title stringclasses 1 value | text stringlengths 30 426k | id stringlengths 27 30 |
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asv_bench/benchmarks/period.py/Indexing/time_shallow_copy
class Indexing:
def time_shallow_copy(self):
self.index._view() | negative_train_query0_00898 | |
asv_bench/benchmarks/period.py/Indexing/time_series_loc
class Indexing:
def time_series_loc(self):
self.series.loc[self.period] | negative_train_query0_00899 | |
asv_bench/benchmarks/period.py/Indexing/time_align
class Indexing:
def time_align(self):
DataFrame({"a": self.series, "b": self.series[:500]}) | negative_train_query0_00900 | |
asv_bench/benchmarks/period.py/Indexing/time_intersection
class Indexing:
def time_intersection(self):
self.index[:750].intersection(self.index[250:]) | negative_train_query0_00901 | |
asv_bench/benchmarks/period.py/Indexing/time_unique
class Indexing:
def time_unique(self):
self.index.unique() | negative_train_query0_00902 | |
asv_bench/benchmarks/strftime.py/DatetimeStrftime/setup
class DatetimeStrftime:
def setup(self, nobs):
d = "2018-11-29"
dt = "2018-11-26 11:18:27.0"
self.data = pd.DataFrame(
{
"dt": [np.datetime64(dt)] * nobs,
"d": [np.datetime64(d)] * nobs,
"r": [np.random.uniform()] * nobs,
}
) | negative_train_query0_00903 | |
asv_bench/benchmarks/strftime.py/DatetimeStrftime/time_frame_date_to_str
class DatetimeStrftime:
def time_frame_date_to_str(self, nobs):
self.data["d"].astype(str) | negative_train_query0_00904 | |
asv_bench/benchmarks/strftime.py/DatetimeStrftime/time_frame_date_formatting_default
class DatetimeStrftime:
def time_frame_date_formatting_default(self, nobs):
self.data["d"].dt.strftime(date_format=None) | negative_train_query0_00905 | |
asv_bench/benchmarks/strftime.py/DatetimeStrftime/time_frame_date_formatting_default_explicit
class DatetimeStrftime:
def time_frame_date_formatting_default_explicit(self, nobs):
self.data["d"].dt.strftime(date_format="%Y-%m-%d") | negative_train_query0_00906 | |
asv_bench/benchmarks/strftime.py/DatetimeStrftime/time_frame_date_formatting_custom
class DatetimeStrftime:
def time_frame_date_formatting_custom(self, nobs):
self.data["d"].dt.strftime(date_format="%Y---%m---%d") | negative_train_query0_00907 | |
asv_bench/benchmarks/strftime.py/DatetimeStrftime/time_frame_datetime_to_str
class DatetimeStrftime:
def time_frame_datetime_to_str(self, nobs):
self.data["dt"].astype(str) | negative_train_query0_00908 | |
asv_bench/benchmarks/strftime.py/DatetimeStrftime/time_frame_datetime_formatting_default
class DatetimeStrftime:
def time_frame_datetime_formatting_default(self, nobs):
self.data["dt"].dt.strftime(date_format=None) | negative_train_query0_00909 | |
asv_bench/benchmarks/strftime.py/DatetimeStrftime/time_frame_datetime_formatting_default_explicit_date_only
class DatetimeStrftime:
def time_frame_datetime_formatting_default_explicit_date_only(self, nobs):
self.data["dt"].dt.strftime(date_format="%Y-%m-%d") | negative_train_query0_00910 | |
asv_bench/benchmarks/strftime.py/DatetimeStrftime/time_frame_datetime_formatting_default_explicit
class DatetimeStrftime:
def time_frame_datetime_formatting_default_explicit(self, nobs):
self.data["dt"].dt.strftime(date_format="%Y-%m-%d %H:%M:%S") | negative_train_query0_00911 | |
asv_bench/benchmarks/strftime.py/DatetimeStrftime/time_frame_datetime_formatting_default_with_float
class DatetimeStrftime:
def time_frame_datetime_formatting_default_with_float(self, nobs):
self.data["dt"].dt.strftime(date_format="%Y-%m-%d %H:%M:%S.%f") | negative_train_query0_00912 | |
asv_bench/benchmarks/strftime.py/DatetimeStrftime/time_frame_datetime_formatting_custom
class DatetimeStrftime:
def time_frame_datetime_formatting_custom(self, nobs):
self.data["dt"].dt.strftime(date_format="%Y-%m-%d --- %H:%M:%S") | negative_train_query0_00913 | |
asv_bench/benchmarks/strftime.py/PeriodStrftime/setup
class PeriodStrftime:
def setup(self, nobs, freq):
self.data = pd.DataFrame(
{
"p": pd.period_range(start="2000-01-01", periods=nobs, freq=freq),
"r": [np.random.uniform()] * nobs,
}
)
self.data["i"] = self.data["p"]
self.data.set_index("i", inplace=True)
if freq == "D":
self.default_fmt = "%Y-%m-%d"
elif freq == "h":
self.default_fmt = "%Y-%m-%d %H:00" | negative_train_query0_00914 | |
asv_bench/benchmarks/strftime.py/PeriodStrftime/time_frame_period_to_str
class PeriodStrftime:
def time_frame_period_to_str(self, nobs, freq):
self.data["p"].astype(str) | negative_train_query0_00915 | |
asv_bench/benchmarks/strftime.py/PeriodStrftime/time_frame_period_formatting_default
class PeriodStrftime:
def time_frame_period_formatting_default(self, nobs, freq):
self.data["p"].dt.strftime(date_format=None) | negative_train_query0_00916 | |
asv_bench/benchmarks/strftime.py/PeriodStrftime/time_frame_period_formatting_default_explicit
class PeriodStrftime:
def time_frame_period_formatting_default_explicit(self, nobs, freq):
self.data["p"].dt.strftime(date_format=self.default_fmt) | negative_train_query0_00917 | |
asv_bench/benchmarks/strftime.py/PeriodStrftime/time_frame_period_formatting_custom
class PeriodStrftime:
def time_frame_period_formatting_custom(self, nobs, freq):
self.data["p"].dt.strftime(date_format="%Y-%m-%d --- %H:%M:%S") | negative_train_query0_00918 | |
asv_bench/benchmarks/strftime.py/PeriodStrftime/time_frame_period_formatting_iso8601_strftime_Z
class PeriodStrftime:
def time_frame_period_formatting_iso8601_strftime_Z(self, nobs, freq):
self.data["p"].dt.strftime(date_format="%Y-%m-%dT%H:%M:%SZ") | negative_train_query0_00919 | |
asv_bench/benchmarks/strftime.py/PeriodStrftime/time_frame_period_formatting_iso8601_strftime_offset
class PeriodStrftime:
def time_frame_period_formatting_iso8601_strftime_offset(self, nobs, freq):
"""Not optimized yet as %z is not supported by `convert_strftime_format`"""
self.data["p"].dt.strftime(date_format="%Y-%m-%dT%H:%M:%S%z") | negative_train_query0_00920 | |
asv_bench/benchmarks/strftime.py/BusinessHourStrftime/setup
class BusinessHourStrftime:
def setup(self, nobs):
self.data = pd.DataFrame(
{
"off": [offsets.BusinessHour()] * nobs,
}
) | negative_train_query0_00921 | |
asv_bench/benchmarks/strftime.py/BusinessHourStrftime/time_frame_offset_str
class BusinessHourStrftime:
def time_frame_offset_str(self, nobs):
self.data["off"].apply(str) | negative_train_query0_00922 | |
asv_bench/benchmarks/strftime.py/BusinessHourStrftime/time_frame_offset_repr
class BusinessHourStrftime:
def time_frame_offset_repr(self, nobs):
self.data["off"].apply(repr) | negative_train_query0_00923 | |
asv_bench/benchmarks/multiindex_object.py/GetLoc/setup
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"]
) | negative_train_query0_00924 | |
asv_bench/benchmarks/multiindex_object.py/GetLoc/time_large_get_loc
class GetLoc:
def time_large_get_loc(self):
self.mi_large.get_loc((999, 19, "Z")) | negative_train_query0_00925 | |
asv_bench/benchmarks/multiindex_object.py/GetLoc/time_large_get_loc_warm
class GetLoc:
def time_large_get_loc_warm(self):
for _ in range(1000):
self.mi_large.get_loc((999, 19, "Z")) | negative_train_query0_00926 | |
asv_bench/benchmarks/multiindex_object.py/GetLoc/time_med_get_loc
class GetLoc:
def time_med_get_loc(self):
self.mi_med.get_loc((999, 9, "A")) | negative_train_query0_00927 | |
asv_bench/benchmarks/multiindex_object.py/GetLoc/time_med_get_loc_warm
class GetLoc:
def time_med_get_loc_warm(self):
for _ in range(1000):
self.mi_med.get_loc((999, 9, "A")) | negative_train_query0_00928 | |
asv_bench/benchmarks/multiindex_object.py/GetLoc/time_string_get_loc
class GetLoc:
def time_string_get_loc(self):
self.mi_small.get_loc((99, "A", "A")) | negative_train_query0_00929 | |
asv_bench/benchmarks/multiindex_object.py/GetLoc/time_small_get_loc_warm
class GetLoc:
def time_small_get_loc_warm(self):
for _ in range(1000):
self.mi_small.get_loc((99, "A", "A")) | negative_train_query0_00930 | |
asv_bench/benchmarks/multiindex_object.py/GetLocs/setup
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"]
) | negative_train_query0_00931 | |
asv_bench/benchmarks/multiindex_object.py/GetLocs/time_large_get_locs
class GetLocs:
def time_large_get_locs(self):
self.mi_large.get_locs([999, 19, "Z"]) | negative_train_query0_00932 | |
asv_bench/benchmarks/multiindex_object.py/GetLocs/time_med_get_locs
class GetLocs:
def time_med_get_locs(self):
self.mi_med.get_locs([999, 9, "A"]) | negative_train_query0_00933 | |
asv_bench/benchmarks/multiindex_object.py/GetLocs/time_small_get_locs
class GetLocs:
def time_small_get_locs(self):
self.mi_small.get_locs([99, "A", "A"]) | negative_train_query0_00934 | |
asv_bench/benchmarks/multiindex_object.py/Duplicates/setup
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] | negative_train_query0_00935 | |
asv_bench/benchmarks/multiindex_object.py/Duplicates/time_remove_unused_levels
class Duplicates:
def time_remove_unused_levels(self):
self.mi_unused_levels.remove_unused_levels() | negative_train_query0_00936 | |
asv_bench/benchmarks/multiindex_object.py/Integer/setup
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),
]
) | negative_train_query0_00937 | |
asv_bench/benchmarks/multiindex_object.py/Integer/time_get_indexer
class Integer:
def time_get_indexer(self):
self.mi_int.get_indexer(self.obj_index) | negative_train_query0_00938 | |
asv_bench/benchmarks/multiindex_object.py/Integer/time_get_indexer_and_backfill
class Integer:
def time_get_indexer_and_backfill(self):
self.mi_int.get_indexer(self.other_mi_many_mismatches, method="backfill") | negative_train_query0_00939 | |
asv_bench/benchmarks/multiindex_object.py/Integer/time_get_indexer_and_pad
class Integer:
def time_get_indexer_and_pad(self):
self.mi_int.get_indexer(self.other_mi_many_mismatches, method="pad") | negative_train_query0_00940 | |
asv_bench/benchmarks/multiindex_object.py/Integer/time_is_monotonic
class Integer:
def time_is_monotonic(self):
self.mi_int.is_monotonic_increasing | negative_train_query0_00941 | |
asv_bench/benchmarks/multiindex_object.py/Duplicated/setup
class Duplicated:
def setup(self):
n, k = 200, 5000
levels = [
np.arange(n),
Index([f"i-{i}" for i in range(n)], dtype=object).values,
1000 + np.arange(n),
]
codes = [np.random.choice(n, (k * n)) for lev in levels]
self.mi = MultiIndex(levels=levels, codes=codes) | negative_train_query0_00942 | |
asv_bench/benchmarks/multiindex_object.py/Duplicated/time_duplicated
class Duplicated:
def time_duplicated(self):
self.mi.duplicated() | negative_train_query0_00943 | |
asv_bench/benchmarks/multiindex_object.py/Sortlevel/setup
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))) | negative_train_query0_00944 | |
asv_bench/benchmarks/multiindex_object.py/Sortlevel/time_sortlevel_int64
class Sortlevel:
def time_sortlevel_int64(self):
self.mi_int.sortlevel() | negative_train_query0_00945 | |
asv_bench/benchmarks/multiindex_object.py/Sortlevel/time_sortlevel_zero
class Sortlevel:
def time_sortlevel_zero(self):
self.mi.sortlevel(0) | negative_train_query0_00946 | |
asv_bench/benchmarks/multiindex_object.py/Sortlevel/time_sortlevel_one
class Sortlevel:
def time_sortlevel_one(self):
self.mi.sortlevel(1) | negative_train_query0_00947 | |
asv_bench/benchmarks/multiindex_object.py/SortValues/setup
class SortValues:
def setup(self, dtype):
a = array(np.tile(np.arange(100), 1000), dtype=dtype)
b = array(np.tile(np.arange(1000), 100), dtype=dtype)
self.mi = MultiIndex.from_arrays([a, b]) | negative_train_query0_00948 | |
asv_bench/benchmarks/multiindex_object.py/SortValues/time_sort_values
class SortValues:
def time_sort_values(self, dtype):
self.mi.sort_values() | negative_train_query0_00949 | |
asv_bench/benchmarks/multiindex_object.py/Values/setup_cache
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 | negative_train_query0_00950 | |
asv_bench/benchmarks/multiindex_object.py/Values/time_datetime_level_values_copy
class Values:
def time_datetime_level_values_copy(self, mi):
mi.copy().values | negative_train_query0_00951 | |
asv_bench/benchmarks/multiindex_object.py/Values/time_datetime_level_values_sliced
class Values:
def time_datetime_level_values_sliced(self, mi):
mi[:10].values | negative_train_query0_00952 | |
asv_bench/benchmarks/multiindex_object.py/CategoricalLevel/setup
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"}) | negative_train_query0_00953 | |
asv_bench/benchmarks/multiindex_object.py/CategoricalLevel/time_categorical_level
class CategoricalLevel:
def time_categorical_level(self):
self.df.set_index(["a", "b"]) | negative_train_query0_00954 | |
asv_bench/benchmarks/multiindex_object.py/Equals/setup
class Equals:
def setup(self):
self.mi = MultiIndex.from_product(
[
date_range("2000-01-01", periods=1000),
RangeIndex(1000),
]
)
self.mi_deepcopy = self.mi.copy(deep=True)
self.idx_non_object = RangeIndex(1) | negative_train_query0_00955 | |
asv_bench/benchmarks/multiindex_object.py/Equals/time_equals_deepcopy
class Equals:
def time_equals_deepcopy(self):
self.mi.equals(self.mi_deepcopy) | negative_train_query0_00956 | |
asv_bench/benchmarks/multiindex_object.py/Equals/time_equals_non_object_index
class Equals:
def time_equals_non_object_index(self):
self.mi.equals(self.idx_non_object) | negative_train_query0_00957 | |
asv_bench/benchmarks/multiindex_object.py/SetOperations/setup
class SetOperations:
def setup(self, index_structure, dtype, method, sort):
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 = Index([f"i-{i}" for i in range(N // 1000)], dtype=object).values
str_left = MultiIndex.from_product([level1, level2])
level2 = range(N // 1000)
ea_int_left = MultiIndex.from_product([level1, Series(level2, dtype="Int64")])
data = {
"datetime": dates_left,
"int": int_left,
"string": str_left,
"ea_int": ea_int_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"] | negative_train_query0_00958 | |
asv_bench/benchmarks/multiindex_object.py/SetOperations/time_operation
class SetOperations:
def time_operation(self, index_structure, dtype, method, sort):
getattr(self.left, method)(self.right, sort=sort) | negative_train_query0_00959 | |
asv_bench/benchmarks/multiindex_object.py/Difference/setup
class Difference:
def setup(self, dtype):
N = 10**4 * 2
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 = Series(range(N // 1000), dtype="Int64")
level2[0] = NA
ea_int_left = MultiIndex.from_product([level1, level2])
level2 = Index([f"i-{i}" for i in range(N // 1000)], dtype=object).values
str_left = MultiIndex.from_product([level1, level2])
data = {
"datetime": dates_left,
"int": int_left,
"ea_int": ea_int_left,
"string": str_left,
}
data = {k: {"left": mi, "right": mi[:5]} for k, mi in data.items()}
self.left = data[dtype]["left"]
self.right = data[dtype]["right"] | negative_train_query0_00960 | |
asv_bench/benchmarks/multiindex_object.py/Difference/time_difference
class Difference:
def time_difference(self, dtype):
self.left.difference(self.right) | negative_train_query0_00961 | |
asv_bench/benchmarks/multiindex_object.py/Unique/setup
class Unique:
def setup(self, dtype_val):
level = Series(
[1, 2, dtype_val[1], dtype_val[1]] + list(range(1_000_000)),
dtype=dtype_val[0],
)
self.midx = MultiIndex.from_arrays([level, level])
level_dups = Series(
[1, 2, dtype_val[1], dtype_val[1]] + list(range(500_000)) * 2,
dtype=dtype_val[0],
)
self.midx_dups = MultiIndex.from_arrays([level_dups, level_dups]) | negative_train_query0_00962 | |
asv_bench/benchmarks/multiindex_object.py/Unique/time_unique
class Unique:
def time_unique(self, dtype_val):
self.midx.unique() | negative_train_query0_00963 | |
asv_bench/benchmarks/multiindex_object.py/Unique/time_unique_dups
class Unique:
def time_unique_dups(self, dtype_val):
self.midx_dups.unique() | negative_train_query0_00964 | |
asv_bench/benchmarks/multiindex_object.py/Isin/setup
class Isin:
def setup(self, dtype):
N = 10**5
level1 = range(1000)
level2 = date_range(start="1/1/2000", periods=N // 1000)
dates_midx = MultiIndex.from_product([level1, level2])
level2 = range(N // 1000)
int_midx = MultiIndex.from_product([level1, level2])
level2 = Index([f"i-{i}" for i in range(N // 1000)], dtype=object).values
str_midx = MultiIndex.from_product([level1, level2])
data = {
"datetime": dates_midx,
"int": int_midx,
"string": str_midx,
}
self.midx = data[dtype]
self.values_small = self.midx[:100]
self.values_large = self.midx[100:] | negative_train_query0_00965 | |
asv_bench/benchmarks/multiindex_object.py/Isin/time_isin_small
class Isin:
def time_isin_small(self, dtype):
self.midx.isin(self.values_small) | negative_train_query0_00966 | |
asv_bench/benchmarks/multiindex_object.py/Isin/time_isin_large
class Isin:
def time_isin_large(self, dtype):
self.midx.isin(self.values_large) | negative_train_query0_00967 | |
asv_bench/benchmarks/multiindex_object.py/Putmask/setup
class Putmask:
def setup(self):
N = 10**5
level1 = range(1_000)
level2 = date_range(start="1/1/2000", periods=N // 1000)
self.midx = MultiIndex.from_product([level1, level2])
level1 = range(1_000, 2_000)
self.midx_values = MultiIndex.from_product([level1, level2])
level2 = date_range(start="1/1/2010", periods=N // 1000)
self.midx_values_different = MultiIndex.from_product([level1, level2])
self.mask = np.array([True, False] * (N // 2)) | negative_train_query0_00968 | |
asv_bench/benchmarks/multiindex_object.py/Putmask/time_putmask
class Putmask:
def time_putmask(self):
self.midx.putmask(self.mask, self.midx_values) | negative_train_query0_00969 | |
asv_bench/benchmarks/multiindex_object.py/Putmask/time_putmask_all_different
class Putmask:
def time_putmask_all_different(self):
self.midx.putmask(self.mask, self.midx_values_different) | negative_train_query0_00970 | |
asv_bench/benchmarks/multiindex_object.py/Append/setup
class Append:
def setup(self, dtype):
N1 = 1000
N2 = 500
left_level1 = range(N1)
right_level1 = range(N1, N1 + N1)
if dtype == "datetime64[ns]":
level2 = date_range(start="2000-01-01", periods=N2)
elif dtype == "int64":
level2 = range(N2)
elif dtype == "string":
level2 = Index([f"i-{i}" for i in range(N2)], dtype=object)
else:
raise NotImplementedError
self.left = MultiIndex.from_product([left_level1, level2])
self.right = MultiIndex.from_product([right_level1, level2]) | negative_train_query0_00971 | |
asv_bench/benchmarks/multiindex_object.py/Append/time_append
class Append:
def time_append(self, dtype):
self.left.append(self.right) | negative_train_query0_00972 | |
asv_bench/benchmarks/dtypes.py/Dtypes/time_pandas_dtype
class Dtypes:
def time_pandas_dtype(self, dtype):
pandas_dtype(dtype) | negative_train_query0_00973 | |
asv_bench/benchmarks/dtypes.py/DtypesInvalid/time_pandas_dtype_invalid
class DtypesInvalid:
def time_pandas_dtype_invalid(self, dtype):
try:
pandas_dtype(self.data_dict[dtype])
except TypeError:
pass | negative_train_query0_00974 | |
asv_bench/benchmarks/dtypes.py/SelectDtypes/setup
class SelectDtypes:
def setup(self, dtype):
N, K = 5000, 50
self.index = Index([f"i-{i}" for i in range(N)], dtype=object)
self.columns = Index([f"i-{i}" for i in range(K)], dtype=object)
def create_df(data):
return DataFrame(data, index=self.index, columns=self.columns)
self.df_int = create_df(np.random.randint(low=100, size=(N, K)))
self.df_float = create_df(np.random.randn(N, K))
self.df_bool = create_df(np.random.choice([True, False], size=(N, K)))
self.df_string = create_df(
np.random.choice(list(string.ascii_letters), size=(N, K))
) | negative_train_query0_00975 | |
asv_bench/benchmarks/dtypes.py/SelectDtypes/setup/create_df
class SelectDtypes:
def create_df(data):
return DataFrame(data, index=self.index, columns=self.columns) | negative_train_query0_00976 | |
asv_bench/benchmarks/dtypes.py/SelectDtypes/time_select_dtype_int_include
class SelectDtypes:
def time_select_dtype_int_include(self, dtype):
self.df_int.select_dtypes(include=dtype) | negative_train_query0_00977 | |
asv_bench/benchmarks/dtypes.py/SelectDtypes/time_select_dtype_int_exclude
class SelectDtypes:
def time_select_dtype_int_exclude(self, dtype):
self.df_int.select_dtypes(exclude=dtype) | negative_train_query0_00978 | |
asv_bench/benchmarks/dtypes.py/SelectDtypes/time_select_dtype_float_include
class SelectDtypes:
def time_select_dtype_float_include(self, dtype):
self.df_float.select_dtypes(include=dtype) | negative_train_query0_00979 | |
asv_bench/benchmarks/dtypes.py/SelectDtypes/time_select_dtype_float_exclude
class SelectDtypes:
def time_select_dtype_float_exclude(self, dtype):
self.df_float.select_dtypes(exclude=dtype) | negative_train_query0_00980 | |
asv_bench/benchmarks/dtypes.py/SelectDtypes/time_select_dtype_bool_include
class SelectDtypes:
def time_select_dtype_bool_include(self, dtype):
self.df_bool.select_dtypes(include=dtype) | negative_train_query0_00981 | |
asv_bench/benchmarks/dtypes.py/SelectDtypes/time_select_dtype_bool_exclude
class SelectDtypes:
def time_select_dtype_bool_exclude(self, dtype):
self.df_bool.select_dtypes(exclude=dtype) | negative_train_query0_00982 | |
asv_bench/benchmarks/dtypes.py/SelectDtypes/time_select_dtype_string_include
class SelectDtypes:
def time_select_dtype_string_include(self, dtype):
self.df_string.select_dtypes(include=dtype) | negative_train_query0_00983 | |
asv_bench/benchmarks/dtypes.py/SelectDtypes/time_select_dtype_string_exclude
class SelectDtypes:
def time_select_dtype_string_exclude(self, dtype):
self.df_string.select_dtypes(exclude=dtype) | negative_train_query0_00984 | |
asv_bench/benchmarks/dtypes.py/CheckDtypes/setup
class CheckDtypes:
def setup(self):
self.ext_dtype = pd.Int64Dtype()
self.np_dtype = np.dtype("int64") | negative_train_query0_00985 | |
asv_bench/benchmarks/dtypes.py/CheckDtypes/time_is_extension_array_dtype_true
class CheckDtypes:
def time_is_extension_array_dtype_true(self):
is_extension_array_dtype(self.ext_dtype) | negative_train_query0_00986 | |
asv_bench/benchmarks/dtypes.py/CheckDtypes/time_is_extension_array_dtype_false
class CheckDtypes:
def time_is_extension_array_dtype_false(self):
is_extension_array_dtype(self.np_dtype) | negative_train_query0_00987 | |
asv_bench/benchmarks/frame_methods.py/AsType/setup
class AsType:
def setup(self, from_to_dtypes, copy):
from_dtype = from_to_dtypes[0]
if from_dtype in ("float64", "Float64", "float64[pyarrow]"):
data = np.random.randn(100, 100)
elif from_dtype in ("int64", "Int64", "int64[pyarrow]"):
data = np.random.randint(0, 1000, (100, 100))
else:
raise NotImplementedError
self.df = DataFrame(data, dtype=from_dtype) | negative_train_query0_00988 | |
asv_bench/benchmarks/frame_methods.py/AsType/time_astype
class AsType:
def time_astype(self, from_to_dtypes, copy):
self.df.astype(from_to_dtypes[1], copy=copy) | negative_train_query0_00989 | |
asv_bench/benchmarks/frame_methods.py/Clip/setup
class Clip:
def setup(self, dtype):
data = np.random.randn(100_000, 10)
df = DataFrame(data, dtype=dtype)
self.df = df | negative_train_query0_00990 | |
asv_bench/benchmarks/frame_methods.py/Clip/time_clip
class Clip:
def time_clip(self, dtype):
self.df.clip(-1.0, 1.0) | negative_train_query0_00991 | |
asv_bench/benchmarks/frame_methods.py/GetNumericData/setup
class GetNumericData:
def setup(self):
self.df = DataFrame(np.random.randn(10000, 25))
self.df["foo"] = "bar"
self.df["bar"] = "baz"
self.df = self.df._consolidate() | negative_train_query0_00992 | |
asv_bench/benchmarks/frame_methods.py/GetNumericData/time_frame_get_numeric_data
class GetNumericData:
def time_frame_get_numeric_data(self):
self.df._get_numeric_data() | negative_train_query0_00993 | |
asv_bench/benchmarks/frame_methods.py/Reindex/setup
class Reindex:
def setup(self):
N = 10**3
self.df = DataFrame(np.random.randn(N * 10, N))
self.idx = np.arange(4 * N, 7 * N)
self.idx_cols = np.random.randint(0, N, N)
self.df2 = DataFrame(
{
c: {
0: np.random.randint(0, 2, N).astype(np.bool_),
1: np.random.randint(0, N, N).astype(np.int16),
2: np.random.randint(0, N, N).astype(np.int32),
3: np.random.randint(0, N, N).astype(np.int64),
}[np.random.randint(0, 4)]
for c in range(N)
}
) | negative_train_query0_00994 | |
asv_bench/benchmarks/frame_methods.py/Reindex/time_reindex_axis0
class Reindex:
def time_reindex_axis0(self):
self.df.reindex(self.idx) | negative_train_query0_00995 | |
asv_bench/benchmarks/frame_methods.py/Reindex/time_reindex_axis1
class Reindex:
def time_reindex_axis1(self):
self.df.reindex(columns=self.idx_cols) | negative_train_query0_00996 | |
asv_bench/benchmarks/frame_methods.py/Reindex/time_reindex_axis1_missing
class Reindex:
def time_reindex_axis1_missing(self):
self.df.reindex(columns=self.idx) | negative_train_query0_00997 |
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