pivot_vs_groupby.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"A\": [\"foo\", \"foo\", \"foo\", \"foo\", \"foo\",\n",
    "                         \"bar\", \"bar\", \"bar\", \"bar\"],\n",
    "                   \"B\": [\"one\", \"one\", \"one\", \"two\", \"two\",\n",
    "                         \"one\", \"one\", \"two\", \"two\"],\n",
    "                   \"C\": [\"small\", \"large\", \"large\", \"small\",\n",
    "                         \"small\", \"large\", \"small\", \"small\",\n",
    "                         \"large\"],\n",
    "                   \"D\": [1, 2, 2, 3, 3, 4, 5, 6, 7]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pivot = pd.pivot_table(df, values='D', index=['A', 'B'],\n",
    "                          columns=['C'], aggfunc=np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gb = df.groupby(['A', 'B', 'C']).agg({'D' : np.sum}).unstack(level = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
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       "      <th>small</th>\n",
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       "  <tbody>\n",
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       "      <th>one</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            D      \n",
       "C       large small\n",
       "A   B              \n",
       "bar one   4.0   5.0\n",
       "    two   7.0   6.0\n",
       "foo one   4.0   1.0\n",
       "    two   NaN   6.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>large</th>\n",
       "      <th>small</th>\n",
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       "    <tr>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "C        large  small\n",
       "A   B                \n",
       "bar one    4.0    5.0\n",
       "    two    7.0    6.0\n",
       "foo one    4.0    1.0\n",
       "    two    NaN    6.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pivot"
   ]
  }
 ],
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