Reading and Writing JSON Files with Python and Pandas: A Complete Tutorial

In this article we learn how to read and write JSON files with Python. First we use the built‑in json module to create a JSON file from a Python dictionary and write it to disk.

JSON (JavaScript Object Notation) is a compact, text‑based data‑exchange format that closely resembles Python dictionaries, making it easy to convert between the two.

Using the json module, we open a new file and call json.dump() to write the dictionary as JSON. The same module can later read the file back with json.load(), converting the JSON text into native Python objects.

Next we load the JSON file into a Pandas dataframe with pd.read_json('data.json'). This allows us to use Pandas' powerful data‑manipulation methods for summarisation and visualisation.

After loading, we demonstrate how to set a column as the index, rename columns using df.rename() combined with regular expressions from the re module, and replace characters to create cleaner column names.

We also show how to save the dataframe to CSV with df.to_csv('output.csv'), which is useful for inspecting data in text editors or Excel.

For JSON hosted on the web, we read it directly from a URL using pd.read_json(url). When the JSON is nested, we can first fetch it with the requests library, then normalise it with json_normalize() to obtain a flat dataframe.

Finally, we visualise the data with Seaborn. Using sns.lineplot() we plot a time‑series of cancelled flights from 2003 to 2016, grouping by airline code, and customise axis labels and legend placement.

In conclusion, the article covered writing a Python dictionary to a JSON file, loading JSON with both the json module and Pandas, reading JSON from local files and URLs, handling nested structures, renaming columns, exporting to CSV, and visualising the results with Seaborn.