将包含列表的 Pandas 列“unstack”成多行
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"unstack" a pandas column containing lists into multiple rows
提问by Alex
Say I have the following Pandas Dataframe:
假设我有以下 Pandas 数据框：
df = pd.DataFrame({"a" : [1,2,3], "b" : [[1,2],[2,3,4],[5]]})
a b
0 1 [1, 2]
1 2 [2, 3, 4]
2 3 [5]
How would I "unstack" the lists in the "b" column in order to transform it into the dataframe:
我将如何“取消堆叠”“b”列中的列表以将其转换为数据帧：
a b
0 1 1
1 1 2
2 2 2
3 2 3
4 2 4
5 3 5
回答by MaxU
UPDATE:generic vectorized approach - will work also for multiple columns DFs:
更新：通用矢量化方法 - 也适用于多列 DF：
assuming we have the following DF:
假设我们有以下 DF：
In [159]: df
Out[159]:
a b c
0 1 [1, 2] 5
1 2 [2, 3, 4] 6
2 3 [5] 7
Solution:
解决方案：
In [160]: lst_col = 'b'
In [161]: pd.DataFrame({
...: col:np.repeat(df[col].values, df[lst_col].str.len())
...: for col in df.columns.difference([lst_col])
...: }).assign(**{lst_col:np.concatenate(df[lst_col].values)})[df.columns.tolist()]
...:
Out[161]:
a b c
0 1 1 5
1 1 2 5
2 2 2 6
3 2 3 6
4 2 4 6
5 3 5 7
Setup:
设置：
df = pd.DataFrame({
"a" : [1,2,3],
"b" : [[1,2],[2,3,4],[5]],
"c" : [5,6,7]
})
Vectorized NumPy approach:
矢量化 NumPy 方法：
In [124]: pd.DataFrame({'a':np.repeat(df.a.values, df.b.str.len()),
'b':np.concatenate(df.b.values)})
Out[124]:
a b
0 1 1
1 1 2
2 2 2
3 2 3
4 2 4
5 3 5
OLD answer:
旧答案：
Try this:
尝试这个：
In [89]: df.set_index('a', append=True).b.apply(pd.Series).stack().reset_index(level=[0, 2], drop=True).reset_index()
Out[89]:
a 0
0 1 1.0
1 1 2.0
2 2 2.0
3 2 3.0
4 2 4.0
5 3 5.0
Or bit nicer solution provided by @Boud:
或者@Boud 提供的更好的解决方案：
In [110]: df.set_index('a').b.apply(pd.Series).stack().reset_index(level=-1, drop=True).astype(int).reset_index()
Out[110]:
a 0
0 1 1
1 1 2
2 2 2
3 2 3
4 2 4
5 3 5
回答by Karvy1
Here is another approach with itertuples-
这是另一种方法itertuples-
df = pd.DataFrame({"a" : [1,2,3], "b" : [[1,2],[2,3,4],[5]]})
data = []
for i in df.itertuples():
lst = i[2]
for col2 in lst:
data.append([i[1], col2])
df_output = pd.DataFrame(data =data, columns=df.columns)
df_output
Output is -
输出是 -
a b
0 1 1
1 1 2
2 2 2
3 2 3
4 2 4
5 3 5
Edit: You can also compress the loops into a single code and populate dataas -
编辑：您还可以将循环压缩为单个代码并填充data为 -
data = [[i[1], col2] for i in df.itertuples() for col2 in i[2]]
