from __future__ import annotations from collections import defaultdict from copy import copy import csv import datetime from enum import Enum import itertools from typing import ( Callable, DefaultDict, Hashable, Iterable, List, Mapping, Sequence, Tuple, cast, final, overload, ) import warnings import numpy as np import pandas._libs.lib as lib import pandas._libs.ops as libops import pandas._libs.parsers as parsers from pandas._libs.parsers import STR_NA_VALUES from pandas._libs.tslibs import parsing from pandas._typing import ( ArrayLike, DtypeArg, ) from pandas.errors import ( ParserError, ParserWarning, ) from pandas.util._exceptions import find_stack_level from pandas.core.dtypes.cast import astype_nansafe from pandas.core.dtypes.common import ( ensure_object, is_bool_dtype, is_categorical_dtype, is_dict_like, is_dtype_equal, is_extension_array_dtype, is_integer, is_integer_dtype, is_list_like, is_object_dtype, is_scalar, is_string_dtype, pandas_dtype, ) from pandas.core.dtypes.dtypes import CategoricalDtype from pandas.core.dtypes.missing import isna from pandas import DataFrame from pandas.core import algorithms from pandas.core.arrays import Categorical from pandas.core.indexes.api import ( Index, MultiIndex, ensure_index_from_sequences, ) from pandas.core.series import Series from pandas.core.tools import datetimes as tools from pandas.io.date_converters import generic_parser class ParserBase: class BadLineHandleMethod(Enum): ERROR = 0 WARN = 1 SKIP = 2 _implicit_index: bool = False _first_chunk: bool def __init__(self, kwds): self.names = kwds.get("names") self.orig_names: list | None = None self.prefix = kwds.pop("prefix", None) self.index_col = kwds.get("index_col", None) self.unnamed_cols: set = set() self.index_names: list | None = None self.col_names = None self.parse_dates = _validate_parse_dates_arg(kwds.pop("parse_dates", False)) self._parse_date_cols: Iterable = [] self.date_parser = kwds.pop("date_parser", None) self.dayfirst = kwds.pop("dayfirst", False) self.keep_date_col = kwds.pop("keep_date_col", False) self.na_values = kwds.get("na_values") self.na_fvalues = kwds.get("na_fvalues") self.na_filter = kwds.get("na_filter", False) self.keep_default_na = kwds.get("keep_default_na", True) self.dtype = copy(kwds.get("dtype", None)) self.true_values = kwds.get("true_values") self.false_values = kwds.get("false_values") self.mangle_dupe_cols = kwds.get("mangle_dupe_cols", True) self.infer_datetime_format = kwds.pop("infer_datetime_format", False) self.cache_dates = kwds.pop("cache_dates", True) self._date_conv = _make_date_converter( date_parser=self.date_parser, dayfirst=self.dayfirst, infer_datetime_format=self.infer_datetime_format, cache_dates=self.cache_dates, ) # validate header options for mi self.header = kwds.get("header") if isinstance(self.header, (list, tuple, np.ndarray)): if not all(map(is_integer, self.header)): raise ValueError("header must be integer or list of integers") if any(i < 0 for i in self.header): raise ValueError( "cannot specify multi-index header with negative integers" ) if kwds.get("usecols"): raise ValueError( "cannot specify usecols when specifying a multi-index header" ) if kwds.get("names"): raise ValueError( "cannot specify names when specifying a multi-index header" ) # validate index_col that only contains integers if self.index_col is not None: is_sequence = isinstance(self.index_col, (list, tuple, np.ndarray)) if not ( is_sequence and all(map(is_integer, self.index_col)) or is_integer(self.index_col) ): raise ValueError( "index_col must only contain row numbers " "when specifying a multi-index header" ) elif self.header is not None: # GH 27394 if self.prefix is not None: raise ValueError( "Argument prefix must be None if argument header is not None" ) # GH 16338 elif not is_integer(self.header): raise ValueError("header must be integer or list of integers") # GH 27779 elif self.header < 0: raise ValueError( "Passing negative integer to header is invalid. " "For no header, use header=None instead" ) self._name_processed = False self._first_chunk = True self.usecols, self.usecols_dtype = self._validate_usecols_arg(kwds["usecols"]) # Fallback to error to pass a sketchy test(test_override_set_noconvert_columns) # Normally, this arg would get pre-processed earlier on self.on_bad_lines = kwds.get("on_bad_lines", self.BadLineHandleMethod.ERROR) def _validate_parse_dates_presence(self, columns: Sequence[Hashable]) -> Iterable: """ Check if parse_dates are in columns. If user has provided names for parse_dates, check if those columns are available. Parameters ---------- columns : list List of names of the dataframe. Returns ------- The names of the columns which will get parsed later if a dict or list is given as specification. Raises ------ ValueError If column to parse_date is not in dataframe. """ cols_needed: Iterable if is_dict_like(self.parse_dates): cols_needed = itertools.chain(*self.parse_dates.values()) elif is_list_like(self.parse_dates): # a column in parse_dates could be represented # ColReference = Union[int, str] # DateGroups = List[ColReference] # ParseDates = Union[DateGroups, List[DateGroups], # Dict[ColReference, DateGroups]] cols_needed = itertools.chain.from_iterable( col if is_list_like(col) and not isinstance(col, tuple) else [col] for col in self.parse_dates ) else: cols_needed = [] cols_needed = list(cols_needed) # get only columns that are references using names (str), not by index missing_cols = ", ".join( sorted( { col for col in cols_needed if isinstance(col, str) and col not in columns } ) ) if missing_cols: raise ValueError( f"Missing column provided to 'parse_dates': '{missing_cols}'" ) # Convert positions to actual column names return [ col if (isinstance(col, str) or col in columns) else columns[col] for col in cols_needed ] def close(self): pass @final @property def _has_complex_date_col(self) -> bool: return isinstance(self.parse_dates, dict) or ( isinstance(self.parse_dates, list) and len(self.parse_dates) > 0 and isinstance(self.parse_dates[0], list) ) @final def _should_parse_dates(self, i: int) -> bool: if isinstance(self.parse_dates, bool): return self.parse_dates else: if self.index_names is not None: name = self.index_names[i] else: name = None j = i if self.index_col is None else self.index_col[i] if is_scalar(self.parse_dates): return (j == self.parse_dates) or ( name is not None and name == self.parse_dates ) else: return (j in self.parse_dates) or ( name is not None and name in self.parse_dates ) @final def _extract_multi_indexer_columns( self, header, index_names: list | None, passed_names: bool = False, ): """ Extract and return the names, index_names, col_names if the column names are a MultiIndex. Parameters ---------- header: list of lists The header rows index_names: list, optional The names of the future index passed_names: bool, default False A flag specifying if names where passed """ if len(header) < 2: return header[0], index_names, None, passed_names # the names are the tuples of the header that are not the index cols # 0 is the name of the index, assuming index_col is a list of column # numbers ic = self.index_col if ic is None: ic = [] if not isinstance(ic, (list, tuple, np.ndarray)): ic = [ic] sic = set(ic) # clean the index_names index_names = header.pop(-1) index_names, _, _ = self._clean_index_names( index_names, self.index_col, self.unnamed_cols ) # extract the columns field_count = len(header[0]) # check if header lengths are equal if not all(len(header_iter) == field_count for header_iter in header[1:]): raise ParserError("Header rows must have an equal number of columns.") def extract(r): return tuple(r[i] for i in range(field_count) if i not in sic) columns = list(zip(*(extract(r) for r in header))) names = columns.copy() for single_ic in sorted(ic): names.insert(single_ic, single_ic) # Clean the column names (if we have an index_col). if len(ic): col_names = [ r[ic[0]] if ((r[ic[0]] is not None) and r[ic[0]] not in self.unnamed_cols) else None for r in header ] else: col_names = [None] * len(header) passed_names = True return names, index_names, col_names, passed_names @final def _maybe_dedup_names(self, names: Sequence[Hashable]) -> Sequence[Hashable]: # see gh-7160 and gh-9424: this helps to provide # immediate alleviation of the duplicate names # issue and appears to be satisfactory to users, # but ultimately, not needing to butcher the names # would be nice! if self.mangle_dupe_cols: names = list(names) # so we can index counts: DefaultDict[Hashable, int] = defaultdict(int) is_potential_mi = _is_potential_multi_index(names, self.index_col) for i, col in enumerate(names): cur_count = counts[col] while cur_count > 0: counts[col] = cur_count + 1 if is_potential_mi: # for mypy assert isinstance(col, tuple) col = col[:-1] + (f"{col[-1]}.{cur_count}",) else: col = f"{col}.{cur_count}" cur_count = counts[col] names[i] = col counts[col] = cur_count + 1 return names @final def _maybe_make_multi_index_columns( self, columns: Sequence[Hashable], col_names: Sequence[Hashable] | None = None, ) -> Sequence[Hashable] | MultiIndex: # possibly create a column mi here if _is_potential_multi_index(columns): list_columns = cast(List[Tuple], columns) return MultiIndex.from_tuples(list_columns, names=col_names) return columns @final def _make_index(self, data, alldata, columns, indexnamerow=False): if not is_index_col(self.index_col) or not self.index_col: index = None elif not self._has_complex_date_col: index = self._get_simple_index(alldata, columns) index = self._agg_index(index) elif self._has_complex_date_col: if not self._name_processed: (self.index_names, _, self.index_col) = self._clean_index_names( list(columns), self.index_col, self.unnamed_cols ) self._name_processed = True index = self._get_complex_date_index(data, columns) index = self._agg_index(index, try_parse_dates=False) # add names for the index if indexnamerow: coffset = len(indexnamerow) - len(columns) assert index is not None index = index.set_names(indexnamerow[:coffset]) # maybe create a mi on the columns columns = self._maybe_make_multi_index_columns(columns, self.col_names) return index, columns @final def _get_simple_index(self, data, columns): def ix(col): if not isinstance(col, str): return col raise ValueError(f"Index {col} invalid") to_remove = [] index = [] for idx in self.index_col: i = ix(idx) to_remove.append(i) index.append(data[i]) # remove index items from content and columns, don't pop in # loop for i in sorted(to_remove, reverse=True): data.pop(i) if not self._implicit_index: columns.pop(i) return index @final def _get_complex_date_index(self, data, col_names): def _get_name(icol): if isinstance(icol, str): return icol if col_names is None: raise ValueError(f"Must supply column order to use {icol!s} as index") for i, c in enumerate(col_names): if i == icol: return c to_remove = [] index = [] for idx in self.index_col: name = _get_name(idx) to_remove.append(name) index.append(data[name]) # remove index items from content and columns, don't pop in # loop for c in sorted(to_remove, reverse=True): data.pop(c) col_names.remove(c) return index def _clean_mapping(self, mapping): """converts col numbers to names""" if not isinstance(mapping, dict): return mapping clean = {} for col, v in mapping.items(): # for mypy assert self.orig_names is not None if isinstance(col, int) and col not in self.orig_names: col = self.orig_names[col] clean[col] = v return clean @final def _agg_index(self, index, try_parse_dates: bool = True) -> Index: arrays = [] for i, arr in enumerate(index): if try_parse_dates and self._should_parse_dates(i): arr = self._date_conv(arr) if self.na_filter: col_na_values = self.na_values col_na_fvalues = self.na_fvalues else: col_na_values = set() col_na_fvalues = set() if isinstance(self.na_values, dict): assert self.index_names is not None col_name = self.index_names[i] if col_name is not None: col_na_values, col_na_fvalues = _get_na_values( col_name, self.na_values, self.na_fvalues, self.keep_default_na ) clean_dtypes = self._clean_mapping(self.dtype) cast_type = None if isinstance(clean_dtypes, dict) and self.index_names is not None: cast_type = clean_dtypes.get(self.index_names[i], None) try_num_bool = not (cast_type and is_string_dtype(cast_type)) arr, _ = self._infer_types( arr, col_na_values | col_na_fvalues, try_num_bool ) arrays.append(arr) names = self.index_names index = ensure_index_from_sequences(arrays, names) return index @final def _convert_to_ndarrays( self, dct: Mapping, na_values, na_fvalues, verbose: bool = False, converters=None, dtypes=None, ): result = {} for c, values in dct.items(): conv_f = None if converters is None else converters.get(c, None) if isinstance(dtypes, dict): cast_type = dtypes.get(c, None) else: # single dtype or None cast_type = dtypes if self.na_filter: col_na_values, col_na_fvalues = _get_na_values( c, na_values, na_fvalues, self.keep_default_na ) else: col_na_values, col_na_fvalues = set(), set() if c in self._parse_date_cols: # GH#26203 Do not convert columns which get converted to dates # but replace nans to ensure to_datetime works mask = algorithms.isin(values, set(col_na_values) | col_na_fvalues) np.putmask(values, mask, np.nan) result[c] = values continue if conv_f is not None: # conv_f applied to data before inference if cast_type is not None: warnings.warn( ( "Both a converter and dtype were specified " f"for column {c} - only the converter will be used." ), ParserWarning, stacklevel=find_stack_level(), ) try: values = lib.map_infer(values, conv_f) except ValueError: # error: Argument 2 to "isin" has incompatible type "List[Any]"; # expected "Union[Union[ExtensionArray, ndarray], Index, Series]" mask = algorithms.isin( values, list(na_values) # type: ignore[arg-type] ).view(np.uint8) values = lib.map_infer_mask(values, conv_f, mask) cvals, na_count = self._infer_types( values, set(col_na_values) | col_na_fvalues, try_num_bool=False ) else: is_ea = is_extension_array_dtype(cast_type) is_str_or_ea_dtype = is_ea or is_string_dtype(cast_type) # skip inference if specified dtype is object # or casting to an EA try_num_bool = not (cast_type and is_str_or_ea_dtype) # general type inference and conversion cvals, na_count = self._infer_types( values, set(col_na_values) | col_na_fvalues, try_num_bool ) # type specified in dtype param or cast_type is an EA if cast_type and ( not is_dtype_equal(cvals, cast_type) or is_extension_array_dtype(cast_type) ): if not is_ea and na_count > 0: try: if is_bool_dtype(cast_type): raise ValueError( f"Bool column has NA values in column {c}" ) except (AttributeError, TypeError): # invalid input to is_bool_dtype pass cast_type = pandas_dtype(cast_type) cvals = self._cast_types(cvals, cast_type, c) result[c] = cvals if verbose and na_count: print(f"Filled {na_count} NA values in column {c!s}") return result @final def _set_noconvert_dtype_columns( self, col_indices: list[int], names: Sequence[Hashable] ) -> set[int]: """ Set the columns that should not undergo dtype conversions. Currently, any column that is involved with date parsing will not undergo such conversions. If usecols is specified, the positions of the columns not to cast is relative to the usecols not to all columns. Parameters ---------- col_indices: The indices specifying order and positions of the columns names: The column names which order is corresponding with the order of col_indices Returns ------- A set of integers containing the positions of the columns not to convert. """ usecols: list[int] | list[str] | None noconvert_columns = set() if self.usecols_dtype == "integer": # A set of integers will be converted to a list in # the correct order every single time. usecols = sorted(self.usecols) elif callable(self.usecols) or self.usecols_dtype not in ("empty", None): # The names attribute should have the correct columns # in the proper order for indexing with parse_dates. usecols = col_indices else: # Usecols is empty. usecols = None def _set(x) -> int: if usecols is not None and is_integer(x): x = usecols[x] if not is_integer(x): x = col_indices[names.index(x)] return x if isinstance(self.parse_dates, list): for val in self.parse_dates: if isinstance(val, list): for k in val: noconvert_columns.add(_set(k)) else: noconvert_columns.add(_set(val)) elif isinstance(self.parse_dates, dict): for val in self.parse_dates.values(): if isinstance(val, list): for k in val: noconvert_columns.add(_set(k)) else: noconvert_columns.add(_set(val)) elif self.parse_dates: if isinstance(self.index_col, list): for k in self.index_col: noconvert_columns.add(_set(k)) elif self.index_col is not None: noconvert_columns.add(_set(self.index_col)) return noconvert_columns def _infer_types(self, values, na_values, try_num_bool=True): """ Infer types of values, possibly casting Parameters ---------- values : ndarray na_values : set try_num_bool : bool, default try try to cast values to numeric (first preference) or boolean Returns ------- converted : ndarray na_count : int """ na_count = 0 if issubclass(values.dtype.type, (np.number, np.bool_)): # If our array has numeric dtype, we don't have to check for strings in isin na_values = np.array([val for val in na_values if not isinstance(val, str)]) mask = algorithms.isin(values, na_values) na_count = mask.astype("uint8", copy=False).sum() if na_count > 0: if is_integer_dtype(values): values = values.astype(np.float64) np.putmask(values, mask, np.nan) return values, na_count if try_num_bool and is_object_dtype(values.dtype): # exclude e.g DatetimeIndex here try: result, _ = lib.maybe_convert_numeric(values, na_values, False) except (ValueError, TypeError): # e.g. encountering datetime string gets ValueError # TypeError can be raised in floatify result = values na_count = parsers.sanitize_objects(result, na_values) else: na_count = isna(result).sum() else: result = values if values.dtype == np.object_: na_count = parsers.sanitize_objects(values, na_values) if result.dtype == np.object_ and try_num_bool: result, _ = libops.maybe_convert_bool( np.asarray(values), true_values=self.true_values, false_values=self.false_values, ) return result, na_count def _cast_types(self, values, cast_type, column): """ Cast values to specified type Parameters ---------- values : ndarray cast_type : string or np.dtype dtype to cast values to column : string column name - used only for error reporting Returns ------- converted : ndarray """ if is_categorical_dtype(cast_type): known_cats = ( isinstance(cast_type, CategoricalDtype) and cast_type.categories is not None ) if not is_object_dtype(values) and not known_cats: # TODO: this is for consistency with # c-parser which parses all categories # as strings values = astype_nansafe(values, np.dtype(str)) cats = Index(values).unique().dropna() values = Categorical._from_inferred_categories( cats, cats.get_indexer(values), cast_type, true_values=self.true_values ) # use the EA's implementation of casting elif is_extension_array_dtype(cast_type): # ensure cast_type is an actual dtype and not a string cast_type = pandas_dtype(cast_type) array_type = cast_type.construct_array_type() try: if is_bool_dtype(cast_type): return array_type._from_sequence_of_strings( values, dtype=cast_type, true_values=self.true_values, false_values=self.false_values, ) else: return array_type._from_sequence_of_strings(values, dtype=cast_type) except NotImplementedError as err: raise NotImplementedError( f"Extension Array: {array_type} must implement " "_from_sequence_of_strings in order to be used in parser methods" ) from err else: try: values = astype_nansafe(values, cast_type, copy=True, skipna=True) except ValueError as err: raise ValueError( f"Unable to convert column {column} to type {cast_type}" ) from err return values @overload def _do_date_conversions( self, names: Index, data: DataFrame, ) -> tuple[Sequence[Hashable] | Index, DataFrame]: ... @overload def _do_date_conversions( self, names: Sequence[Hashable], data: Mapping[Hashable, ArrayLike], ) -> tuple[Sequence[Hashable], Mapping[Hashable, ArrayLike]]: ... def _do_date_conversions( self, names: Sequence[Hashable] | Index, data: Mapping[Hashable, ArrayLike] | DataFrame, ) -> tuple[Sequence[Hashable] | Index, Mapping[Hashable, ArrayLike] | DataFrame]: # returns data, columns if self.parse_dates is not None: data, names = _process_date_conversion( data, self._date_conv, self.parse_dates, self.index_col, self.index_names, names, keep_date_col=self.keep_date_col, ) return names, data def _check_data_length( self, columns: Sequence[Hashable], data: Sequence[ArrayLike], ) -> None: """Checks if length of data is equal to length of column names. One set of trailing commas is allowed. self.index_col not False results in a ParserError previously when lengths do not match. Parameters ---------- columns: list of column names data: list of array-likes containing the data column-wise. """ if not self.index_col and len(columns) != len(data) and columns: if len(columns) == len(data) - 1 and np.all( (is_object_dtype(data[-1]) and data[-1] == "") | isna(data[-1]) ): return warnings.warn( "Length of header or names does not match length of data. This leads " "to a loss of data with index_col=False.", ParserWarning, stacklevel=find_stack_level(), ) @overload def _evaluate_usecols( self, usecols: set[int] | Callable[[Hashable], object], names: Sequence[Hashable], ) -> set[int]: ... @overload def _evaluate_usecols( self, usecols: set[str], names: Sequence[Hashable] ) -> set[str]: ... def _evaluate_usecols( self, usecols: Callable[[Hashable], object] | set[str] | set[int], names: Sequence[Hashable], ) -> set[str] | set[int]: """ Check whether or not the 'usecols' parameter is a callable. If so, enumerates the 'names' parameter and returns a set of indices for each entry in 'names' that evaluates to True. If not a callable, returns 'usecols'. """ if callable(usecols): return {i for i, name in enumerate(names) if usecols(name)} return usecols def _validate_usecols_names(self, usecols, names): """ Validates that all usecols are present in a given list of names. If not, raise a ValueError that shows what usecols are missing. Parameters ---------- usecols : iterable of usecols The columns to validate are present in names. names : iterable of names The column names to check against. Returns ------- usecols : iterable of usecols The `usecols` parameter if the validation succeeds. Raises ------ ValueError : Columns were missing. Error message will list them. """ missing = [c for c in usecols if c not in names] if len(missing) > 0: raise ValueError( f"Usecols do not match columns, columns expected but not found: " f"{missing}" ) return usecols def _validate_usecols_arg(self, usecols): """ Validate the 'usecols' parameter. Checks whether or not the 'usecols' parameter contains all integers (column selection by index), strings (column by name) or is a callable. Raises a ValueError if that is not the case. Parameters ---------- usecols : list-like, callable, or None List of columns to use when parsing or a callable that can be used to filter a list of table columns. Returns ------- usecols_tuple : tuple A tuple of (verified_usecols, usecols_dtype). 'verified_usecols' is either a set if an array-like is passed in or 'usecols' if a callable or None is passed in. 'usecols_dtype` is the inferred dtype of 'usecols' if an array-like is passed in or None if a callable or None is passed in. """ msg = ( "'usecols' must either be list-like of all strings, all unicode, " "all integers or a callable." ) if usecols is not None: if callable(usecols): return usecols, None if not is_list_like(usecols): # see gh-20529 # # Ensure it is iterable container but not string. raise ValueError(msg) usecols_dtype = lib.infer_dtype(usecols, skipna=False) if usecols_dtype not in ("empty", "integer", "string"): raise ValueError(msg) usecols = set(usecols) return usecols, usecols_dtype return usecols, None def _clean_index_names(self, columns, index_col, unnamed_cols): if not is_index_col(index_col): return None, columns, index_col columns = list(columns) # In case of no rows and multiindex columns we have to set index_names to # list of Nones GH#38292 if not columns: return [None] * len(index_col), columns, index_col cp_cols = list(columns) index_names: list[str | int | None] = [] # don't mutate index_col = list(index_col) for i, c in enumerate(index_col): if isinstance(c, str): index_names.append(c) for j, name in enumerate(cp_cols): if name == c: index_col[i] = j columns.remove(name) break else: name = cp_cols[c] columns.remove(name) index_names.append(name) # Only clean index names that were placeholders. for i, name in enumerate(index_names): if isinstance(name, str) and name in unnamed_cols: index_names[i] = None return index_names, columns, index_col def _get_empty_meta( self, columns, index_col, index_names, dtype: DtypeArg | None = None ): columns = list(columns) # Convert `dtype` to a defaultdict of some kind. # This will enable us to write `dtype[col_name]` # without worrying about KeyError issues later on. if not is_dict_like(dtype): # if dtype == None, default will be object. default_dtype = dtype or object # error: Argument 1 to "defaultdict" has incompatible type "Callable[[], # Union[ExtensionDtype, str, dtype[Any], Type[object], Dict[Hashable, # Union[ExtensionDtype, Union[str, dtype[Any]], Type[str], Type[float], # Type[int], Type[complex], Type[bool], Type[object]]]]]"; expected # "Optional[Callable[[], Union[ExtensionDtype, str, dtype[Any], # Type[object]]]]" # error: Incompatible return value type (got "Union[ExtensionDtype, str, # dtype[Any], Type[object], Dict[Hashable, Union[ExtensionDtype, Union[str, # dtype[Any]], Type[str], Type[float], Type[int], Type[complex], Type[bool], # Type[object]]]]", expected "Union[ExtensionDtype, str, dtype[Any], # Type[object]]") dtype = defaultdict( lambda: default_dtype # type: ignore[arg-type, return-value] ) else: dtype = cast(dict, dtype) dtype = defaultdict( lambda: object, {columns[k] if is_integer(k) else k: v for k, v in dtype.items()}, ) # Even though we have no data, the "index" of the empty DataFrame # could for example still be an empty MultiIndex. Thus, we need to # check whether we have any index columns specified, via either: # # 1) index_col (column indices) # 2) index_names (column names) # # Both must be non-null to ensure a successful construction. Otherwise, # we have to create a generic empty Index. if (index_col is None or index_col is False) or index_names is None: index = Index([]) else: data = [Series([], dtype=dtype[name]) for name in index_names] index = ensure_index_from_sequences(data, names=index_names) index_col.sort() for i, n in enumerate(index_col): columns.pop(n - i) col_dict = {col_name: Series([], dtype=dtype[col_name]) for col_name in columns} return index, columns, col_dict def _make_date_converter( date_parser=None, dayfirst=False, infer_datetime_format=False, cache_dates=True ): def converter(*date_cols): if date_parser is None: strs = parsing.concat_date_cols(date_cols) try: return tools.to_datetime( ensure_object(strs), utc=None, dayfirst=dayfirst, errors="ignore", infer_datetime_format=infer_datetime_format, cache=cache_dates, ).to_numpy() except ValueError: return tools.to_datetime( parsing.try_parse_dates(strs, dayfirst=dayfirst), cache=cache_dates ) else: try: result = tools.to_datetime( date_parser(*date_cols), errors="ignore", cache=cache_dates ) if isinstance(result, datetime.datetime): raise Exception("scalar parser") return result except Exception: try: return tools.to_datetime( parsing.try_parse_dates( parsing.concat_date_cols(date_cols), parser=date_parser, dayfirst=dayfirst, ), errors="ignore", ) except Exception: return generic_parser(date_parser, *date_cols) return converter parser_defaults = { "delimiter": None, "escapechar": None, "quotechar": '"', "quoting": csv.QUOTE_MINIMAL, "doublequote": True, "skipinitialspace": False, "lineterminator": None, "header": "infer", "index_col": None, "names": None, "prefix": None, "skiprows": None, "skipfooter": 0, "nrows": None, "na_values": None, "keep_default_na": True, "true_values": None, "false_values": None, "converters": None, "dtype": None, "cache_dates": True, "thousands": None, "comment": None, "decimal": ".", # 'engine': 'c', "parse_dates": False, "keep_date_col": False, "dayfirst": False, "date_parser": None, "usecols": None, # 'iterator': False, "chunksize": None, "verbose": False, "encoding": None, "squeeze": None, "compression": None, "mangle_dupe_cols": True, "infer_datetime_format": False, "skip_blank_lines": True, "encoding_errors": "strict", "on_bad_lines": ParserBase.BadLineHandleMethod.ERROR, "error_bad_lines": None, "warn_bad_lines": None, } def _process_date_conversion( data_dict, converter: Callable, parse_spec, index_col, index_names, columns, keep_date_col: bool = False, ): def _isindex(colspec): return (isinstance(index_col, list) and colspec in index_col) or ( isinstance(index_names, list) and colspec in index_names ) new_cols = [] new_data = {} orig_names = columns columns = list(columns) date_cols = set() if parse_spec is None or isinstance(parse_spec, bool): return data_dict, columns if isinstance(parse_spec, list): # list of column lists for colspec in parse_spec: if is_scalar(colspec) or isinstance(colspec, tuple): if isinstance(colspec, int) and colspec not in data_dict: colspec = orig_names[colspec] if _isindex(colspec): continue # Pyarrow engine returns Series which we need to convert to # numpy array before converter, its a no-op for other parsers data_dict[colspec] = converter(np.asarray(data_dict[colspec])) else: new_name, col, old_names = _try_convert_dates( converter, colspec, data_dict, orig_names ) if new_name in data_dict: raise ValueError(f"New date column already in dict {new_name}") new_data[new_name] = col new_cols.append(new_name) date_cols.update(old_names) elif isinstance(parse_spec, dict): # dict of new name to column list for new_name, colspec in parse_spec.items(): if new_name in data_dict: raise ValueError(f"Date column {new_name} already in dict") _, col, old_names = _try_convert_dates( converter, colspec, data_dict, orig_names ) new_data[new_name] = col # If original column can be converted to date we keep the converted values # This can only happen if values are from single column if len(colspec) == 1: new_data[colspec[0]] = col new_cols.append(new_name) date_cols.update(old_names) data_dict.update(new_data) new_cols.extend(columns) if not keep_date_col: for c in list(date_cols): data_dict.pop(c) new_cols.remove(c) return data_dict, new_cols def _try_convert_dates(parser: Callable, colspec, data_dict, columns): colset = set(columns) colnames = [] for c in colspec: if c in colset: colnames.append(c) elif isinstance(c, int) and c not in columns: colnames.append(columns[c]) else: colnames.append(c) new_name: tuple | str if all(isinstance(x, tuple) for x in colnames): new_name = tuple(map("_".join, zip(*colnames))) else: new_name = "_".join([str(x) for x in colnames]) to_parse = [np.asarray(data_dict[c]) for c in colnames if c in data_dict] new_col = parser(*to_parse) return new_name, new_col, colnames def _get_na_values(col, na_values, na_fvalues, keep_default_na): """ Get the NaN values for a given column. Parameters ---------- col : str The name of the column. na_values : array-like, dict The object listing the NaN values as strings. na_fvalues : array-like, dict The object listing the NaN values as floats. keep_default_na : bool If `na_values` is a dict, and the column is not mapped in the dictionary, whether to return the default NaN values or the empty set. Returns ------- nan_tuple : A length-two tuple composed of 1) na_values : the string NaN values for that column. 2) na_fvalues : the float NaN values for that column. """ if isinstance(na_values, dict): if col in na_values: return na_values[col], na_fvalues[col] else: if keep_default_na: return STR_NA_VALUES, set() return set(), set() else: return na_values, na_fvalues def _is_potential_multi_index( columns: Sequence[Hashable] | MultiIndex, index_col: bool | Sequence[int] | None = None, ) -> bool: """ Check whether or not the `columns` parameter could be converted into a MultiIndex. Parameters ---------- columns : array-like Object which may or may not be convertible into a MultiIndex index_col : None, bool or list, optional Column or columns to use as the (possibly hierarchical) index Returns ------- bool : Whether or not columns could become a MultiIndex """ if index_col is None or isinstance(index_col, bool): index_col = [] return bool( len(columns) and not isinstance(columns, MultiIndex) and all(isinstance(c, tuple) for c in columns if c not in list(index_col)) ) def _validate_parse_dates_arg(parse_dates): """ Check whether or not the 'parse_dates' parameter is a non-boolean scalar. Raises a ValueError if that is the case. """ msg = ( "Only booleans, lists, and dictionaries are accepted " "for the 'parse_dates' parameter" ) if parse_dates is not None: if is_scalar(parse_dates): if not lib.is_bool(parse_dates): raise TypeError(msg) elif not isinstance(parse_dates, (list, dict)): raise TypeError(msg) return parse_dates def is_index_col(col) -> bool: return col is not None and col is not False