from datetime import (
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date,
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datetime,
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timedelta,
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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.cast import (
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infer_dtype_from,
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infer_dtype_from_array,
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infer_dtype_from_scalar,
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)
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from pandas.core.dtypes.common import is_dtype_equal
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from pandas import (
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Categorical,
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Interval,
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Period,
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Series,
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Timedelta,
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Timestamp,
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date_range,
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)
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@pytest.fixture(params=[True, False])
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def pandas_dtype(request):
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return request.param
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def test_infer_dtype_from_int_scalar(any_int_numpy_dtype):
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# Test that infer_dtype_from_scalar is
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# returning correct dtype for int and float.
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data = np.dtype(any_int_numpy_dtype).type(12)
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dtype, val = infer_dtype_from_scalar(data)
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assert dtype == type(data)
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def test_infer_dtype_from_float_scalar(float_numpy_dtype):
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float_numpy_dtype = np.dtype(float_numpy_dtype).type
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data = float_numpy_dtype(12)
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dtype, val = infer_dtype_from_scalar(data)
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assert dtype == float_numpy_dtype
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@pytest.mark.parametrize(
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"data,exp_dtype", [(12, np.int64), (np.float_(12), np.float64)]
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)
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def test_infer_dtype_from_python_scalar(data, exp_dtype):
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dtype, val = infer_dtype_from_scalar(data)
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assert dtype == exp_dtype
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@pytest.mark.parametrize("bool_val", [True, False])
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def test_infer_dtype_from_boolean(bool_val):
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dtype, val = infer_dtype_from_scalar(bool_val)
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assert dtype == np.bool_
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def test_infer_dtype_from_complex(complex_dtype):
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data = np.dtype(complex_dtype).type(1)
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dtype, val = infer_dtype_from_scalar(data)
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assert dtype == np.complex_
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@pytest.mark.parametrize(
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"data", [np.datetime64(1, "ns"), Timestamp(1), datetime(2000, 1, 1, 0, 0)]
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)
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def test_infer_dtype_from_datetime(data):
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dtype, val = infer_dtype_from_scalar(data)
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assert dtype == "M8[ns]"
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@pytest.mark.parametrize("data", [np.timedelta64(1, "ns"), Timedelta(1), timedelta(1)])
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def test_infer_dtype_from_timedelta(data):
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dtype, val = infer_dtype_from_scalar(data)
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assert dtype == "m8[ns]"
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@pytest.mark.parametrize("freq", ["M", "D"])
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def test_infer_dtype_from_period(freq, pandas_dtype):
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p = Period("2011-01-01", freq=freq)
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dtype, val = infer_dtype_from_scalar(p, pandas_dtype=pandas_dtype)
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if pandas_dtype:
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exp_dtype = f"period[{freq}]"
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else:
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exp_dtype = np.object_
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assert dtype == exp_dtype
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assert val == p
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@pytest.mark.parametrize(
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"data", [date(2000, 1, 1), "foo", Timestamp(1, tz="US/Eastern")]
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)
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def test_infer_dtype_misc(data):
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dtype, val = infer_dtype_from_scalar(data)
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assert dtype == np.object_
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@pytest.mark.parametrize("tz", ["UTC", "US/Eastern", "Asia/Tokyo"])
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def test_infer_from_scalar_tz(tz, pandas_dtype):
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dt = Timestamp(1, tz=tz)
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dtype, val = infer_dtype_from_scalar(dt, pandas_dtype=pandas_dtype)
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if pandas_dtype:
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exp_dtype = f"datetime64[ns, {tz}]"
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else:
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exp_dtype = np.object_
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assert dtype == exp_dtype
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assert val == dt
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@pytest.mark.parametrize(
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"left, right, subtype",
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[
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(0, 1, "int64"),
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(0.0, 1.0, "float64"),
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(Timestamp(0), Timestamp(1), "datetime64[ns]"),
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(Timestamp(0, tz="UTC"), Timestamp(1, tz="UTC"), "datetime64[ns, UTC]"),
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(Timedelta(0), Timedelta(1), "timedelta64[ns]"),
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],
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)
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def test_infer_from_interval(left, right, subtype, closed, pandas_dtype):
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# GH 30337
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interval = Interval(left, right, closed)
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result_dtype, result_value = infer_dtype_from_scalar(interval, pandas_dtype)
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expected_dtype = f"interval[{subtype}, {closed}]" if pandas_dtype else np.object_
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assert result_dtype == expected_dtype
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assert result_value == interval
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def test_infer_dtype_from_scalar_errors():
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msg = "invalid ndarray passed to infer_dtype_from_scalar"
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with pytest.raises(ValueError, match=msg):
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infer_dtype_from_scalar(np.array([1]))
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@pytest.mark.parametrize(
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"value, expected, pandas_dtype",
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[
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("foo", np.object_, False),
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(b"foo", np.object_, False),
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(1, np.int64, False),
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(1.5, np.float_, False),
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(np.datetime64("2016-01-01"), np.dtype("M8[ns]"), False),
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(Timestamp("20160101"), np.dtype("M8[ns]"), False),
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(Timestamp("20160101", tz="UTC"), np.object_, False),
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(Timestamp("20160101", tz="UTC"), "datetime64[ns, UTC]", True),
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],
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)
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def test_infer_dtype_from_scalar(value, expected, pandas_dtype):
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dtype, _ = infer_dtype_from_scalar(value, pandas_dtype=pandas_dtype)
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assert is_dtype_equal(dtype, expected)
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with pytest.raises(TypeError, match="must be list-like"):
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infer_dtype_from_array(value, pandas_dtype=pandas_dtype)
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@pytest.mark.parametrize(
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"arr, expected, pandas_dtype",
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[
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([1], np.int_, False),
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(np.array([1], dtype=np.int64), np.int64, False),
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([np.nan, 1, ""], np.object_, False),
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(np.array([[1.0, 2.0]]), np.float_, False),
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(Categorical(list("aabc")), np.object_, False),
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(Categorical([1, 2, 3]), np.int64, False),
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(Categorical(list("aabc")), "category", True),
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(Categorical([1, 2, 3]), "category", True),
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(date_range("20160101", periods=3), np.dtype("=M8[ns]"), False),
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(
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date_range("20160101", periods=3, tz="US/Eastern"),
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"datetime64[ns, US/Eastern]",
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True,
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),
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(Series([1.0, 2, 3]), np.float64, False),
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(Series(list("abc")), np.object_, False),
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(
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Series(date_range("20160101", periods=3, tz="US/Eastern")),
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"datetime64[ns, US/Eastern]",
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True,
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),
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],
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)
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def test_infer_dtype_from_array(arr, expected, pandas_dtype):
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dtype, _ = infer_dtype_from_array(arr, pandas_dtype=pandas_dtype)
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assert is_dtype_equal(dtype, expected)
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@pytest.mark.parametrize("cls", [np.datetime64, np.timedelta64])
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def test_infer_dtype_from_scalar_zerodim_datetimelike(cls):
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# ndarray.item() can incorrectly return int instead of td64/dt64
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val = cls(1234, "ns")
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arr = np.array(val)
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dtype, res = infer_dtype_from_scalar(arr)
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assert dtype.type is cls
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assert isinstance(res, cls)
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dtype, res = infer_dtype_from(arr)
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assert dtype.type is cls
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