标签：link ice font 添加 panda pad 编号 结果 min
重新索引会更改DataFrame的行标签和列标签。
可以通过索引来实现多个操作：
import pandas as pd import numpy as np N=20 df = pd.DataFrame({ ‘A‘: pd.date_range(start=‘2016-01-01‘,periods=N,freq=‘D‘), ‘x‘: np.linspace(0,stop=N-1,num=N), ‘y‘: np.random.rand(N), ‘C‘: np.random.choice([‘Low‘,‘Medium‘,‘High‘],N).tolist(), ‘D‘: np.random.normal(100, 10, size=(N)).tolist() }) print(df) print(‘\n‘) #reindex the DataFrame df_reindexed = df.reindex(index=[0,2,5], columns=[‘A‘, ‘C‘, ‘B‘]) # 将符合的提取出来了 print (df_reindexed)
输出结果：
A x y C D
0 2016-01-01 0.0 0.910736 Low 105.308796
1 2016-01-02 1.0 0.570500 Low 91.024238
2 2016-01-03 2.0 0.930298 High 112.359308
3 2016-01-04 3.0 0.251355 Medium 106.155192
4 2016-01-05 4.0 0.579235 Low 90.079651
5 2016-01-06 5.0 0.623852 High 110.592218
6 2016-01-07 6.0 0.621130 Medium 96.222673
7 2016-01-08 7.0 0.989647 Medium 92.253444
8 2016-01-09 8.0 0.506653 Medium 102.601417
9 2016-01-10 9.0 0.099482 Low 97.721659
10 2016-01-11 10.0 0.254750 Medium 75.502131
11 2016-01-12 11.0 0.543014 Medium 88.895951
12 2016-01-13 12.0 0.911283 Medium 79.526056
13 2016-01-14 13.0 0.255296 Low 92.248119
14 2016-01-15 14.0 0.205302 Low 103.301747
15 2016-01-16 15.0 0.246407 Low 107.158250
16 2016-01-17 16.0 0.202039 High 96.411279
17 2016-01-18 17.0 0.734529 High 88.177103
18 2016-01-19 18.0 0.275703 Medium 82.885365
19 2016-01-20 19.0 0.084449 High 98.803349
A C B
0 2016-01-01 Low NaN
2 2016-01-03 High NaN
5 2016-01-06 High NaN
有时可能希望采取一个对象和重新索引，其轴被标记为与另一个对象相同。 考虑下面的例子来理解这一点。
import pandas as pd import numpy as np df1 = pd.DataFrame(np.random.randn(10,3),columns=[‘col1‘,‘col2‘,‘col3‘]) df2 = pd.DataFrame(np.random.randn(7,3),columns=[‘col1‘,‘col2‘,‘col3‘]) print(df1) print(df2) df1 = df1.reindex_like(df2) # 在df1中，把和df2一样的标签行提取出来 print(df1)
输出结果：
col1 col2 col3
0 0.989992 0.543438 -2.311684
1 -0.704759 -0.555589 -0.570049
2 -0.658263 -0.605368 -0.025520
3 1.533949 -0.936191 -0.071094
4 -0.729812 -0.339670 0.468700
5 -0.164076 0.075098 0.654549
6 -0.491034 1.096496 -0.166250
7 0.230918 -1.561643 1.501326
8 0.703623 -0.407445 -0.792633
9 0.340817 -1.132127 -0.695821
col1 col2 col3
0 0.144380 0.295776 -0.743097
1 -1.597853 0.029949 -1.605222
2 0.626728 -0.077997 -0.167353
3 0.466008 0.695279 -0.047752
4 -1.088821 -0.456605 1.192847
5 -0.020330 1.616297 -0.368196
6 -1.038790 -1.264894 0.059060
col1 col2 col3
0 0.989992 0.543438 -2.311684
1 -0.704759 -0.555589 -0.570049
2 -0.658263 -0.605368 -0.025520
3 1.533949 -0.936191 -0.071094
4 -0.729812 -0.339670 0.468700
5 -0.164076 0.075098 0.654549
6 -0.491034 1.096496 -0.166250
注意 - 在这里，
df1数据帧(DataFrame)被更改并重新编号，如df2。 列名称应该匹配，否则将为整个列标签添加NAN。
reindex()采用可选参数方法，它是一个填充方法，其值如下：
pad/ffill - 向前填充值bfill/backfill - 向后填充值nearest - 从最近的索引值填充import pandas as pd import numpy as np df1 = pd.DataFrame(np.random.randn(6,3),columns=[‘col1‘,‘col2‘,‘col3‘]) df2 = pd.DataFrame(np.random.randn(2,3),columns=[‘col1‘,‘col2‘,‘col3‘]) # Padding NAN‘s print(df2.reindex_like(df1)) print(‘\n‘) # Now Fill the NAN‘s with preceding Values print ("Data Frame with Forward Fill:") print (df2.reindex_like(df1,method=‘ffill‘))
输出结果：
col1 col2 col3
0 1.311620 -0.707176 0.599863
1 -0.423455 -0.700265 1.133371
2 NaN NaN NaN
3 NaN NaN NaN
4 NaN NaN NaN
5 NaN NaN NaN
Data Frame with Forward Fill:
col1 col2 col3
0 1.311620 -0.707176 0.599863
1 -0.423455 -0.700265 1.133371
2 -0.423455 -0.700265 1.133371
3 -0.423455 -0.700265 1.133371
4 -0.423455 -0.700265 1.133371
5 -0.423455 -0.700265 1.133371注 - 最后四行被填充了。
限制参数在重建索引时提供对填充的额外控制。限制指定连续匹配的最大计数。
import pandas as pd import numpy as np df1 = pd.DataFrame(np.random.randn(6,3),columns=[‘col1‘,‘col2‘,‘col3‘]) df2 = pd.DataFrame(np.random.randn(2,3),columns=[‘col1‘,‘col2‘,‘col3‘]) # Padding NAN‘s print(df2.reindex_like(df1)) print(‘\n‘) # Now Fill the NAN‘s with preceding Values print ("Data Frame with Forward Fill limiting to 1:") print(df2.reindex_like(df1,method=‘ffill‘,limit=1))
输出结果：
col1 col2 col3
0 0.247784 2.128727 0.702576
1 -0.055713 -0.021732 -0.174577
2 NaN NaN NaN
3 NaN NaN NaN
4 NaN NaN NaN
5 NaN NaN NaN
Data Frame with Forward Fill limiting to 1:
col1 col2 col3
0 0.247784 2.128727 0.702576
1 -0.055713 -0.021732 -0.174577
2 -0.055713 -0.021732 -0.174577
3 NaN NaN NaN
4 NaN NaN NaN
5 NaN NaN NaN
注意 - 只有第
7行由前6行填充。 然后，其它行按原样保留。
rename()方法允许基于一些映射(字典或者系列)或任意函数来重新标记一个轴。
import pandas as pd import numpy as np df1 = pd.DataFrame(np.random.randn(6,3),columns=[‘col1‘,‘col2‘,‘col3‘]) print(df1) print(‘\n‘) print ("After renaming the rows and columns:") print(df1.rename(columns={‘col1‘ : ‘c1‘, ‘col2‘ : ‘c2‘},index = {0 : ‘apple‘, 1 : ‘banana‘, 2 : ‘durian‘}))
输出结果：
col1 col2 col3
0 0.486791 0.105759 1.540122
1 -0.990237 1.007885 -0.217896
2 -0.483855 -1.645027 -1.194113
3 -0.122316 0.566277 -0.366028
4 -0.231524 -0.721172 -0.112007
5 0.438810 0.000225 0.435479
After renaming the rows and columns:
c1 c2 col3
apple 0.486791 0.105759 1.540122
banana -0.990237 1.007885 -0.217896
durian -0.483855 -1.645027 -1.194113
3 -0.122316 0.566277 -0.366028
4 -0.231524 -0.721172 -0.112007
5 0.438810 0.000225 0.435479
rename()方法提供了一个inplace命名参数，默认为False并复制底层数据。 指定传递inplace = True则表示将数据重命名。
标签：link ice font 添加 panda pad 编号 结果 min
原文地址：https://www.cnblogs.com/Summer-skr--blog/p/11704722.html
