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Taylor Diagram in Python using Matplotlib and Pandas | Visualize Model Performance

Learn how to create a Taylor Diagram in Python using Matplotlib and Pandas. This guide explains model evaluation through standard deviation, correlation, and performance visualization with clear code examples.

🧭 What is a Polar Plot?

A polar plot is a type of plot where data is displayed using angles and radial distances instead of Cartesian x-y coordinates.

📌 Key Features:

Element	Description
Angle (θ)	Represents the direction or correlation (in Taylor diagrams).
Radius (r)	Represents the magnitude, often standard deviation.
Center	Called the origin, where `r = 0`.

🌀 How It Works:

In a polar plot:

A point is located by how far it is from the center (radial distance) and the angle it makes with a reference direction (often 0° or 90°).
Think of a radar screen or compass.

📊 Used In:

Taylor diagrams
Wind direction and speed plots
Circular statistics
Phase angle plots

🎯 Goal: Plot angles and magnitudes (like points on a compass)

import matplotlib.pyplot as plt
import numpy as np

# Example angles (in degrees) and magnitudes
angles_deg = [0, 45, 90, 135, 180, 225, 270, 315]
magnitudes = [1, 2, 1.5, 3, 2.5, 2, 1, 1.8]

# Convert degrees to radians for polar plot
angles_rad = np.deg2rad(angles_deg)

# Create polar plot
plt.figure(figsize=(6, 6))
ax = plt.subplot(111, polar=True)  # 111 means 1x1 grid, 1st plot

# Plot the data
ax.plot(angles_rad, magnitudes, marker='o')

# Add title
ax.set_title("Simple Polar Plot", va='bottom')

# Show the plot
plt.show()

🔍 Explanation:

angles_deg: Compass directions like North (0°), East (90°), South (180°), etc.
magnitudes: Distance from the center for each direction.
np.deg2rad(): Converts degrees to radians since matplotlib uses radians.
polar=True: Tells matplotlib to use polar coordinates.

customized polar plot example with:

✅ Filled area
🎨 Custom colors
🏷️ Labeled angles (like a compass)

🌈 Polar Plot with Area Fill

import matplotlib.pyplot as plt
import numpy as np

# Compass directions in degrees
angles_deg = [0, 45, 90, 135, 180, 225, 270, 315]
magnitudes = [1, 2, 1.5, 3, 2.5, 2, 1, 1.8]

# Convert degrees to radians
angles_rad = np.deg2rad(angles_deg)

# Repeat first point to close the circular shape
angles_rad = np.append(angles_rad, angles_rad[0])
magnitudes.append(magnitudes[0])

# Create plot
plt.figure(figsize=(7, 7))
ax = plt.subplot(111, polar=True)

# Plot line and fill area
ax.plot(angles_rad, magnitudes, color='teal', linewidth=2, label='Measurements')
ax.fill(angles_rad, magnitudes, color='skyblue', alpha=0.4)

# Set title and labels
ax.set_title("🌍 Compass Polar Plot", va='bottom')
ax.set_xticks(np.deg2rad(angles_deg))
ax.set_xticklabels(['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW'])

# Add legend
ax.legend(loc='upper right')

# Show plot
plt.show()

📌 What This Shows:

The shape connects all points and fills the area.
Labels around the circle match compass directions (North, East, etc.).
fill() makes the plot visually intuitive and engaging.

📊 Polar Plot Example: Sales by Region

import matplotlib.pyplot as plt
import numpy as np

# Define sales regions and sales figures
regions = ['North', 'North-East', 'East', 'South-East', 'South', 'South-West', 'West', 'North-West']
sales = [120, 150, 130, 180, 160, 140, 110, 170]

# Convert region positions to angles (in radians)
angles = np.linspace(0, 2 * np.pi, len(regions), endpoint=False).tolist()

# Close the circular plot by repeating the first point
sales += [sales[0]]
angles += [angles[0]]

# Create polar plot
plt.figure(figsize=(8, 6))
ax = plt.subplot(111, polar=True)

# Plot the line and fill the area
ax.plot(angles, sales, color='navy', linewidth=2, label='Sales')
ax.fill(angles, sales, color='skyblue', alpha=0.4)

# Set region names around the circle
ax.set_xticks(angles[:-1])  # exclude last repeated angle
ax.set_xticklabels(regions)

# Title and legend
ax.set_title("📈 Sales Distribution by Region", pad=30)
ax.legend(loc='upper right')

# Show plot
plt.show()

🧠 What’s Happening Here:

Each region is mapped to a direction around the circle.
The distance from the center (radial distance) shows the sales amount.
The filled area makes it easier to compare sales visually across regions.