Why window functions matter in Pandas - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the rolling average of sales over 3 days.

Pandas

df['rolling_avg'] = df['sales'].[1](3).mean()

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

Arolling

Bgroupby

Csort_values

Dcumsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using groupby instead of rolling will group data but not create a moving window.

Using cumsum adds values cumulatively but does not average over a window.

The rolling function creates a rolling window to calculate statistics like the mean over a fixed number of rows.

2fill in blank

medium

Complete the code to calculate the cumulative sum of sales.

Pandas

df['cumulative_sales'] = df['sales'].[1]()

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

Arolling

Bcumsum

Cmean

Dshift

Attempts:

3 left

💡 Hint

Common Mistakes

Using rolling.mean() calculates a moving average, not a cumulative sum.

Using shift() moves data but does not sum values.

The cumsum function calculates the cumulative sum, adding each value to the sum of all previous values.

3fill in blank

hard

Fix the error in the code to calculate the expanding maximum of sales.

Pandas

df['expanding_max'] = df['sales'].[1]().max()

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

Arolling

Bgroupby

Cexpanding

Dcumsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using rolling().max() calculates a fixed window max, not expanding.

Using groupby() groups data but does not create expanding windows.

The expanding function creates a window that expands from the start to the current row, useful for cumulative calculations like max.

4fill in blank

hard

Fill in the blank to calculate the difference between current sales and the previous day's sales.

Pandas

df['sales_diff'] = df['sales'].[1]()

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

Adiff

Bcumsum

Cshift

Drolling

Attempts:

3 left

💡 Hint

Common Mistakes

Using shift() moves the values but does not compute the difference.

Using cumsum() computes cumulative sums, not differences.

Using rolling requires a window size and computes statistics over windows.

The diff() function calculates the difference between the current value and the previous row's value.

5fill in blank

hard

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

Pandas

lengths = { [1]: [2] for [3] in words if len([3]) > 4 }

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

Aword

Blen(word)

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently causes errors.

Using len(word) as a key instead of value reverses the dictionary.

This dictionary comprehension uses word as the variable, maps each word to its length using len(word), and iterates over word in the list words.

Practice

1. What is the main benefit of using window functions in pandas?

easy

A. They allow calculations over a subset of rows without collapsing the data.

B. They delete rows that do not meet a condition.

C. They convert DataFrames into lists.

D. They only work on numeric columns.

Solution

1. Step 1: Understand window function purpose

   Window functions perform calculations across a set of rows related to the current row without reducing the number of rows.

2. Step 2: Compare options

   They allow calculations over a subset of rows without collapsing the data. correctly describes this benefit. Options A, B, and D describe unrelated or incorrect behaviors.

3. Final Answer:

   They allow calculations over a subset of rows without collapsing the data. -> Option A

4. Quick Check:

   Window functions keep all rows while calculating [OK]

Hint: Window functions keep all rows while calculating [OK]

Common Mistakes:

• Thinking window functions remove rows
• Confusing window functions with filtering
• Assuming window functions only work on numbers

2. Which of the following is the correct syntax to calculate a rolling mean over 3 rows in pandas?

easy

A. df.rolling['col'](3).mean()

B. df['col'].rolling(3).mean()

C. df['col'].mean(rolling=3)

D. df.rolling.mean(3)['col']

Solution

1. Step 1: Recall rolling mean syntax

   The correct syntax uses the rolling() method on the Series, then calls mean(): df['col'].rolling(3).mean()

2. Step 2: Check each option

   df['col'].rolling(3).mean() matches the correct syntax. Options B, C, and D misuse method calls or order.

3. Final Answer:

   df['col'].rolling(3).mean() -> Option B

4. Quick Check:

   rolling() then mean() is correct order [OK]

Hint: Use rolling() before mean() on the column [OK]

Common Mistakes:

• Using square brackets with rolling
• Calling mean() before rolling()
• Passing rolling window size inside mean()

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({'value': [10, 20, 30, 40, 50]})
df['rolling_sum'] = df['value'].rolling(3).sum()
print(df)

What is the output of the rolling_sum column?

medium

A. [NaN, 30.0, 60.0, 90.0, 120.0]

B. [10, 30, 60, 90, 120]

C. [NaN, NaN, 60.0, 90.0, 120.0]

D. [10, 20, 30, 40, 50]

Solution

1. Step 1: Understand rolling sum with window=3

   Rolling sum with window 3 sums current and previous 2 rows. First two rows have less than 3 values, so result is NaN.

2. Step 2: Calculate sums

   Row 3: 10+20+30=60, Row 4: 20+30+40=90, Row 5: 30+40+50=120

3. Final Answer:

   [NaN, NaN, 60.0, 90.0, 120.0] -> Option C

4. Quick Check:

   Rolling sum skips incomplete windows [OK]

Hint: Rolling sums start producing values after full window [OK]

Common Mistakes:

• Expecting sums for first two rows
• Confusing rolling sum with cumulative sum
• Ignoring NaN for incomplete windows

4. Which of the following is true about this code snippet?

df['rank'] = df['score'].rolling(3).rank()

medium

A. The rolling window size must be larger than 3.

B. rank() requires a window size argument.

C. The code should use groupby before rolling.

D. rolling() supports rank() directly.

Solution

1. Step 1: Check rolling and rank compatibility

   The rolling() method returns a Rolling object that supports aggregation functions like sum(), mean(), rank(), etc.

2. Step 2: Identify correct statement

   rolling() supports rank() directly. The other options describe incorrect requirements or unrelated behaviors.

3. Final Answer:

   rolling() supports rank() directly. -> Option D

4. Quick Check:

   rank() is valid on rolling objects [OK]

Hint: rank() works directly on rolling objects [OK]

Common Mistakes:

• Thinking rank() is not supported directly on rolling
• Trying to pass window size to rank()
• Confusing rolling with groupby

5. You have daily sales data in a DataFrame and want to calculate a 7-day moving average of sales but keep all days in the result. Which pandas method should you use and why?

hard

A. Use rolling(7).mean() because it calculates moving averages without dropping rows.

B. Use groupby('day').mean() because it groups days and averages sales.

C. Use resample('7D').mean() because it resamples data into 7-day periods.

D. Use cumsum() because it gives cumulative sums over 7 days.

Solution

1. Step 1: Understand the goal

   You want a moving average over 7 days that keeps all original rows (days) in the DataFrame.

2. Step 2: Evaluate methods

   rolling(7).mean() calculates the moving average using a sliding window and keeps all rows. groupby() aggregates and reduces rows. resample() changes frequency and may drop days. cumsum() is cumulative sum, not average.

3. Final Answer:

   Use rolling(7).mean() because it calculates moving averages without dropping rows. -> Option A

4. Quick Check:

   rolling().mean() keeps all rows with moving average [OK]

Hint: rolling().mean() keeps all rows with moving average [OK]

Common Mistakes:

• Using groupby which reduces rows
• Using resample which changes data frequency
• Confusing cumulative sum with moving average