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.

Practice

1. What is the main difference between pandas.cut() and pandas.qcut()?

easy

A. cut() creates bins with fixed ranges; qcut() creates bins with equal counts.

B. cut() sorts data; qcut() filters data.

C. cut() works only on integers; qcut() works only on floats.

D. cut() creates equal-sized bins; qcut() creates fixed range bins.

Solution

1. Step 1: Understand cut() behavior

   cut() divides data into bins with fixed numeric ranges you specify.

2. Step 2: Understand qcut() behavior

   qcut() divides data into bins so that each bin has roughly the same number of data points.

3. Final Answer:

   cut() creates bins with fixed ranges; qcut() creates bins with equal counts. -> Option A

4. Quick Check:

   Fixed ranges vs equal counts = C [OK]

Hint: Remember: cut = fixed ranges, qcut = equal-sized groups [OK]

Common Mistakes:

• Confusing fixed ranges with equal counts
• Thinking qcut sorts data instead of binning
• Assuming cut only works on integers

2. Which of the following is the correct syntax to create 3 equal-width bins from a pandas Series data using cut()?

easy

A. pd.cut(data, bins=3)

B. pd.qcut(data, bins=3)

C. pd.cut(data, q=3)

D. pd.qcut(data, q=3)

Solution

1. Step 1: Recall cut() syntax

   cut() uses the parameter bins to specify the number of equal-width bins.

2. Step 2: Identify correct usage

   Using pd.cut(data, bins=3) creates 3 fixed-width bins.

3. Final Answer:

   pd.cut(data, bins=3) -> Option A

4. Quick Check:

   cut with bins=3 = B [OK]

Hint: cut(): bins=N (equal width); qcut(): bins=N (equal count) [OK]

Common Mistakes:

• Using q parameter with cut()
• Confusing cut() and qcut() syntax
• Using bins parameter with qcut()

3. Given the code:

import pandas as pd
values = [10, 20, 30, 40, 50]
bins = pd.cut(values, bins=[0, 25, 50])
print(bins.tolist())

What is the output?

medium

A. ['(0, 25]', '(0, 25]', '(25, 50]', '(25, 50]', 'NaN']

B. ['[0, 25)', '[0, 25)', '[25, 50)', '[25, 50)', '[25, 50)']

C. ['(0, 25]', '(0, 25]', '(25, 50]', '(25, 50]', '(50, 75]']

D. ['(0, 25]', '(0, 25]', '(25, 50]', '(25, 50]', '(25, 50]']

Solution

1. Step 1: Understand bins parameter

   The bins are [0, 25, 50], so intervals (0, 25], (25, 50]. Values <=25 in first, >25 to <=50 in second.

2. Step 2: Assign values to bins

   10, 20 in (0, 25]; 30, 40, 50 in (25, 50].

3. Final Answer:

   ['(0, 25]', '(0, 25]', '(25, 50]', '(25, 50]', '(25, 50]'] -> Option D

4. Quick Check:

   Bins match values = A [OK]

Hint: Check bin edges and which side is inclusive [OK]

Common Mistakes:

• Confusing inclusive side of intervals
• Assuming last bin includes values >50
• Using wrong interval notation

4. What is wrong with this code snippet?

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

medium

A. No error; code runs correctly.

B. Values list must be a pandas Series, not a list.

C. Using 'bins' parameter with qcut causes an error.

D. qcut requires numeric bins, not number of bins.

Solution

1. Step 1: Check qcut parameters

   qcut() accepts q=int or q=array-like to specify quantiles. The parameter bins is not valid for qcut().

2. Step 2: Verify code runs

   The code uses bins=3 which is invalid for qcut() and will raise a TypeError.

3. Final Answer:

   Using 'bins' parameter with qcut causes an error. -> Option C

4. Quick Check:

   qcut uses q= for quantiles, bins= invalid = D [OK]

Hint: qcut uses q= for quantile count, not bins= [OK]

Common Mistakes:

• Thinking bins= invalid for qcut()
• Believing input must be Series
• Confusing parameter requirements

5. You have a DataFrame column df['scores'] with 1000 exam scores ranging from 0 to 100. You want to create 4 groups with roughly equal number of students in each group. Which code correctly creates these groups?

hard

A. df['grade_bins'] = pd.cut(df['scores'], bins=4)

B. df['grade_bins'] = pd.qcut(df['scores'], q=4)

C. df['grade_bins'] = pd.cut(df['scores'], q=4)

D. df['grade_bins'] = pd.qcut(df['scores'], bins=4)

Solution

1. Step 1: Understand goal

   Roughly equal students per group -> equal counts -> qcut().

2. Step 2: Choose correct function and parameters

   qcut(df['scores'], q=4) creates 4 equal-count bins. The parameter bins does not exist for qcut().

3. Final Answer:

   df['grade_bins'] = pd.qcut(df['scores'], q=4) -> Option B

4. Quick Check:

   Equal counts need qcut q=4 = A [OK]

Hint: qcut(..., q=N) for N equal-count groups [OK]

Common Mistakes:

• Using cut() for equal-sized groups
• Confusing bins and nonexistent q parameters
• Using q= with qcut()