Python & SQL: A Match Made in Data Manipulation Heaven
Published: 2023-05-19 23:51:02

There are several reasons that Python’s popularity has taken off in recent years: it’s simple, readable, and exceptionally versatile. While it wasn’t necessarily created with data management in mind, its extensive library and colossal ecosystem of third-party packages and frameworks have made it an invaluable asset when it comes to data analysis, manipulation and visualization.

SQL, on the other hand, is already operating within its wheelhouse when utilized for aforementioned tasks, as was intended when designed by IBM in the early 1970s. While its early versions focused almost solely on data querying and retrieval, over time it evolved and expanded into the robust database management and manipulation system it’s known for today. However, just because databases are its specialty doesn’t mean it’s without its weaknesses: it isn’t particularly adept at working with external databases nor producing the most effective data visualization, for instance — which is where Python can complement SQL beautifully.

Python, on the other hand, falls rather short when compared to SQL’s ability to manage large datasets and perform complex operations like data retrieval, filtering, indexing, aggregation, etc. Frankly, one could spend days comparing and contrasting the two languages’ strengths and weaknesses which seems like a task both futile and frivolous when the two can simply be paired together to accomplish virtually any and all data-related tasks a programmer (or business) could need. 

In fact, by pairing one of Python’s libraries (like SQLAlchemy or psycopg2, for instance) with SQL’s querying abilities, users are able to write SQL statements within Python code, execute them, and retrieve and process Python objects as results with an ease and grace that I would have dreamt of having at my disposal when previously working in non-profit data evaluation.

Further, the two languages build data pipelines and perform Extract, Transform, Load (ETL) processes beautifully. While Python can take the helm on extracting data from a multitude of sources, perform complex transformations and prepare such data for loading into a database, SQL queries are able to perform further transformations within the databases themselves, which optimizes performance while reducing the net back-and-forth data transfers can require.

Finally, integrating SQL’s data retrieval capabilities with Python’s rich visualization libraries allows one to build informative and visually-appealing charts, plots and reports like never before. There are several Python libraries, like Matplotlib, Plotly or Bokeh that serve as supremely effective tools to chart, plot or visualize data, which can then be paired with other libraries that can provide customization options for colors, labels, axis formatting and other visual elements that can provide an exceptionally appealing visual presentation of data. Further, these visualizations can be exported in several different formats — including image or interactive files — which can then be shared with other and/or embedded in web applications or reports. Other Python frameworks like Flask or Django are indispensable when it comes to building interactive web applications to drive home the importance of particular data statistics.

Ultimately, by pairing Python’s strengths in analytics, external data integrations and data visualization capabilities with SQL’s exceptionally efficient database operations, integrity and scalability both languages are able to overcome their own inherent weaknesses to create impactful, and insightful representations of data that all stakeholders are bound to appreciate when making data-driven decisions.