groupby() basics in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the groupby() function do in pandas?

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

beginner

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

First, use df.groupby('column_name') to group the data, then call .mean() on the grouped object to get the average for each group.

beginner

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

It returns a DataFrameGroupBy object, which is like a container holding the groups but not the final result yet.

intermediate

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

Yes, by passing a list of column names like df.groupby(['col1', 'col2']). This groups data by unique combinations of those columns.

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. Grouping works on the full data or filtered data depending on when you apply it.

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

A Adds up values in each numeric column for each Category group

B Counts the number of rows in each Category

C Sorts the dataframe by Category

D Filters rows where Category is 'sum'

The sum() function adds numeric values for each group created by groupby('Category').

Which object type does groupby() return before aggregation?

A DataFrame

B DataFrameGroupBy

C Series

D List

groupby() returns a DataFrameGroupBy object that holds grouped data.

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

A df.group_by('A', 'B')

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

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

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

Pass a list of column names to groupby() to group by multiple columns.

Which method gives the average value per group?

A mean()

B sum()

C count()

D max()

mean() calculates the average of numeric columns for each group.

If you want to count rows in each group, which method do you use?

A sum()

B mean()

C count()

D group()

count() counts the number of non-null rows in each group.

Explain how groupby() works in pandas and give a simple example.

Think about how you might group a list of items by category and then find the average price.

Describe how to group data by multiple columns and why this might be useful.

Imagine sorting your music collection by artist and album.

Practice

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

easy

A. It splits the 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 what groupby() does.

3. Final Answer:

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

4. Quick Check:

   groupby() = split data into groups [OK]

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

Common Mistakes:

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

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 the correct method name and syntax

   The correct method is groupby() called with parentheses and the column name as a string argument.

2. Step 2: Check each option

   df.groupby('Category') uses df.groupby('Category') which is correct. df.group_by('Category') uses wrong method name group_by. df.groupby['Category'] uses square brackets instead of parentheses. df.groupby(Category) misses quotes around the column name.

3. Final Answer:

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

4. Quick Check:

   Correct syntax uses parentheses and string column name [OK]

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

Common Mistakes:

• Using wrong method name like group_by
• Using square brackets instead of parentheses
• Not quoting the column name string

3. Given the DataFrame:

import pandas as pd
data = {'Team': ['A', 'A', 'B', 'B', 'C'], 'Points': [10, 15, 10, 5, 20]}
df = pd.DataFrame(data)
result = df.groupby('Team').sum()

What is the output of result?

medium

A. Team Points
A 25
B 15
C 20

B. {'Team': ['A', 'B', 'C'], 'Points': [25, 15, 20]}

C. Team Points
A 10
B 10
C 20

D. Team Points
A 15
B 5
C 20

Solution

1. Step 1: Group data by 'Team'

   The groups are: A with points 10 and 15, B with points 10 and 5, C with points 20.

2. Step 2: Sum points in each group

   Sum for A = 10 + 15 = 25, B = 10 + 5 = 15, C = 20.

3. Final Answer:

   Team Points A 25 B 15 C 20 -> Option A

4. Quick Check:

   Sum points per team = 25, 15, 20 [OK]

Hint: Sum points after groupby to get totals per group [OK]

Common Mistakes:

• Not summing points, just taking first value
• Mixing up group sums
• Confusing output format

4. What is wrong with this code snippet?

df = pd.DataFrame({'City': ['X', 'Y', 'X'], 'Sales': [100, 200, 150]})
result = df.groupby.City.sum()

medium

A. DataFrame is missing import statement.

B. The column name 'City' is misspelled.

C. The sum() function cannot be used after groupby.

D. You cannot use dot notation after groupby; it should be parentheses.

Solution

1. Step 1: Check groupby syntax

   The correct way to group by a column is df.groupby('City') with parentheses and quotes.

2. Step 2: Identify the error in dot notation

   Using dot notation like df.groupby.City is invalid because groupby is a method, not an attribute.

3. Final Answer:

   You cannot use dot notation after groupby; it should be parentheses. -> Option D

4. Quick Check:

   Use parentheses with groupby, not dot notation [OK]

Hint: Always use parentheses with groupby, not dot notation [OK]

Common Mistakes:

• Using dot notation instead of parentheses
• Misspelling column names
• Thinking sum() is invalid after groupby

5. You have a sales DataFrame:

import pandas as pd
data = {'Store': ['A', 'A', 'B', 'B', 'B'], 'Product': ['X', 'Y', 'X', 'Y', 'X'], 'Sales': [100, 150, 200, 250, 300]}
df = pd.DataFrame(data)

How do you find the total sales for each store and product combination?

hard

A. df.groupby('Product').sum('Store')

B. df.groupby(['Store', 'Product']).sum()

C. df.groupby('Store').sum('Product')

D. df.groupby(['Store']).sum(['Product'])

Solution

1. Step 1: Understand grouping by multiple columns

   To group by both 'Store' and 'Product', pass a list of column names to groupby().

2. Step 2: Use sum() to aggregate sales

   After grouping, use sum() to add sales values for each group.

3. Final Answer:

   df.groupby(['Store', 'Product']).sum() -> Option B

4. Quick Check:

   Group by multiple columns using list, then sum [OK]

Hint: Group by list of columns for multi-level grouping [OK]

Common Mistakes:

• Passing column names to sum() instead of groupby()
• Grouping by single column only
• Using wrong syntax for groupby