popular names in USA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d72ed577",
   "metadata": {},
   "source": [
    "## Popular names in USA\n",
    "* Data source： https://www.ssa.gov/oact/babynames/limits.html\n",
    "* Include all babies name borned from 1990 to 2021. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8dc23a6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import re as re\n",
    "import scipy\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3b8c3a40",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "\n",
    "years = range(1990, 2022) \n",
    "pieces = [] # create a list \n",
    "columns = ['name', 'gender', 'frequency']\n",
    "\n",
    "for year in years: \n",
    "    path = 'names 1990-2021/yob%d.txt' % year   #%d operator is put where the integer is to be specified\n",
    "    frame = pd.read_csv(path, names=columns) #  load data\n",
    "    frame['year'] = year \n",
    "    pieces.append(frame)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "05867f97",
   "metadata": {},
   "outputs": [],
   "source": [
    "baby_names = pd.concat(pieces, ignore_index=True) # transfer to pd data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "afc192d4",
   "metadata": {},
   "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>gender</th>\n",
       "      <th>frequency</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>F</td>\n",
       "      <td>46481</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ashley</td>\n",
       "      <td>F</td>\n",
       "      <td>45560</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brittany</td>\n",
       "      <td>F</td>\n",
       "      <td>36536</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Amanda</td>\n",
       "      <td>F</td>\n",
       "      <td>34409</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Samantha</td>\n",
       "      <td>F</td>\n",
       "      <td>25868</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982931</th>\n",
       "      <td>Zykell</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982932</th>\n",
       "      <td>Zylus</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982933</th>\n",
       "      <td>Zymari</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982934</th>\n",
       "      <td>Zyn</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982935</th>\n",
       "      <td>Zyran</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>982936 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            name gender  frequency  year\n",
       "0        Jessica      F      46481  1990\n",
       "1         Ashley      F      45560  1990\n",
       "2       Brittany      F      36536  1990\n",
       "3         Amanda      F      34409  1990\n",
       "4       Samantha      F      25868  1990\n",
       "...          ...    ...        ...   ...\n",
       "982931    Zykell      M          5  2021\n",
       "982932     Zylus      M          5  2021\n",
       "982933    Zymari      M          5  2021\n",
       "982934       Zyn      M          5  2021\n",
       "982935     Zyran      M          5  2021\n",
       "\n",
       "[982936 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "43fe14c7",
   "metadata": {},
   "outputs": [
    {
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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>gender</th>\n",
       "      <th>frequency</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>F</td>\n",
       "      <td>46481</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ashley</td>\n",
       "      <td>F</td>\n",
       "      <td>45560</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brittany</td>\n",
       "      <td>F</td>\n",
       "      <td>36536</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Amanda</td>\n",
       "      <td>F</td>\n",
       "      <td>34409</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Samantha</td>\n",
       "      <td>F</td>\n",
       "      <td>25868</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>25821</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Stephanie</td>\n",
       "      <td>F</td>\n",
       "      <td>24863</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jennifer</td>\n",
       "      <td>F</td>\n",
       "      <td>22234</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>20749</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lauren</td>\n",
       "      <td>F</td>\n",
       "      <td>20508</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name gender  frequency  year\n",
       "0    Jessica      F      46481  1990\n",
       "1     Ashley      F      45560  1990\n",
       "2   Brittany      F      36536  1990\n",
       "3     Amanda      F      34409  1990\n",
       "4   Samantha      F      25868  1990\n",
       "5      Sarah      F      25821  1990\n",
       "6  Stephanie      F      24863  1990\n",
       "7   Jennifer      F      22234  1990\n",
       "8  Elizabeth      F      20749  1990\n",
       "9     Lauren      F      20508  1990"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_names.head(10) # see first 10 lines data "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0235b5b",
   "metadata": {},
   "source": [
    "## Specify which names are the most popular from 1990-2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "93781e63",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aaban</th>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aabha</th>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aabid</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aabidah</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aabir</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zyvion</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zyvon</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zyyanna</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zyyon</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zzyzx</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>81388 rows × 1 columns</p>\n",
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      ],
      "text/plain": [
       "         frequency\n",
       "name              \n",
       "Aaban          120\n",
       "Aabha           51\n",
       "Aabid           16\n",
       "Aabidah          5\n",
       "Aabir           10\n",
       "...            ...\n",
       "Zyvion           5\n",
       "Zyvon            7\n",
       "Zyyanna          6\n",
       "Zyyon            6\n",
       "Zzyzx           10\n",
       "\n",
       "[81388 rows x 1 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_names.groupby('name').agg({'frequency':sum})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "24656508",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Michael</th>\n",
       "      <td>879880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jacob</th>\n",
       "      <td>754333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matthew</th>\n",
       "      <td>714963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Joshua</th>\n",
       "      <td>680211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Christopher</th>\n",
       "      <td>679146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Briayla</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Briazia</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nhala</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ngozichukwu</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cansu</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81388 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             frequency\n",
       "name                  \n",
       "Michael         879880\n",
       "Jacob           754333\n",
       "Matthew         714963\n",
       "Joshua          680211\n",
       "Christopher     679146\n",
       "...                ...\n",
       "Briayla              5\n",
       "Briazia              5\n",
       "Nhala                5\n",
       "Ngozichukwu          5\n",
       "Cansu                5\n",
       "\n",
       "[81388 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sort all values in descending order after aggregation\n",
    "baby_names.groupby('name').agg({'frequency':sum}).sort_values(by=['frequency'],ascending=[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78b85985",
   "metadata": {},
   "source": [
    "## The comparison between male and female in the past 10 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4460c2c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "freq_by_gender_year = baby_names.pivot_table(index ='year', columns='gender',\n",
    "                                             values = 'frequency', aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cd67fc4a",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>1759366</td>\n",
       "      <td>1895285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>1752777</td>\n",
       "      <td>1889640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>1784227</td>\n",
       "      <td>1918546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>1782611</td>\n",
       "      <td>1914078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1769164</td>\n",
       "      <td>1894723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1723043</td>\n",
       "      <td>1847191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1696917</td>\n",
       "      <td>1811738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1673030</td>\n",
       "      <td>1788414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1627098</td>\n",
       "      <td>1734277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>1609171</td>\n",
       "      <td>1718248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender        F        M\n",
       "year                    \n",
       "2012    1759366  1895285\n",
       "2013    1752777  1889640\n",
       "2014    1784227  1918546\n",
       "2015    1782611  1914078\n",
       "2016    1769164  1894723\n",
       "2017    1723043  1847191\n",
       "2018    1696917  1811738\n",
       "2019    1673030  1788414\n",
       "2020    1627098  1734277\n",
       "2021    1609171  1718248"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_by_gender_year.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a53ee65",
   "metadata": {},
   "source": [
    "## The visulaization of the comparison "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bac0b3f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Frequency by year and gender'}, xlabel='year'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq_by_gender_year.plot(title='Frequency by year and gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b818e8f5",
   "metadata": {},
   "source": [
    "## The trend analysis of names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fec5aba8",
   "metadata": {},
   "outputs": [],
   "source": [
    "baby_names['ranked'] = baby_names.groupby(['year', 'gender'])['frequency'].rank(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "30ac285b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>frequency</th>\n",
       "      <th>year</th>\n",
       "      <th>ranked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>F</td>\n",
       "      <td>46481</td>\n",
       "      <td>1990</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ashley</td>\n",
       "      <td>F</td>\n",
       "      <td>45560</td>\n",
       "      <td>1990</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brittany</td>\n",
       "      <td>F</td>\n",
       "      <td>36536</td>\n",
       "      <td>1990</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Amanda</td>\n",
       "      <td>F</td>\n",
       "      <td>34409</td>\n",
       "      <td>1990</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Samantha</td>\n",
       "      <td>F</td>\n",
       "      <td>25868</td>\n",
       "      <td>1990</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>25821</td>\n",
       "      <td>1990</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Stephanie</td>\n",
       "      <td>F</td>\n",
       "      <td>24863</td>\n",
       "      <td>1990</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jennifer</td>\n",
       "      <td>F</td>\n",
       "      <td>22234</td>\n",
       "      <td>1990</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>20749</td>\n",
       "      <td>1990</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lauren</td>\n",
       "      <td>F</td>\n",
       "      <td>20508</td>\n",
       "      <td>1990</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name gender  frequency  year  ranked\n",
       "0    Jessica      F      46481  1990     1.0\n",
       "1     Ashley      F      45560  1990     2.0\n",
       "2   Brittany      F      36536  1990     3.0\n",
       "3     Amanda      F      34409  1990     4.0\n",
       "4   Samantha      F      25868  1990     5.0\n",
       "5      Sarah      F      25821  1990     6.0\n",
       "6  Stephanie      F      24863  1990     7.0\n",
       "7   Jennifer      F      22234  1990     8.0\n",
       "8  Elizabeth      F      20749  1990     9.0\n",
       "9     Lauren      F      20508  1990    10.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_names.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e1debf81",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_pct(group):# define a function to show percentage of each name \n",
    "    group['pct'] = group.frequency / group.frequency.sum()*100\n",
    "    return group\n",
    "\n",
    "# apply function \n",
    "baby_names = baby_names.groupby(['year', 'gender']).apply(add_pct) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5167d2d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>frequency</th>\n",
       "      <th>year</th>\n",
       "      <th>ranked</th>\n",
       "      <th>pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>F</td>\n",
       "      <td>46481</td>\n",
       "      <td>1990</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.448496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ashley</td>\n",
       "      <td>F</td>\n",
       "      <td>45560</td>\n",
       "      <td>1990</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.399980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brittany</td>\n",
       "      <td>F</td>\n",
       "      <td>36536</td>\n",
       "      <td>1990</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.924620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Amanda</td>\n",
       "      <td>F</td>\n",
       "      <td>34409</td>\n",
       "      <td>1990</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.812575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Samantha</td>\n",
       "      <td>F</td>\n",
       "      <td>25868</td>\n",
       "      <td>1990</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.362658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>25821</td>\n",
       "      <td>1990</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.360182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Stephanie</td>\n",
       "      <td>F</td>\n",
       "      <td>24863</td>\n",
       "      <td>1990</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.309717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jennifer</td>\n",
       "      <td>F</td>\n",
       "      <td>22234</td>\n",
       "      <td>1990</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.171228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>20749</td>\n",
       "      <td>1990</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.093002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lauren</td>\n",
       "      <td>F</td>\n",
       "      <td>20508</td>\n",
       "      <td>1990</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.080307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name gender  frequency  year  ranked       pct\n",
       "0    Jessica      F      46481  1990     1.0  2.448496\n",
       "1     Ashley      F      45560  1990     2.0  2.399980\n",
       "2   Brittany      F      36536  1990     3.0  1.924620\n",
       "3     Amanda      F      34409  1990     4.0  1.812575\n",
       "4   Samantha      F      25868  1990     5.0  1.362658\n",
       "5      Sarah      F      25821  1990     6.0  1.360182\n",
       "6  Stephanie      F      24863  1990     7.0  1.309717\n",
       "7   Jennifer      F      22234  1990     8.0  1.171228\n",
       "8  Elizabeth      F      20749  1990     9.0  1.093002\n",
       "9     Lauren      F      20508  1990    10.0  1.080307"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_names.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "95748e32",
   "metadata": {},
   "outputs": [],
   "source": [
    "dff = baby_names[baby_names.gender == 'F'] #divide data into male and female\n",
    "dfm = baby_names[baby_names.gender == 'M']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0566f44f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the top names in both male and female\n",
    "rank1m = dfm[dfm.ranked == 1]\n",
    "rank1f = dff[dff.ranked == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "83c30d76",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/ns/h76j0lmj3f36gyjjdrtlhg1r0000gn/T/ipykernel_33714/3939879099.py:5: DeprecationWarning: scipy.arange is deprecated and will be removed in SciPy 2.0.0, use numpy.arange instead\n",
      "  plt.xticks(scipy.arange(1990,2021,2), rotation=70)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(rank1m.year, rank1m.pct, color=\"red\", linewidth = 2, label = 'Men')\n",
    "plt.fill_between(rank1m.year, rank1m.pct, color=\"red\", alpha = 0.2, interpolate=True)\n",
    "plt.xlim(1990,2021)\n",
    "plt.ylim(0,4)\n",
    "plt.xticks(scipy.arange(1990,2021,2), rotation=70)\n",
    "plt.title(\"Popularity of top mens' name by year\", size=18, color=\"red\")\n",
    "plt.xlabel('Year', size=15)\n",
    "plt.ylabel('% of male births', size=15)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a26489c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/ns/h76j0lmj3f36gyjjdrtlhg1r0000gn/T/ipykernel_33714/3483182847.py:5: DeprecationWarning: scipy.arange is deprecated and will be removed in SciPy 2.0.0, use numpy.arange instead\n",
      "  plt.xticks(scipy.arange(1990,2021,2), rotation=70)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(rank1f.year, rank1f.pct, color=\"blue\", linewidth = 2, label = 'Women')\n",
    "plt.fill_between(rank1f.year, rank1f.pct, color=\"blue\", alpha = 0.1, interpolate=True)\n",
    "plt.xlim(1990,2021)\n",
    "plt.ylim(0,3)\n",
    "plt.xticks(scipy.arange(1990,2021,2), rotation=70)\n",
    "plt.title(\"Popularity of top female' name by year\", size=18, color=\"blue\")\n",
    "plt.xlabel('Year', size=15)\n",
    "plt.ylabel('% of female births', size=15)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0afd65ea",
   "metadata": {},
   "source": [
    "# Why do the popularity of top male and female names descrease every year? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2d3857b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>gender</th>\n",
       "      <th>name_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990</td>\n",
       "      <td>F</td>\n",
       "      <td>15232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1990</td>\n",
       "      <td>M</td>\n",
       "      <td>9485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1991</td>\n",
       "      <td>F</td>\n",
       "      <td>15467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1991</td>\n",
       "      <td>M</td>\n",
       "      <td>9648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1992</td>\n",
       "      <td>F</td>\n",
       "      <td>15611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year gender  name_count\n",
       "0  1990      F       15232\n",
       "1  1990      M        9485\n",
       "2  1991      F       15467\n",
       "3  1991      M        9648\n",
       "4  1992      F       15611"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "name_count = baby_names.groupby(['year', 'gender']).size() \n",
    "name_count = name_count.to_frame(name=\"name_count\").reset_index()\n",
    "name_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e889cdc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of names')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name_count_m = name_count[name_count.gender == \"M\"]\n",
    "\n",
    "plt.plot(name_count_m.year, name_count_m.name_count, color=\"blue\", linewidth = 2)\n",
    "\n",
    "plt.fill_between(name_count_m.year, name_count_m.name_count, color=\"blue\", alpha = 0.1)\n",
    "\n",
    "plt.xlim(1990,2021)\n",
    "plt.ylim(0,21000)\n",
    "\n",
    "plt.title(\"Number of boy's names by year\", size=18, color=\"blue\")\n",
    "plt.xlabel('Year', size=15)\n",
    "plt.ylabel('Number of names', size=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "afca2dff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of names')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name_count_f = name_count[name_count.gender == \"F\"]\n",
    "plt.plot(name_count_f.year, name_count_f.name_count, color=\"red\", linewidth = 2)\n",
    "plt.fill_between(name_count_f.year, name_count_f.name_count, color=\"red\", alpha = 0.1)\n",
    "plt.xlim(1990,2021)\n",
    "plt.ylim(0,21000)\n",
    "plt.title(\"Number of girl's names by year\", size=18, color=\"red\")\n",
    "plt.xlabel('Year', size=15)\n",
    "plt.ylabel('Number of names', size=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "240a9ce0",
   "metadata": {},
   "source": [
    "# Conslusion: the choice of male and female names are rising, thus causes the phenomenon. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "727629b9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d32b8f96",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}