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MDExOlB1bGxSZXF1ZXN0NTMzMDgxMDgx
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Removed unnecessary dict calls
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| 0
| 2020-12-05T19:30:38Z
| 2020-12-07T13:34:34Z
| 2020-12-07T13:34:34Z
|
CONTRIBUTOR
| null |
xref #38138
File modified:
pandas/tests/io/parser/test_quoting.py
pandas/tests/io/parser/test_usecols.py
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NTMzMDgyNDA5
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CLN: fix C408 #38138
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| 1
| 2020-12-05T19:42:07Z
| 2020-12-07T13:32:06Z
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|
MEMBER
| null |
xref #38138
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MDExOlB1bGxSZXF1ZXN0NTMzMDk0Mjg2
| 38,321
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CLN: remove now-unnecesary coerce_to_dtypes
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| 2020-12-05T21:23:26Z
| 2020-12-07T15:37:10Z
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MEMBER
| null |
Made unnecessary bc we now operate blockwise, so coercion is handled at a lower level.
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MDExOlB1bGxSZXF1ZXN0NTMzMDk0NzI1
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REF: use lighter-weight casting function
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MDExOlB1bGxSZXF1ZXN0NTMzMDk3MjY5
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CLN: Implement multiindex handling for get_op_result_name
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| 2020-12-05T21:50:19Z
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MEMBER
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
@jbrockmendel
This would solve the inconsistency in ``get_op_result_name`` for MultiIndexes, but adds a bit more complexity here.
I think this would allow us to remove the now duplicated code in ``intersection``
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BUG: BDay() offsetting not behaving as expected
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MDExOlB1bGxSZXF1ZXN0NTMzMTEwOTA3
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BUG: read_csv raising IndexError with multiple header cols, specified index_col and no data rows
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| 2020-12-05T23:43:55Z
| 2020-12-13T20:02:42Z
| 2020-12-13T20:01:38Z
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MEMBER
| null |
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Parser returned an empty list instead of a list filled with None, which caused the IndexError.
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CLN: use with instead of try finally in `io/stata.py`
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MEMBER
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This is reported by lgtm.com.
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MDU6SXNzdWU3NTc5MDY3MDQ=
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ENH: Adding highlighting options to `to_latex` function
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| 2020-12-06T11:44:51Z
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NONE
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Following #3196, it would be extremely useful to add the possibility of highlighting the best value in a row or column of a DataFrame object when converting into a latex table (the `to_latex` function)
A convenient api for that would be to add a parameter to the `to_latex()` function, such as `highlight_rows=TYPE`, where TYPE can be `bold|italics|...` and than each row's best value would be highlighted.
There could also be a scenario where the best value is the lowest one, so maybe an additional parameter should be `best_highlight: str` between `high|low`.
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REF/TYP: Rename ABCIndexClass->ABCIndex and use cast
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REGR: Groupby first/last/nth treats None as an observation
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MEMBER
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This is a regression from 1.0.x to 1.1.x, introduced by #33462. Assuming it doesn't get into the 1.2 rc, not sure if it should go into 1.2 during the rc phase or wait until 1.3.
Had to decide on some edge-case behaviors in odd situations with missing values, see https://github.com/pandas-dev/pandas/issues/38286#issuecomment-739521117
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BUG: first("1M") returning two months when first day is last day of month
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MDExOlB1bGxSZXF1ZXN0NTMzMjQxMDgx
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REGR: Fix Index construction from Sparse["datetime64[ns]"]
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Backport PR #38330: REGR: Groupby first/last/nth treats None as an observation
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Backport PR #38330
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BUG: read_csv not converting to float for python engine with decimal sep, usecols and parse_dates
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BUG: iloc.__setitem__ with dict value and mixed dtypes
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- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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rewrite dict as literal in tests/io/parser/test_index_col.py
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| 2020-12-06T23:07:29Z
| 2020-12-09T10:13:53Z
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|
NONE
| null |
for issue # 38138
rewrite unnecessary dict call as a literal in pandas/tests/io/parser/test_index_col.py
Please let me know if anything needs to be amended, changed or fixed. I'm new to contributing.
- [ ] issue #38138
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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REF: avoid accessing private iloc methods
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| 1
| 2020-12-06T23:08:34Z
| 2020-12-07T20:54:00Z
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MEMBER
| null |
I'm pretty sure this is made possible by @phofl's recent fix to only call `_align_series` in `_setitem_with_indexer` when `name != "iloc"`
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BUG: `df.set_index()` doesn't maintain ExtensionArray dtype
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| 1
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| 2020-12-07T01:53:04Z
| 2020-12-07T01:53:04Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
`dtype` of `ExtensionArray` is lost after `set_index()`/`reset_index()`.
Note: I'm using cyberpandas as a "minimal" implementation of an `ExtensionArray` example (I think it's minimal enough to convey and reproduce the issue - this also happens with my own `ExtensionArray` implementation which in turn is inspired by cyberpandas' `IPArray`/`ExtensionArray`).
```python
>>> from cyberpandas import IPArray
>>> import pandas as pd
>>> df = pd.DataFrame({"address": IPArray(['192.168.1.1', '192.168.1.10']), 'a': [1, 2]})
>>> df
address a
0 192.168.1.1 1
1 192.168.1.10 2
>>> dfa = df.set_index(['address'])
>>> # the dtype is already lost here, so reset_index() can't do much to restore the original dtype
>>> dfa
a
address
192.168.1.1 1
192.168.1.10 2
>>> dfa.index.dtype
dtype('O')
>>> dfb = dfa.reset_index(drop=False)
>>> dfb
address a
0 192.168.1.1 1
1 192.168.1.10 2
>>> dfb.address.dtype
dtype('O')
>>> df.address.dtype
<cyberpandas.ip_array.IPType object at 0x7ff3689988e0>
```
I found this in `Index.__new__` which always sets the Index dtype of an `ExtensionArray` to `object`. What's the reason for this?
```
# extension dtype
elif is_extension_array_dtype(data_dtype) or is_extension_array_dtype(dtype):
if not (dtype is None or is_object_dtype(dtype)):
# coerce to the provided dtype
ea_cls = dtype.construct_array_type()
data = ea_cls._from_sequence(data, dtype=dtype, copy=False)
else:
data = np.asarray(data, dtype=object)
# coerce to the object dtype
data = data.astype(object)
return Index(data, dtype=object, copy=copy, name=name, **kwargs)
```
#### Problem description
Type information is only stored in dtype (and holds additional meta information). current workaround is to capture type info before `set_index()` (which I use to assign column values by index) and cleanup/fix dtype afterwards. This is potentially error prone (as all places where set_index is used need to implement the cleanup)
#### Expected Output
`isinstance(dfb.address.dtype, cyberpandas.ip_array.IPType) is True`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.9.0.final.0
python-bits : 64
OS : Linux
OS-release : 5.9.11-200.fc33.x86_64
Version : #1 SMP Tue Nov 24 18:18:01 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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TYP: pandas/core/missing.py
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|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU3NTgyODE4NTE=
| 38,340
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ENH: implement fast isin() for nullable dtypes
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| 2021-01-20T01:44:33Z
| 2021-01-20T01:44:33Z
|
MEMBER
| null |
Currently, you can get quite a slowdown:
```
In [41]: arr = np.random.randint(0, 10, 1_000_001)
In [42]: s = pd.Series(arr)
In [43]: %timeit s.isin([1, 2, 3, 20])
2.71 ms ± 175 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [44]: s = pd.Series(arr, dtype="Int64")
In [45]: %timeit s.isin([1, 2, 3, 20])
22.9 ms ± 96.7 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
```
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Backport PR #38332 on branch 1.1.x (REGR: Fix Index construction from Sparse["datetime64[ns]"])
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NONE
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Backport PR #38332: REGR: Fix Index construction from Sparse["datetime64[ns]"]
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Backport PR #38318 on branch 1.1.x (DOC: 1.1.5 release date)
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NONE
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Backport PR #38318: DOC: 1.1.5 release date
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ENH: Functions for creating data frames and series "like" an existing one with different values
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| 2020-12-07T11:17:15Z
| 2020-12-12T21:09:25Z
| 2020-12-12T21:09:25Z
|
NONE
| null |
#### Problem
Sometimes I find myself needing a copy of a `DataFrame` or a `Series` with different values to the original. That is to say, with a similar index and column structure and naming. For example, it could occur when doing some numerical transformations which return NumPy arrays. The thing can of course be done by creating new objects by hand, but it becomes quite repetitive after a few lines.
#### Solution
Utilities like `np.zeros_like` are neat shorthands, and looks like Pandas has used them, at least in Indexing ([`reindex_like`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.reindex_like.html)). I would like to see similar ways of constructing frames and series. I've written such shorthands for my personal use, but I think others could benefit from them being included in the library.
#### API breaking implications
This would introduce new functionality without affecting other parts of the system.
#### Describe alternatives you've considered
Doing it by hand is possible and pretty easy. It's only cumbersome, which the shorthand would solve.
#### Additional context
I'm not in the know of Pandas internal functionality, but the shorthands I've written are as follows:
```python
def df_like(data: Union[np.ndarray, pd.DataFrame], like: pd.DataFrame) -> pd.DataFrame:
if isinstance(data, pd.DataFrame):
data = data.values
return pd.DataFrame(data, index=like.index, columns=like.columns)
def series_like(data: Union[np.ndarray, pd.Series], like: pd.Series) -> pd.Series:
if isinstance(data, pd.Series):
data = data.values
return pd.Series(data, index=like.index, name=like.name)
```
If there's something else to account for, I would expect the functions to handle that as well. Maybe it could also be class method.
```python
class DataFrame:
@classmethod
def like(cls, data, like: "DataFrame") -> "DataFrame":
if isinstance(data, cls):
data = data.values
return cls(data, index=like.index, columns=like.columns)
```
Looking forward to hearing your thoughts! I'd also be open to contributing these changes if an agreement is reached.
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CI: Move sql builds from Travis to Github actions
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try to move builds with databases to GitHub actions
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TST: revert too-ambitious
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MDExOlB1bGxSZXF1ZXN0NTMzOTQwOTgw
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PERF: isin casting integers to object
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| 2020-12-07T20:52:06Z
| 2020-12-08T15:45:01Z
| 2020-12-08T08:41:40Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
xref https://github.com/pandas-dev/pandas/pull/38279#issuecomment-739973166 cc @jorisvandenbossche
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Extend check namespace usage
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| 2021-01-04T09:32:19Z
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NONE
| null |
- [ ] xref #38093
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
TLDR: This code changes might be not in a mergeable state as false positives remain when updating the pattern.
The change is a bit bigger to what is described in #38093 as more files needed to be fixed, which is fine I think.
The stack displaying the files to fix might be limited to a few items (9 items in my case) when running the command `pre-commit run inconsistent-namespace-usage --all-files`. Every time a set of files was fixed a few others appeared which is why I ended fixing up more files and why my list of false positive is not exhaustive.
I have committed the change to the PATTERN in a separate commit as we have false positives. Such as the following files which contain:
1. a similar function in the numpy package (ex: np.array see https://github.com/pandas-dev/pandas/blob/862cd05df4452592a99dd1a4fa10ce8cfb3766f7/pandas/tests/test_strings.py#L188)
2. a similar function in the pyarrow package (ex: pyarrow.array see https://github.com/pandas-dev/pandas/blob/862cd05df4452592a99dd1a4fa10ce8cfb3766f7/pandas/tests/io/test_parquet.py#L839)
3. a function can be called on dataframe or a serie (ex: `df.isna` and `aggr.isna` see https://github.com/pandas-dev/pandas/blob/862cd05df4452592a99dd1a4fa10ce8cfb3766f7/pandas/tests/groupby/test_categorical.py#L528 and https://github.com/pandas-dev/pandas/blob/862cd05df4452592a99dd1a4fa10ce8cfb3766f7/pandas/tests/groupby/test_categorical.py#L1570
4. or a function is called with any prefixes (see the few calls to `factorize` in https://github.com/pandas-dev/pandas/blob/862cd05df4452592a99dd1a4fa10ce8cfb3766f7/pandas/tests/test_algos.py#L52)
5. `eval` vs `pd.eval` (see https://github.com/pandas-dev/pandas/blob/862cd05df4452592a99dd1a4fa10ce8cfb3766f7/pandas/tests/computation/test_eval.py)
I'm probably missing a few other cases but 1) 2) and 3) can be added in the negative lookahead (ignore function/methods which start with np/pyarrow/df). However the regex might grow bigger and bigger. 5) can be ignored entirely either within the regex or with an exclusion list.
Let me know you're thinking as I see three solutions:
- update the regex and maybe add an exclusion list (for case like eval) and see where that leads
- remove last commit for PATTERN and create another ticket to address the other cases
- close the ticket and find/investigate another solution (ex: separate class vs function check/check import instead...)
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Removed unused strict struct member in tokenizer
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| 2
| 2020-12-07T21:57:04Z
| 2020-12-08T19:43:18Z
| 2020-12-08T19:43:14Z
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MEMBER
| null |
I don't see where this would ever be assigned a value
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MDExOlB1bGxSZXF1ZXN0NTM0MDE3ODMz
| 38,349
|
REF: call _ensure_valid_index inside _sanitize_column
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| 2020-12-07T23:37:43Z
| 2020-12-08T16:23:13Z
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|
MEMBER
| null |
They are only ever called together.
|
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| 758,964,737
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MDExOlB1bGxSZXF1ZXN0NTM0MDQxOTMw
| 38,350
|
TST: add message matches to pytest.raises in various tests GH30999
|
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| 2
| 2020-12-08T00:46:57Z
| 2020-12-08T21:33:42Z
| 2020-12-08T21:31:51Z
|
CONTRIBUTOR
| null |
- [x] ref [#30999 ](https://github.com/pandas-dev/pandas/issues/30999)
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Files changed:
pandas/tests/io/pytables/test_complex.py
pandas/tests/io/test_clipboard.py
pandas/tests/plotting/frame/test_frame_subplots.py
|
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| 758,976,748
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MDExOlB1bGxSZXF1ZXN0NTM0MDUxMjkx
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|
BUG: item_cache invalidation
|
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| 2
| 2020-12-08T01:14:28Z
| 2020-12-08T19:46:08Z
| 2020-12-08T19:07:17Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
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|
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MDExOlB1bGxSZXF1ZXN0NTM0MTEwMzM2
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|
ENH: column-wise DataFrame.fillna with Series/Dict value
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| 2021-07-11T23:34:31Z
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|
MEMBER
| null |
- [x] closes #4514
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Picking up #30922
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PERF: IntervalIndex.isin
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| 2020-12-14T15:52:36Z
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|
MEMBER
| null |
```
In [1]: import pandas as pd
In [2]: ii = pd.IntervalIndex.from_breaks(range(100000))
In [3]: values = ii[:100]
In [4]: from pandas.core.algorithms import isin
In [5]: %timeit isin(ii, values)
91.3 ms ± 5.24 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) # <-- master
3.84 ms ± 57.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <-- PR
```
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TST: a weird bug with numpy + DataFrame with duplicate columns
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MEMBER
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- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Picking up #37125
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|
BUG: pylint with merge of pandas==1.1.5 raises a RecursionError
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|
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
```sh
pip install pandas==1.1.5 pylint
```
```python
# pandas_merge_sample.py
import pandas as pd
pd.merge(None, None)
```
```sh
pylint pandas_merge_sample.py
```
#### Problem description
Performing `pylint` on Python codes that include `pandas.merge` makes a RecursionError like below.
I'm not sure the error was caused by pandas itself, but at least I can say `pylint` with `pandas==1.1.4` doesn't raise the error, and `pylint` with `pandas==1.1.5` raises.
For `pylint` side, an issue about it was already opened, so I've opened it just in case for those who will face the same problem.
https://github.com/PyCQA/pylint/issues/3969
I might think the commit can be related to the problem.
https://github.com/pandas-dev/pandas/commit/e99e5ab32c4e831e7bbac0346189f4d6d86a6225#diff-7c4c2c4161d35fe8d1d71cc86b45abcc642271069f05cf860d9195a304d2ed33
```
...
res = next(generator)
File "/private/tmp/pandas-pylint/venv/lib/python3.8/site-packages/astroid/inference.py", line 293, in infer_attribute
for owner in self.expr.infer(context):
File "/private/tmp/pandas-pylint/venv/lib/python3.8/site-packages/astroid/util.py", line 160, in limit_inference
yield from islice(iterator, size)
File "/private/tmp/pandas-pylint/venv/lib/python3.8/site-packages/astroid/context.py", line 113, in cache_generator
for result in generator:
File "/private/tmp/pandas-pylint/venv/lib/python3.8/site-packages/astroid/decorators.py", line 132, in raise_if_nothing_inferred
yield next(generator)
File "/private/tmp/pandas-pylint/venv/lib/python3.8/site-packages/astroid/decorators.py", line 93, in wrapped
generator = _func(node, context, **kwargs)
File "/private/tmp/pandas-pylint/venv/lib/python3.8/site-packages/astroid/inference.py", line 203, in infer_name
context = contextmod.copy_context(context)
File "/private/tmp/pandas-pylint/venv/lib/python3.8/site-packages/astroid/context.py", line 155, in copy_context
return context.clone()
File "/private/tmp/pandas-pylint/venv/lib/python3.8/site-packages/astroid/context.py", line 102, in clone
clone = InferenceContext(self.path, inferred=self.inferred)
RecursionError: maximum recursion depth exceeded
```
#### Expected Output
No error will be raised.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 19.3.0
Version : Darwin Kernel Version 19.3.0: Thu Jan 9 20:58:23 PST 2020; root:xnu-6153.81.5~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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ENH: Add an `assert` method to Series and DataFrame to assert on the fly in chaining
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| 2020-12-08T11:35:32Z
| 2021-08-14T22:04:27Z
| 2021-08-14T22:04:27Z
|
NONE
| null |
#### Is your feature request related to a problem?
When chaining methods, one may want to make some check and raise errors accordingly. Currently it is not possible to define these checks on the fly but one rather has to define a function like :
```
def assert_a_positive(df):
if not (df.a > 0).all():
raise ValueError()
return df
```
so as to be able to do later, for example:
```
(
pd.DataFrame({"a": [1, 2]})
.pipe(assert_a_positive)
.assign(b=lambda df: 1 / df.a)
)
```
This lakes flexibility and readability. Eventually it refrains from writing more test for data scientist that, even though then don't write unit test, at least are used to write sanity check like that.
#### Describe the solution you'd like
Adding an `assert` method to `pd.Series` and `pd.DataFrame` such that the above example could be written:
```
(
pd.DataFrame({"a": [1, 2]})
.assert(lambda df: (df.a > 0).all())
.assign(b=lambda df: 1 / df.a)
)
```
#### API breaking implications
It doest not break a thing but just add a new method
#### Describe alternatives you've considered
There is actually a [great-expectation](https://github.com/great-expectations/great_expectations) package that does this sort of thing but with pre-built check. It is not as natural as writing down directly `assert` on-the-fly and requires an extra dep.
#### Additional context
In my current projects, I have just added these few lines at init:
```
from pandas.core.frame import DataFrame
from pandas.core.series import Series
def pandas_assert(df, func, *args, error_type=AssertionError):
if not func(df):
raise error_type(*[arg(df) if callable(arg) else arg for arg in args])
return df
setattr(DataFrame, "assert_", pandas_assert) # assert cannot be used
setattr(Series, "assert_", pandas_assert)
```
so that I can then do for example:
```
(
pd.DataFrame({"a": [-1, 1, 2, 0]})
.assert_(lambda df: (df.a > 0).all())
.assign(b=lambda df: 1 / df.a)
)
(
pd.DataFrame({"a": [-1, 1, 2, 0]})
.assert_(lambda df: (df.a > 0).all(), "some a are lower than 0")
.assign(b=lambda df: 1 / df.a)
)
(
pd.DataFrame({"a": [-1, 1, 2, 0]})
.assert_(
lambda df: (df.a > 0).all(), "some a are lower than 0", error_type=ValueError
)
.assign(b=lambda df: 1 / df.a)
)
(
pd.DataFrame({"a": [-1, 1, 2, 0]})
.assert_(
lambda df: (df.a > 0).all(),
lambda df: f"some a are lower than 0:\n{df[df.a <= 0]}",
error_type=ValueError,
)
.assign(b=lambda df: 1 / df.a)
)
```
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MDU6SXNzdWU3NTkzODkxNjI=
| 38,357
|
DOC: Clarify stability of sorting in documentation of DataFrame.sort_values for multiple columns / labels
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| 4
| 2020-12-08T12:02:54Z
| 2020-12-22T20:32:57Z
| 2020-12-22T20:32:57Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
Description of the argument `kind` in the documentation of [pandas.DataFrame.sort_values](https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.sort_values.html).
#### Documentation problem
The current documentation states (emphasize mine):
>Choice of sorting algorithm. See also ndarray.np.sort for more information. **mergesort is the only stable algorithm. For DataFrames, this option is only applied when sorting on a single column or label**.
To me that means, that the `kind` argument is ignored if you sort by multiple columns / labels. But I think one could even interpret it as `mergesort` explicitely is not done in that case. Running some tests, it does look like the sorting is stable when sorting multiple columns / labels. But it is not written anywhere. If it really is guaranteed that the sorting is stable for sorting by multiple columns / labels, it would be nice to have this written down explicitely in the documentation.
#### Suggested fix for documentation
IF that is actually the case, I would suggest to add a sentence to the effect that the sorting is stable if sorting is done by multiple columns / labels. E.g. change the `kind` argument description to:
>Choice of sorting algorithm. See also ndarray.np.sort for more information. mergesort is the only stable algorithm. ~~For DataFrames, this option is only applied when sorting on a single column or label.~~ **For DataFrames, if sorting by multiple columns or labels, this argument is ignored, defaulting to a stable sorting algorithm.**
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MDU6SXNzdWU3NTk0NjUzNjU=
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Performance regressions in GroupBy.fillna
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| 2021-09-15T15:26:50Z
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|
CONTRIBUTOR
| null |
https://pandas.pydata.org/speed/pandas/#groupby.FillNA.time_df_ffill?python=3.8&Cython=0.29.21&commits=03e58585
From https://github.com/pandas-dev/pandas/commit/03e58585
cc @GYHHAHA
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DOC: Start v1.3.0 release notes
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MEMBER
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CI: activate azure pipelines/github actions on 1.2.x
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MDExOlB1bGxSZXF1ZXN0NTM0NDg0NjY4
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DOC: minor fixes to 1.2 release notes
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MDU6SXNzdWU3NTk1MDcwODE=
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|
BUG: Groupby column and min aggregation over period column yields NaT for all rows
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|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample:
```python
d = {
'user_id': {
0: 4628132781164409,
1: 7075307556966343,
2: 8013098674514720,
3: 7690164883842520,
4: 4985766983843363,
5: 3038805367505476,
6: 2541787162631756,
7: 4985766983843363,
8: 2541787162631756,
9: 3558493739463398},
'date': {
0: '2020-02-12',
1: '2020-02-12',
2: '2020-02-12',
3: '2020-02-12',
4: '2020-02-12',
5: '2020-02-12',
6: '2020-02-12',
7: '2020-02-12',
8: '2020-02-12',
9: '2020-02-12'
}
}
df = pd.DataFrame(d).astype({'date': 'datetime64'})
df['week'] = df['date'].dt.to_period('W')
df['cohort'] = df.groupby('user_id')['week'].min()
```
#### Problem description
The above code returns NaT values for the `cohort` column, but it was expected to return the minimum values for `week`, which in this case would be `2020-02-10/2020-02-16` for all rows.
#### Expected Output
If we change the last line to:
```
df['cohort'] = df.groupby('user_id')['week'].transform('min')
```
We get the expected value for the cohort column, i.e., `2020-02-10/2020-02-16`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-1029-aws
Version : #30~18.04.1-Ubuntu SMP Tue Oct 20 11:09:25 UTC 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 : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0
Cython : 0.29.15
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.20
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : 0.52.0
</details>
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Backport PR #38361 on branch 1.2.x (DOC: minor fixes to 1.2 release notes)
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NONE
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Backport PR #38361: DOC: minor fixes to 1.2 release notes
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BUG: weird tick spacing when plotting with time (instead of datetime) on x-axis
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[] | null | 0
| 2020-12-08T15:52:14Z
| 2021-08-14T22:06:23Z
| 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
import pandas as pd
import numpy as np
ts = pd.date_range("2020-01-01", "2020-01-02", freq='15T', closed="left")
pd.Series(np.random.rand(len(ts)), ts).plot()
pd.Series(np.random.rand(len(ts)), ts.time).plot()
```
First plot:
Second plot:
#### Problem description and expected output
I'd expect the tick marks of the second plot to be at more natural positions, like is the case in the first plot.
#### Note
This may be an issue in `matplotlib`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : de_DE.cp1252
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3
setuptools : 50.3.2.post20201202
Cython : None
pytest : 6.1.2
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.1
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
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.20
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NTM0NTQ1ODgy
| 38,365
|
BUG FIX- C408
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| 2020-12-08T15:54:17Z
| 2020-12-08T17:29:03Z
| 2020-12-08T17:29:03Z
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NONE
| null |
- [ ] closes #c408
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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Backport PR #38360 on branch 1.2.x (CI: activate azure pipelines/github actions on 1.2.x)
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NONE
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Backport PR #38360: CI: activate azure pipelines/github actions on 1.2.x
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BUG: Pandas 1.1.5 location-based indexing error with quantized pivot table
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| 7
| 2020-12-08T16:08:38Z
| 2020-12-21T13:55:18Z
| 2020-12-21T13:55:18Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
input_df = pd.DataFrame(**{
'index': [0, 1],
'columns': ['loss', 'category_64973.fc_size', 'category_64973.num_fc_layers', 'training.learning_rate'],
'data': [[1.0549572706222534, 240, 2, 0.0014908184659929895], [1.225046157836914, 160, 2, 0.0013734204727201226]]
})
input_df['training.learning_rate'] = pd.qcut(
input_df['training.learning_rate'],
q=10,
precision=3,
duplicates='drop',
)
data = input_df.pivot_table(
index='category_64973.fc_size',
columns='training.learning_rate',
values='loss',
aggfunc='mean'
)
# Seaborn code starts here
mask = np.zeros(data.shape, bool)
mask = pd.DataFrame(mask,
index=data.index,
columns=data.columns,
dtype=bool)
mask | pd.isnull(data)
```
#### Problem description
An error occurs when attempting to plot a quantized pivot table using Seaborn with the latest version of Pandas (`1.1.5`).
The code above is a self-contained example showing what Seaborn is doing when `heatmap()` is called on the input pivot table (`data`). See this usage in the Ludwig framework: https://github.com/uber/ludwig/blob/master/ludwig/utils/visualization_utils.py#L1392. Prior to v1.1.5, this code was working fine and used to generate plots in Ludwig.
The stack trace is as follows:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~/repos/ludwig/env/lib/python3.7/site-packages/pandas/core/indexing.py in _has_valid_tuple(self, key)
701 try:
--> 702 self._validate_key(k, i)
703 except ValueError as err:
~/repos/ludwig/env/lib/python3.7/site-packages/pandas/core/indexing.py in _validate_key(self, key, axis)
1368 else:
-> 1369 raise ValueError(f"Can only index by location with a [{self._valid_types}]")
1370
ValueError: Can only index by location with a [integer, integer slice (START point is INCLUDED, END point is EXCLUDED), listlike of integers, boolean array]
The above exception was the direct cause of the following exception:
ValueError Traceback (most recent call last)
<ipython-input-1-e654830c5b85> in <module>
32 dtype=bool)
33
---> 34 mask | pd.isnull(data)
~/repos/ludwig/env/lib/python3.7/site-packages/pandas/core/ops/__init__.py in f(self, other, axis, level, fill_value)
638 self, other, op, axis, default_axis, fill_value, level
639 ):
--> 640 return _frame_arith_method_with_reindex(self, other, op)
641
642 if isinstance(other, ABCSeries) and fill_value is not None:
~/repos/ludwig/env/lib/python3.7/site-packages/pandas/core/ops/__init__.py in _frame_arith_method_with_reindex(left, right, op)
572 )
573
--> 574 new_left = left.iloc[:, lcols]
575 new_right = right.iloc[:, rcols]
576 result = op(new_left, new_right)
~/repos/ludwig/env/lib/python3.7/site-packages/pandas/core/indexing.py in __getitem__(self, key)
871 # AttributeError for IntervalTree get_value
872 pass
--> 873 return self._getitem_tuple(key)
874 else:
875 # we by definition only have the 0th axis
~/repos/ludwig/env/lib/python3.7/site-packages/pandas/core/indexing.py in _getitem_tuple(self, tup)
1441 def _getitem_tuple(self, tup: Tuple):
1442
-> 1443 self._has_valid_tuple(tup)
1444 try:
1445 return self._getitem_lowerdim(tup)
~/repos/ludwig/env/lib/python3.7/site-packages/pandas/core/indexing.py in _has_valid_tuple(self, key)
705 "Location based indexing can only have "
706 f"[{self._valid_types}] types"
--> 707 ) from err
708
709 def _is_nested_tuple_indexer(self, tup: Tuple) -> bool:
ValueError: Location based indexing can only have [integer, integer slice (START point is INCLUDED, END point is EXCLUDED), listlike of integers, boolean array] types
```
Note that this last `mask | pd.isnull(data)` operations succeeds with Pandas 1.1.4 and all other dependencies being left the same.
#### Expected Output
The `mask | pd.isnull(data)` call should succeed.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.7.8.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.0
Cython : 0.29.21
pytest : 6.1.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.2
html5lib : None
pymysql : 0.10.1
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.2
bottleneck : None
fsspec : 0.8.4
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.52.0
</details>
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Shruti bug fix
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DOC: sync xarray version
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[WIP] ENH: add Pyarrow csv engine
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MEMBER
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- [x] closes #23697
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Picking up #31817.
I'm putting this up to use CI for debugging. There are a bunch of comments in #31817 that I haven't addressed yet (working on them) - but I'll take feedback at any time
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Backport PR #38369 on branch 1.2.x (DOC: sync xarray version)
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Backport PR #38369: DOC: sync xarray version
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MDExOlB1bGxSZXF1ZXN0NTM0NzY3NjU0
| 38,373
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REF: share IntervalIndex.intersection with Index.intersection
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| 1
| 2020-12-08T22:26:56Z
| 2020-12-14T01:22:44Z
| 2020-12-12T23:21:45Z
|
MEMBER
| null |
- [x] closes #38299
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This changes the return dtype of IntervalIndex.intersection when that intersection is empty.
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MDExOlB1bGxSZXF1ZXN0NTM0NzY5MTEx
| 38,374
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CLN: typo fixup in 1.2.0 whatsnew
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MEMBER
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- [ ] closes #xxxx
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cc @simonjayhawkins
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DOC: infer_dtypes document is inconsistent with behaviour regarding boolean
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| 9
| 2020-12-08T23:08:13Z
| 2020-12-16T02:14:18Z
| 2020-12-16T02:14:18Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
https://github.com/pandas-dev/pandas/blob/0711b7adb3150f14461c5d1a111a87a83a45bc90/pandas/_libs/lib.pyx#L1315
#### Documentation problem
In this example the return value should be 'boolean' as mentioned in this test case https://github.com/pandas-dev/pandas/blob/1db3aa2b8ae8bd3b20dea14b92e1e7355bfa266a/pandas/tests/dtypes/test_inference.py#L679
#### Suggested fix for documentation
change 'mix' to 'boolean'
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Add py39 target in Black's configuration, bump Black to 21.5b2
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MDExOlB1bGxSZXF1ZXN0NTM0ODE2Mjk0
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CLN: lighter-weight casting
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- [ ] closes #xxxx
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- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU3NTk4OTQ3NDc=
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|
BUG: is_numeric_dtype return bool as TRUE
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|
NONE
| null |
#### Code Sample,
```
from pandas.api.types import is_integer_dtype, is_numeric_dtype, is_string_dtype, is_bool_dtype
import numpy as np
is_numeric_dtype(bool)
# True
is_numeric_dtype(np.bool)
# True
```
#### Problem description
is_numeric_dtype is returning true for boolean values
source code below
https://github.com/pandas-dev/pandas/blob/v1.1.5/pandas/core/dtypes/common.py#L1223-L1262
```
def is_numeric_dtype(arr_or_dtype) -> bool:
"""
Check whether the provided array or dtype is of a numeric dtype.
Parameters
----------
arr_or_dtype : array-like
The array or dtype to check.
Returns
-------
boolean
Whether or not the array or dtype is of a numeric dtype.
Examples
--------
>>> is_numeric_dtype(str)
False
>>> is_numeric_dtype(int)
True
>>> is_numeric_dtype(float)
True
>>> is_numeric_dtype(np.uint64)
True
>>> is_numeric_dtype(np.datetime64)
False
>>> is_numeric_dtype(np.timedelta64)
False
>>> is_numeric_dtype(np.array(['a', 'b']))
False
>>> is_numeric_dtype(pd.Series([1, 2]))
True
>>> is_numeric_dtype(pd.Index([1, 2.]))
True
>>> is_numeric_dtype(np.array([], dtype=np.timedelta64))
False
"""
return _is_dtype_type(
arr_or_dtype, classes_and_not_datetimelike(np.number, np.bool_)
)
```
#### Expected Output
is_numeric_dtype(np.bool)
# False
####
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.2.9-98_fbk11_hardened_3627_g39307fe48d19
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL :
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : None
setuptools : 46.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.2.14
tables : None
tabulate : 0.8.6
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NTM0ODI2NDkw
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|
fix series.isin slow issue with Dtype IntegerArray
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| 29
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| 2021-02-01T11:30:00Z
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|
CONTRIBUTOR
| null |
- [ ] closes #38340
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BUG: item_cache invalidation on DataFrame.insert
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MEMBER
| null |
- [x] closes #26985
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Something wonky is going on with tm.assert_produces_warning, will see if it shows up on the CI.
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CLN: C408 Unnecessary dict call, last files #38138
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REF: unify Index union methods
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| 2020-12-09T02:53:00Z
| 2020-12-14T16:15:04Z
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MEMBER
| null |
- [ ] closes #xxxx
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- [ ] whatsnew entry
Sits on top of #38373 which focuses on intersection.
@simonjayhawkins this changes corner-case behavior for IntervalIndex.union, which we already changed in #38282 for 1.2.0. I think this is unambiguously More Correct. Question for you: should we try to get it in to 1.2.0 to avoid back-to-back behavior changes?
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CLN: C408 Unnecessary dict call - rewrite as a literal #38138
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| 9
| 2020-12-09T04:32:51Z
| 2020-12-09T10:14:53Z
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CONTRIBUTOR
| null |
Fix in:
./pandas/tests/io/generate_legacy_storage_files.py
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Backport PR #38374 on branch 1.2.x (CLN: typo fixup in 1.2.0 whatsnew)
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NONE
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Backport PR #38374: CLN: typo fixup in 1.2.0 whatsnew
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| 1
| 2020-12-09T14:10:52Z
| 2020-12-09T17:09:29Z
| 2020-12-09T17:09:16Z
|
MEMBER
| null |
- [x] closes #38138
|
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MDU6SXNzdWU3NjAzOTYzMTU=
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REGR: assigning column with name that is registered as extension dtype errors
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| 2
| 2020-12-09T14:51:18Z
| 2020-12-14T14:11:03Z
| 2020-12-14T14:11:03Z
|
MEMBER
| null |
```
In [34]: df = pd.DataFrame([0])
In [35]: df["Int64"] = [1]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
~/scipy/pandas/pandas/core/generic.py in _set_item(self, key, value)
3822 try:
-> 3823 loc = self._info_axis.get_loc(key)
3824 except KeyError:
~/scipy/pandas/pandas/core/indexes/range.py in get_loc(self, key, method, tolerance)
353 raise KeyError(key) from err
--> 354 raise KeyError(key)
355 return super().get_loc(key, method=method, tolerance=tolerance)
KeyError: 'Int64'
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
<ipython-input-35-26205ed43ee5> in <module>
----> 1 df["Int64"] = [1]
~/scipy/pandas/pandas/core/frame.py in __setitem__(self, key, value)
3161 else:
3162 # set column
-> 3163 self._set_item(key, value)
3164
3165 def _setitem_slice(self, key: slice, value):
~/scipy/pandas/pandas/core/frame.py in _set_item(self, key, value)
3238 self._ensure_valid_index(value)
3239 value = self._sanitize_column(key, value)
-> 3240 NDFrame._set_item(self, key, value)
3241
3242 # check if we are modifying a copy
~/scipy/pandas/pandas/core/generic.py in _set_item(self, key, value)
3824 except KeyError:
3825 # This item wasn't present, just insert at end
-> 3826 self._mgr.insert(len(self._info_axis), key, value)
3827 return
3828
~/scipy/pandas/pandas/core/internals/managers.py in insert(self, loc, item, value, allow_duplicates)
1195
1196 # insert to the axis; this could possibly raise a TypeError
-> 1197 new_axis = self.items.insert(loc, item)
1198
1199 if value.ndim == self.ndim - 1 and not is_extension_array_dtype(value.dtype):
~/scipy/pandas/pandas/core/indexes/numeric.py in insert(self, loc, item)
172 def insert(self, loc: int, item):
173 try:
--> 174 item = self._validate_fill_value(item)
175 except TypeError:
176 return self.astype(object).insert(loc, item)
~/scipy/pandas/pandas/core/indexes/numeric.py in _validate_fill_value(self, value)
117 Convert value to be insertable to ndarray.
118 """
--> 119 if is_bool(value) or is_bool_dtype(value):
120 # force conversion to object
121 # so we don't lose the bools
~/scipy/pandas/pandas/core/dtypes/common.py in is_bool_dtype(arr_or_dtype)
1398 return arr_or_dtype.is_object and arr_or_dtype.inferred_type == "boolean"
1399 elif is_extension_array_dtype(arr_or_dtype):
-> 1400 return getattr(arr_or_dtype, "dtype", arr_or_dtype)._is_boolean
1401
1402 return issubclass(dtype.type, np.bool_)
AttributeError: 'str' object has no attribute '_is_boolean'
```
This is something that started to fail recently. Getting this from the geopandas CI testing with pandas master.
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MDExOlB1bGxSZXF1ZXN0NTM1MzA2MjAz
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Updated docstring for DataFrame.plot.bar graph clearify the use of color list
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| 3
| 2020-12-09T16:32:58Z
| 2020-12-14T17:27:45Z
| 2020-12-14T17:27:38Z
|
CONTRIBUTOR
| null |
- [X] closes #31691
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BUG: `DataFrame.__repr__` not showing correct `MultiIndex` values
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| 2020-12-09T17:24:53Z
| 2020-12-09T23:32:05Z
| 2020-12-09T23:32:04Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
>>> import pandas as pd
>>> df = pd.DataFrame()
>>> df['a'] = pd.Series([1, 2, None], dtype='Int64')
>>> df['b'] = pd.Series([10, 11, 12])
>>> df['c'] = pd.Series(['dsf', 'bac', 'abc'])
>>> df
a b c
0 1 10 dsf
1 2 11 bac
2 <NA> 12 abc
>>> df.set_index(['a','b'])
c
a b
1 10 dsf
2 11 bac
NaN 12 abc
>>> df.set_index(['a','b']).index
MultiIndex([(1.0, 10),
(2.0, 11),
(nan, 12)],
names=['a', 'b'])
```
#### Problem description
The actual underlying values in the multiIndex in first level are all `floats` but the `DataFrame.__repr__` outputs the values as integers.
#### Expected Output
The expected output should be inline with `MultiIndex.__repr__`.
```python
>>> df.set_index(['a','b'])
c
a b
1.0 10 dsf
2.0 11 bac
NaN 12 abc
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 20.1.0
Version : Darwin Kernel Version 20.1.0: Sat Oct 31 00:07:11 PDT 2020; root:xnu-7195.50.7~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0
Cython : None
pytest : None
hypothesis : 5.29.0
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
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 : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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REF: consolidate NDFrame._iset_item, _set_item with DataFrame methods
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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BUG: ExtensionArray with 2D datetime64 values errors on display formatting
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| 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.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
import pandas.io.formats.format as fmt
x = pd.date_range('2018-01-01', periods=5, freq='H').to_numpy()
x = np.tile(x, (3, 1))
formatter = fmt.Datetime64Formatter(x)
result = formatter.get_result()
```
#### Problem description
When using a Pandas ExtensionArray that has 2D values of Datetime64 dtype, Pandas errors on formatting the values with the following:
```
File "/ib/python3.8/site-packages/pandas/io/formats/printing.py", line 73, in <listcomp>
return [x.rjust(max_len) for x in texts]
AttributeError: 'list' object has no attribute 'rjust'
```
The above is also true when using the `Datetime64Formatter` directly with values of ndim > 1.
#### Expected Output
Formatting that takes into account dimensionality:
```
[' [2018-01-01 00:00:00, 2018-01-01 01:00:00, 201...', ' [2018-01-01 05:00:00, 2018-01-01 06:00:00, 201...']
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-54-generic
Version : #60-Ubuntu SMP Fri Nov 6 10:37:59 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20201009
Cython : None
pytest : 6.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
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[#38390] Bug fix for Datetime64Formatter with values of ndim > 1
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| 2020-12-09T19:28:54Z
| 2021-03-09T07:25:14Z
| 2021-03-09T07:25:14Z
|
CONTRIBUTOR
| null |
- [x] closes #38390
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This change fixes a bug in `Datetime64Formatter` that errors with values of ndim > 1. This was discovered when using an ExtensionArray with 2D values of datetime64 and attempting to format/display. The formatter currently allows for 2D values, but will end up returning nested strings instead of a flat list of string. The same error occurs when using the `Datetime64Formatter` directly on 2D values.
The fix is to flatten the values to get a flat list of formatted datetime64 strings, then reshape, and then format a second time over the outer dimension to produce the properly nested list format and the final flat list of strings.
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BUG: read_csv interpreting NA value as comment when NA contains comment string
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| 2020-12-09T20:46:56Z
| 2020-12-14T08:21:46Z
| 2020-12-13T23:44:23Z
|
MEMBER
| null |
- [x] xref #34002
- [x] tests added / passed
- [x] passes `black pandas`
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- [x] whatsnew entry
I looked into the ``c`` case, but this is not that trivial as here. Comments are removed way before checking for na values. Would have to move that part way down to be able to do this.
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MDExOlB1bGxSZXF1ZXN0NTM1NDc4Nzc5
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Solved desing problem with to_datetime when "ignore" error was being used.
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NONE
| null |
- [x] closes #28120
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- [x] Now when u use some array of integers on to_datetime function passing as arguments: error="ignore" and format="%Y%m%d" it only skips none fitting values instead of all the values.
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ENH: make "closed" part of IntervalDtype
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| 2020-12-10T00:08:54Z
| 2021-01-10T02:07:08Z
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MEMBER
| null |
- [x] closes #19371
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Most of the work here is in accomodating backwards-compat.
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Backport PR #35100 on branch 1.2.x (Document Tips for Debugging C Extensions)
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Backport PR #35100: Document Tips for Debugging C Extensions
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BUG: algos.rank with readonly values
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MEMBER
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One of the two current npdev failures is caused by https://github.com/numpy/numpy/pull/17884
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MDU6SXNzdWU3NjA4MzMzNDA=
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DOC: Correct pandas sources should be discoverable by sphinx autodoc
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[] | null | 1
| 2020-12-10T02:43:40Z
| 2021-08-14T22:08:09Z
| 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.
---
**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
"""
A module demonstrating the inability to automatically discover link targets of references to pandas objects.
.. autofunction:: foo
"""
import pandas as pd
def foo(df: pd.DataFrame) -> None:
"""
:param df: A data frame
"""
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
In the current state of things, sphinx autodoc will load in the module and via runtime inspection of `pd.DataFrame.__module__` discover that the fully qualified name of the type of the parameter `df` is `pandas.core.frame.DataFrame`. However, the documentation for `DataFrame` is indexed as `pandas.DataFrame` as per the pandas `objects.inv` file, so sphinx fails to make the connection between those two names for the same type.
Instead I have to resort to manually specifying the type as `:type df: pandas.DataFrame`, which reduces the value gained from using autodoc in the first place. It also leads to hours spent trying to figure out how my setup was misconfigured.
I notice also that if I do:
```python
"""
A module demonstrating the ability to automatically discover link targets of references to pandas objects.
.. autofunction:: foo
"""
import pandas as pd
pd.DataFrame.__module__ = "pandas"
def foo(df: pd.DataFrame) -> None:
"""
:param df: A data frame
"""
```
Then the automatic references are recognised properly, and I get a clickable hyperlink to the official pandas docs for DataFrame.
#### Expected Output
I expected the indexed names in `objects.inv` to align with the runtime inspected type names.
I don't know if the code in sphinx or the code in pandas ought to change, but my feeling is that it would be unproductive to expect sphinx to divine meaning from the full dynamic breadth of possibilities that python offers. It would seem to me that there should be some structuring convention that, if adhered to would fix this.
#### Output of ``pd.show_versions()``
(I don't think this is relevant for this bug)
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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REF: implement sanitize_masked_array
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| 0
| 2020-12-10T02:46:55Z
| 2020-12-11T23:08:13Z
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|
MEMBER
| null |
The recarray handling in internals.construction is inconsistent with the other recarray handling. Hopefully I can get that sorted out and then we can re-use sanitize_masked_array there too.
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BUG: DataFrame(MaskedRecords) inconsistent with Series behavior
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| 1
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| 2021-03-15T00:09:58Z
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|
MEMBER
| null |
Example based on tests.frame.test_constructors.TestDataFrameConstructors.test_constructor_mrecarray, which has comment "Ensure mrecarray produces frame identical to dict of masked arrays from GH3479"
```
import numpy as np
from numpy.ma import mrecords
import pandas as pd
arrays = [
("float", np.array([1.5, 2.0])),
("int", np.array([1, 2])),
("str", np.array(["abc", "def"])),
]
for name, arr in arrays[:]:
arrays.append(
("masked1_" + name, np.ma.masked_array(arr, mask=[False, True]))
)
arrays.append(("masked_all", np.ma.masked_all((2,))))
arrays.append(("masked_none", np.ma.masked_array([1.0, 2.5], mask=False)))
comb = [arrays[0], arrays[1], arrays[3]]
names, data = zip(*comb)
mrecs = mrecords.fromarrays(data, names=names)
result = pd.DataFrame(mrecs)
alt = {k: pd.Series(v) for k, v in comb}
expected = pd.DataFrame(alt, columns=names)
tm.assert_frame_equal(result, expected) # <-- nope!
>>> result
float int masked1_float
0 1.5 1 1.500000e+00
1 2.0 2 1.000000e+20
>>> expected
float int masked1_float
0 1.5 1 1.5
1 2.0 2 NaN
```
I would expect these two to match.
Note that `alt["masked1_float"]._values` is a masked_array, so that is non-lossy (though `alt["masked1_float"]`'s repr does not make that obvious). `expected["masked1_float"]._values` is a regular ndarray, so that is lossy.
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REF: simplify internals.construction
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MDU6SXNzdWU3NjEwMDg0MzM=
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BUG: .groupby().min() .max() .agg('min') .agg('max') ERROR
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[] | null | 8
| 2020-12-10T08:03:48Z
| 2021-01-22T10:17:41Z
| 2020-12-16T06:58:25Z
|
NONE
| null |
- [O] I have checked that this issue has not already been reported.
I checked it but this bug reported that already solved. but in my pandas, still occur.
(https://github.com/pandas-dev/pandas/issues/31522)
- [O] I have confirmed this bug exists on the latest version of pandas.
I've checked latest version at anaconda
- [ ] (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
# data given from https://www.kaggle.com/hesh97/titanicdataset-traincsv/data
import pandas as pd
df = pd.read_csv('./train.csv')
df.head()
class_group = df.groupby('Pclass')
print(class_group.groups) # pass
print(class_group.mean()['Survived']) # pass
print(class_group.median()) # pass
print(class_group.agg('min')) # error occur. also .agg('max'), .min(), .max()
```
#### Problem description
I found same error occurred at older version, 1.0.0, 1.0.1. (https://github.com/pandas-dev/pandas/issues/31522)
At that ticket, after 1.0.3, this bug solved.
But I tested, Windows10 64bit python3.6 pandas 1.0.1, 1.0.3, 1.0.5, 1.1.3 and CentOS7.5 python3.8 pandas 1.0.5, same error occured.
Just like this. Same code, same data at Windows 10 and CentOS 7
* Windows
""""""""""""""""""""""""""""""""""""""""""""""
Traceback (most recent call last):
File "E:/PythonProjects/Deepphi/local-dev/pandaserror.py", line 11, in <module>
print(class_group.agg('min')) # error occur. also .agg('max'), .min(), .max()
File "C:\Users\deepnoid-workstation\Anaconda3_64bit\envs\dev\lib\site-packages\pandas\core\groupby\generic.py", line 951, in aggregate
result, how = self._aggregate(func, *args, **kwargs)
File "C:\Users\deepnoid-workstation\Anaconda3_64bit\envs\dev\lib\site-packages\pandas\core\base.py", line 307, in _aggregate
return self._try_aggregate_string_function(arg, *args, **kwargs), None
File "C:\Users\deepnoid-workstation\Anaconda3_64bit\envs\dev\lib\site-packages\pandas\core\base.py", line 263, in _try_aggregate_string_function
return f(*args, **kwargs)
File "C:\Users\deepnoid-workstation\Anaconda3_64bit\envs\dev\lib\site-packages\pandas\core\groupby\groupby.py", line 1552, in min
numeric_only=numeric_only, min_count=min_count, alias="min", npfunc=np.min
File "C:\Users\deepnoid-workstation\Anaconda3_64bit\envs\dev\lib\site-packages\pandas\core\groupby\groupby.py", line 1000, in _agg_general
how=alias, alt=npfunc, numeric_only=numeric_only, min_count=min_count,
File "C:\Users\deepnoid-workstation\Anaconda3_64bit\envs\dev\lib\site-packages\pandas\core\groupby\generic.py", line 1022, in _cython_agg_general
how, alt=alt, numeric_only=numeric_only, min_count=min_count
File "C:\Users\deepnoid-workstation\Anaconda3_64bit\envs\dev\lib\site-packages\pandas\core\groupby\generic.py", line 1135, in _cython_agg_blocks
assert len(locs) == result.shape[1]
AssertionError
Process finished with exit code 1
""""""""""""""""""""""""""""""""""""""""""""""
CentOS
""""""""""""""""""""""""""""""""""""""""""""""
Traceback (most recent call last):
File "pandaserror.py", line 24, in <module>
print(class_group.agg('min'))
File "/conda/anaconda3/lib/python3.8/site-packages/pandas/core/groupby/generic.py", line 928, in aggregate
result, how = self._aggregate(func, *args, **kwargs)
File "/conda/anaconda3/lib/python3.8/site-packages/pandas/core/base.py", line 311, in _aggregate
return self._try_aggregate_string_function(arg, *args, **kwargs), None
File "/conda/anaconda3/lib/python3.8/site-packages/pandas/core/base.py", line 267, in _try_aggregate_string_function
return f(*args, **kwargs)
File "/conda/anaconda3/lib/python3.8/site-packages/pandas/core/groupby/groupby.py", line 1372, in f
return self._cython_agg_general(alias, alt=npfunc, **kwargs)
File "/conda/anaconda3/lib/python3.8/site-packages/pandas/core/groupby/generic.py", line 993, in _cython_agg_general
agg_blocks, agg_items = self._cython_agg_blocks(
File "/conda/anaconda3/lib/python3.8/site-packages/pandas/core/groupby/generic.py", line 1100, in _cython_agg_blocks
assert len(locs) == result.shape[1]
AssertionError
""""""""""""""""""""""""""""""""""""""""""""""
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
* Windows
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.6.10.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : AMD64 Family 23 Model 113 Stepping 0, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.4.0.post20200518
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.49.1
None
* CentOS
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1062.12.1.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : ko_KR.UTF-8
LOCALE : ko_KR.UTF-8
pandas : 1.0.5
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200714
Cython : 0.29.21
pytest : 5.4.3
hypothesis : None
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.2
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.3
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.9
numba : 0.50.1
None
</details>
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ENH: Naming inconsistency of functions like read_csv() and to_csv()
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[] | null | 4
| 2020-12-10T12:57:24Z
| 2021-08-14T22:09:47Z
| 2021-08-14T22:09:47Z
|
NONE
| null |
#### Is your feature request related to a problem?
Inconsistent naming of functions related to read and write data like `pd.read_csv()` and `to_csv()`. This is confusing for users because of the naming: **read vs. to** . For users who are new to pandas, those function names `read` and `to` are confusing and not consistent.
#### Describe the solution you'd like
Those functions should either be named `read() `and `write()` or `from()` and `to()`. My solution is to add a function `write()` like `write_csv()` that just wraps the existing `to()` function. Then the old way would still work. Another solutions is to add a `from()` function like `from_csv()`.
#### API breaking implications
We could have a depreciation warning on the `to()` functions or just keep it.
#### Describe alternatives you've considered
Keeping naming inconsistency.
#### Additional context
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TST: Add test to ensure DF describe does not throw an error (#32409)
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| 2021-01-20T18:57:36Z
| 2021-01-20T18:57:33Z
|
CONTRIBUTOR
| null |
- [x] closes #32409
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Notes:
- Tested using `pytest pandas/tests/frame/methods/test_describe.py::TestDataFrameDescribe::test_describe_does_not_raise_error` and tested error format by calling a raise error line in the try block `raise TypeError("Test message")`
- Re-created PR initial PR with rebase attempt in the following link: https://github.com/pandas-dev/pandas/pull/35270 -- but the diff didn't seem reasonable
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REF: simplify casting
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| 0
| 2020-12-10T18:10:42Z
| 2020-12-11T22:54:01Z
| 2020-12-11T22:47:03Z
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MEMBER
| null |
This gets us down to only 3 usages of maybe_cast_to_datetime
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MDExOlB1bGxSZXF1ZXN0NTM2NDMwNTc1
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BUG: Series/DataFrame construction from scalars
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| 2020-12-11T02:44:24Z
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MEMBER
| null |
- [x] closes #38032
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NTM2NDk4NDM5
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BUG: DataFrame.to_markdown with an empty frame
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| 1
| 2020-12-11T04:41:13Z
| 2020-12-12T23:28:09Z
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|
MEMBER
| null |
- [x] closes #31771
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- [x] whatsnew entry
Picking up #36233.
Bumping the minimum version of tabulate as per https://github.com/pandas-dev/pandas/issues/31771#issuecomment-685401053
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CLN: remove coerce_float keyword from construction.internals
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| 2020-12-11T04:55:45Z
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MEMBER
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This sits on top of #38400
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ENH: add end and end_day origin for resample
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| 7
| 2020-12-11T06:22:06Z
| 2020-12-22T14:07:34Z
| 2020-12-22T14:02:49Z
|
CONTRIBUTOR
| null |
- [x] closes #37804
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
|
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Backport PR #38396 on branch 1.2.x (BUG: algos.rank with readonly values)
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NONE
| null |
Backport PR #38396: BUG: algos.rank with readonly values
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BUG: Cannot read XLSX files with xlrd version 2.0.0
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| 6
| 2020-12-11T12:53:57Z
| 2021-03-01T11:26:18Z
| 2020-12-11T14:10:37Z
|
NONE
| null |
Code that fails with xlrd==2.0.0:
`pd.read_excel("file.xlsx")
`
raises the error: `XLRDError: Excel xlsx file; not supported`
The documentation for xlrd==2.0.0 says that it does not support any other files other than "xls"
xlrd updated their version today on 2020-12-11
```
/local_disk0/.ephemeral_nfs/envs/pythonEnv-1e568db2-af63-4098-8ee9-a8375bd77616/lib/python3.7/site-packages/pandas/io/excel/_base.py in read_excel(io, sheet_name, header, names, index_col, usecols, squeeze, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, verbose, parse_dates, date_parser, thousands, comment, skipfooter, convert_float, mangle_dupe_cols, **kwds)
302
303 if not isinstance(io, ExcelFile):
--> 304 io = ExcelFile(io, engine=engine)
305 elif engine and engine != io.engine:
306 raise ValueError(
/local_disk0/.ephemeral_nfs/envs/pythonEnv-1e568db2-af63-4098-8ee9-a8375bd77616/lib/python3.7/site-packages/pandas/io/excel/_base.py in __init__(self, io, engine)
819 self._io = stringify_path(io)
820
--> 821 self._reader = self._engines[engine](self._io)
822
823 def __fspath__(self):
/local_disk0/.ephemeral_nfs/envs/pythonEnv-1e568db2-af63-4098-8ee9-a8375bd77616/lib/python3.7/site-packages/pandas/io/excel/_xlrd.py in __init__(self, filepath_or_buffer)
19 err_msg = "Install xlrd >= 1.0.0 for Excel support"
20 import_optional_dependency("xlrd", extra=err_msg)
---> 21 super().__init__(filepath_or_buffer)
22
23 @property
/local_disk0/.ephemeral_nfs/envs/pythonEnv-1e568db2-af63-4098-8ee9-a8375bd77616/lib/python3.7/site-packages/pandas/io/excel/_base.py in __init__(self, filepath_or_buffer)
351 self.book = self.load_workbook(filepath_or_buffer)
352 elif isinstance(filepath_or_buffer, str):
--> 353 self.book = self.load_workbook(filepath_or_buffer)
354 elif isinstance(filepath_or_buffer, bytes):
355 self.book = self.load_workbook(BytesIO(filepath_or_buffer))
/local_disk0/.ephemeral_nfs/envs/pythonEnv-1e568db2-af63-4098-8ee9-a8375bd77616/lib/python3.7/site-packages/pandas/io/excel/_xlrd.py in load_workbook(self, filepath_or_buffer)
34 return open_workbook(file_contents=data)
35 else:
---> 36 return open_workbook(filepath_or_buffer)
37
38 @property
/local_disk0/.ephemeral_nfs/envs/pythonEnv-1e568db2-af63-4098-8ee9-a8375bd77616/lib/python3.7/site-packages/xlrd/__init__.py in open_workbook(filename, logfile, verbosity, use_mmap, file_contents, encoding_override, formatting_info, on_demand, ragged_rows, ignore_workbook_corruption)
168 # files that xlrd can parse don't start with the expected signature.
169 if file_format and file_format != 'xls':
--> 170 raise XLRDError(FILE_FORMAT_DESCRIPTIONS[file_format]+'; not supported')
171
172 bk = open_workbook_xls(
XLRDError: Excel xlsx file; not supported
```
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MDExOlB1bGxSZXF1ZXN0NTM2OTIxMjk1
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REF: move __from_arrow__ to common base class for numeric masked arrays
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| 1
| 2020-12-11T14:04:06Z
| 2020-12-13T07:30:24Z
| 2020-12-12T22:54:06Z
|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/issues/38110
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MDExOlB1bGxSZXF1ZXN0NTM2OTgzMzgy
| 38,412
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REF: move __array_ufunc__ to base NumericArray
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| 5
| 2020-12-11T15:02:30Z
| 2021-01-13T13:59:37Z
| 2021-01-13T13:58:36Z
|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/issues/38110
The implementations for integer and float array is almost identical, so move to base class. In addition, this also enables that ufuncs on IntegerArray can return FloatingArray (instead of float numpy array)
I should probably consolidate the tests as well, and move those to `tests/arrays/masked/test_ufunc.py` or so.
In addition, `BooleanArray.__array_ufunc__` is actually also very similar, but didn't include that here, yet. I should probably do that, but wondering if I should move the common `__array_ufunc__` implementation to BaseMaskedArray, or rather make BooleanArray subclass NumericArray.
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ENH: better error message when trying to do improper aggregation on dataframe
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[] | null | 2
| 2020-12-11T15:42:23Z
| 2021-08-14T22:12:38Z
| 2021-08-14T22:12:38Z
|
NONE
| null |
#### Is your feature request related to a problem?
I have an automated pipeline at work that does some data processing, and this morning I woke up to many notifications that the pipeline had failed. I checked the logs and the stack trace, and found that the script was throwing the exception `pandas.core.base.DataError: No numeric types to aggregate` when trying to take the mean of a column over a groupby. Due to this error message, I assumed that the problem was due to data being coerced to the wrong data type somewhere else, so I tried to hunt down the source of that. After being unable to find any error, I tried force-coercing the data to `float` type, but this still did not fix the issue. So I finally went to inspect the actual data in the dataframe, and it turns out it was empty! So there was no aggregation to be done. I think if the error message had been a bit more descriptive, I might have been able to identify the actual problem a bit quicker.
#### Describe the solution you'd like
I think it would be great if the error message could be a bit more informative. In the case I ran into, rather than saying that there are `No numeric types to aggregate`, perhaps it could have said something like `Unable to aggregate over empty DataFrame`. In other potential scenarios, like where the data types are actually not numeric, perhaps the error might also mention which column(s) (if you are aggregating multiple at the same time) is(are) causing the problem. Perhaps something like `No numeric types to aggregate in columns ['col1', 'col2', ...]`
#### API breaking implications
Don't think this will affect the API at all, just the error message.
#### Describe alternatives you've considered
I have added code to my pipeline to check for this edge case, which I suppose is an "alternative".
#### Additional context
This is the offending bit of code that was causing my error. `costs_df` ended up having zero rows when I pulled the data from one of my databases, so taking the mean over `cost_per_mile` threw the above exception.
```python
costs_df.groupby(["organization_cd", "route_id"])["cost_per_mile"].mean()
```
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TST: add messages to bare pytest raises
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| 1
| 2020-12-11T16:24:17Z
| 2020-12-14T19:11:26Z
| 2020-12-14T19:11:21Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This PR adds messages to the bare pytest raises in pandas/tests/generic/methods/test_pipe.py and pandas/tests/reshape/merge/test_merge_asof.py. This PR references #30999.
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BUG: MultiIndex.__repr__ is broken when `display.max_seq_items` = 1
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"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/80"
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| 4
| 2020-12-11T18:30:16Z
| 2020-12-28T18:38:47Z
| 2020-12-28T18:38:47Z
|
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
In[11]: pd.set_option("display.max_seq_items", 2)
In[12]: pd.MultiIndex.from_tuples(tuples)
Out[12]:
MultiIndex([(1, 'red'),
...
(2, 'blue')],
length=40)
In[13]: tuples = [(1, 'red'), (1, 'blue'), (2, 'red'), (2, 'blue')]
In[14]: pd.MultiIndex.from_tuples(tuples)
Out[14]:
MultiIndex([(1, 'red'),
...
(2, 'blue')],
length=4)
In[15]: pd.set_option("display.max_seq_items", 1)
In[16]: pd.MultiIndex.from_tuples(tuples)
Out[16]:
MultiIndex([...
(1, 'red'),
(1, 'blue'),
(2, 'red'),
(2, 'blue')],
length=4)
```
#### Problem description
The output of `MultiIndex.__repr__` is dependent on `display.max_seq_items`, the output is correct for different values of `max_seq_items` except when it is set to 1. When it is set to 1, the entire records in MultiIndex are being printed and also `"..."` are being prepended to the data output.
#### Expected Output
```python
In[12]: pd.MultiIndex.from_tuples(tuples)
Out[12]:
MultiIndex([(1, 'red'),
...],
length=40)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.7.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-53-generic
Version : #59-Ubuntu SMP Wed Oct 21 09:38:44 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.1
setuptools : 49.6.0.post20201009
Cython : 0.29.21
pytest : 6.1.2
hypothesis : 5.43.3
sphinx : 3.3.1
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 : 0.8.4
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.52.0
</details>
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TYP: pandas/core/frame.py
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| 2021-03-15T15:32:40Z
| 2021-03-15T15:32:28Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I plan to do a pass through the whole file - putting it up in case anybody has feedback on what I have
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ENH: Add method argument to rolling constructor to allow table-wise rolling
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| 8
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| 2020-12-27T23:38:35Z
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|
MEMBER
| null |
- [x] closes #15095
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MDU6SXNzdWU3NjI5NTM4MDM=
| 38,418
|
PERF/QST: Pandas merge is an order of magnitude slower than map for joining a Series to a DataFrame on a column
|
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[] | null | 2
| 2020-12-11T21:51:36Z
| 2021-08-14T22:13:47Z
| null |
NONE
| null |
Summary of situation:
We have some large ```DataFrame``` and a ```Series``` with some additional data corresponding to particular groups in the ```DataFrame``` (for example, from some supplementary data source, the result of a ```groupby``` or just from some other computation regarding the groups). To make a toy example, here we have 5000 different groups and 1e6 rows:
```
df = pd.DataFrame({"group": [i for i in range(5000)]*200, "value": np.random.random(int(1e6))})
grp_data = pd.DataFrame({"group":[i for i in range(5000)], "grp_value":np.random.random(5000)})
```
```
df
group value
0 0 0.760855
1 1 0.811482
2 2 0.115438
3 3 0.766736
4 4 0.651614
... ... ...
999995 4995 0.679567
999996 4996 0.016641
999997 4997 0.159281
999998 4998 0.762351
999999 4999 0.352805
[1000000 rows x 2 columns]
```
```
grp_data
group grp_value
0 0 0.628370
1 1 0.457861
2 2 0.954026
3 3 0.509669
4 4 0.297275
... ... ...
4995 4995 0.415900
4996 4996 0.511556
4997 4997 0.698350
4998 4998 0.905850
4999 4999 0.197737
[5000 rows x 2 columns]
```
Here are two methods for joining on the extra group data, one using ```merge``` and the other using ```map```.
```
tic = time.perf_counter()
merge_df = df.merge(grp_data, on="group", how="left")
print(str((time.perf_counter() - tic)*1000) + " milliseconds")
```
```
96.42482600065705 milliseconds
```
```
tic = time.perf_counter()
map_df = df.copy()
map_df["grp_value"] = map_df.group.map(grp_data.set_index("group").grp_value)
print(str((time.perf_counter() - tic)*1000) + " milliseconds")
```
```
16.401605999988078 milliseconds
```
Each produce the same DataFrame as desired with the ```grp_value```'s paired with their corresponding ```group``` for each row:
```
group value grp_value
0 0 0.760855 0.628370
1 1 0.811482 0.457861
2 2 0.115438 0.954026
3 3 0.766736 0.509669
4 4 0.651614 0.297275
... ... ... ...
999995 4995 0.679567 0.415900
999996 4996 0.016641 0.511556
999997 4997 0.159281 0.698350
999998 4998 0.762351 0.905850
999999 4999 0.352805 0.197737
[1000000 rows x 3 columns]
```
But map is an order of magnitude faster for the same operation.
With the actual DataFrame that I found this difference (which has more columns and is longer), the performance difference was even worse at 15x slower. From what I've read online, I only ever see merge suggested for this kind of thing and I find myself doing this kind of operation a lot. My question is twofold:
1) What is the natural way to combine data in the way described above using pandas?
2) Why is merge so much slower than map in this case?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.49+
Version : #1 SMP Mon Nov 30 19:42:49 PST 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : C.UTF-8
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.18.5
pytz : 2019.3
dateutil : 2.8.1
pip : 20.2.4
setuptools : 46.1.3.post20200325
Cython : 0.29.21
pytest : 5.4.1
hypothesis : 5.10.0
sphinx : 3.0.2
blosc : None
feather : 0.4.1
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: 0.9.0
bs4 : 4.9.0
bottleneck : 1.3.2
fsspec : 0.8.4
fastparquet : None
gcsfs : 0.6.1
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pyxlsb : None
s3fs : 0.5.1
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
</details>
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