001_Seaborn_Loading_Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i><small><small>\n",
    "All the IPython Notebooks in **[Python Seaborn Module](https://github.com/milaan9/12_Python_Seaborn_Module)** lecture series by **[Dr. Milaan Parmar](https://www.linkedin.com/in/milaanparmar/)** are available @ **[GitHub](https://github.com/milaan9)**\n",
    "</small></small></i>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/milaan9/12_Python_Seaborn_Module/blob/main/001_Seaborn_Loading_Dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Datasets in Seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When working with Seaborn, we can either use one of the **[built-in datasets](https://github.com/mwaskom/seaborn-data)** that Seaborn offers or we can load a **Pandas** DataFrame. Seaborn is part of the **[PyData](https://pydata.org/)** stack hence accepts Pandas data structures.\n",
    "\n",
    "Let us begin by importing few built-in datasets but before that we shall import few other libraries as well that our Seaborn would depend upon:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-16T14:14:25.714457Z",
     "start_time": "2021-07-16T14:14:22.363422Z"
    }
   },
   "outputs": [],
   "source": [
    "# Importing intrinsic libraries:\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have imported the required libraries, now it is time to load built-in dataset. The dataset we would be dealing with in this illustration is **[Iris Flower Dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set)**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-16T14:14:25.743756Z",
     "start_time": "2021-07-16T14:14:25.717390Z"
    }
   },
   "outputs": [],
   "source": [
    "# Loading built-in Datasets:\n",
    "iris = sns.load_dataset(\"iris\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly we may load other dataset as well and for illustration sake, I shall code few of them down here (though won't be referencing to):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-16T14:14:25.929299Z",
     "start_time": "2021-07-16T14:14:25.760359Z"
    }
   },
   "outputs": [],
   "source": [
    "# Refer to 'Dataset Source Reference' for list of all built-in Seaborn datasets.\n",
    "tips = sns.load_dataset(\"tips\")\n",
    "exercise = sns.load_dataset(\"exercise\")\n",
    "titanic = sns.load_dataset(\"titanic\")\n",
    "flights = sns.load_dataset(\"flights\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us take a sneak peek as to how this Iris dataset looks like and we shall be using Pandas to do so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-16T14:14:26.022561Z",
     "start_time": "2021-07-16T14:14:25.933208Z"
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   "outputs": [
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     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa\n",
       "5           5.4          3.9           1.7          0.4  setosa\n",
       "6           4.6          3.4           1.4          0.3  setosa\n",
       "7           5.0          3.4           1.5          0.2  setosa\n",
       "8           4.4          2.9           1.4          0.2  setosa\n",
       "9           4.9          3.1           1.5          0.1  setosa"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Iris dataset actually has 50 samples from each of three species of Iris flower (Setosa, Virginica and Versicolor). Four features were measured (in centimetres) from each sample: Length and Width of the Sepals and Petals. Let us try to have a summarized view of this dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-16T14:14:26.143654Z",
     "start_time": "2021-07-16T14:14:26.031351Z"
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   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.057333      3.758000     1.199333\n",
       "std        0.828066     0.435866      1.765298     0.762238\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`.describe()`** is a very useful method in Pandas as it generates descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values. Without getting in-depth into analysis here, let us try to plot something simple from this dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-16T14:14:26.768646Z",
     "start_time": "2021-07-16T14:14:26.156350Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 14.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='species', ylabel='petal_length'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "%matplotlib inline\n",
    "# Later in the course I shall explain why above 2 lines of code have been added.\n",
    "\n",
    "sns.swarmplot(x=\"species\", y=\"petal_length\", data=iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This beautiful representation of data we see above is known as a **`Swarm Plot`** with minimal parameters. I shall be covering this in detail later on but for now I just wanted you to have a feel of serenity we're getting into. \n",
    "\n",
    "Let us now try to load a random dataset and the one I've picked for this illustration is **[PoliceKillingsUS](https://github.com/washingtonpost/data-police-shootings)** dataset. This dataset has been prepared by The Washington Post (they keep updating it on runtime) with every fatal shooting in the United States by a police officer in the line of duty since Jan. 1, 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-16T14:14:26.844816Z",
     "start_time": "2021-07-16T14:14:26.790621Z"
    }
   },
   "outputs": [],
   "source": [
    "# Loading Pandas DataFrame:\n",
    "df = pd.read_csv(\"datasets/PoliceKillingsUS.csv\", encoding=\"windows-1252\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just the way we looked into Iris Data set, let us know have a preview of this dataset as well. We won't be getting into deep analysis of this dataset because our agenda is only to visualize the content within. So, let's do this: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-16T14:14:26.951750Z",
     "start_time": "2021-07-16T14:14:26.855562Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>02/01/15</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>02/01/15</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>03/01/15</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>04/01/15</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>04/01/15</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "      <td>Kenneth Joe Brown</td>\n",
       "      <td>04/01/15</td>\n",
       "      <td>shot</td>\n",
       "      <td>gun</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "      <td>W</td>\n",
       "      <td>Guthrie</td>\n",
       "      <td>OK</td>\n",
       "      <td>False</td>\n",
       "      <td>attack</td>\n",
       "      <td>Not fleeing</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13</td>\n",
       "      <td>Kenneth Arnold Buck</td>\n",
       "      <td>05/01/15</td>\n",
       "      <td>shot</td>\n",
       "      <td>gun</td>\n",
       "      <td>22.0</td>\n",
       "      <td>M</td>\n",
       "      <td>H</td>\n",
       "      <td>Chandler</td>\n",
       "      <td>AZ</td>\n",
       "      <td>False</td>\n",
       "      <td>attack</td>\n",
       "      <td>Car</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15</td>\n",
       "      <td>Brock Nichols</td>\n",
       "      <td>06/01/15</td>\n",
       "      <td>shot</td>\n",
       "      <td>gun</td>\n",
       "      <td>35.0</td>\n",
       "      <td>M</td>\n",
       "      <td>W</td>\n",
       "      <td>Assaria</td>\n",
       "      <td>KS</td>\n",
       "      <td>False</td>\n",
       "      <td>attack</td>\n",
       "      <td>Not fleeing</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16</td>\n",
       "      <td>Autumn Steele</td>\n",
       "      <td>06/01/15</td>\n",
       "      <td>shot</td>\n",
       "      <td>unarmed</td>\n",
       "      <td>34.0</td>\n",
       "      <td>F</td>\n",
       "      <td>W</td>\n",
       "      <td>Burlington</td>\n",
       "      <td>IA</td>\n",
       "      <td>False</td>\n",
       "      <td>other</td>\n",
       "      <td>Not fleeing</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>17</td>\n",
       "      <td>Leslie Sapp III</td>\n",
       "      <td>06/01/15</td>\n",
       "      <td>shot</td>\n",
       "      <td>toy weapon</td>\n",
       "      <td>47.0</td>\n",
       "      <td>M</td>\n",
       "      <td>B</td>\n",
       "      <td>Knoxville</td>\n",
       "      <td>PA</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>"
      ],
      "text/plain": [
       "   id                 name      date   manner_of_death       armed   age  \\\n",
       "0   3           Tim Elliot  02/01/15              shot         gun  53.0   \n",
       "1   4     Lewis Lee Lembke  02/01/15              shot         gun  47.0   \n",
       "2   5   John Paul Quintero  03/01/15  shot and Tasered     unarmed  23.0   \n",
       "3   8      Matthew Hoffman  04/01/15              shot  toy weapon  32.0   \n",
       "4   9    Michael Rodriguez  04/01/15              shot    nail gun  39.0   \n",
       "5  11    Kenneth Joe Brown  04/01/15              shot         gun  18.0   \n",
       "6  13  Kenneth Arnold Buck  05/01/15              shot         gun  22.0   \n",
       "7  15        Brock Nichols  06/01/15              shot         gun  35.0   \n",
       "8  16        Autumn Steele  06/01/15              shot     unarmed  34.0   \n",
       "9  17      Leslie Sapp III  06/01/15              shot  toy weapon  47.0   \n",
       "\n",
       "  gender race           city state  signs_of_mental_illness threat_level  \\\n",
       "0      M    A        Shelton    WA                     True       attack   \n",
       "1      M    W          Aloha    OR                    False       attack   \n",
       "2      M    H        Wichita    KS                    False        other   \n",
       "3      M    W  San Francisco    CA                     True       attack   \n",
       "4      M    H          Evans    CO                    False       attack   \n",
       "5      M    W        Guthrie    OK                    False       attack   \n",
       "6      M    H       Chandler    AZ                    False       attack   \n",
       "7      M    W        Assaria    KS                    False       attack   \n",
       "8      F    W     Burlington    IA                    False        other   \n",
       "9      M    B      Knoxville    PA                    False       attack   \n",
       "\n",
       "          flee  body_camera  \n",
       "0  Not fleeing        False  \n",
       "1  Not fleeing        False  \n",
       "2  Not fleeing        False  \n",
       "3  Not fleeing        False  \n",
       "4  Not fleeing        False  \n",
       "5  Not fleeing        False  \n",
       "6          Car        False  \n",
       "7  Not fleeing        False  \n",
       "8  Not fleeing         True  \n",
       "9  Not fleeing        False  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset is pretty self-descriptive and has limited number of features (may read as columns).\n",
    "\n",
    "**`race`**:\n",
    "**`W`**: White, non-Hispanic\n",
    "**`B`**: Black, non-Hispanic\n",
    "**`A`**: Asian\n",
    "**`N`**: Native American\n",
    "**`H`**: Hispanic\n",
    "**`O`**: Other\n",
    "**`None`**: unknown\n",
    "\n",
    "And, **`gender`** indicates:\n",
    "**`M`**: Male\n",
    "**`F`**: Female\n",
    "**`None`**: unknown\n",
    "The threat_level column include incidents where officers or others were shot at, threatened with a gun, attacked with other weapons or physical force, etc. The attack category is meant to flag the highest level of threat. The **`other`** and **`undetermined`** categories represent all remaining cases. **`Other`** includes many incidents where officers or others faced significant threats.\n",
    "\n",
    "The **`threat column`** and the **`fleeing column`** are not necessarily related. Also, **`attacks`** represent a status immediately before fatal shots by police; while **`fleeing`** could begin slightly earlier and involve a chase. Lately, **`body_camera`** indicates if an officer was wearing a body camera and it may have recorded some portion of the incident.\n",
    "\n",
    "Let us now look into the descriptive statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-16T14:14:27.128016Z",
     "start_time": "2021-07-16T14:14:26.978606Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2535.000000</td>\n",
       "      <td>2458.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1445.731755</td>\n",
       "      <td>36.605370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>794.259490</td>\n",
       "      <td>13.030774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>768.500000</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1453.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2126.500000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2822.000000</td>\n",
       "      <td>91.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                id          age\n",
       "count  2535.000000  2458.000000\n",
       "mean   1445.731755    36.605370\n",
       "std     794.259490    13.030774\n",
       "min       3.000000     6.000000\n",
       "25%     768.500000    26.000000\n",
       "50%    1453.000000    34.000000\n",
       "75%    2126.500000    45.000000\n",
       "max    2822.000000    91.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These stats in particular do not really make much sense. Instead let us try to visualize age of people who were claimed to be armed as per this dataset.\n",
    "\n",
    ">**Note:** Two special lines of code that we added earlier won't be required again. As promised, I shall reason that in upcoming lectures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-16T14:14:36.342768Z",
     "start_time": "2021-07-16T14:14:27.134367Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='armed', ylabel='age'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(x=\"armed\", y=\"age\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you would have guessed by now, this plot is known as a **Strip plot** and pretty ideal for categorical values. Even this shall be dealt in length later on.\n",
    "\n",
    "I hope these sample plots have intrigued you enough to dive deeper into statistical visual inference with Seaborn. And in next lecture, we shall learn to **[Control Aesthetics](https://github.com/milaan9/12_Python_Seaborn_Module/blob/main/002_Seaborn_Controlling_Aesthetics.ipynb)** of our plot and few other important aspects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
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   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
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   "types_to_exclude": [
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