Aggregation-based features in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the mean of the 'sales' column.

Data Analysis Python

mean_sales = df['sales'].[1]()

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Asum

Bmax

Cmean

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() instead of mean()

Using count() which counts entries, not averages

The mean() function calculates the average value of the 'sales' column.

2fill in blank

medium

Complete the code to group data by 'category' and calculate the sum of 'amount'.

Data Analysis Python

grouped = df.groupby('[1]')['amount'].sum()

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Aregion

Bdate

Csales

Dcategory

Attempts:

3 left

💡 Hint

Common Mistakes

Grouping by wrong column like 'date' or 'region'

Not grouping at all before summing

Grouping by 'category' allows us to sum 'amount' values for each category.

3fill in blank

hard

Fix the error in the code to calculate the maximum 'score' per 'team'.

Data Analysis Python

max_scores = df.groupby('team')['[1]'].max()

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Ascores

Bscore

CScore

Dteam

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'scores' or 'Score' which do not match the column name

Grouping by the wrong column

The column name is exactly 'score' in lowercase. Using the correct column name avoids errors.

4fill in blank

hard

Fill both blanks to create a dictionary of average 'price' for items with 'quantity' greater than 5.

Data Analysis Python

avg_price = {item: [1] for item, qty in data.items() if qty [2] 5}

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Aprice_data[item]

B>

C<

Dquantity_data[item]

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong variable names for price or quantity

Using wrong comparison operator

We use price_data[item] to get the price for each item and filter items where quantity is greater than 5 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary of total 'sales' for each 'region' where sales are above 1000.

Data Analysis Python

total_sales = { [1]: [2] for [3], sales in sales_data.items() if sales > 1000 }

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Aregion

Bsales

Dregion_name

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently

Confusing keys and values in the dictionary comprehension

The dictionary keys are regions, so region is used for both key and loop variable. The values are sales amounts.