Pandas count number of occurrences of each value between ranges

user_6074035 0
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I have a dataset where I have age as a continuous variable and I want to county the number of occurrences of 1's and 0's in "Mental Health" for a number of age group ranges, e.g. 18-25, 26-33, and so on.

A sample code is as below:

df = pd.DataFrame([[18, 1], [45, 1], [56, 0], [26, 0], [35, 1]], columns=['Age', 'Mental_Health'])

What is the easiest way to do this? I don't really want to convert the age into a range if I can avoid it, if I have to I will but I'm ideally looking for something which comes out with 18-25 suffering = 24, not suffering = 21, and so on for all age ranges.

What is the easiest way of doing this?

pandas range python
2 Answers

You want pd.cut. You can define arbitrary bins (I've used range below). This will cut the passed series, and you can count the distinct "cut" ranges to see how many rows fall therein:

df["age_range"] = pd.cut(df.Age, bins=[0,18,25,33,99], right=False)
df2 = df.groupby("age_range").Mental_Health.sum().to_frame(name="suffering")
df2["not_suffering"] = df.groupby("age_range").Mental_Health.count() - df2.suffering

output:

           suffering  not_suffering
age_range
[0, 18)            0              0
[18, 25)           1              0
[25, 33)           0              1
[33, 99)           2              1
user_50329410

Try this:

import pandas as pd
import numpy as np
df = pd.DataFrame([[18, 1], [45, 1], [56, 0], [26, 0], [35, 1]], columns=['Age', 'Mental_Health'])

df['cuts'] = pd.cut(df['Age'], np.arange(0,100,15))

df.pivot_table(index='cuts', columns='Mental_Health', values='Age', aggfunc='count').fillna(0).astype(int)

Output:

Mental_Health  0  1
cuts               
(15, 30]       1  1
(30, 45]       0  2
(45, 60]       1  0
user_81623940
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