Source code for tabledata._core

"""
.. codeauthor:: Tsuyoshi Hombashi <tsuyoshi.hombashi@gmail.com>
"""

import copy
import re
from collections import OrderedDict, namedtuple
from typing import Any, Dict, List, Optional, Sequence

import dataproperty as dp
import typepy
from dataproperty.typing import TypeHint
from typepy import Nan

from ._constant import PatternMatch
from ._converter import to_value_matrix
from ._logger import logger


[docs]class TableData: """ Class to represent a table data structure. :param table_name: Name of the table. :param headers: Table header names. :param rows: Data of the table. """ def __init__( self, table_name: Optional[str], headers: Sequence[str], rows: Sequence, dp_extractor: Optional[dp.DataPropertyExtractor] = None, type_hints: Optional[Sequence[TypeHint]] = None, max_workers: Optional[int] = None, ): self.__table_name = table_name self.__value_matrix = None # type: Optional[Sequence] self.__value_dp_matrix = None # type: Optional[Sequence[Sequence[dp.DataProperty]]] if rows: self.__rows = rows else: self.__rows = [] if dp_extractor: self.__dp_extractor = copy.deepcopy(dp_extractor) else: self.__dp_extractor = dp.DataPropertyExtractor() if type_hints: self.__dp_extractor.column_type_hints = type_hints self.__dp_extractor.strip_str_header = '"' if max_workers: self.__dp_extractor.max_workers = max_workers if not headers: self.__dp_extractor.headers = [] else: self.__dp_extractor.headers = headers def __repr__(self) -> str: element_list = ["table_name={}".format(self.table_name)] try: element_list.append("headers=[{}]".format(", ".join(self.headers))) except TypeError: element_list.append("headers=None") element_list.extend(["cols={}".format(self.num_columns), "rows={}".format(self.num_rows)]) return ", ".join(element_list) def __eq__(self, other) -> bool: return self.equals(other, cmp_by_dp=False) def __ne__(self, other) -> bool: return not self.equals(other, cmp_by_dp=False) @property def table_name(self) -> Optional[str]: """ :return: Name of the table. :rtype: str """ return self.__table_name @table_name.setter def table_name(self, value: Optional[str]) -> None: self.__table_name = value @property def headers(self) -> Sequence[str]: """Get the table header names. Returns: |list| or |tuple|: Table header names. """ return self.__dp_extractor.headers @property def rows(self) -> Sequence: """Original rows of tabular data. Returns: |list| or |tuple|: Table rows. """ return self.__rows @property def value_matrix(self) -> Sequence: """Converted rows of tabular data. Returns: |list| or |tuple|: Table rows. """ if self.__value_matrix: return self.__value_matrix self.__value_matrix = [ [value_dp.data for value_dp in value_dp_list] for value_dp_list in self.value_dp_matrix ] return self.__value_matrix @property def has_value_dp_matrix(self) -> bool: return self.__value_dp_matrix is not None @property def max_workers(self) -> int: return self.__dp_extractor.max_workers @max_workers.setter def max_workers(self, value: Optional[int]) -> None: self.__dp_extractor.max_workers = value # type: ignore @property def num_rows(self) -> Optional[int]: """ :return: Number of rows in the tabular data. |None| if the ``rows`` is neither list nor tuple. :rtype: int """ try: return len(self.rows) except TypeError: return None @property def num_columns(self) -> Optional[int]: if typepy.is_not_empty_sequence(self.headers): return len(self.headers) try: return len(self.rows[0]) except TypeError: return None except IndexError: return 0 @property def value_dp_matrix(self) -> Sequence[Sequence[dp.DataProperty]]: """ :return: DataProperty for table data. :rtype: list """ if self.__value_dp_matrix is None: self.__value_dp_matrix = self.__dp_extractor.to_dp_matrix( to_value_matrix(self.headers, self.rows) ) return self.__value_dp_matrix @property def header_dp_list(self) -> List[dp.DataProperty]: return self.__dp_extractor.to_header_dp_list() @property def column_dp_list(self) -> List[dp.ColumnDataProperty]: return self.__dp_extractor.to_column_dp_list(self.value_dp_matrix) @property def dp_extractor(self) -> dp.DataPropertyExtractor: return self.__dp_extractor
[docs] def is_empty_header(self) -> bool: """ :return: |True| if the data :py:attr:`.headers` is empty. :rtype: bool """ return typepy.is_empty_sequence(self.headers)
[docs] def is_empty_rows(self) -> bool: """ :return: |True| if the tabular data has no rows. :rtype: bool """ return self.num_rows == 0
[docs] def is_empty(self) -> bool: """ :return: |True| if the data :py:attr:`.headers` or :py:attr:`.value_matrix` is empty. :rtype: bool """ return any([self.is_empty_header(), self.is_empty_rows()])
[docs] def equals(self, other, cmp_by_dp: bool = True) -> bool: if cmp_by_dp: return self.__equals_dp(other) return self.__equals_raw(other)
def __equals_base(self, other) -> bool: compare_item_list = [self.table_name == other.table_name] if self.num_rows is not None: compare_item_list.append(self.num_rows == other.num_rows) return all(compare_item_list) def __equals_raw(self, other) -> bool: if not self.__equals_base(other): return False if self.headers != other.headers: return False for lhs_row, rhs_row in zip(self.rows, other.rows): if len(lhs_row) != len(rhs_row): return False if not all( [ lhs == rhs for lhs, rhs in zip(lhs_row, rhs_row) if not Nan(lhs).is_type() and not Nan(rhs).is_type() ] ): return False return True def __equals_dp(self, other) -> bool: if not self.__equals_base(other): return False if self.header_dp_list != other.header_dp_list: return False if (self.value_dp_matrix is None and other) or (self.value_dp_matrix and other is None): return False for lhs_list, rhs_list in zip(self.value_dp_matrix, other.value_dp_matrix): if len(lhs_list) != len(rhs_list): return False if any([lhs != rhs for lhs, rhs in zip(lhs_list, rhs_list)]): return False return True
[docs] def in_tabledata_list(self, other: Sequence, cmp_by_dp: bool = True) -> bool: for table_data in other: if self.equals(table_data, cmp_by_dp=cmp_by_dp): return True return False
[docs] def validate_rows(self) -> None: """ :raises ValueError: """ invalid_row_idx_list = [] for row_idx, row in enumerate(self.rows): if isinstance(row, (list, tuple)) and len(self.headers) != len(row): invalid_row_idx_list.append(row_idx) if isinstance(row, dict): if not all([header in row for header in self.headers]): invalid_row_idx_list.append(row_idx) if not invalid_row_idx_list: return for invalid_row_idx in invalid_row_idx_list: logger.debug( "invalid row (line={}): {}".format(invalid_row_idx, self.rows[invalid_row_idx]) ) raise ValueError( "table header length and row length are mismatch:\n" + " header(len={}): {}\n".format(len(self.headers), self.headers) + " # of miss match rows: {} ouf of {}\n".format( len(invalid_row_idx_list), self.num_rows ) )
[docs] def as_dict(self, default_key: str = "table") -> "Dict[str, List[OrderedDict[str, Any]]]": """ Args: default_key: Key of a returning dictionary when the ``table_name`` is empty. Returns: dict: Table data as a |dict| instance. Sample Code: .. code:: python from tabledata import TableData TableData( "sample", ["a", "b"], [[1, 2], [3.3, 4.4]] ).as_dict() Output: .. code:: json {'sample': [OrderedDict([('a', 1), ('b', 2)]), OrderedDict([('a', 3.3), ('b', 4.4)])]} """ # noqa dict_body = [] for row in self.value_matrix: if not row: continue values = [ (header, value) for header, value in zip(self.headers, row) if value is not None ] if not values: continue dict_body.append(OrderedDict(values)) table_name = self.table_name if not table_name: table_name = default_key return {table_name: dict_body}
[docs] def as_tuple(self): """ :return: Rows of the table. :rtype: list of |namedtuple| :Sample Code: .. code:: python from tabledata import TableData records = TableData( "sample", ["a", "b"], [[1, 2], [3.3, 4.4]] ).as_tuple() for record in records: print(record) :Output: .. code-block:: none Row(a=1, b=2) Row(a=Decimal('3.3'), b=Decimal('4.4')) """ Row = namedtuple("Row", self.headers) for value_dp_list in self.value_dp_matrix: if typepy.is_empty_sequence(value_dp_list): continue row = Row(*[value_dp.data for value_dp in value_dp_list]) yield row
[docs] def as_dataframe(self): """ :return: Table data as a ``pandas.DataFrame`` instance. :rtype: pandas.DataFrame :Sample Code: .. code-block:: python from tabledata import TableData TableData( "sample", ["a", "b"], [[1, 2], [3.3, 4.4]] ).as_dataframe() :Output: .. code-block:: none a b 0 1 2 1 3.3 4.4 :Dependency Packages: - `pandas <https://pandas.pydata.org/>`__ """ import pandas dataframe = pandas.DataFrame(self.value_matrix) if not self.is_empty_header(): dataframe.columns = self.headers return dataframe
[docs] def transpose(self): return TableData( self.table_name, self.headers, [row for row in zip(*self.rows)], max_workers=self.max_workers, )
[docs] def filter_column( self, patterns: Optional[str] = None, is_invert_match: bool = False, is_re_match: bool = False, pattern_match: PatternMatch = PatternMatch.OR, ): logger.debug( "filter_column: patterns={}, is_invert_match={}, " "is_re_match={}, pattern_match={}".format( patterns, is_invert_match, is_re_match, pattern_match ) ) if not patterns: return self match_header_list = [] match_column_matrix = [] if pattern_match == PatternMatch.OR: match_method = any elif pattern_match == PatternMatch.AND: match_method = all else: raise ValueError("unknown matching: {}".format(pattern_match)) for header, column in zip(self.headers, zip(*self.rows)): is_match_list = [] for pattern in patterns: is_match = self.__is_match(header, pattern, is_re_match) is_match_list.append( any([is_match and not is_invert_match, not is_match and is_invert_match]) ) if match_method(is_match_list): match_header_list.append(header) match_column_matrix.append(column) logger.debug( "filter_column: table={}, match_header_list={}".format( self.table_name, match_header_list ) ) return TableData( self.table_name, match_header_list, list(zip(*match_column_matrix)), max_workers=self.max_workers, )
[docs] @staticmethod def from_dataframe( dataframe, table_name: str = "", type_hints: Optional[Sequence[TypeHint]] = None, max_workers: Optional[int] = None, ): """ Initialize TableData instance from a pandas.DataFrame instance. :param pandas.DataFrame dataframe: :param str table_name: Table name to create. """ return TableData( table_name, list(dataframe.columns.values), dataframe.values.tolist(), type_hints=type_hints, max_workers=max_workers, )
@staticmethod def __is_match(header: str, pattern: str, is_re_match: bool) -> bool: if is_re_match: return re.search(pattern, header) is not None return header == pattern