Summary  
This chapter covers customizing plot appearance by adjusting colors (uniform or per-element using the `color` parameter) and transparency (`alpha`) for different chart elements.  

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

Master the foundational skills of data visualization using the object-oriented approach in Matplotlib. Learn to control every aspect of your figures, from axes and legends to colors and styles. Create professional-quality static charts, handle time-series data, and export high-resolution graphics for reports and publications.

Adjusting Colors and Styles

Colors

Transparency

解答