Binning with cut() and qcut() in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of pandas cut() with custom bins

What is the output of the following code snippet?

Pandas

import pandas as pd
import numpy as np

values = pd.Series([1, 7, 5, 4, 6, 3])
bins = [0, 3, 6, 9]
categories = pd.cut(values, bins)
print(categories.value_counts().to_dict())

A {'(0, 3]': 2, '(3, 6]': 2, '(6, 9]': 2}

B {'(0, 3]': 1, '(3, 6]': 4, '(6, 9]': 1}

C {'(0, 3]': 3, '(3, 6]': 2, '(6, 9]': 1}

D {'(0, 3]': 2, '(3, 6]': 3, '(6, 9]': 1}

💡 Hint

Remember that cut() bins are right-inclusive by default.

The bins are (0,3], (3,6], and (6,9]. Values 1 and 3 fall into (0,3], values 4,5,6 fall into (3,6], and value 7 falls into (6,9].

❓ data_output

intermediate

Number of bins created by qcut() with duplicates

Given the following code, how many unique bins does qcut() create?

Pandas

import pandas as pd
values = pd.Series([1, 2, 2, 3, 4, 5, 6, 7, 8, 9])
categories = pd.qcut(values, 4, duplicates='drop')
print(categories.cat.categories)

A 2

B 4

C 3

D 5

💡 Hint

qcut tries to create equal-sized bins but drops duplicates in bin edges.

Because the value 2 appears twice, some quantile edges coincide, so qcut drops duplicates and creates 3 bins instead of 4.

🔧 Debug

advanced

Identify the error in this cut() usage

What error does the following code raise?

Pandas

import pandas as pd
values = pd.Series([1, 2, 3, 4, 5])
bins = [0, 2, 4]
categories = pd.cut(values, bins, labels=["Low", "Medium", "High"])
print(categories)

A ValueError: Bin labels must be one fewer than the number of bin edges

B TypeError: labels must be a list of strings

C IndexError: list index out of range

D No error, prints categories with labels

💡 Hint

Check the number of labels vs number of bins.

The number of labels must be exactly one less than the number of bin edges. Here, bins has length 3, so labels must have length 2.

❓ visualization

advanced

Interpreting qcut() bin edges from output

Given this code, which option correctly describes the bin edges printed?

Pandas

import pandas as pd
values = pd.Series([10, 20, 30, 40, 50, 60, 70, 80, 90, 100])
categories = pd.qcut(values, 5)
print(categories.cat.categories)

A Bins split data into 4 groups with equal counts

B Bins split data into 5 equal-sized groups by value ranges with edges at 10, 28, 46, 64, 82, 100

C Bins split data into 5 equal-width intervals from 10 to 100

D Bins split data into 5 groups but edges are arbitrary and not quantiles

💡 Hint

qcut creates bins with equal number of data points, edges are quantiles.

qcut divides data into equal-sized groups by rank, so edges correspond to quantiles, not equal width.

🚀 Application

expert

Choosing binning method for skewed data

You have a dataset with highly skewed income values. You want to create 4 bins that each contain roughly the same number of people. Which method and parameters should you use?

A Use pandas qcut() with 4 bins and duplicates='drop'

B Use pandas cut() with 4 equal-width bins

C Use pandas cut() with custom bins based on income quantiles

D Use pandas qcut() with 4 bins and labels=False

💡 Hint

Think about how to get bins with equal counts despite skew.

qcut creates bins with equal counts by default. Using duplicates='drop' handles any duplicate edges due to skew.