Pandas缺失数据处理

Pandas用np.nan代表缺失数据

import pandas as pd
import numpy as np

dates = pd.date_range('20130101',periods=10)
df = pd.DataFrame(np.random.randn(10,4),index=dates,columns=['A','B','C','D'])
df.head()

reindex() 可以修改 索引，会返回一个数据的副本:

df1 = df.reindex(index = dates[0:4], columns = ['A', 'B', 'C', 'D', 'E'])
df1

df2 = df.reindex(index=dates[0:4], columns=['A','B','C','D']+['E'])
df2

df3 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])
df3

df3.loc[dates[0]:dates[1],'E'] = 1
df3

对缺失值进行填充

df1.fillna(value=5)

df2['E'] = df1['E'].fillna(value=5)
df2

丢掉含有缺失项的行:

df3.dropna(how = 'any')

对缺失项布尔赋值

df4 = df1.isnull()
df4

df5 = pd.isnull(df1)
df5