{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame({\"A\": [\"female\", \"female\", \"female\", \"female\", \"female\",\n",
    "                         \"male\", \"male\", \"male\", \"male\"],\n",
    "                   \"B\": [\"CS\", \"CS\", \"CS\", \"DS\", \"DS\",\n",
    "                         \"CS\", \"CS\", \"DS\", \"DS\"],\n",
    "                   \"C\": [\"MS\", \"PhD\", \"PhD\", \"MS\",\n",
    "                         \"MS\", \"PhD\", \"MS\", \"MS\", \"PhD\"],\n",
    "                   \"D\": [1, 7, 2, 5, 3, 2, 5, 3, 7]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rows are indexed by \"index\"\n",
    "# columns are indexed by \"columns\"\n",
    "# entries are the \"sum\" of all D values\n",
    "table = df.pivot_table('D', index=['A', 'B'],\n",
    "                    columns='C', aggfunc='sum')\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = df.pivot_table('D', index=['C', 'B'],\n",
    "                    columns='A', aggfunc='mean')\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = df.pivot_table('D', index=['C', 'B'],\n",
    "                    columns='A', aggfunc='count')\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = df.pivot_table('D', index=['A', 'B'],\n",
    "                    columns='C', aggfunc='sum', fill_value=0)\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = df.pivot_table('D', index=['C', 'B'],\n",
    "                    columns='A', aggfunc='max')\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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