Return the lowr or upper bound of a range after binning in Python

Alex Man :

I convert the following df into bins using pd.cut in following:

import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randint(0,100,size=(5, 4)), columns=list('ABCD'))
print(df)
newDF = pd.cut(df.A, 2, precision=0)
print(newDF)

A   B   C   D
0  83  43  99  85
1   6  57  44  45
2   5  72  10  53
3  24  50  23  18
4  75  25  96  27
0    (44.0, 83.0]
1     (5.0, 44.0]
2     (5.0, 44.0]
3     (5.0, 44.0]
4    (44.0, 83.0]

Is there any way to return the lower bound or upper bound of the range instead of the whole range? For example, from the above example:

0    44.0
1    5.0
2    5.0
3    5.0
4    44.0

ansev :

Use Series.map:

pd.cut(df.A, 2, precision=0).map(lambda x: x.left)

or pd.IntervalIndex

s = pd.cut(df.A, 2, precision=0)
pd.Series(data=pd.IntervalIndex(s).left, index = s.index)

---

#print(df)
#
#
#    A   B   C   D
#0  26  70  28   2
#1  49  42  56  28
#2  48  26  40  19
#3   3  50  17   3
#4  20  34  54  42
#
#
#pd.cut(df.A, 2, precision=0).map(lambda x: x.left)
#
#0     3.0
#1    26.0
#2    26.0
#3     3.0
#4     3.0
#Name: A, dtype: category
#Categories (2, float64): [3.0 < 26.0]

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