programmerKey.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DataTables, Indexes, Pandas, and Seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use('fivethirtyeight')\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from glob import glob\n",
    "sns.set_context(\"notebook\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.ssa.gov/OACT/babynames/index.html\n",
    "\n",
    "https://www.ssa.gov/data/\n",
    "\n",
    "We can run terminal/shell commands directly in a notebook! Here's the code to download the dataset (not running since it takes a while):\n",
    "\n",
    "    !wget https://www.ssa.gov/oact/babynames/state/namesbystate.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !wget https://www.ssa.gov/oact/babynames/state/namesbystate.zip\n",
    "!unzip namesbystate.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "03-datatables-indexes-pandas.ipynb     NC.TXT\r\n",
      "03live-datatables-indexes-pandas.ipynb ND.TXT\r\n",
      "AK.TXT                                 NE.TXT\r\n",
      "AL.TXT                                 NH.TXT\r\n",
      "AR.TXT                                 NJ.TXT\r\n",
      "AZ.TXT                                 NM.TXT\r\n",
      "CA.TXT                                 NV.TXT\r\n",
      "CO.TXT                                 NY.TXT\r\n",
      "CT.TXT                                 OH.TXT\r\n",
      "DC.TXT                                 OK.TXT\r\n",
      "DE.TXT                                 OR.TXT\r\n",
      "FL.TXT                                 PA.TXT\r\n",
      "GA.TXT                                 RI.TXT\r\n",
      "HI.TXT                                 SC.TXT\r\n",
      "IA.TXT                                 SD.TXT\r\n",
      "ID.TXT                                 StateReadMe.pdf\r\n",
      "IL.TXT                                 TN.TXT\r\n",
      "IN.TXT                                 TX.TXT\r\n",
      "KS.TXT                                 UT.TXT\r\n",
      "KY.TXT                                 VA.TXT\r\n",
      "LA.TXT                                 VT.TXT\r\n",
      "MA.TXT                                 WA.TXT\r\n",
      "MD.TXT                                 WI.TXT\r\n",
      "ME.TXT                                 WV.TXT\r\n",
      "MI.TXT                                 WY.TXT\r\n",
      "MN.TXT                                 lec03.ipynb\r\n",
      "MO.TXT                                 lec03_live.ipynb\r\n",
      "MS.TXT                                 namesbystate.zip\r\n",
      "MT.TXT\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CA,F,1910,Mary,295\r",
      "\r\n",
      "CA,F,1910,Helen,239\r",
      "\r\n",
      "CA,F,1910,Dorothy,220\r",
      "\r\n",
      "CA,F,1910,Margaret,163\r",
      "\r\n",
      "CA,F,1910,Frances,134\r",
      "\r\n",
      "CA,F,1910,Ruth,128\r",
      "\r\n",
      "CA,F,1910,Evelyn,126\r",
      "\r\n",
      "CA,F,1910,Alice,118\r",
      "\r\n",
      "CA,F,1910,Virginia,101\r",
      "\r\n",
      "CA,F,1910,Elizabeth,93\r",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!head CA.TXT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  367931 CA.TXT\r\n"
     ]
    }
   ],
   "source": [
    "!wc -l CA.TXT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# !cat CA.TXT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1: What was the most popular name in CA last year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Mary</td>\n",
       "      <td>295</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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State Sex  Year      Name  Count\n",
       "0    CA   F  1910      Mary    295\n",
       "1    CA   F  1910     Helen    239\n",
       "2    CA   F  1910   Dorothy    220\n",
       "3    CA   F  1910  Margaret    163\n",
       "4    CA   F  1910   Frances    134"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca = pd.read_csv('CA.TXT', header=None, names=['State', 'Sex', 'Year', 'Name', 'Count'])\n",
    "ca.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    295\n",
       "1    239\n",
       "2    220\n",
       "3    163\n",
       "4    134\n",
       "Name: Count, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca['Count'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Mary</td>\n",
       "      <td>295</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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State Sex  Year     Name  Count\n",
       "0    CA   F  1910     Mary    295\n",
       "1    CA   F  1910    Helen    239\n",
       "2    CA   F  1910  Dorothy    220"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ca[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State Sex\n",
       "0    CA   F\n",
       "1    CA   F\n",
       "2    CA   F"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca.iloc[0:3, 0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    CA\n",
       "1    CA\n",
       "2    CA\n",
       "3    CA\n",
       "Name: State, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca.loc[0:3, 'State']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Year</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Mary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Helen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Dorothy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Margaret</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Frances</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Ruth</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sex  Year      Name\n",
       "0   F  1910      Mary\n",
       "1   F  1910     Helen\n",
       "2   F  1910   Dorothy\n",
       "3   F  1910  Margaret\n",
       "4   F  1910   Frances\n",
       "5   F  1910      Ruth"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca.loc[0:5, 'Sex':'Name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the leftmost column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>a@gmail.com</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Mary</td>\n",
       "      <td>295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b@gmail.com</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Helen</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c@gmail.com</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Dorothy</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d@gmail.com</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Margaret</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e@gmail.com</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Frances</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            State Sex  Year      Name  Count\n",
       "a@gmail.com    CA   F  1910      Mary    295\n",
       "b@gmail.com    CA   F  1910     Helen    239\n",
       "c@gmail.com    CA   F  1910   Dorothy    220\n",
       "d@gmail.com    CA   F  1910  Margaret    163\n",
       "e@gmail.com    CA   F  1910   Frances    134"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emails = ca.head()\n",
    "emails.index = ['a@gmail.com', 'b@gmail.com', 'c@gmail.com', 'd@gmail.com', 'e@gmail.com']\n",
    "emails"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b@gmail.com</th>\n",
       "      <td>1910</td>\n",
       "      <td>Helen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c@gmail.com</th>\n",
       "      <td>1910</td>\n",
       "      <td>Dorothy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d@gmail.com</th>\n",
       "      <td>1910</td>\n",
       "      <td>Margaret</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Year      Name\n",
       "b@gmail.com  1910     Helen\n",
       "c@gmail.com  1910   Dorothy\n",
       "d@gmail.com  1910  Margaret"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emails.loc['b@gmail.com':'d@gmail.com', 'Year':'Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Mary</td>\n",
       "      <td>295</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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State Sex  Year      Name  Count\n",
       "0    CA   F  1910      Mary    295\n",
       "1    CA   F  1910     Helen    239\n",
       "2    CA   F  1910   Dorothy    220\n",
       "3    CA   F  1910  Margaret    163\n",
       "4    CA   F  1910   Frances    134"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "Name: Year, dtype: bool"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ca['Year'] == 2016).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>213461</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>2016</td>\n",
       "      <td>Mia</td>\n",
       "      <td>2785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213462</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>2016</td>\n",
       "      <td>Sophia</td>\n",
       "      <td>2747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213463</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>2016</td>\n",
       "      <td>Emma</td>\n",
       "      <td>2592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213464</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>2016</td>\n",
       "      <td>Olivia</td>\n",
       "      <td>2533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213465</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>2016</td>\n",
       "      <td>Isabella</td>\n",
       "      <td>2350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Sex  Year      Name  Count\n",
       "213461    CA   F  2016       Mia   2785\n",
       "213462    CA   F  2016    Sophia   2747\n",
       "213463    CA   F  2016      Emma   2592\n",
       "213464    CA   F  2016    Olivia   2533\n",
       "213465    CA   F  2016  Isabella   2350"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca[ca['Year'] == 2016].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sorting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>213461</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>2016</td>\n",
       "      <td>Mia</td>\n",
       "      <td>2785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213462</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>2016</td>\n",
       "      <td>Sophia</td>\n",
       "      <td>2747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365035</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>2016</td>\n",
       "      <td>Noah</td>\n",
       "      <td>2683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213463</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>2016</td>\n",
       "      <td>Emma</td>\n",
       "      <td>2592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213464</th>\n",
       "      <td>CA</td>\n",
       "      <td>F</td>\n",
       "      <td>2016</td>\n",
       "      <td>Olivia</td>\n",
       "      <td>2533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Sex  Year    Name  Count\n",
       "213461    CA   F  2016     Mia   2785\n",
       "213462    CA   F  2016  Sophia   2747\n",
       "365035    CA   M  2016    Noah   2683\n",
       "213463    CA   F  2016    Emma   2592\n",
       "213464    CA   F  2016  Olivia   2533"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca_sorted = ca[ca['Year'] == 2016]\n",
    "ca_sorted.sort_values('Count', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2: Most popular names in all states for each year?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Put all DFs together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--  1 sam  staff   548K Mar 10 00:00 AK.TXT\r\n",
      "-rw-r--r--  1 sam  staff   2.6M Mar 10 00:00 AL.TXT\r\n",
      "-rw-r--r--  1 sam  staff   1.9M Mar 10 00:00 AR.TXT\r\n",
      "-rw-r--r--  1 sam  staff   2.2M Mar 10 00:00 AZ.TXT\r\n",
      "-rw-r--r--  1 sam  staff   7.3M Mar 10 00:00 CA.TXT\r\n",
      "-rw-r--r--  1 sam  staff   2.0M Mar 10 00:00 CO.TXT\r\n",
      "-rw-r--r--  1 sam  staff   1.6M Mar 10 00:00 CT.TXT\r\n",
      "-rw-r--r--  1 sam  staff   1.1M Mar 10 00:00 DC.TXT\r\n",
      "-rw-r--r--  1 sam  staff   628K Mar 10 00:00 DE.TXT\r\n",
      "-rw-r--r--  1 sam  staff   3.9M Mar 10 00:00 FL.TXT\r\n"
     ]
    }
   ],
   "source": [
    "# Make sure that filesizes are managable\n",
    "!ls -alh *.TXT | head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "glob('*.TXT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>0</th>\n",
       "      <td>AK</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Mary</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Annie</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AK</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Anna</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AK</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Margaret</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AK</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Helen</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State Sex  Year      Name  Count\n",
       "0    AK   F  1910      Mary     14\n",
       "1    AK   F  1910     Annie     12\n",
       "2    AK   F  1910      Anna     10\n",
       "3    AK   F  1910  Margaret      8\n",
       "4    AK   F  1910     Helen      7"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_names = glob('*.TXT')\n",
    "\n",
    "baby_names = pd.concat(\n",
    "    (pd.read_csv(f, names=['State', 'Sex', 'Year', 'Name', 'Count']) for f in file_names)\n",
    ").reset_index(drop=True)\n",
    "baby_names.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5838786"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(baby_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Group by state and year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>675533</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1995</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>5003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675534</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1995</td>\n",
       "      <td>Michael</td>\n",
       "      <td>4783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675535</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1995</td>\n",
       "      <td>Jose</td>\n",
       "      <td>4572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675536</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1995</td>\n",
       "      <td>Christopher</td>\n",
       "      <td>4096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675537</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1995</td>\n",
       "      <td>David</td>\n",
       "      <td>4029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Sex  Year         Name  Count\n",
       "675533    CA   M  1995       Daniel   5003\n",
       "675534    CA   M  1995      Michael   4783\n",
       "675535    CA   M  1995         Jose   4572\n",
       "675536    CA   M  1995  Christopher   4096\n",
       "675537    CA   M  1995        David   4029"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_names[\n",
    "    (baby_names['State'] == 'CA')\n",
    "    & (baby_names['Year'] == 1995)\n",
    "    & (baby_names['Sex'] == 'M')\n",
    "].head()\n",
    "\n",
    "# Now I could write 3 nested for loops..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State\n",
       "AK     27624\n",
       "AL    130297\n",
       "AR     98853\n",
       "AZ    110866\n",
       "CA    367931\n",
       "dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_names.groupby('State').size().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AK</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AK</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AK</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Count\n",
       "0    AK     14\n",
       "1    AK     12\n",
       "2    AK     10\n",
       "3    AK      8\n",
       "4    AK      7"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_counts = baby_names.loc[:, ('State', 'Count')]\n",
    "state_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AK</th>\n",
       "      <td>424852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>5773719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AR</th>\n",
       "      <td>3408590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>3532872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>30115165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Count\n",
       "State          \n",
       "AK       424852\n",
       "AL      5773719\n",
       "AR      3408590\n",
       "AZ      3532872\n",
       "CA     30115165"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_counts.groupby('State').sum().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    state_counts.group('State', np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AK</th>\n",
       "      <td>424852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>5773719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AR</th>\n",
       "      <td>3408590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>3532872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>30115165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Count\n",
       "State          \n",
       "AK       424852\n",
       "AL      5773719\n",
       "AR      3408590\n",
       "AZ      3532872\n",
       "CA     30115165"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_counts.groupby('State').agg(np.sum).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a custom function to aggregate.\n",
    "\n",
    "Equivalent to this code from Data 8:\n",
    "\n",
    "    state_and_groups.group('State', np.sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping by multiple columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State  Year\n",
       "AK     1910    16\n",
       "       1911    11\n",
       "       1912    20\n",
       "       1913    12\n",
       "       1914    32\n",
       "dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_names.groupby(['State', 'Year']).size().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">AK</th>\n",
       "      <th>1910</th>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1911</th>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1913</th>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1914</th>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Count\n",
       "State Year       \n",
       "AK    1910    115\n",
       "      1911     84\n",
       "      1912    141\n",
       "      1913    110\n",
       "      1914    245"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_names.groupby(['State', 'Year']).sum().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th>Year</th>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">AK</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">1910</th>\n",
       "      <th>F</th>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1911</th>\n",
       "      <th>F</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <th>F</th>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Count\n",
       "State Year Sex       \n",
       "AK    1910 F       68\n",
       "           M       47\n",
       "      1911 F       44\n",
       "           M       40\n",
       "      1912 F       82"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_names.groupby(['State', 'Year', 'Sex']).sum().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th>Year</th>\n",
       "      <th>Sex</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">AK</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">1910</th>\n",
       "      <th>F</th>\n",
       "      <td>Mary</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>John</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1911</th>\n",
       "      <th>F</th>\n",
       "      <td>Mary</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>John</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1912</th>\n",
       "      <th>F</th>\n",
       "      <td>Mary</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>John</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1913</th>\n",
       "      <th>F</th>\n",
       "      <td>Mary</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>John</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1914</th>\n",
       "      <th>F</th>\n",
       "      <td>Mary</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>John</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1915</th>\n",
       "      <th>F</th>\n",
       "      <td>Mary</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>John</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1916</th>\n",
       "      <th>F</th>\n",
       "      <td>Mary</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>John</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1917</th>\n",
       "      <th>F</th>\n",
       "      <td>Mary</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>John</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1918</th>\n",
       "      <th>F</th>\n",
       "      <td>Mary</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>John</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1919</th>\n",
       "      <th>F</th>\n",
       "      <td>Mary</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>John</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1920</th>\n",
       "      <th>F</th>\n",
       "      <td>Mary</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>John</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1921</th>\n",
       "      <th>F</th>\n",
       "      <td>Mary</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>John</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1922</th>\n",
       "      <th>F</th>\n",
       "      <td>Mary</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>Robert</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1923</th>\n",
       "      <th>F</th>\n",
       "      <td>Mary</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>John</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1924</th>\n",
       "      <th>F</th>\n",
       "      <td>Mary</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>John</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">WY</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">2002</th>\n",
       "      <th>F</th>\n",
       "      <td>Madison</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2003</th>\n",
       "      <th>F</th>\n",
       "      <td>Emma</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>Jacob</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2004</th>\n",
       "      <th>F</th>\n",
       "      <td>Madison</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>Michael</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2005</th>\n",
       "      <th>F</th>\n",
       "      <td>Madison</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>Jacob</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2006</th>\n",
       "      <th>F</th>\n",
       "      <td>Emily</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2007</th>\n",
       "      <th>F</th>\n",
       "      <td>Madison</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>James</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2008</th>\n",
       "      <th>F</th>\n",
       "      <td>Madison</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>James</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2009</th>\n",
       "      <th>F</th>\n",
       "      <td>Isabella</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>Wyatt</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2010</th>\n",
       "      <th>F</th>\n",
       "      <td>Isabella</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>James</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2011</th>\n",
       "      <th>F</th>\n",
       "      <td>Emma</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>William</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2012</th>\n",
       "      <th>F</th>\n",
       "      <td>Emma</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>Liam</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
       "      <th>F</th>\n",
       "      <td>Sophia</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>Liam</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
       "      <th>F</th>\n",
       "      <td>Olivia</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>Jackson</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2015</th>\n",
       "      <th>F</th>\n",
       "      <td>Emma</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>Liam</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2016</th>\n",
       "      <th>F</th>\n",
       "      <td>Emma</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>Wyatt</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10914 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Name  Count\n",
       "State Year Sex                 \n",
       "AK    1910 F        Mary     14\n",
       "           M        John      8\n",
       "      1911 F        Mary     12\n",
       "           M        John     15\n",
       "      1912 F        Mary      9\n",
       "           M        John     16\n",
       "      1913 F        Mary     21\n",
       "           M        John     19\n",
       "      1914 F        Mary     22\n",
       "           M        John     17\n",
       "      1915 F        Mary     23\n",
       "           M        John     21\n",
       "      1916 F        Mary     18\n",
       "           M        John     25\n",
       "      1917 F        Mary     21\n",
       "           M        John     26\n",
       "      1918 F        Mary     27\n",
       "           M        John     23\n",
       "      1919 F        Mary     22\n",
       "           M        John     24\n",
       "      1920 F        Mary     38\n",
       "           M        John     21\n",
       "      1921 F        Mary     36\n",
       "           M        John     35\n",
       "      1922 F        Mary     29\n",
       "           M      Robert     22\n",
       "      1923 F        Mary     26\n",
       "           M        John     27\n",
       "      1924 F        Mary     41\n",
       "           M        John     36\n",
       "...                  ...    ...\n",
       "WY    2002 F     Madison     35\n",
       "           M       Ethan     52\n",
       "      2003 F        Emma     38\n",
       "           M       Jacob     47\n",
       "      2004 F     Madison     40\n",
       "           M     Michael     34\n",
       "      2005 F     Madison     38\n",
       "           M       Jacob     41\n",
       "      2006 F       Emily     39\n",
       "           M       Ethan     44\n",
       "      2007 F     Madison     36\n",
       "           M       James     38\n",
       "      2008 F     Madison     35\n",
       "           M       James     41\n",
       "      2009 F    Isabella     36\n",
       "           M       Wyatt     42\n",
       "      2010 F    Isabella     44\n",
       "           M       James     36\n",
       "      2011 F        Emma     43\n",
       "           M     William     32\n",
       "      2012 F        Emma     40\n",
       "           M        Liam     41\n",
       "      2013 F      Sophia     42\n",
       "           M        Liam     33\n",
       "      2014 F      Olivia     40\n",
       "           M     Jackson     34\n",
       "      2015 F        Emma     39\n",
       "           M        Liam     38\n",
       "      2016 F        Emma     36\n",
       "           M       Wyatt     46\n",
       "\n",
       "[10914 rows x 2 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def first(series):\n",
    "    '''Returns the first value in the series.'''\n",
    "    return series.iloc[0]\n",
    "\n",
    "most_popular_names = (\n",
    "    baby_names\n",
    "    .groupby(['State', 'Year', 'Sex'])\n",
    "    .agg(first)\n",
    ")\n",
    "most_popular_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This creates a multilevel index. It is quite complex, but just know that you can still slice:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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></th>\n",
       "      <th>Name</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th>Year</th>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th>2010</th>\n",
       "      <th>M</th>\n",
       "      <td>Samuel</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name  Count\n",
       "State Year Sex               \n",
       "ID    2010 M    Samuel    114"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_popular_names[most_popular_names['Name'] == 'Samuel']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And you can use `.loc` as so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name     Daniel\n",
       "Count      4452\n",
       "Name: (CA, 1997, M), dtype: object"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_popular_names.loc['CA', 1997, 'M']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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></th>\n",
       "      <th>Name</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th>Year</th>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">CA</th>\n",
       "      <th>1995</th>\n",
       "      <th>M</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>5003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <th>M</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>4705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <th>M</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>4452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <th>M</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>4317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <th>M</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>4352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <th>M</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>4339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name  Count\n",
       "State Year Sex               \n",
       "CA    1995 M    Daniel   5003\n",
       "      1996 M    Daniel   4705\n",
       "      1997 M    Daniel   4452\n",
       "      1998 M    Daniel   4317\n",
       "      1999 M    Daniel   4352\n",
       "      2000 M    Daniel   4339"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_popular_names.loc['CA', 1995:2000, 'M']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3: Can I deduce gender from the last letter of a person’s name?\n",
    "\n",
    "Survey question time!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute last letter of each name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>0</th>\n",
       "      <td>AK</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Mary</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Annie</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AK</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Anna</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AK</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Margaret</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AK</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Helen</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State Sex  Year      Name  Count\n",
       "0    AK   F  1910      Mary     14\n",
       "1    AK   F  1910     Annie     12\n",
       "2    AK   F  1910      Anna     10\n",
       "3    AK   F  1910  Margaret      8\n",
       "4    AK   F  1910     Helen      7"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_names.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    5\n",
       "2    4\n",
       "3    8\n",
       "4    5\n",
       "Name: Name, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_names['Name'].apply(len).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    5\n",
       "2    4\n",
       "3    8\n",
       "4    5\n",
       "Name: Name, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_names['Name'].str.len().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    y\n",
       "1    e\n",
       "2    a\n",
       "3    t\n",
       "4    n\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_names['Name'].str[-1].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To add column to dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>Last letter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Mary</td>\n",
       "      <td>14</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Annie</td>\n",
       "      <td>12</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AK</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Anna</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AK</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Margaret</td>\n",
       "      <td>8</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AK</td>\n",
       "      <td>F</td>\n",
       "      <td>1910</td>\n",
       "      <td>Helen</td>\n",
       "      <td>7</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State Sex  Year      Name  Count Last letter\n",
       "0    AK   F  1910      Mary     14           y\n",
       "1    AK   F  1910     Annie     12           e\n",
       "2    AK   F  1910      Anna     10           a\n",
       "3    AK   F  1910  Margaret      8           t\n",
       "4    AK   F  1910     Helen      7           n"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_names['Last letter'] = baby_names['Name'].str[-1]\n",
    "baby_names.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Group by last letter and sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>Last letter</th>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>F</th>\n",
       "      <td>49128453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>1585024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>F</th>\n",
       "      <td>9666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>1369244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <th>F</th>\n",
       "      <td>18211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Count\n",
       "Last letter Sex          \n",
       "a           F    49128453\n",
       "            M     1585024\n",
       "b           F        9666\n",
       "            M     1369244\n",
       "c           F       18211"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letter_counts = (baby_names\n",
    "                 .loc[:, ('Sex', 'Count', 'Last letter')]\n",
    "                 .groupby(['Last letter', 'Sex'])\n",
    "                 .sum())\n",
    "letter_counts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize our result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use .plot to get some basic plotting functionality:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x111b5fd30>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ARjJDfvMuSV6vV5WVlSooKJAkjRkzRo2NjV1ec+DAAXm93h7nNzU1dbvncrnk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\npk+frjfffFOBQEDV1dW6995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Ne1tbm+bPn6/BgwdrypQpeuqpp/Ta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081NTXdxlm2bFl02/yePXskSQ6HQydOnNDhw4cHLH8AAAAAAMxg6Lb5lpYWJScnt7t38cUX64IL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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1128db9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Why is this not good?\n",
    "letter_counts.plot.barh(figsize=(15, 15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the docs shows me that pandas will make one set of bars for each column in my table. How do I move each sex into its own column? I have to use pivot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>Last letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>49128453</td>\n",
       "      <td>1585024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>9666</td>\n",
       "      <td>1369244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>18211</td>\n",
       "      <td>1565621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>564804</td>\n",
       "      <td>15423771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>31212081</td>\n",
       "      <td>12778932</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex                 F         M\n",
       "Last letter                    \n",
       "a            49128453   1585024\n",
       "b                9666   1369244\n",
       "c               18211   1565621\n",
       "d              564804  15423771\n",
       "e            31212081  12778932"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For comparison, the group above:\n",
    "# letter_counts = (baby_names\n",
    "#                  .loc[:, ('Sex', 'Count', 'Last letter')]\n",
    "#                  .groupby(['Last letter', 'Sex'])\n",
    "#                  .sum())\n",
    "\n",
    "last_letter_pivot = baby_names.pivot_table(\n",
    "    index='Last letter', # the rows (turned into index)\n",
    "    columns='Sex', # the column values\n",
    "    values='Count', # the field(s) to processed in each group\n",
    "    aggfunc=sum, # group operation\n",
    ")\n",
    "last_letter_pivot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1127b9080>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y2WyC7gAA6AyZTGaL25OGz6OOOir69esXt912WwwcODBFWUpYNpvN+xeUrs+c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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1127a1b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "last_letter_pivot.plot.barh(figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why is this still not ideal?\n",
    "\n",
    "- Plotting raw counts\n",
    "- Not sorted by any order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Last letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.968746</td>\n",
       "      <td>0.031254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>0.830335</td>\n",
       "      <td>0.169665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.709510</td>\n",
       "      <td>0.290490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>0.645210</td>\n",
       "      <td>0.354790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.571341</td>\n",
       "      <td>0.428659</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    F         M\n",
       "Last letter                    \n",
       "a            0.968746  0.031254\n",
       "i            0.830335  0.169665\n",
       "e            0.709510  0.290490\n",
       "z            0.645210  0.354790\n",
       "y            0.571341  0.428659"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totals = last_letter_pivot['F'] + last_letter_pivot['M']\n",
    "\n",
    "last_letter_props = pd.DataFrame({\n",
    "    'F': last_letter_pivot['F'] / totals,\n",
    "    'M': last_letter_pivot['M'] / totals,\n",
    "}).sort_values('M')\n",
    "last_letter_props.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112c6fac8>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OOigmTpyYoiwAADmT7LT7JZdcEpdcckmqcgAA5FDur/mszG3KugUAAGrEQ+YBAEhG+AQA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\naRuG1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1119c17f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "last_letter_props.plot.barh(figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do you notice?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use a subset of our dataset for now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ca_and_ny = baby_names[\n",
    "    (baby_names['Year'] == 2016)\n",
    "    & (baby_names['State'].isin(['CA', 'NY']))\n",
    "]\n",
    "ca_and_ny.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We actually don't need to do any pivoting / grouping for seaborn!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.barplot(x=..., y=..., data=...)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the automatic confidence interval generation. Many seaborn functions have these nifty statistical features.\n",
    "\n",
    "(It actually isn't useful for our case since we have a census. It also makes seaborn functions run slower since they use bootstrap to generate the CI, so sometimes you want to turn it off.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Going to work with tips data just to demonstrate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips = sns.load_dataset(\"tips\")\n",
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.distplot(...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.lmplot(x=..., y=..., data=...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  }
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