---> Done\n",
      "Run time for entity Card_Merchnum_desc in field Card_dow ---> Done\n",
      "Run time for entity Card_Merchnum_desc in field Merchnum_desc ---> Done\n",
      "Run time for entity Card_Merchnum_desc in field Merchnum_dow ---> Done\n",
      "Run time for entity Card_Merchnum_desc in field Merchdesc_dow ---> Done\n",
      "Run time for entity Card_Merchnum_desc in field Card_Merchnum_Zip ---> Done\n",
      "Run time for entity Card_Merchnum_desc in field Card_Merchdesc_Zip ---> Done\n",
      "Run time for entity Card_Merchnum_desc in field Merchnum_desc_State ---> Done\n",
      "Run time for entity Card_Merchnum_Zip in field Cardnum ---> Done\n",
      "Run time for entity Card_Merchnum_Zip in field Merchnum ---> Done\n",
      "Run time for entity Card_Merchnum_Zip in field card_merch ---> Done\n",
      "Run time for entity Card_Merchnum_Zip in field card_zip ---> Done\n",
      "Run time for entity Card_Merchnum_Zip in field card_state ---> Done\n",
      "Run time for entity Card_Merchnum_Zip in field merch_zip ---> Done\n",
      "Run time for entity Card_Merchnum_Zip in field merch_state ---> Done\n",
      "Run time for entity Card_Merchnum_Zip in field state_des ---> Done\n",
      "Run time for entity Card_Merchnum_Zip in field card_zip3 ---> Done\n",
      "Run time for entity Card_Merchnum_Zip in field Card_Merchdesc ---> Done\n",
      "Run time for entity Card_Merchnum_Zip in field Card_dow ---> Done\n",
      "Run time for entity Card_Merchnum_Zip in field Merchnum_desc ---> Done\n",
      "Run time for entity Card_Merchnum_Zip in field Merchnum_dow ---> Done\n",
      "Run time for entity Card_Merchnum_Zip in field Merchdesc_dow ---> Done\n",
      "Run time for entity Card_Merchnum_Zip in field Card_Merchnum_desc ---> Done\n",
      "Run time for entity Card_Merchnum_Zip in field Card_Merchdesc_Zip ---> Done\n",
      "Run time for entity Card_Merchnum_Zip in field Merchnum_desc_State ---> Done\n",
      "Run time for entity Card_Merchdesc_Zip in field Cardnum ---> Done\n",
      "Run time for entity Card_Merchdesc_Zip in field Merchnum ---> Done\n",
      "Run time for entity Card_Merchdesc_Zip in field card_merch ---> Done\n",
      "Run time for entity Card_Merchdesc_Zip in field card_zip ---> Done\n",
      "Run time for entity Card_Merchdesc_Zip in field card_state ---> Done\n",
      "Run time for entity Card_Merchdesc_Zip in field merch_zip ---> Done\n",
      "Run time for entity Card_Merchdesc_Zip in field merch_state ---> Done\n",
      "Run time for entity Card_Merchdesc_Zip in field state_des ---> Done\n",
      "Run time for entity Card_Merchdesc_Zip in field card_zip3 ---> Done\n",
      "Run time for entity Card_Merchdesc_Zip in field Card_Merchdesc ---> Done\n",
      "Run time for entity Card_Merchdesc_Zip in field Card_dow ---> Done\n",
      "Run time for entity Card_Merchdesc_Zip in field Merchnum_desc ---> Done\n",
      "Run time for entity Card_Merchdesc_Zip in field Merchnum_dow ---> Done\n",
      "Run time for entity Card_Merchdesc_Zip in field Merchdesc_dow ---> Done\n",
      "Run time for entity Card_Merchdesc_Zip in field Card_Merchnum_desc ---> Done\n",
      "Run time for entity Card_Merchdesc_Zip in field Card_Merchnum_Zip ---> Done\n",
      "Run time for entity Card_Merchdesc_Zip in field Merchnum_desc_State ---> Done\n",
      "Run time for entity Merchnum_desc_State in field Cardnum ---> Done\n",
      "Run time for entity Merchnum_desc_State in field Merchnum ---> Done\n",
      "Run time for entity Merchnum_desc_State in field card_merch ---> Done\n",
      "Run time for entity Merchnum_desc_State in field card_zip ---> Done\n",
      "Run time for entity Merchnum_desc_State in field card_state ---> Done\n",
      "Run time for entity Merchnum_desc_State in field merch_zip ---> Done\n",
      "Run time for entity Merchnum_desc_State in field merch_state ---> Done\n",
      "Run time for entity Merchnum_desc_State in field state_des ---> Done\n",
      "Run time for entity Merchnum_desc_State in field card_zip3 ---> Done\n",
      "Run time for entity Merchnum_desc_State in field Card_Merchdesc ---> Done\n",
      "Run time for entity Merchnum_desc_State in field Card_dow ---> Done\n",
      "Run time for entity Merchnum_desc_State in field Merchnum_desc ---> Done\n",
      "Run time for entity Merchnum_desc_State in field Merchnum_dow ---> Done\n",
      "Run time for entity Merchnum_desc_State in field Merchdesc_dow ---> Done\n",
      "Run time for entity Merchnum_desc_State in field Card_Merchnum_desc ---> Done\n",
      "Run time for entity Merchnum_desc_State in field Card_Merchnum_Zip ---> Done\n",
      "Run time for entity Merchnum_desc_State in field Card_Merchdesc_Zip ---> Done\n",
      "Total run time: 0.29769826318333326mins\n"
     ]
    }
   ],
   "source": [
    "start = timeit.default_timer()\n",
    "# # Cross entity uniqueness variables\n",
    "for entity in entities: \n",
    "     for field in entities:\n",
    "        st = timeit.default_timer()\n",
    "        if entity != field:\n",
    "            new_attributes = f'{entity}_{field}_nunique'\n",
    "            if new_attributes not in list(final.columns):\n",
    "                mapper3 = final.groupby(entity)[field].nunique()\n",
    "                final[new_attributes] = final[entity].map(mapper3)\n",
    "            print(f'Run time for entity {entity} in field {field}'+ ' ---> Done')\n",
    "print('Total run time: {}mins'.format((timeit.default_timer() - start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96397, 1922)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(96397, 1922)\n",
      "# new variables is  306\n"
     ]
    }
   ],
   "source": [
    "print(final.shape)\n",
    "print('# new variables is ',len(final.columns) - numstart)\n",
    "numstart = len(final.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# print(final.shape)\n",
    "# final = final.T.drop_duplicates().T\n",
    "# final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df2 = data.copy()\n",
    "# df2['check_date'] = df2.Date\n",
    "# df2['check_recnum'] = df2.Recnum\n",
    "# df_2 = df2[['Recnum', 'Date', 'Amount', 'Cardnum', 'Merchnum']]\n",
    "# df_s = df2[['check_recnum', 'check_date', 'Amount', 'Cardnum', 'Merchnum']]\n",
    "# temp2 = pd.merge(df_2, df_s, left_on = 'Cardnum', right_on = 'Cardnum')\n",
    "\n",
    "# #Frequency Mappers\n",
    "# # groupers = ['Cardnum', 'Merchnum']\n",
    "# groupers = ['Cardnum']\n",
    "# for grouper in groupers: \n",
    "#     for d in [0,1]:\n",
    "#         for dd in [3,7,14,30]:\n",
    "#             numerator_df = temp2[(temp2.check_date >= (temp2.Date - dt.timedelta(d)))\n",
    "#                                & (temp2.Recnum >= temp2.check_recnum)]\n",
    "#             denominator_df = temp2[(temp2.check_date >= (temp2.Date - dt.timedelta(dd)))\n",
    "#                                & (temp2.Recnum >= temp2.check_recnum)]\n",
    "\n",
    "#             numerator = numerator_df.groupby(grouper)['Recnum'].count()\n",
    "#             denominator = denominator_df.groupby(grouper)['Recnum'].count()/dd\n",
    "\n",
    "#             colname = 'relative_velocity_count_by_' + grouper + '_' + str(d) + '_days_over_' + str(dd)\n",
    "\n",
    "#             final[colname] = final[grouper].map(numerator)/final[grouper].map(denominator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(96397, 1922)\n",
      "# new variables is  0\n"
     ]
    }
   ],
   "source": [
    "print(final.shape)\n",
    "print('# new variables is ',len(final.columns) - numstart)\n",
    "numstart = len(final.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run time for the last entity ----------------- 0.02721662499999411s\n",
      "Cardnum_variability_avg_0 ---> Done\n",
      "Cardnum_variability_max_0 ---> Done\n",
      "Cardnum_variability_med_0 ---> Done\n",
      "Cardnum amount variables over past 0 ---> Done\n",
      "Cardnum_variability_avg_1 ---> Done\n",
      "Cardnum_variability_max_1 ---> Done\n",
      "Cardnum_variability_med_1 ---> Done\n",
      "Cardnum amount variables over past 1 ---> Done\n",
      "Cardnum_variability_avg_3 ---> Done\n",
      "Cardnum_variability_max_3 ---> Done\n",
      "Cardnum_variability_med_3 ---> Done\n",
      "Cardnum amount variables over past 3 ---> Done\n",
      "Cardnum_variability_avg_7 ---> Done\n",
      "Cardnum_variability_max_7 ---> Done\n",
      "Cardnum_variability_med_7 ---> Done\n",
      "Cardnum amount variables over past 7 ---> Done\n",
      "Cardnum_variability_avg_14 ---> Done\n",
      "Cardnum_variability_max_14 ---> Done\n",
      "Cardnum_variability_med_14 ---> Done\n",
      "Cardnum amount variables over past 14 ---> Done\n",
      "Cardnum_variability_avg_30 ---> Done\n",
      "Cardnum_variability_max_30 ---> Done\n",
      "Cardnum_variability_med_30 ---> Done\n",
      "Cardnum amount variables over past 30 ---> Done\n",
      "Run time for the last entity ----------------- 2.5443698329999904s\n",
      "Merchnum_variability_avg_0 ---> Done\n",
      "Merchnum_variability_max_0 ---> Done\n",
      "Merchnum_variability_med_0 ---> Done\n",
      "Merchnum amount variables over past 0 ---> Done\n",
      "Merchnum_variability_avg_1 ---> Done\n",
      "Merchnum_variability_max_1 ---> Done\n",
      "Merchnum_variability_med_1 ---> Done\n",
      "Merchnum amount variables over past 1 ---> Done\n",
      "Merchnum_variability_avg_3 ---> Done\n",
      "Merchnum_variability_max_3 ---> Done\n",
      "Merchnum_variability_med_3 ---> Done\n",
      "Merchnum amount variables over past 3 ---> Done\n",
      "Merchnum_variability_avg_7 ---> Done\n",
      "Merchnum_variability_max_7 ---> Done\n",
      "Merchnum_variability_med_7 ---> Done\n",
      "Merchnum amount variables over past 7 ---> Done\n",
      "Merchnum_variability_avg_14 ---> Done\n",
      "Merchnum_variability_max_14 ---> Done\n",
      "Merchnum_variability_med_14 ---> Done\n",
      "Merchnum amount variables over past 14 ---> Done\n",
      "Merchnum_variability_avg_30 ---> Done\n",
      "Merchnum_variability_max_30 ---> Done\n",
      "Merchnum_variability_med_30 ---> Done\n",
      "Merchnum amount variables over past 30 ---> Done\n",
      "Run time for the last entity ----------------- 24.904272332999994s\n",
      "card_merch_variability_avg_0 ---> Done\n",
      "card_merch_variability_max_0 ---> Done\n",
      "card_merch_variability_med_0 ---> Done\n",
      "card_merch amount variables over past 0 ---> Done\n",
      "card_merch_variability_avg_1 ---> Done\n",
      "card_merch_variability_max_1 ---> Done\n",
      "card_merch_variability_med_1 ---> Done\n",
      "card_merch amount variables over past 1 ---> Done\n",
      "card_merch_variability_avg_3 ---> Done\n",
      "card_merch_variability_max_3 ---> Done\n",
      "card_merch_variability_med_3 ---> Done\n",
      "card_merch amount variables over past 3 ---> Done\n",
      "card_merch_variability_avg_7 ---> Done\n",
      "card_merch_variability_max_7 ---> Done\n",
      "card_merch_variability_med_7 ---> Done\n",
      "card_merch amount variables over past 7 ---> Done\n",
      "card_merch_variability_avg_14 ---> Done\n",
      "card_merch_variability_max_14 ---> Done\n",
      "card_merch_variability_med_14 ---> Done\n",
      "card_merch amount variables over past 14 ---> Done\n",
      "card_merch_variability_avg_30 ---> Done\n",
      "card_merch_variability_max_30 ---> Done\n",
      "card_merch_variability_med_30 ---> Done\n",
      "card_merch amount variables over past 30 ---> Done\n",
      "Run time for the last entity ----------------- 1.3704199990000063s\n",
      "card_zip_variability_avg_0 ---> Done\n",
      "card_zip_variability_max_0 ---> Done\n",
      "card_zip_variability_med_0 ---> Done\n",
      "card_zip amount variables over past 0 ---> Done\n",
      "card_zip_variability_avg_1 ---> Done\n",
      "card_zip_variability_max_1 ---> Done\n",
      "card_zip_variability_med_1 ---> Done\n",
      "card_zip amount variables over past 1 ---> Done\n",
      "card_zip_variability_avg_3 ---> Done\n",
      "card_zip_variability_max_3 ---> Done\n",
      "card_zip_variability_med_3 ---> Done\n",
      "card_zip amount variables over past 3 ---> Done\n",
      "card_zip_variability_avg_7 ---> Done\n",
      "card_zip_variability_max_7 ---> Done\n",
      "card_zip_variability_med_7 ---> Done\n",
      "card_zip amount variables over past 7 ---> Done\n",
      "card_zip_variability_avg_14 ---> Done\n",
      "card_zip_variability_max_14 ---> Done\n",
      "card_zip_variability_med_14 ---> Done\n",
      "card_zip amount variables over past 14 ---> Done\n",
      "card_zip_variability_avg_30 ---> Done\n",
      "card_zip_variability_max_30 ---> Done\n",
      "card_zip_variability_med_30 ---> Done\n",
      "card_zip amount variables over past 30 ---> Done\n",
      "Run time for the last entity ----------------- 1.0757113749999974s\n",
      "card_state_variability_avg_0 ---> Done\n",
      "card_state_variability_max_0 ---> Done\n",
      "card_state_variability_med_0 ---> Done\n",
      "card_state amount variables over past 0 ---> Done\n",
      "card_state_variability_avg_1 ---> Done\n",
      "card_state_variability_max_1 ---> Done\n",
      "card_state_variability_med_1 ---> Done\n",
      "card_state amount variables over past 1 ---> Done\n",
      "card_state_variability_avg_3 ---> Done\n",
      "card_state_variability_max_3 ---> Done\n",
      "card_state_variability_med_3 ---> Done\n",
      "card_state amount variables over past 3 ---> Done\n",
      "card_state_variability_avg_7 ---> Done\n",
      "card_state_variability_max_7 ---> Done\n",
      "card_state_variability_med_7 ---> Done\n",
      "card_state amount variables over past 7 ---> Done\n",
      "card_state_variability_avg_14 ---> Done\n",
      "card_state_variability_max_14 ---> Done\n",
      "card_state_variability_med_14 ---> Done\n",
      "card_state amount variables over past 14 ---> Done\n",
      "card_state_variability_avg_30 ---> Done\n",
      "card_state_variability_max_30 ---> Done\n",
      "card_state_variability_med_30 ---> Done\n",
      "card_state amount variables over past 30 ---> Done\n",
      "Run time for the last entity ----------------- 1.2538705419999872s\n",
      "merch_zip_variability_avg_0 ---> Done\n",
      "merch_zip_variability_max_0 ---> Done\n",
      "merch_zip_variability_med_0 ---> Done\n",
      "merch_zip amount variables over past 0 ---> Done\n",
      "merch_zip_variability_avg_1 ---> Done\n",
      "merch_zip_variability_max_1 ---> Done\n",
      "merch_zip_variability_med_1 ---> Done\n",
      "merch_zip amount variables over past 1 ---> Done\n",
      "merch_zip_variability_avg_3 ---> Done\n",
      "merch_zip_variability_max_3 ---> Done\n",
      "merch_zip_variability_med_3 ---> Done\n",
      "merch_zip amount variables over past 3 ---> Done\n",
      "merch_zip_variability_avg_7 ---> Done\n",
      "merch_zip_variability_max_7 ---> Done\n",
      "merch_zip_variability_med_7 ---> Done\n",
      "merch_zip amount variables over past 7 ---> Done\n",
      "merch_zip_variability_avg_14 ---> Done\n",
      "merch_zip_variability_max_14 ---> Done\n",
      "merch_zip_variability_med_14 ---> Done\n",
      "merch_zip amount variables over past 14 ---> Done\n",
      "merch_zip_variability_avg_30 ---> Done\n",
      "merch_zip_variability_max_30 ---> Done\n",
      "merch_zip_variability_med_30 ---> Done\n",
      "merch_zip amount variables over past 30 ---> Done\n",
      "Run time for the last entity ----------------- 24.96129054100001s\n",
      "merch_state_variability_avg_0 ---> Done\n",
      "merch_state_variability_max_0 ---> Done\n",
      "merch_state_variability_med_0 ---> Done\n",
      "merch_state amount variables over past 0 ---> Done\n",
      "merch_state_variability_avg_1 ---> Done\n",
      "merch_state_variability_max_1 ---> Done\n",
      "merch_state_variability_med_1 ---> Done\n",
      "merch_state amount variables over past 1 ---> Done\n",
      "merch_state_variability_avg_3 ---> Done\n",
      "merch_state_variability_max_3 ---> Done\n",
      "merch_state_variability_med_3 ---> Done\n",
      "merch_state amount variables over past 3 ---> Done\n",
      "merch_state_variability_avg_7 ---> Done\n",
      "merch_state_variability_max_7 ---> Done\n",
      "merch_state_variability_med_7 ---> Done\n",
      "merch_state amount variables over past 7 ---> Done\n",
      "merch_state_variability_avg_14 ---> Done\n",
      "merch_state_variability_max_14 ---> Done\n",
      "merch_state_variability_med_14 ---> Done\n",
      "merch_state amount variables over past 14 ---> Done\n",
      "merch_state_variability_avg_30 ---> Done\n",
      "merch_state_variability_max_30 ---> Done\n",
      "merch_state_variability_med_30 ---> Done\n",
      "merch_state amount variables over past 30 ---> Done\n",
      "Run time for the last entity ----------------- 24.878636416999996s\n",
      "state_des_variability_avg_0 ---> Done\n",
      "state_des_variability_max_0 ---> Done\n",
      "state_des_variability_med_0 ---> Done\n",
      "state_des amount variables over past 0 ---> Done\n",
      "state_des_variability_avg_1 ---> Done\n",
      "state_des_variability_max_1 ---> Done\n",
      "state_des_variability_med_1 ---> Done\n",
      "state_des amount variables over past 1 ---> Done\n",
      "state_des_variability_avg_3 ---> Done\n",
      "state_des_variability_max_3 ---> Done\n",
      "state_des_variability_med_3 ---> Done\n",
      "state_des amount variables over past 3 ---> Done\n",
      "state_des_variability_avg_7 ---> Done\n",
      "state_des_variability_max_7 ---> Done\n",
      "state_des_variability_med_7 ---> Done\n",
      "state_des amount variables over past 7 ---> Done\n",
      "state_des_variability_avg_14 ---> Done\n",
      "state_des_variability_max_14 ---> Done\n",
      "state_des_variability_med_14 ---> Done\n",
      "state_des amount variables over past 14 ---> Done\n",
      "state_des_variability_avg_30 ---> Done\n",
      "state_des_variability_max_30 ---> Done\n",
      "state_des_variability_med_30 ---> Done\n",
      "state_des amount variables over past 30 ---> Done\n",
      "Run time for the last entity ----------------- 2.8472433749999766s\n",
      "card_zip3_variability_avg_0 ---> Done\n",
      "card_zip3_variability_max_0 ---> Done\n",
      "card_zip3_variability_med_0 ---> Done\n",
      "card_zip3 amount variables over past 0 ---> Done\n",
      "card_zip3_variability_avg_1 ---> Done\n",
      "card_zip3_variability_max_1 ---> Done\n",
      "card_zip3_variability_med_1 ---> Done\n",
      "card_zip3 amount variables over past 1 ---> Done\n",
      "card_zip3_variability_avg_3 ---> Done\n",
      "card_zip3_variability_max_3 ---> Done\n",
      "card_zip3_variability_med_3 ---> Done\n",
      "card_zip3 amount variables over past 3 ---> Done\n",
      "card_zip3_variability_avg_7 ---> Done\n",
      "card_zip3_variability_max_7 ---> Done\n",
      "card_zip3_variability_med_7 ---> Done\n",
      "card_zip3 amount variables over past 7 ---> Done\n",
      "card_zip3_variability_avg_14 ---> Done\n",
      "card_zip3_variability_max_14 ---> Done\n",
      "card_zip3_variability_med_14 ---> Done\n",
      "card_zip3 amount variables over past 14 ---> Done\n",
      "card_zip3_variability_avg_30 ---> Done\n",
      "card_zip3_variability_max_30 ---> Done\n",
      "card_zip3_variability_med_30 ---> Done\n",
      "card_zip3 amount variables over past 30 ---> Done\n",
      "Run time for the last entity ----------------- 1.1229678340000078s\n",
      "Card_Merchdesc_variability_avg_0 ---> Done\n",
      "Card_Merchdesc_variability_max_0 ---> Done\n",
      "Card_Merchdesc_variability_med_0 ---> Done\n",
      "Card_Merchdesc amount variables over past 0 ---> Done\n",
      "Card_Merchdesc_variability_avg_1 ---> Done\n",
      "Card_Merchdesc_variability_max_1 ---> Done\n",
      "Card_Merchdesc_variability_med_1 ---> Done\n",
      "Card_Merchdesc amount variables over past 1 ---> Done\n",
      "Card_Merchdesc_variability_avg_3 ---> Done\n",
      "Card_Merchdesc_variability_max_3 ---> Done\n",
      "Card_Merchdesc_variability_med_3 ---> Done\n",
      "Card_Merchdesc amount variables over past 3 ---> Done\n",
      "Card_Merchdesc_variability_avg_7 ---> Done\n",
      "Card_Merchdesc_variability_max_7 ---> Done\n",
      "Card_Merchdesc_variability_med_7 ---> Done\n",
      "Card_Merchdesc amount variables over past 7 ---> Done\n",
      "Card_Merchdesc_variability_avg_14 ---> Done\n",
      "Card_Merchdesc_variability_max_14 ---> Done\n",
      "Card_Merchdesc_variability_med_14 ---> Done\n",
      "Card_Merchdesc amount variables over past 14 ---> Done\n",
      "Card_Merchdesc_variability_avg_30 ---> Done\n",
      "Card_Merchdesc_variability_max_30 ---> Done\n",
      "Card_Merchdesc_variability_med_30 ---> Done\n",
      "Card_Merchdesc amount variables over past 30 ---> Done\n",
      "Run time for the last entity ----------------- 0.5881499999999846s\n",
      "Card_dow_variability_avg_0 ---> Done\n",
      "Card_dow_variability_max_0 ---> Done\n",
      "Card_dow_variability_med_0 ---> Done\n",
      "Card_dow amount variables over past 0 ---> Done\n",
      "Card_dow_variability_avg_1 ---> Done\n",
      "Card_dow_variability_max_1 ---> Done\n",
      "Card_dow_variability_med_1 ---> Done\n",
      "Card_dow amount variables over past 1 ---> Done\n",
      "Card_dow_variability_avg_3 ---> Done\n",
      "Card_dow_variability_max_3 ---> Done\n",
      "Card_dow_variability_med_3 ---> Done\n",
      "Card_dow amount variables over past 3 ---> Done\n",
      "Card_dow_variability_avg_7 ---> Done\n",
      "Card_dow_variability_max_7 ---> Done\n",
      "Card_dow_variability_med_7 ---> Done\n",
      "Card_dow amount variables over past 7 ---> Done\n",
      "Card_dow_variability_avg_14 ---> Done\n",
      "Card_dow_variability_max_14 ---> Done\n",
      "Card_dow_variability_med_14 ---> Done\n",
      "Card_dow amount variables over past 14 ---> Done\n",
      "Card_dow_variability_avg_30 ---> Done\n",
      "Card_dow_variability_max_30 ---> Done\n",
      "Card_dow_variability_med_30 ---> Done\n",
      "Card_dow amount variables over past 30 ---> Done\n",
      "Run time for the last entity ----------------- 0.8715843749999976s\n",
      "Merchnum_desc_variability_avg_0 ---> Done\n",
      "Merchnum_desc_variability_max_0 ---> Done\n",
      "Merchnum_desc_variability_med_0 ---> Done\n",
      "Merchnum_desc amount variables over past 0 ---> Done\n",
      "Merchnum_desc_variability_avg_1 ---> Done\n",
      "Merchnum_desc_variability_max_1 ---> Done\n",
      "Merchnum_desc_variability_med_1 ---> Done\n",
      "Merchnum_desc amount variables over past 1 ---> Done\n",
      "Merchnum_desc_variability_avg_3 ---> Done\n",
      "Merchnum_desc_variability_max_3 ---> Done\n",
      "Merchnum_desc_variability_med_3 ---> Done\n",
      "Merchnum_desc amount variables over past 3 ---> Done\n",
      "Merchnum_desc_variability_avg_7 ---> Done\n",
      "Merchnum_desc_variability_max_7 ---> Done\n",
      "Merchnum_desc_variability_med_7 ---> Done\n",
      "Merchnum_desc amount variables over past 7 ---> Done\n",
      "Merchnum_desc_variability_avg_14 ---> Done\n",
      "Merchnum_desc_variability_max_14 ---> Done\n",
      "Merchnum_desc_variability_med_14 ---> Done\n",
      "Merchnum_desc amount variables over past 14 ---> Done\n",
      "Merchnum_desc_variability_avg_30 ---> Done\n",
      "Merchnum_desc_variability_max_30 ---> Done\n",
      "Merchnum_desc_variability_med_30 ---> Done\n",
      "Merchnum_desc amount variables over past 30 ---> Done\n",
      "Run time for the last entity ----------------- 2.4318592500000022s\n",
      "Merchnum_dow_variability_avg_0 ---> Done\n",
      "Merchnum_dow_variability_max_0 ---> Done\n",
      "Merchnum_dow_variability_med_0 ---> Done\n",
      "Merchnum_dow amount variables over past 0 ---> Done\n",
      "Merchnum_dow_variability_avg_1 ---> Done\n",
      "Merchnum_dow_variability_max_1 ---> Done\n",
      "Merchnum_dow_variability_med_1 ---> Done\n",
      "Merchnum_dow amount variables over past 1 ---> Done\n",
      "Merchnum_dow_variability_avg_3 ---> Done\n",
      "Merchnum_dow_variability_max_3 ---> Done\n",
      "Merchnum_dow_variability_med_3 ---> Done\n",
      "Merchnum_dow amount variables over past 3 ---> Done\n",
      "Merchnum_dow_variability_avg_7 ---> Done\n",
      "Merchnum_dow_variability_max_7 ---> Done\n",
      "Merchnum_dow_variability_med_7 ---> Done\n",
      "Merchnum_dow amount variables over past 7 ---> Done\n",
      "Merchnum_dow_variability_avg_14 ---> Done\n",
      "Merchnum_dow_variability_max_14 ---> Done\n",
      "Merchnum_dow_variability_med_14 ---> Done\n",
      "Merchnum_dow amount variables over past 14 ---> Done\n",
      "Merchnum_dow_variability_avg_30 ---> Done\n",
      "Merchnum_dow_variability_max_30 ---> Done\n",
      "Merchnum_dow_variability_med_30 ---> Done\n",
      "Merchnum_dow amount variables over past 30 ---> Done\n",
      "Run time for the last entity ----------------- 3.050272791000026s\n",
      "Merchdesc_dow_variability_avg_0 ---> Done\n",
      "Merchdesc_dow_variability_max_0 ---> Done\n",
      "Merchdesc_dow_variability_med_0 ---> Done\n",
      "Merchdesc_dow amount variables over past 0 ---> Done\n",
      "Merchdesc_dow_variability_avg_1 ---> Done\n",
      "Merchdesc_dow_variability_max_1 ---> Done\n",
      "Merchdesc_dow_variability_med_1 ---> Done\n",
      "Merchdesc_dow amount variables over past 1 ---> Done\n",
      "Merchdesc_dow_variability_avg_3 ---> Done\n",
      "Merchdesc_dow_variability_max_3 ---> Done\n",
      "Merchdesc_dow_variability_med_3 ---> Done\n",
      "Merchdesc_dow amount variables over past 3 ---> Done\n",
      "Merchdesc_dow_variability_avg_7 ---> Done\n",
      "Merchdesc_dow_variability_max_7 ---> Done\n",
      "Merchdesc_dow_variability_med_7 ---> Done\n",
      "Merchdesc_dow amount variables over past 7 ---> Done\n",
      "Merchdesc_dow_variability_avg_14 ---> Done\n",
      "Merchdesc_dow_variability_max_14 ---> Done\n",
      "Merchdesc_dow_variability_med_14 ---> Done\n",
      "Merchdesc_dow amount variables over past 14 ---> Done\n",
      "Merchdesc_dow_variability_avg_30 ---> Done\n",
      "Merchdesc_dow_variability_max_30 ---> Done\n",
      "Merchdesc_dow_variability_med_30 ---> Done\n",
      "Merchdesc_dow amount variables over past 30 ---> Done\n",
      "Run time for the last entity ----------------- 0.9844405409999695s\n",
      "Card_Merchnum_desc_variability_avg_0 ---> Done\n",
      "Card_Merchnum_desc_variability_max_0 ---> Done\n",
      "Card_Merchnum_desc_variability_med_0 ---> Done\n",
      "Card_Merchnum_desc amount variables over past 0 ---> Done\n",
      "Card_Merchnum_desc_variability_avg_1 ---> Done\n",
      "Card_Merchnum_desc_variability_max_1 ---> Done\n",
      "Card_Merchnum_desc_variability_med_1 ---> Done\n",
      "Card_Merchnum_desc amount variables over past 1 ---> Done\n",
      "Card_Merchnum_desc_variability_avg_3 ---> Done\n",
      "Card_Merchnum_desc_variability_max_3 ---> Done\n",
      "Card_Merchnum_desc_variability_med_3 ---> Done\n",
      "Card_Merchnum_desc amount variables over past 3 ---> Done\n",
      "Card_Merchnum_desc_variability_avg_7 ---> Done\n",
      "Card_Merchnum_desc_variability_max_7 ---> Done\n",
      "Card_Merchnum_desc_variability_med_7 ---> Done\n",
      "Card_Merchnum_desc amount variables over past 7 ---> Done\n",
      "Card_Merchnum_desc_variability_avg_14 ---> Done\n",
      "Card_Merchnum_desc_variability_max_14 ---> Done\n",
      "Card_Merchnum_desc_variability_med_14 ---> Done\n",
      "Card_Merchnum_desc amount variables over past 14 ---> Done\n",
      "Card_Merchnum_desc_variability_avg_30 ---> Done\n",
      "Card_Merchnum_desc_variability_max_30 ---> Done\n",
      "Card_Merchnum_desc_variability_med_30 ---> Done\n",
      "Card_Merchnum_desc amount variables over past 30 ---> Done\n",
      "Run time for the last entity ----------------- 0.5769458340000142s\n",
      "Card_Merchnum_Zip_variability_avg_0 ---> Done\n",
      "Card_Merchnum_Zip_variability_max_0 ---> Done\n",
      "Card_Merchnum_Zip_variability_med_0 ---> Done\n",
      "Card_Merchnum_Zip amount variables over past 0 ---> Done\n",
      "Card_Merchnum_Zip_variability_avg_1 ---> Done\n",
      "Card_Merchnum_Zip_variability_max_1 ---> Done\n",
      "Card_Merchnum_Zip_variability_med_1 ---> Done\n",
      "Card_Merchnum_Zip amount variables over past 1 ---> Done\n",
      "Card_Merchnum_Zip_variability_avg_3 ---> Done\n",
      "Card_Merchnum_Zip_variability_max_3 ---> Done\n",
      "Card_Merchnum_Zip_variability_med_3 ---> Done\n",
      "Card_Merchnum_Zip amount variables over past 3 ---> Done\n",
      "Card_Merchnum_Zip_variability_avg_7 ---> Done\n",
      "Card_Merchnum_Zip_variability_max_7 ---> Done\n",
      "Card_Merchnum_Zip_variability_med_7 ---> Done\n",
      "Card_Merchnum_Zip amount variables over past 7 ---> Done\n",
      "Card_Merchnum_Zip_variability_avg_14 ---> Done\n",
      "Card_Merchnum_Zip_variability_max_14 ---> Done\n",
      "Card_Merchnum_Zip_variability_med_14 ---> Done\n",
      "Card_Merchnum_Zip amount variables over past 14 ---> Done\n",
      "Card_Merchnum_Zip_variability_avg_30 ---> Done\n",
      "Card_Merchnum_Zip_variability_max_30 ---> Done\n",
      "Card_Merchnum_Zip_variability_med_30 ---> Done\n",
      "Card_Merchnum_Zip amount variables over past 30 ---> Done\n",
      "Run time for the last entity ----------------- 0.9053837090000343s\n",
      "Card_Merchdesc_Zip_variability_avg_0 ---> Done\n",
      "Card_Merchdesc_Zip_variability_max_0 ---> Done\n",
      "Card_Merchdesc_Zip_variability_med_0 ---> Done\n",
      "Card_Merchdesc_Zip amount variables over past 0 ---> Done\n",
      "Card_Merchdesc_Zip_variability_avg_1 ---> Done\n",
      "Card_Merchdesc_Zip_variability_max_1 ---> Done\n",
      "Card_Merchdesc_Zip_variability_med_1 ---> Done\n",
      "Card_Merchdesc_Zip amount variables over past 1 ---> Done\n",
      "Card_Merchdesc_Zip_variability_avg_3 ---> Done\n",
      "Card_Merchdesc_Zip_variability_max_3 ---> Done\n",
      "Card_Merchdesc_Zip_variability_med_3 ---> Done\n",
      "Card_Merchdesc_Zip amount variables over past 3 ---> Done\n",
      "Card_Merchdesc_Zip_variability_avg_7 ---> Done\n",
      "Card_Merchdesc_Zip_variability_max_7 ---> Done\n",
      "Card_Merchdesc_Zip_variability_med_7 ---> Done\n",
      "Card_Merchdesc_Zip amount variables over past 7 ---> Done\n",
      "Card_Merchdesc_Zip_variability_avg_14 ---> Done\n",
      "Card_Merchdesc_Zip_variability_max_14 ---> Done\n",
      "Card_Merchdesc_Zip_variability_med_14 ---> Done\n",
      "Card_Merchdesc_Zip amount variables over past 14 ---> Done\n",
      "Card_Merchdesc_Zip_variability_avg_30 ---> Done\n",
      "Card_Merchdesc_Zip_variability_max_30 ---> Done\n",
      "Card_Merchdesc_Zip_variability_med_30 ---> Done\n",
      "Card_Merchdesc_Zip amount variables over past 30 ---> Done\n",
      "Run time for the last entity ----------------- 0.578162041999974s\n",
      "Merchnum_desc_State_variability_avg_0 ---> Done\n",
      "Merchnum_desc_State_variability_max_0 ---> Done\n",
      "Merchnum_desc_State_variability_med_0 ---> Done\n",
      "Merchnum_desc_State amount variables over past 0 ---> Done\n",
      "Merchnum_desc_State_variability_avg_1 ---> Done\n",
      "Merchnum_desc_State_variability_max_1 ---> Done\n",
      "Merchnum_desc_State_variability_med_1 ---> Done\n",
      "Merchnum_desc_State amount variables over past 1 ---> Done\n",
      "Merchnum_desc_State_variability_avg_3 ---> Done\n",
      "Merchnum_desc_State_variability_max_3 ---> Done\n",
      "Merchnum_desc_State_variability_med_3 ---> Done\n",
      "Merchnum_desc_State amount variables over past 3 ---> Done\n",
      "Merchnum_desc_State_variability_avg_7 ---> Done\n",
      "Merchnum_desc_State_variability_max_7 ---> Done\n",
      "Merchnum_desc_State_variability_med_7 ---> Done\n",
      "Merchnum_desc_State amount variables over past 7 ---> Done\n",
      "Merchnum_desc_State_variability_avg_14 ---> Done\n",
      "Merchnum_desc_State_variability_max_14 ---> Done\n",
      "Merchnum_desc_State_variability_med_14 ---> Done\n",
      "Merchnum_desc_State amount variables over past 14 ---> Done\n",
      "Merchnum_desc_State_variability_avg_30 ---> Done\n",
      "Merchnum_desc_State_variability_max_30 ---> Done\n",
      "Merchnum_desc_State_variability_med_30 ---> Done\n",
      "Merchnum_desc_State amount variables over past 30 ---> Done\n",
      "Total run time: 1.62297104235mins\n"
     ]
    }
   ],
   "source": [
    "start = timeit.default_timer()\n",
    "for entity in entities:\n",
    "    try: print('Run time for the last entity ----------------- {}s'.format(timeit.default_timer() - st))\n",
    "    except: \n",
    "        print('')\n",
    "    st = timeit.default_timer() \n",
    "    df_l = df1[['Recnum', 'Date', entity,'Amount']]\n",
    "    df_r = df1[['check_record', 'check_date', entity, 'Amount']]\n",
    "    temp = pd.merge(df_l, df_r, left_on = entity, right_on = entity)\n",
    "    \n",
    "    for time in [0,1,3,7,14,30]:\n",
    "        temp2 = temp[(temp.check_date >= (temp.Date - dt.timedelta(time))) &\\\n",
    "                       (temp.Recnum >= temp.check_record)][['Recnum', 'check_record',entity, 'Amount_x','Amount_y']]\n",
    "        temp2['Amount_diff']=temp2['Amount_y']-temp2['Amount_x']\n",
    "\n",
    "        col_name = entity + '_variability_avg_' + str(time)\n",
    "        mapper2 = temp2.groupby('Recnum')['Amount_diff'].mean()\n",
    "        final[col_name] = final.Recnum.map(mapper2)        \n",
    "        print(col_name + ' ---> Done')\n",
    "        \n",
    "        col_name = entity + '_variability_max_' + str(time)\n",
    "        mapper2 = temp2.groupby('Recnum')['Amount_diff'].max()\n",
    "        final[col_name] = final.Recnum.map(mapper2)        \n",
    "        print(col_name + ' ---> Done')\n",
    "        \n",
    "        col_name = entity + '_variability_med_' + str(time)\n",
    "        mapper2 = temp2.groupby('Recnum')['Amount_diff'].median()\n",
    "        final[col_name] = final.Recnum.map(mapper2)        \n",
    "        print(col_name + ' ---> Done')\n",
    "        \n",
    "        print(entity + ' amount variables over past ' + str(time) + ' ---> Done')\n",
    "    del df_l\n",
    "    del df_r\n",
    "    del temp\n",
    "    del temp2\n",
    "        \n",
    "print('Total run time: {}mins'.format((timeit.default_timer() - start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96397, 2246)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(96397, 2246)\n",
      "# new variables is  324\n"
     ]
    }
   ],
   "source": [
    "print(final.shape)\n",
    "print('# new variables is ',len(final.columns) - numstart)\n",
    "numstart = len(final.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total run time: 36.16697837916666mins\n",
      "CPU times: user 28min 26s, sys: 8min 47s, total: 37min 13s\n",
      "Wall time: 36min 10s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# this cell can take a long time.\n",
    "## Counts by Entity\n",
    "start = timeit.default_timer()\n",
    "for i in entities:\n",
    "    for v in entities:\n",
    "        if i==v:\n",
    "            continue\n",
    "        else:\n",
    "            df_c=df1[['Recnum','Date',i]]\n",
    "            df_d=df1[['check_record','check_date',i,v]]\n",
    "            temp=pd.merge(df_c,df_d,left_on=i,right_on=i)\n",
    "            \n",
    "        for t in [1,3,7,14,30,60]:\n",
    "            count_day_df=temp[(temp.check_date>=(temp.Date-dt.timedelta(t)))&(temp.Recnum>=temp.check_record)]\n",
    "            col_name=f'{i}_unique_count_for_{v}_{t}'\n",
    "            mapper=count_day_df.groupby(['Recnum'])[v].nunique()\n",
    "            final[col_name]=final.Recnum.map(mapper)\n",
    "\n",
    "print('Total run time: {}mins'.format((timeit.default_timer() - start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96397, 4082)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cardnum\n",
      "Merchnum\n",
      "card_merch\n",
      "card_zip\n",
      "card_state\n",
      "merch_zip\n",
      "merch_state\n",
      "state_des\n",
      "card_zip3\n",
      "Card_Merchdesc\n",
      "Card_dow\n",
      "Merchnum_desc\n",
      "Merchnum_dow\n",
      "Merchdesc_dow\n",
      "Card_Merchnum_desc\n",
      "Card_Merchnum_Zip\n",
      "Card_Merchdesc_Zip\n",
      "Merchnum_desc_State\n",
      "run time: 0.18228908300034163s\n"
     ]
    }
   ],
   "source": [
    "## Relative Velocity (squared)\n",
    "start = timeit.default_timer()\n",
    "for ent in entities:\n",
    "    print(ent)\n",
    "    for d in ['0', '1']:\n",
    "        for dd in ['7', '14', '30', '60']:\n",
    "            final[ent + '_count_' + d + '_by_' + dd + \"_sq\"] =\\\n",
    "            final[ent + '_count_' + d]/(final[ent + '_count_' + dd])/pow(float(dd),2)\n",
    "print('run time: {}s'.format(timeit.default_timer() - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96397, 4226)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Binning Amounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiUAAAGdCAYAAADNHANuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAcQ0lEQVR4nO3dbZCVdd3A8d+BXWSRRwXdZWATDDQtDXPsQRu8s4eZhslxRnxKrVHMCSqZhtKGSNEssSIMnUwkFcoHQrehuV/EYOYIFU7G5CQOmFAQ7QYYCwIiZ3ev+4XjuVlY0MOePee/nM/nFefstdf1/+21y365zuGcXJZlWQAAVFifSi8AACBClAAAiRAlAEASRAkAkARRAgAkQZQAAEkQJQBAEkQJAJAEUQIAJEGUAABJqKnUgXfs2BFtbW2VOnzZjRgxIrZt21bpZZSVmauDmauDmavDkWauqamJYcOG9ejxKxYlbW1tkc/nK3X4ssrlchHx1szV8lZDZjbzscrMZj5WpTCzh28AgCSIEgAgCaIEAEiCKAEAkiBKAIAkiBIAIAmiBABIgigBAJIgSgCAJIgSACAJogQASIIoAQCSIEoAgCSIEgAgCaIEAEhCTaUO3H7H9Gh/dV2lDl92myu9gAowc3Uwc3Uwc7r6LlhW6SWUjCslAEASRAkAkARRAgAkQZQAAEkQJQBAEkQJAJAEUQIAJEGUAABJECUAQBJECQCQBFECACRBlAAASRAlAEASinqX4OXLl8fy5ctj27ZtERExatSouPTSS2PChAk9sjgAoHoUFSUnnHBCXHXVVVFfXx8REc8++2zcfffdcffdd8fo0aN7ZIEAQHUoKkrOPffcTrevvPLKWL58ebzyyiuiBADolqKi5EAdHR3xxz/+Md58880YP358KdcEAFShoqNk06ZNMXPmzMjn89G/f/+YMWNGjBo16rDb5/P5yOfzhdu5XC7q6uqObrUAQCe5XK6k+ynV/o5qDVmWZcV8QltbW2zfvj327NkTq1evjqeffjpmz5592DBZsmRJLF26tHB7zJgxMWfOnGj52ucj/+q67q0eAKrc6P/9c6WXUDJFR8nB7rjjjjj55JPjS1/6UpcfP9yVElECAN1X8+BvSrKfXC4X9fX10dLSEl2lQU1NTYwYMaIkxzqco35OyduyLOsUHQerra2N2tra7h4GAOhCN68tdLm/Uu/z3SrqxdMeffTRePnll2Pr1q2xadOmeOyxx+Kll16Kj3/84z21PgCgShR1pWTnzp1x7733xo4dO2LAgAHxnve8J2bOnBlnnXVWT60PAKgSRUXJl7/85Z5aBwBQ5bz3DQCQBFECACRBlAAASRAlAEASRAkAkARRAgAkQZQAAEkQJQBAEkQJAJAEUQIAJEGUAABJECUAQBKKekO+Uuo7a1505POVOnxZ5XK5aGhoiObm5siyrNLLKQszm/lYZWYz03NcKQEAkiBKAIAkiBIAIAmiBABIgigBAJIgSgCAJIgSACAJogQASIIoAQCSIEoAgCSIEgAgCaIEAEiCKAEAkiBKAIAkiBIAIAmiBABIgigBAJIgSgCAJIgSACAJogQASIIoAQCSIEoAgCSIEgAgCaIEAEiCKAEAkiBKAIAkiBIAIAmiBABIgigBAJIgSgCAJIgSACAJogQASIIoAQCSIEoAgCSIEgAgCaIEAEhCTaUO3H7H9Gh/dV2lDl92myu9gAowc3Uwc3Uwc9r6LlhW6SWUhCslAEASRAkAkARRAgAkQZQAAEkQJQBAEkQJAJAEUQIAJEGUAABJECUAQBJECQCQBFECACRBlAAASRAlAEASuhUlTU1Ncdlll8XDDz9couUAANXqqKPk73//e6xYsSLe8573lHI9AECVOqoo2bdvX8yfPz9uvPHGOP7440u9JgCgCh1VlDz44IMxYcKEOOuss0q9HgCgStUU+wmrVq2KjRs3xve///13tX0+n498Pl+4ncvloq6urtjDAgCHkcvlSraPUuzraBUVJdu3b4+HH344Zs6cGf369XtXn9PU1BRLly4t3B4zZkzMmTOnuFUCAIfV0NBQsn3V19eXbF/FymVZlr3bjZ9//vn44Q9/GH36/P+jPh0dHZHL5SKXy8Wjjz7a6WMRh79S0vK1z0f+1XUlGAEAqlvNg7/p9j5yuVzU19dHS0tLdJUGNTU1MWLEiG4f50iKulLygQ98IH74wx92uu+nP/1pjBw5Mi6++OJDgiQiora2Nmpra7u3SgDgsIq4vvCu9lXK/RWjqCipq6uLxsbGTvcdd9xxMWjQoEPuBwAohld0BQCSUPT/vjnYbbfdVoJlAADVzpUSACAJogQASIIoAQCSIEoAgCSIEgAgCaIEAEiCKAEAkiBKAIAkiBIAIAmiBABIgigBAJIgSgCAJHT7DfmOVt9Z86Ijn6/U4csql8tFQ0NDNDc3R5ZllV5OWZjZzMcqM5uZnuNKCQCQBFECACRBlAAASRAlAEASRAkAkARRAgAkQZQAAEkQJQBAEkQJAJAEUQIAJEGUAABJECUAQBJECQCQBFECACRBlAAASRAlAEASRAkAkARRAgAkQZQAAEkQJQBAEkQJAJAEUQIAJEGUAABJECUAQBJECQCQBFECACRBlAAASRAlAEASRAkAkARRAgAkQZQAAEkQJQBAEkQJAJAEUQIAJEGUAABJECUAQBJqKnXg9jumR/ur6yp1+LLbXOkFVICZq4OZq4OZ09Z3wbJKL6EkXCkBAJIgSgCAJIgSACAJogQASIIoAQCSIEoAgCSIEgAgCaIEAEiCKAEAkiBKAIAkiBIAIAmiBABIgigBAJJQ1LsEL1myJJYuXdrpviFDhsSCBQtKuigAoPoUFSUREaNHj45Zs2YVbvfp42ILANB9RUdJnz59YujQoT2wFACgmhUdJS0tLXHjjTdGTU1NjBs3Lq688so4+eSTD7t9Pp+PfD5fuJ3L5aKuru7oVgsAHCKXy5VsH6XY11GvIcuy7N1uvGbNmnjzzTdj5MiR0draGk899VRs2bIl5s6dG4MGDerycw5+HsqYMWNizpw50fK1z0f+1XXdnwAAqtzo//1zpZdQEkVFycH27dsXX/3qV+Piiy+OSZMmdbnN4a6UiBIAKI2aB3/T7X3kcrmor6+PlpaW6CoNampqYsSIEd0+zpEU/fDNgfr37x+NjY3R3Nx82G1qa2ujtra2O4cBAI6gG9cXutxXKfdXjG7915l8Ph9btmyJYcOGlWo9AECVKupKyaJFi+Lcc8+N4cOHx86dO+PJJ5+MN954IyZOnNhT6wMAqkRRUfLf//437rnnnti1a1cMHjw4xo0bF3feeWePP8YEABz7ioqS6dOn99AyAIBq5+VYAYAkiBIAIAmiBABIgigBAJIgSgCAJIgSACAJogQASIIoAQCSIEoAgCSIEgAgCaIEAEiCKAEAklDUG/KVUt9Z86Ijn6/U4csql8tFQ0NDNDc3R5ZllV5OWZjZzMcqM5uZnuNKCQCQBFECACRBlAAASRAlAEASRAkAkARRAgAkQZQAAEkQJQBAEkQJAJAEUQIAJEGUAABJECUAQBJECQCQBFECACRBlAAASRAlAEASRAkAkARRAgAkQZQAAEkQJQBAEkQJAJAEUQIAJEGUAABJECUAQBJECQCQBFECACRBlAAASRAlAEASRAkAkARRAgAkQZQAAEkQJQBAEkQJAJAEUQIAJEGUAABJECUAQBJqKnXg9jumR/ur6yp1+LLbXOkFVICZq4OZq4OZ09Z3wbJKL6EkXCkBAJIgSgCAJIgSACAJogQASIIoAQCSIEoAgCSIEgAgCaIEAEiCKAEAkiBKAIAkiBIAIAmiBABIgigBAJJQ9LsEr127NpYtWxYbN26MHTt2xIwZM+K8887ribUBAFWk6Cslb775Zpxyyilx3XXX9cR6AIAqVfSVkgkTJsSECRN6Yi0AQBUrOkqKlc/nI5/PF27ncrmoq6vr6cMCQNXI5XIl20cp9nW0ejxKmpqaYunSpYXbY8aMiTlz5vT0YQGgajQ0NJRsX/X19SXbV7F6PEouueSSmDRpUuF2JQsMAI5Fzc3N3d5HLpeL+vr6aGlpiSzLDvl4TU1NjBgxotvHOZIej5La2tqora3t6cMAQNXqKiK6s69S7q8YXqcEAEhC0VdK9u3bFy0tLYXbW7dujX/84x8xcODAGD58eEkXBwBUj6Kj5NVXX43Zs2cXbi9atCgiIiZOnBjTpk0r3coAgKpSdJSceeaZsWTJkp5YCwBQxTynBABIgigBAJIgSgCAJIgSACAJogQASIIoAQCSIEoAgCSIEgAgCaIEAEiCKAEAkiBKAIAkiBIAIAlFvyFfqfSdNS868vlKHb6scrlcNDQ0RHNzc2RZVunllIWZzXysMrOZ6TmulAAASRAlAEASRAkAkARRAgAkQZQAAEkQJQBAEkQJAJAEUQIAJEGUAABJECUAQBJECQCQBFECACRBlAAASRAlAEASRAkAkARRAgAkQZQAAEkQJQBAEkQJAJAEUQIAJEGUAABJECUAQBJECQCQBFECACRBlAAASRAlAEASRAkAkARRAgAkQZQAAEkQJQBAEkQJAJAEUQIAJEGUAABJECUAQBJECQCQBFECACShplIHbr9jerS/uq5Shy+7zZVeQAWYuTqYuTqYuXfou2BZpZfQLa6UAABJECUAQBJECQCQBFECACRBlAAASRAlAEASRAkAkARRAgAkQZQAAEkQJQBAEkQJAJAEUQIAJEGUAABJKOpdgpuamuL555+PLVu2RL9+/WL8+PFx9dVXx8iRI3tqfQBAlSgqStauXRuf+cxn4tRTT4329vZ4/PHH47vf/W7MnTs3+vfv31NrBACqQFFRMnPmzE63p06dGlOmTIkNGzbEGWecUdKFAQDVpagoOdjevXsjImLgwIGH3Safz0c+ny/czuVyUVdX153DAgBdyOVy3f7c7uyju446SrIsi0ceeSROP/30aGxsPOx2TU1NsXTp0sLtMWPGxJw5c472sADAYTQ0NHR7H/X19SVYydE56ihZuHBhbNq0KW6//fYjbnfJJZfEpEmTCrcrWWAAcCxrbm4+6s/N5XJRX18fLS0tkWXZIR+vqamJESNGdGd57+ioouTnP/95vPDCCzF79uw48cQTj7htbW1t1NbWHtXiAIB3r6uYOJp9lGI/R6Oo1ynJsiwWLlwYq1evju985ztx0kkn9dS6AIAqU1SULFy4MJ577rm46aaboq6uLlpbW6O1tTX279/fU+sDAKpEUQ/fLF++PCIibrvttk73T506NS688MJSrQkAqEJFRcmSJUt6ah0AQJXz3jcAQBJECQCQBFECACRBlAAASRAlAEASRAkAkARRAgAkQZQAAEkQJQBAEkQJAJAEUQIAJEGUAABJKOoN+Uqp76x50ZHPV+rwZZXL5aKhoSGam5sjy7JKL6cszGzmY5WZzUzPcaUEAEiCKAEAkiBKAIAkiBIAIAmiBABIgigBAJIgSgCAJIgSACAJogQASIIoAQCSIEoAgCSIEgAgCaIEAEiCKAEAkiBKAIAkiBIAIAk1FTtwTcUOXTFmrg5mrg5mrg5mfuf7SymXZVnW40c5QD6fj9ra2nIeEgDoBcr+8E0+n4977rkn3njjjXIfumLeeOONuPnmm818jDNzdTBzdTBzZVTkOSWrVq2KMl+gqagsy2Ljxo1mPsaZuTqYuTqYuTI80RUASIIoAQCSUPYoqa2tjUsvvbSqnuxq5upg5upg5upg5soo+/++AQDoiodvAIAkiBIAIAmiBABIgigBAJJQ1hf1/+1vfxvLli2L1tbWGDVqVHzxi1+M973vfeVcwlFpamqK559/PrZs2RL9+vWL8ePHx9VXXx0jR44sbHPffffFs88+2+nzxo0bF3feeWfhdj6fj8WLF8eqVati//798f73vz+mTJkSJ554YmGb3bt3x0MPPRR//vOfIyLi3HPPjeuuuy6OP/74Hp6ysyVLlsTSpUs73TdkyJBYsGBBRLz1Iju/+tWv4umnn47du3fHuHHj4vrrr4/Ro0cXtu9N80ZETJs2LbZt23bI/Z/+9KdjypQpx8Q5Xrt2bSxbtiw2btwYO3bsiBkzZsR5551X+Hg5z+v27dvjwQcfjJdeein69esX559/flx77bUlf3+NI83c1tYWjz/+eKxZsya2bt0aAwYMiA984ANx1VVXxQknnFDYx2233RZr167ttN+PfexjMX369F43c0R5/75KZebLLrusy8+7+uqr43Of+1xE9L7z/G5+N/W2n+my/e+bP/zhDzF//vyYMmVKnHbaabFixYp4+umn48c//nEMHz68HEs4anfeeWecf/75ceqpp0Z7e3s8/vjjsWnTppg7d270798/It76Id+5c2dMnTq18Hk1NTUxcODAwu0FCxbECy+8EFOnTo1BgwbFokWLYvfu3TFnzpzo0+eti1bf+9734rXXXosbb7wxIiJ+9rOfxYgRI+KWW24p48RvRcnq1atj1qxZhfv69OkTgwcPjoiIX//619HU1BRTp06NhoaGeOqpp+Lll1+OefPmRV1dXa+bNyJi165d0dHRUbi9adOm+O53vxu33nprnHnmmcfEOV6zZk2sW7cuxowZEz/60Y8O+Yu7XOe1o6MjvvGNb8TgwYPj2muvjddffz3uu++++PCHPxzXXXdd2Wbeu3dv/OhHP4qLLrooTjnllNi9e3c88sgj0d7eHnfddVdhH7fddls0NDTE5ZdfXrivX79+MWDAgMLt3jJzRPn+vkpp5tbW1kO2v//+++MnP/lJnHzyyRHR+87zu/nd1Ot+prMy+da3vpU98MADne6bPn169stf/rJcSyiZnTt3ZpMnT85eeumlwn333ntvNmfOnMN+zp49e7IrrrgiW7VqVeG+1157LbvsssuyNWvWZFmWZZs3b84mT56crV+/vrDNunXrssmTJ2dbtmwp/SBH8MQTT2QzZszo8mMdHR3ZDTfckDU1NRXu279/f/aFL3whW758eZZlvW/erjz00EPZV77ylayjoyPLsmPvHE+ePDlbvXp14XY5z+tf/vKX7LLLLstee+21wjYrV67MrrrqqmzPnj09MW6WZYfO3JVXXnklmzx5crZt27bCfbfeemv20EMPHfZzetvM5fpeTmnmg82ZMyebPXt2p/t683nOskN/N/XGn+myPKekra0tNmzYEGeffXan+88666xYt25dOZZQUnv37o2I6PSvioi3Lh9OmTIlbrrpprj//vtj586dhY9t2LAh2tvb46yzzircd8IJJ0RjY2OsX78+IiLWr18fAwYMiHHjxhW2GT9+fAwYMKAiX6eWlpa48cYbY9q0aTFv3rz4z3/+ExERW7dujdbW1k7ns7a2Ns4444zCOnvjvAdqa2uL5557Lv7nf/4ncrlc4f5j7RwfqJzndf369dHY2NjpIZKzzz478vl8bNiwoUfnfCd79+6NXC7X6V/HERHPPfdcXH/99fH1r389Fi1a1OlNy3rjzOX4Xk5t5re1trbGmjVr4hOf+MQhH+vN5/ng30298We6LM8pefuy+JAhQzrdP2TIkEMuqaUuy7J45JFH4vTTT4/GxsbC/RMmTIiPfvSjMXz48Ni6dWs88cQTcfvtt8ddd90VtbW10draesjl0YjOX4PW1tZDvkYHb1Mu48aNi2nTpsXIkSOjtbU1nnrqqfj2t78dc+fOLaylq/O5ffv2iIheN+/Bnn/++dizZ09ceOGFhfuOtXN8sHKe1662GThwYNTU1FT067B///549NFH4/zzz+8UJRdccEGcdNJJMXTo0Ni8eXM8+uij8c9//rPw8GZvm7lc38spzXygZ599Nvr379/p4Z2I3n2eu/rd1Bt/psv6RNcD/8V5pPtStnDhwti0aVPcfvvtne7/2Mc+VvhzY2NjnHrqqTF16tT4y1/+Eh/+8IcPu7/sXTylJ8uysn+dJkyYUPhzY2NjjB8/Pr761a/Gs88+W6jlg9f0bmd5N9tU+vvimWeeiQ9+8IOdqv9YO8eHU67z2tW8lfw6tLW1xbx58yLLspgyZUqnj33yk58s/LmxsTEaGhrilltuiQ0bNsTYsWMPu89UZy7n93IqMx/omWeeiY9//OPRr1+/Tvf35vN8uN9NXa0n5Z/psjx8M3jw4OjTp88htbRz584u6ytVP//5z+OFF16IW2+9tdOzkrsybNiwGDFiRDQ3N0dExNChQ6OtrS12797dabtdu3bF0KFDC9sceAn1wG0q/XXq379/NDY2RnNzc2G9B5/PA9fZm+fdtm1bvPjii3HRRRcdcbtj7RyX87wOHTr0kOPs3r072tvbK/J1aGtrix//+Mexbdu2+Pa3v33IQzcHGzNmTPTt2zdaWloionfOfKCe+l5OceaXX345/v3vf3f50M3Best5Ptzvpt74M12WKKmpqYmxY8fGiy++2On+F198MU477bRyLKFbsiyLhQsXxurVq+M73/lOnHTSSe/4Oa+//nq89tprMWzYsIiIGDt2bPTt27fT12DHjh2xadOmGD9+fES89Rjd3r174+9//3thm1deeSX27t1b8a9TPp+PLVu2xLBhwwqXNw+cpa2tLdauXVtYZ2+e95lnnokhQ4bEOeecc8TtjrVzXM7zOn78+Ni0aVPs2LGjsM2LL74YtbW1R/wXaU94O0haWlpi1qxZMWjQoHf8nM2bN0d7e3vhL+3eNvPBeup7OcWZf/e738XYsWPjlFNOecdtUz/P7/S7qTf+TJft4ZtJkybF/PnzY+zYsTF+/PhYsWJFbN++PT71qU+VawlHbeHChbFy5cr45je/GXV1dYUaHDBgQPTr1y/27dsXS5YsiY985CMxdOjQ2LZtWzz22GMxaNCgwmOWAwYMiE984hOxePHiGDRoUAwcODAWL14cjY2NhScYjRo1Kj74wQ/Gz372s7jhhhsiIuKBBx6Ic845p9P/Oy+HRYsWxbnnnhvDhw+PnTt3xpNPPhlvvPFGTJw4MXK5XHz2s5+NpqamaGhoiPr6+mhqaorjjjsuLrjggl4579s6Ojri97//fUycODH69u1buP9YOcf79u0r/Ksv4q0nwv3jH/+IgQMHxvDhw8t2Xs8+++wYNWpU3HvvvXH11VfH7t27Y/HixXHRRRe941WKUs48bNiwmDt3bmzcuDFuvvnm6OjoKPx8v/14eEtLS6xcuTImTJgQgwYNin/961+xePHiGDNmTJx++um9buaBAweW7Xs5lZnfftmJvXv3xp/+9Ke45pprDvn83nie3+l3Uzn/ri7V3GV9l+C3Xzxtx44dMXr06PjCF74QZ5xxRrkOf9QO96I7U6dOjQsvvDD2798fP/jBD2Ljxo2xZ8+eGDZsWJx55plx+eWXd3oNlv3798cvfvGLWLlyZacXqDlwm927dxcuxUVEfOhDH4rrr7++7C8mNm/evHj55Zdj165dMXjw4Bg3blxcccUVMWrUqIj4/xfkWbFiRezZsyfe+973xvXXX9/pyb+9ad63/fWvf40777wz5s2b1ykSjpVz/NJLL8Xs2bMPuX/ixIkxbdq0sp7Xt19o6W9/+1v069cvLrjggrjmmmtK/rbpR5p58uTJ8ZWvfKXLz3v79Wm2b98e8+fPj82bN8e+ffvixBNPjHPOOScmT57c6cmBvWXmG264oazfyynMPG3atIiIWLFiRTz88MPxwAMPHPKLsjee53f63RRR3r+rSzF3WaMEAOBwvPcNAJAEUQIAJEGUAABJECUAQBJECQCQBFECACRBlAAASRAlAEASRAkAkARRAgAkQZQAAEkQJQBAEv4P67UJl/PuP8gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Amount bins\n",
    "AMOUNT = True\n",
    "if AMOUNT:\n",
    "      final['amount_cat'] = pd.qcut(final.Amount, q=5,labels=[1,2,3,4,5])\n",
    "      \n",
    "      final['amount_cat'].value_counts().plot(kind='barh')\n",
    "      plt.show()\n",
    "      \n",
    "      qcut_series, qcut_intervals = pd.qcut(final.Amount, q=5,labels=[1,2,3,4,5],retbins=True)\n",
    "      \n",
    "      qcut_series.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 0.01\n",
      "2 21.74\n",
      "3 85.0\n",
      "4 216.0\n",
      "5 550.57\n"
     ]
    }
   ],
   "source": [
    "bins = [1,2,3,4,5]\n",
    "for bin, interval in zip(bins, qcut_intervals):\n",
    "    print(bin, round(interval,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "if AMOUNT:\n",
    "    final[['Amount', 'amount_cat']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "if AMOUNT:\n",
    "    final['amount_cat'] = final['amount_cat'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['501.0', '544.0', '601.0', ..., '99928.0', '99929.0', '99950.0'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Foreign zipcode\n",
    "zip_state = pd.read_csv('zip_code_database.csv')[['zip','state']]\n",
    "zip_state.sample(5)\n",
    "# Check if the zipcode of merchant is in the US\n",
    "zip_state = pd.read_csv('zip_code_database.csv')['zip'].astype(float).astype(str).values\n",
    "zip_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = list(map(lambda x: x not in zip_state, final['Merch zip']))\n",
    "final = pd.concat([final, pd.DataFrame({'foreign': mapping})], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "final.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recnum</th>\n",
       "      <th>Cardnum</th>\n",
       "      <th>Date</th>\n",
       "      <th>Merchnum</th>\n",
       "      <th>Merch description</th>\n",
       "      <th>Merch state</th>\n",
       "      <th>Merch zip</th>\n",
       "      <th>Transtype</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Fraud</th>\n",
       "      <th>...</th>\n",
       "      <th>Merchnum_desc_State_count_0_by_7_sq</th>\n",
       "      <th>Merchnum_desc_State_count_0_by_14_sq</th>\n",
       "      <th>Merchnum_desc_State_count_0_by_30_sq</th>\n",
       "      <th>Merchnum_desc_State_count_0_by_60_sq</th>\n",
       "      <th>Merchnum_desc_State_count_1_by_7_sq</th>\n",
       "      <th>Merchnum_desc_State_count_1_by_14_sq</th>\n",
       "      <th>Merchnum_desc_State_count_1_by_30_sq</th>\n",
       "      <th>Merchnum_desc_State_count_1_by_60_sq</th>\n",
       "      <th>amount_cat</th>\n",
       "      <th>foreign</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5142190439</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEXSHP12/23/09AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.005102</td>\n",
       "      <td>0.001111</td>\n",
       "      <td>0.000278</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.005102</td>\n",
       "      <td>0.001111</td>\n",
       "      <td>0.000278</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5142183973</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>61003026333</td>\n",
       "      <td>SERVICEMERCHANDISE#81</td>\n",
       "      <td>MA</td>\n",
       "      <td>1803.0</td>\n",
       "      <td>P</td>\n",
       "      <td>31.42</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.005102</td>\n",
       "      <td>0.001111</td>\n",
       "      <td>0.000278</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.005102</td>\n",
       "      <td>0.001111</td>\n",
       "      <td>0.000278</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5142131721</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>4503082993600</td>\n",
       "      <td>OFFICEDEPOT#191</td>\n",
       "      <td>MD</td>\n",
       "      <td>20706.0</td>\n",
       "      <td>P</td>\n",
       "      <td>178.49</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.005102</td>\n",
       "      <td>0.001111</td>\n",
       "      <td>0.000278</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.005102</td>\n",
       "      <td>0.001111</td>\n",
       "      <td>0.000278</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5142148452</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEXSHP12/28/09AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.005102</td>\n",
       "      <td>0.001111</td>\n",
       "      <td>0.000278</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.005102</td>\n",
       "      <td>0.001111</td>\n",
       "      <td>0.000278</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5142190439</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEXSHP12/23/09AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.005102</td>\n",
       "      <td>0.001111</td>\n",
       "      <td>0.000278</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.005102</td>\n",
       "      <td>0.001111</td>\n",
       "      <td>0.000278</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 4228 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Recnum     Cardnum       Date       Merchnum      Merch description  \\\n",
       "0       1  5142190439 2010-01-01  5509006296254    FEDEXSHP12/23/09AB#   \n",
       "1       2  5142183973 2010-01-01    61003026333  SERVICEMERCHANDISE#81   \n",
       "2       3  5142131721 2010-01-01  4503082993600        OFFICEDEPOT#191   \n",
       "3       4  5142148452 2010-01-01  5509006296254    FEDEXSHP12/28/09AB#   \n",
       "4       5  5142190439 2010-01-01  5509006296254    FEDEXSHP12/23/09AB#   \n",
       "\n",
       "  Merch state Merch zip Transtype  Amount  Fraud  ...  \\\n",
       "0          TN   38118.0         P    3.62      0  ...   \n",
       "1          MA    1803.0         P   31.42      0  ...   \n",
       "2          MD   20706.0         P  178.49      0  ...   \n",
       "3          TN   38118.0         P    3.62      0  ...   \n",
       "4          TN   38118.0         P    3.62      0  ...   \n",
       "\n",
       "  Merchnum_desc_State_count_0_by_7_sq  Merchnum_desc_State_count_0_by_14_sq  \\\n",
       "0                            0.020408                              0.005102   \n",
       "1                            0.020408                              0.005102   \n",
       "2                            0.020408                              0.005102   \n",
       "3                            0.020408                              0.005102   \n",
       "4                            0.020408                              0.005102   \n",
       "\n",
       "  Merchnum_desc_State_count_0_by_30_sq  Merchnum_desc_State_count_0_by_60_sq  \\\n",
       "0                             0.001111                              0.000278   \n",
       "1                             0.001111                              0.000278   \n",
       "2                             0.001111                              0.000278   \n",
       "3                             0.001111                              0.000278   \n",
       "4                             0.001111                              0.000278   \n",
       "\n",
       "   Merchnum_desc_State_count_1_by_7_sq Merchnum_desc_State_count_1_by_14_sq  \\\n",
       "0                             0.020408                             0.005102   \n",
       "1                             0.020408                             0.005102   \n",
       "2                             0.020408                             0.005102   \n",
       "3                             0.020408                             0.005102   \n",
       "4                             0.020408                             0.005102   \n",
       "\n",
       "  Merchnum_desc_State_count_1_by_30_sq Merchnum_desc_State_count_1_by_60_sq  \\\n",
       "0                             0.001111                             0.000278   \n",
       "1                             0.001111                             0.000278   \n",
       "2                             0.001111                             0.000278   \n",
       "3                             0.001111                             0.000278   \n",
       "4                             0.001111                             0.000278   \n",
       "\n",
       "  amount_cat foreign  \n",
       "0          1   False  \n",
       "1          2   False  \n",
       "2          3   False  \n",
       "3          1   False  \n",
       "4          1   False  \n",
       "\n",
       "[5 rows x 4228 columns]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96397, 4228)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove any redundant columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "final.set_index('Recnum', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(96397, 4227)\n",
      "CPU times: user 1min 40s, sys: 19.8 s, total: 2min\n",
      "Wall time: 2min 5s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(96397, 3047)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# if the kernel dies in this cell it's likely due to memory problems.\n",
    "# In that case, just write out the data file as is and you can read it in another notebook that just does deduping\n",
    "print(final.shape)\n",
    "final = final.T.drop_duplicates().T\n",
    "final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cardnum',\n",
       " 'Date',\n",
       " 'Merchnum',\n",
       " 'Merch description',\n",
       " 'Merch state',\n",
       " 'Merch zip',\n",
       " 'Transtype',\n",
       " 'Amount',\n",
       " 'Fraud',\n",
       " 'Dow',\n",
       " 'Dow_Risk',\n",
       " 'Month',\n",
       " 'Month_Risk',\n",
       " 'state_risk',\n",
       " 'card_merch',\n",
       " 'card_zip',\n",
       " 'card_state',\n",
       " 'merch_zip',\n",
       " 'merch_state',\n",
       " 'state_des',\n",
       " 'zip3',\n",
       " 'card_zip3',\n",
       " 'Card_Merchdesc',\n",
       " 'Card_dow',\n",
       " 'Merchnum_desc',\n",
       " 'Merchnum_dow',\n",
       " 'Merchdesc_dow',\n",
       " 'Card_Merchnum_desc',\n",
       " 'Card_Merchnum_Zip',\n",
       " 'Card_Merchdesc_Zip',\n",
       " 'Merchnum_desc_State',\n",
       " 'Cardnum_day_since',\n",
       " 'Cardnum_count_0',\n",
       " 'Cardnum_avg_0',\n",
       " 'Cardnum_max_0',\n",
       " 'Cardnum_med_0',\n",
       " 'Cardnum_total_0',\n",
       " 'Cardnum_actual/avg_0',\n",
       " 'Cardnum_actual/max_0',\n",
       " 'Cardnum_actual/med_0',\n",
       " 'Cardnum_actual/toal_0',\n",
       " 'Cardnum_count_1',\n",
       " 'Cardnum_avg_1',\n",
       " 'Cardnum_max_1',\n",
       " 'Cardnum_med_1',\n",
       " 'Cardnum_total_1',\n",
       " 'Cardnum_actual/avg_1',\n",
       " 'Cardnum_actual/max_1',\n",
       " 'Cardnum_actual/med_1',\n",
       " 'Cardnum_actual/toal_1',\n",
       " 'Cardnum_count_3',\n",
       " 'Cardnum_avg_3',\n",
       " 'Cardnum_max_3',\n",
       " 'Cardnum_med_3',\n",
       " 'Cardnum_total_3',\n",
       " 'Cardnum_actual/avg_3',\n",
       " 'Cardnum_actual/max_3',\n",
       " 'Cardnum_actual/med_3',\n",
       " 'Cardnum_actual/toal_3',\n",
       " 'Cardnum_count_7',\n",
       " 'Cardnum_avg_7',\n",
       " 'Cardnum_max_7',\n",
       " 'Cardnum_med_7',\n",
       " 'Cardnum_total_7',\n",
       " 'Cardnum_actual/avg_7',\n",
       " 'Cardnum_actual/max_7',\n",
       " 'Cardnum_actual/med_7',\n",
       " 'Cardnum_actual/toal_7',\n",
       " 'Cardnum_count_14',\n",
       " 'Cardnum_avg_14',\n",
       " 'Cardnum_max_14',\n",
       " 'Cardnum_med_14',\n",
       " 'Cardnum_total_14',\n",
       " 'Cardnum_actual/avg_14',\n",
       " 'Cardnum_actual/max_14',\n",
       " 'Cardnum_actual/med_14',\n",
       " 'Cardnum_actual/toal_14',\n",
       " 'Cardnum_count_30',\n",
       " 'Cardnum_avg_30',\n",
       " 'Cardnum_max_30',\n",
       " 'Cardnum_med_30',\n",
       " 'Cardnum_total_30',\n",
       " 'Cardnum_actual/avg_30',\n",
       " 'Cardnum_actual/max_30',\n",
       " 'Cardnum_actual/med_30',\n",
       " 'Cardnum_actual/toal_30',\n",
       " 'Cardnum_count_60',\n",
       " 'Cardnum_avg_60',\n",
       " 'Cardnum_max_60',\n",
       " 'Cardnum_med_60',\n",
       " 'Cardnum_total_60',\n",
       " 'Cardnum_actual/avg_60',\n",
       " 'Cardnum_actual/max_60',\n",
       " 'Cardnum_actual/med_60',\n",
       " 'Cardnum_actual/toal_60',\n",
       " 'Merchnum_day_since',\n",
       " 'Merchnum_count_0',\n",
       " 'Merchnum_avg_0',\n",
       " 'Merchnum_max_0',\n",
       " 'Merchnum_med_0',\n",
       " 'Merchnum_total_0',\n",
       " 'Merchnum_actual/avg_0',\n",
       " 'Merchnum_actual/max_0',\n",
       " 'Merchnum_actual/med_0',\n",
       " 'Merchnum_actual/toal_0',\n",
       " 'Merchnum_count_1',\n",
       " 'Merchnum_avg_1',\n",
       " 'Merchnum_max_1',\n",
       " 'Merchnum_med_1',\n",
       " 'Merchnum_total_1',\n",
       " 'Merchnum_actual/avg_1',\n",
       " 'Merchnum_actual/max_1',\n",
       " 'Merchnum_actual/med_1',\n",
       " 'Merchnum_actual/toal_1',\n",
       " 'Merchnum_count_3',\n",
       " 'Merchnum_avg_3',\n",
       " 'Merchnum_max_3',\n",
       " 'Merchnum_med_3',\n",
       " 'Merchnum_total_3',\n",
       " 'Merchnum_actual/avg_3',\n",
       " 'Merchnum_actual/max_3',\n",
       " 'Merchnum_actual/med_3',\n",
       " 'Merchnum_actual/toal_3',\n",
       " 'Merchnum_count_7',\n",
       " 'Merchnum_avg_7',\n",
       " 'Merchnum_max_7',\n",
       " 'Merchnum_med_7',\n",
       " 'Merchnum_total_7',\n",
       " 'Merchnum_actual/avg_7',\n",
       " 'Merchnum_actual/max_7',\n",
       " 'Merchnum_actual/med_7',\n",
       " 'Merchnum_actual/toal_7',\n",
       " 'Merchnum_count_14',\n",
       " 'Merchnum_avg_14',\n",
       " 'Merchnum_max_14',\n",
       " 'Merchnum_med_14',\n",
       " 'Merchnum_total_14',\n",
       " 'Merchnum_actual/avg_14',\n",
       " 'Merchnum_actual/max_14',\n",
       " 'Merchnum_actual/med_14',\n",
       " 'Merchnum_actual/toal_14',\n",
       " 'Merchnum_count_30',\n",
       " 'Merchnum_avg_30',\n",
       " 'Merchnum_max_30',\n",
       " 'Merchnum_med_30',\n",
       " 'Merchnum_total_30',\n",
       " 'Merchnum_actual/avg_30',\n",
       " 'Merchnum_actual/max_30',\n",
       " 'Merchnum_actual/med_30',\n",
       " 'Merchnum_actual/toal_30',\n",
       " 'Merchnum_count_60',\n",
       " 'Merchnum_avg_60',\n",
       " 'Merchnum_max_60',\n",
       " 'Merchnum_med_60',\n",
       " 'Merchnum_total_60',\n",
       " 'Merchnum_actual/avg_60',\n",
       " 'Merchnum_actual/max_60',\n",
       " 'Merchnum_actual/med_60',\n",
       " 'Merchnum_actual/toal_60',\n",
       " 'card_merch_day_since',\n",
       " 'card_merch_count_0',\n",
       " 'card_merch_avg_0',\n",
       " 'card_merch_max_0',\n",
       " 'card_merch_med_0',\n",
       " 'card_merch_total_0',\n",
       " 'card_merch_actual/avg_0',\n",
       " 'card_merch_actual/max_0',\n",
       " 'card_merch_actual/med_0',\n",
       " 'card_merch_actual/toal_0',\n",
       " 'card_merch_count_1',\n",
       " 'card_merch_avg_1',\n",
       " 'card_merch_max_1',\n",
       " 'card_merch_med_1',\n",
       " 'card_merch_total_1',\n",
       " 'card_merch_actual/avg_1',\n",
       " 'card_merch_actual/max_1',\n",
       " 'card_merch_actual/med_1',\n",
       " 'card_merch_actual/toal_1',\n",
       " 'card_merch_count_3',\n",
       " 'card_merch_avg_3',\n",
       " 'card_merch_max_3',\n",
       " 'card_merch_med_3',\n",
       " 'card_merch_total_3',\n",
       " 'card_merch_actual/avg_3',\n",
       " 'card_merch_actual/max_3',\n",
       " 'card_merch_actual/med_3',\n",
       " 'card_merch_actual/toal_3',\n",
       " 'card_merch_count_7',\n",
       " 'card_merch_avg_7',\n",
       " 'card_merch_max_7',\n",
       " 'card_merch_med_7',\n",
       " 'card_merch_total_7',\n",
       " 'card_merch_actual/avg_7',\n",
       " 'card_merch_actual/max_7',\n",
       " 'card_merch_actual/med_7',\n",
       " 'card_merch_actual/toal_7',\n",
       " 'card_merch_count_14',\n",
       " 'card_merch_avg_14',\n",
       " 'card_merch_max_14',\n",
       " 'card_merch_med_14',\n",
       " 'card_merch_total_14',\n",
       " 'card_merch_actual/avg_14',\n",
       " 'card_merch_actual/max_14',\n",
       " 'card_merch_actual/med_14',\n",
       " 'card_merch_actual/toal_14',\n",
       " 'card_merch_count_30',\n",
       " 'card_merch_avg_30',\n",
       " 'card_merch_max_30',\n",
       " 'card_merch_med_30',\n",
       " 'card_merch_total_30',\n",
       " 'card_merch_actual/avg_30',\n",
       " 'card_merch_actual/max_30',\n",
       " 'card_merch_actual/med_30',\n",
       " 'card_merch_actual/toal_30',\n",
       " 'card_merch_count_60',\n",
       " 'card_merch_avg_60',\n",
       " 'card_merch_max_60',\n",
       " 'card_merch_med_60',\n",
       " 'card_merch_total_60',\n",
       " 'card_merch_actual/avg_60',\n",
       " 'card_merch_actual/max_60',\n",
       " 'card_merch_actual/med_60',\n",
       " 'card_merch_actual/toal_60',\n",
       " 'card_zip_day_since',\n",
       " 'card_zip_count_0',\n",
       " 'card_zip_avg_0',\n",
       " 'card_zip_max_0',\n",
       " 'card_zip_med_0',\n",
       " 'card_zip_total_0',\n",
       " 'card_zip_actual/avg_0',\n",
       " 'card_zip_actual/max_0',\n",
       " 'card_zip_actual/med_0',\n",
       " 'card_zip_actual/toal_0',\n",
       " 'card_zip_count_1',\n",
       " 'card_zip_avg_1',\n",
       " 'card_zip_max_1',\n",
       " 'card_zip_med_1',\n",
       " 'card_zip_total_1',\n",
       " 'card_zip_actual/avg_1',\n",
       " 'card_zip_actual/max_1',\n",
       " 'card_zip_actual/med_1',\n",
       " 'card_zip_actual/toal_1',\n",
       " 'card_zip_count_3',\n",
       " 'card_zip_avg_3',\n",
       " 'card_zip_max_3',\n",
       " 'card_zip_med_3',\n",
       " 'card_zip_total_3',\n",
       " 'card_zip_actual/avg_3',\n",
       " 'card_zip_actual/max_3',\n",
       " 'card_zip_actual/med_3',\n",
       " 'card_zip_actual/toal_3',\n",
       " 'card_zip_count_7',\n",
       " 'card_zip_avg_7',\n",
       " 'card_zip_max_7',\n",
       " 'card_zip_med_7',\n",
       " 'card_zip_total_7',\n",
       " 'card_zip_actual/avg_7',\n",
       " 'card_zip_actual/max_7',\n",
       " 'card_zip_actual/med_7',\n",
       " 'card_zip_actual/toal_7',\n",
       " 'card_zip_count_14',\n",
       " 'card_zip_avg_14',\n",
       " 'card_zip_max_14',\n",
       " 'card_zip_med_14',\n",
       " 'card_zip_total_14',\n",
       " 'card_zip_actual/avg_14',\n",
       " 'card_zip_actual/max_14',\n",
       " 'card_zip_actual/med_14',\n",
       " 'card_zip_actual/toal_14',\n",
       " 'card_zip_count_30',\n",
       " 'card_zip_avg_30',\n",
       " 'card_zip_max_30',\n",
       " 'card_zip_med_30',\n",
       " 'card_zip_total_30',\n",
       " 'card_zip_actual/avg_30',\n",
       " 'card_zip_actual/max_30',\n",
       " 'card_zip_actual/med_30',\n",
       " 'card_zip_actual/toal_30',\n",
       " 'card_zip_count_60',\n",
       " 'card_zip_avg_60',\n",
       " 'card_zip_max_60',\n",
       " 'card_zip_med_60',\n",
       " 'card_zip_total_60',\n",
       " 'card_zip_actual/avg_60',\n",
       " 'card_zip_actual/max_60',\n",
       " 'card_zip_actual/med_60',\n",
       " 'card_zip_actual/toal_60',\n",
       " 'card_state_day_since',\n",
       " 'card_state_count_0',\n",
       " 'card_state_avg_0',\n",
       " 'card_state_max_0',\n",
       " 'card_state_med_0',\n",
       " 'card_state_total_0',\n",
       " 'card_state_actual/avg_0',\n",
       " 'card_state_actual/max_0',\n",
       " 'card_state_actual/med_0',\n",
       " 'card_state_actual/toal_0',\n",
       " 'card_state_count_1',\n",
       " 'card_state_avg_1',\n",
       " 'card_state_max_1',\n",
       " 'card_state_med_1',\n",
       " 'card_state_total_1',\n",
       " 'card_state_actual/avg_1',\n",
       " 'card_state_actual/max_1',\n",
       " 'card_state_actual/med_1',\n",
       " 'card_state_actual/toal_1',\n",
       " 'card_state_count_3',\n",
       " 'card_state_avg_3',\n",
       " 'card_state_max_3',\n",
       " 'card_state_med_3',\n",
       " 'card_state_total_3',\n",
       " 'card_state_actual/avg_3',\n",
       " 'card_state_actual/max_3',\n",
       " 'card_state_actual/med_3',\n",
       " 'card_state_actual/toal_3',\n",
       " 'card_state_count_7',\n",
       " 'card_state_avg_7',\n",
       " 'card_state_max_7',\n",
       " 'card_state_med_7',\n",
       " 'card_state_total_7',\n",
       " 'card_state_actual/avg_7',\n",
       " 'card_state_actual/max_7',\n",
       " 'card_state_actual/med_7',\n",
       " 'card_state_actual/toal_7',\n",
       " 'card_state_count_14',\n",
       " 'card_state_avg_14',\n",
       " 'card_state_max_14',\n",
       " 'card_state_med_14',\n",
       " 'card_state_total_14',\n",
       " 'card_state_actual/avg_14',\n",
       " 'card_state_actual/max_14',\n",
       " 'card_state_actual/med_14',\n",
       " 'card_state_actual/toal_14',\n",
       " 'card_state_count_30',\n",
       " 'card_state_avg_30',\n",
       " 'card_state_max_30',\n",
       " 'card_state_med_30',\n",
       " 'card_state_total_30',\n",
       " 'card_state_actual/avg_30',\n",
       " 'card_state_actual/max_30',\n",
       " 'card_state_actual/med_30',\n",
       " 'card_state_actual/toal_30',\n",
       " 'card_state_count_60',\n",
       " 'card_state_avg_60',\n",
       " 'card_state_max_60',\n",
       " 'card_state_med_60',\n",
       " 'card_state_total_60',\n",
       " 'card_state_actual/avg_60',\n",
       " 'card_state_actual/max_60',\n",
       " 'card_state_actual/med_60',\n",
       " 'card_state_actual/toal_60',\n",
       " 'merch_zip_day_since',\n",
       " 'merch_zip_count_0',\n",
       " 'merch_zip_avg_0',\n",
       " 'merch_zip_max_0',\n",
       " 'merch_zip_med_0',\n",
       " 'merch_zip_total_0',\n",
       " 'merch_zip_actual/avg_0',\n",
       " 'merch_zip_actual/max_0',\n",
       " 'merch_zip_actual/med_0',\n",
       " 'merch_zip_actual/toal_0',\n",
       " 'merch_zip_count_1',\n",
       " 'merch_zip_avg_1',\n",
       " 'merch_zip_max_1',\n",
       " 'merch_zip_med_1',\n",
       " 'merch_zip_total_1',\n",
       " 'merch_zip_actual/avg_1',\n",
       " 'merch_zip_actual/max_1',\n",
       " 'merch_zip_actual/med_1',\n",
       " 'merch_zip_actual/toal_1',\n",
       " 'merch_zip_count_3',\n",
       " 'merch_zip_avg_3',\n",
       " 'merch_zip_max_3',\n",
       " 'merch_zip_med_3',\n",
       " 'merch_zip_total_3',\n",
       " 'merch_zip_actual/avg_3',\n",
       " 'merch_zip_actual/max_3',\n",
       " 'merch_zip_actual/med_3',\n",
       " 'merch_zip_actual/toal_3',\n",
       " 'merch_zip_count_7',\n",
       " 'merch_zip_avg_7',\n",
       " 'merch_zip_max_7',\n",
       " 'merch_zip_med_7',\n",
       " 'merch_zip_total_7',\n",
       " 'merch_zip_actual/avg_7',\n",
       " 'merch_zip_actual/max_7',\n",
       " 'merch_zip_actual/med_7',\n",
       " 'merch_zip_actual/toal_7',\n",
       " 'merch_zip_count_14',\n",
       " 'merch_zip_avg_14',\n",
       " 'merch_zip_max_14',\n",
       " 'merch_zip_med_14',\n",
       " 'merch_zip_total_14',\n",
       " 'merch_zip_actual/avg_14',\n",
       " 'merch_zip_actual/max_14',\n",
       " 'merch_zip_actual/med_14',\n",
       " 'merch_zip_actual/toal_14',\n",
       " 'merch_zip_count_30',\n",
       " 'merch_zip_avg_30',\n",
       " 'merch_zip_max_30',\n",
       " 'merch_zip_med_30',\n",
       " 'merch_zip_total_30',\n",
       " 'merch_zip_actual/avg_30',\n",
       " 'merch_zip_actual/max_30',\n",
       " 'merch_zip_actual/med_30',\n",
       " 'merch_zip_actual/toal_30',\n",
       " 'merch_zip_count_60',\n",
       " 'merch_zip_avg_60',\n",
       " 'merch_zip_max_60',\n",
       " 'merch_zip_med_60',\n",
       " 'merch_zip_total_60',\n",
       " 'merch_zip_actual/avg_60',\n",
       " 'merch_zip_actual/max_60',\n",
       " 'merch_zip_actual/med_60',\n",
       " 'merch_zip_actual/toal_60',\n",
       " 'merch_state_day_since',\n",
       " 'merch_state_count_0',\n",
       " 'merch_state_avg_0',\n",
       " 'merch_state_max_0',\n",
       " 'merch_state_med_0',\n",
       " 'merch_state_total_0',\n",
       " 'merch_state_actual/avg_0',\n",
       " 'merch_state_actual/max_0',\n",
       " 'merch_state_actual/med_0',\n",
       " 'merch_state_actual/toal_0',\n",
       " 'merch_state_count_1',\n",
       " 'merch_state_avg_1',\n",
       " 'merch_state_max_1',\n",
       " 'merch_state_med_1',\n",
       " 'merch_state_total_1',\n",
       " 'merch_state_actual/avg_1',\n",
       " 'merch_state_actual/max_1',\n",
       " 'merch_state_actual/med_1',\n",
       " 'merch_state_actual/toal_1',\n",
       " 'merch_state_count_3',\n",
       " 'merch_state_avg_3',\n",
       " 'merch_state_max_3',\n",
       " 'merch_state_med_3',\n",
       " 'merch_state_total_3',\n",
       " 'merch_state_actual/avg_3',\n",
       " 'merch_state_actual/max_3',\n",
       " 'merch_state_actual/med_3',\n",
       " 'merch_state_actual/toal_3',\n",
       " 'merch_state_count_7',\n",
       " 'merch_state_avg_7',\n",
       " 'merch_state_max_7',\n",
       " 'merch_state_med_7',\n",
       " 'merch_state_total_7',\n",
       " 'merch_state_actual/avg_7',\n",
       " 'merch_state_actual/max_7',\n",
       " 'merch_state_actual/med_7',\n",
       " 'merch_state_actual/toal_7',\n",
       " 'merch_state_count_14',\n",
       " 'merch_state_avg_14',\n",
       " 'merch_state_max_14',\n",
       " 'merch_state_med_14',\n",
       " 'merch_state_total_14',\n",
       " 'merch_state_actual/avg_14',\n",
       " 'merch_state_actual/max_14',\n",
       " 'merch_state_actual/med_14',\n",
       " 'merch_state_actual/toal_14',\n",
       " 'merch_state_count_30',\n",
       " 'merch_state_avg_30',\n",
       " 'merch_state_max_30',\n",
       " 'merch_state_med_30',\n",
       " 'merch_state_total_30',\n",
       " 'merch_state_actual/avg_30',\n",
       " 'merch_state_actual/max_30',\n",
       " 'merch_state_actual/med_30',\n",
       " 'merch_state_actual/toal_30',\n",
       " 'merch_state_count_60',\n",
       " 'merch_state_avg_60',\n",
       " 'merch_state_max_60',\n",
       " 'merch_state_med_60',\n",
       " 'merch_state_total_60',\n",
       " 'merch_state_actual/avg_60',\n",
       " 'merch_state_actual/max_60',\n",
       " 'merch_state_actual/med_60',\n",
       " 'merch_state_actual/toal_60',\n",
       " 'state_des_day_since',\n",
       " 'state_des_count_0',\n",
       " 'state_des_avg_0',\n",
       " 'state_des_max_0',\n",
       " 'state_des_med_0',\n",
       " 'state_des_total_0',\n",
       " 'state_des_actual/avg_0',\n",
       " 'state_des_actual/max_0',\n",
       " 'state_des_actual/med_0',\n",
       " 'state_des_actual/toal_0',\n",
       " 'state_des_count_1',\n",
       " 'state_des_avg_1',\n",
       " 'state_des_max_1',\n",
       " 'state_des_med_1',\n",
       " 'state_des_total_1',\n",
       " 'state_des_actual/avg_1',\n",
       " 'state_des_actual/max_1',\n",
       " 'state_des_actual/med_1',\n",
       " 'state_des_actual/toal_1',\n",
       " 'state_des_count_3',\n",
       " 'state_des_avg_3',\n",
       " 'state_des_max_3',\n",
       " 'state_des_med_3',\n",
       " 'state_des_total_3',\n",
       " 'state_des_actual/avg_3',\n",
       " 'state_des_actual/max_3',\n",
       " 'state_des_actual/med_3',\n",
       " 'state_des_actual/toal_3',\n",
       " 'state_des_count_7',\n",
       " 'state_des_avg_7',\n",
       " 'state_des_max_7',\n",
       " 'state_des_med_7',\n",
       " 'state_des_total_7',\n",
       " 'state_des_actual/avg_7',\n",
       " 'state_des_actual/max_7',\n",
       " 'state_des_actual/med_7',\n",
       " 'state_des_actual/toal_7',\n",
       " 'state_des_count_14',\n",
       " 'state_des_avg_14',\n",
       " 'state_des_max_14',\n",
       " 'state_des_med_14',\n",
       " 'state_des_total_14',\n",
       " 'state_des_actual/avg_14',\n",
       " 'state_des_actual/max_14',\n",
       " 'state_des_actual/med_14',\n",
       " 'state_des_actual/toal_14',\n",
       " 'state_des_count_30',\n",
       " 'state_des_avg_30',\n",
       " 'state_des_max_30',\n",
       " 'state_des_med_30',\n",
       " 'state_des_total_30',\n",
       " 'state_des_actual/avg_30',\n",
       " 'state_des_actual/max_30',\n",
       " 'state_des_actual/med_30',\n",
       " 'state_des_actual/toal_30',\n",
       " 'state_des_count_60',\n",
       " 'state_des_avg_60',\n",
       " 'state_des_max_60',\n",
       " 'state_des_med_60',\n",
       " 'state_des_total_60',\n",
       " 'state_des_actual/avg_60',\n",
       " 'state_des_actual/max_60',\n",
       " 'state_des_actual/med_60',\n",
       " 'state_des_actual/toal_60',\n",
       " 'card_zip3_day_since',\n",
       " 'card_zip3_count_0',\n",
       " 'card_zip3_avg_0',\n",
       " 'card_zip3_max_0',\n",
       " 'card_zip3_med_0',\n",
       " 'card_zip3_total_0',\n",
       " 'card_zip3_actual/avg_0',\n",
       " 'card_zip3_actual/max_0',\n",
       " 'card_zip3_actual/med_0',\n",
       " 'card_zip3_actual/toal_0',\n",
       " 'card_zip3_count_1',\n",
       " 'card_zip3_avg_1',\n",
       " 'card_zip3_max_1',\n",
       " 'card_zip3_med_1',\n",
       " 'card_zip3_total_1',\n",
       " 'card_zip3_actual/avg_1',\n",
       " 'card_zip3_actual/max_1',\n",
       " 'card_zip3_actual/med_1',\n",
       " 'card_zip3_actual/toal_1',\n",
       " 'card_zip3_count_3',\n",
       " 'card_zip3_avg_3',\n",
       " 'card_zip3_max_3',\n",
       " 'card_zip3_med_3',\n",
       " 'card_zip3_total_3',\n",
       " 'card_zip3_actual/avg_3',\n",
       " 'card_zip3_actual/max_3',\n",
       " 'card_zip3_actual/med_3',\n",
       " 'card_zip3_actual/toal_3',\n",
       " 'card_zip3_count_7',\n",
       " 'card_zip3_avg_7',\n",
       " 'card_zip3_max_7',\n",
       " 'card_zip3_med_7',\n",
       " 'card_zip3_total_7',\n",
       " 'card_zip3_actual/avg_7',\n",
       " 'card_zip3_actual/max_7',\n",
       " 'card_zip3_actual/med_7',\n",
       " 'card_zip3_actual/toal_7',\n",
       " 'card_zip3_count_14',\n",
       " 'card_zip3_avg_14',\n",
       " 'card_zip3_max_14',\n",
       " 'card_zip3_med_14',\n",
       " 'card_zip3_total_14',\n",
       " 'card_zip3_actual/avg_14',\n",
       " 'card_zip3_actual/max_14',\n",
       " 'card_zip3_actual/med_14',\n",
       " 'card_zip3_actual/toal_14',\n",
       " 'card_zip3_count_30',\n",
       " 'card_zip3_avg_30',\n",
       " 'card_zip3_max_30',\n",
       " 'card_zip3_med_30',\n",
       " 'card_zip3_total_30',\n",
       " 'card_zip3_actual/avg_30',\n",
       " 'card_zip3_actual/max_30',\n",
       " 'card_zip3_actual/med_30',\n",
       " 'card_zip3_actual/toal_30',\n",
       " 'card_zip3_count_60',\n",
       " 'card_zip3_avg_60',\n",
       " 'card_zip3_max_60',\n",
       " 'card_zip3_med_60',\n",
       " 'card_zip3_total_60',\n",
       " 'card_zip3_actual/avg_60',\n",
       " 'card_zip3_actual/max_60',\n",
       " 'card_zip3_actual/med_60',\n",
       " 'card_zip3_actual/toal_60',\n",
       " 'Card_Merchdesc_day_since',\n",
       " 'Card_Merchdesc_count_0',\n",
       " 'Card_Merchdesc_avg_0',\n",
       " 'Card_Merchdesc_max_0',\n",
       " 'Card_Merchdesc_med_0',\n",
       " 'Card_Merchdesc_total_0',\n",
       " 'Card_Merchdesc_actual/avg_0',\n",
       " 'Card_Merchdesc_actual/max_0',\n",
       " 'Card_Merchdesc_actual/med_0',\n",
       " 'Card_Merchdesc_actual/toal_0',\n",
       " 'Card_Merchdesc_count_1',\n",
       " 'Card_Merchdesc_avg_1',\n",
       " 'Card_Merchdesc_max_1',\n",
       " 'Card_Merchdesc_med_1',\n",
       " 'Card_Merchdesc_total_1',\n",
       " 'Card_Merchdesc_actual/avg_1',\n",
       " 'Card_Merchdesc_actual/max_1',\n",
       " 'Card_Merchdesc_actual/med_1',\n",
       " 'Card_Merchdesc_actual/toal_1',\n",
       " 'Card_Merchdesc_count_3',\n",
       " 'Card_Merchdesc_avg_3',\n",
       " 'Card_Merchdesc_max_3',\n",
       " 'Card_Merchdesc_med_3',\n",
       " 'Card_Merchdesc_total_3',\n",
       " 'Card_Merchdesc_actual/avg_3',\n",
       " 'Card_Merchdesc_actual/max_3',\n",
       " 'Card_Merchdesc_actual/med_3',\n",
       " 'Card_Merchdesc_actual/toal_3',\n",
       " 'Card_Merchdesc_count_7',\n",
       " 'Card_Merchdesc_avg_7',\n",
       " 'Card_Merchdesc_max_7',\n",
       " 'Card_Merchdesc_med_7',\n",
       " 'Card_Merchdesc_total_7',\n",
       " 'Card_Merchdesc_actual/avg_7',\n",
       " 'Card_Merchdesc_actual/max_7',\n",
       " 'Card_Merchdesc_actual/med_7',\n",
       " 'Card_Merchdesc_actual/toal_7',\n",
       " 'Card_Merchdesc_count_14',\n",
       " 'Card_Merchdesc_avg_14',\n",
       " 'Card_Merchdesc_max_14',\n",
       " 'Card_Merchdesc_med_14',\n",
       " 'Card_Merchdesc_total_14',\n",
       " 'Card_Merchdesc_actual/avg_14',\n",
       " 'Card_Merchdesc_actual/max_14',\n",
       " 'Card_Merchdesc_actual/med_14',\n",
       " 'Card_Merchdesc_actual/toal_14',\n",
       " 'Card_Merchdesc_count_30',\n",
       " 'Card_Merchdesc_avg_30',\n",
       " 'Card_Merchdesc_max_30',\n",
       " 'Card_Merchdesc_med_30',\n",
       " 'Card_Merchdesc_total_30',\n",
       " 'Card_Merchdesc_actual/avg_30',\n",
       " 'Card_Merchdesc_actual/max_30',\n",
       " 'Card_Merchdesc_actual/med_30',\n",
       " 'Card_Merchdesc_actual/toal_30',\n",
       " 'Card_Merchdesc_count_60',\n",
       " 'Card_Merchdesc_avg_60',\n",
       " 'Card_Merchdesc_max_60',\n",
       " 'Card_Merchdesc_med_60',\n",
       " 'Card_Merchdesc_total_60',\n",
       " 'Card_Merchdesc_actual/avg_60',\n",
       " 'Card_Merchdesc_actual/max_60',\n",
       " 'Card_Merchdesc_actual/med_60',\n",
       " 'Card_Merchdesc_actual/toal_60',\n",
       " 'Card_dow_day_since',\n",
       " 'Card_dow_count_7',\n",
       " 'Card_dow_avg_7',\n",
       " 'Card_dow_max_7',\n",
       " 'Card_dow_med_7',\n",
       " 'Card_dow_total_7',\n",
       " 'Card_dow_actual/avg_7',\n",
       " 'Card_dow_actual/max_7',\n",
       " 'Card_dow_actual/med_7',\n",
       " 'Card_dow_actual/toal_7',\n",
       " 'Card_dow_count_14',\n",
       " 'Card_dow_avg_14',\n",
       " 'Card_dow_max_14',\n",
       " 'Card_dow_med_14',\n",
       " 'Card_dow_total_14',\n",
       " 'Card_dow_actual/avg_14',\n",
       " 'Card_dow_actual/max_14',\n",
       " 'Card_dow_actual/med_14',\n",
       " 'Card_dow_actual/toal_14',\n",
       " 'Card_dow_count_30',\n",
       " 'Card_dow_avg_30',\n",
       " 'Card_dow_max_30',\n",
       " 'Card_dow_med_30',\n",
       " 'Card_dow_total_30',\n",
       " 'Card_dow_actual/avg_30',\n",
       " 'Card_dow_actual/max_30',\n",
       " 'Card_dow_actual/med_30',\n",
       " 'Card_dow_actual/toal_30',\n",
       " 'Card_dow_count_60',\n",
       " 'Card_dow_avg_60',\n",
       " 'Card_dow_max_60',\n",
       " 'Card_dow_med_60',\n",
       " 'Card_dow_total_60',\n",
       " 'Card_dow_actual/avg_60',\n",
       " 'Card_dow_actual/max_60',\n",
       " 'Card_dow_actual/med_60',\n",
       " 'Card_dow_actual/toal_60',\n",
       " 'Merchnum_desc_day_since',\n",
       " 'Merchnum_desc_count_0',\n",
       " 'Merchnum_desc_avg_0',\n",
       " 'Merchnum_desc_max_0',\n",
       " 'Merchnum_desc_med_0',\n",
       " 'Merchnum_desc_total_0',\n",
       " 'Merchnum_desc_actual/avg_0',\n",
       " 'Merchnum_desc_actual/max_0',\n",
       " 'Merchnum_desc_actual/med_0',\n",
       " 'Merchnum_desc_actual/toal_0',\n",
       " 'Merchnum_desc_count_1',\n",
       " 'Merchnum_desc_avg_1',\n",
       " 'Merchnum_desc_max_1',\n",
       " 'Merchnum_desc_med_1',\n",
       " 'Merchnum_desc_total_1',\n",
       " 'Merchnum_desc_actual/avg_1',\n",
       " 'Merchnum_desc_actual/max_1',\n",
       " 'Merchnum_desc_actual/med_1',\n",
       " 'Merchnum_desc_actual/toal_1',\n",
       " 'Merchnum_desc_count_3',\n",
       " 'Merchnum_desc_avg_3',\n",
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       " 'Merchnum_desc_med_3',\n",
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       " 'Merchnum_desc_total_14',\n",
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       " 'Merchnum_desc_actual/toal_14',\n",
       " 'Merchnum_desc_count_30',\n",
       " 'Merchnum_desc_avg_30',\n",
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       " 'Merchdesc_dow_actual/toal_14',\n",
       " 'Merchdesc_dow_count_30',\n",
       " 'Merchdesc_dow_avg_30',\n",
       " 'Merchdesc_dow_max_30',\n",
       " 'Merchdesc_dow_med_30',\n",
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       " 'Merchdesc_dow_actual/avg_30',\n",
       " 'Merchdesc_dow_actual/max_30',\n",
       " 'Merchdesc_dow_actual/med_30',\n",
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       " 'Merchdesc_dow_med_60',\n",
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       " 'Merchdesc_dow_actual/max_60',\n",
       " 'Merchdesc_dow_actual/med_60',\n",
       " 'Merchdesc_dow_actual/toal_60',\n",
       " 'Card_Merchnum_desc_day_since',\n",
       " 'Card_Merchnum_desc_count_0',\n",
       " 'Card_Merchnum_desc_avg_0',\n",
       " 'Card_Merchnum_desc_max_0',\n",
       " 'Card_Merchnum_desc_med_0',\n",
       " 'Card_Merchnum_desc_total_0',\n",
       " 'Card_Merchnum_desc_actual/avg_0',\n",
       " 'Card_Merchnum_desc_actual/max_0',\n",
       " 'Card_Merchnum_desc_actual/med_0',\n",
       " 'Card_Merchnum_desc_actual/toal_0',\n",
       " 'Card_Merchnum_desc_count_1',\n",
       " 'Card_Merchnum_desc_avg_1',\n",
       " 'Card_Merchnum_desc_max_1',\n",
       " 'Card_Merchnum_desc_med_1',\n",
       " 'Card_Merchnum_desc_total_1',\n",
       " 'Card_Merchnum_desc_actual/avg_1',\n",
       " 'Card_Merchnum_desc_actual/max_1',\n",
       " 'Card_Merchnum_desc_actual/med_1',\n",
       " 'Card_Merchnum_desc_actual/toal_1',\n",
       " 'Card_Merchnum_desc_count_3',\n",
       " 'Card_Merchnum_desc_avg_3',\n",
       " 'Card_Merchnum_desc_max_3',\n",
       " 'Card_Merchnum_desc_med_3',\n",
       " 'Card_Merchnum_desc_total_3',\n",
       " 'Card_Merchnum_desc_actual/avg_3',\n",
       " 'Card_Merchnum_desc_actual/max_3',\n",
       " 'Card_Merchnum_desc_actual/med_3',\n",
       " 'Card_Merchnum_desc_actual/toal_3',\n",
       " 'Card_Merchnum_desc_count_7',\n",
       " 'Card_Merchnum_desc_avg_7',\n",
       " 'Card_Merchnum_desc_max_7',\n",
       " 'Card_Merchnum_desc_med_7',\n",
       " 'Card_Merchnum_desc_total_7',\n",
       " 'Card_Merchnum_desc_actual/avg_7',\n",
       " 'Card_Merchnum_desc_actual/max_7',\n",
       " 'Card_Merchnum_desc_actual/med_7',\n",
       " 'Card_Merchnum_desc_actual/toal_7',\n",
       " 'Card_Merchnum_desc_count_14',\n",
       " 'Card_Merchnum_desc_avg_14',\n",
       " 'Card_Merchnum_desc_max_14',\n",
       " 'Card_Merchnum_desc_med_14',\n",
       " 'Card_Merchnum_desc_total_14',\n",
       " 'Card_Merchnum_desc_actual/avg_14',\n",
       " 'Card_Merchnum_desc_actual/max_14',\n",
       " 'Card_Merchnum_desc_actual/med_14',\n",
       " 'Card_Merchnum_desc_actual/toal_14',\n",
       " 'Card_Merchnum_desc_count_30',\n",
       " 'Card_Merchnum_desc_avg_30',\n",
       " 'Card_Merchnum_desc_max_30',\n",
       " 'Card_Merchnum_desc_med_30',\n",
       " 'Card_Merchnum_desc_total_30',\n",
       " 'Card_Merchnum_desc_actual/avg_30',\n",
       " 'Card_Merchnum_desc_actual/max_30',\n",
       " 'Card_Merchnum_desc_actual/med_30',\n",
       " 'Card_Merchnum_desc_actual/toal_30',\n",
       " 'Card_Merchnum_desc_count_60',\n",
       " 'Card_Merchnum_desc_avg_60',\n",
       " 'Card_Merchnum_desc_max_60',\n",
       " 'Card_Merchnum_desc_med_60',\n",
       " 'Card_Merchnum_desc_total_60',\n",
       " 'Card_Merchnum_desc_actual/avg_60',\n",
       " 'Card_Merchnum_desc_actual/max_60',\n",
       " 'Card_Merchnum_desc_actual/med_60',\n",
       " 'Card_Merchnum_desc_actual/toal_60',\n",
       " 'Card_Merchnum_Zip_day_since',\n",
       " 'Card_Merchnum_Zip_count_0',\n",
       " 'Card_Merchnum_Zip_avg_0',\n",
       " 'Card_Merchnum_Zip_max_0',\n",
       " 'Card_Merchnum_Zip_med_0',\n",
       " 'Card_Merchnum_Zip_total_0',\n",
       " 'Card_Merchnum_Zip_actual/avg_0',\n",
       " 'Card_Merchnum_Zip_actual/max_0',\n",
       " 'Card_Merchnum_Zip_actual/med_0',\n",
       " 'Card_Merchnum_Zip_actual/toal_0',\n",
       " 'Card_Merchnum_Zip_count_1',\n",
       " 'Card_Merchnum_Zip_avg_1',\n",
       " 'Card_Merchnum_Zip_max_1',\n",
       " 'Card_Merchnum_Zip_med_1',\n",
       " 'Card_Merchnum_Zip_total_1',\n",
       " 'Card_Merchnum_Zip_actual/avg_1',\n",
       " 'Card_Merchnum_Zip_actual/max_1',\n",
       " 'Card_Merchnum_Zip_actual/med_1',\n",
       " 'Card_Merchnum_Zip_actual/toal_1',\n",
       " 'Card_Merchnum_Zip_count_3',\n",
       " 'Card_Merchnum_Zip_avg_3',\n",
       " 'Card_Merchnum_Zip_max_3',\n",
       " 'Card_Merchnum_Zip_med_3',\n",
       " 'Card_Merchnum_Zip_total_3',\n",
       " 'Card_Merchnum_Zip_actual/avg_3',\n",
       " 'Card_Merchnum_Zip_actual/max_3',\n",
       " 'Card_Merchnum_Zip_actual/med_3',\n",
       " 'Card_Merchnum_Zip_actual/toal_3',\n",
       " 'Card_Merchnum_Zip_count_7',\n",
       " 'Card_Merchnum_Zip_avg_7',\n",
       " 'Card_Merchnum_Zip_max_7',\n",
       " 'Card_Merchnum_Zip_med_7',\n",
       " 'Card_Merchnum_Zip_total_7',\n",
       " 'Card_Merchnum_Zip_actual/avg_7',\n",
       " 'Card_Merchnum_Zip_actual/max_7',\n",
       " 'Card_Merchnum_Zip_actual/med_7',\n",
       " 'Card_Merchnum_Zip_actual/toal_7',\n",
       " 'Card_Merchnum_Zip_count_14',\n",
       " 'Card_Merchnum_Zip_avg_14',\n",
       " 'Card_Merchnum_Zip_max_14',\n",
       " 'Card_Merchnum_Zip_med_14',\n",
       " 'Card_Merchnum_Zip_total_14',\n",
       " 'Card_Merchnum_Zip_actual/avg_14',\n",
       " 'Card_Merchnum_Zip_actual/max_14',\n",
       " 'Card_Merchnum_Zip_actual/med_14',\n",
       " 'Card_Merchnum_Zip_actual/toal_14',\n",
       " 'Card_Merchnum_Zip_count_30',\n",
       " 'Card_Merchnum_Zip_avg_30',\n",
       " 'Card_Merchnum_Zip_max_30',\n",
       " 'Card_Merchnum_Zip_med_30',\n",
       " 'Card_Merchnum_Zip_total_30',\n",
       " 'Card_Merchnum_Zip_actual/avg_30',\n",
       " 'Card_Merchnum_Zip_actual/max_30',\n",
       " 'Card_Merchnum_Zip_actual/med_30',\n",
       " 'Card_Merchnum_Zip_actual/toal_30',\n",
       " 'Card_Merchnum_Zip_count_60',\n",
       " 'Card_Merchnum_Zip_avg_60',\n",
       " 'Card_Merchnum_Zip_max_60',\n",
       " 'Card_Merchnum_Zip_med_60',\n",
       " 'Card_Merchnum_Zip_total_60',\n",
       " 'Card_Merchnum_Zip_actual/avg_60',\n",
       " 'Card_Merchnum_Zip_actual/max_60',\n",
       " 'Card_Merchnum_Zip_actual/med_60',\n",
       " 'Card_Merchnum_Zip_actual/toal_60',\n",
       " 'Card_Merchdesc_Zip_day_since',\n",
       " 'Card_Merchdesc_Zip_count_0',\n",
       " 'Card_Merchdesc_Zip_avg_0',\n",
       " 'Card_Merchdesc_Zip_max_0',\n",
       " 'Card_Merchdesc_Zip_med_0',\n",
       " 'Card_Merchdesc_Zip_total_0',\n",
       " 'Card_Merchdesc_Zip_actual/avg_0',\n",
       " 'Card_Merchdesc_Zip_actual/max_0',\n",
       " 'Card_Merchdesc_Zip_actual/med_0',\n",
       " 'Card_Merchdesc_Zip_actual/toal_0',\n",
       " 'Card_Merchdesc_Zip_count_1',\n",
       " 'Card_Merchdesc_Zip_avg_1',\n",
       " 'Card_Merchdesc_Zip_max_1',\n",
       " 'Card_Merchdesc_Zip_med_1',\n",
       " 'Card_Merchdesc_Zip_total_1',\n",
       " 'Card_Merchdesc_Zip_actual/avg_1',\n",
       " 'Card_Merchdesc_Zip_actual/max_1',\n",
       " ...]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.columns.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "# careful about this line. Modify it so you only keep the variables (including the record # and dependent variable)\n",
    "final_vars = final.iloc[:, np.r_[8, 10, 11, len(entities)+10:len(final.columns)]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0.025994</td>\n",
       "      <td>January</td>\n",
       "      <td>5142190439550900629625438118.0</td>\n",
       "      <td>5142190439FEDEXSHP12/23/09AB#38118.0</td>\n",
       "      <td>5509006296254FEDEXSHP12/23/09AB#TN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.005102</td>\n",
       "      <td>0.001111</td>\n",
       "      <td>0.000278</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.005102</td>\n",
       "      <td>0.001111</td>\n",
       "      <td>0.000278</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3022 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Fraud  Dow_Risk    Month               Card_Merchnum_Zip  \\\n",
       "Recnum                                                            \n",
       "1          0  0.025994  January  5142190439550900629625438118.0   \n",
       "2          0  0.025994  January     5142183973610030263331803.0   \n",
       "3          0  0.025994  January  5142131721450308299360020706.0   \n",
       "4          0  0.025994  January  5142148452550900629625438118.0   \n",
       "5          0  0.025994  January  5142190439550900629625438118.0   \n",
       "\n",
       "                           Card_Merchdesc_Zip  \\\n",
       "Recnum                                          \n",
       "1        5142190439FEDEXSHP12/23/09AB#38118.0   \n",
       "2       5142183973SERVICEMERCHANDISE#811803.0   \n",
       "3            5142131721OFFICEDEPOT#19120706.0   \n",
       "4        5142148452FEDEXSHP12/28/09AB#38118.0   \n",
       "5        5142190439FEDEXSHP12/23/09AB#38118.0   \n",
       "\n",
       "                       Merchnum_desc_State Cardnum_day_since Cardnum_count_0  \\\n",
       "Recnum                                                                         \n",
       "1       5509006296254FEDEXSHP12/23/09AB#TN            1461.0               1   \n",
       "2       61003026333SERVICEMERCHANDISE#81MA            1461.0               1   \n",
       "3           4503082993600OFFICEDEPOT#191MD            1461.0               1   \n",
       "4       5509006296254FEDEXSHP12/28/09AB#TN            1461.0               1   \n",
       "5       5509006296254FEDEXSHP12/23/09AB#TN               0.0               2   \n",
       "\n",
       "       Cardnum_avg_0 Cardnum_max_0  ... Merchnum_desc_State_count_0_by_7_sq  \\\n",
       "Recnum                              ...                                       \n",
       "1               3.62          3.62  ...                            0.020408   \n",
       "2              31.42         31.42  ...                            0.020408   \n",
       "3             178.49        178.49  ...                            0.020408   \n",
       "4               3.62          3.62  ...                            0.020408   \n",
       "5               3.62          3.62  ...                            0.020408   \n",
       "\n",
       "       Merchnum_desc_State_count_0_by_14_sq  \\\n",
       "Recnum                                        \n",
       "1                                  0.005102   \n",
       "2                                  0.005102   \n",
       "3                                  0.005102   \n",
       "4                                  0.005102   \n",
       "5                                  0.005102   \n",
       "\n",
       "       Merchnum_desc_State_count_0_by_30_sq  \\\n",
       "Recnum                                        \n",
       "1                                  0.001111   \n",
       "2                                  0.001111   \n",
       "3                                  0.001111   \n",
       "4                                  0.001111   \n",
       "5                                  0.001111   \n",
       "\n",
       "       Merchnum_desc_State_count_0_by_60_sq  \\\n",
       "Recnum                                        \n",
       "1                                  0.000278   \n",
       "2                                  0.000278   \n",
       "3                                  0.000278   \n",
       "4                                  0.000278   \n",
       "5                                  0.000278   \n",
       "\n",
       "       Merchnum_desc_State_count_1_by_7_sq  \\\n",
       "Recnum                                       \n",
       "1                                 0.020408   \n",
       "2                                 0.020408   \n",
       "3                                 0.020408   \n",
       "4                                 0.020408   \n",
       "5                                 0.020408   \n",
       "\n",
       "       Merchnum_desc_State_count_1_by_14_sq  \\\n",
       "Recnum                                        \n",
       "1                                  0.005102   \n",
       "2                                  0.005102   \n",
       "3                                  0.005102   \n",
       "4                                  0.005102   \n",
       "5                                  0.005102   \n",
       "\n",
       "       Merchnum_desc_State_count_1_by_30_sq  \\\n",
       "Recnum                                        \n",
       "1                                  0.001111   \n",
       "2                                  0.001111   \n",
       "3                                  0.001111   \n",
       "4                                  0.001111   \n",
       "5                                  0.001111   \n",
       "\n",
       "       Merchnum_desc_State_count_1_by_60_sq amount_cat foreign  \n",
       "Recnum                                                          \n",
       "1                                  0.000278          1   False  \n",
       "2                                  0.000278          2   False  \n",
       "3                                  0.000278          3   False  \n",
       "4                                  0.000278          1   False  \n",
       "5                                  0.000278          1   False  \n",
       "\n",
       "[5 rows x 3022 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_vars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96397, 3022)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_vars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#final_vars.to_csv('candidate_variables.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duration:  0:50:06.742062\n"
     ]
    }
   ],
   "source": [
    "print('Duration: ', pd.datetime.now() - start_time)"
   ]
  }
 ],
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