Matplotlib

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

# Simple plot using the direct state-based interface
x = [1, 2, 3]
y = [4, 5, 6]

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

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()

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()

# 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()

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()

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()

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()

# 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()

# 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()