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MDExOlB1bGxSZXF1ZXN0NTQzNjEwNDIy
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CI: move arm64 build to azure #38617
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MEMBER
| null |
xref #38617
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BUG: tm.assert_produces_warning() doesn't work with expected_warning=None or False
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| 2
| 2020-12-21T18:29:49Z
| 2020-12-22T13:40:41Z
| 2020-12-22T13:40:41Z
|
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
```python
import warnings
import pandas._testing as tm
with tm.assert_produces_warning(None):
warnings.warn(UserWarning)
with tm.assert_produces_warning(False):
warnings.warn(RuntimeWarning)
```
#### Problem description
Docs for this function say "To check that no warning is returned, specify ``False`` or ``None``". It also includes the second example as something that should raise an `AssertionError`. However, both these examples do not raise.
#### Expected Output
Raise `AssertionError` for both.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9a46a4b700d1b794d9a944928ac95f4cb742b259
python : 3.8.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Oct 29 22:56:45 PDT 2020; root:xnu-6153.141.2.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.3.0.dev0+125.g9a46a4b70
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.1
setuptools : 49.6.0.post20201009
Cython : 0.29.21
pytest : 6.2.0
hypothesis : 5.43.3
sphinx : 3.3.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.4
fastparquet : 0.4.1
gcsfs : 0.7.1
matplotlib : 3.3.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.3
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.2
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.52.0
</details>
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MDExOlB1bGxSZXF1ZXN0NTQzNjgxNTUx
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BUG: join not working correctly with MultiIndex and one dimension categorical
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| 1
| 2020-12-21T19:31:30Z
| 2021-01-13T16:55:33Z
| 2021-01-13T13:25:53Z
|
MEMBER
| null |
- [x] closes #38502
- [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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| 2021-01-21T15:15:19Z
| 2021-01-21T15:15:19Z
|
MEMBER
| null |
xref #33401 which discusses astype, #24559 which discusses constructors. I think these need to be considered jointly.
Two big-picture questions.
1) when we pass `constructor(dt64values, tz=tz)`, do we interpret dt64values as UTC or wall-times?
2) when we pass constructor(dt64values).astype("M8[ns, tz]")`, do we interpret dt64values as UTC or wall-times?
Let's check the current behavior on master for Timestamp, DatetimeIndex, and Series (see code block below if you want to check my work)
cls | Timestamp | DatetimeIndex | Series |
------------------------ | ------------- | ------------- | ------- |
cls(dt64, dtype=dt64tz) | UTC | Wall | UTC |
cls(dt64).astype(dt64tz) | N/A | Wall | UTC |
cls(dt64tz).astype("M8[ns]") | N/A | UTC | UTC |
If we were starting fresh, I would probably lean towards making these all interpret dt64 as wall-times. But because these are already mostly-UTC, it would be an easier change to bring the DatetimeIndex behavior in line with the others.
Thoughts? cc @jorisvandenbossche @mroeschke @jreback
```
values = (3600 * 10**9 * np.arange(4)).astype("datetime64[ns]")
dt64 = values[-1]
tz = "US/Pacific"
dtype = pd.DatetimeTZDtype(tz=tz)
ts = pd.Timestamp(dt64, tz=tz)
dti = pd.DatetimeIndex(values, dtype=dtype)
ser = pd.Series(values, dtype=dtype)
if_wall = pd.DatetimeIndex(values).tz_localize(tz)
if_utc = pd.DatetimeIndex(values).tz_localize("UTC").tz_convert(tz)
assert ts == if_utc[-1]
assert (dti == if_wall).all()
assert (ser == if_utc).all()
dti_naive = pd.DatetimeIndex(values)
ser_naive = pd.Series(values)
res_dti = dti_naive.astype(dtype)
res_ser = ser.astype(dtype)
assert (res_dti == dti).all()
assert (res_ser == ser).all()
# astype from tzaware -> tznaive
assert (dti.astype(dti_naive.dtype) == dti.tz_convert("UTC").tz_localize(None)).all()
assert (ser_naive.astype(ser.dtype) == ser).all()
```
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BUG: Index and MultiIndex ops treating nan differently
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[] | null | 1
| 2020-12-21T19:56:31Z
| 2021-06-17T03:04:45Z
| null |
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.
---
**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
``Index.union`` treats ``nan`` differently than ``MultiIndex.union``. Same goes for intersection.
```python
idx1 = Index([1, np.nan])
idx2 = Index([1, np.nan, 3])
idx1.union(idx2)
idx1.intersection(idx2)
mi1 = MultiIndex.from_arrays([[1, np.nan]])
mi2 = MultiIndex.from_arrays([[1, np.nan, 3]])
mi1.union(mi2)
mi1.intersection(mi2)
```
Output:
```
Float64Index([1.0, 3.0, nan], dtype='float64')
Float64Index([1.0, nan], dtype='float64')
MultiIndex([(1.0,),
(nan,),
(nan,),
(3.0,)],
)
MultiIndex([(1.0,)],
)
```
#### Problem description
Inconsistent behavior is a problem. Additionally if both MultiIndexes are *equal* nan is only returned once. When not matching nan is the desired output, we have to adjust for theses cases before checking for equality.
#### Expected Output
I am not quite sure what to expect, but taking the current behavior of merges into account, I think it would be more reasonable to return
```
MultiIndex([(1.0,),
(nan,),
(3.0,)],
)
MultiIndex([(1.0,),
(nan,)],
)
```
for the MultiIndex operations. Thoughts @jbrockmendel ?
related to #38439
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c6b05c3ad771da775011647f66687a398e11c533
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-58-generic
Version : #64-Ubuntu SMP Wed Dec 9 08:16:25 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0.dev0+129.gc6b05c3ad7
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20201009
Cython : 0.29.21
pytest : 6.1.1
hypothesis : 5.37.1
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : 0.4.1
gcsfs : 0.7.1
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.2
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
None
Process finished with exit code 0
</details>
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Backport PR #38613 on branch 1.2.x (CI: move py38 slow to azure #38429)
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NONE
| null |
Backport PR #38613: CI: move py38 slow to azure #38429
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TYP: pd.eval, DataFrame.(eval, query)
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
xref https://github.com/pandas-dev/pandas/pull/38416#discussion_r546736072
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MDExOlB1bGxSZXF1ZXN0NTQzNzAwNTg5
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Bug: assert_produces_warning(None) not raising AssertionError with warning
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- [x] closes #38620
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDU6SXNzdWU3NzI0MzkxODc=
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|
DOC: example for doing a reversed rolling window
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|
CONTRIBUTOR
| null |
https://github.com/pandas-dev/pandas/issues/34226#issuecomment-690887774 is the example, should just add to window.rst
also would be good to show using https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.api.indexers.FixedForwardWindowIndexer.html?highlight=forward#pandas.api.indexers.FixedForwardWindowIndexer
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MDExOlB1bGxSZXF1ZXN0NTQzNzk2NDc5
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TST/REF: Delete duplicate hist tests
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| 2020-12-22T00:24:13Z
| 2020-12-22T01:21:35Z
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|
MEMBER
| null |
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
The tests deleted in this pr are also found starting here: https://github.com/pandas-dev/pandas/blob/e4f425dddda6b299fe5f5e05f18f128302f8604c/pandas/tests/plotting/test_hist_method.py#L26-L27
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MDExOlB1bGxSZXF1ZXN0NTQzODIwNzgw
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REF: collected dtypes.cast and construction simplifications
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| 1
| 2020-12-22T02:03:44Z
| 2020-12-22T15:31:17Z
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MEMBER
| null |
Also catch a warning in an io test that has been bugging me.
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MDU6SXNzdWU3NzI1ODcwOTE=
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TST/BUG: Unexpected warnings causing failures on master with fixed `assert_produces_warnings(None)` behavior
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[] | null | 4
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| 2021-06-11T18:30:09Z
| null |
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/38626#issuecomment-749295555
Making sure `assert_produces_warning(None)` raises if a warning is emitted gives the following failures in master due to unexpected warnings:
```
FAILED pandas/tests/arithmetic/test_timedelta64.py::TestTimedelta64ArithmeticUnsorted::test_tda_add_dt64_object_array[DataFrame-'UTC']
FAILED pandas/tests/arithmetic/test_timedelta64.py::TestTimedelta64ArithmeticUnsorted::test_tda_add_dt64_object_array[DataFrame-'US/Eastern']
FAILED pandas/tests/arithmetic/test_timedelta64.py::TestTimedelta64ArithmeticUnsorted::test_tda_add_dt64_object_array[DataFrame-'Asia/Tokyo']
FAILED pandas/tests/arithmetic/test_timedelta64.py::TestTimedelta64ArithmeticUnsorted::test_tda_add_dt64_object_array[DataFrame-'dateutil/US/Pacific']
FAILED pandas/tests/arithmetic/test_timedelta64.py::TestTimedelta64ArithmeticUnsorted::test_tda_add_dt64_object_array[DataFrame-'dateutil/Asia/Singapore']
FAILED pandas/tests/arithmetic/test_timedelta64.py::TestTimedelta64ArithmeticUnsorted::test_tda_add_dt64_object_array[DataFrame-'+01:15']
FAILED pandas/tests/arithmetic/test_timedelta64.py::TestTimedelta64ArithmeticUnsorted::test_tda_add_dt64_object_array[DataFrame-'-02:15']
FAILED pandas/tests/arithmetic/test_timedelta64.py::TestTimedelta64ArithmeticUnsorted::test_tda_add_dt64_object_array[DataFrame-'UTC+01:15']
FAILED pandas/tests/arithmetic/test_timedelta64.py::TestTimedelta64ArithmeticUnsorted::test_tda_add_dt64_object_array[DataFrame-'UTC-02:15']
FAILED pandas/tests/arithmetic/test_timedelta64.py::TestTimedelta64ArithmeticUnsorted::test_tda_add_dt64_object_array[DataFrame-tzutc()]
FAILED pandas/tests/arithmetic/test_timedelta64.py::TestTimedelta64ArithmeticUnsorted::test_tda_add_dt64_object_array[DataFrame-tzlocal()]
FAILED pandas/tests/arithmetic/test_timedelta64.py::TestTimedelta64ArithmeticUnsorted::test_tda_add_dt64_object_array[DataFrame-pytz.FixedOffset(300)]
FAILED pandas/tests/arithmetic/test_timedelta64.py::TestTimedelta64ArithmeticUnsorted::test_tda_add_dt64_object_array[DataFrame-<UTC>]
FAILED pandas/tests/arithmetic/test_timedelta64.py::TestTimedelta64ArithmeticUnsorted::test_tda_add_dt64_object_array[DataFrame-pytz.FixedOffset(-300)]
FAILED pandas/tests/arithmetic/test_timedelta64.py::TestTimedelta64ArithmeticUnsorted::test_tda_add_dt64_object_array[DataFrame-datetime.timezone.utc]
FAILED pandas/tests/arithmetic/test_timedelta64.py::TestTimedelta64ArithmeticUnsorted::test_tda_add_dt64_object_array[DataFrame-datetime.timezone(datetime.timedelta(seconds=3600))]
FAILED pandas/tests/arithmetic/test_timedelta64.py::TestTimedelta64ArithmeticUnsorted::test_tda_add_dt64_object_array[DataFrame-datetime.timezone(datetime.timedelta(days=-1, seconds=82800), 'foo')]
FAILED pandas/tests/frame/test_arithmetic.py::TestFrameArithmetic::test_binop_other[add-True-bool]
FAILED pandas/tests/frame/test_arithmetic.py::TestFrameArithmetic::test_binop_other[mul-True-bool]
FAILED pandas/tests/indexes/test_common.py::TestCommon::test_asi8_deprecation[int]
FAILED pandas/tests/indexes/test_common.py::TestCommon::test_asi8_deprecation[uint]
FAILED pandas/tests/indexes/test_common.py::TestCommon::test_asi8_deprecation[range]
FAILED pandas/tests/indexes/test_common.py::TestCommon::test_asi8_deprecation[repeats]
```
Tests which have been xfailed in #38626 are:
- [x] pandas/tests/arithmetic/test_timedelta64.py::TestTimedelta64ArithmeticUnsorted::test_tda_add_dt64_object_array
- [ ] pandas/tests/frame/test_arithmetic.py::TestFrameArithmetic::test_binop_other
- [ ] pandas/tests/indexes/test_common.py::TestCommon::test_asi8_deprecation
- [ ] pandas/tests/io/parser/test_common.py::test_chunks_have_consistent_numerical_type
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DOC: capitalize NumPy as proper noun
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| 5
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CONTRIBUTOR
| null |
In a similar vein to https://github.com/pandas-dev/pandas/pull/37808
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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Backport PR #37534: BUG: error raise when column contains percentage
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Backport PR #37905 on branch 1.2.x (BUG: groupby resample different results with .agg() vs .mean() )
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Backport PR #37905: BUG: groupby resample different results with .agg() vs .mean()
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MDExOlB1bGxSZXF1ZXN0NTQ0MjAyMzY1
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TYP: Extend typing to allow passing Index to_datetime
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| 2021-04-14T07:14:01Z
| 2020-12-23T15:52:41Z
|
CONTRIBUTOR
| null |
ArrayConvertible should be able to accept Index or Series type as well
This extends it to use AnyArrayLike instead of ArrayLike.
- [ x] passes `black pandas`
- [ x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
|
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MDU6SXNzdWU3NzMwNzY2MTA=
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ValueError: Only callable can be used as callback
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| 2020-12-22T16:27:13Z
| 2021-05-26T15:54:53Z
| 2021-01-27T12:08:11Z
|
NONE
| null |
- [ ] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, 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 question.
```python
# Your code here, if applicable
#Importing Libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
#Importing Dataset
dataset = pd.read_csv("dataset.csv")
```
When I'm running the last line which imports m dataset it gives me error and I do not know how to resolve that.
ERROR:
dataset = pd.read_csv("C:/Users/saraj/OneDrive/Desktop/AI/dataset.csv")
Traceback (most recent call last):
File "<ipython-input-33-9a3340a25db3>", line 1, in <module>
dataset = pd.read_csv("C:/Users/saraj/OneDrive/Desktop/AI/dataset.csv")
File "C:\Users\saraj\anaconda3\envs\AI project\lib\site-packages\pandas\io\parsers.py", line 688, in read_csv
return _read(filepath_or_buffer, kwds)
File "C:\Users\saraj\anaconda3\envs\AI project\lib\site-packages\pandas\io\parsers.py", line 460, in _read
data = parser.read(nrows)
File "C:\Users\saraj\anaconda3\envs\AI project\lib\site-packages\pandas\io\parsers.py", line 1213, in read
df = DataFrame(col_dict, columns=columns, index=index)
File "C:\Users\saraj\anaconda3\envs\AI project\lib\site-packages\pandas\core\frame.py", line 468, in __init__
mgr = init_dict(data, index, columns, dtype=dtype)
File "C:\Users\saraj\anaconda3\envs\AI project\lib\site-packages\pandas\core\internals\construction.py", line 259, in init_dict
if missing.any() and not is_integer_dtype(dtype):
File "C:\Users\saraj\anaconda3\envs\AI project\lib\site-packages\pandas\core\generic.py", line 11580, in logical_func
return self._reduce(
File "C:\Users\saraj\anaconda3\envs\AI project\lib\site-packages\pandas\core\series.py", line 4248, in _reduce
with np.errstate(all="ignore"):
File "C:\Users\saraj\anaconda3\envs\AI project\lib\site-packages\numpy\core\_ufunc_config.py", line 436, in __enter__
self.oldcall = seterrcall(self.call)
File "C:\Users\saraj\anaconda3\envs\AI project\lib\site-packages\numpy\core\_ufunc_config.py", line 308, in seterrcall
raise ValueError("Only callable can be used as callback")
ValueError: Only callable can be used as callback
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MDExOlB1bGxSZXF1ZXN0NTQ0MjY0Mzk3
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CI: clean azure pipeline
|
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| 2021-07-17T02:18:38Z
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MEMBER
| null |
We save about 2-3 min build time by using Azure's conda.
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| 2020-12-22T21:04:09Z
| 2020-12-22T21:04:05Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Simplified logic in ``assert_produces_warning`` by extracting functions ``_assert_caught_expected_warning``, ``_assert_caught_no_extra_warnings`` and ``_is_unexpected_warning``.
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| 2
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| 2020-12-22T20:02:03Z
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MEMBER
| null |
- [x] closes #38616
- [x] tests added / passed
- [x] passes `black pandas`
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Since this is a regression on master I think a whatsnew is not necessary?
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Backport PR #38638 on branch 1.2.x (BUG: Fix regression in pandas merge)
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| 2020-12-22T19:22:50Z
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NONE
| null |
Backport PR #38638: BUG: Fix regression in pandas merge
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NONE
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Backport PR #38533: BUG&TST: HTML formatting error in Styler.render() in rowspan attribute
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| 2021-01-04T17:50:11Z
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MEMBER
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- [x] closes #15969
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- [x] whatsnew entry
Also in-lining methods that were only used once and moving a BaseIndexer test that was using `win_type`
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| 12
| 2020-12-22T21:32:49Z
| 2020-12-29T23:16:30Z
| 2020-12-29T23:16:30Z
|
MEMBER
| null |
Does anybody know if this was an intentional change? (I don't directly find something about it in the whatsnew)
```
In [9]: pd.__version__
Out[9]: '1.0.5'
In [10]: df = pd.DataFrame({"key": pd.Categorical(["b"]*5, categories=["a", "b", "c", "d"]), "col": range(5)})
In [11]: gb = df.groupby("key")
In [12]: list(gb.indices)
Out[12]: ['a', 'b', 'c', 'd']
```
vs
```
In [1]: pd.__version__
Out[1]: '1.3.0.dev0+92.ga2d10ba88a'
In [2]: df = pd.DataFrame({"key": pd.Categorical(["b"]*5, categories=["a", "b", "c", "d"]), "col": range(5)})
In [3]: gb = df.groupby("key")
In [4]: list(gb.indices)
Out[4]: ['b']
```
This already changed in pandas 1.1, so not a recent change.
The consequence of this is that iterating over `gb` vs iterating over `gb.indices` is not consistent anymore.
cc @mroeschke @rhshadrach
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REF: use _should_compare to short-circuit set operations
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BUG: Roundtrip with openpyxl and datetime precision
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| 3
| 2020-12-22T22:16:06Z
| 2021-08-14T22:30:50Z
| 2021-08-14T22:30:43Z
|
MEMBER
| null |
The test `tests.io.excel.test_writers.test_datetimes` fails with openpyxl (now the default, xref #35029):
```
path = "test.xlsx"
datetimes = [
datetime(2013, 1, 13, 1, 2, 3),
datetime(2013, 1, 13, 2, 45, 56),
datetime(2013, 1, 13, 4, 29, 49), # <---
datetime(2013, 1, 13, 6, 13, 42),
datetime(2013, 1, 13, 7, 57, 35), # <---
datetime(2013, 1, 13, 9, 41, 28),
datetime(2013, 1, 13, 11, 25, 21),
datetime(2013, 1, 13, 13, 9, 14),
datetime(2013, 1, 13, 14, 53, 7),
datetime(2013, 1, 13, 16, 37, 0),
datetime(2013, 1, 13, 18, 20, 52),
]
write_frame = DataFrame({"A": datetimes})
write_frame.to_excel(path, "Sheet1")
read_frame = pd.read_excel(path, sheet_name="Sheet1", header=0, engine="openpyxl")
tm.assert_series_equal(write_frame["A"], read_frame["A"])
```
The highlighted lines give
2013-01-13 04:29:48.999999
2013-01-13 07:57:35.000001
With these two lines removed, the results are of the form e.g. `2013-01-13 11:25:21` and the test passes.
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|
REGR: passing dask arrays to Series or DataFrame
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| 23
| 2020-12-22T22:40:34Z
| 2021-07-20T12:17:05Z
| 2021-07-20T12:17:05Z
|
NONE
| null |
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import dask.array as da
a = da.ones((12,), chunks=4)
s = pd.Series(a, index=range(12))
print(s.dtype)
```
#### Problem description
This has been detected by `xarray`'s upstream-dev CI ([environment](https://github.com/pydata/xarray/runs/1592176067?check_suite_focus=true#step:5:1177)): with `1.1.3`, the `dtype` is `float64` while on `master` (installed from `scipy-wheels-nightly`) this became `object` (and the series / dataframe contains `dask` scalars). Was that change intentional? Poking around on the merged PR list, this might have been #38563 (not sure, though).
To be clear, for us this only affects test code and since it would compute anyways we can easily work around this by computing the `dask` array before passing it to `pd.Series` or `pd.DataFrame`.
See also pydata/xarray#4717.
cc @TomAugspurger
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TST/BUG: _gen_two_subplots always adding subplot even with passed axis
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| 2020-12-23T14:49:02Z
| 2020-12-23T14:34:06Z
|
MEMBER
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
`kwargs.get("ax", fig.add_subplot(211))` was always adding a subplot (since the argument to `.get()` will be evaluated even if it doesn't need to be used). Assuming the intended behavior was for `fig.add_subplot(211)` to be called only if `ax` is not passed. This change also gets rid of half of remaining `matplotlib` warnings from
`pandas/tests/plotting/test_hist_method.py::TestSeriesPlots::test_hist_legacy`
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BUG: `DataFrame.corrwith` seems to be broken
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| 1
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| 2020-12-23T13:50:59Z
| 2020-12-23T13:50:59Z
|
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.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
>>> import numpy as np; import pandas as pd
>>> x = pd.DataFrame(np.random.randn(100, 3), columns=['A', 'B', 'C'])
>>> y = pd.DataFrame(np.random.randn(100, 3), columns=['X', 'Y', 'Z'])
>>> x.corrwith(y)
A NaN
B NaN
C NaN
D NaN
E NaN
F NaN
dtype: float64
```
#### Problem description
I believe that this is a bug, and that if it is intended behavior, it should not be so.
#### Expected Output
Expected output is the cross-correlations between `self` and `other`:
```
>>> x.corrwith(y)
A B C
X -0.144123 -0.063604 0.008358
Y -0.097303 -0.032101 -0.127265
Z 0.032475 0.037129 0.082559
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.8.6.final.0
python-bits : 64
OS : Darwin
OS-release : 20.2.0
Version : Darwin Kernel Version 20.2.0: Wed Dec 2 20:39:59 PST 2020; root:xnu-7195.60.75~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.0.0
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.52.0
</details>
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REF: standardize get_indexer/get_indexer_non_unique code
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BUG: Fix regression for groupby.indices in case of unused categories
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| 12
| 2020-12-22T23:58:34Z
| 2020-12-30T09:10:38Z
| 2020-12-29T23:16:30Z
|
MEMBER
| null |
- [x] closes #38642
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
To show the unused we categories, we can dispatch back as we did before #36842. Since Categorical does not support nan, the reason for the switch does not exist in this case. I think handling unused categories in ``get_indexer_dict`` is not desirable, because it would introduce a lot of convolution for one corner case we could handle here pretty easily.
Since we are only using ``indices`` in grouper only for categoricals, we do not have to worry about sorting as the test shows.
cc @jorisvandenbossche @mroeschke
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ENH: improve partial key indexing performance
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| 2
| 2020-12-23T01:03:41Z
| 2021-03-07T19:52:17Z
| null |
CONTRIBUTOR
| null |
Indexing for entries in a series or dataframe with a multi-index has dramatically worse performance when using partial keys.
For example, for a series with a 3-level multi-index, `series[x]` is dramatically slower than `series[(x,y,z)]`. Please see the session below for a minimal reproduction:
```In [1]: %paste
from datetime import datetime
import pandas as pd
import numpy as np
import timeit
dates = pd.date_range('1997-01-01', '2020-12-31')
cats = list('abcdefghijklmnop')
multi = pd.MultiIndex.from_product([dates, cats, cats])
series = pd.Series(np.random.rand(len(multi)), index=multi)
date = datetime(year=2020, month=1, day=1)
# generate cache
series.loc[(date, 'a', 'a')]
repeats = 1000
len(series)
## -- End pasted text --
Out[1]: 2244096
In [2]: # Performance of indexing with full keys
In [3]: timeit.timeit(lambda: series.loc[(date, 'a', 'a')], number=repeats)
Out[3]: 0.08493588299825205
In [4]: # Performance of indexing with partial keys
In [5]: timeit.timeit(lambda: series.loc[date], number=repeats)
Out[5]: 4.771169905001443
```
From looking at the code, it seems that `BaseMultiIndexCodesEngine` (pandas/_libs/index.pyx) essentially creates a hash table (a `libindex.UInt64Engine`) with entries for each full key. This makes full-key lookups fast. Meanwhile, for partial key lookups on a sorted index, `MultiIndex._get_level_indexer` will do two binary searches for each provided level of the key.
Instead of a flattened-index approach (again, via `libindex.UInt64Engine`), it would be *excellent* if Pandas implemented nested indeces (nested hash tables, if you will) for this. This would have a small impact on the performance of full key lookups (current lookup complexity is O(1), new complexity would be O(number_of_levels)). However, partial key lookups would greatly benefit, going from O(log(size_of_series)) to O(number_of_levels_in_key).
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MDExOlB1bGxSZXF1ZXN0NTQ0NDg0MzQ5
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REF: use DatetimeArray._validate_setitem_value for DatetimeBlock._can_hold_element
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| 2020-12-23T17:02:34Z
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MEMBER
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DOC: example for doing a reversed rolling window
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NONE
| null |
Trying to fix #38627
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BUG: MultiIndex, IntervalIndex intersection with Categorical
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MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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[BUG] Concat duplicates errors (or lack there of)
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| 2021-01-12T23:04:12Z
| 2020-12-24T15:08:03Z
|
CONTRIBUTOR
| null |
- [ ] closes #6963
- [ ] xref #36263, #36290
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
@jreback
So here is a basic proposal for making `concat` throw a more useful error when there are duplicate values on the index not being concatenated along. Below are some examples of how this error message is improved. I'm including a comparison to 1.2.0rc1, since it includes a pr which was supposed to change some of these errors to working cases, but those cases are extremely limited. That's why the first commit of this PR reverts that change.
I haven't gone too far with this since it a) reverts a merged PR and b) affects the release candidate, so should be conservative. I would like to get feedback on whether I should proceed with this approach.
My thinking right now is that a better error and revert should go into 1.2 – no behaviour change from 1.1, just better errors. A question of whether `pd.concat(..., join="inner")` means an intersection of indices (the current [and documented](https://pandas.pydata.org/docs/user_guide/merging.html#concatenating-objects) behaviour) or an actual inner join seems like something that could benefit from more discussion.
If they are set operations, it should probably error on duplicates. If they are relational algebra operations, they should work with duplicates. It would probably also be useful to then add more arguments to `concat`, like `merge`s `validate`.
# One dataframe has repeated column names (str)
```python
pd.concat([
pd.DataFrame(np.ones((4, 4)), columns=list("aabc")),
pd.DataFrame(np.ones((4, 3)), columns=list("abc")),
])
```
This PR
```pytb
InvalidIndexError: Reindexing only valid with uniquely valued Index objects
```
Pandas v1.1.5
```pytb
ValueError: Plan shapes are not aligned
```
pandas 1.2.0rc1
```pytb
ValueError: Plan shapes are not aligned
```
-------------------------
# One dataframe has repeated column names (int)
```python
pd.concat([ # One dataframe has repeated column names
pd.DataFrame(np.ones((4, 4)), columns=[1, 1, 2, 3]),
pd.DataFrame(np.ones((4, 3)), columns=[1, 2, 3]),
)
```
This PR
```pytb
InvalidIndexError: Reindexing only valid with uniquely valued Index objects
```
Pandas v1.1.5
```pytb
ValueError: Plan shapes are not aligned
```
pandas 1.2.0rc1
```pytb
ValueError: Plan shapes are not aligned
```
--------------------------
# Repeated columns (same amount) different column ordering
```python
pd.concat([
pd.DataFrame(np.ones((2, 4)), columns=list("aabc")),
pd.DataFrame(np.ones((2, 4)), columns=list("abca")),
])
```
This PR:
```pytb
InvalidIndexError: Reindexing only valid with uniquely valued Index objects
```
In pandas v1.15
```pytb
AssertionError: Number of manager items must equal union of block items
# manager items: 3, # tot_items: 4
```
In pandas v1.2.0rc1
```pytb
AssertionError: Number of manager items must equal union of block items
# manager items: 3, # tot_items: 4
```
----------------------------
# Repeated columns of different values in each
This point is what the reverted PR was trying to fix. This demonstrates that it didn't seem to fix what it set out to, and that the intended fix introduced strange behaviour anyways. To do this, I'll use the reverted test case.
```python
# Test case from previous PR
a_int = pd.DataFrame([1, 2, 3, 4], index=[0, 1, 1, 4], columns=["a"])
b_int = pd.DataFrame([6, 7, 8, 9], index=[0, 0, 1, 3], columns=["b"])
# Same, but with string indexes
a_str = a_int.set_index(letters[a_int.index])
b_str = b_int.set_index(letters[b_int.index])
```
## Int index
```python
pd.concat([a_int, b_int], axis=1)
```
This PR
```pytb
InvalidIndexError: Reindexing only valid with uniquely valued Index objects
```
Pandas v1.1.5
```pytb
ValueError: Shape of passed values is (8, 2), indices imply (6, 2)
```
pandas 1.2.0rc1
```pytb
a b
0 1.0 6.0
0 1.0 7.0
1 2.0 8.0
1 3.0 8.0
3 NaN 9.0
4 4.0 NaN
```
<details>
<summary> This behaviour is a bit strange </summary>
First, the `concat` documentation [defines the options here as set operations, like intersect and union](https://pandas.pydata.org/docs/user_guide/merging.html#concatenating-objects). Is this a union, or is this an outer join?
If these are joins, then inner joins only seem to work when they are equivalent to intersections:
```python
>>> pd.concat([a_int, b_int], axis=1, join="inner")
...
ValueError: Shape of passed values is (3, 2), indices imply (2, 2)
>>> pd.merge(a_int, b_int, how="inner", left_index=True, right_index=True)
a b
0 1 6
0 1 7
1 2 8
1 3 8
```
</details>
## str index
This is mostly just to show the behaviour in 1.2.0rc1 is different for string and int indices
```python
pd.concat([a_str, b_str], axis=1)
```
This PR
```pytb
InvalidIndexError: Reindexing only valid with uniquely valued Index objects
```
Pandas v1.1.5
```pytb
ValueError: Shape of passed values is (5, 2), indices imply (4, 2)
```
pandas 1.2.0rc1
```pytb
ValueError: Shape of passed values is (5, 2), indices imply (4, 2)
```
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BUG: multiple index dataframe with index names groupy rolling computing reture wrong index
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| 2020-12-23T14:06:46Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
import pandas as pd
arrays = [np.array(['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux']), np.array(['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two'])]
df = pd.DataFrame(np.random.randn(8, 4), index=arrays)
df.groupby(level=0).rolling(window=2).mean()
```
the result:
```
0 1 2 3
bar bar one NaN NaN NaN NaN
two -0.361421 -0.696686 -0.746137 -0.053621
baz baz one NaN NaN NaN NaN
two 1.014213 0.575803 0.199990 -0.596995
foo foo one NaN NaN NaN NaN
two -0.337821 0.921825 0.256034 -0.572588
qux qux one NaN NaN NaN NaN
two -0.404824 -1.209763 0.581025 -0.741480
```
```
#now set the name to the index
df.index.names=['a','b']
df.groupby(level=0).rolling(window=2).mean()
```
the result:
```
0 1 2 3
a
bar NaN NaN NaN NaN
bar -0.361421 -0.696686 -0.746137 -0.053621
baz NaN NaN NaN NaN
baz 1.014213 0.575803 0.199990 -0.596995
foo NaN NaN NaN NaN
foo -0.337821 0.921825 0.256034 -0.572588
qux NaN NaN NaN NaN
qux -0.404824 -1.209763 0.581025 -0.741480
```
#### Problem description
multiple index dataframe with index names groupy rolling computing reture wrong index
#### Expected Output
the return result should keep the original index
#### Output of ``pd.show_versions()``
<details>
In [36]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : zh_CN.UTF-8
LOCALE : zh_CN.UTF-8
pandas : 1.1.5
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.2
Cython : None
pytest : 6.2.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.2.14
tables : None
tabulate : None
xarray : None
xlrd : 1.1.0
xlwt : None
numba : None
</details>
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why not check the column order and type consistent before use ‘appand’ mode in to_csv or equivalent ops such as to_sql?
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NONE
| null |
as the title say, when i append a column permutation of a exists dataframe to a exists(saved)
csv file, the column permutation df not reordered by the exist header in csv file.
one reason i can understand is that not all csv file have header in the first row, so
this design seems reasonable, but for many cases, if the source code reordered for
our append op which have header or table column, this may be more convenient.
same problem to type.
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DOC/CI: fix class alias docstrings for sphinx 3.4 + unpin sphinx
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| 5
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| 2020-12-24T09:07:29Z
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MEMBER
| null |
Closes #38600
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Backport PR #38293 on branch 1.2.x (BUG: read_json does not respect chunksize)
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NONE
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TST: fix rest of mpl warnings
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| 1
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MEMBER
| null |
- [x] closes #18306
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Backport PR #38456 on branch 1.2.x (DOC: update wording about when xlrd engine can be used)
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NONE
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Backport PR #38456: DOC: update wording about when xlrd engine can be used
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CLN: numba.prange usage
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MEMBER
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xref https://github.com/pandas-dev/pandas/pull/38417#discussion_r547925255
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MDExOlB1bGxSZXF1ZXN0NTQ0OTg3NTUz
| 38,662
|
REF: collect dt64<->dt64tz astype in dtypes.cast
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| 0
| 2020-12-23T19:07:44Z
| 2020-12-23T23:49:52Z
| 2020-12-23T23:30:33Z
|
MEMBER
| null |
From here its straightforward to put the rest of Block._astype directly in astype_nansafe
|
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Develop pre check
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| 1
| 2020-12-23T19:20:30Z
| 2020-12-28T03:49:56Z
| 2020-12-27T12:19:34Z
|
CONTRIBUTOR
| null |
- [ x] closes #37957
- [ ] tests added / passed
- [ x passes `black pandas`
- [ x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0NTQ0OTk3Mzkw
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CLN: Rolling helper attributes
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| 1
| 2020-12-23T19:36:27Z
| 2021-01-04T16:32:07Z
| 2020-12-23T21:40:20Z
|
MEMBER
| null |
- [x] tests added / passed
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- [ ] whatsnew entry
`is_datetimelike` was just an internal check and can be in-lined
`_on` can just be calculated once in the init
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MDExOlB1bGxSZXF1ZXN0NTQ1MDEyNTMx
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REF: Index.__new__
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| 0
| 2020-12-23T20:20:08Z
| 2020-12-29T18:25:51Z
| 2020-12-29T18:21:55Z
|
MEMBER
| null |
Aiming towards sharing more of this with Series and pd.array
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MDExOlB1bGxSZXF1ZXN0NTQ1MDEyODE1
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CLN: Rolling _prep_values
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BUG: read_excel does not use pyxlsb for xlsb files when engine is None
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| 2020-12-27T23:54:54Z
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|
MEMBER
| null |
From `pandas.tests.io.excel.test_excel_read_binary_via_read_excel` (currently only in #38571):
```
if read_ext == ".xlsb" and engine == "pyxlsb":
with pytest.raises(IOError, match="File contains no valid workbook part"):
with open("test1" + read_ext, "rb") as f:
pd.read_excel(f)
```
This should instead determine that pyxlsb should be used when installed and no error should be raised.
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BUG: `scatter_matrix` raising with 2d axis passed
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MEMBER
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Backport PR #38657 on branch 1.2.x (DOC/CI: fix class alias docstrings for sphinx 3.4 + unpin sphinx)
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| 2020-12-23T22:37:19Z
| 2020-12-24T01:11:25Z
| 2020-12-24T01:11:24Z
|
NONE
| null |
Backport PR #38657: DOC/CI: fix class alias docstrings for sphinx 3.4 + unpin sphinx
|
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Backport PR #38571 on branch 1.2.x (DEPR: Adjust read excel behavior for xlrd >= 2.0)
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NONE
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Backport PR #38571: DEPR: Adjust read excel behavior for xlrd >= 2.0
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REF: share Index.union
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MEMBER
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MDU6SXNzdWU3NzQxMzgzMDg=
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BUG: SeriesGroupBy.value_counts is inconsistent with Series.value_counts when the Series is categorical
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| 1
| 2020-12-24T02:27:43Z
| 2020-12-31T16:41:17Z
| 2020-12-31T16:41:17Z
|
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.
- [ ] (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()
df["col1"] = ["A", "A", "B", "B"]
df["col2"] = pd.Categorical(["1", "2", "1", "1"], categories=["1", "2", "3"])
print(df.groupby("col1").col2.value_counts())
```
#### Problem description
A *value_counts* applied to the col2 Series:
```python
df.col2.value_counts()
```
yields:
```
1 3
2 1
3 0
Name: col2, dtype: int64
```
Because col2 is categorical we get the 0 count for value 3, as desired.
If we groupby col1 first
```python
df.groupby("col1").col2.value_counts()
```
we get the following output:
```
col1 col2
A 1 1
2 1
B 1 2
Name: col2, dtype: int64
```
#### Expected Output
The expected output can be achieved with a workaround:
```python
df.groupby("col1").col2.apply(pd.Series.value_counts)
```
which yields:
```
col1
A 2 1
1 1
3 0
B 1 2
3 0
2 0
Name: col2, dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.4
numpy : 1.19.1
pytz : 2019.3
dateutil : 2.8.0
pip : 20.2.3
setuptools : 41.2.0
Cython : 0.29.21
pytest : 5.3.5
hypothesis : None
sphinx : 2.4.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fsspec : 0.6.0
fastparquet : None
gcsfs : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.10
tables : None
tabulate : 0.8.6
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
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MDExOlB1bGxSZXF1ZXN0NTQ1MTEzNzM5
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TST: arithmetic test parametrization/cleanup
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REF: dispatch TDBlock._can_hold_element to TimedeltaArray._validate_setitem_value
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MDU6SXNzdWU3NzQxNTAwNTc=
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BUG: dataframe.pivot()
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| 2020-12-24T07:13:12Z
| 2020-12-24T07:13:12Z
|
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.
---
**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
# official doc, https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot.html
# first code with a bug
df = pd.DataFrame({
"lev1": [1, 1, 1, 2, 2, 2],
"lev2": [1, 1, 2, 1, 1, 2],
"lev3": [1, 2, 1, 2, 1, 2],
"lev4": [1, 2, 3, 4, 5, 6],
"values": [0, 1, 2, 3, 4, 5]})
df.pivot(index=["lev1", "lev2"], columns=["lev3"],values="values")
# ValueError: Length of passed values is 6, index implies 2.
# second code with a bug
>>> month_stats_melt.columns
[Out] Index(['stockcode', 'ym', 'variable', 'value'], dtype='object')
>>> month_stats_melt.head()
[Out]
stockcode ym variable value
0 70001 2020/01 月初占比 0.078306
1 70001 2020/02 月初占比 0.082309
2 70001 2020/03 月初占比 0.083712
3 70001 2020/04 月初占比 0.086247
4 70001 2020/05 月初占比 0.087852
>>> month_stats_melt.pivot(index='stockcode',columns=['ym', 'variable'],values='value')
# KeyError: 'Level ym not found'
```
#### Problem description
# first bug
ValueError Traceback (most recent call last)
<ipython-input-86-9830d2bb712c> in <module>
5 "lev4": [1, 2, 3, 4, 5, 6],
6 "values": [0, 1, 2, 3, 4, 5]})
----> 7 df.pivot(index=["lev1", "lev2"], columns=["lev3"],values="values")
~\anaconda3\lib\site-packages\pandas\core\frame.py in pivot(self, index, columns, values)
5921 from pandas.core.reshape.pivot import pivot
5922
-> 5923 return pivot(self, index=index, columns=columns, values=values)
5924
5925 _shared_docs[
~\anaconda3\lib\site-packages\pandas\core\reshape\pivot.py in pivot(data, index, columns, values)
447 )
448 else:
--> 449 indexed = data._constructor_sliced(data[values].values, index=index)
450 return indexed.unstack(columns)
451
~\anaconda3\lib\site-packages\pandas\core\series.py in __init__(self, data, index, dtype, name, copy, fastpath)
289 try:
290 if len(index) != len(data):
--> 291 raise ValueError(
292 f"Length of passed values is {len(data)}, "
293 f"index implies {len(index)}."
ValueError: Length of passed values is 6, index implies 2.
# second bug
ValueError Traceback (most recent call last)
~\anaconda3\lib\site-packages\pandas\core\indexes\multi.py in _get_level_number(self, level)
1294 try:
-> 1295 level = self.names.index(level)
1296 except ValueError:
ValueError: 'ym' is not in list
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
<ipython-input-21-42dbf3cc39b4> in <module>
----> 1 month_stats_pivot = month_stats_melt.pivot(index='stockcode',columns=['ym', 'variable'],values='value')
2 month_stats_pivot
~\anaconda3\lib\site-packages\pandas\core\frame.py in pivot(self, index, columns, values)
5921 from pandas.core.reshape.pivot import pivot
5922
-> 5923 return pivot(self, index=index, columns=columns, values=values)
5924
5925 _shared_docs[
~\anaconda3\lib\site-packages\pandas\core\reshape\pivot.py in pivot(data, index, columns, values)
448 else:
449 indexed = data._constructor_sliced(data[values].values, index=index)
--> 450 return indexed.unstack(columns)
451
452
~\anaconda3\lib\site-packages\pandas\core\series.py in unstack(self, level, fill_value)
3548 from pandas.core.reshape.reshape import unstack
3549
-> 3550 return unstack(self, level, fill_value)
3551
3552 # ----------------------------------------------------------------------
~\anaconda3\lib\site-packages\pandas\core\reshape\reshape.py in unstack(obj, level, fill_value)
396 # _unstack_multiple only handles MultiIndexes,
397 # and isn't needed for a single level
--> 398 return _unstack_multiple(obj, level, fill_value=fill_value)
399 else:
400 level = level[0]
~\anaconda3\lib\site-packages\pandas\core\reshape\reshape.py in _unstack_multiple(data, clocs, fill_value)
318 index = data.index
319
--> 320 clocs = [index._get_level_number(i) for i in clocs]
321
322 rlocs = [i for i in range(index.nlevels) if i not in clocs]
~\anaconda3\lib\site-packages\pandas\core\reshape\reshape.py in <listcomp>(.0)
318 index = data.index
319
--> 320 clocs = [index._get_level_number(i) for i in clocs]
321
322 rlocs = [i for i in range(index.nlevels) if i not in clocs]
~\anaconda3\lib\site-packages\pandas\core\indexes\multi.py in _get_level_number(self, level)
1296 except ValueError:
1297 if not is_integer(level):
-> 1298 raise KeyError(f"Level {level} not found")
1299 elif level < 0:
1300 level += self.nlevels
KeyError: 'Level ym not found'
#### Expected Output
# first
df.pivot(index=["lev1", "lev2"], columns=["lev3"],values="values")
lev3 1 2
lev1 lev2
1 1 0.0 1.0
2 2.0 NaN
2 1 4.0 3.0
2 NaN 5.0
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Chinese (Simplified)_China.936
pandas : 1.0.5
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200714
Cython : 0.29.21
pytest : 5.4.3
hypothesis : None
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.2
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.3
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.9
numba : 0.50.1
</details>
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REF: share Index.difference
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CLN: simplify Block.putmask
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MDExOlB1bGxSZXF1ZXN0NTQ1Mjc0MzY3
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DOC: 1.2.0 release date
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MDExOlB1bGxSZXF1ZXN0NTQ1MjkxMDYw
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Revert "Deprecated casting an object-dtype index of datetime objects to DatetimeIndex in the Series constructor (23598)"
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CLN: Add typing for dtype argument in io/sql.py
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| 8
| 2020-12-24T14:55:29Z
| 2020-12-29T14:56:38Z
| 2020-12-29T14:08:23Z
|
CONTRIBUTOR
| null |
Follow up PR for #37546:
- Added typing `Optional[DtypeArg]` for dtype arg in pandas/io, since those functions accept single values and dicts as `dtype` args
- Added typing `Optional[Dtype]` for dtype arg in pandas/core, since those functions only accept a single value as `dtype` args
- Added typing `Optional[NpDtype]` for dtype arg in pandas/core for functions that only accept numpy dtypes as `dtype` args
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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BUG: DataFrame.rank with np.inf and np.nan
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| 4
| 2020-12-24T15:03:41Z
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MEMBER
| null |
- [x] closes #32593
- [x] tests added / passed
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- [x] whatsnew entry
Seemed like two distinct issues here - first `np.inf` wasn't distinguished from `np.nan` (https://github.com/pandas-dev/pandas/issues/32593#issuecomment-597376199). The changes to `skip_condition` logic handles that, but doesn't fix issue from OP. Rest of diff is to handle when `np.inf` and `np.nan` are both included.
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Backport PR #38654 on branch 1.2.x ([BUG] Concat duplicates errors (or lack there of))
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| 0
| 2020-12-24T15:08:49Z
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NONE
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Backport PR #38654: [BUG] Concat duplicates errors (or lack there of)
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REF: simplify coerce_to_target_dtype
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Add match=msg to pytest.raises or convert to external_error_raised for modules in tests/plotting
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| 2020-12-24T16:07:43Z
| 2020-12-24T19:10:37Z
| 2020-12-24T19:03:02Z
|
MEMBER
| null |
This pull request xref #30999 to remove bare `pytest.raises`. It doesn't close that issue as I have only addressed files within `pandas/tests/plotting/`.
Things I did that were not simple adding `match = msg` to `pytest.raises` or converting them to `tm.external_error_raised`:
- In `pandas/plotting/_matplotlib/_core.py` I found two error messages somewhat unclear. I changed the error message in the `ValueError` raised by `_get_stacked_values` to be, imo, a little bit clearer. In addition, in the constructor for `PiePlot`, the previous error message for the `ValueError` was "None doesn't allow negative values" which I changed to "pie plot doesn't allow negative values" because I believe that was the original intent.
- In `test_frame.py`, in the `test_errorbar_plot` method, the `pytest.raises` was allowing matplotlib to raise either a `TypeError` or a `ValueError`. I changed it to an `external_error_raised` but also specified it to be a `TypeError` because that is what it is, and I didn't see the reason it was allowed to be either error.
- Also in `test_frame.py`, the test `test_invalid_xy_args` was pytest-parameterized to test two different kinds of errors: x not being a label or position, or the label being bad. I split it into two tests, calling the new test `test_bad_label`
- Also in `test_frame.py`, the test `test_partially_invalid_plot_data` looped over a list with one element in it. I guessed that this was a remnant of an older test strategy that is no longer employed. I removed the list and made the kind of plot being tested more explicit.
- In a couple of test modules I moved a few lines out of the context of the `pytest.raises` because they were setup lines that didn't raise the error: I wanted to make it clear exactly which pandas method was raising the error.
I will note that another developer, [DylanBrdt](https://github.com/DylanBrdt) commented on the issue that they would address these files but they haven't been active on github since February.
- [ ] xref #30999
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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post-merge fixup
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| 2020-12-24T17:57:07Z
| 2020-12-24T19:05:46Z
| 2020-12-24T18:59:22Z
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MEMBER
| null |
#38674 combined with #38677 lead to a NameError this fixes
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CLN: algos.searchsorted
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Split up pandas/tests/io/pytables/test_store.py into smaller modules
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| 2020-12-25T04:56:22Z
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MEMBER
| null |
pandas/tests/io/pytables/test_store.py is more than 5000 lines long, which is pretty long for a test module. It would be good to break it up into smaller, logical pieces, each in its own test module.
Came out of PR 38609.
OT: @moink love to split this file up: pandas/tests/io/pytables/test_store.py in a separate PR :->
_Originally posted by @jreback in https://github.com/pandas-dev/pandas/issues/38609#issuecomment-749256512_
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| 1
| 2020-12-24T21:49:55Z
| 2020-12-28T15:40:13Z
| 2020-12-28T15:27:03Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0NTQ1NDcyMTI1
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|
REF: make pd._testing a directory
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| 2
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|
MEMBER
| null |
We've accumulated some other helpers outside of pd._testing that id like to move there (xref #38688). This is a good time to split the large file.
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ASV: Add benchmark for indexing with .loc for sorted/unsorted DatetimeIndex
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| 4
| 2020-12-25T00:05:41Z
| 2021-10-09T15:17:17Z
| null |
MEMBER
| null |
xref #17754, a benchmark should be added for indexing with .loc for sorted/unsorted `DatetimeIndex`. With a constructed `DatetimeIndex`, for example
```python
dates = pd.date_range('2011-1-1', periods=500000, freq='min')
index = np.random.choice(dates, 500000, replace=True)
df = pd.DataFrame(index=index, data={'a': 1})
df_sorted = df.sort_index()
```
the behavior of interest to benchmark is
```python
df.loc['2011-6-11']
```
and
```python
df_sorted.loc['2011-6-11']
```
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BUG: Pandas pivot_table fails with columns and margin
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| 4
| 2020-12-25T03:30:32Z
| 2020-12-28T20:40:09Z
| null |
NONE
| null |
- [x ] I have checked that this issue has not already been reported.
- [ x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
arrays = [['bar', 'bar', 'foo', 'foo'],
['one', 'two', 'one', 'two'], ['A','A','B','B']]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second', 'third'])
values = np.array([[1,2,3,4],[5,6,7,8]])
df = pd.DataFrame(values.T, index=index, columns = ['0', '1'])
# here comes the pivot_table, this one works, it has 'colums'
df.pivot_table(index = ['first', 'second'], columns = 'third', aggfunc = 'sum')
#this one works, it has 'margins'
df.pivot_table(index = ['first', 'second'], aggfunc = 'sum', margins=True)
#this one fails, it has both 'columns' and 'margins'
df.pivot_table(index = ['first', 'second'], columns = 'third', aggfunc = 'sum', margins=True)
```
#### Problem description
---------------------------------------------------------------------------
```python-traceback
KeyError Traceback (most recent call last)
<ipython-input-5-a9c12a5808db> in <module>
13
14 #this one fails, it has both 'columns' and 'margins'
---> 15 df.pivot_table(index = ['first', 'second'], columns = 'third', aggfunc = 'sum', margins=True)
~\Anaconda3\lib\site-packages\pandas\core\frame.py in pivot_table(self, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed)
6068 from pandas.core.reshape.pivot import pivot_table
6069
-> 6070 return pivot_table(
6071 self,
6072 values=values,
~\Anaconda3\lib\site-packages\pandas\core\reshape\pivot.py in pivot_table(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed)
156 if dropna:
157 data = data[data.notna().all(axis=1)]
--> 158 table = _add_margins(
159 table,
160 data,
~\Anaconda3\lib\site-packages\pandas\core\reshape\pivot.py in _add_margins(table, data, values, rows, cols, aggfunc, observed, margins_name, fill_value)
226
227 elif values:
--> 228 marginal_result_set = _generate_marginal_results(
229 table,
230 data,
~\Anaconda3\lib\site-packages\pandas\core\reshape\pivot.py in _generate_marginal_results(table, data, values, rows, cols, aggfunc, observed, grand_margin, margins_name)
323
324 if len(rows) > 0:
--> 325 margin = data[rows + values].groupby(rows, observed=observed).agg(aggfunc)
326 cat_axis = 1
327
~\Anaconda3\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
2804 if is_iterator(key):
2805 key = list(key)
-> 2806 indexer = self.loc._get_listlike_indexer(key, axis=1, raise_missing=True)[1]
2807
2808 # take() does not accept boolean indexers
~\Anaconda3\lib\site-packages\pandas\core\indexing.py in _get_listlike_indexer(self, key, axis, raise_missing)
1550 keyarr, indexer, new_indexer = ax._reindex_non_unique(keyarr)
1551
-> 1552 self._validate_read_indexer(
1553 keyarr, indexer, o._get_axis_number(axis), raise_missing=raise_missing
1554 )
~\Anaconda3\lib\site-packages\pandas\core\indexing.py in _validate_read_indexer(self, key, indexer, axis, raise_missing)
1644 if not (self.name == "loc" and not raise_missing):
1645 not_found = list(set(key) - set(ax))
-> 1646 raise KeyError(f"{not_found} not in index")
1647
1648 # we skip the warning on Categorical/Interval
KeyError: "['first', 'second'] not in index"
```
#### Expected Output
No error thrown.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.0.5
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200714
Cython : 0.29.21
pytest : 5.4.3
hypothesis : None
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.2
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.3
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.9
numba : 0.50.1
</details>
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MDU6SXNzdWU3NzQ2MjAyMjE=
| 38,692
|
API: setting np.nan into Series[bool]
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| 11
| 2020-12-25T04:06:58Z
| 2021-01-28T03:29:55Z
| 2021-01-28T03:29:55Z
|
MEMBER
| null |
```
ser = pd.Series([True, False, True, False])
ser[0] = np.nan
>>> ser
0 NaN
1 0.0
2 1.0
3 0.0
dtype: float64
```
It would be more consistent (and would simplify the code) if this cast to object and did not convert True/False to 1.0/0.0. ATM we have 2 tests that such a change would break.
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MDExOlB1bGxSZXF1ZXN0NTQ1NTU0NDQw
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TST: Inconsistent behavior of .replace() in Int64 series with NA
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| 4
| 2020-12-25T08:47:35Z
| 2020-12-28T14:55:49Z
| 2020-12-28T14:55:46Z
|
CONTRIBUTOR
| null |
- [ ] closes #38267
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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DOC add command to run pre-commit checks to pull request template
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| 2020-12-25T11:13:22Z
| 2021-01-10T18:30:49Z
| 2021-01-10T18:30:49Z
|
MEMBER
| null |
I think we could add
```bash
passes `pre-commit run --from-ref=upstream/master --to-ref=HEAD --all-files`
```
to the pull request template (maybe it could even replace the current `black` and `flake8` ones).
The file to change would be
```
.github/PULL_REQUEST_TEMPLATE.md
```
Some of the contributing guide may also need updating if it mentions the `flake8 --diff` command.
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|
TST: GH30999 Add match=msg to all pytest.raises in tests/generic/methods/test_sample.py
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MEMBER
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This pull request partially addresses xref #30999 to remove bare `pytest.raises` by adding `match=msg`. It doesn't close that issue as I have only addressed one test module: pandas/tests/generic/methods/test_sample.py
The only other thing I did was move a few lines out of the `pytest.raises` context as they were setup lines that did not raise the error, in order to make it clear which pandas method was raising the error.
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| 2020-12-25T13:59:23Z
| 2021-01-04T04:45:57Z
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|
CONTRIBUTOR
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- [ ] closes #38696
- [ ] tests added / passed
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TST: GH30999 Add match=msg to all pytest.raises in tests/indexes
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MEMBER
| null |
This pull request partially addresses xref #30999 to remove bare pytest.raises by adding match=msg. It doesn't close that issue as I have only addressed modules in pandas/tests/indexes
I made a few of the raised errors more specific types. There are two other changes that are a bit more complex but I will put specific comments on the code lines to explain those.
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BUG: Fix pd.json_normalize to not skip the first element of a generator input
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CONTRIBUTOR
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Backport PR #38679 on branch 1.2.x (Revert "Deprecated casting an object-dtype index of datetime objects to DatetimeIndex in the Series constructor (23598)")
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NONE
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Backport PR #38679: Revert "Deprecated casting an object-dtype index of datetime objects to DatetimeIndex in the Series constructor (23598)"
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CLN: remove unnecessary casting from Block methods
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DEPR MultiIndex.is_lexsorted and MultiIndex.lexsort_depth
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| 2020-12-26T09:49:04Z
| 2021-01-05T08:49:42Z
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MEMBER
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- [x] closes #32259
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
The issue is that `is_lexsorted` only looks at whether the _codes_ are sorted - the order of the codes doesn't necessarily reflect the sorting of the index values, e.g. if the index is created from a `.groupby(sort=False)` operation (see the linked issue for an example).
So, instead of `is_lexsorted`, users should use `is_monotonic_increasing`. There isn't an equivalent function to point them to for `lexsort_depth`, right?
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Backport PR #38678 on branch 1.2.x (DOC: 1.2.0 release date)
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NONE
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Backport PR #38678: DOC: 1.2.0 release date
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CI: jedi v0.18.0 breaks a number of ipython test_tab_complete_warning tests
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| 5
| 2020-12-26T13:32:15Z
| 2021-08-02T10:55:41Z
| 2021-08-02T10:54:40Z
|
MEMBER
| null |
It looks like jedi version 0.18.0 was released yesterday and broke the following tests:
FAILED pandas/tests/arrays/categorical/test_warnings.py::TestCategoricalWarnings::test_tab_complete_warning
FAILED pandas/tests/frame/test_api.py::TestDataFrameMisc::test_tab_complete_warning[DataFrame]
FAILED pandas/tests/frame/test_api.py::TestDataFrameMisc::test_tab_complete_warning[Series]
FAILED pandas/tests/indexes/test_base.py::TestIndex::test_tab_complete_warning
FAILED pandas/tests/resample/test_resampler_grouper.py::test_tab_complete_ipython6_warning
in the Linux py38_Locale pipeline only.
I have looked into it and it seems that it is caused by this commit which is part of version 0.18.0 of Jedi
https://github.com/davidhalter/jedi/commit/abb2250bf5b11cd0b0733949311df54ca0adbbf1
Ipython has a commit that I believe will fix the error:
https://github.com/ipython/ipython/commit/dcd9dc90aee7e4c5c52ce44c18e7518934790612
but it's slated for inclusion in version 8.0 of ipython:
https://github.com/ipython/ipython/issues/11816
(version 7.19.0 is the most recent release, on 2020-10-30)
It's causing failures in all PRs that were submitted today (2020-12-26) starting with #38696
- [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.
---
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DOC: Start v1.2.1 release notes
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TST: Add match=msg to all pytest.raises in pandas/tests/io/json
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| 1
| 2020-12-26T15:53:44Z
| 2020-12-27T22:57:19Z
| 2020-12-27T22:57:01Z
|
MEMBER
| null |
This pull request partially addresses xref #30999 to remove bare `pytest.raises` by adding `match=msg`. It doesn't close that issue as I have only addressed test modules in tje pandas/tests/io/json directory.
I also moved a couple of lines out of the `pytest.raises` context to make clear which pandas method was raising the error. And I deleted a couple of unreachable asserts in tests.
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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Backport PR #38704 on branch 1.2.x (DOC: Start v1.2.1 release notes)
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Backport PR #38704: DOC: Start v1.2.1 release notes
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MDExOlB1bGxSZXF1ZXN0NTQ1NzMwMzg2
| 38,707
|
TST: Remove broken test for deprecated functionality
|
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| 5
| 2020-12-26T20:10:28Z
| 2020-12-28T14:26:14Z
| 2020-12-28T14:26:10Z
|
CONTRIBUTOR
| null |
Registering converters by default on import is deprecated since #28722.
The test did not fail as the subprocess call contains errors in the python code. Specifically, the code is in double and single quotes. This means that the python code passed to the subprocess call is never actually excecuted. Python in the subprocess is called to "excecute" a string. This is will simply finish with exit code 0 as there is nothing to do basically.
Additonally, the test code contains a stray closing bracket at the end which would make the code fail if it was excecuted.
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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| 774,935,269
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MDU6SXNzdWU3NzQ5MzUyNjk=
| 38,708
|
REGR: repr of stringified floats (e.g. "3.50") drop ending "0"
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| 1
| 2020-12-26T21:24:11Z
| 2020-12-29T03:18:49Z
| 2020-12-29T03:18:49Z
|
CONTRIBUTOR
| null |
Example:
```python
>>> ser = pd.Series(["3.50"])
>>> ser
0 3.5 # wrong repr, should show 3.50
dtype: object
>>> ser[0]
'3.50' # this is ok
```
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API: Series[bool][key] = np.nan -> cast to object
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| 6
| 2020-12-27T00:16:16Z
| 2021-06-30T14:39:48Z
| 2021-01-28T03:29:55Z
|
MEMBER
| null |
- [x] closes #38692
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
sits on top of #38688
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MDExOlB1bGxSZXF1ZXN0NTQ1NzQ4Nzgy
| 38,710
|
ENH: Add xlsb auto detection to read_excel and respect default options
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| 8
| 2020-12-27T00:39:30Z
| 2021-01-03T17:19:47Z
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|
MEMBER
| null |
- [x] closes #35416 , closes #34252
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Note: Even though most of the reader options for read_excel only have 1 option, I think it is still useful to respect pandas.set_option.
Also, if some people have older xlrd(e.g. 1.2.0) and don't have openpyxl, they can use options to force pandas to read xlsx files with xlrd. (this is possible now, but xlrd's maintainer has made it clear he doesn't support for things like this)
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BUG: loc with empty multiindex raises exception
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- [x] closes #36936
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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BUG: Pandas 1.2.0 not importing numpy
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| 2
| 2020-12-27T03:17:13Z
| 2020-12-27T03:30:16Z
| 2020-12-27T03:23:29Z
|
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.
---
**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.
Collecting pandas
222
Downloading http://*private-pypi-repo*/root/pypi/%2Bf/e03/386615b970b8b/pandas-1.2.0.tar.gz (5.4 MB)
223
Installing build dependencies: started
224
Installing build dependencies: finished with status 'done'
225
Getting requirements to build wheel: started
226
Getting requirements to build wheel: finished with status 'error'
227
ERROR: Command errored out with exit status 1:
228
command: /usr/local/bin/python /usr/local/lib/python3.6/site-packages/pip/_vendor/pep517/_in_process.py get_requires_for_build_wheel /tmp/tmpyzo8hnvu
229
cwd: /tmp/pip-install-wsvw3tn6/pandas_0107e3e2e0984808803b03b19130c9e4
230
Complete output (18 lines):
231
Traceback (most recent call last):
232
File "/usr/local/lib/python3.6/site-packages/pip/_vendor/pep517/_in_process.py", line 280, in <module>
233
main()
234
File "/usr/local/lib/python3.6/site-packages/pip/_vendor/pep517/_in_process.py", line 263, in main
235
json_out['return_val'] = hook(**hook_input['kwargs'])
236
File "/usr/local/lib/python3.6/site-packages/pip/_vendor/pep517/_in_process.py", line 114, in get_requires_for_build_wheel
237
return hook(config_settings)
238
File "/tmp/pip-build-env-isjnq9ut/overlay/lib/python3.6/site-packages/setuptools/build_meta.py", line 150, in get_requires_for_build_wheel
239
config_settings, requirements=['wheel'])
240
File "/tmp/pip-build-env-isjnq9ut/overlay/lib/python3.6/site-packages/setuptools/build_meta.py", line 130, in _get_build_requires
241
self.run_setup()
242
File "/tmp/pip-build-env-isjnq9ut/overlay/lib/python3.6/site-packages/setuptools/build_meta.py", line 254, in run_setup
243
self).run_setup(setup_script=setup_script)
244
File "/tmp/pip-build-env-isjnq9ut/overlay/lib/python3.6/site-packages/setuptools/build_meta.py", line 145, in run_setup
245
exec(compile(code, __file__, 'exec'), locals())
246
File "setup.py", line 20, in <module>
247
import numpy
248
ModuleNotFoundError: No module named 'numpy'
#### Problem description
When I am downloading pandas 1.2.0 to build my docker image for my python project, I am getting the following error above. Numpy seems to be not importing for some reason. The excepted output would be what was when I building the image yesterday (before the release)
I am using the following command to download my requirements:
RUN pip install --no-cache-dir -r requirements.txt --extra-index-url $INDEX_URL --trusted-host $TRUSTED_HOST_URL
And the requirements.txt is as follows:
connexion
python_dateutil
setuptools
Flask-Testing
libmctwist >= 1.0.0
swagger-ui-bundle
libmctwist requirements.txt contains:
libinfinityd >= 1.1.0
azure-cosmos
libinfinityd requirements.txt contains:
pandas
requests
pyyaml
six
mysql-connector-python
I've tried altering libinfinityd's requirements.txt to contain NumPy, at this point though, I think I'm going to revert to pandas 1.1.5 in the libinfinityd's requirements.txt
#### Expected Output
Collecting pandas
214
Downloading http://*private-pypi-repo*/root/pypi/%2Bf/f10/fc41ee3c75a47/pandas-1.1.5.tar.gz (5.2 MB)
215
Installing build dependencies: started
216
Installing build dependencies: still running...
217
Installing build dependencies: still running...
218
Installing build dependencies: still running...
219
Installing build dependencies: finished with status 'done'
220
Getting requirements to build wheel: started
221
Getting requirements to build wheel: still running...
222
Getting requirements to build wheel: finished with status 'done'
223
Preparing wheel metadata: started
224
Preparing wheel metadata: finished with status 'done'
225
WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='pypi.org', port=443): Read timed out. (read timeout=15)",)': /simple/pytz/
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MDExOlB1bGxSZXF1ZXN0NTQ1NzYzMDAx
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DOC: fix link redirect to NumFOCUS
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| 2020-12-27T04:37:16Z
| 2020-12-27T15:37:08Z
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CONTRIBUTOR
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDU6SXNzdWU3NzQ5ODgzNzg=
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REGR: to_csv problems with zip compression and large dataframes
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| 8
| 2020-12-27T06:53:59Z
| 2020-12-29T00:05:07Z
| 2020-12-29T00:05:07Z
|
NONE
| null |
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import io
f = io.BytesIO()
d = pd.DataFrame({'a':[1]*5000})
d.to_csv(f, compression='zip')
f.seek(0)
pd.read_csv(f, compression='zip')
```
#### Problem description
Writing large (over 1163 rows) dataframes to csv with zip compression (inferred or explicit; to file or io.BytesIO) creates a corrupted zip file.
ValueError: Multiple files found in ZIP file. Only one file per ZIP: ['zip', 'zip', 'zip', 'zip', 'zip']
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Polish_Poland.1250
pandas : 1.2.0
numpy : 1.19.3
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.1.0.post20201221
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.16.2
xlrd : None
xlwt : None
numba : 0.52.0
</details>
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MDExOlB1bGxSZXF1ZXN0NTQ1Nzc3Nzgw
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CI: pin jedi version<0.18.0
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| 2
| 2020-12-27T08:05:17Z
| 2020-12-27T17:55:01Z
| 2020-12-27T10:51:12Z
|
MEMBER
| null |
... so CI jobs pass.
xref #38703. OP should be kept open until upstream fix comes through.
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Backport PR #38715 on branch 1.2.x (CI: pin jedi version<0.18.0)
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| 0
| 2020-12-27T10:53:32Z
| 2020-12-27T12:55:06Z
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|
NONE
| null |
Backport PR #38715: CI: pin jedi version<0.18.0
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MDExOlB1bGxSZXF1ZXN0NTQ1Nzk0MTg3
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|
Slicing columns with mixed types <str>,<int> fails with ValueError #20975
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| 3
| 2020-12-27T11:12:59Z
| 2020-12-27T22:55:04Z
| 2020-12-27T22:55:00Z
|
CONTRIBUTOR
| null |
- [ ] closes #20975
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU3NzUwMzc3MDY=
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Change tests of casting an interval range index from float to int to reflect current behaviour
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| 2
| 2020-12-27T13:23:52Z
| 2020-12-30T03:27:23Z
| 2020-12-30T03:27:23Z
|
MEMBER
| null |
This is to address an issue I came across in PR #38697 where there is a test, `test_subtype_integer_errors`, in pandas/tests/indexes/interval/test_astype.py marked as failing because it is not raising `ValueErrors` when casting an interval range index from float to int. My reading of #15832 is that it is unnecessary to raise a `ValueError` in this case.
As I wrote in the comments to #38697:
The question is, as a user of pandas, if you had an interval range such as [(0.0, 0.25], (0.25, 0.5], (0.5, 0.75], (0.75, 1.0], (1.0, 1.25], (1.25, 1.5], (1.5, 1.75], (1.75, 2.0], (2.0, 2.25], (2.25, 2.5]], and you cast it to int, what would you want or expect? The old (failing) version of the test said you expect a ValueError. I am saying you expect [(0, 0], (0, 0], (0, 0], (0, 1], (1, 1], (1, 1], (1, 1], (1, 2], (2, 2], (2, 2]] which, as I said, looks not that useful as an IntervalIndex, but is not wrong either.
So what I plan to do is delete two `pytest.raises` calls in `test_subtype_integer_errors` but also add a test to ensure that casting an interval range from float to int works as expected.
>
> Your logic makes sense to me, but I'd defer to someone with more experience on how we handle `IntervalIndex`.
>
> If being removed from this test, we definitely want to make sure this specific casting behavior is being tested somewhere. To keep this pr simple and easier to merge, I'd suggest reverting modification of this test, then making a followup pr to clean up this test and add tests for any untested casting behavior.
>
> _Originally posted by @mzeitlin11 in https://github.com/pandas-dev/pandas/pull/38697#discussion_r549023944_
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