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

Subplots

Up until now we only created multiple plots on one Axes object using multiple calls of the plotting functions (we can combine different plot types).

Now it’s time to learn how to create multiple Axes objects and thus multiple plots on different Axes objects.

pyplot has a subplots() function exactly for this purpose. We have already used this function when we created a canvas in the first section, now we'll have a more detailed look at it.

Rows and Columns

The two most important arguments of this function are nrows and ncolumns which specify the number of rows and columns of the subplot grid respectively (their default values are 1 and 1 resulting in just one Axes object).

subplots() returns a Figure object and either an Axes object or an array of Axes objects.

Let’s have a look at an example:


              123
            
import matplotlib.pyplot as plt
fig, axs = plt.subplots(2, 2)
plt.show()

We have just created a 2 by 2 subplot grid.

Note

The subplots function here returns an array of Axes objects, since there is more than one subplot. When there is an array of Axes returned the variable for storing it is often called axs (ax is mostly used for a single Axes object).

axs in our case is a two-dimensional array, hence why we should use both a row index and a column index to access a particular Axes object.

Let’s create a few plots:


              123456789101112
            
import matplotlib.pyplot as plt
import numpy as np
data_linear = np.arange(1, 11)
data_squared = data_linear ** 2
# Creating a 2x2 subplot grid
fig, axs = plt.subplots(2, 2)
# Creating a different plot for each Axes object
axs[0, 0].plot(data_linear)
axs[0, 1].plot(data_squared)
axs[1, 0].scatter(data_linear, data_linear)
axs[1, 1].scatter(data_linear, data_squared)
plt.show()

The first row of the subplot grid (row 0) has two line plots and the second row (row 1) has two scatter plots.

Remember that we cannot use plt.plot() or plt.scatter() here, since we want to place each plot on a separate Axes object (subplot).

Converting to 1D Array

It is also possible to use the .ravel() method to convert 2D Axes array to 1D contiguous flattened array:


              1234567891011121314
            
import matplotlib.pyplot as plt
import numpy as np
data_linear = np.arange(1, 11)
data_squared = data_linear ** 2
# Creating a 2x2 subplot grid
fig, axs = plt.subplots(2, 2)
# Converting axs to 1D array of 4 elements
axs = axs.ravel()
# Creating a different plot for each Axes object
axs[0].plot(data_linear)
axs[1].plot(data_squared)
axs[2].scatter(data_linear, data_linear)
axs[3].scatter(data_linear, data_squared)
plt.show()

Since we have a 2x2 array, axs.ravel() returns a 1D array with four elements.

Sharing an Axis

Another two important parameters of the subplots() function are sharex and sharey which specify whether the properties should be shared across the x or y axes respectively. They both have a default value of False. Let’s change one of them in our example:


              1234567891011121314
            
import matplotlib.pyplot as plt
import numpy as np
data_linear = np.arange(1, 11)
data_squared = data_linear ** 2
# Share x-axis among all the subplots
fig, axs = plt.subplots(2, 2, sharex=True)
# Converting axs to 1D array of 4 elements
axs = axs.ravel()
# Creating a different plot for each Axes object
axs[0].plot(data_linear)
axs[1].plot(data_squared)
axs[2].scatter(data_linear, data_linear)
axs[3].scatter(data_linear, data_squared)
plt.show()

With the True value for sharex x-axis will be shared among all subplots, which makes sense here, since we have the same x-axis coordinates for all of the subplots.

Moreover, we can set sharex or sharey parameters to row ( each subplot row will share a respective axis) or col (each subplot column will share a respective axis).

As usual feel free to explore more in the documentation in case you want to.

Task

Swipe to show code editor

Use the correct function to create a subplot grid.
The grid should have 3 rows and 1 column (specify the first two parameters).
Specify the rightmost keyword argument, so that x-axis will be shared among all the subplots.
Store the result of the function for creating subplots in the fig and axs variables (from left to right).
Place the first line plot for data_linear on the first row (row 0) of the subplot grid.
Place the second line plot for data_squared on the second row (row 1) of the subplot grid.
Place the third line plot for data_exp on the third row (row 2) of the subplot grid.

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 6

Subplots

Up until now we only created multiple plots on one Axes object using multiple calls of the plotting functions (we can combine different plot types).

Now it’s time to learn how to create multiple Axes objects and thus multiple plots on different Axes objects.

pyplot has a subplots() function exactly for this purpose. We have already used this function when we created a canvas in the first section, now we'll have a more detailed look at it.

Rows and Columns

subplots() returns a Figure object and either an Axes object or an array of Axes objects.

Let’s have a look at an example:


              123
            
import matplotlib.pyplot as plt
fig, axs = plt.subplots(2, 2)
plt.show()

We have just created a 2 by 2 subplot grid.

Note

The subplots function here returns an array of Axes objects, since there is more than one subplot. When there is an array of Axes returned the variable for storing it is often called axs (ax is mostly used for a single Axes object).

axs in our case is a two-dimensional array, hence why we should use both a row index and a column index to access a particular Axes object.

Let’s create a few plots:


              123456789101112
            
import matplotlib.pyplot as plt
import numpy as np
data_linear = np.arange(1, 11)
data_squared = data_linear ** 2
# Creating a 2x2 subplot grid
fig, axs = plt.subplots(2, 2)
# Creating a different plot for each Axes object
axs[0, 0].plot(data_linear)
axs[0, 1].plot(data_squared)
axs[1, 0].scatter(data_linear, data_linear)
axs[1, 1].scatter(data_linear, data_squared)
plt.show()

The first row of the subplot grid (row 0) has two line plots and the second row (row 1) has two scatter plots.

Remember that we cannot use plt.plot() or plt.scatter() here, since we want to place each plot on a separate Axes object (subplot).

Converting to 1D Array

It is also possible to use the .ravel() method to convert 2D Axes array to 1D contiguous flattened array:


              1234567891011121314
            
import matplotlib.pyplot as plt
import numpy as np
data_linear = np.arange(1, 11)
data_squared = data_linear ** 2
# Creating a 2x2 subplot grid
fig, axs = plt.subplots(2, 2)
# Converting axs to 1D array of 4 elements
axs = axs.ravel()
# Creating a different plot for each Axes object
axs[0].plot(data_linear)
axs[1].plot(data_squared)
axs[2].scatter(data_linear, data_linear)
axs[3].scatter(data_linear, data_squared)
plt.show()

Since we have a 2x2 array, axs.ravel() returns a 1D array with four elements.

Sharing an Axis


              1234567891011121314
            
import matplotlib.pyplot as plt
import numpy as np
data_linear = np.arange(1, 11)
data_squared = data_linear ** 2
# Share x-axis among all the subplots
fig, axs = plt.subplots(2, 2, sharex=True)
# Converting axs to 1D array of 4 elements
axs = axs.ravel()
# Creating a different plot for each Axes object
axs[0].plot(data_linear)
axs[1].plot(data_squared)
axs[2].scatter(data_linear, data_linear)
axs[3].scatter(data_linear, data_squared)
plt.show()

With the True value for sharex x-axis will be shared among all subplots, which makes sense here, since we have the same x-axis coordinates for all of the subplots.

Moreover, we can set sharex or sharey parameters to row ( each subplot row will share a respective axis) or col (each subplot column will share a respective axis).

As usual feel free to explore more in the documentation in case you want to.

Task

Swipe to show code editor

Use the correct function to create a subplot grid.
The grid should have 3 rows and 1 column (specify the first two parameters).
Specify the rightmost keyword argument, so that x-axis will be shared among all the subplots.
Store the result of the function for creating subplots in the fig and axs variables (from left to right).
Place the first line plot for data_linear on the first row (row 0) of the subplot grid.
Place the second line plot for data_squared on the second row (row 1) of the subplot grid.
Place the third line plot for data_exp on the third row (row 2) of the subplot grid.

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 6

Subplots

Up until now we only created multiple plots on one Axes object using multiple calls of the plotting functions (we can combine different plot types).

Now it’s time to learn how to create multiple Axes objects and thus multiple plots on different Axes objects.

pyplot has a subplots() function exactly for this purpose. We have already used this function when we created a canvas in the first section, now we'll have a more detailed look at it.

Rows and Columns

subplots() returns a Figure object and either an Axes object or an array of Axes objects.

Let’s have a look at an example:


              123
            
import matplotlib.pyplot as plt
fig, axs = plt.subplots(2, 2)
plt.show()

We have just created a 2 by 2 subplot grid.

Note

The subplots function here returns an array of Axes objects, since there is more than one subplot. When there is an array of Axes returned the variable for storing it is often called axs (ax is mostly used for a single Axes object).

axs in our case is a two-dimensional array, hence why we should use both a row index and a column index to access a particular Axes object.

Let’s create a few plots:


              123456789101112
            
import matplotlib.pyplot as plt
import numpy as np
data_linear = np.arange(1, 11)
data_squared = data_linear ** 2
# Creating a 2x2 subplot grid
fig, axs = plt.subplots(2, 2)
# Creating a different plot for each Axes object
axs[0, 0].plot(data_linear)
axs[0, 1].plot(data_squared)
axs[1, 0].scatter(data_linear, data_linear)
axs[1, 1].scatter(data_linear, data_squared)
plt.show()

The first row of the subplot grid (row 0) has two line plots and the second row (row 1) has two scatter plots.

Remember that we cannot use plt.plot() or plt.scatter() here, since we want to place each plot on a separate Axes object (subplot).

Converting to 1D Array

It is also possible to use the .ravel() method to convert 2D Axes array to 1D contiguous flattened array:


              1234567891011121314
            
import matplotlib.pyplot as plt
import numpy as np
data_linear = np.arange(1, 11)
data_squared = data_linear ** 2
# Creating a 2x2 subplot grid
fig, axs = plt.subplots(2, 2)
# Converting axs to 1D array of 4 elements
axs = axs.ravel()
# Creating a different plot for each Axes object
axs[0].plot(data_linear)
axs[1].plot(data_squared)
axs[2].scatter(data_linear, data_linear)
axs[3].scatter(data_linear, data_squared)
plt.show()

Since we have a 2x2 array, axs.ravel() returns a 1D array with four elements.

Sharing an Axis


              1234567891011121314
            
import matplotlib.pyplot as plt
import numpy as np
data_linear = np.arange(1, 11)
data_squared = data_linear ** 2
# Share x-axis among all the subplots
fig, axs = plt.subplots(2, 2, sharex=True)
# Converting axs to 1D array of 4 elements
axs = axs.ravel()
# Creating a different plot for each Axes object
axs[0].plot(data_linear)
axs[1].plot(data_squared)
axs[2].scatter(data_linear, data_linear)
axs[3].scatter(data_linear, data_squared)
plt.show()

With the True value for sharex x-axis will be shared among all subplots, which makes sense here, since we have the same x-axis coordinates for all of the subplots.

Moreover, we can set sharex or sharey parameters to row ( each subplot row will share a respective axis) or col (each subplot column will share a respective axis).

As usual feel free to explore more in the documentation in case you want to.

Task

Swipe to show code editor

Use the correct function to create a subplot grid.
The grid should have 3 rows and 1 column (specify the first two parameters).
Specify the rightmost keyword argument, so that x-axis will be shared among all the subplots.
Store the result of the function for creating subplots in the fig and axs variables (from left to right).
Place the first line plot for data_linear on the first row (row 0) of the subplot grid.
Place the second line plot for data_squared on the second row (row 1) of the subplot grid.
Place the third line plot for data_exp on the third row (row 2) of the subplot grid.

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

Everything was clear?

Thanks for your feedback!

Up until now we only created multiple plots on one Axes object using multiple calls of the plotting functions (we can combine different plot types).

Now it’s time to learn how to create multiple Axes objects and thus multiple plots on different Axes objects.

pyplot has a subplots() function exactly for this purpose. We have already used this function when we created a canvas in the first section, now we'll have a more detailed look at it.

Rows and Columns

subplots() returns a Figure object and either an Axes object or an array of Axes objects.

Let’s have a look at an example:


              123
            
import matplotlib.pyplot as plt
fig, axs = plt.subplots(2, 2)
plt.show()

We have just created a 2 by 2 subplot grid.

Note

The subplots function here returns an array of Axes objects, since there is more than one subplot. When there is an array of Axes returned the variable for storing it is often called axs (ax is mostly used for a single Axes object).

axs in our case is a two-dimensional array, hence why we should use both a row index and a column index to access a particular Axes object.

Let’s create a few plots:


              123456789101112
            
import matplotlib.pyplot as plt
import numpy as np
data_linear = np.arange(1, 11)
data_squared = data_linear ** 2
# Creating a 2x2 subplot grid
fig, axs = plt.subplots(2, 2)
# Creating a different plot for each Axes object
axs[0, 0].plot(data_linear)
axs[0, 1].plot(data_squared)
axs[1, 0].scatter(data_linear, data_linear)
axs[1, 1].scatter(data_linear, data_squared)
plt.show()

The first row of the subplot grid (row 0) has two line plots and the second row (row 1) has two scatter plots.

Remember that we cannot use plt.plot() or plt.scatter() here, since we want to place each plot on a separate Axes object (subplot).

Converting to 1D Array

It is also possible to use the .ravel() method to convert 2D Axes array to 1D contiguous flattened array:


              1234567891011121314
            
import matplotlib.pyplot as plt
import numpy as np
data_linear = np.arange(1, 11)
data_squared = data_linear ** 2
# Creating a 2x2 subplot grid
fig, axs = plt.subplots(2, 2)
# Converting axs to 1D array of 4 elements
axs = axs.ravel()
# Creating a different plot for each Axes object
axs[0].plot(data_linear)
axs[1].plot(data_squared)
axs[2].scatter(data_linear, data_linear)
axs[3].scatter(data_linear, data_squared)
plt.show()

Since we have a 2x2 array, axs.ravel() returns a 1D array with four elements.

Sharing an Axis


              1234567891011121314
            
import matplotlib.pyplot as plt
import numpy as np
data_linear = np.arange(1, 11)
data_squared = data_linear ** 2
# Share x-axis among all the subplots
fig, axs = plt.subplots(2, 2, sharex=True)
# Converting axs to 1D array of 4 elements
axs = axs.ravel()
# Creating a different plot for each Axes object
axs[0].plot(data_linear)
axs[1].plot(data_squared)
axs[2].scatter(data_linear, data_linear)
axs[3].scatter(data_linear, data_squared)
plt.show()

With the True value for sharex x-axis will be shared among all subplots, which makes sense here, since we have the same x-axis coordinates for all of the subplots.

Moreover, we can set sharex or sharey parameters to row ( each subplot row will share a respective axis) or col (each subplot column will share a respective axis).

As usual feel free to explore more in the documentation in case you want to.

Task

Swipe to show code editor

Use the correct function to create a subplot grid.
The grid should have 3 rows and 1 column (specify the first two parameters).
Specify the rightmost keyword argument, so that x-axis will be shared among all the subplots.
Store the result of the function for creating subplots in the fig and axs variables (from left to right).
Place the first line plot for data_linear on the first row (row 0) of the subplot grid.
Place the second line plot for data_squared on the second row (row 1) of the subplot grid.
Place the third line plot for data_exp on the third row (row 2) of the subplot grid.

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

Section 3. Chapter 6

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