Binning with cut() and qcut() in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create 3 equal-width bins for the data using cut().

Pandas

import pandas as pd

data = [5, 10, 15, 20, 25]
bins = pd.cut(data, [1])
print(bins)

Drag options to blanks, or click blank then click option'

A3

B[3, 6, 9]

C5

Dpd.qcut

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a list of numbers instead of the number of bins.

Using qcut instead of cut for equal-width bins.

Using pd.cut with an integer creates that many equal-width bins.

2fill in blank

medium

Complete the code to create 4 bins with equal number of data points using qcut().

Pandas

import pandas as pd

data = [5, 10, 15, 20, 25, 30, 35, 40]
bins = pd.[1](data, 4)
print(bins)

Drag options to blanks, or click blank then click option'

Acut

Bqcut

Cbin

Dgroupby

Attempts:

3 left

💡 Hint

Common Mistakes

Using cut() which creates equal-width bins, not equal-sized bins.

Using incorrect function names like bin or groupby.

qcut() creates bins with equal number of data points (quantiles).

3fill in blank

hard

Fix the error in the code to create bins with cut() using custom bin edges.

Pandas

import pandas as pd

values = [1, 2, 3, 4, 5]
bins = pd.cut(values, bins=[1])
print(bins)

Drag options to blanks, or click blank then click option'

Apd.qcut

B3

C[0, 2, 4, 6]

D['a', 'b', 'c']

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a single integer instead of a list for bins.

Passing non-numeric bin edges.

Custom bins must be a list of numbers defining bin edges.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Pandas

words = ['apple', 'bat', 'carrot', 'dog', 'elephant']
lengths = {word: [1] for word in words if len(word) [2] 3}
print(lengths)

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Alen(word)

Bword

C>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself instead of its length as the value.

Using '<=' instead of '>' in the condition.

The dictionary comprehension maps each word to its length if the word length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words with their lengths for words longer than 4 characters.

Pandas

words = ['apple', 'bat', 'carrot', 'dog', 'elephant']
result = { [1]: [2] for word in words if len(word) [3] 4 }
print(result)

Drag options to blanks, or click blank then click option'

Aword.upper()

Blen(word)

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original word instead of uppercase for keys.

Using '<' or '<=' instead of '>' in the condition.

The dictionary comprehension creates keys as uppercase words and values as their lengths, filtering words longer than 4 characters.

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()