Data Visualization with Matplotlib: Advanced Techniques and Examples

pip install matplotlib

Installing NumPy (for the examples using numerical operations)

pip install numpy

For the 3D plotting example, you may also need to install the mpl_toolkits library:

pip install mpl_toolkits

If you’re using Jupyter Notebook or a similar environment, you can include %matplotlib inline at the beginning of your notebook to display the plots directly in the notebook.

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

# Create a figure and a 3D axis
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# Generate data
t = np.linspace(0, 20, 100)
x = np.sin(t)
y = np.cos(t)
z = t

# Plot 3D curve
ax.plot(x, y, z, label='3D curve')

# Customize the plot
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
ax.set_title('3D Plotting with Matplotlib')

# Show the plot
plt.legend()
plt.show()

import matplotlib.pyplot as plt
import numpy as np

# Generate data
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = 2 * np.cos(x)

# Create subplots with different y-axis scales
fig, ax1 = plt.subplots()

# Plot data on the first subplot
ax1.plot(x, y1, 'b-')
ax1.set_xlabel('X Axis')
ax1.set_ylabel('Blue Curve', color='b')
ax1.tick_params('y', colors='b')

# Create a second y-axis and plot data on it
ax2 = ax1.twinx()
ax2.plot(x, y2, 'r-')
ax2.set_ylabel('Red Curve', color='r')
ax2.tick_params('y', colors='r')

# Title and display the plot
plt.title('Subplots with Different Y-axis Scales')
plt.show()

import matplotlib.pyplot as plt
import numpy as np

# Create a grid of x and y values
x = np.linspace(-2, 2, 100)
y = np.linspace(-2, 2, 100)
X, Y = np.meshgrid(x, y)

# Define a function to calculate Z values
def f(x, y):
    return np.sin(np.sqrt(x**2 + y**2))

# Calculate Z values
Z = f(X, Y)

# Create a contour plot
plt.contour(X, Y, Z, cmap='viridis')

# Customize the plot
plt.title('Contour Plot with Matplotlib')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')

# Show the plot
plt.colorbar(label='Z Values')
plt.show()