파이썬 UCI Auto MPG 시각화 연습

머신러닝 실습 Data인 Auto MPG르 간단히 전처리 단계 Data 시각화를 해봄.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['font.family']='DejaVu Sans'
plt.rcParams['axes.unicode_minus']=False

 

Data는 본문 하단 출처를 참고하여 다운로드...

df=pd.read_csv('drive/My Drive/data/cars/uci_auto_mpg.csv')
df.info()
========== Result ==========
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 398 entries, 0 to 397
Data columns (total 8 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   MPG           398 non-null    float64
 1   Cylinders     398 non-null    int64  
 2   Displacement  398 non-null    float64
 3   Horsepower    392 non-null    float64
 4   Weight        398 non-null    float64
 5   Acceleration  398 non-null    float64
 6   Model Year    398 non-null    int64  
 7   Origin        398 non-null    int64  
dtypes: float64(5), int64(3)
memory usage: 25.0 KB

 

null 값 있는 column 확인 후 삭제

df.isna().sum() # null값 여부 확인
========== Result ==========
MPG             0
Cylinders       0
Displacement    0
Horsepower      6
Weight          0
Acceleration    0
Model Year      0
Origin          0
dtype: int64

df.dropna(axis=0,inplace=True) # nan row 삭제

df.isna().sum() # null값 여부 확인
========== Result ==========
MPG             0
Cylinders       0
Displacement    0
Horsepower      0
Weight          0
Acceleration    0
Model Year      0
Origin          0
dtype: int64

 

heatmap 그려보기

sns.heatmap(df.corr(),vmin=-1,vmax=1,
            linewidth=0.5,annot=True,cmap=plt.cm.gist_heat)
plt.show()

 

pairplot 그려보기

sns.pairplot(df[['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight']],diag_kind='kde')
plt.show()

 

 

Data출처 : archive.ics.uci.edu/ml/datasets/Auto+MPG

UCI Machine Learning Repository: Auto MPG Data Set