cut() and qcut() for binning in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of pd.cut() with specified bins

What is the output of this code snippet using pd.cut() to bin data into intervals?

Data Analysis Python

import pandas as pd
import numpy as np

values = [1, 7, 5, 4, 6, 3]
bins = [0, 3, 6, 9]
binned = pd.cut(values, bins)
print(binned.tolist())

A ['(0, 3]', '(6, 9]', '(0, 3]', '(3, 6]', '(6, 9]', '(0, 3]']

B ['(0, 3]', '(3, 6]', '(3, 6]', '(3, 6]', '(3, 6]', '(0, 3]']

C ['(0, 3]', '(6, 9]', '(3, 6]', '(3, 6]', '(6, 9]', '(0, 3]']

D ['[0, 3)', '[6, 9)', '[3, 6)', '[3, 6)', '[6, 9)', '[0, 3)']

💡 Hint

Remember that pd.cut creates intervals that are right-inclusive by default.

The pd.cut() function bins values into intervals defined by bins. The intervals are right-inclusive by default, so 3 belongs to the first bin (0,3], 5 belongs to (3,6], and so on.

❓ data_output

intermediate

Number of bins created by pd.qcut()

Given this code using pd.qcut(), how many unique bins are created?

Data Analysis Python

import pandas as pd
values = [10, 15, 14, 20, 18, 30, 25, 40]
binned = pd.qcut(values, 3)
unique_bins = binned.unique()
print(len(unique_bins))

A 3

B 4

C 2

D 5

💡 Hint

pd.qcut divides data into quantiles, so the number of bins equals the number of quantiles.

pd.qcut() splits data into equal-sized groups based on rank. Here, 3 quantiles means 3 bins.

🔧 Debug

advanced

Error raised by pd.cut() with overlapping bins

What error does this code raise when trying to bin data with overlapping bins using pd.cut()?

Data Analysis Python

import pandas as pd
values = [1, 2, 3, 4]
bins = [0, 2, 2, 5]
pd.cut(values, bins)

A TypeError: bins must be a list of integers.

B No error, returns binned data.

C IndexError: bin index out of range.

D ValueError: bins must increase monotonically.

💡 Hint

Bins must be strictly increasing numbers.

The bins list has repeated values (2, 2), which is not allowed. pd.cut() requires bins to be strictly increasing.

🚀 Application

advanced

Using qcut() to create equal-sized groups

You want to split a dataset of 12 values into 4 groups with equal number of values using pd.qcut(). Which code snippet correctly achieves this?

A pd.cut(data, 4, labels=False)

B pd.qcut(data, 4, labels=False)

C pd.qcut(data, bins=[0, 3, 6, 9, 12])

D pd.cut(data, bins=[0, 3, 6, 9, 12])

💡 Hint

pd.qcut() splits data into quantiles, not fixed intervals.

pd.qcut(data, 4) splits data into 4 equal-sized groups by rank. pd.cut() splits by fixed intervals, not equal counts.

🧠 Conceptual

expert

Difference between cut() and qcut()

Which statement correctly describes the main difference between pd.cut() and pd.qcut()?

A <code>pd.cut()</code> bins data into equal-width intervals; <code>pd.qcut()</code> bins data into equal-sized groups based on data distribution.

B <code>pd.cut()</code> bins data into equal-sized groups; <code>pd.qcut()</code> bins data into equal-width intervals.

C <code>pd.cut()</code> only works with numeric data; <code>pd.qcut()</code> works with categorical data.

D <code>pd.cut()</code> automatically labels bins; <code>pd.qcut()</code> does not support labels.

💡 Hint

Think about how the bins are created: by width or by count.

pd.cut() creates bins with fixed width intervals. pd.qcut() creates bins so that each bin has roughly the same number of data points.