505 lines
17 KiB
Python
505 lines
17 KiB
Python
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from copy import (
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copy,
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deepcopy,
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)
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import numpy as np
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import pytest
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from pandas.core.dtypes.common import is_scalar
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from pandas import (
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DataFrame,
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Index,
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Series,
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date_range,
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)
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import pandas._testing as tm
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# ----------------------------------------------------------------------
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# Generic types test cases
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def construct(box, shape, value=None, dtype=None, **kwargs):
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"""
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construct an object for the given shape
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if value is specified use that if its a scalar
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if value is an array, repeat it as needed
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"""
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if isinstance(shape, int):
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shape = tuple([shape] * box._AXIS_LEN)
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if value is not None:
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if is_scalar(value):
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if value == "empty":
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arr = None
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dtype = np.float64
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# remove the info axis
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kwargs.pop(box._info_axis_name, None)
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else:
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arr = np.empty(shape, dtype=dtype)
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arr.fill(value)
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else:
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fshape = np.prod(shape)
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arr = value.ravel()
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new_shape = fshape / arr.shape[0]
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if fshape % arr.shape[0] != 0:
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raise Exception("invalid value passed in construct")
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arr = np.repeat(arr, new_shape).reshape(shape)
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else:
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arr = np.random.default_rng(2).standard_normal(shape)
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return box(arr, dtype=dtype, **kwargs)
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class TestGeneric:
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@pytest.mark.parametrize(
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"func",
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[
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str.lower,
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{x: x.lower() for x in list("ABCD")},
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Series({x: x.lower() for x in list("ABCD")}),
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],
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)
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def test_rename(self, frame_or_series, func):
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# single axis
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idx = list("ABCD")
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for axis in frame_or_series._AXIS_ORDERS:
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kwargs = {axis: idx}
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obj = construct(frame_or_series, 4, **kwargs)
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# rename a single axis
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result = obj.rename(**{axis: func})
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expected = obj.copy()
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setattr(expected, axis, list("abcd"))
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tm.assert_equal(result, expected)
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def test_get_numeric_data(self, frame_or_series):
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n = 4
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kwargs = {
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frame_or_series._get_axis_name(i): list(range(n))
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for i in range(frame_or_series._AXIS_LEN)
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}
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# get the numeric data
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o = construct(frame_or_series, n, **kwargs)
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result = o._get_numeric_data()
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tm.assert_equal(result, o)
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# non-inclusion
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result = o._get_bool_data()
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expected = construct(frame_or_series, n, value="empty", **kwargs)
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if isinstance(o, DataFrame):
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# preserve columns dtype
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expected.columns = o.columns[:0]
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# https://github.com/pandas-dev/pandas/issues/50862
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tm.assert_equal(result.reset_index(drop=True), expected)
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# get the bool data
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arr = np.array([True, True, False, True])
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o = construct(frame_or_series, n, value=arr, **kwargs)
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result = o._get_numeric_data()
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tm.assert_equal(result, o)
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def test_nonzero(self, frame_or_series):
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# GH 4633
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# look at the boolean/nonzero behavior for objects
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obj = construct(frame_or_series, shape=4)
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msg = f"The truth value of a {frame_or_series.__name__} is ambiguous"
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with pytest.raises(ValueError, match=msg):
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bool(obj == 0)
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with pytest.raises(ValueError, match=msg):
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bool(obj == 1)
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with pytest.raises(ValueError, match=msg):
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bool(obj)
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obj = construct(frame_or_series, shape=4, value=1)
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with pytest.raises(ValueError, match=msg):
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bool(obj == 0)
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with pytest.raises(ValueError, match=msg):
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bool(obj == 1)
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with pytest.raises(ValueError, match=msg):
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bool(obj)
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obj = construct(frame_or_series, shape=4, value=np.nan)
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with pytest.raises(ValueError, match=msg):
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bool(obj == 0)
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with pytest.raises(ValueError, match=msg):
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bool(obj == 1)
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with pytest.raises(ValueError, match=msg):
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bool(obj)
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# empty
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obj = construct(frame_or_series, shape=0)
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with pytest.raises(ValueError, match=msg):
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bool(obj)
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# invalid behaviors
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obj1 = construct(frame_or_series, shape=4, value=1)
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obj2 = construct(frame_or_series, shape=4, value=1)
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with pytest.raises(ValueError, match=msg):
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if obj1:
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pass
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with pytest.raises(ValueError, match=msg):
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obj1 and obj2
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with pytest.raises(ValueError, match=msg):
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obj1 or obj2
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with pytest.raises(ValueError, match=msg):
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not obj1
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def test_frame_or_series_compound_dtypes(self, frame_or_series):
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# see gh-5191
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# Compound dtypes should raise NotImplementedError.
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def f(dtype):
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return construct(frame_or_series, shape=3, value=1, dtype=dtype)
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msg = (
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"compound dtypes are not implemented "
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f"in the {frame_or_series.__name__} constructor"
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)
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with pytest.raises(NotImplementedError, match=msg):
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f([("A", "datetime64[h]"), ("B", "str"), ("C", "int32")])
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# these work (though results may be unexpected)
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f("int64")
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f("float64")
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f("M8[ns]")
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def test_metadata_propagation(self, frame_or_series):
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# check that the metadata matches up on the resulting ops
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o = construct(frame_or_series, shape=3)
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o.name = "foo"
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o2 = construct(frame_or_series, shape=3)
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o2.name = "bar"
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# ----------
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# preserving
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# ----------
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# simple ops with scalars
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for op in ["__add__", "__sub__", "__truediv__", "__mul__"]:
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result = getattr(o, op)(1)
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tm.assert_metadata_equivalent(o, result)
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# ops with like
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for op in ["__add__", "__sub__", "__truediv__", "__mul__"]:
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result = getattr(o, op)(o)
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tm.assert_metadata_equivalent(o, result)
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# simple boolean
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for op in ["__eq__", "__le__", "__ge__"]:
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v1 = getattr(o, op)(o)
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tm.assert_metadata_equivalent(o, v1)
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tm.assert_metadata_equivalent(o, v1 & v1)
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tm.assert_metadata_equivalent(o, v1 | v1)
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# combine_first
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result = o.combine_first(o2)
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tm.assert_metadata_equivalent(o, result)
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# ---------------------------
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# non-preserving (by default)
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# ---------------------------
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# add non-like
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result = o + o2
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tm.assert_metadata_equivalent(result)
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# simple boolean
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for op in ["__eq__", "__le__", "__ge__"]:
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# this is a name matching op
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v1 = getattr(o, op)(o)
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v2 = getattr(o, op)(o2)
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tm.assert_metadata_equivalent(v2)
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tm.assert_metadata_equivalent(v1 & v2)
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tm.assert_metadata_equivalent(v1 | v2)
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def test_size_compat(self, frame_or_series):
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# GH8846
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# size property should be defined
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o = construct(frame_or_series, shape=10)
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assert o.size == np.prod(o.shape)
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assert o.size == 10 ** len(o.axes)
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def test_split_compat(self, frame_or_series):
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# xref GH8846
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o = construct(frame_or_series, shape=10)
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with tm.assert_produces_warning(
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FutureWarning, match=".swapaxes' is deprecated", check_stacklevel=False
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):
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assert len(np.array_split(o, 5)) == 5
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assert len(np.array_split(o, 2)) == 2
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# See gh-12301
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def test_stat_unexpected_keyword(self, frame_or_series):
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obj = construct(frame_or_series, 5)
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starwars = "Star Wars"
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errmsg = "unexpected keyword"
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with pytest.raises(TypeError, match=errmsg):
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obj.max(epic=starwars) # stat_function
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with pytest.raises(TypeError, match=errmsg):
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obj.var(epic=starwars) # stat_function_ddof
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with pytest.raises(TypeError, match=errmsg):
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obj.sum(epic=starwars) # cum_function
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with pytest.raises(TypeError, match=errmsg):
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obj.any(epic=starwars) # logical_function
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@pytest.mark.parametrize("func", ["sum", "cumsum", "any", "var"])
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def test_api_compat(self, func, frame_or_series):
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# GH 12021
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# compat for __name__, __qualname__
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obj = construct(frame_or_series, 5)
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f = getattr(obj, func)
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assert f.__name__ == func
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assert f.__qualname__.endswith(func)
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def test_stat_non_defaults_args(self, frame_or_series):
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obj = construct(frame_or_series, 5)
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out = np.array([0])
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errmsg = "the 'out' parameter is not supported"
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with pytest.raises(ValueError, match=errmsg):
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obj.max(out=out) # stat_function
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with pytest.raises(ValueError, match=errmsg):
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obj.var(out=out) # stat_function_ddof
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with pytest.raises(ValueError, match=errmsg):
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obj.sum(out=out) # cum_function
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with pytest.raises(ValueError, match=errmsg):
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obj.any(out=out) # logical_function
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def test_truncate_out_of_bounds(self, frame_or_series):
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# GH11382
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# small
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shape = [2000] + ([1] * (frame_or_series._AXIS_LEN - 1))
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small = construct(frame_or_series, shape, dtype="int8", value=1)
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tm.assert_equal(small.truncate(), small)
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tm.assert_equal(small.truncate(before=0, after=3e3), small)
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tm.assert_equal(small.truncate(before=-1, after=2e3), small)
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# big
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shape = [2_000_000] + ([1] * (frame_or_series._AXIS_LEN - 1))
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big = construct(frame_or_series, shape, dtype="int8", value=1)
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tm.assert_equal(big.truncate(), big)
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tm.assert_equal(big.truncate(before=0, after=3e6), big)
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tm.assert_equal(big.truncate(before=-1, after=2e6), big)
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@pytest.mark.parametrize(
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"func",
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[copy, deepcopy, lambda x: x.copy(deep=False), lambda x: x.copy(deep=True)],
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)
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@pytest.mark.parametrize("shape", [0, 1, 2])
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def test_copy_and_deepcopy(self, frame_or_series, shape, func):
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# GH 15444
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obj = construct(frame_or_series, shape)
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obj_copy = func(obj)
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assert obj_copy is not obj
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tm.assert_equal(obj_copy, obj)
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def test_data_deprecated(self, frame_or_series):
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obj = frame_or_series()
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msg = "(Series|DataFrame)._data is deprecated"
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with tm.assert_produces_warning(DeprecationWarning, match=msg):
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mgr = obj._data
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assert mgr is obj._mgr
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class TestNDFrame:
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# tests that don't fit elsewhere
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@pytest.mark.parametrize(
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"ser",
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[
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Series(range(10), dtype=np.float64),
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Series([str(i) for i in range(10)], dtype=object),
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],
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)
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def test_squeeze_series_noop(self, ser):
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# noop
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tm.assert_series_equal(ser.squeeze(), ser)
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def test_squeeze_frame_noop(self):
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# noop
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df = DataFrame(np.eye(2))
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tm.assert_frame_equal(df.squeeze(), df)
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def test_squeeze_frame_reindex(self):
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# squeezing
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df = DataFrame(
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np.random.default_rng(2).standard_normal((10, 4)),
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columns=Index(list("ABCD"), dtype=object),
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index=date_range("2000-01-01", periods=10, freq="B"),
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).reindex(columns=["A"])
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tm.assert_series_equal(df.squeeze(), df["A"])
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def test_squeeze_0_len_dim(self):
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# don't fail with 0 length dimensions GH11229 & GH8999
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empty_series = Series([], name="five", dtype=np.float64)
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empty_frame = DataFrame([empty_series])
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tm.assert_series_equal(empty_series, empty_series.squeeze())
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tm.assert_series_equal(empty_series, empty_frame.squeeze())
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def test_squeeze_axis(self):
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# axis argument
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df = DataFrame(
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np.random.default_rng(2).standard_normal((1, 4)),
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columns=Index(list("ABCD"), dtype=object),
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index=date_range("2000-01-01", periods=1, freq="B"),
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).iloc[:, :1]
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assert df.shape == (1, 1)
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tm.assert_series_equal(df.squeeze(axis=0), df.iloc[0])
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|
tm.assert_series_equal(df.squeeze(axis="index"), df.iloc[0])
|
||
|
|
tm.assert_series_equal(df.squeeze(axis=1), df.iloc[:, 0])
|
||
|
|
tm.assert_series_equal(df.squeeze(axis="columns"), df.iloc[:, 0])
|
||
|
|
assert df.squeeze() == df.iloc[0, 0]
|
||
|
|
msg = "No axis named 2 for object type DataFrame"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
df.squeeze(axis=2)
|
||
|
|
msg = "No axis named x for object type DataFrame"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
df.squeeze(axis="x")
|
||
|
|
|
||
|
|
def test_squeeze_axis_len_3(self):
|
||
|
|
df = DataFrame(
|
||
|
|
np.random.default_rng(2).standard_normal((3, 4)),
|
||
|
|
columns=Index(list("ABCD"), dtype=object),
|
||
|
|
index=date_range("2000-01-01", periods=3, freq="B"),
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(df.squeeze(axis=0), df)
|
||
|
|
|
||
|
|
def test_numpy_squeeze(self):
|
||
|
|
s = Series(range(2), dtype=np.float64)
|
||
|
|
tm.assert_series_equal(np.squeeze(s), s)
|
||
|
|
|
||
|
|
df = DataFrame(
|
||
|
|
np.random.default_rng(2).standard_normal((10, 4)),
|
||
|
|
columns=Index(list("ABCD"), dtype=object),
|
||
|
|
index=date_range("2000-01-01", periods=10, freq="B"),
|
||
|
|
).reindex(columns=["A"])
|
||
|
|
tm.assert_series_equal(np.squeeze(df), df["A"])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"ser",
|
||
|
|
[
|
||
|
|
Series(range(10), dtype=np.float64),
|
||
|
|
Series([str(i) for i in range(10)], dtype=object),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_transpose_series(self, ser):
|
||
|
|
# calls implementation in pandas/core/base.py
|
||
|
|
tm.assert_series_equal(ser.transpose(), ser)
|
||
|
|
|
||
|
|
def test_transpose_frame(self):
|
||
|
|
df = DataFrame(
|
||
|
|
np.random.default_rng(2).standard_normal((10, 4)),
|
||
|
|
columns=Index(list("ABCD"), dtype=object),
|
||
|
|
index=date_range("2000-01-01", periods=10, freq="B"),
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(df.transpose().transpose(), df)
|
||
|
|
|
||
|
|
def test_numpy_transpose(self, frame_or_series):
|
||
|
|
obj = DataFrame(
|
||
|
|
np.random.default_rng(2).standard_normal((10, 4)),
|
||
|
|
columns=Index(list("ABCD"), dtype=object),
|
||
|
|
index=date_range("2000-01-01", periods=10, freq="B"),
|
||
|
|
)
|
||
|
|
obj = tm.get_obj(obj, frame_or_series)
|
||
|
|
|
||
|
|
if frame_or_series is Series:
|
||
|
|
# 1D -> np.transpose is no-op
|
||
|
|
tm.assert_series_equal(np.transpose(obj), obj)
|
||
|
|
|
||
|
|
# round-trip preserved
|
||
|
|
tm.assert_equal(np.transpose(np.transpose(obj)), obj)
|
||
|
|
|
||
|
|
msg = "the 'axes' parameter is not supported"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
np.transpose(obj, axes=1)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"ser",
|
||
|
|
[
|
||
|
|
Series(range(10), dtype=np.float64),
|
||
|
|
Series([str(i) for i in range(10)], dtype=object),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_take_series(self, ser):
|
||
|
|
indices = [1, 5, -2, 6, 3, -1]
|
||
|
|
out = ser.take(indices)
|
||
|
|
expected = Series(
|
||
|
|
data=ser.values.take(indices),
|
||
|
|
index=ser.index.take(indices),
|
||
|
|
dtype=ser.dtype,
|
||
|
|
)
|
||
|
|
tm.assert_series_equal(out, expected)
|
||
|
|
|
||
|
|
def test_take_frame(self):
|
||
|
|
indices = [1, 5, -2, 6, 3, -1]
|
||
|
|
df = DataFrame(
|
||
|
|
np.random.default_rng(2).standard_normal((10, 4)),
|
||
|
|
columns=Index(list("ABCD"), dtype=object),
|
||
|
|
index=date_range("2000-01-01", periods=10, freq="B"),
|
||
|
|
)
|
||
|
|
out = df.take(indices)
|
||
|
|
expected = DataFrame(
|
||
|
|
data=df.values.take(indices, axis=0),
|
||
|
|
index=df.index.take(indices),
|
||
|
|
columns=df.columns,
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(out, expected)
|
||
|
|
|
||
|
|
def test_take_invalid_kwargs(self, frame_or_series):
|
||
|
|
indices = [-3, 2, 0, 1]
|
||
|
|
|
||
|
|
obj = DataFrame(range(5))
|
||
|
|
obj = tm.get_obj(obj, frame_or_series)
|
||
|
|
|
||
|
|
msg = r"take\(\) got an unexpected keyword argument 'foo'"
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
obj.take(indices, foo=2)
|
||
|
|
|
||
|
|
msg = "the 'out' parameter is not supported"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
obj.take(indices, out=indices)
|
||
|
|
|
||
|
|
msg = "the 'mode' parameter is not supported"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
obj.take(indices, mode="clip")
|
||
|
|
|
||
|
|
def test_axis_classmethods(self, frame_or_series):
|
||
|
|
box = frame_or_series
|
||
|
|
obj = box(dtype=object)
|
||
|
|
values = box._AXIS_TO_AXIS_NUMBER.keys()
|
||
|
|
for v in values:
|
||
|
|
assert obj._get_axis_number(v) == box._get_axis_number(v)
|
||
|
|
assert obj._get_axis_name(v) == box._get_axis_name(v)
|
||
|
|
assert obj._get_block_manager_axis(v) == box._get_block_manager_axis(v)
|
||
|
|
|
||
|
|
def test_flags_identity(self, frame_or_series):
|
||
|
|
obj = Series([1, 2])
|
||
|
|
if frame_or_series is DataFrame:
|
||
|
|
obj = obj.to_frame()
|
||
|
|
|
||
|
|
assert obj.flags is obj.flags
|
||
|
|
obj2 = obj.copy()
|
||
|
|
assert obj2.flags is not obj.flags
|
||
|
|
|
||
|
|
def test_bool_dep(self) -> None:
|
||
|
|
# GH-51749
|
||
|
|
msg_warn = (
|
||
|
|
"DataFrame.bool is now deprecated and will be removed "
|
||
|
|
"in future version of pandas"
|
||
|
|
)
|
||
|
|
with tm.assert_produces_warning(FutureWarning, match=msg_warn):
|
||
|
|
DataFrame({"col": [False]}).bool()
|