Manually opening and closing cursors? Iterating through DB output by hand? Remembering which function is the actual one that matches the Python data structure you're gonna be using?
There has to be a better way!
There totally is.
One of Pandas' most useful abilities is easy I/O. Whether it's a CSV, JSON, an Excel file, or a database -
In one corner we have Pandas: Python's beloved data analysis library. In the other, AWS: the unstoppable cloud provider we're obligated to use for all eternity. We should have know this day would come.
While not the prettiest workflow, uploaded Python package dependencies for usage in AWS Lambda is typically straightforward. We install the packages locally to a virtual env,
You've heard the cliché before: it is often cited that roughly %80~ of a data scientist's role is dedicated to cleaning data sets. I Personally haven't looked in to the papers or clinical trials which prove this number (that was a joke), but the idea holds true: in the data profession, we find ourselves doing away with blatantly corrupt or
Let's say you have two obscenely large sets of data.
These sets of data contain information on a similar topic, such as customers. Dataset #1 might contain a high-level view of all customers of a business, while Datatset #2 contains a lifetime history of orders for a company. Unsurprisingly, the customers in Dataset #1 appear in
Let’s face it: the last thing the world needs is another “Intro to Pandas” post. Anybody strange enough to read this blog surely had the same reaction to discovering Pandas as I did: a manic euphoria that can only be described as love at first sight. We wanted to tell the world, and that we did. A lot. Yet
