mirror of
https://github.com/PiBrewing/craftbeerpi4.git
synced 2024-11-23 07:28:13 +01:00
411 lines
13 KiB
Python
411 lines
13 KiB
Python
|
import datetime
|
||
|
|
import decimal
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import pytest
|
||
|
|
import pytz
|
||
|
|
|
||
|
|
from pandas.core.dtypes.base import registry
|
||
|
|
|
||
|
|
import pandas as pd
|
||
|
|
import pandas._testing as tm
|
||
|
|
from pandas.api.extensions import register_extension_dtype
|
||
|
|
from pandas.api.types import is_scalar
|
||
|
|
from pandas.arrays import (
|
||
|
|
BooleanArray,
|
||
|
|
DatetimeArray,
|
||
|
|
FloatingArray,
|
||
|
|
IntegerArray,
|
||
|
|
IntervalArray,
|
||
|
|
SparseArray,
|
||
|
|
StringArray,
|
||
|
|
TimedeltaArray,
|
||
|
|
)
|
||
|
|
from pandas.core.arrays import PandasArray, integer_array, period_array
|
||
|
|
from pandas.tests.extension.decimal import DecimalArray, DecimalDtype, to_decimal
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"data, dtype, expected",
|
||
|
|
[
|
||
|
|
# Basic NumPy defaults.
|
||
|
|
([1, 2], None, IntegerArray._from_sequence([1, 2])),
|
||
|
|
([1, 2], object, PandasArray(np.array([1, 2], dtype=object))),
|
||
|
|
(
|
||
|
|
[1, 2],
|
||
|
|
np.dtype("float32"),
|
||
|
|
PandasArray(np.array([1.0, 2.0], dtype=np.dtype("float32"))),
|
||
|
|
),
|
||
|
|
(np.array([1, 2], dtype="int64"), None, IntegerArray._from_sequence([1, 2])),
|
||
|
|
(
|
||
|
|
np.array([1.0, 2.0], dtype="float64"),
|
||
|
|
None,
|
||
|
|
FloatingArray._from_sequence([1.0, 2.0]),
|
||
|
|
),
|
||
|
|
# String alias passes through to NumPy
|
||
|
|
([1, 2], "float32", PandasArray(np.array([1, 2], dtype="float32"))),
|
||
|
|
# Period alias
|
||
|
|
(
|
||
|
|
[pd.Period("2000", "D"), pd.Period("2001", "D")],
|
||
|
|
"Period[D]",
|
||
|
|
period_array(["2000", "2001"], freq="D"),
|
||
|
|
),
|
||
|
|
# Period dtype
|
||
|
|
(
|
||
|
|
[pd.Period("2000", "D")],
|
||
|
|
pd.PeriodDtype("D"),
|
||
|
|
period_array(["2000"], freq="D"),
|
||
|
|
),
|
||
|
|
# Datetime (naive)
|
||
|
|
(
|
||
|
|
[1, 2],
|
||
|
|
np.dtype("datetime64[ns]"),
|
||
|
|
DatetimeArray._from_sequence(np.array([1, 2], dtype="datetime64[ns]")),
|
||
|
|
),
|
||
|
|
(
|
||
|
|
np.array([1, 2], dtype="datetime64[ns]"),
|
||
|
|
None,
|
||
|
|
DatetimeArray._from_sequence(np.array([1, 2], dtype="datetime64[ns]")),
|
||
|
|
),
|
||
|
|
(
|
||
|
|
pd.DatetimeIndex(["2000", "2001"]),
|
||
|
|
np.dtype("datetime64[ns]"),
|
||
|
|
DatetimeArray._from_sequence(["2000", "2001"]),
|
||
|
|
),
|
||
|
|
(
|
||
|
|
pd.DatetimeIndex(["2000", "2001"]),
|
||
|
|
None,
|
||
|
|
DatetimeArray._from_sequence(["2000", "2001"]),
|
||
|
|
),
|
||
|
|
(
|
||
|
|
["2000", "2001"],
|
||
|
|
np.dtype("datetime64[ns]"),
|
||
|
|
DatetimeArray._from_sequence(["2000", "2001"]),
|
||
|
|
),
|
||
|
|
# Datetime (tz-aware)
|
||
|
|
(
|
||
|
|
["2000", "2001"],
|
||
|
|
pd.DatetimeTZDtype(tz="CET"),
|
||
|
|
DatetimeArray._from_sequence(
|
||
|
|
["2000", "2001"], dtype=pd.DatetimeTZDtype(tz="CET")
|
||
|
|
),
|
||
|
|
),
|
||
|
|
# Timedelta
|
||
|
|
(
|
||
|
|
["1H", "2H"],
|
||
|
|
np.dtype("timedelta64[ns]"),
|
||
|
|
TimedeltaArray._from_sequence(["1H", "2H"]),
|
||
|
|
),
|
||
|
|
(
|
||
|
|
pd.TimedeltaIndex(["1H", "2H"]),
|
||
|
|
np.dtype("timedelta64[ns]"),
|
||
|
|
TimedeltaArray._from_sequence(["1H", "2H"]),
|
||
|
|
),
|
||
|
|
(
|
||
|
|
pd.TimedeltaIndex(["1H", "2H"]),
|
||
|
|
None,
|
||
|
|
TimedeltaArray._from_sequence(["1H", "2H"]),
|
||
|
|
),
|
||
|
|
# Category
|
||
|
|
(["a", "b"], "category", pd.Categorical(["a", "b"])),
|
||
|
|
(
|
||
|
|
["a", "b"],
|
||
|
|
pd.CategoricalDtype(None, ordered=True),
|
||
|
|
pd.Categorical(["a", "b"], ordered=True),
|
||
|
|
),
|
||
|
|
# Interval
|
||
|
|
(
|
||
|
|
[pd.Interval(1, 2), pd.Interval(3, 4)],
|
||
|
|
"interval",
|
||
|
|
IntervalArray.from_tuples([(1, 2), (3, 4)]),
|
||
|
|
),
|
||
|
|
# Sparse
|
||
|
|
([0, 1], "Sparse[int64]", SparseArray([0, 1], dtype="int64")),
|
||
|
|
# IntegerNA
|
||
|
|
([1, None], "Int16", integer_array([1, None], dtype="Int16")),
|
||
|
|
(pd.Series([1, 2]), None, PandasArray(np.array([1, 2], dtype=np.int64))),
|
||
|
|
# String
|
||
|
|
(["a", None], "string", StringArray._from_sequence(["a", None])),
|
||
|
|
(["a", None], pd.StringDtype(), StringArray._from_sequence(["a", None])),
|
||
|
|
# Boolean
|
||
|
|
([True, None], "boolean", BooleanArray._from_sequence([True, None])),
|
||
|
|
([True, None], pd.BooleanDtype(), BooleanArray._from_sequence([True, None])),
|
||
|
|
# Index
|
||
|
|
(pd.Index([1, 2]), None, PandasArray(np.array([1, 2], dtype=np.int64))),
|
||
|
|
# Series[EA] returns the EA
|
||
|
|
(
|
||
|
|
pd.Series(pd.Categorical(["a", "b"], categories=["a", "b", "c"])),
|
||
|
|
None,
|
||
|
|
pd.Categorical(["a", "b"], categories=["a", "b", "c"]),
|
||
|
|
),
|
||
|
|
# "3rd party" EAs work
|
||
|
|
([decimal.Decimal(0), decimal.Decimal(1)], "decimal", to_decimal([0, 1])),
|
||
|
|
# pass an ExtensionArray, but a different dtype
|
||
|
|
(
|
||
|
|
period_array(["2000", "2001"], freq="D"),
|
||
|
|
"category",
|
||
|
|
pd.Categorical([pd.Period("2000", "D"), pd.Period("2001", "D")]),
|
||
|
|
),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_array(data, dtype, expected):
|
||
|
|
result = pd.array(data, dtype=dtype)
|
||
|
|
tm.assert_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_array_copy():
|
||
|
|
a = np.array([1, 2])
|
||
|
|
# default is to copy
|
||
|
|
b = pd.array(a, dtype=a.dtype)
|
||
|
|
assert np.shares_memory(a, b._ndarray) is False
|
||
|
|
|
||
|
|
# copy=True
|
||
|
|
b = pd.array(a, dtype=a.dtype, copy=True)
|
||
|
|
assert np.shares_memory(a, b._ndarray) is False
|
||
|
|
|
||
|
|
# copy=False
|
||
|
|
b = pd.array(a, dtype=a.dtype, copy=False)
|
||
|
|
assert np.shares_memory(a, b._ndarray) is True
|
||
|
|
|
||
|
|
|
||
|
|
cet = pytz.timezone("CET")
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"data, expected",
|
||
|
|
[
|
||
|
|
# period
|
||
|
|
(
|
||
|
|
[pd.Period("2000", "D"), pd.Period("2001", "D")],
|
||
|
|
period_array(["2000", "2001"], freq="D"),
|
||
|
|
),
|
||
|
|
# interval
|
||
|
|
([pd.Interval(0, 1), pd.Interval(1, 2)], IntervalArray.from_breaks([0, 1, 2])),
|
||
|
|
# datetime
|
||
|
|
(
|
||
|
|
[pd.Timestamp("2000"), pd.Timestamp("2001")],
|
||
|
|
DatetimeArray._from_sequence(["2000", "2001"]),
|
||
|
|
),
|
||
|
|
(
|
||
|
|
[datetime.datetime(2000, 1, 1), datetime.datetime(2001, 1, 1)],
|
||
|
|
DatetimeArray._from_sequence(["2000", "2001"]),
|
||
|
|
),
|
||
|
|
(
|
||
|
|
np.array([1, 2], dtype="M8[ns]"),
|
||
|
|
DatetimeArray(np.array([1, 2], dtype="M8[ns]")),
|
||
|
|
),
|
||
|
|
(
|
||
|
|
np.array([1, 2], dtype="M8[us]"),
|
||
|
|
DatetimeArray(np.array([1000, 2000], dtype="M8[ns]")),
|
||
|
|
),
|
||
|
|
# datetimetz
|
||
|
|
(
|
||
|
|
[pd.Timestamp("2000", tz="CET"), pd.Timestamp("2001", tz="CET")],
|
||
|
|
DatetimeArray._from_sequence(
|
||
|
|
["2000", "2001"], dtype=pd.DatetimeTZDtype(tz="CET")
|
||
|
|
),
|
||
|
|
),
|
||
|
|
(
|
||
|
|
[
|
||
|
|
datetime.datetime(2000, 1, 1, tzinfo=cet),
|
||
|
|
datetime.datetime(2001, 1, 1, tzinfo=cet),
|
||
|
|
],
|
||
|
|
DatetimeArray._from_sequence(
|
||
|
|
["2000", "2001"], dtype=pd.DatetimeTZDtype(tz=cet)
|
||
|
|
),
|
||
|
|
),
|
||
|
|
# timedelta
|
||
|
|
(
|
||
|
|
[pd.Timedelta("1H"), pd.Timedelta("2H")],
|
||
|
|
TimedeltaArray._from_sequence(["1H", "2H"]),
|
||
|
|
),
|
||
|
|
(
|
||
|
|
np.array([1, 2], dtype="m8[ns]"),
|
||
|
|
TimedeltaArray(np.array([1, 2], dtype="m8[ns]")),
|
||
|
|
),
|
||
|
|
(
|
||
|
|
np.array([1, 2], dtype="m8[us]"),
|
||
|
|
TimedeltaArray(np.array([1000, 2000], dtype="m8[ns]")),
|
||
|
|
),
|
||
|
|
# integer
|
||
|
|
([1, 2], IntegerArray._from_sequence([1, 2])),
|
||
|
|
([1, None], IntegerArray._from_sequence([1, None])),
|
||
|
|
([1, pd.NA], IntegerArray._from_sequence([1, pd.NA])),
|
||
|
|
([1, np.nan], IntegerArray._from_sequence([1, np.nan])),
|
||
|
|
# float
|
||
|
|
([0.1, 0.2], FloatingArray._from_sequence([0.1, 0.2])),
|
||
|
|
([0.1, None], FloatingArray._from_sequence([0.1, pd.NA])),
|
||
|
|
([0.1, np.nan], FloatingArray._from_sequence([0.1, pd.NA])),
|
||
|
|
([0.1, pd.NA], FloatingArray._from_sequence([0.1, pd.NA])),
|
||
|
|
# integer-like float
|
||
|
|
([1.0, 2.0], FloatingArray._from_sequence([1.0, 2.0])),
|
||
|
|
([1.0, None], FloatingArray._from_sequence([1.0, pd.NA])),
|
||
|
|
([1.0, np.nan], FloatingArray._from_sequence([1.0, pd.NA])),
|
||
|
|
([1.0, pd.NA], FloatingArray._from_sequence([1.0, pd.NA])),
|
||
|
|
# mixed-integer-float
|
||
|
|
([1, 2.0], FloatingArray._from_sequence([1.0, 2.0])),
|
||
|
|
([1, np.nan, 2.0], FloatingArray._from_sequence([1.0, None, 2.0])),
|
||
|
|
# string
|
||
|
|
(["a", "b"], StringArray._from_sequence(["a", "b"])),
|
||
|
|
(["a", None], StringArray._from_sequence(["a", None])),
|
||
|
|
# Boolean
|
||
|
|
([True, False], BooleanArray._from_sequence([True, False])),
|
||
|
|
([True, None], BooleanArray._from_sequence([True, None])),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_array_inference(data, expected):
|
||
|
|
result = pd.array(data)
|
||
|
|
tm.assert_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"data",
|
||
|
|
[
|
||
|
|
# mix of frequencies
|
||
|
|
[pd.Period("2000", "D"), pd.Period("2001", "A")],
|
||
|
|
# mix of closed
|
||
|
|
[pd.Interval(0, 1, closed="left"), pd.Interval(1, 2, closed="right")],
|
||
|
|
# Mix of timezones
|
||
|
|
[pd.Timestamp("2000", tz="CET"), pd.Timestamp("2000", tz="UTC")],
|
||
|
|
# Mix of tz-aware and tz-naive
|
||
|
|
[pd.Timestamp("2000", tz="CET"), pd.Timestamp("2000")],
|
||
|
|
np.array([pd.Timestamp("2000"), pd.Timestamp("2000", tz="CET")]),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_array_inference_fails(data):
|
||
|
|
result = pd.array(data)
|
||
|
|
expected = PandasArray(np.array(data, dtype=object))
|
||
|
|
tm.assert_extension_array_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("data", [np.array([[1, 2], [3, 4]]), [[1, 2], [3, 4]]])
|
||
|
|
def test_nd_raises(data):
|
||
|
|
with pytest.raises(ValueError, match="PandasArray must be 1-dimensional"):
|
||
|
|
pd.array(data, dtype="int64")
|
||
|
|
|
||
|
|
|
||
|
|
def test_scalar_raises():
|
||
|
|
with pytest.raises(ValueError, match="Cannot pass scalar '1'"):
|
||
|
|
pd.array(1)
|
||
|
|
|
||
|
|
|
||
|
|
# ---------------------------------------------------------------------------
|
||
|
|
# A couple dummy classes to ensure that Series and Indexes are unboxed before
|
||
|
|
# getting to the EA classes.
|
||
|
|
|
||
|
|
|
||
|
|
@register_extension_dtype
|
||
|
|
class DecimalDtype2(DecimalDtype):
|
||
|
|
name = "decimal2"
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def construct_array_type(cls):
|
||
|
|
"""
|
||
|
|
Return the array type associated with this dtype.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
type
|
||
|
|
"""
|
||
|
|
return DecimalArray2
|
||
|
|
|
||
|
|
|
||
|
|
class DecimalArray2(DecimalArray):
|
||
|
|
@classmethod
|
||
|
|
def _from_sequence(cls, scalars, dtype=None, copy=False):
|
||
|
|
if isinstance(scalars, (pd.Series, pd.Index)):
|
||
|
|
raise TypeError("scalars should not be of type pd.Series or pd.Index")
|
||
|
|
|
||
|
|
return super()._from_sequence(scalars, dtype=dtype, copy=copy)
|
||
|
|
|
||
|
|
|
||
|
|
def test_array_unboxes(index_or_series):
|
||
|
|
box = index_or_series
|
||
|
|
|
||
|
|
data = box([decimal.Decimal("1"), decimal.Decimal("2")])
|
||
|
|
# make sure it works
|
||
|
|
with pytest.raises(
|
||
|
|
TypeError, match="scalars should not be of type pd.Series or pd.Index"
|
||
|
|
):
|
||
|
|
DecimalArray2._from_sequence(data)
|
||
|
|
|
||
|
|
result = pd.array(data, dtype="decimal2")
|
||
|
|
expected = DecimalArray2._from_sequence(data.values)
|
||
|
|
tm.assert_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.fixture
|
||
|
|
def registry_without_decimal():
|
||
|
|
idx = registry.dtypes.index(DecimalDtype)
|
||
|
|
registry.dtypes.pop(idx)
|
||
|
|
yield
|
||
|
|
registry.dtypes.append(DecimalDtype)
|
||
|
|
|
||
|
|
|
||
|
|
def test_array_not_registered(registry_without_decimal):
|
||
|
|
# check we aren't on it
|
||
|
|
assert registry.find("decimal") is None
|
||
|
|
data = [decimal.Decimal("1"), decimal.Decimal("2")]
|
||
|
|
|
||
|
|
result = pd.array(data, dtype=DecimalDtype)
|
||
|
|
expected = DecimalArray._from_sequence(data)
|
||
|
|
tm.assert_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
class TestArrayAnalytics:
|
||
|
|
def test_searchsorted(self, string_dtype):
|
||
|
|
arr = pd.array(["a", "b", "c"], dtype=string_dtype)
|
||
|
|
|
||
|
|
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)
|