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

Axes Customization

Ticks Customization

For adjusting ticks, the pyplot module has two functions with identical syntax:

xticks() for the x-axis;
yticks() for the y-axis.

Both functions have two most important parameters:

ticks specifies where you want the ticks to appear (use an array-like type). You can remove ticks by passing an empty list;
labels assigns labels to the ticks at their specified locations. This parameter must be used alongside the ticks parameter.

You can also provide additional keyword arguments to control the appearance of the labels.

Now it’s time for some examples. Here is one of the graphs we have recently created:

Everything seems to be pretty fine with this plot, however, it would be better to have more years on the x-axis in this range (1995-2020). Let’s use xticks() for this purpose:


              123456789101112
            
import pandas as pd
import matplotlib.pyplot as plt
url = 'https://content-media-cdn.codefinity.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv'
# Loading the dataset with the average yearly temperatures in Boston and Seattle
weather_df = pd.read_csv(url, index_col=0)
plt.plot(weather_df['Boston'], label='Boston')
plt.plot(weather_df['Seattle'], label='Seattle')
plt.title('Boston and Seattle average yearly temperatures')
plt.legend(loc='upper left')
# Setting the x-ticks coordinates and their rotation
plt.xticks(range(1995, 2021, 2), rotation=30)
plt.show()

Now there is every second year in this range on the x-axis. We accomplished this using the range() function (with the step parameter 2) for the ticks argument.

Moreover, we used a keyword argument rotation to rotate the ticks labels 30 degrees counterclockwise for better readability.

We could also specify the list of labels for the ticks via setting the labels (e.g, labels = ['label1', 'label2']).

Axes Labels Customization

You can use xlabel() and ylabel() functions from the pyplot module to set the labels for the x-axis and y-axis. These functions require only one parameter: the label itself (a string).


              123456789101112
            
import pandas as pd
import matplotlib.pyplot as plt
url = 'https://content-media-cdn.codefinity.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv'
weather_df = pd.read_csv(url, index_col=0)
plt.plot(weather_df['Boston'], label='Boston')
plt.plot(weather_df['Seattle'], label='Seattle')
plt.title('Boston and Seattle average yearly temperatures')
plt.legend(loc='upper left')
plt.xticks(range(1995, 2021, 2), rotation=30)
# Setting the label for the y-axis
plt.ylabel('Temperature, °F')
plt.show()

It is also possible to modify the label apperance, for instance, set the font size via fontsize keyword argument or its color via color keyword argument.

In addition, there is loc parameter which specifies the label location (center by default).

For x-axis label 'left', 'center' and 'right' are possible values;
For y-axis instead of 'left' and 'right' there is 'top' and 'bottom'.

You can explore more in the documentation: xlabel, ylabel.

Task

Swipe to start coding

Use the correct function to set data_linear as x-axis ticks.
Use the correct function to set 'x' as the x-axis label.
Use 'right' as the location for the x-axis label.
Use the correct function to set 'y' as the y-axis label.
Use 'top' as the location for the y-axis label.
Set rotation parameter to 0 for the y-axis label.

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 3. Chapter 3

Axes Customization

Ticks Customization

For adjusting ticks, the pyplot module has two functions with identical syntax:

xticks() for the x-axis;
yticks() for the y-axis.

Both functions have two most important parameters:

ticks specifies where you want the ticks to appear (use an array-like type). You can remove ticks by passing an empty list;
labels assigns labels to the ticks at their specified locations. This parameter must be used alongside the ticks parameter.

You can also provide additional keyword arguments to control the appearance of the labels.

Now it’s time for some examples. Here is one of the graphs we have recently created:

Everything seems to be pretty fine with this plot, however, it would be better to have more years on the x-axis in this range (1995-2020). Let’s use xticks() for this purpose:


              123456789101112
            
import pandas as pd
import matplotlib.pyplot as plt
url = 'https://content-media-cdn.codefinity.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv'
# Loading the dataset with the average yearly temperatures in Boston and Seattle
weather_df = pd.read_csv(url, index_col=0)
plt.plot(weather_df['Boston'], label='Boston')
plt.plot(weather_df['Seattle'], label='Seattle')
plt.title('Boston and Seattle average yearly temperatures')
plt.legend(loc='upper left')
# Setting the x-ticks coordinates and their rotation
plt.xticks(range(1995, 2021, 2), rotation=30)
plt.show()

Now there is every second year in this range on the x-axis. We accomplished this using the range() function (with the step parameter 2) for the ticks argument.

Moreover, we used a keyword argument rotation to rotate the ticks labels 30 degrees counterclockwise for better readability.

We could also specify the list of labels for the ticks via setting the labels (e.g, labels = ['label1', 'label2']).

Axes Labels Customization

You can use xlabel() and ylabel() functions from the pyplot module to set the labels for the x-axis and y-axis. These functions require only one parameter: the label itself (a string).


              123456789101112
            
import pandas as pd
import matplotlib.pyplot as plt
url = 'https://content-media-cdn.codefinity.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv'
weather_df = pd.read_csv(url, index_col=0)
plt.plot(weather_df['Boston'], label='Boston')
plt.plot(weather_df['Seattle'], label='Seattle')
plt.title('Boston and Seattle average yearly temperatures')
plt.legend(loc='upper left')
plt.xticks(range(1995, 2021, 2), rotation=30)
# Setting the label for the y-axis
plt.ylabel('Temperature, °F')
plt.show()

It is also possible to modify the label apperance, for instance, set the font size via fontsize keyword argument or its color via color keyword argument.

In addition, there is loc parameter which specifies the label location (center by default).

For x-axis label 'left', 'center' and 'right' are possible values;
For y-axis instead of 'left' and 'right' there is 'top' and 'bottom'.

You can explore more in the documentation: xlabel, ylabel.

Task

Swipe to start coding

Use the correct function to set data_linear as x-axis ticks.
Use the correct function to set 'x' as the x-axis label.
Use 'right' as the location for the x-axis label.
Use the correct function to set 'y' as the y-axis label.
Use 'top' as the location for the y-axis label.
Set rotation parameter to 0 for the y-axis label.

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 3. Chapter 3

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