Implement _is_utc in timezones
This is the first of a bunch of PRs to take the place of #17274.

- [ ] 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/17419?src=pr&el=h1) Report
> Merging [#17419](https://codecov.io/gh/pandas-dev/pandas/pull/17419?src=pr&el=desc) into [master](https://codecov.io/gh/pandas-dev/pandas/commit/e6aed2ebb7374ed2a6a7c284750d47728aec285e?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/17419/graphs/tree.svg?width=650&height=150&src=pr&token=eZ4WkYLtcO)](https://codecov.io/gh/pandas-dev/pandas/pull/17419?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master   #17419      +/-   ##
==========================================
- Coverage   91.15%   91.13%   -0.02%     
==========================================
  Files         163      163              
  Lines       49534    49534              
==========================================
- Hits        45153    45144       -9     
- Misses       4381     4390       +9
```

| Flag | Coverage Δ | |
|---|---|---|
| #multiple | `88.92% <ø> (ø)` | :arrow_up: |
| #single | `40.22% <ø> (-0.07%)` | :arrow_down: |

| [Impacted Files](https://codecov.io/gh/pandas-dev/pandas/pull/17419?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [pandas/io/gbq.py](https://codecov.io/gh/pandas-dev/pandas/pull/17419?src=pr&el=tree#diff-cGFuZGFzL2lvL2dicS5weQ==) | `25% <0%> (-58.34%)` | :arrow_down: |
| [pandas/core/frame.py](https://codecov.io/gh/pandas-dev/pandas/pull/17419?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvZnJhbWUucHk=) | `97.72% <0%> (-0.1%)` | :arrow_down: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/17419?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/17419?src=pr&el=footer). Last update [e6aed2e...fb55a8c](https://codecov.io/gh/pandas-dev/pandas/pull/17419?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
 In order to get asv to run, I had to move timezones.pyx from _libs/tslibs/ to just _libs/.

```
asv continuous -f 1.1 -E virtualenv master HEAD -b ^timeseries
      before           after         ratio
     [8351f86a]       [29669bb5]
+        1.09±0ms      1.42±0.06ms     1.30  timeseries.ResampleSeries.time_1min_5min_ohlc
+     1.78±0.04ms      1.99±0.01ms     1.11  timeseries.ResampleSeries.time_resample_datetime64
+     2.96±0.01ms      3.26±0.04ms     1.10  timeseries.ToDatetime.time_iso8601_format
-        49.0±4μs       43.5±0.2μs     0.89  timeseries.SemiMonthOffset.time_end_apply
-     2.55±0.01ms      2.23±0.04ms     0.87  timeseries.ResampleDataFrame.time_max_numpy
-     6.62±0.05μs         5.61±0μs     0.85  timeseries.DatetimeIndex.time_timestamp_tzinfo_cons
-      28.3±0.2μs      23.9±0.09μs     0.84  timeseries.Offsets.time_custom_bday_cal_incr
-        17.9±1μs      14.6±0.05μs     0.82  timeseries.Offsets.time_custom_bday_apply
-      19.9±0.8ms       13.5±0.4ms     0.68  timeseries.AsOfDataFrame.time_asof_single
-        21.4±2ms       13.8±0.7ms     0.64  timeseries.AsOfDataFrame.time_asof_nan_single
-           1.98s          337±7ms     0.17  timeseries.AsOfDataFrame.time_asof
-           1.98s          326±5ms     0.16  timeseries.AsOfDataFrame.time_asof_nan

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
```

As usual, I don't see anything meaningful here, since the one function being defined in timezones.pyx is verbatim identical to the one it is replacing. we'll did your code affect asof?

that is a non trivial diff
 you can test multiple things via a regex in the bench

timezone is also relevant here > we'll did your code affect asof?

I don't see anything in `asof` that depends directly on timezones, and the `AsOf` and `AsOfDataFrame` benchmarks use tz-naive indexes.  So I have trouble imagining how that would be affected.

> that is a non trivial diff

One function is implemented in timezones.pyx:

```
cdef inline bint _is_utc(object tz):
     return tz is UTC or isinstance(tz, _dateutil_tzutc)
```

This replaces identical (up to a different alias for `_dateutil_tzutc`) functions in `tslib` and `_libs.index`.  The rest is boilerplate. > timezone is also relevant here

A search for tz|timezone in the benchmarks brought up the timeseries.py benchmarks above, 2 hits in binary_ops.py, and 1 hit in groupby.py.  Are there others I'm missing?

```
asv continuous -f 1.1 -E virtualenv master HEAD -b ^binary_ops
       before           after         ratio
     [8351f86a]       [29669bb5]
+        120±10ms         198±30ms     1.65  binary_ops.Ops.time_frame_multi_and(False, 'default')
+         142±8ms         221±30ms     1.56  binary_ops.Ops.time_frame_multi_and(True, 1)
+        88.4±2ms         132±20ms     1.49  binary_ops.Ops2.time_frame_float_floor_by_zero
+        85.0±1ms          126±5ms     1.49  binary_ops.Ops2.time_frame_float_div
+         136±5ms         186±20ms     1.36  binary_ops.Ops.time_frame_multi_and(False, 1)
-     1.08±0.05ms         922±10μs     0.85  binary_ops.Timeseries.time_series_timestamp_compare
```

Of these, `binary_ops.Timeseries.time_series_timestamp_compare` is the only one with tz/tzinfo involved.

```
asv continuous -f 1.1 -E virtualenv master HEAD -b ^groupby.groupby_datetimetz
[...]
BENCHMARKS NOT SIGNIFICANTLY CHANGED.
```
 since this code touches virtually all time related code, pls run with ``-b time`` to see. OK, I just started this running.

For my own edification: I was under the impression that since this PR is moving a function but not changing it that it would obviously not affect perf.  Is there some theoretical reason that is wrong?  Is this something cython-specific? > For my own edification: I was under the impression that since this PR is moving a function but not changing it that it would obviously not affect perf. Is there some theoretical reason that is wrong? Is this something cython-specific?

whenever you touch cython in a non-trivial way, we need to assure perf is comparable. in this case you are moving *lots* of code it is very easy to drop things. just as you always tests, always run perf. > pls run with -b time to see.

This is running, but it looks like the "time" regex is catching _everything_ since all the bench methods start with "time_". ```
     before           after         ratio
     [8351f86a]       [29669bb5]
!           15.6s           failed      n/a  gil.nogil_datetime_fields.time_datetime_field_day
!           13.2s           failed      n/a  gil.nogil_datetime_fields.time_datetime_field_daysinmonth
!           29.7s           failed      n/a  gil.nogil_datetime_fields.time_datetime_field_normalize
!           15.0s           failed      n/a  gil.nogil_datetime_fields.time_datetime_field_year
!           38.2s           failed      n/a  gil.nogil_datetime_fields.time_period_to_datetime
!           36.5s           failed      n/a  panel_methods.PanelMethods.time_pct_change_items
+        49.3±1μs       1.09±0.03s 22102.18  indexing.Int64Indexing.time_loc_scalar
+         407±7μs       1.17±0.02s  2866.16  indexing.Int64Indexing.time_ix_list_like
+        361±10μs          877±9ms  2427.51  indexing.Int64Indexing.time_getitem_list_like
+         557±9μs       1.15±0.05s  2064.80  indexing.Int64Indexing.time_loc_list_like
+     1.34±0.04ms       1.08±0.02s   807.81  indexing.Int64Indexing.time_ix_array
+     1.68±0.02ms       1.04±0.02s   619.33  indexing.Int64Indexing.time_loc_array
+         222±5μs        97.8±30ms   440.73  indexing.MultiIndexing.time_series_xs_mi_ix
+      3.44±0.2ms         966±10ms   280.50  indexing.Int64Indexing.time_getitem_lists
+       18.1±70ms            3.54s   196.26  join_merge.ConcatFrames.time_c_ordered_axis0
+       24.8±70ms            3.70s   149.36  join_merge.ConcatFrames.time_f_ordered_axis0
+      1.94±0.2ms         230±10ms   118.68  categoricals.Categoricals.time_concat
+        267±10μs       5.14±0.7ms    19.23  indexing.MultiIndexing.time_frame_xs_mi_ix
+        13.0±1ms        234±100ms    17.96  binary_ops.TimeseriesTZ.time_timestamp_ops_diff1
+        284±10ms            4.04s    14.23  gil.NoGilGroupby.time_sum_8
+        9.09±2ms         102±20ms    11.25  algorithms.Algorithms.time_factorize_float
+        6.49±2ms        56.8±10ms     8.76  algorithms.Algorithms.time_factorize_int
+        122±20ms            945ms     7.72  frame_methods.Shift.time_shift_axis0
+        129±10ms         829±30ms     6.44  frame_methods.Shift.time_shift_axis_1
+        259±20ms            1.65s     6.37  frame_methods.Reindex.time_reindex_both_axes
+      1.41±0.02s            8.55s     6.07  gil.NoGilGroupby.time_groups_2
+         247±2ms       1.49±0.09s     6.05  frame_methods.Reindex.time_reindex_both_axes_ix
+           1.08s            6.47s     6.00  join_merge.ConcatPanels.time_c_ordered_axis0
+         400±6ms            2.35s     5.86  indexing.MultiIndexing.time_multiindex_large_get_loc
+        74.8±1ms         436±20ms     5.83  index_object.SetOperations.time_int64_union
+        187±20ms            1.02s     5.48  index_object.Multi1.time_duplicated
+           1.19s            6.35s     5.33  join_merge.ConcatPanels.time_f_ordered_axis0
+           1.13s            5.86s     5.17  join_merge.ConcatPanels.time_f_ordered_axis1
+         172±9ms        887±0.1ms     5.16  indexing.Int64Indexing.time_getitem_array
+       666±200ms            3.11s     4.66  gil.NoGilGroupby.time_sum_8_notp
+       86.6±20ms         398±10ms     4.60  index_object.SetOperations.time_int64_intersection
+           1.81s            8.29s     4.57  join_merge.ConcatPanels.time_c_ordered_axis2
+        535±40ms            2.39s     4.47  indexing.MultiIndexing.time_multiindex_large_get_loc_warm
+         174±2ms         775±10ms     4.45  join_merge.Align.time_series_align_left_monotonic
+        95.2±1ms       416±0.09ms     4.37  inference.to_numeric_downcast.time_downcast('datetime64', 'unsigned')
+         240±5ms       1.03±0.01s     4.31  join_merge.Align.time_series_align_int64_index
+         713±9ms            3.04s     4.26  index_object.Multi2.time_sortlevel_int64
+         124±2ms         516±20ms     4.18  panel_ctor.Constructors1.time_panel_from_dict_all_different_indexes
+         113±1ms         458±20ms     4.06  panel_ctor.Constructors4.time_panel_from_dict_two_different_indexes
+      3.08±0.03s            12.4s     4.03  gil.NoGilGroupby.time_groups_4
+           1.12s            4.45s     3.96  join_merge.ConcatPanels.time_c_ordered_axis1
+        213±20ms         835±20ms     3.92  frame_methods.frame_duplicated.time_frame_duplicated
+           7.47s            25.9s     3.47  gil.NoGilGroupby.time_groups_8
+        322±10ms       1.11±0.03s     3.46  index_object.SetOperations.time_int64_symmetric_difference
+         174±6ms         597±20ms     3.43  index_object.SetOperations.time_int64_difference
+         370±2ms       1.10±0.01s     2.96  indexing.StringIndexing.time_get_value
+       187±0.4ms          523±2ms     2.80  inference.to_numeric_downcast.time_downcast('string-nint', None)
+           13.7s            37.6s     2.76  gil.nogil_datetime_fields.time_datetime_to_period
+       149±0.5ms          392±8ms     2.63  inference.to_numeric_downcast.time_downcast('string-float', 'integer')
+         152±2ms         398±10ms     2.63  inference.to_numeric_downcast.time_downcast('string-float', 'signed')
+        205±20ms         514±10ms     2.50  inference.to_numeric_downcast.time_downcast('string-int', None)
+       158±0.8ms         362±10ms     2.30  inference.to_numeric_downcast.time_downcast('string-float', 'unsigned')
+       288±0.2ms         620±40ms     2.16  inference.to_numeric_downcast.time_downcast('string-int', 'signed')
+      263±0.03ms         552±20ms     2.10  inference.to_numeric_downcast.time_downcast('string-int', 'integer')
+      11.4±0.3ms         23.4±5ms     2.06  gil.nogil_read_csv.time_read_csv_object
+       242±0.9ms         470±10ms     1.94  inference.to_numeric_downcast.time_downcast('string-int', 'float')
+           755ms            1.44s     1.91  join_merge.ConcatPanels.time_f_ordered_axis2
+      4.00±0.2ms       6.96±0.8ms     1.74  algorithms.Algorithms.time_duplicated_int
+           603ms            958ms     1.59  frame_methods.frame_nunique.time_frame_nunique
+           1.45s            2.29s     1.58  eval.Eval.time_mult('python', 'all')
+         137±7ms            215ms     1.58  gil.NoGilGroupby.time_sum_4
+       145±0.8μs         224±50μs     1.55  period.period_standard_indexing.time_intersection
+      49.4±0.1μs         75.7±4μs     1.53  indexing.Int64Indexing.time_loc_slice
+      54.9±0.6μs       82.0±0.5μs     1.49  indexing.Int64Indexing.time_ix_slice
+     31.1±0.03ms        46.1±50ms     1.48  panel_ctor.Constructors3.time_panel_from_dict_same_index
+         473±7ms         680±20ms     1.44  gil.nogil_take1d_float64.time_nogil_take1d_float64
+         312±3ms          441±3ms     1.41  timeseries.AsOfDataFrame.time_asof_nan
+     5.49±0.03ms       7.77±0.6ms     1.41  io_bench.frame_to_csv_date_formatting.time_frame_to_csv_date_formatting
+         558±1μs        780±0.7μs     1.40  groupby.GroupBySuite.time_head('int', 100)
+          91.7ms            127ms     1.39  packers.JSON.time_write_json_mixed_float_int
+      33.3±0.3ms         46.1±2ms     1.38  eval.Eval.time_and('numexpr', 'all')
+        320±10ms          442±5ms     1.38  timeseries.AsOfDataFrame.time_asof
+      23.0±0.2ms      31.5±0.04ms     1.37  parser_vb.read_csv2.time_comment
+      66.5±0.1ms       90.6±0.3ms     1.36  packers.STATA.time_write_stata_with_validation
+      82.8±0.3ms          109±7ms     1.32  io_bench.frame_to_csv_mixed.time_frame_to_csv_mixed
+     23.9±0.08μs       30.9±0.5μs     1.29  timeseries.Offsets.time_custom_bday_cal_incr_n
+           1.21s            1.53s     1.27  join_merge.i8merge.time_i8merge
+        65.1±2ms         80.7±1ms     1.24  parser_vb.read_csv_categorical.time_convert_direct
+      5.52±0.2ms       6.73±0.3ms     1.22  algorithms.Algorithms.time_duplicated_float
+           29.0s            35.3s     1.22  panel_methods.PanelMethods.time_pct_change_major
+           1.23s            1.47s     1.19  groupby.Groups.time_groupby_groups('object_large')
+       286±0.2ms        338±0.4ms     1.18  inference.to_numeric.time_from_numeric_str
+     1.14±0.03ms      1.33±0.01ms     1.16  index_object.Float64.time_mul
+           1.51s            1.74s     1.15  timeseries.Iteration.time_iter_periodindex
+      1.10±0.01s            1.27s     1.15  reshape.reshape_unstack_large_single_dtype.time_unstack_with_mask
+      28.4±0.2μs      32.6±0.08μs     1.15  timeseries.Offsets.time_custom_bday_cal_incr_neg_n
+       875±0.4μs       1.00±0.1ms     1.15  period.period_standard_indexing.time_align
+         292±2μs          333±7μs     1.14  inference.DtypeInfer.time_uint32
+           26.0s            29.4s     1.13  panel_methods.PanelMethods.time_pct_change_minor
+      23.7±0.9ms       26.8±0.3ms     1.13  packers.packers_read_hdf_store.time_packers_read_hdf_store
+           7.31s            8.24s     1.13  join_merge.JoinIndex.time_left_outer_join_index
+          86.6ms           96.9ms     1.12  packers.JSON.time_write_json_mixed_delta_int_tstamp
+      10.1±0.1ms      11.3±0.09ms     1.12  algorithms.Algorithms.time_add_overflow_both_arg_nan
+       457±0.5ms          508±3ms     1.11  timeseries.ToDatetime.time_format_no_exact
+           240ms            266ms     1.11  gil.nogil_read_csv.time_read_csv_datetime
-      17.1±0.2ms       15.5±0.2ms     0.91  categoricals.Categoricals2.time_value_counts
-     3.95±0.05ms      3.58±0.08ms     0.91  frame_methods.frame_fillna_many_columns_pad.time_frame_fillna_many_columns_pad
-      3.58±0.1ms      3.22±0.07ms     0.90  timeseries.AsOf.time_asof
-     1.53±0.03ms         1.37±0ms     0.89  frame_ctor.FrameConstructorDTIndexFromOffsets.time_frame_ctor('MonthBegin', 2)
-         132±3ms        118±0.6ms     0.89  strings.StringMethods.time_get_dummies
-      2.67±0.2ms         2.36±0ms     0.89  groupby.GroupBySuite.time_median('int', 10000)
-           2.18s            1.93s     0.88  frame_methods.Dropna.time_dropna_axis1_all
-      20.1±0.1μs       17.8±0.2μs     0.88  timeseries.Offsets.time_custom_bday_incr
-      32.7±0.2μs       28.4±0.1μs     0.87  timeseries.Offsets.time_custom_bday_cal_decr
-        459±30ms        398±0.6ms     0.87  timeseries.DatetimeIndex.time_add_offset_slow
-      17.2±0.4μs      14.5±0.06μs     0.84  timeseries.Offsets.time_custom_bday_apply
-      3.08±0.2ms      2.55±0.01ms     0.83  strings.StringMethods.time_title
-      29.8±0.1μs       24.1±0.1μs     0.81  timeseries.Offsets.time_custom_bday_cal_incr
-           356ms            271ms     0.76  gil.nogil_read_csv.time_read_csv
-       104±0.4ms       73.1±0.7ms     0.70  series_methods.series_isin_int64.time_series_isin_int64_large
-           109ms           74.0ms     0.68  packers.JSON.time_write_json_date_index
-     1.63±0.03ms      1.05±0.03ms     0.65  binary_ops.Timeseries.time_series_timestamp_compare
-     1.75±0.03ms      1.11±0.02ms     0.63  binary_ops.TimeseriesTZ.time_series_timestamp_compare
-       254±100ms            159ms     0.63  gil.NoGilGroupby.time_sum_4_notp
-     1.73±0.01ms      1.06±0.02ms     0.61  binary_ops.Timeseries.time_timestamp_series_compare
-           275ms            163ms     0.59  frame_methods.Dropna.time_dropna_axis0_any
-         494±5ms          243±3ms     0.49  inference.to_numeric_downcast.time_downcast('string-nint', 'float')
-         474±3ms          220±2ms     0.46  inference.to_numeric_downcast.time_downcast('string-nint', 'unsigned')
-         276±5ms          116±2ms     0.42  reshape.reshape_pivot_time_series.time_reshape_pivot_time_series
-           1.22s            478ms     0.39  frame_methods.Dropna.time_dropna_axis0_any_mixed_dtypes
-           448ms            100ms     0.22  packers.JSON.time_write_json_mixed_float_int_T
-           312ms           62.3ms     0.20  frame_methods.Dropna.time_dropna_axis1_any
-        257±30ms         49.8±2ms     0.19  categoricals.Categoricals.time_union
-           386ms           74.7ms     0.19  packers.JSON.time_write_json_mixed_float_int_str

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
``` these benches look really unstable. not sure why. These benches should be very stable unless you have something odd going on. you can try setting processor afinity so it runs on a single (same) processor, I recall you have a 'big' machine? Affinity is worth a shot.  

```
taskset 03 asv continuous -f 1.1 -E virtualenv master HEAD -b timeseries -b tz -b timezone
[...]
        before           after         ratio
     [8351f86a]       [29669bb5]
+     5.46±0.02ms       6.28±0.1ms     1.15  timeseries.DatetimeIndex.time_dti_tz_factorize
+     29.9±0.07μs       33.3±0.3μs     1.11  timeseries.Offsets.time_custom_bday_cal_decr
-     31.0±0.05μs       28.1±0.1μs     0.91  timeseries.Offsets.time_custom_bday_decr
-         438±2ms          397±1ms     0.91  timeseries.SemiMonthOffset.time_end_incr_rng
-        448±20ms        404±0.1ms     0.90  timeseries.DatetimeIndex.time_add_offset_slow
-     22.5±0.06μs       19.9±0.1μs     0.88  timeseries.AsOf.time_asof_single
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
``` Tinkering around with setup.py on an unrelated branch I found that .pyx files like `'pandas/_libs/tslibs/timezones'` don't need to go into the `depends` section of the `ext_data`.  This raises three questions:

1) Is this observation accurate?  Is it platform or version specific?

2) If so, should I remove the extraneous entries from the `ext_data` in this PR?

3) If so, should I make a separate PR removing other extraneous entries currently in `ext_data`? > If so, should I remove the extraneous entries from the ext_data in this PR?

not sure what you think is extraneous. these a represent c dependencies. sure some of ``tseries_depends`` / ``lib_depends`` might be able to be simplied a bit. new PR.

needs a rebase

 > not sure what you think is extraneous. these a represent c dependencies. sure some of tseries_depends / lib_depends might be able to be simplied a bit. new PR.

New PR sounds good.  This will be easier to just show than describe. rebase this and can merge next ok lgtm. ping on green. ping thanks.

a tip for you. squashing commits makes rebasing much easier. generally commits should follow logical flow of code and be purposeful.