Скачать или смотреть How to Solve Date Conversion Issues When Using excel2json in Python

Learn how to effectively convert Excel date formats to JSON using `excel2json`. This guide helps you tackle the common issue of dates appearing as random numbers in your JSON output!
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Solving Date Conversion Issues in JSON Output with excel2json

When working with data in Excel, converting your spreadsheets into JSON can streamline data processing and analysis. However, one common pitfall many users encounter is the way excel2json handles date formats. Instead of the expected date string, you might see seemingly random numbers when converting the dates during the Excel to JSON transformation. In this post, we’ll explore why this happens and how to properly address it so you can retain meaningful date representations in your JSON files.

Understanding the Problem

The Scenario

Suppose you have an Excel sheet with the following three columns:

Column 1Column 2Column 31BOB1/1/20202SALLY1/4/2020When you try to convert this .xls file to JSON using the excel2json library, here's the basic code you might use:

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

The Issue

While the conversion works well for numeric and text data, you may find that the dates (in Column 3) appear in the JSON output as random numbers instead of their formatted string representation. For instance, you might see something like this:

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

So, where are these numbers coming from, and how do you fix this problem?

Why Are Dates Displayed as Numbers?

Excel stores dates as serial numbers (often referred to as ordinal representation). For example, the date 1/1/2020 corresponds to a serial number like 43831, which counts the days since a specific baseline date (i.e., January 1, 1900). To convert these ordinal date representations into human-readable strings, you'll need to apply some additional processing to your JSON output.

The Solution: Converting Excel Dates to JSON

Step-by-Step Guide to Retaining Date Formats

To effectively handle the date conversion, follow these steps:

Import Required Module: First, you'll need to import the datetime module in Python.

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

Convert Excel Date to Standard Format: You can convert the ordinal date into a Python datetime object, and then format it as a string. Here’s the code snippet for this conversion process:

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

Create a Conversion Function: It’s a good idea to encapsulate this logic in a function that can map through the list of your ordinal dates:

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

Final Thoughts

By following the steps above, you can ensure your JSON data retains the correct date formats, making it much more human-readable and useful for data analysis. Remember, processing Excel dates can be tricky due to their internal representation, but with this approach, you'll be able to handle it confidently. Now you're ready to convert those Excel dates effectively!

Whether you're preparing data for analysis or sharing it with others, ensuring the dates are handled properly can save a lot of confusion down the line. Happy coding!