Зміст курсу

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

Grouped Bar Charts

In the previous chapter we used a stacked bar chart to compare several categories for each value on the x-axis. However, there is another option to accomplish this task by using a grouped bar chart. It allows us to visualize such data via placing bars on the sides of other bars instead of placing them on top of each other.

Here are the steps to achieve this:

Start by specifying the bar width and creating an array of x-axis coordinates (you can use np.arange() for this);
Create a two-dimensional array to hold the arrays for each category;
Use a for loop to call the bar() function multiple times, once for each category (e.g., three times for three categories).

Let's have a look at an example:


              12345678910111213141516
            
import matplotlib.pyplot as plt
import numpy as np
countries = ['USA', 'China', 'Japan']
positions = np.arange(len(countries))
primary_sector = np.array([1.4, 4.8, 0.4])
secondary_sector = np.array([11.3, 6.2, 0.8])
tertiary_sector = np.array([14.2, 8.4, 3.2])
sectors = np.array([primary_sector, secondary_sector, tertiary_sector])
# Setting the width of the bars
width = 0.3
for i in range(len(sectors)):
# Plotting the bars for each category (sector)
   plt.bar(positions + width * i, sectors[i], width)
# Setting the x-axis ticks position and labels
plt.xticks(positions + width * (len(sectors) - 1) / 2, countries)
plt.show()

The xticks() function customizes the x-axis in the following way:

The first argument positions + width * (len(sectors) - 1) / 2 is an array of x-axis coordinates representing the centers of the bar groups;
The second argument provides labels (names) for these x-axis ticks, using the countries array.

Note
The code in our example is rather flexible and works for an arbitrary number of categories (you may only need to adjust width to avoid overlapping).

Завдання

Pass the correct array to the len() function.
Use the correct function to plot bars.
Use the correct variable which should be multiplied by i.
Use the correct variable as an index for answers array.
Pass the correct variable as the rightmost arguments of the plotting function.

Завдання

Pass the correct array to the len() function.
Use the correct function to plot bars.
Use the correct variable which should be multiplied by i.
Use the correct variable as an index for answers array.
Pass the correct variable as the rightmost arguments of the plotting function.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Секція 2. Розділ 6

Grouped Bar Charts

Here are the steps to achieve this:

Start by specifying the bar width and creating an array of x-axis coordinates (you can use np.arange() for this);
Create a two-dimensional array to hold the arrays for each category;
Use a for loop to call the bar() function multiple times, once for each category (e.g., three times for three categories).

Let's have a look at an example:


              12345678910111213141516
            
import matplotlib.pyplot as plt
import numpy as np
countries = ['USA', 'China', 'Japan']
positions = np.arange(len(countries))
primary_sector = np.array([1.4, 4.8, 0.4])
secondary_sector = np.array([11.3, 6.2, 0.8])
tertiary_sector = np.array([14.2, 8.4, 3.2])
sectors = np.array([primary_sector, secondary_sector, tertiary_sector])
# Setting the width of the bars
width = 0.3
for i in range(len(sectors)):
# Plotting the bars for each category (sector)
   plt.bar(positions + width * i, sectors[i], width)
# Setting the x-axis ticks position and labels
plt.xticks(positions + width * (len(sectors) - 1) / 2, countries)
plt.show()

The xticks() function customizes the x-axis in the following way:

The first argument positions + width * (len(sectors) - 1) / 2 is an array of x-axis coordinates representing the centers of the bar groups;
The second argument provides labels (names) for these x-axis ticks, using the countries array.

Note
The code in our example is rather flexible and works for an arbitrary number of categories (you may only need to adjust width to avoid overlapping).

Завдання

Pass the correct array to the len() function.
Use the correct function to plot bars.
Use the correct variable which should be multiplied by i.
Use the correct variable as an index for answers array.
Pass the correct variable as the rightmost arguments of the plotting function.

Завдання

Pass the correct array to the len() function.
Use the correct function to plot bars.
Use the correct variable which should be multiplied by i.
Use the correct variable as an index for answers array.
Pass the correct variable as the rightmost arguments of the plotting function.

Все було зрозуміло?

Секція 2. Розділ 6

Grouped Bar Charts

Here are the steps to achieve this:

Start by specifying the bar width and creating an array of x-axis coordinates (you can use np.arange() for this);
Create a two-dimensional array to hold the arrays for each category;
Use a for loop to call the bar() function multiple times, once for each category (e.g., three times for three categories).

Let's have a look at an example:


              12345678910111213141516
            
import matplotlib.pyplot as plt
import numpy as np
countries = ['USA', 'China', 'Japan']
positions = np.arange(len(countries))
primary_sector = np.array([1.4, 4.8, 0.4])
secondary_sector = np.array([11.3, 6.2, 0.8])
tertiary_sector = np.array([14.2, 8.4, 3.2])
sectors = np.array([primary_sector, secondary_sector, tertiary_sector])
# Setting the width of the bars
width = 0.3
for i in range(len(sectors)):
# Plotting the bars for each category (sector)
   plt.bar(positions + width * i, sectors[i], width)
# Setting the x-axis ticks position and labels
plt.xticks(positions + width * (len(sectors) - 1) / 2, countries)
plt.show()

The xticks() function customizes the x-axis in the following way:

The first argument positions + width * (len(sectors) - 1) / 2 is an array of x-axis coordinates representing the centers of the bar groups;
The second argument provides labels (names) for these x-axis ticks, using the countries array.

Note
The code in our example is rather flexible and works for an arbitrary number of categories (you may only need to adjust width to avoid overlapping).

Завдання

Pass the correct array to the len() function.
Use the correct function to plot bars.
Use the correct variable which should be multiplied by i.
Use the correct variable as an index for answers array.
Pass the correct variable as the rightmost arguments of the plotting function.

Завдання

Pass the correct array to the len() function.
Use the correct function to plot bars.
Use the correct variable which should be multiplied by i.
Use the correct variable as an index for answers array.
Pass the correct variable as the rightmost arguments of the plotting function.

Все було зрозуміло?

Here are the steps to achieve this:

Start by specifying the bar width and creating an array of x-axis coordinates (you can use np.arange() for this);
Create a two-dimensional array to hold the arrays for each category;
Use a for loop to call the bar() function multiple times, once for each category (e.g., three times for three categories).

Let's have a look at an example:


              12345678910111213141516
            
import matplotlib.pyplot as plt
import numpy as np
countries = ['USA', 'China', 'Japan']
positions = np.arange(len(countries))
primary_sector = np.array([1.4, 4.8, 0.4])
secondary_sector = np.array([11.3, 6.2, 0.8])
tertiary_sector = np.array([14.2, 8.4, 3.2])
sectors = np.array([primary_sector, secondary_sector, tertiary_sector])
# Setting the width of the bars
width = 0.3
for i in range(len(sectors)):
# Plotting the bars for each category (sector)
   plt.bar(positions + width * i, sectors[i], width)
# Setting the x-axis ticks position and labels
plt.xticks(positions + width * (len(sectors) - 1) / 2, countries)
plt.show()

The xticks() function customizes the x-axis in the following way:

The first argument positions + width * (len(sectors) - 1) / 2 is an array of x-axis coordinates representing the centers of the bar groups;
The second argument provides labels (names) for these x-axis ticks, using the countries array.

Note
The code in our example is rather flexible and works for an arbitrary number of categories (you may only need to adjust width to avoid overlapping).

Завдання

Pass the correct array to the len() function.
Use the correct function to plot bars.
Use the correct variable which should be multiplied by i.
Use the correct variable as an index for answers array.
Pass the correct variable as the rightmost arguments of the plotting function.

Секція 2. Розділ 6