From hello at noatamir.com  Fri Oct  7 08:46:27 2022
From: hello at noatamir.com (Noa Tamir)
Date: Fri, 07 Oct 2022 12:46:27 +0000
Subject: [Pandas-dev] pandas slack
Message-ID: <gL6sjR_WBgsVa4H-GTMDQpWiGGpJBMruqu6YABoFMY5lrD7gRP3iy_8jVCpn5vGLig9YYfHX26jkjE5He5H4ct7kooNtMe7_VzCK-Uc8AIA=@noatamir.com>

Hello folks ?

pandas is happy to announce we now have a slack for developers, maintainers and potential contributors. This is not a space for user questions, rather for discussions about:

- How to contribute, including onboarding to different roles as people become more active
- Internal team announcements

- Mirroring public announcement, or sharing private information like who is planning to attend a conference, a meeting, etc.
- A space to share ideas about the project
- Coordinating project work and activity as needs arise or change

You are welcome to join! Email us at slack at pandas.paydata.org and let us know that you read and agree to our [code of conduct](https://pandas.pydata.org/community/coc.html) ?

If you have ideas for improvements, feel free to share! As a side note, this is not meant to replace the mailing list ? all important announcements and conversations should still happen here.
Cheers,
Noa
she/???/sie
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From hello at noatamir.com  Sat Oct  8 11:04:05 2022
From: hello at noatamir.com (Noa Tamir)
Date: Sat, 08 Oct 2022 15:04:05 +0000
Subject: [Pandas-dev] pandas slack
In-Reply-To: <gL6sjR_WBgsVa4H-GTMDQpWiGGpJBMruqu6YABoFMY5lrD7gRP3iy_8jVCpn5vGLig9YYfHX26jkjE5He5H4ct7kooNtMe7_VzCK-Uc8AIA=@noatamir.com>
References: <gL6sjR_WBgsVa4H-GTMDQpWiGGpJBMruqu6YABoFMY5lrD7gRP3iy_8jVCpn5vGLig9YYfHX26jkjE5He5H4ct7kooNtMe7_VzCK-Uc8AIA=@noatamir.com>
Message-ID: <cY-4kBCEBXiHjE6QHqpLnKgGd0_gfnuRJ2EU70pLe42hkOHNT6bHbMNDIipPGgkasA86zcCz5RPdmWtJikgV17Y6YOD4C2KReoyzQIyyEis=@noatamir.com>

Whoops! There's a typo in the email address ?.

Please email us at slack at pandas.pydata.org.

Thanks to the person who brought it to my attention ?.

Cheers,
Noa
she/???/sie

------- Original Message -------
On Friday, October 7th, 2022 at 14:46, Noa Tamir <hello at noatamir.com> wrote:

> Hello folks ?
>
> pandas is happy to announce we now have a slack for developers, maintainers and potential contributors. This is not a space for user questions, rather for discussions about:
>
> - How to contribute, including onboarding to different roles as people become more active
> - Internal team announcements
>
> - Mirroring public announcement, or sharing private information like who is planning to attend a conference, a meeting, etc.
> - A space to share ideas about the project
> - Coordinating project work and activity as needs arise or change
>
> You are welcome to join! Email us at slack at pandas.paydata.org and let us know that you read and agree to our [code of conduct](https://pandas.pydata.org/community/coc.html) ?
>
> If you have ideas for improvements, feel free to share! As a side note, this is not meant to replace the mailing list ? all important announcements and conversations should still happen here.
> Cheers,
> Noa
> she/???/sie
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From hello at noatamir.com  Tue Oct 11 07:09:56 2022
From: hello at noatamir.com (Noa Tamir)
Date: Tue, 11 Oct 2022 11:09:56 +0000
Subject: [Pandas-dev] 1st New Contributor Meeting - October 19th
Message-ID: <9rrVvuyQfTsv4AeyApKEEbbvWQtRY5sfMQYerfb_9SIbqXCizQzgA4pPHEzom2xy3zw1EEahyANH8RTKVKsqt6vbvD9ItN5axqDcaMS_KDo=@noatamir.com>

Hi ?

We?re happy to invite you to the first pandas New Contributor Meeting. ?

The first meeting will take place next week, October 19th (Wednesday) at 4:00 PM UTC [1] and it will be scheduled to repeat monthly after that. ?

This meeting is for any new contributors, who want to meet more experienced contributors and maintainers, in order to get support with getting started as a pandas contributors. Or if you started contributing recently, perhaps you?d like to discuss your ongoing pull requests (PRs), or anything else you might want to discuss ? you decide what?s on the agenda!

To join us, please check our meeting calendar [2], which has meeting times, with links to the agenda [3], and the meeting zoom link [4].
? you all are invited
? everyone can present (add yourself to the hackMD agenda)
? anyone can sit in and listen

We?re looking forward to meeting with you!

Cheers,
Marco (he/him), Richard (he/him), and Noa (she/they)

PS- if you?re a more experienced contributor or maintainer, and want to join to help new contributors, you are also welcome to join us!

[1[ ? 4pm UTC in your local time: https://dateful.com/convert/utc?t=4pm
[2] ? Meeting calendar: https://pandas.pydata.org/docs/dev/development/meeting.html
[3] ? Agenda: https://hackmd.io/@pandas-dev/HJgQt1Tei
[4] ? Zoom link (October 19th (Wednesday) at 4:00 PM UTC): https://us06web.zoom.us/j/85437327422?pwd=WDZaVXo2ZlJ4dDBPa092R3NSY01tdz09
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From jorisvandenbossche at gmail.com  Tue Oct 11 13:06:09 2022
From: jorisvandenbossche at gmail.com (Joris Van den Bossche)
Date: Tue, 11 Oct 2022 19:06:09 +0200
Subject: [Pandas-dev] October 2022 monthly community meeting (Wednesday
 October 12, UTC 18:00)
In-Reply-To: <CALQtMBZdwuDMk=BxxTUjjYyorM-Rp1BwHHw_HKtrf_LxzbZJSQ@mail.gmail.com>
References: <CALQtMBZA3vEp3qx5xgj5LZeY2egnAe1zSdNv=HPOQPeFqKzGAg@mail.gmail.com>
 <CALQtMBZdwuDMk=BxxTUjjYyorM-Rp1BwHHw_HKtrf_LxzbZJSQ@mail.gmail.com>
Message-ID: <CALQtMBZyki2NGqxOLk-a2ooouG_dQaMpaR1EENvz21vWaJU1sQ@mail.gmail.com>

Hi all,

A reminder that the next monthly dev call is tomorrow (Wednesday, October
12) at 18:00 UTC (1pm Central). Our calendar is at
https://pandas.pydata.org/docs/development/meeting.html#calendar to check
your local time.
All are welcome to attend!

Video Call:
https://us06web.zoom.us/j/84484803210?pwd=TjUxNmcyNHcvcG9SNGJvbE53Y21GZz09
Meeting notes:
https://docs.google.com/document/u/1/d/1tGbTiYORHiSPgVMXawiweGJlBw5dOkVJLY-licoBmBU/edit?ouid=102771015311436394588&usp=docs_home&ths=true

Joris
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From m.e.gorelli at gmail.com  Fri Oct 14 06:13:06 2022
From: m.e.gorelli at gmail.com (Marco Gorelli)
Date: Fri, 14 Oct 2022 11:13:06 +0100
Subject: [Pandas-dev] Is it time for a bi-weekly pandas call?
Message-ID: <CA+gwkEAkMaRJK5X8BiaEyeDeOT8UWGH22pEo42mSrnvJ+P+VvQ@mail.gmail.com>

Currently, there's a monthly pandas call, which has been in place for
several years. Seeing as there's now more people working on pandas as part
of their jobs, might it be time to increase the frequency? E.g. to meet
every 2 weeks, instead of once a month?
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From garcia.marc at gmail.com  Mon Oct 17 00:34:50 2022
From: garcia.marc at gmail.com (Marc Garcia)
Date: Mon, 17 Oct 2022 11:34:50 +0700
Subject: [Pandas-dev] Is it time for a bi-weekly pandas call?
In-Reply-To: <CA+gwkEAkMaRJK5X8BiaEyeDeOT8UWGH22pEo42mSrnvJ+P+VvQ@mail.gmail.com>
References: <CA+gwkEAkMaRJK5X8BiaEyeDeOT8UWGH22pEo42mSrnvJ+P+VvQ@mail.gmail.com>
Message-ID: <CAEk5N5sQ0CAis5wqNhUz65BHsHZ137RvetLX8pUtsH-C3vW+rw@mail.gmail.com>

I'm personally happy with both frequencies, no preference.

Couple of related things:
- Since this call is where a lot of the decision making happens, would it
make sense to discuss this as part of the governance discussions?
- Maybe worth also discussing the time of the call? I think the current
time is quite reasonable for many time zones, and it necessarily need to be
night time in some places during the call. But I wonder if it'd make a
difference if we have the call a bit later to contributors or potential
contributors in India, China... if we move the call one or two hours
earlier, and how this affects people in California or Hawaii. This shows
the current time in different time zones:
https://www.timeanddate.com/worldclock/converter.html?iso=20221107T180000&p1=103&p2=224&p3=179&p4=233&p5=1440&p6=125&p7=166&p8=776&p9=176&p10=28&p11=237&p12=240

On Fri, Oct 14, 2022 at 5:13 PM Marco Gorelli <m.e.gorelli at gmail.com> wrote:

> Currently, there's a monthly pandas call, which has been in place for
> several years. Seeing as there's now more people working on pandas as part
> of their jobs, might it be time to increase the frequency? E.g. to meet
> every 2 weeks, instead of once a month?
> _______________________________________________
> Pandas-dev mailing list
> Pandas-dev at python.org
> https://mail.python.org/mailman/listinfo/pandas-dev
>
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From jbrockmendel at gmail.com  Mon Oct 17 14:38:30 2022
From: jbrockmendel at gmail.com (Brock Mendel)
Date: Mon, 17 Oct 2022 11:38:30 -0700
Subject: [Pandas-dev] asv vs 1.4.0
Message-ID: <CAKf8g9SVfo6nnzxcAHx30EmPUtRCr9chYNiBGOE_G1KQrZJNxw@mail.gmail.com>

I ran the asv suite (a few times with --record-samples) with main (as of a
few days ago) vs 1.4.0.  Below are the full results.  Emailing bc it won't
fit in a GH issue.

Some of these have easy explanations.
 - time_tz_localize_to_utc with utc slowed down bc a fastpath was moved
from inside the function to the calling function
- TimestampProperties slowed down because of Timestamp.freq deprecation
(~10% of all slowed asvs)
- OffestDatetimeArithmetic.time_apply* slowed down bc of a DateOffset.apply
deprecation

Not sure what's going on with select_dtypes (~20% of slowed asvs) or
algos.isin.

       before           after         ratio
     [bb1f6515]       [56d82a9b]
     <v1.4.0^0>       <main>
+         480?7ns         460?20?s   960.13
 tslibs.tz_convert.TimeTZConvert.time_tz_localize_to_utc(1000000,
datetime.timezone.utc)
+      58.5?0.9?s       20.5?0.4ms   349.36
 algos.isin.IsIn.time_isin_mismatched_dtype('bool')
+        79.5?2?s       20.6?0.5ms   259.38
 algos.isin.IsIn.time_isin_mismatched_dtype('boolean')
+      58.4?0.9?s       9.22?0.2ms   157.81
 algos.isin.IsIn.time_isin_mismatched_dtype('uint64')
+         142?1?s      6.78?0.03ms    47.71
 inference.ToNumericDowncast.time_downcast('int32', 'float')
+         285?2?s       9.21?0.2ms    32.30
 algos.isin.IsIn.time_isin_mismatched_dtype('object')
+     2.83?0.03?s       76.4?0.7?s    27.02
 categoricals.Contains.time_categorical_index_contains
+      36.1?0.9?s         948?30?s    26.26
 dtypes.SelectDtypes.time_select_dtype_string_exclude(<class 'int'>)
+        36.6?1?s         941?30?s    25.72
 dtypes.SelectDtypes.time_select_dtype_string_exclude(<class 'float'>)
+      44.5?0.9?s      1.01?0.04ms    22.81
 dtypes.SelectDtypes.time_select_dtype_string_exclude(<class 'complex'>)
+      44.6?0.9?s         961?40?s    21.52
 dtypes.SelectDtypes.time_select_dtype_string_exclude(<class 'bool'>)
+      49.1?0.9?s      1.03?0.04ms    21.00
 dtypes.SelectDtypes.time_select_dtype_string_exclude('Int16')
+        616?30?s       12.9?0.3ms    20.93
 inference.ToNumericDowncast.time_downcast('datetime64', 'float')
+      48.2?0.6?s         974?40?s    20.19
 dtypes.SelectDtypes.time_select_dtype_string_exclude('Int8')
+      49.7?0.6?s         973?30?s    19.59
 dtypes.SelectDtypes.time_select_dtype_string_exclude('Int32')
+      52.1?0.7?s      1.01?0.04ms    19.44
 dtypes.SelectDtypes.time_select_dtype_string_exclude('UInt32')
+      50.4?0.7?s         969?30?s    19.22
 dtypes.SelectDtypes.time_select_dtype_string_exclude('Int64')
+      51.0?0.8?s         980?40?s    19.22
 dtypes.SelectDtypes.time_select_dtype_string_exclude('UInt8')
+      52.8?0.7?s      1.01?0.04ms    19.10
 dtypes.SelectDtypes.time_select_dtype_string_exclude('UInt64')
+      51.3?0.6?s         976?40?s    19.02
 dtypes.SelectDtypes.time_select_dtype_string_exclude('UInt16')
+      60.6?0.7?s      1.05?0.04ms    17.38
 dtypes.SelectDtypes.time_select_dtype_string_exclude('M8[ns]')
+      60.5?0.9?s      1.05?0.04ms    17.36
 dtypes.SelectDtypes.time_select_dtype_string_exclude('float32')
+      60.5?0.7?s      1.05?0.04ms    17.35
 dtypes.SelectDtypes.time_select_dtype_string_exclude('complex128')
+      60.6?0.8?s      1.05?0.04ms    17.33
 dtypes.SelectDtypes.time_select_dtype_string_exclude('complex64')
+      60.5?0.9?s      1.02?0.04ms    16.88
 dtypes.SelectDtypes.time_select_dtype_string_exclude('uint16')
+      60.4?0.9?s         992?40?s    16.43
 dtypes.SelectDtypes.time_select_dtype_string_exclude('uint64')
+      60.5?0.7?s         990?40?s    16.35
 dtypes.SelectDtypes.time_select_dtype_string_exclude('m8[ns]')
+      60.4?0.8?s         987?40?s    16.35
 dtypes.SelectDtypes.time_select_dtype_string_exclude('int8')
+      60.5?0.7?s         989?40?s    16.35
 dtypes.SelectDtypes.time_select_dtype_string_exclude('bool')
+      60.7?0.9?s         992?40?s    16.35
 dtypes.SelectDtypes.time_select_dtype_string_exclude('int32')
+      60.6?0.7?s         990?40?s    16.34
 dtypes.SelectDtypes.time_select_dtype_string_exclude('float64')
+        60.8?1?s         992?40?s    16.31
 dtypes.SelectDtypes.time_select_dtype_string_exclude('int64')
+      60.6?0.6?s         987?20?s    16.30
 dtypes.SelectDtypes.time_select_dtype_string_exclude('uint32')
+      60.6?0.9?s         986?20?s    16.29
 dtypes.SelectDtypes.time_select_dtype_string_exclude('uint8')
+      60.8?0.9?s         987?40?s    16.24
 dtypes.SelectDtypes.time_select_dtype_string_exclude('int16')
+      61.2?0.7?s         992?40?s    16.21
 dtypes.SelectDtypes.time_select_dtype_string_exclude('timedelta64[ns]')
+      61.6?0.7?s         996?40?s    16.17
 dtypes.SelectDtypes.time_select_dtype_string_exclude('datetime64[ns]')
+      36.0?0.8?s          567?3?s    15.77
 dtypes.SelectDtypes.time_select_dtype_int_include(<class 'int'>)
+      36.3?0.7?s          569?4?s    15.66
 dtypes.SelectDtypes.time_select_dtype_float_include(<class 'float'>)
+      36.3?0.6?s          566?4?s    15.60
 dtypes.SelectDtypes.time_select_dtype_float_exclude(<class 'int'>)
+      36.7?0.7?s          568?5?s    15.48
 dtypes.SelectDtypes.time_select_dtype_int_exclude(<class 'float'>)
+      44.4?0.8?s          578?4?s    13.02
 dtypes.SelectDtypes.time_select_dtype_int_exclude(<class 'bool'>)
+        44.4?1?s          577?4?s    13.00
 dtypes.SelectDtypes.time_select_dtype_int_exclude(<class 'complex'>)
+      44.4?0.7?s          577?3?s    12.99
 dtypes.SelectDtypes.time_select_dtype_float_exclude(<class 'bool'>)
+      44.5?0.8?s          576?3?s    12.94
 dtypes.SelectDtypes.time_select_dtype_float_exclude(<class 'complex'>)
+         281?4?s      3.41?0.06ms    12.14
 algos.isin.IsIn.time_isin_mismatched_dtype('str')
+      48.1?0.8?s          583?4?s    12.11
 dtypes.SelectDtypes.time_select_dtype_int_exclude('Int8')
+      48.3?0.8?s          583?4?s    12.06
 dtypes.SelectDtypes.time_select_dtype_float_exclude('Int8')
+      49.0?0.6?s          584?4?s    11.92
 dtypes.SelectDtypes.time_select_dtype_int_exclude('Int16')
+      49.0?0.9?s          582?3?s    11.88
 dtypes.SelectDtypes.time_select_dtype_float_exclude('Int16')
+         288?5?s      3.40?0.07ms    11.82
 algos.isin.IsIn.time_isin_mismatched_dtype('string[python]')
+      49.5?0.7?s          584?4?s    11.80
 dtypes.SelectDtypes.time_select_dtype_int_exclude('Int32')
+      49.9?0.6?s          585?4?s    11.73
 dtypes.SelectDtypes.time_select_dtype_int_include('Int64')
+      49.8?0.7?s          584?4?s    11.73
 dtypes.SelectDtypes.time_select_dtype_float_exclude('Int32')
+      50.2?0.9?s          583?4?s    11.61
 dtypes.SelectDtypes.time_select_dtype_float_exclude('Int64')
+      50.8?0.8?s          585?4?s    11.51
 dtypes.SelectDtypes.time_select_dtype_int_exclude('UInt8')
+      50.9?0.9?s          584?4?s    11.47
 dtypes.SelectDtypes.time_select_dtype_float_exclude('UInt8')
+      51.2?0.7?s          586?4?s    11.44
 dtypes.SelectDtypes.time_select_dtype_int_exclude('UInt16')
+      51.2?0.6?s          584?4?s    11.40
 dtypes.SelectDtypes.time_select_dtype_float_exclude('UInt16')
+      52.1?0.7?s          588?3?s    11.28
 dtypes.SelectDtypes.time_select_dtype_int_exclude('UInt32')
+      52.1?0.7?s          586?4?s    11.25
 dtypes.SelectDtypes.time_select_dtype_float_exclude('UInt32')
+      22.2?0.3?s         248?20?s    11.17
 indexing.NumericSeriesIndexing.time_getitem_slice(<class
'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
+      52.6?0.7?s          585?5?s    11.13
 dtypes.SelectDtypes.time_select_dtype_int_exclude('UInt64')
+      52.7?0.8?s          585?4?s    11.12
 dtypes.SelectDtypes.time_select_dtype_float_exclude('UInt64')
+      22.2?0.3?s          247?4?s    11.11
 indexing.NumericSeriesIndexing.time_getitem_slice(<class
'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
+      60.4?0.7?s          599?3?s     9.91
 dtypes.SelectDtypes.time_select_dtype_int_exclude('bool')
+      60.4?0.8?s          597?4?s     9.90
 dtypes.SelectDtypes.time_select_dtype_int_include('int64')
+      60.5?0.8?s          598?4?s     9.89
 dtypes.SelectDtypes.time_select_dtype_int_exclude('int32')
+      60.4?0.9?s          597?3?s     9.89
 dtypes.SelectDtypes.time_select_dtype_int_exclude('float64')
+      60.5?0.7?s          598?5?s     9.89
 dtypes.SelectDtypes.time_select_dtype_int_exclude('M8[ns]')
+      60.4?0.6?s          597?4?s     9.89
 dtypes.SelectDtypes.time_select_dtype_float_exclude('m8[ns]')
+      60.5?0.9?s          598?4?s     9.88
 dtypes.SelectDtypes.time_select_dtype_int_exclude('uint16')
+      60.3?0.7?s          596?4?s     9.87
 dtypes.SelectDtypes.time_select_dtype_float_exclude('bool')
+      60.4?0.8?s          596?4?s     9.87
 dtypes.SelectDtypes.time_select_dtype_float_exclude('uint8')
+      60.7?0.8?s          599?4?s     9.87
 dtypes.SelectDtypes.time_select_dtype_int_exclude('uint64')
+      60.5?0.7?s          597?4?s     9.87
 dtypes.SelectDtypes.time_select_dtype_float_exclude('complex64')
+      60.3?0.8?s          595?4?s     9.86
 dtypes.SelectDtypes.time_select_dtype_float_include('float64')
+      60.5?0.9?s          596?4?s     9.86
 dtypes.SelectDtypes.time_select_dtype_float_exclude('int64')
+      60.5?0.8?s          597?3?s     9.85
 dtypes.SelectDtypes.time_select_dtype_int_exclude('complex64')
+      60.6?0.9?s          598?5?s     9.85
 dtypes.SelectDtypes.time_select_dtype_int_exclude('uint8')
+      60.5?0.9?s          596?4?s     9.85
 dtypes.SelectDtypes.time_select_dtype_float_exclude('int8')
+      60.5?0.8?s          596?4?s     9.85
 dtypes.SelectDtypes.time_select_dtype_float_exclude('complex128')
+      60.5?0.7?s          596?4?s     9.85
 dtypes.SelectDtypes.time_select_dtype_int_exclude('m8[ns]')
+      60.7?0.9?s          598?4?s     9.85
 dtypes.SelectDtypes.time_select_dtype_int_exclude('float32')
+      60.6?0.7?s          596?3?s     9.85
 dtypes.SelectDtypes.time_select_dtype_int_exclude('uint32')
+      60.6?0.7?s          597?4?s     9.85
 dtypes.SelectDtypes.time_select_dtype_int_exclude('int8')
+      60.5?0.8?s          595?5?s     9.84
 dtypes.SelectDtypes.time_select_dtype_float_exclude('M8[ns]')
+      60.6?0.6?s          596?4?s     9.84
 dtypes.SelectDtypes.time_select_dtype_float_exclude('int32')
+      60.6?0.7?s          596?5?s     9.84
 dtypes.SelectDtypes.time_select_dtype_int_exclude('complex128')
+      60.5?0.8?s          595?4?s     9.84
 dtypes.SelectDtypes.time_select_dtype_float_exclude('uint32')
+        60.5?1?s          594?4?s     9.83
 dtypes.SelectDtypes.time_select_dtype_float_exclude('float32')
+      60.8?0.8?s          597?4?s     9.82
 dtypes.SelectDtypes.time_select_dtype_float_exclude('uint64')
+      60.7?0.8?s          595?4?s     9.81
 dtypes.SelectDtypes.time_select_dtype_float_exclude('int16')
+      60.9?0.7?s          597?4?s     9.81
 dtypes.SelectDtypes.time_select_dtype_int_exclude('int16')
+      60.6?0.8?s          595?5?s     9.81
 dtypes.SelectDtypes.time_select_dtype_float_exclude('uint16')
+      61.3?0.6?s          598?5?s     9.74
 dtypes.SelectDtypes.time_select_dtype_int_exclude('timedelta64[ns]')
+      61.2?0.8?s          596?5?s     9.74
 dtypes.SelectDtypes.time_select_dtype_float_exclude('timedelta64[ns]')
+      61.5?0.9?s          598?4?s     9.72
 dtypes.SelectDtypes.time_select_dtype_float_exclude('datetime64[ns]')
+      61.7?0.7?s          598?5?s     9.69
 dtypes.SelectDtypes.time_select_dtype_int_exclude('datetime64[ns]')
+         482?8ns      4.55?0.08?s     9.44
 tslibs.tz_convert.TimeTZConvert.time_tz_localize_to_utc(10000,
datetime.timezone.utc)
+        718?30?s      6.12?0.04ms     8.52
 frame_methods.ToDict.time_to_dict_ints('list')
+      22.3?0.3?s          186?4?s     8.35
 indexing.NumericSeriesIndexing.time_getitem_slice(<class
'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
+      36.8?0.7?s         298?10?s     8.11
 dtypes.SelectDtypes.time_select_dtype_bool_exclude(<class 'float'>)
+     2.37?0.01ms       18.7?0.7ms     7.90
 ctors.SeriesConstructors.time_series_constructor(<class 'list'>, False,
'int')
+         296?5?s      2.33?0.01ms     7.89
 tslibs.tz_convert.TimeTZConvert.time_tz_localize_to_utc(10000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+      36.6?0.8?s         284?20?s     7.77
 dtypes.SelectDtypes.time_select_dtype_bool_exclude(<class 'int'>)
+     2.41?0.01ms       18.5?0.6ms     7.67
 ctors.SeriesConstructors.time_series_constructor(<class 'list'>, True,
'int')
+      44.3?0.9?s         302?20?s     6.82
 dtypes.SelectDtypes.time_select_dtype_bool_include(<class 'bool'>)
+      22.1?0.3?s          149?4?s     6.74
 indexing.NumericSeriesIndexing.time_getitem_slice(<class
'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
+        44.7?1?s         287?20?s     6.42
 dtypes.SelectDtypes.time_select_dtype_bool_exclude(<class 'complex'>)
+      49.3?0.8?s         314?20?s     6.36
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('Int16')
+        37.6?1ms        237?0.5ms     6.30
 tslibs.tz_convert.TimeTZConvert.time_tz_localize_to_utc(1000000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+      48.5?0.8?s         299?20?s     6.17
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('Int8')
+        50.1?1?s         308?20?s     6.15
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('Int32')
+      51.4?0.7?s         314?20?s     6.10
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('UInt8')
+      51.6?0.9?s         301?20?s     5.83
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('UInt16')
+      50.7?0.7?s         294?20?s     5.79
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('Int64')
+      52.4?0.7?s         297?20?s     5.67
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('UInt32')
+      53.0?0.8?s         296?20?s     5.59
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('UInt64')
+      61.2?0.8?s         332?10?s     5.43
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('uint8')
+        780?20ns      4.20?0.05?s     5.38
 tslibs.timestamp.TimestampProperties.time_freqstr(tzlocal(), 'B')
+      60.9?0.9?s         326?20?s     5.36
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('uint16')
+        786?20ns      4.20?0.07?s     5.35
 tslibs.timestamp.TimestampProperties.time_freqstr(None, 'B')
+        788?10ns      4.22?0.04?s     5.35
 tslibs.timestamp.TimestampProperties.time_freqstr(<DstTzInfo 'US/Pacific'
LMT-1 day, 16:07:00 STD>, 'B')
+        788?20ns      4.20?0.06?s     5.33
 tslibs.timestamp.TimestampProperties.time_freqstr(datetime.timezone(datetime.timedelta(seconds=3600)),
'B')
+        791?20ns      4.21?0.04?s     5.32
 tslibs.timestamp.TimestampProperties.time_freqstr(datetime.timezone.utc,
'B')
+      61.0?0.9?s         323?20?s     5.30
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('int16')
+        790?10ns      4.18?0.05?s     5.29
 tslibs.timestamp.TimestampProperties.time_freqstr(tzfile('/usr/share/zoneinfo/Asia/Tokyo'),
'B')
+      61.0?0.8?s         323?20?s     5.29
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('float64')
+      60.8?0.7?s         318?20?s     5.24
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('uint32')
+      60.9?0.8?s         318?20?s     5.22
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('float32')
+      61.9?0.9?s         321?20?s     5.19
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('datetime64[ns]')
+        60.7?1?s         315?20?s     5.19
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('complex64')
+      60.0?0.8?s         311?20?s     5.19
 dtypes.SelectDtypes.time_select_dtype_bool_include('bool')
+     1.81?0.02ms       9.36?0.2ms     5.17
 stat_ops.FrameOps.time_op('skew', 'int', 0)
+      60.7?0.8?s         314?20?s     5.17
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('M8[ns]')
+      60.8?0.6?s         314?20?s     5.16
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('m8[ns]')
+      61.7?0.8?s         318?20?s     5.15
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('timedelta64[ns]')
+     1.77?0.01ms       9.12?0.4ms     5.14
 stat_ops.FrameOps.time_op('kurt', 'int', 0)
+      60.9?0.8?s         313?20?s     5.14
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('int64')
+      61.0?0.9?s         311?20?s     5.10
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('int32')
+      61.0?0.7?s         310?20?s     5.09
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('uint64')
+      60.8?0.9?s         307?20?s     5.05
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('complex128')
+      60.7?0.9?s         304?20?s     5.01
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('int8')
+     4.30?0.02ms       21.1?0.2ms     4.90
 ctors.SeriesConstructors.time_series_constructor(<function arr_dict at
0x7f68db67e700>, False, 'int')
+         907?8ns      4.42?0.06?s     4.87
 tslibs.timestamp.TimestampProperties.time_freqstr(<DstTzInfo 'US/Pacific'
LMT-1 day, 16:07:00 STD>, None)
+        908?10ns      4.40?0.07?s     4.84
 tslibs.timestamp.TimestampProperties.time_freqstr(None, None)
+     4.39?0.02ms       21.2?0.2ms     4.83
 ctors.SeriesConstructors.time_series_constructor(<function arr_dict at
0x7f68db67e700>, True, 'int')
+         912?8ns      4.38?0.04?s     4.81
 tslibs.timestamp.TimestampProperties.time_freqstr(tzlocal(), None)
+        915?10ns      4.38?0.05?s     4.79
 tslibs.timestamp.TimestampProperties.time_freqstr(datetime.timezone(datetime.timedelta(seconds=3600)),
None)
+         913?8ns      4.37?0.07?s     4.79
 tslibs.timestamp.TimestampProperties.time_freqstr(datetime.timezone.utc,
None)
+        916?10ns      4.38?0.05?s     4.78
 tslibs.timestamp.TimestampProperties.time_freqstr(tzfile('/usr/share/zoneinfo/Asia/Tokyo'),
None)
+      18.8?0.3?s         86.0?2?s     4.57
 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(10000,
datetime.timezone(datetime.timedelta(seconds=3600)))
+         415?6?s      1.74?0.05ms     4.20
 stat_ops.FrameOps.time_op('sum', 'int', 0)
+     1.22?0.02ms       5.02?0.2ms     4.13
 stat_ops.FrameOps.time_op('var', 'int', 0)
+         616?7?s      2.53?0.04ms     4.10
 stat_ops.FrameOps.time_op('mean', 'int', 0)
+         716?4?s      2.93?0.01ms     4.09
 tslibs.tz_convert.TimeTZConvert.time_tz_localize_to_utc(10000, <DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>)
+     1.23?0.02ms       4.98?0.2ms     4.06
 stat_ops.FrameOps.time_op('std', 'int', 0)
+     1.04?0.02?s       4.12?0.2?s     3.98
 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(0,
datetime.timezone(datetime.timedelta(seconds=3600)))
+        629?10?s      2.49?0.09ms     3.96
 stat_ops.FrameOps.time_op('mean', 'int', 1)
+     2.41?0.03ms       9.46?0.2ms     3.93
 stat_ops.FrameOps.time_op('mad', 'int', 0)
+         409?5?s      1.60?0.04ms     3.90
 stat_ops.FrameOps.time_op('prod', 'int', 1)
+      34.5?0.5?s          133?1?s     3.86
 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('string',
'nonunique_monotonic_inc')
+         360?3?s      1.37?0.01ms     3.80
 indexing.NonNumericSeriesIndexing.time_getitem_list_like('string',
'unique_monotonic_inc')
+         360?3?s      1.36?0.01ms     3.78
 indexing.NonNumericSeriesIndexing.time_getitem_list_like('string',
'non_monotonic')
+      19.8?0.3?s       74.6?0.4?s     3.76
 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('string',
'unique_monotonic_inc')
+      79.3?0.6ms        297?0.6ms     3.74
 tslibs.tz_convert.TimeTZConvert.time_tz_localize_to_utc(1000000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+     1.16?0.01ms      4.32?0.06ms     3.74
 timeseries.TzLocalize.time_infer_dst('US/Eastern')
+      20.0?0.2?s       74.5?0.4?s     3.73
 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('string',
'non_monotonic')
+      20.1?0.3?s       74.8?0.4?s     3.72
 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('string',
'nonunique_monotonic_inc')
+     9.45?0.04ms       34.6?0.1ms     3.66
 timeseries.DatetimeIndex.time_normalize('tz_aware')
+         330?4?s         1.21?0ms     3.66
 indexing.NonNumericSeriesIndexing.time_getitem_list_like('string',
'nonunique_monotonic_inc')
+      5.38?0.1?s       19.6?0.5?s     3.65
 timeseries.TzLocalize.time_infer_dst(tzutc())
+      27.8?0.4?s        101?0.5?s     3.63
 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('string',
'unique_monotonic_inc')
+      26.4?0.4?s       95.7?0.5?s     3.62
 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('string',
'non_monotonic')
+     9.99?0.05ms       35.1?0.1ms     3.51
 timeseries.DatetimeAccessor.time_dt_accessor_normalize('US/Eastern')
+        21.7?1ms         75.9?2ms     3.49
 frame_methods.MaskBool.time_frame_mask_bools
+     2.52?0.02ms      8.73?0.02ms     3.47
 stat_ops.Correlation.time_corr_wide('pearson')
+      6.05?0.1?s       20.9?0.6?s     3.46
 timeseries.TzLocalize.time_infer_dst('UTC')
+     4.84?0.07?s       16.6?0.6?s     3.43
 timeseries.TzLocalize.time_infer_dst(None)
+         415?3?s      1.39?0.07ms     3.35
 stat_ops.FrameOps.time_op('sum', 'int', 1)
+     2.93?0.05ms       9.58?0.3ms     3.27
 stat_ops.FrameOps.time_op('mad', 'int', 1)
+     1.63?0.02ms       5.26?0.2ms     3.23
 stat_ops.FrameOps.time_op('var', 'int', 1)
+      22.2?0.2?s       70.2?0.7?s     3.16  arithmetic.Ops2.time_series_dot
+         634?4?s      1.95?0.06ms     3.08
 stat_ops.FrameOps.time_op('prod', 'int', 0)
+     4.17?0.08?s       12.8?0.3?s     3.07
 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<Day>)
+     1.83?0.02ms       5.45?0.1ms     2.98
 stat_ops.FrameOps.time_op('std', 'int', 1)
+         109?1?s          294?3?s     2.70
 algos.isin.IsIn.time_isin('bool')
+     4.25?0.05?s       10.9?0.3?s     2.56
 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<Day>)
+         134?2?s          327?2?s     2.44
 algos.isin.IsIn.time_isin('boolean')
+      15.8?0.7?s         37.2?1?s     2.36
 series_methods.NanOps.time_func('mean', 1000, 'Int64')
+         480?6ns      1.08?0.02?s     2.26
 tslibs.tz_convert.TimeTZConvert.time_tz_localize_to_utc(100,
datetime.timezone.utc)
+      4.28?0.1?s       9.61?0.2?s     2.24
 index_cached_properties.IndexCache.time_engine('UInt64Index')
+         479?9ns      1.07?0.02?s     2.23
 tslibs.tz_convert.TimeTZConvert.time_tz_localize_to_utc(0,
datetime.timezone.utc)
+      5.11?0.1?s       11.4?0.2?s     2.22
 tslibs.timestamp.TimestampProperties.time_is_quarter_end(tzlocal(), 'B')
+      5.04?0.1?s       11.2?0.5?s     2.22
 tslibs.timestamp.TimestampProperties.time_is_year_start(tzfile('/usr/share/zoneinfo/Asia/Tokyo'),
'B')
+      5.04?0.1?s       11.2?0.3?s     2.22
 tslibs.timestamp.TimestampProperties.time_is_quarter_start(tzfile('/usr/share/zoneinfo/Asia/Tokyo'),
'B')
+      5.05?0.1?s       11.2?0.5?s     2.21
 tslibs.timestamp.TimestampProperties.time_is_year_start(datetime.timezone(datetime.timedelta(seconds=3600)),
'B')
+      5.07?0.1?s       11.2?0.5?s     2.21
 tslibs.timestamp.TimestampProperties.time_is_year_start(tzlocal(), 'B')
+      5.02?0.1?s       11.1?0.5?s     2.21
 tslibs.timestamp.TimestampProperties.time_is_quarter_start(<DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>, 'B')
+         481?7ns      1.06?0.03?s     2.21
 tslibs.tz_convert.TimeTZConvert.time_tz_localize_to_utc(1,
datetime.timezone.utc)
+     5.08?0.09?s       11.2?0.3?s     2.20
 tslibs.timestamp.TimestampProperties.time_is_month_start(tzfile('/usr/share/zoneinfo/Asia/Tokyo'),
'B')
+      5.07?0.1?s       11.2?0.4?s     2.20
 tslibs.timestamp.TimestampProperties.time_is_quarter_end(tzfile('/usr/share/zoneinfo/Asia/Tokyo'),
'B')
+      5.04?0.2?s       11.1?0.4?s     2.20
 tslibs.timestamp.TimestampProperties.time_is_year_start(<DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>, 'B')
+      5.05?0.1?s       11.1?0.3?s     2.20
 tslibs.timestamp.TimestampProperties.time_is_month_start(tzlocal(), 'B')
+      5.06?0.1?s       11.1?0.4?s     2.20
 tslibs.timestamp.TimestampProperties.time_is_month_start(<DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>, 'B')
+     5.02?0.09?s       11.0?0.5?s     2.20
 tslibs.timestamp.TimestampProperties.time_is_month_start(datetime.timezone(datetime.timedelta(seconds=3600)),
'B')
+      5.10?0.1?s       11.2?0.3?s     2.20
 tslibs.timestamp.TimestampProperties.time_is_month_end(tzlocal(), 'B')
+     1.04?0.01ms      2.29?0.02ms     2.19
 timeseries.DatetimeIndex.time_normalize('repeated')
+      3.89?0.1?s       8.53?0.2?s     2.19
 index_cached_properties.IndexCache.time_engine('Float64Index')
+      5.08?0.1?s       11.1?0.5?s     2.19
 tslibs.timestamp.TimestampProperties.time_is_year_end(datetime.timezone(datetime.timedelta(seconds=3600)),
'B')
+      5.07?0.2?s       11.1?0.4?s     2.19
 tslibs.timestamp.TimestampProperties.time_is_month_end(datetime.timezone(datetime.timedelta(seconds=3600)),
'B')
+      5.07?0.1?s       11.1?0.4?s     2.19
 tslibs.timestamp.TimestampProperties.time_is_month_end(tzfile('/usr/share/zoneinfo/Asia/Tokyo'),
'B')
+      5.05?0.1?s       11.0?0.4?s     2.18
 tslibs.timestamp.TimestampProperties.time_is_quarter_start(tzlocal(), 'B')
+      5.08?0.1?s       11.1?0.5?s     2.18
 tslibs.timestamp.TimestampProperties.time_is_month_end(<DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>, 'B')
+     1.05?0.02ms      2.30?0.02ms     2.18
 timeseries.DatetimeIndex.time_normalize('tz_naive')
+      5.07?0.1?s       11.0?0.4?s     2.18
 tslibs.timestamp.TimestampProperties.time_is_quarter_end(<DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>, 'B')
+      5.07?0.1?s       11.0?0.4?s     2.18
 tslibs.timestamp.TimestampProperties.time_is_quarter_start(datetime.timezone(datetime.timedelta(seconds=3600)),
'B')
+      5.06?0.1?s       11.0?0.5?s     2.17
 tslibs.timestamp.TimestampProperties.time_is_year_end(<DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>, 'B')
+      5.09?0.1?s       11.0?0.4?s     2.17
 tslibs.timestamp.TimestampProperties.time_is_quarter_end(datetime.timezone(datetime.timedelta(seconds=3600)),
'B')
+     5.13?0.07?s       11.1?0.4?s     2.17
 tslibs.timestamp.TimestampProperties.time_is_year_end(tzlocal(), 'B')
+      5.08?0.1?s       11.0?0.3?s     2.17
 tslibs.timestamp.TimestampProperties.time_is_year_end(tzfile('/usr/share/zoneinfo/Asia/Tokyo'),
'B')
+      4.91?0.1?s       10.6?0.5?s     2.16
 tslibs.timestamp.TimestampProperties.time_is_month_end(datetime.timezone.utc,
'B')
+     4.87?0.06?s       10.5?0.4?s     2.16
 tslibs.timestamp.TimestampProperties.time_is_year_start(datetime.timezone.utc,
'B')
+      4.88?0.1?s       10.5?0.3?s     2.15
 tslibs.timestamp.TimestampProperties.time_is_month_end(None, 'B')
+      4.85?0.1?s       10.4?0.4?s     2.15
 tslibs.timestamp.TimestampProperties.time_is_quarter_start(datetime.timezone.utc,
'B')
+      17.3?0.5?s         37.1?1?s     2.14
 series_methods.NanOps.time_func('mean', 1000, 'boolean')
+      4.88?0.1?s       10.4?0.5?s     2.13
 tslibs.timestamp.TimestampProperties.time_is_month_start(datetime.timezone.utc,
'B')
+     1.24?0.01ms      2.64?0.02ms     2.13
 timeseries.DatetimeAccessor.time_dt_accessor_normalize(tzutc())
+     4.91?0.07?s       10.4?0.4?s     2.13
 tslibs.timestamp.TimestampProperties.time_is_year_end(None, 'B')
+        950?10?s      2.02?0.03ms     2.12
 series_methods.NanOps.time_func('mean', 1000000, 'Int64')
+      4.92?0.1?s       10.4?0.6?s     2.12
 tslibs.timestamp.TimestampProperties.time_is_year_start(None, 'B')
+      4.93?0.1?s       10.4?0.4?s     2.12
 tslibs.timestamp.TimestampProperties.time_is_quarter_end(None, 'B')
+      4.89?0.1?s       10.3?0.4?s     2.12
 tslibs.timestamp.TimestampProperties.time_is_month_start(None, 'B')
+      4.91?0.1?s       10.4?0.4?s     2.11
 tslibs.timestamp.TimestampProperties.time_is_quarter_start(None, 'B')
+     1.25?0.01ms      2.64?0.01ms     2.11
 timeseries.DatetimeAccessor.time_dt_accessor_normalize('UTC')
+     4.92?0.08?s       10.4?0.4?s     2.11
 tslibs.timestamp.TimestampProperties.time_is_quarter_end(datetime.timezone.utc,
'B')
+     4.92?0.08?s       10.3?0.4?s     2.10
 tslibs.timestamp.TimestampProperties.time_is_year_end(datetime.timezone.utc,
'B')
+      12.3?0.2?s       25.8?0.3?s     2.09
 dtypes.SelectDtypes.time_select_dtype_float_include(<class 'int'>)
+      12.2?0.2?s       25.5?0.4?s     2.08
 dtypes.SelectDtypes.time_select_dtype_string_include(<class 'int'>)
+      12.2?0.2?s       25.4?0.4?s     2.08
 dtypes.SelectDtypes.time_select_dtype_float_exclude(<class 'float'>)
+      12.4?0.2?s       25.8?0.3?s     2.07
 dtypes.SelectDtypes.time_select_dtype_bool_include(<class 'float'>)
+      12.3?0.2?s       25.6?0.3?s     2.07
 dtypes.SelectDtypes.time_select_dtype_bool_include(<class 'int'>)
+      12.5?0.2?s       25.9?0.4?s     2.07
 dtypes.SelectDtypes.time_select_dtype_string_include(<class 'float'>)
+      12.3?0.2?s       25.4?0.4?s     2.06
 dtypes.SelectDtypes.time_select_dtype_int_exclude(<class 'int'>)
+      12.5?0.2?s       25.7?0.4?s     2.06
 dtypes.SelectDtypes.time_select_dtype_int_include(<class 'float'>)
+     1.23?0.01ms      2.54?0.02ms     2.06
 timeseries.DatetimeAccessor.time_dt_accessor_normalize(None)
+      10.1?0.2?s       20.5?0.5?s     2.03
 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<BusinessDay>)
+      5.66?0.9ms       11.0?0.4ms     1.95
 stat_ops.FrameOps.time_op('sem', 'int', 0)
+      10.1?0.1?s       19.5?0.5?s     1.93
 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessDay>)
+        967?10ns      1.86?0.02?s     1.93
 array.IntegerArray.time_constructor
+      5.17?0.2?s       9.83?0.3?s     1.90
 index_cached_properties.IndexCache.time_engine('TimedeltaIndex')
+        29.9?3ms         55.7?7ms     1.87
 gil.ParallelDatetimeFields.time_datetime_field_normalize
+         133?2?s        246?0.6?s     1.85
 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(10000, <DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>)
+      1.13?0.1ms       2.06?0.2ms     1.83
 inference.ToDatetimeFromIntsFloats.time_nanosec_int64
+      18.0?0.3?s       32.8?0.4?s     1.83
 dtypes.SelectDtypes.time_select_dtype_string_include(<class 'bool'>)
+      18.0?0.4?s       32.9?0.4?s     1.83
 dtypes.SelectDtypes.time_select_dtype_float_include(<class 'complex'>)
+        4.34?1ms       7.92?0.2ms     1.82
 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(1000000,
datetime.timezone(datetime.timedelta(seconds=3600)))
+      18.0?0.3?s       32.9?0.4?s     1.82
 dtypes.SelectDtypes.time_select_dtype_float_include(<class 'bool'>)
+      18.1?0.3?s       32.8?0.4?s     1.81
 dtypes.SelectDtypes.time_select_dtype_int_include(<class 'complex'>)
+      18.1?0.3?s       32.8?0.3?s     1.81
 dtypes.SelectDtypes.time_select_dtype_string_include(<class 'complex'>)
+      1.16?0.1ms       2.11?0.2ms     1.81
 inference.ToDatetimeFromIntsFloats.time_nanosec_uint64
+      18.2?0.3?s       32.8?0.4?s     1.80
 dtypes.SelectDtypes.time_select_dtype_bool_include(<class 'complex'>)
+      18.2?0.3?s       32.6?0.4?s     1.79
 dtypes.SelectDtypes.time_select_dtype_int_include(<class 'bool'>)
+      18.1?0.3?s       32.4?0.4?s     1.79
 dtypes.SelectDtypes.time_select_dtype_bool_exclude(<class 'bool'>)
+      21.0?0.3?s       37.5?0.6?s     1.78
 dtypes.SelectDtypes.time_select_dtype_float_include('Int8')
+      21.3?0.3?s       37.7?0.4?s     1.77
 dtypes.SelectDtypes.time_select_dtype_string_include('Int8')
+      21.6?0.4?s       38.3?0.5?s     1.77
 dtypes.SelectDtypes.time_select_dtype_float_include('Int16')
+      21.2?0.3?s       37.5?0.4?s     1.77
 dtypes.SelectDtypes.time_select_dtype_int_include('Int8')
+      21.6?0.5?s       38.2?0.5?s     1.76
 dtypes.SelectDtypes.time_select_dtype_int_include('Int16')
+      21.8?0.3?s       38.4?0.4?s     1.76
 dtypes.SelectDtypes.time_select_dtype_string_include('Int16')
+      21.3?0.3?s       37.1?0.5?s     1.74
 dtypes.SelectDtypes.time_select_dtype_bool_include('Int8')
+      22.0?0.4?s       38.1?0.4?s     1.73
 dtypes.SelectDtypes.time_select_dtype_bool_include('Int16')
+         225?6ms          389?2ms     1.73
 join_merge.Merge.time_merge_dataframes_cross(False)
+         225?7ms          390?4ms     1.73
 join_merge.Merge.time_merge_dataframes_cross(True)
+      22.4?0.4?s       38.6?0.6?s     1.73
 dtypes.SelectDtypes.time_select_dtype_string_include('Int32')
+      22.4?0.4?s       38.7?0.6?s     1.73
 dtypes.SelectDtypes.time_select_dtype_float_include('Int32')
+      22.9?0.5?s       39.4?0.7?s     1.72
 dtypes.SelectDtypes.time_select_dtype_float_include('Int64')
+      22.6?0.5?s       38.9?0.7?s     1.72
 dtypes.SelectDtypes.time_select_dtype_int_include('Int32')
+      7.28?0.3ms       12.5?0.3ms     1.71
 stat_ops.FrameOps.time_op('kurt', 'int', 1)
+      22.9?0.5?s       39.1?0.6?s     1.71
 dtypes.SelectDtypes.time_select_dtype_int_exclude('Int64')
+      23.0?0.4?s       39.3?0.6?s     1.71
 dtypes.SelectDtypes.time_select_dtype_string_include('Int64')
+      23.8?0.3?s       40.6?0.7?s     1.71
 dtypes.SelectDtypes.time_select_dtype_int_include('UInt16')
+      23.8?0.4?s       40.7?0.5?s     1.71
 dtypes.SelectDtypes.time_select_dtype_float_include('UInt16')
+      1.22?0.2ms       2.08?0.2ms     1.71
 inference.ToDatetimeFromIntsFloats.time_nanosec_float64
+      4.84?0.1?s       8.26?0.2?s     1.71
 index_cached_properties.IndexCache.time_engine('PeriodIndex')
+      6.55?0.2?s       11.2?0.3?s     1.71
 index_cached_properties.IndexCache.time_engine('DatetimeIndex')
+      23.7?0.4?s       40.5?0.5?s     1.70
 dtypes.SelectDtypes.time_select_dtype_string_include('UInt16')
+      23.7?0.3?s       40.3?0.4?s     1.70
 dtypes.SelectDtypes.time_select_dtype_float_include('UInt8')
+         176?2?s          299?2?s     1.70
 stat_ops.Correlation.time_corr('pearson')
+      23.7?0.4?s       40.2?0.6?s     1.70
 dtypes.SelectDtypes.time_select_dtype_string_include('UInt8')
+         734?8?s      1.24?0.01ms     1.69
 stat_ops.FrameOps.time_op('mean', 'Int64', 0)
+      22.7?0.4?s       38.4?0.6?s     1.69
 dtypes.SelectDtypes.time_select_dtype_bool_include('Int32')
+      23.7?0.5?s       40.0?0.6?s     1.69
 dtypes.SelectDtypes.time_select_dtype_int_include('UInt8')
+      70.0?0.9?s          118?2?s     1.69
 tslibs.timestamp.TimestampOps.time_floor(None)
+      24.5?0.4?s       41.3?0.5?s     1.69
 dtypes.SelectDtypes.time_select_dtype_float_include('UInt32')
+         171?1?s          288?2?s     1.69
 algos.isin.IsIn.time_isin_empty('bool')
+      23.3?0.3?s       39.4?0.4?s     1.69
 dtypes.SelectDtypes.time_select_dtype_bool_include('Int64')
+      23.8?0.3?s       40.1?0.6?s     1.69
 dtypes.SelectDtypes.time_select_dtype_bool_include('UInt8')
+      8.25?0.1ms       13.9?0.4ms     1.68
 stat_ops.FrameOps.time_op('skew', 'int', 1)
+      24.5?0.3?s       41.1?0.6?s     1.68
 dtypes.SelectDtypes.time_select_dtype_string_include('UInt32')
+      25.0?0.3?s       41.7?0.5?s     1.67
 dtypes.SelectDtypes.time_select_dtype_string_include('UInt64')
+      25.1?0.4?s       41.9?0.6?s     1.67
 dtypes.SelectDtypes.time_select_dtype_int_include('UInt64')
+      24.7?0.4?s       41.2?0.7?s     1.67
 dtypes.SelectDtypes.time_select_dtype_int_include('UInt32')
+      25.1?0.3?s       41.9?0.5?s     1.67
 dtypes.SelectDtypes.time_select_dtype_float_include('UInt64')
+      24.2?0.3?s       40.3?0.5?s     1.66
 dtypes.SelectDtypes.time_select_dtype_bool_include('UInt16')
+      24.7?0.3?s       41.0?0.5?s     1.66
 dtypes.SelectDtypes.time_select_dtype_bool_include('UInt32')
+        71.4?1?s          119?2?s     1.66
 tslibs.timestamp.TimestampOps.time_ceil(None)
+      25.4?0.4?s       41.7?0.5?s     1.64
 dtypes.SelectDtypes.time_select_dtype_bool_include('UInt64')
+      7.88?0.3ms       12.8?0.2ms     1.63
 join_merge.Join.time_join_dataframe_index_single_key_small(True)
+     3.42?0.07?s       5.57?0.2?s     1.63
 tslibs.offsets.OffestDatetimeArithmetic.time_add(<Day>)
+         330?2ms          536?3ms     1.62
 join_merge.MergeCategoricals.time_merge_object
+      79.5?0.7?s          129?2?s     1.62
 tslibs.timestamp.TimestampOps.time_floor(datetime.timezone.utc)
+      80.5?0.7?s          130?2?s     1.61
 tslibs.timestamp.TimestampOps.time_ceil(datetime.timezone.utc)
+         199?2?s          321?3?s     1.61
 algos.isin.IsIn.time_isin_empty('boolean')
+         309?2ms          496?2ms     1.61
 join_merge.MergeCategoricals.time_merge_on_cat_col
+      10.3?0.1?s       16.0?0.5?s     1.56
 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<YearEnd: month=12>)
+      4.32?0.1?s       6.71?0.2?s     1.55
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<Day>)
+      10.3?0.2?s       16.0?0.7?s     1.55
 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<QuarterBegin:
startingMonth=3>)
+      10.2?0.2?s       15.8?0.7?s     1.55
 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<YearBegin: month=1>)
+      10.4?0.1?s       16.0?0.5?s     1.55
 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<BYearEnd: month=12>)
+      10.1?0.2?s       15.6?0.5?s     1.55
 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<MonthBegin>)
+      32.2?0.6?s       49.5?0.6?s     1.54
 dtypes.SelectDtypes.time_select_dtype_float_include('complex64')
+      32.1?0.5?s       49.5?0.5?s     1.54
 dtypes.SelectDtypes.time_select_dtype_int_include('complex128')
+      10.3?0.1?s       15.9?0.4?s     1.54
 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<BYearBegin: month=1>)
+      32.2?0.4?s       49.5?0.5?s     1.54
 dtypes.SelectDtypes.time_select_dtype_float_include('complex128')
+      10.4?0.2?s       16.1?0.5?s     1.54
 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<QuarterEnd:
startingMonth=3>)
+      68.1?0.5?s        105?0.7?s     1.54
 categoricals.Concat.time_append_overlapping_index
+      32.3?0.3?s       49.6?0.6?s     1.54
 dtypes.SelectDtypes.time_select_dtype_string_include('uint16')
+      32.2?0.4?s       49.4?0.4?s     1.53
 dtypes.SelectDtypes.time_select_dtype_string_include('complex128')
+      33.1?0.5?s       50.8?0.7?s     1.53
 dtypes.SelectDtypes.time_select_dtype_float_include('datetime64[ns]')
+      10.4?0.3?s       16.0?0.7?s     1.53
 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<BusinessQuarterEnd:
startingMonth=3>)
+      32.3?0.4?s       49.5?0.6?s     1.53
 dtypes.SelectDtypes.time_select_dtype_float_include('uint16')
+      32.3?0.4?s       49.6?0.5?s     1.53
 dtypes.SelectDtypes.time_select_dtype_float_include('uint32')
+      32.3?0.5?s       49.5?0.5?s     1.53
 dtypes.SelectDtypes.time_select_dtype_int_include('uint32')
+      32.2?0.4?s       49.4?0.5?s     1.53
 dtypes.SelectDtypes.time_select_dtype_string_include('int8')
+      10.3?0.2?s       15.8?0.5?s     1.53
 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<SemiMonthEnd:
day_of_month=15>)
+      32.4?0.5?s       49.7?0.5?s     1.53
 dtypes.SelectDtypes.time_select_dtype_float_include('uint8')
+      10.1?0.2?s       15.4?0.5?s     1.53
 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<MonthBegin>)
+      32.3?0.4?s       49.4?0.6?s     1.53
 dtypes.SelectDtypes.time_select_dtype_string_include('float32')
+      32.4?0.5?s       49.6?0.5?s     1.53
 dtypes.SelectDtypes.time_select_dtype_string_include('uint64')
+      32.2?0.4?s       49.3?0.5?s     1.53
 dtypes.SelectDtypes.time_select_dtype_int_include('M8[ns]')
+     10.2?0.09?s       15.7?0.5?s     1.53
 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<BusinessMonthEnd>)
+      32.4?0.5?s       49.6?0.6?s     1.53
 dtypes.SelectDtypes.time_select_dtype_string_include('float64')
+      32.4?0.4?s       49.6?0.5?s     1.53
 dtypes.SelectDtypes.time_select_dtype_float_include('uint64')
+      32.4?0.6?s       49.6?0.6?s     1.53
 dtypes.SelectDtypes.time_select_dtype_int_include('float64')
+      32.5?0.5?s       49.7?0.5?s     1.53
 dtypes.SelectDtypes.time_select_dtype_float_include('int32')
+      10.2?0.2?s       15.7?0.3?s     1.53
 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<SemiMonthBegin:
day_of_month=15>)
+      13.3?0.2ms       20.3?0.1ms     1.53
 frame_methods.ToDict.time_to_dict_ints('index')
+      10.5?0.2?s       16.1?0.6?s     1.53
 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<BusinessQuarterBegin:
startingMonth=3>)
+      32.7?0.4?s       50.1?0.6?s     1.53
 dtypes.SelectDtypes.time_select_dtype_string_include('timedelta64[ns]')
+      32.3?0.5?s       49.4?0.6?s     1.53
 dtypes.SelectDtypes.time_select_dtype_string_include('bool')
+         347?1ms          531?3ms     1.53
 join_merge.MergeCategoricals.time_merge_on_cat_idx
+      32.4?0.4?s       49.5?0.5?s     1.53
 dtypes.SelectDtypes.time_select_dtype_int_include('uint8')
+     1.24?0.01ms      1.90?0.02ms     1.53
 series_methods.NanOps.time_func('mean', 1000000, 'boolean')
+      32.3?0.4?s       49.3?0.5?s     1.53
 dtypes.SelectDtypes.time_select_dtype_string_include('complex64')
+      32.3?0.5?s       49.3?0.7?s     1.53
 dtypes.SelectDtypes.time_select_dtype_float_include('bool')
+      32.7?0.4?s       50.0?0.4?s     1.53
 dtypes.SelectDtypes.time_select_dtype_string_include('uint8')
+      32.4?0.5?s       49.4?0.4?s     1.53
 dtypes.SelectDtypes.time_select_dtype_float_include('int8')
+      32.4?0.5?s       49.4?0.5?s     1.53
 dtypes.SelectDtypes.time_select_dtype_int_include('uint16')
+      32.5?0.6?s       49.5?0.5?s     1.53
 dtypes.SelectDtypes.time_select_dtype_string_include('m8[ns]')
+      32.4?0.5?s       49.3?0.5?s     1.53
 dtypes.SelectDtypes.time_select_dtype_int_include('complex64')
+      32.5?0.3?s       49.5?0.5?s     1.53
 dtypes.SelectDtypes.time_select_dtype_int_include('int8')
+      32.5?0.5?s       49.6?0.6?s     1.52
 dtypes.SelectDtypes.time_select_dtype_int_include('uint64')
+      32.5?0.3?s       49.6?0.7?s     1.52
 dtypes.SelectDtypes.time_select_dtype_float_include('int16')
+      33.1?0.5?s       50.5?0.4?s     1.52
 dtypes.SelectDtypes.time_select_dtype_string_include('datetime64[ns]')
+      32.4?0.4?s       49.4?0.6?s     1.52
 dtypes.SelectDtypes.time_select_dtype_bool_include('M8[ns]')
+      32.5?0.4?s       49.5?0.5?s     1.52
 dtypes.SelectDtypes.time_select_dtype_string_include('int32')
+      10.4?0.2?s       15.9?0.6?s     1.52
 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<QuarterBegin:
startingMonth=3>)
+      32.4?0.4?s       49.4?0.5?s     1.52
 dtypes.SelectDtypes.time_select_dtype_string_include('M8[ns]')
+      32.4?0.4?s       49.3?0.8?s     1.52
 dtypes.SelectDtypes.time_select_dtype_string_include('uint32')
+      10.2?0.2?s       15.5?0.6?s     1.52
 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<MonthEnd>)
+      32.3?0.5?s       49.2?0.6?s     1.52
 dtypes.SelectDtypes.time_select_dtype_float_include('m8[ns]')
+      32.3?0.5?s       49.3?0.5?s     1.52
 dtypes.SelectDtypes.time_select_dtype_int_exclude('int64')
+      32.4?0.3?s       49.3?0.5?s     1.52
 dtypes.SelectDtypes.time_select_dtype_bool_include('m8[ns]')
+      10.3?0.1?s       15.7?0.6?s     1.52
 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<YearBegin:
month=1>)
+      32.6?0.4?s       49.6?0.4?s     1.52
 dtypes.SelectDtypes.time_select_dtype_int_include('float32')
+     1.04?0.02?s       1.58?0.1?s     1.52
 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(0,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+      32.5?0.3?s       49.5?0.6?s     1.52
 dtypes.SelectDtypes.time_select_dtype_bool_include('int64')
+      32.5?0.4?s       49.4?0.5?s     1.52
 dtypes.SelectDtypes.time_select_dtype_float_include('int64')
+      32.2?0.4?s       49.0?0.5?s     1.52
 dtypes.SelectDtypes.time_select_dtype_float_exclude('float64')
+      32.6?0.4?s       49.5?0.6?s     1.52
 dtypes.SelectDtypes.time_select_dtype_int_include('int16')
+      32.5?0.4?s       49.3?0.7?s     1.52
 dtypes.SelectDtypes.time_select_dtype_bool_include('complex64')
+      32.5?0.4?s       49.5?0.7?s     1.52
 dtypes.SelectDtypes.time_select_dtype_float_include('float32')
+      32.6?0.4?s       49.6?0.5?s     1.52
 dtypes.SelectDtypes.time_select_dtype_bool_include('uint16')
+      32.5?0.4?s       49.4?0.5?s     1.52
 dtypes.SelectDtypes.time_select_dtype_bool_include('complex128')
+      32.4?0.4?s       49.3?0.5?s     1.52
 dtypes.SelectDtypes.time_select_dtype_string_include('int64')
+      32.5?0.5?s       49.4?0.5?s     1.52
 dtypes.SelectDtypes.time_select_dtype_float_include('M8[ns]')
+      32.6?0.3?s       49.5?0.6?s     1.52
 dtypes.SelectDtypes.time_select_dtype_bool_include('uint32')
+      32.4?0.4?s       49.2?0.6?s     1.52
 dtypes.SelectDtypes.time_select_dtype_int_include('bool')
+      32.5?0.4?s       49.4?0.4?s     1.52
 dtypes.SelectDtypes.time_select_dtype_int_include('m8[ns]')
+      10.4?0.2?s       15.8?0.5?s     1.52
 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<BYearBegin:
month=1>)
+      32.5?0.3?s       49.3?0.5?s     1.52
 dtypes.SelectDtypes.time_select_dtype_string_include('int16')
+      19.7?0.6?s       29.9?0.8?s     1.52
 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<CustomBusinessDay>)
+      33.0?0.3?s       50.1?0.6?s     1.52
 dtypes.SelectDtypes.time_select_dtype_float_include('timedelta64[ns]')
+      32.2?0.3?s       48.8?0.5?s     1.52
 dtypes.SelectDtypes.time_select_dtype_bool_exclude('bool')
+      33.4?0.4?s       50.6?0.6?s     1.52
 dtypes.SelectDtypes.time_select_dtype_int_include('datetime64[ns]')
+      17.9?0.3?s       27.1?0.2?s     1.52
 tslibs.tz_convert.TimeTZConvert.time_tz_localize_to_utc(100,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+      32.6?0.6?s       49.5?0.6?s     1.52
 dtypes.SelectDtypes.time_select_dtype_int_include('int32')
+      10.3?0.1?s       15.5?0.5?s     1.51
 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<BusinessMonthBegin>)
+      32.8?0.5?s       49.6?0.4?s     1.51
 dtypes.SelectDtypes.time_select_dtype_bool_include('uint64')
+      10.5?0.2?s       15.8?0.5?s     1.51
 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<YearEnd:
month=12>)
+      32.6?0.3?s       49.4?0.7?s     1.51
 dtypes.SelectDtypes.time_select_dtype_bool_include('float32')
+      23.8?0.6?s       36.0?0.7?s     1.51
 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<DateOffset: days=2,
months=2>)
+      10.1?0.2?s       15.3?0.5?s     1.51
 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<MonthEnd>)
+      33.2?0.4?s       50.1?0.6?s     1.51
 dtypes.SelectDtypes.time_select_dtype_int_include('timedelta64[ns]')
+      32.6?0.4?s       49.3?0.5?s     1.51
 dtypes.SelectDtypes.time_select_dtype_bool_include('int8')
+      32.7?0.4?s       49.5?0.6?s     1.51
 dtypes.SelectDtypes.time_select_dtype_bool_include('int32')
+     4.32?0.08?s       6.52?0.2?s     1.51
 tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<Day>)
+      10.5?0.1?s       15.9?0.6?s     1.51
 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessQuarterEnd:
startingMonth=3>)
+      32.8?0.3?s       49.5?0.6?s     1.51
 dtypes.SelectDtypes.time_select_dtype_bool_include('uint8')
+      33.2?0.4?s       50.1?0.6?s     1.51
 dtypes.SelectDtypes.time_select_dtype_bool_include('timedelta64[ns]')
+      32.7?0.3?s       49.3?0.7?s     1.51
 dtypes.SelectDtypes.time_select_dtype_bool_include('float64')
+      10.4?0.2?s       15.7?0.6?s     1.51
 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<QuarterEnd:
startingMonth=3>)
+      10.5?0.2?s       15.9?0.4?s     1.51
 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessQuarterBegin:
startingMonth=3>)
+      24.5?0.6?s       36.8?0.6?s     1.50
 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<DateOffset:
days=2, months=2>)
+      32.8?0.4?s       49.4?0.7?s     1.50
 dtypes.SelectDtypes.time_select_dtype_bool_include('int16')
+      10.5?0.2?s       15.7?0.6?s     1.50
 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<BYearEnd:
month=12>)
+      21.9?0.4?s       32.9?0.2?s     1.50
 tslibs.tz_convert.TimeTZConvert.time_tz_localize_to_utc(100, <DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>)
+      10.3?0.1?s       15.5?0.3?s     1.50
 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessMonthEnd>)
+      33.7?0.3?s       50.6?0.6?s     1.50
 dtypes.SelectDtypes.time_select_dtype_bool_include('datetime64[ns]')
+      10.2?0.1?s       15.3?0.5?s     1.50
 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessMonthBegin>)
+      10.4?0.2?s       15.5?0.5?s     1.49
 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<SemiMonthEnd:
day_of_month=15>)
+      8.29?0.1ms       12.4?0.1ms     1.49
 indexing.InsertColumns.time_assign_list_like_with_setitem
+      10.4?0.2?s       15.5?0.6?s     1.49
 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<SemiMonthBegin:
day_of_month=15>)
+         156?2?s          231?3?s     1.48
 algos.isin.IsIn.time_isin_empty('uint64')
+      10.8?0.4ms       16.0?0.3ms     1.48
 join_merge.Join.time_join_dataframe_index_shuffle_key_bigger_sort(True)
+       102?0.9?s          151?2?s     1.48
 tslibs.timestamp.TimestampOps.time_floor(datetime.timezone(datetime.timedelta(seconds=3600)))
+       102?0.8?s          152?2?s     1.48
 tslibs.timestamp.TimestampOps.time_ceil(datetime.timezone(datetime.timedelta(seconds=3600)))
+      19.7?0.7?s       29.1?0.9?s     1.48
 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<CustomBusinessDay>)
+      10.7?0.3ms       15.8?0.3ms     1.47
 join_merge.Join.time_join_dataframe_index_single_key_bigger(True)
+     18.8?0.08ms       27.6?0.8ms     1.47
 index_object.SetOperations.time_operation('monotonic', 'date_string',
'symmetric_difference')
+      4.92?0.1?s       7.23?0.2?s     1.47
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<Day>)
+         230?4?s          337?4?s     1.46
 arithmetic.Ops2.time_frame_series_dot
+      9.15?0.4ms       13.3?0.8ms     1.46
 arithmetic.AddOverflowArray.time_add_overflow_arr_mask_nan
+      9.20?0.4ms       13.3?0.8ms     1.45
 arithmetic.AddOverflowArray.time_add_overflow_b_mask_nan
+         656?8?s        946?100?s     1.44
 stat_ops.SeriesOps.time_op('mad', 'float')
+         640?9?s        923?100?s     1.44
 stat_ops.SeriesOps.time_op('mad', 'int')
+     20.7?0.07ms       29.7?0.2ms     1.44
 index_object.SetOperations.time_operation('non_monotonic', 'date_string',
'intersection')
+        734?20?s      1.05?0.02ms     1.44
 strings.StringArrayConstruction.time_string_array_with_nan_construction
+      26.3?0.1ms       37.7?0.6ms     1.43
 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1000000,
6000)
+     3.92?0.02ms      5.56?0.04ms     1.42
 series_methods.NanOps.time_func('var', 1000000, 'boolean')
+     2.46?0.08ms      3.48?0.03ms     1.42
 stat_ops.FrameOps.time_op('mad', 'float', 0)
+       271?0.9?s          383?3?s     1.41
 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(10000,
6000)
+     2.35?0.03?s      3.28?0.03?s     1.40
 series_methods.SeriesGetattr.time_series_datetimeindex_repr
+        755?20?s      1.04?0.04ms     1.37
 strings.StringArrayConstruction.time_string_array_construction
+        63.8?1?s       87.5?0.9?s     1.37
 timedelta.TimedeltaIndexing.time_union
+      37.9?0.6ms       51.5?0.4ms     1.36
 groupby.GroupByMethods.time_dtype_as_group('int16', 'mad',
'transformation', 1)
+      39.0?0.8ms       52.9?0.7ms     1.36
 groupby.GroupByMethods.time_dtype_as_field('int16', 'unique', 'direct', 1)
+         247?2?s          334?4?s     1.35
 algos.isin.IsIn.time_isin('uint64')
+      24.4?0.4ms       33.0?0.3ms     1.35
 groupby.GroupByMethods.time_dtype_as_field('float', 'mad',
'transformation', 1)
+      57.8?0.9ms         78.2?1ms     1.35
 groupby.GroupByMethods.time_dtype_as_group('int', 'unique', 'direct', 1)
+      56.4?0.8ms       76.2?0.9ms     1.35
 groupby.GroupByMethods.time_dtype_as_group('float', 'mad',
'transformation', 1)
+      38.9?0.9ms       52.5?0.7ms     1.35
 groupby.GroupByMethods.time_dtype_as_field('uint', 'unique', 'direct', 1)
+      39.5?0.8ms       53.2?0.6ms     1.35
 groupby.GroupByMethods.time_dtype_as_field('float', 'unique', 'direct', 1)
+      24.7?0.4ms       33.3?0.4ms     1.35
 groupby.GroupByMethods.time_dtype_as_field('uint', 'mad',
'transformation', 1)
+      39.2?0.9ms       52.8?0.7ms     1.35
 groupby.GroupByMethods.time_dtype_as_field('int', 'unique', 'direct', 1)
+      57.8?0.9ms         77.9?1ms     1.35
 groupby.GroupByMethods.time_dtype_as_group('uint', 'unique', 'direct', 1)
+      36.8?0.6ms       49.5?0.6ms     1.35
 groupby.GroupByMethods.time_dtype_as_group('int', 'mad', 'transformation',
1)
+      24.7?0.4ms       33.3?0.3ms     1.35
 groupby.GroupByMethods.time_dtype_as_field('int', 'mad', 'transformation',
1)
+      36.8?0.6ms       49.5?0.5ms     1.35
 groupby.GroupByMethods.time_dtype_as_group('uint', 'mad',
'transformation', 1)
+      57.3?0.9ms         77.0?1ms     1.34
 groupby.GroupByMethods.time_dtype_as_group('int16', 'unique', 'direct', 1)
+      24.2?0.4ms       32.5?0.4ms     1.34
 groupby.GroupByMethods.time_dtype_as_field('float', 'mad', 'direct', 1)
+      25.4?0.4ms       34.1?0.4ms     1.34
 groupby.GroupByMethods.time_dtype_as_field('float', 'mad',
'transformation', 5)
+      24.8?0.3ms       33.2?0.3ms     1.34
 groupby.GroupByMethods.time_dtype_as_field('int16', 'mad',
'transformation', 1)
+        984?10?s      1.32?0.02ms     1.34
 rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'int', 'sum')
+      11.7?0.5ms       15.7?0.5ms     1.34
 stat_ops.FrameOps.time_op('sem', 'int', 1)
+      25.7?0.4ms       34.4?0.3ms     1.34
 groupby.GroupByMethods.time_dtype_as_field('int', 'mad', 'transformation',
5)
+        91.1?2ms          122?2ms     1.34
 groupby.GroupByMethods.time_dtype_as_group('float', 'unique', 'direct', 1)
+      25.7?0.4ms       34.3?0.3ms     1.33
 groupby.GroupByMethods.time_dtype_as_field('int16', 'mad',
'transformation', 5)
+      36.7?0.6ms       48.9?0.5ms     1.33
 groupby.GroupByMethods.time_dtype_as_group('int', 'mad', 'direct', 1)
+      25.1?0.4ms       33.5?0.3ms     1.33
 groupby.GroupByMethods.time_dtype_as_field('float', 'mad', 'direct', 5)
+      24.5?0.4ms       32.7?0.3ms     1.33
 groupby.GroupByMethods.time_dtype_as_field('int16', 'mad', 'direct', 1)
+      24.6?0.4ms       32.8?0.3ms     1.33
 groupby.GroupByMethods.time_dtype_as_field('int', 'mad', 'direct', 1)
+      38.0?0.6ms       50.6?0.4ms     1.33
 groupby.GroupByMethods.time_dtype_as_group('int16', 'mad', 'direct', 1)
+        56.5?1ms       75.2?0.9ms     1.33
 groupby.GroupByMethods.time_dtype_as_group('float', 'mad', 'direct', 1)
+      25.8?0.4ms       34.3?0.3ms     1.33
 groupby.GroupByMethods.time_dtype_as_field('uint', 'mad',
'transformation', 5)
+        930?10?s      1.24?0.02ms     1.33
 rolling.ForwardWindowMethods.time_rolling('Series', 10, 'float', 'sum')
+      36.7?0.6ms       48.8?0.4ms     1.33
 groupby.GroupByMethods.time_dtype_as_group('uint', 'mad', 'direct', 1)
+        935?10?s      1.24?0.02ms     1.33
 rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'float', 'sum')
+         319?4ms          424?3ms     1.33
 stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'mad')
+      24.6?0.4ms       32.7?0.2ms     1.33
 groupby.GroupByMethods.time_dtype_as_field('uint', 'mad', 'direct', 1)
+      25.4?0.4ms       33.7?0.3ms     1.32
 groupby.GroupByMethods.time_dtype_as_field('int16', 'mad', 'direct', 5)
+      25.4?0.4ms       33.5?0.3ms     1.32
 groupby.GroupByMethods.time_dtype_as_field('uint', 'mad', 'direct', 5)
+     1.06?0.01ms      1.41?0.02ms     1.32
 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'int', 'sum')
+      25.4?0.4ms       33.5?0.4ms     1.32
 groupby.GroupByMethods.time_dtype_as_field('int', 'mad', 'direct', 5)
+      36.8?0.2ms       48.5?0.6ms     1.32
 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1000000,
7000)
+        987?10?s      1.30?0.02ms     1.32
 rolling.ForwardWindowMethods.time_rolling('Series', 10, 'int', 'sum')
+        94.9?1ms          125?2ms     1.32
 groupby.GroupByMethods.time_dtype_as_group('datetime', 'unique', 'direct',
1)
+      3.88?0.1?s       5.09?0.2?s     1.31
 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('datetime', 0,
datetime.timezone(datetime.timedelta(seconds=3600)))
+     1.08?0.01ms      1.41?0.02ms     1.31
 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'int', 'sum')
+      42.8?0.3ms       56.1?0.4ms     1.31
 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1000000,
8000)
+      18.5?0.6ms       24.3?0.1ms     1.31
 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(1000000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+     1.00?0.01ms      1.31?0.01ms     1.31
 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'float', 'sum')
+         812?3?s         1.06?0ms     1.31
 tslibs.resolution.TimeResolution.time_get_resolution('D', 10000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+        9.98?4ms         13.0?1ms     1.31  eval.Eval.time_mult('python',
'all')
+      81.3?0.3ms        106?0.3ms     1.30
 tslibs.resolution.TimeResolution.time_get_resolution('D', 1000000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+         377?2?s          490?2?s     1.30
 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(10000,
7000)
+      47.0?0.1ms       61.2?0.5ms     1.30
 frame_methods.Rename.time_dict_rename_both_axes
+      47.1?0.2ms       61.1?0.6ms     1.30
 frame_methods.Rename.time_rename_both_axes
+     1.01?0.01ms      1.31?0.01ms     1.30
 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'float',
'sum')
+         867?3?s         1.12?0ms     1.30
 tslibs.resolution.TimeResolution.time_get_resolution('h', 10000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+         436?1?s          565?3?s     1.30
 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(10000,
8000)
+      86.7?0.3ms        112?0.3ms     1.29
 tslibs.resolution.TimeResolution.time_get_resolution('h', 1000000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+      3.95?0.1?s       5.10?0.2?s     1.29
 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 0,
datetime.timezone(datetime.timedelta(seconds=3600)))
+      86.7?0.4ms        112?0.3ms     1.29
 tslibs.resolution.TimeResolution.time_get_resolution('s', 1000000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+         865?3?s         1.12?0ms     1.29
 tslibs.resolution.TimeResolution.time_get_resolution('m', 10000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+         868?2?s         1.12?0ms     1.29
 tslibs.resolution.TimeResolution.time_get_resolution('s', 10000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+      14.2?0.2ms       18.4?0.2ms     1.29
 groupby.Cumulative.time_frame_transform('Float64', 'cummax')
+      3.96?0.2?s       5.10?0.2?s     1.29
 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 0,
datetime.timezone(datetime.timedelta(seconds=3600)))
+      86.7?0.4ms        112?0.3ms     1.29
 tslibs.resolution.TimeResolution.time_get_resolution('m', 1000000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+         161?2?s          207?3?s     1.28
 tslibs.timestamp.TimestampOps.time_floor(tzlocal())
+        90.4?5ms          116?5ms     1.28
 gil.ParallelDatetimeFields.time_period_to_datetime
+      9.30?0.4ms       11.9?0.8ms     1.28
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+         144?1ms          184?1ms     1.28
 series_methods.SeriesConstructor.time_constructor_dict
+         163?1?s          208?3?s     1.28
 tslibs.timestamp.TimestampOps.time_ceil(tzlocal())
+     1.07?0.02ms      1.36?0.02ms     1.28
 rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'float', 'mean')
+      11.3?0.2ms       14.3?0.4ms     1.27
 groupby.Cumulative.time_frame_transform_many_nulls('Int64', 'cummin')
+     6.42?0.04ms      8.15?0.07ms     1.27
 join_merge.Concat.time_concat_small_frames(1)
+     1.09?0.01ms      1.38?0.02ms     1.27
 rolling.ForwardWindowMethods.time_rolling('Series', 10, 'float', 'mean')
+      52.2?0.2ms       66.2?0.5ms     1.27
 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1000000,
9000)
+     2.52?0.06ms       3.18?0.2ms     1.26
 rolling.Apply.time_rolling('DataFrame', 300, 'int', <function sum at
0x7f68e553ac10>, True)
+      11.5?0.2ms       14.5?0.3ms     1.26
 groupby.Cumulative.time_frame_transform_many_nulls('Int64', 'cummax')
+        23.0?3ms         28.9?3ms     1.26
 algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>,
20, 'outside')
+         198?2ms          249?4ms     1.26
 frame_methods.Iteration.time_iterrows
+      66.3?0.1ms       83.4?0.2ms     1.26
 tslibs.resolution.TimeResolution.time_get_resolution('h', 1000000,
datetime.timezone(datetime.timedelta(seconds=3600)))
+     1.03?0.02?s      1.30?0.06?s     1.26
 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(0, <DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>)
+     2.49?0.08ms       3.13?0.2ms     1.26
 rolling.Apply.time_rolling('Series', 300, 'int', <function sum at
0x7f68e553ac10>, True)
+         530?1?s          667?2?s     1.26
 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(10000,
9000)
+         657?1?s          826?3?s     1.26
 tslibs.resolution.TimeResolution.time_get_resolution('D', 10000,
datetime.timezone.utc)
+      65.7?0.1ms       82.5?0.2ms     1.26
 tslibs.resolution.TimeResolution.time_get_resolution('D', 1000000, None)
+     65.7?0.07ms       82.5?0.2ms     1.26
 tslibs.resolution.TimeResolution.time_get_resolution('D', 1000000,
datetime.timezone.utc)
+         668?2?s          839?3?s     1.26
 tslibs.resolution.TimeResolution.time_get_resolution('h', 10000,
datetime.timezone(datetime.timedelta(seconds=3600)))
+         661?2?s          830?4?s     1.26
 tslibs.resolution.TimeResolution.time_get_resolution('s', 10000, None)
+      67.1?0.1ms       84.2?0.3ms     1.26
 tslibs.resolution.TimeResolution.time_get_resolution('D', 1000000,
datetime.timezone(datetime.timedelta(seconds=3600)))
+      66.0?0.1ms       82.9?0.4ms     1.26
 tslibs.resolution.TimeResolution.time_get_resolution('s', 1000000, None)
+         658?2?s          826?3?s     1.25
 tslibs.resolution.TimeResolution.time_get_resolution('D', 10000, None)
+      66.0?0.1ms       82.8?0.3ms     1.25
 tslibs.resolution.TimeResolution.time_get_resolution('s', 1000000,
datetime.timezone.utc)
+      3.52?0.2ms       4.41?0.2ms     1.25
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+         676?1?s          847?3?s     1.25
 tslibs.resolution.TimeResolution.time_get_resolution('D', 10000,
datetime.timezone(datetime.timedelta(seconds=3600)))
+      21.8?0.4ms       27.4?0.4ms     1.25
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+         662?1?s          829?4?s     1.25
 tslibs.resolution.TimeResolution.time_get_resolution('s', 10000,
datetime.timezone.utc)
+      32.6?0.6?s       40.8?0.5?s     1.25
 boolean.TimeLogicalOps.time_or_scalar
+         337?3?s          421?2?s     1.25
 frame_methods.Quantile.time_frame_quantile(0)
+      67.3?0.1ms       84.2?0.2ms     1.25
 tslibs.resolution.TimeResolution.time_get_resolution('s', 1000000,
datetime.timezone(datetime.timedelta(seconds=3600)))
+      33.8?0.6?s       42.3?0.7?s     1.25
 boolean.TimeLogicalOps.time_and_scalar
+     1.16?0.02ms      1.45?0.02ms     1.25
 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'float',
'mean')
+         678?1?s          847?4?s     1.25
 tslibs.resolution.TimeResolution.time_get_resolution('s', 10000,
datetime.timezone(datetime.timedelta(seconds=3600)))
+     66.2?0.06ms       82.6?0.3ms     1.25
 tslibs.resolution.TimeResolution.time_get_resolution('m', 1000000, None)
+      66.2?0.1ms       82.6?0.3ms     1.25
 tslibs.resolution.TimeResolution.time_get_resolution('m', 1000000,
datetime.timezone.utc)
+         665?2?s          831?4?s     1.25
 tslibs.resolution.TimeResolution.time_get_resolution('h', 10000,
datetime.timezone.utc)
+         662?1?s          826?2?s     1.25
 tslibs.resolution.TimeResolution.time_get_resolution('m', 10000,
datetime.timezone.utc)
+         663?1?s          826?2?s     1.25
 tslibs.resolution.TimeResolution.time_get_resolution('m', 10000, None)
+     66.4?0.08ms       82.7?0.4ms     1.25
 tslibs.resolution.TimeResolution.time_get_resolution('h', 1000000,
datetime.timezone.utc)
+       163?0.7ms          204?5ms     1.25
 join_merge.MergeCategoricals.time_merge_cat
+     66.4?0.09ms       82.7?0.3ms     1.25
 tslibs.resolution.TimeResolution.time_get_resolution('h', 1000000, None)
+         665?2?s          828?4?s     1.25
 tslibs.resolution.TimeResolution.time_get_resolution('h', 10000, None)
+      4.00?0.1?s       4.98?0.3?s     1.24
 tslibs.resolution.TimeResolution.time_get_resolution('us', 1,
datetime.timezone(datetime.timedelta(seconds=3600)))
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datetime.timezone(datetime.timedelta(seconds=3600)))
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datetime.timezone(datetime.timedelta(seconds=3600)))
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+      4.68?0.2?s       5.81?0.4?s     1.24
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datetime.timezone(datetime.timedelta(seconds=3600)))
+      41.5?0.9ms       51.4?0.6ms     1.24
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datetime.timezone(datetime.timedelta(seconds=3600)))
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0x7f68e553ac10>, True)
+     1.94?0.04ms      2.40?0.05ms     1.24
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'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
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datetime.timezone(datetime.timedelta(seconds=3600)))
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 tslibs.resolution.TimeResolution.time_get_resolution('s', 100,
datetime.timezone(datetime.timedelta(seconds=3600)))
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datetime.timezone(datetime.timedelta(seconds=3600)))
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0x7f68e553ac10>, True)
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datetime.timezone.utc)
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+      3.89?0.1?s       4.79?0.3?s     1.23
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datetime.timezone(datetime.timedelta(seconds=3600)))
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datetime.timezone.utc)
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datetime.timezone.utc)
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datetime.timezone(datetime.timedelta(seconds=3600)))
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0x7f68e553ac10>, True)
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datetime.timezone(datetime.timedelta(seconds=3600)))
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datetime.timezone(datetime.timedelta(seconds=3600)))
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datetime.timezone.utc)
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datetime.timezone(datetime.timedelta(seconds=3600)))
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datetime.timezone(datetime.timedelta(seconds=3600)))
+     3.94?0.09?s       4.82?0.2?s     1.22
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datetime.timezone(datetime.timedelta(seconds=3600)))
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+      12.7?0.6ms       15.5?0.6ms     1.22
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<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+      10.8?0.1?s       13.2?0.3?s     1.22
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datetime.timezone(datetime.timedelta(seconds=3600)))
+     1.79?0.03ms      2.19?0.04ms     1.22
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'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
+     3.80?0.05ms       4.65?0.3ms     1.22
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Apply.<lambda> at 0x7f68d77f9280>, True)
+      64.4?0.3ms       78.7?0.4ms     1.22
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10000)
+     1.16?0.01?s      1.41?0.01?s     1.22
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+     1.15?0.01?s      1.40?0.01?s     1.22
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+      8.85?0.6ms       10.8?0.3ms     1.22
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+      4.07?0.1?s       4.96?0.3?s     1.22
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datetime.timezone(datetime.timedelta(seconds=3600)))
+     1.16?0.01?s      1.42?0.01?s     1.22
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+      3.43?0.2ms       4.18?0.3ms     1.22
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0x7f68e553ac10>, True)
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datetime.timezone(datetime.timedelta(seconds=3600)))
+      90.3?0.4ms        110?0.3ms     1.22
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<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+      3.93?0.1?s       4.78?0.3?s     1.22
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datetime.timezone(datetime.timedelta(seconds=3600)))
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+      3.96?0.2?s       4.82?0.3?s     1.22
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datetime.timezone(datetime.timedelta(seconds=3600)))
+      66.9?0.9ms         81.3?1ms     1.22
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1)
+         652?1?s          793?3?s     1.22
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10000)
+      39.6?0.4?s       48.0?0.7?s     1.21
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+      2.68?0.07s       3.25?0.09s     1.21
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+     1.16?0.01?s      1.41?0.01?s     1.21
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+      64.4?0.3ms       78.0?0.4ms     1.21
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11000)
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0x7f68e553ac10>, True)
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+      85.6?0.4ms        103?0.2ms     1.21
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<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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11000)
+      45.7?0.1ms       55.1?0.5ms     1.21
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datetime.timezone(datetime.timedelta(seconds=3600)))
+     3.86?0.08ms       4.65?0.2ms     1.20
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0x7f68d77f9280>, True)
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<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+     2.86?0.05ms       3.42?0.2ms     1.20
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Apply.<lambda> at 0x7f68d77f9280>, True)
+     7.40?0.02ms      8.87?0.05ms     1.20
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at 0x7f68d77f9280>, True)
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0x7f68e553ac10>, True)
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at 0x7f68d77f9280>, True)
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at 0x7f68d77f9280>, True)
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5)
+      11.8?0.2?s       14.0?0.4?s     1.19
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5)
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Apply.<lambda> at 0x7f68d77f9280>, True)
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3000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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datetime.timezone(datetime.timedelta(seconds=3600)))
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2000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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2011, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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1011, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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1000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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'float', 'mean')
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'transformation', 5)
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 groupby.GroupByMethods.time_dtype_as_field('float', 'skew',
'transformation', 1)
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 groupby.GroupByMethods.time_dtype_as_group('float', 'cumsum', 'direct', 5)
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 groupby.GroupByMethods.time_dtype_as_group('datetime', 'size', 'direct', 5)
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 groupby.GroupByMethods.time_dtype_as_field('int16', 'size', 'direct', 1)
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 groupby.GroupByMethods.time_dtype_as_group('int', 'size', 'direct', 5)
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 ctors.SeriesConstructors.time_series_constructor(<function no_change at
0x7f68db67e550>, False, 'float')
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 index_cached_properties.IndexCache.time_engine('CategoricalIndex')
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 groupby.GroupByMethods.time_dtype_as_group('int', 'cumsum', 'direct', 5)
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 ctors.SeriesConstructors.time_series_constructor(<function no_change at
0x7f68db67e550>, False, 'int')
+         452?5?s          528?5?s     1.17
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'float', 'sum')
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 rolling.Methods.time_method('Series', ('rolling', {'window': 10}),
'float', 'sum')
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 groupby.GroupByMethods.time_dtype_as_group('float', 'size', 'direct', 5)
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 groupby.GroupByMethods.time_dtype_as_field('datetime', 'size', 'direct', 1)
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 groupby.GroupByMethods.time_dtype_as_group('uint', 'cumsum', 'direct', 5)
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'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
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day, 16:07:00 STD>)
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 groupby.GroupByMethods.time_dtype_as_group('object', 'size', 'direct', 1)
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'transformation', 5)
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 groupby.Cumulative.time_frame_transform_many_nulls('int64', 'cummax')
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 rolling.Methods.time_method('Series', ('expanding', {}), 'int', 'count')
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'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
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 indexing.NumericSeriesIndexing.time_getitem_list_like(<class
'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
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16:07:00 STD>)
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 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1, 7000)
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datetime.timezone.utc)
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datetime.timezone(datetime.timedelta(seconds=3600)))
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7000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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'int', 'sum')
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datetime.timezone.utc)
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'float', 'sum')
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6000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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9000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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11000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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datetime.timezone(datetime.timedelta(seconds=3600)))
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datetime.timezone(datetime.timedelta(seconds=3600)))
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8000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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'transformation', 5)
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12000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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'int', 'sum')
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10000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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'transformation', 5)
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tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
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'count')
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4000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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4006, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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'int', 'sum')
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tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
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tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
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tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
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'transformation', 1)
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 tslibs.resolution.TimeResolution.time_get_resolution('us', 100,
datetime.timezone.utc)
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'transformation', 1)
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Apply.<lambda> at 0x7f68d77f9280>, True)
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3000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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'transformation', 1)
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'transformation', 1)
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tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
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 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'float',
'mean')
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datetime.timezone.utc)
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tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
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 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1, 10000)
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'transformation', 1)
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5000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'int', 'mean')
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datetime.timezone.utc)
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tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
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 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1000000,
2000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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'transformation', 1)
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2011, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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'sum')
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datetime.timezone(datetime.timedelta(seconds=3600)))
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1011, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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1000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
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datetime.timezone(datetime.timedelta(seconds=3600)))
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'transformation', 1)
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1)
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 algorithms.Factorize.time_factorize(False, True, 'datetime64[ns, tz]')
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 rolling.Methods.time_method('Series', ('expanding', {}), 'float', 'std')
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1)
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 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('add')
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datetime.timezone(datetime.timedelta(seconds=3600)))
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1)
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datetime.timezone.utc)
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'float', 'mean')
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7000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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9000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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6000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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8000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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12000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
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datetime.timezone(datetime.timedelta(seconds=3600)))
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10000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+       103?0.5ms        116?0.2ms     1.12
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11000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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'float', 'sum')
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'float', 'mean')
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'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
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'sum')
+         950?5?s      1.07?0.01ms     1.12  groupby.FillNA.time_df_bfill
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'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
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'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
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datetime.timezone(datetime.timedelta(seconds=3600)))
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4006, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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'float', 'mean')
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datetime.timezone(datetime.timedelta(seconds=3600)))
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4000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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1)
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5)
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'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
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'int', 'mean')
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'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
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5)
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datetime.timezone(datetime.timedelta(seconds=3600)))
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datetime.timezone(datetime.timedelta(seconds=3600)))
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'sum')
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datetime.timezone(datetime.timedelta(seconds=3600)))
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'days')
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datetime.timezone(datetime.timedelta(seconds=3600)))
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datetime.timezone(datetime.timedelta(seconds=3600)))
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datetime.timezone(datetime.timedelta(seconds=3600)))
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'kurt')
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'seconds')
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datetime.timezone(datetime.timedelta(seconds=3600)))
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datetime.timezone(datetime.timedelta(seconds=3600)))
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'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
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'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
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datetime.timezone(datetime.timedelta(seconds=3600)))
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datetime.timezone(datetime.timedelta(seconds=3600)))
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5000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
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'sum')
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'mean')
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 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('date', 1, None)
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datetime.timezone(datetime.timedelta(seconds=3600)))
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'microseconds')
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datetime.timezone.utc)
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datetime.timezone(datetime.timedelta(seconds=3600)))
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None)
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datetime.timezone.utc)
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datetime.timezone.utc)
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datetime.timezone(datetime.timedelta(seconds=3600)))
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datetime.timezone.utc)
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datetime.timezone(datetime.timedelta(seconds=3600)))
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datetime.timezone(datetime.timedelta(seconds=3600)))
+      16.4?0.5ms       18.0?0.5ms     1.10
 join_merge.Join.time_join_dataframe_index_multi(False)
+      1.97?0.5ms       2.17?0.5ms     1.10
 rolling.VariableWindowMethods.time_method('Series', '50s', 'int', 'count')
+      45.5?0.4ms       50.1?0.8ms     1.10
 rolling.Pairwise.time_groupby(({'window': 1000}, 'rolling'), 'corr', True)
+      1.99?0.5ms       2.19?0.5ms     1.10
 rolling.VariableWindowMethods.time_method('Series', '1h', 'float', 'count')
+      1.95?0.5ms       2.15?0.5ms     1.10
 rolling.VariableWindowMethods.time_method('Series', '1d', 'int', 'count')
+      12.8?0.6ms       14.0?0.3ms     1.10
 groupby.Cumulative.time_frame_transform('float64', 'cumsum')
+        925?50?s      1.02?0.06ms     1.10
 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class
'numpy.int64'>, 4, <built-in function eq>)
+     1.77?0.06?s      1.95?0.07?s     1.10
 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 1,
datetime.timezone.utc)
+      4.05?0.1?s       4.46?0.3?s     1.10
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 10000,
datetime.timezone(datetime.timedelta(seconds=3600)))
+      4.02?0.2?s       4.43?0.3?s     1.10
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 4000,
datetime.timezone(datetime.timedelta(seconds=3600)))
+        32.9?4ms         36.2?6ms     1.10
 gil.ParallelGroupbyMethods.time_loop(2, 'last')
-        80.0?2?s         72.6?2?s     0.91
 indexing.CategoricalIndexIndexing.time_getitem_list_like('monotonic_incr')
-      75.2?0.8?s       68.3?0.7?s     0.91
 tslibs.period.PeriodUnaryMethods.time_to_timestamp('min')
-     1.40?0.02ms      1.27?0.01ms     0.91
 reindex.DropDuplicates.time_frame_drop_dups_bool(True)
-      16.4?0.3ms       14.9?0.2ms     0.91
 rolling.Groupby.time_method('sum', ('expanding', {}))
-         275?2ns          249?4ns     0.91
 tslibs.timestamp.TimestampProperties.time_is_month_end(<DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>, None)
-      4.93?0.1?s       4.47?0.1?s     0.91
 indexing.NumericSeriesIndexing.time_getitem_scalar(<class
'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
-      16.5?0.3ms       14.9?0.2ms     0.91
 rolling.Groupby.time_method('kurt', ('expanding', {}))
-        79.8?1?s         72.4?2?s     0.91
 indexing.CategoricalIndexIndexing.time_getitem_list_like('non_monotonic')
-      55.6?0.4ms         50.4?3ms     0.91
 io.stata.Stata.time_read_stata('tm')
-        76.0?1ms         68.8?1ms     0.91
 groupby.GroupByMethods.time_dtype_as_group('float', 'skew',
'transformation', 5)
-        80.0?2?s         72.5?2?s     0.91
 indexing.CategoricalIndexIndexing.time_getitem_list_like('monotonic_decr')
-      16.4?0.4ms       14.9?0.3ms     0.91
 rolling.Groupby.time_method('max', ('expanding', {}))
-         275?1ns          249?4ns     0.91
 tslibs.timestamp.TimestampProperties.time_is_month_end(datetime.timezone.utc,
None)
-      54.5?0.4ms         49.4?3ms     0.91
 io.stata.Stata.time_read_stata('td')
-      36.5?0.2ms       33.0?0.4ms     0.91
 strings.Methods.time_join('str')
-      12.2?0.2?s       11.0?0.2?s     0.90
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<QuarterEnd:
startingMonth=3>)
-      19.4?0.9ms       17.5?0.4ms     0.90
 rolling.Groupby.time_method('kurt', ('rolling', {'window': '30s', 'on':
'C'}))
-     5.08?0.09?s      4.59?0.07?s     0.90
 indexing.CategoricalIndexIndexing.time_get_loc_scalar('monotonic_incr')
-      54.5?0.7ms         49.3?1ms     0.90
 frame_ctor.FromDicts.time_nested_dict_int64
-        51.8?3ms         46.8?3ms     0.90  strings.Cat.time_cat(3, None,
None, 0.15)
-         217?4?s          196?4?s     0.90
 algos.isin.IsIn.time_isin_empty('string[python]')
-      9.10?0.2?s      8.23?0.09?s     0.90
 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(100, 8000)
-         274?1ns          248?4ns     0.90
 tslibs.timestamp.TimestampProperties.time_is_month_end(None, None)
-      50.1?0.4ms       45.2?0.1ms     0.90
 frame_methods.Count.time_count_level_multi(0)
-      49.1?0.3ms       44.4?0.3ms     0.90
 frame_methods.Count.time_count_level_multi(1)
-      55.8?0.6ms         50.4?3ms     0.90
 io.stata.Stata.time_read_stata('tq')
-        18.7?2ms       16.9?0.8ms     0.90
 categoricals.Constructor.time_regular
-     1.31?0.01ms      1.19?0.01ms     0.90
 groupby.GroupByMethods.time_dtype_as_group('int', 'cumprod',
'transformation', 5)
-      34.8?0.2ms       31.4?0.1ms     0.90
 strings.Methods.time_fullmatch('str')
-         202?5?s          182?4?s     0.90
 algos.isin.IsIn.time_isin_empty('str')
-     2.37?0.02ms      2.14?0.02ms     0.90
 stat_ops.FrameOps.time_op('var', 'Int64', 0)
-     1.31?0.01ms      1.18?0.01ms     0.90
 groupby.GroupByMethods.time_dtype_as_group('uint', 'cumprod',
'transformation', 5)
-      8.36?0.2?s       7.53?0.1?s     0.90
 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(100, 7000)
-         278?2ns          251?4ns     0.90
 tslibs.timestamp.TimestampProperties.time_is_month_end(tzlocal(), None)
-      29.4?0.3?s       26.5?0.6?s     0.90
 tslibs.timestamp.TimestampOps.time_tz_convert(tzlocal())
-         253?3ns          228?4ns     0.90
 tslibs.timestamp.TimestampProperties.time_dayofyear(tzfile('/usr/share/zoneinfo/Asia/Tokyo'),
None)
-         252?3ns          227?4ns     0.90
 tslibs.timestamp.TimestampProperties.time_dayofyear(None, 'B')
-        73.9?1?s         66.4?2?s     0.90
 categoricals.CategoricalSlicing.time_getitem_list_like('monotonic_incr')
-         253?3ns          228?5ns     0.90
 tslibs.timestamp.TimestampProperties.time_dayofyear(<DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>, None)
-         253?3ns          228?4ns     0.90
 tslibs.timestamp.TimestampProperties.time_dayofyear(datetime.timezone.utc,
None)
-         262?2ns          236?5ns     0.90
 tslibs.timestamp.TimestampProperties.time_week(datetime.timezone(datetime.timedelta(seconds=3600)),
'B')
-         254?3ns          228?4ns     0.90
 tslibs.timestamp.TimestampProperties.time_dayofyear(datetime.timezone(datetime.timedelta(seconds=3600)),
None)
-      33.5?0.2ms       30.1?0.2ms     0.90
 strings.Methods.time_match('str')
-         184?2ms          165?1ms     0.90
 io.excel.WriteExcel.time_write_excel('xlwt')
-         253?3ns          228?4ns     0.90
 tslibs.timestamp.TimestampProperties.time_dayofyear(datetime.timezone.utc,
'B')
-      2.63?0.1ms       2.36?0.1ms     0.90
 frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'datetime64[ns]')
-      39.5?0.4?s       35.4?0.3?s     0.90
 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 100,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      12.0?0.2?s       10.8?0.3?s     0.90
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<BYearEnd:
month=12>)
-         254?3ns          228?3ns     0.90
 tslibs.timestamp.TimestampProperties.time_dayofyear(tzfile('/usr/share/zoneinfo/Asia/Tokyo'),
'B')
-         253?4ns          227?4ns     0.90
 tslibs.timestamp.TimestampProperties.time_dayofyear(datetime.timezone(datetime.timedelta(seconds=3600)),
'B')
-         256?3ns          230?5ns     0.90
 tslibs.timestamp.TimestampProperties.time_dayofyear(tzlocal(), None)
-         262?3ns          235?5ns     0.90
 tslibs.timestamp.TimestampProperties.time_week(tzfile('/usr/share/zoneinfo/Asia/Tokyo'),
'B')
-     3.41?0.02?s      3.06?0.05?s     0.90
 indexing.NonNumericSeriesIndexing.time_getitem_scalar('string',
'unique_monotonic_inc')
-      16.1?0.2ms       14.5?0.1ms     0.90
 stat_ops.FrameMultiIndexOps.time_op(0, 'skew')
-        74.0?2?s         66.4?1?s     0.90
 categoricals.CategoricalSlicing.time_getitem_list_like('non_monotonic')
-      11.8?0.2?s       10.5?0.4?s     0.90
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<QuarterBegin:
startingMonth=3>)
-      1.28?0.2ms      1.15?0.07ms     0.90
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-      8.57?0.2ms       7.68?0.2ms     0.90
 series_methods.NanOps.time_func('median', 1000000, 'int8')
-         265?2ns          238?5ns     0.90
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-      55.8?0.5ms         49.9?3ms     0.90
 io.stata.Stata.time_read_stata('th')
-         263?2ns          235?5ns     0.90
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None)
-         253?3ns          226?4ns     0.89
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-     1.33?0.01ms      1.19?0.01ms     0.89
 groupby.GroupByMethods.time_dtype_as_group('int16', 'cumprod',
'transformation', 5)
-         263?2ns          235?5ns     0.89
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LMT-1 day, 16:07:00 STD>, 'B')
-      56.0?0.6?s       50.1?0.6?s     0.89
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-         191?2?s          171?2?s     0.89
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-      6.57?0.4ms       5.87?0.2ms     0.89
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'numpy.int64'>, 5.0, <built-in function pow>)
-      13.3?0.1?s      11.9?0.06?s     0.89
 tslibs.resolution.TimeResolution.time_get_resolution('ns', 100, <DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      37.6?0.3?s       33.6?0.8?s     0.89
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-         378?6?s          338?7?s     0.89
 reindex.Reindex.time_reindex_dates
-      12.0?0.2?s       10.7?0.3?s     0.89
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<BusinessQuarterBegin:
startingMonth=3>)
-        74.2?2?s         66.3?2?s     0.89
 categoricals.CategoricalSlicing.time_getitem_list_like('monotonic_decr')
-      37.1?0.5?s       33.1?0.6?s     0.89
 indexing.NumericSeriesIndexing.time_iloc_list_like(<class
'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
-         255?3ns          228?4ns     0.89
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'US/Pacific' LMT-1 day, 16:07:00 STD>, 'B')
-         236?3ns          211?5ns     0.89
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'B')
-         235?3ns          210?4ns     0.89
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'B')
-      31.2?0.5ms       27.8?0.5ms     0.89  strings.Methods.time_pad('str')
-      54.3?0.6ms         48.4?3ms     0.89
 io.stata.Stata.time_read_stata('ty')
-         262?3ns          234?5ns     0.89
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-         322?2?s          287?2?s     0.89
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-      11.9?0.2?s       10.6?0.2?s     0.89
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startingMonth=3>)
-         236?2ns          210?5ns     0.89
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None)
-         235?2ns          209?4ns     0.89
 tslibs.timestamp.TimestampProperties.time_days_in_month(None, 'B')
-     7.17?0.08ms      6.39?0.09ms     0.89
 io.csv.ToCSVDatetime.time_frame_date_formatting
-         264?3ns          236?5ns     0.89
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-     1.37?0.05?s      1.23?0.05?s     0.89
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-      11.8?0.2?s       10.5?0.3?s     0.89
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month=1>)
-     5.30?0.05ms      4.72?0.05ms     0.89
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-     1.80?0.06?s      1.60?0.06?s     0.89
 index_cached_properties.IndexCache.time_shape('PeriodIndex')
-         235?3ns          209?4ns     0.89
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'B')
-      12.0?0.2?s       10.7?0.3?s     0.89
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month=1>)
-        57.0?1ms         50.8?1ms     0.89
 indexing.MultiIndexing.time_loc_all_null_slices(False)
-      2.60?0.1ms       2.32?0.1ms     0.89
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-         236?2ns          210?4ns     0.89
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'US/Pacific' LMT-1 day, 16:07:00 STD>, 'B')
-      12.0?0.3?s       10.7?0.4?s     0.89
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-      6.34?0.1?s       5.64?0.2?s     0.89
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-         237?3ns          211?5ns     0.89
 tslibs.timestamp.TimestampProperties.time_days_in_month(tzlocal(), 'B')
-         262?2ns          233?4ns     0.89
 tslibs.timestamp.TimestampProperties.time_week(None, 'B')
-         262?2ns          233?6ns     0.89
 tslibs.timestamp.TimestampProperties.time_week(datetime.timezone(datetime.timedelta(seconds=3600)),
None)
-      31.1?0.3ms       27.7?0.4ms     0.89
 strings.Methods.time_center('str')
-         257?3ns          228?5ns     0.89
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-      16.4?0.3?s       14.6?0.6?s     0.89
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-     7.20?0.04ms      6.40?0.02ms     0.89
 inference.ToTimedelta.time_convert_string_seconds
-         263?3ns          234?5ns     0.89
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LMT-1 day, 16:07:00 STD>, None)
-         246?1ms          219?1ms     0.89
 groupby.GroupByCythonAggEaDtypes.time_frame_agg('Float64', 'median')
-         262?3ns          233?4ns     0.89
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-      30.6?0.2ms       27.1?0.1ms     0.89  strings.Methods.time_get('str')
-         591?3?s          524?4?s     0.89
 indexing.NonNumericSeriesIndexing.time_getitem_list_like('period',
'unique_monotonic_inc')
-         262?3ns          233?5ns     0.89
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-     4.16?0.06ms      3.69?0.05ms     0.89
 algorithms.Factorize.time_factorize(True, False, 'Int64')
-       43.1?10ms        38.2?10ms     0.89
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19, 'inside')
-      8.13?0.1ms       7.20?0.2ms     0.89
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'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
-       133?0.7?s          118?1?s     0.89
 tslibs.period.PeriodProperties.time_property('M', 'end_time')
-      11.0?0.2?s       9.73?0.2?s     0.89
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startingMonth=3>)
-      25.1?0.1ms       22.2?0.1ms     0.88  strings.Methods.time_len('str')
-         235?3ns          208?5ns     0.88
 tslibs.timestamp.TimestampProperties.time_days_in_month(None, None)
-         598?3?s          529?3?s     0.88
 indexing.NonNumericSeriesIndexing.time_getitem_list_like('period',
'non_monotonic')
-      12.4?0.3?s       11.0?0.2?s     0.88
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<YearEnd:
month=12>)
-      11.7?0.2?s       10.4?0.6?s     0.88
 tslibs.timestamp.TimestampOps.time_normalize(datetime.timezone(datetime.timedelta(seconds=3600)))
-     1.85?0.03ms      1.64?0.02ms     0.88
 reindex.DropDuplicates.time_frame_drop_dups_bool(False)
-      10.9?0.2?s       9.60?0.3?s     0.88
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<YearBegin: month=1>)
-      37.3?0.5?s       33.0?0.4?s     0.88
 indexing.NumericSeriesIndexing.time_iloc_list_like(<class
'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
-      7.31?0.2?s       6.46?0.1?s     0.88
 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(100, 6000)
-      4.22?0.1?s      3.72?0.07?s     0.88
 libs.InferDtype.time_infer_dtype('empty')
-         113?1?s         99.8?2?s     0.88
 algos.isin.IsIn.time_isin_mismatched_dtype('string[pyarrow]')
-         624?4?s          550?4?s     0.88
 indexing.NonNumericSeriesIndexing.time_getitem_list_like('datetime',
'unique_monotonic_inc')
-         236?3ns          208?6ns     0.88
 tslibs.timestamp.TimestampProperties.time_days_in_month(<DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>, None)
-       120?0.4ms          106?1ms     0.88
 groupby.GroupByCythonAggEaDtypes.time_frame_agg('Int64', 'var')
-         292?1ms          258?3ms     0.88
 join_merge.MergeMultiIndex.time_merge_sorted_multiindex(('datetime64[ns]',
'int64'), 'outer')
-      37.4?0.5?s       32.9?0.4?s     0.88
 indexing.NumericSeriesIndexing.time_iloc_list_like(<class
'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
-      10.8?0.2?s       9.47?0.3?s     0.88
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<MonthEnd>)
-         629?3?s          554?4?s     0.88
 indexing.NonNumericSeriesIndexing.time_getitem_list_like('datetime',
'non_monotonic')
-      37.4?0.5?s       32.9?0.4?s     0.88
 indexing.NumericSeriesIndexing.time_iloc_list_like(<class
'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
-      37.5?0.6?s       33.0?0.4?s     0.88
 indexing.NumericSeriesIndexing.time_iloc_list_like(<class
'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
-      10.8?0.2?s       9.53?0.2?s     0.88
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<BusinessMonthBegin>)
-      11.0?0.1?s       9.67?0.2?s     0.88
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<BusinessQuarterEnd:
startingMonth=3>)
-      27.2?0.1ms       23.9?0.2ms     0.88
 strings.Contains.time_contains('str', True)
-         236?3ns          208?5ns     0.88
 tslibs.timestamp.TimestampProperties.time_days_in_month(datetime.timezone(datetime.timedelta(seconds=3600)),
None)
-      10.9?0.2?s       9.61?0.2?s     0.88
 tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<BusinessQuarterBegin:
startingMonth=3>)
-      10.8?0.1?s       9.52?0.3?s     0.88
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<QuarterBegin:
startingMonth=3>)
-      10.7?0.1?s       9.42?0.3?s     0.88
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<MonthBegin>)
-      40.1?0.6?s       35.2?0.5?s     0.88
 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(1, tzlocal())
-      10.9?0.2?s       9.58?0.2?s     0.88
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<BusinessQuarterBegin:
startingMonth=3>)
-         212?3ns          186?4ns     0.88
 tslibs.timestamp.TimestampProperties.time_is_leap_year(datetime.timezone(datetime.timedelta(seconds=3600)),
'B')
-      11.0?0.2?s       9.62?0.3?s     0.88
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<SemiMonthBegin:
day_of_month=15>)
-         236?3ns          207?5ns     0.88
 tslibs.timestamp.TimestampProperties.time_days_in_month(datetime.timezone.utc,
None)
-      10.9?0.2?s       9.52?0.2?s     0.88
 tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<BusinessQuarterEnd:
startingMonth=3>)
-      15.3?0.1?s      13.4?0.08?s     0.88
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 10000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      10.8?0.2?s       9.47?0.3?s     0.87
 tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<QuarterBegin:
startingMonth=3>)
-      10.9?0.2?s       9.58?0.3?s     0.87
 tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<BYearBegin: month=1>)
-         661?6?s          578?2?s     0.87
 algorithms.Factorize.time_factorize(True, False, 'boolean')
-      11.0?0.2?s       9.57?0.2?s     0.87
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<BusinessMonthEnd>)
-      11.6?0.3ms       10.1?0.3ms     0.87
 multiindex_object.GetLoc.time_small_get_loc_warm
-       117?0.4ms          102?1ms     0.87
 groupby.GroupByCythonAggEaDtypes.time_frame_agg('Int64', 'mean')
-      15.2?0.1?s       13.2?0.1?s     0.87
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 9000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      37.5?0.7?s       32.7?0.4?s     0.87
 indexing.NumericSeriesIndexing.time_iloc_list_like(<class
'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
-      15.2?0.1?s      13.3?0.08?s     0.87
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 4000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-         213?4ns          186?5ns     0.87
 tslibs.timestamp.TimestampProperties.time_is_leap_year(tzfile('/usr/share/zoneinfo/Asia/Tokyo'),
None)
-     2.53?0.03?s      2.21?0.03?s     0.87  period.Indexing.time_get_loc
-      15.3?0.2?s      13.4?0.08?s     0.87
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 12000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      10.2?0.1?s       8.88?0.2?s     0.87
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<BusinessMonthBegin>)
-      24.6?0.6?s       21.4?0.7?s     0.87
 tslibs.timestamp.TimestampOps.time_replace_tz(tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      12.1?0.3?s       10.5?0.3?s     0.87
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<SemiMonthBegin:
day_of_month=15>)
-      15.2?0.2?s      13.2?0.08?s     0.87
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 4006,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      11.1?0.2?s       9.70?0.2?s     0.87
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<BYearBegin:
month=1>)
-      11.1?0.3?s       9.62?0.3?s     0.87
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<BYearEnd: month=12>)
-     1.37?0.01ms      1.20?0.01ms     0.87
 categoricals.Concat.time_concat_non_overlapping_index
-      10.3?0.1?s       8.98?0.2?s     0.87
 tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<BusinessMonthEnd>)
-      15.2?0.1?s       13.2?0.1?s     0.87
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 11000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-         240?2ns          209?4ns     0.87
 tslibs.timestamp.TimestampProperties.time_days_in_month(tzlocal(), None)
-      15.0?0.1?s      13.1?0.08?s     0.87
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 7000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      10.2?0.1?s       8.83?0.2?s     0.87
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<MonthBegin>)
-         199?6ms          173?6ms     0.87
 index_object.Indexing.time_get_loc_non_unique_sorted('String')
-      15.9?0.1?s      13.8?0.09?s     0.87
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 5000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      10.2?0.2?s       8.83?0.2?s     0.87
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<MonthEnd>)
-      10.2?0.2?s       8.90?0.3?s     0.87
 tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<MonthEnd>)
-     1.37?0.06?s      1.19?0.06?s     0.87
 index_cached_properties.IndexCache.time_inferred_type('PeriodIndex')
-         143?2?s          125?2?s     0.87
 tslibs.period.PeriodProperties.time_property('min', 'end_time')
-      11.1?0.2?s       9.61?0.2?s     0.87
 tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<SemiMonthEnd:
day_of_month=15>)
-        84.7?2?s         73.5?1?s     0.87
 multiindex_object.GetLoc.time_large_get_loc
-      6.93?0.2?s       6.02?0.2?s     0.87
 index_object.Indexing.time_get_loc_non_unique_sorted('Int')
-      11.3?0.3?s       9.76?0.2?s     0.87
 tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<YearEnd: month=12>)
-         704?6?s          611?3?s     0.87
 groupby.GroupByMethods.time_dtype_as_group('float', 'cumprod',
'transformation', 1)
-      21.9?0.7ms       19.0?0.4ms     0.87
 io.style.Render.time_tooltips_render(36, 12)
-      15.1?0.1?s       13.1?0.1?s     0.87
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 8000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      11.0?0.2?s       9.52?0.3?s     0.87
 tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<BYearEnd: month=12>)
-         214?3ns          185?5ns     0.87
 tslibs.timestamp.TimestampProperties.time_is_leap_year(<DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>, None)
-      10.3?0.2?s       8.96?0.2?s     0.87
 tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<BusinessMonthBegin>)
-      10.2?0.1?s       8.82?0.2?s     0.87
 tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<MonthBegin>)
-      11.3?0.2?s       9.77?0.2?s     0.87
 tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<QuarterEnd:
startingMonth=3>)
-      11.0?0.3?s       9.53?0.2?s     0.87
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<SemiMonthEnd:
day_of_month=15>)
-      15.1?0.2?s      13.1?0.09?s     0.87
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 6000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-         212?3ns          184?5ns     0.87
 tslibs.timestamp.TimestampProperties.time_is_leap_year(datetime.timezone(datetime.timedelta(seconds=3600)),
None)
-      9.49?0.2?s       8.21?0.2?s     0.87
 tslibs.offsets.OffestDatetimeArithmetic.time_add(<SemiMonthEnd:
day_of_month=15>)
-      11.3?0.2?s       9.81?0.2?s     0.87
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<YearEnd: month=12>)
-         212?3ns          183?6ns     0.87
 tslibs.timestamp.TimestampProperties.time_is_leap_year(None, 'B')
-         225?2?s          195?1?s     0.86
 join_merge.Concat.time_concat_empty_left(0)
-         225?1?s          194?2?s     0.86
 join_merge.Concat.time_concat_empty_right(0)
-      9.39?0.1?s       8.11?0.2?s     0.86
 tslibs.offsets.OffestDatetimeArithmetic.time_add(<YearBegin: month=1>)
-         213?3ns          184?4ns     0.86
 tslibs.timestamp.TimestampProperties.time_is_leap_year(datetime.timezone.utc,
'B')
-         215?4ns          185?4ns     0.86
 tslibs.timestamp.TimestampProperties.time_is_leap_year(tzlocal(), 'B')
-      13.9?0.1?s       12.0?0.1?s     0.86
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 3000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-         296?2ms          256?4ms     0.86
 io.csv.ReadCSVEngine.time_read_bytescsv('python')
-      9.28?0.2?s       8.00?0.2?s     0.86
 tslibs.offsets.OffestDatetimeArithmetic.time_add(<MonthBegin>)
-      9.43?0.1?s       8.14?0.2?s     0.86
 tslibs.offsets.OffestDatetimeArithmetic.time_add(<SemiMonthBegin:
day_of_month=15>)
-      11.8?0.4?s       10.2?0.4?s     0.86
 multiindex_object.GetLoc.time_string_get_loc
-      10.9?0.1?s       9.36?0.2?s     0.86
 tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<YearBegin: month=1>)
-      9.47?0.1?s       8.16?0.2?s     0.86
 tslibs.offsets.OffestDatetimeArithmetic.time_add(<YearEnd: month=12>)
-      10.9?0.3?s       9.43?0.3?s     0.86
 tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<SemiMonthBegin:
day_of_month=15>)
-         213?4ns          184?5ns     0.86
 tslibs.timestamp.TimestampProperties.time_is_leap_year(datetime.timezone.utc,
None)
-      12.3?0.2ms      10.6?0.09ms     0.86
 reindex.DropDuplicates.time_frame_drop_dups_na(False)
-         216?4ns          186?5ns     0.86
 tslibs.timestamp.TimestampProperties.time_is_leap_year(tzlocal(), None)
-      10.3?0.1?s       8.83?0.3?s     0.86
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<BusinessMonthEnd>)
-         213?3ns          183?5ns     0.86
 tslibs.timestamp.TimestampProperties.time_is_leap_year(None, None)
-      9.83?0.7ms       8.45?0.1ms     0.86
 timeseries.Iteration.time_iter_preexit(<function period_range at
0x7f68e010eaf0>)
-     13.9?0.09?s      11.9?0.09?s     0.86
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 1000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-     2.60?0.03?s      2.24?0.03?s     0.86
 indexing.NonNumericSeriesIndexing.time_getitem_scalar('string',
'non_monotonic')
-      12.1?0.2?s       10.4?0.2?s     0.86
 tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<SemiMonthEnd:
day_of_month=15>)
-         214?3ns          184?5ns     0.86
 tslibs.timestamp.TimestampProperties.time_is_leap_year(<DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>, 'B')
-      13.9?0.1ms       11.9?0.1ms     0.86
 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'median')
-      4.34?0.1ms       3.72?0.1ms     0.86
 reindex.DropDuplicates.time_frame_drop_dups(True)
-      24.2?0.1ms       20.7?0.1ms     0.86
 strings.Methods.time_endswith('str')
-      12.4?0.1?s       10.6?0.1?s     0.86
 tslibs.resolution.TimeResolution.time_get_resolution('s', 100,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      9.47?0.2?s       8.11?0.2?s     0.86
 tslibs.offsets.OffestDatetimeArithmetic.time_add(<QuarterBegin:
startingMonth=3>)
-      14.0?0.1?s      12.0?0.09?s     0.86
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 2011,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      9.54?0.2?s       8.16?0.2?s     0.86
 tslibs.offsets.OffestDatetimeArithmetic.time_add(<BYearEnd: month=12>)
-      9.64?0.2?s       8.24?0.2?s     0.86
 tslibs.offsets.OffestDatetimeArithmetic.time_add(<BusinessQuarterEnd:
startingMonth=3>)
-      22.2?0.3ms       18.9?0.2ms     0.86
 strings.Methods.time_slice('str')
-        31.0?1ms       26.5?0.7ms     0.86
 hash_functions.Float64GroupIndex.time_groupby
-      9.33?0.1?s       7.98?0.1?s     0.86
 tslibs.offsets.OffestDatetimeArithmetic.time_add(<BusinessMonthBegin>)
-      9.51?0.2?s       8.13?0.2?s     0.85
 tslibs.offsets.OffestDatetimeArithmetic.time_add(<BYearBegin: month=1>)
-         216?4ns          184?5ns     0.85
 tslibs.timestamp.TimestampProperties.time_is_leap_year(tzfile('/usr/share/zoneinfo/Asia/Tokyo'),
'B')
-      24.2?0.1ms       20.6?0.1ms     0.85
 strings.Methods.time_startswith('str')
-      9.50?0.1?s      8.12?0.09?s     0.85
 tslibs.offsets.OffestDatetimeArithmetic.time_add(<QuarterEnd:
startingMonth=3>)
-      9.32?0.1?s       7.97?0.1?s     0.85
 tslibs.offsets.OffestDatetimeArithmetic.time_add(<MonthEnd>)
-      24.9?0.3ms       21.3?0.3ms     0.85
 strings.Methods.time_title('str')
-      13.9?0.1?s       11.9?0.1?s     0.85
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 1011,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      11.9?0.3ms       10.2?0.3ms     0.85
 multiindex_object.GetLoc.time_med_get_loc_warm
-      14.0?0.1?s      11.9?0.08?s     0.85
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 2000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      10.6?0.1ms      9.07?0.09ms     0.85
 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'sem')
-        53.3?1ms         45.4?1ms     0.85
 join_merge.MergeMultiIndex.time_merge_sorted_multiindex(('Int64',
'Int64'), 'right')
-      9.50?0.1?s       8.10?0.1?s     0.85
 tslibs.offsets.OffestDatetimeArithmetic.time_add(<BusinessQuarterBegin:
startingMonth=3>)
-      9.33?0.2?s       7.95?0.2?s     0.85
 tslibs.offsets.OffestDatetimeArithmetic.time_add(<BusinessMonthEnd>)
-      14.6?0.3ms       12.5?0.3ms     0.85
 indexing.ChainIndexing.time_chained_indexing('warn')
-      2.56?0.1ms       2.18?0.1ms     0.85
 frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'timedelta64[ns]')
-      12.4?0.1?s       10.6?0.1?s     0.85
 tslibs.resolution.TimeResolution.time_get_resolution('D', 100,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-     1.43?0.05?s      1.22?0.05?s     0.85
 index_cached_properties.IndexCache.time_values('DatetimeIndex')
-      1.89?0.1?s      1.61?0.07?s     0.85
 index_cached_properties.IndexCache.time_shape('DatetimeIndex')
-      97.0?0.8?s         82.1?1?s     0.85
 indexing.IndexSingleRow.time_iloc_row(True)
-        9.09?2ms         7.70?2ms     0.85
 algorithms.Factorize.time_factorize(False, False, 'Int64')
-      12.3?0.2?s       10.4?0.1?s     0.85
 tslibs.resolution.TimeResolution.time_get_resolution('h', 100,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      21.3?0.3ms       18.0?0.3ms     0.85
 strings.Methods.time_upper('str')
-      24.4?0.8?s       20.6?0.6?s     0.85
 tslibs.timestamp.TimestampOps.time_replace_tz(<DstTzInfo 'US/Pacific'
LMT-1 day, 16:07:00 STD>)
-        14.5?2ms         12.2?2ms     0.85
 algorithms.Factorize.time_factorize(False, True, 'Int64')
-      97.5?0.8?s         82.4?1?s     0.85
 indexing.IndexSingleRow.time_iloc_row(False)
-         621?5?s          525?3?s     0.84
 groupby.GroupByMethods.time_dtype_as_group('uint', 'cumprod',
'transformation', 1)
-      11.7?0.6ms      9.92?0.06ms     0.84
 algos.isin.IsinWithArange.time_isin(<class 'numpy.object_'>, 8000, -2)
-         618?4?s          522?3?s     0.84
 groupby.GroupByMethods.time_dtype_as_group('int', 'cumprod',
'transformation', 1)
-      12.4?0.1?s       10.5?0.1?s     0.84
 tslibs.resolution.TimeResolution.time_get_resolution('m', 100,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      11.7?0.6ms       9.81?0.1ms     0.84
 algos.isin.IsinWithArange.time_isin(<class 'numpy.object_'>, 2000, -2)
-      11.6?0.6ms      9.78?0.02ms     0.84
 algos.isin.IsinWithArange.time_isin(<class 'numpy.object_'>, 1000, -2)
-      5.52?0.2?s       4.65?0.3?s     0.84
 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(1,
datetime.timezone(datetime.timedelta(seconds=3600)))
-         105?1?s       88.1?0.7?s     0.84
 indexing.IndexSingleRow.time_loc_row(False)
-      22.7?0.2ms       19.1?0.1ms     0.84
 strings.Methods.time_normalize('str')
-        39.9?1ms       33.5?0.5ms     0.84
 io.sql.WriteSQLDtypes.time_to_sql_dataframe_column('sqlite', 'time')
-     1.42?0.05?s      1.19?0.06?s     0.84
 index_cached_properties.IndexCache.time_inferred_type('DatetimeIndex')
-       140?0.8ms        117?0.4ms     0.84  strings.Slice.time_vector_slice
-       105?0.8?s       87.9?0.9?s     0.84
 indexing.IndexSingleRow.time_loc_row(True)
-      25.7?0.3ms       21.5?0.2ms     0.84
 io.style.Render.time_apply_format_hide_render(36, 120)
-      22.5?0.1ms       18.8?0.1ms     0.84
 strings.Methods.time_replace('str')
-        910?60ms          761?5ms     0.84
 timeseries.Iteration.time_iter(<function period_range at 0x7f68e010eaf0>)
-        696?30?s         581?20?s     0.83
 timeseries.DatetimeIndex.time_unique('repeated')
-      62.7?0.3ms       52.3?0.2ms     0.83
 groupby.GroupByCythonAggEaDtypes.time_frame_agg('Int32', 'sum')
-         574?6?s          478?3?s     0.83
 groupby.GroupByMethods.time_dtype_as_field('uint', 'cumprod',
'transformation', 1)
-     2.65?0.04?s      2.21?0.04?s     0.83
 index_object.Indexing.time_get_loc_sorted('Int')
-      8.16?0.2?s       6.78?0.2?s     0.83
 timeseries.AsOf.time_asof_single_early('Series')
-     2.65?0.04?s      2.20?0.03?s     0.83
 index_object.Indexing.time_get_loc('Int')
-         645?5?s          536?3?s     0.83
 groupby.GroupByMethods.time_dtype_as_group('int16', 'cumprod',
'transformation', 1)
-      17.2?0.3ms       14.3?0.3ms     0.83
 io.style.Render.time_tooltips_render(24, 12)
-     1.01?0.01ms          842?5?s     0.83
 categoricals.Concat.time_append_non_overlapping_index
-         576?4?s          477?3?s     0.83
 groupby.GroupByMethods.time_dtype_as_field('int', 'cumprod',
'transformation', 1)
-        53.6?2ms         44.3?1ms     0.83
 join_merge.MergeMultiIndex.time_merge_sorted_multiindex(('Int64',
'Int64'), 'left')
-      35.4?0.4ms       29.1?0.4ms     0.82
 groupby.GroupByCythonAgg.time_frame_agg('float64', 'min')
-         579?5?s          476?3?s     0.82
 groupby.GroupByMethods.time_dtype_as_field('int16', 'cummax',
'transformation', 1)
-      18.6?0.1ms       15.3?0.1ms     0.82
 strings.Methods.time_lstrip('str')
-      66.2?0.4ms         54.3?1ms     0.82
 groupby.GroupByCythonAggEaDtypes.time_frame_agg('Int32', 'var')
-      21.1?0.2ms       17.3?0.1ms     0.82
 strings.Methods.time_isalnum('str')
-         523?3?s          429?5?s     0.82
 indexing.DatetimeIndexIndexing.time_get_indexer_mismatched_tz
-         578?4?s          474?3?s     0.82
 groupby.GroupByMethods.time_dtype_as_field('int16', 'cummin',
'transformation', 1)
-      19.6?0.2ms      16.1?0.08ms     0.82
 strings.Methods.time_zfill('str')
-      7.55?0.1ms      6.18?0.08ms     0.82
 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'sum')
-      7.59?0.1ms      6.20?0.06ms     0.82
 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'mean')
-      7.66?0.8ms       6.25?0.2ms     0.82
 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('ge')
-      18.8?0.1ms       15.4?0.1ms     0.82
 strings.Contains.time_contains('str', False)
-      7.28?0.1ms      5.94?0.07ms     0.82
 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'prod')
-      7.77?0.1ms      6.34?0.09ms     0.82
 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'var')
-      18.3?0.1ms      15.0?0.08ms     0.82
 strings.Methods.time_strip('str')
-      5.77?0.3ms      4.70?0.09ms     0.81
 series_methods.NanOps.time_func('var', 1000000, 'Int64')
-      18.4?0.1ms      15.0?0.05ms     0.81
 strings.Methods.time_rstrip('str')
-      18.3?0.2ms       14.9?0.1ms     0.81
 strings.Methods.time_islower('str')
-      18.2?0.1ms       14.9?0.1ms     0.81
 strings.Methods.time_isalpha('str')
-      14.2?0.2?s       11.6?0.3?s     0.81
 indexing.NonNumericSeriesIndexing.time_getitem_scalar('datetime',
'nonunique_monotonic_inc')
-      9.03?0.1ms       7.34?0.1ms     0.81  strings.Cat.time_cat(0, ',',
'-', 0.15)
-      7.95?0.1ms       6.47?0.3ms     0.81  groupby.Float32.time_sum
-      8.90?0.2?s       7.16?0.1?s     0.80
 inference.MaybeConvertObjects.time_maybe_convert_objects
-         165?2?s          132?2?s     0.80
 timeseries.DatetimeIndex.time_add_timedelta('dst')
-      12.9?0.1?s       10.4?0.1?s     0.80
 tslibs.resolution.TimeResolution.time_get_resolution('us', 100,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      18.8?0.2ms       15.1?0.2ms     0.80
 strings.Methods.time_istitle('str')
-     6.57?0.06ms      5.27?0.06ms     0.80
 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}),
'float', 'sem')
-      7.68?0.6ms       6.16?0.1ms     0.80
 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('ne')
-      17.5?0.3ms       14.0?0.3ms     0.80
 strings.Methods.time_lower('str')
-      17.5?0.1ms       14.0?0.1ms     0.80
 strings.Methods.time_isupper('str')
-      55.0?0.2ms       44.0?0.3ms     0.80
 groupby.GroupByCythonAggEaDtypes.time_frame_agg('Int32', 'first')
-      7.63?0.8ms       6.09?0.1ms     0.80
 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('eq')
-      8.46?0.1ms       6.75?0.1ms     0.80  strings.Cat.time_cat(0, None,
None, 0.15)
-      8.90?0.1ms      7.10?0.04ms     0.80  strings.Cat.time_cat(0, None,
'-', 0.15)
-      13.3?0.3?s       10.6?0.3?s     0.80
 indexing.NonNumericSeriesIndexing.time_getitem_scalar('datetime',
'unique_monotonic_inc')
-      63.2?0.3ms         50.3?1ms     0.80
 groupby.GroupByCythonAggEaDtypes.time_frame_agg('Int32', 'mean')
-      19.3?0.2ms       15.3?0.1ms     0.80
 io.style.Render.time_apply_format_hide_render(24, 120)
-      7.86?0.2ms       6.25?0.2ms     0.79
 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('le')
-      8.47?0.2ms       6.73?0.4ms     0.79
 frame_methods.Fillna.time_frame_fillna(True, 'pad', 'datetime64[ns, tz]')
-        525?10?s          417?9?s     0.79
 index_cached_properties.IndexCache.time_is_monotonic_decreasing('MultiIndex')
-     6.64?0.05ms      5.27?0.05ms     0.79
 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}),
'int', 'sem')
-      8.70?0.1ms       6.91?0.1ms     0.79  strings.Cat.time_cat(0, ',',
None, 0.15)
-      9.97?0.2?s       7.91?0.2?s     0.79
 indexing.NonNumericSeriesIndexing.time_getitem_scalar('datetime',
'non_monotonic')
-      7.87?0.2ms       6.24?0.1ms     0.79
 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('gt')
-      16.2?0.2ms       12.8?0.1ms     0.79
 strings.Methods.time_isnumeric('str')
-      8.37?0.1ms      6.63?0.09ms     0.79  strings.Cat.time_cat(0, ',',
None, 0.001)
-      7.66?0.9ms       6.07?0.1ms     0.79
 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('lt')
-     6.65?0.06ms      5.26?0.04ms     0.79
 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}),
'int', 'sem')
-      8.13?0.1ms      6.44?0.09ms     0.79  strings.Cat.time_cat(0, None,
None, 0.001)
-     5.41?0.07?s       4.27?0.1?s     0.79
 timeseries.DatetimeIndex.time_get('repeated')
-     7.65?0.08ms      6.03?0.04ms     0.79
 stat_ops.SeriesMultiIndexOps.time_op(0, 'skew')
-         212?5ns          167?3ns     0.79
 tslibs.timedelta.TimedeltaProperties.time_timedelta_nanoseconds
-     6.62?0.05ms      5.22?0.04ms     0.79
 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}),
'float', 'sem')
-       140?0.4ms        110?0.4ms     0.79
 index_object.Indexing.time_get_loc_sorted('String')
-      8.38?0.3ms      6.59?0.06ms     0.79
 stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'median')
-      8.59?0.1ms      6.76?0.06ms     0.79
 stat_ops.FrameMultiIndexOps.time_op(1, 'median')
-       126?0.7ms       99.4?0.8ms     0.79
 reindex.Reindex.time_reindex_multiindex_with_cache
-      1.33?0.01s       1.04?0.01s     0.79
 io.excel.WriteExcelStyled.time_write_excel_style('openpyxl')
-       636?200?s         499?70?s     0.78
 series_methods.NanOps.time_func('sum', 1000000, 'int8')
-     8.40?0.08ms      6.59?0.07ms     0.78
 stat_ops.FrameMultiIndexOps.time_op(0, 'median')
-      8.15?0.1ms       6.39?0.1ms     0.78  strings.Cat.time_cat(0, None,
'-', 0.001)
-     1.43?0.02?s      1.12?0.02?s     0.78
 timedelta.TimedeltaIndexing.time_get_loc
-     1.04?0.02?s         813?20ns     0.78
 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(0,
datetime.timezone.utc)
-      16.3?0.1ms       12.8?0.1ms     0.78
 strings.Methods.time_isdecimal('str')
-      55.1?0.3ms       43.1?0.3ms     0.78
 groupby.GroupByCythonAggEaDtypes.time_frame_agg('Int32', 'prod')
-         386?2?s          302?2?s     0.78
 groupby.GroupByMethods.time_dtype_as_field('int16', 'rank', 'direct', 5)
-         349?2?s          273?2?s     0.78
 groupby.GroupByMethods.time_dtype_as_field('int16', 'rank', 'direct', 1)
-      3.76?0.1ms      2.93?0.05ms     0.78
 strftime.DatetimeStrftime.time_frame_datetime_formatting_custom(1000)
-      5.49?0.1?s       4.28?0.2?s     0.78
 timeseries.DatetimeIndex.time_get('tz_naive')
-      47.0?0.3ms       36.6?0.3ms     0.78
 groupby.GroupByCythonAggEaDtypes.time_frame_agg('Int32', 'max')
-      12.3?0.2?s       9.58?0.1?s     0.78
 tslibs.resolution.TimeResolution.time_get_resolution('ns', 100,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-     6.31?0.06ms      4.91?0.05ms     0.78
 rolling.Methods.time_method('Series', ('rolling', {'window': 1000}),
'float', 'sem')
-         407?5ns          317?4ns     0.78
 tslibs.timestamp.TimestampProperties.time_dayofweek(tzfile('/usr/share/zoneinfo/Asia/Tokyo'),
'B')
-     6.82?0.03ms      5.30?0.03ms     0.78
 libs.InferDtype.time_infer_dtype_skipna('py-null')
-      8.53?0.1ms       6.61?0.1ms     0.78  strings.Cat.time_cat(0, ',',
'-', 0.001)
-         407?4ns          315?6ns     0.78
 tslibs.timestamp.TimestampProperties.time_dayofweek(datetime.timezone(datetime.timedelta(seconds=3600)),
None)
-         406?3ns          315?4ns     0.78
 tslibs.timestamp.TimestampProperties.time_dayofweek(None, 'B')
-         407?4ns          315?5ns     0.77
 tslibs.timestamp.TimestampProperties.time_dayofweek(datetime.timezone.utc,
'B')
-         408?4ns          316?4ns     0.77
 tslibs.timestamp.TimestampProperties.time_dayofweek(<DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>, None)
-      5.50?0.1?s       4.26?0.1?s     0.77
 timeseries.DatetimeIndex.time_get('dst')
-      16.5?0.1ms       12.8?0.1ms     0.77
 strings.Methods.time_isdigit('str')
-         409?5ns          316?5ns     0.77
 tslibs.timestamp.TimestampProperties.time_dayofweek(<DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>, 'B')
-      16.1?0.1ms      12.4?0.09ms     0.77
 strings.Methods.time_isspace('str')
-     6.35?0.06ms      4.91?0.06ms     0.77
 rolling.Methods.time_method('Series', ('rolling', {'window': 1000}),
'int', 'sem')
-         407?3ns          315?5ns     0.77
 tslibs.timestamp.TimestampProperties.time_dayofweek(None, None)
-         408?3ns          315?4ns     0.77
 tslibs.timestamp.TimestampProperties.time_dayofweek(datetime.timezone(datetime.timedelta(seconds=3600)),
'B')
-         750?4ns          579?4ns     0.77
 index_object.Float64IndexMethod.time_get_loc
-         410?5ns          316?4ns     0.77
 tslibs.timestamp.TimestampProperties.time_dayofweek(tzlocal(), None)
-         839?6?s          647?5?s     0.77
 series_methods.ValueCountsEA.time_value_counts(1000, True)
-         654?4ms          504?5ms     0.77
 io.excel.ReadExcelNRows.time_read_excel('odf')
-         409?5ns          315?4ns     0.77
 tslibs.timestamp.TimestampProperties.time_dayofweek(tzfile('/usr/share/zoneinfo/Asia/Tokyo'),
None)
-         409?4ns          315?5ns     0.77
 tslibs.timestamp.TimestampProperties.time_dayofweek(datetime.timezone.utc,
None)
-     6.37?0.06ms      4.90?0.06ms     0.77
 rolling.Methods.time_method('Series', ('rolling', {'window': 10}), 'int',
'sem')
-      3.70?0.1ms      2.85?0.07ms     0.77
 strftime.DatetimeStrftime.time_frame_date_formatting_custom(1000)
-      14.5?0.2?s       11.2?0.1?s     0.77
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 5000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-         412?5ns          317?4ns     0.77
 tslibs.timestamp.TimestampProperties.time_dayofweek(tzlocal(), 'B')
-         603?6?s          463?3?s     0.77
 groupby.GroupByMethods.time_dtype_as_group('uint', 'sum', 'direct', 5)
-     6.33?0.07ms      4.86?0.04ms     0.77
 rolling.Methods.time_method('Series', ('rolling', {'window': 10}),
'float', 'sem')
-     1.45?0.03?s      1.12?0.04?s     0.77
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 12000,
tzlocal())
-        778?10?s          597?4?s     0.77
 indexing.MultiIndexing.time_loc_all_scalars(True)
-      13.8?0.2?s       10.6?0.1?s     0.77
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 11000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-         611?3?s          469?3?s     0.77
 groupby.GroupByMethods.time_dtype_as_group('int', 'sum', 'direct', 5)
-         602?4?s          462?4?s     0.77
 groupby.GroupByMethods.time_dtype_as_group('int16', 'sum', 'direct', 5)
-        74.7?2?s         57.1?2?s     0.76
 series_methods.NanOps.time_func('var', 1000, 'boolean')
-     1.45?0.02?s      1.11?0.04?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 2000,
tzlocal())
-     1.46?0.02?s      1.12?0.03?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 7000,
tzlocal())
-     6.42?0.06ms      4.91?0.04ms     0.76
 stat_ops.FrameMultiIndexOps.time_op(0, 'sem')
-     1.43?0.03?s      1.09?0.03?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 4000,
None)
-     1.46?0.03?s      1.11?0.03?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 2011,
tzlocal())
-     7.73?0.07ms      5.89?0.04ms     0.76
 stat_ops.SeriesMultiIndexOps.time_op(0, 'kurt')
-         589?5?s          449?3?s     0.76
 groupby.GroupByMethods.time_dtype_as_group('int', 'prod', 'direct', 5)
-      13.7?0.2?s       10.4?0.1?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 8000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      7.48?0.2ms      5.69?0.04ms     0.76
 stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'var')
-      13.6?0.2?s       10.4?0.1?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 7000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      13.7?0.1?s       10.4?0.1?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 9000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-         585?6?s          445?2?s     0.76
 groupby.GroupByMethods.time_dtype_as_group('uint', 'prod', 'direct', 5)
-      13.9?0.1?s      10.5?0.08?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 12000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-     1.46?0.04?s      1.11?0.04?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 2000,
tzlocal())
-     1.44?0.03?s      1.09?0.03?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 11000,
None)
-     1.46?0.02?s      1.11?0.04?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 5000,
tzlocal())
-      7.49?0.1ms      5.69?0.07ms     0.76  strings.Cat.time_cat(0, None,
'-', 0.0)
-     7.78?0.08ms      5.91?0.07ms     0.76  strings.Cat.time_cat(0, ',',
'-', 0.0)
-     1.46?0.04?s      1.11?0.05?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 8000,
tzlocal())
-      7.78?0.1ms      5.91?0.07ms     0.76  strings.Cat.time_cat(0, ',',
None, 0.0)
-         585?5?s          444?3?s     0.76
 groupby.GroupByMethods.time_dtype_as_group('int16', 'prod', 'direct', 5)
-      13.8?0.2?s       10.5?0.1?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 4000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-     1.46?0.04?s      1.10?0.03?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 6000,
tzlocal())
-     1.46?0.03?s      1.11?0.04?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 1000,
tzlocal())
-     1.45?0.03?s      1.10?0.03?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 1011,
None)
-     1.47?0.03?s      1.11?0.04?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 4000,
tzlocal())
-      13.5?0.2?s      10.3?0.08?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 6000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-     6.42?0.06ms      4.87?0.06ms     0.76
 stat_ops.FrameMultiIndexOps.time_op(1, 'sem')
-     1.46?0.03?s      1.11?0.04?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 3000,
tzlocal())
-     1.46?0.03?s      1.11?0.03?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4000,
tzlocal())
-     1.44?0.03?s      1.09?0.03?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 2011,
None)
-     1.46?0.03?s      1.11?0.03?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 1011,
tzlocal())
-     1.46?0.03?s      1.10?0.03?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 11000,
tzlocal())
-     1.46?0.04?s      1.11?0.04?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 12000,
tzlocal())
-     1.43?0.04?s      1.08?0.03?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4006,
None)
-     1.46?0.02?s      1.10?0.04?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 9000,
tzlocal())
-     1.46?0.04?s      1.11?0.03?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 10000,
tzlocal())
-     1.44?0.03?s      1.09?0.04?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 2000,
None)
-     1.46?0.02?s      1.10?0.04?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4006,
tzlocal())
-     1.46?0.02?s      1.10?0.03?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 10000,
tzlocal())
-     1.43?0.03?s      1.08?0.03?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 5000,
None)
-      13.9?0.2?s       10.5?0.1?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 4006,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-     1.43?0.03?s      1.08?0.03?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 2000,
None)
-     1.47?0.04?s      1.11?0.04?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 7000,
tzlocal())
-     1.44?0.03?s      1.09?0.03?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 1011,
None)
-     1.46?0.03?s      1.10?0.04?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 6000,
tzlocal())
-     1.46?0.03?s      1.10?0.04?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 2011,
tzlocal())
-     1.46?0.04?s      1.10?0.04?s     0.76
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 3000,
tzlocal())
-     1.47?0.02?s      1.11?0.04?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 4006,
tzlocal())
-      47.0?0.3ms       35.5?0.4ms     0.75
 groupby.GroupByCythonAggEaDtypes.time_frame_agg('Int32', 'min')
-     1.44?0.03?s      1.09?0.03?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 3000,
None)
-     1.46?0.03?s      1.10?0.04?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 1000,
tzlocal())
-      34.8?0.9ms       26.2?0.7ms     0.75
 strftime.DatetimeStrftime.time_frame_date_formatting_custom(10000)
-     1.47?0.04?s      1.11?0.03?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 1011,
tzlocal())
-      13.9?0.1?s       10.5?0.1?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 10000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      8.84?0.1ms       6.66?0.1ms     0.75
 frame_methods.Apply.time_apply_ref_by_name
-     1.44?0.03?s      1.08?0.03?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 3000,
None)
-     1.46?0.03?s      1.10?0.04?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 8000,
tzlocal())
-     1.45?0.04?s      1.09?0.03?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 9000,
None)
-     1.46?0.03?s      1.10?0.04?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 9000,
tzlocal())
-     1.45?0.03?s      1.09?0.03?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 8000,
None)
-      7.53?0.1ms      5.66?0.08ms     0.75
 ctors.DatetimeIndexConstructor.time_from_list_of_str
-     1.44?0.03?s      1.08?0.03?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 10000,
None)
-     1.45?0.02?s      1.09?0.03?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 6000,
None)
-     1.47?0.03?s      1.10?0.03?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 11000,
tzlocal())
-     1.47?0.04?s      1.10?0.04?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 5000,
tzlocal())
-     1.44?0.03?s      1.08?0.04?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 10000,
None)
-     1.43?0.03?s      1.08?0.03?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 2011,
None)
-      12.4?0.1ms       9.27?0.1ms     0.75
 io.style.Render.time_tooltips_render(12, 12)
-      7.59?0.1ms      5.70?0.08ms     0.75  strings.Cat.time_cat(0, None,
None, 0.0)
-     1.45?0.03?s      1.09?0.04?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 4006,
None)
-      7.24?0.3ms      5.43?0.07ms     0.75
 stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'mean')
-     1.44?0.03?s      1.08?0.03?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 12000,
None)
-     1.44?0.03?s      1.08?0.04?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4000,
None)
-         645?5?s          483?3?s     0.75
 groupby.GroupByMethods.time_dtype_as_group('float', 'sum', 'direct', 5)
-     1.45?0.03?s      1.08?0.03?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 11000,
None)
-      13.0?0.2?s       9.74?0.1?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 3000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-     1.45?0.03?s      1.08?0.03?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 1000,
None)
-     1.45?0.03?s      1.08?0.03?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 6000,
None)
-     1.44?0.04?s      1.08?0.04?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 9000,
None)
-     8.25?0.07ms      6.17?0.06ms     0.75
 stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'sem')
-      7.53?0.2ms      5.63?0.04ms     0.75
 stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'std')
-      13.0?0.1?s       9.73?0.1?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 2011,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-     1.46?0.03?s      1.09?0.03?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 1000,
None)
-     1.45?0.04?s      1.08?0.03?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 7000,
None)
-         557?4?s          416?3?s     0.75
 indexing.MultiIndexing.time_xs_full_key(True)
-     1.44?0.03?s      1.08?0.03?s     0.75
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 5000,
None)
-     1.45?0.04?s      1.08?0.03?s     0.74
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 7000,
None)
-     1.45?0.04?s      1.08?0.03?s     0.74
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 12000,
None)
-      7.12?0.2ms      5.30?0.08ms     0.74
 stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'prod')
-      13.2?0.2ms      9.81?0.08ms     0.74
 io.style.Render.time_apply_format_hide_render(36, 12)
-      13.0?0.2?s      9.68?0.09?s     0.74
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 2000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-     1.45?0.04?s      1.08?0.03?s     0.74
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 8000,
None)
-      7.36?0.2ms      5.48?0.06ms     0.74
 stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'sum')
-      58.1?0.3ms       43.0?0.4ms     0.74
 groupby.GroupByCythonAggEaDtypes.time_frame_agg('Float64', 'first')
-      13.0?0.1?s       9.59?0.1?s     0.74
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 1000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      47.2?0.3ms       34.8?0.5ms     0.74
 array.ArrowStringArray.time_setitem(True)
-         627?7?s          462?3?s     0.74
 groupby.GroupByMethods.time_dtype_as_group('float', 'prod', 'direct', 5)
-      5.02?0.1?s       3.70?0.2?s     0.74
 tslibs.timestamp.TimestampOps.time_replace_None(tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      13.0?0.2?s       9.58?0.1?s     0.74
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(100, 1011,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      65.0?0.4ms         47.8?1ms     0.74
 groupby.GroupByCythonAggEaDtypes.time_frame_agg('Float64', 'sum')
-      4.27?0.2?s      3.11?0.06?s     0.73
 tslibs.timedelta.TimedeltaConstructor.time_from_components
-      37.2?0.9ms       27.1?0.8ms     0.73
 strftime.DatetimeStrftime.time_frame_datetime_formatting_custom(10000)
-     1.34?0.04?s         977?30ns     0.73
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 9000,
datetime.timezone.utc)
-     1.34?0.03?s         973?30ns     0.73
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 9000,
datetime.timezone.utc)
-      55.2?0.3ms       40.1?0.5ms     0.73
 groupby.GroupByCythonAggEaDtypes.time_frame_agg('Int32', 'last')
-     1.34?0.03?s         970?30ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 4000,
datetime.timezone.utc)
-        600?20?s         435?20?s     0.72
 indexing.MultiIndexing.time_xs_full_key(False)
-     1.34?0.03?s         968?30ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 2011,
datetime.timezone.utc)
-     1.34?0.03?s         970?30ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 1000,
datetime.timezone.utc)
-     1.34?0.03?s         968?30ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 2000,
datetime.timezone.utc)
-     1.35?0.02?s         974?30ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 2000,
datetime.timezone.utc)
-     1.34?0.03?s         966?30ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 2011,
datetime.timezone.utc)
-     1.34?0.03?s         966?20ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 12000,
datetime.timezone.utc)
-     1.34?0.03?s         970?30ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 11000,
datetime.timezone.utc)
-     1.35?0.02?s         971?30ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 12000,
datetime.timezone.utc)
-     1.34?0.03?s         964?30ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 6000,
datetime.timezone.utc)
-      24.7?0.2ms       17.8?0.2ms     0.72
 array.ArrowStringArray.time_setitem(False)
-     1.33?0.02?s         962?30ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 1011,
datetime.timezone.utc)
-     1.34?0.03?s         969?30ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 3000,
datetime.timezone.utc)
-     1.34?0.03?s         967?30ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 7000,
datetime.timezone.utc)
-     1.34?0.03?s         963?30ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 10000,
datetime.timezone.utc)
-     1.34?0.02?s         965?30ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4000,
datetime.timezone.utc)
-     1.35?0.03?s         968?20ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 5000,
datetime.timezone.utc)
-     1.35?0.03?s         969?30ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 7000,
datetime.timezone.utc)
-       139?0.6ms       99.4?0.8ms     0.72
 reindex.Reindex.time_reindex_multiindex_no_cache
-        77.7?2?s         55.7?1?s     0.72
 series_methods.NanOps.time_func('var', 1000, 'Int64')
-     1.34?0.04?s         964?30ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 6000,
datetime.timezone.utc)
-     1.34?0.02?s         963?30ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 5000,
datetime.timezone.utc)
-     1.35?0.03?s         965?40ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 3000,
datetime.timezone.utc)
-     1.34?0.03?s         963?30ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 10000,
datetime.timezone.utc)
-     1.35?0.04?s         966?30ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 1000,
datetime.timezone.utc)
-     1.35?0.02?s         963?30ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 1011,
datetime.timezone.utc)
-     1.35?0.03?s         963?20ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 4006,
datetime.timezone.utc)
-     1.35?0.03?s         966?30ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 8000,
datetime.timezone.utc)
-     1.35?0.03?s         965?30ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 11000,
datetime.timezone.utc)
-      12.7?0.2ms      9.07?0.09ms     0.72
 io.style.Render.time_apply_format_hide_render(12, 120)
-     1.34?0.03?s         961?30ns     0.72
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4006,
datetime.timezone.utc)
-        880?30?s         629?30?s     0.72
 indexing.MultiIndexing.time_loc_all_scalars(False)
-      5.81?0.2?s      4.14?0.09?s     0.71
 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(10000,
datetime.timezone.utc)
-     1.35?0.04?s         960?20ns     0.71
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 8000,
datetime.timezone.utc)
-     6.12?0.07ms      4.36?0.05ms     0.71
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-      58.3?0.4ms         41.5?2ms     0.71
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-      30.6?0.7?s       21.7?0.4?s     0.71
 algorithms.SortIntegerArray.time_argsort(1000)
-      30.4?0.2ms       21.4?0.1ms     0.71
 io.excel.ReadExcelNRows.time_read_excel('xlrd')
-     2.03?0.03?s      1.43?0.03?s     0.71
 tslibs.timestamp.TimestampOps.time_tz_convert(datetime.timezone.utc)
-       110?0.4ms       76.4?0.6ms     0.70
 groupby.GroupByCythonAggEaDtypes.time_frame_agg('Int64', 'first')
-       110?0.5ms       76.1?0.6ms     0.69
 groupby.GroupByCythonAggEaDtypes.time_frame_agg('Int64', 'last')
-         201?3ms          139?7ms     0.69
 join_merge.MergeMultiIndex.time_merge_sorted_multiindex(('int64',
'int64'), 'outer')
-      27.3?0.2ms       18.8?0.2ms     0.69
 ctors.DatetimeIndexConstructor.time_from_list_of_timestamps
-        878?20?s         599?10?s     0.68
 groupby.Categories.time_groupby_sort
-      10.6?0.1ms      7.22?0.06ms     0.68
 io.style.Render.time_apply_format_hide_render(24, 12)
-        13.0?3ms       8.76?0.6ms     0.67
 arithmetic.FrameWithFrameWide.time_op_different_blocks(<built-in function
add>, (10000, 1000))
-         110?2ms       74.2?0.7ms     0.67
 groupby.GroupByCythonAggEaDtypes.time_frame_agg('Int64', 'prod')
-      68.9?0.5ms       46.3?0.6ms     0.67
 groupby.GroupByCythonAggEaDtypes.time_frame_agg('Float64', 'var')
-      4.90?0.3ms       3.27?0.3ms     0.67
 replace.FillNa.time_fillna(False)
-        893?20?s         593?10?s     0.66
 groupby.Categories.time_groupby_ordered_sort
-      10.9?0.2?s       7.22?0.1?s     0.66
 series_methods.SeriesConstructor.time_constructor_fastpath
-        855?20?s         564?10?s     0.66
 groupby.Categories.time_groupby_extra_cat_sort
-     3.56?0.04?s      2.34?0.02?s     0.66
 tslibs.timestamp.TimestampOps.time_normalize(datetime.timezone.utc)
-       117?0.9ms       75.6?0.6ms     0.65
 groupby.GroupByCythonAggEaDtypes.time_frame_agg('Int64', 'sum')
-      5.14?0.2?s       3.33?0.1?s     0.65
 tslibs.timestamp.TimestampOps.time_replace_None(<DstTzInfo 'US/Pacific'
LMT-1 day, 16:07:00 STD>)
-        37.6?1ms         24.3?3ms     0.65
 series_methods.ValueCountsEA.time_value_counts(100000, True)
-     2.20?0.06?s      1.42?0.02?s     0.64
 tslibs.timestamp.TimestampOps.time_tz_localize(None)
-      66.1?0.4ms       42.5?0.7ms     0.64
 groupby.GroupByCythonAggEaDtypes.time_frame_agg('Float64', 'mean')
-        38.7?1ms         24.7?3ms     0.64
 series_methods.ValueCountsEA.time_value_counts(100000, False)
-     3.57?0.05?s      2.26?0.03?s     0.63
 tslibs.timestamp.TimestampOps.time_normalize(None)
-      13.8?0.8ms       8.72?0.1ms     0.63
 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(1000000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-     4.67?0.09ms      2.90?0.04ms     0.62
 stat_ops.SeriesMultiIndexOps.time_op(1, 'median')
-     4.52?0.04ms      2.81?0.04ms     0.62
 stat_ops.SeriesMultiIndexOps.time_op(0, 'median')
-      4.93?0.1ms      3.05?0.03ms     0.62
 algorithms.Factorize.time_factorize(False, True, 'boolean')
-      10.4?0.3?s       6.45?0.5?s     0.62
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-     4.71?0.07ms       2.90?0.3ms     0.62
 algorithms.DuplicatedMaskedArray.time_duplicated(True, False, 'Int64')
-         283?4?s          174?2?s     0.61
 groupby.GroupByMethods.time_dtype_as_field('int16', 'min', 'direct', 5)
-         282?4?s          172?3?s     0.61
 groupby.GroupByMethods.time_dtype_as_field('int16', 'first', 'direct', 5)
-      7.35?0.1ms      4.45?0.02ms     0.61
 io.csv.ToCSVDatetimeBig.time_frame(1000)
-     3.06?0.05ms      1.84?0.04ms     0.60
 series_methods.ValueCountsEA.time_value_counts(10000, True)
-     3.66?0.04ms      2.20?0.03ms     0.60
 stat_ops.FrameMultiIndexOps.time_op(0, 'mean')
-     3.49?0.03ms      2.09?0.03ms     0.60
 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}),
'float', 'count')
-     3.91?0.03ms      2.34?0.05ms     0.60
 stat_ops.FrameMultiIndexOps.time_op(1, 'var')
-         278?4?s          167?2?s     0.60
 groupby.GroupByMethods.time_dtype_as_field('int', 'sum', 'direct', 5)
-      6.50?0.1ms      3.89?0.01ms     0.60
 categoricals.ValueCounts.time_value_counts(True)
-     3.88?0.05ms      2.32?0.04ms     0.60
 stat_ops.FrameMultiIndexOps.time_op(0, 'var')
-     3.47?0.02ms      2.07?0.02ms     0.60
 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}),
'int', 'count')
-      6.52?0.1ms      3.89?0.01ms     0.60
 categoricals.ValueCounts.time_value_counts(False)
-         269?4?s          160?2?s     0.60
 groupby.GroupByMethods.time_dtype_as_field('int', 'prod', 'direct', 5)
-     3.73?0.03ms      2.22?0.02ms     0.60
 stat_ops.FrameMultiIndexOps.time_op(1, 'mean')
-         287?4?s          171?2?s     0.60
 groupby.GroupByMethods.time_dtype_as_field('int16', 'max', 'direct', 5)
-         270?4?s          160?3?s     0.59
 groupby.GroupByMethods.time_dtype_as_field('uint', 'prod', 'direct', 5)
-      2.55?0.1ms       1.51?0.5ms     0.59
 algorithms.SortIntegerArray.time_argsort(100000)
-     3.48?0.03ms      2.06?0.02ms     0.59
 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}),
'int', 'count')
-         250?4?s          148?2?s     0.59
 groupby.GroupByMethods.time_dtype_as_field('int16', 'min', 'direct', 1)
-         278?3?s          165?3?s     0.59
 groupby.GroupByMethods.time_dtype_as_field('uint', 'sum', 'direct', 5)
-         247?4?s          146?2?s     0.59
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-      57.8?0.4ms         34.2?1ms     0.59
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-     3.51?0.02ms      2.08?0.03ms     0.59
 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}),
'float', 'count')
-     3.69?0.04ms      2.17?0.03ms     0.59
 stat_ops.FrameMultiIndexOps.time_op(0, 'sum')
-     4.33?0.03ms      2.55?0.02ms     0.59
 stat_ops.SeriesMultiIndexOps.time_op(1, 'sem')
-     3.73?0.05ms      2.19?0.02ms     0.59
 stat_ops.FrameMultiIndexOps.time_op(1, 'sum')
-     4.36?0.05ms      2.55?0.01ms     0.58
 stat_ops.SeriesMultiIndexOps.time_op(0, 'sem')
-         103?2ms         60.0?2ms     0.58
 join_merge.MergeMultiIndex.time_merge_sorted_multiindex(('Int64',
'Int64'), 'inner')
-     3.39?0.02ms      1.97?0.03ms     0.58
 rolling.Methods.time_method('Series', ('rolling', {'window': 1000}),
'float', 'count')
-     3.35?0.03ms      1.95?0.03ms     0.58
 rolling.Methods.time_method('Series', ('rolling', {'window': 1000}),
'int', 'count')
-     3.86?0.04ms      2.24?0.04ms     0.58
 stat_ops.FrameMultiIndexOps.time_op(1, 'std')
-     3.41?0.02ms      1.98?0.03ms     0.58
 rolling.Methods.time_method('Series', ('rolling', {'window': 10}),
'float', 'count')
-         242?3?s          140?2?s     0.58
 groupby.GroupByMethods.time_dtype_as_field('uint', 'sum', 'direct', 1)
-     3.79?0.05ms      2.20?0.03ms     0.58
 stat_ops.FrameMultiIndexOps.time_op(0, 'std')
-         252?4?s          146?2?s     0.58
 groupby.GroupByMethods.time_dtype_as_field('int16', 'max', 'direct', 1)
-     3.37?0.03ms      1.94?0.02ms     0.58
 rolling.Methods.time_method('Series', ('rolling', {'window': 10}), 'int',
'count')
-         235?4?s          136?2?s     0.58
 groupby.GroupByMethods.time_dtype_as_field('int', 'prod', 'direct', 1)
-     3.48?0.04ms      2.01?0.02ms     0.58
 stat_ops.FrameMultiIndexOps.time_op(0, 'prod')
-         236?5?s          136?3?s     0.58
 groupby.GroupByMethods.time_dtype_as_field('uint', 'prod', 'direct', 1)
-         243?4?s          140?2?s     0.58
 groupby.GroupByMethods.time_dtype_as_field('int', 'sum', 'direct', 1)
-         690?9ms          397?2ms     0.58
 io.csv.ToCSVDatetimeBig.time_frame(100000)
-         258?4?s          148?3?s     0.58
 groupby.GroupByMethods.time_dtype_as_group('float', 'sum', 'direct', 1)
-         250?4?s          144?2?s     0.58
 groupby.GroupByMethods.time_dtype_as_group('int', 'sum', 'direct', 1)
-      7.93?0.1ms      4.56?0.04ms     0.58
 io.style.Render.time_apply_format_hide_render(12, 12)
-        68.5?1ms       39.3?0.1ms     0.57
 io.csv.ToCSVDatetimeBig.time_frame(10000)
-     3.49?0.08ms      2.00?0.05ms     0.57
 strftime.BusinessHourStrftime.time_frame_offset_repr(1000)
-         251?4?s          144?3?s     0.57
 groupby.GroupByMethods.time_dtype_as_group('uint', 'sum', 'direct', 1)
-         251?3?s          144?3?s     0.57
 groupby.GroupByMethods.time_dtype_as_group('int16', 'sum', 'direct', 1)
-     1.09?0.01ms          624?6?s     0.57
 series_methods.ValueCountsEA.time_value_counts(1000, False)
-     3.62?0.09ms      2.07?0.05ms     0.57
 strftime.BusinessHourStrftime.time_frame_offset_str(1000)
-         275?5?s          156?3?s     0.57
 groupby.GroupByMethods.time_dtype_as_field('int16', 'last', 'direct', 5)
-         243?5?s          138?3?s     0.57
 groupby.GroupByMethods.time_dtype_as_group('int', 'prod', 'direct', 1)
-         243?4?s          138?3?s     0.57
 groupby.GroupByMethods.time_dtype_as_group('uint', 'prod', 'direct', 1)
-         243?5?s          137?3?s     0.57
 groupby.GroupByMethods.time_dtype_as_group('int16', 'prod', 'direct', 1)
-     3.45?0.04ms      1.95?0.02ms     0.57
 stat_ops.FrameMultiIndexOps.time_op(1, 'prod')
-         250?5?s          141?2?s     0.57
 groupby.GroupByMethods.time_dtype_as_group('float', 'prod', 'direct', 1)
-      4.48?0.4ms      2.53?0.01ms     0.56
 algorithms.Factorize.time_factorize(False, False, 'boolean')
-         245?7?s          137?3?s     0.56
 groupby.GroupByMethods.time_dtype_as_field('int16', 'last', 'direct', 1)
-      53.7?0.4ms       29.9?0.5ms     0.56
 index_cached_properties.IndexCache.time_values('MultiIndex')
-     3.38?0.05ms      1.87?0.04ms     0.55
 series_methods.ValueCountsEA.time_value_counts(10000, False)
-     1.62?0.04?s         895?10ns     0.55
 tslibs.timedelta.TimedeltaConstructor.time_from_np_timedelta
-        78.6?1ms       43.5?0.6ms     0.55
 multiindex_object.GetLoc.time_large_get_loc_warm
-      6.75?0.2?s      3.73?0.04?s     0.55
 libs.InferDtype.time_infer_dtype_skipna('empty')
-      7.37?0.2?s       4.07?0.1?s     0.55
 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(100, <DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      34.2?0.8ms       18.5?0.3ms     0.54
 strftime.BusinessHourStrftime.time_frame_offset_repr(10000)
-      35.7?0.7ms       19.2?0.4ms     0.54
 strftime.BusinessHourStrftime.time_frame_offset_str(10000)
-     3.87?0.05ms      2.07?0.03ms     0.54
 stat_ops.SeriesMultiIndexOps.time_op(0, 'var')
-      40.2?0.3?s       21.2?0.2?s     0.53
 frame_methods.XS.time_frame_xs(0)
-     3.90?0.06ms      2.06?0.02ms     0.53
 stat_ops.SeriesMultiIndexOps.time_op(1, 'var')
-        37.4?1ms       19.8?0.8ms     0.53
 multiindex_object.SetOperations.time_operation('monotonic', 'string',
'symmetric_difference', None)
-        38.3?1ms       20.2?0.4ms     0.53
 multiindex_object.SetOperations.time_operation('non_monotonic', 'string',
'symmetric_difference', None)
-     3.83?0.07ms      2.01?0.02ms     0.53
 stat_ops.SeriesMultiIndexOps.time_op(0, 'std')
-      37.8?0.7ms       19.8?0.5ms     0.52
 multiindex_object.SetOperations.time_operation('monotonic', 'ea_int',
'symmetric_difference', None)
-        37.9?1ms       19.8?0.5ms     0.52
 multiindex_object.SetOperations.time_operation('non_monotonic', 'ea_int',
'symmetric_difference', None)
-         334?2ms          174?2ms     0.52
 groupby.Transform.time_transform_lambda_max_tall
-        87.0?6ms         44.6?5ms     0.51
 multiindex_object.SetOperations.time_operation('non_monotonic', 'int',
'union', None)
-        37.6?1ms       19.2?0.7ms     0.51
 multiindex_object.SetOperations.time_operation('non_monotonic', 'string',
'symmetric_difference', False)
-         154?1?s       78.5?0.7?s     0.51
 tslibs.period.PeriodConstructor.time_period_constructor('D', True)
-         154?1?s         78.4?1?s     0.51
 tslibs.period.PeriodConstructor.time_period_constructor('D', False)
-     3.62?0.05ms      1.85?0.02ms     0.51
 stat_ops.SeriesMultiIndexOps.time_op(1, 'mean')
-     3.91?0.08ms      1.99?0.03ms     0.51
 stat_ops.SeriesMultiIndexOps.time_op(1, 'std')
-     3.67?0.05ms      1.86?0.04ms     0.51
 stat_ops.SeriesMultiIndexOps.time_op(0, 'sum')
-     3.59?0.08ms      1.81?0.02ms     0.50
 stat_ops.SeriesMultiIndexOps.time_op(0, 'mean')
-      6.98?0.1ms      3.51?0.07ms     0.50
 algorithms.DuplicatedMaskedArray.time_duplicated(True, False, 'Float64')
-     3.65?0.08ms      1.83?0.02ms     0.50
 stat_ops.SeriesMultiIndexOps.time_op(1, 'sum')
-        87.8?6ms         43.9?1ms     0.50
 multiindex_object.SetOperations.time_operation('monotonic', 'int',
'union', None)
-     1.06?0.02ms          529?7?s     0.50
 replace.FillNa.time_fillna(True)
-        38.6?1ms       19.1?0.8ms     0.50
 multiindex_object.SetOperations.time_operation('monotonic', 'ea_int',
'symmetric_difference', False)
-     4.33?0.07ms      2.14?0.03ms     0.49
 algorithms.DuplicatedMaskedArray.time_duplicated(True, 'last', 'Float64')
-        38.6?1ms       19.0?0.7ms     0.49
 multiindex_object.SetOperations.time_operation('monotonic', 'string',
'symmetric_difference', False)
-     5.22?0.09?s       2.57?0.2?s     0.49
 tslibs.timestamp.TimestampOps.time_tz_convert(<DstTzInfo 'US/Pacific'
LMT-1 day, 16:07:00 STD>)
-     1.66?0.02?s         820?20ns     0.49
 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(1,
datetime.timezone.utc)
-     3.34?0.05ms      1.64?0.03ms     0.49
 stat_ops.SeriesMultiIndexOps.time_op(0, 'prod')
-        88.7?5ms         43.5?5ms     0.49
 multiindex_object.SetOperations.time_operation('non_monotonic', 'ea_int',
'union', None)
-        88.9?1ms       43.4?0.7ms     0.49
 multiindex_object.SetOperations.time_operation('monotonic', 'ea_int',
'union', None)
-     3.07?0.03ms      1.50?0.02ms     0.49
 frame_methods.Lookup.time_frame_fancy_lookup
-     1.73?0.03?s         835?10ns     0.48
 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(100,
datetime.timezone.utc)
-        10.7?3ms       5.12?0.1ms     0.48
 arithmetic.FrameWithFrameWide.time_op_same_blocks(<built-in function add>,
(10000, 1000))
-      25.6?0.2ms       12.3?0.1ms     0.48  io.sas.SAS.time_read_sas7bdat
-         338?3?s          162?2?s     0.48
 indexing.SortedAndUnsortedDatetimeIndexLoc.time_loc_unsorted
-        94.7?3ms         45.3?3ms     0.48
 algorithms.Factorize.time_factorize(False, True, 'string[pyarrow]')
-     3.42?0.05ms      1.62?0.03ms     0.47
 stat_ops.SeriesMultiIndexOps.time_op(1, 'prod')
-         351?3?s          166?2?s     0.47
 algos.isin.IsIn.time_isin_categorical('boolean')
-     1.72?0.01ms          810?3?s     0.47
 indexing.CategoricalIndexIndexing.time_getitem_list('non_monotonic')
-     1.72?0.01ms          810?3?s     0.47
 indexing.CategoricalIndexIndexing.time_getitem_list('monotonic_decr')
-     1.72?0.01ms          808?3?s     0.47
 indexing.CategoricalIndexIndexing.time_getitem_list('monotonic_incr')
-     1.71?0.01ms          802?4?s     0.47
 categoricals.CategoricalSlicing.time_getitem_list('monotonic_decr')
-     1.71?0.01ms          802?5?s     0.47
 categoricals.CategoricalSlicing.time_getitem_list('non_monotonic')
-     1.71?0.01ms          801?4?s     0.47
 categoricals.CategoricalSlicing.time_getitem_list('monotonic_incr')
-     5.81?0.03ms      2.72?0.02ms     0.47
 libs.InferDtype.time_infer_dtype_skipna('py-object')
-      6.28?0.1?s       2.91?0.2?s     0.46
 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 1,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-     3.39?0.03ms      1.57?0.01ms     0.46
 join_merge.Merge.time_merge_dataframe_empty_left(False)
-     3.23?0.05ms      1.49?0.04ms     0.46
 algorithms.DuplicatedMaskedArray.time_duplicated(True, 'last', 'Int64')
-      5.73?0.2?s       2.63?0.2?s     0.46
 tslibs.timestamp.TimestampOps.time_tz_convert(tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-        76.1?6ms         34.7?4ms     0.46
 multiindex_object.SetOperations.time_operation('non_monotonic', 'string',
'union', False)
-         103?6ms         47.0?5ms     0.45
 multiindex_object.SetOperations.time_operation('non_monotonic', 'string',
'union', None)
-      28.0?0.2ms       12.7?0.2ms     0.45
 ctors.DatetimeIndexConstructor.time_from_list_of_datetimes
-        76.8?7ms         34.8?5ms     0.45
 multiindex_object.SetOperations.time_operation('monotonic', 'string',
'union', False)
-         103?7ms       46.5?0.8ms     0.45
 multiindex_object.SetOperations.time_operation('monotonic', 'string',
'union', None)
-      6.19?0.2?s       2.79?0.2?s     0.45
 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 1,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-         327?2?s          146?2?s     0.45
 algos.isin.IsIn.time_isin_categorical('bool')
-        85.1?1ms         38.0?1ms     0.45
 algorithms.Factorize.time_factorize(True, True, 'string[pyarrow]')
-         214?4?s         95.3?2?s     0.45
 groupby.GroupByMethods.time_dtype_as_group('float', 'cumprod', 'direct', 1)
-      27.5?0.2ms      12.2?0.04ms     0.44
 ctors.DatetimeIndexConstructor.time_from_list_of_dates
-         233?4?s          103?1?s     0.44
 groupby.GroupByMethods.time_dtype_as_field('uint', 'cumprod', 'direct', 5)
-         213?4?s         93.6?1?s     0.44
 groupby.GroupByMethods.time_dtype_as_field('uint', 'cumprod', 'direct', 1)
-     4.98?0.07ms      2.19?0.03ms     0.44
 algorithms.DuplicatedMaskedArray.time_duplicated(True, 'first', 'Float64')
-         213?4?s         93.4?1?s     0.44
 groupby.GroupByMethods.time_dtype_as_group('uint', 'cumprod', 'direct', 1)
-         213?4?s         93.4?1?s     0.44
 groupby.GroupByMethods.time_dtype_as_group('int', 'cumprod', 'direct', 1)
-         214?4?s         93.3?1?s     0.44
 groupby.GroupByMethods.time_dtype_as_group('int16', 'cumprod', 'direct', 1)
-     5.78?0.02ms      2.51?0.02ms     0.44
 libs.InferDtype.time_infer_dtype_skipna('bytes')
-         213?4?s         92.7?1?s     0.43
 groupby.GroupByMethods.time_dtype_as_field('int', 'cumprod', 'direct', 1)
-         234?4?s          102?1?s     0.43
 groupby.GroupByMethods.time_dtype_as_field('int', 'cumprod', 'direct', 5)
-     3.84?0.07ms      1.67?0.02ms     0.43  io.sas.SAS.time_read_xpt
-      5.91?0.2?s       2.56?0.2?s     0.43
 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('datetime', 1,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      6.05?0.2?s       2.60?0.2?s     0.43
 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('datetime', 1,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-         298?4?s          126?2?s     0.42
 groupby.GroupByMethods.time_dtype_as_group('float', 'cumprod', 'direct', 5)
-      5.72?0.1?s       2.39?0.2?s     0.42
 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 1,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-         218?4?s         90.9?1?s     0.42
 groupby.GroupByMethods.time_dtype_as_field('int16', 'cummax', 'direct', 1)
-         295?5?s          123?2?s     0.42
 groupby.GroupByMethods.time_dtype_as_group('int16', 'cumprod', 'direct', 5)
-         299?5?s          124?2?s     0.42
 groupby.GroupByMethods.time_dtype_as_group('int', 'cumprod', 'direct', 5)
-         241?4?s         99.8?1?s     0.41
 groupby.GroupByMethods.time_dtype_as_field('int16', 'cummax', 'direct', 5)
-         296?4?s          122?2?s     0.41
 groupby.GroupByMethods.time_dtype_as_group('uint', 'cumprod', 'direct', 5)
-      5.95?0.2?s       2.46?0.2?s     0.41
 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 1,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-        41.6?1ms       17.1?0.4ms     0.41
 join_merge.MergeMultiIndex.time_merge_sorted_multiindex(('int64',
'int64'), 'right')
-         315?2?s          130?3?s     0.41
 indexing.SortedAndUnsortedDatetimeIndexLoc.time_loc_sorted
-         218?5?s         89.0?1?s     0.41
 groupby.GroupByMethods.time_dtype_as_field('int16', 'cummin', 'direct', 1)
-         240?4?s       97.7?0.9?s     0.41
 groupby.GroupByMethods.time_dtype_as_field('int16', 'cummin', 'direct', 5)
-        42.0?1ms       16.8?0.3ms     0.40
 join_merge.MergeMultiIndex.time_merge_sorted_multiindex(('int64',
'int64'), 'left')
-        85.0?7ms         33.8?5ms     0.40
 multiindex_object.SetOperations.time_operation('monotonic', 'int',
'union', False)
-        85.6?2ms         33.8?5ms     0.39
 multiindex_object.SetOperations.time_operation('non_monotonic', 'ea_int',
'union', False)
-        19.0?1ms      7.45?0.07ms     0.39
 frame_methods.Duplicated.time_frame_duplicated_subset
-      5.10?0.2?s       1.99?0.1?s     0.39
 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('datetime', 0,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      37.4?0.9ms       14.6?0.3ms     0.39
 multiindex_object.SetOperations.time_operation('non_monotonic', 'ea_int',
'symmetric_difference', False)
-      3.82?0.1ms      1.49?0.04ms     0.39
 algorithms.DuplicatedMaskedArray.time_duplicated(True, 'first', 'Int64')
-      5.11?0.2?s       1.99?0.1?s     0.39
 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 0,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.09?0.2?s       1.97?0.1?s     0.39
 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 0,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      7.09?0.2?s       2.73?0.1?s     0.39
 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(100,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.11?0.2?s       1.95?0.1?s     0.38
 tslibs.resolution.TimeResolution.time_get_resolution('D', 1,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-        35.0?1ms       13.3?0.2ms     0.38
 multiindex_object.SetOperations.time_operation('non_monotonic', 'int',
'symmetric_difference', None)
-        35.3?1ms       13.4?0.2ms     0.38
 multiindex_object.SetOperations.time_operation('monotonic', 'int',
'symmetric_difference', None)
-        84.2?6ms         31.8?4ms     0.38
 multiindex_object.SetOperations.time_operation('non_monotonic', 'int',
'union', False)
-      5.15?0.1?s       1.93?0.1?s     0.37
 tslibs.resolution.TimeResolution.time_get_resolution('h', 1,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.20?0.1?s       1.95?0.2?s     0.37
 tslibs.resolution.TimeResolution.time_get_resolution('m', 1,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      4.72?0.2?s       1.74?0.1?s     0.37
 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 0,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      5.22?0.2?s       1.93?0.1?s     0.37
 tslibs.resolution.TimeResolution.time_get_resolution('s', 1,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-        86.2?2ms         31.7?1ms     0.37
 multiindex_object.SetOperations.time_operation('monotonic', 'ea_int',
'union', False)
-      4.86?0.1?s       1.79?0.1?s     0.37
 tslibs.resolution.TimeResolution.time_get_resolution('D', 0,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      4.84?0.1?s       1.77?0.1?s     0.37
 tslibs.resolution.TimeResolution.time_get_resolution('m', 0,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      4.87?0.1?s       1.79?0.1?s     0.37
 tslibs.resolution.TimeResolution.time_get_resolution('ns', 0,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-        34.9?1ms       12.7?0.2ms     0.36
 multiindex_object.SetOperations.time_operation('non_monotonic', 'int',
'symmetric_difference', False)
-      4.87?0.1?s       1.77?0.1?s     0.36
 tslibs.resolution.TimeResolution.time_get_resolution('s', 0,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      4.87?0.1?s       1.77?0.1?s     0.36
 tslibs.resolution.TimeResolution.time_get_resolution('us', 0,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      4.88?0.1?s       1.77?0.1?s     0.36
 tslibs.resolution.TimeResolution.time_get_resolution('h', 0,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.21?0.2?s       1.89?0.1?s     0.36
 tslibs.resolution.TimeResolution.time_get_resolution('us', 1,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.17?0.1?s       1.86?0.1?s     0.36
 tslibs.resolution.TimeResolution.time_get_resolution('ns', 1,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      4.83?0.2?s       1.73?0.1?s     0.36
 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('datetime', 0,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.75?0.1?s       1.69?0.1?s     0.36
 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 0,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-        35.6?1ms       12.7?0.2ms     0.36
 multiindex_object.SetOperations.time_operation('monotonic', 'int',
'symmetric_difference', False)
-        35.7?5ms         12.6?4ms     0.35
 reindex.DropDuplicates.time_frame_drop_dups_int(False)
-     7.21?0.06ms      2.54?0.03ms     0.35
 groupby.Categories.time_groupby_ordered_nosort
-         131?4ms       45.8?0.2ms     0.35
 frame_methods.ToDict.time_to_dict_datetimelike('index')
-      4.81?0.2?s      1.66?0.08?s     0.34
 tslibs.resolution.TimeResolution.time_get_resolution('us', 1, <DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      3.45?0.1?s      1.18?0.01?s     0.34
 tslibs.timedelta.TimedeltaConstructor.time_from_datetime_timedelta
-      4.88?0.2?s      1.66?0.09?s     0.34
 tslibs.resolution.TimeResolution.time_get_resolution('m', 1, <DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.85?0.2?s      1.65?0.08?s     0.34
 tslibs.resolution.TimeResolution.time_get_resolution('ns', 1, <DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>)
-     4.48?0.07?s      1.51?0.08?s     0.34
 tslibs.resolution.TimeResolution.time_get_resolution('s', 0, <DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.91?0.1?s      1.66?0.07?s     0.34
 tslibs.resolution.TimeResolution.time_get_resolution('s', 1, <DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.89?0.2?s      1.65?0.07?s     0.34
 tslibs.resolution.TimeResolution.time_get_resolution('h', 1, <DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.49?0.1?s      1.51?0.06?s     0.34
 tslibs.resolution.TimeResolution.time_get_resolution('D', 0, <DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.89?0.2?s      1.65?0.08?s     0.34
 tslibs.resolution.TimeResolution.time_get_resolution('D', 1, <DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      5.07?0.1?s       1.71?0.1?s     0.34
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4006,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      4.29?0.1ms      1.44?0.01ms     0.34
 strftime.DatetimeStrftime.time_frame_datetime_formatting_default_with_float(1000)
-      4.52?0.2?s      1.51?0.07?s     0.33
 tslibs.resolution.TimeResolution.time_get_resolution('us', 0, <DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      5.03?0.2?s       1.68?0.1?s     0.33
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 9000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      4.53?0.1?s      1.51?0.07?s     0.33
 tslibs.resolution.TimeResolution.time_get_resolution('m', 0, <DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>)
-     4.53?0.09?s      1.51?0.07?s     0.33
 tslibs.resolution.TimeResolution.time_get_resolution('h', 0, <DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      5.11?0.2?s       1.70?0.1?s     0.33
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      4.49?0.1?s      1.49?0.08?s     0.33
 tslibs.resolution.TimeResolution.time_get_resolution('ns', 0, <DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      33.3?0.7?s       11.1?0.7?s     0.33
 tslibs.timestamp.TimestampOps.time_normalize(<DstTzInfo 'US/Pacific' LMT-1
day, 16:07:00 STD>)
-      5.11?0.2?s       1.70?0.1?s     0.33
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 10000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.13?0.2?s       1.70?0.1?s     0.33
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 12000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.10?0.2?s       1.69?0.1?s     0.33
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 5000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.15?0.2?s       1.70?0.1?s     0.33
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 6000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.07?0.2?s       1.67?0.1?s     0.33
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 4006,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.12?0.2?s       1.69?0.1?s     0.33
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 4000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.06?0.2?s       1.67?0.1?s     0.33
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 10000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.16?0.2?s       1.70?0.1?s     0.33
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 8000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.10?0.1?s       1.67?0.1?s     0.33
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 1011,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.13?0.2?s       1.68?0.1?s     0.33
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 3000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.14?0.2?s       1.68?0.1?s     0.33
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 1011,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.17?0.1?s       1.69?0.1?s     0.33
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 11000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.18?0.2?s       1.69?0.1?s     0.33
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 8000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.15?0.2?s       1.68?0.1?s     0.33
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 9000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.11?0.1?s       1.67?0.1?s     0.33
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 1000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.17?0.2?s       1.69?0.1?s     0.33
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 11000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.16?0.1?s       1.68?0.1?s     0.33
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 3000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.15?0.1?s       1.67?0.1?s     0.33
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 2000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.14?0.2?s       1.67?0.1?s     0.32
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 6000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.16?0.1?s       1.67?0.1?s     0.32
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 7000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.14?0.2?s       1.67?0.1?s     0.32
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 2011,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.14?0.1?s       1.67?0.1?s     0.32
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 5000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.18?0.2?s       1.68?0.1?s     0.32
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 1000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.17?0.2?s       1.68?0.1?s     0.32
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 7000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-     7.64?0.06ms      2.48?0.04ms     0.32
 groupby.Categories.time_groupby_nosort
-         144?4ms       46.5?0.2ms     0.32
 frame_methods.ToDict.time_to_dict_datetimelike('records')
-      5.15?0.2?s       1.67?0.1?s     0.32
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 2000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.21?0.1?s       1.68?0.1?s     0.32
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 12000,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      5.18?0.2?s       1.67?0.1?s     0.32
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 2011,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      3.78?0.1ms      1.21?0.01ms     0.32
 strftime.DatetimeStrftime.time_frame_datetime_formatting_default(1000)
-     3.94?0.02?s      1.26?0.01?s     0.32
 index_object.Range.time_sort_values_des
-      34.2?0.9?s       10.8?0.7?s     0.31
 tslibs.timestamp.TimestampOps.time_normalize(tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      45.9?0.6ms       14.3?0.2ms     0.31
 join_merge.JoinMultiindexSubset.time_join_multiindex_subset
-      4.68?0.1?s      1.45?0.09?s     0.31
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 8000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.68?0.1?s      1.45?0.08?s     0.31
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 2011,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.68?0.1?s      1.45?0.09?s     0.31
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 6000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.65?0.2?s      1.43?0.09?s     0.31
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 12000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.70?0.1?s      1.45?0.09?s     0.31
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 5000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.69?0.1?s      1.44?0.08?s     0.31
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 2000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.71?0.2?s      1.44?0.09?s     0.31
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.68?0.2?s      1.43?0.08?s     0.31
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 1011,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-        41.8?1ms      12.8?0.08ms     0.31
 strftime.DatetimeStrftime.time_frame_datetime_formatting_default_with_float(10000)
-      4.70?0.1?s      1.44?0.09?s     0.31
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 3000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      33.7?0.1ms       10.3?0.1ms     0.31
 index_object.SetOperations.time_operation('monotonic', 'ea_int',
'intersection')
-     6.84?0.07ms      2.09?0.05ms     0.31
 groupby.Categories.time_groupby_extra_cat_nosort
-     4.69?0.09?s       1.43?0.1?s     0.30
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 12000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-     4.70?0.09?s      1.43?0.09?s     0.30
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 9000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.69?0.2?s       1.43?0.1?s     0.30
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 3000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.69?0.2?s      1.43?0.09?s     0.30
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 9000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.73?0.2?s      1.44?0.07?s     0.30
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 4000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.70?0.2?s      1.43?0.09?s     0.30
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 2011,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.68?0.2?s      1.42?0.08?s     0.30
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 6000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.71?0.1?s      1.43?0.09?s     0.30
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 10000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.72?0.2?s      1.43?0.07?s     0.30
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 7000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.73?0.1?s      1.43?0.07?s     0.30
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 5000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-     4.71?0.06?s      1.43?0.08?s     0.30
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 1000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-        91.2?2ms       27.6?0.6ms     0.30
 multiindex_object.SetOperations.time_operation('monotonic', 'ea_int',
'intersection', None)
-      4.75?0.1?s      1.44?0.07?s     0.30
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 2000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.73?0.2?s      1.43?0.09?s     0.30
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 1011,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.71?0.1?s      1.42?0.09?s     0.30
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 7000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.72?0.1?s      1.42?0.09?s     0.30
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 1000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.74?0.1?s      1.43?0.09?s     0.30
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 11000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.77?0.2?s      1.44?0.09?s     0.30
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 11000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-     4.73?0.09?s      1.42?0.09?s     0.30
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4006,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.78?0.1?s      1.44?0.09?s     0.30
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 10000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      4.75?0.1?s      1.42?0.09?s     0.30
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 8000,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-     4.96?0.05ms      1.48?0.02ms     0.30
 categoricals.Rank.time_rank_string_cat_ordered
-      4.40?0.1ms       1.31?0.2ms     0.30
 reindex.DropDuplicates.time_frame_drop_dups_int(True)
-      4.80?0.1?s      1.42?0.08?s     0.30
 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 4006,
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-        90.1?2ms       26.2?0.6ms     0.29
 multiindex_object.SetOperations.time_operation('monotonic', 'int',
'intersection', None)
-         137?4ms       39.4?0.3ms     0.29
 frame_methods.ToDict.time_to_dict_datetimelike('dict')
-      21.1?0.3ms       6.02?0.1ms     0.28
 index_object.UnionWithDuplicates.time_union_with_duplicates
-      36.9?0.9ms      10.4?0.08ms     0.28
 strftime.DatetimeStrftime.time_frame_datetime_formatting_default(10000)
-         135?4ms       38.0?0.2ms     0.28
 frame_methods.ToDict.time_to_dict_datetimelike('split')
-      6.51?0.2?s       1.83?0.1?s     0.28
 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(1,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-         121?4ms       34.1?0.3ms     0.28
 frame_methods.ToDict.time_to_dict_datetimelike('list')
-     4.97?0.05ms      1.39?0.02ms     0.28
 categoricals.Rank.time_rank_int_cat_ordered
-         304?2ms         84.0?4ms     0.28
 multiindex_object.SetOperations.time_operation('monotonic', 'datetime',
'union', None)
-         305?3ms       83.6?0.4ms     0.27
 multiindex_object.SetOperations.time_operation('non_monotonic',
'datetime', 'union', None)
-        91.6?2ms       24.5?0.3ms     0.27
 join_merge.MergeMultiIndex.time_merge_sorted_multiindex(('int64',
'int64'), 'inner')
-      6.17?0.2?s      1.61?0.09?s     0.26
 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(1, <DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>)
-     2.17?0.02ms          564?3?s     0.26
 join_merge.Append.time_append_mixed
-        46.2?2ms       11.7?0.7ms     0.25
 indexing.MultiIndexing.time_loc_slice_plus_null_slice(False)
-         296?2ms         74.5?2ms     0.25
 multiindex_object.SetOperations.time_operation('monotonic', 'datetime',
'union', False)
-         296?3ms         73.8?2ms     0.25
 multiindex_object.SetOperations.time_operation('non_monotonic',
'datetime', 'union', False)
-      15.4?0.3?s       3.81?0.2?s     0.25
 tslibs.tz_convert.TimeTZConvert.time_tz_localize_to_utc(1,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      18.3?0.4?s       4.48?0.4?s     0.24
 tslibs.tz_convert.TimeTZConvert.time_tz_localize_to_utc(0,
datetime.timezone(datetime.timedelta(seconds=3600)))
-      15.1?0.3?s       3.68?0.2?s     0.24
 tslibs.tz_convert.TimeTZConvert.time_tz_localize_to_utc(1, <DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      29.8?0.1ms       7.16?0.1ms     0.24
 index_object.SetOperations.time_operation('monotonic', 'ea_int', 'union')
-      14.4?0.2?s       3.41?0.1?s     0.24
 tslibs.tz_convert.TimeTZConvert.time_tz_localize_to_utc(0,
tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-     4.01?0.01ms          939?4?s     0.23
 libs.InferDtype.time_infer_dtype_skipna('np-object')
-      24.9?0.8ms       5.79?0.2ms     0.23
 algorithms.DuplicatedMaskedArray.time_duplicated(False, 'last', 'Float64')
-      13.8?0.1ms      3.19?0.04ms     0.23
 multiindex_object.Difference.time_difference('int')
-      19.6?0.3?s       4.52?0.4?s     0.23
 tslibs.tz_convert.TimeTZConvert.time_tz_localize_to_utc(1,
datetime.timezone(datetime.timedelta(seconds=3600)))
-      14.7?0.2ms      3.40?0.03ms     0.23
 multiindex_object.Difference.time_difference('ea_int')
-        30.8?2ms         7.09?2ms     0.23
 algorithms.DuplicatedMaskedArray.time_duplicated(False, False, 'Float64')
-      14.2?0.2?s       3.17?0.1?s     0.22
 tslibs.tz_convert.TimeTZConvert.time_tz_localize_to_utc(0, <DstTzInfo
'US/Pacific' LMT-1 day, 16:07:00 STD>)
-     6.74?0.07ms      1.49?0.01ms     0.22
 indexing.MultiIndexing.time_loc_all_slices(True)
-      64.3?0.4ms      13.9?0.01ms     0.22
 timeseries.Iteration.time_iter_preexit(<function timedelta_range at
0x7f68e0104670>)
-      21.8?0.3?s       4.57?0.4?s     0.21
 tslibs.tz_convert.TimeTZConvert.time_tz_localize_to_utc(100,
datetime.timezone(datetime.timedelta(seconds=3600)))
-         182?6ms       38.2?0.4ms     0.21
 indexing.MultiIndexing.time_loc_all_lists(True)
-      1.53?0.4ms         313?10?s     0.20
 indexing.MultiIndexing.time_loc_partial_key_scalar(False)
-      3.13?0.06s          639?1ms     0.20
 timeseries.Iteration.time_iter(<function timedelta_range at
0x7f68e0104670>)
-      1.53?0.4ms         307?10?s     0.20
 indexing.MultiIndexing.time_xs_level_0(False)
-      16.6?0.1ms      3.34?0.03ms     0.20
 multiindex_object.Difference.time_difference('string')
-     3.70?0.08ms          728?3?s     0.20
 strftime.DatetimeStrftime.time_frame_datetime_formatting_default_date_only(1000)
-         147?1ms       28.9?0.4ms     0.20
 multiindex_object.SetOperations.time_operation('monotonic', 'string',
'intersection', None)
-      3.69?0.1ms          724?3?s     0.20
 strftime.DatetimeStrftime.time_frame_date_formatting_default(1000)
-        28.3?1ms       5.46?0.3ms     0.19
 algorithms.DuplicatedMaskedArray.time_duplicated(False, 'first', 'Float64')
-         151?1ms       28.9?0.4ms     0.19
 multiindex_object.SetOperations.time_operation('non_monotonic', 'string',
'intersection', None)
-      21.8?0.1ms       3.85?0.1ms     0.18
 algorithms.DuplicatedMaskedArray.time_duplicated(False, 'last', 'Int64')
-     3.57?0.07ms          630?2?s     0.18
 strftime.DatetimeStrftime.time_frame_date_to_str(1000)
-        72.9?1ms       12.8?0.2ms     0.18
 multiindex_object.SetOperations.time_operation('non_monotonic', 'string',
'intersection', False)
-         152?1ms       26.5?0.3ms     0.17
 io.excel.ReadExcelNRows.time_read_excel('openpyxl')
-     1.71?0.07ms          298?3?s     0.17
 indexing.MultiIndexing.time_loc_partial_key_scalar(True)
-      58.8?0.2ms       10.2?0.5ms     0.17
 hash_functions.Unique.time_unique('Float64')
-     1.68?0.09ms          292?3?s     0.17
 indexing.MultiIndexing.time_xs_level_0(True)
-     1.90?0.01ms          328?2?s     0.17
 join_merge.Append.time_append_homogenous
-        73.0?1ms       12.6?0.2ms     0.17
 multiindex_object.SetOperations.time_operation('monotonic', 'string',
'intersection', False)
-      31.8?0.1ms      5.30?0.02ms     0.17
 hash_functions.Unique.time_unique_with_duplicates('Float64')
-         162?1ms       26.7?0.5ms     0.17
 multiindex_object.SetOperations.time_operation('non_monotonic', 'ea_int',
'intersection', None)
-         160?1ms       25.5?0.6ms     0.16
 multiindex_object.SetOperations.time_operation('non_monotonic', 'int',
'intersection', None)
-        34.7?1ms      5.51?0.02ms     0.16
 strftime.DatetimeStrftime.time_frame_datetime_formatting_default_date_only(10000)
-        26.7?2ms       4.23?0.9ms     0.16
 algorithms.DuplicatedMaskedArray.time_duplicated(False, False, 'Int64')
-      34.8?0.9ms      5.48?0.02ms     0.16
 strftime.DatetimeStrftime.time_frame_date_formatting_default(10000)
-        25.4?1ms       3.99?0.1ms     0.16
 algorithms.DuplicatedMaskedArray.time_duplicated(False, 'first', 'Int64')
-         597?6?s         93.3?2?s     0.16
 multiindex_object.GetLocs.time_small_get_locs
-      59.2?0.7ms       9.17?0.2ms     0.15
 period.PeriodIndexConstructor.time_from_ints_daily('D', False)
-      59.3?0.6ms       9.08?0.2ms     0.15
 period.PeriodIndexConstructor.time_from_ints_daily('D', True)
-      35.8?0.9ms      5.47?0.01ms     0.15
 strftime.DatetimeStrftime.time_frame_date_to_str(10000)
-        81.8?1ms       12.3?0.2ms     0.15
 multiindex_object.SetOperations.time_operation('non_monotonic', 'ea_int',
'intersection', False)
-     2.03?0.03?s          289?5ns     0.14
 index_object.Range.time_sort_values_asc
-       145?0.3ms       20.5?0.5ms     0.14
 groupby.Cumulative.time_frame_transform('Float64', 'cumsum')
-      1.39?0.4ms          196?6?s     0.14
 indexing.MultiIndexing.time_loc_partial_key_slice(False)
-         774?8?s          107?2?s     0.14
 multiindex_object.GetLocs.time_med_get_locs
-        71.6?1ms       9.85?0.6ms     0.14
 indexing.MultiIndexing.time_loc_all_slices(False)
-        90.8?2ms       12.4?0.3ms     0.14
 multiindex_object.SetOperations.time_operation('monotonic', 'ea_int',
'intersection', False)
-      25.8?0.2ms       3.48?0.1ms     0.14
 hash_functions.Unique.time_unique_with_duplicates('Int64')
-        80.1?1ms       10.8?0.2ms     0.13
 multiindex_object.SetOperations.time_operation('non_monotonic', 'int',
'intersection', False)
-      63.1?0.3ms      7.90?0.07ms     0.13
 multiindex_object.Values.time_datetime_level_values_copy
-     1.64?0.08ms          205?2?s     0.12
 indexing.MultiIndexing.time_loc_partial_key_slice(True)
-      58.5?0.3ms       7.28?0.3ms     0.12
 hash_functions.Unique.time_unique('Int64')
-      12.5?0.1ms      1.56?0.01ms     0.12
 join_merge.Merge.time_merge_dataframe_empty_right(False)
-        88.7?2ms       11.0?0.2ms     0.12
 multiindex_object.SetOperations.time_operation('monotonic', 'int',
'intersection', False)
-         201?1ms       24.6?0.3ms     0.12
 join_merge.MergeMultiIndex.time_merge_sorted_multiindex(('datetime64[ns]',
'int64'), 'inner')
-         102?4ms       12.4?0.8ms     0.12
 indexing.MultiIndexing.time_loc_null_slice_plus_slice(False)
-        725?20ms         87.1?3ms     0.12
 groupby.Transform.time_transform_lambda_max_wide
-       160?0.4ms       18.9?0.5ms     0.12
 groupby.Cumulative.time_frame_transform('Int64', 'cumsum')
-     13.6?0.08ms      1.57?0.01ms     0.12
 join_merge.Merge.time_merge_dataframe_empty_left(True)
-     5.61?0.07ms          645?5?s     0.12
 indexing.MultiIndexing.time_loc_slice_plus_null_slice(True)
-     28.1?0.08ms      3.09?0.01ms     0.11
 frame_ctor.FromScalar.time_frame_from_scalar_ea_float64
-         157?2ms       17.1?0.3ms     0.11
 join_merge.MergeMultiIndex.time_merge_sorted_multiindex(('datetime64[ns]',
'int64'), 'right')
-       191?0.7ms       20.7?0.2ms     0.11
 groupby.Cumulative.time_frame_transform_many_nulls('Float64', 'cumsum')
-      30.9?0.2ms      3.27?0.04ms     0.11
 multiindex_object.Difference.time_difference('datetime')
-         158?1ms       16.6?0.3ms     0.11
 join_merge.MergeMultiIndex.time_merge_sorted_multiindex(('datetime64[ns]',
'int64'), 'left')
-         672?3ms       69.2?0.8ms     0.10
 reindex.Reindex.time_reindex_multiindex_no_cache_dates
-         145?2ms       13.8?0.2ms     0.10
 multiindex_object.SetOperations.time_operation('non_monotonic',
'datetime', 'symmetric_difference', None)
-         145?2ms       13.7?0.2ms     0.09
 multiindex_object.SetOperations.time_operation('monotonic', 'datetime',
'symmetric_difference', None)
-     1.83?0.02ms        169?0.8?s     0.09
 series_methods.SeriesConstructor.time_constructor_no_data
-         145?2ms       13.1?0.2ms     0.09
 multiindex_object.SetOperations.time_operation('non_monotonic',
'datetime', 'symmetric_difference', False)
-       198?0.7ms       17.8?0.4ms     0.09
 groupby.Cumulative.time_frame_transform_many_nulls('Int64', 'cumsum')
-         870?3ms       77.8?0.8ms     0.09
 multiindex_object.Unique.time_unique_dups(('Int64', <NA>))
-         146?2ms       13.0?0.2ms     0.09
 multiindex_object.SetOperations.time_operation('monotonic', 'datetime',
'symmetric_difference', False)
-      5.53?0.1ms          487?3?s     0.09
 join_merge.JoinEmpty.time_inner_join_left_empty
-         841?2ms         73.0?1ms     0.09
 multiindex_object.Unique.time_unique_dups(('int64', 0))
-       307?0.8ms       26.4?0.7ms     0.09
 multiindex_object.SetOperations.time_operation('monotonic', 'datetime',
'intersection', None)
-      6.33?0.1ms          486?2?s     0.08
 join_merge.JoinEmpty.time_inner_join_right_empty
-         378?2ms       25.9?0.6ms     0.07
 multiindex_object.SetOperations.time_operation('non_monotonic',
'datetime', 'intersection', None)
-      23.9?0.3ms      1.55?0.01ms     0.06
 join_merge.Merge.time_merge_dataframe_empty_right(True)
-         312?2ms       19.6?0.6ms     0.06
 rolling.TableMethod.time_ewm_mean('single')
-      76.0?0.5ms      4.70?0.05ms     0.06
 multiindex_object.Isin.time_isin('datetime')
-         1.40?0s         83.2?1ms     0.06
 multiindex_object.Unique.time_unique(('Int64', <NA>))
-      1.41?0.01s         81.3?1ms     0.06
 multiindex_object.Unique.time_unique(('int64', 0))
-      83.5?0.3ms       4.80?0.5ms     0.06
 multiindex_object.SortValues.time_sort_values('int64')
-      84.8?0.3ms       4.76?0.5ms     0.06
 multiindex_object.SortValues.time_sort_values('Int64')
-        519?30ms       28.6?0.6ms     0.06
 indexing.MultiIndexing.time_loc_all_lists(False)
-      22.8?0.5ms      1.20?0.03ms     0.05
 multiindex_object.GetLocs.time_large_get_locs
-     7.62?0.09ms          386?2?s     0.05
 array.ArrowStringArray.time_setitem_list(False)
-         287?5?s       13.4?0.7?s     0.05
 tslibs.tz_convert.TimeTZConvert.time_tz_localize_to_utc(10000,
datetime.timezone(datetime.timedelta(seconds=3600)))
-         243?3ms       11.0?0.2ms     0.05
 multiindex_object.SetOperations.time_operation('non_monotonic',
'datetime', 'intersection', False)
-       307?0.7ms       11.2?0.2ms     0.04
 multiindex_object.SetOperations.time_operation('monotonic', 'datetime',
'intersection', False)
-     1.60?0.01ms       39.3?0.5?s     0.02
 index_cached_properties.IndexCache.time_is_all_dates('MultiIndex')
-     1.61?0.01ms         38.7?3?s     0.02
 index_cached_properties.IndexCache.time_is_all_dates('IntervalIndex')
-        36.4?1ms         843?50?s     0.02
 tslibs.tz_convert.TimeTZConvert.time_tz_localize_to_utc(1000000,
datetime.timezone(datetime.timedelta(seconds=3600)))
-        50.8?2ms      1.11?0.01ms     0.02
 indexing.MultiIndexing.time_loc_null_slice_plus_slice(True)
-      18.8?0.2ms          384?3?s     0.02
 array.ArrowStringArray.time_setitem_list(True)
-      83.6?0.6ms      1.68?0.01ms     0.02
 groupby.GroupByMethods.time_dtype_as_field('int16', 'diff',
'transformation', 5)
-      79.5?0.7ms      1.58?0.01ms     0.02
 groupby.GroupByMethods.time_dtype_as_field('uint', 'diff',
'transformation', 5)
-      79.4?0.4ms      1.55?0.01ms     0.02
 groupby.GroupByMethods.time_dtype_as_field('float', 'diff',
'transformation', 5)
-      83.4?0.6ms      1.57?0.01ms     0.02
 groupby.GroupByMethods.time_dtype_as_field('int', 'diff',
'transformation', 5)
-       177?0.3ms      3.12?0.01ms     0.02
 frame_ctor.FromScalar.time_frame_from_scalar_ea_float64_na
-        98.8?1ms      1.41?0.01ms     0.01
 groupby.GroupByMethods.time_dtype_as_group('int16', 'diff',
'transformation', 5)
-        99.4?1ms      1.42?0.01ms     0.01
 groupby.GroupByMethods.time_dtype_as_group('int', 'diff',
'transformation', 5)
-        99.4?1ms      1.40?0.01ms     0.01
 groupby.GroupByMethods.time_dtype_as_group('uint', 'diff',
'transformation', 5)
-      84.7?0.6ms      1.17?0.02ms     0.01  series_methods.ClipDt.time_clip
-     1.58?0.01ms       20.0?0.3?s     0.01
 index_cached_properties.IndexCache.time_is_all_dates('UInt64Index')
-     1.60?0.01ms       20.1?0.5?s     0.01
 index_cached_properties.IndexCache.time_is_all_dates('DatetimeIndex')
-     1.61?0.01ms       20.0?0.4?s     0.01
 index_cached_properties.IndexCache.time_is_all_dates('PeriodIndex')
-     1.58?0.01ms       19.3?0.6?s     0.01
 series_methods.ToFrame.time_to_frame('datetime64[ns]', None)
-         144?1ms      1.73?0.01ms     0.01
 groupby.GroupByMethods.time_dtype_as_field('datetime', 'diff',
'transformation', 5)
-     1.57?0.01ms       18.3?0.4?s     0.01
 index_cached_properties.IndexCache.time_is_all_dates('TimedeltaIndex')
-     1.57?0.01ms       18.1?0.4?s     0.01
 index_cached_properties.IndexCache.time_is_all_dates('Float64Index')
-     1.57?0.01ms       17.9?0.4?s     0.01
 series_methods.ToFrame.time_to_frame('int64', None)
-     1.57?0.01ms       17.2?0.4?s     0.01
 series_methods.ToFrame.time_to_frame('Int64', None)
-     1.58?0.01ms       17.3?0.5?s     0.01
 series_methods.ToFrame.time_to_frame('category', None)
-      58.1?0.1ms         606?30?s     0.01
 array.IntegerArray.time_from_integer_array
-     4.55?0.07ms         47.3?3?s     0.01
 index_cached_properties.IndexCache.time_is_all_dates('CategoricalIndex')
-         155?2ms      1.47?0.01ms     0.01
 groupby.GroupByMethods.time_dtype_as_group('float', 'diff',
'transformation', 5)
-         152?2ms      1.38?0.01ms     0.01
 groupby.GroupByMethods.time_dtype_as_group('datetime', 'diff',
'transformation', 5)
-       154?0.6ms      1.37?0.02ms     0.01
 frame_methods.ToRecords.time_to_records_multiindex
-      79.3?0.7ms          663?4?s     0.01
 groupby.GroupByMethods.time_dtype_as_field('int16', 'diff',
'transformation', 1)
-      75.7?0.7ms          563?4?s     0.01
 groupby.GroupByMethods.time_dtype_as_field('uint', 'diff',
'transformation', 1)
-     1.55?0.01ms       11.0?0.5?s     0.01
 index_cached_properties.IndexCache.time_is_all_dates('Int64Index')
-     1.55?0.01ms       10.9?0.5?s     0.01
 index_cached_properties.IndexCache.time_is_all_dates('RangeIndex')
-      75.4?0.5ms          530?3?s     0.01
 groupby.GroupByMethods.time_dtype_as_field('float', 'diff',
'transformation', 1)
-      79.5?0.5ms          558?4?s     0.01
 groupby.GroupByMethods.time_dtype_as_field('int', 'diff',
'transformation', 1)
-         119?1ms          742?8?s     0.01
 indexing.MultiIndexing.time_loc_all_bool_indexers(True)
-         122?1ms         728?10?s     0.01
 indexing.MultiIndexing.time_loc_all_bool_indexers(False)
-       116?0.8ms          622?4?s     0.01
 groupby.GroupByMethods.time_dtype_as_group('int16', 'diff',
'transformation', 1)
-         1.25?0s      6.67?0.09ms     0.01
 array.ArrowStringArray.time_setitem_slice(True)
-         117?1ms          605?4?s     0.01
 groupby.GroupByMethods.time_dtype_as_group('int', 'diff',
'transformation', 1)
-       117?0.7ms          605?3?s     0.01
 groupby.GroupByMethods.time_dtype_as_group('uint', 'diff',
'transformation', 1)
-         139?1ms          704?5?s     0.01
 groupby.GroupByMethods.time_dtype_as_field('datetime', 'diff',
'transformation', 1)
-         294?2ms      1.36?0.04ms     0.00
 rolling.GroupbyEWMEngine.time_groupby_mean('numba')
-      83.3?0.5ms          356?3?s     0.00
 groupby.GroupByMethods.time_dtype_as_field('int16', 'diff', 'direct', 5)
-      79.5?0.8ms          335?6?s     0.00
 groupby.GroupByMethods.time_dtype_as_field('int16', 'diff', 'direct', 1)
-        98.1?1ms          387?3?s     0.00
 groupby.GroupByMethods.time_dtype_as_group('int', 'diff', 'direct', 5)
-        97.1?1ms          375?3?s     0.00
 groupby.GroupByMethods.time_dtype_as_group('int16', 'diff', 'direct', 5)
-        97.8?1ms          374?2?s     0.00
 groupby.GroupByMethods.time_dtype_as_group('uint', 'diff', 'direct', 5)
-         184?1ms          681?4?s     0.00
 groupby.GroupByMethods.time_dtype_as_group('float', 'diff',
'transformation', 1)
-         187?2ms          585?5?s     0.00
 groupby.GroupByMethods.time_dtype_as_group('datetime', 'diff',
'transformation', 1)
-      75.8?0.9ms          227?5?s     0.00
 groupby.GroupByMethods.time_dtype_as_field('uint', 'diff', 'direct', 1)
-      79.3?0.8ms          237?3?s     0.00
 groupby.GroupByMethods.time_dtype_as_field('uint', 'diff', 'direct', 5)
-      2.72?0.01s      7.82?0.09ms     0.00
 groupby.ApplyNonUniqueUnsortedIndex.time_groupby_apply_non_unique_unsorted_index
-      83.2?0.6ms          234?2?s     0.00
 groupby.GroupByMethods.time_dtype_as_field('int', 'diff', 'direct', 5)
-         143?1ms          402?3?s     0.00
 groupby.GroupByMethods.time_dtype_as_field('datetime', 'diff', 'direct', 5)
-         140?1ms          389?7?s     0.00
 groupby.GroupByMethods.time_dtype_as_field('datetime', 'diff', 'direct', 1)
-      79.7?0.9ms          219?4?s     0.00
 groupby.GroupByMethods.time_dtype_as_field('int', 'diff', 'direct', 1)
-      79.1?0.4ms          203?2?s     0.00
 groupby.GroupByMethods.time_dtype_as_field('float', 'diff', 'direct', 5)
-      75.3?0.7ms          191?3?s     0.00
 groupby.GroupByMethods.time_dtype_as_field('float', 'diff', 'direct', 1)
-         152?2ms          363?3?s     0.00
 groupby.GroupByMethods.time_dtype_as_group('float', 'diff', 'direct', 5)
-         152?2ms          356?2?s     0.00
 groupby.GroupByMethods.time_dtype_as_group('datetime', 'diff', 'direct', 5)
-       115?0.8ms          225?5?s     0.00
 groupby.GroupByMethods.time_dtype_as_group('int16', 'diff', 'direct', 1)
-       117?0.8ms          225?4?s     0.00
 groupby.GroupByMethods.time_dtype_as_group('int', 'diff', 'direct', 1)
-       117?0.8ms          225?5?s     0.00
 groupby.GroupByMethods.time_dtype_as_group('uint', 'diff', 'direct', 1)
-         183?2ms          212?5?s     0.00
 groupby.GroupByMethods.time_dtype_as_group('float', 'diff', 'direct', 1)
-         187?1ms          215?5?s     0.00
 groupby.GroupByMethods.time_dtype_as_group('datetime', 'diff', 'direct', 1)
-         665?2ms          228?4?s     0.00
 rolling.TableMethod.time_ewm_mean('table')
-         1.18?0s          345?2?s     0.00
 array.ArrowStringArray.time_setitem_slice(False)
brock at Falcon:~/pd/pandas/asv_bench$ Connection to falcon.local closed by
remote host.
Connection to falcon.local closed.
brock at EnterpriseB fixmes58 %
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From emailformattr at gmail.com  Tue Oct 18 12:34:11 2022
From: emailformattr at gmail.com (Matthew Roeschke)
Date: Tue, 18 Oct 2022 09:34:11 -0700
Subject: [Pandas-dev] Is it time for a bi-weekly pandas call?
In-Reply-To: <CAEk5N5sQ0CAis5wqNhUz65BHsHZ137RvetLX8pUtsH-C3vW+rw@mail.gmail.com>
References: <CA+gwkEAkMaRJK5X8BiaEyeDeOT8UWGH22pEo42mSrnvJ+P+VvQ@mail.gmail.com>
 <CAEk5N5sQ0CAis5wqNhUz65BHsHZ137RvetLX8pUtsH-C3vW+rw@mail.gmail.com>
Message-ID: <CACvdwiNY+UoiK8zxuHD7HKnWY21s0uL-esLvGk+6XyPJBeFNtw@mail.gmail.com>

I would be interested in meeting twice a month.

On Sun, Oct 16, 2022 at 9:35 PM Marc Garcia <garcia.marc at gmail.com> wrote:

> I'm personally happy with both frequencies, no preference.
>
> Couple of related things:
> - Since this call is where a lot of the decision making happens, would it
> make sense to discuss this as part of the governance discussions?
> - Maybe worth also discussing the time of the call? I think the current
> time is quite reasonable for many time zones, and it necessarily need to be
> night time in some places during the call. But I wonder if it'd make a
> difference if we have the call a bit later to contributors or potential
> contributors in India, China... if we move the call one or two hours
> earlier, and how this affects people in California or Hawaii. This shows
> the current time in different time zones:
> https://www.timeanddate.com/worldclock/converter.html?iso=20221107T180000&p1=103&p2=224&p3=179&p4=233&p5=1440&p6=125&p7=166&p8=776&p9=176&p10=28&p11=237&p12=240
>
> On Fri, Oct 14, 2022 at 5:13 PM Marco Gorelli <m.e.gorelli at gmail.com>
> wrote:
>
>> Currently, there's a monthly pandas call, which has been in place for
>> several years. Seeing as there's now more people working on pandas as part
>> of their jobs, might it be time to increase the frequency? E.g. to meet
>> every 2 weeks, instead of once a month?
>> _______________________________________________
>> Pandas-dev mailing list
>> Pandas-dev at python.org
>> https://mail.python.org/mailman/listinfo/pandas-dev
>>
> _______________________________________________
> Pandas-dev mailing list
> Pandas-dev at python.org
> https://mail.python.org/mailman/listinfo/pandas-dev
>


-- 
Matthew Roeschke
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From rhshadrach at gmail.com  Wed Oct 19 08:46:31 2022
From: rhshadrach at gmail.com (Richard Shadrach)
Date: Wed, 19 Oct 2022 08:46:31 -0400
Subject: [Pandas-dev] Is it time for a bi-weekly pandas call?
In-Reply-To: <CACvdwiNY+UoiK8zxuHD7HKnWY21s0uL-esLvGk+6XyPJBeFNtw@mail.gmail.com>
References: <CA+gwkEAkMaRJK5X8BiaEyeDeOT8UWGH22pEo42mSrnvJ+P+VvQ@mail.gmail.com>
 <CAEk5N5sQ0CAis5wqNhUz65BHsHZ137RvetLX8pUtsH-C3vW+rw@mail.gmail.com>
 <CACvdwiNY+UoiK8zxuHD7HKnWY21s0uL-esLvGk+6XyPJBeFNtw@mail.gmail.com>
Message-ID: <CAJ=56R+hG16HE65FLOKjZeCKq2xctd_FOxwTqTKNJqRhURWmMA@mail.gmail.com>

I would also like bi-weekly.

Best,
Richard

On Tue, Oct 18, 2022, 12:34 Matthew Roeschke <emailformattr at gmail.com>
wrote:

> I would be interested in meeting twice a month.
>
> On Sun, Oct 16, 2022 at 9:35 PM Marc Garcia <garcia.marc at gmail.com> wrote:
>
>> I'm personally happy with both frequencies, no preference.
>>
>> Couple of related things:
>> - Since this call is where a lot of the decision making happens, would it
>> make sense to discuss this as part of the governance discussions?
>> - Maybe worth also discussing the time of the call? I think the current
>> time is quite reasonable for many time zones, and it necessarily need to be
>> night time in some places during the call. But I wonder if it'd make a
>> difference if we have the call a bit later to contributors or potential
>> contributors in India, China... if we move the call one or two hours
>> earlier, and how this affects people in California or Hawaii. This shows
>> the current time in different time zones:
>> https://www.timeanddate.com/worldclock/converter.html?iso=20221107T180000&p1=103&p2=224&p3=179&p4=233&p5=1440&p6=125&p7=166&p8=776&p9=176&p10=28&p11=237&p12=240
>>
>> On Fri, Oct 14, 2022 at 5:13 PM Marco Gorelli <m.e.gorelli at gmail.com>
>> wrote:
>>
>>> Currently, there's a monthly pandas call, which has been in place for
>>> several years. Seeing as there's now more people working on pandas as part
>>> of their jobs, might it be time to increase the frequency? E.g. to meet
>>> every 2 weeks, instead of once a month?
>>> _______________________________________________
>>> Pandas-dev mailing list
>>> Pandas-dev at python.org
>>> https://mail.python.org/mailman/listinfo/pandas-dev
>>>
>> _______________________________________________
>> Pandas-dev mailing list
>> Pandas-dev at python.org
>> https://mail.python.org/mailman/listinfo/pandas-dev
>>
>
>
> --
> Matthew Roeschke
> _______________________________________________
> Pandas-dev mailing list
> Pandas-dev at python.org
> https://mail.python.org/mailman/listinfo/pandas-dev
>
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From garcia.marc at gmail.com  Wed Oct 19 23:36:00 2022
From: garcia.marc at gmail.com (Marc Garcia)
Date: Thu, 20 Oct 2022 10:36:00 +0700
Subject: [Pandas-dev] ANN: pandas v1.5.1
Message-ID: <CAEk5N5sT8dJMwi5QWOjTJmPB6uVmya==oyF7Sjeu73u153Wdig@mail.gmail.com>

We are pleased to announce the release of pandas v1.5.1.

This is a patch release in the 1.5.x series and includes some regression
fixes and bug fixes. We recommend that all users in the 1.5.x series
upgrade to this version.

See the release notes <https://pandas.pydata.org/docs/whatsnew/v1.5.1.html>
for a list of all the changes.

The release can be installed from PyPI

    python -m pip install --upgrade pandas==1.5.1

Or from conda-forge

    mamba install -c conda-forge pandas==1.5.1

Please report any issues with the release on the pandas issue tracker
<https://github.com/pandas-dev/pandas/issues/new/choose>.

Thanks to all the contributors
<https://pandas.pydata.org/docs/whatsnew/v1.5.1.html#contributors> who made
this release possible.
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From hello at noatamir.com  Mon Oct 31 04:12:02 2022
From: hello at noatamir.com (Noa Tamir)
Date: Mon, 31 Oct 2022 08:12:02 +0000
Subject: [Pandas-dev] =?utf-8?q?Proposal_to_host_a_PyData_Global_sprint?=
 =?utf-8?b?OiBjYWxsIGZvciBtZW50b3JzIPCfk6M=?=
Message-ID: <WnhFi_a4XMYK2_noBi2ah0R6bawUV4TeOb0JXe7XZhJTdE-AnbUwnlRaYcxdHtVno_BBZMIrRutkz0-sLv_1Ke5DHZrR6y419FOvwQoCMtg=@noatamir.com>

Hi folks,

We?ve been asked if we?d like to host a pandas sprint at [PyData global](https://pydata.org/global2022/). There?s [an informal guide for the sprint](https://hackmd.io/lkkx0XCcQb2tZd_wO23WYw?view).

We can do a short sprint, in one time zone, or a long one crossing timezones (Dec 1st & 2nd) ? depending on the availability of maintainers and mentors to participate.

I?m happy to coordinate this activity. Please let me know by the end of the week (Sunday 6.11) if you are interested in mentoring the sprint. If we have enough maintainers and mentors in one or two timezones, I'll prepare the sprint registration next week.

Cheers,
Noa
she/they/???/sie
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