BUG: Timestamp retains frequency of input Timestamps
State: closed

- [x] closes #22311
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry


Labels:
name: Datetime
description: Datetime data dtype
name: Frequency
description: DateOffsets

Comments:
Hello @mroeschke! Thanks for submitting the PR.

 - There are no PEP8 issues in the file [`pandas/tests/scalar/timestamp/test_timestamp.py`](https://github.com/mroeschke/pandas/blob/232c29b367168522c796fe30dac17476ed098270/pandas/tests/scalar/timestamp/test_timestamp.py) !


LGTM.

Do any of the other constructors lose freq?  (Eg to_datetime)
`to_datetime` looks good on master:

```
In [1]: pd.to_datetime(pd.Timestamp('2010-08-08', freq='D')).freq
Out[1]: <Day>
```

`DatetimeIndex` looks like it drops the frequency (but I am not sure if it should be kept in this case?)

```
In [3]: pd.DatetimeIndex([pd.Timestamp('2010-08-08', freq='D')]).freq
```
# [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/23503?src=pr&el=h1) Report
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> The diff coverage is `n/a`.

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```diff
@@           Coverage Diff           @@
##           master   #23503   +/-   ##
=======================================
  Coverage   92.25%   92.25%           
=======================================
  Files         161      161           
  Lines       51207    51207           
=======================================
  Hits        47239    47239           
  Misses       3968     3968
```

| Flag | Coverage Δ | |
|---|---|---|
| #multiple | `90.63% <ø> (ø)` | :arrow_up: |
| #single | `42.28% <ø> (ø)` | :arrow_up: |


------

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lgtm. can you rebase and also do a quick perf check.
Looks like there's a _little_ hit (I think the asvs make it seem worse than reality), but imo not too bad 
```
$ asv continuous -f 1.1 upstream/master HEAD -b ^groupby

       before           after         ratio
     [d0691e05]       [2dcb1fbb]
+     1.29±0.04μs       2.21±0.6μs     1.71  timestamp.TimestampConstruction.time_parse_iso8601_no_tz
+     1.27±0.03μs       2.07±0.2μs     1.62  timestamp.TimestampConstruction.time_fromordinal
+     1.63±0.04μs       2.29±0.1μs     1.41  timestamp.TimestampConstruction.time_parse_today
+     1.59±0.01μs      2.22±0.06μs     1.40  timestamp.TimestampConstruction.time_parse_now
+      1.56±0.1μs       2.08±0.1μs     1.34  timestamp.TimestampConstruction.time_fromtimestamp

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.


# These two are the benchmark from timestamp.TimestampConstruction.time_parse_iso8601_no_tz
--branch
In [1]: %timeit pd.Timestamp('2017-08-25 08:16:14')
1.62 µs ± 42.1 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

--master
In [1]: %timeit pd.Timestamp('2017-08-25 08:16:14')
1.12 µs ± 13.4 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

```
thanks @mroeschke