Python pandas.api.types.is_float_dtype() Examples

The following are 9 code examples of pandas.api.types.is_float_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.api.types , or try the search function .
Example #1
Source File: scales.py    From plotnine with GNU General Public License v2.0 6 votes vote down vote up 
def make_scale(ae, series, *args, **kwargs):
    """
    Return a proper scale object for the series

    The scale is for the aesthetic ae, and args & kwargs
    are passed on to the scale creating class
    """
    if pdtypes.is_float_dtype(series) and np.isinf(series).all():
        raise PlotnineError("Cannot create scale for infinite data")

    stype = scale_type(series)

    # filter parameters by scale type
    if stype in ('discrete', 'ordinal'):
        with suppress(KeyError):
            del kwargs['trans']

    scale_name = 'scale_{}_{}'.format(ae, stype)
    scale_klass = Registry[scale_name]
    return scale_klass(*args, **kwargs)
Example #2
Source File: test_create_ingest.py    From cooler with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def test_cload_field(bins_path, pairs_path):
    kwargs = dict(
        metadata=None,
        assembly="toy",
        chunksize=10,
        zero_based=False,
        comment_char="#",
        input_copy_status="unique",
        no_symmetric_upper=False,
        temp_dir=None,
        no_delete_temp=False,
        storage_options=None,
        no_count=True,
        max_merge=200,
        chrom1=2,
        pos1=3,
        chrom2=4,
        pos2=5,
    )
    cload_pairs.callback(
        bins_path, pairs_path, testcool_path, field=("score=8:dtype=float",), **kwargs
    )
    pixels = cooler.Cooler(testcool_path).pixels()[:]
    assert "count" in pixels.columns and types.is_integer_dtype(pixels.dtypes["count"])
    assert "score" in pixels.columns and types.is_float_dtype(pixels.dtypes["score"])
Example #3
Source File: test_integer.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def _check_op(self, s, op_name, other, exc=None):
        op = self.get_op_from_name(op_name)
        result = op(s, other)

        # compute expected
        mask = s.isna()

        # if s is a DataFrame, squeeze to a Series
        # for comparison
        if isinstance(s, pd.DataFrame):
            result = result.squeeze()
            s = s.squeeze()
            mask = mask.squeeze()

        # other array is an Integer
        if isinstance(other, IntegerArray):
            omask = getattr(other, 'mask', None)
            mask = getattr(other, 'data', other)
            if omask is not None:
                mask |= omask

        # 1 ** na is na, so need to unmask those
        if op_name == '__pow__':
            mask = np.where(s == 1, False, mask)

        elif op_name == '__rpow__':
            mask = np.where(other == 1, False, mask)

        # float result type or float op
        if ((is_float_dtype(other) or is_float(other) or
             op_name in ['__rtruediv__', '__truediv__',
                         '__rdiv__', '__div__'])):
            rs = s.astype('float')
            expected = op(rs, other)
            self._check_op_float(result, expected, mask, s, op_name, other)

        # integer result type
        else:
            rs = pd.Series(s.values._data)
            expected = op(rs, other)
            self._check_op_integer(result, expected, mask, s, op_name, other)
Example #4
Source File: test_integer.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def _check_op(self, s, op_name, other, exc=None):
        op = self.get_op_from_name(op_name)
        result = op(s, other)

        # compute expected
        mask = s.isna()

        # if s is a DataFrame, squeeze to a Series
        # for comparison
        if isinstance(s, pd.DataFrame):
            result = result.squeeze()
            s = s.squeeze()
            mask = mask.squeeze()

        # other array is an Integer
        if isinstance(other, IntegerArray):
            omask = getattr(other, 'mask', None)
            mask = getattr(other, 'data', other)
            if omask is not None:
                mask |= omask

        # 1 ** na is na, so need to unmask those
        if op_name == '__pow__':
            mask = np.where(s == 1, False, mask)

        elif op_name == '__rpow__':
            mask = np.where(other == 1, False, mask)

        # float result type or float op
        if ((is_float_dtype(other) or is_float(other) or
             op_name in ['__rtruediv__', '__truediv__',
                         '__rdiv__', '__div__'])):
            rs = s.astype('float')
            expected = op(rs, other)
            self._check_op_float(result, expected, mask, s, op_name, other)

        # integer result type
        else:
            rs = pd.Series(s.values._data)
            expected = op(rs, other)
            self._check_op_integer(result, expected, mask, s, op_name, other)
Example #5
Source File: test_dataframe.py    From direct-access-py with MIT License 5 votes vote down vote up 
def test_dataframe():
    d2 = DirectAccessV2(
        api_key=DIRECTACCESS_API_KEY,
        client_id=DIRECTACCESS_CLIENT_ID,
        client_secret=DIRECTACCESS_CLIENT_SECRET,
        access_token=DIRECTACCESS_TOKEN,
    )
    df = d2.to_dataframe("rigs", pagesize=10000, deleteddate="null")

    # Check index is set to API endpoint "primary key"
    assert df.index.name == "RigID"

    # Check datetime64 dtypes
    assert is_datetime64_ns_dtype(df.CreatedDate)
    assert is_datetime64_ns_dtype(df.DeletedDate)
    assert is_datetime64_ns_dtype(df.SpudDate)
    assert is_datetime64_ns_dtype(df.UpdatedDate)

    # Check Int64 dtypes
    assert is_int64_dtype(df.PermitDepth)
    assert is_int64_dtype(df.FormationDepth)

    # Check float dtypes
    assert is_float_dtype(df.RigLatitudeWGS84)
    assert is_float_dtype(df.RigLongitudeWGS84)

    return
Example #6
Source File: test_cli_ingest.py    From cooler with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def test_load_field():
    runner = CliRunner()
    with runner.isolated_filesystem():
        extra_args = ["--field", "count=7:dtype=float"]
        result = _run_load(runner, "toy.symm.upper.2.bg2", "bg2", 2, extra_args)
        assert result.exit_code == 0
        pixels1 = cooler.Cooler(op.join(datadir, "toy.symm.upper.2.cool")).pixels()[:]
        pixels2 = cooler.Cooler("toy.2.cool").pixels()[:]
        assert "count" in pixels2.columns and types.is_float_dtype(
            pixels2.dtypes["count"]
        )
        assert np.allclose(pixels1["count"][:], pixels2["count"][:])
Example #7
Source File: test_cli_ingest.py    From cooler with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def test_load_field2():
    runner = CliRunner()
    with runner.isolated_filesystem():
        extra_args = ["--count-as-float"]
        result = _run_load(runner, "toy.symm.upper.2.bg2", "bg2", 2, extra_args)
        assert result.exit_code == 0
        pixels1 = cooler.Cooler(op.join(datadir, "toy.symm.upper.2.cool")).pixels()[:]
        pixels2 = cooler.Cooler("toy.2.cool").pixels()[:]
        assert "count" in pixels2.columns and types.is_float_dtype(
            pixels2.dtypes["count"]
        )
        assert np.allclose(pixels1["count"][:], pixels2["count"][:])
Example #8
Source File: test_integer.py    From coffeegrindsize with MIT License 5 votes vote down vote up 
def _check_op(self, s, op_name, other, exc=None):
        op = self.get_op_from_name(op_name)
        result = op(s, other)

        # compute expected
        mask = s.isna()

        # if s is a DataFrame, squeeze to a Series
        # for comparison
        if isinstance(s, pd.DataFrame):
            result = result.squeeze()
            s = s.squeeze()
            mask = mask.squeeze()

        # other array is an Integer
        if isinstance(other, IntegerArray):
            omask = getattr(other, 'mask', None)
            mask = getattr(other, 'data', other)
            if omask is not None:
                mask |= omask

        # 1 ** na is na, so need to unmask those
        if op_name == '__pow__':
            mask = np.where(s == 1, False, mask)

        elif op_name == '__rpow__':
            mask = np.where(other == 1, False, mask)

        # float result type or float op
        if ((is_float_dtype(other) or is_float(other) or
             op_name in ['__rtruediv__', '__truediv__',
                         '__rdiv__', '__div__'])):
            rs = s.astype('float')
            expected = op(rs, other)
            self._check_op_float(result, expected, mask, s, op_name, other)

        # integer result type
        else:
            rs = pd.Series(s.values._data)
            expected = op(rs, other)
            self._check_op_integer(result, expected, mask, s, op_name, other)
Example #9
Source File: test_cli_ingest.py    From cooler with BSD 3-Clause "New" or "Revised" License 4 votes vote down vote up 
def test_cload_field():
    runner = CliRunner()
    with runner.isolated_filesystem():
        extra_args = ["--field", "score=8"]
        result = _run_cload_pairs(runner, 2, extra_args)
        assert result.exit_code == 0
        pixels = cooler.Cooler("toy.2.cool").pixels()[:]
        assert "count" in pixels.columns and types.is_integer_dtype(
            pixels.dtypes["count"]
        )
        assert "score" in pixels.columns and types.is_float_dtype(
            pixels.dtypes["score"]
        )

        extra_args = ["--field", "count=8"]
        result = _run_cload_pairs(runner, 2, extra_args)
        assert result.exit_code == 0
        pixels = cooler.Cooler("toy.2.cool").pixels()[:]
        assert "count" in pixels.columns and types.is_integer_dtype(
            pixels.dtypes["count"]
        )
        assert np.allclose(pixels["count"][:], 0)

        extra_args = ["--field", "count=8:dtype=float"]
        result = _run_cload_pairs(runner, 2, extra_args)
        assert result.exit_code == 0
        pixels = cooler.Cooler("toy.2.cool").pixels()[:]
        assert "count" in pixels.columns and types.is_float_dtype(
            pixels.dtypes["count"]
        )
        assert np.allclose(pixels["count"][:], 0.2)

        extra_args = ["--field", "count=8:agg=min,dtype=float"]
        result = _run_cload_pairs(runner, 2, extra_args)
        assert result.exit_code == 0
        pixels = cooler.Cooler("toy.2.cool").pixels()[:]
        assert "count" in pixels.columns and types.is_float_dtype(
            pixels.dtypes["count"]
        )
        assert np.allclose(pixels["count"][:], 0.1)

        ## don't implement the --no-count for now
        # extra_args =  ['--field', 'score=7:dtype=float', '--no-count']
        # result = _run_cload_pairs(runner, 2, extra_args)
        # assert result.exit_code == 0
        # pixels = cooler.Cooler('toy.2.cool').pixels()[:]
        # assert 'count' not in pixels.columns
        # assert 'score' in pixels.columns and types.is_float_dtype(pixels.dtypes['score'])


# '--metadata', '',
# '--zero-based',
# '--comment-char', '',
# '--storage-options', '',