Skip to content

Goal of this section

By the end of this section you will be able to:

  • Create simple line and scatter plots
  • Label axes and add titles
  • Understand how plotting helps interpret biological data

We will use matplotlib and seaborn only. These are the most widely used plotting tools in Python for scientific work.

Import plotting libraries

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

A simple line plot

Create some data:

x = np.arange(1, 11)
y = 1 / x

Plot it:

plt.plot(x, y)
plt.show()

Adding labels and a title

plt.plot(x, y)
plt.xlabel("x")
plt.ylabel("1 / x")
plt.title("Simple line plot")
plt.show()

Good plots always have: - a title - labeled axes

Changing the style

You can change how points and lines look:

plt.plot(x, y, marker="o", color="red")
plt.xlabel("x")
plt.ylabel("1 / x")
plt.title("Line plot with markers")
plt.show()

Scatter plot

Scatter plots are useful to show relationships between variables.

plt.scatter(x, y)
plt.xlabel("x")
plt.ylabel("1 / x")
plt.title("Scatter plot")
plt.show()

Using seaborn (built on matplotlib)

Seaborn works directly with tables (DataFrames).

import pandas as pd

df = pd.DataFrame({"x": x, "y": y})
df

Plot with seaborn:

sns.lineplot(data=df, x="x", y="y")
plt.title("Line plot with seaborn")
plt.show()

Scatter plot with seaborn:

sns.scatterplot(data=df, x="x", y="y")
plt.title("Scatter plot with seaborn")
plt.show()

Plotting real biological data (Iris example)

Load the iris dataset:

from sklearn.datasets import load_iris

iris_data = load_iris(as_frame=True)
iris = iris_data.frame
iris.head()

Add species names:

iris["species"] = iris_data.target_names[iris_data.target]
iris.head()

Make a scatter plot:

sns.scatterplot(data=iris,
                x="sepal length (cm)",
                y="sepal width (cm)",
                hue="species")

plt.title("Sepal length vs width")
plt.show()

Each point is one flower.
Color shows the species.

Exercise

  1. Make a scatter plot of:
  2. x = petal length (cm)
  3. y = petal width (cm)

  4. color = species

  5. Change the plot title and axis labels.

Why this matters for bioinformatics

Plots help us: - see patterns - detect outliers - compare groups

In gene expression analysis, we use plots to: - compare samples - detect clusters - visualize trends

In the next section, we will learn how to work with matrices and tables of data.