CLN: ASV binary ops benchmark
- Utilized `params` of timezones in the `Timeseries` class instead of creating a subclass of `TimeseriesTZ`

- Division was being tested instead of modulo in `time_frame_int_mod` and `time_frame_float_mod`

- Added `np.random.seed(1234)` in setup classes where random data is created xref #8144

- Renamed `time_timestamp_ops_diff2` to `time_timestamp_ops_diff_with_shift`

- Replaced the depreciated `np.random.random_integers` with `np.random.randint`

- Ran flake8 and replaced star imports

```
asv run -q -b ^binary_ops
[  0.00%] ·· Benchmarking conda-py3.6-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[  7.14%] ··· Running binary_ops.Ops.time_frame_add                                                                                 46.1ms;...
[ 14.29%] ··· Running binary_ops.Ops.time_frame_comparison                                                                          16.5ms;...
[ 21.43%] ··· Running binary_ops.Ops.time_frame_mult                                                                                55.3ms;...
[ 28.57%] ··· Running binary_ops.Ops.time_frame_multi_and                                                                            126ms;...
[ 35.71%] ··· Running binary_ops.Ops2.time_frame_float_div                                                                              73.0ms
[ 42.86%] ··· Running binary_ops.Ops2.time_frame_float_div_by_zero                                                                      80.7ms
[ 50.00%] ··· Running binary_ops.Ops2.time_frame_float_floor_by_zero                                                                    83.9ms
[ 57.14%] ··· Running binary_ops.Ops2.time_frame_float_mod                                                                              25.3ms
[ 64.29%] ··· Running binary_ops.Ops2.time_frame_int_div_by_zero                                                                        97.2ms
[ 71.43%] ··· Running binary_ops.Ops2.time_frame_int_mod                                                                                46.1ms
[ 78.57%] ··· Running binary_ops.Timeseries.time_series_timestamp_compare                                                           5.40ms;...
[ 85.71%] ··· Running binary_ops.Timeseries.time_timestamp_ops_diff                                                                 42.8ms;...
[ 92.86%] ··· Running binary_ops.Timeseries.time_timestamp_ops_diff_with_shift                                                       157ms;...
[100.00%] ··· Running binary_ops.Timeseries.time_timestamp_series_compare                                                           5.55ms;...
```

# [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/18444?src=pr&el=h1) Report
> Merging [#18444](https://codecov.io/gh/pandas-dev/pandas/pull/18444?src=pr&el=desc) into [master](https://codecov.io/gh/pandas-dev/pandas/commit/412988ed972a929203b2e867c8fdc7fcb0e7d312?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/18444/graphs/tree.svg?token=eZ4WkYLtcO&src=pr&width=650&height=150)](https://codecov.io/gh/pandas-dev/pandas/pull/18444?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master   #18444      +/-   ##
==========================================
- Coverage   91.34%   91.32%   -0.02%     
==========================================
  Files         163      163              
  Lines       49717    49717              
==========================================
- Hits        45413    45404       -9     
- Misses       4304     4313       +9
```

| Flag | Coverage Δ | |
|---|---|---|
| #multiple | `89.12% <ø> (ø)` | :arrow_up: |
| #single | `40.51% <ø> (-0.07%)` | :arrow_down: |

| [Impacted Files](https://codecov.io/gh/pandas-dev/pandas/pull/18444?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [pandas/io/gbq.py](https://codecov.io/gh/pandas-dev/pandas/pull/18444?src=pr&el=tree#diff-cGFuZGFzL2lvL2dicS5weQ==) | `25% <0%> (-58.34%)` | :arrow_down: |
| [pandas/core/frame.py](https://codecov.io/gh/pandas-dev/pandas/pull/18444?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvZnJhbWUucHk=) | `97.8% <0%> (-0.1%)` | :arrow_down: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/18444?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/18444?src=pr&el=footer). Last update [412988e...371138a](https://codecov.io/gh/pandas-dev/pandas/pull/18444?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
 Yeah I have noticed ASV has some interesting quirks. Nonetheless for consistency, I changed `timezone` to `tz` thanks!