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https://api.github.com/repos/pandas-dev/pandas/issues/26613 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26613/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26613/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26613/events | https://github.com/pandas-dev/pandas/issues/26613 | 451,137,239 | MDU6SXNzdWU0NTExMzcyMzk= | 26,613 | to_csv does not fail with specified columns not in dataframe | {
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} | [] | closed | false | null | [] | null | 3 | 2019-06-02T00:09:24Z | 2020-02-18T03:44:15Z | 2019-06-02T02:52:19Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
In [4]: data = pd.DataFrame({'a': [1, 2, 3]})
In [5]: data.to_csv('test.csv', columns=['a', 'x'])
/Users/amcphers/Projects/scgenome/venv/lib/python3.7/site-packages/pandas/core/indexing.py:1494: FutureWarning:
Passing list-likes to .loc or [] with any missing label will raise
KeyError in the future, you can use .reindex() as an alternative.
See the documentation here:
https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike
return self._getitem_tuple(key)
...
$ head test.csv
,a,x
0,1,
1,2,
2,3,
```
#### Problem description
The documentation specified that the columns kwarg is "Columns to write." If there is no matching column to write this would seem to warent an error of some kind. Instead an unrelated warning is printed.
#### Expected Output
An exception should be thrown.
#### Output of ``pd.show_versions()``
<details>
In [6]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Darwin
OS-release: 16.7.0
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
setuptools: 41.0.1
Cython: 0.29
numpy: 1.16.2
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 5.8.0
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.3
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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cc @pandas-dev/pandas-core | {
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} | 6 | 2019-06-02T02:28:38Z | 2021-07-28T22:55:13Z | 2021-07-28T22:55:13Z | CONTRIBUTOR | null | Test code:
```python
SIZE_MULT = 5
data = np.random.randint(0, 255, size=10**SIZE_MULT, dtype='uint8')
index = pd.MultiIndex.from_product(
[list(range(10**(SIZE_MULT-1))), list('ABCDEFGHIJ')],
names = ['d', 'l'])
test = pd.DataFrame(data, index, columns = ['data'])
test.head()
test['data'].dtype
```
```python
data
d l
0 A 137
B 156
C 48
D 186
E 170
dtype('uint8')
```
And suppose we want group by 0-level of index and shift each group (shift step = 2, for example).
```python
%%time
shifted = test.groupby(axis=0, level=[0]).shift(2)
print(shifted['data'].dtype)
```
```
float64
CPU times: user 9.43 ms, sys: 56 Β΅s, total: 9.49 ms
Wall time: 8.29 ms
```
Now to the problem: if we want to preserve our dtype 'uint8', we have to get rid of `None`s, and set our fill value with 0, for example. But we will get HUGE time of code execution now:
```python
%%time
shifted = test.groupby(axis=0, level=[0]).shift(2, fill_value = 0)
shifted.head()
print(shifted['data'].dtype)
```
```
uint8
CPU times: user 5.9 s, sys: 38.4 ms, total: 5.94 s
Wall time: 5.89 s
```
Expected time of execution should be comparable with following:
If we take 1st shifted dataframe without `fill_value`, and add few code lines to achieve same result:
```python
%%time
shifted = test.groupby(axis=0, level=[0]).shift(2)
shifted.fillna(0, inplace=True)
shifted = shifted.astype(np.uint8)
print(shifted['data'].dtype)
```
**Output:**
```
uint8
CPU times: user 9.64 ms, sys: 3.68 ms, total: 13.3 ms
Wall time: 11.3 ms
```
It will add only few ms, not 5 seconds.
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 5.0.0-16-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.5.0
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.7
numpy: 1.16.3
scipy: 1.2.1
pyarrow: None
xarray: 0.12.1
IPython: 7.2.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
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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} | 4 | 2019-06-02T04:31:14Z | 2019-06-05T19:13:12Z | 2019-06-02T17:03:47Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import pyarrow as pa
df = pd.DataFrame({'x': pd.Categorical(['a', 'a', 'b', 'b'])})
df.dtypes # category
# This works:
pa.Table.from_pandas(df).to_pandas().dtypes # category
df.to_parquet("categories.parquet")
# This reads back object, but I expected category
pd.read_parquet("categories.parquet").dtypes # object
# Numeric categories have the same issue:
df_num = pd.DataFrame({'x': pd.Categorical([1, 1, 2, 2])})
df_num.dtypes # category
pa.Table.from_pandas(df_num).to_pandas().dtypes # category
df_num.to_parquet("categories_num.parquet")
# This reads back int64, but I expected category
pd.read_parquet("categories_num.parquet").dtypes # int64
```
#### Problem description
Writing a string categorical variable to parquet is read back as string (`object` dtype). I expected it to be read as `category`.
The same thing happens if the category is numeric -- a numeric category is read back as int64.
In the code above, I tried out an in-memory arrow table, which successfully translates categories back to pandas. Please let me know if you think this is an issue with the arrow project rather than pandas.
In the scheme of things, this isn't a big deal, but it's a small surprise.
#### Expected Output
I expected the data read by `pd.read_parquet` to be exactly the same as the data written by `df.to_parquet`. The values are the same, but the dtypes are not.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 5.0.0-15-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.5.0
pip: 19.1.1
setuptools: 41.0.1
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numpy: 1.16.4
scipy: 1.2.1
pyarrow: 0.11.1
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: 3.1.0
openpyxl: 2.4.10
xlrd: 1.2.0
xlwt: None
xlsxwriter: 1.1.8
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.3.3
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10.1
s3fs: None
fastparquet: 0.3.0
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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} | 4 | 2019-06-02T09:14:11Z | 2019-06-19T13:43:48Z | 2019-06-06T17:38:21Z | CONTRIBUTOR | null | - [x] xref #26565
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Follow-up to #26565. Make ``RangeIndex._data`` be created in fewer cases.
Performance examples (but the larger gain is from memory savings by not creating the ``_data`` array):
```python
>>> rng = pd.RangeIndex(-5_000_000, 0, 6)
>>> %timeit rng.all()
789 Β΅s Β± 5.24 Β΅s per loop # master
216 ns Β± 0.957 ns per loop # this PR
>>> %timeit rng.any()
763 Β΅s Β± 6.08 Β΅s per loop # master
124 ns Β± 9.15 ns per loop # this PR
>>> %timeit (-2 in rng)
380 ns Β± 1.85 ns per loop # master
689 ns Β± 44.7 ns per loop # this PR, slightly slower
```
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I assume this must be a Python 3.5 failure, though I haven't been able to verify that, as I can't seem to be able to build pandas with Python 3.5 locally. (So I can't fix this either, as everything works fine on my Python3.7). | {
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} | 3 | 2019-06-02T10:44:29Z | 2019-06-03T11:15:59Z | 2019-06-02T11:47:35Z | MEMBER | null | closes #26614 | {
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Is it better to put `pip install pytest-azurepipelines` inside `setup_env.sh`? | {
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} | 7 | 2019-06-02T15:38:29Z | 2019-06-03T07:28:29Z | 2019-06-03T05:35:26Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/26591#issuecomment-498040997
This is one option (adding back the specific doc build environment yml file), another option would be to actually update the main `environment.yaml` file to be suitable for the doc build.
At the moment, my preference is to add back the separate yml file because 1) those additional dependencies are not necessarily needed for a dev env setup, and just makes that env even more heavy and 2) we might want to pin to certain versions from time to time for the doc build (eg we should consider pinning sphinx to < 2.0, which is not necessarily needed for all contributors to do as well).
Another thing is that this env could actually use an update (eg python 3.6 -> 3.7)
cc @datapythonista
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"description": "Incorrect or improved er... | open | false | null | [] | null | 0 | 2019-06-02T18:56:12Z | 2021-07-10T02:12:40Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
mix = pd.MultiIndex.from_product([['foo', 'bar'], ['one', 'two'], ['A', 'B']])
mix.isin([('foo', 'one', 'B'), ('bar', 'two', 'A')]) # returns `[False, True, False, False, False, False, True, False]`
mix.isin([('bar',), ('foo', 'one', 'B'), ('bar',)]) # should raise, returns [False, True, False, False, False, False, False, False] instead
```
#### Problem description
I got bit this morning by some improperly structured arguments for `MultiIndex.isin`. It turns out that as long as one value in the input `values` is the right length, `isin` doesn't care if every other value is too short. On the other hand, `isin` will error out in many other cases relating to the lengths of elements of `input`. With respect to my above example, these all raise an error:
```python
mix.isin([('foo', 'one'), ('bar', 'two')]) # raises `ValueError: Length of names must match number of levels in MultiIndex.`
mix.isin([('foo', 'one', 'B'), ('bar', 'two', 'A', 'alpha')]) # raises `ValueError: Length of names ...`
mix.isin([('bar',), ('foo', 'one', 'B'), ('bar', 'two', 'A', 'alpha')]) # raises `ValueError: Length of names ...`
```
but this hot mess executes normally:
```python
mix.isin([('foo', 'one', 'B'), *(('bar',),)*33]) # returns [False, True, False, False, False, False, False, False]
```
I'd like to see two fixes:
- If elements of `values` are too short, `isin` should raise an error, just like it does in cases when elements are too long.
- The current error message for invalid value length is a little confusing, since it refers to `Length of names`, even though `names` isn't an argument for `isin`. It turns out the reason for this is that the error is raised by the function `_set_names` during the construction of a new `MultiIndex` that `isin` uses to do validation.
I'll submit a PR | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26623 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26623/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26623/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26623/events | https://github.com/pandas-dev/pandas/pull/26623 | 451,223,552 | MDExOlB1bGxSZXF1ZXN0Mjg0MzYzNTQw | 26,623 | fixes #26622: pd.MultiIndex.isin should fail when input args are too short | {
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"color": "207d... | closed | false | null | [] | null | 9 | 2019-06-02T19:05:18Z | 2019-11-07T23:34:43Z | 2019-11-07T23:34:42Z | NONE | null | - [x] closes #26622
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This fixes #26622 by making `pd.MultiIndex.isin` fail when input args are too short, as well as when they are too long (the current behavior). It also replaces existing error message with something a little bit clearly and more directly related to `isin` and its parameters.
Currently, `isin` does some validation by [constructing a new `MultiIndex` and then taking its `values`](https://github.com/pandas-dev/pandas/blob/addc5fcd95064b765d4ee4260304d44822fdee3b/pandas/core/indexes/multi.py#L3178):
values = MultiIndex.from_tuples(values, names=self.names).values
Though the code I wrote should handle all validation relating to the length of the elements of `values`, I left this line in since I was unsure if it was validating/fixing anything else. Does anyone know if that line is safe to remove now? | {
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... | closed | false | null | [] | null | 8 | 2019-06-03T00:13:26Z | 2019-12-25T20:21:41Z | 2019-06-05T00:20:48Z | CONTRIBUTOR | null | Follow up from #26619. Since pytest have just done a point release for 4.6.1 we should be good to remove the pin.
https://github.com/pytest-dev/pytest/issues/5358
cc @simonjayhawkins | {
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} | 5 | 2019-06-03T00:29:23Z | 2019-10-15T11:38:53Z | 2019-10-15T11:38:53Z | CONTRIBUTOR | null | As the python 3.8 release date draws closer ( currently [scheduled](https://www.python.org/dev/peps/pep-0569/#release-schedule) for October this year ).
I was wondering if we want to set up python3.8 builds?
Given the 3.8 dev branch is available on [travis](https://docs.travis-ci.com/user/languages/python/) and shortly [azure](https://github.com/Microsoft/azure-pipelines-image-generation/issues/905).
Might be abit early for this - but it will mean we can catch any potential issues early + support python 3.8 upon release date.
Related issues: https://github.com/pandas-dev/pandas/issues/22714
I am happy to work on this - but would be good to get people's thoughts first.
Thanks! | {
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} | 2 | 2019-06-03T07:51:31Z | 2019-06-04T11:23:43Z | 2019-06-04T11:23:42Z | MEMBER | null | - [X] xref #25632
- [ ] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 13 | 2019-06-03T08:00:14Z | 2019-06-28T14:10:28Z | 2019-06-28T14:10:21Z | CONTRIBUTOR | null | - [x] closes #15481
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I'm not sure why, but after rebasing on latest master my previous PR #26400 is automatically closed. So I created a new PR here. I have edited as per all the comments there. | {
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} | 3 | 2019-06-03T12:38:30Z | 2019-11-08T00:25:46Z | 2019-11-08T00:24:45Z | MEMBER | null | ref https://github.com/pandas-dev/pandas/pull/26414#issuecomment-498224395 it appears that mypy and the Appender may not be playing nice together. A solution to this may be tucked away in https://github.com/python/mypy/issues/3157 | {
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... | open | false | null | [] | null | 5 | 2019-06-03T14:03:03Z | 2020-04-02T04:34:49Z | null | NONE | null | pandas version 0.24.2
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import datetime
thisdf = pd.DataFrame({'date':['12.01.1999','','28.04.2012']})
def parseDate(date):
try:
return datetime.datetime.strptime(date,'%d.%m.%Y')
except Exception:
return None
print(thisdf.date.apply(parseDate, convert_dtype=False))
```
Output:
```
0 1999-01-12
1 NaT
2 2012-04-28
Name: date, dtype: datetime64[ns]
```
#### Problem description
I want to parse a pandas Series containing strings to datetime using the above function `parseDate`. The important thing is that `None` values **must not** be converted to `NaT` because I put it into a SQLAlchemy database afterwards which only recognizes `None` values as NULL. Hence the returned Series must be of `dtype object`. However, all methods I tried return the Series with `dtype: datetime64[ns]` after parsing.
The solution of #14559 (using `.astype(object)` after the apply) does not help because the `NaT` values are still `NaT` values instead of None. Of course I could do another round of transformation after that but looping through twice does not seem very performant to me.
Any help appreciated .
#### Expected Output
A pandas Series with dtype=object
#### 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: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
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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... | closed | false | null | [] | null | 15 | 2019-06-03T14:17:30Z | 2019-10-06T23:45:16Z | 2019-10-06T23:45:16Z | CONTRIBUTOR | null | Added support for stride and offset aliases to the 'unit' keyword in
'to_datetime'. Documented support for additional 'unit' intervals that
were available.
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/26632 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26632/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26632/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26632/events | https://github.com/pandas-dev/pandas/issues/26632 | 451,518,758 | MDU6SXNzdWU0NTE1MTg3NTg= | 26,632 | pd.DataFrame.replace regression causes dtype to remain object | {
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} | 8 | 2019-06-03T14:34:31Z | 2019-11-20T05:13:38Z | 2019-11-20T05:13:38Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
# broken after pandas 0.23.4 if only "a" is replaced
def return_replace(initial):
return initial.replace({"a": 1.0, "b": 0.0})
# working
def return_replace_just_one(initial):
return initial.replace({"a": 1.0})
# the following should all be float64
print("problem:", return_replace(pd.DataFrame(["a"])).dtypes[0])
print("works:", return_replace(pd.DataFrame(["b"])).dtypes[0])
print("works:", return_replace_just_one(pd.DataFrame(["a"])).dtypes[0])
print("works:", return_replace(pd.DataFrame(["a", "b"])).dtypes[0])
```
#### Problem description
The behaviour shown above is inconsistent and hard to spot. In my case, it broke one of my tests due to mismatching types (as I was expecting a float64).
The problem seems to involve a regression when upgrading from pandas 0.23.4 to any later version (tested with 0.24.0, 0.24.1 and 0.24.2, all of which have the same issue).
Returning back to the old behaviour of changing the type also in the first case where it fails above (i.e. changing the type whenever possible?) would be more consistent and not require a manual type definition.
This is probably related to https://github.com/pandas-dev/pandas/issues/23305.
#### Expected Output
```
problem: float64
works: float64
works: float64
works: float64
```
#### Output of ``pd.show_versions()``
Note: The below is for the working version of pandas!
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 5.1.6-arch1-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
LOCALE: de_DE.UTF-8
pandas: 0.23.4
pytest: 4.4.2
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: 1.3.0
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: 3.1.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | 4 | 2019-06-03T14:38:45Z | 2019-06-05T07:22:31Z | 2019-06-05T07:22:09Z | CONTRIBUTOR | null | Follow up https://github.com/pandas-dev/pandas/pull/26556#discussion_r289056741 | {
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} | 13 | 2019-06-03T14:46:46Z | 2019-06-13T18:08:53Z | 2019-06-12T18:40:48Z | CONTRIBUTOR | null | - [x] closes #26545
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
BugFix:
When using `engine='python'`, columns were handled incorrectly if the first header had in the bom.
Bug:
```
In [1]: import pandas as pd
In [2]: pd.read_csv('test.txt', engine='python', delimiter='\t')
Out[2]:
Empty DataFrame
Columns: [Project ID]
Index: []
In [3]: pd.read_csv('test.txt', engine='python', delimiter='\t').shape
Out[3]: (0, 1)
In [4]: pd.read_csv('test.txt', delimiter='\t')
Out[4]:
Empty DataFrame
Columns: [Project ID, Project Name, Product Name]
Index: []
In [5]: pd.read_csv('test, delimiter='\t').shape
Out[5]: (0, 3)
```
Fix:
```
In [1]: import pandas as pd
In [2]: pd.read_csv('test.txt', engine='python', delimiter='\t')
Out[2]:
Empty DataFrame
Columns: [Project ID, Project Name, Product Name]
Index: []
In [3]: pd.read_csv('test.txt', engine='python', delimiter='\t').shape
Out[3]: (0, 3)
In [4]: pd.read_csv('test.txt', delimiter='\t')
Out[4]:
Empty DataFrame
Columns: [Project ID, Project Name, Product Name]
Index: []
In [5]: pd.read_csv('test, delimiter='\t').shape
Out[5]: (0, 3)
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26635 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26635/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26635/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26635/events | https://github.com/pandas-dev/pandas/pull/26635 | 451,588,316 | MDExOlB1bGxSZXF1ZXN0Mjg0NjQ4OTYz | 26,635 | CLN/API: clean-up of level argument of isin for indexes | {
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"description": nu... | closed | false | null | [] | null | 6 | 2019-06-03T16:55:43Z | 2019-06-09T17:51:30Z | 2019-06-03T19:59:48Z | MEMBER | null | The docstring for Index.isin() states:
```
level : str or int, optional
Name or position of the index level to use (if the index is a
`MultiIndex`).
```
But the keyword is accepted by the other indexes, validated and raises if the level parameter does not match the only level in the index.
This PR removes the levels argument from the indexes (except MultiIndex)
Series.isin() and DataFrame.isin() do not have a level parameter, so isin for indexes will be consistent.
The implementation for MultiIndex.isin is independent from Index.isin, so i think that a separate docstring for MultiIndex would be much clearer. (but out of scope for this PR)
The docstring for datetimelike indexes does not include the level parameter but is not part of the published api, so has been removed to avoid confusion.
NOTE: This is not a change to the api, but a clean-up to match the api to the published api. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26636 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26636/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26636/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26636/events | https://github.com/pandas-dev/pandas/issues/26636 | 451,596,098 | MDU6SXNzdWU0NTE1OTYwOTg= | 26,636 | Enhancement request: read_html() parameter to control contents of DataFrame | {
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"description": "read_htm... | open | false | null | [] | null | 20 | 2019-06-03T17:15:18Z | 2021-10-11T09:16:30Z | null | NONE | null | The current read_html() method is useful when parsing simple tables, however in practice, not every html table has been created with simple parsing in mind. It is often required that we identify the type or purpose of data in a table cell. HTML tables generally express information this via the assignment of a CSS class name to a th or td element.
I propose an optional read_html() string parameter called "get_attribute" that indicates if the parser should get the text of the cell or if it should get the value of an attribute. If get_attribute is None, behavior of read_html() is the same as its current functionality: th/td cell values will be read into the dataframe. If it is a string value, read_html() will retrieve attribute values rather than cell text. If a th or td element has no matching attribute, the value returned is None.
I would use this functionality as follows:
```
myTableData = pandas.read_html( table )[ 0 ]
myTableClasses = pandas.read_html( table, get_attribute = 'class' )[ 0 ]
```
The dataframe myTableClasses would contain information that I could use to map cell styling to the values in myTableData. I imagine it could be useful for other types of information as well, conveyed via other attributes. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26637 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26637/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26637/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26637/events | https://github.com/pandas-dev/pandas/issues/26637 | 451,628,388 | MDU6SXNzdWU0NTE2MjgzODg= | 26,637 | Improve docs for pandas_version in Dataframe to_json(orient='table') | {
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} | 7 | 2019-06-03T18:36:16Z | 2020-10-14T12:33:58Z | 2020-10-14T12:33:58Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas as pd
df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6], 'C': [7,8,9] })
df.to_json(orient='table', index='False')
```
#### Problem description
I was attempting to serialize a dataframe and omit the indexes.
In the generated JSON the indexes remained and the pandas_version value in the JSON was "0.20.0"
The results I obtained on Windows 10 and MacOS 10.14.5 was:
{"schema": {"fields":[{"name":"values","type":"integer"},{"name":"A","type":"integer"},{"name":"B","type":"integer"},{"name":"C","type":"integer"}],"primaryKey":[null]**,"pandas_version":"0.20.0"}**, "data": [{"index":0,"A":1,"B":4,"C":7},{"index":1,"A":2,"B":5,"C":8},{"index":2,"A":3,"B":6,"C":9}]}'
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
{"schema": {"fields":[{"name":"values","type":"integer"},{"name":"A","type":"integer"},{"name":"B","type":"integer"},{"name":"C","type":"integer"}],"primaryKey":[null],"pandas_version":"0.24.2"}, "data": [{"A":1,"B":4,"C":7},{"A":2,"B":5,"C":8},{"A":3,"B":6,"C":9}]}'
#### 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: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
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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} | 2 | 2019-06-03T21:22:48Z | 2019-06-03T22:17:46Z | 2019-06-03T22:17:41Z | CONTRIBUTOR | null | "Other data structures..." is not usuful anymore, as ``Panel`` and ``Panel4D`` are have been removed for the code base,
Plus other small stuff. | {
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} | 17 | 2019-06-03T21:23:24Z | 2019-12-30T17:45:24Z | 2019-12-30T17:45:24Z | CONTRIBUTOR | null | Possible regression in https://github.com/pandas-dev/pandas/commit/9a42cbe85461c28417a5130bc80b035044c5575a
ASV: http://pandas.pydata.org/speed/pandas/index.html#strings.Methods.time_normalize?commits=9a42cbe85461c28417a5130bc80b035044c5575a
A bunch of benchmarks are ~20% slower. Not sure if that was talked about in the issue.
cc @h-vetinari | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26640 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26640/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26640/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26640/events | https://github.com/pandas-dev/pandas/issues/26640 | 451,697,600 | MDU6SXNzdWU0NTE2OTc2MDA= | 26,640 | Investigate performance regression in TimeSeries.infer_freq | {
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"default"... | open | false | null | [] | null | 2 | 2019-06-03T21:27:49Z | 2021-07-10T02:15:40Z | null | CONTRIBUTOR | null | A whole bunch of them:
- [ ] InferFreq: http://pandas.pydata.org/speed/pandas/index.html#timeseries.InferFreq.time_infer_freq?commits=9c5165e26260483ae64300c4d98939bf575aab7e-8154efb0c1a64295cf54e00025b4ab09bcd02752&p-freq=%27D%27
- [ ] DatetimeIndex http://pandas.pydata.org/speed/pandas/index.html#timeseries.DatetimeIndex.time_timeseries_is_month_start?commits=9c5165e26260483ae64300c4d98939bf575aab7e-8154efb0c1a64295cf54e00025b4ab09bcd02752&p-index_type=%27repeated%27
- [ ] offsets.ApplyIndex: http://pandas.pydata.org/speed/pandas/index.html#offset.ApplyIndex.time_apply_index?p-offset=%3CMonthEnd%3E&commits=9c5165e26260483ae64300c4d98939bf575aab7e-8154efb0c1a64295cf54e00025b4ab09bcd02752
- [ ] io.json.ToJSON.time_delta_int_tstamp: http://pandas.pydata.org/speed/pandas/index.html#io.json.ToJSON.time_delta_int_tstamp?p-orient=%27columns%27&commits=9c5165e26260483ae64300c4d98939bf575aab7e-8154efb0c1a64295cf54e00025b4ab09bcd02752
- [ ] index_object.Indexing.time_get('String')?: http://pandas.pydata.org/speed/pandas/index.html#index_object.Indexing.time_get?commits=9c5165e26260483ae64300c4d98939bf575aab7e-8154efb0c1a64295cf54e00025b4ab09bcd02752&p-dtype=%27String%27
- [ ] http://pandas.pydata.org/speed/pandas/index.html#groupby.GroupByMethods.time_dtype_as_group?commits=9c5165e26260483ae64300c4d98939bf575aab7e-8154efb0c1a64295cf54e00025b4ab09bcd02752&p-method=%27all%27&p-application=%27transformation%27&p-dtype=%27datetime%27
- [ ] http://pandas.pydata.org/speed/pandas/index.html#gil.ParallelDatetimeFields.time_datetime_to_period?commits=9c5165e26260483ae64300c4d98939bf575aab7e-8154efb0c1a64295cf54e00025b4ab09bcd02752
Unfortunately, the benchmark machine was down for ~1 week (we lost power during a storm, and I forgot to turn it back on). The window is https://github.com/pandas-dev/pandas/compare/9c5165e26260483ae64300c4d98939bf575aab7e...8154efb0c1a64295cf54e00025b4ab09bcd02752. | {
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} | 3 | 2019-06-03T23:56:09Z | 2019-06-05T09:06:32Z | 2019-06-04T23:59:02Z | CONTRIBUTOR | null | - [x] xref #17633
- [x] whatsnew entry
Removes ``NDFrame.select``, that was deprecated in 0.21. | {
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"description": "R... | open | false | null | [] | null | 6 | 2019-06-04T00:19:28Z | 2021-07-10T02:18:03Z | null | CONTRIBUTOR | null | I've made a PR to remove ``NDFrame.select``, see #26641. That method was deprecated in 0.21.
There was a discussion in #12401, where many participants agreed that removing the old ``select`` and having ``NDFrame.filter`` renamed to ``NDFrame.select`` would be a better naming scheme. I agree on that for several of the reasons mentioned in the thread.
What do people think of such a name change and a related deprecation of the name (not functionality) ``.filter``?
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} | 2 | 2019-06-04T00:27:07Z | 2019-06-04T02:22:47Z | 2019-06-04T01:23:50Z | MEMBER | null | Some small fixes I noticed reading over the whatsnew | {
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https://mypy.readthedocs.io/en/latest/running_mypy.html#missing-imports
This PR makes the missing imports explicit. Third party libraries I think we'll have to keep until they become available in typeshed. Internal imports may be resolved through either stubfiles or internal import machinery (would need review as follow ups) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26646 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26646/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26646/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26646/events | https://github.com/pandas-dev/pandas/pull/26646 | 452,041,973 | MDExOlB1bGxSZXF1ZXN0Mjg1MDEwMjIz | 26,646 | catch iterator input in io.json.json_normalize | {
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} | [] | closed | false | null | [] | null | 6 | 2019-06-04T14:49:14Z | 2019-06-04T17:24:15Z | 2019-06-04T17:24:14Z | NONE | null | The data argument in json_normalize should be an dictionary or list of dictionaries. If an iterator is passed, unexpected behavior can occur. In particular, this will result in the loss of the first row. See https://stackoverflow.com/questions/56362810/missing-first-document-when-loading-multi-document-yaml-file-in-pandas-dataframe.
In my proposal, an error is thrown. Alternatively, the iterator could be turned into a list:
```
if data is iter(data):
data = [x for x in data]
```
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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] | closed | false | null | [] | null | 7 | 2019-06-04T15:06:31Z | 2020-08-08T21:15:17Z | 2020-04-17T00:17:16Z | NONE | null | The data argument in json_normalize requires be an dictionary or list of dictionaries. If an iterator is passed, unexpected behavior can occur. In particular, this will result in the loss of the first row. See https://stackoverflow.com/questions/56362810/missing-first-document-when-loading-multi-document-yaml-file-in-pandas-dataframe.
I think this should be catched, either by throwing an error (as proposed in https://github.com/pandas-dev/pandas/pull/26646) or by ensuring that the output for the iterator is the same as for the list created by "materializing" the iterator.
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} | 15 | 2019-06-04T16:29:15Z | 2019-06-13T10:07:14Z | 2019-06-13T08:54:38Z | MEMBER | null | - [X] xref #22766, #26574
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Looks like the problem in #26591 was that the `HostName` input of the azure task that installs the key, is not the host name, but the `know_hosts` line for the host name where to push.
I set up azure-pipelines in my pandas branch, to be able to see the `master` builds and avoid surprises, and this branch is successfully pushing the docs to https://dev.pandas.io
@TomAugspurger since there were differences between the travis docs dependencies and `environment.yml` I think it makes more sense to change and use `environment.yml` here, so we can detect and fix any problem while we still have the travis docs, and then simply remove the travis docs dependencies with the build when we are happy with this.
@jorisvandenbossche the `ImportError` for `pyarrow` doesn't seem to be because `pyarrow` is not in `environment.yml`, it is. There is something weird with that, need to have a look. | {
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} | 5 | 2019-06-04T18:08:00Z | 2019-07-05T14:29:35Z | 2019-07-05T14:29:35Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
left = pd.DataFrame({
'by_col1': pd.DatetimeIndex(['2018-01-01']).tz_localize('UTC'),
'by_col2': ['HELLO'],
'on_col': [2],
'value': ['a']})
right = pd.DataFrame({
'by_col1': pd.DatetimeIndex(['2018-01-01']).tz_localize('UTC'),
'by_col2': ['WORLD'],
'on_col': [1],
'value': ['b']})
pd.merge_asof(left, right, by=['by_col1', 'by_col2'], on='on_col')
```
#### Problem description
This is very similar to: https://github.com/pandas-dev/pandas/issues/21184
The only difference is that the `merge_asof` `by` is made of 2 columns (instead of one):
* one is tz-aware
* the other one is something else (string, number etc...)
When running this, I get:
```
Traceback (most recent call last):
File "test.py", line 13, in <module>
pd.merge_asof(left, right, by=['by_col1', 'by_col2'], on='on_col')
File "myenv/lib/python3.6/site-packages/pandas/core/reshape/merge.py", line 462, in merge_asof
return op.get_result()
File "myenv/lib/python3.6/site-packages/pandas/core/reshape/merge.py", line 1256, in get_result
join_index, left_indexer, right_indexer = self._get_join_info()
File "myenv/lib/python3.6/site-packages/pandas/core/reshape/merge.py", line 756, in _get_join_info
right_indexer) = self._get_join_indexers()
File "myenv/lib/python3.6/site-packages/pandas/core/reshape/merge.py", line 1504, in _get_join_indexers
left_by_values = flip(left_by_values)
File "myenv/lib/python3.6/site-packages/pandas/core/reshape/merge.py", line 1457, in flip
return np.array(lzip(*xs), labeled_dtypes)
File "myenv/lib/python3.6/site-packages/pandas/core/dtypes/dtypes.py", line 150, in __repr__
return str(self)
File "myenv/lib/python3.6/site-packages/pandas/core/dtypes/dtypes.py", line 129, in __str__
return self.__unicode__()
File "myenv/lib/python3.6/site-packages/pandas/core/dtypes/dtypes.py", line 704, in __unicode__
return "datetime64[{unit}, {tz}]".format(unit=self.unit, tz=self.tz)
SystemError: PyEval_EvalFrameEx returned a result with an error set
```
#### Expected Output
I expect the merge_asof to work, and pick up the by column accordingly
#### Output of ``0.24.2``
<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: Linux
OS-release: 3.10.0-862.3.3.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: 4.5.0
pip: 19.1.1
setuptools: 40.8.0
Cython: 0.28.5
numpy: 1.16.4
scipy: 1.1.0
pyarrow: 0.12.1
xarray: None
IPython: 7.3.0
sphinx: 1.4.6
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.3
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.1
bs4: None
html5lib: None
sqlalchemy: 1.2.18
pymysql: None
psycopg2: 2.7.1 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.7.0
gcsfs: None
</details>
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"c... | open | false | null | [] | null | 3 | 2019-06-04T18:51:44Z | 2021-07-10T02:19:53Z | null | NONE | null | #### Code Sample
```python
import pandas as pd
import numpy as np
d = {'col1': [0.0037448, -0.002455324, 0.003062666, 0.000768503, 0.002309015]}
df = pd.DataFrame(data=d)
dfT = df.T
np.matmul(dfT, df)
```
#### Problem description
Issue originally raised here: https://github.com/numpy/numpy/issues/13712. In version 1.16 of NumPy ```np.matmul``` was implemented as a ufunc which caused compatibility issues with pandas. I get an error when trying to use ```np.matmul``` with single dimension pandas DataFrames. In the linked issue someone suggested that ```DataFrame.__array_ufunc__``` needs to handle ```numpy.matmul```.
Here is the error message I receive when running the sample code:
```
Traceback (most recent call last):
File "MatmulBugTest.py", line 8, in <module>
np.matmul(dfT, df)
File "C:\Users\bryant_n\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\generic.py", line 1909, in __array_wrap__
return self._constructor(result, **d).__finalize__(self)
File "C:\Users\bryant_n\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\frame.py", line 424, in __init__
copy=copy)
File "C:\Users\bryant_n\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\internals\construction.py", line 167, in init_ndarray
return create_block_manager_from_blocks([values], [columns, index])
File "C:\Users\bryant_n\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\internals\managers.py", line 1660, in create_block_manager_from_blocks
construction_error(tot_items, blocks[0].shape[1:], axes, e)
File "C:\Users\bryant_n\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\internals\managers.py", line 1691, in construction_error
passed, implied))
ValueError: Shape of passed values is (1, 1), indices imply (1, 5)
```
#### Expected Output
Expected output of sample code is ```array([[3.53542131e-05]])```.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.0.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: 3.8.0
pip: 19.1
setuptools: 40.2.0
Cython: 0.28.5
numpy: 1.16.3
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.5.0
sphinx: 1.7.9
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 2.2.3
openpyxl: 2.5.6
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.1.0
lxml.etree: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.11
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 7 | 2019-06-05T09:16:18Z | 2019-06-05T23:47:57Z | 2019-06-05T23:47:57Z | CONTRIBUTOR | null | - [x] xref #18262
- [x] tests added / passed
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``.swapaxes`` are not needed, now that ``Panel`` is being removed. | {
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} | 9 | 2019-06-05T09:35:43Z | 2019-06-05T21:53:15Z | 2019-06-05T21:37:55Z | CONTRIBUTOR | null | - [ X ] tests passed
- [ X ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Checking whether `if arr_or_dtype is None` is done in the beginning of the function so the following check is redundant and can be safely removed.
This issue (among a few others) were flagged up by LGTM.com website: https://lgtm.com/projects/g/pandas-dev/pandas/alerts/?mode=tree. Some of the other numerical computing repositories have been analyzed there as well such as [numpy](https://lgtm.com/projects/g/numpy/numpy/alerts/?mode=tree) and [scipy](https://lgtm.com/projects/g/scipy/scipy/alerts/?mode=tree).
If you like, you can use LGTM for automatically reviewing code in pull requests. Here's an example of how Google's AMPHTML use that to flag up security vulnerabilities in their code base: ampproject/amphtml#13060.
(full disclosure: I'm a huge fan of `pandas` and also part of the team that runs LGTM.com)
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```
ImportError: /home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pyarrow/lib.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _ZNK5arrow6Status8ToStringB5cxx11Ev
```
There are few differences between the created environments, like Python 3.6 vs 3.7, arrow-cpp being installed from defaults vs conda-forge... But it's unclear what's the exact problem or what is causing it.
Ideally, we'd like to use `environment.yml` to build the docs, so the environment is exactly the same as the local installations we use.
What we need to do is:
- Identify what is causing the error
- Implement the required changes so `pyarrow` can be imported
- Replace `ci/deps/travis-36-docs.yaml` by `environment.yml` in the doc builds in Travis and Azure. | {
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} | 5 | 2019-06-05T11:13:22Z | 2019-06-05T13:36:09Z | 2019-06-05T12:46:38Z | MEMBER | null | In #26648 we detected that `environment.yml` didn't contain some of the dependencies required to build the documentation. `environment.yml` is what we use to build the documentation locally. Adding them here.
CC: @jorisvandenbossche
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```python
In [45]: a.loc[[True, False]] # too few?
Out[45]:
a 1
dtype: int64
In [46]: a.loc[[True, False, True, False, False]] # too many?
Out[46]:
a 1
c 3
dtype: int64
```
The fact that we raise here, when we have too many true values makes me think this is a bug
```python
In [53]: a.loc[[True, False, True, False, True]]
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
~/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/indexing.py in _getbool_axis(self, key, axis)
1517 try:
-> 1518 return self.obj._take(inds, axis=axis)
1519 except Exception as detail:
~/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/series.py in _take(self, indices, axis, is_copy)
3925 indices = ensure_platform_int(indices)
-> 3926 new_index = self.index.take(indices)
3927
~/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/indexes/base.py in take(self, indices, axis, allow_fill, fill_value, **kwargs)
793 fill_value=fill_value,
--> 794 na_value=self._na_value)
795 else:
~/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/indexes/base.py in _assert_take_fillable(self, values, indices, allow_fill, fill_value, na_value)
819 else:
--> 820 taken = values.take(indices)
821 return taken
IndexError: index 4 is out of bounds for axis 0 with size 3
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
<ipython-input-53-9fd96bbd72ce> in <module>
----> 1 a.loc[[True, False, True, False, True]]
~/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/indexing.py in __getitem__(self, key)
1498
1499 maybe_callable = com.apply_if_callable(key, self.obj)
-> 1500 return self._getitem_axis(maybe_callable, axis=axis)
1501
1502 def _is_scalar_access(self, key):
~/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/indexing.py in _getitem_axis(self, key, axis)
1867 return self._get_slice_axis(key, axis=axis)
1868 elif com.is_bool_indexer(key):
-> 1869 return self._getbool_axis(key, axis=axis)
1870 elif is_list_like_indexer(key):
1871
~/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/indexing.py in _getbool_axis(self, key, axis)
1518 return self.obj._take(inds, axis=axis)
1519 except Exception as detail:
-> 1520 raise self._exception(detail)
1521
1522 def _get_slice_axis(self, slice_obj, axis=None):
KeyError: IndexError('index 4 is out of bounds for axis 0 with size 3')
```
In the docs at http://pandas-docs.github.io/pandas-docs-travis/user_guide/indexing.html#boolean-indexing we say
> You may select rows from a DataFrame using a boolean vector the same length as the DataFrameβs index
Note that this only affects `.loc`. `Series.__getitem__` and `DataFrame.__getitem__` are unaffected. | {
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} | 0 | 2019-06-05T13:08:15Z | 2019-06-06T15:46:22Z | 2019-06-06T15:46:22Z | MEMBER | null | Our dependencies file `environment.yml` (https://github.com/pandas-dev/pandas/blob/master/environment.yml) is growing, and it's difficult to keep track on what each dependency is required for, and if there is any that can be removed.
We already provide some groups:
- required: pandas requires them to work
- development: they are used by pandas contributors and CI, but not by pandas itself
- optional: pandas can use them if they are installed (e.g. `fastparquet` will be used if `pandas.read_parquet()` is called, but it's not required otherwise)
But those don't provide enough information, and could be improved. Also, not sure if the current division is correct, for example `nbsphinx` should probably be in `development`, I don't think pandas uses it.
Some groups we could have are:
- testing (e.g. `pytest`, `hypothesis`,...)
- documentation (e.g. `sphinx`, `numpydoc`,...)
- code checks, see `ci/code_checks.sh` (e.g. `flake8`, `mypy`, `isort`,...)
And for the optional, I'd personally add a comment for the ones that make sense to inform in which part of pandas they are used, for example:
```
- fastparquet>=0.2.1 # pandas.read_parquet, DataFrame.to_parquet
- matplotlib>=2.2.2 # DataFrame.plot, Series.plot, pandas.plotting
``` | {
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} | 2 | 2019-06-05T13:43:06Z | 2019-06-11T00:16:58Z | 2019-06-11T00:16:58Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
df = pd.DataFrame(columns=["a"], data=np.random.randint(10, size=30).reshape(-1, 1))
pct = np.linspace(0, 1, 20 + 1)
desc = df.describe(percentiles=pct)
print(desc.index)
print(desc.loc['25%'])
print(desc.loc['30%'])
```
#### Problem description
If desc.index is printed out, some percentile indexes are written as decimal numbers (e.g. 30.0%, 55.0%, 85.0%), while others are whole numbers (e.g. 35%, 70%, 100%). Printing percentiles of distribution therefore requires hardcoding some decimal numbers, e.g.
```python
print(desc.loc['0%'])
print(desc.loc['5%'])
print(desc.loc['10%'])
print(desc.loc['15.0%'])
print(desc.loc['20%'])
print(desc.loc['25%'])
print(desc.loc['30.0%'])
print(desc.loc['35%'])
print(desc.loc['40%'])
print(desc.loc['45%'])
print(desc.loc['50%'])
print(desc.loc['55.0%'])
print(desc.loc['60.0%'])
print(desc.loc['65%'])
print(desc.loc['70%'])
print(desc.loc['75%'])
print(desc.loc['80%'])
print(desc.loc['85.0%'])
print(desc.loc['90%'])
print(desc.loc['95%'])
print(desc.loc['100%'])
```
#### Expected Output
Indexes of percentiles should be either all whole numbers or either all decimal numbers.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-50-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: None
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.3
scipy: 1.2.1
pyarrow: None
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: 3.0.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.7.3
lxml.etree: 3.5.0
bs4: 4.4.1
html5lib: 0.999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 1 | 2019-06-05T13:46:49Z | 2019-06-08T19:52:20Z | 2019-06-08T19:52:20Z | MEMBER | null | In #25455 we added `isort` as a dependency to all our builds. Looks like the CI was failing, and adding `isort` fixed the problem. Not sure what the problem is, but `isort` is something we should only have in `environment.yml`, as it's only used in `ci/code_checks.sh` and locally.
We should remove `isort` from all files in `ci/deps/` and if the CI complains, find out what's the problem, and fix it. | {
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} | 11 | 2019-06-05T13:49:22Z | 2019-06-08T16:16:13Z | 2019-06-08T16:15:58Z | MEMBER | null | Follow up to #26579
Removing the frame2 fixture which is sparsely used. I've also removed inheritance of `ReadingTestsBase` and made that it's own discoverable class. Previously these tests were only being generated when `TestXlrdReader` was being run which tightly couples reading with xlrd.
The goal here in subsequent PRs would be to better parametrize `TestReaders` for new engines that come in and truly make `TestXlrdReader` only deal with xlrd specific tests (will have to move around some tests to make that happen)
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} | 10 | 2019-06-05T13:56:27Z | 2019-09-05T15:34:44Z | 2019-09-05T15:34:44Z | MEMBER | null | Not entirely sure how to do it with asv, but at the command line its just `python3 -X importtime -c "import pandas"`
- [ ] import time with `python3 -X importtime -c "import pandas"`
- [ ] import size with something like `import pandas, psutil; psutil.Process().memory_info()` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26664 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26664/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26664/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26664/events | https://github.com/pandas-dev/pandas/issues/26664 | 452,519,211 | MDU6SXNzdWU0NTI1MTkyMTE= | 26,664 | Using LGTM.com to find and fix issues in Python and C code | {
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] | closed | false | null | [] | null | 2 | 2019-06-05T14:02:02Z | 2019-06-06T10:01:25Z | 2019-06-05T17:58:42Z | CONTRIBUTOR | null | There are a few issues in the code that were flagged up by LGTM.com website: https://lgtm.com/projects/g/pandas-dev/pandas/alerts/?mode=tree. Some of the other numerical computing repositories have been analyzed there as well such as [numpy](https://lgtm.com/projects/g/numpy/numpy/alerts/?mode=tree) and [scipy](https://lgtm.com/projects/g/scipy/scipy/alerts/?mode=tree).
You can see the issues in both Python and C code. For instance, here is the issue [Implicit scaling of pointer arithmetic expressions can cause buffer overflow conditions](https://lgtm.com/projects/g/scipy/scipy/alerts/?mode=tree&ruleFocus=2156240610) that is found in SciPy source C code. Some of the issues may be of higher importance to the project (e.g. [Allocating memory with a size controlled by an external user can result in integer overflow](https://lgtm.com/projects/g/scipy/scipy/alerts/?mode=tree&ruleFocus=2157860315) vs [A pure expression whose value is ignored is likely to be the result of a typo.](https://lgtm.com/projects/g/scipy/scipy/alerts/?mode=tree&ruleFocus=2165170567)). It is completely up to the developers to pick what's relevant.
Due to the dynamic nature of Python and some heavy use of all kinds of magic in `numpy` (e.g. [Comparison of identical values, the intent of which is unclear](https://lgtm.com/projects/g/scipy/scipy/alerts/?mode=tree&ruleFocus=4840093) and `scipy` (e.g. [Using a named argument whose name does not correspond to a parameter of the init method of the class being instantiated, will result in a TypeError at runtime](https://lgtm.com/projects/g/scipy/scipy/alerts/?mode=tree&ruleFocus=1506312116236), some of the alerts may look like false positives and I would love to find out which of them are to be able to fix the code to avoid raising them.
If you like, you can use LGTM for automatically reviewing code in pull requests. Here's an example of how Google's AMPHTML use that to flag up security vulnerabilities in their code base: ampproject/amphtml#13060. This will prevent introducing new issues as you can see if there are any issues introduced before merging.
(full disclosure: I'm a huge fan of `pandas` and also part of the team that runs LGTM.com) | {
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} | 2 | 2019-06-05T14:07:17Z | 2019-06-05T20:33:37Z | 2019-06-05T18:44:39Z | CONTRIBUTOR | null | - [x] closes #23868
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Modifies the try-catch for imports of required dependencies (`numpy`, `pytz`, and `dateutil`) to record any exceptions raised, and then to include the message from these when reporting that required dependencies are missing.
Previously, the error message simply claimed the dependency was missing, which was somewhat misleading if the dependency was installed but raised its own ImportError (missing sub-dependencies, bad install, etc.). | {
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Not sure how much trouble it will be to do this conditionally for py37+ only. | {
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} | 8 | 2019-06-05T15:13:17Z | 2020-05-12T14:54:10Z | 2020-05-12T14:54:10Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
Data usage agreements prevent me from attaching a Stata file of format version 104, so I cannot easily provide a copy/paste example. This is unfortunate as I have several of these files...
```
. dtaversion CIV_LSMS_1987_ANTHROPOMETRICS_A.DTA
(file "CIV_LSMS_1987_ANTHROPOMETRICS_A.DTA" is .dta-format 104 from Stata 4)
```
#### Problem description
The `stata.py` module will provide error messages that it can only read certain versions and explicitly enumerates version 104 both in the [`_version_error` string](https://github.com/pandas-dev/pandas/blob/c07d71d13b21e0b6e22146f0f546f1f8e24a64b3/pandas/io/stata.py#L40-L42) and in [`StataReader._read_old_header`](https://github.com/pandas-dev/pandas/blob/c07d71d13b21e0b6e22146f0f546f1f8e24a64b3/pandas/io/stata.py#L1224)
However, `StataReader` cannot read files with version 104. This is because
1. `StataReader.__init__` calls `_read_header` [code](https://github.com/pandas-dev/pandas/blob/c07d71d13b21e0b6e22146f0f546f1f8e24a64b3/pandas/io/stata.py#L993)
1. `_read_header` calls `_read_old_header` [code](https://github.com/pandas-dev/pandas/blob/c07d71d13b21e0b6e22146f0f546f1f8e24a64b3/pandas/io/stata.py#L1025)
1. `_read_old_header` calls `_get_time_stamp` [code](https://github.com/pandas-dev/pandas/blob/c07d71d13b21e0b6e22146f0f546f1f8e24a64b3/pandas/io/stata.py#L1238)
1. `_get_time_stamp` raises a `ValueError` for format versions > 104 [code](https://github.com/pandas-dev/pandas/blob/c07d71d13b21e0b6e22146f0f546f1f8e24a64b3/pandas/io/stata.py#L1197-L1207)
None of this behavior can be overridden or otherwise configured as part of instantiating `pandas.io.stata.StataReader`
#### Expected Output
Remove 104 as a supported format and error: `ValueError: Version of given Stata file is not 105, 108, 111 (Stata 7SE), 113 (Stata 8/9), 114 (Stata 10/11), 115 (Stata 12), 117 (Stata 13), or 118 (Stata 14)`
Alternatively `_get_time_stamp` could be changed to return `""` or a similar value to reflect that Stata did not include a file timestamp in that format version. My experiments subclassing `StataReader` to suppress the error, read the data file in as a df, and export to CSV yield a file equivalent to an equivalent `export delimited` call from Stata.
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-50-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: 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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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Marks the methods mentioned in #26557 as deprecated, with proper messages. | {
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- [X] closes #26656
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This issue seems to be ubiquitous, and below link suggest solution:
https://github.com/apache/arrow/issues/2213
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Will troubleshoot later, just parking this here for now. | {
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} | 0 | 2019-06-05T19:35:11Z | 2019-06-09T21:59:56Z | 2019-06-09T21:59:56Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
index = pd.DatetimeIndex([], name='foo')
index.isin([], level='foobar')
```
#### Problem description
on master:
```
array([], dtype=bool)
```
on 0.23.4
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-2-82f8aeda6721> in <module>
1 index = pd.DatetimeIndex([], name='foo')
----> 2 index.isin([], level='foobar')
TypeError: isin() got an unexpected keyword argument 'level'
```
#### Expected Output
to validate input and raise same message as other non-datetimelike indexes
```
KeyError: 'Level foobar must be same as name (foo)'
```
the api for `isin` is defined in #7892
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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"description": "pandas t... | open | false | null | [] | null | 2 | 2019-06-05T19:41:21Z | 2020-05-22T03:34:29Z | null | MEMBER | null | tests.plotting.test_frame.TestDataFramePlots.test_subplots_timeseries_y_axis issues a warning when plotting a datetime64[tz] column:
```
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_subplots_timeseries_y_axis
/Users/bmendel/Desktop/pd/matwarns/pandas/plotting/_core.py:384: FutureWarning: Converting timezone-aware DatetimeArray to timezone-naive ndarray with 'datetime64[ns]' dtype. In the future, this will return an ndarray with 'object' dtype where each element is a 'pandas.Timestamp' with the correct 'tz'.
To accept the future behavior, pass 'dtype=object'.
To keep the old behavior, pass 'dtype="datetime64[ns]"'.
numeric_data[col] = np.asarray(numeric_data[col])
```
The relevant code is:
```
data = {"numeric": np.array([1, 2, 5]),
"timedelta": [pd.Timedelta(-10, unit="s"),
pd.Timedelta(10, unit="m"),
pd.Timedelta(10, unit="h")],
"datetime_no_tz": [pd.to_datetime("2017-08-01 00:00:00"),
pd.to_datetime("2017-08-01 02:00:00"),
pd.to_datetime("2017-08-02 00:00:00")],
"datetime_all_tz": [pd.to_datetime("2017-08-01 00:00:00",
utc=True),
pd.to_datetime("2017-08-01 02:00:00",
utc=True),
pd.to_datetime("2017-08-02 00:00:00",
utc=True)],
"text": ["This", "should", "fail"]}
testdata = pd.DataFrame(data)
ax_datetime_no_tz = testdata.plot(y="datetime_no_tz")
ax_datetime_all_tz = testdata.plot(y="datetime_all_tz")
```
Checking the actual images generated by these last two:
The UTC is completely lost for the tz-aware case, and in both cases the dates are rendered without year and with weird-looking hour. | {
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} | 5 | 2019-06-05T19:41:28Z | 2019-06-10T07:57:50Z | 2019-06-09T21:59:57Z | MEMBER | null | #LondonPythonSprints
- [x] closes #26675
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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"d... | closed | false | null | [] | null | 6 | 2019-06-05T19:44:38Z | 2019-06-07T19:58:18Z | 2019-06-07T19:45:00Z | MEMBER | null | In master we get the following warnings in this file:
```
$ pytest pandas/tests/plotting/test_frame.py --skip-slow
[...]
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_mpl2_color_cycle_str
pandas/pandas/plotting/_style.py:60: MatplotlibDeprecationWarning: Support for uppercase single-letter colors is deprecated since Matplotlib 3.1 and will be removed in 3.3; please use lowercase instead.
[conv.to_rgba(c) for c in colors]
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_subplots_timeseries_y_axis
pandas/pandas/plotting/_core.py:384: FutureWarning: Converting timezone-aware DatetimeArray to timezone-naive ndarray with 'datetime64[ns]' dtype. In the future, this will return an ndarray with 'object' dtype where each element is a 'pandas.Timestamp' with the correct 'tz'.
To accept the future behavior, pass 'dtype=object'.
To keep the old behavior, pass 'dtype="datetime64[ns]"'.
numeric_data[col] = np.asarray(numeric_data[col])
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_line_area_stacked
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_line_area_stacked
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_line_area_stacked
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_line_area_stacked
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_line_area_stacked
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_line_area_stacked
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_line_area_stacked
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_line_area_stacked
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_line_area_stacked
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_line_area_stacked
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_line_area_stacked
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_line_area_stacked
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_line_area_stacked
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_line_area_stacked
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_line_area_stacked
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_line_area_stacked
pandas/pandas/plotting/_core.py:1014: UserWarning: Attempted to set non-positive left xlim on a log-scaled axis.
Invalid limit will be ignored.
ax.set_xlim(left, right)
```
This PR addresses first and third of these. The middle one is the topic of #26676. | {
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] | closed | false | null | [] | null | 7 | 2019-06-05T19:55:46Z | 2019-07-31T11:49:59Z | 2019-06-14T13:58:17Z | NONE | null | `convert_objects` doesn't exist anymore, and the example in the release notes when it was added is failing and generating a warning in the doc build:
```
Exception in /home/travis/build/pandas-dev/pandas/doc/source/whatsnew/v0.11.0.rst at block ending on line None
Specify :okexcept: as an option in the ipython:: block to suppress this message
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-20-d11827bcdf31> in <module>
----> 1 s.convert_objects(convert_dates='coerce')
~/build/pandas-dev/pandas/pandas/core/generic.py in __getattr__(self, name)
5095 if self._info_axis._can_hold_identifiers_and_holds_name(name):
5096 return self[name]
-> 5097 return object.__getattribute__(self, name)
5098
5099 def __setattr__(self, name, value):
AttributeError: 'Series' object has no attribute 'convert_objects'
```
Open during the London sprint.
- [X] xref #24173
- [ ] 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/26680 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26680/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26680/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26680/events | https://github.com/pandas-dev/pandas/issues/26680 | 452,695,648 | MDU6SXNzdWU0NTI2OTU2NDg= | 26,680 | derivative method for Series and DataFrame | {
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} | [] | closed | false | null | [] | null | 3 | 2019-06-05T20:15:57Z | 2019-06-05T21:30:36Z | 2019-06-05T21:30:35Z | CONTRIBUTOR | null | Hello,
when dealing with sensors data (energy meter, volumetric meter, ...) and having such data in a `DataFrame` (or in a `Series`) it can be convenient to easily calculate derivative of values (generally over local time) ie calculate power from energy consumption for example or flow rate from volume over time.
This question have been asked on StackOverflow (at least 2 times)
https://stackoverflow.com/questions/39235712/calculate-local-time-derivative-of-series
https://stackoverflow.com/questions/26245242/time-differentiation-in-pandas/26246562
Maybe `Series` and `DataFrame` could have a `derivative` method.
I wrote some convenient methods (both for `DataFrame` and for `Series`) to calculate derivative.
I currently [monkey patch](https://en.wikipedia.org/wiki/Monkey_patch) Pandas using the following code:
```python
import pandas as pd
def _derivative_series(self):
if isinstance(self.index, pd.DatetimeIndex):
den = self.index.to_series(keep_tz=True).diff().dt.total_seconds()
else:
den = self.index.to_series().diff()
num = self.diff()
return num.div(den, axis=0)
def _derivative_dataframe(self, var=None):
if var is None:
if isinstance(self.index, pd.DatetimeIndex):
den = self.index.to_series(keep_tz=True).diff().dt.total_seconds()
else:
den = self.index.to_series().diff()
num = self.diff()
return num.div(den, axis=0)
else:
if pd.api.types.is_datetime64_any_dtype(self[var]):
den = self[var].diff().dt.total_seconds()
else:
den = self[var].diff()
num = self.loc[:, self.columns != var].diff()
result = num.div(den, axis=0)
result[var] = self[var]
return result.loc[:, self.columns]
def monkey_patch_pandas(pd):
pd.Series.derivative = _derivative_series
pd.DataFrame.derivative = _derivative_dataframe
```
So you can now use `derivative` method with DataFrame with `DatetimeIndex`
```python
import pandas as pd
monkey_patch_pandas(pd)
from io import StringIO
dat = """time,sensor1,sensor2
2019-05-27 13:49:47.703850+02:00,0.0,100.2
2019-05-27 13:49:47.827518+02:00,0.4,102.2
2019-05-27 13:49:47.974124+02:00,0.8,102.4
2019-05-27 13:49:48.097793+02:00,1.1,104.1
2019-05-27 13:49:48.222461+02:00,1.2,101.1
2019-05-27 13:49:48.355105+02:00,1.4,102.0
"""
df = pd.read_csv(StringIO(dat), index_col='time', parse_dates=True)
print("df:")
print(df)
print("")
print("df.derivative():")
print(df.derivative())
print("")
```
it should also work with `Series` with `DatetimeIndex`
```python
print("df['sensor1'].derivative():")
print(df['sensor1'].derivative())
print("")
```
but you can also derivate using a column name as variable
```python
print("df.reset_index().derivative(var='time'):")
print(df.reset_index().derivative(var='time'))
```
`derivate` method should also work fine with `DataFrame` with float index like
```python
dat2 = """x,y1,y2
0.0,0.0,100.2
0.2,0.4,102.2
0.3,0.8,102.4
0.45,1.1,104.1
0.5,1.2,101.1
0.7,1.4,102.0
"""
df = pd.read_csv(StringIO(dat2), index_col='x', parse_dates=True)
print("df:")
print(df)
print("")
print("df.derivative():")
print(df.derivative())
print("")
```
with `Series` also
```python
print("df['y1'].derivative():")
print(df['y1'].derivative())
print("")
```
and given a variable name (a `DataFrame` column name)
```python
print("df.reset_index().derivative(var='x'):")
print(df.reset_index().derivative(var='x'))
```
I wonder if some other Pandas users have similar use cases and / if adding such a feature directly in Pandas code could be valuable.
Kind regards
PS : An other approach could be to make use of [`numpy.gradient`](https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.gradient.html) function | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26681 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26681/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26681/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26681/events | https://github.com/pandas-dev/pandas/issues/26681 | 452,700,560 | MDU6SXNzdWU0NTI3MDA1NjA= | 26,681 | integrate method for Series and DataFrame | {
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} | [] | closed | false | null | [] | null | 1 | 2019-06-05T20:27:33Z | 2019-06-06T19:25:53Z | 2019-06-05T21:31:11Z | CONTRIBUTOR | null | Hello,
Similar to #26680 when dealing with sensors data such as power meter or flow meter and having such data in a `DataFrame` (or in a `Series`) it can be convenient to easily calculate integral of values ie calculate energy consumption from power usage, volume consumption from flow rate for example.
Maybe `Series` and `DataFrame` could have an `integrate` method.
Similar question have been asked on StackOverflow
https://stackoverflow.com/questions/50239878/pandas-dataframe-column-numerical-integration
Maybe it could be worth to provide a better integration with Scipy and make use of `scipy.integrate.trapz`.
Kind regards
PS : related issue may be #3759 | {
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} | 4 | 2019-06-05T23:28:46Z | 2019-06-06T16:12:35Z | 2019-06-06T12:08:41Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas
import numpy
import sqlite3
con = sqlite3.connect('example.db')
df = pandas.DataFrame(
{
'filled': [1.0, 2.0, 3.0],
'partial': [1.0, numpy.nan, numpy.nan],
'empty': [numpy.nan] * 3
}
)
df.to_sql('test', con, index=False)
returned_df = pandas.read_sql_query('select * from test', con)
for col in ['filled', 'partial', 'empty']:
assert df[col].dtype == returned_df[col].dtype, f'Problematic column: {col}, Expected dtype: {df[col].dtype}, actual dtype: {returned_df[col].dtype}'
```
#### Problem description
When a float column queried from a SQL database contains NULL, the type of that column in the resulting DataFrame is `object`, filled with `None` values ('empty' column in the example). NULL values in a column containing at least one non-NULL row ('partial' column in the example) are converted to `float64` dtype, as expected.
This itself is not necessarily an issue, but without a way to assign dtypes to a column, some future uses of the column (such as joining to another numeric column) raise an exception unless the column type is checked and (if necessary) changed. In `read_csv` and `read_excel`, both the `dtype` parameter and the `converters` parameter are capable of the expected behaviour, so implementing one or both of these in `read_sql_query` (and probably `read_sql_table`, which I believe has the same issue) would provide a reasonable workaround.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.19.34-04457-g5b63d4390e96
machine: x86_64
processor:
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: 1.16.3
scipy: None
pyarrow: None
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: 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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} | 3 | 2019-06-06T01:13:19Z | 2020-02-03T03:48:29Z | 2020-02-03T03:48:29Z | NONE | null | Reindexing a tz aware dataframe using method='nearest' raise an internal warning.
```python
from pandas.util.testing import makeTimeDataFrame
df = makeTimeDataFrame(freq='1h')
df = df.tz_localize('UTC')
df.reindex(df.index[1:4], method='nearest')
```
raises a warning:
```
/lib/python3.7/site-packages/pandas/core/indexes/base.py:2820: FutureWarning: Converting timezone-aware DatetimeArray to timezone-naive ndarray with 'datetime64[ns]' dtype. In the future, this will return an ndarray with 'object' dtype where each element is a 'pandas.Timestamp' with the correct 'tz'.
To accept the future behavior, pass 'dtype=object'.
To keep the old behavior, pass 'dtype="datetime64[ns]"'.
target = np.asarray(target)
```
in pandas 0.24.2 | {
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} | 16 | 2019-06-06T03:49:53Z | 2019-06-19T00:41:11Z | 2019-06-19T00:41:04Z | CONTRIBUTOR | null | - [x] closes #26557
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] add whatsnew entry
- [x] add tests
Marks the methods mentioned in #26557 as deprecated, with proper messages. | {
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} | 7 | 2019-06-06T07:42:15Z | 2019-06-11T08:27:06Z | 2019-06-11T08:27:05Z | CONTRIBUTOR | null | closes #23868
Resubmit of PR #26665, which was merged in a bit too eagerly!
Change to test hopefully avoids recursion, although like others, it doesn't reproduce locally for me.
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- [x] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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] | closed | false | null | [] | null | 2 | 2019-06-06T11:35:37Z | 2020-01-21T04:27:19Z | 2020-01-21T04:27:18Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
pd.Series([1, 2, 3, 4]).rolling(2).apply(lambda n: str(n))
```
#### Problem description
When using `rolling(x),apply(func)`, an error is thrown if the return value of `func` is non-numeric. Is this necessary? There are valid cases for a rolling apply function returning a string value.
#### Expected Output
```
0 1
1 2
2 3
3 4
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Linux
pandas: 0.24.2
```
</details>
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] | closed | false | null | [] | null | 1 | 2019-06-06T11:46:41Z | 2019-06-07T01:53:30Z | 2019-06-07T01:52:27Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
def fn(window):
return(0)
pd.Series([1, 2, 3, np.inf, 4, 5, 6]).rolling(2).apply(fn)
```
output:
```
0 NaN
1 0.0
2 0.0
3 NaN
4 NaN
5 0.0
6 0.0
```
#### Problem description
If `series.rolling(x).apply(fn)` does not create a window if any of the values in said window are inf/NaN, and instead returns NaN for the window in question. This seems like an optimisation to reduce the number of window comparisons, but this may not be the behaviour intended by the user.
#### Expected Output
```
0 NaN
1 0.0
2 0.0
3 0.0
4 0.0
5 0.0
6 0.0
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Linux
pandas: 0.24.2
```
</details>
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} | 1 | 2019-06-06T12:02:38Z | 2019-06-07T15:14:35Z | 2019-06-07T15:14:35Z | CONTRIBUTOR | null | ```python
In [17]: a = pd.timedelta_range('2H', periods=4)
In [18]: a <= np.array(a.to_numpy()[0])
```
```pytb
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-e196011a0cff> in <module>
----> 1 a <= np.array(a.to_numpy()[0])
~/sandbox/pandas/pandas/core/indexes/datetimelike.py in wrapper(self, other)
116 other = other._values
117
--> 118 result = op(self._data, maybe_unwrap_index(other))
119 return result
120
~/sandbox/pandas/pandas/core/arrays/timedeltas.py in wrapper(self, other)
78
79 elif not is_list_like(other):
---> 80 return ops.invalid_comparison(self, other, op)
81
82 elif len(other) != len(self):
~/sandbox/pandas/pandas/core/ops.py in invalid_comparison(left, right, op)
1196 else:
1197 raise TypeError("Invalid comparison between dtype={dtype} and {typ}"
-> 1198 .format(dtype=left.dtype, typ=type(right).__name__))
1199 return res_values
1200
TypeError: Invalid comparison between dtype=timedelta64[ns] and ndarray
```
Works for DatetimeIndex.
Raises a different error for PeriodIndex
```python
In [24]: a = pd.period_range('2000', periods=4)
In [25]: a <= np.array(a.to_numpy()[0])
```
```pytb
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-25-e196011a0cff> in <module>
----> 1 a <= np.array(a.to_numpy()[0])
~/sandbox/pandas/pandas/core/indexes/datetimelike.py in wrapper(self, other)
116 other = other._values
117
--> 118 result = op(self._data, maybe_unwrap_index(other))
119 return result
120
~/sandbox/pandas/pandas/core/arrays/period.py in wrapper(self, other)
76 result.fill(nat_result)
77 else:
---> 78 other = Period(other, freq=self.freq)
79 result = op(other.ordinal)
80
~/sandbox/pandas/pandas/_libs/tslibs/period.pyx in pandas._libs.tslibs.period.Period.__new__()
2448 ordinal = converted.ordinal
2449
-> 2450 elif is_null_datetimelike(value) or value in nat_strings:
2451 ordinal = NPY_NAT
2452
TypeError: unhashable type: 'numpy.ndarray'
```
@jbrockmendel would you be interested in looking at this? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26690 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26690/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26690/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26690/events | https://github.com/pandas-dev/pandas/issues/26690 | 453,005,600 | MDU6SXNzdWU0NTMwMDU2MDA= | 26,690 | ImportError: cannot import name 'Timedelta' | {
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] | closed | false | null | [] | null | 7 | 2019-06-06T12:35:18Z | 2019-06-11T06:41:33Z | 2019-06-11T06:41:33Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# X@ubuntu:~/Desktop$ pylivetrader run -f algo.py --backend-config config.yaml
```
#### Problem description
I'm trying to run pylivetrader locally in a conda environment with all requisites met, so far as I can see.
Pandas continually fails.
I have tried multiple Pandas versions and explored both the help sections here as well as thoroughly searched online for possible solutions.
I believe there is a conflict of Pandas versions going on, but based on the output of ``pd.show_versions()``, as I detail below, I'm not quite sure what is going on.
#### Expected Output
I expect pylivetrader to be able to run the algo.py, instead I am faced with ``` ImportError: cannot import name 'Timedelta' ```.
#### Output of ``pd.show_versions()``
Furthermore, the output of the above is:
details>
```AttributeError: module 'pandas' has no attribute 'show_versions'```
However, if I run ```conda list``` in the specific env then I see that:
``` pandas 0.24.2 py37he6710b0_0 ```
/details>
```
Traceback (most recent call last):
File "/home/X.local/bin/pylivetrader", line 7, in <module>
from pylivetrader.__main__ import main
File "/home/X/.local/lib/python3.5/site-packages/pylivetrader/__init__.py", line 16, in <module>
from pylivetrader import api # noqa
File "/home/X/.local/lib/python3.5/site-packages/pylivetrader/api.py", line 25, in <module>
from pylivetrader.finance.asset_restrictions import (
File "/home/X/.local/lib/python3.5/site-packages/pylivetrader/finance/asset_restrictions.py", line 20, in <module>
import pandas as pd
File "/home/X/.local/lib/python3.5/site-packages/pandas/__init__.py", line 42, in <module>
from pandas.core.api import *
File "/home/X/.local/lib/python3.5/site-packages/pandas/core/api.py", line 10, in <module>
from pandas.core.groupby.groupby import Grouper
File "/home/X/.local/lib/python3.5/site-packages/pandas/core/groupby/__init__.py", line 2, in <module>
from pandas.core.groupby.groupby import (
File "/home/X/.local/lib/python3.5/site-packages/pandas/core/groupby/groupby.py", line 49, in <module>
from pandas.core.frame import DataFrame
File "/home/X/.local/lib/python3.5/site-packages/pandas/core/frame.py", line 66, in <module>
from pandas.core.generic import NDFrame, _shared_docs
File "/home/X/.local/lib/python3.5/site-packages/pandas/core/generic.py", line 43, in <module>
from pandas.core.internals import BlockManager
File "/home/X/.local/lib/python3.5/site-packages/pandas/core/internals/__init__.py", line 2, in <module>
from .blocks import ( # noqa:F401
File "/home/X/.local/lib/python3.5/site-packages/pandas/core/internals/blocks.py", line 11, in <module>
from pandas._libs.tslibs import Timedelta, conversion, is_null_datetimelike
ImportError: cannot import name 'Timedelta'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/26691 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26691/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26691/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26691/events | https://github.com/pandas-dev/pandas/pull/26691 | 453,058,466 | MDExOlB1bGxSZXF1ZXN0Mjg1ODI1NjM3 | 26,691 | DEPS: Grouping deps and adding information to environment.yml | {
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} | 6 | 2019-06-06T14:19:50Z | 2019-06-07T07:42:20Z | 2019-06-06T15:46:22Z | MEMBER | null | - [X] closes #26659
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I didn't add or remove any dependency here. I think it makes more sense to keep exactly the same here that the diff is not so clear, and make changes in a follow up PR if needed.
CC: @jreback @TomAugspurger @jorisvandenbossche | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26692 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26692/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26692/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26692/events | https://github.com/pandas-dev/pandas/issues/26692 | 453,081,056 | MDU6SXNzdWU0NTMwODEwNTY= | 26,692 | If tuples used as index pd.read_json( orient='split') does not read file saved by df.to_json(orient='split) | {
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"description": "read_json, to_json, json... | open | false | null | [] | null | 2 | 2019-06-06T15:00:11Z | 2020-11-04T21:51:51Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
a = pd.DataFrame()
a['example'] = [0, 1 ,2, 3 ,4 ,5 ,6]
a.index = [(i, i) for i in range(7)]
a.to_json('test2.json', orient='split')
b = pd.read_json('test2.json', orient='split')
```
#### Problem description
After using tuples as index and saving DataFrame with orient='split', error is thrown during loading via pd.read_json( orient='split').
Full traceback:
--------------------------------------------------------
ValueError Traceback (most recent call last)
~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py in create_block_manager_from_arrays(arrays, names, axes)
1666 blocks = form_blocks(arrays, names, axes)
-> 1667 mgr = BlockManager(blocks, axes)
1668 mgr._consolidate_inplace()
~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py in __init__(self, blocks, axes, do_integrity_check)
113 if do_integrity_check:
--> 114 self._verify_integrity()
115
~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py in _verify_integrity(self)
310 if block._verify_integrity and block.shape[1:] != mgr_shape[1:]:
--> 311 construction_error(tot_items, block.shape[1:], self.axes)
312 if len(self.items) != tot_items:
~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py in construction_error(tot_items, block_shape, axes, e)
1690 raise ValueError("Shape of passed values is {0}, indices imply {1}".format(
-> 1691 passed, implied))
1692
ValueError: Shape of passed values is (7, 1), indices imply (3, 1)
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-36-f5b064e2d9e4> in <module>
3 a.index = [(i, i, i) for i in range(7)]
4 a.to_json('test2.json', orient='split')
----> 5 b = pd.read_json('test2.json', orient='split')
~/anaconda3/lib/python3.7/site-packages/pandas/io/json/json.py in read_json(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit, encoding, lines, chunksize, compression)
425 return json_reader
426
--> 427 result = json_reader.read()
428 if should_close:
429 try:
~/anaconda3/lib/python3.7/site-packages/pandas/io/json/json.py in read(self)
535 )
536 else:
--> 537 obj = self._get_object_parser(self.data)
538 self.close()
539 return obj
~/anaconda3/lib/python3.7/site-packages/pandas/io/json/json.py in _get_object_parser(self, json)
554 obj = None
555 if typ == 'frame':
--> 556 obj = FrameParser(json, **kwargs).parse()
557
558 if typ == 'series' or obj is None:
~/anaconda3/lib/python3.7/site-packages/pandas/io/json/json.py in parse(self)
650
651 else:
--> 652 self._parse_no_numpy()
653
654 if self.obj is None:
~/anaconda3/lib/python3.7/site-packages/pandas/io/json/json.py in _parse_no_numpy(self)
874 loads(json, precise_float=self.precise_float))}
875 self.check_keys_split(decoded)
--> 876 self.obj = DataFrame(dtype=None, **decoded)
877 elif orient == "index":
878 self.obj = DataFrame(
~/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)
446
447 mgr = arrays_to_mgr(arrays, columns, index, columns,
--> 448 dtype=dtype)
449 else:
450 mgr = init_ndarray(data, index, columns, dtype=dtype,
~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/construction.py in arrays_to_mgr(arrays, arr_names, index, columns, dtype)
59 axes = [ensure_index(columns), index]
60
---> 61 return create_block_manager_from_arrays(arrays, arr_names, axes)
62
63
~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py in create_block_manager_from_arrays(arrays, names, axes)
1669 return mgr
1670 except ValueError as e:
-> 1671 construction_error(len(arrays), arrays[0].shape, axes, e)
1672
1673
~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py in construction_error(tot_items, block_shape, axes, e)
1689 raise ValueError("Empty data passed with indices specified.")
1690 raise ValueError("Shape of passed values is {0}, indices imply {1}".format(
-> 1691 passed, implied))
1692
1693
ValueError: Shape of passed values is (7, 1), indices imply (2, 1)
2 in last tuple is number of elements of tuples I use as index.
#### Expected Output
b = a -- dataframe with same content as a
example
(0, 0) 0
(1, 1) 1
(2, 2) 2
(3, 3) 3
(4, 4) 4
(5, 5) 5
(6, 6) 6
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-20-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.2
pytest: 4.5.0
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.7
numpy: 1.16.3
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: 2.0.1
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.1.0
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.8
lxml.etree: 4.3.3
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.3
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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} | 5 | 2019-06-06T15:52:05Z | 2019-06-07T11:29:27Z | 2019-06-07T11:29:24Z | MEMBER | null | Follow up of #26691. `cpplint` was installed from pip, but it's now available in conda-forge.
CC: @TomAugspurger | {
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} | 5 | 2019-06-06T18:47:36Z | 2019-06-07T17:24:15Z | 2019-06-07T17:01:59Z | MEMBER | null | While discussion on other support avenues are still ongoing, I think it would still be beneficial to add the a Sponsors Button to the repo that points to https://pandas.pydata.org/donate.html
Note: https://www.flipcause.com/secure/cause_pdetails/MzA3OA== is the direct page to donate but this link doesn't appear as "donate friendly" IMO
Let me know if there's a better link to reference
Reference to the Support Button on Numpy's page: https://github.com/numpy/numpy/pull/13700
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} | 0 | 2019-06-06T18:58:04Z | 2019-06-10T15:48:30Z | 2019-06-10T15:48:04Z | MEMBER | null | We have a `ci/print_skipped.py` script to print the skipped tests, that is currently used in Travis (see end of `.travis.yml` file). It would be to also do this in Azure as an extra step. | {
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} | [] | closed | false | null | [] | null | 1 | 2019-06-06T19:25:05Z | 2019-06-06T19:29:32Z | 2019-06-06T19:25:27Z | CONTRIBUTOR | null | I'm getting an error when running the tests suite. I've locatet the relevant PR to be #26585.
It is failing on my machine, but not on the CI, so I assume some configuration differences. I'm on a Windows machine, which could be the relevant difference. See my cinfuΓguration at the bottom of this issue.
```
================================================================= FAILURES ==================================================================
______________________ TestTimeConversionFormats.test_int_to_datetime_format_YYYYMMDD_typeerror[2012010101-2012010101] ______________________
self = <pandas.tests.indexes.datetimes.test_tools.TestTimeConversionFormats object at 0x000002950CC92E80>, int_date = 2012010101
expected = 2012010101
@pytest.mark.parametrize('int_date, expected', [
# valid date, length == 8
[20121030, datetime(2012, 10, 30)],
# short valid date, length == 6
[199934, datetime(1999, 3, 4)],
# long integer date partially parsed to datetime(2012,1,1), length > 8
[2012010101, 2012010101],
# invalid date partially parsed to datetime(2012,9,9), length == 8
[20129930, 20129930],
# short integer date partially parsed to datetime(2012,9,9), length < 8
[2012993, 2012993],
# short invalid date, length == 4
[2121, 2121]])
def test_int_to_datetime_format_YYYYMMDD_typeerror(self, int_date,
expected):
# GH 26583
> result = to_datetime(int_date, format='%Y%m%d', errors='ignore')
pandas\tests\indexes\datetimes\test_tools.py:152:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas\util\_decorators.py:188: in wrapper
return func(*args, **kwargs)
pandas\core\tools\datetimes.py:626: in to_datetime
result = convert_listlike(np.array([arg]), box, format)[0]
pandas\core\tools\datetimes.py:270: in _convert_listlike_datetimes
arg, format, exact=exact, errors=errors)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> val[found.end():])
E TypeError: 'int' object is unsliceable
pandas\_libs\tslibs\strptime.pyx:149: TypeError
________________________ TestTimeConversionFormats.test_int_to_datetime_format_YYYYMMDD_typeerror[20129930-20129930] ________________________
self = <pandas.tests.indexes.datetimes.test_tools.TestTimeConversionFormats object at 0x000002950D025C18>, int_date = 20129930
expected = 20129930
@pytest.mark.parametrize('int_date, expected', [
# valid date, length == 8
[20121030, datetime(2012, 10, 30)],
# short valid date, length == 6
[199934, datetime(1999, 3, 4)],
# long integer date partially parsed to datetime(2012,1,1), length > 8
[2012010101, 2012010101],
# invalid date partially parsed to datetime(2012,9,9), length == 8
[20129930, 20129930],
# short integer date partially parsed to datetime(2012,9,9), length < 8
[2012993, 2012993],
# short invalid date, length == 4
[2121, 2121]])
def test_int_to_datetime_format_YYYYMMDD_typeerror(self, int_date,
expected):
# GH 26583
> result = to_datetime(int_date, format='%Y%m%d', errors='ignore')
pandas\tests\indexes\datetimes\test_tools.py:152:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas\util\_decorators.py:188: in wrapper
return func(*args, **kwargs)
pandas\core\tools\datetimes.py:626: in to_datetime
result = convert_listlike(np.array([arg]), box, format)[0]
pandas\core\tools\datetimes.py:270: in _convert_listlike_datetimes
arg, format, exact=exact, errors=errors)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> val[found.end():])
E TypeError: 'int' object is unsliceable
pandas\_libs\tslibs\strptime.pyx:149: TypeError
_________________________ TestTimeConversionFormats.test_int_to_datetime_format_YYYYMMDD_typeerror[2012993-2012993] _________________________
self = <pandas.tests.indexes.datetimes.test_tools.TestTimeConversionFormats object at 0x000002950CEC7908>, int_date = 2012993
expected = 2012993
@pytest.mark.parametrize('int_date, expected', [
# valid date, length == 8
[20121030, datetime(2012, 10, 30)],
# short valid date, length == 6
[199934, datetime(1999, 3, 4)],
# long integer date partially parsed to datetime(2012,1,1), length > 8
[2012010101, 2012010101],
# invalid date partially parsed to datetime(2012,9,9), length == 8
[20129930, 20129930],
# short integer date partially parsed to datetime(2012,9,9), length < 8
[2012993, 2012993],
# short invalid date, length == 4
[2121, 2121]])
def test_int_to_datetime_format_YYYYMMDD_typeerror(self, int_date,
expected):
# GH 26583
> result = to_datetime(int_date, format='%Y%m%d', errors='ignore')
pandas\tests\indexes\datetimes\test_tools.py:152:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas\util\_decorators.py:188: in wrapper
return func(*args, **kwargs)
pandas\core\tools\datetimes.py:626: in to_datetime
result = convert_listlike(np.array([arg]), box, format)[0]
pandas\core\tools\datetimes.py:270: in _convert_listlike_datetimes
arg, format, exact=exact, errors=errors)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> val[found.end():])
E TypeError: 'int' object is unsliceable
pandas\_libs\tslibs\strptime.pyx:149: TypeError
================================================= 3 failed, 394 deselected in 2.40 seconds ==================================================
```
<details>
INSTALLED VERSIONS
------------------
commit: 649ad5c91aabb95d7f200eec84b82cae1459fc65
python: 3.7.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.25.0.dev0+616.g7f0a7435e
pytest: 4.3.0
pip: 18.1
setuptools: 40.6.2
Cython: 0.29
numpy: 1.16.4
scipy: 1.1.0
pyarrow: 0.11.1
xarray: None
IPython: 7.5.0
sphinx: 1.8.2
patsy: None
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: None
tables: 3.5.1
numexpr: 2.6.8
feather: None
matplotlib: 3.0.1
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: None
lxml.etree: 4.3.3
bs4: 4.7.1
html5lib: 1.0.1
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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} | 3 | 2019-06-06T21:18:16Z | 2019-10-15T20:09:12Z | 2019-06-07T11:35:18Z | CONTRIBUTOR | null | Follow-up to #26565. I didn't ensure a python int in ``RangeIndex.get_loc`` in the original PR, causing a slow performance when using a key with a wrong type:
```python
In [1]: rng = pd.RangeIndex(1_000_000)
In [2]: %timeit rng.get_loc(np.int64(900_000))
156 ms Β± 1.03 ms per loop # master
478 ns Β± 6.3 ns per loop # this PR
```
Also some cleanups in ``__getitem__``. | {
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} | 12 | 2019-06-07T01:44:49Z | 2019-06-10T23:09:33Z | 2019-06-10T15:48:05Z | CONTRIBUTOR | null | - [x] closes #26695
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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I mean something like:
```python
def columns_as_multiindex(df, inplace=False):
"""Replace flat (:obj:`pandas.Index`) columns with hierarchical (:obj:`pandas.MultiIndex`).
Parameters
----------
df : pandas.DataFrame
Dataframe wich contains the columns to be converted to MultiIndex.
inplace : bool (default False)
If ``True``, do operation inplace and return ``None``.
Returns
-------
pandas.DataFrame or None
Returns dataframe with modifed columns or ``None`` (depends on `inplace` parameter value).
Examples
--------
>>> df0 = pd.DataFrame([[1,2], [10, 20], [100, 200]], columns=["A", "B"])
>>> df0
... A B
... 0 1 2
... 1 10 20
... 2 100 200
>>> columns = pd.MultiIndex.from_tuples([("C", "C0"), ("D", "D0")])
>>> df1 = pd.DataFrame([[1,3], [10, 30], [100, 300]], columns=columns)
>>> df1
... C D
... C0 D0
... 0 1 3
... 1 10 30
... 2 100 300
Merging ``df0`` with ``df1`` returns the dataframe contains tuples in the column names
>>> df = pd.merge(df0, df1, left_on="A", right_on=[("C", "C0")])
>>> df
... A B (C, C0) (D, D0)
... 0 1 2 1 3
... 1 10 20 10 30
... 2 100 200 100 300
It's a bit ugly. Let's beautify the header
>>> columns_as_multiindex(df)
>>> df
... A B C D
... C0 D0
... 0 1 2 1 3
... 1 10 20 10 30
... 2 100 200 100 300
"""
columns = df.columns.tolist()
columns = [(c,) if isinstance(c, str) or not np.iterable(c) else c for c in columns]
max_len = max([len(c) for c in columns])
columns = [tuple(c + ("",) * (max_len - len(c))) for c in columns]
columns = pd.MultiIndex.from_tuples(columns)
if inplace:
df.columns = columns
else:
df_new = df.copy()
df_new.columns = columns
return df_new
```
**UPD 2019-06-13** A bit more robust function on input data.
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} | 11 | 2019-06-07T07:56:12Z | 2019-06-09T12:07:27Z | 2019-06-08T23:07:30Z | MEMBER | null | - [X] xref #26648, #26103
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
The clipboard example in the docs is failing when building in azure (#26648), and had to be previously fixed in travis (#26103). I don't think there is any need to run those examples, and we can keep things simpler. This adds the same value to the doc reader, and doesn't require to set up a clipboard service in the doc builds. Also, we're thinking on having a doc build for windows (#26574) not sure if that could cause new problems.
CC: @jorisvandenbossche
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```python
import pandas
data = ([1, 2, 3], [4, 5, 6], [7, 8, 9])
pandas.DataFrame(data)
# the whole process crashes
```
#### Problem description
the whole python process crashes when pandas.DataFrame() is called with a tuple for the data argument.
#### Expected Output
```
1 2 3
0 1 2 3
1 4 5 6
2 7 8 9
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: AMD64 Family 21 Model 19 Stepping 1, AuthenticAMD
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 4.5.0
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.8
numpy: 1.16.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: 2.0.1
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.1.0
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.8
lxml.etree: 4.3.3
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.3
pymysql: 0.9.3
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/26702 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26702/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26702/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26702/events | https://github.com/pandas-dev/pandas/pull/26702 | 453,403,521 | MDExOlB1bGxSZXF1ZXN0Mjg2MDk4NjQz | 26,702 | PERF: restore DatetimeIndex.__iter__ performance by using non-EA implementation | {
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} | 8 | 2019-06-07T08:46:53Z | 2019-06-07T19:35:04Z | 2019-06-07T19:01:02Z | CONTRIBUTOR | null | The `timeseries.TimeIteration.time_iter` benchmark shows a `6.4x` regression here: https://qwhelan.github.io/pandas/#timeseries.Iteration.time_iter?p-time_index=%3Cfunction%20date_range%3E&commits=08395af4-fc24c2cd . An iteration of `asv find` blames https://github.com/pandas-dev/pandas/commit/1fc76b80#diff-26a6d2ca7adfca586aabbb1c9dd8bf36R74 . I believe what is happening is:
- `pandas.core.indexes.datetimelike.ea_passthrough` was modified from calling `getattr()` to being passed a classmethod directly
- The corresponding change in `DatetimeIndexOpsMixin.__init__` then implicitly changed `__iter__` from `DatetimeArray.__iter__` to `DatetimeLikeArrayMixin.__iter__`. The latter must assume an `ExtensionArray`, so is intrinsically slower.
- This regression was then hidden in `asv` due to inadvertent inclusion of memory addresses when using certain parameters. This is resolved in https://github.com/airspeed-velocity/asv/pull/771 and explains why my `asv` results shows this regression while http://pandas.pydata.org/speed/pandas/#timeseries.Iteration.time_iter? does not
Reverting back to old behavior yields a `7x` speedup:
```
$ asv compare HEAD^ HEAD --only-changed
before after ratio
[649ad5c9] [4d0c13cc]
<any_all_fix> <datetime_iter>
- 3.30Β±0.02s 469Β±6ms 0.14 timeseries.Iteration.time_iter(<function date_range>)
- 34.7Β±1ms 11.2Β±0.2ms 0.32 timeseries.Iteration.time_iter_preexit(<function date_range>)
```
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 7 | 2019-06-07T10:44:44Z | 2019-06-10T22:28:20Z | 2019-06-10T22:28:16Z | MEMBER | null | The CI job that is checking that the benchmarks don't break is reporting that an `asv` setting has been renamed.
I think we've got a server where we run the benchmarks, not sure if we replicate the environment there at every run from `environment.yml`. Otherwise I guess we should do it manually before merging this.
@TomAugspurger I think you manage that server? does this make sense?
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https://api.github.com/repos/pandas-dev/pandas/issues/26705 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26705/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26705/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26705/events | https://github.com/pandas-dev/pandas/issues/26705 | 453,510,633 | MDU6SXNzdWU0NTM1MTA2MzM= | 26,705 | Deprecate Series.ftype / DataFrame.ftypes | {
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} | 6 | 2019-06-07T13:20:34Z | 2019-06-11T00:18:58Z | 2019-06-11T00:18:58Z | MEMBER | null | With the `ftype(s)` property, you could check if data was sparse:
```
In [26]: df = pd.DataFrame({'a': pd.SparseArray([1, np.nan, 1])})
In [27]: df
Out[27]:
a
0 1.0
1 NaN
2 1.0
In [28]: df.dtypes
Out[28]:
a Sparse[float64, nan]
dtype: object
In [29]: df.ftypes
Out[29]:
a float64:sparse
dtype: object
```
But now that the dtype itself is a EA SparseDtype, I don't think those `ftype` and `ftypes` attributes serve any good purpose. So let's deprecate them. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26706 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26706/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26706/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26706/events | https://github.com/pandas-dev/pandas/issues/26706 | 453,514,291 | MDU6SXNzdWU0NTM1MTQyOTE= | 26,706 | Recommended way to check for sparse data (DataFrame or Series) | {
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"following_url": "https://api.github.com/users/avinashpancham/following{/oth... | null | 35 | 2019-06-07T13:28:33Z | 2021-07-10T02:28:27Z | null | MEMBER | null | Previously, you check for the SparseDataFrame class, but now we want people to store sparse data in normal DataFrames/Series.
So for a library developer, what is the recommended way to check for sparse data?
For a Series (or SparseArray), the `pd.api.types.is_sparse` still works on a plain Series with sparse data:
```
In [42]: df = pd.DataFrame({'a': pd.SparseArray([1, np.nan, 1])})
In [43]: s = df['a']
In [44]: type(s)
Out[44]: pandas.core.series.Series
In [45]: pd.api.types.is_sparse(s)
Out[45]: True
```
but for a DataFrame that function does not work. So you could apply that method on each column/dtype, and check with `any` or `all` depending on your requirements:
```
In [56]: df = pd.DataFrame({'a': pd.SparseArray([1, np.nan, 1]), 'b': [1, 2, 3]})
In [57]: df.dtypes.apply(pd.api.types.is_sparse)
Out[57]:
a True
b False
dtype: bool
In [58]: df.dtypes.apply(pd.api.types.is_sparse).any()
Out[58]: True
In [59]: df.dtypes.apply(pd.api.types.is_sparse).all()
Out[59]: False
```
So that actually works quite OK, now I am writing it.
Do we want to make this even easier somehow? Or document this? | {
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} | 3 | 2019-06-07T14:30:53Z | 2019-06-07T16:03:10Z | 2019-06-07T15:14:35Z | MEMBER | null | - [x] closes #26689
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
The long-term solution to problems of this kind will be #23853. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26708 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26708/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26708/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26708/events | https://github.com/pandas-dev/pandas/pull/26708 | 453,547,450 | MDExOlB1bGxSZXF1ZXN0Mjg2MjE1Njc5 | 26,708 | set eval(engine="python") for python2 | {
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} | [] | closed | false | null | [] | null | 5 | 2019-06-07T14:37:59Z | 2019-06-08T21:52:54Z | 2019-06-08T21:52:53Z | NONE | null | see issue https://github.com/pandas-dev/pandas/issues/26549
- [ ] 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/26709 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26709/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26709/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26709/events | https://github.com/pandas-dev/pandas/issues/26709 | 453,598,969 | MDU6SXNzdWU0NTM1OTg5Njk= | 26,709 | Broken benchmarks | {
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"description": ... | closed | false | null | [] | null | 2 | 2019-06-07T16:34:40Z | 2019-06-07T20:34:08Z | 2019-06-07T20:34:08Z | MEMBER | null | The benchmarks are currently broken, and are making red the CI of all PRs that modify something in `asv_bench` (we run the CI check for benchmarks when something is modified in that directory).
This has probably been caused by #26225. But it's strange that the CI for that PR wasn't red, since it run the benchmark checks.
This is blocking #26414, #26703. The error can be seen here: https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=12407
```
[ 16.10%] Β·Β·Β· ...ervalIndexMethod.time_intersection_duplicate 1/3 failed
[ 16.10%] Β·Β·Β· ========== ==========
param1
---------- ----------
1000 1.76Β±0ms
100000 73.3Β±0ms
10000000 failed
========== ==========
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26710 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26710/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26710/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26710/events | https://github.com/pandas-dev/pandas/issues/26710 | 453,629,263 | MDU6SXNzdWU0NTM2MjkyNjM= | 26,710 | New accessor API | {
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... | open | false | null | [] | null | 17 | 2019-06-07T17:58:19Z | 2021-07-10T02:32:49Z | null | MEMBER | null | Currently, to extend pandas `Series`, `DataFrame` and `Index` with user-defined methods, we use accessors in the next way:
```python
@pandas.api.extensions.register_series_accessor('emoji')
class Emoji:
def __init__(self, data):
self.data = data
def is_monkey(self):
"""
This would create `Series().emoji.is_monkey`
"""
return self.data.isin(['π', 'π', 'π'])
```
While this works well, I think there are two problems with this approach:
- The API looks somehow intimidating, and it's not well known. I think because `pandas.api.extensions.register_series_accessor` is too long and lives in `pandas.api`, separate of functionality most users know.
- It's not possible to register methods directly (`Series().is_monkey` instead of `Series().emoji.is_monkey`)
I think all the projects extending pandas I've seen, simply "inject" the methods (except the ones implemented by pandas maintainers). For example:
- https://github.com/PatrikHlobil/Pandas-Bokeh/blob/master/pandas_bokeh/__init__.py#L20
- https://github.com/nalepae/pandarallel/blob/master/pandarallel/pandarallel.py#L52
What I propose is to have a easier/simpler API for the user. To be specific, this is the syntax I'd like when extending `Series`...
```python
import pandas
@pandas.Series.extend('emoji')
class Emoji:
def __init__(self, data):
self.data = data
def is_monkey(self):
"""
This would create `Series().emoji.is_monkey`
"""
return self.data.isin(['π', 'π', 'π'])
@pandas.Series.extend(namespace='emoji')
def is_monkey(data):
"""
This would also create `Series().emoji.is_monkey`
"""
return data.isin(['π', 'π', 'π'])
@pandas.Series.extend
class Emoji:
def __init__(self, data):
self.data = data
def is_monkey(self):
"""
This would directly create `Series().is_monkey`
"""
return self.data.isin(['π', 'π', 'π'])
@pandas.Series.extend
def is_monkey(data):
"""
This would create `Series().emoji.is_monkey`
"""
return data.isin(['π', 'π', 'π'])
```
This would make things much easier for the user, because:
- The name `pandas.Series.extend` is much easier to remember
- A single function can be used (without creating a class)
- A direct method of `Series`... can be created
CC: @pandas-dev/pandas-core | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26711 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26711/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26711/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26711/events | https://github.com/pandas-dev/pandas/pull/26711 | 453,654,805 | MDExOlB1bGxSZXF1ZXN0Mjg2MzAyMTU3 | 26,711 | PERF: perform a unique intersection with IntervalIndex if at least one side is unique | {
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} | 6 | 2019-06-07T19:08:56Z | 2019-07-20T09:39:54Z | 2019-06-10T22:20:53Z | CONTRIBUTOR | null | Resolves https://github.com/pandas-dev/pandas/issues/26709
```
before after ratio
[3ff4f38f] [30bd5369]
<datetime_iter~1>
- 749Β±100ΞΌs 365Β±70ΞΌs 0.49 index_object.IntervalIndexMethod.time_intersection_duplicate(1000)
- 59.6Β±3ms 8.04Β±0.2ms 0.13 index_object.IntervalIndexMethod.time_intersection_duplicate(100000)
failed 2.50Β±0.01s n/a index_object.IntervalIndexMethod.time_intersection_duplicate(10000000)
```
- [ ] 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/26712 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26712/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26712/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26712/events | https://github.com/pandas-dev/pandas/pull/26712 | 453,657,592 | MDExOlB1bGxSZXF1ZXN0Mjg2MzA0NDMy | 26,712 | ASV: remove large IntervalIndex benchmark | {
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} | 3 | 2019-06-07T19:17:00Z | 2019-06-07T20:37:18Z | 2019-06-07T20:34:08Z | CONTRIBUTOR | null | Closes #26709
The `setup` for that `N` took 30s+ on my machine. I doubt that was intended. | {
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