difference between cut and qcut pandas
pd.cut(df['ext price'], bins=4).value_counts() #bin range size afre equal
#res
(87998.212, 120263.375] 12 #different no. of observation
(55603.989, 87998.212] 5
(120263.375, 152528.538] 2
(152528.538, 184793.7] 1
Name: ext price, dtype: int64
#If you want equal distribution of the items in your bins, use qcut . If you want to define your own numeric bin ranges, then use cut .
pd.cut(df['ext price'], bins=4)
#res
0 (55603.989, 87998.212]
1 (87998.212, 120263.375]
2 (55603.989, 87998.212]
3 (120263.375, 152528.538]
4 (87998.212, 120263.375]
....
14 (87998.212, 120263.375]
15 (120263.375, 152528.538]
16 (87998.212, 120263.375]
17 (87998.212, 120263.375]
18 (87998.212, 120263.375]
19 (87998.212, 120263.375]
Name: ext price, dtype: category
Categories (4, interval[float64]): [(55603.989, 87998.212] < (87998.212, 120263.375] < (120263.375, 152528.538] < (152528.538, 184793.7]]