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

The following are 28 code examples of pandas.core.dtypes.common.is_integer_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: test_nanops.py    From twitter-stock-recommendation with MIT License 6 votes vote down vote up 
def test_returned_dtype(self):

        dtypes = [np.int16, np.int32, np.int64, np.float32, np.float64]
        if hasattr(np, 'float128'):
            dtypes.append(np.float128)

        for dtype in dtypes:
            s = Series(range(10), dtype=dtype)
            group_a = ['mean', 'std', 'var', 'skew', 'kurt']
            group_b = ['min', 'max']
            for method in group_a + group_b:
                result = getattr(s, method)()
                if is_integer_dtype(dtype) and method in group_a:
                    assert result.dtype == np.float64
                else:
                    assert result.dtype == dtype
Example #2
Source File: test_indexing.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def test_setitem_dtype_upcast(self):

        # GH3216
        df = DataFrame([{"a": 1}, {"a": 3, "b": 2}])
        df['c'] = np.nan
        assert df['c'].dtype == np.float64

        df.loc[0, 'c'] = 'foo'
        expected = DataFrame([{"a": 1, "c": 'foo'},
                              {"a": 3, "b": 2, "c": np.nan}])
        tm.assert_frame_equal(df, expected)

        # GH10280
        df = DataFrame(np.arange(6, dtype='int64').reshape(2, 3),
                       index=list('ab'),
                       columns=['foo', 'bar', 'baz'])

        for val in [3.14, 'wxyz']:
            left = df.copy()
            left.loc['a', 'bar'] = val
            right = DataFrame([[0, val, 2], [3, 4, 5]], index=list('ab'),
                              columns=['foo', 'bar', 'baz'])

            tm.assert_frame_equal(left, right)
            assert is_integer_dtype(left['foo'])
            assert is_integer_dtype(left['baz'])

        left = DataFrame(np.arange(6, dtype='int64').reshape(2, 3) / 10.0,
                         index=list('ab'),
                         columns=['foo', 'bar', 'baz'])
        left.loc['a', 'bar'] = 'wxyz'

        right = DataFrame([[0, 'wxyz', .2], [.3, .4, .5]], index=list('ab'),
                          columns=['foo', 'bar', 'baz'])

        tm.assert_frame_equal(left, right)
        assert is_float_dtype(left['foo'])
        assert is_float_dtype(left['baz'])
Example #3
Source File: test_common.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_is_integer_dtype():
    assert not com.is_integer_dtype(str)
    assert not com.is_integer_dtype(float)
    assert not com.is_integer_dtype(np.datetime64)
    assert not com.is_integer_dtype(np.timedelta64)
    assert not com.is_integer_dtype(pd.Index([1, 2.]))
    assert not com.is_integer_dtype(np.array(['a', 'b']))
    assert not com.is_integer_dtype(np.array([], dtype=np.timedelta64))

    assert com.is_integer_dtype(int)
    assert com.is_integer_dtype(np.uint64)
    assert com.is_integer_dtype(pd.Series([1, 2]))
Example #4
Source File: test_constructors.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_constructor_list_of_lists(self):
        # GH #484
        l = [[1, 'a'], [2, 'b']]
        df = DataFrame(data=l, columns=["num", "str"])
        assert is_integer_dtype(df['num'])
        assert df['str'].dtype == np.object_

        # GH 4851
        # list of 0-dim ndarrays
        expected = DataFrame({0: np.arange(10)})
        data = [np.array(x) for x in range(10)]
        result = DataFrame(data)
        tm.assert_frame_equal(result, expected)
Example #5
Source File: test_multilevel.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_delevel_infer_dtype(self):
        tuples = [tuple
                  for tuple in cart_product(
                      ['foo', 'bar'], [10, 20], [1.0, 1.1])]
        index = MultiIndex.from_tuples(tuples, names=['prm0', 'prm1', 'prm2'])
        df = DataFrame(np.random.randn(8, 3), columns=['A', 'B', 'C'],
                       index=index)
        deleveled = df.reset_index()
        assert is_integer_dtype(deleveled['prm1'])
        assert is_float_dtype(deleveled['prm2'])
Example #6
Source File: test_multilevel.py    From coffeegrindsize with MIT License 5 votes vote down vote up 
def test_delevel_infer_dtype(self):
        tuples = [tuple
                  for tuple in cart_product(
                      ['foo', 'bar'], [10, 20], [1.0, 1.1])]
        index = MultiIndex.from_tuples(tuples, names=['prm0', 'prm1', 'prm2'])
        df = DataFrame(np.random.randn(8, 3), columns=['A', 'B', 'C'],
                       index=index)
        deleveled = df.reset_index()
        assert is_integer_dtype(deleveled['prm1'])
        assert is_float_dtype(deleveled['prm2'])
Example #7
Source File: test_common.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def test_is_integer_dtype():
    assert not com.is_integer_dtype(str)
    assert not com.is_integer_dtype(float)
    assert not com.is_integer_dtype(np.datetime64)
    assert not com.is_integer_dtype(np.timedelta64)
    assert not com.is_integer_dtype(pd.Index([1, 2.]))
    assert not com.is_integer_dtype(np.array(['a', 'b']))
    assert not com.is_integer_dtype(np.array([], dtype=np.timedelta64))

    assert com.is_integer_dtype(int)
    assert com.is_integer_dtype(np.uint64)
    assert com.is_integer_dtype(pd.Series([1, 2]))
Example #8
Source File: test_constructors.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def test_constructor_list_of_lists(self):
        # GH #484
        l = [[1, 'a'], [2, 'b']]
        df = DataFrame(data=l, columns=["num", "str"])
        assert is_integer_dtype(df['num'])
        assert df['str'].dtype == np.object_

        # GH 4851
        # list of 0-dim ndarrays
        expected = DataFrame({0: np.arange(10)})
        data = [np.array(x) for x in range(10)]
        result = DataFrame(data)
        tm.assert_frame_equal(result, expected)
Example #9
Source File: test_nanops.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def test_returned_dtype(self):

        dtypes = [np.int16, np.int32, np.int64, np.float32, np.float64]
        if hasattr(np, 'float128'):
            dtypes.append(np.float128)

        for dtype in dtypes:
            s = Series(range(10), dtype=dtype)
            group_a = ['mean', 'std', 'var', 'skew', 'kurt']
            group_b = ['min', 'max']
            for method in group_a + group_b:
                result = getattr(s, method)()
                if is_integer_dtype(dtype) and method in group_a:
                    assert result.dtype == np.float64
                else:
                    assert result.dtype == dtype
Example #10
Source File: test_multilevel.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def test_delevel_infer_dtype(self):
        tuples = [tuple
                  for tuple in cart_product(
                      ['foo', 'bar'], [10, 20], [1.0, 1.1])]
        index = MultiIndex.from_tuples(tuples, names=['prm0', 'prm1', 'prm2'])
        df = DataFrame(np.random.randn(8, 3), columns=['A', 'B', 'C'],
                       index=index)
        deleveled = df.reset_index()
        assert is_integer_dtype(deleveled['prm1'])
        assert is_float_dtype(deleveled['prm2'])
Example #11
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_signed_integer_dtype(dtype):
    assert com.is_integer_dtype(dtype)
Example #12
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_not_integer_dtype(dtype):
    assert not com.is_integer_dtype(dtype)
Example #13
Source File: test_constructors.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def test_constructor_list_of_lists(self):
        # GH #484
        df = DataFrame(data=[[1, 'a'], [2, 'b']], columns=["num", "str"])
        assert is_integer_dtype(df['num'])
        assert df['str'].dtype == np.object_

        # GH 4851
        # list of 0-dim ndarrays
        expected = DataFrame({0: np.arange(10)})
        data = [np.array(x) for x in range(10)]
        result = DataFrame(data)
        tm.assert_frame_equal(result, expected)
Example #14
Source File: test_multilevel.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def test_delevel_infer_dtype(self):
        tuples = [tuple
                  for tuple in cart_product(
                      ['foo', 'bar'], [10, 20], [1.0, 1.1])]
        index = MultiIndex.from_tuples(tuples, names=['prm0', 'prm1', 'prm2'])
        df = DataFrame(np.random.randn(8, 3), columns=['A', 'B', 'C'],
                       index=index)
        deleveled = df.reset_index()
        assert is_integer_dtype(deleveled['prm1'])
        assert is_float_dtype(deleveled['prm2'])
Example #15
Source File: test_nanops.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def test_returned_dtype(self):

        dtypes = [np.int16, np.int32, np.int64, np.float32, np.float64]
        if hasattr(np, 'float128'):
            dtypes.append(np.float128)

        for dtype in dtypes:
            s = Series(range(10), dtype=dtype)
            group_a = ['mean', 'std', 'var', 'skew', 'kurt']
            group_b = ['min', 'max']
            for method in group_a + group_b:
                result = getattr(s, method)()
                if is_integer_dtype(dtype) and method in group_a:
                    assert result.dtype == np.float64
                else:
                    assert result.dtype == dtype
Example #16
Source File: test_multilevel.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def test_delevel_infer_dtype(self):
        tuples = [tuple
                  for tuple in cart_product(
                      ['foo', 'bar'], [10, 20], [1.0, 1.1])]
        index = MultiIndex.from_tuples(tuples, names=['prm0', 'prm1', 'prm2'])
        df = DataFrame(np.random.randn(8, 3), columns=['A', 'B', 'C'],
                       index=index)
        deleveled = df.reset_index()
        assert is_integer_dtype(deleveled['prm1'])
        assert is_float_dtype(deleveled['prm2'])
Example #17
Source File: test_common.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_is_integer_dtype():
    assert not com.is_integer_dtype(str)
    assert not com.is_integer_dtype(float)
    assert not com.is_integer_dtype(np.datetime64)
    assert not com.is_integer_dtype(np.timedelta64)
    assert not com.is_integer_dtype(pd.Index([1, 2.]))
    assert not com.is_integer_dtype(np.array(['a', 'b']))
    assert not com.is_integer_dtype(np.array([], dtype=np.timedelta64))

    assert com.is_integer_dtype(int)
    assert com.is_integer_dtype(np.uint64)
    assert com.is_integer_dtype(pd.Series([1, 2]))
Example #18
Source File: test_constructors.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_constructor_list_of_lists(self):
        # GH #484
        l = [[1, 'a'], [2, 'b']]
        df = DataFrame(data=l, columns=["num", "str"])
        assert is_integer_dtype(df['num'])
        assert df['str'].dtype == np.object_

        # GH 4851
        # list of 0-dim ndarrays
        expected = DataFrame({0: np.arange(10)})
        data = [np.array(x) for x in range(10)]
        result = DataFrame(data)
        tm.assert_frame_equal(result, expected)
Example #19
Source File: test_nanops.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_returned_dtype(self):

        dtypes = [np.int16, np.int32, np.int64, np.float32, np.float64]
        if hasattr(np, 'float128'):
            dtypes.append(np.float128)

        for dtype in dtypes:
            s = Series(range(10), dtype=dtype)
            group_a = ['mean', 'std', 'var', 'skew', 'kurt']
            group_b = ['min', 'max']
            for method in group_a + group_b:
                result = getattr(s, method)()
                if is_integer_dtype(dtype) and method in group_a:
                    assert result.dtype == np.float64
                else:
                    assert result.dtype == dtype
Example #20
Source File: test_multilevel.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_delevel_infer_dtype(self):
        tuples = [tuple
                  for tuple in cart_product(
                      ['foo', 'bar'], [10, 20], [1.0, 1.1])]
        index = MultiIndex.from_tuples(tuples, names=['prm0', 'prm1', 'prm2'])
        df = DataFrame(np.random.randn(8, 3), columns=['A', 'B', 'C'],
                       index=index)
        deleveled = df.reset_index()
        assert is_integer_dtype(deleveled['prm1'])
        assert is_float_dtype(deleveled['prm2'])
Example #21
Source File: test_common.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def test_is_signed_integer_dtype(dtype):
    assert com.is_integer_dtype(dtype)
Example #22
Source File: test_common.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def test_is_not_integer_dtype(dtype):
    assert not com.is_integer_dtype(dtype)
Example #23
Source File: test_constructors.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def test_constructor_list_of_lists(self):
        # GH #484
        df = DataFrame(data=[[1, 'a'], [2, 'b']], columns=["num", "str"])
        assert is_integer_dtype(df['num'])
        assert df['str'].dtype == np.object_

        # GH 4851
        # list of 0-dim ndarrays
        expected = DataFrame({0: np.arange(10)})
        data = [np.array(x) for x in range(10)]
        result = DataFrame(data)
        tm.assert_frame_equal(result, expected)
Example #24
Source File: test_indexing.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def test_setitem_dtype_upcast(self):

        # GH3216
        df = DataFrame([{"a": 1}, {"a": 3, "b": 2}])
        df['c'] = np.nan
        assert df['c'].dtype == np.float64

        df.loc[0, 'c'] = 'foo'
        expected = DataFrame([{"a": 1, "c": 'foo'},
                              {"a": 3, "b": 2, "c": np.nan}])
        tm.assert_frame_equal(df, expected)

        # GH10280
        df = DataFrame(np.arange(6, dtype='int64').reshape(2, 3),
                       index=list('ab'),
                       columns=['foo', 'bar', 'baz'])

        for val in [3.14, 'wxyz']:
            left = df.copy()
            left.loc['a', 'bar'] = val
            right = DataFrame([[0, val, 2], [3, 4, 5]], index=list('ab'),
                              columns=['foo', 'bar', 'baz'])

            tm.assert_frame_equal(left, right)
            assert is_integer_dtype(left['foo'])
            assert is_integer_dtype(left['baz'])

        left = DataFrame(np.arange(6, dtype='int64').reshape(2, 3) / 10.0,
                         index=list('ab'),
                         columns=['foo', 'bar', 'baz'])
        left.loc['a', 'bar'] = 'wxyz'

        right = DataFrame([[0, 'wxyz', .2], [.3, .4, .5]], index=list('ab'),
                          columns=['foo', 'bar', 'baz'])

        tm.assert_frame_equal(left, right)
        assert is_float_dtype(left['foo'])
        assert is_float_dtype(left['baz'])
Example #25
Source File: test_reshape.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 4 votes vote down vote up 
def test_basic_types(self, sparse, dtype):
        # GH 10531
        s_list = list('abc')
        s_series = Series(s_list)
        s_df = DataFrame({'a': [0, 1, 0, 1, 2],
                          'b': ['A', 'A', 'B', 'C', 'C'],
                          'c': [2, 3, 3, 3, 2]})

        expected = DataFrame({'a': [1, 0, 0],
                              'b': [0, 1, 0],
                              'c': [0, 0, 1]},
                             dtype=self.effective_dtype(dtype),
                             columns=list('abc'))
        if sparse:
            if is_integer_dtype(dtype):
                fill_value = 0
            elif dtype == bool:
                fill_value = False
            else:
                fill_value = 0.0

            expected = expected.apply(SparseArray, fill_value=fill_value)
        result = get_dummies(s_list, sparse=sparse, dtype=dtype)
        tm.assert_frame_equal(result, expected)

        result = get_dummies(s_series, sparse=sparse, dtype=dtype)
        tm.assert_frame_equal(result, expected)

        result = get_dummies(s_df, columns=s_df.columns,
                             sparse=sparse, dtype=dtype)
        if sparse:
            dtype_name = 'Sparse[{}, {}]'.format(
                self.effective_dtype(dtype).name,
                fill_value
            )
        else:
            dtype_name = self.effective_dtype(dtype).name

        expected = Series({dtype_name: 8})
        tm.assert_series_equal(result.get_dtype_counts(), expected)

        result = get_dummies(s_df, columns=['a'], sparse=sparse, dtype=dtype)

        expected_counts = {'int64': 1, 'object': 1}
        expected_counts[dtype_name] = 3 + expected_counts.get(dtype_name, 0)

        expected = Series(expected_counts).sort_index()
        tm.assert_series_equal(result.get_dtype_counts().sort_index(),
                               expected)
Example #26
Source File: test_reshape.py    From coffeegrindsize with MIT License 4 votes vote down vote up 
def test_basic_types(self, sparse, dtype):
        # GH 10531
        s_list = list('abc')
        s_series = Series(s_list)
        s_df = DataFrame({'a': [0, 1, 0, 1, 2],
                          'b': ['A', 'A', 'B', 'C', 'C'],
                          'c': [2, 3, 3, 3, 2]})

        expected = DataFrame({'a': [1, 0, 0],
                              'b': [0, 1, 0],
                              'c': [0, 0, 1]},
                             dtype=self.effective_dtype(dtype),
                             columns=list('abc'))
        if sparse:
            if is_integer_dtype(dtype):
                fill_value = 0
            elif dtype == bool:
                fill_value = False
            else:
                fill_value = 0.0

            expected = expected.apply(SparseArray, fill_value=fill_value)
        result = get_dummies(s_list, sparse=sparse, dtype=dtype)
        tm.assert_frame_equal(result, expected)

        result = get_dummies(s_series, sparse=sparse, dtype=dtype)
        tm.assert_frame_equal(result, expected)

        result = get_dummies(s_df, columns=s_df.columns,
                             sparse=sparse, dtype=dtype)
        if sparse:
            dtype_name = 'Sparse[{}, {}]'.format(
                self.effective_dtype(dtype).name,
                fill_value
            )
        else:
            dtype_name = self.effective_dtype(dtype).name

        expected = Series({dtype_name: 8})
        tm.assert_series_equal(result.get_dtype_counts(), expected)

        result = get_dummies(s_df, columns=['a'], sparse=sparse, dtype=dtype)

        expected_counts = {'int64': 1, 'object': 1}
        expected_counts[dtype_name] = 3 + expected_counts.get(dtype_name, 0)

        expected = Series(expected_counts).sort_index()
        tm.assert_series_equal(result.get_dtype_counts().sort_index(),
                               expected)
Example #27
Source File: test_nanops.py    From recruit with Apache License 2.0 4 votes vote down vote up 
def test_returned_dtype(self):

        dtypes = [np.int16, np.int32, np.int64, np.float32, np.float64]
        if hasattr(np, 'float128'):
            dtypes.append(np.float128)

        for dtype in dtypes:
            s = Series(range(10), dtype=dtype)
            group_a = ['mean', 'std', 'var', 'skew', 'kurt']
            group_b = ['min', 'max']
            for method in group_a + group_b:
                result = getattr(s, method)()
                if is_integer_dtype(dtype) and method in group_a:
                    assert result.dtype == np.float64
                else:
                    assert result.dtype == dtype
Example #28
Source File: test_reshape.py    From recruit with Apache License 2.0 4 votes vote down vote up 
def test_basic_types(self, sparse, dtype):
        # GH 10531
        s_list = list('abc')
        s_series = Series(s_list)
        s_df = DataFrame({'a': [0, 1, 0, 1, 2],
                          'b': ['A', 'A', 'B', 'C', 'C'],
                          'c': [2, 3, 3, 3, 2]})

        expected = DataFrame({'a': [1, 0, 0],
                              'b': [0, 1, 0],
                              'c': [0, 0, 1]},
                             dtype=self.effective_dtype(dtype),
                             columns=list('abc'))
        if sparse:
            if is_integer_dtype(dtype):
                fill_value = 0
            elif dtype == bool:
                fill_value = False
            else:
                fill_value = 0.0

            expected = expected.apply(SparseArray, fill_value=fill_value)
        result = get_dummies(s_list, sparse=sparse, dtype=dtype)
        tm.assert_frame_equal(result, expected)

        result = get_dummies(s_series, sparse=sparse, dtype=dtype)
        tm.assert_frame_equal(result, expected)

        result = get_dummies(s_df, columns=s_df.columns,
                             sparse=sparse, dtype=dtype)
        if sparse:
            dtype_name = 'Sparse[{}, {}]'.format(
                self.effective_dtype(dtype).name,
                fill_value
            )
        else:
            dtype_name = self.effective_dtype(dtype).name

        expected = Series({dtype_name: 8})
        tm.assert_series_equal(result.get_dtype_counts(), expected)

        result = get_dummies(s_df, columns=['a'], sparse=sparse, dtype=dtype)

        expected_counts = {'int64': 1, 'object': 1}
        expected_counts[dtype_name] = 3 + expected_counts.get(dtype_name, 0)

        expected = Series(expected_counts).sort_index()
        tm.assert_series_equal(result.get_dtype_counts().sort_index(),
                               expected)