关于pandas中groupby的参数as_index的True与False


关于pandas中groupby的参数as_index的True与False

grouby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, **kwargs)

下面部分是从https://stackoverflow.com/questions/41236370/what-is-as-index-in-groupby-in-pandas搬运

import pandas as pd

df = pd.DataFrame(data={'books':['bk1','bk1','bk1','bk2','bk2','bk3'], 'price': [12,12,12,15,15,17]})
print df
print
print df.groupby('books', as_index=True).sum()
print
print df.groupby('books', as_index=False).sum()

注意两次print输出中‘book’和‘price’的位置

  books  price
0   bk1     12
1   bk1     12
2   bk1     12
3   bk2     15
4   bk2     15
5   bk3     17

       price
books       
bk1       36
bk2       30
bk3       17

  books  price
0   bk1     36
1   bk2     30
2   bk3     17

When as_index=True the key(s) you use in groupby will become an index in the new dataframe.

The benefit of asindex=True is that you can yank out the rows you want by using key names. For eg. if you want 'bk1' you can get it like this: df.loc['bk1'] as opposed to when asindex=Falsethen you will have to get it like this: df.loc[df.books=='bk1']

Including the other main benefit of using as_index=True raised by @ayhan in comments: df.loc['bk1'] would be faster because it doesn't have to traverse the entire books column to find 'bk1' when it's indexed. It will just calculate the hash value of 'bk1' and find it in 1 go.