拆分没有多索引的 pandas 系列 [英] split a Pandas series without a multiindex

问题描述

我想采用一个具有单级索引的Pandas系列，并将该索引拆分为具有多列的数据框.例如，输入:

I would like to take a Pandas Series with a single-level index and split on that index into a dataframe with multiple columns. For instance, for input:

s = pd.Series(range(10,17), index=['a','a','b','b','c','c','c'])

s
a    10
a    11
b    12
b    13
c    14
c    15
c    16
dtype: int64

我想要的输出是:

    a    b    c
0   10   12   14
1   11   13   15
2   NaN  NaN  16

我不能直接使用unstack命令，因为它需要一个多索引，而我只有一个单级索引.我尝试放入一个都具有相同值的虚拟索引，但出现错误"ReshapeError:索引包含重复的条目，无法重塑".

I cannot directly use the unstack command because it requires a multiindex and I only have a single-level index. I tried putting in a dummy index that all had the same value, but I got an error "ReshapeError: Index contains duplicate entries, cannot reshape".

我知道这有点不寻常，因为1)大熊猫不喜欢粗糙的数组，因此需要填充，2)需要任意重置索引，3)我不能真正初始化" "，直到我知道最长的列将是多长为止.但这似乎仍然是我应该能够做的事情.我也考虑过通过groupby进行操作，但是似乎没有任何类型的聚合函数之类的grouped_df.values()之类的东西-可能是出于上述原因.

I know that this is a little bit unusual because 1) pandas doesn't like ragged arrays, so there will need to be padding, 2) the index needs to be arbitrarily reset, 3) I can't really "initialize" the dataframe until I know how long the longest column is going to be. But this still seems like something that I should be able to do somehow. I also thought about doing it via groupby, but it doesn't seem like there is anything like grouped_df.values() without any kind of aggregating function- probably for the above reasons.

推荐答案

您可以使用groupby，apply，reset_index创建多索引系列，然后调用unstack:

You can use groupby, apply, reset_index to create a multiindex Series, and then call unstack:

import pandas as pd
s = pd.Series(range(10,17), index=['a','a','b','b','c','c','c'])
df = s.groupby(level=0).apply(pd.Series.reset_index, drop=True).unstack(0)
print df

输出:

   a   b   c
0  10  12  14
1  11  13  15
2 NaN NaN  16

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