填补缺失值的两种方法ffill和bfill

Pandas填充缺失值两种方法：bfill/ffill对比

import pandas as pd
import numpy as np

"""
在dataframe中建立缺失值的两种方法：
np.nan和None
"""
df_=pd.DataFrame({'ID':range(5)
                ,'Height':range(5,10)
                ,'Gender':['male',np.nan,None,'famle',np.nan]
                 ,'col3':[1.3,np.nan,3.6,np.nan,5.8]}).set_index('ID')

df_

	Height	Gender	col3
ID
0	5	male	1.3
1	6	NaN	NaN
2	7	None	3.6
3	8	famle	NaN
4	9	NaN	5.8

'''
bfill:向前填充
ffill:向后填充
默认 axis=0(列方向)  vs  axis=1(行方向)
'''
df1=df_.fillna(method='bfill',axis=1)
df1

	Height	Gender	col3
ID
0	5	male	1.3
1	6	NaN	NaN
2	7	3.6	3.6
3	8	famle	NaN
4	9	5.8	5.8

df2=df_.fillna(method='bfill',axis=0)
df2

	Height	Gender	col3
ID
0	5	male	1.3
1	6	famle	3.6
2	7	famle	3.6
3	8	famle	5.8
4	9	NaN	5.8

df3=df_.fillna(method='ffill',axis=0)
df3

	Height	Gender	col3
ID
0	5	male	1.3
1	6	male	1.3
2	7	male	3.6
3	8	famle	3.6
4	9	famle	5.8

df3['col3'].replace([5.8,1.3],[np.nan,np.nan],inplace=True)
df3

	Height	Gender	col3
ID
0	5	male	NaN
1	6	male	NaN
2	7	male	3.6
3	8	famle	3.6
4	9	famle	NaN

df4=df3.fillna(method='ffill',axis=1)

df4

	Height	Gender	col3
ID
0	5	male	male
1	6	male	male
2	7	male	3.6
3	8	famle	3.6
4	9	famle	famle