Summary  
The chapter describes how to customize plot appearance by specifying colors for plot elements and adjusting their transparency using the `color` and `alpha` parameters.  

General domain of usage  
Data visualization

## Colors

When bar charts were discussed, the **colors** of individual bars were customized. To change the color for all plots uniformly, use the `color` keyword argument.


import matplotlib.pyplot as plt
import numpy as np

data_linear = np.arange(0, 11)
data_squared = data_linear ** 2
data_log = np.exp(data_linear)

# Setting the color of the first line plot
plt.plot(data_linear, label='linear function', color='red')

# Setting the color of the second line plot
plt.plot(data_squared, '-o', label='quadratic function', color='blue')

plt.xticks(data_linear)
plt.xlabel('x', loc='right')
plt.ylabel('y', loc='top', rotation=0)
plt.legend()
plt.show()

In this case, the `red` color was set for the first line plot, and the second one was set to `blue`. Unlike scatter or bar plots, which consist of multiple elements, a line plot represents a single element, so only **one** color can be assigned to it. For comparison, consider the following **bar chart** example from an earlier section:


import matplotlib.pyplot as plt

programming_languages = ['Python', 'Java', 'C#', 'C++']
shares = [40, 30, 17, 13]

# Setting a separate color for each bar
plt.bar(programming_languages, shares, color=['b', 'green', 'red', 'yellow'])
plt.title('Percentage of users of programming languages')
plt.show()

## Transparency
Another appearance parameter is `alpha` (transparency of the plot). Its default value is `1` (opaque), which is its maximum possible value. Basically, its possible values range from `0` to `1`, where `0` makes the plot fully transparent.

import matplotlib.pyplot as plt
import numpy as np

data_linear = np.arange(0, 11)
data_squared = data_linear ** 2

# Changing the transparency of the first line plot
plt.plot(data_linear, label='linear function', color='red', alpha=0.5)
plt.plot(data_squared, '-o', label='quadratic function', color='blue')

plt.xticks(data_linear)
plt.xlabel('x', loc='right')
plt.ylabel('y', loc='top', rotation=0)
plt.legend()
plt.show()

Using `alpha=0.5` we made the plot for the linear function more transparent in order to focus more attention on the quadratic function plot. Modifying **transparency** is mostly used exactly for this purpose. 

import unittest
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import user_code  # Ensure user_code.py is in the same directory or accessible via PYTHONPATH

def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

class TestBarColorsAndTransparency(unittest.TestCase):
    def test_primary_sector_color(self):
        bars = plt.gca().patches[:3]
        expected_color = mcolors.to_rgba('darkslateblue')[:3]
        _dynamic_test(
            self,
            all(bar.get_facecolor()[:3] == expected_color for bar in bars),
            "Primary sector bars are correctly colored darkslateblue",
            "Primary sector bars are not colored darkslateblue"
        )

    def test_secondary_sector_color(self):
        bars = plt.gca().patches[3:6]
        expected_color = mcolors.to_rgba('steelblue')[:3]
        _dynamic_test(
            self,
            all(bar.get_facecolor()[:3] == expected_color for bar in bars),
            "Secondary sector bars are correctly colored steelblue",
            "Secondary sector bars are not colored steelblue"
        )

    def test_secondary_sector_alpha(self):
        bars = plt.gca().patches[3:6]
        _dynamic_test(
            self,
            all(round(bar.get_facecolor()[3], 2) == 0.7 for bar in bars),
            "Secondary sector bars have correct transparency alpha=0.7",
            "Secondary sector bars do not have correct transparency alpha=0.7"
        )


    def test_tertiary_sector_color(self):
        bars = plt.gca().patches[6:]
        expected_color = mcolors.to_rgba('goldenrod')[:3]
        _dynamic_test(
            self,
            all(bar.get_facecolor()[:3] == expected_color for bar in bars),
            "Tertiary sector bars are correctly colored goldenrod",
            "Tertiary sector bars are not colored goldenrod"
        )

if __name__ == "__main__":
    unittest.main(argv=[''], exit=False)


test_color_transparency.py

Learn how to explore and communicate data through visual analysis. The course covers creating common plots, customizing visualizations, and using statistical charts to examine data distributions, relationships, and patterns. Exploratory data analysis techniques are applied to identify trends, anomalies, and insights in real-world datasets.

Colors

Colors

Transparency

Oplossing