![]() In the dataset above, each row represents a country, and each column represents some fact about that country.īut as the amount of data we capture increases, we often don’t know the exact structure of the data at the time we store it. When data is stored in SQL databases, it tends to follow a rigid structure that looks like a table. We’ll start with a look at the JSON data, then segue into exploration and analysis of the JSON with Python. In this post, focused on learning python programming, we’ll look at how to leverage tools like Pandas to explore and map out police activity in Montgomery County, Maryland. ![]() In cases like this, a combination of command line tools and Python can make for an efficient way to explore and analyze the data.
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