Data aggregation reporting in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is data aggregation in pandas?

Data aggregation in pandas means combining data from multiple rows or columns to get a summary, like sums, averages, or counts.

beginner

Which pandas function is commonly used to group data before aggregation?

The groupby() function is used to split data into groups based on one or more columns before applying aggregation.

beginner

How do you calculate the average of a column after grouping in pandas?

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

intermediate

What does the agg() function do in pandas?

agg() lets you apply one or more aggregation functions like sum, mean, or count to grouped data in a flexible way.

beginner

Why is data aggregation useful in reporting?

Aggregation helps to simplify large data sets by summarizing key information, making reports easier to understand and decisions faster.

Which pandas method groups data for aggregation?

A groupby()

B merge()

C pivot()

D sort_values()

groupby() is used to group data before aggregation.

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

A Sorts data by Category

B Counts rows in each Category

C Filters rows where Category is 'sum'

D Sums all numeric columns for each Category group

This sums numeric columns for each group in 'Category'.

Which function calculates the average of grouped data?

A mean()

B count()

C max()

D min()

mean() calculates the average value.

What is the purpose of agg() in pandas?

A Join tables

B Sort data

C Apply multiple aggregation functions at once

D Filter rows

agg() applies one or more aggregation functions to grouped data.

Which of these is NOT a typical aggregation function?

A mean()

B plot()

C sum()

D count()

plot() is for visualization, not aggregation.

Explain how to use pandas to group data by a column and calculate the sum and average of another column.

Think about chaining methods after grouping.

Describe why data aggregation is important in creating reports from large datasets.

Consider how summaries help understand big data.

Practice

1. What is the main purpose of using groupby() in pandas for data aggregation reporting?

easy

A. To filter rows based on a condition

B. To sort data in ascending order

C. To split data into groups based on column values

D. To merge two dataframes

Solution

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

   The groupby() function splits the dataframe into groups based on unique values in one or more columns.

2. Step 2: Identify the purpose in aggregation

   After grouping, aggregation functions like sum or mean can be applied to each group separately.

3. Final Answer:

   To split data into groups based on column values -> Option C

4. Quick Check:

   groupby() splits data for aggregation [OK]

Hint: Remember: groupby splits data, agg summarizes groups [OK]

Common Mistakes:

• Confusing groupby with sorting
• Thinking groupby filters rows
• Mixing groupby with merging dataframes

2. Which of the following is the correct syntax to calculate the mean of column 'Sales' grouped by 'Region' in pandas?

easy

A. df.groupby('Region').agg('Sales'.mean)

B. df.group_by('Region').mean('Sales')

C. df.groupby('Sales')['Region'].mean()

D. df.groupby('Region')['Sales'].mean()

Solution

1. Step 1: Identify correct groupby syntax

   The correct method is groupby('Region') to group by 'Region'.

2. Step 2: Select column and apply mean

   Use ['Sales'].mean() to calculate mean of 'Sales' per group.

3. Final Answer:

   df.groupby('Region')['Sales'].mean() -> Option D

4. Quick Check:

   Correct method chaining for groupby and mean [OK]

Hint: Use df.groupby('col')['col2'].mean() for grouped mean [OK]

Common Mistakes:

• Using group_by instead of groupby
• Swapping group and aggregation columns
• Incorrect agg syntax with string method

3. Given the dataframe:

import pandas as pd
data = {'Team': ['A', 'A', 'B', 'B', 'C'], 'Points': [10, 15, 10, 20, 30]}

What is the output of:

df = pd.DataFrame(data)
df.groupby('Team').agg({'Points': 'sum'})

medium

A. {'Team': ['A', 'B', 'C'], 'Points': [25, 30, 30]}

B. {'Team': ['A', 'B', 'C'], 'Points': [10, 10, 30]}

C. {'Team': ['A', 'B', 'C'], 'Points': [15, 20, 30]}

D. Error: agg() requires a list of functions

Solution

1. Step 1: Group data by 'Team'

   Teams are A, B, and C. Grouping splits rows accordingly.

2. Step 2: Sum 'Points' per group

   For A: 10+15=25, B: 10+20=30, C: 30.

3. Final Answer:

   {'Team': ['A', 'B', 'C'], 'Points': [25, 30, 30]} -> Option A

4. Quick Check:

   Sum points per team = [25, 30, 30] [OK]

Hint: Sum points after groupby sums values per group [OK]

Common Mistakes:

• Summing only first value per group
• Confusing sum with mean
• Expecting agg() needs list always

4. You run this code:

df.groupby('Category').agg('sum', 'Price')

But get an error. What is the problem?

medium

A. agg() expects a dictionary or list, not multiple arguments

B. groupby() requires numeric columns only

C. sum is not a valid aggregation function

D. Price column does not exist

Solution

1. Step 1: Check agg() function signature

   The agg() method expects a single function, list of functions, or dict mapping columns to functions.

2. Step 2: Identify incorrect argument usage

   Passing two separate arguments ('sum', 'Price') is invalid syntax and causes error.

3. Final Answer:

   agg() expects a dictionary or list, not multiple arguments -> Option A

4. Quick Check:

   agg() needs one argument: function or dict [OK]

Hint: Use agg({'Price': 'sum'}) or agg('sum') only [OK]

Common Mistakes:

• Passing multiple arguments to agg()
• Assuming groupby needs numeric columns only
• Using invalid function names

5. You have a sales dataframe with columns 'Store', 'Product', and 'Revenue'. You want to find the total revenue per store and the average revenue per product within each store. Which pandas code correctly achieves this?

hard

A. df.groupby('Store').agg({'Revenue': ['sum', 'mean']})

B. df.groupby(['Store', 'Product']).agg({'Revenue': 'mean'}).groupby('Store').agg({'Revenue': 'sum'})

C. df.groupby('Store').agg({'Revenue': 'sum'}).groupby('Product').agg({'Revenue': 'mean'})

D. df.groupby('Product').agg({'Revenue': ['sum', 'mean']})

Solution

1. Step 1: Calculate average revenue per product within each store

   Group by both 'Store' and 'Product', then calculate mean revenue.

2. Step 2: Calculate total revenue per store

   Group the result by 'Store' and sum the mean revenues of products.

3. Final Answer:

   df.groupby(['Store', 'Product']).agg({'Revenue': 'mean'}).groupby('Store').agg({'Revenue': 'sum'}) -> Option B

4. Quick Check:

   Nested groupby for product mean then store sum [OK]

Hint: Group by both, then aggregate stepwise for nested summaries [OK]

Common Mistakes:

• Trying to do sum and mean in one groupby without nesting
• Grouping by wrong columns order
• Mixing aggregation levels incorrectly