Скачать или смотреть How to Parse a JSON with No Keys in Python using Pandas

Learn how to effectively parse a JSON without keys in Python and transform it into a structured dataframe using Pandas.
---
This video is based on the question https://stackoverflow.com/q/75442653/ asked by the user 'user3362705' ( https://stackoverflow.com/u/3362705/ ) and on the answer https://stackoverflow.com/a/75442762/ provided by the user 'Tim Roberts' ( https://stackoverflow.com/u/1883316/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: python parse a json with no keys

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
How to Parse a JSON with No Keys in Python using Pandas

Working with JSON data in Python can sometimes feel daunting, especially when your JSON structure lacks keys or is nested in complex ways. Recently, I encountered a situation where I needed to parse an unusual JSON file and transform it into a usable Pandas dataframe. This experience inspired me to share the process step-by-step so that you, too, can make sense of similar data challenges.

The Problem

I received a JSON file that contained a series of records without typical key-value layouts. The challenge was to extract all necessary information and format it correctly in a Pandas dataframe. Below is the JSON structure I was dealing with:

[[See Video to Reveal this Text or Code Snippet]]

The desired output for the dataframe should look like the table below:

idtimeauthornewoldpropertysender01mSeHpsjSTHuHSGhpCj1675348581375sync11991903358nullhs_object_idhs_sync01mSeHpsjSTHuHSGhpCj1675348610656syncdailycohort__child_1_hs_sync02b85apv47W1PRHFCXDM1662788673128syncnullnullapp_content_category_child_1_hs_sync02b85apv47W1PRHFCXDM1662788673129syncnullnullapp_content_category_child_2_hs_syncUnfortunately, attempts to use the json_normalize function did not yield the results I expected, as the keys were lost and data was being dumped into a single column of the dataframe. So, how could I fix this?

The Solution

The key to successfully parsing this JSON structure is to manually iterate through its nested keys and gather all relevant information. Below, I provide a detailed breakdown of the code to achieve this.

Step 1: Import Necessary Libraries

You will need to import JSON and Pandas to manipulate the data efficiently.

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Load the JSON Data

Next, we define our JSON data as a string and load it using the json.loads() method.

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Prepare Data for DataFrame

We create an empty list to store each record as a dictionary. Then, we need to loop through the JSON structure to gather the data.

[[See Video to Reveal this Text or Code Snippet]]

Step 4: Create the DataFrame

Once the rows list is populated with structured data, we can easily create our dataframe.

[[See Video to Reveal this Text or Code Snippet]]

Final Output

When the above code is executed, it produces a dataframe similar to the one we desire:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

Parsing complex JSON data isn't as intimidating once you understand how to iterate through the structure and extract the necessary details. With this method, you can handle JSON files lacking intuitive key-value pairs and convert them into easily manageable dataframes using Pandas. Feel free to adapt this approach to fit your unique data needs and don't hesitate to dive deeper into JSON parsing in Python!