Adding Title to a Pandas DataFrame Table
Are you creating a table using Pandas DataFrame and wondering how to add a title to it? Well, the good news is that it is an easy feat to achieve.
In this article, we will look at how to add a title to a Pandas DataFrame table using the set_title() function in the Matplotlib library. We will also provide a simple example of implementing this process to aid your understanding.
Functionality of set_title() in Matplotlib
Matplotlib is a popular plotting library in Python designed to create high-quality visualizations. One of the features of this library is set_title(), which lets you add a title to a plot.
A plot may contain one or more axes, and set_title() can be used on any axes within the plot. The set_title() function takes a title string as an argument and can also have additional optional arguments such as fontdict and loc to customize the title’s appearance and position, respectively.
Example of Adding a Title to a Pandas DataFrame Table using set_title()
Suppose we have a Pandas DataFrame containing basketball teams’ data on points and assists in different seasons. Our goal is to create a table that displays this data and add a title to it.
Creating a Pandas DataFrame with Points and Assists Data for Basketball Teams
To create a DataFrame with basketball teams’ data on points and assists, we can use the following code:
import pandas as pd
data = {'Team':['LA Lakers', 'Golden State Warriors', 'Houston Rockets', 'Phoenix Suns', 'Milwaukee Bucks'],
'Points in 2019-20 Season':[114.3, 105.7, 117.8, 113.6, 118.7],
'Assists in 2019-20 Season':[25.4, 27.4, 21.5, 27.2, 25.9]}
basketball_df = pd.DataFrame(data)
print(basketball_df)Output:
Team Points in 2019-20 Season Assists in 2019-20 Season
0 LA Lakers 114.3 25.4
1 Golden State Warriors 105.7 27.4
2 Houston Rockets 117.8 21.5
3 Phoenix Suns 113.6 27.2
4 Milwaukee Bucks 118.7 25.9Creating a Table in Matplotlib to Display the Data from the Pandas DataFrame
Next, we will create a table in Matplotlib to display the basketball teams’ data from the Pandas DataFrame. We will use the table() function in Matplotlib, which takes two arguments: cellText and colLabels.
cellText contains the data to be displayed in the table, while colLabels contains the column headers. import matplotlib.pyplot as plt
fig, ax = plt.subplots()
table_data = [basketball_df.columns.to_list()] + basketball_df.values.tolist()
ax.axis('off')
table = ax.table(cellText=table_data, colLabels=None, cellLoc='center', loc='center')
table.set_fontsize(14)
table.scale(1, 1.5)
plt.show()Output:
TABLE IMAGE TO BE ADDED HERE
Using set_title() to Specify a Title for the Table Created from the Pandas DataFrame
Finally, we will use set_title() to specify a title for the table created from the Pandas DataFrame. We will use the ax object from the previous code snippet and call the set_title() function.
Suppose we want to set the title as “Points and Assists Data for Basketball Teams.” The code for this would be:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
table_data = [basketball_df.columns.to_list()] + basketball_df.values.tolist()
ax.axis('off')
table = ax.table(cellText=table_data, colLabels=None, cellLoc='center', loc='center')
table.set_fontsize(14)
table.scale(1, 1.5)
ax.set_title('Points and Assists Data for Basketball Teams', fontdict={'fontsize':18, 'fontweight':'bold'}, loc='center')
plt.show()Output:
TABLE IMAGE TO BE ADDED HERE
Modifying the Title Font and Location using fontdict and loc Arguments
In the previous code snippet, we have modified the title’s font and location using the fontdict and loc arguments of set_title(). The fontdict argument lets us pass a dictionary of properties such as fontsize, fontweight, fontfamily, etc., that apply to the title’s font.
Here, we have set the title’s fontsize to 18 and the fontweight to bold using the fontdict={'fontsize':18, 'fontweight':'bold'}. The loc argument specifies the title’s location within the plot axes, and the value ‘center’ centers the title horizontally in the axes.
We have also used the same argument to center the table within the plot axes.
Conclusion
Adding a title to a Pandas DataFrame table is essential to provide context and an overview of the data being displayed. In this article, we have learned how to add a table title using set_title() in Matplotlib.
We have also provided a simple example of implementing this process to aid the reader’s understanding. With this knowledge, you can now create beautiful and informative tables for your data analysis projects.
In this addition, we will provide additional resources to further enhance your knowledge of adding a title to a Pandas DataFrame table using the set_title() function in Matplotlib. We will provide a link to the complete documentation for the table() function in Matplotlib, and also reference the Matplotlib documentation for modifying the appearance of the title.
Link to the Complete Documentation for the table() Function in Matplotlib
The table() function in Matplotlib is a powerful tool for creating tables from Pandas DataFrames. While we have briefly covered its usage in this article, the documentation provides more in-depth information on the function’s parameters and how to customize the table’s appearance.
You can find the complete documentation for the table() function in Matplotlib here: https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.table.html. The documentation covers the various parameters of the table() function, such as cellText, cellColours, and cellLoc, and how to use them to customize the table.
It also includes examples of creating tables with different styles and characteristics, such as multi-colored tables and tables with merged cells. Additionally, the documentation provides information on how to add row and column headers to the table and how to format them.
You can also find detailed information on how to specify the column widths and individual cell sizes for the table.
Reference to the Matplotlib Documentation for Modifying the Appearance of the Title
Modifying the appearance of the title adds visual appeal and can make the table more informative and appealing. In our example, we used the fontdict and loc arguments to modify the appearance of the title.
However, the Matplotlib documentation provides several other options for modifying the appearance of the title in a plot. The documentation provides details on various attributes such as color, font family, font size, and font weight, and how to use them to customize the title’s appearance.
You can find the documentation on modifying the appearance of the title in Matplotlib here: https://matplotlib.org/stable/tutorials/text/text_props.html. The documentation explains how to use the set_fontsize() function to modify the title’s font size and the set_color() function to modify its color.
You can also use the set_weight() function to adjust the thickness and boldness of the title’s font. In addition to this, the documentation provides details on how to use the set_fontfamily() function to modify the font family of the title, such as Times New Roman or Arial.
Furthermore, the Matplotlib documentation provides other helpful tips on how to modify the text appearance within a plot, such as aligning text, wrapping text, and making text bold or italicized.
Conclusion
In conclusion, the Matplotlib documentation provides comprehensive information on the table() function and how to customize the appearance of tables created from Pandas DataFrames. It also provides details on how to modify the appearance of plot titles and other text in a plot.
With these additional resources, you can further enhance your knowledge of adding a title to a Pandas DataFrame table and customize your plots to make them more informative and visually appealing. In summary, adding a title to a Pandas DataFrame table is essential to provide context and an overview of the data being presented.
This article has provided a simple example of how to add a table title using set_title() in Matplotlib and also included additional resources, such as the complete documentation for the table() function in Matplotlib and reference to the Matplotlib documentation for modifying the appearance of the title. With this knowledge, you can easily create beautiful and informative tables for your data analysis projects.
Always remember to customize the appearance of your tables to make them more visually appealing and memorable.