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Скачать или смотреть How to Efficiently Read a txt File with JSON Lines in Pandas

  • vlogize
  • 2025-08-22
  • 0
How to Efficiently Read a txt File with JSON Lines in Pandas
In pandas how do I open a txt file where each line is formatted like a json object?pythonpandas
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Описание к видео How to Efficiently Read a txt File with JSON Lines in Pandas

Discover how to open a text file formatted as JSON objects in Pandas and convert it into a structured DataFrame with columns and rows.
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This video is based on the question https://stackoverflow.com/q/64112700/ asked by the user 'SantoshGupta7' ( https://stackoverflow.com/u/3259896/ ) and on the answer https://stackoverflow.com/a/64112757/ provided by the user 'Dishin H Goyani' ( https://stackoverflow.com/u/6075699/ ) 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: In pandas, how do I open a txt file where each line is formatted like a json object?

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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.

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How to Efficiently Read a txt File with JSON Lines in Pandas

When working with data in Python, especially using the Pandas library, you may encounter text files where each line is structured like a JSON object. This can present a unique challenge when you want to transform that data into a Pandas DataFrame. In this guide, we'll explore how to read a text file in Pandas that contains lines formatted as JSON, and convert it into a more manageable DataFrame format.

The Problem

You have a text file (let’s call it data.txt) that looks like this:

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

What You Want

Your goal is to read this file into a Pandas DataFrame such that the keys become the column headers, and the values for each line become the corresponding rows. The desired output in the DataFrame should look like this:

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

However, using the standard pd.read_json method with the various orientations often leads to a ValueError: Trailing data, making it difficult to achieve the desired format.

The Solution

To correctly read a JSON formatted text file where each line is a separate JSON object, you can utilize the pd.read_json() function with specific parameters that allow reading JSON objects line by line.

Step-by-Step Guide

Import Pandas:
Ensure you have the Pandas library installed and import it in your Python script.

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

Use pd.read_json:
You will use the pd.read_json method with the lines=True parameter. This tells Pandas to treat each line of the text file as a separate JSON object.

Here’s how to do it:

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

Check Your DataFrame:
Once you execute the above code, you can print the DataFrame to check if it has been read as expected:

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

The output should show a DataFrame structured like this:

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

Key Parameters Explained

orient='records': This specifies the JSON string format to interpret. The records orientation signifies that each line should be treated as a record (a separate JSON object).

lines=True: This parameter is crucial as it allows pd.read_json to parse the file line by line instead of attempting to read it all as a single JSON object.

Conclusion

Reading a text file formatted as JSON objects in Pandas is straightforward with the right approach. By applying the correct parameters to the pd.read_json() function, you can efficiently convert each line into rows within a DataFrame.

Now you're equipped to handle similar data formats, making data manipulation and analysis more streamlined and effective. Don't forget to explore other functionalities of Pandas to enhance your data handling capabilities!

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