Python - Matplotlib & Seaborn

Matplotlib & Seaborn in Python

Data visualization is a fundamental step in data analysis and machine learning. It helps in identifying patterns, trends, and anomalies. Python offers powerful libraries for data visualization, among which Matplotlib and Seaborn are the most popular. This document provides a comprehensive overview of both libraries, explaining how to use them to create a variety of plots and customize visualizations effectively.

Introduction to Matplotlib

Matplotlib is a 2D plotting library for Python. It allows you to generate plots, histograms, bar charts, error charts, scatterplots, etc., with just a few lines of code. The core object in Matplotlib is the Figure and Axes.

Importing Matplotlib

import matplotlib.pyplot as plt

Basic Line Plot

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

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

Matplotlib Plot Types

Bar Chart

categories = ['A', 'B', 'C', 'D']
values = [10, 24, 36, 40]

plt.bar(categories, values)
plt.title('Bar Chart')
plt.xlabel('Categories')
plt.ylabel('Values')
plt.show()

Horizontal Bar Chart

plt.barh(categories, values)
plt.title('Horizontal Bar Chart')
plt.xlabel('Values')
plt.ylabel('Categories')
plt.show()

Histogram

data = [22, 55, 62, 45, 21, 22, 34, 42, 52, 48, 25, 27]

plt.hist(data, bins=5, color='skyblue', edgecolor='black')
plt.title('Histogram')
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.show()

Scatter Plot

x = [5, 7, 8, 7, 2, 17, 2, 9]
y = [99, 86, 87, 88, 100, 86, 103, 87]

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

Pie Chart

labels = ['Python', 'Java', 'C++', 'Ruby']
sizes = [215, 130, 245, 210]

plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90)
plt.title('Pie Chart')
plt.axis('equal')
plt.show()

Customizing Matplotlib Plots

Changing Colors and Linestyles

plt.plot(x, y, color='green', linestyle='--', linewidth=2)
plt.title('Customized Line Plot')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()

Adding Grid

plt.plot(x, y)
plt.grid(True)
plt.title('Line Plot with Grid')
plt.show()

Saving Figures

plt.plot(x, y)
plt.title('Saved Plot')
plt.savefig('line_plot.png')
plt.show()

Subplots in Matplotlib

Using subplot()

plt.subplot(1, 2, 1)
plt.plot(x, y, color='blue')
plt.title('Plot 1')

plt.subplot(1, 2, 2)
plt.bar(categories, values, color='orange')
plt.title('Plot 2')

plt.tight_layout()
plt.show()

Using subplots() Function

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

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

axs[0, 1].bar(categories, values)
axs[0, 1].set_title('Bar')

axs[1, 0].scatter(x, y)
axs[1, 0].set_title('Scatter')

axs[1, 1].hist(data, bins=4)
axs[1, 1].set_title('Histogram')

plt.tight_layout()
plt.show()

Introduction to Seaborn

Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. It works well with Pandas DataFrames and supports themes, color palettes, and complex plot types with simple syntax.

Importing Seaborn

import seaborn as sns
import pandas as pd

Loading Built-in Datasets

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

Seaborn Plot Types

Scatter Plot with Seaborn

sns.scatterplot(x='total_bill', y='tip', data=df)
plt.title('Total Bill vs Tip')
plt.show()

Line Plot

sns.lineplot(x='size', y='tip', data=df)
plt.title('Size vs Tip')
plt.show()

Bar Plot

sns.barplot(x='day', y='total_bill', data=df)
plt.title('Bar Plot of Total Bill by Day')
plt.show()

Count Plot

sns.countplot(x='day', data=df)
plt.title('Count of Records per Day')
plt.show()

Box Plot

sns.boxplot(x='day', y='total_bill', data=df)
plt.title('Boxplot of Total Bill by Day')
plt.show()

Violin Plot

sns.violinplot(x='day', y='total_bill', data=df)
plt.title('Violin Plot of Total Bill by Day')
plt.show()

Customizing Seaborn Plots

Changing Themes

sns.set_theme(style="darkgrid")
sns.boxplot(x='day', y='total_bill', data=df)
plt.title('Themed Boxplot')
plt.show()

Setting Color Palettes

sns.set_palette('pastel')
sns.barplot(x='day', y='total_bill', data=df)
plt.title('Pastel Color Palette')
plt.show()

Adding Hue for Categorical Splits

sns.scatterplot(x='total_bill', y='tip', hue='sex', data=df)
plt.title('Scatter with Hue by Sex')
plt.show()

Advanced Seaborn Visualizations

Pair Plot

sns.pairplot(df)
plt.show()

Heatmap

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

Facet Grid

g = sns.FacetGrid(df, col='sex', row='time')
g.map_dataframe(sns.scatterplot, x='total_bill', y='tip')
plt.show()

Joint Plot

sns.jointplot(x='total_bill', y='tip', data=df, kind='hex')
plt.show()

Combining Matplotlib with Seaborn

plt.figure(figsize=(8, 5))
sns.boxplot(x='day', y='total_bill', data=df)
plt.title('Seaborn with Matplotlib')
plt.xlabel('Day of Week')
plt.ylabel('Total Bill')
plt.grid(True)
plt.show()

Matplotlib and Seaborn are powerful tools for creating compelling and informative data visualizations in Python. Matplotlib provides low-level control over plot elements, making it suitable for custom visualizations, while Seaborn simplifies the process of creating beautiful statistical plots with minimal code. By mastering both libraries, data scientists and analysts can present data insights more clearly and effectively. Whether you need a simple line plot or a complex multi-panel visualization, these libraries equip you with the necessary tools to bring your data to life.