Missing Data

Let’s show a few convenient methods to deal with Missing Data in pandas:

import numpy as np
import pandas as pd

df = pd.DataFrame({'A':[1,2,np.nan],
                  'B':[5,np.nan,np.nan],
                  'C':[1,2,3]})

df

	A	B	C
0	1.0	5.0	1
1	2.0	NaN	2
2	NaN	NaN	3

df.dropna()

	A	B	C
0	1.0	5.0	1

df.dropna(axis=1)

	C
0	1
1	2
2	3

df.dropna(thresh=2)

	A	B	C
0	1.0	5.0	1
1	2.0	NaN	2

df.fillna(value='FILL VALUE')

	A	B	C
0	1	5	1
1	2	FILL VALUE	2
2	FILL VALUE	FILL VALUE	3

df['A'].fillna(value=df['A'].mean())

0    1.0
1    2.0
2    1.5
Name: A, dtype: float64

Great Job!