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BUG: value_counts Int64 zero-size array to reduction
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| 3
| 2020-04-06T12:12:18Z
| 2020-04-06T21:58:59Z
| 2020-04-06T21:58:59Z
|
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.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
```python
pd.Series([], dtype='int64').value_counts() # Series([], dtype: int64)
pd.Series([], dtype='Int64').value_counts() # ValueError: zero-size array to reduction ...
```
#### Problem description
Found this while using `Int64` types in dask. Error occurred in `_meta.value_counts()`
#### Expected Output
Error not to occur.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.113-1-MANJARO
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 : 46.1.3.post20200325
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : None
blosc : 1.9.0
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 : 4.9.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : None
tabulate : None
xarray : 0.15.1
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0Mzk5NjA3NTM5
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Changed files permissions to be the same
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MEMBER
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Not 100% sure about this change, feel free to close at anytime.
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MDU6SXNzdWU1OTUwODE5NTU=
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|
BUG: ExtensionArray formatting of datetime-like forces nanosecond precision
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CONTRIBUTOR
| null |
When passing in an `ExtensionArray` column into the formatting code, we always do a cast with `numpy.asarray(…)` and then re-enter the if-clauses in `format_array`. This enables us to re-use some of the formatting code that already is there for the existing columns but also is problematic for columns that are slightly different than the existing ones but can coerce to them.
In my current example I have a `FletcherContinuousDtype(timestamp[us])` with values that are not representable in the nanosecond range (e.g. year 3000 or year 9999, typical dates used in traditional DB setups for the very far future). It is passed into `ExtensionArrayFormatter` then transformed into an `np.ndarray[datetime64[us]]` and then passed again into `format_array` where it steps into the `Datetime64Formatter`. There a forceful cast to nanoseconds is done through https://github.com/pandas-dev/pandas/blob/01f73100d5f7b942a796ffd000962dee28b43f9c/pandas/io/formats/format.py#L1438-L1439
We could either fix this by:
- The specific approach: Somehow allow non-nanoseconds timestamps in the `Datetime64Formatter`
- The general approach: For an ExtensionArray the formatting should be `fmt_values = [values._formatter(x) for x in self.values]`
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MDU6SXNzdWU1OTUxMzMxODg=
| 33,321
|
BUG: DataFrame.mode index dtype is not type stable
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| 2
| 2020-04-06T13:49:14Z
| 2020-12-29T19:51:23Z
| 2020-12-29T19:51:23Z
|
CONTRIBUTOR
| 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.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
The `.index.dtype` is not stable for DataFrame.mode. It depends on whether the DataFrame is empty and possible the dtypes
```python
In [60]: pd.DataFrame([], columns=['a', 'b']).mode().index.dtype
Out[60]: dtype('O')
```
#### Problem description
The index dtype should always be Int64, to match the non-empty case
#### Expected Output
```python
In [61]: pd.DataFrame({"A": ['a']}).mode().index.dtype
Out[61]: dtype('int64')
```
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MDExOlB1bGxSZXF1ZXN0Mzk5Njc2MjY3
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TST: Don't use 'is' on strings to avoid SyntaxWarning
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| 1
| 2020-04-06T14:13:00Z
| 2020-04-06T21:35:00Z
| 2020-04-06T21:34:55Z
|
CONTRIBUTOR
| null |
This avoids the below warning (in Python 3.8, [user-visible in Debian](https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=956021) because they byte-compile everything on install), and possibly unspecified behaviour (though I haven't seen that in practice).
```
/usr/lib/python3/dist-packages/pandas/tests/frame/test_alter_axes.py:241: SyntaxWarning: "is" with a literal. Did you mean "=="?
False if (keys[0] is "A" and keys[1] is "A") else drop # noqa: F632
```
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MDU6SXNzdWU1OTUxNTc4NzA=
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PERF: performance regression in slicing a Series
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| 1
| 2020-04-06T14:19:57Z
| 2020-04-06T16:11:08Z
| 2020-04-06T16:11:08Z
|
MEMBER
| null |
All of the slice indexing benchmarks are showing regressions, eg https://pandas.pydata.org/speed/pandas/#indexing.NumericSeriesIndexing.time_loc_slice?p-index_dtype=%3Cclass%20'pandas.core.indexes.numeric.Int64Index'%3E&p-index_structure='unique_monotonic_inc'&commits=80d37adc-4f89c261
Replicating one of them with a small snippet confirms this:
```
In [1]: pd.__version__
Out[1]: '1.1.0.dev0+1122.gc7c640ec7'
In [2]: N = 10 ** 6
In [3]: data = pd.Series(np.random.rand(N), index=pd.Int64Index(range(N)))
In [4]: %timeit data.iloc[:800000]
103 ms ± 8.33 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [5]: %timeit data[:800000]
97.2 ms ± 10.2 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
```
vs
```
In [1]: pd.__version__
Out[1]: '1.0.3'
In [2]: N = 10 ** 6
In [3]: data = pd.Series(np.random.rand(N), index=pd.Int64Index(range(N)))
In [4]: %timeit data.iloc[:800000]
55.7 µs ± 2.72 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
In [5]: %timeit data[:800000]
69.6 µs ± 2.48 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```
Checking with snakeviz shows that on master, quasi all time the indexing operation takes is spent in setting the `mgr_locs` (creating the BlockPlacement object), which is certainly not as expected:
Putting a breakpoint in the Block init just before creating the BlockPlacement, showed that the value being passed on master is a `range` object, while this should be a `slice` object, I assume.
Going up the stack to see where this range object is created, points to:
https://github.com/pandas-dev/pandas/blob/a9c105a7a6dfa210f2706e2d8df6a6222964ff26/pandas/core/internals/managers.py#L1571
which according to `git blame` is last touched by this PR: https://github.com/pandas-dev/pandas/pull/32421. And indeed, a range object was added there instead of a slice (other places in the PR *did* use a slice, so this was probably kind of a typo).
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PERF: fix placement when slicing a Series
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| 2
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| 2020-04-06T16:11:08Z
| 2020-04-06T16:11:08Z
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MEMBER
| null |
Closes https://github.com/pandas-dev/pandas/issues/33323
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MDExOlB1bGxSZXF1ZXN0Mzk5Njk3MzYy
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CI: Add argument doc/source/development to formatting docstrings in code_checks.sh
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[] | null | 2
| 2020-04-06T14:45:07Z
| 2020-04-07T00:59:55Z
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CONTRIBUTOR
| null |
Regarding issue #32550
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CI: Doc build failing
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[] | null | 3
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CONTRIBUTOR
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Raw log: https://pipelines.actions.githubusercontent.com/xZyE9jtmkxWlfCAbyu1SHPJOlsa2huNFYcxohSTomy6EbdNZT9/_apis/pipelines/1/runs/25615/signedlogcontent/5?urlExpires=2020-04-06T15%3A02%3A02.2235397Z&urlSigningMethod=HMACV1&urlSignature=eEVJM7I2bg9PFf1apMIamBRrB1gG01K%2F12iKl%2Fe%2Bp5k%3D
```
2020-04-06T13:50:55.3962303Z looking for now-outdated files... none found
2020-04-06T13:50:55.6426712Z pickling environment... done
2020-04-06T13:50:55.6437496Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.BDay.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6440109Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.BMonthBegin.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6440725Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.BMonthEnd.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6441280Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.BQuarterBegin.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6441822Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.BQuarterEnd.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6442356Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.BYearBegin.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6442955Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.BYearEnd.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6443493Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.BusinessDay.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6444257Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.BusinessHour.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6444841Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.BusinessMonthBegin.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6445386Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.BusinessMonthEnd.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6445944Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.CBMonthBegin.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6448129Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.CBMonthEnd.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6448680Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.CDay.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6449227Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.CustomBusinessDay.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6449763Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.CustomBusinessHour.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6450302Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.CustomBusinessMonthBegin.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6450916Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.CustomBusinessMonthEnd.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6451574Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.DateOffset.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6453847Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.Day.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6456375Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.Easter.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6456758Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.FY5253.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6457151Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.FY5253Quarter.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6457538Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.Hour.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6465081Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.LastWeekOfMonth.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6465522Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.Micro.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6465912Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.Milli.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6466283Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.Minute.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6466680Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.MonthBegin.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6467071Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.MonthEnd.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6467648Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.MonthOffset.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6468033Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.Nano.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6468754Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.QuarterBegin.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6468876Z checking consistency... done
2020-04-06T13:50:55.6476990Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.QuarterEnd.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6477700Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.QuarterOffset.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6478266Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.Second.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6478810Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.SemiMonthBegin.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6479349Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.SemiMonthEnd.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6524416Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.SemiMonthOffset.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6525343Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.Tick.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6526474Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.Week.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6529160Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.WeekOfMonth.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6529727Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.YearBegin.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6530263Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.YearEnd.__call__.rst: WARNING: document isn't included in any toctree
2020-04-06T13:50:55.6530778Z /home/runner/work/pandas/pandas/doc/source/reference/api/pandas.tseries.offsets.YearOffset.__call__.rst: WARNING: document isn't included in any toctree
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BUG: User-facing AssertionError in Series.to_timestamp
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| 10
| 2020-04-06T16:03:45Z
| 2020-05-20T14:05:36Z
| 2020-05-20T14:05:36Z
|
CONTRIBUTOR
| 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.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
In [5]: import pandas as pd
In [6]: pd.Series([0]).to_timestamp()
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-6-1a9d08d7daba> in <module>
----> 1 pd.Series([0]).to_timestamp()
~/sandbox/pandas/pandas/core/series.py in to_timestamp(self, freq, how, copy)
4565 new_values = new_values.copy()
4566
-> 4567 assert isinstance(self.index, (ABCDatetimeIndex, ABCPeriodIndex))
4568 new_index = self.index.to_timestamp(freq=freq, how=how)
4569 return self._constructor(new_values, index=new_index).__finalize__(
AssertionError:
```
#### Problem description
Users shouldn't see AssertionErrors like this, since they may be disabled when the interpreter starts up.
#### Expected Output
We probably want this to be a `TypeError`.
|
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DOC: Fix examples in `pandas/core/strings.py`
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MDExOlB1bGxSZXF1ZXN0Mzk5NzU5MDk4
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CLN: Static types in `pandas/_lib/lib.pyx`
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MEMBER
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BUG: Inconsistent replacement of values
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| 2020-04-07T18:35:04Z
|
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.
---
**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.
#### Problem description
Value at a given position don't change using "values" indexing when the column in cuestion has object data type. However, the value change using "at".
I known that values return numpy array, then I test the same behaviour on numpy and everything is ok.
#### Normal Behaviour:
```python
import pandas as pd
#Create DataFrame
columns = ["A", "B"]
data = [[0, 1], [2, 3]]
dataset = pd.DataFrame(data=data, columns=columns)
print(dataset)
print("\n", dataset.dtypes)
print("\n", dataset.values.flags)
print("="*25)
#Modify value
dataset.values[0, 1] = "4"
print(dataset)
print("\n", dataset.dtypes)
print("\n", dataset.values.flags)
print("="*25)
#Modify value
dataset.values[0, 1] = 4
print(dataset)
print("\n", dataset.dtypes)
print("\n", dataset.values.flags)
```
#### Output:
```python
A B
0 0 1
1 2 3
A int64
B int64
dtype: object
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
=========================
A B
0 0 4
1 2 3
A int64
B int64
dtype: object
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
=========================
A B
0 0 4
1 2 3
A int64
B int64
dtype: object
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
```
#### Inconsistent Behaviour:
```python
import pandas as pd
#Create DataFrame
columns = ["A", "B"]
data = [[0, 1], [2, "3"]]
dataset = pd.DataFrame(data=data, columns=columns)
print(dataset)
print("\n", dataset.dtypes)
print("\n", dataset.values.flags)
print("="*25)
#Modify value
dataset.values[0, 1] = "4"
print(dataset)
print("\n", dataset.dtypes)
print("\n", dataset.values.flags)
print("="*25)
#Modify value
dataset.values[0, 1] = 4
print(dataset)
print("\n", dataset.dtypes)
print("\n", dataset.values.flags)
print("="*25)
#Modify value
dataset.at[0, "B"] = "4"
print(dataset)
print("\n", dataset.dtypes)
print("\n", dataset.values.flags)
```
#### Output:
```python
A B
0 0 1
1 2 3
A int64
B object
dtype: object
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
=========================
A B
0 0 1
1 2 3
A int64
B object
dtype: object
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
=========================
A B
0 0 1
1 2 3
A int64
B object
dtype: object
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
=========================
A B
0 0 4
1 2 3
A int64
B object
dtype: object
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
```
#### Normal Behaviour on Numpy:
```python
import numpy as np
data = [[0, 1], [2, "3"]]
#Create array
dataset = np.array(data)
print(dataset)
print("\n", dataset.dtype)
print("\n", dataset.flags)
print("="*25)
#Modify value
dataset[0, 1] = 4
print(dataset)
print("\n", dataset.dtype)
print("\n", dataset.flags)
print("="*25)
#Create array
dataset = np.array(data, dtype=object)
print(dataset)
print("\n", dataset.dtype)
print("\n", dataset.flags)
print("="*25)
#Modify value
dataset[0, 1] = 4
print(dataset)
print("\n", dataset.dtype)
print("\n", dataset.flags)
```
#### Output:
```python
[['0' '1']
['2' '3']]
<U21
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
=========================
[['0' '4']
['2' '3']]
<U21
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
=========================
[[0 1]
[2 '3']]
object
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
=========================
[[0 4]
[2 '3']]
object
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
```
#### Output of ``pd.__version__``
```python
'1.0.3'
```
#### Output of ``pd.show_versions()``
<details>
```python
/usr/local/lib/python3.6/dist-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use "pip install psycopg2-binary" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.
""")
/usr/local/lib/python3.6/dist-packages/pandas_datareader/compat/__init__.py:7: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
from pandas.util.testing import assert_frame_equal
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.104+
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 : 2018.9
dateutil : 2.8.1
pip : 19.3.1
setuptools : 46.0.0
Cython : 0.29.16
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 5.5.0
pandas_datareader: 0.8.1
bs4 : 4.6.3
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.2.6
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.11.0
pyarrow : 0.14.1
pytables : None
pytest : 3.6.4
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : 3.4.4
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : None
numba : 0.47.0
```
</details>
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MDU6SXNzdWU1OTUyOTUzMzk=
| 33,331
|
BUG: concat_{categorical, compat} leads to erroneous result on non-ns datetime-EA
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| 2020-04-06T17:31:41Z
| 2020-06-07T00:31:39Z
| null |
CONTRIBUTOR
| null |
Code to reproduce:
```python
import numpy as np
import pandas as pd
np_datetimes = np.array([datetime.date(2010, 1, 1)], dtype="datetime64[D]")
other = pd.array(["a", "b"], dtype="category")
pd.core.dtypes.concat.concat_categorical([np_datetimes, other])
# outputs:
# array([Timestamp('1970-01-01 00:00:00.000014610'), 'a', 'b'], dtype=object)
# expected either one of
# a) array([Timestamp('2010-01-01 00:00:00'), 'a', 'b'], dtype=object)
# b) array([datetime.date(2010, 1, 1), 'a', 'b'], dtype=object)
```
This happens as `concat_datetime` / `_convert_datetimelike_to_object` assumes that datetimes are nanoseconds only.
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MDExOlB1bGxSZXF1ZXN0Mzk5ODA1MDYw
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REF: BlockManager.delete -> idelete
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MEMBER
| null |
Moving towards all-locational inside BlockManager, also makes it easier to grep for where methods are used.
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DEPR: deprecate _data when getting BlockManager
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| 2021-07-31T02:11:55Z
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CONTRIBUTOR
| null |
we renamed `_data` -> `_mgr` to provide more clarity on the internals; we should add a `DeprecationWarning` on calling this from the pandas objects (`DataFrame/Series`) to have downstream update to use `_mgr` at some point: https://github.com/pandas-dev/pandas/pull/33054#issuecomment-609948311
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BLD: Increase minimum version of Cython to 0.29.16
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| 4
| 2020-04-06T18:26:27Z
| 2020-04-08T04:52:18Z
| 2020-04-07T23:24:23Z
|
CONTRIBUTOR
| null |
- In particular, this fixes a bug in code returning ctuples
- cython/cython#2745
- cython/cython#1427
- This is a prereq for #33220
- [x] whatsnew entry
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Selenium By Python--> Getting error while running program
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NONE
| null |
pip latest version is also installed
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MDExOlB1bGxSZXF1ZXN0Mzk5ODQyOTU5
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PLT: Order of plots does not preserve the column orders in df.hist
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- [x] closes #29235
- [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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| 595,372,118
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MDExOlB1bGxSZXF1ZXN0Mzk5ODU5Njc2
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DOC/PLT: Add `stacked` in doc and doc example for barh and bar plot
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MDU6SXNzdWU1OTUzODUyNjk=
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|
DOC/API: Discussion on whether __finalize__ is public
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[] | null | 2
| 2020-04-06T19:53:42Z
| 2020-12-07T08:10:53Z
| null |
CONTRIBUTOR
| null |
Currently `NDFrame.__finalize__` is not documented. We only have a couple references to it in the whatsnew.
I have a few questions around its API
1. Is it considered public? Can subclasses call `self.__finalize__()`?
2. Are the values passed in `method='...'` considered public?
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MDExOlB1bGxSZXF1ZXN0Mzk5OTA0MDc0
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BUG: Don't raise on value_counts for empty Int64
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| 1
| 2020-04-06T21:02:15Z
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MEMBER
| null |
- [ ] closes #33317
- [ ] tests added / passed
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- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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|
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REF: replace column-wise, remove BlockManager.apply filter kwarg
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Follow-up to #33279.
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| 2020-04-07T11:57:43Z
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MEMBER
| null |
- [X] closes #28528
This forgets about the OVH server, and after every merge to master will synchronize the repo web and docs in the production server.
**Note** that this will delete any file in the server that is not known, except the cheat sheets, and all the docs versions in `pandas-doc`. I don't think anything else should be kept, but if there is anything else that we upload manually, please let me know. I made a backup in the server, so if we delete anything by accident with this, it can be easily restored.
CC: @TomAugspurger @jorisvandenbossche
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MEMBER
| null |
cc @jorisvandenbossche this fixes the issue with `DatetimeLikeBlockMixin.iget` discussed in #33252
Note: this sits on top of #33290.
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- [ ] closes #xxxx
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TST: add read_json test for issue #32383
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| 1
| 2020-04-06T23:03:42Z
| 2020-04-14T20:06:20Z
| 2020-04-12T21:50:14Z
|
CONTRIBUTOR
| null |
- [x] closes #32383
- [x] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BUG: Timestamp comparison with ndarray[dt64]
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- [ ] closes #xxxx
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MDExOlB1bGxSZXF1ZXN0Mzk5OTUwODc5
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REF: remove BlockManager.set
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| 2
| 2020-04-06T23:10:14Z
| 2020-04-17T22:12:10Z
| 2020-04-17T21:23:53Z
|
MEMBER
| null |
xref #33052, #33332.
This touches several DataFrame/NDFrame methods. Those methods need a refactor in part because the ones defined on NDFrame are only ever called for DataFrame. Will refactor in follow-up.
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TST: implement test_setitem for Series
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| 1
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MEMBER
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Lots of scattered tests that belong here. Also lots of scattered local branches.
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REF: misplaced tests in test_partial_slicing files
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Whats left in these after this are all testing Series methods, so belong elsewhere.
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REF: DatetimeIndex test_insert, test_delete
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ENH: Implement StringArray.min / max
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| 7
| 2020-04-07T02:03:12Z
| 2020-04-25T13:31:46Z
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|
MEMBER
| null |
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Using the new masked reductions from @jorisvandenbossche to implement these for StringArray. Part of https://github.com/pandas-dev/pandas/issues/31746 but doesn't close because we're not adding sum here.
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CI: Add argument doc/source/development to formatting docstrings in code_checks.sh
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| 2020-04-07T02:34:42Z
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CONTRIBUTOR
| null |
Issue #32550
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TST: implement test_getitem for Series
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MEMBER
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Lime with #33348, lots of scattered stuff is going to end up here
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REF: collect Index constructor tests
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|
BUG/API: should MultiIndex with leading integer level fallback to positional?
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| 10
| 2020-04-07T03:10:42Z
| 2020-04-10T16:00:57Z
| 2020-04-10T16:00:57Z
|
MEMBER
| null |
```
idx = pd.Index(range(3))
dti = pd.date_range("2016-01-01", periods=2)
mi = pd.MultiIndex.from_product([idx, dti])
ser = pd.Series(range(len(mi))[::-1], index=mi)
>>> ser
0 2016-01-01 5
2016-01-02 4
1 2016-01-01 3
2016-01-02 2
2 2016-01-01 1
2016-01-02 0
dtype: int64
>>> ser[3]
2
```
My intuition is that `ser[3]` should behave like indexing on just the first level of the MultIIndex:
```
ser2 = ser.droplevel(1)
>>> ser2
0 5
0 4
1 3
1 2
2 1
2 0
dtype: int64
>>> ser2[3]
KeyError: 3
```
Should we avoid the fallback-to-positional behavior in this case?
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BUG/REF: unstack with EA dtypes
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| 2
| 2020-04-07T03:22:44Z
| 2020-04-10T17:48:07Z
| 2020-04-10T16:12:06Z
|
MEMBER
| null |
1) `ser.unstack(args)` should behave like `ser.to_frame().unstack(args).droplevel(level=0, axis=1)`. This fails on master, is fixed by this PR.
2) `reshape._unstack_extension_series` currently re-implements logic that is in `ExtensionBlock._unstack`. This de-duplicates by dispatching.
3) We currently transpose in cases where we shouldn't. This implements `DataFrame._can_fast_transpose` to avoid that.
4) `test_astype_object_series` has what looks like a typo that is making us test not-the-intended-thing; @TomAugspurger can you confirm im reading this right?
5) The dtype comparisons in `test_astype_object_frame` currently raise, are commented out for exposition, should be fixed in a separate PR.
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|
PERF: fastpath DataFrame constructor from BlockManager
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[] | null | 9
| 2020-04-07T03:42:11Z
| 2020-04-09T18:03:02Z
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|
MEMBER
| null |
When trying to make `fast_apply` unnecessary I found that there is non-trivial overhead in the constructors that we can avoid. Moreover, since users shouldn't be passing BlockManagers around anyway, we _might_ be able to get that case out of the DataFrame constructor entirely.
```
In [2]: ser = pd.Series(range(5))
In [3]: df = ser.to_frame()
In [4]: mgr = df._mgr
In [5]: %timeit pd.DataFrame(mgr)
2.08 µs ± 51.4 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
In [6]: %timeit pd.DataFrame._from_mgr(mgr)
1.26 µs ± 17.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
```
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DatetimeIndex.to_period with freq
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| 3
| 2020-04-07T07:12:43Z
| 2020-04-12T23:00:01Z
| 2020-04-12T23:00:01Z
|
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 (1.0.3).
---
#### Code Sample
```python
# Input date range:
pi5m = pd.date_range(start='2019-12-22 06:40:00+00:00', end='2019-12-22 08:45:00+00:00', freq='5min')
# Converting to PeriodIndex
pi5m.to_period()
File "/home/p/.local/lib/python3.7/site-packages/pandas/core/arrays/period.py", line 938, in dt64arr_to_periodarr
freq = Period._maybe_convert_freq(freq)
File "pandas/_libs/tslibs/period.pyx", line 1580, in pandas._libs.tslibs.period._Period._maybe_convert_freq
AttributeError: 'NoneType' object has no attribute 'n'
# Working
pi5m.to_period('5min')
```
#### Problem description
It is specified in documentation that if not provided, `freq` is inferred.
However, with above-provided input, I get the error message indicated.
Is this a bug?
Thanks for your feedback.
Bests,
|
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PERF: improve IntegerArray fast constructor
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| 11
| 2020-04-07T09:07:13Z
| 2020-04-10T17:57:07Z
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|
MEMBER
| null |
Trying to improve the IntegerArray constructor from its constituents.
Example test case:
```
a = pd.array([1, 2, 3], dtype="Int64")
values = a._data
mask = a._mask
pd.arrays.IntegerArray(values, mask)
```
```
In [5]: %timeit pd.arrays.IntegerArray(values, mask)
2.9 µs ± 45.4 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # master
775 ns ± 41.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # PR
```
So what is in the PR is *one possible* solution, to show the impact of using "general dtype checking functions" (that eg also need to deal with EA dtypes, getting the dtype of values being passed, etc). It is clear that here we can do a much more specialized check, and that this gives a considerable performance boost (in eg block/column-wise operations on IntegerArray, this is reconstructed many times). Some options:
- do an inline specialized check as done here
- define separate helper functions for those specialized checks (eg restricting to numpy dtypes)
- improve performance of `is_bool_dtype` / `is_integer_dtype` for those cases (maybe putting a check like this as a "fastpath" at the beginning of those functions might avoid more costly checks done in those functions for this specific case (eg "is_bool_np_dtype")
- simply don't do any validation at all in the IntegerArray constructor, and assume this is the responsibility of the caller
- provide a keyword in the IntegerArray constructor to turn off validation
- add a private constructor (similar as the `_simple_new` we have for others) that uses this fastpath
For optimal performance, having a way to don't do any validation at all (so also don't check the passed values are ndarray, or their dtypes) would actually even be better than what this PR does. But I also like the clean constructor we have now (eg no separate private constructor)
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BUG: Can't disable progress bar for read_gbq
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| 5
| 2020-04-07T09:13:24Z
| 2020-06-04T17:41:34Z
| 2020-06-04T17:41:34Z
|
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.
---
**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
# Your code here
import pandas as pd
pd.read_gbq(..., progress_bar_type=None)
```
(Due to the BigQuery dependency it's not copy-pastable)
Warning message displayed or tqdm is used:
```
/path/to/python/site-packages/pandas_gbq/gbq.py:555: UserWarning: A progress bar was requested, but there was an error loading the tqdm library. Please install tqdm to use the progress bar functionality.
progress_bar_type=progress_bar_type,
```
That is because if `progress_bar_type` is set to `None` it is not passed to `pandas_gbq.read_gbq` (see https://github.com/pandas-dev/pandas/pull/29858#discussion_r352260559). But the default for `pandas_gbq.read_gbq` is `tqdm`.
#### Problem description
It should be possible to disable the progress bar.
#### Expected Output
No progress bar.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-91-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 : 39.0.1
Cython : None
pytest : 4.6.9
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 : 0.13.1
pyarrow : None
pytables : None
pytest : 4.6.9
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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Fix read parquet import error message
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| 8
| 2020-04-07T09:46:52Z
| 2020-04-08T20:10:58Z
| 2020-04-08T16:45:46Z
|
CONTRIBUTOR
| null |
- [x] closes #33313
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry N/A
`pandas.io.parquet.get_engine()` uses handling of `ImportError`s for flow control to decide which parquet reader engine is used. In doing so, it quashed lower-level error messages that would have been helpful to the user attempting to diagnose the error, replacing it with a misleading error message.
I refactored the error handling, allowing for these lower-level error messages to be collected and explicitly "bubbled up". Thus fixing the incorrect error message.
No tests added -- this behaviour is not worthy of testing. Presumably not worthy of a whatsnew entry either.
|
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TST: iat with duplicate column names
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| 3
| 2020-04-07T10:34:43Z
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MEMBER
| null |
- [x] closes #11754
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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DEP: Bump min version of dateutil to 2.7.3
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MEMBER
| null |
- [x] xref https://github.com/pandas-dev/pandas/pull/32465#discussion_r404449469
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- [ ] whatsnew entry
---
Not sure where to put the note in the "whatsnew"
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PERF: statically define classes for is_dtype checks
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| 10
| 2020-04-07T11:28:13Z
| 2020-07-17T11:18:57Z
| 2020-07-17T11:18:57Z
|
CONTRIBUTOR
| null |
This moves the definition of the functions passed to `_is_dtype_type` from dynamically generated functions is `is_integer_dtype` to top-level functions. I can't run asv right now (https://github.com/pandas-dev/pandas/issues/33315#issuecomment-610329344) but here are some timeits
master
```python
In [2]: t = np.dtype('int64')
In [3]: %timeit pd.api.types.is_integer_dtype(t)
2.22 µs ± 51.6 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
```
This PR
```python
In [3]: %timeit pd.api.types.is_integer_dtype(t)
1.54 µs ± 39.6 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
```
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MDU6SXNzdWU1OTU4MDIxNjI=
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PERF: Regression in Series.is_monotonic_increasing for categorical
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| 1
| 2020-04-07T11:37:18Z
| 2020-04-15T17:41:38Z
| 2020-04-15T17:41:38Z
|
CONTRIBUTOR
| 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.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
Pandas 1.0.1
```python
In [2]: N = 1000
In [3]: c = pd.CategoricalIndex(list("a" * N + "b" * N + "c" * N))
In [4]: s = pd.Series(c)
In [5]: %timeit s.is_monotonic_increasing
35.3 µs ± 663 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```
Master
```python
In [5]: %timeit s.is_monotonic_increasing
73.6 µs ± 782 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```
#### Problem description
https://pandas.pydata.org/speed/pandas/#categoricals.IsMonotonic.time_categorical_series_is_monotonic_increasing
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CI: Not deleting docs symlink in prod
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[] | null | 1
| 2020-04-07T14:25:00Z
| 2020-04-07T14:43:13Z
| 2020-04-07T14:43:00Z
|
MEMBER
| null |
In #33341 I forgot to exclude the `docs` symlink for deletion, meaning that it'll be deleted when a PR is merged. And the docs will be unavailable. Fixing it here.
@pandas-dev/pandas-core can we prioritize this please.
|
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Unpinned openpyxl from env files
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| 1
| 2020-04-07T14:48:09Z
| 2020-04-08T17:23:42Z
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|
MEMBER
| null |
follow up to #31525 - looks like these were overlooked
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MDU6SXNzdWU1OTU5MjcwMzU=
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PERF/DISC: restrict inputs to is_foo_dtype
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| 2020-04-07T14:49:11Z
| 2020-06-05T19:58:12Z
| 2020-06-05T19:58:12Z
|
MEMBER
| null |
xref #33364
A lot of the overhead in is_foo_dtype (which we call _a lot_) comes from checking for dtype objects, arrays, and strings. We could tighten these to be e.g.
```
def is_extension_arrray_dtype(obj):
return isinstance(obj, ExtensionDtype)
```
The trouble with this is some of these are user-facing, so this would require a deprecation cycle, and in the interim the `warnings` call would actually hurt performance.
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BUG: Adding `label` argument to `plot()` removes error bars
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| 2021-07-31T02:13:00Z
| 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.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import matplotlib.pyplot as plt
cols = ['x']
val = pd.DataFrame([1,2], columns=cols)
err = pd.DataFrame([0.5,0.5], columns=cols)
# Has error bars
val.plot(y=cols, yerr=err)
# Does not have error bars, but should
val.plot(y=cols, yerr=err, label=["label"])
plt.show()
```
#### Problem description
When adding both `yerr` and `label` as parameters to the `plot()` function call, no error bars are plotted.
#### Expected Output
Both plots should look identical apart from differing labels, but the second one does not have error bars.
#### Output of ``pd.show_versions()``
<details>
commit : bd91f4577c5b54a72a4e092a7d7db43fec1b46bb
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.5.13-arch2-1
Version : #1 SMP PREEMPT Mon, 30 Mar 2020 20:42:41 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.0.dev0+1159.gbd91f4577c
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3
Cython : 0.29.16
pytest : 5.4.1
hypothesis : None
sphinx : 3.0.0
blosc : None
feather : None
xlsxwriter : None
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 : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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QST: Why is resample with high frequency faster than lower frequency
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[] | null | 0
| 2020-04-07T15:26:27Z
| 2021-07-31T02:13:41Z
| null |
NONE
| null |
When resampling a pandas DatetimeIndex, why would resampling daily (rule="D") or hourly (rule="H") between two dates be faster than resampling weekly ("W"), monthly ("M") or yearly ("Y")?
```python
df = pd.DataFrame({"data": ["A", "A"]}, index = pd.DatetimeIndex(["2010-01-01", "2020-01-01"]))
df.resample("D").bfill()
1.27 ms ± 121 µs per loop (mean ± std. dev. of 10 runs, 10 loops each)
df.resample("H").bfill()
2.46 ms ± 115 µs per loop (mean ± std. dev. of 10 runs, 10 loops each)
df.resample("W").bfill()
12.2 ms ± 446 µs per loop (mean ± std. dev. of 10 runs, 10 loops each)
df.resample("M").bfill()
5.1 ms ± 506 µs per loop (mean ± std. dev. of 10 runs, 10 loops each)
df.resample("Y").bfill()
2.99 ms ± 160 µs per loop (mean ± std. dev. of 10 runs, 10 loops each)
```
One would expect that resampling with yearly frequency would be much faster than hourly because there is only 11 rows to compute vs 87k+ rows in the case of an hourly resampling.
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ENH: Add query functionality to skiprows param in read_csv
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| 2020-04-07T15:37:36Z
| 2020-06-01T01:55:28Z
| 2020-06-01T01:55:28Z
|
NONE
| null |
This allows users to pass in strings to query through the ``skiprows`` parameter in ``pandas.read_csv``. This is done through querying the Dataframe as soon as it is created, and returning a Dataframe with the desired columns to the user.
Included in this PR are the relevant whatsnew entry, documentation changes, and new tests.
- [X] closes #32072
- [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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BLD: Compatibility issues with master branch cython
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| 2020-04-07T15:47:14Z
| 2020-04-08T09:12:34Z
| 2020-04-08T09:12:34Z
|
MEMBER
| null |
Continuation of issue #33224
---
With the latest version of cython, running ```python setup.py build_ext --inplace -j 0``` gives the following errors:
---
#### ```pandas/_libs/join.pyx```:
<details>
```
building 'pandas._libs.join' extension
gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -march=x86-64 -mtune=generic -O3 -pipe -fno-plt -fno-semantic-interposition -march=x86-64 -mtune=generic -O3 -pipe -fno-plt -march=x86-64 -mtune=generic -O3 -pipe -fno-plt -fPIC -DNPY_NO_DEPRECATED_API=0 -Ipandas/_libs/src/klib -I/home/user/Venvs/cythonM-pandas/lib/python3.8/site-packages/numpy/core/include -I/home/user/Venvs/cythonM-pandas/include -I/usr/include/python3.8 -c pandas/_libs/join.c -o build/temp.linux-x86_64-3.8/pandas/_libs/join.o
In file included from /usr/include/python3.8/tupleobject.h:41,
from /usr/include/python3.8/Python.h:105,
from pandas/_libs/join.c:32:
pandas/_libs/join.c: In function ‘__pyx_pf_6pandas_5_libs_4join_12inner_join_indexer.isra.0’:
/usr/include/python3.8/cpython/tupleobject.h:27:60: warning: array subscript -1 is below array bounds of ‘PyObject *[1]’ {aka ‘struct _object *[1]’} [-Warray-bounds]
27 | #define PyTuple_GET_ITEM(op, i) (_PyTuple_CAST(op)->ob_item[i])
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~
pandas/_libs/join.c:204503:27: note: in expansion of macro ‘PyTuple_GET_ITEM’
204503 | PyObject *r = PyTuple_GET_ITEM(o, n);
| ^~~~~~~~~~~~~~~~
pandas/_libs/join.c: In function ‘__pyx_pf_6pandas_5_libs_4join_10left_join_indexer.isra.0’:
/usr/include/python3.8/cpython/tupleobject.h:27:60: warning: array subscript -1 is below array bounds of ‘PyObject *[1]’ {aka ‘struct _object *[1]’} [-Warray-bounds]
27 | #define PyTuple_GET_ITEM(op, i) (_PyTuple_CAST(op)->ob_item[i])
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~
pandas/_libs/join.c:204503:27: note: in expansion of macro ‘PyTuple_GET_ITEM’
204503 | PyObject *r = PyTuple_GET_ITEM(o, n);
| ^~~~~~~~~~~~~~~~
pandas/_libs/join.c: In function ‘__pyx_pf_6pandas_5_libs_4join_8left_join_indexer_unique.isra.0’:
/usr/include/python3.8/cpython/tupleobject.h:27:60: warning: array subscript -1 is below array bounds of ‘PyObject *[1]’ {aka ‘struct _object *[1]’} [-Warray-bounds]
27 | #define PyTuple_GET_ITEM(op, i) (_PyTuple_CAST(op)->ob_item[i])
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~
pandas/_libs/join.c:204503:27: note: in expansion of macro ‘PyTuple_GET_ITEM’
204503 | PyObject *r = PyTuple_GET_ITEM(o, n);
| ^~~~~~~~~~~~~~~~
pandas/_libs/join.c: In function ‘__pyx_pf_6pandas_5_libs_4join_14outer_join_indexer.isra.0’:
/usr/include/python3.8/cpython/tupleobject.h:27:60: warning: array subscript -1 is below array bounds of ‘PyObject *[1]’ {aka ‘struct _object *[1]’} [-Warray-bounds]
27 | #define PyTuple_GET_ITEM(op, i) (_PyTuple_CAST(op)->ob_item[i])
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~
pandas/_libs/join.c:204503:27: note: in expansion of macro ‘PyTuple_GET_ITEM’
204503 | PyObject *r = PyTuple_GET_ITEM(o, n);
| ^~~~~~~~~~~~~~~~
```
</details>
---
#### ```pandas/_libs/reshape.pyx```:
<details>
```
building 'pandas._libs.reshape' extension
gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -march=x86-64 -mtune=generic -O3
-pipe -fno-plt -fno-semantic-interposition -march=x86-64 -mtune=generic -O3 -pipe -fno-plt -march=x86-64 -mtun
e=generic -O3 -pipe -fno-plt -fPIC -DNPY_NO_DEPRECATED_API=0 -I/home/user/Venvs/cythonM-pandas/lib/python3.8/
site-packages/numpy/core/include -I/home/user/Venvs/cythonM-pandas/include -I/usr/include/python3.8 -c pandas
/_libs/reshape.c -o build/temp.linux-x86_64-3.8/pandas/_libs/reshape.o
In file included from /usr/include/python3.8/tupleobject.h:41,
from /usr/include/python3.8/Python.h:105,
from pandas/_libs/reshape.c:30:
pandas/_libs/reshape.c: In function ‘__pyx_pf_6pandas_5_libs_7reshape_unstack.isra.0’:
/usr/include/python3.8/cpython/tupleobject.h:27:60: warning: array subscript -1 is below array bounds of ‘PyObject *[1]’ {aka ‘struct _object *[1]’} [-Warray-bounds]
27 | #define PyTuple_GET_ITEM(op, i) (_PyTuple_CAST(op)->ob_item[i])
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~
pandas/_libs/reshape.c:29728:27: note: in expansion of macro ‘PyTuple_GET_ITEM’
29728 | PyObject *r = PyTuple_GET_ITEM(o, n);
| ^~~~~~~~~~~~~~~~
```
</details>
---
[join_zip.zip](https://github.com/pandas-dev/pandas/files/4445598/join_zip.zip)
[reshape_zip.zip](https://github.com/pandas-dev/pandas/files/4445599/reshape_zip.zip)
---
Output of ```pd.show_versions()```:
<details>
INSTALLED VERSIONS
------------------
commit : bd91f4577c5b54a72a4e092a7d7db43fec1b46bb
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.5.13.b-1-hardened
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0.dev0+1159.gbd91f4577
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3
Cython : 3.0a0 (https://github.com/cython/cython/tree/30c891e0dc36a553455df9064ecb1f13414a12d2)
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
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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BUG: #31464 Fix error when parsing JSON list of bool into Series
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| 1
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| 2020-04-08T19:04:29Z
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CONTRIBUTOR
| null |
Add a missing exception type to the except clause, to cover
the TypeError that is thrown by Cythonized array_to_datetime
function when trying to convert bool to nonseconds.
- [x] closes #31464
- [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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DOC/TYP: Fixed typing and doc
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| null |
- [x] xref https://github.com/pandas-dev/pandas/pull/33263#pullrequestreview-389118991
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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CLN: json_table_schema remove unused arg, annotate
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MEMBER
| null |
cc @WillAyd
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DOC: Fix capitalization among headings in doc/source/whatsnew (#32550)
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| 2020-04-10T19:23:18Z
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CONTRIBUTOR
| null |
Updated headers for the following files:
```
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- [x] doc/source/whatsnew/v0.14.0.rst
- [x] doc/source/whatsnew/v0.15.0.rst
- [x] doc/source/whatsnew/v0.18.0.rst
- [x] doc/source/whatsnew/v0.18.1.rst
```
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BUG: is_categorical shouldnt recognize Dtype objects
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|
MEMBER
| null |
is_categorical(obj) is used in a couple places where we then go on to access `obj.dtype`, so this should not return `True` if we have a `CategoricalDtype` object.
|
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| 1
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| 2020-04-10T19:23:39Z
| 2020-04-10T17:14:53Z
|
CONTRIBUTOR
| null |
Updated headers for the following files:
```
- [x] doc/source/whatsnew/v0.19.0.rst
- [x] doc/source/whatsnew/v0.20.0.rst
- [x] doc/source/whatsnew/v0.24.0.rst
- [x] doc/source/whatsnew/v0.25.0.rst
- [x] doc/source/whatsnew/v1.0.0.rst
```
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API/TST: Call __finalize__ in more places
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| 2
| 2020-04-07T19:57:53Z
| 2020-04-10T17:53:12Z
| 2020-04-10T17:53:08Z
|
CONTRIBUTOR
| null |
Progress towards https://github.com/pandas-dev/pandas/issues/28283. This
adds tests that ensures `NDFrame.__finalize__` is called in more places.
Thus far I've added tests for anything that meets the following rule:
> Pandas calls `NDFrame.__finalize__` on any NDFrame method that returns
> another NDFrame.
I think that given the generality of `__finalize__`, making any kind of
list of which methods should call it is going to be somewhat arbitrary.
That rule errs on the side of calling it too often, which I think is my
preference.
*For reviewers* I think the two most helpful pieces would be
1. Do you agree with the general rule of `NDFrame -> NDFrame` should call `__finalize__`?
2. A spot check on the tests I've added
If you don't agree with the general rule, then we'll need to go through method by method and determine whether that method should call `__finalize__`.
Once we have agreement that the tests I've added accurately reflect the desired outcome, this can be merged and we can fix `xfails` in smaller batches.
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BUG: `pandas.Series.eq` doc and result mismatch for fill_value
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| 1
| 2020-04-07T20:22:34Z
| 2020-04-25T21:48:50Z
| 2020-04-25T21:48:50Z
|
NONE
| null |
- [ ] 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
```
>>> import pandas as pd
>>> import numpy as np
>>> pd.Series([np.nan]).eq(pd.Series([np.nan]), fill_value=5.0)
0 False
dtype: bool
```
#### Problem description
As per the doc, if fill_value is provided and if the element at a position is missing in both series, then result will also be missing, but as per the result, False is being returned rather than `nan`.
#### Expected Output
So, not sure if doc is wrong or output is wrong. So opening this issue just to clarify this and fix appropriate issue.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-26-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.12
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.6.1
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.0.1
pyxlsb : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.5
tables : 3.5.2
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
numba : 0.44.1
```
</details>
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BUG: iloc setting columns not taking effect
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| 2021-05-03T12:28:30Z
| 2020-04-16T17:24:40Z
|
MEMBER
| null |
- [x] closes #33198
- [ ] 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 capitalisation in doc/source/whatsnew - part1 (issue #32550)
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| 2020-04-07T21:21:14Z
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|
CONTRIBUTOR
| null |
- [ ] modify files index.rst, v0.4.x.rst, v0.5.0.rst, v0.6.0.rst
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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FEATURE: pandas.Series.query()(#22347)
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FEATURE: pandas.Series.query() #22347
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MDExOlB1bGxSZXF1ZXN0NDAwNTc1NzIx
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DEPR: is_categorical
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MEMBER
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xref #33377 the current behavior of is_categorical is buggy, besides its redundant with is_categorical_dtype
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BUG: "nan" as a string instead of missing value.
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NONE
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*Sorry I cannot share the data. I tried to make test data but it does not gives same error or different missing values as described below.*
*Added more info at bottom about `pd.NA`*
I am loading data with code:
df = pd.read_csv("C:/data.csv")
When loading data I am getting this warning:
C:\Users\User1\AppData\Local\Continuum\anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3063: DtypeWarning: Columns (162,247,274,292,304,316,321,335,345,347,357,379,389,390,393,395,400,401,420,424,447,462,465,467,478,481,534,536,538,570,616,632,653,666,675,691,707,754,758,762,766,770,774,778,782,784,785,786,788,789,790,792,793,794,796,797,798,800,801,802,804,805,806,808,809,810,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,867,868,871,872,875,876,880,1367,1368,1370,1371,1373,1374,1376,1377,1379,1380,1382,1383,1385,1386,1388,1389,1391,1392,1394,1395,1397,1398,1400,1401,1403,1404,1406,1407,1409,1410,1412,1413,1415,1416,1418,1419,1421,1422,1424,1425,2681) have mixed types.Specify dtype option on import or set low_memory=False.
interactivity=interactivity, compiler=compiler, result=result)
As I understood this warning is not a problem and I can ignore it.
*For example code with expected output look at the bottom of question (error wich is described below is not replicated with that example because of different data!)*
After I am running this code:
import pandas as pd
import numpy as np
col1 = ["var1", "var3", "var5"]
col2 = ["var2", "var4", "var6"]
colR = ["Result1", "Result2", "Result3"]
s1 = df[col1].isnull().to_numpy()
s2 = df[col2].isnull().to_numpy()
conditions = [~s1 & ~s2, s1 & s2, ~s1 & s2, s1 & ~s2]
choices = ["Both values", np.nan, df[col1], df[col2]]
df = pd.concat([df, pd.DataFrame(np.select(conditions, choices), columns=colR, index=df.index)], axis=1)
Newly created columns apon running code above contain `nan` but colums that are loaded from `csv` file contain `NaN`.
After running `df['var1'].value_counts(dropna=False)`, I am getting output:
NaN 3453
0.0 3002
1.0 314
Name: var1, dtype: int64
After running `df['Result1'].value_counts(dropna=False)`, I am getting output:
0.0 3655
nan 2665
1.0 407
Both values 42
Name: Result1, dtype: int64
Notice that `var1` contains `NaN` values but `Result1` contains `nan` values.
When I run `df['var1'].value_counts(dropna=False).loc[[np.nan]]` I am getting output:
NaN 3453
Name: weeklyivr_q1, dtype: int64
When I run `df['Result1'].value_counts(dropna=False).loc[[np.nan]]` I am getting error (variable names in error are different but key idea is that there are no missing values):
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-52-0daeac75fdb4> in <module>
27 #combined_IVR["weeklyivr_q1"].value_counts(dropna=False)
28 #combined_IVR["my_weekly_ivr_1"].value_counts(dropna=False).loc[["Both values"]]
---> 29 combined_IVR["my_weekly_ivr_1"].value_counts(dropna=False).loc[[np.nan]]
30 #combined_IVR["weeklyivr_q1"].value_counts(dropna=False).loc[[np.nan]]
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\indexing.py in __getitem__(self, key)
1764
1765 maybe_callable = com.apply_if_callable(key, self.obj)
-> 1766 return self._getitem_axis(maybe_callable, axis=axis)
1767
1768 def _is_scalar_access(self, key: Tuple):
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\indexing.py in _getitem_axis(self, key, axis)
1950 raise ValueError("Cannot index with multidimensional key")
1951
-> 1952 return self._getitem_iterable(key, axis=axis)
1953
1954 # nested tuple slicing
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\indexing.py in _getitem_iterable(self, key, axis)
1591 else:
1592 # A collection of keys
-> 1593 keyarr, indexer = self._get_listlike_indexer(key, axis, raise_missing=False)
1594 return self.obj._reindex_with_indexers(
1595 {axis: [keyarr, indexer]}, copy=True, allow_dups=True
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\indexing.py in _get_listlike_indexer(self, key, axis, raise_missing)
1549
1550 self._validate_read_indexer(
-> 1551 keyarr, indexer, o._get_axis_number(axis), raise_missing=raise_missing
1552 )
1553 return keyarr, indexer
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\indexing.py in _validate_read_indexer(self, key, indexer, axis, raise_missing)
1636 if missing == len(indexer):
1637 axis_name = self.obj._get_axis_name(axis)
-> 1638 raise KeyError(f"None of [{key}] are in the [{axis_name}]")
1639
1640 # We (temporarily) allow for some missing keys with .loc, except in
KeyError: "None of [Float64Index([nan], dtype='float64')] are in the [index]"
When I am running `df['Result1'].value_counts(dropna=False).loc[['nan']]` I am getting:
nan 2665
Name: my_weekly_ivr_1, dtype: int64
So `nan` in 'Result1' column is string.
If i replace `choices = ["Both values", np.nan, df[col1], df[col2]]` with `choices = ["Both values", pd.NA, df[col1], df[col2]]` and after run:
df['Result1'].value_counts(dropna=False).loc[[np.nan]]
I am getting output:
NaN 2665
Name: Result1, dtype: int64
So in this case `np.nan` produces string and `pd.NA` missing value.
<br>
**Question:**
Why am getting `nan` in 'Result1' column when using np.nan? What can be a reason and how to fix this?
--------
Example code with expected output:
import pandas as pd
import numpy as np
df = pd.DataFrame({ 'var1': ['a', 'b', 'c',np.nan, np.nan],
'var2': [1, 2, np.nan , 4, np.nan],
'var3': [np.nan , "x", np.nan, "y", "z"],
'var4': [np.nan , 4, np.nan, 5, 6],
'var5': ["a", np.nan , "b", np.nan, "c"],
'var6': [1, np.nan , 2, np.nan, 3]
})
col1 = ["var1", "var3", "var5"]
col2 = ["var2", "var4", "var6"]
colR = ["Result1", "Result2", "Result3"]
s1 = df[col1].isnull().to_numpy()
s2 = df[col2].isnull().to_numpy()
conditions = [~s1 & ~s2, s1 & s2, ~s1 & s2, s1 & ~s2]
choices = ["Both values", np.nan, df[col1], df[col2]]
df = pd.concat([df, pd.DataFrame(np.select(conditions, choices), columns=colR, index=df.index)], axis=1)
The result ( `df` ) looks like:
var1 var2 var3 var4 var5 var6 Result1 Result2 Result3
0 a 1.0 NaN NaN a 1.0 Both values NaN Both values
1 b 2.0 x 4.0 NaN NaN Both values Both values NaN
2 c NaN NaN NaN b 2.0 c NaN Both values
3 NaN 4.0 y 5.0 NaN NaN 4 Both values NaN
4 NaN NaN z 6.0 c 3.0 NaN Both values Both values
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REF: numeric index test_indexing
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| 2020-04-08T15:09:18Z
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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ENH: Add numba engine to groupby.aggregate
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| 2020-04-08T05:48:14Z
| 2020-04-26T04:21:57Z
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MEMBER
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
In the same spirit of #31845 and #32854, adding `engine` and `engine_kwargs` arguments to `groupby.aggreate`.
This PR has some functionality that is waiting to be merged in #32854
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BUG: when adding multiple plots with different cmap, colorbars legends use first cmap
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| 2020-04-08T07:36:12Z
| 2020-04-10T17:00:49Z
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|
MEMBER
| null |
For example, when doing twice a scatter plot with a different colormap, it adds a colorbar for each, but with the wrong colors:
```
df = pd.DataFrame({'x': [1, 2, 3], 'y': [1, 3, 2], 'c': [1, 2, 3]}
df['x2'] = df['x'] + 1
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
df.plot('x', 'y', c='c', kind='scatter', cmap="cividis", ax=ax)
df.plot('x2', 'y', c='c', kind='scatter', cmap="magma", ax=ax)
```
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QST:What's wrong with SHA256? Be hacked?
|
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| 1
| 2020-04-08T10:35:28Z
| 2020-05-06T19:47:01Z
| 2020-04-08T10:57:39Z
|
NONE
| null |
When I pip3 install pandas using raspberrypi,it reports:
THESE PACKAGES DO NOT MATCH THE HASHES FROM THE REQUIREMENTS FILE. If you have updated the package versions, please update the hashes. Otherwise, examine the package contents carefully; someone may have tampered with them.
pandas from https://www.piwheels.org/simple/pandas/pandas-1.0.3-cp37-cp37m-linux_armv7l.whl#sha256=f1b6b335a4aefff03ce1aefffd24934d3e93ce0dc88e387efa761de1981fdcf1:
Expected sha256 f1b6b335a4aefff03ce1aefffd24934d3e93ce0dc88e387efa761de1981fdcf1
Got e2621af914f20cbe948a6bfe74d009dc28b8fa1511f50ac46ab70c24a14f4176
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April 2020 Dev Meeting
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| 2020-04-08T11:20:23Z
| 2020-04-10T13:12:05Z
| 2020-04-10T13:12:05Z
|
CONTRIBUTOR
| null |
The monthly dev meeting is today, April 8th, at 18:00 UTC. Note that your local time may be different than last month due to daylight savings time. Our calendar is at https://pandas.pydata.org/docs/development/meeting.html#calendar
Video Call: https://zoom.us/j/942410248
Minutes: https://docs.google.com/document/u/1/d/1tGbTiYORHiSPgVMXawiweGJlBw5dOkVJLY-licoBmBU/edit?ouid=102771015311436394588&usp=docs_home&ths=true
Please add items you'd like to see discussed to the agenda.
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MDExOlB1bGxSZXF1ZXN0NDAwODI2OTI0
| 33,392
|
BUG/PLT: Multiple plots with different cmap, colorbars legends use first cmap
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| 2020-04-10T17:00:52Z
| 2020-04-10T17:00:49Z
|
MEMBER
| null |
- [x] closes #33389
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Since this is a plot PR, just post what it looks like after the change
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CI: Check private function not used across modules
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MEMBER
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- [ ] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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(WIP) CI: Check private function not used across modules
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MEMBER
| null |
- [x] xref https://github.com/pandas-dev/pandas/pull/32942#issuecomment-609893282
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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---
cc @jbrockmendel
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|
BUG: Test failures with CPython master
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CONTRIBUTOR
| null |
We have some failures on CPython master (commit c6e5c1123bac6cbb4c85265155af5349dcea522e to be precise):
```
===================================================================================== FAILURES =====================================================================================
pandas/_libs/hashtable_class_helper.pxi:1614: KeyError: 'one'
pandas/_libs/hashtable_class_helper.pxi:1614: KeyError: 'one'
pandas/_libs/hashtable_class_helper.pxi:1614: KeyError: 'one'
pandas/_libs/hashtable_class_helper.pxi:1614: KeyError: 'one'
/Users/taugspurger/sandbox/cpython-dev/pandas/pandas/util/testing.py:394: AssertionError: Series Expected type <class 'pandas.core.series.Series'>, found <class 'pandas.core.arrays.datetimes.DatetimeArray'> instead
pandas/_libs/hashtable_class_helper.pxi:1614: KeyError: 'one'
pandas/_libs/hashtable_class_helper.pxi:1614: KeyError: 'one'
pandas/_libs/hashtable_class_helper.pxi:1614: KeyError: 'one'
pandas/_libs/hashtable_class_helper.pxi:1614: KeyError: 'one'
pandas/_libs/hashtable_class_helper.pxi:1614: KeyError: 'one'
pandas/_libs/hashtable_class_helper.pxi:1614: KeyError: 'one'
pandas/_libs/hashtable_class_helper.pxi:1614: KeyError: 'one'
pandas/_libs/hashtable_class_helper.pxi:1614: KeyError: 'one'
pandas/_libs/groupby_helper.pxi:1014: TypeError: Expected dict, got int
pandas/_libs/groupby_helper.pxi:951: TypeError: Expected dict, got int
pandas/_libs/groupby_helper.pxi:1014: TypeError: Expected dict, got int
pandas/_libs/groupby_helper.pxi:951: TypeError: Expected dict, got int
pandas/_libs/groupby_helper.pxi:1014: TypeError: Expected dict, got int
pandas/_libs/groupby_helper.pxi:951: TypeError: Expected dict, got int
pandas/_libs/groupby_helper.pxi:1014: TypeError: Expected dict, got int
pandas/_libs/groupby_helper.pxi:951: TypeError: Expected dict, got int
pandas/_libs/groupby_helper.pxi:1014: TypeError: Expected dict, got int
pandas/_libs/groupby_helper.pxi:1014: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:750: TypeError: Expected dict, got int
pandas/_libs/join.pyx:819: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
pandas/_libs/join.pyx:681: TypeError: Expected dict, got int
================================================================================= warnings summary =================================================================================
pandas/tests/frame/test_alter_axes.py:241
pandas/tests/frame/test_alter_axes.py:241
/Users/taugspurger/sandbox/cpython-dev/pandas/pandas/tests/frame/test_alter_axes.py:241: SyntaxWarning: "is" with a literal. Did you mean "=="?
False if (keys[0] is "A" and keys[1] is "A") else drop # noqa: F632
pandas/tests/frame/test_missing.py::TestDataFrameMissingData::test_fillna_categorical_nan
/Users/taugspurger/sandbox/cpython-dev/numpy/numpy/lib/nanfunctions.py:1114: RuntimeWarning: All-NaN slice encountered
r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out,
pandas/tests/groupby/test_filters.py::test_filter_and_transform_with_non_unique_float_index
/Users/taugspurger/sandbox/cpython-dev/pandas/pandas/core/indexes/base.py:2821: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
return self._engine.get_loc(key)
pandas/tests/io/json/test_json_table_schema.py: 12 tests with warnings
pandas/tests/io/json/test_pandas.py: 3 tests with warnings
/Users/taugspurger/sandbox/cpython-dev/pandas/pandas/io/json/_json.py:148: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.
return dumps(
pandas/tests/io/json/test_pandas.py::TestPandasContainer::test_tz_is_utc
/Users/taugspurger/sandbox/cpython-dev/pandas/pandas/tests/io/json/test_pandas.py:1324: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.
assert dumps(ts, iso_dates=True) == exp
pandas/tests/io/json/test_pandas.py::TestPandasContainer::test_tz_is_utc
/Users/taugspurger/sandbox/cpython-dev/pandas/pandas/tests/io/json/test_pandas.py:1326: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.
assert dumps(dt, iso_dates=True) == exp
pandas/tests/io/json/test_pandas.py::TestPandasContainer::test_tz_is_utc
/Users/taugspurger/sandbox/cpython-dev/pandas/pandas/tests/io/json/test_pandas.py:1329: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.
assert dumps(ts, iso_dates=True) == exp
pandas/tests/io/json/test_pandas.py::TestPandasContainer::test_tz_is_utc
/Users/taugspurger/sandbox/cpython-dev/pandas/pandas/tests/io/json/test_pandas.py:1331: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.
assert dumps(dt, iso_dates=True) == exp
pandas/tests/io/json/test_pandas.py::TestPandasContainer::test_tz_is_utc
/Users/taugspurger/sandbox/cpython-dev/pandas/pandas/tests/io/json/test_pandas.py:1334: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.
assert dumps(ts, iso_dates=True) == exp
pandas/tests/io/json/test_pandas.py::TestPandasContainer::test_tz_is_utc
/Users/taugspurger/sandbox/cpython-dev/pandas/pandas/tests/io/json/test_pandas.py:1336: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.
assert dumps(dt, iso_dates=True) == exp
pandas/tests/io/json/test_pandas.py::TestPandasContainer::test_tz_range_is_utc
pandas/tests/io/json/test_pandas.py::TestPandasContainer::test_tz_range_is_utc
/Users/taugspurger/sandbox/cpython-dev/pandas/pandas/tests/io/json/test_pandas.py:1353: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.
result = dumps(df, iso_dates=True)
pandas/tests/io/json/test_pandas.py::TestPandasContainer::test_tz_range_is_utc
pandas/tests/io/json/test_pandas.py::TestPandasContainer::test_tz_range_is_utc
/Users/taugspurger/sandbox/cpython-dev/pandas/pandas/tests/io/json/test_pandas.py:1361: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.
assert dumps(df, iso_dates=True) == dfexp
pandas/tests/io/json/test_pandas.py::TestPandasContainer::test_tz_range_is_utc
pandas/tests/io/json/test_pandas.py::TestPandasContainer::test_tz_range_is_utc
/Users/taugspurger/sandbox/cpython-dev/pandas/pandas/tests/io/json/test_pandas.py:1368: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.
assert dumps(df, iso_dates=True) == dfexp
pandas/tests/sparse/test_pivot.py::TestPivotTable::test_pivot_table
/Users/taugspurger/sandbox/cpython-dev/pandas/pandas/core/arrays/sparse.py:1645: RuntimeWarning: invalid value encountered in double_scalars
return sp_sum / ct
-- Docs: https://docs.pytest.org/en/latest/warnings.html
============================================================================= short test summary info ==============================================================================
FAILED pandas/tests/test_multilevel.py::TestMultiLevel::test_frame_group_ops[True-True-1-1-min] - KeyError: 'one'
FAILED pandas/tests/test_multilevel.py::TestMultiLevel::test_frame_group_ops[True-True-1-1-max] - KeyError: 'one'
FAILED pandas/tests/test_multilevel.py::TestMultiLevel::test_frame_group_ops[False-True-1-1-min] - KeyError: 'one'
FAILED pandas/tests/test_multilevel.py::TestMultiLevel::test_frame_group_ops[False-True-1-1-max] - KeyError: 'one'
FAILED pandas/tests/groupby/test_timegrouper.py::TestGroupBy::test_groupby_with_timezone_selection - AssertionError: Series Expected type <class 'pandas.core.series.Series'>, fo...
FAILED pandas/tests/groupby/test_whitelist.py::test_regression_whitelist_methods[True-True-1-1-min] - KeyError: 'one'
FAILED pandas/tests/groupby/test_whitelist.py::test_regression_whitelist_methods[True-True-1-1-max] - KeyError: 'one'
FAILED pandas/tests/groupby/test_whitelist.py::test_regression_whitelist_methods[True-False-1-1-min] - KeyError: 'one'
FAILED pandas/tests/groupby/test_whitelist.py::test_regression_whitelist_methods[True-False-1-1-max] - KeyError: 'one'
FAILED pandas/tests/groupby/test_whitelist.py::test_regression_whitelist_methods[False-True-1-1-min] - KeyError: 'one'
FAILED pandas/tests/groupby/test_whitelist.py::test_regression_whitelist_methods[False-True-1-1-max] - KeyError: 'one'
FAILED pandas/tests/groupby/test_whitelist.py::test_regression_whitelist_methods[False-False-1-1-min] - KeyError: 'one'
FAILED pandas/tests/groupby/test_whitelist.py::test_regression_whitelist_methods[False-False-1-1-max] - KeyError: 'one'
FAILED pandas/tests/groupby/aggregate/test_cython.py::test__cython_agg_general[min-amin] - TypeError: Expected dict, got int
FAILED pandas/tests/groupby/aggregate/test_cython.py::test__cython_agg_general[max-amax] - TypeError: Expected dict, got int
FAILED pandas/tests/groupby/aggregate/test_cython.py::test_cython_agg_empty_buckets[True-min-amin] - TypeError: Expected dict, got int
FAILED pandas/tests/groupby/aggregate/test_cython.py::test_cython_agg_empty_buckets[True-max-amax] - TypeError: Expected dict, got int
FAILED pandas/tests/groupby/aggregate/test_cython.py::test_cython_agg_empty_buckets[False-min-amin] - TypeError: Expected dict, got int
FAILED pandas/tests/groupby/aggregate/test_cython.py::test_cython_agg_empty_buckets[False-max-amax] - TypeError: Expected dict, got int
FAILED pandas/tests/groupby/aggregate/test_cython.py::test_cython_agg_empty_buckets[None-min-amin] - TypeError: Expected dict, got int
FAILED pandas/tests/groupby/aggregate/test_cython.py::test_cython_agg_empty_buckets[None-max-amax] - TypeError: Expected dict, got int
FAILED pandas/tests/resample/test_datetime_index.py::test_custom_grouper - TypeError: Expected dict, got int
FAILED pandas/tests/resample/test_datetime_index.py::test_resample_frame_basic - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_examples2 - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_basic - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_basic_categorical - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_basic_left_index - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_basic_right_index - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_basic_left_index_right_index - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_basic_left_by_right_by - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_missing_right_by - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_multiby - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_multiby_heterogeneous_types - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_multiby_indexed - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_basic2 - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_with_duplicates - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_valid_tolerance - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_non_sorted - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_tolerance[pd.Timedelta] - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_index_tolerance - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_allow_exact_matches - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_allow_exact_matches_and_tolerance - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_forward_by - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_nearest_by - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_by_int - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_on_specialized_type_by_int[float] - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_on_specialized_type_by_int[float32] - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_on_specialized_type_by_int[float64] - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_on_specialized_type_by_int[uint8] - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_on_specialized_type_by_int[uint16] - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_on_specialized_type_by_int[uint32] - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_on_specialized_type_by_int[uint64] - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_on_specialized_type_by_int[int] - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_on_specialized_type_by_int[int8] - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_on_specialized_type_by_int[int16] - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_on_specialized_type_by_int[int32] - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_on_specialized_type_by_int[int64] - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_on_float_by_int - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_merge_by_col_tz_aware - TypeError: Expected dict, got int
FAILED pandas/tests/reshape/merge/test_merge_asof.py::TestAsOfMerge::test_by_mixed_tz_aware - TypeError: Expected dict, got int
======================================== 60 failed, 54929 passed, 3971 skipped, 832 xfailed, 10 xpassed, 32 warnings in 1229.76s (0:20:29) =========================================
```
Short tracebacks in the details
<details>
```
===================================================================================== FAILURES =====================================================================================
______________________________________________________________ TestMultiLevel.test_frame_group_ops[True-True-1-1-min] ______________________________________________________________
pandas/core/indexes/base.py:2821: in get_loc
return self._engine.get_loc(key)
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
During handling of the above exception, another exception occurred:
pandas/tests/test_multilevel.py:1031: in test_frame_group_ops
rightside = getattr(frame, op)(level=level, axis=axis, skipna=skipna)
pandas/core/generic.py:11600: in stat_func
return self._agg_by_level(name, axis=axis, level=level, skipna=skipna)
pandas/core/generic.py:10415: in _agg_by_level
return getattr(grouped, name)(**kwargs)
pandas/core/groupby/groupby.py:1383: in f
result[col] = self._try_cast(result[col], self.obj[col])
pandas/core/frame.py:2846: in __getitem__
return self._getitem_multilevel(key)
pandas/core/frame.py:2896: in _getitem_multilevel
loc = self.columns.get_loc(key)
pandas/core/indexes/multi.py:2662: in get_loc
loc = self._get_level_indexer(key, level=0)
pandas/core/indexes/multi.py:2928: in _get_level_indexer
code = level_index.get_loc(key)
pandas/core/indexes/base.py:2823: in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
______________________________________________________________ TestMultiLevel.test_frame_group_ops[True-True-1-1-max] ______________________________________________________________
pandas/core/indexes/base.py:2821: in get_loc
return self._engine.get_loc(key)
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
During handling of the above exception, another exception occurred:
pandas/tests/test_multilevel.py:1031: in test_frame_group_ops
rightside = getattr(frame, op)(level=level, axis=axis, skipna=skipna)
pandas/core/generic.py:11600: in stat_func
return self._agg_by_level(name, axis=axis, level=level, skipna=skipna)
pandas/core/generic.py:10415: in _agg_by_level
return getattr(grouped, name)(**kwargs)
pandas/core/groupby/groupby.py:1383: in f
result[col] = self._try_cast(result[col], self.obj[col])
pandas/core/frame.py:2846: in __getitem__
return self._getitem_multilevel(key)
pandas/core/frame.py:2896: in _getitem_multilevel
loc = self.columns.get_loc(key)
pandas/core/indexes/multi.py:2662: in get_loc
loc = self._get_level_indexer(key, level=0)
pandas/core/indexes/multi.py:2928: in _get_level_indexer
code = level_index.get_loc(key)
pandas/core/indexes/base.py:2823: in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
_____________________________________________________________ TestMultiLevel.test_frame_group_ops[False-True-1-1-min] ______________________________________________________________
pandas/core/indexes/base.py:2821: in get_loc
return self._engine.get_loc(key)
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
During handling of the above exception, another exception occurred:
pandas/tests/test_multilevel.py:1031: in test_frame_group_ops
rightside = getattr(frame, op)(level=level, axis=axis, skipna=skipna)
pandas/core/generic.py:11600: in stat_func
return self._agg_by_level(name, axis=axis, level=level, skipna=skipna)
pandas/core/generic.py:10415: in _agg_by_level
return getattr(grouped, name)(**kwargs)
pandas/core/groupby/groupby.py:1383: in f
result[col] = self._try_cast(result[col], self.obj[col])
pandas/core/frame.py:2846: in __getitem__
return self._getitem_multilevel(key)
pandas/core/frame.py:2896: in _getitem_multilevel
loc = self.columns.get_loc(key)
pandas/core/indexes/multi.py:2662: in get_loc
loc = self._get_level_indexer(key, level=0)
pandas/core/indexes/multi.py:2928: in _get_level_indexer
code = level_index.get_loc(key)
pandas/core/indexes/base.py:2823: in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
_____________________________________________________________ TestMultiLevel.test_frame_group_ops[False-True-1-1-max] ______________________________________________________________
pandas/core/indexes/base.py:2821: in get_loc
return self._engine.get_loc(key)
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
During handling of the above exception, another exception occurred:
pandas/tests/test_multilevel.py:1031: in test_frame_group_ops
rightside = getattr(frame, op)(level=level, axis=axis, skipna=skipna)
pandas/core/generic.py:11600: in stat_func
return self._agg_by_level(name, axis=axis, level=level, skipna=skipna)
pandas/core/generic.py:10415: in _agg_by_level
return getattr(grouped, name)(**kwargs)
pandas/core/groupby/groupby.py:1383: in f
result[col] = self._try_cast(result[col], self.obj[col])
pandas/core/frame.py:2846: in __getitem__
return self._getitem_multilevel(key)
pandas/core/frame.py:2896: in _getitem_multilevel
loc = self.columns.get_loc(key)
pandas/core/indexes/multi.py:2662: in get_loc
loc = self._get_level_indexer(key, level=0)
pandas/core/indexes/multi.py:2928: in _get_level_indexer
code = level_index.get_loc(key)
pandas/core/indexes/base.py:2823: in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
_________________________________________________________________ TestGroupBy.test_groupby_with_timezone_selection _________________________________________________________________
pandas/tests/groupby/test_timegrouper.py:673: in test_groupby_with_timezone_selection
tm.assert_series_equal(df1, df2)
pandas/util/testing.py:394: in _check_isinstance
raise AssertionError(
E AssertionError: Series Expected type <class 'pandas.core.series.Series'>, found <class 'pandas.core.arrays.datetimes.DatetimeArray'> instead
_______________________________________________________________ test_regression_whitelist_methods[True-True-1-1-min] _______________________________________________________________
pandas/core/indexes/base.py:2821: in get_loc
return self._engine.get_loc(key)
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
During handling of the above exception, another exception occurred:
pandas/tests/groupby/test_whitelist.py:209: in test_regression_whitelist_methods
result = getattr(grouped, op)()
pandas/core/groupby/groupby.py:1383: in f
result[col] = self._try_cast(result[col], self.obj[col])
pandas/core/frame.py:2846: in __getitem__
return self._getitem_multilevel(key)
pandas/core/frame.py:2896: in _getitem_multilevel
loc = self.columns.get_loc(key)
pandas/core/indexes/multi.py:2662: in get_loc
loc = self._get_level_indexer(key, level=0)
pandas/core/indexes/multi.py:2928: in _get_level_indexer
code = level_index.get_loc(key)
pandas/core/indexes/base.py:2823: in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
_______________________________________________________________ test_regression_whitelist_methods[True-True-1-1-max] _______________________________________________________________
pandas/core/indexes/base.py:2821: in get_loc
return self._engine.get_loc(key)
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
During handling of the above exception, another exception occurred:
pandas/tests/groupby/test_whitelist.py:209: in test_regression_whitelist_methods
result = getattr(grouped, op)()
pandas/core/groupby/groupby.py:1383: in f
result[col] = self._try_cast(result[col], self.obj[col])
pandas/core/frame.py:2846: in __getitem__
return self._getitem_multilevel(key)
pandas/core/frame.py:2896: in _getitem_multilevel
loc = self.columns.get_loc(key)
pandas/core/indexes/multi.py:2662: in get_loc
loc = self._get_level_indexer(key, level=0)
pandas/core/indexes/multi.py:2928: in _get_level_indexer
code = level_index.get_loc(key)
pandas/core/indexes/base.py:2823: in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
______________________________________________________________ test_regression_whitelist_methods[True-False-1-1-min] _______________________________________________________________
pandas/core/indexes/base.py:2821: in get_loc
return self._engine.get_loc(key)
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
During handling of the above exception, another exception occurred:
pandas/tests/groupby/test_whitelist.py:209: in test_regression_whitelist_methods
result = getattr(grouped, op)()
pandas/core/groupby/groupby.py:1383: in f
result[col] = self._try_cast(result[col], self.obj[col])
pandas/core/frame.py:2846: in __getitem__
return self._getitem_multilevel(key)
pandas/core/frame.py:2896: in _getitem_multilevel
loc = self.columns.get_loc(key)
pandas/core/indexes/multi.py:2662: in get_loc
loc = self._get_level_indexer(key, level=0)
pandas/core/indexes/multi.py:2928: in _get_level_indexer
code = level_index.get_loc(key)
pandas/core/indexes/base.py:2823: in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
______________________________________________________________ test_regression_whitelist_methods[True-False-1-1-max] _______________________________________________________________
pandas/core/indexes/base.py:2821: in get_loc
return self._engine.get_loc(key)
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
During handling of the above exception, another exception occurred:
pandas/tests/groupby/test_whitelist.py:209: in test_regression_whitelist_methods
result = getattr(grouped, op)()
pandas/core/groupby/groupby.py:1383: in f
result[col] = self._try_cast(result[col], self.obj[col])
pandas/core/frame.py:2846: in __getitem__
return self._getitem_multilevel(key)
pandas/core/frame.py:2896: in _getitem_multilevel
loc = self.columns.get_loc(key)
pandas/core/indexes/multi.py:2662: in get_loc
loc = self._get_level_indexer(key, level=0)
pandas/core/indexes/multi.py:2928: in _get_level_indexer
code = level_index.get_loc(key)
pandas/core/indexes/base.py:2823: in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
______________________________________________________________ test_regression_whitelist_methods[False-True-1-1-min] _______________________________________________________________
pandas/core/indexes/base.py:2821: in get_loc
return self._engine.get_loc(key)
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
During handling of the above exception, another exception occurred:
pandas/tests/groupby/test_whitelist.py:209: in test_regression_whitelist_methods
result = getattr(grouped, op)()
pandas/core/groupby/groupby.py:1383: in f
result[col] = self._try_cast(result[col], self.obj[col])
pandas/core/frame.py:2846: in __getitem__
return self._getitem_multilevel(key)
pandas/core/frame.py:2896: in _getitem_multilevel
loc = self.columns.get_loc(key)
pandas/core/indexes/multi.py:2662: in get_loc
loc = self._get_level_indexer(key, level=0)
pandas/core/indexes/multi.py:2928: in _get_level_indexer
code = level_index.get_loc(key)
pandas/core/indexes/base.py:2823: in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
______________________________________________________________ test_regression_whitelist_methods[False-True-1-1-max] _______________________________________________________________
pandas/core/indexes/base.py:2821: in get_loc
return self._engine.get_loc(key)
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
During handling of the above exception, another exception occurred:
pandas/tests/groupby/test_whitelist.py:209: in test_regression_whitelist_methods
result = getattr(grouped, op)()
pandas/core/groupby/groupby.py:1383: in f
result[col] = self._try_cast(result[col], self.obj[col])
pandas/core/frame.py:2846: in __getitem__
return self._getitem_multilevel(key)
pandas/core/frame.py:2896: in _getitem_multilevel
loc = self.columns.get_loc(key)
pandas/core/indexes/multi.py:2662: in get_loc
loc = self._get_level_indexer(key, level=0)
pandas/core/indexes/multi.py:2928: in _get_level_indexer
code = level_index.get_loc(key)
pandas/core/indexes/base.py:2823: in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
______________________________________________________________ test_regression_whitelist_methods[False-False-1-1-min] ______________________________________________________________
pandas/core/indexes/base.py:2821: in get_loc
return self._engine.get_loc(key)
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
During handling of the above exception, another exception occurred:
pandas/tests/groupby/test_whitelist.py:209: in test_regression_whitelist_methods
result = getattr(grouped, op)()
pandas/core/groupby/groupby.py:1383: in f
result[col] = self._try_cast(result[col], self.obj[col])
pandas/core/frame.py:2846: in __getitem__
return self._getitem_multilevel(key)
pandas/core/frame.py:2896: in _getitem_multilevel
loc = self.columns.get_loc(key)
pandas/core/indexes/multi.py:2662: in get_loc
loc = self._get_level_indexer(key, level=0)
pandas/core/indexes/multi.py:2928: in _get_level_indexer
code = level_index.get_loc(key)
pandas/core/indexes/base.py:2823: in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
______________________________________________________________ test_regression_whitelist_methods[False-False-1-1-max] ______________________________________________________________
pandas/core/indexes/base.py:2821: in get_loc
return self._engine.get_loc(key)
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
During handling of the above exception, another exception occurred:
pandas/tests/groupby/test_whitelist.py:209: in test_regression_whitelist_methods
result = getattr(grouped, op)()
pandas/core/groupby/groupby.py:1383: in f
result[col] = self._try_cast(result[col], self.obj[col])
pandas/core/frame.py:2846: in __getitem__
return self._getitem_multilevel(key)
pandas/core/frame.py:2896: in _getitem_multilevel
loc = self.columns.get_loc(key)
pandas/core/indexes/multi.py:2662: in get_loc
loc = self._get_level_indexer(key, level=0)
pandas/core/indexes/multi.py:2928: in _get_level_indexer
code = level_index.get_loc(key)
pandas/core/indexes/base.py:2823: in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
pandas/_libs/index.pyx:103: in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
pandas/_libs/index.pyx:127: in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
pandas/_libs/hashtable_class_helper.pxi:1607: in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
pandas/_libs/hashtable_class_helper.pxi:1614: in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
E KeyError: 'one'
============================================================================= short test summary info ==============================================================================
FAILED pandas/tests/test_multilevel.py::TestMultiLevel::test_frame_group_ops[True-True-1-1-min] - KeyError: 'one'
FAILED pandas/tests/test_multilevel.py::TestMultiLevel::test_frame_group_ops[True-True-1-1-max] - KeyError: 'one'
FAILED pandas/tests/test_multilevel.py::TestMultiLevel::test_frame_group_ops[False-True-1-1-min] - KeyError: 'one'
FAILED pandas/tests/test_multilevel.py::TestMultiLevel::test_frame_group_ops[False-True-1-1-max] - KeyError: 'one'
FAILED pandas/tests/groupby/test_timegrouper.py::TestGroupBy::test_groupby_with_timezone_selection - AssertionError: Series Expected type <class 'pandas.core.series.Series'>, fo...
FAILED pandas/tests/groupby/test_whitelist.py::test_regression_whitelist_methods[True-True-1-1-min] - KeyError: 'one'
FAILED pandas/tests/groupby/test_whitelist.py::test_regression_whitelist_methods[True-True-1-1-max] - KeyError: 'one'
FAILED pandas/tests/groupby/test_whitelist.py::test_regression_whitelist_methods[True-False-1-1-min] - KeyError: 'one'
FAILED pandas/tests/groupby/test_whitelist.py::test_regression_whitelist_methods[True-False-1-1-max] - KeyError: 'one'
FAILED pandas/tests/groupby/test_whitelist.py::test_regression_whitelist_methods[False-True-1-1-min] - KeyError: 'one'
FAILED pandas/tests/groupby/test_whitelist.py::test_regression_whitelist_methods[False-True-1-1-max] - KeyError: 'one'
FAILED pandas/tests/groupby/test_whitelist.py::test_regression_whitelist_methods[False-False-1-1-min] - KeyError: 'one'
FAILED pandas/tests/groupby/test_whitelist.py::test_regression_whitelist_methods[False-False-1-1-max] - KeyError: 'one'
```
</details>
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MDU6SXNzdWU1OTY2MjY4OTY=
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|
DEPR: Remove aliased Resampler methods
|
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open
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[] | null | 0
| 2020-04-08T14:33:56Z
| 2020-04-08T14:34:29Z
| null |
MEMBER
| null |
It seems the forward and backward filling methods are duplicated for Resampler objects (e.g., bfill is an alias for backfill, and ffill is an alias for pad). Does it make sense to deprecate backfill and pad in favor of the bfill and ffill, as these names are more consistent with other methods in pandas (e.g., DataFrame.ffill)?
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CI: pandas warnings in tests about keyword only arguments
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| 2020-04-12T21:48:32Z
|
CONTRIBUTOR
| null |
e.g. https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=32821&view=logs&j=76104ccd-8dcc-5006-a17c-28bcdd709542&t=cd420693-444f-5c55-14e0-1be238e189a1
```
pandas\tests\io\test_html.py:886
D:\a\1\s\pandas\tests\io\test_html.py:886: FutureWarning: Starting with Pandas version 2.0 all arguments of read_html except for the argument 'io' will be keyword-only
result = self.read_html(data, "Arizona", index_col=0)[0]
```
We'll need to track down places where we're passing positional arguments like https://github.com/pandas-dev/pandas/blob/def6f6d67c18d6b76ef79defd9e3bd86385ef9de/pandas/tests/io/test_html.py#L886 and update them to use keyword arguments. That may be the only one, not sure.
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ENH: Support passing compression args to gzip and bz2
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CONTRIBUTOR
| null |
This commit closes #33196 but takes a more generic approach than the suggested
solution. Instead of providing a 'fast' kwarg or global compression level
setting, this commit extends the ability to pass compression settings as a
dict to the gzip and bz2 compression methods. In this way, if the user
wants faster compression, they can pass
compression={'method': 'gzip', 'compresslevel'=1} rather than
just compression='gzip'.
Note: For the API to be consistent when passing paths vs. filelikes, GZipFile and gzip2.open() must accept the same kwargs.
- [x] closes #33196
- [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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DOC: DatetimeTZDtype astype behavior with Series.astype
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NONE
| null |
#### Code Sample
```python
import pandas as pd
typ = pd.DatetimeTZDtype(tz = 'EST')
s = pd.Series([pd.Timestamp('2020-01-01 15:00')])
print(s.astype(typ))
```
```
0 2020-01-01 10:00:00-05:00
dtype: datetime64[ns, EST]
```
#### Problem description
Hello,
I noticed a strange behavior using `DatetimeTZDtype` , when specifying a timezone as argument. When the type is applied to a tz-naive datetime column using `astype`, it will first localize the timestamps to `UTC` and then convert the timezone, whereas I was expecting it would just localize my tz-naive timestamps.
My situation is I am getting data from a database and need to convert the columns to the desired types. Using `DatetimeTZDtype` and `astype` is a nice way to convert and localize in just one line, but I encountered this unexpected behavior (though it is easy to work around that).
I tracked the issue to these lines [https://github.com/pandas-dev/pandas/blob/master/pandas/core/internals/blocks.py#L2090](url)
Thanks
#### Expected Output
```
0 2020-01-01 15:00:00-05:00
dtype: datetime64[ns, EST]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0
Cython : 0.29.16
pytest : 5.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 0.9.3
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 : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.48.0
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
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PERF: fastpaths in is_foo_dtype checks
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| 2020-04-17T03:01:07Z
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|
MEMBER
| null |
xref #33364, partially addresses #33368
For exposition I used the new type checking functions in parsers.pyx.
For the implementation I chose `.type` and `.kind` checks in order to avoid needing the imports from `dtypes.dtypes`, which has dependencies on a bunch of other parts of the code.
```
In [1]: import pandas as pd
In [2]: from pandas.core.dtypes.common import *
In [3]: cat = pd.Categorical([])
In [4]: arr = np.arange(5)
In [5]: %timeit is_categorical_dtype(cat.dtype)
1.57 µs ± 34.5 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
In [6]: %timeit is_cat_dtype(cat.dtype)
316 ns ± 3.78 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
In [7]: %timeit is_extension_array_dtype(cat.dtype)
364 ns ± 5.39 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
In [8]: %timeit is_ea_dtype(cat.dtype)
270 ns ± 7.31 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
In [9]: %timeit is_extension_array_dtype(arr.dtype)
759 ns ± 5.16 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
In [10]: %timeit is_ea_dtype(arr.dtype)
199 ns ± 8.77 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
```
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BUG: Index.astype does not localize to UTC first like Series.astype with datetime64[ns, tz] dtypes
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| 2021-06-11T06:01:55Z
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|
MEMBER
| 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.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```
In [4]: pd.__version__
Out[4]: '1.1.0.dev0+1202.g1de2cf1ac'
In [5]: pd.Series([pd.Timestamp('2020-01-01 15:00')]).astype('datetime64[ns, EST]')
Out[5]:
0 2020-01-01 10:00:00-05:00
dtype: datetime64[ns, EST]
In [6]: pd.Index([pd.Timestamp('2020-01-01 15:00')]).astype('datetime64[ns, EST]')
Out[6]: DatetimeIndex(['2020-01-01 15:00:00-05:00'], dtype='datetime64[ns, EST]', freq=None)
```
#### Problem description
`Out[5]` is the expected output as documented in https://pandas.pydata.org/docs/user_guide/timeseries.html#time-zone-series-operations. `Out[6]` should align therefore with `Out[5]`
#### Expected Output
```
In [6]: pd.Index([pd.Timestamp('2020-01-01 15:00')]).astype('datetime64[ns, EST]')
Out[6]: DatetimeIndex(['2020-01-01 10:00:00-05:00'], dtype='datetime64[ns, EST]', freq=None)
```
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CONTRIBUTOR
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- [ ] Modify files v0.6.1.rst, v0.7.0.rst, v0.7.1.rst, v0.7.2.rst, v0.7.3.rst
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BUG: to_period() freq was not infered
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MEMBER
| null |
- [x] closes #33358
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
---
When running ```pytest -q --cache-clear pandas/tests/ -k "period"``` some tests in ```pandas/tests/indexing/``` are failing, but when running ```pytest -q --cache-clear pandas/tests/indexing/``` __all__ the tests are passing (on my local machine), so I figured and debug from here, thoughts?
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REF: call pandas_dtype up-front in Index.__new__
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MEMBER
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This leaves us in position to drop in dtype-only is_foo_dtype checks
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REF: collect DataFrame.__setitem__ tests
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MDExOlB1bGxSZXF1ZXN0NDAxMTIyODQ1
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BUG: DatetimeIndex.astype matches Series.dtype with datetime tz dtype
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- [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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MDU6SXNzdWU1OTY5MTU0MDM=
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BUG: ValueError: buffer source array is read-only during groupby
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| 6
| 2020-04-08T23:08:24Z
| 2020-04-10T16:57:10Z
| 2020-04-10T16:57:10Z
|
CONTRIBUTOR
| 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.
- [x] (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
df = pd.DataFrame(data={'x': [1], 'y': [2]})
df.to_parquet('pq_df', partition_cols='x')
df = pd.read_parquet('pq_df')
df.groupby('x', sort=False)
```
#### Problem description
The above code raises an exception:
```
Traceback (most recent call last):
File "mve.py", line 5, in <module>
df.groupby('x', sort=False)
File "/Users/ehasse/.local/lib/python3.8/site-packages/pandas/core/frame.py", line 5798, in groupby
return groupby_generic.DataFrameGroupBy(
File "/Users/ehasse/.local/lib/python3.8/site-packages/pandas/core/groupby/groupby.py", line 402, in __init__
grouper, exclusions, obj = get_grouper(
File "/Users/ehasse/.local/lib/python3.8/site-packages/pandas/core/groupby/grouper.py", line 615, in get_grouper
Grouping(
File "/Users/ehasse/.local/lib/python3.8/site-packages/pandas/core/groupby/grouper.py", line 312, in __init__
self.grouper, self.all_grouper = recode_for_groupby(
File "/Users/ehasse/.local/lib/python3.8/site-packages/pandas/core/groupby/categorical.py", line 72, in recode_for_groupby
cat = cat.add_categories(c.categories[~c.categories.isin(cat.categories)])
File "/Users/ehasse/.local/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 4667, in isin
return algos.isin(self, values)
File "/Users/ehasse/.local/lib/python3.8/site-packages/pandas/core/algorithms.py", line 447, in isin
return f(comps, values)
File "pandas/_libs/hashtable_func_helper.pxi", line 555, in pandas._libs.hashtable.ismember_int64
File "stringsource", line 658, in View.MemoryView.memoryview_cwrapper
File "stringsource", line 349, in View.MemoryView.memoryview.__cinit__
ValueError: buffer source array is read-only
```
This specifically requires the following:
* The dataframe is loaded from a parquet file.
* The column being grouped by was used to partition the file.
* sort=False is passed.
In addtion, passing observed=True stops the error from occurring.
I believe this is related to #31710, but they were unable to provide an example for groupby, and the issue remains on 1.0.3.
#### Expected Output
A DataFrameGroupBy object.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
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 : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0.post20200309
Cython : None
pytest : 5.4.1
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 : 0.16.0
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 : 0.47.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDAxMTQyODEw
| 33,411
|
To numeric
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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`
- [x] whatsnew entry
Addressing GH #33013. Supports downcasting of nullable dtypes by transferring elements in extension array to ndarray to avoid TypeError thrown by np.allclose. Enhancement documented on v1.1.0 in the "other enhancements" section. Unit test written in test_to_numeric.
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MDU6SXNzdWU1OTY5NDAzMDY=
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|
ENH: More permissable conversion to StringDtype
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| 2020-04-09T00:24:37Z
| 2020-04-09T08:47:32Z
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|
CONTRIBUTOR
| null |
Currently, converting directly to StringDtype is not really safe unless you know you've got a ``str`` type:
```python
>>> pd.Series([1,2, np.nan], dtype=str)[0]
'1' # string, works
>>> pd.Series([1,2, np.nan], dtype="string")
ValueError # does not work
>>> pd.Series([1,2, np.nan], dtype=str).astype("string")
# works
>>> pd.Series([1,2, np.nan]).astype("string")
ValueError # does not work
>>> pd.Series([1,2, np.nan]).astype(str).astype("string")
# works
```
The net result is that we have to wrap conversions, so in general will have to do ``x.astype(str).astype("string")``, which seems unnecessary.
This extends to other conversions to StringDtype, e.g. for ``read_excel`` we have to do:
```python
>>> df = pd.read_excel(..., dtype={"col_x": str}).astype({"col_x": "string"})
```
where directly setting ``dtype={"col_x": "string"}`` would seem more natural.
I think we should be able to convert directly to StringDtype by internally converting first to ``str``, if needed. I might be missing some nuance why this is not allowed, though.
Any comment on loosening up here?
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MDExOlB1bGxSZXF1ZXN0NDAxMTYwODA1
| 33,413
|
use https link for Anaconda
|
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| 1
| 2020-04-09T01:35:50Z
| 2020-04-09T02:22:12Z
| 2020-04-09T02:22:07Z
|
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDU6SXNzdWU1OTY5ODA4Njk=
| 33,414
|
ENH: Optionally pass dtypes as a dict into json_normalize
|
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open
| false
| null |
[] | null | 2
| 2020-04-09T02:42:32Z
| 2020-10-09T00:13:03Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
In some of my projects, the data I need to load as DataFrame comes in a json list.
We switched to `json_normalize` for its ease of use, but some memory/performance issues came as the default typing within this method outputs mostly `object`, `int64` and `float64` column dtypes for us - all of which are the most memory-demanding of their categories.
#### Describe the solution you'd like
Depends on #4464.
I envision passing `dtype` as a dict, so for a format like
```json
json = [{
"nest": {
"int": 10,
"float": 2.5,
"str": "category_one"
}
}]
```
Normalizing would look like
```python
dtype = {'nest.int': np.int8, 'nest.float': np.float32, 'next.str': 'category'}
df = pd.json_normalize(json, dtype=dtype)
```
Following the conclusion of #4464, the `dtype` arg would be passed to [the DataFrame constructor within `json_normalize`](https://github.com/pandas-dev/pandas/blob/983fae65fbadb83a064dfec53031d875d8e13928/pandas/io/json/_normalize.py#L338).
#### API breaking implications
As of now, [the code itself mentions a problem](https://github.com/pandas-dev/pandas/blob/983fae65fbadb83a064dfec53031d875d8e13928/pandas/io/json/_normalize.py#L343-L352) regarding metadata field typing.
I didn't dive in enough to determine how to deal with this. I see no breaking change coming from this feature, but if I understand correctly that will always override metadata field types with `object`.
If so the condition could be changed to
```python
if k in result and k not in dtype.keys():
```
#### Describe alternatives you've considered
I've made a separate module which runs `json_normalize` and then overrides the resulting DataFrame's dtypes dynamically through `as_type` and `apply`.
|
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| 596,986,419
|
MDExOlB1bGxSZXF1ZXN0NDAxMTgwMzc1
| 33,415
|
WIP: Timestamp/DTA match stdlib tzawareness-compat behavior
|
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closed
| false
| null |
[] | null | 1
| 2020-04-09T03:01:58Z
| 2021-01-04T06:20:18Z
| 2020-04-15T18:44:42Z
|
MEMBER
| null |
- [x] closes #28507
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I'm not super-happy with the implementation adding `op` kwarg to check_compatible_with and assert_tzawareness_compat, looking for something cleaner.
|
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| 596,993,171
|
MDExOlB1bGxSZXF1ZXN0NDAxMTg1Nzgz
| 33,416
|
DOC: Fix EX01 in DataFrame.duplicated
|
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| 1
| 2020-04-09T03:24:13Z
| 2020-04-09T17:53:40Z
| 2020-04-09T17:53:33Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Related to #27977.
```
################################################################################
################################## Validation ##################################
################################################################################
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