Скачать или смотреть How to Combine Two JSON Fields into a List of Floats in Python with Negatives

Learn how to effectively merge two fields from a JSON file into a list of floats using Python, ensuring that one field is converted to negative values while the other remains positive.
---
This video is based on the question https://stackoverflow.com/q/63968256/ asked by the user 'Swag Mand' ( https://stackoverflow.com/u/14304868/ ) and on the answer https://stackoverflow.com/a/63968393/ provided by the user 'AbbasEbadian' ( https://stackoverflow.com/u/6899925/ ) 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: Add two Fields from json File into one list, turn both into floats one of the Fields into a negative one

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 Combine Two JSON Fields into a List of Floats in Python with Negatives

In programming, particularly when dealing with data processing in Python, you may encounter scenarios where you need to combine data fields from a JSON file into a single list. This task often becomes complex, especially if you want to modify the values, such as converting one field into negative numbers. In this guide, we will look at a real problem and break down a solution step by step.

The Problem

Suppose you have a JSON file containing financial transactions which record amounts in two separate fields, FIELD7 and FIELD8. The challenge is to combine these two fields into a single list while ensuring that:

Values from FIELD7 are converted to negative floats.

Values from FIELD8 are converted to positive floats.

Example JSON Data

To put this into context, consider the following JSON file data:

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

Current Output

When running an existing function on this data, you might receive incorrect output such as:

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

Wanted Output

The correct output you want to achieve is:

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

The Solution

The core issue lies in how we are attempting to retrieve and manipulate the values in fields FIELD7 and FIELD8. Let’s break down the solution step by step.

1. Understanding Data Retrieval

The following line from the initial implementation was causing the problems:

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

This line creates a tuple instead of retrieving values separately, which leads to unexpected outputs. To solve this, we need to revise the approach.

2. Correcting the Code

Let’s rewrite the problematic line to achieve the desired behavior:

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

3. Explanation of Changes

Condition Checking: The new line checks whether FIELD7 has a value. If it does, it casts that value to a float directly.

Negative Conversion: If FIELD7 is empty, it takes the value from FIELD8, converting it to a negative float.

Dot Replacement: The replacement step for commas ensures that any numeric formatting issues are rectified.

4. Complete Function

Here’s how the complete function should look like:

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

5. Expected Result

Upon executing the revised function, your output should now correctly reflect the desired result:

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

Conclusion

By carefully manipulating the structure of how we access and convert the fields in the JSON data, we can achieve a clean and functional outcome. This result not only meets the initial requirements, but it also opens up doors for further data processing tasks in Python.

We hope this breakdown helps you handle similar data processing tasks efficiently in your projects! Happy coding!