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Ultimate Visualization with Python
course content

Course Content

Ultimate Visualization with Python

Ultimate Visualization with Python

1. Matplotlib Introduction
Plotting ApplicationsWhy Use Matplotlib?Creating a CanvasBasic Plotting
2. Creating Commonly Used Plots
Line PlotMultiple Line PlotsScatter PlotBar ChartStacked Bar ChartsGrouped Bar Charts
3. Plots Customization
Adding Title to the PlotAdding LegendAxes CustomizationColors and Transparency Customizing GridSubplots
4. More Statistical Plots
HistogramBox PlotPie Chart
5. Plotting with Seaborn
Seaborn IntroductionCountplotPlots DecorationKDE PlotJoint PlotPair PlotHeatmap

bookHeatmap

A heatmap is a method for visualizing two-dimensional data using colors to represent the magnitude of each value. Here is an example of a heatmap:

In this example we used a heatmap to represent pairwise correlation between the variables in a dataset. With such a heatmap everything is neat and clear, exactly what we need from a good visulization.

Creating a Simple Heatmap

seaborn has a function called heatmap(). Its only required parameter is data which should be a 2D (rectangle) dataset.

Perhaps the most common use case of a heatmap is with a correlation matrix like in the example above. Given a DataFrame, we should first call its corr() method to get a correlation matrix and only then pass this matrix as an argument for the heatmap() function:


              
1234567891011

            
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/countries_data.csv'
# Loading the dataset with the countries data
countries_df = pd.read_csv(url, index_col=0)
# Creating a correlation matrix with all numeric variables
correlation_matrix = countries_df.corr(numeric_only=True)
# Creating a heatmap based on the correlation matrix
sns.heatmap(correlation_matrix)
plt.show()
copy

Everything is pretty straightforward here, however, we didn’t use all the columns of the DataFrame to create a correlation matrix (numeric_only=True), since they have strings and are not numeric.

Annotation and Colors

This heatmap can be made more informative via writing the appropriate value (correlation coefficient in our case) in each cell . That can be done simply by setting the annot parameter to True.

It is also possible to change the colors for our heatmap via setting the cmap parameter (you can explore the palettes here).

With this in mind, let’s now modify our example:


              
1234567891011

            
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/countries_data.csv'
# Loading the dataset with the countries data
countries_df = pd.read_csv(url, index_col=0)
# Creating a correlation matrix with all numeric variables
correlation_matrix = countries_df.corr(numeric_only=True)
# Setting annotation and color palette
sns.heatmap(correlation_matrix, annot=True, cmap='viridis')
plt.show()
copy

We could also remove the color bar on the right by setting cbar=False.

In most of the cases that’s all you will need from a heatmap customization, however, you can always explore more in its documentation.

Improving Readability

The final thing that would improve the readability of our heatmap is rotating the ticks using already familiar xticks() and yticks() functions:


              
1234567891011121314

            
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/countries_data.csv'
# Loading the dataset with the countries data
countries_df = pd.read_csv(url, index_col=0)
# Creating a correlation matrix with all numeric variables
correlation_matrix = countries_df.corr(numeric_only=True)
# Creating a heatmap based on the correlation matrix
sns.heatmap(correlation_matrix, annot=True, cmap='viridis')
# Rotating the ticks by 20 degrees counterclockwise
plt.xticks(rotation=20)
plt.yticks(rotation=20)
plt.show()
copy
Task
test

Swipe to show code editor

  1. Use the correct method to create a correlation matrix.
  2. Set the argument of the method to include only numeric variables.
  3. Use the correct function to create a heatmap.
  4. Set correlation_matrix to be the data for the heatmap via specifying the first argument.
  5. Add the values in each cell of the matrix via specifying the second argument.
  6. Set the palette (color map) of the heatmap to 'crest' via specifying the third (rightmost) argument.
  7. Rotate x-axis and y-axis ticks by 15 degrees counterclockwise via specifying a keyword argument in xticks() and yticks().

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 5. Chapter 7
toggle bottom row

bookHeatmap

A heatmap is a method for visualizing two-dimensional data using colors to represent the magnitude of each value. Here is an example of a heatmap:

In this example we used a heatmap to represent pairwise correlation between the variables in a dataset. With such a heatmap everything is neat and clear, exactly what we need from a good visulization.

Creating a Simple Heatmap

seaborn has a function called heatmap(). Its only required parameter is data which should be a 2D (rectangle) dataset.

Perhaps the most common use case of a heatmap is with a correlation matrix like in the example above. Given a DataFrame, we should first call its corr() method to get a correlation matrix and only then pass this matrix as an argument for the heatmap() function:


              
1234567891011

            
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/countries_data.csv'
# Loading the dataset with the countries data
countries_df = pd.read_csv(url, index_col=0)
# Creating a correlation matrix with all numeric variables
correlation_matrix = countries_df.corr(numeric_only=True)
# Creating a heatmap based on the correlation matrix
sns.heatmap(correlation_matrix)
plt.show()
copy

Everything is pretty straightforward here, however, we didn’t use all the columns of the DataFrame to create a correlation matrix (numeric_only=True), since they have strings and are not numeric.

Annotation and Colors

This heatmap can be made more informative via writing the appropriate value (correlation coefficient in our case) in each cell . That can be done simply by setting the annot parameter to True.

It is also possible to change the colors for our heatmap via setting the cmap parameter (you can explore the palettes here).

With this in mind, let’s now modify our example:


              
1234567891011

            
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/countries_data.csv'
# Loading the dataset with the countries data
countries_df = pd.read_csv(url, index_col=0)
# Creating a correlation matrix with all numeric variables
correlation_matrix = countries_df.corr(numeric_only=True)
# Setting annotation and color palette
sns.heatmap(correlation_matrix, annot=True, cmap='viridis')
plt.show()
copy

We could also remove the color bar on the right by setting cbar=False.

In most of the cases that’s all you will need from a heatmap customization, however, you can always explore more in its documentation.

Improving Readability

The final thing that would improve the readability of our heatmap is rotating the ticks using already familiar xticks() and yticks() functions:


              
1234567891011121314

            
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/countries_data.csv'
# Loading the dataset with the countries data
countries_df = pd.read_csv(url, index_col=0)
# Creating a correlation matrix with all numeric variables
correlation_matrix = countries_df.corr(numeric_only=True)
# Creating a heatmap based on the correlation matrix
sns.heatmap(correlation_matrix, annot=True, cmap='viridis')
# Rotating the ticks by 20 degrees counterclockwise
plt.xticks(rotation=20)
plt.yticks(rotation=20)
plt.show()
copy
Task
test

Swipe to show code editor

  1. Use the correct method to create a correlation matrix.
  2. Set the argument of the method to include only numeric variables.
  3. Use the correct function to create a heatmap.
  4. Set correlation_matrix to be the data for the heatmap via specifying the first argument.
  5. Add the values in each cell of the matrix via specifying the second argument.
  6. Set the palette (color map) of the heatmap to 'crest' via specifying the third (rightmost) argument.
  7. Rotate x-axis and y-axis ticks by 15 degrees counterclockwise via specifying a keyword argument in xticks() and yticks().

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 5. Chapter 7
toggle bottom row

bookHeatmap

A heatmap is a method for visualizing two-dimensional data using colors to represent the magnitude of each value. Here is an example of a heatmap:

In this example we used a heatmap to represent pairwise correlation between the variables in a dataset. With such a heatmap everything is neat and clear, exactly what we need from a good visulization.

Creating a Simple Heatmap

seaborn has a function called heatmap(). Its only required parameter is data which should be a 2D (rectangle) dataset.

Perhaps the most common use case of a heatmap is with a correlation matrix like in the example above. Given a DataFrame, we should first call its corr() method to get a correlation matrix and only then pass this matrix as an argument for the heatmap() function:


              
1234567891011

            
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/countries_data.csv'
# Loading the dataset with the countries data
countries_df = pd.read_csv(url, index_col=0)
# Creating a correlation matrix with all numeric variables
correlation_matrix = countries_df.corr(numeric_only=True)
# Creating a heatmap based on the correlation matrix
sns.heatmap(correlation_matrix)
plt.show()
copy

Everything is pretty straightforward here, however, we didn’t use all the columns of the DataFrame to create a correlation matrix (numeric_only=True), since they have strings and are not numeric.

Annotation and Colors

This heatmap can be made more informative via writing the appropriate value (correlation coefficient in our case) in each cell . That can be done simply by setting the annot parameter to True.

It is also possible to change the colors for our heatmap via setting the cmap parameter (you can explore the palettes here).

With this in mind, let’s now modify our example:


              
1234567891011

            
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/countries_data.csv'
# Loading the dataset with the countries data
countries_df = pd.read_csv(url, index_col=0)
# Creating a correlation matrix with all numeric variables
correlation_matrix = countries_df.corr(numeric_only=True)
# Setting annotation and color palette
sns.heatmap(correlation_matrix, annot=True, cmap='viridis')
plt.show()
copy

We could also remove the color bar on the right by setting cbar=False.

In most of the cases that’s all you will need from a heatmap customization, however, you can always explore more in its documentation.

Improving Readability

The final thing that would improve the readability of our heatmap is rotating the ticks using already familiar xticks() and yticks() functions:


              
1234567891011121314

            
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/countries_data.csv'
# Loading the dataset with the countries data
countries_df = pd.read_csv(url, index_col=0)
# Creating a correlation matrix with all numeric variables
correlation_matrix = countries_df.corr(numeric_only=True)
# Creating a heatmap based on the correlation matrix
sns.heatmap(correlation_matrix, annot=True, cmap='viridis')
# Rotating the ticks by 20 degrees counterclockwise
plt.xticks(rotation=20)
plt.yticks(rotation=20)
plt.show()
copy
Task
test

Swipe to show code editor

  1. Use the correct method to create a correlation matrix.
  2. Set the argument of the method to include only numeric variables.
  3. Use the correct function to create a heatmap.
  4. Set correlation_matrix to be the data for the heatmap via specifying the first argument.
  5. Add the values in each cell of the matrix via specifying the second argument.
  6. Set the palette (color map) of the heatmap to 'crest' via specifying the third (rightmost) argument.
  7. Rotate x-axis and y-axis ticks by 15 degrees counterclockwise via specifying a keyword argument in xticks() and yticks().

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

A heatmap is a method for visualizing two-dimensional data using colors to represent the magnitude of each value. Here is an example of a heatmap:

In this example we used a heatmap to represent pairwise correlation between the variables in a dataset. With such a heatmap everything is neat and clear, exactly what we need from a good visulization.

Creating a Simple Heatmap

seaborn has a function called heatmap(). Its only required parameter is data which should be a 2D (rectangle) dataset.

Perhaps the most common use case of a heatmap is with a correlation matrix like in the example above. Given a DataFrame, we should first call its corr() method to get a correlation matrix and only then pass this matrix as an argument for the heatmap() function:


              
1234567891011

            
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/countries_data.csv'
# Loading the dataset with the countries data
countries_df = pd.read_csv(url, index_col=0)
# Creating a correlation matrix with all numeric variables
correlation_matrix = countries_df.corr(numeric_only=True)
# Creating a heatmap based on the correlation matrix
sns.heatmap(correlation_matrix)
plt.show()
copy

Everything is pretty straightforward here, however, we didn’t use all the columns of the DataFrame to create a correlation matrix (numeric_only=True), since they have strings and are not numeric.

Annotation and Colors

This heatmap can be made more informative via writing the appropriate value (correlation coefficient in our case) in each cell . That can be done simply by setting the annot parameter to True.

It is also possible to change the colors for our heatmap via setting the cmap parameter (you can explore the palettes here).

With this in mind, let’s now modify our example:


              
1234567891011

            
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/countries_data.csv'
# Loading the dataset with the countries data
countries_df = pd.read_csv(url, index_col=0)
# Creating a correlation matrix with all numeric variables
correlation_matrix = countries_df.corr(numeric_only=True)
# Setting annotation and color palette
sns.heatmap(correlation_matrix, annot=True, cmap='viridis')
plt.show()
copy

We could also remove the color bar on the right by setting cbar=False.

In most of the cases that’s all you will need from a heatmap customization, however, you can always explore more in its documentation.

Improving Readability

The final thing that would improve the readability of our heatmap is rotating the ticks using already familiar xticks() and yticks() functions:


              
1234567891011121314

            
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/countries_data.csv'
# Loading the dataset with the countries data
countries_df = pd.read_csv(url, index_col=0)
# Creating a correlation matrix with all numeric variables
correlation_matrix = countries_df.corr(numeric_only=True)
# Creating a heatmap based on the correlation matrix
sns.heatmap(correlation_matrix, annot=True, cmap='viridis')
# Rotating the ticks by 20 degrees counterclockwise
plt.xticks(rotation=20)
plt.yticks(rotation=20)
plt.show()
copy
Task
test

Swipe to show code editor

  1. Use the correct method to create a correlation matrix.
  2. Set the argument of the method to include only numeric variables.
  3. Use the correct function to create a heatmap.
  4. Set correlation_matrix to be the data for the heatmap via specifying the first argument.
  5. Add the values in each cell of the matrix via specifying the second argument.
  6. Set the palette (color map) of the heatmap to 'crest' via specifying the third (rightmost) argument.
  7. Rotate x-axis and y-axis ticks by 15 degrees counterclockwise via specifying a keyword argument in xticks() and yticks().

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 5. Chapter 7
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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