transform discrete and continuous data to percentile in pandas python code example

Example 1: difference between cut and qcut pandas

df['ext price'].value_counts(bins=4, sort=False)

#res
(55603.988000000005, 87998.212]     5
(87998.212, 120263.375]            12
(120263.375, 152528.538]            2
(152528.538, 184793.7]              1
Name: ext price, dtype: int64

Example 2: difference between cut and qcut pandas

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]]