{"cells":[{"metadata":{},"cell_type":"markdown","source":"<p  style=\"text-align: center;\"><font size=\"8\"><b>POLICE KILLINGS IN THE USA <br>2015 to 2019</b></font></p>\n\n<img src=\"https://github.com/miltonsuggs/EDA_USA-Police-Shootings/blob/master/Don't%20Shoot.jpg?raw=true\" alt=\"Don't Shoot\">  \n\n\n## **INTRODUCTION**\n\nSince its inception, police in America have continued to have a tenuous and volatile relationship with many of its citizens, especially those of lower economic status and those whose race is not classified as White. \n\nIn this notebook I will perform a exploratory data analysis on a dataset that consists of people killed by police throughout the United States. I have also added US census data to get a clearer picture of how the racial statistics compare to those of the United States as a whole. \n\nI hope this notebook provides you with insights and I hope that we can work toward rectifying the racial and economic disparity that continues to fuel police brutality in these United States of America. "},{"metadata":{},"cell_type":"markdown","source":"<a id=\"top\"></a>\n\n<h3 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\"  role=\"tab\" aria-controls=\"home\">Table of Contents</h3>\n\n* <a href='#1'>I. LOAD LIBRARIES & PACKAGES</a>\n* <a href='#2'>II. DATA OVERVIEW & INSIGHTS</a>\n* <a href='#3'>III. MISSING VALUES</a>\n* <a href='#4'>IV. FEATURE ENGINEERING</a>\n* <a href='#5'>V. EXPLORATORY DATA ANALYSIS</a>  \n    * [Univariate Data Exploration](#univariate)\n    * [Timewise Data Exploration](#timewise)\n    * [Bivariate Data Exploration](#bivariate)\n* <a href='#6'>VI. CONCLUSION</a>"},{"metadata":{},"cell_type":"markdown","source":"# <a id='1'>I. LOAD PACKAGES & LIBRARIES</a>"},{"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np \nimport pandas as pd \nimport os\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport plotly.express as px\nimport missingno as msno\nimport plotly.graph_objects as go\nimport plotly.figure_factory as ff\nfrom plotly.subplots import make_subplots\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":505,"outputs":[{"output_type":"stream","text":"/kaggle/input/state-census-quickfacts/Missouri QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Montana QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Kansas QuickFacts.csv\n/kaggle/input/state-census-quickfacts/West Virginia QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Arizona QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Connecticut QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Virginia QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Massachusetts QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Oregon QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Maryland QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Delaware QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Utah QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Vermont QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Oklahoma QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Kentucky QuickFacts.csv\n/kaggle/input/state-census-quickfacts/California quickfacts.csv\n/kaggle/input/state-census-quickfacts/North Dakota QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Idaho QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Minnesota QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Texas QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Wisconsin QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Rhode Island QuickFacts.csv\n/kaggle/input/state-census-quickfacts/New Mexico QuickFacts .csv\n/kaggle/input/state-census-quickfacts/South Dakota QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Indiana QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Pennsylvania QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Mississippi QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Maine QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Hawaii QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Wyoming QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Ohio QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Unted States QuickFacts.csv\n/kaggle/input/state-census-quickfacts/New Jersey QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Washington QuickFacts.csv\n/kaggle/input/state-census-quickfacts/North Carolina QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Florida QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Georga QuickFacts.csv\n/kaggle/input/state-census-quickfacts/New Hampshire QuickFacts.csv\n/kaggle/input/state-census-quickfacts/New York QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Michigan QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Alabama QuickFacts.csv\n/kaggle/input/state-census-quickfacts/South Carolina QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Louisiana QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Arkansas QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Colorado QuickFacts .csv\n/kaggle/input/state-census-quickfacts/Alaska QuickFacts .csv\n/kaggle/input/state-census-quickfacts/Nevada QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Nebraska QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Tennessee QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Iowa QuickFacts.csv\n/kaggle/input/state-census-quickfacts/Illinois QuickFacts.csv\n/kaggle/input/data-police-shootings/fatal-police-shootings-data.csv\n/kaggle/input/us-census-2019-population-est/US_census_2019_quickfacts.csv\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"# <a id='2'>II. DATA OVERVIEW & INSIGHTS</a>"},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"df = pd.read_csv('/kaggle/input/data-police-shootings/fatal-police-shootings-data.csv')\ndf.columns","execution_count":506,"outputs":[{"output_type":"execute_result","execution_count":506,"data":{"text/plain":"Index(['id', 'name', 'date', 'manner_of_death', 'armed', 'age', 'gender',\n       'race', 'city', 'state', 'signs_of_mental_illness', 'threat_level',\n       'flee', 'body_camera'],\n      dtype='object')"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_population = pd.read_csv('../input/us-census-2019-population-est/US_census_2019_quickfacts.csv')\ndf_population.head(25)","execution_count":507,"outputs":[{"output_type":"execute_result","execution_count":507,"data":{"text/plain":"                                                 Fact United States  Alabama  \\\n0           Population estimates July 1 2019  (V2019)     328239523  4903185   \n1     Population estimates base April 1 2010  (V2019)     308758105  4780125   \n2   Population  change - April 1 2010 (estimates b...           6.3      2.6   \n3                      Population Census April 1 2010     308745538  4779736   \n4                              Persons under 5 years            6.0      6.0   \n5                             Persons under 18 years           22.3     22.2   \n6                          Persons 65 years and over           16.5     17.3   \n7                                     Female persons           50.8     51.7   \n8                                        White alone           76.3     69.1   \n9                    Black or African American alone           13.4     26.8   \n10           American Indian and Alaska Native alone            1.3      0.7   \n11                                       Asian alone            5.9      1.5   \n12  Native Hawaiian and Other Pacific Islander alone            0.2      0.1   \n13                                 Two or More Races            2.8      1.8   \n14                                Hispanic or Latino           18.5      4.6   \n15                White alone not Hispanic or Latino           60.1     65.3   \n16                                 Veterans 2015-2019      18230322   330207   \n17                    Foreign born persons  2015-2019          13.6      3.5   \n18                Housing units  July 1 2019  (V2019)     139684244  2284847   \n19         Owner-occupied housing unit rate 2015-2019          64.0     68.8   \n20  Median value of owner-occupied housing units 2...       $217500  $142700   \n21  Median selected monthly owner costs -with a mo...         $1595    $1186   \n22  Median selected monthly owner costs -without a...          $500     $363   \n23                        Median gross rent 2015-2019         $1062     $792   \n24                              Building permits 2019       1386048    17748   \n\n     Alaska  Arizona Arkansas California Colorado Connecticut Delaware  ...  \\\n0    731545  7278717  3017804   39512223  5758736     3565287   973764  ...   \n1    710249  6392288  2916031   37254519  5029319     3574147   897937  ...   \n2       3.0     13.9      3.5        6.1     14.5        -0.2      8.4  ...   \n3    710231  6392017  2915918   37253956  5029196     3574097   897934  ...   \n4       7.0      5.9      6.2        6.0      5.8         5.1      5.6  ...   \n5      24.6     22.5     23.2       22.5     21.9        20.4     20.9  ...   \n6      12.5     18.0     17.4       14.8     14.6        17.7     19.4  ...   \n7      47.9     50.3     50.9       50.3     49.6        51.2     51.7  ...   \n8      65.3     82.6     79.0       71.9     86.9        79.7     69.2  ...   \n9       3.7      5.2     15.7        6.5      4.6        12.2     23.2  ...   \n10     15.6      5.3      1.0        1.6      1.6         0.6      0.7  ...   \n11      6.5      3.7      1.7       15.5      3.5         5.0      4.1  ...   \n12      1.4      0.3      0.4        0.5      0.2         0.1      0.1  ...   \n13      7.5      2.9      2.2        4.0      3.1         2.5      2.7  ...   \n14      7.3     31.7      7.8       39.4     21.8        16.9      9.6  ...   \n15     60.2     54.1     72.0       36.5     67.7        65.9     61.7  ...   \n16    65186   488061   197138    1574531   373795      167521    65438  ...   \n17      7.8     13.3      4.8       26.8      9.7        14.6      9.6  ...   \n18   319854  3075981  1389129   14366336  2464164     1524992   443781  ...   \n19     64.3     64.4     65.6       54.8     65.2        66.1     71.2  ...   \n20  $270400  $225500  $127800    $505000  $343300     $275400  $251100  ...   \n21    $1933    $1434    $1089      $2357    $1744       $2119    $1587  ...   \n22     $582     $419     $353       $594     $474        $894     $470  ...   \n23    $1244    $1052     $745      $1503    $1271       $1180    $1130  ...   \n24     1680    46580    12723     110197    38633        5854     6539  ...   \n\n   South Dakota Tennessee     Texas     Utah  Vermont Virginia Washington  \\\n0        884659   6829174  28995881  3205958   623989  8535519    7614893   \n1        814198   6346276  25146091  2763891   625737  8001049    6724540   \n2           8.7       7.6      15.3     16.0     -0.3      6.7       13.2   \n3        814180   6346105  25145561  2763885   625741  8001024    6724540   \n4           6.9       6.0       6.9      7.7      4.7      5.9        6.0   \n5          24.5      22.1      25.5     29.0     18.3     21.8       21.8   \n6          17.2      16.7      12.9     11.4     20.0     15.9       15.9   \n7          49.5      51.2      50.3     49.6     50.6     50.8       49.9   \n8          84.6      78.4      78.7     90.6     94.2     69.4       78.5   \n9           2.3      17.1      12.9      1.5      1.4     19.9        4.4   \n10          9.0       0.5       1.0      1.6      0.4      0.5        1.9   \n11          1.5       2.0       5.2      2.7      1.9      6.9        9.6   \n12          0.1       0.1       0.1      1.1        Z      0.1        0.8   \n13          2.5       2.0       2.1      2.6      2.0      3.2        4.9   \n14          4.2       5.7      39.7     14.4      2.0      9.8       13.0   \n15         81.5      73.5      41.2     77.8     92.6     61.2       67.5   \n16        57550    431274   1453450   120447    36988   677533     529784   \n17          3.7       5.1      17.0      8.5      4.7     12.4       14.3   \n18       401862   3028213  11283353  1133521   339439  3562143    3195004   \n19         67.8      66.3      62.0     70.2     70.8     66.3       63.0   \n20      $167100   $167200   $172500  $279100  $227700  $273100    $339000   \n21        $1340     $1244     $1606    $1551    $1621    $1799      $1886   \n22         $483      $388      $514     $430     $677     $479       $583   \n23         $747      $869     $1045    $1037     $985    $1234      $1258   \n24         4415     41361    209895    28779     1801    32418      48424   \n\n   West Virginia Wisconsin  Wyoming  \n0        1792147   5822434   578759  \n1        1853018   5687285   563775  \n2           -3.3       2.4      2.7  \n3        1852994   5686986   563626  \n4            5.2       5.7      6.0  \n5           20.1      21.8     23.1  \n6           20.5      17.5     17.1  \n7           50.5      50.2     49.1  \n8           93.5      87.0     92.5  \n9            3.6       6.7      1.3  \n10           0.3       1.2      2.7  \n11           0.8       3.0      1.1  \n12             Z       0.1      0.1  \n13           1.8       2.0      2.2  \n14           1.7       7.1     10.1  \n15          92.0      80.9     83.7  \n16        130536    331340    44999  \n17           1.7       5.0      3.4  \n18        894956   2725296   280291  \n19          73.2      67.0     70.4  \n20       $119600   $180600  $220500  \n21         $1050     $1430    $1459  \n22          $326      $553     $420  \n23          $725      $856     $855  \n24          3010     17480     1708  \n\n[25 rows x 52 columns]","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>Fact</th>\n      <th>United States</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 2019  (V2019)</td>\n      <td>328239523</td>\n      <td>4903185</td>\n      <td>731545</td>\n      <td>7278717</td>\n      <td>3017804</td>\n      <td>39512223</td>\n      <td>5758736</td>\n      <td>3565287</td>\n      <td>973764</td>\n      <td>...</td>\n      <td>884659</td>\n      <td>6829174</td>\n      <td>28995881</td>\n      <td>3205958</td>\n      <td>623989</td>\n      <td>8535519</td>\n      <td>7614893</td>\n      <td>1792147</td>\n      <td>5822434</td>\n      <td>578759</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Population estimates base April 1 2010  (V2019)</td>\n      <td>308758105</td>\n      <td>4780125</td>\n      <td>710249</td>\n      <td>6392288</td>\n      <td>2916031</td>\n      <td>37254519</td>\n      <td>5029319</td>\n      <td>3574147</td>\n      <td>897937</td>\n      <td>...</td>\n      <td>814198</td>\n      <td>6346276</td>\n      <td>25146091</td>\n      <td>2763891</td>\n      <td>625737</td>\n      <td>8001049</td>\n      <td>6724540</td>\n      <td>1853018</td>\n      <td>5687285</td>\n      <td>563775</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Population  change - April 1 2010 (estimates b...</td>\n      <td>6.3</td>\n      <td>2.6</td>\n      <td>3.0</td>\n      <td>13.9</td>\n      <td>3.5</td>\n      <td>6.1</td>\n      <td>14.5</td>\n      <td>-0.2</td>\n      <td>8.4</td>\n      <td>...</td>\n      <td>8.7</td>\n      <td>7.6</td>\n      <td>15.3</td>\n      <td>16.0</td>\n      <td>-0.3</td>\n      <td>6.7</td>\n      <td>13.2</td>\n      <td>-3.3</td>\n      <td>2.4</td>\n      <td>2.7</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Population Census April 1 2010</td>\n      <td>308745538</td>\n      <td>4779736</td>\n      <td>710231</td>\n      <td>6392017</td>\n      <td>2915918</td>\n      <td>37253956</td>\n      <td>5029196</td>\n      <td>3574097</td>\n      <td>897934</td>\n      <td>...</td>\n      <td>814180</td>\n      <td>6346105</td>\n      <td>25145561</td>\n      <td>2763885</td>\n      <td>625741</td>\n      <td>8001024</td>\n      <td>6724540</td>\n      <td>1852994</td>\n      <td>5686986</td>\n      <td>563626</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Persons under 5 years</td>\n      <td>6.0</td>\n      <td>6.0</td>\n      <td>7.0</td>\n      <td>5.9</td>\n      <td>6.2</td>\n      <td>6.0</td>\n      <td>5.8</td>\n      <td>5.1</td>\n      <td>5.6</td>\n      <td>...</td>\n      <td>6.9</td>\n      <td>6.0</td>\n      <td>6.9</td>\n      <td>7.7</td>\n      <td>4.7</td>\n      <td>5.9</td>\n      <td>6.0</td>\n      <td>5.2</td>\n      <td>5.7</td>\n      <td>6.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Persons under 18 years</td>\n      <td>22.3</td>\n      <td>22.2</td>\n      <td>24.6</td>\n      <td>22.5</td>\n      <td>23.2</td>\n      <td>22.5</td>\n      <td>21.9</td>\n      <td>20.4</td>\n      <td>20.9</td>\n      <td>...</td>\n      <td>24.5</td>\n      <td>22.1</td>\n      <td>25.5</td>\n      <td>29.0</td>\n      <td>18.3</td>\n      <td>21.8</td>\n      <td>21.8</td>\n      <td>20.1</td>\n      <td>21.8</td>\n      <td>23.1</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Persons 65 years and over</td>\n      <td>16.5</td>\n      <td>17.3</td>\n      <td>12.5</td>\n      <td>18.0</td>\n      <td>17.4</td>\n      <td>14.8</td>\n      <td>14.6</td>\n      <td>17.7</td>\n      <td>19.4</td>\n      <td>...</td>\n      <td>17.2</td>\n      <td>16.7</td>\n      <td>12.9</td>\n      <td>11.4</td>\n      <td>20.0</td>\n      <td>15.9</td>\n      <td>15.9</td>\n      <td>20.5</td>\n      <td>17.5</td>\n      <td>17.1</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Female persons</td>\n      <td>50.8</td>\n      <td>51.7</td>\n      <td>47.9</td>\n      <td>50.3</td>\n      <td>50.9</td>\n      <td>50.3</td>\n      <td>49.6</td>\n      <td>51.2</td>\n      <td>51.7</td>\n      <td>...</td>\n      <td>49.5</td>\n      <td>51.2</td>\n      <td>50.3</td>\n      <td>49.6</td>\n      <td>50.6</td>\n      <td>50.8</td>\n      <td>49.9</td>\n      <td>50.5</td>\n      <td>50.2</td>\n      <td>49.1</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>White alone</td>\n      <td>76.3</td>\n      <td>69.1</td>\n      <td>65.3</td>\n      <td>82.6</td>\n      <td>79.0</td>\n      <td>71.9</td>\n      <td>86.9</td>\n      <td>79.7</td>\n      <td>69.2</td>\n      <td>...</td>\n      <td>84.6</td>\n      <td>78.4</td>\n      <td>78.7</td>\n      <td>90.6</td>\n      <td>94.2</td>\n      <td>69.4</td>\n      <td>78.5</td>\n      <td>93.5</td>\n      <td>87.0</td>\n      <td>92.5</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Black or African American alone</td>\n      <td>13.4</td>\n      <td>26.8</td>\n      <td>3.7</td>\n      <td>5.2</td>\n      <td>15.7</td>\n      <td>6.5</td>\n      <td>4.6</td>\n      <td>12.2</td>\n      <td>23.2</td>\n      <td>...</td>\n      <td>2.3</td>\n      <td>17.1</td>\n      <td>12.9</td>\n      <td>1.5</td>\n      <td>1.4</td>\n      <td>19.9</td>\n      <td>4.4</td>\n      <td>3.6</td>\n      <td>6.7</td>\n      <td>1.3</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>American Indian and Alaska Native alone</td>\n      <td>1.3</td>\n      <td>0.7</td>\n      <td>15.6</td>\n      <td>5.3</td>\n      <td>1.0</td>\n      <td>1.6</td>\n      <td>1.6</td>\n      <td>0.6</td>\n      <td>0.7</td>\n      <td>...</td>\n      <td>9.0</td>\n      <td>0.5</td>\n      <td>1.0</td>\n      <td>1.6</td>\n      <td>0.4</td>\n      <td>0.5</td>\n      <td>1.9</td>\n      <td>0.3</td>\n      <td>1.2</td>\n      <td>2.7</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Asian alone</td>\n      <td>5.9</td>\n      <td>1.5</td>\n      <td>6.5</td>\n      <td>3.7</td>\n      <td>1.7</td>\n      <td>15.5</td>\n      <td>3.5</td>\n      <td>5.0</td>\n      <td>4.1</td>\n      <td>...</td>\n      <td>1.5</td>\n      <td>2.0</td>\n      <td>5.2</td>\n      <td>2.7</td>\n      <td>1.9</td>\n      <td>6.9</td>\n      <td>9.6</td>\n      <td>0.8</td>\n      <td>3.0</td>\n      <td>1.1</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Native Hawaiian and Other Pacific Islander alone</td>\n      <td>0.2</td>\n      <td>0.1</td>\n      <td>1.4</td>\n      <td>0.3</td>\n      <td>0.4</td>\n      <td>0.5</td>\n      <td>0.2</td>\n      <td>0.1</td>\n      <td>0.1</td>\n      <td>...</td>\n      <td>0.1</td>\n      <td>0.1</td>\n      <td>0.1</td>\n      <td>1.1</td>\n      <td>Z</td>\n      <td>0.1</td>\n      <td>0.8</td>\n      <td>Z</td>\n      <td>0.1</td>\n      <td>0.1</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Two or More Races</td>\n      <td>2.8</td>\n      <td>1.8</td>\n      <td>7.5</td>\n      <td>2.9</td>\n      <td>2.2</td>\n      <td>4.0</td>\n      <td>3.1</td>\n      <td>2.5</td>\n      <td>2.7</td>\n      <td>...</td>\n      <td>2.5</td>\n      <td>2.0</td>\n      <td>2.1</td>\n      <td>2.6</td>\n      <td>2.0</td>\n      <td>3.2</td>\n      <td>4.9</td>\n      <td>1.8</td>\n      <td>2.0</td>\n      <td>2.2</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Hispanic or Latino</td>\n      <td>18.5</td>\n      <td>4.6</td>\n      <td>7.3</td>\n      <td>31.7</td>\n      <td>7.8</td>\n      <td>39.4</td>\n      <td>21.8</td>\n      <td>16.9</td>\n      <td>9.6</td>\n      <td>...</td>\n      <td>4.2</td>\n      <td>5.7</td>\n      <td>39.7</td>\n      <td>14.4</td>\n      <td>2.0</td>\n      <td>9.8</td>\n      <td>13.0</td>\n      <td>1.7</td>\n      <td>7.1</td>\n      <td>10.1</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>White alone not Hispanic or Latino</td>\n      <td>60.1</td>\n      <td>65.3</td>\n      <td>60.2</td>\n      <td>54.1</td>\n      <td>72.0</td>\n      <td>36.5</td>\n      <td>67.7</td>\n      <td>65.9</td>\n      <td>61.7</td>\n      <td>...</td>\n      <td>81.5</td>\n      <td>73.5</td>\n      <td>41.2</td>\n      <td>77.8</td>\n      <td>92.6</td>\n      <td>61.2</td>\n      <td>67.5</td>\n      <td>92.0</td>\n      <td>80.9</td>\n      <td>83.7</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Veterans 2015-2019</td>\n      <td>18230322</td>\n      <td>330207</td>\n      <td>65186</td>\n      <td>488061</td>\n      <td>197138</td>\n      <td>1574531</td>\n      <td>373795</td>\n      <td>167521</td>\n      <td>65438</td>\n      <td>...</td>\n      <td>57550</td>\n      <td>431274</td>\n      <td>1453450</td>\n      <td>120447</td>\n      <td>36988</td>\n      <td>677533</td>\n      <td>529784</td>\n      <td>130536</td>\n      <td>331340</td>\n      <td>44999</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Foreign born persons  2015-2019</td>\n      <td>13.6</td>\n      <td>3.5</td>\n      <td>7.8</td>\n      <td>13.3</td>\n      <td>4.8</td>\n      <td>26.8</td>\n      <td>9.7</td>\n      <td>14.6</td>\n      <td>9.6</td>\n      <td>...</td>\n      <td>3.7</td>\n      <td>5.1</td>\n      <td>17.0</td>\n      <td>8.5</td>\n      <td>4.7</td>\n      <td>12.4</td>\n      <td>14.3</td>\n      <td>1.7</td>\n      <td>5.0</td>\n      <td>3.4</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Housing units  July 1 2019  (V2019)</td>\n      <td>139684244</td>\n      <td>2284847</td>\n      <td>319854</td>\n      <td>3075981</td>\n      <td>1389129</td>\n      <td>14366336</td>\n      <td>2464164</td>\n      <td>1524992</td>\n      <td>443781</td>\n      <td>...</td>\n      <td>401862</td>\n      <td>3028213</td>\n      <td>11283353</td>\n      <td>1133521</td>\n      <td>339439</td>\n      <td>3562143</td>\n      <td>3195004</td>\n      <td>894956</td>\n      <td>2725296</td>\n      <td>280291</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Owner-occupied housing unit rate 2015-2019</td>\n      <td>64.0</td>\n      <td>68.8</td>\n      <td>64.3</td>\n      <td>64.4</td>\n      <td>65.6</td>\n      <td>54.8</td>\n      <td>65.2</td>\n      <td>66.1</td>\n      <td>71.2</td>\n      <td>...</td>\n      <td>67.8</td>\n      <td>66.3</td>\n      <td>62.0</td>\n      <td>70.2</td>\n      <td>70.8</td>\n      <td>66.3</td>\n      <td>63.0</td>\n      <td>73.2</td>\n      <td>67.0</td>\n      <td>70.4</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Median value of owner-occupied housing units 2...</td>\n      <td>$217500</td>\n      <td>$142700</td>\n      <td>$270400</td>\n      <td>$225500</td>\n      <td>$127800</td>\n      <td>$505000</td>\n      <td>$343300</td>\n      <td>$275400</td>\n      <td>$251100</td>\n      <td>...</td>\n      <td>$167100</td>\n      <td>$167200</td>\n      <td>$172500</td>\n      <td>$279100</td>\n      <td>$227700</td>\n      <td>$273100</td>\n      <td>$339000</td>\n      <td>$119600</td>\n      <td>$180600</td>\n      <td>$220500</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Median selected monthly owner costs -with a mo...</td>\n      <td>$1595</td>\n      <td>$1186</td>\n      <td>$1933</td>\n      <td>$1434</td>\n      <td>$1089</td>\n      <td>$2357</td>\n      <td>$1744</td>\n      <td>$2119</td>\n      <td>$1587</td>\n      <td>...</td>\n      <td>$1340</td>\n      <td>$1244</td>\n      <td>$1606</td>\n      <td>$1551</td>\n      <td>$1621</td>\n      <td>$1799</td>\n      <td>$1886</td>\n      <td>$1050</td>\n      <td>$1430</td>\n      <td>$1459</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Median selected monthly owner costs -without a...</td>\n      <td>$500</td>\n      <td>$363</td>\n      <td>$582</td>\n      <td>$419</td>\n      <td>$353</td>\n      <td>$594</td>\n      <td>$474</td>\n      <td>$894</td>\n      <td>$470</td>\n      <td>...</td>\n      <td>$483</td>\n      <td>$388</td>\n      <td>$514</td>\n      <td>$430</td>\n      <td>$677</td>\n      <td>$479</td>\n      <td>$583</td>\n      <td>$326</td>\n      <td>$553</td>\n      <td>$420</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Median gross rent 2015-2019</td>\n      <td>$1062</td>\n      <td>$792</td>\n      <td>$1244</td>\n      <td>$1052</td>\n      <td>$745</td>\n      <td>$1503</td>\n      <td>$1271</td>\n      <td>$1180</td>\n      <td>$1130</td>\n      <td>...</td>\n      <td>$747</td>\n      <td>$869</td>\n      <td>$1045</td>\n      <td>$1037</td>\n      <td>$985</td>\n      <td>$1234</td>\n      <td>$1258</td>\n      <td>$725</td>\n      <td>$856</td>\n      <td>$855</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Building permits 2019</td>\n      <td>1386048</td>\n      <td>17748</td>\n      <td>1680</td>\n      <td>46580</td>\n      <td>12723</td>\n      <td>110197</td>\n      <td>38633</td>\n      <td>5854</td>\n      <td>6539</td>\n      <td>...</td>\n      <td>4415</td>\n      <td>41361</td>\n      <td>209895</td>\n      <td>28779</td>\n      <td>1801</td>\n      <td>32418</td>\n      <td>48424</td>\n      <td>3010</td>\n      <td>17480</td>\n      <td>1708</td>\n    </tr>\n  </tbody>\n</table>\n<p>25 rows × 52 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.head()","execution_count":508,"outputs":[{"output_type":"execute_result","execution_count":508,"data":{"text/plain":"   id                name        date   manner_of_death       armed   age  \\\n0   3          Tim Elliot  2015-01-02              shot         gun  53.0   \n1   4    Lewis Lee Lembke  2015-01-02              shot         gun  47.0   \n2   5  John Paul Quintero  2015-01-03  shot and Tasered     unarmed  23.0   \n3   8     Matthew Hoffman  2015-01-04              shot  toy weapon  32.0   \n4   9   Michael Rodriguez  2015-01-04              shot    nail gun  39.0   \n\n  gender race           city state  signs_of_mental_illness threat_level  \\\n0      M    A        Shelton    WA                     True       attack   \n1      M    W          Aloha    OR                    False       attack   \n2      M    H        Wichita    KS                    False        other   \n3      M    W  San Francisco    CA                     True       attack   \n4      M    H          Evans    CO                    False       attack   \n\n          flee  body_camera  \n0  Not fleeing        False  \n1  Not fleeing        False  \n2  Not fleeing        False  \n3  Not fleeing        False  \n4  Not fleeing        False  ","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>id</th>\n      <th>name</th>\n      <th>date</th>\n      <th>manner_of_death</th>\n      <th>armed</th>\n      <th>age</th>\n      <th>gender</th>\n      <th>race</th>\n      <th>city</th>\n      <th>state</th>\n      <th>signs_of_mental_illness</th>\n      <th>threat_level</th>\n      <th>flee</th>\n      <th>body_camera</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3</td>\n      <td>Tim Elliot</td>\n      <td>2015-01-02</td>\n      <td>shot</td>\n      <td>gun</td>\n      <td>53.0</td>\n      <td>M</td>\n      <td>A</td>\n      <td>Shelton</td>\n      <td>WA</td>\n      <td>True</td>\n      <td>attack</td>\n      <td>Not fleeing</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4</td>\n      <td>Lewis Lee Lembke</td>\n      <td>2015-01-02</td>\n      <td>shot</td>\n      <td>gun</td>\n      <td>47.0</td>\n      <td>M</td>\n      <td>W</td>\n      <td>Aloha</td>\n      <td>OR</td>\n      <td>False</td>\n      <td>attack</td>\n      <td>Not fleeing</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5</td>\n      <td>John Paul Quintero</td>\n      <td>2015-01-03</td>\n      <td>shot and Tasered</td>\n      <td>unarmed</td>\n      <td>23.0</td>\n      <td>M</td>\n      <td>H</td>\n      <td>Wichita</td>\n      <td>KS</td>\n      <td>False</td>\n      <td>other</td>\n      <td>Not fleeing</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>8</td>\n      <td>Matthew Hoffman</td>\n      <td>2015-01-04</td>\n      <td>shot</td>\n      <td>toy weapon</td>\n      <td>32.0</td>\n      <td>M</td>\n      <td>W</td>\n      <td>San Francisco</td>\n      <td>CA</td>\n      <td>True</td>\n      <td>attack</td>\n      <td>Not fleeing</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>9</td>\n      <td>Michael Rodriguez</td>\n      <td>2015-01-04</td>\n      <td>shot</td>\n      <td>nail gun</td>\n      <td>39.0</td>\n      <td>M</td>\n      <td>H</td>\n      <td>Evans</td>\n      <td>CO</td>\n      <td>False</td>\n      <td>attack</td>\n      <td>Not fleeing</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.info()","execution_count":509,"outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 5416 entries, 0 to 5415\nData columns (total 14 columns):\n #   Column                   Non-Null Count  Dtype  \n---  ------                   --------------  -----  \n 0   id                       5416 non-null   int64  \n 1   name                     5416 non-null   object \n 2   date                     5416 non-null   object \n 3   manner_of_death          5416 non-null   object \n 4   armed                    5189 non-null   object \n 5   age                      5181 non-null   float64\n 6   gender                   5414 non-null   object \n 7   race                     4895 non-null   object \n 8   city                     5416 non-null   object \n 9   state                    5416 non-null   object \n 10  signs_of_mental_illness  5416 non-null   bool   \n 11  threat_level             5416 non-null   object \n 12  flee                     5167 non-null   object \n 13  body_camera              5416 non-null   bool   \ndtypes: bool(2), float64(1), int64(1), object(10)\nmemory usage: 518.5+ KB\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.describe(include='all')","execution_count":510,"outputs":[{"output_type":"execute_result","execution_count":510,"data":{"text/plain":"                 id   name        date manner_of_death armed          age  \\\ncount   5416.000000   5416        5416            5416  5189  5181.000000   \nunique          NaN   5206        1844               2    93          NaN   \ntop             NaN  TK TK  2018-01-06            shot   gun          NaN   \nfreq            NaN    187           9            5146  3060          NaN   \nmean    3010.398264    NaN         NaN             NaN   NaN    37.117931   \nstd     1695.786456    NaN         NaN             NaN   NaN    13.116135   \nmin        3.000000    NaN         NaN             NaN   NaN     6.000000   \n25%     1545.750000    NaN         NaN             NaN   NaN    27.000000   \n50%     3009.500000    NaN         NaN             NaN   NaN    35.000000   \n75%     4486.250000    NaN         NaN             NaN   NaN    46.000000   \nmax     5927.000000    NaN         NaN             NaN   NaN    91.000000   \n\n       gender  race         city state signs_of_mental_illness threat_level  \\\ncount    5414  4895         5416  5416                    5416         5416   \nunique      2     6         2470    51                       2            3   \ntop         M     W  Los Angeles    CA                   False       attack   \nfreq     5176  2476           85   799                    4200         3495   \nmean      NaN   NaN          NaN   NaN                     NaN          NaN   \nstd       NaN   NaN          NaN   NaN                     NaN          NaN   \nmin       NaN   NaN          NaN   NaN                     NaN          NaN   \n25%       NaN   NaN          NaN   NaN                     NaN          NaN   \n50%       NaN   NaN          NaN   NaN                     NaN          NaN   \n75%       NaN   NaN          NaN   NaN                     NaN          NaN   \nmax       NaN   NaN          NaN   NaN                     NaN          NaN   \n\n               flee body_camera  \ncount          5167        5416  \nunique            4           2  \ntop     Not fleeing       False  \nfreq           3411        4798  \nmean            NaN         NaN  \nstd             NaN         NaN  \nmin             NaN         NaN  \n25%             NaN         NaN  \n50%             NaN         NaN  \n75%             NaN         NaN  \nmax             NaN         NaN  ","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>id</th>\n      <th>name</th>\n      <th>date</th>\n      <th>manner_of_death</th>\n      <th>armed</th>\n      <th>age</th>\n      <th>gender</th>\n      <th>race</th>\n      <th>city</th>\n      <th>state</th>\n      <th>signs_of_mental_illness</th>\n      <th>threat_level</th>\n      <th>flee</th>\n      <th>body_camera</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>5416.000000</td>\n      <td>5416</td>\n      <td>5416</td>\n      <td>5416</td>\n      <td>5189</td>\n      <td>5181.000000</td>\n      <td>5414</td>\n      <td>4895</td>\n      <td>5416</td>\n      <td>5416</td>\n      <td>5416</td>\n      <td>5416</td>\n      <td>5167</td>\n      <td>5416</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>NaN</td>\n      <td>5206</td>\n      <td>1844</td>\n      <td>2</td>\n      <td>93</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>6</td>\n      <td>2470</td>\n      <td>51</td>\n      <td>2</td>\n      <td>3</td>\n      <td>4</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>NaN</td>\n      <td>TK TK</td>\n      <td>2018-01-06</td>\n      <td>shot</td>\n      <td>gun</td>\n      <td>NaN</td>\n      <td>M</td>\n      <td>W</td>\n      <td>Los Angeles</td>\n      <td>CA</td>\n      <td>False</td>\n      <td>attack</td>\n      <td>Not fleeing</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>NaN</td>\n      <td>187</td>\n      <td>9</td>\n      <td>5146</td>\n      <td>3060</td>\n      <td>NaN</td>\n      <td>5176</td>\n      <td>2476</td>\n      <td>85</td>\n      <td>799</td>\n      <td>4200</td>\n      <td>3495</td>\n      <td>3411</td>\n      <td>4798</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>3010.398264</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>37.117931</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>std</th>\n      <td>1695.786456</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>13.116135</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>min</th>\n      <td>3.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>6.000000</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>25%</th>\n      <td>1545.750000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>27.000000</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>50%</th>\n      <td>3009.500000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>35.000000</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>4486.250000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>46.000000</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>max</th>\n      <td>5927.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>91.000000</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</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### **CARDINALITY**"},{"metadata":{"trusted":true},"cell_type":"code","source":"cardinality={}\nfor col in df.columns:\n    cardinality[col] = df[col].nunique()\n\ncardinality","execution_count":511,"outputs":[{"output_type":"execute_result","execution_count":511,"data":{"text/plain":"{'id': 5416,\n 'name': 5206,\n 'date': 1844,\n 'manner_of_death': 2,\n 'armed': 93,\n 'age': 77,\n 'gender': 2,\n 'race': 6,\n 'city': 2470,\n 'state': 51,\n 'signs_of_mental_illness': 2,\n 'threat_level': 3,\n 'flee': 4,\n 'body_camera': 2}"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('MANNER OF DEATH')\nprint(df['manner_of_death'].unique())\nprint('-'*40)\nprint('RACE')\nprint(df['race'].unique())\nprint('-'*40)\nprint('THREAT LEVEL')\nprint(df['threat_level'].unique())\nprint('-'*40)\nprint('FLEE')\nprint(df['flee'].unique())\n","execution_count":512,"outputs":[{"output_type":"stream","text":"MANNER OF DEATH\n['shot' 'shot and Tasered']\n----------------------------------------\nRACE\n['A' 'W' 'H' 'B' 'O' nan 'N']\n----------------------------------------\nTHREAT LEVEL\n['attack' 'other' 'undetermined']\n----------------------------------------\nFLEE\n['Not fleeing' 'Car' 'Foot' 'Other' nan]\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"# <a id=\"3\">III. MISSING VALUES</a>\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"missing_values = df.isnull()\nmissing_values.head()","execution_count":513,"outputs":[{"output_type":"execute_result","execution_count":513,"data":{"text/plain":"      id   name   date  manner_of_death  armed    age  gender   race   city  \\\n0  False  False  False            False  False  False   False  False  False   \n1  False  False  False            False  False  False   False  False  False   \n2  False  False  False            False  False  False   False  False  False   \n3  False  False  False            False  False  False   False  False  False   \n4  False  False  False            False  False  False   False  False  False   \n\n   state  signs_of_mental_illness  threat_level   flee  body_camera  \n0  False                    False         False  False        False  \n1  False                    False         False  False        False  \n2  False                    False         False  False        False  \n3  False                    False         False  False        False  \n4  False                    False         False  False        False  ","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>id</th>\n      <th>name</th>\n      <th>date</th>\n      <th>manner_of_death</th>\n      <th>armed</th>\n      <th>age</th>\n      <th>gender</th>\n      <th>race</th>\n      <th>city</th>\n      <th>state</th>\n      <th>signs_of_mental_illness</th>\n      <th>threat_level</th>\n      <th>flee</th>\n      <th>body_camera</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"for column in missing_values.columns.tolist():\n    print(column)\n    print(missing_values[column].value_counts())\n    print('')","execution_count":514,"outputs":[{"output_type":"stream","text":"id\nFalse    5416\nName: id, dtype: int64\n\nname\nFalse    5416\nName: name, dtype: int64\n\ndate\nFalse    5416\nName: date, dtype: int64\n\nmanner_of_death\nFalse    5416\nName: manner_of_death, dtype: int64\n\narmed\nFalse    5189\nTrue      227\nName: armed, dtype: int64\n\nage\nFalse    5181\nTrue      235\nName: age, dtype: int64\n\ngender\nFalse    5414\nTrue        2\nName: gender, dtype: int64\n\nrace\nFalse    4895\nTrue      521\nName: race, dtype: int64\n\ncity\nFalse    5416\nName: city, dtype: int64\n\nstate\nFalse    5416\nName: state, dtype: int64\n\nsigns_of_mental_illness\nFalse    5416\nName: signs_of_mental_illness, dtype: int64\n\nthreat_level\nFalse    5416\nName: threat_level, dtype: int64\n\nflee\nFalse    5167\nTrue      249\nName: flee, dtype: int64\n\nbody_camera\nFalse    5416\nName: body_camera, dtype: int64\n\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"missing_percentage = (missing_values.sum()*100)/df.shape[0]\nmissing_percentage","execution_count":515,"outputs":[{"output_type":"execute_result","execution_count":515,"data":{"text/plain":"id                         0.000000\nname                       0.000000\ndate                       0.000000\nmanner_of_death            0.000000\narmed                      4.191285\nage                        4.338996\ngender                     0.036928\nrace                       9.619645\ncity                       0.000000\nstate                      0.000000\nsigns_of_mental_illness    0.000000\nthreat_level               0.000000\nflee                       4.597489\nbody_camera                0.000000\ndtype: float64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"msno.matrix(df)","execution_count":516,"outputs":[{"output_type":"execute_result","execution_count":516,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f4400645d10>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1800x720 with 2 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#DROP NULL VALUES\n\ndf.dropna(inplace=True)","execution_count":517,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# <a id=\"4\">IV. FEATURE ENGINEERING</a>"},{"metadata":{"trusted":true},"cell_type":"code","source":"# SEPARATE DAY, MONTH, YEAR INTO INDIVIDUAL COLUMNS\ndf['date']=pd.to_datetime(df['date'])\ndf['year']=pd.to_datetime(df['date']).dt.year\ndf['month']=pd.to_datetime(df['date']).dt.month\ndf['month_name']=df['date'].dt.strftime('%B')\ndf['month_num']=df['date'].dt.strftime('%m')\ndf['weekday']=df['date'].dt.strftime('%A')  \ndf['date_num']=df['date'].dt.strftime('%d').astype(int)\ndf['year_month']=df.date.dt.to_period(\"M\")\n\n# CLASSIFY VICTIM AGES INTO AGE RANGE GROUPS\ndf['age_range']=np.where(df['age']<18,'<18',np.where((df['age']>=18)&(df['age']<=35),'18-35',\nnp.where((df['age']>=36)&(df['age']<=50),'36-50', np.where(df['age']>65,'65+',\nnp.where((df['age']>=51)&(df['age']<=65),'51-65',\"Not Specified\")))))\n\n# CHANGE ORDER OF COLUMNS\ncols = ['id', 'name', 'age', 'age_range', 'gender', 'race', 'manner_of_death', 'armed', 'flee', \n        'signs_of_mental_illness', 'threat_level', 'body_camera', 'city', 'state',\n        'date', 'date_num', 'year', 'year_month', 'month', 'month_name', 'month_num', 'weekday']\ndf=df[cols]\n\n#REPLACE VALUES OF RACE COLUMN WITH FULL NAME\nfor i in df['race']:\n        df['race'].replace({'A':'Asian', 'W':'White', 'H':'Hispanic', 'B':'Black', 'O':'Other', 'N':'Native'}, inplace=True)\n\n# DROP YEAR 2020 From dataset\n\ndf = df[df['year'] != 2020]\n\ndf.head(3)","execution_count":518,"outputs":[{"output_type":"execute_result","execution_count":518,"data":{"text/plain":"   id                name   age age_range gender      race   manner_of_death  \\\n0   3          Tim Elliot  53.0     51-65      M     Asian              shot   \n1   4    Lewis Lee Lembke  47.0     36-50      M     White              shot   \n2   5  John Paul Quintero  23.0     18-35      M  Hispanic  shot and Tasered   \n\n     armed         flee  signs_of_mental_illness  ...     city  state  \\\n0      gun  Not fleeing                     True  ...  Shelton     WA   \n1      gun  Not fleeing                    False  ...    Aloha     OR   \n2  unarmed  Not fleeing                    False  ...  Wichita     KS   \n\n        date date_num  year  year_month  month month_name  month_num   weekday  \n0 2015-01-02        2  2015     2015-01      1    January         01    Friday  \n1 2015-01-02        2  2015     2015-01      1    January         01    Friday  \n2 2015-01-03        3  2015     2015-01      1    January         01  Saturday  \n\n[3 rows x 22 columns]","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>id</th>\n      <th>name</th>\n      <th>age</th>\n      <th>age_range</th>\n      <th>gender</th>\n      <th>race</th>\n      <th>manner_of_death</th>\n      <th>armed</th>\n      <th>flee</th>\n      <th>signs_of_mental_illness</th>\n      <th>...</th>\n      <th>city</th>\n      <th>state</th>\n      <th>date</th>\n      <th>date_num</th>\n      <th>year</th>\n      <th>year_month</th>\n      <th>month</th>\n      <th>month_name</th>\n      <th>month_num</th>\n      <th>weekday</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3</td>\n      <td>Tim Elliot</td>\n      <td>53.0</td>\n      <td>51-65</td>\n      <td>M</td>\n      <td>Asian</td>\n      <td>shot</td>\n      <td>gun</td>\n      <td>Not fleeing</td>\n      <td>True</td>\n      <td>...</td>\n      <td>Shelton</td>\n      <td>WA</td>\n      <td>2015-01-02</td>\n      <td>2</td>\n      <td>2015</td>\n      <td>2015-01</td>\n      <td>1</td>\n      <td>January</td>\n      <td>01</td>\n      <td>Friday</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4</td>\n      <td>Lewis Lee Lembke</td>\n      <td>47.0</td>\n      <td>36-50</td>\n      <td>M</td>\n      <td>White</td>\n      <td>shot</td>\n      <td>gun</td>\n      <td>Not fleeing</td>\n      <td>False</td>\n      <td>...</td>\n      <td>Aloha</td>\n      <td>OR</td>\n      <td>2015-01-02</td>\n      <td>2</td>\n      <td>2015</td>\n      <td>2015-01</td>\n      <td>1</td>\n      <td>January</td>\n      <td>01</td>\n      <td>Friday</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5</td>\n      <td>John Paul Quintero</td>\n      <td>23.0</td>\n      <td>18-35</td>\n      <td>M</td>\n      <td>Hispanic</td>\n      <td>shot and Tasered</td>\n      <td>unarmed</td>\n      <td>Not fleeing</td>\n      <td>False</td>\n      <td>...</td>\n      <td>Wichita</td>\n      <td>KS</td>\n      <td>2015-01-03</td>\n      <td>3</td>\n      <td>2015</td>\n      <td>2015-01</td>\n      <td>1</td>\n      <td>January</td>\n      <td>01</td>\n      <td>Saturday</td>\n    </tr>\n  </tbody>\n</table>\n<p>3 rows × 22 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# DROP UNNECESSARY ROWS AND COLUMNS\npopulation1 = df_population\npopulation1.drop(population1.index[1:7], inplace=True)\npopulation1.reset_index(inplace=True)\npopulation1 = population1[0:10]\npopulation1.drop(columns=['index'], inplace=True)\n\n# REMOVE UNNECESSARY WORDS AND REPLACE NONSENSICAL VALUES\npopulation1 = population1.replace({'alone':''}, regex=True)\npopulation1.replace('Z', 0, inplace=True) #Replace values Z with number 0 so we can convert columns to float\n\n# CONVERT NUMBERS FROM STRING TO FLOAT\nfor col in population1.columns[1:52]:\n     population1[col] = pd.to_numeric(population1[col])","execution_count":519,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.info()","execution_count":520,"outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nInt64Index: 4063 entries, 0 to 4934\nData columns (total 22 columns):\n #   Column                   Non-Null Count  Dtype         \n---  ------                   --------------  -----         \n 0   id                       4063 non-null   int64         \n 1   name                     4063 non-null   object        \n 2   age                      4063 non-null   float64       \n 3   age_range                4063 non-null   object        \n 4   gender                   4063 non-null   object        \n 5   race                     4063 non-null   object        \n 6   manner_of_death          4063 non-null   object        \n 7   armed                    4063 non-null   object        \n 8   flee                     4063 non-null   object        \n 9   signs_of_mental_illness  4063 non-null   bool          \n 10  threat_level             4063 non-null   object        \n 11  body_camera              4063 non-null   bool          \n 12  city                     4063 non-null   object        \n 13  state                    4063 non-null   object        \n 14  date                     4063 non-null   datetime64[ns]\n 15  date_num                 4063 non-null   int64         \n 16  year                     4063 non-null   int64         \n 17  year_month               4063 non-null   period[M]     \n 18  month                    4063 non-null   int64         \n 19  month_name               4063 non-null   object        \n 20  month_num                4063 non-null   object        \n 21  weekday                  4063 non-null   object        \ndtypes: bool(2), datetime64[ns](1), float64(1), int64(4), object(13), period[M](1)\nmemory usage: 674.5+ KB\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"# <a id=\"5\">V. EXPLORATORY DATA ANALYSIS</a>"},{"metadata":{},"cell_type":"markdown","source":"<a id=\"univariate\"></a>\n## **UNIVARIATE DATA EXPLORATION & VISUALIZATION**\n\nIn this section we'll create visualizations for numeric and categorical data on an individual basis.  "},{"metadata":{},"cell_type":"markdown","source":"### **AGE AND AGE RANGES**"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = ff.create_distplot([df['age']], ['age'], bin_size=5, colors=['blue'])\nfig.update_layout(title_text=\"Distribution of Age\", title_x=0.5)\nfig.show()","execution_count":521,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"d2337b0d-ebbd-4bb0-a536-0ce6713df953\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n            <script type=\"text/javascript\">\n                require([\"plotly\"], function(Plotly) {\n                    window.PLOTLYENV=window.PLOTLYENV || {};\n                    \n                if (document.getElementById(\"d2337b0d-ebbd-4bb0-a536-0ce6713df953\")) {\n                    Plotly.newPlot(\n                        'd2337b0d-ebbd-4bb0-a536-0ce6713df953',\n                        [{\"autobinx\": false, \"histnorm\": \"probability 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marker=dict(color=colors, line=dict(color='#000000',width=1))), row=1, col=1)\n\nfig.add_trace(go.Pie(labels=age_count['age_range'], \n                     values=age_count['count'],\n                     textfont=dict(size=15), opacity = 0.8,\n                     hole = 0.5, \n                     hoverinfo = \"label+percent+name\",\n                     domain  = dict(x = [.0,.48]),\n                     name    = \"Age Range Percent\",\n                     marker  = dict(colors=colors, line = dict(width = 1.5))), \n              row=1, col=2)\n\nfig.update_layout(height=500, showlegend=True)\n\nfig.show()","execution_count":522,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"8c9055e5-6bee-4e53-a6cb-2403ec8702e8\" class=\"plotly-graph-div\" style=\"height:500px; width:100%;\"></div>\n            <script type=\"text/javascript\">\n                require([\"plotly\"], function(Plotly) {\n                    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\"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 0.45]}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0]}},\n                        {\"responsive\": true}\n                    ).then(function(){\n                            \nvar gd = document.getElementById('8c9055e5-6bee-4e53-a6cb-2403ec8702e8');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })\n                };\n                });\n            </script>\n        </div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### **GENDER DISTRIBUTION**"},{"metadata":{"trusted":true},"cell_type":"code","source":"gender_count = df['gender'].value_counts().to_frame().reset_index()\ngender_count.rename(columns={'index':'gender', 'gender':'count'}, inplace=True)\n\nfig = make_subplots(rows=1, cols=2, \n                    specs=[[{\"type\": \"xy\"}, {\"type\": \"domain\"}]], \n                    subplot_titles=(\"Gender Count\", \"Gender Percentages\"))\n\n# bar_colors=['#3b76a3', '#3ba372', '#a3873b', '#a33b3b', '#863ba3', '#3ba3a1']\n\nfig.add_trace(go.Bar(x=gender_count['gender'], \n                     y=gender_count['count'],\n                     text=gender_count['count'],\n                     textposition = 'auto',\n                     name='Gender Count',\n                     opacity = 0.8, \n                     marker=dict(color= ['#398fcc','#cf4485'], line=dict(color='#000000',width=1))), row=1, col=1)\n\nfig.add_trace(go.Pie(labels=gender_count['gender'], \n                     values=gender_count['count'],\n                     textfont=dict(size=15), opacity = 0.8,\n                     hole = 0.5, \n                     hoverinfo = \"label+percent+name\",\n                     domain  = dict(x = [.0,.48]),\n                     name    = \"Gender Percent\",\n                     marker  = dict(colors = ['#398fcc','#cf4485'], line = dict(width = 1.5))), \n              row=1, col=2)\n\nfig.update_layout(height=500, showlegend=True)\n\nfig.show()","execution_count":523,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"bb5e46f3-3287-4e31-9053-db1e3dbc31b1\" class=\"plotly-graph-div\" style=\"height:500px; width:100%;\"></div>\n            <script type=\"text/javascript\">\n                require([\"plotly\"], function(Plotly) {\n 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\"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": 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\"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": 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              ).then(function(){\n                            \nvar gd = document.getElementById('bb5e46f3-3287-4e31-9053-db1e3dbc31b1');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })\n                };\n                });\n            </script>\n        </div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### **RACE DISTRIBUTION**"},{"metadata":{"trusted":true},"cell_type":"code","source":"    race_percent = population1[2:10]\n    race_percent.sort_values(by='United States', ascending=False, inplace=True)\n    \n    colors=['#c44560', '#3b76a3', '#3ba372', '#a3873b', '#a33b3b', '#863ba3', '#3ba3a1']\n\n#     labels=['White', 'Black', 'Native', 'Asian', 'Hispanic']\n#     values=population['California'][2:4]\n    \n    fig = go.Figure(data=(go.Bar(x=race_percent['Fact'], \n                                 y=race_percent['United States'],\n                                 text=race_percent['United States'],\n                                 textposition = 'auto',\n                                 name=('Percentage of Population'),\n                                 opacity = 0.8, \n                                 marker=dict(color=colors, line=dict(color='#000000',width=1)))))\n\n    \n    fig.update_layout(height=500,\n                      yaxis_title=\"% of Total Population\",\n                      title_text=('United States Total Population: ' + df_population['United States'][0]),\n                      showlegend=False)\n    fig.show()\n\n","execution_count":524,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"759bb40b-09f2-45a5-b38a-b7fa000b8215\" class=\"plotly-graph-div\" style=\"height:500px; width:100%;\"></div>\n            <script type=\"text/javascript\">\n                require([\"plotly\"], function(Plotly) {\n                    window.PLOTLYENV=window.PLOTLYENV || {};\n                    \n                if (document.getElementById(\"759bb40b-09f2-45a5-b38a-b7fa000b8215\")) {\n                    Plotly.newPlot(\n                        '759bb40b-09f2-45a5-b38a-b7fa000b8215',\n                        [{\"marker\": {\"color\": [\"#c44560\", \"#3b76a3\", \"#3ba372\", \"#a3873b\", \"#a33b3b\", \"#863ba3\", \"#3ba3a1\"], \"line\": {\"color\": \"#000000\", \"width\": 1}}, \"name\": \"Percentage of 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\"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 0.45]}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0]}},\n                        {\"responsive\": true}\n                    ).then(function(){\n                            \nvar gd = document.getElementById('f7fa9ea4-4c71-49f0-9909-61cbe5cad613');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })\n                };\n                });\n            </script>\n        </div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### **MANNER OF DEATH DISTRIBUTION**"},{"metadata":{"trusted":true},"cell_type":"code","source":"manner_count = df['manner_of_death'].value_counts().to_frame().reset_index()\nmanner_count.rename(columns={'index':'manner_of_death', 'manner_of_death':'count'}, inplace=True)\n\nfig = make_subplots(rows=1, cols=2, \n                    specs=[[{\"type\": \"xy\"}, {\"type\": \"domain\"}]], \n                    subplot_titles=(\"Manner of Death Count\", \"Manner of Death Percentages\"))\n\nfig.add_trace(go.Bar(x=manner_count['manner_of_death'], \n                     y=manner_count['count'],\n                     text=manner_count['count'],\n                     textposition = 'auto',\n                     name='Manner of Death Count',\n                     opacity = 0.8, \n                     marker=dict(color= ['#ad3131','#089ebf'], line=dict(color='#000000',width=1))), row=1, col=1)\n\nfig.add_trace(go.Pie(labels=manner_count['manner_of_death'], \n                     values=gender_count['count'],\n                     textfont=dict(size=15), opacity = 0.8,\n                     hole = 0.5, \n                     hoverinfo = \"label+percent+name\",\n                     domain  = dict(x = [.0,.48]),\n                     name    = \"Manner of Death Percent\",\n                     marker  = dict(colors = ['#ad3131','#089ebf'], line = dict(width = 1.5))), \n              row=1, col=2)\n\nfig.update_layout(height=500, showlegend=True)\n\nfig.show()","execution_count":526,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"278b5bd0-fab4-458b-8986-1a70d2fc6ea2\" class=\"plotly-graph-div\" style=\"height:500px; width:100%;\"></div>\n            <script type=\"text/javascript\">\n                require([\"plotly\"], function(Plotly) {\n                    window.PLOTLYENV=window.PLOTLYENV || {};\n        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\"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 0.45]}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0]}},\n                        {\"responsive\": true}\n                    ).then(function(){\n                            \nvar gd = document.getElementById('278b5bd0-fab4-458b-8986-1a70d2fc6ea2');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })\n                };\n                });\n            </script>\n        </div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### **TOP 15 WEAPONS**"},{"metadata":{"trusted":true},"cell_type":"code","source":"top_armed = df['armed'].value_counts().to_frame()\ntop_armed.reset_index(inplace=True)\ntop_armed = top_armed.rename(columns={'index':'armed', 'armed':'count'})\n\nfig = px.histogram(top_armed[0:15], x='armed', y='count', color='armed')\n\nfig.update_layout(title_text='Weapon of Victim', title_x=0.5)\nfig.show()","execution_count":527,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"419fefa1-54b3-4705-be3f-d90ba5851483\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n            <script type=\"text/javascript\">\n                require([\"plotly\"], function(Plotly) {\n                    window.PLOTLYENV=window.PLOTLYENV || {};\n                    \n                if (document.getElementById(\"419fefa1-54b3-4705-be3f-d90ba5851483\")) {\n                    Plotly.newPlot(\n                        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\"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Was Victim Fleeing?\", \"x\": 0.5}, \"xaxis\": {\"anchor\": \"y\", \"categoryarray\": [\"Not fleeing\", \"Car\", \"Foot\", \"Other\"], \"categoryorder\": \"array\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"flee\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"count\"}}},\n                        {\"responsive\": true}\n                    ).then(function(){\n                            \nvar gd = document.getElementById('46446124-e2eb-461d-9f67-b8d7f96cad1f');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = 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Percentages\"))\n\nfig.add_trace(go.Bar(x=mental_illness['signs_of_mental_illness'], \n                     y=mental_illness['count'],\n                     text=mental_illness['count'],\n                     textposition = 'auto',\n                     name='Mental Illness Count',\n                     opacity = 0.8, \n                     marker=dict(color=['#089ebf','#ad3131'], line=dict(color='#000000',width=1))), row=1, col=1)\n\nfig.add_trace(go.Pie(labels=mental_illness['signs_of_mental_illness'], \n                     values=mental_illness['count'],\n                     textfont=dict(size=15), opacity = 0.8,\n                     hole = 0.5, \n                     hoverinfo = \"label+percent+name\",\n                     domain  = dict(x = [.0,.48]),\n                     name    = \"Mental Illness Percent\",\n                     marker  = dict(colors = ['#089ebf','#ad3131'], line = dict(width = 1.5))), \n              row=1, col=2)\n\nfig.update_layout(height=500, showlegend=True)\n\nfig.show()","execution_count":529,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"746ff0b4-6c4a-4c12-9e88-2e30f695ae0b\" class=\"plotly-graph-div\" style=\"height:500px; width:100%;\"></div>\n            <script type=\"text/javascript\">\n                require([\"plotly\"], function(Plotly) {\n                    window.PLOTLYENV=window.PLOTLYENV || {};\n                    \n                if (document.getElementById(\"746ff0b4-6c4a-4c12-9e88-2e30f695ae0b\")) {\n                    Plotly.newPlot(\n                        '746ff0b4-6c4a-4c12-9e88-2e30f695ae0b',\n                        [{\"marker\": {\"color\": [\"#089ebf\", \"#ad3131\"], \"line\": {\"color\": \"#000000\", \"width\": 1}}, \"name\": \"Mental Illness Count\", \"opacity\": 0.8, \"text\": [3065.0, 998.0], \"textposition\": \"auto\", \"type\": \"bar\", \"x\": [false, true], \"xaxis\": \"x\", \"y\": [3065, 998], \"yaxis\": \"y\"}, 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  x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })\n                };\n                });\n            </script>\n        </div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### **THREAT LEVEL DISTRIBUTION**"},{"metadata":{"trusted":true},"cell_type":"code","source":"threat = df['threat_level'].value_counts().to_frame().reset_index()\nthreat.rename(columns={'index':'threat_level', 'threat_level':'count'}, inplace=True)\n\nfig = make_subplots(rows=1, cols=2, \n                    specs=[[{\"type\": \"xy\"}, {\"type\": \"domain\"}]], \n                    subplot_titles=(\"Threat Level Count\", \"Threat Level Percentages\"))\n\nfig.add_trace(go.Bar(x=threat['threat_level'], \n                     y=threat['count'],\n                     text=threat['count'],\n                     textposition = 'auto',\n                     name='Threat Level Count',\n                     opacity = 0.8, \n                     marker=dict(color=['#ba2d2d','#2d97ba', '#5c663f'], line=dict(color='#000000',width=1))), row=1, col=1)\n\nfig.add_trace(go.Pie(labels=threat['threat_level'], \n                     values=threat['count'],\n                     textfont=dict(size=15), opacity = 0.8,\n                     hole = 0.5, \n                     hoverinfo = \"label+percent+name\",\n                     domain  = dict(x = [.0,.48]),\n                     name    = \"Threat Level Percent\",\n                     marker  = dict(colors = ['#ba2d2d','#2d97ba', '#5c663f'], line = dict(width = 1.5))), \n              row=1, col=2)\n\nfig.update_layout(height=500, showlegend=True)\n\nfig.show()","execution_count":530,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"f1d60295-47ee-4af9-9653-48fbf8810a27\" 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\"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 0.45]}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0]}},\n                        {\"responsive\": true}\n                    ).then(function(){\n                            \nvar gd = document.getElementById('f1d60295-47ee-4af9-9653-48fbf8810a27');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })\n                };\n                });\n            </script>\n        </div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### **WAS OFFICER'S BODY CAMERA ON?**"},{"metadata":{"trusted":true},"cell_type":"code","source":"body_cam = df['body_camera'].value_counts().to_frame().reset_index()\nbody_cam.rename(columns={'index':'body_camera', 'body_camera':'count'}, inplace=True)\n\nfig = make_subplots(rows=1, cols=2, \n                    specs=[[{\"type\": \"xy\"}, {\"type\": \"domain\"}]], \n                    subplot_titles=(\"Body Camera Count\", \"Body Camera Percentages\"))\n\nfig.add_trace(go.Bar(x=body_cam['body_camera'], \n                     y=body_cam['count'],\n                     text=body_cam['count'],\n                     textposition = 'auto',\n                     name='Body Camera Count',\n                     opacity = 0.8, \n                     marker=dict(color=['#ad3131', '#089ebf'], line=dict(color='#000000',width=1))), row=1, col=1)\n\nfig.add_trace(go.Pie(labels=body_cam['body_camera'], \n                     values=body_cam['count'],\n                     textfont=dict(size=15), opacity = 0.8,\n                     hole = 0.5, \n                     hoverinfo = \"label+percent+name\",\n                     domain  = dict(x = [.0,.48]),\n                     name    = \"Body Camera Percent\",\n                     marker  = dict(colors = ['#ad3131', '#089ebf'], line = dict(width = 1.5))), \n              row=1, col=2)\n\nfig.update_layout(height=500,\n                  title_text=\"Was the Officer's Body Camera On?\",\n                  showlegend=True)\n\nfig.show()","execution_count":531,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"af598710-c260-4c3d-90d1-42ffe43c42c5\" class=\"plotly-graph-div\" 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xaxis_title='States', title_x=0.5, height=600)\n\nfig.show()","execution_count":532,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"689358c0-edda-4db1-90fe-206ad6992b58\" class=\"plotly-graph-div\" style=\"height:600px; width:100%;\"></div>\n            <script type=\"text/javascript\">\n                require([\"plotly\"], function(Plotly) {\n                    window.PLOTLYENV=window.PLOTLYENV || {};\n                    \n                if (document.getElementById(\"689358c0-edda-4db1-90fe-206ad6992b58\")) {\n                    Plotly.newPlot(\n                        '689358c0-edda-4db1-90fe-206ad6992b58',\n                        [{\"marker\": {\"color\": [39512223.0, 28995881.0, 21477737.0, 19453561.0, 12801989.0, 12671821.0, 11689100.0, 10617423.0, 10488084.0, 9986857.0, 8882190.0, 8535519.0, 7614893.0, 7278717.0, 6892503.0, 6829174.0, 6732219.0, 6137428.0, 6045680.0, 5822434.0, 5758736.0, 5639632.0, 5148714.0, 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height=600)\n\nfig.show()","execution_count":533,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"edcc3ddc-1a15-4b78-bb45-215073e7f6c1\" class=\"plotly-graph-div\" style=\"height:600px; width:100%;\"></div>\n            <script type=\"text/javascript\">\n                require([\"plotly\"], function(Plotly) {\n                    window.PLOTLYENV=window.PLOTLYENV || {};\n                    \n                if (document.getElementById(\"edcc3ddc-1a15-4b78-bb45-215073e7f6c1\")) {\n                    Plotly.newPlot(\n                        'edcc3ddc-1a15-4b78-bb45-215073e7f6c1',\n                        [{\"marker\": {\"color\": [587, 354, 265, 186, 136, 131, 128, 127, 123, 108, 105, 104, 85, 82, 81, 78, 78, 78, 77, 76, 75, 68, 66, 65, 64, 64, 57, 55, 53, 50, 49, 49, 43, 37, 33, 28, 27, 27, 22, 20, 19, 14, 13, 13, 13, 13, 11, 8, 8, 8, 2]}, \"text\": [587.0, 354.0, 265.0, 186.0, 136.0, 131.0, 128.0, 127.0, 123.0, 108.0, 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"},{"metadata":{"trusted":true},"cell_type":"code","source":"#STATE WHERE SHOOTINGS TOOK PLACE\ncities = df['city'].value_counts().to_frame().reset_index()\ncities.rename(columns={'index':'city', 'city':'count'}, inplace=True)\n# states = states.sort_values(by='count', ascending=False)\ncities = cities[:25]\n\n\nfig = go.Figure(go.Bar(x=cities['city'].sort_index(ascending=True), \n                       y=cities['count'].sort_index(ascending=True),\n                        text=cities['count'].sort_index(ascending=True),\n                       textposition='outside', marker_color=cities['count'].sort_index(ascending=True)))\n\n\nfig.update_xaxes(showline=True, linewidth=2, linecolor='black', mirror=True)\nfig.update_yaxes(showline=True, linewidth=2, linecolor='black', mirror=True)\nfig.update_layout(title_text='Police Killings, Organized by Cities',yaxis_title='Cities',\n                 xaxis_title='Total number of victims', title_x=0.5, 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by Cities\", \"x\": 0.5}, \"xaxis\": {\"linecolor\": \"black\", \"linewidth\": 2, \"mirror\": true, \"showline\": true, \"title\": {\"text\": \"Total number of victims\"}}, \"yaxis\": {\"linecolor\": \"black\", \"linewidth\": 2, \"mirror\": true, \"showline\": true, \"title\": {\"text\": \"Cities\"}}},\n                        {\"responsive\": true}\n                    ).then(function(){\n                            \nvar gd = document.getElementById('17c139b7-4e78-4f7b-8903-b2e169c73437');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: 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We'll look at averages and totals to determine which years, months, and days have the most and least number of deaths. "},{"metadata":{},"cell_type":"markdown","source":"### **POLICE KILLINGS BY YEAR**"},{"metadata":{"trusted":true},"cell_type":"code","source":"df_years = df['year'].value_counts().to_frame().reset_index()\ndf_years.rename(columns={'index':'year', 'year':'count'}, inplace=True)\ndf_years = df_years.sort_values(by='year')\n\nfig = go.Figure()\n\nfig.add_trace(go.Scatter(x=df_years['year'], \n                         y=df_years['count'], \n                         mode='lines+markers', \n                         marker_color=\"red\"))\n\nfig.update_layout(title_text='Police Killings by Year',\n                  xaxis_title='Years',\n                  yaxis_title='Total number of kills', \n                  title_x=0.5)\n\nfig.show()\n","execution_count":535,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"c0e05359-bea6-4d4f-92e3-411dfd1243a7\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n            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Killings by Year\", \"x\": 0.5}, \"xaxis\": {\"title\": {\"text\": \"Years\"}}, \"yaxis\": {\"title\": {\"text\": \"Total number of kills\"}}},\n                        {\"responsive\": true}\n                    ).then(function(){\n                            \nvar gd = document.getElementById('c0e05359-bea6-4d4f-92e3-411dfd1243a7');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })\n                };\n                });\n            </script>\n        </div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### **MONTHLY DEATHS BY YEAR**\n\nLet's visualize how many police killings occurred each month from 2015 to 2020"},{"metadata":{"trusted":true},"cell_type":"code","source":"df_monthly = df['date'].groupby(df.date.dt.to_period(\"M\")).agg('count').to_frame(name=\"count\").reset_index()\ndf_monthly = df_monthly.sort_values(by='date')\n\nyear_month=[]\nfor i in df_monthly['date']:\n    year_month.append(str(i))\n    \ndf_monthly.head()","execution_count":536,"outputs":[{"output_type":"execute_result","execution_count":536,"data":{"text/plain":"      date  count\n0  2015-01     68\n1  2015-02     73\n2  2015-03     84\n3  2015-04     80\n4  2015-05     65","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>date</th>\n      <th>count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2015-01</td>\n      <td>68</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2015-02</td>\n      <td>73</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2015-03</td>\n      <td>84</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2015-04</td>\n      <td>80</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2015-05</td>\n      <td>65</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = make_subplots(rows=2, cols=1, subplot_titles=(\"Monthly series\", \"Distribution of monthly count\"))\n\nfig.add_trace(go.Scatter(x=year_month, y=df_monthly['count'], \n                         name=\"Monthly Deaths\", mode='lines+markers'),row=1,col=1)\n\nfig.add_trace(go.Box(y=df_monthly['count'], name='Count',\n                marker_color = 'indianred',boxmean='sd'),row=2,col=1)\n\nfig.update_xaxes(title_text=\"Year\", row=1, col=1,showline=True, linewidth=2, linecolor='black', mirror=True)\nfig.update_xaxes(title_text=\" \", row=2, col=1,showline=True, linewidth=2, linecolor='black', mirror=True)\nfig.update_yaxes(title_text=\"Number of Victims\", row=1, col=1,showline=True, linewidth=2, linecolor='black', mirror=True)\nfig.update_yaxes(title_text=\"Number of Victims\", row=2, col=1,showline=True, linewidth=2, linecolor='black', mirror=True)\n\nfig.update_layout(title_text='Fatal Killing Monthly Count 2015 - 2019', title_x=0.5,showlegend=False,height=1000)\nfig.show()","execution_count":537,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"6c3e91ab-0bf5-4661-8b3c-d3bc426a4b1a\" 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{\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Fatal Killing Monthly Count 2015 - 2019\", \"x\": 0.5}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"linecolor\": \"black\", \"linewidth\": 2, \"mirror\": true, \"showline\": true, \"title\": {\"text\": \"Year\"}}, \"xaxis2\": {\"anchor\": \"y2\", \"domain\": [0.0, 1.0], \"linecolor\": \"black\", \"linewidth\": 2, \"mirror\": true, \"showline\": true, \"title\": {\"text\": \" \"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.625, 1.0], \"linecolor\": \"black\", \"linewidth\": 2, \"mirror\": true, \"showline\": true, \"title\": {\"text\": \"Number of Victims\"}}, \"yaxis2\": {\"anchor\": \"x2\", \"domain\": [0.0, 0.375], \"linecolor\": \"black\", \"linewidth\": 2, \"mirror\": true, \"showline\": true, \"title\": {\"text\": \"Number of Victims\"}}},\n                        {\"responsive\": true}\n                    ).then(function(){\n                            \nvar gd = document.getElementById('6c3e91ab-0bf5-4661-8b3c-d3bc426a4b1a');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })\n                };\n                });\n            </script>\n        </div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### **MONTHLY DEATHS ORGANIZED BY YEAR**\nFor a better comparison, let's visualize the number of killings each month for every year. "},{"metadata":{"trusted":true},"cell_type":"code","source":"df_monthly['year'] = df_monthly['date'].dt.strftime('%Y')\n\ndef plot_month(year, color):\n    temp_month = []\n    for i in df_monthly.loc[df_monthly['year']==year]['date']:\n        temp_month.append(str(i))\n    trace=go.Bar(x=temp_month, y=df_monthly.loc[df_monthly['year']==year]['count'], \n                 name=year, marker_color=color)\n    return trace","execution_count":538,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = make_subplots(rows=3, cols=2, subplot_titles=('2015', '2016', '2017', '2018', '2019'))\n\nfig.add_trace(plot_month('2015', 'blue'), row=1, col=1)\nfig.add_trace(plot_month('2016', 'red'), row=1, col=2)\nfig.add_trace(plot_month('2017', 'green'), row=2, col=1)\nfig.add_trace(plot_month('2018', 'orange'), row=2, col=2)\nfig.add_trace(plot_month('2019', 'purple'), row=3, col=1)\n\nfig.update_xaxes(showline=True, linewidth=2, linecolor='black', mirror=True)\nfig.update_yaxes(showline=True, linewidth=2, linecolor='black', mirror=True)\nfig.update_layout(title_text='Distribution of Monthly Killings by Year', title_x=0.5, showlegend=False)\nfig.show()","execution_count":539,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"587d92ae-dc13-4a1d-8321-c946c2271a13\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n            <script type=\"text/javascript\">\n                require([\"plotly\"], function(Plotly) {\n                    window.PLOTLYENV=window.PLOTLYENV || {};\n                    \n                if (document.getElementById(\"587d92ae-dc13-4a1d-8321-c946c2271a13\")) {\n                    Plotly.newPlot(\n                        '587d92ae-dc13-4a1d-8321-c946c2271a13',\n                        [{\"marker\": {\"color\": \"blue\"}, \"name\": \"2015\", \"type\": \"bar\", \"x\": [\"2015-01\", \"2015-02\", \"2015-03\", \"2015-04\", 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KILLINGS**"},{"metadata":{"trusted":true},"cell_type":"code","source":"only_month = df.groupby(['year','month_name', 'month'])[['month_name']].count()\nonly_month.rename(columns={'month_name':'count'}, inplace=True)\nonly_month.reset_index(inplace=True)\nonly_month = only_month.groupby(['month_name', 'month'])[['count']].mean()\nonly_month.sort_values(by='month', inplace=True)\nonly_month = only_month.round(2)\nonly_month.reset_index(inplace=True)\n\nfig = go.Figure(data=[go.Bar(x=only_month['month_name'], \n                             y=only_month['count'], \n                             name='Months', \n                             marker_color='blue', \n                             text=only_month['count'],\n                             textposition='auto')])\n\nfig.update_xaxes(showline=True, linewidth=1, linecolor='black', mirror=True)\nfig.update_yaxes(showline=True, linewidth=1, linecolor='black', mirror=True)\nfig.update_layout(title_text='Average Deaths - All Months', xaxis_title='Months',\n                 yaxis_title='Average Number of Killings', title_x=0.5,barmode='stack')\n\nfig.show()","execution_count":540,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"bbdb3753-05d1-4cbc-960e-0e9955e2717e\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n            <script type=\"text/javascript\">\n                require([\"plotly\"], function(Plotly) {\n                    window.PLOTLYENV=window.PLOTLYENV || {};\n                    \n                if (document.getElementById(\"bbdb3753-05d1-4cbc-960e-0e9955e2717e\")) {\n                    Plotly.newPlot(\n                        'bbdb3753-05d1-4cbc-960e-0e9955e2717e',\n                        [{\"marker\": {\"color\": \"blue\"}, \"name\": \"Months\", \"text\": [77.8, 72.4, 77.4, 66.0, 61.2, 66.0, 75.8, 68.8, 60.6, 67.0, 59.2, 60.4], \"textposition\": \"auto\", \"type\": \"bar\", \"x\": [\"January\", \"February\", 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\"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, 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\"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Average Deaths - All Months\", \"x\": 0.5}, \"xaxis\": {\"linecolor\": \"black\", \"linewidth\": 1, \"mirror\": true, \"showline\": true, \"title\": {\"text\": \"Months\"}}, \"yaxis\": {\"linecolor\": \"black\", \"linewidth\": 1, \"mirror\": true, \"showline\": true, \"title\": {\"text\": \"Average Number of Killings\"}}},\n                        {\"responsive\": true}\n                    ).then(function(){\n                            \nvar gd = document.getElementById('bbdb3753-05d1-4cbc-960e-0e9955e2717e');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })\n                };\n                });\n            </script>\n        </div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### **YEARS WITH MOST KILLINGS**"},{"metadata":{"trusted":true},"cell_type":"code","source":"year_count = df.groupby(['year'])[['id']].agg('count')\nyear_count.reset_index(inplace=True)\nyear_count.rename(columns={'id':'count'}, inplace=True)\n\nfig = go.Figure(data=[go.Bar(x=year_count['year'], \n                             y=year_count['count'], \n                             name='Years', \n                             marker_color='blue',\n                             text=year_count['count'],\n                             textposition='auto')])\n\nfig.update_xaxes(showline=True, linewidth=1, linecolor='black', mirror=True)\nfig.update_yaxes(showline=True, linewidth=1, linecolor='black', mirror=True)\nfig.update_layout(title_text='Deaths - All Years',xaxis_title='Years',\n                 yaxis_title='Total number of kills', title_x=0.5,barmode='stack')\nfig.show()","execution_count":541,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"58916dfa-0532-4647-b25e-1a948da49a70\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n            <script type=\"text/javascript\">\n                require([\"plotly\"], function(Plotly) {\n                    window.PLOTLYENV=window.PLOTLYENV || {};\n                    \n                if (document.getElementById(\"58916dfa-0532-4647-b25e-1a948da49a70\")) {\n                    Plotly.newPlot(\n                        '58916dfa-0532-4647-b25e-1a948da49a70',\n                        [{\"marker\": {\"color\": \"blue\"}, \"name\": \"Years\", \"text\": [895.0, 820.0, 784.0, 800.0, 764.0], \"textposition\": \"auto\", \"type\": \"bar\", \"x\": [2015, 2016, 2017, 2018, 2019], \"y\": [895, 820, 784, 800, 764]}],\n                        {\"barmode\": \"stack\", \"template\": {\"data\": 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\"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": 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[0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Deaths - All Years\", \"x\": 0.5}, \"xaxis\": {\"linecolor\": \"black\", \"linewidth\": 1, \"mirror\": true, \"showline\": true, \"title\": {\"text\": \"Years\"}}, \"yaxis\": {\"linecolor\": \"black\", \"linewidth\": 1, \"mirror\": true, \"showline\": true, \"title\": {\"text\": \"Total number of kills\"}}},\n                        {\"responsive\": true}\n                    ).then(function(){\n                            \nvar gd = document.getElementById('58916dfa-0532-4647-b25e-1a948da49a70');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })\n                };\n                });\n            </script>\n        </div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### **MOST KILLINGS BY DAY**"},{"metadata":{"trusted":true},"cell_type":"code","source":"weekday_count = df.groupby(['weekday'])[['id']].agg('count')\nweekday_count = weekday_count.reindex(['Sunday','Monday','Tuesday','Wednesday','Thursday','Friday','Saturday'])\nweekday_count.reset_index(inplace=True)\nweekday_count.rename(columns={'id':'count'}, inplace=True)\n\nfig = go.Figure(data=[go.Bar(x=weekday_count['weekday'], \n                             y=weekday_count['count'],\n                             name='Weekdays', \n                             marker_color='blue',\n                             text=weekday_count['count'],\n                             textposition='auto')])\n\nfig.update_xaxes(showline=True, linewidth=1, linecolor='black', mirror=True)\nfig.update_yaxes(showline=True, linewidth=1, linecolor='black', mirror=True)\nfig.update_layout(title_text='Deaths - Days of the Week',xaxis_title='Weekdays',\n                 yaxis_title='Total Number of Killings', title_x=0.5,barmode='stack')\nfig.show()","execution_count":542,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div 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race_armed.groupby(['race','armed'])[['armed']].count()\nrace_armed.rename(columns={race_armed.columns[0] : 'count'}, inplace=True)\nrace_armed.reset_index(inplace=True)\nrace_armed.sort_values(by='count', ascending=False, inplace=True)","execution_count":543,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"race_gun = race_armed.loc[race_armed['armed'] == 'gun']\nrace_knife = race_armed.loc[race_armed['armed'] == 'knife']\nrace_unarmed = race_armed.loc[race_armed['armed'] == 'unarmed']\n\nfig = make_subplots(rows=3, cols=2, \n                    specs=[[{\"type\": \"xy\"}, {\"type\": \"domain\"}], \n                           [{\"type\": \"xy\"}, {\"type\": \"domain\"}],\n                           [{\"type\": \"xy\"}, {\"type\": \"domain\"}]], \n                    subplot_titles=(\"Gun Count\", \"Gun Percentages\",\n                                    \"Knife Count\", \"Knife Percentages\",\n                                   \"Unarmed Count\", 'Unarmed Percentages'))\n\ncolors=['#f299ac', '#1c1811', '#a3873b', '#f7ea72', '#c95742', '#3ba3a1']\n\n\nfig.add_trace(go.Bar(x=race_gun['race'], \n                     y=race_gun['count'],\n                     text=race_gun['count'],\n                     textposition = 'auto',\n                     name='Gun Count',\n                     opacity = 0.8, \n                     marker=dict(color=colors, line=dict(color='#000000',width=1))), row=1, col=1)\n\nfig.add_trace(go.Bar(x=race_knife['race'], \n                     y=race_knife['count'],\n                     text=race_knife['count'],\n                     textposition = 'auto',\n                     name='Knife Count',\n                     opacity = 0.8, \n                     marker=dict(color=colors, line=dict(color='#000000',width=1))), row=2, col=1)\n\nfig.add_trace(go.Bar(x=race_unarmed['race'], \n                     y=race_unarmed['count'],\n                     text=race_unarmed['count'],\n                     textposition = 'auto',\n                     name='Unarmed Count',\n                     opacity = 0.8, \n                     marker=dict(color=colors, line=dict(color='#000000',width=1))), row=3, col=1)\n\n\nfig.add_trace(go.Pie(labels=race_gun['race'], \n                     values=race_gun['count'],\n                     textfont=dict(size=15), opacity = 0.8,\n                     hole = 0.5, \n                     hoverinfo = \"label+percent+name\",\n                     domain  = dict(x = [.0,.48]),\n                     name    = \"Gun Percent\",\n                     marker  = dict(colors = colors, line = dict(width = 1.5))), \n              row=1, col=2)\n\nfig.add_trace(go.Pie(labels=race_knife['race'], \n                     values=race_knife['count'],\n                     textfont=dict(size=15), opacity = 0.8,\n                     hole = 0.5, \n                     hoverinfo = \"label+percent+name\",\n                     domain  = dict(x = [.0,.48]),\n                     name    = \"Knife Percent\",\n                     marker  = dict(colors = colors, line = dict(width = 1.5))), \n              row=2, col=2)\n\nfig.add_trace(go.Pie(labels=race_gun['race'], \n                     values=race_unarmed['count'],\n                     textfont=dict(size=15), opacity = 0.8,\n                     hole = 0.5, \n                     hoverinfo = \"label+percent+name\",\n                     domain  = dict(x = [.0,.48]),\n                     name    = \"Unarmed Percent\",\n                     marker  = dict(colors = colors, line = dict(width = 1.5))), \n              row=3, col=2)\n\nfig.update_layout(height=1000, showlegend=True)\n\nfig.show()","execution_count":544,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"1dc73094-20c9-4b1e-9b9b-18c5a1113f8b\" class=\"plotly-graph-div\" style=\"height:1000px; width:100%;\"></div>\n            <script type=\"text/javascript\">\n                require([\"plotly\"], 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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>age_range</th>\n      <th>18-35</th>\n      <th>36-50</th>\n      <th>51-65</th>\n      <th>65+</th>\n      <th>&lt;18</th>\n    </tr>\n    <tr>\n      <th>race</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>Asian</th>\n      <td>36.0</td>\n      <td>25.0</td>\n      <td>13.0</td>\n      <td>NaN</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>Black</th>\n      <td>685.0</td>\n      <td>268.0</td>\n      <td>69.0</td>\n      <td>15.0</td>\n      <td>31.0</td>\n    </tr>\n    <tr>\n      <th>Hispanic</th>\n      <td>431.0</td>\n      <td>228.0</td>\n      <td>48.0</td>\n      <td>6.0</td>\n      <td>19.0</td>\n    </tr>\n    <tr>\n      <th>Native</th>\n      <td>45.0</td>\n      <td>19.0</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>Other</th>\n      <td>25.0</td>\n      <td>11.0</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>White</th>\n      <td>867.0</td>\n      <td>703.0</td>\n      <td>406.0</td>\n      <td>76.0</td>\n      <td>28.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_race_age = df.groupby(['race', 'age_range']).agg('count')['id'].to_frame('count').reset_index()\ndf_black = df_race_age.loc[df_race_age['race'] == 'Black']\ndf_white = df_race_age.loc[df_race_age['race'] == 'White']\ndf_hispanic = df_race_age.loc[df_race_age['race'] == 'Hispanic']\ndf_native = df_race_age.loc[df_race_age['race'] == 'Native']\ndf_asian = df_race_age.loc[df_race_age['race'] == 'Asian']\ndf_other = df_race_age.loc[df_race_age['race'] == 'Other']","execution_count":546,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"black = go.Bar(x = df_black['age_range'], y = df_black['count'], \n             marker=dict(color='black'),name=\"black\")\n\nwhite = go.Bar(x=df_white['age_range'],y=df_white['count'],\n               marker=dict(color='pink'),name=\"white\")\n\nhispanic = go.Bar(x=df_hispanic['age_range'],y=df_hispanic['count'],\n               marker=dict(color='tan'),name=\"hispanic\")\n\nasian = go.Bar(x=df_asian['age_range'],y=df_asian['count'],\n               marker=dict(color='yellow'),name=\"asian\")\n\nnative = go.Bar(x=df_native['age_range'],y=df_native['count'],\n               marker=dict(color='red'),name=\"native\")\n\nother = go.Bar(x=df_other['age_range'],y=df_other['count'],\n               marker=dict(color='teal'),name=\"other\")\n\ndata=[white,black,hispanic,asian,native,other]\n\nfig = go.Figure(data)\nfig.update_xaxes(showline=True, linewidth=2, linecolor='black', mirror=True)\nfig.update_yaxes(showline=True, linewidth=2, linecolor='black', mirror=True)\nfig.update_layout(title=\"Race & Age Range\",title_x=0.5,xaxis=dict(title=\"Age Range\"),yaxis=dict(title=\"Number of Victims\"),\n                   barmode=\"group\")\nfig.show()","execution_count":547,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"00b7d81e-0120-44ea-8014-6ff48f99145a\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n            <script type=\"text/javascript\">\n                require([\"plotly\"], function(Plotly) {\n                    window.PLOTLYENV=window.PLOTLYENV || {};\n                    \n                if (document.getElementById(\"00b7d81e-0120-44ea-8014-6ff48f99145a\")) {\n                    Plotly.newPlot(\n                        '00b7d81e-0120-44ea-8014-6ff48f99145a',\n                      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\"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 0.45]}, \"xaxis2\": {\"anchor\": \"y2\", \"domain\": [0.0, 0.45]}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.625, 1.0]}, \"yaxis2\": {\"anchor\": \"x2\", \"domain\": [0.0, 0.375]}},\n                        {\"responsive\": true}\n                    ).then(function(){\n                            \nvar gd = document.getElementById('643c0750-45c7-4b67-84cc-cbff6905d7e0');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })\n                };\n                });\n            </script>\n        </div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### **RACE & GENDER**"},{"metadata":{"trusted":true},"cell_type":"code","source":"df_race_gender = df.groupby(['race', 'gender']).agg('count')['id'].to_frame('count').reset_index()\n\ndf_black_gender = df_race_gender.loc[df_race_gender['race'] == 'Black']\ndf_black_gender = df_black_gender.sort_values(by='count', ascending=False)\n\ndf_white_gender = df_race_gender.loc[df_race_gender['race'] == 'White']\ndf_white_gender = df_white_gender.sort_values(by='count', ascending=False)\n\ndf_hispanic_gender = df_race_gender.loc[df_race_gender['race'] == 'Hispanic']\ndf_hispanic_gender = df_hispanic_gender.sort_values(by='count', ascending=False)\n\ndf_asian_gender = df_race_gender.loc[df_race_gender['race'] == 'Asian']\ndf_asian_gender = df_asian_gender.sort_values(by='count', ascending=False)\n\ndf_native_gender = df_race_gender.loc[df_race_gender['race'] == 'Native']\ndf_native_gender = df_native_gender.sort_values(by='count', ascending=False)\n\ndf_other_gender = df_race_gender.loc[df_race_gender['race'] == 'Other']\ndf_other_gender = df_other_gender.sort_values(by='count', ascending=False)","execution_count":550,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"black = go.Bar(x=df_black_gender['gender'], y=df_black_gender['count'], \n              marker=dict(color='black'),name=\"black\", \n              text=df_black_gender['count'], textposition='auto')\n\nwhite = go.Bar(x=df_white_gender['gender'], y=df_white_gender['count'], \n              marker=dict(color='pink'),name=\"white\",\n              text=df_white_gender['count'], textposition='auto')\n\nhispanic = go.Bar(x=df_hispanic_gender['gender'], y=df_hispanic_gender['count'], \n              marker=dict(color='tan'),name=\"hispanic\",\n            text=df_hispanic_gender['count'], textposition='auto')\n\nasian = go.Bar(x=df_asian_gender['gender'], y=df_asian_gender['count'], \n              marker=dict(color='yellow'),name=\"asian\",\n              text=df_asian_gender['count'], textposition='auto')\n\nnative = go.Bar(x=df_native_gender['gender'], y=df_native_gender['count'], \n              marker=dict(color='red'),name=\"native\",\n               text=df_native_gender['count'], textposition='auto')\n\nother = go.Bar(x=df_other_gender['gender'], y=df_other_gender['count'], \n              marker=dict(color='teal'),name=\"other\",\n              text=df_other_gender['count'], textposition='auto')\n\ndata=[white, black, hispanic, asian, native, other]\n\nfig = go.Figure(data)\nfig.update_xaxes(showline=True, linewidth=2, linecolor='black', mirror=True)\nfig.update_yaxes(showline=True, linewidth=2, linecolor='black', mirror=True)\nfig.update_layout(title=\"Race & Gender\", \n                  title_x=0.5, \n                  xaxis=dict(title=\"Gender\"), \n                  yaxis=dict(title=\"Number of Victims\"),\n                  barmode=\"group\")\nfig.show()","execution_count":551,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div 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Gender\", \"x\": 0.5}, \"xaxis\": {\"linecolor\": \"black\", \"linewidth\": 2, \"mirror\": true, \"showline\": true, \"title\": {\"text\": \"Gender\"}}, \"yaxis\": {\"linecolor\": \"black\", \"linewidth\": 2, \"mirror\": true, \"showline\": true, \"title\": {\"text\": \"Number of Victims\"}}},\n                        {\"responsive\": true}\n                    ).then(function(){\n                            \nvar gd = document.getElementById('a6d41ef7-3cc8-432d-9fbc-48a286ae0ae6');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })\n                };\n                });\n            </script>\n        </div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### **RACE & LOCATION**\n\nLet's take a look at the racial distribution of the 7 states with the most deaths.\n\nFirst lets create some plotting functions to vizualize data from the census and our original dataset."},{"metadata":{"trusted":true},"cell_type":"code","source":"def pop_plot(state):\n    \n    state_pop = population1[['Fact',state]][2:9]\n    state_pop.sort_values(by=state, ascending=False, inplace=True)\n    \n    colors=['#3b76a3', '#3ba372', '#a3873b', '#a33b3b', '#863ba3', '#3ba3a1']\n\n#     labels=['White', 'Black', 'Native', 'Asian', 'Hispanic']\n#     values=population['California'][2:4]\n    \n    fig = go.Figure(data=(go.Bar(x=state_pop['Fact'], \n                                 y=state_pop[state],\n                                 text=state_pop[state],\n                                 textposition = 'auto',\n                                 name=('Percentage of Population'),\n                                 opacity = 0.8, \n                                 marker=dict(color=colors, line=dict(color='#000000',width=1)))))\n    \n    fig.update_layout(height=500,\n                      yaxis_title=\"% of Total Population\",\n                      title_text=(state + ' Total Population: ' + df_population[state][0]),\n                      showlegend=False)\n    fig.show()\n\n","execution_count":552,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"state_count = df['state'].value_counts().to_frame()[:10]\nstate_count.reset_index(inplace=True)\nstate_count.rename(columns={'index':'state', 'state':'count'}, inplace=True)\n\nrace_state = df.groupby(['race','state'])[['state']].count()\nrace_state.rename(columns={'state':'count'}, inplace=True)\nrace_state.reset_index(inplace=True)\nrace_state = race_state.loc[race_state['state'].isin(state_count['state'])]\nrace_state.sort_values(by='count', ascending=False, inplace=True)","execution_count":553,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def race_state_count(state):\n\n    fig = make_subplots(rows=1, cols=2, \n                        specs=[[{\"type\": \"xy\"}, {\"type\": \"domain\"}]] ,\n                        subplot_titles=((state + \" Race Count\"), (state +\" Race Percentages\")))\n\n    colors=['#3b76a3', '#3ba372', '#a3873b', '#a33b3b', '#863ba3', '#3ba3a1']\n\n    fig.add_trace(go.Bar(x=race_state['race'].loc[race_state['state'] == state], \n                         y=race_state['count'].loc[race_state['state'] == state],\n                         text=race_state['count'].loc[race_state['state'] == state],\n                         textposition = 'auto',\n                         name=(state +' Race Count'),\n                         opacity = 0.8, \n                         marker=dict(color=colors, line=dict(color='#000000',width=1))), row=1, col=1)\n\n    fig.add_trace(go.Pie(labels=race_state['race'].loc[race_state['state'] == state], \n                         values=race_state['count'].loc[race_state['state'] == state],\n                         textfont=dict(size=15), opacity = 0.8,\n                         hole = 0.5, \n                         hoverinfo = \"label+percent+name\",\n                         domain  = dict(x = [.0,.48]),\n                         name    = (state + \" Race Percent\"),\n                         marker  = dict(colors = colors, line = dict(width = 1.5))), \n                  row=1, col=2)\n\n    fig.update_layout(height=500, showlegend=True)\n\n    fig.show()","execution_count":554,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### 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their population number. "},{"metadata":{},"cell_type":"markdown","source":"### **TEXAS**"},{"metadata":{"trusted":true},"cell_type":"code","source":"pop_plot('Texas')","execution_count":557,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"2b7de14b-9339-4056-8c51-2d6e63a99a43\" class=\"plotly-graph-div\" style=\"height:500px; width:100%;\"></div>\n            <script type=\"text/javascript\">\n                require([\"plotly\"], function(Plotly) {\n                    window.PLOTLYENV=window.PLOTLYENV || {};\n                    \n                if (document.getElementById(\"2b7de14b-9339-4056-8c51-2d6e63a99a43\")) {\n                    Plotly.newPlot(\n                        '2b7de14b-9339-4056-8c51-2d6e63a99a43',\n                        [{\"marker\": {\"color\": [\"#3b76a3\", \"#3ba372\", \"#a3873b\", \"#a33b3b\", \"#863ba3\", \"#3ba3a1\"], \"line\": {\"color\": \"#000000\", \"width\": 1}}, \"name\": \"Percentage of Population\", 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\"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 0.45]}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0]}},\n                        {\"responsive\": true}\n                    ).then(function(){\n                            \nvar gd = document.getElementById('bd282f2b-3329-4694-808f-e11c340370aa');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n 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\"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Colorado Total Population: 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\"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 0.45]}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0]}},\n                        {\"responsive\": true}\n                    ).then(function(){\n                            \nvar gd = document.getElementById('b3a98585-05a5-4ba5-a9b3-1b473721aea8');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if 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class=\"plotly-graph-div\" style=\"height:500px; width:100%;\"></div>\n            <script type=\"text/javascript\">\n                require([\"plotly\"], function(Plotly) {\n                    window.PLOTLYENV=window.PLOTLYENV || {};\n                    \n                if (document.getElementById(\"2c3c4bd7-faa9-48a3-b0e7-eea30b4f570d\")) {\n                    Plotly.newPlot(\n                        '2c3c4bd7-faa9-48a3-b0e7-eea30b4f570d',\n                        [{\"marker\": {\"color\": [\"#3b76a3\", \"#3ba372\", \"#a3873b\", \"#a33b3b\", \"#863ba3\", \"#3ba3a1\"], \"line\": {\"color\": \"#000000\", \"width\": 1}}, \"name\": \"Percentage of Population\", \"opacity\": 0.8, \"text\": [82.6, 31.7, 5.3, 5.2, 3.7, 2.9, 0.3], \"textposition\": \"auto\", \"type\": \"bar\", \"x\": [\"White  \", \"Hispanic or Latino \", \"American Indian and Alaska Native  \", \"Black or African American  \", \"Asian  \", \"Two or More Races \", \"Native Hawaiian and Other Pacific Islander  \"], 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{\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 0.45]}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0]}},\n                        {\"responsive\": true}\n                    ).then(function(){\n                            \nvar gd = document.getElementById('ac6c2c75-3bc9-4941-b5f1-8144ba1b1fdb');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n  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= race_mental.loc[race_mental['race'] == 'Native']\nnative_mental = native_mental.sort_values(by='count', ascending=False)\n\nother_mental = race_mental.loc[race_mental['race'] == 'Other']\nother_mental = other_mental.sort_values(by='count', ascending=False)","execution_count":569,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"black = go.Bar(x=black_mental['signs_of_mental_illness'], y=black_mental['count'],\n              marker=dict(color='black'),name=\"black\")\n\nwhite = go.Bar(x=white_mental['signs_of_mental_illness'], y=white_mental['count'],\n              marker=dict(color='pink'),name=\"white\")\n\nhispanic = go.Bar(x=hispanic_mental['signs_of_mental_illness'], y=hispanic_mental['count'],\n              marker=dict(color='tan'),name=\"hispanic\")\n\nasian = go.Bar(x=asian_mental['signs_of_mental_illness'], y=asian_mental['count'],\n              marker=dict(color='yellow'),name=\"asian\")\n\nnative = go.Bar(x=native_mental['signs_of_mental_illness'], y=native_mental['count'],\n              marker=dict(color='red'),name=\"native\")\n\nother = go.Bar(x=other_mental['signs_of_mental_illness'], y=other_mental['count'],\n              marker=dict(color='teal'),name=\"other\")\n\ndata = [white,black,hispanic,asian,native,other]\n\nfig = go.Figure(data)\nfig.update_xaxes(showline=True, linewidth=2, linecolor='black', mirror=True)\nfig.update_yaxes(showline=True, linewidth=2, linecolor='black', mirror=True)\nfig.update_layout(title=\"Death Toll - Race & Mental Illness\", title_x=0.5,\n                  xaxis=dict(title=\"Signs of Mental Illness\"),\n                  yaxis=dict(title=\"Number of Victims\"),\n                   barmode=\"group\")\nfig.show()","execution_count":570,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"42e0dcf4-c7c5-494c-a900-d7d9543c6f3c\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n            <script 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\"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Death Toll - Race & Mental Illness\", \"x\": 0.5}, \"xaxis\": {\"linecolor\": \"black\", \"linewidth\": 2, \"mirror\": true, \"showline\": true, \"title\": {\"text\": \"Signs of Mental Illness\"}}, \"yaxis\": {\"linecolor\": \"black\", \"linewidth\": 2, \"mirror\": true, \"showline\": true, \"title\": {\"text\": \"Number of Victims\"}}},\n                        {\"responsive\": true}\n                    ).then(function(){\n        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LEVEL**"},{"metadata":{"trusted":true},"cell_type":"code","source":"threat_count = df['threat_level'].value_counts().to_frame()[:10]\nthreat_count.reset_index(inplace=True)\nthreat_count.rename(columns={'index':'threat_level', 'threat_level':'count'}, inplace=True)\n\nrace_threat = df.groupby(['race','threat_level'])[['threat_level']].count()\nrace_threat.rename(columns={'threat_level':'count'}, inplace=True)\nrace_threat.reset_index(inplace=True)\nrace_threat = race_threat.loc[race_threat['threat_level'].isin(threat_count['threat_level'])]\nrace_threat.sort_values(by='count', ascending=False, inplace=True)","execution_count":571,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"attack = race_threat.loc[race_threat['threat_level'] == 'attack']\nundetermined = race_threat.loc[race_threat['threat_level'] == 'undetermined']\nother = race_threat.loc[race_threat['threat_level'] == 'other']\n\nfig = make_subplots(rows=3, cols=2, \n                    specs=[[{\"type\": \"xy\"}, {\"type\": \"domain\"}], \n                           [{\"type\": \"xy\"}, {\"type\": \"domain\"}],\n                           [{\"type\": \"xy\"}, {\"type\": \"domain\"}]], \n                    subplot_titles=(\"Attack Count\", \"Attack Percentages\",\n                                    \"Other Count\", \"Other Percentages\",\n                                   \"Undetermined Count\", 'Undetermined Percentages'))\n\nbar_colors=['#3b76a3', '#3ba372', '#a3873b', '#a33b3b', '#863ba3', '#3ba3a1']\npie_colors=['#3b76a3', '#3ba372', '#a3873b', '#a33b3b', '#863ba3', '#3ba3a1']\n\nfig.add_trace(go.Bar(x=attack['race'], \n                     y=attack['count'],\n                     text=attack['count'],\n                     textposition = 'auto',\n                     name='Attack Count',\n                     opacity = 0.8, \n                     marker=dict(color=bar_colors, line=dict(color='#000000',width=1))), row=1, col=1)\n\nfig.add_trace(go.Bar(x=undetermined['race'], \n                     y=undetermined['count'],\n                     text=undetermined['count'],\n                     textposition = 'auto',\n                     name='Undetermined Count',\n                     opacity = 0.8, \n                     marker=dict(color=bar_colors, line=dict(color='#000000',width=1))), row=2, col=1)\n\nfig.add_trace(go.Bar(x=other['race'], \n                     y=other['count'],\n                     text=other['count'],\n                     textposition = 'auto',\n                     name='Unarmed Count',\n                     opacity = 0.8, \n                     marker=dict(color=bar_colors, line=dict(color='#000000',width=1))), row=3, col=1)\n\n\nfig.add_trace(go.Pie(labels=attack['race'], \n                     values=attack['count'],\n                     textfont=dict(size=15), opacity = 0.8,\n                     hole = 0.5, \n                     hoverinfo = \"label+percent+name\",\n                     domain  = dict(x = [.0,.48]),\n                     name    = \"Attack Percent\",\n                     marker  = dict(colors = pie_colors, line = dict(width = 1.5))), \n              row=1, col=2)\n\nfig.add_trace(go.Pie(labels=undetermined['race'], \n                     values=undetermined['count'],\n                     textfont=dict(size=15), opacity = 0.8,\n                     hole = 0.5, \n                     hoverinfo = \"label+percent+name\",\n                     domain  = dict(x = [.0,.48]),\n                     name    = \"Undetermined Percent\",\n                     marker  = dict(colors = pie_colors, line = dict(width = 1.5))), \n              row=2, col=2)\n\nfig.add_trace(go.Pie(labels=other['race'], \n                     values=other['count'],\n                     textfont=dict(size=15), opacity = 0.8,\n                     hole = 0.5, \n                     hoverinfo = \"label+percent+name\",\n                     domain  = dict(x = [.0,.48]),\n                     name    = \"Other Percent\",\n                     marker  = dict(colors = pie_colors, line = dict(width = 1.5))), \n              row=3, col=2)\n\nfig.update_layout(height=1000, showlegend=True)\n\nfig.show()","execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"fec7afcf-4e85-43e2-8d1e-1b989b895e84\" class=\"plotly-graph-div\" style=\"height:1000px; width:100%;\"></div>\n            <script type=\"text/javascript\">\n                require([\"plotly\"], function(Plotly) {\n                    window.PLOTLYENV=window.PLOTLYENV || {};\n                    \n                if (document.getElementById(\"fec7afcf-4e85-43e2-8d1e-1b989b895e84\")) {\n                    Plotly.newPlot(\n                        'fec7afcf-4e85-43e2-8d1e-1b989b895e84',\n                        [{\"marker\": {\"color\": [\"#3b76a3\", \"#3ba372\", \"#a3873b\", \"#a33b3b\", \"#863ba3\", \"#3ba3a1\"], \"line\": {\"color\": \"#000000\", \"width\": 1}}, \"name\": \"Attack 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CONCLUSION</a>"},{"metadata":{},"cell_type":"markdown","source":"I consider the above analysis to be only partially complete as there are many other aspects of the data to explore. But it is certainly a good start! I will continue to revisit the data and create new visualizations to gain further insight into the data.\n\nThe data set does not include population information, so the numbers of killings by race does not reflect any disparities or biases that may be present. However, census data reveals that Black and Hispanic people make up about 13% & 16% of the United States population respectively and White people make up about 70%; each are killed disproportionately to their population. Of the people listed in the data set, 51% are White, 26% are Black (twice the population!) and 18% are Hispanic.\n\nIt would also be very interesting to have knowledge of the economic status of each of the victims, although that is far outside the scope of this dataset. \n\nThanks for stopping by!"}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4}