Rolling window calculations in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of rolling mean calculation

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

Data Analysis Python

import pandas as pd
s = pd.Series([1, 2, 3, 4, 5])
result = s.rolling(window=3).mean()
print(result.tolist())

A [nan, 2.0, 3.0, 4.0, 5.0]

B [nan, nan, 2.0, 3.0, 4.0]

C [1.0, 1.5, 2.0, 3.0, 4.0]

D [nan, nan, 1.0, 2.0, 3.0]

💡 Hint

Remember that rolling mean with window 3 needs 3 values before it can compute a mean.

The rolling mean with window 3 returns NaN for the first two values because there are not enough data points. Starting from the third value, it computes the mean of the current and previous two values.

❓ data_output

intermediate

Count of rolling sums above threshold

Given this pandas DataFrame, how many rolling sums with window size 4 are greater than 10?

Data Analysis Python

import pandas as pd
df = pd.DataFrame({'values': [2, 3, 5, 7, 1, 4, 6]})
rolling_sums = df['values'].rolling(window=4).sum()
count = (rolling_sums > 10).sum()
print(count)

A 4

B 3

C 2

D 1

💡 Hint

Calculate rolling sums and count how many exceed 10.

The rolling sums with window 4 are [nan, nan, nan, 17, 16, 17, 18]. All four valid sums are greater than 10, so the count is 4.

🔧 Debug

advanced

Identify the error in rolling apply function

What error does this code raise when applying a custom function with rolling window?

Data Analysis Python

import pandas as pd
s = pd.Series([1, 2, 3, 4, 5])
def custom_func(x):
    return x[0] + x[1]
result = s.rolling(window=3).apply(custom_func)
print(result.tolist())

A TypeError: unsupported operand type(s) for +: 'float' and 'float'

B ValueError: wrong number of items passed 2, placement implies 3

C IndexError: index out of range

D No error, outputs [nan, nan, 3.0, 5.0, 7.0]

💡 Hint

Check how the rolling window passes data to the function.

The rolling apply function passes a numpy array whose length equals the number of available data points (less than window size at the start). The function tries to access x[1], which causes an IndexError when fewer than 2 points are available.

❓ visualization

advanced

Interpret rolling standard deviation plot

Given this code that plots rolling standard deviation, what pattern will the plot show?

Data Analysis Python

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
data = pd.Series(np.random.normal(0, 1, 100))
rolling_std = data.rolling(window=10).std()
plt.plot(data, label='Data')
plt.plot(rolling_std, label='Rolling Std')
plt.legend()
plt.show()

A Rolling std increases linearly from 0 to 10

B Rolling std starts high and decreases steadily

C Rolling std is constant zero throughout

D Rolling std starts with NaN values then fluctuates around 1

💡 Hint

Think about standard deviation of normal random data and rolling window behavior.

The rolling standard deviation with window 10 will be NaN for the first 9 points, then it will fluctuate around the true std deviation of 1 for normal data.

🚀 Application

expert

Calculate rolling correlation between two series

Which option correctly calculates the rolling correlation with window size 5 between two pandas Series s1 and s2?

Data Analysis Python

import pandas as pd
s1 = pd.Series([1, 2, 3, 4, 5, 6, 7])
s2 = pd.Series([7, 6, 5, 4, 3, 2, 1])

A s1.rolling(window=5).corr(s2)

B s1.corr(s2).rolling(window=5)

C pd.rolling_corr(s1, s2, window=5)

D s1.rolling_corr(s2, window=5)

💡 Hint

Check pandas rolling correlation syntax in recent versions.

The correct method is s1.rolling(window=5).corr(s2). Other options are either deprecated or invalid syntax.

Practice

1. What does the rolling(window=3).mean() function do on a data series?

easy

A. Calculates the average of every group of 3 consecutive values in the series

B. Calculates the sum of all values in the series

C. Returns the maximum value in the entire series

D. Sorts the series in ascending order

Solution

1. Step 1: Understand rolling window

   The rolling function with window=3 looks at every group of 3 consecutive values in the series.

2. Step 2: Apply mean function

   For each group of 3 values, mean() calculates their average, producing a smoothed series.

3. Final Answer:

   Calculates the average of every group of 3 consecutive values in the series -> Option A

4. Quick Check:

   Rolling mean = average over window [OK]

Hint: Rolling mean smooths data by averaging fixed-size groups [OK]

Common Mistakes:

• Thinking it sums all values instead of averaging groups
• Confusing rolling mean with overall mean
• Assuming it sorts or finds max values

2. Which of the following is the correct syntax to calculate a rolling sum with a window size of 4 on a pandas Series named data?

easy

A. data.rolling(4).sum

B. rolling(data, window=4).sum()

C. data.rolling(window=4).sum()

D. data.rolling_sum(window=4)

Solution

1. Step 1: Recall pandas rolling syntax

   The rolling function requires the window size as a named argument: rolling(window=4).

2. Step 2: Apply sum function correctly

   After rolling, call sum() to get the rolling sum over each window.

3. Final Answer:

   data.rolling(window=4).sum() -> Option C

4. Quick Check:

   Correct rolling sum syntax uses rolling(window=) [OK]

Hint: Use rolling(window=) with function, not rolling_sum() [OK]

Common Mistakes:

• Omitting 'window=' keyword argument
• Using non-existent rolling_sum method
• Calling rolling as a standalone function

3. Given the pandas Series data = pd.Series([2, 4, 6, 8, 10]), what is the output of data.rolling(window=2).sum()?

medium

A. [2, 6, 12, 20, 30]

B. [2, 4, 6, 8, 10]

C. [NaN, 3, 5, 7, 9]

D. [NaN, 6, 10, 14, 18]

Solution

1. Step 1: Understand rolling sum with window=2

   Each value is the sum of the current and previous value. The first value has no previous, so result is NaN.

2. Step 2: Calculate sums step-by-step

   Positions: NaN (no previous), 2+4=6, 4+6=10, 6+8=14, 8+10=18.

3. Final Answer:

   [NaN, 6, 10, 14, 18] -> Option D

4. Quick Check:

   Rolling sum window=2 sums pairs [OK]

Hint: First rolling sum is NaN, then sums of pairs follow [OK]

Common Mistakes:

• Ignoring NaN for first value
• Confusing sum with mean
• Adding values cumulatively instead of rolling pairs

4. What is wrong with this code snippet for calculating a rolling mean with window size 3 on a pandas DataFrame column df['values']?

rolling_means = df.rolling(3).mean()['values']

medium

A. The window size must be passed as a string, not an integer

B. The rolling function should be applied on the column, not the whole DataFrame

C. The mean() function cannot be used after rolling()

D. The syntax is correct and will work without errors

Solution

1. Step 1: Identify rolling application target

   Rolling should be applied on the Series (column) directly, not on the entire DataFrame.

2. Step 2: Correct the code

   Use df['values'].rolling(3).mean() to get rolling mean of that column.

3. Final Answer:

   The rolling function should be applied on the column, not the whole DataFrame -> Option B

4. Quick Check:

   Apply rolling on Series, not DataFrame for single column [OK]

Hint: Apply rolling on the column Series, not the whole DataFrame [OK]

Common Mistakes:

• Applying rolling on DataFrame then selecting column
• Passing window size as string
• Thinking mean() is invalid after rolling

5. You have daily sales data in a pandas Series sales. You want to calculate a 7-day rolling average but only for days where the window is fully available (no partial windows). Which parameter should you set in rolling() to achieve this?

hard

A. min_periods=7

B. center=True

C. window=7, min_periods=1

D. window=7, center=False

Solution

1. Step 1: Understand min_periods parameter

   min_periods sets the minimum number of observations in window required to have a value.

2. Step 2: Set min_periods=7 for full windows only

   Setting min_periods=7 means rolling average is calculated only when all 7 days are present, avoiding partial windows.

3. Final Answer:

   min_periods=7 -> Option A

4. Quick Check:

   min_periods=window size ensures full window data [OK]

Hint: Use min_periods equal to window size for full windows only [OK]

Common Mistakes:

• Using min_periods=1 allows partial windows
• Confusing center parameter with min_periods
• Not setting min_periods at all