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How to use is_categorical_dtype method in hypothesis

Best Python code snippet using hypothesis

parsers.pyi

Source:parsers.pyi

...178    ...179def is_bool_dtype(arr_or_dtype) -> bool:180    "\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    "181    ...182def is_categorical_dtype(arr_or_dtype) -> bool:183    '\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    '184    ...185def is_datetime64_dtype(arr_or_dtype) -> bool:186    '\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    '187    ...188def is_extension_array_dtype(arr_or_dtype) -> bool:189    "\n    Check if an object is a pandas extension array type.\n\n    See the :ref:`Use Guide <extending.extension-types>` 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    "190    ...191def is_float_dtype(arr_or_dtype) -> bool:192    "\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    "193    ...194def is_integer_dtype(arr_or_dtype) -> bool:195    "\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    "196    ...197def is_object_dtype(arr_or_dtype) -> bool:...

table_schema.py

Source:table_schema.py

...42    elif (is_datetime64_dtype(x) or is_datetime64tz_dtype(x)):43        return 'datetime'44    elif is_timedelta64_dtype(x):45        return 'duration'46    elif is_categorical_dtype(x):47        return 'any'48    elif is_string_dtype(x):49        return 'string'50    else:51        return 'any'52def set_default_names(data):53    """Sets index names to 'index' for regular, or 'level_x' for Multi"""54    if all(name is not None for name in data.index.names):55        return data56    data = data.copy()57    if data.index.nlevels > 1:58        names = [name if name is not None else 'level_{}'.format(i)59                 for i, name in enumerate(data.index.names)]60        data.index.names = names61    else:62        data.index.name = data.index.name or 'index'63    return data64def make_field(arr, dtype=None):65    dtype = dtype or arr.dtype66    if arr.name is None:67        name = 'values'68    else:69        name = arr.name70    field = {'name': name,71             'type': as_json_table_type(dtype)}72    if is_categorical_dtype(arr):73        if hasattr(arr, 'categories'):74            cats = arr.categories75            ordered = arr.ordered76        else:77            cats = arr.cat.categories78            ordered = arr.cat.ordered79        field['constraints'] = {"enum": list(cats)}80        field['ordered'] = ordered81    elif is_datetime64tz_dtype(arr):82        if hasattr(arr, 'dt'):83            field['tz'] = arr.dt.tz.zone84        else:85            field['tz'] = arr.tz.zone86    return field...

api.pyi

Source:api.pyi

1from pandas.core.dtypes.common import (2    is_array_like as is_array_like,3    is_bool as is_bool,4    is_bool_dtype as is_bool_dtype,5    is_categorical as is_categorical,6    is_categorical_dtype as is_categorical_dtype,7    is_complex as is_complex,8    is_complex_dtype as is_complex_dtype,9    is_datetime64_any_dtype as is_datetime64_any_dtype,10    is_datetime64_dtype as is_datetime64_dtype,11    is_datetime64_ns_dtype as is_datetime64_ns_dtype,12    is_datetime64tz_dtype as is_datetime64tz_dtype,13    is_dict_like as is_dict_like,14    is_dtype_equal as is_dtype_equal,15    is_extension_array_dtype as is_extension_array_dtype,16    is_extension_type as is_extension_type,17    is_file_like as is_file_like,18    is_float as is_float,19    is_float_dtype as is_float_dtype,20    is_hashable as is_hashable,21    is_int64_dtype as is_int64_dtype,22    is_integer as is_integer,23    is_integer_dtype as is_integer_dtype,24    is_interval as is_interval,25    is_interval_dtype as is_interval_dtype,26    is_iterator as is_iterator,27    is_list_like as is_list_like,28    is_named_tuple as is_named_tuple,29    is_number as is_number,30    is_numeric_dtype as is_numeric_dtype,31    is_object_dtype as is_object_dtype,32    is_period_dtype as is_period_dtype,33    is_re as is_re,34    is_re_compilable as is_re_compilable,35    is_scalar as is_scalar,36    is_signed_integer_dtype as is_signed_integer_dtype,37    is_sparse as is_sparse,38    is_string_dtype as is_string_dtype,39    is_timedelta64_dtype as is_timedelta64_dtype,40    is_timedelta64_ns_dtype as is_timedelta64_ns_dtype,41    is_unsigned_integer_dtype as is_unsigned_integer_dtype,...

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