binning in python pandas 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: dataframe cut based on range

test = pd.DataFrame({'days': [0,20,30,31,45,60]})

test['range1'] = pd.cut(test.days, [0,30,60], include_lowest=True)
#30 value is in [30, 60) group
test['range2'] = pd.cut(test.days, [0,30,60], right=False)
#30 value is in (0, 30] group
test['range3'] = pd.cut(test.days, [0,30,60])
print (test)
   days          range1    range2    range3
0     0  (-0.001, 30.0]   [0, 30)       NaN
1    20  (-0.001, 30.0]   [0, 30)   (0, 30]
2    30  (-0.001, 30.0]  [30, 60)   (0, 30]
3    31    (30.0, 60.0]  [30, 60)  (30, 60]
4    45    (30.0, 60.0]  [30, 60)  (30, 60]
5    60    (30.0, 60.0]       NaN  (30, 60]

Example 3: 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]]