Fastpaths for Timestamp properties
State: closed

Addresses a bunch of the TimestampProperties regressions in #18532 .  ASV vs 0.21.0

```
asv continuous -f 1.1 -E virtualenv 81372093 HEAD -b TimestampProperties
[...]
       before           after         ratio
     [81372093]       [5fc79fb0]
+     5.73±0.02μs      10.5±0.03μs     1.84  timestamp.TimestampProperties.time_dayofyear(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>)
+     5.96±0.04μs      10.9±0.03μs     1.82  timestamp.TimestampProperties.time_is_month_end(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>)
+     5.80±0.01μs       10.5±0.1μs     1.82  timestamp.TimestampProperties.time_days_in_month(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>)
+     6.18±0.01μs       10.6±0.6μs     1.72  timestamp.TimestampProperties.time_week(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>)
-     5.82±0.01μs          337±4ns     0.06  timestamp.TimestampProperties.time_is_leap_year(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>)
-     5.99±0.09μs         326±10ns     0.05  timestamp.TimestampProperties.time_quarter(None)
-     5.99±0.01μs        324±0.6ns     0.05  timestamp.TimestampProperties.time_is_year_end(None)
-     5.90±0.01μs        319±0.5ns     0.05  timestamp.TimestampProperties.time_is_month_start(None)
-     6.02±0.02μs          321±1ns     0.05  timestamp.TimestampProperties.time_is_leap_year(None)
-     6.08±0.05μs          321±2ns     0.05  timestamp.TimestampProperties.time_is_year_end(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>)
-     6.06±0.01μs        316±0.8ns     0.05  timestamp.TimestampProperties.time_is_month_start(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>)
-     6.19±0.09μs        319±0.7ns     0.05  timestamp.TimestampProperties.time_is_quarter_start(None)
-      6.09±0.2μs        310±0.6ns     0.05  timestamp.TimestampProperties.time_quarter(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>)
-      6.38±0.2μs        323±0.7ns     0.05  timestamp.TimestampProperties.time_is_year_start(None)
-      6.90±0.4μs          337±4ns     0.05  timestamp.TimestampProperties.time_is_quarter_end(None)
-      6.73±0.1μs          325±2ns     0.05  timestamp.TimestampProperties.time_is_year_start(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>)
-      6.75±0.2μs          321±1ns     0.05  timestamp.TimestampProperties.time_is_quarter_start(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>)
-      7.14±0.1μs        325±0.4ns     0.05  timestamp.TimestampProperties.time_is_quarter_end(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>)
```

Timestamps with freqs are still SOL, but that's a relatively rare case.

Labels:
name: Datetime
description: Datetime data dtype
name: Performance
description: Memory or execution speed performance

Comments:
Or just against master(ish):

```
asv continuous -f 1.1 -E virtualenv 34b036c6 HEAD -b TimestampProperties
[...]
       before           after         ratio
     [34b036c6]       [5fc79fb0]
-     6.16±0.01μs          325±1ns     0.05  timestamp.TimestampProperties.time_is_quarter_start(None)
-     6.16±0.07μs          318±2ns     0.05  timestamp.TimestampProperties.time_is_month_start(None)
-     6.39±0.02μs          325±2ns     0.05  timestamp.TimestampProperties.time_is_leap_year(None)
-      6.19±0.1μs          313±1ns     0.05  timestamp.TimestampProperties.time_quarter(None)
-     6.44±0.01μs          320±1ns     0.05  timestamp.TimestampProperties.time_is_year_end(None)
-     6.64±0.01μs          328±3ns     0.05  timestamp.TimestampProperties.time_is_year_start(None)
-      6.80±0.2μs          327±1ns     0.05  timestamp.TimestampProperties.time_is_quarter_end(None)
-     10.0±0.03μs          327±3ns     0.03  timestamp.TimestampProperties.time_quarter(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>)
-      10.7±0.2μs          330±3ns     0.03  timestamp.TimestampProperties.time_is_year_end(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>)
-     11.0±0.08μs          325±2ns     0.03  timestamp.TimestampProperties.time_is_leap_year(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>)
-     11.3±0.08μs          329±2ns     0.03  timestamp.TimestampProperties.time_is_year_start(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>)
-     11.1±0.07μs          314±2ns     0.03  timestamp.TimestampProperties.time_is_month_start(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>)
-     11.3±0.06μs          319±5ns     0.03  timestamp.TimestampProperties.time_is_quarter_start(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>)
-      11.9±0.1μs          329±3ns     0.03  timestamp.TimestampProperties.time_is_quarter_end(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>)
```
# [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/18539?src=pr&el=h1) Report
> Merging [#18539](https://codecov.io/gh/pandas-dev/pandas/pull/18539?src=pr&el=desc) into [master](https://codecov.io/gh/pandas-dev/pandas/commit/88ab6934e78117359719eb09c7d580906155575d?src=pr&el=desc) will **decrease** coverage by `0.01%`.
> The diff coverage is `n/a`.

[![Impacted file tree graph](https://codecov.io/gh/pandas-dev/pandas/pull/18539/graphs/tree.svg?token=eZ4WkYLtcO&width=650&height=150&src=pr)](https://codecov.io/gh/pandas-dev/pandas/pull/18539?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master   #18539      +/-   ##
==========================================
- Coverage   91.35%   91.33%   -0.02%     
==========================================
  Files         164      164              
  Lines       49801    49802       +1     
==========================================
- Hits        45494    45487       -7     
- Misses       4307     4315       +8
```

| Flag | Coverage Δ | |
|---|---|---|
| #multiple | `89.13% <ø> (ø)` | :arrow_up: |
| #single | `40.81% <ø> (-0.07%)` | :arrow_down: |

| [Impacted Files](https://codecov.io/gh/pandas-dev/pandas/pull/18539?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [pandas/io/gbq.py](https://codecov.io/gh/pandas-dev/pandas/pull/18539?src=pr&el=tree#diff-cGFuZGFzL2lvL2dicS5weQ==) | `25% <0%> (-58.34%)` | :arrow_down: |
| [pandas/core/frame.py](https://codecov.io/gh/pandas-dev/pandas/pull/18539?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvZnJhbWUucHk=) | `97.81% <0%> (-0.1%)` | :arrow_down: |
| [pandas/core/base.py](https://codecov.io/gh/pandas-dev/pandas/pull/18539?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvYmFzZS5weQ==) | `96.55% <0%> (ø)` | :arrow_up: |
| [pandas/core/groupby.py](https://codecov.io/gh/pandas-dev/pandas/pull/18539?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvZ3JvdXBieS5weQ==) | `92.04% <0%> (+0.01%)` | :arrow_up: |
| [pandas/core/indexes/base.py](https://codecov.io/gh/pandas-dev/pandas/pull/18539?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvaW5kZXhlcy9iYXNlLnB5) | `96.47% <0%> (+0.04%)` | :arrow_up: |

------

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can you confirm we have tests for all of these for both with and w/o freq 

and asv's for same

```
In [13]: t = pd.Timestamp('20130101')

In [14]: %timeit t.is_month_start
11.2 µs ± 178 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

In [15]: t = pd.Timestamp('20130101', freq='B')

In [16]: %timeit t.is_month_start
12.6 µs ± 60.9 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
```


> can you confirm we have tests for all of these for both with and w/o freq

Just pushed with these added.
thanks!