For our benchmark dataset, we use the infamous TPC-H data set. We run the benchmark entirely from within the Google Colab environment.
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Both the DuckDB code and the Pandas code operates fully on a Pandas-in, Pandas-out basis. In these benchmarks, we operate purely on Pandas DataFrames. The source code for the benchmarks is available for interactive use in Google Colab. To demonstrate the performance of DuckDB when executing SQL on Pandas DataFrames, we now present a number of benchmarks. Unlike when using an external database system such as Postgres, the data transfer time of the input or the output is negligible (see Appendix A for details). For many queries, you can use DuckDB to process data faster than Pandas, and with a much lower total memory usage, without ever leaving the Pandas DataFrame binary format (“Pandas-in, Pandas-out”). Not only is this process painless, it is highly efficient. The column names and types are also extracted automatically from the DataFrame. The SQL table name mydf is interpreted as the local Python variable mydf that happens to be a Pandas DataFrame, which DuckDB can read and query directly. Import pandas as pd import duckdb mydf = pd.
Using DuckDB, it is possible to run SQL efficiently right on top of Pandas DataFrames.Īs a short teaser, here is a code snippet that allows you to do exactly that: run arbitrary SQL queries directly on Pandas DataFrames using DuckDB. SQL is a very powerful tool for performing these types of data transformations. SQL on PandasĪfter your data has been converted into a Pandas DataFrame often additional data wrangling and analysis still need to be performed. Apache Arrow is gaining significant traction in this domain as well, and DuckDB also quacks Arrow. a CSV or Parquet file) often your data will never be loaded into an external database system at all, and will instead be directly loaded into a Pandas DataFrame. These libraries serve as the standard for data exchange between the vast ecosystem of Data Science libraries in Python 1 such as scikit-learn or TensorFlow. While you can very effectively perform aggregations and data transformations in an external database system such as Postgres if your data is stored there, at some point you will need to convert that data back into Pandas and NumPy.
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It is a versatile and flexible language that allows the user to efficiently perform a wide variety of data transformations, without having to care about how the data is physically represented or how to do these data transformations in the most optimal way. Here at team DuckDB, we are huge fans of SQL. Recently, an article was published advocating for using SQL for Data Analysis.
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TLDR: DuckDB, a free and open source analytical data management system, can efficiently run SQL queries directly on Pandas DataFrames. Mark Raasveldt and Hannes Mühleisen Efficient SQL on Pandas with DuckDB
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