Data visualization

Your next task is to create a function that visualizes the earlier created dataset as a line plot. It’s the most appropriate visualization type, as you’re dealing with time series data.

Here’s the function for data visualization and an example call:

In a nutshell — you’re creating data visualization, setting the title, playing around with fonts — nothing special. The visualization isn’t shown to the user but is instead saved to the machine. You’ll see later how powerful this can be.

And that’s your visualization function. There’s only one step remaining before you can create PDF documents, and that is to save all the visualization and define the report page structure.

Create a PDF page structure

The task now is to create a function that does the following:

• Creates a folder for charts — deletes if it exists and re-creates it
• Saves a data visualization for every month in 2020 except for January — so you can see how to work with different number of elements per page (feel free to include January too)
• Creates a PDF matrix from the visualizations — a 2-dimensional matrix where a row represents a single page in the PDF report

Here’s the code snippet for the function:

It’s possibly a lot to digest, so go over it line by line. The comments should help. The idea behind sorting is to obtain the month integer representation from the string.

Here’s an example call of the construct() function:

You should see the following in your Notebook after running the above snippet:

In case you’re wondering — here’s how the plots/ folder looks on my machine.

And that’s all you need to construct PDF reports — you’ll learn how to do that next.

Create PDF reports

This is where everything comes together. You’ll now create a custom PDF class that inherits from the FPDF. This way, all properties and methods are available in our class, if you don’t forget to call  the constructor. The constructor will also hold values for page width and height (A4 paper).

Your PDF class will have a couple of methods:

• header() – used to define the document header. A custom logo is placed on the left (make sure to have one or delete this code line), and a hardcoded text is placed on the right
• footer() – used to define the document footer. It will simply show the page number
• page_body() – used to define how the page looks like. This will depend on the number of visualizations shown per page, so positions are margins are set accordingly (feel free to play around with the values)
• print_page() – used to add a blank page and fill it with content

Here’s the entire code snippet for the class:

Now it’s time to instantiate it and to append pages from the 2-dimensional content matrix:

The above cell will take some time to execute, and will return an empty string when done. That’s expected, as your report is saved to the folder where the Notebook is stored.

Of course, yours will look different due to the different logo and due to sales data being completely random.

And that’s how you create data-visualization-powered PDF reports with Python. Let’s wrap things up next.

Conclusion

You’ve learned many things today  how to create dummy data for any occasion, how to visualize it, and how to embed visualizations into a single PDF report. Embedding your visualizations will require minimal code changes  mostly for positioning and margins.