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import string
import numpy as np
from pandas import (
DataFrame,
MultiIndex,
RangeIndex,
date_range,
)
from .pandas_vb_common import tm
class GetLoc:
def setup(self):
self.mi_large = MultiIndex.from_product(
[np.arange(1000), np.arange(20), list(string.ascii_letters)],
names=["one", "two", "three"],
)
self.mi_med = MultiIndex.from_product(
[np.arange(1000), np.arange(10), list("A")], names=["one", "two", "three"]
)
self.mi_small = MultiIndex.from_product(
[np.arange(100), list("A"), list("A")], names=["one", "two", "three"]
)
def time_large_get_loc(self):
self.mi_large.get_loc((999, 19, "Z"))
def time_large_get_loc_warm(self):
for _ in range(1000):
self.mi_large.get_loc((999, 19, "Z"))
def time_med_get_loc(self):
self.mi_med.get_loc((999, 9, "A"))
def time_med_get_loc_warm(self):
for _ in range(1000):
self.mi_med.get_loc((999, 9, "A"))
def time_string_get_loc(self):
self.mi_small.get_loc((99, "A", "A"))
def time_small_get_loc_warm(self):
for _ in range(1000):
self.mi_small.get_loc((99, "A", "A"))
class GetLocs:
def setup(self):
self.mi_large = MultiIndex.from_product(
[np.arange(1000), np.arange(20), list(string.ascii_letters)],
names=["one", "two", "three"],
)
self.mi_med = MultiIndex.from_product(
[np.arange(1000), np.arange(10), list("A")], names=["one", "two", "three"]
)
self.mi_small = MultiIndex.from_product(
[np.arange(100), list("A"), list("A")], names=["one", "two", "three"]
)
def time_large_get_locs(self):
self.mi_large.get_locs([999, 19, "Z"])
def time_med_get_locs(self):
self.mi_med.get_locs([999, 9, "A"])
def time_small_get_locs(self):
self.mi_small.get_locs([99, "A", "A"])
class Duplicates:
def setup(self):
size = 65536
arrays = [np.random.randint(0, 8192, size), np.random.randint(0, 1024, size)]
mask = np.random.rand(size) < 0.1
self.mi_unused_levels = MultiIndex.from_arrays(arrays)
self.mi_unused_levels = self.mi_unused_levels[mask]
def time_remove_unused_levels(self):
self.mi_unused_levels.remove_unused_levels()
class Integer:
def setup(self):
self.mi_int = MultiIndex.from_product(
[np.arange(1000), np.arange(1000)], names=["one", "two"]
)
self.obj_index = np.array(
[
(0, 10),
(0, 11),
(0, 12),
(0, 13),
(0, 14),
(0, 15),
(0, 16),
(0, 17),
(0, 18),
(0, 19),
],
dtype=object,
)
self.other_mi_many_mismatches = MultiIndex.from_tuples(
[
(-7, 41),
(-2, 3),
(-0.7, 5),
(0, 0),
(0, 1.5),
(0, 340),
(0, 1001),
(1, -4),
(1, 20),
(1, 1040),
(432, -5),
(432, 17),
(439, 165.5),
(998, -4),
(998, 24065),
(999, 865.2),
(999, 1000),
(1045, -843),
]
)
def time_get_indexer(self):
self.mi_int.get_indexer(self.obj_index)
def time_get_indexer_and_backfill(self):
self.mi_int.get_indexer(self.other_mi_many_mismatches, method="backfill")
def time_get_indexer_and_pad(self):
self.mi_int.get_indexer(self.other_mi_many_mismatches, method="pad")
def time_is_monotonic(self):
self.mi_int.is_monotonic_increasing
class Duplicated:
def setup(self):
n, k = 200, 5000
levels = [np.arange(n), tm.makeStringIndex(n).values, 1000 + np.arange(n)]
codes = [np.random.choice(n, (k * n)) for lev in levels]
self.mi = MultiIndex(levels=levels, codes=codes)
def time_duplicated(self):
self.mi.duplicated()
class Sortlevel:
def setup(self):
n = 1182720
low, high = -4096, 4096
arrs = [
np.repeat(np.random.randint(low, high, (n // k)), k)
for k in [11, 7, 5, 3, 1]
]
self.mi_int = MultiIndex.from_arrays(arrs)[np.random.permutation(n)]
a = np.repeat(np.arange(100), 1000)
b = np.tile(np.arange(1000), 100)
self.mi = MultiIndex.from_arrays([a, b])
self.mi = self.mi.take(np.random.permutation(np.arange(100000)))
def time_sortlevel_int64(self):
self.mi_int.sortlevel()
def time_sortlevel_zero(self):
self.mi.sortlevel(0)
def time_sortlevel_one(self):
self.mi.sortlevel(1)
class Values:
def setup_cache(self):
level1 = range(1000)
level2 = date_range(start="1/1/2012", periods=100)
mi = MultiIndex.from_product([level1, level2])
return mi
def time_datetime_level_values_copy(self, mi):
mi.copy().values
def time_datetime_level_values_sliced(self, mi):
mi[:10].values
class CategoricalLevel:
def setup(self):
self.df = DataFrame(
{
"a": np.arange(1_000_000, dtype=np.int32),
"b": np.arange(1_000_000, dtype=np.int64),
"c": np.arange(1_000_000, dtype=float),
}
).astype({"a": "category", "b": "category"})
def time_categorical_level(self):
self.df.set_index(["a", "b"])
class Equals:
def setup(self):
idx_large_fast = RangeIndex(100000)
idx_small_slow = date_range(start="1/1/2012", periods=1)
self.mi_large_slow = MultiIndex.from_product([idx_large_fast, idx_small_slow])
self.idx_non_object = RangeIndex(1)
def time_equals_non_object_index(self):
self.mi_large_slow.equals(self.idx_non_object)
class SetOperations:
params = [
("monotonic", "non_monotonic"),
("datetime", "int", "string"),
("intersection", "union", "symmetric_difference"),
]
param_names = ["index_structure", "dtype", "method"]
def setup(self, index_structure, dtype, method):
N = 10**5
level1 = range(1000)
level2 = date_range(start="1/1/2000", periods=N // 1000)
dates_left = MultiIndex.from_product([level1, level2])
level2 = range(N // 1000)
int_left = MultiIndex.from_product([level1, level2])
level2 = tm.makeStringIndex(N // 1000).values
str_left = MultiIndex.from_product([level1, level2])
data = {
"datetime": dates_left,
"int": int_left,
"string": str_left,
}
if index_structure == "non_monotonic":
data = {k: mi[::-1] for k, mi in data.items()}
data = {k: {"left": mi, "right": mi[:-1]} for k, mi in data.items()}
self.left = data[dtype]["left"]
self.right = data[dtype]["right"]
def time_operation(self, index_structure, dtype, method):
getattr(self.left, method)(self.right)
from .pandas_vb_common import setup # noqa: F401 isort:skip
|
