Pandas - Melt -  Stack - Unstack.ipynb

{
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  {
   "cell_type": "code",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>chicago</th>\n",
       "      <th>chennai</th>\n",
       "      <th>berlin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>32</td>\n",
       "      <td>75</td>\n",
       "      <td>41</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>30</td>\n",
       "      <td>77</td>\n",
       "      <td>43</td>\n",
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       "      <th>2</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>28</td>\n",
       "      <td>75</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>22</td>\n",
       "      <td>82</td>\n",
       "      <td>38</td>\n",
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       "      <td>Friday</td>\n",
       "      <td>30</td>\n",
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       "      <td>30</td>\n",
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       "      <td>Sunday</td>\n",
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      "text/plain": [
       "         day  chicago  chennai  berlin\n",
       "0     Monday       32       75      41\n",
       "1    Tuesday       30       77      43\n",
       "2  Wednesday       28       75      45\n",
       "3   Thursday       22       82      38\n",
       "4     Friday       30       83      30\n",
       "5   Saturday       20       81      45\n",
       "6     Sunday       25       77      47"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"data/weather4.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "40f7d1a3",
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       "      <td>Monday</td>\n",
       "      <td>chicago</td>\n",
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       "      <td>Thursday</td>\n",
       "      <td>chicago</td>\n",
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       "      <td>Sunday</td>\n",
       "      <td>chicago</td>\n",
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       "      <th>7</th>\n",
       "      <td>Monday</td>\n",
       "      <td>chennai</td>\n",
       "      <td>75</td>\n",
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       "      <td>Tuesday</td>\n",
       "      <td>chennai</td>\n",
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       "      <td>Wednesday</td>\n",
       "      <td>chennai</td>\n",
       "      <td>75</td>\n",
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       "      <td>Thursday</td>\n",
       "      <td>chennai</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Friday</td>\n",
       "      <td>chennai</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
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       "      <th>12</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>chennai</td>\n",
       "      <td>81</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>chennai</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Monday</td>\n",
       "      <td>berlin</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>berlin</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>berlin</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>berlin</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Friday</td>\n",
       "      <td>berlin</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>berlin</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>berlin</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          day variable  value\n",
       "0      Monday  chicago     32\n",
       "1     Tuesday  chicago     30\n",
       "2   Wednesday  chicago     28\n",
       "3    Thursday  chicago     22\n",
       "4      Friday  chicago     30\n",
       "5    Saturday  chicago     20\n",
       "6      Sunday  chicago     25\n",
       "7      Monday  chennai     75\n",
       "8     Tuesday  chennai     77\n",
       "9   Wednesday  chennai     75\n",
       "10   Thursday  chennai     82\n",
       "11     Friday  chennai     83\n",
       "12   Saturday  chennai     81\n",
       "13     Sunday  chennai     77\n",
       "14     Monday   berlin     41\n",
       "15    Tuesday   berlin     43\n",
       "16  Wednesday   berlin     45\n",
       "17   Thursday   berlin     38\n",
       "18     Friday   berlin     30\n",
       "19   Saturday   berlin     45\n",
       "20     Sunday   berlin     47"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.melt(df, id_vars=[\"day\"])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d6952ce9",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>chicago</td>\n",
       "      <td>32</td>\n",
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       "      <th>1</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>chicago</td>\n",
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      "text/plain": [
       "         day variable  value\n",
       "0     Monday  chicago     32\n",
       "1    Tuesday  chicago     30\n",
       "2  Wednesday  chicago     28\n",
       "3   Thursday  chicago     22\n",
       "4     Friday  chicago     30\n",
       "5   Saturday  chicago     20\n",
       "6     Sunday  chicago     25"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1[\"variable\"] == \"chicago\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "52d8cf94",
   "metadata": {},
   "outputs": [
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       "      <th>temperature</th>\n",
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       "      <td>Thursday</td>\n",
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       "      <td>Friday</td>\n",
       "      <td>chennai</td>\n",
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       "      <td>Saturday</td>\n",
       "      <td>chennai</td>\n",
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       "      <td>chennai</td>\n",
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       "      <td>Monday</td>\n",
       "      <td>berlin</td>\n",
       "      <td>41</td>\n",
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       "      <th>15</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>berlin</td>\n",
       "      <td>43</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>berlin</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>berlin</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Friday</td>\n",
       "      <td>berlin</td>\n",
       "      <td>30</td>\n",
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       "      <th>19</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>berlin</td>\n",
       "      <td>45</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>berlin</td>\n",
       "      <td>47</td>\n",
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      ],
      "text/plain": [
       "          day     city  temperature\n",
       "0      Monday  chicago           32\n",
       "1     Tuesday  chicago           30\n",
       "2   Wednesday  chicago           28\n",
       "3    Thursday  chicago           22\n",
       "4      Friday  chicago           30\n",
       "5    Saturday  chicago           20\n",
       "6      Sunday  chicago           25\n",
       "7      Monday  chennai           75\n",
       "8     Tuesday  chennai           77\n",
       "9   Wednesday  chennai           75\n",
       "10   Thursday  chennai           82\n",
       "11     Friday  chennai           83\n",
       "12   Saturday  chennai           81\n",
       "13     Sunday  chennai           77\n",
       "14     Monday   berlin           41\n",
       "15    Tuesday   berlin           43\n",
       "16  Wednesday   berlin           45\n",
       "17   Thursday   berlin           38\n",
       "18     Friday   berlin           30\n",
       "19   Saturday   berlin           45\n",
       "20     Sunday   berlin           47"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "melted = pd.melt(df, id_vars=[\"day\"], var_name='city', value_name='temperature')\n",
    "melted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f05d0cd1",
   "metadata": {},
   "outputs": [],
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  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9629fa1e",
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    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0_level_0</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Price</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Price to earnings ratio (P/E)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Facebook</th>\n",
       "      <th>Google</th>\n",
       "      <th>Microsoft</th>\n",
       "      <th>Facebook</th>\n",
       "      <th>Google</th>\n",
       "      <th>Microsoft</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-06-05</td>\n",
       "      <td>155</td>\n",
       "      <td>955</td>\n",
       "      <td>66</td>\n",
       "      <td>37.10</td>\n",
       "      <td>32.0</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-06-06</td>\n",
       "      <td>150</td>\n",
       "      <td>987</td>\n",
       "      <td>69</td>\n",
       "      <td>36.98</td>\n",
       "      <td>31.3</td>\n",
       "      <td>30.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-06-07</td>\n",
       "      <td>153</td>\n",
       "      <td>963</td>\n",
       "      <td>62</td>\n",
       "      <td>36.78</td>\n",
       "      <td>31.7</td>\n",
       "      <td>30.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-06-08</td>\n",
       "      <td>155</td>\n",
       "      <td>1000</td>\n",
       "      <td>61</td>\n",
       "      <td>36.11</td>\n",
       "      <td>31.2</td>\n",
       "      <td>30.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-06-09</td>\n",
       "      <td>156</td>\n",
       "      <td>1012</td>\n",
       "      <td>66</td>\n",
       "      <td>37.07</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Unnamed: 0_level_0    Price                  Price to earnings ratio (P/E)  \\\n",
       "             Company Facebook Google Microsoft                      Facebook   \n",
       "0         2017-06-05      155    955        66                         37.10   \n",
       "1         2017-06-06      150    987        69                         36.98   \n",
       "2         2017-06-07      153    963        62                         36.78   \n",
       "3         2017-06-08      155   1000        61                         36.11   \n",
       "4         2017-06-09      156   1012        66                         37.07   \n",
       "\n",
       "                    \n",
       "  Google Microsoft  \n",
       "0   32.0     30.31  \n",
       "1   31.3     30.56  \n",
       "2   31.7     30.46  \n",
       "3   31.2     30.11  \n",
       "4   30.0     31.00  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\"data/stocks.xlsx\",header=[0,1])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "26aafb4a",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Google</th>\n",
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       "      <td>NaT</td>\n",
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       "      <th>Microsoft</th>\n",
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      "text/plain": [
       "              Price  Price to earnings ratio (P/E) Unnamed: 0_level_0\n",
       "0 Company       NaN                            NaN         2017-06-05\n",
       "  Facebook    155.0                          37.10                NaT\n",
       "  Google      955.0                          32.00                NaT\n",
       "  Microsoft    66.0                          30.31                NaT\n",
       "1 Company       NaN                            NaN         2017-06-06\n",
       "  Facebook    150.0                          36.98                NaT\n",
       "  Google      987.0                          31.30                NaT\n",
       "  Microsoft    69.0                          30.56                NaT\n",
       "2 Company       NaN                            NaN         2017-06-07\n",
       "  Facebook    153.0                          36.78                NaT\n",
       "  Google      963.0                          31.70                NaT\n",
       "  Microsoft    62.0                          30.46                NaT\n",
       "3 Company       NaN                            NaN         2017-06-08\n",
       "  Facebook    155.0                          36.11                NaT\n",
       "  Google     1000.0                          31.20                NaT\n",
       "  Microsoft    61.0                          30.11                NaT\n",
       "4 Company       NaN                            NaN         2017-06-09\n",
       "  Facebook    156.0                          37.07                NaT\n",
       "  Google     1012.0                          30.00                NaT\n",
       "  Microsoft    66.0                          31.00                NaT"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7e36347c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>963.0</td>\n",
       "      <td>62.00</td>\n",
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       "    <tr>\n",
       "      <th>Price to earnings ratio (P/E)</th>\n",
       "      <td>NaT</td>\n",
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       "                                   Company  Facebook  Google  Microsoft\n",
       "0 Price                                NaT    155.00   955.0      66.00\n",
       "  Price to earnings ratio (P/E)        NaT     37.10    32.0      30.31\n",
       "  Unnamed: 0_level_0            2017-06-05       NaN     NaN        NaN\n",
       "1 Price                                NaT    150.00   987.0      69.00\n",
       "  Price to earnings ratio (P/E)        NaT     36.98    31.3      30.56\n",
       "  Unnamed: 0_level_0            2017-06-06       NaN     NaN        NaN\n",
       "2 Price                                NaT    153.00   963.0      62.00\n",
       "  Price to earnings ratio (P/E)        NaT     36.78    31.7      30.46\n",
       "  Unnamed: 0_level_0            2017-06-07       NaN     NaN        NaN\n",
       "3 Price                                NaT    155.00  1000.0      61.00\n",
       "  Price to earnings ratio (P/E)        NaT     36.11    31.2      30.11\n",
       "  Unnamed: 0_level_0            2017-06-08       NaN     NaN        NaN\n",
       "4 Price                                NaT    156.00  1012.0      66.00\n",
       "  Price to earnings ratio (P/E)        NaT     37.07    30.0      31.00\n",
       "  Unnamed: 0_level_0            2017-06-09       NaN     NaN        NaN"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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    "df.stack(level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "afbbf512",
   "metadata": {},
   "outputs": [
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Price  Price to earnings ratio (P/E) Unnamed: 0_level_0\n",
       "0 Company       NaN                            NaN         2017-06-05\n",
       "  Facebook    155.0                          37.10                NaT\n",
       "  Google      955.0                          32.00                NaT\n",
       "  Microsoft    66.0                          30.31                NaT\n",
       "1 Company       NaN                            NaN         2017-06-06\n",
       "  Facebook    150.0                          36.98                NaT\n",
       "  Google      987.0                          31.30                NaT\n",
       "  Microsoft    69.0                          30.56                NaT\n",
       "2 Company       NaN                            NaN         2017-06-07\n",
       "  Facebook    153.0                          36.78                NaT\n",
       "  Google      963.0                          31.70                NaT\n",
       "  Microsoft    62.0                          30.46                NaT\n",
       "3 Company       NaN                            NaN         2017-06-08\n",
       "  Facebook    155.0                          36.11                NaT\n",
       "  Google     1000.0                          31.20                NaT\n",
       "  Microsoft    61.0                          30.11                NaT\n",
       "4 Company       NaN                            NaN         2017-06-09\n",
       "  Facebook    156.0                          37.07                NaT\n",
       "  Google     1012.0                          30.00                NaT\n",
       "  Microsoft    66.0                          31.00                NaT"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stacked=df.stack()\n",
    "df_stacked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2916572a",
   "metadata": {},
   "outputs": [
    {
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       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.78</td>\n",
       "      <td>31.7</td>\n",
       "      <td>30.46</td>\n",
       "      <td>2017-06-07</td>\n",
       "      <td>NaT</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>156.0</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.07</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31.00</td>\n",
       "      <td>2017-06-09</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
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      "text/plain": [
       "    Price                            Price to earnings ratio (P/E)           \\\n",
       "  Company Facebook  Google Microsoft                       Company Facebook   \n",
       "0     NaN    155.0   955.0      66.0                           NaN    37.10   \n",
       "1     NaN    150.0   987.0      69.0                           NaN    36.98   \n",
       "2     NaN    153.0   963.0      62.0                           NaN    36.78   \n",
       "3     NaN    155.0  1000.0      61.0                           NaN    36.11   \n",
       "4     NaN    156.0  1012.0      66.0                           NaN    37.07   \n",
       "\n",
       "                   Unnamed: 0_level_0                            \n",
       "  Google Microsoft            Company Facebook Google Microsoft  \n",
       "0   32.0     30.31         2017-06-05      NaT    NaT       NaT  \n",
       "1   31.3     30.56         2017-06-06      NaT    NaT       NaT  \n",
       "2   31.7     30.46         2017-06-07      NaT    NaT       NaT  \n",
       "3   31.2     30.11         2017-06-08      NaT    NaT       NaT  \n",
       "4   30.0     31.00         2017-06-09      NaT    NaT       NaT  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stacked.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "24eb5b86",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.67</td>\n",
       "      <td>6.50</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Q1 2017</td>\n",
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       "      <td>1012</td>\n",
       "      <td>66</td>\n",
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       "      <td>22.34</td>\n",
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       "      <td>6.40</td>\n",
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      "text/plain": [
       "  Unnamed: 0_level_0 Price Ratios                   \\\n",
       "  Unnamed: 0_level_1        Price                    \n",
       "             Company     Facebook Google Microsoft   \n",
       "0            Q1 2016          155    955        66   \n",
       "1            Q2 2016          150    987        69   \n",
       "2            Q3 2016          153    963        62   \n",
       "3            Q4 2016          155   1000        61   \n",
       "4            Q1 2017          156   1012        66   \n",
       "\n",
       "                                                 Income Statement         \\\n",
       "  Price to earnings ratio (P/E)                         Net Sales          \n",
       "                       Facebook Google Microsoft         Facebook Google   \n",
       "0                         37.10   32.0     30.31              2.6     20   \n",
       "1                         36.98   31.3     30.56              3.1     22   \n",
       "2                         36.78   31.7     30.46              4.3     24   \n",
       "3                         36.11   31.2     30.11              6.7     26   \n",
       "4                         37.07   30.0     31.00              8.1     31   \n",
       "\n",
       "                                         \n",
       "            Net Profit                   \n",
       "  Microsoft   Facebook Google Microsoft  \n",
       "0     18.70       0.80   5.43      4.56  \n",
       "1     21.30       0.97   5.89      5.10  \n",
       "2     21.45       1.20   6.10      5.43  \n",
       "3     21.88       1.67   6.50      5.89  \n",
       "4     22.34       2.03   6.40      6.09  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df2 = pd.read_excel(\"data/stocks_3_levels.xlsx\",header=[0,1,2])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "66b84d9e",
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
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       "            Income Statement           Price Ratios  \\\n",
       "                  Net Profit Net Sales        Price   \n",
       "0 Company                NaN       NaN          NaN   \n",
       "  Facebook              0.80      2.60        155.0   \n",
       "  Google                5.43     20.00        955.0   \n",
       "  Microsoft             4.56     18.70         66.0   \n",
       "1 Company                NaN       NaN          NaN   \n",
       "  Facebook              0.97      3.10        150.0   \n",
       "  Google                5.89     22.00        987.0   \n",
       "  Microsoft             5.10     21.30         69.0   \n",
       "2 Company                NaN       NaN          NaN   \n",
       "  Facebook              1.20      4.30        153.0   \n",
       "  Google                6.10     24.00        963.0   \n",
       "  Microsoft             5.43     21.45         62.0   \n",
       "3 Company                NaN       NaN          NaN   \n",
       "  Facebook              1.67      6.70        155.0   \n",
       "  Google                6.50     26.00       1000.0   \n",
       "  Microsoft             5.89     21.88         61.0   \n",
       "4 Company                NaN       NaN          NaN   \n",
       "  Facebook              2.03      8.10        156.0   \n",
       "  Google                6.40     31.00       1012.0   \n",
       "  Microsoft             6.09     22.34         66.0   \n",
       "\n",
       "                                          Unnamed: 0_level_0  \n",
       "            Price to earnings ratio (P/E) Unnamed: 0_level_1  \n",
       "0 Company                             NaN            Q1 2016  \n",
       "  Facebook                          37.10                NaN  \n",
       "  Google                            32.00                NaN  \n",
       "  Microsoft                         30.31                NaN  \n",
       "1 Company                             NaN            Q2 2016  \n",
       "  Facebook                          36.98                NaN  \n",
       "  Google                            31.30                NaN  \n",
       "  Microsoft                         30.56                NaN  \n",
       "2 Company                             NaN            Q3 2016  \n",
       "  Facebook                          36.78                NaN  \n",
       "  Google                            31.70                NaN  \n",
       "  Microsoft                         30.46                NaN  \n",
       "3 Company                             NaN            Q4 2016  \n",
       "  Facebook                          36.11                NaN  \n",
       "  Google                            31.20                NaN  \n",
       "  Microsoft                         30.11                NaN  \n",
       "4 Company                             NaN            Q1 2017  \n",
       "  Facebook                          37.07                NaN  \n",
       "  Google                            30.00                NaN  \n",
       "  Microsoft                         31.00                NaN  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "12bc451b",
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    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>987.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>36.98</td>\n",
       "      <td>31.3</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>Income Statement</th>\n",
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       "      <td>5.43</td>\n",
       "      <td>4.3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>21.45</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>153.0</td>\n",
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       "      <td>62.0</td>\n",
       "      <td>36.78</td>\n",
       "      <td>31.7</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q3 2016</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">3</th>\n",
       "      <th>Income Statement</th>\n",
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       "      <td>26.0</td>\n",
       "      <td>21.88</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>36.11</td>\n",
       "      <td>31.2</td>\n",
       "      <td>30.11</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Unnamed: 0_level_0</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q4 2016</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>Income Statement</th>\n",
       "      <td>2.03</td>\n",
       "      <td>6.40</td>\n",
       "      <td>6.09</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>Price Ratios</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156.0</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>37.07</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31.00</td>\n",
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       "    <tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q1 2017</td>\n",
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       "</table>\n",
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      "text/plain": [
       "                     Net Profit                  Net Sales                   \\\n",
       "                       Facebook Google Microsoft  Facebook Google Microsoft   \n",
       "0 Income Statement         0.80   5.43      4.56       2.6   20.0     18.70   \n",
       "  Price Ratios              NaN    NaN       NaN       NaN    NaN       NaN   \n",
       "  Unnamed: 0_level_0        NaN    NaN       NaN       NaN    NaN       NaN   \n",
       "1 Income Statement         0.97   5.89      5.10       3.1   22.0     21.30   \n",
       "  Price Ratios              NaN    NaN       NaN       NaN    NaN       NaN   \n",
       "  Unnamed: 0_level_0        NaN    NaN       NaN       NaN    NaN       NaN   \n",
       "2 Income Statement         1.20   6.10      5.43       4.3   24.0     21.45   \n",
       "  Price Ratios              NaN    NaN       NaN       NaN    NaN       NaN   \n",
       "  Unnamed: 0_level_0        NaN    NaN       NaN       NaN    NaN       NaN   \n",
       "3 Income Statement         1.67   6.50      5.89       6.7   26.0     21.88   \n",
       "  Price Ratios              NaN    NaN       NaN       NaN    NaN       NaN   \n",
       "  Unnamed: 0_level_0        NaN    NaN       NaN       NaN    NaN       NaN   \n",
       "4 Income Statement         2.03   6.40      6.09       8.1   31.0     22.34   \n",
       "  Price Ratios              NaN    NaN       NaN       NaN    NaN       NaN   \n",
       "  Unnamed: 0_level_0        NaN    NaN       NaN       NaN    NaN       NaN   \n",
       "\n",
       "                        Price                   Price to earnings ratio (P/E)  \\\n",
       "                     Facebook  Google Microsoft                      Facebook   \n",
       "0 Income Statement        NaN     NaN       NaN                           NaN   \n",
       "  Price Ratios          155.0   955.0      66.0                         37.10   \n",
       "  Unnamed: 0_level_0      NaN     NaN       NaN                           NaN   \n",
       "1 Income Statement        NaN     NaN       NaN                           NaN   \n",
       "  Price Ratios          150.0   987.0      69.0                         36.98   \n",
       "  Unnamed: 0_level_0      NaN     NaN       NaN                           NaN   \n",
       "2 Income Statement        NaN     NaN       NaN                           NaN   \n",
       "  Price Ratios          153.0   963.0      62.0                         36.78   \n",
       "  Unnamed: 0_level_0      NaN     NaN       NaN                           NaN   \n",
       "3 Income Statement        NaN     NaN       NaN                           NaN   \n",
       "  Price Ratios          155.0  1000.0      61.0                         36.11   \n",
       "  Unnamed: 0_level_0      NaN     NaN       NaN                           NaN   \n",
       "4 Income Statement        NaN     NaN       NaN                           NaN   \n",
       "  Price Ratios          156.0  1012.0      66.0                         37.07   \n",
       "  Unnamed: 0_level_0      NaN     NaN       NaN                           NaN   \n",
       "\n",
       "                                      Unnamed: 0_level_1  \n",
       "                     Google Microsoft            Company  \n",
       "0 Income Statement      NaN       NaN                NaN  \n",
       "  Price Ratios         32.0     30.31                NaN  \n",
       "  Unnamed: 0_level_0    NaN       NaN            Q1 2016  \n",
       "1 Income Statement      NaN       NaN                NaN  \n",
       "  Price Ratios         31.3     30.56                NaN  \n",
       "  Unnamed: 0_level_0    NaN       NaN            Q2 2016  \n",
       "2 Income Statement      NaN       NaN                NaN  \n",
       "  Price Ratios         31.7     30.46                NaN  \n",
       "  Unnamed: 0_level_0    NaN       NaN            Q3 2016  \n",
       "3 Income Statement      NaN       NaN                NaN  \n",
       "  Price Ratios         31.2     30.11                NaN  \n",
       "  Unnamed: 0_level_0    NaN       NaN            Q4 2016  \n",
       "4 Income Statement      NaN       NaN                NaN  \n",
       "  Price Ratios         30.0     31.00                NaN  \n",
       "  Unnamed: 0_level_0    NaN       NaN            Q1 2017  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
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   ],
   "source": [
    "df2.stack(level=0)"
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  },
  {
   "cell_type": "code",
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   "id": "b1897ed9",
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       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>Price</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>150.00</td>\n",
       "      <td>987.0</td>\n",
       "      <td>69.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price to earnings ratio (P/E)</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.98</td>\n",
       "      <td>31.3</td>\n",
       "      <td>30.56</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0_level_1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q2 2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2</th>\n",
       "      <th>Net Profit</th>\n",
       "      <td>1.20</td>\n",
       "      <td>6.10</td>\n",
       "      <td>5.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Net Sales</th>\n",
       "      <td>4.30</td>\n",
       "      <td>24.00</td>\n",
       "      <td>21.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>153.00</td>\n",
       "      <td>963.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price to earnings ratio (P/E)</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.78</td>\n",
       "      <td>31.7</td>\n",
       "      <td>30.46</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0_level_1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q3 2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">3</th>\n",
       "      <th>Net Profit</th>\n",
       "      <td>1.67</td>\n",
       "      <td>6.50</td>\n",
       "      <td>5.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Net Sales</th>\n",
       "      <td>6.70</td>\n",
       "      <td>26.00</td>\n",
       "      <td>21.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>61.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price to earnings ratio (P/E)</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.11</td>\n",
       "      <td>31.2</td>\n",
       "      <td>30.11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0_level_1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q4 2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">4</th>\n",
       "      <th>Net Profit</th>\n",
       "      <td>2.03</td>\n",
       "      <td>6.40</td>\n",
       "      <td>6.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Net Sales</th>\n",
       "      <td>8.10</td>\n",
       "      <td>31.00</td>\n",
       "      <td>22.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156.00</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>66.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price to earnings ratio (P/E)</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.07</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0_level_1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q1 2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Income Statement                   \\\n",
       "                                        Facebook Google Microsoft   \n",
       "0 Net Profit                                0.80   5.43      4.56   \n",
       "  Net Sales                                 2.60  20.00     18.70   \n",
       "  Price                                      NaN    NaN       NaN   \n",
       "  Price to earnings ratio (P/E)              NaN    NaN       NaN   \n",
       "  Unnamed: 0_level_1                         NaN    NaN       NaN   \n",
       "1 Net Profit                                0.97   5.89      5.10   \n",
       "  Net Sales                                 3.10  22.00     21.30   \n",
       "  Price                                      NaN    NaN       NaN   \n",
       "  Price to earnings ratio (P/E)              NaN    NaN       NaN   \n",
       "  Unnamed: 0_level_1                         NaN    NaN       NaN   \n",
       "2 Net Profit                                1.20   6.10      5.43   \n",
       "  Net Sales                                 4.30  24.00     21.45   \n",
       "  Price                                      NaN    NaN       NaN   \n",
       "  Price to earnings ratio (P/E)              NaN    NaN       NaN   \n",
       "  Unnamed: 0_level_1                         NaN    NaN       NaN   \n",
       "3 Net Profit                                1.67   6.50      5.89   \n",
       "  Net Sales                                 6.70  26.00     21.88   \n",
       "  Price                                      NaN    NaN       NaN   \n",
       "  Price to earnings ratio (P/E)              NaN    NaN       NaN   \n",
       "  Unnamed: 0_level_1                         NaN    NaN       NaN   \n",
       "4 Net Profit                                2.03   6.40      6.09   \n",
       "  Net Sales                                 8.10  31.00     22.34   \n",
       "  Price                                      NaN    NaN       NaN   \n",
       "  Price to earnings ratio (P/E)              NaN    NaN       NaN   \n",
       "  Unnamed: 0_level_1                         NaN    NaN       NaN   \n",
       "\n",
       "                                Price Ratios                    \\\n",
       "                                    Facebook  Google Microsoft   \n",
       "0 Net Profit                             NaN     NaN       NaN   \n",
       "  Net Sales                              NaN     NaN       NaN   \n",
       "  Price                               155.00   955.0     66.00   \n",
       "  Price to earnings ratio (P/E)        37.10    32.0     30.31   \n",
       "  Unnamed: 0_level_1                     NaN     NaN       NaN   \n",
       "1 Net Profit                             NaN     NaN       NaN   \n",
       "  Net Sales                              NaN     NaN       NaN   \n",
       "  Price                               150.00   987.0     69.00   \n",
       "  Price to earnings ratio (P/E)        36.98    31.3     30.56   \n",
       "  Unnamed: 0_level_1                     NaN     NaN       NaN   \n",
       "2 Net Profit                             NaN     NaN       NaN   \n",
       "  Net Sales                              NaN     NaN       NaN   \n",
       "  Price                               153.00   963.0     62.00   \n",
       "  Price to earnings ratio (P/E)        36.78    31.7     30.46   \n",
       "  Unnamed: 0_level_1                     NaN     NaN       NaN   \n",
       "3 Net Profit                             NaN     NaN       NaN   \n",
       "  Net Sales                              NaN     NaN       NaN   \n",
       "  Price                               155.00  1000.0     61.00   \n",
       "  Price to earnings ratio (P/E)        36.11    31.2     30.11   \n",
       "  Unnamed: 0_level_1                     NaN     NaN       NaN   \n",
       "4 Net Profit                             NaN     NaN       NaN   \n",
       "  Net Sales                              NaN     NaN       NaN   \n",
       "  Price                               156.00  1012.0     66.00   \n",
       "  Price to earnings ratio (P/E)        37.07    30.0     31.00   \n",
       "  Unnamed: 0_level_1                     NaN     NaN       NaN   \n",
       "\n",
       "                                Unnamed: 0_level_0  \n",
       "                                           Company  \n",
       "0 Net Profit                                   NaN  \n",
       "  Net Sales                                    NaN  \n",
       "  Price                                        NaN  \n",
       "  Price to earnings ratio (P/E)                NaN  \n",
       "  Unnamed: 0_level_1                       Q1 2016  \n",
       "1 Net Profit                                   NaN  \n",
       "  Net Sales                                    NaN  \n",
       "  Price                                        NaN  \n",
       "  Price to earnings ratio (P/E)                NaN  \n",
       "  Unnamed: 0_level_1                       Q2 2016  \n",
       "2 Net Profit                                   NaN  \n",
       "  Net Sales                                    NaN  \n",
       "  Price                                        NaN  \n",
       "  Price to earnings ratio (P/E)                NaN  \n",
       "  Unnamed: 0_level_1                       Q3 2016  \n",
       "3 Net Profit                                   NaN  \n",
       "  Net Sales                                    NaN  \n",
       "  Price                                        NaN  \n",
       "  Price to earnings ratio (P/E)                NaN  \n",
       "  Unnamed: 0_level_1                       Q4 2016  \n",
       "4 Net Profit                                   NaN  \n",
       "  Net Sales                                    NaN  \n",
       "  Price                                        NaN  \n",
       "  Price to earnings ratio (P/E)                NaN  \n",
       "  Unnamed: 0_level_1                       Q1 2017  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.stack(level=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f75ace0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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