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test_array.py
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test_array.py
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import datetime
import decimal
import numpy as np
import pytest
import pytz
from pandas.core.dtypes.dtypes import registry
import pandas as pd
from pandas.api.extensions import register_extension_dtype
from pandas.core.arrays import PandasArray, integer_array, period_array
from pandas.tests.extension.decimal import (
DecimalArray, DecimalDtype, to_decimal)
import pandas.util.testing as tm
@pytest.mark.parametrize("data, dtype, expected", [
# Basic NumPy defaults.
([1, 2], None, PandasArray(np.array([1, 2]))),
([1, 2], object, PandasArray(np.array([1, 2], dtype=object))),
([1, 2], np.dtype('float32'),
PandasArray(np.array([1., 2.0], dtype=np.dtype('float32')))),
(np.array([1, 2]), None, PandasArray(np.array([1, 2]))),
# 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]'),
pd.arrays.DatetimeArray._from_sequence(
np.array([1, 2], dtype='datetime64[ns]'))),
(np.array([1, 2], dtype='datetime64[ns]'), None,
pd.arrays.DatetimeArray._from_sequence(
np.array([1, 2], dtype='datetime64[ns]'))),
(pd.DatetimeIndex(['2000', '2001']), np.dtype('datetime64[ns]'),
pd.arrays.DatetimeArray._from_sequence(['2000', '2001'])),
(pd.DatetimeIndex(['2000', '2001']), None,
pd.arrays.DatetimeArray._from_sequence(['2000', '2001'])),
(['2000', '2001'], np.dtype('datetime64[ns]'),
pd.arrays.DatetimeArray._from_sequence(['2000', '2001'])),
# Datetime (tz-aware)
(['2000', '2001'], pd.DatetimeTZDtype(tz="CET"),
pd.arrays.DatetimeArray._from_sequence(
['2000', '2001'], dtype=pd.DatetimeTZDtype(tz="CET"))),
# Timedelta
(['1H', '2H'], np.dtype('timedelta64[ns]'),
pd.arrays.TimedeltaArray._from_sequence(['1H', '2H'])),
(pd.TimedeltaIndex(['1H', '2H']), np.dtype('timedelta64[ns]'),
pd.arrays.TimedeltaArray._from_sequence(['1H', '2H'])),
(pd.TimedeltaIndex(['1H', '2H']), None,
pd.arrays.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',
pd.arrays.IntervalArray.from_tuples([(1, 2), (3, 4)])),
# Sparse
([0, 1], 'Sparse[int64]', pd.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))),
# 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)
assert np.shares_memory(a, b._ndarray) is False
# copy=True
b = pd.array(a, copy=True)
assert np.shares_memory(a, b._ndarray) is False
# copy=False
b = pd.array(a, 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)],
pd.arrays.IntervalArray.from_breaks([0, 1, 2])),
# datetime
([pd.Timestamp('2000',), pd.Timestamp('2001')],
pd.arrays.DatetimeArray._from_sequence(['2000', '2001'])),
([datetime.datetime(2000, 1, 1), datetime.datetime(2001, 1, 1)],
pd.arrays.DatetimeArray._from_sequence(['2000', '2001'])),
(np.array([1, 2], dtype='M8[ns]'),
pd.arrays.DatetimeArray(np.array([1, 2], dtype='M8[ns]'))),
(np.array([1, 2], dtype='M8[us]'),
pd.arrays.DatetimeArray(np.array([1000, 2000], dtype='M8[ns]'))),
# datetimetz
([pd.Timestamp('2000', tz='CET'), pd.Timestamp('2001', tz='CET')],
pd.arrays.DatetimeArray._from_sequence(
['2000', '2001'], dtype=pd.DatetimeTZDtype(tz='CET'))),
([datetime.datetime(2000, 1, 1, tzinfo=cet),
datetime.datetime(2001, 1, 1, tzinfo=cet)],
pd.arrays.DatetimeArray._from_sequence(['2000', '2001'],
tz=cet)),
# timedelta
([pd.Timedelta('1H'), pd.Timedelta('2H')],
pd.arrays.TimedeltaArray._from_sequence(['1H', '2H'])),
(np.array([1, 2], dtype='m8[ns]'),
pd.arrays.TimedeltaArray(np.array([1, 2], dtype='m8[ns]'))),
(np.array([1, 2], dtype='m8[us]'),
pd.arrays.TimedeltaArray(np.array([1000, 2000], dtype='m8[ns]'))),
])
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)
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 DecimalArray2
class DecimalArray2(DecimalArray):
@classmethod
def _from_sequence(cls, scalars, dtype=None, copy=False):
if isinstance(scalars, (pd.Series, pd.Index)):
raise TypeError
return super(DecimalArray2, cls)._from_sequence(
scalars, dtype=dtype, copy=copy
)
@pytest.mark.parametrize("box", [pd.Series, pd.Index])
def test_array_unboxes(box):
data = box([decimal.Decimal('1'), decimal.Decimal('2')])
# make sure it works
with pytest.raises(TypeError):
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)