Mean, Median and Mode Explained Simply

What Are Mean, Median and Mode?

When working with data, we often want to understand what is normal, common or typical. Mean, median and mode help us summarise data using a single value.

1. Mean

The mean is the average of all values. To calculate it, add all values together and divide by the number of values.

The mean is useful when the data is fairly balanced. However, it can be affected by very large or very small values.

2. Median

The median is the middle value after sorting the data from smallest to largest.

The median is often better than the mean when the data contains outliers.

3. Mode

The mode is the value that appears most often in a dataset.

Mode is useful for categorical data such as product names, customer types or most selected options.

Mean vs Median: Why the Difference Matters

Consider the following salaries:

The mean salary is pulled upward by the very high salary of £150,000. The median gives a better idea of the typical salary in this case.

Why Are These Important in Data Science?

Mean, median and mode are used regularly in data analysis and machine learning.

• Understanding typical values in a dataset
• Filling missing values
• Detecting outliers
• Summarising business data
• Preparing data for machine learning models

Python Example

You can calculate mean, median and mode using Pandas:

import pandas as pd

data = [10, 20, 30, 30, 100]

s = pd.Series(data)

print("Mean:", s.mean())
print("Median:", s.median())
print("Mode:", s.mode()[0])

Quick Practice

Find the mean, median and mode for the following data:

Answers:

• Mean = 6.8
• Median = 6
• Mode = 6

Key Takeaway

Mean, median and mode help us understand the central tendency of data. The mean gives the average, the median gives the middle value, and the mode gives the most common value.