Python Matplotlib Basics

The pyplot Module

Most beginner work uses matplotlib.pyplot, usually imported as plt. It offers simple functions to build a chart step by step: plot the data, add labels, then display or save the result.

pip install matplotlib

Line Plot

A line plot is great for showing a trend over time, such as sales by month or temperature by day.

import matplotlib.pyplot as plt

months = ["Jan", "Feb", "Mar", "Apr"]
sales = [200, 240, 210, 300]

plt.plot(months, sales)
plt.title("Monthly Sales")
plt.xlabel("Month")
plt.ylabel("Sales (in units)")
plt.show()

Bar and Scatter Plots

Use plt.bar() to compare values across categories, like scores of different students, and plt.scatter() to show the relationship between two numeric variables, like hours studied versus marks scored.

import matplotlib.pyplot as plt

students = ["Aarav", "Diya", "Kabir"]
scores = [78, 92, 85]

plt.bar(students, scores)
plt.title("Test Scores")
plt.show()

import matplotlib.pyplot as plt

hours = [1, 2, 3, 4, 5]
marks = [40, 50, 55, 70, 90]

plt.scatter(hours, marks)
plt.xlabel("Hours Studied")
plt.ylabel("Marks")
plt.title("Study Hours vs Marks")
plt.show()

Showing and Saving a Chart

plt.show() opens a window (or renders inline in a notebook) to display the chart. To save it as an image file for a report or presentation instead, use plt.savefig() before or instead of plt.show().

import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [10, 20, 15])
plt.title("Example Chart")
plt.savefig("chart.png")