Python Pandas 将一组初始列融合为多个目标列 [英] Python Pandas Melt Groups of Initial Columns Into Multiple Target Columns

问题描述

我需要将一组初始列融合到未规范化的数据集中的多个目标列中.这是一个例子(来自这个问题 pandas dataframe将多个值变量重塑/堆叠到单独的列中):

I have a need to melt groups of initial columns into multiple target columns in a dataset that is not normalized well. Here is an example (from this question pandas dataframe reshaping/stacking of multiple value variables into seperate columns):

         des1 des2 des3 interval1 interval2 interval3
value   
aaa       a    b    c     ##1         ##2       ##3
bbb       d    e    f     ##4         ##5       ##6
ccc       g    h    i     ##7         ##8       ##9

我正在尝试将其融入这样的方向:

I am trying to melt this into something like this orientation:

         des      interval
value   
aaa       a         ##1
aaa       b         ##2
aaa       c         ##3
bbb       d         ##4
bbb       e         ##5
bbb       f         ##6
ccc       g         ##7
ccc       h         ##8
ccc       i         ##9

我希望使用melt而不是stack来避免手动设置大量数据的子集.以下是我迄今为止的开始:

I was hoping to use melt instead of stack to avoid manually subsetting a lot of data. Here is what I have started out with thus far:

import pandas as pd
import numpy as np
import fnmatch

column_list = list(df_initial.columns.values)

question_sources = [c for c in fnmatch.filter(column_list, "measure*question*source")]     
question_ranks = [c for c in fnmatch.filter(column_list, "measure*rank")]
question_targets = [c for c in fnmatch.filter(column_list, "measure*targeted")]
question_statuses = [c for c in fnmatch.filter(column_list, "measure*status")]

place = [c for c in fnmatch.filter(column_list, "place")]
measure_statuses = [c for c in fnmatch.filter(column_list, "measureInfo_status")]

starter_list = place + measure_statuses

df_gpro_melt_1 = (pd.melt(df_initial, id_vars=starter_list,      
                    value_vars=question_sources, var_name="question_sources", 
                    value_name="question_sources_values"))      

是否可以将初始列的组融合为多个目标列?非常感谢任何建议.

Is it possible to melt groups of initial columns into multiple target columns? Any advice is much appreciated.

推荐答案

这应该适用于您的示例，如果您的列遵循示例数据框中的模式:

This should work for your example, if your columns follow the pattern in your example data frame:

pd.concat((pd.DataFrame({'des':df.iloc[:,i], 
                         'interval':df.iloc[:,i+3]}) 
             for i in range(3)))

如果对不同，你可以使用这个模式，但是遍历一个列表

If the pairs are different, you can use this pattern, but iterate through a list

tuples = [(0,3),(1,4),(2,5)]

pd.concat((pd.DataFrame({'des':df.iloc[:,i], 
                          'interval':df.iloc[:,j]}) 
             for i,j in tuples))

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