title stringclasses 1
value | text stringlengths 30 426k | id stringlengths 27 30 |
|---|---|---|
asv_bench/benchmarks/groupby.py/Size/setup
class Size:
def setup(self):
n = 10**5
offsets = np.random.randint(n, size=n).astype("timedelta64[ns]")
dates = np.datetime64("now") + offsets
self.df = DataFrame(
{
"key1": np.random.randint(0, 500, size=n),
... | negative_train_query0_00398 | |
asv_bench/benchmarks/groupby.py/Size/time_multi_size
class Size:
def time_multi_size(self):
self.df.groupby(["key1", "key2"]).size() | negative_train_query0_00399 | |
asv_bench/benchmarks/groupby.py/Size/time_category_size
class Size:
def time_category_size(self):
self.draws.groupby(self.cats, observed=True).size() | negative_train_query0_00400 | |
asv_bench/benchmarks/groupby.py/Shift/setup
class Shift:
def setup(self):
N = 18
self.df = DataFrame({"g": ["a", "b"] * 9, "v": list(range(N))}) | negative_train_query0_00401 | |
asv_bench/benchmarks/groupby.py/Shift/time_defaults
class Shift:
def time_defaults(self):
self.df.groupby("g").shift() | negative_train_query0_00402 | |
asv_bench/benchmarks/groupby.py/Shift/time_fill_value
class Shift:
def time_fill_value(self):
self.df.groupby("g").shift(fill_value=99) | negative_train_query0_00403 | |
asv_bench/benchmarks/groupby.py/Fillna/setup
class Fillna:
def setup(self):
N = 100
self.df = DataFrame(
{"group": [1] * N + [2] * N, "value": [np.nan, 1.0] * N}
).set_index("group") | negative_train_query0_00404 | |
asv_bench/benchmarks/groupby.py/Fillna/time_df_ffill
class Fillna:
def time_df_ffill(self):
self.df.groupby("group").ffill() | negative_train_query0_00405 | |
asv_bench/benchmarks/groupby.py/Fillna/time_df_bfill
class Fillna:
def time_df_bfill(self):
self.df.groupby("group").bfill() | negative_train_query0_00406 | |
asv_bench/benchmarks/groupby.py/Fillna/time_srs_ffill
class Fillna:
def time_srs_ffill(self):
self.df.groupby("group")["value"].ffill() | negative_train_query0_00407 | |
asv_bench/benchmarks/groupby.py/Fillna/time_srs_bfill
class Fillna:
def time_srs_bfill(self):
self.df.groupby("group")["value"].bfill() | negative_train_query0_00408 | |
asv_bench/benchmarks/groupby.py/GroupByMethods/setup
class GroupByMethods:
def setup(self, dtype, method, application, ncols, engine):
if method in method_blocklist.get(dtype, {}):
raise NotImplementedError # skip benchmark
if ncols != 1 and method in ["value_counts", "unique"]:
... | negative_train_query0_00409 | |
asv_bench/benchmarks/groupby.py/GroupByMethods/time_dtype_as_group
class GroupByMethods:
def time_dtype_as_group(self, dtype, method, application, ncols, engine):
self.as_group_method() | negative_train_query0_00410 | |
asv_bench/benchmarks/groupby.py/GroupByMethods/time_dtype_as_field
class GroupByMethods:
def time_dtype_as_field(self, dtype, method, application, ncols, engine):
self.as_field_method() | negative_train_query0_00411 | |
asv_bench/benchmarks/groupby.py/GroupByCythonAgg/setup
class GroupByCythonAgg:
def setup(self, dtype, method):
N = 1_000_000
df = DataFrame(np.random.randn(N, 10), columns=list("abcdefghij"))
df["key"] = np.random.randint(0, 100, size=N)
self.df = df | negative_train_query0_00412 | |
asv_bench/benchmarks/groupby.py/GroupByCythonAgg/time_frame_agg
class GroupByCythonAgg:
def time_frame_agg(self, dtype, method):
self.df.groupby("key").agg(method) | negative_train_query0_00413 | |
asv_bench/benchmarks/groupby.py/GroupByNumbaAgg/setup
class GroupByNumbaAgg:
def setup(self, dtype, method):
if method in _numba_unsupported_methods:
raise NotImplementedError
super().setup(dtype, method) | negative_train_query0_00414 | |
asv_bench/benchmarks/groupby.py/GroupByNumbaAgg/time_frame_agg
class GroupByNumbaAgg:
def time_frame_agg(self, dtype, method):
self.df.groupby("key").agg(method, engine="numba") | negative_train_query0_00415 | |
asv_bench/benchmarks/groupby.py/GroupByCythonAggEaDtypes/setup
class GroupByCythonAggEaDtypes:
def setup(self, dtype, method):
N = 1_000_000
df = DataFrame(
np.random.randint(0, high=100, size=(N, 10)),
columns=list("abcdefghij"),
dtype=dtype,
)
df.loc... | negative_train_query0_00416 | |
asv_bench/benchmarks/groupby.py/GroupByCythonAggEaDtypes/time_frame_agg
class GroupByCythonAggEaDtypes:
def time_frame_agg(self, dtype, method):
self.df.groupby("key").agg(method) | negative_train_query0_00417 | |
asv_bench/benchmarks/groupby.py/Cumulative/setup
class Cumulative:
def setup(self, dtype, method, with_nans):
if with_nans and dtype == "int64":
raise NotImplementedError("Construction of df would raise")
N = 500_000
keys = np.random.randint(0, 100, size=N)
vals = np.random.... | negative_train_query0_00418 | |
asv_bench/benchmarks/groupby.py/Cumulative/time_frame_transform
class Cumulative:
def time_frame_transform(self, dtype, method, with_nans):
self.df.groupby("key").transform(method) | negative_train_query0_00419 | |
asv_bench/benchmarks/groupby.py/RankWithTies/setup
class RankWithTies:
def setup(self, dtype, tie_method):
N = 10**4
if dtype == "datetime64":
data = np.array([Timestamp("2011/01/01")] * N, dtype=dtype)
else:
data = np.ones(N, dtype=dtype)
self.df = DataFrame({"va... | negative_train_query0_00420 | |
asv_bench/benchmarks/groupby.py/RankWithTies/time_rank_ties
class RankWithTies:
def time_rank_ties(self, dtype, tie_method):
self.df.groupby("key").rank(method=tie_method) | negative_train_query0_00421 | |
asv_bench/benchmarks/groupby.py/Float32/setup
class Float32:
def setup(self):
tmp1 = (np.random.random(10000) * 0.1).astype(np.float32)
tmp2 = (np.random.random(10000) * 10.0).astype(np.float32)
tmp = np.concatenate((tmp1, tmp2))
arr = np.repeat(tmp, 10)
self.df = DataFrame({"a":... | negative_train_query0_00422 | |
asv_bench/benchmarks/groupby.py/Float32/time_sum
class Float32:
def time_sum(self):
self.df.groupby(["a"])["b"].sum() | negative_train_query0_00423 | |
asv_bench/benchmarks/groupby.py/String/setup
class String:
def setup(self, dtype, method):
cols = list("abcdefghjkl")
self.df = DataFrame(
np.random.randint(0, 100, size=(10_000, len(cols))),
columns=cols,
dtype=dtype,
) | negative_train_query0_00424 | |
asv_bench/benchmarks/groupby.py/String/time_str_func
class String:
def time_str_func(self, dtype, method):
self.df.groupby("a")[self.df.columns[1:]].agg(method) | negative_train_query0_00425 | |
asv_bench/benchmarks/groupby.py/Categories/setup
class Categories:
def setup(self, observed):
N = 10**5
arr = np.random.random(N)
data = {"a": Categorical(np.random.randint(10000, size=N)), "b": arr}
self.df = DataFrame(data)
data = {
"a": Categorical(np.random.randin... | negative_train_query0_00426 | |
asv_bench/benchmarks/groupby.py/Categories/time_groupby_sort
class Categories:
def time_groupby_sort(self, observed):
self.df.groupby("a", observed=observed)["b"].count() | negative_train_query0_00427 | |
asv_bench/benchmarks/groupby.py/Categories/time_groupby_nosort
class Categories:
def time_groupby_nosort(self, observed):
self.df.groupby("a", observed=observed, sort=False)["b"].count() | negative_train_query0_00428 | |
asv_bench/benchmarks/groupby.py/Categories/time_groupby_ordered_sort
class Categories:
def time_groupby_ordered_sort(self, observed):
self.df_ordered.groupby("a", observed=observed)["b"].count() | negative_train_query0_00429 | |
asv_bench/benchmarks/groupby.py/Categories/time_groupby_ordered_nosort
class Categories:
def time_groupby_ordered_nosort(self, observed):
self.df_ordered.groupby("a", observed=observed, sort=False)["b"].count() | negative_train_query0_00430 | |
asv_bench/benchmarks/groupby.py/Categories/time_groupby_extra_cat_sort
class Categories:
def time_groupby_extra_cat_sort(self, observed):
self.df_extra_cat.groupby("a", observed=observed)["b"].count() | negative_train_query0_00431 | |
asv_bench/benchmarks/groupby.py/Categories/time_groupby_extra_cat_nosort
class Categories:
def time_groupby_extra_cat_nosort(self, observed):
self.df_extra_cat.groupby("a", observed=observed, sort=False)["b"].count() | negative_train_query0_00432 | |
asv_bench/benchmarks/groupby.py/MultipleCategories/setup
class MultipleCategories:
def setup(self):
N = 10**3
arr = np.random.random(N)
data = {
"a1": Categorical(np.random.randint(10000, size=N)),
"a2": Categorical(np.random.randint(10000, size=N)),
"b": arr,... | negative_train_query0_00433 | |
asv_bench/benchmarks/groupby.py/MultipleCategories/time_groupby_sort
class MultipleCategories:
def time_groupby_sort(self):
self.df.groupby(["a1", "a2"], observed=False)["b"].count() | negative_train_query0_00434 | |
asv_bench/benchmarks/groupby.py/MultipleCategories/time_groupby_nosort
class MultipleCategories:
def time_groupby_nosort(self):
self.df.groupby(["a1", "a2"], observed=False, sort=False)["b"].count() | negative_train_query0_00435 | |
asv_bench/benchmarks/groupby.py/MultipleCategories/time_groupby_ordered_sort
class MultipleCategories:
def time_groupby_ordered_sort(self):
self.df_ordered.groupby(["a1", "a2"], observed=False)["b"].count() | negative_train_query0_00436 | |
asv_bench/benchmarks/groupby.py/MultipleCategories/time_groupby_ordered_nosort
class MultipleCategories:
def time_groupby_ordered_nosort(self):
self.df_ordered.groupby(["a1", "a2"], observed=False, sort=False)["b"].count() | negative_train_query0_00437 | |
asv_bench/benchmarks/groupby.py/MultipleCategories/time_groupby_extra_cat_sort
class MultipleCategories:
def time_groupby_extra_cat_sort(self):
self.df_extra_cat.groupby(["a1", "a2"], observed=False)["b"].count() | negative_train_query0_00438 | |
asv_bench/benchmarks/groupby.py/MultipleCategories/time_groupby_extra_cat_nosort
class MultipleCategories:
def time_groupby_extra_cat_nosort(self):
self.df_extra_cat.groupby(["a1", "a2"], observed=False, sort=False)["b"].count() | negative_train_query0_00439 | |
asv_bench/benchmarks/groupby.py/MultipleCategories/time_groupby_transform
class MultipleCategories:
def time_groupby_transform(self):
self.df_extra_cat.groupby(["a1", "a2"], observed=False)["b"].cumsum() | negative_train_query0_00440 | |
asv_bench/benchmarks/groupby.py/Datelike/setup
class Datelike:
def setup(self, grouper):
N = 10**4
rng_map = {
"period_range": period_range,
"date_range": date_range,
"date_range_tz": partial(date_range, tz="US/Central"),
}
self.grouper = rng_map[group... | negative_train_query0_00441 | |
asv_bench/benchmarks/groupby.py/Datelike/time_sum
class Datelike:
def time_sum(self, grouper):
self.df.groupby(self.grouper).sum() | negative_train_query0_00442 | |
asv_bench/benchmarks/groupby.py/SumBools/setup
class SumBools:
def setup(self):
N = 500
self.df = DataFrame({"ii": range(N), "bb": [True] * N}) | negative_train_query0_00443 | |
asv_bench/benchmarks/groupby.py/SumBools/time_groupby_sum_booleans
class SumBools:
def time_groupby_sum_booleans(self):
self.df.groupby("ii").sum() | negative_train_query0_00444 | |
asv_bench/benchmarks/groupby.py/SumMultiLevel/setup
class SumMultiLevel:
def setup(self):
N = 50
self.df = DataFrame(
{"A": list(range(N)) * 2, "B": range(N * 2), "C": 1}
).set_index(["A", "B"]) | negative_train_query0_00445 | |
asv_bench/benchmarks/groupby.py/SumMultiLevel/time_groupby_sum_multiindex
class SumMultiLevel:
def time_groupby_sum_multiindex(self):
self.df.groupby(level=[0, 1]).sum() | negative_train_query0_00446 | |
asv_bench/benchmarks/groupby.py/SumTimeDelta/setup
class SumTimeDelta:
def setup(self):
N = 10**4
self.df = DataFrame(
np.random.randint(1000, 100000, (N, 100)),
index=np.random.randint(200, size=(N,)),
).astype("timedelta64[ns]")
self.df_int = self.df.copy().asty... | negative_train_query0_00447 | |
asv_bench/benchmarks/groupby.py/SumTimeDelta/time_groupby_sum_timedelta
class SumTimeDelta:
def time_groupby_sum_timedelta(self):
self.df.groupby(lambda x: x).sum() | negative_train_query0_00448 | |
asv_bench/benchmarks/groupby.py/SumTimeDelta/time_groupby_sum_int
class SumTimeDelta:
def time_groupby_sum_int(self):
self.df_int.groupby(lambda x: x).sum() | negative_train_query0_00449 | |
asv_bench/benchmarks/groupby.py/Transform/setup
class Transform:
def setup(self):
n1 = 400
n2 = 250
index = MultiIndex(
levels=[np.arange(n1), Index([f"i-{i}" for i in range(n2)], dtype=object)],
codes=[np.repeat(range(n1), n2).tolist(), list(range(n2)) * n1],
... | negative_train_query0_00450 | |
asv_bench/benchmarks/groupby.py/Transform/time_transform_lambda_max
class Transform:
def time_transform_lambda_max(self):
self.df.groupby(level="lev1").transform(lambda x: max(x)) | negative_train_query0_00451 | |
asv_bench/benchmarks/groupby.py/Transform/time_transform_str_max
class Transform:
def time_transform_str_max(self):
self.df.groupby(level="lev1").transform("max") | negative_train_query0_00452 | |
asv_bench/benchmarks/groupby.py/Transform/time_transform_lambda_max_tall
class Transform:
def time_transform_lambda_max_tall(self):
self.df_tall.groupby(level=0).transform(lambda x: np.max(x, axis=0)) | negative_train_query0_00453 | |
asv_bench/benchmarks/groupby.py/Transform/time_transform_lambda_max_wide
class Transform:
def time_transform_lambda_max_wide(self):
self.df_wide.groupby(level=0).transform(lambda x: np.max(x, axis=0)) | negative_train_query0_00454 | |
asv_bench/benchmarks/groupby.py/Transform/time_transform_multi_key1
class Transform:
def time_transform_multi_key1(self):
self.df1.groupby(["jim", "joe"])["jolie"].transform("max") | negative_train_query0_00455 | |
asv_bench/benchmarks/groupby.py/Transform/time_transform_multi_key2
class Transform:
def time_transform_multi_key2(self):
self.df2.groupby(["jim", "joe"])["jolie"].transform("max") | negative_train_query0_00456 | |
asv_bench/benchmarks/groupby.py/Transform/time_transform_multi_key3
class Transform:
def time_transform_multi_key3(self):
self.df3.groupby(["jim", "joe"])["jolie"].transform("max") | negative_train_query0_00457 | |
asv_bench/benchmarks/groupby.py/Transform/time_transform_multi_key4
class Transform:
def time_transform_multi_key4(self):
self.df4.groupby(["jim", "joe"])["jolie"].transform("max") | negative_train_query0_00458 | |
asv_bench/benchmarks/groupby.py/TransformBools/setup
class TransformBools:
def setup(self):
N = 120000
transition_points = np.sort(np.random.choice(np.arange(N), 1400))
transitions = np.zeros(N, dtype=np.bool_)
transitions[transition_points] = True
self.g = transitions.cumsum()
... | negative_train_query0_00459 | |
asv_bench/benchmarks/groupby.py/TransformBools/time_transform_mean
class TransformBools:
def time_transform_mean(self):
self.df["signal"].groupby(self.g).transform("mean") | negative_train_query0_00460 | |
asv_bench/benchmarks/groupby.py/TransformNaN/setup
class TransformNaN:
def setup(self):
self.df_nans = DataFrame(
{"key": np.repeat(np.arange(1000), 10), "B": np.nan, "C": np.nan}
)
self.df_nans.loc[4::10, "B":"C"] = 5 | negative_train_query0_00461 | |
asv_bench/benchmarks/groupby.py/TransformNaN/time_first
class TransformNaN:
def time_first(self):
self.df_nans.groupby("key").transform("first") | negative_train_query0_00462 | |
asv_bench/benchmarks/groupby.py/TransformEngine/setup
class TransformEngine:
def setup(self, parallel):
N = 10**3
data = DataFrame(
{0: [str(i) for i in range(100)] * N, 1: list(range(100)) * N},
columns=[0, 1],
)
self.parallel = parallel
self.grouper = da... | negative_train_query0_00463 | |
asv_bench/benchmarks/groupby.py/TransformEngine/time_series_numba
class TransformEngine:
def time_series_numba(self, parallel):
def function(values, index):
return values * 5
self.grouper[1].transform(
function, engine="numba", engine_kwargs={"parallel": self.parallel}
) | negative_train_query0_00464 | |
asv_bench/benchmarks/groupby.py/TransformEngine/time_series_numba/function
class TransformEngine:
def function(values, index):
return values * 5 | negative_train_query0_00465 | |
asv_bench/benchmarks/groupby.py/TransformEngine/time_series_cython
class TransformEngine:
def time_series_cython(self, parallel):
def function(values):
return values * 5
self.grouper[1].transform(function, engine="cython") | negative_train_query0_00466 | |
asv_bench/benchmarks/groupby.py/TransformEngine/time_series_cython/function
class TransformEngine:
def function(values):
return values * 5 | negative_train_query0_00467 | |
asv_bench/benchmarks/groupby.py/TransformEngine/time_dataframe_numba
class TransformEngine:
def time_dataframe_numba(self, parallel):
def function(values, index):
return values * 5
self.grouper.transform(
function, engine="numba", engine_kwargs={"parallel": self.parallel}
... | negative_train_query0_00468 | |
asv_bench/benchmarks/groupby.py/TransformEngine/time_dataframe_numba/function
class TransformEngine:
def function(values, index):
return values * 5 | negative_train_query0_00469 | |
asv_bench/benchmarks/groupby.py/TransformEngine/time_dataframe_cython
class TransformEngine:
def time_dataframe_cython(self, parallel):
def function(values):
return values * 5
self.grouper.transform(function, engine="cython") | negative_train_query0_00470 | |
asv_bench/benchmarks/groupby.py/TransformEngine/time_dataframe_cython/function
class TransformEngine:
def function(values):
return values * 5 | negative_train_query0_00471 | |
asv_bench/benchmarks/groupby.py/AggEngine/setup
class AggEngine:
def setup(self, parallel):
N = 10**3
data = DataFrame(
{0: [str(i) for i in range(100)] * N, 1: list(range(100)) * N},
columns=[0, 1],
)
self.parallel = parallel
self.grouper = data.groupby(0... | negative_train_query0_00472 | |
asv_bench/benchmarks/groupby.py/AggEngine/time_series_numba
class AggEngine:
def time_series_numba(self, parallel):
def function(values, index):
total = 0
for i, value in enumerate(values):
if i % 2:
total += value + 5
else:
... | negative_train_query0_00473 | |
asv_bench/benchmarks/groupby.py/AggEngine/time_series_numba/function
class AggEngine:
def function(values, index):
total = 0
for i, value in enumerate(values):
if i % 2:
total += value + 5
else:
total += value * 2
... | negative_train_query0_00474 | |
asv_bench/benchmarks/groupby.py/AggEngine/time_series_cython
class AggEngine:
def time_series_cython(self, parallel):
def function(values):
total = 0
for i, value in enumerate(values):
if i % 2:
total += value + 5
else:
... | negative_train_query0_00475 | |
asv_bench/benchmarks/groupby.py/AggEngine/time_series_cython/function
class AggEngine:
def function(values):
total = 0
for i, value in enumerate(values):
if i % 2:
total += value + 5
else:
total += value * 2
retu... | negative_train_query0_00476 | |
asv_bench/benchmarks/groupby.py/AggEngine/time_dataframe_numba
class AggEngine:
def time_dataframe_numba(self, parallel):
def function(values, index):
total = 0
for i, value in enumerate(values):
if i % 2:
total += value + 5
else:
... | negative_train_query0_00477 | |
asv_bench/benchmarks/groupby.py/AggEngine/time_dataframe_numba/function
class AggEngine:
def function(values, index):
total = 0
for i, value in enumerate(values):
if i % 2:
total += value + 5
else:
total += value * 2
... | negative_train_query0_00478 | |
asv_bench/benchmarks/groupby.py/AggEngine/time_dataframe_cython
class AggEngine:
def time_dataframe_cython(self, parallel):
def function(values):
total = 0
for i, value in enumerate(values):
if i % 2:
total += value + 5
else:
... | negative_train_query0_00479 | |
asv_bench/benchmarks/groupby.py/AggEngine/time_dataframe_cython/function
class AggEngine:
def function(values):
total = 0
for i, value in enumerate(values):
if i % 2:
total += value + 5
else:
total += value * 2
r... | negative_train_query0_00480 | |
asv_bench/benchmarks/groupby.py/Sample/setup
class Sample:
def setup(self):
N = 10**3
self.df = DataFrame({"a": np.zeros(N)})
self.groups = np.arange(0, N)
self.weights = np.ones(N) | negative_train_query0_00481 | |
asv_bench/benchmarks/groupby.py/Sample/time_sample
class Sample:
def time_sample(self):
self.df.groupby(self.groups).sample(n=1) | negative_train_query0_00482 | |
asv_bench/benchmarks/groupby.py/Sample/time_sample_weights
class Sample:
def time_sample_weights(self):
self.df.groupby(self.groups).sample(n=1, weights=self.weights) | negative_train_query0_00483 | |
asv_bench/benchmarks/groupby.py/Resample/setup
class Resample:
def setup(self):
num_timedeltas = 20_000
num_groups = 3
index = MultiIndex.from_product(
[
np.arange(num_groups),
to_timedelta(np.arange(num_timedeltas), unit="s"),
],
... | negative_train_query0_00484 | |
asv_bench/benchmarks/groupby.py/Resample/time_resample
class Resample:
def time_resample(self):
self.df.groupby(level="groups").resample("10s", on="timedeltas").mean() | negative_train_query0_00485 | |
asv_bench/benchmarks/groupby.py/Resample/time_resample_multiindex
class Resample:
def time_resample_multiindex(self):
self.df_multiindex.groupby(level="groups").resample(
"10s", level="timedeltas"
).mean() | negative_train_query0_00486 | |
asv_bench/benchmarks/ctors.py/no_change
def no_change(arr):
return arr | negative_train_query0_00487 | |
asv_bench/benchmarks/ctors.py/list_of_str
def list_of_str(arr):
return list(arr.astype(str)) | negative_train_query0_00488 | |
asv_bench/benchmarks/ctors.py/gen_of_str
def gen_of_str(arr):
return (x for x in arr.astype(str)) | negative_train_query0_00489 | |
asv_bench/benchmarks/ctors.py/arr_dict
def arr_dict(arr):
return dict(zip(range(len(arr)), arr)) | negative_train_query0_00490 | |
asv_bench/benchmarks/ctors.py/list_of_tuples
def list_of_tuples(arr):
return [(i, -i) for i in arr] | negative_train_query0_00491 | |
asv_bench/benchmarks/ctors.py/gen_of_tuples
def gen_of_tuples(arr):
return ((i, -i) for i in arr) | negative_train_query0_00492 | |
asv_bench/benchmarks/ctors.py/list_of_lists
def list_of_lists(arr):
return [[i, -i] for i in arr] | negative_train_query0_00493 | |
asv_bench/benchmarks/ctors.py/list_of_tuples_with_none
def list_of_tuples_with_none(arr):
return [(i, -i) for i in arr][:-1] + [None] | negative_train_query0_00494 | |
asv_bench/benchmarks/ctors.py/list_of_lists_with_none
def list_of_lists_with_none(arr):
return [[i, -i] for i in arr][:-1] + [None] | negative_train_query0_00495 | |
asv_bench/benchmarks/ctors.py/SeriesConstructors/setup
class SeriesConstructors:
def setup(self, data_fmt, with_index, dtype):
if data_fmt in (gen_of_str, gen_of_tuples) and with_index:
raise NotImplementedError(
"Series constructors do not support using generators with indexes"
... | negative_train_query0_00496 | |
asv_bench/benchmarks/ctors.py/SeriesConstructors/time_series_constructor
class SeriesConstructors:
def time_series_constructor(self, data_fmt, with_index, dtype):
Series(self.data, index=self.index) | negative_train_query0_00497 |
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