> ## Documentation Index > Fetch the complete documentation index at: https://genai.codewithsiva.dev/llms.txt > Use this file to discover all available pages before exploring further. # Matplotlib > Creating static, animated, and interactive visualizations in Python Matplotlib is the foundational plotting library for Python. It provides full control over every element of a chart—from axes and labels to grids and legends—allowing you to create publication-quality charts. *** ## 1. Introduction and Core Interfaces Matplotlib offers two main interfaces to create plots: 1. **State-Based Interface (using `plt.xxx`)**: The simplest and most common method for single plots. You call functions directly on `pyplot`, and Matplotlib automatically manages the figure and plot details under the hood. 2. **Object-Oriented Interface (using `fig, ax`)**: Recommended for advanced setups, such as creating grid layouts (subplots) or managing multiple charts simultaneously. For single charts, the **State-Based (`plt`)** style is highly preferred due to its simplicity. ### Importing Matplotlib By convention, the plotting module `matplotlib.pyplot` is imported under the alias `plt`: ```python theme={null} import matplotlib.pyplot as plt import numpy as np import pandas as pd ``` ### Creating Your First Plot ```python theme={null} # Simple plot using the direct state-based interface x = [1, 2, 3] y = [4, 5, 6] plt.plot(x, y) plt.show() ``` *** ## 2. Anatomy and Customization of a Plot You can easily customize titles, labels, legends, grids, and boundaries using direct `plt.` commands: ```python theme={null} x = np.linspace(0, 10, 100) y = np.sin(x) # 1. Plotting the data plt.plot(x, y, label="Sine Wave", color="teal", linestyle="--", linewidth=2) # 2. Customizing Labels and Title plt.title("Customized Sine Wave Plot", fontsize=14, fontweight="bold", pad=15) plt.xlabel("X-Axis Values (Time)", fontsize=11) plt.ylabel("Y-Axis Values (Amplitude)", fontsize=11) # 3. Boundaries and Grid plt.xlim(0, 10) # X-axis limits plt.ylim(-1.5, 1.5) # Y-axis limits plt.grid(True, linestyle=":", alpha=0.6) # 4. Legend plt.legend(loc="upper right") # 5. Ticks Customization plt.xticks([0, np.pi, 2*np.pi, 3*np.pi], ["0", "π", "2π", "3π"]) # Display the result plt.show() ``` *** ## 3. Analyzing Numerical Data Numerical data is continuous and is best visualized using plots that show trends, distributions, or correlations. ### Line Plots (Trends Over Time) Line plots are used to show how numerical values change over a continuous interval (typically time). ```python theme={null} years = [2021, 2022, 2023, 2024, 2025] sales = [150, 220, 290, 410, 500] plt.plot(years, sales, marker="o", color="blue") plt.title("Sales Growth Over Time") plt.xlabel("Year") plt.ylabel("Sales") plt.show() ``` ### Histograms (Distributions) Histograms show the frequency distribution of a continuous numerical variable by grouping values into "bins". ```python theme={null} # Generate 1000 random values following a normal distribution scores = np.random.normal(loc=75, scale=10, size=1000) plt.hist(scores, bins=20, color="skyblue", edgecolor="black", alpha=0.7) plt.title("Distribution of Student Scores") plt.xlabel("Exam Scores") plt.ylabel("Number of Students") plt.show() ``` ### Scatter Plots (Correlation) Scatter plots show the relationship (correlation) between two numerical variables. ```python theme={null} study_hours = [2, 4, 5, 7, 8, 10, 11, 12] exam_scores = [55, 62, 70, 78, 85, 92, 95, 100] plt.scatter(study_hours, exam_scores, color="orange", s=100, edgecolor="red") plt.title("Study Hours vs. Exam Scores") plt.xlabel("Hours Studied") plt.ylabel("Exam Score") plt.show() ``` *** ## 4. Analyzing Categorical Data Categorical data represents discrete groups (like gender, courses, or jobs) and is best visualized using bar charts. ### Vertical Bar Charts Used to compare numerical values across different categorical groups. ```python theme={null} courses = ["Python", "SQL", "Machine Learning", "Git"] students = [450, 320, 540, 180] plt.bar(courses, students, color=["#10B981", "#3B82F6", "#8B5CF6", "#F59E0B"]) plt.title("Student Enrollment by Course") plt.ylabel("Number of Students") plt.show() ``` ### Horizontal Bar Charts (`barh`) Highly useful when category names are long, preventing text overlap on the X-axis. ```python theme={null} categories = ["Software Engineer", "Data Scientist", "Product Manager", "UI/UX Designer"] salaries = [110, 120, 105, 90] # in thousands plt.barh(categories, salaries, color="purple") plt.title("Median Salaries by Role ($k)") plt.xlabel("Salary ($k)") plt.show() ``` *** ## 5. Analyzing Numerical vs. Categorical Data To compare the distribution of a numerical variable across different categories, we use **Box Plots**. ### Box Plots (Whisker Plots) A box plot summarizes a dataset using five statistics: minimum, first quartile (Q1), median, third quartile (Q3), and maximum. It is also excellent for identifying outliers. ```python theme={null} # Sample data: Exam scores for three different classes class_A = [55, 62, 70, 78, 85, 90, 92, 100] class_B = [40, 50, 55, 60, 72, 80, 85, 90] class_C = [65, 75, 80, 85, 90, 95, 98, 100] data_to_plot = [class_A, class_B, class_C] # Create boxplot plt.boxplot(data_to_plot, labels=["Class A", "Class B", "Class C"], patch_artist=True) plt.title("Exam Score Distribution by Class") plt.ylabel("Scores") plt.show() ``` *** ## 6. Multi-Plots and Subplots To display multiple plots side-by-side or stacked in a grid, we transition to the **Object-Oriented Interface** using `plt.subplots(rows, columns)`. ### Grid Layouts with Subplots ```python theme={null} # Create a grid of 2 rows and 2 columns of subplots fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10, 8)) # Data x = np.linspace(0, 5, 50) # 1. Top-Left (Row 0, Col 0) - Linear Plot axes[0, 0].plot(x, x, color="blue") axes[0, 0].set_title("Linear") # 2. Top-Right (Row 0, Col 1) - Quadratic Plot axes[0, 1].plot(x, x**2, color="green") axes[0, 1].set_title("Quadratic") # 3. Bottom-Left (Row 1, Col 0) - Cubic Plot axes[1, 0].plot(x, x**3, color="red") axes[1, 0].set_title("Cubic") # 4. Bottom-Right (Row 1, Col 1) - Exponential Plot axes[1, 1].plot(x, np.exp(x), color="purple") axes[1, 1].set_title("Exponential") # Adjust spacing so titles and labels don't overlap plt.tight_layout() plt.show() ``` *** ## Practice and Next Steps Before moving to the next section, make sure to practice your Matplotlib skills using the interactive notebook: Practice your skills using the interactive notebook. [💻 VS Code](vscode://file/Users/sivaprasad/Downloads/python%20material/python4ai/public/notebooks/matplotlib_student_exercise.ipynb) | [🚀 Colab](https://colab.research.google.com/github/prasad230776/python4ai/blob/master/public/notebooks/matplotlib_student_exercise.ipynb) | 📥 Download Learn how to create beautiful, advanced statistical charts with Seaborn.