# Python: 3.9.2 (tags/v3.9.2:1a79785, Feb 19 2021, 13:44:55) [MSC v.1928 64 bit (AMD64)] # Library: pandas, version: 1.2.4 # Module: pandas._libs.parsers, version: unspecified import typing import builtins as _mod_builtins import numpy as _mod_numpy import pandas.errors as _mod_pandas_errors DtypeWarning = _mod_pandas_errors.DtypeWarning ENOENT: int EmptyDataError = _mod_pandas_errors.EmptyDataError ParserError = _mod_pandas_errors.ParserError ParserWarning = _mod_pandas_errors.ParserWarning QUOTE_MINIMAL: int QUOTE_NONE: int QUOTE_NONNUMERIC: int STR_NA_VALUES: set class TextReader(_mod_builtins.object): "\n\n # source: StringIO or file object\n\n ..versionchange:: 1.2.0\n removed 'compression', 'memory_map', and 'encoding' argument.\n These arguments are outsourced to CParserWrapper.\n 'source' has to be a file handle.\n " def __init__(self, *args, **kwargs) -> None: "\n\n # source: StringIO or file object\n\n ..versionchange:: 1.2.0\n removed 'compression', 'memory_map', and 'encoding' argument.\n These arguments are outsourced to CParserWrapper.\n 'source' has to be a file handle.\n " ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def _convert_column_data(self) -> typing.Any: ... def _get_converter(self) -> typing.Any: ... def _set_quoting(self) -> typing.Any: ... @property def allow_leading_cols(self) -> typing.Any: ... @property def buffer_lines(self) -> typing.Any: ... def close(self) -> typing.Any: ... @property def converters(self) -> typing.Any: ... @property def delim_whitespace(self) -> typing.Any: ... @property def delimiter(self) -> typing.Any: ... @property def dtype(self) -> typing.Any: ... @property def dtype_cast_order(self) -> typing.Any: ... @property def header(self) -> typing.Any: ... @property def header_end(self) -> typing.Any: ... @property def header_start(self) -> typing.Any: ... @property def index_col(self) -> typing.Any: ... @property def leading_cols(self) -> typing.Any: ... @property def low_memory(self) -> typing.Any: ... @property def mangle_dupe_cols(self) -> typing.Any: ... @property def na_values(self) -> typing.Any: ... @property def names(self) -> typing.Any: ... @property def noconvert(self) -> typing.Any: ... @property def orig_header(self) -> typing.Any: ... def read(self) -> typing.Any: '\n rows=None --> read all rows\n ' ... def remove_noconvert(self) -> typing.Any: ... def set_error_bad_lines(self) -> typing.Any: ... def set_noconvert(self) -> typing.Any: ... @property def skipfooter(self) -> typing.Any: ... @property def skiprows(self) -> typing.Any: ... @property def table_width(self) -> typing.Any: ... @property def unnamed_cols(self) -> typing.Any: ... @property def usecols(self) -> typing.Any: ... def __getattr__(self, name) -> typing.Any: ... _NA_VALUES: list __doc__: typing.Any __file__: str __name__: str __package__: str def __pyx_unpickle_Enum() -> typing.Any: ... __test__: dict def _compute_na_values() -> typing.Any: ... def _concatenate_chunks() -> typing.Any: ... def _ensure_encoded() -> typing.Any: ... def _maybe_encode() -> typing.Any: ... def _maybe_upcast() -> typing.Any: '\n\n ' ... def is_bool_dtype(arr_or_dtype) -> bool: "\n Check whether the provided array or dtype is of a boolean dtype.\n\n Parameters\n ----------\n arr_or_dtype : array-like\n The array or dtype to check.\n\n Returns\n -------\n boolean\n Whether or not the array or dtype is of a boolean dtype.\n\n Notes\n -----\n An ExtensionArray is considered boolean when the ``_is_boolean``\n attribute is set to True.\n\n Examples\n --------\n >>> is_bool_dtype(str)\n False\n >>> is_bool_dtype(int)\n False\n >>> is_bool_dtype(bool)\n True\n >>> is_bool_dtype(np.bool_)\n True\n >>> is_bool_dtype(np.array(['a', 'b']))\n False\n >>> is_bool_dtype(pd.Series([1, 2]))\n False\n >>> is_bool_dtype(np.array([True, False]))\n True\n >>> is_bool_dtype(pd.Categorical([True, False]))\n True\n >>> is_bool_dtype(pd.arrays.SparseArray([True, False]))\n True\n " ... def is_categorical_dtype(arr_or_dtype) -> bool: '\n Check whether an array-like or dtype is of the Categorical dtype.\n\n Parameters\n ----------\n arr_or_dtype : array-like\n The array-like or dtype to check.\n\n Returns\n -------\n boolean\n Whether or not the array-like or dtype is of the Categorical dtype.\n\n Examples\n --------\n >>> is_categorical_dtype(object)\n False\n >>> is_categorical_dtype(CategoricalDtype())\n True\n >>> is_categorical_dtype([1, 2, 3])\n False\n >>> is_categorical_dtype(pd.Categorical([1, 2, 3]))\n True\n >>> is_categorical_dtype(pd.CategoricalIndex([1, 2, 3]))\n True\n ' ... def is_datetime64_dtype(arr_or_dtype) -> bool: '\n Check whether an array-like or dtype is of the datetime64 dtype.\n\n Parameters\n ----------\n arr_or_dtype : array-like\n The array-like or dtype to check.\n\n Returns\n -------\n boolean\n Whether or not the array-like or dtype is of the datetime64 dtype.\n\n Examples\n --------\n >>> is_datetime64_dtype(object)\n False\n >>> is_datetime64_dtype(np.datetime64)\n True\n >>> is_datetime64_dtype(np.array([], dtype=int))\n False\n >>> is_datetime64_dtype(np.array([], dtype=np.datetime64))\n True\n >>> is_datetime64_dtype([1, 2, 3])\n False\n ' ... def is_extension_array_dtype(arr_or_dtype) -> bool: "\n Check if an object is a pandas extension array type.\n\n See the :ref:`Use Guide ` for more.\n\n Parameters\n ----------\n arr_or_dtype : object\n For array-like input, the ``.dtype`` attribute will\n be extracted.\n\n Returns\n -------\n bool\n Whether the `arr_or_dtype` is an extension array type.\n\n Notes\n -----\n This checks whether an object implements the pandas extension\n array interface. In pandas, this includes:\n\n * Categorical\n * Sparse\n * Interval\n * Period\n * DatetimeArray\n * TimedeltaArray\n\n Third-party libraries may implement arrays or types satisfying\n this interface as well.\n\n Examples\n --------\n >>> from pandas.api.types import is_extension_array_dtype\n >>> arr = pd.Categorical(['a', 'b'])\n >>> is_extension_array_dtype(arr)\n True\n >>> is_extension_array_dtype(arr.dtype)\n True\n\n >>> arr = np.array(['a', 'b'])\n >>> is_extension_array_dtype(arr.dtype)\n False\n " ... def is_float_dtype(arr_or_dtype) -> bool: "\n Check whether the provided array or dtype is of a float dtype.\n\n This function is internal and should not be exposed in the public API.\n\n Parameters\n ----------\n arr_or_dtype : array-like\n The array or dtype to check.\n\n Returns\n -------\n boolean\n Whether or not the array or dtype is of a float dtype.\n\n Examples\n --------\n >>> is_float_dtype(str)\n False\n >>> is_float_dtype(int)\n False\n >>> is_float_dtype(float)\n True\n >>> is_float_dtype(np.array(['a', 'b']))\n False\n >>> is_float_dtype(pd.Series([1, 2]))\n False\n >>> is_float_dtype(pd.Index([1, 2.]))\n True\n " ... def is_integer_dtype(arr_or_dtype) -> bool: "\n Check whether the provided array or dtype is of an integer dtype.\n\n Unlike in `in_any_int_dtype`, timedelta64 instances will return False.\n\n .. versionchanged:: 0.24.0\n\n The nullable Integer dtypes (e.g. pandas.Int64Dtype) are also considered\n as integer by this function.\n\n Parameters\n ----------\n arr_or_dtype : array-like\n The array or dtype to check.\n\n Returns\n -------\n boolean\n Whether or not the array or dtype is of an integer dtype and\n not an instance of timedelta64.\n\n Examples\n --------\n >>> is_integer_dtype(str)\n False\n >>> is_integer_dtype(int)\n True\n >>> is_integer_dtype(float)\n False\n >>> is_integer_dtype(np.uint64)\n True\n >>> is_integer_dtype('int8')\n True\n >>> is_integer_dtype('Int8')\n True\n >>> is_integer_dtype(pd.Int8Dtype)\n True\n >>> is_integer_dtype(np.datetime64)\n False\n >>> is_integer_dtype(np.timedelta64)\n False\n >>> is_integer_dtype(np.array(['a', 'b']))\n False\n >>> is_integer_dtype(pd.Series([1, 2]))\n True\n >>> is_integer_dtype(np.array([], dtype=np.timedelta64))\n False\n >>> is_integer_dtype(pd.Index([1, 2.])) # float\n False\n " ... def is_object_dtype(arr_or_dtype) -> bool: '\n Check whether an array-like or dtype is of the object dtype.\n\n Parameters\n ----------\n arr_or_dtype : array-like\n The array-like or dtype to check.\n\n Returns\n -------\n boolean\n Whether or not the array-like or dtype is of the object dtype.\n\n Examples\n --------\n >>> is_object_dtype(object)\n True\n >>> is_object_dtype(int)\n False\n >>> is_object_dtype(np.array([], dtype=object))\n True\n >>> is_object_dtype(np.array([], dtype=int))\n False\n >>> is_object_dtype([1, 2, 3])\n False\n ' ... k = _mod_numpy.object_ na_values: dict def pandas_dtype(dtype) -> Union[numpy.dtype, ForwardRef('ExtensionDtype')]: '\n Convert input into a pandas only dtype object or a numpy dtype object.\n\n Parameters\n ----------\n dtype : object to be converted\n\n Returns\n -------\n np.dtype or a pandas dtype\n\n Raises\n ------\n TypeError if not a dtype\n ' ... def sanitize_objects() -> typing.Any: '\n Convert specified values, including the given set na_values and empty\n strings if convert_empty is True, to np.nan.\n\n Parameters\n ----------\n values : ndarray[object]\n na_values : set\n convert_empty : bool, default True\n ' ... def union_categoricals(to_union, sort_categories, ignore_order) -> typing.Any: '\n Combine list-like of Categorical-like, unioning categories.\n\n All categories must have the same dtype.\n\n Parameters\n ----------\n to_union : list-like\n Categorical, CategoricalIndex, or Series with dtype=\'category\'.\n sort_categories : bool, default False\n If true, resulting categories will be lexsorted, otherwise\n they will be ordered as they appear in the data.\n ignore_order : bool, default False\n If true, the ordered attribute of the Categoricals will be ignored.\n Results in an unordered categorical.\n\n Returns\n -------\n Categorical\n\n Raises\n ------\n TypeError\n - all inputs do not have the same dtype\n - all inputs do not have the same ordered property\n - all inputs are ordered and their categories are not identical\n - sort_categories=True and Categoricals are ordered\n ValueError\n Empty list of categoricals passed\n\n Notes\n -----\n To learn more about categories, see `link\n `__\n\n Examples\n --------\n >>> from pandas.api.types import union_categoricals\n\n If you want to combine categoricals that do not necessarily have\n the same categories, `union_categoricals` will combine a list-like\n of categoricals. The new categories will be the union of the\n categories being combined.\n\n >>> a = pd.Categorical(["b", "c"])\n >>> b = pd.Categorical(["a", "b"])\n >>> union_categoricals([a, b])\n [\'b\', \'c\', \'a\', \'b\']\n Categories (3, object): [\'b\', \'c\', \'a\']\n\n By default, the resulting categories will be ordered as they appear\n in the `categories` of the data. If you want the categories to be\n lexsorted, use `sort_categories=True` argument.\n\n >>> union_categoricals([a, b], sort_categories=True)\n [\'b\', \'c\', \'a\', \'b\']\n Categories (3, object): [\'a\', \'b\', \'c\']\n\n `union_categoricals` also works with the case of combining two\n categoricals of the same categories and order information (e.g. what\n you could also `append` for).\n\n >>> a = pd.Categorical(["a", "b"], ordered=True)\n >>> b = pd.Categorical(["a", "b", "a"], ordered=True)\n >>> union_categoricals([a, b])\n [\'a\', \'b\', \'a\', \'b\', \'a\']\n Categories (2, object): [\'a\' < \'b\']\n\n Raises `TypeError` because the categories are ordered and not identical.\n\n >>> a = pd.Categorical(["a", "b"], ordered=True)\n >>> b = pd.Categorical(["a", "b", "c"], ordered=True)\n >>> union_categoricals([a, b])\n Traceback (most recent call last):\n ...\n TypeError: to union ordered Categoricals, all categories must be the same\n\n New in version 0.20.0\n\n Ordered categoricals with different categories or orderings can be\n combined by using the `ignore_ordered=True` argument.\n\n >>> a = pd.Categorical(["a", "b", "c"], ordered=True)\n >>> b = pd.Categorical(["c", "b", "a"], ordered=True)\n >>> union_categoricals([a, b], ignore_order=True)\n [\'a\', \'b\', \'c\', \'c\', \'b\', \'a\']\n Categories (3, object): [\'a\', \'b\', \'c\']\n\n `union_categoricals` also works with a `CategoricalIndex`, or `Series`\n containing categorical data, but note that the resulting array will\n always be a plain `Categorical`\n\n >>> a = pd.Series(["b", "c"], dtype=\'category\')\n >>> b = pd.Series(["a", "b"], dtype=\'category\')\n >>> union_categoricals([a, b])\n [\'b\', \'c\', \'a\', \'b\']\n Categories (3, object): [\'b\', \'c\', \'a\']\n ' ... def __getattr__(name) -> typing.Any: ...