What is as_index in groupby in pandas?

print() is your friend when you don't understand a thing. It clears out doubts many times.

Take a look:

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(df.groupby('books', as_index=True).sum())

print(df.groupby('books', as_index=False).sum())

Output:

  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 benefits you get when you set the column as index are:

1. Speed. When you filter values based on the index column eg. df.loc['bk1'], it would be faster because of hashing of index column. It doesn't have to traverse the entire books column to find 'bk1'. It will just calculate the hash value of 'bk1' and find it in 1 go.

2. Ease. When as_index=True you can use this syntax df.loc['bk1'] which is shorter and faster as opposed to df.loc[df.books=='bk1'] which is longer and slower.

When using the group by function, as_index can be set to true or false depending on if you want the column by which you grouped to be the index of the output.

import pandas as pd
table_r = pd.DataFrame({
    'colors': ['orange', 'red', 'orange', 'red'],
    'price': [1000, 2000, 3000, 4000],
    'quantity': [500, 3000, 3000, 4000],
})
new_group = table_r.groupby('colors',as_index=True).count().sort('price', ascending=False)
print new_group

output:

        price  quantity
colors                 
orange      2         2
red         2         2

Now with as_index=False

   colors  price  quantity
0  orange      2         2
1     red      2         2

Note how colors is no longer an index when we change as_index=False