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Axes Customization

Ticks Customization

To adjust ticks on a plot, use the pyplot module's functions:

xticks for customizing the x-axis;
yticks for customizing the y-axis.

Both functions follow the same syntax and have two key parameters:

ticks defines the positions where the ticks should appear. You can use any array-like structure. To remove ticks entirely, pass an empty list;
labels assigns custom text to each tick position. This must be used together with the ticks parameter.

You can also include extra keyword arguments to style the labels and control their appearance.

Below is one of the graphs 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:


              1234567891011121314151617181920
            
import pandas as pd
import matplotlib.pyplot as plt

# Loading the dataset with the average yearly temperatures in Boston and Seattle
url = 'https://staging-content-media-cdn.codefinity.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv'
weather_df = pd.read_csv(url, index_col=0)

# Plotting the data
plt.plot(weather_df['Boston'], label='Boston')
plt.plot(weather_df['Seattle'], label='Seattle')

# Adding a title and legend
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)

# Displaying the plot
plt.show()

Now every second year appears on the x-axis. This was achieved using the range() function with a step of 2 for the ticks argument.

Additionally, the tick labels were rotated 30 degrees counterclockwise using the rotation keyword for improved readability.

Tick labels can also be manually specified by passing a list to the labels argument (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).


              123456789101112131415161718
            
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)

# Plotting the data
plt.plot(weather_df['Boston'], label='Boston')
plt.plot(weather_df['Seattle'], label='Seattle')

# Adding title, legend, and labels
plt.title('Boston and Seattle average yearly temperatures')
plt.legend(loc='upper left')
plt.xticks(range(1995, 2021, 2), rotation=30)
plt.ylabel('Temperature, °F')

# Displaying the plot
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'.

Study More

You can explore more in the documentation: xlabel() and 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

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Axes Customization

Ticks Customization

To adjust ticks on a plot, use the pyplot module's functions:

xticks for customizing the x-axis;
yticks for customizing the y-axis.

Both functions follow the same syntax and have two key parameters:

ticks defines the positions where the ticks should appear. You can use any array-like structure. To remove ticks entirely, pass an empty list;
labels assigns custom text to each tick position. This must be used together with the ticks parameter.

You can also include extra keyword arguments to style the labels and control their appearance.

Below is one of the graphs 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:


              1234567891011121314151617181920
            
import pandas as pd
import matplotlib.pyplot as plt

# Loading the dataset with the average yearly temperatures in Boston and Seattle
url = 'https://staging-content-media-cdn.codefinity.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv'
weather_df = pd.read_csv(url, index_col=0)

# Plotting the data
plt.plot(weather_df['Boston'], label='Boston')
plt.plot(weather_df['Seattle'], label='Seattle')

# Adding a title and legend
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)

# Displaying the plot
plt.show()

Now every second year appears on the x-axis. This was achieved using the range() function with a step of 2 for the ticks argument.

Additionally, the tick labels were rotated 30 degrees counterclockwise using the rotation keyword for improved readability.

Tick labels can also be manually specified by passing a list to the labels argument (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).


              123456789101112131415161718
            
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)

# Plotting the data
plt.plot(weather_df['Boston'], label='Boston')
plt.plot(weather_df['Seattle'], label='Seattle')

# Adding title, legend, and labels
plt.title('Boston and Seattle average yearly temperatures')
plt.legend(loc='upper left')
plt.xticks(range(1995, 2021, 2), rotation=30)
plt.ylabel('Temperature, °F')

# Displaying the plot
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'.

Study More

You can explore more in the documentation: xlabel() and 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