219 lines
6.4 KiB
Python
219 lines
6.4 KiB
Python
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import numpy as np
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import pytest
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import pandas as pd
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from pandas import Series
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import pandas._testing as tm
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@pytest.mark.parametrize("operation, expected", [("min", "a"), ("max", "b")])
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def test_reductions_series_strings(operation, expected):
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# GH#31746
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ser = Series(["a", "b"], dtype="string")
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res_operation_serie = getattr(ser, operation)()
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assert res_operation_serie == expected
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@pytest.mark.parametrize("as_period", [True, False])
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def test_mode_extension_dtype(as_period):
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# GH#41927 preserve dt64tz dtype
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ser = Series([pd.Timestamp(1979, 4, n) for n in range(1, 5)])
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if as_period:
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ser = ser.dt.to_period("D")
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else:
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ser = ser.dt.tz_localize("US/Central")
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res = ser.mode()
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assert res.dtype == ser.dtype
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tm.assert_series_equal(res, ser)
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def test_mode_nullable_dtype(any_numeric_ea_dtype):
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# GH#55340
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ser = Series([1, 3, 2, pd.NA, 3, 2, pd.NA], dtype=any_numeric_ea_dtype)
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result = ser.mode(dropna=False)
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expected = Series([2, 3, pd.NA], dtype=any_numeric_ea_dtype)
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tm.assert_series_equal(result, expected)
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result = ser.mode(dropna=True)
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expected = Series([2, 3], dtype=any_numeric_ea_dtype)
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tm.assert_series_equal(result, expected)
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ser[-1] = pd.NA
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result = ser.mode(dropna=True)
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expected = Series([2, 3], dtype=any_numeric_ea_dtype)
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tm.assert_series_equal(result, expected)
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result = ser.mode(dropna=False)
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expected = Series([pd.NA], dtype=any_numeric_ea_dtype)
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tm.assert_series_equal(result, expected)
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def test_mode_infer_string():
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# GH#56183
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pytest.importorskip("pyarrow")
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ser = Series(["a", "b"], dtype=object)
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with pd.option_context("future.infer_string", True):
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result = ser.mode()
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expected = Series(["a", "b"], dtype=object)
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tm.assert_series_equal(result, expected)
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def test_reductions_td64_with_nat():
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# GH#8617
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ser = Series([0, pd.NaT], dtype="m8[ns]")
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exp = ser[0]
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assert ser.median() == exp
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assert ser.min() == exp
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assert ser.max() == exp
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@pytest.mark.parametrize("skipna", [True, False])
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def test_td64_sum_empty(skipna):
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# GH#37151
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ser = Series([], dtype="timedelta64[ns]")
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result = ser.sum(skipna=skipna)
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assert isinstance(result, pd.Timedelta)
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assert result == pd.Timedelta(0)
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def test_td64_summation_overflow():
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# GH#9442
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ser = Series(pd.date_range("20130101", periods=100000, freq="h"))
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ser[0] += pd.Timedelta("1s 1ms")
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# mean
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result = (ser - ser.min()).mean()
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expected = pd.Timedelta((pd.TimedeltaIndex(ser - ser.min()).asi8 / len(ser)).sum())
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# the computation is converted to float so
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# might be some loss of precision
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assert np.allclose(result._value / 1000, expected._value / 1000)
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# sum
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msg = "overflow in timedelta operation"
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with pytest.raises(ValueError, match=msg):
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(ser - ser.min()).sum()
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s1 = ser[0:10000]
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with pytest.raises(ValueError, match=msg):
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(s1 - s1.min()).sum()
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s2 = ser[0:1000]
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(s2 - s2.min()).sum()
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def test_prod_numpy16_bug():
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ser = Series([1.0, 1.0, 1.0], index=range(3))
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result = ser.prod()
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assert not isinstance(result, Series)
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@pytest.mark.parametrize("func", [np.any, np.all])
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@pytest.mark.parametrize("kwargs", [{"keepdims": True}, {"out": object()}])
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def test_validate_any_all_out_keepdims_raises(kwargs, func):
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ser = Series([1, 2])
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param = next(iter(kwargs))
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name = func.__name__
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msg = (
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f"the '{param}' parameter is not "
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"supported in the pandas "
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rf"implementation of {name}\(\)"
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)
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with pytest.raises(ValueError, match=msg):
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func(ser, **kwargs)
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def test_validate_sum_initial():
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ser = Series([1, 2])
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msg = (
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r"the 'initial' parameter is not "
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r"supported in the pandas "
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r"implementation of sum\(\)"
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)
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with pytest.raises(ValueError, match=msg):
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np.sum(ser, initial=10)
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def test_validate_median_initial():
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ser = Series([1, 2])
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msg = (
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r"the 'overwrite_input' parameter is not "
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r"supported in the pandas "
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r"implementation of median\(\)"
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)
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with pytest.raises(ValueError, match=msg):
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# It seems like np.median doesn't dispatch, so we use the
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# method instead of the ufunc.
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ser.median(overwrite_input=True)
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def test_validate_stat_keepdims():
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ser = Series([1, 2])
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msg = (
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r"the 'keepdims' parameter is not "
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r"supported in the pandas "
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r"implementation of sum\(\)"
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)
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with pytest.raises(ValueError, match=msg):
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np.sum(ser, keepdims=True)
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def test_mean_with_convertible_string_raises(using_array_manager, using_infer_string):
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# GH#44008
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ser = Series(["1", "2"])
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if using_infer_string:
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msg = "does not support"
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with pytest.raises(TypeError, match=msg):
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ser.sum()
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else:
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assert ser.sum() == "12"
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msg = "Could not convert string '12' to numeric|does not support"
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with pytest.raises(TypeError, match=msg):
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ser.mean()
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df = ser.to_frame()
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if not using_array_manager:
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msg = r"Could not convert \['12'\] to numeric|does not support"
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with pytest.raises(TypeError, match=msg):
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df.mean()
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def test_mean_dont_convert_j_to_complex(using_array_manager):
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# GH#36703
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df = pd.DataFrame([{"db": "J", "numeric": 123}])
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if using_array_manager:
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msg = "Could not convert string 'J' to numeric"
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else:
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msg = r"Could not convert \['J'\] to numeric|does not support"
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with pytest.raises(TypeError, match=msg):
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df.mean()
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with pytest.raises(TypeError, match=msg):
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df.agg("mean")
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msg = "Could not convert string 'J' to numeric|does not support"
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with pytest.raises(TypeError, match=msg):
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df["db"].mean()
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msg = "Could not convert string 'J' to numeric|ufunc 'divide'"
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with pytest.raises(TypeError, match=msg):
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np.mean(df["db"].astype("string").array)
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def test_median_with_convertible_string_raises(using_array_manager):
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# GH#34671 this _could_ return a string "2", but definitely not float 2.0
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msg = r"Cannot convert \['1' '2' '3'\] to numeric|does not support"
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ser = Series(["1", "2", "3"])
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with pytest.raises(TypeError, match=msg):
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ser.median()
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if not using_array_manager:
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msg = r"Cannot convert \[\['1' '2' '3'\]\] to numeric|does not support"
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df = ser.to_frame()
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with pytest.raises(TypeError, match=msg):
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df.median()
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