#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming  #Keras

https://xn--r1a.website/CodeProgrammer ✅

pip install matplotlib

import matplotlib.pyplot as plt
import numpy as np

x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]

plt.plot(x, y)
plt.title("Simple Line Plot")
plt.xlabel("X Axis")
plt.ylabel("Y Axis")
plt.grid(True)
plt.show()

plt.plot(x, y, color='green', linestyle='--', marker='o', label='Data')
plt.title("Styled Line Plot")
plt.xlabel("X Axis")
plt.ylabel("Y Axis")
plt.legend()
plt.show()

x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

plt.plot(x, y1, label="sin(x)", color='blue')
plt.plot(x, y2, label="cos(x)", color='red')
plt.title("Multiple Line Plot")
plt.xlabel("X Axis")
plt.ylabel("Y Axis")
plt.legend()
plt.grid(True)
plt.show()

x = np.random.rand(100)
y = np.random.rand(100)

plt.scatter(x, y, color='purple', alpha=0.6)
plt.title("Scatter Plot")
plt.xlabel("X Axis")
plt.ylabel("Y Axis")
plt.show()

categories = ['A', 'B', 'C', 'D']
values = [4, 7, 2, 5]

plt.bar(categories, values, color='skyblue')
plt.title("Bar Chart Example")
plt.xlabel("Category")
plt.ylabel("Value")
plt.show()

data = np.random.randn(1000)

plt.hist(data, bins=30, color='orange', edgecolor='black')
plt.title("Histogram")
plt.xlabel("Value")
plt.ylabel("Frequency")
plt.show()

plt.plot([1, 2, 3], [4, 5, 6])
plt.savefig("plot.png")