groupby() basics in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the groupby() function do in data analysis?

It splits data into groups based on one or more columns, so you can perform calculations on each group separately.

beginner

How do you use groupby() to find the average of a column for each group?

First, use groupby() on the column to group by, then call .mean() on the grouped object to get averages.

beginner

What type of object does groupby() return before applying an aggregation?

It returns a GroupBy object, which is like a special container holding the groups but no calculations done yet.

intermediate

Can you group by multiple columns using groupby()? How?

Yes, by passing a list of column names to groupby(), like df.groupby(['col1', 'col2']).

intermediate

What is the difference between groupby() and filtering data before grouping?

groupby() organizes data into groups for aggregation, while filtering removes rows before grouping.

What does df.groupby('Category').sum() do?

A Sorts the dataframe by 'Category'

B Counts the number of rows in 'Category'

C Adds up values in each group of 'Category'

D Filters rows where 'Category' is sum

The groupby() groups rows by 'Category', and sum() adds values in each group.

Which method is used to get the average value of groups after groupby()?

A sort()

B count()

C max()

D mean()

mean() calculates the average of each group.

What type of object is returned immediately after calling groupby()?

A DataFrame

B GroupBy object

C Series

D List

A GroupBy object is returned, which holds grouped data but no calculations yet.

How do you group data by two columns 'A' and 'B'?

A df.groupby(['A', 'B'])

B df.group(['A', 'B'])

C df.groupby('A', 'B')

D df.groupby('A' & 'B')

Pass a list of columns to groupby() like ['A', 'B'].

Which of these is NOT a typical aggregation function used after groupby()?

A filter()

B sum()

C mean()

D count()

filter() is used to filter groups, not aggregate values.

Explain how groupby() works and why it is useful in data analysis.

Think about sorting your data into buckets to analyze each bucket separately.

Describe how to group data by multiple columns and get the average of another column.

Use square brackets to group by more than one 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