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84 lines
2.8 KiB
Python
84 lines
2.8 KiB
Python
import pyarrow as pa
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import xlntpyarrow.lib as xpa
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COLUMN_TYPE_FIELD = {
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xpa.Cell.Type.Number: pa.float64,
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xpa.Cell.Type.SharedString: pa.string,
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xpa.Cell.Type.InlineString: pa.string,
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xpa.Cell.Type.FormulaString: pa.string,
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xpa.Cell.Type.Error: pa.string,
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xpa.Cell.Type.Boolean: pa.bool_,
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xpa.Cell.Type.Date: pa.date32,
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xpa.Cell.Type.Empty: pa.string,
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}
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def cell_to_pyarrow_array(cell, type):
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if cell.data_type() == xpa.Cell.Type.Number:
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return pa.array([cell.value_long_double()], type)
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elif cell.data_type() == xpa.Cell.Type.SharedString:
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return pa.array([cell.value_string()], type)
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elif cell.data_type() == xpa.Cell.Type.InlineString:
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return pa.array([cell.value_string()], type)
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elif cell.data_type() == xpa.Cell.Type.FormulaString:
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return pa.array([cell.value_string()], type)
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elif cell.data_type() == xpa.Cell.Type.Error:
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return pa.array([cell.value_string()], type)
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elif cell.data_type() == xpa.Cell.Type.Boolean:
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return pa.array([cell.value_bool()], type)
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elif cell.data_type() == xpa.Cell.Type.Date:
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return pa.array([cell.value_unsigned_int()], type)
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elif cell.data_type() == xpa.Cell.Type.Empty:
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return pa.array([cell.value_string()], type)
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def xlsx2arrow(io, sheetname):
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reader = xpa.StreamingWorkbookReader()
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reader.open(io)
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sheet_titles = reader.sheet_titles()
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sheet_title = sheet_titles[0]
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if sheetname is not None:
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if isinstance(sheetname, int):
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sheet_title = sheet_titles[sheetname]
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elif isinstance(sheetname, str):
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sheet_title = sheetname
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reader.begin_worksheet(sheet_title)
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column_names = []
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fields = []
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batches = []
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schema = None
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first_batch = []
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max_column = 0
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while reader.has_cell():
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if schema is None:
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cell = reader.read_cell()
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type = cell.data_type()
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if cell.row() == 1:
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column_names.append(cell.value_string())
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max_column = max(max_column, cell.column())
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continue
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elif cell.row() == 2:
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column_name = column_names[cell.column() - 1]
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fields.append(pa.field(column_name, COLUMN_TYPE_FIELD[type]()))
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first_batch.append(cell_to_pyarrow_array(cell, fields[-1].type))
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if cell.column() == max_column:
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schema = pa.schema(fields)
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print(schema)
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batches.append(pa.RecordBatch.from_arrays(first_batch, column_names))
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continue
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batches.append(reader.read_batch(schema, 10000))
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reader.end_worksheet()
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return pa.Table.from_batches(batches)
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if __name__ == '__main__':
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file = open('tmp.xlsx', 'rb')
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table = xlsx2arrow(file, 'Sheet1')
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print(table.to_pandas())
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