Python / Pandas - 对多维数据进行透视

Question

我是python / pandas的新手。 我正在尝试将行转置为columns.Apologies任何误解input_df（pic） output_df（pic）

input_df:
Date    Project    Processes                Time_in_sec Time_measures
-----------------------------------------------------------------------
7/6/2017    FE      eBanking .aspx          157         Average Response Time 
9/2/2017    PCB     eBanking Frontpage.fi   227         Call per hour
1/23/2017   ICC     Acct Transfer.dc        28          Average Response Time 
1/24/2017   PCB     Transaction .com        0           Number of calls
1/23/2017   ICC     eBanking Logon.no       0           Number of calls

output_df:
Date    Project Processes   Average Response Time   Call per hour   Number of calls
-----------------------------------------------------------------------------
7/6/2017    FE  eBanking .aspx        157       
9/2/2017    PCB eBanking Frontpage.fi                    227    
1/24/2017   PCB Transaction .com                                        0
1/23/2017   ICC Acct Transfer.dc      28        
1/23/2017   ICC eBanking Logon.no                                       0

我尝试了以下代码，并在输出below_code's_output中只获得了3列“平均响应时间”，“每小时呼叫数”'呼叫数'。 但我很困惑如何获得所有必要的6列。 能否请你帮忙？

    output_df = input_df.pivot(columns = 'Time_measures',  values= 'Time_in_sec')

Answer 1

IIUC您可以将pivot_table与reset_index pivot_table使用

df.pivot_table(columns='Time_measures', values='Time_in_sec',index=['Date','Project','Processes'],fill_value='').reset_index()
Out[98]: 
Time_measures       Date Project              Processes  \
0              1/23/2017     ICC       Acct Transfer.dc   
1              1/23/2017     ICC      eBanking Logon.no   
2              1/24/2017     PCB       Transaction .com   
3               7/6/2017      FE         eBanking .aspx   
4               9/2/2017     PCB  eBanking Frontpage.fi   
Time_measures Average Response Time  Call per hour Number of calls  
0                                 28                                
1                                                                0  
2                                                                0  
3                                157                                
4                                              227