Rename column names of groupby and count result with Pandas

Question

Given the following dataframe:

import numpy as np
df = pd.DataFrame({'price': np.random.random_integers(0, high=100, size=100)})
ranges = [0,10,20,30,40,50,60,70,80,90,100]
df.groupby(pd.cut(df.price, ranges)).count()

Out:

          price
 price  
(0, 10]     9
(10, 20]    11
(20, 30]    11
(30, 40]    9
(40, 50]    16
(50, 60]    7
(60, 70]    10
(70, 80]    9
(80, 90]    14
(90, 100]   4

How could I reset_index the result and rename column names as bins and counts? Thanks.

      bins    counts
0   (0, 10]     9
1   (10, 20]    11
2   (20, 30]    11
3   (30, 40]    9
4   (40, 50]    16
5   (50, 60]    7
6   (60, 70]    10
7   (70, 80]    9
8   (80, 90]    14
9   (90, 100]   4

Answer 1

One idea is use rename for Series from pd.cut, so if select column price for processing groups output is Series, so add Series.reset_index with name parameter for 2 columns DataFrame:

df1 = (df.groupby(pd.cut(df.price, ranges).rename('bins'))['price'].count()
         .reset_index(name='counts'))
print (df1)
        bins  counts
0    (0, 10]      13
1   (10, 20]      13
2   (20, 30]       9
3   (30, 40]       9
4   (40, 50]       7
5   (50, 60]       9
6   (60, 70]       9
7   (70, 80]      12
8   (80, 90]       9
9  (90, 100]       9

Answer 2

This code works but not concise enough, if you have other options, welcome to share:

df.groupby(pd.cut(df.price, ranges)).count()\
.rename(columns={'price' : 'counts'})\
.reset_index()\
.rename(columns={'price': 'bins'})

Out:

      bins    counts
0   (0, 10]     9
1   (10, 20]    11
2   (20, 30]    11
3   (30, 40]    9
4   (40, 50]    16
5   (50, 60]    7
6   (60, 70]    10
7   (70, 80]    9
8   (80, 90]    14
9   (90, 100]   4