250 lines
7.8 KiB
Python
250 lines
7.8 KiB
Python
from __future__ import annotations
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from typing import (
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TYPE_CHECKING,
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cast,
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)
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import numpy as np
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from pandas._typing import (
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FilePath,
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ReadBuffer,
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Scalar,
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StorageOptions,
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)
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from pandas.compat._optional import import_optional_dependency
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from pandas.util._decorators import doc
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import pandas as pd
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from pandas.core.shared_docs import _shared_docs
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from pandas.io.excel._base import BaseExcelReader
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if TYPE_CHECKING:
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from pandas._libs.tslibs.nattype import NaTType
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@doc(storage_options=_shared_docs["storage_options"])
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class ODFReader(BaseExcelReader):
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def __init__(
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self,
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filepath_or_buffer: FilePath | ReadBuffer[bytes],
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storage_options: StorageOptions = None,
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) -> None:
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"""
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Read tables out of OpenDocument formatted files.
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Parameters
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----------
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filepath_or_buffer : str, path to be parsed or
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an open readable stream.
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{storage_options}
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"""
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import_optional_dependency("odf")
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super().__init__(filepath_or_buffer, storage_options=storage_options)
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@property
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def _workbook_class(self):
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from odf.opendocument import OpenDocument
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return OpenDocument
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def load_workbook(self, filepath_or_buffer: FilePath | ReadBuffer[bytes]):
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from odf.opendocument import load
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return load(filepath_or_buffer)
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@property
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def empty_value(self) -> str:
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"""Property for compat with other readers."""
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return ""
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@property
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def sheet_names(self) -> list[str]:
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"""Return a list of sheet names present in the document"""
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from odf.table import Table
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tables = self.book.getElementsByType(Table)
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return [t.getAttribute("name") for t in tables]
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def get_sheet_by_index(self, index: int):
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from odf.table import Table
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self.raise_if_bad_sheet_by_index(index)
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tables = self.book.getElementsByType(Table)
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return tables[index]
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def get_sheet_by_name(self, name: str):
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from odf.table import Table
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self.raise_if_bad_sheet_by_name(name)
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tables = self.book.getElementsByType(Table)
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for table in tables:
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if table.getAttribute("name") == name:
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return table
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self.close()
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raise ValueError(f"sheet {name} not found")
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def get_sheet_data(
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self, sheet, file_rows_needed: int | None = None
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) -> list[list[Scalar | NaTType]]:
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"""
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Parse an ODF Table into a list of lists
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"""
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from odf.table import (
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CoveredTableCell,
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TableCell,
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TableRow,
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)
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covered_cell_name = CoveredTableCell().qname
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table_cell_name = TableCell().qname
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cell_names = {covered_cell_name, table_cell_name}
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sheet_rows = sheet.getElementsByType(TableRow)
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empty_rows = 0
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max_row_len = 0
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table: list[list[Scalar | NaTType]] = []
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for sheet_row in sheet_rows:
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sheet_cells = [
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x
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for x in sheet_row.childNodes
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if hasattr(x, "qname") and x.qname in cell_names
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]
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empty_cells = 0
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table_row: list[Scalar | NaTType] = []
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for sheet_cell in sheet_cells:
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if sheet_cell.qname == table_cell_name:
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value = self._get_cell_value(sheet_cell)
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else:
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value = self.empty_value
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column_repeat = self._get_column_repeat(sheet_cell)
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# Queue up empty values, writing only if content succeeds them
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if value == self.empty_value:
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empty_cells += column_repeat
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else:
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table_row.extend([self.empty_value] * empty_cells)
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empty_cells = 0
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table_row.extend([value] * column_repeat)
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if max_row_len < len(table_row):
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max_row_len = len(table_row)
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row_repeat = self._get_row_repeat(sheet_row)
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if self._is_empty_row(sheet_row):
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empty_rows += row_repeat
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else:
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# add blank rows to our table
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table.extend([[self.empty_value]] * empty_rows)
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empty_rows = 0
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for _ in range(row_repeat):
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table.append(table_row)
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if file_rows_needed is not None and len(table) >= file_rows_needed:
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break
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# Make our table square
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for row in table:
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if len(row) < max_row_len:
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row.extend([self.empty_value] * (max_row_len - len(row)))
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return table
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def _get_row_repeat(self, row) -> int:
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"""
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Return number of times this row was repeated
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Repeating an empty row appeared to be a common way
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of representing sparse rows in the table.
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"""
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from odf.namespaces import TABLENS
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return int(row.attributes.get((TABLENS, "number-rows-repeated"), 1))
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def _get_column_repeat(self, cell) -> int:
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from odf.namespaces import TABLENS
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return int(cell.attributes.get((TABLENS, "number-columns-repeated"), 1))
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def _is_empty_row(self, row) -> bool:
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"""
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Helper function to find empty rows
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"""
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for column in row.childNodes:
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if len(column.childNodes) > 0:
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return False
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return True
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def _get_cell_value(self, cell) -> Scalar | NaTType:
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from odf.namespaces import OFFICENS
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if str(cell) == "#N/A":
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return np.nan
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cell_type = cell.attributes.get((OFFICENS, "value-type"))
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if cell_type == "boolean":
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if str(cell) == "TRUE":
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return True
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return False
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if cell_type is None:
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return self.empty_value
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elif cell_type == "float":
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# GH5394
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cell_value = float(cell.attributes.get((OFFICENS, "value")))
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val = int(cell_value)
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if val == cell_value:
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return val
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return cell_value
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elif cell_type == "percentage":
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cell_value = cell.attributes.get((OFFICENS, "value"))
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return float(cell_value)
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elif cell_type == "string":
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return self._get_cell_string_value(cell)
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elif cell_type == "currency":
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cell_value = cell.attributes.get((OFFICENS, "value"))
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return float(cell_value)
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elif cell_type == "date":
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cell_value = cell.attributes.get((OFFICENS, "date-value"))
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return pd.Timestamp(cell_value)
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elif cell_type == "time":
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stamp = pd.Timestamp(str(cell))
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# cast needed here because Scalar doesn't include datetime.time
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return cast(Scalar, stamp.time())
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else:
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self.close()
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raise ValueError(f"Unrecognized type {cell_type}")
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def _get_cell_string_value(self, cell) -> str:
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"""
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Find and decode OpenDocument text:s tags that represent
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a run length encoded sequence of space characters.
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"""
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from odf.element import Element
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from odf.namespaces import TEXTNS
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from odf.text import S
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text_s = S().qname
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value = []
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for fragment in cell.childNodes:
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if isinstance(fragment, Element):
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if fragment.qname == text_s:
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spaces = int(fragment.attributes.get((TEXTNS, "c"), 1))
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value.append(" " * spaces)
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else:
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# recursive impl needed in case of nested fragments
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# with multiple spaces
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# https://github.com/pandas-dev/pandas/pull/36175#discussion_r484639704
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value.append(self._get_cell_string_value(fragment))
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else:
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value.append(str(fragment).strip("\n"))
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return "".join(value)
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