CLN: ASV string by mroeschke · Pull Request #19069 · pandas-dev/pandas (original) (raw)
I still have 3 more timeseries benchmarks left to clean up, but it looks like there's some benchmarks in GroupByMethods that are taking upwards of 30s on my machine (due to large test sizes) that can be reduced:
[ 0.30%] ··· Running ...ns.time_different_python_functions_multicol 35.6ms
[ 0.45%] ··· Running ...s.time_different_python_functions_singlecol 574ms
[ 0.60%] ··· Running ...y.AggFunctions.time_different_str_functions 23.7ms
[ 0.60%] ··· Setting up /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/inference.py:39
[ 0.75%] ··· Running inference.DateInferOps.time_add_timedeltas 459ms
[ 0.90%] ··· Running inference.DateInferOps.time_subtract_datetimes 487ms
[ 1.05%] ··· Running ...e.DateInferOps.time_timedelta_plus_datetime 578ms
[ 1.05%] ··· Setting up /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/groupby.py:66
[ 1.20%] ··· Running groupby.Groups.time_series_groups ok
[ 1.20%] ····
============== ========
key
-------------- --------
int64_small 88.5ms
int64_large 292ms
object_small 137ms
object_large 484ms
============== ========
[ 1.35%] ··· Running algorithms.Duplicated.time_duplicated_float ok
[ 1.35%] ····
======= ========
keep
------- --------
first 34.1ms
last 35.5ms
False 39.7ms
======= ========
[ 1.50%] ··· Running algorithms.Duplicated.time_duplicated_int ok
[ 1.50%] ····
======= ========
keep
------- --------
first 21.4ms
last 22.4ms
False 31.3ms
======= ========
[ 1.65%] ··· Running algorithms.Duplicated.time_duplicated_string ok
[ 1.65%] ····
======= ========
keep
------- --------
first 24.8ms
last 25.2ms
False 23.8ms
======= ========
[ 1.80%] ··· Running ...catedUniqueIndex.time_duplicated_unique_int 349μs
[ 1.95%] ··· Running algorithms.Factorize.time_factorize_float ok
[ 1.95%] ····
======= ========
sort
------- --------
True 53.8ms
False 29.3ms
======= ========
[ 2.11%] ··· Running algorithms.Factorize.time_factorize_int ok
[ 2.11%] ····
======= ========
sort
------- --------
True 34.7ms
False 20.0ms
======= ========
[ 2.26%] ··· Running algorithms.Factorize.time_factorize_string ok
[ 2.26%] ····
======= ========
sort
------- --------
True 265ms
False 42.4ms
======= ========
[ 2.41%] ··· Running algorithms.Match.time_match_string 711μs
[ 2.41%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/algorithms.py:86: FutureWarning: pd.match() is deprecated and will be removed in a future version
pd.match(self.all, self.uniques)
[ 2.56%] ··· Running ...s_caching.CacheReadonly.time_cache_readonly 10.5μs
[ 2.71%] ··· Running ..._caching.DataFrameAttributes.time_get_index 10.9μs
[ 2.86%] ··· Running ..._caching.DataFrameAttributes.time_set_index 24.7μs
[ 3.01%] ··· Running ...verflowArray.time_add_overflow_arr_mask_nan 38.0ms
[ 3.16%] ··· Running ....AddOverflowArray.time_add_overflow_arr_rev 24.8ms
[ 3.31%] ··· Running ...dOverflowArray.time_add_overflow_b_mask_nan 38.2ms
[ 3.46%] ··· Running ...verflowArray.time_add_overflow_both_arg_nan 39.9ms
[ 3.61%] ··· Running ....AddOverflowScalar.time_add_overflow_scalar ok
[ 3.61%] ····
======== ========
scalar
-------- --------
1 19.4ms
-1 19.9ms
0 20.2ms
======== ========
[ 3.76%] ··· Running binary_ops.Ops.time_frame_add ok
[ 3.76%] ····
============= ========= ========
-- threads
------------- ------------------
use_numexpr default 1
============= ========= ========
True 27.1ms 21.4ms
False 22.2ms 21.1ms
============= ========= ========
[ 3.91%] ··· Running binary_ops.Ops.time_frame_comparison ok
[ 3.91%] ····
============= ========= ========
-- threads
------------- ------------------
use_numexpr default 1
============= ========= ========
True 16.2ms 10.2ms
False 125ms 124ms
============= ========= ========
[ 4.06%] ··· Running binary_ops.Ops.time_frame_mult ok
[ 4.06%] ····
============= ========= ========
-- threads
------------- ------------------
use_numexpr default 1
============= ========= ========
True 55.5ms 21.2ms
False 20.6ms 20.6ms
============= ========= ========
[ 4.21%] ··· Running binary_ops.Ops.time_frame_multi_and ok
[ 4.21%] ····
============= ========= =======
-- threads
------------- -----------------
use_numexpr default 1
============= ========= =======
True 135ms 110ms
False 97.4ms 105ms
============= ========= =======
[ 4.36%] ··· Running binary_ops.Ops2.time_frame_float_div 71.0ms
[ 4.51%] ··· Running binary_ops.Ops2.time_frame_float_div_by_zero 23.4ms
[ 4.66%] ··· Running binary_ops.Ops2.time_frame_float_floor_by_zero 84.1ms
[ 4.81%] ··· Running binary_ops.Ops2.time_frame_float_mod 25.6ms
[ 4.96%] ··· Running binary_ops.Ops2.time_frame_int_div_by_zero 23.3ms
[ 5.11%] ··· Running binary_ops.Ops2.time_frame_int_mod 45.2ms
[ 5.26%] ··· Running ...ps.Timeseries.time_series_timestamp_compare ok
[ 5.26%] ····
============ ========
tz
------------ --------
None 5.33ms
US/Eastern 5.58ms
============ ========
[ 5.41%] ··· Running binary_ops.Timeseries.time_timestamp_ops_diff ok
[ 5.41%] ····
============ ========
tz
------------ --------
None 36.9ms
US/Eastern 42.1ms
============ ========
[ 5.56%] ··· Running ...meseries.time_timestamp_ops_diff_with_shift ok
[ 5.56%] ····
============ ========
tz
------------ --------
None 118ms
US/Eastern 60.5ms
============ ========
[ 5.71%] ··· Running ...ps.Timeseries.time_timestamp_series_compare ok
[ 5.71%] ····
============ ========
tz
------------ --------
None 5.48ms
US/Eastern 5.57ms
============ ========
[ 5.86%] ··· Running categoricals.Concat.time_concat 16.1ms
[ 6.02%] ··· Running categoricals.Concat.time_union 11.1ms
[ 6.17%] ··· Running categoricals.Constructor.time_all_nan 113ms
[ 6.32%] ··· Running categoricals.Constructor.time_datetimes 2.37ms
[ 6.47%] ··· Running ...oricals.Constructor.time_datetimes_with_nat 2.47ms
[ 6.62%] ··· Running categoricals.Constructor.time_fastpath 1.55ms
[ 6.77%] ··· Running categoricals.Constructor.time_regular 32.1ms
[ 6.92%] ··· Running categoricals.Constructor.time_with_nan 332ms
[ 7.07%] ··· Running categoricals.Rank.time_rank_int 10.6ms
[ 7.07%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/categoricals.py:122: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead
self.s_str_cat_ordered = self.s_str.astype('category', ordered=True)
/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/categoricals.py:126: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead
self.s_int_cat_ordered = self.s_int.astype('category', ordered=True)
[ 7.22%] ··· Running categoricals.Rank.time_rank_int_cat 12.7ms
[ 7.22%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/categoricals.py:122: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead
self.s_str_cat_ordered = self.s_str.astype('category', ordered=True)
/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/categoricals.py:126: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead
self.s_int_cat_ordered = self.s_int.astype('category', ordered=True)
[ 7.37%] ··· Running categoricals.Rank.time_rank_int_cat_ordered 12.2ms
[ 7.37%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/categoricals.py:122: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead
self.s_str_cat_ordered = self.s_str.astype('category', ordered=True)
/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/categoricals.py:126: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead
self.s_int_cat_ordered = self.s_int.astype('category', ordered=True)
[ 7.52%] ··· Running categoricals.Rank.time_rank_string 338ms
[ 7.52%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/categoricals.py:122: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead
self.s_str_cat_ordered = self.s_str.astype('category', ordered=True)
/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/categoricals.py:126: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead
self.s_int_cat_ordered = self.s_int.astype('category', ordered=True)
[ 7.67%] ··· Running categoricals.Rank.time_rank_string_cat 16.2ms
[ 7.67%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/categoricals.py:122: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead
self.s_str_cat_ordered = self.s_str.astype('category', ordered=True)
/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/categoricals.py:126: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead
self.s_int_cat_ordered = self.s_int.astype('category', ordered=True)
/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/pandas/core/categorical.py:1591: FutureWarning: Treating Series 'new_categories' as a list-like and using the values. In a future version, 'rename_categories' will treat Series like a dictionary.
For dict-like, use 'new_categories.to_dict()'
For list-like, use 'new_categories.values'.
self.rename_categories(Series(self.categories).rank())
[ 7.82%] ··· Running categoricals.Rank.time_rank_string_cat_ordered 11.6ms
[ 7.82%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/categoricals.py:122: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead
self.s_str_cat_ordered = self.s_str.astype('category', ordered=True)
/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/categoricals.py:126: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead
self.s_int_cat_ordered = self.s_int.astype('category', ordered=True)
[ 7.97%] ··· Running categoricals.Repr.time_rendering 1.80ms
[ 8.12%] ··· Running categoricals.SetCategories.time_set_categories 50.1ms
[ 8.27%] ··· Running categoricals.ValueCounts.time_value_counts ok
[ 8.27%] ····
======== ========
dropna
-------- --------
True 61.6ms
False 60.1ms
======== ========
[ 8.42%] ··· Running ...xConstructor.time_multiindex_from_iterables 20.5ms
[ 8.57%] ··· Running ....SeriesConstructors.time_series_constructor ok
[ 8.57%] ····
======================================= ======== ========
-- with_index
--------------------------------------- -----------------
data_fmt False True
======================================= ======== ========
<function <lambda> at 0x7f9c849e3a28> 101μs 141μs
<type 'list'> 3.48ms 3.62ms
<function <lambda> at 0x7f9c849e3cf8> 835μs 871μs
<function <lambda> at 0x7f9c849e3e60> 11.1ms 8.96ms
<function <lambda> at 0x7f9c849e3ed8> 1.06ms 1.16ms
<function <lambda> at 0x7f9c849e3f50> 1.19ms 1.10ms
<function <lambda> at 0x7f9c849e8050> 1.03ms 1.11ms
<function <lambda> at 0x7f9c849e80c8> 1.10ms 1.08ms
======================================= ======== ========
[ 8.72%] ··· Running ...ructors.time_dtindex_from_index_with_series 195μs
[ 8.87%] ··· Running ...DtypesConstructors.time_dtindex_from_series 115μs
[ 9.02%] ··· Running ...esConstructors.time_index_from_array_floats 140μs
[ 9.17%] ··· Running ...esConstructors.time_index_from_array_string 203μs
[ 9.32%] ··· Running eval.Eval.time_add ok
[ 9.32%] ····
========= ======== ========
-- threads
--------- -----------------
engine 1 all
========= ======== ========
numexpr 39.3ms 77.1ms
python 59.6ms 136ms
========= ======== ========
[ 9.47%] ··· Running eval.Eval.time_and ok
[ 9.47%] ····
========= ======== ========
-- threads
--------- -----------------
engine 1 all
========= ======== ========
numexpr 55.5ms 59.8ms
python 110ms 111ms
========= ======== ========
[ 9.62%] ··· Running eval.Eval.time_chained_cmp ok
[ 9.62%] ····
========= ======== ========
-- threads
--------- -----------------
engine 1 all
========= ======== ========
numexpr 48.5ms 56.9ms
python 54.2ms 74.8ms
========= ======== ========
[ 9.77%] ··· Running eval.Eval.time_mult ok
[ 9.77%] ····
========= ======== ========
-- threads
--------- -----------------
engine 1 all
========= ======== ========
numexpr 39.1ms 73.1ms
python 59.5ms 136ms
========= ======== ========
[ 9.92%] ··· Running eval.Query.time_query_datetime_column 19.6ms
[ 10.08%] ··· Running eval.Query.time_query_datetime_index 41.5ms
[ 10.23%] ··· Running eval.Query.time_query_with_boolean_selection 69.1ms
[ 10.38%] ··· Running frame_ctor.FromDicts.time_dict 4.83ms
[ 10.53%] ··· Running frame_ctor.FromDicts.time_list_of_dict 149ms
[ 10.68%] ··· Running frame_ctor.FromDicts.time_nested_dict 90.0ms
[ 10.83%] ··· Running frame_ctor.FromDicts.time_nested_dict_int64 204ms
[ 10.98%] ··· Running ...hTimestamp.time_dict_with_timestamp_offsets ok
[ 10.98%] ····
======== ========
offset
-------- --------
<Nano> 71.5ms
<Hour> 93.3ms
======== ========
[ 11.13%] ··· Running frame_ctor.FromNDArray.time_frame_from_ndarray 374μs
[ 11.28%] ··· Running ...omRecords.time_frame_from_records_generator ok
[ 11.28%] ····
======= ========
nrows
------- --------
None 141ms
1000 2.36ms
======= ========
[ 11.43%] ··· Running frame_ctor.FromSeries.time_mi_series 289μs
[ 11.58%] ··· Running frame_methods.Apply.time_apply_axis_1 725ms
[ 11.73%] ··· Running frame_methods.Apply.time_apply_lambda_mean 12.2ms
[ 11.88%] ··· Running frame_methods.Apply.time_apply_np_mean 13.6ms
[ 12.03%] ··· Running frame_methods.Apply.time_apply_pass_thru 14.2ms
[ 12.18%] ··· Running frame_methods.Apply.time_apply_ref_by_name 65.1ms
[ 12.33%] ··· Running frame_methods.Apply.time_apply_user_func 262ms
[ 12.48%] ··· Running ...s.Count.time_count_level_mixed_dtypes_multi ok
[ 12.48%] ····
====== =======
axis
------ -------
0 145ms
1 127ms
====== =======
[ 12.63%] ··· Running frame_methods.Count.time_count_level_multi ok
[ 12.63%] ····
====== =======
axis
------ -------
0 114ms
1 155ms
====== =======
[ 12.78%] ··· Running frame_methods.Dropna.time_dropna ok
[ 12.78%] ····
===== ======== ========
-- axis
----- -----------------
how 0 1
===== ======== ========
all 141ms 152ms
any 61.3ms 63.3ms
===== ======== ========
[ 12.93%] ··· Running ...ethods.Dropna.time_dropna_axis_mixed_dtypes ok
[ 12.93%] ····
===== ======= =======
-- axis
----- ---------------
how 0 1
===== ======= =======
all 429ms 440ms
any 331ms 339ms
===== ======= =======
[ 13.08%] ··· Running frame_methods.Dtypes.time_frame_dtypes 338μs
[ 13.23%] ··· Running frame_methods.Duplicated.time_frame_duplicated 361ms
[ 13.38%] ··· Running ...thods.Duplicated.time_frame_duplicated_wide 393ms
[ 13.53%] ··· Running frame_methods.Equals.time_frame_float_equal 7.63ms
[ 13.68%] ··· Running frame_methods.Equals.time_frame_float_unequal 21.7ms
[ 13.83%] ··· Running ...e_methods.Equals.time_frame_nonunique_equal 11.8ms
[ 13.98%] ··· Running ...methods.Equals.time_frame_nonunique_unequal 12.0ms
[ 14.14%] ··· Running frame_methods.Equals.time_frame_object_equal 42.1ms
[ 14.29%] ··· Running frame_methods.Equals.time_frame_object_unequal 26.3ms
[ 14.44%] ··· Running frame_methods.Fillna.time_frame_fillna ok
[ 14.44%] ····
========= ======== ========
-- method
--------- -----------------
inplace pad bfill
========= ======== ========
True 16.6ms 16.8ms
False 17.1ms 16.8ms
========= ======== ========
[ 14.59%] ··· Running ....GetDtypeCounts.time_frame_get_dtype_counts 476μs
[ 14.74%] ··· Running frame_methods.GetDtypeCounts.time_info 972ms
[ 14.74%] ····· <class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Columns: 10000 entries, 0 to 9999
dtypes: float64(10000)
memory usage: 781.3 KB
[ 14.89%] ··· Running ....GetNumericData.time_frame_get_numeric_data 321μs
[ 14.89%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/frame_methods.py🔞 FutureWarning: consolidate is deprecated and will be removed in a future release.
self.df = self.df.consolidate()
[ 15.04%] ··· Running frame_methods.Interpolate.time_interpolate ok
[ 15.04%] ····
========== =======
downcast
---------- -------
None 105ms
infer 162ms
========== =======
[ 15.19%] ··· Running ...hods.Interpolate.time_interpolate_some_good ok
[ 15.19%] ····
========== ========
downcast
---------- --------
None 3.07ms
infer 6.04ms
========== ========
[ 15.34%] ··· Running frame_methods.Isnull.time_isnull 2.48ms
[ 15.49%] ··· Running ...e_methods.Isnull.time_isnull_floats_no_null 2.53ms
[ 15.64%] ··· Running frame_methods.Isnull.time_isnull_obj 92.4ms
[ 15.79%] ··· Running frame_methods.Isnull.time_isnull_strngs 84.3ms
[ 15.94%] ··· Running frame_methods.Iteration.time_iteritems 84.4ms
[ 16.09%] ··· Running frame_methods.Iteration.time_iteritems_cached 84.9ms
[ 16.24%] ··· Running ...e_methods.Iteration.time_iteritems_indexing 445ms
[ 16.39%] ··· Running frame_methods.Iteration.time_iterrows 587ms
[ 16.54%] ··· Running frame_methods.Iteration.time_itertuples 91.6ms
[ 16.69%] ··· Running frame_methods.Lookup.time_frame_fancy_lookup 7.61ms
[ 16.84%] ··· Running ..._methods.Lookup.time_frame_fancy_lookup_all 48.3ms
[ 16.99%] ··· Running frame_methods.MaskBool.time_frame_mask_bools 32.7ms
[ 17.14%] ··· Running frame_methods.MaskBool.time_frame_mask_floats 25.8ms
[ 17.29%] ··· Running frame_methods.NSort.time_nlargest ok
[ 17.29%] ····
======= ========
keep
------- --------
first 3.63ms
last 3.60ms
======= ========
[ 17.44%] ··· Running frame_methods.NSort.time_nsmallest ok
[ 17.44%] ····
======= ========
keep
------- --------
first 3.58ms
last 3.57ms
======= ========
[ 17.59%] ··· Running frame_methods.Nunique.time_frame_nunique 804ms
[ 17.74%] ··· Running frame_methods.Quantile.time_frame_quantile ok
[ 17.74%] ····
====== ========
axis
------ --------
0 1.05ms
1 1.67ms
====== ========
[ 17.89%] ··· Running frame_methods.Reindex.time_reindex_axis0 19.8ms
[ 18.05%] ··· Running frame_methods.Reindex.time_reindex_axis1 140ms
[ 18.20%] ··· Running frame_methods.Reindex.time_reindex_both_axes 54.7ms
[ 18.35%] ··· Running ...e_methods.Reindex.time_reindex_both_axes_ix 54.2ms
[ 18.50%] ··· Running frame_methods.Reindex.time_reindex_upcast 19.5ms
[ 18.65%] ··· Running frame_methods.Repr.time_frame_repr_wide 33.5ms
[ 18.80%] ··· Running frame_methods.Repr.time_html_repr_trunc_mi 907ms
[ 18.95%] ··· Running frame_methods.Repr.time_html_repr_trunc_si 829ms
[ 19.10%] ··· Running frame_methods.Repr.time_repr_tall 55.2ms
[ 19.25%] ··· Running frame_methods.Shift.time_shift ok
[ 19.25%] ····
====== ========
axis
------ --------
0 39.8ms
1 45.4ms
====== ========
[ 19.40%] ··· Running ...ByColumns.time_frame_sort_values_by_columns 68.9ms
[ 19.55%] ··· Running ...e_methods.SortValues.time_frame_sort_values ok
[ 19.55%] ····
=========== =======
ascending
----------- -------
True 387ms
False 393ms
=========== =======
[ 19.70%] ··· Running frame_methods.ToHTML.time_to_html_mixed failed
[ 19.70%] ····· Traceback (most recent call last):
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 818, in <module>
commands[mode](args)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 789, in main_run
skip = benchmark.do_setup()
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 393, in do_setup
result = Benchmark.do_setup(self)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 327, in do_setup
setup(*self._current_params)
File "/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/frame_methods.py", line 130, in setup
self.df2[0] = period_range('2000', '2010', nrows)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/pandas/core/indexes/period.py", line 1226, in period_range
raise ValueError('Of the three parameters: start, end, and periods, '
ValueError: Of the three parameters: start, end, and periods, exactly two must be specified
[ 19.85%] ··· Running frame_methods.ToString.time_to_string_floats 73.5ms
[ 20.00%] ··· Running frame_methods.XS.time_frame_xs ok
[ 20.00%] ····
====== =======
axis
------ -------
0 887μs
1 752μs
====== =======
[ 20.15%] ··· Running ...allelDatetimeFields.time_datetime_field_day 226ms
[ 20.30%] ··· Running ...etimeFields.time_datetime_field_daysinmonth 236ms
[ 20.45%] ··· Running ...atetimeFields.time_datetime_field_normalize 327ms
[ 20.60%] ··· Running ...llelDatetimeFields.time_datetime_field_year 223ms
[ 20.75%] ··· Running ...allelDatetimeFields.time_datetime_to_period 237ms
[ 20.90%] ··· Running ...allelDatetimeFields.time_period_to_datetime 325ms
[ 21.05%] ··· Running gil.ParallelFactorize.time_loop ok
[ 21.05%] ····
========= ========
threads
--------- --------
2 83.9ms
4 167ms
8 332ms
========= ========
[ 21.20%] ··· Running gil.ParallelFactorize.time_parallel ok
[ 21.20%] ····
========= =======
threads
--------- -------
2 103ms
4 229ms
8 491ms
========= =======
[ 21.35%] ··· Running gil.ParallelGroupbyMethods.time_loop ok
[ 21.35%] ····
========= ======== ========
threads method
--------- -------- --------
2 count 112ms
2 last 102ms
2 max 99.4ms
2 mean 102ms
2 min 99.0ms
2 prod 101ms
2 sum 102ms
2 var 125ms
4 count 226ms
4 last 201ms
4 max 196ms
4 mean 205ms
4 min 199ms
4 prod 201ms
4 sum 200ms
4 var 250ms
8 count 492ms
8 last 447ms
8 max 432ms
8 mean 444ms
8 min 433ms
8 prod 443ms
8 sum 438ms
8 var 535ms
========= ======== ========
[ 21.50%] ··· Running gil.ParallelGroupbyMethods.time_parallel ok
[ 21.50%] ····
========= ======== =======
threads method
--------- -------- -------
2 count 168ms
2 last 136ms
2 max 124ms
2 mean 136ms
2 min 136ms
2 prod 139ms
2 sum 135ms
2 var 159ms
4 count 360ms
4 last 292ms
4 max 283ms
4 mean 295ms
4 min 276ms
4 prod 282ms
4 sum 285ms
4 var 331ms
8 count 744ms
8 last 600ms
8 max 568ms
8 mean 604ms
8 min 590ms
8 prod 595ms
8 sum 535ms
8 var 726ms
========= ======== =======
[ 21.65%] ··· Running gil.ParallelGroups.time_get_groups ok
[ 21.65%] ····
========= =======
threads
--------- -------
2 1.49s
4 2.96s
8 5.93s
========= =======
[ 21.80%] ··· Running gil.ParallelKth.time_kth_smallest 292ms
[ 21.95%] ··· Running gil.ParallelReadCSV.time_read_csv ok
[ 21.95%] ····
========== ========
dtype
---------- --------
float 587ms
object 31.8ms
datetime 600ms
========== ========
[ 22.11%] ··· Running gil.ParallelRolling.time_rolling ok
[ 22.11%] ····
================ ========
method
---------------- --------
rolling_median 232ms
rolling_mean 10.2ms
rolling_min 18.6ms
rolling_max 15.4ms
rolling_var 15.2ms
rolling_skew 27.2ms
rolling_kurt 20.2ms
rolling_std 8.49ms
================ ========
[ 22.26%] ··· Running gil.ParallelTake1D.time_take1d ok
[ 22.26%] ····
========= ========
dtype
--------- --------
int64 23.5ms
float64 19.7ms
========= ========
[ 22.41%] ··· Running ...lyDictReturn.time_groupby_apply_dict_return 92.4ms
[ 22.56%] ··· Running ...by.Categories.time_groupby_extra_cat_nosort 13.9ms
[ 22.71%] ··· Running groupby.Categories.time_groupby_extra_cat_sort 4.00ms
[ 22.86%] ··· Running groupby.Categories.time_groupby_nosort 17.3ms
[ 23.01%] ··· Running groupby.Categories.time_groupby_ordered_nosort 26.3ms
[ 23.16%] ··· Running groupby.Categories.time_groupby_ordered_sort 4.54ms
[ 23.31%] ··· Running groupby.Categories.time_groupby_sort 4.89ms
[ 23.46%] ··· Running groupby.DateAttributes.time_len_groupby_object 330ms
[ 23.61%] ··· Running groupby.Datelike.time_sum ok
[ 23.61%] ····
=============== ========
grouper
--------------- --------
period_range 62.9ms
date_range 5.12ms
date_range_tz 8.15ms
=============== ========
[ 23.76%] ··· Running groupby.FirstLast.time_groupby_first ok
[ 23.76%] ····
========== ========
dtype
---------- --------
float32 11.3ms
float64 10.9ms
datetime 23.0ms
object 29.0ms
========== ========
[ 23.91%] ··· Running groupby.FirstLast.time_groupby_last ok
[ 23.91%] ····
========== ========
dtype
---------- --------
float32 10.8ms
float64 10.7ms
datetime 23.1ms
object 28.4ms
========== ========
[ 24.06%] ··· Running groupby.FirstLast.time_groupby_nth_all ok
[ 24.06%] ····
========== ========
dtype
---------- --------
float32 57.3ms
float64 58.3ms
datetime 83.2ms
object 95.1ms
========== ========
[ 24.21%] ··· Running groupby.FirstLast.time_groupby_nth_none ok
[ 24.21%] ····
========== ========
dtype
---------- --------
float32 31.4ms
float64 31.6ms
datetime 44.8ms
object 47.6ms
========== ========
[ 24.36%] ··· Running groupby.Float32.time_sum 21.4ms
[ 24.51%] ··· Running groupby.GroupByMethods.time_all ok
[ 24.51%] ····
======= ======== =======
-- ngroups
------- ----------------
dtype 100 10000
======= ======== =======
int 27.3ms 2.52s
float 41.6ms 3.99s
======= ======== =======
[ 24.66%] ··· Running groupby.GroupByMethods.time_any ok
[ 24.66%] ····
======= ======== =======
-- ngroups
------- ----------------
dtype 100 10000
======= ======== =======
int 27.6ms 2.52s
float 41.5ms 3.97s
======= ======== =======
[ 24.81%] ··· Running groupby.GroupByMethods.time_count ok
[ 24.81%] ····
======= ======== ========
-- ngroups
------- -----------------
dtype 100 10000
======= ======== ========
int 1.09ms 4.10ms
float 1.21ms 5.67ms
======= ======== ========
[ 24.96%] ··· Running groupby.GroupByMethods.time_cumcount ok
[ 24.96%] ····
======= ======== ========
-- ngroups
------- -----------------
dtype 100 10000
======= ======== ========
int 1.23ms 5.49ms
float 1.28ms 6.77ms
======= ======== ========
[ 25.11%] ··· Running groupby.GroupByMethods.time_cummax ok
[ 25.11%] ····
======= ======== ========
-- ngroups
------- -----------------
dtype 100 10000
======= ======== ========
int 1.27ms 4.50ms
float 1.32ms 5.80ms
======= ======== ========
[ 25.26%] ··· Running groupby.GroupByMethods.time_cummin ok
[ 25.26%] ····
======= ======== ========
-- ngroups
------- -----------------
dtype 100 10000
======= ======== ========
int 1.32ms 4.54ms
float 1.35ms 5.96ms
======= ======== ========
[ 25.41%] ··· Running groupby.GroupByMethods.time_cumprod ok
[ 25.41%] ····
======= ======== ========
-- ngroups
------- -----------------
dtype 100 10000
======= ======== ========
int 1.65ms 5.16ms
float 1.73ms 6.68ms
======= ======== ========
[ 25.56%] ··· Running groupby.GroupByMethods.time_cumsum ok
[ 25.56%] ····
======= ======== ========
-- ngroups
------- -----------------
dtype 100 10000
======= ======== ========
int 1.33ms 4.30ms
float 1.38ms 6.00ms
======= ======== ========
[ 25.71%] ··· Running groupby.GroupByMethods.time_describe ok
[ 25.71%] ····
======= ======= =======
-- ngroups
------- ---------------
dtype 100 10000
======= ======= =======
int 323ms 31.7s
float 503ms 50.0s
======= ======= =======
[ 25.86%] ··· Running groupby.GroupByMethods.time_diff ok
[ 25.86%] ····
======= ======== =======
-- ngroups
------- ----------------
dtype 100 10000
======= ======== =======
int 35.9ms 3.26s
float 54.0ms 5.16s
======= ======== =======
[ 26.02%] ··· Running groupby.GroupByMethods.time_first ok
[ 26.02%] ····
======= ======== ========
-- ngroups
------- -----------------
dtype 100 10000
======= ======== ========
int 1.25ms 4.58ms
float 1.29ms 6.19ms
======= ======== ========
[ 26.17%] ··· Running groupby.GroupByMethods.time_head ok
[ 26.17%] ····
======= ======== ========
-- ngroups
------- -----------------
dtype 100 10000
======= ======== ========
int 1.73ms 5.91ms
float 1.78ms 7.20ms
======= ======== ========
[ 26.32%] ··· Running groupby.GroupByMethods.time_last ok
[ 26.32%] ····
======= ======== ========
-- ngroups
------- -----------------
dtype 100 10000
======= ======== ========
int 1.22ms 4.45ms
float 1.29ms 6.10ms
======= ======== ========
[ 26.47%] ··· Running groupby.GroupByMethods.time_mad ok
[ 26.47%] ····
======= ======= =======
-- ngroups
------- ---------------
dtype 100 10000
======= ======= =======
int 533ms 10.4s
float 577ms 16.3s
======= ======= =======
[ 26.62%] ··· Running groupby.GroupByMethods.time_max ok
[ 26.62%] ····
======= ======== ========
-- ngroups
------- -----------------
dtype 100 10000
======= ======== ========
int 1.27ms 4.60ms
float 1.35ms 6.09ms
======= ======== ========
[ 26.77%] ··· Running groupby.GroupByMethods.time_mean ok
[ 26.77%] ····
======= ======== ========
-- ngroups
------- -----------------
dtype 100 10000
======= ======== ========
int 1.42ms 4.78ms
float 1.69ms 7.08ms
======= ======== ========
[ 26.92%] ··· Running groupby.GroupByMethods.time_median ok
[ 26.92%] ····
======= ======== ========
-- ngroups
------- -----------------
dtype 100 10000
======= ======== ========
int 1.43ms 5.52ms
float 1.70ms 7.61ms
======= ======== ========
[ 27.07%] ··· Running groupby.GroupByMethods.time_min ok
[ 27.07%] ····
======= ======== ========
-- ngroups
------- -----------------
dtype 100 10000
======= ======== ========
int 1.31ms 4.66ms
float 1.34ms 6.13ms
======= ======== ========
[ 27.22%] ··· Running groupby.GroupByMethods.time_nunique ok
[ 27.22%] ····
======= ======== ========
-- ngroups
------- -----------------
dtype 100 10000
======= ======== ========
int 1.22ms 8.95ms
float 1.24ms 10.6ms
======= ======== ========
[ 27.37%] ··· Running groupby.GroupByMethods.time_pct_change ok
[ 27.37%] ····
======= ======= =======
-- ngroups
------- ---------------
dtype 100 10000
======= ======= =======
int 535ms 11.5s
float 595ms 17.9s
======= ======= =======
[ 27.52%] ··· Running groupby.GroupByMethods.time_prod ok
[ 27.52%] ····
======= ======== ========
-- ngroups
------- -----------------
dtype 100 10000
======= ======== ========
int 1.63ms 5.17ms
float 1.65ms 7.08ms
======= ======== ========
[ 27.67%] ··· Running groupby.GroupByMethods.time_rank ok
[ 27.67%] ····
======= ======== =======
-- ngroups
------- ----------------
dtype 100 10000
======= ======== =======
int 40.0ms 3.75s
float 60.2ms 5.90s
======= ======== =======
[ 27.82%] ··· Running groupby.GroupByMethods.time_sem ok
[ 27.82%] ····
======= ======= =======
-- ngroups
------- ---------------
dtype 100 10000
======= ======= =======
int 416ms 423ms
float 418ms 427ms
======= ======= =======
[ 27.97%] ··· Running groupby.GroupByMethods.time_shift ok
[ 27.97%] ····
======= ======== ========
-- ngroups
------- -----------------
dtype 100 10000
======= ======== ========
int 1.07ms 4.77ms
float 1.12ms 6.47ms
======= ======== ========
[ 28.12%] ··· Running groupby.GroupByMethods.time_size ok
[ 28.12%] ····
======= ======== ========
-- ngroups
------- -----------------
dtype 100 10000
======= ======== ========
int 1.04ms 3.93ms
float 1.09ms 5.42ms
======= ======== ========
[ 28.27%] ··· Running groupby.GroupByMethods.time_skew ok
[ 28.27%] ····
======= ======== =======
-- ngroups
------- ----------------
dtype 100 10000
======= ======== =======
int 43.5ms 4.12s
float 66.3ms 6.44s
======= ======== =======
[ 28.42%] ··· Running groupby.GroupByMethods.time_std ok
[ 28.42%] ····
======= ======== ========
-- ngroups
------- -----------------
dtype 100 10000
======= ======== ========
int 1.56ms 5.22ms
float 1.49ms 6.64ms
======= ======== ========
[ 28.57%] ··· Running groupby.GroupByMethods.time_sum ok
[ 28.57%] ····
======= ======== ========
-- ngroups
------- -----------------
dtype 100 10000
======= ======== ========
int 1.64ms 5.26ms
float 1.73ms 6.91ms
======= ======== ========
[ 28.72%] ··· Running groupby.GroupByMethods.time_tail ok
[ 28.72%] ····
======= ======== ========
-- ngroups
------- -----------------
dtype 100 10000
======= ======== ========
int 1.74ms 6.02ms
float 1.81ms 7.21ms
======= ======== ========
[ 28.87%] ··· Running groupby.GroupByMethods.time_unique ok
[ 28.87%] ····
======= ======== =======
-- ngroups
------- ----------------
dtype 100 10000
======= ======== =======
int 31.5ms 2.86s
float 47.3ms 4.51s
======= ======== =======
[ 29.02%] ··· Running groupby.GroupByMethods.time_value_counts ok
[ 29.02%] ····
======= ======== ========
-- ngroups
------- -----------------
dtype 100 10000
======= ======== ========
int 1.85ms 14.9ms
float 1.91ms 16.7ms
======= ======== ========
[ 29.17%] ··· Running groupby.GroupByMethods.time_var ok
[ 29.17%] ····
======= ======== ========
-- ngroups
------- -----------------
dtype 100 10000
======= ======== ========
int 1.39ms 5.23ms
float 1.39ms 6.31ms
======= ======== ========
[ 29.32%] ··· Running groupby.GroupManyLabels.time_sum ok
[ 29.32%] ····
======= ========
ncols
------- --------
1 6.73ms
1000 15.6ms
======= ========
[ 29.47%] ··· Running groupby.GroupStrings.time_multi_columns failed
[ 29.47%] ····· Traceback (most recent call last):
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 818, in <module>
commands[mode](args)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 789, in main_run
skip = benchmark.do_setup()
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 393, in do_setup
result = Benchmark.do_setup(self)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 327, in do_setup
setup(*self._current_params)
File "/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/groupby.py", line 286, in setup
(n // 1)), 1)})
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/pandas/core/frame.py", line 330, in __init__
mgr = self._init_dict(data, index, columns, dtype=dtype)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/pandas/core/frame.py", line 461, in _init_dict
return _arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/pandas/core/frame.py", line 6163, in _arrays_to_mgr
index = extract_index(arrays)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/pandas/core/frame.py", line 6211, in extract_index
raise ValueError('arrays must all be same length')
ValueError: arrays must all be same length
[ 29.62%] ··· Running groupby.Int64.time_overflow 596ms
[ 29.77%] ··· Running groupby.Size.time_category_size 15.2ms
[ 29.92%] ··· Running groupby.Size.time_dt_size 19.7ms
[ 30.08%] ··· Running groupby.Size.time_dt_timegrouper_size 57.8ms
[ 30.08%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/groupby.py:349: FutureWarning: pd.TimeGrouper is deprecated and will be removed; Please use pd.Grouper(freq=...)
self.df.groupby(TimeGrouper(key='dates', freq='M')).size()
[ 30.23%] ··· Running groupby.Size.time_multi_size 27.2ms
[ 30.38%] ··· Running groupby.SumBools.time_groupby_sum_booleans 3.48ms
[ 30.53%] ··· Running ...y.SumMultiLevel.time_groupby_sum_multiindex 2.79ms
[ 30.68%] ··· Running groupby.Transform.time_transform_lambda_max 724ms
[ 30.83%] ··· Running groupby.Transform.time_transform_multi_key1 17.1ms
[ 30.98%] ··· Running groupby.Transform.time_transform_multi_key2 14.1ms
[ 31.13%] ··· Running groupby.Transform.time_transform_multi_key3 13.5ms
[ 31.28%] ··· Running groupby.Transform.time_transform_multi_key4 6.91ms
[ 31.43%] ··· Running groupby.Transform.time_transform_ufunc_max 20.6ms
[ 31.58%] ··· Running groupby.TransformBools.time_transform_mean 10.2ms
[ 31.73%] ··· Running groupby.TransformNaN.time_first 6.32ms
[ 31.88%] ··· Running index_object.Datetime.time_is_dates_only 349μs
[ 32.03%] ··· Running index_object.Float64IndexMethod.time_get_loc 7.95ms
[ 32.18%] ··· Running index_object.IndexAppend.time_append_int_list 323ms
[ 32.33%] ··· Running index_object.IndexAppend.time_append_obj_list 305ms
[ 32.48%] ··· Running ...x_object.IndexAppend.time_append_range_list 164ms
[ 32.63%] ··· Running index_object.Indexing.time_boolean_array ok
[ 32.63%] ····
======== ========
dtype
-------- --------
String 35.8ms
Float 6.97ms
Int 6.96ms
======== ========
[ 32.78%] ··· Running index_object.Indexing.time_boolean_series ok
[ 32.78%] ····
======== ========
dtype
-------- --------
String 35.9ms
Float 7.03ms
Int 7.00ms
======== ========
[ 32.93%] ··· Running index_object.Indexing.time_get ok
[ 32.93%] ····
======== ========
dtype
-------- --------
String 24.1μs
Float 26.5μs
Int 27.4μs
======== ========
[ 33.08%] ··· Running index_object.Indexing.time_slice ok
[ 33.08%] ····
======== ========
dtype
-------- --------
String 55.5μs
Float 56.6μs
Int 55.4μs
======== ========
[ 33.23%] ··· Running index_object.Indexing.time_slice_step ok
[ 33.23%] ····
======== ========
dtype
-------- --------
String 83.2μs
Float 57.3μs
Int 55.9μs
======== ========
[ 33.38%] ··· Running index_object.Ops.time_add ok
[ 33.38%] ····
======= ========
dtype
------- --------
float 5.50ms
int 5.43ms
======= ========
[ 33.53%] ··· Running index_object.Ops.time_divide ok
[ 33.53%] ····
======= ========
dtype
------- --------
float 5.38ms
int 20.6ms
======= ========
[ 33.68%] ··· Running index_object.Ops.time_modulo ok
[ 33.68%] ····
======= ========
dtype
------- --------
float 15.7ms
int 19.3ms
======= ========
[ 33.83%] ··· Running index_object.Ops.time_multiply ok
[ 33.83%] ····
======= ========
dtype
------- --------
float 5.41ms
int 5.39ms
======= ========
[ 33.98%] ··· Running index_object.Ops.time_subtract ok
[ 33.98%] ····
======= ========
dtype
------- --------
float 5.45ms
int 5.38ms
======= ========
[ 34.14%] ··· Running index_object.Range.time_max 13.3μs
[ 34.29%] ··· Running index_object.Range.time_max_trivial 13.2μs
[ 34.44%] ··· Running index_object.Range.time_min 13.4μs
[ 34.59%] ··· Running index_object.Range.time_min_trivial 13.0μs
[ 34.74%] ··· Running ...tDisjoint.time_datetime_difference_disjoint 10.2ms
[ 34.89%] ··· Running index_object.SetOperations.time_operation ok
[ 34.89%] ····
============= ====================== ========
dtype method
------------- ---------------------- --------
datetime intersection 5.97ms
datetime union 8.34ms
datetime symmetric_difference 32.8ms
date_string intersection 69.7ms
date_string union 68.8ms
date_string symmetric_difference 135ms
int intersection 2.55ms
int union 2.53ms
int symmetric_difference 24.3ms
strings intersection 70.0ms
strings union 253ms
strings symmetric_difference 96.3ms
============= ====================== ========
[ 35.04%] ··· Running ...iesIndex.time_frame_assign_timeseries_index 5.91ms
[ 35.19%] ··· Running ....DataFrameNumericIndexing.time_bool_indexer 1.45ms
[ 35.34%] ··· Running indexing.DataFrameNumericIndexing.time_iloc 525μs
[ 35.49%] ··· Running ...ing.DataFrameNumericIndexing.time_iloc_dups 537μs
[ 35.64%] ··· Running indexing.DataFrameNumericIndexing.time_loc 871μs
[ 35.79%] ··· Running ...xing.DataFrameNumericIndexing.time_loc_dups 6.69ms
[ 35.94%] ··· Running ...g.DataFrameStringIndexing.time_boolean_rows 680μs
[ 36.09%] ··· Running ...rameStringIndexing.time_boolean_rows_object 677μs
[ 36.24%] ··· Running ...xing.DataFrameStringIndexing.time_get_value 214μs
[ 36.24%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/indexing.py:115: FutureWarning: get_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead
self.df.get_value(self.idx_scalar, self.col_scalar)
[ 36.39%] ··· Running ...DataFrameStringIndexing.time_getitem_scalar 236μs
[ 36.54%] ··· Running indexing.DataFrameStringIndexing.time_ix 360μs
[ 36.69%] ··· Running indexing.DataFrameStringIndexing.time_loc 274μs
[ 36.84%] ··· Running ...Column.time_frame_getitem_single_column_int 189μs
[ 36.99%] ··· Running ...lumn.time_frame_getitem_single_column_label 168μs
[ 37.14%] ··· Running ...xing.InsertColumns.time_assign_with_setitem 55.3ms
[ 37.29%] ··· Running indexing.InsertColumns.time_insert 103ms
[ 37.44%] ··· Running indexing.MultiIndexing.time_frame_ix 19.3ms
[ 37.59%] ··· Running indexing.MultiIndexing.time_index_slice 11.7ms
[ 37.74%] ··· Running indexing.MultiIndexing.time_series_ix 17.3ms
[ 37.89%] ··· Running ...ing.NonNumericSeriesIndexing.time_get_value ok
[ 37.89%] ····
========== ========
index
---------- --------
string 23.9ms
datetime 4.92ms
========== ========
[ 37.89%] ·····
For parameters: 'string'
/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/indexing.py:94: FutureWarning: get_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead
self.s.get_value(self.lbl)
For parameters: 'datetime'
/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/indexing.py:94: FutureWarning: get_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead
self.s.get_value(self.lbl)
[ 38.05%] ··· Running ...ericSeriesIndexing.time_getitem_label_slice ok
[ 38.05%] ····
========== ========
index
---------- --------
string 26.0ms
datetime 5.67ms
========== ========
[ 38.20%] ··· Running ...umericSeriesIndexing.time_getitem_pos_slice ok
[ 38.20%] ····
========== ========
index
---------- --------
string 2.94ms
datetime 429μs
========== ========
[ 38.35%] ··· Running ...onNumericSeriesIndexing.time_getitem_scalar ok
[ 38.35%] ····
========== ========
index
---------- --------
string 23.4ms
datetime 4.78ms
========== ========
[ 38.50%] ··· Running ...ng.NumericSeriesIndexing.time_getitem_array ok
[ 38.50%] ····
========================================== ========
param1
------------------------------------------ --------
pandas.core.indexes.numeric.Int64Index 58.6ms
pandas.core.indexes.numeric.Float64Index 269ms
========================================== ========
[ 38.65%] ··· Running ...umericSeriesIndexing.time_getitem_list_like ok
[ 38.65%] ····
========================================== ========
param1
------------------------------------------ --------
pandas.core.indexes.numeric.Int64Index 57.0ms
pandas.core.indexes.numeric.Float64Index 265ms
========================================== ========
[ 38.80%] ··· Running ...ng.NumericSeriesIndexing.time_getitem_lists ok
[ 38.80%] ····
========================================== ========
param1
------------------------------------------ --------
pandas.core.indexes.numeric.Int64Index 65.1ms
pandas.core.indexes.numeric.Float64Index 271ms
========================================== ========
[ 38.95%] ··· Running ...g.NumericSeriesIndexing.time_getitem_scalar ok
[ 38.95%] ····
========================================== ========
param1
------------------------------------------ --------
pandas.core.indexes.numeric.Int64Index 2.60ms
pandas.core.indexes.numeric.Float64Index 3.42ms
========================================== ========
[ 39.10%] ··· Running ...ng.NumericSeriesIndexing.time_getitem_slice ok
[ 39.10%] ····
========================================== ========
param1
------------------------------------------ --------
pandas.core.indexes.numeric.Int64Index 282μs
pandas.core.indexes.numeric.Float64Index 3.63ms
========================================== ========
[ 39.25%] ··· Running indexing.NumericSeriesIndexing.time_iloc_array ok
[ 39.25%] ····
========================================== =======
param1
------------------------------------------ -------
pandas.core.indexes.numeric.Int64Index 377μs
pandas.core.indexes.numeric.Float64Index 337μs
========================================== =======
[ 39.40%] ··· Running ...g.NumericSeriesIndexing.time_iloc_list_like ok
[ 39.40%] ····
========================================== =======
param1
------------------------------------------ -------
pandas.core.indexes.numeric.Int64Index 266μs
pandas.core.indexes.numeric.Float64Index 283μs
========================================== =======
[ 39.55%] ··· Running ...xing.NumericSeriesIndexing.time_iloc_scalar ok
[ 39.55%] ····
========================================== =======
param1
------------------------------------------ -------
pandas.core.indexes.numeric.Int64Index 140μs
pandas.core.indexes.numeric.Float64Index 127μs
========================================== =======
[ 39.70%] ··· Running indexing.NumericSeriesIndexing.time_iloc_slice ok
[ 39.70%] ····
========================================== =======
param1
------------------------------------------ -------
pandas.core.indexes.numeric.Int64Index 225μs
pandas.core.indexes.numeric.Float64Index 352μs
========================================== =======
[ 39.85%] ··· Running indexing.NumericSeriesIndexing.time_ix_array ok
[ 39.85%] ····
========================================== ========
param1
------------------------------------------ --------
pandas.core.indexes.numeric.Int64Index 57.7ms
pandas.core.indexes.numeric.Float64Index 266ms
========================================== ========
[ 40.00%] ··· Running ...ing.NumericSeriesIndexing.time_ix_list_like ok
[ 40.00%] ····
========================================== ========
param1
------------------------------------------ --------
pandas.core.indexes.numeric.Int64Index 56.8ms
pandas.core.indexes.numeric.Float64Index 266ms
========================================== ========
[ 40.15%] ··· Running indexing.NumericSeriesIndexing.time_ix_scalar ok
[ 40.15%] ····
========================================== ========
param1
------------------------------------------ --------
pandas.core.indexes.numeric.Int64Index 2.81ms
pandas.core.indexes.numeric.Float64Index 3.59ms
========================================== ========
[ 40.30%] ··· Running indexing.NumericSeriesIndexing.time_ix_slice ok
[ 40.30%] ····
========================================== ========
param1
------------------------------------------ --------
pandas.core.indexes.numeric.Int64Index 2.94ms
pandas.core.indexes.numeric.Float64Index 3.71ms
========================================== ========
[ 40.45%] ··· Running indexing.NumericSeriesIndexing.time_loc_array ok
[ 40.45%] ····
========================================== ========
param1
------------------------------------------ --------
pandas.core.indexes.numeric.Int64Index 59.6ms
pandas.core.indexes.numeric.Float64Index 268ms
========================================== ========
[ 40.60%] ··· Running ...ng.NumericSeriesIndexing.time_loc_list_like ok
[ 40.60%] ····
========================================== ========
param1
------------------------------------------ --------
pandas.core.indexes.numeric.Int64Index 57.0ms
pandas.core.indexes.numeric.Float64Index 266ms
========================================== ========
[ 40.75%] ··· Running indexing.NumericSeriesIndexing.time_loc_scalar ok
[ 40.75%] ····
========================================== ========
param1
------------------------------------------ --------
pandas.core.indexes.numeric.Int64Index 58.7ms
pandas.core.indexes.numeric.Float64Index 113ms
========================================== ========
[ 40.90%] ··· Running indexing.NumericSeriesIndexing.time_loc_slice ok
[ 40.90%] ····
========================================== ========
param1
------------------------------------------ --------
pandas.core.indexes.numeric.Int64Index 2.82ms
pandas.core.indexes.numeric.Float64Index 3.67ms
========================================== ========
[ 41.05%] ··· Running indexing.PanelIndexing.time_subset 5.11ms
[ 41.20%] ··· Running indexing.Take.time_take ok
[ 41.20%] ····
========== ========
index
---------- --------
int 11.1ms
datetime 11.0ms
========== ========
[ 41.35%] ··· Running inference.NumericInferOps.time_add ok
[ 41.35%] ····
======================== =======
dtype
------------------------ -------
<type 'numpy.int64'> 431ms
<type 'numpy.int32'> 419ms
<type 'numpy.uint32'> 420ms
<type 'numpy.uint64'> 421ms
<type 'numpy.float32'> 421ms
<type 'numpy.float64'> 431ms
<type 'numpy.int16'> 418ms
<type 'numpy.int8'> 419ms
<type 'numpy.uint16'> 419ms
<type 'numpy.uint8'> 419ms
======================== =======
[ 41.50%] ··· Running inference.NumericInferOps.time_divide ok
[ 41.50%] ····
======================== =======
dtype
------------------------ -------
<type 'numpy.int64'> 433ms
<type 'numpy.int32'> 428ms
<type 'numpy.uint32'> 425ms
<type 'numpy.uint64'> 427ms
<type 'numpy.float32'> 425ms
<type 'numpy.float64'> 430ms
<type 'numpy.int16'> 426ms
<type 'numpy.int8'> 424ms
<type 'numpy.uint16'> 423ms
<type 'numpy.uint8'> 422ms
======================== =======
[ 41.65%] ··· Running inference.NumericInferOps.time_modulo ok
[ 41.65%] ····
======================== =======
dtype
------------------------ -------
<type 'numpy.int64'> 442ms
<type 'numpy.int32'> 427ms
<type 'numpy.uint32'> 426ms
<type 'numpy.uint64'> 445ms
<type 'numpy.float32'> 431ms
<type 'numpy.float64'> 428ms
<type 'numpy.int16'> 432ms
<type 'numpy.int8'> 432ms
<type 'numpy.uint16'> 426ms
<type 'numpy.uint8'> 433ms
======================== =======
[ 41.80%] ··· Running inference.NumericInferOps.time_multiply ok
[ 41.80%] ····
======================== =======
dtype
------------------------ -------
<type 'numpy.int64'> 427ms
<type 'numpy.int32'> 419ms
<type 'numpy.uint32'> 419ms
<type 'numpy.uint64'> 422ms
<type 'numpy.float32'> 423ms
<type 'numpy.float64'> 425ms
<type 'numpy.int16'> 420ms
<type 'numpy.int8'> 416ms
<type 'numpy.uint16'> 419ms
<type 'numpy.uint8'> 417ms
======================== =======
[ 41.95%] ··· Running inference.NumericInferOps.time_subtract ok
[ 41.95%] ····
======================== =======
dtype
------------------------ -------
<type 'numpy.int64'> 427ms
<type 'numpy.int32'> 422ms
<type 'numpy.uint32'> 419ms
<type 'numpy.uint64'> 424ms
<type 'numpy.float32'> 421ms
<type 'numpy.float64'> 425ms
<type 'numpy.int16'> 417ms
<type 'numpy.int8'> 417ms
<type 'numpy.uint16'> 418ms
<type 'numpy.uint8'> 418ms
======================== =======
[ 42.11%] ··· Running inference.ToNumeric.time_from_float ok
[ 42.11%] ····
======== =======
errors
-------- -------
ignore 154μs
coerce 156μs
======== =======
[ 42.26%] ··· Running inference.ToNumeric.time_from_numeric_str ok
[ 42.26%] ····
======== ========
errors
-------- --------
ignore 7.67ms
coerce 7.74ms
======== ========
[ 42.41%] ··· Running inference.ToNumeric.time_from_str ok
[ 42.41%] ····
======== ========
errors
-------- --------
ignore 378μs
coerce 22.8ms
======== ========
[ 42.56%] ··· Running inference.ToNumericDowncast.time_downcast ok
[ 42.56%] ····
============== ========== ========
dtype downcast
-------------- ---------- --------
string-float None 284ms
string-float integer 281ms
string-float signed 284ms
string-float unsigned 281ms
string-float float 281ms
string-int None 552ms
string-int integer 595ms
string-int signed 588ms
string-int unsigned 586ms
string-int float 553ms
string-nint None 524ms
string-nint integer 568ms
string-nint signed 572ms
string-nint unsigned 535ms
string-nint float 545ms
datetime64 None 5.13ms
datetime64 integer 73.3ms
datetime64 signed 72.3ms
datetime64 unsigned 74.0ms
datetime64 float 8.82ms
int-list None 68.9ms
int-list integer 102ms
int-list signed 100ms
int-list unsigned 102ms
int-list float 69.4ms
int32 None 26.0μs
int32 integer 32.5ms
int32 signed 32.5ms
int32 unsigned 33.4ms
int32 float 1.37ms
============== ========== ========
[ 42.71%] ··· Running io.csv.ReadCSVCategorical.time_convert_direct 96.8ms
[ 42.86%] ··· Running io.csv.ReadCSVCategorical.time_convert_post 142ms
[ 43.01%] ··· Running io.csv.ReadCSVComment.time_comment 56.3ms
[ 43.16%] ··· Running ...v.ReadCSVDInferDatetimeFormat.time_read_csv ok
[ 43.16%] ····
======================= ======== ========= ========
-- format
----------------------- ---------------------------
infer_datetime_format custom iso8601 ymd
======================= ======== ========= ========
True 24.2ms 4.79ms 5.00ms
False 654ms 3.59ms 3.26ms
======================= ======== ========= ========
[ 43.31%] ··· Running io.csv.ReadCSVFloatPrecision.time_read_csv ok
[ 43.31%] ····
===== ========= ======== ======== ============
-- float_precision
--------------- ------------------------------
sep decimal None high round_trip
===== ========= ======== ======== ============
, . 3.98ms 3.86ms 5.61ms
, _ 4.21ms 4.38ms 4.28ms
; . 4.00ms 3.80ms 5.56ms
; _ 4.24ms 4.23ms 4.21ms
===== ========= ======== ======== ============
[ 43.46%] ··· Running ...VFloatPrecision.time_read_csv_python_engine ok
[ 43.46%] ····
===== ========= ======== ======== ============
-- float_precision
--------------- ------------------------------
sep decimal None high round_trip
===== ========= ======== ======== ============
, . 8.67ms 8.48ms 8.54ms
, _ 7.03ms 7.04ms 7.12ms
; . 8.55ms 8.81ms 8.56ms
; _ 7.05ms 7.05ms 7.29ms
===== ========= ======== ======== ============
[ 43.61%] ··· Running io.csv.ReadCSVParseDates.time_baseline 2.86ms
[ 43.76%] ··· Running io.csv.ReadCSVParseDates.time_multiple_date 2.84ms
[ 43.91%] ··· Running io.csv.ReadCSVSkipRows.time_skipprows ok
[ 43.91%] ····
========== ========
skiprows
---------- --------
None 45.0ms
10000 30.4ms
========== ========
[ 44.06%] ··· Running io.csv.ReadCSVThousands.time_thousands ok
[ 44.06%] ····
===== ======== ========
-- thousands
----- -----------------
sep None ,
===== ======== ========
, 39.2ms 36.1ms
| 38.5ms 38.4ms
===== ======== ========
[ 44.21%] ··· Running io.csv.ReadUint64Integers.time_read_uint64 8.91ms
[ 44.36%] ··· Running ...adUint64Integers.time_read_uint64_na_values 13.1ms
[ 44.51%] ··· Running ...dUint64Integers.time_read_uint64_neg_values 13.3ms
[ 44.66%] ··· Running io.csv.S3.time_read_csv_10_rows ok
[ 44.66%] ····
============= ======== =======
-- engine
------------- ----------------
compression python c
============= ======== =======
None 6.86s 9.88s
gzip 8.80s 7.93s
bz2 33.1s n/a
============= ======== =======
[ 44.81%] ··· Running io.csv.ToCSV.time_frame ok
[ 44.81%] ····
======= ========
kind
------- --------
wide 88.6ms
long 174ms
mixed 38.9ms
======= ========
[ 44.96%] ··· Running ...sv.ToCSVDatetime.time_frame_date_formatting 23.1ms
[ 45.11%] ··· Running io.excel.Excel.time_read_excel ok
[ 45.11%] ····
============ =======
engine
------------ -------
openpyxl 571ms
xlsxwriter 561ms
xlwt 160ms
============ =======
[ 45.26%] ··· Running io.excel.Excel.time_write_excel ok
[ 45.26%] ····
============ =======
engine
------------ -------
openpyxl 1.25s
xlsxwriter 968ms
xlwt 698ms
============ =======
[ 45.41%] ··· Running io.hdf.HDF.time_read_hdf ok
[ 45.41%] ····
======== ========
format
-------- --------
table 62.7ms
fixed 76.1ms
======== ========
[ 45.56%] ··· Running io.hdf.HDF.time_write_hdf ok
[ 45.56%] ····
======== =======
format
-------- -------
table 121ms
fixed 147ms
======== =======
[ 45.71%] ··· Running ...df.HDFStoreDataFrame.time_query_store_table 24.7ms
[ 45.86%] ··· Running ...FStoreDataFrame.time_query_store_table_wide 33.7ms
[ 46.02%] ··· Running io.hdf.HDFStoreDataFrame.time_read_store 12.7ms
[ 46.17%] ··· Running io.hdf.HDFStoreDataFrame.time_read_store_mixed 25.2ms
[ 46.32%] ··· Running io.hdf.HDFStoreDataFrame.time_read_store_table 18.2ms
[ 46.47%] ··· Running ...FStoreDataFrame.time_read_store_table_mixed 36.9ms
[ 46.62%] ··· Running ...DFStoreDataFrame.time_read_store_table_wide 29.5ms
[ 46.77%] ··· Running io.hdf.HDFStoreDataFrame.time_store_info 52.3ms
[ 46.92%] ··· Running io.hdf.HDFStoreDataFrame.time_store_repr 111μs
[ 47.07%] ··· Running io.hdf.HDFStoreDataFrame.time_store_str 108μs
[ 47.22%] ··· Running io.hdf.HDFStoreDataFrame.time_write_store 14.0ms
[ 47.37%] ··· Running ...df.HDFStoreDataFrame.time_write_store_mixed 31.5ms
[ 47.52%] ··· Running ...df.HDFStoreDataFrame.time_write_store_table 45.1ms
[ 47.67%] ··· Running ...HDFStoreDataFrame.time_write_store_table_dc 358ms
[ 47.82%] ··· Running ...StoreDataFrame.time_write_store_table_mixed 56.5ms
[ 47.97%] ··· Running ...FStoreDataFrame.time_write_store_table_wide 145ms
[ 48.12%] ··· Running ...f.HDFStorePanel.time_read_store_table_panel 54.9ms
[ 48.27%] ··· Running ....HDFStorePanel.time_write_store_table_panel 86.9ms
[ 48.42%] ··· Running io.json.ReadJSON.time_read_json ok
[ 48.42%] ····
========= ======= ==========
-- index
--------- ------------------
orient int datetime
========= ======= ==========
split 253ms 269ms
index 7.78s 7.89s
records 619ms 624ms
========= ======= ==========
[ 48.57%] ··· Running io.json.ReadJSONLines.peakmem_read_json_lines ok
[ 48.57%] ····
========== ======
index
---------- ------
int 192M
datetime 192M
========== ======
[ 48.72%] ··· Running ...eadJSONLines.peakmem_read_json_lines_concat ok
[ 48.72%] ····
========== ======
index
---------- ------
int 164M
datetime 164M
========== ======
[ 48.87%] ··· Running io.json.ReadJSONLines.time_read_json_lines ok
[ 48.87%] ····
========== =======
index
---------- -------
int 720ms
datetime 721ms
========== =======
[ 49.02%] ··· Running ...n.ReadJSONLines.time_read_json_lines_concat ok
[ 49.02%] ····
========== =======
index
---------- -------
int 745ms
datetime 749ms
========== =======
[ 49.17%] ··· Running io.json.ToJSON.time_delta_int_tstamp ok
[ 49.17%] ····
========= =======
orient
--------- -------
split 241ms
columns 231ms
index 392ms
========= =======
[ 49.32%] ··· Running io.json.ToJSON.time_delta_int_tstamp_lines ok
[ 49.32%] ····
========= =======
orient
--------- -------
split 559ms
columns 567ms
index 561ms
========= =======
[ 49.47%] ··· Running io.json.ToJSON.time_float_int ok
[ 49.47%] ····
========= =======
orient
--------- -------
split 222ms
columns 216ms
index 379ms
========= =======
[ 49.62%] ··· Running io.json.ToJSON.time_float_int_lines ok
[ 49.62%] ····
========= =======
orient
--------- -------
split 593ms
columns 595ms
index 598ms
========= =======
[ 49.77%] ··· Running io.json.ToJSON.time_float_int_str ok
[ 49.77%] ····
========= =======
orient
--------- -------
split 346ms
columns 221ms
index 392ms
========= =======
[ 49.92%] ··· Running io.json.ToJSON.time_float_int_str_lines ok
[ 49.92%] ····
========= =======
orient
--------- -------
split 618ms
columns 619ms
index 616ms
========= =======
[ 50.08%] ··· Running io.json.ToJSON.time_floats_with_dt_index ok
[ 50.08%] ····
========= =======
orient
--------- -------
split 183ms
columns 209ms
index 214ms
========= =======
[ 50.23%] ··· Running io.json.ToJSON.time_floats_with_dt_index_lines ok
[ 50.23%] ····
========= =======
orient
--------- -------
split 467ms
columns 467ms
index 471ms
========= =======
[ 50.38%] ··· Running io.json.ToJSON.time_floats_with_int_idex_lines ok
[ 50.38%] ····
========= =======
orient
--------- -------
split 464ms
columns 460ms
index 464ms
========= =======
[ 50.53%] ··· Running io.json.ToJSON.time_floats_with_int_index ok
[ 50.53%] ····
========= =======
orient
--------- -------
split 160ms
columns 173ms
index 191ms
========= =======
[ 50.68%] ··· Running io.msgpack.MSGPack.time_read_msgpack 42.6ms
[ 50.83%] ··· Running io.msgpack.MSGPack.time_write_msgpack 69.7ms
[ 50.98%] ··· Running io.pickle.Pickle.time_read_pickle 104ms
[ 51.13%] ··· Running io.pickle.Pickle.time_write_pickle 154ms
[ 51.28%] ··· Running io.sas.SAS.time_read_msgpack ok
[ 51.28%] ····
========== ========
format
---------- --------
sas7bdat 608ms
xport 9.91ms
========== ========
[ 51.43%] ··· Running io.sql.ReadSQLTable.time_read_sql_table_all 101ms
[ 51.58%] ··· Running ...eadSQLTable.time_read_sql_table_parse_dates 30.8ms
[ 51.73%] ··· Running ...adSQLTableDtypes.time_read_sql_table_column ok
[ 51.73%] ····
================ ========
dtype
---------------- --------
float 23.7ms
float_with_nan 23.8ms
string 25.2ms
bool 23.6ms
int 25.3ms
datetime 46.0ms
================ ========
[ 51.88%] ··· Running io.sql.SQL.time_read_sql_query ok
[ 51.88%] ····
============ ========
connection
------------ --------
sqlalchemy 70.5ms
sqlite 57.8ms
============ ========
[ 52.03%] ··· Running io.sql.SQL.time_to_sql_dataframe ok
[ 52.03%] ····
============ =======
connection
------------ -------
sqlalchemy 436ms
sqlite 187ms
============ =======
[ 52.18%] ··· Running ...SQLDtypes.time_read_sql_query_select_column ok
[ 52.18%] ····
============ ================ ========
connection dtype
------------ ---------------- --------
sqlalchemy float 18.0ms
sqlalchemy float_with_nan 18.6ms
sqlalchemy string 18.8ms
sqlalchemy bool 18.5ms
sqlalchemy int 18.9ms
sqlalchemy datetime 20.0ms
sqlite float 10.0ms
sqlite float_with_nan 9.75ms
sqlite string 11.0ms
sqlite bool 10.7ms
sqlite int 11.0ms
sqlite datetime 11.8ms
============ ================ ========
[ 52.33%] ··· Running ...WriteSQLDtypes.time_to_sql_dataframe_column ok
[ 52.33%] ····
============ ================ ========
connection dtype
------------ ---------------- --------
sqlalchemy float 137ms
sqlalchemy float_with_nan 149ms
sqlalchemy string 137ms
sqlalchemy bool 148ms
sqlalchemy int 132ms
sqlalchemy datetime 253ms
sqlite float 53.8ms
sqlite float_with_nan 61.9ms
sqlite string 55.5ms
sqlite bool 92.9ms
sqlite int 52.4ms
sqlite datetime 119ms
============ ================ ========
[ 52.48%] ··· Running io.stata.Stata.time_read_stata ok
[ 52.48%] ····
=============== =======
convert_dates
--------------- -------
tc 511ms
td 512ms
tm 1.35s
tw 1.27s
th 1.35s
tq 1.35s
ty 1.78s
=============== =======
[ 52.63%] ··· Running io.stata.Stata.time_write_stata ok
[ 52.63%] ····
=============== =======
convert_dates
--------------- -------
tc 607ms
td 594ms
tm 603ms
tw 1.34s
th 604ms
tq 613ms
ty 602ms
=============== =======
[ 52.78%] ··· Running join_merge.Align.time_series_align_int64_index 668ms
[ 52.93%] ··· Running ...erge.Align.time_series_align_left_monotonic 198ms
[ 53.08%] ··· Running join_merge.Append.time_append_homogenous 1.66ms
[ 53.08%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/join_merge.py:29: FutureWarning: consolidate is deprecated and will be removed in a future release.
self.mdf1.consolidate(inplace=True)
[ 53.23%] ··· Running join_merge.Append.time_append_mixed 2.54ms
[ 53.23%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/join_merge.py:29: FutureWarning: consolidate is deprecated and will be removed in a future release.
self.mdf1.consolidate(inplace=True)
[ 53.38%] ··· Running join_merge.Concat.time_concat_empty_left ok
[ 53.38%] ····
====== =======
axis
------ -------
0 392μs
1 484μs
====== =======
[ 53.53%] ··· Running join_merge.Concat.time_concat_empty_right ok
[ 53.53%] ····
====== =======
axis
------ -------
0 403μs
1 481μs
====== =======
[ 53.68%] ··· Running join_merge.Concat.time_concat_series ok
[ 53.68%] ····
====== ========
axis
------ --------
0 27.6ms
1 218ms
====== ========
[ 53.83%] ··· Running join_merge.Concat.time_concat_small_frames ok
[ 53.83%] ····
====== ========
axis
------ --------
0 108ms
1 81.1ms
====== ========
[ 53.98%] ··· Running join_merge.ConcatDataFrames.time_c_ordered ok
[ 53.98%] ····
====== ======= =======
-- ignore_index
------ ---------------
axis True False
====== ======= =======
0 146ms 148ms
1 224ms 226ms
====== ======= =======
[ 54.14%] ··· Running join_merge.ConcatDataFrames.time_f_ordered ok
[ 54.14%] ····
====== ======= =======
-- ignore_index
------ ---------------
axis True False
====== ======= =======
0 172ms 173ms
1 162ms 154ms
====== ======= =======
[ 54.29%] ··· Running join_merge.ConcatPanels.time_c_ordered ok
[ 54.29%] ····
====== ======= =======
-- ignore_index
------ ---------------
axis True False
====== ======= =======
0 319ms 314ms
1 368ms 369ms
2 1.68s 1.67s
====== ======= =======
[ 54.44%] ··· Running join_merge.ConcatPanels.time_f_ordered ok
[ 54.44%] ····
====== ======= =======
-- ignore_index
------ ---------------
axis True False
====== ======= =======
0 667ms 671ms
1 288ms 296ms
2 298ms 292ms
====== ======= =======
[ 54.59%] ··· Running join_merge.I8Merge.time_i8merge ok
[ 54.59%] ····
======= =======
how
------- -------
inner 1.59s
outer 1.60s
left 1.68s
right 1.59s
======= =======
[ 54.74%] ··· Running ..._merge.Join.time_join_dataframe_index_multi ok
[ 54.74%] ····
======= ========
sort
------- --------
True 52.9ms
False 42.6ms
======= ========
[ 54.89%] ··· Running ...oin_dataframe_index_shuffle_key_bigger_sort ok
[ 54.89%] ····
======= ========
sort
------- --------
True 37.8ms
False 30.7ms
======= ========
[ 55.04%] ··· Running ...time_join_dataframe_index_single_key_bigger ok
[ 55.04%] ····
======= ========
sort
------- --------
True 36.8ms
False 31.7ms
======= ========
[ 55.19%] ··· Running ....time_join_dataframe_index_single_key_small ok
[ 55.19%] ····
======= ========
sort
------- --------
True 29.9ms
False 28.1ms
======= ========
[ 55.34%] ··· Running ..._merge.JoinIndex.time_left_outer_join_index 3.80s
[ 55.49%] ··· Running ...ge.JoinNonUnique.time_join_non_unique_equal 419ms
[ 55.64%] ··· Running join_merge.Merge.time_merge_2intkey ok
[ 55.64%] ····
======= ========
sort
------- --------
True 71.4ms
False 39.6ms
======= ========
[ 55.79%] ··· Running ...rge.Merge.time_merge_dataframe_integer_2key ok
[ 55.79%] ····
======= ========
sort
------- --------
True 23.1ms
False 9.88ms
======= ========
[ 55.94%] ··· Running ...erge.Merge.time_merge_dataframe_integer_key ok
[ 55.94%] ····
======= ========
sort
------- --------
True 5.66ms
False 4.87ms
======= ========
[ 56.09%] ··· Running join_merge.MergeAsof.time_by_int 49.9ms
[ 56.24%] ··· Running join_merge.MergeAsof.time_by_object 83.8ms
[ 56.39%] ··· Running join_merge.MergeAsof.time_multiby 1.33s
[ 56.54%] ··· Running join_merge.MergeAsof.time_on_int 30.2ms
[ 56.69%] ··· Running join_merge.MergeAsof.time_on_int32 34.2ms
[ 56.84%] ··· Running join_merge.MergeCategoricals.time_merge_cat 776ms
[ 56.99%] ··· Running join_merge.MergeCategoricals.time_merge_object 1.45s
[ 57.14%] ··· Running join_merge.MergeOrdered.time_merge_ordered 148ms
[ 57.29%] ··· Running multiindex_object.Duplicated.time_duplicated 224ms
[ 57.44%] ··· Running ...object.Duplicates.time_remove_unused_levels 1.12ms
[ 57.59%] ··· Running multiindex_object.GetLoc.time_large_get_loc 421ms
[ 57.74%] ··· Running ...index_object.GetLoc.time_large_get_loc_warm 889ms
[ 57.89%] ··· Running multiindex_object.GetLoc.time_med_get_loc 4.01ms
[ 58.05%] ··· Running multiindex_object.GetLoc.time_med_get_loc_warm 18.9ms
[ 58.20%] ··· Running ...index_object.GetLoc.time_small_get_loc_warm 15.3ms
[ 58.35%] ··· Running multiindex_object.GetLoc.time_string_get_loc 703μs
[ 58.50%] ··· Running multiindex_object.Integer.time_get_indexer 342ms
[ 58.65%] ··· Running multiindex_object.Integer.time_is_monotonic 233ms
[ 58.80%] ··· Running ...index_object.Sortlevel.time_sortlevel_int64 773ms
[ 58.95%] ··· Running multiindex_object.Sortlevel.time_sortlevel_one 18.9ms
[ 59.10%] ··· Running ...iindex_object.Sortlevel.time_sortlevel_zero 19.1ms
[ 59.25%] ··· Running offset.ApplyIndex.time_apply_index ok
[ 59.25%] ····
============================================= ========
offset
--------------------------------------------- --------
<YearEnd: month=12> 171ms
<YearBegin: month=1> 6.79ms
<QuarterEnd: startingMonth=3> 192ms
<QuarterBegin: startingMonth=3> 7.12ms
<MonthEnd> 1.27ms
<MonthBegin> 1.20ms
<DateOffset: kwds={'months': 2, 'days': 2}> 2.49ms
<BusinessDay> 134ms
<SemiMonthEnd: day_of_month=15> 138ms
<SemiMonthBegin: day_of_month=15> 137ms
============================================= ========
[ 59.40%] ··· Running offset.OffestDatetimeArithmetic.time_add ok
[ 59.40%] ····
============================================= ========
offset
--------------------------------------------- --------
<Day> 104μs
<BusinessYearEnd: month=12> 201μs
<BusinessYearBegin: month=1> 122μs
<BusinessQuarterEnd: startingMonth=3> 107μs
<BusinessQuarterBegin: startingMonth=3> 126μs
<BusinessMonthEnd> 124μs
<BusinessMonthBegin> 137μs
<CustomBusinessDay> 93.8μs
<CustomBusinessDay> 89.0μs
<CustomBusinessMonthBegin> 413μs
<CustomBusinessMonthEnd> 440μs
<CustomBusinessMonthEnd> 421μs
<YearEnd: month=12> 61.9μs
<YearBegin: month=1> 60.8μs
<QuarterEnd: startingMonth=3> 168μs
<QuarterBegin: startingMonth=3> 121μs
<MonthEnd> 176μs
<MonthBegin> 120μs
<DateOffset: kwds={'months': 2, 'days': 2}> 70.0μs
<BusinessDay> 86.2μs
<SemiMonthEnd: day_of_month=15> 179μs
<SemiMonthBegin: day_of_month=15> 176μs
============================================= ========
[ 59.55%] ··· Running offset.OffestDatetimeArithmetic.time_add_10 ok
[ 59.55%] ····
============================================= ========
offset
--------------------------------------------- --------
<Day> 120μs
<BusinessYearEnd: month=12> 186μs
<BusinessYearBegin: month=1> 160μs
<BusinessQuarterEnd: startingMonth=3> 204μs
<BusinessQuarterBegin: startingMonth=3> 151μs
<BusinessMonthEnd> 139μs
<BusinessMonthBegin> 156μs
<CustomBusinessDay> 110μs
<CustomBusinessDay> 103μs
<CustomBusinessMonthBegin> 455μs
<CustomBusinessMonthEnd> 461μs
<CustomBusinessMonthEnd> 464μs
<YearEnd: month=12> 130μs
<YearBegin: month=1> 99.4μs
<QuarterEnd: startingMonth=3> 116μs
<QuarterBegin: startingMonth=3> 131μs
<MonthEnd> 192μs
<MonthBegin> 136μs
<DateOffset: kwds={'months': 2, 'days': 2}> 277μs
<BusinessDay> 94.6μs
<SemiMonthEnd: day_of_month=15> 187μs
<SemiMonthBegin: day_of_month=15> 182μs
============================================= ========
[ 59.70%] ··· Running offset.OffestDatetimeArithmetic.time_apply ok
[ 59.70%] ····
============================================= ========
offset
--------------------------------------------- --------
<Day> 98.7μs
<BusinessYearEnd: month=12> 190μs
<BusinessYearBegin: month=1> 115μs
<BusinessQuarterEnd: startingMonth=3> 96.2μs
<BusinessQuarterBegin: startingMonth=3> 116μs
<BusinessMonthEnd> 113μs
<BusinessMonthBegin> 127μs
<CustomBusinessDay> 79.7μs
<CustomBusinessDay> 81.0μs
<CustomBusinessMonthBegin> 407μs
<CustomBusinessMonthEnd> 411μs
<CustomBusinessMonthEnd> 415μs
<YearEnd: month=12> 53.5μs
<YearBegin: month=1> 51.1μs
<QuarterEnd: startingMonth=3> 95.4μs
<QuarterBegin: startingMonth=3> 111μs
<MonthEnd> 165μs
<MonthBegin> 110μs
<DateOffset: kwds={'months': 2, 'days': 2}> 60.4μs
<BusinessDay> 76.1μs
<SemiMonthEnd: day_of_month=15> 167μs
<SemiMonthBegin: day_of_month=15> 166μs
============================================= ========
[ 59.85%] ··· Running ...OffestDatetimeArithmetic.time_apply_np_dt64 ok
[ 59.85%] ····
============================================= ========
offset
--------------------------------------------- --------
<Day> 104μs
<BusinessYearEnd: month=12> 198μs
<BusinessYearBegin: month=1> 120μs
<BusinessQuarterEnd: startingMonth=3> 104μs
<BusinessQuarterBegin: startingMonth=3> 121μs
<BusinessMonthEnd> 120μs
<BusinessMonthBegin> 134μs
<CustomBusinessDay> 85.6μs
<CustomBusinessDay> 86.6μs
<CustomBusinessMonthBegin> 410μs
<CustomBusinessMonthEnd> 417μs
<CustomBusinessMonthEnd> 419μs
<YearEnd: month=12> 59.6μs
<YearBegin: month=1> 58.5μs
<QuarterEnd: startingMonth=3> 103μs
<QuarterBegin: startingMonth=3> 119μs
<MonthEnd> 173μs
<MonthBegin> 118μs
<DateOffset: kwds={'months': 2, 'days': 2}> 68.8μs
<BusinessDay> 83.1μs
<SemiMonthEnd: day_of_month=15> 176μs
<SemiMonthBegin: day_of_month=15> 173μs
============================================= ========
[ 60.00%] ··· Running offset.OffestDatetimeArithmetic.time_subtract ok
[ 60.00%] ····
============================================= ========
offset
--------------------------------------------- --------
<Day> 126μs
<BusinessYearEnd: month=12> 165μs
<BusinessYearBegin: month=1> 164μs
<BusinessQuarterEnd: startingMonth=3> 116μs
<BusinessQuarterBegin: startingMonth=3> 135μs
<BusinessMonthEnd> 144μs
<BusinessMonthBegin> 145μs
<CustomBusinessDay> 112μs
<CustomBusinessDay> 104μs
<CustomBusinessMonthBegin> 374μs
<CustomBusinessMonthEnd> 566μs
<CustomBusinessMonthEnd> 569μs
<YearEnd: month=12> 104μs
<YearBegin: month=1> 103μs
<QuarterEnd: startingMonth=3> 113μs
<QuarterBegin: startingMonth=3> 130μs
<MonthEnd> 196μs
<MonthBegin> 139μs
<DateOffset: kwds={'months': 2, 'days': 2}> 128μs
<BusinessDay> 86.1μs
<SemiMonthEnd: day_of_month=15> 188μs
<SemiMonthBegin: day_of_month=15> 189μs
============================================= ========
[ 60.15%] ··· Running ...t.OffestDatetimeArithmetic.time_subtract_10 ok
[ 60.15%] ····
============================================= =======
offset
--------------------------------------------- -------
<Day> 139μs
<BusinessYearEnd: month=12> 196μs
<BusinessYearBegin: month=1> 197μs
<BusinessQuarterEnd: startingMonth=3> 125μs
<BusinessQuarterBegin: startingMonth=3> 145μs
<BusinessMonthEnd> 158μs
<BusinessMonthBegin> 159μs
<CustomBusinessDay> 132μs
<CustomBusinessDay> 115μs
<CustomBusinessMonthBegin> 389μs
<CustomBusinessMonthEnd> 479μs
<CustomBusinessMonthEnd> 486μs
<YearEnd: month=12> 155μs
<YearBegin: month=1> 134μs
<QuarterEnd: startingMonth=3> 125μs
<QuarterBegin: startingMonth=3> 140μs
<MonthEnd> 209μs
<MonthBegin> 152μs
<DateOffset: kwds={'months': 2, 'days': 2}> 512μs
<BusinessDay> 114μs
<SemiMonthEnd: day_of_month=15> 197μs
<SemiMonthBegin: day_of_month=15> 193μs
============================================= =======
[ 60.30%] ··· Running ...fsetDatetimeIndexArithmetic.time_add_offset ok
[ 60.30%] ····
============================================= ========
offset
--------------------------------------------- --------
<Day> 687μs
<BusinessYearEnd: month=12> 199ms
<BusinessYearBegin: month=1> 110ms
<BusinessQuarterEnd: startingMonth=3> 93.2ms
<BusinessQuarterBegin: startingMonth=3> 111ms
<BusinessMonthEnd> 108ms
<BusinessMonthBegin> 123ms
<CustomBusinessDay> 76.1ms
<CustomBusinessDay> 76.7ms
<CustomBusinessMonthBegin> 397ms
<CustomBusinessMonthEnd> 404ms
<CustomBusinessMonthEnd> 404ms
<YearEnd: month=12> 21.1ms
<YearBegin: month=1> 3.09ms
<QuarterEnd: startingMonth=3> 22.9ms
<QuarterBegin: startingMonth=3> 3.11ms
<MonthEnd> 665μs
<MonthBegin> 658μs
<DateOffset: kwds={'months': 2, 'days': 2}> 1.33ms
<BusinessDay> 16.8ms
<SemiMonthEnd: day_of_month=15> 17.9ms
<SemiMonthBegin: day_of_month=15> 17.4ms
============================================= ========
[ 60.45%] ··· Running offset.OffsetSeriesArithmetic.time_add_offset ok
[ 60.45%] ····
============================================= =======
offset
--------------------------------------------- -------
<Day> 419ms
<BusinessYearEnd: month=12> 615ms
<BusinessYearBegin: month=1> 530ms
<BusinessQuarterEnd: startingMonth=3> 515ms
<BusinessQuarterBegin: startingMonth=3> 533ms
<BusinessMonthEnd> 528ms
<BusinessMonthBegin> 544ms
<CustomBusinessDay> 499ms
<CustomBusinessDay> 495ms
<CustomBusinessMonthBegin> 816ms
<CustomBusinessMonthEnd> 826ms
<CustomBusinessMonthEnd> 824ms
<YearEnd: month=12> 443ms
<YearBegin: month=1> 422ms
<QuarterEnd: startingMonth=3> 442ms
<QuarterBegin: startingMonth=3> 419ms
<MonthEnd> 414ms
<MonthBegin> 415ms
<DateOffset: kwds={'months': 2, 'days': 2}> 418ms
<BusinessDay> 433ms
<SemiMonthEnd: day_of_month=15> 438ms
<SemiMonthBegin: day_of_month=15> 436ms
============================================= =======
[ 60.60%] ··· Running offset.OnOffset.time_on_offset ok
[ 60.60%] ····
============================================= ========
offset
--------------------------------------------- --------
<Day> 28.3μs
<BusinessYearEnd: month=12> 6.95ms
<BusinessYearBegin: month=1> 5.24ms
<BusinessQuarterEnd: startingMonth=3> 5.61ms
<BusinessQuarterBegin: startingMonth=3> 4.63ms
<BusinessMonthEnd> 5.31ms
<BusinessMonthBegin> 35.1μs
<CustomBusinessDay> 405μs
<CustomBusinessDay> 407μs
<CustomBusinessMonthBegin> 16.1ms
<CustomBusinessMonthEnd> 16.2ms
<CustomBusinessMonthEnd> 16.6ms
<YearEnd: month=12> 35.0μs
<YearBegin: month=1> 21.2μs
<QuarterEnd: startingMonth=3> 249μs
<QuarterBegin: startingMonth=3> 4.48ms
<MonthEnd> 34.2μs
<MonthBegin> 18.9μs
<DateOffset: kwds={'months': 2, 'days': 2}> 21.0μs
<BusinessDay> 20.5μs
<SemiMonthEnd: day_of_month=15> 38.8μs
<SemiMonthBegin: day_of_month=15> 23.3μs
============================================= ========
[ 60.75%] ··· Running panel_ctor.DifferentIndexes.time_from_dict 384ms
[ 60.90%] ··· Running panel_ctor.SameIndexes.time_from_dict 32.4ms
[ 61.05%] ··· Running panel_ctor.TwoIndexes.time_from_dict 104ms
[ 61.20%] ··· Running panel_methods.PanelMethods.time_pct_change ok
[ 61.20%] ····
======= =======
axis
------- -------
items 1.56s
major 1.45s
minor 1.42s
======= =======
[ 61.35%] ··· Running panel_methods.PanelMethods.time_shift ok
[ 61.35%] ····
======= =======
axis
------- -------
items 476μs
major 384μs
minor 427μs
======= =======
[ 61.50%] ··· Running period.Algorithms.time_drop_duplicates ok
[ 61.50%] ····
======== ========
typ
-------- --------
index 483μs
series 7.28ms
======== ========
[ 61.65%] ··· Running period.Algorithms.time_value_counts ok
[ 61.65%] ····
======== ========
typ
-------- --------
index 1.39ms
series 8.01ms
======== ========
[ 61.80%] ··· Running ...ramePeriodColumn.time_setitem_period_column 78.9ms
[ 61.95%] ··· Running period.Indexing.time_align 2.62ms
[ 62.11%] ··· Running period.Indexing.time_get_loc 207μs
[ 62.26%] ··· Running period.Indexing.time_intersection 561μs
[ 62.41%] ··· Running period.Indexing.time_series_loc 416μs
[ 62.56%] ··· Running period.Indexing.time_shallow_copy 52.7μs
[ 62.71%] ··· Running period.Indexing.time_shape 13.8μs
[ 62.86%] ··· Running ...PeriodIndexConstructor.time_from_date_range ok
[ 62.86%] ····
====== =======
freq
------ -------
D 368μs
====== =======
[ 63.01%] ··· Running ...PeriodIndexConstructor.time_from_pydatetime ok
[ 63.01%] ····
====== ========
freq
------ --------
D 15.3ms
====== ========
[ 63.16%] ··· Running period.PeriodProperties.time_property ok
[ 63.16%] ····
====== ============== ========
freq attr
------ -------------- --------
M year 16.9μs
M month 17.5μs
M day 17.2μs
M hour 17.7μs
M minute 18.0μs
M second 16.5μs
M is_leap_year 18.1μs
M quarter 16.7μs
M qyear 17.0μs
M week 18.2μs
M daysinmonth 17.4μs
M dayofweek 16.7μs
M dayofyear 17.6μs
M start_time 246μs
M end_time 283μs
min year 17.8μs
min month 17.9μs
min day 18.2μs
min hour 17.5μs
min minute 17.5μs
min second 17.4μs
min is_leap_year 19.0μs
min quarter 18.8μs
min qyear 17.1μs
min week 17.9μs
min daysinmonth 18.0μs
min dayofweek 18.9μs
min dayofyear 18.1μs
min start_time 238μs
min end_time 274μs
====== ============== ========
[ 63.31%] ··· Running period.PeriodUnaryMethods.time_asfreq ok
[ 63.31%] ····
====== =======
freq
------ -------
M 160μs
min 167μs
====== =======
[ 63.46%] ··· Running period.PeriodUnaryMethods.time_now ok
[ 63.46%] ····
====== =======
freq
------ -------
M 124μs
min 227μs
====== =======
[ 63.61%] ··· Running period.PeriodUnaryMethods.time_to_timestamp ok
[ 63.61%] ····
====== =======
freq
------ -------
M 238μs
min 238μs
====== =======
[ 63.76%] ··· Running plotting.Misc.time_plot_andrews_curves 1.87s
[ 63.91%] ··· Running plotting.Plotting.time_frame_plot 413ms
[ 64.06%] ··· Running plotting.Plotting.time_series_plot 420ms
[ 64.21%] ··· Running ...ting.TimeseriesPlotting.time_plot_irregular 128ms
[ 64.36%] ··· Running plotting.TimeseriesPlotting.time_plot_regular 390ms
[ 64.51%] ··· Running ...TimeseriesPlotting.time_plot_regular_compat 125ms
[ 64.66%] ··· Running ...ex.Align.time_align_series_irregular_string 567ms
[ 64.81%] ··· Running reindex.DropDuplicates.time_frame_drop_dups ok
[ 64.81%] ····
========= ========
inplace
--------- --------
True 24.2ms
False 24.3ms
========= ========
[ 64.96%] ··· Running ...ex.DropDuplicates.time_frame_drop_dups_bool ok
[ 64.96%] ····
========= ========
inplace
--------- --------
True 6.43ms
False 6.63ms
========= ========
[ 65.11%] ··· Running ...dex.DropDuplicates.time_frame_drop_dups_int ok
[ 65.11%] ····
========= ========
inplace
--------- --------
True 61.0ms
False 57.4ms
========= ========
[ 65.26%] ··· Running reindex.DropDuplicates.time_frame_drop_dups_na ok
[ 65.26%] ····
========= ========
inplace
--------- --------
True 27.6ms
False 27.2ms
========= ========
[ 65.41%] ··· Running ...ex.DropDuplicates.time_series_drop_dups_int ok
[ 65.41%] ····
========= ========
inplace
--------- --------
True 1.43ms
False 1.37ms
========= ========
[ 65.56%] ··· Running ...DropDuplicates.time_series_drop_dups_string ok
[ 65.56%] ····
========= ========
inplace
--------- --------
True 1.57ms
False 1.61ms
========= ========
[ 65.71%] ··· Running reindex.Fillna.time_float_32 ok
[ 65.71%] ····
========== =======
method
---------- -------
pad 815μs
backfill 812μs
========== =======
[ 65.86%] ··· Running reindex.Fillna.time_reindexed ok
[ 65.86%] ····
========== ========
method
---------- --------
pad 1.20ms
backfill 1.17ms
========== ========
[ 66.02%] ··· Running reindex.LevelAlign.time_align_level 27.8ms
[ 66.17%] ··· Running reindex.LevelAlign.time_reindex_level 27.0ms
[ 66.32%] ··· Running reindex.LibFastZip.time_lib_fast_zip 27.1ms
[ 66.47%] ··· Running reindex.LibFastZip.time_lib_fast_zip_fillna 30.1ms
[ 66.62%] ··· Running reindex.Reindex.time_reindex_columns 2.01ms
[ 66.77%] ··· Running reindex.Reindex.time_reindex_dates 1.55ms
[ 66.92%] ··· Running reindex.Reindex.time_reindex_multiindex 545ms
[ 67.07%] ··· Running reindex.ReindexMethod.time_reindex_method ok
[ 67.07%] ····
========== ========
method
---------- --------
pad 5.53ms
backfill 5.75ms
========== ========
[ 67.22%] ··· Running replace.Convert.time_replace ok
[ 67.22%] ····
============ =========== ===========
-- replace_data
------------ -----------------------
contructor Timestamp Timedelta
============ =========== ===========
DataFrame 437ms 429ms
Series 364ms 365ms
============ =========== ===========
[ 67.37%] ··· Running replace.FillNa.time_fillna ok
[ 67.37%] ····
========= ========
inplace
--------- --------
True 6.55ms
False 15.7ms
========= ========
[ 67.52%] ··· Running replace.FillNa.time_replace ok
[ 67.52%] ····
========= ========
inplace
--------- --------
True 6.50ms
False 23.6ms
========= ========
[ 67.67%] ··· Running replace.ReplaceDict.time_replace_series ok
[ 67.67%] ····
========= =======
inplace
--------- -------
True 7.82s
False 7.72s
========= =======
[ 67.82%] ··· Running reshape.Melt.time_melt_dataframe 6.23ms
[ 67.97%] ··· Running reshape.Pivot.time_reshape_pivot_time_series 329ms
[ 68.12%] ··· Running reshape.PivotTable.time_pivot_table 40.6ms
[ 68.27%] ··· Running reshape.SimpleReshape.time_stack 5.75ms
[ 68.42%] ··· Running reshape.SimpleReshape.time_unstack 6.72ms
[ 68.57%] ··· Running reshape.SparseIndex.time_unstack 2.93ms
[ 68.72%] ··· Running reshape.Unstack.time_full_product 196ms
[ 68.87%] ··· Running reshape.Unstack.time_without_last_row 339ms
[ 69.02%] ··· Running reshape.WideToLong.time_wide_to_long_big 273ms
[ 69.17%] ··· Running rolling.Methods.time_rolling ok
[ 69.17%] ····
============ ======== ======= ======== ========
contructor window dtype method
------------ -------- ------- -------- --------
DataFrame 10 int median 60.2ms
DataFrame 10 int mean 6.38ms
DataFrame 10 int max 6.40ms
DataFrame 10 int min 6.35ms
DataFrame 10 int std 7.49ms
DataFrame 10 int count 9.31ms
DataFrame 10 int skew 6.51ms
DataFrame 10 int kurt 6.57ms
DataFrame 10 int sum 6.08ms
DataFrame 10 int corr 57.3s
DataFrame 10 int cov 56.8s
DataFrame 10 float median 63.0ms
DataFrame 10 float mean 5.81ms
DataFrame 10 float max 6.81ms
DataFrame 10 float min 6.89ms
DataFrame 10 float std 8.57ms
DataFrame 10 float count 9.15ms
DataFrame 10 float skew 11.0ms
DataFrame 10 float kurt 10.8ms
DataFrame 10 float sum 5.36ms
DataFrame 10 float corr 57.0s
DataFrame 10 float cov 56.7s
DataFrame 1000 int median 89.4ms
DataFrame 1000 int mean 6.35ms
DataFrame 1000 int max 6.44ms
DataFrame 1000 int min 6.36ms
DataFrame 1000 int std 7.56ms
DataFrame 1000 int count 9.37ms
DataFrame 1000 int skew 6.50ms
DataFrame 1000 int kurt 6.56ms
DataFrame 1000 int sum 6.02ms
DataFrame 1000 int corr 57.4s
DataFrame 1000 int cov 56.9s
DataFrame 1000 float median 118ms
DataFrame 1000 float mean 5.82ms
DataFrame 1000 float max 6.82ms
DataFrame 1000 float min 6.90ms
DataFrame 1000 float std 8.55ms
DataFrame 1000 float count 9.20ms
DataFrame 1000 float skew 10.9ms
DataFrame 1000 float kurt 11.0ms
DataFrame 1000 float sum 5.41ms
DataFrame 1000 float corr 57.2s
DataFrame 1000 float cov 57.0s
Series 10 int median 63.2ms
Series 10 int mean 9.56ms
Series 10 int max 9.51ms
Series 10 int min 9.52ms
Series 10 int std 11.0ms
Series 10 int count 18.2ms
Series 10 int skew 9.68ms
Series 10 int kurt 9.91ms
Series 10 int sum 9.15ms
Series 10 int corr 515ms
Series 10 int cov 487ms
Series 10 float median 66.3ms
Series 10 float mean 9.39ms
Series 10 float max 10.4ms
Series 10 float min 10.7ms
Series 10 float std 12.7ms
Series 10 float count 18.0ms
Series 10 float skew 14.7ms
Series 10 float kurt 14.2ms
Series 10 float sum 8.91ms
Series 10 float corr 518ms
Series 10 float cov 489ms
Series 1000 int median 93.4ms
Series 1000 int mean 9.56ms
Series 1000 int max 9.59ms
Series 1000 int min 9.59ms
Series 1000 int std 11.1ms
Series 1000 int count 18.2ms
Series 1000 int skew 9.60ms
Series 1000 int kurt 9.66ms
Series 1000 int sum 9.27ms
Series 1000 int corr 514ms
Series 1000 int cov 490ms
Series 1000 float median 121ms
Series 1000 float mean 9.38ms
Series 1000 float max 10.4ms
Series 1000 float min 10.4ms
Series 1000 float std 12.6ms
Series 1000 float count 18.0ms
Series 1000 float skew 14.5ms
Series 1000 float kurt 14.2ms
Series 1000 float sum 8.93ms
Series 1000 float corr 515ms
Series 1000 float cov 483ms
============ ======== ======= ======== ========
[ 69.32%] ··· Running rolling.Quantile.time_quantile ok
[ 69.32%] ····
============ ======== ======= ======== ======= ========
-- percentile
----------------------------- -------------------------
contructor window dtype 0 0.5 1
============ ======== ======= ======== ======= ========
DataFrame 10 int 6.46ms 303ms 6.40ms
DataFrame 10 float 6.89ms 315ms 6.85ms
DataFrame 1000 int 6.37ms 614ms 6.46ms
DataFrame 1000 float 6.94ms 711ms 6.83ms
Series 10 int 9.49ms 308ms 9.64ms
Series 10 float 10.6ms 304ms 10.3ms
Series 1000 int 9.51ms 621ms 9.57ms
Series 1000 float 10.3ms 674ms 10.3ms
============ ======== ======= ======== ======= ========
[ 69.47%] ··· Running series_methods.Clip.time_clip 291μs
[ 69.62%] ··· Running series_methods.Dir.time_dir_strings 30.7ms
[ 69.77%] ··· Running series_methods.Dropna.time_dropna ok
[ 69.77%] ····
========== ========
dtype
---------- --------
int 4.21ms
datetime 19.4ms
========== ========
[ 69.92%] ··· Running series_methods.IsIn.time_isin ok
[ 69.92%] ····
======== ========
dtype
-------- --------
int64 2.59ms
object 4.92ms
======== ========
[ 70.08%] ··· Running series_methods.Map.time_map ok
[ 70.08%] ····
======== ========
m
-------- --------
dict 3.48ms
Series 2.33ms
======== ========
[ 70.23%] ··· Running series_methods.NSort.time_nlargest ok
[ 70.23%] ····
======= ========
keep
------- --------
last 5.36ms
first 5.63ms
======= ========
[ 70.38%] ··· Running series_methods.NSort.time_nsmallest ok
[ 70.38%] ····
======= ========
keep
------- --------
last 4.37ms
first 5.01ms
======= ========
[ 70.53%] ··· Running ..._methods.SeriesConstructor.time_constructor ok
[ 70.53%] ····
====== =======
data
------ -------
None 652μs
dict 1.56s
====== =======
[ 70.68%] ··· Running series_methods.ValueCounts.time_value_counts ok
[ 70.68%] ····
======== ========
dtype
-------- --------
int 4.53ms
float 7.80ms
object 24.6ms
======== ========
[ 70.83%] ··· Running sparse.Arithmetic.time_add ok
[ 70.83%] ····
================== ======== ========
-- fill_value
------------------ -----------------
dense_proportion 0 nan
================== ======== ========
0.1 82.2ms 69.5ms
0.01 8.58ms 68.4ms
================== ======== ========
[ 70.98%] ··· Running sparse.Arithmetic.time_divide ok
[ 70.98%] ····
================== ======== ========
-- fill_value
------------------ -----------------
dense_proportion 0 nan
================== ======== ========
0.1 87.2ms 68.0ms
0.01 9.06ms 67.8ms
================== ======== ========
[ 71.13%] ··· Running sparse.Arithmetic.time_intersect ok
[ 71.13%] ····
================== ======== =======
-- fill_value
------------------ ----------------
dense_proportion 0 nan
================== ======== =======
0.1 5.63ms 340ms
0.01 357μs 366ms
================== ======== =======
[ 71.28%] ··· Running sparse.Arithmetic.time_make_union ok
[ 71.28%] ····
================== ======== =======
-- fill_value
------------------ ----------------
dense_proportion 0 nan
================== ======== =======
0.1 79.5ms 728ms
0.01 8.20ms 715ms
================== ======== =======
[ 71.43%] ··· Running sparse.ArithmeticBlock.time_addition ok
[ 71.43%] ····
============ ========
fill_value
------------ --------
nan 8.24ms
0 7.98ms
============ ========
[ 71.58%] ··· Running sparse.ArithmeticBlock.time_division ok
[ 71.58%] ····
============ ========
fill_value
------------ --------
nan 8.41ms
0 7.90ms
============ ========
[ 71.73%] ··· Running sparse.ArithmeticBlock.time_intersect ok
[ 71.73%] ····
============ ========
fill_value
------------ --------
nan 3.77ms
0 3.75ms
============ ========
[ 71.88%] ··· Running sparse.ArithmeticBlock.time_make_union ok
[ 71.88%] ····
============ ========
fill_value
------------ --------
nan 7.93ms
0 8.14ms
============ ========
[ 72.03%] ··· Running sparse.FromCoo.time_sparse_series_from_coo 3.47ms
[ 72.18%] ··· Running ...se.SparseArrayConstructor.time_sparse_array ok
[ 72.18%] ····
================== ============ ======================== ========
dense_proportion fill_value dtype
------------------ ------------ ------------------------ --------
0.1 0 <type 'numpy.int64'> 47.9ms
0.1 0 <type 'numpy.float64'> 45.7ms
0.1 0 <type 'object'> 106ms
0.1 nan <type 'numpy.int64'> 376ms
0.1 nan <type 'numpy.float64'> 47.0ms
0.1 nan <type 'object'> 122ms
0.01 0 <type 'numpy.int64'> 8.95ms
0.01 0 <type 'numpy.float64'> 8.94ms
0.01 0 <type 'object'> 68.0ms
0.01 nan <type 'numpy.int64'> 357ms
0.01 nan <type 'numpy.float64'> 9.63ms
0.01 nan <type 'object'> 65.1ms
================== ============ ======================== ========
[ 72.33%] ··· Running ...SparseDataFrameConstructor.time_constructor 6.42s
[ 72.48%] ··· Running ...e.SparseDataFrameConstructor.time_from_dict 246ms
[ 72.63%] ··· Running ....SparseDataFrameConstructor.time_from_scipy 566ms
[ 72.78%] ··· Running ...se.SparseSeriesToFrame.time_series_to_frame 285ms
[ 72.93%] ··· Running sparse.ToCoo.time_sparse_series_to_coo 44.3ms
[ 73.08%] ··· Running stat_ops.Correlation.time_corr ok
[ 73.08%] ····
========== ========
method
---------- --------
spearman 117ms
kendall 696ms
pearson 5.82ms
========== ========
[ 73.23%] ··· Running stat_ops.FrameMultiIndexOps.time_op ok
[ 73.23%] ····
======== ======== ========
level op
-------- -------- --------
0 mean 8.40ms
0 sum 8.41ms
0 median 22.2ms
0 std 12.0ms
0 skew 75.3ms
0 kurt 135ms
0 mad 507ms
0 prod 8.55ms
0 sem 435ms
0 var 11.3ms
1 mean 8.44ms
1 sum 8.56ms
1 median 24.9ms
1 std 11.9ms
1 skew 139ms
1 kurt 214ms
1 mad 683ms
1 prod 8.60ms
1 sem 434ms
1 var 11.6ms
[0, 1] mean 17.4ms
[0, 1] sum 17.2ms
[0, 1] median 32.3ms
[0, 1] std 20.7ms
[0, 1] skew 953ms
[0, 1] kurt 1.04s
[0, 1] mad 2.66s
[0, 1] prod 17.3ms
[0, 1] sem 443ms
[0, 1] var 20.4ms
======== ======== ========
[ 73.38%] ··· Running stat_ops.FrameOps.time_op ok
[ 73.38%] ····
======== ======= ====== ======== ========
-- use_bottleneck
----------------------- -----------------
op dtype axis True False
======== ======= ====== ======== ========
mean float 0 1.21ms 12.0ms
mean float 1 2.08ms 12.3ms
mean int 0 1.22ms 10.2ms
mean int 1 2.06ms 11.3ms
sum float 0 11.6ms 11.7ms
sum float 1 11.9ms 11.6ms
sum int 0 7.35ms 7.44ms
sum int 1 8.31ms 8.42ms
median float 0 6.88ms 23.7ms
median float 1 6.07ms 7.46s
median int 0 4.47ms 24.6ms
median int 1 5.61ms 7.44s
std float 0 1.93ms 23.4ms
std float 1 4.49ms 26.1ms
std int 0 3.42ms 24.5ms
std int 1 6.13ms 26.0ms
skew float 0 33.5ms 33.3ms
skew float 1 53.8ms 53.4ms
skew int 0 34.7ms 34.9ms
skew int 1 53.0ms 52.5ms
kurt float 0 33.6ms 32.7ms
kurt float 1 44.7ms 45.0ms
kurt int 0 34.5ms 34.0ms
kurt int 1 46.6ms 46.9ms
mad float 0 433ms 448ms
mad float 1 434ms 455ms
mad int 0 433ms 455ms
mad int 1 431ms 458ms
prod float 0 9.99ms 10.0ms
prod float 1 9.86ms 10.1ms
prod int 0 3.82ms 3.85ms
prod int 1 5.35ms 5.38ms
sem float 0 5.63ms 48.5ms
sem float 1 12.0ms 55.5ms
sem int 0 9.55ms 51.1ms
sem int 1 15.4ms 58.1ms
var float 0 1.93ms 23.4ms
var float 1 3.30ms 25.9ms
var int 0 3.41ms 24.3ms
var int 1 4.76ms 25.5ms
======== ======= ====== ======== ========
[ 73.53%] ··· Running stat_ops.Rank.time_average_old ok
[ 73.53%] ····
============= ======= =======
-- pct
------------- ---------------
constructor True False
============= ======= =======
DataFrame 435ms 438ms
Series 435ms 434ms
============= ======= =======
[ 73.68%] ··· Running stat_ops.Rank.time_rank ok
[ 73.68%] ····
============= ======== ========
-- pct
------------- -----------------
constructor True False
============= ======== ========
DataFrame 19.0ms 18.8ms
Series 19.2ms 18.1ms
============= ======== ========
[ 73.83%] ··· Running stat_ops.SeriesMultiIndexOps.time_op ok
[ 73.83%] ····
======== ======== ========
level op
-------- -------- --------
0 mean 21.8ms
0 sum 20.9ms
0 median 24.5ms
0 std 22.7ms
0 skew 33.3ms
0 kurt 32.7ms
0 mad 462ms
0 prod 21.3ms
0 sem 438ms
0 var 22.4ms
1 mean 21.2ms
1 sum 21.2ms
1 median 24.7ms
1 std 23.0ms
1 skew 76.1ms
1 kurt 78.1ms
1 mad 567ms
1 prod 21.2ms
1 sem 444ms
1 var 22.6ms
[0, 1] mean 15.6ms
[0, 1] sum 15.2ms
[0, 1] median 19.1ms
[0, 1] std 16.7ms
[0, 1] skew 523ms
[0, 1] kurt 524ms
[0, 1] mad 1.61s
[0, 1] prod 15.2ms
[0, 1] sem 435ms
[0, 1] var 16.6ms
======== ======== ========
[ 73.98%] ··· Running stat_ops.SeriesOps.time_op ok
[ 73.98%] ····
======== ======= ======== ========
-- use_bottleneck
---------------- -----------------
op dtype True False
======== ======= ======== ========
mean float 398μs 2.07ms
mean int 399μs 2.20ms
sum float 2.00ms 2.01ms
sum int 2.11ms 2.20ms
median float 2.24ms 6.27ms
median int 1.47ms 6.37ms
std float 604μs 3.19ms
std int 1.05ms 3.90ms
skew float 4.81ms 4.84ms
skew int 5.37ms 5.57ms
kurt float 4.74ms 4.61ms
kurt int 5.14ms 5.23ms
mad float 426ms 437ms
mad int 431ms 428ms
prod float 1.92ms 1.98ms
prod int 1.05ms 1.04ms
sem float 1.55ms 6.71ms
sem int 2.74ms 8.11ms
var float 601μs 3.28ms
var int 947μs 3.77ms
======== ======= ======== ========
[ 74.14%] ··· Running strings.Contains.time_contains ok
[ 74.14%] ····
======= ========
regex
------- --------
True 105ms
False 38.5ms
======= ========
[ 74.29%] ··· Running strings.Dummies.time_get_dummies 7.78s
[ 74.44%] ··· Running strings.Encode.time_encode_decode 711μs
[ 74.59%] ··· Running strings.Methods.time_cat 24.1ms
[ 74.74%] ··· Running strings.Methods.time_center 85.5ms
[ 74.89%] ··· Running strings.Methods.time_count 121ms
[ 75.04%] ··· Running strings.Methods.time_endswith 67.9ms
[ 75.19%] ··· Running strings.Methods.time_extract 479ms
[ 75.19%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/strings.py:26: FutureWarning: currently extract(expand=None) means expand=False (return Index/Series/DataFrame) but in a future version of pandas this will be changed to expand=True (return DataFrame)
self.s.str.extract('(\\w*)A(\\w*)')
[ 75.34%] ··· Running strings.Methods.time_findall 176ms
[ 75.49%] ··· Running strings.Methods.time_get 57.5ms
[ 75.64%] ··· Running strings.Methods.time_len 40.9ms
[ 75.79%] ··· Running strings.Methods.time_lower 56.7ms
[ 75.94%] ··· Running strings.Methods.time_lstrip 55.2ms
[ 76.09%] ··· Running strings.Methods.time_match 141ms
[ 76.24%] ··· Running strings.Methods.time_pad 83.0ms
[ 76.39%] ··· Running strings.Methods.time_replace 72.6ms
[ 76.54%] ··· Running strings.Methods.time_rstrip 60.1ms
[ 76.69%] ··· Running strings.Methods.time_slice 52.2ms
[ 76.84%] ··· Running strings.Methods.time_startswith 68.4ms
[ 76.99%] ··· Running strings.Methods.time_strip 57.2ms
[ 77.14%] ··· Running strings.Methods.time_title 59.1ms
[ 77.29%] ··· Running strings.Methods.time_upper 56.6ms
[ 77.44%] ··· Running strings.Repeat.time_repeat ok
[ 77.44%] ····
========= ========
repeats
--------- --------
int 81.9ms
array 70.1ms
========= ========
[ 77.59%] ··· Running strings.Slice.time_vector_slice 284ms
[ 77.74%] ··· Running strings.Split.time_split ok
[ 77.74%] ····
======== =======
expand
-------- -------
True 771ms
False 353ms
======== =======
[ 77.89%] ··· Running timedelta.DatetimeAccessor.time_dt_accessor 167μs
[ 78.05%] ··· Running ...imeAccessor.time_timedelta_dt_accessor_days 2.17s
[ 78.20%] ··· Running ...sor.time_timedelta_dt_accessor_microseconds 2.14s
[ 78.35%] ··· Running ...ssor.time_timedelta_dt_accessor_nanoseconds 2.11s
[ 78.50%] ··· Running ...Accessor.time_timedelta_dt_accessor_seconds 2.17s
[ 78.65%] ··· Running ...a.TimedeltaConstructor.time_from_components 63.1μs
[ 78.80%] ··· Running ...ltaConstructor.time_from_datetime_timedelta 35.5μs
[ 78.95%] ··· Running timedelta.TimedeltaConstructor.time_from_int 31.7μs
[ 79.10%] ··· Running ...a.TimedeltaConstructor.time_from_iso_format failed
[ 79.10%] ····· Traceback (most recent call last):
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 818, in <module>
commands[mode](args)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 795, in main_run
result = benchmark.do_run()
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 349, in do_run
return self.run(*self._current_params)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 424, in run
samples, number = self.benchmark_timing(timer, repeat, warmup_time, number=number)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 471, in benchmark_timing
timing = timer.timeit(number)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/timeit.py", line 202, in timeit
timing = self.inner(it, self.timer)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/timeit.py", line 100, in inner
_func()
File "/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timedelta.py", line 32, in time_from_iso_format
Timedelta('P4DT12H30M5S')
File "pandas/_libs/tslib.pyx", line 2588, in pandas._libs.tslib.Timedelta.__new__
File "pandas/_libs/tslibs/timedeltas.pyx", line 159, in pandas._libs.tslibs.timedeltas.parse_timedelta_string
File "pandas/_libs/tslibs/timedeltas.pyx", line 296, in pandas._libs.tslibs.timedeltas.timedelta_from_spec
ValueError: invalid abbreviation: P
[ 79.25%] ··· Running ...elta.TimedeltaConstructor.time_from_missing 18.9μs
[ 79.40%] ··· Running ...TimedeltaConstructor.time_from_np_timedelta 27.3μs
[ 79.55%] ··· Running ...delta.TimedeltaConstructor.time_from_string 41.3μs
[ 79.70%] ··· Running timedelta.TimedeltaConstructor.time_from_unit 31.5μs
[ 79.85%] ··· Running timedelta.TimedeltaOps.time_add_td_ts 21.0ms
[ 80.00%] ··· Running ...lta.TimedeltaProperties.time_timedelta_days 10.7μs
[ 80.15%] ··· Running ...deltaProperties.time_timedelta_microseconds 10.6μs
[ 80.30%] ··· Running ...edeltaProperties.time_timedelta_nanoseconds 10.6μs
[ 80.45%] ··· Running ....TimedeltaProperties.time_timedelta_seconds 10.9μs
[ 80.60%] ··· Running timedelta.ToTimedelta.time_convert_coerce 399ms
[ 80.75%] ··· Running timedelta.ToTimedelta.time_convert_ignore 397ms
[ 80.90%] ··· Running timedelta.ToTimedelta.time_convert_int 497μs
[ 81.05%] ··· Running timedelta.ToTimedelta.time_convert_string 156ms
[ 81.20%] ··· Running ...lta.ToTimedelta.time_convert_string_seconds 143ms
[ 81.35%] ··· Running timeseries.AsOf.time_asof 14.3ms
[ 81.50%] ··· Running timeseries.AsOf.time_asof_nan 14.1ms
[ 81.65%] ··· Running timeseries.AsOf.time_asof_nan_single 6.69ms
[ 81.80%] ··· Running timeseries.AsOf.time_asof_single 247μs
[ 81.95%] ··· Running timeseries.AsOf.time_asof_single_early 224μs
[ 82.11%] ··· Running timeseries.AsOfDataFrame.time_asof 106ms
[ 82.26%] ··· Running timeseries.AsOfDataFrame.time_asof_nan 123ms
[ 82.41%] ··· Running timeseries.AsOfDataFrame.time_asof_nan_single 34.4ms
[ 82.56%] ··· Running timeseries.AsOfDataFrame.time_asof_single 32.7ms
[ 82.71%] ··· Running ...series.AsOfDataFrame.time_asof_single_early 564μs
[ 82.86%] ··· Running timeseries.DatetimeAccessor.time_dt_accessor 143μs
[ 83.01%] ··· Running ...DatetimeAccessor.time_dt_accessor_normalize 15.4ms
[ 83.16%] ··· Running timeseries.DatetimeIndex.time_add_timedelta 5.03ms
[ 83.31%] ··· Running timeseries.DatetimeIndex.time_dti_factorize 28.2ms
[ 83.46%] ··· Running timeseries.DatetimeIndex.time_dti_time 18.6ms
[ 83.61%] ··· Running timeseries.DatetimeIndex.time_dti_tz_factorize 27.4ms
[ 83.76%] ··· Running timeseries.DatetimeIndex.time_infer_dst 4.82ms
[ 83.76%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timeseries.py:94: FutureWarning: the infer_dst=True keyword is deprecated, use ambiguous='infer' instead
self.index.tz_localize('US/Eastern', infer_dst=True)
[ 83.91%] ··· Running ...ries.DatetimeIndex.time_infer_freq_business 19.5ms
[ 84.06%] ··· Running timeseries.DatetimeIndex.time_infer_freq_daily 19.8ms
[ 84.21%] ··· Running timeseries.DatetimeIndex.time_infer_freq_none 39.9ms
[ 84.36%] ··· Running timeseries.DatetimeIndex.time_normalize 5.85ms
[ 84.51%] ··· Running timeseries.DatetimeIndex.time_reset_index 1.27ms
[ 84.66%] ··· Running timeseries.DatetimeIndex.time_reset_index_tz 1.57ms
[ 84.81%] ··· Running ...atetimeIndex.time_timeseries_is_month_start 1.13ms
[ 84.96%] ··· Running ...es.DatetimeIndex.time_timestamp_tzinfo_cons 120μs
[ 85.11%] ··· Running timeseries.DatetimeIndex.time_to_date 498ms
[ 85.26%] ··· Running timeseries.DatetimeIndex.time_to_pydatetime 62.8ms
[ 85.41%] ··· Running timeseries.DatetimeIndex.time_unique 458μs
[ 85.56%] ··· Running timeseries.Iteration.time_iter_datetimeindex 1.27s
[ 85.71%] ··· Running ...s.Iteration.time_iter_datetimeindex_preexit 24.8ms
[ 85.86%] ··· Running timeseries.Iteration.time_iter_periodindex 6.45s
[ 86.02%] ··· Running ...ies.Iteration.time_iter_periodindex_preexit 66.2ms
[ 86.17%] ··· Running timeseries.ResampleDataFrame.time_max_numpy 7.08ms
[ 86.17%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timeseries.py:166: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...)..apply(<func>)
self.df.resample('1s', how=np.max)
[ 86.32%] ··· Running timeseries.ResampleDataFrame.time_max_string 7.05ms
[ 86.32%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timeseries.py:169: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...).max()
self.df.resample('1s', how='max')
[ 86.47%] ··· Running timeseries.ResampleDataFrame.time_mean_numpy 6.62ms
[ 86.47%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timeseries.py:172: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...)..apply(<func>)
self.df.resample('1s', how=np.mean)
[ 86.62%] ··· Running timeseries.ResampleDataFrame.time_mean_string 6.60ms
[ 86.62%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timeseries.py:175: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...).mean()
self.df.resample('1s', how='mean')
[ 86.77%] ··· Running timeseries.ResampleDataFrame.time_min_numpy 7.02ms
[ 86.77%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timeseries.py:178: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...)..apply(<func>)
self.df.resample('1s', how=np.min)
[ 86.92%] ··· Running timeseries.ResampleDataFrame.time_min_string 7.10ms
[ 86.92%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timeseries.py:181: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...).min()
self.df.resample('1s', how='min')
[ 87.07%] ··· Running timeseries.ResampleSeries.time_1min_5min_mean 2.96ms
[ 87.07%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timeseries.py:210: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...).mean()
self.ts2[:10000].resample('5min', how='mean')
[ 87.22%] ··· Running timeseries.ResampleSeries.time_1min_5min_ohlc 3.25ms
[ 87.22%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timeseries.py:213: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...).ohlc()
self.ts2[:10000].resample('5min', how='ohlc')
[ 87.37%] ··· Running ....ResampleSeries.time_period_downsample_mean 29.3ms
[ 87.37%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timeseries.py:200: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...).mean()
self.ts1.resample('D', how='mean')
[ 87.52%] ··· Running ...ies.ResampleSeries.time_resample_datetime64 6.72ms
[ 87.52%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timeseries.py:207: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...).last()
self.dt_ts.resample('1S', how='last')
[ 87.67%] ··· Running ...sampleSeries.time_timestamp_downsample_mean 17.0ms
[ 87.67%] ····· /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timeseries.py:203: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...).mean()
self.ts2.resample('D', how='mean')
[ 87.82%] ··· Running timeseries.TimeDatetimeConverter.time_convert 12.1ms
[ 87.97%] ··· Running timeseries.TimeSeries.time_add_irregular 443ms
[ 88.12%] ··· Running timeseries.TimeSeries.time_large_lookup_value 6.15ms
[ 88.27%] ··· Running ...series.TimeSeries.time_sort_index_monotonic 1.22ms
[ 88.42%] ··· Running ...es.TimeSeries.time_sort_index_non_monotonic 18.5ms
[ 88.57%] ··· Running ...s.TimeSeries.time_timeseries_slice_minutely 366μs
[ 88.72%] ··· Running ....time_cache_false_with_dup_seconds_and_unit failed
[ 88.72%] ····· Traceback (most recent call last):
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 818, in <module>
commands[mode](args)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 795, in main_run
result = benchmark.do_run()
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 349, in do_run
return self.run(*self._current_params)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 424, in run
samples, number = self.benchmark_timing(timer, repeat, warmup_time, number=number)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 471, in benchmark_timing
timing = timer.timeit(number)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/timeit.py", line 202, in timeit
timing = self.inner(it, self.timer)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/timeit.py", line 100, in inner
_func()
File "/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timeseries.py", line 388, in time_cache_false_with_dup_seconds_and_unit
to_datetime(self.dup_numeric_seconds, unit='s', cache=False)
TypeError: to_datetime() got an unexpected keyword argument 'cache'
[ 88.87%] ··· Running ...time.time_cache_false_with_dup_string_dates failed
[ 88.87%] ····· Traceback (most recent call last):
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 818, in <module>
commands[mode](args)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 795, in main_run
result = benchmark.do_run()
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 349, in do_run
return self.run(*self._current_params)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 424, in run
samples, number = self.benchmark_timing(timer, repeat, warmup_time, number=number)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 471, in benchmark_timing
timing = timer.timeit(number)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/timeit.py", line 202, in timeit
timing = self.inner(it, self.timer)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/timeit.py", line 100, in inner
_func()
File "/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timeseries.py", line 394, in time_cache_false_with_dup_string_dates
to_datetime(self.dup_string_dates, cache=False)
TypeError: to_datetime() got an unexpected keyword argument 'cache'
[ 89.02%] ··· Running ...ache_false_with_dup_string_dates_and_format failed
[ 89.02%] ····· Traceback (most recent call last):
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 818, in <module>
commands[mode](args)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 795, in main_run
result = benchmark.do_run()
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 349, in do_run
return self.run(*self._current_params)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 424, in run
samples, number = self.benchmark_timing(timer, repeat, warmup_time, number=number)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 471, in benchmark_timing
timing = timer.timeit(number)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/timeit.py", line 202, in timeit
timing = self.inner(it, self.timer)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/timeit.py", line 100, in inner
_func()
File "/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timeseries.py", line 400, in time_cache_false_with_dup_string_dates_and_format
to_datetime(self.dup_string_dates, format='%Y-%m-%d', cache=False)
TypeError: to_datetime() got an unexpected keyword argument 'cache'
[ 89.17%] ··· Running ..._cache_false_with_dup_string_tzoffset_dates failed
[ 89.17%] ····· Traceback (most recent call last):
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 818, in <module>
commands[mode](args)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 795, in main_run
result = benchmark.do_run()
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 349, in do_run
return self.run(*self._current_params)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 424, in run
samples, number = self.benchmark_timing(timer, repeat, warmup_time, number=number)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 471, in benchmark_timing
timing = timer.timeit(number)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/timeit.py", line 202, in timeit
timing = self.inner(it, self.timer)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/timeit.py", line 100, in inner
_func()
File "/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timeseries.py", line 406, in time_cache_false_with_dup_string_tzoffset_dates
to_datetime(self.dup_string_with_tz, cache=False)
TypeError: to_datetime() got an unexpected keyword argument 'cache'
[ 89.32%] ··· Running ...me_cache_false_with_unique_seconds_and_unit failed
[ 89.32%] ····· Traceback (most recent call last):
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 818, in <module>
commands[mode](args)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 795, in main_run
result = benchmark.do_run()
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 349, in do_run
return self.run(*self._current_params)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 424, in run
samples, number = self.benchmark_timing(timer, repeat, warmup_time, number=number)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 471, in benchmark_timing
timing = timer.timeit(number)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/timeit.py", line 202, in timeit
timing = self.inner(it, self.timer)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/timeit.py", line 100, in inner
_func()
File "/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timeseries.py", line 382, in time_cache_false_with_unique_seconds_and_unit
to_datetime(self.unique_numeric_seconds, unit='s', cache=False)
TypeError: to_datetime() got an unexpected keyword argument 'cache'
[ 89.47%] ··· Running ...e.time_cache_true_with_dup_seconds_and_unit failed
[ 89.47%] ····· Traceback (most recent call last):
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 818, in <module>
commands[mode](args)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 795, in main_run
result = benchmark.do_run()
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 349, in do_run
return self.run(*self._current_params)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 424, in run
samples, number = self.benchmark_timing(timer, repeat, warmup_time, number=number)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 471, in benchmark_timing
timing = timer.timeit(number)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/timeit.py", line 202, in timeit
timing = self.inner(it, self.timer)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/timeit.py", line 100, in inner
_func()
File "/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timeseries.py", line 385, in time_cache_true_with_dup_seconds_and_unit
to_datetime(self.dup_numeric_seconds, unit='s', cache=True)
TypeError: to_datetime() got an unexpected keyword argument 'cache'
[ 89.62%] ··· Running ...etime.time_cache_true_with_dup_string_dates failed
[ 89.62%] ····· Traceback (most recent call last):
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 818, in <module>
commands[mode](args)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 795, in main_run
result = benchmark.do_run()
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 349, in do_run
return self.run(*self._current_params)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 424, in run
samples, number = self.benchmark_timing(timer, repeat, warmup_time, number=number)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 471, in benchmark_timing
timing = timer.timeit(number)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/timeit.py", line 202, in timeit
timing = self.inner(it, self.timer)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/timeit.py", line 100, in inner
_func()
File "/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timeseries.py", line 391, in time_cache_true_with_dup_string_dates
to_datetime(self.dup_string_dates, cache=True)
TypeError: to_datetime() got an unexpected keyword argument 'cache'
[ 89.77%] ··· Running ...cache_true_with_dup_string_dates_and_format failed
[ 89.77%] ····· Traceback (most recent call last):
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 818, in <module>
commands[mode](args)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 795, in main_run
result = benchmark.do_run()
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 349, in do_run
return self.run(*self._current_params)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 424, in run
samples, number = self.benchmark_timing(timer, repeat, warmup_time, number=number)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 471, in benchmark_timing
timing = timer.timeit(number)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/timeit.py", line 202, in timeit
timing = self.inner(it, self.timer)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/timeit.py", line 100, in inner
_func()
File "/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timeseries.py", line 397, in time_cache_true_with_dup_string_dates_and_format
to_datetime(self.dup_string_dates, format='%Y-%m-%d', cache=True)
TypeError: to_datetime() got an unexpected keyword argument 'cache'
[ 89.92%] ··· Running ...e_cache_true_with_dup_string_tzoffset_dates failed
[ 89.92%] ····· Traceback (most recent call last):
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 818, in <module>
commands[mode](args)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 795, in main_run
result = benchmark.do_run()
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 349, in do_run
return self.run(*self._current_params)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 424, in run
samples, number = self.benchmark_timing(timer, repeat, warmup_time, number=number)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 471, in benchmark_timing
timing = timer.timeit(number)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/timeit.py", line 202, in timeit
timing = self.inner(it, self.timer)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/timeit.py", line 100, in inner
_func()
File "/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timeseries.py", line 403, in time_cache_true_with_dup_string_tzoffset_dates
to_datetime(self.dup_string_with_tz, cache=True)
TypeError: to_datetime() got an unexpected keyword argument 'cache'
[ 90.08%] ··· Running ...ime_cache_true_with_unique_seconds_and_unit failed
[ 90.08%] ····· Traceback (most recent call last):
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 818, in <module>
commands[mode](args)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 795, in main_run
result = benchmark.do_run()
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 349, in do_run
return self.run(*self._current_params)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 424, in run
samples, number = self.benchmark_timing(timer, repeat, warmup_time, number=number)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/site-packages/asv/benchmark.py", line 471, in benchmark_timing
timing = timer.timeit(number)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/timeit.py", line 202, in timeit
timing = self.inner(it, self.timer)
File "/home/matt/anaconda/envs/pandas_dev/lib/python2.7/timeit.py", line 100, in inner
_func()
File "/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timeseries.py", line 379, in time_cache_true_with_unique_seconds_and_unit
to_datetime(self.unique_numeric_seconds, unit='s', cache=True)
TypeError: to_datetime() got an unexpected keyword argument 'cache'
[ 90.23%] ··· Running timeseries.ToDatetime.time_format_YYYYMMDD 14.6ms
[ 90.38%] ··· Running timeseries.ToDatetime.time_format_exact 2.03s
[ 90.53%] ··· Running timeseries.ToDatetime.time_format_no_exact 1.91s
[ 90.68%] ··· Running timeseries.ToDatetime.time_iso8601 9.82ms
[ 90.83%] ··· Running timeseries.ToDatetime.time_iso8601_format 9.98ms
[ 90.98%] ··· Running ...eries.ToDatetime.time_iso8601_format_no_sep 9.52ms
[ 91.13%] ··· Running timeseries.ToDatetime.time_iso8601_nosep 9.66ms
[ 91.28%] ··· Running ...ries.ToDatetime.time_iso8601_tz_spaceformat 620ms
[ 91.43%] ··· Running ....TimestampAcrossDst.time_replace_across_dst 37.9μs
[ 91.58%] ··· Running ...p.TimestampConstruction.time_parse_dateutil 432μs
[ 91.73%] ··· Running ...estampConstruction.time_parse_iso8601_no_tz 14.6μs
[ 91.88%] ··· Running ...TimestampConstruction.time_parse_iso8601_tz 49.7μs
[ 92.03%] ··· Running timestamp.TimestampConstruction.time_parse_now 22.4μs
[ 92.18%] ··· Running ...tamp.TimestampConstruction.time_parse_today 22.6μs
[ 92.33%] ··· Running timestamp.TimestampOps.time_replace_None ok
[ 92.33%] ····
============ ========
tz
------------ --------
None 18.2μs
US/Eastern 39.0μs
============ ========
[ 92.48%] ··· Running timestamp.TimestampOps.time_replace_tz ok
[ 92.48%] ····
============ ========
tz
------------ --------
None 46.8μs
US/Eastern 70.6μs
============ ========
[ 92.63%] ··· Running timestamp.TimestampOps.time_to_pydatetime ok
[ 92.63%] ····
============ ========
tz
------------ --------
None 12.4μs
US/Eastern 13.9μs
============ ========
[ 92.78%] ··· Running timestamp.TimestampProperties.time_dayofweek ok
[ 92.78%] ····
================================================ ====== ========
tz freq
------------------------------------------------ ------ --------
None None 10.0μs
None B 11.4μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> None 10.1μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> B 11.2μs
================================================ ====== ========
[ 92.93%] ··· Running timestamp.TimestampProperties.time_dayofyear ok
[ 92.93%] ····
================================================ ====== ========
tz freq
------------------------------------------------ ------ --------
None None 40.3μs
None B 44.4μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> None 39.9μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> B 45.5μs
================================================ ====== ========
[ 93.08%] ··· Running ...tamp.TimestampProperties.time_days_in_month ok
[ 93.08%] ····
================================================ ====== ========
tz freq
------------------------------------------------ ------ --------
None None 41.6μs
None B 45.3μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> None 41.1μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> B 45.6μs
================================================ ====== ========
[ 93.23%] ··· Running timestamp.TimestampProperties.time_freqstr ok
[ 93.23%] ····
================================================ ====== ========
tz freq
------------------------------------------------ ------ --------
None None 11.0μs
None B 13.3μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> None 11.2μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> B 13.2μs
================================================ ====== ========
[ 93.38%] ··· Running ...stamp.TimestampProperties.time_is_leap_year ok
[ 93.38%] ····
================================================ ====== ========
tz freq
------------------------------------------------ ------ --------
None None 45.2μs
None B 51.2μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> None 43.2μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> B 50.5μs
================================================ ====== ========
[ 93.53%] ··· Running ...stamp.TimestampProperties.time_is_month_end ok
[ 93.53%] ····
================================================ ====== ========
tz freq
------------------------------------------------ ------ --------
None None 42.2μs
None B 49.9μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> None 42.2μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> B 51.4μs
================================================ ====== ========
[ 93.68%] ··· Running ...amp.TimestampProperties.time_is_month_start ok
[ 93.68%] ····
================================================ ====== ========
tz freq
------------------------------------------------ ------ --------
None None 41.9μs
None B 49.4μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> None 42.1μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> B 49.6μs
================================================ ====== ========
[ 93.83%] ··· Running ...amp.TimestampProperties.time_is_quarter_end ok
[ 93.83%] ····
================================================ ====== ========
tz freq
------------------------------------------------ ------ --------
None None 43.3μs
None B 50.9μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> None 42.0μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> B 50.3μs
================================================ ====== ========
[ 93.98%] ··· Running ...p.TimestampProperties.time_is_quarter_start ok
[ 93.98%] ····
================================================ ====== ========
tz freq
------------------------------------------------ ------ --------
None None 41.8μs
None B 49.6μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> None 42.3μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> B 50.0μs
================================================ ====== ========
[ 94.14%] ··· Running timestamp.TimestampProperties.time_is_year_end ok
[ 94.14%] ····
================================================ ====== ========
tz freq
------------------------------------------------ ------ --------
None None 42.3μs
None B 50.9μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> None 43.0μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> B 49.9μs
================================================ ====== ========
[ 94.29%] ··· Running ...tamp.TimestampProperties.time_is_year_start ok
[ 94.29%] ····
================================================ ====== ========
tz freq
------------------------------------------------ ------ --------
None None 42.8μs
None B 50.9μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> None 42.4μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> B 50.7μs
================================================ ====== ========
[ 94.44%] ··· Running timestamp.TimestampProperties.time_microsecond ok
[ 94.44%] ····
================================================ ====== ========
tz freq
------------------------------------------------ ------ --------
None None 8.78μs
None B 9.44μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> None 9.03μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> B 9.95μs
================================================ ====== ========
[ 94.59%] ··· Running timestamp.TimestampProperties.time_offset ok
[ 94.59%] ····
================================================ ====== ========
tz freq
------------------------------------------------ ------ --------
None None 14.9μs
None B 16.3μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> None 14.6μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> B 16.4μs
================================================ ====== ========
[ 94.59%] ·····
For parameters: None, None
/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timestamp.py:40: FutureWarning: .offset is deprecated. Use .freq instead
self.ts.offset
For parameters: None, 'B'
/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timestamp.py:40: FutureWarning: .offset is deprecated. Use .freq instead
self.ts.offset
For parameters: <DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, None
/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timestamp.py:40: FutureWarning: .offset is deprecated. Use .freq instead
self.ts.offset
For parameters: <DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, 'B'
/home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/timestamp.py:40: FutureWarning: .offset is deprecated. Use .freq instead
self.ts.offset
[ 94.74%] ··· Running timestamp.TimestampProperties.time_quarter ok
[ 94.74%] ····
================================================ ====== ========
tz freq
------------------------------------------------ ------ --------
None None 40.3μs
None B 45.4μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> None 40.0μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> B 45.0μs
================================================ ====== ========
[ 94.89%] ··· Running timestamp.TimestampProperties.time_tz ok
[ 94.89%] ····
================================================ ====== ========
tz freq
------------------------------------------------ ------ --------
None None 9.69μs
None B 11.4μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> None 9.98μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> B 10.8μs
================================================ ====== ========
[ 95.04%] ··· Running timestamp.TimestampProperties.time_week ok
[ 95.04%] ····
================================================ ====== ========
tz freq
------------------------------------------------ ------ --------
None None 40.2μs
None B 43.9μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> None 40.7μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> B 46.3μs
================================================ ====== ========
[ 95.19%] ··· Running ...stamp.TimestampProperties.time_weekday_name ok
[ 95.19%] ····
================================================ ====== ========
tz freq
------------------------------------------------ ------ --------
None None 11.9μs
None B 13.4μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> None 11.8μs
<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD> B 13.6μs
================================================ ====== ========
[ 95.19%] ··· Setting up /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/groupby.py:299
[ 95.34%] ··· Running groupby.MultiColumn.time_col_select_lambda_sum 254ms
[ 95.49%] ··· Running groupby.MultiColumn.time_col_select_numpy_sum 25.4ms
[ 95.64%] ··· Running groupby.MultiColumn.time_cython_sum 29.2ms
[ 95.79%] ··· Running groupby.MultiColumn.time_lambda_sum 495ms
[ 95.79%] ··· Setting up /home/matt/Projects/pandas-mroeschke/asv_bench/benchmarks/multiindex_object.py:129
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