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} | 1 | 2020-03-19T04:11:32Z | 2020-03-20T14:18:28Z | 2020-03-20T10:29:24Z | CONTRIBUTOR | null | GH30999
- [ ] ref #30999
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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... | closed | false | null | [] | null | 3 | 2020-03-19T04:23:13Z | 2021-07-30T03:52:02Z | 2021-07-30T03:52:02Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
>>> series = pd.Series(['2.5', '3', np.nan, 'A'])
>>> pd.to_numeric(series)
Traceback (most recent call last):
File "pandas/_libs/lib.pyx", line 1926, in pandas._libs.lib.maybe_convert_numeric
ValueError: Unable to parse string "A"
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/dwales/anaconda3/lib/python3.6/site-packages/pandas/core/tools/numeric.py", line 150, in to_numeric
values, set(), coerce_numeric=coerce_numeric
File "pandas/_libs/lib.pyx", line 1963, in pandas._libs.lib.maybe_convert_numeric
ValueError: Unable to parse string "A" at position 3
>>> pd.to_numeric(series, errors='coerce')
0 2.5
1 3.0
2 NaN
3 NaN
dtype: float64
# Index just the rows which were coerced by `to_numeric`
>>> just_coerced_na = pd.to_numeric(series.dropna(), errors='coerce')
>>> just_coerced_na
0 2.5
1 3.0
3 NaN
dtype: float64
>>> series.loc[just_coerced_na[just_coerced_na.isna()].index]
3 A
dtype: object
# Alternative way to mask just the rows coerced by `to_numeric`
>>> overall_coerced = pd.to_numeric(series, errors='coerce')
>>> numeric_coerced = pd.to_numeric(series.dropna(), errors='coerce')
>>> mask = overall_coerced.isna() & numeric_coerced.isna()
>>> series[mask]
3 A
dtype: object
```
#### Problem description
As shown in the code above, it is possible to create an index mask to determine which rows of an original series cause `to_numeric` coerce to `NA` or raise `ValueError`. However, the creation of this mask is not immediately obvious, and requires first performing `pd.to_numeric(series, errors='coerce')` on the `dropna` subset of the original data, then subsetting this subset with `isna`, then using the `index` of this second subset to mask the original series.
One might imagine that `series.str.isnumeric()` might reveal which values were or were not numeric, but in fact none of the standard Python string functions use the same definition of 'numeric' as the `pd.to_numeric` function.
A function called something like `pd.is_numeric()` which returns a nullable boolean mask would make this much easier. For example, see the following:
#### Expected Output
```python
>>> series = pd.Series(['2.5', '3', np.nan, 'A'])
>>> series
0 2.5
1 3
2 NaN
3 A
dtype: object
>>> series.is_numeric()
0 True
1 True
2 <NA>
3 False
dtype: boolean
>>> series.loc[series.is_numeric()]
0 2.5
1 3
dtype: object
>>> series.loc[~series.is_numeric()]
3 A
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 1.0.2
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0.post20200309
Cython : 0.29.15
pytest : 5.4.1
hypothesis : 5.5.4
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : 3.6.1
tabulate : 0.8.3
xarray : None
xlrd : 1.2.0
xlwt : 1.2.0
xlsxwriter : 1.2.8
numba : 0.48.0
</details>
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```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | 0 | 2020-03-19T06:46:40Z | 2020-03-19T11:09:03Z | 2020-03-19T11:09:03Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Related to #27977.
Output for pandas.Series.corr, pandas.Series.cov, pandas.Series.count:
```
################################################################################
################################## Validation ##################################
################################################################################
1 Errors found:
No extended summary found
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} | 4 | 2020-03-19T07:26:36Z | 2020-03-19T12:02:46Z | 2020-03-19T10:47:16Z | MEMBER | null | Apart from this being more idiomatic, it also avoids creating a SparseArray through the normal machinery (including validation of the input etc) for the empty list.
With this PR:
```
In [1]: data = np.array([1, 2, 3], dtype=float)
In [2]: index = pd.core.arrays.sparse.IntIndex(5, np.array([0, 2, 4]))
In [3]: dtype = pd.SparseDtype("float64", 0)
In [4]: pd.arrays.SparseArray._simple_new(data, index, dtype)
Out[4]:
[1.0, 0, 2.0, 0, 3.0]
Fill: 0
IntIndex
Indices: array([0, 2, 4], dtype=int32)
In [5]: %timeit pd.arrays.SparseArray._simple_new(data, index, dtype)
381 ns ± 4.83 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
```
while on released version this gives around 50µs (100x slower)
Noticed while investigating https://github.com/pandas-dev/pandas/issues/32196 | {
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"de... | open | false | null | [] | null | 6 | 2020-03-19T09:28:33Z | 2021-10-06T09:24:58Z | null | NONE | null | #### Code Sample
```python
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: s = pd.Series([1,2,np.nan], dtype='Int64')
In [4]: t = pd.Series([1,2,3], dtype='Int64')
In [5]: s-t
Out[5]:
0 0
1 0
2 <NA>
dtype: Int64
In [6]: (s-t).dtype
Out[6]: Int64Dtype()
In [7]: s.to_frame() - t.to_frame()
Out[7]:
0
0 0
1 0
2 <NA>
In [8]: (s.to_frame() - t.to_frame())[0].dtype
Out[8]: dtype('O')
```
#### Problem description
The Int64 dtype is changed when doing operations on two dataframes whereas it is kept (expected behavior) on series operations.
#### Expected Output
```
# In [8]: (s.to_frame() - t.to_frame())[0].dtype
# Out[8]: dtype('Int64Dtype()')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : 0.4.0
scipy : None
sqlalchemy : None
tables : None
tabulate : 0.8.6
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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} | 6 | 2020-03-19T11:13:11Z | 2020-04-02T12:38:22Z | 2020-03-31T15:18:11Z | CONTRIBUTOR | null | - [x] xref #28792
Would like feedback on a few issues before continuing to work on this issue :
- File pandas/core/accessor.py . How to handle multiline text? Couldn't find anywhere good recomendations.
- File pandas/core/ops/docstrings.py . Changed logic of templates. Is this okay? Also new lines with formatted text are too long, would like a reccomended sollution for this.
- File pandas/core/window/rolling.py and some others in regard to window functions have broken links that can be fixed by adding pandas. prefix to them(as in changed code). Is this right thing to do and should I make this changes whenever i see this?
Sorry for obvious mistakes, just starting to contribute. Thanks. | {
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} | 13 | 2020-03-19T11:59:13Z | 2020-03-22T20:33:51Z | 2020-03-22T20:33:47Z | CONTRIBUTOR | null | This optimizes `DataFrame.sparse.from_spmatrix` performance, using an approach proposed by @jorisvandenbossche in https://github.com/pandas-dev/pandas/issues/32196#issuecomment-600824238
Bulds on top of https://github.com/pandas-dev/pandas/pull/32821 and adds another ~4.5x speed up in addition to that PR. Benchmarks for run time (in seconds) are done by running `pd.DataFrame.sparse.from_spmatrix` on a random sparse CSR array of given n_samples, n_features with a density=0.01:
```
label PR master Speed-up master/PR
n_samples n_features
100 100000 2.3624 10.1247 4.29x
10000 10000 0.2391 1.1037 4.62x
100000 100 0.0031 0.0134 4.32x
```
with the benchmarking code below,
<details>
```py
import pandas as pd
import numpy as np
import scipy.sparse
from neurtu import timeit, delayed
def bench_cases():
for n_samples, n_features in [(100, 100000), (10000, 10000), (100000, 100)]:
X = scipy.sparse.rand(
n_samples, n_features, random_state=0, density=0.01, format="csr"
)
tags = {"n_samples": n_samples, "n_features": n_features, "label": "PR"}
yield delayed(pd.DataFrame.sparse.from_spmatrix, tags=tags.copy())(X)
res = timeit(bench_cases())
res = (
res.reset_index()
.set_index(["n_samples", "n_features", "label"])["wall_time"]
.unstack(-1)[["PR"]]
.round(4)
)
# res["master"] = np.array([10.1247, 1.1037, 0.0134])
# res["Speed-up master/PR"] = (res["master"] / res["PR"]).round(2).astype("str") + "x"
print(res)
```
</details>
Closes https://github.com/pandas-dev/pandas/issues/32196 although further optimization might be possible. Around 90% of remaining run time happens in `DataFrame._from_arrays` which goes deeper into pandas internals. Maybe some checks could be disabled there, but that looks less straightforward. | {
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} | 6 | 2020-03-19T13:51:36Z | 2020-03-20T10:30:19Z | 2020-03-19T20:16:31Z | MEMBER | null | This skips the non-consolidatable blocks to determine if the blocks are consolidated. So meaning that if you have multiple EA columns with the same dtype, we do not do an unnecessary consolidation.
From investigating https://github.com/pandas-dev/pandas/issues/32196#issuecomment-600824238
@rth this should give another speed-up to your benchmark | {
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to the maintenance branch.
```
Contributors
============
A total of 8 people contributed patches to this release. People with a
"+" by their names contributed a patch for the first time.
* Daniel Saxton
* Joris Van den Bossche
* MeeseeksMachine
* MomIsBestFriend
* Pandas Development Team
* Simon Hawkins
* Tom Augspurger
* jbrockmendel
```
Fixed
```
Contributors
============
A total of 22 people contributed patches to this release. People with a
"+" by their names contributed a patch for the first time.
* Anna Daglis +
* Daniel Saxton
* Irv Lustig
* Jan Škoda +
* Joris Van den Bossche
* Justin Zheng +
* Kaiqi Dong
* Kendall Masse +
* Marco Gorelli
* Matthew Roeschke +
* Pedro Reys +
* Prakhar Pandey +
* Robert de Vries +
* Rushabh Vasani +
* Simon Hawkins +
* Stijn Van Hoey +
* Terji Petersen +
* Tom Augspurger
* William Ayd
* alimcmaster1
* gfyoung +
* jbrockmendel
```
Closes https://github.com/pandas-dev/pandas/issues/31717 | {
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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2020-03-19T17:57:06Z | 2020-03-21T15:17:51Z | 2020-03-20T16:09:27Z | MEMBER | null | - [x] ref #30755
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 5 | 2020-03-19T18:14:57Z | 2020-04-10T17:11:36Z | 2020-03-21T21:00:31Z | MEMBER | null | In trying to figure out the difference between Block.set vs Block.setitem I found that ExtensionBlock.set is not inplace like it is supposed to be. Traced this back to a problem in CategoricalBlock.should_store, which this fixes+tests.
In separate passes I would like to
- rename set and setitem to something like "setitem_inplace" and "setitem_newobj"
- ATM setitem is _sometimes_ inplace; I'd like to make that consistent. | {
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} | 5 | 2020-03-19T18:46:12Z | 2020-03-20T11:55:08Z | 2020-03-20T00:22:27Z | MEMBER | null | There is CI failure in the ```Web/Docs```, under ```check ipython directive errors``` on github actions, noticed on #32831 and on #32830
The error that is being shown:
```
Run ! grep -B1 "^<<<-------------------------------------------------------------------------$" sphinx.log
FutureWarning,
<<<-------------------------------------------------------------------------
##[error]Process completed with exit code 1.
```
---
The root cause of the problem, is a warning that is generated during the creation of the docs.
```
>>>-------------------------------------------------------------------------
Warning in /home/runner/work/pandas/pandas/doc/source/user_guide/scale.rst at block ending on line 254
Specify :okwarning: as an option in the ipython:: block to suppress this message
----------------------------------------------------------------------------
/home/runner/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/fsspec/implementations/local.py:33: FutureWarning: The default value of auto_mkdir=True has been deprecated and will be changed to auto_mkdir=False by default in a future release.
FutureWarning,
<<<-------------------------------------------------------------------------
```
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} | 5 | 2020-03-19T19:09:43Z | 2020-03-20T00:40:23Z | 2020-03-20T00:22:27Z | MEMBER | null | - [x] closes #32832
- [ ] tests added / passed
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Not sure if we need to totally re-implement cut. Please feel free to give any advice, I am new to contributing. | {
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} | 3 | 2020-03-19T20:18:08Z | 2020-03-29T05:06:01Z | 2020-03-22T20:34:44Z | CONTRIBUTOR | null | More descriptive SpecificationError message that reports to user non-existing columns causing error
- [x] closes #32755
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/32837 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32837/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32837/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32837/events | https://github.com/pandas-dev/pandas/pull/32837 | 584,696,798 | MDExOlB1bGxSZXF1ZXN0MzkxMjM0ODYw | 32,837 | DOC: Remove `# doctest: +SKIP` | {
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- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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---
I want to be sure that the output of describe is deterministic, I will rebase regularly and if it passes all the times, I will mark it as ready for review. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/32838 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32838/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32838/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32838/events | https://github.com/pandas-dev/pandas/issues/32838 | 584,701,580 | MDU6SXNzdWU1ODQ3MDE1ODA= | 32,838 | DataFrame.replace fails to replace value when columns are specified and only non-replacement columns contain pd.NA | {
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"description": "Related to pd.NA and nul... | closed | false | null | [] | null | 4 | 2020-03-19T21:09:43Z | 2020-04-14T20:33:19Z | 2020-04-14T20:33:19Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
In [87]: df2 = pd.DataFrame([['a', 1], ['b', pd.NA]])
In [108]: df2
Out[108]:
0 1
0 a 1
1 b <NA>
In [109]: df2.replace({0: 'a'}, np.nan)
/home/torstein/anaconda3/lib/python3.7/site-packages/pandas/core/missing.py:47: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
mask = arr == x
Out[111]:
0 1
0 a 1
1 b <NA>
In [112]: df2.dtypes
Out[112]:
0 object
1 object
dtype: object
# This works:
In [121]: df2[0].replace('a', np.nan)
Out[121]:
0 NaN
1 b
Name: 0, dtype: object
# This also works:
In [126]: pd.DataFrame([['a', 1], ['b', np.nan]]).replace({0: 'a'}, 'b')
Out[126]:
0 1
0 b 1.0
1 b NaN
```
#### Problem description
There are multiple similar issues, but in this case, there are no NaNs in the column specified by the replacement-dictionary. If the dataframe is created without additional column `1` with a `pd.NA`, even though no replacement is performed on that column, then replace works.
#### Expected Output
'a' to be replaced with nan
#### Output of ``pd.show_versions()``
<details>
pandas : 1.0.2
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0.post20191030
Cython : 0.29.13
pytest : 5.2.2
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : 1.2.2
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 2.2.3
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.2.2
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.10
tables : 3.5.2
tabulate : 0.8.5
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.2
numba : 0.46.0
</details>
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} | 1 | 2020-03-19T21:11:33Z | 2020-03-25T20:36:11Z | 2020-03-22T20:30:22Z | CONTRIBUTOR | null | - [x] closes #26218
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 1 | 2020-03-19T21:31:13Z | 2020-04-06T09:26:08Z | 2020-03-19T22:20:36Z | MEMBER | null | Following https://github.com/pandas-dev/pydata-sphinx-theme/issues/102, need to use the new package name
We could actually also start using the released version. But going to merge this soon when CI passes (as I think doc build in other PRs will start to fail) | {
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} | 0 | 2020-03-19T21:32:49Z | 2020-04-04T00:01:46Z | 2020-04-04T00:01:46Z | CONTRIBUTOR | null | #### Code Sample
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({"A": [0, 1, np.NaN], "B": [1, 2, 3]})
df.groupby(["A"]).count()
```
#### Problem description
This randomly causes a SegFault or Bus error.
With bounds checking enabled I get the following IndexError.
https://github.com/pandas-dev/pandas/blob/master/pandas/_libs/lib.pyx#L778
```python
@cython.boundscheck(True)
@cython.wraparound(False)
def count_level_2d(ndarray[uint8_t, ndim=2, cast=True] mask,
...
```
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/pandas/pandas/core/groupby/generic.py", line 1784, in count
blocks = [make_block(val, placement=loc) for val, loc in zip(counted, locs)]
File "/pandas/pandas/core/groupby/generic.py", line 1784, in <listcomp>
blocks = [make_block(val, placement=loc) for val, loc in zip(counted, locs)]
File "/pandas/pandas/core/groupby/generic.py", line 1782, in <genexpr>
lib.count_level_2d(x, labels=ids, max_bin=ngroups, axis=1) for x in vals
File "pandas/_libs/lib.pyx", line 803, in pandas._libs.lib.count_level_2d
counts[i, labels[j]] += mask[i, j]
IndexError: Out of bounds on buffer access (axis 1)
```
#### Expected Output
```python
B
A
0.0 1
1.0 1
```
### Note
This issue is related to https://github.com/pandas-dev/pandas/issues/21824. The Segfault happens in the same function, but the call stack is different.
Code sample from https://github.com/pandas-dev/pandas/issues/21824:
```python
import numpy as np
import pandas as pd
df = pd.DataFrame({"Person": ["John", "Myla", None, "John", "Myla"],
"Age": [24., np.nan, 21., 33, 26],
"Single": [False, True, True, True, False]})
res = df.set_index(["Person", "Single"]).count(level="Person")
```
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/pandas/pandas/core/frame.py", line 7786, in count
return self._count_level(level, axis=axis, numeric_only=numeric_only)
File "/pandas/pandas/core/frame.py", line 7842, in _count_level
counts = lib.count_level_2d(mask, level_codes, len(level_index), axis=0)
File "pandas/_libs/lib.pyx", line 796, in pandas._libs.lib.count_level_2d
counts[labels[i], j] += mask[i, j]
IndexError: Out of bounds on buffer access (axis 0)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 981252e7adb74b3aca2d9dfd075b0b64c3552975
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Thu Jan 23 06:52:12 PST 2020; root:xnu-4903.278.25~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.0.dev0+870.g981252e7a
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0.post20200311
Cython : 0.29.15
pytest : 5.4.1
hypothesis : 5.6.0
sphinx : 2.4.4
blosc : 1.8.3
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : 0.3.3
gcsfs : None
matplotlib : 3.2.0
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.15.0
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
</details>
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} | 9 | 2020-03-19T21:33:52Z | 2020-04-04T00:01:54Z | 2020-04-04T00:01:47Z | CONTRIBUTOR | null | - [x] closes #32841
- [x] closes #21824
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 2 | 2020-03-19T21:37:48Z | 2020-03-23T17:26:56Z | 2020-03-23T17:24:45Z | CONTRIBUTOR | null | - [x] closes #32550
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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"description": "Requires dis... | closed | false | null | [] | null | 15 | 2020-03-19T21:55:08Z | 2020-06-15T14:06:22Z | 2020-06-14T15:42:03Z | CONTRIBUTOR | null | This is an attempt to add extensibility to the `DataFrame` constructor so that third party libraries can register their own ways of converting to a Pandas Dataframe. It does this by creating a [`singledispatch` function](https://docs.python.org/3/library/functools.html#functools.singledispatch) that is used in the constructor.
For example, Dask could implement the function like this:
```python
from pandas.core.construction import create_dataframe
import dask.datafame
@create_dataframe.register
def _create_dataframe_dask(data: dask.datafame.DataFrame, *args, **kwargs):
return create_dataframe(data.compute(), *args, **kwargs)
```
Then, if a downstream library tries to construct a Pandas dataframe from a dask dataframe, it will work:
```python
import dask
import pandas
df = dask.datasets.timeseries()
assert isinstance(pandas.DataFrame(df), pandas.DataFrame)
```
This is response to [the thread about providing a protocol for dataframes](https://discuss.ossdata.org/t/a-dataframe-protocol-for-the-pydata-ecosystem/267) to present an alternative for the underlying use case. The alternative is:
1. Force libraries like sk learn to depend on pandas
2. Have them called `pandas.Dataframe` on their input data to see if it can be turned into a dataframe
3. Have third party libraries with alternative dataframe implementations register themselves with this function provided here.
It doesn't try to solve any sort of out-of-core dataframe API conversation and it does require all libraries to have Pandas as a hard dependency.
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
- [x] Review name of function
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} | 1 | 2020-03-19T23:33:08Z | 2020-03-21T21:15:37Z | 2020-03-21T20:40:12Z | MEMBER | null | The upcoming branch that implements the last frame-with-series arithmetic ops block-wise is going to need this | {
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} | 1 | 2020-03-20T01:18:59Z | 2020-03-23T20:25:07Z | 2020-03-22T00:12:46Z | MEMBER | null | This makes method-chaining a little bit nicer.
Moving away from using _from_sequence and towards using the constructors where feasible. This will make it easier to stricten what we accept in these _from_sequence methods.
We also should be inferring freq _less_ in arithmetic methods, but that is a behavior change that will be done separately. | {
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} | 2 | 2020-03-20T02:43:02Z | 2020-03-20T20:40:44Z | 2020-03-20T20:40:28Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
ATM we go through the wrong path when setting a listlike entry in a single position.
The broader goal: separate out the cases where setitem is inplace vs copying. | {
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] | closed | false | null | [] | null | 1 | 2020-03-20T02:55:19Z | 2020-09-16T12:31:31Z | 2020-09-16T12:31:20Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
Python 3.8.2 (default, Mar 14 2020, 03:36:42)
[GCC 7.5.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> pd.__version__
'1.0.3'
>>> x = pd.DataFrame()
>>> x[0] = None
>>> x.loc[0, 0] = False
>>> x.loc[1, 0] = True
>>> x
0
0 False
1 True
>>> ~x
0
0 -1
1 -2
>>>
```
#### Problem description
Unexpected behavior when inverting column of dtype object with boolean values.
#### Expected Output
```
>>> ~x
0
0 True
1 False
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-18362-Microsoft
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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} | 1 | 2020-03-20T03:14:09Z | 2020-03-21T21:05:19Z | 2020-03-21T20:35:26Z | MEMBER | null | Make a few tests marginally clearer, avoid some clutter when grepping for `.getitem` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/32853 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32853/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32853/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32853/events | https://github.com/pandas-dev/pandas/issues/32853 | 584,835,951 | MDU6SXNzdWU1ODQ4MzU5NTE= | 32,853 | pandas transforms a datetime column. | {
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} | [] | closed | false | null | [] | null | 4 | 2020-03-20T04:23:09Z | 2020-03-21T03:24:53Z | 2020-03-20T17:54:18Z | NONE | null | There is a csv file with a column in string datetime.
1. I want to load this csv file with pandas and set the string datetime to index, in the mean time, I want to change the datetime year-month-day. for example as the below.
5:06:12
6:08:23
21:09:59
==================================
I want the right result as below
2017-01-01 5:06:12
2017-01-01 6:08:23
2017-01-01 21:09:59
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} | 8 | 2020-03-20T04:39:44Z | 2020-04-16T19:57:01Z | 2020-04-16T19:43:25Z | MEMBER | null | - [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
In the same spirit of https://github.com/pandas-dev/pandas/issues/31845, adding `engine` and `engine_kwargs` arguments to `groupby.transform` (which was easier to tackle first than `groupby.apply`). This signature is the same as what was added to `rolling.apply`.
Constraints:
- The user defined function's first two arguments must be `def f(values, index, ...)`, explicitly those names, as we will pass in the the values and the pandas index (as a numpy array) into the udf | {
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"color": "ff... | closed | false | null | [] | null | 3 | 2020-03-20T06:18:23Z | 2020-06-09T22:50:28Z | 2020-06-09T22:50:28Z | CONTRIBUTOR | null | - [x] closes #9259
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Hopefully this is a more informative message. The original bug report mentions that the index key also fails for Series but didn't report the error type, so I took my best guess at the reporter's intent in the test. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/32856 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32856/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32856/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32856/events | https://github.com/pandas-dev/pandas/pull/32856 | 584,933,875 | MDExOlB1bGxSZXF1ZXN0MzkxNDI0NTA3 | 32,856 | PERF: faster placement creating extension blocks from arrays | {
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} | 10 | 2020-03-20T09:13:09Z | 2020-03-21T21:05:19Z | 2020-03-21T20:20:58Z | MEMBER | null | When creating a DataFrame from many arrays stored in ExtensionBlocks, it seems quite some time is taken inside BlockPlacement using `np.require` on the passed list. Specifying the placement as a slice instead gives a much faster creation of the BlockPlacement. This delays the conversion to an array, though, but afterwards the conversion of the slice to an array inside BlockPlacement when neeeded is faster than an initial creation of a BlockPlacement from a list/array of 1 element.
From investigating https://github.com/pandas-dev/pandas/issues/32196#issuecomment-600824238
@rth this reduces it with another third! (only from the dataframe creation, to be clear) | {
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... | closed | false | null | [] | null | 52 | 2020-03-20T09:39:09Z | 2020-07-28T13:21:55Z | 2020-07-28T13:21:54Z | NONE | null | When trying to install `pandas 1.0.2` or newer versions on a docker, I run into the following import error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Python\lib\site-packages\pandas\__init__.py", line 55, in <module>
from pandas.core.api import (
File "C:\Python\lib\site-packages\pandas\core\api.py", line 29, in <module>
from pandas.core.groupby import Grouper, NamedAgg
File "C:\Python\lib\site-packages\pandas\core\groupby\__init__.py", line 1, in <module>
from pandas.core.groupby.generic import DataFrameGroupBy, NamedAgg, SeriesGroupBy
File "C:\Python\lib\site-packages\pandas\core\groupby\generic.py", line 60, in <module>
from pandas.core.frame import DataFrame
File "C:\Python\lib\site-packages\pandas\core\frame.py", line 124, in <module>
from pandas.core.series import Series
File "C:\Python\lib\site-packages\pandas\core\series.py", line 4572, in <module>
Series._add_series_or_dataframe_operations()
File "C:\Python\lib\site-packages\pandas\core\generic.py", line 10349, in _add_series_or_dataframe_operations
from pandas.core.window import EWM, Expanding, Rolling, Window
File "C:\Python\lib\site-packages\pandas\core\window\__init__.py", line 1, in <module>
from pandas.core.window.ewm import EWM # noqa:F401
File "C:\Python\lib\site-packages\pandas\core\window\ewm.py", line 5, in <module>
import pandas._libs.window.aggregations as window_aggregations
ImportError: DLL load failed while importing aggregations: The specified module could not be found.
```
I've reproduced this error on a separate pc by running:
```cmd
docker run -it python cmd.exe
pip install pandas
python
import pandas
```
This error only occurs when trying to import pandas from the docker. Python and pip versions:
```
python 3.8.2
pip 20.0.2
``` | {
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} | 7 | 2020-03-20T09:48:39Z | 2020-03-20T20:06:07Z | 2020-03-20T20:06:03Z | MEMBER | null | For cases where you know to have valid data (eg you just created them yourself, or they are already validated), it can be useful to skip the validation checks when creating a DataFrame from arrays.
Use case is for example https://github.com/pandas-dev/pandas/pull/32825
From investigating https://github.com/pandas-dev/pandas/issues/32196#issuecomment-600824238
@rth this gives another 20% improvement on the dataframe creation part. Together with https://github.com/pandas-dev/pandas/pull/32856, it gives a bit more than a 2x improvement on the dataframe creation part (once the sparse arrays are created) | {
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} | 4 | 2020-03-20T10:29:01Z | 2021-03-29T14:21:48Z | 2021-03-29T14:21:48Z | NONE | null | #### Code to recreate the issue
```python
import pandas as pd
import numpy as np
s = pd.Series(np.array([np.inf, np.nan, -np.inf]).astype(np.int32))
print(s)
print(s.rank())
s = pd.Series(np.array([np.inf, np.nan, -np.inf]).astype(np.int64))
print(s)
print(s.rank())
s = pd.Series([-9223372036854775808, -9223372036854775808, -9223372036854775808], dtype=np.int64)
print(s.rank())
```
```text
0 -2147483648
1 -2147483648
2 -2147483648
dtype: int32
0 2.0
1 2.0
2 2.0
dtype: float64
0 -9223372036854775808
1 -9223372036854775808
2 -9223372036854775808
dtype: int64
0 NaN
1 NaN
2 NaN
dtype: float64
0 NaN
1 NaN
2 NaN
dtype: float64
```
#### Problem description
rank for **`np.int64`** most negative value gives **NaN** as rank.
`np.int32` works fine for similar case.
#### Expected Output
```text
0 -2147483648
1 -2147483648
2 -2147483648
dtype: int32
0 2.0
1 2.0
2 2.0
dtype: float64
0 -9223372036854775808
1 -9223372036854775808
2 -9223372036854775808
dtype: int64
0 2.0
1 2.0
2 2.0
dtype: float64
0 2.0
1 2.0
2 2.0
dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.2
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0.post20191030
Cython : 0.29.13
pytest : 5.2.2
hypothesis : None
sphinx : 1.8.2
blosc : None
feather : None
xlsxwriter : 1.2.2
lxml.etree : 4.3.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.2.1
fastparquet : 0.1.6
gcsfs : None
lxml.etree : 4.3.0
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 2.5.12
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
pytest : 5.2.2
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.10
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.2
numba : 0.42.0
</details>
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... | open | false | null | [] | null | 8 | 2020-03-20T11:51:21Z | 2021-07-30T03:57:26Z | null | CONTRIBUTOR | null | #### Code Sample
```python
import pandas as pd
# Define a subclass for pd.Series with metadata myprop
class SubclassedSeries(pd.Series):
_metadata = ['myprop']
@property
def _constructor(self):
return SubclassedSeries
@property
def _constructor_expanddim(self):
return SubclassedDataFrame
# Define a subclass for pd.DataFrame that slices to SubclassedSeries
class SubclassedDataFrame(pd.DataFrame):
@property
def _constructor(self):
return SubclassedDataFrame
@property
def _constructor_sliced(self):
return SubclassedSeries
# make an instance of SubclassedSeries and set myprop
sr = SubclassedSeries([1,2,3])
sr.myprop = 'foo'
print("myprob is", sr.myprop) # Works
# put the SubclassedSeries object into a SubclassedDataFrame and try to get myprop
df = SubclassedDataFrame({'a': sr})
print(type(df.a)) # Works (prints <class '__main__.SubclassedSeries'>)
print("myprob is", df.a.myprop) # does not work (AttributeError: 'SubclassedSeries' object has no attribute 'myprop')
```
#### Problem description
`_metadata` items of `pd.Series` subclasses are not propagated when the `SubclassedSeries` object is put into a `SubclassedDataFrame`. I would expect `myprop` to be available in the new `SubclassedDataFrame`.
#### Expected Output
```
myprob is foo
<class '__main__.SubclassedSeries'>
myprob is foo
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-42-lowlatency
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 1.0.2
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0.post20200309
Cython : 0.29.15
pytest : 5.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : 0.15.0
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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} | 5 | 2020-03-20T12:00:27Z | 2020-05-11T13:09:37Z | 2020-05-09T21:54:28Z | CONTRIBUTOR | null | ```python
test_df = pd.DataFrame({'foo' : ['a'], 'bar': [1]})
test_df['bar'] = test_df['bar'].astype('Int64')
test_df.groupby('foo')['bar'].sum(min_count=2)
# output:
foo
a -9223372036854775808
Name: bar, dtype: Int64
```
#### Problem description
Per the documentation, `sum` should return `NA` if there are fewer than `min_count` values. This works fine on the dataframe itself:
```python
test_df['bar'].sum(min_count=2)
# output
nan
```
but gives what looks like an overflow error when called after `groupby`.
I ran into this with real data when calling with `min_count=1` on a dataframe where some of the values were missing, but I thought the minimal example above was clearer.
#### Expected Output
foo
a NaN
Name: bar, dtype: Int64
#### 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 : Linux
OS-release : 4.15.0-74-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : None
pytest : 5.3.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.4
pyxlsb : None
s3fs : None
scipy : 1.3.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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] | closed | false | null | [] | null | 13 | 2020-03-20T13:12:29Z | 2020-03-26T14:03:02Z | 2020-03-26T14:03:02Z | NONE | null | import pandas as pd
import dask.distributed as dd
The above code was working fine till afternoon IST and now, it has started throwing errors.
Perhaps, the version?
AttributeError: module 'pandas._libs.missing' has no attribute 'NAType'
>>> df_train = dd.read_csv(filename_train, blocksize = "60MB")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'dd' is not defined
>>> df_test = dd.read_csv(filename_test, blocksize = "60MB")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'dd' is not defined
>>> df_train = df_train.drop(columns = ['INTERVAL_ID'], axis=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'df_train' is not defined
version
pandas=1.0.2=py37h0573a6f_0 | {
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} | 0 | 2020-03-20T13:15:16Z | 2020-03-26T01:19:31Z | 2020-03-26T01:19:31Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
class MyMapping(Mapping):
def __init__(self, content=None):
if content:
self._content = content
else:
self._content = {}
def __getitem__(self, key):
return self._content[key]
def __iter__(self):
return iter(self._content)
def __len__(self):
return len(self._content)
objs = MyMapping({"a": pd.DataFrame({"_0": [0.], "_1": [0.]}), "b": pd.DataFrame({"_0": [0.], "_1": [0.]})}
concat_objs = pd.concat(objs, axis=1)
```
#### Problem description
The above code raises a TypeError with the message "cannot concatenate object of type '<class 'str'>'; only Series and DataFrame objs are valid".
As the documentation explicitly specifies that the argument `objs` can be a mapping of Series or DataFrame objects, the expected behaviour is to just proceed.
#### Expected Output
The concatenated data frame.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.3.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_GB.UTF-8
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.0.0
Cython : None
pytest : 5.3.2
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.1
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : 3.6.1
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 7 | 2020-03-20T13:16:23Z | 2020-03-25T08:49:10Z | 2020-03-25T00:19:17Z | CONTRIBUTOR | null | While the documentation of ``pandas.DataFrame.concat`` specifies that
any mapping of ``Label`` to ``FrameOrSeries`` is acceptable as the first
argument, but the code actually checks for an instance of ``dict``.
This changeset fixes that restriction.
- [x] closes #32863
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 15 | 2020-03-20T13:45:34Z | 2020-04-27T18:59:43Z | 2020-04-27T18:59:43Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
class ForwardIndexer(BaseIndexer):
def get_window_bounds(self, num_values, min_periods, center, closed):
start = np.empty(num_values, dtype=np.int64)
end = np.empty(num_values, dtype=np.int64)
for i in range(num_values):
if i + min_periods <= num_values:
start[i] = i
end[i] = min(i + self.window_size, num_values)
else:
start[i] = i
end[i] = i + 1
return start, end
x = pd.DataFrame({"a": [1,2,3,4,5,6,7,8,9]})
rolling = x["a"].rolling(ForwardIndexer(window_size=3), min_periods=2)
result = rolling.min()
result
OUT:
0 0.0
1 0.0
2 1.0
3 2.0
4 3.0
5 4.0
6 5.0
7 7.0
8 NaN
Name: a, dtype: float64
IN:
expected = rolling.apply(lambda x: min(x))
expected
OUT:
0 1.0
1 2.0
2 3.0
3 4.0
4 5.0
5 6.0
6 7.0
7 8.0
8 NaN
Name: a, dtype: float64
```
#### Problem description
We state [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html) that we support supplying a custom Indexer when building a `pandas.DataFrame.rolling` object. While the object does get built, and it returns the correct windows, it doesn't support many rolling window functions. The problem is that our implementations of these aggregation functions expect a standard backward-looking window and we support centered windows via a bit of a crutch.
For example, `rolling.min` eventually falls through to `_roll_min_max_variable` in `aggregations.pyx`, which uses [this bit of code](https://github.com/pandas-dev/pandas/blob/6812842d3653e93f9191a59446b1b4b19c77c428/pandas/_libs/window/aggregations.pyx#L1091) to record the output:
```python
for i in range(endi[0], endi[N-1]):
if not Q.empty() and curr_win_size > 0:
output[i-1+close_offset] = calc_mm(
minp, nobs, values[Q.front()])
else:
output[i-1+close_offset] = NaN
```
This indexing of output means that the window minimum gets written near the end of the window, even if the window is forward-looking. I've investigated a bit, and there is a similar issue in `rolling.std` - it also isn't adapted to more flexible rolling windows.
While it's not possible to make rolling window aggregation functions completely universal without loss of efficiency, it's possible to adapt them to most useful cases: forward-looking, smoothly contracting and expanding. We'd still have to think on how we would check that we support a custom Indexer, and whether we would check at all. It might be possible to just specify the supported kinds in the docs and throw a warning or do something similar.
If we choose this path, I'd be happy to deal with the problem over a series of PRs or share the load with someone. Looks like a fair bit of work, but the pandemic freed up a lot of time.
#### Expected Output
```
OUT:
0 1.0
1 2.0
2 3.0
3 4.0
4 5.0
5 6.0
6 7.0
7 8.0
8 NaN
Name: a, dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d308712c8edef078524b8a65df7cb74e9019218e
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : ru_RU.UTF-8
LOCALE : None.None
pandas : 0.26.0.dev0+2635.gd308712c8
numpy : 1.17.5
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 44.0.0.post20200106
Cython : 0.29.14
pytest : 5.3.4
hypothesis : 5.2.0
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : 0.4.0
scipy : 1.3.1
sqlalchemy : 1.3.12
tables : 3.6.1
tabulate : 0.8.6
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.47.0
</details>
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} | 1 | 2020-03-20T14:13:52Z | 2020-03-21T20:19:10Z | 2020-03-21T20:19:05Z | CONTRIBUTOR | null | - [x] closes #14042
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` | {
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"default"... | closed | false | null | [] | null | 45 | 2020-03-20T14:47:53Z | 2021-06-16T14:00:02Z | 2021-06-16T14:00:02Z | MEMBER | null | Currently, for reductions on a DataFrame, we convert the full DataFrame to a single "interleaved" array and then perform the operation. That's the default, but when `numeric_only=True` is specified, it is done block-wise.
Enabling column-wise reductions (or block-wise for EAs):
- Gives better performance in common cases / no need to create a new 2D array (which goes through object dtype for nullable ints)
- Ensures to use the reduction implementation of the EA itself, which can be more correct / more efficient than converting to an ndarray and using our generic nanops.
For illustration purposes, I added a `column_wise` keyword in this PR (not meant to keep this, just for testing), so we can compare a few cases:
```
In [9]: df_wide = pd.DataFrame(np.random.randint(1000, size=(1000,100))).astype("Int64").copy()
In [15]: %timeit df_wide.mean()
9.68 ms ± 100 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [16]: %timeit df_wide.mean(numeric_only=True)
10.1 ms ± 345 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [17]: %timeit df_wide.mean(column_wise=True)
5.22 ms ± 29.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [18]: df_long = pd.DataFrame(np.random.randint(1000, size=(10000,10))).astype("Int64").copy()
In [19]: %timeit df_long.mean()
7.77 ms ± 115 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [20]: %timeit df_long.mean(numeric_only=True)
2.07 ms ± 31.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [21]: %timeit df_long.mean(column_wise=True)
1.04 ms ± 4.54 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
So I experimented with two approaches:
- First by iterating through the columns and calling the `_reduce` of the underlying EA (this path gets taken by using the temporary keyword `column_wise=True`)
- Fixing the block-wise case for extension blocks (triggered by `numeric_only=True`) by changing that to also use `_reduce` of the EA (currently this was failing by calling nanops functions on the EA)
The first gives better performance (it is simpler in implementation by not involding the blocks), but requires some more new code (it uses less the existing machinery).
Ideally, for EA columns, we should always use their own reduction implementation (thus call `EA._reduce`), I think. So for both approaches, the question will be how to trigger this behaviour.
Closes #32651, closes #34520 | {
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} | 1 | 2020-03-20T14:53:32Z | 2020-03-20T15:56:46Z | 2020-03-20T15:56:39Z | CONTRIBUTOR | null | IIUC, type() will report pandas.core.series.Series to me
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | [] | closed | false | null | [] | null | 1 | 2020-03-20T15:39:55Z | 2020-03-21T03:36:04Z | 2020-03-21T03:36:04Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
pd.show_versions()
df = pd.DataFrame([[1.0,20.0,3.0], [30.0,26.0,0.0], [32.0,0.0,86.0], [17.0, 3.0, 9.0]])
df1 = df.iloc[1:]
df2 = pd.DataFrame([[30.0,26.0,0.0], [32.0,0.0,86.0], [17.0, 3.0, 9.0]])
print("\n*** df ***")
print(df)
print("\n*** df1 ***")
print(df1)
print("\n*** df2 ***")
print(df2)
print("\ndf.corr(df1)")
print(df.corrwith(df1, axis=1, drop = True))
print("\ndf.corr(df2)")
print(df.corrwith(df2, axis=1, drop = True))
```
#### Problem description
The method corrwith returns incorrect values (1) when "other" is the same table shifted on one row.
```
df.corr(df1)
1 1.0
2 1.0
3 1.0
```
#### Expected Output
```
df.corr(df2)
0 0.299889
1 -0.877555
2 0.290426
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.5
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.9
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
</details>
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Closes https://github.com/pandas-dev/pandas/issues/32748 | {
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} | 1 | 2020-03-20T17:49:25Z | 2020-03-21T21:02:57Z | 2020-03-21T20:18:16Z | MEMBER | null | some parametrization along the way | {
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- [ ] tests added / passed
- [ ] passes `black pandas`
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} | 2 | 2020-03-20T19:53:29Z | 2020-03-22T22:02:33Z | 2020-03-22T22:02:15Z | MEMBER | null | - [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
xref: https://github.com/pandas-dev/pandas/issues/32845 | {
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} | 1 | 2020-03-20T21:21:23Z | 2020-03-22T00:41:08Z | 2020-03-22T00:16:45Z | MEMBER | null | Pretty much re-wrote the comparison tests to match the patterns we use elsewhere, avoided bare pytest.raises.
A bunch of tests use `Period("NaT", freq)` which ends up being very duplicative. Cut that down a bit and marked a couple tests that belong elsewhere, will move in a separate pass. | {
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} | 1 | 2020-03-20T22:44:11Z | 2020-03-21T21:03:30Z | 2020-03-21T20:14:05Z | MEMBER | null | small unrelated cleanup in test_timeseries | {
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} | 5 | 2020-03-21T00:01:58Z | 2020-03-22T20:58:28Z | 2020-03-22T20:31:05Z | MEMBER | null | I'm split on where to put test_to_xarray. Since its testing one method, tests/generic/methods/ makes sense. BUT we could try to reserve that directory for tests that are nicely parametrized over Series/DataFrame, which these are not. Thoughts? | {
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arr = np.arange(5).astype("c8")
ser = pd.Series(arr)
val = np.finfo(np.float64).max
val = val.astype("c16")
>>> val
(1.7976931348623157e+308+0j)
ser[0] = val
>>> ser
0 inf+0.000000e+00j
1 1.000000e+00+0.000000e+00j
2 2.000000e+00+0.000000e+00j
3 3.000000e+00+0.000000e+00j
4 4.000000e+00+0.000000e+00j
dtype: complex64
```
For IntBlock we check the itemsize to avoid overflows, but for FloatBlock and ComplexBlock we do not, so we get the `inf` here. Should we instead be upcasting? | {
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} | 2 | 2020-03-21T02:55:48Z | 2020-03-22T01:57:40Z | 2020-03-22T00:15:45Z | CONTRIBUTOR | null | * [x] ref #30999
* [x] tests added / passed
* [x] passes `black pandas`
* [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Please note that this file had an error message that was pretty ugly, so I changed
it to only include the class name as I have done for other tests. | {
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} | 3 | 2020-03-21T06:05:14Z | 2020-03-23T02:51:54Z | 2020-03-23T02:51:38Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Related to #27977.
output of `python scripts/validate_docstrings.py pandas.DataFrame.sort_index`:
```
################################################################################
################################## Validation ##################################
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```
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Related to #27977.
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################################################################################
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MacOS 10.14
python 3.6.6
I tried to install pandas from source in a setup virtual environment using pip.
I cloned the repository, created the virtual environment with python -m venv.
I ran pip install -r requirements-dev.txt
Had a few problems with versions of dependencies being too recent and not satisfying requirements, but I manually downgraded them.
And when I ran the above "python setup.py build_ext --inplace -j 0" I got the issue mentioned.
Thanks for your help | {
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} | 1 | 2020-03-21T13:33:34Z | 2020-03-21T20:49:05Z | 2020-03-21T20:06:06Z | CONTRIBUTOR | null | Makes ``MultiIndex.copy`` call ``MultiIndex._shallow_copy`` rather than the other way around. This is cleaner and let's us copy the existing ``.cache``, so may give performance boost when operating on copied MultiIndexes:
```python
>>> n = 100_000
>>> df = pd.DataFrame({'a': range(n), 'b': range(1, n+1)})
>>> mi = pd.MultiIndex.from_frame(df)
>>> mi.get_loc(mi[0]) # also sets up the cache
>>> %timeit mi.copy().get_loc(mi[0])
8.57 ms ± 157 µs per loop # master
57.9 µs ± 798 ns per loop # this PR
```
Also cleans kwargs from the ``MultiIndex._shallow_copy`` signature. This PR is somewhat related to #32669.
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} | 1 | 2020-03-21T13:47:18Z | 2020-03-23T13:42:02Z | 2020-03-21T20:04:41Z | CONTRIBUTOR | null | - [ ] ref #30999
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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`pandas` does a wonderful job when working with time series - e.g. it is easy to find periodic patterns. There are straight forward methods to find these patterns for standard time intervals such as days, weeks, months and years.
However, it is trickier to find these patterns when they occur in other periodicities or irregular but defined intervals. Often religious holidays depend on the moon or a calendar different from the western calendar ([ISO 8601](https://en.wikipedia.org/wiki/ISO_8601#Usage)). Easter is such an example and already has an offset in `pandas`. It would be very practical to get offsets to other holidays as well, like [iftar](https://en.wikipedia.org/wiki/Iftar), [Chinese New Year](https://en.wikipedia.org/wiki/Chinese_New_Year), [Passover](https://en.wikipedia.org/wiki/Passover), [Krishna Janmashtami](https://en.wikipedia.org/wiki/Krishna_Janmashtami) and [Kanamara Matsuri](https://en.wikipedia.org/wiki/Kanamara_Matsuri) to find patterns relative to their timing.
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} | 3 | 2020-03-21T15:06:29Z | 2020-03-22T00:12:11Z | 2020-03-22T00:12:06Z | CONTRIBUTOR | null | Inspired by #30999
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 1 | 2020-03-21T16:01:03Z | 2020-03-23T18:36:59Z | 2020-03-21T20:31:08Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
---
This PR is doing basicly two things:
* Getting rid of ```noqa: E128``` comments.
* Unifying use of```util.foo```
in some places there was a use of ```is_integer_object(foo)``` and in some places there was a use of ```util.is_integer_object(foo)```, now ```util.is_integer_object(foo)``` is being in use instead.
---
Benchmarks:
```
In [1]: from pandas._libs.tslibs.nattype import _make_nat_func
In [2]: %timeit _make_nat_func("foo", "bar")
120 ns ± 1.01 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # Master
117 ns ± 1.47 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # PR
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- [X] closes #32621
- [X] tests passed
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} | 8 | 2020-03-21T19:22:49Z | 2020-07-11T16:02:15Z | 2020-03-26T23:45:57Z | MEMBER | null | - [x] closes #7001
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Would appreciate any feedback on this attempt.
The strategy is to modify the dtype after the aggregation is computed in certain cases when casting. In order for this to work, the cast functions need to be made aware of how the data was aggregated. I've added an optional "how" argument to maybe_downcast_numeric and _try_cast. Because this dtype change is needed in two places, I've added the function groupby_result_dtype to dtypes/common.py to handle the logic.
I wasn't sure where the mapping information needed by groupby_result_dtype should be stored. Currently it is in the function itself, but maybe there is a better place for it.
If this is a good approach, it could potentially be expanded for other aggregations and datatypes. One thought is that perhaps groupby(-).mean() should always return a float for numeric types. | {
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} | 5 | 2020-03-21T19:43:03Z | 2020-03-21T23:00:05Z | 2020-03-21T22:59:56Z | CONTRIBUTOR | null | I applied [this awesome automatic spell fixer](https://github.com/vlajos/misspell-fixer) to our codebase. I needed to do a manual review as it was attempting to fix some names, but the false-positive rate was pretty low!
They also provide a GitHub action for automatic checks on PRs. Might be helpful, especially for the `docs`.
If this is merged, I can create a follow-up issue for a discussion on the CI hook.
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`test_setitem_ndarray_3d` in `pandas/tests/indexing/test_indexing.py` isn't raising for empty index, iloc and DataFrame parametrization.
The mentioned PR added a new `xfail` test for this combination: `test_setitem_ndarray_3d_does_not_fail_for_iloc_empty_dataframe`. The goal of this PR is to remove the `xfail` test and remove the associated `pytest.skip` in `test_setitem_ndarray_3d`. | {
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} | 4 | 2020-03-21T20:58:54Z | 2020-03-23T18:36:08Z | 2020-03-22T20:40:46Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
---
Follow up to https://github.com/pandas-dev/pandas/pull/32794#discussion_r394045101
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} | 1 | 2020-03-21T21:26:36Z | 2020-03-22T01:15:15Z | 2020-03-22T00:17:11Z | CONTRIBUTOR | null | In #32883 I made a PR to fix an issue that ``MultiIndex.copy`` didn't copy the ``_cache``.
This adds a test for this for all index types (there's no futher issue, but it should still be tested for). | {
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- [X] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
These are emitted by golangs `time.Duration` printing: https://github.com/golang/go/blob/36b815edd6cd23d5aabdb488c24db2033bbdeea2/src/time/time.go#L669 | {
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- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Related to #27977.
output of `python scripts/validate_docstrings.py pandas.Series.factorize`:
```
################################################################################
################################## Validation ##################################
################################################################################
```
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Related to #27977.
output of `python scripts/validate_docstrings.py pandas.Series.factorize`:
```
################################################################################
################################## Validation ##################################
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```
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```python
df = pd.DataFrame(np.random.rand(100))
df.columns = pd.MultiIndex.from_tuples([((1,), 1)])
df.loc[:, ((1,), 1)]
# results in an empty dataframe
```
#### Problem description
If a MultiIndex has tuples in one of its levels, you can not use a nested tuple to get that column. It returns empty.
#### Expected Output
Column(s) from the original dataframe.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.3.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.1
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : 0.29.14
pytest : 5.3.5
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.1.8
numba : None
</details>
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} | 8 | 2020-03-22T06:07:56Z | 2020-06-20T11:08:08Z | 2020-06-18T15:21:38Z | CONTRIBUTOR | null | - [ ] closes #28917
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
In to_html, to_latex and to_string, ```col_space``` arg now accepts a list or dict where we can mention the column name to change only a specific column's width.
Haven't add test for to_latex and to_string since they too don't have test for the old col_space and I cannot think of anything less cumbersome than to count added whitespace. | {
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} | 5 | 2020-03-22T14:07:53Z | 2020-03-31T21:26:47Z | 2020-03-31T21:26:47Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame(np.random.random((10,3))*100, columns=['a', 'b', 'c'])
plt.scatter(x='a', y='b', s='c', data=df, alpha=0.3)
df.plot.scatter(x='a', y='b', s='c')
```
#### Problem description
Calling plot.scatter on a pandas dataframe with option for size results in a TypeError. The same data can be plotted directly with matplotlib.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.3.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 46.0.0.post20200311
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : 0.3.3
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.0
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.48.0
```
</details>
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} | 10 | 2020-03-22T14:57:14Z | 2020-05-06T14:32:23Z | 2020-03-26T22:59:45Z | CONTRIBUTOR | null | @jbrockmendel thanks for encouraging me to submit a PR. I'm totally open to any suggested changes; this is just a sketch of something that would work for our needs and hopefully not break anything in pandas.
To those not familiar, some changes introduced in https://github.com/pandas-dev/pandas/pull/31511 broke some functionality of an Index subclass we define in xarray. More details/discussion can be found in https://github.com/pydata/xarray/issues/3751 and https://github.com/pydata/xarray/pull/3764, but the gist of the issue is that for better or for worse, we have been relying on left and right distances in `_get_nearest_indexer` being determined using NumPy arrays rather than Index objects (as they were prior to https://github.com/pandas-dev/pandas/pull/31511).
One way to fix this is to override `_get_nearest_indexer` within our Index subclass, which we've temporarily done in https://github.com/pydata/xarray/pull/3764, but this potentially fragile. It would be great if we could find a solution here.
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 3 | 2020-03-22T15:23:04Z | 2020-03-24T20:00:53Z | 2020-03-24T19:59:40Z | CONTRIBUTOR | null | * [x] ref #30999
* [x] tests added / passed
* [x] passes `black pandas`
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"description": "Related to non-user acce... | open | false | null | [] | null | 2 | 2020-03-22T16:34:50Z | 2020-10-19T11:54:16Z | null | NONE | null | As I read file `pandas/_libs/src/klib/khash.h`, I notice lines 226, 227 in macro `kh_resize_##name` ([source](https://github.com/pandas-dev/pandas/blob/master/pandas/_libs/src/klib/khash.h#L226))
```
h->keys = (khkey_t*)realloc(h->keys, new_n_buckets * sizeof(khkey_t)); \
if (kh_is_map) h->vals = (khval_t*)realloc(h->vals, new_n_buckets * sizeof(khval_t)); \
```
If realloc fail in either statement, it set h->keys or h->vals to NULL and leak original pointer value. Any attempt to deref new value of h->keys or h->vals will lead to undefined behaviors.
Should we assign result of memory allocation to a temporary variable and perform check before reassign to HashTable's internal data? | {
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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- [x] add support for startrow and startcol parameters | {
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] | closed | false | null | [] | null | 2 | 2020-03-22T20:32:32Z | 2020-03-23T14:56:40Z | 2020-03-23T14:56:40Z | NONE | null | When calling the get_daily_adjusted() function from the Alpha Vantage web API to building a DataFrame and calling the pd.pct_change() function on the resulting DataFrame, a SettingWithCopyWarning is raised.
Alternatively, if a similar DataFrame is built calling the DataFrame() constructor method with a call to the pd.pct_change() function, no SettingWithCopyWarning is raised.
```python
Python 3.6.5 |Anaconda, Inc.| (default, Mar 29 2018, 13:32:41) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> from alpha_vantage.timeseries import TimeSeries
>>> import pandas as pd
>>> import datetime
>>>
>>> ts = TimeSeries(key="XXXX", output_format='pandas')
>>> df, meta_data = ts.get_daily_adjusted(symbol='TSLA', outputsize='full')
>>>
>>> df.columns = df.columns.str[3:]
>>> tesla = df[(df.index > '2019-05-01')]
>>>
>>> print(isinstance(tesla, pd.DataFrame))
True
>>>
>>> print(tesla.info())
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 224 entries, 2020-03-20 to 2019-05-02
Data columns (total 8 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 open 224 non-null float64
1 high 224 non-null float64
2 low 224 non-null float64
3 close 224 non-null float64
4 adjusted close 224 non-null float64
5 volume 224 non-null float64
6 dividend amount 224 non-null float64
7 split coefficient 224 non-null float64
dtypes: float64(8)
memory usage: 15.8 KB
None
>>>
>>> tesla.close = tesla.close.pct_change(periods=1)
C:\Users\randy\Anaconda3\lib\site-packages\pandas\core\generic.py:5292: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
self[name] = value
>>>
>>> df2 = pd.DataFrame({
... 'open': [14.97,15.1,1.5,14.82],
... 'high': [14.97,15.15,15,15.47],
... 'low': [14.97,14.86,14.16,14.62],
... 'close': [14.97,13.08,15,15.14]},
... index=['2019-12-31', '2020-01-02', '2020-01-03','2020-01-04'])
>>>
>>> print(isinstance(df, pd.DataFrame))
True
>>>
>>> df2.close = df2.close.pct_change(periods = 1)
>>>
>>> print(df2.info())
<class 'pandas.core.frame.DataFrame'>
Index: 4 entries, 2019-12-31 to 2020-01-04
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 open 4 non-null float64
1 high 4 non-null float64
2 low 4 non-null float64
3 close 3 non-null float64
dtypes: float64(4)
memory usage: 160.0+ bytes
None
```
#### Problem description
I can't determine why the method for DataFrame construction seems to influence the behavior of the pd.pct_change() method. What is different between these two instances? Chaining a copy() method call to the first example still raises the SettingWithCopyWarning.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.0rc0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : 0.29.15
pytest : 5.3.5
hypothesis : 5.5.4
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : 0.8.6
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.48.0
</details>
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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