您可以使用
Dataframe与构造函数
lists通过转换为创建
numpy array通过
values使用
tolist：
import pandas as pdd1 = {'teams': [['SF', 'NYG'],['SF', 'NYG'],['SF', 'NYG'], ['SF', 'NYG'],['SF', 'NYG'],['SF', 'NYG'],['SF', 'NYG']]}df2 = pd.Dataframe(d1)print (df2) teams0 [SF, NYG]1 [SF, NYG]2 [SF, NYG]3 [SF, NYG]4 [SF, NYG]5 [SF, NYG]6 [SF, NYG]df2[['team1','team2']] = pd.Dataframe(df2.teams.values.tolist(), index= df2.index)print (df2) teams team1 team20 [SF, NYG] SF NYG1 [SF, NYG] SF NYG2 [SF, NYG] SF NYG3 [SF, NYG] SF NYG4 [SF, NYG] SF NYG5 [SF, NYG] SF NYG6 [SF, NYG] SF NYG对于新的Dataframe：
df3 = pd.Dataframe(df2['teams'].values.tolist(), columns=['team1','team2'])print (df3) team1 team20 SF NYG1 SF NYG2 SF NYG3 SF NYG4 SF NYG5 SF NYG6 SF NYG
解决方案apply(pd.Series)非常慢：
#7k rowsdf2 = pd.concat([df2]*1000).reset_index(drop=True)In [89]: %timeit df2['teams'].apply(pd.Series)1 loop, best of 3: 1.15 s per loopIn [90]: %timeit pd.Dataframe(df2['teams'].values.tolist(), columns=['team1','team2'])1000 loops, best of 3: 820 µs per loop
