Expanding window operations in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of expanding sum with min_periods

What is the output of the following code snippet using pandas expanding sum with min_periods=2?

Pandas

import pandas as pd
s = pd.Series([1, 2, 3, 4, 5])
result = s.expanding(min_periods=2).sum()
print(result.tolist())

A [nan, nan, 6.0, 10.0, 15.0]

B [1.0, 3.0, 6.0, 10.0, 15.0]

C [1, 3, 6, 10, 15]

D [nan, 3.0, 6.0, 10.0, 15.0]

💡 Hint

Remember that min_periods=2 means the expanding window will return NaN until at least 2 values are included.

The expanding sum with min_periods=2 returns NaN for the first element because there is only one value. From the second element onward, it sums all values up to that point.

❓ data_output

intermediate

Length of expanding window result

Given a pandas Series of length 7, what is the length of the Series returned by expanding().mean()?

Pandas

import pandas as pd
s = pd.Series(range(7))
result = s.expanding().mean()
print(len(result))

A 7

B 6

C 8

D 0

💡 Hint

The expanding window operation returns a result for each original element.

The expanding mean returns a Series of the same length as the original Series because it computes the mean for each expanding window ending at each element.

🔧 Debug

advanced

Identify the error in expanding window code

What error does the following code raise when executed?

Pandas

import pandas as pd
s = pd.Series([1, 2, 3])
result = s.expanding(min_periods=0).sum()
print(result)

A AttributeError: 'Series' object has no attribute 'expanding'

B ValueError: min_periods must be >= 1

C No error, prints [1, 3, 6]

D TypeError: unsupported operand type(s) for +: 'int' and 'NoneType'

💡 Hint

Check the allowed values for the min_periods parameter in expanding.

The min_periods parameter must be at least 1. Setting it to 0 raises a ValueError.

❓ visualization

advanced

Plotting expanding mean of a time series

Which option produces a line plot showing the original data and its expanding mean on the same graph?

Pandas

import pandas as pd
import matplotlib.pyplot as plt
s = pd.Series([2, 4, 6, 8, 10])
exp_mean = s.expanding().mean()
plt.figure()
plt.plot(s, label='Original')
plt.plot(exp_mean, label='Expanding Mean')
plt.legend()
plt.show()

A The plot shows two lines but both are flat at zero.

B The plot shows only one line identical to the original data.

C The plot shows two lines: the original increasing values and a smoother line starting at the first value and gradually increasing.

D The plot shows an error because matplotlib is not imported.

💡 Hint

The expanding mean smooths the data by averaging all values up to each point.

The expanding mean line starts at the first value and gradually approaches the original data values, showing a smoothing effect.

🚀 Application

expert

Using expanding window to detect trend changes

You have daily sales data in a pandas Series. You want to detect when the sales trend changes by comparing the expanding mean to the current value. Which code snippet correctly creates a boolean Series indicating if the current value is above the expanding mean?

A sales > sales.expanding().mean()

B sales.expanding().mean() > sales

C sales > sales.rolling(window=3).mean()

D sales.expanding(min_periods=0).mean() > sales

💡 Hint

Expanding mean includes all data up to current point; compare current value to it.

Option A compares the current sales value to the expanding mean correctly, producing a boolean Series indicating if sales are above the trend.