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Conversion between DuckDB and Python – DuckDB 9357: This page is for DuckDB 1.3, which is outdated. Switch to the current version . Search Shortcut cmd + k | ctrl + k Installation Documentation Getting Started Connect Overview Concurrency Data Import Overview Data Sources CSV Files Overview ... 2 Fetch as dictionary of NumPy arrays: arr = con . execute ( "SELECT * FROM items" ). fetchnumpy () print ( arr ) {'item': masked_array(data=['jeans', 'hammer', 'laptop', 'chainsaw', 'iphone'], mask=[False, False, False, False, False], fill_value='?', ... dtype=object), 'value': masked_array(data=[20.0, 42.2, 2000.0, 500.0, 300.0], mask=[False, False, False, False, False], fill_value=1e+20), 'count': maske ...; https://man.mtool.work/duckdb/1.3/clients/python/conversion.html

Export to Numpy – DuckDB 9357: This page is for DuckDB 1.3, which is outdated. Switch to the current version . Search Shortcut cmd + k | ctrl + k Installation Documentation Getting Started Connect Overview Concurrency Data Import Overview Data Sources CSV Files Overview ... duckdb . sql ( "SELECT unnest([1, 2, 3]) AS x, 5.0 AS y" ). fetchnumpy () my_arr {'x': array([1, 2, 3], dtype=int32), 'y': masked_array(data=[5.0, 5.0, 5.0], mask=[False, False, False], fill_value=1e+20)} Then, the array can be processed using Numpy fu ... Numpy using fetchnumpy import duckdb Documentation Guides Copy Markdown Export query converted example numpy unnest dtype masked mask fill processed functions Also DuckDB also supports 関連するページ Import from Numpy Export to Pandas Python API Execu ...; https://man.mtool.work/duckdb/1.3/guides/python/export_numpy.html

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