276 lines
8.3 KiB
Python
276 lines
8.3 KiB
Python
|
import re
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import pytest
|
||
|
|
|
||
|
|
from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
|
||
|
|
|
||
|
|
import pandas as pd
|
||
|
|
from pandas import IntervalIndex, MultiIndex, RangeIndex
|
||
|
|
import pandas._testing as tm
|
||
|
|
|
||
|
|
|
||
|
|
def test_labels_dtypes():
|
||
|
|
|
||
|
|
# GH 8456
|
||
|
|
i = MultiIndex.from_tuples([("A", 1), ("A", 2)])
|
||
|
|
assert i.codes[0].dtype == "int8"
|
||
|
|
assert i.codes[1].dtype == "int8"
|
||
|
|
|
||
|
|
i = MultiIndex.from_product([["a"], range(40)])
|
||
|
|
assert i.codes[1].dtype == "int8"
|
||
|
|
i = MultiIndex.from_product([["a"], range(400)])
|
||
|
|
assert i.codes[1].dtype == "int16"
|
||
|
|
i = MultiIndex.from_product([["a"], range(40000)])
|
||
|
|
assert i.codes[1].dtype == "int32"
|
||
|
|
|
||
|
|
i = MultiIndex.from_product([["a"], range(1000)])
|
||
|
|
assert (i.codes[0] >= 0).all()
|
||
|
|
assert (i.codes[1] >= 0).all()
|
||
|
|
|
||
|
|
|
||
|
|
def test_values_boxed():
|
||
|
|
tuples = [
|
||
|
|
(1, pd.Timestamp("2000-01-01")),
|
||
|
|
(2, pd.NaT),
|
||
|
|
(3, pd.Timestamp("2000-01-03")),
|
||
|
|
(1, pd.Timestamp("2000-01-04")),
|
||
|
|
(2, pd.Timestamp("2000-01-02")),
|
||
|
|
(3, pd.Timestamp("2000-01-03")),
|
||
|
|
]
|
||
|
|
result = MultiIndex.from_tuples(tuples)
|
||
|
|
expected = construct_1d_object_array_from_listlike(tuples)
|
||
|
|
tm.assert_numpy_array_equal(result.values, expected)
|
||
|
|
# Check that code branches for boxed values produce identical results
|
||
|
|
tm.assert_numpy_array_equal(result.values[:4], result[:4].values)
|
||
|
|
|
||
|
|
|
||
|
|
def test_values_multiindex_datetimeindex():
|
||
|
|
# Test to ensure we hit the boxing / nobox part of MI.values
|
||
|
|
ints = np.arange(10 ** 18, 10 ** 18 + 5)
|
||
|
|
naive = pd.DatetimeIndex(ints)
|
||
|
|
|
||
|
|
aware = pd.DatetimeIndex(ints, tz="US/Central")
|
||
|
|
|
||
|
|
idx = MultiIndex.from_arrays([naive, aware])
|
||
|
|
result = idx.values
|
||
|
|
|
||
|
|
outer = pd.DatetimeIndex([x[0] for x in result])
|
||
|
|
tm.assert_index_equal(outer, naive)
|
||
|
|
|
||
|
|
inner = pd.DatetimeIndex([x[1] for x in result])
|
||
|
|
tm.assert_index_equal(inner, aware)
|
||
|
|
|
||
|
|
# n_lev > n_lab
|
||
|
|
result = idx[:2].values
|
||
|
|
|
||
|
|
outer = pd.DatetimeIndex([x[0] for x in result])
|
||
|
|
tm.assert_index_equal(outer, naive[:2])
|
||
|
|
|
||
|
|
inner = pd.DatetimeIndex([x[1] for x in result])
|
||
|
|
tm.assert_index_equal(inner, aware[:2])
|
||
|
|
|
||
|
|
|
||
|
|
def test_values_multiindex_periodindex():
|
||
|
|
# Test to ensure we hit the boxing / nobox part of MI.values
|
||
|
|
ints = np.arange(2007, 2012)
|
||
|
|
pidx = pd.PeriodIndex(ints, freq="D")
|
||
|
|
|
||
|
|
idx = MultiIndex.from_arrays([ints, pidx])
|
||
|
|
result = idx.values
|
||
|
|
|
||
|
|
outer = pd.Int64Index([x[0] for x in result])
|
||
|
|
tm.assert_index_equal(outer, pd.Int64Index(ints))
|
||
|
|
|
||
|
|
inner = pd.PeriodIndex([x[1] for x in result])
|
||
|
|
tm.assert_index_equal(inner, pidx)
|
||
|
|
|
||
|
|
# n_lev > n_lab
|
||
|
|
result = idx[:2].values
|
||
|
|
|
||
|
|
outer = pd.Int64Index([x[0] for x in result])
|
||
|
|
tm.assert_index_equal(outer, pd.Int64Index(ints[:2]))
|
||
|
|
|
||
|
|
inner = pd.PeriodIndex([x[1] for x in result])
|
||
|
|
tm.assert_index_equal(inner, pidx[:2])
|
||
|
|
|
||
|
|
|
||
|
|
def test_consistency():
|
||
|
|
# need to construct an overflow
|
||
|
|
major_axis = list(range(70000))
|
||
|
|
minor_axis = list(range(10))
|
||
|
|
|
||
|
|
major_codes = np.arange(70000)
|
||
|
|
minor_codes = np.repeat(range(10), 7000)
|
||
|
|
|
||
|
|
# the fact that is works means it's consistent
|
||
|
|
index = MultiIndex(
|
||
|
|
levels=[major_axis, minor_axis], codes=[major_codes, minor_codes]
|
||
|
|
)
|
||
|
|
|
||
|
|
# inconsistent
|
||
|
|
major_codes = np.array([0, 0, 1, 1, 1, 2, 2, 3, 3])
|
||
|
|
minor_codes = np.array([0, 1, 0, 1, 1, 0, 1, 0, 1])
|
||
|
|
index = MultiIndex(
|
||
|
|
levels=[major_axis, minor_axis], codes=[major_codes, minor_codes]
|
||
|
|
)
|
||
|
|
|
||
|
|
assert index.is_unique is False
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.arm_slow
|
||
|
|
def test_hash_collisions():
|
||
|
|
# non-smoke test that we don't get hash collisions
|
||
|
|
|
||
|
|
index = MultiIndex.from_product(
|
||
|
|
[np.arange(1000), np.arange(1000)], names=["one", "two"]
|
||
|
|
)
|
||
|
|
result = index.get_indexer(index.values)
|
||
|
|
tm.assert_numpy_array_equal(result, np.arange(len(index), dtype="intp"))
|
||
|
|
|
||
|
|
for i in [0, 1, len(index) - 2, len(index) - 1]:
|
||
|
|
result = index.get_loc(index[i])
|
||
|
|
assert result == i
|
||
|
|
|
||
|
|
|
||
|
|
def test_dims():
|
||
|
|
pass
|
||
|
|
|
||
|
|
|
||
|
|
def take_invalid_kwargs():
|
||
|
|
vals = [["A", "B"], [pd.Timestamp("2011-01-01"), pd.Timestamp("2011-01-02")]]
|
||
|
|
idx = MultiIndex.from_product(vals, names=["str", "dt"])
|
||
|
|
indices = [1, 2]
|
||
|
|
|
||
|
|
msg = r"take\(\) got an unexpected keyword argument 'foo'"
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
idx.take(indices, foo=2)
|
||
|
|
|
||
|
|
msg = "the 'out' parameter is not supported"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
idx.take(indices, out=indices)
|
||
|
|
|
||
|
|
msg = "the 'mode' parameter is not supported"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
idx.take(indices, mode="clip")
|
||
|
|
|
||
|
|
|
||
|
|
def test_isna_behavior(idx):
|
||
|
|
# should not segfault GH5123
|
||
|
|
# NOTE: if MI representation changes, may make sense to allow
|
||
|
|
# isna(MI)
|
||
|
|
msg = "isna is not defined for MultiIndex"
|
||
|
|
with pytest.raises(NotImplementedError, match=msg):
|
||
|
|
pd.isna(idx)
|
||
|
|
|
||
|
|
|
||
|
|
def test_large_multiindex_error():
|
||
|
|
# GH12527
|
||
|
|
df_below_1000000 = pd.DataFrame(
|
||
|
|
1, index=MultiIndex.from_product([[1, 2], range(499999)]), columns=["dest"]
|
||
|
|
)
|
||
|
|
with pytest.raises(KeyError, match=r"^\(-1, 0\)$"):
|
||
|
|
df_below_1000000.loc[(-1, 0), "dest"]
|
||
|
|
with pytest.raises(KeyError, match=r"^\(3, 0\)$"):
|
||
|
|
df_below_1000000.loc[(3, 0), "dest"]
|
||
|
|
df_above_1000000 = pd.DataFrame(
|
||
|
|
1, index=MultiIndex.from_product([[1, 2], range(500001)]), columns=["dest"]
|
||
|
|
)
|
||
|
|
with pytest.raises(KeyError, match=r"^\(-1, 0\)$"):
|
||
|
|
df_above_1000000.loc[(-1, 0), "dest"]
|
||
|
|
with pytest.raises(KeyError, match=r"^\(3, 0\)$"):
|
||
|
|
df_above_1000000.loc[(3, 0), "dest"]
|
||
|
|
|
||
|
|
|
||
|
|
def test_million_record_attribute_error():
|
||
|
|
# GH 18165
|
||
|
|
r = list(range(1000000))
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{"a": r, "b": r}, index=MultiIndex.from_tuples([(x, x) for x in r])
|
||
|
|
)
|
||
|
|
|
||
|
|
msg = "'Series' object has no attribute 'foo'"
|
||
|
|
with pytest.raises(AttributeError, match=msg):
|
||
|
|
df["a"].foo()
|
||
|
|
|
||
|
|
|
||
|
|
def test_can_hold_identifiers(idx):
|
||
|
|
key = idx[0]
|
||
|
|
assert idx._can_hold_identifiers_and_holds_name(key) is True
|
||
|
|
|
||
|
|
|
||
|
|
def test_metadata_immutable(idx):
|
||
|
|
levels, codes = idx.levels, idx.codes
|
||
|
|
# shouldn't be able to set at either the top level or base level
|
||
|
|
mutable_regex = re.compile("does not support mutable operations")
|
||
|
|
with pytest.raises(TypeError, match=mutable_regex):
|
||
|
|
levels[0] = levels[0]
|
||
|
|
with pytest.raises(TypeError, match=mutable_regex):
|
||
|
|
levels[0][0] = levels[0][0]
|
||
|
|
# ditto for labels
|
||
|
|
with pytest.raises(TypeError, match=mutable_regex):
|
||
|
|
codes[0] = codes[0]
|
||
|
|
with pytest.raises(ValueError, match="assignment destination is read-only"):
|
||
|
|
codes[0][0] = codes[0][0]
|
||
|
|
# and for names
|
||
|
|
names = idx.names
|
||
|
|
with pytest.raises(TypeError, match=mutable_regex):
|
||
|
|
names[0] = names[0]
|
||
|
|
|
||
|
|
|
||
|
|
def test_level_setting_resets_attributes():
|
||
|
|
ind = MultiIndex.from_arrays([["A", "A", "B", "B", "B"], [1, 2, 1, 2, 3]])
|
||
|
|
assert ind.is_monotonic
|
||
|
|
with tm.assert_produces_warning(FutureWarning):
|
||
|
|
ind.set_levels([["A", "B"], [1, 3, 2]], inplace=True)
|
||
|
|
# if this fails, probably didn't reset the cache correctly.
|
||
|
|
assert not ind.is_monotonic
|
||
|
|
|
||
|
|
|
||
|
|
def test_rangeindex_fallback_coercion_bug():
|
||
|
|
# GH 12893
|
||
|
|
foo = pd.DataFrame(np.arange(100).reshape((10, 10)))
|
||
|
|
bar = pd.DataFrame(np.arange(100).reshape((10, 10)))
|
||
|
|
df = pd.concat({"foo": foo.stack(), "bar": bar.stack()}, axis=1)
|
||
|
|
df.index.names = ["fizz", "buzz"]
|
||
|
|
|
||
|
|
str(df)
|
||
|
|
expected = pd.DataFrame(
|
||
|
|
{"bar": np.arange(100), "foo": np.arange(100)},
|
||
|
|
index=MultiIndex.from_product([range(10), range(10)], names=["fizz", "buzz"]),
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(df, expected, check_like=True)
|
||
|
|
|
||
|
|
result = df.index.get_level_values("fizz")
|
||
|
|
expected = pd.Int64Index(np.arange(10), name="fizz").repeat(10)
|
||
|
|
tm.assert_index_equal(result, expected)
|
||
|
|
|
||
|
|
result = df.index.get_level_values("buzz")
|
||
|
|
expected = pd.Int64Index(np.tile(np.arange(10), 10), name="buzz")
|
||
|
|
tm.assert_index_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_memory_usage(idx):
|
||
|
|
result = idx.memory_usage()
|
||
|
|
if len(idx):
|
||
|
|
idx.get_loc(idx[0])
|
||
|
|
result2 = idx.memory_usage()
|
||
|
|
result3 = idx.memory_usage(deep=True)
|
||
|
|
|
||
|
|
# RangeIndex, IntervalIndex
|
||
|
|
# don't have engines
|
||
|
|
if not isinstance(idx, (RangeIndex, IntervalIndex)):
|
||
|
|
assert result2 > result
|
||
|
|
|
||
|
|
if idx.inferred_type == "object":
|
||
|
|
assert result3 > result2
|
||
|
|
|
||
|
|
else:
|
||
|
|
|
||
|
|
# we report 0 for no-length
|
||
|
|
assert result == 0
|
||
|
|
|
||
|
|
|
||
|
|
def test_nlevels(idx):
|
||
|
|
assert idx.nlevels == 2
|