Window functions (expanding, ewm) in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the expanding sum of the 'sales' column.

Data Analysis Python

import pandas as pd

data = {'sales': [10, 20, 30, 40, 50]}
df = pd.DataFrame(data)
df['expanding_sum'] = df['sales'].[1]().sum()
print(df)

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

Arolling

Bewm

Cexpanding

Dcumsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using rolling() instead of expanding()

Using cumsum() which is similar but not a window function

Using ewm() which is for exponential weighted calculations

The expanding() function creates an expanding window that includes all previous rows. Using sum() on it calculates the cumulative sum up to each row.

2fill in blank

medium

Complete the code to calculate the exponentially weighted mean with a span of 3.

Data Analysis Python

import pandas as pd

data = {'values': [1, 2, 3, 4, 5]}
df = pd.DataFrame(data)
df['ewm_mean'] = df['values'].[1](span=3).mean()
print(df)

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

Arolling

Bewm

Cexpanding

Dcumsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using rolling() which uses fixed window size

Using expanding() which uses all previous data equally

Using cumsum() which is a simple cumulative sum

The ewm() function calculates exponentially weighted statistics. Setting span=3 controls the decay rate for weighting.

3fill in blank

hard

Fix the error in the code to calculate the expanding mean of the 'temperature' column.

Data Analysis Python

import pandas as pd

data = {'temperature': [22, 21, 23, 24, 25]}
df = pd.DataFrame(data)
df['expanding_mean'] = df['temperature'].[1]().mean
print(df)

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

Aexpanding

Brolling

Cewm

Dcumsum

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting parentheses after mean

Using rolling() instead of expanding()

Using cumsum() which is not a window function

The mean is a method and needs parentheses () to be called. Using expanding() creates the expanding window.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Data Analysis Python

words = ['data', 'science', 'is', 'fun']
lengths = {word: [1] for word in words if len(word) [2] 3}
print(lengths)

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using word instead of len(word) for values

Using < instead of > in the condition

Not filtering words by length

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values greater than 0.

Data Analysis Python

data = {'a': 1, 'b': -2, 'c': 3}
result = {{ [1]: [2] for k, v in data.items() if v [3] 0 }}
print(result)

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Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using k instead of k.upper() for keys

Filtering with < instead of >

Using k instead of v for values

The dictionary comprehension uses k.upper() for keys, keeps values v, and filters for values greater than zero with >.