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https://api.github.com/repos/pandas-dev/pandas/issues/38919
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MDExOlB1bGxSZXF1ZXN0NTQ3ODY5MDI1
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BUG: fix the bad error raised by HDFStore.put()
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| 5
| 2021-01-03T12:00:19Z
| 2021-01-05T02:22:38Z
| 2021-01-05T02:22:33Z
|
CONTRIBUTOR
| null |
- [x] closes #34274
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
*P.S. something was wrong with `git diff upstream/master` so I directly ran `flake8 ./pandas/io/pytables.py` as it's the only file changed*
------
I was running into the same problem as #34274 and found where the error is. But (as I've just started using pandas for several days:sweet_smile:) I have few knowledge about pandas, so maybe I didn't make the full use of pandas' components.
As seen in #34274, if a `DataFrame` contains non-string elements and is about to be written into an HDF5 file by `HDFStore.put()`, a `TypeError: object of type 'int' has no len()` error is raised.
But it's not the right "error" expected. `HDFStore.put()` can't serialize some types of element, `so report an exception on a column by column basis` is actually needed.
This commit fixes this, now it raises `TypeError: Cannot serialize the column [{column_No}] because its data contents are not string but [{non_string_type}] object dtype` as expected.
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MDExOlB1bGxSZXF1ZXN0NTQ3ODcxNDE5
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TST: GH30999 Add placeholder messages to pandas/tests/io/test_sql.py and remove test for numexpr < 2.6.8
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| 1
| 2021-01-03T12:22:31Z
| 2021-01-05T17:01:47Z
| 2021-01-03T17:24:50Z
|
MEMBER
| null |
This is my attempt to finally finish off #30999
In pandas/tests/io/test_sql.py, there is a whole test class skipped. It looks like xref #20536 is supposed to address that, but no one has commented there since March 2018, so I don't think that's going to be fixed any time soon. I noticed that there were other tests in the same module with `match="<insert message here>"` so I decided to put it in the two tests that I can't figure out the correct error message for.
In pandas/tests/computation/test_compat.py there was an if statement that the numexpr library is at least 2.6.8. I tried to set up an environment with a lower version but conda couldn't resolve the dependencies. That test isn't running in the CI (xref #38876) and that test was last touched in a substantive way in 2016. I think that portion of the test is no longer required.
Not sure that I actually addressed these correctly, but I made an attempt so we could have a conversation about it in a more concrete way.
- [x] 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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MDU6SXNzdWU3Nzc2NjkzNjM=
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COMPAT/BLD: rolling failed on Arm64 Linux
|
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[] | null | 16
| 2021-01-03T16:17:40Z
| 2021-08-14T23:12:21Z
| null |
MEMBER
| null |
The test `pandas/tests/window/test_rolling.py::test_rolling_var_numerical_issues` has failed on arm64 build.
```bash
_______________ test_rolling_var_numerical_issues[var-1-values0] _______________
[gw2] linux -- Python 3.7.9 /home/travis/miniconda3/envs/pandas-dev/bin/python
func = 'var', third_value = 1, values = [5e+33, 0, 0.5, 0.5, 2, 0]
@pytest.mark.parametrize(
("func", "third_value", "values"),
[
("var", 1, [5e33, 0, 0.5, 0.5, 2, 0]),
("std", 1, [7.071068e16, 0, 0.7071068, 0.7071068, 1.414214, 0]),
("var", 2, [5e33, 0.5, 0, 0.5, 2, 0]),
("std", 2, [7.071068e16, 0.7071068, 0, 0.7071068, 1.414214, 0]),
],
)
def test_rolling_var_numerical_issues(func, third_value, values):
# GH: 37051
ds = Series([99999999999999999, 1, third_value, 2, 3, 1, 1])
result = getattr(ds.rolling(2), func)()
expected = Series([np.nan] + values)
> tm.assert_series_equal(result, expected)
pandas/tests/window/test_rolling.py:908:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/_libs/testing.pyx:46: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
E AssertionError: Series are different
E
E Series values are different (42.85714 %)
E [index]: [0, 1, 2, 3, 4, 5, 6]
E [left]: [nan, 5e+33, 0.0, 0.0, 0.0, 0.0, 0.0]
E [right]: [nan, 5e+33, 0.0, 0.5, 0.5, 2.0, 0.0]
pandas/_libs/testing.pyx:161: AssertionError
_______________ test_rolling_var_numerical_issues[std-1-values1] _______________
[gw2] linux -- Python 3.7.9 /home/travis/miniconda3/envs/pandas-dev/bin/python
func = 'std', third_value = 1
values = [7.071068e+16, 0, 0.7071068, 0.7071068, 1.414214, 0]
@pytest.mark.parametrize(
("func", "third_value", "values"),
[
("var", 1, [5e33, 0, 0.5, 0.5, 2, 0]),
("std", 1, [7.071068e16, 0, 0.7071068, 0.7071068, 1.414214, 0]),
("var", 2, [5e33, 0.5, 0, 0.5, 2, 0]),
("std", 2, [7.071068e16, 0.7071068, 0, 0.7071068, 1.414214, 0]),
],
)
def test_rolling_var_numerical_issues(func, third_value, values):
# GH: 37051
ds = Series([99999999999999999, 1, third_value, 2, 3, 1, 1])
result = getattr(ds.rolling(2), func)()
expected = Series([np.nan] + values)
> tm.assert_series_equal(result, expected)
pandas/tests/window/test_rolling.py:908:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/_libs/testing.pyx:46: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
E AssertionError: Series are different
E
E Series values are different (42.85714 %)
E [index]: [0, 1, 2, 3, 4, 5, 6]
E [left]: [nan, 7.071067811865475e+16, 0.0, 0.0, 0.0, 0.0, 0.0]
E [right]: [nan, 7.071068e+16, 0.0, 0.7071068, 0.7071068, 1.414214, 0.0]
pandas/_libs/testing.pyx:161: AssertionError
_______________ test_rolling_var_numerical_issues[var-2-values2] _______________
[gw2] linux -- Python 3.7.9 /home/travis/miniconda3/envs/pandas-dev/bin/python
func = 'var', third_value = 2, values = [5e+33, 0.5, 0, 0.5, 2, 0]
@pytest.mark.parametrize(
("func", "third_value", "values"),
[
("var", 1, [5e33, 0, 0.5, 0.5, 2, 0]),
("std", 1, [7.071068e16, 0, 0.7071068, 0.7071068, 1.414214, 0]),
("var", 2, [5e33, 0.5, 0, 0.5, 2, 0]),
("std", 2, [7.071068e16, 0.7071068, 0, 0.7071068, 1.414214, 0]),
],
)
def test_rolling_var_numerical_issues(func, third_value, values):
# GH: 37051
ds = Series([99999999999999999, 1, third_value, 2, 3, 1, 1])
result = getattr(ds.rolling(2), func)()
expected = Series([np.nan] + values)
> tm.assert_series_equal(result, expected)
pandas/tests/window/test_rolling.py:908:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/_libs/testing.pyx:46: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
E AssertionError: Series are different
E
E Series values are different (42.85714 %)
E [index]: [0, 1, 2, 3, 4, 5, 6]
E [left]: [nan, 5e+33, 0.0, 0.0, 0.0, 0.0, 0.0]
E [right]: [nan, 5e+33, 0.5, 0.0, 0.5, 2.0, 0.0]
pandas/_libs/testing.pyx:161: AssertionError
_______________ test_rolling_var_numerical_issues[std-2-values3] _______________
[gw2] linux -- Python 3.7.9 /home/travis/miniconda3/envs/pandas-dev/bin/python
func = 'std', third_value = 2
values = [7.071068e+16, 0.7071068, 0, 0.7071068, 1.414214, 0]
@pytest.mark.parametrize(
("func", "third_value", "values"),
[
("var", 1, [5e33, 0, 0.5, 0.5, 2, 0]),
("std", 1, [7.071068e16, 0, 0.7071068, 0.7071068, 1.414214, 0]),
("var", 2, [5e33, 0.5, 0, 0.5, 2, 0]),
("std", 2, [7.071068e16, 0.7071068, 0, 0.7071068, 1.414214, 0]),
],
)
def test_rolling_var_numerical_issues(func, third_value, values):
# GH: 37051
ds = Series([99999999999999999, 1, third_value, 2, 3, 1, 1])
result = getattr(ds.rolling(2), func)()
expected = Series([np.nan] + values)
> tm.assert_series_equal(result, expected)
pandas/tests/window/test_rolling.py:908:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/_libs/testing.pyx:46: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
E AssertionError: Series are different
E
E Series values are different (42.85714 %)
E [index]: [0, 1, 2, 3, 4, 5, 6]
E [left]: [nan, 7.071067811865475e+16, 0.0, 0.0, 0.0, 0.0, 0.0]
E [right]: [nan, 7.071068e+16, 0.7071068, 0.0, 0.7071068, 1.414214, 0.0]
pandas/_libs/testing.pyx:161: AssertionError
```
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MDU6SXNzdWU3Nzc2Njk1MzQ=
| 38,922
|
BUG: KeyError on Column Name - Column Name Is 100% There
|
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| 4
| 2021-01-03T16:18:34Z
| 2021-02-15T10:46:05Z
| 2021-02-14T14:12:24Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
Yes
- [x] I have confirmed this bug exists on the latest version of pandas.
Yes 1.2.0
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
No
---
**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.
```
AAPL
MultiIndex([('AAPL', 'open'),
('AAPL', 'high'),
('AAPL', 'low'),
('AAPL', 'close'),
('AAPL', 'volume')],
)
<class 'pandas.core.frame.DataFrame'>
[False False False False False False False False False False False False
False False False False True True True True True True True True
False False False False False False False False False False False False
False False False False False False False False False False False False
False False False False False False False False False False False False
False False False False False False False False False False False False
False False False False False False False False False False False False
False False False False False False False False False False False False
False False False False]
<class 'numpy.ndarray'>
MultiIndex([('AAPL', 'open'),
('AAPL', 'high'),
('AAPL', 'low'),
('AAPL', 'close'),
('AAPL', 'volume')],
)
<class 'pandas.core.frame.DataFrame'>
```
```python
import sqlite3
import config
import alpaca_trade_api as tradeapi
import pandas as pd
import numpy as np
from datetime import date
symbols = [stock['symbol'] for stock in stocks]
api = tradeapi.REST(config.API_KEY, config.SECRET_KEY, base_url=config.API_URL)
current_date = "2020-12-31"
start_minute_bar = "2020-12-31 09:00:00-05:00"
end_minute_bar = "2020-12-31 09:45:00-05:00"
for symbol in symbols:
minute_bars = api.get_barset(symbol, '5Min', start=pd.Timestamp(current_date), end=pd.Timestamp(current_date)).df
print(symbol)
print(minute_bars.columns)
print(type(minute_bars))
opening_range_mask = (minute_bars.index >= start_minute_bar) & (minute_bars.index < end_minute_bar)
print(type(opening_range_mask))
opening_range_bars = minute_bars.loc[opening_range_mask]
print(opening_range_bars.columns)
print(type(opening_range_bars))
opening_range_low = opening_range_bars['low'].min() <<<<<< ERROR @ THIS LINE ('low') KeyERROR
```
#### Problem description
I keep getting a key error when calling the code line:
opening_range_low = opening_range_bars['low'].min()
the error state being KeyERROR on the 'low' brackets. I've used
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 71 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : English_United Kingdom.1252
pandas : 1.2.0
numpy : 1.19.4
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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|
COMPAT: test in `tests/indexes/interval/test_astype.py` failed on arm64
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[] | null | 0
| 2021-01-03T16:32:30Z
| 2021-08-14T23:12:53Z
| null |
MEMBER
| null |
```bash
_________________ TestFloatSubtype.test_subtype_integer_errors _________________
[gw3] linux -- Python 3.7.9 /home/travis/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.indexes.interval.test_astype.TestFloatSubtype object at 0xffff290887d0>
def test_subtype_integer_errors(self):
# float64 -> uint64 fails with negative values
index = interval_range(-10.0, 10.0)
dtype = IntervalDtype("uint64")
msg = re.escape(
"Cannot convert interval[float64] to interval[uint64]; subtypes are "
"incompatible"
)
with pytest.raises(TypeError, match=msg):
> index.astype(dtype)
E Failed: DID NOT RAISE <class 'TypeError'>
pandas/tests/indexes/interval/test_astype.py:177: Failed
```
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|
TST: 26807 split pandas/tests/tseries/offsets/test_offsets.py into multiple smaller test modules
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| 3
| 2021-01-03T17:59:12Z
| 2021-01-05T17:03:38Z
| 2021-01-03T23:22:06Z
|
MEMBER
| null |
This is to address xref #26807 specifically for pandas/tests/tseries/offsets/test_offsets.py . I tried to get all the new modules below about 1000 lines and break it up in logical ways.
Other than moving code, I have changed some list construction for pytest parameterizing to be a tiny bit more compact by changing a bunch of sequential `append`s to a single list literal. Other than that no changes.
- [ ] 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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|
ENH: Add support to import optional submodule and specify different min_version than default
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MEMBER
| null |
- [ ] closes #38888
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
cc @jreback, @arw2019
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MDU6SXNzdWU3Nzc2OTQxNTA=
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CLN: Unify number recognition tests in read_csv for all parsers
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| 2021-01-03T18:42:54Z
| 2021-01-04T23:24:44Z
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MEMBER
| null |
Follow up for #38420
We should parametrize over all parsers for the test sets to ensure a consistent behavior
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TST: stricten xfails
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- [ ] closes #xxxx
- [ ] tests added / passed
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BUG: UnknownTimezoneWarning from df.to_datetime()
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| 2021-01-03T19:08:29Z
| 2021-01-04T01:22:05Z
| 2021-01-04T01:16:46Z
|
NONE
| null |
I have `test.csv`:
```
datetime
Oct 19, 2020 06:58:20 PM EDT
Oct 19, 2020 04:45:33 PM EDT
```
and `test.py`:
```
import pandas as pd
from datetime import datetime
input_file_path = 'test.csv'
df = pd.read_csv(input_file_path, delimiter=';')
print(df)
df['datetime'] = pd.to_datetime(df['datetime'])
print(df)
```
Running `python test.py` results in:
```
datetime
0 Oct 19, 2020 06:58:20 PM EDT
1 Oct 19, 2020 04:45:33 PM EDT
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/dateutil/parser/_parser.py:1218: UnknownTimezoneWarning: tzname EDT identified but not understood. Pass `tzinfos` argument in order to correctly return a timezone-aware datetime. In a future version, this will raise an exception.
category=UnknownTimezoneWarning)
datetime
0 2020-10-19 18:58:20
1 2020-10-19 16:45:33
```
The `UnknownTimezoneWarning` from `dateutil` is what I am concerned about. I don't believe there is currently a way to pass an argument to `panda`'s `to_datetime()` to avoid this. Am I missing something or does this need to be addressed?
Running with `pandas` v1.2.0, `python` v3.7.3.
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TST: Replace pytest.xfail
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| 1
| 2021-01-03T21:10:21Z
| 2021-01-04T21:05:18Z
| 2021-01-04T01:26:12Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Part of #38902.
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MDU6SXNzdWU3Nzc3MTk4ODc=
| 38,930
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TST/REF: splitting pandas/io/parsers.py into multiple files
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| 1
| 2021-01-03T21:29:21Z
| 2021-01-21T23:48:43Z
| 2021-01-21T23:48:43Z
|
MEMBER
| null |
`pandas/io/parsers.py` is close to 4k LOC (3986 on 1.3 master). Would it be reasonable to split it into multiple files (located, say, in a `pandas/io/parsers` directory)?
AFAICT the file contains 4 logical pieces:
- `read_...` methods (~700 LOC)
- `ParserBase` and related module-level methods ( ~1100 LOC)
- `CParser` and related module-level methods (~400LOC)
- `PythonParser`, its derived classes and related module-level methods (~1800 LOC)
Once #38370 goes in these would be joined by
- `PyarrowParser` (~100 LOC right now)
IMO this kind of refactor would make the module somewhat easier to grok
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MDExOlB1bGxSZXF1ZXN0NTQ3OTQyNzcy
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BUG: DataFrame.__setitem__ raising ValueError with string indexer and empty df and df to set
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| 0
| 2021-01-03T22:14:19Z
| 2021-01-06T01:13:51Z
| 2021-01-06T00:35:39Z
|
MEMBER
| null |
- [x] closes #38831
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Dont have to convert df to series, df has an index.
cc @jbrockmendel
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BUG: rank_2d raising with mixed dtypes
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| 1
| 2021-01-03T22:21:58Z
| 2021-01-05T01:30:14Z
| 2021-01-05T00:45:16Z
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MEMBER
| null |
- [x] tests added / passed
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MDExOlB1bGxSZXF1ZXN0NTQ3OTUwNjY1
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DOC: improve shared content between comparison pages
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| 2
| 2021-01-03T23:31:09Z
| 2021-01-04T04:53:19Z
| 2021-01-04T03:55:07Z
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MEMBER
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This pull request does a few things between the SAS and Stata pages, in separate commits:
- Makes the headings match, where it makes sense for them to
- Create more shared includes, as a follow-up to https://github.com/pandas-dev/pandas/pull/38887
- Improves some wording and ensures more methods are linked in the comparison includes
The motivation here is that I'm working on adding the other sections to the Comparison to Spreadsheets page, and want to ensure they're consistent.
---
- [ ] ~~closes #xxxx~~
- [x] tests added / passed
- [ ] ~~passes `black pandas`~~
- [ ] ~~passes `git diff upstream/master -u -- "*.py" | flake8 --diff`~~
- [ ] ~~whatsnew entry~~
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MDExOlB1bGxSZXF1ZXN0NTQ3OTkyMjIx
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ENH: Improve numerical stability for groupby.mean and groupby.cumsum
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MEMBER
| null |
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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DOC: remove use of head() in the comparison docs
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| 2
| 2021-01-04T03:43:50Z
| 2021-01-04T13:21:20Z
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|
MEMBER
| null |
This helps to clarify the examples by removing code that isn't relevant. Added a dedicated section to the SAS, SQL, and Stata pages.
This builds on https://github.com/pandas-dev/pandas/pull/38933; ~~will rebase and mark as ready for review once that's merged. In the meantime, the last commit is the one that can be reviewed.~~ Thanks!
- [ ] ~~closes #xxxx~~
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] ~~whatsnew entry~~
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CLN: Consolidate raise_on_missing and on_version into a single errors parameter in import_optional_dependency
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| 1
| 2021-01-04T03:50:52Z
| 2021-01-11T13:59:41Z
| 2021-01-11T13:59:41Z
|
MEMBER
| null |
xref [#38925 comment](https://github.com/pandas-dev/pandas/pull/38925#discussion_r551048418)
The new parameter should errors should have options of either "raise", "warn", or "ignore".
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ENH: pd.read_excel with table parameter
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| 2
| 2021-01-04T04:22:24Z
| 2021-08-14T23:13:35Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
Excel has [tables ](https://support.microsoft.com/en-us/office/overview-of-excel-tables-7ab0bb7d-3a9e-4b56-a3c9-6c94334e492c#:~:text=To%20quickly%20create%20a%20table,row%2C%20and%20then%20click%20OK.), that makes data managing within Excel easier and offers some other features.
At the moment, pandas cannot access those tables, it simply reads in all the data. I think it would be helpful if a `table` parameter was added to `pd.read_excel` to capture table(s) defined in a sheet.
#### Sample Data:
Attached is a sample excel file: [016-MSPTDA-Excel.xlsx](https://github.com/pandas-dev/pandas/files/5768126/016-MSPTDA-Excel.xlsx)
With the current implementation of `read_excel`, we cannot select specific tables (dSalesReps, dProduct, dCategory, or dSupplier). Even if we read the `Tables` sheet, it becomes quite hard to separate the data into individual tables.
#### Describe the solution you'd like
```pd.read_excel(io=filename, sheet=sheetname, table=tablename, ...)```
The table parameter can have a default of `None`, in which case the entire sheet is read in; if however table is not `None`, then the table or lists of tables only are read in. The `openpyxl` library would be used to implement this.
#### API breaking implications
I am not aware of any API breaking implications
#### Describe alternatives you've considered
It could be done outside Pandas, where you read in the data first through `openpyxl`, before passing it to Pandas. I wrote a [blog post](https://samukweku.github.io/data-wrangling-blog/spreadsheet/python/pandas/openpyxl/2020/05/19/Access-Tables-In-Excel.html) about it; I feel, however, that it may be more convenient to provide that same functionality within Pandas and let the user worry less about the abstractions.
#### Additional context
I did check to see if this has been raised before, but did not find any. If it already has, kindly point me to that issue, and I will gladly close this.
Thanks.
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MDU6SXNzdWU3Nzc4MDg3OTA=
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DOC: Update contributing.rst
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| 2
| 2021-01-04T04:42:01Z
| 2021-01-06T15:02:41Z
| 2021-01-06T15:02:41Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
doc/source/development/contributing.rst
#### Documentation problem
Need additional information about the command that verifies the linting of code files.
#### Suggested fix for documentation
`pre-commit run --from-ref=upstream/master --to-ref=HEAD --all-files`
The command verifies the linting of code files, it looks for common mistake patterns
*NOTE: An additional paragraph is preferred for this*
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API: honor copy=True when passing dict to DataFrame
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| 18
| 2021-01-04T06:26:44Z
| 2021-03-31T01:05:17Z
| 2021-03-31T01:03:22Z
|
MEMBER
| null |
- [x] closes #32960
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
xref #34872 cc @TomAugspurger used the test_dict_nocopy you wrote but it ended up pretty mangled
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⬆️ UPGRADE: Autoupdate pre-commit config
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| 2
| 2021-01-04T07:40:09Z
| 2021-01-05T09:41:33Z
| 2021-01-05T09:41:28Z
|
CONTRIBUTOR
| null |
<!-- START pr-commits -->
<!-- END pr-commits -->
## Base PullRequest
default branch (https://github.com/pandas-dev/pandas/tree/master)
## Command results
<details>
<summary>Details: </summary>
<details>
<summary><em>add path</em></summary>
```Shell
/home/runner/work/_actions/technote-space/create-pr-action/v2/node_modules/npm-check-updates/bin
```
</details>
<details>
<summary><em>pip install pre-commit</em></summary>
```Shell
Collecting pre-commit
Downloading pre_commit-2.9.3-py2.py3-none-any.whl (184 kB)
Collecting cfgv>=2.0.0
Using cached cfgv-3.2.0-py2.py3-none-any.whl (7.3 kB)
Collecting identify>=1.0.0
Downloading identify-1.5.11-py2.py3-none-any.whl (97 kB)
Collecting nodeenv>=0.11.1
Using cached nodeenv-1.5.0-py2.py3-none-any.whl (21 kB)
Collecting pyyaml>=5.1
Using cached PyYAML-5.3.1-cp39-cp39-linux_x86_64.whl
Collecting toml
Using cached toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting virtualenv>=20.0.8
Downloading virtualenv-20.2.2-py2.py3-none-any.whl (5.7 MB)
Collecting appdirs<2,>=1.4.3
Using cached appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)
Collecting distlib<1,>=0.3.1
Using cached distlib-0.3.1-py2.py3-none-any.whl (335 kB)
Collecting filelock<4,>=3.0.0
Using cached filelock-3.0.12-py3-none-any.whl (7.6 kB)
Collecting six<2,>=1.9.0
Using cached six-1.15.0-py2.py3-none-any.whl (10 kB)
Installing collected packages: six, filelock, distlib, appdirs, virtualenv, toml, pyyaml, nodeenv, identify, cfgv, pre-commit
Successfully installed appdirs-1.4.4 cfgv-3.2.0 distlib-0.3.1 filelock-3.0.12 identify-1.5.11 nodeenv-1.5.0 pre-commit-2.9.3 pyyaml-5.3.1 six-1.15.0 toml-0.10.2 virtualenv-20.2.2
```
### stderr:
```Shell
WARNING: You are using pip version 20.3.1; however, version 20.3.3 is available.
You should consider upgrading via the '/opt/hostedtoolcache/Python/3.9.1/x64/bin/python -m pip install --upgrade pip' command.
```
</details>
<details>
<summary><em>pre-commit autoupdate || (exit 0);</em></summary>
```Shell
Updating https://github.com/python/black ... already up to date.
Updating https://gitlab.com/pycqa/flake8 ... already up to date.
Updating https://github.com/PyCQA/isort ... [INFO] Initializing environment for https://github.com/PyCQA/isort.
updating 5.6.4 -> 5.7.0.
Updating https://github.com/asottile/pyupgrade ... [INFO] Initializing environment for https://github.com/asottile/pyupgrade.
already up to date.
Updating https://github.com/pre-commit/pygrep-hooks ... already up to date.
Updating https://github.com/asottile/yesqa ... already up to date.
Updating https://github.com/pre-commit/pre-commit-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pre-commit-hooks.
already up to date.
Updating https://github.com/codespell-project/codespell ... [INFO] Initializing environment for https://github.com/codespell-project/codespell.
already up to date.
```
</details>
<details>
<summary><em>pre-commit run -a || (exit 0);</em></summary>
```Shell
[INFO] Installing environment for https://github.com/python/black.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/PyCQA/isort.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/pyupgrade.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/yesqa.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/pre-commit/pre-commit-hooks.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/codespell-project/codespell.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
black..................................................................................................Passed
flake8.................................................................................................Passed
flake8 (cython)........................................................................................Passed
flake8 (cython template)...............................................................................Passed
isort..................................................................................................Passed
pyupgrade..............................................................................................Passed
rst ``code`` is two backticks..........................................................................Passed
rst directives end with two colons.....................................................................Passed
rst ``inline code`` next to normal text................................................................Passed
Generate pip dependency from conda.....................................................................Passed
flake8-rst.............................................................................................Passed
Check for non-standard imports.........................................................................Passed
Check for non-standard numpy.random-related imports excluding pandas/_testing.py.......................Passed
Check for non-standard imports in test suite...........................................................Passed
Check for incorrect code block or IPython directives...................................................Passed
Check for use of not concatenated strings..............................................................Passed
Check for strings with wrong placed spaces.............................................................Passed
Check for import of private attributes across modules..................................................Passed
Check for use of private functions across modules......................................................Passed
Check for use of bare pytest raises....................................................................Passed
Check for inconsistent use of pandas namespace in tests................................................Passed
Check for use of Union[Series, DataFrame] instead of FrameOrSeriesUnion alias..........................Passed
Check for use of foo.__class__ instead of type(foo)....................................................Passed
Check for use of comment-based annotation syntax and missing error codes...............................Passed
Check code for instances of os.remove..................................................................Passed
Strip unnecessary `# noqa`s............................................................................Passed
Fix End of Files.......................................................................................Passed
Trim Trailing Whitespace...............................................................................Passed
codespell..............................................................................................Passed
```
</details>
</details>
## Changed files
<details>
<summary>Changed file: </summary>
- .pre-commit-config.yaml
</details>
<hr>
[:octocat: Repo](https://github.com/technote-space/create-pr-action) | [:memo: Issues](https://github.com/technote-space/create-pr-action/issues) | [:department_store: Marketplace](https://github.com/marketplace/actions/create-pr-action)
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DOC: minor tweaks to formatting on SQL comparison page
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| 2021-01-04T08:41:33Z
| 2021-01-04T13:33:16Z
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MEMBER
| null |
Adding code formatting, missing punctuation, etc. No changes of substance.
- [ ] ~~closes #xxxx~~
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] ~~whatsnew entry~~
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MEMBER
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Backport PR #38893
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CI: Travis not running (no credits)
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| 7
| 2021-01-04T12:02:24Z
| 2021-06-01T13:51:18Z
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MEMBER
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https://travis-ci.com/github/pandas-dev/pandas/requests
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REGR: empty CatgoricalIndex constructor by passing no data (but passing categories) no longer working
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| 1
| 2021-01-04T13:29:38Z
| 2021-05-25T08:14:34Z
| 2021-05-25T08:14:34Z
|
MEMBER
| null |
See https://github.com/pandas-dev/pandas/pull/38614/files#r551313565
https://github.com/pandas-dev/pandas/pull/38614/ changed the CategoricalIndex constructor to disallow scalar values, but at the same time it also disallowed passing *no* values to create an empty index (which in practice means passing the default of `None`):
```
In [17]: pd.CategoricalIndex(categories=['a', 'b'])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-17-6f0ae73d9374> in <module>
----> 1 pd.CategoricalIndex(categories=['a', 'b'])
~/scipy/pandas/pandas/core/indexes/category.py in __new__(cls, data, categories, ordered, dtype, copy, name)
187 dtype: Optional[Dtype] = None,
188 copy=False,
--> 189 name=None,
190 ):
191
TypeError: CategoricalIndex(...) must be called with a collection of some kind, None was passed
```
If we make this change, this is at least something that should be deprecated IMO (eg it breaks dask parquet reading).
But I am not sure if we should change it, since other constructors like `Series()` allow similar creation of empty objects:
cc @jbrockmendel
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Backport PR #38909 on branch 1.2.x (BUG: Fixed regression in rolling.skew and rolling.kurt modifying object)
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NONE
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Backport PR #38909: BUG: Fixed regression in rolling.skew and rolling.kurt modifying object
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REGR: comparison op with dask data structure fails
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[] | null | 9
| 2021-01-04T14:29:39Z
| 2021-08-14T23:14:12Z
| null |
MEMBER
| null |
Using pandas 1.0.5 and latest dask 2020.12.0:
```
In [2]: import dask.dataframe as dd
In [3]: df = pd.DataFrame({"x": ["a", "b", "c"] * 100}, dtype="category")
...: ddf = dd.from_pandas(df, npartitions=3)
In [4]: df.x
Out[4]:
0 a
1 b
2 c
3 a
4 b
..
295 b
296 c
297 a
298 b
299 c
Name: x, Length: 300, dtype: category
Categories (3, object): [a, b, c]
In [5]: ddf.x
Out[5]:
Dask Series Structure:
npartitions=3
0 category[known]
100 ...
200 ...
299 ...
Name: x, dtype: category
Dask Name: getitem, 6 tasks
In [6]: df.x == ddf.x
Out[6]:
0 True
1 True
2 True
3 True
4 True
...
295 True
296 True
297 True
298 True
299 True
Name: x, Length: 300, dtype: bool
In [9]: (df.x == ddf.x).all()
Out[9]: True
```
But with master (using same dask version), this gives:
```
In [3]: df.x == ddf.x
Out[3]:
0 False
1 False
2 False
3 False
4 False
...
295 False
296 False
297 False
298 False
299 False
Name: x, Length: 300, dtype: bool
```
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REGR: Barplot broken on Index(dtype='object')
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| 2021-09-23T21:51:37Z
| null |
NONE
| null |
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame(
{
'a': [1, 2],
},
index=pd.Index([0, 'Total'])
)
df.plot.bar()
```
#### Problem description
On pandas 1.2.0, this raises:
```
Traceback (most recent call last):
File "/home/raillard/WFH/LISA/.lisa-venv-3.8/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 706, in astype
casted = self._values.astype(dtype, copy=copy)
ValueError: invalid literal for int() with base 10: 'Total'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "testplot.py", line 21, in <module>
df.plot.bar()
File "/home/raillard/WFH/LISA/.lisa-venv-3.8/lib/python3.8/site-packages/pandas/plotting/_core.py", line 1113, in bar
return self(kind="bar", x=x, y=y, **kwargs)
File "/home/raillard/WFH/LISA/.lisa-venv-3.8/lib/python3.8/site-packages/pandas/plotting/_core.py", line 955, in __call__
return plot_backend.plot(data, kind=kind, **kwargs)
File "/home/raillard/WFH/LISA/.lisa-venv-3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/__init__.py", line 61, in plot
plot_obj.generate()
File "/home/raillard/WFH/LISA/.lisa-venv-3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py", line 280, in generate
self._make_plot()
File "/home/raillard/WFH/LISA/.lisa-venv-3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py", line 1434, in _make_plot
self.tick_pos = ax.convert_xunits(self.ax_index).astype(np.int)
File "/home/raillard/WFH/LISA/.lisa-venv-3.8/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 708, in astype
raise TypeError(
TypeError: Cannot cast Index to dtype int64
```
#### Expected Output
On pandas 1.1.5, it works without issues and produces a plot with 2 bars, one labeled "0" and the other one "Total" as expected.
#### Output of ``pd.show_versions()``
<details>
Python 3.8.7 (default, Dec 24 2020, 17:53:09)
[GCC 10.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.3-arch1-1
Version : #1 SMP PREEMPT Sun, 27 Dec 2020 10:50:46 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 1.2.0
numpy : 1.19.4
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.1.1
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : 3.4.2
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 : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU3NzgyNTExNDY=
| 38,948
|
ENH: Return type of pandas.DataFrame.dropna is incorrectly None
|
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"color": "ea91a4",
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"description": "type annotations, mypy/pyright type checking",
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[] | null | 2
| 2021-01-04T17:39:10Z
| 2021-03-14T10:55:05Z
| 2021-03-14T10:55:05Z
|
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 numpy as np
example = pd.DataFrame({'a': [1, 2, 3], 'b': [4, np.nan, 6]})
example.dropna()
```
#### Problem description
Currently, the return type of `pandas.DataFrame.dropna` is missing. The documentation correctly states that the return type should be `Optional[DataFrame]`, but the function definition lacks a return type, so type checkers (in my case pyright) incorrectly throw the type as `None`.
The issue occurs in the source code here:
https://github.com/pandas-dev/pandas/blob/3e89b4c4b1580aa890023fc550774e63d499da25/pandas/core/frame.py#L5041
It should be typed as seen in `pandas.DataFrame.drop_duplicates`, seen below:
https://github.com/pandas-dev/pandas/blob/3e89b4c4b1580aa890023fc550774e63d499da25/pandas/core/frame.py#L5183-L5189
More broadly, it seems that the `dropna` function is missing typing for its arguments as well.
#### Expected Output
The return type of `pandas.DataFrame.dropna` should be `Optional[DataFrame]`
I don't think this should be too hard to fix. I'll work in it tonight and submit a PR in the next day or two.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.9.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-33-generic
Version : #36-Ubuntu SMP Wed Dec 9 09:14:40 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.2.0
numpy : 1.19.4
pytz : 2020.5
dateutil : 2.8.1
pip : 20.2.2
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 : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU3NzgyNTc2NTk=
| 38,949
|
ENH: Explode like feature for spliting column containing iterable values into multiple columns
|
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[] | null | 5
| 2021-01-04T17:51:59Z
| 2021-08-14T23:18:43Z
| 2021-08-14T23:18:43Z
|
NONE
| null |
#### Describe the solution you'd like
```py
from collections.abc import Iterable
import pandas as pd
def split_columns(df):
iterable_cols = [c for c in df.columns if isinstance(df[c].iloc[0], Iterable)]
for c in iterable_cols:
n = len(df[c].iloc[0])
if isinstance(df[c].iloc[0], dict):
tmp_df = pd.DataFrame(df[c].tolist(), index=df.index)
tmp_df.columns = c + "_" + tmp_df.columns
df = pd.concat([df, tmp_df], 1)
else:
df[[f"{c}_{i}" for i in range(n)]] = pd.DataFrame(
df[c].tolist(), index=df.index
)
return df.drop(iterable_cols, 1)
```
I think the code is not robust and optimized, but it will give an idea of the desired feature.
<table>
<tr>
<td>
Input:
<img src="https://user-images.githubusercontent.com/6646693/103563159-45780100-4ee2-11eb-976b-fbeea5a3922f.png" height="200">
<td>Output:
<img src="https://user-images.githubusercontent.com/6646693/103563202-5cb6ee80-4ee2-11eb-9df9-4c75b80eeb8a.png" height="200">
<tr>
<td>
<img src="https://user-images.githubusercontent.com/6646693/103565357-3bf09800-4ee6-11eb-9f6b-22d7f5bd3e89.png" width=150>
<td>
<img src="https://user-images.githubusercontent.com/6646693/103565412-50349500-4ee6-11eb-8c4b-ad53e3fe89df.png" width=200>
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REF: de-duplicate tslibs.fields
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| 2021-01-04T18:19:52Z
| 2021-01-04T19:30:14Z
| 2021-01-04T19:20:16Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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DOC: clarify and spellcheck indexing documentation
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| 1
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| 2021-01-05T17:09:34Z
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CONTRIBUTOR
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|
BUG: DataFrame.iloc.setitem with IntegerArray values
|
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| 0
| 2021-01-04T18:45:33Z
| 2021-01-09T22:26:22Z
| 2021-01-09T22:26:22Z
|
MEMBER
| null |
```
arr = pd.array([4, 5, 6])
df = pd.DataFrame(np.arange(3))
>>> df.iloc[:2] = arr[:2]
ValueError: could not broadcast input array from shape (2) into shape (2,1)
>>> df.iloc[:2] = arr[:2].to_numpy() # <-- works fine
```
This bug would not occur with 2D EAs.
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MDU6SXNzdWU3NzgyODkxNTI=
| 38,953
|
DOC: MultiIndex.is_lexsorted not linked
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| 6
| 2021-01-04T18:50:32Z
| 2021-01-06T04:27:04Z
| 2021-01-06T04:26:43Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
https://pandas.pydata.org/docs/dev/reference/api/pandas.MultiIndex.html?highlight=is_lexsorted
#### Documentation problem
On the API page for `MultiIndex`, `is_lexsorted` does not link to an API page for that method. That method has been deprecated and does not contain a docstring, perhaps that is why there is no link?
#### Suggested fix for documentation
Since this method is deprecated, suggest removing `is_lexsorted` from the list of methods on the `MultiIndex` API page. Alternatively could add a docstring and API page.
What is preferred?
|
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MDExOlB1bGxSZXF1ZXN0NTQ4NDA4MDIx
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CLN: Unify number recognition tests for all parsers
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| 1
| 2021-01-04T19:03:38Z
| 2021-01-04T23:23:01Z
| 2021-01-04T23:15:17Z
|
MEMBER
| null |
- [x] closes #38926
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Minor regex improvements.
Is a fixture the right thing to do here?
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BUG: read_excel() fails when checking __version__ of older xlrd versions
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| 3
| 2021-01-04T20:00:06Z
| 2021-02-01T13:45:48Z
| 2021-02-01T13:45:48Z
|
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.
---
#### When xlrd is installed but is an "older" version (e.g. 1.1.0):
```python
pandas.read_excel("file.xlsx", engine="openpyxl")
```
raises
```python
AttributeError: module 'xlrd' has no attribute '__version__'
```
This specifically occurs in ``pandas/io/excel/_base.py:336``
#### Problem description
This bug occurs regardless of the ``engine`` input because ``pandas.read_excel()`` _always_ checks ``xlrd.__version__`` if any version of xlrd is installed. Older versions of xlrd use ``__VERSION__`` instead of ``__version__`` instead, which results in the AttributeError.
This exception does not occur and everything behaves as expected if:
- xlrd is not installed
- A "newer" version of xlrd is installed (that has the ``__version__`` attribute)
#### Expected Output
According to the documentation,
```
Changed in version 1.2.0: When engine=None, the following logic will be used to determine the engine:
- If path_or_buffer is an OpenDocument format (.odf, .ods, .odt), then odf will be used.
- Otherwise if path_or_buffer is an xls format, xlrd will be used.
- Otherwise if openpyxl is installed, then openpyxl will be used.
- Otherwise if xlrd >= 2.0 is installed, a ValueError will be raised.
- Otherwise xlrd will be used and a FutureWarning will be raised. This case will raise a ValueError in a future version of pandas.
```
Based on this, I believe the intended behavior is:
- read_excel() should _not_ check the xlrd version if engine is ``"openpyxl"``
- If read_excel() does need to know the version of xlrd, it should try both ``__version__`` and ``__VERSION__``
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1062.4.1.el7.x86_64
Version : #1 SMP Wed Sep 25 09:42:57 EDT 2019
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US
LOCALE : en_US.ISO8859-1
pandas : 1.2.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.3.3
setuptools : 40.8.0
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.1
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 : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : 1.1.0
xlwt : None
numba : None
</details>
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| 38,956
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BUG: read_excel() using openpyxl engine header argument not working as expected
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| 13
| 2021-01-04T20:35:24Z
| 2021-02-05T03:15:59Z
| 2021-02-05T03:15:59Z
|
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.
---
#### Code Sample, a copy-pastable example
I am happy to provide the file I am using privately, offline if necessary. This would be necessary to directly reproduce this error.
```
import pandas as pd
df = pd.read_excel(myfile.xlsx, engine='openpyxl', header=2)
```
Throws error:
```...ValueError: Passed header=2 but only 2 lines in file```
#### Problem description
I changed my `read_excel()` code to be now using `engine='openpyxl` with the 1.2 `read_excel()` changes, but the `header` argument is no longer working as it was before. I am trying to read in a .xlsx file that has 2 initial rows that should be skipped, and the 3rd row contains the headers. Thus, I am passing `header=2`, which should automatically skip the first two rows and use the 3rd (0-indexed) row as the headers. However, I am getting the following error: `ValueError: Passed header=2 but only 2 lines in file`. I have confirmed multiple times that this file contains more than 2 lines - it contains thousands (happy to provide this file offline if necessary). I was able to previously run this code with the same file (before the `xlrd` deprecation, using the same header specification. With the openpyxl changes in 1.2, should we be using the `header` argument any differently? Thank you.
This is related to changes in https://github.com/pandas-dev/pandas/pull/35029.
#### Expected Output
Intended behavior would be that the .xlsx file is successfully read in as a dataframe using headers in row 3 and does not throw the above error.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
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 : 1.19.4
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 50.3.2
Cython : None
pytest : 4.3.1
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 : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 4.3.1
pyxlsb : None
s3fs : 0.4.2
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
xlsxwriter : None
numba : None
```
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DOC: move API breaking "check_freq" section from v1.2.1rst to v1.1.0.rst
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| 3
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| 2021-01-05T15:37:43Z
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|
CONTRIBUTOR
| null |
and add reference to 1.1.0 whats new update in v1.2.1.rst
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
This moves the doc notes for `assert_series_equal` and `assert_frame_equal` from the 1.2.1 whats new to the 1.1.0 whats new and adds a small reference to the section in the 1.1.0 page in the 1.2.1 page. This came up in #38471 and makes more sense to me than the current approach, but happy to close if current state is preferred.
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Backport PR #38957 on branch 1.2.x (DOC: move API breaking "check_freq" section from v1.2.1rst to v1.1.0.rst)
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NONE
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Backport PR #38957: DOC: move API breaking "check_freq" section from v1.2.1rst to v1.1.0.rst
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TYP/CLN: assorted cleanups
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- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Part of #38902.
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BUG: DataFrame.any() with not returning Boolean series with skipna=False across columns with numeric and string types
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| 2021-01-10T04:42:03Z
| 2021-01-10T04:02:23Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
If the dataframe has columns have different numeric types
```python
import pandas as pd
import numpy as np
df = pd.DataFrame([[1, 0], [np.nan, 1]], columns=list('ab'))
df.any(axis=1, skipna=False)
```
the behavior is as expected
```python
0 True
1 True
dtype: bool
```
But if the columns have numeric and string types
```python
df = pd.DataFrame([[1, 's'], [np.nan, 't']], columns=list('ab'))
df.any(axis=1, skipna=False)
```
it looks like the first column is returned
```python
0 1
1 NaN
dtype: object
```
The mixed types across rows doesn't seem to be a problem, if I do
```python
df = pd.DataFrame([['s', 1], [np.nan, 't']], columns=list('ab'))
df.any(axis=0, skipna=False)
```
I get
```python
a True
b True
dtype: bool
```
Also don't have any issues if `skipna=True`.
Expected behavior is to get a series of booleans either way.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.9.1.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 : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.2
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.0.0.post20201207
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 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 : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [x] whatsnew entry
xref: https://github.com/pandas-dev/pandas/pull/38641#issuecomment-754118989
xref: https://github.com/pandas-dev/pandas/pull/38664/files#r551419130
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MDU6SXNzdWU3Nzg2MDE2OTc=
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BUG: Timedelta.min.ceil("us") returns NaT
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MEMBER
| null |
```
>>> Timedelta.min.ceil("us")
NaT
>>> pd.Timedelta.max.floor("us")
OverflowError: Python int too large to convert to C long
```
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BUG: Timedelta(td64_out_of_bounds) silently overflowing
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- [ ] closes #xxxx
- [x] tests added / passed
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- [x] whatsnew entry
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BUG: Fillna doesn't work on large dataframes
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| 2021-08-14T23:19:26Z
| 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.
---
I'm using the fillna function as follows
```
ax = data.xtrain.fillna({"A": 123}, inplace=False)
```
Here data.xtrain is pandas dataframe
```
ipdb> type(data.xtrain)
<class 'pandas.core.frame.DataFrame'>
ipdb> data.xtrain.shape
(1317, 2728)
```
I noticed that the fillna method doesn't on my dataframe. It works okay on the samples provided in documentation. By digging into the codebase, I was able to isolate the error to the following line https://github.com/pandas-dev/pandas/blob/bbffcb95cf8a9ecc190ed8f467c7e283bc7d50b8/pandas/core/generic.py#L6424
Here `result[k]` seems to return a new copy of the column `k`
```
ipdb> obj is result[k]
False
```
`fillna` wouldn't work if the above attribute access returns a copy of the column.
I don't see a similar effect when the dataframe is a small 4*5 matrix.
#### Code Sample, a copy-pastable example
Unfortunately, the dataframe contains sensitive information which I can't share.
#### Problem description
`fillna` method uses attribute access when passed a dict of na values. In some cases, attribute access returns a new copy of the column which leads to the method not working as expected.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.7.6.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 : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.7.4
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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TST: add missing iloc label indexing tests
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| 1
| 2021-01-05T09:00:38Z
| 2021-01-10T11:05:52Z
| 2021-01-05T14:12:11Z
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CONTRIBUTOR
| null |
- [x] closes #38967
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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ENH: Add typing for pandas.core.frame.dropna
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[] | null | 13
| 2021-01-05T10:02:32Z
| 2021-04-24T13:31:51Z
| 2021-04-24T13:31:51Z
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NONE
| null |
This PR adds type hinting for pandas.core.frame.dropna. Additionally, an explicit `return None` was added to the if/else block for in-place changes to meet mypy standards and make the types explicit.
I haven't yet run the tests, because I didn't modify any source code. Once/if the CI builds pass, I'll check it off the list.
- [x] closes #38948
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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REGR: Bar plot from Series with IntervalIndex fails in pandas 1.2.0
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| 7
| 2021-01-05T11:41:55Z
| 2021-08-14T23:19:45Z
| null |
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
pd.Series(
[1,2],
index=pd.interval_range(0,2)
).plot.bar()
```
#### Problem description
The following error is raised in pandas 1.2.0
TypeError: Cannot cast IntervalIndex to dtype int32
pandas 1.1.5 does not have the same issue
stack trace:
<details>
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
c:\users\rclement\documents\.venv\lib\site-packages\pandas\core\arrays\interval.py in astype(self, dtype, copy)
777 try:
--> 778 return np.asarray(self).astype(dtype, copy=copy)
779 except (TypeError, ValueError) as err:
TypeError: int() argument must be a string, a bytes-like object or a number, not 'pandas._libs.interval.Interval'
The above exception was the direct cause of the following exception:
TypeError Traceback (most recent call last)
<ipython-input-4-fc9e0f28d9be> in <module>
1 pd.Series(
2 [1,2],
----> 3 index=pd.interval_range(0,2)
4 ).plot.bar()
c:\users\rclement\documents\.venv\lib\site-packages\pandas\plotting\_core.py in bar(self, x, y, **kwargs)
1111 other axis represents a measured value.
1112 """
-> 1113 return self(kind="bar", x=x, y=y, **kwargs)
1114
1115 @Appender(
c:\users\rclement\documents\.venv\lib\site-packages\pandas\plotting\_core.py in __call__(self, *args, **kwargs)
953 data.columns = label_name
954
--> 955 return plot_backend.plot(data, kind=kind, **kwargs)
956
957 __call__.__doc__ = __doc__
c:\users\rclement\documents\.venv\lib\site-packages\pandas\plotting\_matplotlib\__init__.py in plot(data, kind, **kwargs)
59 kwargs["ax"] = getattr(ax, "left_ax", ax)
60 plot_obj = PLOT_CLASSES[kind](data, **kwargs)
---> 61 plot_obj.generate()
62 plot_obj.draw()
63 return plot_obj.result
c:\users\rclement\documents\.venv\lib\site-packages\pandas\plotting\_matplotlib\core.py in generate(self)
278 self._compute_plot_data()
279 self._setup_subplots()
--> 280 self._make_plot()
281 self._add_table()
282 self._make_legend()
c:\users\rclement\documents\.venv\lib\site-packages\pandas\plotting\_matplotlib\core.py in _make_plot(self)
1432 if self.orientation == "vertical":
1433 ax.xaxis.update_units(self.ax_index)
-> 1434 self.tick_pos = ax.convert_xunits(self.ax_index).astype(np.int)
1435 elif self.orientation == "horizontal":
1436 ax.yaxis.update_units(self.ax_index)
c:\users\rclement\documents\.venv\lib\site-packages\pandas\core\indexes\interval.py in astype(self, dtype, copy)
368 def astype(self, dtype, copy: bool = True):
369 with rewrite_exception("IntervalArray", type(self).__name__):
--> 370 new_values = self._values.astype(dtype, copy=copy)
371 return Index(new_values, dtype=new_values.dtype, name=self.name)
372
c:\users\rclement\documents\.venv\lib\site-packages\pandas\core\arrays\interval.py in astype(self, dtype, copy)
779 except (TypeError, ValueError) as err:
780 msg = f"Cannot cast {type(self).__name__} to dtype {dtype}"
--> 781 raise TypeError(msg) from err
782
783 def equals(self, other) -> bool:
TypeError: Cannot cast IntervalIndex to dtype int32
</details>
#### Expected Output
A bar plot (produced with pandas 1.1.5):
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
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 : 1.2.0
numpy : 1.19.4
pytz : 2020.5
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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Backport PR #38841 on branch 1.2.x (Update conf.py to execute imports during pdf building)
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| 0
| 2021-01-05T12:58:45Z
| 2021-01-05T14:12:30Z
| 2021-01-05T14:12:29Z
|
NONE
| null |
Backport PR #38841: Update conf.py to execute imports during pdf building
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Backport PR #38803 on branch 1.2.x (BUG: avoid attribute error with pyarrow >=0.16.0 and <1.0.0)
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NONE
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Backport PR #38803: BUG: avoid attribute error with pyarrow >=0.16.0 and <1.0.0
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pandas.core.groupby.DataFrameGroupBy.rank() dense_rank does not work
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| 11
| 2021-01-05T14:00:59Z
| 2021-07-12T19:30:38Z
| 2021-07-12T19:30:38Z
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NONE
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TST: update pre-commit config to only exclude extension from bare pytest.raises check
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| 1
| 2021-01-05T16:56:16Z
| 2021-01-05T19:10:57Z
| 2021-01-05T18:47:08Z
|
MEMBER
| null |
With #38920 I eliminated all instances of `pytest.raise` without `match=msg` in pandas/tests/computation and pandas/tests/io. #38799 was happening around the same time and missed that they were fixed. So this closes the loop and now only pandas/tests/extension needs to be excluded from the linting check.
I don't think the bare `pytest.raise`s in pandas/tests/extensions will be removed. They are in a pretty complex inheritance hierarchy and reused for many different types of errors and error messages. So I propose that this PR closes #30999.
- [x] closes #30999
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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BUG: pd.to_numeric does not copy _mask for ExtensionArrays
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| 0
| 2021-01-05T17:15:32Z
| 2021-01-09T21:22:56Z
| 2021-01-09T21:22:56Z
|
MEMBER
| null |
In #38746 while implementing `to_numeric` for ExtensionArrays I do not copy the mask of the original input. This means that the (potentially) cast array shares a mask with the input. We should copy the mask.
```python
In [9]: import pandas as pd
...: import pandas._testing as tm
...:
...: arr = pd.array([1, 2, pd.NA], dtype="Int64")
...:
...: result = pd.to_numeric(arr, downcast="integer")
...: expected = pd.array([1, 2, pd.NA], dtype="Int8")
...: tm.assert_extension_array_equal(result, expected)
...:
...: arr[1] = pd.NA # should not modify result
...: tm.assert_extension_array_equal(result, expected)
...:
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-9-f72c43e18273> in <module>
9
10 arr[1] = pd.NA
---> 11 tm.assert_extension_array_equal(result, expected)
~/repos/pandas/pandas/_testing/asserters.py in assert_extension_array_equal(left, right, check_dtype, index_values, check_less_precise, check_exact, rtol, atol)
794 left_na = np.asarray(left.isna())
795 right_na = np.asarray(right.isna())
--> 796 assert_numpy_array_equal(
797 left_na, right_na, obj="ExtensionArray NA mask", index_values=index_values
798 )
[... skipping hidden 1 frame]
~/repos/pandas/pandas/_testing/asserters.py in _raise(left, right, err_msg)
699 diff = diff * 100.0 / left.size
700 msg = f"{obj} values are different ({np.round(diff, 5)} %)"
--> 701 raise_assert_detail(obj, msg, left, right, index_values=index_values)
702
703 raise AssertionError(err_msg)
~/repos/pandas/pandas/_testing/asserters.py in raise_assert_detail(obj, message, left, right, diff, index_values)
629 msg += f"\n[diff]: {diff}"
630
--> 631 raise AssertionError(msg)
632
633
AssertionError: ExtensionArray NA mask are different
ExtensionArray NA mask values are different (33.33333 %)
[left]: [False, True, True]
[right]: [False, False, True]
```
Thanks @jorisvandenbossche for pointing this out!
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MDExOlB1bGxSZXF1ZXN0NTQ5MzEwODU4
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DOC: remove is_lexsorted from MultiIndex docstring
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| 1
| 2021-01-05T17:31:37Z
| 2021-01-06T00:15:31Z
| 2021-01-06T00:15:27Z
|
CONTRIBUTOR
| null |
`is_lexsorted` has been deprecated (https://github.com/pandas-dev/pandas/pull/38701)
- [x] closes https://github.com/pandas-dev/pandas/issues/38953
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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DOC: Scatter and Hexbin from Series plot documentation
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| 4
| 2021-01-05T18:18:35Z
| 2021-01-06T18:36:30Z
| 2021-01-06T18:36:16Z
|
NONE
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.plot.html
#### Documentation problem
As the scatter and hexbin kinds are not supported for Series, it shouldn't appear in the docs for Series, just for DataFrames.
#### Suggested fix for documentation
Simply removing them.
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BUG: MultiIndex.union dropping duplicates from result
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| 13
| 2021-01-05T19:42:42Z
| 2021-05-27T10:26:30Z
| 2021-05-26T02:02:27Z
|
MEMBER
| null |
- [x] xref #38745
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
This more or less sits on top of #36299. The current base-_union implementation works only for sorted indexes correctly if indexes containing duplicates. Hence I've only added tests for sorted indexes,
|
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MDExOlB1bGxSZXF1ZXN0NTQ5NDYzOTY1
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CLN: Multiindex tests
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MEMBER
| null |
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Found a few wrong tests and parametrized the similar ones
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MDU6SXNzdWU3Nzk1NDQ0MDM=
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BUG: Different results from DataFrame.apply and str accessor
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| 6
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| 2021-01-16T01:12:46Z
| 2021-01-16T01:12:46Z
|
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.
- [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
import pandas as pd
print(pd.__version__)
df = pd.DataFrame(zip('abc', 'def'))
print(df.apply(lambda f: "/".join(f), axis=1))
print(df.apply(lambda f: "/".join(f.str.upper()), axis=1))
```
#### Problem description
Using the `str` accessor gives different results on 1.1.5 and 1.2.x:
```
1.1.5
0 a/d
1 b/e
2 c/f
dtype: object
0 A/D
1 B/E
2 C/F
dtype: object
1.2.0+22.g6cdb4e7fb
0 a/d
1 b/e
2 c/f
dtype: object
0 A/D
1 A/D
2 A/D
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 6cdb4e7fbf4ba9a8fc83af550d866765f64ecdd9
python : 3.7.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.2.0+22.g6cdb4e7fb
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.0.0.post20201207
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU3Nzk2ODcyOTY=
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REGR: ExtensionArray aggregation on non-numeric types fails
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| 5
| 2021-01-05T22:51:01Z
| 2021-01-13T13:18:40Z
| 2021-01-13T13:18:40Z
|
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
df = pd.DataFrame({'key': ['a', 'a', 'b', 'b'], 'val': ['a', 'b', 'c', 'd']}, dtype="string")
df.groupby("key").agg({'val': 'first'})
```
Expected:
```
val
key
a a
b c
```
Actual fails with error:
```
lib/python3.8/site-packages/pandas/core/groupby/ops.py in _ea_wrap_cython_operation(self, kind, values, how, axis, min_count, **kwargs)
538 return result
539
--> 540 raise NotImplementedError(values.dtype)
541
NotImplementedError: string
```
#### Problem description
Calling groupby on an ExtensionArray and aggregation on a string column fails with the error: `NotImplementedError: string`. This is because an optimized cython aggregation is attempted and when fails, the error string is improperly checked and does not fallback to a non-cython aggregation.
This was found with the extension arrays from https://github.com/CODAIT/text-extensions-for-pandas/blob/master/text_extensions_for_pandas/io/bert.py#L217 and full error log can be seen here https://github.com/CODAIT/text-extensions-for-pandas/pull/157/checks?check_run_id=1646927381#step:5:1438
#### Expected Output
Pandas should handle the NotImplementedError and fallback to a non-cython aggregation implementation.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
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 : 1.2.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 49.6.0.post20201009
Cython : None
pytest : 6.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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| 779,689,697
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MDU6SXNzdWU3Nzk2ODk2OTc=
| 38,981
|
to_csv - parameter decimal
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| 2021-01-05T22:53:23Z
| 2021-01-27T12:09:24Z
| 2021-01-27T12:09:24Z
|
NONE
| null |
I think that pandas.to_csv does not properly process the decimal parameter. It seems it is ignored whenever the dataframe to be exported contains a NaN value. This means it is not possible to export pandas in European CSV format.
I am aware that problems with to_csv have been discussed earlier and from these discussions I found the hint to check for NaN values. I am afraid I do not understand the final outcome of alle these previous discussions but I think the current behaviour is not quite correct.
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MDExOlB1bGxSZXF1ZXN0NTQ5NjQ2ODk4
| 38,982
|
REGR: Bug fix for ExtensionArray groupby aggregation on non-numeric types
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| 2021-01-14T19:08:56Z
| 2021-01-13T13:18:40Z
|
CONTRIBUTOR
| null |
- [X] closes #38980
- [x] tests added / passed
- [X] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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MDU6SXNzdWU3Nzk3NTUwOTQ=
| 38,983
|
BUG: IntegerDtype.__eq__(np.dtype(object)) returns True
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| 2021-01-06T16:31:16Z
| 2021-01-06T16:21:47Z
|
MEMBER
| null |
Also looks like `IntegerArray.astype(other_nullable_integer_dtype)` is returning an ndarray
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MDExOlB1bGxSZXF1ZXN0NTQ5NzMzMDAx
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BUG: MultiIndex.intersection duplicating nans in result
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| 2021-01-06T00:34:12Z
| 2021-01-06T15:24:33Z
| 2021-01-06T14:44:46Z
|
MEMBER
| null |
- [x] xref #38623
- [x] tests added / passed
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- [x] whatsnew entry
This also aligns the multiindex implementation with the base implementation a bit more.
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REF: de-duplication in libperiod
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MEMBER
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BUG: Datetimelike equality comparisons with Categorical
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| 2
| 2021-01-06T01:37:23Z
| 2021-01-08T14:10:07Z
| 2021-01-08T14:10:03Z
|
CONTRIBUTOR
| null |
- [ ] closes #30699
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NTQ5ODA3OTM4
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Fix bug on master
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| 2
| 2021-01-06T02:02:54Z
| 2021-01-06T02:53:30Z
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|
MEMBER
| null |
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
The banklist file was removed and replaced with banklist.csv. This causes failures on master.
This is just a temporary fix for the user guide.
cc @jreback
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| 4
| 2021-01-06T02:10:49Z
| 2021-09-01T16:15:36Z
| 2021-09-01T16:15:36Z
|
MEMBER
| null |
**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
The banklist.html file was removed from "https://www.fdic.gov/resources/resolutions/bank-failures/failed-bank-list/
#38987 marked the tests as xfail and removed the userguide entry.
#### Expected
Replace the xfail tests with a new html file url and adjust the userguide appropriately.
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| 6
| 2021-01-06T02:24:24Z
| 2021-01-07T18:48:20Z
| 2021-01-07T18:48:20Z
|
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
I have a few weird csv files with null bytes in the first row. Since 1.2.0 they are raising even if I skip the first row,
```python
pd.read_csv("test.csv", engine="python", skiprows=[0])
pd.read_csv("test.csv", engine="c", skiprows=[0])
```
Could not get this to fail with an example created in code.
#### Problem description
Both engines raising
```
Traceback (most recent call last):
File "/home/developer/.config/JetBrains/PyCharm2020.3/scratches/scratch_4.py", line 377, in <module>
pd.read_csv("/media/sf_Austausch/test.csv", engine="c", skiprows=[0], nrows=2)
File "/home/developer/PycharmProjects/pandas/pandas/io/parsers.py", line 605, in read_csv
return _read(filepath_or_buffer, kwds)
File "/home/developer/PycharmProjects/pandas/pandas/io/parsers.py", line 457, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/home/developer/PycharmProjects/pandas/pandas/io/parsers.py", line 814, in __init__
self._engine = self._make_engine(self.engine)
File "/home/developer/PycharmProjects/pandas/pandas/io/parsers.py", line 1045, in _make_engine
return mapping[engine](self.f, **self.options) # type: ignore[call-arg]
File "/home/developer/PycharmProjects/pandas/pandas/io/parsers.py", line 1894, in __init__
self._reader = parsers.TextReader(self.handles.handle, **kwds)
File "pandas/_libs/parsers.pyx", line 517, in pandas._libs.parsers.TextReader.__cinit__
File "pandas/_libs/parsers.pyx", line 619, in pandas._libs.parsers.TextReader._get_header
File "pandas/_libs/parsers.pyx", line 813, in pandas._libs.parsers.TextReader._tokenize_rows
File "pandas/_libs/parsers.pyx", line 1942, in pandas._libs.parsers.raise_parser_error
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xe4 in position 8: invalid continuation byte
Process finished with exit code 1
```
This worked on 1.1.5.
Relevant changes are:
https://github.com/pandas-dev/pandas/blob/9b16b1e1ee049b042a7d59cddd8fbd913137223f/pandas/io/common.py#L556:L557
infers encoding now, which leads to
https://github.com/pandas-dev/pandas/blob/9b16b1e1ee049b042a7d59cddd8fbd913137223f/pandas/io/common.py#L643:L651
where errors is always strict for read_csv. On 1.1.5 in case of no encoding given, ``errors`` was set to ``replace``, which caused this to work. Was this an intended change? Labeling as Regression for now.
#### Expected Output
```
! xyz 0 1
0 * 2000 1 2
1 * 2001 0 0
```
cc @gfyoung @twoertwein
This was caused by #36997
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : cd0224d26c6050f0e638861b9e557086673c14c1
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+356.ge0cb09e917
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
</details>
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DOC: add comparison to spreadsheets
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| 9
| 2021-01-06T02:49:22Z
| 2021-10-05T16:30:58Z
| null |
MEMBER
| null |
#### Location of the documentation
https://pandas.pydata.org/docs/dev/getting_started/comparison/comparison_with_spreadsheets.html
#### Documentation problem
From https://github.com/pandas-dev/pandas/pull/38554:
> I teach [a class on pandas for public policy students](https://github.com/afeld/python-public-policy/blob/master/syllabus.md#readme), and for many of them, spreadsheets are the only point of reference they have for working with tabular data.
There are [a number of unofficial resources](https://www.google.com/search?q=excel+vs+pandas), but no official, living documentation to point them to.
#### Suggested fix for documentation
The idea is to add a "Comparison to spreadsheets" page alongside [the other comparison pages](https://pandas.pydata.org/pandas-docs/stable/getting_started/comparison/index.html). That new page already got merged in https://github.com/pandas-dev/pandas/pull/38554; creating this issue to keep a running checklist of the TODOs.
- [x] start with what @rotuna did in https://github.com/pandas-dev/pandas/pull/23042
- [x] link from Getting Started pages
- [ ] incorporate structure from [SAS](https://pandas.pydata.org/pandas-docs/stable/getting_started/comparison/comparison_with_sas.html)/[STATA](https://pandas.pydata.org/pandas-docs/stable/getting_started/comparison/comparison_with_stata.html) comparison pages
- [x] Data structures
- [x] Data input / output
- [x] Mention `read_excel()`
- [x] Data operations
- [x] String processing
- [x] Merging
- [ ] Missing data
- [ ] GroupBy
- [x] Other considerations
- [ ] standardize datasets used for examples, for greater consistency and reduced cognitive burden on the readers
- [`tips`](https://raw.github.com/pandas-dev/pandas/master/pandas/tests/io/data/csv/tips.csv) probably works for most
- [ ] add info about charting
- [x] [add example showing round trip to/from Excel](https://github.com/pandas-dev/pandas/pull/38554#discussion_r549409566)
- [ ] [have pivot table examples match](https://github.com/pandas-dev/pandas/pull/38554#issuecomment-752767183)
- [ ] add [quick reference](https://pandas.pydata.org/docs/getting_started/comparison/comparison_with_r.html#quick-reference)
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Backport PR #38987 on branch 1.2.x (Fix bug on master)
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| 0
| 2021-01-06T02:53:25Z
| 2021-01-06T12:53:03Z
| 2021-01-06T12:53:02Z
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NONE
| null |
Backport PR #38987: Fix bug on master
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ENH: 2D support for MaskedArray
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| 2021-10-19T11:58:27Z
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|
MEMBER
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This doesn't in any way _use_ the 2D support, but opens up the option of incrementally fleshing out the tests.
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DOC: add more sections to spreadsheet comparison
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| 2021-01-06T14:53:19Z
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[Preview (link to PDF on Google Drive)](https://drive.google.com/file/d/1uUyTQyEAX3F6h4EJqKz7Mz4KehGBnAig/view?usp=sharing)
This pull request gets closer to full parity with [SAS](https://pandas.pydata.org/pandas-docs/stable/getting_started/comparison/comparison_with_sas.html)/[STATA](https://pandas.pydata.org/pandas-docs/stable/getting_started/comparison/comparison_with_stata.html) comparison pages by adding the Data Input/Output through Merging sections. It still needs Missing Data and GroupBy, but wanted to get this in while I was at a good stopping place. Each section was done in its own commit, if it's easier to review that way.
---
- [x] ~~closes~~ part of https://github.com/pandas-dev/pandas/issues/38990
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] ~~whatsnew entry~~
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DOC: elaborate on copies vs in place operations in comparison docs
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| 2021-01-10T01:13:26Z
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MEMBER
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- [ ] ~~closes #xxxx~~
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- [ ] ~~whatsnew entry~~
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ENH: Add table-wise numba rolling to other agg funcions
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| 2021-05-26T15:54:22Z
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MEMBER
| null |
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Timings for a wide table
```
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(10, 10**5))
roll_single = df.rolling(2, method="single")
roll_table = df.rolling(2, method="table")
%timeit roll_single.mean()
4.92 s ± 463 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
roll_single.mean(engine="numba", engine_kwargs={"nopython": True, "nogil": True, "parallel": True})
%timeit roll_single.mean(engine="numba", engine_kwargs={"nopython": True, "nogil": True, "parallel": True})
5.72 s ± 430 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
roll_table.mean(engine="numba", engine_kwargs={"nopython": True, "nogil": True, "parallel": True})
%timeit roll_table.mean(engine="numba", engine_kwargs={"nopython": True, "nogil": True, "parallel": True})
10.3 ms ± 1.23 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)
```
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|
ENH: branch deployments of documentation
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| 2021-01-06T05:47:51Z
| 2021-09-17T14:06:18Z
| null |
MEMBER
| null |
#### Is your feature request related to a problem?
When submitting large/complex pull requests around documentation, I've been (reasonably) asked to submit a screenshot of the change. These are tedious to capture, the links in screenshots aren't clickable, the text isn't searchable, etc.
#### Describe the solution you'd like
When changes to a branch are pushed up, the documentation is built and available at a stable URL, a la ReadTheDocs. I believe the first part happens already via the `Web and docs` job in GitHub Actions, so I _think_ it would mostly be a matter of deploying (and later cleaning up?) the built docs. https://pandas.pydata.org/docs/dev/ shows the latest on `master`, so maybe the URL structure is something like:
```
https://pandas.pydata.org/docs/preview/<user>/<branch>/
```
Bonus: Adding a commit status that links to the generated docs for that branch, [as Netlify does](https://www.netlify.com/blog/2016/07/20/introducing-deploy-previews-in-netlify/).
#### API breaking implications
n/a
#### Describe alternatives you've considered
In one pull request, I:
1. Generated the page
2. Opened it in my browser
3. Exported it as a PDF
4. Uploaded it to Google Drive
5. Changed the sharing settings
6. Pasted the link in the comment
This solved the clickability/searchability problems, but is even more work than taking a screenshot.
#### Additional context
none
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REGR: errors='replace' when encoding/errors are not specified
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| 2021-01-06T06:02:23Z
| 2021-01-07T19:07:02Z
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|
CONTRIBUTOR
| null |
- [x] closes #38989
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Should 1.3 use `errors='replace'` when no `encoding/errors` are specified or use `errors=None` (strict)?
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DOC: Update contributing.rst
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| 1
| 2021-01-06T10:52:38Z
| 2021-01-10T11:03:10Z
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|
CONTRIBUTOR
| null |
- [x] closes #38938
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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BUG:
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| 2021-01-06T10:58:25Z
| 2021-01-26T17:38:09Z
| 2021-01-26T17:38:08Z
|
NONE
| null |
- [Checked ] I have checked that this issue has not already been reported.
- [ Checked] I have confirmed this bug exists on the latest version of pandas.
- [Not Checked ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
df.to_csv("test.csv")
ImportError: cannot import name 'CompressionOptions' from 'pandas._typing
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
Breaking error on printing csvs right after upgrading to 1.1.5
#### Expected Output
CSV output of dataframe
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Remove Scatter and Hexbin from Series plot documentation
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| 4
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| 2021-01-06T18:36:21Z
| 2021-01-06T18:36:17Z
|
CONTRIBUTOR
| null |
- [x] closes #38976
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] Remove scatter and hexbin entry from https://github.com/pandas-dev/pandas/blob/v1.2.0/pandas/plotting/_core.py#L603-L1708
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| 24
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| 2021-02-05T03:15:59Z
| 2021-02-05T03:15:59Z
|
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.
-----
#### Old behavior with xlrd 1.2.0 (last version with XLSX support)
```
>>> import pandas as pd
>>> df=pd.read_excel('infile.xlsx', engine='xlrd')
<stdin>:1: FutureWarning: Your version of xlrd is 1.2.0. In xlrd >= 2.0, only the xls format is supported. As a result, the openpyxl engine will be used if it is installed and the engine argument is not specified. Install openpyxl instead.
>>> df.shape
(843, 11)
```
File is read perfectly and dataframe is ok, with all 11 columns.
#### Problem description
When using openpyxl as engine for read_excel:
```
>>> import pandas as pd
>>> df=pd.read_excel('infile.xlsx', engine='openpyxl')
>>> print(df)
Empty DataFrame
Columns: [Column1, Column2, Column3]
Index: []
>>> df.shape
(0, 3)
```
Only the 3 first headers are read, and it stops.
However, if the file is opened directly with openpyxl, it works fine, I'm using it to open and save the file with a temporary name to make openpyxl work as engine on Pandas:
```
>>> wb = load_workbook('infile.xlsx')
>>> wb.save(filename = 'temp.xlsx')
>>> pd.read_excel('temp.xlsx', engine='openpyxl')
>>> df.shape
(843, 11)
```
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.128-microsoft-standard
Version : #1 SMP Tue Jun 23 12:58:10 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.3.1
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : None
</details>
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| 15
| 2021-01-06T15:27:36Z
| 2021-07-22T03:38:16Z
| 2021-01-26T23:31:50Z
|
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
df = pd.DataFrame(range(100000))
df.to_pickle("df.pkl.xz", protocol=5)
```
#### Problem description
the above code raises `TypeError`
```
TypeError Traceback (most recent call last)
<ipython-input-220-8c4b6e9cecfc> in <module>
2
3 df = pd.DataFrame(range(100000))
----> 4 df.to_pickle("df.pkl.xz", protocol=5)
/python3.9/site-packages/pandas/core/generic.py in to_pickle(self, path, compression, protocol, storage_options)
2859 from pandas.io.pickle import to_pickle
2860
-> 2861 to_pickle(
2862 self,
2863 path,
/python3.9/site-packages/pandas/io/pickle.py in to_pickle(obj, filepath_or_buffer, compression, protocol, storage_options)
95 storage_options=storage_options,
96 ) as handles:
---> 97 pickle.dump(obj, handles.handle, protocol=protocol) # type: ignore[arg-type]
98
99
/python3.9/lzma.py in write(self, data)
232 compressed = self._compressor.compress(data)
233 self._fp.write(compressed)
--> 234 self._pos += len(data)
235 return len(data)
236
TypeError: object of type 'pickle.PickleBuffer' has no len()
```
Note that in order to reproduce the bug, it have to:
* the data frame is large enough
* it is using compressing ("*.xz" or "*.zip")
* it is using latest pickle protocol (default). set protocol to 4 explicity will workaround this bug
#### Expected Output
the pickle should save ok
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.9.0.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 : 1.2.0
numpy : 1.19.4
pytz : 2020.5
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0
Cython : 0.29.21
pytest : 6.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: 0.9.0
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
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TST: add note about scope of base extension tests to all files
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| 2021-01-12T08:05:04Z
| 2021-01-06T18:34:18Z
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MEMBER
| null |
We already had this note in about half of the files in this directory, copied it to include in the other files as well.
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BUG: IndexError: positional indexers are out-of-bounds iloc boolean indexing
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| 35
| 2021-01-06T15:49:44Z
| 2021-03-02T23:22:00Z
| 2021-03-02T23:21:59Z
|
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.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame([[0, 1, 2]], columns=['a', 'b', 'c'])
mask = pd.DataFrame([[False, True, False]], columns=['a', 'b', 'c'])
df.iloc[mask] = 3 # Works fine with assignment
print(df.iloc[mask]) # Throws IndexError, but should give similar result as df.values[mask]
```
#### Problem description
Documentation of Pandas says I can use boolean array with iloc. The iloc call with the assignment works fine, but without assignment fails. Just as a note here: In pandas 1.0.1 not even the assignment works (it starts working again if there are more than 1 rows of data).
#### Expected Output
3 (similar as df.values[mask])
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.5.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 : 1.2.0
numpy : 1.18.4
pytz : 2019.3
dateutil : 2.7.3
pip : 20.2
setuptools : 46.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fsspec : 0.7.3
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.49.1
</details>
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BUG: thousands separator in read_html alters data even though converter is set for a specific column
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| 0
| 2021-01-06T17:33:11Z
| 2021-01-26T16:49:08Z
| 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.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
def convert_date_fix(date_str):
return pd.to_datetime(date_str, format='%d0%m%Y')
html_table = """
<table class="gridview" style="width:600px;border-collapse:collapse;" cellspacing="0" cellpadding="4" border="0">
<tbody>
<tr>
<th scope="col" align="left">Portfolio</th>
<th scope="col" align="right">Waehrung</th>
<th scope="col" align="right">Volumen</th>
<th scope="col" align="right">Datum</th>
</tr>
<tr>
<td align="left">Aktie A</td>
<td align="right">EUR</td>
<td align="right">20.320.945,77</td>
<td align="right">05.01.2021</td>
</tr>
<tr>
<td align="left">Aktie B</td>
<td align="right">EUR</td>
<td align="right">4.133.996,41</td>
<td align="right">05.01.2021</td>
</tr>
<tr>
<td align="left">Aktie C</td>
<td align="right">EUR</td>
<td align="right">3.855.218,70</td>
<td align="right">05.01.2021</td>
</tr>
</tbody>
</table>"""
#ex1: date-string is converted to int64 with . separator (05.01.2021 becomes 5012021)
data_frame = pd.read_html(html_table, thousands='.', decimal=',')[0]
print(data_frame)
print(data_frame.dtypes)
#ex2: date-string is converted to object but string is altered same as above (05.01.2021 becomes 5012021)
data_frame = pd.read_html(html_table, thousands='.', decimal=',', converters={'Datum' : str})[0]
print(data_frame)
print(data_frame.dtypes)
#ex3: ugly fix that reverts conversion and acutally turns the column into a date
data_frame = pd.read_html(html_table, thousands='.', decimal=',', converters={'Datum' : convert_date_fix})[0]
print(data_frame)
print(data_frame.dtypes)
```
#### Problem description
I've set `thousands` and `decimal` separator parameters to fit the general number format that is used in Germany and also in my html-table example.
ex1: although the date column gets wrongfully interpreted as integer value, the behaviour is reasonable since no specific `converters` were defined for that column.
ex2 (the actual problem/bug): when setting a specific `converter` for the date column and thereby define it as string I'd expect the parser to keep the value untouched and return '05.01.2021', however it also interprets the string as integer and applies the corresponding conversion as above.
ex3: even when setting an own function as `converter` the original value is still altered and the integer conversion needs to be reverted in my helper function.
#### Expected Output
When a `converter` is defined for a specific column, the original value should be passed to the conversion function and conversions that come from thousands-separator for numeric values should not be applied.
#### Additional improvement/feature request
Adapt [`read_html`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_html.html) to accept a `date_parser` as in [`read_csv`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html) which would make it easier to automatically parse dates in various formats. The `read_html` function currently has a boolean `parse_dates` param (same as `read_csv`) but in contrast does not support conversion of unconventional date formats.
see also #10684 or [this](https://stackoverflow.com/questions/33111299/pandas-read-html-can-i-specify-format-for-parse-dates) stackoverflow post.
#### 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 : AMD64 Family 23 Model 113 Stepping 0, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : German_Austria.1252
pandas : 1.2.0
numpy : 1.20.0rc2
pytz : 2020.5
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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DOC: Clarify index_col behavior for read_csv
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| 1
| 2021-01-06T19:53:22Z
| 2021-01-06T23:01:11Z
| 2021-01-06T22:56:26Z
|
MEMBER
| null |
- [x] closes #38830
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
|
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| 780,840,365
|
MDU6SXNzdWU3ODA4NDAzNjU=
| 39,007
|
BUG: Inconsistent results of value_counts and mode for different nan-values
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| 1
| 2021-01-06T20:54:29Z
| 2021-01-27T14:03:52Z
| 2021-01-27T14:03:52Z
|
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.
```python
import pandas as pd
import numpy as np
s=pd.Series([True, pd.NA, np.nan])
s.value_counts(dropna=False)
print(s.value_counts(dropna=False))
print(s.mode(dropna=False))
```
#### Problem description
For `s.value_counts(...)` the result is
```
NaN 2
True 1
dtype: int64
```
but for `s.mode(...)` the output is:
```
0 NaN
1 True
2 <NA>
dtype: object
```
#### Expected Output
for the sake of consistancy one would expect `mode` to be only `NaN`, as it was counted twice with `value_counts`, i.e.
```
0 NaN
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-53-generic
Version : #74-Ubuntu SMP Fri Dec 2 15:59:10 UTC 2016
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.2
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.0.0.post20201207
Cython : 0.29.21
pytest : 5.4.3
hypothesis : 5.36.0
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 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.50.1
</details>
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MDExOlB1bGxSZXF1ZXN0NTUwNjcyMDkz
| 39,008
|
CLN: inspect_excel_format
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| 2021-01-06T21:05:55Z
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| 2021-01-08T21:39:23Z
|
MEMBER
| null |
- [x] closes #38823
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
cc @rhshadrach
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ENH: making value_counts stable/keeping original ordering
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| 8
| 2021-01-06T21:07:00Z
| 2021-01-27T19:05:04Z
| 2021-01-22T21:42:34Z
|
CONTRIBUTOR
| null |
closes #12679
closes #11227
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
The order of the returned keys for `value_counts` aren't arbitrary (i.e. depending on the used hash function) but are original ordering (when sorted this applies for the keys with the same number of values).
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|
BUG: 1.2. version fails in adding column if number on column name beginning.
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| 2021-01-08T00:19:44Z
| 2021-01-08T00:19:44Z
|
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 that raise traceback
```python
import pandas as pd
df = pd.DataFrame([[1, 2], [3, 4]])
df['0 - Name'] = [1, 2]
```
Result:
ValueError: format number 1 of "0 - Name" is not recognized
If other name witho no mumber on column name, it will pass.
In older version, worked correctly
Tested on new virtualenv - just `pip install pandas`
Failed also on Travis (Python 3.6 passed, 3.7 and 3.8 failed), so probably not my IDE setup or something similar
Full traceback here...
```python-traceback
Traceback (most recent call last):
File "c:\Users\truton\ownCloud\Github\test\del\lib\site-packages\pandas\core\generic.py", line 3823, in _set_item
loc = self._info_axis.get_loc(key)
File "c:\Users\truton\ownCloud\Github\test\del\lib\site-packages\pandas\core\indexes\range.py", line 354, in get_loc
raise KeyError(key)
KeyError: '0 - Name'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "c:\Users\truton\ownCloud\Github\test\del.py", line 4, in <module>
df['0 - Name'] = [1, 2]
File "c:\Users\truton\ownCloud\Github\test\del\lib\site-packages\pandas\core\frame.py", line 3163, in __setitem__
self._set_item(key, value)
File "c:\Users\truton\ownCloud\Github\test\del\lib\site-packages\pandas\core\frame.py", line 3240, in _set_item
NDFrame._set_item(self, key, value)
File "c:\Users\truton\ownCloud\Github\test\del\lib\site-packages\pandas\core\generic.py", line 3826, in _set_item
self._mgr.insert(len(self._info_axis), key, value)
File "c:\Users\truton\ownCloud\Github\test\del\lib\site-packages\pandas\core\internals\managers.py", line 1197,
in insert
new_axis = self.items.insert(loc, item)
File "c:\Users\truton\ownCloud\Github\test\del\lib\site-packages\pandas\core\indexes\numeric.py", line 174, in insert
item = self._validate_fill_value(item)
File "c:\Users\truton\ownCloud\Github\test\del\lib\site-packages\pandas\core\indexes\numeric.py", line 119, in _validate_fill_value
if is_bool(value) or is_bool_dtype(value):
File "c:\Users\truton\ownCloud\Github\test\del\lib\site-packages\pandas\core\dtypes\common.py", line 1384, in is_bool_dtype
dtype = get_dtype(arr_or_dtype)
File "c:\Users\truton\ownCloud\Github\test\del\lib\site-packages\pandas\core\dtypes\common.py", line 1607, in get_dtype
return pandas_dtype(arr_or_dtype)
File "c:\Users\truton\ownCloud\Github\test\del\lib\site-packages\pandas\core\dtypes\common.py", line 1799, in pandas_dtype
npdtype = np.dtype(dtype)
File "c:\Users\truton\ownCloud\Github\test\del\lib\site-packages\numpy\core\_internal.py", line 177, in _commastring
(len(result)+1, astr))
ValueError: format number 1 of "0 - Name" is not recognized
```
#### Problem description
Very strange is than it happens only if it's first assignment, so this will work correctly with no error
```python
import pandas as pd
df = pd.DataFrame([[1, 2], [3, 4]])
df['a - Name'] = [1, 2]
df['0 - Name'] = [1, 2]
```
#### Expected Output
I do not create dataframe with numeric names, but thing is, that if source data are numpy, than created dataframe has numeric range index. If user in my library insert data - i create some derivations - want to keep names + name of derivation - thus if somebody insert numpy, it will fail.
Expected output is creating new column with number in column name start - or generating columns like ['1', '2', '3'] from arrays which works...
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17763
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : cs_CZ.UTF-8
LOCALE : None.None
pandas : 1.2.0
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.1
setuptools : 46.1.3
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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ENH: enhance query syntax
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| 2021-08-14T23:24:44Z
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|
CONTRIBUTOR
| null |
#### Is your feature request related to a problem?
No, it's additional behavior.
#### Describe the solution you'd like
```df.query(key=value)```
Add new query behavior for simple key lookup. Helps improve readability.
#### API breaking implications
Will not break API. Make the query expression optional, new calls hit this feature if and only if the query expression is not provided.
#### Describe alternatives you've considered
Just not adding this. It's sugar and adds some complexity.
#### Additional context
Single value lookups can be:
```
info = df[df['zipcode'] == adress.zip]
```
Or if you want to use the query API:
```
info = df.query(f'zipcode == {address.zip}')
```
I've found myself writing (on accident)
```
info = df.query(zipcode=adress.zip)
```
The third approach is cleanest and easiest to read. The first approach (via bitmask) is fastest. We can just short-circuit the new special case to use a bitmask, rather than going through the "query" machinery each time.
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BUG: read_csv raising ValueError for tru_values/false_values and boolean dtype
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MEMBER
| null |
- [x] closes #34655
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
I am not really experienced with cython, so I would appreciate feedback on the switiching function. This was not done previously in case of ea boolean dtype, hence why this was failing before.
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ENH/SQL: Allow 'dtype' argument in to_sql() to take dictionary including a default datatype
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NONE
| null |
Related to #11886
Could we modify the dictionary interpretation in `to_sql()` so that the datatype specified by a key like `'default'` would apply to all other columns not already specified in the dictionary? Or, alternatively, we could break this out to two different arguments: a dictionary for specific columns and a datatype for all the rest?
Instead of choosing between specifying every column having a specific type and using one datatype for all columns, I need a hybrid of the two.
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MDExOlB1bGxSZXF1ZXN0NTUwOTIwMzg4
| 39,014
|
DOC: Add whatsnew
|
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| 2021-01-10T11:00:28Z
| 2021-01-08T14:17:28Z
|
CONTRIBUTOR
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- [x] closes #38878
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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BUG: DataFrame.rank() wrong result for inf
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| 2
| 2021-01-07T09:00:15Z
| 2021-01-07T10:32:39Z
| 2021-01-07T10:32:19Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(3, 4))
df.iloc[0, 0] = np.inf
df
0 1 2 3
0 inf -0.614812 -0.316211 -0.049311
1 1.254238 -0.082490 -0.199442 -0.373851
2 -1.440219 0.723572 0.028726 -0.904849
df.rank()
0 1 2 3
0 NaN 1.0 1.0 3.0
1 2.0 2.0 2.0 2.0
2 1.0 3.0 3.0 1.0
df.iloc[0, 0] = -np.inf
df.rank()
0 1 2 3
0 1.0 1.0 1.0 3.0
1 3.0 2.0 2.0 2.0
2 2.0 3.0 3.0 1.0
```
#### Problem description
as you can see, -inf and inf get different results.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-118-generic
Version : #119-Ubuntu SMP Tue Sep 8 12:30:01 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0
numpy : 1.18.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.0.0.post20201207
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : 0.10.1
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: 0.9.0
bs4 : None
bottleneck : None
fsspec : 0.8.4
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.52.0
</details>
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MDU6SXNzdWU3ODExNzk3Mzk=
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|
BUG: pd.DataFrame.pivot() alters external list (v 1.1.5)
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| 3
| 2021-01-07T09:48:12Z
| 2021-01-07T11:15:58Z
| 2021-01-07T11:15:37Z
|
NONE
| null |
I just discovered a strange behavior, pd.DataFrame.pivot() changes an external list by adding an element:
```
import numpy as np
import pandas as pd
size = 10
a = np.random.randint(0, 100, size = size)
b = np.random.randint(0, 100, size = size)
c = np.random.randint(0, 100, size = size)
x = np.random.randint(0, 3, size = size)
y = np.random.randint(0, 100, size = size)
data = {'a': a, 'b': b, 'c': c, 'x': x, 'y': y}
group_fields = ['a', 'b', 'c']
group_fields_copy = group_fields.copy()
df = pd.DataFrame.from_dict(data)
df_pivot = df.pivot(index = group_fields, columns = 'x').reset_index()
print(group_fields)
print(group_fields_copy)
```
```
['a', 'b', 'c', 'x']
['a', 'b', 'c']
```
However, this doesn't happen if I change `group_fields = ['a', 'b', 'c']` to a set `group_fields = ('a', 'b', 'c')`
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ENH: Add encoding errors option in pandas.read_csv
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| 3
| 2021-01-07T12:01:20Z
| 2021-03-09T22:03:54Z
| 2021-03-09T22:03:54Z
|
NONE
| null |
#### Related to problem:
```python
df.to_csv('abc.csv', errors='surrogatepass') # saving works fine.
# Try to load:
# Attempt 1:
pd.read_csv('abc.csv')
# Fails. UnicodeEncodeError: 'utf-8' codec can't encode characters in position 30682-30685: surrogates not allowed
# Attempt 2:
pd.read_csv('abc.csv', errors='surrogatepass')
# Fails. No `errors` parameter.
# Attempt 3:
with open('abc.csv', errors='surrogatepass') as _file:
df = pd.read_csv(_file)
# Fails. UnicodeEncodeError: 'utf-8' codec can't encode characters in position 30682-30685: surrogates not allowed
```
#### Describe the solution you'd like
Recently, we added `errors` as a function parameter to `to_csv` in this [merged PR](https://github.com/pandas-dev/pandas/pull/27899). Can we do the same for `read_csv`? This solution would make Attempt 2 work.
(Not sure why Attempt 3 doesn't work since `read_csv` accepts a file handler object.)
#### API breaking implications
Should not break.
#### Describe alternatives you've considered
see (futile) Attempt 3 above.
#### Additional context
Section "Error handlers" in https://docs.python.org/3/library/codecs.html says:
<img width="681" alt="Screenshot 2021-01-07 at 7 57 20 PM" src="https://user-images.githubusercontent.com/1690072/103890172-91b18400-5122-11eb-8475-dbaa6738bdff.png">
Example of encoding & decoding:
```python
x="\ud83d\ude4f".encode('utf-16', 'surrogatepass').decode('utf-16')
print(x)
# prints 🙏
```
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| 781,281,220
|
MDExOlB1bGxSZXF1ZXN0NTUxMDMxOTY4
| 39,018
|
CLN: add typing to dtype arg in core/common.py (GH38808)
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| 1
| 2021-01-07T12:33:52Z
| 2021-01-07T14:07:44Z
| 2021-01-07T14:07:40Z
|
CONTRIBUTOR
| null |
Follow the issue - https://github.com/pandas-dev/pandas/issues/38808
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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