How to make each bin of data as column of dataframe - python-3.x

Question

I have have dataframe with column A , i want to divide column in bins and count of each bin as column of dataframe , for example bin from 0 to how many points and add this in in dataframe.
i used this code for binning but i am not sure how to insert count column in df.
df=pd.DataFrame({'max':[0.2,0.3,1,1.5,2.5,0.2]})
print(df)
max
0 0.2
1 0.3
2 1.0
3 1.5
4 2.5
5 0.2
bins = [0, 0.5, 1, 1.5, 2, 2.5]
x=pd.cut(df['max'], bins)
desired output
print(df)
0_0.5_count 0.5_1_count
0 3 1

Answer 1

First add parameter label to cut, then count by Series.value_counts and for DataFrame use Series.to_frame with transpose by DataFrame.T:
bins = [0, 0.5, 1, 1.5, 2, 2.5]
labels = ['{}_{}_count'.format(i, j) for i, j in zip(bins[:-1], bins[1:])]
x=pd.cut(df['max'], bins, labels=labels).value_counts().sort_index().to_frame(0).T
print (x)
0_0.5_count 0.5_1_count 1_1.5_count 1.5_2_count 2_2.5_count
0 3 1 1 0 1
Details:
print (pd.cut(df['max'], bins, labels=labels))
0 0_0.5_count
1 0_0.5_count
2 0.5_1_count
3 1_1.5_count
4 2_2.5_count
5 0_0.5_count
Name: max, dtype: category
Categories (5, object): [0_0.5_count < 0.5_1_count < 1_1.5_count < 1.5_2_count < 2_2.5_count]
print (pd.cut(df['max'], bins, labels=labels).value_counts())
0_0.5_count 3
2_2.5_count 1
1_1.5_count 1
0.5_1_count 1
1.5_2_count 0
Name: max, dtype: int64
Alternative solution with GroupBy.size:
bins = [0, 0.5, 1, 1.5, 2, 2.5]
labels = ['{}_{}_count'.format(i, j) for i, j in zip(bins[:-1], bins[1:])]
x= df.groupby(pd.cut(df['max'], bins, labels=labels)).size().rename_axis(None).to_frame().T
print (x)
0_0.5_count 0.5_1_count 1_1.5_count 1.5_2_count 2_2.5_count
0 3 1 1 0 1