**π‘ Problem Formulation:** When visualizing data or providing graphical representations, one might need to emphasize specific areas within a plot by filling in polygons. In Python, using Matplotlib, this can be accomplished in various ways. For example, if given the vertices of a pentagon, the desired output would be a plot with the pentagon filled in a color of choice, showing clear demarcation of that area from the rest of the plot.

## Method 1: Using the `fill()`

function

Matplotlibβs `fill()`

function allows you to fill the area of a polygon by specifying the vertices. The function takes in the x and y coordinates of the polygon’s vertices and an optional color to fill the polygon. This method provides a straightforward way to fill in simple polygons quickly.

Here’s an example:

import matplotlib.pyplot as plt # Coordinates of the vertices of the polygon x = [1, 2, 3, 2, 1] y = [1, 3, 1, 0, 1] plt.fill(x, y, color='skyblue') plt.plot(x, y, color='blue') # Optional: plot the outline plt.show()

This code snippet creates a plot with a sky-blue filled pentagon.

The example demonstrates creating a pentagon by specifying the coordinates of its vertices to `fill()`

, with the ‘skyblue’ color filling the inside of the polygon. The `plot()`

function is also used to draw the outline of the pentagon for better visual contrast.

## Method 2: Using the `fill_between()`

function

For filling area between two horizontal curves, Matplotlib provides the `fill_between()`

function. This is effective for shading areas under a line graph, between two lines, or between a line and a constant (like the x-axis).

Here’s an example:

import numpy as np import matplotlib.pyplot as plt # Creating the x-axis values and two y-axis values to fill between x = np.linspace(0, 2 * np.pi, 500) y1 = np.sin(x) y2 = np.sin(3 * x) plt.fill_between(x, y1, y2, color='violet', alpha=0.5) plt.plot(x, y1, x, y2, color='darkviolet') plt.show()

This code snippet fills the area between two sine waves with a violet color.

This method leverages the `fill_between()`

function to fill the area between the curve `y1 = np.sin(x)`

and `y2 = np.sin(3 * x)`

with semi-transparent violet color, giving it a nice visual effect.

## Method 3: Using the `Polygon`

class

The `Polygon`

class from Matplotlib’s patches module allows for more control and customization when creating and filling polygons. You can instantiate a `Polygon`

object with the list of vertices and additional style parameters.

Here’s an example:

import matplotlib.pyplot as plt import matplotlib.patches as patches # Coordinates of the vertices of the polygon polygon_coords = [(1, 1), (2, 3), (3, 1), (2, 0)] # Creating a Polygon patch polygon = patches.Polygon(polygon_coords, closed=True, facecolor='limegreen', edgecolor='forestgreen') # Adding the patch to the current axes ax = plt.gca() ax.add_patch(polygon) plt.axis('scaled') plt.show()

This code snippet creates a plot with a lime-green filled polygon with a forest-green edge.

In the example, a `Polygon`

patch is created with the specified vertices and color attributes. It is then added to the current Axes object using `add_patch()`

method. Using the `Polygon`

class allows for extensive customization and is more appropriate for complex shapes and figures.

## Method 4: Using `PathPatch`

from the `path`

Module

`PathPatch`

objects from the Matplotlib’s path module are similar to `Polygon`

patches but offer a different interface that can be more convenient for complex paths. It allows you to create a polygon by defining a path and a patch from that path.

Here’s an example:

import matplotlib.pyplot as plt from matplotlib.path import Path import matplotlib.patches as patches verts = [(1, 1), (2, 3), (3, 1), (2, 0), (1, 1)] codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY] path = Path(verts, codes) patch = patches.PathPatch(path, facecolor='orange', lw=2) ax = plt.gca() ax.add_patch(patch) plt.axis('scaled') plt.show()

This code snippet draws an orange filled polygon created from a path.

By defining the vertices and codes for how the path should be constructed, `PathPatch`

allows for more sophisticated paths. The path is converted into a patch which is added to the plot. The code snippet illustrates a flexible way to define and fill complex shapes.

## Bonus One-Liner Method 5: Using `plt.fill()`

with Star Expansion

Python’s star expansion can be deployed in combination with matplotlib’s `plt.fill()`

function for a concise one-liner to fill a polygon given a list of coordinate tuples.

Here’s an example:

import matplotlib.pyplot as plt # List of coordinates polygon_coords = [(1, 1), (2, 3), (3, 1), (2, 0), (1, 1)] plt.fill(*zip(*polygon_coords), color='gold') plt.show()

This code snippet quickly fills a polygon with a gold color.

The `*zip(*polygon_coords)`

syntax unpacks and transposes the list of tuples into separate x and y coordinate sequences, which are passed to `plt.fill()`

. This one-liner is handy for quickly filling polygons when the coordinates list is already available.

## Summary/Discussion

**Method 1: Using**Simple and effective for basic shapes. Limited to non-complex polygons.`fill()`

.**Method 2: Using**Ideal for filling areas between lines, particularly in line plots. Not suitable for arbitrary polygons.`fill_between()`

.**Method 3: Using the**Offers customization and control. Can be overkill for simple tasks.`Polygon`

class.**Method 4: Using**Best for complex figures and intricate paths. More verbose than simple`PathPatch`

.`fill()`

.**Bonus Method 5: One-Liner with star expansion.**Most succinct for quick plotting. Requires understanding of Pythonβs unpacking features.

Emily Rosemary Collins is a tech enthusiast with a strong background in computer science, always staying up-to-date with the latest trends and innovations. Apart from her love for technology, Emily enjoys exploring the great outdoors, participating in local community events, and dedicating her free time to painting and photography. Her interests and passion for personal growth make her an engaging conversationalist and a reliable source of knowledge in the ever-evolving world of technology.