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Data Science with Python

Data Visualization with Matplotlib

Python Basics · Lesson 7 of 30

A chart shows a pattern that a table of numbers hides. Matplotlib is the standard plotting library for Python, and pandas uses it behind the scenes.

Charts need a place to draw, so the examples in this lesson are for your own computer. Install Python, run pip install matplotlib pandas, and paste the code into a Jupyter notebook or a .py file.

The basic pattern

import matplotlib.pyplot as plt

months = ["Jan", "Feb", "Mar", "Apr", "May"]
revenue = [2550, 1410, 1300, 2380, 1690]

fig, ax = plt.subplots(figsize=(7, 4))
ax.plot(months, revenue, marker="o")
ax.set_title("Monthly revenue")
ax.set_xlabel("Month")
ax.set_ylabel("Revenue")
plt.show()

plt.subplots() returns a figure (the whole image) and an axes (one chart inside it). You call plotting methods on the axes.

Choosing a chart

Question Chart Method
How does a value change over time? Line ax.plot
How do categories compare? Bar ax.bar or ax.barh
How are two numbers related? Scatter ax.scatter
How are values distributed? Histogram ax.hist
How do groups differ in spread? Box plot ax.boxplot

Bar chart

categories = ["Computers", "Accessories", "Office"]
revenue = [5600, 1050, 210]

fig, ax = plt.subplots()
ax.barh(categories, revenue)
ax.set_title("Revenue by category")
ax.invert_yaxis()   # largest at the top
plt.show()

Histogram and scatter

import numpy as np

rng = np.random.default_rng(1)
heights = rng.normal(170, 8, 500)
weights = heights * 0.9 - 85 + rng.normal(0, 6, 500)

fig, (left, right) = plt.subplots(1, 2, figsize=(10, 4))
left.hist(heights, bins=25)
left.set_title("Distribution of height")
right.scatter(heights, weights, alpha=0.4)
right.set_title("Height and weight")
plt.show()

plt.subplots(1, 2) creates two charts side by side.

Plotting straight from pandas

A DataFrame has a plot method, which is usually the quickest route.

import pandas as pd

df = pd.DataFrame({
    "category": ["Computers", "Accessories", "Office"],
    "revenue": [5600, 1050, 210],
})
df.plot(kind="bar", x="category", y="revenue", legend=False, title="Revenue by category")
plt.tight_layout()
plt.savefig("revenue.png", dpi=150)

savefig writes the chart to a file for a report or slide.

Making a chart honest and readable

  • Give every chart a title and label both axes, with units.
  • Start the value axis of a bar chart at zero.
  • Sort bars by value unless the categories have a natural order.
  • Use colour to highlight, not to decorate.
  • Remove anything that does not help the reader: extra gridlines, borders, legends for a single series.

Other libraries

Seaborn builds on Matplotlib and produces statistical charts in one line. Plotly produces interactive charts for web pages. Both follow the same ideas as this lesson.

Practice

On your computer, plot the price column of the products DataFrame from the previous lesson as a sorted horizontal bar chart.