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"""
Benchmarks for code in pandas/_libs, excluding pandas/_libs/tslibs,
which has its own directory.
If a PR does not edit anything in _libs/, then it is unlikely that the
benchmarks will be affected.
"""
import numpy as np
from pandas._libs.lib import (
infer_dtype,
is_list_like,
is_scalar,
)
from pandas import (
NA,
Index,
NaT,
)
from .pandas_vb_common import lib
try:
from pandas.util import cache_readonly
except ImportError:
from pandas.util.decorators import cache_readonly
# TODO: share with something in pd._testing?
scalars = [
0,
1.0,
1 + 2j,
True,
"foo",
b"bar",
None,
np.datetime64(123, "ns"),
np.timedelta64(123, "ns"),
NaT,
NA,
]
zero_dims = [np.array("123")]
listlikes = [np.array([1, 2, 3]), {0: 1}, {1, 2, 3}, [1, 2, 3], (1, 2, 3)]
class ScalarListLike:
params = scalars + zero_dims + listlikes
def time_is_list_like(self, param):
is_list_like(param)
def time_is_scalar(self, param):
is_scalar(param)
class FastZip:
def setup(self):
N = 10000
K = 10
key1 = Index([f"i-{i}" for i in range(N)], dtype=object).values.repeat(K)
key2 = Index([f"i-{i}" for i in range(N)], dtype=object).values.repeat(K)
col_array = np.vstack([key1, key2, np.random.randn(N * K)])
col_array2 = col_array.copy()
col_array2[:, :10000] = np.nan
self.col_array_list = list(col_array)
def time_lib_fast_zip(self):
lib.fast_zip(self.col_array_list)
class InferDtype:
param_names = ["dtype"]
data_dict = {
"np-object": np.array([1] * 100000, dtype="O"),
"py-object": [1] * 100000,
"np-null": np.array([1] * 50000 + [np.nan] * 50000),
"py-null": [1] * 50000 + [None] * 50000,
"np-int": np.array([1] * 100000, dtype=int),
"np-floating": np.array([1.0] * 100000, dtype=float),
"empty": [],
"bytes": [b"a"] * 100000,
}
params = list(data_dict.keys())
def time_infer_dtype_skipna(self, dtype):
infer_dtype(self.data_dict[dtype], skipna=True)
def time_infer_dtype(self, dtype):
infer_dtype(self.data_dict[dtype], skipna=False)
class CacheReadonly:
def setup(self):
class Foo:
@cache_readonly
def prop(self):
return 5
self.obj = Foo()
def time_cache_readonly(self):
self.obj.prop
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