Uni-Variate_and_Bi-Variate_Analysis_Various_Techniques.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A2nHwAAAA2oh9GADoJl7VAQC14+ABAAC0EfswANBNvKoDAGrHwQMAAGgj9mEAoJt4VQcA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\nt9xyS1q7dm064YQT0qGHHtrJvO51rxv8vV/4whcWx3cx+ntedNFFg7/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XrpcS73fUc+Fx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\ne85LdHOlq5PlA0XeUx2zfUov+WCQBwDF07XjKL5Qn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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename=\"Downloads/EDA_Diagram.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Univariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('iris.csv')\n",
    "df.shape\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</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>Iris-setosa</th>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "Species                                                                      \n",
       "Iris-setosa      50             50            50             50            50\n",
       "Iris-versicolor  50             50            50             50            50\n",
       "Iris-virginica   50             50            50             50            50"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Species']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Species</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Species  Count\n",
       "0      Iris-setosa     50\n",
       "1  Iris-versicolor     50\n",
       "2   Iris-virginica     50"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_table = df.groupby(['Species']).size().reset_index(name='Count').rename(columns={'Sepcies':'Species'})\n",
    "freq_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ufv1DdE+Sw4CXjfwJOAANMydt6CeAtzG4XHVL6/sS8MfTrp2eMOwx9hoyCbw8yW+18ata\n/9UMPv4C4NXAV6dv1P7S+uG0a8evAa7kILHAudufrzI4myfJ84Ej51niNcBzkhzX9rVnXg9n8EsI\nBpd+DmgLulO0cycBb03yS2A3sPeZGgzOIs5sY/4beFdV/SDJXzD4B9tDgF8Crwe+w+DOtpuTfL2q\nXp3kAuDatq8PV9UNSX4X+Nskv2rb/mFV/SjJeQzO2ncw+PP1YHQSs88JwKeAv2dwdrXHu4EPMHj+\nw+B5fOFcj1FVtyX5K+DKJPcDNzB4of8JcH6StwJTwOtm2PdG4F+SPAK4cx9jenUS85+7/Xkn8PEM\n3jxwJbATmOlyzn5V1VQGnwz7mfa63QWcCvwNMJHkTcBX5rrfB9tB+bZFSX1I8hvA/TX4bKlnAP9c\nVccvdV1LxTN0ScvZMcDF7az6F8AfLHE9S8ozdEnqhP8UlaROGOiS1AkDXZI6YaBLUicMdEnqhIEu\nSZ34P+U7PO+RhOFFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(freq_table['Species'], freq_table['Count'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Species</th>\n",
       "      <th>Count</th>\n",
       "      <th>Count%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>50</td>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>50</td>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>50</td>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Species  Count     Count%\n",
       "0      Iris-setosa     50  33.333333\n",
       "1  Iris-versicolor     50  33.333333\n",
       "2   Iris-virginica     50  33.333333"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_table['Count%'] = freq_table['Count']/sum(freq_table['Count'])*100\n",
    "freq_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5.5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>6.4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>6.7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6.9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>7.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>7.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>7.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>7.7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>7.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SepalLengthCm  Count\n",
       "0             4.3      1\n",
       "1             4.4      3\n",
       "2             4.5      1\n",
       "3             4.6      4\n",
       "4             4.7      2\n",
       "5             4.8      5\n",
       "6             4.9      6\n",
       "7             5.0     10\n",
       "8             5.1      9\n",
       "9             5.2      4\n",
       "10            5.3      1\n",
       "11            5.4      6\n",
       "12            5.5      7\n",
       "13            5.6      6\n",
       "14            5.7      8\n",
       "15            5.8      7\n",
       "16            5.9      3\n",
       "17            6.0      6\n",
       "18            6.1      6\n",
       "19            6.2      4\n",
       "20            6.3      9\n",
       "21            6.4      7\n",
       "22            6.5      5\n",
       "23            6.6      2\n",
       "24            6.7      8\n",
       "25            6.8      3\n",
       "26            6.9      4\n",
       "27            7.0      1\n",
       "28            7.1      1\n",
       "29            7.2      3\n",
       "30            7.3      1\n",
       "31            7.4      1\n",
       "32            7.6      1\n",
       "33            7.7      4\n",
       "34            7.9      1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['SepalLengthCm']).size().reset_index(name='Count').rename(columns={'SepalLengthCm':'SepalLengthCm'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Zktd2d1fVO9fYD3CQEMKAuZiCxhuSvGEKFI/JLKBc1t0nrLXZKu9/I8nru/uH\nq2rHtM/rqtbp90vT76/m638X6zrs/0vLXn81yTecjtzgtl9b9v5r8TcaDmpORwL7XVUdP81c7XHn\nJB9K8p4kS9OF+6mqG1bVHZa1+7Fp+X2SXN3dVyf55iQfndY/dh9LWq/f1fxzkkdM7W+f5LuXrfvK\ndIrzunhtkidM+zukqo64jtsDBxkhDJiHm2Z2CvFd02m82yf59e7+cpKTkzyrqt6R5OIkJy7b7j+q\n6s1JXpDk8dOy303yzKr6l8xOI27ESVW1e89PZtdz7a3f1Twvs+B2SZL/neSSJFdP605PcsmyC/M3\n4klJ7jfNCl6YZL0QCBzkqnvl7D/A4lXVG5I8pbsvGF1LMputSnLD7v7PqrpNZjNZt5uCJMD15noD\ngNUdluT102nHSvIEAQzYn8yEAQAM4JowAIABhDAAgAGEMACAAYQwAIABhDAAgAH+H7sIinfBqp5n\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,7))\n",
    "x = df[\"SepalLengthCm\"]\n",
    "plt.hist(x, bins=20, color=\"green\")\n",
    "plt.title(\"Sepal Length in cm\")\n",
    "plt.xlabel(\"Sepal Length cm\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "0            5.1           3.5            1.4           0.2\n",
       "1            4.9           3.0            1.4           0.2\n",
       "2            4.7           3.2            1.3           0.2\n",
       "3            4.6           3.1            1.5           0.2\n",
       "4            5.0           3.6            1.4           0.2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df[[\"SepalLengthCm\",\"SepalWidthCm\", \"PetalLengthCm\",\"PetalWidthCm\"]]\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9abc6c9e8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lOy4apV2AWSc0wRmMeWLLZBrYxq4BgEezew4AoIPQBADQQWgCAOggNAEAdBCaAAA6CE0A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\nGhu7CUMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,7))\n",
    "new_df.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.8"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"SepalLengthCm\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0999999999999996"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(df.SepalLengthCm, 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.4000000000000004"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(df.SepalLengthCm, 75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Binning or Discretization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['[4 - 5.0)', '[5 - 6.0)', '[6 - 7.0)', '[7 - 8.0)']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sepal_length_ranges = [\"[{0} - {1})\".format(SepalLengthCm, SepalLengthCm + 1.0) for SepalLengthCm in range(4, 8, 1)]\n",
    "sepal_length_ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_sepal_length_ranges = len(sepal_length_ranges)\n",
    "count_sepal_length_ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['SepalLengthCm_Range'] = pd.cut(x=df['SepalLengthCm'], bins=count_sepal_length_ranges, labels=sepal_length_ranges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "      <th>SepalLengthCm_Range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>[4 - 5.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>[4 - 5.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>[4 - 5.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>[4 - 5.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>[4 - 5.0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species  \\\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa   \n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa   \n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa   \n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa   \n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa   \n",
       "\n",
       "  SepalLengthCm_Range  \n",
       "0           [4 - 5.0)  \n",
       "1           [4 - 5.0)  \n",
       "2           [4 - 5.0)  \n",
       "3           [4 - 5.0)  \n",
       "4           [4 - 5.0)  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5 - 6.0)    50\n",
       "[4 - 5.0)    45\n",
       "[6 - 7.0)    43\n",
       "[7 - 8.0)    12\n",
       "Name: SepalLengthCm_Range, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sepal_len_hist_df = df['SepalLengthCm_Range'].value_counts()\n",
    "sepal_len_hist_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm_Range</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[5 - 6.0)</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[4 - 5.0)</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[6 - 7.0)</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[7 - 8.0)</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  SepalLengthCm_Range  Count\n",
       "0           [5 - 6.0)     50\n",
       "1           [4 - 5.0)     45\n",
       "2           [6 - 7.0)     43\n",
       "3           [7 - 8.0)     12"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_range_hist = pd.DataFrame(sepal_len_hist_df).reset_index()\n",
    "df_range_hist.columns = ['SepalLengthCm_Range', 'Count']\n",
    "df_range_hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PIm8F9m829m829m8Kbc/QJel80/kMXZLOKwa6JDVhoEtSEwa6JDVhoEtSE/8Pu8Lk0eSW\nt/0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(df_range_hist[\"SepalLengthCm_Range\"], df_range_hist[\"Count\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bi-Variate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical-Numerical Variable Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x13553402d30>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9zKRj2LlNJYNgzOwvgSeByQQPxO8NfMXdH47oY8Be7r4tLI/zIHBJcR8zOxP4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\nRFJJCVBERFLp/wP1Ejb2vXqTnAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 463.5x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatter Plot\n",
    "sns.lmplot(x='SepalLengthCm', y='SepalWidthCm', hue='Species', fit_reg=False, data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x135534f1550>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G8DvG2Ec55wk535zzhwE8DABz587lhbzP9j4fTqq1F+z8TdVCgO3Dfp+xcz4g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\nkSwbISpHVQfnPYfHgnMr09qVwuHvZaj9xLRvtbjLl9beGzpMrc2NQ0/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HFrKiV4pHyPqDaZ+vz6levZ+KeNbO7azJzaOdy04iYe2fYIa49fCxB/XdKMRT6FjKlNribWHr+W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OwLZuOU3FOQqcuEy+MKSiinl8TxDFoD7v/k/qBkcdtt2/Hcl88F7S/LMg63HKbhx2koMLaG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\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7zuqdeeUOmPmj1DSTiumhICQjVrOTToPjro52aHeuEgDxmkukQdGnUCj68y52i+tWN/el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\nCCHk7P8BaAdGhxDIpMgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1003.5x900 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatter Plot Matrix\n",
    "sns.pairplot(hue='Species', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.716676</td>\n",
       "      <td>-0.397729</td>\n",
       "      <td>0.882747</td>\n",
       "      <td>0.899759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <td>0.716676</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109369</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <td>-0.397729</td>\n",
       "      <td>-0.109369</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>-0.356544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <td>0.882747</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <td>0.899759</td>\n",
       "      <td>0.817954</td>\n",
       "      <td>-0.356544</td>\n",
       "      <td>0.962757</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  \\\n",
       "Id             1.000000       0.716676     -0.397729       0.882747   \n",
       "SepalLengthCm  0.716676       1.000000     -0.109369       0.871754   \n",
       "SepalWidthCm  -0.397729      -0.109369      1.000000      -0.420516   \n",
       "PetalLengthCm  0.882747       0.871754     -0.420516       1.000000   \n",
       "PetalWidthCm   0.899759       0.817954     -0.356544       0.962757   \n",
       "\n",
       "               PetalWidthCm  \n",
       "Id                 0.899759  \n",
       "SepalLengthCm      0.817954  \n",
       "SepalWidthCm      -0.356544  \n",
       "PetalLengthCm      0.962757  \n",
       "PetalWidthCm       1.000000  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Correlation \n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical-Categorical Variable Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13553406358>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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E9inRzMxKUTTQI+J+QNu5e3RlyzEzs7bySlEzs0Q40M3MEuFANzNLhAPdzCwRDnQzs0Q4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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2 = pd.read_csv('iris_with_colors.csv')\n",
    "df_plot = df2.groupby(['Color', 'Species']).size().reset_index().pivot(columns='Color', index='Species', values=0)\n",
    "df_plot.plot(x=df_plot.index, kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Color</th>\n",
       "      <th>Blue</th>\n",
       "      <th>Green</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Iris-setosa</th>\n",
       "      <td>34</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <td>32</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>12</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Color            Blue  Green\n",
       "Species                     \n",
       "Iris-setosa        34     16\n",
       "Iris-versicolor    32     18\n",
       "Iris-virginica     12     38"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df2.Species,df2.Color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Species</th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>25.5</td>\n",
       "      <td>5.006</td>\n",
       "      <td>3.418</td>\n",
       "      <td>1.464</td>\n",
       "      <td>0.244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>75.5</td>\n",
       "      <td>5.936</td>\n",
       "      <td>2.770</td>\n",
       "      <td>4.260</td>\n",
       "      <td>1.326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>125.5</td>\n",
       "      <td>6.588</td>\n",
       "      <td>2.974</td>\n",
       "      <td>5.552</td>\n",
       "      <td>2.026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Species     Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  \\\n",
       "0      Iris-setosa   25.5          5.006         3.418          1.464   \n",
       "1  Iris-versicolor   75.5          5.936         2.770          4.260   \n",
       "2   Iris-virginica  125.5          6.588         2.974          5.552   \n",
       "\n",
       "   PetalWidthCm  \n",
       "0         0.244  \n",
       "1         1.326  \n",
       "2         2.026  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mean = df.groupby(['Species']).mean().reset_index()\n",
    "df_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13555943f98>]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FZhZrZm3c/dsI1ytSqW3Ymc1Tc9J4b/m3xNavxS9/0JMbBnfQkNJS6YWzZ9AZ2AG8YmZ9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\ngP8D8BbNmu5OVysAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_mean['Species'], df_mean['SepalLengthCm'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-22.370745708143382"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mean = df.groupby(['Species']).mean().reset_index()\n",
    "df_var = df.groupby(['Species']).var().reset_index()\n",
    "z = (5.006 - 5.936)/np.sqrt(np.square(0.1242)/50 + np.square(0.266433)/50)\n",
    "z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical-Categorical Variable Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_Species</th>\n",
       "      <th>mean_SepalLengthCm</th>\n",
       "      <th>mean_SepalWidthCm</th>\n",
       "      <th>mean_PetalLengthCm</th>\n",
       "      <th>mean_PetalWidthCm</th>\n",
       "      <th>var_SepalLengthCm</th>\n",
       "      <th>var_SepalWidthCm</th>\n",
       "      <th>var_PetalLengthCm</th>\n",
       "      <th>var_PetalWidthCm</th>\n",
       "      <th>count_SepalLengthCm</th>\n",
       "      <th>count_SepalWidthCm</th>\n",
       "      <th>count_PetalLengthCm</th>\n",
       "      <th>count_PetalWidthCm</th>\n",
       "      <th>count_SepalLengthCm_Range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.006</td>\n",
       "      <td>3.418</td>\n",
       "      <td>1.464</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.124249</td>\n",
       "      <td>0.145180</td>\n",
       "      <td>0.030106</td>\n",
       "      <td>0.011494</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>5.936</td>\n",
       "      <td>2.770</td>\n",
       "      <td>4.260</td>\n",
       "      <td>1.326</td>\n",
       "      <td>0.266433</td>\n",
       "      <td>0.098469</td>\n",
       "      <td>0.220816</td>\n",
       "      <td>0.039106</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>6.588</td>\n",
       "      <td>2.974</td>\n",
       "      <td>5.552</td>\n",
       "      <td>2.026</td>\n",
       "      <td>0.404343</td>\n",
       "      <td>0.104004</td>\n",
       "      <td>0.304588</td>\n",
       "      <td>0.075433</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mean_Species  mean_SepalLengthCm  mean_SepalWidthCm  mean_PetalLengthCm  \\\n",
       "0      Iris-setosa               5.006              3.418               1.464   \n",
       "1  Iris-versicolor               5.936              2.770               4.260   \n",
       "2   Iris-virginica               6.588              2.974               5.552   \n",
       "\n",
       "   mean_PetalWidthCm  var_SepalLengthCm  var_SepalWidthCm  var_PetalLengthCm  \\\n",
       "0              0.244           0.124249          0.145180           0.030106   \n",
       "1              1.326           0.266433          0.098469           0.220816   \n",
       "2              2.026           0.404343          0.104004           0.304588   \n",
       "\n",
       "   var_PetalWidthCm  count_SepalLengthCm  count_SepalWidthCm  \\\n",
       "0          0.011494                   50                  50   \n",
       "1          0.039106                   50                  50   \n",
       "2          0.075433                   50                  50   \n",
       "\n",
       "   count_PetalLengthCm  count_PetalWidthCm  count_SepalLengthCm_Range  \n",
       "0                   50                  50                         50  \n",
       "1                   50                  50                         50  \n",
       "2                   50                  50                         50  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mean = df.groupby(['Species']).mean().reset_index().drop(['Id'], axis=1).add_prefix('mean_')\n",
    "df_var = df.groupby(['Species']).var().reset_index().drop(['Id','Species'], axis=1).add_prefix('var_')\n",
    "df_count = df.groupby(['Species']).count().reset_index().drop(['Id', 'Species'], axis=1).add_prefix('count_')\n",
    "pd.concat([df_mean, df_var, df_count], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.63212149999999989"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k=3\n",
    "N = 50+50+50\n",
    "Numerator = np.sum([np.square(5.006-5.843)/(k-1), np.square(5.936-5.843)/(k-1), np.square(6.588-5.843)/(k-1)])\n",
    "Numerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26500816326530613"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Setosa = np.sum(np.square(df[df['Species'] == 'Iris-setosa'].SepalLengthCm - 5.006)/(N-k))\n",
    "Versicolor = np.sum(np.square(df[df['Species'] == 'Iris-versicolor'].SepalLengthCm - 5.936)/(N-k))\n",
    "Verginica = np.sum(np.square(df[df['Species'] == 'Iris-virginica'].SepalLengthCm - 6.588)/(N-k))\n",
    "Denominator = Setosa+Versicolor+Verginica\n",
    "Denominator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3852906726015366"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F = Numerator/Denominator\n",
    "F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Link to Calculate F-Statistics\n",
    "http://onlinestatbook.com/2/calculators/F_dist.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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