Seaborn - Loading Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seaborn | Part-1: Loading Datasets:"
   ]
  },
  {
   "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": {},
   "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": {},
   "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": {},
   "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": {},
   "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",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <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": {},
   "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29a6eea4668>"
      ]
     },
     "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": {},
     "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading Pandas DataFrame:\n",
    "df = pd.read_csv(\"C:/Users/Alok/Downloads/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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 11,
     "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. Latly, `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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 12,
     "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",
    "Quick 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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29a72b2d128>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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 of our plot and few other important aspects."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}