python - Pandas 表示跨多个列

Question

我有一个使用 numpy 数组(MnthIdx、Val1、Val2、Val3)创建的数据框:

import pandas as pd
import numpy as np

dfout3 = pd.DataFrame({'Idx': MnthIdx,
                       'Col1': Val1,
                       'Col2': Val2,
                       'Col3': Val3)})

MeanTable1 = pd.pivot_table(dfout3, index=['Idx'], values=['Val1'], aggfunc=[np.mean])
MeanVal1 = np.asarray(MeanTable1['mean'])
MeanTable2 = pd.pivot_table(dfout3, index=['Idx'], values=['Val2'], aggfunc=[np.mean])
MeanVal2 = np.asarray(MeanTable2['mean'])
MeanTable2 = pd.pivot_table(dfout3, index=['Idx'], values=['Val3'], aggfunc=[np.mean])
MeanVal3 = np.asarray(MeanTable3['mean'])

是否有可能我只需 1 步即可完成上述 3 步。

Answer 1

您可以使用pivot_table有 3 列作为参数值。 Aggfunc=[np.mean] 可以省略，因为这是默认聚合函数。最后，如果需要输出为 numpy 数组，请使用 values :

print (pd.pivot_table(dfout3, index=['Idx'], values=['Col1', 'Col2', 'Col3']))

示例:

import pandas as pd
import numpy as np

MnthIdx = [1,2,2,3,3]
Val1 =    [2,5,2,3,4]
Val2 =    [6,1,5,3,5]
Val3 =    [3,9,5,7,8]


dfout3 = pd.DataFrame({'Idx': MnthIdx,
                       'Col1': Val1,
                       'Col2': Val2,
                       'Col3': Val3})

MeanTable1 = pd.pivot_table(dfout3, index=['Idx'], values=['Col1'], aggfunc=[np.mean])
MeanVal1 = np.asarray(MeanTable1['mean'])
MeanTable2 = pd.pivot_table(dfout3, index=['Idx'], values=['Col2'], aggfunc=[np.mean])
MeanVal2 = np.asarray(MeanTable2['mean'])
MeanTable3 = pd.pivot_table(dfout3, index=['Idx'], values=['Col3'], aggfunc=[np.mean])
MeanVal3 = np.asarray(MeanTable3['mean'])

print (MeanTable1)
    mean
    Col1
Idx     
1    2.0
2    3.5
3    3.5

print (MeanTable2)
    mean
    Col2
Idx     
1      6
2      3
3      4

print (MeanTable3)
    mean
    Col3
Idx     
1    3.0
2    7.0
3    7.5

print (pd.pivot_table(dfout3, index=['Idx'], values=['Col1', 'Col2', 'Col3']))
     Col1  Col2  Col3
Idx                  
1     2.0   6.0   3.0
2     3.5   3.0   7.0
3     3.5   4.0   7.5

print (pd.pivot_table(dfout3, index=['Idx'], values=['Col1', 'Col2', 'Col3']).values)
[[ 2.   6.   3. ]
 [ 3.5  3.   7. ]
 [ 3.5  4.   7.5]]