Challenge 4: Customizing Plots

While Matplotlib provides excellent tools for visualizing data, it's often the fine-tuning and personalization of these visualizations that make them stand out. Understanding how to customize and style plots can bring numerous benefits:

Clarity: Thoughtfully styled plots can emphasize important data points and patterns, making it easier for viewers to grasp the key message.
Aesthetics: A visually appealing plot can engage the audience more effectively and can better reflect the context or theme of the data.
Brand Integration: For businesses and publications, styling plots to match brand colors and themes ensures consistent representation.

Learning the intricacies of Matplotlib's customization options can elevate your data presentations to new heights.

Oppgave

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Using Matplotlib, perform the following tasks to customize and style a plot:

Plot a simple line graph.
Set the title to Customized Sinusoidal Curve with fontsize 16; set x-label name to X-axis and y-label name to Y-axis with fontsize 14.
Change the line style to dashed, width to 2, and color to purple.
Customize the x and y axis ticks and their labels. Use fonsize 12.
Add a grid with a specific style: gray color, solid lines, width of lines equals 0.5, transparency equals 0.5.

Løsning

import matplotlib.pyplot as plt

import numpy as np

x = np.linspace(0, 10, 100)

y = np.sin(x)

# 1. Set the title, x-label, and y-label with custom font sizes.

plt.title('Customized Sinusoidal Curve', fontsize=16)

plt.xlabel('X-axis', fontsize=14)

plt.ylabel('Y-axis', fontsize=14)

# 2. Change the line style, width, and color.

plt.plot(x, y, linestyle='--', linewidth=2, color='purple')

# 3. Customize the x and y axis ticks and their labels.

plt.xticks(np.arange(0, 11, 2), fontsize=12) # X axis

plt.yticks(np.linspace(-1, 1, 5), fontsize=12) # Y axis

# 4. Add a grid with a specific style.

plt.grid(color='gray', linestyle='-', linewidth=0.5, alpha=0.5)

plt.show()

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Seksjon 4. Kapittel 4

import matplotlib.pyplot as plt

import numpy as np

x = np.linspace(0, 10, 100)

y = np.sin(x)

# 1. Set the title, x-label, and y-label with custom font sizes.

plt.title('___', fontsize=___)

plt.xlabel('___', fontsize=___)

plt.ylabel('___', fontsize=___)

# 2. Change the line style, width, and color.

plt.plot(x, y, linestyle='___', linewidth=___, color='___')

# 3. Customize the x and y axis ticks and their labels.

plt.___(np.arange(0, 11, 2), fontsize=___) # X axis

plt.___(np.linspace(-1, 1, 5), fontsize=___) # Y axis

# 4. Add a grid with a specific style.

plt.___(color='___', linestyle='___', linewidth=___, alpha=___)

plt.show()

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