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} | 2 | 2019-07-09T21:05:09Z | 2019-08-02T15:04:44Z | 2019-07-10T02:41:01Z | NONE | null | #23750, and various issues on supporting upcoming python 3.8.
Azure (multiple):
```
[warning] Using or importing the ABCs from 'collections' instead
of from 'collections.abc' is deprecated, and in 3.8 it will stop working
```
From https://docs.python.org/3/library/collections.html?highlight=collections#module-collections:
```
Changed in version 3.3: Moved Collections Abstract Base Classes to the
collections.abc module. For backwards compatibility, they continue to be
visible in this module through Python 3.7.
Subsequently, they will be removed entirely.
```
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] | closed | false | null | [] | null | 2 | 2019-07-09T21:16:05Z | 2019-07-09T21:43:35Z | 2019-07-09T21:40:09Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
query = """
SELECT risk_state_code, COUNT(*) as num_records
FROM EDW.vw_policy_coverage_deductible_transaction
GROUP BY risk_state_code
"""
bquery = pd.read_sql(query, bvt_conn)
pquery = pd.read_sql(query, prod_conn)
merged = pd.merge(bquery, pquery, on='risk_state_code', how='outer')
merged
```
#### Problem description
When performing pandas merge on categorical column name, it duplicates unique values (different outcome every time I run the cell). Have tried in multiple environments, pandas is updated to latest version.
*Executing the **SAME** code produces different outputs pictured below.
Here are the dataframes being merged:
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 0 | 2019-07-09T22:50:21Z | 2020-11-17T01:26:21Z | 2020-11-17T01:26:21Z | NONE | null | ```python
import pandas as pd
from datetime import datetime, timedelta
end = datetime.utcnow()
begin = end - timedelta(minutes=1)
data_interval = 10
date_index = pd.date_range(start=begin,
end=end,
freq='{} s'.format(data_interval))
df = pd.DataFrame([], columns=['time','a','b'])
df = df.set_index('time', drop=True)
tol = timedelta(seconds=9)
df = df.reindex(date_index, method='pad', tolerance=tol)
# IndexError: index -1 is out of bounds for axis 0 with size 0
df = pd.DataFrame([], columns=['time','a','b'])
df = df.reindex(date_index, method='nearest')
# IndexError: index -1 is out of bounds for axis 0 with size 0
```
You get an index error when a dataframe is empty using the `tolerance=` or `method='nearest'` .
This is not something that happens with other usages of reindex and can come up as a surprise when reindexing an empty window of data. I would expect it to behave the same as it does without tolerance here.
#### Expected Output
Should be same as reindex with no args in this case which returns...
```
a b
2019-07-09 22:35:05.165640 NaN NaN
2019-07-09 22:35:15.165640 NaN NaN
2019-07-09 22:35:25.165640 NaN NaN
2019-07-09 22:35:35.165640 NaN NaN
2019-07-09 22:35:45.165640 NaN NaN
2019-07-09 22:35:55.165640 NaN NaN
2019-07-09 22:36:05.165640 NaN NaN
```
#### Temp Solution
Simple user solution is to check length... but this is a problem that might surprise someone at a bad time like it did for us.
``` python
if(len(df) is 0):
df = df.reindex(date_index)
else:
df = df.reindex(date_index, method='pad', tolerance=tol)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-54-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 5.0.1
pip: 18.0
setuptools: 40.4.1
Cython: None
numpy: 1.16.4
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 1.1.8
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.3.5
pymysql: 0.9.3
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: 0.6.1
pandas_datareader: None
gcsfs: None
</details>
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} | 1 | 2019-07-10T04:44:36Z | 2019-07-10T16:49:34Z | 2019-07-10T16:39:22Z | MEMBER | null | - [X] closes #27295
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 6 | 2019-07-10T06:29:45Z | 2019-07-25T17:34:00Z | 2019-07-25T17:33:53Z | CONTRIBUTOR | null | Adds some typing to ``Categorical``.
This PR does the simpler typing of ``Categorical``. In addition there are some more thorny typing that I've stayed away from for now to see if others can implement the basic types (list-likes etc.). | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27319 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27319/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27319/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27319/events | https://github.com/pandas-dev/pandas/issues/27319 | 466,155,645 | MDU6SXNzdWU0NjYxNTU2NDU= | 27,319 | DOC: Error in pd.cut documentation example regarding IntervalIndex usage. | {
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"id":... | open | false | null | [] | null | 6 | 2019-07-10T07:51:30Z | 2020-09-21T21:26:24Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.cut.html
```
bins = pd.IntervalIndex.from_tuples([(0, 1), (2, 3), (4, 5)])
pd.cut([0, 0.5, 1.5, 2.5, 4.5], bins)
[NaN, (0, 1], NaN, (2, 3], (4, 5]]
Categories (3, interval[int64]): [(0, 1] < (2, 3] < (4, 5]]
```
#### Problem description
Proposed example in pd.cut IntervalIndex section does not take into consideration actual pd.cut behaviour which in above example results produces ranges with lots of missing values and nans in results. In docs example above for example intermediate values like 2 and 4 WILL NOT be included in any bins, so the actual values of 2 and 4 in the data will produce nans after cutting the attribute using pd.cut.
I assume here that user in 99% of the time when using cut to bucketize value space wants all values in the spectrum to be included. This usage example can lead to data loss.
Actual example should be along the lines:
```
bins = pd.IntervalIndex.from_tuples([(0, 1), (1, 3), (3, 5)])
```
resulting in the following bins:
```
Categories (3, interval[int64]): [(0, 1] < (1, 3] < (3, 5]]
```
EDIT:
There is a correct/proper example in IntervalIndex docs:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.IntervalIndex.from_tuples.html#pandas.IntervalIndex.from_tuples | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27320 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27320/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27320/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27320/events | https://github.com/pandas-dev/pandas/issues/27320 | 466,183,320 | MDU6SXNzdWU0NjYxODMzMjA= | 27,320 | [Feature request] slice a sorted dataframe | {
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} | [] | closed | false | null | [] | null | 2 | 2019-07-10T08:50:48Z | 2019-07-10T12:28:37Z | 2019-07-10T11:57:11Z | NONE | null | Say I have a dataframe `df` with two columns `guest_name`, `room_nr` and I want the room numbers of a specific guest, we can slice that `df.loc[df["guest_name"]==guest],"room_nr"]`.
I have (to be honest) not looked into how the slicing is done as such, but I could imagine parsing the argument "sorted=True" could speed it up, such that it is possible to sort on `guest_name` and we do not need to loop through the entire dataset at each time, but we can stop after we see a new instance of `guest_name` | {
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} | 5 | 2019-07-10T14:27:59Z | 2019-07-11T14:34:54Z | 2019-07-11T14:32:15Z | MEMBER | null | - [x] closes #13843
We have two very-similar functions for masking division by zero for Index vs Series. The Index version is the more correct of the two, so I'm trying to make the Series one behave more like the Index one, eventually just using the Index one for both.
The hard part here is that IntegerArray, SparseArray, and SparseSeries have their own arithmetic implementations, and we test that they behave the same as Series. Several attempts to patch these implementations have failed (and motivated #27302, which this is rebased on top of), so for now this PR patches the tests instead of the implementations.
I think the IntegerArray implementation should be relatively easy to patch. SparseArray I'll need some help (cc @TomAugspurger) with `__setitem__` and/or `putmask`.
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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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} | 6 | 2019-07-10T14:52:06Z | 2019-07-17T22:18:06Z | 2019-07-17T22:16:31Z | MEMBER | null | Broken off from #27311 to troubleshoot build-specific failures. Also re-wrote test to parametrize and be more succinct | {
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] | closed | false | null | [] | null | 2 | 2019-07-10T16:06:20Z | 2020-01-21T04:29:47Z | 2020-01-21T04:29:47Z | NONE | null | #### Code Sample
```
df = pd.read_hdf(file_path, key="alarms")
df['acqTime'] = pd.to_datetime(df['acqTime'], unit='s')
df.set_index('acqTime', inplace= True)
df.sort_index(inplace=True)
df = df.resample("2S").fillna("backfill")
```
#### Problem description
I am trying to fill gaps in my time-series by resampling data every 2 seconds. Sometimes it may be the case the number of missing data points are even or odd. I want to even it out for 2 seconds.
But using the code above I get the following error:
```
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
BigQuery.py", line 94, in <module>
data = p.map(worker_partial, alarms[:2])
lib\multiprocessing\pool.py", line 268, in map
return self._map_async(func, iterable, mapstar, chunksize).get()
File lib\multiprocessing\pool.py", line 657, in get
raise self._value
ValueError: cannot reindex a non-unique index with a method or limit
```
#### Output Description
## Before Resampling:
```
[Timestamp('2018-10-16 05:13:38'),
Timestamp('2018-10-16 05:13:40'),
Timestamp('2018-10-16 05:13:42'),
Timestamp('2018-10-16 05:13:44'),
Timestamp('2018-10-16 05:13:47'),
Timestamp('2018-10-16 05:13:55),
]
```
After Resampling Expected Output:
```
[Timestamp('2018-10-16 05:13:38'),
Timestamp('2018-10-16 05:13:40'),
Timestamp('2018-10-16 05:13:42'),
Timestamp('2018-10-16 05:13:44'),
Timestamp('2018-10-16 05:13:46'),
Timestamp('2018-10-16 05:13:47'),
Timestamp('2018-10-16 05:13:49'),
Timestamp('2018-10-16 05:13:51'),
Timestamp('2018-10-16 05:13:53'),
Timestamp('2018-10-16 05:13:55'),
]
```
#### Output of ``pd.show_versions()``
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: 1.3.0
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: 3.5.2
numexpr: 2.6.9
feather: None
matplotlib: 3.1.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: 0.10.0
pandas_datareader: None
gcsfs: None
```
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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- [x] closes #27315
- [ ] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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] | closed | false | null | [] | null | 9 | 2019-07-10T20:37:21Z | 2019-07-11T14:29:04Z | 2019-07-11T14:29:04Z | CONTRIBUTOR | null | Happens with some data frames only. I cannot produce a minimal reproducible example, but can pickle the data frame in question if it helps.
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} | 6 | 2019-07-10T21:03:53Z | 2021-06-05T00:05:52Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```
>>> import pandas as pd
>>> mydf = pd.DataFrame.from_dict(dict(dt=[pd.Timestamp('2000-01-01'),pd.Timestamp('2000-01-01 00:00:00.000000001')]))
>>> print(mydf.to_csv(index=False))
dt
2000-01-01 00:00:00.000000000
2000-01-01 00:00:00.000000001
>>> print(mydf.to_csv(index=False,date_format='%Y-%m-%dT%H:%M:%S.%f'))
dt
2000-01-01T00:00:00.000000
2000-01-01T00:00:00.000000
```
#### Problem description
ISO-8601 specifies that the 'T' separator is optional in contexts where there is no ambiguity with other 8601 date or time formats. I'm trying to make some CSV files and am trying to be very strict with what I emit because I don't know how they'll be consumed.
It'd be *really* nice to be able to preserve full precision in the data but still have that separator character present and as far as I am aware, there is no Python Datetime format string which displays nanoseconds.
The shortest alternative I can come up with for this is the following (and I am not 100% sure this will work correctly):
```
tmp_df = mydf
dt_cols = mydf.select_dtypes(include='datetime64').columns
tmp_df[dt_cols] = tmp_df[dt_cols].applymap(lambda x:str(x.to_datetime64()))
tmp_df.to_csv(index=False)
```
#### Expected Output
```
dt
2000-01-01T00:00:00.000000000
2000-01-01T00:00:00.000000001
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-327.36.3.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 3.6.4
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: None
pyarrow: 0.13.0
xarray: None
IPython: 7.5.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.4
openpyxl: 2.5.14
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.2.19
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27329 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27329/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27329/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27329/events | https://github.com/pandas-dev/pandas/issues/27329 | 466,534,326 | MDU6SXNzdWU0NjY1MzQzMjY= | 27,329 | BUG?: namedtuples fields not checked on DataFrame constructor | {
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"description": "Series/DataFrame/Index/p... | open | false | null | [] | null | 2 | 2019-07-10T21:36:47Z | 2019-07-23T19:38:06Z | null | MEMBER | null | I personally find it very surprising that we assume that if the first element is a NamedTuple, that all elements of the list will be exactly the same NamedTuples:
```
In [29]: Record1 = namedtuple('Record1', ('b','a'))
In [30]: Record2 = namedtuple('Record2', ('a','c'))
In [31]: records = [Record1(1, 2), Record2(3, 4)]
In [32]: pd.DataFrame(records)
Out[32]:
b a
0 1 2
1 3 4
```
So the field names of the subsequent namedtuples are simply ignored.
The case of all the same tuples might be the most typical case, but it doesn't seem very safe for a default behaviour to not check this. | {
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} | 1 | 2019-07-11T00:06:58Z | 2019-07-11T20:25:31Z | 2019-07-11T20:15:04Z | MEMBER | null | Broken off from #27311 for cleaner scope and to troubleshoot platform-specific failures. Related but orthogonal to #27323. | {
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} | 3 | 2019-07-11T03:25:46Z | 2019-07-11T16:30:47Z | 2019-07-11T16:28:02Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/27333 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27333/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27333/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27333/events | https://github.com/pandas-dev/pandas/issues/27333 | 466,644,477 | MDU6SXNzdWU0NjY2NDQ0Nzc= | 27,333 | Automatically remove invalid characters from Bigquery identifiers in df.to_gbq() | {
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} | [] | closed | false | null | [] | null | 1 | 2019-07-11T03:41:01Z | 2019-07-11T03:58:00Z | 2019-07-11T03:58:00Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# BQ supports limited characters in names
import pandas as pd
import os
from google.oauth2 import service_account
credentialPath = os.path.join('build','keys','build.json')
df = pd.DataFrame({'good_name':[1,3,5,7,4,5,6,4,7,8,9], '*bad*name*':[3,5,6,2,4,6,7,8,7,8,9]})
df.to_gbq(destination_table='dataset_name.table_name',credentials=credentials)
```
#### Problem description
Bigquery responds with an error
```
pandas_gbq.gbq.GenericGBQException: Reason: 400 POST https://www.googleapis.com/bigquery/v2/projects/poco-analytics-experimental/datasets/dataset_name/tables: Invalid field name "*bad*name*". Fields must contain only letters, numbers, and underscores, start with a letter or underscore, and be at most 128 characters long.
```
#### Expected Output
It would be nice if pandas either removed all of the invalid characters or replaced them with underscores (which are the only permitted punctuation character) so that identifiers match https://cloud.google.com/bigquery/docs/reference/standard-sql/lexical#identifiers
ie.
```
import re
df.columns = [re.sub(r'[^A-Za-z_0-9]', '_', cn) for cn in df.columns]
table=re.sub(r'[^A-Za-z_0-9]', '_', '{}.{}'.format(dataset, tableName))
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Darwin
OS-release: 18.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.2
pytest: 5.0.1
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: 0.10.0
pandas_datareader: None
gcsfs: None
</details>
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This is a new attempt to solve issue #16918 after an attempt with PR #27073 | {
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```plain
hypothesis\_settings.py:193: HypothesisDeprecationWarning: The timeout setting can
safely be removed with no effect.
```
Starting with 4.0.0 released in Jan, the timeout setting does [nothing](https://github.com/HypothesisWorks/hypothesis/commit/2b9506b7120c2f0593539c1e5d9b526d7ec1377f).
https://hypothesis.readthedocs.io/en/latest/settings.html#hypothesis.settings.timeout
```plain
The timeout setting has been deprecated and no longer does anything.
default value: not_set
The timeout setting can safely be removed with no effect.
```
Bump dependency from `>=3.8.2` and remove setting.
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] | closed | false | null | [] | null | 11 | 2019-07-11T10:31:13Z | 2019-10-11T22:36:19Z | 2019-10-11T22:36:19Z | NONE | null | 'elif end and not offset.onOffset(end): end = offset.rollback(end)' gives
unwanted results for e.g. offset=MonthEnd when start.time() > end.time()
EXAMPLE:
In [1]: import pandas as pd
In [2]: from datetime import datetime
In [3]: pd.date_range(freq='M', start=datetime(2019, 4, 30, 0, 2), end=datetime(2019, 7, 1, 0, 1))
Out[3]: DatetimeIndex(['2019-04-30 00:02:00', '2019-05-31 00:02:00'], dtype='datetime64[ns]', freq='M')
In [4]: pd.date_range(freq='M', start=datetime(2019, 4, 30, 0, 1), end=datetime(2019, 7, 1, 0, 1))
Out[4]: DatetimeIndex(['2019-04-30 00:01:00', '2019-05-31 00:01:00', '2019-06-30 00:01:00'],
dtype='datetime64[ns]', freq='M')
line 3 output expected to include 2019-06-30 as only 1 minute difference between start dates in lines 3 and 4
- [ ] closes #xxxx
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 8 | 2019-07-11T14:35:26Z | 2021-01-03T17:10:41Z | 2021-01-03T17:10:41Z | MEMBER | null | ref https://github.com/pandas-dev/pandas/pull/27318#discussion_r302265250 it appears that CategoricalDtype might want to accept a Categorical as an argument but fails to handle that properly. Not sure intent here but we either want to fix to allow Categorical or alternately simplify and only allow `str/CategoricalDtype` input | {
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} | 1 | 2019-07-11T15:11:41Z | 2019-07-11T19:14:50Z | 2019-07-11T19:14:50Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame()
pd.to_parquet('empty.parquet')
```
#### Problem description
`DataFrame.to_parquet` raises `ValueError: parquet must have string column names` when the dataframe has no columns. `pyarrow` and `fastparquet` both support writing empty dataframes so I don't think this restriction is necessary (see below script).
```py
import fastparquet as fp
import pyarrow as pa
import pyarrow.parquet as pq
import pandas as pd
df = pd.DataFrame()
fn = 'empty.parquet'
# Test fastparquet
fp.write(fn, df)
# Test pyarrow
table = pa.Table.from_pandas(df)
pq.write_table(table, fn)
# This will fail
df.to_parquet(fn)
```
I'm happy to open a PR if it's agreed that this is an issue.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Darwin
OS-release: 18.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: None
pyarrow: 0.14.0
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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"color": "AD... | closed | false | null | [] | null | 2 | 2019-07-11T16:51:35Z | 2019-07-12T16:35:52Z | 2019-07-12T16:35:39Z | CONTRIBUTOR | null | - [x] closes #26642
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Adds a method ``select_str`` to supplement ``select_dtypes``. This PR is intended to be followed by a PR that deprecate ``NDFrame.filter``.
I miss the most having a string focused axis-filtering method, and is ``NDFrame.filter`` a bit unfocused and badly named IMO. I think usage of parameter ``items`` in ``NDFrame.filter`` can just be replaced by recommending ``NDFrame.__getitem__`` and parameter ``like`` is less useful than ``startswith`` and ``endswith`` (is less precise). So this is not a 100 % replacement for ``NDFrame.filter``, but intends to make something that is more useful (IMO, of course).
This is not fully tested and need a whatsnew etc. but I would like to get some response before I go further.
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} | 5 | 2019-07-11T17:08:09Z | 2019-07-11T20:07:45Z | 2019-07-11T19:14:50Z | CONTRIBUTOR | null | Fixes #27339
I wrote two tests because `fastparquet` adds a name to the index when it deserializes.
- [x] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 8 | 2019-07-11T18:11:33Z | 2021-07-10T18:04:03Z | null | CONTRIBUTOR | null | I think it might be nice to put the ci/code_checks.sh into its own repo / library where others could use these.
I think we have quite sophisticated doc string validations, potentially these could migrate to numpydoc.
cc @datapythonista | {
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"id": 211840... | open | false | null | [] | null | 5 | 2019-07-11T18:18:58Z | 2020-07-21T12:53:46Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame.from_records({'ref':['a','a','a','b','b'],
'time':[dt.datetime(2014,12,31), dt.datetime(2015,12,31), dt.datetime(2016,12,31),
dt.datetime(2012,12,31), dt.datetime(2014,12,31)],
'value':5*[1]})
# These frames differs
pd.DataFrame.equals(df.groupby("ref").resample(rule='M', on='time')['value'].sum(),
df.set_index('time').groupby("ref").resample(rule='M')['value'].sum())
# Similarly to .set_index() .apply produces the correct output
pd.DataFrame.equals(df.groupby("ref").apply(lambda f :f.resample(rule='M', on='time')['value'].sum()),
df.set_index('time').groupby("ref").resample(rule='M')['value'].sum())
#This is the incorrect frame
df.groupby("ref").resample(rule='5T', on='time')['value'].sum().to_frame()
```
#### Problem description
DataFrameGroupby.resample with the `on` keyword (last line) produces an incorrect output: it differs from the output produce with a DateTimeIndex and has incorrect values. It seems that it does not handle the group properly:
- the result has an entry for `('a', 2012-12-31 00:00:00):1` that is incorrect `ref =='a'` only starts in 2014 so not only should there be no entry but value of 1 is incorrect
- the result has entries for 'b' starting in 2015 when
- `ref == 'b'` has a min time entry in 2015 so there should not be any entry before that regardless of its value
#### Expected Output
`True` for `pd.DataFrame.equals(df.groupby("ref").resample(rule='M', on='time')['value'].sum(),
df.set_index('time').groupby("ref").resample(rule='M')['value'].sum())`
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Darwin
OS-release: 18.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
LOCALE: en_CA.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: None
pyarrow: None
xarray: None
IPython: 7.6.1
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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$ git log --format="%s" | head -n 10
CLN: revisit build warnings in cython templates
ENH: Raise ValueError for unsupported Window functions (#27275)
BUG: fix inserting tz-aware datetime to Series, closes #12862 (#27322)
CLN: requested follow-ups (#27332)
BUG: Consistent division by zero behavior for Index/Series (#27321)
TST: suppress rolling warnings correctly for raw= (#27330)
CLN: replace usage internally of .iteritems with .items (#26114)
BUG: appending a Timedelta to Series incorrectly casts to integer (#27303)
CLN: Split test_window.py (#27305)
check early for non-scalar default_fill_value (#27302)
```
```
$ git log --format="%s" | grep "^ " | wc -l
44
```
There are multiple recent instances. | {
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} | 4 | 2019-07-11T19:34:25Z | 2019-07-31T16:19:41Z | 2019-07-12T14:43:47Z | NONE | null | - [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Retrying dropped portions from #27157, which amend cython templates, to deal with special-cases based on type. In this Pr, compiler warnings about `x==x` being useless in the case of ints, but not floats (since `nan!=nan`).
In https://github.com/pandas-dev/pandas/pull/27157#discussion_r299103166 these were rejected because there wasn't agreement about how this should done and a separate discussion was requested. Annoyingly, these changes agree with what already exists in these files (in multiple places):
https://github.com/pandas-dev/pandas/blob/355e322b5ed4a31c9e1cab9652510e85a54f23a3/pandas/_libs/groupby_helper.pxi.in#L212-L216
But the file is already inconsistent in how these type checks are done:
https://github.com/pandas-dev/pandas/blob/355e322b5ed4a31c9e1cab9652510e85a54f23a3/pandas/_libs/groupby_helper.pxi.in#L391
Making a decision here also required to get rid of the warnings mentioned in https://github.com/pandas-dev/pandas/issues/27169, in that case fused types which mix signed/unsigned trigger warning when the code later includes comparisons with a signed quantity. So there too, type checks in the template are the obvious fix.
cc @WillAyd, @jreback
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} | 9 | 2019-07-11T20:48:41Z | 2019-07-15T21:06:19Z | 2019-07-15T20:13:03Z | MEMBER | null | I've got a bunch of branches going that touch this, easier to just get it right once. | {
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} | 6 | 2019-07-11T21:43:48Z | 2019-07-12T18:45:35Z | 2019-07-12T14:29:38Z | CONTRIBUTOR | null | closes #19228
xref #26807
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] passes `black pandas`
Just splitted code into files by classes. No changes made to code
cc @jreback | {
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} | 1 | 2019-07-11T21:54:49Z | 2019-07-12T14:32:33Z | 2019-07-12T14:26:55Z | CONTRIBUTOR | null | - [x] closes #27174
- [ ] tests added / passed
- [X] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Getting rid the `_take` private method that seems to be no longer needed.
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} | 16 | 2019-07-11T22:38:50Z | 2019-11-07T21:15:58Z | 2019-11-07T21:15:58Z | NONE | null | closes #5377
(This is a feature request stated in the form of a PR.)
This change makes it easy to count the number of times each unique row appears in a data frame. It also removes an unnecessary difference between DataFrame and Series (i.e. the existence of the `value_counts` methods).
```
>>> df = pd.DataFrame({'num_legs': [2, 4, 4], 'num_wings': [2, 0, 0]},
... index=['falcon', 'dog', 'cat'])
>>> df
num_legs num_wings
falcon 2 2
dog 4 0
cat 4 0
>>> df.value_counts()
(4, 0) 2
(2, 2) 1
dtype: int64
>>> df1col = df[['num_legs']]
>>> df1col
num_legs
falcon 2
dog 4
cat 4
>>> df1col.value_counts()
(4,) 2
(2,) 1
dtype: int64
```
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 15 | 2019-07-12T05:44:16Z | 2020-05-08T11:50:12Z | 2020-05-08T11:50:12Z | MEMBER | null | Here's a quick little script I wrote to check for things like isinstance(obj, MultiIndex)
```python
import ast
import pathlib
# Taken from pandas.core.dtypes.generic
BAD_NAMES = {'Index', 'Int64Index', 'UInt64Index', 'RangeIndex',
'Float64Index', 'MultiIndex', 'DatetimeIndex', 'TimedeltaIndex',
'PeriodIndex', 'CategoricalIndex', 'IntervalIndex', 'IndexClass',
'Series', 'DataFrame', 'SparseDataFrame', 'SparseSeries',
'SparseArray', 'Categorical', 'DatetimeArray', 'TimedeltaArray',
'PeriodArray', 'Period', 'DateOffset', 'Interval', 'ExtensionArray',
'PandasArray'}
def check_name_or_attr(filename, node):
if isinstance(node, ast.Name): # direct name
if node.id in BAD_NAMES:
print(f"{filename}:{node.lineno} isinstance check of {node.id}")
elif isinstance(node, ast.Attribute): # ex: pd.DataFrame
if node.attr in BAD_NAMES:
print(f"{filename}:{node.lineno} isinstance check of {node.attr}")
for path in pathlib.Path('.').glob('**/*.py'):
filename = path.resolve()
with open(path) as fh:
contents = fh.read()
tree = ast.parse(contents)
for node in ast.walk(tree):
try:
isinstance_check = node.value.func.id == 'isinstance'
except AttributeError:
continue
if isinstance_check:
types = node.value.args[1]
if isinstance(types, ast.Tuple):
for elt in types.elts:
check_name_or_attr(filename, elt)
else:
check_name_or_attr(filename, types)
```
Running that from project root yields all of the following errors:
```sh
/Users/williamayd/clones/pandas/pandas/core/frame.py:4681 isinstance check of MultiIndex
/Users/williamayd/clones/pandas/pandas/core/indexing.py:2499 isinstance check of MultiIndex
/Users/williamayd/clones/pandas/pandas/core/indexing.py:564 isinstance check of MultiIndex
/Users/williamayd/clones/pandas/pandas/core/reshape/tile.py:540 isinstance check of Series
/Users/williamayd/clones/pandas/pandas/core/reshape/concat.py:373 isinstance check of DataFrame
/Users/williamayd/clones/pandas/pandas/core/reshape/concat.py:377 isinstance check of Series
/Users/williamayd/clones/pandas/pandas/core/computation/align.py:72 isinstance check of Series
/Users/williamayd/clones/pandas/pandas/core/arrays/period.py:931 isinstance check of Period
/Users/williamayd/clones/pandas/pandas/core/arrays/period.py:932 isinstance check of Period
/Users/williamayd/clones/pandas/pandas/core/indexes/base.py:3626 isinstance check of MultiIndex
/Users/williamayd/clones/pandas/pandas/core/indexes/base.py:3627 isinstance check of MultiIndex
/Users/williamayd/clones/pandas/pandas/io/formats/excel.py:453 isinstance check of Index
/Users/williamayd/clones/pandas/pandas/io/formats/excel.py:501 isinstance check of Index
/Users/williamayd/clones/pandas/pandas/io/formats/excel.py:552 isinstance check of Index
/Users/williamayd/clones/pandas/pandas/io/formats/excel.py:592 isinstance check of Index
```
So I think these need to be swapped out for their ABC equivalents | {
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only KeyError in this PR as precursor to #25996 | {
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N/A
#### Problem description
I'd like to suggest that the documentation about formatting Excel sheets in the I/O tools page here: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html?highlight=xlsxwriter#style-and-formatting be improved by putting in a reference to the numerous examples provided by the xlsxwriter documentation here:
https://xlsxwriter.readthedocs.io/pandas_examples.html
I'm willing to do a PR, but I didn't know if there was some reason for not including the external reference on that particular page.
#### Expected Output
N/A
#### Output of ``pd.show_versions()``
pandas 0.24.2
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} | 4 | 2019-07-12T14:43:45Z | 2019-07-12T19:38:24Z | 2019-07-12T19:38:24Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
# each line of `data` is a list containing a single dict
data = pd.Series([[{"a": 1, "b": 2}], [{"a": 3, "b": 4}]])
crash = pd.DataFrame.from_records(data)
```
#### Problem description
The above code results in a crash of the jupyter kernel
#### Expected Output
`crash` should be a DataFrame
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-54-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
LOCALE: fr_FR.UTF-8
pandas: 0.24.2
pytest: 5.0.0
pip: 19.0.3
setuptools: 41.0.0
Cython: None
numpy: 1.16.4
scipy: None
pyarrow: None
xarray: None
IPython: 7.6.1
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.0
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.3.5
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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Adds a ``regex`` parameter to ``NDFrame.loc.__call__``, so we can now do:
```python
>>> df = pd.DataFrame(1, index=["A", "AB", "BC", "CD"], columns=["CD", "BC", "AB", "A"])
>>> df.loc(regex=True)["B", "B"]
BC AB
AB 1 1
BC 1 1
```
This is intended to be followed up by deprecating ``NDFrame.filter``.
This implementation does not allow setting values using ``regex=True``, as the most urgent motivation is to get a replacement for ``NDFrame.filter``. Setting values can come later, even after version 1.0.
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"color": "e11... | closed | false | null | [] | null | 2 | 2019-07-12T18:52:27Z | 2019-07-13T19:07:18Z | 2019-07-13T19:07:08Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
from pandas import Timestamp, DataFrame
x = dict(a={0: 0.45})
y = dict(b={0: Timestamp('2015-06-19 00:00:00')})
combined = DataFrame(x).combine_first(DataFrame(y))
print(combined)
print(combined.b.dtype)
assert combined.b.dtype.name == 'datetime64[ns]'
```
#### Problem description
In version `0.23.4`, the code above works. Starting with release `0.24.0rc1`, this code breaks.
The problem is that a when you call `x.combine_first(y)` and `y` has timestamp columns that are not present at all in `x`, the result will have those columns cast to `float64`. That might be fine, but the fact that behavior changed without warning in `0.24.0rc1` makes it seem like a bug, especially since `combine_first` has special case handling for timestamp-like columns.
#### Expected Output
```
a b
0 0.45 2015-06-19
datetime64[ns]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-24-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.4
pytest: 4.5.0
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.27.1
numpy: 1.16.2
scipy: 1.2.1
pyarrow: 0.14.0
xarray: 0.12.1
IPython: 7.4.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 1.0.1
sqlalchemy: 1.3.2
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.8.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27365 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27365/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27365/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27365/events | https://github.com/pandas-dev/pandas/issues/27365 | 467,560,211 | MDU6SXNzdWU0Njc1NjAyMTE= | 27,365 | ERR: missing key not displayed in exception message for IntervalIndex | {
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} | 3 | 2019-07-12T19:09:52Z | 2020-11-26T00:40:36Z | 2020-11-26T00:40:36Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
adapted from `test_non_matching` in `pandas\tests\indexing\interval\test_interval.py`
```python
>>> import numpy as np
>>> import pandas as pd
>>> pd.__version__
'0.25.0rc0+59.g0437f6899'
>>>
>>> s=pd.Series(np.arange(5), pd.IntervalIndex.from_breaks(np.arange(6)))
>>> s
(0, 1] 0
(1, 2] 1
(2, 3] 2
(3, 4] 3
(4, 5] 4
dtype: int32
>>>
>>> s.loc[[4,5]]
(3, 4] 3
(4, 5] 4
dtype: int32
>>>
>>> s.loc[[4,5,6]]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 1409, in __getitem__
return self._getitem_axis(maybe_callable, axis=axis)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 1816, in _getitem_axis
return self._getitem_iterable(key, axis=axis)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 1118, in _getitem_iterable
keyarr, indexer = self._get_listlike_indexer(key, axis, raise_missing=False)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 1060, in _get_listlike_indexer
indexer, keyarr = ax._convert_listlike_indexer(key, kind=self.name)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexes\base.py", line 3239, in _convert_listlike_indexer
indexer = self._convert_list_indexer(keyarr, kind=kind)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexes\interval.py", line 626, in _convert_list_indexer
raise KeyError
KeyError
>>>
>>> s.to_frame().loc[[4,5,6]]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 1409, in __getitem__
return self._getitem_axis(maybe_callable, axis=axis)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 1816, in _getitem_axis
return self._getitem_iterable(key, axis=axis)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 1118, in _getitem_iterable
keyarr, indexer = self._get_listlike_indexer(key, axis, raise_missing=False)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 1060, in _get_listlike_indexer
indexer, keyarr = ax._convert_listlike_indexer(key, kind=self.name)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexes\base.py", line 3239, in _convert_listlike_indexer
indexer = self._convert_list_indexer(keyarr, kind=kind)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexes\interval.py", line 626, in _convert_list_indexer
raise KeyError
KeyError
>>>
```
#### Problem description
With a list indexer, the missing key should be in the exception message
#### Expected Output
```python
KeyError: 6
```
another example - from `test_loc_with_overlap` in `pandas\tests\indexing\interval\test_interval_new.py`
```python
>>> idx = pd.IntervalIndex.from_tuples([(1, 5), (3, 7)])
>>> s = pd.Series(range(len(idx)), index=idx)
>>> s
(1, 5] 0
(3, 7] 1
dtype: int64
>>>
>>> s.loc[pd.Interval(3, 5)]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 1409, in __getitem__
return self._getitem_axis(maybe_callable, axis=axis)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 1827, in _getitem_axis
return self._get_label(key, axis=axis)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 152, in _get_label
return self.obj._xs(label, axis=axis)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\generic.py", line 3738, in xs
loc = self.index.get_loc(key)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexes\interval.py", line 875, in get_loc
raise KeyError(key)
KeyError: Interval(3, 5, closed='right')
>>>
>>> s.loc[[pd.Interval(3, 5)]]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 1409, in __getitem__
return self._getitem_axis(maybe_callable, axis=axis)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 1816, in _getitem_axis
return self._getitem_iterable(key, axis=axis)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 1118, in _getitem_iterable
keyarr, indexer = self._get_listlike_indexer(key, axis, raise_missing=False)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 1060, in _get_listlike_indexer
indexer, keyarr = ax._convert_listlike_indexer(key, kind=self.name)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexes\base.py", line 3239, in _convert_listlike_indexer
indexer = self._convert_list_indexer(keyarr, kind=kind)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexes\interval.py", line 626, in _convert_list_indexer
raise KeyError
KeyError
>>>
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | 1 | 2019-07-12T19:26:09Z | 2019-12-31T17:20:19Z | 2019-07-12T22:19:37Z | CONTRIBUTOR | null | - [x ] closes #27356
- [ ] tests added / passed
- Not needed. Just a doc change
- [ ] passes `black pandas`
- Not needed. Just a doc change
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- Not needed. Just a doc change
- [ ] whatsnew entry
- Not needed. Just a doc change | {
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} | 1 | 2019-07-12T20:58:12Z | 2019-07-17T16:15:25Z | 2019-07-17T11:48:31Z | MEMBER | null | - [x] closes #15552
- [x] closes #20441
- [x] closes #17173
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
1 timezone plotting bug, 1 indexing bug with tz data, 1 timezone data regression test | {
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} | 11 | 2019-07-12T21:32:24Z | 2019-10-12T17:45:10Z | 2019-10-12T17:44:51Z | CONTRIBUTOR | null | https://github.com/pandas-dev/pandas/issues/24760
https://github.com/pandas-dev/pandas/issues/23638
There are some special characters encoded with 'window-1252' in lists
created by 'locale -a'. The two know locales with this problem are
Norwegian 'bokmål', and 'français'.
- [x] closes #24760 Already closed but previous resolution was a work-around
- [x] closed #23638 Already closed without resolution
- [ ] tests added / passed
- [ ] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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... | open | false | null | [] | null | 0 | 2019-07-12T21:44:59Z | 2021-07-10T18:05:57Z | null | CONTRIBUTOR | null | ``df.groupby(..., observed=True)`` behaves inconsistently for unordered Categoricals:
```python
>>> df = pd.DataFrame({'A': pd.Categorical(['b', 'a']), 'B': [1,2]})
>>> df.groupby('A', observed=False).sum() # ok
B
A
a 2
b 1
>>> df.groupby('A', observed=True).sum() # not ok
B
A
b 1
a 2
```
Because the ``sort`` parameter in the groupby is implicitly True, the second result should be the same as if ``sort_index`` had been called:
```python
>>> df.groupby('A', observed=True).sum().sort_index() # ok result, but shouldn't be needed
B
A
a 2
b 1
```
My guess is that somewhere there is missing a ``if sort: obj.sort_index()`` block or similar.
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- Fixed the bad path in reset_index where the dtype was ignored if there are only
missing values.
- Simplified the structure of _maybe_casted_values in reset_index by
using as much as possible the take method of ExtensionArray's
- [ ] closes #19602
- [ ] closes #24206
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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commit 681e6a9b07271a0955e6780e476ab2d7101e549c
Author: Phil Ruffwind <rf@rufflewind.com>
BUG: Segfault due to float_precision='round_trip'
`round_trip` calls back into Python, so the GIL must be held. It also
fails to silence the Python exception, leading to spurious errors.
Closes #15140.
```
681e6a9b07271a0955e6 closed #15140 but introduced a cython warning (seen with 0.29.12)
```
warning: pandas/_libs/parsers.pyx:1724:34: Casting a GIL-requiring
function into a nogil function circumvents GIL validation
```
The commit says it fixes a segfault, while the cython warning suggests this is still unsafe.
The lines in question are https://github.com/pandas-dev/pandas/blob/8913b7e84ac75b1093bf708e0d1a9a0789e8cfcd/pandas/_libs/parsers.pyx#L1723-L1727
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- [ ] Cython 0.30 released
**Warning**: this requires Cython 0.30 (not yet released as of Jul 2019) (the CI currently fails to build)
The [next release](https://cython.readthedocs.io/en/latest/src/changes.html#features-added) of Cython 0.30.0 [introduced](https://github.com/cython/cython/issues/2579) conditional gil acqusition with support for fused types.
This makes a clean solution for #27372 possible, eliminating the cython warning
```
warning: pandas/_libs/parsers.pyx:1724:34: Casting a GIL-requiring
function into a nogil function circumvents GIL validation
```
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"description": ... | closed | false | null | [] | null | 1 | 2019-07-13T03:21:25Z | 2019-08-21T14:21:45Z | 2019-08-21T14:21:45Z | NONE | null | from "setup environment" stage:
```
conda env create -q --file=ci/deps/azure-macos-35.yaml
Warning: you have pip-installed dependencies in your environment file,
but you do not list pip itself as one of your conda dependencies.
Conda may not use the correct pip to install your packages,
and they may end up in the wrong place.
Please add an explicit pip dependency. I'm adding one for you, but still nagging you.
```
```
You are using pip version 10.0.1, however version 19.1.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
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} | 0 | 2019-07-13T03:48:41Z | 2019-07-15T14:50:06Z | 2019-07-15T12:25:28Z | MEMBER | null | - [x] closes #17192
- [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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} | 3 | 2019-07-13T04:56:28Z | 2019-07-15T21:06:49Z | 2019-07-15T20:19:28Z | MEMBER | null | The only noticeable thing should be a couple of fixed build warnings from khash_python | {
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- [x] tests added / passed
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Just so I'm clear, you're suggesting something like:
`test.groupby('groups').transform({'value1': [np.mean, max], 'value2': max})` which should return something like:
```
value1 value2
mean max max
2012-02-01 14:00:00 2 3 30
2012-02-01 14:01:00 2 3 30
2012-03-05 14:04:00 2 3 30
2012-03-05 14:01:00 5 6 60
2012-03-10 14:02:00 5 6 60
2012-03-11 14:07:50 5 6 60
```
_Originally posted by @brianhuey in https://github.com/pandas-dev/pandas/issues/17309#issuecomment-429669023_
Exactly this is my feature request. | {
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} | 7 | 2019-07-13T14:47:18Z | 2019-07-17T13:57:56Z | 2019-07-17T11:50:47Z | CONTRIBUTOR | null | Fixes: https://github.com/pandas-dev/pandas/issues/26934
Signed-off-by: Antonio Gutierrez <chibby0ne@gmail.com>
- [x] closes #xxxx
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- [ ] passes `black pandas`
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I looked at several different options for providing this functionality. In the end it seemed best to create a a new function that returns a validation function to only allow positive integers. So now we have is_pos_int available along side the existing is_int and is_text function. display.max_rows has been modified to use this new validation. I went through the other integer based display options and tested each with a negative int to see what else might be broken. display.precision and display.max_columns both had issues when set to a negative number prior to displaying a dataframe. They have been changed as well. | {
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} | 9 | 2019-07-14T02:50:05Z | 2019-07-24T00:58:19Z | 2019-07-24T00:44:15Z | MEMBER | null | Works toward fixing #27344
Adds types where unambiguous | {
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} | 1 | 2019-07-14T08:13:45Z | 2019-07-18T15:00:08Z | 2019-07-18T15:00:08Z | CONTRIBUTOR | null | `MultiIndex.shape` is currently extremely slow as it triggers the creation of `._values`, which can be quite expensive for datetime levels. The one mitigating factor is that this result is cached and thus making `._values.shape` near-instant on subsequent calls, but also hard to catch in `asv` benchmarks; this commit adds a suite dedicated to measuring such cached properties on `Index` objects.
`asv` results show a `~400,000x` speedup for a relatively straightforward case:
```
before after ratio
[269d3681] [d205acf6]
<master> <shape>
- 3.52±0.02s 8.33±0.2μs 0.00 index_cached_properties.MultiIndexCached.time_shape
```
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/27385 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27385/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27385/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27385/events | https://github.com/pandas-dev/pandas/issues/27385 | 467,825,299 | MDU6SXNzdWU0Njc4MjUyOTk= | 27,385 | AttributeError: module 'pandas' has no attribute 'tslib' | {
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#### Problem description
I just installed ggplot on my Windows x64 running Python 3.5 using pip. When I try to run:
from ggplot import *
from sklearn.metrics import roc_curve, auc
preds = log_mod.predict_proba(X_test)[:,1]
fpr, tpr, _ = roc_curve(y_test, preds)
df_dummies = pd.DataFrame(dict(fpr=fpr, tpr=tpr))
ggplot(df_dummies, aes(x='fpr', y='tpr')) +\
geom_line() +\
geom_abline(linetype='dashed')
I got the error: AttributeError: module 'pandas' has no attribute 'tslib'
Here is the traceback:
AttributeError Traceback (most recent call last)
<ipython-input-23-9083c102bdc2> in <module>
----> 1 from ggplot import *
2 from sklearn.metrics import roc_curve, auc
3
4
5 preds = log_mod.predict_proba(X_test)[:,1]
~\Anaconda3\lib\site-packages\ggplot\__init__.py in <module>
17
18
---> 19 from .geoms import geom_area, geom_blank, geom_boxplot, geom_line, geom_point, geom_jitter, geom_histogram, geom_density, geom_hline, geom_vline, geom_bar, geom_abline, geom_tile, geom_rect, geom_bin2d, geom_step, geom_text, geom_path, geom_ribbon, geom_now_its_art, geom_violin, geom_errorbar, geom_polygon
20 from .stats import stat_smooth, stat_density
21
~\Anaconda3\lib\site-packages\ggplot\geoms\__init__.py in <module>
----> 1 from .geom_abline import geom_abline
2 from .geom_area import geom_area
3 from .geom_bar import geom_bar
4 from .geom_bin2d import geom_bin2d
5 from .geom_blank import geom_blank
~\Anaconda3\lib\site-packages\ggplot\geoms\geom_abline.py in <module>
----> 1 from .geom import geom
2
3 class geom_abline(geom):
4 """
5 Line specified by slope and intercept
~\Anaconda3\lib\site-packages\ggplot\geoms\geom.py in <module>
1 from __future__ import (absolute_import, division, print_function,
2 unicode_literals)
----> 3 from ..ggplot import ggplot
4 from ..aes import aes
5
~\Anaconda3\lib\site-packages\ggplot\ggplot.py in <module>
11 import warnings
12
---> 13 from .aes import aes
14 from .legend import make_legend
15 from .themes import theme_gray
~\Anaconda3\lib\site-packages\ggplot\aes.py in <module>
9 from patsy.eval import EvalEnvironment
10
---> 11 from . import utils
12
13 import numpy as np
~\Anaconda3\lib\site-packages\ggplot\utils.py in <module>
79
80 date_types = (
---> 81 pd.tslib.Timestamp,
82 pd.DatetimeIndex,
83 pd.Period,
AttributeError: module 'pandas' has no attribute 'tslib'
I have tried to :
change
date_types = (
pd.tslib.Timestamp,
pd.DatetimeIndex,
pd.Period,
pd.PeriodIndex,
datetime.datetime,
datetime.time
)
to
date_types = (
pd.Timestamp,
pd.DatetimeIndex,
pd.Period,
pd.PeriodIndex,
datetime.datetime,
datetime.time
)
Then do the same in the ggplot/stats/smoothers.py file
AND
comment out
from pandas.lib import Timestamp
But It didn't work.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27386 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27386/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27386/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27386/events | https://github.com/pandas-dev/pandas/pull/27386 | 467,848,286 | MDExOlB1bGxSZXF1ZXN0Mjk3NDAyMzI5 | 27,386 | in groupby sorting index when length of groupings is one | {
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"description": "Categorical Dat... | closed | false | null | [] | null | 3 | 2019-07-14T14:48:05Z | 2019-09-13T01:33:03Z | 2019-09-13T01:33:03Z | NONE | null | #### Reference Issues/PRs
Example: Fixes #27369 "Bug: groupby(..., observed=True) doesn't respect sort keys"
#### What does this implement/fix? Explain your changes.
Sorts the index when the length of groupings is one (if `sefl.sorted` equals `True`)
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https://api.github.com/repos/pandas-dev/pandas/issues/27387 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27387/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27387/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27387/events | https://github.com/pandas-dev/pandas/issues/27387 | 467,849,160 | MDU6SXNzdWU0Njc4NDkxNjA= | 27,387 | Slicing Series takes from sparse DataFrame with missing values slow | {
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"color": "a1... | open | false | null | [] | null | 3 | 2019-07-14T14:56:14Z | 2020-09-21T21:27:10Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
import time
import tabulate
def timeIt(df, rangeExact, rangeApprox):
start = time.time()
df["A"].loc[rangeExact[0]]
end = time.time()
times = {"select exact label": end-start}
start = time.time()
df["A"].loc[rangeExact[0]:rangeExact[1]]
end = time.time()
times["slice exact label"] = end-start
# With approximate labels no select is possible -> KeyError
start = time.time()
df["A"].loc[rangeApprox[0]:rangeApprox[1]]
end = time.time()
times["slice approximate labels"] = end-start
return times
# Data
nn = 10**7
df = pd.DataFrame(np.random.rand(nn, 2), index=np.linspace(0.1, 99.1, nn), columns=("A", "B"))
offIndexSeries = pd.Series(np.random.rand(int(nn/4)), index=np.linspace(0.10002345, 99.10002345, int(nn/4)), name="C")
dfWithOffIndex = pd.concat([df, offIndexSeries], axis=1)
rangeExact = (df.iloc[4].name, df.iloc[58].name)
rangeApprox = [v + 0.00034 for v in rangeExact]
print("--- DATA ---")
print(dfWithOffIndex.head())
# Experiments
cases = {}
cases["dense dataframe no offIndex"] = timeIt(df, rangeExact, rangeApprox)
cases["dense dataframe with offIndex"] = timeIt(dfWithOffIndex, rangeExact, rangeApprox)
cases["sparse dataframe no offIndex"] = timeIt(df.to_sparse(), rangeExact, rangeApprox)
cases["sparse dataframe with offIndex"] = timeIt(dfWithOffIndex.to_sparse(), rangeExact, rangeApprox)
# Table
print("--- RESULTS ---")
headers = ["↓: case →: operation"] + [k for k in list(cases.values())[0]]
rows = [[caseName] + [v for k, v in case.items()] for caseName, case in cases.items()]
print(tabulate.tabulate(rows, headers=headers))
# Faster way
print("--- ALTERNATIVE ---")
start = time.time()
df["A"].to_dense().loc[rangeApprox[0]:rangeApprox[1]]
end = time.time()
print(f"when converting to dense first: {end-start}")
```
#### Problem description
When a DataFrame contains Series with different indices (missing values for certain cells - called case "withOffIndex" below) slicing becomes orders of magnitude slower, when the DataFrame is spase, compared to dense. See the test results below. Converting the Series into the dense representation before slicing seems to give consistently better to much better results (including the time required for the conversion). At least in the range between 10^2...10^7 rows (tested - results below for 10^7).
Since missing values make sparse dataframes shine this is somewhat painful.
```
--- DATA ---
A B C
0.100000 0.217638 0.034994 NaN
0.100010 0.584343 0.823189 NaN
0.100020 0.315356 0.926022 NaN
0.100023 NaN NaN 0.192552
0.100030 0.246858 0.310810 NaN
--- RESULTS ---
↓: case →: operation select exact label slice exact label slice approximate labels
------------------------------ -------------------- ------------------- --------------------------
dense dataframe no offIndex 0.00120592 0.000380039 0.000725031
dense dataframe with offIndex 0.0456889 0.000464201 0.000380278
sparse dataframe no offIndex 0.00103784 0.0166719 0.0147569
sparse dataframe with offIndex 0.000307083 7.17341 7.33061
```
#### Expected Output
```
--- ALTERNATIVE ---
when converting to dense first: 0.0004820823669433594
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Darwin
OS-release: 17.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27388 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27388/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27388/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27388/events | https://github.com/pandas-dev/pandas/issues/27388 | 467,857,609 | MDU6SXNzdWU0Njc4NTc2MDk= | 27,388 | PeriodDType should require freq argument | {
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} | 1 | 2019-07-14T16:13:39Z | 2021-07-10T18:07:07Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> pd.PeriodDtype()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/tesla/Projects/pandas/pandas/core/dtypes/dtypes.py", line 149, in __repr__
return str(self)
File "/home/tesla/Projects/pandas/pandas/core/dtypes/dtypes.py", line 880, in __str__
return self.name
File "/home/tesla/Projects/pandas/pandas/core/dtypes/dtypes.py", line 884, in name
return "period[{freq}]".format(freq=self.freq.freqstr)
AttributeError: 'NoneType' object has no attribute 'freqstr'
```
#### Problem description
Currently if you instantiate a `PeriodDtype` it is not required to set the `freq` since the `__new__` method passes a `freq=None`, which eventually leads to the error shown in the first code snippet.
Having a default `None` frequency doesn't really make sense. In fact there shouldn't be any default value of frequency. The *freq* should be a required argument.
#### Expected Output
```python
>>> import pandas as pd
>>> pd.Period
pd.Period( pd.PeriodDtype( pd.PeriodIndex(
>>> pd.PeriodDtype()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: __new__() missing 1 required positional argument: 'freq'
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 269d36814e471880627109a5e5ea63d4a5fdc5a6
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.2.0-arch2-1-ARCH
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0rc0+62.g269d36814
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.11
pytest : 5.0.0
hypothesis : 4.23.6
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.6.1
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : 0.3.0
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.11.1
pytables : None
s3fs : 0.2.1
scipy : 1.2.1
sqlalchemy : 1.3.5
tables : 3.5.2
xarray : 0.12.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27389 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27389/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27389/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27389/events | https://github.com/pandas-dev/pandas/issues/27389 | 467,881,480 | MDU6SXNzdWU0Njc4ODE0ODA= | 27,389 | Discuss: transformation vs. aggregation in `agg` vs. `transform` | {
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{... | closed | false | null | [] | null | 6 | 2019-07-14T20:31:04Z | 2021-07-10T18:11:15Z | 2021-07-10T18:11:15Z | NONE | null | There have been multiple issues regarding
- Confusion about relative respective roles of `agg` and `transform` (and `apply`).
https://github.com/pandas-dev/pandas/issues/26960, https://github.com/pandas-dev/pandas/issues/18103 in both the DataFrame and Grou
- `transform('rank')` and others returning the wrong answer (first issue from 2016): https://github.com/pandas-dev/pandas/issues/14274, https://github.com/pandas-dev/pandas/issues/19354, https://github.com/pandas-dev/pandas/issues/22509,
I'd like to prepare a PR to fix this, but I need to know what the consensus is first.
## 1. should `Groupby/DataFrame/Series.agg` disallow transformations?
https://github.com/pandas-dev/pandas/issues/14741#issuecomment-262953242 about `Groupby.agg`
```
you need to use .transform. .agg is by definition a reducer.
```
`agg` currently accepts transformations as well.
`DataFrame.agg` was merged in #27389 despite repeated objections to mixing transformations and aggregations, https://github.com/pandas-dev/pandas/pull/14668#issuecomment-260893757 and https://github.com/pandas-dev/pandas/pull/14668#issuecomment-269468649.
## 2. What should `transform('rank')` return?
**updated**
```
g.min # one value per group. ok.
g.transform('min') # one value per group, broadcasted. ok.
g.rank() # like-index result. Ok
g.transform('rank') # Currently, a nonsense result.
```
On the one hand, users have been told that transformations don't belong in `transform` (!): https://github.com/pandas-dev/pandas/issues/22509#issuecomment-501937245, https://github.com/pandas-dev/pandas/issues/14274#issuecomment-248760874. This makes sense if you think of the `transform('name')` form as solely for broadcasting aggregations.
On the other hand, the documentation for `transform`, as well as Wes Mckinney's excellent pandas book portray `transform` as the dedicated tool for shape-preserving operations, so excluding them from the `transform('name')` case would be a little surprising.
Personally, I'm +0 for deprecating `transform('rank')` and with a warning to use `g.rank()`, as well as for the other transformation ops. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27390 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27390/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27390/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27390/events | https://github.com/pandas-dev/pandas/issues/27390 | 467,890,223 | MDU6SXNzdWU0Njc4OTAyMjM= | 27,390 | Period - Frequency conversion extremely slow | {
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] | closed | false | null | [] | null | 4 | 2019-07-14T22:10:52Z | 2019-07-14T22:45:32Z | 2019-07-14T22:45:26Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
# Create series with 372,363 daily periods
series_day = pd.Series(pd.period_range(start='1000-01-01', end='2019-06-30', freq='D'))
# Convert daily periods to monthly periods
# HERE IS THE PROBLEM - CONVERSION IS UNUSUALLY SLOW
series_month = series_day.apply(lambda d: d.asfreq('M'))
```
#### Problem description
Conversion between Periods is unusually slow operation.
(I mean conversion between Period frequencies like: daily period -> monthly period)
It can easily take longs hours to convert 100M periods (from daily -> monthly frequency) on modern i7 CPU.
Compared to other common date/time operations on same dataset, which can finish in seconds / or max a few minutes, conversion period frequencies takes extremely long time.
**Result of :**
```
%%timeit -r 3
series_month = series_day.apply(lambda d: d.asfreq('M'))
```
was:
`1min 51s ± 3.74 s per loop (mean ± std. dev. of 3 runs, 1 loop each)`
When ~350k conversions takes 2 mins, then 100M conversions takes above 7 hours.
**So let's compare:**
- conversion from datetime[ns] -> daily/monthly Period - runs very quickly
- conversion from Period -> Period - runs extremely slow
There really has to be some unusually slow or inefficient code in the period conversion,
which slows down the conversion process.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 79 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.1
pytest: None
pip: 19.0.1
setuptools: 40.8.0
Cython: None
numpy: 1.15.4
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: None
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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"de... | closed | false | null | [] | null | 1 | 2019-07-15T02:56:16Z | 2019-08-02T14:14:35Z | 2019-07-18T00:06:01Z | MEMBER | null | - [x] tests passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Includes simplifying some code paths | {
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} | 4 | 2019-07-15T03:41:51Z | 2019-07-31T12:31:18Z | 2019-07-31T12:31:00Z | CONTRIBUTOR | null | - [x] closes #27283
- [x] tests added/passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
As mentioned in #27335, this PR serves as an infrastructure for future changes in the default int type
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} | 13 | 2019-07-15T08:06:33Z | 2020-02-03T15:52:27Z | 2020-02-03T15:52:27Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```test.csv
field,data,values
text1,34,hh,fail
text2,76,tt,fail2
```
```python
import pandas as pd
if __name__ == "__main__":
data_list = pd.read_csv('test.csv', prefix="X")
print(data_list)
```
#### Problem description
A documentation said:
```
prefix : str, optional
Prefix to add to column numbers when no header, e.g. ‘X’ for X0, X1, …
```
If I understood correctly this documentation I should get this
#### Expected Output
```
field data values X0
text1 34 hh fail
text2 76 tt fail2
```
But I got this anyway:
#### Current Output
```
field data values
text1 34 hh fail
text2 76 tt fail2
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Windows
OS-release: 8.1
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 40.1.0
Cython: None
numpy: 1.15.0
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.3
openpyxl: 2.5.5
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.1.1
bs4: 4.6.0
html5lib: None
sqlalchemy: 1.2.19
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
None
</details>
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} | 3 | 2019-07-15T09:49:35Z | 2019-09-13T13:19:38Z | 2019-09-13T13:19:38Z | NONE | null | ```python
# This code is failing
df = pd.DataFrame({'timestamp': [np.datetime64('2017-01-01 01:00:00'), np.datetime64('2017-01-01 02:00:00')],
'value': ['a', 'b']})
df.loc[:, 'day'] = df.loc[:, 'timestamp'].values.astype('datetime64[D]')
```
#### Error
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-91-7e5c52b6ed28> in <module>()
----> 1 df.loc[:, 'day'] = df.loc[:, 'timestamp'].values.astype('datetime64[D]')
/home/homola/env/venv/local/lib/python2.7/site-packages/pandas/core/indexing.pyc in __setitem__(self, key, value)
188 key = com.apply_if_callable(key, self.obj)
189 indexer = self._get_setitem_indexer(key)
--> 190 self._setitem_with_indexer(indexer, value)
191
192 def _validate_key(self, key, axis):
/home/homola/env/venv/local/lib/python2.7/site-packages/pandas/core/indexing.pyc in _setitem_with_indexer(self, indexer, value)
360
361 # add a new item with the dtype setup
--> 362 self.obj[key] = _infer_fill_value(value)
363
364 new_indexer = convert_from_missing_indexer_tuple(
/home/homola/env/venv/local/lib/python2.7/site-packages/pandas/core/frame.pyc in __setitem__(self, key, value)
3368 else:
3369 # set column
-> 3370 self._set_item(key, value)
3371
3372 def _setitem_slice(self, key, value):
/home/homola/env/venv/local/lib/python2.7/site-packages/pandas/core/frame.pyc in _set_item(self, key, value)
3443
3444 self._ensure_valid_index(value)
-> 3445 value = self._sanitize_column(key, value)
3446 NDFrame._set_item(self, key, value)
3447
/home/homola/env/venv/local/lib/python2.7/site-packages/pandas/core/frame.pyc in _sanitize_column(self, key, value, broadcast)
3652 # upcast
3653 value = cast_scalar_to_array(len(self.index), value)
-> 3654 value = maybe_cast_to_datetime(value, infer_dtype)
3655
3656 # return internal types directly
/home/homola/env/venv/local/lib/python2.7/site-packages/pandas/core/dtypes/cast.pyc in maybe_cast_to_datetime(value, dtype, errors)
979 else:
980 raise TypeError("cannot convert datetimelike to "
--> 981 "dtype [{dtype}]".format(dtype=dtype))
982 elif is_datetime64tz:
983
TypeError: cannot convert datetimelike to dtype [datetime64[D]]
```
```python
# This code is ok
df = pd.DataFrame({'timestamp': [np.datetime64('2017-01-01 01:00:00'), np.datetime64('2017-01-01 02:00:00')],
'value': ['a', 'b']})
df['day'] = df.loc[:, 'timestamp'].values.astype('datetime64[D]')
```
#### Problem description
When assigning to a column and using pandas.DataFrame.loc, it fails due to 'TypeError: cannot convert datetimelike to dtype [datetime64[D]]'. When using simple assignment, it doesn't fail.
If it is a bad practice what is done above in the first example, the error message should be different.
#### Expected Outcome
Assignment is successful for both below.
```python
df.loc[:, 'day'] = df.loc[:, 'timestamp'].values.astype('datetime64[D]')
df['day'] = df.loc[:, 'timestamp'].values.astype('datetime64[D]')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-51-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.utf8
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.24.2
numpy: 1.16.4
...
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27396 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27396/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27396/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27396/events | https://github.com/pandas-dev/pandas/issues/27396 | 468,064,525 | MDU6SXNzdWU0NjgwNjQ1MjU= | 27,396 | TYPING: Unreachable code | {
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"description": "type annotat... | open | false | null | [] | null | 8 | 2019-07-15T11:00:29Z | 2021-07-10T18:12:34Z | null | MEMBER | null | mypy 0.720 introduced `--warn-unreachable` http://mypy-lang.blogspot.com/2019/07/mypy-0720-released.html
```
$ mypy pandas --warn-unreachable
pandas\core\computation\expressions.py:68: error: Statement is unreachable
pandas\core\computation\expressions.py:123: error: Statement is unreachable
pandas\core\dtypes\dtypes.py:98: error: Statement is unreachable
pandas\core\dtypes\dtypes.py:102: error: Statement is unreachable
pandas\core\dtypes\dtypes.py:316: error: Statement is unreachable
pandas\core\dtypes\dtypes.py:541: error: Right operand of 'and' is never evaluated
pandas\core\dtypes\dtypes.py:543: error: Statement is unreachable
pandas\core\indexers.py:105: error: Statement is unreachable
pandas\core\indexers.py:233: error: Statement is unreachable
pandas\core\arrays\array_.py:230: error: Statement is unreachable
pandas\core\arrays\_ranges.py:40: error: Statement is unreachable
pandas\core\arrays\period.py:243: error: Statement is unreachable
pandas\core\indexes\interval.py:1212: error: Statement is unreachable
pandas\core\computation\pytables.py:93: error: Left operand of 'and' is always true
pandas\core\computation\pytables.py:103: error: Left operand of 'and' is always true
pandas\core\indexing.py:185: error: Left operand of 'or' is always false
pandas\core\indexing.py:246: error: Statement is unreachable
pandas\core\internals\managers.py:1491: error: Statement is unreachable
pandas\core\internals\managers.py:1505: error: Statement is unreachable
pandas\core\internals\managers.py:1517: error: Statement is unreachable
pandas\io\json\_normalize.py:255: error: Statement is unreachable
pandas\io\json\_normalize.py:284: error: Statement is unreachable
(pandas-dev)
```
should we include this in `ci\code_checks.sh`? | {
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] | closed | false | null | [] | null | 1 | 2019-07-15T14:04:10Z | 2021-07-10T18:18:58Z | 2021-07-10T18:18:58Z | NONE | null | This is a suggestion to include a `how` option to [`first_valid_index`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.first_valid_index.html#pandas-dataframe-first-valid-index) ([similar to `dropna`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.dropna.html#pandas-dataframe-dropna)), such that the first index having at least one non-`nan` value (`how="any"`, current behaviour), or no `nan` value (`how="all", new behavious), is returned.
#### Code Sample, a copy-pastable example if possible
Currently, this can be accomplished as follows:
```python
df.dropna().first_valid_index() # would become df.first_valid_index(how="all")
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-54-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: pt_BR.UTF-8
LOCALE: pt_BR.UTF-8
pandas: 0.22.0
pytest: 3.3.2
pip: 9.0.1
setuptools: 39.0.1
Cython: None
numpy: 1.13.3
scipy: 0.19.1
pyarrow: None
xarray: None
IPython: 5.5.0
sphinx: 1.6.7
patsy: 0.4.1+dev
dateutil: 2.6.1
pytz: 2018.3
blosc: 1.4.4
bottleneck: None
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 2.1.1
openpyxl: 2.4.9
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml: 4.2.1
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | 1 | 2019-07-15T14:08:10Z | 2019-12-11T17:09:38Z | 2019-12-11T17:09:38Z | CONTRIBUTOR | null | ```python
In [4]: np.array([1]) / np.array([0])
/Users/taugspurger/.virtualenvs/pandas-dev/bin/ipython:1: RuntimeWarning: divide by zero encountered in true_divide
#!/Users/taugspurger/Envs/pandas-dev/bin/python
Out[4]: array([inf])
In [7]: pd.array([1], dtype='Int64') / 0
Out[7]: array([nan])
``` | {
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} | 14 | 2019-07-15T15:42:30Z | 2019-10-03T19:09:06Z | 2019-10-03T19:09:06Z | CONTRIBUTOR | null | - [x] closes #11704
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Hi, I have yet to implements tests, use flake8 and create a whatsnew entry as I am uncertain whether this implementation would be correct.
I would really appreciate it if someone could give me some feedback on the code.
This PR will implement iterators for window functions.
Some questions I have:
* Does DataFrame.rolling(x) yield DataFrames?
* Does Series.rolling(x) yield Series? | {
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"description": "R... | closed | false | null | [] | null | 12 | 2019-07-15T16:13:55Z | 2019-07-21T09:52:54Z | 2019-07-17T16:24:46Z | NONE | null | Could pandas include as a submodule "SSPipe" Package?
https://pypi.org/project/sspipe/
https://sspipe.github.io/
DataFrame object already has a .pipe() method, but the objective is to make the "pipe" (Equivalent to R's magrittr %>% functional pipe) a standard that can be extrapolated to all Python objects and casuistics. | {
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} | 1 | 2019-07-15T16:46:40Z | 2020-01-16T00:34:59Z | 2019-07-15T18:17:38Z | MEMBER | null | xref #27396 @simonjayhawkins some of these were just easy cleanups.
Didn't look through all but some of the others are more complicated and probably require in depth review | {
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} | 7 | 2019-07-15T17:07:27Z | 2019-07-31T14:00:45Z | 2019-07-31T12:49:01Z | CONTRIBUTOR | null | - [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
`roll_window` function is split into two to enable implementing other weighted functions e.g. var, std (ref #26597)
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} | 7 | 2019-07-15T17:41:08Z | 2020-09-21T22:13:51Z | 2020-09-21T22:13:50Z | MEMBER | null | As a follow up to #27393 I think I made a mistake in just typing some of the return values to be `Callable`. The following script highlights that issue:
```python
from pandas.util._decorators import Appender
def func1(num: int) -> int:
return num
@Appender("")
def func2(num: int) -> int:
return num
reveal_type(func1)
reveal_type(func2)
```
If you run that from the project root you'll get something like the following:
```sh
$ mypy foo.py
foo.py:10: note: Revealed type is 'def (num: builtins.int) -> builtins.int'
foo.py:11: note: Revealed type is 'def (*Any, **Any) -> Any'
```
We probably want to maintain the types of the signature for `func2` so I think need to use TypeVar for generic support.
More info on the mypy side in:
https://github.com/python/mypy/pull/3113
https://github.com/python/mypy/issues/1551
So I think need something like:
```python
T = TypeVar('T', bound=Callable[..., Any])
# then decorators can return
-> Callable[[T], T]
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] | closed | false | null | [] | null | 4 | 2019-07-15T19:56:31Z | 2019-07-16T09:57:44Z | 2019-07-16T09:57:44Z | CONTRIBUTOR | null | This is not a problem but an observation and possibly something for a deprecation (as discussed with @datapythonista at PyDataLondon this weekend).
Pandas supports a `.query` syntax including the `.query("my_col < @some_var")` syntax to bring in a local `some_var`. This predates Python's f-string syntax which was introduced in Python 3.6.
I wonder if Python's f-string syntax is a more standard way to tackle variable substitution going forwards? The equivalent would be `.query(f"my_col < {some_var}")`.
The benefit would be to reduce the amount of code that's custom to Pandas and to stick to a more-standard (IMHO) approach to variable substitution.
#### Code Sample, a copy-pastable example if possible
```python
In [4]: df = pd.DataFrame({'col': [1, 2, 3, 4]})
In [5]: value = 3
In [6]: df.query("col < @value")
Out[6]:
col
0 1
1 2
In [7]: df.query(f"col < {value}")
Out[7]:
col
0 1
1 2
```
#### Output of ``pd.show_versions()``
<details>
In [10]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Linux
OS-release: 5.0.0-19-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.23.4
...
</details>
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} | 1 | 2019-07-15T21:22:32Z | 2019-07-16T19:19:30Z | 2019-07-16T19:19:30Z | CONTRIBUTOR | null | https://travis-ci.org/pandas-dev/pandas/jobs/559114855
quick look suggests if we do more limiting of pytest <5.0.0 things will work (not sure what exactly is wrong with the newer pytest). | {
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] | open | false | null | [] | null | 2 | 2019-07-15T22:29:25Z | 2021-07-10T18:19:27Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({'x': [1, 2, 3, 4], 'y': [10, 20, 30, 40]},
index=pd.MultiIndex.from_product([['a', 'b'], ['c', 'd']],
names=['ab', 'cd']))
df
Out[2]:
x y
ab cd
a c 1 10
d 2 20
b c 3 30
d 4 40
In [3]: for it in df.itertuples():
print(it)
Pandas(Index=('a', 'c'), x=1, y=10)
Pandas(Index=('a', 'd'), x=2, y=20)
Pandas(Index=('b', 'c'), x=3, y=30)
Pandas(Index=('b', 'd'), x=4, y=40)
```
#### Problem description
When iterating through a `DataFrame`, the names of the `Index` are lost.
It would be really convenient if when a `MultiIndex` is used, the names of the `MultiIndex` were included in the result of `itertuples()`.
Propose to add named argument to `itertuples()` called `nameIndex` with default value `False` to retain current behavior, and `nameIndex=True` causing output as shown below.
#### Expected Output
```
Pandas(Index=Index(ab='a', cd='c'), x=1, y=10)
Pandas(Index=Index(ab='a', cd='d'), x=2, y=20)
Pandas(Index=Index(ab='b', cd='c'), x=3, y=30)
Pandas(Index=Index(ab='b', cd='d'), x=4, y=40)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b57d523b3006445170c865a6f5c8a5cde43e64f8
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.0rc0+62.gb57d523b3
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.11
pytest : 5.0.0
hypothesis : 4.23.6
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.6.1
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : 0.3.0
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.11.1
pytables : None
s3fs : 0.2.1
scipy : 1.2.1
sqlalchemy : 1.3.5
tables : 3.5.2
xarray : 0.12.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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} | 2 | 2019-07-15T22:48:19Z | 2019-07-25T22:36:22Z | 2019-07-25T22:10:35Z | MEMBER | null | {
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there's Also already `Window` and `Rolling` classes, so `roll_window` doesn't make clear where it fits in.
I'd also like to rename `Window` to `WeightedWindow`. But that class is returned to the user from `df.rolling` so not sure if that's considered a breaking change. | {
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} | 2 | 2019-07-16T01:37:56Z | 2019-07-22T21:31:32Z | 2019-07-22T21:13:57Z | MEMBER | null | Working towards minimizing the amount of special-casing we do within internals | {
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} | 2 | 2019-07-16T04:00:59Z | 2019-07-16T20:17:40Z | 2019-07-16T19:42:50Z | NONE | null | Fixes
```
pandas/_libs/tslibs/tzconversion.c:3157:21: warning: ‘__pyx_pybuffernd_dst_hours.diminfo[0].strides’ may be used uninitialized in this function [-Wmaybe-uninitialized]
3157 | __Pyx_LocalBuf_ND __pyx_pybuffernd_dst_hours;
| ^~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/tslibs/tzconversion.c:3149:21: warning: ‘__pyx_pybuffernd_ambiguous_array.diminfo[0].strides’ may be used uninitialized in this function [-Wmaybe-uninitialized]
3149 | __Pyx_LocalBuf_ND __pyx_pybuffernd_ambiguous_array;
``` | {
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