关于pandas中groupby的参数as_index的True与False

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在完成作业的过程中遇到了一些困难，在参考别的同学代码中发现他比我多了一条as_index=False，就把index的标题位置上移，为实现后面的工作提供了基础。上面说的比较抽象，在下面有实例说明。

首先看一下pandas官方给出的groupby函数，可以看到默认值为as_index=True

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

Output：

注意两次print输出中‘book’和‘price’的位置

  books  price0   bk1     121   bk1     122   bk1     123   bk2     154   bk2     155   bk3     17       pricebooks       bk1       36bk2       30bk3       17  books  price0   bk1     361   bk2     302   bk3     17

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

The benefit of as_index=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 as_index=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.

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