Notebooks/Python_Tasks/02_EDA_Pandas_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Python  \n",
    "\n",
    "## Pandas - Exploratory Data Analysis (EDA) part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "metadata": {
    "id": "ESmCc8HRL8Zu"
   },
   "outputs": [],
   "source": [
    "names1880 = pd.read_csv(os.path.join('../../Data','txt','names','yob1880.txt'), names=['name', 'sex', 'births'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880[0:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#names1880.head()\n",
    "names1880.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880.groupby('sex').births.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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', 'txt', '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": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.groupby('sex').births.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "jlc5pO5mL8Z2"
   },
   "outputs": [],
   "source": [
    "total_births = names.pivot_table('births', index='year', columns='sex', aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "JhtPzkjoL8Z7",
    "outputId": "e30a32ca-521d-4fb4-b4e9-87b45d81e937"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_births.plot(title='Total births by sex and year', figsize=(12,6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "rEb5MJBzL8Z8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\WZHRERO\\AppData\\Local\\Temp\\1\\ipykernel_11632\\688551148.py:7: FutureWarning: Not prepending group keys to the result index of transform-like apply. In the future, the group keys will be included in the index, regardless of whether the applied function returns a like-indexed object.\n",
      "To preserve the previous behavior, use\n",
      "\n",
      "\t>>> .groupby(..., group_keys=False)\n",
      "\n",
      "To adopt the future behavior and silence this warning, use \n",
      "\n",
      "\t>>> .groupby(..., group_keys=True)\n",
      "  names = names.groupby(['year', 'sex']).apply(add_prop)\n"
     ]
    }
   ],
   "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": 19,
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#names\n",
    "names.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[names.percent > 0.085]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "AF9caZ_IL8aA",
    "outputId": "2afe9e46-7817-4858-f9f3-add03014dff5"
   },
   "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",
       "      <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",
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       "    <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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[names.name.str.startswith('Stephany')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "BPs3OWg6L8aE",
    "outputId": "6e5e78b7-d35c-47d1-d691-13e34f61d2d3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(names.groupby(['year', 'sex']).percent.sum(), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 24,
   "metadata": {
    "id": "8Ct5ta2AL8aG",
    "outputId": "eec3cc70-6bd4-42c8-b639-8d7538ee7f70"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th>name</th>\n",
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       "      <th>percent</th>\n",
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       "    <tr>\n",
       "      <th>year</th>\n",
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       "      <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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#top1000\n",
    "pd.options.display.float_format = '{:,.3f}'.format\n",
    "top1000[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "zdaQsQBfL8aH"
   },
   "outputs": [],
   "source": [
    "boys = top1000[top1000.sex == 'M']\n",
    "girls = top1000[top1000.sex == 'F']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "gUN_-69hL8aI",
    "outputId": "55b51f64-75b6-4197-d2fa-87452b33d2a8"
   },
   "outputs": [
    {
     "data": {
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       "      <th>1883</th>\n",
       "      <th>M</th>\n",
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       "    <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",
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       "      <td>M</td>\n",
       "      <td>1808</td>\n",
       "      <td>1885</td>\n",
       "      <td>0.017</td>\n",
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       "    <tr>\n",
       "      <th>1886</th>\n",
       "      <th>M</th>\n",
       "      <th>14031</th>\n",
       "      <td>Walter</td>\n",
       "      <td>M</td>\n",
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       "      <td>1886</td>\n",
       "      <td>0.017</td>\n",
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       "    <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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Walter_names = boys[boys.name=='Walter']\n",
    "Walter_names[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>name</th>\n",
       "      <th>Aaden</th>\n",
       "      <th>Aadhya</th>\n",
       "      <th>Aaliyah</th>\n",
       "      <th>Aanya</th>\n",
       "      <th>Aarav</th>\n",
       "      <th>Aaron</th>\n",
       "      <th>Aarush</th>\n",
       "      <th>Aayan</th>\n",
       "      <th>Ab</th>\n",
       "      <th>Abagail</th>\n",
       "      <th>...</th>\n",
       "      <th>Zoey</th>\n",
       "      <th>Zoie</th>\n",
       "      <th>Zola</th>\n",
       "      <th>Zollie</th>\n",
       "      <th>Zona</th>\n",
       "      <th>Zora</th>\n",
       "      <th>Zula</th>\n",
       "      <th>Zuri</th>\n",
       "      <th>Zyaire</th>\n",
       "      <th>Zyon</th>\n",
       "    </tr>\n",
       "    <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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births_top1000.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "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": 30,
   "metadata": {
    "id": "Mp4ohkV0L8aK"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Sum of table1000.percent by year and sex'}, xlabel='year'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 31,
   "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": 32,
   "metadata": {
    "id": "51uc51PHL8aM"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "154"
      ]
     },
     "execution_count": 32,
     "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": 33,
   "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": 33,
     "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": 34,
   "metadata": {
    "id": "NeejVpMUL8aP"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Number of popular names in top 50%'}, xlabel='year'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diversity.plot(title=\"Number of popular names in top 50%\", figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": 35,
     "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": 36,
   "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "Dr-MxbNFL8aR"
   },
   "outputs": [],
   "source": [
    "letter_prop = subtable / subtable.sum().astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "Kwz1A8TlL8aT"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Female'}, xlabel='last_letter'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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": 39,
   "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": 39,
     "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": 40,
   "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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letter_prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "Hxs-_XFfL8aW"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='year'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dny_ts.plot(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "1kEtIc7zL8aY"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Leslie', 'Lesley', 'Leslee', 'Lesli', 'Lesly'], dtype=object)"
      ]
     },
     "execution_count": 42,
     "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": 43,
   "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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered = top1000[top1000.name.isin(lesley_like)]\n",
    "filtered.groupby('name').births.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": 44,
     "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": 45,
   "metadata": {
    "id": "A-MpFyxzL8ae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='year'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table.plot(style={'M': 'k-', 'F': 'k--'}, figsize=(12,8))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.8 64-bit (microsoft store)",
   "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": "3.10.8"
  },
  "vscode": {
   "interpreter": {
    "hash": "a3a5d4f9651fa550758ade4a473ec6b18daeb3d63067db8abd62b64f4493ea9c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}