我在下面的数据帧nbr2:
Postal_Code Borough Neighborhood
0 M1B Scarborough Rouge, Malvern
1 M4C East York Woodbine Heights
2 M4E East Toronto The Beaches
3 M4L East Toronto The Beaches West, India Bazaar
4 M4M East Toronto Studio District
5 M4N Central Toronto Lawrence Park
在应用以下代码以过滤出行时:
neighbor = nbr2.drop(nbr2[nbr2['Borough'].str.contains("Toronto")==False].index, axis=0, inplace=True)
数据框的分布如下:
Postal_Code Borough \
37 M4E East Toronto
41 M4K East Toronto
42 M4L East Toronto
43 M4M East Toronto
Neighborhood
37 The Beaches
41 The Danforth West\n, Riverdale
42 The Beaches West\n, India Bazaar
43 Studio District\n
下面的代码也导致类似的结构:
# define the dataframe columns
column_names = ['Postal_Code','Borough', 'Neighborhood']
# instantiate the dataframe
neighbor = pd.DataFrame(columns=column_names)
neighbor = nbr2.drop(nbr2[nbr2['Borough'].str.contains("Toronto")==False].index, axis=0, inplace=True)
最佳答案
采用
pd.set_option('display.expand_frame_repr', False)