Aggregation functions (sum, mean, count) in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the sum aggregation function do in data analysis?

The sum function adds up all the values in a column or list to give a total amount.

beginner

How would you describe the mean aggregation function?

The mean function calculates the average value by adding all numbers and dividing by how many numbers there are.

beginner

What does the count aggregation function measure?

The count function counts how many items or rows are in a dataset or column, ignoring empty or missing values.

beginner

In Python's pandas library, which method would you use to get the sum of a DataFrame column named 'sales'?

You would use df['sales'].sum() to get the total sales amount.

beginner

Why are aggregation functions useful in data analysis?

They help summarize large data sets into simple numbers like totals, averages, or counts, making it easier to understand the data.

Which aggregation function would you use to find the average value of a column?

A mean

B sum

C count

D max

The mean function calculates the average by dividing the sum of values by the number of values.

What does the count function do when applied to a data column?

A Adds all values

B Finds the highest value

C Calculates the average

D Counts the number of non-empty entries

Count returns how many non-empty entries are in the column.

If you want to know the total sales from a list of sales numbers, which function should you use?

A mean

B count

C sum

D min

Sum adds all sales numbers to give the total.

In pandas, how do you get the count of rows in a DataFrame column 'age'?

A df['age'].count()

B df['age'].sum()

C df['age'].mean()

D df['age'].max()

The count() method counts non-missing values in the 'age' column.

Which aggregation function ignores missing or empty values when calculating?

A mean

B All of the above

C count

D sum

Sum, mean, and count in pandas all ignore missing values by default.

Explain in your own words what the sum, mean, and count aggregation functions do and give a simple example for each.

Think about adding numbers, finding average, and counting items.

Describe how aggregation functions help when working with large datasets.

Imagine you have a big list of numbers and want just one number to describe it.

Practice

1. What does the sum() function do when applied to a column of numbers in a DataFrame?

easy

A. Counts how many numbers are in the column

B. Finds the middle value of the numbers

C. Adds all the numbers together to get a total

D. Sorts the numbers from smallest to largest

Solution

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

   The sum() function adds all values in a column to get their total.

2. Step 2: Compare with other functions

   mean() finds average, count() counts entries, so they do different things.

3. Final Answer:

   Adds all the numbers together to get a total -> Option C

4. Quick Check:

   sum() = total of values [OK]

Hint: Sum adds values, mean averages, count counts entries [OK]

Common Mistakes:

• Confusing sum() with mean()
• Thinking sum() counts entries
• Assuming sum() sorts data

2. Which of the following is the correct way to calculate the average of a DataFrame column named sales using pandas?

easy

A. df.sales.sum() / df.sales.count()

B. df.mean('sales')

C. mean(df.sales)

D. df['sales'].mean()

Solution

1. Step 1: Identify correct pandas syntax for mean()

   In pandas, to get the mean of a column, use df['column'].mean().

2. Step 2: Check other options

   df.mean('sales') is invalid syntax; mean(df.sales) is not pandas method; df.sales.sum() / df.sales.count() works but is longer and less direct.

3. Final Answer:

   df['sales'].mean() -> Option D

4. Quick Check:

   Use df['column'].mean() for average [OK]

Hint: Use df['col'].mean() to get average quickly [OK]

Common Mistakes:

• Using mean() as a standalone function
• Passing column name as argument to df.mean()
• Dividing sum by count manually instead of mean()

3. Given the DataFrame df below:

import pandas as pd
data = {'score': [10, 20, 30, 40, 50]}
df = pd.DataFrame(data)

What is the output of df['score'].count()?

medium

A. 5

B. 150

C. 30

D. Error

Solution

1. Step 1: Understand what count() does on a column

   count() counts the number of non-missing entries in the column. Here, all 5 values are present.

2. Step 2: Count the entries in 'score'

   There are 5 numbers: 10, 20, 30, 40, 50, so count() returns 5.

3. Final Answer:

   5 -> Option A

4. Quick Check:

   count() = number of entries [OK]

Hint: count() returns number of non-empty entries [OK]

Common Mistakes:

• Confusing count() with sum()
• Expecting count() to return total value
• Thinking count() returns average

4. Consider this code snippet:

import pandas as pd
data = {'values': [5, None, 15, 20]}
df = pd.DataFrame(data)
result = df['values'].sum()

What is the value of result?

medium

A. 40

B. None

C. Error

D. 20

Solution

1. Step 1: Understand how sum() handles None values

   In pandas, sum() ignores None (missing) values and sums only the numbers.

2. Step 2: Calculate sum ignoring None

   Values are 5, None, 15, 20. Sum = 5 + 15 + 20 = 40.

3. Final Answer:

   40 -> Option A

4. Quick Check:

   sum() skips None and adds numbers [OK]

Hint: sum() ignores None/null values automatically [OK]

Common Mistakes:

• Expecting sum() to return None if any missing
• Getting error due to None value
• Counting None as zero explicitly

5. You have a DataFrame df with a column sales containing daily sales numbers. You want to find the average sales but only for days where sales were above zero. Which code correctly calculates this?

hard

A. df['sales'].mean()

B. df[df['sales'] > 0]['sales'].mean()

C. df['sales'].sum() / df['sales'].count()

D. df['sales'].count() / df['sales'].sum()

Solution

1. Step 1: Filter rows where sales > 0

   Use df[df['sales'] > 0] to keep only positive sales days.

2. Step 2: Calculate mean of filtered sales

   Apply ['sales'].mean() on filtered data to get average of positive sales only.

3. Final Answer:

   df[df['sales'] > 0]['sales'].mean() -> Option B

4. Quick Check:

   Filter then mean() for conditional average [OK]

Hint: Filter data first, then apply mean() for conditional average [OK]

Common Mistakes:

• Using mean() without filtering
• Dividing sum by count without filtering
• Swapping sum and count in division