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Resolving pandas to_sql Creates Empty Tables Issue in MySQL

Are you struggling with `pandas to_sql` creating empty tables in MySQL? Discover the reasons behind this issue and the simple fix to ensure your data frames are correctly inserted into your MySQL database! --- This video is based on the question https://stackoverflow.com/q/75749226/ asked by the user 'the_dude' ( https://stackoverflow.com/u/7182511/ ) and on the answer https://stackoverflow.com/a/75782096/ provided by the user 'bodyCoder99' ( https://stackoverflow.com/u/21157232/ ) 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: pandas to_sql creates empty tables, can't replace tables 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 Fix pandas to_sql Creating Empty Tables in MySQL If you’re utilizing Python's pandas library to write data to a MySQL database but are encountering an issue where the tables are created empty or cannot be replaced, you’re not alone! This common problem can stem from a few key areas. Below, we'll explore the root cause and provide a straightforward solution to ensure that your data is correctly inserted into your database. Understanding the Problem You've set up your code to transfer a DataFrame into a MySQL table, but upon executing it, you realize the resulting table is empty. A further attempt to replace the table results in the process getting stuck due to a metadata lock. The SQL output reflects an empty set and a waiting state, which can be frustrating and confusing, especially for those newer to using MySQL with pandas. Example Scenario Here’s a snippet of the code that you might be using: [[See Video to Reveal this Text or Code Snippet]] Running this code results in an empty table, and upon querying it with SELECT * FROM name;, you find no data. Re-running the code may even get your database stuck in a waiting state, hindering future operations. Root Cause of the Issue The underlying problem occurs because the data types of your DataFrame need to be properly defined in the to_sql function. By default, pandas may not correctly infer the SQL data types that correspond to your DataFrame's structure, leading to empty inserts. Solution: Defining Data Types Explicitly To resolve this issue, you'll want to specify the data types of your DataFrame explicitly using the dtype parameter in the to_sql() method. Here’s a breakdown of how you can accomplish this: Step-by-Step Fix Import the Required Types from SQLAlchemy: You need to ensure that you have the necessary SQL data types available through SQLAlchemy. Install SQLAlchemy types module if you don’t have it: [[See Video to Reveal this Text or Code Snippet]] Define the Data Types: You can create a dictionary where the keys are the column names and the values are the corresponding SQL data types. For example, if your DataFrame consists of integers, define your data type as so: [[See Video to Reveal this Text or Code Snippet]] Modify the to_sql call: Pass the dtype dictionary as an argument in the to_sql() method: [[See Video to Reveal this Text or Code Snippet]] Final Resulting Code Here’s how the final code should look after integrating the data type specification: [[See Video to Reveal this Text or Code Snippet]] Once these adjustments are made, executing your code should successfully populate your MySQL table with the intended data. Conclusion Dealing with empty tables when using pandas to_sql can be a smooth process once you understand the importance of defining data types. By implementing the solutions outlined above, you'll save time and frustration in your data management tasks. Embrace these tips for a better experience managing your data in MySQL through pandas!