P7- Python for Data Descriptive Statistics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4f3ba579",
   "metadata": {},
   "source": [
    "Descriptive statistics are measures that summarize important features of data, often with a single number. Producing descriptive statistics is a common first step to take after cleaning and preparing a data set for analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "715b603e",
   "metadata": {},
   "source": [
    "**Measures of Center**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74c18602",
   "metadata": {},
   "source": [
    "Measures of center are statistics that give us a sense of the \"middle\" of a numeric variable. In other words, centrality measures give you a sense of a typical value you'd expect to see. Common measures of center include the mean, median and mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e175c0ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "af392e1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('mtcars.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4c06a62d",
   "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>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>drat</th>\n",
       "      <th>wt</th>\n",
       "      <th>qsec</th>\n",
       "      <th>vs</th>\n",
       "      <th>am</th>\n",
       "      <th>gear</th>\n",
       "      <th>carb</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mazda RX4</th>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.620</td>\n",
       "      <td>16.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mazda RX4 Wag</th>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.875</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Datsun 710</th>\n",
       "      <td>22.8</td>\n",
       "      <td>4</td>\n",
       "      <td>108.0</td>\n",
       "      <td>93</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.320</td>\n",
       "      <td>18.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hornet 4 Drive</th>\n",
       "      <td>21.4</td>\n",
       "      <td>6</td>\n",
       "      <td>258.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.215</td>\n",
       "      <td>19.44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hornet Sportabout</th>\n",
       "      <td>18.7</td>\n",
       "      <td>8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>175</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.440</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    mpg  cyl   disp   hp  drat     wt   qsec  vs  am  gear  \\\n",
       "model                                                                        \n",
       "Mazda RX4          21.0    6  160.0  110  3.90  2.620  16.46   0   1     4   \n",
       "Mazda RX4 Wag      21.0    6  160.0  110  3.90  2.875  17.02   0   1     4   \n",
       "Datsun 710         22.8    4  108.0   93  3.85  2.320  18.61   1   1     4   \n",
       "Hornet 4 Drive     21.4    6  258.0  110  3.08  3.215  19.44   1   0     3   \n",
       "Hornet Sportabout  18.7    8  360.0  175  3.15  3.440  17.02   0   0     3   \n",
       "\n",
       "                   carb  \n",
       "model                    \n",
       "Mazda RX4             4  \n",
       "Mazda RX4 Wag         4  \n",
       "Datsun 710            1  \n",
       "Hornet 4 Drive        1  \n",
       "Hornet Sportabout     2  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df =  df.rename(columns={'Unnamed: 0': 'model'})\n",
    "df.index = df.model\n",
    "del df[\"model\"]\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6220ff59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg      20.090625\n",
       "cyl       6.187500\n",
       "disp    230.721875\n",
       "hp      146.687500\n",
       "drat      3.596563\n",
       "wt        3.217250\n",
       "qsec     17.848750\n",
       "vs        0.437500\n",
       "am        0.406250\n",
       "gear      3.687500\n",
       "carb      2.812500\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mean of the each column\n",
    "\n",
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6bea745a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model\n",
       "Mazda RX4              29.907273\n",
       "Mazda RX4 Wag          29.981364\n",
       "Datsun 710             23.598182\n",
       "Hornet 4 Drive         38.739545\n",
       "Hornet Sportabout      53.664545\n",
       "Valiant                35.049091\n",
       "Duster 360             59.720000\n",
       "Merc 240D              24.634545\n",
       "Merc 230               27.233636\n",
       "Merc 280               31.860000\n",
       "Merc 280C              31.787273\n",
       "Merc 450SE             46.430909\n",
       "Merc 450SL             46.500000\n",
       "Merc 450SLC            46.350000\n",
       "Cadillac Fleetwood     66.232727\n",
       "Lincoln Continental    66.058545\n",
       "Chrysler Imperial      65.972273\n",
       "Fiat 128               19.440909\n",
       "Honda Civic            17.742273\n",
       "Toyota Corolla         18.814091\n",
       "Toyota Corona          24.888636\n",
       "Dodge Challenger       47.240909\n",
       "AMC Javelin            46.007727\n",
       "Camaro Z28             58.752727\n",
       "Pontiac Firebird       57.379545\n",
       "Fiat X1-9              18.928636\n",
       "Porsche 914-2          24.779091\n",
       "Lotus Europa           24.880273\n",
       "Ford Pantera L         60.971818\n",
       "Ferrari Dino           34.508182\n",
       "Maserati Bora          63.155455\n",
       "Volvo 142E             26.262727\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the mean along the rows \n",
    "df.mean(axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50d953e4",
   "metadata": {},
   "source": [
    "The median of a distribution is the value where 50% of the data lies below it and 50% lies above it. In essence, the median splits the data in half. The median is also known as the 50% percentile since 50% of the observations are found below it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1a978394",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg      19.200\n",
       "cyl       6.000\n",
       "disp    196.300\n",
       "hp      123.000\n",
       "drat      3.695\n",
       "wt        3.325\n",
       "qsec     17.710\n",
       "vs        0.000\n",
       "am        0.000\n",
       "gear      4.000\n",
       "carb      2.000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the median of the each column\n",
    "df.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2d355cbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model\n",
       "Mazda RX4              4.000\n",
       "Mazda RX4 Wag          4.000\n",
       "Datsun 710             4.000\n",
       "Hornet 4 Drive         3.215\n",
       "Hornet Sportabout      3.440\n",
       "Valiant                3.460\n",
       "Duster 360             4.000\n",
       "Merc 240D              4.000\n",
       "Merc 230               4.000\n",
       "Merc 280               4.000\n",
       "Merc 280C              4.000\n",
       "Merc 450SE             4.070\n",
       "Merc 450SL             3.730\n",
       "Merc 450SLC            3.780\n",
       "Cadillac Fleetwood     5.250\n",
       "Lincoln Continental    5.424\n",
       "Chrysler Imperial      5.345\n",
       "Fiat 128               4.000\n",
       "Honda Civic            4.000\n",
       "Toyota Corolla         4.000\n",
       "Toyota Corona          3.700\n",
       "Dodge Challenger       3.520\n",
       "AMC Javelin            3.435\n",
       "Camaro Z28             4.000\n",
       "Pontiac Firebird       3.845\n",
       "Fiat X1-9              4.000\n",
       "Porsche 914-2          4.430\n",
       "Lotus Europa           4.000\n",
       "Ford Pantera L         5.000\n",
       "Ferrari Dino           6.000\n",
       "Maserati Bora          8.000\n",
       "Volvo 142E             4.000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the median of the each row\n",
    "df.median(axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd37b553",
   "metadata": {},
   "source": [
    "Although the mean and median both give us some sense of the center of a distribution, they aren't always the same. The median always gives us a value that splits the data into two halves while the mean is a numeric average so extreme values can have a significant impact on the mean. In a symmetric distribution, the mean and median will be the same. Let's investigate with a density plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fb14d4e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x28e8e001570>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a normal data with the denstiy plot\n",
    "\n",
    "normal_data = pd.DataFrame(np.random.normal(size = 1000000))\n",
    "normal_data.plot(kind = 'density',\n",
    "                figsize = (10,10))\n",
    "\n",
    "# Plot black line at the mean\n",
    "\n",
    "plt.vlines(normal_data.mean(),\n",
    "          ymin = 0,\n",
    "          ymax = 0.4,\n",
    "          linewidth = 5.0,\n",
    "          color = 'black')\n",
    "\n",
    "# Plot red line at the median\n",
    "\n",
    "plt.vlines(normal_data.median(),\n",
    "          ymin = 0,\n",
    "          ymax = 0.4,\n",
    "          linewidth = 2.5,\n",
    "          color = 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0847eff1",
   "metadata": {},
   "source": [
    "* In the plot above the mean and median are both so close to zero that the red median line lies on top of the thicker black line drawn at the mean.\n",
    "\n",
    "* In skewed distributions, the mean tends to get pulled in the direction of the skew, while the median tends to resist the effects of skew:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "649b0b48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x28e91083dc0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the skewed dat using denstiy plot \n",
    "skewed_data = pd.DataFrame(np.random.exponential(size = 100000))\n",
    "skewed_data.plot(kind  = 'density',\n",
    "                figsize = (10,10),\n",
    "                xlim = (-1,5))\n",
    "\n",
    "\n",
    "# plot the mean by black line \n",
    "plt.vlines(skewed_data.mean(),\n",
    "           ymin = 0,\n",
    "           ymax = 0.7,\n",
    "           linewidth = 5.0,\n",
    "           color = 'black'\n",
    "          )\n",
    "\n",
    "# plot the median by red line\n",
    "plt.vlines(skewed_data.median(),\n",
    "           ymin = 0,\n",
    "           ymax = 0.7,\n",
    "           linewidth = 2.5,\n",
    "           color = 'red'\n",
    "          )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcd75349",
   "metadata": {},
   "source": [
    "* The mean is also influenced heavily by outliers, while the median resists the influence of outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "803c4105",
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_data = np.random.normal(size = 50)\n",
    "outlier =np.random.uniform(15,size = 3)\n",
    "\n",
    "combined_data = np.concatenate((norm_data,outlier),axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9d5bdf2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_data = pd.DataFrame(combined_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9cb45758",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.192864</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.339230</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.543664</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <th>6</th>\n",
       "      <td>-0.493143</td>\n",
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       "      <th>9</th>\n",
       "      <td>0.321990</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.661023</td>\n",
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       "      <th>17</th>\n",
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       "      <th>21</th>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.597820</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.792197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-0.715689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.298811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-0.240609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-0.850311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.823733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>-0.904743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>-0.152157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1.333294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>-0.522125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>-0.838234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.191596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.275239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.845066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.543474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>-1.222897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.078508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1.027314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>-1.647578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1.447343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>-0.674599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.341499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.320473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.289404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>-0.389032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>9.786153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2.528102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>14.233517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0\n",
       "0   -0.192864\n",
       "1   -0.339230\n",
       "2   -0.715361\n",
       "3   -1.556008\n",
       "4   -0.543664\n",
       "5    1.271400\n",
       "6   -0.493143\n",
       "7   -1.899731\n",
       "8    0.638197\n",
       "9    0.321990\n",
       "10  -0.053693\n",
       "11   0.560063\n",
       "12  -0.260076\n",
       "13  -1.561454\n",
       "14  -0.352439\n",
       "15  -0.787884\n",
       "16  -0.661023\n",
       "17   0.239231\n",
       "18  -0.552692\n",
       "19   0.420661\n",
       "20   0.079592\n",
       "21   1.898908\n",
       "22   0.509813\n",
       "23   0.346117\n",
       "24  -0.597820\n",
       "25   0.792197\n",
       "26  -0.715689\n",
       "27   0.298811\n",
       "28  -0.240609\n",
       "29  -0.850311\n",
       "30   0.823733\n",
       "31  -0.904743\n",
       "32  -0.152157\n",
       "33   1.333294\n",
       "34  -0.522125\n",
       "35  -0.838234\n",
       "36   0.191596\n",
       "37   0.275239\n",
       "38   0.845066\n",
       "39   0.543474\n",
       "40  -1.222897\n",
       "41   0.078508\n",
       "42   1.027314\n",
       "43  -1.647578\n",
       "44   1.447343\n",
       "45  -0.674599\n",
       "46   0.341499\n",
       "47   0.320473\n",
       "48   0.289404\n",
       "49  -0.389032\n",
       "50   9.786153\n",
       "51   2.528102\n",
       "52  14.233517"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7711da12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x28e914d5660>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the above data \n",
    "\n",
    "combined_data.plot(kind = 'density',\n",
    "                  figsize = (10,10),\n",
    "                  xlim = (-5,20))\n",
    "\n",
    "# plot mean by black line \n",
    "\n",
    "plt.vlines(combined_data.mean(),\n",
    "          ymin = 0,\n",
    "          ymax = 0.25,\n",
    "          linewidth = 5.0,\n",
    "          color = 'black')\n",
    "\n",
    "# plot median by th red line \n",
    "\n",
    "plt.vlines(combined_data.median(),\n",
    "          ymin = 0,\n",
    "          ymax = 0.25,\n",
    "          linewidth = 2.5,\n",
    "          color = 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "650f71f4",
   "metadata": {},
   "source": [
    "Since the median tends to resist the effects of skewness and outliers, it is known a \"robust\" statistic. The median generally gives a better sense of the typical value in a distribution with significant skew or outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "681649b6",
   "metadata": {},
   "source": [
    "**mode**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48c34a64",
   "metadata": {},
   "source": [
    "The mode of a variable is simply the value that appears most frequently. Unlike mean and median, you can take the mode of a categorical variable and it is possible to have multiple modes. Find the mode with df.mode():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "322b3e0e",
   "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>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>drat</th>\n",
       "      <th>wt</th>\n",
       "      <th>qsec</th>\n",
       "      <th>vs</th>\n",
       "      <th>am</th>\n",
       "      <th>gear</th>\n",
       "      <th>carb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>275.8</td>\n",
       "      <td>110.0</td>\n",
       "      <td>3.07</td>\n",
       "      <td>3.44</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175.0</td>\n",
       "      <td>3.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.0</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>3</th>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>22.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cyl   disp     hp  drat    wt   qsec   vs   am  gear  carb\n",
       "0  10.4  8.0  275.8  110.0  3.07  3.44  17.02  0.0  0.0   3.0   2.0\n",
       "1  15.2  NaN    NaN  175.0  3.92   NaN  18.90  NaN  NaN   NaN   4.0\n",
       "2  19.2  NaN    NaN  180.0   NaN   NaN    NaN  NaN  NaN   NaN   NaN\n",
       "3  21.0  NaN    NaN    NaN   NaN   NaN    NaN  NaN  NaN   NaN   NaN\n",
       "4  21.4  NaN    NaN    NaN   NaN   NaN    NaN  NaN  NaN   NaN   NaN\n",
       "5  22.8  NaN    NaN    NaN   NaN   NaN    NaN  NaN  NaN   NaN   NaN\n",
       "6  30.4  NaN    NaN    NaN   NaN   NaN    NaN  NaN  NaN   NaN   NaN"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mode()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d34e9184",
   "metadata": {},
   "source": [
    "* The columns with multiple modes (multiple values with the same count) return multiple values as the mode. Columns with no mode (no value that appears more than once) return NaN."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf154fdc",
   "metadata": {},
   "source": [
    "**Measures of Spread**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0a7f6e9",
   "metadata": {},
   "source": [
    "Measures of spread (dispersion) are statistics that describe how data varies. While measures of center give us an idea of the typical value, measures of spread give us a sense of how much the data tends to diverge from the typical value.\n",
    "\n",
    "One of the simplest measures of spread is the range. Range is the distance between the maximum and minimum observations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b9d39415",
   "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>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>drat</th>\n",
       "      <th>wt</th>\n",
       "      <th>qsec</th>\n",
       "      <th>vs</th>\n",
       "      <th>am</th>\n",
       "      <th>gear</th>\n",
       "      <th>carb</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mazda RX4</th>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.620</td>\n",
       "      <td>16.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mazda RX4 Wag</th>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.875</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                mpg  cyl   disp   hp  drat     wt   qsec  vs  am  gear  carb\n",
       "model                                                                       \n",
       "Mazda RX4      21.0    6  160.0  110   3.9  2.620  16.46   0   1     4     4\n",
       "Mazda RX4 Wag  21.0    6  160.0  110   3.9  2.875  17.02   0   1     4     4"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "01b7f632",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.5"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(df['mpg']) - min(df['mpg'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5da9399",
   "metadata": {},
   "source": [
    "The median represents the 50th percentile of a data set. A summary of several percentiles can be used to describe a variable's spread. We can extract the minimum value (0th percentile), first quartile (25th percentile), median, third quartile(75th percentile) and maximum value (100th percentile) using the quantile() function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e9cf3ddb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10.4, 15.425, 19.2, 22.8, 33.9]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "five_num = [df['mpg'].quantile(0),\n",
    "           df['mpg'].quantile(0.25),\n",
    "           df['mpg'].quantile(0.50),\n",
    "           df['mpg'].quantile(0.75),\n",
    "           df['mpg'].quantile(1.00)]\n",
    "five_num"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43b8050e",
   "metadata": {},
   "source": [
    "Since these values are so commonly used to describe data, they are known as the \"five number summary\". They are the same percentile values returned by df.describe():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "7630e20d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    32.000000\n",
       "mean     20.090625\n",
       "std       6.026948\n",
       "min      10.400000\n",
       "25%      15.425000\n",
       "50%      19.200000\n",
       "75%      22.800000\n",
       "max      33.900000\n",
       "Name: mpg, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mpg'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb8ad870",
   "metadata": {},
   "source": [
    "Interquartile (IQR) range is another common measure of spread. IQR is the distance between the 3rd quartile and the 1st quartile:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a0f6817d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.375"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mpg'].quantile(0.75) - df['mpg'].quantile(0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "15420e1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.65, 19.2, 'IQR')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column = 'mpg',\n",
    "          return_type = 'axes',\n",
    "          figsize = (8,8))\n",
    "\n",
    "plt.text(x = 0.85 , y= 10.4, s ='min')\n",
    "plt.text(x = 0.75 , y= 15.5, s = '1st Quartile')\n",
    "plt.text(x = 0.75 , y= 19.2, s = 'median')\n",
    "plt.text(x = 0.75 , y= 22.8, s = '3rd Quartile')\n",
    "plt.text(x = 0.85 , y= 33.9, s = 'max')\n",
    "plt.text(x = 0.65 , y= 19.2, s = 'IQR', rotation = 90, size = 25)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd57fe87",
   "metadata": {},
   "source": [
    "Variance and standard deviation are two other common measures of spread. The variance of a distribution is the average of the squared deviations (differences) from the mean. Use df.var() to check variance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "fafe2175",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36.32410282258064"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mpg'].var()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ce9120b",
   "metadata": {},
   "source": [
    "The standard deviation is the square root of the variance. Standard deviation can be more interpretable than variance, since the standard deviation is expressed in terms of the same units as the variable in question while variance is expressed in terms of units squared. Use df.std() to check the standard deviation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "21128153",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.026948052089104"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mpg'].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "948b8576",
   "metadata": {},
   "source": [
    "Since variance and standard deviation are both derived from the mean, they are susceptible to the influence of data skew and outliers. Median absolute deviation is an alternative measure of spread based on the median, which inherits the median's robustness against the influence of skew and outliers. It is the median of the absolute value of the deviations from the median:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "24b5d7e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.411490000000001"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs_median_deviation = abs(df['mpg']-df['mpg'].median())\n",
    "\n",
    "(abs_median_deviation.median() *1.4826) #Median_Absolute_deviation(MAD) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8aac191",
   "metadata": {},
   "source": [
    "Note: The MAD is often multiplied by a scaling factor of 1.4826."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "512fa631",
   "metadata": {},
   "source": [
    "**Skewness and Kurtosis**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9db02e78",
   "metadata": {},
   "source": [
    "Beyond measures of center and spread, descriptive statistics include measures that give you a sense of the shape of a distribution. Skewness measures the skew or asymmetry of a distribution while kurtosis measures how much data is in the tails of a distribution v.s. the center. We won't go into the exact calculations behind skewness and kurtosis, but they are essentially just statistics that take the idea of variance a step further: while variance involves squaring deviations from the mean, skewness involves cubing deviations from the mean and kurtosis involves raising deviations from the mean to the 4th power.\n",
    "\n",
    "Pandas has built in functions for checking skewness and kurtosis, df.skew() and df.kurt() respectively:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "d07424e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6723771376290805"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mpg'].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "afefdace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0220062914240855"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mpg'].kurt()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d34bb9ec",
   "metadata": {},
   "source": [
    "To explore these two measures further, let's create some dummy data and inspect it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "72fdecbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_data = np.random.normal(size=100000)\n",
    "skewed_data = np.concatenate((np.random.normal(size=35000)+2, \n",
    "                             np.random.exponential(size=65000)), \n",
    "                             axis=0)\n",
    "uniform_data = np.random.uniform(0,2, size=100000)\n",
    "peaked_data = np.concatenate((np.random.exponential(size=50000),\n",
    "                             np.random.exponential(size=50000)*(-1)),\n",
    "                             axis=0)\n",
    "\n",
    "data_df = pd.DataFrame({\"norm\":norm_data,\n",
    "                       \"skewed\":skewed_data,\n",
    "                       \"uniform\":uniform_data,\n",
    "                       \"peaked\":peaked_data})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "0f4ab38b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_df.plot(kind=\"density\",\n",
    "            figsize=(10,10),\n",
    "            xlim=(-5,5));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ada3637f",
   "metadata": {},
   "source": [
    "* Now let's check the skewness of each of the distributions. Since skewness measures asymmetry, we'd expect to see low skewness for all of the distributions except the skewed one, because all the others are roughly symmetric:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "8638b27c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "norm       0.008725\n",
       "skewed     1.018160\n",
       "uniform   -0.002077\n",
       "peaked    -0.026025\n",
       "dtype: float64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "16ac59b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "norm      -0.001046\n",
       "skewed     1.455543\n",
       "uniform   -1.197730\n",
       "peaked     3.076886\n",
       "dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.kurt()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f911f52d",
   "metadata": {},
   "source": [
    "* As we can see from the output, the normally distributed data has a kurtosis near zero, the flat distribution has negative kurtosis and the two distributions with more data in the tails vs the center have higher kurtosis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12577bf0",
   "metadata": {},
   "source": [
    "* Descriptive statistics help us to explore features of our data, like center, spread and shape by summarizing them with numerical measurements. \n",
    "\n",
    "* Descriptive statistics help inform the direction of an analysis and let us communicate our insights to others quickly and succinctly. In addition, certain values, like the mean and variance, are used in all sorts of statistical tests and predictive models."
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