从另一个（使用枢轴）创建一个数据框

问题描述

我有一个熊猫的问题。我有一个包含三列的数据框：'id1'，'id2'，'amount'。

从这里，我想创建另一个数据框，索引为'id1 '，哪些列是id2，单元格包含相应的金额。

我们来看一个例子：

$ b pre pre pre $ p $ d d = = pd.DataFrame（[['first_person'，'first_item'，10]，['first_person'，'second_item' 6]，['second_person'，'first_item'，18]，['second_person'，'second_item'，36]]，columns = ['id1'，'id2'，'amount']）

其中：

  id1 id2 amount 
 0 first_person first_item 10 
 1 first_person second_item 6 
 2 second_person first_item 18 
 3 second_person second_item 36 
  

从此我想创建一个第二个数据框，它是：

  first_item second_item 
 first_person 10 6 
 second_person 18 36 
  

当然，在发布之前，我已经做了一段时间，但是我已经设法做到这一点是一个双for循环...对于我的数据帧的大小是无处可计算你会知道如何用更多的pythonic方式做到这一点吗？ （这显然比'for'循环更有效！）

解决方案

我想你可以使用 （ pandas中的新增内容 0.18.0 ）：

  print df 
 id1 id2 amount 
 0 first_person first_item 10 
 1 first_person second_item 6 
 2 second_person first_item 18 
 3 second_person second_item 36 
 
 print df.pivot（index ='id1'，columns ='id2'，values ='amount'）
 .rename_axis（无）
 .rename_axis（无，轴= 1）
 
 first_item second_item 
 first_person 10 6 
 second_person 18 36 
  

I'm having a problem with pandas. I have a dataframe with three columns: 'id1','id2','amount'.

From this, I would like to create another dataframe which index is 'id1', which columns is 'id2', and the cells contain the corresponding 'amount'.

Let's go for an example:

import pandas as pd
df = pd.DataFrame([['first_person','first_item',10],['first_person','second_item',6],['second_person','first_item',18],['second_person','second_item',36]],columns = ['id1','id2','amount'])

which yields:

     id1              id2             amount
0    first_person     first_item      10
1    first_person     second_item     6
2    second_person    first_item      18
3    second_person    second_item     36

And from this I would like to create a second dataframe which is:

                 first_item    second_item
first_person     10            6
second_person    18            36

Of course, before posting I've worked on it for a time, but all I've managed to do for this is a double 'for loop'... Which for the size of my dataframes is nowhere to be computable. Would you know how to do this in a more pythonic way? (which would obviously be far more efficient than 'for' loops!)

解决方案

I think you can use pivot with rename_axis (new in pandas 0.18.0):

print df
             id1          id2  amount
0   first_person   first_item      10
1   first_person  second_item       6
2  second_person   first_item      18
3  second_person  second_item      36

print df.pivot(index='id1', columns='id2', values='amount')
        .rename_axis(None)
        .rename_axis(None, axis=1)

               first_item  second_item
first_person           10            6
second_person          18           36

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