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mde-2590/Exploratory Data Analysis Lab.ipynb

Created June 2, 2019 15:54
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Exploratory Data Analysis Lab.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"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>symboling</th>\n",
" <th>normalized-losses</th>\n",
" <th>make</th>\n",
" <th>aspiration</th>\n",
" <th>num-of-doors</th>\n",
" <th>body-style</th>\n",
" <th>drive-wheels</th>\n",
" <th>engine-location</th>\n",
" <th>wheel-base</th>\n",
" <th>length</th>\n",
" <th>...</th>\n",
" <th>compression-ratio</th>\n",
" <th>horsepower</th>\n",
" <th>peak-rpm</th>\n",
" <th>city-mpg</th>\n",
" <th>highway-mpg</th>\n",
" <th>price</th>\n",
" <th>city-L/100km</th>\n",
" <th>horsepower-binned</th>\n",
" <th>diesel</th>\n",
" <th>gas</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3</td>\n",
" <td>122</td>\n",
" <td>alfa-romero</td>\n",
" <td>std</td>\n",
" <td>two</td>\n",
" <td>convertible</td>\n",
" <td>rwd</td>\n",
" <td>front</td>\n",
" <td>88.6</td>\n",
" <td>0.811148</td>\n",
" <td>...</td>\n",
" <td>9.0</td>\n",
" <td>111.0</td>\n",
" <td>5000.0</td>\n",
" <td>21</td>\n",
" <td>27</td>\n",
" <td>13495.0</td>\n",
" <td>11.190476</td>\n",
" <td>Medium</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3</td>\n",
" <td>122</td>\n",
" <td>alfa-romero</td>\n",
" <td>std</td>\n",
" <td>two</td>\n",
" <td>convertible</td>\n",
" <td>rwd</td>\n",
" <td>front</td>\n",
" <td>88.6</td>\n",
" <td>0.811148</td>\n",
" <td>...</td>\n",
" <td>9.0</td>\n",
" <td>111.0</td>\n",
" <td>5000.0</td>\n",
" <td>21</td>\n",
" <td>27</td>\n",
" <td>16500.0</td>\n",
" <td>11.190476</td>\n",
" <td>Medium</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>122</td>\n",
" <td>alfa-romero</td>\n",
" <td>std</td>\n",
" <td>two</td>\n",
" <td>hatchback</td>\n",
" <td>rwd</td>\n",
" <td>front</td>\n",
" <td>94.5</td>\n",
" <td>0.822681</td>\n",
" <td>...</td>\n",
" <td>9.0</td>\n",
" <td>154.0</td>\n",
" <td>5000.0</td>\n",
" <td>19</td>\n",
" <td>26</td>\n",
" <td>16500.0</td>\n",
" <td>12.368421</td>\n",
" <td>Medium</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2</td>\n",
" <td>164</td>\n",
" <td>audi</td>\n",
" <td>std</td>\n",
" <td>four</td>\n",
" <td>sedan</td>\n",
" <td>fwd</td>\n",
" <td>front</td>\n",
" <td>99.8</td>\n",
" <td>0.848630</td>\n",
" <td>...</td>\n",
" <td>10.0</td>\n",
" <td>102.0</td>\n",
" <td>5500.0</td>\n",
" <td>24</td>\n",
" <td>30</td>\n",
" <td>13950.0</td>\n",
" <td>9.791667</td>\n",
" <td>Medium</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>164</td>\n",
" <td>audi</td>\n",
" <td>std</td>\n",
" <td>four</td>\n",
" <td>sedan</td>\n",
" <td>4wd</td>\n",
" <td>front</td>\n",
" <td>99.4</td>\n",
" <td>0.848630</td>\n",
" <td>...</td>\n",
" <td>8.0</td>\n",
" <td>115.0</td>\n",
" <td>5500.0</td>\n",
" <td>18</td>\n",
" <td>22</td>\n",
" <td>17450.0</td>\n",
" <td>13.055556</td>\n",
" <td>Medium</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 29 columns</p>\n",
"</div>"
],
"text/plain": [
" symboling normalized-losses make aspiration num-of-doors \\\n",
"0 3 122 alfa-romero std two \n",
"1 3 122 alfa-romero std two \n",
"2 1 122 alfa-romero std two \n",
"3 2 164 audi std four \n",
"4 2 164 audi std four \n",
"\n",
" body-style drive-wheels engine-location wheel-base length ... \\\n",
"0 convertible rwd front 88.6 0.811148 ... \n",
"1 convertible rwd front 88.6 0.811148 ... \n",
"2 hatchback rwd front 94.5 0.822681 ... \n",
"3 sedan fwd front 99.8 0.848630 ... \n",
"4 sedan 4wd front 99.4 0.848630 ... \n",
"\n",
" compression-ratio horsepower peak-rpm city-mpg highway-mpg price \\\n",
"0 9.0 111.0 5000.0 21 27 13495.0 \n",
"1 9.0 111.0 5000.0 21 27 16500.0 \n",
"2 9.0 154.0 5000.0 19 26 16500.0 \n",
"3 10.0 102.0 5500.0 24 30 13950.0 \n",
"4 8.0 115.0 5500.0 18 22 17450.0 \n",
"\n",
" city-L/100km horsepower-binned diesel gas \n",
"0 11.190476 Medium 0 1 \n",
"1 11.190476 Medium 0 1 \n",
"2 12.368421 Medium 0 1 \n",
"3 9.791667 Medium 0 1 \n",
"4 13.055556 Medium 0 1 \n",
"\n",
"[5 rows x 29 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path='https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/automobileEDA.csv'\n",
"df = pd.read_csv(path)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%%capture\n",
"! pip install seaborn"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline "
]
},
{
"cell_type": "code",
"execution_count": 5,
"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>symboling</th>\n",
" <th>normalized-losses</th>\n",
" <th>wheel-base</th>\n",
" <th>length</th>\n",
" <th>width</th>\n",
" <th>height</th>\n",
" <th>curb-weight</th>\n",
" <th>engine-size</th>\n",
" <th>bore</th>\n",
" <th>stroke</th>\n",
" <th>compression-ratio</th>\n",
" <th>horsepower</th>\n",
" <th>peak-rpm</th>\n",
" <th>city-mpg</th>\n",
" <th>highway-mpg</th>\n",
" <th>price</th>\n",
" <th>city-L/100km</th>\n",
" <th>diesel</th>\n",
" <th>gas</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>symboling</th>\n",
" <td>1.000000</td>\n",
" <td>0.466264</td>\n",
" <td>-0.535987</td>\n",
" <td>-0.365404</td>\n",
" <td>-0.242423</td>\n",
" <td>-0.550160</td>\n",
" <td>-0.233118</td>\n",
" <td>-0.110581</td>\n",
" <td>-0.140019</td>\n",
" <td>-0.008245</td>\n",
" <td>-0.182196</td>\n",
" <td>0.075819</td>\n",
" <td>0.279740</td>\n",
" <td>-0.035527</td>\n",
" <td>0.036233</td>\n",
" <td>-0.082391</td>\n",
" <td>0.066171</td>\n",
" <td>-0.196735</td>\n",
" <td>0.196735</td>\n",
" </tr>\n",
" <tr>\n",
" <th>normalized-losses</th>\n",
" <td>0.466264</td>\n",
" <td>1.000000</td>\n",
" <td>-0.056661</td>\n",
" <td>0.019424</td>\n",
" <td>0.086802</td>\n",
" <td>-0.373737</td>\n",
" <td>0.099404</td>\n",
" <td>0.112360</td>\n",
" <td>-0.029862</td>\n",
" <td>0.055563</td>\n",
" <td>-0.114713</td>\n",
" <td>0.217299</td>\n",
" <td>0.239543</td>\n",
" <td>-0.225016</td>\n",
" <td>-0.181877</td>\n",
" <td>0.133999</td>\n",
" <td>0.238567</td>\n",
" <td>-0.101546</td>\n",
" <td>0.101546</td>\n",
" </tr>\n",
" <tr>\n",
" <th>wheel-base</th>\n",
" <td>-0.535987</td>\n",
" <td>-0.056661</td>\n",
" <td>1.000000</td>\n",
" <td>0.876024</td>\n",
" <td>0.814507</td>\n",
" <td>0.590742</td>\n",
" <td>0.782097</td>\n",
" <td>0.572027</td>\n",
" <td>0.493244</td>\n",
" <td>0.158502</td>\n",
" <td>0.250313</td>\n",
" <td>0.371147</td>\n",
" <td>-0.360305</td>\n",
" <td>-0.470606</td>\n",
" <td>-0.543304</td>\n",
" <td>0.584642</td>\n",
" <td>0.476153</td>\n",
" <td>0.307237</td>\n",
" <td>-0.307237</td>\n",
" </tr>\n",
" <tr>\n",
" <th>length</th>\n",
" <td>-0.365404</td>\n",
" <td>0.019424</td>\n",
" <td>0.876024</td>\n",
" <td>1.000000</td>\n",
" <td>0.857170</td>\n",
" <td>0.492063</td>\n",
" <td>0.880665</td>\n",
" <td>0.685025</td>\n",
" <td>0.608971</td>\n",
" <td>0.124139</td>\n",
" <td>0.159733</td>\n",
" <td>0.579821</td>\n",
" <td>-0.285970</td>\n",
" <td>-0.665192</td>\n",
" <td>-0.698142</td>\n",
" <td>0.690628</td>\n",
" <td>0.657373</td>\n",
" <td>0.211187</td>\n",
" <td>-0.211187</td>\n",
" </tr>\n",
" <tr>\n",
" <th>width</th>\n",
" <td>-0.242423</td>\n",
" <td>0.086802</td>\n",
" <td>0.814507</td>\n",
" <td>0.857170</td>\n",
" <td>1.000000</td>\n",
" <td>0.306002</td>\n",
" <td>0.866201</td>\n",
" <td>0.729436</td>\n",
" <td>0.544885</td>\n",
" <td>0.188829</td>\n",
" <td>0.189867</td>\n",
" <td>0.615077</td>\n",
" <td>-0.245800</td>\n",
" <td>-0.633531</td>\n",
" <td>-0.680635</td>\n",
" <td>0.751265</td>\n",
" <td>0.673363</td>\n",
" <td>0.244356</td>\n",
" <td>-0.244356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>height</th>\n",
" <td>-0.550160</td>\n",
" <td>-0.373737</td>\n",
" <td>0.590742</td>\n",
" <td>0.492063</td>\n",
" <td>0.306002</td>\n",
" <td>1.000000</td>\n",
" <td>0.307581</td>\n",
" <td>0.074694</td>\n",
" <td>0.180449</td>\n",
" <td>-0.062704</td>\n",
" <td>0.259737</td>\n",
" <td>-0.087027</td>\n",
" <td>-0.309974</td>\n",
" <td>-0.049800</td>\n",
" <td>-0.104812</td>\n",
" <td>0.135486</td>\n",
" <td>0.003811</td>\n",
" <td>0.281578</td>\n",
" <td>-0.281578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>curb-weight</th>\n",
" <td>-0.233118</td>\n",
" <td>0.099404</td>\n",
" <td>0.782097</td>\n",
" <td>0.880665</td>\n",
" <td>0.866201</td>\n",
" <td>0.307581</td>\n",
" <td>1.000000</td>\n",
" <td>0.849072</td>\n",
" <td>0.644060</td>\n",
" <td>0.167562</td>\n",
" <td>0.156433</td>\n",
" <td>0.757976</td>\n",
" <td>-0.279361</td>\n",
" <td>-0.749543</td>\n",
" <td>-0.794889</td>\n",
" <td>0.834415</td>\n",
" <td>0.785353</td>\n",
" <td>0.221046</td>\n",
" <td>-0.221046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>engine-size</th>\n",
" <td>-0.110581</td>\n",
" <td>0.112360</td>\n",
" <td>0.572027</td>\n",
" <td>0.685025</td>\n",
" <td>0.729436</td>\n",
" <td>0.074694</td>\n",
" <td>0.849072</td>\n",
" <td>1.000000</td>\n",
" <td>0.572609</td>\n",
" <td>0.209523</td>\n",
" <td>0.028889</td>\n",
" <td>0.822676</td>\n",
" <td>-0.256733</td>\n",
" <td>-0.650546</td>\n",
" <td>-0.679571</td>\n",
" <td>0.872335</td>\n",
" <td>0.745059</td>\n",
" <td>0.070779</td>\n",
" <td>-0.070779</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bore</th>\n",
" <td>-0.140019</td>\n",
" <td>-0.029862</td>\n",
" <td>0.493244</td>\n",
" <td>0.608971</td>\n",
" <td>0.544885</td>\n",
" <td>0.180449</td>\n",
" <td>0.644060</td>\n",
" <td>0.572609</td>\n",
" <td>1.000000</td>\n",
" <td>-0.055390</td>\n",
" <td>0.001263</td>\n",
" <td>0.566936</td>\n",
" <td>-0.267392</td>\n",
" <td>-0.582027</td>\n",
" <td>-0.591309</td>\n",
" <td>0.543155</td>\n",
" <td>0.554610</td>\n",
" <td>0.054458</td>\n",
" <td>-0.054458</td>\n",
" </tr>\n",
" <tr>\n",
" <th>stroke</th>\n",
" <td>-0.008245</td>\n",
" <td>0.055563</td>\n",
" <td>0.158502</td>\n",
" <td>0.124139</td>\n",
" <td>0.188829</td>\n",
" <td>-0.062704</td>\n",
" <td>0.167562</td>\n",
" <td>0.209523</td>\n",
" <td>-0.055390</td>\n",
" <td>1.000000</td>\n",
" <td>0.187923</td>\n",
" <td>0.098462</td>\n",
" <td>-0.065713</td>\n",
" <td>-0.034696</td>\n",
" <td>-0.035201</td>\n",
" <td>0.082310</td>\n",
" <td>0.037300</td>\n",
" <td>0.241303</td>\n",
" <td>-0.241303</td>\n",
" </tr>\n",
" <tr>\n",
" <th>compression-ratio</th>\n",
" <td>-0.182196</td>\n",
" <td>-0.114713</td>\n",
" <td>0.250313</td>\n",
" <td>0.159733</td>\n",
" <td>0.189867</td>\n",
" <td>0.259737</td>\n",
" <td>0.156433</td>\n",
" <td>0.028889</td>\n",
" <td>0.001263</td>\n",
" <td>0.187923</td>\n",
" <td>1.000000</td>\n",
" <td>-0.214514</td>\n",
" <td>-0.435780</td>\n",
" <td>0.331425</td>\n",
" <td>0.268465</td>\n",
" <td>0.071107</td>\n",
" <td>-0.299372</td>\n",
" <td>0.985231</td>\n",
" <td>-0.985231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>horsepower</th>\n",
" <td>0.075819</td>\n",
" <td>0.217299</td>\n",
" <td>0.371147</td>\n",
" <td>0.579821</td>\n",
" <td>0.615077</td>\n",
" <td>-0.087027</td>\n",
" <td>0.757976</td>\n",
" <td>0.822676</td>\n",
" <td>0.566936</td>\n",
" <td>0.098462</td>\n",
" <td>-0.214514</td>\n",
" <td>1.000000</td>\n",
" <td>0.107885</td>\n",
" <td>-0.822214</td>\n",
" <td>-0.804575</td>\n",
" <td>0.809575</td>\n",
" <td>0.889488</td>\n",
" <td>-0.169053</td>\n",
" <td>0.169053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>peak-rpm</th>\n",
" <td>0.279740</td>\n",
" <td>0.239543</td>\n",
" <td>-0.360305</td>\n",
" <td>-0.285970</td>\n",
" <td>-0.245800</td>\n",
" <td>-0.309974</td>\n",
" <td>-0.279361</td>\n",
" <td>-0.256733</td>\n",
" <td>-0.267392</td>\n",
" <td>-0.065713</td>\n",
" <td>-0.435780</td>\n",
" <td>0.107885</td>\n",
" <td>1.000000</td>\n",
" <td>-0.115413</td>\n",
" <td>-0.058598</td>\n",
" <td>-0.101616</td>\n",
" <td>0.115830</td>\n",
" <td>-0.475812</td>\n",
" <td>0.475812</td>\n",
" </tr>\n",
" <tr>\n",
" <th>city-mpg</th>\n",
" <td>-0.035527</td>\n",
" <td>-0.225016</td>\n",
" <td>-0.470606</td>\n",
" <td>-0.665192</td>\n",
" <td>-0.633531</td>\n",
" <td>-0.049800</td>\n",
" <td>-0.749543</td>\n",
" <td>-0.650546</td>\n",
" <td>-0.582027</td>\n",
" <td>-0.034696</td>\n",
" <td>0.331425</td>\n",
" <td>-0.822214</td>\n",
" <td>-0.115413</td>\n",
" <td>1.000000</td>\n",
" <td>0.972044</td>\n",
" <td>-0.686571</td>\n",
" <td>-0.949713</td>\n",
" <td>0.265676</td>\n",
" <td>-0.265676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>highway-mpg</th>\n",
" <td>0.036233</td>\n",
" <td>-0.181877</td>\n",
" <td>-0.543304</td>\n",
" <td>-0.698142</td>\n",
" <td>-0.680635</td>\n",
" <td>-0.104812</td>\n",
" <td>-0.794889</td>\n",
" <td>-0.679571</td>\n",
" <td>-0.591309</td>\n",
" <td>-0.035201</td>\n",
" <td>0.268465</td>\n",
" <td>-0.804575</td>\n",
" <td>-0.058598</td>\n",
" <td>0.972044</td>\n",
" <td>1.000000</td>\n",
" <td>-0.704692</td>\n",
" <td>-0.930028</td>\n",
" <td>0.198690</td>\n",
" <td>-0.198690</td>\n",
" </tr>\n",
" <tr>\n",
" <th>price</th>\n",
" <td>-0.082391</td>\n",
" <td>0.133999</td>\n",
" <td>0.584642</td>\n",
" <td>0.690628</td>\n",
" <td>0.751265</td>\n",
" <td>0.135486</td>\n",
" <td>0.834415</td>\n",
" <td>0.872335</td>\n",
" <td>0.543155</td>\n",
" <td>0.082310</td>\n",
" <td>0.071107</td>\n",
" <td>0.809575</td>\n",
" <td>-0.101616</td>\n",
" <td>-0.686571</td>\n",
" <td>-0.704692</td>\n",
" <td>1.000000</td>\n",
" <td>0.789898</td>\n",
" <td>0.110326</td>\n",
" <td>-0.110326</td>\n",
" </tr>\n",
" <tr>\n",
" <th>city-L/100km</th>\n",
" <td>0.066171</td>\n",
" <td>0.238567</td>\n",
" <td>0.476153</td>\n",
" <td>0.657373</td>\n",
" <td>0.673363</td>\n",
" <td>0.003811</td>\n",
" <td>0.785353</td>\n",
" <td>0.745059</td>\n",
" <td>0.554610</td>\n",
" <td>0.037300</td>\n",
" <td>-0.299372</td>\n",
" <td>0.889488</td>\n",
" <td>0.115830</td>\n",
" <td>-0.949713</td>\n",
" <td>-0.930028</td>\n",
" <td>0.789898</td>\n",
" <td>1.000000</td>\n",
" <td>-0.241282</td>\n",
" <td>0.241282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>diesel</th>\n",
" <td>-0.196735</td>\n",
" <td>-0.101546</td>\n",
" <td>0.307237</td>\n",
" <td>0.211187</td>\n",
" <td>0.244356</td>\n",
" <td>0.281578</td>\n",
" <td>0.221046</td>\n",
" <td>0.070779</td>\n",
" <td>0.054458</td>\n",
" <td>0.241303</td>\n",
" <td>0.985231</td>\n",
" <td>-0.169053</td>\n",
" <td>-0.475812</td>\n",
" <td>0.265676</td>\n",
" <td>0.198690</td>\n",
" <td>0.110326</td>\n",
" <td>-0.241282</td>\n",
" <td>1.000000</td>\n",
" <td>-1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gas</th>\n",
" <td>0.196735</td>\n",
" <td>0.101546</td>\n",
" <td>-0.307237</td>\n",
" <td>-0.211187</td>\n",
" <td>-0.244356</td>\n",
" <td>-0.281578</td>\n",
" <td>-0.221046</td>\n",
" <td>-0.070779</td>\n",
" <td>-0.054458</td>\n",
" <td>-0.241303</td>\n",
" <td>-0.985231</td>\n",
" <td>0.169053</td>\n",
" <td>0.475812</td>\n",
" <td>-0.265676</td>\n",
" <td>-0.198690</td>\n",
" <td>-0.110326</td>\n",
" <td>0.241282</td>\n",
" <td>-1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" symboling normalized-losses wheel-base length \\\n",
"symboling 1.000000 0.466264 -0.535987 -0.365404 \n",
"normalized-losses 0.466264 1.000000 -0.056661 0.019424 \n",
"wheel-base -0.535987 -0.056661 1.000000 0.876024 \n",
"length -0.365404 0.019424 0.876024 1.000000 \n",
"width -0.242423 0.086802 0.814507 0.857170 \n",
"height -0.550160 -0.373737 0.590742 0.492063 \n",
"curb-weight -0.233118 0.099404 0.782097 0.880665 \n",
"engine-size -0.110581 0.112360 0.572027 0.685025 \n",
"bore -0.140019 -0.029862 0.493244 0.608971 \n",
"stroke -0.008245 0.055563 0.158502 0.124139 \n",
"compression-ratio -0.182196 -0.114713 0.250313 0.159733 \n",
"horsepower 0.075819 0.217299 0.371147 0.579821 \n",
"peak-rpm 0.279740 0.239543 -0.360305 -0.285970 \n",
"city-mpg -0.035527 -0.225016 -0.470606 -0.665192 \n",
"highway-mpg 0.036233 -0.181877 -0.543304 -0.698142 \n",
"price -0.082391 0.133999 0.584642 0.690628 \n",
"city-L/100km 0.066171 0.238567 0.476153 0.657373 \n",
"diesel -0.196735 -0.101546 0.307237 0.211187 \n",
"gas 0.196735 0.101546 -0.307237 -0.211187 \n",
"\n",
" width height curb-weight engine-size bore \\\n",
"symboling -0.242423 -0.550160 -0.233118 -0.110581 -0.140019 \n",
"normalized-losses 0.086802 -0.373737 0.099404 0.112360 -0.029862 \n",
"wheel-base 0.814507 0.590742 0.782097 0.572027 0.493244 \n",
"length 0.857170 0.492063 0.880665 0.685025 0.608971 \n",
"width 1.000000 0.306002 0.866201 0.729436 0.544885 \n",
"height 0.306002 1.000000 0.307581 0.074694 0.180449 \n",
"curb-weight 0.866201 0.307581 1.000000 0.849072 0.644060 \n",
"engine-size 0.729436 0.074694 0.849072 1.000000 0.572609 \n",
"bore 0.544885 0.180449 0.644060 0.572609 1.000000 \n",
"stroke 0.188829 -0.062704 0.167562 0.209523 -0.055390 \n",
"compression-ratio 0.189867 0.259737 0.156433 0.028889 0.001263 \n",
"horsepower 0.615077 -0.087027 0.757976 0.822676 0.566936 \n",
"peak-rpm -0.245800 -0.309974 -0.279361 -0.256733 -0.267392 \n",
"city-mpg -0.633531 -0.049800 -0.749543 -0.650546 -0.582027 \n",
"highway-mpg -0.680635 -0.104812 -0.794889 -0.679571 -0.591309 \n",
"price 0.751265 0.135486 0.834415 0.872335 0.543155 \n",
"city-L/100km 0.673363 0.003811 0.785353 0.745059 0.554610 \n",
"diesel 0.244356 0.281578 0.221046 0.070779 0.054458 \n",
"gas -0.244356 -0.281578 -0.221046 -0.070779 -0.054458 \n",
"\n",
" stroke compression-ratio horsepower peak-rpm \\\n",
"symboling -0.008245 -0.182196 0.075819 0.279740 \n",
"normalized-losses 0.055563 -0.114713 0.217299 0.239543 \n",
"wheel-base 0.158502 0.250313 0.371147 -0.360305 \n",
"length 0.124139 0.159733 0.579821 -0.285970 \n",
"width 0.188829 0.189867 0.615077 -0.245800 \n",
"height -0.062704 0.259737 -0.087027 -0.309974 \n",
"curb-weight 0.167562 0.156433 0.757976 -0.279361 \n",
"engine-size 0.209523 0.028889 0.822676 -0.256733 \n",
"bore -0.055390 0.001263 0.566936 -0.267392 \n",
"stroke 1.000000 0.187923 0.098462 -0.065713 \n",
"compression-ratio 0.187923 1.000000 -0.214514 -0.435780 \n",
"horsepower 0.098462 -0.214514 1.000000 0.107885 \n",
"peak-rpm -0.065713 -0.435780 0.107885 1.000000 \n",
"city-mpg -0.034696 0.331425 -0.822214 -0.115413 \n",
"highway-mpg -0.035201 0.268465 -0.804575 -0.058598 \n",
"price 0.082310 0.071107 0.809575 -0.101616 \n",
"city-L/100km 0.037300 -0.299372 0.889488 0.115830 \n",
"diesel 0.241303 0.985231 -0.169053 -0.475812 \n",
"gas -0.241303 -0.985231 0.169053 0.475812 \n",
"\n",
" city-mpg highway-mpg price city-L/100km diesel \\\n",
"symboling -0.035527 0.036233 -0.082391 0.066171 -0.196735 \n",
"normalized-losses -0.225016 -0.181877 0.133999 0.238567 -0.101546 \n",
"wheel-base -0.470606 -0.543304 0.584642 0.476153 0.307237 \n",
"length -0.665192 -0.698142 0.690628 0.657373 0.211187 \n",
"width -0.633531 -0.680635 0.751265 0.673363 0.244356 \n",
"height -0.049800 -0.104812 0.135486 0.003811 0.281578 \n",
"curb-weight -0.749543 -0.794889 0.834415 0.785353 0.221046 \n",
"engine-size -0.650546 -0.679571 0.872335 0.745059 0.070779 \n",
"bore -0.582027 -0.591309 0.543155 0.554610 0.054458 \n",
"stroke -0.034696 -0.035201 0.082310 0.037300 0.241303 \n",
"compression-ratio 0.331425 0.268465 0.071107 -0.299372 0.985231 \n",
"horsepower -0.822214 -0.804575 0.809575 0.889488 -0.169053 \n",
"peak-rpm -0.115413 -0.058598 -0.101616 0.115830 -0.475812 \n",
"city-mpg 1.000000 0.972044 -0.686571 -0.949713 0.265676 \n",
"highway-mpg 0.972044 1.000000 -0.704692 -0.930028 0.198690 \n",
"price -0.686571 -0.704692 1.000000 0.789898 0.110326 \n",
"city-L/100km -0.949713 -0.930028 0.789898 1.000000 -0.241282 \n",
"diesel 0.265676 0.198690 0.110326 -0.241282 1.000000 \n",
"gas -0.265676 -0.198690 -0.110326 0.241282 -1.000000 \n",
"\n",
" gas \n",
"symboling 0.196735 \n",
"normalized-losses 0.101546 \n",
"wheel-base -0.307237 \n",
"length -0.211187 \n",
"width -0.244356 \n",
"height -0.281578 \n",
"curb-weight -0.221046 \n",
"engine-size -0.070779 \n",
"bore -0.054458 \n",
"stroke -0.241303 \n",
"compression-ratio -0.985231 \n",
"horsepower 0.169053 \n",
"peak-rpm 0.475812 \n",
"city-mpg -0.265676 \n",
"highway-mpg -0.198690 \n",
"price -0.110326 \n",
"city-L/100km 0.241282 \n",
"diesel -1.000000 \n",
"gas 1.000000 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.corr()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"#float64"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>bore</th>\n",
" <th>stroke</th>\n",
" <th>compression-ratio</th>\n",
" <th>horsepower</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>bore</th>\n",
" <td>1.000000</td>\n",
" <td>-0.055390</td>\n",
" <td>0.001263</td>\n",
" <td>0.566936</td>\n",
" </tr>\n",
" <tr>\n",
" <th>stroke</th>\n",
" <td>-0.055390</td>\n",
" <td>1.000000</td>\n",
" <td>0.187923</td>\n",
" <td>0.098462</td>\n",
" </tr>\n",
" <tr>\n",
" <th>compression-ratio</th>\n",
" <td>0.001263</td>\n",
" <td>0.187923</td>\n",
" <td>1.000000</td>\n",
" <td>-0.214514</td>\n",
" </tr>\n",
" <tr>\n",
" <th>horsepower</th>\n",
" <td>0.566936</td>\n",
" <td>0.098462</td>\n",
" <td>-0.214514</td>\n",
" <td>1.000000</td>\n",
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],
"text/plain": [
" bore stroke compression-ratio horsepower\n",
"bore 1.000000 -0.055390 0.001263 0.566936\n",
"stroke -0.055390 1.000000 0.187923 0.098462\n",
"compression-ratio 0.001263 0.187923 1.000000 -0.214514\n",
"horsepower 0.566936 0.098462 -0.214514 1.000000"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[['bore', 'stroke', 'compression-ratio', 'horsepower']].corr() "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jupyterlab/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
]
},
{
"data": {
"text/plain": [
"(0, 56345.0442767281)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Engine size as potential predictor variable of price\n",
"sns.regplot(x=\"engine-size\", y=\"price\", data=df)\n",
"plt.ylim(0,)"
]
},
{
"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>engine-size</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>engine-size</th>\n",
" <td>1.000000</td>\n",
" <td>0.872335</td>\n",
" </tr>\n",
" <tr>\n",
" <th>price</th>\n",
" <td>0.872335</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" engine-size price\n",
"engine-size 1.000000 0.872335\n",
"price 0.872335 1.000000"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[[\"engine-size\", \"price\"]].corr()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f75ec040278>"
]
},
"execution_count": 10,
"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": [
"sns.regplot(x=\"highway-mpg\", y=\"price\", data=df)"
]
},
{
"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>highway-mpg</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>highway-mpg</th>\n",
" <td>1.000000</td>\n",
" <td>-0.704692</td>\n",
" </tr>\n",
" <tr>\n",
" <th>price</th>\n",
" <td>-0.704692</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" highway-mpg price\n",
"highway-mpg 1.000000 -0.704692\n",
"price -0.704692 1.000000"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[['highway-mpg', 'price']].corr()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f75e6393e48>"
]
},
"execution_count": 12,
"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": [
"sns.regplot(x=\"peak-rpm\", y=\"price\", data=df)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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>peak-rpm</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>peak-rpm</th>\n",
" <td>1.000000</td>\n",
" <td>-0.101616</td>\n",
" </tr>\n",
" <tr>\n",
" <th>price</th>\n",
" <td>-0.101616</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" peak-rpm price\n",
"peak-rpm 1.000000 -0.101616\n",
"price -0.101616 1.000000"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[['peak-rpm','price']].corr()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"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>stroke</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>stroke</th>\n",
" <td>1.00000</td>\n",
" <td>0.08231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>price</th>\n",
" <td>0.08231</td>\n",
" <td>1.00000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" stroke price\n",
"stroke 1.00000 0.08231\n",
"price 0.08231 1.00000"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[[\"stroke\",\"price\"]].corr() "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f75e630c940>"
]
},
"execution_count": 15,
"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": [
"sns.regplot(x=\"stroke\", y=\"price\", data=df)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f75e62e6cc0>"
]
},
"execution_count": 16,
"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": [
"sns.boxplot(x=\"body-style\", y=\"price\", data=df)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f75e61f46d8>"
]
},
"execution_count": 17,
"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": [
"sns.boxplot(x=\"engine-location\", y=\"price\", data=df)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f75e618df60>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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N/PUREcANwEfT/hXg6gmet9mU0tnZSW9vL52dnWVXxWyPjHtPJbUoztiNYx8L/DFw3Ijuw/9T0t2SeoH3A/8tfc69wFXAfcC/AqenFs3O9PkbgfuBq1JZgHOAs9NN/XlkScysrgwODtLV1UVE0NXV5daK1bWJ9v7qlvQp4Ergl8PBiNg+2g4R8UNq3/fYMMY+FwAX1IhvqLVf6hF29Mi4WT3p7Owka3jD0NAQnZ2dnH322SXXymz3TPQ5lT8h6/X1A6Cn6mVme6i7u5sdO3YAsGPHDjZt2lRyjcx230STyhFkDyDeBWwBLiJ7SNHM9tCyZctobGwEoLGxkfb29pJrZLb7JppUOoHDgTVkCeXwFDOzPVSpVMj6tUBDQwOVSqXkGpntvokmlbdFxH+NiBvSaxXwtiIrZjZTNDU10dHRgSQ6OjqYN29e2VUy220TTSp3Slo6vCLpGODfiqmS2cxTqVRYvHixWylW9zTc62TMQtk4W28DfpZCh5B17x0CIiIWF1bDnLW1tUVPz/h9DNasWUNfX98k1Cg/W7duBaC1tbXkmkzcokWLWL16ddnVMLNxSNocEW3jlZtol+Lle1ifutPX18edd9/H0BsOKLsqE6aXsh8Im3/y85JrMjENz43aI93M6tSEkkpE/LToikxFQ284gBeOGHc0GttN+953TdlVMLOcTfSeipmZ2bicVMzMLDdOKmZmlhsnFTMzy42TipmZ5cZJxczMcuOkYmZmuSksqUg6WNINku6XdK+ks1L8AEndkram97kpLklrJPVJ6pX0rqpjVVL5rZIqVfGj0oRffWnfWvO3mJnZJCmypbIT+POIOBxYCpwu6QjgXOC6iGgFrkvrAB1kUwi3AquAiyFLQsD5wDFkE3KdP5yIUplVVfvNuCf/zcymksKSSkRsi4gfpeVnyMYKWwCs5NVh8zuBE9PySuCKyNwKzEnz2Z8AdEfE9oh4EugGlqdt+0fELWm++iuqjmVmZiWYlHsqkhYC7wRuA+ZHxDbIEg/w5lRsAfBI1W79KTZWvL9G3MzMSlJ4UpG0H/A94JMR8YuxitaIxW7Ea9VhlaQeST0DAwPjVdnMzHZToUlFUiNZQvlORHw/hR9Ll65I74+neD9wcNXuLcCj48RbasRfJyIujYi2iGhrbm7es5MyK8Dg4CBnnnkmTzzxRNlVMdsjRfb+EnA5cH9EfLlq03pguAdXBbi6Kn5y6gW2FHg6XR7bCLRLmptu0LcDG9O2ZyQtTZ91ctWxzOpKZ2cnvb29dHZ6lm6rb0W2VI4F/hg4TtKW9FoBfBFYJmkrsCytA2wAHgT6gK8DfwYQEduBzwN3pNfnUgzgE8BlaZ+fAF0Fno9ZIQYHB9mwYQMRwYYNG9xasbo20Um6dllE/JDa9z0APlCjfACnj3KstcDaGvEe4Mg9qKZZ6To7O9m5cycAO3bsoLOzk7PPPrvkWpntHj9Rb1ayTZs2MTytd0SwcePGkmtktvucVMxKNn/+/DHXzeqJk4pZyX7+85+PuW5WT5xUzEp24IEHjrluU5+7hL/KScWsZG6p1D93CX+Vk4pZydxSqW+Dg4N0dXUREXR1dc341oqTilnJHnvssTHXbWrr7Ox8pffe0NDQjG+tOKmYlay9vZ3hqYAkccIJJ5RcI9sV3d3d7NixA8ieM9q0aVPJNSqXk4pZySqVCnvtlT2H3NjYSKVSGWcPm0qWLVtGY2MjkH1/7e3tJdeoXIU9UV/v+vv7aXjuafa975qyqzJtNTz3BP39O8uuRumamppYsWIF69evZ8WKFcybN6/sKtkuqFQqdHVlI0Q1NDTM+B8FbqmYTQGVSoXFixfP+D9I9aipqYmOjg4k0dHRMeN/FLilMoqWlhYee3EvXjjig2VXZdra975raGlxTyfI/jBddNFFZVfDdlOlUuHhhx/2jwKcVMzM9ph/FLzKl7/MpgA/kW3ThZOK2RTgJ7JtunBSMSuZn8i26aTI6YTXSnpc0j1Vsc9I+o8RM0EObztPUp+kBySdUBVfnmJ9ks6tih8q6TZJWyVdKWnvos7FrEh+ItumkyJv1H8T+DvgihHxCyPif1cHJB0BnAS8A3gLcK2kt6bNXyGbdrgfuEPS+oi4D/hSOtY6SV8DTgUuLupkzADWrFlDX19frsfs7e1laGgIyJ7IXr9+PQ8//HBux1+0aBGrV6/O7XhmYymspRIRNwHbxy2YWQmsi4gXI+Ihsjnnj06vvoh4MCJeAtYBK5WNaXEc8N20fydwYq4nYDZJ5s6dO+a6WT0po0vxGZJOBnqAP4+IJ4EFwK1VZfpTDOCREfFjgHnAUxGxs0b515G0ClgFcMghh+RxDjZDFfGLf3BwkI985CNEBPvssw+XXXbZjH+AzurXZN+ovxg4DFgCbAP+JsVVo2zsRrymiLg0Itoioq25uXnXamxWsKamJg444AAAP5FtdW9SWyoR8cqY3pK+DgwPrNUPHFxVtAV4NC3Xig8CcyTtlVor1eXN6s6BBx7ICy+84Ceyre5NaktF0kFVq78LDPcMWw+cJGkfSYcCrcDtwB1Aa+rptTfZzfz1kXWVuQH4aNq/Alw9GedgVoTGxkZaW1vdSrG6V1hLRdI/AO8DmiT1A+cD75O0hOxS1cPAaQARca+kq4D7gJ3A6RHxcjrOGcBGYBawNiLuTR9xDrBO0heAO4HLizoXMzObmMKSSkR8rEZ41D/8EXEBcEGN+AZgQ434g2S9w8zMbIrwE/VmZpYbj1I8hobnttfVJF164RcAxL77l1yTiWl4bjvgoe/NphMnlVEsWrSo7Crssq1bnwGg9bB6+UN9YF3+dzaz0TmpjKIeh7UYrvOaNWtKromZzVS+p2JmZrlxUjEzs9w4qZiZWW6cVMzMLDdOKmZmlhsnFTMzy427FJvZjFDErJ3D+vv7AWhpacn92PU2c6eTiplNKUX98e/v7+f555/P/bjAK8ct4vj9/f2F/PcoKlk5qZjZlNLX18ed994Jc3I+sIA35HzMYUPZ27NveDb3Qz/Lswz8x0C+B30q38NVc1Ixs6lnDgy9b6jsWkxbDTcWdzvdN+rNzCw3hSUVSWslPS7pnqrYAZK6JW1N73NTXJLWSOqT1CvpXVX7VFL5rZIqVfGjJN2d9lkjqda89WZmNomKvPz1TeDvgCuqYucC10XEFyWdm9bPATrIphBuBY4BLgaOkXQA2YyRbWSzRW6WtD4inkxlVgG3kk3itRzoKvB8rE4U2cunKFu3bgXqbyDTeuuZZMUrcubHmyQtHBFeSTbFMEAncCNZUlkJXJHmnr9V0pw0n/37gO6I2A4gqRtYLulGYP+IuCXFrwBOxEnFyG70/vs9P+KQ/V4uuyoTtveO7KLBCw/fUXJNJu5nz84quwo2BU32jfr5EbENICK2SXpzii8AHqkq159iY8X7a8TNADhkv5f5q7b8e+LYq77Qs1/ZVbApaKrcqK91PyR2I1774NIqST2SegYGcu6aZ2Zmr5jspPJYuqxFen88xfuBg6vKtQCPjhNvqRGvKSIujYi2iGhrbm7e45MwM7PaJjuprAeGe3BVgKur4ienXmBLgafTZbKNQLukuamnWDuwMW17RtLS1Ovr5KpjmZlZSQq7pyLpH8hutDdJ6ifrxfVF4CpJpwI/A34/Fd8ArAD6gOeAUwAiYrukzwPDdy8/N3zTHvgEWQ+z2WQ36H2T3sysZEX2/vrYKJs+UKNsAKePcpy1wNoa8R7gyD2po5lNPf39/fB0sU99z3hPQX/0j19uN/hbMzOz3HjsLzObUlpaWhjQgMf+KlDDjQ20LMh/mH5wS8XMzHLklopNO/39/fzymVl+OK9gP31mFm/sL+a6vNUvJxUzm3qeqrMb9cODN9TL75inKGwMEicVm3ZaWlp4Yec2D9NSsC/07Me+BU2fW2+GBwRtXdBack0maEFx/52dVMxsSilq1ON6HL0a6m8kaCcVM7M9NHv27LKrMGU4qZjZjFBPv/brmZOKTUs/e7a+en899lx2U3r+G+rn2YyfPTuLt5ZdCZtynFQmWZHXdYucPbCeruvW443el9J3t+/COrnRC7yV+vxvbcVyUplGfF03Uy/Jr9pwndesWVNyTcz2jJPKJKvHP3hmZhNVR08XmZnZVOekYmZmuXFSMTOz3JSSVCQ9LOluSVsk9aTYAZK6JW1N73NTXJLWSOqT1CvpXVXHqaTyWyVVRvs8MzObHGW2VN4fEUsioi2tnwtcFxGtwHVpHaADaE2vVcDFkCUhsimKjwGOBs4fTkRmZlaOqdT7ayXZnPYAncCNwDkpfkWacvhWSXMkHZTKdg/PWS+pG1gO/MPkVttmkqKeM/IzRjZdlNVSCWCTpM2SVqXY/IjYBpDe35ziC4BHqvbtT7HR4q8jaZWkHkk9AwMDOZ6GWT5mz57t54xsWiirpXJsRDwq6c1At6Qfj1FWNWIxRvz1wYhLgUsB2traapYxmwj/4jcbWyktlYh4NL0/DvwT2T2Rx9JlLdL746l4P3Bw1e4twKNjxM3MrCSTnlQkvVHSrwwvA+3APcB6YLgHVwW4Oi2vB05OvcCWAk+ny2MbgXZJc9MN+vYUMzOzkpRx+Ws+8E+Shj//7yPiXyXdAVwl6VTgZ8Dvp/IbgBVAH/AccApARGyX9HngjlTuc8M37c3MrBzKOlXNHG1tbdHT01N2NczM6oqkzVWPgIzKT9SbmVlunFTMzCw3TipmZpYbJxUzM8vNjLtRL2kA+GnZ9ShQEzBYdiVst/i7q2/T/fv71YhoHq/QjEsq052knon00LCpx99dffP3l/HlLzMzy42TipmZ5cZJZfq5tOwK2G7zd1ff/P3heypmZpYjt1TMzCw3TiozhKT3Sbqm7HrMZJJWS7pf0nd2cb8bJc34XkVlkzRL0p278+9I0kJJ9xRRr6lmKk0nbLtA2TDPioihsutiE/ZnQEdEPFR2RWy3nAXcD+xfdkWmMrdU6kj6tXO/pK8CDwKXp/hZkh5My4dJ+mFaXi7px2n990qruCHpa8CvAeslPSNpTpoj6AlJJ6cy35J0vKTZktZJ6pV0JeB5hksmqQX4HeCytH60pO+n5ZWSnpe0t6R9q/4tHiXpLkm3AKeXVvlJ5qRSf94GXAG8Bzgyxd4LPCFpAfCbwM2S9gW+DnwobT+whLpaEhEfJ5uZ9P3Ad4BjgXeQ/Th4byq2FLgV+ATwXEQsBi4Ajpr0CttIfwt8Ghi+MvAj4J1p+b1kEw2+GzgGuC3FvwGsjoj3TGI9S+ekUn9+GhG3RsTPgf3SLJoHA38P/BbZ/+A3A28HHoqIrZF18ft2aTW2kW4m+65+C7gY+PX0g2B7RDyb4t8GiIheoLesihpI+iDweERsHo5FxE6gT9LhZNOhf5mqf3+S3gTMiYgfpF2+NcnVLo2TSv35ZdXyLWQzYT5A9ofqvWQtmH9L291ffGq6iey7ei9wIzAAfJTsOxzm727qOBb4sKSHgXXAcZK+TfZ9dQA7gGvJrhL8Jtn3K2bod+ikUt9uAj6V3u8ku7TyYkQ8DfwYOFTSYansx8qpoo0UEY+QDT7YGhEPAj8k+x6Hk8pNwB8CSDoSWFxGPS0TEedFREtELAROAq6PiD8i+54+CdwSEQPAPLIrBPdGxFPA05J+Mx3mD0uoeimcVOrbzWSXvm6KiJeBR8j+QBERLwCrgH9JN+qn88jM9eg24N/T8s3AAtJ3R3ZJbD9JvWTX8W+f/OrZBNwGzCdLLpBdpuyNV58oPwX4SrpR/3wJ9SuFn6g3M7PcuKViZma5cVIxM7PcOKmYmVlunFTMzCw3TipmZpYbJxWzMUj6jKRP1Yh/fHjMrkmow7M5HccjVVvhPEqx2S6StFdEfK3sephNRW6pmI0g6S8lPSDpWrIBPIfnNPlrST8AzhpuwUg6XNLtVfsuTA8tDo9S+wNJmyVtlHRQjc/6tKTVaflCSden5Q+koUCGy12QRry9VdL8FGuW9D1Jd6TXsSn+RklrU+xOSStrfO5vS9qSXnemMeTM9piTilkVSUeRDcXxTrLpAt5dtXlORPx2RPzNcCAi7gf2lvRrKfQHwFWSGoGLgI9GxFHAWrIRh0caHgcMoI3sSfpG0mjTKf5G4NaI+I38gmdJAAAB/ElEQVRU/k9T/P8AF0bEu4GPkIZlB/6SbCiRd5MN3fO/JL1xxOd+Cjg9Ipakz58xT3xbsXz5y+y13gv8U0Q8ByBpfdW2K0fZ5yrgPwNfJEsqf0DWwjkS6M7mU2MWsK3GvpuBo1JL4UWyIdXbUj1WpzIvAddUlV+Wlo8HjkjHB9g/HaedbADE4XtB+wKHjPjcfwO+nGah/H5E9I9ybma7xEnF7PVGG7vol6PErwT+MU3aFBGxVdKvkw0s+Jq5NCQdDPxzWv1aRHwtjX57CvD/yMaPej9wGNksgwA7qsaTeplX/902AO+JiNe0MtKsoB+JiAdGxOe/coIRX5T0L8AK4FZJx0fEj0c5P7MJ8+Uvs9e6CfjdNPvir5BNcjamiPgJ2R/7/8GrrZkHgGZJ7wGQ1CjpHRHxSEQsSa/hm/3Vo03fDHwc2BLjD8y3CThjeEXSkrS4ETgzJRckvXPkjpIOi4i7I+JLQA/Z6Lpme8xJxaxKRPyILDFsAb7Ha+c4GcuVwB+RXQojIl4imyPlS5LuSsf7T6PsezNwENkQ6o8BL0zwc1cDbWna4fvIkhHA54FGoFfSPWl9pE9KuifV7XmgawKfZzYuj1JsZma5cUvFzMxy46RiZma5cVIxM7PcOKmYmVlunFTMzCw3TipmZpYbJxUzM8uNk4qZmeXm/wOJ4wILNo1GjAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.boxplot(x=\"drive-wheels\", y=\"price\", data=df)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"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>symboling</th>\n",
" <th>normalized-losses</th>\n",
" <th>wheel-base</th>\n",
" <th>length</th>\n",
" <th>width</th>\n",
" <th>height</th>\n",
" <th>curb-weight</th>\n",
" <th>engine-size</th>\n",
" <th>bore</th>\n",
" <th>stroke</th>\n",
" <th>compression-ratio</th>\n",
" <th>horsepower</th>\n",
" <th>peak-rpm</th>\n",
" <th>city-mpg</th>\n",
" <th>highway-mpg</th>\n",
" <th>price</th>\n",
" <th>city-L/100km</th>\n",
" <th>diesel</th>\n",
" <th>gas</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>201.000000</td>\n",
" <td>201.00000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>197.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.840796</td>\n",
" <td>122.00000</td>\n",
" <td>98.797015</td>\n",
" <td>0.837102</td>\n",
" <td>0.915126</td>\n",
" <td>53.766667</td>\n",
" <td>2555.666667</td>\n",
" <td>126.875622</td>\n",
" <td>3.330692</td>\n",
" <td>3.256904</td>\n",
" <td>10.164279</td>\n",
" <td>103.405534</td>\n",
" <td>5117.665368</td>\n",
" <td>25.179104</td>\n",
" <td>30.686567</td>\n",
" <td>13207.129353</td>\n",
" <td>9.944145</td>\n",
" <td>0.099502</td>\n",
" <td>0.900498</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.254802</td>\n",
" <td>31.99625</td>\n",
" <td>6.066366</td>\n",
" <td>0.059213</td>\n",
" <td>0.029187</td>\n",
" <td>2.447822</td>\n",
" <td>517.296727</td>\n",
" <td>41.546834</td>\n",
" <td>0.268072</td>\n",
" <td>0.319256</td>\n",
" <td>4.004965</td>\n",
" <td>37.365700</td>\n",
" <td>478.113805</td>\n",
" <td>6.423220</td>\n",
" <td>6.815150</td>\n",
" <td>7947.066342</td>\n",
" <td>2.534599</td>\n",
" <td>0.300083</td>\n",
" <td>0.300083</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-2.000000</td>\n",
" <td>65.00000</td>\n",
" <td>86.600000</td>\n",
" <td>0.678039</td>\n",
" <td>0.837500</td>\n",
" <td>47.800000</td>\n",
" <td>1488.000000</td>\n",
" <td>61.000000</td>\n",
" <td>2.540000</td>\n",
" <td>2.070000</td>\n",
" <td>7.000000</td>\n",
" <td>48.000000</td>\n",
" <td>4150.000000</td>\n",
" <td>13.000000</td>\n",
" <td>16.000000</td>\n",
" <td>5118.000000</td>\n",
" <td>4.795918</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>101.00000</td>\n",
" <td>94.500000</td>\n",
" <td>0.801538</td>\n",
" <td>0.890278</td>\n",
" <td>52.000000</td>\n",
" <td>2169.000000</td>\n",
" <td>98.000000</td>\n",
" <td>3.150000</td>\n",
" <td>3.110000</td>\n",
" <td>8.600000</td>\n",
" <td>70.000000</td>\n",
" <td>4800.000000</td>\n",
" <td>19.000000</td>\n",
" <td>25.000000</td>\n",
" <td>7775.000000</td>\n",
" <td>7.833333</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1.000000</td>\n",
" <td>122.00000</td>\n",
" <td>97.000000</td>\n",
" <td>0.832292</td>\n",
" <td>0.909722</td>\n",
" <td>54.100000</td>\n",
" <td>2414.000000</td>\n",
" <td>120.000000</td>\n",
" <td>3.310000</td>\n",
" <td>3.290000</td>\n",
" <td>9.000000</td>\n",
" <td>95.000000</td>\n",
" <td>5125.369458</td>\n",
" <td>24.000000</td>\n",
" <td>30.000000</td>\n",
" <td>10295.000000</td>\n",
" <td>9.791667</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>2.000000</td>\n",
" <td>137.00000</td>\n",
" <td>102.400000</td>\n",
" <td>0.881788</td>\n",
" <td>0.925000</td>\n",
" <td>55.500000</td>\n",
" <td>2926.000000</td>\n",
" <td>141.000000</td>\n",
" <td>3.580000</td>\n",
" <td>3.410000</td>\n",
" <td>9.400000</td>\n",
" <td>116.000000</td>\n",
" <td>5500.000000</td>\n",
" <td>30.000000</td>\n",
" <td>34.000000</td>\n",
" <td>16500.000000</td>\n",
" <td>12.368421</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>3.000000</td>\n",
" <td>256.00000</td>\n",
" <td>120.900000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>59.800000</td>\n",
" <td>4066.000000</td>\n",
" <td>326.000000</td>\n",
" <td>3.940000</td>\n",
" <td>4.170000</td>\n",
" <td>23.000000</td>\n",
" <td>262.000000</td>\n",
" <td>6600.000000</td>\n",
" <td>49.000000</td>\n",
" <td>54.000000</td>\n",
" <td>45400.000000</td>\n",
" <td>18.076923</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" symboling normalized-losses wheel-base length width \\\n",
"count 201.000000 201.00000 201.000000 201.000000 201.000000 \n",
"mean 0.840796 122.00000 98.797015 0.837102 0.915126 \n",
"std 1.254802 31.99625 6.066366 0.059213 0.029187 \n",
"min -2.000000 65.00000 86.600000 0.678039 0.837500 \n",
"25% 0.000000 101.00000 94.500000 0.801538 0.890278 \n",
"50% 1.000000 122.00000 97.000000 0.832292 0.909722 \n",
"75% 2.000000 137.00000 102.400000 0.881788 0.925000 \n",
"max 3.000000 256.00000 120.900000 1.000000 1.000000 \n",
"\n",
" height curb-weight engine-size bore stroke \\\n",
"count 201.000000 201.000000 201.000000 201.000000 197.000000 \n",
"mean 53.766667 2555.666667 126.875622 3.330692 3.256904 \n",
"std 2.447822 517.296727 41.546834 0.268072 0.319256 \n",
"min 47.800000 1488.000000 61.000000 2.540000 2.070000 \n",
"25% 52.000000 2169.000000 98.000000 3.150000 3.110000 \n",
"50% 54.100000 2414.000000 120.000000 3.310000 3.290000 \n",
"75% 55.500000 2926.000000 141.000000 3.580000 3.410000 \n",
"max 59.800000 4066.000000 326.000000 3.940000 4.170000 \n",
"\n",
" compression-ratio horsepower peak-rpm city-mpg highway-mpg \\\n",
"count 201.000000 201.000000 201.000000 201.000000 201.000000 \n",
"mean 10.164279 103.405534 5117.665368 25.179104 30.686567 \n",
"std 4.004965 37.365700 478.113805 6.423220 6.815150 \n",
"min 7.000000 48.000000 4150.000000 13.000000 16.000000 \n",
"25% 8.600000 70.000000 4800.000000 19.000000 25.000000 \n",
"50% 9.000000 95.000000 5125.369458 24.000000 30.000000 \n",
"75% 9.400000 116.000000 5500.000000 30.000000 34.000000 \n",
"max 23.000000 262.000000 6600.000000 49.000000 54.000000 \n",
"\n",
" price city-L/100km diesel gas \n",
"count 201.000000 201.000000 201.000000 201.000000 \n",
"mean 13207.129353 9.944145 0.099502 0.900498 \n",
"std 7947.066342 2.534599 0.300083 0.300083 \n",
"min 5118.000000 4.795918 0.000000 0.000000 \n",
"25% 7775.000000 7.833333 0.000000 1.000000 \n",
"50% 10295.000000 9.791667 0.000000 1.000000 \n",
"75% 16500.000000 12.368421 0.000000 1.000000 \n",
"max 45400.000000 18.076923 1.000000 1.000000 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"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>make</th>\n",
" <th>aspiration</th>\n",
" <th>num-of-doors</th>\n",
" <th>body-style</th>\n",
" <th>drive-wheels</th>\n",
" <th>engine-location</th>\n",
" <th>engine-type</th>\n",
" <th>num-of-cylinders</th>\n",
" <th>fuel-system</th>\n",
" <th>horsepower-binned</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>201</td>\n",
" <td>201</td>\n",
" <td>201</td>\n",
" <td>201</td>\n",
" <td>201</td>\n",
" <td>201</td>\n",
" <td>201</td>\n",
" <td>201</td>\n",
" <td>201</td>\n",
" <td>200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
" <td>22</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\n",
" <td>toyota</td>\n",
" <td>std</td>\n",
" <td>four</td>\n",
" <td>sedan</td>\n",
" <td>fwd</td>\n",
" <td>front</td>\n",
" <td>ohc</td>\n",
" <td>four</td>\n",
" <td>mpfi</td>\n",
" <td>Low</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>32</td>\n",
" <td>165</td>\n",
" <td>115</td>\n",
" <td>94</td>\n",
" <td>118</td>\n",
" <td>198</td>\n",
" <td>145</td>\n",
" <td>157</td>\n",
" <td>92</td>\n",
" <td>115</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" make aspiration num-of-doors body-style drive-wheels \\\n",
"count 201 201 201 201 201 \n",
"unique 22 2 2 5 3 \n",
"top toyota std four sedan fwd \n",
"freq 32 165 115 94 118 \n",
"\n",
" engine-location engine-type num-of-cylinders fuel-system \\\n",
"count 201 201 201 201 \n",
"unique 2 6 7 8 \n",
"top front ohc four mpfi \n",
"freq 198 145 157 92 \n",
"\n",
" horsepower-binned \n",
"count 200 \n",
"unique 3 \n",
"top Low \n",
"freq 115 "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe(include=['object'])"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"fwd 118\n",
"rwd 75\n",
"4wd 8\n",
"Name: drive-wheels, dtype: int64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['drive-wheels'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"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>drive-wheels</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>fwd</th>\n",
" <td>118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rwd</th>\n",
" <td>75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4wd</th>\n",
" <td>8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" drive-wheels\n",
"fwd 118\n",
"rwd 75\n",
"4wd 8"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['drive-wheels'].value_counts().to_frame()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"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>value_counts</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>fwd</th>\n",
" <td>118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rwd</th>\n",
" <td>75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4wd</th>\n",
" <td>8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" value_counts\n",
"fwd 118\n",
"rwd 75\n",
"4wd 8"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"drive_wheels_counts = df['drive-wheels'].value_counts().to_frame()\n",
"drive_wheels_counts.rename(columns={'drive-wheels': 'value_counts'}, inplace=True)\n",
"drive_wheels_counts"
]
},
{
"cell_type": "code",
"execution_count": 27,
"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>value_counts</th>\n",
" </tr>\n",
" <tr>\n",
" <th>drive-wheels</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>fwd</th>\n",
" <td>118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rwd</th>\n",
" <td>75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4wd</th>\n",
" <td>8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" value_counts\n",
"drive-wheels \n",
"fwd 118\n",
"rwd 75\n",
"4wd 8"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"drive_wheels_counts.index.name = 'drive-wheels'\n",
"drive_wheels_counts"
]
},
{
"cell_type": "code",
"execution_count": 28,
"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>value_counts</th>\n",
" </tr>\n",
" <tr>\n",
" <th>engine-location</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>front</th>\n",
" <td>198</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rear</th>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" value_counts\n",
"engine-location \n",
"front 198\n",
"rear 3"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# engine-location as variable\n",
"engine_loc_counts = df['engine-location'].value_counts().to_frame()\n",
"engine_loc_counts.rename(columns={'engine-location': 'value_counts'}, inplace=True)\n",
"engine_loc_counts.index.name = 'engine-location'\n",
"engine_loc_counts.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['rwd', 'fwd', '4wd'], dtype=object)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['drive-wheels'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"df_group_one = df[['drive-wheels','body-style','price']]"
]
},
{
"cell_type": "code",
"execution_count": 31,
"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>drive-wheels</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4wd</td>\n",
" <td>10241.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>fwd</td>\n",
" <td>9244.779661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>rwd</td>\n",
" <td>19757.613333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" drive-wheels price\n",
"0 4wd 10241.000000\n",
"1 fwd 9244.779661\n",
"2 rwd 19757.613333"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# grouping results\n",
"df_group_one = df_group_one.groupby(['drive-wheels'],as_index=False).mean()\n",
"df_group_one"
]
},
{
"cell_type": "code",
"execution_count": 32,
"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>drive-wheels</th>\n",
" <th>body-style</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4wd</td>\n",
" <td>hatchback</td>\n",
" <td>7603.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4wd</td>\n",
" <td>sedan</td>\n",
" <td>12647.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4wd</td>\n",
" <td>wagon</td>\n",
" <td>9095.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>fwd</td>\n",
" <td>convertible</td>\n",
" <td>11595.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>fwd</td>\n",
" <td>hardtop</td>\n",
" <td>8249.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>fwd</td>\n",
" <td>hatchback</td>\n",
" <td>8396.387755</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>fwd</td>\n",
" <td>sedan</td>\n",
" <td>9811.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>fwd</td>\n",
" <td>wagon</td>\n",
" <td>9997.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>rwd</td>\n",
" <td>convertible</td>\n",
" <td>23949.600000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>rwd</td>\n",
" <td>hardtop</td>\n",
" <td>24202.714286</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>rwd</td>\n",
" <td>hatchback</td>\n",
" <td>14337.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>rwd</td>\n",
" <td>sedan</td>\n",
" <td>21711.833333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>rwd</td>\n",
" <td>wagon</td>\n",
" <td>16994.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" drive-wheels body-style price\n",
"0 4wd hatchback 7603.000000\n",
"1 4wd sedan 12647.333333\n",
"2 4wd wagon 9095.750000\n",
"3 fwd convertible 11595.000000\n",
"4 fwd hardtop 8249.000000\n",
"5 fwd hatchback 8396.387755\n",
"6 fwd sedan 9811.800000\n",
"7 fwd wagon 9997.333333\n",
"8 rwd convertible 23949.600000\n",
"9 rwd hardtop 24202.714286\n",
"10 rwd hatchback 14337.777778\n",
"11 rwd sedan 21711.833333\n",
"12 rwd wagon 16994.222222"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# grouping results\n",
"df_gptest = df[['drive-wheels','body-style','price']]\n",
"grouped_test1 = df_gptest.groupby(['drive-wheels','body-style'],as_index=False).mean()\n",
"grouped_test1"
]
},
{
"cell_type": "code",
"execution_count": 33,
"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",
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" }\n",
"\n",
" .dataframe thead tr th {\n",
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" }\n",
"\n",
" .dataframe thead tr:last-of-type th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"5\" halign=\"left\">price</th>\n",
" </tr>\n",
" <tr>\n",
" <th>body-style</th>\n",
" <th>convertible</th>\n",
" <th>hardtop</th>\n",
" <th>hatchback</th>\n",
" <th>sedan</th>\n",
" <th>wagon</th>\n",
" </tr>\n",
" <tr>\n",
" <th>drive-wheels</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>4wd</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>7603.000000</td>\n",
" <td>12647.333333</td>\n",
" <td>9095.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>fwd</th>\n",
" <td>11595.0</td>\n",
" <td>8249.000000</td>\n",
" <td>8396.387755</td>\n",
" <td>9811.800000</td>\n",
" <td>9997.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rwd</th>\n",
" <td>23949.6</td>\n",
" <td>24202.714286</td>\n",
" <td>14337.777778</td>\n",
" <td>21711.833333</td>\n",
" <td>16994.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" price \\\n",
"body-style convertible hardtop hatchback sedan \n",
"drive-wheels \n",
"4wd NaN NaN 7603.000000 12647.333333 \n",
"fwd 11595.0 8249.000000 8396.387755 9811.800000 \n",
"rwd 23949.6 24202.714286 14337.777778 21711.833333 \n",
"\n",
" \n",
"body-style wagon \n",
"drive-wheels \n",
"4wd 9095.750000 \n",
"fwd 9997.333333 \n",
"rwd 16994.222222 "
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grouped_pivot = grouped_test1.pivot(index='drive-wheels',columns='body-style')\n",
"grouped_pivot"
]
},
{
"cell_type": "code",
"execution_count": 34,
"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 tr th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe thead tr:last-of-type th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"5\" halign=\"left\">price</th>\n",
" </tr>\n",
" <tr>\n",
" <th>body-style</th>\n",
" <th>convertible</th>\n",
" <th>hardtop</th>\n",
" <th>hatchback</th>\n",
" <th>sedan</th>\n",
" <th>wagon</th>\n",
" </tr>\n",
" <tr>\n",
" <th>drive-wheels</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>4wd</th>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>7603.000000</td>\n",
" <td>12647.333333</td>\n",
" <td>9095.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>fwd</th>\n",
" <td>11595.0</td>\n",
" <td>8249.000000</td>\n",
" <td>8396.387755</td>\n",
" <td>9811.800000</td>\n",
" <td>9997.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rwd</th>\n",
" <td>23949.6</td>\n",
" <td>24202.714286</td>\n",
" <td>14337.777778</td>\n",
" <td>21711.833333</td>\n",
" <td>16994.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" price \\\n",
"body-style convertible hardtop hatchback sedan \n",
"drive-wheels \n",
"4wd 0.0 0.000000 7603.000000 12647.333333 \n",
"fwd 11595.0 8249.000000 8396.387755 9811.800000 \n",
"rwd 23949.6 24202.714286 14337.777778 21711.833333 \n",
"\n",
" \n",
"body-style wagon \n",
"drive-wheels \n",
"4wd 9095.750000 \n",
"fwd 9997.333333 \n",
"rwd 16994.222222 "
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grouped_pivot = grouped_pivot.fillna(0) #fill missing values with 0\n",
"grouped_pivot"
]
},
{
"cell_type": "code",
"execution_count": 35,
"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>body-style</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>convertible</td>\n",
" <td>21890.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>hardtop</td>\n",
" <td>22208.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>hatchback</td>\n",
" <td>9957.441176</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>sedan</td>\n",
" <td>14459.755319</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>wagon</td>\n",
" <td>12371.960000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" body-style price\n",
"0 convertible 21890.500000\n",
"1 hardtop 22208.500000\n",
"2 hatchback 9957.441176\n",
"3 sedan 14459.755319\n",
"4 wagon 12371.960000"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# grouping results\n",
"df_gptest2 = df[['body-style','price']]\n",
"grouped_test_bodystyle = df_gptest2.groupby(['body-style'],as_index= False).mean()\n",
"grouped_test_bodystyle"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline "
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#use the grouped results\n",
"plt.pcolor(grouped_pivot, cmap='RdBu')\n",
"plt.colorbar()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"im = ax.pcolor(grouped_pivot, cmap='RdBu')\n",
"\n",
"#label names\n",
"row_labels = grouped_pivot.columns.levels[1]\n",
"col_labels = grouped_pivot.index\n",
"\n",
"#move ticks and labels to the center\n",
"ax.set_xticks(np.arange(grouped_pivot.shape[1]) + 0.5, minor=False)\n",
"ax.set_yticks(np.arange(grouped_pivot.shape[0]) + 0.5, minor=False)\n",
"\n",
"#insert labels\n",
"ax.set_xticklabels(row_labels, minor=False)\n",
"ax.set_yticklabels(col_labels, minor=False)\n",
"\n",
"#rotate label if too long\n",
"plt.xticks(rotation=90)\n",
"\n",
"fig.colorbar(im)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"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>symboling</th>\n",
" <th>normalized-losses</th>\n",
" <th>wheel-base</th>\n",
" <th>length</th>\n",
" <th>width</th>\n",
" <th>height</th>\n",
" <th>curb-weight</th>\n",
" <th>engine-size</th>\n",
" <th>bore</th>\n",
" <th>stroke</th>\n",
" <th>compression-ratio</th>\n",
" <th>horsepower</th>\n",
" <th>peak-rpm</th>\n",
" <th>city-mpg</th>\n",
" <th>highway-mpg</th>\n",
" <th>price</th>\n",
" <th>city-L/100km</th>\n",
" <th>diesel</th>\n",
" <th>gas</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>symboling</th>\n",
" <td>1.000000</td>\n",
" <td>0.466264</td>\n",
" <td>-0.535987</td>\n",
" <td>-0.365404</td>\n",
" <td>-0.242423</td>\n",
" <td>-0.550160</td>\n",
" <td>-0.233118</td>\n",
" <td>-0.110581</td>\n",
" <td>-0.140019</td>\n",
" <td>-0.008245</td>\n",
" <td>-0.182196</td>\n",
" <td>0.075819</td>\n",
" <td>0.279740</td>\n",
" <td>-0.035527</td>\n",
" <td>0.036233</td>\n",
" <td>-0.082391</td>\n",
" <td>0.066171</td>\n",
" <td>-0.196735</td>\n",
" <td>0.196735</td>\n",
" </tr>\n",
" <tr>\n",
" <th>normalized-losses</th>\n",
" <td>0.466264</td>\n",
" <td>1.000000</td>\n",
" <td>-0.056661</td>\n",
" <td>0.019424</td>\n",
" <td>0.086802</td>\n",
" <td>-0.373737</td>\n",
" <td>0.099404</td>\n",
" <td>0.112360</td>\n",
" <td>-0.029862</td>\n",
" <td>0.055563</td>\n",
" <td>-0.114713</td>\n",
" <td>0.217299</td>\n",
" <td>0.239543</td>\n",
" <td>-0.225016</td>\n",
" <td>-0.181877</td>\n",
" <td>0.133999</td>\n",
" <td>0.238567</td>\n",
" <td>-0.101546</td>\n",
" <td>0.101546</td>\n",
" </tr>\n",
" <tr>\n",
" <th>wheel-base</th>\n",
" <td>-0.535987</td>\n",
" <td>-0.056661</td>\n",
" <td>1.000000</td>\n",
" <td>0.876024</td>\n",
" <td>0.814507</td>\n",
" <td>0.590742</td>\n",
" <td>0.782097</td>\n",
" <td>0.572027</td>\n",
" <td>0.493244</td>\n",
" <td>0.158502</td>\n",
" <td>0.250313</td>\n",
" <td>0.371147</td>\n",
" <td>-0.360305</td>\n",
" <td>-0.470606</td>\n",
" <td>-0.543304</td>\n",
" <td>0.584642</td>\n",
" <td>0.476153</td>\n",
" <td>0.307237</td>\n",
" <td>-0.307237</td>\n",
" </tr>\n",
" <tr>\n",
" <th>length</th>\n",
" <td>-0.365404</td>\n",
" <td>0.019424</td>\n",
" <td>0.876024</td>\n",
" <td>1.000000</td>\n",
" <td>0.857170</td>\n",
" <td>0.492063</td>\n",
" <td>0.880665</td>\n",
" <td>0.685025</td>\n",
" <td>0.608971</td>\n",
" <td>0.124139</td>\n",
" <td>0.159733</td>\n",
" <td>0.579821</td>\n",
" <td>-0.285970</td>\n",
" <td>-0.665192</td>\n",
" <td>-0.698142</td>\n",
" <td>0.690628</td>\n",
" <td>0.657373</td>\n",
" <td>0.211187</td>\n",
" <td>-0.211187</td>\n",
" </tr>\n",
" <tr>\n",
" <th>width</th>\n",
" <td>-0.242423</td>\n",
" <td>0.086802</td>\n",
" <td>0.814507</td>\n",
" <td>0.857170</td>\n",
" <td>1.000000</td>\n",
" <td>0.306002</td>\n",
" <td>0.866201</td>\n",
" <td>0.729436</td>\n",
" <td>0.544885</td>\n",
" <td>0.188829</td>\n",
" <td>0.189867</td>\n",
" <td>0.615077</td>\n",
" <td>-0.245800</td>\n",
" <td>-0.633531</td>\n",
" <td>-0.680635</td>\n",
" <td>0.751265</td>\n",
" <td>0.673363</td>\n",
" <td>0.244356</td>\n",
" <td>-0.244356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>height</th>\n",
" <td>-0.550160</td>\n",
" <td>-0.373737</td>\n",
" <td>0.590742</td>\n",
" <td>0.492063</td>\n",
" <td>0.306002</td>\n",
" <td>1.000000</td>\n",
" <td>0.307581</td>\n",
" <td>0.074694</td>\n",
" <td>0.180449</td>\n",
" <td>-0.062704</td>\n",
" <td>0.259737</td>\n",
" <td>-0.087027</td>\n",
" <td>-0.309974</td>\n",
" <td>-0.049800</td>\n",
" <td>-0.104812</td>\n",
" <td>0.135486</td>\n",
" <td>0.003811</td>\n",
" <td>0.281578</td>\n",
" <td>-0.281578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>curb-weight</th>\n",
" <td>-0.233118</td>\n",
" <td>0.099404</td>\n",
" <td>0.782097</td>\n",
" <td>0.880665</td>\n",
" <td>0.866201</td>\n",
" <td>0.307581</td>\n",
" <td>1.000000</td>\n",
" <td>0.849072</td>\n",
" <td>0.644060</td>\n",
" <td>0.167562</td>\n",
" <td>0.156433</td>\n",
" <td>0.757976</td>\n",
" <td>-0.279361</td>\n",
" <td>-0.749543</td>\n",
" <td>-0.794889</td>\n",
" <td>0.834415</td>\n",
" <td>0.785353</td>\n",
" <td>0.221046</td>\n",
" <td>-0.221046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>engine-size</th>\n",
" <td>-0.110581</td>\n",
" <td>0.112360</td>\n",
" <td>0.572027</td>\n",
" <td>0.685025</td>\n",
" <td>0.729436</td>\n",
" <td>0.074694</td>\n",
" <td>0.849072</td>\n",
" <td>1.000000</td>\n",
" <td>0.572609</td>\n",
" <td>0.209523</td>\n",
" <td>0.028889</td>\n",
" <td>0.822676</td>\n",
" <td>-0.256733</td>\n",
" <td>-0.650546</td>\n",
" <td>-0.679571</td>\n",
" <td>0.872335</td>\n",
" <td>0.745059</td>\n",
" <td>0.070779</td>\n",
" <td>-0.070779</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bore</th>\n",
" <td>-0.140019</td>\n",
" <td>-0.029862</td>\n",
" <td>0.493244</td>\n",
" <td>0.608971</td>\n",
" <td>0.544885</td>\n",
" <td>0.180449</td>\n",
" <td>0.644060</td>\n",
" <td>0.572609</td>\n",
" <td>1.000000</td>\n",
" <td>-0.055390</td>\n",
" <td>0.001263</td>\n",
" <td>0.566936</td>\n",
" <td>-0.267392</td>\n",
" <td>-0.582027</td>\n",
" <td>-0.591309</td>\n",
" <td>0.543155</td>\n",
" <td>0.554610</td>\n",
" <td>0.054458</td>\n",
" <td>-0.054458</td>\n",
" </tr>\n",
" <tr>\n",
" <th>stroke</th>\n",
" <td>-0.008245</td>\n",
" <td>0.055563</td>\n",
" <td>0.158502</td>\n",
" <td>0.124139</td>\n",
" <td>0.188829</td>\n",
" <td>-0.062704</td>\n",
" <td>0.167562</td>\n",
" <td>0.209523</td>\n",
" <td>-0.055390</td>\n",
" <td>1.000000</td>\n",
" <td>0.187923</td>\n",
" <td>0.098462</td>\n",
" <td>-0.065713</td>\n",
" <td>-0.034696</td>\n",
" <td>-0.035201</td>\n",
" <td>0.082310</td>\n",
" <td>0.037300</td>\n",
" <td>0.241303</td>\n",
" <td>-0.241303</td>\n",
" </tr>\n",
" <tr>\n",
" <th>compression-ratio</th>\n",
" <td>-0.182196</td>\n",
" <td>-0.114713</td>\n",
" <td>0.250313</td>\n",
" <td>0.159733</td>\n",
" <td>0.189867</td>\n",
" <td>0.259737</td>\n",
" <td>0.156433</td>\n",
" <td>0.028889</td>\n",
" <td>0.001263</td>\n",
" <td>0.187923</td>\n",
" <td>1.000000</td>\n",
" <td>-0.214514</td>\n",
" <td>-0.435780</td>\n",
" <td>0.331425</td>\n",
" <td>0.268465</td>\n",
" <td>0.071107</td>\n",
" <td>-0.299372</td>\n",
" <td>0.985231</td>\n",
" <td>-0.985231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>horsepower</th>\n",
" <td>0.075819</td>\n",
" <td>0.217299</td>\n",
" <td>0.371147</td>\n",
" <td>0.579821</td>\n",
" <td>0.615077</td>\n",
" <td>-0.087027</td>\n",
" <td>0.757976</td>\n",
" <td>0.822676</td>\n",
" <td>0.566936</td>\n",
" <td>0.098462</td>\n",
" <td>-0.214514</td>\n",
" <td>1.000000</td>\n",
" <td>0.107885</td>\n",
" <td>-0.822214</td>\n",
" <td>-0.804575</td>\n",
" <td>0.809575</td>\n",
" <td>0.889488</td>\n",
" <td>-0.169053</td>\n",
" <td>0.169053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>peak-rpm</th>\n",
" <td>0.279740</td>\n",
" <td>0.239543</td>\n",
" <td>-0.360305</td>\n",
" <td>-0.285970</td>\n",
" <td>-0.245800</td>\n",
" <td>-0.309974</td>\n",
" <td>-0.279361</td>\n",
" <td>-0.256733</td>\n",
" <td>-0.267392</td>\n",
" <td>-0.065713</td>\n",
" <td>-0.435780</td>\n",
" <td>0.107885</td>\n",
" <td>1.000000</td>\n",
" <td>-0.115413</td>\n",
" <td>-0.058598</td>\n",
" <td>-0.101616</td>\n",
" <td>0.115830</td>\n",
" <td>-0.475812</td>\n",
" <td>0.475812</td>\n",
" </tr>\n",
" <tr>\n",
" <th>city-mpg</th>\n",
" <td>-0.035527</td>\n",
" <td>-0.225016</td>\n",
" <td>-0.470606</td>\n",
" <td>-0.665192</td>\n",
" <td>-0.633531</td>\n",
" <td>-0.049800</td>\n",
" <td>-0.749543</td>\n",
" <td>-0.650546</td>\n",
" <td>-0.582027</td>\n",
" <td>-0.034696</td>\n",
" <td>0.331425</td>\n",
" <td>-0.822214</td>\n",
" <td>-0.115413</td>\n",
" <td>1.000000</td>\n",
" <td>0.972044</td>\n",
" <td>-0.686571</td>\n",
" <td>-0.949713</td>\n",
" <td>0.265676</td>\n",
" <td>-0.265676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>highway-mpg</th>\n",
" <td>0.036233</td>\n",
" <td>-0.181877</td>\n",
" <td>-0.543304</td>\n",
" <td>-0.698142</td>\n",
" <td>-0.680635</td>\n",
" <td>-0.104812</td>\n",
" <td>-0.794889</td>\n",
" <td>-0.679571</td>\n",
" <td>-0.591309</td>\n",
" <td>-0.035201</td>\n",
" <td>0.268465</td>\n",
" <td>-0.804575</td>\n",
" <td>-0.058598</td>\n",
" <td>0.972044</td>\n",
" <td>1.000000</td>\n",
" <td>-0.704692</td>\n",
" <td>-0.930028</td>\n",
" <td>0.198690</td>\n",
" <td>-0.198690</td>\n",
" </tr>\n",
" <tr>\n",
" <th>price</th>\n",
" <td>-0.082391</td>\n",
" <td>0.133999</td>\n",
" <td>0.584642</td>\n",
" <td>0.690628</td>\n",
" <td>0.751265</td>\n",
" <td>0.135486</td>\n",
" <td>0.834415</td>\n",
" <td>0.872335</td>\n",
" <td>0.543155</td>\n",
" <td>0.082310</td>\n",
" <td>0.071107</td>\n",
" <td>0.809575</td>\n",
" <td>-0.101616</td>\n",
" <td>-0.686571</td>\n",
" <td>-0.704692</td>\n",
" <td>1.000000</td>\n",
" <td>0.789898</td>\n",
" <td>0.110326</td>\n",
" <td>-0.110326</td>\n",
" </tr>\n",
" <tr>\n",
" <th>city-L/100km</th>\n",
" <td>0.066171</td>\n",
" <td>0.238567</td>\n",
" <td>0.476153</td>\n",
" <td>0.657373</td>\n",
" <td>0.673363</td>\n",
" <td>0.003811</td>\n",
" <td>0.785353</td>\n",
" <td>0.745059</td>\n",
" <td>0.554610</td>\n",
" <td>0.037300</td>\n",
" <td>-0.299372</td>\n",
" <td>0.889488</td>\n",
" <td>0.115830</td>\n",
" <td>-0.949713</td>\n",
" <td>-0.930028</td>\n",
" <td>0.789898</td>\n",
" <td>1.000000</td>\n",
" <td>-0.241282</td>\n",
" <td>0.241282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>diesel</th>\n",
" <td>-0.196735</td>\n",
" <td>-0.101546</td>\n",
" <td>0.307237</td>\n",
" <td>0.211187</td>\n",
" <td>0.244356</td>\n",
" <td>0.281578</td>\n",
" <td>0.221046</td>\n",
" <td>0.070779</td>\n",
" <td>0.054458</td>\n",
" <td>0.241303</td>\n",
" <td>0.985231</td>\n",
" <td>-0.169053</td>\n",
" <td>-0.475812</td>\n",
" <td>0.265676</td>\n",
" <td>0.198690</td>\n",
" <td>0.110326</td>\n",
" <td>-0.241282</td>\n",
" <td>1.000000</td>\n",
" <td>-1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gas</th>\n",
" <td>0.196735</td>\n",
" <td>0.101546</td>\n",
" <td>-0.307237</td>\n",
" <td>-0.211187</td>\n",
" <td>-0.244356</td>\n",
" <td>-0.281578</td>\n",
" <td>-0.221046</td>\n",
" <td>-0.070779</td>\n",
" <td>-0.054458</td>\n",
" <td>-0.241303</td>\n",
" <td>-0.985231</td>\n",
" <td>0.169053</td>\n",
" <td>0.475812</td>\n",
" <td>-0.265676</td>\n",
" <td>-0.198690</td>\n",
" <td>-0.110326</td>\n",
" <td>0.241282</td>\n",
" <td>-1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" symboling normalized-losses wheel-base length \\\n",
"symboling 1.000000 0.466264 -0.535987 -0.365404 \n",
"normalized-losses 0.466264 1.000000 -0.056661 0.019424 \n",
"wheel-base -0.535987 -0.056661 1.000000 0.876024 \n",
"length -0.365404 0.019424 0.876024 1.000000 \n",
"width -0.242423 0.086802 0.814507 0.857170 \n",
"height -0.550160 -0.373737 0.590742 0.492063 \n",
"curb-weight -0.233118 0.099404 0.782097 0.880665 \n",
"engine-size -0.110581 0.112360 0.572027 0.685025 \n",
"bore -0.140019 -0.029862 0.493244 0.608971 \n",
"stroke -0.008245 0.055563 0.158502 0.124139 \n",
"compression-ratio -0.182196 -0.114713 0.250313 0.159733 \n",
"horsepower 0.075819 0.217299 0.371147 0.579821 \n",
"peak-rpm 0.279740 0.239543 -0.360305 -0.285970 \n",
"city-mpg -0.035527 -0.225016 -0.470606 -0.665192 \n",
"highway-mpg 0.036233 -0.181877 -0.543304 -0.698142 \n",
"price -0.082391 0.133999 0.584642 0.690628 \n",
"city-L/100km 0.066171 0.238567 0.476153 0.657373 \n",
"diesel -0.196735 -0.101546 0.307237 0.211187 \n",
"gas 0.196735 0.101546 -0.307237 -0.211187 \n",
"\n",
" width height curb-weight engine-size bore \\\n",
"symboling -0.242423 -0.550160 -0.233118 -0.110581 -0.140019 \n",
"normalized-losses 0.086802 -0.373737 0.099404 0.112360 -0.029862 \n",
"wheel-base 0.814507 0.590742 0.782097 0.572027 0.493244 \n",
"length 0.857170 0.492063 0.880665 0.685025 0.608971 \n",
"width 1.000000 0.306002 0.866201 0.729436 0.544885 \n",
"height 0.306002 1.000000 0.307581 0.074694 0.180449 \n",
"curb-weight 0.866201 0.307581 1.000000 0.849072 0.644060 \n",
"engine-size 0.729436 0.074694 0.849072 1.000000 0.572609 \n",
"bore 0.544885 0.180449 0.644060 0.572609 1.000000 \n",
"stroke 0.188829 -0.062704 0.167562 0.209523 -0.055390 \n",
"compression-ratio 0.189867 0.259737 0.156433 0.028889 0.001263 \n",
"horsepower 0.615077 -0.087027 0.757976 0.822676 0.566936 \n",
"peak-rpm -0.245800 -0.309974 -0.279361 -0.256733 -0.267392 \n",
"city-mpg -0.633531 -0.049800 -0.749543 -0.650546 -0.582027 \n",
"highway-mpg -0.680635 -0.104812 -0.794889 -0.679571 -0.591309 \n",
"price 0.751265 0.135486 0.834415 0.872335 0.543155 \n",
"city-L/100km 0.673363 0.003811 0.785353 0.745059 0.554610 \n",
"diesel 0.244356 0.281578 0.221046 0.070779 0.054458 \n",
"gas -0.244356 -0.281578 -0.221046 -0.070779 -0.054458 \n",
"\n",
" stroke compression-ratio horsepower peak-rpm \\\n",
"symboling -0.008245 -0.182196 0.075819 0.279740 \n",
"normalized-losses 0.055563 -0.114713 0.217299 0.239543 \n",
"wheel-base 0.158502 0.250313 0.371147 -0.360305 \n",
"length 0.124139 0.159733 0.579821 -0.285970 \n",
"width 0.188829 0.189867 0.615077 -0.245800 \n",
"height -0.062704 0.259737 -0.087027 -0.309974 \n",
"curb-weight 0.167562 0.156433 0.757976 -0.279361 \n",
"engine-size 0.209523 0.028889 0.822676 -0.256733 \n",
"bore -0.055390 0.001263 0.566936 -0.267392 \n",
"stroke 1.000000 0.187923 0.098462 -0.065713 \n",
"compression-ratio 0.187923 1.000000 -0.214514 -0.435780 \n",
"horsepower 0.098462 -0.214514 1.000000 0.107885 \n",
"peak-rpm -0.065713 -0.435780 0.107885 1.000000 \n",
"city-mpg -0.034696 0.331425 -0.822214 -0.115413 \n",
"highway-mpg -0.035201 0.268465 -0.804575 -0.058598 \n",
"price 0.082310 0.071107 0.809575 -0.101616 \n",
"city-L/100km 0.037300 -0.299372 0.889488 0.115830 \n",
"diesel 0.241303 0.985231 -0.169053 -0.475812 \n",
"gas -0.241303 -0.985231 0.169053 0.475812 \n",
"\n",
" city-mpg highway-mpg price city-L/100km diesel \\\n",
"symboling -0.035527 0.036233 -0.082391 0.066171 -0.196735 \n",
"normalized-losses -0.225016 -0.181877 0.133999 0.238567 -0.101546 \n",
"wheel-base -0.470606 -0.543304 0.584642 0.476153 0.307237 \n",
"length -0.665192 -0.698142 0.690628 0.657373 0.211187 \n",
"width -0.633531 -0.680635 0.751265 0.673363 0.244356 \n",
"height -0.049800 -0.104812 0.135486 0.003811 0.281578 \n",
"curb-weight -0.749543 -0.794889 0.834415 0.785353 0.221046 \n",
"engine-size -0.650546 -0.679571 0.872335 0.745059 0.070779 \n",
"bore -0.582027 -0.591309 0.543155 0.554610 0.054458 \n",
"stroke -0.034696 -0.035201 0.082310 0.037300 0.241303 \n",
"compression-ratio 0.331425 0.268465 0.071107 -0.299372 0.985231 \n",
"horsepower -0.822214 -0.804575 0.809575 0.889488 -0.169053 \n",
"peak-rpm -0.115413 -0.058598 -0.101616 0.115830 -0.475812 \n",
"city-mpg 1.000000 0.972044 -0.686571 -0.949713 0.265676 \n",
"highway-mpg 0.972044 1.000000 -0.704692 -0.930028 0.198690 \n",
"price -0.686571 -0.704692 1.000000 0.789898 0.110326 \n",
"city-L/100km -0.949713 -0.930028 0.789898 1.000000 -0.241282 \n",
"diesel 0.265676 0.198690 0.110326 -0.241282 1.000000 \n",
"gas -0.265676 -0.198690 -0.110326 0.241282 -1.000000 \n",
"\n",
" gas \n",
"symboling 0.196735 \n",
"normalized-losses 0.101546 \n",
"wheel-base -0.307237 \n",
"length -0.211187 \n",
"width -0.244356 \n",
"height -0.281578 \n",
"curb-weight -0.221046 \n",
"engine-size -0.070779 \n",
"bore -0.054458 \n",
"stroke -0.241303 \n",
"compression-ratio -0.985231 \n",
"horsepower 0.169053 \n",
"peak-rpm 0.475812 \n",
"city-mpg -0.265676 \n",
"highway-mpg -0.198690 \n",
"price -0.110326 \n",
"city-L/100km 0.241282 \n",
"diesel -1.000000 \n",
"gas 1.000000 "
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.corr()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"from scipy import stats"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Pearson Correlation Coefficient is 0.5846418222655081 with a P-value of P = 8.076488270732955e-20\n"
]
}
],
"source": [
"pearson_coef, p_value = stats.pearsonr(df['wheel-base'], df['price'])\n",
"print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value) "
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Pearson Correlation Coefficient is 0.8095745670036559 with a P-value of P = 6.36905742825998e-48\n"
]
}
],
"source": [
"pearson_coef, p_value = stats.pearsonr(df['horsepower'], df['price'])\n",
"print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value) "
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Pearson Correlation Coefficient is 0.690628380448364 with a P-value of P = 8.016477466159053e-30\n"
]
}
],
"source": [
"pearson_coef, p_value = stats.pearsonr(df['length'], df['price'])\n",
"print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value) "
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Pearson Correlation Coefficient is 0.8344145257702846 with a P-value of P = 2.1895772388936997e-53\n"
]
}
],
"source": [
"pearson_coef, p_value = stats.pearsonr(df['curb-weight'], df['price'])\n",
"print( \"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value) "
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Pearson Correlation Coefficient is 0.8723351674455185 with a P-value of P = 9.265491622197996e-64\n"
]
}
],
"source": [
"pearson_coef, p_value = stats.pearsonr(df['engine-size'], df['price'])\n",
"print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value) "
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Pearson Correlation Coefficient is 0.5431553832626602 with a P-value of P = 8.049189483935364e-17\n"
]
}
],
"source": [
"pearson_coef, p_value = stats.pearsonr(df['bore'], df['price'])\n",
"print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value ) "
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Pearson Correlation Coefficient is -0.6865710067844677 with a P-value of P = 2.3211320655676368e-29\n"
]
}
],
"source": [
"pearson_coef, p_value = stats.pearsonr(df['city-mpg'], df['price'])\n",
"print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value) "
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Pearson Correlation Coefficient is -0.7046922650589529 with a P-value of P = 1.7495471144476807e-31\n"
]
}
],
"source": [
"pearson_coef, p_value = stats.pearsonr(df['highway-mpg'], df['price'])\n",
"print( \"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value ) "
]
},
{
"cell_type": "code",
"execution_count": 49,
"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>drive-wheels</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>rwd</td>\n",
" <td>13495.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>rwd</td>\n",
" <td>16500.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>fwd</td>\n",
" <td>13950.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4wd</td>\n",
" <td>17450.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>fwd</td>\n",
" <td>15250.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>136</th>\n",
" <td>4wd</td>\n",
" <td>7603.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" drive-wheels price\n",
"0 rwd 13495.0\n",
"1 rwd 16500.0\n",
"3 fwd 13950.0\n",
"4 4wd 17450.0\n",
"5 fwd 15250.0\n",
"136 4wd 7603.0"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grouped_test2=df_gptest[['drive-wheels', 'price']].groupby(['drive-wheels'])\n",
"grouped_test2.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"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>drive-wheels</th>\n",
" <th>body-style</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>rwd</td>\n",
" <td>convertible</td>\n",
" <td>13495.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>rwd</td>\n",
" <td>convertible</td>\n",
" <td>16500.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>rwd</td>\n",
" <td>hatchback</td>\n",
" <td>16500.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>fwd</td>\n",
" <td>sedan</td>\n",
" <td>13950.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4wd</td>\n",
" <td>sedan</td>\n",
" <td>17450.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>fwd</td>\n",
" <td>sedan</td>\n",
" <td>15250.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>fwd</td>\n",
" <td>sedan</td>\n",
" <td>17710.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>fwd</td>\n",
" <td>wagon</td>\n",
" <td>18920.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>fwd</td>\n",
" <td>sedan</td>\n",
" <td>23875.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>rwd</td>\n",
" <td>sedan</td>\n",
" <td>16430.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>rwd</td>\n",
" <td>sedan</td>\n",
" <td>16925.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>rwd</td>\n",
" <td>sedan</td>\n",
" <td>20970.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>rwd</td>\n",
" <td>sedan</td>\n",
" <td>21105.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>rwd</td>\n",
" <td>sedan</td>\n",
" <td>24565.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>rwd</td>\n",
" <td>sedan</td>\n",
" <td>30760.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>rwd</td>\n",
" <td>sedan</td>\n",
" <td>41315.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>rwd</td>\n",
" <td>sedan</td>\n",
" <td>36880.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>fwd</td>\n",
" <td>hatchback</td>\n",
" <td>5151.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>fwd</td>\n",
" <td>hatchback</td>\n",
" <td>6295.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>fwd</td>\n",
" <td>sedan</td>\n",
" <td>6575.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>fwd</td>\n",
" <td>hatchback</td>\n",
" <td>5572.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>fwd</td>\n",
" <td>hatchback</td>\n",
" <td>6377.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>fwd</td>\n",
" <td>hatchback</td>\n",
" <td>7957.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>fwd</td>\n",
" <td>hatchback</td>\n",
" <td>6229.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>fwd</td>\n",
" <td>sedan</td>\n",
" <td>6692.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>fwd</td>\n",
" <td>sedan</td>\n",
" <td>7609.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>fwd</td>\n",
" <td>sedan</td>\n",
" <td>8558.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>fwd</td>\n",
" <td>wagon</td>\n",
" <td>8921.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>fwd</td>\n",
" <td>hatchback</td>\n",
" <td>12964.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>fwd</td>\n",
" <td>hatchback</td>\n",
" <td>6479.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>171</th>\n",
" <td>fwd</td>\n",
" <td>hatchback</td>\n",
" <td>9988.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>172</th>\n",
" <td>fwd</td>\n",
" <td>sedan</td>\n",
" <td>10898.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>173</th>\n",
" <td>fwd</td>\n",
" <td>hatchback</td>\n",
" <td>11248.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>174</th>\n",
" <td>rwd</td>\n",
" <td>hatchback</td>\n",
" <td>16558.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>175</th>\n",
" <td>rwd</td>\n",
" <td>hatchback</td>\n",
" <td>15998.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>176</th>\n",
" <td>rwd</td>\n",
" <td>sedan</td>\n",
" <td>15690.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>177</th>\n",
" <td>rwd</td>\n",
" <td>wagon</td>\n",
" <td>15750.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>178</th>\n",
" <td>fwd</td>\n",
" <td>sedan</td>\n",
" <td>7775.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>179</th>\n",
" <td>fwd</td>\n",
" <td>sedan</td>\n",
" <td>7975.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>180</th>\n",
" <td>fwd</td>\n",
" <td>sedan</td>\n",
" <td>7995.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>181</th>\n",
" <td>fwd</td>\n",
" <td>sedan</td>\n",
" <td>8195.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>182</th>\n",
" <td>fwd</td>\n",
" <td>sedan</td>\n",
" <td>8495.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>183</th>\n",
" <td>fwd</td>\n",
" <td>sedan</td>\n",
" <td>9495.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>184</th>\n",
" <td>fwd</td>\n",
" <td>sedan</td>\n",
" <td>9995.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>185</th>\n",
" <td>fwd</td>\n",
" <td>convertible</td>\n",
" <td>11595.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>186</th>\n",
" <td>fwd</td>\n",
" <td>hatchback</td>\n",
" <td>9980.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>187</th>\n",
" <td>fwd</td>\n",
" <td>sedan</td>\n",
" <td>13295.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>188</th>\n",
" <td>fwd</td>\n",
" <td>sedan</td>\n",
" <td>13845.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>189</th>\n",
" <td>fwd</td>\n",
" <td>wagon</td>\n",
" <td>12290.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>190</th>\n",
" <td>rwd</td>\n",
" <td>sedan</td>\n",
" <td>12940.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>191</th>\n",
" <td>rwd</td>\n",
" <td>wagon</td>\n",
" <td>13415.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>192</th>\n",
" <td>rwd</td>\n",
" <td>sedan</td>\n",
" <td>15985.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>193</th>\n",
" <td>rwd</td>\n",
" <td>wagon</td>\n",
" <td>16515.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>rwd</td>\n",
" <td>sedan</td>\n",
" <td>18420.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>195</th>\n",
" <td>rwd</td>\n",
" <td>wagon</td>\n",
" <td>18950.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>196</th>\n",
" <td>rwd</td>\n",
" <td>sedan</td>\n",
" <td>16845.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>197</th>\n",
" <td>rwd</td>\n",
" <td>sedan</td>\n",
" <td>19045.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>198</th>\n",
" <td>rwd</td>\n",
" <td>sedan</td>\n",
" <td>21485.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>199</th>\n",
" <td>rwd</td>\n",
" <td>sedan</td>\n",
" <td>22470.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>200</th>\n",
" <td>rwd</td>\n",
" <td>sedan</td>\n",
" <td>22625.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>201 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" drive-wheels body-style price\n",
"0 rwd convertible 13495.0\n",
"1 rwd convertible 16500.0\n",
"2 rwd hatchback 16500.0\n",
"3 fwd sedan 13950.0\n",
"4 4wd sedan 17450.0\n",
"5 fwd sedan 15250.0\n",
"6 fwd sedan 17710.0\n",
"7 fwd wagon 18920.0\n",
"8 fwd sedan 23875.0\n",
"9 rwd sedan 16430.0\n",
"10 rwd sedan 16925.0\n",
"11 rwd sedan 20970.0\n",
"12 rwd sedan 21105.0\n",
"13 rwd sedan 24565.0\n",
"14 rwd sedan 30760.0\n",
"15 rwd sedan 41315.0\n",
"16 rwd sedan 36880.0\n",
"17 fwd hatchback 5151.0\n",
"18 fwd hatchback 6295.0\n",
"19 fwd sedan 6575.0\n",
"20 fwd hatchback 5572.0\n",
"21 fwd hatchback 6377.0\n",
"22 fwd hatchback 7957.0\n",
"23 fwd hatchback 6229.0\n",
"24 fwd sedan 6692.0\n",
"25 fwd sedan 7609.0\n",
"26 fwd sedan 8558.0\n",
"27 fwd wagon 8921.0\n",
"28 fwd hatchback 12964.0\n",
"29 fwd hatchback 6479.0\n",
".. ... ... ...\n",
"171 fwd hatchback 9988.0\n",
"172 fwd sedan 10898.0\n",
"173 fwd hatchback 11248.0\n",
"174 rwd hatchback 16558.0\n",
"175 rwd hatchback 15998.0\n",
"176 rwd sedan 15690.0\n",
"177 rwd wagon 15750.0\n",
"178 fwd sedan 7775.0\n",
"179 fwd sedan 7975.0\n",
"180 fwd sedan 7995.0\n",
"181 fwd sedan 8195.0\n",
"182 fwd sedan 8495.0\n",
"183 fwd sedan 9495.0\n",
"184 fwd sedan 9995.0\n",
"185 fwd convertible 11595.0\n",
"186 fwd hatchback 9980.0\n",
"187 fwd sedan 13295.0\n",
"188 fwd sedan 13845.0\n",
"189 fwd wagon 12290.0\n",
"190 rwd sedan 12940.0\n",
"191 rwd wagon 13415.0\n",
"192 rwd sedan 15985.0\n",
"193 rwd wagon 16515.0\n",
"194 rwd sedan 18420.0\n",
"195 rwd wagon 18950.0\n",
"196 rwd sedan 16845.0\n",
"197 rwd sedan 19045.0\n",
"198 rwd sedan 21485.0\n",
"199 rwd sedan 22470.0\n",
"200 rwd sedan 22625.0\n",
"\n",
"[201 rows x 3 columns]"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_gptest"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4 17450.0\n",
"136 7603.0\n",
"140 9233.0\n",
"141 11259.0\n",
"144 8013.0\n",
"145 11694.0\n",
"150 7898.0\n",
"151 8778.0\n",
"Name: price, dtype: float64"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grouped_test2.get_group('4wd')['price']"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANOVA results: F= 67.95406500780399 , P = 3.3945443577151245e-23\n"
]
}
],
"source": [
"# ANOVA\n",
"f_val, p_val = stats.f_oneway(grouped_test2.get_group('fwd')['price'], grouped_test2.get_group('rwd')['price'], grouped_test2.get_group('4wd')['price']) \n",
" \n",
"print( \"ANOVA results: F=\", f_val, \", P =\", p_val) "
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANOVA results: F= 130.5533160959111 , P = 2.2355306355677845e-23\n"
]
}
],
"source": [
"f_val, p_val = stats.f_oneway(grouped_test2.get_group('fwd')['price'], grouped_test2.get_group('rwd')['price']) \n",
" \n",
"print( \"ANOVA results: F=\", f_val, \", P =\", p_val )"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANOVA results: F= 8.580681368924756 , P = 0.004411492211225333\n"
]
}
],
"source": [
"f_val, p_val = stats.f_oneway(grouped_test2.get_group('4wd')['price'], grouped_test2.get_group('rwd')['price']) \n",
" \n",
"print( \"ANOVA results: F=\", f_val, \", P =\", p_val) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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