Python - Data Visualization with Matplotlib and Seaborn

Data Visualization with Matplotlib and Seaborn in Python

Introduction

Data visualization is a crucial step in data analysis. It helps to communicate data findings, identify patterns, understand trends, and present insights in an effective and intuitive manner. In the Python ecosystem, Matplotlib and Seaborn are two of the most widely used libraries for creating high-quality visualizations.

Matplotlib is a comprehensive library for static, animated, and interactive plots. Seaborn, built on top of Matplotlib, provides a high-level interface for drawing attractive and informative statistical graphics.

This guide covers:

• Introduction to Matplotlib and Seaborn
• Creating plots with Matplotlib
• Customizing plots
• Seaborn overview and plotting functions
• Working with categorical and statistical plots
• Styling and aesthetics
• Multi-plot layouts
• Best practices

Getting Started

Installation

pip install matplotlib seaborn

Importing Libraries

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

Matplotlib Basics

Creating a Simple Line Plot

x = [1, 2, 3, 4, 5]
y = [10, 20, 25, 30, 40]

plt.plot(x, y)
plt.title('Line Plot')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.show()

Figure and Axes API

fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_title('Using Object-Oriented API')
ax.set_xlabel('X-axis')
ax.set_ylabel('Y-axis')
plt.show()

Matplotlib Plot Types

Bar Chart

categories = ['A', 'B', 'C']
values = [5, 7, 3]

plt.bar(categories, values)
plt.title('Bar Chart')
plt.show()

Scatter Plot

x = np.random.rand(50)
y = np.random.rand(50)

plt.scatter(x, y)
plt.title('Scatter Plot')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()

Histogram

data = np.random.randn(1000)

plt.hist(data, bins=30)
plt.title('Histogram')
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.show()

Pie Chart

labels = ['Python', 'Java', 'C++', 'Ruby']
sizes = [45, 30, 15, 10]

plt.pie(sizes, labels=labels, autopct='%1.1f%%')
plt.title('Pie Chart')
plt.show()

Box Plot

data = np.random.rand(10, 4)

plt.boxplot(data)
plt.title('Box Plot')
plt.show()

Customization in Matplotlib

Titles, Labels, and Legends

plt.plot(x, y, label='Line')
plt.title('Custom Plot')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.legend()
plt.show()

Colors and Line Styles

plt.plot(x, y, color='red', linestyle='--', marker='o')
plt.title('Styled Line Plot')
plt.show()

Subplots

fig, axs = plt.subplots(2, 1)

axs[0].plot(x, y)
axs[0].set_title('Top Plot')

axs[1].bar(categories, values)
axs[1].set_title('Bottom Plot')

plt.tight_layout()
plt.show()

Introduction to Seaborn

Loading Built-in Datasets

tips = sns.load_dataset('tips')
print(tips.head())

Seaborn Styles

sns.set_style('darkgrid')

Seaborn Plotting Functions

Distribution Plots

sns.histplot(tips['total_bill'], kde=True)
plt.title('Histogram with KDE')
plt.show()

Box Plot

sns.boxplot(x='day', y='total_bill', data=tips)
plt.title('Box Plot by Day')
plt.show()

Violin Plot

sns.violinplot(x='day', y='total_bill', data=tips)
plt.title('Violin Plot')
plt.show()

Bar Plot

sns.barplot(x='day', y='total_bill', data=tips, ci=None)
plt.title('Bar Plot')
plt.show()

Count Plot

sns.countplot(x='day', data=tips)
plt.title('Count Plot')
plt.show()

Scatter Plot (relplot)

sns.relplot(x='total_bill', y='tip', data=tips)

Line Plot (relplot)

sns.relplot(x='size', y='tip', kind='line', data=tips)

Pair Plot

sns.pairplot(tips)
plt.suptitle('Pair Plot')
plt.show()

Heatmap

corr = tips.corr()
sns.heatmap(corr, annot=True, cmap='coolwarm')
plt.title('Correlation Heatmap')
plt.show()

Advanced Seaborn Usage

FacetGrid

g = sns.FacetGrid(tips, col='sex')
g.map_dataframe(sns.scatterplot, x='total_bill', y='tip')

Hue Parameter

sns.scatterplot(x='total_bill', y='tip', hue='sex', data=tips)

Style and Size Parameters

sns.relplot(x='total_bill', y='tip', hue='sex', style='smoker', size='size', data=tips)

Combining Seaborn and Matplotlib

Customizing Seaborn with Matplotlib

ax = sns.boxplot(x='day', y='total_bill', data=tips)
ax.set_title('Box Plot with Custom Title')
ax.set_xlabel('Weekday')
ax.set_ylabel('Total Bill')

Styling and Themes

Setting Styles

sns.set_style('whitegrid')

Available Styles

• white
• dark
• whitegrid
• darkgrid
• ticks

Color Palettes

sns.set_palette('Set2')

Saving Plots

Save with Matplotlib

plt.figure()
sns.histplot(tips['total_bill'])
plt.title('Histogram')
plt.savefig('histogram.png')

Real-World Examples

1. Sales Data Visualization

sales = pd.DataFrame({
    'Month': ['Jan', 'Feb', 'Mar', 'Apr'],
    'Revenue': [1000, 1500, 1300, 1700]
})

sns.barplot(x='Month', y='Revenue', data=sales)
plt.title('Monthly Revenue')
plt.show()

2. COVID-19 Cases

covid = pd.DataFrame({
    'Date': pd.date_range(start='2021-01-01', periods=7),
    'Cases': [100, 120, 150, 180, 170, 200, 220]
})

plt.plot(covid['Date'], covid['Cases'], marker='o')
plt.title('Daily COVID-19 Cases')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

3. Exam Scores Comparison

scores = pd.DataFrame({
    'Student': ['Alice', 'Bob', 'Charlie', 'David'],
    'Math': [85, 78, 92, 88],
    'Science': [90, 85, 88, 91]
})

scores_melted = pd.melt(scores, id_vars='Student', value_vars=['Math', 'Science'])
sns.barplot(x='Student', y='value', hue='variable', data=scores_melted)
plt.title('Exam Scores')
plt.show()

Best Practices

• Start with Seaborn for high-level plots
• Use Matplotlib for low-level customizations
• Use FacetGrid and subplots for comparing multiple dimensions
• Always label axes and use descriptive titles
• Choose appropriate color palettes for accessibility
• Save your figures using plt.savefig()

Data visualization is an indispensable part of data analysis and storytelling. Matplotlib and Seaborn together provide a powerful toolkit for creating a wide variety of static and dynamic plots. Matplotlib offers low-level control, while Seaborn simplifies statistical visualization and improves aesthetics.

By mastering these tools, you can effectively communicate data insights and build intuitive dashboards and reports. The key is understanding your data, choosing the right visual representation, and ensuring clarity and accessibility in your plots.