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ENH: Infer Enums as categorical
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NONE
| null |
#### Is your feature request related to a problem?
This relates to #36124, but I think has other benefits.
#### Describe the solution you'd like
I would like pandas to infer an array of Enums as categorical data. Today, IntEnums are turned into Ints, which is not ideal behavior.
#### API breaking implications
This is a change to the way part of the current API behaves, but I believe it provides a more intuitive processing of Enum data.
#### Describe alternatives you've considered
#### Additional context
```python
# Your code here, if applicable
```
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Issue36124 display of int enums
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- [x] closes #36124
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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DOC: update code style for development doc and user guide #36777
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CONTRIBUTOR
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Addresses part of #36777
Ran blacken-tools and checked for warnings from flake8-rst for 4 additional doc files.
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BUG: Raise ValueError with nan in timeaware windows
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MEMBER
| null |
- [x] closes #34617
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Raising a ValueError when NaN is in timeaware window.
cc @mroeschke
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CONTRIBUTOR
| null |
Addresses part of #36777
Ran blacken-tools and checked for warnings from flake8-rst for 5 additional doc files.
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CONTRIBUTOR
| null |
Partially addresses #36777.
Fixed the following files:
- `doc/source/development/extending.rst`
- `doc/source/user_guide/duplicates.rst`
- `doc/source/user_guide/gotchas.rst`
- `doc/source/user_guide/scale.rst`
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MDExOlB1bGxSZXF1ZXN0NDk3MjI1Nzg0
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|
DOC: Fix extending.rst code style #36777
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| 2020-10-03T06:31:44Z
| 2020-10-03T14:48:26Z
| 2020-10-03T14:46:51Z
|
CONTRIBUTOR
| null |
ref #36777
I tried to format development/extending.rst, please review
cc @dsaxton
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MDExOlB1bGxSZXF1ZXN0NDk3MjI3MDcx
| 36,826
|
CI add end-of-file-fixer
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| 2020-10-03T06:43:25Z
| 2020-10-07T07:26:31Z
| 2020-10-06T22:54:42Z
|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/36600#issuecomment-702976850
> ok if you'd fix the EOF issue can merge (and yes good idea to add to the pre-commit hook / separate PR pls)
Note: in addition to adding the end-of-file-fixer pre-commit hook, I applied the following
```diff
diff --git a/scripts/generate_pip_deps_from_conda.py b/scripts/generate_pip_deps_from_conda.py
index c417f58f6..c6d00eb58 100755
--- a/scripts/generate_pip_deps_from_conda.py
+++ b/scripts/generate_pip_deps_from_conda.py
@@ -94,7 +94,7 @@ def main(conda_fname, pip_fname, compare=False):
f"# This file is auto-generated from {fname}, do not modify.\n"
"# See that file for comments about the need/usage of each dependency.\n\n"
)
- pip_content = header + "\n".join(pip_deps)
+ pip_content = header + "\n".join(pip_deps) + "\n"
if compare:
with open(pip_fname) as pip_fd:
```
----
I don't know how many of these we want to apply this to, for now I'm just excluding licenses (which I don't think should be touched?), html, txt, svg,and csv files
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BUG: python engine in pd.read_csv fails to use header=None correctly
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| 2020-10-03T09:56:06Z
| 2021-08-13T03:47:44Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
For the following example file:
```
# comment
data_block_1
300.000000 2.700000 0.070000 1 400 1.04000 2
# comment
data_block_2
000001@0001_sum_particles.mrcs 0001_sum.mrc 587.000000 1268.000000 -51.52270
000001@0001_sum_particles.mrcs 0002_sum.mrc 685.000000 1268.000000 -51.52270
```
```python
df1 = pd.read_csv(fname, skiprows=3, skipfooter=7, delim_whitespace=True, engine='python', comment='#', header=None)
df2 = pd.read_csv(fname, skiprows=3, skipfooter=7, delim_whitespace=True, engine='python', comment='#', header=0)
```
```python
>>> df1
Empty DataFrame
Columns: [0, 1, 2, 3, 4, 5, 6]
Index: []
>>> df2
Empty DataFrame
Columns: [300.000000, 2.700000, 0.070000, 1, 400, 1.04000, 2]
Index: []
```
#### Problem description
Trying to read data_block_1 with the python engine (to be able to use skipfooter) yields an empty DataFrame
##### Expected behaviour
- When `header=None` is passed to `read_csv()`, the dataframe returned should have shape `(1,7)` and contain the values from `data_block_1` (this does not happen)
- When `header=0` is passed, the values in `data_block_1` end up as `df.columns` of an empty DataFrame (this happens)
#### Expected Output
```
0 1 2 3 4 5 6
0 300.000000 2.700000 0.070000 1 400 1.04000 2
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-7642-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.5
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
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 : 3.0.5
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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MDU6SXNzdWU3MTQwNjEzMDc=
| 36,828
|
to_csv fails silently when writing to S3 (only 1 time out of 50,000)
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| 2020-10-03T10:38:17Z
| 2021-08-13T03:56:33Z
| 2021-08-13T03:56:33Z
|
NONE
| null |
#### Code Sample, a copy-pastable example
```python
def save_df_to_s3(df, bucket, key):
s3_url = f"s3://{bucket}/{key}"
try:
response = df.to_csv(s3_url, index=True, header=True, encoding='utf-8')
except Exception as e:
error_type = type(e).__name__
logger.error(f"{error_type}: {e}")
logger.info(response)
else:
file_name = re.split('/', key)[-1]
logger.info(f"'{file_name}' was uploaded/updated to bucket '{bucket}'")
return response
```
#### Problem description
This code is executed by a lambda which is triggered every 10 seconds. This code works perfectly for days on end. But in rare cases, the writing of the csv file on the bucket s3 fails silently (approximately 1 time out of 50,000).
Does this relate to the following information provided by AWS "S3 Standard is designed for 99.99% availability"?
I activated "Server access logging" on my bucket to try to have more information on the next failure.
#### Expected Output
Raise some exception
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.7.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.177-104.253.amzn2.x86_64
Version : #1 SMP Fri May 1 02:01:13 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.1
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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DOC: update code style for remaining intro tutorial docs for #36777 computation.rst
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MDU6SXNzdWU3MTQwNjMyNjQ=
| 36,830
|
QST: while trying to convert categorical variables which are ordinal , i ran the following code
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| 2020-10-03T12:17:16Z
| 2020-10-03T12:17:15Z
|
NONE
| null |
- [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, please read [this guide](
https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your question.
```python
# Your code here, if applicable
```
|
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MDExOlB1bGxSZXF1ZXN0NDk3MjU4ODQ0
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|
DOC: reformat doc groupby.rst
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| 2020-10-04T22:33:14Z
| 2020-10-03T18:09:22Z
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CONTRIBUTOR
| null |
ref #36777
|
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DOC: update code style for remaining intro tutorial docs for #36777 computation.rst
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CONTRIBUTOR
| null |
update code style computation.rst for #36777
|
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MDExOlB1bGxSZXF1ZXN0NDk3MjY4OTM4
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REF: NDFrame describe
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| 2021-01-11T12:27:21Z
| 2020-12-29T20:46:38Z
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MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Refactor ``NDFrame.describe`` method.
- Create module ``pandas/io/formats/describe.py``
- Delegate ``NDFrame.describe`` to function ``describe_ndframe`` in the new module
- Implement polymorphism for describing series and dataframe
- Implement strategy pattern for describing series of different kinds (categorical, numeric, timestamp)
Benefits:
- Reduce complexity in ``pandas/core/generic.py``
- Straightforward logic how to treat each datatype (reduced if/elif/else workflow)
- Enable potential for further extension
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Fix/empty string datetimelike conversion/issue 36550
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|
CONTRIBUTOR
| null |
- [x] xref #36550
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
# Empty string conversion
The actual comportment of Timedelta, Timestamp and to_datetime is to return NaT.
```python
import pandas as pd
pd.to_datetime("", errors="raise")
# NaT
pd.to_datetime("", errors="coerce")
# NaT
pd.to_datetime("", errors="ignore")
# NaT
pd.Timedelta("")
# NaT
pd.Timestamp("")
# NaT
```
I would propose to raise a ValueError instead, same as for string that are not datetimelike, as follow:
```python
import pandas as pd
import pytest
with pytest.raises(ValueError):
pd.to_datetime("", errors="raise")
pd.to_datetime("", errors="coerce")
# NaT
pd.to_datetime("", errors="ignore")
# ''
with pytest.raises(ValueError):
pd.Timedelta("")
with pytest.raises(ValueError):
pd.Timestamp("")
```
# Tests
I added the relevant test for the expected comportment.
I still will have to fix some test that rely on the current behaviors.
# Benchmark
I ran the following command
`asv continuous -E virtualenv -f 1.1 upstream/master HEAD -b "^tslibs.(timestamp|timedelta|tslib)"`
<details>
```
before after ratio
[8f6ec1e8] [4393cc9e]
<master> <fix/empty-string-datetimelike-conversion/issue-36550>
+ 173±1ns 202±10ns 1.17 tslibs.timestamp.TimestampProperties.time_is_leap_year(tzlocal(), 'B')
+ 162±2ns 188±4ns 1.16 tslibs.timestamp.TimestampProperties.time_is_quarter_start(tzlocal(), None)
+ 178±1ns 205±20ns 1.15 tslibs.timestamp.TimestampProperties.time_dayofweek(tzfile('/usr/share/zoneinfo/US/Central'), 'B')
+ 163±2ns 186±10ns 1.15 tslibs.timestamp.TimestampProperties.time_is_year_end(tzlocal(), None)
+ 162±1ns 185±10ns 1.15 tslibs.timestamp.TimestampProperties.time_is_year_end(tzfile('/usr/share/zoneinfo/US/Central'), None)
+ 162±2ns 185±10ns 1.14 tslibs.timestamp.TimestampProperties.time_is_year_end(<UTC>, None)
+ 169±2ns 193±10ns 1.14 tslibs.timestamp.TimestampProperties.time_is_quarter_end(tzlocal(), None)
+ 164±2ns 186±4ns 1.14 tslibs.timestamp.TimestampProperties.time_is_quarter_start(tzutc(), None)
+ 4.60±0.03μs 5.16±0.3μs 1.12 tslibs.timestamp.TimestampProperties.time_is_year_end(datetime.timezone(datetime.timedelta(seconds=3600)), 'B')
+ 147±1ns 163±4ns 1.11 tslibs.timestamp.TimestampProperties.time_microsecond(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, 'B')
+ 170±2ns 188±7ns 1.11 tslibs.timestamp.TimestampProperties.time_is_quarter_end(tzfile('/usr/share/zoneinfo/US/Central'), None)
+ 179±0.7ns 197±10ns 1.10 tslibs.timestamp.TimestampProperties.time_dayofweek(datetime.timezone(datetime.timedelta(seconds=3600)), None)
+ 174±0.3ns 191±10ns 1.10 tslibs.timestamp.TimestampProperties.time_is_leap_year(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, 'B')
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE DECREASED.
```
</details>
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| 714,099,055
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MDExOlB1bGxSZXF1ZXN0NDk3MjgyNjI5
| 36,835
|
DOC: update code style for remaining intro tutorial docs for #36777 computation.rst
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| 2020-10-03T14:59:04Z
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|
CONTRIBUTOR
| null |
…omputation.rst #36832
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0NDk3Mjk0MDY4
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DOC: Update docs on estimated time of running full asv suite (36344)
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| null |
- [x] closes #36344
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDU6SXNzdWU3MTQxMzM1MzA=
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|
Add hacktoberfest topic to the repo
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| 2021-02-28T12:22:32Z
|
CONTRIBUTOR
| null |
Hacktoberfest 2020 has undergone some changes due to the excessive spamming that occurred this year. The rules for a PR to be accepted have been modified. Detailed post on their official website can be found [here](https://hacktoberfest.digitalocean.com/hacktoberfest-update).
Here is the most important part for maintainers:
As one can see, the maintainers need to add the `hacktoberfest` topic to the repo in order for all the PRs to be considered valid. This is completely opt-in and this issue serves as a notification for the maintainers in case they want to take part.
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MDExOlB1bGxSZXF1ZXN0NDk3MzExNTgw
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DEPR: Deprecate use of strings denoting units with 'M', 'Y' or 'y' in pd.to_timedelta (36666)
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| 2020-10-03T17:35:09Z
| 2020-10-31T18:52:13Z
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CONTRIBUTOR
| null |
- [x] closes #36666
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0NDk3MzM0NTI1
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PERF: Add asv benchmarks for select_dtypes (14588)
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| 2020-10-05T17:41:34Z
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CONTRIBUTOR
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| 2020-10-03T22:20:37Z
| 2020-11-28T18:25:04Z
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|
CONTRIBUTOR
| null |
#32640 copied the cache when copying indexes. Indexes are immutable, so some refactoring allows us to __share__ the cache instead. This means potentially fewer expensive index ops.
```python
>>> idx = pd.CategoricalIndex(np.arange(100_000))
>>> copied = idx.copy()
>>> copied._cache
{}
>>> idx.get_loc(99_999)
>>> copied._cache
{'_engine': <pandas._libs.index.Int32Engine at 0x1fd3a6934c8>} # don't need to recreate _engine on copied
```
Performance example:
```python
>>> idx = pd.CategoricalIndex(np.arange(100_000))
>>> %timeit idx._shallow_copy().get_loc(99_999)
4.09 ms ± 65 µs per loop # master
7.71 µs ± 355 ns per loop # this PR
```
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| null |
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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| 2020-10-03T23:31:55Z
| 2020-11-04T21:12:38Z
| 2020-11-04T02:59:03Z
|
MEMBER
| null |
- [x] closes #35646
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- [x] tests added / passed
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When grouping over 2 columns with only ``nans`` this raised an error previously. Realised this, when running tests for my fix, because this case was tests with one grouping column. Added test to cover fixed behavior for more than one column.
cc @mroeschke Takes same path as two or more grouping columns now.
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|
MEMBER
| null |
- [x] closes #36796
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Unless supporting this is intentional, in which case we should deprecate and explicitly implement flex-bool operations.
If/when this is done, we can stop passing `special` and even `cls`.
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MDExOlB1bGxSZXF1ZXN0NDk3MzU0MzY0
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DOC: Typo fix
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| 1
| 2020-10-04T00:09:36Z
| 2020-10-04T03:27:58Z
| 2020-10-04T03:27:50Z
|
CONTRIBUTOR
| null |
Noticed a minor typo when using the docs
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MDExOlB1bGxSZXF1ZXN0NDk3MzU2NTgz
| 36,845
|
DOC: normalize usage of word "pandas"
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| 2020-10-04T00:34:52Z
| 2020-10-04T23:16:35Z
| 2020-10-04T20:13:32Z
|
CONTRIBUTOR
| null |
changes references to the library, pandas, to match the standard lowercase spelling. This changes applicable `.rst` files under the `doc/source` path.
References such as Pandas, **pandas**, *pandas*, and ``pandas`` have been replaced with pandas.
- [x] closes #32316
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0NDk3MzY1OTkx
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|
Update documentation
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|
NONE
| null |
Capitalization error
|
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MDExOlB1bGxSZXF1ZXN0NDk3MzY2ODA1
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CI: pin pymysql #36465
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MEMBER
| null |
Part of #36465
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MDExOlB1bGxSZXF1ZXN0NDk3MzY2OTQy
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REF: IndexOpsMixin wrapping
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MEMBER
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NDk3Mzc0ODA0
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DOC: black enhancingperf.rst and 10min.rst code style
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CONTRIBUTOR
| null |
ref #36777
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DOC: `.loc` behavior undocumented for Index argument
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| 2
| 2020-10-04T05:00:52Z
| 2020-11-29T18:06:24Z
| 2020-11-29T18:06:24Z
|
NONE
| null |
#### Location of the documentation
[pandas.Series.loc](https://pandas.pydata.org/docs/dev/reference/api/pandas.Series.loc.html)
[pandas.DataFrame.loc](https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.loc.html)
#### Documentation problem
The documentation does not mention `pandas.Index` as a valid argument for `.loc`. I expected the result to be the same as passing the values of the index, but the result is instead the same as `reindex` (as long as the result has at least one element, otherwise it raises `KeyError`). I discovered the difference when relying on the index name to remain unchanged after `.loc`. Below is a minimal demonstration of the current behavior for `pandas.Series`. Analogous behavior occurs for both axes of `pandas.DataFrame`.
```python
In [2]: foo = pd.Index(range(2), name='foo')
bar = pd.Index(range(1), name='bar')
baz = pd.Index([2], name='baz')
s = pd.Series(list('ab'), index=foo)
s
Out [2]:
foo
0 a
1 b
dtype: object
In [3]: s.loc[bar]
Out [3]:
bar
0 a
dtype: object
In [4]: s.loc[bar].index is bar
Out [4]: True
In [5]: s.reindex(bar).index is bar
Out [5]: True
In [6]: s.loc[baz]
Out [6]: KeyError: "None of [Int64Index([2], dtype='int64', name='baz')] are in the [index]"
```
Output of `pd.show_versions`
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.6.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.193-149.317.amzn2.x86_64
Version : #1 SMP Thu Sep 3 19:04:44 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.2.12
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.51.0
</details>
#### Suggested fix for documentation
Add an entry to explain the current/intended behavior when passing an `Index` to `.loc`, especially mentioning that the index will be replaced. Doing so would complement the clarity recently provided by #35506 for alignable boolean series.
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DOC: Standardize references to pandas in the documentation for #32316
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CONTRIBUTOR
| null |
Standardize references to pandas in the documentation for #32316
|
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REF/CLN: pandas/io/parsers.py
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| 7
| 2020-10-04T07:17:43Z
| 2020-10-22T07:31:10Z
| 2020-10-14T17:54:46Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Refactor/cleanup ``pandas/io/parsers.py``
- Extract method _refresh_kwargs_based_on_dialect
- Extract method _validate_skipfooter
- Drop local variable engine_specified
- Clean-up FutureWarning issue
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MDU6SXNzdWU3MTQyNjA1MjE=
| 36,853
|
DOC: Add freq strings reference link for Period
|
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[] | null | 2
| 2020-10-04T08:07:26Z
| 2021-08-13T03:56:58Z
| null |
CONTRIBUTOR
| null |
#### Location of the documentation
[pandas.Period](https://pandas.pydata.org/docs/reference/api/pandas.Period.html)
#### Documentation problem
The doc does not provide much information on the possible offset-aliases for the `freq` parameter
#### Suggested fix for documentation
A link to [offset-aliases](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases) for the `freq` parameter for quick reference
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MDExOlB1bGxSZXF1ZXN0NDk3Mzk3Mjcz
| 36,854
|
TYP: update setup.cfg
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MEMBER
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MDU6SXNzdWU3MTQyNjUwNTA=
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|
BUG: Column name is missing after concat if the key contain only one column of string data
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| 2020-10-04T08:36:33Z
| 2020-10-07T09:00:17Z
| 2020-10-07T09:00:17Z
|
NONE
| null |
I'm going to develop a simple application to compare two dataframe with given keys (multiple index columns).
I need to generate 3 dataframe as below,
- final_1 : contain all rows in df1, plus empty row with keys in df2 only
- final_2 : contain all rows in df2, plus empty row with keys in df1 only
- compare_result: contain information of merge ('both','left_only','right_only'), together with column comparison result
Please find the source beblow, there has some problem with particular keys combination.
I try to make a simple dataset and include in the source.
In the attached dataset,
- It works find with the keys ['org_code','sub_key'], but with error if only ['org_code']
- It also work after I convert the 'org_code' to create a int column of 'dummy_code'.
- It also failed using ['user_name'] as keys, but no error for ['amount'].
It seems that there will have problem if the keys contain only one column of string data (e.g. 'org_code').
It was found that the index column name is missing for df_all in those fail cases.
Which cause the error like: **KeyError: "['org_code'] not in index"**
df_all is generated using concat command, and it need to get a merged dataframe with columns of df1 and df2 under different group, and join by the key_columns.
```python
df_all = pd.concat([df1.set_index(key_columns), df2.set_index(key_columns)], axis='columns', keys=['df1', 'df2'])
```
The workaround is to set the index name after df_all is generated if the key contain only one column.
i.e.
```Python
if len(key_columns) == 1:
df_all.index.name = key_columns[0]
```
But it doesn't work if similar problem occurred in multiple columns situation. So it'd be better if concat can keep the original column name.
May I know if this is the problem in concat command, or anything wrong in my case? Thanks.
#### Code Sample, a copy-pastable example
```python
import tkinter as tk
from TableChecker import TableChecker
import pandas as pd
df1 = pd.DataFrame({
'org_code': ['A222', 'A252', 'B100019'],
'dummy_code': [222, 252, 100019],
'sub_key': [1,2,3],
'user_name': ['mary', 'john', 'david'],
'amount': [222, 313, 100019],
})
df2= pd.DataFrame({
'org_code': ['S6258', 'B100019'],
'dummy_code': [6258, 100019],
'sub_key': [4,3],
'user_name': ['peter', 'david'],
'amount': [2232, 126],
})
org_columns = list(df1.keys())
result_columns = list(df1.keys())
result_columns.append('_merge')
key_columns = ['org_code']
df_merge = pd.merge(df1[key_columns], df2[key_columns], how='outer', indicator=True)
df_merge.set_index(key_columns, inplace=True)
df_all = pd.concat([df1.set_index(key_columns), df2.set_index(key_columns)], axis='columns', keys=['df1', 'df2'])
df_diff = df_all['df1'] != df_all['df2']
result = pd.concat([df_merge, df_diff], axis=1)
compare_result = result.reset_index()[result_columns]
final_1 = df_all['df1'].reset_index()[org_columns]
final_2 = df_all['df2'].reset_index()[org_columns]
```
#### Problem description
By modifying the line key_columns = ['org_code'] to change the key columns, it will have different result.
1) Error for single string data column: e.g., ['org_code'] or ['user_name']
2) No error for single numeric data column: e.g., ['dummy_code'], ['sub_key'] or ['amount']
3) No error for combined keys: e.g., ['org_code','sub_key'] ,['org_code','user_name'] or ['org_code','dummy_code','sub_key']
#### Expected Output
The column name of index column in df_all should not be empty, it should contain the original column name.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.950
pandas : 1.1.2
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0
Cython : None
pytest : 5.4.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDk3NDA0ODIx
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DOC: blacken-docs doc/source/ecosystem.rst
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CONTRIBUTOR
| null |
- [x] xref #36777
- [ ] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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DOC: doc/source/whatsnew
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CONTRIBUTOR
| null |
- [x] xref #36777
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- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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DOC: the source link of CategoricalIndex's methods are wrong.
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[] | null | 5
| 2020-10-04T15:27:55Z
| 2021-08-13T03:58:22Z
| null |
CONTRIBUTOR
| null |
#### Location of the documentation
- [ ] https://pandas.pydata.org/docs/dev/reference/api/pandas.CategoricalIndex.add_categories.html
- [ ] https://pandas.pydata.org/docs/dev/reference/api/pandas.CategoricalIndex.rename_categories.html
- [ ] https://pandas.pydata.org/docs/dev/reference/api/pandas.CategoricalIndex.reorder_categories.html
- [ ] https://pandas.pydata.org/docs/dev/reference/api/pandas.CategoricalIndex.remove_categories.html
- [ ] https://pandas.pydata.org/docs/dev/reference/api/pandas.CategoricalIndex.remove_unused_categories.html
- [ ] https://pandas.pydata.org/docs/dev/reference/api/pandas.CategoricalIndex.set_categories.html
- [ ] https://pandas.pydata.org/docs/dev/reference/api/pandas.CategoricalIndex.as_ordered.html
- [ ] https://pandas.pydata.org/docs/dev/reference/api/pandas.CategoricalIndex.as_unordered.html
#### Documentation problem
For example, in pandas.CategoricalIndex.add_categories, the source link jumps to the following source.This link is wrong.
As far as I've observed, the above methods all jump to the same wrong link.
https://github.com/pandas-dev/pandas/blob/40b2aa56c3779d0178069e8182dcc3bcdc102e57/pandas/core/accessor.py#L98-L99
The source link should jump to the following location.
https://github.com/pandas-dev/pandas/blob/40daf00721f358105f744e8aad7b0c524d1c417b/pandas/core/arrays/categorical.py#L943
#### Suggested fix for documentation
Correct the source link.
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MDExOlB1bGxSZXF1ZXN0NDk3NDU5MTI0
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REF/CLN: pandas/io/pytables.py
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| 1
| 2020-10-04T16:17:03Z
| 2020-10-07T09:44:10Z
| 2020-10-06T22:44:23Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Clean-up and minor refactor of ``pandas/io/pytables.py``.
- Extract method ``_identify_group``
- Clean-up some docstrings (spacing)
- Use suppress instead of try/except/pass
- Format error message using dedent
- Remove unnecessary empty lines as they compromise readability of some blocks
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MDU6SXNzdWU3MTQzNTEyNjE=
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CI: Continuous benchmarking
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| 2020-10-04T16:33:11Z
| 2020-10-08T21:13:31Z
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|
MEMBER
| null |
I think it would be helpful if pandas had a set of performance benchmarks to run automatically during every CI run (it looks like there is something for the asvs, but it seems they almost never actually run?). It would reduce some of the friction involved in manually running the asv suite and pasting results as comments, and also help prevent certain things from slipping by just not thinking to run benchmarks.
There exists the pytest plugin pytest-benchmark which seems even more lightweight than asv, and appears to be what RAPIDS uses for a benchmark tool of their own https://github.com/rapidsai/benchmark. Could it be an option to have a GitHub Action that runs pytest-benchmark on every PR (I don't know a lot about the plugin, but I am assuming it could be configured to either take a delta between master and the given branch, as well as possibly between that branch and some baseline commit on master such as a major release)? It may also be possible to cache the result from master somewhere to prevent it from being rerun every time.
What constitutes "failure" is another question, and maybe it's best to configure things to only warn on failure instead of making the whole run red (would help address flaky benchmarks as well). Also we would presumably need fine-grained control over the architecture used by GitHub to make sure it doesn't drift, and I'm not sure if that's possible.
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ENH: Rolling mode
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[] | null | 3
| 2020-10-04T16:34:56Z
| 2021-11-15T00:35:36Z
| 2021-11-15T00:35:36Z
|
CONTRIBUTOR
| null |
#### Is your feature request related to a problem?
I wish I could use pandas to do `df.rolling(window_size).mode()`. Similar to #12430.
#### Describe the solution you'd like
All rolling methods in pandas seem to be implemented in cython. I implemented a working draft for rolling mode in cython (I'm new to cython).
Before continuing my implementation, I would like some feedback:
1. What type should mode expect? I assume that float64 would be best: handles NaNs and non-numeric object could be categorized and converted to floats.
2. `pd.DataFrame.mode` returns all possible modes. Is that something we would also expect from a rolling mode? It is probably much faster to return only one possible mode (the current draft could easily be extended for that: replace `output` with a cvector[clist[float]] and in the end convert it to a np.ndarray).
3. TODO: implement mode for '_variable' version. If I understand it correctly this is needed for `pd.DataFrame.expanding()`.
#### Additional context
```cython
# cython: language_level=3, boundscheck=False, wraparound=False, cdivision=True
# distutils: language = c++
from libcpp.vector cimport vector as cvector
from libcpp.unordered_map cimport unordered_map
from libcpp.list cimport list as clist
from libcpp.pair cimport pair as cpair
from libc.math cimport isnan
cimport cython
from cython.operator cimport dereference, postincrement, postdecrement
import numpy as np
cimport numpy as np
ctypedef np.float_t DTYPE_FLOAT_t
def rolling_mode_fixed(np.ndarray[DTYPE_FLOAT_t, ndim=1] values, const cython.int window_size, const cython.bint dropna):
# https://cs.stackexchange.com/a/96768
cdef:
cython.int N = values.size
double NaN = float("NaN")
np.ndarray[DTYPE_FLOAT_t, ndim=1] output = np.empty(N, dtype=np.float)
cython.int largest_frequency, nan_count, i, frequency = 0
double value, old_value
unordered_map[double, cython.int] get_frequency
cvector[clist[double]] get_objects = cvector[clist[double]](window_size-1)
cpair[double, cython.int] pair
with nogil:
# pre-allocate + 20% free space
get_frequency.reserve(<int>(1.2 * window_size))
# new items always have a frequency of 1
pair.second = 1
# set NaN and initialize data structures
for i in range(window_size):
output[i] = NaN
value = values[i]
if isnan(value):
# either skip NaNs or count them seperate
nan_count += 1
continue
# insert with frequency 1 or increment
pair.first = value
insert_pair = get_frequency.insert(pair)
if not insert_pair.second:
postincrement(dereference(insert_pair.first).second)
frequency = dereference(insert_pair.first).second
if frequency != 1:
# remove form current set
get_objects[frequency - 2].remove(value)
# add to next set
get_objects[frequency-1].push_back(value)
# update largest frequency
largest_frequency = max(frequency, largest_frequency)
# populate first valid output
if (dropna and largest_frequency != 0) or (largest_frequency >= nan_count):
output[window_size - 1] = get_objects[largest_frequency - 1].front()
# process
for i in range(window_size, N):
value = values[i]
old_value = values[i - window_size]
value_isnan = isnan(value)
old_value_isnan = isnan(old_value)
if (value == old_value) or (value_isnan and old_value_isnan):
# no change
output[i] = output[i-1]
continue
# decrement old value
if old_value_isnan:
nan_count -= 1
else:
# find the existing key and decrement it
find_pair = get_frequency.find(old_value)
frequency = dereference(find_pair).second
get_objects[frequency - 1].remove(old_value)
if frequency > 1:
postdecrement(dereference(find_pair).second)
get_objects[frequency - 2].push_back(old_value)
else:
get_frequency.erase(find_pair)
# increment new value
if value_isnan:
nan_count += 1
else:
# insert with frequency 1 or increment
pair.first = value
insert_pair = get_frequency.insert(pair)
if not insert_pair.second:
postincrement(dereference(insert_pair.first).second)
get_objects[dereference(insert_pair.first).second - 2].remove(value)
get_objects[dereference(insert_pair.first).second - 1].push_back(value)
# update largest set
if (largest_frequency != window_size) and not get_objects[
largest_frequency
].empty():
# increased
largest_frequency += 1
elif get_objects[largest_frequency-1].empty():
# decreased
largest_frequency -= 1
# write output
if (dropna and largest_frequency != 0) or (largest_frequency >= nan_count):
output[i] = get_objects[largest_frequency - 1].front()
else:
output[i] = NaN
return output
```
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DOC: make return type documentation of series methods consistent #35409
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| 4
| 2020-10-04T16:49:03Z
| 2020-10-15T06:09:08Z
| 2020-10-10T19:41:57Z
|
CONTRIBUTOR
| null |
- [x] closes #35409
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
|
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|
CI: Show ipython directive errors
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| 3
| 2020-10-04T17:29:04Z
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|
MEMBER
| null |
Current output of ipython directive errors in CI is essentially a blank line, so adding a bit more to try to show what error is happening.
```
(pandas-dev) ➜ ~ grep -B1 "^<<<-------------------------------------------------------------------------$" sphinx.log
<<<-------------------------------------------------------------------------
```
vs.
```
(pandas-dev) ➜ ~ grep -B10 "^<<<-------------------------------------------------------------------------$" sphinx.log
>>>-------------------------------------------------------------------------
Exception in /Users/danielsaxton/pandas/doc/source/user_guide/enhancingperf.rst at block ending on line 834
Specify :okexcept: as an option in the ipython:: block to suppress this message
File "<ipython-input-66-d3ab2b9618fd>", line 1
df.query("strings == "a" and nums == 1")
^
SyntaxError: invalid syntax
<<<-------------------------------------------------------------------------
```
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TST: insert 'match' to bare pytest raises in pandas/tests/tseries/off…
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| 12
| 2020-10-04T18:27:10Z
| 2020-10-11T19:21:32Z
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|
CONTRIBUTOR
| null |
…sets/test_offsets.py
- [ ] ref https://github.com/pandas-dev/pandas/issues/30999
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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BUG: rolling.apply substitutes for applied function
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| 2020-10-04T18:40:45Z
| 2020-10-04T18:44:32Z
| 2020-10-04T18:44:32Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import datetime
import pandas as pd
import numpy as np
index = pd.date_range(datetime.datetime.now(), periods=5, freq="1s")
ser = pd.Series(1, index=index)
ser.rolling("2s").apply(np.sum, kwargs={"initial": 5})
```
results in:
```
ValueError: the 'initial' parameter is not supported in the pandas implementation of sum()
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
When I explicitly specify the numpy version of sum, pandas should not substitute it for me.
#### Expected Output
Sums in window with 5 added.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.1.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDk3NDgyMDc2
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BUG: df.replace over pd.Period columns (#34871)
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| 2020-10-04T19:01:03Z
| 2020-11-17T01:31:42Z
| 2020-11-17T01:17:29Z
|
CONTRIBUTOR
| null |
- [x] closes #34871
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This commit ensures that PeriodArrays return False for _can_hold_element for any element that is not a pd.Period. This prevents upstream code from casting the dtype to object. Also un-xfail test written in #34904
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conflict with PyPDF2?
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[] | null | 3
| 2020-10-04T19:02:50Z
| 2020-10-17T13:13:31Z
| 2020-10-17T13:13:31Z
|
NONE
| null |
I am not sure if it is a bug of pandas or mine.
but when I test my code I have the following peculiar error: **an error that disappears if I run again the exact same line of code of pandas**
After I successfully ran my code, I try the following block of code
```
import pandas as pd
df = pd.DataFrame([['a', 'b', 'c'], ['d', '6', '8']])
df[df.apply(lambda x: x.str.contains('e')).any()]
```
and it hit an error, strange is that the code block behaves as expected if I try it again after it hit the error, see below
```
~/Documents/cs_project/toc(master*) » python3 -i corp_gov_report.py macone@Macs-MacBook-Pro
Bounding box (Decimal('124.724'), Decimal('46.220'), Decimal('752.754'), Decimal('753.770')) is not fully within parent page bounding box (Decimal('0'), Decimal('0'), Decimal('595.276'), Decimal('807.874'))
Bounding box (Decimal('124.724'), Decimal('46.220'), Decimal('752.754'), Decimal('753.770')) is not fully within parent page bounding box (Decimal('0'), Decimal('0'), Decimal('595.276'), Decimal('807.874'))
currency HK$’000
5 Audit and audit related services 1,800
8 financial statements 600
Independent Auditor’s Remuneration (iv) 獨立核數師之酬金
During the year, the remuneration paid or payable to the 於本年度，已付或應付予獨立核數師安
independent auditor, Ernst & Young, is set out as follows: 永會計師事務所之酬金載列如下：
Services rendered HK$’000
所提供服務 千港元
Audit and audit related services 審核及審核相關服務 1,800
Non-audit services 非審核服務
Review of interim condensed consolidated 審閱中期簡明綜合財務報告
financial statements 600
Tax advisory services 稅務諮詢服務 113
>>> df = pd.DataFrame([['a', 'b', 'c'], ['d', '6', '8']])
>>> df[df.apply(lambda x: x.str.contains('e')).any()]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/frame.py", line 2890, in __getitem__
return self._getitem_bool_array(key)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/frame.py", line 2933, in _getitem_bool_array
stacklevel=3,
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/warnings.py", line 110, in _showwarnmsg
msg.file, msg.line)
File "/Users/macone/Library/Python/3.7/lib/python/site-packages/PyPDF2/pdf.py", line 1069, in _showwarning
file.write(formatWarning(message, category, filename, lineno, line))
File "/Users/macone/Library/Python/3.7/lib/python/site-packages/PyPDF2/utils.py", line 69, in formatWarning
file = filename.replace("/", "\\").rsplit("\\", 1)[1] # find the file name
IndexError: list index out of range
>>> df[df.apply(lambda x: x.str.contains('e')).any()]
Empty DataFrame
Columns: [0, 1, 2]
Index: []
```
but if i ran `df[df.apply(lambda x: x.str.contains('e')).any()]` again, the error appear again, but if i trid again, it disappear
```
>>> df[df.apply(lambda x: x.str.contains('e')).any()]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/frame.py", line 2890, in __getitem__
return self._getitem_bool_array(key)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/frame.py", line 2933, in _getitem_bool_array
stacklevel=3,
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/warnings.py", line 110, in _showwarnmsg
msg.file, msg.line)
File "/Users/macone/Library/Python/3.7/lib/python/site-packages/PyPDF2/pdf.py", line 1069, in _showwarning
file.write(formatWarning(message, category, filename, lineno, line))
File "/Users/macone/Library/Python/3.7/lib/python/site-packages/PyPDF2/utils.py", line 69, in formatWarning
file = filename.replace("/", "\\").rsplit("\\", 1)[1] # find the file name
IndexError: list index out of range
>>> df[df.apply(lambda x: x.str.contains('e')).any()]
Empty DataFrame
Columns: [0, 1, 2]
Index: []
>>> df[df.apply(lambda x: x.str.contains('e')).any()]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/frame.py", line 2890, in __getitem__
return self._getitem_bool_array(key)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/frame.py", line 2933, in _getitem_bool_array
stacklevel=3,
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/warnings.py", line 110, in _showwarnmsg
msg.file, msg.line)
File "/Users/macone/Library/Python/3.7/lib/python/site-packages/PyPDF2/pdf.py", line 1069, in _showwarning
file.write(formatWarning(message, category, filename, lineno, line))
File "/Users/macone/Library/Python/3.7/lib/python/site-packages/PyPDF2/utils.py", line 69, in formatWarning
file = filename.replace("/", "\\").rsplit("\\", 1)[1] # find the file name
IndexError: list index out of range
>>> df[df.apply(lambda x: x.str.contains('e')).any()]
Empty DataFrame
Columns: [0, 1, 2]
Index: []
>>> df[df.apply(lambda x: x.str.contains('e')).any()]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
```
see here for the image:
In addition, I encounter this error when I was trying to do this during the project development.
```
@property
def df_clean_table(self):
df_filtered_table = self.df_filtered_table
currency_cols = self.get_cond_cols(df_filtered_table, self.currency_element)
financial_num_cols = self.get_cond_cols(df_filtered_table, self.financial_num_element)
currency_rows= self.get_cond_rows(df_filtered_table, self.currency_element)
year_rows = self.get_cond_rows(df_filtered_table, self.year_element)
currency_row_idx = df_filtered_table.loc[currency_rows, financial_num_cols].index
currency_row = df_filtered_table.loc[currency_row_idx]
currency_header = currency_row[currency_row.apply(self.currency_element, axis=1)].fillna('').values.flatten()
year_row_idx = df_filtered_table.loc[year_rows, currency_cols].index
year_row = df_filtered_table.loc[year_row_idx]
year_header = year_row[year_row.apply(self.year_element, axis=1)].fillna('').values.flatten()
if year_header:
df_filtered_table.columns = pd.MultiIndex.from_arrays([year_header, currency_header], names = ['year', 'currency'])
else:
df_filtered_table.columns = pd.MultiIndex.from_arrays([currency_header], names=['currency'])
return df_filtered_table.drop(year_row_idx.append(currency_row_idx))
```
see below
but where I was debugging, I tried:
```
df_filtered_table = table.df_filtered_table
year_rows = table.get_cond_rows(df_filtered_table, table.year_element)
currency_cols = table.get_cond_cols(df_filtered_table, table.currency_element)
year_row_idx = df_filtered_table.loc[year_rows, currency_cols].index
year_row = df_filtered_table.loc[year_row_idx]
year_header = year_row[year_row.apply(table.year_element, axis=1)].fillna('').values.flatten()
if not year_header:
print('here is the error!')
if not year_header:
print('here no the error!')
```
see below:
here is the full error and the debugging..
```
~/Documents/cs_project/toc(master*) » python3 -i corp_gov_report.py macone@Macs-MacBook-Pro
Bounding box (Decimal('124.724'), Decimal('46.220'), Decimal('752.754'), Decimal('753.770')) is not fully within parent page bounding box (Decimal('0'), Decimal('0'), Decimal('595.276'), Decimal('807.874'))
Bounding box (Decimal('124.724'), Decimal('46.220'), Decimal('752.754'), Decimal('753.770')) is not fully within parent page bounding box (Decimal('0'), Decimal('0'), Decimal('595.276'), Decimal('807.874'))
Traceback (most recent call last):
File "corp_gov_report.py", line 161, in <module>
print(table.df_clean_table)
File "corp_gov_report.py", line 128, in df_clean_table
if year_header:
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/warnings.py", line 110, in _showwarnmsg
msg.file, msg.line)
File "/Users/macone/Library/Python/3.7/lib/python/site-packages/PyPDF2/pdf.py", line 1069, in _showwarning
file.write(formatWarning(message, category, filename, lineno, line))
File "/Users/macone/Library/Python/3.7/lib/python/site-packages/PyPDF2/utils.py", line 69, in formatWarning
file = filename.replace("/", "\\").rsplit("\\", 1)[1] # find the file name
IndexError: list index out of range
>>> df_filtered_table = table.df_filtered_table
year_rows = table.get_cond_rows(df_filtered_table, table.year_element)
currency_cols = table.get_cond_cols(df_filtered_table, table.currency_element)
year_row_idx = df_filtered_table.loc[year_rows, currency>>> year_rows = table.get_cond_rows(df_filtered_table, table.year_element)
currency_cols = table.get_cond_cols(df_filtered_table, table.currency_element)
year_row_idx = df_filtered_table.loc[year_rows, currency_cols].index
year_row = df_filtered_table.loc[year_row_idx]
year_header = year_row[year_row.apply(ta>>> currency_cols = table.get_cond_cols(df_filtered_table, table.currency_element)
year_row_idx = df_filtered_table.loc[year_rows, currency_cols].index
year_row = df_filtered_table.loc[year_row_idx]
year_header = year_row[year_row.apply(table.year_element, axis=1)].fillna('').values.flatt>>> year_row_idx = df_filtered_table.loc[year_rows, currency_cols].index
>>> year_row = df_filtered_table.loc[year_row_idx]
>>> year_header = year_row[year_row.apply(table.year_element, axis=1)].fillna('').values.flatten()
if not year_header:
print('here is the error!')
if not year_header:
print('here no the>>> if not year_header:
... print('here is the error!')
...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/warnings.py", line 110, in _showwarnmsg
msg.file, msg.line)
File "/Users/macone/Library/Python/3.7/lib/python/site-packages/PyPDF2/pdf.py", line 1069, in _showwarning
file.write(formatWarning(message, category, filename, lineno, line))
File "/Users/macone/Library/Python/3.7/lib/python/site-packages/PyPDF2/utils.py", line 69, in formatWarning
file = filename.replace("/", "\\").rsplit("\\", 1)[1] # find the file name
IndexError: list index out of range
>>> if not year_header:
... print('here no the error!')
...
here no the error!
```
you may see the error disappears when I try again to check the if-clause:`if not year_header`.
Here is the error message
```
Traceback (most recent call last):
File "corp_gov_report.py", line 161, in <module>
print(table.df_clean_table)
File "corp_gov_report.py", line 128, in df_clean_table
if year_header:
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/warnings.py", line 110, in _showwarnmsg
msg.file, msg.line)
File "/Users/macone/Library/Python/3.7/lib/python/site-packages/PyPDF2/pdf.py", line 1069, in _showwarning
file.write(formatWarning(message, category, filename, lineno, line))
File "/Users/macone/Library/Python/3.7/lib/python/site-packages/PyPDF2/utils.py", line 69, in formatWarning
file = filename.replace("/", "\\").rsplit("\\", 1)[1] # find the file name
IndexError: list index out of range
```
The error message is very similar to the replicated example I showed at the beginning, except the following lines.
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/frame.py", line 2890, in __getitem__
return self._getitem_bool_array(key)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/frame.py", line 2933, in _getitem_bool_array
stacklevel=3,
```
If any worth, the following is my developing project, you may see the full project in this [repo](https://github.com/fdq09eca/cs-project/tree/master/toc) (the file hitting the error is `corp_gov_report.py` and its content is shown as below)
```
from pdf import PDF, Outline, ReportOutline, PageWithSection
from helper import flatten
import datetime
import re
import pandas as pd
class CorporateGovReport(ReportOutline):
title_regex = r'^(?=.*report).*corporate governance.*$'
def __init__(self, outline):
super().__init__(outline)
@property
def audit_fee(self):
return AuditFee.retrieve(self.pages)
class AuditFee(PageWithSection):
section_regex = r"^(?!.*Nomination|.*Report)(?=.*REMUNERATION|.*independent|.*external|.*Accountability).*auditor.*$"
def __init__(self, pages):
super().__init__(pages)
@property
def sections(self):
return flatten([page.get_section(AuditFee.section_regex) for page in self.pages])
@property
def tables(self):
return [AuditFeeTable.retrieve(section) for section in self.sections]
class AuditFeeTable:
setting = {
"vertical_strategy": "text",
"horizontal_strategy": "text"
}
currency_regex = 'HK'
financial_num_regex = '\d,?\d*'
year_regex = '|'.join(map(str, range(int(datetime.datetime.now().year) - 2, int(datetime.datetime.now().year) + 1)))
def __init__(self, section):
self.section = section
@classmethod
def retrieve(cls, section):
return cls(section.page)
@classmethod
def set_year_regex(cls, current_year: int):
cls.year_regex = '|'.join(
map(str, range(current_year - 2, current_year + 1)))
print(f'Set {cls.__name__}.year_regex to {cls.year_regex}')
@property
def raw_table(self):
return self.section.extract_table(AuditFeeTable.setting)
@property
def df_raw_table(self):
df = pd.DataFrame(self.raw_table)
return df
@staticmethod
def currency_element(x):
return x.str.contains(AuditFeeTable.currency_regex)
@staticmethod
def financial_num_element(x):
return x.str.contains(AuditFeeTable.financial_num_regex)
@staticmethod
def year_element(x):
return x.str.contains(AuditFeeTable.year_regex)
@staticmethod
def eng_element(x):
return x.str.contains(r'[A-Za-z0-9]+')
@staticmethod
def get_cond_cols(df, element_cond_func):
return df.apply(element_cond_func).any()
@staticmethod
def get_cond_rows(df, element_cond_func):
return df.apply(element_cond_func).any(axis=1)
@property
def df_fee(self):
df = self.df_raw_table
currency_cols = self.get_cond_cols(df, self.currency_element)
currency_rows = self.get_cond_rows(df, self.currency_element)
financial_num_rows = self.get_cond_rows(df, self.financial_num_element)
year_rows = self.get_cond_rows(df, self.year_element)
df_fee = df.loc[(financial_num_rows|year_rows) , currency_cols]
return df_fee
@property
def df_filtered_table(self):
df_fee = self.df_fee
df_filtered_table = self.df_raw_table.iloc[df_fee.index]
df_filtered_table = df_filtered_table.loc[:, self.get_cond_cols(df_filtered_table, self.eng_element)]
return df_filtered_table
@property
def df_clean_table(self):
df_filtered_table = self.df_filtered_table
currency_cols = self.get_cond_cols(df_filtered_table, self.currency_element)
financial_num_cols = self.get_cond_cols(df_filtered_table, self.financial_num_element)
currency_rows= self.get_cond_rows(df_filtered_table, self.currency_element)
year_rows = self.get_cond_rows(df_filtered_table, self.year_element)
currency_row_idx = df_filtered_table.loc[currency_rows, financial_num_cols].index
currency_row = df_filtered_table.loc[currency_row_idx]
currency_header = currency_row[currency_row.apply(self.currency_element, axis=1)].fillna('')
year_row_idx = df_filtered_table.loc[year_rows, currency_cols].index
year_row = df_filtered_table.loc[year_row_idx]
year_header = year_row[year_row.apply(self.year_element, axis=1)].fillna('')
if not year_header.empty and not currency_header.enpty:
df_filtered_table.columns = pd.MultiIndex.from_arrays([year_header.iloc[0], currency_header.iloc[0]], names = ['year', 'currency'])
else:
df_filtered_table.columns = pd.MultiIndex.from_arrays([currency_header.iloc[0]], names=['currency'])
return df_filtered_table.drop(year_row_idx.append(currency_row_idx))
@property
def currency(self):
pass
@property
def currency_unit(self):
pass
@property
def total(self):
pass
def __repr__(self):
return f'{self.__class__.__name__} - {self.section}'
if __name__ == '__main__':
url, p = 'https://www1.hkexnews.hk/listedco/listconews/sehk/2020/0721/2020072100713.pdf', 61
# url, p = 'https://www1.hkexnews.hk/listedco/listconews/sehk/2020/0721/2020072100653.pdf', 94
pdf = PDF.create(url)
corp_gov_report = pdf.get_outline(CorporateGovReport.title_regex)
corp_gov_report = CorporateGovReport.create(corp_gov_report[0])
page = corp_gov_report.audit_fee.pages[0]
sec = corp_gov_report.audit_fee.sections[0]
table = corp_gov_report.audit_fee.tables[0]
print(table.df_clean_table)
print(sec.text)
# print(corp_gov_report.pages)
# page = pdf.
```
here are the dependencies
```
~/Documents/cs_project/toc(master*) » pip3 freeze
chardet==3.0.4
numpy==1.19.1
pandas==1.1.0
pdfminer.six==20200517
pdfplumber==0.5.23
Pillow==7.2.0
pycryptodome==3.9.8
PyPDF2==1.26.0
python-dateutil==2.8.1
pytz==2020.1
six==1.15.0
sortedcontainers==2.2.2
Wand==0.6.2
```
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MDExOlB1bGxSZXF1ZXN0NDk3NDk4MzQw
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DOC: Distinguish between different types of boolean indexing #10492
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- [x] closes #10492
- [x] tests added / passed
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TST: xfailed arithmetic tests
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REF: IntervalIndex.equals defer to IntervalArray.equals
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MEMBER
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- [ ] closes #xxxx
- [ ] tests added / passed
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- [ ] whatsnew entry
Small behavior change in that `interval_index.equals(series[interval])` will now return False, matching other Index subclasses.
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PERF: Improve RollingGroupby.count
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| 2020-10-05T02:27:35Z
| 2020-10-05T16:52:34Z
| 2020-10-05T13:01:38Z
|
MEMBER
| null |
- [x] closes #35625
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
```
In [1]: import pandas as pd
...:
...: # Generate sample df
...: df = pd.DataFrame({'column1': range(600), 'group': 5*['l'+str(i) for i in range(120)]})
...:
...: # sort by group for easy/efficient joining of new columns to df
...: df=df.sort_values('group',kind='mergesort').reset_index(drop=True)
In [2]: %timeit df['mean']=df.groupby('group').rolling(3,min_periods=1)['column1'].mean().values
5.59 ms ± 310 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [3]: %timeit df['sum']=df.groupby('group').rolling(3,min_periods=1)['column1'].sum().values
...:
5.34 ms ± 343 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [4]: %timeit df['count']=df.groupby('group').rolling(3,min_periods=1)['column1'].count().values
...:
4.97 ms ± 51.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```
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MDExOlB1bGxSZXF1ZXN0NDk3NTQ5MjAy
| 36,873
|
REF: dont consolidate in is_mixed_type
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| 2020-10-05T02:44:21Z
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Avoid copies, side-effects.
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MDU6SXNzdWU3MTQ0ODc2Nzk=
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|
DOC: GL01 errors in shared_docs.py
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| 2020-10-05T03:49:00Z
| 2020-10-16T01:32:19Z
| 2020-10-16T01:32:19Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
[`shared_docs.py`](https://github.com/pandas-dev/pandas/blob/master/pandas/core/shared_docs.py)
https://github.com/pandas-dev/pandas/blob/32d79ef46621de51e13732690c2e52566a5c67b6/pandas/core/shared_docs.py#L6-L8
https://github.com/pandas-dev/pandas/blob/32d79ef46621de51e13732690c2e52566a5c67b6/pandas/core/shared_docs.py#L48-L50
https://github.com/pandas-dev/pandas/blob/32d79ef46621de51e13732690c2e52566a5c67b6/pandas/core/shared_docs.py#L77-L79
https://github.com/pandas-dev/pandas/blob/32d79ef46621de51e13732690c2e52566a5c67b6/pandas/core/shared_docs.py#L146-L148
https://github.com/pandas-dev/pandas/blob/32d79ef46621de51e13732690c2e52566a5c67b6/pandas/core/shared_docs.py#L260-L262
#### Documentation problem
GL01 pandas docstring errors. `Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between.`
#### Suggested fix for documentation
It's really unnecessary using `\` to break the line when using `"""` as the docstring. It also treats the next line as the same line. Remove the `\` instead.
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MDExOlB1bGxSZXF1ZXN0NDk3NTYzNTI4
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DOC: Fix GL01 docstring errors in some functions
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| 2
| 2020-10-05T03:49:23Z
| 2020-10-16T01:32:24Z
| 2020-10-16T01:32:19Z
|
CONTRIBUTOR
| null |
- [x] fixes #36874
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
There are some GL01 pandas docstring errors in `shared_docs.py`.
```
GL01
Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between.
```
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MDExOlB1bGxSZXF1ZXN0NDk3NTY3ODU2
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|
BUG: in DataFrame.reset_index() only call maybe_upcast_putmask with ndarrays
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| null |
- [x] closes #24206
- [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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CLN: Remove the duplicate configuration of flake8-rst in setup.cfg
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CONTRIBUTOR
| null |
Remove duplicate `ignore = E203` configuration in setup.cfg[flake8-rst]
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New branch
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NONE
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- [ ] ref #36777
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Update flake8 to 3.8.4 in pre-commit-config.yaml
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|
MEMBER
| null |
Steps here are:
- upgrade flake8 in `.pre-commit-config.yaml`
- run `pre-commit run flake8 --all`
If everything passes, open a PR. If there's some error, let us know
Comment `take` if you want to work on this
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BUG: any() and all() behavior on string series is different from python
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NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
pd.Series(["kkkk", "llll"]).any() # "kkkk"
any(["kkkk", "llll"]) # True
pd.Series(["kkkk", "llll"]).all() # "llll"
all(["kkkk", "llll"]) # True
```
#### Problem description
Python returns boolean not the value. So we should follow that.
https://stackoverflow.com/questions/10180344/why-does-python-any-return-a-bool-instead-of-the-value
#### Expected Output
The expected behavior is to match `any` and `all` built-in functions.
https://docs.python.org/3/library/functions.html#any
```python
def any(iterable):
for element in iterable:
if element:
return True
return False
```
https://docs.python.org/3/library/functions.html#all
```python
def all(iterable):
for element in iterable:
if not element:
return False
return True
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.112+
Version : #1 SMP Fri Sep 4 12:06:06 PDT 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200712
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.18
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU3MTQ2ODkyOTA=
| 36,881
|
Why prompt me “name 'pd' is not defined”
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[] | null | 5
| 2020-10-05T09:46:37Z
| 2020-10-05T09:58:42Z
| 2020-10-05T09:57:08Z
|
NONE
| null |
### Question about pandas
I have customized a Python interpreter, on which I try to run pandas's apply method, but when executing this method, there will be an exception. I don't know why, who knows why
```python
[1] import pandas as pd
[2] titanic = pd.read_csv('../data/titanic.csv')
[3] def not_null_count(columns):
return len(columns[pd.isnull(columns)])
[4] res = titanic.apply(not_null_count)
[5]print(res)
run_code:
comp_code = compile(code, '<zmq-kernel>', 'single')
if comp_code:
exec(comp_code, self.user_global_ns, self.user_ns)
```
File "<zmq-kernel>", line 1, in <module>
File "/usr/local/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py", line 6878, in apply return op.get_result()
File "/usr/local/anaconda3/lib/python3.8/site-packages/pandas/core/apply.py", line 186, in get_result return self.apply_standard()
File "/usr/local/anaconda3/lib/python3.8/site-packages/pandas/core/apply.py", line 295, in apply_standard result = libreduction.compute_reduction(
File "pandas/_libs/reduction.pyx", line 620, in pandas._libs.reduction.compute_reduction
File "pandas/_libs/reduction.pyx", line 128, in pandas._libs.reduction.Reducer.get_result
File "<zmq-kernel>", line 2, in not_null_count
NameError: name 'pd' is not defined
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| 714,700,891
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MDExOlB1bGxSZXF1ZXN0NDk3NzM4ODc2
| 36,882
|
upgrade flake8 to 3.8.4 #36879
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| 5
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| 2020-10-08T12:47:02Z
| 2020-10-05T10:15:12Z
|
CONTRIBUTOR
| null |
closes #36879
Here are the logs after the upgrade to 3.8.4:
```console
(base) root@33d4c5d14a5b:/home/pandas-tazminia# pre-commit run flake8 --all
[INFO] Initializing environment for https://github.com/python/black.
[INFO] Initializing environment for https://gitlab.com/pycqa/flake8.
[INFO] Initializing environment for https://gitlab.com/pycqa/flake8:flake8-comprehensions>=3.1.0.
[INFO] Initializing environment for https://github.com/PyCQA/isort.
[INFO] Initializing environment for https://github.com/asottile/pyupgrade.
[INFO] Initializing environment for https://github.com/pre-commit/pygrep-hooks.
[INFO] Initializing environment for https://github.com/asottile/yesqa.
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
flake8...................................................................Passed
flake8-pyx...............................................................Passed
flake8-pxd...............................................................Passed
```
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|
DOC: sync release notes on 1.1.x with master
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| 0
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|
MEMBER
| null |
xref #36845
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| 36,884
|
TYP: check_untyped_defs compat.pickle_compat
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| 2020-10-05T10:24:51Z
| 2020-10-05T13:12:30Z
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| null |
pandas\compat\pickle_compat.py:277: error: Incompatible types in assignment (expression has type "Callable[[bytes, DefaultNamedArg(bool, 'fix_imports'), DefaultNamedArg(str, 'encoding'), DefaultNamedArg(str, 'errors')], Any]", variable has type "Callable[[bytes, DefaultNamedArg(bool, 'fix_imports'), DefaultNamedArg(str, 'encoding'), DefaultNamedArg(str, 'errors'), DefaultNamedArg(Optional[Iterable[Any]], 'buffers')], Any]") [assignment]
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TYP: check_untyped_defs core.arrays.base
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| 1
| 2020-10-05T10:33:22Z
| 2020-10-05T13:15:12Z
| 2020-10-05T12:58:39Z
|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/issues/31160
from https://docs.python.org/3/library/functions.html?highlight=setattr#setattr
> For example, setattr(x, 'foobar', 123) is equivalent to x.foobar = 123.
The changes here, don't 'fix' the issue. The correct solution is to create these methods with a statically defined interface, see https://github.com/pandas-dev/pandas/issues/31160#issuecomment-699193016
In the meantime, the changes here allows untyped defs in this module to be checked. (without adding type ignores)
pandas\core\arrays\base.py:1179: error: Unsupported left operand type for + ("Type[ExtensionOpsMixin]") [operator]
pandas\core\arrays\base.py:1180: error: "Type[ExtensionOpsMixin]" has no attribute "__radd__" [attr-defined]
pandas\core\arrays\base.py:1181: error: Unsupported left operand type for - ("Type[ExtensionOpsMixin]") [operator]
pandas\core\arrays\base.py:1182: error: "Type[ExtensionOpsMixin]" has no attribute "__rsub__" [attr-defined]
pandas\core\arrays\base.py:1183: error: Unsupported left operand type for * ("Type[ExtensionOpsMixin]") [operator]
pandas\core\arrays\base.py:1184: error: "Type[ExtensionOpsMixin]" has no attribute "__rmul__" [attr-defined]
pandas\core\arrays\base.py:1185: error: Unsupported left operand type for ** ("Type[ExtensionOpsMixin]") [operator]
pandas\core\arrays\base.py:1186: error: "Type[ExtensionOpsMixin]" has no attribute "__rpow__" [attr-defined]
pandas\core\arrays\base.py:1187: error: Unsupported left operand type for % ("Type[ExtensionOpsMixin]") [operator]
pandas\core\arrays\base.py:1188: error: "Type[ExtensionOpsMixin]" has no attribute "__rmod__" [attr-defined]
pandas\core\arrays\base.py:1189: error: Unsupported left operand type for // ("Type[ExtensionOpsMixin]") [operator]
pandas\core\arrays\base.py:1190: error: "Type[ExtensionOpsMixin]" has no attribute "__rfloordiv__" [attr-defined]
pandas\core\arrays\base.py:1191: error: Unsupported left operand type for / ("Type[ExtensionOpsMixin]") [operator]
pandas\core\arrays\base.py:1192: error: "Type[ExtensionOpsMixin]" has no attribute "__rtruediv__" [attr-defined]
pandas\core\arrays\base.py:1193: error: Unsupported left operand type for divmod ("Type[ExtensionOpsMixin]") [operator]
pandas\core\arrays\base.py:1194: error: "Type[ExtensionOpsMixin]" has no attribute "__rdivmod__" [attr-defined]
pandas\core\arrays\base.py:1202: error: Cannot assign to a method [assignment]
pandas\core\arrays\base.py:1203: error: Cannot assign to a method [assignment]
pandas\core\arrays\base.py:1204: error: Unsupported left operand type for < ("Type[ExtensionOpsMixin]") [operator]
pandas\core\arrays\base.py:1205: error: Unsupported left operand type for > ("Type[ExtensionOpsMixin]") [operator]
pandas\core\arrays\base.py:1206: error: Unsupported left operand type for <= ("Type[ExtensionOpsMixin]") [operator]
pandas\core\arrays\base.py:1207: error: Unsupported left operand type for >= ("Type[ExtensionOpsMixin]") [operator]
pandas\core\arrays\base.py:1215: error: Unsupported left operand type for & ("Type[ExtensionOpsMixin]") [operator]
pandas\core\arrays\base.py:1216: error: "Type[ExtensionOpsMixin]" has no attribute "__rand__" [attr-defined]
pandas\core\arrays\base.py:1217: error: Unsupported left operand type for | ("Type[ExtensionOpsMixin]") [operator]
pandas\core\arrays\base.py:1218: error: "Type[ExtensionOpsMixin]" has no attribute "__ror__" [attr-defined]
pandas\core\arrays\base.py:1219: error: Unsupported left operand type for ^ ("Type[ExtensionOpsMixin]") [operator]
pandas\core\arrays\base.py:1220: error: "Type[ExtensionOpsMixin]" has no attribute "__rxor__" [attr-defined]
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Backport PR #36864 on branch 1.1.x (CI: Update error message for np_dev)
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NONE
| null |
Backport PR #36864: CI: Update error message for np_dev
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DOC: 1.1.3 release date
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| 2020-10-05T12:24:21Z
| 2020-10-05T14:03:43Z
| 2020-10-05T14:03:30Z
|
MEMBER
| null |
removed mention of py39 support @jreback
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BUG: to_json(lines=True) does not add a trailing newline
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| 1
| 2020-10-05T13:19:25Z
| 2020-10-10T22:55:57Z
| 2020-10-10T22:55:57Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame({'AAA': [4, 5, 6, 7],
'BBB': [10, 20, 30, 40],
'CCC': [100, 50, -30, -50]})
df.to_json('foo.jsonl', orient='records', lines=True)
open('foo.jsonl').read()
```
output (Notice - no newline after the last line):
```
'{"AAA":4,"BBB":10,"CCC":100}\n{"AAA":5,"BBB":20,"CCC":50}\n{"AAA":6,"BBB":30,"CCC":-30}\n{"AAA":7,"BBB":40,"CCC":-50}'
```
#### Problem description
When outputting JSON lines, i.e. `lines=True`, every record should end with a newline, including the last record. Otherwise, files cannot be concatenated, the number of records will be counted incorrectly by `wc -l`, etc.
#### Expected Output
There should be a newline at the end, i.e.:
```
'{"AAA":4,"BBB":10,"CCC":100}\n{"AAA":5,"BBB":20,"CCC":50}\n{"AAA":6,"BBB":30,"CCC":-30}\n{"AAA":7,"BBB":40,"CCC":-50}\n'
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1127.19.1.el7.x86_64
Version : #1 SMP Tue Aug 11 19:12:04 EDT 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : None
pytest : None
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.0
fastparquet : 0.4.1
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.50.1
</details>
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MDU6SXNzdWU3MTQ4NTg0ODU=
| 36,889
|
BUG: RollingGroupby no longer respects sort being disabled
|
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| 5
| 2020-10-05T13:48:19Z
| 2020-10-09T00:46:15Z
| 2020-10-09T00:46:15Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
pd.DataFrame({'foo': [2,1], 'bar': [2, 1]}).groupby('foo', sort=False).rolling(1).min()
foo bar
foo
1 1 1.0 1.0
2 0 2.0 2.0
```
#### Problem description
RollingGroupby no longer respects sort being disabled
#### Expected Output
Behaviour in 1.0.5
```python
pd.DataFrame({'foo': [2,1], 'bar': [2, 1]}).groupby('foo', sort=False).rolling(1).min()
foo bar
foo
2 0 2.0 2.0
1 1 1.0 1.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.6.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.2
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 9.0.1
setuptools : 50.3.0
Cython : 0.29.21
pytest : 6.0.2
hypothesis : None
sphinx : 1.7.9
blosc : 1.7.0
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : 0.4.1
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : 0.3.3
scipy : 1.5.2
sqlalchemy : 1.2.12
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.46.0
</details>
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MDU6SXNzdWU3MTQ4NjQzMDg=
| 36,890
|
ENH: Intermediate join results that preserve data frame alias, similar to SQL joins
|
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[] | null | 4
| 2020-10-05T13:55:26Z
| 2021-08-13T04:01:25Z
| 2021-08-13T04:01:25Z
|
NONE
| null |
Please take a look at this stackoverflow post (this is my post) and let me know what you think. You can answer the question if you like, but I'm guessing the answer is that there is currently no other way to handle the column conflicts other than using the suffixes keyword.
https://stackoverflow.com/questions/64126437/pandas-merges-and-suffixes
As such, I have been enhancing my Pandas code generator to deal with column conflicts, but would be a really nice Pandas feature to be more "SQL like" in how merges are handled in that the "intermediate results" preserve the original column names and then allow you to reference by dataset alias.
Thanks.
|
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| 36,891
|
Backport PR #36887 on branch 1.1.x (DOC: 1.1.3 release date)
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| 2020-10-05T15:17:12Z
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NONE
| null |
Backport PR #36887: DOC: 1.1.3 release date
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pandas dataset for practice and understanding
- [ ] closes #xxxx
- [ ] tests added / passed
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- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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ENH: Pluggable SQL performance
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CONTRIBUTOR
| null |
Currently the pandas SQL logic is using SQLAlchemy with results being returned as Python objects before being converted to a DataFrame. While the API is simple, it doesn't have good performance characteristics due to the intermediate Python objects. There exist currently some faster alternatives with inconsistent and more complicated APIs.
In addition to not having a uniform API, these implementations are only concerned about fast result en-/decoding. Functionality like automatic table creation as we have in `pandas.DataFrame.to_sql` doesn't exist there.
Thus it would be nice to have a way to use these connector implementations behind the standard pandas API.
## Faster alternatives
* [bcpandas](https://github.com/yehoshuadimarsky/bcpandas): Use BCP to insert data into MS SQL Server
* [turbodbc](https://turbodbc.readthedocs.io/en/latest/): Fast access to databases which have an ODBC driver via Apache Arrow (`fetchallarrow().to_pandas()`), e.g. MS SQL or Exasol.
* [snowflake-connector-python](https://github.com/snowflakedb/snowflake-connector-python): Brings native Apache Arrow to speed via `fetch_all_pandas()`
* [pyarrow.jvm / JDBC](https://uwekorn.com/2019/11/17/fast-jdbc-access-in-python-using-pyarrow-jvm.html): Use `pyarrow`'s JVM module to get faster access to JDBC results via Arrow
* `postgres-copy-arrow-decode`: Not yet opensourced (shame on me): Cython-based encoder/decoder for Postgres' `COPY BINARY` command that decodes Postgres' binary protocol from/to Arrow. Works together with `psycopg2` and gives roughly a 2x speedup and type stability on the `COPY CSV` method in `pandas`'s docs.
* [PostgresAdapter](https://github.com/ContinuumIO/PostgresAdapter): NumPy support for Postgres connections
* [d6tstack](https://github.com/d6t/d6tstack/blob/master/examples-sql.ipynb): Fast insert into Postgres/MySQL/MSSQL via CSV files
## General implementation idea
* `pandas` users should only deal with `read_sql` and `to_sql` in its current fashion.
* There shouldn't be any new hard dependencies in pandas.
* The SQLAlchemy engine is a nice uniform interface to specify a database connection, keep this.
* We only need a limited set of operations implemented by the performance backend, basically `to_sql(DataFrame)` and `read_sql(query) -> DataFrame`. Table creation, index adjustment and further convenience functionality can still be handled by the high-level SQLAlchemy layer.
### Implementation idea (1) – Dispatch on `type(engine.raw_connection().connection)`
SQLAlchemy exposes the underlying connection of the database driver via `engine.raw_connection()`. This is a useful way to detect how we connect to the database. We could provide a registry where each backend implementation provides a function `supports_connection(engine.raw_connection().connection) -> bool` to determine whether it can be used.
Pro:
* Users doesn't need to change their code. If the backend is loaded, they will automatically get the speedup.
Con:
* Users need to take care that the backend is loaded, otherwise queries will work but stay slow.
* Only one implementation per database connection class can be implemented
### Implementation idea (2) – Extend the `method=` param
`pandas.DataFrame.to_sql` already has a `method` parameter where the user can supply a callable that is used to insert the data into the Database. Currently the callable gets a row-iterator and not a DataFrame. Thus this interface is already hard-wired that the intermediate result needs to be converted into Python objects. Instead of providing a row-iterator, we could pass the original DataFrame to this method
Pro:
* Clear control on which method is used
* Backend implementations could be used via `method=turbodbc.pandas.to_sql`
Con:
* Potentially a breaking change on the `method` paramter or a second parameter needs to be added that is doing nearly the same things is introduced.
* Needs explicit usage for the speedup.
### Implementation idea (3) - Introduce `engine=` param
As we have with the the Parquet and CSV IO implementations, we could also go for providing an `engine` parameter where users could easily switch based on the name of an implementation. A prototype implementation would look like:
```python
import pandas as pd
class DatabaseEngine:
name = "fastengine"
@staticmethod
def supports_connecton(connection) -> bool: # for engine="auto"
return isinstance(connection, FastConnection)
def to_sql(engine, df: pd.DataFrame, table: str):
…
def from_sql(engine, query: str) -> pd.DataFrame:
…
pd.register_sql_backend(DatabaseEngine.name, DatabaseEngine)
```
Pro:
* In contrast to (1), here you would get an error when the backend was not loaded.
* Clear control on which method is used.
* User doesn't need to provide the exact function but only the name of the engine
* We could provide an `engine="auto"` setting which on explicit usage tries to find a matching backend and will otherwise fallback to the plain SQLAlchemy implementation.
* We can provide some of these engines as part of pandas, others can come from third-party libraries.
Con:
* Needs explicit usage for the speedup.
Personally, I would prefer this approach.
## Related issues
* [Use Turbodbc/Arrow for read_sql_table](https://github.com/pandas-dev/pandas/issues/17790), would be fixed by this proposal
* [Faster SQL implementation in d6stack library](https://github.com/pandas-dev/pandas/issues/28817), would be fixed by this proposal
* [Improve type handling in read_sql and read_sql_table](https://github.com/pandas-dev/pandas/issues/13049), related to this proposal but won't be fully fixed
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ENH: allow non-consolidation in constructors
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MEMBER
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- [ ] closes #xxxx
- [ ] tests added / passed
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cc @TomAugspurger I think something like this is what is needed for #34872.
If this were a "real" PR, I'd probably not put `consolidate` as a `DataFrame.__init__` kwarg but instead set it to `True` and only change it to `False` based on the `copy` kwarg.
The usage in core.ops is not necessary, but is an example of where we can get a perf bump.
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| 1
| 2020-10-05T18:31:47Z
| 2020-11-03T13:30:04Z
| 2020-11-03T13:30:04Z
|
CONTRIBUTOR
| null |
- [X ] I have checked that this issue has not already been reported.
- [ X] I have confirmed this bug exists on the latest version of pandas.
- [X ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
df = pd.DataFrame(np.zeros((5, 5)))
df.columns = list('abcde')
df2 = pd.DataFrame(columns=list('abcfg'))
df['a'] = pd.Timestamp("2020-05-05")
df = df.set_index(['a', 'b', 'c'])
df2 = df2.set_index(['a', 'b', 'c'])
# error
df.merge(df2, on=['a'], how='left')
# no error
out = df.reset_index().merge(df2.reset_index(), how='left', on=['a'])
# no error
df.join(df2, how='left')
```
#### Problem description
We pull a lot of data from SQL into data frames. The current pandas.from_sql does not automatically set column data types, and they get reset anyway when we call a `set_index`. So when our getters return an empty result, none of the data types are set in the multi-index.
We have a lot of instances in our production codebase where we merge two dataframes in the above pattern. In pandas 0.25.3 (last used version), a DF missing values in a level of the datetime index was not a problem. However, in pandas 1.1.2, we get an error thrown whenever one of the levels in the multi-index is a datetime index, and one of the DFs in the join has a missing dtype.
#### Expected Output
Same as df.join(df2) or the other example shown.
#### Output of ``pd.show_versions()``
<details>
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.17.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 41.2.0
Cython : 0.29.21
pytest : 5.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : 0.4.1
gcsfs : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.17
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDk4MDYzNzk3
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regression fix for merging DF with datetime index with empty DF
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CONTRIBUTOR
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- [X ] tests added / passed
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- [ X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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BUG: Add trailing trailing newline in to_json
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Backport PR #36689 on branch 1.1.x (DOC: Start v1.1.4 release notes)
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NONE
| null |
Backport PR #36689: DOC: Start v1.1.4 release notes
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Make Index.ravel() behavior match every other ravel() method in existence.
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| 715,253,740
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MDU6SXNzdWU3MTUyNTM3NDA=
| 36,904
|
REGR: change in Series.astype(str) behavior for None
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| 2
| 2020-10-06T00:39:53Z
| 2020-10-10T18:24:59Z
| 2020-10-10T18:24:59Z
|
NONE
| null |
#### Question about pandas
Let's say we have a `Series` with `None` like the below.
```python
pser = pd.Series(["hi", "hi ", " ", " \t", "", None], name="x")
print(pser)
0 hi
1 hi
2
3 \t
4
5 None
Name: x, dtype: object
```
the last value is `None`, but it is casted to `nan` after using `astype` with `str` parameter.
```python
print(pser.astype(str))
0 hi
1 hi
2
3 \t
4
5 nan
Name: x, dtype: object
```
Is it intended behavior in pandas 1.1.1. ??
Thanks :)
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CLN: value -> key
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| 0
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| 2020-10-29T10:52:25Z
| 2020-10-29T01:06:25Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
pdf = pd.DataFrame()
pdf['a'] = pd.Series(['2020-01-01 08:00:00', '1920-02-01 09:00:00'], dtype='datetime64[ns]')
pdf['b'] = pd.Series(['2020-02-01 08:00:00', pd.NaT], dtype='datetime64[ns]')
pdf.max(axis=1, skipna=False)
```
#### Problem description
Current output is
```
0 2020-02-01 08:00:00
1 1920-02-01 09:00:00
dtype: datetime64[ns]
```
In general, missing values involved in reduction should propagate.
#### Expected Output
```
0 2020-02-01 08:00:00
1 NaT
dtype: datetime64[ns]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-48-generic
Version : #52~18.04.1-Ubuntu SMP Thu Sep 10 12:50:22 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20200917
Cython : 0.29.21
pytest : 6.1.1
hypothesis : 5.28.0
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.3.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.51.0rc1
</details>
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MDExOlB1bGxSZXF1ZXN0NDk4MjIyODI3
| 36,908
|
BUG: IntervalArray.__eq__ not deferring to Series
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| 2
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| 2020-10-13T16:28:09Z
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MEMBER
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- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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BUG: Left join is broken with a MultiIndex with only one level
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| 0
| 2020-10-06T05:20:33Z
| 2021-01-03T22:22:53Z
| 2021-01-03T22:22:53Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
foo = pd.DataFrame(data={'c': 3}, index=pd.MultiIndex.from_tuples([(1, 2)], names=('a', 'b')))
bar = pd.DataFrame(data={'d': 4}, index=pd.MultiIndex.from_tuples([(2,)], names=('b',)))
joined = foo.join(bar, how='left')
print(joined)
```
#### Expected Output
```
c d
b a
2 1 3 4
```
(Throwing an exception on the line where `bar` is created is also possibly acceptable, although confusing.)
#### Produced Output
```
c d
b a
2 1 3 NaN
```
#### Problem description
When joining with a data frame with a MultiIndex with a single level in it, left joins seem to not work properly. This behavior is confusing (why would a MultiIndex with a single index not work?), and is inconsistent with similar code which produces expected output.
Possibly related issues: #34292, #29252, #34357
#### Examples of similar code with expected behavior
Example 1:
<details>
```python
import pandas as pd
foo = pd.DataFrame(data={'c': 3}, index=pd.MultiIndex.from_tuples([(1, 2)], names=('a', 'b')))
bar = pd.DataFrame(data={'d': 4}, index=pd.Index([2], name='b'))
joined = foo.join(bar, how='left')
print(joined)
```
produces
```
c d
a b
1 2 3 4
```
</details>
Example 2:
<details>
```python
import pandas as pd
foo = pd.DataFrame(data={'c': 3}, index=pd.MultiIndex.from_tuples([(1, 2, 99)], names=('a', 'b', 'x')))
bar = pd.DataFrame(data={'d': 4}, index=pd.MultiIndex.from_tuples([(2, 99)], names=('b', 'x')))
joined = foo.join(bar, how='left')
print(joined)
```
produces
```
c d
b x a
2 99 1 3 4
```
</details>
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_United States.1252
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 50.3.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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|
BUG: Joining data frames with MultiIndex results in non-deterministic level order.
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| 0
| 2020-10-06T05:37:57Z
| 2020-10-23T00:20:33Z
| 2020-10-23T00:20:33Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
foo = pd.DataFrame(data={'e': 5}, index=pd.MultiIndex.from_tuples([(1, 2, 4)], names=('a', 'b', 'd')))
bar = pd.DataFrame(data={'f': 6}, index=pd.MultiIndex.from_tuples([(2, 3)], names=('b', 'c')))
joined = foo.join(bar, how='inner')
print(foo.join(bar))
```
#### Expected Output
Something deterministic
#### Produced Output
When running the code multiple times (restart the python interpreter), the output switches (apparently randomly) between these two outputs:
```
e f
b d a c
2 4 1 3 5 6
```
```
e f
b a d c
2 1 4 3 5 6
```
#### Problem description
When joining with a data frame with a MultiIndex, the order of the levels in the resulting data frame is non-deterministic. This is very surprising, and resulted in a hard-to-debug issue in my code. This issue happens with both inner joins and left joins.
Possibly related issues: #36909, #25760
Past discussion on non-determinism in pandas: #32514, #32449, #12679
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_United States.1252
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 50.3.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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| 2020-10-10T09:54:00Z
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MEMBER
| null |
- [x] closes #36889
- [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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ENH: Add option for `to_json` function to discard null values
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| 2021-08-23T08:34:11Z
| 2021-08-23T08:34:11Z
|
NONE
| null |
#### Is your feature request related to a problem?
No, at least not that I am aware of.
#### Describe the solution you'd like
The [documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_json.html) states that "NaN’s and None will be converted to null" when using `pandas.DataFrame.to_json`. I'd like to have an option for the `to_json` function, so that keys that have a null value will be discarded. See "'Additional context" for an example.
#### API breaking implications
New optional parameter for `pandas.DataFrame.to_json`. However by default this option should be disabled, so no breaking implications, I think.
#### Additional context
``` python
import pandas as pd
import numpy as np
data = {
# None and np.nan will result in null values when converting to JSON
"a": [None, 1, 2],
"b": [0, np.nan, 0]
}
df = pd.DataFrame(data)
# Note: `discard_null` doesn't exist yet
print(df.to_json(orient="records", discard_null=True))
# current result: [{"a":null,"b":0.0},{"a":1.0,"b":null},{"a":2.0,"b":0.0}]
# desired result: [{"b":0.0},{"a":1.0},{"a":2.0,"b":0.0}]
```
Currently I am only working with `orient="records"`. Does it make sense to restrict the option only to this orientation or does it make sense to implement this option for other orientations as well?
I'd like to implement this feature by myself and contribute a PR. If there are any tips about how I can do this, please let me know 👌
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BUG: Fixed IntegerArray.__array_ufunc__ with nout
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| 2020-10-06T18:42:14Z
| 2020-10-06T18:41:36Z
|
CONTRIBUTOR
| null |
We forgot to return.
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|
BUG: groupby dropna for multiindex dataframe
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|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
Hello,
I'm not sure if it's a bug report, a feature request, or if there's already another way to do it. In which case, I would be happy to hear it and close this issue.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
test_df = pd.DataFrame.from_dict({
'gene_1' : ['Bacteria', 'Lactobacillus', 11, 17],
'gene_2' : ['Bacteria', 'Lactobacillus', 9, 3],
'gene_3' : [np.nan, np.nan, 1, 2],
'gene_4' : [np.nan, np.nan, 3, 4],
'gene_5' : ['Bacteria', np.nan, 10, 11]
},
orient='index', columns=['kingdom', 'genus', 'sample_1', 'sample_2'])
test_df = test_df.set_index(['kingdom', 'genus'])
```
```python
test_df.groupby(level=[0,1], dropna = False).sum()
```
#### Current output
#### Problem description
I would like the dropna option of groupby to handle multipleindex dataframe
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-48-generic
Version : #52-Ubuntu SMP Thu Sep 10 10:58:49 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
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 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
Related issues : #3729 #29716
Thank you,
Agnès
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| 2
| 2020-10-06T13:48:54Z
| 2020-11-29T17:21:53Z
| 2020-11-29T17:21:53Z
|
NONE
| null |
```python
import pandas as pd
arraysA = [['val1', 'val1', 'val1', 'val1'], ['val2', 'val2', 'val2', 'val2']]
arraysB = [['val1'], ['val2']]
# MultiIndex([('val1', 'val2'),
# ('val1', 'val2'),
# ('val1', 'val2'),
# ('val1', 'val2')],
# names=['idx1', 'idx2'])
indexA = pd.MultiIndex.from_arrays(arraysA, names=('idx1', 'idx2'))
# MultiIndex([('val1', 'val2')],
# names=['idx1', 'idx2'])
indexB = pd.MultiIndex.from_arrays(arraysB, names=('idx1', 'idx2'))
res = indexA.intersection(indexB)
```
#### Problem description
Intersection of multiindexes **must** produce result without duplicates. According to definition the intersection of two sets A and B, denoted by A ∩ B, is the set containing all elements of A that also belong to B (or equivalently, all elements of B that also belong to A.
Version 1.0.3 gave correct output, versions 1.1.2 , 1,1,3 - not.
#### Output: Pandas 1.1.3
MultiIndex([('val1', 'val2'),
('val1', 'val2'),
('val1', 'val2'),
('val1', 'val2')],
names=['idx1', 'idx2'])
#### Output: Pandas 1.0.3
MultiIndex([('val1', 'val2')],
names=['idx1', 'idx2'])
#### Expected Output
I assume correct output should look like pandas 1.0.3 version:
MultiIndex([('val1', 'val2')], names=['idx1', 'idx2'])
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17763
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.3
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 46.0.0
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.0
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.14.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : 1.0.6
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : None
None
</details>
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MDU6SXNzdWU3MTU3MDg3MjU=
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ENH: pd.unstack(level, values=["a", "b", "c"])
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| 13
| 2020-10-06T14:02:32Z
| 2021-03-25T11:54:11Z
| 2021-03-25T11:54:11Z
|
NONE
| null |
#### Is your feature request related to a problem?
I would like to unstack only certain values from a DataFrame to know exactly which columns will be in the resulting dataframe.
For example, when the dataframe is empty, `df.unstack(0)` will just return an empty dataframe without columns:
https://github.com/pandas-dev/pandas/issues/21255
Advantages:
- predictable column layout
- subsetting for certain values
#### Describe the solution you'd like
I'd propose a values option for `pd.DataFrame.unstack()` that lets one specify which values will be unstacked into columns.
E.g. `df.unstack(0, values=["A", "B", "C"])` would ensure that the resulting dataframe has exactly the toplevel columns `A`, `B` and `C`.
#### API breaking implications
N/A
#### Describe alternatives you've considered
I checked out `pd.DataFrame.pivot()`, however, it is more complicated to use and cannot use index levels for unstacking.
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TYP: check_untyped_defs core.computation.expressions
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| 1
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| 2020-10-06T19:25:07Z
| 2020-10-06T18:03:14Z
|
MEMBER
| null |
pandas\core\computation\expressions.py:251: error: "None" not callable [misc]
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BUG: Horizontal boxplots on subplots throws ValueError
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| 5
| 2020-10-06T16:32:21Z
| 2020-11-17T01:27:56Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
df1 = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))
df2 = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))
f, axs = plt.subplots(1, 2, figsize=(10,7), sharey=True)
df1.boxplot(ax=axs[0], showfliers=False, vert=False)
df2.boxplot(ax=axs[1], showfliers=False, vert=False)
```
#### Problem description
Above code snippet throws `ValueError: The number of FixedLocator locations (8), usually from a call to set_ticks, does not match the number of ticklabels (4).` due to changes introduced in matplotlib 3.3.0.
It looks like fixes were implemented as part of #35393 in `boxplot.py` for `vert=True` but not for `vert=False`. Performing the `len(ticks) != len(keys)` check for `ticks = ax.get_yticks()` and the associated keys list doubling snippet fixes the issue.
Not sure if that is the right fix, but works for my use case.
#### Expected Output
Two sets of horizontal boxplot charts
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-48-generic
Version : #52-Ubuntu SMP Thu Sep 10 10:58:49 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 19.1.1
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.2
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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