Contenido del Curso

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

Customizing Grid

Another important part of the customization is grid customization. pyplot module has a grid() function for this purpose.

Visibility and Axes

Its first parameter visible specifies whether to show the grid lines (by default, they are not shown).

axis parameter specifies whether to apply the customization to the grid lines parallel to the x-axis (set to 'y'), to the y-axis (set to 'x') or both of them (set to 'both'), which is the default value. Let’s clarify all of this with an example:


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import matplotlib.pyplot as plt
import numpy as np
data_linear = np.arange(0, 11)
data_squared = data_linear ** 2
plt.plot(data_linear, label='linear function', color='red', alpha=0.5)
plt.plot(data_squared, '-o', label='quadratic function', color='blue')
plt.xticks(data_linear)
plt.xlabel('x', loc='right')
plt.ylabel('y', loc='top', rotation=0)
# Setting the horizontal grid lines to be visible
plt.grid(True, axis='x')
plt.legend()
plt.show()

In this example we set visible to True, however, since we also set axis='x', it only applies to the grid lines parallel to the y-axis. Hence why there are only vertical grid lines shown, which makes the plot more detailed, yet it is not cluttered with perpendicular grid lines, which are not necessary here.

Color and Transparency

It is also possible to change the color of the grid lines using the color parameter and their transparency using the alpha parameter. Both of these parameters are already familiar to us, so let’s see them in action:


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import matplotlib.pyplot as plt
import numpy as np
data_linear = np.arange(0, 11)
data_squared = data_linear ** 2
plt.plot(data_linear, label='linear function', color='red', alpha=0.5)
plt.plot(data_squared, '-o', label='quadratic function', color='blue')
plt.xticks(data_linear)
plt.xlabel('x', loc='right')
plt.ylabel('y', loc='top', rotation=0)
# Customizing the horizontal grid lines
plt.grid(True, axis='x', alpha=0.2, color='black')
plt.legend()
plt.show()

Now our grid lines are black (color='black') and are more transparent (alpha=0.2) which makes the plot look even better.

There are still more possible parameters for the grid() functions (they are not used so often), so here is its documentation in case you want to explore more.

Tarea

Use the appropriate function to customize the grid lines.
Make the grid lines visible via correctly specifying the leftmost argument.
Apply the customization only to the grid lines parallel to the x-axis via specifying the second parameter.
Set the color of the grid lines to 'slategrey' via specifying the third parameter.
Set the transparency of the grid lines to 0.5 via specifying the rightmost parameter.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 3. Capítulo 5

Customizing Grid

Another important part of the customization is grid customization. pyplot module has a grid() function for this purpose.

Visibility and Axes

Its first parameter visible specifies whether to show the grid lines (by default, they are not shown).


              12345678910111213
            
import matplotlib.pyplot as plt
import numpy as np
data_linear = np.arange(0, 11)
data_squared = data_linear ** 2
plt.plot(data_linear, label='linear function', color='red', alpha=0.5)
plt.plot(data_squared, '-o', label='quadratic function', color='blue')
plt.xticks(data_linear)
plt.xlabel('x', loc='right')
plt.ylabel('y', loc='top', rotation=0)
# Setting the horizontal grid lines to be visible
plt.grid(True, axis='x')
plt.legend()
plt.show()

Color and Transparency


              12345678910111213
            
import matplotlib.pyplot as plt
import numpy as np
data_linear = np.arange(0, 11)
data_squared = data_linear ** 2
plt.plot(data_linear, label='linear function', color='red', alpha=0.5)
plt.plot(data_squared, '-o', label='quadratic function', color='blue')
plt.xticks(data_linear)
plt.xlabel('x', loc='right')
plt.ylabel('y', loc='top', rotation=0)
# Customizing the horizontal grid lines
plt.grid(True, axis='x', alpha=0.2, color='black')
plt.legend()
plt.show()

Now our grid lines are black (color='black') and are more transparent (alpha=0.2) which makes the plot look even better.

There are still more possible parameters for the grid() functions (they are not used so often), so here is its documentation in case you want to explore more.

Tarea

Use the appropriate function to customize the grid lines.
Make the grid lines visible via correctly specifying the leftmost argument.
Apply the customization only to the grid lines parallel to the x-axis via specifying the second parameter.
Set the color of the grid lines to 'slategrey' via specifying the third parameter.
Set the transparency of the grid lines to 0.5 via specifying the rightmost parameter.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 3. Capítulo 5

Customizing Grid

Another important part of the customization is grid customization. pyplot module has a grid() function for this purpose.

Visibility and Axes

Its first parameter visible specifies whether to show the grid lines (by default, they are not shown).


              12345678910111213
            
import matplotlib.pyplot as plt
import numpy as np
data_linear = np.arange(0, 11)
data_squared = data_linear ** 2
plt.plot(data_linear, label='linear function', color='red', alpha=0.5)
plt.plot(data_squared, '-o', label='quadratic function', color='blue')
plt.xticks(data_linear)
plt.xlabel('x', loc='right')
plt.ylabel('y', loc='top', rotation=0)
# Setting the horizontal grid lines to be visible
plt.grid(True, axis='x')
plt.legend()
plt.show()

Color and Transparency


              12345678910111213
            
import matplotlib.pyplot as plt
import numpy as np
data_linear = np.arange(0, 11)
data_squared = data_linear ** 2
plt.plot(data_linear, label='linear function', color='red', alpha=0.5)
plt.plot(data_squared, '-o', label='quadratic function', color='blue')
plt.xticks(data_linear)
plt.xlabel('x', loc='right')
plt.ylabel('y', loc='top', rotation=0)
# Customizing the horizontal grid lines
plt.grid(True, axis='x', alpha=0.2, color='black')
plt.legend()
plt.show()

Now our grid lines are black (color='black') and are more transparent (alpha=0.2) which makes the plot look even better.

There are still more possible parameters for the grid() functions (they are not used so often), so here is its documentation in case you want to explore more.

Tarea

Use the appropriate function to customize the grid lines.
Make the grid lines visible via correctly specifying the leftmost argument.
Apply the customization only to the grid lines parallel to the x-axis via specifying the second parameter.
Set the color of the grid lines to 'slategrey' via specifying the third parameter.
Set the transparency of the grid lines to 0.5 via specifying the rightmost parameter.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Another important part of the customization is grid customization. pyplot module has a grid() function for this purpose.

Visibility and Axes

Its first parameter visible specifies whether to show the grid lines (by default, they are not shown).


              12345678910111213
            
import matplotlib.pyplot as plt
import numpy as np
data_linear = np.arange(0, 11)
data_squared = data_linear ** 2
plt.plot(data_linear, label='linear function', color='red', alpha=0.5)
plt.plot(data_squared, '-o', label='quadratic function', color='blue')
plt.xticks(data_linear)
plt.xlabel('x', loc='right')
plt.ylabel('y', loc='top', rotation=0)
# Setting the horizontal grid lines to be visible
plt.grid(True, axis='x')
plt.legend()
plt.show()

Color and Transparency


              12345678910111213
            
import matplotlib.pyplot as plt
import numpy as np
data_linear = np.arange(0, 11)
data_squared = data_linear ** 2
plt.plot(data_linear, label='linear function', color='red', alpha=0.5)
plt.plot(data_squared, '-o', label='quadratic function', color='blue')
plt.xticks(data_linear)
plt.xlabel('x', loc='right')
plt.ylabel('y', loc='top', rotation=0)
# Customizing the horizontal grid lines
plt.grid(True, axis='x', alpha=0.2, color='black')
plt.legend()
plt.show()

Now our grid lines are black (color='black') and are more transparent (alpha=0.2) which makes the plot look even better.

There are still more possible parameters for the grid() functions (they are not used so often), so here is its documentation in case you want to explore more.

Tarea

Use the appropriate function to customize the grid lines.
Make the grid lines visible via correctly specifying the leftmost argument.
Apply the customization only to the grid lines parallel to the x-axis via specifying the second parameter.
Set the color of the grid lines to 'slategrey' via specifying the third parameter.
Set the transparency of the grid lines to 0.5 via specifying the rightmost parameter.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

Sección 3. Capítulo 5

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones