import numpy as np
|
import pytest
|
|
import pandas as pd
|
from pandas import MultiIndex
|
import pandas._testing as tm
|
|
|
def test_numeric_compat(idx):
|
with pytest.raises(TypeError, match="cannot perform __mul__"):
|
idx * 1
|
|
with pytest.raises(TypeError, match="cannot perform __rmul__"):
|
1 * idx
|
|
div_err = "cannot perform __truediv__"
|
with pytest.raises(TypeError, match=div_err):
|
idx / 1
|
|
div_err = div_err.replace(" __", " __r")
|
with pytest.raises(TypeError, match=div_err):
|
1 / idx
|
|
with pytest.raises(TypeError, match="cannot perform __floordiv__"):
|
idx // 1
|
|
with pytest.raises(TypeError, match="cannot perform __rfloordiv__"):
|
1 // idx
|
|
|
@pytest.mark.parametrize("method", ["all", "any", "__invert__"])
|
def test_logical_compat(idx, method):
|
msg = f"cannot perform {method}"
|
|
with pytest.raises(TypeError, match=msg):
|
getattr(idx, method)()
|
|
|
def test_inplace_mutation_resets_values():
|
levels = [["a", "b", "c"], [4]]
|
levels2 = [[1, 2, 3], ["a"]]
|
codes = [[0, 1, 0, 2, 2, 0], [0, 0, 0, 0, 0, 0]]
|
|
mi1 = MultiIndex(levels=levels, codes=codes)
|
mi2 = MultiIndex(levels=levels2, codes=codes)
|
|
# instantiating MultiIndex should not access/cache _.values
|
assert "_values" not in mi1._cache
|
assert "_values" not in mi2._cache
|
|
vals = mi1.values.copy()
|
vals2 = mi2.values.copy()
|
|
# accessing .values should cache ._values
|
assert mi1._values is mi1._cache["_values"]
|
assert mi1.values is mi1._cache["_values"]
|
assert isinstance(mi1._cache["_values"], np.ndarray)
|
|
# Make sure level setting works
|
new_vals = mi1.set_levels(levels2).values
|
tm.assert_almost_equal(vals2, new_vals)
|
|
# Doesn't drop _values from _cache [implementation detail]
|
tm.assert_almost_equal(mi1._cache["_values"], vals)
|
|
# ...and values is still same too
|
tm.assert_almost_equal(mi1.values, vals)
|
|
# Make sure label setting works too
|
codes2 = [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]
|
exp_values = np.empty((6,), dtype=object)
|
exp_values[:] = [(1, "a")] * 6
|
|
# Must be 1d array of tuples
|
assert exp_values.shape == (6,)
|
|
new_mi = mi2.set_codes(codes2)
|
assert "_values" not in new_mi._cache
|
new_values = new_mi.values
|
assert "_values" in new_mi._cache
|
|
# Shouldn't change cache
|
tm.assert_almost_equal(mi2._cache["_values"], vals2)
|
|
# Should have correct values
|
tm.assert_almost_equal(exp_values, new_values)
|
|
|
def test_boxable_categorical_values():
|
cat = pd.Categorical(pd.date_range("2012-01-01", periods=3, freq="h"))
|
result = MultiIndex.from_product([["a", "b", "c"], cat]).values
|
expected = pd.Series(
|
[
|
("a", pd.Timestamp("2012-01-01 00:00:00")),
|
("a", pd.Timestamp("2012-01-01 01:00:00")),
|
("a", pd.Timestamp("2012-01-01 02:00:00")),
|
("b", pd.Timestamp("2012-01-01 00:00:00")),
|
("b", pd.Timestamp("2012-01-01 01:00:00")),
|
("b", pd.Timestamp("2012-01-01 02:00:00")),
|
("c", pd.Timestamp("2012-01-01 00:00:00")),
|
("c", pd.Timestamp("2012-01-01 01:00:00")),
|
("c", pd.Timestamp("2012-01-01 02:00:00")),
|
]
|
).values
|
tm.assert_numpy_array_equal(result, expected)
|
result = pd.DataFrame({"a": ["a", "b", "c"], "b": cat, "c": np.array(cat)}).values
|
expected = pd.DataFrame(
|
{
|
"a": ["a", "b", "c"],
|
"b": [
|
pd.Timestamp("2012-01-01 00:00:00"),
|
pd.Timestamp("2012-01-01 01:00:00"),
|
pd.Timestamp("2012-01-01 02:00:00"),
|
],
|
"c": [
|
pd.Timestamp("2012-01-01 00:00:00"),
|
pd.Timestamp("2012-01-01 01:00:00"),
|
pd.Timestamp("2012-01-01 02:00:00"),
|
],
|
}
|
).values
|
tm.assert_numpy_array_equal(result, expected)
|
