steliosNB.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HomeWork 1 Due at Mid-night 9/16/2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Late Policy: Take off 50% after one day, 80% after two days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Make sure you have pandasql installed. If not, make sure you run the code in the following cell**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "try:\n",
    "    import pandasql as ps\n",
    "except:\n",
    "    print(\"Failed in import pandasql\")\n",
    "    os.system(\"pip install pandasql\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import all standard libaries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pandasql as ps\n",
    "from pandasql import sqldf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading MPG dataset ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(400, 9)\n"
     ]
    },
    {
     "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>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "0  18.0          8         307.0       130.0    3504          12.0   \n",
       "1  15.0          8         350.0       165.0    3693          11.5   \n",
       "2  18.0          8         318.0       150.0    3436          11.0   \n",
       "3  16.0          8         304.0       150.0    3433          12.0   \n",
       "4  17.0          8         302.0       140.0    3449          10.5   \n",
       "\n",
       "   model_year origin                       name  \n",
       "0          70    usa  chevrolet chevelle malibu  \n",
       "1          70    usa          buick skylark 320  \n",
       "2          70    usa         plymouth satellite  \n",
       "3          70    usa              amc rebel sst  \n",
       "4          70    usa                ford torino  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg = pd.read_csv(\"mpg2.csv\")\n",
    "print(mpg.shape)\n",
    "mpg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1. Check if there are any null or NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>113.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2228</td>\n",
       "      <td>14.0</td>\n",
       "      <td>71</td>\n",
       "      <td>japan</td>\n",
       "      <td>toyota corona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>25.0</td>\n",
       "      <td>4</td>\n",
       "      <td>98.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2046</td>\n",
       "      <td>19.0</td>\n",
       "      <td>71</td>\n",
       "      <td>usa</td>\n",
       "      <td>ford pinto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2875</td>\n",
       "      <td>17.0</td>\n",
       "      <td>74</td>\n",
       "      <td>usa</td>\n",
       "      <td>ford maverick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>40.9</td>\n",
       "      <td>4</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1835</td>\n",
       "      <td>17.3</td>\n",
       "      <td>80</td>\n",
       "      <td>europe</td>\n",
       "      <td>renault lecar deluxe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>23.6</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2905</td>\n",
       "      <td>14.3</td>\n",
       "      <td>80</td>\n",
       "      <td>usa</td>\n",
       "      <td>ford mustang cobra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>34.5</td>\n",
       "      <td>4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2320</td>\n",
       "      <td>15.8</td>\n",
       "      <td>81</td>\n",
       "      <td>europe</td>\n",
       "      <td>renault 18i</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>23.0</td>\n",
       "      <td>4</td>\n",
       "      <td>151.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3035</td>\n",
       "      <td>20.5</td>\n",
       "      <td>82</td>\n",
       "      <td>usa</td>\n",
       "      <td>amc concord dl</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "33    NaN          4         113.0        95.0    2228          14.0   \n",
       "34   25.0          4          98.0         NaN    2046          19.0   \n",
       "128  21.0          6         200.0         NaN    2875          17.0   \n",
       "332  40.9          4          85.0         NaN    1835          17.3   \n",
       "338  23.6          4         140.0         NaN    2905          14.3   \n",
       "356  34.5          4         100.0         NaN    2320          15.8   \n",
       "376  23.0          4         151.0         NaN    3035          20.5   \n",
       "\n",
       "     model_year  origin                  name  \n",
       "33           71   japan         toyota corona  \n",
       "34           71     usa            ford pinto  \n",
       "128          74     usa         ford maverick  \n",
       "332          80  europe  renault lecar deluxe  \n",
       "338          80     usa    ford mustang cobra  \n",
       "356          81  europe           renault 18i  \n",
       "376          82     usa        amc concord dl  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for null values\n",
    "mpg.loc[mpg.isnull().any(1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2. Remove any rows that has null or NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop null values\n",
    "mpg = mpg.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3. Check if there is any outliers in the mpg field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19565770>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Output\n",
    "sns.boxplot(x=mpg['mpg'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 4. Display and then Remove the one outlier row. You should now have 392 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>90.0</td>\n",
       "      <td>8</td>\n",
       "      <td>451.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>2555</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>usa</td>\n",
       "      <td>tesla model Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "9  90.0          8         451.0       450.0    2555           8.0   \n",
       "\n",
       "   model_year origin           name  \n",
       "9        2020    usa  tesla model Y  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output\n",
    "mpg[mpg['mpg'] >= 50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get index of outlier\n",
    "outlier = mpg[mpg['mpg'] >= 50].index\n",
    "# Drop row based on index\n",
    "mpg.drop(outlier, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 5. Calculate the average acceleration cars made in usa using SQL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>origin</th>\n",
       "      <th>avg_acceleration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>usa</td>\n",
       "      <td>14.990204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  origin  avg_acceleration\n",
       "0    usa         14.990204"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build sql query\n",
    "qry = \"\"\"\n",
    " SELECT\n",
    " origin, AVG(acceleration) as avg_acceleration\n",
    " FROM mpg\n",
    " WHERE origin == 'usa'\n",
    " \"\"\"\n",
    "# Build df based on query\n",
    "accelAvg= sqldf(qry, globals())\n",
    "# Output result\n",
    "accelAvg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 6. Calculate the average horsepower for each country using SQL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>origin</th>\n",
       "      <th>avg_horsepower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>europe</td>\n",
       "      <td>80.558824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>japan</td>\n",
       "      <td>79.835443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>usa</td>\n",
       "      <td>119.048980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   origin  avg_horsepower\n",
       "0  europe       80.558824\n",
       "1   japan       79.835443\n",
       "2     usa      119.048980"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build sql query\n",
    "qry = \"\"\"\n",
    " SELECT\n",
    " origin, AVG(horsepower) as avg_horsepower\n",
    " FROM mpg\n",
    " GROUP BY origin\n",
    " \"\"\"\n",
    "# Build df based on query\n",
    "hpAvg = sqldf(qry, globals())\n",
    "# Output result\n",
    "hpAvg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 7. Calculate the number of occurrences, mean, min, max of the weight for each country using SQL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>count</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>europe</td>\n",
       "      <td>68</td>\n",
       "      <td>1825</td>\n",
       "      <td>2433.470588</td>\n",
       "      <td>3820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>japan</td>\n",
       "      <td>79</td>\n",
       "      <td>1613</td>\n",
       "      <td>2221.227848</td>\n",
       "      <td>2930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>usa</td>\n",
       "      <td>245</td>\n",
       "      <td>1800</td>\n",
       "      <td>3372.489796</td>\n",
       "      <td>5140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   origin  count   min         mean   max\n",
       "0  europe     68  1825  2433.470588  3820\n",
       "1   japan     79  1613  2221.227848  2930\n",
       "2     usa    245  1800  3372.489796  5140"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build sql query\n",
    "qry = \"\"\"\n",
    " SELECT\n",
    " origin, COUNT(*) as count, MIN(weight) as min, AVG(weight) as mean, MAX(weight) as max\n",
    " FROM mpg\n",
    " GROUP BY origin\n",
    " \"\"\"\n",
    "# Build df based on query\n",
    "mpg_df = sqldf(qry, globals())\n",
    "# Output result\n",
    "mpg_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 8. Answer Question 3 but use Panda functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.445918</td>\n",
       "      <td>5.471939</td>\n",
       "      <td>194.411990</td>\n",
       "      <td>104.469388</td>\n",
       "      <td>2977.584184</td>\n",
       "      <td>15.541327</td>\n",
       "      <td>75.979592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.805007</td>\n",
       "      <td>1.705783</td>\n",
       "      <td>104.644004</td>\n",
       "      <td>38.491160</td>\n",
       "      <td>849.402560</td>\n",
       "      <td>2.758864</td>\n",
       "      <td>3.683737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>1613.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>2225.250000</td>\n",
       "      <td>13.775000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>93.500000</td>\n",
       "      <td>2803.500000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>76.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>275.750000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>3614.750000</td>\n",
       "      <td>17.025000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>46.600000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>455.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>5140.000000</td>\n",
       "      <td>24.800000</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mpg   cylinders  displacement  horsepower       weight  \\\n",
       "count  392.000000  392.000000    392.000000  392.000000   392.000000   \n",
       "mean    23.445918    5.471939    194.411990  104.469388  2977.584184   \n",
       "std      7.805007    1.705783    104.644004   38.491160   849.402560   \n",
       "min      9.000000    3.000000     68.000000   46.000000  1613.000000   \n",
       "25%     17.000000    4.000000    105.000000   75.000000  2225.250000   \n",
       "50%     22.750000    4.000000    151.000000   93.500000  2803.500000   \n",
       "75%     29.000000    8.000000    275.750000  126.000000  3614.750000   \n",
       "max     46.600000    8.000000    455.000000  230.000000  5140.000000   \n",
       "\n",
       "       acceleration  model_year  \n",
       "count    392.000000  392.000000  \n",
       "mean      15.541327   75.979592  \n",
       "std        2.758864    3.683737  \n",
       "min        8.000000   70.000000  \n",
       "25%       13.775000   73.000000  \n",
       "50%       15.500000   76.000000  \n",
       "75%       17.025000   79.000000  \n",
       "max       24.800000   82.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output\n",
    "mpg.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 9. Calculate the Standard deviation, Skew and Kurtosis for the horsepower, weight and acceleration for each country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dataframes for each country\n",
    "mpg_usa = mpg.loc[(mpg['origin'] == 'usa')]\n",
    "mpg_jpn = mpg.loc[(mpg['origin'] == 'japan')]\n",
    "mpg_eur = mpg.loc[(mpg['origin'] == 'europe')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create lists of key-value pairs for std dev, skew, and kurt for hp, weight, and accel\n",
    "data_usa = {'Std Deviation': [mpg_usa['horsepower'].std(), mpg_usa['weight'].std(), mpg_usa['acceleration'].std()],\n",
    "            'Skew': [mpg_usa['horsepower'].skew(), mpg_usa['weight'].skew(), mpg_usa['acceleration'].skew()],\n",
    "            'Kurtosis': [mpg_usa['horsepower'].kurt(), mpg_usa['weight'].kurt(), mpg_usa['acceleration'].kurt()]}\n",
    "data_jpn = {'Std Deviation': [mpg_jpn['horsepower'].std(), mpg_jpn['weight'].std(), mpg_jpn['acceleration'].std()],\n",
    "            'Skew': [mpg_jpn['horsepower'].skew(), mpg_jpn['weight'].skew(), mpg_jpn['acceleration'].skew()],\n",
    "            'Kurtosis': [mpg_jpn['horsepower'].kurt(), mpg_jpn['weight'].kurt(), mpg_jpn['acceleration'].kurt()]}\n",
    "data_eur = {'Std Deviation': [mpg_eur['horsepower'].std(), mpg_eur['weight'].std(), mpg_eur['acceleration'].std()],\n",
    "            'Skew': [mpg_eur['horsepower'].skew(), mpg_eur['weight'].skew(), mpg_eur['acceleration'].skew()],\n",
    "            'Kurtosis': [mpg_eur['horsepower'].kurt(), mpg_eur['weight'].kurt(), mpg_eur['acceleration'].kurt()]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Std Deviation</th>\n",
       "      <th>Skew</th>\n",
       "      <th>Kurtosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>39.89779</td>\n",
       "      <td>0.763248</td>\n",
       "      <td>-0.189521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>795.34669</td>\n",
       "      <td>0.067009</td>\n",
       "      <td>-0.965494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acceleration</th>\n",
       "      <td>2.73602</td>\n",
       "      <td>0.098737</td>\n",
       "      <td>-0.142774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Std Deviation      Skew  Kurtosis\n",
       "horsepower         39.89779  0.763248 -0.189521\n",
       "weight            795.34669  0.067009 -0.965494\n",
       "acceleration        2.73602  0.098737 -0.142774"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# USA data\n",
    "pd.DataFrame(data_usa, index = ['horsepower', 'weight', 'acceleration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Std Deviation</th>\n",
       "      <th>Skew</th>\n",
       "      <th>Kurtosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>17.819199</td>\n",
       "      <td>0.660375</td>\n",
       "      <td>-0.131696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>320.497248</td>\n",
       "      <td>0.498497</td>\n",
       "      <td>-0.380625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acceleration</th>\n",
       "      <td>1.954937</td>\n",
       "      <td>0.042795</td>\n",
       "      <td>-0.528450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Std Deviation      Skew  Kurtosis\n",
       "horsepower        17.819199  0.660375 -0.131696\n",
       "weight           320.497248  0.498497 -0.380625\n",
       "acceleration       1.954937  0.042795 -0.528450"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Japan data\n",
    "pd.DataFrame(data_jpn, index = ['horsepower', 'weight', 'acceleration'])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Std Deviation</th>\n",
       "      <th>Skew</th>\n",
       "      <th>Kurtosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>20.157871</td>\n",
       "      <td>0.472850</td>\n",
       "      <td>-0.059514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>491.813867</td>\n",
       "      <td>0.812081</td>\n",
       "      <td>-0.249813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acceleration</th>\n",
       "      <td>3.087813</td>\n",
       "      <td>0.945656</td>\n",
       "      <td>0.036290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Std Deviation      Skew  Kurtosis\n",
       "horsepower        20.157871  0.472850 -0.059514\n",
       "weight           491.813867  0.812081 -0.249813\n",
       "acceleration       3.087813  0.945656  0.036290"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Europe data\n",
    "pd.DataFrame(data_eur, index = ['horsepower', 'weight', 'acceleration'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 10. How would you describe the acceleration distribution for different countries based on the Skew?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### USA\n",
    "Moderately Skewed\n",
    "\n",
    "The skew of the acceleration of cars in the US is 0.098737, meaning the data is close to a standard distribution, with slightly more of the entries lying to the left of the median (15).\n",
    "#### Japan\n",
    "Moderately Skewed\n",
    "\n",
    "The skew of the acceleration of cars in the US is 0.042795, meaning the data is closer than the US to a standard distribution, with slightly more of the entries lying to the left of the median (16).\n",
    "#### Europe\n",
    "Highly Skewed\n",
    "\n",
    "The skew of the acceleration of cars in the US is 0.945656, meaning a majority of the data lies to the left of the mean (, with slightly more of the entries lying to the left of the median (19)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 11. How would you describe the acceleration distribution for different countries based on Kurtosis?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### USA\n",
    "The kurtosis of the acceleration of cars in the US is -0.059514, meaning the data is close to a standard distribution, with moderately wider distribution around the tail.\n",
    "#### Japan\n",
    "The kurtosis of the acceleration of cars in the US is -0.528450, meaning the data is much more spread out than a standard distribution, with a highly wider distribution around the tail.\n",
    "#### Europe\n",
    "The kurtosis of the acceleration of cars in the US is 0.036290, meaning the data is close to a standard distribution, with moderately wider distribution around the tail."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 12. Plot the histogram of acceleration distribution for different country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg             0.842750\n",
       "cylinders      -0.264224\n",
       "displacement    0.090515\n",
       "horsepower      0.763248\n",
       "weight          0.067009\n",
       "acceleration    0.098737\n",
       "model_year      0.147762\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# USA histogram\n",
    "mpg.loc[(mpg['origin'] == 'usa')].hist(column='acceleration', bins = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1A7C31D0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Japan histogram\n",
    "mpg.loc[(mpg['origin'] == 'japan')].hist(column='acceleration', bins = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1A73EC30>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Europe histogram\n",
    "mpg.loc[(mpg['origin'] == 'europe')].hist(column='acceleration', bins = 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 13. Calculate correlation of all the factors among themselves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mpg</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.777618</td>\n",
       "      <td>-0.805127</td>\n",
       "      <td>-0.778427</td>\n",
       "      <td>-0.832244</td>\n",
       "      <td>0.423329</td>\n",
       "      <td>0.580541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cylinders</th>\n",
       "      <td>-0.777618</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.950823</td>\n",
       "      <td>0.842983</td>\n",
       "      <td>0.897527</td>\n",
       "      <td>-0.504683</td>\n",
       "      <td>-0.345647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>displacement</th>\n",
       "      <td>-0.805127</td>\n",
       "      <td>0.950823</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.897257</td>\n",
       "      <td>0.932994</td>\n",
       "      <td>-0.543800</td>\n",
       "      <td>-0.369855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>-0.778427</td>\n",
       "      <td>0.842983</td>\n",
       "      <td>0.897257</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.864538</td>\n",
       "      <td>-0.689196</td>\n",
       "      <td>-0.416361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>-0.832244</td>\n",
       "      <td>0.897527</td>\n",
       "      <td>0.932994</td>\n",
       "      <td>0.864538</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.416839</td>\n",
       "      <td>-0.309120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acceleration</th>\n",
       "      <td>0.423329</td>\n",
       "      <td>-0.504683</td>\n",
       "      <td>-0.543800</td>\n",
       "      <td>-0.689196</td>\n",
       "      <td>-0.416839</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.290316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year</th>\n",
       "      <td>0.580541</td>\n",
       "      <td>-0.345647</td>\n",
       "      <td>-0.369855</td>\n",
       "      <td>-0.416361</td>\n",
       "      <td>-0.309120</td>\n",
       "      <td>0.290316</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   mpg  cylinders  displacement  horsepower    weight  \\\n",
       "mpg           1.000000  -0.777618     -0.805127   -0.778427 -0.832244   \n",
       "cylinders    -0.777618   1.000000      0.950823    0.842983  0.897527   \n",
       "displacement -0.805127   0.950823      1.000000    0.897257  0.932994   \n",
       "horsepower   -0.778427   0.842983      0.897257    1.000000  0.864538   \n",
       "weight       -0.832244   0.897527      0.932994    0.864538  1.000000   \n",
       "acceleration  0.423329  -0.504683     -0.543800   -0.689196 -0.416839   \n",
       "model_year    0.580541  -0.345647     -0.369855   -0.416361 -0.309120   \n",
       "\n",
       "              acceleration  model_year  \n",
       "mpg               0.423329    0.580541  \n",
       "cylinders        -0.504683   -0.345647  \n",
       "displacement     -0.543800   -0.369855  \n",
       "horsepower       -0.689196   -0.416361  \n",
       "weight           -0.416839   -0.309120  \n",
       "acceleration      1.000000    0.290316  \n",
       "model_year        0.290316    1.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 14. Which factors (cylinders, horsepower or weight) will affect your mileage the most based on correlation?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the correlation chart above, weight has the greatest effect on car mileage (correlation of -0.832244)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 15. Does the result depend on the country? If yes, how?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mpg</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.824524</td>\n",
       "      <td>-0.834628</td>\n",
       "      <td>-0.751570</td>\n",
       "      <td>-0.846424</td>\n",
       "      <td>0.377239</td>\n",
       "      <td>0.648641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cylinders</th>\n",
       "      <td>-0.824524</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.933885</td>\n",
       "      <td>0.827646</td>\n",
       "      <td>0.881609</td>\n",
       "      <td>-0.563293</td>\n",
       "      <td>-0.463986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>displacement</th>\n",
       "      <td>-0.834628</td>\n",
       "      <td>0.933885</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.902744</td>\n",
       "      <td>0.917588</td>\n",
       "      <td>-0.619890</td>\n",
       "      <td>-0.497591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>-0.751570</td>\n",
       "      <td>0.827646</td>\n",
       "      <td>0.902744</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.838450</td>\n",
       "      <td>-0.719191</td>\n",
       "      <td>-0.495009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>-0.846424</td>\n",
       "      <td>0.881609</td>\n",
       "      <td>0.917588</td>\n",
       "      <td>0.838450</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.440230</td>\n",
       "      <td>-0.406388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acceleration</th>\n",
       "      <td>0.377239</td>\n",
       "      <td>-0.563293</td>\n",
       "      <td>-0.619890</td>\n",
       "      <td>-0.719191</td>\n",
       "      <td>-0.440230</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.380878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year</th>\n",
       "      <td>0.648641</td>\n",
       "      <td>-0.463986</td>\n",
       "      <td>-0.497591</td>\n",
       "      <td>-0.495009</td>\n",
       "      <td>-0.406388</td>\n",
       "      <td>0.380878</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   mpg  cylinders  displacement  horsepower    weight  \\\n",
       "mpg           1.000000  -0.824524     -0.834628   -0.751570 -0.846424   \n",
       "cylinders    -0.824524   1.000000      0.933885    0.827646  0.881609   \n",
       "displacement -0.834628   0.933885      1.000000    0.902744  0.917588   \n",
       "horsepower   -0.751570   0.827646      0.902744    1.000000  0.838450   \n",
       "weight       -0.846424   0.881609      0.917588    0.838450  1.000000   \n",
       "acceleration  0.377239  -0.563293     -0.619890   -0.719191 -0.440230   \n",
       "model_year    0.648641  -0.463986     -0.497591   -0.495009 -0.406388   \n",
       "\n",
       "              acceleration  model_year  \n",
       "mpg               0.377239    0.648641  \n",
       "cylinders        -0.563293   -0.463986  \n",
       "displacement     -0.619890   -0.497591  \n",
       "horsepower       -0.719191   -0.495009  \n",
       "weight           -0.440230   -0.406388  \n",
       "acceleration      1.000000    0.380878  \n",
       "model_year        0.380878    1.000000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# US data\n",
    "mpg_usa.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
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       "      <th>model_year</th>\n",
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       "      <th>mpg</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.139788</td>\n",
       "      <td>-0.366020</td>\n",
       "      <td>-0.673095</td>\n",
       "      <td>-0.564106</td>\n",
       "      <td>0.401114</td>\n",
       "      <td>0.568662</td>\n",
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       "    <tr>\n",
       "      <th>cylinders</th>\n",
       "      <td>-0.139788</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.720992</td>\n",
       "      <td>0.431770</td>\n",
       "      <td>0.489184</td>\n",
       "      <td>-0.219675</td>\n",
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       "    <tr>\n",
       "      <th>displacement</th>\n",
       "      <td>-0.366020</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.730176</td>\n",
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       "      <td>-0.535590</td>\n",
       "      <td>0.120835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>-0.673095</td>\n",
       "      <td>0.431770</td>\n",
       "      <td>0.730176</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>-0.720149</td>\n",
       "      <td>-0.215264</td>\n",
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       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>-0.564106</td>\n",
       "      <td>0.489184</td>\n",
       "      <td>0.841430</td>\n",
       "      <td>0.867589</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.567682</td>\n",
       "      <td>0.045365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acceleration</th>\n",
       "      <td>0.401114</td>\n",
       "      <td>-0.219675</td>\n",
       "      <td>-0.535590</td>\n",
       "      <td>-0.720149</td>\n",
       "      <td>-0.567682</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000944</td>\n",
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       "    <tr>\n",
       "      <th>model_year</th>\n",
       "      <td>0.568662</td>\n",
       "      <td>0.085985</td>\n",
       "      <td>0.120835</td>\n",
       "      <td>-0.215264</td>\n",
       "      <td>0.045365</td>\n",
       "      <td>-0.000944</td>\n",
       "      <td>1.000000</td>\n",
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      "text/plain": [
       "                   mpg  cylinders  displacement  horsepower    weight  \\\n",
       "mpg           1.000000  -0.139788     -0.366020   -0.673095 -0.564106   \n",
       "cylinders    -0.139788   1.000000      0.720992    0.431770  0.489184   \n",
       "displacement -0.366020   0.720992      1.000000    0.730176  0.841430   \n",
       "horsepower   -0.673095   0.431770      0.730176    1.000000  0.867589   \n",
       "weight       -0.564106   0.489184      0.841430    0.867589  1.000000   \n",
       "acceleration  0.401114  -0.219675     -0.535590   -0.720149 -0.567682   \n",
       "model_year    0.568662   0.085985      0.120835   -0.215264  0.045365   \n",
       "\n",
       "              acceleration  model_year  \n",
       "mpg               0.401114    0.568662  \n",
       "cylinders        -0.219675    0.085985  \n",
       "displacement     -0.535590    0.120835  \n",
       "horsepower       -0.720149   -0.215264  \n",
       "weight           -0.567682    0.045365  \n",
       "acceleration      1.000000   -0.000944  \n",
       "model_year       -0.000944    1.000000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Japan data\n",
    "mpg_jpn.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
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       "      <th>model_year</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mpg</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.271719</td>\n",
       "      <td>-0.495594</td>\n",
       "      <td>-0.679575</td>\n",
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       "      <td>0.298047</td>\n",
       "      <td>0.504231</td>\n",
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       "      <th>cylinders</th>\n",
       "      <td>-0.271719</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.657791</td>\n",
       "      <td>0.392653</td>\n",
       "      <td>0.574767</td>\n",
       "      <td>0.024453</td>\n",
       "      <td>0.262851</td>\n",
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       "    <tr>\n",
       "      <th>displacement</th>\n",
       "      <td>-0.495594</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.622043</td>\n",
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       "      <td>0.038032</td>\n",
       "      <td>0.206940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>-0.679575</td>\n",
       "      <td>0.392653</td>\n",
       "      <td>0.622043</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>-0.544388</td>\n",
       "      <td>-0.132330</td>\n",
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       "      <th>weight</th>\n",
       "      <td>-0.512011</td>\n",
       "      <td>0.574767</td>\n",
       "      <td>0.891560</td>\n",
       "      <td>0.611619</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.166325</td>\n",
       "      <td>0.178178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acceleration</th>\n",
       "      <td>0.298047</td>\n",
       "      <td>0.024453</td>\n",
       "      <td>0.038032</td>\n",
       "      <td>-0.544388</td>\n",
       "      <td>0.166325</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.175077</td>\n",
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       "    <tr>\n",
       "      <th>model_year</th>\n",
       "      <td>0.504231</td>\n",
       "      <td>0.262851</td>\n",
       "      <td>0.206940</td>\n",
       "      <td>-0.132330</td>\n",
       "      <td>0.178178</td>\n",
       "      <td>0.175077</td>\n",
       "      <td>1.000000</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "                   mpg  cylinders  displacement  horsepower    weight  \\\n",
       "mpg           1.000000  -0.271719     -0.495594   -0.679575 -0.512011   \n",
       "cylinders    -0.271719   1.000000      0.657791    0.392653  0.574767   \n",
       "displacement -0.495594   0.657791      1.000000    0.622043  0.891560   \n",
       "horsepower   -0.679575   0.392653      0.622043    1.000000  0.611619   \n",
       "weight       -0.512011   0.574767      0.891560    0.611619  1.000000   \n",
       "acceleration  0.298047   0.024453      0.038032   -0.544388  0.166325   \n",
       "model_year    0.504231   0.262851      0.206940   -0.132330  0.178178   \n",
       "\n",
       "              acceleration  model_year  \n",
       "mpg               0.298047    0.504231  \n",
       "cylinders         0.024453    0.262851  \n",
       "displacement      0.038032    0.206940  \n",
       "horsepower       -0.544388   -0.132330  \n",
       "weight            0.166325    0.178178  \n",
       "acceleration      1.000000    0.175077  \n",
       "model_year        0.175077    1.000000  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Europe data\n",
    "mpg_eur.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes, when the data is filtered by origin, the above information gives us the following\n",
    "\n",
    "#### USA\n",
    "Strongest correlation: *Weight* at *-0.846424* \n",
    "\n",
    "#### Japan\n",
    "Strongest correlation: *Horsepower* at *-0.673095*\n",
    "\n",
    "#### Europe\n",
    "Strongest correlation: *Horsepower* at *-0.679575*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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