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test_issues.py
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test_issues.py
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import pickle
import time
import numpy as np
import pandas as pd
import pandas._testing as tm
import pytest
import pint
from pandas.tests.extension.base.base import BaseExtensionTests
from pint.testsuite import helpers
from pint_pandas import PintArray, PintType
from pint_pandas.pint_array import pandas_version_info
ureg = PintType.ureg
class TestIssue165(BaseExtensionTests):
def test_force_ndarray_like(self):
# store previous registries to undo our changes
prev_PintType_ureg = PintType.ureg
prev_appreg = pint.get_application_registry().get()
prev_cache = PintType._cache
try:
# create a temporary registry with force_ndarray_like = True (`pint_xarray` insists on that)
test_ureg = pint.UnitRegistry()
test_ureg.force_ndarray_like = True
# register
pint.set_application_registry(test_ureg)
PintType.ureg = test_ureg
# clear units cache
PintType._cache = {}
# run TestIssue21.test_offset_concat with our test-registry (one of many that currently fails with force_ndarray_like=True)
q_a = ureg.Quantity(np.arange(5), test_ureg.Unit("degC"))
q_b = ureg.Quantity(np.arange(6), test_ureg.Unit("degC"))
q_a_ = np.append(q_a, np.nan)
a = pd.Series(PintArray(q_a))
b = pd.Series(PintArray(q_b))
result = pd.concat([a, b], axis=1)
expected = pd.DataFrame(
{0: PintArray(q_a_), 1: PintArray(q_b)}, dtype="pint[degC]"
)
tm.assert_equal(result, expected)
finally:
# restore registry
PintType.ureg = prev_PintType_ureg
PintType._cache = prev_cache
pint.set_application_registry(prev_appreg)
class TestIssue21(BaseExtensionTests):
@pytest.mark.filterwarnings("ignore::RuntimeWarning")
def test_offset_concat(self):
q_a = ureg.Quantity(np.arange(5), ureg.Unit("degC"))
q_b = ureg.Quantity(np.arange(6), ureg.Unit("degC"))
q_a_ = np.append(q_a, np.nan)
a = pd.Series(PintArray(q_a))
b = pd.Series(PintArray(q_b))
result = pd.concat([a, b], axis=1)
expected = pd.DataFrame(
{0: PintArray(q_a_), 1: PintArray(q_b)}, dtype="pint[degC]"
)
tm.assert_equal(result, expected)
# issue #141
print(PintArray(q_a))
class TestIssue68(BaseExtensionTests):
def test_assignment_add_empty(self):
# GH 68
data = PintArray.from_1darray_quantity(
np.arange(start=1.0, stop=101.0, dtype=float) * ureg.nm
)
result = pd.Series(data)
result[[]] += data[0]
expected = pd.Series(data)
tm.assert_series_equal(result, expected)
class TestIssue80:
@staticmethod
def _timeit(fun, n_runs=5):
run_time = []
for k in range(n_runs):
t_start = time.monotonic_ns()
fun()
t_end = time.monotonic_ns()
run_time.append(t_end - t_start)
return np.median(run_time) * ureg.ns
@staticmethod
def _make_df(size, pint_units=True, dtype=float):
if pint_units:
dist_unit = "pint[m]"
time_unit = "pint[s]"
else:
dist_unit = dtype
time_unit = dtype
return pd.DataFrame(
{
"distance": pd.Series(
np.arange(1, size + 1, dtype=dtype), dtype=dist_unit
),
"time": pd.Series(np.arange(1, size + 1, dtype=dtype), dtype=time_unit),
}
)
def test_div(self):
n = 1_000_000
df_pint = self._make_df(n)
df = self._make_df(n, pint_units=False)
tp = self._timeit(lambda: df_pint["distance"] / df_pint["time"]).to("ms")
t = self._timeit(lambda: df["distance"] / df["time"]).to("ms")
assert tp <= 5 * t
@pytest.mark.parametrize(
"reduction",
["min", "max", "sum", "mean", "median"],
)
def test_reductions(self, reduction):
# before the fix, those reductions could be *very* slow. Fail early.
for n in [10_000, 1_000_000]:
s_pint = self._make_df(n)["time"]
s = self._make_df(n, pint_units=False)["time"]
tp = self._timeit(getattr(s_pint, reduction)).to("ms")
t = self._timeit(getattr(s, reduction)).to("ms")
assert tp <= 5 * t
def test_issue_86():
a = PintArray([1, 2], ureg.m)
b_listlike = [1 * ureg.km, 1 * ureg.m]
units = b_listlike[0].units
b_pa = PintArray([v.m_as(units) for v in b_listlike], units)
assert np.all(a + b_listlike == a + b_pa)
def test_issue_71():
a = PintArray([1, 2], ureg.m)
s = pickle.dumps(a)
b = pickle.loads(s)
assert np.all(a == b)
def test_issue_88():
q_m = ureg.Quantity([1, 2], "m")
a = PintArray(q_m)
helpers.assert_quantity_almost_equal(q_m, a.quantity)
q_mm = ureg.Quantity([1000, 2000], "mm")
b = PintArray(q_mm, "m")
helpers.assert_quantity_almost_equal(q_m, b.quantity)
def test_issue_127():
a = PintType.construct_from_string("pint[dimensionless]")
b = PintType.construct_from_string("pint[]")
assert a == b
class TestIssue174(BaseExtensionTests):
def test_sum(self):
if pandas_version_info < (2, 1):
pytest.skip("Pandas reduce functions strip units prior to version 2.1.0")
a = pd.DataFrame([[0, 1, 2], [3, 4, 5]]).astype("pint[m]")
row_sum = a.sum(axis=0)
expected_1 = pd.Series([3, 5, 7], dtype="pint[m]")
tm.assert_series_equal(row_sum, expected_1)
col_sum = a.sum(axis=1)
expected_2 = pd.Series([3, 12], dtype="pint[m]")
tm.assert_series_equal(col_sum, expected_2)
@pytest.mark.parametrize("dtype", [pd.Float64Dtype(), "float"])
def test_issue_194(dtype):
s0 = pd.Series([1.0, 2.5], dtype=dtype)
s1 = s0.astype("pint[dimensionless]")
s2 = s1.astype(dtype)
tm.assert_series_equal(s0, s2)
class TestIssue202(BaseExtensionTests):
def test_dequantify(self):
df = pd.DataFrame()
df["test"] = pd.Series([1, 2, 3], dtype="pint[kN]")
df.insert(0, "test", df["test"], allow_duplicates=True)
expected = pd.DataFrame.from_dict(
data={
"index": [0, 1, 2],
"columns": [("test", "kilonewton")],
"data": [[1], [2], [3]],
"index_names": [None],
"column_names": [None, "unit"],
},
orient="tight",
dtype="Int64",
)
result = df.iloc[:, 1:].pint.dequantify()
tm.assert_frame_equal(expected, result)
expected = pd.DataFrame.from_dict(
data={
"index": [0, 1, 2],
"columns": [("test", "kilonewton"), ("test", "kilonewton")],
"data": [[1, 1], [2, 2], [3, 3]],
"index_names": [None],
"column_names": [None, "unit"],
},
orient="tight",
dtype="Int64",
)
result = df.pint.dequantify()
tm.assert_frame_equal(expected, result)