Why window functions matter in Pandas - Challenge Your Understanding

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of rolling mean with window size 3

What is the output of the following code that calculates a rolling mean with a window size of 3 on a pandas Series?

Pandas

import pandas as pd
s = pd.Series([10, 20, 30, 40, 50])
result = s.rolling(window=3).mean()
print(result.tolist())

A [nan, 15.0, 25.0, 35.0, 45.0]

B [10.0, 20.0, 30.0, 40.0, 50.0]

C [nan, nan, 20.0, 30.0, 40.0]

D [nan, nan, nan, 30.0, 40.0]

💡 Hint

Think about how rolling mean calculates the average of the current and previous values within the window.

The rolling mean with window=3 calculates the average of the current and previous two values. The first two positions have insufficient data, so they are NaN. Starting from the third value, the mean is computed over the last three values.

❓ data_output

intermediate

Result of cumulative sum grouped by category

Given the DataFrame below, what is the output of the cumulative sum of 'value' grouped by 'category'?

Pandas

import pandas as pd
df = pd.DataFrame({'category': ['A', 'A', 'B', 'B', 'A'], 'value': [1, 2, 3, 4, 5]})
df['cum_sum'] = df.groupby('category')['value'].cumsum()
print(df)

A

  category  value  cum_sum
0        A      1        1
1        A      2        3
2        B      3        3
3        B      4        7
4        A      5        8

B

  category  value  cum_sum
0        A      1        1
1        A      2        2
2        B      3        3
3        B      4        4
4        A      5        5

C

  category  value  cum_sum
0        A      1        1
1        A      2        3
2        B      3        3
3        B      4        4
4        A      5        5

D

  category  value  cum_sum
0        A      1        1
1        A      2        2
2        B      3        7
3        B      4        7
4        A      5        8

💡 Hint

Cumulative sum restarts for each group and adds values in order.

The cumulative sum adds values within each category group. For category A: 1, then 1+2=3, then 3+5=8. For category B: 3, then 3+4=7.

❓ visualization

advanced

Visualizing rolling average impact

Which plot correctly shows the original data and its rolling average with window size 4?

Pandas

import pandas as pd
import matplotlib.pyplot as plt
s = pd.Series([5, 10, 15, 20, 25, 30, 35])
rolling_avg = s.rolling(window=4).mean()
plt.plot(s, label='Original')
plt.plot(rolling_avg, label='Rolling Avg')
plt.legend()
plt.show()

A Scatter plot with original data points and rolling average points overlapping exactly

B Pie chart showing proportion of original data values

C Bar chart showing original data and rolling average side by side for each point

D Line plot with original data as a smooth increasing line and rolling average starting with NaN for first 3 points, then smooth line following original but lagging

💡 Hint

Rolling average smooths data and has NaN for initial points without enough data.

The rolling average line starts with NaN for the first 3 points (window=4), then follows the trend of original data but smoother and slightly lagged. A line plot best shows this.

🧠 Conceptual

advanced

Why use window functions instead of groupby aggregation?

Which statement best explains why window functions are useful compared to simple groupby aggregation?

A Window functions allow calculations across rows while keeping the original row structure, unlike groupby which reduces rows to aggregated results.

B Window functions always run faster than groupby aggregations.

C Groupby aggregation can only be used on numeric data, window functions work on all data types.

D Window functions replace the need for any filtering or sorting in data processing.

💡 Hint

Think about whether the number of rows changes after applying each method.

Window functions compute values across rows but keep the original number of rows, allowing more detailed analysis. Groupby aggregation reduces rows by summarizing groups.

🔧 Debug

expert

Identify the error in this expanding window code

What error does the following code produce and why? import pandas as pd s = pd.Series([2, 4, 6, 8]) result = s.expanding(min_periods=5).mean() print(result.tolist())

Pandas

import pandas as pd
s = pd.Series([2, 4, 6, 8])
result = s.expanding(min_periods=5).mean()
print(result.tolist())

A SyntaxError due to incorrect use of expanding function

B All values are NaN because min_periods=5 is greater than the series length

C TypeError because mean() cannot be applied to Series

D IndexError because expanding window exceeds series length

💡 Hint

Check the length of the series and the min_periods parameter.

The expanding window requires at least 5 data points to compute the mean, but the series has only 4 points, so all results are NaN.