【数据分析可视化】Series和Dataframe的Reindexing

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import numpy as np
import pandas as pd
from pandas import Series, DataFrame

Series reindex

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s1 =  Series([1,2,3,4], index=['A','B','C','D'])
s1

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A    1
B    2
C    3
D    4
dtype: int64

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 # reindex新的Series索引 （查看参数快捷键shift tab）
s1.reindex(index=['A','B','C','D','E'])

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A    1.0
B    2.0
C    3.0
D    4.0
E    NaN
dtype: float64

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# reindex并给空值索引赋值
s1.reindex(index=['A','B','C','D','E'],fill_value=10)

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A     1
B     2
C     3
D     4
E    10
dtype: int64

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# 测试reindex的赋值方式
s2 = Series(['A','B','C'],index=[1,5,10])
s2

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1     A
5     B
10    C
dtype: object

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# 重新索引
s2.reindex(index=range(15))

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0     NaN
1       A
2     NaN
3     NaN
4     NaN
5       B
6     NaN
7     NaN
8     NaN
9     NaN
10      C
11    NaN
12    NaN
13    NaN
14    NaN
dtype: object

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# 函数填充值(ffill函数将重新索引前安装区间范围依次赋值)
s2.reindex(index=range(15),method='ffill')

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0     NaN
1       A
2       A
3       A
4       A
5       B
6       B
7       B
8       B
9       B
10      C
11      C
12      C
13      C
14      C
dtype: object

DataFrame reindex

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# 故意漏掉C
df1 = DataFrame(np.random.rand(25).reshape([5,5]), index=['A','B','D','E','F'], columns=['c1','c2','c3','c4','c5'])
df1

	c1	c2	c3	c4	c5
A	0.916976	0.277428	0.487468	0.392332	0.906246
B	0.112718	0.000009	0.958650	0.890877	0.640683
D	0.715841	0.857899	0.834954	0.134856	0.982175
E	0.375207	0.925308	0.734072	0.583107	0.677676
F	0.627784	0.818094	0.636362	0.417960	0.063043

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# reindex恢复C-重新索引行
df1.reindex(['A','B','C','D','E','F'])

	c1	c2	c3	c4	c5
A	0.916976	0.277428	0.487468	0.392332	0.906246
B	0.112718	0.000009	0.958650	0.890877	0.640683
C	NaN	NaN	NaN	NaN	NaN
D	0.715841	0.857899	0.834954	0.134856	0.982175
E	0.375207	0.925308	0.734072	0.583107	0.677676
F	0.627784	0.818094	0.636362	0.417960	0.063043

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# reindex-重新索引列
df1.reindex(columns=['c1','c2','c3','c4','c5','c6'])

	c1	c2	c3	c4	c5	c6
A	0.916976	0.277428	0.487468	0.392332	0.906246	NaN
B	0.112718	0.000009	0.958650	0.890877	0.640683	NaN
D	0.715841	0.857899	0.834954	0.134856	0.982175	NaN
E	0.375207	0.925308	0.734072	0.583107	0.677676	NaN
F	0.627784	0.818094	0.636362	0.417960	0.063043	NaN

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 # reindex-行列同时重排(新增)
df1.reindex(index=['A','B','C','D','E','F'] ,columns=['c1','c2','c3','c4','c5','c6'])

	c1	c2	c3	c4	c5	c6
A	0.916976	0.277428	0.487468	0.392332	0.906246	NaN
B	0.112718	0.000009	0.958650	0.890877	0.640683	NaN
C	NaN	NaN	NaN	NaN	NaN	NaN
D	0.715841	0.857899	0.834954	0.134856	0.982175	NaN
E	0.375207	0.925308	0.734072	0.583107	0.677676	NaN
F	0.627784	0.818094	0.636362	0.417960	0.063043	NaN

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# Series（减少）
s1

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A    1
B    2
C    3
D    4
dtype: int64

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s1.reindex(index=['A','B'])

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A    1
B    2
dtype: int64

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# reindex-行列同时重排(减少)
df1

	c1	c2	c3	c4	c5
A	0.916976	0.277428	0.487468	0.392332	0.906246
B	0.112718	0.000009	0.958650	0.890877	0.640683
D	0.715841	0.857899	0.834954	0.134856	0.982175
E	0.375207	0.925308	0.734072	0.583107	0.677676
F	0.627784	0.818094	0.636362	0.417960	0.063043

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df1.reindex(['A','B'])

	c1	c2	c3	c4	c5
A	0.916976	0.277428	0.487468	0.392332	0.906246
B	0.112718	0.000009	0.958650	0.890877	0.640683

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# 删掉的方法
s1.drop('A')

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B    2
C    3
D    4
dtype: int64

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# 0行1列
df1.drop('A',axis=0)

	c1	c2	c3	c4	c5
B	0.112718	0.000009	0.958650	0.890877	0.640683
D	0.715841	0.857899	0.834954	0.134856	0.982175
E	0.375207	0.925308	0.734072	0.583107	0.677676
F	0.627784	0.818094	0.636362	0.417960	0.063043

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df1.drop('c1',axis=1)

	c2	c3	c4	c5
A	0.277428	0.487468	0.392332	0.906246
B	0.000009	0.958650	0.890877	0.640683
D	0.857899	0.834954	0.134856	0.982175
E	0.925308	0.734072	0.583107	0.677676
F	0.818094	0.636362	0.417960	0.063043

nan

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