issue#27482 - added a check for if obj is instance of type in _isna-new
- [x] closes #27482 
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry

Can someone help me; when I run `git diff upstream/master -u -- "*.py" | flake8 --diff`
I get: `fatal: bad revision 'upstream/master'` Needs a test, don’t worry too much about the git thing could you point me to the file where the test would go for this? I had a quick look, would it be okay to go in this function: https://github.com/pandas-dev/pandas/blob/master/pandas/tests/dtypes/test_missing.py#L81 > this logic needs to be replicated in _isnew_old as well. tests are needed.
> 
> may want to move this to the last clause of the if/else. performance tests need to be run for this.

I added it above this elif https://github.com/joy-rosie/pandas/blob/issue-27482-isnull/pandas/core/dtypes/missing.py#L138 because that is causing the error. I will add to _isna_old and also add tests, not sure how to add perfomance tests. I have added the tests and the statement to `_isna_old`, please let me know how I can run the performance tests > please let me know how I can run the performance tests

https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#running-the-performance-test-suite Hello, I ran the performance tests as shown in the link and this is the final output:

.       before           after         ratio
     [143bc34a]       [7aef72ce]
     <master>         <issue-27482-isnull>
+      21.7±0.5ms       29.6±0.6ms     1.37  indexing.NumericSeriesIndexing.time_loc_scalar(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
+         409±5μs         498±10μs     1.22  indexing.NumericSeriesIndexing.time_ix_scalar(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
+     12.4±0.09ms       15.0±0.1ms     1.21  index_object.SetOperations.time_operation('int', 'symmetric_difference')
+      6.12±0.2ms       6.99±0.3ms     1.14  stat_ops.SeriesMultiIndexOps.time_op(0, 'median')
+     1.67±0.06μs       1.87±0.1μs     1.12  index_cached_properties.IndexCache.time_values('PeriodIndex')
+       332±0.6ns         369±10ns     1.11  timestamp.TimestampOps.time_tz_localize('US/Eastern')
+     1.01±0.02μs      1.12±0.02μs     1.11  index_cached_properties.IndexCache.time_inferred_type('RangeIndex')
+     2.14±0.05ms      2.37±0.04ms     1.11  reindex.LevelAlign.time_align_level
+         329±2ns          362±9ns     1.10  timestamp.TimestampOps.time_tz_localize(tzutc())
+       331±0.5ns          365±8ns     1.10  timestamp.TimestampOps.time_tz_localize(<UTC>)
-      4.64±0.2μs      4.21±0.05μs     0.91  timedelta.TimedeltaConstructor.time_from_missing
-     1.71±0.02μs      1.55±0.05μs     0.91  period.PeriodProperties.time_property('M', 'year')
-     1.42±0.08ms      1.29±0.01ms     0.90  ctors.SeriesConstructors.time_series_constructor(<function list_of_lists>, False, 'int')
-        567±50μs        511±0.9μs     0.90  groupby.GroupByMethods.time_dtype_as_field('float', 'head', 'transformation')
-      1.04±0.4ms          936±3μs     0.90  groupby.GroupByMethods.time_dtype_as_group('int', 'cumsum', 'direct')
-     1.68±0.01μs       1.51±0.1μs     0.90  period.PeriodProperties.time_property('min', 'second')
-     1.48±0.08ms      1.34±0.02ms     0.90  ctors.SeriesConstructors.time_series_constructor(<function list_of_lists>, True, 'int')
-     2.41±0.05ms      2.16±0.09ms     0.90  reindex.LevelAlign.time_reindex_level
-     1.69±0.04μs      1.52±0.06μs     0.90  period.PeriodProperties.time_property('M', 'month')
-     1.69±0.02μs      1.51±0.06μs     0.89  period.PeriodProperties.time_property('M', 'day')
-        586±60μs        523±0.7μs     0.89  groupby.GroupByMethods.time_dtype_as_field('datetime', 'tail', 'transformation')
-     1.69±0.01μs      1.49±0.06μs     0.88  period.PeriodProperties.time_property('min', 'day')
-      1.43±0.2ms         1.24±0ms     0.87  groupby.GroupByMethods.time_dtype_as_group('int', 'rank', 'transformation')
-        609±90μs        528±0.9μs     0.87  groupby.GroupByMethods.time_dtype_as_group('int', 'quantile', 'transformation')
-      1.46±0.2ms         1.24±0ms     0.85  groupby.GroupByMethods.time_dtype_as_group('int', 'rank', 'direct')
-      7.26±0.1ms       6.01±0.3ms     0.83  algorithms.Duplicated.time_duplicated('first', 'int')
-        348±80μs          287±1μs     0.83  groupby.GroupByMethods.time_dtype_as_field('float', 'last', 'transformation')
-      1.69±0.3ms      1.36±0.01ms     0.81  groupby.GroupByMethods.time_dtype_as_field('int', 'value_counts', 'direct')
-       90.4±20μs       65.4±0.1μs     0.72  algorithms.SortIntegerArray.time_argsort(1000)
-       422±200μs        289±0.9μs     0.68  groupby.GroupByMethods.time_dtype_as_field('float', 'last', 'direct')
-       887±300μs          553±1μs     0.62  groupby.GroupByMethods.time_dtype_as_field('object', 'head', 'transformation')

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE DECREASED.

Machine information:
{
    "arch": "x86_64",
    "cpu": "Intel(R) Core(TM) i5-3230M CPU @ 2.60GHz",
    "machine": "josie-lenovo-ideapad",
    "num_cpu": "4",
    "os": "Linux 4.18.0-25-generic",
    "ram": "3897484",
    "version": 1
} I feel like it is a bit random where the performance has gone down and where its gone up > I feel like it is a bit random where the performance has gone down and where its gone up

Yah, this is a common issue with asv (especially in cases where there are no non-noise changes).  The usual solution is to re-run it a couple of times and pick out the changes that are consistent across runs. this also needs a whatsnew note, put in 1.0 in Missing bug fix section sorry to be a pain but where do i add a whatsnew note?

I am also finding it difficult to get any reliable results for the performance tests, I have ran it a few times and there are different results each time

Is there anything i can do there? > sorry to be a pain but where do i add a whatsnew note?

Don't worry about it, we were all new here at one point.  whatsnew note goes in doc/source/whatsnew/0.25.1.rst

> I am also finding it difficult to get any reliable results for the performance tests, I have ran it a few times and there are different results each time

That usually indicates that there isn't any real impact.  The non-asv alternative is to use Ipython %timeit directly on the affected function(s) > That usually indicates that there isn't any real impact. The non-asv alternative is to use Ipython %timeit directly on the affected function(s)
for benchmarking I am running the test function:

```
import pandas as pd
from pandas.util import testing as tm
isna_f = pd.isna
pd.__version__
```

```
%%timeit -n 10000

assert not isna_f(1.0)
assert isna_f(None)
assert isna_f(pd.np.nan)
assert not isna_f(pd.np.inf)
assert not isna_f(-pd.np.inf)

# # type
# assert not isna_f(type(pd.Series()))
# assert not isna_f(type(pd.DataFrame()))

# series
for s in [
    tm.makeFloatSeries(),
    tm.makeStringSeries(),
    tm.makeObjectSeries(),
    tm.makeTimeSeries(),
    tm.makePeriodSeries(),
]:
    assert isinstance(isna_f(s), pd.Series)

# frame
for df in [
    tm.makeTimeDataFrame(),
    tm.makePeriodFrame(),
    tm.makeMixedDataFrame(),
]:
    result = isna_f(df)
    expected = df.apply(isna_f)
    tm.assert_frame_equal(result, expected)
```

Outputs:

old:
```
'0.25.0'
```
```
33 ms ± 1.74 ms per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```

new:
```
'0.25.0+71.g7aef72ced'
```
```
33 ms ± 1.8 ms per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```

Please let me know if you think I should run the benchmark on anything else

> 
> Don't worry about it, we were all new here at one point. whatsnew note goes in doc/source/whatsnew/0.25.1.rst

Will this go into bugfixes or missing? > Will this go into bugfixes or missing?

Yes. Probably in the "Missing" section I added into whatsnew and it seems like there are some things failing but I am not sure why a change in whatsnew could cause these particular failures? Restarted the build. That test is randomly failing and we haven't diagnosed the cause yet. Is this ready to go now? Let me know if there are any remaining bits. LGTM