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

# Define a random function with two inputs
def random_function(x, y):
    return (np.sin(x) + x * np.cos(y) + y + 3**(x/3))

# Define the number of random samples to generate
n_samples = 1000

# Generate random X and Y values within a specified range
x_min, x_max = -10, 10
y_min, y_max = -10, 10

# Generate random values for X and Y
X_random = np.random.uniform(x_min, x_max, n_samples)
Y_random = np.random.uniform(y_min, y_max, n_samples)

# Evaluate the random function at the generated X and Y values
Z_random = random_function(X_random, Y_random)

# Create a dataset
dataset = pd.DataFrame({
    'X': X_random,
    'Y': Y_random,
    'Z': Z_random
})

# Display the dataset
print(dataset.head())

# Create a 2D scatter plot of the sampled data
plt.figure(figsize=(8, 6))
scatter = plt.scatter(dataset['X'], dataset['Y'], c=dataset['Z'], cmap='viridis', s=10)
plt.colorbar(scatter, label='Function Value')
plt.title('Scatter Plot of Randomly Sampled Data')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.show()