Separate parsing functions out from tslib
This is part 3 in an N part series of PRs to split `tslib` into thematically distinct modules.  The others so far are #17274 and #17342.

Moves parsing functions from _libs/src/inference and `core.tools.datetimes`.

The `tslibs.parsing` module has no within-pandas dependencies.  There are some dateutil workarounds that ideally can be upstreamed.

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

# [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/17363?src=pr&el=h1) Report
> Merging [#17363](https://codecov.io/gh/pandas-dev/pandas/pull/17363?src=pr&el=desc) into [master](https://codecov.io/gh/pandas-dev/pandas/commit/e0fe5cc60b1dc0d777223bba64b8abfc0e0e02ab?src=pr&el=desc) will **decrease** coverage by `0.01%`.
> The diff coverage is `100%`.

[![Impacted file tree graph](https://codecov.io/gh/pandas-dev/pandas/pull/17363/graphs/tree.svg?src=pr&width=650&token=eZ4WkYLtcO&height=150)](https://codecov.io/gh/pandas-dev/pandas/pull/17363?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master   #17363      +/-   ##
==========================================
- Coverage   91.25%   91.24%   -0.02%     
==========================================
  Files         163      163              
  Lines       49808    49734      -74     
==========================================
- Hits        45454    45378      -76     
- Misses       4354     4356       +2
```

| Flag | Coverage Δ | |
|---|---|---|
| #multiple | `89.03% <100%> (-0.01%)` | :arrow_down: |
| #single | `40.33% <54.54%> (-0.05%)` | :arrow_down: |

| [Impacted Files](https://codecov.io/gh/pandas-dev/pandas/pull/17363?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [pandas/io/date\_converters.py](https://codecov.io/gh/pandas-dev/pandas/pull/17363?src=pr&el=tree#diff-cGFuZGFzL2lvL2RhdGVfY29udmVydGVycy5weQ==) | `100% <100%> (ø)` | :arrow_up: |
| [pandas/core/indexes/base.py](https://codecov.io/gh/pandas-dev/pandas/pull/17363?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvaW5kZXhlcy9iYXNlLnB5) | `96.28% <100%> (ø)` | :arrow_up: |
| [pandas/io/parsers.py](https://codecov.io/gh/pandas-dev/pandas/pull/17363?src=pr&el=tree#diff-cGFuZGFzL2lvL3BhcnNlcnMucHk=) | `95.48% <100%> (ø)` | :arrow_up: |
| [pandas/core/tools/datetimes.py](https://codecov.io/gh/pandas-dev/pandas/pull/17363?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvdG9vbHMvZGF0ZXRpbWVzLnB5) | `83.79% <100%> (-1.46%)` | :arrow_down: |
| [pandas/io/gbq.py](https://codecov.io/gh/pandas-dev/pandas/pull/17363?src=pr&el=tree#diff-cGFuZGFzL2lvL2dicS5weQ==) | `25% <0%> (-58.34%)` | :arrow_down: |
| [pandas/core/frame.py](https://codecov.io/gh/pandas-dev/pandas/pull/17363?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvZnJhbWUucHk=) | `97.73% <0%> (-0.1%)` | :arrow_down: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/17363?src=pr&el=continue).
> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)
> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`
> Powered by [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/17363?src=pr&el=footer). Last update [e0fe5cc...f89d11e](https://codecov.io/gh/pandas-dev/pandas/pull/17363?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
 again a full run asv would be nice I'll run the whole suite next; here are results from just `-b timeseries`

```
asv continuous -f 1.1 -E virtualenv master HEAD -b timeseries
[...]
       before           after         ratio
     [36dadd70]       [c52c7968]
+     19.2±0.08μs       22.6±0.1μs     1.17  timeseries.Offsets.time_custom_bday_incr
+           1.49s            1.64s     1.10  timeseries.Iteration.time_iter_periodindex
-      23.9±0.1μs       21.6±0.1μs     0.91  timeseries.Offsets.time_timeseries_year_incr
-     18.1±0.09μs      16.2±0.06μs     0.89  timeseries.Offsets.time_timeseries_year_apply
-         129±5μs        115±0.2μs     0.89  timeseries.DatetimeIndex.time_unique
-      46.2±0.8μs       40.9±0.2μs     0.88  timeseries.SemiMonthOffset.time_end_apply
-     7.23±0.05μs       6.00±0.1μs     0.83  timeseries.DatetimeIndex.time_timestamp_tzinfo_cons
-      2.02±0.2ms         1.67±0ms     0.82  timeseries.DatetimeIndex.time_add_timedelta

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
``` Full results:
```
asv continuous -f 1.1 -E virtualenv master HEAD
[...]

       before           after         ratio
     [36dadd70]       [c52c7968]
!           38.4s           failed      n/a  gil.nogil_datetime_fields.time_period_to_datetime
+         273±3ns         270±30ms 991429.63  indexing.MultiIndexing.time_is_monotonic
+        55.1±2μs       1.02±0.01s 18449.64  indexing.Int64Indexing.time_loc_scalar
+        484±20μs        299±300ms   618.36  indexing.Int64Indexing.time_ix_list_like
+     1.96±0.08ms       1.16±0.03s   590.47  indexing.Int64Indexing.time_loc_array
+         235±5μs         136±40ms   577.51  indexing.MultiIndexing.time_series_xs_mi_ix
+        660±30μs         254±10ms   384.79  indexing.Int64Indexing.time_loc_list_like
+      17.9±200ms            3.66s   204.48  join_merge.ConcatFrames.time_c_ordered_axis0
+       128±100ms            2.94s    22.98  join_merge.ConcatFrames.time_c_ordered_axis1
+        415±50ms            3.90s     9.39  gil.nogil_take1d_float64.time_nogil_take1d_float64
+        436±80ms            3.77s     8.65  gil.nogil_take1d_int64.time_nogil_take1d_int64
+      7.88±0.1ms       57.0±0.8ms     7.24  binary_ops.TimeseriesTZ.time_timestamp_ops_diff1
+       75.6±30ms          416±7ms     5.50  inference.to_numeric_downcast.time_downcast('datetime64', 'integer')
+      80.6±0.9ms         428±40ms     5.30  index_object.SetOperations.time_int64_union
+           124ms            606ms     4.87  packers.JSON.time_write_json_mixed_float_int_T
+           1.36s            6.56s     4.84  join_merge.ConcatPanels.time_c_ordered_axis0
+        526±20ms            2.48s     4.72  indexing.MultiIndexing.time_multiindex_large_get_loc_warm
+           1.62s            7.58s     4.68  join_merge.ConcatPanels.time_f_ordered_axis1
+           1.65s            7.34s     4.46  join_merge.ConcatPanels.time_f_ordered_axis2
+           1.67s            7.35s     4.40  join_merge.ConcatPanels.time_c_ordered_axis1
+        241±20ms         964±10ms     4.00  indexing.Int64Indexing.time_getitem_lists
+        236±10ms         910±10ms     3.85  indexing.Int64Indexing.time_getitem_array
+           2.28s            8.56s     3.75  join_merge.ConcatPanels.time_c_ordered_axis2
+          55.7ms            206ms     3.70  packers.JSON.time_write_json
+        245±10ms          904±4ms     3.69  indexing.Int64Indexing.time_getitem_list_like
+           124ms            456ms     3.69  packers.JSON.time_write_json_T
+         227±3ms         793±20ms     3.50  join_merge.Align.time_series_align_left_monotonic
+         342±8ms       1.17±0.03s     3.41  index_object.SetOperations.time_int64_symmetric_difference
+         119±1ms         404±10ms     3.40  inference.to_numeric_downcast.time_downcast('string-float', None)
+       125±0.7ms         400±10ms     3.19  inference.to_numeric_downcast.time_downcast('string-float', 'signed')
+        306±10ms         974±30ms     3.18  join_merge.Align.time_series_align_int64_index
+        355±10ms            1.10s     3.10  indexing.StringIndexing.time_get_value
+         363±6ms            1.09s     3.00  indexing.StringIndexing.time_getitem_label_slice
+         128±4ms          352±6ms     2.75  inference.to_numeric_downcast.time_downcast('string-float', 'unsigned')
+     1.54±0.04ms       4.19±0.2ms     2.72  indexing.Int64Indexing.time_ix_array
+       218±0.3ms          590±1ms     2.70  inference.to_numeric_downcast.time_downcast('string-nint', 'signed')
+         197±2ms         504±10ms     2.56  inference.to_numeric_downcast.time_downcast('string-nint', 'unsigned')
+       220±0.6ms         548±20ms     2.49  inference.to_numeric_downcast.time_downcast('string-int', 'signed')
+       204±0.5ms          492±4ms     2.41  inference.to_numeric_downcast.time_downcast('string-nint', None)
+          75.5ms            168ms     2.23  packers.JSON.time_write_json_mixed_float_int_str
+       236±0.2ms       504±0.01ms     2.14  inference.to_numeric_downcast.time_downcast('string-int', 'integer')
+       219±0.2ms         448±20ms     2.04  inference.to_numeric_downcast.time_downcast('string-nint', 'float')
+           1.77s            3.60s     2.04  join_merge.ConcatPanels.time_f_ordered_axis0
+       256±0.4ms       500±0.04ms     1.95  inference.to_numeric_downcast.time_downcast('string-nint', 'integer')
+         268±2ms            509ms     1.90  frame_methods.Dropna.time_count_level_axis0_mixed_dtypes_multi
+      54.3±0.5μs       83.8±0.1μs     1.54  indexing.Int64Indexing.time_ix_slice
+      33.6±0.4μs         50.6±3μs     1.51  indexing.Int64Indexing.time_iloc_list_like
+     7.17±0.07ms       10.7±0.5ms     1.49  timeseries.DatetimeIndex.time_add_offset_fast
+        271±80ms         403±30ms     1.49  index_object.SetOperations.time_int64_difference
+         278±8μs         376±30μs     1.36  indexing.MultiIndexing.time_frame_xs_mi_ix
+       457±0.4μs        602±0.4μs     1.32  groupby.GroupBySuite.time_mean('int', 100)
+           10.5s            13.7s     1.30  join_merge.MergeCategoricals.time_merge_cat
+           324ms            417ms     1.28  gil.nogil_read_csv.time_read_csv
+      73.0±0.7ms         91.7±6ms     1.26  gil.nogil_rolling_algos_fast.time_nogil_rolling_kurt
+        341±30ms         425±10ms     1.25  indexing.MultiIndexing.time_multiindex_get_indexer
+      26.1±200ms           32.0ms     1.22  join_merge.ConcatFrames.time_f_ordered_axis0
+        991±70μs      1.21±0.03ms     1.22  inference.to_numeric_downcast.time_downcast('datetime64', None)
+         474±4ms            579ms     1.22  frame_methods.Dropna.time_dropna_axis0_all_mixed_dtypes
+      23.0±0.2ms       27.0±0.3ms     1.18  packers.packers_read_hdf_store.time_packers_read_hdf_store
+     9.43±0.05ms       10.9±0.3ms     1.15  algorithms.Algorithms.time_factorize_string
+     3.52±0.02ms      4.05±0.03ms     1.15  timeseries.SeriesArithmetic.time_add_offset_delta
+        1.26±0ms       1.44±0.1ms     1.15  frame_ctor.FrameConstructorDTIndexFromOffsets.time_frame_ctor('BusinessDay', 2)
+       131±0.9ms          148±1ms     1.13  inference.to_numeric_downcast.time_downcast('string-float', 'integer')
+     41.5±0.06μs       46.5±0.2μs     1.12  timeseries.SemiMonthOffset.time_begin_apply
+     4.51±0.05ms      5.03±0.02ms     1.12  algorithms.Algorithms.time_add_overflow_zero_scalar
+     3.97±0.03ms      4.40±0.01ms     1.11  io_bench.read_uint64_integers.time_read_uint64_neg_values
+     4.04±0.03ms       4.45±0.2ms     1.10  io_bench.read_uint64_integers.time_read_uint64_na_values
+      236±0.04ms         260±40ms     1.10  inference.to_numeric_downcast.time_downcast('string-int', 'unsigned')
-        81.2±2ms         73.8±2ms     0.91  frame_methods.Dropna.time_dropna_axis1_any
-         498±6ms        452±0.2ms     0.91  timeseries.ToDatetime.time_format_no_exact
-     19.4±0.06μs       17.5±0.2μs     0.90  timeseries.Offsets.time_custom_bday_apply_dt64
-      2.69±0.2ms      2.42±0.01ms     0.90  rolling.SeriesRolling.time_rolling_skew
-     1.71±0.03ms      1.54±0.01ms     0.90  reshape.melt_dataframe.time_melt_dataframe
-     1.17±0.03ms      1.05±0.02ms     0.90  replace.replace_replacena.time_replace_replacena
-      9.82±0.2ms      8.76±0.07ms     0.89  groupby.groupby_size.time_groupby_size
-      22.0±0.3ms       19.6±0.2ms     0.89  frame_methods.frame_fancy_lookup.time_frame_fancy_lookup_all
-     27.6±0.06μs      24.5±0.05μs     0.89  timeseries.Offsets.time_custom_bday_cal_incr
-     1.17±0.03ms      1.04±0.02ms     0.89  replace.replace_fillna.time_replace_fillna
-     6.36±0.01μs         5.63±0μs     0.89  timeseries.DatetimeIndex.time_timestamp_tzinfo_cons
-     1.34±0.01ms         1.18±0ms     0.88  frame_ctor.FrameConstructorDTIndexFromOffsets.time_frame_ctor('Day', 2)
-        437±10μs         384±20μs     0.88  frame_methods.FrameIsnull.time_isnull
-         367±2ms          323±1ms     0.88  frame_methods.Dropna.time_dropna_axis0_all
-      17.8±0.1μs      15.3±0.07μs     0.86  timeseries.Offsets.time_timeseries_year_apply
-      10.9±0.2μs      9.10±0.04μs     0.83  period.period_standard_indexing.time_get_loc
-           1.14s          939±2ms     0.82  reshape.reshape_unstack_large_single_dtype.time_unstack_with_mask
-        75.8±3ms         59.8±3ms     0.79  series_methods.series_isin_int64.time_series_isin_int64_large
-      67.4±0.3ms       52.2±0.2ms     0.77  parser_vb.read_csv_categorical.time_convert_post
-           146ms            112ms     0.77  gil.nogil_kth_smallest.time_nogil_kth_smallest
-           791ms        577±0.6ms     0.73  groupby.Groups.time_groupby_groups('int64_small')
-         433±4ms          303±3ms     0.70  timeseries.AsOfDataFrame.time_asof
-         448±9ms          308±6ms     0.69  timeseries.AsOfDataFrame.time_asof_nan
-      54.3±0.2ms      36.6±0.07ms     0.67  parser_vb.read_csv_categorical.time_convert_direct
-           1.48s            906ms     0.61  groupby.Groups.time_groupby_groups('int64_large')
-           1.57s            949ms     0.60  groupby.Groups.time_groupby_groups('object_small')
-        19.3±1ms       10.4±0.7ms     0.54  gil.nogil_read_csv.time_read_csv_object
-      31.0±0.3ms      15.6±0.09ms     0.51  parser_vb.read_csv2.time_comment
-           563ms            204ms     0.36  packers.JSON.time_write_json_mixed_float_int
-         422±4ms          128±2ms     0.30  panel_ctor.Constructors4.time_panel_from_dict_two_different_indexes
-           2.78s          758±1ms     0.27  index_object.Multi2.time_sortlevel_int64
-         873±9ms          227±4ms     0.26  reshape.reshape_pivot_time_series.time_reshape_pivot_time_series
-           793ms          204±4ms     0.26  frame_methods.frame_duplicated.time_frame_duplicated
-           1.14m            15.4s     0.23  gil.nogil_datetime_fields.time_datetime_field_year
-           391ms           85.8ms     0.22  packers.JSON.time_write_json_mixed_delta_int_tstamp
-           20.3s            3.64s     0.18  join_merge.MergeCategoricals.time_merge_object
-           3.85s          577±6ms     0.15  reshape.reshape_unstack_large_single_dtype.time_unstack_full_product
-           912ms          136±7ms     0.15  index_object.Multi1.time_duplicated
-       35.5±20ms         5.24±1ms     0.15  categoricals.Categoricals.time_union

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
```
 so the benchmarks are telling u that something is amiss 

these are generally petty stable

so run the ones that are out of whack and try to narrow it down  ```
asv continuous -f 1.1 -E virtualenv master HEAD -b indexing.MultiIndexing.time_is_monotonic
[...]
       before           after         ratio
     [36dadd70]       [c52c7968]
+         206±6ms         329±40ms     1.60  indexing.MultiIndexing.time_is_monotonic

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
```

Run again immediately:
```
asv continuous -f 1.1 -E virtualenv master HEAD -b indexing.MultiIndexing.time_is_monotonic
[...]
       before           after         ratio
     [36dadd70]       [c52c7968]
-        400±20ms         197±20ms     0.49  indexing.MultiIndexing.time_is_monotonic

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
```

> so the benchmarks are telling u that something is amiss

Something is amiss, but that is orthogonal to the PR, which is just a refactor. > Something is amiss, but that is orthogonal to the PR, which is just a refactor.

it may be, but until these show stable results and we can tell whether something is changed, can't merge these. Setting the affinity seems to have made a difference:

```
taskset 04 asv continuous -f 1.1 -E virtualenv master HEAD -b timeseries
[...]
       before           after         ratio
     [36dadd70]       [c52c7968]
+         321±2ms         459±20ms     1.43  timeseries.AsOfDataFrame.time_asof_nan
+     19.0±0.09μs       24.1±0.1μs     1.27  timeseries.Offsets.time_timeseries_year_incr
+         317±2ms          375±5ms     1.18  timeseries.AsOfDataFrame.time_asof
+      77.0±0.4μs       85.1±0.2μs     1.11  timeseries.AsOfDataFrame.time_asof_single_early
-     27.1±0.06μs      24.4±0.09μs     0.90  timeseries.Offsets.time_custom_bday_cal_incr
-      19.5±0.1μs      16.9±0.09μs     0.87  timeseries.Offsets.time_custom_bday_apply_dt64
-         519±2ms        438±0.7ms     0.84  timeseries.ToDatetime.time_format_no_exact

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
```

Immediate re-run of the ones that showed a change
```
taskset 04 asv continuous -f 1.1 -E virtualenv master HEAD -b timeseries.AsOfDataFrame -b timeseries.Offsets -b timeseries.ToDatetime
[...]
       before           after         ratio
     [36dadd70]       [c52c7968]
+         324±3ms          449±2ms     1.39  timeseries.AsOfDataFrame.time_asof_nan
+     15.6±0.07μs      19.5±0.04μs     1.25  timeseries.Offsets.time_custom_bday_apply
+     19.4±0.03μs      22.4±0.06μs     1.16  timeseries.Offsets.time_timeseries_day_apply
+      20.7±0.1μs      23.9±0.09μs     1.15  timeseries.Offsets.time_timeseries_day_incr
+     28.7±0.09μs       31.9±0.2μs     1.11  timeseries.Offsets.time_custom_bday_cal_incr_neg_n
-      28.2±0.1μs      25.4±0.09μs     0.90  timeseries.Offsets.time_custom_bday_cal_incr_n
-     30.6±0.09μs      27.5±0.03μs     0.90  timeseries.Offsets.time_custom_bday_decr
-         252±6μs          225±3μs     0.89  timeseries.Offsets.time_custom_bmonthbegin_decr_n

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
```

```
taskset 05 asv continuous -f 1.1 -E virtualenv master HEAD -b timeseries.AsOfDataFrame -b timeseries.Offsets -b timeseries.ToDatetime
[...]
       before           after         ratio
     [36dadd70]       [c52c7968]
+       220±0.6μs         246±30μs     1.12  timeseries.Offsets.time_custom_bmonthbegin_decr_n
+       212±0.1μs          236±1μs     1.11  timeseries.Offsets.time_custom_bmonthend_incr_n
+        2.99±0ms      3.30±0.01ms     1.10  timeseries.ToDatetime.time_iso8601
-     20.5±0.04μs      18.2±0.06μs     0.89  timeseries.Offsets.time_custom_bday_incr
-      33.7±0.1μs      28.8±0.06μs     0.86  timeseries.Offsets.time_custom_bday_cal_incr_neg_n

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
```

Of these, the only one that looks related to this PR is `timeseries.ToDatetime.time_iso8601` For each of `parsing`, `strptime`, `frequencies`, `timezones`, and `offsets`, if and when these refactorings are merged I'll put together targeted tests and benchmarks for each module.  Modularity FTW. needs a rebase GH won't let me respond inline to `?` comment following `NAT_SENTINEL`.  Is this a placeholder for `NaT` to avoid circular import to/from `tslib`. ping ok, let's rebase thanks! pls add the followups to the list.