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MDExOlB1bGxSZXF1ZXN0NTg0NjI4ODI2
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BUG: update should try harder to preserve dtypes #4094
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NONE
| null |
- [ ] closes #4094
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
I have to admit, I am not quite sure about the code in the [initial example](https://github.com/pandas-dev/pandas/issues/4094#issue-16212879). But the rest of the given examples ([1](https://github.com/pandas-dev/pandas/issues/4094#issuecomment-297671925), [2](https://github.com/pandas-dev/pandas/issues/4094#issuecomment-583174514)) are converted to tests and they do pass.
I would have loved to make better use of hypothesis in the tests, but currently [column](https://hypothesis.readthedocs.io/en/latest/numpy.html#hypothesis.extra.pandas.columns) does not support dtype strategies and neither does [from_dtype](https://hypothesis.readthedocs.io/en/latest/numpy.html#hypothesis.extra.numpy.from_dtype) (if I would define the elements of the column and infere the dtype).
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STYLE: Inconsistent namespace - resample #39992
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- [ ] closes #xxxx
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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- [ ] closes #xxxx
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TST: use env variable instead of pytest option for testing ArrayManager
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| 4
| 2021-03-04T12:35:21Z
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|
MEMBER
| null |
I noticed a problem with the current `--array-manager` pytest option to test the ArrayManager, while checking the window tests.
The problem with the option is that `conftest.py` files are read first, *before* handling the option in `pytest_sessionstart`. This means that any DataFrame or Series that gets constructed on *import* of a `conftest.py` file, has already been created before the setting is changed, and will thus be created with the wrong manager.
Example of this is a DataFrame that is not created *inside* a (fixture) function, but created top-level in the conftest.py file (or eg used in parametrization).
Luckily we don't have too many of those, but one example in window/conftest.py:
https://github.com/pandas-dev/pandas/blob/74a68980d116c843d04b3ccb6ab9368f19637d9b/pandas/tests/window/conftest.py#L352-L367
or one example in the toplevel conftest.py:
https://github.com/pandas-dev/pandas/blob/74a68980d116c843d04b3ccb6ab9368f19637d9b/pandas/conftest.py#L597-L608
So to avoid needing to rewrite those (and continue to ensure such cases don't get added), I don't see another way as using the environment variable approach instead of the pytest option (although the option is much easier to use interactively ..)
cc @jreback
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- [x] xref #39992
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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CONTRIBUTOR
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- [x] xref #39992
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CLN: Minor cleanup of caching
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| 2021-03-04T17:18:52Z
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CONTRIBUTOR
| null |
Minor cleanup.
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DEPR: restrict downstream usage of core.internals
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| null |
MEMBER
| null |
There are some optimizations I'd like to make in core.internals (mostly making the signatures stricter so we can do less validation at `__init__`-time). But that risks breaking changes for downstream packages that use the existing non-public APIs.
I'd like to start by asking downstream projects that access internals to identify what they actually use/need.
Once we identify what is used, my thought is to 1) encourage usage of public APIs where possible and 2) provide backward-compatible psuedo-public APIs (xref #40182).
cc @jorisvandenbossche (pyarrow), @TomAugspurger (dask), @mdurant (fastparquet), @shoyer (xarray), anyone else?
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PERF: BlockPlacement construction
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| 6
| 2021-03-04T18:00:54Z
| 2021-03-06T03:05:06Z
| 2021-03-06T03:01:22Z
|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/40171#issuecomment-790219422 when disabling fast_apply, this cuts the linked benchmark by 13%
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Check the that SQLAlchemy ResultProxy returns rows before fetching rows
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Check the that SQLAlchemy ResultProxy returns rows before fetching rows
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ENH: Allow overwriting existing sheets when appending to excel files
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| 6
| 2021-03-04T19:16:52Z
| 2021-04-22T01:05:47Z
| 2021-04-22T01:05:47Z
|
CONTRIBUTOR
| null |
#### Is your feature request related to a problem?
As far as I can tell there is no way to write a DataFrame to a specific (named) sheet in an existing Excel file, creating the sheet if it doesn't exist or overwriting if it does. The closest thing to this that exists is the openpyxl append mode, but if you try to write to `sheet_which_already_exists` with this, the data will be written to `sheet_which_already_exists1`. Using write mode overwrites the entire book.
Being able to overwrite existing sheets would be useful for populating "raw data" sheets in existing reports/publications on a regular basis, with pivot tables etc updating automatically.
This seems to be a very common issue:
https://stackoverflow.com/questions/62618680/overwrite-an-excel-sheet-with-pandas-dataframe-without-affecting-other-sheets
https://stackoverflow.com/questions/47602533/append-a-pandas-dataframe-to-an-existing-excel-table?noredirect=1&lq=1
https://stackoverflow.com/questions/34744863/python-how-to-use-excelwriter-to-write-into-an-existing-worksheet?noredirect=1&lq=1
https://github.com/pandas-dev/pandas/issues/28653
https://github.com/pandas-dev/pandas/issues/29990
#### Describe the solution you'd like
There are a few potential behaviours someone might want when they write to an existing sheet in append mode:
* the current behaviour, which creates a new sheet with a different name
* to delete the current contents of the sheet and write their data
* to write the data without deleting anything, for example to add columns or rows to an existing table, or a new table in a different position
Originally I considered adding a flag like `overwrite=True` to `pd.ExcelWriter` but I don't think this would be sufficient to cover all the potential behaviours, so would suggest an enum along the lines of the `if_exists` option in `df.to_sql`, with the following options:
* new_sheet: Create a new sheet with a different name.
* overwrite_sheet: Delete the contents of the sheet, then write to it.
* overwrite_cells: Write directly to the named sheet without deleting the previous contents.
This option would only be relevant when using `mode="a"` with the openpyxl engine.
#### API breaking implications
The `new_sheet` option would replicate the current behaviour, so this could be made the default.
#### Additional context
Demonstration of current behaviour:
```python
import pandas as pd
from openpyxl import load_workbook
import io
df1 = pd.DataFrame({
"col1": ["apple", "banana", "carrot"],
"col2": [1, 2, 3],
})
df2 = pd.DataFrame({"col1": [5]})
excel = io.BytesIO()
with pd.ExcelWriter(excel, engine="openpyxl", mode="w") as writer:
# write two sheets
df1.to_excel(writer, sheet_name="sheet1", index=False)
df1.to_excel(writer, sheet_name="sheet2", index=False)
with pd.ExcelWriter(excel, engine="openpyxl", mode="a") as writer:
# try to overwrite one of them
df2.to_excel(writer, sheet_name="sheet1", index=False)
wb = load_workbook(excel)
# sheet1, sheet2, sheet11
print(wb.sheetnames)
```
Why does it work this way? At the moment, when the openpyxl `ExcelWriter` is initialised in append mode it loads the workbook, but does not update `self.sheets` to reflect the sheets in the loaded file.
https://github.com/pandas-dev/pandas/blob/master/pandas/io/excel/_openpyxl.py#L54-L58
This seems unintentional and means that when `write_cells` is called with a sheet name that already exists in the file, it is not found in `self.sheets`:
https://github.com/pandas-dev/pandas/blob/master/pandas/io/excel/_openpyxl.py#L414-L419
so we follow the else branch, and when assigning to `wks.title` openpyxl suffixes the sheet name to avoid a naming conflict.
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ENH: Add if_sheet_exists parameter to ExcelWriter
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| 8
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| 2021-04-22T01:05:48Z
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|
CONTRIBUTOR
| null |
- [x] closes #40230
- [x] tests added / passed
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- [x] whatsnew entry
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CI autofix_prs: false for pre-commit.ci
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| 6
| 2021-03-04T20:12:46Z
| 2021-04-13T19:35:04Z
| 2021-04-13T12:31:46Z
|
MEMBER
| null |
Somehow, this autoupdate job doesn't trigger all the workflows. I don't know why, because [the same workflow in PyMC3](https://github.com/pymc-devs/pymc3/pull/4466) works just fine.
From [this action's README](https://github.com/technote-space/create-pr-action), all the actions have commit email and commit name set - setting them is all I can think to try at the moment. @afeld any ideas? Maybe there's a different workflow that should be used instead?
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DEPS: skip html tests if html5lib is not installed #40198
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| 3
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| 2021-03-18T16:44:53Z
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MEMBER
| null |
- [x] closes #40198
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BUG: select_dtypes considers numpy int and pandas nullable integer types as the same
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| 0
| 2021-03-04T21:20:18Z
| 2021-03-22T15:09:11Z
| null |
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
Version 1.2.3
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame([{"A": 1, "B": 2}, {"A": 3}]).astype({"B": "Int64"})
print(df.dtypes, end="\n\n")
print(df.select_dtypes("Int64")) # Expect to select only column B
```
#### Actual output
```
A int64
B Int64
dtype: object
A B
0 1 2
1 3 <NA>
```
#### Problem description
Calling `select_dtypes("Int64")` (`Int` with capital "I") selects both `int64` and `Int64` columns.
Also applies to other int types, e.g. `int32` & `Int32`.
Similar behavior is also obtained with `df.select_dtypes(pd.Int64Dtype)`
#### Expected Output
```
A int64
B Int64
dtype: object
B
0 2
1 <NA>
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : French_Canada.1252
pandas : 1.2.3
numpy : 1.19.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : 3.5.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.51.2
```
</details>
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BUG: reindex on empty Series raises ValueError: zero-size array to reduction operation
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| 0
| 2021-03-04T21:52:43Z
| 2021-05-12T01:20:35Z
| 2021-05-12T01:20:35Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
index = pd.MultiIndex.from_tuples([], names=["first", "second"])
s = pd.Series([], index=index, dtype=object)
print(f"s...\n{s}\n")
print(f"reindexed...\n{s.reindex(['a'], level='first')}")
```
prints
```
s...
Series([], dtype: object)
```
and then raises.
#### Problem description
reindex raises `ValueError: zero-size array to reduction operation maximum which has no identity` from `numpy/core/_methods.py`. Traceback:
<details>
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-37-935bb97993f2> in <module>
3 s = pd.Series(range(len(index)), index=index, dtype=object)
4 print(f"s...\n{s}\n")
----> 5 print(f"reindexed...\n{s.reindex(['a'], level='first')}")
~/.pyenv/versions/3.7.9/envs/covid-data-model/lib/python3.7/site-packages/pandas/core/series.py in reindex(self, index, **kwargs)
4343 )
4344 def reindex(self, index=None, **kwargs):
-> 4345 return super().reindex(index=index, **kwargs)
4346
4347 def drop(
~/.pyenv/versions/3.7.9/envs/covid-data-model/lib/python3.7/site-packages/pandas/core/generic.py in reindex(self, *args, **kwargs)
4810 # perform the reindex on the axes
4811 return self._reindex_axes(
-> 4812 axes, level, limit, tolerance, method, fill_value, copy
4813 ).__finalize__(self, method="reindex")
4814
~/.pyenv/versions/3.7.9/envs/covid-data-model/lib/python3.7/site-packages/pandas/core/generic.py in _reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
4826 ax = self._get_axis(a)
4827 new_index, indexer = ax.reindex(
-> 4828 labels, level=level, limit=limit, tolerance=tolerance, method=method
4829 )
4830
~/.pyenv/versions/3.7.9/envs/covid-data-model/lib/python3.7/site-packages/pandas/core/indexes/multi.py in reindex(self, target, method, level, limit, tolerance)
2469 target = ensure_index(target)
2470 target, indexer, _ = self._join_level(
-> 2471 target, level, how="right", return_indexers=True, keep_order=False
2472 )
2473 else:
~/.pyenv/versions/3.7.9/envs/covid-data-model/lib/python3.7/site-packages/pandas/core/indexes/base.py in _join_level(self, other, level, how, return_indexers, keep_order)
3922 else: # tie out the order with other
3923 if level == 0: # outer most level, take the fast route
-> 3924 ngroups = 1 + new_lev_codes.max()
3925 left_indexer, counts = libalgos.groupsort_indexer(
3926 new_lev_codes, ngroups
~/.pyenv/versions/3.7.9/envs/covid-data-model/lib/python3.7/site-packages/numpy/core/_methods.py in _amax(a, axis, out, keepdims, initial, where)
37 def _amax(a, axis=None, out=None, keepdims=False,
38 initial=_NoValue, where=True):
---> 39 return umr_maximum(a, axis, None, out, keepdims, initial, where)
40
41 def _amin(a, axis=None, out=None, keepdims=False,
ValueError: zero-size array to reduction operation maximum which has no identity
</details>
#### Expected Output
I think it makes sense for `s.reindex(['a'], level='first')` to return an empty Series, as it does when `s` is not empty:
```python
index = pd.MultiIndex.from_tuples([('bar', 'one'),('foo', 'two')], names=["first", "second"])
s = pd.Series(range(len(index)), index=index, dtype=object)
print(f"s...\n{s}\n")
print(f"reindexed...\n{s.reindex(['a'], level='first')}")
```
prints
```
s...
first second
bar one 0
foo two 1
dtype: object
reindexed...
Series([], dtype: object)
```
In 1.0.3 I could use df.loc[['label']] to get a slice without an exception when no data was found but that started raising KeyError when I upgraded to 1.2.3. I'm using reindex per https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#reindexing
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.7.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.0-11-amd64
Version : #1 SMP Debian 4.19.146-1 (2020-09-17)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.20.1
pytz : 2020.5
dateutil : 2.8.1
pip : 21.0.1
setuptools : 47.1.0
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : 0.5.0
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.1
sqlalchemy : 1.3.23
tables : None
tabulate : 0.8.7
xarray : 0.16.2
xlrd : 1.2.0
xlwt : None
numba : 0.52.0
</details>
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MDExOlB1bGxSZXF1ZXN0NTg1MjIxMjM3
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PERF: non-cython groupby.apply
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| 0
| 2021-03-05T00:27:15Z
| 2021-03-05T15:02:03Z
| 2021-03-05T14:22:48Z
|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/40171#issuecomment-790219422 when disabling fast_apply, this cuts the linked benchmark by 13-15%
xref #40227 which also gets 13%
Potential pitfall if _constructor is particularly wonky.
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Backport PR #40143 on branch 1.2.x (REGR: reduction operations failing if `min_count` is larger)
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| 2
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NONE
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Backport PR #40143: REGR: reduction operations failing if `min_count` is larger
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| 1
| 2021-03-05T01:29:50Z
| 2021-04-24T12:37:44Z
| 2021-03-05T13:17:01Z
|
MEMBER
| null |
- [x] closes #40211
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
In test_frame_transform.test_transform_partial_failure, two of the tests did not actually test for partial failure as I had intended back in #35964. That is fixed here. Also added tests for Series.
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PERF: enable nullable integer for read_sql via parameter
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NONE
| null |
Function `pandas.read_sql` read SQL query or database table into a DataFrame. However, for nullable integer columns, once there exists one row with null, pandas will infer this column as float, which may lead to subsequent issues (in our case, we use DataFrame for ETL and the integer converted to float, not suitable for our sink database table schema).
Using nullable integer is one of the solution we adopted, which may cause more memory usage. Therefore, I add default False parameter for `pandas.read_sql` to not enable it as default, once you want to enable it, just set it as True.
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CONTRIBUTOR
| null |
- [X] refereces #40131
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ENH: allow start=range_object in RangeIndex constructor
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- [x] closes #12067
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simplifies `Index.__new__` a bit
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ENH: consistent `Styler.highlight_X` arg signature with `props`
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| 3
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| 2021-03-30T05:05:53Z
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CONTRIBUTOR
| null |
This splits #39821 into separate PRs.
The `See Also` doc strings here reference methods created in those PRs - if we push this through now I will fix it later if those methods don't get approved before 1.3.0.
The tests that were edited contained redundant code and were not testing.
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QST: Method for comparing Series[Timestamp] to Interval
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[] | null | 1
| 2021-03-05T08:12:24Z
| 2021-08-19T01:42:44Z
| null |
NONE
| null |
I have recently discovered the IntervalArray type, which has been really useful to replace two or more date comparisons with a single call. But I got a bit stuck in a simpler situation. Is there a natural pandas API for comparing a series of Timestamps to a single `pd.Interval`, that respects the `closed` nature of the Interval? At the minute I am using:
```python
df['end_date'].between(MY_INTERVAL.left, MY_INTERVAL.right)
```
But I feel like I'm looking for something like this:
```python
df['end_date'].dt.isin(MY_INTERVAL)
```
I couldn't find anything in the documentation that would give me a vectorised solution, am I just looking for something that isn't there?
- [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [x] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
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[WIP] ENH:`Styler.highlight_quantile` method
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CONTRIBUTOR
| null |
will update.
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ENH: consistency of input args for boundaries
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| 15
| 2021-03-05T08:28:22Z
| 2021-09-14T10:00:26Z
| null |
CONTRIBUTOR
| null |
There are some methods where boundary inputs are required.
This issue proposes standardising to the keyword arg: `inclusive` and using values `{"both", "neither", "left", "right"}`.
- [x] Styler.highlight_between(): `'inclusive' in {"both", "neither", "left", "right"}`
- [x] Series.between() (#40628): `'inclusive' in {"both", "neither", "left", "right"}`
- [ ] pd.date_range() https://github.com/pandas-dev/pandas/pull/43504
- [x] DataFrame.between_time() https://github.com/pandas-dev/pandas/pull/43248
- [ ] pd.Interval
- [ ] pd.interval_range
- [ ] pd.IntervalIndex
- [ ] pd.cut
More methods yet to be identified.
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PERF: specialized 1D take version
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| 8
| 2021-03-05T08:43:15Z
| 2021-03-08T22:20:37Z
| 2021-03-05T21:34:21Z
|
MEMBER
| null |
A part that I took out of https://github.com/pandas-dev/pandas/pull/39692, doing it separately here.
This gives a bit of code duplication (but all large parts of the function were already factored out into shared helper functions before, like `_take_preprocess_indexer_and_fill_value`), but having a 1D version gives less overhead compared to `take_nd`.
Using the same benchmark case (unstack ASV case) as in https://github.com/pandas-dev/pandas/pull/39692, this gives:
```python
arrays = [np.arange(100).repeat(100), np.roll(np.tile(np.arange(100), 100), 25)]
index = pd.MultiIndex.from_arrays(arrays)
df = pd.DataFrame(np.random.randn(10000, 4), index=index)
df_am = df._as_manager("array")
```
```
In [3]: %timeit df_am.unstack()
3.31 ms ± 60.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- master
2.95 ms ± 57.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- PR
```
It's a small difference (around 10% improvement), but I repeated the timings above multiple times it was quite consistently a bit faster.
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BENCH: add DataFrame.reindex(columns=..) benchmark that selects existing columns
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| 1
| 2021-03-05T09:01:39Z
| 2021-03-05T13:21:13Z
| 2021-03-05T13:16:36Z
|
MEMBER
| null |
I noticed that the current `df.reindex(columns=idx)` benchmark uses an indexer with all-non-existing indices (it uses `range(4000, 7000)` as indexer, but there are only 1000 columns), so basically it is benchmarking to create empty (all-NaN) columns. Therefore added a case of reindexing with an indexer consisting of existing column labels.
For reindexing the rows, it's already using existing row labels.
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PERF: avoid validating indices repeatedly in ArrayManager.reindex_indexer
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| 0
| 2021-03-05T09:14:58Z
| 2021-03-05T13:21:07Z
| 2021-03-05T13:16:18Z
|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/issues/39146
In `ArrayManager.reindex_indexer`, we are currently using `take`, which validates the indices each time. Since we know we have the same indices for each column, we can validate them once, and use the lower level `take_nd`.
From the `frame_methods.py::Reindex` ASV benchmark:
```python
N = 10 ** 3
df = DataFrame(np.random.randn(N * 10, N))
idx = np.arange(4 * N, 7 * N)
df_am = df._as_manager("array")
```
```
In [4]: %timeit df_am.reindex(idx)
21 ms ± 189 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) <-- master
12.6 ms ± 1.5 ms per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- PR
```
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|
ENH: DataFrame.to_hdf() can't save to a subgroup
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| 8
| 2021-03-05T09:58:12Z
| 2021-03-05T13:19:20Z
| 2021-03-05T11:35:19Z
|
NONE
| null |
I am storing a lot of complex hierarchical data to HDF5 files. Thus, my HDF5 files have a hierarchical structure of datasets sitting inside groups sitting inside other groups.
Currently, the [to_hdf](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_hdf.html) function allows to input a path and a key. As far as I have understood, the key refers to the name of the group to be created at the lowest level of HDF5 file. However, I don't want to place it at the lowest level. I want it to be a subgroup of an already created group.
Here is an example functionality that I would expect to work
import pandas as pd
```
df = pd.DataFrame()
with h5py.File('test.h5') as f:
f.create_group('foo')
df.to_hdf('test.h5', 'foo/bar')
```
Here, `foo` is an already existing group, and `bar` is the name of the pandas dataset. As you see, I would like `bar` to be a subgroup of `foo`. Currently, this crashes with the error
`ValueError: Unable to create group (name already exists)`
I would also be happy if somebody could recommend a hack. Currently, I am unable to find how to move groups around using h5py
EDIT: I realize that for this to work, the [read_hdf](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_hdf.html) function will also need to change. I haven't tested, but I suspect that it also can't handle hierarchical keys yet
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|
BUG: ValueError when calling groupby + fillna on StringDType column
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| 2021-05-13T23:27:44Z
| 2021-05-13T23:27:44Z
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NONE
| null |
### Versions
<details>
<summary>pd.show_versions()</summary>
```
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English
pandas : 1.2.3
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.2.1
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
### Description
When doing a groupby and then a fillna on a column of dtype StringDType, pandas throws a `ValueError: StringArray requires a sequence of strings or pandas.NA`. This does not seem to happen with other (extension) dtypes afaik.
The error only occurs if there are still NAs left after the `fillna`. It does not occur without the `groupby`.
### Example
```python3
>> pd.DataFrame({"a": pd.array([None, "a"], dtype="string"), "b": [0, 0]}).groupby("b").ffill()
Traceback (most recent call last):
File "C:\venv\lib\site-packages\IPython\core\interactiveshell.py", line 3437, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-22-a665c967b563>", line 1, in <module>
pd.DataFrame({"a": pd.array([None, "a"], dtype="string"), "b": [0, 0]}).groupby("b").ffill()
File "C:\venv\lib\site-packages\pandas\core\groupby\groupby.py", line 1953, in pad
return self._fill("ffill", limit=limit)
File "C:\venv\lib\site-packages\pandas\core\groupby\groupby.py", line 1919, in _fill
return self._get_cythonized_result(
File "C:\venv\lib\site-packages\pandas\core\groupby\groupby.py", line 2673, in _get_cythonized_result
result = algorithms.take_nd(values, result)
File "C:\venv\lib\site-packages\pandas\core\algorithms.py", line 1699, in take_nd
return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill)
File "C:\venv\lib\site-packages\pandas\core\arrays\_mixins.py", line 78, in take
return self._from_backing_data(new_data)
File "C:\venv\lib\site-packages\pandas\core\arrays\numpy_.py", line 190, in _from_backing_data
return type(self)(arr)
File "C:\venv\lib\site-packages\pandas\core\arrays\string_.py", line 195, in __init__
self._validate()
File "C:\venv\lib\site-packages\pandas\core\arrays\string_.py", line 200, in _validate
raise ValueError("StringArray requires a sequence of strings or pandas.NA")
ValueError: StringArray requires a sequence of strings or pandas.NA
>> pd.DataFrame({"a": pd.array(["a", None], dtype="string"), "b": [0, 0]}).groupby("b").ffill()
Out[31]:
a
0 a
1 a
```
Edit: Updated pandas, still an issue in 1.2.3
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MDExOlB1bGxSZXF1ZXN0NTg1NTE5NjQ0
| 40,251
|
[ArrayManager] Fix window operations with axis=1
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| 0
| 2021-03-05T11:13:03Z
| 2021-03-05T13:31:09Z
| 2021-03-05T13:28:56Z
|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/issues/39146
With this PR, all `pandas/tests/window/` tests are passing. It was basically only the `axis=1` case that was not working (since you can't do that column-wise)
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| 823,076,498
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MDU6SXNzdWU4MjMwNzY0OTg=
| 40,252
|
BUG: Interpolate over time does not work with Int64 or Float64
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| 2021-03-05T13:19:01Z
| 2021-03-24T14:31:57Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
pd.DataFrame({"a": [1, None, 2]}, index=pd.to_datetime([1,2,3], unit="d")).convert_dtypes().interpolate(method="time")
```
```
Traceback (most recent call last):
File "C:\venv\lib\site-packages\IPython\core\interactiveshell.py", line 3437, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-7-4eba02001155>", line 1, in <module>
pd.DataFrame({"a": [1, None, 2]}, index=pd.to_datetime([1,2,3], unit="d")).convert_dtypes().interpolate(method="time")
File "C:\venv\lib\site-packages\pandas\core\generic.py", line 7222, in interpolate
new_data = obj._mgr.interpolate(
File "C:\venv\lib\site-packages\pandas\core\internals\managers.py", line 593, in interpolate
return self.apply("interpolate", **kwargs)
File "C:\venv\lib\site-packages\pandas\core\internals\managers.py", line 427, in apply
applied = getattr(b, f)(**kwargs)
File "C:\venv\lib\site-packages\pandas\core\internals\blocks.py", line 1931, in interpolate
values=values.fillna(value=fill_value, method=method, limit=limit),
File "C:\venv\lib\site-packages\pandas\core\arrays\base.py", line 655, in fillna
value, method = validate_fillna_kwargs(value, method)
File "C:\venv\lib\site-packages\pandas\util\_validators.py", line 367, in validate_fillna_kwargs
method = clean_fill_method(method)
File "C:\venv\lib\site-packages\pandas\core\missing.py", line 82, in clean_fill_method
raise ValueError(f"Invalid fill method. Expecting {expecting}. Got {method}")
ValueError: Invalid fill method. Expecting pad (ffill) or backfill (bfill). Got time
```
#### Problem description
Without the `convert_dtypes()` this works without any problems.
#### Expected Output
```
a
1970-01-02 1.0
1970-01-03 1.5
1970-01-04 2.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English
pandas : 1.2.3
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.2.1
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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Backport PR #40233 on branch 1.2.x (DEPS: skip html tests if html5lib is not installed #40198)
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| 2021-03-05T15:53:07Z
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NONE
| null |
Backport PR #40233: DEPS: skip html tests if html5lib is not installed #40198
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PERF/CLN: restrict _isna_ndarraylike to actual ndarray/EA
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| 1
| 2021-03-05T14:01:33Z
| 2021-03-05T16:36:50Z
| 2021-03-05T16:36:46Z
|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/issues/39146
I am looking at some `isna` related benchmarks, and noticing a lot of overhead related to checking of types. This makes `_isna_ndarraylike` stricter to only handle ndarray and EA, and not Series/Index (and therefore also renamed it to `_isna_array`).
Reproducer from the `frame_methods.py::Isnull` benchmark:
```python
N = 10 ** 3
sample = np.array([np.nan, 1.0])
data = np.random.choice(sample, (N, N))
df = DataFrame(data)
df_am = df._as_manager("array")
```
```
In [3]: %timeit df_am.isna()
5.03 ms ± 125 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- master
4.25 ms ± 9.94 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- PR
```
This gives a small improvement. But it further allows to directly use `_is_array` instead of the general `isna` from Manager.isna, which will give further improvements (but was planning to do this as a follow-up)
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BUG: missing values encoded in pandas.Int64Dtype not supported by json table scheme
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| 2021-03-05T16:19:19Z
| 2021-04-08T16:19:45Z
| null |
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
data = {'A' : [1, 2, 2, pd.NA, 4, 8, 8, 8, 8, 9],
'B': [pd.NA] * 10}
data = pd.DataFrame(data)
data = data.astype(pd.Int64Dtype()) # in my example I get this from data.convert_dtypes()
data_json = data.to_json(orient='table', indent=4)
pd.read_json(data_json, orient='table') #ValueError: Cannot convert non-finite values (NA or inf) to integer
```
#### Problem description
I am investigating different json-ouput formats for my use-case. I expect as an end-user that if I can write a json with pandas, I can also read it in without an error.
Background: I used pd.convert_dtypes on a larger DataFrame, where some columns are populated by identical values. `convert_dtypes` converted some of them having only missing to `pd.Int64Dtype`. I tried understand why this is failing und could replicate the issue using the above example having identical dtypes as in my use-case.
#### Expected Output
```
A B
0 1 <NA>
1 2 <NA>
2 2 <NA>
3 <NA> <NA>
4 4 <NA>
5 8 <NA>
6 8 <NA>
7 8 <NA>
8 8 <NA>
9 9 <NA>
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 165 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Danish_Denmark.1252
pandas : 1.2.3
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.1.post20201107
Cython : None
pytest : None
hypothesis : None
sphinx : 3.5.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NTg1NzM3MjU1
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|
REF: simplify operating-columnwise dispatch
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| 5
| 2021-03-05T16:38:17Z
| 2021-03-16T17:03:19Z
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|
MEMBER
| null |
After this we have one usage of split_and_operate left and 2 places where we can _almost_ use `@maybe_split`. In both cases I'm still trying to untangle things.
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MDU6SXNzdWU4MjMyNjYxMzE=
| 40,257
|
BUG: drop_duplicates() doesn't drop zero-column rows
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open
| false
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[] | null | 0
| 2021-03-05T17:06:02Z
| 2021-08-19T01:44:11Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df_01 = pd.DataFrame([[]])
df_02 = pd.DataFrame([[],[]])
assert df_02.drop_duplicates().equals(df_01)
assert df_02.duplicated().equals(pd.Series([False,True]))
```
#### Problem description
Duplicates checking fails on various zero-column dataframes. They check for `self.empty` for a fast-path bypass, when they really should be checking e.g. `self.index.empty`.
#### Expected Output
The above assertions should succeed.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-17763-Microsoft
Version : #864-Microsoft Thu Nov 07 15:22:00 PST 2019
machine : x86_64
processor :
byteorder : little
LC_ALL : C
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.2.2
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 18.1
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 5.8.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.0.2
numexpr : 2.6.9
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.1.0
sqlalchemy : None
tables : 3.4.4
tabulate : 0.8.2
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU4MjMzOTA0Nzc=
| 40,258
|
BUG: df.sort_values(by=[]) fails with IndexError
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| 2021-03-05T20:28:48Z
| 2021-03-12T01:05:43Z
| 2021-03-12T01:05:43Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({'x': [4, 3]})
assert df.sort_values(by=[]).equals(df)
```
#### Problem description
When a dataframe is sorted by no columns, it should return the original dataframe (or at least a dataframe with some permutation of the original's rows). Instead it currently fails with an IndexError in frame.py:5451.
#### Expected Output
The assertion in the sample code should succeed, or at least the `sort_values()` call should not raise an error.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-17763-Microsoft
Version : #864-Microsoft Thu Nov 07 15:22:00 PST 2019
machine : x86_64
processor :
byteorder : little
LC_ALL : C
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.2.2
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 18.1
setuptools : 40.8.0
Cython : 0.29.22
pytest : None
hypothesis : None
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 5.8.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.0.2
numexpr : 2.6.9
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.1.0
sqlalchemy : None
tables : 3.4.4
tabulate : 0.8.2
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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|
BUG: np.all cannot handle a boolean pd.Index anymore
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| 7
| 2021-03-05T21:07:07Z
| 2021-03-06T16:11:06Z
| 2021-03-06T11:08:31Z
|
NONE
| null |
- [ ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd, numpy as np
print(pd.__version__)
print(np.__version__)
# 1.2.2
# 1.19.5
conditions = pd.Index([True, True, True, True, True, True], dtype='object')
conditions
# Index([True, True, True, True, True, True], dtype='object')
np.all(conditions.values)
# True
np.all(conditions)
# ---------------------------------------------------------------------------
# TypeError Traceback (most recent call last)
# <ipython-input-102-9a6b94382bda> in <module>
# ----> 1 np.all(conditions)
# <__array_function__ internals> in all(*args, **kwargs)
# ~/anaconda3/envs/soothsayer5_env/lib/python3.8/site-packages/numpy/core/fromnumeric.py in all(a, axis, out, keepdims)
# 2409
# 2410 """
# -> 2411 return _wrapreduction(a, np.logical_and, 'all', axis, None, out, keepdims=keepdims)
# 2412
# 2413
# ~/anaconda3/envs/soothsayer5_env/lib/python3.8/site-packages/numpy/core/fromnumeric.py in _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs)
# 83 return reduction(axis=axis, dtype=dtype, out=out, **passkwargs)
# 84 else:
# ---> 85 return reduction(axis=axis, out=out, **passkwargs)
# 86
# 87 return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
# TypeError: all() got an unexpected keyword argument 'axis'
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
For years I've been using pandas index arrays with only True/False entries. `np.all` was always able to handle these and for some reason it cannot anymore. I'm not sure why and the error message is not intuitive to me since I'm not using an axis keyword.
#### Expected Output
True or False
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
Version : Darwin Kernel Version 19.5.0: Tue May 26 20:41:44 PDT 2020; root:xnu-6153.121.2~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.2
numpy : 1.19.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.3.1.post20200810
Cython : 0.29.21
pytest : 6.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.0
fastparquet : 0.4.0
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 1.0.1
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.16
tables : None
tabulate : None
xarray : 0.16.2
xlrd : 1.2.0
xlwt : None
numba : 0.50.1
</details>
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Fixning-Doc-Typos
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NONE
| null |
Docs: changes the documentation
Fix docs typos and small grammatical update
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CLN: Remove _axis from apply
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
The regex `[\W]_axis` is showing this removes the only occurances of the variable `_axis` throughout all of `pandas.core`. It is only set in groupby in a branch where is must be 0, and is only used to check that it is 0.
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CLN: BlockManager.get_slice require only slice arg
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
non-slice objects are currently getting past mypy. This ensures we only get slices, will open up some optimization options.
Also did some docstring/typing cleanup in the neighborhood.
<b>update</b>: updated to implement Index._getitem_slice. Cuts about 10% off of the benchmark discussed in https://github.com/pandas-dev/pandas/pull/40171#issuecomment-790219422 (as compared to current master, not then-master)
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DES: How much perf penalty will we accept to get rid of libreduction?
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| 2021-10-02T03:07:33Z
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MEMBER
| null |
xref some discussion https://github.com/pandas-dev/pandas/pull/40171#issuecomment-790219422
libreduction and the associated callers are a disproportionate maintenance headache [citation needed]. It would be nice to be able to rip it out and just have one path for those methods, but that would entail a non-trivial performance hit. Recently, though, we've managed to optimize the pure-python path a bit, and im optimistic we can shave off some more of the difference.
The question: how much of a perf penalty are we willing to accept in order to remove libreduction?
Copying from https://github.com/pandas-dev/pandas/pull/40171#issuecomment-789077389
> I'll throw out a number: if we could get worst-case down to within about 3x and most-cases within 1.5x, I'd be open to removing the cython paths. They are a disproportionate producer of headaches. (From #36459 (possibly out of date) "entirely disabling the cython path leads to 4 currently-xfailed tests passing")
> Besides which, if/when cython3 becomes available, we may have to get rid of these anyway.
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CI: numpy-dev PerformanceWarning
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xref https://github.com/pandas-dev/pandas/pull/39953#issuecomment-791672194
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TYP: remove ignores for "Cannot assign to a method"
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MEMBER
| null |
xref #37715
This is dynamic generated code. so I am also OK with leaving theses ignores in play to highlight this. these mypy errors could be interpreted as not being false positives.
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DOC: GH39672 Pandas dataframe pct_change function arguments
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Remove pd_array from tests
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TYP: add mypy messages as comments to ignores
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xref #37715
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Add examples to documentation for DataFrame.swaplevel
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| 6
| 2021-03-06T12:42:07Z
| 2021-03-15T13:58:21Z
| 2021-03-12T14:10:16Z
|
CONTRIBUTOR
| null |
- [x ] closes https://github.com/MarcoGorelli/pyladies-sprint-March-2021/issues/13
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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TYP: remove ignore from makeCustomIndex
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MEMBER
| null |
xref #37715
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TYP: remove ignore from PandasObject.__sizeof__
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MEMBER
| null |
xref #37715
we should probably have a base implementation (or make memory_usage an abstract method). without doing this we have no way of enforcing that the method has a `deep` parameter.
I am quite happy leaving the ignore in play to highlight this.
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TYP: remove ignore from MPLPlot._plot
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MEMBER
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xref #37715
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ENH: pandas.read_json Add skip error line
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| 2021-03-21T15:25:59Z
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NONE
| null |
It will be great if pandas.read_json have skip error line like pandas.read_csv do.
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ENH: read_excel (xlrd engine) add parameter for ignore_workbook_corruption
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| 2021-03-06T15:21:02Z
| 2021-03-24T14:31:31Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
yes, sometimes, expecially when created by 3rd party application old excel file can be corrupted:
https://www.codeforests.com/2020/05/28/fix-the-compdocerror-for-xlrd/
https://stackoverflow.com/questions/12705527/reading-excel-files-with-xlrd
https://stackoverflow.com/questions/34550624/python-xlrd-read-excel-file-error/
https://titanwolf.org/Network/Articles/Article?AID=f1370e16-c9ef-4892-a00e-b1bafee2f6a8#gsc.tab=0
#### Describe the solution you'd like
xlrd's team recently in version 2.0 (december 2020) solved this with a flag: `ignore_workbook_corruption`, please see https://xlrd.readthedocs.io/en/latest/changes.html
So my desire would be to just have this flag inside the pandas's function `read_excel`
#### API breaking implications
any, I guess
#### Describe alternatives you've considered
Open the file directly with xlrd and pass it to pandas
I may be unaware of other solutions
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POC: aggregate always aggregates
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MEMBER
| null |
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Proof of concept to show what it would take for #35725 (needs tests, docs, and some comments cleaned up). I wanted to make sure the desired behavior has a viable path before deprecating. I'm planning to put up a PR to deprecate for #35725 in the next week or so, but since this seems to be a somewhat substantial behavior change, I'm wondering if it should be behind an option so that users can opt-in earlier and restore previous behavior for at least 1 release after the deprecation takes effect.
This makes any call to `*.agg` aggregate. For example:
pd.DataFrame({'a': [1, 1], 'b': [2, 2]}).agg(lambda x: x)
will return a Series with 2 rows whose entries are Series themselves. On master, this would be a no-op. Some notes:
- GroupBy.aggregate is now almost entirely handled by either core.apply or _python_agg_general. The only case that isn't is where the result is empty; this should be able to be simplified.
- Also closes #39169, #39436
- Closes the agg portion of #38042
- Series.agg no longer uses "try ser.apply(f), fallback to f(ser)"; it just does the latter now.
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STYLE: Inconsistent namespace - plotting
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CONTRIBUTOR
| null |
- [x] xref #39992
- [x] tests added / passed - plotting
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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Reorder pre-commit-config and normalize id names
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CONTRIBUTOR
| null |
- [ ] closes https://github.com/MarcoGorelli/pyladies-sprint-March-2021/issues/21
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry (not sure where to add this?)
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Move cpplint check to pre-commit
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| 1
| 2021-03-06T17:48:48Z
| 2021-03-08T08:47:47Z
| 2021-03-07T20:12:33Z
|
CONTRIBUTOR
| null |
Currently, cpplint runs from https://github.com/pandas-dev/pandas/blob/master/ci/code_checks.sh
However, cpplint have a pre-commit hook which can be used.
- [x] closes https://github.com/MarcoGorelli/pyladies-sprint-March-2021/issues/2
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
|
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| 823,708,541
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MDExOlB1bGxSZXF1ZXN0NTg2MTA4NzI5
| 40,279
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DOC: Clarify DataFrame.combine_first and Series.combine_first
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CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
|
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| 823,711,612
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MDExOlB1bGxSZXF1ZXN0NTg2MTEwOTQ0
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STYLE: Pandas codespell fix for documentation
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CONTRIBUTOR
| null |
- [x] xref #38802
- [x] tests added / passed
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- [ ] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0NTg2MTY4MTUy
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REF: Block.putmask dont use split_and_operate
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
I find this much clearer. Along with #40256 we'll be able to get rid of split_and_operate altogether
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CI: avoid port-already-in-use errors
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| 2
| 2021-03-07T02:41:42Z
| 2021-03-09T19:03:11Z
| 2021-03-08T13:27:24Z
|
MEMBER
| null |
Should prevent the occasional CI failures e.g. https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=55705&view=ms.vss-test-web.build-test-results-tab&runId=2644226&resultId=178989&paneView=debug
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MDExOlB1bGxSZXF1ZXN0NTg2MjAwNTcw
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STYLE: Inconsistent namespace - tools (pandas-dev#39992)
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| 1
| 2021-03-07T06:54:03Z
| 2021-03-07T11:07:51Z
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|
CONTRIBUTOR
| null |
- [x] xref #39992
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NTg2MjA0NjMz
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|
API: remove `.set_tooltips_class` incorpate to `.set_tooltips`
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| 1
| 2021-03-07T07:35:29Z
| 2021-03-08T19:51:39Z
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|
CONTRIBUTOR
| null |
`.set_tooltips` and `.set_tooltips_class` are introduced to 1.3.0.
Just realised it seems a bit pointless to have two api functions:
- [x] remove `.set_tooltips_class()` and `._init_tooltips()`
- [x] include the `css_name` and `props` arguments in `.set_tooltips` for same functionality.
- [x] no api break since these functions have not yet been released
- [x] make argument names more consistent with other Styler methods.
|
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MDExOlB1bGxSZXF1ZXN0NTg2MjUwNTI5
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BLD: GH30873 Workaround parallel build failures
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| 2021-03-28T18:58:45Z
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|
CONTRIBUTOR
| null |
Fix parallel build race conditions due to the same source files being
used in multiple targets. To workaround the distutils bug, create
unique .c files for each target that just #include the original file.
- [x] closes #30873
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
|
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STYLE: Resolved Inconsistent namespace
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|
CONTRIBUTOR
| null |
- [x] closes #39992
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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ENH: IO support for R data files with `pandas.read_rdata` and `DataFrame.to_rdata`
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| 2021-03-07T14:30:30Z
| 2021-03-29T17:18:43Z
| null |
CONTRIBUTOR
| null |
Currently, [Pandas IO tools](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html) for binary files support largely the commercial statistical packages (SAS, Stata, SPSS). Interestingly, R binary types (.rds, .rda) are not included. Since many data science teams work between the open source stacks, some IO pandas support of R data files may be worthwhile to pursue.
I know there is some history of pandas with rpy2. However, there may be a way to integrate an IO module for R data files without optional dependency (i.e, [pyreadr](https://github.com/ofajardo/pyreadr)) but using a lightweight C library: [librdata](https://github.com/WizardMac/librdata/tree/master/src). Also, R's [`saveRDS`](https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/readRDS) uses compression types (`gzip`, `bzip2`, and `xz`) already handled with pandas io.
Thanks to the authors of pyreadr and librdata (not unlike the `sas7bdat` authors for `read_sas` or `PyDTA` authors for `read_stata`), I was able to implement a demo on an uncompressed rds type.
### R
```r
set.seed(332020)
alpha <- c(LETTERS, letters, c(0:9))
data_tools <- c("sas", "stata", "spss", "python", "r", "julia")
random_df <- data.frame(
group = factor(sample(data_tools, 500, replace=TRUE)),
int = sample(1:15, 500, replace=TRUE),
num = rnorm(500),
char = replicate(500, paste(sample(alpha, 3, replace=TRUE), collapse="")),
bool = sample(c(TRUE, FALSE), 500, replace=TRUE),
date = sample(seq(as.Date("2001-01-01"), Sys.Date(), by="days"), 500, replace=TRUE),
ts= as.POSIXct(sample(1577836800:as.integer(Sys.time()), 500, replace=TRUE), origin="1970-01-01")
)
head(random_df, 5)
# group int num char bool date ts
# 1 r 15 1.2012638 3kp TRUE 2006-12-07 2020-07-04 21:06:23
# 2 spss 5 0.9627570 rf6 TRUE 2010-10-28 2020-03-22 02:04:45
# 3 julia 9 -0.7922929 t9Q FALSE 2003-01-02 2021-02-11 06:33:02
# 4 julia 14 -0.4305794 zWw TRUE 2004-11-24 2020-01-25 11:24:20
# 5 python 5 -1.0262956 LqK TRUE 2020-08-20 2021-01-15 23:22:05
tail(random_df, 5)
# group int num char bool date ts
# 496 python 2 1.1038483 xGh TRUE 2014-01-14 2020-11-30 11:30:00
# 497 sas 1 -0.5588906 BbC TRUE 2011-03-15 2020-11-03 06:30:08
# 498 sas 1 0.3989181 FHi TRUE 2003-03-02 2020-01-16 08:23:56
# 499 python 1 -0.7840641 e3k FALSE 2001-10-23 2020-05-27 06:53:21
# 500 spss 7 0.2351526 Klv TRUE 2002-05-31 2020-05-28 11:42:56
### RDS
saveRDS(random_df, "/path/to/r_df.rds", compress=FALSE)
### RDA
save(random_df, "/path/to/r_df.rda", compress=FALSE)
```
### Python *(using a Cython built module)*
**Parser**
```python
from rparser import Parser, Writer # slight adjustments to pyreadr's pyx w/o 3rd party imports
import pandas as pd
from datetime import datetime as dt, timedelta
class BaseRParser(Parser):
"""
Parses the RData or Rds file using the parser defined
in librdata.pyx which in turn uses the C API of librdata.
"""
def __init__(self):
self.counter = 0
self.is_factor = False
self.col_names = {}
self.row_names = {}
self.col_types = {}
self.col_data = {}
self.text_vals = {}
self.value_labels = {}
def handle_table(self, name):
"""
Every object in the file is called table, this method is evoked once per object.
:param name: str: the name of the table
"""
pass
def handle_column(self, name, data_type, data, count):
"""
Evoked once per each column in the table.
:param name: str: column name, may be None
:param data_type: object of type DataType(Enum) (defined in librdata.pyx)
:param data: a dictionary containing the data in R vector, may be empty
:param count: int: number of elements in the array
"""
self.row_count = count
if self.is_factor:
data = {k:self.value_labels[v] for k,v in data.items()}
self.is_factor = False
if data_type == "bool":
data = {k:True if v==1 else False for k,v in data.items()}
if data_type == "date":
data = {k:dt(1970,1,1,0,0) + timedelta(v) for k,v in data.items()}
if data_type == "datetime":
data = {k:dt.fromtimestamp(v) for k,v in data.items()}
self.col_data = {**self.col_data, **{self.counter: data}}
self.col_types[self.counter] = data_type
self.counter += 1
def handle_column_name(self, name, index):
"""
Some times name is None in handle column but it is recovered with this method.
:param name: str: name of the column
:param index: int: index of the column
"""
self.col_names[index] = name
if index == (self.counter - 1):
self.compile_dataframe()
def handle_dim(self, name, data_type, data, count):
"""
Evoked once to retrieve the number of dimensions
:param name: str: column name, may be None
:param data_type: object of type DataType(Enum) (defined in librdata.pyx)
:param data: a numpy array representing the number of dimensions
:param count: int: number of elements in the array
"""
pass
def handle_dim_name(self, name, index):
"""
Get one dimension name, one at a time, for matrices, arrays, tables.
:param name: str: name of the dimension
:param index: int: index of the dimension
"""
pass
def handle_row_name(self, name, index):
"""
Handles R dataframe's rownames
:param name: str: name of the row
:param index: int: index of the row
"""
self.row_names[index] = name
def handle_text_value(self, name, index):
"""
For character vectors this will be called once per row and will
retrieve the string value for that row.
:param name: str: string value for the row
:param index: int: index of the row
:return:
"""
self.text_vals[index] = name
if index == (self.row_count - 1):
self.col_data = {**self.col_data, **{self.counter-1: self.text_vals}}
self.text_vals = {}
def handle_value_label(self, name, index):
"""
Factors are represented as integer vectors.
For factors, this method is called before reading the integer data
in the Factor column with handle_column and will give all the
string values corresponding to the integer values.
:param name: str: string value
:param index: int: integer value
:return:
"""
self.is_factor = True
self.value_labels[index+1] = name
def compile_dataframe(self):
df_data = {n:v for (k,v),n in zip(self.col_data.items(), list(self.col_names.values()))}
self.r_dataframe = pd.DataFrame(df_data)
```
```python
parser = BaseRParser()
parser.read_rds("/path/to/r_df.rds")
parser.read_rds("/path/to/r_df.rda")
py_df = parser.r_dataframe
print(py_df)
# group int num char bool date ts
# 0 r 15 1.201264 3kp True 2006-12-07 2020-07-04 21:06:23
# 1 spss 5 0.962757 rf6 True 2010-10-28 2020-03-22 02:04:45
# 2 julia 9 -0.792293 t9Q False 2003-01-02 2021-02-11 06:33:02
# 3 julia 14 -0.430579 zWw True 2004-11-24 2020-01-25 11:24:20
# 4 python 5 -1.026296 LqK True 2020-08-20 2021-01-15 23:22:05
# .. ... ... ... ... ... ... ...
# 495 python 2 1.103848 xGh True 2014-01-14 2020-11-30 11:30:00
# 496 sas 1 -0.558891 BbC True 2011-03-15 2020-11-03 06:30:08
# 497 sas 1 0.398918 FHi True 2003-03-02 2020-01-16 08:23:56
# 498 python 1 -0.784064 e3k False 2001-10-23 2020-05-27 06:53:21
# 499 spss 7 0.235153 Klv True 2002-05-31 2020-05-28 11:42:56
# [500 rows x 7 columns]
```
**Writer**
```python
class BaseRWriter(Writer):
"""
Writes pandas data to the RData or Rds file using the writer defined
in librdata.pyx which in turn uses the C API of librdata.
"""
def write_rds(self, path=None, file_format="rds", frame=None, frame_name=None):
"""
write a RData or Rds file.
path: str: path to the file
file_format: str: rdata or rds
df: pandas data frame
df_name = name of the object to write. Irrelevant if rds format.
"""
py_to_r_types = {
"int64": "INTEGER",
"float64": "REAL",
"bool": "LOGICAL",
"object": "CHARACTER",
"datetime64[ns]": "TIMESTAMP"
}
self.open(path, file_format)
self.set_row_count(frame.shape[0])
self.set_table_name(frame_name)
for col_name, col_type in zip(frame.columns.tolist(), frame.dtypes.tolist()):
cur_type = py_to_r_types[str(col_type)]
self.add_column(str(col_name), str(cur_type))
for col in frame.select_dtypes(include=['datetime']).columns:
frame[col] = frame[col].dt.tz_localize("America/Chicago").astype(int).div(10**9)
for col_indx,((col,ser),dtype) in enumerate(zip(frame.to_dict().items(), frame.dtypes.tolist())):
cur_type = py_to_r_types[str(dtype)]
for row_indx, (indx, val) in enumerate(ser.items()):
self.insert_value(row_indx, col_indx, val, cur_type)
self.close()
```
```python
writer = BaseRWriter()
writer.write_rds(
path="/path/to/py_df.rds",
file_format="rds",
frame=py_df,
frame_name="pandas_df"
)
```
**R**
```r
py_df <- readRDS("/path/to//py_df.rds")
str(py_df)
# 'data.frame': 500 obs. of 7 variables:
# $ group: chr "r" "spss" "julia" "julia" ...
# $ int : int 15 5 9 14 5 9 7 7 5 10 ...
# $ num : num 1.201 0.963 -0.792 -0.431 -1.026 ...
# $ char : chr "3kp" "rf6" "t9Q" "zWw" ...
# $ bool : logi TRUE TRUE FALSE TRUE TRUE TRUE ...
# $ date : 'POSIXct' num 2006-12-07 2010-10-28 2003-01-02 2004-11-24 2020-08-20 ...
# $ ts : 'POSIXct' num 2020-07-04 21:06:23 2020-03-22 02:04:45 2021-02-11 06:33:02 2020-01-25 11:24:20 2021-01-15 23:22:05 ...
# - attr(*, "datalabel")= chr "pandas_df"
# - attr(*, "var.labels")= chr [1:7] "" "" "" "" ...
head(py_df, 5)
# group int num char bool date ts
# 1 r 15 1.2012638 3kp TRUE 2006-12-07 2020-07-04 21:06:23
# 2 spss 5 0.9627570 rf6 TRUE 2010-10-28 2020-03-22 02:04:45
# 3 julia 9 -0.7922929 t9Q FALSE 2003-01-02 2021-02-11 06:33:02
# 4 julia 14 -0.4305794 zWw TRUE 2004-11-24 2020-01-25 11:24:20
# 5 python 5 -1.0262956 LqK TRUE 2020-08-20 2021-01-15 23:22:05
tail(py_df, 5)
# group int num char bool date ts
# 496 python 2 1.1038483 xGh TRUE 2014-01-14 2020-11-30 11:30:00
# 497 sas 1 -0.5588906 BbC TRUE 2011-03-15 2020-11-03 06:30:08
# 498 sas 1 0.3989181 FHi TRUE 2003-03-02 2020-01-16 08:23:56
# 499 python 1 -0.7840641 e3k FALSE 2001-10-23 2020-05-27 06:53:21
# 500 spss 7 0.2351526 Klv TRUE 2002-05-31 2020-05-28 11:42:56
```
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DEPR: Series/DataFrame.transform partial failure except TypeError
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| 2021-03-07T17:00:51Z
| 2021-03-26T13:41:36Z
| 2021-03-26T13:36:27Z
|
MEMBER
| null |
- [x] closes #40211
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Partial revert of #40238. I did not realize how `TypeError` was handled differently than other errors in other UDF functions (https://github.com/pandas-dev/pandas/issues/40211#issuecomment-792030597). The deprecation is now only for partial failure when something other than TypeError is raised.
In order to make this work, transform needed to passthrough TypeErrors when they occur so we can correctly decide how to act with a list- or dict-like.
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| 1
| 2021-03-07T19:41:57Z
| 2021-03-09T15:36:27Z
| null |
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import io
df = pd.DataFrame({"A": [0, 1], "B": [10, 11]})
for engine in ["openpyxl", "xlsxwriter", "odf"]:
excel = io.BytesIO()
with pd.ExcelWriter(excel, engine=engine) as writer:
df.to_excel(writer, sheet_name="foo", columns=["A"], index=False)
df.to_excel(writer, sheet_name="foo", columns=["B"], startcol=1, index=False)
result = pd.read_excel(excel, "foo")
print(engine)
print(result)
print()
```
Produces:
```
openpyxl
A B
0 0 10
1 1 11
xlsxwriter
A B
0 0 10
1 1 11
odf
A Unnamed: 1
0 0.0 NaN
1 1.0 NaN
2 NaN B
3 NaN 10
4 NaN 11
```
Adding this test to `test_writers.py` (line 1327) will also catch the issue:
```python
def test_excel_multiple_writes_one_writer(self, path):
df = DataFrame({"A": [0, 1], "B": [10, 11]})
with ExcelWriter(path) as writer:
df.to_excel(writer, columns=["A"], index=False)
df.to_excel(writer, columns=["B"], startcol=1, index=False)
result = pd.read_excel(path)
tm.assert_frame_equal(result, df)
```
#### Problem description
This came up when implementing #40231, the behaviour between the engines here seems to be inconsistent.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 6baec67643923ae93b685bdec01105b51f1c0a94
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.3.0.dev0+979.g6baec67643.dirty
numpy : 1.20.0
pytz : 2019.1
dateutil : 2.8.1
pip : 20.3.3
setuptools : 41.4.0
Cython : 0.29.21
pytest : 6.2.2
hypothesis : 6.1.1
sphinx : 2.4.0
blosc : None
feather : 0.4.0
xlsxwriter : 1.3.7
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.1.0
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 0.13.0
pyxlsb : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NTg2MzE0NTU1
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DOC: fix grammar issues and incorrect link in "Getting Started" guide…
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| 1
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|
CONTRIBUTOR
| null |
… (#40215)
- [x] closes #40215
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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EHN: Refactor pd.read_excel/DataFrame.to_excel like DataFrame.plot
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[] | null | 3
| 2021-03-07T21:33:35Z
| 2021-03-10T00:02:37Z
| 2021-03-10T00:02:37Z
|
MEMBER
| null |
Two issues have recently come up (#40274 and #40230) looking to add engine-specific arguments to either `pd.read_excel` or `DataFrame.to_excel`. Thus far these are either just to pass through the engine or fix/enhance the current pandas layer, and I'm generally positive on doing so, but I do not like adding engine-specific arguments to these methods.
Thus I am proposing to refactor `pd.read_excel` and `DataFrame.to_excel` to mirror (at least, from an API perspective) the design of `DataFrame.plot` so that a user could call, e.g.
- pd.read_excel
- pd.read_excel.openpyxl
- pd.read_excel.xlrd
- pd.read_excel.ods
- etc.
The arguments to `pd.read_excel` would be those that are common to all engines, whereas the arguments to e.g. `pd.read_excel.openpyxl` would be that plus any engine-specific arguments. The new methods would just be interfaces to the backend subclasses that exist today, and only responsible for validating the arguments provided.
The only alternative I see (besides not allowing engine-specific options at all, which is also an option) is to use `**kwargs` instead. This would result in poorer documentation and we would need to react to upstream enhancements in the engine rather than being able opt-in to them.
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CLN: Window code
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MEMBER
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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TYP: Window code
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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ENH: NDArrayBackedExtensionArray.fillna(method) with 2d
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PERF: declare contiguity in libgroubpy
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MEMBER
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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ENH: add fast DataFrame iterator that doesn't rename columns
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| 2021-03-08T05:52:03Z
| 2021-03-20T03:50:41Z
| 2021-03-20T03:50:40Z
|
MEMBER
| null |
Hello, everyone.
#### Is your feature request related to a problem?
So we have come across a problem at work. We often pre-process data by feeding generators into each other. In order to be able to work with rows independently and not store meta information somewhere, these rows are fed through as dicts where the keys are column names. The datasets can be a dozen gigabytes in size.
DataFrame objects are convenient for a lot of purposes, including the ease of vectorized operations, but to use them, we need to be able to feed the DataFrame row by row into the pipeline. None of the DataFrame iterators we have in pandas get the job done without wrappers or cludges:
* `iteritems` iterates over columns, so it's a no-go;
* `iterrows` does type conversion, and we need to preserve original types. It's also slow;
* `itertuples` returns a `namedtuple`, which can't be relied upon, because it renames columns.
#### Describe the solution you'd like
Passing data through the pipeline as a dict of `column: value` pairs is quite common, I believe. Can we add a `DataFrame.iterdicts` method that would return rows as `OrderedDict` to guarantee column order and preserve their names and types?
#### API breaking implications
None. Adding a new method to the `DataFrame` API.
#### Describe alternatives you've considered
We had to cludge this by doing something like:
```python
def iterdicts(df):
"""
Yield a dictionary for each row of `df`.
:param df:
:return:
"""
col = df.columns
for row in df.itertuples(index=False, name=None):
yield dict(zip(col, row))
```
I think anyone can do this for themselves, but it would be nice to have such a convenience method as part of the native API.
Another alternative is to move the warning about `itertuples` renaming columns to the very top of the doc. This, at least, will ensure that someone who needs to read a hundred docs a day at work won't miss it, and then end up with a bug that pops up only on some datasets.
#### Additional context
Heard the same complaint at my previous workplace, but there I could answer with, 'Have you heard of apply and all the pre-programmed Cython functions in pandas?' With most datasets, the preferred approach should be to use vectorized operations, and it makes sense to get pandas iterators convenient and good enough, and then leave them at that.
But when you are pushing around dozens of gigabytes of data in an environment with parallelization, network IO, and a lot of other stuff, it becomes unviable to process the entire dataset in a DataFrame (or an ndarray, for that matter). And other parts of the pipeline take up the majority of its runtime, so processing it row by row is the optimal way to code this, when the preprocessing operations are sufficiently complex and spread out.
I believe that the need for this iterator is widespread enough to add it to the pandas API, but I'm biased as I didn't read the docs far enough down (as you do, when you are in a hurry), and then spent half a day debugging the solution only to discover that some datasets have columns that are invalid python names and get renamed. This is probably the worst part about `itertuples` being the fastest option recommeded for such a use case right now: you code a pipeline, and then it breaks only on some datasets.
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⬆️ UPGRADE: Autoupdate pre-commit config
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| 2021-03-08T07:54:50Z
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|
CONTRIBUTOR
| null |
<!-- START pr-commits -->
<!-- END pr-commits -->
## Base PullRequest
default branch (https://github.com/pandas-dev/pandas/tree/master)
## Command results
<details>
<summary>Details: </summary>
<details>
<summary><em>add path</em></summary>
```Shell
/home/runner/work/_actions/technote-space/create-pr-action/v2/node_modules/npm-check-updates/bin
```
</details>
<details>
<summary><em>pip install pre-commit</em></summary>
```Shell
Collecting pre-commit
Downloading pre_commit-2.11.0-py2.py3-none-any.whl (187 kB)
Collecting cfgv>=2.0.0
Using cached cfgv-3.2.0-py2.py3-none-any.whl (7.3 kB)
Collecting nodeenv>=0.11.1
Using cached nodeenv-1.5.0-py2.py3-none-any.whl (21 kB)
Collecting identify>=1.0.0
Downloading identify-2.1.0-py2.py3-none-any.whl (98 kB)
Collecting toml
Using cached toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting virtualenv>=20.0.8
Downloading virtualenv-20.4.2-py2.py3-none-any.whl (7.2 MB)
Collecting pyyaml>=5.1
Downloading PyYAML-5.4.1-cp39-cp39-manylinux1_x86_64.whl (630 kB)
Collecting six<2,>=1.9.0
Using cached six-1.15.0-py2.py3-none-any.whl (10 kB)
Collecting distlib<1,>=0.3.1
Using cached distlib-0.3.1-py2.py3-none-any.whl (335 kB)
Collecting filelock<4,>=3.0.0
Using cached filelock-3.0.12-py3-none-any.whl (7.6 kB)
Collecting appdirs<2,>=1.4.3
Using cached appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)
Installing collected packages: six, filelock, distlib, appdirs, virtualenv, toml, pyyaml, nodeenv, identify, cfgv, pre-commit
Successfully installed appdirs-1.4.4 cfgv-3.2.0 distlib-0.3.1 filelock-3.0.12 identify-2.1.0 nodeenv-1.5.0 pre-commit-2.11.0 pyyaml-5.4.1 six-1.15.0 toml-0.10.2 virtualenv-20.4.2
```
</details>
<details>
<summary><em>pre-commit autoupdate || (exit 0);</em></summary>
```Shell
Updating https://github.com/MarcoGorelli/absolufy-imports ... [INFO] Initializing environment for https://github.com/MarcoGorelli/absolufy-imports.
updating v0.2.1 -> v0.2.2.
Updating https://github.com/python/black ... already up to date.
Updating https://github.com/codespell-project/codespell ... [INFO] Initializing environment for https://github.com/codespell-project/codespell.
already up to date.
Updating https://github.com/pre-commit/pre-commit-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pre-commit-hooks.
already up to date.
Updating https://github.com/cpplint/cpplint ... [INFO] Initializing environment for https://github.com/cpplint/cpplint.
=====> /home/runner/.cache/pre-commit/repo4yrgmdjk/.pre-commit-hooks.yaml does not exist
Updating https://gitlab.com/pycqa/flake8 ... already up to date.
Updating https://github.com/PyCQA/isort ... [INFO] Initializing environment for https://github.com/PyCQA/isort.
already up to date.
Updating https://github.com/MarcoGorelli/no-string-hints ... [INFO] Initializing environment for https://github.com/MarcoGorelli/no-string-hints.
already up to date.
Updating https://github.com/asottile/pyupgrade ... [INFO] Initializing environment for https://github.com/asottile/pyupgrade.
already up to date.
Updating https://github.com/pre-commit/pygrep-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pygrep-hooks.
already up to date.
Updating https://github.com/asottile/yesqa ... already up to date.
```
</details>
<details>
<summary><em>pre-commit run -a || (exit 0);</em></summary>
```Shell
[INFO] Initializing environment for https://github.com/cpplint/cpplint.
[INFO] Installing environment for https://github.com/MarcoGorelli/absolufy-imports.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/python/black.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/codespell-project/codespell.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/pre-commit/pre-commit-hooks.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/cpplint/cpplint.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/PyCQA/isort.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/MarcoGorelli/no-string-hints.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/pyupgrade.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/yesqa.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
absolufy-imports.......................................................................................Passed
black..................................................................................................Passed
codespell..............................................................................................Passed
Fix End of Files.......................................................................................Passed
Trim Trailing Whitespace...............................................................................Passed
cpplint................................................................................................Passed
flake8.................................................................................................Passed
flake8 (cython)........................................................................................Passed
flake8 (cython template)...............................................................................Passed
isort..................................................................................................Passed
no-string-hints........................................................................................Passed
pyupgrade..............................................................................................Passed
rst ``code`` is two backticks..........................................................................Passed
rst directives end with two colons.....................................................................Passed
rst ``inline code`` next to normal text................................................................Passed
Strip unnecessary `# noqa`s............................................................................Passed
flake8-rst.............................................................................................Passed
Check for use of Union[Series, DataFrame] instead of FrameOrSeriesUnion alias..........................Passed
Check for inconsistent use of pandas namespace in tests................................................Passed
Check for incorrect code block or IPython directives...................................................Passed
Check code for instances of os.remove..................................................................Passed
Check code for instances of pd.api.types...............................................................Passed
Check for non-standard imports.........................................................................Passed
Check for non-standard imports in test suite...........................................................Passed
Check for non-standard numpy.random-related imports excluding pandas/_testing.py.......................Passed
Check for use of np.bool instead of np.bool_...........................................................Passed
Check for use of np.object instead of np.object_.......................................................Passed
Generate pip dependency from conda.....................................................................Passed
Validate correct capitalization among titles in documentation..........................................Passed
Check for use of foo.__class__ instead of type(foo)....................................................Passed
Check for use of bare pytest raises....................................................................Passed
Check for use of private functions across modules......................................................Passed
Check for import of private attributes across modules..................................................Passed
Check for use of pytest.xfail..........................................................................Passed
Check for use of not concatenated strings..............................................................Passed
Check for strings with wrong placed spaces.............................................................Passed
Check for outdated annotation syntax and missing error codes...........................................Passed
```
</details>
</details>
## Changed files
<details>
<summary>Changed file: </summary>
- .pre-commit-config.yaml
</details>
<hr>
[:octocat: Repo](https://github.com/technote-space/create-pr-action) | [:memo: Issues](https://github.com/technote-space/create-pr-action/issues) | [:department_store: Marketplace](https://github.com/marketplace/actions/create-pr-action)
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MDExOlB1bGxSZXF1ZXN0NTg2NTc2NDE0
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|
[ArrayManager] TST: enable IO tests
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[] | null | 5
| 2021-03-08T08:36:53Z
| 2021-03-12T16:10:56Z
| 2021-03-12T16:10:53Z
|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/issues/39146
This basically just adds IO testing to the set of tests we run for ArrayManager. I already added skips for JSON in the past (and now also for PyTables IO and pickle), so those are still skipped now as well.
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PERF: get_dummies create array of 0/1s with appropriate layout for internals
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| 3
| 2021-03-08T08:55:13Z
| 2021-10-14T14:58:19Z
| 2021-03-15T14:05:23Z
|
MEMBER
| null |
This ensures that the 2D ndarray that is created in `get_dummies` for a single column is directly in the "right" layout as the internals prefer it (column-major, so it's row-major in the transposed Block.values).
For ArrayManager, this gives a speed-up for `get_dummies` itself (because we still copy the arrays to ensure proper contiguous memory layout for each column, currently):
```
In [7]: %timeit pd.get_dummies(s, sparse=False)
23.2 ms ± 1.16 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) <-- master
16.6 ms ± 689 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- PR
```
For the current BlockManager, it makes the actual get_dummies a bit slower, but can make a follow-up operation actually faster (where a copy, or consolidation, or a reduction, or .. would happen):
```
In [4]: %timeit pd.get_dummies(s, sparse=False)
10.7 ms ± 397 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- master
14.2 ms ± 713 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- PR
In [5]: %timeit pd.get_dummies(s, sparse=False).copy()
22.3 ms ± 260 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) <-- master
17.8 ms ± 1.92 ms per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- PR
```
The timing is from the following example taken from the `frame_methods.py::GetDummies` ASV case:
```
import string
categories = list(string.ascii_letters[:12])
s = pd.Series(
np.random.choice(categories, size=1000000),
dtype=pd.CategoricalDtype(categories),
)
```
So it makes the actual `get_dummies` a bit slower for the BlockManager, and thus I could also explicitly only do this for the ArrayManager. But since it improves follow-up operations, I think it can actually be beneficial overall for BlockManager as well. But either is fine for me.
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MDExOlB1bGxSZXF1ZXN0NTg2NjA2NzUx
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PERF: further improve take (reindex/unstack) for ArrayManager
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| 13
| 2021-03-08T09:16:06Z
| 2021-03-15T07:24:42Z
| 2021-03-15T00:30:45Z
|
MEMBER
| null |
A few related changes to `take_1d` / `ArrayManager.unstack`:
- Check in `ArrayManager.unstack` whether there will be missing values introduced or not, and if not, pass `allow_fill=False` to the `take` function, so this can take a more performant path (and doesn't need to check repeatedly for `indexer == -1`). And the same for `ArrayManager.reindex_indexer`.
- In the `take_1d`, if `allow_fill=False`, use the numpy `take` instead of our own cython implementation (numpy is a bit faster).
- Move the `ensure_int64` check for the indexer out of the shared `_take_preprocess_indexer_and_fill_value`. This way, we can do this once in ArrayManager.unstack and avoid doing it repeatedly for the same indexer inside `take_1d`.
Case from the `frame_methods.py::Unstack.time_full_product` ASV benchmark:
```python
dtype = 'int'
m = 100
n = 1000
levels = np.arange(m)
index = pd.MultiIndex.from_product([levels] * 2)
columns = np.arange(n)
values = np.arange(m * m * n).reshape(m * m, n)
df = pd.DataFrame(values, index, columns)
df_am = df._as_manager("array")
```
with the combined changes, this gives:
```
In [2]: %timeit df_am.unstack()
1.09 s ± 68.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) <-- master
276 ms ± 19.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) <-- PR
```
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MDExOlB1bGxSZXF1ZXN0NTg2NjM2NDY4
| 40,301
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[ArrayManager] Fix DataFrame.from_records with emtpy size
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| 1
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MEMBER
| null |
xref https://github.com/pandas-dev/pandas/issues/39146, follow-up on https://github.com/pandas-dev/pandas/pull/39991
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BUG: FileCreateError for large excel file created with ExcelWriter
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| 2021-03-08T11:51:28Z
| 2021-03-08T18:20:10Z
| 2021-03-08T18:20:10Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
def generate_multi_mpan_shape(shape_data, discount_data, mpan_eac_data, days_used):
# Evaluate shape volume
shape_volume = shape_data.fillna(0).to_numpy().sum()
num_of_mpans = mpan_eac_data.shape[0]
options = {}
options["strings_to_formulas"] = False
options["strings_to_urls"] = False
excel_obj = io.BytesIO()
writer = pd.ExcelWriter(excel_obj, options=options, engine="xlsxwriter")
for index in range(num_of_mpans):
# Generate a shape for a given MPAN
mpan = mpan_eac_data.iloc[index, 0]
eac = float(mpan_eac_data.iloc[index, 1])
volume_used = eac * days_used / 365
volume_ratio = volume_used / shape_volume
output_data = shape_data.copy(True)
output_data *= volume_ratio
# Apply discounts
if discount_data.iloc[0] != False:
output_data = apply_discounts(output_data, discount_data)
output_data = output_data.round(9)
output_data.insert(0, "MPAN", mpan)
output_data.insert(1, "Date", output_data.index.values)
output_data.to_excel(writer, sheet_name=f"{mpan}", index=False, header=False)
print(index)
writer.close()
excel_obj.seek(0)
return excel_obj
```
#### Problem description
I'm using the following code to generate a multi worksheet excel file using ExcelWriter. This code is being run in a lambda function, which only has 500MB available in the /tmp folder so I've tried to create the file completely in memory. The issue arises when I try to create an excel file that is larger than about 100 MB.
The following error is returned when ```writer.close()``` is called
```
Traceback (most recent call last):\n File \"/opt/python/xlsxwriter/workbook.py\", line 320, in close\n self._store_workbook()\n File \"/opt/python/xlsxwriter/workbook.py\", line 685, in _store_workbook\n xml_files = packager._create_package()\n File \"/opt/python/xlsxwriter/packager.py\", line 135, in _create_package\n self._write_worksheet_files()\n File \"/opt/python/xlsxwriter/packager.py\", line 190, in _write_worksheet_files\n worksheet._assemble_xml_file()\n File \"/opt/python/xlsxwriter/worksheet.py\", line 3875, in _assemble_xml_file\n self._write_sheet_data()\n File \"/opt/python/xlsxwriter/worksheet.py\", line 5483, in _write_sheet_data\n self._write_rows()\n File \"/opt/python/xlsxwriter/worksheet.py\", line 5676, in _write_rows\n self._write_cell(row_num, col_num, col_ref)\n File \"/opt/python/xlsxwriter/worksheet.py\", line 5857, in _write_cell\n self._xml_number_element(cell.number, attributes)\n File \"/opt/python/xlsxwriter/xmlwriter.py\", line 137, in _xml_number_element\n self.fh.write(\"\"\"<c%s><v>%.16G</v></c>\"\"\" % (attr, number))\n File \"/var/lang/lib/python3.8/codecs.py\", line 721, in write\n return self.writer.write(data)\n File \"/var/lang/lib/python3.8/codecs.py\", line 378, in write\n self.stream.write(data)\nOSError: [Errno 28] No space left on device\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \"/var/task/src/forecasting/shape_tool/shape_tool_controller.py\", line 133, in generate_multi_shape\n output_file = service.generate_multi_mpan_shape(\n File \"/var/task/src/forecasting/forecasting_logging.py\", line 32, in wrapper_func\n obj = func(*args)\n File \"/var/task/src/forecasting/shape_tool/shape_tool_service.py\", line 520, in generate_multi_mpan_shape\n writer.close()\n File \"/opt/python/pandas/io/excel/_base.py\", line 898, in close\n content = self.save()\n File \"/opt/python/pandas/io/excel/_xlsxwriter.py\", line 198, in save\n return self.book.close()\n File \"/opt/python/xlsxwriter/workbook.py\", line 322, in close\n raise FileCreateError(e)\nxlsxwriter.exceptions.FileCreateError: [Errno 28] No space left on device"
```
I'm not sure what is causing the issue here. The error seems to be saying that it is trying to save the file to disk but there is no space, but this is what I was trying to avoid by creating the file completely in memory
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-17763-Microsoft
Version : #1432-Microsoft Mon Aug 18 18:18:00 PST 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : 6.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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[ArrayManager] Enable read_parquet to not create 2D blocks when using ArrayManager
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| 2021-04-26T09:44:57Z
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MEMBER
| null |
xref https://github.com/pandas-dev/pandas/issues/39146
I was exploring the Parquet IO, and pyarrow has an option to not created consolidated blocks. If we do this when wanting to create an ArrayManager, we can reduce the memory usage. It's a bit slower, though, because there is still the overhead of creating more blocks (that's something that would need to be changed in pyarrow).
Would still need to add a test that checks the option is honored.
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[ArrayManager] Add Series._as_manager (consolidate with DataFrame)
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MEMBER
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Move the `_as_manager` helper to `generic.py`, and ensure it works for Series as well
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docs: changes to documentation
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docs: changes to documentation
small grammatical update to the documentation
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REF: de-duplicate code for pad/backfill
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MEMBER
| null |
```
before after ratio
[5ef41593] [6a7ac9e4]
<master> <interpolate_2d>
+ 3.44±0.04ms 5.14±0.4ms 1.50 frame_methods.Fillna.time_frame_fillna(True, 'pad', 'Float64')
+ 3.48±0.1ms 5.06±0.3ms 1.45 frame_methods.Fillna.time_frame_fillna(True, 'pad', 'Int64')
+ 3.32±0.05ms 4.59±0.05ms 1.38 frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'Int64')
+ 3.30±0.05ms 4.42±0.1ms 1.34 frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'Float64')
+ 3.37±0.03ms 4.43±0.1ms 1.32 frame_methods.Fillna.time_frame_fillna(False, 'pad', 'Int64')
+ 3.34±0.05ms 4.30±0.08ms 1.29 frame_methods.Fillna.time_frame_fillna(False, 'pad', 'Float64')
+ 2.15±0.04ms 2.48±0.2ms 1.15 frame_methods.Fillna.time_frame_fillna(True, 'bfill', 'Float64')
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE DECREASED.
```
if the general idea of this PR is acceptable, could try to improve these but these were already significantly improved in #39953.
with the cython functions reduced from 4 to 1, it is feasible to add 1 dedicated function for no limit (my numba branch is quicker than cython with that change) instead of 4 extra functions.
also may pave the way to add limit area and limit direction support to EAs
and maybe also for 2D masked arrays? cc @jbrockmendel
also having 1 cython function, I think also paves the way for a cython version for the limit area to remove the python set logic could be investigated.
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Use oldest-supported-numpy in pyproject.toml
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CONTRIBUTOR
| null |
We should be able to replace the build requires section of our pyproject.toml with https://pypi.org/project/oldest-supported-numpy/
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BUG:
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NONE
| null |
- [ ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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|
BUG: I am facing parse error but it was note resolved so can someone please give an idea to resolve this issue?
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| 2021-03-21T15:25:47Z
| 2021-03-21T15:25:47Z
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NONE
| null |
line1 : import pandas as pd
line2 : import numpy as np
line3 :
line4 : gd = pd.read_csv('Desktop/Data_Science/abcd_efgh.csv')
for the above code i was getting the error as follows
---------------------------------------------------------------------------
ParserError Traceback (most recent call last)
<ipython-input-5-61cbc9c13f04> in <module>
2 import numpy as np
3
----> 4 gd = pd.read_csv('Desktop/Data_Science/abcd_efgh.csv')
5 gd
~\anaconda3\lib\site-packages\pandas\io\parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
674 )
675
--> 676 return _read(filepath_or_buffer, kwds)
677
678 parser_f.__name__ = name
~\anaconda3\lib\site-packages\pandas\io\parsers.py in _read(filepath_or_buffer, kwds)
452
453 try:
--> 454 data = parser.read(nrows)
455 finally:
456 parser.close()
~\anaconda3\lib\site-packages\pandas\io\parsers.py in read(self, nrows)
1131 def read(self, nrows=None):
1132 nrows = _validate_integer("nrows", nrows)
-> 1133 ret = self._engine.read(nrows)
1134
1135 # May alter columns / col_dict
~\anaconda3\lib\site-packages\pandas\io\parsers.py in read(self, nrows)
2035 def read(self, nrows=None):
2036 try:
-> 2037 data = self._reader.read(nrows)
2038 except StopIteration:
2039 if self._first_chunk:
pandas\_libs\parsers.pyx in pandas._libs.parsers.TextReader.read()
pandas\_libs\parsers.pyx in pandas._libs.parsers.TextReader._read_low_memory()
pandas\_libs\parsers.pyx in pandas._libs.parsers.TextReader._read_rows()
pandas\_libs\parsers.pyx in pandas._libs.parsers.TextReader._tokenize_rows()
pandas\_libs\parsers.pyx in pandas._libs.parsers.raise_parser_error()
ParserError: Error tokenizing data. C error: Expected 3 fields in line 5, saw 66
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GH39992 update inconsistent namespace usage for reductions
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CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NTg3MDkwMTky
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Added orient="ndarray" to DataFrame.to_dict()
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NONE
| null |
It would be useful to allow Series to be directly converted to numpy arrays instead of having to do this outside.
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API: Add `Styler.to_html`, for saving output to HTML file
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| 2
| 2021-03-08T21:04:33Z
| 2021-05-26T05:28:54Z
| 2021-05-26T02:09:32Z
|
CONTRIBUTOR
| null |
- [x] closes #11700 and closes #13379
## `Styler.to_html` method
This PR adds the above method to `Styler`.
This is a **minimal** introduction with limited arguments to provide just the core file io functionality and basic options, either to write a fully structured HTML file or just the `Styler.render` output. It is very easier to extend in follow ons.
#### Jinja 2 template inheritance
Due to the more complex logic, the jinja2 templates were split into smaller components:
- html_style.tpl: this just deals with the `<style>` html element
- html_table.tpl: this just deals with the core `<table>` element including (or excluding) style identifiers
- html.tpl: this is the controlling template which imports the above based on what is required.
It is more performant to have a separate temple that excludes styling elements by about 33%, rather than include the logic in a single template. I think it is easier to maintain as well.
#### Tests
A new `test_to_html.py` file is added. Some tests are moved from `test_style.py` if they more relate to HTML generation. Some new tests are added.
#### Objective
Ultimately the medium-term aim is to deprecate `DataFrame.to_html`. `Styler` is currently faster at rendering HTML than `DataFrame.to_html` (#39972), and this is further improved in this PR.
There is a standalone purpose for wanting to create HTML to file from Styler.
This PR complements `Styler.to_latex` whose aim is also to deprecate `DataFrame.to_latex`.
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ENH: Consider adding alternative calculation of centered moving averages for even window size
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open
| false
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[] | null | 1
| 2021-03-09T01:04:37Z
| 2021-03-12T00:27:59Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported (originally reported in the now quite old #14425 but reviving here with a more thorough description of the problem)
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
The current `rolling(window, center=True).mean()` behaviour in Pandas first calculates the rolling mean and then shifts the result back to "center" the labels. This does not give the expected result (at least to me):
```python
import pandas as pd
window_size = 2
df = pd.DataFrame([2, 6, 4, 2, 0, 1, 5, 3],
index=["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug"])
df.rolling(window=window_size).mean()
```
| | 0 |
|:----|------:|
| Jan | nan |
| Feb | 4 |
| Mar | 5 |
| Apr | 3 |
| May | 1 |
| Jun | 0.5 |
| Jul | 3 |
| Aug | 4 |
#### Problem description
For rolling averages with an even sized window (e.g., 2, 4, 6, 8, etc.) [it is common](https://otexts.com/fpp2/moving-averages.html#moving-averages-of-moving-averages) to apply a moving average with window size = 2 (`2 x MA`) to the result of the original moving average output to line up results with the original index.
Here's an example animation showing the expected behaviour:
#### Expected Output
The following code achieves the above (correct?) behaviour:
```python
(df.rolling(window=window_size).mean()
.rolling(window=2).mean()
.shift(-window_size // 2)
)
```
| | 0 |
|:----|-------:|
| Jan | nan |
| Feb | 4.5 |
| Mar | 4 |
| Apr | 2 |
| May | 0.75 |
| Jun | 1.75 |
| Jul | 3.5 |
| Aug | nan |
#### Additional context
I'm not sure if this needs to be changed in the codebase as the above workaround is not too bad. But I wanted to document the issue here for anyone who encounters it. I'd also be happy to work on a PR if this did want to be implemented.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.9.2.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Tue Aug 20 16:57:14 PDT 2019; root:xnu-4903.271.2~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_CA.UTF-8
LOCALE : en_CA.UTF-8
pandas : 1.2.3
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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|
ERR: Raise NotImplimentedError for EWMA with times and adjust=False
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| 1
| 2021-03-09T05:10:47Z
| 2021-03-09T17:43:35Z
| 2021-03-09T13:32:23Z
|
MEMBER
| null |
- [x] xref #40098
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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|
BUG: Inconsistent rounding up and rounding down in Timestamp.round (v1.2.3)
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| 2021-03-09T05:47:35Z
| 2021-08-22T17:19:54Z
| 2021-08-22T17:19:54Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
print(pd.Timestamp('2021-03-03T23:20:30').round("1min"))
print(pd.Timestamp('2021-03-03T23:21:30').round("1min"))
print(pd.Timestamp('2021-03-03T23:22:30').round("5min"))
print(pd.Timestamp('2021-03-03T23:27:30').round("5min"))
```
Output:
```
2021-03-03 23:20:00
2021-03-03 23:22:00
2021-03-03 23:20:00
2021-03-03 23:30:00
```
#### Problem description
Rounding a `Timestamp` has inconsistent behavior w.r.t. rounding up vs rounding down when the `Timestamp` is exactly in the middle of the rounding resolution. In the above example, if the minute is even, it rounds down, and if the minute is odd, it rounds up.
#### Expected Output
Round up consistently
```
2021-03-03 23:21:00
2021-03-03 23:22:00
2021-03-03 23:25:00
2021-03-03 23:30:00
```
#### Output of ``pd.show_versions()``
```
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-1032-azure
Version : #34-Ubuntu SMP Mon Feb 10 19:37:25 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.20.1
pytz : 2020.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 50.3.0.post20201006
Cython : None
pytest : 6.1.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.3.3
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.16.0
xlrd : None
xlwt : None
numba : None
```
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| 40,316
|
BUG: Inserting series into dataframe does ignore column names/keys
|
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| 3
| 2021-03-09T08:25:57Z
| 2021-03-11T07:24:32Z
| 2021-03-10T22:59:24Z
|
NONE
| null |
- [ ] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
I am initializing a dataframe with column names and was able to insert a pandas series which is dictionary-like.
In older versions (e.g. 1.1.4), pandas did sort the values of the series according to their keys into the columns of the dataframe.
However, in newer versions (1.2.2 at least, and also 1.2.3) it ignores the keys of the series and assigns the elements of the series in current order of the series keys (no matter their names).
This can lead to data mis-location. I would not have noticed this behaviour if the entries of the series did not have different shapes.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
# init dataframe with column names
df = pd.DataFrame(columns=['a', 'b', 'c'], index=range(3))
# create series on the fly and put it into dataframe
s = pd.Series({'d': 2, 'a': 1, 'c': 3})
df.iloc[0] = s
df
```
#### Problem description
The output in version 1.2.3 looks like
```python
a b c
0 2 1 3
1 NaN NaN NaN
2 NaN NaN NaN
```
#### Expected Output
The output in version 1.1.4 looks like
```python
a b c
0 1 NaN 3
1 NaN NaN NaN
2 NaN NaN NaN
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.9.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17763
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : de_DE.cp1252
pandas : 1.2.3
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 54.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : 3.5.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
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MDExOlB1bGxSZXF1ZXN0NTg3NjMwODQy
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PERF: reduce overhead in groupby _cython_operation
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| 2021-03-09T08:46:34Z
| 2021-03-15T15:14:13Z
| 2021-03-15T15:14:10Z
|
MEMBER
| null |
The `BaseGrouper._cython_operation` has lots of checking of the dtype etc, which gives quite some overhead compared to the actual aggregation function (for small data). This is a first set of small changes to cut down that overhead.
Using the `GroupManyLabels` benchmark we have
```python
ncols = 1000
N = 1000
data = np.random.randn(N, ncols)
labels = np.random.randint(0, 100, size=N)
df = DataFrame(data)
df_am = df._as_manager('array')
```
gives
```
In [2]: %timeit df_am.groupby(labels).sum()
48.9 ms ± 1.92 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) <-- master
39.1 ms ± 695 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) <-- PR
```
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MDU6SXNzdWU4MjU2NTE3OTQ=
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|
BUG: unexpected DataFrame output when LaTeX is used in column names
|
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| 2021-03-09T10:01:36Z
| 2021-04-25T17:43:08Z
| 2021-04-25T17:43:08Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
LaTeX and `int`:
```python
import pandas as pd
pd.DataFrame({'$a$': [1]})
```
LaTeX and `str:`
```python
import pandas as pd
pd.DataFrame({'$a$': ['1']})
```
LaTeX that includes spaces and `float` or `int` or `str`:
```python
import pandas as pd
pd.DataFrame({'$a \: a$': [1.]})
```
#### Problem description
Any dataframe that uses a LaTeX in the column name and data of dtype `int `or `str `in the same column ends up taking the whole width of the jupyter cell. In DataFrames with several columns, all columns with no LaTeX or LaTeX and `float `dtype data have the expected width, while the problematic combination produces a very wide column that takes up the rest of the space.
Furthemore, column names using LaTeX with spaces in them result in the same behaviour for `float`, `int `and `str `dtypes. Additionally, the spaces appear displayed as if a linebreak (\n) and a space were used. This behaviour persists with r- and f-strings.
#### Expected Output
The outputted DataFrame should be identical whether LaTeX was used in the column name or not (other than the column name). The outputted DataFrame when using LaTeX in column names and `str `or `int `dtype data in the column should be the same (apart from data dtype) as when `float `dtype is used.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 1.2.3
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.23
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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