![]() # Use 'try-except' to skip files that may be missing data # Change the glob if you want to only look through files with specific namesįiles = glob. # Place your JSON data in a directory named 'data/' ![]() # To run the script via command line: "python3 json-to-csv-exporter.py" # Place this Python script in your working directory when you have JSON files in a subdirectory. Json-to-csv-exporter.py #!/usr/bin/env python3 I just drop it in my working directory and run it via command line with python3 json-to-csv-exporter.py: The following handy little Python 3 script is useful for sifting through a directory full of JSON files and exporting specific values to a CSV for an ad-hoc analysis. But I often end up with folders full of data that cannot really be analyzed manually: working_directoryįor example, how to compare changes in those metrics over time? Or how to look for a peak in the data? I save test results as JSON, which is fine for looking at individual snapshots at a later time. I often monitor key page speed metrics by testing web pages using WebPagetest or Google Lighthouse using their CLI or Node tools. ijson will iteratively parse the json file instead of reading it all in at once.Using Python to Read Multiple JSON Files and Export Values to a CSVĬomparing data from multiple JSON files can get unweildy – unless you leverage Python to give you the data you need. We can accomplish this using the ijson package. Instead, we’ll need to iteratively read it in in a memory-efficient way. Because we’re assuming that the JSON file won’t fit in memory, we can’t just directly read it in using the json library. Now that we know which key contains information on the columns, we need to read that information in. In this case, the columns key looks interesting, as it potentially contains information on the columns in the list of lists in the data key. This shows us the full key structure associated with md_traffic.json, and tell us which parts of the JSON file are relevant for us. Here’s an example of a list of events sent to a server: ", "-77.105925", "39.03223", null, false ] ] A good example is a list of events from visitors on a website. 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. Working with large JSON datasets can be a pain, particularly when they are too large to fit into memory.
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