np.mean() for average in NumPy

Introduction

We use np.mean() to find the average value of numbers. It helps us understand the typical value in a group of numbers.

To find the average score of students in a class.

To calculate the average temperature over a week.

To get the average sales amount from daily sales data.

To summarize the average height of people in a survey.

Syntax

NumPy

np.mean(array, axis=None, dtype=None, out=None, keepdims=False)

array is the list or array of numbers you want to average.

axis decides if you want the average across rows, columns, or the whole array.

Examples

Finds the average of all numbers in the list.

NumPy

np.mean([1, 2, 3, 4, 5])

Finds the average for each column.

NumPy

np.mean([[1, 2], [3, 4]], axis=0)

Finds the average for each row.

NumPy

np.mean([[1, 2], [3, 4]], axis=1)

Sample Program

This program shows how to use np.mean() to find averages of a simple list and a 2D array. It calculates the overall average, averages by columns, and averages by rows.

NumPy

import numpy as np

# List of numbers
numbers = [10, 20, 30, 40, 50]

# Calculate average
average = np.mean(numbers)
print(f"Average of numbers: {average}")

# 2D array example
data = np.array([[1, 2, 3], [4, 5, 6]])

# Average of all elements
avg_all = np.mean(data)
print(f"Average of all elements: {avg_all}")

# Average by columns
avg_columns = np.mean(data, axis=0)
print(f"Average by columns: {avg_columns}")

# Average by rows
avg_rows = np.mean(data, axis=1)
print(f"Average by rows: {avg_rows}")

Important Notes

If your data has missing values (like np.nan), use np.nanmean() to ignore them.

Setting axis=None (default) calculates the average of all elements.

Summary

np.mean() finds the average value of numbers in arrays or lists.

You can calculate the average across the whole data or along rows or columns using the axis parameter.

It helps summarize data to understand typical values quickly.