Python pandas._libs.lib.is_bool_array() Examples

The following are 10 code examples of pandas._libs.lib.is_bool_array(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module pandas._libs.lib , or try the search function .
Example #1
Source File: common.py    From vnpy_crypto with MIT License 6 votes vote down vote up 
def is_bool_indexer(key):
    if isinstance(key, (ABCSeries, np.ndarray, ABCIndex)):
        if key.dtype == np.object_:
            key = np.asarray(_values_from_object(key))

            if not lib.is_bool_array(key):
                if isna(key).any():
                    raise ValueError('cannot index with vector containing '
                                     'NA / NaN values')
                return False
            return True
        elif key.dtype == np.bool_:
            return True
    elif isinstance(key, list):
        try:
            arr = np.asarray(key)
            return arr.dtype == np.bool_ and len(arr) == len(key)
        except TypeError:  # pragma: no cover
            return False

    return False
Example #2
Source File: common.py    From Splunking-Crime with GNU Affero General Public License v3.0 6 votes vote down vote up 
def is_bool_indexer(key):
    if isinstance(key, (ABCSeries, np.ndarray, ABCIndex)):
        if key.dtype == np.object_:
            key = np.asarray(_values_from_object(key))

            if not lib.is_bool_array(key):
                if isna(key).any():
                    raise ValueError('cannot index with vector containing '
                                     'NA / NaN values')
                return False
            return True
        elif key.dtype == np.bool_:
            return True
    elif isinstance(key, list):
        try:
            arr = np.asarray(key)
            return arr.dtype == np.bool_ and len(arr) == len(key)
        except TypeError:  # pragma: no cover
            return False

    return False
Example #3
Source File: common.py    From elasticintel with GNU General Public License v3.0 6 votes vote down vote up 
def is_bool_indexer(key):
    if isinstance(key, (ABCSeries, np.ndarray, ABCIndex)):
        if key.dtype == np.object_:
            key = np.asarray(_values_from_object(key))

            if not lib.is_bool_array(key):
                if isna(key).any():
                    raise ValueError('cannot index with vector containing '
                                     'NA / NaN values')
                return False
            return True
        elif key.dtype == np.bool_:
            return True
    elif isinstance(key, list):
        try:
            arr = np.asarray(key)
            return arr.dtype == np.bool_ and len(arr) == len(key)
        except TypeError:  # pragma: no cover
            return False

    return False
Example #4
Source File: blocks.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def is_bool(self):
        """ we can be a bool if we have only bool values but are of type
        object
        """
        return lib.is_bool_array(self.values.ravel())

    # TODO: Refactor when convert_objects is removed since there will be 1 path
Example #5
Source File: internals.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def is_bool(self):
        """ we can be a bool if we have only bool values but are of type
        object
        """
        return lib.is_bool_array(self.values.ravel())

    # TODO: Refactor when convert_objects is removed since there will be 1 path
Example #6
Source File: blocks.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def is_bool(self):
        """ we can be a bool if we have only bool values but are of type
        object
        """
        return lib.is_bool_array(self.values.ravel())

    # TODO: Refactor when convert_objects is removed since there will be 1 path
Example #7
Source File: internals.py    From Splunking-Crime with GNU Affero General Public License v3.0 5 votes vote down vote up 
def is_bool(self):
        """ we can be a bool if we have only bool values but are of type
        object
        """
        return lib.is_bool_array(self.values.ravel())

    # TODO: Refactor when convert_objects is removed since there will be 1 path
Example #8
Source File: internals.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def is_bool(self):
        """ we can be a bool if we have only bool values but are of type
        object
        """
        return lib.is_bool_array(self.values.ravel())

    # TODO: Refactor when convert_objects is removed since there will be 1 path
Example #9
Source File: common.py    From recruit with Apache License 2.0 4 votes vote down vote up 
def is_bool_indexer(key):
    # type: (Any) -> bool
    """
    Check whether `key` is a valid boolean indexer.

    Parameters
    ----------
    key : Any
        Only list-likes may be considered boolean indexers.
        All other types are not considered a boolean indexer.
        For array-like input, boolean ndarrays or ExtensionArrays
        with ``_is_boolean`` set are considered boolean indexers.

    Returns
    -------
    bool

    Raises
    ------
    ValueError
        When the array is an object-dtype ndarray or ExtensionArray
        and contains missing values.
    """
    na_msg = 'cannot index with vector containing NA / NaN values'
    if (isinstance(key, (ABCSeries, np.ndarray, ABCIndex)) or
            (is_array_like(key) and is_extension_array_dtype(key.dtype))):
        if key.dtype == np.object_:
            key = np.asarray(values_from_object(key))

            if not lib.is_bool_array(key):
                if isna(key).any():
                    raise ValueError(na_msg)
                return False
            return True
        elif is_bool_dtype(key.dtype):
            # an ndarray with bool-dtype by definition has no missing values.
            # So we only need to check for NAs in ExtensionArrays
            if is_extension_array_dtype(key.dtype):
                if np.any(key.isna()):
                    raise ValueError(na_msg)
            return True
    elif isinstance(key, list):
        try:
            arr = np.asarray(key)
            return arr.dtype == np.bool_ and len(arr) == len(key)
        except TypeError:  # pragma: no cover
            return False

    return False
Example #10
Source File: common.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 4 votes vote down vote up 
def is_bool_indexer(key):
    # type: (Any) -> bool
    """
    Check whether `key` is a valid boolean indexer.

    Parameters
    ----------
    key : Any
        Only list-likes may be considered boolean indexers.
        All other types are not considered a boolean indexer.
        For array-like input, boolean ndarrays or ExtensionArrays
        with ``_is_boolean`` set are considered boolean indexers.

    Returns
    -------
    bool

    Raises
    ------
    ValueError
        When the array is an object-dtype ndarray or ExtensionArray
        and contains missing values.
    """
    na_msg = 'cannot index with vector containing NA / NaN values'
    if (isinstance(key, (ABCSeries, np.ndarray, ABCIndex)) or
            (is_array_like(key) and is_extension_array_dtype(key.dtype))):
        if key.dtype == np.object_:
            key = np.asarray(values_from_object(key))

            if not lib.is_bool_array(key):
                if isna(key).any():
                    raise ValueError(na_msg)
                return False
            return True
        elif is_bool_dtype(key.dtype):
            # an ndarray with bool-dtype by definition has no missing values.
            # So we only need to check for NAs in ExtensionArrays
            if is_extension_array_dtype(key.dtype):
                if np.any(key.isna()):
                    raise ValueError(na_msg)
            return True
    elif isinstance(key, list):
        try:
            arr = np.asarray(key)
            return arr.dtype == np.bool_ and len(arr) == len(key)
        except TypeError:  # pragma: no cover
            return False

    return False