Aggregation functions (sum, mean, std) in Data Analysis Python

Introduction

Aggregation functions help us find simple summaries of data, like totals, averages, or how spread out numbers are.

When you want to find the total sales from a list of daily sales numbers.

When you need to calculate the average temperature over a week.

When checking how much variation there is in students' test scores.

When summarizing data to understand overall trends quickly.

When preparing data for reports or visualizations.

Syntax

Data Analysis Python

data.sum()
data.mean()
data.std()

These functions are often used on columns of data in tables (like pandas DataFrames or Series).

They return a single number summarizing the data.

Examples

This adds all numbers: 10 + 20 + 30 + 40 = 100.

Data Analysis Python

import pandas as pd

numbers = pd.Series([10, 20, 30, 40])
print(numbers.sum())

This calculates the average: (10 + 20 + 30 + 40) / 4 = 25.

Data Analysis Python

import pandas as pd

numbers = pd.Series([10, 20, 30, 40])
print(numbers.mean())

This finds how spread out the numbers are around the average.

Data Analysis Python

import pandas as pd

numbers = pd.Series([10, 20, 30, 40])
print(numbers.std())

Sample Program

This program shows how to use sum, mean, and std to summarize daily sales data.

Data Analysis Python

import pandas as pd

# Create a small dataset of daily sales
sales = pd.Series([100, 150, 200, 130, 170])

# Calculate total sales
total_sales = sales.sum()

# Calculate average sales
average_sales = sales.mean()

# Calculate sales standard deviation
sales_std = sales.std()

print(f"Total sales: {total_sales}")
print(f"Average sales: {average_sales}")
print(f"Sales standard deviation: {sales_std:.2f}")

Important Notes

Standard deviation (std) tells you how much the numbers differ from the average.

If your data has missing values, these functions usually ignore them automatically.

You can use these functions on columns in tables to summarize each column.

Summary

Aggregation functions give quick summaries like total, average, and spread.

They help understand data without looking at every number.

sum(), mean(), and std() are common and easy to use in data analysis.