如果有人来这里找现成的函数，我写了一个。

如果列没有指定，它会找到所有带有列表的列并展开它们; 添加的列命名为column_name_0、column_name_1等; 列的顺序在最终的数据框架中保持不变; if strict=True，它检查给定列中的列表是否大小相等。

感谢改进和评论。

def unfold_columns(df, columns=[], strict=False):
    assert isinstance(columns, list), "Columns should be a list of column names"
    if len(columns) == 0:
        columns = [
            column for column in df.columns 
            if df.applymap(lambda x: isinstance(x, list)).all()[column]
        ]
    else:
        assert(all([(column in df.columns) for column in columns])), \
            "Not all given columns are found in df"
    columns_order = df.columns
    for column_name in columns:
        if df[column_name].apply(lambda x: isinstance(x, list)).all():
            if strict:
                assert len(set(df[column_name].apply(lambda x: len(x)))) == 1, \
                    f"Lists in df['{column_name}'] are not of equal length"
            unfolded = pd.DataFrame(df[column_name].tolist())
            unfolded.columns = [f'{column_name}_{x}' for x in unfolded.columns]
            columns_order = [
                *columns_order[:list(columns_order).index(column_name)], 
                *unfolded.columns, 
                *columns_order[list(columns_order).index(column_name)+1:]
            ]
            df = df.join(unfolded).drop([column_name], axis=1)
    return df[columns_order]

根据前面的回答，下面是另一个解决方案，它返回与df2.teams.apply(pd.Series)相同的结果，但运行时间要快得多:

pd.DataFrame([{x: y for x, y in enumerate(item)} for item in df2['teams'].values.tolist()], index=df2.index)

计时:

In [1]:
import pandas as pd
d1 = {'teams': [['SF', 'NYG'],['SF', 'NYG'],['SF', 'NYG'],
                ['SF', 'NYG'],['SF', 'NYG'],['SF', 'NYG'],['SF', 'NYG']]}
df2 = pd.DataFrame(d1)
df2 = pd.concat([df2]*1000).reset_index(drop=True)

In [2]: %timeit df2['teams'].apply(pd.Series)

8.27 s ± 2.73 s per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [3]: %timeit pd.DataFrame([{x: y for x, y in enumerate(item)} for item in df2['teams'].values.tolist()], index=df2.index)

35.4 ms ± 5.22 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

这是另一个使用df的解。Transform和df.set_index:

>>> from operator import itemgetter
>>> df['teams'].transform({'item1': itemgetter(0), 'item2': itemgetter(1)})

  team1 team2
0    SF   NYG
1    SF   NYG
2    SF   NYG
3    SF   NYG
4    SF   NYG
5    SF   NYG
6    SF   NYG

当然可以概括为:

>>> indices = range(len(df['teams'][0]))

>>> df['teams'].transform({f'team{i+1}': itemgetter(i) for i in indices})

  team1 team2
0    SF   NYG
1    SF   NYG
2    SF   NYG
3    SF   NYG
4    SF   NYG
5    SF   NYG
6    SF   NYG

这种方法具有提取所需指标的额外好处:

>>> df
                 teams
0  [SF, NYG, XYZ, ABC]
1  [SF, NYG, XYZ, ABC]
2  [SF, NYG, XYZ, ABC]
3  [SF, NYG, XYZ, ABC]
4  [SF, NYG, XYZ, ABC]
5  [SF, NYG, XYZ, ABC]
6  [SF, NYG, XYZ, ABC]

>>> indices = [0, 2]
>>> df['teams'].transform({f'team{i+1}': itemgetter(i) for i in indices})

  team1 team3
0    SF   XYZ
1    SF   XYZ
2    SF   XYZ
3    SF   XYZ
4    SF   XYZ
5    SF   XYZ
6    SF   XYZ

这个解决方案保留了df2 DataFrame的索引，不像任何使用tolist()的解决方案:

df3 = df2.teams.apply(pd.Series)
df3.columns = ['team1', 'team2']

结果如下:

  team1 team2
0    SF   NYG
1    SF   NYG
2    SF   NYG
3    SF   NYG
4    SF   NYG
5    SF   NYG
6    SF   NYG

更简单的解决方案:

pd.DataFrame(df2["teams"].to_list(), columns=['team1', 'team2'])

产量,

  team1 team2
-------------
0    SF   NYG
1    SF   NYG
2    SF   NYG
3    SF   NYG
4    SF   NYG
5    SF   NYG
6    SF   NYG
7    SF   NYG

如果你想拆分一列带分隔符的字符串而不是列表，你可以类似地做:

pd.DataFrame(df["teams"].str.split('<delim>', expand=True).values,
             columns=['team1', 'team2'])

与所提出的解决方案相比，似乎有一种语法更简单的方法，因此更容易记住。我假设这个列在数据框架df中叫做“meta”:

df2 = pd.DataFrame(df['meta'].str.split().values.tolist())