[STUDY]Pandas_10minutes to pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 객체 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1, 3, 5, np.nan, 6, 8])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04',\n",
       "               '2020-01-05', '2020-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('20200101', periods=6)\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.888147</td>\n",
       "      <td>1.158443</td>\n",
       "      <td>-1.260765</td>\n",
       "      <td>0.599243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.947061</td>\n",
       "      <td>0.056453</td>\n",
       "      <td>1.403369</td>\n",
       "      <td>0.842185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.500025</td>\n",
       "      <td>-0.212664</td>\n",
       "      <td>-1.420194</td>\n",
       "      <td>0.630546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.782244</td>\n",
       "      <td>0.879595</td>\n",
       "      <td>-1.006656</td>\n",
       "      <td>-0.747239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-0.847207</td>\n",
       "      <td>-0.142760</td>\n",
       "      <td>-0.335974</td>\n",
       "      <td>-0.415355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>-0.493919</td>\n",
       "      <td>0.661829</td>\n",
       "      <td>-0.408328</td>\n",
       "      <td>0.105172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-01-01  0.888147  1.158443 -1.260765  0.599243\n",
       "2020-01-02 -0.947061  0.056453  1.403369  0.842185\n",
       "2020-01-03 -0.500025 -0.212664 -1.420194  0.630546\n",
       "2020-01-04 -0.782244  0.879595 -1.006656 -0.747239\n",
       "2020-01-05 -0.847207 -0.142760 -0.335974 -0.415355\n",
       "2020-01-06 -0.493919  0.661829 -0.408328  0.105172"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A          B  C      E    F\n",
       "0  1.0 2020-01-02  3   test  foo\n",
       "1  1.0 2020-01-02  3  train  foo\n",
       "2  1.0 2020-01-02  3   test  foo\n",
       "3  1.0 2020-01-02  3  train  foo"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'A' : 1.,\n",
    "                   'B' : pd.Timestamp('20200102'),\n",
    "                   'C' : np.array([3] * 4, dtype = 'int32'),\n",
    "                   'E' : pd.Categorical(['test', 'train', 'test', 'train']),\n",
    "                   'F' : 'foo'})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A           float64\n",
       "B    datetime64[ns]\n",
       "C             int32\n",
       "E          category\n",
       "F            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 데이터 확인하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.888147</td>\n",
       "      <td>1.158443</td>\n",
       "      <td>-1.260765</td>\n",
       "      <td>0.599243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.947061</td>\n",
       "      <td>0.056453</td>\n",
       "      <td>1.403369</td>\n",
       "      <td>0.842185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.500025</td>\n",
       "      <td>-0.212664</td>\n",
       "      <td>-1.420194</td>\n",
       "      <td>0.630546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.782244</td>\n",
       "      <td>0.879595</td>\n",
       "      <td>-1.006656</td>\n",
       "      <td>-0.747239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-0.847207</td>\n",
       "      <td>-0.142760</td>\n",
       "      <td>-0.335974</td>\n",
       "      <td>-0.415355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-01-01  0.888147  1.158443 -1.260765  0.599243\n",
       "2020-01-02 -0.947061  0.056453  1.403369  0.842185\n",
       "2020-01-03 -0.500025 -0.212664 -1.420194  0.630546\n",
       "2020-01-04 -0.782244  0.879595 -1.006656 -0.747239\n",
       "2020-01-05 -0.847207 -0.142760 -0.335974 -0.415355"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.947061</td>\n",
       "      <td>0.056453</td>\n",
       "      <td>1.403369</td>\n",
       "      <td>0.842185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.500025</td>\n",
       "      <td>-0.212664</td>\n",
       "      <td>-1.420194</td>\n",
       "      <td>0.630546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.782244</td>\n",
       "      <td>0.879595</td>\n",
       "      <td>-1.006656</td>\n",
       "      <td>-0.747239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-0.847207</td>\n",
       "      <td>-0.142760</td>\n",
       "      <td>-0.335974</td>\n",
       "      <td>-0.415355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>-0.493919</td>\n",
       "      <td>0.661829</td>\n",
       "      <td>-0.408328</td>\n",
       "      <td>0.105172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-01-02 -0.947061  0.056453  1.403369  0.842185\n",
       "2020-01-03 -0.500025 -0.212664 -1.420194  0.630546\n",
       "2020-01-04 -0.782244  0.879595 -1.006656 -0.747239\n",
       "2020-01-05 -0.847207 -0.142760 -0.335974 -0.415355\n",
       "2020-01-06 -0.493919  0.661829 -0.408328  0.105172"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A', 'B', 'C', 'D'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04',\n",
       "               '2020-01-05', '2020-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.447052</td>\n",
       "      <td>0.400149</td>\n",
       "      <td>-0.504758</td>\n",
       "      <td>0.169092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.679742</td>\n",
       "      <td>0.576502</td>\n",
       "      <td>1.033461</td>\n",
       "      <td>0.638177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.947061</td>\n",
       "      <td>-0.212664</td>\n",
       "      <td>-1.420194</td>\n",
       "      <td>-0.747239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.830966</td>\n",
       "      <td>-0.092957</td>\n",
       "      <td>-1.197238</td>\n",
       "      <td>-0.285223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.641135</td>\n",
       "      <td>0.359141</td>\n",
       "      <td>-0.707492</td>\n",
       "      <td>0.352207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-0.495446</td>\n",
       "      <td>0.825154</td>\n",
       "      <td>-0.354062</td>\n",
       "      <td>0.622721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.888147</td>\n",
       "      <td>1.158443</td>\n",
       "      <td>1.403369</td>\n",
       "      <td>0.842185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A         B         C         D\n",
       "count  6.000000  6.000000  6.000000  6.000000\n",
       "mean  -0.447052  0.400149 -0.504758  0.169092\n",
       "std    0.679742  0.576502  1.033461  0.638177\n",
       "min   -0.947061 -0.212664 -1.420194 -0.747239\n",
       "25%   -0.830966 -0.092957 -1.197238 -0.285223\n",
       "50%   -0.641135  0.359141 -0.707492  0.352207\n",
       "75%   -0.495446  0.825154 -0.354062  0.622721\n",
       "max    0.888147  1.158443  1.403369  0.842185"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>2020-01-01 00:00:00</th>\n",
       "      <th>2020-01-02 00:00:00</th>\n",
       "      <th>2020-01-03 00:00:00</th>\n",
       "      <th>2020-01-04 00:00:00</th>\n",
       "      <th>2020-01-05 00:00:00</th>\n",
       "      <th>2020-01-06 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.888147</td>\n",
       "      <td>-0.947061</td>\n",
       "      <td>-0.500025</td>\n",
       "      <td>-0.782244</td>\n",
       "      <td>-0.847207</td>\n",
       "      <td>-0.493919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1.158443</td>\n",
       "      <td>0.056453</td>\n",
       "      <td>-0.212664</td>\n",
       "      <td>0.879595</td>\n",
       "      <td>-0.142760</td>\n",
       "      <td>0.661829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-1.260765</td>\n",
       "      <td>1.403369</td>\n",
       "      <td>-1.420194</td>\n",
       "      <td>-1.006656</td>\n",
       "      <td>-0.335974</td>\n",
       "      <td>-0.408328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.599243</td>\n",
       "      <td>0.842185</td>\n",
       "      <td>0.630546</td>\n",
       "      <td>-0.747239</td>\n",
       "      <td>-0.415355</td>\n",
       "      <td>0.105172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2020-01-01  2020-01-02  2020-01-03  2020-01-04  2020-01-05  2020-01-06\n",
       "A    0.888147   -0.947061   -0.500025   -0.782244   -0.847207   -0.493919\n",
       "B    1.158443    0.056453   -0.212664    0.879595   -0.142760    0.661829\n",
       "C   -1.260765    1.403369   -1.420194   -1.006656   -0.335974   -0.408328\n",
       "D    0.599243    0.842185    0.630546   -0.747239   -0.415355    0.105172"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>D</th>\n",
       "      <th>C</th>\n",
       "      <th>B</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.599243</td>\n",
       "      <td>-1.260765</td>\n",
       "      <td>1.158443</td>\n",
       "      <td>0.888147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>0.842185</td>\n",
       "      <td>1.403369</td>\n",
       "      <td>0.056453</td>\n",
       "      <td>-0.947061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>0.630546</td>\n",
       "      <td>-1.420194</td>\n",
       "      <td>-0.212664</td>\n",
       "      <td>-0.500025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.747239</td>\n",
       "      <td>-1.006656</td>\n",
       "      <td>0.879595</td>\n",
       "      <td>-0.782244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-0.415355</td>\n",
       "      <td>-0.335974</td>\n",
       "      <td>-0.142760</td>\n",
       "      <td>-0.847207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>0.105172</td>\n",
       "      <td>-0.408328</td>\n",
       "      <td>0.661829</td>\n",
       "      <td>-0.493919</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   D         C         B         A\n",
       "2020-01-01  0.599243 -1.260765  1.158443  0.888147\n",
       "2020-01-02  0.842185  1.403369  0.056453 -0.947061\n",
       "2020-01-03  0.630546 -1.420194 -0.212664 -0.500025\n",
       "2020-01-04 -0.747239 -1.006656  0.879595 -0.782244\n",
       "2020-01-05 -0.415355 -0.335974 -0.142760 -0.847207\n",
       "2020-01-06  0.105172 -0.408328  0.661829 -0.493919"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.500025</td>\n",
       "      <td>-0.212664</td>\n",
       "      <td>-1.420194</td>\n",
       "      <td>0.630546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-0.847207</td>\n",
       "      <td>-0.142760</td>\n",
       "      <td>-0.335974</td>\n",
       "      <td>-0.415355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.947061</td>\n",
       "      <td>0.056453</td>\n",
       "      <td>1.403369</td>\n",
       "      <td>0.842185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>-0.493919</td>\n",
       "      <td>0.661829</td>\n",
       "      <td>-0.408328</td>\n",
       "      <td>0.105172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.782244</td>\n",
       "      <td>0.879595</td>\n",
       "      <td>-1.006656</td>\n",
       "      <td>-0.747239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.888147</td>\n",
       "      <td>1.158443</td>\n",
       "      <td>-1.260765</td>\n",
       "      <td>0.599243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-01-03 -0.500025 -0.212664 -1.420194  0.630546\n",
       "2020-01-05 -0.847207 -0.142760 -0.335974 -0.415355\n",
       "2020-01-02 -0.947061  0.056453  1.403369  0.842185\n",
       "2020-01-06 -0.493919  0.661829 -0.408328  0.105172\n",
       "2020-01-04 -0.782244  0.879595 -1.006656 -0.747239\n",
       "2020-01-01  0.888147  1.158443 -1.260765  0.599243"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='B')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 선택"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 얻기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-01-01    0.888147\n",
       "2020-01-02   -0.947061\n",
       "2020-01-03   -0.500025\n",
       "2020-01-04   -0.782244\n",
       "2020-01-05   -0.847207\n",
       "2020-01-06   -0.493919\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.888147</td>\n",
       "      <td>1.158443</td>\n",
       "      <td>-1.260765</td>\n",
       "      <td>0.599243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.947061</td>\n",
       "      <td>0.056453</td>\n",
       "      <td>1.403369</td>\n",
       "      <td>0.842185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-01-01  0.888147  1.158443 -1.260765  0.599243\n",
       "2020-01-02 -0.947061  0.056453  1.403369  0.842185"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.947061</td>\n",
       "      <td>0.056453</td>\n",
       "      <td>1.403369</td>\n",
       "      <td>0.842185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.500025</td>\n",
       "      <td>-0.212664</td>\n",
       "      <td>-1.420194</td>\n",
       "      <td>0.630546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.782244</td>\n",
       "      <td>0.879595</td>\n",
       "      <td>-1.006656</td>\n",
       "      <td>-0.747239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-0.847207</td>\n",
       "      <td>-0.142760</td>\n",
       "      <td>-0.335974</td>\n",
       "      <td>-0.415355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-01-02 -0.947061  0.056453  1.403369  0.842185\n",
       "2020-01-03 -0.500025 -0.212664 -1.420194  0.630546\n",
       "2020-01-04 -0.782244  0.879595 -1.006656 -0.747239\n",
       "2020-01-05 -0.847207 -0.142760 -0.335974 -0.415355"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2020-01-02':'2020-01-05']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Label을 통한 선택"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.888147\n",
       "B    1.158443\n",
       "C   -1.260765\n",
       "D    0.599243\n",
       "Name: 2020-01-01 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.888147</td>\n",
       "      <td>1.158443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.947061</td>\n",
       "      <td>0.056453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.500025</td>\n",
       "      <td>-0.212664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.782244</td>\n",
       "      <td>0.879595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-0.847207</td>\n",
       "      <td>-0.142760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>-0.493919</td>\n",
       "      <td>0.661829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2020-01-01  0.888147  1.158443\n",
       "2020-01-02 -0.947061  0.056453\n",
       "2020-01-03 -0.500025 -0.212664\n",
       "2020-01-04 -0.782244  0.879595\n",
       "2020-01-05 -0.847207 -0.142760\n",
       "2020-01-06 -0.493919  0.661829"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, ['A', 'B']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.947061</td>\n",
       "      <td>0.056453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.500025</td>\n",
       "      <td>-0.212664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.782244</td>\n",
       "      <td>0.879595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2020-01-02 -0.947061  0.056453\n",
       "2020-01-03 -0.500025 -0.212664\n",
       "2020-01-04 -0.782244  0.879595"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2020-01-02':'2020-01-04', ['A', 'B']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.782244\n",
       "B    0.879595\n",
       "Name: 2020-01-04 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2020-01-04', ['A', 'B']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8881471619926138"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0], 'A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8881471619926138"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at[dates[0], 'A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 위치로 선택"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.782244\n",
       "B    0.879595\n",
       "C   -1.006656\n",
       "D   -0.747239\n",
       "Name: 2020-01-04 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.782244</td>\n",
       "      <td>0.879595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-0.847207</td>\n",
       "      <td>-0.142760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2020-01-04 -0.782244  0.879595\n",
       "2020-01-05 -0.847207 -0.142760"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3:5, 0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>A</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.947061</td>\n",
       "      <td>1.403369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.500025</td>\n",
       "      <td>-1.420194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-0.847207</td>\n",
       "      <td>-0.335974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         C\n",
       "2020-01-02 -0.947061  1.403369\n",
       "2020-01-03 -0.500025 -1.420194\n",
       "2020-01-05 -0.847207 -0.335974"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[1,2,4], [0,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.947061</td>\n",
       "      <td>0.056453</td>\n",
       "      <td>1.403369</td>\n",
       "      <td>0.842185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.500025</td>\n",
       "      <td>-0.212664</td>\n",
       "      <td>-1.420194</td>\n",
       "      <td>0.630546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-01-02 -0.947061  0.056453  1.403369  0.842185\n",
       "2020-01-03 -0.500025 -0.212664 -1.420194  0.630546"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>1.158443</td>\n",
       "      <td>-1.260765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>0.056453</td>\n",
       "      <td>1.403369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.212664</td>\n",
       "      <td>-1.420194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>0.879595</td>\n",
       "      <td>-1.006656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-0.142760</td>\n",
       "      <td>-0.335974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>0.661829</td>\n",
       "      <td>-0.408328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   B         C\n",
       "2020-01-01  1.158443 -1.260765\n",
       "2020-01-02  0.056453  1.403369\n",
       "2020-01-03 -0.212664 -1.420194\n",
       "2020-01-04  0.879595 -1.006656\n",
       "2020-01-05 -0.142760 -0.335974\n",
       "2020-01-06  0.661829 -0.408328"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05645325349168395"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05645325349168395"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iat[1,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boolean Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.888147</td>\n",
       "      <td>1.158443</td>\n",
       "      <td>-1.260765</td>\n",
       "      <td>0.599243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-01-01  0.888147  1.158443 -1.260765  0.599243"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.A > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.888147</td>\n",
       "      <td>1.158443</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.599243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.056453</td>\n",
       "      <td>1.403369</td>\n",
       "      <td>0.842185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.630546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.879595</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.661829</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.105172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-01-01  0.888147  1.158443       NaN  0.599243\n",
       "2020-01-02       NaN  0.056453  1.403369  0.842185\n",
       "2020-01-03       NaN       NaN       NaN  0.630546\n",
       "2020-01-04       NaN  0.879595       NaN       NaN\n",
       "2020-01-05       NaN       NaN       NaN       NaN\n",
       "2020-01-06       NaN  0.661829       NaN  0.105172"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.888147</td>\n",
       "      <td>1.158443</td>\n",
       "      <td>-1.260765</td>\n",
       "      <td>0.599243</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.947061</td>\n",
       "      <td>0.056453</td>\n",
       "      <td>1.403369</td>\n",
       "      <td>0.842185</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.500025</td>\n",
       "      <td>-0.212664</td>\n",
       "      <td>-1.420194</td>\n",
       "      <td>0.630546</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.782244</td>\n",
       "      <td>0.879595</td>\n",
       "      <td>-1.006656</td>\n",
       "      <td>-0.747239</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-0.847207</td>\n",
       "      <td>-0.142760</td>\n",
       "      <td>-0.335974</td>\n",
       "      <td>-0.415355</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>-0.493919</td>\n",
       "      <td>0.661829</td>\n",
       "      <td>-0.408328</td>\n",
       "      <td>0.105172</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D      E\n",
       "2020-01-01  0.888147  1.158443 -1.260765  0.599243    one\n",
       "2020-01-02 -0.947061  0.056453  1.403369  0.842185    one\n",
       "2020-01-03 -0.500025 -0.212664 -1.420194  0.630546    two\n",
       "2020-01-04 -0.782244  0.879595 -1.006656 -0.747239  three\n",
       "2020-01-05 -0.847207 -0.142760 -0.335974 -0.415355   four\n",
       "2020-01-06 -0.493919  0.661829 -0.408328  0.105172  three"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "df2['E'] = ['one', 'one', 'two', 'three', 'four', 'three']\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.500025</td>\n",
       "      <td>-0.212664</td>\n",
       "      <td>-1.420194</td>\n",
       "      <td>0.630546</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-0.847207</td>\n",
       "      <td>-0.142760</td>\n",
       "      <td>-0.335974</td>\n",
       "      <td>-0.415355</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D     E\n",
       "2020-01-03 -0.500025 -0.212664 -1.420194  0.630546   two\n",
       "2020-01-05 -0.847207 -0.142760 -0.335974 -0.415355  four"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2['E'].isin(['two', 'four'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-01-02    1\n",
       "2020-01-03    2\n",
       "2020-01-04    3\n",
       "2020-01-05    4\n",
       "2020-01-06    5\n",
       "2020-01-07    6\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series([1,2,3,4,5,6], index=pd.date_range('20200102', periods=6))\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.888147</td>\n",
       "      <td>1.158443</td>\n",
       "      <td>-1.260765</td>\n",
       "      <td>0.599243</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.947061</td>\n",
       "      <td>0.056453</td>\n",
       "      <td>1.403369</td>\n",
       "      <td>0.842185</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.500025</td>\n",
       "      <td>-0.212664</td>\n",
       "      <td>-1.420194</td>\n",
       "      <td>0.630546</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.782244</td>\n",
       "      <td>0.879595</td>\n",
       "      <td>-1.006656</td>\n",
       "      <td>-0.747239</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-0.847207</td>\n",
       "      <td>-0.142760</td>\n",
       "      <td>-0.335974</td>\n",
       "      <td>-0.415355</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>-0.493919</td>\n",
       "      <td>0.661829</td>\n",
       "      <td>-0.408328</td>\n",
       "      <td>0.105172</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D    F\n",
       "2020-01-01  0.888147  1.158443 -1.260765  0.599243  NaN\n",
       "2020-01-02 -0.947061  0.056453  1.403369  0.842185  1.0\n",
       "2020-01-03 -0.500025 -0.212664 -1.420194  0.630546  2.0\n",
       "2020-01-04 -0.782244  0.879595 -1.006656 -0.747239  3.0\n",
       "2020-01-05 -0.847207 -0.142760 -0.335974 -0.415355  4.0\n",
       "2020-01-06 -0.493919  0.661829 -0.408328  0.105172  5.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['F'] = s1\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 라벨에 값 설정"
   ]
  },
  {
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.158443</td>\n",
       "      <td>-1.260765</td>\n",
       "      <td>0.599243</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.947061</td>\n",
       "      <td>0.056453</td>\n",
       "      <td>1.403369</td>\n",
       "      <td>0.842185</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.500025</td>\n",
       "      <td>-0.212664</td>\n",
       "      <td>-1.420194</td>\n",
       "      <td>0.630546</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.782244</td>\n",
       "      <td>0.879595</td>\n",
       "      <td>-1.006656</td>\n",
       "      <td>-0.747239</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-0.847207</td>\n",
       "      <td>-0.142760</td>\n",
       "      <td>-0.335974</td>\n",
       "      <td>-0.415355</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>-0.493919</td>\n",
       "      <td>0.661829</td>\n",
       "      <td>-0.408328</td>\n",
       "      <td>0.105172</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D    F\n",
       "2020-01-01  0.000000  1.158443 -1.260765  0.599243  NaN\n",
       "2020-01-02 -0.947061  0.056453  1.403369  0.842185  1.0\n",
       "2020-01-03 -0.500025 -0.212664 -1.420194  0.630546  2.0\n",
       "2020-01-04 -0.782244  0.879595 -1.006656 -0.747239  3.0\n",
       "2020-01-05 -0.847207 -0.142760 -0.335974 -0.415355  4.0\n",
       "2020-01-06 -0.493919  0.661829 -0.408328  0.105172  5.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at[dates[0], 'A'] = 0\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 위치에 값 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.260765</td>\n",
       "      <td>0.599243</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.947061</td>\n",
       "      <td>0.056453</td>\n",
       "      <td>1.403369</td>\n",
       "      <td>0.842185</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.500025</td>\n",
       "      <td>-0.212664</td>\n",
       "      <td>-1.420194</td>\n",
       "      <td>0.630546</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.782244</td>\n",
       "      <td>0.879595</td>\n",
       "      <td>-1.006656</td>\n",
       "      <td>-0.747239</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-0.847207</td>\n",
       "      <td>-0.142760</td>\n",
       "      <td>-0.335974</td>\n",
       "      <td>-0.415355</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>-0.493919</td>\n",
       "      <td>0.661829</td>\n",
       "      <td>-0.408328</td>\n",
       "      <td>0.105172</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D    F\n",
       "2020-01-01  0.000000  0.000000 -1.260765  0.599243  NaN\n",
       "2020-01-02 -0.947061  0.056453  1.403369  0.842185  1.0\n",
       "2020-01-03 -0.500025 -0.212664 -1.420194  0.630546  2.0\n",
       "2020-01-04 -0.782244  0.879595 -1.006656 -0.747239  3.0\n",
       "2020-01-05 -0.847207 -0.142760 -0.335974 -0.415355  4.0\n",
       "2020-01-06 -0.493919  0.661829 -0.408328  0.105172  5.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iat[0,1] = 0\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.260765</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.947061</td>\n",
       "      <td>0.056453</td>\n",
       "      <td>1.403369</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.500025</td>\n",
       "      <td>-0.212664</td>\n",
       "      <td>-1.420194</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.782244</td>\n",
       "      <td>0.879595</td>\n",
       "      <td>-1.006656</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-0.847207</td>\n",
       "      <td>-0.142760</td>\n",
       "      <td>-0.335974</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>-0.493919</td>\n",
       "      <td>0.661829</td>\n",
       "      <td>-0.408328</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F\n",
       "2020-01-01  0.000000  0.000000 -1.260765  5  NaN\n",
       "2020-01-02 -0.947061  0.056453  1.403369  5  1.0\n",
       "2020-01-03 -0.500025 -0.212664 -1.420194  5  2.0\n",
       "2020-01-04 -0.782244  0.879595 -1.006656  5  3.0\n",
       "2020-01-05 -0.847207 -0.142760 -0.335974  5  4.0\n",
       "2020-01-06 -0.493919  0.661829 -0.408328  5  5.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, 'D'] = np.array([5] * len(df))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### where 연산 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.260765</td>\n",
       "      <td>-5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.947061</td>\n",
       "      <td>-0.056453</td>\n",
       "      <td>-1.403369</td>\n",
       "      <td>-5</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.500025</td>\n",
       "      <td>-0.212664</td>\n",
       "      <td>-1.420194</td>\n",
       "      <td>-5</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.782244</td>\n",
       "      <td>-0.879595</td>\n",
       "      <td>-1.006656</td>\n",
       "      <td>-5</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-0.847207</td>\n",
       "      <td>-0.142760</td>\n",
       "      <td>-0.335974</td>\n",
       "      <td>-5</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>-0.493919</td>\n",
       "      <td>-0.661829</td>\n",
       "      <td>-0.408328</td>\n",
       "      <td>-5</td>\n",
       "      <td>-5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F\n",
       "2020-01-01  0.000000  0.000000 -1.260765 -5  NaN\n",
       "2020-01-02 -0.947061 -0.056453 -1.403369 -5 -1.0\n",
       "2020-01-03 -0.500025 -0.212664 -1.420194 -5 -2.0\n",
       "2020-01-04 -0.782244 -0.879595 -1.006656 -5 -3.0\n",
       "2020-01-05 -0.847207 -0.142760 -0.335974 -5 -4.0\n",
       "2020-01-06 -0.493919 -0.661829 -0.408328 -5 -5.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "df2[df2 > 0] = -df2\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. 결측치"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.260765</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.947061</td>\n",
       "      <td>0.056453</td>\n",
       "      <td>1.403369</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.500025</td>\n",
       "      <td>-0.212664</td>\n",
       "      <td>-1.420194</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.782244</td>\n",
       "      <td>0.879595</td>\n",
       "      <td>-1.006656</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F    E\n",
       "2020-01-01  0.000000  0.000000 -1.260765  5  NaN  1.0\n",
       "2020-01-02 -0.947061  0.056453  1.403369  5  1.0  1.0\n",
       "2020-01-03 -0.500025 -0.212664 -1.420194  5  2.0  NaN\n",
       "2020-01-04 -0.782244  0.879595 -1.006656  5  3.0  NaN"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])\n",
    "df1.loc[dates[0]:dates[1], 'E'] = 1\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.947061</td>\n",
       "      <td>0.056453</td>\n",
       "      <td>1.403369</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F    E\n",
       "2020-01-02 -0.947061  0.056453  1.403369  5  1.0  1.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dropna(how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.260765</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.947061</td>\n",
       "      <td>0.056453</td>\n",
       "      <td>1.403369</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.500025</td>\n",
       "      <td>-0.212664</td>\n",
       "      <td>-1.420194</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.782244</td>\n",
       "      <td>0.879595</td>\n",
       "      <td>-1.006656</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F    E\n",
       "2020-01-01  0.000000  0.000000 -1.260765  5  5.0  1.0\n",
       "2020-01-02 -0.947061  0.056453  1.403369  5  1.0  1.0\n",
       "2020-01-03 -0.500025 -0.212664 -1.420194  5  2.0  5.0\n",
       "2020-01-04 -0.782244  0.879595 -1.006656  5  3.0  5.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.fillna(value=5)"
   ]
  },
  {
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                A      B      C      D      F      E\n",
       "2020-01-01  False  False  False  False   True  False\n",
       "2020-01-02  False  False  False  False  False  False\n",
       "2020-01-03  False  False  False  False  False   True\n",
       "2020-01-04  False  False  False  False  False   True"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isna(df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. 연산"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 통계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.595076\n",
       "B    0.207076\n",
       "C   -0.504758\n",
       "D    5.000000\n",
       "F    3.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-01-01    0.934809\n",
       "2020-01-02    1.302552\n",
       "2020-01-03    0.973423\n",
       "2020-01-04    1.418139\n",
       "2020-01-05    1.534812\n",
       "2020-01-06    1.951917\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-01-01    NaN\n",
       "2020-01-02    NaN\n",
       "2020-01-03    1.0\n",
       "2020-01-04    3.0\n",
       "2020-01-05    5.0\n",
       "2020-01-06    NaN\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.260765</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.947061</td>\n",
       "      <td>0.056453</td>\n",
       "      <td>1.403369</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.500025</td>\n",
       "      <td>-0.212664</td>\n",
       "      <td>-1.420194</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.782244</td>\n",
       "      <td>0.879595</td>\n",
       "      <td>-1.006656</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-0.847207</td>\n",
       "      <td>-0.142760</td>\n",
       "      <td>-0.335974</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>-0.493919</td>\n",
       "      <td>0.661829</td>\n",
       "      <td>-0.408328</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F\n",
       "2020-01-01  0.000000  0.000000 -1.260765  5  NaN\n",
       "2020-01-02 -0.947061  0.056453  1.403369  5  1.0\n",
       "2020-01-03 -0.500025 -0.212664 -1.420194  5  2.0\n",
       "2020-01-04 -0.782244  0.879595 -1.006656  5  3.0\n",
       "2020-01-05 -0.847207 -0.142760 -0.335974  5  4.0\n",
       "2020-01-06 -0.493919  0.661829 -0.408328  5  5.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\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>2020-01-02</th>\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>2020-01-03</th>\n",
       "      <td>-1.500025</td>\n",
       "      <td>-1.212664</td>\n",
       "      <td>-2.420194</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-3.782244</td>\n",
       "      <td>-2.120405</td>\n",
       "      <td>-4.006656</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-5.847207</td>\n",
       "      <td>-5.142760</td>\n",
       "      <td>-5.335974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\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": [
       "                   A         B         C    D    F\n",
       "2020-01-01       NaN       NaN       NaN  NaN  NaN\n",
       "2020-01-02       NaN       NaN       NaN  NaN  NaN\n",
       "2020-01-03 -1.500025 -1.212664 -2.420194  4.0  1.0\n",
       "2020-01-04 -3.782244 -2.120405 -4.006656  2.0  0.0\n",
       "2020-01-05 -5.847207 -5.142760 -5.335974  0.0 -1.0\n",
       "2020-01-06       NaN       NaN       NaN  NaN  NaN"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sub(s, axis='index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.260765</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.947061</td>\n",
       "      <td>0.056453</td>\n",
       "      <td>0.142605</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-1.447087</td>\n",
       "      <td>-0.156211</td>\n",
       "      <td>-1.277589</td>\n",
       "      <td>15</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-2.229331</td>\n",
       "      <td>0.723384</td>\n",
       "      <td>-2.284245</td>\n",
       "      <td>20</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-3.076537</td>\n",
       "      <td>0.580625</td>\n",
       "      <td>-2.620219</td>\n",
       "      <td>25</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>-3.570457</td>\n",
       "      <td>1.242454</td>\n",
       "      <td>-3.028546</td>\n",
       "      <td>30</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C   D     F\n",
       "2020-01-01  0.000000  0.000000 -1.260765   5   NaN\n",
       "2020-01-02 -0.947061  0.056453  0.142605  10   1.0\n",
       "2020-01-03 -1.447087 -0.156211 -1.277589  15   3.0\n",
       "2020-01-04 -2.229331  0.723384 -2.284245  20   6.0\n",
       "2020-01-05 -3.076537  0.580625 -2.620219  25  10.0\n",
       "2020-01-06 -3.570457  1.242454 -3.028546  30  15.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(np.cumsum) # 열방향으로 누적합"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.947061\n",
       "B    1.092259\n",
       "C    2.823563\n",
       "D    0.000000\n",
       "F    4.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda x: x.max() - x.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogramming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6\n",
       "1    5\n",
       "2    3\n",
       "3    6\n",
       "4    0\n",
       "5    1\n",
       "6    1\n",
       "7    4\n",
       "8    4\n",
       "9    6\n",
       "dtype: int32"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(np.random.randint(0, 7, size=10))\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6    3\n",
       "4    2\n",
       "1    2\n",
       "5    1\n",
       "3    1\n",
       "0    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 문자열 메소드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       a\n",
       "1       b\n",
       "2       c\n",
       "3    aaba\n",
       "4    baca\n",
       "5     NaN\n",
       "6    caba\n",
       "7     dog\n",
       "8     cat\n",
       "dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])\n",
    "s.str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Merge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## concat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.801600</td>\n",
       "      <td>-1.577743</td>\n",
       "      <td>-0.077565</td>\n",
       "      <td>-0.444844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.070460</td>\n",
       "      <td>0.357997</td>\n",
       "      <td>-1.498373</td>\n",
       "      <td>-0.226674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.814045</td>\n",
       "      <td>0.917224</td>\n",
       "      <td>0.273669</td>\n",
       "      <td>-2.282675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.871345</td>\n",
       "      <td>-0.414311</td>\n",
       "      <td>1.318121</td>\n",
       "      <td>0.830176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.061914</td>\n",
       "      <td>-1.198102</td>\n",
       "      <td>-2.087422</td>\n",
       "      <td>-0.209200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.784021</td>\n",
       "      <td>-0.613166</td>\n",
       "      <td>-0.515160</td>\n",
       "      <td>0.241125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.251260</td>\n",
       "      <td>0.590695</td>\n",
       "      <td>0.716314</td>\n",
       "      <td>1.751956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.611662</td>\n",
       "      <td>-1.138359</td>\n",
       "      <td>2.178149</td>\n",
       "      <td>0.295532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.478299</td>\n",
       "      <td>2.350008</td>\n",
       "      <td>0.449963</td>\n",
       "      <td>-0.827764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.388971</td>\n",
       "      <td>0.587807</td>\n",
       "      <td>0.635435</td>\n",
       "      <td>0.309550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -0.801600 -1.577743 -0.077565 -0.444844\n",
       "1  1.070460  0.357997 -1.498373 -0.226674\n",
       "2 -1.814045  0.917224  0.273669 -2.282675\n",
       "3 -0.871345 -0.414311  1.318121  0.830176\n",
       "4  0.061914 -1.198102 -2.087422 -0.209200\n",
       "5 -0.784021 -0.613166 -0.515160  0.241125\n",
       "6 -0.251260  0.590695  0.716314  1.751956\n",
       "7 -0.611662 -1.138359  2.178149  0.295532\n",
       "8  0.478299  2.350008  0.449963 -0.827764\n",
       "9  0.388971  0.587807  0.635435  0.309550"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(10,4))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.801600</td>\n",
       "      <td>-1.577743</td>\n",
       "      <td>-0.077565</td>\n",
       "      <td>-0.444844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.070460</td>\n",
       "      <td>0.357997</td>\n",
       "      <td>-1.498373</td>\n",
       "      <td>-0.226674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.814045</td>\n",
       "      <td>0.917224</td>\n",
       "      <td>0.273669</td>\n",
       "      <td>-2.282675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.061914</td>\n",
       "      <td>-1.198102</td>\n",
       "      <td>-2.087422</td>\n",
       "      <td>-0.209200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.784021</td>\n",
       "      <td>-0.613166</td>\n",
       "      <td>-0.515160</td>\n",
       "      <td>0.241125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.251260</td>\n",
       "      <td>0.590695</td>\n",
       "      <td>0.716314</td>\n",
       "      <td>1.751956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.478299</td>\n",
       "      <td>2.350008</td>\n",
       "      <td>0.449963</td>\n",
       "      <td>-0.827764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.388971</td>\n",
       "      <td>0.587807</td>\n",
       "      <td>0.635435</td>\n",
       "      <td>0.309550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -0.801600 -1.577743 -0.077565 -0.444844\n",
       "1  1.070460  0.357997 -1.498373 -0.226674\n",
       "2 -1.814045  0.917224  0.273669 -2.282675\n",
       "4  0.061914 -1.198102 -2.087422 -0.209200\n",
       "5 -0.784021 -0.613166 -0.515160  0.241125\n",
       "6 -0.251260  0.590695  0.716314  1.751956\n",
       "8  0.478299  2.350008  0.449963 -0.827764\n",
       "9  0.388971  0.587807  0.635435  0.309550"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pieces = [df[:3], df[4:7], df[8:]]\n",
    "pd.concat(pieces)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "left = pd.DataFrame({'key' : ['foo', 'foo'], 'lval' : [1,2]})\n",
    "right = pd.DataFrame({'key' : ['foo', 'foo'], 'lval' : [4,5]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>key</th>\n",
       "      <th>lval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval\n",
       "0  foo     1\n",
       "1  foo     2"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>key</th>\n",
       "      <th>lval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval\n",
       "0  foo     4\n",
       "1  foo     5"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>key</th>\n",
       "      <th>lval_x</th>\n",
       "      <th>lval_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval_x  lval_y\n",
       "0  foo       1       4\n",
       "1  foo       1       5\n",
       "2  foo       2       4\n",
       "3  foo       2       5"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on='key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "left = pd.DataFrame({'key' : ['foo', 'bar'], 'lval' : [1,2]})\n",
    "right = pd.DataFrame({'key' : ['foo', 'bar'], 'lval' : [4,5]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>key</th>\n",
       "      <th>lval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval\n",
       "0  foo     1\n",
       "1  bar     2"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>key</th>\n",
       "      <th>lval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval\n",
       "0  foo     4\n",
       "1  bar     5"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>key</th>\n",
       "      <th>lval_x</th>\n",
       "      <th>lval_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval_x  lval_y\n",
       "0  foo       1       4\n",
       "1  bar       2       5"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on='key')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Append"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.604534</td>\n",
       "      <td>-1.114765</td>\n",
       "      <td>-0.044895</td>\n",
       "      <td>1.957436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.441917</td>\n",
       "      <td>-1.795450</td>\n",
       "      <td>-0.003785</td>\n",
       "      <td>-0.845387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.537402</td>\n",
       "      <td>-0.877339</td>\n",
       "      <td>-0.957562</td>\n",
       "      <td>-1.722995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.843970</td>\n",
       "      <td>0.131150</td>\n",
       "      <td>-0.576968</td>\n",
       "      <td>1.847783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.523929</td>\n",
       "      <td>0.821509</td>\n",
       "      <td>0.415841</td>\n",
       "      <td>1.005611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.445699</td>\n",
       "      <td>1.568006</td>\n",
       "      <td>0.022389</td>\n",
       "      <td>0.636877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.303929</td>\n",
       "      <td>-0.123727</td>\n",
       "      <td>-1.195953</td>\n",
       "      <td>1.347366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.627348</td>\n",
       "      <td>1.244304</td>\n",
       "      <td>-0.641737</td>\n",
       "      <td>0.260490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  1.604534 -1.114765 -0.044895  1.957436\n",
       "1  0.441917 -1.795450 -0.003785 -0.845387\n",
       "2 -0.537402 -0.877339 -0.957562 -1.722995\n",
       "3  2.843970  0.131150 -0.576968  1.847783\n",
       "4 -1.523929  0.821509  0.415841  1.005611\n",
       "5  1.445699  1.568006  0.022389  0.636877\n",
       "6 -0.303929 -0.123727 -1.195953  1.347366\n",
       "7 -0.627348  1.244304 -0.641737  0.260490"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(8,4), columns=['A', 'B', 'C', 'D'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    2.843970\n",
       "B    0.131150\n",
       "C   -0.576968\n",
       "D    1.847783\n",
       "Name: 3, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = df.iloc[3]\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.604534</td>\n",
       "      <td>-1.114765</td>\n",
       "      <td>-0.044895</td>\n",
       "      <td>1.957436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.441917</td>\n",
       "      <td>-1.795450</td>\n",
       "      <td>-0.003785</td>\n",
       "      <td>-0.845387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.537402</td>\n",
       "      <td>-0.877339</td>\n",
       "      <td>-0.957562</td>\n",
       "      <td>-1.722995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.843970</td>\n",
       "      <td>0.131150</td>\n",
       "      <td>-0.576968</td>\n",
       "      <td>1.847783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.523929</td>\n",
       "      <td>0.821509</td>\n",
       "      <td>0.415841</td>\n",
       "      <td>1.005611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.445699</td>\n",
       "      <td>1.568006</td>\n",
       "      <td>0.022389</td>\n",
       "      <td>0.636877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.303929</td>\n",
       "      <td>-0.123727</td>\n",
       "      <td>-1.195953</td>\n",
       "      <td>1.347366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.627348</td>\n",
       "      <td>1.244304</td>\n",
       "      <td>-0.641737</td>\n",
       "      <td>0.260490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.843970</td>\n",
       "      <td>0.131150</td>\n",
       "      <td>-0.576968</td>\n",
       "      <td>1.847783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  1.604534 -1.114765 -0.044895  1.957436\n",
       "1  0.441917 -1.795450 -0.003785 -0.845387\n",
       "2 -0.537402 -0.877339 -0.957562 -1.722995\n",
       "3  2.843970  0.131150 -0.576968  1.847783\n",
       "4 -1.523929  0.821509  0.415841  1.005611\n",
       "5  1.445699  1.568006  0.022389  0.636877\n",
       "6 -0.303929 -0.123727 -1.195953  1.347366\n",
       "7 -0.627348  1.244304 -0.641737  0.260490\n",
       "8  2.843970  0.131150 -0.576968  1.847783"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.append(s, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. 그룹화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>-1.708623</td>\n",
       "      <td>2.222794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>1.950608</td>\n",
       "      <td>-0.361138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>2.686426</td>\n",
       "      <td>-0.467859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>-0.322832</td>\n",
       "      <td>-0.283212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.541769</td>\n",
       "      <td>-0.007112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>0.534237</td>\n",
       "      <td>-2.741442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>0.668081</td>\n",
       "      <td>-0.083397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>foo</td>\n",
       "      <td>three</td>\n",
       "      <td>0.923791</td>\n",
       "      <td>-0.090297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B         C         D\n",
       "0  foo    one -1.708623  2.222794\n",
       "1  bar    one  1.950608 -0.361138\n",
       "2  foo    two  2.686426 -0.467859\n",
       "3  bar  three -0.322832 -0.283212\n",
       "4  foo    two -0.541769 -0.007112\n",
       "5  bar    two  0.534237 -2.741442\n",
       "6  foo    one  0.668081 -0.083397\n",
       "7  foo  three  0.923791 -0.090297"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    {\n",
    "        'A' : ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],\n",
    "        'B' : ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],\n",
    "        'C' : np.random.randn(8),\n",
    "        'D' : np.random.randn(8)\n",
    "    })\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <td>2.162014</td>\n",
       "      <td>-3.385792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>2.027905</td>\n",
       "      <td>1.574128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            C         D\n",
       "A                      \n",
       "bar  2.162014 -3.385792\n",
       "foo  2.027905  1.574128"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('A').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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></th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>1.950608</td>\n",
       "      <td>-0.361138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>-0.322832</td>\n",
       "      <td>-0.283212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.534237</td>\n",
       "      <td>-2.741442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>-1.040542</td>\n",
       "      <td>2.139397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>0.923791</td>\n",
       "      <td>-0.090297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2.144657</td>\n",
       "      <td>-0.474971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  C         D\n",
       "A   B                        \n",
       "bar one    1.950608 -0.361138\n",
       "    three -0.322832 -0.283212\n",
       "    two    0.534237 -2.741442\n",
       "foo one   -1.040542  2.139397\n",
       "    three  0.923791 -0.090297\n",
       "    two    2.144657 -0.474971"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['A', 'B']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Reshape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stack 데이터프레임 열들의 계층을 압축"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('bar', 'one'),\n",
       " ('bar', 'two'),\n",
       " ('baz', 'one'),\n",
       " ('baz', 'two'),\n",
       " ('foo', 'one'),\n",
       " ('foo', 'two'),\n",
       " ('qux', 'one'),\n",
       " ('qux', 'two')]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',\n",
    "                     'foo', 'foo', 'qux', 'qux'],\n",
    "                    ['one', 'two', 'one', 'two',\n",
    "                     'one', 'two', 'one', 'two']]))\n",
    "tuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = pd.MultiIndex.from_tuples(tuples, names = ['first', 'second'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.632181</td>\n",
       "      <td>1.704795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-2.062170</td>\n",
       "      <td>-1.315705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.830955</td>\n",
       "      <td>1.136316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.713830</td>\n",
       "      <td>0.747834</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -0.632181  1.704795\n",
       "      two    -2.062170 -1.315705\n",
       "baz   one    -0.830955  1.136316\n",
       "      two    -0.713830  0.747834"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(8,2), index=index, columns=['A', 'B'])\n",
    "df2 = df[:4]\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first  second   \n",
       "bar    one     A   -0.632181\n",
       "               B    1.704795\n",
       "       two     A   -2.062170\n",
       "               B   -1.315705\n",
       "baz    one     A   -0.830955\n",
       "               B    1.136316\n",
       "       two     A   -0.713830\n",
       "               B    0.747834\n",
       "dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked = df2.stack()\n",
    "stacked"
   ]
  },
  {
   "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></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.632181</td>\n",
       "      <td>1.704795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-2.062170</td>\n",
       "      <td>-1.315705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.830955</td>\n",
       "      <td>1.136316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.713830</td>\n",
       "      <td>0.747834</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -0.632181  1.704795\n",
       "      two    -2.062170 -1.315705\n",
       "baz   one    -0.830955  1.136316\n",
       "      two    -0.713830  0.747834"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>second</th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.632181</td>\n",
       "      <td>-2.062170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1.704795</td>\n",
       "      <td>-1.315705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.830955</td>\n",
       "      <td>-0.713830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1.136316</td>\n",
       "      <td>0.747834</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "second        one       two\n",
       "first                      \n",
       "bar   A -0.632181 -2.062170\n",
       "      B  1.704795 -1.315705\n",
       "baz   A -0.830955 -0.713830\n",
       "      B  1.136316  0.747834"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>first</th>\n",
       "      <th>bar</th>\n",
       "      <th>baz</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">one</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.632181</td>\n",
       "      <td>-0.830955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1.704795</td>\n",
       "      <td>1.136316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">two</th>\n",
       "      <th>A</th>\n",
       "      <td>-2.062170</td>\n",
       "      <td>-0.713830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.315705</td>\n",
       "      <td>0.747834</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "first          bar       baz\n",
       "second                      \n",
       "one    A -0.632181 -0.830955\n",
       "       B  1.704795  1.136316\n",
       "two    A -2.062170 -0.713830\n",
       "       B -1.315705  0.747834"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pivot table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>foo</td>\n",
       "      <td>0.116473</td>\n",
       "      <td>0.101690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>B</td>\n",
       "      <td>foo</td>\n",
       "      <td>0.058762</td>\n",
       "      <td>-0.939949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>two</td>\n",
       "      <td>C</td>\n",
       "      <td>foo</td>\n",
       "      <td>0.430735</td>\n",
       "      <td>0.790355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>three</td>\n",
       "      <td>A</td>\n",
       "      <td>bar</td>\n",
       "      <td>0.069930</td>\n",
       "      <td>1.270333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>one</td>\n",
       "      <td>B</td>\n",
       "      <td>bar</td>\n",
       "      <td>0.586978</td>\n",
       "      <td>0.203680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>one</td>\n",
       "      <td>C</td>\n",
       "      <td>bar</td>\n",
       "      <td>-0.747333</td>\n",
       "      <td>-0.639631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>A</td>\n",
       "      <td>foo</td>\n",
       "      <td>0.895960</td>\n",
       "      <td>-0.400455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>three</td>\n",
       "      <td>B</td>\n",
       "      <td>foo</td>\n",
       "      <td>-1.207056</td>\n",
       "      <td>0.632501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>one</td>\n",
       "      <td>C</td>\n",
       "      <td>foo</td>\n",
       "      <td>1.527734</td>\n",
       "      <td>-0.436767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>bar</td>\n",
       "      <td>-0.865987</td>\n",
       "      <td>-1.056407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>two</td>\n",
       "      <td>B</td>\n",
       "      <td>bar</td>\n",
       "      <td>0.676884</td>\n",
       "      <td>0.581229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>three</td>\n",
       "      <td>C</td>\n",
       "      <td>bar</td>\n",
       "      <td>0.315623</td>\n",
       "      <td>0.585387</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        A  B    C         D         E\n",
       "0     one  A  foo  0.116473  0.101690\n",
       "1     one  B  foo  0.058762 -0.939949\n",
       "2     two  C  foo  0.430735  0.790355\n",
       "3   three  A  bar  0.069930  1.270333\n",
       "4     one  B  bar  0.586978  0.203680\n",
       "5     one  C  bar -0.747333 -0.639631\n",
       "6     two  A  foo  0.895960 -0.400455\n",
       "7   three  B  foo -1.207056  0.632501\n",
       "8     one  C  foo  1.527734 -0.436767\n",
       "9     one  A  bar -0.865987 -1.056407\n",
       "10    two  B  bar  0.676884  0.581229\n",
       "11  three  C  bar  0.315623  0.585387"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3,\n",
    "                   'B' : ['A', 'B', 'C'] * 4,\n",
    "                   'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,\n",
    "                   'D' : np.random.randn(12),\n",
    "                   'E' : np.random.randn(12)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>C</th>\n",
       "      <th>bar</th>\n",
       "      <th>foo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">one</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.865987</td>\n",
       "      <td>0.116473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.586978</td>\n",
       "      <td>0.058762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.747333</td>\n",
       "      <td>1.527734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">three</th>\n",
       "      <th>A</th>\n",
       "      <td>0.069930</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.207056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.315623</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">two</th>\n",
       "      <th>A</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.895960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.676884</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.430735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "C             bar       foo\n",
       "A     B                    \n",
       "one   A -0.865987  0.116473\n",
       "      B  0.586978  0.058762\n",
       "      C -0.747333  1.527734\n",
       "three A  0.069930       NaN\n",
       "      B       NaN -1.207056\n",
       "      C  0.315623       NaN\n",
       "two   A       NaN  0.895960\n",
       "      B  0.676884       NaN\n",
       "      C       NaN  0.430735"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Time Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-01-01    25299\n",
       "Freq: 5T, dtype: int32"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('1/1/2020', periods=100, freq='S')\n",
    "ts = pd.Series(np.random.randint(0,500, len(rng)), index=rng)\n",
    "ts.resample('5Min').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 시간대 표현"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-03-06    0.519037\n",
       "2020-03-07    1.164813\n",
       "2020-03-08    0.330202\n",
       "2020-03-09    1.551025\n",
       "2020-03-10    0.618894\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('3/6/2020 00:00', periods=5, freq='D')\n",
    "ts = pd.Series(np.random.randn(len(rng)), rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-03-06 00:00:00+00:00    0.519037\n",
       "2020-03-07 00:00:00+00:00    1.164813\n",
       "2020-03-08 00:00:00+00:00    0.330202\n",
       "2020-03-09 00:00:00+00:00    1.551025\n",
       "2020-03-10 00:00:00+00:00    0.618894\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc = ts.tz_localize('UTC')\n",
    "ts_utc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 다른 시간대로 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-03-05 19:00:00-05:00    0.519037\n",
       "2020-03-06 19:00:00-05:00    1.164813\n",
       "2020-03-07 19:00:00-05:00    0.330202\n",
       "2020-03-08 20:00:00-04:00    1.551025\n",
       "2020-03-09 20:00:00-04:00    0.618894\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc.tz_convert('US/Eastern')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 시간 표현 -> 기간 표현으로 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-01-31    0.155601\n",
       "2020-02-29   -0.152887\n",
       "2020-03-31   -1.008154\n",
       "2020-04-30   -0.650010\n",
       "2020-05-31   -0.225124\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('1/1/2020', periods=5, freq='M')\n",
    "ts = pd.Series(np.random.randn(len(rng)), index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-01    0.155601\n",
       "2020-02   -0.152887\n",
       "2020-03   -1.008154\n",
       "2020-04   -0.650010\n",
       "2020-05   -0.225124\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps = ts.to_period()\n",
    "ps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-01-01    0.155601\n",
       "2020-02-01   -0.152887\n",
       "2020-03-01   -1.008154\n",
       "2020-04-01   -0.650010\n",
       "2020-05-01   -0.225124\n",
       "Freq: MS, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps.to_timestamp()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 예시. 11월에 끝나는 연말 결산의 분기별 빈도를 분기말 익월의 월말일 오전 9시로 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1990-03-01 09:00   -0.093970\n",
       "1990-06-01 09:00    0.224046\n",
       "1990-09-01 09:00    2.043986\n",
       "1990-12-01 09:00   -0.501457\n",
       "1991-03-01 09:00   -0.788555\n",
       "Freq: H, dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prng = pd.period_range('1990Q1', '2020Q4', freq='Q-NOV')\n",
    "ts = pd.Series(np.random.randn(len(prng)), prng)\n",
    "ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9\n",
    "ts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. 범주화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    b\n",
       "3    a\n",
       "4    a\n",
       "5    e\n",
       "Name: grade, dtype: category\n",
       "Categories (3, object): [a, b, e]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'id' : [1,2,3,4,5,6],\n",
    "                  'raw_grade' : ['a', 'b', 'b', 'a', 'a', 'e']})\n",
    "df['grade'] = df['raw_grade'].astype('category')\n",
    "df['grade']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"grade\"].cat.categories = [\"very good\", \"good\", \"very bad\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    very good\n",
       "1         good\n",
       "2         good\n",
       "3    very good\n",
       "4    very good\n",
       "5     very bad\n",
       "Name: grade, dtype: category\n",
       "Categories (5, object): [very bad, bad, medium, good, very good]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"grade\"] = df[\"grade\"].cat.set_categories([\"very bad\", \"bad\", \"medium\", \"good\", \"very good\"])\n",
    "df[\"grade\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>id</th>\n",
       "      <th>raw_grade</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>e</td>\n",
       "      <td>very bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>b</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>very good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "      <td>very good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>a</td>\n",
       "      <td>very good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id raw_grade      grade\n",
       "5   6         e   very bad\n",
       "1   2         b       good\n",
       "2   3         b       good\n",
       "0   1         a  very good\n",
       "3   4         a  very good\n",
       "4   5         a  very good"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='grade')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grade\n",
       "very bad     1\n",
       "bad          0\n",
       "medium       0\n",
       "good         2\n",
       "very good    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('grade').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 11. 그래프"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe88a7b0>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEHCAYAAACtAv3IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe88a3f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2020', periods=1000))\n",
    "ts = ts.cumsum()\n",
    "ts.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,\n",
    "                 columns=['A', 'B', 'C', 'D'])\n",
    "df = df.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xdeae5f0>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xde86090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEHCAYAAACtAv3IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1YQ7fDOzmvgv2Xj8kOIJpF0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdeae030>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "df.plot()\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 12. 데이터 입출력"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('foo.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>Unnamed: 0</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>-1.786738</td>\n",
       "      <td>0.541207</td>\n",
       "      <td>-1.022816</td>\n",
       "      <td>1.380583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>-2.622204</td>\n",
       "      <td>1.453898</td>\n",
       "      <td>-3.347064</td>\n",
       "      <td>1.460343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>-1.455507</td>\n",
       "      <td>3.343049</td>\n",
       "      <td>-4.386752</td>\n",
       "      <td>2.182119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>-2.683006</td>\n",
       "      <td>2.258686</td>\n",
       "      <td>-3.635778</td>\n",
       "      <td>1.518157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>-2.758338</td>\n",
       "      <td>2.029283</td>\n",
       "      <td>-4.640839</td>\n",
       "      <td>4.075687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         A         B         C         D\n",
       "0  2020-01-01 -1.786738  0.541207 -1.022816  1.380583\n",
       "1  2020-01-02 -2.622204  1.453898 -3.347064  1.460343\n",
       "2  2020-01-03 -1.455507  3.343049 -4.386752  2.182119\n",
       "3  2020-01-04 -2.683006  2.258686 -3.635778  1.518157\n",
       "4  2020-01-05 -2.758338  2.029283 -4.640839  4.075687"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('foo.csv').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### hdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_hdf('foo.h5', 'df')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>-1.786738</td>\n",
       "      <td>0.541207</td>\n",
       "      <td>-1.022816</td>\n",
       "      <td>1.380583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-2.622204</td>\n",
       "      <td>1.453898</td>\n",
       "      <td>-3.347064</td>\n",
       "      <td>1.460343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-1.455507</td>\n",
       "      <td>3.343049</td>\n",
       "      <td>-4.386752</td>\n",
       "      <td>2.182119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-2.683006</td>\n",
       "      <td>2.258686</td>\n",
       "      <td>-3.635778</td>\n",
       "      <td>1.518157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-2.758338</td>\n",
       "      <td>2.029283</td>\n",
       "      <td>-4.640839</td>\n",
       "      <td>4.075687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-01-01 -1.786738  0.541207 -1.022816  1.380583\n",
       "2020-01-02 -2.622204  1.453898 -3.347064  1.460343\n",
       "2020-01-03 -1.455507  3.343049 -4.386752  2.182119\n",
       "2020-01-04 -2.683006  2.258686 -3.635778  1.518157\n",
       "2020-01-05 -2.758338  2.029283 -4.640839  4.075687"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_hdf('foo.h5', 'df').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel('foo.xlsx', sheet_name='Sheet1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>Unnamed: 0</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>-1.786738</td>\n",
       "      <td>0.541207</td>\n",
       "      <td>-1.022816</td>\n",
       "      <td>1.380583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>-2.622204</td>\n",
       "      <td>1.453898</td>\n",
       "      <td>-3.347064</td>\n",
       "      <td>1.460343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>-1.455507</td>\n",
       "      <td>3.343049</td>\n",
       "      <td>-4.386752</td>\n",
       "      <td>2.182119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>-2.683006</td>\n",
       "      <td>2.258686</td>\n",
       "      <td>-3.635778</td>\n",
       "      <td>1.518157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>-2.758338</td>\n",
       "      <td>2.029283</td>\n",
       "      <td>-4.640839</td>\n",
       "      <td>4.075687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Unnamed: 0         A         B         C         D\n",
       "0 2020-01-01 -1.786738  0.541207 -1.022816  1.380583\n",
       "1 2020-01-02 -2.622204  1.453898 -3.347064  1.460343\n",
       "2 2020-01-03 -1.455507  3.343049 -4.386752  2.182119\n",
       "3 2020-01-04 -2.683006  2.258686 -3.635778  1.518157\n",
       "4 2020-01-05 -2.758338  2.029283 -4.640839  4.075687"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}