Solving the pandas dataframe.to_sql() Issue: Working in Jupyter but Not in VSCode

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If you’re a data scientist or data engineer, using pandas to manipulate and store your data in SQL databases is probably something you do often. However, you may find yourself running into unexpected behavior, such as getting an error in VSCode that doesn’t occur in Jupyter Notebook, even when the same code is utilized. Let’s understand this common issue and how to solve it.

The Problem

A user ran a piece of code to upload a DataFrame to a SQL Server using to_sql() in a Jupyter Notebook without any issues:

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

However, when executing the same code in Visual Studio Code (VSCode), an error occurred:

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

The error message roughly translates to "precision value not valid" in English, suggesting that there’s a type mismatch between the data being inserted and the table structure. This led the user to question why it would work seamlessly on Jupyter but fail on VSCode, despite seemingly identical setups.

Analyzing the Cause

Environment Differences: The user noted that while the code before to_sql() worked fine, it could be that both environments (Jupyter and VSCode) contained different libraries or versions, which could lead to inconsistency.

Connection Strings: Both the connection string and the SQLAlchemy engine were verified to be identical, eliminating them as potential culprits.

DataFrame Characteristics: The structure and content of df_clean were checked to ensure there were no discrepancies between the two environments.

Virtual Environment: The user confirmed that the same packages were installed in the VSCode virtual environment, raising questions about whether all required dependencies were correctly managed.

The Solution

In their exploration, the user discovered a solution: adding the following parameter when creating the SQLAlchemy engine resolved the issue:

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

What This Does

use_setinputsizes=False: This parameter adjustments the way that SQLAlchemy binds parameters to Python types. Setting it to False can help alleviate type incompatibility issues that arise during the execution of the query.

Updated Code Snippet

The updated line to create the engine with the solution implemented looks like this:

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

After incorporating this change, the user was able to run their code in both Jupyter and VSCode without encountering the previous error.

Conclusion

If you encounter similar issues, make sure to check your packages, settings, and consider modifications as suggested above. Happy coding!