Course Content

Ultimate Visualization with Python

1. Matplotlib Introduction

Plotting Applications Why Use Matplotlib?Creating a Canvas Basic Plotting

2. Creating Commonly Used Plots

Line Plot Multiple Line Plots Scatter Plot Bar Chart Stacked Bar Charts Grouped Bar Charts

3. Plots Customization

Adding Title to the Plot Adding Legend Axes Customization Colors and Transparency Customizing Grid Subplots

4. More Statistical Plots

Histogram Box Plot Pie Chart

5. Plotting with Seaborn

Seaborn Introduction Countplot Plots Decoration KDE Plot Joint Plot Pair Plot Heatmap

Colors and Transparency

Colors

When we discussed bar charts we customized the colors of the bars, setting a unique color for each separate bar. As a matter of fact, it is possible to change the color/colors for all the plots using the color keyword argument. Let’s have a look at an example:


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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()

Here we set the red color for the first line plot, while the second line plot was set to the blue color. Unlike scatter plots or bar plots (they have multiple elements), we can only set one color for the line plot, since it only has one element. Speaking of the bar charts, here is an example from the previous section:


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import matplotlib.pyplot as plt
programming_languages = ['Python', 'Java', 'C#', 'C++']
shares = [40, 30, 17, 13]
# Setting separate a 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.

Let’s modify our line plots with this parameter:


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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.

Task

Swipe to show code editor

Set the color of the lowest bars to 'darkslateblue'.
Set the color of the middle bars to 'steelblue' (the argument should follow the label parameter).
Set the transparency of the middle bars to 0.7 (the rightmost argument).
Set the color of the top bars to 'goldenrod'.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 4

Colors and Transparency

Colors


              1234567891011121314
            
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()


              1234567
            
import matplotlib.pyplot as plt
programming_languages = ['Python', 'Java', 'C#', 'C++']
shares = [40, 30, 17, 13]
# Setting separate a 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

Let’s modify our line plots with this parameter:


              123456789101112
            
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()

Task

Swipe to show code editor

Set the color of the lowest bars to 'darkslateblue'.
Set the color of the middle bars to 'steelblue' (the argument should follow the label parameter).
Set the transparency of the middle bars to 0.7 (the rightmost argument).
Set the color of the top bars to 'goldenrod'.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 4

Colors and Transparency

Colors


              1234567891011121314
            
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()


              1234567
            
import matplotlib.pyplot as plt
programming_languages = ['Python', 'Java', 'C#', 'C++']
shares = [40, 30, 17, 13]
# Setting separate a 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

Let’s modify our line plots with this parameter:


              123456789101112
            
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()

Task

Swipe to show code editor

Set the color of the lowest bars to 'darkslateblue'.
Set the color of the middle bars to 'steelblue' (the argument should follow the label parameter).
Set the transparency of the middle bars to 0.7 (the rightmost argument).
Set the color of the top bars to 'goldenrod'.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Colors


              1234567891011121314
            
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()


              1234567
            
import matplotlib.pyplot as plt
programming_languages = ['Python', 'Java', 'C#', 'C++']
shares = [40, 30, 17, 13]
# Setting separate a 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

Let’s modify our line plots with this parameter:


              123456789101112
            
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()

Task

Swipe to show code editor

Set the color of the lowest bars to 'darkslateblue'.
Set the color of the middle bars to 'steelblue' (the argument should follow the label parameter).
Set the transparency of the middle bars to 0.7 (the rightmost argument).
Set the color of the top bars to 'goldenrod'.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 3. Chapter 4

Switch to desktop for real-world practiceContinue from where you are using one of the options below