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BUG: Fails and or weird aggregation results when using agg with custom functions
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- [x] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame(
[
(1, "a", "f"),
(2, "b", "d"),
(3, None, "f"),
(4, "a", None),
(5, "a", "f"),
(6, None, "d"),
(7, None, "d"),
(8, "b", None),
(9, "a", "f"),
(10, None, None),
(11, None, "f"),
(12, None, "d"),
],
columns=["item", "att1", "att2"],
)
df2 = pd.DataFrame(
[
(1, "a", "f"),
(2, "b", "d"),
(3, None, "f"),]
, columns=["item", "att1", "att2"]
)
def count_not_null(series: pd.Series) -> int:
return series.notnull().astype(int).sum()
def count_all(series: pd.Series) -> int:
""" count all the values (regardless if they are null or nan) """
return len(series)
df.agg("count")
# item 12
# att1 6
# att2 9
# dtype: int64
df.agg(["count", ])
# item att1 att2
# count 12 6 9
df.agg(count_all)
# item 12
# att1 12
# att2 12
# dtype: int64
df.agg([count_all,])
# item att1 att2
# count_all 12 12 12
df.query("item==1").agg(["count",]) # works fine
# item att1 att2
# count 1 1 1
# All seems to work well however same aggregation gives weird results on the other dataframe
df2.aggregate([count_all,])
# item att1 att2
# item att1 count_all
# count_all 3.0 3.0 NaN
# 0 NaN NaN 1.0
# 1 NaN NaN 1.0
# 2 NaN NaN 1.0
# I get the same issues on the other df when I filter some column for exampe
df.query("item==1").agg([count_all,]) # **weird result** ->
# item att1 att2
# item count_all count_all
# count_all 1.0 NaN NaN
# 0 NaN 1.0 1.0
# And it fails if I use more than one custom aggregations
df.query("item==1").agg(["sum", "count"]) # works with standard aggregations
# item att1 att2
# sum 1 a f
# count 1 1 1
# fails with custom aggregations
df.query("item==1").agg([count_all, count_not_null])
/usr/local/anaconda3/envs/hooqu/lib/python3.8/site-packages/pandas/core/base.py in _aggregate_multiple_funcs(self, arg, _axis)
553 result = Series(results, index=keys, name=self.name)
554 if is_nested_object(result):
--> 555 raise ValueError("cannot combine transform and aggregation operations")
556 return result
557
ValueError: cannot combine transform and aggregation operations
# df[df["item"]==1].agg([count_all, count_not_null]) also fails
# It works well if I don't use any filtering via query
df.agg([count_all, count_not_null])
# item att1 att2
# count_all 12 12 12
# count_not_null 12 6 9
# It also fails without filtering on the second dataframe
df2.agg([count_all, count_not_null])
---> 1 df2.agg([count_all, count_not_null])
/usr/local/anaconda3/envs/hooqu/lib/python3.8/site-packages/pandas/core/frame.py in aggregate(self, func, axis, *args, **kwargs)
6704 result = None
6705 try:
-> 6706 result, how = self._aggregate(func, axis=axis, *args, **kwargs)
6707 except TypeError:
6708 pass
/usr/local/anaconda3/envs/hooqu/lib/python3.8/site-packages/pandas/core/frame.py in _aggregate(self, arg, axis, *args, **kwargs)
6718 result = result.T if result is not None else result
6719 return result, how
-> 6720 return super()._aggregate(arg, *args, **kwargs)
6721
6722 agg = aggregate
/usr/local/anaconda3/envs/hooqu/lib/python3.8/site-packages/pandas/core/base.py in _aggregate(self, arg, *args, **kwargs)
475 elif is_list_like(arg):
476 # we require a list, but not an 'str'
--> 477 return self._aggregate_multiple_funcs(arg, _axis=_axis), None
478 else:
479 result = None
/usr/local/anaconda3/envs/hooqu/lib/python3.8/site-packages/pandas/core/base.py in _aggregate_multiple_funcs(self, arg, _axis)
521 colg = self._gotitem(col, ndim=1, subset=obj.iloc[:, index])
522 try:
--> 523 new_res = colg.aggregate(arg)
524 except (TypeError, DataError):
525 pass
/usr/local/anaconda3/envs/hooqu/lib/python3.8/site-packages/pandas/core/series.py in aggregate(self, func, axis, *args, **kwargs)
3686 # Validate the axis parameter
3687 self._get_axis_number(axis)
-> 3688 result, how = self._aggregate(func, *args, **kwargs)
3689 if result is None:
3690
/usr/local/anaconda3/envs/hooqu/lib/python3.8/site-packages/pandas/core/base.py in _aggregate(self, arg, *args, **kwargs)
475 elif is_list_like(arg):
476 # we require a list, but not an 'str'
--> 477 return self._aggregate_multiple_funcs(arg, _axis=_axis), None
478 else:
479 result = None
/usr/local/anaconda3/envs/hooqu/lib/python3.8/site-packages/pandas/core/base.py in _aggregate_multiple_funcs(self, arg, _axis)
553 result = Series(results, index=keys, name=self.name)
554 if is_nested_object(result):
--> 555 raise ValueError("cannot combine transform and aggregation operations")
556 return result
557
```
#### Problem description
I would have expected the output of a custom aggregation upon filtering to be very similar to the one standard ones. Furthermore there seems to be a small bug when passing a single custom aggregation into a collection to the `agg` DataFrame method.
I have narrow down the problem to the call to `_aggregate_multiple_funcs` that works differently based on the size of the dataframe and the number of functions.
In particular when executing the aggregation on the columns (series) different column behave differently. Example:
if I defined:
```python
def count_all(series):
print(type(series))
return len(series)
```
And then called `aggregate` on different columns the function is passed different set of paramters:
```python
df2['att1'].agg(count_all)
# <class 'str'>
# <class 'str'>
# <class 'NoneType'>
# <class 'pandas.core.series.Series'>
# and
df2['att2'].agg(count_all)
# <class 'str'>
# <class 'str'>
# <class 'str'>
```
I don't understand this behaviour. I would expect that both functions receive only the full series data.
#### Workaround
After reading the code I found this line (not sure if it has to do with the problem):
https://github.com/pandas-dev/pandas/blob/v1.0.3/pandas/core/series.py#L3706
So I reimplemented one of the custom aggregation like this:
```
def count_all(series):
if not isinstance(series, pd.Series):
raise TypeError
return len(series)
```
And then all aggregations worked. Was I using a bad implementation of a custom aggregation function? From the docs it was not obvious that the aggregation function is required to check the input type.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Darwin
OS-release : 19.0.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : None
pytest : 5.4.1
hypothesis : 5.8.0
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
|
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MDU6SXNzdWU1OTg4MTg0MTk=
| 33,518
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BUG: Illegal instruction: 4 on mid2010 Mac
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NONE
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#### Code Sample, a copy-pastable example
```python
Python 3.7.7 (default, Mar 26 2020, 10:32:53)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
Illegal instruction: 4
(Pandas) iMac:~ laurent$ sysctl -a | grep machdep.cpu.features
machdep.cpu.features: FPU VME DE PSE TSC MSR PAE MCE CX8 APIC SEP MTRR PGE MCA CMOV PAT PSE36 CLFSH DS ACPI MMX FXSR SSE SSE2 SS HTT TM PBE SSE3 DTES64 MON DSCPL VMX SMX EST TM2 SSSE3 CX16 TPR PDCM SSE4.1 SSE4.2 POPCNT
(Pandas) iMac:~ laurent$ conda list numpy
# packages in environment at /Users/laurent/opt/anaconda3/envs/Pandas:
#
# Name Version Build Channel
numpy 1.18.1 py37h7241aed_0
numpy-base 1.18.1 py37h6575580_1
```
#### Problem description
With a mid 2010 Mac, OSX 10.13.6 (High Sierra)
```
Identifiant du modèle : iMac11,3
Nom du processeur : Intel Core i5
Vitesse du processeur : 2,8 GHz
Nombre de processeurs : 1
Nombre total de cœurs : 4
Cache de niveau 2 (par cœur) : 256 Ko
Cache de niveau 3 : 8 Mo
Mémoire : 16 Go
Version de la ROM de démarrage : 99.0.0.0.0
Version SMC (système) : 1.59f2
```
`import pandas as pd`, crashes with `Illegal instruction: 4`
Old Mac doesn't support VFX, as it is not displayed in sysctl -a | grep machdep.cpu.features output.
This problem is also mentioned here :
https://github.com/conda/conda/issues/9678
https://github.com/das-developers/condaCDF/issues/1
and as it was detected when trying to run inside Anaconda/jupyter, the work in progress to solve is described here :
https://discourse.jupyter.org/t/osx-10-13-6-kernelrestarter-restarting-kernel/3965/8
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MDExOlB1bGxSZXF1ZXN0NDAyNTk4NjEz
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TYP: use overload to refine return type of reset_index
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TYP: disallow decorator preserves function signature
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| 2020-05-02T18:00:27Z
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MEMBER
| null |
xref #33455
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MDExOlB1bGxSZXF1ZXN0NDAyNjA1ODY0
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BUG: Fixed concat with reindex and extension types
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| 10
| 2020-04-13T11:35:47Z
| 2020-07-02T08:12:21Z
| 2020-07-01T01:43:27Z
|
CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/27692
Closes https://github.com/pandas-dev/pandas/issues/33027
I have a larger cleanup planned, but this fixes the linked issues for now.
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MDExOlB1bGxSZXF1ZXN0NDAyNjM0NTI0
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CLN: redundant code in IntegerArray._reduce
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MDU6SXNzdWU1OTg4OTczNjg=
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ENH: Canonic SI frequency
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|
CONTRIBUTOR
| null |
Since pandas is widely used for data science, including working with time series, a canonic representation of the frequency, as defined by SI standards, physics etc. would be a nice improvement.
To summarize differences between the general definition of frequency and the pandas definition of frequency:
- General definition: Frequency is in the unit of `1/time_period`, for SI unis more specifically `1/second=1Hz`.
- pandas definition: Frequency is the time period, so it is exactly the opposite of the physical/general definition.
Since many calculations require the actual frequency, for example summing up velocities to get the length, this would facilitate many calculations.
#### Proposed solution:
Additionally `pd.DatetimeIndex` etc. should get a new attribute, f.i. named `freq_canonic`, that represents the actual frequency of the index in Hertz.
To avoid breaking the API, I'd implement the frequency as a data attribute, which can be accessed by the user, but has no effect on time series calculations.
I could try making a pull request for this feature, if you are interested.
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TST: Add Categorical Series test
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| 2020-04-13T14:20:13Z
| 2020-04-21T18:32:12Z
| 2020-04-21T14:49:08Z
|
MEMBER
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Follow up to https://github.com/pandas-dev/pandas/pull/33513
cc @jreback
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REF: simplify concat_datetime
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| 0
| 2020-04-13T14:27:46Z
| 2020-04-15T01:36:43Z
| 2020-04-15T01:32:20Z
|
MEMBER
| null |
By wrapping dt64/td64 ndarrays in DTA/TDA, we can get rid of a bunch of .astype(object) logic
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CI: xfail rank tests
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| 2020-04-13T16:26:11Z
| 2020-04-13T16:54:59Z
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MEMBER
| null |
With npdev installed, the affected tests arent failing for me on OSX, so this is a shot int he dark.
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MDExOlB1bGxSZXF1ZXN0NDAyNzI3ODA5
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BUG: Grouped-by column loses name when empty list of aggregations is specified
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| 2020-04-13T16:39:41Z
| 2020-05-22T10:22:12Z
| 2020-05-22T10:22:12Z
|
NONE
| null |
- [ ] closes #32580
- [ ] 0 tests added / 0 passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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CLN: .values->._values in hashing
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| 2
| 2020-04-13T16:59:51Z
| 2020-04-15T01:35:22Z
| 2020-04-15T01:28:13Z
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MEMBER
| null |
This sits on top of #33511 (at first thought it could be orthogonal, but this breaks a couple of hash_scalar tests if we dont rip those out first)
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MEMBER
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Moving towards clarifying the EA._concat_same_type behavior with unique vs non-unique dtypes
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BUG: Do not use string Index like Datetimelike Index
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CONTRIBUTOR
| null |
Follow up of conversation in https://github.com/pandas-dev/pandas/pull/32739#issuecomment-602271577.
The goal is to prevent index of string that look like datetimelike to be used as datetimelike.
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CLN: General cleanup in `_libs/lib.pyx`
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CLN: Change isocalendar to be a method
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| 2020-05-27T08:23:27Z
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CONTRIBUTOR
| null |
For consistency with `Timestamp.isocalendar`, `Series.dt.isocalendar` and
`DatetimeIndex.isocalendar` should rather be methods and not attributes.
Followup of #33220, see the discussions following the merge of that PR
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CI: pin cython==0.29.16 in npdev build
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MEMBER
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xref #33507
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REF: put EA concat logic in _concat_arrays
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[] | null | 12
| 2020-04-13T23:54:23Z
| 2020-04-17T14:59:26Z
| 2020-04-17T14:59:08Z
|
MEMBER
| null |
cc @jorisvandenbossche @TomAugspurger per discussion in #32586 (among others) about `_concat_same_type`, this is a proof of concept for a 3-method solution:
- `EA._concat_same_dtype` --> require same type _and_ dtype; DTA/PA do this now
- `EA._concat_same_type` --> require same type but not necessarily same type; we could do without this, but since its already in the API...
- `EA._concat_arrays` --> any ndarray/EAs
For example, `dtypes.concat._concat_sparse` naturally becomes `SparseArray._concat_arrays`. The middle chunk of `union_categoricals` becomes `Categorical._concat_same_dtype`.
Everything described above is just a refactor, putting logic in more reasonable places. The benefit interface-wise is that the dispatching in `concat_compat` looks like
```
if we_have_any_categoricals:
return Categorical._concat_arrays(to_concat)
elif we_have_any_datetimelike:
return DatetimeLike._concat_arrays(to_concat)
elif we_have_any_sparse:
return SparseArray._concat_arrays(to_concat)
[...]
```
ATM the order Categorical -> DTA/TDA/PA -> Sparse is hard-coded, but we could generalize this either with a negotiation logic like Tom [described](https://github.com/pandas-dev/pandas/issues/22994#issuecomment-436279333) or with something simpler like defining `EA.__concat_priority__ = 1, Categorical.__concat_priority__ = 1000, [...]` and the dispatch becomes:
```
eas = {type(x) for x in to_concat if isinstance(x, ExtensionArray)}
eas = list(eas)
eas.sort(lambda x: x.__concat_priority__)
if eas:
return eas[-1]._concat_arrays(to_concat)
```
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ENH: Clear index cache after reindex
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| 2020-04-14T00:26:37Z
| 2020-05-22T10:56:50Z
| 2020-05-22T10:56:05Z
|
NONE
| null |
Add flag to clear index cache after reindex.
By default, reindex causes index to cache values which potentially increases the memory consumption significantly in the case of multiindexes.
```
In [2]: idx = pd.MultiIndex.from_product([pd.date_range('2010-01-01', '2015-01-01'), range(1000)], names=['date', 'id'])
In [3]: idx2 = pd.MultiIndex.from_product([pd.date_range('2010-01-01', '2015-01-01'), range(500)], names=['date', 'id'])
In [4]: df = pd.DataFrame({'a': 1}, index=idx)
In [5]: df.memory_usage(deep=True, index=True) # Original Memory Usage
Out[5]:
Index 7453600
a 14616000
dtype: int64
In [6]: df.reindex(idx2) # df is still the same as original.
In [7]: df.memory_usage(deep=True, index=True) # Memory usage after reindex
Out[7]:
Index 91339680
a 14616000
dtype: int64
```
With clear_cache=True
```
In [20]: idx = pd.MultiIndex.from_product([pd.date_range('2010-01-01', '2015-01-01'), range(1000)], names=['date', 'id'])
In [21]: idx2 = pd.MultiIndex.from_product([pd.date_range('2010-01-01', '2015-01-01'), range(500)], names=['date', 'id'])
In [22]: df = pd.DataFrame({'a': 1}, index=idx)
In [23]: df.memory_usage(deep=True, index=True) # Original Memory Usage
Out[23]:
Index 7453600
a 14616000
dtype: int64
In [24]: df.reindex(idx2) # df is still the same as original.
In [25]: df.memory_usage(deep=True, index=True) # Memory usage after reindex
Out[25]:
Index 7453600
a 14616000
dtype: int64
```
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REF: use cached inferred_type when calling lib.infer_dtype(index)
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| 2
| 2020-04-14T01:30:47Z
| 2020-04-25T22:27:38Z
| 2020-04-25T22:22:08Z
|
MEMBER
| null |
Among other things, this will let us avoid a couple of ugly calls in Series
```
if isinstance(key, Index):
key_type = key.inferred_type
else:
key_type = lib.infer_dtype(key, skipna=False)
```
Cleanup of nearby EA-handling code
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ENH: Implement IntegerArray.sum
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| 2020-04-25T08:05:18Z
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I think this is mostly interesting in that it allows normalize=True for value_counts on an IntegerArray backed Series, which currently doesn't work:
```python
[ins] In [1]: s = pd.Series([1, 2, 3], dtype="Int64")
[ins] In [2]: s.value_counts(normalize=True)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-2-2bf1a78353e5> in <module>
----> 1 s.value_counts(normalize=True)
~/pandas/pandas/core/base.py in value_counts(self, normalize, sort, ascending, bins, dropna)
1252 normalize=normalize,
1253 bins=bins,
-> 1254 dropna=dropna,
1255 )
1256 return result
~/pandas/pandas/core/algorithms.py in value_counts(values, sort, ascending, normalize, bins, dropna)
725
726 if normalize:
--> 727 result = result / float(counts.sum())
728
729 return result
AttributeError: 'IntegerArray' object has no attribute 'sum'
```
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BUG: Series[listlike_of_ints] incorrect on MultiIndex
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| 2020-04-25T21:22:00Z
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|
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| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This is one of the cases that @jorisvandenbossche identified in #33355.
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PERF: Fix performance regression #33365
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| 7
| 2020-04-14T03:28:12Z
| 2020-04-16T05:28:55Z
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|
CONTRIBUTOR
| null |
Fix performance regression in Series.is_monotonic_increasing for categorical
by avoiding Categorical construction for categorical series
- [x] closes #33365
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry: perf regression was introduced after previous version
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MDU6SXNzdWU1OTkzNDY1Nzc=
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|
DownSampling Time Series Data using pandas
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| 2020-04-14T07:08:21Z
| 2020-08-21T17:06:00Z
| 2020-04-14T16:25:39Z
|
NONE
| null |
Hi all, i am still new to machine learning , I have time series data as per below and i need tips/help on how to downsample it using scikit-learn. see my dummy data below ,Payment status is my label(0=missed, 1=paid), i need to downsample using time series strategy and keeping temporal order
```
Policy_no Deduction_date Payment_status
1 01-Jan-2019 1
2 01-Jan-2019 1
3 01-Jan-2019 1
4 01-Jan-2019 1
5 01-Jan-2019 1
1 01-feb-2019 1
2 01-feb-2019 1
3 01-feb-2019 0
4 01-feb-2019 0
5 01-feb-2019 0
1 01-mar-2019 1
2 01-mar-2019 1
3 01-mar-2019 1
```
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MDU6SXNzdWU1OTkzNTA4MTA=
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|
ENH: Use self values in Series groupby
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| 2020-04-14T07:16:53Z
| 2020-04-23T20:36:24Z
| 2020-04-23T20:36:24Z
|
NONE
| null |
#### Is your feature request related to a problem?
I wish I could use pandas to do
```
s = pd.Series([1,2,1,3,1,2])
s.groupby()
```
rather than
```
s.groupby(s)
```
#### Describe the solution you'd like
I would like pandas to interpret the series values as `grouper` when `by` or `level` is not provided.
#### API breaking implications
I can't think of any reason this will affect the API.
#### Describe alternatives you've considered
Grouping using series itself as `grouper`.
`s.groupby(s)`
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Preserving boolean dtype in Series.any/all function with level keyword #33449
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CONTRIBUTOR
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BUG:Timezone lost when assigning Datetime via DataFrame.at
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| 5
| 2020-04-14T09:04:16Z
| 2020-04-21T13:25:22Z
| 2020-04-21T13:25:22Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import datetime as dt
import pandas as pd
df = pd.DataFrame({'foo': [dt.datetime(2000, 1, 1)]})
df.at[0, 'foo'] = dt.datetime(2000, 1, 2, tzinfo=dt.timezone.utc)
print(df.at[0, 'foo']) # Prints 2000-01-02 00:00:00
assert df.at[0, 'foo'].tzinfo is not None # Fails
```
#### Problem description
When assigning a tz-aware datetime via `at` I expect that the tz-information is kept. If that is not possible due to a datatype problem then I expect a warning or error message. Instead it is lost without a warning or error message.
#### Expected Output
I would have expected that the tz-information is kept, i.e. that
```
assert df.at[0, 'foo'].tzinfo is not None # Should not fail
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-46-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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| 599,697,658
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MDU6SXNzdWU1OTk2OTc2NTg=
| 33,545
|
BUG: index name is lost when doing groupby where by is a dict and at least one value in the by dict is a list of of size zero
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| 1
| 2020-04-14T16:22:59Z
| 2020-06-14T18:04:54Z
| 2020-06-14T18:04:54Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
To reproduce:
```
example = pd.DataFrame({'foo':[1,2,3],'bar1':[1,2,3],'bar2':[1,2,3]})
#index name is foo
display(example.groupby('foo').agg({'bar1': ['min'], 'bar2':['max']}))
#index name is None
display(example.groupby('foo').agg({'bar1': ['min'], 'bar2':[]}))
```
#### Output of ``pd.show_versions()``
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-7642-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.9
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
```
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| 599,708,778
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MDExOlB1bGxSZXF1ZXN0NDAzMjk1MzI4
| 33,546
|
TST: rename checknull_old -> test_checknull_old
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| 2
| 2020-04-14T16:41:32Z
| 2020-04-14T19:32:42Z
| 2020-04-14T17:21:42Z
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MEMBER
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| 599,801,617
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MDU6SXNzdWU1OTk4MDE2MTc=
| 33,547
|
Spelling error in docs, Getting Started, section 03
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| 2020-04-14T19:19:41Z
| 2020-04-14T22:05:53Z
| 2020-04-14T22:05:53Z
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NONE
| null |
In the Getting Started tutorials, section 03 "How to get subsets from a DataFrame", the paragraph beginning:
> We now from before...
should be:
> We know from before...
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MDU6SXNzdWU1OTk4MDQ0NDg=
| 33,548
|
BUG: groupby resample different results with .agg() vs .mean()
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| 3
| 2020-04-14T19:24:39Z
| 2020-12-22T14:07:19Z
| 2020-12-22T14:07:19Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
data = pd.DataFrame({
'cat': ['cat_1', 'cat_1', 'cat_2', 'cat_1', 'cat_2', 'cat_1', 'cat_2', 'cat_1'],
'num': [5,20,22,3,4,30,10,50],
'date': ['2019-2-1', '2018-02-03','2020-3-11','2019-2-2', '2019-2-2', '2018-12-4','2020-3-11', '2020-12-12']
})
data['date'] = pd.to_datetime(data['date'])
using_agg = data.groupby('cat').resample('Y', on='date').agg({'num': 'mean'})
using_mean = data.groupby('cat').resample('Y', on='date').mean()
print(using_agg)
print(using_mean)
assert(np.allclose(using_agg['num'], using_mean['num']))
```
#### Problem description
I expect to get the same result from using .agg({col_name: 'mean'}) and I expect to get from .mean()
It's very surprising the results are different here, and really worrying for me, considering historic code for us might be producing incorrect results. Can anyone shed some light on why this may be the case?
#### Expected Output
The output from `using_mean` is correct and should be equal to the output from `using_agg`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Darwin
OS-release : 19.3.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 18.1
setuptools : 40.6.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDAzNDAzOTkw
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[WIP] Add remote file io using fsspec.
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NONE
| null |
- [x] closes #33452
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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DOC: Fix some typos
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| 2
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| 2020-04-14T22:14:07Z
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MEMBER
| null |
Closes #33547, also Titanic should be capitalized
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|
CONTRIBUTOR
| null |
- If timezone is not UTC, then convert to UTC
- This bug was found while deprecating 'week' and 'weekofyear'
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| 2020-04-26T02:11:00Z
| 2020-04-25T20:45:15Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
The issue this addresses is related to #31218.
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BUG: tz_localize needs to invalidate freq
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| null |
- [x] closes #30511
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Identified when trying to add `freq` check to `assert_index_equal` for DatetimeIndex and TimedeltaIndex.
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|
BUG: merge left and merge inner produce different index-order
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[] | null | 5
| 2020-04-14T23:21:35Z
| 2020-10-25T22:26:28Z
| null |
NONE
| null |
According to the `df.merge` docstring and [documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html), concerning the `how` parameter:
> left: use only keys from left frame, similar to a SQL left outer join; **preserve key order**.
> inner: use intersection of keys from both frames, similar to a SQL inner join; **preserve the order of the left keys.**
(emphasis mine.) I understand this to mean, that the order of the left index will be preserved when using `how='left'` and `how='inner'` (or omitted). Of course, some keys might not be present in the second case, but that's beside the point here.
If my understanding is incorrect, my apologies and feel free to discard this report.
If my understanding is correct, however, I noticed today that this is not always true. Here is an example, where the order is not preserved.
#### Code Sample
```python
import pandas as pd
import numpy as np
data = pd.DataFrame({'value':np.random.randint(-10,100,12)}, index=pd.date_range('2020-01-01', periods=12, freq='M'))
data['q'] = data.index.map(lambda ts: ts.quarter)
data['even'] = data.index.map(lambda ts: ts.month % 2 ==0)
cols = ['even', 'q']
av = data.groupby(cols).apply(lambda gr: gr[['value']].mean())
df1 = data.merge(av, how='inner', left_on=cols, suffixes=['', '_av'], right_index=True)
df2 = data.merge(av, how='left', left_on=cols, suffixes=['', '_av'], right_index=True)
```
The dataframes:
```
data:
value q even
2020-01-31 74 1 False
2020-02-29 87 1 True
2020-03-31 79 1 False
2020-04-30 74 2 True
2020-05-31 71 2 False
2020-06-30 80 2 True
2020-07-31 94 3 False
2020-08-31 19 3 True
2020-09-30 58 3 False
2020-10-31 97 4 True
2020-11-30 5 4 False
2020-12-31 16 4 True
av:
value
even q
False 1 76.5
2 71.0
3 76.0
4 5.0
True 1 87.0
2 77.0
3 19.0
4 56.5
```
#### Output
```
df2: #as expected and wanted
value q even value_av
2020-01-31 74 1 False 76.5
2020-02-29 87 1 True 87.0
2020-03-31 79 1 False 76.5
2020-04-30 74 2 True 77.0
2020-05-31 71 2 False 71.0
2020-06-30 80 2 True 77.0
2020-07-31 94 3 False 76.0
2020-08-31 19 3 True 19.0
2020-09-30 58 3 False 76.0
2020-10-31 97 4 True 56.5
2020-11-30 5 4 False 5.0
2020-12-31 16 4 True 56.5
df1: #not as expected
value q even value_av
2020-01-31 74 1 False 76.5
2020-03-31 79 1 False 76.5
2020-02-29 87 1 True 87.0
2020-04-30 74 2 True 77.0
2020-06-30 80 2 True 77.0
2020-05-31 71 2 False 71.0
2020-07-31 94 3 False 76.0
2020-09-30 58 3 False 76.0
2020-08-31 19 3 True 19.0
2020-10-31 97 4 True 56.5
2020-12-31 16 4 True 56.5
2020-11-30 5 4 False 5.0
```
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 42.0.2.post20191203
Cython : 0.29.15
pytest : 5.4.1
hypothesis : 5.8.3
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : None
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDAzNDY4NjEz
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CLN: x-array test warnings
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| 2020-04-14T23:49:57Z
| 2020-04-15T00:33:23Z
| 2020-04-15T00:33:14Z
|
CONTRIBUTOR
| null |
Remove these warnings:
```
pandas/tests/generic/test_to_xarray.py:102
/home/travis/build/pandas-dev/pandas/pandas/tests/generic/test_to_xarray.py:102: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
s = Series(range(len(indices)), index=indices)
```
https://travis-ci.org/github/pandas-dev/pandas/jobs/675056013
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fix
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|
CONTRIBUTOR
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- [x] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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fstring updates. Changing from .format to fstring
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| 2
| 2020-04-15T00:58:53Z
| 2020-04-15T19:40:48Z
| 2020-04-15T19:40:39Z
|
CONTRIBUTOR
| null |
Please accept my updates by converting .format to fstrings.
https://github.com/pandas-dev/pandas/issues/29547
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|
BUG: pd.array([timedelta_like_strings]) should infer TimedeltaArray
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| 19
| 2020-04-15T01:20:13Z
| 2021-06-01T13:53:05Z
| 2021-06-01T13:53:05Z
|
MEMBER
| null |
```
>>> left = pd.array(['59 days', '59 days', pd.NaT]))
>>> left
<StringArray>
['59 days', '59 days', <NA>]
Length: 3, dtype: string
>>> left = pd.array(['59 days', '59 days', pd.NaT], dtype='m8[ns]')
>>> left
<TimedeltaArray>
['59 days', '59 days', NaT]
Length: 3, dtype: timedelta64[ns]
```
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BUG: Series.apply() throws an error on empty series when dtype = pd.StringDtype
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| 1
| 2020-04-15T07:34:30Z
| 2020-05-02T17:49:04Z
| 2020-05-02T17:49:04Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
pd.Series(['a', 'b', 'c']).iloc[0:0].apply(lambda x: x) #works as expected
pd.Series(['a', 'b', 'c']).astype('string').iloc[0:0].apply(lambda x: x) #throws an exception
```
#### Problem description
Applying a function to an empty Series with dtype `pd.StringDtype` throws `ValueError: PandasArray must be 1-dimensional.`.
#### Expected Output
Apply does nothing.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.6.3-arch1-1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU2MDAxMDQxMTc=
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|
ENH: plotting function for "2D histograms"/"dynamic spectra" (similar to heatmap)
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| 2020-04-15T08:06:03Z
| 2020-06-05T18:54:29Z
| null |
CONTRIBUTOR
| null |
#### Is your feature request related to a problem?
I often need to plot a heatmap of a DataFrame which uses an `IntervalIndex` as its columns (and, usually, time as its index). Such a plot could also be called a "dynamic spectrum" or "2D histogram" and is used to quickly get an idea of how a spectrum develops over time.
<img src="https://user-images.githubusercontent.com/5310424/79311884-6042b980-7efe-11ea-86d9-1a24b0f73038.png" width=400/>
This is slightly different from what is usually considered as a heatmap (see #19008 for an example) as the bins are not necessarily equidistant and there is not necessarily a separate label for each bin. The y axis (which is used for the `IntervalIndex`) could even have logarithmic scaling.
#### Describe the solution you'd like
This could use the same API `df.plot(type='heatmap')` as suggested in #19008 and switch between appropriate axis scaling/labeling modes depending on whether a `CategoricalIndex`, `IntervalIndex` or other types of indices are used.
#### Describe alternatives you've considered
My current implementation (see below) uses matplotlib's `pcolormesh`, but needs to do some fiddling with the bin edges to work correctly.
Matplotlib's `hist2d` does not work for this use case, because the data is already stored in histogrammed form - the histogram and its bins don't need to be calculated, just plotted.
Seaborn's `heatmap` function seems to be limited to plotting categorical data, so both `IntervalIndex` and `DatetimeIndex` are displayed as categorical data with one label per bin, equidistant spacing, and values on the y axis sorted from top to bottom instead of bottom to top:
<img src="https://user-images.githubusercontent.com/5310424/79312520-4d7cb480-7eff-11ea-80c2-9d0c120c68a2.png" width=400/>
#### Additional context
My current implementation looks similar to this:
```python
binedges = np.append(df.columns.left, df.columns.right[-1])
X, Y = np.meshgrid(df.index, binedges)
pcm = ax.pcolormesh(X, Y, df.values.T)
# then add labels, colorbar etc.
```
This only works if the `IntervalIndex` has no gaps and is non-overlapping, which would have to be checked first.
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PERF: operate on arrays instead of Series in DataFrame/DataFrame ops
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MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/32779
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BUG: df.style.apply(subset=...) argument only works for explicit slices but not ":"
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| 3
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| 2021-02-04T00:51:34Z
| 2021-02-04T00:51:34Z
|
NONE
| null |
Related to #25858 (if not the same?), reproduced on 0.25.3 and current version 1.0.3.
It seems that the `subset` argument for pd.DataFrame.style only works for slices with explicit labels, e.g. `['A',2]` but not for select-all labels, e.g. `[:,2]`. See MWE below
```python
import pandas as pd
pd.__version__ # 1.0.3
# Taken from
# https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html
def color_negative_red(val):
color = 'red' if val < 0 else 'black'
return 'color: %s' % color
df = pd.DataFrame([
[-1, 3, 6],
[-2,-3, 2]
])
df.index = pd.MultiIndex.from_tuples([('A',1),('A',2)])
# Style works for applymap
df.style.applymap(color_negative_red)
# Works for explicit slice
slicer = pd.IndexSlice[pd.IndexSlice['A',2],:]
df.loc[slicer] # valid .loc slicer
df.style.applymap(color_negative_red, subset=slicer) # works
# Breaks for select-all slice
slicer = pd.IndexSlice[pd.IndexSlice[:,2],:]
df.loc[slicer] # valid .loc slicer
df.style.applymap(color_negative_red, subset=slicer)
# TypeError: unhashable type: 'slice'
```
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CI: Fix Deprecation warning from jedi
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| 2020-04-15T12:24:59Z
| 2020-04-15T12:24:55Z
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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DOC: rename_axis 'columns' param
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NONE
| null |
Example :
```python
import pandas as pd
import numpy as np
s1 = pd.Series(np.random.randn(3), name="First", index=['a', 'b', 'c'])
s1.rename(columns="My Column")
```
Except :
TypeError: rename_axis() got an unexpected keyword argument "columns"
Description :
The documentation shows a "columns" param which is not allowed for a Series. There should be a note to explain this parameter only apply for DataFrame.
Link :
https://github.com/pandas-dev/pandas/blob/3adf3340453d6704d4a2cb47058214cc697a7d29/pandas/core/generic.py#L1110-L1279
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : fr_FR.cp1252
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : None
pytest : None
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDAzODEzNjAz
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REF: simplify broadcasting code
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MDExOlB1bGxSZXF1ZXN0NDAzODE5OTQw
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CI: Fix jedi deprecation warning for 0.17.0 on IPython
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| 4
| 2020-04-15T15:09:09Z
| 2020-05-26T09:40:37Z
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|
MEMBER
| null |
- [ ] closes #33567
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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CI : jedi deprecation warnings fails CI
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| 2020-05-26T09:40:47Z
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MEMBER
| null |
```
ip = <IPython.core.interactiveshell.InteractiveShell object at 0x7f10269bd910>
@async_mark()
async def test_tab_complete_warning(self, ip):
# GH 16409
pytest.importorskip("IPython", minversion="6.0.0")
from IPython.core.completer import provisionalcompleter
code = "import pandas as pd; df = pd.DataFrame()"
await ip.run_code(code)
with tm.assert_produces_warning(None):
with provisionalcompleter("ignore"):
> list(ip.Completer.completions("df.", 1))
pandas/tests/frame/test_api.py:534:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <contextlib._GeneratorContextManager object at 0x7f10264bc190>
type = None, value = None, traceback = None
def __exit__(self, type, value, traceback):
if type is None:
try:
> next(self.gen)
E AssertionError: Caused unexpected warning(s): [('DeprecationWarning', DeprecationWarning('Providing the line is now done in the functions themselves like `Script(...).complete(line, column)`'), '/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/jedi/api/__init__.py', 843), ('DeprecationWarning', DeprecationWarning('Providing the column is now done in the functions themselves like `Script(...).complete(line, column)`'), '/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/jedi/api/__init__.py', 843), ('DeprecationWarning', DeprecationWarning('Deprecated since version 0.16.0. Use Script(...).complete instead.'), '/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/IPython/core/completer.py', 1401)]
```
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DOC: Fix heading capitalization in doc/source/whatsnew - part5 (#32550)
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| 6
| 2020-04-15T15:53:50Z
| 2020-05-06T21:14:57Z
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|
CONTRIBUTOR
| null |
-Quite a lot of complicated issues here, need reviewing. Such as 'Groupby' transformed to 'GroupBy', 'I/O' to 'IO', and some others.
- [ ] Modify files v0.18.0.rst, v0.18.1.rst, v0.19.0.rst, v0.19.1.rst, v0.19.2.rst, v0.20.0.rst, v0.20.2.rst, v0.20.3.rst, v0.21.0.rst, v0.21.1.rst
-File v0.20.0.rst has bad file name as it is actually talking about version v0.20.1.rst
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MDU6SXNzdWU2MDA0MTE2Njc=
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BUG: strange behaviour in quantile with group by
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| 1
| 2020-04-15T16:02:22Z
| 2020-05-25T21:58:07Z
| 2020-05-25T21:58:07Z
|
NONE
| null |
is this a bug in pandas? why null values in the grouped by field break the quantile?
```
df = pd.DataFrame({
'category': ['A', 'A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B', 'B'],
'value': [1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6]
})
quantiles = df.groupby('category')['value'].quantile(0.75)
print(quantiles)
df2 = pd.DataFrame({
'category': ['A', 'A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B', 'B', np.nan],
'value': [1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6,1]
})
quantiles2 = df2.groupby('category')['value'].quantile(0.75)
print(quantiles2)
```
produces this output:
```
category
A 4.75
B 4.75
Name: value, dtype: float64
category
A 3.75
B 3.75
Name: value, dtype: float64
```
im using pandas 1.0.3
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MDExOlB1bGxSZXF1ZXN0NDAzOTUwNTU5
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|
Reverted cython pin
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| 2020-04-15T19:37:17Z
| 2020-06-11T15:40:31Z
| 2020-04-22T04:17:18Z
|
MEMBER
| null |
reverts #33534 and closes #33507
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BUG: Debug grouped quantile with NA values
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- [x] closes #33569
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0NDAzOTY5Nzc2
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|
BUG: set_levels set wrong order levels for MutiIndex
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| 2020-04-17T16:07:47Z
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NONE
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- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Added parameter that allows user to reset codes
|
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BUG: DatetimeIndex.insert on empty can preserve freq
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MDU6SXNzdWU2MDA2MTExMzQ=
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appropriate converting without '1900-01-01'
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| 2020-04-15T21:54:34Z
| 2020-08-21T17:06:00Z
| 2020-04-16T05:29:59Z
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NONE
| null |
hello everybody, i wish that you are in good healthy, so i have one little problem, i wanna to convert one column from my tabular csv (a lot of columns of time HH:MM:SS) so i see that a lot of my columns are object so i wanna to convert them to datetime64 with this method include in pandas library,
`data['DT']=data['DT].apply(pd.to_datetime)`
the problem that he showed me the actual date so i wanna just HH:MM:SS without date and preserving that their type format in datetime.
any kind of help if possible in my case.
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MDExOlB1bGxSZXF1ZXN0NDA0MDE3NDY4
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ENH: Add index to output of assert_series_equal on category and datetime values
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| 2020-04-26T20:22:54Z
| 2020-04-26T20:22:51Z
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CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This is really an extension of #31435, which added index output to the messages produced when series (and DataFrames) are compared. I failed to notice that categorical values and datetime values were evaluated through a different code path and didn't add the index output for these data types. Since the original problem (index reordering) could just as easily happen for these types the functionality should be there as well. Also, it makes the output more consistent.
I added a couple of new tests to surface and check the additional output.
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BUG:
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| 2020-08-21T17:06:01Z
| 2020-04-16T21:13:34Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame([
{'id': 1},
{'id': 2}
])
df2 = pd.DataFrame([
{'id': 2, 'col1': 'text', 'col2': None}
])
df = pd.merge(df, df2, how='left', on=['id'])
print(df)
# id col1 col2
# 0 1 NaN NaN
# 1 2 text None
df = pd.DataFrame([
{'id': 1},
{'id': 2}
])
df2 = pd.DataFrame([
{'id': 2, 'col2': None}
])
df = pd.merge(df, df2, how='left', on=['id'])
print(df)
# id col2
# 0 1 None
# 1 2 None
```
#### Problem description
Not sure is it a "real " bug or just an expected behavior of pandas. From the code fragment below you can see that with different input pandas fills empty columns with different empty values.
#### Expected Output
```
# id col1 col2
# 0 1 NaN None
# 1 2 text None
```
For the first print statement
#### Output of ``pd.show_versions()``
<details>
import pandas as pd
pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.76-linuxkit
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.8
numba : None
</details>
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TST: Added test case for DataFrame.at
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| 5
| 2020-04-15T23:31:59Z
| 2020-04-21T14:53:36Z
| 2020-04-21T13:25:22Z
|
CONTRIBUTOR
| null |
- [x] closes #33544
- [x] tests added
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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DOC: Added check for standard
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CONTRIBUTOR
| null |
- [ ] xref #32316
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This is my first time contributing, if I made any errors please tell me! I tried to continue what @joybhallaa was working on. I made a check to see which files are not using the standard documentation.
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solve bug #32580
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NONE
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…ns is specified.
- [ ] closes #32580
- [ ] 0 tests added / 0 passed
- [ ] passes `black pandas`
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MDExOlB1bGxSZXF1ZXN0NDA0MTM4Nzgx
| 33,580
|
Update pytables version
|
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| 2020-04-16T19:45:01Z
| 2020-04-16T19:45:00Z
|
CONTRIBUTOR
| null |
Based on https://github.com/pandas-dev/pandas/pull/32700#pullrequestreview-391684059 I update pytables minimal required version to 3.4.3
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- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0NDA0MTQ2ODgx
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Issue 30999 fix
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MDExOlB1bGxSZXF1ZXN0NDA0MTQ2OTc3
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fixup some f-strings
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NONE
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xref #29547
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MDExOlB1bGxSZXF1ZXN0NDA0MTQ3MDQy
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Issue 33428 fix
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NONE
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TST: add message check to pytest.raises (tests/arrays/boolean/test_arithmetic.py)
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NONE
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BUG: Raise a TypeError when record_path doesn't point to an array
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| 2
| 2020-04-16T08:19:35Z
| 2020-04-17T05:04:34Z
| 2020-04-16T23:38:21Z
|
CONTRIBUTOR
| null |
When `record_path` points to something that is Iterable but is not
a sequence in JSON world we will receive odd results.
```
>>> json_normalize([{'key': 'value'}], record_path='key')
0
0 v
1 a
2 l
3 u
4 e
```
Based on RFC 8259 (https://tools.ietf.org/html/rfc8259) a JSON value MUST be
object, array, number, or string, false, null, true. But only two of them
should be treated as Iterable.
```
An object is an unordered *collection* of zero or more name/value
pairs, where a name is a string and a value is a string, number,
boolean, null, object, or array.
An array is an ordered *sequence* of zero or more values.
--
https://tools.ietf.org/html/rfc8259#page-3
```
Based on that `[{'key': 'value'}]` and `{'key': 'value'}` should not be
treated in the same way. In `json_normalize` documentation `record_path`
is described as `Path in each object to list of records`.
So when we want to translate JSON to Python like an object we need to take
into consideration a list (sequence). Based on that `record_path` should
point out to `list`, not `Iterable`.
In specs I added all possibile values that are allowed in JSON and
should not be treated as a collection. There is a special case for null
value that is already implemented.
| type | value | Iterable | Should be treated as list |
|--------|---------|----------|---------------------------|
| object | {} | Yes | No (unordered list) |
| array | [] | Yes | Yes |
| number | 1 | No | No |
| string | "value" | Yes | No |
| false | False | No | No |
| null | Null | No | No (Check #30148) |
| true | True | No | No |
- [x] closes #26284
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MDU6SXNzdWU2MDA4OTEyNTc=
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API: Specify the dtype of new columns added in reindex
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[] | null | 3
| 2020-04-16T09:33:23Z
| 2020-05-04T15:56:10Z
| 2020-05-04T15:56:10Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```
df = pd.DataFrame({'x': [np.nan, 1., 2.]}).astype(pd.SparseDtype("float", np.nan))
df = df.reindex(['x', 'y'], axis='columns')
df.info()
```
```
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 2 columns):
x -4 non-null Sparse[float64, nan]
y -6 non-null float64
dtypes: Sparse[float64, nan](1), float64(1)
memory usage: 176.0 bytes
```
#### Problem description
When re-indexing the columns of a sparse dataframe, new columns are not sparse. This is problematic especially since the new columns would be completely sparse.
#### Expected Output
I'd expect that the new column was also of type `Sparse[float64, 0.0]`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-45-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2
Cython : 0.29.15
pytest : 5.3.5
hypothesis : None
sphinx : 2.4.1
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : 0.9.3
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : 1.3.13
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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QST: pd.pivot_table() generates wrong sums
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| 2020-05-07T13:34:35Z
|
NONE
| null |
- [ X ] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ X ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
I have stumbled upon a strange phenomenon...
The same question asked on [StackOverflow](https://stackoverflow.com/questions/60883087/pandas-pivot-table-generates-wrong-sums)
I have a working code to generate pivoted tables from data uploaded from excels. I have stumbled on a strange case where after applying pivot_table to the data, I get different summed values than before pivot...
I generate data from xlsx and xls files, do some stuff with it and then want to generate a pivoted table. Before the pivot I do df.VAL1.sum() and df.VAL2.sum() and the values are the same ones as the ones summed in excels using =sum(), but after I run my pd.pivot_table() code I get a different result when I do pivot.sum()
The data has the following columns:
```
COL1 COL2 COL3 time_period VAL1 VAL2 UNIT_VAL1 COL4 COL5 COL6 UNIT_VAL2 COL7 COL8 COL9
```
where time_period is a string - YYYY-MM
and here is the pivot_table attributes:
```python
pivot = pd.pivot_table(
df[[
'COL1', 'COL2', 'COL3', 'COL9', 'time_period',
'VAL1','COL8', 'COL4', 'COL5', 'COL6', 'COL7', 'UNIT_VAL1'
]],
values='VAL1',
columns='time_period',
index=[
'COL7', 'COL8', 'COL3', 'COL1',
'COL5', 'COL2', 'COL9',
'COL4', 'COL6', 'UNIT_VAL1'
],
aggfunc=np.sum
)
```
Can the string `time_period` be the problem here or the order of the passed column names? Or maybe I am doing something wrong here? I want to note that this happens only with 2 dataframes, as other dataframes work well.
I did observe that this pivot code produces different sums after 4 decimal spaces, but that shouldn't change the sums IMHO... Maybe I should use `df.picot_table` instead of `pd.pivot_table` ? The problem is I need two tables from the data frame - one in KG and one in local CUR...
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TST: added test for GH28597
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| 2
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| 2020-04-17T22:09:13Z
| 2020-04-17T22:08:52Z
|
CONTRIBUTOR
| null |
Added test to ensure that categories stay ordered when grouping
with missing values.
- [ ] closes #28597
- [X] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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| 0
| 2020-04-16T11:26:41Z
| 2021-01-25T16:33:13Z
| 2021-01-25T16:33:13Z
|
NONE
| null |
#### Problem description
Slicing a DataFrame with a datetime index by datetime results in a KeyError when the string contains microseconds.
`df['2017-10-25T16:25:04.252':'2017-10-25T16:50:05.237']`
```
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
<ipython-input-20-482a9c5e8c58> in <module>
----> 1 df['2017-10-25T16:25:04.252':'2017-10-25T16:50:05.237']
/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py in __getitem__(self, key)
2777
2778 # Do we have a slicer (on rows)?
-> 2779 indexer = convert_to_index_sliceable(self, key)
2780 if indexer is not None:
2781 # either we have a slice or we have a string that can be converted
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py in convert_to_index_sliceable(obj, key)
2265 idx = obj.index
2266 if isinstance(key, slice):
-> 2267 return idx._convert_slice_indexer(key, kind="getitem")
2268
2269 elif isinstance(key, str):
/opt/conda/lib/python3.7/site-packages/pandas/core/indexes/base.py in _convert_slice_indexer(self, key, kind)
2960 indexer = key
2961 else:
-> 2962 indexer = self.slice_indexer(start, stop, step, kind=kind)
2963
2964 return indexer
/opt/conda/lib/python3.7/site-packages/pandas/core/indexes/datetimes.py in slice_indexer(self, start, end, step, kind)
823 mask = True
824 if start is not None:
--> 825 start_casted = self._maybe_cast_slice_bound(start, "left", kind)
826 mask = start_casted <= self
827
/opt/conda/lib/python3.7/site-packages/pandas/core/indexes/datetimes.py in _maybe_cast_slice_bound(self, label, side, kind)
761 freq = getattr(self, "freqstr", getattr(self, "inferred_freq", None))
762 _, parsed, reso = parsing.parse_time_string(label, freq)
--> 763 lower, upper = self._parsed_string_to_bounds(reso, parsed)
764 # lower, upper form the half-open interval:
765 # [parsed, parsed + 1 freq)
/opt/conda/lib/python3.7/site-packages/pandas/core/indexes/datetimes.py in _parsed_string_to_bounds(self, reso, parsed)
517 }
518 if reso not in valid_resos:
--> 519 raise KeyError
520 if reso == "year":
521 start = Timestamp(parsed.year, 1, 1)
KeyError:
```
I think that the function .parse_time_string in pandas._libs.tslibs.parsing.pyx is not supporting microseconds and returns `None`
#### Output of ``pd.show_versions()``
pandas : 1.0.3
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MDU6SXNzdWU2MDEwNTI3MzY=
| 33,590
|
BUG: replace in Series not working when pd.NA is present
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| 2020-04-16T13:26:35Z
| 2020-08-21T17:06:02Z
| 2020-04-17T04:39:36Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
# works
pd.Series(['A', None]).replace({'A': 0})
# raises TypeError: Cannot compare types 'ndarray(dtype=object)' and 'str'
pd.Series(['A', pd.NA]).replace({'A': 0})
```
#### Problem description
The `replace` method does not work when `pd.NA` instead of `None` is contained in the series.
#### Expected Output
Both statements should replace 'A' with 0 without touching the missing number.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-96-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU2MDExMzc4MzU=
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|
BUG: groupby first()/last() change datatype of all NaN columns to float; nth() preserves datatype
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| 2020-04-16T15:01:32Z
| 2020-06-17T18:54:45Z
| 2020-06-17T18:54:37Z
|
NONE
| null |
- [x ] I have checked that this issue has not already been reported.
- [x ] I have confirmed this bug exists on the latest version of pandas.
Version 1.0.3 from the conda distro.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
print('pandas version is', pd.__version__, '\n')
df = pd.DataFrame({'id':['a','b','b','c'], 'sym':['ibm','msft','msft','goog'], 'idx':range(4), 'sectype':['E','E','E','E']})
df['osi'] = df.sym.where(df.sectype=='O') # add a column of NaNs that are object/str
print(df)
print(df.info())
# .nth(-1) does the right thing
x = df.groupby('id').nth(-1)
print('\nnth(-1)')
print(x.info())
# .last() converts the osi col to floats
x = df.groupby('id').last()
print('\nlast()')
print(x.info())
# .nth(0) does the right thing
x = df.groupby('id').nth(-1)
print('\nnth(0)')
print(x.info())
# .first() converts the osi col to floats
x = df.groupby('id').first()
print('\nfirst()')
print(x.info())
```
#### Problem description
Given a dataframe with an all-NaN column of str/objects, performing a groupby().first() will convert the all-NaN column to float. This is true for .last() as well, but not for .nth(0) and .nth(-1), so these are workarounds.
This is a problem for me as the groupby().first() is in general code that is iteratively called and the results either pd.concat'd (resulting in a column with mixed types) or appended to an hdf file (causing failure on the write).
#### Expected Output
The resulting dataframe from a groupby().first()/.last() should have the same metadata (column structure and datatypes) as the input dataframe. The result of .first()/.last() should match that of .nth(0)/.nth(-1) .
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : 0.29.15
pytest : 5.4.1
hypothesis : 5.8.3
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDA0NDc4MTUw
| 33,592
|
Added a check for standard documentation
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| 4
| 2020-04-16T15:41:44Z
| 2020-07-17T11:16:19Z
| 2020-07-17T11:16:19Z
|
CONTRIBUTOR
| null |
- [ ] closes #33213
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I created a check to see which files contain Pandas or *pandas* which will be of use for individuals aiming to resolve issue #32316. When the codecheck script is run, it will output the files that contain those strings.
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MDU6SXNzdWU2MDEyMDk0MDI=
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BUG: TypeError: _reconstruct: First argument must be a sub-type of ndarray
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| 2020-11-20T06:40:03Z
| 2020-04-17T12:54:18Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
import pandas as pd
pd.read_pickle("filtered_data.pickle")
```
#### Problem description
I am able to import the dataframe (which can be downloaded in zipped form from [here](https://javierantoran.github.io/assets/datasets/filtered_flight_data.pickle.zip)) with Pandas version 0.24.2 but get the following error with version 1.0.3:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/homes/jua23/.virtualenvs/arch_uncert/lib/python3.7/site-packages/pandas/io/pickle.py", line 182, in read_pickle
return pickle.load(f)
TypeError: _reconstruct: First argument must be a sub-type of ndarray
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-42-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0
Cython : 0.29.15
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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BUG: pandas 1.0 dropna error with categorical data if pd.options.mode.use_inf_as_na = True
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| 3
| 2020-04-16T18:06:38Z
| 2020-05-26T09:34:35Z
| 2020-04-27T21:07:23Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
from pandas.api.types import CategoricalDtype
# with categorical column and use_inf_as_na = True -> ERROR
pd.options.mode.use_inf_as_na = True
df1 = pd.DataFrame([['a1', 'good'], ['b1', 'good'], ['c1', 'good'], ['d1', 'bad']], columns=['C1', 'C2'])
df2 = pd.DataFrame([['a1', 'good'], ['b1', np.inf], ['c1', np.NaN], ['d1', 'bad']], columns=['C1', 'C2'])
categories = CategoricalDtype(categories=['good', 'bad'], ordered=True)
df1.loc[:, 'C2'] = df1['C2'].astype(categories)
df2.loc[:, 'C2'] = df2['C2'].astype(categories)
df1.dropna(axis=0) # ERROR
df2.dropna(axis=0) # ERROR
```
#### Problem description
With the latest version of pandas (1.0.3, installed via pip on python 3.6.8), DataFrame.dropna returns an error if a column is of type **CategoricalDtype** AND **pd.options.mode.use_inf_as_na = True**.
Exception with traceback:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/sbucc/miniconda3/envs/tf114/lib/python3.6/site-packages/pandas/core/frame.py", line 4751, in dropna
count = agg_obj.count(axis=agg_axis)
File "/home/sbucc/miniconda3/envs/tf114/lib/python3.6/site-packages/pandas/core/frame.py", line 7800, in count
result = notna(frame).sum(axis=axis)
File "/home/sbucc/miniconda3/envs/tf114/lib/python3.6/site-packages/pandas/core/dtypes/missing.py", line 376, in notna
res = isna(obj)
File "/home/sbucc/miniconda3/envs/tf114/lib/python3.6/site-packages/pandas/core/dtypes/missing.py", line 126, in isna
return _isna(obj)
File "/home/sbucc/miniconda3/envs/tf114/lib/python3.6/site-packages/pandas/core/dtypes/missing.py", line 185, in _isna_old
return obj._constructor(obj._data.isna(func=_isna_old))
File "/home/sbucc/miniconda3/envs/tf114/lib/python3.6/site-packages/pandas/core/internals/managers.py", line 555, in isna
return self.apply("apply", func=func)
File "/home/sbucc/miniconda3/envs/tf114/lib/python3.6/site-packages/pandas/core/internals/managers.py", line 442, in apply
applied = getattr(b, f)(**kwargs)
File "/home/sbucc/miniconda3/envs/tf114/lib/python3.6/site-packages/pandas/core/internals/blocks.py", line 390, in apply
result = func(self.values, **kwargs)
File "/home/sbucc/miniconda3/envs/tf114/lib/python3.6/site-packages/pandas/core/dtypes/missing.py", line 183, in _isna_old
return _isna_ndarraylike_old(obj)
File "/home/sbucc/miniconda3/envs/tf114/lib/python3.6/site-packages/pandas/core/dtypes/missing.py", line 283, in _isna_ndarraylike_old
vec = libmissing.isnaobj_old(values.ravel())
TypeError: Argument 'arr' has incorrect type (expected numpy.ndarray, got Categorical)
This doesn't happen with pandas 0.24.0 or if pd.options.mode.use_inf_as_na = False (default).
#### Expected Output
no error
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-96-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
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MDExOlB1bGxSZXF1ZXN0NDA0Njc1NjEy
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DEPR: Deprecate week, weekofyear in Series.dt,DatetimeIndex
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| 13
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| 2020-05-26T06:36:11Z
| 2020-05-26T03:22:04Z
|
CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/33503
This PR is a followup to #33220 and implements what was discussed there.
A few comments:
- `DatetimeIndex.isocalendar` does not set the index on the returned dataframe,
leading to some cumbersome syntax. This may be worth changing, please let me
know what you think.
- Should we also deprecate `Timestamp.week/weekofyear`?
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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Update _core.py with missing parameters
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| 2020-04-16T20:15:12Z
| 2020-05-07T11:19:22Z
| 2020-05-06T21:22:15Z
|
CONTRIBUTOR
| null |
added missing parameters:
- ax
- subplots
- sharex
- sharey
- secondary_y
- sort_columns
- stacked
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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PERF: always slice when indexing on columns
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[] | null | 7
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| 2021-08-26T19:21:03Z
| null |
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Touched on in #32779, this makes all\* DataFrame indexing-on-columns do slicing instead of taking, so doesn't make copies.
\* Actually there is a different path in `_slice_take_blocks_ax0` that we go down if we `self._is_single_block`, can update that later if we decide this is something we want to do.
```
In [3]: dti = pd.date_range("2016-01-01", periods=10**5, freq="S")
In [4]: df = pd.DataFrame._from_arrays([dti]*10 + [dti - dti] * 10 + [dti.to_period("D")]*10, columns=range(30), index=range(len(dti)))
In [8]: arr = np.arange(30)
In [9]: np.random.shuffle(arr)
In [10]: %timeit df[arr]
8.35 ms ± 64.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <-- master
650 µs ± 52.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- PR
```
The tradeoff is that we end up with less-consolidated results. I'm OK with that, but there may be downsides I'm not aware of, will wait for others to weigh in.
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CLN: trim unreachable branches in _compare_or_regex_search
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CLN: remove BlockManager._get_counts, get_dtype_counts
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| 6
| 2020-04-17T00:39:34Z
| 2020-04-25T21:46:55Z
| 2020-04-25T21:40:30Z
|
MEMBER
| null |
We have the same optimization in place for the flex ops.
ipython results for the asv this adds:
```
In [3]: arr = np.arange(10 ** 6).reshape(100, -1)
In [4]: df = pd.DataFrame(arr)
In [5]: df["C"] = 1.0
In [6]: row = df.iloc[0]
In [11]: %timeit df + row
1.92 s ± 71.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # <-- master
1 ms ± 19.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- PR
```
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ENH: add pandas.core.groupby.DataFrameGroupBy.nlargest / nsmallest
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[] | null | 2
| 2020-04-17T00:56:04Z
| 2021-07-31T04:24:02Z
| 2021-07-31T04:24:02Z
|
CONTRIBUTOR
| null |
#### Is your feature request related to a problem?
It's common to want to select nlargest/nsmallest within a groupby based on whichever column is supposed to determine the sort order. However, the DataFrameGroupBy API has no such function, and it's very cumbersome to first select the largest records of a Series and then somehow reduce the dataframe based on that (and it's essentially impossible in the case when there's no unique index per record).
Another consideration is speed: for Series, the [docs](https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.SeriesGroupBy.nlargest.html) note:
> **Notes**
> Faster than `.sort_values(ascending=False).head(n)` for small n relative to the size of the Series object.
Hopefully this can be leveraged for DataFrames as well.
#### Describe the solution you'd like
Docstring for `nlargest`, `nsmallest` adapted correspondingly
```python
def nlargest(self, sort_by, n=5, keep="first") -> "DataFrame":
"""
Return the `n` first rows when sorting after `sort_by` in descending order
Parameters
----------
sort_by: list
Column(s) of the DataFrame describing the precedence according to which
the DataFrame shall be sorted. Equivalent to `DataFrame.sort_values(sort_by)`
n : int, default 5
Return this many descending sorted values.
keep : {'first', 'last', 'all'}, default 'first'
When the sorting results in a tie extending beyond the `n`-th record:
- ``first`` : return the first `n` occurrences in order
of appearance.
- ``last`` : return the last `n` occurrences in reverse
order of appearance.
- ``all`` : keep all occurrences. This can result in a Series of
size larger than `n`.
Returns
-------
DataFrame
The first `n` records of the DataFrame, sorted in decreasing order
according to `sort_by`
See Also
--------
DataFrame.nsmallest: Get the `n` smallest elements.
DataFrame.sort_values: Sort Series by values.
DataFrame.head: Return the first `n` rows.
Notes
-----
Faster than ``.sort_values(ascending=False).head(n)`` for small `n`
relative to the size of the ``DataFrame`` object.
[...]
""""
```
#### API breaking implications
No breaking changes, function doesn't exist yet.
Initially, this could directly wrap ``.sort_values(sort_by, ascending=False).head(n)``, but hopefully similar speed ups can be gained as in the Series case.
#### Alternatives
One could allow an argument `ascending` if the columns should be allowed to toggle this per column (same as for `DataFrame.sort_values`). In this case, it might be better to call the methods `nfirst` / `nlast`.
Alternatively, one could just implement a minimal version where `nlargest`/`nsmallest` is only determined based on one column (and not several). In this case, a kwarg `dropna=True` should be added too (like for Series, cf. #28984).
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.format changed to f string
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CONTRIBUTOR
| null |
.format changed to f string
https://github.com/pandas-dev/pandas/issues/29547
- [ ] closes #xxxx
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BUG: TimeDeltaIndex slicing with strings incorrectly includes too much data
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| 2021-07-31T04:24:46Z
| null |
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
fs = 50000
i = pd.to_timedelta(np.arange(900 * fs, dtype=np.int) / fs * 1e9, unit='ns')
df = pd.DataFrame({'dummy': np.arange(len(i))}, index=i)
assert np.isclose(len(df['710s':'711s']) / fs, 2.0)
assert np.isclose(len(df['710s':'719s']) / fs, 10.0)
assert np.isclose(len(df['610s':'620s']) / fs, 11.0)
assert np.isclose(len(df['710s':'720.00000001s']) / fs, 10.00002)
assert np.isclose(len(df['710s':'720s']) / fs, 11.0) # fails! Slices 70 seconds of data??
```
#### Problem description
Slicing a dataframe with a TimeDeltaIndex with the particular right bound '720s' seems to be incorrectly parsed, not returning the time slice as expected. As can be seen in the above example, other bounds work as expected, but using '720s' as the right bound returns 60 more seconds of data than it should have.
#### Expected Output
Slicing between '710s' and '720s' should return 11 seconds of data, as slicing '610s' and '620s' does.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-29-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 9.0.1
setuptools : 46.1.3
Cython : None
pytest : 4.3.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 0.999999999
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : 2.4.11
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
pytest : 4.3.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : None
tabulate : 0.8.6
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : None
numba : 0.48.0
</details>
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BUG: DatetimeIndex.intersection losing freq and tz
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| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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BUG: support count function for custom BaseIndexer rolling windows
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| 3
| 2020-04-17T07:28:21Z
| 2020-04-18T05:45:10Z
| 2020-04-17T21:33:07Z
|
MEMBER
| null |
- [X] xref #32865
- [X] 1 tests added / 1 passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
## Scope of PR
This PR makes sure that when we call `count` with a `BaseIndexer` subclass, custom `start` and `end` arrays get calculated, and the algorithm in the `aggregations.pyx` gets called instead of the `_Window.create_blocks`.
Turns out that we were sending the calculation into `aggregations.pyx` only for frequency-based windows, and custom `BaseIndexer` subclasses got ignored. Fixed it and cleaned the code up a bit.
## Background on the wider issue
We currently don't support several rolling window functions when building a rolling window object using a custom class descended from `pandas.api.indexers.Baseindexer`. The implementations were written with backward-looking windows in mind, and this led to these functions breaking.
Currently, using these functions returns a `NotImplemented` error thanks to #33057, but ideally we want to update the implementations, so that they will work without a performance hit. This is what I aim to do over a series of PRs.
## Perf notes
No changes to the algorithms necessary.
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ENH: Always display column headers in HTML output when scrolling down on long dataframes
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| 2021-02-20T21:01:00Z
|
CONTRIBUTOR
| null |
#### Is your feature request related to a problem?
I sometimes need to visually screen several hundreds of records. For this I do:
```python
pd.set_option('display.max_rows', 300)
```
and then display the dataframe in the output cell of a jupyter notebook.
However if the dataframe is long enough to not fit in a single browser window, scrolling down hides the column headers and it makes it really hard to interpret the content of bottom records.
#### Describe the solution you'd like
It would be very nice to use some CSS wizardry to always keep the column headers visible just below the top edge of the browser viewport when scrolling down in a notebook with a long dataframe that does not fit entirely in the browser viewport at once.
#### Alternatively solution
An alternative it to slice the long DF into chunks and display with 50 or so records and display each chunk sequentially so that each of them always fit on the screen. But this is tedious and generates many jupyter cells which is not necessarily desired.
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| 17
| 2020-04-17T10:05:51Z
| 2020-05-02T17:26:21Z
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|
MEMBER
| null |
Exploring what is being discussed in https://github.com/pandas-dev/pandas/issues/22994. @TomAugspurger your idea seems to be working nicely! (it almost removes as much code than it adds (apart from tests/docs), ànd fixes the EA concat bugs ;))
Few notes compared to proposal in #22994:
- Since we already have a `find_common_type` function, decided to use this as the "get_concat_dtype", since it seems this does what is needed for concat
- Extending the EA interface with a `ExensionDtype._get_common_type` method that is used in pandas' `find_common_type` function to dispatch the logic to the extension type
What I already handled:
- general protocol, using this for concat with axis=0 (eg concatting series) and using it as example for IntegerDtype
- handling categoricals this way (which removes the `concat_categorical` helper). This turned up a few cases where we have value-dependent behaviour right now, which we can't easily preserve (mainly regarding NaNs and int dtype, see below)
Still need to handle sparse (those has failing tests now) and maybe datetime, and check other failures.
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DOC/CLN: Clean/Fix documentation for Window module
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CONTRIBUTOR
| null |
- xref #31661
- xref #28792
- Added '.' before dataframe/series reference to fix missing link problem. Don't know if it's the best approach.
- Changed base template that is used by some functions to return better See Also section. Hope it's ok.
- Also question about Window class. It doesn't has its counterpart from Series/DF modules, its doc containing examples from Rolling part and there're only 4 functions. Is it internal base class for all windows and maybe shoul be excluded from docs or is it used somewhere maybe for creating custom windows?
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TST: make expected DTI/TDI results more specific
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|
MEMBER
| null |
assert_index_equal doesnt check for matching freq. I've got a branch that changes that, but the diff is massive, so i am first going through the tests and updating the `expected`s so that they will pass once that check is in place.
Also in this sequence: xref #33604
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TYP: type NDFrame.(_get_axis|_get_axis_name|_get_axis_number)
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| 2
| 2020-04-17T15:31:35Z
| 2020-04-18T19:07:49Z
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CONTRIBUTOR
| null |
Gives return types to ``NDFrame._get_axis`` etc.
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MDExOlB1bGxSZXF1ZXN0NDA1MjIxODY2
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BUG: set_levels set wrong order levels for MutiIndex
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|
NONE
| null |
- [ ] closes #33420
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Added parameter that allows user to reset codes
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DOC: Remove extra backtick in example in documentation.
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| 1
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| 2020-04-25T17:06:31Z
| 2020-04-17T17:55:09Z
|
CONTRIBUTOR
| null |
:meth:`~Series.idxmax`` --> :meth:`~Series.idxmax`
- [x] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Fixed bug. Added in check for ufunc and evaluates inner expression be…
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| false
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[] | null | 7
| 2020-04-17T20:43:11Z
| 2020-08-07T22:56:51Z
| 2020-08-07T22:56:51Z
|
CONTRIBUTOR
| null |
…fore evaluating outer expression
- [x] closes #24670
- [x] passes `black pandas`
|
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MDExOlB1bGxSZXF1ZXN0NDA1MzM1MDM3
| 33,614
|
CLN: assorted cleanups
|
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| 2020-04-17T20:52:26Z
| 2020-04-17T22:06:38Z
| 2020-04-17T21:39:09Z
|
MEMBER
| null |
Trying to clear out my local CLN branches
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| 602,229,005
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MDExOlB1bGxSZXF1ZXN0NDA1MzM2Nzk5
| 33,615
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CLN: avoid catching AssertionError, AttributeError in NDFrame methods
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| 3
| 2020-04-17T20:56:51Z
| 2020-04-21T14:56:55Z
| 2020-04-21T14:47:58Z
|
MEMBER
| null |
@WillAyd when i run `mypy` manually it passes, but when run via the pre-commit hook I get
```
pandas/core/internals/blocks.py:2739: error: unused 'type: ignore' comment
pandas/core/generic.py:4034: error: unused 'type: ignore' comment
pandas/core/generic.py:4093: error: unused 'type: ignore' comment
Found 3 errors in 2 files (checked 2 source files)
```
Something similar is happening with most branches these days. Have you seen this?
|
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MDExOlB1bGxSZXF1ZXN0NDA1NDI0MTMy
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REF: get .items out of BlockManager.apply
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| 0
| 2020-04-18T01:28:13Z
| 2020-04-19T23:53:03Z
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MEMBER
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