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

Countplot

A countplot is a plot which creates columns (bars) that represent the number of entries for each category of a categorical list. It can be also thought of as a histogram of a categorical variable. Let’s have a look at an example of a count plot:

Here each column represents the number of Titanic passengers of each class. You may have already noticed that this plot is very similar to the bar chart. Indeed, it is a rather specific kind of bar chart representing the frequency of each category.

Note

You still have to import the pyplot module from matplotlib and use the plt.show() function to display the plots created with seaborn.

In order to create a countplot with seaborn, you should use the countplot() function. There are several possible options to pass our data to this function.

Passing a 1D Array

The first option is to simply pass the value for the x parameter which can be some kind of an array:


              12345
            
import seaborn as sns
import matplotlib.pyplot as plt
fruits = ['apple', 'banana', 'orange', 'apple', 'apple', 'apple', 'orange', 'banana', 'banana', 'banana', 'banana', 'banana', 'banana', 'banana']
sns.countplot(x=fruits)
plt.show()

As you can see, the function simply counts the occurrences of each unique element in the list and creates a column with the respective height for each of them.

Note

We may also use y parameter instead of x to change the orientation of the plot from vertical to horizontal.

Passing a 2D Object

Another option is to use the data parameter combined with the x or y parameter. This approach is suitable for working with pandas DataFrame. You can pass a list of arrays or a DataFrame as the value for data. For x or y you can pass a name of the column in the DataFrame, for example:


              123456
            
import seaborn as sns
import matplotlib.pyplot as plt
# Loading a built-in dataset of the Titanic passengers
titanic_df = sns.load_dataset('titanic')
sns.countplot(data=titanic_df, x='class')
plt.show()

Our function in this example creates a countplot based on the 'class' column of a titanic DataFrame and counts the number of entries for each unique value in this column.

Task

Swipe to start coding

Import the seaborn library with the sns alias.
Import the matplotlib.pyplot module with the plt alias.
Use the correct function to create a countplot.
Use the diamonds as the first argument to specify the DataFrame.
Use the 'cut' column of the diamonds DataFrame as the categories for the countplot and display the cateories on the y-axis via the second argument.
Display the plot using the correct function.

Solution

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

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Section 5. Chapter 2

Countplot

Note

You still have to import the pyplot module from matplotlib and use the plt.show() function to display the plots created with seaborn.

In order to create a countplot with seaborn, you should use the countplot() function. There are several possible options to pass our data to this function.

Passing a 1D Array

The first option is to simply pass the value for the x parameter which can be some kind of an array:


              12345
            
import seaborn as sns
import matplotlib.pyplot as plt
fruits = ['apple', 'banana', 'orange', 'apple', 'apple', 'apple', 'orange', 'banana', 'banana', 'banana', 'banana', 'banana', 'banana', 'banana']
sns.countplot(x=fruits)
plt.show()

As you can see, the function simply counts the occurrences of each unique element in the list and creates a column with the respective height for each of them.

Note

We may also use y parameter instead of x to change the orientation of the plot from vertical to horizontal.

Passing a 2D Object


              123456
            
import seaborn as sns
import matplotlib.pyplot as plt
# Loading a built-in dataset of the Titanic passengers
titanic_df = sns.load_dataset('titanic')
sns.countplot(data=titanic_df, x='class')
plt.show()

Our function in this example creates a countplot based on the 'class' column of a titanic DataFrame and counts the number of entries for each unique value in this column.

Task

Swipe to start coding

Import the seaborn library with the sns alias.
Import the matplotlib.pyplot module with the plt alias.
Use the correct function to create a countplot.
Use the diamonds as the first argument to specify the DataFrame.
Use the 'cut' column of the diamonds DataFrame as the categories for the countplot and display the cateories on the y-axis via the second argument.
Display the plot using the correct function.

Solution

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 5. Chapter 2

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