Pandas缺失数据处理

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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()

	A	B	C	D
2013-01-01	-0.031531	1.231280	-1.069298	1.068172
2013-01-02	-0.216581	0.535341	-1.408095	0.677334
2013-01-03	0.262541	-0.034165	0.712012	0.053880
2013-01-04	0.142971	-0.009381	-0.369560	2.142902
2013-01-05	-0.483484	1.896420	-1.087918	-0.608670

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

	A	B	C	D	E
2013-01-01	-0.031531	1.231280	-1.069298	1.068172	NaN
2013-01-02	-0.216581	0.535341	-1.408095	0.677334	NaN
2013-01-03	0.262541	-0.034165	0.712012	0.053880	NaN
2013-01-04	0.142971	-0.009381	-0.369560	2.142902	NaN

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

	A	B	C	D	E
2013-01-01	-0.031531	1.231280	-1.069298	1.068172	NaN
2013-01-02	-0.216581	0.535341	-1.408095	0.677334	NaN
2013-01-03	0.262541	-0.034165	0.712012	0.053880	NaN
2013-01-04	0.142971	-0.009381	-0.369560	2.142902	NaN

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

	A	B	C	D	E
2013-01-01	-0.031531	1.231280	-1.069298	1.068172	NaN
2013-01-02	-0.216581	0.535341	-1.408095	0.677334	NaN
2013-01-03	0.262541	-0.034165	0.712012	0.053880	NaN
2013-01-04	0.142971	-0.009381	-0.369560	2.142902	NaN

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

	A	B	C	D	E
2013-01-01	-0.031531	1.231280	-1.069298	1.068172	1.0
2013-01-02	-0.216581	0.535341	-1.408095	0.677334	1.0
2013-01-03	0.262541	-0.034165	0.712012	0.053880	NaN
2013-01-04	0.142971	-0.009381	-0.369560	2.142902	NaN

---

对缺失值进行填充

df1.fillna(value=5)

	A	B	C	D	E
2013-01-01	-0.031531	1.231280	-1.069298	1.068172	5.0
2013-01-02	-0.216581	0.535341	-1.408095	0.677334	5.0
2013-01-03	0.262541	-0.034165	0.712012	0.053880	5.0
2013-01-04	0.142971	-0.009381	-0.369560	2.142902	5.0

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

	A	B	C	D	E
2013-01-01	-0.031531	1.231280	-1.069298	1.068172	5.0
2013-01-02	-0.216581	0.535341	-1.408095	0.677334	5.0
2013-01-03	0.262541	-0.034165	0.712012	0.053880	5.0
2013-01-04	0.142971	-0.009381	-0.369560	2.142902	5.0

丢掉含有缺失项的行:

df3.dropna(how = 'any')

	A	B	C	D	E
2013-01-01	-0.031531	1.231280	-1.069298	1.068172	1.0
2013-01-02	-0.216581	0.535341	-1.408095	0.677334	1.0

---

对缺失项布尔赋值

df4 = df1.isnull()
df4

	A	B	C	D	E
2013-01-01	False	False	False	False	True
2013-01-02	False	False	False	False	True
2013-01-03	False	False	False	False	True
2013-01-04	False	False	False	False	True

df5 = pd.isnull(df1)
df5

	A	B	C	D	E
2013-01-01	False	False	False	False	True
2013-01-02	False	False	False	False	True
2013-01-03	False	False	False	False	True
2013-01-04	False	False	False	False	True