Python pandas.core.dtypes.common.is_bool_dtype() Examples

The following are 14 code examples of pandas.core.dtypes.common.is_bool_dtype(). 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.core.dtypes.common , or try the search function .
Example #1
Source File: sparse.py    From recruit with Apache License 2.0 6 votes vote down vote up 
def _wrap_result(name, data, sparse_index, fill_value, dtype=None):
    """
    wrap op result to have correct dtype
    """
    if name.startswith('__'):
        # e.g. __eq__ --> eq
        name = name[2:-2]

    if name in ('eq', 'ne', 'lt', 'gt', 'le', 'ge'):
        dtype = np.bool

    fill_value = lib.item_from_zerodim(fill_value)

    if is_bool_dtype(dtype):
        # fill_value may be np.bool_
        fill_value = bool(fill_value)
    return SparseArray(data,
                       sparse_index=sparse_index,
                       fill_value=fill_value,
                       dtype=dtype)
Example #2
Source File: sparse.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 6 votes vote down vote up 
def _wrap_result(name, data, sparse_index, fill_value, dtype=None):
    """
    wrap op result to have correct dtype
    """
    if name.startswith('__'):
        # e.g. __eq__ --> eq
        name = name[2:-2]

    if name in ('eq', 'ne', 'lt', 'gt', 'le', 'ge'):
        dtype = np.bool

    fill_value = lib.item_from_zerodim(fill_value)

    if is_bool_dtype(dtype):
        # fill_value may be np.bool_
        fill_value = bool(fill_value)
    return SparseArray(data,
                       sparse_index=sparse_index,
                       fill_value=fill_value,
                       dtype=dtype)
Example #3
Source File: test_common.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def test_is_bool_dtype():
    assert not com.is_bool_dtype(int)
    assert not com.is_bool_dtype(str)
    assert not com.is_bool_dtype(pd.Series([1, 2]))
    assert not com.is_bool_dtype(np.array(['a', 'b']))
    assert not com.is_bool_dtype(pd.Index(['a', 'b']))

    assert com.is_bool_dtype(bool)
    assert com.is_bool_dtype(np.bool)
    assert com.is_bool_dtype(np.array([True, False]))
    assert com.is_bool_dtype(pd.Index([True, False]))
Example #4
Source File: sparse.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def _is_boolean(self):
        from pandas.core.dtypes.common import is_bool_dtype
        return is_bool_dtype(self.subtype)
Example #5
Source File: sparse.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def __getitem__(self, key):
        if isinstance(key, tuple):
            if len(key) > 1:
                raise IndexError("too many indices for array.")
            key = key[0]

        if is_integer(key):
            return self._get_val_at(key)
        elif isinstance(key, tuple):
            data_slice = self.values[key]
        elif isinstance(key, slice):
            # special case to preserve dtypes
            if key == slice(None):
                return self.copy()
            # TODO: this logic is surely elsewhere
            # TODO: this could be more efficient
            indices = np.arange(len(self), dtype=np.int32)[key]
            return self.take(indices)
        else:
            # TODO: I think we can avoid densifying when masking a
            # boolean SparseArray with another. Need to look at the
            # key's fill_value for True / False, and then do an intersection
            # on the indicies of the sp_values.
            if isinstance(key, SparseArray):
                if is_bool_dtype(key):
                    key = key.to_dense()
                else:
                    key = np.asarray(key)

            if com.is_bool_indexer(key) and len(self) == len(key):
                return self.take(np.arange(len(key), dtype=np.int32)[key])
            elif hasattr(key, '__len__'):
                return self.take(key)
            else:
                raise ValueError("Cannot slice with '{}'".format(key))

        return type(self)(data_slice, kind=self.kind)
Example #6
Source File: test_common.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_is_bool_dtype():
    assert not com.is_bool_dtype(int)
    assert not com.is_bool_dtype(str)
    assert not com.is_bool_dtype(pd.Series([1, 2]))
    assert not com.is_bool_dtype(np.array(['a', 'b']))
    assert not com.is_bool_dtype(pd.Index(['a', 'b']))

    assert com.is_bool_dtype(bool)
    assert com.is_bool_dtype(np.bool)
    assert com.is_bool_dtype(np.array([True, False]))
    assert com.is_bool_dtype(pd.Index([True, False]))
Example #7
Source File: test_common.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def test_is_bool_dtype():
    assert not com.is_bool_dtype(int)
    assert not com.is_bool_dtype(str)
    assert not com.is_bool_dtype(pd.Series([1, 2]))
    assert not com.is_bool_dtype(np.array(['a', 'b']))
    assert not com.is_bool_dtype(pd.Index(['a', 'b']))

    assert com.is_bool_dtype(bool)
    assert com.is_bool_dtype(np.bool)
    assert com.is_bool_dtype(np.array([True, False]))
    assert com.is_bool_dtype(pd.Index([True, False]))
Example #8
Source File: sparse.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def _is_boolean(self):
        from pandas.core.dtypes.common import is_bool_dtype
        return is_bool_dtype(self.subtype)
Example #9
Source File: sparse.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def __getitem__(self, key):
        if isinstance(key, tuple):
            if len(key) > 1:
                raise IndexError("too many indices for array.")
            key = key[0]

        if is_integer(key):
            return self._get_val_at(key)
        elif isinstance(key, tuple):
            data_slice = self.values[key]
        elif isinstance(key, slice):
            # special case to preserve dtypes
            if key == slice(None):
                return self.copy()
            # TODO: this logic is surely elsewhere
            # TODO: this could be more efficient
            indices = np.arange(len(self), dtype=np.int32)[key]
            return self.take(indices)
        else:
            # TODO: I think we can avoid densifying when masking a
            # boolean SparseArray with another. Need to look at the
            # key's fill_value for True / False, and then do an intersection
            # on the indicies of the sp_values.
            if isinstance(key, SparseArray):
                if is_bool_dtype(key):
                    key = key.to_dense()
                else:
                    key = np.asarray(key)

            if com.is_bool_indexer(key) and len(self) == len(key):
                return self.take(np.arange(len(key), dtype=np.int32)[key])
            elif hasattr(key, '__len__'):
                return self.take(key)
            else:
                raise ValueError("Cannot slice with '{}'".format(key))

        return type(self)(data_slice, kind=self.kind)
Example #10
Source File: test_common.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def test_is_bool_dtype():
    assert not com.is_bool_dtype(int)
    assert not com.is_bool_dtype(str)
    assert not com.is_bool_dtype(pd.Series([1, 2]))
    assert not com.is_bool_dtype(np.array(['a', 'b']))
    assert not com.is_bool_dtype(pd.Index(['a', 'b']))

    assert com.is_bool_dtype(bool)
    assert com.is_bool_dtype(np.bool)
    assert com.is_bool_dtype(np.array([True, False]))
    assert com.is_bool_dtype(pd.Index([True, False]))
Example #11
Source File: test_common.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_is_bool_dtype():
    assert not com.is_bool_dtype(int)
    assert not com.is_bool_dtype(str)
    assert not com.is_bool_dtype(pd.Series([1, 2]))
    assert not com.is_bool_dtype(np.array(['a', 'b']))
    assert not com.is_bool_dtype(pd.Index(['a', 'b']))

    assert com.is_bool_dtype(bool)
    assert com.is_bool_dtype(np.bool)
    assert com.is_bool_dtype(np.array([True, False]))
    assert com.is_bool_dtype(pd.Index([True, False]))
Example #12
Source File: test_to_from_scipy.py    From recruit with Apache License 2.0 4 votes vote down vote up 
def test_from_to_scipy(spmatrix, index, columns, fill_value, dtype):
    # GH 4343
    # Make one ndarray and from it one sparse matrix, both to be used for
    # constructing frames and comparing results
    arr = np.eye(3, dtype=dtype)
    # GH 16179
    arr[0, 1] = dtype(2)
    try:
        spm = spmatrix(arr)
        assert spm.dtype == arr.dtype
    except (TypeError, AssertionError):
        # If conversion to sparse fails for this spmatrix type and arr.dtype,
        # then the combination is not currently supported in NumPy, so we
        # can just skip testing it thoroughly
        return

    sdf = SparseDataFrame(spm, index=index, columns=columns,
                          default_fill_value=fill_value)

    # Expected result construction is kind of tricky for all
    # dtype-fill_value combinations; easiest to cast to something generic
    # and except later on
    rarr = arr.astype(object)
    rarr[arr == 0] = np.nan
    expected = SparseDataFrame(rarr, index=index, columns=columns).fillna(
        fill_value if fill_value is not None else np.nan)

    # Assert frame is as expected
    sdf_obj = sdf.astype(object)
    tm.assert_sp_frame_equal(sdf_obj, expected)
    tm.assert_frame_equal(sdf_obj.to_dense(), expected.to_dense())

    # Assert spmatrices equal
    assert dict(sdf.to_coo().todok()) == dict(spm.todok())

    # Ensure dtype is preserved if possible
    # XXX: verify this
    res_dtype = bool if is_bool_dtype(dtype) else dtype
    tm.assert_contains_all(sdf.dtypes.apply(lambda dtype: dtype.subtype),
                           {np.dtype(res_dtype)})
    assert sdf.to_coo().dtype == res_dtype

    # However, adding a str column results in an upcast to object
    sdf['strings'] = np.arange(len(sdf)).astype(str)
    assert sdf.to_coo().dtype == np.object_
Example #13
Source File: test_to_from_scipy.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 4 votes vote down vote up 
def test_from_to_scipy(spmatrix, index, columns, fill_value, dtype):
    # GH 4343
    # Make one ndarray and from it one sparse matrix, both to be used for
    # constructing frames and comparing results
    arr = np.eye(3, dtype=dtype)
    # GH 16179
    arr[0, 1] = dtype(2)
    try:
        spm = spmatrix(arr)
        assert spm.dtype == arr.dtype
    except (TypeError, AssertionError):
        # If conversion to sparse fails for this spmatrix type and arr.dtype,
        # then the combination is not currently supported in NumPy, so we
        # can just skip testing it thoroughly
        return

    sdf = SparseDataFrame(spm, index=index, columns=columns,
                          default_fill_value=fill_value)

    # Expected result construction is kind of tricky for all
    # dtype-fill_value combinations; easiest to cast to something generic
    # and except later on
    rarr = arr.astype(object)
    rarr[arr == 0] = np.nan
    expected = SparseDataFrame(rarr, index=index, columns=columns).fillna(
        fill_value if fill_value is not None else np.nan)

    # Assert frame is as expected
    sdf_obj = sdf.astype(object)
    tm.assert_sp_frame_equal(sdf_obj, expected)
    tm.assert_frame_equal(sdf_obj.to_dense(), expected.to_dense())

    # Assert spmatrices equal
    assert dict(sdf.to_coo().todok()) == dict(spm.todok())

    # Ensure dtype is preserved if possible
    # XXX: verify this
    res_dtype = bool if is_bool_dtype(dtype) else dtype
    tm.assert_contains_all(sdf.dtypes.apply(lambda dtype: dtype.subtype),
                           {np.dtype(res_dtype)})
    assert sdf.to_coo().dtype == res_dtype

    # However, adding a str column results in an upcast to object
    sdf['strings'] = np.arange(len(sdf)).astype(str)
    assert sdf.to_coo().dtype == np.object_
Example #14
Source File: test_to_from_scipy.py    From coffeegrindsize with MIT License 4 votes vote down vote up 
def test_from_to_scipy(spmatrix, index, columns, fill_value, dtype):
    # GH 4343
    # Make one ndarray and from it one sparse matrix, both to be used for
    # constructing frames and comparing results
    arr = np.eye(3, dtype=dtype)
    # GH 16179
    arr[0, 1] = dtype(2)
    try:
        spm = spmatrix(arr)
        assert spm.dtype == arr.dtype
    except (TypeError, AssertionError):
        # If conversion to sparse fails for this spmatrix type and arr.dtype,
        # then the combination is not currently supported in NumPy, so we
        # can just skip testing it thoroughly
        return

    sdf = SparseDataFrame(spm, index=index, columns=columns,
                          default_fill_value=fill_value)

    # Expected result construction is kind of tricky for all
    # dtype-fill_value combinations; easiest to cast to something generic
    # and except later on
    rarr = arr.astype(object)
    rarr[arr == 0] = np.nan
    expected = SparseDataFrame(rarr, index=index, columns=columns).fillna(
        fill_value if fill_value is not None else np.nan)

    # Assert frame is as expected
    sdf_obj = sdf.astype(object)
    tm.assert_sp_frame_equal(sdf_obj, expected)
    tm.assert_frame_equal(sdf_obj.to_dense(), expected.to_dense())

    # Assert spmatrices equal
    assert dict(sdf.to_coo().todok()) == dict(spm.todok())

    # Ensure dtype is preserved if possible
    # XXX: verify this
    res_dtype = bool if is_bool_dtype(dtype) else dtype
    tm.assert_contains_all(sdf.dtypes.apply(lambda dtype: dtype.subtype),
                           {np.dtype(res_dtype)})
    assert sdf.to_coo().dtype == res_dtype

    # However, adding a str column results in an upcast to object
    sdf['strings'] = np.arange(len(sdf)).astype(str)
    assert sdf.to_coo().dtype == np.object_