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.