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EHN: fix unsigned int type problem of the result diff()
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| 2020-02-19T07:05:17Z
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NONE
| null |
An unexpected output type(float64) occurs to diff() when using uint
type. So fix it by restoring original dtype after storing it
Fixes #28909
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
What do you think about this problem with the code? @jreback
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`to_json` of Series with period dtype results in AttributeError
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| 1
| 2020-02-12T10:20:28Z
| 2020-07-10T13:57:42Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
test = pd.Series(pd.period_range('1/1/2011', freq='B', periods=3))
test.to_json(None, orient='table')
```
Leads to:
```
~/miniconda3/envs/qgrid/lib/python3.7/site-packages/pandas/core/generic.py in to_json(self, path_or_buf, orient, date_format, double_precision, force_ascii, date_unit, default_handler, lines, compression, index, indent)
2362 compression=compression,
2363 index=index,
-> 2364 indent=indent,
2365 )
2366
~/miniconda3/envs/qgrid/lib/python3.7/site-packages/pandas/io/json/_json.py in to_json(path_or_buf, obj, orient, date_format, double_precision, force_ascii, date_unit, default_handler, lines, compression, index, indent)
83 default_handler=default_handler,
84 index=index,
---> 85 indent=indent,
86 ).write()
87
~/miniconda3/envs/qgrid/lib/python3.7/site-packages/pandas/io/json/_json.py in __init__(self, obj, orient, date_format, double_precision, ensure_ascii, date_unit, index, default_handler, indent)
289 raise ValueError(msg)
290
--> 291 self.schema = build_table_schema(obj, index=self.index)
292
293 # NotImplemented on a column MultiIndex
~/miniconda3/envs/qgrid/lib/python3.7/site-packages/pandas/io/json/_table_schema.py in build_table_schema(data, index, primary_key, version)
251 if data.ndim > 1:
252 for column, s in data.items():
--> 253 fields.append(convert_pandas_type_to_json_field(s))
254 else:
255 fields.append(convert_pandas_type_to_json_field(data))
~/miniconda3/envs/qgrid/lib/python3.7/site-packages/pandas/io/json/_table_schema.py in convert_pandas_type_to_json_field(arr, dtype)
115 field["ordered"] = ordered
116 elif is_period_dtype(arr):
--> 117 field["freq"] = arr.freqstr
118 elif is_datetime64tz_dtype(arr):
119 if hasattr(arr, "dt"):
~/miniconda3/envs/qgrid/lib/python3.7/site-packages/pandas/core/generic.py in __getattr__(self, name)
5272 if self._info_axis._can_hold_identifiers_and_holds_name(name):
5273 return self[name]
-> 5274 return object.__getattribute__(self, name)
5275
5276 def __setattr__(self, name: str, value) -> None:
AttributeError: 'Series' object has no attribute 'freqstr'
```
This issue also crops up in dataframes where a column is a period Series and `orient='table'`.
#### Problem description
I can't find anything that says this would be unsupported.
#### Expected Output
Json string of series representation.
#### Output of ``pd.show_versions()``
<details>
pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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BUG: fix parquet roundtrip with unsigned integer dtypes
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| 2
| 2020-02-12T10:30:30Z
| 2020-02-12T15:14:59Z
| 2020-02-12T15:04:32Z
|
MEMBER
| null |
Closes #31896
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| 2
| 2020-02-12T11:01:38Z
| 2020-02-15T18:07:32Z
| 2020-02-15T18:07:32Z
|
CONTRIBUTOR
| null |
The documentation for ohlc is missleading. In https://pandas.pydata.org/pandas-docs/stable/reference/groupby.html The following doc appears, which probably references the docstring in the code.
A sentence like `Computes open, high, low and close values, excluding missing values` is probably more complete.
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MDU6SXNzdWU1NjM5MzczNDk=
| 31,920
|
pd.read_csv does not recognize scientific notation if 'decimal' attribute is set with engine=python
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| 0
| 2020-02-12T11:30:37Z
| 2021-01-03T16:36:21Z
| 2021-01-03T16:36:21Z
|
CONTRIBUTOR
| null |
Contents of 'abc.tsv':
```
a b
1,2 3,5E-1
1,2e2 3,5
```
Code:
```python
>>> import pandas as pd
>>> pd.read_csv('abc.tsv', '\t', decimal=',')
a b
0 1.2 0.35
1 120.0 3.50
>>> pd.read_csv('abc.tsv', '\t', decimal=',', engine='python')
a b
0 1.2 3,5E-1
1 1,2e2 3.5
```
#### Problem description
The outputs of the two read_csv commands above should be the same. In this case an obvious "solution" is to just use the C-engine, however I have to process a file where I need to use a regular expression as the delimiter.
#### Expected Output
```python
>>> pd.read_csv('abc.tsv', '\t', decimal=',', engine='python')
a b
0 1.2 0.35
1 120.0 3.50
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : 0.29.15
pytest : 5.3.5
hypothesis : 5.4.1
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0Mzc0Mjg3MDU1
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Backport PR #31794 on branch 1.0.x (BUG: Avoid casting Int to object in Categorical.from_codes)
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| 0
| 2020-02-12T12:42:34Z
| 2020-02-12T13:14:08Z
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NONE
| null |
Backport PR #31794: BUG: Avoid casting Int to object in Categorical.from_codes
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BUG: segmentation fault with pd.NA and np.ndarray.__contains__
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| 7
| 2020-02-12T12:56:45Z
| 2020-11-19T00:32:30Z
| 2020-11-19T00:32:30Z
|
MEMBER
| null |
Testing for membership in an ndarray segfaults:
```
In [1]: pd.NA in np.array(["a"], dtype=object)
Segmentation fault (core dumped)
```
Now, this might be something that needs to be fixed in numpy, as for plain lists it "works":
```
In [3]: pd.NA in ['a']
...
TypeError: boolean value of NA is ambiguous
```
but numpy might not expect an error instead of True/False.
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| 0
| 2020-02-12T13:14:06Z
| 2020-02-12T13:49:09Z
| 2020-02-12T13:49:09Z
|
NONE
| null |
Backport PR #31773: BUG: fix StringArray/PandasArray setitem with slice
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MDU6SXNzdWU1NjM5OTk4ODE=
| 31,924
|
Pandas negative timedelta is 1 day off
|
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| 2020-02-12T13:28:13Z
| 2020-02-12T15:46:48Z
| 2020-02-12T15:46:48Z
|
NONE
| null |
#### Code Sample:
```python
import datetime
import pandas as pd
a=pd.to_datetime('2017-04-03T14:06:00+01:00')
b=pd.to_datetime('2017-04-03T14:00:00+01:00')
print(a-b)
print(b-a)
#Timedelta('0 days 00:06:00')
#Timedelta('-1 days +23:54:00')
```
#### Problem description
Two timestamps obtained from Pandas "to_datetime" and having a time delta of 6 minutes, will have a strange result when the difference is negative.
#### Expected Output
#### Timedelta('0 days -00:06:00')
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-76-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.1
scipy: 1.2.1
pyarrow: 0.14.0
xarray: None
IPython: 7.3.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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| 564,007,092
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MDU6SXNzdWU1NjQwMDcwOTI=
| 31,925
|
pandas.DataFrame.reindex_like throws if called from a derived class instance
|
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| 7
| 2020-02-12T13:40:15Z
| 2020-03-11T21:51:40Z
| 2020-03-11T21:51:40Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import pandas.testing as pdt
class MyDataFrame(pd.DataFrame):
pass
mdf = MyDataFrame()
df = pd.DataFrame()
# In DataFrame.reindex_like, the derived class is compared to
# the base class. The following line will throw
# 'AssertionError: <class 'pandas.core.frame.DataFrame'>'
mdf.reindex_like(df)
# In pdt.assert_frame_equal,
# check_like=True triggers a call to DataFrame.reindex_like.
# The following line will throw the same
# 'AssertionError: <class 'pandas.core.frame.DataFrame'>'
pdt.assert_frame_equal(mdf, df, check_like=True)
```
#### Problem description
The method `reindex_like` throws an `AssertionError` when the caller is an instance of a derived class of `pandas.DataFrame`.
#### Expected Output
No exception thrown.
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-65-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 9.0.1
setuptools : 39.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 0.999999999
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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Backport PR #31877 on branch 1.0.x (BUG: fix infer_dtype for StringDtype)
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NONE
| null |
Backport PR #31877: BUG: fix infer_dtype for StringDtype
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Construction of Categorical from array with pd.NA failing
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| 2
| 2020-02-12T15:11:38Z
| 2020-02-23T14:57:08Z
| 2020-02-23T14:57:08Z
|
MEMBER
| null |
So creating a Categorical from an array with pd.NA fails:
```
In [10]: pd.Categorical(np.array(["a", "b", pd.NA], dtype=object))
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/scipy/pandas/pandas/core/arrays/categorical.py in __init__(self, values, categories, ordered, dtype, fastpath)
354 try:
--> 355 codes, categories = factorize(values, sort=True)
356 except TypeError:
~/scipy/pandas/pandas/core/algorithms.py in factorize(values, sort, na_sentinel, size_hint)
635 codes, uniques = _factorize_array(
--> 636 values, na_sentinel=na_sentinel, size_hint=size_hint, na_value=na_value
637 )
~/scipy/pandas/pandas/core/algorithms.py in _factorize_array(values, na_sentinel, size_hint, na_value)
483 table = hash_klass(size_hint or len(values))
--> 484 uniques, codes = table.factorize(values, na_sentinel=na_sentinel, na_value=na_value)
485
~/scipy/pandas/pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.factorize()
~/scipy/pandas/pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable._unique()
~/scipy/pandas/pandas/_libs/missing.pyx in pandas._libs.missing.NAType.__bool__()
TypeError: boolean value of NA is ambiguous
```
while from a list actually "works":
```
In [11]: pd.Categorical(["a", "b", pd.NA])
Out[11]:
[a, b, NaN]
Categories (2, object): [a, b]
```
This also means that creating a Categorical from a StringArray with missing values won't work (with the same error as above).
I am only not sure if it should work (at least before we can have a "string" dtype index as the categories of the created Categorical).
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MDExOlB1bGxSZXF1ZXN0Mzc0MzYzNDM5
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Backport PR #31918 on branch 1.0.x (BUG: fix parquet roundtrip with unsigned integer dtypes)
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NONE
| null |
Backport PR #31918: BUG: fix parquet roundtrip with unsigned integer dtypes
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BUG: pd.Timestamp constructor fails if tzinfo is specified
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| 2020-02-12T15:29:21Z
| 2021-07-28T05:35:20Z
| null |
CONTRIBUTOR
| null |
The constructor for `pd.Timestamp` has both a `tz=` and `tzinfo=` argument, presumably because `pd.Timestamp` is trying to *both* support the `datetime.datetime` interface and its own constructor. There is evidently logic in the constructor to prevent both `tz` and `tzinfo` being specified, but it seems something has gone wrong, because constructing an aware `pd.Timestamp` by components and including `tzinfo` *always* raises an exception, see:
```python
import pandas as pd
from datetime import timezone
pd.Timestamp(2020, 1, 1, tzinfo=timezone.utc)
```
This raises the following exception:
```
>>> pd.Timestamp(2020, 12, 31, tzinfo=timezone.utc)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-18-9d882de0a384> in <module>
----> 1 pd.Timestamp(2020, 12, 31, tzinfo=timezone.utc)
pandas/_libs/tslibs/timestamps.pyx in pandas._libs.tslibs.timestamps.Timestamp.__new__()
ValueError: Can provide at most one of tz, tzinfo
```
Despite the fact that only `tzinfo` was specified. This seems to only occur when you are using the "per-component" specification, as passing a `datetime` or epoch time as the first component allows you to specify the time zone using `tzinfo`:
```python
pd.Timestamp(0, tzinfo=timezone.utc) # Works
```
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pd.Timestamp constructor ignores missing arguments
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| 2020-02-12T15:35:43Z
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| null |
CONTRIBUTOR
| null |
As part of the discussions in #31563, I came across these strange semantics in `pd.Timestamp`, where it is apparently legal to over-specify a `pd.Timestamp` by specifying both a `datetime` (or another `Timestamp`) *and* pass the by-component construction values, and any irrelevant arguments are *ignored*:
```python
>>> pd.Timestamp(datetime(2020, 12, 31),
year=1, month=1, day=1,
hour=23, minute=59, second=59, microsecond=999999)
Timestamp('2020-12-31 00:00:00')
```
The signature for the function is:
```
pd.Timestamp(
ts_input=<object object at 0x7fd988a10760>,
freq=None,
tz=None,
unit=None,
year=None,
month=None,
day=None,
hour=None,
minute=None,
second=None,
microsecond=None,
nanosecond=None,
tzinfo=None,
)
```
There's actually a decent amount of redundant information in there, because `pd.Timestamp` is attempting to have its own constructor *in addition to being constructable like a datetime. Properly, there are two overloaded constructors here (note that I'm not sure if `nanosecond` belongs to both or just one):
```
pd.Timestamp(ts_input, freq, tz, unit[, nanosecond?])
pd.Timestamp(year, month, day
[, hour, minute, second, microsecond, nanosecond, tzinfo])
```
I think that ideally the correct behavior would be to throw an error if you mix and match between the two, which is at least done in the case of specifying both `tz` and `tzinfo`:
```
>>> pd.Timestamp(datetime.now(), tz=timezone.utc, tzinfo=timezone.utc)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-17-d80c9ce6a89d> in <module>
----> 1 pd.Timestamp(datetime.now(), tz=timezone.utc, tzinfo=timezone.utc)
pandas/_libs/tslibs/timestamps.pyx in pandas._libs.tslibs.timestamps.Timestamp.__new__()
ValueError: Can provide at most one of tz, tzinfo
```
Though confusingly this also fails if you specify `tzinfo` *at all* in the "by-component" constructor. I have filed a separate bug for that at #31929.
## Recommendation
I think that the behavior of `pandas.Timestamp` should probably be brought at least mostly in-line with the concept of two overloaded constructors (possibly with `tz` and `tzinfo` being mutually-exclusive aliases for one another). Any other combination, particularly combinations where the values passed are *ignored*, should raise an exception.
This *may* be a breaking change, since it will start raising exceptions in code that didn't raise exceptions before (though I am not sure I can think of any situation where silently ignoring the values is a desirable condition), so it may be a good idea to have a deprecation period where a warning rather than an exception is raised.
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Revert 31791
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MEMBER
| null |
supersedes #31800
we never back ported this so just cleaning up master (assuming it now works again)
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| 564,127,507
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MDExOlB1bGxSZXF1ZXN0Mzc0NDEzNTQx
| 31,932
|
CLN: remove unreachable in Series._reduce
|
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| 1
| 2020-02-12T16:45:49Z
| 2020-02-14T01:54:05Z
| 2020-02-14T01:27:40Z
|
MEMBER
| null |
these are made unreachable now that Series._values returns DTA/TDA for datetime64/timedelta64 dtypes.
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MDExOlB1bGxSZXF1ZXN0Mzc0NDQ1MTcx
| 31,933
|
CLN: 29547 replace old string formatting 2
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| 1
| 2020-02-12T17:51:56Z
| 2020-02-21T01:30:01Z
| 2020-02-13T05:54:38Z
|
CONTRIBUTOR
| null |
I splitted PR #31844 in batches, this is the second
For this PR I ran the command `grep -l -R -e '%s' -e '%d' -e '\.format(' --include=*.{py,pyx} pandas/` and checked all the files that were returned for `.format(` and changed the old string format for the corresponding `fstrings` to attempt a full clean of, [#29547](https://github.com/pandas-dev/pandas/issues/29547). I may have missed something so is a good idea to double check just in case
- [ x ] tests added / passed
- [ x ] passes `black pandas`
- [ x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDU6SXNzdWU1NjQxODYzMTU=
| 31,934
|
to_csv compression dict option 'archive_name' should accept os.PathLike
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| 2020-02-12T18:30:39Z
| 2021-07-28T05:36:17Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
from pathlib import Path
random_data = np.random.standard_normal(size=(1000, 3))
data = pd.DataFrame(random_data, columns=['A', 'B', 'C'])
csv_file = Path('random_data.zip')
data .to_csv(csv_file.with_suffix('.zip'), index=False, compression={'method': 'zip', 'archive_name': csv_file})
```
#### Problem description
The Python std lib has made a tremendous effort to support os.PathLike objects, especially pathlib.Path, across the standard library. [ZipFile was somewhat recently patched](https://bugs.python.org/issue28231) to add PathLike support for externally facing paths, but it appears they did not add this support to ZipInfo which is used to interface with the files within a zip archive.
DataFrame's `to_csv()` method was [recently updated](https://github.com/pandas-dev/pandas/pull/26024) to handle passing additional arguments when using zip compression. If compression is specified as a `dict` and `archive_name` is passed as a key, the value currently must be a `str` because ZipInfo requires a string. Since pandas has exposed this to the user it would be nice for PathLike objects like `pathlib.Path` to get converted to a `str` before being passed to ZipInfo.
If someone wants to raise this upstream on the Python issue tracker, that's also an acceptable outcome. Perhaps this was just an oversight on the [previous issue](https://bugs.python.org/issue28231) where they neglected to consider the arguments to ZipInfo as externally facing.
#### Expected Output
- Zip file is saved with no exceptions
- Zip file contains a *.csv file (it's only because pandas defaults to creating a zip file containing an identically named zip file that passing `archive_name` is necessary for what should be default behavior. <sigh>)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 40.6.2
Cython : 0.29.14
pytest : 4.2.0
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.2.2
lxml.etree : 4.3.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.1
matplotlib : 3.1.1
numexpr : 2.6.9
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 4.2.0
pyxlsb : None
s3fs : None
scipy : 1.3.2
sqlalchemy : 1.3.6
tables : 3.4.4
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.2
numba : 0.46.0
</details>
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| 564,241,678
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MDExOlB1bGxSZXF1ZXN0Mzc0NTA4MTM3
| 31,935
|
Clean Up C Warnings
|
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| 9
| 2020-02-12T20:10:55Z
| 2020-02-17T19:50:33Z
| 2020-02-17T19:50:33Z
|
MEMBER
| null |
Getting closer to no warnings (at least with clang). Changes in this PR get rid of the following warnings:
```sh
warning: pandas/_libs/window/aggregations.pyx:60:4: Buffer unpacking not optimized away.
warning: pandas/_libs/window/aggregations.pyx:60:4: Buffer unpacking not optimized away.
warning: pandas/_libs/window/aggregations.pyx:60:36: Buffer unpacking not optimized away.
warning: pandas/_libs/window/aggregations.pyx:60:36: Buffer unpacking not optimized away.
pandas/_libs/hashing.c:25100:20: warning: unused function '__pyx_memview_get_object' [-Wunused-function]
static PyObject *__pyx_memview_get_object(const char *itemp) {
^
pandas/_libs/hashing.c:25105:12: warning: unused function '__pyx_memview_set_object' [-Wunused-function]
static int __pyx_memview_set_object(const char *itemp, PyObject *obj) {
pandas/_libs/algos.c:81410:3: warning: code will never be executed [-Wunreachable-code]
__Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_arr.rcbuffer->pybuffer);
^~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:83045:3: warning: code will never be executed [-Wunreachable-code]
__Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_arr.rcbuffer->pybuffer);
^~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:83951:3: warning: code will never be executed [-Wunreachable-code]
__Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_arr.rcbuffer->pybuffer);
^~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:84857:3: warning: code will never be executed [-Wunreachable-code]
__Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_arr.rcbuffer->pybuffer);
^~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:85034:3: warning: code will never be executed [-Wunreachable-code]
__Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_arr.rcbuffer->pybuffer);
^~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:85940:3: warning: code will never be executed [-Wunreachable-code]
__Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_arr.rcbuffer->pybuffer);
^~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/tslibs/conversion.c:33113:20: warning: unused function '__pyx_memview_get_object' [-Wunused-function]
static PyObject *__pyx_memview_get_object(const char *itemp) {
^
pandas/_libs/tslibs/conversion.c:33118:12: warning: unused function '__pyx_memview_set_object' [-Wunused-function]
static int __pyx_memview_set_object(const char *itemp, PyObject *obj) {
```
Only a handful left after this
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MDExOlB1bGxSZXF1ZXN0Mzc0NTIwNTMz
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|
dont skip keyerror for IntervalIndex
|
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| 3
| 2020-02-12T20:38:17Z
| 2020-02-22T17:53:56Z
| 2020-02-22T17:53:45Z
|
MEMBER
| null |
cc @jschendel this is speculative that this is the desired behavior.
This is our only use of `Index.is_interval()`
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MDU6SXNzdWU1NjQyNjI2ODk=
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|
Why does Series.transform() exist?
|
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| 2020-02-12T20:50:16Z
| 2020-02-14T18:24:13Z
| 2020-02-14T01:47:06Z
|
NONE
| null |
This is my first issue on GitHub, so apologies in advance if there's something wrong with the format.
My issue does not have any expected output, I just really want to understand if and why the `Series.transform() ` method is not redundant. Overall, the `transform()` methods are very similar to `apply()` methods, and as I was trying to figure out what the difference between them is ([this](https://stackoverflow.com/questions/27517425/apply-vs-transform-on-a-group-object) Stack Overflow topic was helpful), I managed to pinpoint 3 primary differences:
1) When the DataFrame is grouped on several categories, `apply()` sends the entire sub-DataFrames within the function, while `transform()` sends each column of each sub-DataFrame separately. That's why columns can't access values in other columns within `transform()`;
2) When the input passed to the function is an iterable of a certain length, `apply()` can still have the output of any length, while `transform()` has a limitation of having to output an iterable of the same length as the input;
3) When the function outputs a scalar, `apply()` returns that scalar, while `transform()` propagates that scalar to the iterable of the input length.
I conducted a series of experiments that test these three differences on each applicable pandas object type: Series, DataFrame, SeriesGroupBy, and DataFrameGroupBy. I can send my ipynb with the code and the results if necessary, but it would be sufficient to just look at the conclusion for the Series type:
1 – not applicable. In both cases the function has a scalar input.
2 – not applicable. No matter what the function returns, in both cases the result is assigned to the single cell, even if it means entire DataFrames within cells of a Seires.
3 – not applicable. The input length is always "1" (it's considered "1" even when it's an iterable), so there's no need to propagate.
Inapplicability of 1 is self-explanatory. But 2 was a surprise. Below is the code I tried:
```
import pandas as pd
df = pd.DataFrame({'State':['Texas', 'Texas', 'Florida', 'Florida'],
'a':[4,5,1,3], 'b':[6,10,3,11]})
def return_df(x):
return pd.DataFrame([[4, 5], [3, 2]])
def return_series(x):
return pd.Series([1, 2])
df['a'].transform(return_df)
df['a'].transform(return_series)
```
If you try this code, you'll see that it doesn't matter what the function returns. Whatever it is, it will be put inside the single Series cell in its entirety. Is this behavior intentional? It results in the output size being predetermined by the input size, so all the size checks that `Series.transform()` has within itself become redundant. I can't imagine any situation where `Series.transform()` could behave in a different way from `Series.apply()`. And that raises the question I posed: why does `Series.transform()` exist?
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MDExOlB1bGxSZXF1ZXN0Mzc0NTM0Mzky
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|
CLN: assorted cleanups
|
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| 1
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| 2020-02-13T17:35:33Z
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MEMBER
| null |
broken off of other local branches
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BUG: Fix construction of Categorical from pd.NA
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| 5
| 2020-02-12T21:17:04Z
| 2020-02-23T15:10:32Z
| 2020-02-23T14:57:08Z
|
MEMBER
| null |
- [x] closes #31927
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0Mzc0NTM4ODcz
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REF: use iloc instead of _ixs outside of indexing code
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| 7
| 2020-02-12T21:18:37Z
| 2020-04-05T17:45:44Z
| 2020-02-17T16:31:20Z
|
MEMBER
| null |
Readers are much more likely to be familiar with iloc than _ixs.
This leaves us with exactly one usage of _ixs.
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DOC: update ohlc docstring so that it reflects the real use #31919
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| 6
| 2020-02-13T00:30:02Z
| 2020-02-15T18:07:40Z
| 2020-02-15T18:07:33Z
|
CONTRIBUTOR
| null |
- [x] closes #31919
- [x] tests added / passed
The test python scripts/validate_docstrings.py pandas.core.groupby.GroupBy.ohlc returns errors, but that is something that happens with many functions at the moment
```################################################################################
################# Docstring (pandas.core.groupby.GroupBy.ohlc) #################
################################################################################
Compute open, high, low and close values of a group, excluding missing values.
For multiple groupings, the result index will be a MultiIndex
Returns
-------
DataFrame
Open, high, low and close values within each group.
See Also
--------
Series.groupby
DataFrame.groupby
################################################################################
################################## Validation ##################################
################################################################################
3 Errors found:
Missing description for See Also "Series.groupby" reference
Missing description for See Also "DataFrame.groupby" reference
No examples section found```
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CLN: Update docstring decorator from Appender and Substitution to doc
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| 6
| 2020-02-13T01:34:07Z
| 2020-03-02T07:51:53Z
| null |
CONTRIBUTOR
| null |
We use to use `Appender` and `Substitution` decorators to share docstring cross functions. Now we implemented a new decorator `doc` for that use case, and it would simplify the reuse of docstrings.
The idea here is to replace `Appender` and `Substitution` with `doc` decorator, and move the shared docstring to a better place if possible.
#### Example
The original code will be look like this:
```python
@Substitution(**_shared_doc_kwargs)
@Appender(NDFrame.fillna.__doc__)
def fillna(self, value=None, ...) -> Optional["DataFrame"]:
return super().fillna(...)
```
We would like to change it to this:
```python
@doc(NDFrame.fillna, **_shared_doc_kwargs)
def fillna(self, value=None, ...) -> Optional["DataFrame"]:
return super().fillna(...)
```
#### Reference
- [CLN: Replace Appender and Substitution with simpler doc decorator](https://github.com/pandas-dev/pandas/pull/31060)
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|
sort_index is different from before version
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| 2020-02-13T01:47:09Z
| 2020-02-13T01:46:22Z
|
NONE
| null |
```python
dates = pd.date_range('20170101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
print(df.sort_index())
```
#### version 1 output
D C B A
2017-01-01 -1.418768 0.441179 0.668193 -0.883575
2017-01-02 -0.642718 0.406244 -1.558845 -0.050165
2017-01-03 -0.492745 -1.578937 0.829258 -1.092268
2017-01-04 1.113140 -2.376751 0.608188 -0.080518
2017-01-05 1.003710 1.969951 -1.583741 -0.610659
2017-01-06 -0.154932 -0.375000 0.179116 -0.294583
#### Expected Output
```
D C B A
2017-01-01 0.426359 2.542352 -0.324047 0.418973
2017-01-02 -0.834625 -1.356709 0.150744 -1.690500
2017-01-03 -0.018274 0.900801 1.072851 0.149830
2017-01-04 -1.075027 -0.889379 -0.663223 -1.404002
2017-01-05 -1.273966 -1.335761 -1.356561 -1.135199
2017-01-06 -1.590793 0.693430 -0.504164 0.143386
```
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Errors in pandas._libs.lib.maybe_convert_objects leading to use of uninitialized memory
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| 8
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| 2020-10-06T22:43:17Z
| 2020-10-06T22:43:17Z
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NONE
| null |
#### Code Sample 1
```python
import numpy as np
import pandas as pd
from pandas._libs import lib as lib
print(lib.maybe_convert_objects(np.array([np.nan, True], dtype='object'), safe=1))
```
#### Code Sample 2
```python
import numpy as np
import pandas as pd
from pandas._libs import lib as lib
print(lib.maybe_convert_objects(np.array([np.nan, np.datetime64()], dtype='object'), safe=1, convert_datetime=1))
```
#### Code Sample 3
```python
import math
import pandas as pd
z1 = pd.Series([True, False, pd.NA]).value_counts(dropna=False)
print(z1)
z2 = pd.Series({True: 1, False: 1, math.nan:1})
print(z1)
```
#### Problem description
This problem was originally detected by @benfuja when he noticed inconsistent output in the `value_counts` function when `dropna=False` for otherwise Boolean arrays containing a `pd.NA` indicator. Code Sample 3 is Ben's original example showing the odd behavior (note that z1 is printed twice, and depending on your malloc library, will likely change values between calls). When we tried to isolate what was happening, we ended up down a couple of rabbit holes leading to the `maybe_convert_objects` function.
What I suspect is happening is that when `maybe_convert_objects` is called by the Series index formatter, it is expecting that if a mixture of Booleans and `np.nan` are present, that the presence of `np.nan` will be preserved. Instead, I think what happens is that `maybe_convert_objects` erroneously returns garbage values when either Booleans or datetimes/timedeltas are present along with `np.nan` by returning a corresponding array with uninitialized empty memory in the locations where the `np.nan` values were present. Code Samples 1 and 2 are examples of this.
As noted in https://github.com/pandas-dev/pandas/issues/27417, there are probably a few opportunities to improve this function and its dependencies. I'm not particularly familiar with this code base, but it seems to me that the most correct minimal fix may be in the Seen class:
#### Proposed Minimal Fix
```python
@property
def numeric_(self):
- return self.complex_ or self.float_ or self.int_
+ return self.complex_ or self.float_ or self.int_ or self.null_ or self.nan_
```
#### Rationale
The `maybe_convert_objects` method relies on the `Seen` class to tell it if it is safe to consider an array as containing only values of a specific type. When `maybe_convert_objects` encounters a `NaN` or a `None`, it stores the encountered value in the `floats` and `complexes` arrays and sets the `seen.nan_` / `seen.null_` flags. Later checks for whether an array is of a specific type will miss that these values were encountered if these flags are not checked for. This seemed like the most logical place to put these checks in `Seen`, since both `self.null_` and `self.nan_` are stored in the `floats/complexes` arrays earlier. Another option would be to fix the logic in `maybe_convert_objects` to more carefully check the `self.nan_/seen.null_` flags in the case statements.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 45.0.0.post20200113
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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CLN: 29547 replace old string formatting 3
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| 5
| 2020-02-13T03:45:56Z
| 2020-02-21T01:30:01Z
| 2020-02-13T20:34:48Z
|
CONTRIBUTOR
| null |
I splitted PR #31844 in batches, this is the third
For this PR I ran the command `grep -l -R -e '%s' -e '%d' -e '\.format(' --include=*.{py,pyx} pandas/` and checked all the files that were returned for `.format(` and changed the old string format for the corresponding `fstrings` to attempt a full clean of, [#29547](https://github.com/pandas-dev/pandas/issues/29547). I may have missed something so is a good idea to double check just in case
- [ x ] tests added / passed
- [ x ] passes `black pandas`
- [ x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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API: replace() should raise an exception if invalid argument is given
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| 2
| 2020-02-13T06:13:46Z
| 2020-03-03T04:32:14Z
| 2020-03-03T03:24:23Z
|
CONTRIBUTOR
| null |
- [x] closes #18634
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I used `is_scalar()` instead of `is_numeric()` to allow data structures like pd.Timestamp to be used in `replace()`. See tests in pandas/tests/frame/methods/test_replace.py, pandas/tests/series/methods/test_replace.py for examples.
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Backport PR #31910 on branch 1.0.x (BUG: Handle NA in assert_numpy_array_equal)
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Backport PR #31910: BUG: Handle NA in assert_numpy_array_equal
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| 2021-07-28T05:38:40Z
| null |
MEMBER
| null |
Currently, indexing with a list including pd.NA (so the list version of indexing with a BooleanArray or IntegerArray) works on the array, but not on Series:
("works" = raising the correct error message)
```
In [21]: a = pd.array([1, 2, 3])
In [22]: a[[True, False, pd.NA]]
...
ValueError: Cannot mask with a boolean indexer containing NA values
In [23]: a[[0, 1, pd.NA]]
...
ValueError: Cannot index with an integer indexer containing NA values
```
vs
```
In [24]: s = pd.Series(a)
In [25]: s[[True, False, pd.NA]]
...
KeyError: 'Passing list-likes to .loc or [] with any missing labels is no longer supported, see https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike'
In [26]: s[[0, 1, pd.NA]]
...
TypeError: boolean value of NA is ambiguous
```
Because the validation of the indexer isn't yet updated to handle listlikes that include pd.NA.
And similar problems for setitem.
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pandas.Dataframe.interpolate() does not extrapolate even if it is asked to, depending on interpolation method
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| 2020-02-13T08:54:37Z
| 2021-10-29T23:59:14Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
a = pd.Series([0, 1, np.nan, 3, 4, np.nan, np.nan, np.nan, np.nan])
a_int=a.interpolate(method='cubic', limit_area=None)
```
#### Problem description
Some of the offered methods (it seems all of them that are provided by interp1d) are unable to extrapolate over np.nan. However, the limit_area switch for df.interpolate() indicates you can force extrapolation. A combination of limit_area=None and an incompatible method should raise a warning.
There used to be a similar issue where extrapolation over trailing NaN was done unintentionally, so maybe the fix for that overdid it. https://github.com/pandas-dev/pandas/issues/8000
#### Expected Output
Extrapolation over the NaNs in the array is expected. Using a different method, such as pchip achieves this.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 0.25.3 (also tested with 1.0.0)
numpy : 1.15.4
pytz : 2018.9
dateutil : 2.7.5
pip : 20.0.2
setuptools : 41.0.1
Cython : 0.29.15
pytest : None
hypothesis : None
sphinx : 1.8.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.5.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 3.0.3
numexpr : None
odfpy : None
openpyxl : 2.5.12
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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BUG: groupby-nunique modifies null values
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| 5
| 2020-02-13T10:47:13Z
| 2020-02-23T15:02:17Z
| 2020-02-23T15:02:17Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({"GROUP": 0, "VALUE": [1.0, np.nan]})
df.groupby("GROUP")["VALUE"].nunique()
print(df)
```
#### Problem description
Original dataframe is modified:
```
GROUP VALUE
0 0 1.000000e+00
1 0 -9.223372e+18
```
Issue seems to have been introduced in version 1.0.0, 0.25.3 works as expected.
#### Expected Output
Original dataframe should not be modified.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.9.87-linuxkit-aufs
machine : x86_64
processor :
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : 4.23.0
sphinx : 1.7.9
blosc : None
feather : None
xlsxwriter : 1.2.2
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.2 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : None
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
pytest : 5.2.1
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.11
tables : None
tabulate : 0.8.5
xarray : None
xlrd : None
xlwt : None
xlsxwriter : 1.2.2
numba : 0.45.1
</details>
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MDU6SXNzdWU1NjQ3MTg1Mzk=
| 31,951
|
merge coerces to left dtype even with how='right'
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[] | null | 0
| 2020-02-13T14:25:46Z
| 2021-07-28T05:39:52Z
| null |
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> df1 = pd.DataFrame({'a': [1]})
>>> df2 = pd.DataFrame({'a': [1.0]})
>>> df1.merge(df2, on='a')
>>> df1.merge(df2, on='a', how='left')
a
0 1
>>> df1.merge(df2, on='a', how='right')
a
0 1
```
#### Problem description
I don't know if this is an issue, it's just something I noticed while working on #31278 . The right value `1.0` gets coerced to the dtype of the left dataframe when using `how='left'`, and I would have expected the left value `1` to get coerced to the dtype of the right dataframe when using `how='right'`
#### Expected Output
```
>>> df1.merge(df2, on='a', how='right')
a
0 1.0
```
#### Output of ``pd.show_versions()``
<details>
I'm on the master branch, here's what `pd.show_versions()` shows:
/home/SERILOCAL/m.gorelli/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/fastparquet/dataframe.py:5: FutureWarning: pandas.core.index is deprecated and will be removed in a future version. The public classes are available in the top-level namespace.
from pandas.core.index import CategoricalIndex, RangeIndex, Index, MultiIndex
INSTALLED VERSIONS
------------------
{k:<17}: {stat}
{k:<17}: {stat}
{k:<17}: {stat}
{k:<17}: {stat}
{k:<17}: {stat}
{k:<17}: {stat}
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MDU6SXNzdWU1NjQ3MjEyNjg=
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|
Multiline pd.eval() seems to be broken
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[] | null | 5
| 2020-02-13T14:29:44Z
| 2020-02-13T16:59:47Z
| null |
NONE
| null |
#### Code Sample:
```python
In [93]: df
Out[93]:
A B
0 1 2
1 2 3
2 3 4
3 4 5
In [102]: pd.eval("""A = df.A - df.B
...: B = df.A + df.B
...: """,target=pd.DataFrame())
```
#### Problem description
```python
....
/usr/local/lib64/python3.6/site-packages/pandas/core/computation/scope.py in resolve(self, key, is_local)
201 from pandas.core.computation.ops import UndefinedVariableError
202
--> 203 raise UndefinedVariableError(key, is_local)
204
205 def swapkey(self, old_key: str, new_key: str, new_value=None):
UndefinedVariableError: name 'df' is not defined
```
#### Expected Output
```python
Out[106]:
A B
0 -1 1
1 -1 2
2 -1 3
3 -1 4
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1062.9.1.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.0
pip : 9.0.3
setuptools : 45.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
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pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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|
CLN: index related attributes on Series/DataFrame
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| 3
| 2020-02-13T14:47:06Z
| 2020-02-14T21:50:48Z
| 2020-02-14T01:07:53Z
|
CONTRIBUTOR
| null |
Followup to #31126. IMO the current approach to adding the index related class attributes is too indirect and therefore unnecessary difficult to follow. Just adding the class attributes directly on the relevant class makes reading the code easier, IMO.
Notice that the types are already defined in pandas/core/generic.py:304-316.
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BUG: "cannot reindex from duplicate axis" thrown using unique indexes, duplicated column names and a specific numpy array values
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| 32
| 2020-02-13T16:33:54Z
| 2020-12-08T15:58:34Z
| 2020-12-08T15:58:34Z
|
NONE
| null |
#### Code Sample
```
import pandas
import numpy as np
a = np.array([[1,2],[3,4]])
# DO NOT WORKS
b = np.array([[0.5,6],[7,8]])
# b = np.array([[.5,6],[7,8]]) # The same problem
# This one works fine:
# b = np.array([[5,6],[7,8]])
dfA = pandas.DataFrame(a)
# This works fine EVEN using .5, because the columns name is different
# dfA = pandas.DataFrame(a, columns=['a','b'])
dfB = pandas.DataFrame(b)
df_new = pandas.concat([dfA, dfB], axis = 1)
print(df_new[df_new > 5])
```
#### Problem description
It has a bug that combines numpy specific values and duplicated DataFrame column names when it's used a select operation, such as `df[df > 5]`. A exception is thrown saying "cannot reindex from duplicate axis", however It should not be, because:
- The DataFrame has no duplicated indexes ( `df.index.is_unique` is `True`)
- The DataFrame has duplicated column names, but should not be a problem when we apply the selection operation, such as `df_new[df_new > 5]`
- The DataFrame uses `float `or `int` numpy values, so it should not change the behavior of the code
**However** the values in the numpy array DO changes the behavior of the DataFrame selection, if the DataFrame has duplicated column names.
#### Expected Output
```
0 1 0 1
0 NaN NaN NaN 6
1 NaN NaN 7.0 8
```
#### Current Output
```
~/.local/lib/python3.6/site-packages/pandas/core/indexes/base.py in _can_reindex(self, indexer)
3097 # trying to reindex on an axis with duplicates
3098 if not self.is_unique and len(indexer):
-> 3099 raise ValueError("cannot reindex from a duplicate axis")
3100
3101 def reindex(self, target, method=None, level=None, limit=None, tolerance=None):
ValueError: cannot reindex from a duplicate axis
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-28-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : pt_BR.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0Mzc0OTg5MzA1
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|
add eval examples
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| 1
| 2020-02-13T17:11:26Z
| 2020-02-25T17:11:43Z
| 2020-02-14T01:21:42Z
|
MEMBER
| null |
xref #31952
Screenshot for `pandas.eval`:
Screenshot for `pandas.DataFrame.eval`:
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CLN: D412: No blank lines allowed between a section header and its content
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to_sql defaults to cp1252 on Windows 10 instead of using UTF-8 as per dataframe and Oracle
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| 2020-02-13T17:57:00Z
| 2020-05-25T16:47:05Z
| 2020-05-25T16:47:05Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas as pd
import dask.dataframe as dd
from dask.diagnostics import ProgressBar
from dask.distributed import Client
client = Client('192.168.1.33:8786', processes=True)
path = "D:\\Downloads\\Wikipedia\\Yago\\"
dfFacts = dd.read_parquet(path + "yagoFacts", engine='pyarrow', index=False, encoding="utf-8")
dfFacts = dfFacts.compute()
import cx_Oracle
from sqlalchemy import types, create_engine
conn = create_engine('oracle+cx_oracle://hr:password@localhost:1521/?service_name=XEPDB1', max_identifier_length=128)
dtyp = {c:types.VARCHAR(dfFacts[c].str.len().max()) for c in dfFacts.columns.tolist()}
dfFacts.to_sql('yago_facts', conn, if_exists='replace', dtype=dtyp, index=False)
```
#### Problem description
Pandas to_sql seems to default to cp1252 on Windows 10 instead of using UTF-8 even though the dataframe and Oracle database (AL32UTF8) uses UTF-8. It fails with the following error:
```
---------------------------------------------------------------------------
UnicodeEncodeError Traceback (most recent call last)
<ipython-input-27-17445fb6d38f> in <module>
----> 1 dfFacts.to_sql('yago_facts', conn, if_exists='replace', dtype=dtyp, index=False)
~\AppData\Roaming\Python\Python37\site-packages\pandas\core\generic.py in to_sql(self, name, con, schema, if_exists, index, index_label, chunksize, dtype, method)
2710 chunksize=chunksize,
2711 dtype=dtype,
-> 2712 method=method,
2713 )
2714
~\AppData\Roaming\Python\Python37\site-packages\pandas\io\sql.py in to_sql(frame, name, con, schema, if_exists, index, index_label, chunksize, dtype, method)
516 chunksize=chunksize,
517 dtype=dtype,
--> 518 method=method,
519 )
520
~\AppData\Roaming\Python\Python37\site-packages\pandas\io\sql.py in to_sql(self, frame, name, if_exists, index, index_label, schema, chunksize, dtype, method)
1318 )
1319 table.create()
-> 1320 table.insert(chunksize, method=method)
1321 if not name.isdigit() and not name.islower():
1322 # check for potentially case sensitivity issues (GH7815)
~\AppData\Roaming\Python\Python37\site-packages\pandas\io\sql.py in insert(self, chunksize, method)
754
755 chunk_iter = zip(*[arr[start_i:end_i] for arr in data_list])
--> 756 exec_insert(conn, keys, chunk_iter)
757
758 def _query_iterator(
~\AppData\Roaming\Python\Python37\site-packages\pandas\io\sql.py in _execute_insert(self, conn, keys, data_iter)
668 """
669 data = [dict(zip(keys, row)) for row in data_iter]
--> 670 conn.execute(self.table.insert(), data)
671
672 def _execute_insert_multi(self, conn, keys, data_iter):
e:\wpy-3710\python-3.7.1.amd64\lib\site-packages\sqlalchemy\engine\base.py in execute(self, object_, *multiparams, **params)
980 raise exc.ObjectNotExecutableError(object_)
981 else:
--> 982 return meth(self, multiparams, params)
983
984 def _execute_function(self, func, multiparams, params):
e:\wpy-3710\python-3.7.1.amd64\lib\site-packages\sqlalchemy\sql\elements.py in _execute_on_connection(self, connection, multiparams, params)
291 def _execute_on_connection(self, connection, multiparams, params):
292 if self.supports_execution:
--> 293 return connection._execute_clauseelement(self, multiparams, params)
294 else:
295 raise exc.ObjectNotExecutableError(self)
e:\wpy-3710\python-3.7.1.amd64\lib\site-packages\sqlalchemy\engine\base.py in _execute_clauseelement(self, elem, multiparams, params)
1099 distilled_params,
1100 compiled_sql,
-> 1101 distilled_params,
1102 )
1103 if self._has_events or self.engine._has_events:
e:\wpy-3710\python-3.7.1.amd64\lib\site-packages\sqlalchemy\engine\base.py in _execute_context(self, dialect, constructor, statement, parameters, *args)
1248 except BaseException as e:
1249 self._handle_dbapi_exception(
-> 1250 e, statement, parameters, cursor, context
1251 )
1252
e:\wpy-3710\python-3.7.1.amd64\lib\site-packages\sqlalchemy\engine\base.py in _handle_dbapi_exception(self, e, statement, parameters, cursor, context)
1476 util.raise_from_cause(sqlalchemy_exception, exc_info)
1477 else:
-> 1478 util.reraise(*exc_info)
1479
1480 finally:
e:\wpy-3710\python-3.7.1.amd64\lib\site-packages\sqlalchemy\util\compat.py in reraise(tp, value, tb, cause)
151 if value.__traceback__ is not tb:
152 raise value.with_traceback(tb)
--> 153 raise value
154
155 def u(s):
e:\wpy-3710\python-3.7.1.amd64\lib\site-packages\sqlalchemy\engine\base.py in _execute_context(self, dialect, constructor, statement, parameters, *args)
1224 if not evt_handled:
1225 self.dialect.do_executemany(
-> 1226 cursor, statement, parameters, context
1227 )
1228 elif not parameters and context.no_parameters:
e:\wpy-3710\python-3.7.1.amd64\lib\site-packages\sqlalchemy\dialects\oracle\cx_oracle.py in do_executemany(self, cursor, statement, parameters, context)
1126 if isinstance(parameters, tuple):
1127 parameters = list(parameters)
-> 1128 cursor.executemany(statement, parameters)
1129
1130 def do_begin_twophase(self, connection, xid):
e:\wpy-3710\python-3.7.1.amd64\lib\encodings\cp1252.py in encode(self, input, errors)
10
11 def encode(self,input,errors='strict'):
---> 12 return codecs.charmap_encode(input,errors,encoding_table)
13
14 def decode(self,input,errors='strict'):
UnicodeEncodeError: 'charmap' codec can't encode character '\u010d' in position 11: character maps to <undefined>
```
#### Expected Output
No errors with dataframe loaded in Oracle table.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
```
E:\WPy-3710\python-3.7.1.amd64\lib\site-packages\xarray\core\merge.py:10: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version
PANDAS_TYPES = (pd.Series, pd.DataFrame, pd.Panel)
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.18.1
pytz : 2018.6
dateutil : 2.7.5
pip : 20.0.2
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.3.1
hypothesis : 3.82.1
sphinx : 2.2.1
blosc : 1.6.1
feather : 0.4.0
xlsxwriter : 1.1.2
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.6.1
pandas_datareader: None
bs4 : 4.6.3
bottleneck : 1.2.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.0.3
numexpr : 2.6.8
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.13
tables : 3.4.4
xarray : 0.11.3
xlrd : 1.1.0
xlwt : None
xlsxwriter : 1.1.2
```
</details>
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MDExOlB1bGxSZXF1ZXN0Mzc1MDE2NTEy
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CLN: D409: Section underline should match the length of its name
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CLN: D411: Missing blank line before section
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CLN: remove blocking return
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MEMBER
| null |
just noticed a `return` statement in the middle of a test that prevents it from being executed. This was added in ##23752
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| 564,923,549
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MDExOlB1bGxSZXF1ZXN0Mzc1MDY1Njk2
| 31,962
|
REF: remove _convert_scalar_indexer
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| 8
| 2020-02-13T20:11:32Z
| 2020-03-03T15:53:13Z
| 2020-03-03T15:27:18Z
|
MEMBER
| null |
This sits on top of #31867, so is partially a demonstration of how much complication is caused by our inconsistent error-raising.
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MDExOlB1bGxSZXF1ZXN0Mzc1MDY3Mjg5
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CLN: 29547 replace old string formatting 4
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| 1
| 2020-02-13T20:15:24Z
| 2020-02-21T01:30:00Z
| 2020-02-14T01:04:47Z
|
CONTRIBUTOR
| null |
I splitted PR #31844 in batches, this is the fourth
For this PR I ran the command `grep -l -R -e '%s' -e '%d' -e '\.format(' --include=*.{py,pyx} pandas/` and checked all the files that were returned for `.format(` and changed the old string format for the corresponding `fstrings` to attempt a full clean of, [#29547](https://github.com/pandas-dev/pandas/issues/29547). I may have missed something so is a good idea to double check just in case
- [ x ] tests added / passed
- [ x ] passes `black pandas`
- [ x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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| 564,971,493
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MDU6SXNzdWU1NjQ5NzE0OTM=
| 31,964
|
Reindex on nearest datetime gives UFuncTypeError - subtract with dtype('<M8[ns]') and dtype('O')
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| 6
| 2020-02-13T21:51:23Z
| 2020-02-25T10:13:26Z
| 2020-02-21T18:36:09Z
|
NONE
| null |
#### Code Sample
```python
import pandas as pd
print(pd.__version__)
i1 = pd.DatetimeIndex(['2016-06-26 14:27:26+00:00'])
i2 = pd.DatetimeIndex(['2016-07-04 14:00:59+00:00'])
f2 = pd.DataFrame(index=i2)
f2.reindex(i1, method='nearest')
```
#### Problem description
Running the above code gives:
```
Python 3.7.4 (default, Jul 27 2019, 21:25:02)
[GCC 7.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> print(pd.__version__)
1.0.1
>>> i1 = pd.DatetimeIndex(['2016-06-26 14:27:26+00:00'])
>>> i2 = pd.DatetimeIndex(['2016-07-04 14:00:59+00:00'])
>>> f2 = pd.DataFrame(index=i2)
>>> f2.reindex(i1, method='nearest')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/andrew/project/ch2/choochoo/py/env/lib/python3.7/site-packages/pandas/util/_decorators.py", line 227, in wrapper
return func(*args, **kwargs)
File "/home/andrew/project/ch2/choochoo/py/env/lib/python3.7/site-packages/pandas/core/frame.py", line 3856, in reindex
return self._ensure_type(super().reindex(**kwargs))
File "/home/andrew/project/ch2/choochoo/py/env/lib/python3.7/site-packages/pandas/core/generic.py", line 4544, in reindex
axes, level, limit, tolerance, method, fill_value, copy
File "/home/andrew/project/ch2/choochoo/py/env/lib/python3.7/site-packages/pandas/core/frame.py", line 3744, in _reindex_axes
index, method, copy, level, fill_value, limit, tolerance
File "/home/andrew/project/ch2/choochoo/py/env/lib/python3.7/site-packages/pandas/core/frame.py", line 3760, in _reindex_index
new_index, method=method, level=level, limit=limit, tolerance=tolerance
File "/home/andrew/project/ch2/choochoo/py/env/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 3145, in reindex
target, method=method, limit=limit, tolerance=tolerance
File "/home/andrew/project/ch2/choochoo/py/env/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 2740, in get_indexer
indexer = self._get_nearest_indexer(target, limit, tolerance)
File "/home/andrew/project/ch2/choochoo/py/env/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 2821, in _get_nearest_indexer
left_distances = abs(self.values[left_indexer] - target)
numpy.core._exceptions.UFuncTypeError: ufunc 'subtract' cannot use operands with types dtype('<M8[ns]') and dtype('O')
```
This is a problem because it occurs in a larger dataset that I need to reindex. It used to work fine with earlier versions of Pandas (although I do not know which, sorry).
#### Expected Output
Expected output would be something like
```
>>> f2.reindex(i1)
Empty DataFrame
Columns: []
Index: [2016-06-26 14:27:26+00:00]
```
#### Output of ``pd.show_versions()``
<details>
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.12.14-lp151.28.36-default
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
```
</details>
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MDU6SXNzdWU1NjQ5NzMzNzY=
| 31,965
|
drawstyle with "step" or "step-mid" display error
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| 2020-02-13T21:55:28Z
| 2020-05-22T14:36:26Z
| 2020-05-22T14:36:26Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame(index=pd.date_range(start='2020-01-01', periods=3), data=[1,2,3])
df.plot(drawstyle='steps')
df.plot(drawstyle='steps-mid')
```
`df` is:
```
| | 0 |
|---------------------+---|
| 2020-01-01 00:00:00 | 1 |
| 2020-01-02 00:00:00 | 2 |
| 2020-01-03 00:00:00 | 3 |
```
#### Problem description
For the first plot (`step`), the first data point (value 1) is not displayed, and I question the x-axis labeling. It implies that the value throughout the day of 01 Jan is 2, while the datafram suggests the value is 1 (assuming the value is carried forward from the timestamp until the next observation).
For the second plot (`step-mid`), all data is visible, but the x-axis is incorrect. It implies that the value of 1 that began on 01 Jan changed midway through the day. Visually, both values 1 and 3 are graphically underrepresented and half-width, while value 2 is full-width.
I would expect that when `drawstyle="step"` is used, that the graph looks like this:
```
import pandas as pd
df = pd.DataFrame(index=pd.date_range(start='2020-01-01', periods=3), data=[1,2,3])
df.loc[df.index[-1] + (df.index[-1]-df.index[-2])] = df.iloc[-1]
df.plot(drawstyle='steps-post')
```
#### Expected Output
A graph that shows all data with correct x-axis labels.
#### Output of ``pd.show_versions()``
<details>
pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.3
pytz : 2018.7
dateutil : 2.7.5
pip : 18.1
setuptools : 40.5.0
Cython : None
pytest : 5.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.1 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : 7.1.1
pandas_datareader: None
bs4 : None
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.0.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.2.15
tables : None
xarray : 0.14.1
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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| 31,966
|
sluggish `read_pickle` on pandas 1.0 ?
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| 2020-02-14T00:17:45Z
| 2020-07-20T16:09:46Z
| 2020-07-20T16:09:36Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
auths = pd.read_pickle('auths.pkl')
```
#### Problem description
Suddenly it takes 14-18 minutes to read a mid-size (2.1 GB) pickle file after upgrading to pandas 1.0.
Pandas 0.25 can read the very same file in 90 seconds (the file was created using pd.to_pickle a couple of months ago using pandas 0.25).
To try to make sure it has to do with Pandas 1.0, I created a couple of minimal virtual environments, one with latest pandas and one with 0.25. (I am using conda)
```python
conda create -n min_env_101 -y
conda activate min_env_101
conda install -y pandas
```
```python
conda create -n min_env_025 -y
conda activate min_env_025
conda install -y pandas=0.25.1
```
You can see the output of `pd.show_versions()` below. I noticed the latest pandas was installed along python 3.8.1, and pandas 0.25 with python 3.7.6, so I created another virtual environment with latest pandas, but making sure it uses python 3.7.6.
```python
conda create -n min_env_101_py376 python=3.7.6 -y
conda activate min_env_101_py376
conda install -y pandas
```
Then, reading the pickle file takes more than 14 minutes in the virtual environments with pandas 1.0, and just 93 seconds with pandas 0.25.
I checked the issues already reported and did not find anything similar. At some point I thought it was related to [this one](https://github.com/pandas-dev/pandas/issues/29542), but it cannot be since that one was using pandas 0.25.
So I am not sure what is going on (I did not see in pandas 1.0 release notes major changes related to pickle, but maybe I missed something?) and what to do about it.
EDIT: I cannot share the file and I guess you would not accept it either, 'cause reading pickle files may not be safe.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
**virtual environment with pandas 0.25.1**
--- 93.72341346740723 seconds ---
>>>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
<details>
[paste the output of ``pd.show_versions()`` here below this line]
**virtual environment with pandas 1.0.1**
--- 854.9136216640472 seconds ---
>>>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
<details>
[paste the output of ``pd.show_versions()`` here below this line]
**virtual environment with pandas 1.0.1, making sure it uses python 3.7.6**
--- 1087.5447969436646 seconds ---
>>>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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CLN: 29547 replace old string formatting 5
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| 1
| 2020-02-14T01:22:43Z
| 2020-02-21T01:29:58Z
| 2020-02-14T16:54:33Z
|
CONTRIBUTOR
| null |
I splitted PR #31844 in batches, this is the fifth
For this PR I ran the command `grep -l -R -e '%s' -e '%d' -e '\.format(' --include=*.{py,pyx} pandas/` and checked all the files that were returned for `.format(` and changed the old string format for the corresponding `fstrings` to attempt a full clean of, [#29547](https://github.com/pandas-dev/pandas/issues/29547). I may have missed something so is a good idea to double check just in case
- [ x ] tests added / passed
- [ x ] passes `black pandas`
- [ x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0Mzc1MTY4NTI4
| 31,968
|
DOC: Use use recommended library over deprecated library
|
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| 1
| 2020-02-14T01:28:20Z
| 2020-02-15T01:52:04Z
| 2020-02-14T02:35:08Z
|
CONTRIBUTOR
| null |
Importing `pandas.io.json.json_normalize` in pandas 1.0.1 gives the following warning
```
FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead
```
This PR updates the documentation here https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.json_normalize.html to use the recommended approach.
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Update doc decorator for pandas/core/base.py
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| 2020-02-14T02:02:05Z
| 2020-02-14T02:02:05Z
|
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| null |
- [ ] working on #31942
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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CLN: @doc - base.py & indexing.py
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| 19
| 2020-02-14T02:20:29Z
| 2020-03-23T21:42:50Z
| 2020-03-17T00:10:28Z
|
CONTRIBUTOR
| null |
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDU6SXNzdWU1NjUxNzE3NjI=
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|
shift() broken for datetime64 when used with fill_value
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| 10
| 2020-02-14T08:16:24Z
| 2020-03-12T02:26:13Z
| 2020-03-12T02:26:13Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
from pandas.testing import assert_series_equal
dt_series = pd.Series((pd.to_datetime('2020-01-01'), pd.to_datetime('2020-01-02')))
actual_result = dt_series.shift(1, fill_value=0)
expected_result = pd.Series((pd.to_datetime('1970-01-01'), pd.to_datetime('2020-01-01')))
assert_series_equal(actual_result, expected_result)
```
#### Problem description
Above code snippet works on version 0.25.3 but breaks with 1.0.1.
```
print(actual_result)
0 0
1 1577836800000000000
dtype: object
print(expected_result)
0 1970-01-01
1 2020-01-01
dtype: datetime64[ns]
```
```
AssertionError: Attributes of Series are different
Attribute "dtype" are different
[left]: object
[right]: datetime64[ns]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-72-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : None
pytest : 5.3.2
hypothesis : None
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.3.2
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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|
Boolean diff is slower than float, int diff
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| 2020-02-14T09:20:50Z
| 2020-02-17T13:05:58Z
| 2020-02-17T13:05:58Z
|
NONE
| null |
#### Code Sample
This code in Google Colab (Jupyter Notebook/Lab):
```python
import numpy as np
import pandas as pd
size = int(1e7)
value = np.sin(np.linspace(0, 100, size)) < 0.5
print("Numpy Boolean")
%timeit np.diff(value)
value = value.astype(int)
print("Numpy Integer")
%timeit np.diff(value)
df = pd.Series(np.sin(np.linspace(0, 100, size))) < 0.5
print("Pandas Boolean")
%timeit df.diff()
print("Pandas Integer")
%timeit df.astype(int).diff()
print("Pandas Float")
%timeit df.astype(float).diff()
```
Creates the following output:
```
Numpy Boolean
100 loops, best of 3: 11.8 ms per loop
Numpy Integer
100 loops, best of 3: 19 ms per loop
Pandas Boolean
1 loop, best of 3: 208 ms per loop
Pandas Integer
10 loops, best of 3: 51.7 ms per loop
Pandas Float
10 loops, best of 3: 43.8 ms per loop
```
#### Problem description
The diff function on a boolean series is about four times slower than on an integer or float series. This behavior is not reproducable in numpy, where diff on a boolean series is the fastest. Right now it is even faster to convert the boolean series to integer, do the diff and then convert back to boolean.
#### Expected Output
Boolean diff should be the fastest.
#### Output of ``pd.show_versions()``
Google Colab with pandas 1.0.1 installed.
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.137+
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.17.5
pytz : 2018.9
dateutil : 2.6.1
pip : 19.3.1
setuptools : 45.1.0
Cython : 0.29.15
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 5.5.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.6
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.11.0
pyarrow : 0.14.1
pytables : None
pytest : 3.6.4
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.4.4
tabulate : 0.8.6
xarray : 0.14.1
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : None
numba : 0.47.0
</details>
#### Next steps
- Find out why it is slower
- Quick fix: `df.astype(int).diff().dropna().astype(bool)` which benchmarks to `10 loops, best of 3: 114 ms per loop`
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wide_to_long with NaNs in i argument
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| null |
#### Code and output:
```python
import numpy as np
import pandas as pd
df1 = pd.DataFrame({'id' : ['a', np.NaN, 'c'],
'A_1999' : [1, 2, 3],
'A_2000' : [5, 6, 7]})
df1
id A_1999 A_2000
0 a 1 5
1 NaN 2 6
2 c 3 7
df2 = pd.wide_to_long(df1,
stubnames = ['A'],
sep = '_',
i = 'id',
j = 'year').reset_index()
df2
id year A
0 a 1999 1
1 a 1999 2
2 c 1999 3
3 a 2000 5
4 a 2000 6
5 c 2000 7
```
#### Problem description
df1 has one NaN in the id column. When applying `wide_to_long` to df1, `wide_to_long` seems to fill the NaNs from df1 with the preceding value from the df1 dataframe (namely, **a**).
Why might this be a problem: instances of `id == 'a'` in df1 are not the same as in df2 (because the NaNs from df1 have been replaced by **a** in df2):
```
df1.loc[df1['id'] == 'a',]
id A_1999 A_2000
0 a 1 5
```
versus:
```python
df2.loc[df2['id'] == 'a',]
id year A
0 a 1999 1
1 a 1999 2
3 a 2000 5
4 a 2000 6
```
#### Expected Output
I would expect `wide_to_long` to either
1. throw an error that the column that is passed to its `i` argument contains NaNs, or
2. preserve the NaN case in the second dataframe (not sure whether this is desirable), i.e.:
```python
id year A
0 a 1999 1
1 NaN 1999 2
2 c 1999 3
3 a 2000 5
4 NaN 2000 6
5 c 2000 7
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : 0.29.15
pytest : 5.3.5
hypothesis : 5.4.1
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.43.1
</details>
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MDExOlB1bGxSZXF1ZXN0Mzc1NDU0ODMz
| 31,977
|
Avoid importing from pandas at _libs files
|
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| 2020-02-14T15:54:20Z
| 2020-02-29T10:23:29Z
| 2020-02-16T16:50:01Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Something that brought at to me at first [here](https://github.com/pandas-dev/pandas/pull/30395#discussion_r363101229)
---
cc @jbrockmendel
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MDU6SXNzdWU1NjU0MTE2NjA=
| 31,978
|
Pandas breaks warnings module when printing DataFrame
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[] | null | 4
| 2020-02-14T15:54:50Z
| 2020-05-11T01:18:50Z
| null |
NONE
| null |
```python
>>> import warnings
>>> warnings.warn('asd')
<input>:1: UserWarning: asd
>>> warnings.warn('asd')
>>> warnings.warn('asd')
>>> import pandas as pd
>>> df = pd.DataFrame([1])
>>> warnings.warn('asd')
<input>:1: UserWarning: asd
>>> warnings.warn('asd')
>>> df
0
0 1
>>> warnings.warn('asd')
<input>:1: UserWarning: asd
>>> warnings.warn('asd')
<input>:1: UserWarning: asd
>>> warnings.warn('asd')
<input>:1: UserWarning: asd
>>> warnings.warn('asd')
<input>:1: UserWarning: asd
```
#### Problem description
As you can see from the interactive Python listed above, when `DataFrame` is printed, suddenly all calls to `warnings.warn()` are being reported, instead of being suppressed when identical warning is issued.
The problem does not occur on Pandas version `0.25.3`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1250
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.7
numba : None
</details>
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| 31,979
|
CI: macOS py36_macos build fails
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| 2020-02-14T17:03:48Z
| 2020-02-14T17:53:15Z
| 2020-02-14T17:53:15Z
|
MEMBER
| null |
#### Problem description
Currently macOS py36_macos Azure pipeline fails with this build error:
```
ImportError: dlopen(/Users/runner/miniconda3/lib/python3.7/lib-dynload/_ctypes.cpython-37m-darwin.so, 2): Library not loaded: @rpath/libffi.6.dylib
Referenced from: /Users/runner/miniconda3/lib/python3.7/lib-dynload/_ctypes.cpython-37m-darwin.so
Reason: image not found
```
Seen on #31563 and #31977
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MDExOlB1bGxSZXF1ZXN0Mzc1NDkxNTA3
| 31,980
|
CLN: 29547 replace old string formatting 6
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| 1
| 2020-02-14T17:19:28Z
| 2020-02-21T01:30:05Z
| 2020-02-14T19:11:47Z
|
CONTRIBUTOR
| null |
I splitted PR #31844 in batches, this is the sixth
For this PR I ran the command `grep -l -R -e '%s' -e '%d' -e '\.format(' --include=*.{py,pyx} pandas/` and checked all the files that were returned for `.format(` and changed the old string format for the corresponding `fstrings` to attempt a full clean of, [#29547](https://github.com/pandas-dev/pandas/issues/29547). I may have missed something so is a good idea to double check just in case
- [ x ] tests added / passed
- [ x ] passes `black pandas`
- [ x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDU6SXNzdWU1NjU0NjA3MDY=
| 31,981
|
Segfault in version 1.0.1, read_parquet after creating a clickhouse odbc connection
|
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| 2020-02-14T17:28:30Z
| 2020-02-20T13:38:04Z
| 2020-02-20T02:38:30Z
|
NONE
| null |
We started getting segfaults in upgraded versions of pandas and tracked it down to an interaction with making odbc connections. The specific odbc driver is for Clickhouse built in Dec19 from:
```
https://github.com/ClickHouse/clickhouse-odbc
commit 08b252b93fc771fab607fb973443543479b3d972
```
The core dump is reproducable in the example below:
#### Code Sample, a copy-pastable example if possible
```python
import pyodbc
import pandas as pd
con_str = f"Driver=libclickhouseodbc.so;url=http://clickhouse/query;timeout=600"
with pyodbc.connect(con_str, autocommit=True) as con:
pass
df = pd.DataFrame({'A': [1,1,1], 'B': ['a', 'b', 'c']})
df.to_parquet('/tmp/foo.pq')
# This line core dumps:
pd.read_parquet('/tmp/foo.pq')
```
#### Problem description
In the code above, creating the clickhouse odbc connection (not using it) leads to a segfault when reading the parquet file:
```
terminate called after throwing an instance of 'std::bad_cast'
what(): std::bad_cast
Aborted (core dumped)
```
This does not happen in pandas 0.25.3 but does in pandas 1.0.1.
#### Expected Output
No core dump.
#### Odbc driver dependencies
```xbk@499e30e4f63f:$ ldd libclickhouseodbc.so
linux-vdso.so.1 (0x00007ffe02bee000)
libpthread.so.0 => /lib/x86_64-linux-gnu/libpthread.so.0 (0x00007f246c269000)
librt.so.1 => /lib/x86_64-linux-gnu/librt.so.1 (0x00007f246c061000)
libltdl.so.7 => /usr/lib/x86_64-linux-gnu/libltdl.so.7 (0x00007f246be57000)
libc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007f246ba66000)
/lib64/ld-linux-x86-64.so.2 (0x00007f246cd89000)
libdl.so.2 => /lib/x86_64-linux-gnu/libdl.so.2 (0x00007f246b862000)
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1062.4.3.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.0
pip : 9.0.1
setuptools : 45.2.0
Cython : 0.29.14
pytest : 5.3.2
hypothesis : 5.1.2
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 0.999999999
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : 0.15.1.dev539+g8cf0c8e0a
pytables : None
pytest : 5.3.2
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.11
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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| 565,464,527
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MDExOlB1bGxSZXF1ZXN0Mzc1NDk3ODUw
| 31,982
|
CLN 29547 Replace old string formatting syntax with f-strings
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| 1
| 2020-02-14T17:36:30Z
| 2020-02-15T02:19:42Z
| 2020-02-15T02:19:38Z
|
CONTRIBUTOR
| null |
Addresses #29547 for pandas/io/sas/sas_xport.py. I searched through for instances of the `%` operator and `.format`.
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
I noticed that `_read_sas_doc` doesn't seem to be used anymore. I could delete that as well, or we could save that for a separate PR.
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| 565,506,365
|
MDU6SXNzdWU1NjU1MDYzNjU=
| 31,983
|
DOC: pivot_table example
|
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| 2020-02-14T19:11:45Z
| 2020-02-15T13:09:26Z
| 2020-02-15T13:09:26Z
|
CONTRIBUTOR
| null |
The documentation for pivot table seems limited https://pandas.pydata.org/pandas-docs/version/0.17/generated/pandas.pivot_table.html
It doesn't show how `df` was generated. In addition, should pivot_table be `pd.pivot_table`.
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MDExOlB1bGxSZXF1ZXN0Mzc1NTMyNjc4
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REF: dont use numexpr in places where it doesnt help.
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[] | null | 1
| 2020-02-14T19:14:12Z
| 2021-03-02T21:10:52Z
| 2020-02-17T03:31:50Z
|
MEMBER
| null |
DataFrame ops will still use it when dispatching to series/block ops.
Perf neutral:
```
In [3]: arr = np.arange(10**4).reshape(100, 100)
In [4]: df = pd.DataFrame(arr)
In [5]: df[4] = df[4].astype(float)
In [6]: ser = df[0]
In [8]: %timeit res = df+ser
19.8 ms ± 317 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <-- PR
19.8 ms ± 267 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <-- master
In [10]: %timeit res2 = df.add(ser, axis=0)
19.2 ms ± 276 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <-- PR
19.2 ms ± 301 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <-- master
In [13]: %timeit res3 = df + df
23.6 ms ± 382 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <-- PR
24 ms ± 551 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <-- master
```
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MDU6SXNzdWU1NjU1MjA2ODI=
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Error running pd.show_versions()
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| 2020-02-14T19:40:29Z
| 2020-02-15T05:48:04Z
| 2020-02-15T05:48:04Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
pd.show_versions()
```
#### Problem description
Was trying to import xlsb file...
pd.read_excel('my_file.xlsb', engine='pyxlsb')
when i received this error:
ImportError: Can't determine version for pyxlsb
Next, I tried pd.show_versions() and received the same error after restarting the kernel and running these two lines...
import pandas as pd
pd.show_versions()
ImportError: Can't determine version for pyxlsb
FYI:
Python 3.6.9 (default, Nov 7 2019, 10:44:02)
IPython 5.5.0 -- An enhanced Interactive Python.
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
In [1]: import pandas as pd
In [2]: pd.show_versions()
Traceback (most recent call last):
File "<ipython-input-2-3d232a07e144>", line 1, in <module>
pd.show_versions()
File "/home/lang/.local/lib/python3.6/site-packages/pandas/util/_print_versions.py", line 97, in show_versions
modname, raise_on_missing=False, on_version="ignore"
File "/home/lang/.local/lib/python3.6/site-packages/pandas/compat/_optional.py", line 98, in import_optional_dependency
version = _get_version(module)
File "/home/lang/.local/lib/python3.6/site-packages/pandas/compat/_optional.py", line 43, in _get_version
raise ImportError(f"Can't determine version for {module.__name__}")
ImportError: Can't determine version for pyxlsb
pd.__version__
Out[3]: '1.0.1'
</details>
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CLN: 29547 replace old string formatting 7
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| 3
| 2020-02-14T19:45:33Z
| 2020-02-21T01:29:56Z
| 2020-02-15T19:04:20Z
|
CONTRIBUTOR
| null |
I splitted PR #31844 in batches, this is the seventh
For this PR I ran the command `grep -l -R -e '%s' -e '%d' -e '\.format(' --include=*.{py,pyx} pandas/` and checked all the files that were returned for `.format(` and changed the old string format for the corresponding `fstrings` to attempt a full clean of, [#29547](https://github.com/pandas-dev/pandas/issues/29547). I may have missed something so is a good idea to double check just in case
- [ x ] tests added / passed
- [ x ] passes `black pandas`
- [ x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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| 31,987
|
Feature request: option to set week starting on Sunday instead of Monday
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| 2020-02-14T20:06:38Z
| 2020-03-08T03:34:50Z
| 2020-03-08T03:34:50Z
|
NONE
| null |
#### Problem description
Currently, calling `pd.Series.dt.weekday` returns Monday = 0, Sunday = 6. It would be nice to have a settable option that allows Sunday to be the beginning of the week, e.g.
```python
my_series.dt.weekday # Monday = 0, Sunday = 6
pd.options.week_start = 'Sunday'
my_series.dt.weekday # Sunday = 0, Saturday = 6
```
Pandas version 1.0.1.
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Error in `read_pickle` when loading a DataFrame with MultiIndex columns from a pickle created in py27
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| 1
| 2020-02-14T22:23:16Z
| 2020-02-21T14:52:24Z
| 2020-02-21T14:52:24Z
|
CONTRIBUTOR
| null |
Calling `read_pickle` to load a DataFrame with MultiIndex columns from a pickle file created in py27 throws an error saying:
> UnicodeDecodeError: 'ascii' codec can't decode byte 0xd9 in position 0: ordinal not in range(128)
This issue was introduced by #28645, more specifically by [this change](https://github.com/pandas-dev/pandas/pull/28645/files#diff-7ecbc1597b1273ca207747232d64edad):
```diff
diff --git a/pandas/io/pickle.py b/pandas/io/pickle.py
index adf0aa961..8f9bae0f7 100644
--- a/pandas/io/pickle.py
+++ b/pandas/io/pickle.py
@@ -142,18 +142,24 @@ def read_pickle(path, compression="infer"):
# 1) try standard library Pickle
# 2) try pickle_compat (older pandas version) to handle subclass changes
- # 3) try pickle_compat with latin1 encoding
+
+ excs_to_catch = (AttributeError, ImportError)
+ if PY36:
+ excs_to_catch += (ModuleNotFoundError,)
try:
with warnings.catch_warnings(record=True):
# We want to silence any warnings about, e.g. moved modules.
warnings.simplefilter("ignore", Warning)
return pickle.load(f)
- except Exception:
- try:
- return pc.load(f, encoding=None)
- except Exception:
- return pc.load(f, encoding="latin1")
+ except excs_to_catch:
+ # e.g.
+ # "No module named 'pandas.core.sparse.series'"
+ # "Can't get attribute '__nat_unpickle' on <module 'pandas._libs.tslib"
+ return pc.load(f, encoding=None)
+ except UnicodeDecodeError:
+ # e.g. can occur for files written in py27; see GH#28645
+ return pc.load(f, encoding="latin-1")
finally:
f.close()
for _f in fh:
```
Note how before when there was an Exception trying to load the file with the builtin `pickle.load` function, it would try to load them using `pickle_compat` with `enconding=None` and if that call also threw an Exception, it would then fallback to try to load with `encoding="latin-1"`. With the change from #28645 the fallback to use `encoding="latin-1"` is only in the catch block of the initial `pickle_load` call, not the second one that tries to use pickle_compat with `encoding=None`.
This become an issue for py27 pickles with MultiIndex columns after `FrozenNDArray` was removed by #29840 as `pickle.load(f)` throws an `AttributeError` for `FrozenNDArray` and then `pc.load(f, encoding=None)` throws an `UnicodeDecodeError`.
Here is a full stack trace:
<details>
```python
In [1]: import pandas as pd
...: df = pd.read_pickle('test_mi_py27.pkl')
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/anaconda3/envs/pandas-pickle/lib/python3.7/site-packages/pandas/io/pickle.py in read_pickle(filepath_or_buffer, compression)
180 warnings.simplefilter("ignore", Warning)
--> 181 return pickle.load(f)
182 except excs_to_catch:
AttributeError: Can't get attribute 'FrozenNDArray' on <module 'pandas.core.indexes.frozen' from '~/anaconda3/envs/pandas-pickle/lib/python3.7/site-packages/pandas/core/indexes/frozen.py'>
During handling of the above exception, another exception occurred:
UnicodeDecodeError Traceback (most recent call last)
<ipython-input-1-8c3800ea96cb> in <module>
1 import pandas as pd
----> 2 df = pd.read_pickle('test_mi_py27.pkl')
~/anaconda3/envs/pandas-pickle/lib/python3.7/site-packages/pandas/io/pickle.py in read_pickle(filepath_or_buffer, compression)
184 # "No module named 'pandas.core.sparse.series'"
185 # "Can't get attribute '__nat_unpickle' on <module 'pandas._libs.tslib"
--> 186 return pc.load(f, encoding=None)
187 except UnicodeDecodeError:
188 # e.g. can occur for files written in py27; see GH#28645
~/anaconda3/envs/pandas-pickle/lib/python3.7/site-packages/pandas/compat/pickle_compat.py in load(fh, encoding, is_verbose)
239 up.is_verbose = is_verbose
240
--> 241 return up.load()
242 except (ValueError, TypeError):
243 raise
~/anaconda3/envs/pandas-pickle/lib/python3.7/pickle.py in load(self)
1086 raise EOFError
1087 assert isinstance(key, bytes_types)
-> 1088 dispatch[key[0]](self)
1089 except _Stop as stopinst:
1090 return stopinst.value
~/anaconda3/envs/pandas-pickle/lib/python3.7/pickle.py in load_short_binstring(self)
1262 len = self.read(1)[0]
1263 data = self.read(len)
-> 1264 self.append(self._decode_string(data))
1265 dispatch[SHORT_BINSTRING[0]] = load_short_binstring
1266
~/anaconda3/envs/pandas-pickle/lib/python3.7/pickle.py in _decode_string(self, value)
1202 return value
1203 else:
-> 1204 return value.decode(self.encoding, self.errors)
1205
1206 def load_string(self):
UnicodeDecodeError: 'ascii' codec can't decode byte 0xd9 in position 0: ordinal not in range(128)
```
</details>
I believe the fix for this specific issue can be as simple as:
```diff
diff --git a/pandas/io/pickle.py b/pandas/io/pickle.py
index e51f24b55..5c4f2d8c4 100644
--- a/pandas/io/pickle.py
+++ b/pandas/io/pickle.py
@@ -183,7 +183,11 @@ def read_pickle(
# e.g.
# "No module named 'pandas.core.sparse.series'"
# "Can't get attribute '__nat_unpickle' on <module 'pandas._libs.tslib"
- return pc.load(f, encoding=None)
+ try:
+ return pc.load(f, encoding=None)
+ except UnicodeDecodeError:
+ # e.g. can occur for files written in py27;
+ return pc.load(f, encoding="latin-1")
except UnicodeDecodeError:
# e.g. can occur for files written in py27; see GH#28645
return pc.load(f, encoding="latin-1")
```
I will open a PR with the fix above over the weekend.
Thanks!
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pandas/conftest.py refactoring
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CONTRIBUTOR
| null |
As stated in [this review comment](https://github.com/pandas-dev/pandas/pull/31701#pullrequestreview-359291503) from jreback, we should address some refactoring issues in `pandas/conftest.py`:
- [x] create the indices fixture using `tm.all_index_generator` as suggested [by jbrockmendel in this comment](https://github.com/pandas-dev/pandas/pull/31701#discussion_r378532760)
- [x] all fixtures and functions should have a docstring
- [x] add sections, i.e. "some text markers so reading the file is easy"
- [ ] remove/avoid imports from `conftest.py`
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How should isin behave with pd.NA?
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| 4
| 2020-02-15T05:50:08Z
| 2020-10-30T22:48:45Z
| null |
MEMBER
| null |
```python
import pandas as pd
arr = pd.array(["a", "b", pd.NA], dtype="string")
s = pd.Series(["a", "b", "c"])
print(s.isin(arr))
# 0 True
# 1 True
# 2 False
# dtype: bool
print(pd.Series(arr).isin(["a", "b"]))
# 0 True
# 1 True
# 2 False
# dtype: bool
```
I think a case could be made that the actual output is not correct in these cases, and that both should return the nullable boolean `pd.Series(pd.array([True, True, pd.NA]))`. In the first case we don't know that `"c"` _is not_ in `arr`, and in the second case we don't know if `pd.NA` happens to be `"a"` or `"b"`, so again we should have `pd.NA`.
This is obviously an edge case but may be worth considering for the sake of consistency with the other three-valued logic operations (since `isin` is essentially an "or" statement).
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| 31,991
|
BUG: Fix wrong reading sparse matrix
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| 7
| 2020-02-15T07:29:20Z
| 2020-04-16T22:16:53Z
| 2020-04-16T21:22:53Z
|
CONTRIBUTOR
| null |
- [x] closes #29814
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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|
COMPAT: numpy dev version raises new errors during CI
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[] | null | 5
| 2020-02-15T08:01:15Z
| 2020-03-23T15:13:00Z
| 2020-03-23T15:02:03Z
|
MEMBER
| null |
### Problem description
Looks like something has been changed in numpy, and we now get new errors when running that Numpy dev pipeline during CI. This happens regardless of PR (saw it in #31563 and #31991). Seems to be centered around cumsum and division by nan, but I don't have the time to delve deeper atm.
One of the errors below:
```python
# axis = 0
cummin = datetime_frame.cummin()
expected = datetime_frame.apply(Series.cummin)
> tm.assert_frame_equal(cummin, expected)
pandas/tests/frame/test_cumulative.py:84:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/_libs/testing.pyx:65: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj)
E AssertionError: DataFrame.iloc[:, 0] (column name="A") are different
E
E DataFrame.iloc[:, 0] (column name="A") values are different (46.66667 %)
E [left]: [0.652282807224414, -0.06609607048075124, 6.9221550547283e-310, 4.6661712289503e-310, 4.6661712289503e-310, 0.22575641583650302, 1.5013182000449852, -0.035445399728511705, -0.4007619715825971, -0.4007619715825971, -1.4917178151508124, -0.14834313680366412, 0.545948106375247, 1.495247786960942, 0.2128973463640255, 0.794985190506577, -1.4214210868734076, -1.4214210868734076, -1.019792839223919, -0.28828070056157995, -0.47176522372684804, -0.4267351486193231, -2.816743984494702, 0.4308015055934936, -0.007381576327430263, -0.007381576327430263, -1.121456436411571, -0.25765070817685554, -0.7686775772512485, 1.0963456159671487]
E [right]: [0.652282807224414, -0.06609607048075124, -0.06609607048075124, 0.48729707348930335, 0.48729707348930335, 0.22575641583650302, 0.22575641583650302, -0.035445399728511705, -0.4007619715825971, -0.4007619715825971, 0.3249965661970306, -0.14834313680366412, -0.14834313680366412, 0.545948106375247, 0.2128973463640255, 0.2128973463640255, -1.4214210868734076, -1.4214210868734076, -0.9987072139900786, -0.9987072139900786, -0.47176522372684804, -0.47176522372684804, -2.816743984494702, -2.816743984494702, -0.007381576327430263, -0.007381576327430263, -1.121456436411571, -1.121456436411571, -0.7686775772512485, -0.7686775772512485]
```
[Link](https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=28623&view=logs&j=3a03f79d-0b41-5610-1aa4-b4a014d0bc70&t=4d05ed0e-1ed3-5bff-dd63-1e957f2766a9&l=75) to one of the failed pipelines. You can also just take a look at any recent PR.
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| 31,993
|
Backport PR: CI:Testing whole doctest, and not specific module
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| 1
| 2020-02-15T08:21:19Z
| 2020-02-15T10:15:40Z
| 2020-02-15T09:59:33Z
|
MEMBER
| null |
xref #31975
Doing as mentioned [here](https://github.com/pandas-dev/pandas/pull/31975#issuecomment-586443425)
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|
WEB: the donate page is not loading correctly
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| 2020-02-15T08:31:37Z
| 2020-03-05T12:13:09Z
| 2020-03-05T12:13:09Z
|
MEMBER
| null |
For me, the donate page (and specifically the donate widget) is not loading:
It just keeps saying "loading" for me.
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|
DOC: Add See Also section on series.py
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MDExOlB1bGxSZXF1ZXN0Mzc1Njk1NDIx
| 31,996
|
DOC PR09 Add missing dots at con parameter on io/sql.py file
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| 2020-02-15T08:54:18Z
| 2020-02-15T09:04:32Z
| 2020-02-15T09:04:32Z
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MDExOlB1bGxSZXF1ZXN0Mzc1Njk2MDQy
| 31,997
|
DOC PR09 on freq parameter
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MDExOlB1bGxSZXF1ZXN0Mzc1Njk2MzA0
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DOC PR09 Add missing . on freq parameter on groupby.py
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MDExOlB1bGxSZXF1ZXN0Mzc1Njk3OTUx
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DOC PR07: Add description to parameter skipna
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| 2020-02-15T11:33:47Z
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|
CONTRIBUTOR
| null |
- [ ] closes https://github.com/pandanistas/pandanistas_sprint_jakarta2020/issues/18
- [ ] tests added / passed
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output of `python scripts/validate_docstrings.py pandas.Series.argmax`:
```
################################################################################
################################## Validation ##################################
################################################################################
4 Errors found:
No extended summary found
Parameters {'*args', '**kwargs'} not documented
Missing description for See Also "numpy.ndarray.argmax" reference
No examples section found
```
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DOC: PR09 Add missing . on Parameter con description
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| 3
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| 2020-02-15T10:58:37Z
| 2020-02-15T10:58:12Z
|
CONTRIBUTOR
| null |
- [ ] closes https://github.com/pandanistas/pandanistas_sprint_jakarta2020/issues/22
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Output of `python scripts/validate_docstrings.py pandas.DataFrame.to_sql`:
```
################################################################################
##################### Docstring (pandas.DataFrame.to_sql) #####################
################################################################################
Write records stored in a DataFrame to a SQL database.
Databases supported by SQLAlchemy [1]_ are supported. Tables can be
newly created, appended to, or overwritten.
Parameters
----------
name : str
Name of SQL table.
con : sqlalchemy.engine.Engine or sqlite3.Connection
Using SQLAlchemy makes it possible to use any DB supported by that
library. Legacy support is provided for sqlite3.Connection objects. The user
is responsible for engine disposal and connection closure for the SQLAlchemy
connectable See `here <https://docs.sqlalchemy.org/en/13/core/connections.html>`_.
schema : str, optional
Specify the schema (if database flavor supports this). If None, use
default schema.
if_exists : {'fail', 'replace', 'append'}, default 'fail'
How to behave if the table already exists.
* fail: Raise a ValueError.
* replace: Drop the table before inserting new values.
* append: Insert new values to the existing table.
index : bool, default True
Write DataFrame index as a column. Uses `index_label` as the column
name in the table.
index_label : str or sequence, default None
Column label for index column(s). If None is given (default) and
`index` is True, then the index names are used.
A sequence should be given if the DataFrame uses MultiIndex.
chunksize : int, optional
Specify the number of rows in each batch to be written at a time.
By default, all rows will be written at once.
dtype : dict or scalar, optional
Specifying the datatype for columns. If a dictionary is used, the
keys should be the column names and the values should be the
SQLAlchemy types or strings for the sqlite3 legacy mode. If a
scalar is provided, it will be applied to all columns.
method : {None, 'multi', callable}, optional
Controls the SQL insertion clause used:
* None : Uses standard SQL ``INSERT`` clause (one per row).
* 'multi': Pass multiple values in a single ``INSERT`` clause.
* callable with signature ``(pd_table, conn, keys, data_iter)``.
Details and a sample callable implementation can be found in the
section :ref:`insert method <io.sql.method>`.
.. versionadded:: 0.24.0
Raises
------
ValueError
When the table already exists and `if_exists` is 'fail' (the
default).
See Also
--------
read_sql : Read a DataFrame from a table.
Notes
-----
Timezone aware datetime columns will be written as
``Timestamp with timezone`` type with SQLAlchemy if supported by the
database. Otherwise, the datetimes will be stored as timezone unaware
timestamps local to the original timezone.
.. versionadded:: 0.24.0
References
----------
.. [1] https://docs.sqlalchemy.org
.. [2] https://www.python.org/dev/peps/pep-0249/
Examples
--------
Create an in-memory SQLite database.
>>> from sqlalchemy import create_engine
>>> engine = create_engine('sqlite://', echo=False)
Create a table from scratch with 3 rows.
>>> df = pd.DataFrame({'name' : ['User 1', 'User 2', 'User 3']})
>>> df
name
0 User 1
1 User 2
2 User 3
>>> df.to_sql('users', con=engine)
>>> engine.execute("SELECT * FROM users").fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3')]
>>> df1 = pd.DataFrame({'name' : ['User 4', 'User 5']})
>>> df1.to_sql('users', con=engine, if_exists='append')
>>> engine.execute("SELECT * FROM users").fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3'),
(0, 'User 4'), (1, 'User 5')]
Overwrite the table with just ``df1``.
>>> df1.to_sql('users', con=engine, if_exists='replace',
... index_label='id')
>>> engine.execute("SELECT * FROM users").fetchall()
[(0, 'User 4'), (1, 'User 5')]
Specify the dtype (especially useful for integers with missing values).
Notice that while pandas is forced to store the data as floating point,
the database supports nullable integers. When fetching the data with
Python, we get back integer scalars.
>>> df = pd.DataFrame({"A": [1, None, 2]})
>>> df
A
0 1.0
1 NaN
2 2.0
>>> from sqlalchemy.types import Integer
>>> df.to_sql('integers', con=engine, index=False,
... dtype={"A": Integer()})
>>> engine.execute("SELECT * FROM integers").fetchall()
[(1,), (None,), (2,)]
################################################################################
################################## Validation ##################################
################################################################################
```
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MDExOlB1bGxSZXF1ZXN0Mzc1Njk4MDUx
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DOC PR09 Add . in the description parameter
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| 1
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| 2020-02-15T10:29:08Z
| 2020-02-15T10:28:55Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0Mzc1Njk5MjE5
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DOC: Update the pandas.DataFrame.plot docstring
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| 2020-02-15T09:46:58Z
| 2020-02-26T20:47:44Z
| 2020-02-26T20:47:43Z
|
NONE
| null |
Give description for 'figsize','xlim', and 'ylim'.
- [x] closes https://github.com/pandanistas/pandanistas_sprint_jakarta2020/issues/11
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Output of `python scripts/validate_docstrings.py pandas.DataFrame.plot`:|
################################################################################
###################### Docstring (pandas.DataFrame.plot) ######################
################################################################################
Make plots of Series or DataFrame.
Uses the backend specified by the
option ``plotting.backend``. By default, matplotlib is used.
Parameters
----------
data : Series or DataFrame
The object for which the method is called.
x : label or position, default None
Only used if data is a DataFrame.
y : label, position or list of label, positions, default None
Allows plotting of one column versus another. Only used if data is a
DataFrame.
kind : str
The kind of plot to produce:
- 'line' : line plot (default)
- 'bar' : vertical bar plot
- 'barh' : horizontal bar plot
- 'hist' : histogram
- 'box' : boxplot
- 'kde' : Kernel Density Estimation plot
- 'density' : same as 'kde'
- 'area' : area plot
- 'pie' : pie plot
- 'scatter' : scatter plot
- 'hexbin' : hexbin plot.
figsize : a tuple (width, height) in inches
The size of the figure to create in matplotlib.
use_index : bool, default True
Use index as ticks for x axis.
title : str or list
Title to use for the plot. If a string is passed, print the string
at the top of the figure. If a list is passed and `subplots` is
True, print each item in the list above the corresponding subplot.
grid : bool, default None (matlab style default)
Axis grid lines.
legend : bool or {'reverse'}
Place legend on axis subplots.
style : list or dict
The matplotlib line style per column.
logx : bool or 'sym', default False
Use log scaling or symlog scaling on x axis.
.. versionchanged:: 0.25.0
logy : bool or 'sym' default False
Use log scaling or symlog scaling on y axis.
.. versionchanged:: 0.25.0
loglog : bool or 'sym', default False
Use log scaling or symlog scaling on both x and y axes.
.. versionchanged:: 0.25.0
xticks : sequence
Values to use for the xticks.
yticks : sequence
Values to use for the yticks.
xlim : 2-tuple/list
Set or query x-axis limits.
ylim : 2-tuple/list
Set or query y-axis limits.
rot : int, default None
Rotation for ticks (xticks for vertical, yticks for horizontal
plots).
fontsize : int, default None
Font size for xticks and yticks.
colormap : str or matplotlib colormap object, default None
Colormap to select colors from. If string, load colormap with that
name from matplotlib.
colorbar : bool, optional
If True, plot colorbar (only relevant for 'scatter' and 'hexbin'
plots).
position : float
Specify relative alignments for bar plot layout.
From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5
(center).
table : bool, Series or DataFrame, default False
If True, draw a table using the data in the DataFrame and the data
will be transposed to meet matplotlib's default layout.
If a Series or DataFrame is passed, use passed data to draw a
table.
yerr : DataFrame, Series, array-like, dict and str
See :ref:`Plotting with Error Bars <visualization.errorbars>` for
detail.
xerr : DataFrame, Series, array-like, dict and str
Equivalent to yerr.
mark_right : bool, default True
When using a secondary_y axis, automatically mark the column
labels with "(right)" in the legend.
include_bool : bool, default is False
If True, boolean values can be plotted.
backend : str, default None
Backend to use instead of the backend specified in the option
``plotting.backend``. For instance, 'matplotlib'. Alternatively, to
specify the ``plotting.backend`` for the whole session, set
``pd.options.plotting.backend``.
.. versionadded:: 1.0.0
**kwargs
Options to pass to matplotlib plotting method.
Returns
-------
:class:`matplotlib.axes.Axes` or numpy.ndarray of them
If the backend is not the default matplotlib one, the return value
will be the object returned by the backend.
Notes
-----
- See matplotlib documentation online for more on this subject
- If `kind` = 'bar' or 'barh', you can specify relative alignments
for bar plot layout by `position` keyword.
From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5
(center)
################################################################################
################################## Validation ##################################
################################################################################
3 Errors found:
Unknown parameters {'colorbar', '**kwargs', 'style', 'use_index', 'rot', 'mark_right', 'grid', 'y', 'fontsize', 'position', 'kind', 'include_bool', 'logy', 'x', 'yerr', 'xlim', 'backend', 'table', 'logx', 'ylim', 'yticks', 'xerr', 'loglog', 'legend', 'colormap', 'figsize', 'title', 'xticks'}
See Also section not found
No examples section found
|
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| 565,727,402
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MDExOlB1bGxSZXF1ZXN0Mzc1NzAwOTEz
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DOC: Fix SA04 errors from DataFrame.melt
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NONE
| null |
- [ ] closes https://github.com/pandanistas/pandanistas_sprint_jakarta2020/issues/10
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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DOC: Update pandas.DataFrame.droplevel docstring
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| 2
| 2020-02-15T10:29:42Z
| 2020-02-25T01:54:52Z
| 2020-02-25T01:54:46Z
|
CONTRIBUTOR
| null |
- [ ] closes https://github.com/pandanistas/pandanistas_sprint_jakarta2020/issues/23
- [ ] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Output of `python scripts/validate_docstrings.py pandas.DataFrame.droplevel`:
```
################################################################################
#################### Docstring (pandas.DataFrame.droplevel) ####################
################################################################################
Return DataFrame with requested index / column level(s) removed.
.. versionadded:: 0.24.0
Parameters
----------
level : int, str, or list-like
If a string is given, must be the name of a level
If list-like, elements must be names or positional indexes
of levels.
axis : {0 or 'index', 1 or 'columns'}, default 0
Axis along which the level(s) is removed:
* 0 or 'index': remove level(s) in column.
* 1 or 'columns': remove level(s) in row.
Returns
-------
DataFrame
DataFrame with requested index / column level(s) removed.
Examples
--------
>>> df = pd.DataFrame([
... [1, 2, 3, 4],
... [5, 6, 7, 8],
... [9, 10, 11, 12]
... ]).set_index([0, 1]).rename_axis(['a', 'b'])
>>> df.columns = pd.MultiIndex.from_tuples([
... ('c', 'e'), ('d', 'f')
... ], names=['level_1', 'level_2'])
>>> df
level_1 c d
level_2 e f
a b
1 2 3 4
5 6 7 8
9 10 11 12
>>> df.droplevel('a')
level_1 c d
level_2 e f
b
2 3 4
6 7 8
10 11 12
>>> df.droplevel('level_2', axis=1)
level_1 c d
a b
1 2 3 4
5 6 7 8
9 10 11 12
################################################################################
################################## Validation ##################################
################################################################################
1 Errors found:
See Also section not found
```
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MDExOlB1bGxSZXF1ZXN0Mzc1NzAyNTQ3
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|
doc: Add period to parameter description
|
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| 1
| 2020-02-15T10:34:47Z
| 2020-02-15T19:06:35Z
| 2020-02-15T19:06:26Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
```
there are a few errors left:
################################################################################
################################## Validation ##################################
################################################################################
11 Errors found:
Parameters {'**fields', 'name', 'ordinal'} not documented
Unknown parameters {'second', 'minute', 'year', 'month', 'quarter', 'hour', 'day'}
Parameter "year" has no description
Parameter "month" has no description
Parameter "quarter" has no description
Parameter "day" has no description
Parameter "hour" has no description
Parameter "minute" has no description
Parameter "second" has no description
Parameter "dtype" has no description
Examples do not pass tests
```
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MDExOlB1bGxSZXF1ZXN0Mzc1NzAyNTk1
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|
DOC: Fix pandas.index.copy summary documentation
|
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[] | null | 1
| 2020-02-15T10:35:39Z
| 2020-02-26T20:36:11Z
| 2020-02-26T20:35:42Z
|
CONTRIBUTOR
| null |
- [ ] closes https://github.com/pandanistas/pandanistas_sprint_jakarta2020/issues/8
- [ ] tests added / passed
- [ ] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Output of `python scripts/validate_docstrings.py pandas.Index.copy`
################################################################################
######################## Docstring (pandas.Index.copy) ########################
################################################################################
Make a copy of this object.
Name and dtype sets those attributes on the new object.
Parameters
----------
name : Label, optional
Set name for new object.
deep : bool, default False
dtype : numpy dtype or pandas type, optional
Set dtype for new object.
names : list-like, optional
Kept for compatibility with MultiIndex. Should not be used.
Returns
-------
Index
Index refer to new object which is a copy of this object.
Notes
-----
In most cases, there should be no functional difference from using
``deep``, but if ``deep`` is passed it will attempt to deepcopy.
################################################################################
################################## Validation ##################################
################################################################################
3 Errors found:
Parameter "deep" has no description
See Also section not found
No examples section found
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MDExOlB1bGxSZXF1ZXN0Mzc1NzAyODkx
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|
CLN 29574 Replace old string formating
|
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| 1
| 2020-02-15T10:39:50Z
| 2020-02-16T06:31:37Z
| 2020-02-15T19:05:03Z
|
CONTRIBUTOR
| null |
Its my first contribution, modified the str formating to use fstrings
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] pandas/tests/frame/test_to_csv.py
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MDExOlB1bGxSZXF1ZXN0Mzc1NzAyOTYx
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|
DOC: Add description for tz parameter in DatetimeIndex
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|
NONE
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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DOC: Improve documentation for Index.where
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|
CONTRIBUTOR
| null |
- [x] closes https://github.com/pandanistas/pandanistas_sprint_jakarta2020/issues/15
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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| 565,731,760
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MDExOlB1bGxSZXF1ZXN0Mzc1NzA0MDAz
| 32,010
|
DOC: Fix errors in pandas.Series.argmax
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|
CONTRIBUTOR
| null |
- [x] closes https://github.com/pandanistas/pandanistas_sprint_jakarta2020/issues/18
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
output of `python scripts/validate_docstrings.py pandas.Series.argmax`:
```
################################################################################
################################## Validation ##################################
################################################################################
```
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CI: temporary fix to the CI
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| 2020-02-29T10:30:46Z
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|
MEMBER
| null |
ref #31992
---
This is just a temporary fix to the CI.
Until someone smarter than me fix it :)
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(wip)BUG: make sure combine doesn't alter dtype for nullable dtypes
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| 2020-02-15T10:59:40Z
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|
MEMBER
| null |
- [ ] closes #31899
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Haven't added new tests, but have changed the expected return dtype in existing ones
EDIT
----
This is the wrong fix - for, say
```
s1.combine(s2, lambda x, y: x<y)
```
the dtype _would_ be expected to change (in this case to bool)
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pandas.index.copy remove index name information when specifying new dtype
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| 1
| 2020-02-15T11:04:11Z
| 2020-03-27T00:00:35Z
| 2020-03-27T00:00:35Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> named_idx = pd.Index([17.3, 69.221, 33.1, 15.5, 19.3, 74.8, 10, 5.5]).set_names('old_name')
>>> named_idx
Float64Index([17.3, 69.221, 33.1, 15.5, 19.3, 74.8, 10.0, 5.5], dtype='float64', name='old_name')
>>> named_idx.copy(name='new_name', dtype='int64')
Int64Index([17, 69, 33, 15, 19, 74, 10, 5], dtype='int64')
>>> named_idx.copy(dtype='int64')
Int64Index([17, 69, 33, 15, 19, 74, 10, 5], dtype='int64')
```
#### Problem description
When set new dtype parameter in function pandas.index.copy somehow it remove name information on new object
#### Expected Output
>>> import pandas as pd
>>> named_idx = pd.Index([17.3, 69.221, 33.1, 15.5, 19.3, 74.8, 10, 5.5]).set_names('old_name')
>>> named_idx
Float64Index([17.3, 69.221, 33.1, 15.5, 19.3, 74.8, 10.0, 5.5], dtype='float64', name='old_name')
>>> named_idx.copy(name='new_name', dtype='int64')
Int64Index([17, 69, 33, 15, 19, 74, 10, 5], dtype='int64', name='new_name')
>>> named_idx.copy(dtype='int64')
Int64Index([17, 69, 33, 15, 19, 74, 10, 5], dtype='int64', name='old_name')
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
{k:<17}: {stat}
{k:<17}: {stat}
{k:<17}: {stat}
{k:<17}: {stat}
{k:<17}: {stat}
{k:<17}: {stat}
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</details>
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| 565,732,966
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MDExOlB1bGxSZXF1ZXN0Mzc1NzA0ODcz
| 32,014
|
DOC: Update pandas.Series.between_time docstring params
|
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| 1
| 2020-02-15T11:05:41Z
| 2020-02-15T19:10:34Z
| 2020-02-15T19:10:24Z
|
CONTRIBUTOR
| null |
- [x] closes [https://github.com/pandanistas/pandanistas_sprint_jakarta2020/issues/20](https://github.com/pandanistas/pandanistas_sprint_jakarta2020/issues/20)
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
output from python scripts/validate_docstrings.py pandas.Series.between_time:
################################################################################
#################### Docstring (pandas.Series.between_time) ####################
################################################################################
Select values between particular times of the day (e.g., 9:00-9:30 AM).
By setting ``start_time`` to be later than ``end_time``,
you can get the times that are *not* between the two times.
Parameters
----------
start_time : datetime.time or str
The first time value.
end_time : datetime.time or str
The second time value.
include_start : bool, default True
Adding start_time value in the result.
include_end : bool, default True
Adding end_time value in the result.
axis : {0 or 'index', 1 or 'columns'}, default 0
Determine range time on index or columns value.
.. versionadded:: 0.24.0
Returns
-------
Series or DataFrame
If axis set on columns, it will return DataFrame format, vice versa.
Raises
------
TypeError
If the index is not a :class:`DatetimeIndex`
See Also
--------
at_time : Select values at a particular time of the day.
first : Select initial periods of time series based on a date offset.
last : Select final periods of time series based on a date offset.
DatetimeIndex.indexer_between_time : Get just the index locations for
values between particular times of the day.
Examples
--------
>>> i = pd.date_range('2018-04-09', periods=4, freq='1D20min')
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
A
2018-04-09 00:00:00 1
2018-04-10 00:20:00 2
2018-04-11 00:40:00 3
2018-04-12 01:00:00 4
>>> ts.between_time('0:15', '0:45')
A
2018-04-10 00:20:00 2
2018-04-11 00:40:00 3
You get the times that are *not* between two times by setting
``start_time`` later than ``end_time``:
>>> ts.between_time('0:45', '0:15')
A
2018-04-09 00:00:00 1
2018-04-12 01:00:00 4
################################################################################
################################## Validation ##################################
################################################################################
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MDExOlB1bGxSZXF1ZXN0Mzc1NzA1NDE1
| 32,015
|
DOC: Improve docstring of Index.delete
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| 2
| 2020-02-15T11:12:36Z
| 2020-02-24T15:11:04Z
| 2020-02-15T19:08:13Z
|
CONTRIBUTOR
| null |
- [x] closes https://github.com/pandanistas/pandanistas_sprint_jakarta2020/issues/13
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Output from validate_docstrings.py
################################################################################
####################### Docstring (pandas.Index.delete) #######################
################################################################################
Make new Index with passed location(-s) deleted.
Use array of integer as loc parameter to delete multiple locations.
Parameters
----------
loc : int
Location of item(-s) which will be deleted.
Returns
-------
Index
New Index with passed location(-s) deleted.
See Also
--------
numpy.delete : Delete any rows and column from NumPy array (ndarray).
Examples
--------
>>> idx = pd.Index(['a', 'b', 'c'])
>>> idx.delete(1) # Deleting 'b'
Index(['a', 'c'], dtype='object')
>>> idx = pd.Index(['a', 'b', 'c'])
>>> idx.delete([0, 2])
Index(['b'], dtype='object')
################################################################################
################################## Validation ##################################
################################################################################
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