Writing data from pandas DataFrames to a SQL database is very slow using the built-in to_sqlmethod, even with the newly introducedexecute_manyoption. For Microsoft SQL Server, a far far faster method is to use the BCP utility provided byMicrosoft. This utility is a command line tool that transfers data to/from the database and flattext files.
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This package is a wrapper for seamlessly using the bcp utility from Python using a pandasDataFrame. Despite the IO hits, the fastest option by far is saving the data to a CSV file in thefile system and using the bcp utility to transfer the CSV file to SQL Server. Best of all, youdon't need to know anything about using BCP at all!
The only scope of bcpandas is to read and write between a pandas DataFrame and a MicrosoftSQL Server database. That's it. We do not concern ourselves with reading existing flat filesto/from SQL - that introduces way to much complexity in trying to parse and decode the variousparts of the file, like delimiters, quote characters, and line endings. Instead, to read/write anexiting flat file, just import it via pandas into a DataFrame, and then use bcpandas.
The big benefit of this is that we get to precicely control all the finicky parts of the text filewhen we write/read it to a local file and then in the BCP utility. This lets us set library-widedefaults (maybe configurable in the future) and work with those.
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