Pandas 数据框分组依据，带有列表的列

Question

Im using jupyter notebooks, my current dataframe looks like the following:

players_mentioned  |  tweet_text    |  polarity
______________________________________________
[Mane, Salah]      |  xyz           |    0.12
[Salah]            |  asd           |    0.06

How can I group all players individually and average their polarity?

Currently I have tried to use:

df.groupby(df['players_mentioned'].map(tuple))['polarity'].mean()

But this will return a dataframe grouping all the mentions when together as well as separate, how best can I go about splitting the players up and then grouping them back together.

An expected output would contain

 player  | polarity_average
____________________________
  Mane   |   0.12
  Salah  |   0.09

In other words how to group by each item in the lists in every row.

Answer 1

如果您只是想按players_提到的分组并获得该球员受欢迎度得分的平均值，则应该这样做。

df.groupby('players_mentioned').polarity.agg('mean')

Answer 2

you can use the unnesting idiom from this answer .

def unnesting(df, explode):
    idx = df.index.repeat(df[explode[0]].str.len())
    df1 = pd.concat([
        pd.DataFrame({x: np.concatenate(df[x].values)}) for x in explode], axis=1)
    df1.index = idx

    return df1.join(df.drop(explode, 1), how='left')

You can now call groupby on the unnested "players_mentioned" column.

(unnesting(df, ['players_mentioned'])
    .groupby('players_mentioned', as_index=False).mean())

  players_mentioned  polarity
0              Mane      0.12
1             Salah      0.09