ai-content-maker/.venv/Lib/site-packages/pandas/io/excel/_odfreader.py

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2024-05-03 04:18:51 +03:00
from __future__ import annotations
from typing import (
TYPE_CHECKING,
cast,
)
import numpy as np
from pandas._typing import (
FilePath,
ReadBuffer,
Scalar,
StorageOptions,
)
from pandas.compat._optional import import_optional_dependency
from pandas.util._decorators import doc
import pandas as pd
from pandas.core.shared_docs import _shared_docs
from pandas.io.excel._base import BaseExcelReader
if TYPE_CHECKING:
from pandas._libs.tslibs.nattype import NaTType
@doc(storage_options=_shared_docs["storage_options"])
class ODFReader(BaseExcelReader):
"""
Read tables out of OpenDocument formatted files.
Parameters
----------
filepath_or_buffer : str, path to be parsed or
an open readable stream.
{storage_options}
"""
def __init__(
self,
filepath_or_buffer: FilePath | ReadBuffer[bytes],
storage_options: StorageOptions = None,
) -> None:
import_optional_dependency("odf")
super().__init__(filepath_or_buffer, storage_options=storage_options)
@property
def _workbook_class(self):
from odf.opendocument import OpenDocument
return OpenDocument
def load_workbook(self, filepath_or_buffer: FilePath | ReadBuffer[bytes]):
from odf.opendocument import load
return load(filepath_or_buffer)
@property
def empty_value(self) -> str:
"""Property for compat with other readers."""
return ""
@property
def sheet_names(self) -> list[str]:
"""Return a list of sheet names present in the document"""
from odf.table import Table
tables = self.book.getElementsByType(Table)
return [t.getAttribute("name") for t in tables]
def get_sheet_by_index(self, index: int):
from odf.table import Table
self.raise_if_bad_sheet_by_index(index)
tables = self.book.getElementsByType(Table)
return tables[index]
def get_sheet_by_name(self, name: str):
from odf.table import Table
self.raise_if_bad_sheet_by_name(name)
tables = self.book.getElementsByType(Table)
for table in tables:
if table.getAttribute("name") == name:
return table
self.close()
raise ValueError(f"sheet {name} not found")
def get_sheet_data(
self, sheet, convert_float: bool, file_rows_needed: int | None = None
) -> list[list[Scalar | NaTType]]:
"""
Parse an ODF Table into a list of lists
"""
from odf.table import (
CoveredTableCell,
TableCell,
TableRow,
)
covered_cell_name = CoveredTableCell().qname
table_cell_name = TableCell().qname
cell_names = {covered_cell_name, table_cell_name}
sheet_rows = sheet.getElementsByType(TableRow)
empty_rows = 0
max_row_len = 0
table: list[list[Scalar | NaTType]] = []
for sheet_row in sheet_rows:
sheet_cells = [
x
for x in sheet_row.childNodes
if hasattr(x, "qname") and x.qname in cell_names
]
empty_cells = 0
table_row: list[Scalar | NaTType] = []
for sheet_cell in sheet_cells:
if sheet_cell.qname == table_cell_name:
value = self._get_cell_value(sheet_cell, convert_float)
else:
value = self.empty_value
column_repeat = self._get_column_repeat(sheet_cell)
# Queue up empty values, writing only if content succeeds them
if value == self.empty_value:
empty_cells += column_repeat
else:
table_row.extend([self.empty_value] * empty_cells)
empty_cells = 0
table_row.extend([value] * column_repeat)
if max_row_len < len(table_row):
max_row_len = len(table_row)
row_repeat = self._get_row_repeat(sheet_row)
if self._is_empty_row(sheet_row):
empty_rows += row_repeat
else:
# add blank rows to our table
table.extend([[self.empty_value]] * empty_rows)
empty_rows = 0
for _ in range(row_repeat):
table.append(table_row)
if file_rows_needed is not None and len(table) >= file_rows_needed:
break
# Make our table square
for row in table:
if len(row) < max_row_len:
row.extend([self.empty_value] * (max_row_len - len(row)))
return table
def _get_row_repeat(self, row) -> int:
"""
Return number of times this row was repeated
Repeating an empty row appeared to be a common way
of representing sparse rows in the table.
"""
from odf.namespaces import TABLENS
return int(row.attributes.get((TABLENS, "number-rows-repeated"), 1))
def _get_column_repeat(self, cell) -> int:
from odf.namespaces import TABLENS
return int(cell.attributes.get((TABLENS, "number-columns-repeated"), 1))
def _is_empty_row(self, row) -> bool:
"""
Helper function to find empty rows
"""
for column in row.childNodes:
if len(column.childNodes) > 0:
return False
return True
def _get_cell_value(self, cell, convert_float: bool) -> Scalar | NaTType:
from odf.namespaces import OFFICENS
if str(cell) == "#N/A":
return np.nan
cell_type = cell.attributes.get((OFFICENS, "value-type"))
if cell_type == "boolean":
if str(cell) == "TRUE":
return True
return False
if cell_type is None:
return self.empty_value
elif cell_type == "float":
# GH5394
cell_value = float(cell.attributes.get((OFFICENS, "value")))
if convert_float:
val = int(cell_value)
if val == cell_value:
return val
return cell_value
elif cell_type == "percentage":
cell_value = cell.attributes.get((OFFICENS, "value"))
return float(cell_value)
elif cell_type == "string":
return self._get_cell_string_value(cell)
elif cell_type == "currency":
cell_value = cell.attributes.get((OFFICENS, "value"))
return float(cell_value)
elif cell_type == "date":
cell_value = cell.attributes.get((OFFICENS, "date-value"))
return pd.to_datetime(cell_value)
elif cell_type == "time":
stamp = pd.to_datetime(str(cell))
# cast needed here because Scalar doesn't include datetime.time
return cast(Scalar, stamp.time())
else:
self.close()
raise ValueError(f"Unrecognized type {cell_type}")
def _get_cell_string_value(self, cell) -> str:
"""
Find and decode OpenDocument text:s tags that represent
a run length encoded sequence of space characters.
"""
from odf.element import Element
from odf.namespaces import TEXTNS
from odf.text import S
text_s = S().qname
value = []
for fragment in cell.childNodes:
if isinstance(fragment, Element):
if fragment.qname == text_s:
spaces = int(fragment.attributes.get((TEXTNS, "c"), 1))
value.append(" " * spaces)
else:
# recursive impl needed in case of nested fragments
# with multiple spaces
# https://github.com/pandas-dev/pandas/pull/36175#discussion_r484639704
value.append(self._get_cell_string_value(fragment))
else:
value.append(str(fragment).strip("\n"))
return "".join(value)