notebooks/Dataintro-EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "expected-update"
   },
   "source": [
    "# Load Data - babynames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17,
     "output_embedded_package_id": "1GJoCaGBl-jzwL_4rUPyNfaC-2046fThU"
    },
    "executionInfo": {
     "elapsed": 4340,
     "status": "ok",
     "timestamp": 1629910577028,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "intimate-poland",
    "outputId": "7d922c23-e1d6-4f66-e75a-aad2d4f6be6b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Output hidden; open in https://colab.research.google.com to view."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(24)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 2842,
     "status": "ok",
     "timestamp": 1629910582677,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "reduced-wilderness"
   },
   "outputs": [],
   "source": [
    "import urllib.request\n",
    "import os.path\n",
    "\n",
    "# Download data from the web directly\n",
    "data_url = \"https://www.ssa.gov/oact/babynames/names.zip\"\n",
    "local_filename = \"babynames.zip\"\n",
    "if not os.path.exists(local_filename): # if the data exists don't download again\n",
    "    with urllib.request.urlopen(data_url) as resp, open(local_filename, 'wb') as f:\n",
    "        f.write(resp.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "flexible-walnut"
   },
   "source": [
    "babynames.zip is downloaded in the current folder. Inside the babynames.zip, there are txt files, named from yob1880~yob2020, each txt file contains data like:\n",
    "Olivia,F,17535\n",
    "Emma,F,15581\n",
    "...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "executionInfo": {
     "elapsed": 2199,
     "status": "ok",
     "timestamp": 1629912396351,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "handmade-findings"
   },
   "outputs": [],
   "source": [
    "# Load data without unzipping the file\n",
    "import zipfile\n",
    "babynames = [] \n",
    "local_filename = \"babynames.zip\"\n",
    "with zipfile.ZipFile(local_filename, \"r\") as zf:\n",
    "    data_files = [f for f in zf.filelist if f.filename[-3:] == \"txt\"]\n",
    "    def extract_year_from_filename(fn):\n",
    "        return int(fn[3:7])\n",
    "    for f in data_files:\n",
    "        year = extract_year_from_filename(f.filename)\n",
    "        with zf.open(f) as fp:\n",
    "            df = pd.read_csv(fp, names=[\"Name\", \"Sex\", \"Count\"])\n",
    "            df[\"Year\"] = year\n",
    "            babynames.append(df)\n",
    "babynames = pd.concat(babynames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "executionInfo": {
     "elapsed": 223,
     "status": "ok",
     "timestamp": 1629912399391,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "elder-mozambique",
    "outputId": "8797b5b6-2f35-49b3-e858-3e1e13e00f1e"
   },
   "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>Count</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name Sex  Count  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"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "babynames.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "executionInfo": {
     "elapsed": 127,
     "status": "ok",
     "timestamp": 1629912402843,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "historical-intention",
    "outputId": "82a3465d-d8b8-4855-c05b-c06e3018fa38"
   },
   "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>Count</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31266</th>\n",
       "      <td>Zykell</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31267</th>\n",
       "      <td>Zylus</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31268</th>\n",
       "      <td>Zymari</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31269</th>\n",
       "      <td>Zyn</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31270</th>\n",
       "      <td>Zyran</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name Sex  Count  Year\n",
       "31266  Zykell   M      5  2020\n",
       "31267   Zylus   M      5  2020\n",
       "31268  Zymari   M      5  2020\n",
       "31269     Zyn   M      5  2020\n",
       "31270   Zyran   M      5  2020"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "babynames.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "executionInfo": {
     "elapsed": 1034,
     "status": "ok",
     "timestamp": 1629912407511,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "endless-instrumentation",
    "outputId": "5a9cefb4-69bd-4181-9a1c-15d45393880e"
   },
   "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>Count</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name Sex  Count  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"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "babynames['Name'] = babynames['Name'].str.lower()#change name to lower case\n",
    "babynames.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 127,
     "status": "ok",
     "timestamp": 1629912412488,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "J1_nXdrBWaSJ",
    "outputId": "12910b03-eb54-49c4-dff8-c2f4f9403336"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8083452"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "babynames.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "broad-spyware"
   },
   "source": [
    "# Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 175,
     "status": "ok",
     "timestamp": 1629912416938,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "utility-nomination",
    "outputId": "ce599e82-2c53-4288-a23f-335947aa27ab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "358480709"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "babynames['Count'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "executionInfo": {
     "elapsed": 208,
     "status": "ok",
     "timestamp": 1629912419116,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "deluxe-activation",
    "outputId": "34b047ed-8e40-4ad6-8823-8db05dd62578"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'358,480,709'"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format(babynames['Count'].sum(), ',d')    # total number of babies represented"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "executionInfo": {
     "elapsed": 172,
     "status": "ok",
     "timestamp": 1629912421018,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "searching-median",
    "outputId": "01b6de18-39f8-425b-9680-41181e0e1eee"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'2,020,863'"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format(babynames.shape[0], ',d')    # total number of names represented"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "thorough-march"
   },
   "source": [
    "Trying a simple query:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 110
    },
    "executionInfo": {
     "elapsed": 130,
     "status": "ok",
     "timestamp": 1629912424001,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "higher-suspect",
    "outputId": "fe9c9c49-d96d-4f90-d1b8-9b8c0d034ff3"
   },
   "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>Count</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8119</th>\n",
       "      <td>ethan</td>\n",
       "      <td>F</td>\n",
       "      <td>14</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18856</th>\n",
       "      <td>ethan</td>\n",
       "      <td>M</td>\n",
       "      <td>13834</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name Sex  Count  Year\n",
       "8119   ethan   F     14  2016\n",
       "18856  ethan   M  13834  2016"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "babynames[(babynames['Name'] == 'ethan') & (babynames['Year'] == 2016)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bzmL10kXRYCE"
   },
   "source": [
    "## Pivot Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mpr0DYiLQp_o"
   },
   "source": [
    "How to group data using index in a pivot table?\n",
    "pivot_table requires a data and an index parameter\n",
    "data is the Pandas dataframe you pass to the function\n",
    "index is the feature that allows you to group your data. The index feature will appear as an index in the resultant table\n",
    "ref: https://pandas.pydata.org/docs/reference/api/pandas.pivot_table.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "executionInfo": {
     "elapsed": 136,
     "status": "ok",
     "timestamp": 1629912431253,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "h5eOriZUQ80p"
   },
   "outputs": [],
   "source": [
    "pdsex = pd.pivot_table(babynames, \n",
    "        index=['Sex', 'Year'], # the row index\n",
    "        aggfunc='sum'\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 447
    },
    "executionInfo": {
     "elapsed": 131,
     "status": "ok",
     "timestamp": 1629912432807,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "461-qJgYRH3y",
    "outputId": "99c4284e-658b-4f43-a615-9f30146d923f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">F</th>\n",
       "      <th>1880</th>\n",
       "      <td>90994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>91953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>107847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>112319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>129019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">M</th>\n",
       "      <th>2016</th>\n",
       "      <td>1893917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1846154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1810309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1785527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1706423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>282 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Count\n",
       "Sex Year         \n",
       "F   1880    90994\n",
       "    1881    91953\n",
       "    1882   107847\n",
       "    1883   112319\n",
       "    1884   129019\n",
       "...           ...\n",
       "M   2016  1893917\n",
       "    2017  1846154\n",
       "    2018  1810309\n",
       "    2019  1785527\n",
       "    2020  1706423\n",
       "\n",
       "[282 rows x 1 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdsex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "executionInfo": {
     "elapsed": 167,
     "status": "ok",
     "timestamp": 1629912470990,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "6oJXAdYLU7__"
   },
   "outputs": [],
   "source": [
    "subset=babynames[(babynames['Year'] > 2000)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 417
    },
    "executionInfo": {
     "elapsed": 134,
     "status": "ok",
     "timestamp": 1629912472843,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "QWJK5kuRVNYV",
    "outputId": "406d6e41-050e-49c2-e302-d28ec549d306"
   },
   "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>Count</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>emily</td>\n",
       "      <td>F</td>\n",
       "      <td>25057</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>madison</td>\n",
       "      <td>F</td>\n",
       "      <td>22166</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hannah</td>\n",
       "      <td>F</td>\n",
       "      <td>20720</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ashley</td>\n",
       "      <td>F</td>\n",
       "      <td>16528</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>alexis</td>\n",
       "      <td>F</td>\n",
       "      <td>16402</td>\n",
       "      <td>2001</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>31266</th>\n",
       "      <td>zykell</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31267</th>\n",
       "      <td>zylus</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31268</th>\n",
       "      <td>zymari</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31269</th>\n",
       "      <td>zyn</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31270</th>\n",
       "      <td>zyran</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>658127 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Name Sex  Count  Year\n",
       "0        emily   F  25057  2001\n",
       "1      madison   F  22166  2001\n",
       "2       hannah   F  20720  2001\n",
       "3       ashley   F  16528  2001\n",
       "4       alexis   F  16402  2001\n",
       "...        ...  ..    ...   ...\n",
       "31266   zykell   M      5  2020\n",
       "31267    zylus   M      5  2020\n",
       "31268   zymari   M      5  2020\n",
       "31269      zyn   M      5  2020\n",
       "31270    zyran   M      5  2020\n",
       "\n",
       "[658127 rows x 4 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "executionInfo": {
     "elapsed": 215,
     "status": "ok",
     "timestamp": 1629912492486,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "_gGmeQIiVEXa"
   },
   "outputs": [],
   "source": [
    "pdonesex = pd.pivot_table(subset, \n",
    "        index=['Year'], # the row index\n",
    "        aggfunc='sum'\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 696
    },
    "executionInfo": {
     "elapsed": 134,
     "status": "ok",
     "timestamp": 1629912495126,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "-r_HidXKVHUD",
    "outputId": "56072740-0851-438a-c243-6ecd72296855"
   },
   "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>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>3742401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>3737195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>3801147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>3819591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>3843322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>3954356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>3995740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>3928389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>3817731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>3693163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>3654691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>3653979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>3641604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>3701784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>3695531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>3662277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>3568294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>3505963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>3455946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>3305259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Count\n",
       "Year         \n",
       "2001  3742401\n",
       "2002  3737195\n",
       "2003  3801147\n",
       "2004  3819591\n",
       "2005  3843322\n",
       "2006  3954356\n",
       "2007  3995740\n",
       "2008  3928389\n",
       "2009  3817731\n",
       "2010  3693163\n",
       "2011  3654691\n",
       "2012  3653979\n",
       "2013  3641604\n",
       "2014  3701784\n",
       "2015  3695531\n",
       "2016  3662277\n",
       "2017  3568294\n",
       "2018  3505963\n",
       "2019  3455946\n",
       "2020  3305259"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdonesex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 306
    },
    "executionInfo": {
     "elapsed": 492,
     "status": "ok",
     "timestamp": 1629912499270,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "9Tj7LZT7UnYv",
    "outputId": "10505c81-2693-4fac-cba4-cfde4b8b3707"
   },
   "outputs": [
    {
     "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": [
    "pdonesex.plot(kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "piano-placement"
   },
   "source": [
    "Proportion of Male and Female Individuals Over Time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "protecting-figure"
   },
   "source": [
    "In this example we construct a **pivot table** which aggregates the number of babies registered for each year by `Sex`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 234
    },
    "executionInfo": {
     "elapsed": 129,
     "status": "ok",
     "timestamp": 1629912502295,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "appointed-journalism",
    "outputId": "54c5da1e-4b55-4e12-c763-f92da939af62"
   },
   "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>2001</th>\n",
       "      <td>1800081</td>\n",
       "      <td>1942320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>1796311</td>\n",
       "      <td>1940884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>1826525</td>\n",
       "      <td>1974622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>1835493</td>\n",
       "      <td>1984098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>1846945</td>\n",
       "      <td>1996377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex         F        M\n",
       "Year                  \n",
       "2001  1800081  1942320\n",
       "2002  1796311  1940884\n",
       "2003  1826525  1974622\n",
       "2004  1835493  1984098\n",
       "2005  1846945  1996377"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_sex = pd.pivot_table(subset, \n",
    "        index=['Year'], # the row index\n",
    "        columns=['Sex'], # the column values\n",
    "        values='Count', # the field(s) to processed in each group\n",
    "        aggfunc=np.sum,\n",
    "    )\n",
    "\n",
    "year_sex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 306
    },
    "executionInfo": {
     "elapsed": 457,
     "status": "ok",
     "timestamp": 1629912505625,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "ZASxARAaUuRm",
    "outputId": "38d877e7-2b48-4b27-ec7e-133153b1ce80"
   },
   "outputs": [
    {
     "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": [
    "year_sex.plot(kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-wOhyikyS_-H"
   },
   "source": [
    "margins: bool, default False\n",
    "Add all row / columns (e.g. for subtotal / grand totals)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 417
    },
    "executionInfo": {
     "elapsed": 140,
     "status": "ok",
     "timestamp": 1629912509165,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "Le_fQxGoWCtB",
    "outputId": "5a6e213c-fdcf-4161-faec-590088dc810d"
   },
   "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>Count</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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31266</th>\n",
       "      <td>zykell</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31267</th>\n",
       "      <td>zylus</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31268</th>\n",
       "      <td>zymari</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31269</th>\n",
       "      <td>zyn</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31270</th>\n",
       "      <td>zyran</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2020863 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name Sex  Count  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",
       "...          ...  ..    ...   ...\n",
       "31266     zykell   M      5  2020\n",
       "31267      zylus   M      5  2020\n",
       "31268     zymari   M      5  2020\n",
       "31269        zyn   M      5  2020\n",
       "31270      zyran   M      5  2020\n",
       "\n",
       "[2020863 rows x 4 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "babynames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 234
    },
    "executionInfo": {
     "elapsed": 2214,
     "status": "ok",
     "timestamp": 1629912562913,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "romantic-injection",
    "outputId": "fed38108-30ae-4a58-bcfa-765e358069b8"
   },
   "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",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aaban</th>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aabha</th>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aabid</th>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aabidah</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aabir</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       F    M  All\n",
       "Name                 \n",
       "aaban     0  120  120\n",
       "aabha    46    0   46\n",
       "aabid     0   16   16\n",
       "aabidah   5    0    5\n",
       "aabir     0   10   10"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdname_sex = pd.pivot_table(babynames, index='Name', columns='Sex', values='Count',\n",
    "                            aggfunc='sum', fill_value=0., margins=True)\n",
    "pdname_sex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "executionInfo": {
     "elapsed": 167,
     "status": "ok",
     "timestamp": 1629912574577,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "ZROlb7Q6Tbc-"
   },
   "outputs": [],
   "source": [
    "#Compute proportion of female babies given each name.\n",
    "prop_female = (pdname_sex['F'] / pdname_sex['All'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1629912576441,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "LB6kbSTwTchq",
    "outputId": "55c1fea6-76c6-44e1-ef36-205274e51e20"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "aaban      0.000000\n",
       "aabha      1.000000\n",
       "aabid      0.000000\n",
       "aabidah    1.000000\n",
       "aabir      0.000000\n",
       "             ...   \n",
       "zyvon      0.000000\n",
       "zyyanna    1.000000\n",
       "zyyon      0.000000\n",
       "zzyzx      0.000000\n",
       "All        0.494806\n",
       "Length: 100365, dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_female"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2JF_Du8zT16t"
   },
   "source": [
    "Testing a few names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 152,
     "status": "ok",
     "timestamp": 1629912595132,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "ggZbyKi0TtYQ",
    "outputId": "4698639d-7df8-47aa-ae1d-d586810b638c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_female['aadan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 148,
     "status": "ok",
     "timestamp": 1629912601761,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "x7LR6Z-3Tv9G",
    "outputId": "da3df0d0-fe27-4c50-8e2d-708faa224ede"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_female['zyra']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 132,
     "status": "ok",
     "timestamp": 1629912617756,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "LcIX27crXMzi",
    "outputId": "4843d71e-e8e5-451b-9182-939d80843c51"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1151411838174484"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_female['joey']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 449,
     "status": "ok",
     "timestamp": 1629912626329,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "6nDW0R7bXOxY",
    "outputId": "54a16329-4c0f-4e2e-8e35-72cc9feb3e40"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.002527827840211856"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_female['isaac']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 138,
     "status": "ok",
     "timestamp": 1629912604872,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "-nZJdCLnTxCJ",
    "outputId": "af19c765-4ea4-4efb-903e-fc554cd8cd4b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7021478300997172"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_female['avery']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pressing-making"
   },
   "source": [
    "## Build Simple Classifier (Model)\n",
    "\n",
    "We can define a function to return the most likely `Sex` for a name. If there is an exact tie or the name does not appear in the social security dataset the function returns `Unknown`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "executionInfo": {
     "elapsed": 146,
     "status": "ok",
     "timestamp": 1629912633162,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "charged-aircraft"
   },
   "outputs": [],
   "source": [
    "def sex_from_name(name):\n",
    "    lower_name = name.lower()\n",
    "    if lower_name not in prop_female.index or prop_female[lower_name] == 0.5:\n",
    "        return \"Unknown\"\n",
    "    elif prop_female[lower_name] > 0.5:\n",
    "        return \"F\"\n",
    "    else:\n",
    "        return \"M\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "executionInfo": {
     "elapsed": 129,
     "status": "ok",
     "timestamp": 1629912634852,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "attended-crystal",
    "outputId": "85a74659-528a-4208-87a6-b614074a9645"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'M'"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_from_name(\"isaac\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1629912636675,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "extraordinary-standard",
    "outputId": "a26a84b2-78e1-4f8e-e740-58768e7866d2"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'F'"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_from_name(\"avery\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "superb-multimedia"
   },
   "source": [
    "# Baby name by state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "executionInfo": {
     "elapsed": 484,
     "status": "ok",
     "timestamp": 1629912644794,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "bearing-bradford",
    "outputId": "a39a79d1-c27b-4036-c801-dd3b987bcc0c"
   },
   "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>State</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Year</th>\n",
       "      <th>Name</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>278689</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1972</td>\n",
       "      <td>Marlon</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326964</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1996</td>\n",
       "      <td>Nicolaus</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104271</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1987</td>\n",
       "      <td>Chelsi</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223908</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>2018</td>\n",
       "      <td>Kailynn</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274476</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1969</td>\n",
       "      <td>Victor</td>\n",
       "      <td>518</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Sex  Year      Name  Count\n",
       "278689    CA   M  1972    Marlon     87\n",
       "326964    CA   M  1996  Nicolaus      7\n",
       "104271    CA   F  1987    Chelsi     11\n",
       "223908    CA   F  2018   Kailynn      7\n",
       "274476    CA   M  1969    Victor    518"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import urllib.request\n",
    "import os.path\n",
    "import zipfile\n",
    "\n",
    "data_url = \"https://www.ssa.gov/oact/babynames/state/namesbystate.zip\"\n",
    "local_filename = \"babynamesbystate.zip\"\n",
    "if not os.path.exists(local_filename): # if the data exists don't download again\n",
    "    with urllib.request.urlopen(data_url) as resp, open(local_filename, 'wb') as f:\n",
    "        f.write(resp.read())\n",
    "\n",
    "zf = zipfile.ZipFile(local_filename, 'r')\n",
    "\n",
    "ca_name = 'CA.TXT'\n",
    "field_names = ['State', 'Sex', 'Year', 'Name', 'Count']\n",
    "with zf.open(ca_name) as fh:\n",
    "    babynames = pd.read_csv(fh, header=None, names=field_names)\n",
    "\n",
    "babynames.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "executionInfo": {
     "elapsed": 134,
     "status": "ok",
     "timestamp": 1629912649505,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "dress-miller",
    "outputId": "c372ae8b-00b3-42d3-c904-babe0e282750"
   },
   "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>State</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Year</th>\n",
       "      <th>Name</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>221160</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>2018</td>\n",
       "      <td>Emma</td>\n",
       "      <td>2743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385701</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>2018</td>\n",
       "      <td>Noah</td>\n",
       "      <td>2569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221161</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>2018</td>\n",
       "      <td>Mia</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221162</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>2018</td>\n",
       "      <td>Olivia</td>\n",
       "      <td>2465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385702</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>2018</td>\n",
       "      <td>Liam</td>\n",
       "      <td>2413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Sex  Year    Name  Count\n",
       "221160    CA   F  2018    Emma   2743\n",
       "385701    CA   M  2018    Noah   2569\n",
       "221161    CA   F  2018     Mia   2499\n",
       "221162    CA   F  2018  Olivia   2465\n",
       "385702    CA   M  2018    Liam   2413"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "babynames[babynames[\"Year\"] == 2018].sort_values(by = \"Count\", ascending = False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 504,
     "status": "ok",
     "timestamp": 1629912654330,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "basic-investing",
    "outputId": "f62faf85-697b-4bb0-9533-82ee0d0a795f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2743"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_common_count = babynames[babynames[\"Year\"] == 2018].sort_values(by = \"Count\", ascending = False).iloc[0][\"Count\"]\n",
    "most_common_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "executionInfo": {
     "elapsed": 126,
     "status": "ok",
     "timestamp": 1629912658441,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "nearby-thailand"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "plt.style.use('fivethirtyeight')\n",
    "sns.set_context(\"notebook\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "unlimited-lincoln"
   },
   "source": [
    "Find baby names that start with j."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 130,
     "status": "ok",
     "timestamp": 1629912661063,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "great-alaska",
    "outputId": "b1d21b49-5d69-4d73-83de-0b7b1bb1a73a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Mary\n",
       "1        Helen\n",
       "2      Dorothy\n",
       "3     Margaret\n",
       "4      Frances\n",
       "5         Ruth\n",
       "6       Evelyn\n",
       "7        Alice\n",
       "8     Virginia\n",
       "9    Elizabeth\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "babynames[\"Name\"].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "executionInfo": {
     "elapsed": 335,
     "status": "ok",
     "timestamp": 1629912663769,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "upper-soldier",
    "outputId": "dafe85aa-d5a6-4379-927c-7310d6ac7ee5"
   },
   "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>State</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Year</th>\n",
       "      <th>Name</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>309287</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1989</td>\n",
       "      <td>Jaun</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103046</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1987</td>\n",
       "      <td>Jade</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285348</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1976</td>\n",
       "      <td>Jebediah</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281588</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1974</td>\n",
       "      <td>Josue</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47429</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1962</td>\n",
       "      <td>Jeannett</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Sex  Year      Name  Count\n",
       "309287    CA   M  1989      Jaun      7\n",
       "103046    CA   F  1987      Jade    100\n",
       "285348    CA   M  1976  Jebediah      5\n",
       "281588    CA   M  1974     Josue     31\n",
       "47429     CA   F  1962  Jeannett      5"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "starts_with_j = [x[0] == 'J' for x in babynames[\"Name\"]]\n",
    "babynames[starts_with_j].sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "executionInfo": {
     "elapsed": 129,
     "status": "ok",
     "timestamp": 1629912673119,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "imposed-ozone"
   },
   "outputs": [],
   "source": [
    "j_names = babynames[[x[0] == 'J' for x in babynames[\"Name\"]]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 417
    },
    "executionInfo": {
     "elapsed": 127,
     "status": "ok",
     "timestamp": 1629912675100,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "covered-timer",
    "outputId": "6d151dfb-d3c4-4f04-f981-15f758dd02b3"
   },
   "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>State</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Year</th>\n",
       "      <th>Name</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Josephine</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Jean</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Jessie</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Julia</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Juanita</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393987</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>2020</td>\n",
       "      <td>Jr</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393988</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>2020</td>\n",
       "      <td>Juaquin</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393989</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>2020</td>\n",
       "      <td>June</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393990</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>2020</td>\n",
       "      <td>Juniper</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393991</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>2020</td>\n",
       "      <td>Juventino</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33844 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Sex  Year       Name  Count\n",
       "16        CA   F  1910  Josephine     66\n",
       "44        CA   F  1910       Jean     35\n",
       "46        CA   F  1910     Jessie     32\n",
       "59        CA   F  1910      Julia     28\n",
       "66        CA   F  1910    Juanita     25\n",
       "...      ...  ..   ...        ...    ...\n",
       "393987    CA   M  2020         Jr      5\n",
       "393988    CA   M  2020    Juaquin      5\n",
       "393989    CA   M  2020       June      5\n",
       "393990    CA   M  2020    Juniper      5\n",
       "393991    CA   M  2020  Juventino      5\n",
       "\n",
       "[33844 rows x 5 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 251,
     "status": "ok",
     "timestamp": 1629912679153,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "stock-seeking",
    "outputId": "1b0da016-ee20-4ac8-e1e6-ec7fcb72546d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "6    False\n",
       "7    False\n",
       "8    False\n",
       "9    False\n",
       "Name: Name, dtype: bool"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#option2\n",
    "babynames[\"Name\"].str.startswith('J').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 436,
     "status": "ok",
     "timestamp": 1629912685853,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "extended-organ",
    "outputId": "10bf53d9-94f0-4c9a-e0f3-81c2cb1eb05a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "6    False\n",
       "7    False\n",
       "8    False\n",
       "9    False\n",
       "Name: Name, dtype: bool"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "starts_with_j = babynames[\"Name\"].str.startswith('J')\n",
    "starts_with_j.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "executionInfo": {
     "elapsed": 315,
     "status": "ok",
     "timestamp": 1629912695324,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "white-joseph",
    "outputId": "fd88d10f-07f2-40ae-a770-983c53c49a68"
   },
   "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>State</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Year</th>\n",
       "      <th>Name</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19277</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1942</td>\n",
       "      <td>Jeanine</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307122</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1988</td>\n",
       "      <td>Juanantonio</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282032</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1974</td>\n",
       "      <td>Jaysen</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387513</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>2018</td>\n",
       "      <td>Jullian</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145189</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1998</td>\n",
       "      <td>Jesika</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Sex  Year         Name  Count\n",
       "19277     CA   F  1942      Jeanine     10\n",
       "307122    CA   M  1988  Juanantonio      6\n",
       "282032    CA   M  1974       Jaysen      9\n",
       "387513    CA   M  2018      Jullian      9\n",
       "145189    CA   F  1998       Jesika      5"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "babynames[babynames[\"Name\"].str.startswith('J')].sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "executionInfo": {
     "elapsed": 313,
     "status": "ok",
     "timestamp": 1629912698158,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "identified-following",
    "outputId": "9ec4c683-8ed7-4c4f-82fa-92942b808f01"
   },
   "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>State</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Year</th>\n",
       "      <th>Name</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>201727</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>2013</td>\n",
       "      <td>Madelyn</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319214</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1993</td>\n",
       "      <td>Kaden</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317311</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1992</td>\n",
       "      <td>Gerad</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20851</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1944</td>\n",
       "      <td>Nadine</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179814</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>2007</td>\n",
       "      <td>Madelaine</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Sex  Year       Name  Count\n",
       "201727    CA   F  2013    Madelyn    349\n",
       "319214    CA   M  1993      Kaden      8\n",
       "317311    CA   M  1992      Gerad      5\n",
       "20851     CA   F  1944     Nadine     59\n",
       "179814    CA   F  2007  Madelaine      7"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "babynames[babynames[\"Name\"].str.contains('ad')].sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "executionInfo": {
     "elapsed": 1277,
     "status": "ok",
     "timestamp": 1629912702409,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "victorian-dancing",
    "outputId": "79c3ff0d-6171-4fce-ffbc-b5d2041778bb"
   },
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[M, ry]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[Helen]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Dorothy]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[M, rg, ret]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[Fr, nces]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name\n",
       "0       [M, ry]\n",
       "1       [Helen]\n",
       "2     [Dorothy]\n",
       "3  [M, rg, ret]\n",
       "4    [Fr, nces]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "babynames[\"Name\"].str.split('a').to_frame().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "unlimited-defendant"
   },
   "source": [
    "Sort names by their length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "executionInfo": {
     "elapsed": 555,
     "status": "ok",
     "timestamp": 1629912710038,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "representative-benchmark",
    "outputId": "363968a8-1b6f-4d46-f693-941a1e800d4f"
   },
   "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>State</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Year</th>\n",
       "      <th>Name</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102490</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1986</td>\n",
       "      <td>Mariadelosangel</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305111</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1987</td>\n",
       "      <td>Franciscojavier</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306544</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1988</td>\n",
       "      <td>Franciscojavier</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309451</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1989</td>\n",
       "      <td>Franciscojavier</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314358</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1991</td>\n",
       "      <td>Ryanchristopher</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Sex  Year             Name  Count\n",
       "102490    CA   F  1986  Mariadelosangel      5\n",
       "305111    CA   M  1987  Franciscojavier      5\n",
       "306544    CA   M  1988  Franciscojavier     10\n",
       "309451    CA   M  1989  Franciscojavier      6\n",
       "314358    CA   M  1991  Ryanchristopher      7"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "babynames.iloc[[i for i, m in sorted(enumerate(babynames['Name']), key=lambda x: -len(x[1]))]].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "executionInfo": {
     "elapsed": 304,
     "status": "ok",
     "timestamp": 1629912714048,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "adequate-rebate",
    "outputId": "1868f007-bdd9-45da-fb9d-8610017caecd"
   },
   "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>State</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Year</th>\n",
       "      <th>Name</th>\n",
       "      <th>Count</th>\n",
       "      <th>name_lengths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Mary</td>\n",
       "      <td>295</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Helen</td>\n",
       "      <td>239</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Dorothy</td>\n",
       "      <td>220</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Margaret</td>\n",
       "      <td>163</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Frances</td>\n",
       "      <td>134</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State Sex  Year      Name  Count  name_lengths\n",
       "0    CA   F  1910      Mary    295             4\n",
       "1    CA   F  1910     Helen    239             5\n",
       "2    CA   F  1910   Dorothy    220             7\n",
       "3    CA   F  1910  Margaret    163             8\n",
       "4    CA   F  1910   Frances    134             7"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create a new series of only the lengths\n",
    "babyname_lengths = babynames[\"Name\"].str.len()\n",
    "\n",
    "#add that series to the dataframe as a column\n",
    "babynames[\"name_lengths\"] = babyname_lengths\n",
    "babynames.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "executionInfo": {
     "elapsed": 124,
     "status": "ok",
     "timestamp": 1629912720097,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "complicated-zealand",
    "outputId": "61aefa95-be81-4f07-daa6-ec1623f908b6"
   },
   "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>State</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Year</th>\n",
       "      <th>Name</th>\n",
       "      <th>Count</th>\n",
       "      <th>name_lengths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>332035</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1998</td>\n",
       "      <td>Franciscojavier</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329864</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1997</td>\n",
       "      <td>Franciscojavier</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320036</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1993</td>\n",
       "      <td>Ryanchristopher</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314358</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1991</td>\n",
       "      <td>Ryanchristopher</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319923</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1993</td>\n",
       "      <td>Johnchristopher</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Sex  Year             Name  Count  name_lengths\n",
       "332035    CA   M  1998  Franciscojavier      6            15\n",
       "329864    CA   M  1997  Franciscojavier      5            15\n",
       "320036    CA   M  1993  Ryanchristopher      5            15\n",
       "314358    CA   M  1991  Ryanchristopher      7            15\n",
       "319923    CA   M  1993  Johnchristopher      5            15"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sort by the temporary column\n",
    "babynames = babynames.sort_values(by = \"name_lengths\", ascending=False)\n",
    "babynames.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "executionInfo": {
     "elapsed": 126,
     "status": "ok",
     "timestamp": 1629912723076,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "previous-equivalent",
    "outputId": "7ae27021-824b-4762-bb6f-e69f3de40caa"
   },
   "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>State</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Year</th>\n",
       "      <th>Name</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>332035</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1998</td>\n",
       "      <td>Franciscojavier</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329864</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1997</td>\n",
       "      <td>Franciscojavier</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320036</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1993</td>\n",
       "      <td>Ryanchristopher</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314358</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1991</td>\n",
       "      <td>Ryanchristopher</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319923</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1993</td>\n",
       "      <td>Johnchristopher</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Sex  Year             Name  Count\n",
       "332035    CA   M  1998  Franciscojavier      6\n",
       "329864    CA   M  1997  Franciscojavier      5\n",
       "320036    CA   M  1993  Ryanchristopher      5\n",
       "314358    CA   M  1991  Ryanchristopher      7\n",
       "319923    CA   M  1993  Johnchristopher      5"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop the temporary column\n",
    "babynames = babynames.drop(\"name_lengths\", axis = 1)\n",
    "babynames.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "electric-identity"
   },
   "source": [
    "We can also use the Python map function if we want to use an arbitrarily defined function. Suppose we want to sort by the number of occurrences of \"dr\" plus the number of occurences of \"ea\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "executionInfo": {
     "elapsed": 475,
     "status": "ok",
     "timestamp": 1629912731876,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "viral-waters",
    "outputId": "d06d5b4a-e747-4d4f-e568-0eb2fd2cdda4"
   },
   "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>State</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Year</th>\n",
       "      <th>Name</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>131013</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1994</td>\n",
       "      <td>Leandrea</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101962</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1986</td>\n",
       "      <td>Deandrea</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115942</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1990</td>\n",
       "      <td>Deandrea</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300700</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1985</td>\n",
       "      <td>Deandrea</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108715</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1988</td>\n",
       "      <td>Deandrea</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Sex  Year      Name  Count\n",
       "131013    CA   F  1994  Leandrea      5\n",
       "101962    CA   F  1986  Deandrea      6\n",
       "115942    CA   F  1990  Deandrea      5\n",
       "300700    CA   M  1985  Deandrea      6\n",
       "108715    CA   F  1988  Deandrea      5"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def dr_ea_count(string):\n",
    "    return string.count('dr') + string.count('ea')\n",
    "\n",
    "#create the temporary column\n",
    "babynames[\"dr_ea_count\"] = babynames[\"Name\"].map(dr_ea_count)\n",
    "\n",
    "#sort by the temporary column\n",
    "babynames = babynames.sort_values(by = \"dr_ea_count\", ascending=False)\n",
    "\n",
    "#drop that column\n",
    "babynames = babynames.drop(\"dr_ea_count\", 1)\n",
    "babynames.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sunset-transportation"
   },
   "source": [
    "option2 Generate an index sorted in the desired order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "executionInfo": {
     "elapsed": 195,
     "status": "ok",
     "timestamp": 1629912739668,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "impressed-sponsorship",
    "outputId": "91830d7c-895d-4758-9d0a-1174bcd3e2b8"
   },
   "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>State</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Year</th>\n",
       "      <th>Name</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53330</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1966</td>\n",
       "      <td>Esther</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73706</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1976</td>\n",
       "      <td>Natalie</td>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204747</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>2013</td>\n",
       "      <td>Evoleth</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213551</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>2016</td>\n",
       "      <td>Madison</td>\n",
       "      <td>923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25378</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1948</td>\n",
       "      <td>Esperanza</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Sex  Year       Name  Count\n",
       "53330     CA   F  1966     Esther    143\n",
       "73706     CA   F  1976    Natalie    408\n",
       "204747    CA   F  2013    Evoleth      6\n",
       "213551    CA   F  2016    Madison    923\n",
       "25378     CA   F  1948  Esperanza     41"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's start over by first scrambling the order of babynames\n",
    "babynames = babynames.sample(frac=1)\n",
    "babynames.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "criminal-deputy"
   },
   "source": [
    "Another approach is to take advantage of the fact that .loc can accept an index. That is:\n",
    " + df.loc[idx] returns df with its rows in the same order as the given index.\n",
    " + Only works if the index exactly matches the DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "human-server"
   },
   "source": [
    "The first step was to create a sequence of the lengths of the names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 304,
     "status": "ok",
     "timestamp": 1629912754545,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "civil-exhibit",
    "outputId": "9a004340-319a-467c-90ba-0846440a7619"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53330     6\n",
       "73706     7\n",
       "204747    7\n",
       "213551    7\n",
       "25378     9\n",
       "Name: Name, dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_lengths = babynames[\"Name\"].str.len()\n",
    "name_lengths.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "spatial-freeware"
   },
   "source": [
    "The next step is to sort the new series we just created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 136,
     "status": "ok",
     "timestamp": 1629912762319,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "fossil-payday",
    "outputId": "0de79bf6-b080-42dd-c0a5-06708dddcdb1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "304810    2\n",
       "111624    2\n",
       "110190    2\n",
       "119755    2\n",
       "346558    2\n",
       "Name: Name, dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_lengths_sorted_by_length = name_lengths.sort_values()\n",
    "name_lengths_sorted_by_length.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "billion-seminar"
   },
   "source": [
    "Next, we pass the index of the sorted series to the loc method of the original dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 126,
     "status": "ok",
     "timestamp": 1629912765956,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "adaptive-vegetation",
    "outputId": "d91bc99e-c298-4e0a-efd1-61a5a553f171"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([304810, 111624, 110190, 119755, 346558,  87778, 315715, 254371,\n",
       "            236729, 349719,\n",
       "            ...\n",
       "            326729, 314358, 314479, 309451, 306544, 330040, 337003, 329864,\n",
       "            319923, 102490],\n",
       "           dtype='int64', length=394179)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_sorted_by_length = name_lengths_sorted_by_length.index\n",
    "index_sorted_by_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "executionInfo": {
     "elapsed": 292,
     "status": "ok",
     "timestamp": 1629912773914,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "supported-worth",
    "outputId": "3ef31736-650f-4889-864f-f13c68dc6720"
   },
   "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>State</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Year</th>\n",
       "      <th>Name</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>304810</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1987</td>\n",
       "      <td>An</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111624</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1989</td>\n",
       "      <td>Ma</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110190</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1989</td>\n",
       "      <td>Ka</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119755</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1991</td>\n",
       "      <td>Ia</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346558</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>2004</td>\n",
       "      <td>Bo</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Sex  Year Name  Count\n",
       "304810    CA   M  1987   An      6\n",
       "111624    CA   F  1989   Ma      7\n",
       "110190    CA   F  1989   Ka     23\n",
       "119755    CA   F  1991   Ia      5\n",
       "346558    CA   M  2004   Bo     12"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "babynames.loc[index_sorted_by_length].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "varied-colombia"
   },
   "source": [
    "Note we can also do this all in one line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "executionInfo": {
     "elapsed": 329,
     "status": "ok",
     "timestamp": 1629912777978,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "molecular-orientation",
    "outputId": "638d7e25-e9ef-434b-cb26-59bb9fae38aa"
   },
   "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>State</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Year</th>\n",
       "      <th>Name</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>304810</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1987</td>\n",
       "      <td>An</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111624</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1989</td>\n",
       "      <td>Ma</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110190</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1989</td>\n",
       "      <td>Ka</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119755</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1991</td>\n",
       "      <td>Ia</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346558</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>2004</td>\n",
       "      <td>Bo</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Sex  Year Name  Count\n",
       "304810    CA   M  1987   An      6\n",
       "111624    CA   F  1989   Ma      7\n",
       "110190    CA   F  1989   Ka     23\n",
       "119755    CA   F  1991   Ia      5\n",
       "346558    CA   M  2004   Bo     12"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "babynames.loc[babynames[\"Name\"].str.len().sort_values().index].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "confident-coordinate"
   },
   "source": [
    "## Name whose popularity has changed the most."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 137,
     "status": "ok",
     "timestamp": 1629912781682,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "established-judgment",
    "outputId": "08bca74b-f157-4965-9b50-005503d0f738",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69302     5675\n",
       "54974     1902\n",
       "303888      23\n",
       "287534      24\n",
       "16324        5\n",
       "Name: Count, dtype: int64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jennifer_counts = babynames.query(\"Name == 'Jennifer'\")[\"Count\"]\n",
    "jennifer_counts.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "spanish-bundle"
   },
   "source": [
    "The AMMD for Jennifer is 6,061, as seen below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 129,
     "status": "ok",
     "timestamp": 1629912791034,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "young-family",
    "outputId": "ebccca21-634d-4c67-a93f-5139c87962e2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6059"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(jennifer_counts) - min(jennifer_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "executionInfo": {
     "elapsed": 128,
     "status": "ok",
     "timestamp": 1629912794961,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "parliamentary-piece"
   },
   "outputs": [],
   "source": [
    "def ammd(series): #AMMD (absolute max/min difference): max(count) - min(count).\n",
    "    return max(series) - min(series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 157,
     "status": "ok",
     "timestamp": 1629912798314,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "diagnostic-trade",
    "outputId": "7ec8c94a-4b24-4a0e-ce4e-41a776203a8d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6059"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ammd(jennifer_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3051,
     "status": "ok",
     "timestamp": 1629912803656,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "smaller-uncle",
    "outputId": "9a4f922d-5337-4052-8c38-01a0e6d00e99"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Aadan        2\n",
       "Aadarsh      0\n",
       "Aaden      148\n",
       "Aadhav       2\n",
       "Aadhira      4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#option1\n",
    "#build dictionary where entry i is the ammd for the given name\n",
    "#e.g. ammd[\"jennifer\"] should be 6061\n",
    "ammd_of_babyname_counts = {}\n",
    "for name in sorted(babynames[\"Name\"].unique())[0:100]:\n",
    "    counts_of_current_name = babynames[babynames[\"Name\"] == name][\"Count\"]\n",
    "    ammd_of_babyname_counts[name] = ammd(counts_of_current_name)\n",
    "    \n",
    "#convert to series\n",
    "ammd_of_babyname_counts = pd.Series(ammd_of_babyname_counts) \n",
    "ammd_of_babyname_counts.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "shared-trader"
   },
   "source": [
    "Option2: use the very powerful groupby.agg operation, which allows us to simply and efficiently compute what we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 417
    },
    "executionInfo": {
     "elapsed": 128,
     "status": "ok",
     "timestamp": 1629912806327,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "french-modeling",
    "outputId": "0aeb245b-d9bd-43ae-8899-69707c3c2909"
   },
   "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>State</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Year</th>\n",
       "      <th>Name</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53330</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1966</td>\n",
       "      <td>Esther</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73706</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1976</td>\n",
       "      <td>Natalie</td>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204747</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>2013</td>\n",
       "      <td>Evoleth</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213551</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>2016</td>\n",
       "      <td>Madison</td>\n",
       "      <td>923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25378</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1948</td>\n",
       "      <td>Esperanza</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47086</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1962</td>\n",
       "      <td>Deeanna</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391499</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>2020</td>\n",
       "      <td>Giovanni</td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277898</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1971</td>\n",
       "      <td>Burton</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33094</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1954</td>\n",
       "      <td>Della</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382947</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>2017</td>\n",
       "      <td>Weston</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>394179 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Sex  Year       Name  Count\n",
       "53330     CA   F  1966     Esther    143\n",
       "73706     CA   F  1976    Natalie    408\n",
       "204747    CA   F  2013    Evoleth      6\n",
       "213551    CA   F  2016    Madison    923\n",
       "25378     CA   F  1948  Esperanza     41\n",
       "...      ...  ..   ...        ...    ...\n",
       "47086     CA   F  1962    Deeanna      7\n",
       "391499    CA   M  2020   Giovanni    481\n",
       "277898    CA   M  1971     Burton     10\n",
       "33094     CA   F  1954      Della     49\n",
       "382947    CA   M  2017     Weston    277\n",
       "\n",
       "[394179 rows x 5 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "babynames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "executionInfo": {
     "elapsed": 133,
     "status": "ok",
     "timestamp": 1629912810436,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "simple-campbell",
    "outputId": "6b101e56-a829-4276-8a96-d2d5dd0ed6e6"
   },
   "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>State</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Year</th>\n",
       "      <th>Name</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>361678</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>2009</td>\n",
       "      <td>Aadan</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376558</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>2014</td>\n",
       "      <td>Aadan</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358560</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>2008</td>\n",
       "      <td>Aadan</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Sex  Year   Name  Count\n",
       "361678    CA   M  2009  Aadan      6\n",
       "376558    CA   M  2014  Aadan      5\n",
       "358560    CA   M  2008  Aadan      7"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "babynames[(babynames['Name'] == 'Aadan')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 234
    },
    "executionInfo": {
     "elapsed": 990,
     "status": "ok",
     "timestamp": 1629912813825,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "outdoor-elements",
    "outputId": "f50b2941-d7d3-4fe6-a7b8-4e5a32cc8e52"
   },
   "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>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aadan</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aadarsh</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aaden</th>\n",
       "      <td>13</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aadhav</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aadhira</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Year  Count\n",
       "Name                \n",
       "Aadan       6      2\n",
       "Aadarsh     0      0\n",
       "Aaden      13    148\n",
       "Aadhav      5      2\n",
       "Aadhira     3      4"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "babynames.groupby(\"Name\").agg(ammd).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 318
    },
    "executionInfo": {
     "elapsed": 777,
     "status": "ok",
     "timestamp": 1629912858013,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "caring-detail",
    "outputId": "b3816289-105b-4ec2-8b8d-8f2858abfcae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff224332790>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "babynames.groupby(\"Year\").agg(ammd).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "executionInfo": {
     "elapsed": 131,
     "status": "ok",
     "timestamp": 1629912863443,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "irish-hometown"
   },
   "outputs": [],
   "source": [
    "#https://pandas.pydata.org/pandas-docs/version/0.23/generated/pandas.core.groupby.DataFrameGroupBy.agg.html\n",
    "df = pd.DataFrame({'A': [1, 1, 2, 2],\n",
    "                    'B': [1, 2, 3, 4],\n",
    "                  'C': np.random.randn(4)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 172
    },
    "executionInfo": {
     "elapsed": 134,
     "status": "ok",
     "timestamp": 1629912866015,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "opening-virtue",
    "outputId": "d121caf6-ff37-41c3-bd96-4dfcc3c53111"
   },
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.065446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.233834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.304405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>-1.296380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B         C\n",
       "0  1  1  0.065446\n",
       "1  1  2  0.233834\n",
       "2  2  3  0.304405\n",
       "3  2  4 -1.296380"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "executionInfo": {
     "elapsed": 134,
     "status": "ok",
     "timestamp": 1629912868069,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "optical-motion",
    "outputId": "a376497b-f5b6-4cc2-ef5e-0d1c571971dc"
   },
   "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>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.065446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>-1.296380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   B         C\n",
       "A             \n",
       "1  1  0.065446\n",
       "2  3 -1.296380"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('A').agg('min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 172
    },
    "executionInfo": {
     "elapsed": 173,
     "status": "ok",
     "timestamp": 1629912870379,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "placed-asbestos",
    "outputId": "476996e5-5b82-4d0f-8d2c-a57be38695ea"
   },
   "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></th>\n",
       "      <th colspan=\"2\" halign=\"left\">B</th>\n",
       "      <th colspan=\"2\" halign=\"left\">C</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.065446</td>\n",
       "      <td>0.233834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>-1.296380</td>\n",
       "      <td>0.304405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    B             C          \n",
       "  min max       min       max\n",
       "A                            \n",
       "1   1   2  0.065446  0.233834\n",
       "2   3   4 -1.296380  0.304405"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('A').agg(['min', 'max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "smart-lawsuit"
   },
   "source": [
    "## Other groupby Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "exotic-somerset"
   },
   "source": [
    "It is possible to group a DataFrame by multiple features. For example, if we group by Year and Sex we get back a DataFrame with the total number of babies of each sex born in each year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "executionInfo": {
     "elapsed": 148,
     "status": "ok",
     "timestamp": 1629912873624,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "reflected-scale",
    "outputId": "4fd8bdda-b82e-4b55-97f6-bdd6190e17a3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1910</th>\n",
       "      <th>F</th>\n",
       "      <td>5950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>3213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1911</th>\n",
       "      <th>F</th>\n",
       "      <td>6602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>3381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1912</th>\n",
       "      <th>F</th>\n",
       "      <td>9804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>8142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Count\n",
       "Year Sex       \n",
       "1910 F     5950\n",
       "     M     3213\n",
       "1911 F     6602\n",
       "     M     3381\n",
       "1912 F     9804\n",
       "     M     8142"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "babynames.groupby([\"Year\", \"Sex\"]).agg(sum).head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "trying-marathon"
   },
   "source": [
    "The DataFrame resulting from the aggregation operation is now multi-indexed. That is, it has more than one dimension to its index. We will explore this in next week's exercises."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "handmade-activation"
   },
   "source": [
    "### groupby.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 125,
     "status": "ok",
     "timestamp": 1629912877055,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "dramatic-strength",
    "outputId": "ffcc0d25-78b9-4372-8ac3-add0b3201745",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "2006    7076\n",
       "2007    7250\n",
       "2008    7158\n",
       "2009    7121\n",
       "2010    7010\n",
       "2011    6883\n",
       "2012    7011\n",
       "2013    6864\n",
       "2014    6954\n",
       "2015    6874\n",
       "2016    6777\n",
       "2017    6692\n",
       "2018    6547\n",
       "2019    6510\n",
       "2020    6363\n",
       "dtype: int64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#size returns a Series giving the size of each group\n",
    "babynames.groupby(\"Year\").size().tail(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dangerous-necklace"
   },
   "source": [
    "## Finding the number of babies born in each year of each sex."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "confidential-nitrogen"
   },
   "source": [
    "Suppose we want to build a table showing the total number of babies born of each sex in each year. One way is to groupby using both columns of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "executionInfo": {
     "elapsed": 148,
     "status": "ok",
     "timestamp": 1629912882714,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "limiting-gates",
    "outputId": "ca2250e5-eb0a-4ec9-c475-47601ebe2264",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1910</th>\n",
       "      <th>F</th>\n",
       "      <td>5950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>3213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1911</th>\n",
       "      <th>F</th>\n",
       "      <td>6602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>3381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1912</th>\n",
       "      <th>F</th>\n",
       "      <td>9804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>8142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Count\n",
       "Year Sex       \n",
       "1910 F     5950\n",
       "     M     3213\n",
       "1911 F     6602\n",
       "     M     3381\n",
       "1912 F     9804\n",
       "     M     8142"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "babynames.groupby([\"Year\", \"Sex\"]).agg(sum).head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lonely-silver"
   },
   "source": [
    "A more natural approach is to use a pivot table. https://pandas.pydata.org/docs/reference/api/pandas.pivot_table.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "executionInfo": {
     "elapsed": 127,
     "status": "ok",
     "timestamp": 1629912891070,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "official-richmond",
    "outputId": "361ad91b-b881-485f-9ff5-9f09065a1152"
   },
   "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>1910</th>\n",
       "      <td>295</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1911</th>\n",
       "      <td>390</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td>534</td>\n",
       "      <td>501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1913</th>\n",
       "      <td>584</td>\n",
       "      <td>614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1914</th>\n",
       "      <td>773</td>\n",
       "      <td>769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1915</th>\n",
       "      <td>998</td>\n",
       "      <td>1033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex     F     M\n",
       "Year           \n",
       "1910  295   237\n",
       "1911  390   214\n",
       "1912  534   501\n",
       "1913  584   614\n",
       "1914  773   769\n",
       "1915  998  1033"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "babynames_pivot = babynames.pivot_table(\n",
    "    index='Year', # the rows (turned into index)\n",
    "    columns='Sex', # the column values\n",
    "    values='Count', # the field(s) to processed in each group\n",
    "    aggfunc=np.max, # group operation\n",
    ")\n",
    "babynames_pivot.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "laden-excess"
   },
   "source": [
    "Since our queries are getting pretty long, I've stuck them inside parentheses which allows us to spread them over many lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "executionInfo": {
     "elapsed": 140,
     "status": "ok",
     "timestamp": 1629912917657,
     "user": {
      "displayName": "Kaikai Liu",
      "photoUrl": "",
      "userId": "13976416703683897255"
     },
     "user_tz": 420
    },
    "id": "alpine-westminster",
    "outputId": "cc4098ae-5b1c-4453-d827-9be693f1ab8f",
    "scrolled": true
   },
   "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>State</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Year</th>\n",
       "      <th>Name</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>221160</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>2018</td>\n",
       "      <td>Emma</td>\n",
       "      <td>2743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385701</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>2018</td>\n",
       "      <td>Noah</td>\n",
       "      <td>2569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221161</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>2018</td>\n",
       "      <td>Mia</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221162</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>2018</td>\n",
       "      <td>Olivia</td>\n",
       "      <td>2465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385702</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>2018</td>\n",
       "      <td>Liam</td>\n",
       "      <td>2413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Sex  Year    Name  Count\n",
       "221160    CA   F  2018    Emma   2743\n",
       "385701    CA   M  2018    Noah   2569\n",
       "221161    CA   F  2018     Mia   2499\n",
       "221162    CA   F  2018  Olivia   2465\n",
       "385702    CA   M  2018    Liam   2413"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "babynames[babynames[\"Year\"] == 2018]\n",
    "    .sort_values(by = \"Count\", ascending = False)\n",
    "    .head(5)\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyPWSEd5a6mcG029Vv6qLpqG",
   "collapsed_sections": [],
   "name": "Dataintro.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}