Converting JSONs to Pandas DataFrames: Parsing Them the Right Way

By neub9
3 Min Read

Discover the world of data science and machine learning, where one of the core skills is learning to read and manipulate data. If you have experience in this field, you are likely familiar with JSON (JavaScript Object Notation) – a popular format for data storage and exchange. JSON is commonly used in NoSQL databases like MongoDB and REST APIs.

However, JSON isn’t ideal for in-depth analysis in its raw form. To make it more analytically friendly, we can transform it into a tabular format. In Python terms, working with JSON data means we’re essentially handling dictionaries or lists of dictionaries.

Enter pd.json_normalize() – the magic command that allows us to easily parse any JSON into a tabular format in seconds. This function is useful when dealing with single-leveled JSON, JSON with missing values, and nested JSON.

When transforming simple JSON structures into Pandas DataFrames, the pd.json_normalize() command does the heavy lifting. In cases where some values are missing, the corresponding value in the DataFrame appears as ‘NaN’. If you want to select specific fields for transformation, a small preprocessing of the JSON is required to filter only those columns of interest.

Dealing with multiple-leveled JSON is also manageable with pd.json_normalize(). It allows us to choose how many levels to transform. Additionally, we can specifically define the max_level parameter if we only want to transform the top level of the JSON.

Finally, handling nested lists within a JSON field is achievable using Pandas in Python. The pd.json_normalize() function flattens the JSON data, including the nested list, into a structured format suitable for analysis.

Overall, the transformation of JSON data into CSV files using Python’s Pandas library is straightforward and effective. pd.json_normalize() plays a pivotal role in handling and converting JSON data into pandas DataFrame for better analysis. I hope this guide has been helpful, and that you can now more effectively work with JSON data in the future.

For more practical examples and details, you can check the corresponding Jupyter Notebook in my GitHub repository.

About the Author:
Josep Ferrer is an analytics engineer from Barcelona with a background in physics engineering. He specializes in the field of Data Science applied to human mobility and is a part-time content creator focused on data science and technology. Connect with him on LinkedIn, Twitter, or Medium.

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