A PyQT GUI application for converting InfoLease report outputs into Excel files. Handles parsing and summarizing. Learns where files are meant to be store and compiles monthly and yearly summaries.
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InfoLeaseExtract/venv/Lib/site-packages/pandas/tests/arrays/boolean/test_reduction.py

60 lines
2.0 KiB

import numpy as np
import pytest
import pandas as pd
@pytest.fixture
def data():
return pd.array(
[True, False] * 4 + [np.nan] + [True, False] * 44 + [np.nan] + [True, False],
dtype="boolean",
)
@pytest.mark.parametrize(
"values, exp_any, exp_all, exp_any_noskip, exp_all_noskip",
[
([True, pd.NA], True, True, True, pd.NA),
([False, pd.NA], False, False, pd.NA, False),
([pd.NA], False, True, pd.NA, pd.NA),
([], False, True, False, True),
# GH-33253: all True / all False values buggy with skipna=False
([True, True], True, True, True, True),
([False, False], False, False, False, False),
],
)
def test_any_all(values, exp_any, exp_all, exp_any_noskip, exp_all_noskip):
# the methods return numpy scalars
exp_any = pd.NA if exp_any is pd.NA else np.bool_(exp_any)
exp_all = pd.NA if exp_all is pd.NA else np.bool_(exp_all)
exp_any_noskip = pd.NA if exp_any_noskip is pd.NA else np.bool_(exp_any_noskip)
exp_all_noskip = pd.NA if exp_all_noskip is pd.NA else np.bool_(exp_all_noskip)
for con in [pd.array, pd.Series]:
a = con(values, dtype="boolean")
assert a.any() is exp_any
assert a.all() is exp_all
assert a.any(skipna=False) is exp_any_noskip
assert a.all(skipna=False) is exp_all_noskip
assert np.any(a.any()) is exp_any
assert np.all(a.all()) is exp_all
@pytest.mark.parametrize("dropna", [True, False])
def test_reductions_return_types(dropna, data, all_numeric_reductions):
op = all_numeric_reductions
s = pd.Series(data)
if dropna:
s = s.dropna()
if op == "sum":
assert isinstance(getattr(s, op)(), np.int_)
elif op == "prod":
assert isinstance(getattr(s, op)(), np.int_)
elif op in ("min", "max"):
assert isinstance(getattr(s, op)(), np.bool_)
else:
# "mean", "std", "var", "median", "kurt", "skew"
assert isinstance(getattr(s, op)(), np.float64)