Pandas - Missing Data

Create a dataframe with nan values

dropna
drop rows that have null values
drop columns that have null values
dropna -- thresh -- drop rows or columns with atleast thresh NA values
fillna -- value
Fill NA values with mean of A column

import numpy as np
import pandas as pd

d = {'A':[1,2,np.nan],'B':[5,np.nan,np.nan],'C':[1,2,3]}

df = pd.DataFrame(d)

print(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=10)

	A	B	C
0	1.0	5.0	1
1	2.0	10.0	2
2	10.0	10.0	3

df['A'].fillna(value=15)

0     1.0
1     2.0
2    15.0
Name: A, dtype: float64

df

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

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

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