Pandas 将一列列表转换为哑元 [英] Pandas convert a column of list to dummies

问题描述

我有一个数据框，其中一列是我的每个用户所属的组列表.类似的东西:

索引组0 ['a','b','c']1 ['c']2 ['b','c','e']3 ['a','c']4 ['b','e']

我想做的是创建一系列虚拟列来标识每个用户属于哪些组，以便进行一些分析

index a b c d e0 1 1 1 0 01 0 0 1 0 02 0 1 1 0 13 1 0 1 0 04 0 1 0 0 0pd.get_dummies(df['groups'])

不起作用，因为这只会为我的列中的每个不同列表返回一列.

解决方案需要高效，因为数据帧将包含 500,000 多行.任何建议将不胜感激！

解决方案

将 s 用于您的 df['groups']:

In [21]: s = pd.Series({0: ['a', 'b', 'c'], 1:['c'], 2: ['b', 'c', 'e'], 3: ['a', 'c'], 4: ['b', 'e'] })在 [22]: s出[22]:0 [a, b, c]1 [c]2 [b, c, e]3 [a, c]4 [b, e]数据类型:对象

这是一个可能的解决方案:

在[23]中:pd.get_dummies(s.apply(pd.Series).stack()).sum(level=0)出[23]:a b c e0 1 1 1 01 0 0 1 02 0 1 1 13 1 0 1 04 0 1 0 1

这样做的逻辑是:

• .apply(Series) 将一系列列表转换为数据框
• .stack() 再次将所有内容放在一列中(创建多级索引)
• pd.get_dummies( ) 创建假人
• .sum(level=0) 用于重新合并本应为一行的不同行(通过总结第二级，只保留原始级别(level=0)>))

稍微等效的是 pd.get_dummies(s.apply(pd.Series), prefix='', prefix_sep='').sum(level=0,axis=1)>

我不知道这是否足够有效，但无论如何，如果性能很重要，将列表存储在数据框中并不是一个好主意.

I have a dataframe where one column is a list of groups each of my users belongs to. Something like:

index groups  
0     ['a','b','c']
1     ['c']
2     ['b','c','e']
3     ['a','c']
4     ['b','e']

And what I would like to do is create a series of dummy columns to identify which groups each user belongs to in order to run some analyses

index  a   b   c   d   e
0      1   1   1   0   0
1      0   0   1   0   0
2      0   1   1   0   1
3      1   0   1   0   0
4      0   1   0   0   0


pd.get_dummies(df['groups'])

won't work because that just returns a column for each different list in my column.

The solution needs to be efficient as the dataframe will contain 500,000+ rows. Any advice would be appreciated!

解决方案

Using s for your df['groups']:

In [21]: s = pd.Series({0: ['a', 'b', 'c'], 1:['c'], 2: ['b', 'c', 'e'], 3: ['a', 'c'], 4: ['b', 'e'] })

In [22]: s
Out[22]:
0    [a, b, c]
1          [c]
2    [b, c, e]
3       [a, c]
4       [b, e]
dtype: object

This is a possible solution:

In [23]: pd.get_dummies(s.apply(pd.Series).stack()).sum(level=0)
Out[23]:
   a  b  c  e
0  1  1  1  0
1  0  0  1  0
2  0  1  1  1
3  1  0  1  0
4  0  1  0  1

The logic of this is:

• .apply(Series) converts the series of lists to a dataframe
• .stack() puts everything in one column again (creating a multi-level index)
• pd.get_dummies( ) creating the dummies
• .sum(level=0) for remerging the different rows that should be one row (by summing up the second level, only keeping the original level (level=0))

An slight equivalent is pd.get_dummies(s.apply(pd.Series), prefix='', prefix_sep='').sum(level=0, axis=1)

If this will be efficient enough, I don't know, but in any case, if performance is important, storing lists in a dataframe is not a very good idea.

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