FBI Gun Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FBI Gun Data - Investigate a Dataset \n",
    "## By: Sumaya Altamimi\n",
    "### Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "I want to investigate two datasets:\n",
    "<br>1)**The NICS data** which contains the number of firearm checks by month, state, and type. \n",
    "<br>2)**The U.S. census data** which contains several variables at the state level.<br>\n",
    "\n",
    "My goal is to answer these questions:<br>\n",
    "<ul>\n",
    "<li><a href=\"#q1\">Which states have had the highest growth in gun registrations?</a></li>\n",
    "<li><a href=\"#q2\">Which years have had the highest growth in gun registrations?</a></li>\n",
    "<li><a href=\"#q3\">What census data is most associated with long gun per capita?</a></li>\n",
    "<li><a href=\"#q4\">What is the overall trend of gun purchases?</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data wrangling\n",
    "In this section I will load in the data, check for cleanliness, and then trim and clean the dataset for analysis. \n",
    "steps carefully and justify your cleaning decisions.\n",
    "\n",
    "#### General Properties\n",
    "First, will import the important libraries and load our datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "NICS_data = pd.read_excel('gun_data.xlsx')\n",
    "US_census_data = pd.read_csv('U.S. Census Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>state</th>\n",
       "      <th>permit</th>\n",
       "      <th>permit_recheck</th>\n",
       "      <th>handgun</th>\n",
       "      <th>long_gun</th>\n",
       "      <th>other</th>\n",
       "      <th>multiple</th>\n",
       "      <th>admin</th>\n",
       "      <th>prepawn_handgun</th>\n",
       "      <th>...</th>\n",
       "      <th>returned_other</th>\n",
       "      <th>rentals_handgun</th>\n",
       "      <th>rentals_long_gun</th>\n",
       "      <th>private_sale_handgun</th>\n",
       "      <th>private_sale_long_gun</th>\n",
       "      <th>private_sale_other</th>\n",
       "      <th>return_to_seller_handgun</th>\n",
       "      <th>return_to_seller_long_gun</th>\n",
       "      <th>return_to_seller_other</th>\n",
       "      <th>totals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>16717.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5734.0</td>\n",
       "      <td>6320.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>317</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>209.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2320.0</td>\n",
       "      <td>2930.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>160</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     month    state   permit  permit_recheck  handgun  long_gun  other  \\\n",
       "0  2017-09  Alabama  16717.0             0.0   5734.0    6320.0  221.0   \n",
       "1  2017-09   Alaska    209.0             2.0   2320.0    2930.0  219.0   \n",
       "\n",
       "   multiple  admin  prepawn_handgun  ...  returned_other  rentals_handgun  \\\n",
       "0       317    0.0             15.0  ...             0.0              0.0   \n",
       "1       160    0.0              5.0  ...             0.0              0.0   \n",
       "\n",
       "   rentals_long_gun  private_sale_handgun  private_sale_long_gun  \\\n",
       "0               0.0                   9.0                   16.0   \n",
       "1               0.0                  17.0                   24.0   \n",
       "\n",
       "   private_sale_other  return_to_seller_handgun  return_to_seller_long_gun  \\\n",
       "0                 3.0                       0.0                        0.0   \n",
       "1                 1.0                       0.0                        0.0   \n",
       "\n",
       "   return_to_seller_other  totals  \n",
       "0                     3.0   32019  \n",
       "1                     0.0    6303  \n",
       "\n",
       "[2 rows x 27 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NICS_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fact</th>\n",
       "      <th>Fact Note</th>\n",
       "      <th>Alabama</th>\n",
       "      <th>Alaska</th>\n",
       "      <th>Arizona</th>\n",
       "      <th>Arkansas</th>\n",
       "      <th>California</th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Connecticut</th>\n",
       "      <th>Delaware</th>\n",
       "      <th>...</th>\n",
       "      <th>South Dakota</th>\n",
       "      <th>Tennessee</th>\n",
       "      <th>Texas</th>\n",
       "      <th>Utah</th>\n",
       "      <th>Vermont</th>\n",
       "      <th>Virginia</th>\n",
       "      <th>Washington</th>\n",
       "      <th>West Virginia</th>\n",
       "      <th>Wisconsin</th>\n",
       "      <th>Wyoming</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Population estimates, July 1, 2016,  (V2016)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4,863,300</td>\n",
       "      <td>741,894</td>\n",
       "      <td>6,931,071</td>\n",
       "      <td>2,988,248</td>\n",
       "      <td>39,250,017</td>\n",
       "      <td>5,540,545</td>\n",
       "      <td>3,576,452</td>\n",
       "      <td>952,065</td>\n",
       "      <td>...</td>\n",
       "      <td>865454</td>\n",
       "      <td>6651194</td>\n",
       "      <td>27,862,596</td>\n",
       "      <td>3,051,217</td>\n",
       "      <td>624,594</td>\n",
       "      <td>8,411,808</td>\n",
       "      <td>7,288,000</td>\n",
       "      <td>1,831,102</td>\n",
       "      <td>5,778,708</td>\n",
       "      <td>585,501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Population estimates base, April 1, 2010,  (V2...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4,780,131</td>\n",
       "      <td>710,249</td>\n",
       "      <td>6,392,301</td>\n",
       "      <td>2,916,025</td>\n",
       "      <td>37,254,522</td>\n",
       "      <td>5,029,324</td>\n",
       "      <td>3,574,114</td>\n",
       "      <td>897,936</td>\n",
       "      <td>...</td>\n",
       "      <td>814195</td>\n",
       "      <td>6346298</td>\n",
       "      <td>25,146,100</td>\n",
       "      <td>2,763,888</td>\n",
       "      <td>625,741</td>\n",
       "      <td>8,001,041</td>\n",
       "      <td>6,724,545</td>\n",
       "      <td>1,853,011</td>\n",
       "      <td>5,687,289</td>\n",
       "      <td>563,767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Fact Fact Note    Alabama  \\\n",
       "0       Population estimates, July 1, 2016,  (V2016)       NaN  4,863,300   \n",
       "1  Population estimates base, April 1, 2010,  (V2...       NaN  4,780,131   \n",
       "\n",
       "    Alaska    Arizona   Arkansas  California   Colorado Connecticut Delaware  \\\n",
       "0  741,894  6,931,071  2,988,248  39,250,017  5,540,545   3,576,452  952,065   \n",
       "1  710,249  6,392,301  2,916,025  37,254,522  5,029,324   3,574,114  897,936   \n",
       "\n",
       "   ... South Dakota Tennessee       Texas       Utah  Vermont   Virginia  \\\n",
       "0  ...       865454   6651194  27,862,596  3,051,217  624,594  8,411,808   \n",
       "1  ...       814195   6346298  25,146,100  2,763,888  625,741  8,001,041   \n",
       "\n",
       "  Washington West Virginia  Wisconsin  Wyoming  \n",
       "0  7,288,000     1,831,102  5,778,708  585,501  \n",
       "1  6,724,545     1,853,011  5,687,289  563,767  \n",
       "\n",
       "[2 rows x 52 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "US_census_data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.Data Cleaning \n",
    "Check for nulls, duplicates, and data types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "will check and then remove duplicates from `US_census_data` data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(US_census_data.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fact</th>\n",
       "      <th>Fact Note</th>\n",
       "      <th>Alabama</th>\n",
       "      <th>Alaska</th>\n",
       "      <th>Arizona</th>\n",
       "      <th>Arkansas</th>\n",
       "      <th>California</th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Connecticut</th>\n",
       "      <th>Delaware</th>\n",
       "      <th>...</th>\n",
       "      <th>South Dakota</th>\n",
       "      <th>Tennessee</th>\n",
       "      <th>Texas</th>\n",
       "      <th>Utah</th>\n",
       "      <th>Vermont</th>\n",
       "      <th>Virginia</th>\n",
       "      <th>Washington</th>\n",
       "      <th>West Virginia</th>\n",
       "      <th>Wisconsin</th>\n",
       "      <th>Wyoming</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fact Fact Note Alabama Alaska Arizona Arkansas California Colorado  \\\n",
       "67  NaN       NaN     NaN    NaN     NaN      NaN        NaN      NaN   \n",
       "70  NaN       NaN     NaN    NaN     NaN      NaN        NaN      NaN   \n",
       "75  NaN       NaN     NaN    NaN     NaN      NaN        NaN      NaN   \n",
       "\n",
       "   Connecticut Delaware  ... South Dakota Tennessee Texas Utah Vermont  \\\n",
       "67         NaN      NaN  ...          NaN       NaN   NaN  NaN     NaN   \n",
       "70         NaN      NaN  ...          NaN       NaN   NaN  NaN     NaN   \n",
       "75         NaN      NaN  ...          NaN       NaN   NaN  NaN     NaN   \n",
       "\n",
       "   Virginia Washington West Virginia Wisconsin Wyoming  \n",
       "67      NaN        NaN           NaN       NaN     NaN  \n",
       "70      NaN        NaN           NaN       NaN     NaN  \n",
       "75      NaN        NaN           NaN       NaN     NaN  \n",
       "\n",
       "[3 rows x 52 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "US_census_data[US_census_data.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like `US_census_data` has 3 duplicated NaN rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "US_census_data.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "US_census_data.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(US_census_data.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(US_census_data.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, I will check the data types of `US_census_data`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "US_census_data.dtypes.all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, I will change to float, but only the first row(2016 puplication), wich is what we need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "states = US_census_data.columns[2:]\n",
    "us_census_states = US_census_data[states]\n",
    "\n",
    "for state in states:\n",
    "    string_value = us_census_states[state][0]\n",
    "    string_value = string_value.replace(\"'\",'')\n",
    "    string_value = string_value.replace(\",\",\"\")\n",
    "    string_value = string_value.replace(\"%\",\"\")\n",
    "    string_value = float(string_value)\n",
    "    US_census_data[state][0] = string_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "will check and then remove duplicates from `NICS_data` data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(NICS_data.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "154595"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(NICS_data.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like `NICS_data` has no duplicates but many null values!\n",
    "I will replace them with zeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "NICS_data.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(NICS_data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12485 entries, 0 to 12484\n",
      "Data columns (total 27 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   month                      12485 non-null  object \n",
      " 1   state                      12485 non-null  object \n",
      " 2   permit                     12485 non-null  float64\n",
      " 3   permit_recheck             12485 non-null  float64\n",
      " 4   handgun                    12485 non-null  float64\n",
      " 5   long_gun                   12485 non-null  float64\n",
      " 6   other                      12485 non-null  float64\n",
      " 7   multiple                   12485 non-null  int64  \n",
      " 8   admin                      12485 non-null  float64\n",
      " 9   prepawn_handgun            12485 non-null  float64\n",
      " 10  prepawn_long_gun           12485 non-null  float64\n",
      " 11  prepawn_other              12485 non-null  float64\n",
      " 12  redemption_handgun         12485 non-null  float64\n",
      " 13  redemption_long_gun        12485 non-null  float64\n",
      " 14  redemption_other           12485 non-null  float64\n",
      " 15  returned_handgun           12485 non-null  float64\n",
      " 16  returned_long_gun          12485 non-null  float64\n",
      " 17  returned_other             12485 non-null  float64\n",
      " 18  rentals_handgun            12485 non-null  float64\n",
      " 19  rentals_long_gun           12485 non-null  float64\n",
      " 20  private_sale_handgun       12485 non-null  float64\n",
      " 21  private_sale_long_gun      12485 non-null  float64\n",
      " 22  private_sale_other         12485 non-null  float64\n",
      " 23  return_to_seller_handgun   12485 non-null  float64\n",
      " 24  return_to_seller_long_gun  12485 non-null  float64\n",
      " 25  return_to_seller_other     12485 non-null  float64\n",
      " 26  totals                     12485 non-null  int64  \n",
      "dtypes: float64(23), int64(2), object(2)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "NICS_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I will add a column `year` to simplify the analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>state</th>\n",
       "      <th>permit</th>\n",
       "      <th>permit_recheck</th>\n",
       "      <th>handgun</th>\n",
       "      <th>long_gun</th>\n",
       "      <th>other</th>\n",
       "      <th>multiple</th>\n",
       "      <th>admin</th>\n",
       "      <th>prepawn_handgun</th>\n",
       "      <th>...</th>\n",
       "      <th>rentals_handgun</th>\n",
       "      <th>rentals_long_gun</th>\n",
       "      <th>private_sale_handgun</th>\n",
       "      <th>private_sale_long_gun</th>\n",
       "      <th>private_sale_other</th>\n",
       "      <th>return_to_seller_handgun</th>\n",
       "      <th>return_to_seller_long_gun</th>\n",
       "      <th>return_to_seller_other</th>\n",
       "      <th>totals</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>16717.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5734.0</td>\n",
       "      <td>6320.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>317</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32019</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>209.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2320.0</td>\n",
       "      <td>2930.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>160</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6303</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     month    state   permit  permit_recheck  handgun  long_gun  other  \\\n",
       "0  2017-09  Alabama  16717.0             0.0   5734.0    6320.0  221.0   \n",
       "1  2017-09   Alaska    209.0             2.0   2320.0    2930.0  219.0   \n",
       "\n",
       "   multiple  admin  prepawn_handgun  ...  rentals_handgun  rentals_long_gun  \\\n",
       "0       317    0.0             15.0  ...              0.0               0.0   \n",
       "1       160    0.0              5.0  ...              0.0               0.0   \n",
       "\n",
       "   private_sale_handgun  private_sale_long_gun  private_sale_other  \\\n",
       "0                   9.0                   16.0                 3.0   \n",
       "1                  17.0                   24.0                 1.0   \n",
       "\n",
       "   return_to_seller_handgun  return_to_seller_long_gun  \\\n",
       "0                       0.0                        0.0   \n",
       "1                       0.0                        0.0   \n",
       "\n",
       "   return_to_seller_other  totals  year  \n",
       "0                     3.0   32019  2017  \n",
       "1                     0.0    6303  2017  \n",
       "\n",
       "[2 rows x 28 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NICS_data['year'] = NICS_data['month'].str.slice(stop=4)\n",
    "NICS_data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "Now, I will answer the research questions.\n",
    "<a id='q1'></a>\n",
    "### Which states have had the highest growth in gun registrations?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28617553"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_totals = NICS_data['totals'].mean()\n",
    "high_total_states = NICS_data.query('totals>@mean_totals')\n",
    "high_total_states = high_total_states.groupby(by='state').sum()\n",
    "high_total = high_total_states['totals'].max()\n",
    "high_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>permit</th>\n",
       "      <th>permit_recheck</th>\n",
       "      <th>handgun</th>\n",
       "      <th>long_gun</th>\n",
       "      <th>other</th>\n",
       "      <th>multiple</th>\n",
       "      <th>admin</th>\n",
       "      <th>prepawn_handgun</th>\n",
       "      <th>prepawn_long_gun</th>\n",
       "      <th>prepawn_other</th>\n",
       "      <th>...</th>\n",
       "      <th>returned_other</th>\n",
       "      <th>rentals_handgun</th>\n",
       "      <th>rentals_long_gun</th>\n",
       "      <th>private_sale_handgun</th>\n",
       "      <th>private_sale_long_gun</th>\n",
       "      <th>private_sale_other</th>\n",
       "      <th>return_to_seller_handgun</th>\n",
       "      <th>return_to_seller_long_gun</th>\n",
       "      <th>return_to_seller_other</th>\n",
       "      <th>totals</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kentucky</th>\n",
       "      <td>24926779.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1350007.0</td>\n",
       "      <td>1657082.0</td>\n",
       "      <td>19869.0</td>\n",
       "      <td>85452</td>\n",
       "      <td>28236.0</td>\n",
       "      <td>1619.0</td>\n",
       "      <td>2871.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>28617553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              permit  permit_recheck    handgun   long_gun    other  multiple  \\\n",
       "state                                                                           \n",
       "Kentucky  24926779.0             0.0  1350007.0  1657082.0  19869.0     85452   \n",
       "\n",
       "            admin  prepawn_handgun  prepawn_long_gun  prepawn_other  ...  \\\n",
       "state                                                                ...   \n",
       "Kentucky  28236.0           1619.0            2871.0           24.0  ...   \n",
       "\n",
       "          returned_other  rentals_handgun  rentals_long_gun  \\\n",
       "state                                                         \n",
       "Kentucky             1.0              0.0               0.0   \n",
       "\n",
       "          private_sale_handgun  private_sale_long_gun  private_sale_other  \\\n",
       "state                                                                       \n",
       "Kentucky                 341.0                  271.0                11.0   \n",
       "\n",
       "          return_to_seller_handgun  return_to_seller_long_gun  \\\n",
       "state                                                           \n",
       "Kentucky                      13.0                       14.0   \n",
       "\n",
       "          return_to_seller_other    totals  \n",
       "state                                       \n",
       "Kentucky                     7.0  28617553  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_total_states.query('totals==@high_total')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "high_total_states['totals'].plot(kind='bar',figsize=(15,8),title='Gun Registerations for each State',ylabel='Number of Gun Registerations');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reasoning/ Findings\n",
    "As you might see in the scatter plot above, it's very obvious that the state **Kentucky** is the highest in growth in gun registrations.\n",
    "Now , will move to the second question:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q2'></a>\n",
    "### Which years have had the highest growth in gun registrations?\n",
    "I will create new data set `NICS_data_per_year` <br>\n",
    "Then I will check the statistics of the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>permit</th>\n",
       "      <th>permit_recheck</th>\n",
       "      <th>handgun</th>\n",
       "      <th>long_gun</th>\n",
       "      <th>other</th>\n",
       "      <th>multiple</th>\n",
       "      <th>admin</th>\n",
       "      <th>prepawn_handgun</th>\n",
       "      <th>prepawn_long_gun</th>\n",
       "      <th>prepawn_other</th>\n",
       "      <th>...</th>\n",
       "      <th>returned_other</th>\n",
       "      <th>rentals_handgun</th>\n",
       "      <th>rentals_long_gun</th>\n",
       "      <th>private_sale_handgun</th>\n",
       "      <th>private_sale_long_gun</th>\n",
       "      <th>private_sale_other</th>\n",
       "      <th>return_to_seller_handgun</th>\n",
       "      <th>return_to_seller_long_gun</th>\n",
       "      <th>return_to_seller_other</th>\n",
       "      <th>totals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.996012e+06</td>\n",
       "      <td>64127.600000</td>\n",
       "      <td>3.702654e+06</td>\n",
       "      <td>4.868501e+06</td>\n",
       "      <td>99129.700000</td>\n",
       "      <td>167675.650000</td>\n",
       "      <td>36699.400000</td>\n",
       "      <td>2544.850000</td>\n",
       "      <td>4128.600000</td>\n",
       "      <td>42.350000</td>\n",
       "      <td>...</td>\n",
       "      <td>93.250000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>2053.700000</td>\n",
       "      <td>1595.400000</td>\n",
       "      <td>141.650000</td>\n",
       "      <td>49.750000</td>\n",
       "      <td>60.750000</td>\n",
       "      <td>11.950000</td>\n",
       "      <td>1.348113e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.095401e+06</td>\n",
       "      <td>201310.993106</td>\n",
       "      <td>2.120440e+06</td>\n",
       "      <td>1.315503e+06</td>\n",
       "      <td>159655.420278</td>\n",
       "      <td>78686.944107</td>\n",
       "      <td>45354.159068</td>\n",
       "      <td>1420.486102</td>\n",
       "      <td>2790.245648</td>\n",
       "      <td>68.701701</td>\n",
       "      <td>...</td>\n",
       "      <td>236.513018</td>\n",
       "      <td>11.932265</td>\n",
       "      <td>11.882761</td>\n",
       "      <td>5021.595271</td>\n",
       "      <td>3865.373018</td>\n",
       "      <td>343.455394</td>\n",
       "      <td>115.559452</td>\n",
       "      <td>136.555319</td>\n",
       "      <td>30.655085</td>\n",
       "      <td>6.497589e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.816900e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.199990e+05</td>\n",
       "      <td>5.827910e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8720.000000</td>\n",
       "      <td>2219.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.918980e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.359956e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.185712e+06</td>\n",
       "      <td>4.554734e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>101297.750000</td>\n",
       "      <td>6418.500000</td>\n",
       "      <td>2210.500000</td>\n",
       "      <td>2558.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.760006e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.388912e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.983414e+06</td>\n",
       "      <td>4.875076e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>174999.000000</td>\n",
       "      <td>12535.000000</td>\n",
       "      <td>2727.000000</td>\n",
       "      <td>4083.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.192763e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.805118e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.352768e+06</td>\n",
       "      <td>5.454335e+06</td>\n",
       "      <td>175311.250000</td>\n",
       "      <td>231623.750000</td>\n",
       "      <td>56155.750000</td>\n",
       "      <td>3147.250000</td>\n",
       "      <td>5573.500000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>96.250000</td>\n",
       "      <td>102.250000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.835885e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.113465e+07</td>\n",
       "      <td>763287.000000</td>\n",
       "      <td>8.085498e+06</td>\n",
       "      <td>7.128798e+06</td>\n",
       "      <td>586137.000000</td>\n",
       "      <td>324401.000000</td>\n",
       "      <td>148115.000000</td>\n",
       "      <td>5146.000000</td>\n",
       "      <td>11080.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>791.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>17227.000000</td>\n",
       "      <td>13312.000000</td>\n",
       "      <td>1152.000000</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>416.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>2.742551e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             permit  permit_recheck       handgun      long_gun  \\\n",
       "count  2.000000e+01       20.000000  2.000000e+01  2.000000e+01   \n",
       "mean   3.996012e+06    64127.600000  3.702654e+06  4.868501e+06   \n",
       "std    3.095401e+06   201310.993106  2.120440e+06  1.315503e+06   \n",
       "min    7.816900e+04        0.000000  2.199990e+05  5.827910e+05   \n",
       "25%    1.359956e+06        0.000000  2.185712e+06  4.554734e+06   \n",
       "50%    3.388912e+06        0.000000  2.983414e+06  4.875076e+06   \n",
       "75%    5.805118e+06        0.000000  5.352768e+06  5.454335e+06   \n",
       "max    1.113465e+07   763287.000000  8.085498e+06  7.128798e+06   \n",
       "\n",
       "               other       multiple          admin  prepawn_handgun  \\\n",
       "count      20.000000      20.000000      20.000000        20.000000   \n",
       "mean    99129.700000  167675.650000   36699.400000      2544.850000   \n",
       "std    159655.420278   78686.944107   45354.159068      1420.486102   \n",
       "min         0.000000    8720.000000    2219.000000         0.000000   \n",
       "25%         0.000000  101297.750000    6418.500000      2210.500000   \n",
       "50%         0.000000  174999.000000   12535.000000      2727.000000   \n",
       "75%    175311.250000  231623.750000   56155.750000      3147.250000   \n",
       "max    586137.000000  324401.000000  148115.000000      5146.000000   \n",
       "\n",
       "       prepawn_long_gun  prepawn_other  ...  returned_other  rentals_handgun  \\\n",
       "count         20.000000      20.000000  ...       20.000000        20.000000   \n",
       "mean        4128.600000      42.350000  ...       93.250000         3.800000   \n",
       "std         2790.245648      68.701701  ...      236.513018        11.932265   \n",
       "min            0.000000       0.000000  ...        0.000000         0.000000   \n",
       "25%         2558.000000       0.000000  ...        0.000000         0.000000   \n",
       "50%         4083.000000       0.000000  ...        0.000000         0.000000   \n",
       "75%         5573.500000      50.750000  ...        0.000000         0.000000   \n",
       "max        11080.000000     193.000000  ...      791.000000        52.000000   \n",
       "\n",
       "       rentals_long_gun  private_sale_handgun  private_sale_long_gun  \\\n",
       "count         20.000000             20.000000              20.000000   \n",
       "mean           3.600000           2053.700000            1595.400000   \n",
       "std           11.882761           5021.595271            3865.373018   \n",
       "min            0.000000              0.000000               0.000000   \n",
       "25%            0.000000              0.000000               0.000000   \n",
       "50%            0.000000              0.000000               0.000000   \n",
       "75%            0.000000             96.250000             102.250000   \n",
       "max           50.000000          17227.000000           13312.000000   \n",
       "\n",
       "       private_sale_other  return_to_seller_handgun  \\\n",
       "count           20.000000                 20.000000   \n",
       "mean           141.650000                 49.750000   \n",
       "std            343.455394                115.559452   \n",
       "min              0.000000                  0.000000   \n",
       "25%              0.000000                  0.000000   \n",
       "50%              0.000000                  0.000000   \n",
       "75%             17.000000                  0.500000   \n",
       "max           1152.000000                362.000000   \n",
       "\n",
       "       return_to_seller_long_gun  return_to_seller_other        totals  \n",
       "count                  20.000000               20.000000  2.000000e+01  \n",
       "mean                   60.750000               11.950000  1.348113e+07  \n",
       "std                   136.555319               30.655085  6.497589e+06  \n",
       "min                     0.000000                0.000000  8.918980e+05  \n",
       "25%                     0.000000                0.000000  8.760006e+06  \n",
       "50%                     0.000000                0.000000  1.192763e+07  \n",
       "75%                     4.750000                0.500000  1.835885e+07  \n",
       "max                   416.000000              121.000000  2.742551e+07  \n",
       "\n",
       "[8 rows x 25 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NICS_data_per_year = NICS_data.groupby('year').sum()\n",
    "NICS_data_per_year.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown in the above table, the mean of the long gun is grater than the mean of the hand gun data <br>\n",
    "I will check the hand gun data for each year to detirmin what year has the highest registeration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "NICS_data_per_year['handgun'].plot(kind='bar',legend=True,figsize=(20,8),title='Hand Guns per month',ylabel='Sum of Hand Guns');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "NICS_data_per_year['long_gun'].plot(kind='bar',legend=True,figsize=(20,8),title='Long Guns per month',ylabel='Sum of Long Guns');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reasoning/ Findings\n",
    "As you see, almost **2015-2016**, has the highest registeration of ***hand guns***<br>\n",
    "and **2012-2013** has the highest registeration of **long guns** <br>\n",
    "and the overall comparison by year , **long gun is the dominating**.<br>\n",
    "The shown plot below illustrate the idea."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def PlotFunc(data,title,x,xlabel,y1,y2,ylabel):\n",
    "    fig, ax = plt.subplots(figsize=(15,8))\n",
    "    ax.yaxis.set_major_formatter('{x:.1f}')\n",
    "    ax.set(xticks=[])\n",
    "    data.plot(x = x, y = y1, ylabel=ylabel,ax = ax,title=title);\n",
    "    data.plot(x = x, y = y2, c='r',xlabel=xlabel,ax = ax,title=title);\n",
    "    return \n",
    "PlotFunc(NICS_data,'Hand guns vs Long Guns','year','Year','handgun','long_gun','counts of guns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reasoning/ Findings\n",
    "As you see,when we compare by year , Long gun is the overpowering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q3'></a>\n",
    "### What census data is most associated with long gun per capita?\n",
    "I will check the maximum value of long gun, in 2015, December"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>permit</th>\n",
       "      <th>permit_recheck</th>\n",
       "      <th>handgun</th>\n",
       "      <th>long_gun</th>\n",
       "      <th>other</th>\n",
       "      <th>multiple</th>\n",
       "      <th>admin</th>\n",
       "      <th>prepawn_handgun</th>\n",
       "      <th>prepawn_long_gun</th>\n",
       "      <th>prepawn_other</th>\n",
       "      <th>...</th>\n",
       "      <th>returned_other</th>\n",
       "      <th>rentals_handgun</th>\n",
       "      <th>rentals_long_gun</th>\n",
       "      <th>private_sale_handgun</th>\n",
       "      <th>private_sale_long_gun</th>\n",
       "      <th>private_sale_other</th>\n",
       "      <th>return_to_seller_handgun</th>\n",
       "      <th>return_to_seller_long_gun</th>\n",
       "      <th>return_to_seller_other</th>\n",
       "      <th>totals</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>765096.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1806155.0</td>\n",
       "      <td>2239883.0</td>\n",
       "      <td>35009.0</td>\n",
       "      <td>86544</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2661.0</td>\n",
       "      <td>3890.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5455986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>228934.0</td>\n",
       "      <td>3784.0</td>\n",
       "      <td>605521.0</td>\n",
       "      <td>491701.0</td>\n",
       "      <td>30261.0</td>\n",
       "      <td>28925</td>\n",
       "      <td>633.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1486894</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           permit  permit_recheck    handgun   long_gun    other  multiple  \\\n",
       "state                                                                        \n",
       "Alabama  765096.0             0.0  1806155.0  2239883.0  35009.0     86544   \n",
       "Arizona  228934.0          3784.0   605521.0   491701.0  30261.0     28925   \n",
       "\n",
       "         admin  prepawn_handgun  prepawn_long_gun  prepawn_other  ...  \\\n",
       "state                                                             ...   \n",
       "Alabama   31.0           2661.0            3890.0           19.0  ...   \n",
       "Arizona  633.0            317.0             208.0            8.0  ...   \n",
       "\n",
       "         returned_other  rentals_handgun  rentals_long_gun  \\\n",
       "state                                                        \n",
       "Alabama             0.0              1.0               0.0   \n",
       "Arizona             3.0              0.0               0.0   \n",
       "\n",
       "         private_sale_handgun  private_sale_long_gun  private_sale_other  \\\n",
       "state                                                                      \n",
       "Alabama                  86.0                   93.0                10.0   \n",
       "Arizona                 150.0                   94.0                15.0   \n",
       "\n",
       "         return_to_seller_handgun  return_to_seller_long_gun  \\\n",
       "state                                                          \n",
       "Alabama                      11.0                        6.0   \n",
       "Arizona                      12.0                        7.0   \n",
       "\n",
       "         return_to_seller_other   totals  \n",
       "state                                     \n",
       "Alabama                     0.0  5455986  \n",
       "Arizona                     2.0  1486894  \n",
       "\n",
       "[2 rows x 25 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_long_gun = NICS_data['long_gun'].mean()\n",
    "high_long_gun_states = NICS_data.query('long_gun>@mean_long_gun')\n",
    "high_long_gun_states = high_long_gun_states.groupby(by='state').sum()\n",
    "high_long_gun_states.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will check the state of the maximum value of Long gun."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>permit</th>\n",
       "      <th>permit_recheck</th>\n",
       "      <th>handgun</th>\n",
       "      <th>long_gun</th>\n",
       "      <th>other</th>\n",
       "      <th>multiple</th>\n",
       "      <th>admin</th>\n",
       "      <th>prepawn_handgun</th>\n",
       "      <th>prepawn_long_gun</th>\n",
       "      <th>prepawn_other</th>\n",
       "      <th>...</th>\n",
       "      <th>returned_other</th>\n",
       "      <th>rentals_handgun</th>\n",
       "      <th>rentals_long_gun</th>\n",
       "      <th>private_sale_handgun</th>\n",
       "      <th>private_sale_long_gun</th>\n",
       "      <th>private_sale_other</th>\n",
       "      <th>return_to_seller_handgun</th>\n",
       "      <th>return_to_seller_long_gun</th>\n",
       "      <th>return_to_seller_other</th>\n",
       "      <th>totals</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <td>1387955.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1944501.0</td>\n",
       "      <td>9343700.0</td>\n",
       "      <td>5378.0</td>\n",
       "      <td>59382</td>\n",
       "      <td>4387.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12762672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 permit  permit_recheck    handgun   long_gun   other  \\\n",
       "state                                                                   \n",
       "Pennsylvania  1387955.0             0.0  1944501.0  9343700.0  5378.0   \n",
       "\n",
       "              multiple   admin  prepawn_handgun  prepawn_long_gun  \\\n",
       "state                                                               \n",
       "Pennsylvania     59382  4387.0              3.0               9.0   \n",
       "\n",
       "              prepawn_other  ...  returned_other  rentals_handgun  \\\n",
       "state                        ...                                    \n",
       "Pennsylvania            0.0  ...            31.0              0.0   \n",
       "\n",
       "              rentals_long_gun  private_sale_handgun  private_sale_long_gun  \\\n",
       "state                                                                         \n",
       "Pennsylvania               0.0                   0.0                    0.0   \n",
       "\n",
       "              private_sale_other  return_to_seller_handgun  \\\n",
       "state                                                        \n",
       "Pennsylvania                 0.0                       0.0   \n",
       "\n",
       "              return_to_seller_long_gun  return_to_seller_other    totals  \n",
       "state                                                                      \n",
       "Pennsylvania                        0.0                     0.0  12762672  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_gun = high_long_gun_states['long_gun'].max()\n",
    "high_long_gun_states.query('long_gun==@max_gun')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will check the population estimates of Texas in July 1, 2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12784227.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pennsylvania_publication = US_census_data['Pennsylvania']\n",
    "Pennsylvania_publication = Pennsylvania_publication[0]\n",
    "Pennsylvania_publication"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I Will see the proportion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7308771973463863"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proportion = max_gun/Pennsylvania_publication\n",
    "proportion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "high_long_gun_states['long_gun'].plot(kind='bar',figsize=(15,8),title='Maximum Long Gun for each state',ylabel='Maximum Long Gun');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reasoning/ Findings\n",
    "As you see, almost **73%** of census in **Pennsylvania** is associated with long gun "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, I will answer my last question <br> \n",
    "<a id='q4'></a>\n",
    "### What is the overall trend of gun purchases?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['state', 'returned_handgun', 'returned_long_gun', 'returned_other',\n",
       "       'rentals_handgun', 'rentals_long_gun', 'private_sale_handgun',\n",
       "       'private_sale_long_gun', 'private_sale_other',\n",
       "       'return_to_seller_handgun', 'return_to_seller_long_gun',\n",
       "       'return_to_seller_other'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = NICS_data.columns[1:2]\n",
    "cols = cols.append(NICS_data.columns[15:26])\n",
    "purchases_data = pd.DataFrame(columns=cols,data = NICS_data)\n",
    "purchases_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>returned_handgun</th>\n",
       "      <th>returned_long_gun</th>\n",
       "      <th>returned_other</th>\n",
       "      <th>rentals_handgun</th>\n",
       "      <th>rentals_long_gun</th>\n",
       "      <th>private_sale_handgun</th>\n",
       "      <th>private_sale_long_gun</th>\n",
       "      <th>private_sale_other</th>\n",
       "      <th>return_to_seller_handgun</th>\n",
       "      <th>return_to_seller_long_gun</th>\n",
       "      <th>return_to_seller_other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12485.000000</td>\n",
       "      <td>12485.000000</td>\n",
       "      <td>12485.000000</td>\n",
       "      <td>12485.000000</td>\n",
       "      <td>12485.000000</td>\n",
       "      <td>12485.000000</td>\n",
       "      <td>12485.000000</td>\n",
       "      <td>12485.000000</td>\n",
       "      <td>12485.000000</td>\n",
       "      <td>12485.000000</td>\n",
       "      <td>12485.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.218102</td>\n",
       "      <td>1.298438</td>\n",
       "      <td>0.149379</td>\n",
       "      <td>0.006087</td>\n",
       "      <td>0.005767</td>\n",
       "      <td>3.289868</td>\n",
       "      <td>2.555707</td>\n",
       "      <td>0.226912</td>\n",
       "      <td>0.079696</td>\n",
       "      <td>0.097317</td>\n",
       "      <td>0.019143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>36.194758</td>\n",
       "      <td>9.590870</td>\n",
       "      <td>1.710795</td>\n",
       "      <td>0.179790</td>\n",
       "      <td>0.173912</td>\n",
       "      <td>33.987054</td>\n",
       "      <td>25.908792</td>\n",
       "      <td>2.139596</td>\n",
       "      <td>0.663611</td>\n",
       "      <td>0.740136</td>\n",
       "      <td>0.186114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>603.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1017.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       returned_handgun  returned_long_gun  returned_other  rentals_handgun  \\\n",
       "count      12485.000000       12485.000000    12485.000000     12485.000000   \n",
       "mean           5.218102           1.298438        0.149379         0.006087   \n",
       "std           36.194758           9.590870        1.710795         0.179790   \n",
       "min            0.000000           0.000000        0.000000         0.000000   \n",
       "25%            0.000000           0.000000        0.000000         0.000000   \n",
       "50%            0.000000           0.000000        0.000000         0.000000   \n",
       "75%            0.000000           0.000000        0.000000         0.000000   \n",
       "max          603.000000         168.000000       64.000000        12.000000   \n",
       "\n",
       "       rentals_long_gun  private_sale_handgun  private_sale_long_gun  \\\n",
       "count      12485.000000          12485.000000           12485.000000   \n",
       "mean           0.005767              3.289868               2.555707   \n",
       "std            0.173912             33.987054              25.908792   \n",
       "min            0.000000              0.000000               0.000000   \n",
       "25%            0.000000              0.000000               0.000000   \n",
       "50%            0.000000              0.000000               0.000000   \n",
       "75%            0.000000              0.000000               0.000000   \n",
       "max           12.000000           1017.000000             777.000000   \n",
       "\n",
       "       private_sale_other  return_to_seller_handgun  \\\n",
       "count        12485.000000              12485.000000   \n",
       "mean             0.226912                  0.079696   \n",
       "std              2.139596                  0.663611   \n",
       "min              0.000000                  0.000000   \n",
       "25%              0.000000                  0.000000   \n",
       "50%              0.000000                  0.000000   \n",
       "75%              0.000000                  0.000000   \n",
       "max             71.000000                 28.000000   \n",
       "\n",
       "       return_to_seller_long_gun  return_to_seller_other  \n",
       "count               12485.000000            12485.000000  \n",
       "mean                    0.097317                0.019143  \n",
       "std                     0.740136                0.186114  \n",
       "min                     0.000000                0.000000  \n",
       "25%                     0.000000                0.000000  \n",
       "50%                     0.000000                0.000000  \n",
       "75%                     0.000000                0.000000  \n",
       "max                    17.000000                4.000000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchases_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotFunc(purchases_data,'Private Sales of guns','state','State','private_sale_handgun','private_sale_long_gun','counts of Sales')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reasoning/ Findings\n",
    "Private Sales of **Hand guns** is **moere than** Private Sales of **Long guns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotFunc(purchases_data,'Rentals of Guns','state','State','rentals_handgun','rentals_long_gun','counts of rentals')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reasoning/ Findings\n",
    "Rentals of **Hand guns** is **moere than** rentals of **Long guns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotFunc(purchases_data,'Returned Guns','state','State','returned_handgun','returned_long_gun','counts of returned guns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reasoning/ Findings\n",
    "Returned **Hand guns** is **moere than** returned **Long guns**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusion and Limitations\n",
    "**I noticed these important trends on theses datasets:**\n",
    "\n",
    "1) The census data in **Pennsylvania** is most associated with high gun per capita with **73%**<br>\n",
    "2) **Kentucky** states have had the highest growth in gun registrations.<br>\n",
    "3) The overall trend of gun purchases appears to be skewed towards **Hand Gun**.<br>\n",
    "4) **2015-2016**, has the highest registeration of **Hand Guns**<br>\n",
    "5) **2012-2013** has the highest registeration of **Long Guns**\n",
    "\n",
    "**There are also some limitations:**\n",
    "<ol>\n",
    "<li>Long guns totals is greater than Hand guns when comparing by year</li>\n",
    "<li>Hand guns totals is greater than Long guns when comparing by state</li>\n",
    "\n",
    "</ol>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:project1] *",
   "language": "python",
   "name": "conda-env-project1-py"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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