Case Study (autos) : Checking Missing Values¶

import pandas as pd
import numpy as np

df= pd.read_csv('carsinfo.csv')

Columns with missing values:¶

Showing all columns containing at least one missing value : Source

# Following columns have at leat one missing value

df.loc[:, df.isnull().any()][:6]

Number of missing values in each column:¶

Source of Function

def missing_values(dataframe):
    print (round((dataframe.isnull().sum()),2).sort_values(ascending=False))

missing_values(df)

normalized_losses    41
price                 4
stroke                4
bore                  4
peak_rpm              2
horsepower            2
length                0
make                  0
fuel_type             0
aspiration            0
num_of_doors          0
body_style            0
drive_wheels          0
engine_location       0
wheel_base            0
height                0
width                 0
highway_mpg           0
curb_weight           0
engine_type           0
num_of_cylinders      0
engine_size           0
fuel_system           0
compression_ratio     0
city_mpg              0
symboling             0
dtype: int64

	normalized_losses	bore	stroke	horsepower	peak_rpm	price
0	NaN	3.47	2.68	111.0	5000.0	13495.0
1	NaN	3.47	2.68	111.0	5000.0	16500.0
2	NaN	2.68	3.47	154.0	5000.0	16500.0
3	164.0	3.19	3.40	102.0	5500.0	13950.0
4	164.0	3.19	3.40	115.0	5500.0	17450.0
5	NaN	3.19	3.40	110.0	5500.0	15250.0