The paper compares the management of relational databases using the Pandas library for the Python programming language with management by using the SQL programming language. For the sake of simplicity, the implementation of SQL using the SQLite library for the Python programming language was also used. The basic operations in Pandas and SQL are described and compared, along with the specific features of SQLite. Comparative examples are presented and executed in a Jupyter Notebook. The execution time of specific queries in SQLite with corresponding queries implemented in Pandas is analyzed, depending on the number of rows in the database and the presence of indexes. This was done for database sizes that fit into RAM, i.e., up to one million rows.The measurements confirm the expected result that the execution of SQLite is generally faster than the execution of the equivalent code using Pandas, especially when the data is indexed. In examples, it has been shown that in some cases SQLite can be several orders of magnitude faster. On the other hand, the advantage of Pandas as a flexible tool that enables high productivity is illustrated through an example of calculating linear regression. From this example, it is evident that performing regression calculations in SQL itself is relatively complicated, assuming that Pandas is integrated with Python and a multitude of libraries that, among other things, facilitate easy calculation of linear regression.