如何在大 pandas 复杂的条件下进行合并 [英] How to pivot with binning with complicated condition in pandas

问题描述

我有数据框如下

age  type days 
1    a    1
2    b    3
2    b    4
3    a    5   
4    b    2
6    c    1
7    f    0
7    d    4
10   e    2
14   a    1

首先，我想与年龄合并

年龄

[0〜4]

age  type days  
1    a    1
2    b    3
2    b    4
3    a    5   
4    b    2

然后通过使用类型

   sum count
a   6   2
b   9   3
c   0   0
d   0   0
e   0   0
f   0   0

然后我想将此方法应用于另一个binns。

Then I would like to apply this method to another binns.

[5〜9]
[11〜14]

[5~9] [11~14]

我想要的结果在

   [0~4]         [5~9]      [10~14]
   sum count  sum count  sum count
a   6   2      0   0      1   1
b   9   3      0   0      0   0
c   0   0      1   1      0   0
d   0   0      4   1      0   0
e   0   0      0   0      2   1
f   0   0      0   1      0   0

如何做到这一点？
这对我来说非常复杂。

How can this be done? It is very complicated for me..

推荐答案

考虑一个 pivot_table 与 pd.cut 如果您不关心列排序，因为计数和总和不是在箱子下配对。使用操作，您可以更改这样的顺序。

Consider a pivot_table with pd.cut if you do not care too much about column ordering as count and sum are not paired together under the bin. With manipulation you can change such ordering.

df['bin'] = pd.cut(df.age, [0,4,9,14])

pvtdf = df.pivot_table(index='type', columns=['bin'], values='days', 
                       aggfunc=('count', 'sum')).fillna(0)

#       count                   sum               
# bin  (0, 4] (4, 9] (9, 14] (0, 4] (4, 9] (9, 14]
# type                                            
# a       2.0    0.0     1.0    6.0    0.0     1.0
# b       3.0    0.0     0.0    9.0    0.0     0.0
# c       0.0    1.0     0.0    0.0    1.0     0.0
# d       0.0    1.0     0.0    0.0    4.0     0.0
# e       0.0    0.0     1.0    0.0    0.0     2.0
# f       0.0    1.0     0.0    0.0    0.0     0.0

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