411 lines
13 KiB
Python
411 lines
13 KiB
Python
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import datetime
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import decimal
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import numpy as np
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import pytest
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import pytz
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from pandas.core.dtypes.base import registry
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import pandas as pd
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import pandas._testing as tm
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from pandas.api.extensions import register_extension_dtype
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from pandas.api.types import is_scalar
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from pandas.arrays import (
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BooleanArray,
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DatetimeArray,
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FloatingArray,
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IntegerArray,
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IntervalArray,
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SparseArray,
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StringArray,
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TimedeltaArray,
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)
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from pandas.core.arrays import PandasArray, integer_array, period_array
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from pandas.tests.extension.decimal import DecimalArray, DecimalDtype, to_decimal
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@pytest.mark.parametrize(
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"data, dtype, expected",
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[
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# Basic NumPy defaults.
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([1, 2], None, IntegerArray._from_sequence([1, 2])),
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([1, 2], object, PandasArray(np.array([1, 2], dtype=object))),
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(
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[1, 2],
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np.dtype("float32"),
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PandasArray(np.array([1.0, 2.0], dtype=np.dtype("float32"))),
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),
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(np.array([1, 2], dtype="int64"), None, IntegerArray._from_sequence([1, 2])),
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(
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np.array([1.0, 2.0], dtype="float64"),
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None,
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FloatingArray._from_sequence([1.0, 2.0]),
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),
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# String alias passes through to NumPy
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([1, 2], "float32", PandasArray(np.array([1, 2], dtype="float32"))),
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# Period alias
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(
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[pd.Period("2000", "D"), pd.Period("2001", "D")],
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"Period[D]",
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period_array(["2000", "2001"], freq="D"),
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),
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# Period dtype
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(
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[pd.Period("2000", "D")],
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pd.PeriodDtype("D"),
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period_array(["2000"], freq="D"),
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),
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# Datetime (naive)
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(
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[1, 2],
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np.dtype("datetime64[ns]"),
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DatetimeArray._from_sequence(np.array([1, 2], dtype="datetime64[ns]")),
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),
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(
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np.array([1, 2], dtype="datetime64[ns]"),
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None,
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DatetimeArray._from_sequence(np.array([1, 2], dtype="datetime64[ns]")),
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),
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(
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pd.DatetimeIndex(["2000", "2001"]),
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np.dtype("datetime64[ns]"),
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DatetimeArray._from_sequence(["2000", "2001"]),
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),
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(
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pd.DatetimeIndex(["2000", "2001"]),
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None,
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DatetimeArray._from_sequence(["2000", "2001"]),
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),
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(
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["2000", "2001"],
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np.dtype("datetime64[ns]"),
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DatetimeArray._from_sequence(["2000", "2001"]),
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),
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# Datetime (tz-aware)
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(
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["2000", "2001"],
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pd.DatetimeTZDtype(tz="CET"),
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DatetimeArray._from_sequence(
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["2000", "2001"], dtype=pd.DatetimeTZDtype(tz="CET")
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),
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),
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# Timedelta
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(
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["1H", "2H"],
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np.dtype("timedelta64[ns]"),
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TimedeltaArray._from_sequence(["1H", "2H"]),
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),
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(
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pd.TimedeltaIndex(["1H", "2H"]),
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np.dtype("timedelta64[ns]"),
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TimedeltaArray._from_sequence(["1H", "2H"]),
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),
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(
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pd.TimedeltaIndex(["1H", "2H"]),
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None,
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TimedeltaArray._from_sequence(["1H", "2H"]),
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),
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# Category
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(["a", "b"], "category", pd.Categorical(["a", "b"])),
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(
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["a", "b"],
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pd.CategoricalDtype(None, ordered=True),
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pd.Categorical(["a", "b"], ordered=True),
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),
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# Interval
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(
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[pd.Interval(1, 2), pd.Interval(3, 4)],
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"interval",
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IntervalArray.from_tuples([(1, 2), (3, 4)]),
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),
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# Sparse
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([0, 1], "Sparse[int64]", SparseArray([0, 1], dtype="int64")),
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# IntegerNA
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([1, None], "Int16", integer_array([1, None], dtype="Int16")),
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(pd.Series([1, 2]), None, PandasArray(np.array([1, 2], dtype=np.int64))),
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# String
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(["a", None], "string", StringArray._from_sequence(["a", None])),
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(["a", None], pd.StringDtype(), StringArray._from_sequence(["a", None])),
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# Boolean
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([True, None], "boolean", BooleanArray._from_sequence([True, None])),
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([True, None], pd.BooleanDtype(), BooleanArray._from_sequence([True, None])),
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# Index
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(pd.Index([1, 2]), None, PandasArray(np.array([1, 2], dtype=np.int64))),
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# Series[EA] returns the EA
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(
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pd.Series(pd.Categorical(["a", "b"], categories=["a", "b", "c"])),
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None,
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pd.Categorical(["a", "b"], categories=["a", "b", "c"]),
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),
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# "3rd party" EAs work
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([decimal.Decimal(0), decimal.Decimal(1)], "decimal", to_decimal([0, 1])),
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# pass an ExtensionArray, but a different dtype
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(
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period_array(["2000", "2001"], freq="D"),
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"category",
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pd.Categorical([pd.Period("2000", "D"), pd.Period("2001", "D")]),
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),
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],
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)
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def test_array(data, dtype, expected):
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result = pd.array(data, dtype=dtype)
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tm.assert_equal(result, expected)
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def test_array_copy():
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a = np.array([1, 2])
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# default is to copy
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b = pd.array(a, dtype=a.dtype)
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assert np.shares_memory(a, b._ndarray) is False
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# copy=True
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b = pd.array(a, dtype=a.dtype, copy=True)
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assert np.shares_memory(a, b._ndarray) is False
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# copy=False
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b = pd.array(a, dtype=a.dtype, copy=False)
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assert np.shares_memory(a, b._ndarray) is True
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cet = pytz.timezone("CET")
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@pytest.mark.parametrize(
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"data, expected",
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[
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# period
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(
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[pd.Period("2000", "D"), pd.Period("2001", "D")],
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period_array(["2000", "2001"], freq="D"),
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),
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# interval
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([pd.Interval(0, 1), pd.Interval(1, 2)], IntervalArray.from_breaks([0, 1, 2])),
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# datetime
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(
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[pd.Timestamp("2000"), pd.Timestamp("2001")],
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DatetimeArray._from_sequence(["2000", "2001"]),
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),
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(
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[datetime.datetime(2000, 1, 1), datetime.datetime(2001, 1, 1)],
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DatetimeArray._from_sequence(["2000", "2001"]),
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),
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(
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np.array([1, 2], dtype="M8[ns]"),
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DatetimeArray(np.array([1, 2], dtype="M8[ns]")),
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),
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(
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np.array([1, 2], dtype="M8[us]"),
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DatetimeArray(np.array([1000, 2000], dtype="M8[ns]")),
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),
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# datetimetz
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(
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[pd.Timestamp("2000", tz="CET"), pd.Timestamp("2001", tz="CET")],
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DatetimeArray._from_sequence(
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["2000", "2001"], dtype=pd.DatetimeTZDtype(tz="CET")
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),
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),
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(
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[
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datetime.datetime(2000, 1, 1, tzinfo=cet),
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datetime.datetime(2001, 1, 1, tzinfo=cet),
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],
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DatetimeArray._from_sequence(
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["2000", "2001"], dtype=pd.DatetimeTZDtype(tz=cet)
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),
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),
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# timedelta
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(
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[pd.Timedelta("1H"), pd.Timedelta("2H")],
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TimedeltaArray._from_sequence(["1H", "2H"]),
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),
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(
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np.array([1, 2], dtype="m8[ns]"),
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TimedeltaArray(np.array([1, 2], dtype="m8[ns]")),
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),
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(
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np.array([1, 2], dtype="m8[us]"),
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TimedeltaArray(np.array([1000, 2000], dtype="m8[ns]")),
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),
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# integer
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([1, 2], IntegerArray._from_sequence([1, 2])),
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([1, None], IntegerArray._from_sequence([1, None])),
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([1, pd.NA], IntegerArray._from_sequence([1, pd.NA])),
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([1, np.nan], IntegerArray._from_sequence([1, np.nan])),
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# float
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([0.1, 0.2], FloatingArray._from_sequence([0.1, 0.2])),
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([0.1, None], FloatingArray._from_sequence([0.1, pd.NA])),
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([0.1, np.nan], FloatingArray._from_sequence([0.1, pd.NA])),
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([0.1, pd.NA], FloatingArray._from_sequence([0.1, pd.NA])),
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# integer-like float
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([1.0, 2.0], FloatingArray._from_sequence([1.0, 2.0])),
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([1.0, None], FloatingArray._from_sequence([1.0, pd.NA])),
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([1.0, np.nan], FloatingArray._from_sequence([1.0, pd.NA])),
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([1.0, pd.NA], FloatingArray._from_sequence([1.0, pd.NA])),
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# mixed-integer-float
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([1, 2.0], FloatingArray._from_sequence([1.0, 2.0])),
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([1, np.nan, 2.0], FloatingArray._from_sequence([1.0, None, 2.0])),
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# string
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(["a", "b"], StringArray._from_sequence(["a", "b"])),
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(["a", None], StringArray._from_sequence(["a", None])),
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# Boolean
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([True, False], BooleanArray._from_sequence([True, False])),
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([True, None], BooleanArray._from_sequence([True, None])),
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],
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)
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def test_array_inference(data, expected):
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result = pd.array(data)
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tm.assert_equal(result, expected)
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@pytest.mark.parametrize(
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"data",
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[
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# mix of frequencies
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[pd.Period("2000", "D"), pd.Period("2001", "A")],
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# mix of closed
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[pd.Interval(0, 1, closed="left"), pd.Interval(1, 2, closed="right")],
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# Mix of timezones
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[pd.Timestamp("2000", tz="CET"), pd.Timestamp("2000", tz="UTC")],
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# Mix of tz-aware and tz-naive
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[pd.Timestamp("2000", tz="CET"), pd.Timestamp("2000")],
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np.array([pd.Timestamp("2000"), pd.Timestamp("2000", tz="CET")]),
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],
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)
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def test_array_inference_fails(data):
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result = pd.array(data)
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expected = PandasArray(np.array(data, dtype=object))
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tm.assert_extension_array_equal(result, expected)
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@pytest.mark.parametrize("data", [np.array([[1, 2], [3, 4]]), [[1, 2], [3, 4]]])
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def test_nd_raises(data):
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with pytest.raises(ValueError, match="PandasArray must be 1-dimensional"):
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pd.array(data, dtype="int64")
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def test_scalar_raises():
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with pytest.raises(ValueError, match="Cannot pass scalar '1'"):
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pd.array(1)
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# ---------------------------------------------------------------------------
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# A couple dummy classes to ensure that Series and Indexes are unboxed before
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# getting to the EA classes.
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@register_extension_dtype
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class DecimalDtype2(DecimalDtype):
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name = "decimal2"
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@classmethod
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def construct_array_type(cls):
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"""
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Return the array type associated with this dtype.
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Returns
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-------
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type
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"""
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return DecimalArray2
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class DecimalArray2(DecimalArray):
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@classmethod
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def _from_sequence(cls, scalars, dtype=None, copy=False):
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if isinstance(scalars, (pd.Series, pd.Index)):
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raise TypeError("scalars should not be of type pd.Series or pd.Index")
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return super()._from_sequence(scalars, dtype=dtype, copy=copy)
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def test_array_unboxes(index_or_series):
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box = index_or_series
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data = box([decimal.Decimal("1"), decimal.Decimal("2")])
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# make sure it works
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with pytest.raises(
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TypeError, match="scalars should not be of type pd.Series or pd.Index"
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):
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DecimalArray2._from_sequence(data)
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result = pd.array(data, dtype="decimal2")
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expected = DecimalArray2._from_sequence(data.values)
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tm.assert_equal(result, expected)
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@pytest.fixture
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def registry_without_decimal():
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idx = registry.dtypes.index(DecimalDtype)
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registry.dtypes.pop(idx)
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yield
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registry.dtypes.append(DecimalDtype)
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def test_array_not_registered(registry_without_decimal):
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# check we aren't on it
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assert registry.find("decimal") is None
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data = [decimal.Decimal("1"), decimal.Decimal("2")]
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result = pd.array(data, dtype=DecimalDtype)
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expected = DecimalArray._from_sequence(data)
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tm.assert_equal(result, expected)
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class TestArrayAnalytics:
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def test_searchsorted(self, string_dtype):
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arr = pd.array(["a", "b", "c"], dtype=string_dtype)
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result = arr.searchsorted("a", side="left")
|
||
|
|
assert is_scalar(result)
|
||
|
|
assert result == 0
|
||
|
|
|
||
|
|
result = arr.searchsorted("a", side="right")
|
||
|
|
assert is_scalar(result)
|
||
|
|
assert result == 1
|
||
|
|
|
||
|
|
def test_searchsorted_numeric_dtypes_scalar(self, any_real_dtype):
|
||
|
|
arr = pd.array([1, 3, 90], dtype=any_real_dtype)
|
||
|
|
result = arr.searchsorted(30)
|
||
|
|
assert is_scalar(result)
|
||
|
|
assert result == 2
|
||
|
|
|
||
|
|
result = arr.searchsorted([30])
|
||
|
|
expected = np.array([2], dtype=np.intp)
|
||
|
|
tm.assert_numpy_array_equal(result, expected)
|
||
|
|
|
||
|
|
def test_searchsorted_numeric_dtypes_vector(self, any_real_dtype):
|
||
|
|
arr = pd.array([1, 3, 90], dtype=any_real_dtype)
|
||
|
|
result = arr.searchsorted([2, 30])
|
||
|
|
expected = np.array([1, 2], dtype=np.intp)
|
||
|
|
tm.assert_numpy_array_equal(result, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"arr, val",
|
||
|
|
[
|
||
|
|
[
|
||
|
|
pd.date_range("20120101", periods=10, freq="2D"),
|
||
|
|
pd.Timestamp("20120102"),
|
||
|
|
],
|
||
|
|
[
|
||
|
|
pd.date_range("20120101", periods=10, freq="2D", tz="Asia/Hong_Kong"),
|
||
|
|
pd.Timestamp("20120102", tz="Asia/Hong_Kong"),
|
||
|
|
],
|
||
|
|
[
|
||
|
|
pd.timedelta_range(start="1 day", end="10 days", periods=10),
|
||
|
|
pd.Timedelta("2 days"),
|
||
|
|
],
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_search_sorted_datetime64_scalar(self, arr, val):
|
||
|
|
arr = pd.array(arr)
|
||
|
|
result = arr.searchsorted(val)
|
||
|
|
assert is_scalar(result)
|
||
|
|
assert result == 1
|
||
|
|
|
||
|
|
def test_searchsorted_sorter(self, any_real_dtype):
|
||
|
|
arr = pd.array([3, 1, 2], dtype=any_real_dtype)
|
||
|
|
result = arr.searchsorted([0, 3], sorter=np.argsort(arr))
|
||
|
|
expected = np.array([0, 2], dtype=np.intp)
|
||
|
|
tm.assert_numpy_array_equal(result, expected)
|