groupby() basics in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to group the DataFrame by the 'Category' column.

Data Analysis Python

grouped = df.groupby([1])

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

A'Category'

B'Value'

C'Date'

D'Index'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a column name that does not exist in the DataFrame.

Forgetting to put the column name inside quotes.

The groupby() function groups data based on the column name given. Here, we want to group by the 'Category' column.

2fill in blank

medium

Complete the code to calculate the mean of each group after grouping by 'Category'.

Data Analysis Python

mean_values = df.groupby('Category').[1]()

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

Asum

Bmax

Ccount

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() instead of mean().

Forgetting the parentheses after the function name.

The mean() function calculates the average value for each group.

3fill in blank

hard

Fix the error in the code to get the size of each group after grouping by 'Category'.

Data Analysis Python

group_sizes = df.groupby('Category').[1]

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

Asize()

Bsize

Ccount()

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using size without parentheses, which returns a method object.

Using count() which counts non-null values per column, not total rows.

The size() function returns the number of rows in each group. It requires parentheses to call the function.

4fill in blank

hard

Fill both blanks to create a dictionary with group names as keys and the sum of 'Value' as values.

Data Analysis Python

result = {group: data['Value'].[1]() for group, data in df.groupby([2])}

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

Asum

Bmean

C'Category'

D'Value'

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean instead of sum for aggregation.

Grouping by the wrong column name.

The dictionary comprehension loops over groups. We sum the 'Value' column for each group. The grouping is done by 'Category'.

5fill in blank

hard

Fill all three blanks to create a dictionary with group names as keys and the max 'Value' for each group.

Data Analysis Python

result = {group: data[[1]].[2]() for group, data in df.groupby([3])}

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

A'Value'

Bmax

C'Category'

D'Date'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong column name in the first blank.

Using min or mean instead of max.

Grouping by a column other than 'Category'.

We select the 'Value' column from each group, then find the maximum value using max(). The groups are formed by the 'Category' column.

Practice

1. What does the groupby() function do in pandas?

easy

A. It splits data into groups based on column values.

B. It sorts the data in ascending order.

C. It removes duplicate rows from the data.

D. It changes the data type of columns.

Solution

1. Step 1: Understand the purpose of groupby()

   The groupby() function is used to split data into groups based on the values in one or more columns.

2. Step 2: Compare with other options

   Sorting, removing duplicates, and changing data types are different operations and not the main function of groupby().

3. Final Answer:

   It splits data into groups based on column values. -> Option A

4. Quick Check:

   groupby() = split data by column [OK]

Hint: Remember: groupby splits data into groups by column values [OK]

Common Mistakes:

• Confusing groupby with sorting or filtering
• Thinking groupby changes data types
• Assuming groupby removes duplicates

2. Which of the following is the correct syntax to group a DataFrame df by the column 'Category'?

easy

A. df.group_by('Category')

B. df.groupby('Category')

C. df.groupby['Category']

D. df.groupby(Category)

Solution

1. Step 1: Recall correct method syntax

   The correct method name is groupby without underscore, called with parentheses and column name as a string.

2. Step 2: Check each option

   The correct syntax is df.groupby('Category'). Incorrect versions include df.group_by('Category') (wrong method name), df.groupby['Category'] (wrong brackets), and df.groupby(Category) (missing quotes).

3. Final Answer:

   df.groupby('Category') -> Option B

4. Quick Check:

   Correct syntax uses parentheses and quotes [OK]

Hint: Use df.groupby('column_name') with quotes and parentheses [OK]

Common Mistakes:

• Using underscore in groupby (group_by)
• Using square brackets instead of parentheses
• Not quoting the column name

3. Given the DataFrame df below, what is the output of df.groupby('Team')['Points'].sum()?

   Team  Points
0  A     10
1  B     15
2  A     5
3  B     10
4  C     7

medium

A. Team
A 20
B 25
C 7
Name: Points, dtype: int64

B. Team
A 10
B 15
C 7
Name: Points, dtype: int64

C. Team
A 15
B 25
C 7
Name: Points, dtype: int64

D. Team
A 5
B 10
C 7
Name: Points, dtype: int64

Solution

1. Step 1: Group data by 'Team'

   The groups are: A with points 10 and 5, B with 15 and 10, C with 7.

2. Step 2: Sum points in each group

   Sum for A = 10 + 5 = 15, for B = 15 + 10 = 25, for C = 7.

3. Final Answer:

   Team A 15 B 25 C 7 Name: Points, dtype: int64 -> Option C

4. Quick Check:

   Sum points per team = 15, 25, 7 [OK]

Hint: Add values in each group to get sum quickly [OK]

Common Mistakes:

• Not summing all values per group
• Mixing group labels and sums
• Using count instead of sum

4. What is wrong with this code snippet?

grouped = df.groupby('Category')
result = grouped.sum['Sales']
print(result)

medium

A. Using square brackets instead of parentheses after sum.

B. Missing quotes around 'Category' in groupby.

C. Calling sum() without parentheses.

D. Using print() incorrectly.

Solution

1. Step 1: Check method call syntax

   The sum function must be called with parentheses: sum(), not accessed with square brackets.

2. Step 2: Verify other parts

   The groupby column is correctly quoted, and print usage is correct.

3. Final Answer:

   Using square brackets instead of parentheses after sum. -> Option A

4. Quick Check:

   sum() needs parentheses, not brackets [OK]

Hint: Call aggregation functions with parentheses like sum() [OK]

Common Mistakes:

• Using brackets instead of parentheses for functions
• Forgetting parentheses after sum
• Misquoting column names

5. You have a DataFrame df with columns 'City', 'Category', and 'Sales'. How do you find the average sales for each city and category combination?

hard

A. df.groupby('City' & 'Category')['Sales'].mean()

B. df.groupby('City', 'Category')['Sales'].mean()

C. df.groupby('City' + 'Category')['Sales'].mean()

D. df.groupby(['City', 'Category'])['Sales'].mean()

Solution

1. Step 1: Group by multiple columns

   To group by both 'City' and 'Category', pass a list of column names: ['City', 'Category'].

2. Step 2: Calculate mean of 'Sales'

   After grouping, select 'Sales' column and apply mean() to get average sales per group.

3. Final Answer:

   df.groupby(['City', 'Category'])['Sales'].mean() -> Option D

4. Quick Check:

   Group by list of columns, then mean() [OK]

Hint: Group by list of columns: df.groupby(['col1','col2']) [OK]

Common Mistakes:

• Passing multiple columns as separate arguments
• Using bitwise AND (&) instead of list
• Using string concatenation (+ ) instead of list