Mastering Database Queries with pandas read_sql and Parameters

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Summary: Unlock the power of pandas read_sql with parameters for efficient and secure database querying in Python. Learn best practices and usage examples.
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Mastering Database Queries with pandas read_sql and Parameters

Basic Syntax

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Parameterized Queries

Instead of formatting strings to include user inputs, you should use the parameters argument in read_sql. This ensures that user inputs are treated as data, not executable code.

Example with SQLite

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In the above example, the ? is a placeholder for the parameter, and param_value is a list containing the actual value. This method secures your query against SQL injection.

Example with PostgreSQL

If you are using PostgreSQL, the placeholder for parameters is %(param_name)s.

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Benefits of Using Parameters

Using parameters in read_sql offers several advantages:

Security: Prevents SQL injection attacks.

Readability: Makes the code easier to understand and maintain.

Flexibility: Allows dynamic querying based on user input or other runtime variables.

Conclusion

Happy querying!