Exploratory Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "cars_data=pd.read_csv(\"toyota.csv\",index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>age</th>\n",
       "      <th>km</th>\n",
       "      <th>fudtype</th>\n",
       "      <th>hp</th>\n",
       "      <th>metcolor</th>\n",
       "      <th>automatic</th>\n",
       "      <th>cc</th>\n",
       "      <th>doors</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13500</td>\n",
       "      <td>23</td>\n",
       "      <td>46986</td>\n",
       "      <td>diesel</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>three</td>\n",
       "      <td>1165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13750</td>\n",
       "      <td>23</td>\n",
       "      <td>72937</td>\n",
       "      <td>diesel</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>3</td>\n",
       "      <td>1165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13950</td>\n",
       "      <td>24</td>\n",
       "      <td>41711</td>\n",
       "      <td>diesel</td>\n",
       "      <td>90</td>\n",
       "      <td>??</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>3</td>\n",
       "      <td>1165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14950</td>\n",
       "      <td>26</td>\n",
       "      <td>48000</td>\n",
       "      <td>diesel</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>3</td>\n",
       "      <td>1165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       price  age     km fudtype  hp metcolor  automatic    cc  doors  weight\n",
       "index                                                                        \n",
       "0      13500   23  46986  diesel  90        1          0  2000  three    1165\n",
       "1      13750   23  72937  diesel  90        1          0  2000      3    1165\n",
       "2      13950   24  41711  diesel  90       ??          0  2000      3    1165\n",
       "3      14950   26  48000  diesel  90        0          0  2000      3    1165"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cars_data=pd.read_csv(\"toyota.csv\",index_col=0,na_values=[\"??\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>age</th>\n",
       "      <th>km</th>\n",
       "      <th>fudtype</th>\n",
       "      <th>hp</th>\n",
       "      <th>metcolor</th>\n",
       "      <th>automatic</th>\n",
       "      <th>cc</th>\n",
       "      <th>doors</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>13500</td>\n",
       "      <td>23</td>\n",
       "      <td>46986</td>\n",
       "      <td>diesel</td>\n",
       "      <td>90</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>three</td>\n",
       "      <td>1165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13750</td>\n",
       "      <td>23</td>\n",
       "      <td>72937</td>\n",
       "      <td>diesel</td>\n",
       "      <td>90</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>3</td>\n",
       "      <td>1165</td>\n",
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       "      <th>2</th>\n",
       "      <td>13950</td>\n",
       "      <td>24</td>\n",
       "      <td>41711</td>\n",
       "      <td>diesel</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>3</td>\n",
       "      <td>1165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14950</td>\n",
       "      <td>26</td>\n",
       "      <td>48000</td>\n",
       "      <td>diesel</td>\n",
       "      <td>90</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>3</td>\n",
       "      <td>1165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       price  age     km fudtype  hp  metcolor  automatic    cc  doors  weight\n",
       "index                                                                         \n",
       "0      13500   23  46986  diesel  90       1.0          0  2000  three    1165\n",
       "1      13750   23  72937  diesel  90       1.0          0  2000      3    1165\n",
       "2      13950   24  41711  diesel  90       NaN          0  2000      3    1165\n",
       "3      14950   26  48000  diesel  90       0.0          0  2000      3    1165"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cars_data2=cars_data.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Frequency Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fudtype</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>diesel</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0    count\n",
       "fudtype       \n",
       "diesel       4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " pd.crosstab(index=cars_data2[\"fudtype\"],columns='count',dropna=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two way Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>fudtype</th>\n",
       "      <th>diesel</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>automatic</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
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      "text/plain": [
       "fudtype    diesel\n",
       "automatic        \n",
       "0               4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=cars_data2['automatic'],columns=cars_data2['fudtype']\n",
    "           ,dropna=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two way Table joint probability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# probability that a car has automatic gear box and fuel type diesel is 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>fudtype</th>\n",
       "      <th>diesel</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>automatic</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "fudtype    diesel\n",
       "automatic        \n",
       "0             1.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=cars_data2['automatic'],columns=cars_data2['fudtype']\n",
    "           ,normalize=True,dropna=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two way Table marginal probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>fudtype</th>\n",
       "      <th>diesel</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>automatic</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "fudtype    diesel  All\n",
       "automatic             \n",
       "0             1.0  1.0\n",
       "All           1.0  1.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=cars_data2['automatic'],columns=cars_data2['fudtype']\n",
    "           ,margins=True,normalize=True,dropna=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two way Table conditional probability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# If the gear box is automatic then probability that fuel type will be diesel is 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>fudtype</th>\n",
       "      <th>diesel</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>automatic</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "fudtype    diesel\n",
       "automatic        \n",
       "0             1.0\n",
       "All           1.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=cars_data2['automatic'],columns=cars_data2['fudtype']\n",
    "           ,margins=True,normalize='index',dropna=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# If the fuel type is diesel then probability that gear box will be automatic is 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>fudtype</th>\n",
       "      <th>diesel</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>automatic</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "fudtype    diesel  All\n",
       "automatic             \n",
       "0             1.0  1.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=cars_data2['automatic'],columns=cars_data2['fudtype']\n",
    "           ,margins=True,normalize='columns',dropna=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_data=cars_data2.select_dtypes(exclude=[object])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>age</th>\n",
       "      <th>km</th>\n",
       "      <th>hp</th>\n",
       "      <th>metcolor</th>\n",
       "      <th>automatic</th>\n",
       "      <th>cc</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</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>0</th>\n",
       "      <td>13500</td>\n",
       "      <td>23</td>\n",
       "      <td>46986</td>\n",
       "      <td>90</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>1165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13750</td>\n",
       "      <td>23</td>\n",
       "      <td>72937</td>\n",
       "      <td>90</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>1165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13950</td>\n",
       "      <td>24</td>\n",
       "      <td>41711</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>1165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14950</td>\n",
       "      <td>26</td>\n",
       "      <td>48000</td>\n",
       "      <td>90</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>1165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       price  age     km  hp  metcolor  automatic    cc  weight\n",
       "index                                                          \n",
       "0      13500   23  46986  90       1.0          0  2000    1165\n",
       "1      13750   23  72937  90       1.0          0  2000    1165\n",
       "2      13950   24  41711  90       NaN          0  2000    1165\n",
       "3      14950   26  48000  90       0.0          0  2000    1165"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix=num_data.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>hp</th>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metcolor</th>\n",
       "      <td>-0.986912</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.469831</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "             price       age        km  hp  metcolor  automatic  cc  weight\n",
       "price     1.000000  0.982746 -0.228185 NaN -0.986912        NaN NaN     NaN\n",
       "age       0.982746  1.000000 -0.403902 NaN -1.000000        NaN NaN     NaN\n",
       "km       -0.228185 -0.403902  1.000000 NaN  0.469831        NaN NaN     NaN\n",
       "hp             NaN       NaN       NaN NaN       NaN        NaN NaN     NaN\n",
       "metcolor -0.986912 -1.000000  0.469831 NaN  1.000000        NaN NaN     NaN"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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