Selecting data from large MySQL database where value of one column is found in a large list of values
I am not familiar with pandas but strictly speaking from a database point of view you could just have your panda values inserted in a PANDA_VALUES table and then join that PANDA_VALUES table with the table(s) you want to grab your data from.
Assuming you will have some indexes in place on both PANDA_VALUES table and the table with your column the JOIN would be quite fast.
Of course you will have to have a process in place to keep PANDA_VALUES tables updated as the business needs change.
Hope it helps.
I generally use Pandas to extract data from MySQL into a dataframe. This works well and allows me to manipulate the data before analysis. This workflow works well for me.
I'm in a situation where I have a large MySQL database (multiple tables that will yield several million rows). I want to extract the data where one of the columns matches a value in a Pandas series. This series could be of variable length and may change frequently. How can I extract data from the MySQL database where one of the columns of data is found in the Pandas series? The two options I've explored are:

Extract all the data from MySQL into a Pandas dataframe (using pymysql, for example) and then keep only the rows I need (using df.isin()).

or

Query the MySQL database using a query with multiple WHERE ... OR ... OR statements (and load this into Pandas dataframe). This query could be generated using Python to join items of a list with ORs.

I guess both these methods would work but they both seem to have high overheads. Method 1 downloads a lot of unnecessary data (which could be slow and is, perhaps, a higher security risk) whilst method 2 downloads only the desired records but it requires an unwieldy query that contains potentially thousands of OR statements.
Is there a better alternative? If not, which of the two above would be preferred?