将列表的列分解为多行 [英] Explode column of list to multiple rows

问题描述

我想将某个列(在示例column_x中)的列表扩展为多行.

I want to expand the list in a certain column (in the example column_x) to multiple rows.

所以

df = pd.DataFrame({'column_a': ['a_1', 'a_2'], 
                   'column_b': ['b_1', 'b_2'], 
                   'column_x': [['c_1', 'c_2'], ['d_1', 'd_2']]
                  })

应从

    column_a    column_b    column_x
0   a_1         b_1         [c_1, c_2]
1   a_2         b_2         [d_1, d_2]

到

    column_a    column_b    column_x
0   a_1         b_1         c_1
1   a_1         b_1         c_2
2   a_2         b_2         d_1
3   a_2         b_2         d_2

到目前为止，我所拥有的代码正是这样做的，并且它是

The code I have so far does exactly this, and it does it fast.

lens = [len(item) for item in df['column_x']]
pd.DataFrame( {"column_a" : np.repeat(df['column_a'].values, lens), 
               "column_b" : np.repeat(df['column_b'].values, lens), 
               "column_x" : np.concatenate(df['column_x'].values)})

但是，我有很多专栏.是否有一个简洁而优雅的解决方案来重复整个数据帧而无需再次指定每一列?

However, I have lots of columns. Is there a neat and elegant solution for repeating the whole data frame without specifying each column again?

推荐答案

您可以重复索引值:

lens = df['column_x'].str.len()
a = np.repeat(df.index.values, lens)
print (a)
[0 0 1 1]

df = df.loc[a].assign(column_x=np.concatenate(df['column_x'].values)).reset_index(drop=True)
print (df)
  column_a column_b column_x
0      a_1      b_1      c_1
1      a_1      b_1      c_2
2      a_2      b_2      d_1
3      a_2      b_2      d_2

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