how to find and deal with NaN values in a pandas dataframe

import pandas as pd
import numpy as np

df = pd.DataFrame({'Date':['10/2/2011', np.nan, '11/2/2011', '12/2/2011', '13/2/2011'],
                   'Product':['umbrella', 'matress', 'badminton', 'shuttle', np.nan],
                   'Updated_Price':[np.nan, 1250, 1450, 1550, 400],
                   'Discount':[10, 8, np.nan, 15, 10]})

df

	Date	Product	Updated_Price	Discount
0	10/2/2011	umbrella	NaN	10.0
1	NaN	matress	1250.0	8.0
2	11/2/2011	badminton	1450.0	NaN
3	12/2/2011	shuttle	1550.0	15.0
4	13/2/2011	NaN	400.0	10.0

display rows with NaN values from any of the columns

df[df.isnull().any(axis=1)]

	Date	Product	Updated_Price	Discount
0	10/2/2011	umbrella	NaN	10.0
1	NaN	matress	1250.0	8.0
2	11/2/2011	badminton	1450.0	NaN
4	13/2/2011	NaN	400.0	10.0

display number of rows with NaN values per column

df.isnull().sum()

Date             1
Product          1
Updated_Price    1
Discount         1
dtype: int64

fill NaN values with the mean of a column they belong

cols = ['Updated_Price', 'Discount']
df[cols] = df[cols].fillna(df.mean()) 
df

	Date	Product	Updated_Price	Discount
0	10/2/2011	umbrella	1162.5	10.00
1	NaN	matress	1250.0	8.00
2	11/2/2011	badminton	1450.0	10.75
3	12/2/2011	shuttle	1550.0	15.00
4	13/2/2011	NaN	400.0	10.00

fill NaN values with the mean of the values before and after NaN

df['Discount'] = df['Discount'].interpolate()
df

	Date	Product	Updated_Price	Discount
0	10/2/2011	umbrella	NaN	10.0
1	NaN	matress	1250.0	8.0
2	11/2/2011	badminton	1450.0	11.5
3	12/2/2011	shuttle	1550.0	15.0
4	13/2/2011	NaN	400.0	10.0

fill NaN values with the value of the previous row

df['Date'] = df['Date'].fillna(method='ffill')
df

	Date	Product	Updated_Price	Discount
0	10/2/2011	umbrella	NaN	10.0
1	10/2/2011	matress	1250.0	8.0
2	11/2/2011	badminton	1450.0	11.5
3	12/2/2011	shuttle	1550.0	15.0
4	13/2/2011	NaN	400.0	10.0

delete rows with NaN values

df.dropna(subset=['Product'], inplace=True)
df

	Date	Product	Updated_Price	Discount
0	10/2/2011	umbrella	1162.5	10.00
1	10/2/2011	matress	1250.0	8.00
2	11/2/2011	badminton	1450.0	10.75
3	12/2/2011	shuttle	1550.0	15.00