Pandas Series.mean()

Introduction to pandas Series.mean()

Calculating the average of data points is a fundamental operation in data analysis. The mean() method in pandas Series makes it straightforward to compute the arithmetic mean of values stored in a Series. Whether you're dealing with numbers or time-based data, this method is a handy tool for quick summarization.

What is Series.mean()?

The mean() function calculates the average value of the elements in a pandas Series. It sums all the numeric values and divides by the count of non-missing elements, returning a single scalar value representing the central tendency of the data.

Syntax of Series.mean()

The syntax is quite simple:

Series.mean(axis=None, skipna=True, numeric_only=None, **kwargs)

• axis: Not typically used in Series as it is one-dimensional.
• skipna: Whether to exclude NaN values from the calculation (default is True).
• numeric_only: Include only numeric data if set to True.

Basic Usage Example

Here is an example showing how to calculate the mean of a simple numeric Series:

import pandas as pd

data = pd.Series([10, 20, 30, 40, 50])
average = data.mean()
print("Mean value:", average)

Handling Missing Values

By default, mean() ignores missing values (NaN) during calculation. This ensures that incomplete data does not affect your results. You can control this behavior using the skipna parameter.

Example with Missing Data

Consider a Series with some missing entries:

data = pd.Series([15, None, 25, 35, None])
mean_value = data.mean()
print("Mean excluding NaN:", mean_value)

When to Use Series.mean()

Use Series.mean() whenever you need a quick average of values in a Series. It’s useful in exploratory data analysis, feature engineering, and statistical summaries.

Summary

The pandas Series mean() method is a simple yet essential function for calculating the average of data points in a Series. Its built-in handling of missing data and ease of use make it a reliable choice for many data science tasks.