title stringclasses 1
value | text stringlengths 30 426k | id stringlengths 27 30 |
|---|---|---|
asv_bench/benchmarks/timeseries.py/SortIndex/setup
class SortIndex:
def setup(self, monotonic):
N = 10**5
idx = date_range(start="1/1/2000", periods=N, freq="s")
self.s = Series(np.random.randn(N), index=idx)
if not monotonic:
self.s = self.s.sample(frac=1) | negative_train_query0_01198 | |
asv_bench/benchmarks/timeseries.py/SortIndex/time_sort_index
class SortIndex:
def time_sort_index(self, monotonic):
self.s.sort_index() | negative_train_query0_01199 | |
asv_bench/benchmarks/timeseries.py/SortIndex/time_get_slice
class SortIndex:
def time_get_slice(self, monotonic):
self.s[:10000] | negative_train_query0_01200 | |
asv_bench/benchmarks/timeseries.py/Lookup/setup
class Lookup:
def setup(self):
N = 1500000
rng = date_range(start="1/1/2000", periods=N, freq="s")
self.ts = Series(1, index=rng)
self.lookup_val = rng[N // 2] | negative_train_query0_01201 | |
asv_bench/benchmarks/timeseries.py/Lookup/time_lookup_and_cleanup
class Lookup:
def time_lookup_and_cleanup(self):
self.ts[self.lookup_val]
self.ts.index._cleanup() | negative_train_query0_01202 | |
asv_bench/benchmarks/timeseries.py/DatetimeAccessor/setup
class DatetimeAccessor:
def setup(self, tz):
N = 100000
self.series = Series(date_range(start="1/1/2000", periods=N, freq="min", tz=tz)) | negative_train_query0_01203 | |
asv_bench/benchmarks/timeseries.py/DatetimeAccessor/time_dt_accessor
class DatetimeAccessor:
def time_dt_accessor(self, tz):
self.series.dt | negative_train_query0_01204 | |
asv_bench/benchmarks/timeseries.py/DatetimeAccessor/time_dt_accessor_normalize
class DatetimeAccessor:
def time_dt_accessor_normalize(self, tz):
self.series.dt.normalize() | negative_train_query0_01205 | |
asv_bench/benchmarks/timeseries.py/DatetimeAccessor/time_dt_accessor_month_name
class DatetimeAccessor:
def time_dt_accessor_month_name(self, tz):
self.series.dt.month_name() | negative_train_query0_01206 | |
asv_bench/benchmarks/timeseries.py/DatetimeAccessor/time_dt_accessor_day_name
class DatetimeAccessor:
def time_dt_accessor_day_name(self, tz):
self.series.dt.day_name() | negative_train_query0_01207 | |
asv_bench/benchmarks/timeseries.py/DatetimeAccessor/time_dt_accessor_time
class DatetimeAccessor:
def time_dt_accessor_time(self, tz):
self.series.dt.time | negative_train_query0_01208 | |
asv_bench/benchmarks/timeseries.py/DatetimeAccessor/time_dt_accessor_date
class DatetimeAccessor:
def time_dt_accessor_date(self, tz):
self.series.dt.date | negative_train_query0_01209 | |
asv_bench/benchmarks/timeseries.py/DatetimeAccessor/time_dt_accessor_year
class DatetimeAccessor:
def time_dt_accessor_year(self, tz):
self.series.dt.year | negative_train_query0_01210 | |
asv_bench/benchmarks/pandas_vb_common.py/setup
def setup(*args, **kwargs):
# This function just needs to be imported into each benchmark file to
# set up the random seed before each function.
# https://asv.readthedocs.io/en/latest/writing_benchmarks.html
np.random.seed(1234) | negative_train_query0_01211 | |
asv_bench/benchmarks/pandas_vb_common.py/BaseIO/remove
class BaseIO:
def remove(self, f):
"""Remove created files"""
try:
os.remove(f)
except OSError:
# On Windows, attempting to remove a file that is in use
# causes an exception to be raised
pass | negative_train_query0_01212 | |
asv_bench/benchmarks/pandas_vb_common.py/BaseIO/teardown
class BaseIO:
def teardown(self, *args, **kwargs):
self.remove(self.fname) | negative_train_query0_01213 | |
asv_bench/benchmarks/reshape.py/Melt/setup
class Melt:
def setup(self, dtype):
self.df = DataFrame(
np.random.randn(100_000, 3), columns=["A", "B", "C"], dtype=dtype
)
self.df["id1"] = pd.Series(np.random.randint(0, 10, 10000))
self.df["id2"] = pd.Series(np.random.randint(100... | negative_train_query0_01214 | |
asv_bench/benchmarks/reshape.py/Melt/time_melt_dataframe
class Melt:
def time_melt_dataframe(self, dtype):
melt(self.df, id_vars=["id1", "id2"]) | negative_train_query0_01215 | |
asv_bench/benchmarks/reshape.py/Pivot/setup
class Pivot:
def setup(self):
N = 10000
index = date_range("1/1/2000", periods=N, freq="h")
data = {
"value": np.random.randn(N * 50),
"variable": np.arange(50).repeat(N),
"date": np.tile(index.values, 50),
}... | negative_train_query0_01216 | |
asv_bench/benchmarks/reshape.py/Pivot/time_reshape_pivot_time_series
class Pivot:
def time_reshape_pivot_time_series(self):
self.df.pivot(index="date", columns="variable", values="value") | negative_train_query0_01217 | |
asv_bench/benchmarks/reshape.py/SimpleReshape/setup
class SimpleReshape:
def setup(self):
arrays = [np.arange(100).repeat(100), np.roll(np.tile(np.arange(100), 100), 25)]
index = MultiIndex.from_arrays(arrays)
self.df = DataFrame(np.random.randn(10000, 4), index=index)
self.udf = self.df... | negative_train_query0_01218 | |
asv_bench/benchmarks/reshape.py/SimpleReshape/time_stack
class SimpleReshape:
def time_stack(self):
self.udf.stack() | negative_train_query0_01219 | |
asv_bench/benchmarks/reshape.py/SimpleReshape/time_unstack
class SimpleReshape:
def time_unstack(self):
self.df.unstack(1) | negative_train_query0_01220 | |
asv_bench/benchmarks/reshape.py/ReshapeExtensionDtype/setup
class ReshapeExtensionDtype:
def setup(self, dtype):
lev = pd.Index(list("ABCDEFGHIJ"))
ri = pd.Index(range(1000))
mi = MultiIndex.from_product([lev, ri], names=["foo", "bar"])
index = date_range("2016-01-01", periods=10000, fr... | negative_train_query0_01221 | |
asv_bench/benchmarks/reshape.py/ReshapeExtensionDtype/time_stack
class ReshapeExtensionDtype:
def time_stack(self, dtype):
self.df.stack() | negative_train_query0_01222 | |
asv_bench/benchmarks/reshape.py/ReshapeExtensionDtype/time_unstack_fast
class ReshapeExtensionDtype:
def time_unstack_fast(self, dtype):
# last level -> doesn't have to make copies
self.ser.unstack("bar") | negative_train_query0_01223 | |
asv_bench/benchmarks/reshape.py/ReshapeExtensionDtype/time_unstack_slow
class ReshapeExtensionDtype:
def time_unstack_slow(self, dtype):
# first level -> must make copies
self.ser.unstack("foo") | negative_train_query0_01224 | |
asv_bench/benchmarks/reshape.py/ReshapeExtensionDtype/time_transpose
class ReshapeExtensionDtype:
def time_transpose(self, dtype):
self.df.T | negative_train_query0_01225 | |
asv_bench/benchmarks/reshape.py/ReshapeMaskedArrayDtype/setup
class ReshapeMaskedArrayDtype:
def setup(self, dtype):
lev = pd.Index(list("ABCDEFGHIJ"))
ri = pd.Index(range(1000))
mi = MultiIndex.from_product([lev, ri], names=["foo", "bar"])
values = np.random.randn(10_000).astype(int)
... | negative_train_query0_01226 | |
asv_bench/benchmarks/reshape.py/Unstack/setup
class Unstack:
def setup(self, dtype):
m = 100
n = 1000
levels = np.arange(m)
index = MultiIndex.from_product([levels] * 2)
columns = np.arange(n)
if dtype == "int":
values = np.arange(m * m * n).reshape(m * m, n)... | negative_train_query0_01227 | |
asv_bench/benchmarks/reshape.py/Unstack/time_full_product
class Unstack:
def time_full_product(self, dtype):
self.df.unstack() | negative_train_query0_01228 | |
asv_bench/benchmarks/reshape.py/Unstack/time_without_last_row
class Unstack:
def time_without_last_row(self, dtype):
self.df2.unstack() | negative_train_query0_01229 | |
asv_bench/benchmarks/reshape.py/SparseIndex/setup
class SparseIndex:
def setup(self):
NUM_ROWS = 1000
self.df = DataFrame(
{
"A": np.random.randint(50, size=NUM_ROWS),
"B": np.random.randint(50, size=NUM_ROWS),
"C": np.random.randint(-10, 10, s... | negative_train_query0_01230 | |
asv_bench/benchmarks/reshape.py/SparseIndex/time_unstack
class SparseIndex:
def time_unstack(self):
self.df.unstack() | negative_train_query0_01231 | |
asv_bench/benchmarks/reshape.py/WideToLong/setup
class WideToLong:
def setup(self):
nyrs = 20
nidvars = 20
N = 5000
self.letters = list("ABCD")
yrvars = [
letter + str(num)
for letter, num in product(self.letters, range(1, nyrs + 1))
]
colu... | negative_train_query0_01232 | |
asv_bench/benchmarks/reshape.py/WideToLong/time_wide_to_long_big
class WideToLong:
def time_wide_to_long_big(self):
wide_to_long(self.df, self.letters, i="id", j="year") | negative_train_query0_01233 | |
asv_bench/benchmarks/reshape.py/PivotTable/setup
class PivotTable:
def setup(self):
N = 100000
fac1 = np.array(["A", "B", "C"], dtype="O")
fac2 = np.array(["one", "two"], dtype="O")
ind1 = np.random.randint(0, 3, size=N)
ind2 = np.random.randint(0, 2, size=N)
self.df = Da... | negative_train_query0_01234 | |
asv_bench/benchmarks/reshape.py/PivotTable/time_pivot_table
class PivotTable:
def time_pivot_table(self):
self.df.pivot_table(index="key1", columns=["key2", "key3"]) | negative_train_query0_01235 | |
asv_bench/benchmarks/reshape.py/PivotTable/time_pivot_table_agg
class PivotTable:
def time_pivot_table_agg(self):
self.df.pivot_table(
index="key1", columns=["key2", "key3"], aggfunc=["sum", "mean"]
) | negative_train_query0_01236 | |
asv_bench/benchmarks/reshape.py/PivotTable/time_pivot_table_margins
class PivotTable:
def time_pivot_table_margins(self):
self.df.pivot_table(index="key1", columns=["key2", "key3"], margins=True) | negative_train_query0_01237 | |
asv_bench/benchmarks/reshape.py/PivotTable/time_pivot_table_categorical
class PivotTable:
def time_pivot_table_categorical(self):
self.df2.pivot_table(
index="col1", values="col3", columns="col2", aggfunc="sum", fill_value=0
) | negative_train_query0_01238 | |
asv_bench/benchmarks/reshape.py/PivotTable/time_pivot_table_categorical_observed
class PivotTable:
def time_pivot_table_categorical_observed(self):
self.df2.pivot_table(
index="col1",
values="col3",
columns="col2",
aggfunc="sum",
fill_value=0,
... | negative_train_query0_01239 | |
asv_bench/benchmarks/reshape.py/PivotTable/time_pivot_table_margins_only_column
class PivotTable:
def time_pivot_table_margins_only_column(self):
self.df.pivot_table(columns=["key1", "key2", "key3"], margins=True) | negative_train_query0_01240 | |
asv_bench/benchmarks/reshape.py/Crosstab/setup
class Crosstab:
def setup(self):
N = 100000
fac1 = np.array(["A", "B", "C"], dtype="O")
fac2 = np.array(["one", "two"], dtype="O")
self.ind1 = np.random.randint(0, 3, size=N)
self.ind2 = np.random.randint(0, 2, size=N)
self.v... | negative_train_query0_01241 | |
asv_bench/benchmarks/reshape.py/Crosstab/time_crosstab
class Crosstab:
def time_crosstab(self):
pd.crosstab(self.vec1, self.vec2) | negative_train_query0_01242 | |
asv_bench/benchmarks/reshape.py/Crosstab/time_crosstab_values
class Crosstab:
def time_crosstab_values(self):
pd.crosstab(self.vec1, self.vec2, values=self.ind1, aggfunc="sum") | negative_train_query0_01243 | |
asv_bench/benchmarks/reshape.py/Crosstab/time_crosstab_normalize
class Crosstab:
def time_crosstab_normalize(self):
pd.crosstab(self.vec1, self.vec2, normalize=True) | negative_train_query0_01244 | |
asv_bench/benchmarks/reshape.py/Crosstab/time_crosstab_normalize_margins
class Crosstab:
def time_crosstab_normalize_margins(self):
pd.crosstab(self.vec1, self.vec2, normalize=True, margins=True) | negative_train_query0_01245 | |
asv_bench/benchmarks/reshape.py/GetDummies/setup
class GetDummies:
def setup(self):
categories = list(string.ascii_letters[:12])
s = pd.Series(
np.random.choice(categories, size=1000000),
dtype=CategoricalDtype(categories),
)
self.s = s | negative_train_query0_01246 | |
asv_bench/benchmarks/reshape.py/GetDummies/time_get_dummies_1d
class GetDummies:
def time_get_dummies_1d(self):
pd.get_dummies(self.s, sparse=False) | negative_train_query0_01247 | |
asv_bench/benchmarks/reshape.py/GetDummies/time_get_dummies_1d_sparse
class GetDummies:
def time_get_dummies_1d_sparse(self):
pd.get_dummies(self.s, sparse=True) | negative_train_query0_01248 | |
asv_bench/benchmarks/reshape.py/Cut/setup
class Cut:
def setup(self, bins):
N = 10**5
self.int_series = pd.Series(np.arange(N).repeat(5))
self.float_series = pd.Series(np.random.randn(N).repeat(5))
self.timedelta_series = pd.Series(
np.random.randint(N, size=N), dtype="timede... | negative_train_query0_01249 | |
asv_bench/benchmarks/reshape.py/Cut/time_cut_int
class Cut:
def time_cut_int(self, bins):
pd.cut(self.int_series, bins) | negative_train_query0_01250 | |
asv_bench/benchmarks/reshape.py/Cut/time_cut_float
class Cut:
def time_cut_float(self, bins):
pd.cut(self.float_series, bins) | negative_train_query0_01251 | |
asv_bench/benchmarks/reshape.py/Cut/time_cut_timedelta
class Cut:
def time_cut_timedelta(self, bins):
pd.cut(self.timedelta_series, bins) | negative_train_query0_01252 | |
asv_bench/benchmarks/reshape.py/Cut/time_cut_datetime
class Cut:
def time_cut_datetime(self, bins):
pd.cut(self.datetime_series, bins) | negative_train_query0_01253 | |
asv_bench/benchmarks/reshape.py/Cut/time_qcut_int
class Cut:
def time_qcut_int(self, bins):
pd.qcut(self.int_series, bins) | negative_train_query0_01254 | |
asv_bench/benchmarks/reshape.py/Cut/time_qcut_float
class Cut:
def time_qcut_float(self, bins):
pd.qcut(self.float_series, bins) | negative_train_query0_01255 | |
asv_bench/benchmarks/reshape.py/Cut/time_qcut_timedelta
class Cut:
def time_qcut_timedelta(self, bins):
pd.qcut(self.timedelta_series, bins) | negative_train_query0_01256 | |
asv_bench/benchmarks/reshape.py/Cut/time_qcut_datetime
class Cut:
def time_qcut_datetime(self, bins):
pd.qcut(self.datetime_series, bins) | negative_train_query0_01257 | |
asv_bench/benchmarks/reshape.py/Cut/time_cut_interval
class Cut:
def time_cut_interval(self, bins):
# GH 27668
pd.cut(self.int_series, self.interval_bins) | negative_train_query0_01258 | |
asv_bench/benchmarks/reshape.py/Cut/peakmem_cut_interval
class Cut:
def peakmem_cut_interval(self, bins):
# GH 27668
pd.cut(self.int_series, self.interval_bins) | negative_train_query0_01259 | |
asv_bench/benchmarks/reshape.py/Explode/setup
class Explode:
def setup(self, n_rows, max_list_length):
data = [np.arange(np.random.randint(max_list_length)) for _ in range(n_rows)]
self.series = pd.Series(data) | negative_train_query0_01260 | |
asv_bench/benchmarks/reshape.py/Explode/time_explode
class Explode:
def time_explode(self, n_rows, max_list_length):
self.series.explode() | negative_train_query0_01261 | |
asv_bench/benchmarks/index_cached_properties.py/IndexCache/setup
class IndexCache:
def setup(self, index_type):
N = 10**5
if index_type == "MultiIndex":
self.idx = pd.MultiIndex.from_product(
[pd.date_range("1/1/2000", freq="min", periods=N // 2), ["a", "b"]]
)
... | negative_train_query0_01262 | |
asv_bench/benchmarks/index_cached_properties.py/IndexCache/time_values
class IndexCache:
def time_values(self, index_type):
self.idx._values | negative_train_query0_01263 | |
asv_bench/benchmarks/index_cached_properties.py/IndexCache/time_shape
class IndexCache:
def time_shape(self, index_type):
self.idx.shape | negative_train_query0_01264 | |
asv_bench/benchmarks/index_cached_properties.py/IndexCache/time_is_monotonic_decreasing
class IndexCache:
def time_is_monotonic_decreasing(self, index_type):
self.idx.is_monotonic_decreasing | negative_train_query0_01265 | |
asv_bench/benchmarks/index_cached_properties.py/IndexCache/time_is_monotonic_increasing
class IndexCache:
def time_is_monotonic_increasing(self, index_type):
self.idx.is_monotonic_increasing | negative_train_query0_01266 | |
asv_bench/benchmarks/index_cached_properties.py/IndexCache/time_is_unique
class IndexCache:
def time_is_unique(self, index_type):
self.idx.is_unique | negative_train_query0_01267 | |
asv_bench/benchmarks/index_cached_properties.py/IndexCache/time_engine
class IndexCache:
def time_engine(self, index_type):
self.idx._engine | negative_train_query0_01268 | |
asv_bench/benchmarks/index_cached_properties.py/IndexCache/time_inferred_type
class IndexCache:
def time_inferred_type(self, index_type):
self.idx.inferred_type | negative_train_query0_01269 | |
asv_bench/benchmarks/arithmetic.py/IntFrameWithScalar/setup
class IntFrameWithScalar:
def setup(self, dtype, scalar, op):
arr = np.random.randn(20000, 100)
self.df = DataFrame(arr.astype(dtype)) | negative_train_query0_01270 | |
asv_bench/benchmarks/arithmetic.py/IntFrameWithScalar/time_frame_op_with_scalar
class IntFrameWithScalar:
def time_frame_op_with_scalar(self, dtype, scalar, op):
op(self.df, scalar) | negative_train_query0_01271 | |
asv_bench/benchmarks/arithmetic.py/OpWithFillValue/setup
class OpWithFillValue:
def setup(self):
# GH#31300
arr = np.arange(10**6)
df = DataFrame({"A": arr})
ser = df["A"]
self.df = df
self.ser = ser | negative_train_query0_01272 | |
asv_bench/benchmarks/arithmetic.py/OpWithFillValue/time_frame_op_with_fill_value_no_nas
class OpWithFillValue:
def time_frame_op_with_fill_value_no_nas(self):
self.df.add(self.df, fill_value=4) | negative_train_query0_01273 | |
asv_bench/benchmarks/arithmetic.py/OpWithFillValue/time_series_op_with_fill_value_no_nas
class OpWithFillValue:
def time_series_op_with_fill_value_no_nas(self):
self.ser.add(self.ser, fill_value=4) | negative_train_query0_01274 | |
asv_bench/benchmarks/arithmetic.py/MixedFrameWithSeriesAxis/setup
class MixedFrameWithSeriesAxis:
def setup(self, opname):
arr = np.arange(10**6).reshape(1000, -1)
df = DataFrame(arr)
df["C"] = 1.0
self.df = df
self.ser = df[0]
self.row = df.iloc[0] | negative_train_query0_01275 | |
asv_bench/benchmarks/arithmetic.py/MixedFrameWithSeriesAxis/time_frame_op_with_series_axis0
class MixedFrameWithSeriesAxis:
def time_frame_op_with_series_axis0(self, opname):
getattr(self.df, opname)(self.ser, axis=0) | negative_train_query0_01276 | |
asv_bench/benchmarks/arithmetic.py/MixedFrameWithSeriesAxis/time_frame_op_with_series_axis1
class MixedFrameWithSeriesAxis:
def time_frame_op_with_series_axis1(self, opname):
getattr(operator, opname)(self.df, self.ser) | negative_train_query0_01277 | |
asv_bench/benchmarks/arithmetic.py/FrameWithFrameWide/setup
class FrameWithFrameWide:
def setup(self, op, shape):
# we choose dtypes so as to make the blocks
# a) not perfectly match between right and left
# b) appreciably bigger than single columns
n_rows, n_cols = shape
if o... | negative_train_query0_01278 | |
asv_bench/benchmarks/arithmetic.py/FrameWithFrameWide/time_op_different_blocks
class FrameWithFrameWide:
def time_op_different_blocks(self, op, shape):
# blocks (and dtypes) are not aligned
op(self.left, self.right) | negative_train_query0_01279 | |
asv_bench/benchmarks/arithmetic.py/FrameWithFrameWide/time_op_same_blocks
class FrameWithFrameWide:
def time_op_same_blocks(self, op, shape):
# blocks (and dtypes) are aligned
op(self.left, self.left) | negative_train_query0_01280 | |
asv_bench/benchmarks/arithmetic.py/Ops/setup
class Ops:
def setup(self, use_numexpr, threads):
self.df = DataFrame(np.random.randn(20000, 100))
self.df2 = DataFrame(np.random.randn(20000, 100))
if threads != "default":
expr.set_numexpr_threads(threads)
if not use_numexpr:
... | negative_train_query0_01281 | |
asv_bench/benchmarks/arithmetic.py/Ops/time_frame_add
class Ops:
def time_frame_add(self, use_numexpr, threads):
self.df + self.df2 | negative_train_query0_01282 | |
asv_bench/benchmarks/arithmetic.py/Ops/time_frame_mult
class Ops:
def time_frame_mult(self, use_numexpr, threads):
self.df * self.df2 | negative_train_query0_01283 | |
asv_bench/benchmarks/arithmetic.py/Ops/time_frame_multi_and
class Ops:
def time_frame_multi_and(self, use_numexpr, threads):
self.df[(self.df > 0) & (self.df2 > 0)] | negative_train_query0_01284 | |
asv_bench/benchmarks/arithmetic.py/Ops/time_frame_comparison
class Ops:
def time_frame_comparison(self, use_numexpr, threads):
self.df > self.df2 | negative_train_query0_01285 | |
asv_bench/benchmarks/arithmetic.py/Ops/teardown
class Ops:
def teardown(self, use_numexpr, threads):
expr.set_use_numexpr(True)
expr.set_numexpr_threads() | negative_train_query0_01286 | |
asv_bench/benchmarks/arithmetic.py/Ops2/setup
class Ops2:
def setup(self):
N = 10**3
self.df = DataFrame(np.random.randn(N, N))
self.df2 = DataFrame(np.random.randn(N, N))
self.df_int = DataFrame(
np.random.randint(
np.iinfo(np.int16).min, np.iinfo(np.int16).... | negative_train_query0_01287 | |
asv_bench/benchmarks/arithmetic.py/Ops2/time_frame_float_div
class Ops2:
def time_frame_float_div(self):
self.df // self.df2 | negative_train_query0_01288 | |
asv_bench/benchmarks/arithmetic.py/Ops2/time_frame_float_div_by_zero
class Ops2:
def time_frame_float_div_by_zero(self):
self.df / 0 | negative_train_query0_01289 | |
asv_bench/benchmarks/arithmetic.py/Ops2/time_frame_float_floor_by_zero
class Ops2:
def time_frame_float_floor_by_zero(self):
self.df // 0 | negative_train_query0_01290 | |
asv_bench/benchmarks/arithmetic.py/Ops2/time_frame_int_div_by_zero
class Ops2:
def time_frame_int_div_by_zero(self):
self.df_int / 0 | negative_train_query0_01291 | |
asv_bench/benchmarks/arithmetic.py/Ops2/time_frame_int_mod
class Ops2:
def time_frame_int_mod(self):
self.df_int % self.df2_int | negative_train_query0_01292 | |
asv_bench/benchmarks/arithmetic.py/Ops2/time_frame_float_mod
class Ops2:
def time_frame_float_mod(self):
self.df % self.df2 | negative_train_query0_01293 | |
asv_bench/benchmarks/arithmetic.py/Ops2/time_frame_dot
class Ops2:
def time_frame_dot(self):
self.df.dot(self.df2) | negative_train_query0_01294 | |
asv_bench/benchmarks/arithmetic.py/Ops2/time_series_dot
class Ops2:
def time_series_dot(self):
self.s.dot(self.s) | negative_train_query0_01295 | |
asv_bench/benchmarks/arithmetic.py/Ops2/time_frame_series_dot
class Ops2:
def time_frame_series_dot(self):
self.df.dot(self.s) | negative_train_query0_01296 | |
asv_bench/benchmarks/arithmetic.py/Timeseries/setup
class Timeseries:
def setup(self, tz):
N = 10**6
halfway = (N // 2) - 1
self.s = Series(date_range("20010101", periods=N, freq="min", tz=tz))
self.ts = self.s[halfway]
self.s2 = Series(date_range("20010101", periods=N, freq="s"... | negative_train_query0_01297 |
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