vander_plus_3_Pandas_09.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "16fa92c7",
   "metadata": {},
   "source": [
    "## Сводные таблицы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "71770df4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d1176fa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic = sns.load_dataset('titanic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d8fed11b",
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "0      man        True  NaN  Southampton    no  False  \n",
       "1    woman       False    C    Cherbourg   yes  False  \n",
       "2    woman       False  NaN  Southampton   yes   True  \n",
       "3    woman       False    C  Southampton   yes  False  \n",
       "4      man        True  NaN  Southampton    no   True  \n",
       "..     ...         ...  ...          ...   ...    ...  \n",
       "886    man        True  NaN  Southampton    no   True  \n",
       "887  woman       False    B  Southampton   yes   True  \n",
       "888  woman       False  NaN  Southampton    no  False  \n",
       "889    man        True    C    Cherbourg   yes   True  \n",
       "890    man        True  NaN   Queenstown    no   True  \n",
       "\n",
       "[891 rows x 15 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b56514f",
   "metadata": {},
   "source": [
    "### Сводные таблицы \"вручную\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1cc5726a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        survived\n",
       "sex             \n",
       "female       233\n",
       "male         109"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby('sex')[['survived']].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b8f5e6c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "female    233\n",
       "male      109\n",
       "Name: survived, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby('sex')['survived'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "18642895",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex     class \n",
       "female  First     0.968085\n",
       "        Second    0.921053\n",
       "        Third     0.500000\n",
       "male    First     0.368852\n",
       "        Second    0.157407\n",
       "        Third     0.135447\n",
       "Name: survived, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby(['sex', 'class'])['survived'].aggregate('mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "258892f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</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>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "class      First    Second     Third\n",
       "sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby(['sex', 'class'])['survived'].aggregate('mean').unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f6c83e1",
   "metadata": {},
   "source": [
    "### Синтаксис сводных таблиц"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d2f14b64",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</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>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "class      First    Second     Third\n",
       "sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table('survived', index='sex', columns='class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6c214451",
   "metadata": {},
   "outputs": [],
   "source": [
    "# titanic.pivot_table?\n",
    "# values : column to aggregate, optional\n",
    "# index : column, Grouper, array, or list of the previous\n",
    "\n",
    "#     If an array is passed, it must be the same length as the data. The\n",
    "#     list can contain any of the other types (except list).\n",
    "#     Keys to group by on the pivot table index.  If an array is passed,\n",
    "#     it is being used as the same manner as column values.\n",
    "\n",
    "# columns : column, Grouper, array, or list of the previous\n",
    "\n",
    "#     If an array is passed, it must be the same length as the data. The\n",
    "#     list can contain any of the other types (except list).\n",
    "#     Keys to group by on the pivot table column.  If an array is passed,\n",
    "#     it is being used as the same manner as column values.\n",
    "\n",
    "# aggfunc : function, list of functions, dict, default numpy.mean✌\n",
    "\n",
    "#     If list of functions passed, the resulting pivot table will have\n",
    "#     hierarchical columns whose top level are the function names\n",
    "#     (inferred from the function objects themselves)\n",
    "#     If dict is passed, the key is column to aggregate and value\n",
    "#     is function or list of functions.\n",
    "\n",
    "# fill_value : scalar, default None\n",
    "\n",
    "#     Value to replace missing values with (in the resulting pivot table,\n",
    "#     after aggregation).\n",
    "\n",
    "# margins : bool, default False\n",
    "\n",
    "#     Add all row / columns (e.g. for subtotal / grand totals).\n",
    "\n",
    "# dropna : bool, default True\n",
    "\n",
    "#     Do not include columns whose entries are all NaN.\n",
    "\n",
    "# margins_name : str, default 'All'\n",
    "\n",
    "#     Name of the row / column that will contain the totals\n",
    "#     when margins is True.\n",
    "\n",
    "# observed : bool, default False\n",
    "\n",
    "#     This only applies if any of the groupers are Categoricals.\n",
    "#     If True: only show observed values for categorical groupers.\n",
    "#     If False: show all values for categorical groupers.\n",
    "\n",
    "#     .. versionchanged:: 0.25.0\n",
    "\n",
    "# sort : bool, default True\n",
    "\n",
    "#     Specifies if the result should be sorted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dd53b0dc",
   "metadata": {},
   "outputs": [
    {
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       "\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>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</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>female</th>\n",
       "      <td>91</td>\n",
       "      <td>70</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>45</td>\n",
       "      <td>17</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class   First  Second  Third\n",
       "sex                         \n",
       "female     91      70     72\n",
       "male       45      17     47"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table('survived', index='sex', columns='class', aggfunc='sum')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "132ea683",
   "metadata": {},
   "source": [
    "#### Многоуровневые сводные таблицы\n",
    "    возраст в качестве третьего измерения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e3867b8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      (18.0, 80.0]\n",
       "1      (18.0, 80.0]\n",
       "2      (18.0, 80.0]\n",
       "3      (18.0, 80.0]\n",
       "4      (18.0, 80.0]\n",
       "           ...     \n",
       "886    (18.0, 80.0]\n",
       "887    (18.0, 80.0]\n",
       "888             NaN\n",
       "889    (18.0, 80.0]\n",
       "890    (18.0, 80.0]\n",
       "Name: age, Length: 891, dtype: category\n",
       "Categories (2, interval[int64, right]): [(0, 18] < (18, 80]]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = pd.cut(titanic['age'], [0, 18, 80])\n",
    "age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d624cd8c",
   "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",
       "        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>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">female</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>0.972973</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.423729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.215686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.133663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class               First    Second     Third\n",
       "sex    age                                   \n",
       "female (0, 18]   0.909091  1.000000  0.511628\n",
       "       (18, 80]  0.972973  0.900000  0.423729\n",
       "male   (0, 18]   0.800000  0.600000  0.215686\n",
       "       (18, 80]  0.375000  0.071429  0.133663"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table('survived', ['sex', age], 'class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "04bc0497",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.cut?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6d062049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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 th {\n",
       "        text-align: right;\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>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">female</th>\n",
       "      <th>0.75</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">male</th>\n",
       "      <th>70.00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70.50</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71.00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74.00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80.00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "class         First  Second  Third\n",
       "sex    age                        \n",
       "female 0.75     NaN     NaN   1.00\n",
       "       1.00     NaN     NaN   1.00\n",
       "       2.00     0.0     1.0   0.25\n",
       "       3.00     NaN     1.0   0.00\n",
       "       4.00     NaN     1.0   1.00\n",
       "...             ...     ...    ...\n",
       "male   70.00    0.0     0.0    NaN\n",
       "       70.50    NaN     NaN   0.00\n",
       "       71.00    0.0     NaN    NaN\n",
       "       74.00    NaN     NaN   0.00\n",
       "       80.00    1.0     NaN    NaN\n",
       "\n",
       "[145 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table('survived', ['sex', 'age'], 'class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "03267cdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       (-0.001, 14.454]\n",
       "1      (14.454, 512.329]\n",
       "2       (-0.001, 14.454]\n",
       "3      (14.454, 512.329]\n",
       "4       (-0.001, 14.454]\n",
       "             ...        \n",
       "886     (-0.001, 14.454]\n",
       "887    (14.454, 512.329]\n",
       "888    (14.454, 512.329]\n",
       "889    (14.454, 512.329]\n",
       "890     (-0.001, 14.454]\n",
       "Name: fare, Length: 891, dtype: category\n",
       "Categories (2, interval[float64, right]): [(-0.001, 14.454] < (14.454, 512.329]]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# автоматическое вычисление квантилий\n",
    "fare = pd.qcut(titanic['fare'], 2)\n",
    "fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "19644d96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>fare</th>\n",
       "      <th colspan=\"3\" halign=\"left\">(-0.001, 14.454]</th>\n",
       "      <th colspan=\"3\" halign=\"left\">(14.454, 512.329]</th>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>age</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\">female</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.318182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.880000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.972973</td>\n",
       "      <td>0.914286</td>\n",
       "      <td>0.391304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.178571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.098039</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.192308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "fare            (-0.001, 14.454]                     (14.454, 512.329]  \\\n",
       "class                      First    Second     Third             First   \n",
       "sex    age                                                               \n",
       "female (0, 18]               NaN  1.000000  0.714286          0.909091   \n",
       "       (18, 80]              NaN  0.880000  0.444444          0.972973   \n",
       "male   (0, 18]               NaN  0.000000  0.260870          0.800000   \n",
       "       (18, 80]              0.0  0.098039  0.125000          0.391304   \n",
       "\n",
       "fare                                 \n",
       "class              Second     Third  \n",
       "sex    age                           \n",
       "female (0, 18]   1.000000  0.318182  \n",
       "       (18, 80]  0.914286  0.391304  \n",
       "male   (0, 18]   0.818182  0.178571  \n",
       "       (18, 80]  0.030303  0.192308  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table('survived', ['sex', age], [fare, 'class'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91770931",
   "metadata": {},
   "source": [
    "#### Дополнительные параметры сводных таблиц"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "14a2e33c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">fare</th>\n",
       "      <th colspan=\"3\" halign=\"left\">survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</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>female</th>\n",
       "      <td>106.125798</td>\n",
       "      <td>21.970121</td>\n",
       "      <td>16.118810</td>\n",
       "      <td>91</td>\n",
       "      <td>70</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>67.226127</td>\n",
       "      <td>19.741782</td>\n",
       "      <td>12.661633</td>\n",
       "      <td>45</td>\n",
       "      <td>17</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              fare                       survived             \n",
       "class        First     Second      Third    First Second Third\n",
       "sex                                                           \n",
       "female  106.125798  21.970121  16.118810       91     70    72\n",
       "male     67.226127  19.741782  12.661633       45     17    47"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table(index='sex', columns='class', aggfunc={'survived': sum, 'fare': 'mean'})\n",
    "# первый параметр values пропущен, т.к. при задании aggfunc происходит его автоматическое определение"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b2aac47b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.629630</td>\n",
       "      <td>0.472826</td>\n",
       "      <td>0.242363</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class      First    Second     Third       All\n",
       "sex                                           \n",
       "female  0.968085  0.921053  0.500000  0.742038\n",
       "male    0.368852  0.157407  0.135447  0.188908\n",
       "All     0.629630  0.472826  0.242363  0.383838"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# итоги по каждой группе margins=True\n",
    "titanic.pivot_table('survived', index='sex', columns='class', margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a60bddf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>91</td>\n",
       "      <td>70</td>\n",
       "      <td>72</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>45</td>\n",
       "      <td>17</td>\n",
       "      <td>47</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>136</td>\n",
       "      <td>87</td>\n",
       "      <td>119</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "class   First  Second  Third  All\n",
       "sex                              \n",
       "female     91      70     72  233\n",
       "male       45      17     47  109\n",
       "All       136      87    119  342"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table('survived', index='sex', columns='class', margins=True, aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "56e87ac3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>91</td>\n",
       "      <td>70</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>45</td>\n",
       "      <td>17</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "class   First  Second  Third\n",
       "sex                         \n",
       "female     91      70     72\n",
       "male       45      17     47"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table('survived', index='sex', columns='class', aggfunc=sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28aff4e9",
   "metadata": {},
   "source": [
    "### Пример: данные о рождаемости"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0b88bf36",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
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       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1969</td>\n",
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       "      <td>1969</td>\n",
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       "      <th>2</th>\n",
       "      <td>1969</td>\n",
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       "      <th>3</th>\n",
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       "      <td>M</td>\n",
       "      <td>4548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>15542</th>\n",
       "      <td>2008</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>183219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15543</th>\n",
       "      <td>2008</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>158939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15544</th>\n",
       "      <td>2008</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>165468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15545</th>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>173215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15546</th>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>181235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15547 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  month  day gender  births\n",
       "0      1969      1  1.0      F    4046\n",
       "1      1969      1  1.0      M    4440\n",
       "2      1969      1  2.0      F    4454\n",
       "3      1969      1  2.0      M    4548\n",
       "4      1969      1  3.0      F    4548\n",
       "...     ...    ...  ...    ...     ...\n",
       "15542  2008     10  NaN      M  183219\n",
       "15543  2008     11  NaN      F  158939\n",
       "15544  2008     11  NaN      M  165468\n",
       "15545  2008     12  NaN      F  173215\n",
       "15546  2008     12  NaN      M  181235\n",
       "\n",
       "[15547 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "birth = pd.read_csv('data/births.csv')\n",
    "birth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "030590d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1969</td>\n",
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       "      <td>4440</td>\n",
       "      <td>1960</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1969</td>\n",
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       "      <td>M</td>\n",
       "      <td>4548</td>\n",
       "      <td>1960</td>\n",
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       "      <td>4548</td>\n",
       "      <td>1960</td>\n",
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       "      <th>...</th>\n",
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       "      <th>15542</th>\n",
       "      <td>2008</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>183219</td>\n",
       "      <td>2000</td>\n",
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       "    <tr>\n",
       "      <th>15543</th>\n",
       "      <td>2008</td>\n",
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       "      <td>158939</td>\n",
       "      <td>2000</td>\n",
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       "    <tr>\n",
       "      <th>15544</th>\n",
       "      <td>2008</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>165468</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15545</th>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>173215</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15546</th>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>181235</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15547 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  month  day gender  births  decade\n",
       "0      1969      1  1.0      F    4046    1960\n",
       "1      1969      1  1.0      M    4440    1960\n",
       "2      1969      1  2.0      F    4454    1960\n",
       "3      1969      1  2.0      M    4548    1960\n",
       "4      1969      1  3.0      F    4548    1960\n",
       "...     ...    ...  ...    ...     ...     ...\n",
       "15542  2008     10  NaN      M  183219    2000\n",
       "15543  2008     11  NaN      F  158939    2000\n",
       "15544  2008     11  NaN      M  165468    2000\n",
       "15545  2008     12  NaN      F  173215    2000\n",
       "15546  2008     12  NaN      M  181235    2000\n",
       "\n",
       "[15547 rows x 6 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Добавим столбец для десятилетия\n",
    "birth['decade'] = 10 * (birth['year'] // 10)\n",
    "birth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "18cc6dc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decade</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>1753634</td>\n",
       "      <td>1846572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>16263075</td>\n",
       "      <td>17121550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>18310351</td>\n",
       "      <td>19243452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>19479454</td>\n",
       "      <td>20420553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>18229309</td>\n",
       "      <td>19106428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "gender         F         M\n",
       "decade                    \n",
       "1960     1753634   1846572\n",
       "1970    16263075  17121550\n",
       "1980    18310351  19243452\n",
       "1990    19479454  20420553\n",
       "2000    18229309  19106428"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "birth.pivot_table('births', index='decade', columns='gender', aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "efdf11a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'total births per year')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "sns.set()  # используем стили библиотеки seaborn\n",
    "birth.pivot_table('births', index='decade', columns='gender', aggfunc=sum).plot()\n",
    "plt.ylabel('total births per year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4c8a3f24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'total births per year')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline # один хрен\n",
    "sns.set()  # используем стили библиотеки seaborn\n",
    "birth.pivot_table('births', index='decade', columns='gender', aggfunc=sum).plot()\n",
    "plt.ylabel('total births per year')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62ddb5d7",
   "metadata": {},
   "source": [
    "###### Дальнейшее исследование данных.\n",
    "    Отсечь аномальные данные можно при помощи аглогиртма:\n",
    "    \n",
    "# сигма-отсечения (sigma-clipping)\n",
    "Кванти́ль в математической статистике — значение, которое заданная случайная величина не превышает с фиксированной вероятностью. Если вероятность задана в процентах, то квантиль называется процентилем или перцентилем (см. ниже).\n",
    "\n",
    "Например, фраза «90-й процентиль массы тела у новорожденных мальчиков составляет 4 кг» означает, что 90 % мальчиков рождаются с весом, меньшим либо равным 4 кг, а 10 % мальчиков рождаются с весом, большим либо равным 4 кг.\n",
    "\n",
    "0,25-квантиль называется первым (или нижним) кварти́лем (от лат. quarta — четверть);\n",
    "0,5-квантиль называется медианой (от лат. mediāna — середина) или вторым кварти́лем;\n",
    "0,75-квантиль называется третьим (или верхним) кварти́лем.\n",
    "\n",
    "Интеркварти́льным размахом (англ. Interquartile range) называется разность между третьим и первым квартилями, то есть \n",
    "x_0,75-x_0,25. Интерквартильный размах является характеристикой разброса распределения величины и является робастным аналогом дисперсии. Вместе, медиана и интерквартильный размах могут быть использованы вместо математического ожидания и дисперсии в случае распределений с большими выбросами, либо при невозможности вычисления последних.\n",
    "\n",
    "95-й прецентиль — это такое число, что 95% элементов массива меньше или равны этому числу.\n",
    "\n",
    "Короче так мы убираем всплески."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3e3a792b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4262. , 4814. , 5289.5])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quartiles = np.percentile(birth['births'], [20, 50, 75])\n",
    "quartiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a679ff72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4814.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mu = quartiles[1]\n",
    "mu\n",
    "# т.е. мы взяли 50 процентиль = 4814.0\n",
    "# и предположили, что все норм значения должны входить в размах +-760.35\n",
    "# а остальные выбросили... на мой взгляд это очень грубо"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0abfdb98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "760.35"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# грубая оценка среднего значения выборки в котором 0.74 - Межквартильный размах Гауссового распределения.\n",
    "sig = 0.74 * (quartiles[2] - quartiles[0])\n",
    "sig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "554dfa66",
   "metadata": {},
   "outputs": [
    {
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       "      <td>4435</td>\n",
       "      <td>1980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15066</th>\n",
       "      <td>1988</td>\n",
       "      <td>12</td>\n",
       "      <td>31.0</td>\n",
       "      <td>M</td>\n",
       "      <td>4698</td>\n",
       "      <td>1980</td>\n",
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       "  </tbody>\n",
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       "<p>14610 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  month   day gender  births  decade\n",
       "0      1969      1   1.0      F    4046    1960\n",
       "1      1969      1   1.0      M    4440    1960\n",
       "2      1969      1   2.0      F    4454    1960\n",
       "3      1969      1   2.0      M    4548    1960\n",
       "4      1969      1   3.0      F    4548    1960\n",
       "...     ...    ...   ...    ...     ...     ...\n",
       "15062  1988     12  29.0      M    5944    1980\n",
       "15063  1988     12  30.0      F    5742    1980\n",
       "15064  1988     12  30.0      M    6095    1980\n",
       "15065  1988     12  31.0      F    4435    1980\n",
       "15066  1988     12  31.0      M    4698    1980\n",
       "\n",
       "[14610 rows x 6 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# для фильтрации строк, в которых кол-во новорожденных выходит за пределы этих значений, используем метод .query \n",
    "birth = birth.query('(births > @mu - 5 * @sig) & (births < @mu + 5 * @sig)')\n",
    "birth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "efbef2a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp/ipykernel_5176/3933919233.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  birth['day'] = birth['day'].astype(int)\n"
     ]
    }
   ],
   "source": [
    "# установим целочисленный тип для столбца day\n",
    "# изначально он был строчным из-за пустых значений\n",
    "birth['day'] = birth['day'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d918b0d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year       int64\n",
       "month      int64\n",
       "day        int32\n",
       "gender    object\n",
       "births     int64\n",
       "decade     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "birth.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "535a51fb",
   "metadata": {},
   "outputs": [
    {
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       "      <td>31</td>\n",
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       "      <td>M</td>\n",
       "      <td>4698</td>\n",
       "      <td>1980</td>\n",
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       "<p>14610 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            year  month  day gender  births  decade\n",
       "1969-01-01  1969      1    1      F    4046    1960\n",
       "1969-01-01  1969      1    1      M    4440    1960\n",
       "1969-01-02  1969      1    2      F    4454    1960\n",
       "1969-01-02  1969      1    2      M    4548    1960\n",
       "1969-01-03  1969      1    3      F    4548    1960\n",
       "...          ...    ...  ...    ...     ...     ...\n",
       "1988-12-29  1988     12   29      M    5944    1980\n",
       "1988-12-30  1988     12   30      F    5742    1980\n",
       "1988-12-30  1988     12   30      M    6095    1980\n",
       "1988-12-31  1988     12   31      F    4435    1980\n",
       "1988-12-31  1988     12   31      M    4698    1980\n",
       "\n",
       "[14610 rows x 6 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# мы можем создать индекс для этих дат, объединив день, месяц и год\n",
    "birth.index = pd.to_datetime(10000 * birth.year + \n",
    "                             100 * birth.month +\n",
    "                             birth.day, format='%Y%m%d')\n",
    "birth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "733b8e62",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp/ipykernel_5176/2502938730.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  birth['day_of_week'] = birth.index.dayofweek\n"
     ]
    },
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       "      <td>6095</td>\n",
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      ],
      "text/plain": [
       "            year  month  day gender  births  decade  day_of_week\n",
       "1969-01-01  1969      1    1      F    4046    1960            2\n",
       "1969-01-01  1969      1    1      M    4440    1960            2\n",
       "1969-01-02  1969      1    2      F    4454    1960            3\n",
       "1969-01-02  1969      1    2      M    4548    1960            3\n",
       "1969-01-03  1969      1    3      F    4548    1960            4\n",
       "...          ...    ...  ...    ...     ...     ...          ...\n",
       "1988-12-29  1988     12   29      M    5944    1980            3\n",
       "1988-12-30  1988     12   30      F    5742    1980            4\n",
       "1988-12-30  1988     12   30      M    6095    1980            4\n",
       "1988-12-31  1988     12   31      F    4435    1980            5\n",
       "1988-12-31  1988     12   31      M    4698    1980            5\n",
       "\n",
       "[14610 rows x 7 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "birth['day_of_week'] = birth.index.dayofweek\n",
    "birth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0a6b5f90",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "25ef00dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mean birth by day')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "birth.pivot_table('births', index='day_of_week', columns='decade', aggfunc='mean').plot()\n",
    "# plt.gca().set_xticklabels([1,2,3,4,5,6,7])\n",
    "plt.ylabel('mean birth by day')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cb1f2e2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp/ipykernel_5176/152063366.py:2: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  plt.gca().set_xticklabels([0,1,2,3,4,5,6,7])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mean birth by day')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "birth.pivot_table('births', index='day_of_week', columns='decade', aggfunc='mean').plot()\n",
    "plt.gca().set_xticklabels([0,1,2,3,4,5,6,7])\n",
    "plt.ylabel('mean birth by day')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a87a917",
   "metadata": {},
   "source": [
    "    В зависимости от дня года:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "09e1b6e1",
   "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>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>4009.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4247.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4500.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4571.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4603.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">12</th>\n",
       "      <th>27</th>\n",
       "      <td>4850.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5044.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5120.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>5172.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4859.200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         births\n",
       "1  1   4009.225\n",
       "   2   4247.400\n",
       "   3   4500.900\n",
       "   4   4571.350\n",
       "   5   4603.625\n",
       "...         ...\n",
       "12 27  4850.150\n",
       "   28  5044.200\n",
       "   29  5120.150\n",
       "   30  5172.350\n",
       "   31  4859.200\n",
       "\n",
       "[366 rows x 1 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# сначала сгруппируем данные отдельно по месяцу и дню\n",
    "birth_by_date = birth.pivot_table('births', [birth.index.month, birth.index.day])\n",
    "birth_by_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a0f956ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp/ipykernel_5176/1902077255.py:2: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
      "  birth_by_date.index = [pd.datetime(2012, month, day)\n"
     ]
    },
    {
     "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>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>4009.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>4247.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>4500.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>4571.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>4603.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-27</th>\n",
       "      <td>4850.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-28</th>\n",
       "      <td>5044.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-29</th>\n",
       "      <td>5120.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-30</th>\n",
       "      <td>5172.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>4859.200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              births\n",
       "2012-01-01  4009.225\n",
       "2012-01-02  4247.400\n",
       "2012-01-03  4500.900\n",
       "2012-01-04  4571.350\n",
       "2012-01-05  4603.625\n",
       "...              ...\n",
       "2012-12-27  4850.150\n",
       "2012-12-28  5044.200\n",
       "2012-12-29  5120.150\n",
       "2012-12-30  5172.350\n",
       "2012-12-31  4859.200\n",
       "\n",
       "[366 rows x 1 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# преобразуем этот мультииндекс в даты, связав их с фиктивным високосным годом.\n",
    "birth_by_date.index = [pd.datetime(2012, month, day) \n",
    "                       for (month, day) in birth_by_date.index]\n",
    "birth_by_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "41152acc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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S+iYQ6/k3Nv6NX+f/8fXRJuOXVXtx1wfJipZdmPfaV/xzyzG27ZcNuuL5Q6zxl48zw57EyEGZ7DlSQzB0+oykQDCMJcb4K5q/Qa/DaGi9WVEeTCLo23qUh39LCOMvELQhgWjwNSstCY8vpNHVA8Ew6TYLqSlmTlbFGHmXn2Aogj3ZpAm4KsZ/YI4dk1Gv8fzrvAG+PVrD2BG5mvE/334CgLEjtcuVQHKqzUxmtJSykuGjyD9pKWaG9E8nEIpwzHn6lM9AMILZ1GA+LCYDRoOeJLMh4bpdsfSL5vcfLe+42j49jTph/AWCjkcJ+CqdrGJz/QOhCGaTgZwMK+Uxxv9E9N8jB2USkaQmHnm6TZZqKmI8///srUCS5F64sWzYdoK8rGTO7ZeuWa4Y/7QUM1aLkWSLUR2nLhowTrOZGZArV5xsqS6MP6iVfXQ6HfZkU6vTPBWy0pJIsZo42sL4guaRJAmXN6B5MJ8KYfwFgjZEychRatXHZvwEgmHMRj05mcmcjKm7c6C0FoALh8jFDiujgd0al58kswGrxUif9CScMZ7/lm+d9ElLYkCOjbEjcjAaZG87EIowclCWKsn0SUvCajGoQWFlvkB2hpVjUe+61hNAB9iTTTjSkrBajBwpO73nHQiGsRi15sNmNZ1Rpg/ID5Gz+6ZxuIXxBc3j9YcIhSX1+jsdwvgLBG2IkgWjaNexGTqBYASL2UBuZjJ1ngD1fvmtYG9pLfl9UhiQI2f1qMbfLeflAzjSrDhr6vl4aymVtfXsOlTFRUMd6HQ6fjR5BPPvvFgdJzvDSv9sG8MGpPPzovNVjd9s0pMSDcyOGJTJ/tI6PL4gtZ4A9mQTBr0enU7HgGxbi563Pxhp0qoxNzOZPmlnXs7i7Pw0jjndGslMEB+K5JMZjfmcDmH8BYI2RJFD+mWnoNM1pHdCNEPGKMs+IAdnI5LE/tI6zumXRobdgk6nDcSm22RPvU96EvX+MEvXf8tjb2wiHJEYfW5D46TYejpZUe/9oekXMiDHrj6Izu2Xrtb3v2BwHyKSxI4DVdS6A6SmNBiLATl2jjpP3z8jENIGfAH+68bh/GTKiFZ9b7GclWcnGIqoD0FB/CjGX2mReTqE8RcI2hA5C0ZPktlIXlaKZjavEiTNyUwG4GSVl7IqL/X+EOfkp2E06Em3WbSev73B81c4FvXK+zkaGnVYYypp9knVvvKbotk3wwakq8vO7ptKssXI7iPV1HoaHjIAA3JsBIIRTVC6MfJDTms+zCZDkwdCa3Cky99Pov18BQ2ZPkL2EQg6mNhA6Fm5dg7FyCdKemR2uhUdcq6/EtRVvPOstCQqan1IkkSN209GVPbJSmu4mX2BsBy0TWrw9pMaef6xKNlD5+Snqcv0eh25WcmUV9dT6/FragcNyJGDvk//dSt/aKYOkXwuTWWftiIrmg5b7fLz8dZSlq7/tl3G6YkI2Ucg6CSUjB6Agbl2at0Batx+QuEI4YiE2ajHbDKQmZqk5uRDQ558n9QkKut8eHxy4E7R/POykjWedlYj714pswBNC6t9/7oh3Dh2YJMMIEe6HEeodQdIjfH887KS0et01LoDfBEtLNfkPINNZZ+2QokbVLl8/H3TET7ZWkpdM0XyBE1Rrqe0FGH8BYIOJTYLJi8q7zhr6mOqYMoGMyfTSlmVV50IpsyQzc1KprLWxz82yxO4FNknyWzkfx68Wu181Set5dd6hex0K9OuGqx5QIDcgrGi1kc4IpEeYyyMBv1pyyvEPsjagySLkZQkIyWHqymrrkcCdh6sapexehqKtGiK47cRxl8gaCXVLj//2npMMxvVHwxjjqY7xs6kVco+NBj/ZE5W1atlDJQsnOsK+nN231Te/+IQgEaLj91nY8+/NcTGEQblpWo+O7dfWuPVVRo/yNqDDLuFXYfkGkNJZgPb97dcckIgB+LNRgMGfcsT7eKakTFjxgyqqqowGuXVH330UTweD4sWLcLv93PDDTfwwAMPAFBSUsK8efPweDwUFBSwcOFCjEYjx48fZ86cOVRWVjJo0CCeeeYZUlJEtx5B9+W9T/fzxY6TnN03lUdmFgCyYcy0K8a/YSZtQC2BLPtbuRnJ1PtDnKjyYrUY1ZIIVouRq0fns/+4HChWZB8FZZ+NdX2ART+5lKQEDLIjpsvYWXl2zWf3TvsO7/xjD1/uKlNLRle7/NisJk0jl/Yiw57EMaeHAdk2crOS2Xuspt3G6kkEQhFMRn1cJTZaXEOSJA4dOsTq1avV/4YOHcrDDz/MkiVLKC4uZseOHXz66acAzJkzh/nz57N+/XokSWLZsmUALFy4kOnTp7Nu3TpGjhzJkiVLzvA0BYLORSlB4I6p3hkb8E0yy/JFZZ1P9ZYtMbIPwL5jtZqiaKANzDb2/GMDw43JyUgmzday1qsQa/wbGwub1cSgvvLbQL0/xJEyFw++8jkfbDzU8CCLYxZpa1GC1GPOy+Gs3FQq6/zNNscRaAmG5HkmBkPLnn+Lv96BAwcAuOuuu7j55pt5++232bZtGwMHDqR///4YjUYmT57MunXrKC0txefzMWrUKACKiopYt24dwWCQzZs3M2HCBM1ygaC7Eo5E1AqcsY1WlFRPhczUJKrr/PibkX1AloRiG6GAPElLwWTUeteD+6Zxdn4ag/tqZZrWoOSCX1fQv9nPlbeIen+IP35QAsDBEy61W5nZ2H6ev/KQu3hYNgOVkhMnRcmHlpD7LLSR7FNXV8fYsWP5zW9+QzAYZObMmdx99904HA0TTLKzsykrK6O8vFyz3OFwUFZWRnV1NTabTZWNlOWJkJVla3mlNsbhsLe8Ujcap7PG7Innd6zcRSgsYU82EQhF1PGC4QhpqVb179w+KVTU1JMcDahmO2w4HHYyM1MwG/UEQhEy06xNjjfNZqbWHWiy3OGw88Ivrm6z81jx5E0Y9PomwWDlWAH2HKnmiFJoTadTz8XRx9Zu3/O9t4/mlnHnMHRgJgPqg8BWyl3+dhuvx9wPOh0pVhPZcYzRovEfPXo0o0ePVv/+7ne/y4svvshFF12kLpMkCZ1ORyQS0VT0U5Yr/9ceY2KV/yorTz/jsK1xOOw4ne3vaXTUOJ01Zk89v+3fyg3QhwzI4N+7yykvlzV6nz9MOBRWx7clGdlV5aXcKRvPeo9f/Wxgrp29x2qxGHRNjvd3P7qUcERq9jw66jv1R+Wsj7fImUfDBqRzstLNvsNy5o0+EmmX43A47HhcPjKTTer+czKslOyvxHlB+4zXU+4HjzeAXq+jtrblbmwtyj5ff/01GzduVP+WJIn8/HycTqe6zOl0kp2dTW5urmZ5RUUF2dnZZGZm4nK5CIfDmvUFgu7K8UoPOuCc/ulIyIG2UFhCAk0KZFaqXNpZmXwT+5mi7VuaKYZmtRg13bE6A6VI2+ZdZQzKS+XsvmlU1fk55nSj1+nIjUpXHUHfPikcr/RQ6wk06VksaEAO+Bow6tsg4OtyuXjqqafw+/243W5WrlzJL37xCw4ePMjhw4cJh8OsXbuWwsJC8vPzsVgsbNmyBYDVq1dTWFiIyWSioKCA4uJiAFatWkVhYeEZnqZA0HnUeQIkJxnVbBx/MKwGKWODp9nRoKoSHI5Nj+zfqJBbVyO2Qmf/7BSyUi2EIxIlh6vJybTGlUveViizkR94aQN/+nB3h43bFYhEJLy++DqbJRLwbVH2GTduHN988w233HILkUiE6dOnM3r0aJ544gnuvfde/H4/V111FRMnTgTgmWee4ZFHHsHtdjNixAhmzpwJwIIFC5g7dy6vvvoqeXl5PPfcc3GdjEDQFfH4QqTElDD2B8Loook5xpgbT/GOj0TLPMQa/2EDMgAoGNY134JjjX9qipmMaBD2wPE6CoY6TrVZu5CXmUI4KvtuP9C7cv7/vvkoa784xBOzxlJRW48OnRoEb0wgFMZkii/VM648/9mzZzN79mzNsrFjx7JmzZom6w4bNozly5c3WZ6fn8/SpUvjGU4g6PJ46oOkJJnUZuX+YFi94TSef4Zcx+ew4vnHeMvpNgt/eGhcs8HWroAlpl5QbO9fgHxHxyZg5GY1SEx9+/Su+UGHTtbh9Yf4bNtx3v14PwB/nDu+2XUDiucfxzUlZvgKBK3A4wuRkmRUC6r5A2HCzcg+Sh0fJR20cW58VzX8oPX801LM9ElLUs8tdi5CRxAbXzjThjHdjbJo45/1Xx1pcd1gMKr5t4XsIxAImuLxBcnOsDbIPsEwoags0fjGy81KprLOh9GgwxBHIK6rEPuWkhpt//j8vZcTkejwYLTNaiLDbqHa5cfnD7W8QQ9BkiTKq7040pNw1rQcG2rw/EVtH4GgXfD6QiQnGRtkn0DzAV9o8FpD4Y5LVW4LYtOx06IzjZOTTJ2WhfSL741icN9UvH75LcrrC7HyXwc0k+x6Gi5vkHp/mGsu7Kcp5tdc2ntEkgiF5fIOer2OlrLphfEXCBIkIkl4fFHNP8bzD0eNe+NMi+sK+mFPNnF2G8zK7Sxi6/13Fvl9UsjOSMYXkD3/TbvLeP+LQ7z7yb5OPrL2Q2mok5uVwt03ncd5Z8lJAu5msn+UFFglqcDQQtBXGH+BIEF8/jCSBClJRrV2vu8UqZ4A2RnJvHDflcybcVGTfXUXYpvFdCZWi0HtfRyIevyfbTuhfvc9jfKo3p+TaWVI/3Su/E5fALUabCyK8VdScI0txJOE8RcIEsQT9bpSkkxqRoxG9jnFTZforHZBU6wWI/X+cLTTmTxxLhiKaMpq9ySUYnbKfBKlDpS7mSJ3jSvH6lvQ/YXxFwgSRDX+VqM24BuVfYwdOPmpt2G1GIlIEoFQhBpPQ49fT33PDAJ7/SH0Op1q0JUKsLGVZBVU2ceoyD6ndza6xrucQNBFkSSJt/++hwvO6cN3BmcBcponyJ6/0SBPqDldwLc789zPLyczM4VIoGsYV0VmK6+up8blx2jQEwpHcNX3zHLPPn8Yq8WgvjXak+XYS3OyT6Cx7NPCm2bPuUoFgnZAaSL+/LvfsP94LSBP8AJZ8wewmPQazz+eCTbdhXSbhayYjl+djTX6prXgj5vYfaSGfg55wldznnBPwOsPaXoyK5lWrmbOt6FbXFT2acHzF8ZfIDgNZVUN1RG/2SeXFfBGPf/kJPlGTDIb5ElekZ7n+Xc1Gjen75ctzzTuqbKPLxDSBNtNRj1JZkOzjW2CQcXzlx+QLV2H4irtArjrg1S7/C2vKOhwTkazLZLMBo5FSzTsL63FajFgjwbfzCZDo8JuPcfz72o0Nv65mckY9LoeK/vU+0MkW7Qzmu3JJtynkX3UgK+Qfbo+c/9nIw++8nlnH0avJhKRmpUOyqq8mIx6vjM4iyPlLnyBEF9/6+TiYdmqZ5VkNkRTPZUZvuK2ai8aG/9QOILNalKlOAVJav8JdZIkseVbZ7ummdb7w+pEQoU0m4Uad1NnMRiVfVTNX8g+7Uu1y8+sZz+Jq8XckTJXsxeKN5q3HOmAC1bQFK8vyNz/3cgvXt6g5pArlFfXk51hZWCOnao6P+u+OoI/GOaykXnqOga9nnBYUmv7tDS5RtB6rI3q+ow+14HNasLdSPa559lP+WNxSbsey86DVbyycjsbtp1otzHqAyGSGxn/TLuFqrqmxj/QaJKXXsg+7UtVtDn3iSrPadcrdbr57RubWfP5oVOuU+vuma+uXZ0te5xU1PoIhSV1Uo3CySovuRnJau39NZ8fYsSgTM7t11DYTK/XEYlIBKOev0nIPu2GNRpkz3ek8Me54+mfbZONf4wGXusJEAhFEjbKxys8aq58PGzdVwHA19Gubu1BvT/UxPPPTE2iyuVv8nYTbCT7nDcg/bT7Fsb/DFG+8EDw9K9+e0vlTBGl6XdzlFe33HpN0PbEBgvLYn6DiCRRUSt7/kP7p3PNhf0YPjCDH04cqpmwpdfJ6wrPv/1JSTJx903D+eXtDa1lbVYTbl/Db7jvWG3C+3V5Ayz44yY++vexJp9t3HmSVZ8d0CyTJIlt+yrQAbsP1zQbgG0L6v3hJm87mXaLnN7aSPdXbJEyz2Tchf1Ou29xlZ4hwegN31Jxqb1H5QvSeppp8uU9dJZiV8frb7iJYrN7at0BQmGJPulWTEYDd1w/hDnfH02fRqmPer1OLaoFIuDb3lw2Mk9TayjFatLEa/aV1qj/jrfv956jNYQjEkfK3JrlB0/U8dr7u1jz+SGNp11Z56Oyzs/l38kjIknsOFjV4hi+QIi//fPbuB8UwVCEUDjSJM6RGe2rUOXSVvlUmt20VNZBQRj/M0R52vpbeF3cc7QGoNlAjeJEJjJFfffhao453S2vKNDw1vpv+fQ/pZpl3mht/gy7hbLqev6++Sgr/rWfilr594itptgcsuwjV+3U0XKWhaBtsSfLAV/FOO8/Xqd+1thAnqol4u4jNQCUOrVv5p9vb5COYh8wNS7ZgBcMlWMOO+Mw/iWHq3n7w908+MrnanD2dNRHJ9Y1Nv4ZdrnUQ3Uj3V950MVbRkQY/1ZS5w3w9F+2sm2/rPspxr+qzsfWvU7NuvX+EJV18kXY2PgHQ3KRMKCJ3nw6nvrLVua/vqm1h98riUgSG7ad4N97KjTLvX65PHNOhpWyai+bS8rYVFJORbS3bovGXydr/qFIBINBL2r4dDB9s+QWj59FNf6TlXL9e9D2R67zBpjz6hf84uXPm7ypfxs1/iervOp8DUBTQ1+5h6HhPs6wJ3HeWRnsPFjVYoaREtMLhSVKK04fIwTUvgWNm9c0eP5aW6Icd7yTDOM2/k8++SRz584FYMOGDdx8883cdNNNPPTQQwQC8kmVlJRQVFTEhAkTmDdvHqGQfPDHjx/njjvuYOLEidxzzz14PC2feGv5fPsJ/vrR3nbbP8h63ysrtlNyuJqvd8uGXrmYFvxxEy+9t12T1aNcKFaLsUk+vzdGqzxZKTT/9qSqzkcoHKGqTusNen0hki0mcjKTKauqp8rlx+UNqMY/tn1hc+h1uqjmLwnJpxMYMyKHYQPSeffjfdT7Q7jrg5x3ViagNd5fbD9JtctPIBTRSKySJHGi0kOazUwoHNFsU1Fbrz78Yx8k1dF7Ot1mZuSgLGo9AQ61kPEX6/jFk9xRH+1b0Djbx55swmjQNbmOFYUr3u5wcRn/jRs3snLlSvXvefPmsXjxYtauXYvP52P16tUAzJkzh/nz57N+/XokSWLZsmUALFy4kOnTp7Nu3TpGjhzJkiVL4jq41rB1b4XmVa092Hmwir3RoJLyrFc8f6XuS2wwps4j/9ADc2x4fCHNK59SJMyebOJ4pTdujVKhI/KZO5OIJPH+F4cSCqjtOVLd7PeitMOrqPNpPlcas+RkJOOuD1Lj8lPvD3Oy0kNqilnTdL05DDGav8jx73j0Oh3fGdwHjy+kplwPG5CBDlTpDmT9XiH2LbveHyIckRjSLx1okH4ikoSzxsewgXIN/VjjX+P2Y9DrsFlNXDikD0aDni+2nzztcdZ6As3+G2RJqXGmkZJ23DjbR6/TkZJkUm2NgmI72sz419TUsHjxYmbNmqUuC4fDuN1uwuEwfr8fi8VCaWkpPp+PUaNGAVBUVMS6desIBoNs3ryZCRMmaJa3Fy5vAI8vpHl1a2vWbz5Kus2MxdRQW9wfDFMb82Sv0fxb/qHPyk3V/A0Nnv85+WmEwpG4gr6xbxWNL4CexokKDyv/dYAte5wtr4wcW3nwhX+xftPRJp8pwVx/IKzOrQCt7AMND/RDJ10tSj4AumiqZ0h4/p1Gul0OAO85VgNAXlYyGakWjRd/6GQdw6OGPDa+pjhnSl9iJetODvhHOCvXjsVkoCJW9nEFSLdZ0Ol0JCeZGH1uH74qKTut3al1B9TG97WN5N/fvrGJe1/4TOP8KbalsecPYDEbmsQZw5HEYk4tVvWcP38+DzzwACdONHjTv/3tb5kxYwY2m41+/foxceJEdu7cicPhUNdxOByUlZVRXV2NzWbDaDRqlidKVpYtrvXqo/KLxWohI+Z1XZIktn7r5IIhjrg1MYfD3uzy0goPl4zI4+uSMvUHkHQ6TsYEYCS9Xt0+rJPzgM8f4mDdpiNIBoP62Yl9slG7YEg2W/dW4PKHOf8U4yrUxXgN4ZhxEqE127SWMxnrZK38nUbQxbWf7YdrAPiypIwZN43QfFYX86CM/Q18gTCZaVaGDe6jWf9EpZcrLujb4rjJSSZ0Oh1GkwGz2dgh321H/n7dYbyz+snXyaGTchLE8HMc5PWxUVcfxOGwU+cJ4KzxcePlZ3Ok3I3LH1LH2BdNxjh7QAapKWbqfPJn5dGg7rkDs8jOPIHb17CNNxDCkWFV/x49LIfNu8uxpiSRFq293xiPP0R2hpU6jx9/WNKcozJp6/++OcH3rh3C+q8OQ7Qef9+8VBx9tPYvxWoCnfaeSEoyYTDEd59AC8b/3XffJS8vj7Fjx7JixQoAnE4nzzzzDGvXrqVfv34sWrSIRYsWcdNNN2kCXZIkodPp1P/H0pqAWGWlOy5JpCaqqR86Wk0ou+EL27jjJK+t3cUPJw7lqlH5Le7H4bDjdDbV8CRJos4dwKTXpvS53H5K9jcEEo8cr+Xs6MSg0pN1GA06bNHAzZHSGrKjnori+edGA1Ql+ys4N0/+8eo8Af5YXEJEkrhnykg16h/rtew7VEma5fSyRLzn1h6c6VhHT8jy2kmnO679HDpWLW9X5qL0eI1GsjlUWotBryMckdh7qBJbtPqh2xtAj4RRiqCjwfMHSE8xn3Zch8NOIChLeW6PHx20+3fbkb9fdxlPF5VSdxyoJDXZhMflIy3ZRMnhapxOl5qN08dupk9aEkdO1Klj1EUlxXAghCMtiYPHavjgX/vUuTsmnUSGzczxcjcle8tJshhxVteTl5Ws7iMQTRc+WVZHoL75t8WKmnr659ixJ5spq/Co20qSpMaN/rJ+NyfKXXy8tVS93+vdfpyNZEyDXked26/5ntweP3qdTl2m1+tO6zSf1vgXFxfjdDqZMmUKtbW1eL1eNm3axMiRIxkwYAAAt912G7Nnz+buu+/G6Wx4Na+oqCA7O5vMzExcLhfhcBiDwYDT6SQ7O/t0w7aaSERSa3w01oh3HJQrMtac4Szaen+IiCRht5rUpgkA/mCEKpdfLbqkCe54AqSlmNVyrLH9N5W0szSbfFEej5kEduB4Hdv2y8f9zf4KLj0vVz0GhYpabdAH5O/BFwiTnNT92zUoMzfr4tT8ldd8SZILsA2PBv5A9q4G5to5cLxO9bSCoQiBUITkJBMmo4HM1CRNVkdeVnKLYxp0cqqnCPh2HkqD+VA4Qnam7Dz1SUuixuUnGIpwPJpd089hIzvdqinHolxjNqsJR4aVL3eWsedYLf2zbRgNOvqkJZGVmsT+0loe+p+N2JNNhMMSwwdkqPswRWM9SomFxkQkiTpPgMzUJNJSzJpGNL5AmIgkccuVg/jwyyN8vFVORVbu88apnoBGclbHiEhx6/3Qgub/xhtvsHbtWlavXs19993H+PHjee2119i2bRsVFbKX+9FHH3H++eeTn5+PxWJhy5YtAKxevZrCwkJMJhMFBQUUFxcDsGrVKgoLC+M+wETw+IKq11YXE3CVJEnN462sa2osX/9gF6s3HIxrDCWQa0s2qXWzQW6hVl3no0+aFXuySRvccftJs1nU+u+xRagUz99qMdInTWt4YvOKt0XLCYN8sSjETkpS+OeWY8z9343tGvfoKJTvu7nmFc1RUVtPXpZc4/2Y08NXu8p4cfk2QJ7MlZeZjNmoV2fyNtZVczKtGs00N7Nl46+LDfi20DpP0D4kmY1qQbOz8+TYmiPdioSc5XWiyktKkhF7spzVVVHrUzP0XNF7NcVqIju9YQLf0XI3A3LsGA16stKSNMkcXn+IzNQGeUcZ+1TlIdz1QcIRiYxUC2k2sybbR7EHGTaLGndSMBp06r5jsZia1/wT6SWR8JU6ePBg7r//fmbOnMnkyZPZsWMHDz30EADPPPMMixYtYuLEiXi9XmbOnAnAggULWLZsGZMmTeLrr79m9uzZiQ4bF7HGMtbzP1nlVVMsyxsZS38wzOfbTzYx/p76IO9+vI9PtpZqMkOUJgo2q1mtoaHsp8rlJ9NukavuuZp6/maTAbNRryknoGT7WM1GslKTNAWblPMZfW4fth+oVGUvxWDZk02URB9qsRw6WYe7Pths8afuRoPxj9/zHxLVbo+Wu9lxsJL/7KsgHIng8YVIsZo4u2+qmq2lBH6Vt6RR5/ShYFhD7Coe49+Q5y88/85EmXA5OBq4VYL1+0prOVHhITcrGZ1Ox+C+qUQkiUMn5ewfV0xzHke61vgO7ivvq7l03+FnNXj+iiMYPEWFz7qosc+wJ5GeYqHWE1DtitoZzio/mGJJOkVFAIvJ0GSuQiTSVGI/HXHrAkVFRRQVFQEwdepUpk6d2mSdYcOGsXz58ibL8/PzWbp0adwH1VpivcPYfx8uk1/xzsq1q+l+CsrM28as//IQH351BIBBeakMzJVfJZU62vZkk0ZP9gXCuLxBRgzKxB8Ks+dYLV5fiKo6H+U19ZwbTSNLsZo0sk+9L4TRoMdk1JORmkSN2084EsGg1+PxBTHodYwclMnWvRXUegJk2C3qzL+LhmbzydbSaC5yw0WrpLGV19Q3uZi7G0qd9ro4PP9QOEKVy0dOVjL9HSkcdbpVqa3OE8QflcKGDshgzecH8fqCDY1Zop7/tQX9ubagP5tK/g9o/pW7MUqqZ1ikenYJBveVPX/lnnj9A7m65xXfkSuxKg+HfaW1DB2QgcsTIMlskD38RkZ+cL6yL+3ydJuZgTkNgVWlgUrwFDW+lAdMuk32/IMh2RmxWU0NPaGTjE2Mf3OZPtB8tk9EamfPvyujNf4NnuKRMjdGg55R5/ah1hPQaGU7DjRMy1ZKKkuSxP99fZTUaP2QAzH5wYoxkjX/hq/PXR/EHwyTaU/CkW6l3h/iheXf8Ie1u7CajVxzkVxkKSXJqL7mRSSJfcdqVDkoK9WCJDVMHXfXB0lJMsqRfRq8VF908sdFQ2UPddehas33oBh/5ylmDNd5zizu0ZEov6nbG2yx5HWtO4AkQXaGlX7ZNo5XeNSJMEr6XkqSiWED0pEk2HO0Vo0lpES7crUG1fMXmn+nMiCa4KGUP8hItTD+wobkDsURsllN5GYms79Uvq/rvAHVSRg2MINfTR/NvdPOx2zUq05bViPjP3qIQ+NlK7bgVJq/8hZvTzGrcSSlyGNsT+jGsk/SKZI5kk4h+7SZ5t/dcEcNs9Vi0DwIjpS5yHek0DeqBcdmy8R6/soPdLLKy+GTLiZfdhb2ZBMHY2qFKOvYkk3q0z6WzFQL0woHk51hpbymHmdtPRcPz6ZvH3ns2MkZG7ad4Ju9Fdxw6cDottqCTZ76IClWk/r0r49up3j+g/umYk82sTfmHDy+oHqMzdUKqnb5mfnbdew61HItkq6AO+ZB6W00p+FklVdT30j5XlKsJvo5bARDEU5EZ00rb3zJSUYGRTXhI2Uu9h6rwaDX0S87RbPvp++5jGd/dnlcx6jTo2r+oqJn5/HQ9At59meXq0ZZr9Pxg+uHMmuKnPLb39GQ+TIoz64qAm5vUHWwAIYOyGD0uQ5evP9K9UGSmmLGaNBhtRh4YtZYbr16sGZsRZc/Vc0eJaicmmImP5q2qUwmUz3/6EMpllN5/maTnkAwonGIIu2t+XdlFIOfl5WienSSJFfqG5BtIz36QyoZP/5AmKPlbvVVSwnCKEHX/tk2BuWlamYGurxBjAY9FpMBi6np15eZmkRykpGLh2Xj8gSp94exJ2srECo/dqnTg9Vi5LqCfuq2seO7o8ZfqWGuVJ+s94fQ63RYTAbOzkvVvJnEzlxsbsJYVZ2PcERqNlDcFXF5A1ii8lrjN5Z3/rmHP37Q0LBD0UCTzEb6ObQpbmWq52/EbDKQkmSk1hug5FA1g/umNtFWs9KS1Bu/JfQ6pbCbkH06k+Rocb7GXDI8hyd+cikXnJOlLku3WajzBKiq8/Ht4SpszWTGxcq6ep2OzFT5rT473drkejG14Pkrso892UxmqoUks4G31n/Li8u3qUpASpKRvn1SNNLTqTR/ZXlsgDkSkRIqKtijrtSqOh/JFjlwqhiKk1Ve3PVBBuWlqjJOrUeZEFJHRJK4aIgsnyiz7lwxuv6gvFSOV3hUw+L2BrEny5N6mvP8lSe3PdmsPpVTkxu8iljZp94vV5NUPJXM6IX7ydbjuOuDuOtD2JIaPH/F8633h7FaDOh0Os7um8qJSi9f7jpJMBRRjVxWqqWJ5//tkWr1gdBcy8KuhiRJuLxB9TW5xu3XeP81Lr8mO0p5DU4yG+jbJ1lzIygPRUXeSU0xc7JSfsOLTQdtDQ3lHYTs01XJzkjWyDT2ZDPhiMTz736DyxskOQ7Z75qL+jH+FDXylbTv4GlkH6vFgMkoF/5T4hH/2VdBnUd2KM0mA1aLkad/ehmjz5UnHFpPIfsojmds0LdXyz77Sms5u28q6TaL6t1vj2r6Iwdlkhb1wJUHg1L69cKo8VeKNSmpX6lRfU4CjdFU9EFzI8/fajGon8Ua/NQmnr9ixEMkx7xuWi1G0mxm9hyt4eHff8mxaMBSuTA/3lrKnz7cjc8fUp/8Z0ezEX6/Zhf/3uPkyEk3RoOOkWdnUVZdr2YUBENhnnxnK6+9v0s+x25g/L3RmisjBmWSZDaweNk3/Pz5f6lzKFzeIC5vUC13Eev5m4wGcmNy9MuqGmQfgLQU+XuWgHNiunK1Br1a3kEO1Au6Pvbo/XksKr3cdNlZLW5zXUF/Ci/o2+xnJiXb5zTG32Zt/gFztNxFilXr4SsO36kSDpS3kljdv9cEfCtrfY1q3AQpdXo4t18a6TYz/mCYen+I7QcqyctKpk+6FYvZgMVsoM4jG75t++XP+jlkvVeRfeq8cpZNsqUh9UvxHGODQ+ZG+bdKWhjIgZ3m/m2zmuSJRcEw9YFQk0DjvBkX8eDto9TXOZvVRHL06b/3WC0btp1QvQiAs/umqkGiitp69hyr4ay8VAZk2/AHwmqKa+Pyr40bXq/41/5273maKMp3flZuKpMuHag2q9h3rJZI9K0AGn431fOPfjfK7yrvqyHgC/KDXdlf9hlmROmiAd9wRMJkFJ5/dyBWiv3edUPon207zdot0xDwPZXmrzX+d904TK0zdLjMha2RHbC2YPyVMs/+mOyiNp3k1VVx1weZ8+oXvPvxfnXZ/tJaJODcfumkR2trVLv87D1aw3kDG17r05LN1HkDlFV72XO0hstG5qqvW4oRcXkDpNnM6HQ6sqOGVZFQyqq86jLl6au8TV4yPEcdJ9bbbyz7KOdQ7w+per5CnzQrI87KVFNLU6yyF6toihFJ4pjTrXr+VouRRT8ZK1cFrfBw+KSLIf3S1QCzMmO4cc5/bMPriCSx9ovDzfY89fiC/OnD3Wqcoj0IRyLN5vErElZOppVJlw7kwe+NwqDXceB4HV5fSJXVFr/7DZ9vPxEj+8jfzeC+aZiNelKSjKoWq3j+igQoa7nxafunwqDXISF7fSLg2z1ITWm4J7PSzjwd2hxHqqfN2mATzspN5a5JwwFZxm08G78l46/EwZrIPj1d81emasdmrOw+ImdtDOqbSnp0qvfhMheBUIS8Pg2v/6kpZuo8Ab7aVYYOuSUcyHq7kmXj8gZJTZENQkqSiZQkI+U19WrFUEXXV572Y87L4ZGZBWoeMWgNvibgG33Cv7p6By5v8JQphgOyo0Xhok3BY6P+lXX+JqlnWalJ0clMEkP6p6mzXIs3HuY/+yqa1P5WMqMATTZT42yFNRsO8a9vjvPlzsSL8cXLJ1uPM/d/NzaZtFIelWqy063o9TpGDMqMlmeo1QR/j1d4eP2DkhjZR74xxl2Yz2M/GqMG0i0mgxqQVdoAZqZazliqUZytYCiS0Gu3oPOwxxjilvo1xINer8Og15061dPbVPaJDU4rzp6C8jBo3L9XwWJuRvbpDZ6/kt5njzGwuw5VcW6/NCwmg1pVT2nkHDvRSTH+R8vdZGdY1R8gM2Z2rcsbUN8elO2d1V5ORjNkVOMfffqajXrOjk4sUbBFj81k1Gs68eRHpYj9pXVU1PpOWX9HmWWqvGU09gAGNbpY+qQlUe8Po0MuTat8N7uP1PDi8m3NGP8GT/6bmIJ0jeUhZRakL9C0dPSOg5WaTKiW+PQ/pZQ0k2J66GQd9f4wxyq0bSnLqr1kplo0WRdn56VyqMzVbDtM5UawRD1/o0FPnzSretPFfteK5x9PyeaWUG64QCgsyjt0E2JtR2YbGH+Q7/VTyj6+oGZM0NbdLxiqrXem3O+Na/krWHqr5n+sXDYSyit2nTfAkTK3mrWhGO59pbLxj73BU1PM1HoCnKzyanJqs1ItauZInTdAqq3BM1By9pVOW0ogUTFKJkPTp7NBr8dmNZEazQxSyMtKYd7Mi9S/T+X5Dx2QwVP3jGXMebKU1Lg5yVl52oeN8iaQ77CpJYZjadxlyFnjo/jLw0QiEsfKG4rJxfYF9QVCqnFv3GJSkiReX1vC8k/2Ey9vrvuWp//6H46WNzLyUQ//aLmbo+VujkTzr8uq68nJ0OY9n9MvjUAwwvYDlZrlmakW/EHZ825cC0XJ4Y79rtOib3Z92mAGtPKqLUlgENk+3QKzyaB6z5lt4ACA7AQ2F/ANhsL4A+FmA77Kbar0ElBoKeDbnOzTSzx/2VgpWTl7ovVtzovW2rBa5Bo6ipHRGP9kE+76ICcqvKo0AvLTX5ml6/IGNZ5/brQQ1KEyFwa9Tt2fYmSaK7wEsncRK/kopMUEgJOtzf+48nFbVSMe+zqp08GAHFuTdQGG9G+4iH56y0hGnSOnjG3dq+1bC7D8k/3sPVZDWbWX/GiMILbNpLPGRygqOzU2/hW1Pmo9AU0V0tMR+/D6YOMhzWeKtv/ZN8dZ8MdN/PaNzYTCEcqqvE1mPA6LVlLcVFKuWa7X6fAHwupNEcuV38njkuHZTLniLHWZ8hs42tDzh/j7pwo6H7vVhF6nO2X9/UQxGQ3NGn8lvtac8X/8R5cy/86CJkZbnaV8imNrTvZp98JunY0kNTQ/rvVqJ2XlRT15nU6nGu8Mu0WTj6/c9BFJ0qQCKp5zWZUXXyCs8fzPyU9DkmDTrjJyMpNVjdgSNfrGU2R4nDcwU20BF4vG+FviKyugZP/kZiZzdl7zk5IAdTo6QMGwbO65ZeRpJx75gxGcNfWqUVXiHtBQQC7DbmkyYUx5q6p1B5rMvG3++Btuiv/sq1A9Fq8vqGbtHDzR8Hby+fYTeHwhsht5/vIMyRSqXXLt/IJh2dH9hPAHw+pNEcv5Z2cxa8pILop5tc7OsJKdYWXogKa/T6LEBtmE8e8+pKaYSbOZ2+w3M5v0zWr+SnyqsewD8v2sdPiLZXB+Go/+1yVNYgEKipPja+z5J3Aq3c741/vlFE6jQYfLE1RT/gx6neYVSSnIFGpUZS/WGGtlH9l4KvJIrOc/OD8NnU6uwfGdwQ2zBE2q7NP813jH9UO4bdw5TZbHPowa5/eeCuUJ//Oi8/nF90Y1+XzEWZl8b/w56pyFhrH0XHmBHIhu7uI7UuYiFJbon2MjJcmo0fyVWkIDc+xy4+sYL0OJp0BDjRKQH1L/+uZ4kzo8yr4uHZFDIBhR4wxK2QXFK/rNDwvIsFtY8/khQM70aXKug2R5T0J+u5l82VnU+0P4AqFmPf/msFqMPPGTsQzpnx7X+qdD4/mLbJ9uQ9+slCYzwc8Ek1FPsJmSzmo9sGZUgNNxumNTrvNAI81fn0DMqdt1+1AmYg3MtbO/tA5PfRCXN4CtkbZ+27hz2LizTM2lVcjLSiEnw0pZdb3G81fS/RSPNjb9y2oxMjDHzqGTLi4bkasuN6uyT2KdtGKJZ2YhyA+nsup6HOlJzY5nMuqZcMmAZredcf1Q7rh2CBt3nuT1D0rQ6WR9GuSGMQA5GVYy7ElazV8x/rl2/rOvAmdNvdqD9KjTTYbdQrXLz/FKj1opsfjLw6z5/BBJZgNmo4H9nx1k2pWD1KY1Fwzuw9Y9Few9Vsslw3PUIPp93/0OKUlG8rJSGDYgnY3R7KLGmj/AlCsGqecMcvqshFy2oznPv70Rsk/3ZObEobRQKzAhzEYD3+yv5O2/f0uG3cLAXDsjB2VpKga0FXq9DqNBm12UqOzT7Yy/Uid/QI5s/OuiszxjU7cA0mwWFt97RbP1dx75YQH7S+s0ufgZdgs6nTxfAJpW8bvygr5kZ1TTL2YyiBrwPYXmfzqUdoHxVpP85e2jOXiirtUPGr1ep4517cUDGOBI4fUPStSHXU5mMo70JNUYQ4Pso5SuLa9uMP7VdX7O7ZfGv/c41eJp0PCGUl5dz8ETdWzdW8GEgn6q559iNdI/x6YW1TpQWofFZGBQnl2V0wbm2Nm4swydjmZLUlstRm6/5lz1b+UBWuXyaUpbdxSx91siATdB59LWdZgUO/B//5Y7cQ3MaWz8E/P8Wx7PoMku6vEBXyUgqRikOk8AV32g2adqWoq52cJIKUkmjXwDcnZOVmqSasgaZ4GMG53PrCkjNctsVhMGva5VT3TlwdF4ktepyEpLUvXt1qJITLZkM5efn4fVYsRdH8Rs0pOWYibfkUJZVb0atKqP6omK7njgRB07D1URkSRq3H6yUpPISrNqWkkqD6c6j5yBBbK01FA338TAHDtHy+SezHuP1TA4P1WTa6+Ml5WaFNeDVZk4V13nj1v2aUtiNX+jMP69FuVaPadfGsMGpKtzaVzeAHqdrs3bqjbOLgr3dM2/OhqQ1Bh/b9Mc2tagxABMRv0p63DEYrOaeOzuMU1ydONBqQt0JnXkE0UZSzGWNqvSRyAJnU5HP4eNiCSpGn69P4RBryPdZiYlycgHGw/z7F//Q0WtXBk03W4h025RfxNoKBtx8ESdGog/fNKl6Zg1MMeOPxjm0EkXR8vdmiA1yG91QJNMn1Oh3FQSdL7sIzT/XktsG8nB+WnUuANEIpIqSycy+zbe8WITKSJSYm+e3e5KrXbLtXUyohq9uz4q+7TBK5Vi/GUJKL4vMSczuVWv+kodoFM1a2gPUlPM6HSopa2VB1zsHAFoqDNe7w+RZDZoylwAav+ADJtF1f0VlIJx+2NmDR866dL0yj0r6tmv+fwgEjCkUWE1q8XI6HP7cP7gPnGdV+wDtDmZr70Rmr8AGqru5mRYybRbCEck6ryyc5rahnq/gtlk0MzIj0QSm2Ee953y5JNPMnfuXAC2bt3Kbbfdxo033sgvfvELAgH59aakpISioiImTJjAvHnzCIXkL+P48ePccccdTJw4kXvuuQePJ77c8OaocfnJsDc0Q6/1+Kn3h9rE81fq+mfGWcf9TPjxzecx5/ZRZNjbZoJJPNisJubNKGDcRf2BBqPZJ5rplJNhxaDX8dG/j+HxRWsPWZQqmA3fidIAJ8NuITPVQo0roPYXdjeq0XPeoEyN7GO1GMl3pNA/28a2/ZVkpSZxbjMZN/dO+w7XX9w/rvOKfZ3ubNlHGP/ei+IEZWckq/d1tcvfZs5pY+QZxe1c2G3jxo2sXLkSALfbzb333sujjz7KBx98AKD27Z0zZw7z589n/fr1SJLEsmXLAFi4cCHTp09n3bp1jBw5kiVLlsR9gI2pjhp/g15PssWozrpta8+/vUkyG8+4jnxrOLtvqhpvUAJeiudvNMhlKg4cr+Ptv++RC05ZGgrRKcQa/wx7EhFJ1u7lAm1BcjKTuWBwFndcN4SRg/vgrPHh8gYxG/VqPfMbxsiZSZMuHXDGgTeN59/Jso8I+PZelEzE2LIxVXV+XN7mY5JnSvOafxsa/5qaGhYvXsysWbMA+Pzzzxk1ahTDhg0D4JFHHuG6666jtLQUn8/HqFGjALnh+7p16wgGg2zevJkJEyZolreGiCRRXlOv5uTbrCZORLNT7HFo9C2h5JR3pDfemSiZObGZTQ9+bxSXj8xl6x4n1S6/WlvklisHyXIYcm6+DllGUi7yJ9/ZyqrPDuKqDzJsQDr333oB11zUj3xHChFJ4kiZSxPcHnNeDg/ePoqrRjX0WG0tZpNe1VstZ5B221q0nn+3U1IFbcTw6ITBzFSLKktXu3zUNZON2BaYG9USSrSNY4vh5/nz5/PAAw9w4oRc7vfw4cMkJyfzwAMPcODAAS688ELmzp3Lrl27cDgaJhg5HA7Kysqorq7GZrNhNBo1yxMlK8vGsXJZOz7/XAcOh530VIs62ah/3zQcjuZnwyUyxsSxZ3FNNF/+TPcXLx01TuMxw9Ek50H9MjTHMOXqc/l8x0kOl7m45LxcHA47VznsXHXxQB5Y/An7jtWSbreQl5tGfcycln9uOYY/ECanj03dX99o74SDJ13kZFo142RnN53Z2FoemlHA2x+WcOF5uer5dRQZMZlhmZnJHTJ2R18zYryWmXfXGGrcfnKzUpAkCaNBjzsgT0rNzW64J9rq3GwpFuqr69X9SehISbHEvf/TGv93332XvLw8xo4dy4oVKwAIh8Ns2LCBv/3tb/Tt25d58+bx+9//nssuu0wTJJUkCZ1Op/4/lniDqbFUVrrZslN+ADnsZpxOFxajQdWadeEwTqfrdLuIi9uuOlv9d1vsryUcDnuHjNPcmAOz7ew5UoNBimiOISvFqFY/1eskzWdKiekLBmfJy0MNpR2Ukg16qWGbvtEgciAYxmzUt9u5Ds6xseDOi9W/O+o7dTjsuGKznVy+dh+7o68ZMV78GGi49jLsZvZEq9gq90RbjiVFInh9QXV/oXAEv7/hb71eR1bWqWcJn9b4FxcX43Q6mTJlCrW1tXi9XiRJoqCggP795WDcDTfcwNtvv01RURFOp1PdtqKiguzsbDIzM3G5XITDYQwGA06nk+zs1uWrHzheh8VsUAuyKamKOl3blWXtTdw6bjCFF+Q1mRil0+kY3DeVrXsrmlQVnHBJfzaXlKuTrGxWEzodTLxkAJtKyqis86vBeJClIXM0MHWqCoXdHYPI9hE0Q1qKRS1C2RaydGNMRn2jbJ821PzfeOMN1q5dy+rVq7nvvvsYP348a9asYefOnaoM9PHHHzNixAjy8/OxWCxs2bIFgNWrV1NYWIjJZKKgoIDi4mIAVq1aRWFhYcInCnK++Fk5djWoppTqzbBb2ny2Xm/AaNCr6Z2NUfoTKG9WCpcMz+FnRefHdDHT8YeHxvHdqwdzY7QPamqKVt9U+hlccX4ePRGdCPgKmiE1xawmSrRPwNfQseUd8vLyePTRR5k1axZ+v5/hw4fzq1/9CoBnnnmGRx55BLfbzYgRI5g5cyYACxYsYO7cubz66qvk5eXx3HPPJTosIDcaUco2Q0Oeekdk5/Q2+kc7icWWbjgViox39ah8zs5LbdIP9adTz8cXCDeZVd1T0AR8hRMiiBLrBDV2iNqCxqmekpRYqmfcxr+oqIiioiIArr76aq6++uom6wwbNkxN+4wlPz+fpUuXxn1QzRGRJOo82g5bivG3NlPCQXBmnNsvjZQkI5Mubb5Y3KlQZufG0haVM7sysfebkH0ECqdq5dpWmE16tWewJEk9t7Cb1xciHJE0tfAVOeFUrc4ErcdqMfLS7NbJc72N2PROYfwFCkqTmPao6wNyHa2IJBEKR9S3zx5Z3kFpiBDr+fujT71TNTkWCDoCXcxdJGQfgYJSNdhmNbZ5XR9oKCkfDEUIR2NzbTrJq6uglEVNi+mwVTDUwdl9U9VAo0DQGYiqnoLmUFQKezvo/dBg/AOhiNo8qUfKPnVeeep0bL9Ne7KZR2YWdNYhCQSA9oYT2T4ChdQUWfNvjzRPaCifHozOoYEeKvu4ojNF09rpKSoQtBZR2E3QHEqGT3sEe6GhLHwgVvbpkcbfG8RqMXRK1UaB4HToRD1/QTMkmY2kJBnbLRXdFKP5R1qh+Xcj2SegKSssEHQVRKqn4FTM+f7odqs+YI7KPoFQuGdr/r5ASG1DKBB0JUR5B8GpaG7eS1thig349mTZJxiWMIlXakEXRGj+gs5A0fyDwQbNv106eXU24XBE1O8RdEm0PXyF8Rd0DKZY2acn5/kHw5Iw/oIuiaaTVztM5hEImqPZSV491fNXNC6BoCuhGHyDXteqXhUCQWs400le3caahoTsI+iiKN6WkHwEHYmSVhwK9/CAbygiYTKKm0vQ9VDuN9G/V9CRKF5+OCz1cNknJDx/QddE9fxFpo+gAzFG3zTDkdZN8uo21jQYEcZf0DWJ1fwFgo5CedMMR6SerfmHQ5II+Aq6JELzF3QGer0OHbLs066a/5NPPsncuXM1y95++21mzJih/l1SUkJRURETJkxg3rx5hEIhAI4fP84dd9zBxIkTueeee/B4PHEfoEJEEpO8BF0TIfsIOguDQUc4IrXfJK+NGzeycuVKzbJ9+/bx+9//XrNszpw5zJ8/n/Xr1yNJEsuWLQNg4cKFTJ8+nXXr1jFy5EiWLFkS9wHGYhSev6AL0iD7iOtT0LEY9Hpttk9bav41NTUsXryYWbNmqcsCgQDz58/nvvvuU5eVlpbi8/kYNWoUIPf8XbduHcFgkM2bNzNhwgTN8tYgNH9BV0Wv0wnPX9DhGPQ6jebfprLP/PnzeeCBB0hNTVWXPfvss0ybNo3+/fury8rLy3E4HOrfDoeDsrIyqqursdlsGI1GzfLWYBKaqqCLotcL4y/oeIxnIPuctkzmu+++S15eHmPHjmXFihUAfP7555w4cYJf//rXfPXVV+q6kUhEM7tRkiR0Op36/1haOwsyIz0Zh6P9quQ1pqPG6shz6owxe8P5GQw6LBZjj71mxHhdcyyTyYDJZMBmk8tGZ2WlxL3/0xr/4uJinE4nU6ZMoba2Fq/Xi06nY+/evUyZMgWv10tFRQWzZ89mzpw5OJ1OdduKigqys7PJzMzE5XIRDocxGAw4nU6ys7NbdaL19QGcTlertk0Uh8PeIWN11DidNWZvOT8dIEWkHnnNiPG67lg6wOMNUF3jBaC2xovT1NDSMSvLdsptT2v833jjDfXfK1asYNOmTSxatEhd9tVXX/Hyyy/z/PPPA2CxWNiyZQsXXXQRq1evprCwEJPJREFBAcXFxUyePJlVq1ZRWFjYqhMV2T6CrorQ/AWdQbtq/onwzDPPsGjRIiZOnIjX62XmzJkALFiwgGXLljFp0iS+/vprZs+e3ar9i2wfQVdFr9eJ5u2CDsdg0Le6tk/crbGKioooKirSLBszZgxjxoxR/x42bBjLly9vsm1+fj5Lly6N+6BOhfD8BV0VvV4nJnkJOhyDXhed5CX/3SPLO0BDLQuBoKuh14FR5PkLOpgmsk+PNf5C9hF0UQwi1VPQCRgNermwW2dr/u2NkH0EXRWdTsg+go6nQfZpR82/KyBm+Aq6KiMGZdLPceq0OoGgPTAYdASCseUd4t+2exl/IfsIuig/nDissw9B0Asx6PWEIyGitl/IPgKBQNAbMOh1hGJln54a8BX1/AUCgaABpaRzjw/4ilRPgUAgaEAO+PbwNo4gAr4CgUAQi5zqGev5x79tt7GmOkSnJIFAIIhFneTVkz1/g1Hf6lLQAoFA0BMxGPSy7CNJ6HSJlcvvNsbfJKbOCwQCgYaGbJ/EvH7oRsbfIDJ9BAKBQENsbZ9Eq8p2G4tqEnq/QCAQaJBTPeVsnx5r/EXdFIFAINBi0OvV2j49VvYRpR0EAoFAi9GgQwLCESmhuj7QjYy/QQR8BQKBQIOS/h4IhRNOhe82FlVM8BIIBAItilMcDEXQtZfxf/LJJ5k7dy4Af/vb37jpppuYPHkyv/71rwkEAgCUlJRQVFTEhAkTmDdvHqFQCIDjx49zxx13MHHiRO655x48Hk9CBwlgEpq/QCAQaFBiocFQpH00/40bN7Jy5UoADh48yOuvv85f//pX1qxZQyQS4Z133gFgzpw5zJ8/n/Xr1yNJEsuWLQNg4cKFTJ8+nXXr1jFy5EiWLFmS0EGCPJlBIBAIBA0Y9e1o/Gtqali8eDGzZs0CwGw2s2DBAmw2GzqdjiFDhnD8+HFKS0vx+XyMGjUKkBu+r1u3jmAwyObNm5kwYYJmeaKIom4CgUCgRXGKA6FIwpp/i81c5s+fzwMPPMCJEycAyM/PJz8/H4Cqqir+/Oc/s2jRIsrLy3E4HOp2DoeDsrIyqqursdlsGI1GzfJESbaacTjsCW93JnTUeB19Xh09pji/7j+mGK9rjpWeZpX/oQOTSZ/Qvk9r/N99913y8vIYO3YsK1as0HxWVlbG3XffzbRp0xgzZgxbtmzR1JWQJAmdTqf+P5bW1OiJhMM4na6Et2stDoe9Q8brqHE6a0xxft1/TDFe1x3L6/XL/68PEolImn3r9Tqysk7dWvS0xr+4uBin08mUKVOora3F6/Xyu9/9ju9973vcfffdzJgxg7vuuguA3NxcnE6num1FRQXZ2dlkZmbicrkIh8MYDAacTifZ2dkJn6RI9RQIBAItxphsn0TnQp3W+L/xxhvqv1esWMGmTZu47777uOmmm5g9eza33HKL+nl+fj4Wi4UtW7Zw0UUXsXr1agoLCzGZTBQUFFBcXMzkyZNZtWoVhYWFCR0kiC5eAoFA0JiGPP8IFpMhoW0TtqjLly+noqKCN954gylTpjBlyhReeOEFAJ555hkWLVrExIkT8Xq9zJw5E4AFCxawbNkyJk2axNdff83s2bMTHVZ9wgkEAoFAJjbVM9E8/xYDvgpFRUUUFRUBcOeddza7zrBhw1i+fHmT5fn5+SxdujShA2uMwSiyfQQCgSAWdZJXuJ3y/LsCItVTIBAItCiyTygUSaiFI3Qn4y9kH4FAINCgyD7hiISh53r+3eZQBQKBoEOItYs9uJ5/tzlUgUAg6BBiZ/UmOn+q21hUUdhNIBAItBh6h+cvjL9AIBDEEpsI02Pr+ZuE7CMQCAQaYhNhemyqp9D8BQKBQEus559oybRuY1FFnr9AIBBo6RWav0j1FAgEAi2xcniP1fyF8RcIBAItsYkwPVbzF8ZfIBAItPSKPH+R6ikQCARadDqdGg/tsbKPKcETEwgEgt6Aoor02MJuBtHMRSAQCJqgGv+eKvuIqp4CgUDQFEUST7SZS7exqCLgKxAIBE1RHOMeXNK5s49AIBAIuh5K4/Z2m+T15JNPMnfuXAC++OILJk+ezPXXX8/ixYvVdUpKSigqKmLChAnMmzePUCgEwPHjx7njjjuYOHEi99xzDx6PJ6GDBNAL2UcgEAiaoGT7tIvmv3HjRlauXAmAz+fj4YcfZsmSJRQXF7Njxw4+/fRTAObMmcP8+fNZv349kiSxbNkyABYuXMj06dNZt24dI0eOZMmSJQkdpEAgEAiaR5F9dG2d7VNTU8PixYuZNWsWANu2bWPgwIH0798fo9HI5MmTWbduHaWlpfh8PkaNGgXIDd/XrVtHMBhk8+bNTJgwQbNcIBAIBGdOa/P8jS2tMH/+fB544AFOnDgBQHl5OQ6HQ/08OzubsrKyJssdDgdlZWVUV1djs9kwGo2a5YmSlWVLeJszxeGw96hxOmtMcX7df0wxXtcdKynJBIAtJSmhfZ/W+L/77rvk5eUxduxYVqxYAUAkEtFMI5YkCZ1Od8rlyv9jSXQaMkBlpZtIREp4u9bicNhxOl09ZpzOGlOcX/cfU4zXtceKhCMA+OoDmn3r9brTOs2nNf7FxcU4nU6mTJlCbW0tXq+X0tJSDIaG1Bun00l2dja5ubk4nU51eUVFBdnZ2WRmZuJyuQiHwxgMBnV9gUAgEJw5itrTpnn+b7zxBmvXrmX16tXcd999jB8/nj/84Q8cPHiQw4cPEw6HWbt2LYWFheTn52OxWNiyZQsAq1evprCwEJPJREFBAcXFxQCsWrWKwsLCVpyiQCAQCBqjGP021/wbY7FYeOKJJ7j33nvx+/1cddVVTJw4EYBnnnmGRx55BLfbzYgRI5g5cyYACxYsYO7cubz66qvk5eXx3HPPJTqsQCAQCJpBSfFMNNUzbuNfVFREUVERAGPHjmXNmjVN1hk2bBjLly9vsjw/P5+lS5cmdGACgUAgaJkG45/gdu1wLAKBQCDoIJSZvT22to9AIBAImqLY/B5bz18gEAgETVE8/x5b0lkgEAgETVE1f+H5CwQCQe9BmTSb6NxZYfwFAoGgG6M6/AkWQBDGXyAQCLoxitwTkRKz/sL4CwQCQTemwfgnuF07HItAIBAIOggl4CsJz18gEAh6D0qgN9Gqx8L4CwQCQTdGlX2E8RcIBILegyL7CM1fIBAIehGK5y80f4FAIOhFqJq/MP4CgUDQe7BFe/iaTYYW1tSScDMXgUAgEHQdrru4Pwa9jnGj8xPaThh/gUAg6MYYDXquv2RAwtsJ2UcgEAh6IXEZ/xdeeIFJkyZx44038sYbbwCwYcMGbr75Zm666SYeeughAoEAACUlJRQVFTFhwgTmzZtHKBQC4Pjx49xxxx1MnDiRe+65B4/H006nJBAIBIKWaNH4b9q0iS+//JI1a9bw3nvvsXTpUg4cOMC8efNYvHgxa9euxefzsXr1agDmzJnD/PnzWb9+PZIksWzZMgAWLlzI9OnTWbduHSNHjmTJkiXte2YCgUAgOCUtGv9LLrmEt956C6PRSGVlJeFwmOTkZMLhMG63m3A4jN/vx2KxUFpais/nY9SoUYDc9H3dunUEg0E2b97MhAkTNMsFAoFA0DnEFfA1mUy8+OKL/PGPf2TixInk5OTw29/+lhkzZmCz2ejXrx8TJ05k586dOBwOdTuHw0FZWRnV1dXYbDaMRqNmeSJkZdkSWr8tcDjsPWqczhpTnF/3H1OM1z3HOh1xZ/vcd999/OhHP2LWrFm88sorrF27lrVr19KvXz8WLVrEokWLuOmmm9SuMiDPONPpdOr/Y2n8d0tUVroTrl1xJjgcdpxOV48Zp7PGFOfX/ccU43XPsfR63Wmd5hZln/3791NSUgKA1Wrl+uuv58MPP2TIkCEMGDAAvV7PbbfdxqZNm8jNzcXpdKrbVlRUkJ2dTWZmJi6Xi3A4DIDT6SQ7O/tMz00gEAgEraRFz//YsWO8+OKL/OUvfwHgo48+4uabb+Yvf/kLFRUV9OnTh48++ojzzz+f/Px8LBYLW7Zs4aKLLmL16tUUFhZiMpkoKCiguLiYyZMns2rVKgoLCxM60ESbE7cFHTVmTz63jh6rM8bs6ecnxuueY7U0jk6KoxrQSy+9xIcffojBYOD666/n3nvvZeXKlbz22msYDAYGDhzIo48+SmZmJrt37+aRRx7B7XYzYsQIFi1ahNlsprS0lLlz51JZWUleXh7PPfccaWlpbXaiAoFAIIifuIy/QCAQCHoWYoavQCAQ9EKE8RcIBIJeiDD+AoFA0AsRxl8gEAh6IcL4CwQCQS9EGH+BQCDohQjjLxAIBL0QYfwFAoGgF9LljP+KFSuYO3duu45x7NgxRo4cyZQpUzT/nThxotn1X3rpJV566aVWjzV06FDmz5+vWV5SUsLQoUNZsWJFq/YbD3v27GHo0KGsX7++XfbfmecGHXOtJDrm3Llz2+S82/u3i2XdunUUFRVx8803M3nyZP7whz+063gej4eFCxdy3XXXcfPNNzN9+nQ2btx4yvVdLhc/+9nPWjWWco1+/vnnmuXjx4/n2LFjrdpnS+PF2pYJEybw61//moqKijYf60zptT18s7Oz1QY07U16ejqfffYZ4XAYg8EAQHFxMZmZme067nvvvcfEiRP529/+pvZSaGs669x6Oh3x2wGUlZXx5JNPsmLFCjIyMvB4PMyYMYNBgwZxzTXXtPl4kiQxa9Yshg8fzgcffIDZbGbXrl38+Mc/5tlnn2XMmDFNtqmtrVWLS7YGk8nEb37zG9asWYPN1v6l4WNtiyRJPPfcc9x3332888477T52InQ5z19h06ZNfP/732fq1Klcc801/POf/wRkz+qxxx7j+9//PuPHj+e9995rszErKir46U9/SlFREdOmTeOLL75QP9u2bRu33norN954I2+++WZC+01JSWH48OFs3rxZXfb5559z2WWXAfD2229z6623ctNNNzF16lQOHDgAyN7J7NmzmTBhApWVlQmNGQwGef/995k9ezY7d+7kyJEj6j6feuopbrnlFm655RZ27doFwIwZM/j5z3/OhAkTErrRWnNuGzdu5Pbbb1fXX7FiBQsWLEjo/GKZMWMGX331FSB7XuPHjwfa91o51Zhtwel+O8Vb/eqrr5gxYwYgvyUUFRUxZcoU/vu//5vrrrsu7rGqq6sJBoP4fD5A/j2feOIJzjnnHLZt26beg3fddRdHjx5Vz/3xxx9n6tSpTJo0iQ0bNsQ93qZNmzh+/Di//vWvMZvNAJx33nncc889LFmyhJKSEm699VYmT57MD37wA06ePMljjz1GeXl5q73/7OxsLrvsMp588skmn/3P//wPkyZNYvLkyTzxxBOEw2EWLVrEH//4R3Wde++9l3/84x+tGlun03Hvvfeyd+9edu/eze9//3umTp3KzTffzFNPPYVSXedPf/oTEyZMYNKkSTz99NOtGitRuqzxf/vtt3nsscdYuXIljz32GC+88IL62cmTJ3nnnXd49dVXeeqpp1q1//Lyco3k84c//IHHH3+cadOmsWLFCl599VXmz5+P2+0G5DLUb775Jn/729/485//nLAncsMNN6iv8Nu2bWPo0KGYTCbcbjf//Oc/Wbp0KWvXruXqq6/mz3/+s7pdYWEh69evJysrK6HxPv30U/r27cugQYO49tpr+dvf/qZ+lpyczKpVq7jvvvv41a9+pS5XZIbhw4e367ldeumlOJ1O1aitWrWKoqKihMaMl7a4Vjqa0/12zTF37lzuv/9+Vq9eTf/+/dXS6fEwbNgwrrnmGq699lq++93v8vTTTxOJRMjLy+ORRx7h2WefZeXKlfy///f/+M1vfqNu53a7WblyJc8++yxz585Ve3i3xPbt2xk5cmSTfh4XX3wx27dv55e//CU//elPef/995k0aRJvvvkmjzzyCNnZ2bzyyitxn1dj5s6dy4YNGzTyz7/+9S/+7//+j/fee4+VK1dy+PBh/vrXvzJlyhTWrl2rnufWrVu56qqrWj222Wxm4MCB7N69mx07drB8+XJWrVpFWVkZa9asYdu2bbzzzjssX76cNWvWsHPnTnbs2NHq8eKly8o+Tz/9NB9//DHr1q3jm2++0TR8v/zyy9HpdAwZMoSamppW7b852WfMmDEcOHCAF198EYBQKKR6O5MmTSI5ORmAcePGsWnTpoSM5Pjx43n++eeJRCJ8+OGH3HDDDRQXF2Oz2Xj22Wf54IMPOHToEJ999plmvxdccEGrzu+9997jpptuUo/9l7/8Jffffz8At912m3pMc+fOpaqqCoDvfOc7rRor0XPT6XRMnTqVNWvWUFRURGVlZavPsyXa4lrpaE732zWmpqaG0tJS1ThNmzaNt956K6HxFi5cyE9/+lM2bNjAhg0buO222/jxj3/M0aNHueeee9T1FEcIGq6h4cOH43A4+Pbbbzn//PNbHEun0zX7cAoGg0QiEZxOJ+PGjQNg+vTpAG2izdtsNv77v/9blX8AvvzyS2688UasVisgf3erVq3ijjvuIBAIcPjwYbZu3cr48ePVt5TWotPpeOutt6iqqlIdHZ/PR9++famoqGDcuHHY7XKHrz/96U9nNFa8dAnj//XXX9O/f39ycnKQJAmDwcD06dMZM2YMY8aMYezYsfzyl79U17dYLEDi3cBaIhKJ8Oabb5Keng7IbwdZWVn885//VFtQKuvF/h0PKSkpDBs2jC1btvDll1/y4IMPUlxczIkTJ/je977HD37wAwoLC+nTp4/mrUI510SorKzks88+Y+fOnbz11ltIkkRdXZ366tr4XBStPikpKeGxWntuU6dO5e6778ZsNjNlypS4x2ruWgHU1+dQKKRZvy2ulUTHPBNa+u0aj2kwGDiTwryffPIJXq+XSZMmMW3aNKZNm8ayZct4//336devn+oghcNhTdBS+Q4gsfvhggsuYOnSpQSDQUwmk7r8P//5D+effz579uxRl/n9fsrLy9vsPr/iiis08k8kEmmyjvK93nzzzRQXF7N161Z+/OMfn9G4gUCAgwcPMmbMGCZPnsz/+3//D4C6ujoMBgPLly/XnGNZWRlWq5XU1NQzGrcluoTs895776ma/rfffkv//v05dOgQ999/P4WFhXz00UcJvcq2lksvvVQNyuzbt4/JkydTX18PwPr16wkEAtTW1vLJJ59w6aWXJrz/G264gWeffZaRI0eqN0tycjIDBw7kzjvv5Pzzz+ef//znGZ/r6tWrufTSS9XX2o8//phZs2bx17/+FYAPPvgAgH/84x8MHjy4TfoqJHpu+fn55Obmqq/Z8dLctZKRkcG+ffsA1M/ako4c83S/XeyYH330EQB2u53+/fvz6aefAvD+++8nNF5SUhLPPvus6l1LkkRJSQmjRo2itraWr7/+GpC/g1gHrLi4GJBlnLq6OoYMGRLXeAUFBZxzzjn87ne/IxgMArBjxw5effVVfv7zn5OTk6PGEFavXs0LL7yA0WhsswesIv+Ul5dz6aWX8sEHH+Dz+QiFQrz33nvqfT158mSKi4s5fPgwF110UavHi0QivPTSS1xwwQVMmzaN1atX4/F4CIVC/OxnP2P9+vUUFBTw6aefqssffPDB3iP7/PjHP+ahhx7i7bffJjc3l+eff57q6mpuvPFGjEYjl156KT6fD6/X267H8cgjjzB//nwmT54MwFNPPaVmB/Tt25fbb78dv9/PT37yEwYPHpzw/seNG8e8efM0r/Amk4lIJMKkSZOQJImLL76YvXv3ntF5rFy5kgceeECz7I477uAPf/gDNpuNf//73yxfvhyr1coTTzxxRmMptObcJk2axN///ndycnLiHqe5a+Xw4cPMnTuX9957r10yVDpyzNP9dg899BCPP/44L7/8MldccYX6+VNPPcXDDz/M888/z9ChQxN6g7v00kv5+c9/zqxZs1RjfOWVV3Lvvfcyfvx4Hn/8cfx+PzabTRMwPXr0KFOnTgVg8eLFmjeBlnj55ZdZvHgxN910EwaDgbS0NJ5++mnGjBnD008/zW9/+1uefvppMjIyeOqpp8jIyKBv377MmDGDpUuXxj1Ocyjyz3/9139x9dVXU1dXx7Rp0wiFQlxxxRX84Ac/ACAvL4+MjAxGjx6d8JuHEk8E2fgPHz5cbV61e/dubrvtNsLhMFdeeSVTp05Fp9Pxgx/8gNtvv51IJMJ1112nJky0J6KZSy9j/PjxvPXWW/Tr169TjyMUCvHQQw8xceJErr/++k49lu7Oyy+/zG233UZ2djZ///vfef/991s9LyUelMyw5tIyBd2HLuH5C3oXkiRx5ZVXctlll3Httdd29uF0e/r27ctdd92F0WgkNTWVxx9/vLMPSdANEJ6/QCAQ9EK6RMBXIBAIBB1Lpxn/l19+mRtvvJEbb7xRnXzzxRdfMHnyZK6//noWL17cZJuHHnpIUzdly5YtfPe732XKlCn88Ic/pLS0tMOOXyAQCLoznWL8v/jiCzZs2MDKlStZtWoVO3fuZO3atTz88MMsWbKE4uJiduzYoaavlZWVMWvWrCZFrubMmcNjjz3G6tWrmTx5Mo899lhnnI5AIBB0OzrF+DscDubOnYvZbMZkMjF48GAOHTrEwIED6d+/P0ajkcmTJ7Nu3TpAzl2+5ppruOGGG9R9BAIB7r//foYNGwbIpQlOVZVTIBAIBFo6xfife+65jBo1CoBDhw7x4YcfotPpcDgc6jrZ2dmUlZUBcPfdd3Prrbdq9hE7MzQSifDyyy+LzBGBQCCIk04N+O7du5e77rqLhx56iP79+2smU0iSFNfkikAgwC9/+UtCoRA/+clP2vNwBQKBoMfQacZ/y5Yt3HnnnTz44INMnTqV3NxcnE6n+rnT6SQ7O/u0+/B4PNx9992EQiFeffVVTa0QgUAgEJyaTjH+J06c4Gc/+xnPPPMMN954IyAXfDp48CCHDx8mHA6zdu1aCgsLT7ufOXPmMHDgQJ5//vkzrronEAgEvYlOmeH7+uuv4/f7NXVlbr/9dp544gnuvfde/H4/V111FRMnTjzlPnbt2sVHH33EOeeco9YYyc7O5rXXXmv34xcIBILujpjhKxAIBL0QMcNXIBAIeiHC+AsEAkEvRBh/gUAg6IUI4y8QCAS9EGH8BQKBoBcijL9AIBD0QoTxFwgEgl7I/wf7F87VfF5b/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig, ax = plt.subplots(figsize=(12,4))\n",
    "birth_by_date.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dd3fa2b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,4))\n",
    "birth_by_date.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae44866f",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}