数据清洗--DataFrame中的空值处理方法

> import numpy as np
> import pandas as pd
> from pandas import Series,DataFrame
> from numpy import nan as NaN
> data = DataFrame([[12,'man','13865626962'],[19,'woman',NaN],[17,NaN,NaN],[NaN,NaN,NaN]],columns=['age','sex','phone'])
> data
 age sex  phone
0 12.0 man 13865626962
1 19.0 woman   NaN
2 17.0 NaN   NaN
3 NaN NaN   NaN

> data.dropna(axis=0, how='all')
 age sex  phone
0 12.0 man 13865626962
1 19.0 woman   NaN
2 17.0 NaN   NaN

> data.dropna(axis=0, how='any')
 age sex  phone
0 12.0 man 13865626962

> data.dropna(axis=1, how='all')
 age sex  phone
0 12.0 man 13865626962
1 19.0 woman   NaN
2 17.0 NaN   NaN
3 NaN NaN   NaN

> data.dropna(axis=1, how='any')
Empty DataFrame
Columns: []
Index: [0, 1, 2, 3]

> data.fillna(0)
 age sex  phone
0 12.0 man 13865626962
1 19.0 woman   0
2 17.0  0   0
3 0.0  0   0

> data.fillna({'sex':233,'phone':666})
 age sex  phone
0 12.0 man 13865626962
1 19.0 woman   666
2 17.0 233   666
3 NaN 233   666