[5]MultiIndex Series and DataFrames-.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Series is 1D and DataFrames are 2D objects\n",
    "But why?\n",
    "And what exactly is index?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(cse, 2019)    1\n",
       "(cse, 2020)    2\n",
       "(cse, 2021)    3\n",
       "(cse, 2022)    4\n",
       "(ece, 2019)    5\n",
       "(ece, 2020)    6\n",
       "(ece, 2021)    7\n",
       "(ece, 2022)    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# can we have multiple index? Let's try\n",
    "index_val = [('cse',2019),('cse',2020),('cse',2021),('cse',2022),('ece',2019),('ece',2020),('ece',2021),('ece',2022)]\n",
    "a = pd.Series([1,2,3,4,5,6,7,8],index=index_val)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'cse'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3801\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'cse'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[3], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# The problem?\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m a[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcse\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\series.py:981\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    978\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[key]\n\u001b[0;32m    980\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[1;32m--> 981\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_value(key)\n\u001b[0;32m    983\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_hashable(key):\n\u001b[0;32m    984\u001b[0m     \u001b[38;5;66;03m# Otherwise index.get_value will raise InvalidIndexError\u001b[39;00m\n\u001b[0;32m    985\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    986\u001b[0m         \u001b[38;5;66;03m# For labels that don't resolve as scalars like tuples and frozensets\u001b[39;00m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\series.py:1089\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[1;34m(self, label, takeable)\u001b[0m\n\u001b[0;32m   1086\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[label]\n\u001b[0;32m   1088\u001b[0m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[1;32m-> 1089\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mget_loc(label)\n\u001b[0;32m   1090\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39m_get_values_for_loc(\u001b[38;5;28mself\u001b[39m, loc, label)\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m-> 3804\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3805\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3806\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3807\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3808\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3809\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: 'cse'"
     ]
    }
   ],
   "source": [
    "# The problem?\n",
    "a['cse']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The solution -> multiindex series(also known as Hierarchical Indexing)\n",
    "# multiple index levels within a single index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### how to create multiindex object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FrozenList([['cse', 'ece'], [2019, 2020, 2021, 2022]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. pd.MultiIndex.from_tuples()\n",
    "index_val = [('cse',2019),('cse',2020),('cse',2021),('cse',2022),('ece',2019),('ece',2020),('ece',2021),('ece',2022)]\n",
    "multiindex = pd.MultiIndex.from_tuples(index_val)\n",
    "multiindex.levels[0]\n",
    "multiindex.levels[1]\n",
    "multiindex.levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('cse', 2019),\n",
       "            ('cse', 2020),\n",
       "            ('cse', 2021),\n",
       "            ('cse', 2022),\n",
       "            ('ece', 2019),\n",
       "            ('ece', 2020),\n",
       "            ('ece', 2021),\n",
       "            ('ece', 2022)],\n",
       "           )"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. pd.MultiIndex.from_product()\n",
    "pd.MultiIndex.from_product([['cse','ece'],[2019,2020,2021,2022]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### multiindex series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cse  2019    1\n",
       "     2020    2\n",
       "     2021    3\n",
       "     2022    4\n",
       "ece  2019    5\n",
       "     2020    6\n",
       "     2021    7\n",
       "     2022    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,2,3,4,5,6,7,8],index=multiindex)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019    1\n",
       "2020    2\n",
       "2021    3\n",
       "2022    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how to fetch items from such a series\n",
    "s[('cse',2022)]\n",
    "s['cse']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### unstack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cse</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ece</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     2019  2020  2021  2022\n",
       "cse     1     2     3     4\n",
       "ece     5     6     7     8"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#can convert multiIndex series into dataframe\n",
    "temp = s.unstack()\n",
    "temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### stack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cse  2019    1\n",
       "     2020    2\n",
       "     2021    3\n",
       "     2022    4\n",
       "ece  2019    5\n",
       "     2020    6\n",
       "     2021    7\n",
       "     2022    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Then what was the point of multiindex series?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### multiindex dataframe"
   ]
  },
  {
   "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></th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">cse</th>\n",
       "      <th>2019</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">ece</th>\n",
       "      <th>2019</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          avg_package  students\n",
       "cse 2019            1         2\n",
       "    2020            3         4\n",
       "    2021            5         6\n",
       "    2022            7         8\n",
       "ece 2019            9        10\n",
       "    2020           11        12\n",
       "    2021           13        14\n",
       "    2022           15        16"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df1 = pd.DataFrame(\n",
    "    [\n",
    "        [1,2],\n",
    "        [3,4],\n",
    "        [5,6],\n",
    "        [7,8],\n",
    "        [9,10],\n",
    "        [11,12],\n",
    "        [13,14],\n",
    "        [15,16],\n",
    "    ],\n",
    "    index = multiindex,\n",
    "    columns = ['avg_package','students']\n",
    ")\n",
    "\n",
    "branch_df1"
   ]
  },
  {
   "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>avg_package</th>\n",
       "      <th>students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      avg_package  students\n",
       "2019            1         2\n",
       "2020            3         4\n",
       "2021            5         6\n",
       "2022            7         8"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df1.loc['cse']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "avg_package    7\n",
       "students       8\n",
       "Name: (cse, 2022), dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df1.loc['cse',2022]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cse  2019     1\n",
       "     2020     3\n",
       "     2021     5\n",
       "     2022     7\n",
       "ece  2019     9\n",
       "     2020    11\n",
       "     2021    13\n",
       "     2022    15\n",
       "Name: avg_package, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df1['avg_package']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Are columns really different from index?"
   ]
  },
  {
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">delhi</th>\n",
       "      <th colspan=\"2\" halign=\"left\">mumbai</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delhi               mumbai         \n",
       "     avg_package students avg_package students\n",
       "2019           1        2           0        0\n",
       "2020           3        4           0        0\n",
       "2021           5        6           0        0\n",
       "2022           7        8           0        0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# multiindex df from columns perspective\n",
    "branch_df2 = pd.DataFrame(\n",
    "    [\n",
    "        [1,2,0,0],\n",
    "        [3,4,0,0],\n",
    "        [5,6,0,0],\n",
    "        [7,8,0,0],\n",
    "    ],\n",
    "    index = [2019,2020,2021,2022],\n",
    "    columns = pd.MultiIndex.from_product([['delhi','mumbai'],['avg_package','students']])\n",
    ")\n",
    "\n",
    "branch_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>avg_package</th>\n",
       "      <th>students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      avg_package  students\n",
       "2019            1         2\n",
       "2020            3         4\n",
       "2021            5         6\n",
       "2022            7         8"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df2['delhi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019    2\n",
       "2020    4\n",
       "2021    6\n",
       "2022    8\n",
       "Name: students, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df2['delhi']['students']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "delhi   avg_package    7\n",
       "        students       8\n",
       "mumbai  avg_package    0\n",
       "        students       0\n",
       "Name: 2022, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df2.loc[2022]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">delhi</th>\n",
       "      <th colspan=\"2\" halign=\"left\">mumbai</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">cse</th>\n",
       "      <th>2019</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">ece</th>\n",
       "      <th>2019</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               delhi               mumbai         \n",
       "         avg_package students avg_package students\n",
       "cse 2019           1        2           0        0\n",
       "    2020           3        4           0        0\n",
       "    2021           5        6           0        0\n",
       "    2022           7        8           0        0\n",
       "ece 2019           9       10           0        0\n",
       "    2020          11       12           0        0\n",
       "    2021          13       14           0        0\n",
       "    2022          15       16           0        0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multiindex df in terms of both cols and index\n",
    "\n",
    "branch_df3 = pd.DataFrame(\n",
    "    [\n",
    "        [1,2,0,0],\n",
    "        [3,4,0,0],\n",
    "        [5,6,0,0],\n",
    "        [7,8,0,0],\n",
    "        [9,10,0,0],\n",
    "        [11,12,0,0],\n",
    "        [13,14,0,0],\n",
    "        [15,16,0,0],\n",
    "    ],\n",
    "    index = multiindex,\n",
    "    columns = pd.MultiIndex.from_product([['delhi','mumbai'],['avg_package','students']])\n",
    ")\n",
    "\n",
    "branch_df3                #4D data represented in 2D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stacking and Unstacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>avg_package</th>\n",
       "      <th>students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">cse</th>\n",
       "      <th>2019</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">ece</th>\n",
       "      <th>2019</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          avg_package  students\n",
       "cse 2019            1         2\n",
       "    2020            3         4\n",
       "    2021            5         6\n",
       "    2022            7         8\n",
       "ece 2019            9        10\n",
       "    2020           11        12\n",
       "    2021           13        14\n",
       "    2022           15        16"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">avg_package</th>\n",
       "      <th colspan=\"4\" halign=\"left\">students</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cse</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ece</th>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    avg_package                students               \n",
       "           2019 2020 2021 2022     2019 2020 2021 2022\n",
       "cse           1    3    5    7        2    4    6    8\n",
       "ece           9   11   13   15       10   12   14   16"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df1.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "avg_package  2019  cse     1\n",
       "                   ece     9\n",
       "             2020  cse     3\n",
       "                   ece    11\n",
       "             2021  cse     5\n",
       "                   ece    13\n",
       "             2022  cse     7\n",
       "                   ece    15\n",
       "students     2019  cse     2\n",
       "                   ece    10\n",
       "             2020  cse     4\n",
       "                   ece    12\n",
       "             2021  cse     6\n",
       "                   ece    14\n",
       "             2022  cse     8\n",
       "                   ece    16\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df1.unstack().unstack()"
   ]
  },
  {
   "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></th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">cse</th>\n",
       "      <th>2019</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">ece</th>\n",
       "      <th>2019</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          avg_package  students\n",
       "cse 2019            1         2\n",
       "    2020            3         4\n",
       "    2021            5         6\n",
       "    2022            7         8\n",
       "ece 2019            9        10\n",
       "    2020           11        12\n",
       "    2021           13        14\n",
       "    2022           15        16"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df1.unstack().stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cse  2019  avg_package     1\n",
       "           students        2\n",
       "     2020  avg_package     3\n",
       "           students        4\n",
       "     2021  avg_package     5\n",
       "           students        6\n",
       "     2022  avg_package     7\n",
       "           students        8\n",
       "ece  2019  avg_package     9\n",
       "           students       10\n",
       "     2020  avg_package    11\n",
       "           students       12\n",
       "     2021  avg_package    13\n",
       "           students       14\n",
       "     2022  avg_package    15\n",
       "           students       16\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df1.unstack().stack().stack()"
   ]
  },
  {
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">delhi</th>\n",
       "      <th colspan=\"2\" halign=\"left\">mumbai</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delhi               mumbai         \n",
       "     avg_package students avg_package students\n",
       "2019           1        2           0        0\n",
       "2020           3        4           0        0\n",
       "2021           5        6           0        0\n",
       "2022           7        8           0        0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "delhi   avg_package  2019    1\n",
       "                     2020    3\n",
       "                     2021    5\n",
       "                     2022    7\n",
       "        students     2019    2\n",
       "                     2020    4\n",
       "                     2021    6\n",
       "                     2022    8\n",
       "mumbai  avg_package  2019    0\n",
       "                     2020    0\n",
       "                     2021    0\n",
       "                     2022    0\n",
       "        students     2019    0\n",
       "                     2020    0\n",
       "                     2021    0\n",
       "                     2022    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df2.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>delhi</th>\n",
       "      <th>mumbai</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2019</th>\n",
       "      <th>avg_package</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>students</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2020</th>\n",
       "      <th>avg_package</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>students</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2021</th>\n",
       "      <th>avg_package</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>students</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2022</th>\n",
       "      <th>avg_package</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>students</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  delhi  mumbai\n",
       "2019 avg_package      1       0\n",
       "     students         2       0\n",
       "2020 avg_package      3       0\n",
       "     students         4       0\n",
       "2021 avg_package      5       0\n",
       "     students         6       0\n",
       "2022 avg_package      7       0\n",
       "     students         8       0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df2.stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019  avg_package  delhi     1\n",
       "                   mumbai    0\n",
       "      students     delhi     2\n",
       "                   mumbai    0\n",
       "2020  avg_package  delhi     3\n",
       "                   mumbai    0\n",
       "      students     delhi     4\n",
       "                   mumbai    0\n",
       "2021  avg_package  delhi     5\n",
       "                   mumbai    0\n",
       "      students     delhi     6\n",
       "                   mumbai    0\n",
       "2022  avg_package  delhi     7\n",
       "                   mumbai    0\n",
       "      students     delhi     8\n",
       "                   mumbai    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df2.stack().stack()         #3D series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th colspan=\"2\" halign=\"left\">mumbai</th>\n",
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       "    <tr>\n",
       "      <th></th>\n",
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       "      <th>avg_package</th>\n",
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       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">cse</th>\n",
       "      <th>2019</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">ece</th>\n",
       "      <th>2019</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               delhi               mumbai         \n",
       "         avg_package students avg_package students\n",
       "cse 2019           1        2           0        0\n",
       "    2020           3        4           0        0\n",
       "    2021           5        6           0        0\n",
       "    2022           7        8           0        0\n",
       "ece 2019           9       10           0        0\n",
       "    2020          11       12           0        0\n",
       "    2021          13       14           0        0\n",
       "    2022          15       16           0        0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">delhi</th>\n",
       "      <th colspan=\"8\" halign=\"left\">mumbai</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">avg_package</th>\n",
       "      <th colspan=\"4\" halign=\"left\">students</th>\n",
       "      <th colspan=\"4\" halign=\"left\">avg_package</th>\n",
       "      <th colspan=\"4\" halign=\"left\">students</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cse</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ece</th>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          delhi                                             mumbai            \\\n",
       "    avg_package                students                avg_package             \n",
       "           2019 2020 2021 2022     2019 2020 2021 2022        2019 2020 2021   \n",
       "cse           1    3    5    7        2    4    6    8           0    0    0   \n",
       "ece           9   11   13   15       10   12   14   16           0    0    0   \n",
       "\n",
       "                                  \n",
       "         students                 \n",
       "    2022     2019 2020 2021 2022  \n",
       "cse    0        0    0    0    0  \n",
       "ece    0        0    0    0    0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df3.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "delhi   avg_package  2019  cse     1\n",
       "                           ece     9\n",
       "                     2020  cse     3\n",
       "                           ece    11\n",
       "                     2021  cse     5\n",
       "                           ece    13\n",
       "                     2022  cse     7\n",
       "                           ece    15\n",
       "        students     2019  cse     2\n",
       "                           ece    10\n",
       "                     2020  cse     4\n",
       "                           ece    12\n",
       "                     2021  cse     6\n",
       "                           ece    14\n",
       "                     2022  cse     8\n",
       "                           ece    16\n",
       "mumbai  avg_package  2019  cse     0\n",
       "                           ece     0\n",
       "                     2020  cse     0\n",
       "                           ece     0\n",
       "                     2021  cse     0\n",
       "                           ece     0\n",
       "                     2022  cse     0\n",
       "                           ece     0\n",
       "        students     2019  cse     0\n",
       "                           ece     0\n",
       "                     2020  cse     0\n",
       "                           ece     0\n",
       "                     2021  cse     0\n",
       "                           ece     0\n",
       "                     2022  cse     0\n",
       "                           ece     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df3.unstack().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th>delhi</th>\n",
       "      <th>mumbai</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">cse</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">2019</th>\n",
       "      <th>avg_package</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>students</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2020</th>\n",
       "      <th>avg_package</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>students</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2021</th>\n",
       "      <th>avg_package</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>students</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2022</th>\n",
       "      <th>avg_package</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>students</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">ece</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">2019</th>\n",
       "      <th>avg_package</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>students</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2020</th>\n",
       "      <th>avg_package</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>students</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2021</th>\n",
       "      <th>avg_package</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>students</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2022</th>\n",
       "      <th>avg_package</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>students</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      delhi  mumbai\n",
       "cse 2019 avg_package      1       0\n",
       "         students         2       0\n",
       "    2020 avg_package      3       0\n",
       "         students         4       0\n",
       "    2021 avg_package      5       0\n",
       "         students         6       0\n",
       "    2022 avg_package      7       0\n",
       "         students         8       0\n",
       "ece 2019 avg_package      9       0\n",
       "         students        10       0\n",
       "    2020 avg_package     11       0\n",
       "         students        12       0\n",
       "    2021 avg_package     13       0\n",
       "         students        14       0\n",
       "    2022 avg_package     15       0\n",
       "         students        16       0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df3.stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cse  2019  avg_package  delhi      1\n",
       "                        mumbai     0\n",
       "           students     delhi      2\n",
       "                        mumbai     0\n",
       "     2020  avg_package  delhi      3\n",
       "                        mumbai     0\n",
       "           students     delhi      4\n",
       "                        mumbai     0\n",
       "     2021  avg_package  delhi      5\n",
       "                        mumbai     0\n",
       "           students     delhi      6\n",
       "                        mumbai     0\n",
       "     2022  avg_package  delhi      7\n",
       "                        mumbai     0\n",
       "           students     delhi      8\n",
       "                        mumbai     0\n",
       "ece  2019  avg_package  delhi      9\n",
       "                        mumbai     0\n",
       "           students     delhi     10\n",
       "                        mumbai     0\n",
       "     2020  avg_package  delhi     11\n",
       "                        mumbai     0\n",
       "           students     delhi     12\n",
       "                        mumbai     0\n",
       "     2021  avg_package  delhi     13\n",
       "                        mumbai     0\n",
       "           students     delhi     14\n",
       "                        mumbai     0\n",
       "     2022  avg_package  delhi     15\n",
       "                        mumbai     0\n",
       "           students     delhi     16\n",
       "                        mumbai     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df3.stack().stack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with multiindex dataframes"
   ]
  },
  {
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">delhi</th>\n",
       "      <th colspan=\"2\" halign=\"left\">mumbai</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">cse</th>\n",
       "      <th>2019</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">ece</th>\n",
       "      <th>2019</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               delhi               mumbai         \n",
       "         avg_package students avg_package students\n",
       "cse 2019           1        2           0        0\n",
       "    2020           3        4           0        0\n",
       "    2021           5        6           0        0\n",
       "    2022           7        8           0        0\n",
       "ece 2019           9       10           0        0\n",
       "    2020          11       12           0        0\n",
       "    2021          13       14           0        0\n",
       "    2022          15       16           0        0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df3                      #can perform all operations like a normal dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">delhi</th>\n",
       "      <th colspan=\"2\" halign=\"left\">mumbai</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">cse</th>\n",
       "      <th>2019</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ece</th>\n",
       "      <th>2019</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               delhi               mumbai         \n",
       "         avg_package students avg_package students\n",
       "cse 2019           1        2           0        0\n",
       "    2020           3        4           0        0\n",
       "    2021           5        6           0        0\n",
       "    2022           7        8           0        0\n",
       "ece 2019           9       10           0        0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 4)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 8 entries, ('cse', 2019) to ('ece', 2022)\n",
      "Data columns (total 4 columns):\n",
      " #   Column                 Non-Null Count  Dtype\n",
      "---  ------                 --------------  -----\n",
      " 0   (delhi, avg_package)   8 non-null      int64\n",
      " 1   (delhi, students)      8 non-null      int64\n",
      " 2   (mumbai, avg_package)  8 non-null      int64\n",
      " 3   (mumbai, students)     8 non-null      int64\n",
      "dtypes: int64(4)\n",
      "memory usage: 932.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "branch_df3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2 entries, cse to ece\n",
      "Data columns (total 16 columns):\n",
      " #   Column                       Non-Null Count  Dtype\n",
      "---  ------                       --------------  -----\n",
      " 0   (delhi, avg_package, 2019)   2 non-null      int64\n",
      " 1   (delhi, avg_package, 2020)   2 non-null      int64\n",
      " 2   (delhi, avg_package, 2021)   2 non-null      int64\n",
      " 3   (delhi, avg_package, 2022)   2 non-null      int64\n",
      " 4   (delhi, students, 2019)      2 non-null      int64\n",
      " 5   (delhi, students, 2020)      2 non-null      int64\n",
      " 6   (delhi, students, 2021)      2 non-null      int64\n",
      " 7   (delhi, students, 2022)      2 non-null      int64\n",
      " 8   (mumbai, avg_package, 2019)  2 non-null      int64\n",
      " 9   (mumbai, avg_package, 2020)  2 non-null      int64\n",
      " 10  (mumbai, avg_package, 2021)  2 non-null      int64\n",
      " 11  (mumbai, avg_package, 2022)  2 non-null      int64\n",
      " 12  (mumbai, students, 2019)     2 non-null      int64\n",
      " 13  (mumbai, students, 2020)     2 non-null      int64\n",
      " 14  (mumbai, students, 2021)     2 non-null      int64\n",
      " 15  (mumbai, students, 2022)     2 non-null      int64\n",
      "dtypes: int64(16)\n",
      "memory usage: 272.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "branch_df3.unstack().info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cse  2019    False\n",
       "     2020    False\n",
       "     2021    False\n",
       "     2022    False\n",
       "ece  2019    False\n",
       "     2020    False\n",
       "     2021    False\n",
       "     2022    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df3.duplicated()"
   ]
  },
  {
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">delhi</th>\n",
       "      <th colspan=\"2\" halign=\"left\">mumbai</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">cse</th>\n",
       "      <th>2019</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">ece</th>\n",
       "      <th>2019</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               delhi               mumbai         \n",
       "         avg_package students avg_package students\n",
       "cse 2019       False    False       False    False\n",
       "    2020       False    False       False    False\n",
       "    2021       False    False       False    False\n",
       "    2022       False    False       False    False\n",
       "ece 2019       False    False       False    False\n",
       "    2020       False    False       False    False\n",
       "    2021       False    False       False    False\n",
       "    2022       False    False       False    False"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df3.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "delhi   avg_package    7\n",
       "        students       8\n",
       "mumbai  avg_package    0\n",
       "        students       0\n",
       "Name: (cse, 2022), dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting rows single\n",
    "branch_df3.loc[('cse',2022)]       #loc bcoz of labelled index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "delhi   avg_package    7\n",
       "        students       8\n",
       "mumbai  avg_package    0\n",
       "        students       0\n",
       "Name: (cse, 2022), dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df3.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">delhi</th>\n",
       "      <th colspan=\"2\" halign=\"left\">mumbai</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cse</th>\n",
       "      <th>2019</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ece</th>\n",
       "      <th>2020</th>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               delhi               mumbai         \n",
       "         avg_package students avg_package students\n",
       "cse 2019           1        2           0        0\n",
       "ece 2020          11       12           0        0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#extracting multiple rows\n",
    "branch_df3.loc[('cse',2019):('ece',2020)]\n",
    "branch_df3.loc[[('cse',2019),('ece',2020)]]"
   ]
  },
  {
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">delhi</th>\n",
       "      <th colspan=\"2\" halign=\"left\">mumbai</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">cse</th>\n",
       "      <th>2019</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ece</th>\n",
       "      <th>2019</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               delhi               mumbai         \n",
       "         avg_package students avg_package students\n",
       "cse 2019           1        2           0        0\n",
       "    2021           5        6           0        0\n",
       "ece 2019           9       10           0        0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df3.iloc[0:3]\n",
    "branch_df3.iloc[0:5:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">cse</th>\n",
       "      <th>2019</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">ece</th>\n",
       "      <th>2019</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          avg_package  students\n",
       "cse 2019            1         2\n",
       "    2020            3         4\n",
       "    2021            5         6\n",
       "    2022            7         8\n",
       "ece 2019            9        10\n",
       "    2020           11        12\n",
       "    2021           13        14\n",
       "    2022           15        16"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting cols\n",
    "branch_df3['delhi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cse  2019     2\n",
       "     2020     4\n",
       "     2021     6\n",
       "     2022     8\n",
       "ece  2019    10\n",
       "     2020    12\n",
       "     2021    14\n",
       "     2022    16\n",
       "Name: students, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df3['delhi']['students']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>delhi</th>\n",
       "      <th>mumbai</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>students</th>\n",
       "      <th>avg_package</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">cse</th>\n",
       "      <th>2019</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">ece</th>\n",
       "      <th>2019</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            delhi      mumbai\n",
       "         students avg_package\n",
       "cse 2019        2           0\n",
       "    2020        4           0\n",
       "    2021        6           0\n",
       "    2022        8           0\n",
       "ece 2019       10           0\n",
       "    2020       12           0\n",
       "    2021       14           0\n",
       "    2022       16           0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df3.iloc[:,1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>delhi</th>\n",
       "      <th>mumbai</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>students</th>\n",
       "      <th>avg_package</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cse</th>\n",
       "      <th>2019</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ece</th>\n",
       "      <th>2019</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            delhi      mumbai\n",
       "         students avg_package\n",
       "cse 2019        2           0\n",
       "ece 2019       10           0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting both\n",
    "branch_df3.iloc[[0,4],[1,2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sorting index of multiindex DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">delhi</th>\n",
       "      <th colspan=\"2\" halign=\"left\">mumbai</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">ece</th>\n",
       "      <th>2022</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">cse</th>\n",
       "      <th>2022</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               delhi               mumbai         \n",
       "         avg_package students avg_package students\n",
       "ece 2022          15       16           0        0\n",
       "    2021          13       14           0        0\n",
       "    2020          11       12           0        0\n",
       "    2019           9       10           0        0\n",
       "cse 2022           7        8           0        0\n",
       "    2021           5        6           0        0\n",
       "    2020           3        4           0        0\n",
       "    2019           1        2           0        0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df3\n",
    "branch_df3.sort_index(ascending=False)   #both level index sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th colspan=\"2\" halign=\"left\">mumbai</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">ece</th>\n",
       "      <th>2019</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>11</td>\n",
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       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">cse</th>\n",
       "      <th>2019</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               delhi               mumbai         \n",
       "         avg_package students avg_package students\n",
       "ece 2019           9       10           0        0\n",
       "    2020          11       12           0        0\n",
       "    2021          13       14           0        0\n",
       "    2022          15       16           0        0\n",
       "cse 2019           1        2           0        0\n",
       "    2020           3        4           0        0\n",
       "    2021           5        6           0        0\n",
       "    2022           7        8           0        0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df3.sort_index(ascending=[False,True]) #diff. sorting in diff level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th colspan=\"2\" halign=\"left\">mumbai</th>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
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       "      <th>ece</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>cse</th>\n",
       "      <th>2022</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>ece</th>\n",
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       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>cse</th>\n",
       "      <th>2021</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ece</th>\n",
       "      <th>2020</th>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cse</th>\n",
       "      <th>2020</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ece</th>\n",
       "      <th>2019</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cse</th>\n",
       "      <th>2019</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               delhi               mumbai         \n",
       "         avg_package students avg_package students\n",
       "ece 2022          15       16           0        0\n",
       "cse 2022           7        8           0        0\n",
       "ece 2021          13       14           0        0\n",
       "cse 2021           5        6           0        0\n",
       "ece 2020          11       12           0        0\n",
       "cse 2020           3        4           0        0\n",
       "ece 2019           9       10           0        0\n",
       "cse 2019           1        2           0        0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df3.sort_index(level=1,ascending=False) #sorting in only one level"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### transpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">cse</th>\n",
       "      <th colspan=\"4\" halign=\"left\">ece</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">delhi</th>\n",
       "      <th>avg_package</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>students</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">mumbai</th>\n",
       "      <th>avg_package</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>students</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    cse                 ece               \n",
       "                   2019 2020 2021 2022 2019 2020 2021 2022\n",
       "delhi  avg_package    1    3    5    7    9   11   13   15\n",
       "       students       2    4    6    8   10   12   14   16\n",
       "mumbai avg_package    0    0    0    0    0    0    0    0\n",
       "       students       0    0    0    0    0    0    0    0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df3\n",
    "branch_df3.transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### swaplevel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">delhi</th>\n",
       "      <th colspan=\"2\" halign=\"left\">mumbai</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <th>cse</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <th>cse</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <th>cse</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <th>cse</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <th>ece</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <th>ece</th>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <th>ece</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <th>ece</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               delhi               mumbai         \n",
       "         avg_package students avg_package students\n",
       "2019 cse           1        2           0        0\n",
       "2020 cse           3        4           0        0\n",
       "2021 cse           5        6           0        0\n",
       "2022 cse           7        8           0        0\n",
       "2019 ece           9       10           0        0\n",
       "2020 ece          11       12           0        0\n",
       "2021 ece          13       14           0        0\n",
       "2022 ece          15       16           0        0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#swaping between levels\n",
    "branch_df3.swaplevel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "      <th>avg_package</th>\n",
       "      <th>students</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>delhi</th>\n",
       "      <th>delhi</th>\n",
       "      <th>mumbai</th>\n",
       "      <th>mumbai</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">cse</th>\n",
       "      <th>2019</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">ece</th>\n",
       "      <th>2019</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         avg_package students avg_package students\n",
       "               delhi    delhi      mumbai   mumbai\n",
       "cse 2019           1        2           0        0\n",
       "    2020           3        4           0        0\n",
       "    2021           5        6           0        0\n",
       "    2022           7        8           0        0\n",
       "ece 2019           9       10           0        0\n",
       "    2020          11       12           0        0\n",
       "    2021          13       14           0        0\n",
       "    2022          15       16           0        0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_df3.swaplevel(axis=1) #columnwise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Long Vs Wide Data"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Wide format is where we have a single row for every data point with multiple columns to hold the values of various attributes.\n",
    "\n",
    "Long format is where, for each data point we have as many rows as the number of attributes and each row contains the value of a particular attribute for a given data point."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### melt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>cse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cse\n",
       "0  120"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'cse':[120]})"
   ]
  },
  {
   "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>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cse</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  variable  value\n",
       "0      cse    120"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'cse':[120]}).melt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>cse</th>\n",
       "      <th>ece</th>\n",
       "      <th>mech</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>120</td>\n",
       "      <td>100</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cse  ece  mech\n",
       "0  120  100    50"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'cse':[120],'ece':[100],'mech':[50]})  #wide"
   ]
  },
  {
   "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>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cse</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ece</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mech</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  variable  value\n",
       "0      cse    120\n",
       "1      ece    100\n",
       "2     mech     50"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'cse':[120],'ece':[100],'mech':[50]}).melt() #long format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>branch</th>\n",
       "      <th>num_students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cse</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ece</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mech</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  branch  num_students\n",
       "0    cse           120\n",
       "1    ece           100\n",
       "2   mech            50"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'cse':[120],'ece':[100],'mech':[50]}).melt(var_name='branch',value_name='num_students')"
   ]
  },
  {
   "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>branch</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cse</td>\n",
       "      <td>100</td>\n",
       "      <td>120</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ece</td>\n",
       "      <td>150</td>\n",
       "      <td>130</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mech</td>\n",
       "      <td>60</td>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  branch  2020  2021  2022\n",
       "0    cse   100   120   150\n",
       "1    ece   150   130   140\n",
       "2   mech    60    80    70"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    {\n",
    "        'branch':['cse','ece','mech'],\n",
    "        '2020':[100,150,60],\n",
    "        '2021':[120,130,80],\n",
    "        '2022':[150,140,70]\n",
    "    }\n",
    ")"
   ]
  },
  {
   "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",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>branch</td>\n",
       "      <td>cse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>branch</td>\n",
       "      <td>ece</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>branch</td>\n",
       "      <td>mech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2022</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2022</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2022</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   variable value\n",
       "0    branch   cse\n",
       "1    branch   ece\n",
       "2    branch  mech\n",
       "3      2020   100\n",
       "4      2020   150\n",
       "5      2020    60\n",
       "6      2021   120\n",
       "7      2021   130\n",
       "8      2021    80\n",
       "9      2022   150\n",
       "10     2022   140\n",
       "11     2022    70"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    {\n",
    "        'branch':['cse','ece','mech'],\n",
    "        '2020':[100,150,60],\n",
    "        '2021':[120,130,80],\n",
    "        '2022':[150,140,70]\n",
    "    }\n",
    ").melt()"
   ]
  },
  {
   "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>branch</th>\n",
       "      <th>year</th>\n",
       "      <th>students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cse</td>\n",
       "      <td>2020</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ece</td>\n",
       "      <td>2020</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mech</td>\n",
       "      <td>2020</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cse</td>\n",
       "      <td>2021</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ece</td>\n",
       "      <td>2021</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mech</td>\n",
       "      <td>2021</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cse</td>\n",
       "      <td>2022</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ece</td>\n",
       "      <td>2022</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mech</td>\n",
       "      <td>2022</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  branch  year  students\n",
       "0    cse  2020       100\n",
       "1    ece  2020       150\n",
       "2   mech  2020        60\n",
       "3    cse  2021       120\n",
       "4    ece  2021       130\n",
       "5   mech  2021        80\n",
       "6    cse  2022       150\n",
       "7    ece  2022       140\n",
       "8   mech  2022        70"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    {\n",
    "        'branch':['cse','ece','mech'],\n",
    "        '2020':[100,150,60],\n",
    "        '2021':[120,130,80],\n",
    "        '2022':[150,140,70]\n",
    "    }\n",
    ").melt(id_vars=['branch'],var_name='year',value_name='students')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# melt -> real world example\n",
    "death = pd.read_csv('time_series_covid19_deaths_global.csv')\n",
    "confirm = pd.read_csv('time_series_covid19_confirmed_global.csv')"
   ]
  },
  {
   "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",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>...</th>\n",
       "      <th>12/24/22</th>\n",
       "      <th>12/25/22</th>\n",
       "      <th>12/26/22</th>\n",
       "      <th>12/27/22</th>\n",
       "      <th>12/28/22</th>\n",
       "      <th>12/29/22</th>\n",
       "      <th>12/30/22</th>\n",
       "      <th>12/31/22</th>\n",
       "      <th>1/1/23</th>\n",
       "      <th>1/2/23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.93911</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7845</td>\n",
       "      <td>7846</td>\n",
       "      <td>7846</td>\n",
       "      <td>7846</td>\n",
       "      <td>7846</td>\n",
       "      <td>7847</td>\n",
       "      <td>7847</td>\n",
       "      <td>7849</td>\n",
       "      <td>7849</td>\n",
       "      <td>7849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.15330</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3595</td>\n",
       "      <td>3595</td>\n",
       "      <td>3595</td>\n",
       "      <td>3595</td>\n",
       "      <td>3595</td>\n",
       "      <td>3595</td>\n",
       "      <td>3595</td>\n",
       "      <td>3595</td>\n",
       "      <td>3595</td>\n",
       "      <td>3595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.03390</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6881</td>\n",
       "      <td>6881</td>\n",
       "      <td>6881</td>\n",
       "      <td>6881</td>\n",
       "      <td>6881</td>\n",
       "      <td>6881</td>\n",
       "      <td>6881</td>\n",
       "      <td>6881</td>\n",
       "      <td>6881</td>\n",
       "      <td>6881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.50630</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.20270</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1928</td>\n",
       "      <td>1928</td>\n",
       "      <td>1928</td>\n",
       "      <td>1930</td>\n",
       "      <td>1930</td>\n",
       "      <td>1930</td>\n",
       "      <td>1930</td>\n",
       "      <td>1930</td>\n",
       "      <td>1930</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1081 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region       Lat       Long  1/22/20  1/23/20  \\\n",
       "0            NaN    Afghanistan  33.93911  67.709953        0        0   \n",
       "1            NaN        Albania  41.15330  20.168300        0        0   \n",
       "2            NaN        Algeria  28.03390   1.659600        0        0   \n",
       "3            NaN        Andorra  42.50630   1.521800        0        0   \n",
       "4            NaN         Angola -11.20270  17.873900        0        0   \n",
       "\n",
       "   1/24/20  1/25/20  1/26/20  1/27/20  ...  12/24/22  12/25/22  12/26/22  \\\n",
       "0        0        0        0        0  ...      7845      7846      7846   \n",
       "1        0        0        0        0  ...      3595      3595      3595   \n",
       "2        0        0        0        0  ...      6881      6881      6881   \n",
       "3        0        0        0        0  ...       165       165       165   \n",
       "4        0        0        0        0  ...      1928      1928      1928   \n",
       "\n",
       "   12/27/22  12/28/22  12/29/22  12/30/22  12/31/22  1/1/23  1/2/23  \n",
       "0      7846      7846      7847      7847      7849    7849    7849  \n",
       "1      3595      3595      3595      3595      3595    3595    3595  \n",
       "2      6881      6881      6881      6881      6881    6881    6881  \n",
       "3       165       165       165       165       165     165     165  \n",
       "4      1930      1930      1930      1930      1930    1930    1930  \n",
       "\n",
       "[5 rows x 1081 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "death.head()\n",
    "#death.shape \n",
    "#3 year data of each country...death cases... 1 row for each country.. wide format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>...</th>\n",
       "      <th>12/24/22</th>\n",
       "      <th>12/25/22</th>\n",
       "      <th>12/26/22</th>\n",
       "      <th>12/27/22</th>\n",
       "      <th>12/28/22</th>\n",
       "      <th>12/29/22</th>\n",
       "      <th>12/30/22</th>\n",
       "      <th>12/31/22</th>\n",
       "      <th>1/1/23</th>\n",
       "      <th>1/2/23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.93911</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>207310</td>\n",
       "      <td>207399</td>\n",
       "      <td>207438</td>\n",
       "      <td>207460</td>\n",
       "      <td>207493</td>\n",
       "      <td>207511</td>\n",
       "      <td>207550</td>\n",
       "      <td>207559</td>\n",
       "      <td>207616</td>\n",
       "      <td>207627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.15330</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>333749</td>\n",
       "      <td>333749</td>\n",
       "      <td>333751</td>\n",
       "      <td>333751</td>\n",
       "      <td>333776</td>\n",
       "      <td>333776</td>\n",
       "      <td>333806</td>\n",
       "      <td>333806</td>\n",
       "      <td>333811</td>\n",
       "      <td>333812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.03390</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>271194</td>\n",
       "      <td>271198</td>\n",
       "      <td>271198</td>\n",
       "      <td>271202</td>\n",
       "      <td>271208</td>\n",
       "      <td>271217</td>\n",
       "      <td>271223</td>\n",
       "      <td>271228</td>\n",
       "      <td>271229</td>\n",
       "      <td>271229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.50630</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>47686</td>\n",
       "      <td>47686</td>\n",
       "      <td>47686</td>\n",
       "      <td>47686</td>\n",
       "      <td>47751</td>\n",
       "      <td>47751</td>\n",
       "      <td>47751</td>\n",
       "      <td>47751</td>\n",
       "      <td>47751</td>\n",
       "      <td>47751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.20270</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>104973</td>\n",
       "      <td>104973</td>\n",
       "      <td>104973</td>\n",
       "      <td>105095</td>\n",
       "      <td>105095</td>\n",
       "      <td>105095</td>\n",
       "      <td>105095</td>\n",
       "      <td>105095</td>\n",
       "      <td>105095</td>\n",
       "      <td>105095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1081 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region       Lat       Long  1/22/20  1/23/20  \\\n",
       "0            NaN    Afghanistan  33.93911  67.709953        0        0   \n",
       "1            NaN        Albania  41.15330  20.168300        0        0   \n",
       "2            NaN        Algeria  28.03390   1.659600        0        0   \n",
       "3            NaN        Andorra  42.50630   1.521800        0        0   \n",
       "4            NaN         Angola -11.20270  17.873900        0        0   \n",
       "\n",
       "   1/24/20  1/25/20  1/26/20  1/27/20  ...  12/24/22  12/25/22  12/26/22  \\\n",
       "0        0        0        0        0  ...    207310    207399    207438   \n",
       "1        0        0        0        0  ...    333749    333749    333751   \n",
       "2        0        0        0        0  ...    271194    271198    271198   \n",
       "3        0        0        0        0  ...     47686     47686     47686   \n",
       "4        0        0        0        0  ...    104973    104973    104973   \n",
       "\n",
       "   12/27/22  12/28/22  12/29/22  12/30/22  12/31/22  1/1/23  1/2/23  \n",
       "0    207460    207493    207511    207550    207559  207616  207627  \n",
       "1    333751    333776    333776    333806    333806  333811  333812  \n",
       "2    271202    271208    271217    271223    271228  271229  271229  \n",
       "3     47686     47751     47751     47751     47751   47751   47751  \n",
       "4    105095    105095    105095    105095    105095  105095  105095  \n",
       "\n",
       "[5 rows x 1081 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirm.head()  #confirm cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#death = death.melt()\n",
    "#death.shape\n",
    "#death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>date</th>\n",
       "      <th>num_deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.93911</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.15330</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.03390</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.50630</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.20270</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region       Lat       Long     date  num_deaths\n",
       "0            NaN    Afghanistan  33.93911  67.709953  1/22/20           0\n",
       "1            NaN        Albania  41.15330  20.168300  1/22/20           0\n",
       "2            NaN        Algeria  28.03390   1.659600  1/22/20           0\n",
       "3            NaN        Andorra  42.50630   1.521800  1/22/20           0\n",
       "4            NaN         Angola -11.20270  17.873900  1/22/20           0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "death = death.melt(id_vars=['Province/State','Country/Region','Lat','Long'],var_name='date',value_name='num_deaths')\n",
    "death.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "confirm = confirm.melt(id_vars=['Province/State','Country/Region','Lat','Long'],var_name='date',value_name='num_cases')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>date</th>\n",
       "      <th>num_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.939110</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.153300</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.033900</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.506300</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.202700</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311248</th>\n",
       "      <td>NaN</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>31.952200</td>\n",
       "      <td>35.233200</td>\n",
       "      <td>1/2/23</td>\n",
       "      <td>703228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311249</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Winter Olympics 2022</td>\n",
       "      <td>39.904200</td>\n",
       "      <td>116.407400</td>\n",
       "      <td>1/2/23</td>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311250</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>15.552727</td>\n",
       "      <td>48.516388</td>\n",
       "      <td>1/2/23</td>\n",
       "      <td>11945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311251</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>-13.133897</td>\n",
       "      <td>27.849332</td>\n",
       "      <td>1/2/23</td>\n",
       "      <td>334661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311252</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>-19.015438</td>\n",
       "      <td>29.154857</td>\n",
       "      <td>1/2/23</td>\n",
       "      <td>259981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>311253 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Province/State        Country/Region        Lat        Long     date  \\\n",
       "0                 NaN           Afghanistan  33.939110   67.709953  1/22/20   \n",
       "1                 NaN               Albania  41.153300   20.168300  1/22/20   \n",
       "2                 NaN               Algeria  28.033900    1.659600  1/22/20   \n",
       "3                 NaN               Andorra  42.506300    1.521800  1/22/20   \n",
       "4                 NaN                Angola -11.202700   17.873900  1/22/20   \n",
       "...               ...                   ...        ...         ...      ...   \n",
       "311248            NaN    West Bank and Gaza  31.952200   35.233200   1/2/23   \n",
       "311249            NaN  Winter Olympics 2022  39.904200  116.407400   1/2/23   \n",
       "311250            NaN                 Yemen  15.552727   48.516388   1/2/23   \n",
       "311251            NaN                Zambia -13.133897   27.849332   1/2/23   \n",
       "311252            NaN              Zimbabwe -19.015438   29.154857   1/2/23   \n",
       "\n",
       "        num_cases  \n",
       "0               0  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               0  \n",
       "...           ...  \n",
       "311248     703228  \n",
       "311249        535  \n",
       "311250      11945  \n",
       "311251     334661  \n",
       "311252     259981  \n",
       "\n",
       "[311253 rows x 6 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>date</th>\n",
       "      <th>num_cases</th>\n",
       "      <th>num_deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311248</th>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>1/2/23</td>\n",
       "      <td>703228</td>\n",
       "      <td>5708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311249</th>\n",
       "      <td>Winter Olympics 2022</td>\n",
       "      <td>1/2/23</td>\n",
       "      <td>535</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311250</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>1/2/23</td>\n",
       "      <td>11945</td>\n",
       "      <td>2159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311251</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1/2/23</td>\n",
       "      <td>334661</td>\n",
       "      <td>4024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311252</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1/2/23</td>\n",
       "      <td>259981</td>\n",
       "      <td>5637</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>311253 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Country/Region     date  num_cases  num_deaths\n",
       "0                Afghanistan  1/22/20          0           0\n",
       "1                    Albania  1/22/20          0           0\n",
       "2                    Algeria  1/22/20          0           0\n",
       "3                    Andorra  1/22/20          0           0\n",
       "4                     Angola  1/22/20          0           0\n",
       "...                      ...      ...        ...         ...\n",
       "311248    West Bank and Gaza   1/2/23     703228        5708\n",
       "311249  Winter Olympics 2022   1/2/23        535           0\n",
       "311250                 Yemen   1/2/23      11945        2159\n",
       "311251                Zambia   1/2/23     334661        4024\n",
       "311252              Zimbabwe   1/2/23     259981        5637\n",
       "\n",
       "[311253 rows x 4 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirm.merge(death,on=['Province/State','Country/Region','Lat','Long','date'])[['Country/Region','date','num_cases','num_deaths']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pivot Table"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# The pivot table takes simple column-wise data as input, and groups the entries into a two-dimensional table that provides a multidimensional summarization of the data.\n",
    "#GENERALLY USED ON CATEGORICAL COLUMNS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sns.load_dataset('tips')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "Male      20.744076\n",
       "Female    18.056897\n",
       "Name: total_bill, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#avg 'total bill' based on gender\n",
    "df.groupby('sex')['total_bill'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex     smoker\n",
       "Male    Yes       22.284500\n",
       "        No        19.791237\n",
       "Female  Yes       17.977879\n",
       "        No        18.105185\n",
       "Name: total_bill, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['sex','smoker'])['total_bill'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>smoker</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>22.284500</td>\n",
       "      <td>19.791237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>17.977879</td>\n",
       "      <td>18.105185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "smoker        Yes         No\n",
       "sex                         \n",
       "Male    22.284500  19.791237\n",
       "Female  17.977879  18.105185"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['sex','smoker'])['total_bill'].mean().unstack()"
   ]
  },
  {
   "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",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>smoker</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>22.284500</td>\n",
       "      <td>19.791237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>17.977879</td>\n",
       "      <td>18.105185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "smoker        Yes         No\n",
       "sex                         \n",
       "Male    22.284500  19.791237\n",
       "Female  17.977879  18.105185"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#by using pivot table,                    ......shortcut way\n",
    "df.pivot_table(index='sex',columns='smoker',values='total_bill') #by default, aggfunc=mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>smoker</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>1337.07</td>\n",
       "      <td>1919.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>593.27</td>\n",
       "      <td>977.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "smoker      Yes       No\n",
       "sex                     \n",
       "Male    1337.07  1919.75\n",
       "Female   593.27   977.68"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# aggfunc\n",
    "df.pivot_table(index='sex',columns='smoker',values='total_bill',aggfunc='sum') #count,median,var,std,can also use custom fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_10152\\175908820.py:1: FutureWarning: pivot_table dropped a column because it failed to aggregate. This behavior is deprecated and will raise in a future version of pandas. Select only the columns that can be aggregated.\n",
      "  df.pivot_table(index='sex',columns='smoker') #if value is not specified, analysis takes place on all numerical columns\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">size</th>\n",
       "      <th colspan=\"2\" halign=\"left\">tip</th>\n",
       "      <th colspan=\"2\" halign=\"left\">total_bill</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoker</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.711340</td>\n",
       "      <td>3.051167</td>\n",
       "      <td>3.113402</td>\n",
       "      <td>22.284500</td>\n",
       "      <td>19.791237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>2.242424</td>\n",
       "      <td>2.592593</td>\n",
       "      <td>2.931515</td>\n",
       "      <td>2.773519</td>\n",
       "      <td>17.977879</td>\n",
       "      <td>18.105185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            size                 tip           total_bill           \n",
       "smoker       Yes        No       Yes        No        Yes         No\n",
       "sex                                                                 \n",
       "Male    2.500000  2.711340  3.051167  3.113402  22.284500  19.791237\n",
       "Female  2.242424  2.592593  2.931515  2.773519  17.977879  18.105185"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(index='sex',columns='smoker') #if value is not specified, analysis takes place on all numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Thur</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Fri</th>\n",
       "      <th>Sat</th>\n",
       "      <th>Sun</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>Lunch</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Lunch</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Dinner</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
       "      <th>Yes</th>\n",
       "      <td>19.171000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.386667</td>\n",
       "      <td>25.892</td>\n",
       "      <td>21.837778</td>\n",
       "      <td>26.141333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>18.486500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.475</td>\n",
       "      <td>19.929063</td>\n",
       "      <td>20.403256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Female</th>\n",
       "      <th>Yes</th>\n",
       "      <td>19.218571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.260000</td>\n",
       "      <td>12.200</td>\n",
       "      <td>20.266667</td>\n",
       "      <td>16.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>15.899167</td>\n",
       "      <td>18.78</td>\n",
       "      <td>15.980000</td>\n",
       "      <td>22.750</td>\n",
       "      <td>19.003846</td>\n",
       "      <td>20.824286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "day                 Thur               Fri                Sat        Sun\n",
       "time               Lunch Dinner      Lunch  Dinner     Dinner     Dinner\n",
       "sex    smoker                                                           \n",
       "Male   Yes     19.171000    NaN  11.386667  25.892  21.837778  26.141333\n",
       "       No      18.486500    NaN        NaN  17.475  19.929063  20.403256\n",
       "Female Yes     19.218571    NaN  13.260000  12.200  20.266667  16.540000\n",
       "       No      15.899167  18.78  15.980000  22.750  19.003846  20.824286"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# multidimensional\n",
    "df.pivot_table(index=['sex','smoker'],columns=['day','time'],values='total_bill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th colspan=\"6\" halign=\"left\">tip</th>\n",
       "      <th colspan=\"8\" halign=\"left\">total_bill</th>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Thur</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Fri</th>\n",
       "      <th>Sat</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>Lunch</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Lunch</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Lunch</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Lunch</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Lunch</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Lunch</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Lunch</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Lunch</th>\n",
       "      <th>Dinner</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
       "      <th>Yes</th>\n",
       "      <td>2.300000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.629630</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>5.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.20</td>\n",
       "      <td>4.73</td>\n",
       "      <td>10.00</td>\n",
       "      <td>6.5</td>\n",
       "      <td>191.71</td>\n",
       "      <td>0.00</td>\n",
       "      <td>34.16</td>\n",
       "      <td>129.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>589.62</td>\n",
       "      <td>0.0</td>\n",
       "      <td>392.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.656250</td>\n",
       "      <td>2.883721</td>\n",
       "      <td>6.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.50</td>\n",
       "      <td>9.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>369.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>34.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>637.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>877.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Female</th>\n",
       "      <th>Yes</th>\n",
       "      <td>2.428571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>5.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.48</td>\n",
       "      <td>4.30</td>\n",
       "      <td>6.50</td>\n",
       "      <td>4.0</td>\n",
       "      <td>134.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>39.78</td>\n",
       "      <td>48.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>304.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.307692</td>\n",
       "      <td>3.071429</td>\n",
       "      <td>5.17</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.25</td>\n",
       "      <td>4.67</td>\n",
       "      <td>5.2</td>\n",
       "      <td>381.58</td>\n",
       "      <td>18.78</td>\n",
       "      <td>15.98</td>\n",
       "      <td>22.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>247.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>291.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   size                                               tip  \\\n",
       "day                Thur              Fri              Sat       Sun  Thur   \n",
       "time              Lunch Dinner     Lunch Dinner    Dinner    Dinner Lunch   \n",
       "sex    smoker                                                               \n",
       "Male   Yes     2.300000    NaN  1.666667    2.4  2.629630  2.600000  5.00   \n",
       "       No      2.500000    NaN       NaN    2.0  2.656250  2.883721  6.70   \n",
       "Female Yes     2.428571    NaN  2.000000    2.0  2.200000  2.500000  5.00   \n",
       "       No      2.500000    2.0  3.000000    2.0  2.307692  3.071429  5.17   \n",
       "\n",
       "                                                total_bill                \\\n",
       "day                    Fri           Sat    Sun       Thur           Fri   \n",
       "time          Dinner Lunch Dinner Dinner Dinner      Lunch Dinner  Lunch   \n",
       "sex    smoker                                                              \n",
       "Male   Yes       NaN  2.20   4.73  10.00    6.5     191.71   0.00  34.16   \n",
       "       No        NaN   NaN   3.50   9.00    6.0     369.73   0.00   0.00   \n",
       "Female Yes       NaN  3.48   4.30   6.50    4.0     134.53   0.00  39.78   \n",
       "       No        3.0  3.00   3.25   4.67    5.2     381.58  18.78  15.98   \n",
       "\n",
       "                                                   \n",
       "day                     Sat           Sun          \n",
       "time           Dinner Lunch  Dinner Lunch  Dinner  \n",
       "sex    smoker                                      \n",
       "Male   Yes     129.46   0.0  589.62   0.0  392.12  \n",
       "       No       34.95   0.0  637.73   0.0  877.34  \n",
       "Female Yes      48.80   0.0  304.00   0.0   66.16  \n",
       "       No       22.75   0.0  247.05   0.0  291.54  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(index=['sex','smoker'],columns=['day','time'],aggfunc={'size':'mean','tip':'max','total_bill':'sum'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>smoker</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>1337.07</td>\n",
       "      <td>1919.75</td>\n",
       "      <td>3256.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>593.27</td>\n",
       "      <td>977.68</td>\n",
       "      <td>1570.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>1930.34</td>\n",
       "      <td>2897.43</td>\n",
       "      <td>4827.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "smoker      Yes       No      All\n",
       "sex                              \n",
       "Male    1337.07  1919.75  3256.82\n",
       "Female   593.27   977.68  1570.95\n",
       "All     1930.34  2897.43  4827.77"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# margins\n",
    "df.pivot_table(index='sex',columns='smoker',values='total_bill',aggfunc='sum',margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"7\" halign=\"left\">size</th>\n",
       "      <th colspan=\"5\" halign=\"left\">tip</th>\n",
       "      <th colspan=\"9\" halign=\"left\">total_bill</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Thur</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Fri</th>\n",
       "      <th>Sat</th>\n",
       "      <th>Sun</th>\n",
       "      <th>All</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Thur</th>\n",
       "      <th>Fri</th>\n",
       "      <th>...</th>\n",
       "      <th>All</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Thur</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Fri</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Sat</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Sun</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>Lunch</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Lunch</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Dinner</th>\n",
       "      <th></th>\n",
       "      <th>Lunch</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Lunch</th>\n",
       "      <th>...</th>\n",
       "      <th></th>\n",
       "      <th>Lunch</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Lunch</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Lunch</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Lunch</th>\n",
       "      <th>Dinner</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
       "      <th>Yes</th>\n",
       "      <td>2.300000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>2.629630</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>5.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.20</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>191.71</td>\n",
       "      <td>0.00</td>\n",
       "      <td>34.16</td>\n",
       "      <td>129.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>589.62</td>\n",
       "      <td>0.0</td>\n",
       "      <td>392.12</td>\n",
       "      <td>1337.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.656250</td>\n",
       "      <td>2.883721</td>\n",
       "      <td>2.711340</td>\n",
       "      <td>6.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>369.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>34.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>637.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>877.34</td>\n",
       "      <td>1919.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Female</th>\n",
       "      <th>Yes</th>\n",
       "      <td>2.428571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.242424</td>\n",
       "      <td>5.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.48</td>\n",
       "      <td>...</td>\n",
       "      <td>6.5</td>\n",
       "      <td>134.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>39.78</td>\n",
       "      <td>48.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>304.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.16</td>\n",
       "      <td>593.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.307692</td>\n",
       "      <td>3.071429</td>\n",
       "      <td>2.592593</td>\n",
       "      <td>5.17</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>...</td>\n",
       "      <td>5.2</td>\n",
       "      <td>381.58</td>\n",
       "      <td>18.78</td>\n",
       "      <td>15.98</td>\n",
       "      <td>22.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>247.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>291.54</td>\n",
       "      <td>977.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <th></th>\n",
       "      <td>2.459016</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.166667</td>\n",
       "      <td>2.517241</td>\n",
       "      <td>2.842105</td>\n",
       "      <td>2.569672</td>\n",
       "      <td>6.70</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.48</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1077.55</td>\n",
       "      <td>18.78</td>\n",
       "      <td>89.92</td>\n",
       "      <td>235.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1778.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1627.16</td>\n",
       "      <td>4827.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   size                                                 \\\n",
       "day                Thur              Fri                 Sat       Sun   \n",
       "time              Lunch Dinner     Lunch    Dinner    Dinner    Dinner   \n",
       "sex    smoker                                                            \n",
       "Male   Yes     2.300000    NaN  1.666667  2.400000  2.629630  2.600000   \n",
       "       No      2.500000    NaN       NaN  2.000000  2.656250  2.883721   \n",
       "Female Yes     2.428571    NaN  2.000000  2.000000  2.200000  2.500000   \n",
       "       No      2.500000    2.0  3.000000  2.000000  2.307692  3.071429   \n",
       "All            2.459016    2.0  2.000000  2.166667  2.517241  2.842105   \n",
       "\n",
       "                          tip               ...       total_bill         \\\n",
       "day                 All  Thur          Fri  ...   All       Thur          \n",
       "time                    Lunch Dinner Lunch  ...            Lunch Dinner   \n",
       "sex    smoker                               ...                           \n",
       "Male   Yes     2.500000  5.00    NaN  2.20  ...  10.0     191.71   0.00   \n",
       "       No      2.711340  6.70    NaN   NaN  ...   9.0     369.73   0.00   \n",
       "Female Yes     2.242424  5.00    NaN  3.48  ...   6.5     134.53   0.00   \n",
       "       No      2.592593  5.17    3.0  3.00  ...   5.2     381.58  18.78   \n",
       "All            2.569672  6.70    3.0  3.48  ...  10.0    1077.55  18.78   \n",
       "\n",
       "                                                                     \n",
       "day              Fri           Sat            Sun               All  \n",
       "time           Lunch  Dinner Lunch   Dinner Lunch   Dinner           \n",
       "sex    smoker                                                        \n",
       "Male   Yes     34.16  129.46   0.0   589.62   0.0   392.12  1337.07  \n",
       "       No       0.00   34.95   0.0   637.73   0.0   877.34  1919.75  \n",
       "Female Yes     39.78   48.80   0.0   304.00   0.0    66.16   593.27  \n",
       "       No      15.98   22.75   0.0   247.05   0.0   291.54   977.68  \n",
       "All            89.92  235.96   NaN  1778.40   NaN  1627.16  4827.77  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(index=['sex','smoker'],columns=['day','time'],aggfunc={'size':'mean','tip':'max','total_bill':'sum'},margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>Date</th>\n",
       "      <th>Account</th>\n",
       "      <th>Category</th>\n",
       "      <th>Subcategory</th>\n",
       "      <th>Note</th>\n",
       "      <th>INR</th>\n",
       "      <th>Income/Expense</th>\n",
       "      <th>Note.1</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Account.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3/2/2022 10:11</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Food</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brownie</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>INR</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3/2/2022 10:11</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>To lended people</td>\n",
       "      <td>300.0</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>INR</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3/1/2022 19:50</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Food</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.0</td>\n",
       "      <td>INR</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3/1/2022 18:56</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Metro</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>INR</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3/1/2022 18:22</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Food</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snacks</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.0</td>\n",
       "      <td>INR</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date               Account        Category  Subcategory  \\\n",
       "0  3/2/2022 10:11  CUB - online payment            Food          NaN   \n",
       "1  3/2/2022 10:11  CUB - online payment           Other          NaN   \n",
       "2  3/1/2022 19:50  CUB - online payment            Food          NaN   \n",
       "3  3/1/2022 18:56  CUB - online payment  Transportation          NaN   \n",
       "4  3/1/2022 18:22  CUB - online payment            Food          NaN   \n",
       "\n",
       "               Note    INR Income/Expense  Note.1  Amount Currency  Account.1  \n",
       "0           Brownie   50.0        Expense     NaN    50.0      INR       50.0  \n",
       "1  To lended people  300.0        Expense     NaN   300.0      INR      300.0  \n",
       "2            Dinner   78.0        Expense     NaN    78.0      INR       78.0  \n",
       "3             Metro   30.0        Expense     NaN    30.0      INR       30.0  \n",
       "4            Snacks   67.0        Expense     NaN    67.0      INR       67.0  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plotting graphs\n",
    "df = pd.read_csv('expense_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Food                156\n",
       "Other                60\n",
       "Transportation       31\n",
       "Apparel               7\n",
       "Household             6\n",
       "Allowance             6\n",
       "Social Life           5\n",
       "Education             1\n",
       "Salary                1\n",
       "Self-development      1\n",
       "Beauty                1\n",
       "Gift                  1\n",
       "Petty cash            1\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 277 entries, 0 to 276\n",
      "Data columns (total 11 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Date            277 non-null    object \n",
      " 1   Account         277 non-null    object \n",
      " 2   Category        277 non-null    object \n",
      " 3   Subcategory     0 non-null      float64\n",
      " 4   Note            273 non-null    object \n",
      " 5   INR             277 non-null    float64\n",
      " 6   Income/Expense  277 non-null    object \n",
      " 7   Note.1          0 non-null      float64\n",
      " 8   Amount          277 non-null    float64\n",
      " 9   Currency        277 non-null    object \n",
      " 10  Account.1       277 non-null    float64\n",
      "dtypes: float64(5), object(6)\n",
      "memory usage: 23.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info() #date col is a string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 277 entries, 0 to 276\n",
      "Data columns (total 11 columns):\n",
      " #   Column          Non-Null Count  Dtype         \n",
      "---  ------          --------------  -----         \n",
      " 0   Date            277 non-null    datetime64[ns]\n",
      " 1   Account         277 non-null    object        \n",
      " 2   Category        277 non-null    object        \n",
      " 3   Subcategory     0 non-null      float64       \n",
      " 4   Note            273 non-null    object        \n",
      " 5   INR             277 non-null    float64       \n",
      " 6   Income/Expense  277 non-null    object        \n",
      " 7   Note.1          0 non-null      float64       \n",
      " 8   Amount          277 non-null    float64       \n",
      " 9   Currency        277 non-null    object        \n",
      " 10  Account.1       277 non-null    float64       \n",
      "dtypes: datetime64[ns](1), float64(5), object(5)\n",
      "memory usage: 23.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['month'] = df['Date'].dt.month_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Account</th>\n",
       "      <th>Category</th>\n",
       "      <th>Subcategory</th>\n",
       "      <th>Note</th>\n",
       "      <th>INR</th>\n",
       "      <th>Income/Expense</th>\n",
       "      <th>Note.1</th>\n",
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       "      <th>Currency</th>\n",
       "      <th>Account.1</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-03-02 10:11:00</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Food</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brownie</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>INR</td>\n",
       "      <td>50.0</td>\n",
       "      <td>March</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-03-02 10:11:00</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>To lended people</td>\n",
       "      <td>300.0</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>INR</td>\n",
       "      <td>300.0</td>\n",
       "      <td>March</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-03-01 19:50:00</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Food</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.0</td>\n",
       "      <td>INR</td>\n",
       "      <td>78.0</td>\n",
       "      <td>March</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-03-01 18:56:00</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Metro</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>INR</td>\n",
       "      <td>30.0</td>\n",
       "      <td>March</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-03-01 18:22:00</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Food</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snacks</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.0</td>\n",
       "      <td>INR</td>\n",
       "      <td>67.0</td>\n",
       "      <td>March</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Date               Account        Category  Subcategory  \\\n",
       "0 2022-03-02 10:11:00  CUB - online payment            Food          NaN   \n",
       "1 2022-03-02 10:11:00  CUB - online payment           Other          NaN   \n",
       "2 2022-03-01 19:50:00  CUB - online payment            Food          NaN   \n",
       "3 2022-03-01 18:56:00  CUB - online payment  Transportation          NaN   \n",
       "4 2022-03-01 18:22:00  CUB - online payment            Food          NaN   \n",
       "\n",
       "               Note    INR Income/Expense  Note.1  Amount Currency  Account.1  \\\n",
       "0           Brownie   50.0        Expense     NaN    50.0      INR       50.0   \n",
       "1  To lended people  300.0        Expense     NaN   300.0      INR      300.0   \n",
       "2            Dinner   78.0        Expense     NaN    78.0      INR       78.0   \n",
       "3             Metro   30.0        Expense     NaN    30.0      INR       30.0   \n",
       "4            Snacks   67.0        Expense     NaN    67.0      INR       67.0   \n",
       "\n",
       "   month  \n",
       "0  March  \n",
       "1  March  \n",
       "2  March  \n",
       "3  March  \n",
       "4  March  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Category</th>\n",
       "      <th>Allowance</th>\n",
       "      <th>Apparel</th>\n",
       "      <th>Beauty</th>\n",
       "      <th>Education</th>\n",
       "      <th>Food</th>\n",
       "      <th>Gift</th>\n",
       "      <th>Household</th>\n",
       "      <th>Other</th>\n",
       "      <th>Petty cash</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Self-development</th>\n",
       "      <th>Social Life</th>\n",
       "      <th>Transportation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>11000.0</td>\n",
       "      <td>2590.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6440.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400.0</td>\n",
       "      <td>513.72</td>\n",
       "      <td>914.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>NaN</td>\n",
       "      <td>798.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5579.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2808.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1800.00</td>\n",
       "      <td>5078.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>9112.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4580.0</td>\n",
       "      <td>13178.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.00</td>\n",
       "      <td>2850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>195.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3174.40</td>\n",
       "      <td>115.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>331.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Category  Allowance  Apparel  Beauty  Education     Food   Gift  Household  \\\n",
       "month                                                                        \n",
       "December    11000.0   2590.0   196.0        NaN  6440.72    NaN     4800.0   \n",
       "February        NaN    798.0     NaN        NaN  5579.85    NaN     2808.0   \n",
       "January      1000.0      NaN     NaN     1400.0  9112.51    NaN     4580.0   \n",
       "March           NaN      NaN     NaN        NaN   195.00    NaN        NaN   \n",
       "November     2000.0      NaN     NaN        NaN  3174.40  115.0        NaN   \n",
       "\n",
       "Category    Other  Petty cash  Salary  Self-development  Social Life  \\\n",
       "month                                                                  \n",
       "December   1790.0         NaN     NaN             400.0       513.72   \n",
       "February  20000.0         NaN     NaN               NaN      1800.00   \n",
       "January   13178.0         NaN  8000.0               NaN       200.00   \n",
       "March       900.0         NaN     NaN               NaN          NaN   \n",
       "November   2000.0         3.0     NaN               NaN          NaN   \n",
       "\n",
       "Category  Transportation  \n",
       "month                     \n",
       "December           914.0  \n",
       "February          5078.8  \n",
       "January           2850.0  \n",
       "March               30.0  \n",
       "November           331.0  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(index='month',columns='Category',values='INR',aggfunc='sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Household</th>\n",
       "      <th>Other</th>\n",
       "      <th>Petty cash</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Self-development</th>\n",
       "      <th>Social Life</th>\n",
       "      <th>Transportation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>11000</td>\n",
       "      <td>2590</td>\n",
       "      <td>196</td>\n",
       "      <td>0</td>\n",
       "      <td>6440.72</td>\n",
       "      <td>0</td>\n",
       "      <td>4800</td>\n",
       "      <td>1790</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>513.72</td>\n",
       "      <td>914.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>0</td>\n",
       "      <td>798</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5579.85</td>\n",
       "      <td>0</td>\n",
       "      <td>2808</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1800.00</td>\n",
       "      <td>5078.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1400</td>\n",
       "      <td>9112.51</td>\n",
       "      <td>0</td>\n",
       "      <td>4580</td>\n",
       "      <td>13178</td>\n",
       "      <td>0</td>\n",
       "      <td>8000</td>\n",
       "      <td>0</td>\n",
       "      <td>200.00</td>\n",
       "      <td>2850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>195.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3174.40</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>331.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Category  Allowance  Apparel  Beauty  Education     Food  Gift  Household  \\\n",
       "month                                                                       \n",
       "December      11000     2590     196          0  6440.72     0       4800   \n",
       "February          0      798       0          0  5579.85     0       2808   \n",
       "January        1000        0       0       1400  9112.51     0       4580   \n",
       "March             0        0       0          0   195.00     0          0   \n",
       "November       2000        0       0          0  3174.40   115          0   \n",
       "\n",
       "Category  Other  Petty cash  Salary  Self-development  Social Life  \\\n",
       "month                                                                \n",
       "December   1790           0       0               400       513.72   \n",
       "February  20000           0       0                 0      1800.00   \n",
       "January   13178           0    8000                 0       200.00   \n",
       "March       900           0       0                 0         0.00   \n",
       "November   2000           3       0                 0         0.00   \n",
       "\n",
       "Category  Transportation  \n",
       "month                     \n",
       "December           914.0  \n",
       "February          5078.8  \n",
       "January           2850.0  \n",
       "March               30.0  \n",
       "November           331.0  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(index='month',columns='Category',values='INR',aggfunc='sum',fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='month'>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.pivot_table(index='month',columns='Category',values='INR',aggfunc='sum',fill_value=0).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>Income/Expense</th>\n",
       "      <th>Expense</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>16334.44</td>\n",
       "      <td>12310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>17864.65</td>\n",
       "      <td>18200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>20679.51</td>\n",
       "      <td>19641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>525.00</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>3620.40</td>\n",
       "      <td>4003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Income/Expense   Expense  Income\n",
       "month                           \n",
       "December        16334.44   12310\n",
       "February        17864.65   18200\n",
       "January         20679.51   19641\n",
       "March             525.00     600\n",
       "November         3620.40    4003"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(index='month',columns='Income/Expense',values='INR',aggfunc='sum',fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='month'>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YW1tXGdFfafbs2Tz33HPG+zk5ORKQhKhmSin2pWSzJOY06xLTjQOsXe2tGNPVj/H3+tPU3f6u1snPzZ4Ofi4cSM3ml6R0IsMC7urrCyHqltsOR9OnTychIaHKWhDADRfPuv7Y9a4vc6Pyt1PmWjY2NtjYyDonQtSEK8Wl/Bx/nq9jznAoLcd4vIOfC5Gh/gwJ8am2cUS3Y0iwDwdSs1mbkCbhSAjxp24rHD3zzDP8/PPPbNu2jSZNmhiPe3t7A4ZWHR8fH+PxjIwMYyuPt7c3xcXFZGVlmbQeZWRk0L17d2OZCxcuVHndzMxMk/Ps2rXL5PGsrCxKSkpq57RAIeqpU5l5LI1NYWVcKrmFpQDYWFowrINhBeuQJi7mrWCF8GBv3lp3mN2nL5ORU4ins625qySEqKVuacyRUorp06fzww8/8Ouvv9KsWTOTx5s1a4a3t7dx52MwjMDfunWrMfh07twZKysrkzJpaWkkJSUZy4SFhaHX69m9e7exzK5du9Dr9SZlkpKSSEu7uuP2hg0bsLGxoXPnzrdyWUKIW1RaVs6Gg+lEfrmL++dv5avfk8ktLKWpmz0vPdCG2Nn9eH90h1oTjACauNrTsakLSsH6JJm1JoT4Y7fUcvT000/zzTff8NNPP+Hk5GQc26PT6bCzs0Oj0fDss8/y9ttv06pVK1q1asXbb7+Nvb0948aNM5adMmUKM2fOxN3dHTc3N2bNmkVwcLBxkau2bdsyePBgpk6dymeffQbAtGnTGDJkCIGBgQAMHDiQdu3aERkZyfvvv8/ly5eZNWsWU6dOxdnZudq+QUKIqzJzi1ixN5VlsWc4rzcMbNZo4P5ATyaE+dO7VSMsbmEF62p35TKc2wfn9sK5OHD0hCH/D7SGX3URwT7sT8kmKiGNSd0DzFdPIUStdkvh6NNPPwUMI9+vtXDhQuPKoc8//zwFBQU89dRTZGVl0a1bNzZs2ICTk5Ox/IIFC7C0tGTMmDEUFBTQr18/Fi1aZLJnzbJly5gxY4ZxVtuwYcP4+OOPjY9rtVqioqJ46qmn6NGjB3Z2dowbN44PPvjglr4BQog/p5Qi7kwWS2LPsC4xjZIywwacrvZWjO3alPHdmuLndncHWANQWgTpiXC2Igid2wuXT1Ut12YoBA4G4IFgH96MOsyeM5dJ1xfirZOuNSFEVRqlbrDVcAORk5ODTqdDr9dLa5MQ17lSXMqP+8+zJPYMh68ZYH2PnwsTw/x5IPguDrAuL4fLJw0hqDIMpSdCeUnVsm4toHFnyM+AU1sg6CEY9ZXx4Yc+3UncmSzmDG3Hoz2aVX2+qJUKCwtJTk6mWbNm2NrWr1A7efJkFi9eXOX4oEGDiI6ONkONaoc/e89r+vNb9lYTQpg4mZnH0tgzfB931mSA9fB7fIkMDSC4ia7mK5GXebU16Fyc4Vaor1rO3t0QhBp3qfi3E9gb1l3jXJwhHB1ZB0W5YGNovY4I9iHuTBZRCWkSjkStMXjwYBYuXGhyTGZXm88dLQIphKgfSsvKiU5KZ8L/dtFv/lYW/n6a3MJS/N3t+VdEW3a91I/3RnWomWBUfAXOxMDOj2HlZPgoGD5oCd+OhW3vw8lfDcFIawNN7oXQp+ChL2FGPPzzJIxfCX1egFb9rwYjAN9Ohlak0gJDQKoQHmyYVbv3TBbpelkQUtQONjY2eHt7m9xcXV3ZsmUL1tbWbN++3Vh2/vz5eHh4GCck9enTh+nTpzN9+nRcXFxwd3fnX//6F9d2DBUXF/P888/TuHFjHBwc6NatG1u2bDE+vmjRIlxcXPjll19o27Ytjo6ODB482GTS05YtW7j33ntxcHDAxcWFHj16GNcwBFizZg2dO3fG1taW5s2b89prr1FaWlqD37WaIy1HQjRgmblFLN+dwje7U0i7ZoB1vzaeRIYF0LOlR/UOsC4vg4vHTLvHLhwEVVa1rEegoTWoSWfDv15BoLW6+dfSaCBkDGyZB4kroINhd3EfnR1d/F3ZeyaLdYlpPHaftB7VV0opCkpu8H+rhtlZaf9ybb+b1adPH5599lkiIyM5cOAAp0+f5uWXX+bbb781WTJn8eLFTJkyhV27drF3716mTZuGv78/U6dOBeDRRx/l9OnTLF++HF9fX1avXs3gwYNJTEykVatWAFy5coUPPviAJUuWYGFhwYQJE5g1axbLli2jtLSUESNGMHXqVL799luKi4vZvXu38Tp/+eUXJkyYwL///W969uzJyZMnmTZtGgBz5syplu/F3SRjjmTMkWhglFLsPZPFkpgzrE+6OsDazcGasV39GHdvNQ6wzkm72jV2di+cj4fi3KrlHDyhSUXXWJMu4NsRbKuhlerSSfhPJ9BoYeZRcGwEwMLfk3ltzSE6+7uy6m/d7/x1RI27nTFHV4pLaffqLzVcs6oOvT4Ie+ubb3uYPHkyS5curXJdL7zwAq+88grFxcWEhobSqlUrDh48SFhYGF988YWxXJ8+fcjIyODgwYPGsPLiiy/y888/c+jQIU6ePEmrVq04e/asyZZb/fv359577+Xtt99m0aJFPProo5w4cYIWLVoA8Mknn/D666+Tnp7O5cuXcXd3Z8uWLfTu3bvKNfTq1Yvw8HBmz55tPLZ06VKef/55zp8/f9Pfi2vJmCMhRI3LLyrlx/hzLIk5w5H0qwGlY1PDAOvwoDscYF2UB+f3XzNWaB/knKtazsreEH4ad7o6VkjXxNDSU93cKwZnn4uDgz9AtycAw15rr689RNyZLM5nF+DrYlf9ry3ELejbt69xRnilyn1Lra2tWbp0KSEhIfj7+/PRRx9VeX5oaKhJa1VYWBjz58+nrKyMffv2oZSidevWJs8pKirC3d3deN/e3t4YjAB8fHzIyMgw1mXy5MkMGjSIAQMG0L9/f8aMGWNsvYqLi2PPnj289dZbxueXlZVRWFjIlStXsLc3w4zWOyDhSIh67kSGYYD1qriz5BYZ+v9trSwY3qExkWH+BDW+jRaaslLIPGzaPZZ5BFS5aTmNBTRqawhCTboYwlCjNsZ1h+6K4NGG+iWsMIYjb50tXf3d2H36MusS03i8Z/O7Vx9x19hZaTn0+iCzvO6tcnBwoGXLln/4eOWm65cvX+by5cs4ODjc9LnLy8vRarXExcWZLJkD4OjoaPzaysq021qj0ZiMW1q4cCEzZswgOjqa7777jn/9619s3LiR0NBQysvLee211xg5cmSV16+LswslHAlRD5WWlbPp8AW+jjnDzpOXjMcD3O2ZEOrP6M5+6OxvcvyOUqA/e033WBykxUPJlaplnRtXzBqr6B7zuQdsHKuWu5vaj4RfXjLU/9JJQ2sSEBHiw+7Tl4mScFRvaTSaW+reqq1OnjzJP/7xD7744gtWrFjBxIkT2bx5MxYWV+dUxcbGmjwnNjaWVq1aodVq6dixI2VlZWRkZNCzZ887qkvHjh3p2LEjs2fPJiwsjG+++YbQ0FA6derE0aNH/zTg1SV1/3+NEMIoI7eQ5btT+WZXCuk5hgHWFhq4v40XkWH+NzfAulBfscp03NWWofyMquWsnaBxR9Op9M4+VcuZm5MXNO9jmPWW+L1hZhsQHuTN3DUH2Z+SzdmsKzRxrVvN/qJ+KSoqMu46UcnS0hJXV1ciIyMZOHAgjz76KOHh4QQHBzN//nz++c9/Gsumpqby3HPP8cQTT7Bv3z7+85//MH/+fABat27N+PHjmThxIvPnz6djx45cvHiRX3/9leDgYB544IG/rF9ycjKff/45w4YNw9fXl6NHj3Ls2DEmTpwIwKuvvsqQIUPw8/Nj9OjRWFhYkJCQQGJiIm+++WY1fqfuDglHQtRxSin2nM7i65jTRCelU1puaAZ3rxxg3a3pH3/wlxZDxsGKrrGKbTcuHqtaTqMFr/ZXB0037gIercDiLi0CeaeCx1SEoxXQ+3nQaPB0tuXeADd2JV9mfWI6U3tJ65Ewn+joaJPZZwCBgYGMGzeO06dPs2bNGsCw6fr//vc/xowZw4ABA7jnnnsAmDhxIgUFBdx7771otVqeeeYZ42wxMHSJvfnmm8ycOZNz587h7u5OWFjYTQUjMIxHOnLkCIsXL+bSpUv4+Pgwffp0nnjC0FU9aNAg1q5dy+uvv857772HlZUVbdq04fHHH6+G787dJ7PVZLaaqKPyi0pZvf8cS2NNB1h3aurCxLAAwoO9sbG8JrwoBVmnTVuE0g5AWVHVk7s0vdoa1KQLeIeAdR1uWSnKhfdbGdY8mvqbYQwU8HXMaV796SAd/Fz46ekeZq6k+DP1eYXsO9WnTx/uueeeGw7UrstktpoQ4qadyMhlScwZVu07R941A6xH3NOYCaHXDLC+chmS95muMn3lUtUT2uquW2W6s3HKe71h4wSB4YYZa4krjeFocJA3c34+yIHUbFIvXzHPHnFCiFpHwpEQdUBJWTmbDhkGWMecuhpwmnk4MCHUn1EhjdDlHIHUbyH2TzZhtbAC72DT7jG35mDRABbLDxljCEdJq2Dgm2ChxdPJlm7N3Ig9dZn1SWlM69Xir88jhKj3JBwJUYtl5BTy7e5Uvtl9hgs5hu4vraacR1qUEumXSevSY2gOxcGvf7EJa2UY8g4Gywa6X1OLfmDnCnkXIHkbtOgLQESIL7GnLhOVIOFI1E3XbgMiqoeEIyFqGaUUu5Mv83XsGX5JSkdXnk0Hi5NMtUumn/NZ/AuPYHFWD2eve+KfbcIqwNIa2j8Ie78ydK1VhKPB7b2Z81MSB87qpWtNCAFIOBKi1sgrKuXnvSfYu/M3XLMTCbc4yYuWJ/GzyDQUUEDlxvRaG/DpcE33WGdwDaiZVabrk+AxhnB06GeImA9WdjRysiG0uTs7T14iKjGNJ3tL65EQDZ2EIyHMpWIT1guHfyclYRsOFxMYwxnGacrh+vUZ73QTVmHg1w10TUGfAseiDS1JGBaE3HnyElEJEo6EEBKOhLh7rtmEtfzsXsrO7sOqNB8vwAugotHnirU7Vk27YtW0a/VuwioMA8+DH4IdCyBhpTEcDW7vzSs/JpF4Ts+ZS/n4u9/81gxCiPpHwpEQNeEvNmG1qLhdUTYkqmZku4bQNKQXbTr3xl7nJ91jNSl4jCEcHd9gWO7A3g13Rxu6t/Bgx4mLRCWm8VSf+rEFghDi9kg4EuJOVW7Cevaa9YRusAlrORYcK29MfHkL4lVLTtu2oWvX7jwS2pxusiv83ePVztAteSEJDv0EXR4F4IFgH0M4SpBwJERDJ+FIiFtxC5uwljv5kmrfjg3ZjdmU05RE1Ywr2NLF35XIMH9eD/LB2rIBrC9UGwWPNoSjxJXGcDSovRev/JTEwfM5nL6YT4CHdK0J0VBJOBLiz9zGJqznHNuzLNWDxYlF5GeWAWBnpWVEx8ZEhvrTzle2qjG74FGwaS6c+R2yU8HFr6JrzZ3txw1da0/3ldYjcXdMnjyZ7OxsfvzxR3NXRVSQcCTEjSSshG3vw8WjVR+7wSasJa4t+OVwJktizrAr+TJgaElq3siByFB/RnZqgs5OZpfVGrom4N8DzuyApO/hvn8AEBHsYwhHCRKOhGjIpE1fiGspBVvegR8evxqMXJpC+5Ew8C147BeYfRae3A5DFpDefBQfHrCgx3tbmf7NfnYlX8ZCY+iiWfZ4NzY/15tHezSTYFQbhYw2/Juw0nhoUHtvtBYaDqXlcCozz0wVEw1Znz59mDFjBs8//zxubm54e3szd+5ckzLZ2dlMmzYNLy8vbG1tCQoKYu3atcbHV61aRfv27bGxsSEgIID58+ebPD8gIIA333yTiRMn4ujoiL+/Pz/99BOZmZkMHz4cR0dHgoOD2bt3r8nzdu7cSa9evbCzs8PPz48ZM2aQn59fY98Lc5JwJESl0mL46WnYMs9wv8ffYdYJeDYRRi+E7tOhaSjKyo6dJy/y1LI4erz7K//efJyM3CI8HG145v6W7Hjhfj6L7EKPlh5oZNZZ7dVuOGitIeMgXDgIgKuDNT1aegCwLjHNnLUT1UEpKM6/+zel7qjaixcvxsHBgV27dvHee+/x+uuvs3HjRgDKy8sJDw9n586dLF26lEOHDvHOO++g1WoBiIuLY8yYMTz88MMkJiYyd+5cXnnlFRYtWmTyGgsWLKBHjx7s37+fiIgIIiMjmThxIhMmTGDfvn20bNmSiRMnoiquJTExkUGDBjFy5EgSEhL47rvv2LFjB9OnT7+ja62tNErd4btYh+Xk5KDT6dDr9Tg7yziQBq1QD99FQvJWQ7dZxHzjQN1KuYUl/LDvHEtiz3Ai42qrQtcAVyLDAhjc3lsGWNc1y8fDkbWGbrX+cwFYsSeV51cl0Mbbiehne5m3fsKosLCQ5ORkmjVrhq2t7c09qTgf3vat2YrdyEvnwfrmB/RfO+aoT58+lJWVsX37duPj9957L/fffz/vvPMOGzZsIDw8nMOHD9O6desq5xo/fjyZmZls2LDBeOz5558nKiqKgwcNfwQEBATQs2dPlixZAkB6ejo+Pj688sorvP766wDExsYSFhZGWloa3t7eTJw4ETs7Oz777DPjeXfs2EHv3r3Jz8+/+ffkFvzZe17Tn98y5kiI7FRYNtowHd/KAcYshlYDjA8fTc/l65jTrN5/jivFhgHW9tZXB1i39ZFgXWcFjzaEo8Tv4f5XwcKCge29eGm1hiPpuZzIyKOlp6O5aykamJCQEJP7Pj4+ZGQYJoLEx8fTpEmTGwYjgMOHDzN8+HCTYz169OCjjz6irKzM2MJ07Wt4eXkBEBwcXOVYRkYG3t7exMXFceLECZYtW2Yso5SivLyc5ORk2rZte7uXWytJOBINW9oBWDYG8tLB0RvGrwCfDlwpLmXz4QyWxJ5hd/JlY/EWlQOsOzfB2VbGEdV5rQeDjTPoUyE1Fvy742JvzX2tPNhyNJN1iWnM6NfK3LUUt8vK3tCKY47XvZOnW5n+btFoNJSXG9ZNs7P78zXRlFJVuvNv1EF07WtUlr/RscrXLS8v54knnmDGjBlVztW0adM/rVNdJOFINFzHNsDKyVCST4l7G37r/DG/79YSl7Kdw2m5lJUbfqFoLTQMaOvFxDB/wlq4yzii+sTKFtoOg/ilkLAC/LsDhgUhtxzNJCpBwlGdptHcUvdWXRASEsLZs2c5duzYDVuP2rVrx44dO0yO7dy5k9atWxtbjW5Hp06dOHjwIC1bNoxZnBKORINTXFpO+q+f0CTmVSxUGbs1wUw593dyz5muX+Srs2VU5yY80q0pPjpZwbreCh5lCEeHfoTw98DSmkHtvHlZm8jRC7mcyMilpaeTuWspBAC9e/emV69ePPTQQ3z44Ye0bNmSI0eOoNFoGDx4MDNnzqRr16688cYbjB07lpiYGD7++GM++eSTO3rdF154gdDQUJ5++mmmTp2Kg4MDhw8fZuPGjfznP/+ppqurPSQciXovM7eIfSlZ7EvJYv/pS/RP+4xpFj8D8H1ZL2aXPE65hRUhvs50aupKZ3/DzVe29GgYmvUydKnmpcOJTdDmAXT2VtzX0oPfjmYSlZDO3/tLOBK1x6pVq5g1axaPPPII+fn5tGzZknfeeQcwtPCsWLGCV199lTfeeAMfHx9ef/11Jk+efEevGRISwtatW3n55Zfp2bMnSilatGjB2LFjq+GKap9bnq22bds23n//feLi4khLS2P16tWMGDHi6gn/oMvhvffe45///CdgWMdh69atJo+PHTuW5cuXG+9nZWUxY8YMfv7Z8CE2bNgw/vOf/+Di4mIsk5KSwtNPP82vv/6KnZ0d48aN44MPPsDa2vqmrkVmq9U/ZeWKYxdyiTuTxb4zWcSlZHHmkmFBRhuKed/qM4ZpYwD4yWUiZ0Nm0DnAjZAmOuyt5W+FBiv6JYj9P2j/IIxeBMD3cWeZtfIArb0c2fCP3uatn7i92WqiTqtTs9Xy8/Pp0KEDjz76KA899FCVx9PSTNcGWb9+PVOmTKlSdurUqcYpg1B1kNm4ceM4e/Ys0dHRAEybNo3IyEjWrFkDQFlZGRERETRq1IgdO3Zw6dIlJk2ahFKqXjbxiRvLKSwhPiXbEIZSstifkk1eUWmVcp0bKT4o+5BmVxJQFpYw9N8M7zjeDDUWtVLIaEM4OroeCnPA1pkB7byw0mo4diGPYxdyae0lrUdCNBS3HI7Cw8MJDw//w8e9vb1N7v/000/07duX5s2bmxy3t7evUrbS4cOHiY6OJjY2lm7dugHwxRdfEBYWxtGjRwkMDGTDhg0cOnSI1NRUfH0N61jMnz+fyZMn89Zbb0lLUD2klOLMpSvEVbQI7TuTxdELuVXWW7O31tKxqQudm7rSyd+VTk56nFc9ApeOg40zmrFLoHkfs1yDqKV87gH3Vob/I0fWwj3j0NlZ0atVIzYfySAqIY3WAyQcCdFQ1Gg/woULF4iKimLx4sVVHlu2bBlLly7Fy8uL8PBw5syZg5OT4ZdPTEwMOp3OGIwAQkND0el07Ny5k8DAQGJiYggKCjIGI4BBgwZRVFREXFwcffv2rfKaRUVFFBUVGe/n5ORU5+WKalZYUkbCWT37UrKM3WSX8ourlPNzs6NzxVihTv6uBHo5YamtWIzxbBwsHQNXLoJzExi/Erza3eUrEbWeRgMhY+C3twyz1u4ZB0BEiI8hHCWm8Wz/VjJTUYgGokbD0eLFi3FycmLkyJEmx8ePH0+zZs3w9vYmKSmJ2bNnc+DAAePy6Onp6Xh6elY5n6enJ+np6cYylYtUVXJ1dcXa2tpY5nrz5s3jtddeq45LEzUgXV9oaBWqaBk6dF5PSZlps5C11oLgJjpDEGrqSid/Fzyd/mD8weG1sOpxKC0A7xAYtwKcfe7ClYg6KXiUIRwlb4XcC+DkRf92XlhrLTiRkcexC3kEekvrkRANQY2Go6+++orx48dXGUg1depU49dBQUG0atWKLl26sG/fPjp16gTceGD39Ytb3UyZa82ePZvnnnvOeD8nJwc/P79buyhRLUrKyjmSlkvcmcvEpWSz70wW57ILqpTzcLShi//VVqGgxs7YWN7EWh2xn0L0bEBBq4EwaiHYyErH4k+4NYcmXeHsHjj4A4T+DWdbK3q1bsSmwxeISjhPoHeguWvZ4DXgHa8aHHO+1zUWjrZv387Ro0f57rvv/rJsp06dsLKy4vjx43Tq1Alvb28uXLhQpVxmZqaxtcjb25tdu3aZPJ6VlUVJSUmVFqVKNjY22NjY3MbViDuVlV/M/tQsY8vQgVQ9BSVlJmUsNNDG29k4lb6zvytNXO1urSujvAx+eRl2fWq43/lReOAD0MpMNHETgscYwlHCCgj9GwARId5sOnyBtYlp/GNAa+laM5PK1ZuvXLnyl6tEi/rhyhXDTOPrVwy/G2rsE+PLL7+kc+fOdOjQ4S/LHjx4kJKSEnx8DF0eYWFh6PV6du/ezb333gvArl270Ov1dO/e3VjmrbfeIi0tzfi8DRs2YGNjQ+fOnWvoqsTNKC9XnMzMM84gizuTxcnM/CrlnG0t6eTvahw43cHPBUebO/gvWXwFfphqGFAL0P816PF3w3gSIW5G+wch+kU4vw8unQT3FvRv64W1pQWnMvM5eiGXNt4y2cMctFotLi4uxj3G7O3tJajWU0oprly5QkZGBi4uLne0svftuuVPory8PE6cOGG8n5ycTHx8PG5ubsb9VXJycli5ciXz58+v8vyTJ0+ybNkyHnjgATw8PDh06BAzZ86kY8eO9OjRA4C2bdsyePBgpk6datwBeNq0aQwZMoTAQEOz9sCBA2nXrh2RkZG8//77XL58mVmzZjF16lSZqXaX5ReVciA122QWWU5h1en0zRs5GAdOd/Z3pUUjRywsqumXW14mfPswnNsLWmsY8alhDIkQt8KxEbS4H05sNLQe9Z2Nk60VvVs3YuOhC0QlpEk4MqPKGc6VAUnUby4uLn84q72m3fIikFu2bLnhTLBJkyaxaNEiAD7//HOeffZZ0tLS0Ol0JuVSU1OZMGECSUlJ5OXl4efnR0REBHPmzMHNzc1Y7vLly1UWgfz444+rLAL51FNPVVkE8ma7zmQRyFunlOJsVoGxRSjuTBaH03Iov+5/ka2VBR2auBiDUMemrrg53NzinLfs4nFYNgqyToOtCzzyrXGPLCFu2YHvYPU0wxikZ/aBRsNP8ef4+/J4mns4sHlmb2mxMLOysjJKSkrMXQ1Rg6ysrP60xaimP79vORzVJxKO/lpRaRkHz+cYVpuuuGXkFlUp19jFzrCmUFNDIGrr44xV5XT6mnQmBpY/AgVZ4OIPE1aBh2wUKu5AUR580ApKrsDjv0KTzuQVldLpjY0Ul5azbkZP2vnK7wshzKnWrZAt6rfM3CKTsUKJ5/QUl5ablLG00NC+se6atYVczLMxa9IqWP03KCuCxp3hke8M3SJC3AkbRwh8AJK+h8QV0KQzjjaW9A1sxC8HLxCVeF7CkRD1nISjBqysXHE0Pdc4TijuTBYpl69UKefmYG2yIWtIEx22Vnd/gJyRUvD7R7BpruF+myEw8guwtjdfnUT9EjLGEI6SVsHAt0BrSUSIryEcJaQxa2CgdK0JUY9JOGpA9AUlxFcMnN53Jov9KVnkF5tOp9doINDLiY7XhKEA91o0K6SsFNb/E/Z+Zbjf7W8w6C2wMGNYE/VPi/vB3h3yMyF5C7TsT782nthYWnD60hUOns8hqLHuL08jhKibJBzVU0opTlfuQ1YRho5lVN2HzNHGko5NXYwtQ/c0dcHZ9u6vKXFTivLg+0fh+AZAA4PnGdeiEaJaaa0M0/r3/A8SVkLL/jjYWHJ/G0/WJ6UTlZgm4UiIekzCUT1RUFxGwtlsYxfZvpRsLt9gHzJ/d3vjukKd/V1p7eWEtrqm09eknDT4ZgykJ4ClHTz0P2g7xNy1EvVZ8BhDODqy1rCGlrU9DwT7GMJRQhrPD5KuNSHqKwlHdVSavsCkVejg+RxKr5tPb21pQUhjnXHrjU5NXWnkVAdXCL9wCJaNhpyzYO8B476DJl3MXStR3/nda5gBmX0Gjq2HoIe4v40ntlYWpFyWrjUh6jMJR3VASVk5h9NyTMLQeX1hlXKeTjZ0CXA1dpG1873Jfchqs1Nb4LtIKMoB95YwfqVh/RkhappGA8GjYfsHhq61oIeMXWvrEtNZmyBda0LUVxKOaqHL+cWG2WMVXWQHzmZTWGI6nV5roaGtj5NJF1ljl1vch6y2i/8Gfn4GykuhaRg8/A3Yu/3184SoLiFjDOHoxEa4chns3YgI9mVdYjpRied5YbB0rQlRH0k4MrPycsWJin3IKluFTl2sug+Zzs7KuMBiJ39XOjRxweFO9iGrzZSCre/ClnmG++1HGrYDsbI1b71Ew9MoELxDDGPdDq6GrlPo26YRdlZaUi8XkHhOT0gTF3PXUghRzerpp2vtlXftPmQViy3m3mAfspaejtcssuhKcw+H6tuHrDYrLYa1z0L8MsP9Hs9CvzlgcRdW2xbiRoJHG8JR4kroOgV7a0vub+tJVEIaUQlpEo6EqIckHNWgyn3I4q7ZeuNIetV9yOystNzjd+0+ZC642NfQPmS1WaEeVkw0jDPSWEDEfOjymLlrJRq64FGw8VVIiYHsFHBpypBgH6IS0libkMaL4W2ka02IekbCUQ3YeiyTb3elEJeSReYf7ENWGYQ6+7vSxtsJy7uxD1ltpj9rmJGWcQisHGD0Img90Ny1EgKcfSHgPji93dB61HMmfQI9sbfWci67gANn9dzj52LuWgohqpGEoxpwPruA6IPpAFhpNQSZ7EPmipezjJ0xkXYAlo2BvHRw9DZM1fe9x9y1EuKqkDGGcJSwEu57DjtrLf3aerHmwHmiEs5LOBKinpFwVAPua+nB7PA2dPZ3Jaixmfchq+2Ob4SVk6E4Dxq1NUzVd/Ezd62EMNV2GETNhMzDcCEJvIOJCPZmzYHzrEtM56UH2krXmhD1SAPvy6kZfm72PNG7BV0C3CQY/Zm9C+GbsYZg1Kw3TPlFgpGonexcoPUgw9cJKwBMutbiU7PNVjUhRPWTcCTuvvJy2DTXMCtNlUGHcTD+e7CVBfVELRY8xvBv0iooL8fWSkv/tl4ARCWkmbFiQojqJuFI3F2lRfDD47BjgeF+7xdhxCdg2QBn54m6pdVAsNFBzjlI2QlARIgPAOsS0yi/fhqqEKLOknAk7p4rl+HrEYa/vC0sYfgn0He2YZsGIWo7K1toN8zwdUXXWu/WjXCw1nJeX8h+6VoTot6QcCTujsvJ8OVAw1/cNs6GbrSO481dKyFuTUhF19qhH6G0CFsrLQPaSdeaEPWNhCNR887GwZcD4NJxcG4Cj0VDi77mrpUQt86/Bzj5GhYsPb4RgIgQX0C61oSoTyQciZp1JAoWRUB+JngHw+ObwKu9uWslxO2x0ELQSMPXiYautZ6tPHCysSQ9p5B9KVlmrJwQorpIOBI1J/a/sHw8lBZAywHw6Hpw9jF3rYS4M5Vda0ejoVBvmLVW0bW2VrrWhKgXJByJ6ldeBtGzIfoFQEHnR+GR5WDjZO6aCXHnvEPAIxDKiuDwGgAigg2hf32SdK0JUR9IOBLVq/iKYfPY2E8M9/vPhSELQCuLsYt6QqOBkNGGrytmrfVsbehau5BTRJx0rQlR50k4EtUnLxMWD4Uja0FrDQ99Cff9Q6bqi/onuCIcJW+DnDRsLLUMaC+z1oSoLyQciepx8QR82R/O7QVbF4j8EYJHmbtWQtQM1wDw6wYow7pdwJBrFoQsk641Ieo0CUfizp2JMQSjrNPg4g9TNkJAD3PXSoiaVdl6lLgSgPtaNsLJ1pKM3CL2nr5sxooJIe6UhCNxZ5JWwdfDoSALGnc2TNVv1NrctRKi5rUfaVjpPS0eLh7H2tKCQe29AYhKlK41IeoyCUfi9igFOz6C7x8zzNoJjIBJa8HR09w1E+LucHCHFv0MX1cMzL6611q6dK0JUYdJOBK3rqwUop6DTXMM97s9CWOXgLW9eeslxN1WueZR4gpQih4tPNDZWXExr4jdydK1JkRdJeFI3JqiPFg+DvZ+BWhg8DsQ/q5h5WAhGprAcLByMIy3O7sXa0sLBlbutZZ43rx1E0LctlsOR9u2bWPo0KH4+vqi0Wj48ccfTR6fPHkyGo3G5BYaGmpSpqioiGeeeQYPDw8cHBwYNmwYZ8+eNSmTlZVFZGQkOp0OnU5HZGQk2dnZJmVSUlIYOnQoDg4OeHh4MGPGDIqLi2/1ksTNyk2HRQ/A8V/A0tbQWhT6N3PXSgjzsXaAtkMMXyeadq1FJ0nXmhB11S2Ho/z8fDp06MDHH3/8h2UGDx5MWlqa8bZu3TqTx5999llWr17N8uXL2bFjB3l5eQwZMoSysjJjmXHjxhEfH090dDTR0dHEx8cTGRlpfLysrIyIiAjy8/PZsWMHy5cvZ9WqVcycOfNWL0ncjIzD8L/+kHYA7D1gchS0HWruWglhfpWz1pJ+gLISerSs7ForZlfyJfPWTQhxW2552eLw8HDCw8P/tIyNjQ3e3t43fEyv1/Pll1+yZMkS+vfvD8DSpUvx8/Nj06ZNDBo0iMOHDxMdHU1sbCzdunUD4IsvviAsLIyjR48SGBjIhg0bOHToEKmpqfj6GnbFnj9/PpMnT+att97C2dn5Vi9N/JFTW+G7SCjSg1sLmPA9uDU3d62EqB2a9zX8wXDlIpzaglWrAQxu7813e1OJSkijewsPc9dQCHGLamTM0ZYtW/D09KR169ZMnTqVjIwM42NxcXGUlJQwcOBA4zFfX1+CgoLYuXMnADExMeh0OmMwAggNDUWn05mUCQoKMgYjgEGDBlFUVERcXNwN61VUVEROTo7JTfyF+G9h6UOGYOQXapiqL8FIiKu0lhA00vB1QtWutdKycnPVTAhxm6o9HIWHh7Ns2TJ+/fVX5s+fz549e7j//vspKioCID09HWtra1xdXU2e5+XlRXp6urGMp2fVKeGenp4mZby8vEwed3V1xdra2ljmevPmzTOOYdLpdPj5+d3x9dZbSsGWd+HHJ6G8BNo/CBN/Ans3c9dMiNonuGLW2pEoKM4nrIU7rvZWXMovZpfMWhOizqn2cDR27FgiIiIICgpi6NChrF+/nmPHjhEVFfWnz1NKoblmDy7NDfbjup0y15o9ezZ6vd54S01NvdnLalhKi+Gnp2HL24b7Pf4OD30FVrbmrZcQtVWTLuDaDEry4cg6rLQWDA4yDC1YK3utCVHn1PhUfh8fH/z9/Tl+/DgA3t7eFBcXk5VlunN1RkaGsSXI29ubCxcuVDlXZmamSZnrW4iysrIoKSmp0qJUycbGBmdnZ5ObuE6hHr4ZDfHLQGMBER/CgNfBQlZ9EOIPaTRVthOJCDZ0+UcnpUnXmhB1TI1/4l26dInU1FR8fAx98J07d8bKyoqNGzcay6SlpZGUlET37t0BCAsLQ6/Xs3v3bmOZXbt2odfrTcokJSWRlnb1r7INGzZgY2ND586da/qy6if9WfgqHE5tMazd8sh30HWKuWslRN1QuSDkyc2Qf4nQ5m64OViTdaWEmFMya02IuuSWw1FeXh7x8fHEx8cDkJycTHx8PCkpKeTl5TFr1ixiYmI4ffo0W7ZsYejQoXh4ePDggw8CoNPpmDJlCjNnzmTz5s3s37+fCRMmEBwcbJy91rZtWwYPHszUqVOJjY0lNjaWqVOnMmTIEAIDAwEYOHAg7dq1IzIykv3797N582ZmzZrF1KlTpUXodqQlGKbqZxwERy94dB20HvjXzxNCGHi0Ap97oLwUDv6Apfaavdaka02IOuWWw9HevXvp2LEjHTt2BOC5556jY8eOvPrqq2i1WhITExk+fDitW7dm0qRJtG7dmpiYGJycnIznWLBgASNGjGDMmDH06NEDe3t71qxZg1Z7dZXlZcuWERwczMCBAxk4cCAhISEsWbLE+LhWqyUqKgpbW1t69OjBmDFjGDFiBB988MGdfD8apuObYGE45KZBo7bw+GbwvcfctRKi7jFuJ2LoWhtSMWvtl4PplEjXmhB1hkYp1WCXcM3JyUGn06HX6xtua1PcIlj7HKgyaNYLxiwBOxdz10qIuik3HT5sC6oc/n6AUuemdHt7M5fyi/n6sXvp1bqRuWsoRL1Q05/fMsq2oSovh02vwZq/G4JRh0dg/CoJRkLcCSdvwx8ZAIkrsbxm1pp0rQlRd0g4aohKi+CHqbDjQ8P93i/CiE/B0tq89RKiPqictZawEpS6uiCkdK0JUWdIOGporlyGJQ9C0vdgYQnD/w/6zjZMRRZC3Lm2Q0FrAxePQnoC3Zq54+Fojb6ghN9PXDR37YQQN0HCUUNyORm+HAhnfgcbZxj/PXScYO5aCVG/2OogcLDh64QVaC00hAcZWo+ka02IukHCUUNxNg6+HACXjoNzY3gsGlr0NXethKifKrcTSVoF5WXGrrVfDqZTXCpda0LUdhKOGoIjUbAoAvIzwTvYMFXfq725ayVE/dVqgKEFKTcNTu+ga4AbjZxsyCksla41IeoACUf13a7PYPl4KC2Alv3h0fXg7GPuWglRv1naQLsRhq8TK7vWZK81IeoKCUf1VXk5RL8E658HFHSebNgOxMbpr54phKgOlQtCHloDJYVEBBv+KNlwSLrWhKjtJBzVRyUFsHIixP6f4X6/OTDkI9BamrVaQjQoTbuDcxMo0sPxDXQJcMPTyYbcwlJ2nMg0d+2EEH9CwlF9k38RFg+Fw2tAaw0PfQk9n5Op+kLcbRYWEPyQ4euKrrUHKlqPpGtNiNpNwlF9cvGEYfPYs3vA1gUif4TgUeaulRANV+WstWO/QEG2cdbaxoMXKCotM2PFhBB/RsJRfZESC1/2h6xkcGkKUzZCQA9z10qIhs07CDzbQVkxHP6Zzk1d8XK2IbeolO3HZNaaELWVhKP6IOkHWDwMCrLAt5Nhqn6j1uaulRACrrbeJqzA4pqutahE6VoToraScFSXKQU7PoLvH4WyIgiMgMlrwdHT3DUTQlSq3Gvt9A7IOc+Qyq61QxcoLJGuNSFqIwlHdVVZKUTNhE1zDPe7PQljl4C1g3nrJYQw5dIUmoYBChK/p6OfKz46W/KKStl2TGatCVEbSTiqi4ryYPk42PsloIFB8yD8XbDQmrtmQogbqWw9SjR0rRn3WpOuNSFqJQlHdU1uOix6AI7/Apa2MOZrCHvK3LUSQvyZ9g+ChSWkJ0LGEeOstU3StSZErSThqC7JOGyYqp92AOzdYdJaaDfM3LUSQvwVezdoOcDwdeIKOvq54KuzJb+4jK3StSZErSPhqK5I3gZfDgJ9Kri1gMc3gV9Xc9dKCHGzQiq71lZioeHqrDVZEFKIWkfCUV1wYDksGWnYhsAv1BCM3Jqbu1ZCiFvROhysHSE7BVJ3X+1aOyxda0LUNhKOajOlYOt7sPoJKC8x7PI98SdDE70Qom6xtoe2Qw1fJ67gHj8XGrvYcaW4jC1HM8xbNyGECQlHtVVZCfw0HX57y3C/+wwYtRCsbM1bLyHE7auctXZwNZryUmPrkey1JkTtIuGoNirUw7JREL8UNBYQMR8GvmHYyFIIUXc16w0OnnDlEpz8lYiKcUebD2dQUCxda0LUFvJpW9voz8JX4XBqC1g5wCPLoevj5q6VEKI6aC0haKTh64QVhDTR0cTVjoKSMn6TrjUhag0JR7VJWoJhqn7GQXD0gkejoPUgc9dKCFGdgscY/j26Dk1xvrH1SGatCVF7SDiqLU5sgoXhkJsGjdoYZqT5djR3rYQQ1a1xJ8Ns05IrcCTKOO7o1yMZXCkuNXPlhBAg4ah2iFsMy8ZAcR4E9ITHfjHsxySEqH80mqutR4krCG6sw8+tomvtiCwIKURtIOHInJSCza/DmhmgyiDkYZjwA9i5mLtmQoiaFFIRjk7+hib/IhHBvgBEJZ43Y6WEEJUkHJlLaRH8MBW2zzfc7/0CPPhfsLQ2b72EEDXPvQX4djL8UXTwB4Zc07WWXyRda0KYm4Qjc7hyGZY8CIkrDZtRDv8/6PuSobldCNEwVLYeJa6kva8z/u72FJaU8+sRmbUmhLlJOLrbsk7DV4PgzO9g7QTjV0LHCeaulRDibms/0rCO2dk9aLKSZdaaELXILYejbdu2MXToUHx9fdFoNPz444/Gx0pKSnjhhRcIDg7GwcEBX19fJk6cyPnzpv3offr0QaPRmNwefvhhkzJZWVlERkai0+nQ6XRERkaSnZ1tUiYlJYWhQ4fi4OCAh4cHM2bMoLi4+FYv6e45F2eYqn/xGDg3him/QIv7zV0rIYQ5OHlB8z6GrxO/N85a++1oBnnStSaEWd1yOMrPz6dDhw58/PHHVR67cuUK+/bt45VXXmHfvn388MMPHDt2jGHDhlUpO3XqVNLS0oy3zz77zOTxcePGER8fT3R0NNHR0cTHxxMZGWl8vKysjIiICPLz89mxYwfLly9n1apVzJw581Yv6e44EgULIyA/E7yCDVP1vdqbu1ZCCHOqnLWWsIJ23k4EuNtTVFrO5sMXzFsvIRo4y1t9Qnh4OOHh4Td8TKfTsXHjRpNj//nPf7j33ntJSUmhadOr09Pt7e3x9va+4XkOHz5MdHQ0sbGxdOvWDYAvvviCsLAwjh49SmBgIBs2bODQoUOkpqbi62uY6TF//nwmT57MW2+9hbOzc5XzFhUVUVRUZLyfk5Nzaxd/u3Z9DuufBxS07A+jF4GN0915bSFE7dV2CKy1hUvH0aQfICLEh//77SRRCWkMv6exuWsnRINV42OO9Ho9Go0GFxcXk+PLli3Dw8OD9u3bM2vWLHJzc42PxcTEoNPpjMEIIDQ0FJ1Ox86dO41lgoKCjMEIYNCgQRQVFREXF3fDusybN8/YTafT6fDz86vGK72B8nL45WVY/09AQadJhu1AJBgJIcDwuyCw4o/NhJXGKf1bjmVK15oQZlSj4aiwsJAXX3yRcePGmbTkjB8/nm+//ZYtW7bwyiuvsGrVKkaOHGl8PD09HU9Pzyrn8/T0JD093VjGy8vL5HFXV1esra2NZa43e/Zs9Hq98Zaamlodl3ljJQWwchLEVHQ/9nsVhv4/0FrV3GsKIeqeyq61pFW09bKnuYcDxdK1JoRZ3XK32s0qKSnh4Ycfpry8nE8++cTksalTpxq/DgoKolWrVnTp0oV9+/bRqVMnADQ3mNaulDI5fjNlrmVjY4ONjc1tXc8tyb8I3z4CZ3eD1hqGfwIho2v+dYUQdU/L/mDnCnnpaE5vJyLEl//8eoK10rUmhNnUSMtRSUkJY8aMITk5mY0bN95w/M+1OnXqhJWVFcePHwfA29ubCxeq/tWUmZlpbC3y9vau0kKUlZVFSUlJlRalu27LO4ZgZOsCkT9KMBJC/DFLa2g3wvB14krjrLWtRzPJLSwxX72EaMCqPRxVBqPjx4+zadMm3N3d//I5Bw8epKSkBB8fwy+FsLAw9Ho9u3fvNpbZtWsXer2e7t27G8skJSWRlnZ1TZANGzZgY2ND586dq/mqbtGA1wy/7KZshIAe5q2LEKL2q1wQ8tDPBLpZ0qKRA8Vl5WySrjUhzOKWw1FeXh7x8fHEx8cDkJycTHx8PCkpKZSWljJq1Cj27t3LsmXLKCsrIz09nfT0dOP6QydPnuT1119n7969nD59mnXr1jF69Gg6duxIjx6GING2bVsGDx7M1KlTiY2NJTY2lqlTpzJkyBACAwMBGDhwIO3atSMyMpL9+/ezefNmZs2axdSpU/+yparGWTvAmMXQqLV56yGEqBv8QkHnB8W5aI7/QkRIxV5rsiCkEOahbtFvv/2mgCq3SZMmqeTk5Bs+BqjffvtNKaVUSkqK6tWrl3Jzc1PW1taqRYsWasaMGerSpUsmr3Pp0iU1fvx45eTkpJycnNT48eNVVlaWSZkzZ86oiIgIZWdnp9zc3NT06dNVYWHhTV+LXq9XgNLr9bf6bRBCiOq1cY5Sc5yV+nacOpqeo/xfWKtavbROZV8pNnfNhKh1avrzW6OUUmZJZbVATk4OOp0OvV5v/tYmIUTDduEQfBpmmMQx6xj9P03gREYe80d34KHOTcxdOyFqlZr+/Ja91YQQojbwagdeQVBWDId+urrXWqJ0rQlxt0k4EkKI2iK4YmZrwtVZa9uPZ6IvkFlrQtxNEo6EEKK2CB4FaODMDlrb6mnt5UhJmWLjIZm1JsTdJOFICCFqC10T8K9Y/iPxe+N2IlEJ581YKSEaHglHQghRmwSPMvybuJKIEMPm3NuPX0R/RbrWhLhbJBwJIURt0m44WFjBhSRaqlTaeDtRWq745dCN94wUQlQ/CUdCCFGb2LtBq4GGrxNXXJ21JgtCCnHXSDgSQojapnI/xsTveSDYsFfk7ycukpVfbMZKCdFwSDgSQojapvVgsHYCfSotCpKMXWsbpGtNiLtCwpEQQtQ2VnbQbpjh68SVDKlY82itdK0JcVdIOBJCiNqockHIg6t5oJ07ADtPXpKuNSHuAglHQghRGzXrBY7eUJBFc/0u2vk4U1au+OWgdK0JUdMkHAkhRG1koYWghwxfJ6wwbicie60JUfMkHAkhRG1VOWvt6HqGBDoBhq61S3lFZqyUEPWfhCMhhKitfO4B91ZQWoB/xq8ENa7sWpO91oSoSRKOhBCittJorg7MTlxxda+1RNlrTYiaJOFICCFqs8q91k5tYWhzLQAxJy9xUbrWhKgxEo6EEKI2c28BjbuAKqfJ+fUEN9ZRriA6SWatCVFTJBwJIURtFzLG8O+1s9ZkQUghaoyEIyGEqO3ajwSNFs7vY7hfAQC7ki+RmStda0LUBAlHQghR2zk2ghZ9AfBJWUuHJhVda7IgpBA1QsKREELUBcHXdK0FewMQlSCz1oSoCRKOhBCiLmgTAVb2cPkkwz0zANiVfJmM3EIzV0yI+kfCkRBC1AU2jhD4AABep3/mHj8XlMxaE6JGSDgSQoi6onLWWtIqhgY1AmCtzFoTotpJOBJCiLqixf1g5wb5GQx3OQnAntOXuZAjXWtCVCcJR0IIUVdoraD9gwB4nPqJjk0NXWvrE6X1SIjqJOFICCHqksqutcNrGNbOBYAoCUdCVCsJR0IIUZf4dQOXplCcx3C7BAD2nskiXS9da0JUFwlHQghRl2g0EDwaALeTP9HZ39XQtZYkrUdCVJdbDkfbtm1j6NCh+Pr6otFo+PHHH00eV0oxd+5cfH19sbOzo0+fPhw8eNCkTFFREc888wweHh44ODgwbNgwzp49a1ImKyuLyMhIdDodOp2OyMhIsrOzTcqkpKQwdOhQHBwc8PDwYMaMGRQXF9/qJQkhRN1SuSDkiY2MDLQDZK81IarTLYej/Px8OnTowMcff3zDx9977z0+/PBDPv74Y/bs2YO3tzcDBgwgNzfXWObZZ59l9erVLF++nB07dpCXl8eQIUMoKyszlhk3bhzx8fFER0cTHR1NfHw8kZGRxsfLysqIiIggPz+fHTt2sHz5clatWsXMmTNv9ZKEEKJu8WwD3sFQXsoQqz2AoWstTV9g5ooJUU+oOwCo1atXG++Xl5crb29v9c477xiPFRYWKp1Op/773/8qpZTKzs5WVlZWavny5cYy586dUxYWFio6OloppdShQ4cUoGJjY41lYmJiFKCOHDmilFJq3bp1ysLCQp07d85Y5ttvv1U2NjZKr9ffVP31er0Cbrq8EELUGjv+n1JznJX6crAa9envyv+Ftep/20+Zu1ZC3BU1/fldrWOOkpOTSU9PZ+DAgcZjNjY29O7dm507dwIQFxdHSUmJSRlfX1+CgoKMZWJiYtDpdHTr1s1YJjQ0FJ1OZ1ImKCgIX19fY5lBgwZRVFREXFzcDetXVFRETk6OyU0IIeqk4FGABlJ2MqalAmSvNSGqS7WGo/R0wzL2Xl5eJse9vLyMj6Wnp2NtbY2rq+uflvH09Kxyfk9PT5My17+Oq6sr1tbWxjLXmzdvnnEMk06nw8/P7zauUgghagFnXwi4D4DB/I5GA/tSsjmXLV1rQtypGpmtptFoTO4rpaocu971ZW5U/nbKXGv27Nno9XrjLTU19U/rJIQQtVrFmkdOx1bT1d8NkAUhhagO1RqOvL29Aaq03GRkZBhbeby9vSkuLiYrK+tPy1y4cKHK+TMzM03KXP86WVlZlJSUVGlRqmRjY4Ozs7PJTQgh6qy2w0BrDRmHmNDMMOlF9loT4s5Vazhq1qwZ3t7ebNy40XisuLiYrVu30r17dwA6d+6MlZWVSZm0tDSSkpKMZcLCwtDr9ezevdtYZteuXej1epMySUlJpKVd/UWwYcMGbGxs6Ny5c3VelhBC1E52LtDKMH6zX8lWNBqIT83mbNYV89ZLiDrulsNRXl4e8fHxxMfHA4ZB2PHx8aSkpKDRaHj22Wd5++23Wb16NUlJSUyePBl7e3vGjRsHgE6nY8qUKcycOZPNmzezf/9+JkyYQHBwMP379wegbdu2DB48mKlTpxIbG0tsbCxTp05lyJAhBAYGAjBw4EDatWtHZGQk+/fvZ/PmzcyaNYupU6dKi5AQouGo6FpzOPYj3fxdAFifeONxl0KIm3Sr09t+++03BVS5TZo0SSllmM4/Z84c5e3trWxsbFSvXr1UYmKiyTkKCgrU9OnTlZubm7Kzs1NDhgxRKSkpJmUuXbqkxo8fr5ycnJSTk5MaP368ysrKMilz5swZFRERoezs7JSbm5uaPn26KiwsvOlrkan8Qog6r7hAqbf9lJrjrKLXrlT+L6xVwz7eYe5aCVGjavrzW6OUUmbMZmaVk5ODTqdDr9dLa5MQou766WnYv5SC4Am03/sA5Qq2P98XPzd7c9dMiBpR05/fsreaEELUdRXbidgdX0OPACcA1smsNSFum4QjIYSo6wLuAycfKNQzxfsUAFESjoS4bRKOhBCirrPQQtBDAITlb8ZCAwln9aRckllrQtwOCUdCCFEfVMxaszm1gb4BtoC0HglxuyQcCSFEfeAdAh6BUFrI4x4HAYhKlL3WhLgdEo6EEKI+0GggZDQAnXM2YqGBpHM5nLmUb+aKCVH3SDgSQoj6ItgQjqxTdvBAgGGPSelaE+LWSTgSQoj6wjUAmtwLqpzHXPYBECV7rQlxyyQcCSFEfVIxMDvk8ga0FhoOns8h+aJ0rQlxKyQcCSFEfdL+QdBosbxwgJFNDVP5ZUFIIW6NhCMhhKhPHDygZT8AJjvuBmCtdK0JcUskHAkhRH1TsZ1Im8xf0FrA4bQcTmbmmblSQtQdEo6EEKK+afMAWDmg1Z8m0u8iAOuk9UiImybhSAgh6htrB2gTAcB4u1hApvQLcSskHAkhRH1UMWutRcZGbCzKOZKey4kM6VoT4mZIOBJCiPqoeV+w98DiSibTmqQAMmtNiJsl4UgIIeojrSUEjQRglNVOQBaEFOJmSTgSQoj6qmLWWtOM33DWFnH0Qi7HL+SauVJC1H4SjoQQor5q0gVcA9CU5PO0z3FABmYLcTMkHAkhRH2l0Rg3ox1m8TsgXWtC3AwJR0IIUZ9VdK15X/wdT20uxzPyOCZda0L8KQlHQghRnzVqDT4d0JSXMsMrCZDtRIT4KxKOhBCivqtoPQpX2wHDlH6llDlrJEStJuFICCHqu6CHAA3uWfE0117kREYexy7IgpBC/BEJR0IIUd85+0CzXgBM94wHICrhvBkrJETtJuFICCEagortRPqXbgUUa6VrTYg/JOFICCEagrZDQWuDc+5JOlimcioznyPpMmtNiBuRcCSEEA2BrQ4CBwPwlPs+QNY8EuKPSDgSQoiGomLWWq+irVhQTpR0rQlxQxKOhBCioWg1AGx12BVeoIfVUZIv5nMoLcfctRKi1pFwJIQQDYWlDbQbDsATLnsB6VoT4kaqPRwFBASg0Wiq3J5++mkAJk+eXOWx0NBQk3MUFRXxzDPP4OHhgYODA8OGDePs2bMmZbKysoiMjESn06HT6YiMjCQ7O7u6L0cIIeqXiq61boU7sKFYFoQU4gaqPRzt2bOHtLQ0423jxo0AjB492lhm8ODBJmXWrVtnco5nn32W1atXs3z5cnbs2EFeXh5DhgyhrKzMWGbcuHHEx8cTHR1NdHQ08fHxREZGVvflCCFE/eLfA5wbY1WSywCrBE5fusLB89K1JsS1LKv7hI0aNTK5/84779CiRQt69+5tPGZjY4O3t/cNn6/X6/nyyy9ZsmQJ/fv3B2Dp0qX4+fmxadMmBg0axOHDh4mOjiY2NpZu3boB8MUXXxAWFsbRo0cJDAys7ssSQoj6wcLCsGL2zn8zxXkPay91ISoxjaDGOnPXTIhao0bHHBUXF7N06VIee+wxNBqN8fiWLVvw9PSkdevWTJ06lYyMDONjcXFxlJSUMHDgQOMxX19fgoKC2LlzJwAxMTHodDpjMAIIDQ1Fp9MZy9xIUVEROTk5JjchhGhwKhaEDCmIxZl8ohKka02Ia9VoOPrxxx/Jzs5m8uTJxmPh4eEsW7aMX3/9lfnz57Nnzx7uv/9+ioqKAEhPT8fa2hpXV1eTc3l5eZGenm4s4+npWeX1PD09jWVuZN68ecYxSjqdDj8/v2q4SiGEqGO8gqBRW7TlJQyx3kvK5SsknZM/FoWoVKPh6MsvvyQ8PBxfX1/jsbFjxxIREUFQUBBDhw5l/fr1HDt2jKioqD89l1LKpPXp2q//qMz1Zs+ejV6vN95SU1Nv46qEEKKO02ggxDAOdJLDbgDWJspea0JUqrFwdObMGTZt2sTjjz/+p+V8fHzw9/fn+PHjAHh7e1NcXExWVpZJuYyMDLy8vIxlLly4UOVcmZmZxjI3YmNjg7Ozs8lNCCEapGBDOGpdEI8Xl6VrTYhr1Fg4WrhwIZ6enkRERPxpuUuXLpGamoqPjw8AnTt3xsrKyjjLDSAtLY2kpCS6d+8OQFhYGHq9nt27dxvL7Nq1C71ebywjhBDiT7g0haZhaFCMtI7lbFYBCWf15q6VELVCjYSj8vJyFi5cyKRJk7C0vDohLi8vj1mzZhETE8Pp06fZsmULQ4cOxcPDgwcffBAAnU7HlClTmDlzJps3b2b//v1MmDCB4OBg4+y1tm3bMnjwYKZOnUpsbCyxsbFMnTqVIUOGyEw1IYS4WRWtR4/Y7QIgKlEWhBQCaigcbdq0iZSUFB577DGT41qtlsTERIYPH07r1q2ZNGkSrVu3JiYmBicnJ2O5BQsWMGLECMaMGUOPHj2wt7dnzZo1aLVaY5lly5YRHBzMwIEDGThwICEhISxZsqQmLkcIIeqn9g+ChSVNi47TQnNOutaEqKBRDfgnIScnB51Oh16vl/FHQoiG6ZuxcCya/6oHeadoND8+3YN7/FzMXSsh/lRNf37L3mpCCNGQVXStjbKOBRRRCTJrTQgJR0II0ZAFPgDWjniUpNFJc1y61oRAwpEQQjRs1vbQZggAo613cl5fyP7UbPPWSQgzk3AkhBANXcWCkEMtd2FJKVEJMmtNNGwSjoQQoqFr1gccGuFYpuc+i0TWJaZRXi5da6LhknAkhBANndYSgh4CYJTVTtL0hexPzfqLJwlRf0k4EkIIAcFjABhgEYc9hayVrjXRgEk4EkIIAY07gVtzbFQhAyz2sj4xXbrWRIMl4UgIIQRoNMbWo1FWMaTnFLIvRbrWRMMk4UgIIYRBxYKQ3TUJuKOXrjXRYEk4EkIIYeDREnw7oqWMCG2szFoTDZaEIyGEEFdVdK09ZLWTjNwi9p6RrjXR8Eg4EkIIcVXQQ6CxoAPHaaq5IHutiQZJwpEQQoirnLygWW8Ahlv8zrqkdMqka000MBKOhBBCmAoxdK2NtNpJZm4he05fNnOFhLi7JBwJIYQw1WYIWNrSjPO015yWvdZEgyPhSAghhClbZwgMB2CE9nfWS9eaaGAkHAkhhKiqYtbacMsYLucVsDtZutZEwyHhSAghRFUt+4OdK55kEWpxiKhEmbUmGg4JR0IIIaqytIZ2IwAYYfE70UnplJaVm7dOQtwlEo6EEELcWMWstXDL3eTm5UnXmmgwJBwJIYS4Mb9QcG6CEwXcb7GftYkya000DBKOhBBC3JiFBQSPAgyz1qRrTTQUEo6EEEL8sYqutb7aeErzLxN7SrrWRP0n4UgIIcQf82oPnu2xppQHtLtl1ppoECQcCSGE+HMhowHpWhMNh4QjIYQQfy7IMO7oXosj2F5JJ+bUJTNXSIiaJeFICCHEn3PxA/8eWKAYqt0pe62Jek/CkRBCiL8WXNm1tpPog+mUSNeaqMckHAkhhPhr7YajLKxoZ3GGRgXJ7DwpXWui/pJwJIQQ4q/Zu6FpNRCAEdodRCXIrDVRf1V7OJo7dy4ajcbk5u3tbXxcKcXcuXPx9fXFzs6OPn36cPDgQZNzFBUV8cwzz+Dh4YGDgwPDhg3j7NmzJmWysrKIjIxEp9Oh0+mIjIwkOzu7ui9HCCFEpYpZa8O1O9mQlEZxqXStifqpRlqO2rdvT1pamvGWmJhofOy9997jww8/5OOPP2bPnj14e3szYMAAcnNzjWWeffZZVq9ezfLly9mxYwd5eXkMGTKEsrIyY5lx48YRHx9PdHQ00dHRxMfHExkZWROXI4QQAqD1YJS1E000F2lZdJDfT140d42EqBGWNXJSS0uT1qJKSik++ugjXn75ZUaOHAnA4sWL8fLy4ptvvuGJJ55Ar9fz5ZdfsmTJEvr37w/A0qVL8fPzY9OmTQwaNIjDhw8THR1NbGws3bp1A+CLL74gLCyMo0ePEhgYeMN6FRUVUVRUZLyfk5NT3ZcuhBD1l5UdmrZD4cA3jND+TlRCf/oGepq7VkJUuxppOTp+/Di+vr40a9aMhx9+mFOnTgGQnJxMeno6AwcONJa1sbGhd+/e7Ny5E4C4uDhKSkpMyvj6+hIUFGQsExMTg06nMwYjgNDQUHQ6nbHMjcybN8/YDafT6fDz86vW6xZCiHqvomstQruLXw+ela41US9Vezjq1q0bX3/9Nb/88gtffPEF6enpdO/enUuXLpGeng6Al5eXyXO8vLyMj6Wnp2NtbY2rq+uflvH0rPrXiqenp7HMjcyePRu9Xm+8paam3tG1CiFEg9OsN8rRC1dNHh2L4/j9hHStifqn2rvVwsPDjV8HBwcTFhZGixYtWLx4MaGhoQBoNBqT5yilqhy73vVlblT+r85jY2ODjY3NTV2HEEKIG7DQogl6CGI/YYT2d9YmDKdvG+laE/VLjU/ld3BwIDg4mOPHjxvHIV3fupORkWFsTfL29qa4uJisrKw/LXPhwoUqr5WZmVmlVUoIIUQ1q1gQsr/FPnYcSqaotOwvniBE3VLj4aioqIjDhw/j4+NDs2bN8Pb2ZuPGjcbHi4uL2bp1K927dwegc+fOWFlZmZRJS0sjKSnJWCYsLAy9Xs/u3buNZXbt2oVerzeWEUIIUUN8O6LcW2KnKaZ7cSw7jkvXmqhfqj0czZo1i61bt5KcnMyuXbsYNWoUOTk5TJo0CY1Gw7PPPsvbb7/N6tWrSUpKYvLkydjb2zNu3DgAdDodU6ZMYebMmWzevJn9+/czYcIEgoODjbPX2rZty+DBg5k6dSqxsbHExsYydepUhgwZ8ocz1YQQQlQTjQZN8BiAillrsteaqF+qfczR2bNneeSRR7h48SKNGjUiNDSU2NhY/P39AXj++ecpKCjgqaeeIisri27durFhwwacnJyM51iwYAGWlpaMGTOGgoIC+vXrx6JFi9BqtcYyy5YtY8aMGcZZbcOGDePjjz+u7ssRQghxI8GjYMvb3GeRyJxDRyksCcbWSvvXzxOiDtAopZS5K2EuOTk56HQ69Ho9zs7O5q6OEELUKeqLfmjO7WVuyUR6jP8XA9rJmE9xd9T057fsrSaEEOK2aEKu7VqTvdZE/SHhSAghxO1p/yBKo+Uei5McO3yAwhKZtSbqBwlHQgghbo+jJzTvA8CA0m1sO5Zp3voIUU0kHAkhhLhtlV1rw6VrTdQjEo6EEELcvjYRlGttaW6RTvrhndK1JuoFCUdCCCFun40TmrYRAAwq386Wo9K1Juo+CUdCCCHuSOWCkEO1MaxLOGvm2ghx5yQcCSGEuDMt+1Fq40ojjZ78I79SUCxda6LCuTgoyDZ3LW6ZhCMhhBB3RmuFNngkAOFqO1uOZpi5QsLszu2DZWPgi/th13/NXZtbJuFICCHEHauctTbIYg+/HDht3soI8zkfD988DF/0heO/gMYCCrLMXatbVu17qwkhhGiA/LpR5NgEp7yzaI5FU1B8L3bWstdag5F2ALa8C0ejDPc1FhA8Bno/D+4tzFu32yDhSAghxJ3TaLC+ZyzsmM8Daju/Hc3ggWAfc9dK1LT0RNjyDhxZa7ivsYCgUYZQ5NHKvHW7A9KtJoQQolpUdq31tojnt/1HzVwbUaPSk+C7CfDf+yqCkQaCR8NTu+ChL+p0MAJpORJCCFFdPNtQ4NYOu8uHsD+xhivF92FvLR8z9cqFg7D1XTj0U8UBDQSNhN4vQKPAKsWVUigFFhaau1vPOyQtR0IIIaqNbaeHAXiAHfx6RGat1RsZh2HFJPi0e0Uw0kD7B+GpGBj1VZVgVFpWzk/x5xjynx38cjDdPHW+AxLphRBCVBtN8CjUpjl0szjCy3H7GRLia+4qiTuRccTQUnRwNaAMx9qNMLQUebWrUjy/qJTv9qTy5Y5kzmUXALA45jThdWz8mYQjIYQQ1UfXmHyfUBzTYnA9tYb8okE42MhHTZ2TeRS2vgdJqzCGorbDoM+L4NW+SvGM3EIW7zzN0tgU9AUlALg7WDOpewCRof53seLVQ/7HCiGEqFYOXR6BNTFEaHaw+UgGwzpI61GdcfG4oaUo8XuMoajNEEMo8g6uUvxkZh7/236KVfvOUVxaDkAzDwce79mMhzo1wdaqbi7nIOFICCFEtdK0G07p2pm0tUhlxZ4dDOswxtxVEn/l4gnY9h4krgRlCDm0GWLoPvMJqVJ87+nLfLbtFJsOX0BVZKiOTV14olcLBrTzQlvHBmBfT8KREEKI6mXnwhX/fjifjsYnZQ15RSNxlK612unSSdj2PiR8dzUUBT5gaCny6WBStLxcseHQBT7fdpJ9KdnG4/3bevFE7+Z08XdFo6nboaiS/G8VQghR7Zy6PgKno4nQ/M7mQ2kM7+hn7iqJa10+BVsrQ1HFRsGtBxtCkW9Hk6KFJWX8sO8c/9t+ilMX8wGw1lowslNjHu/ZnJaejne79jVOwpEQQohqp2k9mEKtI43LLvHN7o3Q8TFzV0kAXE6GbR/AgW+vhqJWg6DPC9C4s0nR7CvFLIk5w+KY01zMKwbA2daSCaH+TO4egKez7d2u/V0j4UgIIUT1s7KlsGUEtke/w+/cWnILI3GytTJ3rRqurNNXQ1F5qeFYywHQZzY0MQ1FqZev8OWOZFbsTeVKsSFANXax47H7mjG2q1+D6CKt/1cohBDCLHTdxsHR7xisieXXg2cZ3rmZuavU8GSdge0fQPw3V0NRi36GUOTX1aRo0jk9n207xbrENMrKDaOs2/o480Sv5kSE+GClbTjrRks4EkIIUSM0AT3Js/LApeQiqbt+hs5/N3eVGo7sVEMo2r/0mlB0f0UoutdYTCnFtuMX+WzrSXaevGQ83rOVB9N6Nee+lh71ZpD1rZBwJIQQomZYaClu+yAkfEHz9HXkFD6Fs3St1azsVNjxIexbAuWGxRhp3scQipqGGouVlJWz5sB5Pt92iiPpuQBoLTQMCfFhas/mBDXWmaHytYeEIyGEEDXGNXQ8JHzB/Zo4Nhw4ybBubcxdpfpJfw62z4d9X18NRc16GUKRf3djsbyiUpbvTuHLHcmk6QsBsLfW8nDXpjx2XwBNXO3NUftaR8KREEKIGqPxuYdLdgG4F5zm4p7vodu/zF2l+iXnPGz/EPYthjLDjDICehpCUUAPY7ELOYUs/P00y3adIbfQ0M3m4WjDoz0CmNDNH529tOhdS8KREEKImqPRoIJHw+73aZ0Rjb7gBXR28kF8x3LSYMcCiFsEZUWGY/49DKGoWU9jseMXcvl82yl+jD9HSZlhkHXzRg5M69mcER0b19ntPWqahCMhhBA1yiN0POx+nzBNEuv3JTGkR8e/fpK4sdx0Qyjau/BqKGraHfrONrQYaTQopdidfJnPt51i85EM41O7BrgyrVcL+rXxxKKOb+9R06p9Xt68efPo2rUrTk5OeHp6MmLECI4ePWpSZvLkyWg0GpNbaGioSZmioiKeeeYZPDw8cHBwYNiwYZw9e9akTFZWFpGRkeh0OnQ6HZGRkWRnZ1f3JQkhhLgTbs047xSMVqPI3fuduWtTN+VegOjZ8P86wK7/GoKRXyhM/AkeXQfNelGmYF1iGiM+2cnYz2PZfCQDjQYGtfdi1d+6s/LJ7gxo5yXB6CZUe8vR1q1befrpp+natSulpaW8/PLLDBw4kEOHDuHg4GAsN3jwYBYuXGi8b21tbXKeZ599ljVr1rB8+XLc3d2ZOXMmQ4YMIS4uDq3W0Aw4btw4zp49S3R0NADTpk0jMjKSNWvWVPdlCSGEuAOW94yF7Ym0v/QL+itvyBiXm5WXAb//P9jzJZQWGI41udfQUtS8L2g0FBSX8X1cKv/bkcyZS1cAsLa0YFTnJjx+XzOaN6p/23vUNI1Slfvp1ozMzEw8PT3ZunUrvXr1AgwtR9nZ2fz44483fI5er6dRo0YsWbKEsWPHAnD+/Hn8/PxYt24dgwYN4vDhw7Rr147Y2Fi6desGQGxsLGFhYRw5coTAwMC/rFtOTg46nQ69Xo+zs3P1XLAQQoiq8i9S9n4rtJSzvs9awvv0/OvnNGR5mfD7R9eFoq6GMUUt7geNhsv5xXwdc5qvY85wOd8wGNvF3oqJof5M7B6Ah6ON+epfw2r687vGxxzp9XoA3NzcTI5v2bIFT09PXFxc6N27N2+99Raenp4AxMXFUVJSwsCBA43lfX19CQoKYufOnQwaNIiYmBh0Op0xGAGEhoai0+nYuXPnDcNRUVERRUVFxvs5OTnVeq1CCCH+gIMHKa5hNMv6naL9y0HC0Y3lX6xoKfoflBhagWjcGfq8BC37gUbDmUv5/G97MivjUiksKQegiasdj9/XjDFd/bC3luHEd6pGv4NKKZ577jnuu+8+goKCjMfDw8MZPXo0/v7+JCcn88orr3D//fcTFxeHjY0N6enpWFtb4+rqanI+Ly8v0tPTAUhPTzeGqWt5enoay1xv3rx5vPbaa9V4hUIIIW6WXeeHYdPvdMzeSHZ+ES4O9bdl45blX4Kd/4bdX0BJvuGYb0dDKGo1ADQaDqRm8/m2U6xPSqNidw+CG+uY1qs54UHeWDag7T1qWo2Go+nTp5OQkMCOHTtMjld2lQEEBQXRpUsX/P39iYqKYuTIkX94PqWUyTLmN1rS/Poy15o9ezbPPfec8X5OTg5+fn43fT1CCCFun3fXkRRumoW/5gKbft9E/4ER5q6S+V25bAhFuz6/Gop87oG+L0GrgZQr2HI0g8+2nmJX8mXj03q3bsQTvZsT1ty9QW7vUdNqLBw988wz/Pzzz2zbto0mTZr8aVkfHx/8/f05fvw4AN7e3hQXF5OVlWXSepSRkUH37t2NZS5cuFDlXJmZmXh5ed3wdWxsbLCxkb9UhBDCLGwcOdOoL4GZ0ZQnrICGHI6uXIaYj2HXZ1CcZzjm08Ewpqj1YIrKyvkp7ixfbDvF8QzD45YWGobd48u0Xs1p4y3jZGtStYcjpRTPPPMMq1evZsuWLTRr9te7MF+6dInU1FR8fHwA6Ny5M1ZWVmzcuJExY8YAkJaWRlJSEu+99x4AYWFh6PV6du/ezb33GjbR27VrF3q93highBBC1C7O946DqGg65f5KVu4VXJ0a2HYVBVkQ838Q+18oNuxphneIIRQFhpNTVMo3206x8PdkLuQYxsg62ljyyL1+PNqjGb4udmasfMNR7bPVnnrqKb755ht++uknk0HROp0OOzs78vLymDt3Lg899BA+Pj6cPn2al156iZSUFA4fPoyTkxMAf/vb31i7di2LFi3Czc2NWbNmcenSJZOp/OHh4Zw/f57PPvsMMEzl9/f3v+mp/DJbTQgh7rKyEvRvNkenctja9VN6R4wzd43ujoIsiPnEsEZRUcVkIK9g6PMitIkgLaeQr3Yk8+3uVPKKDNt7eDnb8GiPZozr1lQ27L1OnZut9umnnwLQp08fk+MLFy5k8uTJaLVaEhMT+frrr8nOzsbHx4e+ffvy3XffGYMRwIIFC7C0tGTMmDEUFBTQr18/Fi1aZAxGAMuWLWPGjBnGWW3Dhg3j448/ru5LEkIIUV20VpzxGUzI+RVoD66C+h6OCrIh9lPDrcgwexvP9hWhaAhHMvL4fOUBfo4/T2nFKOvWXo5M7dmc4fc0xtpSBlmbQ42vc1SbScuREELcfecTt+K7ahh5ypbifxzFzcXF3FWqfoV6Q9dZ7P8ZvgbwbAd9XkS1GULMqSw+23aKrccyjU/p1syNJ3o3p09r2d7jr9S5liMhhBDiz/gG9SJttTc+5en8vuU7eox4wtxVqj6FOYaus5iPr4aiRm2hzwuUBg5l/cEMPvu/nSSdM3StWWggPMiHab2a08HPxXz1FiYkHAkhhLi7NBrONonAJ+VL7I6sAupBOCrKNYSinR9DYbbhmEcg9HmBK62GsmLvOf43fxtnswyrXdtaWTC6sx+P92yGv7vDH59XmIWEIyGEEHedb8+JsOxLggv2cjnjPG6evuau0u0pyoXdn8PO/xgGXQN4tIbeL3DR/wG+jk3l6x+2kH2lBAA3B2smhvkzMSwANwfrPzmxMCcJR0IIIe66xq3u4YS2BS3LTnJiy1LuHfO8uat0a4ryrglFFYszureC3i+Q7D2IL34/w6rvtlJUatjew9/dnsfva8aozn7YWWv/5MSiNpBwJIQQwiwuBAyn5ckP0Z34Eagj4ag437DFx85/w5VLhmNuLaD3C8Q59+PzHafZ8O12Kqc6dfBz4YlezRnU3hutDLKuMyQcCSGEMIuAXpGUn1hAYPFBLp87jlvjVuau0h8rzoc9Xxo2hb1y0XDMrTnlvZ7nV8te/Hf7Gfae2WUs3q+NJ9N6NefeZm6yvUcdJOFICCGEWTT2b84B6xA6lBzgzJbFuI1/09xVqqr4CuytCEX5FdPuXZtRfN8sVpd05/PNKZzMjAfASqthxD2NmdarOa28nP74nKLWk3AkhBDCbLJaDIcjB2iU/BOoN6C2tLKUFMDer2DHR5CfYTjm4s+VsJkszruXL9ef42LeYQCcbC0Z382fR3sE4OVsa746i2oj4UgIIYTZtOw9nqLD82hSmsLlU3G4tehi3gqVFEDcItixAPIqNjd3acrlLs/yyeWufBN1nivFpwDw0dky5b5mjO3qh5Ns71GvSDgSQghhNk18vNlpcy/di38nbfsS84WjksJrQlG64ZiuKec6PM0HFzrz87pMyspTAWjj7cS0Xs0Z2sEXK61s71EfSTgSQghhVnmBD0Li73ilrIXyBWBxFwNHSSHs+xp2fAi5aQAoXRNOBD7JW+c7smWDHjB0q3Vv4c4TvVvQq5WHDLKu5yQcCSGEMKt2vUahT3gVj/KLZB3+Ddf2/Wr+RUuLDKFo+4eQex4A5dyYA80e59Uz95CwrQDQY6GBiBBfnujVnKDGupqvl6gVJBwJIYQwqyaNXNlg15OBhb+QuXNpzYaj0iLYv9QQinLOAlDu5EuM7yRePt2R07tKgQLsrLSM7erHlPua4edmX3P1EbWShCMhhBBmV9JuFOz7Bd/zGwwBxtKmel+gtBjil8K2+cZQVOboza8eE3jxdEcuHdAApXg4WjMpLIAJof64yvYeDZaEIyGEEGZ3T88Izse54ctlsg9E4dJ5ZPWcuKwE4pcZQpE+BYBSey+idA/zckpn8i4aPgabezjweM/mjOzUGFsr2d6joZNwJIQQwuwauzqw2qEvD15ZRfauZXcejspK4MC3sO19yDaEomK7RqyyG8Pc810pumxoFerU1IUnerdgQFsvLGR7D1FBwpEQQojaIXgM7FqFb8ZWKNSD7W0MgC4rgQPLK0LRGQCKbDxYajWS9y52pyjLEIoGtPPiiV7N6RLgVp1XIOoJCUdCCCFqhdCwXhyLaUxri3Po41ah6/HYzT+5rBQSvjOEoqxkAAqs3VjICP6t70khNlhrLXi4U2Me79mclp6ONXQVoj6QcCSEEKJW8HGx51fn/rTOW0z+3m9vLhyVlULiStj2Hlw2rFx9xcqVz0qH8llOXwqxwdnWkilh/kzqHoCnk2zvIf6ahCMhhBC1hlWHMfD7Yryz9kDOeXD2vXHB8jJDKNr6Hlw+CUC+pQufFEfwVW4/CrClsYudcXsPBxv5uBM3T/63CCGEqDV63duFPdtb09XiGPo9y9H1e860QHkZJK2Cre/CpRMA5Fk483/FD7C4cCBXsKWdjzNP9G7OA8E+sr2HuC0SjoQQQtQa3jpb1rgMoGvOMUriV0BlOCovg4OrDaHo4jEAcjVOfFIcweIyQyjq2cqDJ3q1oEdLd9neQ9wRCUdCCCFqFceOoyjZ8l88cg9DxhG4kGToPrt4FIBcjSOfFj/A4rJBFFrYM6yjL1N7Nqedr7OZay7qCwlHQgghapX7O7dj268h9NPup/yL+7EoyQcgBwc+L3mARWWDUNZOPBzWlMfua0ZjFzsz11jUNxKOhBBC1CpezrZ86z6Iftn7sSjJJ0c58EVpOIvKBmPr5MpTPQIYf68/Onsrc1dV1FMSjoQQQtQ67l1H8en6g+QqO5aWDaBRI0/+1as5Izo2xsZStvcQNUvCkRBCiFpnaEd/JsX/DTsrCz68rzn3t/GU7T3EXSPhSAghRK3jYm/NT0/3MHc1RAMlC0AIIYQQQlxDwpEQQgghxDUkHAkhhBBCXKPOh6NPPvmEZs2aYWtrS+fOndm+fbu5qySEEEKIOqxOh6PvvvuOZ599lpdffpn9+/fTs2dPwsPDSUlJMXfVhBBCCFFHaZRSytyVuF3dunWjU6dOfPrpp8Zjbdu2ZcSIEcybN+8vn5+Tk4NOp0Ov1+PsLMvOCyGEEHVBTX9+19mWo+LiYuLi4hg4cKDJ8YEDB7Jz584bPqeoqIicnByTmxBCCCHEtepsOLp48SJlZWV4eXmZHPfy8iI9Pf2Gz5k3bx46nc548/PzuxtVFUIIIUQdUmfDUSWNxnTFVKVUlWOVZs+ejV6vN95SU1PvRhWFEEIIUYfU2RWyPTw80Gq1VVqJMjIyqrQmVbKxscHGxuZuVE8IIYQQdVSdbTmytramc+fObNy40eT4xo0b6d69u5lqJYQQQoi6rs62HAE899xzREZG0qVLF8LCwvj8889JSUnhySefNHfVhBBCCFFH1elwNHbsWC5dusTrr79OWloaQUFBrFu3Dn9/f3NXTQghhBB1VJ1e5+hOyTpHQgghRN1T05/fdbrl6E5V5kJZ70gIIYSoOyo/t2uqfadBh6Pc3FwAWe9ICCGEqINyc3PR6XTVft4G3a1WXl7O+fPncXJy+sO1kW5XTk4Ofn5+pKam1ssuO7m+uq++X6NcX91X36+xvl8f1Nw1KqXIzc3F19cXC4vqn3jfoFuOLCwsaNKkSY2+hrOzc739Tw9yffVBfb9Gub66r75fY32/PqiZa6yJFqNKdXadIyGEEEKImiDhSAghhBDiGhKOaoiNjQ1z5sypt9uVyPXVffX9GuX66r76fo31/fqg7l5jgx6QLYQQQghxPWk5EkIIIYS4hoQjIYQQQohrSDgSQgghhLiGhKNaJiAggI8++sjc1ahXNBoNP/74o7mrIYS4S+Rnvu6bPHkyI0aMMNvr17twNHnyZDQaDRqNBisrK7y8vBgwYABfffUV5eXl5q6euE3Xvq/X3k6cOGHuqonbZO5ffqJmVP6sPvnkk1Uee+qpp9BoNEyePPnuV6yBqXwf3nnnHZPjP/74Y7XvCFEf1btwBDB48GDS0tI4ffo069evp2/fvvz9739nyJAhlJaWmrt6d11JSYm5q1AtKt/Xa2/NmjWrkdeqye9ZfXk/Ggp5v26dn58fy5cvp6CgwHissLCQb7/9lqZNm97RueX9uHm2tra8++67ZGVlmbsqtYJS6qYzQL0MRzY2Nnh7e9O4cWM6derESy+9xE8//cT69etZtGgRAHq9nmnTpuHp6YmzszP3338/Bw4cMDnPzz//TJcuXbC1tcXDw4ORI0caHysuLub555+ncePGODg40K1bN7Zs2WJ8fNGiRbi4uLB27VoCAwOxt7dn1KhR5Ofns3jxYgICAnB1deWZZ56hrKzM5HVzc3MZN24cjo6O+Pr68p///Mfk8b+q+9y5c7nnnnv46quvaN68OTY2NjW2c/HdVPm+XnvTarWsWbOGzp07Y2trS/PmzXnttdeq/ACkpaURHh6OnZ0dzZo1Y+XKlcbHTp8+jUajYcWKFfTp0wdbW1uWLl1q/D5e66OPPiIgIMB4f8+ePQwYMAAPDw90Oh29e/dm3759Js/RaDT897//Zfjw4Tg4OPDmm2/SsmVLPvjgA5NySUlJWFhYcPLkyer5htUh0dHR3Hfffbi4uODu7s6QIUNMvg+V79EPP/xA3759sbe3p0OHDsTExBjLyPtVe3Tq1ImmTZvyww8/GI/98MMP+Pn50bFjR+Oxm33fr//ZBPjqq69o3749NjY2+Pj4MH36dJM6XLx4kQcffBB7e3tatWrFzz//XMNXXfv0798fb29v5s2b94dlVq1aZfw+BgQEMH/+fONjs2fPJjQ0tMpzQkJCmDNnjvH+woULadu2Lba2trRp04ZPPvnE+Ni172HPnj2xs7Oja9euHDt2jD179tClSxccHR0ZPHgwmZmZVV7rtddeM37WPfHEExQXFxsfU0rx3nvv0bx5c+zs7OjQoQPff/+98fEtW7ag0Wj45Zdf6NKlCzY2Nmzfvv3mvnmqnpk0aZIaPnz4DR/r0KGDCg8PV+Xl5apHjx5q6NChas+ePerYsWNq5syZyt3dXV26dEkppdTatWuVVqtVr776qjp06JCKj49Xb731lvFc48aNU927d1fbtm1TJ06cUO+//76ysbFRx44dU0optXDhQmVlZaUGDBig9u3bp7Zu3arc3d3VwIED1ZgxY9TBgwfVmjVrlLW1tVq+fLnxvP7+/srJyUnNmzdPHT16VP373/9WWq1WbdiwQSmlbqruc+bMUQ4ODmrQoEFq37596sCBA6q8vLwmvt13zR+9r9HR0crZ2VktWrRInTx5Um3YsEEFBASouXPnGssAyt3dXX3xxRfq6NGj6l//+pfSarXq0KFDSimlkpOTFaACAgLUqlWr1KlTp9S5c+fUnDlzVIcOHUxeb8GCBcrf3994f/PmzWrJkiXq0KFD6tChQ2rKlCnKy8tL5eTkmLy+p6en+vLLL9XJkyfV6dOn1VtvvaXatWtncu5//OMfqlevXnf+zaojrn1Pv//+e7Vq1Sp17NgxtX//fjV06FAVHBysysrKlFJX36M2bdqotWvXqqNHj6pRo0Ypf39/VVJSopRS8n7VEpXv64cffqj69etnPN6vXz+1YMECNXz4cDVp0iSl1M2/79f/bH7yySfK1tZWffTRR+ro0aNq9+7dasGCBcbXAlSTJk3UN998o44fP65mzJihHB0djb8jG4LK9+GHH35Qtra2KjU1VSml1OrVq1XlR//evXuVhYWFev3119XRo0fVwoULlZ2dnVq4cKFSSqnExEQFqBMnThjPm5SUpAB19OhRpZRSn3/+ufLx8TG+P6tWrVJubm5q0aJFSinTn93o6Gh16NAhFRoaqjp16qT69OmjduzYofbt26datmypnnzySZP6Ozo6qrFjx6qkpCS1du1a1ahRI/XSSy8Zy7z00kvG8548eVItXLhQ2djYqC1btiillPrtt98UoEJCQtSGDRvUiRMn1MWLF2/q+9egwtHYsWNV27Zt1ebNm5Wzs7MqLCw0ebxFixbqs88+U0opFRYWpsaPH3/D85w4cUJpNBp17tw5k+P9+vVTs2fPVkoZwtH1/6meeOIJZW9vr3Jzc43HBg0apJ544gnjfX9/fzV48OAq9Q4PD1dKqZuq+5w5c5SVlZXKyMi4Yf3rokmTJimtVqscHByMt1GjRqmePXuqt99+26TskiVLlI+Pj/E+YPJDp5RS3bp1U3/729+UUld/eD/66COTMjfzYXu90tJS5eTkpNasWWPy+s8++6xJufPnzyutVqt27dqllFKquLhYNWrUyPgLpSH4s5/VjIwMBajExESl1NX36H//+5+xzMGDBxWgDh8+rJSS96u2qHxfMzMzlY2NjUpOTlanT59Wtra2KjMz0yQcXe+P3vfrfzZ9fX3Vyy+//Id1ANS//vUv4/28vDyl0WjU+vXr7/wC64hrf75CQ0PVY489ppQyDUfjxo1TAwYMMHneP//5T5M/BEJCQtTrr79uvD979mzVtWtX430/Pz/1zTffmJzjjTfeUGFhYUqpG//sfvvttwpQmzdvNh6bN2+eCgwMNKm/m5ubys/PNx779NNPlaOjoyorK1N5eXnK1tZW7dy50+S1p0yZoh555BGl1NVw9OOPP/7Vt6uKetmt9keUUmg0GuLi4sjLy8Pd3R1HR0fjLTk52dikGx8fT79+/W54nn379qGUonXr1ibP37p1q0mTsL29PS1atDDe9/LyIiAgAEdHR5NjGRkZJucPCwurcv/w4cMAN1V3AH9/fxo1anSb36naqW/fvsTHxxtv//73v4mLi+P11183+V5MnTqVtLQ0rly5Ynzun31PK3Xp0uWW65SRkcGTTz5J69at0el06HQ68vLySElJ+dNz+/j4EBERwVdffQXA2rVrKSwsZPTo0bdch/rg5MmTjBs3jubNm+Ps7GwcS3b99zEkJMT4tY+PD0CVn58/I+/X3ePh4UFERASLFy9m4cKFRERE4OHhYVLmZt/3a9+PjIwMzp8//4e/nytd+3/FwcEBJyenW/q/Up+8++67LF68mEOHDpkcP3z4MD169DA51qNHD44fP24c7jF+/HiWLVsGGD5Dv/32W8aPHw9AZmYmqampTJkyxeR38Jtvvlmlu/na98PLywuA4OBgk2PXvz8dOnTA3t7eeD8sLIy8vDxSU1M5dOgQhYWFDBgwwOS1v/766yqvfTu/2y1v+Rl12OHDh2nWrBnl5eX4+PiYjBGq5OLiAoCdnd0fnqe8vBytVktcXBxardbksWuDj5WVlcljlTPorj92M7PoKmcX3EzdwfDLoL5xcHCgZcuWJsfKy8t57bXXTMaDVbK1tf3T810/Y+P675mFhUWVsVrXDwadPHkymZmZfPTRR/j7+2NjY0NYWJhJv/iNzg3w+OOPExkZyYIFC1i4cCFjx441+UXQkAwdOhQ/Pz+++OILfH19KS8vJygoqMr38dqfn2t/JkDer9roscceM44F+r//+78qj9/s+37t+/Fnv5uvdbu/a+ujXr16MWjQIF566SWTmYKVDQbXuv5naNy4cbz44ovs27ePgoICUlNTefjhh4GrP3tffPEF3bp1M3ne9Z+NN/rZvf7Yzb4/15aNioqicePGJo9fv4/b7XweNphw9Ouvv5KYmMg//vEPmjRpQnp6OpaWliaDNa8VEhLC5s2befTRR6s81rFjR8rKysjIyKBnz57VXtfY2Ngq99u0aQMYBjr+Vd0bkk6dOnH06NEqoel6sbGxTJw40eT+tQNDb6RRo0akp6eb/AKJj483KbN9+3Y++eQTHnjgAQBSU1O5ePHiTdX9gQcewMHBgU8//ZT169ezbdu2m3pefXPp0iUOHz7MZ599Zvx52rFjxy2fR96v2mfw4MHGoDNo0CCTx273fXdyciIgIIDNmzfTt2/f6q90PfXOO+9wzz330Lp1a+Oxdu3aVfme79y5k9atWxvDTZMmTejVqxfLli2joKCA/v37G1t+vLy8aNy4MadOnTK2JlWnAwcOUFBQYAzEsbGxODo60qRJE1xdXbGxsSElJYXevXtX+2vXy3BUVFREeno6ZWVlXLhwgejoaObNm8eQIUOYOHEiFhYWhIWFMWLECN59910CAwM5f/4869atY8SIEXTp0oU5c+bQr18/WrRowcMPP0xpaSnr16/n+eefp3Xr1owfP56JEycyf/58OnbsyMWLF/n1118JDg42/uK9Xb///jvvvfceI0aMYOPGjaxcuZKoqCjAMPvgr+rekLz66qsMGTIEPz8/Ro8ejYWFBQkJCSQmJvLmm28ay61cuZIuXbpw3333sWzZMnbv3s2XX375p+fu06cPmZmZvPfee4waNYro6GjWr1+Ps7OzsUzLli1ZsmQJXbp0IScnh3/+8583/ZetVqtl8uTJzJ49m5YtW1bp+msoXF1dcXd35/PPP8fHx4eUlBRefPHFWz6PvF+1j1arNXZfX9+ScCfv+9y5c3nyySfx9PQkPDyc3Nxcfv/9d5555plqv4b6Ijg4mPHjx5vMfp45cyZdu3bljTfeYOzYscTExPDxxx+bzDYDQ9fa3LlzKS4uZsGCBSaPzZ07lxkzZuDs7Ex4eDhFRUXs3buXrKwsnnvuuTuqc/H/b+/eQ5rq4ziOv89M+qOWdCEbIg26iOY1ULphwwIluoPiPybNoAsp2cT6w9Q0KBQjogyiUotIikjTLBhRFq00DPrLoIIiIxFC7CIkbT1/Nc6eHp7Hni7T9nn9tXM453u+Oz/YPvud7WxkhIKCAsrKynj58iUVFRXs3LkTi8WC1WqlpKSE4uJifD4fy5Yt4927d3g8HiZPnkx+fv4PHfuP/M7RjRs3sNls2O12srKyuHXrFkePHqW1tZWwsDAMw6Cjo4P09HScTifz588nNzeXFy9e+BOxw+Hg0qVLXL16leTkZDIyMujq6vIfo6GhgU2bNuFyuYiJiWHt2rV0dXURHR39w/27XC56enpISUmhurqauro6/6eu0fQeSjIzM2lvb8ftdpOamsqiRYs4fPgws2fPDthu//79NDc3k5iYSFNTE+fPnycuLu5fa8fGxlJfX8/x48dJSkqiu7ubkpKSgG3OnDnD4OAgKSkp5OXlUVRUxMyZM0fdf0FBASMjIzidztE/6T+Ez+djwoQJWCwWmpub6enpIT4+nuLiYmpra7+7nsZrbJoyZUpAQP3qR8Y9Pz+fI0eOUF9fz4IFC1i9ejVPnz792a3/caqrqwMumy1cuJCLFy/S3NxMfHw85eXlVFVVfXOTzuzsbN6+fcvw8PA3N27dsmULp06dorGxkYSEBJYvX05jY+NPuQfdihUrmDdvHunp6eTk5LBmzRoqKysDnk95eTkHDx4kNjaWzMxM2trafsqxjS9/v8AoIr/NvXv3cDgc9PX1hVy4zcrKYu7cuRw7dizYrYxaKI+XSCj5I2eORMa6T58+8ezZM/bt20dOTk5IvdEODg5y7do1bt++zcqVK4PdzqiE8niJhCKFI5EguHDhAjExMQwNDVFTUxPsdn4rp9PJ1q1bcblcrFu3LtjtjEooj5dIKNJlNRERERETzRyJiIiImCgciYiIiJgoHImIiIiYKByJiIiImCgciYiIiJgoHImI/E1lZSXJycnBbkNEgkThSERCmmEYtLS0BLsNERlDFI5ERERETBSORGRMcDgcFBYWsmvXLqZOnUpkZCQnT57k48ePbN68GavVypw5c7h+/bp/n87OTtLS0pg4cSI2m429e/fy+fPngJpFRUWUlpYybdo0Zs2aFfDHlXa7HYANGzZgGIZ/+atz585ht9uJiIggNzeX9+/f/8pTICJjhMKRiIwZTU1NzJgxg+7ubgoLC9m+fTvZ2dksWbKER48ekZmZSV5eHsPDw7x+/ZpVq1aRmprK48ePOXHiBKdPn+bAgQPf1Jw0aRJdXV3U1NRQVVWF2+0G4OHDhwA0NDTw5s0b/zLA8+fPaWlpob29nfb2djo7Ozl06NDvOxkiEjT6+xARGRMcDgder5e7d+8C4PV6iYiIYOPGjZw9exaA/v5+bDYb9+/fp62tjcuXL9Pb24thGADU19ezZ88ehoaGsFgs39QESEtLIyMjwx90DMPgypUrrF+/3r9NZWUltbW19Pf3Y7VaASgtLeXOnTs8ePDgd5wOEQkizRyJyJiRmJjofxwWFsb06dNJSEjwr4uMjARgYGCA3t5eFi9e7A9GAEuXLuXDhw/09fX9Y00Am83GwMDAf/Zit9v9weh79hOR8U/hSETGjPDw8IBlwzAC1n0NQj6fjy9fvgQEI4CvE+Hm9f9U0+fz/a9eRrOfiIx/CkciMi7FxcXh8XgwfzPA4/FgtVqJiooadZ3w8HC8Xu+vaFFEximFIxEZl3bs2MGrV68oLCzkyZMntLa2UlFRwe7du7FYRv/SZrfbuXnzJv39/QwODv7CjkVkvFA4EpFxKSoqio6ODrq7u0lKSmLbtm0UFBRQVlb2XXXq6upwu91ER0eTkpLyi7oVkfFEv1YTERERMdHMkYiIiIiJwpGIiIiIicKRiIiIiInCkYiIiIiJwpGIiIiIicKRiIiIiInCkYiIiIiJwpGIiIiIicKRiIiIiInCkYiIiIiJwpGIiIiIyV+9thrEZr1z+gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.pivot_table(index='month',columns='Income/Expense',values='INR',aggfunc='sum',fill_value=0).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='month'>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.pivot_table(index='month',columns='Account',values='INR',aggfunc='sum',fill_value=0).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}