from __future__ import annotations from collections import defaultdict from copy import copy import csv from enum import Enum import itertools from typing import ( TYPE_CHECKING, Any, cast, final, overload, ) import warnings import numpy as np from pandas._libs import ( lib, parsers, ) import pandas._libs.ops as libops from pandas._libs.parsers import STR_NA_VALUES from pandas.compat._optional import import_optional_dependency from pandas.errors import ( ParserError, ParserWarning, ) from pandas.util._exceptions import find_stack_level from pandas.core.dtypes.common import ( is_bool_dtype, is_dict_like, is_float_dtype, is_integer, is_integer_dtype, is_list_like, is_object_dtype, is_string_dtype, ) from pandas.core.dtypes.missing import isna from pandas import ( DataFrame, DatetimeIndex, StringDtype, ) from pandas.core import algorithms from pandas.core.arrays import ( ArrowExtensionArray, BaseMaskedArray, BooleanArray, FloatingArray, IntegerArray, ) from pandas.core.indexes.api import ( Index, MultiIndex, default_index, ensure_index_from_sequences, ) from pandas.core.series import Series from pandas.core.tools import datetimes as tools from pandas.io.common import is_potential_multi_index if TYPE_CHECKING: from collections.abc import ( Callable, Iterable, Mapping, Sequence, ) from pandas._typing import ( ArrayLike, DtypeArg, Hashable, HashableT, Scalar, SequenceT, ) class ParserBase: class BadLineHandleMethod(Enum): ERROR = 0 WARN = 1 SKIP = 2 _implicit_index: bool _first_chunk: bool keep_default_na: bool dayfirst: bool cache_dates: bool usecols_dtype: str | None def __init__(self, kwds) -> None: self._implicit_index = False self.names = kwds.get("names") self.orig_names: Sequence[Hashable] | None = None self.index_col = kwds.get("index_col", None) self.unnamed_cols: set = set() self.index_names: Sequence[Hashable] | None = None self.col_names: Sequence[Hashable] | None = None parse_dates = kwds.pop("parse_dates", False) if parse_dates is None or lib.is_bool(parse_dates): parse_dates = bool(parse_dates) elif not isinstance(parse_dates, list): raise TypeError( "Only booleans and lists are accepted for the 'parse_dates' parameter" ) self.parse_dates: bool | list = parse_dates self.date_parser = kwds.pop("date_parser", lib.no_default) self.date_format = kwds.pop("date_format", None) self.dayfirst = kwds.pop("dayfirst", 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.converters = kwds.get("converters") self.dtype_backend = kwds.get("dtype_backend") self.true_values = kwds.get("true_values") self.false_values = kwds.get("false_values") self.cache_dates = kwds.pop("cache_dates", True) # validate header options for mi self.header = kwds.get("header") if is_list_like(self.header, allow_sets=False): 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: # In this case we can pin down index_col as list[int] if is_integer(self.index_col): self.index_col = [self.index_col] elif not ( is_list_like(self.index_col, allow_sets=False) and all(map(is_integer, self.index_col)) ): raise ValueError( "index_col must only contain integers of column positions " "when specifying a multi-index header" ) else: self.index_col = list(self.index_col) self._first_chunk = True self.usecols, self.usecols_dtype = _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 close(self) -> None: pass @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] 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: Sequence[Hashable] | None, passed_names: bool = False, ) -> tuple[ Sequence[Hashable], Sequence[Hashable] | None, Sequence[Hashable] | None, bool ]: """ 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) # 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), strict=True)) 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 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_make_multi_index_columns( self, columns: SequenceT, col_names: Sequence[Hashable] | None = None, ) -> SequenceT | MultiIndex: # possibly create a column mi here if is_potential_multi_index(columns): columns_mi = cast("Sequence[tuple[Hashable, ...]]", columns) return MultiIndex.from_tuples(columns_mi, names=col_names) return columns @final def _make_index( self, alldata, columns, indexnamerow: list[Scalar] | None = None ) -> tuple[Index | None, Sequence[Hashable] | MultiIndex]: index: Index | None if isinstance(self.index_col, list) and len(self.index_col): to_remove = [] indexes = [] for idx in self.index_col: if isinstance(idx, str): raise ValueError(f"Index {idx} invalid") to_remove.append(idx) indexes.append(alldata[idx]) # remove index items from content and columns, don't pop in # loop for i in sorted(to_remove, reverse=True): alldata.pop(i) if not self._implicit_index: columns.pop(i) index = self._agg_index(indexes) # add names for the index if indexnamerow: coffset = len(indexnamerow) - len(columns) index = index.set_names(indexnamerow[:coffset]) else: index = None # maybe create a mi on the columns columns = self._maybe_make_multi_index_columns(columns, self.col_names) return index, columns @final def _clean_mapping(self, mapping): """converts col numbers to names""" if not isinstance(mapping, dict): return mapping clean = {} # for mypy assert self.orig_names is not None for col, v in mapping.items(): if isinstance(col, int) and col not in self.orig_names: col = self.orig_names[col] clean[col] = v if isinstance(mapping, defaultdict): remaining_cols = set(self.orig_names) - set(clean.keys()) clean.update({col: mapping[col] for col in remaining_cols}) return clean @final def _agg_index(self, index) -> Index: arrays = [] converters = self._clean_mapping(self.converters) clean_dtypes = self._clean_mapping(self.dtype) if self.index_names is not None: names: Iterable = self.index_names zip_strict = True else: names = itertools.cycle([None]) zip_strict = False for i, (arr, name) in enumerate(zip(index, names, strict=zip_strict)): if self._should_parse_dates(i): arr = date_converter( arr, col=self.index_names[i] if self.index_names is not None else None, dayfirst=self.dayfirst, cache_dates=self.cache_dates, date_format=self.date_format, ) 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 ) else: col_na_values, col_na_fvalues = set(), set() cast_type = None index_converter = False if self.index_names is not None: if isinstance(clean_dtypes, dict): cast_type = clean_dtypes.get(self.index_names[i], None) if isinstance(converters, dict): index_converter = converters.get(self.index_names[i]) is not None try_num_bool = not ( (cast_type and is_string_dtype(cast_type)) or index_converter ) arr, _ = self._infer_types( arr, col_na_values | col_na_fvalues, cast_type is None, try_num_bool ) if cast_type is not None: # Don't perform RangeIndex inference idx = Index(arr, name=name, dtype=cast_type, copy=False) else: idx = ensure_index_from_sequences([arr], [name]) arrays.append(idx) if len(arrays) == 1: return arrays[0] else: return MultiIndex.from_arrays(arrays) @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): validate_parse_dates_presence(self.parse_dates, names) for val in self.parse_dates: 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 @final def _infer_types( self, values, na_values, no_dtype_specified, try_num_bool: bool = True ) -> tuple[ArrayLike, int]: """ Infer types of values, possibly casting Parameters ---------- values : ndarray na_values : set no_dtype_specified: Specifies if we want to cast explicitly try_num_bool : bool, default try try to cast values to numeric (first preference) or boolean Returns ------- converted : ndarray or ExtensionArray 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 dtype_backend = self.dtype_backend non_default_dtype_backend = ( no_dtype_specified and dtype_backend is not lib.no_default ) result: ArrayLike if try_num_bool and is_object_dtype(values.dtype): # exclude e.g DatetimeIndex here try: result, result_mask = lib.maybe_convert_numeric( values, na_values, False, convert_to_masked_nullable=non_default_dtype_backend, # type: ignore[arg-type] ) except (ValueError, TypeError): # e.g. encountering datetime string gets ValueError # TypeError can be raised in floatify na_count = parsers.sanitize_objects(values, na_values) result = values else: if non_default_dtype_backend: if result_mask is None: result_mask = np.zeros(result.shape, dtype=np.bool_) if result_mask.all(): result = IntegerArray( np.ones(result_mask.shape, dtype=np.int64), result_mask ) elif is_integer_dtype(result): result = IntegerArray(result, result_mask) elif is_bool_dtype(result): result = BooleanArray(result, result_mask) elif is_float_dtype(result): result = FloatingArray(result, result_mask) na_count = result_mask.sum() 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 and (len(result) == 0 or not isinstance(result[0], int)) ): result, bool_mask = libops.maybe_convert_bool( np.asarray(values), true_values=self.true_values, false_values=self.false_values, convert_to_masked_nullable=non_default_dtype_backend, # type: ignore[arg-type] ) if result.dtype == np.bool_ and non_default_dtype_backend: if bool_mask is None: bool_mask = np.zeros(result.shape, dtype=np.bool_) result = BooleanArray(result, bool_mask) elif result.dtype == np.object_ and non_default_dtype_backend: # read_excel sends array of datetime objects if not lib.is_datetime_array(result, skipna=True): dtype = StringDtype() cls = dtype.construct_array_type() result = cls._from_sequence(values, dtype=dtype) if dtype_backend == "pyarrow": pa = import_optional_dependency("pyarrow") if isinstance(result, np.ndarray): result = ArrowExtensionArray(pa.array(result, from_pandas=True)) elif isinstance(result, BaseMaskedArray): if result._mask.all(): # We want an arrow null array here result = ArrowExtensionArray(pa.array([None] * len(result))) else: result = ArrowExtensionArray( pa.array(result._data, mask=result._mask) ) else: result = ArrowExtensionArray( pa.array(result.to_numpy(), from_pandas=True) ) return result, na_count @overload def _do_date_conversions( self, names: Index, data: DataFrame, ) -> DataFrame: ... @overload def _do_date_conversions( self, names: Sequence[Hashable], data: Mapping[Hashable, ArrayLike], ) -> Mapping[Hashable, ArrayLike]: ... @final def _do_date_conversions( self, names: Sequence[Hashable] | Index, data: Mapping[Hashable, ArrayLike] | DataFrame, ) -> Mapping[Hashable, ArrayLike] | DataFrame: if not isinstance(self.parse_dates, list): return data for colspec in self.parse_dates: if isinstance(colspec, int) and colspec not in data: colspec = names[colspec] if (isinstance(self.index_col, list) and colspec in self.index_col) or ( isinstance(self.index_names, list) and colspec in self.index_names ): continue result = date_converter( data[colspec], col=colspec, dayfirst=self.dayfirst, cache_dates=self.cache_dates, date_format=self.date_format, ) # error: Unsupported target for indexed assignment # ("Mapping[Hashable, ExtensionArray | ndarray[Any, Any]] | DataFrame") data[colspec] = result # type: ignore[index] return data @final 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: empty_str = is_object_dtype(data[-1]) and data[-1] == "" # error: No overload variant of "__ror__" of "ndarray" matches # argument type "ExtensionArray" empty_str_or_na = empty_str | isna(data[-1]) # type: ignore[operator] if len(columns) == len(data) - 1 and np.all(empty_str_or_na): 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(), ) @final def _validate_usecols_names(self, usecols: SequenceT, names: Sequence) -> SequenceT: """ 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 @final def _clean_index_names(self, columns, index_col) -> tuple[list | None, list, list]: 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 self.unnamed_cols: index_names[i] = None return index_names, columns, index_col @final def _get_empty_meta( self, columns: Sequence[HashableT], dtype: DtypeArg | None = None ) -> tuple[Index, list[HashableT], dict[HashableT, Series]]: columns = list(columns) index_col = self.index_col index_names = self.index_names # Convert `dtype` to a defaultdict of some kind. # This will enable us to write `dtype[col_name]` # without worrying about KeyError issues later on. dtype_dict: defaultdict[Hashable, Any] if not is_dict_like(dtype): # if dtype == None, default will be object. dtype_dict = defaultdict(lambda: dtype) else: dtype = cast(dict, dtype) dtype_dict = defaultdict( lambda: None, {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. index: Index if (index_col is None or index_col is False) or index_names is None: index = default_index(0) else: # TODO: We could return default_index(0) if dtype_dict[name] is None data = [ Index([], name=name, dtype=dtype_dict[name]) for name in index_names ] if len(data) == 1: index = data[0] else: index = MultiIndex.from_arrays(data) index_col.sort() for i, n in enumerate(index_col): columns.pop(n - i) col_dict = { col_name: Series([], dtype=dtype_dict[col_name]) for col_name in columns } return index, columns, col_dict def date_converter( date_col, col: Hashable, dayfirst: bool = False, cache_dates: bool = True, date_format: dict[Hashable, str] | str | None = None, ): if date_col.dtype.kind in "Mm": return date_col date_fmt = date_format.get(col) if isinstance(date_format, dict) else date_format str_objs = lib.ensure_string_array(np.asarray(date_col)) try: result = tools.to_datetime( str_objs, format=date_fmt, utc=False, dayfirst=dayfirst, cache=cache_dates, ) except (ValueError, TypeError): # test_usecols_with_parse_dates4 # test_multi_index_parse_dates return str_objs if isinstance(result, DatetimeIndex): arr = result.to_numpy() arr.flags.writeable = True return arr return result._values parser_defaults = { "delimiter": None, "escapechar": None, "quotechar": '"', "quoting": csv.QUOTE_MINIMAL, "doublequote": True, "skipinitialspace": False, "lineterminator": None, "header": "infer", "index_col": None, "names": 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, "dayfirst": False, "date_format": None, "usecols": None, # 'iterator': False, "chunksize": None, "encoding": None, "compression": None, "skip_blank_lines": True, "encoding_errors": "strict", "on_bad_lines": ParserBase.BadLineHandleMethod.ERROR, "dtype_backend": lib.no_default, } def get_na_values(col, na_values, na_fvalues, keep_default_na: bool): """ 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_index_col(col) -> bool: return col is not None and col is not False def validate_parse_dates_presence( parse_dates: bool | list, columns: Sequence[Hashable] ) -> set: """ 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 list is given as specification. Raises ------ ValueError If column to parse_date is not in dataframe. """ if not isinstance(parse_dates, list): return set() missing = set() unique_cols = set() for col in parse_dates: if isinstance(col, str): if col not in columns: missing.add(col) else: unique_cols.add(col) elif col in columns: unique_cols.add(col) else: unique_cols.add(columns[col]) if missing: missing_cols = ", ".join(sorted(missing)) raise ValueError(f"Missing column provided to 'parse_dates': '{missing_cols}'") return unique_cols def _validate_usecols_arg(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 @overload def evaluate_callable_usecols( usecols: Callable[[Hashable], object], names: Iterable[Hashable], ) -> set[int]: ... @overload def evaluate_callable_usecols( usecols: SequenceT, names: Iterable[Hashable] ) -> SequenceT: ... def evaluate_callable_usecols( usecols: Callable[[Hashable], object] | SequenceT, names: Iterable[Hashable], ) -> SequenceT | 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