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Implementing the converter currently, see https://github.com/fiboa/cli/tree/jp . We have a challenge handling large datasets. Dataframes are fast, but all data is loaded in memory at once. Given the Japan-2024 32GB parquet file, and reading only a limited set of columns in the dataframe, the python converter process consumer 64GB of RAM.
From @cholmes
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