Notebooks/Python_Tasks/17_Pandas_EDAb.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Python  \n",
    "\n",
    "## Pandas - Exploratory Data Analysis (EDA) part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import zipfile\n",
    "import requests\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from IPython.display import display, Image\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "#InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ft4zZh3AL8Zu"
   },
   "source": [
    "## Pandas Data Manipulation: Baby names in the USA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "ESmCc8HRL8Zu"
   },
   "outputs": [],
   "source": [
    "names1880 = pd.read_csv(os.path.join('../Data','CSV','names','yob1880.txt'), names=['name', 'sex', 'births'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "0hugWvuwL8Zv",
    "outputId": "4cf49023-0b37-42f8-cb5e-cca5c8c0af97"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Margaret</td>\n",
       "      <td>F</td>\n",
       "      <td>1578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ida</td>\n",
       "      <td>F</td>\n",
       "      <td>1472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alice</td>\n",
       "      <td>F</td>\n",
       "      <td>1414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bertha</td>\n",
       "      <td>F</td>\n",
       "      <td>1320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>1288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Annie</td>\n",
       "      <td>F</td>\n",
       "      <td>1258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Clara</td>\n",
       "      <td>F</td>\n",
       "      <td>1226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ella</td>\n",
       "      <td>F</td>\n",
       "      <td>1156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Florence</td>\n",
       "      <td>F</td>\n",
       "      <td>1063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cora</td>\n",
       "      <td>F</td>\n",
       "      <td>1045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Martha</td>\n",
       "      <td>F</td>\n",
       "      <td>1040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Laura</td>\n",
       "      <td>F</td>\n",
       "      <td>1012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Nellie</td>\n",
       "      <td>F</td>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Grace</td>\n",
       "      <td>F</td>\n",
       "      <td>982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Carrie</td>\n",
       "      <td>F</td>\n",
       "      <td>949</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name sex  births\n",
       "0        Mary   F    7065\n",
       "1        Anna   F    2604\n",
       "2        Emma   F    2003\n",
       "3   Elizabeth   F    1939\n",
       "4      Minnie   F    1746\n",
       "5    Margaret   F    1578\n",
       "6         Ida   F    1472\n",
       "7       Alice   F    1414\n",
       "8      Bertha   F    1320\n",
       "9       Sarah   F    1288\n",
       "10      Annie   F    1258\n",
       "11      Clara   F    1226\n",
       "12       Ella   F    1156\n",
       "13   Florence   F    1063\n",
       "14       Cora   F    1045\n",
       "15     Martha   F    1040\n",
       "16      Laura   F    1012\n",
       "17     Nellie   F     995\n",
       "18      Grace   F     982\n",
       "19     Carrie   F     949"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880[0:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "UWHNM01OL8Zw",
    "outputId": "e7b9fb02-aa1d-4450-cc13-09fee1b299c8"
   },
   "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>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>Woodie</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>Worthy</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>Wright</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>York</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>Zachariah</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name sex  births\n",
       "1995     Woodie   M       5\n",
       "1996     Worthy   M       5\n",
       "1997     Wright   M       5\n",
       "1998       York   M       5\n",
       "1999  Zachariah   M       5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#names1880.head()\n",
    "names1880.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "YTz48vuQL8Zx",
    "outputId": "c3a1711c-2d56-4ab2-cf30-0cc3b572bf87"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "F     90993\n",
       "M    110491\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880.groupby('sex').births.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "-_BbBIPaL8Zy"
   },
   "outputs": [],
   "source": [
    "years = range(1880, 2020)\n",
    "pieces = []\n",
    "columns = ['name', 'sex', 'births']\n",
    "for year in years:\n",
    "    path = os.path.join('../Data', 'CSV', 'names','yob{}.txt'.format(year))\n",
    "    frame = pd.read_csv(path, names=columns)\n",
    "    frame['year'] = year\n",
    "    pieces.append(frame)\n",
    "    \n",
    "# Concatenate everything into a single DataFrame\n",
    "names = pd.concat(pieces, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "W9by5LhfL8Z0",
    "outputId": "c220ebaa-5c19-46dc-b66b-50d767e5976a"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Margaret</td>\n",
       "      <td>F</td>\n",
       "      <td>1578</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ida</td>\n",
       "      <td>F</td>\n",
       "      <td>1472</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alice</td>\n",
       "      <td>F</td>\n",
       "      <td>1414</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bertha</td>\n",
       "      <td>F</td>\n",
       "      <td>1320</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>1288</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name sex  births  year\n",
       "0       Mary   F    7065  1880\n",
       "1       Anna   F    2604  1880\n",
       "2       Emma   F    2003  1880\n",
       "3  Elizabeth   F    1939  1880\n",
       "4     Minnie   F    1746  1880\n",
       "5   Margaret   F    1578  1880\n",
       "6        Ida   F    1472  1880\n",
       "7      Alice   F    1414  1880\n",
       "8     Bertha   F    1320  1880\n",
       "9      Sarah   F    1288  1880"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1988203 entries, 0 to 1988202\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Dtype \n",
      "---  ------  ----- \n",
      " 0   name    object\n",
      " 1   sex     object\n",
      " 2   births  int64 \n",
      " 3   year    int64 \n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 60.7+ MB\n"
     ]
    }
   ],
   "source": [
    "names.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "5uBms8McL8Z1",
    "outputId": "acf006bc-0149-4f01-f569-ff00d2b52786"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "F    175690160\n",
       "M    179285222\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.groupby('sex').births.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "jlc5pO5mL8Z2"
   },
   "outputs": [],
   "source": [
    "total_births = names.pivot_table('births', index='year', columns='sex', aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "gKcryoTnL8Z3",
    "outputId": "1e385c0f-84b6-43eb-ecdc-fc4a166fa314"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>90993</td>\n",
       "      <td>110491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>91955</td>\n",
       "      <td>100746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>107850</td>\n",
       "      <td>113687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>112322</td>\n",
       "      <td>104630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>129022</td>\n",
       "      <td>114445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>1781725</td>\n",
       "      <td>1913059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1767902</td>\n",
       "      <td>1893471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1721550</td>\n",
       "      <td>1845472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1694640</td>\n",
       "      <td>1809166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1665373</td>\n",
       "      <td>1779948</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sex         F        M\n",
       "year                  \n",
       "1880    90993   110491\n",
       "1881    91955   100746\n",
       "1882   107850   113687\n",
       "1883   112322   104630\n",
       "1884   129022   114445\n",
       "...       ...      ...\n",
       "2015  1781725  1913059\n",
       "2016  1767902  1893471\n",
       "2017  1721550  1845472\n",
       "2018  1694640  1809166\n",
       "2019  1665373  1779948\n",
       "\n",
       "[140 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "M6hBV2zQL8Z5",
    "outputId": "dd9f9081-5af2-43fe-d44b-b9b1c16b8ef6"
   },
   "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>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>1781725</td>\n",
       "      <td>1913059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1767902</td>\n",
       "      <td>1893471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1721550</td>\n",
       "      <td>1845472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1694640</td>\n",
       "      <td>1809166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1665373</td>\n",
       "      <td>1779948</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex         F        M\n",
       "year                  \n",
       "2015  1781725  1913059\n",
       "2016  1767902  1893471\n",
       "2017  1721550  1845472\n",
       "2018  1694640  1809166\n",
       "2019  1665373  1779948"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "JhtPzkjoL8Z7",
    "outputId": "e30a32ca-521d-4fb4-b4e9-87b45d81e937"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_births.plot(title='Total births by sex and year', figsize=(12,6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "rEb5MJBzL8Z8"
   },
   "outputs": [],
   "source": [
    "def add_prop(group):\n",
    "    # Integer division floors\n",
    "    births = group.births.astype(float)\n",
    "    group['percent'] = births / births.sum()\n",
    "    return group\n",
    "\n",
    "names = names.groupby(['year', 'sex']).apply(add_prop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "G17cNsnBL8Z9",
    "outputId": "637b85af-8eae-4932-eba0-d189b4d647c5"
   },
   "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>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.077643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name sex  births  year   percent\n",
       "0       Mary   F    7065  1880  0.077643\n",
       "1       Anna   F    2604  1880  0.028618\n",
       "2       Emma   F    2003  1880  0.022013\n",
       "3  Elizabeth   F    1939  1880  0.021309\n",
       "4     Minnie   F    1746  1880  0.019188"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#names\n",
    "names.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "RkKT-q6aL8Z_",
    "outputId": "a5329651-2760-4780-9626-c79d98c823e7"
   },
   "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>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>John</td>\n",
       "      <td>M</td>\n",
       "      <td>9655</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.087383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>943</th>\n",
       "      <td>William</td>\n",
       "      <td>M</td>\n",
       "      <td>9532</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.086269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2938</th>\n",
       "      <td>John</td>\n",
       "      <td>M</td>\n",
       "      <td>8769</td>\n",
       "      <td>1881</td>\n",
       "      <td>0.087041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7116</th>\n",
       "      <td>John</td>\n",
       "      <td>M</td>\n",
       "      <td>8894</td>\n",
       "      <td>1883</td>\n",
       "      <td>0.085004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name sex  births  year   percent\n",
       "942      John   M    9655  1880  0.087383\n",
       "943   William   M    9532  1880  0.086269\n",
       "2938     John   M    8769  1881  0.087041\n",
       "7116     John   M    8894  1883  0.085004"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[names.percent > 0.085]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "AF9caZ_IL8aA",
    "outputId": "2afe9e46-7817-4858-f9f3-add03014dff5"
   },
   "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>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91312</th>\n",
       "      <td>Stephany</td>\n",
       "      <td>F</td>\n",
       "      <td>6</td>\n",
       "      <td>1910</td>\n",
       "      <td>0.000015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95509</th>\n",
       "      <td>Stephany</td>\n",
       "      <td>F</td>\n",
       "      <td>8</td>\n",
       "      <td>1911</td>\n",
       "      <td>0.000019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101809</th>\n",
       "      <td>Stephany</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>1912</td>\n",
       "      <td>0.000009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106541</th>\n",
       "      <td>Stephany</td>\n",
       "      <td>F</td>\n",
       "      <td>13</td>\n",
       "      <td>1913</td>\n",
       "      <td>0.000021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113864</th>\n",
       "      <td>Stephany</td>\n",
       "      <td>F</td>\n",
       "      <td>12</td>\n",
       "      <td>1914</td>\n",
       "      <td>0.000016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1826773</th>\n",
       "      <td>Stephany</td>\n",
       "      <td>F</td>\n",
       "      <td>155</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.000087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860288</th>\n",
       "      <td>Stephany</td>\n",
       "      <td>F</td>\n",
       "      <td>111</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.000063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893459</th>\n",
       "      <td>Stephany</td>\n",
       "      <td>F</td>\n",
       "      <td>99</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926348</th>\n",
       "      <td>Stephany</td>\n",
       "      <td>F</td>\n",
       "      <td>81</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.000048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958607</th>\n",
       "      <td>Stephany</td>\n",
       "      <td>F</td>\n",
       "      <td>76</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.000046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>136 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             name sex  births  year   percent\n",
       "91312    Stephany   F       6  1910  0.000015\n",
       "95509    Stephany   F       8  1911  0.000019\n",
       "101809   Stephany   F       5  1912  0.000009\n",
       "106541   Stephany   F      13  1913  0.000021\n",
       "113864   Stephany   F      12  1914  0.000016\n",
       "...           ...  ..     ...   ...       ...\n",
       "1826773  Stephany   F     155  2015  0.000087\n",
       "1860288  Stephany   F     111  2016  0.000063\n",
       "1893459  Stephany   F      99  2017  0.000058\n",
       "1926348  Stephany   F      81  2018  0.000048\n",
       "1958607  Stephany   F      76  2019  0.000046\n",
       "\n",
       "[136 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[names.name.str.startswith('Stephany')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "BPs3OWg6L8aE",
    "outputId": "6e5e78b7-d35c-47d1-d691-13e34f61d2d3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(names.groupby(['year', 'sex']).percent.sum(), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "2tk37xdzL8aF"
   },
   "outputs": [],
   "source": [
    "def get_top1000(group):\n",
    "    return group.sort_values(by='births', ascending=False)[:1000]\n",
    "\n",
    "grouped = names.groupby(['year', 'sex'])\n",
    "top1000 = grouped.apply(get_top1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "8Ct5ta2AL8aG",
    "outputId": "eec3cc70-6bd4-42c8-b639-8d7538ee7f70"
   },
   "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></th>\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"15\" valign=\"top\">1880</th>\n",
       "      <th rowspan=\"15\" valign=\"top\">F</th>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Margaret</td>\n",
       "      <td>F</td>\n",
       "      <td>1578</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ida</td>\n",
       "      <td>F</td>\n",
       "      <td>1472</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alice</td>\n",
       "      <td>F</td>\n",
       "      <td>1414</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bertha</td>\n",
       "      <td>F</td>\n",
       "      <td>1320</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>1288</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Annie</td>\n",
       "      <td>F</td>\n",
       "      <td>1258</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Clara</td>\n",
       "      <td>F</td>\n",
       "      <td>1226</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ella</td>\n",
       "      <td>F</td>\n",
       "      <td>1156</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Florence</td>\n",
       "      <td>F</td>\n",
       "      <td>1063</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cora</td>\n",
       "      <td>F</td>\n",
       "      <td>1045</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  name sex  births  year  percent\n",
       "year sex                                         \n",
       "1880 F   0        Mary   F    7065  1880    0.078\n",
       "         1        Anna   F    2604  1880    0.029\n",
       "         2        Emma   F    2003  1880    0.022\n",
       "         3   Elizabeth   F    1939  1880    0.021\n",
       "         4      Minnie   F    1746  1880    0.019\n",
       "         5    Margaret   F    1578  1880    0.017\n",
       "         6         Ida   F    1472  1880    0.016\n",
       "         7       Alice   F    1414  1880    0.016\n",
       "         8      Bertha   F    1320  1880    0.015\n",
       "         9       Sarah   F    1288  1880    0.014\n",
       "         10      Annie   F    1258  1880    0.014\n",
       "         11      Clara   F    1226  1880    0.013\n",
       "         12       Ella   F    1156  1880    0.013\n",
       "         13   Florence   F    1063  1880    0.012\n",
       "         14       Cora   F    1045  1880    0.011"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#top1000\n",
    "pd.options.display.float_format = '{:,.3f}'.format\n",
    "top1000[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "zdaQsQBfL8aH"
   },
   "outputs": [],
   "source": [
    "boys = top1000[top1000.sex == 'M']\n",
    "girls = top1000[top1000.sex == 'F']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "gUN_-69hL8aI",
    "outputId": "55b51f64-75b6-4197-d2fa-87452b33d2a8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
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       "    <tr>\n",
       "      <th>year</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <th>M</th>\n",
       "      <th>954</th>\n",
       "      <td>Walter</td>\n",
       "      <td>M</td>\n",
       "      <td>1755</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <th>M</th>\n",
       "      <th>2950</th>\n",
       "      <td>Walter</td>\n",
       "      <td>M</td>\n",
       "      <td>1670</td>\n",
       "      <td>1881</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <th>M</th>\n",
       "      <th>4975</th>\n",
       "      <td>Walter</td>\n",
       "      <td>M</td>\n",
       "      <td>1805</td>\n",
       "      <td>1882</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <th>M</th>\n",
       "      <th>7128</th>\n",
       "      <td>Walter</td>\n",
       "      <td>M</td>\n",
       "      <td>1705</td>\n",
       "      <td>1883</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <th>M</th>\n",
       "      <th>9330</th>\n",
       "      <td>Walter</td>\n",
       "      <td>M</td>\n",
       "      <td>1902</td>\n",
       "      <td>1884</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1885</th>\n",
       "      <th>M</th>\n",
       "      <th>11652</th>\n",
       "      <td>Walter</td>\n",
       "      <td>M</td>\n",
       "      <td>1808</td>\n",
       "      <td>1885</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1886</th>\n",
       "      <th>M</th>\n",
       "      <th>14031</th>\n",
       "      <td>Walter</td>\n",
       "      <td>M</td>\n",
       "      <td>1880</td>\n",
       "      <td>1886</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1887</th>\n",
       "      <th>M</th>\n",
       "      <th>16447</th>\n",
       "      <td>Walter</td>\n",
       "      <td>M</td>\n",
       "      <td>1705</td>\n",
       "      <td>1887</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1888</th>\n",
       "      <th>M</th>\n",
       "      <th>18988</th>\n",
       "      <td>Walter</td>\n",
       "      <td>M</td>\n",
       "      <td>2040</td>\n",
       "      <td>1888</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <th>M</th>\n",
       "      <th>21644</th>\n",
       "      <td>Walter</td>\n",
       "      <td>M</td>\n",
       "      <td>1916</td>\n",
       "      <td>1889</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  name sex  births  year  percent\n",
       "year sex                                         \n",
       "1880 M   954    Walter   M    1755  1880    0.016\n",
       "1881 M   2950   Walter   M    1670  1881    0.017\n",
       "1882 M   4975   Walter   M    1805  1882    0.016\n",
       "1883 M   7128   Walter   M    1705  1883    0.016\n",
       "1884 M   9330   Walter   M    1902  1884    0.017\n",
       "1885 M   11652  Walter   M    1808  1885    0.017\n",
       "1886 M   14031  Walter   M    1880  1886    0.017\n",
       "1887 M   16447  Walter   M    1705  1887    0.017\n",
       "1888 M   18988  Walter   M    2040  1888    0.017\n",
       "1889 M   21644  Walter   M    1916  1889    0.017"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Walter_names = boys[boys.name=='Walter']\n",
    "Walter_names[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "OUJjOp_FL8aI",
    "outputId": "87d5b8d3-171f-4ea0-a1ac-bc7f94116dde"
   },
   "outputs": [],
   "source": [
    "top1000.drop(['sex','year'], axis=1, inplace=True)\n",
    "total_births_top1000 = top1000.pivot_table('births', index='year', columns='name', aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>name</th>\n",
       "      <th>Aaden</th>\n",
       "      <th>Aadhya</th>\n",
       "      <th>Aaliyah</th>\n",
       "      <th>Aanya</th>\n",
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       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.000</td>\n",
       "      <td>28.000</td>\n",
       "      <td>27.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000</td>\n",
       "      <td>21.000</td>\n",
       "      <td>27.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.000</td>\n",
       "      <td>32.000</td>\n",
       "      <td>21.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.000</td>\n",
       "      <td>35.000</td>\n",
       "      <td>25.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>8.000</td>\n",
       "      <td>58.000</td>\n",
       "      <td>27.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 7211 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "name  Aaden  Aadhya  Aaliyah  Aanya  Aarav   Aaron  Aarush  Aayan  Ab  \\\n",
       "year                                                                    \n",
       "1880    NaN     NaN      NaN    NaN    NaN 102.000     NaN    NaN NaN   \n",
       "1881    NaN     NaN      NaN    NaN    NaN  94.000     NaN    NaN NaN   \n",
       "1882    NaN     NaN      NaN    NaN    NaN  85.000     NaN    NaN NaN   \n",
       "1883    NaN     NaN      NaN    NaN    NaN 105.000     NaN    NaN NaN   \n",
       "1884    NaN     NaN      NaN    NaN    NaN  97.000     NaN    NaN NaN   \n",
       "\n",
       "name  Abagail  ...  Zoey  Zoie   Zola  Zollie   Zona   Zora   Zula  Zuri  \\\n",
       "year           ...                                                         \n",
       "1880      NaN  ...   NaN   NaN  7.000     NaN  8.000 28.000 27.000   NaN   \n",
       "1881      NaN  ...   NaN   NaN 10.000     NaN  9.000 21.000 27.000   NaN   \n",
       "1882      NaN  ...   NaN   NaN  9.000     NaN 17.000 32.000 21.000   NaN   \n",
       "1883      NaN  ...   NaN   NaN 10.000     NaN 11.000 35.000 25.000   NaN   \n",
       "1884      NaN  ...   NaN   NaN 14.000   6.000  8.000 58.000 27.000   NaN   \n",
       "\n",
       "name  Zyaire  Zyon  \n",
       "year                \n",
       "1880     NaN   NaN  \n",
       "1881     NaN   NaN  \n",
       "1882     NaN   NaN  \n",
       "1883     NaN   NaN  \n",
       "1884     NaN   NaN  \n",
       "\n",
       "[5 rows x 7211 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births_top1000.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "TNmOR2YIL8aJ"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:xlabel='year'>, <AxesSubplot:xlabel='year'>,\n",
       "       <AxesSubplot:xlabel='year'>, <AxesSubplot:xlabel='year'>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset = total_births_top1000[['John', 'Harry', 'Mary', 'Marilyn']]\n",
    "subset.plot(subplots=True, figsize=(12, 12), grid=False,\n",
    "title=\"Number of births per year\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "Mp4ohkV0L8aK"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Sum of table1000.percent by year and sex'}, xlabel='year'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table = top1000.pivot_table('percent', index='year', columns='sex', aggfunc=sum)\n",
    "\n",
    "table.plot(title='Sum of table1000.percent by year and sex', \n",
    "           yticks=np.linspace(0, 1.2, 13), \n",
    "           xticks=range(1880, 2020, 10), \n",
    "           figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "1wwbs_jiL8aL"
   },
   "outputs": [],
   "source": [
    "df = boys[boys.year == 2019]\n",
    "prop_cumsum = df.sort_values(by='percent', ascending=False).percent.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "51uc51PHL8aM"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "154"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_quantile_count(group, q=0.5):\n",
    "    group = group.sort_values(by='percent', ascending=False)\n",
    "    return group.percent.cumsum().values.searchsorted(q) + 1\n",
    "\n",
    "prop_cumsum.values.searchsorted(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "tN0-6CoIL8aO"
   },
   "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>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>38</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>39</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex    F   M\n",
       "year        \n",
       "1880  38  14\n",
       "1881  38  14\n",
       "1882  38  15\n",
       "1883  39  15\n",
       "1884  39  16"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diversity = top1000.groupby(['year', 'sex']).apply(get_quantile_count)\n",
    "diversity = diversity.unstack('sex')\n",
    "diversity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "NeejVpMUL8aP"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Number of popular names in top 50%'}, xlabel='year'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diversity.plot(title=\"Number of popular names in top 50%\", figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "2n2qOHH4L8aQ"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "      <th colspan=\"3\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</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>a</th>\n",
       "      <td>108,395.000</td>\n",
       "      <td>691,251.000</td>\n",
       "      <td>675,059.000</td>\n",
       "      <td>977.000</td>\n",
       "      <td>5,207.000</td>\n",
       "      <td>28,772.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>694.000</td>\n",
       "      <td>454.000</td>\n",
       "      <td>411.000</td>\n",
       "      <td>3,914.000</td>\n",
       "      <td>39,149.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>5.000</td>\n",
       "      <td>49.000</td>\n",
       "      <td>953.000</td>\n",
       "      <td>482.000</td>\n",
       "      <td>15,472.000</td>\n",
       "      <td>23,279.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6,751.000</td>\n",
       "      <td>3,732.000</td>\n",
       "      <td>2,633.000</td>\n",
       "      <td>22,114.000</td>\n",
       "      <td>262,140.000</td>\n",
       "      <td>44,690.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>133,592.000</td>\n",
       "      <td>435,050.000</td>\n",
       "      <td>315,891.000</td>\n",
       "      <td>28,662.000</td>\n",
       "      <td>178,814.000</td>\n",
       "      <td>129,916.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                   F                                  M              \\\n",
       "year               1910        1960        2010       1910        1960   \n",
       "last_letter                                                              \n",
       "a           108,395.000 691,251.000 675,059.000    977.000   5,207.000   \n",
       "b                   NaN     694.000     454.000    411.000   3,914.000   \n",
       "c                 5.000      49.000     953.000    482.000  15,472.000   \n",
       "d             6,751.000   3,732.000   2,633.000 22,114.000 262,140.000   \n",
       "e           133,592.000 435,050.000 315,891.000 28,662.000 178,814.000   \n",
       "\n",
       "sex                      \n",
       "year               2010  \n",
       "last_letter              \n",
       "a            28,772.000  \n",
       "b            39,149.000  \n",
       "c            23,279.000  \n",
       "d            44,690.000  \n",
       "e           129,916.000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract last letter from name column\n",
    "\n",
    "get_last_letter = lambda x: x[-1]\n",
    "last_letters = names['name'].map(get_last_letter)\n",
    "\n",
    "last_letters.name = 'last_letter'\n",
    "\n",
    "table = names.pivot_table('births', index=last_letters, columns=['sex', 'year'], aggfunc=sum)\n",
    "\n",
    "subtable = table.reindex(columns=[1910, 1960, 2010], level='year')\n",
    "subtable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "yMjnU0j5L8aR"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex  year\n",
       "F    1910     396,482.000\n",
       "     1960   2,022,126.000\n",
       "     2010   1,770,632.000\n",
       "M    1910     194,214.000\n",
       "     1960   2,132,733.000\n",
       "     2010   1,911,572.000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "Dr-MxbNFL8aR"
   },
   "outputs": [],
   "source": [
    "letter_prop = subtable / subtable.sum().astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "Kwz1A8TlL8aT"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Female'}, xlabel='last_letter'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Hdtttx7HHHst3vvOdIednkSVJkka9FStWcP3113PYYYfx61//mhe84AUAPP/5z+fXv/71M167atUq9t577/X7U6ZMYdWqVUPOySJLkiSNao888ghvfetb+exnP8suu+yywbkkJBmWvCyyJEnSqPXkk0/y1re+lXe/+9285S1vAeD3fu/3uOeeewC455572GuvvZ4xxuTJk1m5cuX6/YGBASZPnjzk3CyyJEnSqFRVvOc97+GAAw7gAx/4wPrjxxxzDBdccAEAF1xwAXPnzn3GOIcccgh33HEHd999N0888QQXXXQRxxxzzJDz8+lCSZI0Kv30pz/lK1/5CjNnzuTlL385AH/7t3/L6aefzn/6T/+JL33pS+y777584xvfAODf//3fmTVrFg899BDPec5z+OxnP8uyZcvYZZdd+Md//EeOOuoonnrqKU466SQOPPDAIednkSVJkoaslykX+u1Vr3oVVbXRc1deeeXTjj3/+c9nYGBgo+2PPvpojj766L7m5+1CSZKkFvRUZCWZk+S2JMuTnL6R8x9IsizJTUmuTLJv17mnktzQfCzsZ/KSJEkj1WZvFyaZAJwDzAYGgEVJFlbVsq5m1wOzqurRJO8DPg28ozn3u6p6eX/TliRJGtl66ck6FFheVXdV1RPARcAGw/Sr6odV9Wizew0wpb9pSpKkkWZT46HGoq15rb0UWZOBlV37A82xTXkP8L2u/YlJFie5JsmbtzhDSZI04kycOJHVq1ePi0Krqli9ejUTJ07couv6+nRhkuOAWcCruw7vW1WrkuwH/CDJ0qq6c9B1JwMnA+yzzz79TEmSJLVgypQpDAwMcN999w13Ks+KiRMnMmXKlt2o66XIWgXs3bU/pTm2gSSvBz4MvLqqHl93vKpWNf/eleQq4CBggyKrqs4HzgeYNWvW2C+JJUka5bbddlumTZs23GmMaL3cLlwETE8yLcl2wLHABk8JJjkIOA84pqru7Tq+W5Ltm+09gT8CugfMS5IkjUmb7cmqqjVJ5gGXAxOABVV1S5L5wOKqWgicDewEXNwswvirqjoGOAA4L8laOgXdWYOeSpQkSRqTehqTVVWXApcOOnZG1/brN3Hdz4CZQ0lQkiRpNHLGd0mSpBZYZEmSJLXAIkuSJKkFFlmSJEktsMiSJElqgUWWJElSCyyyJEmSWmCRJUmS1AKLLEmSpBZYZEmSJLXAIkuSJKkFFlmSJEktsMiSJElqgUWWJElSCyyyJEmSWmCRJUmS1AKLLEmSpBb0VGQlmZPktiTLk5y+kfMfSLIsyU1Jrkyyb9e5E5Lc0Xyc0M/kJUmSRqrNFllJJgDnAG8AZgDvTDJjULPrgVlV9QfAN4FPN9fuDnwUOAw4FPhokt36l74kSdLI1EtP1qHA8qq6q6qeAC4C5nY3qKofVtWjze41wJRm+yjg+1V1f1U9AHwfmNOf1CVJkkauXoqsycDKrv2B5timvAf43lZeK0mSNCZs089gSY4DZgGv3sLrTgZOBthnn336mZIkSdKw6KUnaxWwd9f+lObYBpK8HvgwcExVPb4l11bV+VU1q6pmTZo0qdfcJUmSRqxeiqxFwPQk05JsBxwLLOxukOQg4Dw6Bda9XacuB45Mslsz4P3I5pgkSdKYttnbhVW1Jsk8OsXRBGBBVd2SZD6wuKoWAmcDOwEXJwH4VVUdU1X3J/k4nUINYH5V3d/KK5EkSRpBehqTVVWXApcOOnZG1/brn+HaBcCCrU1QkiRpNHLGd0mSpBZYZEmSJLXAIkuSJKkFFlmSJEktsMiSJElqgUWWJElSCyyyJEmSWmCRJUmS1AKLLEmSpBZYZEmSJLXAIkuSJKkFFlmSJEktsMiSJElqgUWWJElSCyyyJEmSWrDNcCcgSZK0xc7cddD+g8OTxzOwJ0uSJKkFPRVZSeYkuS3J8iSnb+T8EUmuS7ImydsGnXsqyQ3Nx8J+JS5JkjSSbfZ2YZIJwDnAbGAAWJRkYVUt62r2K+BE4IMbCfG7qnr50FOVJEkaPXoZk3UosLyq7gJIchEwF1hfZFXViubc2hZylKRnx+AxHtP2GZ48JI0JvdwunAys7NofaI71amKSxUmuSfLmLUlOkiRptHo2ni7ct6pWJdkP+EGSpVV1Z3eDJCcDJwPss4//c5QkSaNfL0XWKmDvrv0pzbGeVNWq5t+7klwFHATcOajN+cD5ALNmzapeY2vopp7+3fXbK8564zBmIknS2NLL7cJFwPQk05JsBxwL9PSUYJLdkmzfbO8J/BFdY7kkSZLGqs32ZFXVmiTzgMuBCcCCqrolyXxgcVUtTHII8G1gN+BNST5WVQcCBwDnNQPinwOcNeipREkaVhv05k4cxkQkjTk9jcmqqkuBSwcdO6NrexGd24iDr/sZMHOIOUqSJI06zvguSZLUAossSZKkFlhkSZIktcAiS5IkqQUWWZIkSS14NmZ8lyRpyLqn2wAnUNbIZ0+WJElSCyyyJEmSWmCRJUmS1ALHZI1WZ+7atf3g8OUhSZI2yp4sSZKkFlhkSZIktcAiS5IkqQUWWZIkSS1w4Pso8bRJ+CYOUyKSJKkn9mRJkiS1wCJLkiSpBT0VWUnmJLktyfIkp2/k/BFJrkuyJsnbBp07IckdzccJ/UpckiRpJNvsmKwkE4BzgNnAALAoycKqWtbV7FfAicAHB127O/BRYBZQwJLm2gf6k76kEa974lxw8lxJ40YvA98PBZZX1V0ASS4C5gLri6yqWtGcWzvo2qOA71fV/c357wNzgH8ecuaSpPHNlS80wvVyu3AysLJrf6A51ouhXCtJkjRqjYiB70lOTrI4yeL77rtvuNORJEkasl6KrFXA3l37U5pjvejp2qo6v6pmVdWsSZMm9RhakiRp5OqlyFoETE8yLcl2wLHAwh7jXw4cmWS3JLsBRzbHJEmSxrTNFllVtQaYR6c4uhX4RlXdkmR+kmMAkhySZAB4O3Bekluaa+8HPk6nUFsEzF83CF6SJGks62lZnaq6FLh00LEzurYX0bkVuLFrFwALhpCjJEnSqDMiBr5LkiSNNS4QLUl6Vs28YOb67aUnLB3GTKR22ZMlSZLUAnuy9B9c/kSSpL6xJ0uSJKkFFlmSJEktsMiSJElqgWOyJEnSqDD19O+u314xcRgT6ZE9WZIkSS2wJ0uSNK5t0Dty1huHMRONNRZZkp5V3RNRgpNRShq7LLK0Sc7KLEnS1nNMliRJUgvsyZLUd6PtCSD137M9zqlvt6Fd+UJ9ZE+WJElSCyyyJEmSWuDtQkmSNOqNxCeXeyqykswBPgdMAP6pqs4adH574ELgYGA18I6qWpFkKnArcFvT9JqqOqVPuUsaR7rH+IDzGUka+TZbZCWZAJwDzAYGgEVJFlbVsq5m7wEeqKr9kxwLfAp4R3Puzqp6eX/TliRJGtl6GZN1KLC8qu6qqieAi4C5g9rMBS5otr8JvC5J+pemJEnS6NLL7cLJwMqu/QHgsE21qao1SR4E9mjOTUtyPfAQ8JGq+snQUpaeHSPx/r4kafRoe+D7PcA+VbU6ycHAJUkOrKqHuhslORk4GWCfffZpOSVJm+QcQZLUN73cLlwF7N21P6U5ttE2SbYBdgVWV9XjVbUaoKqWAHcCvz/4E1TV+VU1q6pmTZo0actfhSRJ0gjTS0/WImB6kml0iqljgXcNarMQOAH4OfA24AdVVUkmAfdX1VNJ9gOmA3f1LXtpiHxiTZLUls0WWc0Yq3nA5XSmcFhQVbckmQ8srqqFwJeAryRZDtxPpxADOAKYn+RJYC1wSlXd38YLkSRJGkl6GpNVVZcClw46dkbX9mPA2zdy3beAbw0xR0kaM3ygQho/XFZHkiSpBS6rI3Xrfrpumk+6SpK2nkWWNI49beD/xGFKRJLGIIssSaOTc3pJGuEckyVJktQCe7IkSe0a3OvoeEeNE/ZkSZIktcAiS5IkqQXeLpQ0JnRP8ukEn5JGAousMcAZpEcXiwFp9PD3VUNhkbUJFi6SJGkoLLIkbZL/i5ekrWeRJUl99rSZ9M964zBlIg2Nd3WGZkwUWf4QSJKkkWZMFFmjibdfpHHIhcelccl5siRJklpgT5ZGJxcHlqRRa7zc1empyEoyB/gcMAH4p6o6a9D57YELgYOB1cA7qmpFc+5DwHuAp4DTquryvmU/GrhmlyRpBHvagxoT3/UfO/7NGpLNFllJJgDnALOBAWBRkoVVtayr2XuAB6pq/yTHAp8C3pFkBnAscCDwQuDfkvx+VT21pYl2/xD4pM4o011o2uMk9Ye9udKI10tP1qHA8qq6CyDJRcBcoLvImguc2Wx/E/jHJGmOX1RVjwN3J1nexPv5kLIe4b1DGxSEE4cxEUljiu8tGi2cxqSjlyJrMrCya38AOGxTbapqTZIHgT2a49cMunbyVmfbZ3aRtuPpX9f/2B7KdBvP9AdmpN3f3zDXd214soWfLacx0Uj7HdCmtfX7OqJ/BkZ458gzGcrXNVX1zA2StwFzquq9zf6fA4dV1byuNjc3bQaa/TvpFGJnAtdU1Veb418CvldV3xz0OU4GTm52Xwzc1mP+ewK/6bHtlmgj7njPdby//rbimqu5jvdcx/vrbyuuufYed9+qmrSxE730ZK0C9u7an9Ic21ibgSTbALvSGQDfy7VU1fnA+T3ksoEki6tq1pZeNxxxx3uu4/31txXXXM11vOc63l9/W3HNtT9xe5knaxEwPcm0JNvRGci+cFCbhcAJzfbbgB9Up4tsIXBsku2TTAOmA9cOJWFJkqTRYLM9Wc0Yq3nA5XSmcFhQVbckmQ8srqqFwJeArzQD2++nU4jRtPsGnUHya4BTt+bJQkmSpNGmp3myqupS4NJBx87o2n4MePsmrv0k8Mkh5PhMtvgW4zDGHe+5jvfX31ZcczXX8Z7reH/9bcU11z7E3ezAd0mSJG051y6UJElqgUVWI8nUZiqKUSfJmUk+ONx5PJMkpyW5NcnXhjuXTWnzZyDJz9qI2+/YLX8NHmkjrtRPSZ6X5K+GOw+NDRZZerb8FTC7qt493IkMh6r6w9EYW2pDOkbq35/n0Xm/koZspP6Qb1aSS5IsSXJLM5lpP2yT5GtNj8s3k+zQj6BJjk9yU5Ibk3ylTzE/nOT2JFfTmcC1HzGPS3JtkhuSnNesW9mPuF8A9gO+l+S/9Cnm/0hyW5Krk/xzH3vyJiT5YvNzdUWS5/YjaJu9OG3FTrJfkuuTHNJG/K3V9Lb9IsmXm9+BryV5fZKfJrkjyaFDjH1rv38Gknwgyc3Nx18PNV4Tc93Xoa/vWd3vLf383WryvS3JhcDNbDiH4tbE2zHJd5v31ZuTvKMfeQJnAS9q3gfP7kfAwT3EST6Y5Mwhxjwryald+0O+o5Hkb5Kc1mz/Q5IfNNuvHcpdiCSHNH8DJzbft1uSvHQouTZx53f/PiX5ZJL39yHuKc33/4Ykdyf54VYHq6pR+QHs3vz7XDq/sHsMMd5UoIA/avYXAB/sQ54HArcDe3bnPcSYBwNLgR2AXYDlQ80VOAD4V2DbZv9c4Pg+fr9WrPsa9CHWIcANwERgZ+COPn2vptKZauTlzf43gOP6lPMj/fpathm7+RrcTKdwvx542UjLs+v7NJPOfxSXNL+v69ZLvWQk/Qx0/b7uCOwE3AIc1KevQ1/fs9p4bxmU71rglX2K91bgi137u/Yxz5v7EWtTMYEPAmcOMeZBwI+69pcBew8x5iuBi5vtn9CZ13Jb4KPAXw4x9ieAzwDnAB/q49f1umb7OcCdDLEWGBR/2+br8KatjTFqe7KA05LcSGdtxL3pTHQ6VCur6qfN9leBV/Uh5mvp/ND+BqCq7u9DzD8Gvl1Vj1bVQzx9ctit8To6b7CLktzQ7O/Xh7ht+CPgO1X1WFU9TKc47Je7q+qGZnsJnV/i8WYS8B3g3VV143Answl3V9XSqlpLp2i5sjrviksZ+ves3z8Dr6Lz+/rbqnoE+F90fof7od/vWW28t3T7ZVVds/lmPVkKzE7yqSR/XFUP9inuqFBV1wN7JXlhkpcBD1TVys1dtxlLgIOT7AI8DvwcmEXn5+InQ4w9H5jdxPv0EGMBUFUrgNVJDgKOBK6vqtX9iN34HJ3J1bf6b0xP82SNNEn+BHg9cHhVPZrkKjq9GkM1eD6L8TS/RYALqupDw53IMHu8a/spOj2l482DwK/o/MFeNsy5bEr392lt1/5ahv6+Npp+Bkbbe9Zv+xWoqm5P8grgaOATSa6sqvn9it9na9hweE4//l4BXExnlZXnA/8y1GBV9WSSu4ETgZ8BNwGvAfYHbh1i+D3o9ORuS+f19+tn4Z/o5Pt8Or25fZHkRGBfYN5mmj6j0dqTtSudqv3RJC+h08XZD/skObzZfhdwdR9i/gB4e5I9AJLs3oeYPwbenOS5SXYG3tSHmFcCb0uyF3TyTLJvH+K24afAm5r7+zsBfzrcCY0xTwB/Bhyf5F3DncwY8BM6v687JNmRztd2qL0C6/T7PauN95ZWJHkh8GhVfRU4G3hFn0I/TGcYQj/9mk6v0x5Jtqd/71n/QmeFlbfRKbj64Sd0bmf+uNk+hU4P0VAL+POA/wF8DfjUEGN1+zYwh84wksv7ETDJwXS+Bsc1veVbbVT2ZAGXAackuRW4jc4tw364DTg1yQI6/4P/n0MNWJ2lhT4J/CjJU3TGuZw4xJjXJfkX4EbgXjrrSw41z2VJPgJckc5TP08CpwK/HGrsfquqRUkW0vlf1q/p3DYYV7cKBul770VV/TbJnwLfT/JIdZbP0lZofl+/zH+s2/pPza2efujre1Yb7y0tmgmcnWQtnfer9/UjaFWtbh6iuBn4XlX9TR9iPpnOUnTXAquAXww1ZhP3lqYYXlVV9/QjJp3C6sPAz5v3gccY4n8KkhwPPFlVX0/ngaqfJXltVf1gqMlW1RPNwPT/W/1btm8esDvwwyTQWULwvVsTyBnfNSol2amqHmmepvoxcHJVXTfceT3bmh7S66pqpPY6qiVJpgL/u6qG/JTWM3yOM+k8sPCZtj6HNBRNp8B1wNur6o7hzmew0Xq7UDq/GaB/HfCtcVpgvZDOwFT/AEoad5LMoPME7JUjscACe7IkSZJaYU+WJElSCyyyJEmSWmCRJUmS1AKLLEmSpBZYZEkaEbKVC10n+evNLYycZEWSPTfT5r93bT8vyV9tTT6StI5FlqTR7q/pLGg8VP+9a/ulwDlJep6wOR2+p0pazzcESSNKkp2SXJnk8SRrk/wuySPNx78luTHJzUnekeQ04IV0Zmb+YY/xj0tybZIbkpyXZEKSs4DnNse+Bvy3pvniJGc31/1NkkVJbkrysebY1CS3JbkQuJnOYvWSBDhPlqQRolm+Z6em92gHOssmfQD4NDAdeAswp6r+c9N+16p6MMkKYFZV/eYZYq8AZgGTmnhvaZY6ORe4pqouXPf5m/avorOUyLZVtSbJkXTWh/tLOoupL2zi/Aq4C/jDqurX8l6Sxgh7siSNNAH+lk4P1aeBycDv0Vmj8simJ+s3wLIkn1h/UXJis+bcPyT5v0nuSvKHSU4EpgC3Ah8CDgYWJbkLOAn4YpKVwHbPkNOfAu+ks3j2Y8AfAr/fnPulBZakjbHIkjTSvJtOj9P/B5xCZxHwiVV1O51CazWdgumrwJHATl3XHkanB2wP4OvARcAhwACdBYT/E/D1qno5nQLrEOC5wBuBbZK8eRM5vYHO4u7Po1OwLQcmNOd+O7SXK2msssiSNNLsCtzbbP8rsC9wU5IfALPpFESfAl4M/AMwEdi5aX93Vf2/VfUU8C90xkjNb85dRacX6u1J9qqqq4BVwN5VdROdXqrXNG3XP+mY5PeaHCbSGWJxL/Bl4M/7+qoljTk9PzkjSc+Sr9Eprl4I/IBOgfMG4Bg6RdAjQAG/A14LPAxcBqyl0+u1zu8AqurXSdYdewT4EnBFkh2B5wNrmqcCtwFObKZ6+HDT/gY6PVjb0Hni8MEmVtEZiyVJm+TAd0kjUjNY/b1V9W/N/gvoFDY7V9WajbQ/sWn/qmZ/f+COqkpXmwHg2Kq6OsmdwD8C/7OqHkvyWWDPqjouyVTgbmBbOrcuN/l5JWlTvF0oaVSoqnuAK4C/S7JLkuckeVGSV29lyJ2B+5sC61DgXc/S55U0TlhkSRpNjqfzFOAy4AHgm8ALus6/rJnr6gY60yyQZOYmYv0VMD/Jw8AZwDeG8Hkl6Wm8XShJktQCe7IkSZJaYJElSZLUgp6KrCRzmvW5lic5/RnavTVJJZnVdexDzXW3JTmqH0lLkiSNdJudJyvJBOAcOpMADtBZjmJhVS0b1G5n4P3A/+k6NgM4FjiQzpw3/5bk95uJAiVJksasXnqyDgWWV9VdVfUEnWUq5m6k3cfpzML8WNexucBFVfV4Vd1NZymKQ4eYsyRJ0ojXy4zvk4GVXfsDdNYHWy/JK+gsTfHdJH8z6NprBl07efAnSHIycDLAjjvuePBLXvKS3rKXJEkaRkuWLPlNVU3a2LkhL6vTLEfx98CJWxujqs4HzgeYNWtWLV68eKhpSZIktS7JLzd1rpciaxWdRVbXmdIcW2dnOmt6XdWs6fV8YGGSY3q4VpIkaUzqZUzWImB6kmlJtqMzkH3hupNV9WBV7VlVU6tqKp3bg8dU1eKm3bFJtk8yDZgOXNv3VyFJkjTCbLYnq6rWJJkHXA5MABZU1S1J5gOLq2rhM1x7S5Jv0FmKYg1wqk8WSpKk8WDELavjmCxJkjRaJFlSVbM2ds4Z3yVJklpgkSVJktQCiyxJkqQWWGRJkiS1YMiTkY4EMy+YucH+0hOWDlMmkiRJHfZkSZIktcAiS5IkqQUWWZIkSS2wyJIkSWqBRZYkSVILLLIkSZJaYJElSZLUAossSZKkFlhkSZIktcAiS5IkqQU9FVlJ5iS5LcnyJKdv5PwpSZYmuSHJ1UlmNMenJvldc/yGJF/o9wuQJEkaiTa7dmGSCcA5wGxgAFiUZGFVLetq9vWq+kLT/hjg74E5zbk7q+rlfc1akiRphOulJ+tQYHlV3VVVTwAXAXO7G1TVQ127OwLVvxQlSZJGn16KrMnAyq79gebYBpKcmuRO4NPAaV2npiW5PsmPkvzxkLKVJEkaJfo28L2qzqmqFwH/DfhIc/geYJ+qOgj4APD1JLsMvjbJyUkWJ1l833339SslSZKkYdNLkbUK2Ltrf0pzbFMuAt4MUFWPV9XqZnsJcCfw+4MvqKrzq2pWVc2aNGlSj6lLkiSNXL0UWYuA6UmmJdkOOBZY2N0gyfSu3TcCdzTHJzUD50myHzAduKsfiUuSJI1km326sKrWJJkHXA5MABZU1S1J5gOLq2ohMC/J64EngQeAE5rLjwDmJ3kSWAucUlX3t/FCJEmSRpLNFlkAVXUpcOmgY2d0bb9/E9d9C/jWUBLcqDN33XB/2j59/xSSJElD4YzvkiRJLbDIkiRJakFPtws1dk09/bvrt1ec9cZhzESSpLHFnixJkqQWWGRJkiS1wCJLkiSpBRZZkiRJLbDIkiRJaoFFliRJUgsssiRJklpgkSVJktQCiyxJkqQWWGRJkiS1wCJLkiSpBRZZkiRJLbDIkiRJakFPRVaSOUluS7I8yekbOX9KkqVJbkhydZIZXec+1Fx3W5Kj+pm8JEnSSLXZIivJBOAc4A3ADOCd3UVU4+tVNbOqXg58Gvj75toZwLHAgcAc4NwmniRJ0pjWS0/WocDyqrqrqp4ALgLmdjeoqoe6dncEqtmeC1xUVY9X1d3A8iaeJEnSmLZND20mAyu79geAwwY3SnIq8AFgO+C1XddeM+jayVuVqSRJ0ijSt4HvVXVOVb0I+G/AR7bk2iQnJ1mcZPF9993Xr5QkSZKGTS9F1ipg7679Kc2xTbkIePOWXFtV51fVrKqaNWnSpB5SkiRJGtl6KbIWAdOTTEuyHZ2B7Au7GySZ3rX7RuCOZnshcGyS7ZNMA6YD1w49bUmSpJFts2OyqmpNknnA5cAEYEFV3ZJkPrC4qhYC85K8HngSeAA4obn2liTfAJYBa4BTq+qpll6LJEnSiNHLwHeq6lLg0kHHzujafv8zXPtJ4JNbm6AkSdJo5IzvkiRJLbDIkiRJaoFFliRJUgsssiRJklpgkSVJktQCiyxJkqQWWGRJkiS1wCJLkiSpBT1NRjoSTD39u+u3V0wcxkQkSZJ6YE+WJElSCyyyJEmSWmCRJUmS1AKLLEmSpBZYZEmSJLXAIkuSJKkFFlmSJEkt6KnISjInyW1Jlic5fSPnP5BkWZKbklyZZN+uc08luaH5WNjP5CVJkkaqzU5GmmQCcA4wGxgAFiVZWFXLuppdD8yqqkeTvA/4NPCO5tzvqurl/U1bkiRpZOulJ+tQYHlV3VVVTwAXAXO7G1TVD6vq0Wb3GmBKf9OUJEkaXXopsiYDK7v2B5pjm/Ie4Htd+xOTLE5yTZI3b+yCJCc3bRbfd999PaQkSZI0svV17cIkxwGzgFd3Hd63qlYl2Q/4QZKlVXVn93VVdT5wPsCsWbOqnzlJkiQNh156slYBe3ftT2mObSDJ64EPA8dU1ePrjlfVqubfu4CrgIOGkK8kSdKo0EuRtQiYnmRaku2AY4ENnhJMchBwHp0C696u47sl2b7Z3hP4I6B7wLwkSdKYtNnbhVW1Jsk84HJgArCgqm5JMh9YXFULgbOBnYCLkwD8qqqOAQ4Azkuylk5Bd9agpxIlSZLGpJ7GZFXVpcClg46d0bX9+k1c9zNg5lASlCRJGo2c8V2SJKkFFlmSJEktsMiSJElqgUWWJElSCyyyJEmSWmCRJUmS1AKLLEmSpBZYZEmSJLXAIkuSJKkFFlmSJEktsMiSJElqgUWWJElSC3paIFrjxJm7Dtp/cHjykCRpDLAnS5IkqQUWWZIkSS3oqchKMifJbUmWJzl9I+c/kGRZkpuSXJlk365zJyS5o/k4oZ/JS5IkjVSbLbKSTADOAd4AzADemWTGoGbXA7Oq6g+AbwKfbq7dHfgocBhwKPDRJLv1L31JkqSRqZeerEOB5VV1V1U9AVwEzO1uUFU/rKpHm91rgCnN9lHA96vq/qp6APg+MKc/qUuSJI1cvRRZk4GVXfsDzbFNeQ/wva28VpIkaUzo6xQOSY4DZgGv3sLrTgZOBthnn336mZIkSdKw6KUnaxWwd9f+lObYBpK8HvgwcExVPb4l11bV+VU1q6pmTZo0qdfcJUmSRqxeiqxFwPQk05JsBxwLLOxukOQg4Dw6Bda9XacuB45Mslsz4P3I5pgkSdKYttnbhVW1Jsk8OsXRBGBBVd2SZD6wuKoWAmcDOwEXJwH4VVUdU1X3J/k4nUINYH5V3d/KK5EkSRpBehqTVVWXApcOOnZG1/brn+HaBcCCrU1QkiRpNHLGd0mSpBZYZEmSJLXAIkuSJKkFFlmSJEktsMiSJElqgUWWJElSC/q6rI7GlpkXzFy/vfSEpcOYiSRJo489WZIkSS2wyJIkSWqBRZYkSVILLLIkSZJaYJElSZLUAossSZKkFlhkSZIktcAiS5IkqQUWWZIkSS3oqchKMifJbUmWJzl9I+ePSHJdkjVJ3jbo3FNJbmg+FvYrcUmSpJFss8vqJJkAnAPMBgaARUkWVtWyrma/Ak4EPriREL+rqpcPPVVJkqTRo5e1Cw8FllfVXQBJLgLmAuuLrKpa0Zxb20KOkiRJo04vRdZkYGXX/gBw2BZ8jolJFgNrgLOq6pItuFaSJOnpztx10P6Dw5PHM+ilyBqqfatqVZL9gB8kWVpVd3Y3SHIycDLAPvvs8yykJEmS1K5eBr6vAvbu2p/SHOtJVa1q/r0LuAo4aCNtzq+qWVU1a9KkSb2GliRJGrF6KbIWAdOTTEuyHXAs0NNTgkl2S7J9s70n8Ed0jeWSJEkaqzZbZFXVGmAecDlwK/CNqrolyfwkxwAkOSTJAPB24LwktzSXHwAsTnIj8EM6Y7IssiRJ0pjX05isqroUuHTQsTO6thfRuY04+LqfATOHmKMkSdKo82wMfJekUWnmBRv+H3HpCUuHKRNJo5HL6kiSJLXAIkuSJKkFFlmSJEktcEyWJK0zeAbpaU6OLGnr2ZMlSZLUAossSZKkFlhkSZIktcAiS5IkqQUWWZIkSS2wyJIkSWqBUzhIkqRRbyQug2VPliRJUgsssiRJklrg7UJJkjQqTD39u+u3V0wcxkR6ZE+WJElSC3oqspLMSXJbkuVJTt/I+SOSXJdkTZK3DTp3QpI7mo8T+pW4JEnSSLbZIivJBOAc4A3ADOCdSWYMavYr4ETg64Ou3R34KHAYcCjw0SS7DT1tSZKkka2XnqxDgeVVdVdVPQFcBMztblBVK6rqJmDtoGuPAr5fVfdX1QPA94E5fchbkiRpROulyJoMrOzaH2iO9WIo10qSJI1aI2Lge5KTkyxOsvi+++4b7nQkSZKGrJciaxWwd9f+lOZYL3q6tqrOr6pZVTVr0qRJPYaWJEkauXopshYB05NMS7IdcCywsMf4lwNHJtmtGfB+ZHNMkiRpTNtskVVVa4B5dIqjW4FvVNUtSeYnOQYgySFJBoC3A+cluaW59n7g43QKtUXA/OaYJEnSmNbTjO9VdSlw6aBjZ3RtL6JzK3Bj1y4AFgwhR0mSpFFnRAx8lyRJGmsssiRJklpgkSVJktQCiyxJkqQWWGRJkiS1oKenCyVprJp6+nfXb6+YOIyJSBpzLLKkTZh5wcwN9peesHSYMpEkjUbeLpQkSWqBRZYkSVILLLIkSZJaYJElSZLUAossSZKkFlhkSZIktcAiS5IkqQUWWZIkSS3oqchKMifJbUmWJzl9I+e3T/Ivzfn/k2Rqc3xqkt8luaH5+EKf85ckSRqRNjvje5IJwDnAbGAAWJRkYVUt62r2HuCBqto/ybHAp4B3NOfurKqX9zdtSZKkka2XnqxDgeVVdVdVPQFcBMwd1GYucEGz/U3gdUnSvzQlSZJGl17WLpwMrOzaHwAO21SbqlqT5EFgj+bctCTXAw8BH6mqnwwtZal/uhcHBlhx1huHKRNJ0ljT9gLR9wD7VNXqJAcDlyQ5sKoe6m6U5GTgZIB99tmn5ZQkSZLa18vtwlXA3l37U5pjG22TZBtgV2B1VT1eVasBqmoJcCfw+4M/QVWdX1WzqmrWpEmTtvxVSJIkjTC9FFmLgOlJpiXZDjgWWDiozULghGb7bcAPqqqSTGoGzpNkP2A6cFd/UpckSRq5Nnu7sBljNQ+4HJgALKiqW5LMBxZX1ULgS8BXkiwH7qdTiAEcAcxP8iSwFjilqu5v44VIkiSNJD2NyaqqS4FLBx07o2v7MeDtG7nuW8C3hpijpFGm+4ECHyaQNF61PfBd0pm7Dtp/cHjykCQ9qyyyJEmjglOuaLSxyJK6dfc6TXM6EUnS1rPIktQub5dKGqcssqRn2cwLZq7fXnrC0mHMRJLUpl7myZIkSdIWsidLkjSuOeWI2mKRJelZ1X27FLxlKmns8nahJElSCyyyJEmSWmCRJUmS1ALHZI0SznQsaaxwGhONFxZZkiSt4+S56iOLLElS3z3b0yL41KpGIossSdLo5FqjGuEc+C5JktSCnnqykswBPgdMAP6pqs4adH574ELgYGA18I6qWtGc+xDwHuAp4LSqurxv2Usavxw7o2eBg/Q1FJstspJMAM4BZgMDwKIkC6tqWVez9wAPVNX+SY4FPgW8I8kM4FjgQOCFwL8l+f2qeqrfL0SSNEINLoi9tadxopfbhYcCy6vqrqp6ArgImDuozVzggmb7m8DrkqQ5flFVPV5VdwPLm3iSJEljWi+3CycDK7v2B4DDNtWmqtYkeRDYozl+zaBrJ291tvoPXf8znDnof4V2aQ+/DZ6smjiMiYwhT5srbtDXdSTd1nl6ru9av+3vq0aT8fDUZpvzUKaqnrlB8jZgTlW9t9n/c+CwqprX1ebmps1As38nnULsTOCaqvpqc/xLwPeq6puDPsfJwMnN7ouB23rMf0/gNz223RJtxB3vuY73199WXHM11/Ge63h//W3FNdfe4+5bVZM2dqKXnqxVwN5d+1OaYxtrM5BkG2BXOgPge7mWqjofOL+HXDaQZHFVzdrS64Yj7njPdby//rbimqu5jvdcx/vrbyuuufYnbi9jshYB05NMS7IdnYHsCwe1WQic0Gy/DfhBdbrIFgLHJtk+yTRgOnDtUBKWJEkaDTbbk9WMsZoHXE5nCocFVXVLkvnA4qpaCHwJ+EqS5cD9dAoxmnbfAJYBa4BTfbJQkiSNBz3Nk1VVlwKXDjp2Rtf2Y8DbN3HtJ4FPDiHHZ7LFtxiHMe54z3W8v/624pqruY73XMf7628rrrn2Ie5mB75LkiRpy7msjiRJUgssshpJpjZTUYw6Sc5M8sHhzuOZJDktya1JvjbcuWxKmz8DSX7WRtx+x275a/BIG3GlfkryvCR/Ndx5aGywyNKz5a+A2VX17uFOZDhU1R+OxthSG9IxUv/+PI/O+5U0ZCP1h3yzklySZEmSW5rJTPthmyRfa3pcvplkh34ETXJ8kpuS3JjkK32K+eEktye5ms4Erv2IeVySa5PckOS8Zt3KfsT9ArAf8L0k/6VPMf9HktuSXJ3kn/vYkzchyRebn6srkjy3H0Hb7MVpK3aS/ZJcn+SQNuJvraa37RdJvtz8DnwtyeuT/DTJHUm2eumuJvat/f4ZSPKBJDc3H3891HhNzHVfh76+Z3W/t/Tzd6vJ97YkFwI3s+EcilsTb8ck323eV29O8o5+5AmcBbyoeR88ux8BB/cQJ/lgkjOHGPOsJKd27Q/5jkaSv0lyWrP9D0l+0Gy/dih3IZIc0vwNnNh8325J8tKh5NrEnd/9+5Tkk0ne34e4pzTf/xuS3J3kh1sdrKpG5Qewe/Pvc+n8wu4xxHhTgQL+qNlfAHywD3keCNwO7Nmd9xBjHgwsBXYAdqGzJuSQcgUOAP4V2LbZPxc4vo/frxXrvgZ9iHUIcAMwEdgZuKNP36updKYaeXmz/w3guD7l/Ei/vpZtxm6+BjfTKdyvB1420vLs+j7NpPMfxSXN7+u69VIvGUk/A12/rzsCOwG3AAf16evQ1/esNt5bBuW7Fnhln+K9Ffhi1/6ufczz5n7E2lRM4IPAmUOMeRDwo679ZcDeQ4z5SuDiZvsndOa13Bb4KPCXQ4z9CeAzwDnAh/r4db2u2X4OcCdDrAUGxd+2+Tq8aWtjjNqeLOC0JDfSWRtxbzoTnQ7Vyqr6abP9VeBVfYj5Wjo/tL8BqKr7+xDzj4FvV9WjVfUQT58cdmu8js4b7KIkNzT7+/Uhbhv+CPhOVT1WVQ/TKQ775e6quqHZXkLnl3i8mQR8B3h3Vd043Mlswt1VtbSq1tIpWq6szrviUob+Pev3z8Cr6Py+/raqHgH+F53f4X7o93tWG+8t3X5ZVddsvllPlgKzk3wqyR9X1YN9ijsqVNX1wF5JXpjkZcADVbVyc9dtxhLg4CS7AI8DPwdm0fm5+MkQY88HZjfxPj3EWABU1QpgdZKDgCOB66tqdT9iNz5HZ3L1rf4b09M8WSNNkj8BXg8cXlWPJrmKTq/GUA2ez2I8zW8R4IKq+tBwJzLMHu/afopOT+l48yDwKzp/sJcNcy6b0v19Wtu1v5ahv6+Npp+B0fae9dt+Baqq25O8Ajga+ESSK6tqfr/i99kaNhye069l4y+ms8rK84F/GWqwqnoyyd3AicDPgJuA1wD7A7cOMfwedHpyt6Xz+vv1s/BPdPJ9Pp3e3L5IciKwLzBvM02f0WjtydqVTtX+aJKX0Oni7Id9khzebL8LuLoPMX8AvD3JHgBJdu9DzB8Db07y3CQ7A2/qQ8wrgbcl2Qs6eSbZtw9x2/BT4E3N/f2dgD8d7oTGmCeAPwOOT/Ku4U5mDPgJnd/XHZLsSOdrO9RegXX6/Z7VxntLK5K8EHi0qr4KnA28ok+hH6YzDKGffk2n12mPJNvTv/esf6Gzwsrb6BRc/fATOrczf9xsn0Knh2ioBfx5wP8AvgZ8aoixun0bmENnGMnl/QiY5GA6X4Pjmt7yrTYqe7KAy4BTktwK3EbnlmE/3AacmmQBnf/B/8+hBqzO0kKfBH6U5Ck641xOHGLM65L8C3AjcC+d9SWHmueyJB8BrkjnqZ8ngVOBXw41dr9V1aIkC+n8L+vXdG4bjKtbBYP0vfeiqn6b5E+B7yd5pDrLZ2krNL+vX+Y/1m39p+ZWTz/09T2rjfeWFs0Ezk6yls771fv6EbSqVjcPUdwMfK+q/qYPMZ9MZym6a4FVwC+GGrOJe0tTDK+qqnv6EZNOYfVh4OfN+8BjDPE/BUmOB56sqq+n80DVz5K8tqp+MNRkq+qJZmD6/63+Lds3D9gd+GES6Cwh+N6tCeSM7xqVkuxUVY80T1P9GDi5qq4b7ryebU0P6XVVNVJ7HdWSJFOB/11VQ35K6xk+x5l0Hlj4TFufQxqKplPgOuDtVXXHcOcz2Gi9XSid3wzQvw741jgtsF5IZ2CqfwAljTtJZtB5AvbKkVhggT1ZkiRJrbAnS5IkqQUWWZIkSS2wyJIkSWqBRZYkSVILLLIkjQjZyoWuk/z15hZGTrIiyZ6bafPfu7afl+SvtiYfSVrHIkvSaPfXdBY0Hqr/3rX9PGCLiqx0+J4qaT3fECSNKEl2SnJlkuuSLE0ytzm+Y5LvJrkxyc1J3pHkNOCFdGZm/mGP8Y9Lcm2SG5Kcl2RCkrOA5zbHvgacBbyo2T+7ue5vkixKclOSjzXHpia5LcmFwM10FquXJMB5siSNEM3yPTsl2QbYoaoeam7xXQNMB94CzKmq/9y037WqHkyyAphVVb95htgrgFnAJODTwFuapU7OBa6pqgvXff6m/VS6ZlNPciSd9eH+ks5i6gubOL8C7gL+sKr6tbyXpDFitK5dKGnsCvC3SY4A1gKTgd+js0bl3yX5FJ0CaGvWU3sdcDCwqFmT7Ll01ujbnCObj3VrDu5Ep/D7FfBLCyxJG2ORJWmkeTedHqeDm96mFcDEqro9ySuAo4FPJLmyquZvYewAF1TVh7biuv+nqs7b4GCnx+u3WxhL0jjhmCxJI82uwL1NgfUaYF9Yv1bjo1X1VeBs4BVN+4eBnXuMfSXwtiR7NTF3T7Juce0nk2y7iZiXAyclWXc7cfK6GJK0KfZkSRppvgb8a5KlwGLgF83xmcDZSdYCTwLva46fD1yW5P+rqtc8U+CqWpbkI8AVzZOATwKnAr9s4tyU5LqqeneSnya5GfheVf1NkgOAnze3GR8BjgOe6uPrljTGOPBdkiSpBd4ulCRJaoG3CyWNGUn+D7D9oMN/XlVLhyMfSeObtwslSZJa4O1CSZKkFlhkSZIktcAiS5IkqQUWWZIkSS2wyJIkSWrB/w8bHay6/Hi67AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1, figsize=(10, 8))\n",
    "letter_prop['M'].plot(kind='bar', rot=0, ax=axes[0], title='Male')\n",
    "letter_prop['F'].plot(kind='bar', rot=0, ax=axes[1], title='Female',legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "zz_HovDGL8aU"
   },
   "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>last_letter</th>\n",
       "      <th>d</th>\n",
       "      <th>n</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>0.083</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>0.083</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>0.085</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>0.084</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>0.086</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "last_letter     d     n     y\n",
       "year                         \n",
       "1880        0.083 0.153 0.076\n",
       "1881        0.083 0.153 0.077\n",
       "1882        0.085 0.150 0.078\n",
       "1883        0.084 0.152 0.079\n",
       "1884        0.086 0.150 0.080"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letter_prop = table / table.sum().astype(float)\n",
    "dny_ts = letter_prop.loc[['d', 'n', 'y'], 'M'].T\n",
    "dny_ts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"10\" halign=\"left\">F</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1880</th>\n",
       "      <th>1881</th>\n",
       "      <th>1882</th>\n",
       "      <th>1883</th>\n",
       "      <th>1884</th>\n",
       "      <th>1885</th>\n",
       "      <th>1886</th>\n",
       "      <th>1887</th>\n",
       "      <th>1888</th>\n",
       "      <th>1889</th>\n",
       "      <th>...</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.346</td>\n",
       "      <td>0.343</td>\n",
       "      <td>0.339</td>\n",
       "      <td>0.341</td>\n",
       "      <td>0.339</td>\n",
       "      <td>0.341</td>\n",
       "      <td>0.340</td>\n",
       "      <td>0.335</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.329</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.007</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.008</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.367</td>\n",
       "      <td>0.371</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.373</td>\n",
       "      <td>0.373</td>\n",
       "      <td>0.373</td>\n",
       "      <td>0.373</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.374</td>\n",
       "      <td>...</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>0.053</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.049</td>\n",
       "      <td>...</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l</th>\n",
       "      <td>0.028</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.047</td>\n",
       "      <td>...</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>0.033</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.040</td>\n",
       "      <td>...</td>\n",
       "      <td>0.363</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.351</td>\n",
       "      <td>0.346</td>\n",
       "      <td>0.342</td>\n",
       "      <td>0.338</td>\n",
       "      <td>0.334</td>\n",
       "      <td>0.324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>o</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>0.005</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s</th>\n",
       "      <td>0.015</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.020</td>\n",
       "      <td>...</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>0.024</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.021</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>u</th>\n",
       "      <td>0.004</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.115</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.099</td>\n",
       "      <td>...</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 280 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sex             F                                                        ...  \\\n",
       "year         1880  1881  1882  1883  1884  1885  1886  1887  1888  1889  ...   \n",
       "last_letter                                                              ...   \n",
       "a           0.346 0.343 0.339 0.341 0.339 0.341 0.340 0.335 0.333 0.329  ...   \n",
       "b             NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "c             NaN   NaN 0.000 0.000   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "d           0.007 0.007 0.007 0.007 0.007 0.006 0.007 0.007 0.007 0.008  ...   \n",
       "e           0.367 0.371 0.375 0.373 0.373 0.373 0.373 0.372 0.374 0.374  ...   \n",
       "f             NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "g           0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  ...   \n",
       "h           0.053 0.052 0.052 0.051 0.051 0.050 0.049 0.049 0.048 0.049  ...   \n",
       "i           0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001  ...   \n",
       "j             NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "k           0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  ...   \n",
       "l           0.028 0.032 0.033 0.034 0.037 0.039 0.040 0.042 0.044 0.047  ...   \n",
       "m           0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001  ...   \n",
       "n           0.033 0.032 0.033 0.034 0.035 0.036 0.036 0.038 0.038 0.040  ...   \n",
       "o           0.000 0.001 0.000 0.000 0.001 0.001 0.001 0.001 0.001 0.001  ...   \n",
       "p             NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "q             NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "r           0.005 0.005 0.005 0.006 0.006 0.006 0.006 0.006 0.007 0.007  ...   \n",
       "s           0.015 0.014 0.015 0.016 0.016 0.016 0.017 0.019 0.020 0.020  ...   \n",
       "t           0.024 0.024 0.022 0.023 0.022 0.022 0.021 0.022 0.021 0.021  ...   \n",
       "u           0.004 0.005 0.004 0.004 0.004 0.004 0.004 0.003 0.003 0.003  ...   \n",
       "v             NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "w             NaN 0.000   NaN   NaN   NaN   NaN 0.000   NaN   NaN   NaN  ...   \n",
       "x             NaN   NaN   NaN 0.000   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "y           0.115 0.113 0.113 0.107 0.108 0.105 0.103 0.103 0.100 0.099  ...   \n",
       "z           0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.002  ...   \n",
       "\n",
       "sex             M                                                        \n",
       "year         2010  2011  2012  2013  2014  2015  2016  2017  2018  2019  \n",
       "last_letter                                                              \n",
       "a           0.015 0.014 0.014 0.015 0.015 0.015 0.016 0.016 0.017 0.018  \n",
       "b           0.020 0.019 0.018 0.018 0.016 0.015 0.014 0.013 0.013 0.012  \n",
       "c           0.012 0.012 0.013 0.013 0.012 0.012 0.012 0.011 0.011 0.010  \n",
       "d           0.023 0.023 0.022 0.023 0.023 0.023 0.023 0.023 0.023 0.023  \n",
       "e           0.068 0.068 0.068 0.070 0.069 0.067 0.067 0.066 0.066 0.066  \n",
       "f           0.001 0.001 0.001 0.001 0.002 0.001 0.002 0.002 0.002 0.002  \n",
       "g           0.001 0.001 0.002 0.002 0.002 0.003 0.003 0.003 0.003 0.003  \n",
       "h           0.052 0.052 0.052 0.053 0.053 0.054 0.054 0.054 0.054 0.054  \n",
       "i           0.023 0.023 0.025 0.024 0.024 0.026 0.026 0.027 0.028 0.030  \n",
       "j           0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001  \n",
       "k           0.019 0.018 0.018 0.018 0.019 0.019 0.019 0.020 0.020 0.020  \n",
       "l           0.070 0.070 0.068 0.067 0.067 0.067 0.067 0.066 0.065 0.065  \n",
       "m           0.025 0.027 0.029 0.030 0.031 0.031 0.031 0.032 0.033 0.033  \n",
       "n           0.363 0.364 0.361 0.354 0.351 0.346 0.342 0.338 0.334 0.324  \n",
       "o           0.043 0.042 0.041 0.042 0.043 0.045 0.048 0.050 0.054 0.057  \n",
       "p           0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001  \n",
       "q           0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  \n",
       "r           0.087 0.088 0.089 0.090 0.093 0.095 0.094 0.094 0.094 0.094  \n",
       "s           0.065 0.064 0.064 0.066 0.067 0.068 0.070 0.072 0.073 0.076  \n",
       "t           0.023 0.024 0.025 0.026 0.027 0.029 0.030 0.032 0.032 0.033  \n",
       "u           0.001 0.001 0.001 0.002 0.002 0.002 0.002 0.002 0.003 0.003  \n",
       "v           0.001 0.001 0.001 0.002 0.002 0.002 0.002 0.002 0.002 0.002  \n",
       "w           0.016 0.016 0.015 0.015 0.014 0.013 0.013 0.012 0.011 0.011  \n",
       "x           0.009 0.009 0.009 0.009 0.010 0.010 0.011 0.011 0.012 0.012  \n",
       "y           0.058 0.058 0.058 0.056 0.055 0.053 0.051 0.050 0.049 0.048  \n",
       "z           0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002  \n",
       "\n",
       "[26 rows x 280 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letter_prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "Hxs-_XFfL8aW"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dny_ts.plot(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "1kEtIc7zL8aY"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Leslie', 'Lesley', 'Leslee', 'Lesli', 'Lesly'], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_names = top1000.name.unique()\n",
    "mask = np.array(['lesl' in x.lower() for x in all_names])\n",
    "lesley_like = all_names[mask]\n",
    "lesley_like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "KkNkrgfKL8ab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Leslee      1082\n",
       "Lesley     35028\n",
       "Lesli        929\n",
       "Leslie    378647\n",
       "Lesly      11429\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered = top1000[top1000.name.isin(lesley_like)]\n",
    "filtered.groupby('name').births.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "Zs6nVWDoL8ad"
   },
   "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>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>1.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex      F   M\n",
       "year          \n",
       "2015 1.000 NaN\n",
       "2016 1.000 NaN\n",
       "2017 1.000 NaN\n",
       "2018 1.000 NaN\n",
       "2019 1.000 NaN"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = filtered.pivot_table('births', index='year', columns='sex', aggfunc='sum')\n",
    "table = table.div(table.sum(1), axis=0)\n",
    "table.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "A-MpFyxzL8ae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table.plot(style={'M': 'k-', 'F': 'k--'}, figsize=(12,8))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}