This text discusses converting a government PDF into a financial planning tool using treemaps, Python, Plotly Express, and tabula-py. It outlines the process of extracting data from a Bureau of Labor Statistics PDF, cleaning it, and creating treemaps to visualize expenditure data for different age brackets. The article emphasizes the utility of treemaps for visualizing and communicating hierarchical data.
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Turn a government PDF into a financial planning tool
Hierarchical data and Treemaps
Hierarchical data is a data model where items are linked to each other in parent-child relationships, forming a tree structure. Treemaps are diagrams that represent hierarchical data using nested rectangles, with the area of each rectangle corresponding to its numerical value. Treemaps are useful for displaying hierarchical data, highlighting proportions, distinguishing categories, spotting patterns, and bringing a fresh look to data visualization.
Consumer Expenditure Surveys
With the pandemic and subsequent inflationary surge, consumer spending has drawn a lot of attention. To help track income and spending, the Census Bureau uses Consumer Expenditure Surveys to collect information on US consumers’ expenditures, income, and demographic characteristics. The Bureau of Labor Statistics (BLS) compiles these statistics into annual reports published in September.
The Plotly Express and Tabula Libraries
Plotly Express is a higher-level version of the Plotly graphing library, allowing easy production of attractive figures with built-in functionality. Tabula-py is a Python wrapper of tabula-java, enabling table extraction from a PDF.
The Process
Our job will be to convert the BLS “Table 1300” PDF into a pandas DataFrame and then into a treemap. We’ll use tabula-py to read the PDF from the web and convert it to a CSV, then pandas to convert the CSV to a DataFrame. We’ll clean and prepare the DataFrame for plotting and use Plotly Express to create the treemap.
Importing Libraries
We’ll import the necessary libraries and set up Jupyter Notebook to show the maximum rows in a DataFrame and round float values in DataFrames to two decimal places.
Converting the PDF Table into a DataFrame
We’ll use tabula-py to convert the web-based PDF into a CSV file and then use pandas to convert the CSV file into a DataFrame.
Wrangling the Data
We’ll clean the DataFrame and prepare it for plotting by extracting and organizing the necessary data.
Plotting the Treemap
We’ll use Plotly Express to create an interactive treemap for visualizing the average expenditures for different age brackets.
Caveat Emptor!
The values in the BLS table represent nationwide averages, and local variations may occur. Therefore, this data should be used directionally, rather than absolutely, for any financial planning purposes.
Summary
The tabula-py module greatly simplifies the process of converting PDF tables into useful formats like CSV. Likewise, Plotly Express, paired with a pandas DataFrame, makes it easy to generate interactive treemaps. With a treemap, you can easily visualize and communicate relationships in hierarchical data.
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