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EPD_analysis.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Import Panda & numpy Libraries\n",
"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": [
"# Read Data File by providing Url\n",
"path='https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Data%20files/automobileEDA.csv'\n",
"df = pd.read_csv(path)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Read Data File by providing Url\n",
"%%capture\n",
"! pip install seaborn"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Import matplot & seaborn Library\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"symboling int64\n",
"normalized-losses int64\n",
"make object\n",
"aspiration object\n",
"num-of-doors object\n",
"body-style object\n",
"drive-wheels object\n",
"engine-location object\n",
"wheel-base float64\n",
"length float64\n",
"width float64\n",
"height float64\n",
"curb-weight int64\n",
"engine-type object\n",
"num-of-cylinders object\n",
"engine-size int64\n",
"fuel-system object\n",
"bore float64\n",
"stroke float64\n",
"compression-ratio float64\n",
"horsepower float64\n",
"peak-rpm float64\n",
"city-mpg int64\n",
"highway-mpg int64\n",
"price float64\n",
"city-L/100km float64\n",
"horsepower-binned object\n",
"diesel int64\n",
"gas int64\n",
"dtype: object\n"
]
}
],
"source": [
"# list the data types for each column to choose right visualization\n",
"print(df.dtypes)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# calculate the correlation between variables \n",
"# of type \"int64\" or \"float64\" using the method \"corr\"\n",
"df.corr()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <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",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Find the correlation between the following\n",
"df[['bore','stroke' ,'compression-ratio','horsepower']].corr()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.0, 56261.11009494285)"
]
},
"execution_count": 9,
"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": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[[\"engine-size\", \"price\"]].corr()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='highway-mpg', ylabel='price'>"
]
},
"execution_count": 11,
"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": [
"# Draw regplot\n",
"sns.regplot(x=\"highway-mpg\", y=\"price\", data=df)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='peak-rpm', ylabel='price'>"
]
},
"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/plain": [
"<AxesSubplot:xlabel='stroke', ylabel='price'>"
]
},
"execution_count": 14,
"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": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='body-style', ylabel='price'>"
]
},
"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": [
"# Draw Boxplot\n",
"sns.boxplot(x=\"body-style\", y=\"price\", data=df)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='engine-location', ylabel='price'>"
]
},
"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=\"engine-location\", y=\"price\", data=df)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='drive-wheels', ylabel='price'>"
]
},
"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": [
"# drive-wheels\n",
"sns.boxplot(x=\"drive-wheels\", y=\"price\", data=df)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"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": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"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>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": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe(include=['object'])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"fwd 118\n",
"rwd 75\n",
"4wd 8\n",
"Name: drive-wheels, dtype: int64"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['drive-wheels'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"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": 21,
"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": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"drive_wheels_counts.index.name = 'drive-wheels'\n",
"drive_wheels_counts"
]
},
{
"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>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": 23,
"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": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['rwd', 'fwd', '4wd'], dtype=object)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The \"groupby\" method groups data by different categories\n",
"df['drive-wheels'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"# assign it to the variable \"df_group_one\n",
"df_group_one = df[['drive-wheels','body-style','price']]"
]
},
{
"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>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": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# grouping results to calculate the average price\n",
"df_group_one = df_group_one.groupby(['drive-wheels'],as_index=False).mean()\n",
"df_group_one"
]
},
{
"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",
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" 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": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# grouping results to be stored to grouped_test1\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": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" <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": 28,
"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": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\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": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# To replace missing data with 0\n",
"grouped_pivot = grouped_pivot.fillna(0) #fill missing values with 0\n",
"grouped_pivot"
]
},
{
"cell_type": "code",
"execution_count": 30,
"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": 30,
"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": 31,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 32,
"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 using Heat Map\n",
"plt.pcolor(grouped_pivot, cmap='RdBu')\n",
"plt.colorbar()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"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": [
"# Change default Labels\n",
"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": 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 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": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# calculate the Pearson Correlation\n",
"df.corr()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"# Import Scipy Library\n",
"from scipy import stats"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Pearson Correlation Coefficient is 0.584641822265508 with a P-value of P = 8.076488270733218e-20\n"
]
}
],
"source": [
"# To calculate the Pearson Correlation Coefficient and P-value of 'wheel-base' and 'price'.\n",
"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": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Pearson Correlation Coefficient is 0.8095745670036559 with a P-value of P = 6.369057428260101e-48\n"
]
}
],
"source": [
"# HP versus price\n",
"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": null,
"metadata": {},
"outputs": [],
"source": [
"# Length versus price\n",
"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) "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python",
"language": "python",
"name": "conda-env-python-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.12"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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