96 lines
2.9 KiB
Python
96 lines
2.9 KiB
Python
|
import numpy as np
|
||
|
|
|
||
|
|
import pandas._libs.index as _index
|
||
|
|
from pandas.errors import PerformanceWarning
|
||
|
|
|
||
|
|
import pandas as pd
|
||
|
|
from pandas import DataFrame, Index, MultiIndex, Series
|
||
|
|
import pandas._testing as tm
|
||
|
|
|
||
|
|
|
||
|
|
class TestMultiIndexBasic:
|
||
|
|
def test_multiindex_perf_warn(self):
|
||
|
|
|
||
|
|
df = DataFrame(
|
||
|
|
{
|
||
|
|
"jim": [0, 0, 1, 1],
|
||
|
|
"joe": ["x", "x", "z", "y"],
|
||
|
|
"jolie": np.random.rand(4),
|
||
|
|
}
|
||
|
|
).set_index(["jim", "joe"])
|
||
|
|
|
||
|
|
with tm.assert_produces_warning(PerformanceWarning):
|
||
|
|
df.loc[(1, "z")]
|
||
|
|
|
||
|
|
df = df.iloc[[2, 1, 3, 0]]
|
||
|
|
with tm.assert_produces_warning(PerformanceWarning):
|
||
|
|
df.loc[(0,)]
|
||
|
|
|
||
|
|
def test_indexing_over_hashtable_size_cutoff(self):
|
||
|
|
n = 10000
|
||
|
|
|
||
|
|
old_cutoff = _index._SIZE_CUTOFF
|
||
|
|
_index._SIZE_CUTOFF = 20000
|
||
|
|
|
||
|
|
s = Series(np.arange(n), MultiIndex.from_arrays((["a"] * n, np.arange(n))))
|
||
|
|
|
||
|
|
# hai it works!
|
||
|
|
assert s[("a", 5)] == 5
|
||
|
|
assert s[("a", 6)] == 6
|
||
|
|
assert s[("a", 7)] == 7
|
||
|
|
|
||
|
|
_index._SIZE_CUTOFF = old_cutoff
|
||
|
|
|
||
|
|
def test_multi_nan_indexing(self):
|
||
|
|
|
||
|
|
# GH 3588
|
||
|
|
df = DataFrame(
|
||
|
|
{
|
||
|
|
"a": ["R1", "R2", np.nan, "R4"],
|
||
|
|
"b": ["C1", "C2", "C3", "C4"],
|
||
|
|
"c": [10, 15, np.nan, 20],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
result = df.set_index(["a", "b"], drop=False)
|
||
|
|
expected = DataFrame(
|
||
|
|
{
|
||
|
|
"a": ["R1", "R2", np.nan, "R4"],
|
||
|
|
"b": ["C1", "C2", "C3", "C4"],
|
||
|
|
"c": [10, 15, np.nan, 20],
|
||
|
|
},
|
||
|
|
index=[
|
||
|
|
Index(["R1", "R2", np.nan, "R4"], name="a"),
|
||
|
|
Index(["C1", "C2", "C3", "C4"], name="b"),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_nested_tuples_duplicates(self):
|
||
|
|
# GH#30892
|
||
|
|
|
||
|
|
dti = pd.to_datetime(["20190101", "20190101", "20190102"])
|
||
|
|
idx = Index(["a", "a", "c"])
|
||
|
|
mi = pd.MultiIndex.from_arrays([dti, idx], names=["index1", "index2"])
|
||
|
|
|
||
|
|
df = DataFrame({"c1": [1, 2, 3], "c2": [np.nan, np.nan, np.nan]}, index=mi)
|
||
|
|
|
||
|
|
expected = DataFrame({"c1": df["c1"], "c2": [1.0, 1.0, np.nan]}, index=mi)
|
||
|
|
|
||
|
|
df2 = df.copy(deep=True)
|
||
|
|
df2.loc[(dti[0], "a"), "c2"] = 1.0
|
||
|
|
tm.assert_frame_equal(df2, expected)
|
||
|
|
|
||
|
|
df3 = df.copy(deep=True)
|
||
|
|
df3.loc[[(dti[0], "a")], "c2"] = 1.0
|
||
|
|
tm.assert_frame_equal(df3, expected)
|
||
|
|
|
||
|
|
def test_multiindex_with_datatime_level_preserves_freq(self):
|
||
|
|
# https://github.com/pandas-dev/pandas/issues/35563
|
||
|
|
idx = Index(range(2), name="A")
|
||
|
|
dti = pd.date_range("2020-01-01", periods=7, freq="D", name="B")
|
||
|
|
mi = MultiIndex.from_product([idx, dti])
|
||
|
|
df = DataFrame(np.random.randn(14, 2), index=mi)
|
||
|
|
result = df.loc[0].index
|
||
|
|
tm.assert_index_equal(result, dti)
|
||
|
|
assert result.freq == dti.freq
|