1. Mastering Data Visualization with Matplotlib

1.1. Introduction

This comprehensive lecture series focuses on mastering Matplotlib, one of the most popular libraries in Python for data visualization. We will explore various functionalities of Matplotlib, understanding how to create a wide array of plots to effectively communicate data insights.

1.2. Part 1: Understanding Matplotlib Basics

1.2.1. Overview

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. It provides a low-level interface for creating a variety of plots, giving you full control over every aspect of the figure.

1.2.2. Installing Matplotlib

Install Matplotlib using pip:

pip install matplotlib

1.2.3. Importing Matplotlib

Import Matplotlib in your Python script:

import matplotlib.pyplot as plt

1.2.4. Creating Basic Plots

Line Plot:

# Sample Data
x = [1, 2, 3, 4, 5]
y = [10, 20, 25, 30, 35]

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

Bar Plot:

# Sample Data
categories = ['A', 'B', 'C', 'D']
values = [4, 7, 1, 8]

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

Scatter Plot:

# Sample Data
x = [5, 7, 8, 7, 2, 17, 2, 9, 4, 11, 12, 9, 6]
y = [99, 86, 87, 88, 100, 86, 103, 87, 94, 78, 77, 85, 86]

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

1.3. Part 2: Advanced Plotting Techniques

1.3.1. Histograms

Histogram:

# Sample Data
data = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4]

# Plot
plt.hist(data, bins=4)
plt.title('Histogram Example')
plt.xlabel('Data Bins')
plt.ylabel('Frequency')
plt.show()

1.3.2. Box Plots

Box Plot:

# Sample Data
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

# Plot
plt.boxplot(data)
plt.title('Box Plot Example')
plt.ylabel('Values')
plt.show()

1.3.3. Pie Charts

Pie Chart:

# Sample Data
labels = 'A', 'B', 'C', 'D'
sizes = [15, 30, 45, 10]
colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue']

# Plot
plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=140)
plt.title('Pie Chart Example')
plt.show()

1.4. Part 3: Customizing Plots

1.4.1. Adding Grid

Add grid to your plot:

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

1.4.2. Changing Line Styles and Colors

Change line style and color:

plt.plot(x, y, linestyle='--', color='r')
plt.show()

1.4.3. Adding Annotations

Add annotations to your plot:

plt.plot(x, y)
plt.annotate('Highest Point', xy=(4, 30), xytext=(3, 35),
             arrowprops=dict(facecolor='black', shrink=0.05))
plt.show()

1.5. Part 4: Real-world Applications and Case Studies

1.5.1. Healthcare Data Analysis

• Visualize patient wait times using histograms.

• Analyze service distribution with bar plots.

1.5.2. Financial Data Analysis

• Visualize stock market trends with line charts.

• Analyze portfolio performance using scatter plots.

1.5.3. Social Media Data Analysis

• Perform sentiment analysis with bar plots.

• Detect trends using time series visualizations.

1.6. Part 5: Best Practices for Data Visualization

1.6.1. Clarity

Ensure your visualization is easy to understand.

1.6.2. Accuracy

Represent data accurately without misleading.

1.6.3. Aesthetics

Use color and design effectively but avoid over-complicating.

1.6.4. Context

Provide context with titles, labels, and legends.

1.7. Part 6: Example

Here is an example of visualizing sales data using Matplotlib. The example uses sales data from a CSV file and then visualizes it using various types of plots.

import pandas as pd import matplotlib.pyplot as plt from pyscript import display # Sample data sales_data = pd.DataFrame({ 'class': ['First', 'Second', 'Third', 'First', 'Second', 'Third', 'First', 'Second', 'Third', 'First', 'Second', 'Third', 'First', 'Second', 'Third'], 'fare': [71.2833, 10.5, 7.25, 53.1, 8.05, 7.8542, 51.8625, 21.0, 11.1333, 26.55, 31.275, 7.8542, 61.3792, 14.4542, 7.7417] }) # Create the plot fig, ax = plt.subplots(figsize=(10, 6)) ax.bar(sales_data['class'], sales_data['fare'], color='skyblue') ax.set_title("Sales Data Visualization") ax.set_xlabel("Class") ax.set_ylabel("Fare") # Display the plot display(fig)

import matplotlib.pyplot as plt import matplotlib.tri as tri import numpy as np from pyscript import display # First create the x and y coordinates of the points. n_angles = 36 n_radii = 8 min_radius = 0.25 radii = np.linspace(min_radius, 0.95, n_radii) angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False) angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1) angles[:, 1::2] += np.pi / n_angles x = (radii * np.cos(angles)).flatten() y = (radii * np.sin(angles)).flatten() z = (np.cos(radii) * np.cos(3 * angles)).flatten() # Create the Triangulation; no triangles so Delaunay triangulation created. triang = tri.Triangulation(x, y) # Mask off unwanted triangles. triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1), y[triang.triangles].mean(axis=1)) < min_radius) fig1, ax1 = plt.subplots() ax1.set_aspect('equal') tpc = ax1.tripcolor(triang, z, shading='flat') fig1.colorbar(tpc) ax1.set_title('tripcolor of Delaunay triangulation, flat shading') display(fig1)

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