Section 1.1: Getting Started with Matplotlib

Matplotlib is a powerful library that supports both static and interactive visualizations, making it essential for data analysis and machine learning.

To install Matplotlib, use the following command:

python -m pip install -U matplotlib

Matplotlib relies on several dependencies, including numpy, cycler, dateutil, pillow, kiwisolver, and pyparsing. For demonstration purposes, we will utilize the famous Titanic dataset. Here's how to import it while excluding the unique passenger ID:

import pandas as pd

df = pd.read_csv('../../data/titanic.csv')

df = df.drop('PassengerId', axis=1)

Section 1.2: Understanding Matplotlib's Architecture

The architecture of Matplotlib is composed of three main layers:

1. Backend Layer: This layer facilitates user interaction with the graphical user interface.
2. Artist Layer: This layer is responsible for rendering visual objects, creating impactful images.
3. Scripting Layer: This layer allows programmatic interaction, enabling users to manipulate figures based on their requirements.

Section 2.1: Bar Charts

Bar charts are essential for visualizing comparative data. Below is an example of how to create a bar chart comparing male and female passengers' mean fares:

width = 0.5 # Width of the bars

men_means = list(df[df['Sex'] == 'male'].mean())

women_means = list(df[df['Sex'] == 'female'].mean())

labels = list(df[df['Sex'] == 'female'].std().index)

x = np.arange(len(labels))

fig, ax = plt.subplots()

rects1 = ax.bar(x - width/2, men_means, width, label='Men')

rects2 = ax.bar(x + width/2, women_means, width, label='Women')

ax.set_xticklabels(labels)

ax.set_xticks(x)

plt.show()

To create a stacked bar chart, simply adjust the code slightly to stack the values:

ax.bar(labels, men_means, width, label='Men')

ax.bar(labels, women_means, width, bottom=men_means, label='Women')

Section 2.2: Histograms

Histograms provide a visual representation of the distribution of a single variable. Here’s how to create a histogram for the age distribution of passengers:

plt.hist(df['Age'])

plt.xlabel('Age')

plt.ylabel('Number of People')

plt.show()

This visualization helps us understand the density of the data and assess its normalization.

Section 2.3: Box Plots

Box plots are crucial for identifying outliers and understanding data distribution through quartiles. Here's how to create a box plot:

plt.boxplot(df[['Fare', 'SibSp', 'Pclass', 'Survived']])

plt.show()

This example clearly highlights outliers at the upper end.

Section 2.4: Scatter Plots

Scatter plots allow us to visualize the relationship between two variables. This example shows how to create a scatter plot comparing fare against age:

plt.scatter(df['Fare'], df['Age'])

plt.show()

This visualization helps analyze the spread and density of data points.