Pandas | 08 重建索引

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：

• 重新排序现有数据以匹配一组新的标签。
• 在没有标签数据的标签位置插入缺失值(NA)标记。

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

 

重建索引与其他对象对齐

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。

填充时重新加注

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。

重建索引时的填充限制

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。

重命名

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。