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} | 2 | 2020-10-10T02:41:27Z | 2020-10-10T16:29:24Z | 2020-10-10T16:27:02Z | MEMBER | null | - [ ] closes #xxxx
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Move towards avoiding special treatment for pandas-internal EAs.
xref #24020 | {
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>>>pd.Series(['A',pd.NA],dtype='string').str.replace(pd.NA,0)
TypeError: repl must be a string or callable
>>>pd.Series(['A',pd.NA],dtype='string').str.replace(0,pd.NA)
TypeError: repl must be a string or callable
```
Now both `str.replace(pd.NA,0)` and `str.replace(0,pd.NA)` raise.
I think it will be better to allow these replacements since `replace` method can handle this. | {
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closes #35509
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} | 2 | 2020-10-10T09:02:17Z | 2020-10-10T16:03:01Z | 2020-10-10T16:02:57Z | CONTRIBUTOR | null | - [ ] ref https://github.com/pandas-dev/pandas/issues/30999
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
from pandas.tseries.offsets import DateOffset
print(pd.__version__)
df = pd.DataFrame(pd.date_range('2020-10-01', periods=3, freq='D'))
print(df[0] + DateOffset(month=1))
1.2.0.dev0+690.g3a043f2d2
0 2020-01-01
1 2020-01-02
2 2020-01-03
Name: 0, dtype: datetime64[ns]
```
#### Problem description
DateOffset replaces the month instead of adding to the current month
#### Expected Output
```python
0
0 2020-11-01
1 2020-11-02
2 2020-11-03
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https://api.github.com/repos/pandas-dev/pandas/issues/37029 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/37029/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/37029/comments | https://api.github.com/repos/pandas-dev/pandas/issues/37029/events | https://github.com/pandas-dev/pandas/pull/37029 | 718,616,194 | MDExOlB1bGxSZXF1ZXN0NTAwOTczNzUx | 37,029 | DOC: Improve what the `axis=` kwarg does for generic methods | {
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} | 5 | 2020-10-10T13:04:41Z | 2020-10-12T15:35:49Z | 2020-10-12T15:35:01Z | CONTRIBUTOR | null | ### [`axis=0` or `axis=1`, which is it?](https://github.com/pandas-dev/pandas/issues/29203#issue-511923432)
Opening a PR to discuss #29203 on what it means to do `df.sum(axis=0)` versus `df.sum(axis=1)`. The docs does not make it intuitively clear to the user. I propose a simple change where **"summing across`axis=0`"** makes it clear that you're summing across indexes (i.e. **column sums**).
- [x] Closes #29203
- [x] Passes pytest
- [x] Passes `black pandas`
- [x] Passes `git diff upstream/master -u -- "*.py" | flake8 --diff` | {
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} | 3 | 2020-10-10T14:36:37Z | 2020-10-17T02:51:06Z | 2020-10-12T15:09:05Z | CONTRIBUTOR | null | - [x] closes #21004
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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https://api.github.com/repos/pandas-dev/pandas/issues/37031 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/37031/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/37031/comments | https://api.github.com/repos/pandas-dev/pandas/issues/37031/events | https://github.com/pandas-dev/pandas/issues/37031 | 718,636,689 | MDU6SXNzdWU3MTg2MzY2ODk= | 37,031 | PERF: Memory leaks after migrating from 0.25.3 to 1.1.3 | {
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] | open | false | null | [] | null | 0 | 2020-10-10T14:54:54Z | 2020-10-11T00:56:03Z | null | NONE | null | hi,
#### Code Sample,
```
step = 100
requestlist = []
headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
for i in range(0, len(df), step):
tmp_df = df.iloc[i:i + step].copy()
final_json_string = []
tmp_df.apply(lambda row: row_to_json_str(row), axis=1)
final_json_string = "\n".join(final_json_string)
final_json_string += "\n"
url = f'{ES_URL}/{table_name}/_bulk'
requestlist.append(
grequests.post(url, headers=headersdata=final_json_string))
del tmp_df
gc.collect()
r = grequests.imap(requestlist, size=4, exception_handler=exception_handler)
del final_json_string
del requestlist
del df
cnx.close()
gc.collect()
```
#### Problem description
os : Ubuntu 16.04.7 LTS
Python 3.8.1 (default, Jan 6 2020, 09:57:21)
[GCC 5.4.0 20160609] on linux
after migrating from 0.25.3 to 1.1.3 the memory usage increase after each method call
back to 0.25.3 the script the memory usage is stable
the source of dataframe is Pandas.read_sql.
the goal of the script is to convert a dataframe to Elasticsearch json request
the loop below isn't exactly the same but the memory profiler give same result : 1669.2 MiB to 6218.0 MiB when for loop comes
memory_usage
108 1669.2 MiB 0.0 MiB list_df = [df[i:i + n] for i in range(0, df.shape[0], n)]
109
110 1669.2 MiB 0.0 MiB requestlist = []
111
112 1669.2 MiB 0.0 MiB headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
113
116 6218.0 MiB 0.0 MiB for tmp_df in list_df:
117 6214.4 MiB 0.0 MiB final_json_string = []
118
119 6215.6 MiB 1.5 MiB tmp_df.apply(lambda row: row_to_json_str(row), axis=1)
120
121 6216.9 MiB 1.3 MiB final_json_string = "\n".join(final_json_string)
122 6218.0 MiB 1.3 MiB final_json_string += "\n"
123
124 6218.0 MiB 0.2 MiB url = f'{ES_URL}/{table_name}/_bulk'
125
127
128 6218.0 MiB 0.0 MiB requestlist.append(
129 6218.0 MiB 0.2 MiB grequests.post(url, headers=headers, data=final_json_string))
130
132 6218.0 MiB 0.0 MiB grequests.imap(requestlist, size=4, exception_handler=exception_handler)
133 6218.0 MiB 0.0 MiB del tmp_df
134 6218.0 MiB 0.0 MiB del requestlist
135 6218.0 MiB 0.0 MiB del list_df
137 6218.0 MiB 0.0 MiB del df
139 6218.0 MiB 0.0 MiB cnx.close()
thanks | {
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} | 2 | 2020-10-10T15:02:19Z | 2020-10-11T02:35:32Z | 2020-10-10T17:26:25Z | CONTRIBUTOR | null | df.mask() and df.where() are complementary and so they share docs, see [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.mask.html). I grouped related examples together.
- [x] Close #25187
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` | {
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} | 5 | 2020-10-10T15:54:29Z | 2020-10-10T22:30:54Z | 2020-10-10T22:30:51Z | CONTRIBUTOR | null | - [x] closes #36040
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This PR replaces [36132](https://github.com/pandas-dev/pandas/pull/36132) following @mroeschke's [36567](https://github.com/pandas-dev/pandas/pull/36567).
Originally, my PR was to fix Issue [36040](https://github.com/pandas-dev/pandas/issues/36040). But it appears that Matthew's PR already fixed it! Nonetheless, I still included two things from my previous PR:
1. I added tests to cover the bug outlined in Issue [36040](https://github.com/pandas-dev/pandas/issues/36040).
2. I reimplemented FixedWindowIndexer.get_window_bounds in a way that I believe if a lot more intuitive and simple. Please lmk if you disagree, in which case I could remove this from the PR.
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} | 1 | 2020-10-10T22:35:53Z | 2020-10-15T01:45:22Z | 2020-10-15T01:45:10Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
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The interpolate method was missing a short summary. I hope this addition is reasonable. Happy to change it with feedback. | {
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} | 6 | 2020-10-10T22:39:15Z | 2020-10-12T19:01:47Z | 2020-10-12T15:30:03Z | MEMBER | null | - [x] closes #26476
- [x] tests added / passed
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I added sem to the Rolling and Expanding API as mentioned in the issue linked above. | {
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} | 2 | 2020-10-10T22:53:56Z | 2020-10-11T21:57:38Z | 2020-10-11T21:00:37Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
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made possible by #36483 | {
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Was not quick enough to add a whatsnew with the PR when 1.1.4 whatsnew was merged. | {
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The main motivation here is for perf for methods that current cast to object dtype, including value_counts, set ops, values_for_(argsort|factorize)
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} | 16 | 2020-10-11T07:38:15Z | 2021-03-21T21:06:58Z | 2021-03-21T21:06:58Z | MEMBER | null | Currently we have a check here that artificially handles a numerical precision issue in `rolling.var` and `rolling.std` where our rolling variance calculation is carrying forward floating point artifacts. Ideally we should be using a more numerically stable algorithm (maybe Kahan summation) so this check isn't so arbitrary.
https://github.com/pandas-dev/pandas/blob/601eff1768380313ec452ba8677cc7e825706796/pandas/_libs/window/aggregations.pyx#L305 | {
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} | 3 | 2020-10-11T14:13:19Z | 2020-10-11T16:08:26Z | 2020-10-11T16:05:13Z | MEMBER | null | A few things here:
- updating isort version
- updating to use `types` instead of `files` (see https://github.com/pre-commit/pre-commit/issues/1436#issuecomment-624219819 and https://github.com/PyCQA/isort/pull/1549#issuecomment-706621474)
- removing `exclude: ^pandas/__init__\.py$|^pandas/core/api\.py$` as it's no longer necessary
- replacing `files: \.(pyx|pxd)$` with `types: [cython]`:
```bash
$ identify-cli pandas/_libs/groupby.pyx
["cython", "file", "non-executable", "text"]
$ identify-cli pandas/_libs/lib.pxd
["cython", "file", "non-executable", "text"]
```
- replacing the name `flake8-pxd` with `flake8 (cython template)`, as that hook wasn't actually checking `.pxd` files (the `flake8-pyx` one was) | {
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} | 7 | 2020-10-11T14:50:31Z | 2020-10-30T13:22:51Z | 2020-10-30T13:22:51Z | CONTRIBUTOR | null | #### Location of the imports
https://github.com/pandas-dev/pandas/blob/8a25b23dd69d1e24840337797e1d20184920b5f3/pandas/tests/plotting/test_hist_method.py#L3-L4
#### Imports problem
~~All numpy-related imports are only about `numpy.random` including `randint`, `choice`, `normal`, etc~~
Part of numpy.random-related imports use aliases
#### Suggested fix for imports
eg: ~~`import numpy as np
` && `np.ranom.randn` --> `from numpy.random import randn`~~
`from numpy.random import randn` --> `import numpy as np
` && `np.ranom.randn` | {
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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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} | 15 | 2020-10-11T15:56:35Z | 2020-10-14T20:04:13Z | 2020-10-12T22:53:38Z | MEMBER | null | - [x] xref #37051
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
As suggested by @mroeschke Kahan summation fixes the numerical problems. Additionally I used Welfords Method to calculate ``ssqdm``, because previously the tests I have added would return
```
0 NaN
1 3.500000e+34
2 3.000000e+34
3 3.000000e+34
4 3.000000e+34
5 3.000000e+34
6 3.000000e+34
7 3.000000e+34
8 3.000000e+34
9 3.000000e+34
dtype: float64
```
for ``var()``. I am running the asv and will post the results when available | {
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} | 9 | 2020-10-11T16:15:57Z | 2020-10-14T12:27:44Z | 2020-10-14T12:27:39Z | CONTRIBUTOR | null | Before this change, calling `pickle.dumps()` created an in-memory byte buffer, negating the advantage
of zero-copy pickle protocol 5. After this change, `pickle.dump` writes directly to open file(-like),
cutting peak memory in half in most cases.
Profiling was done with pandas@master and python 3.8.5
Related issues:
- https://github.com/pandas-dev/pandas/issues/34244
## Update: ASV results
`$ asv continuous -f 1.1 origin/master HEAD -b pickle` yields:
```
before after ratio
[58f74686] [3c1ee270]
<zero-copy-pickle>
- 91.9M 80.1M 0.87 io.pickle.Pickle.peakmem_write_pickle
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
```
- [ ] closes #34244
- [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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"description": "Issue that has not been ... | closed | false | null | [] | null | 16 | 2020-10-11T17:45:43Z | 2021-05-05T03:28:20Z | 2020-10-11T20:12:30Z | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
```
#### Problem description
When importing pandas using python 3.9.0 I get the following error:
```
python(81328,0x10d755dc0) malloc: can't allocate region
:*** mach_vm_map(size=18446744071827312640, flags: 100) failed (error code=3)
python(81328,0x10d755dc0) malloc: *** set a breakpoint in malloc_error_break to debug
init_dgelsd failed init
Traceback (most recent call last):
File "/Users/paul/python/test/t.py", line 2, in <module>
import pandas as pd
File "/Users/paul/python/test/venv/lib/python3.9/site-packages/pandas/__init__.py", line 11, in <module>
__import__(dependency)
File "/Users/paul/python/test/venv/lib/python3.9/site-packages/numpy/__init__.py", line 286, in <module>
raise RuntimeError(msg)
RuntimeError: Polyfit sanity test emitted a warning, most likely due to using a buggy Accelerate backend. If you compiled yourself, see site.cfg.example for information. Otherwise report this to the vendor that provided NumPy.
RankWarning: Polyfit may be poorly conditioned
```
#### Expected Output
Pandas should import without an error.
#### Output of ``pd.show_versions()``
I am unable to run pd.show_versions() because the program crashes as soon as pandas is imported. I am installing pandas 1.1.3 on a Mac.
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} | 0 | 2020-10-11T18:04:07Z | 2020-10-12T17:01:31Z | 2020-10-12T15:05:16Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Annotating Block as `Union[np.ndarray, ExtensionArray]`, mypy gives tons of complaints about EA not having `.T`, so this adds that, then updates tests as appropriate | {
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} | 4 | 2020-10-11T18:37:56Z | 2020-10-14T14:41:51Z | 2020-10-14T12:27:19Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
cc @simonjayhawkins can i get your thoughts on two remaining mypy complaints
```
pandas/io/excel/_base.py:795: error: "Callable[[ExcelWriter], Tuple[str, ...]]" has no attribute "__iter__" (not iterable) [attr-defined]
pandas/io/stata.py:892: error: unused 'type: ignore' comment
```
The excel one is because it doesn't recognize
```
@property
@abstractmethod
def supported_extensions(self) -> Tuple[str, ...]:
```
as returning `Tuple[str, ...]`. I expect if that were resolved it would still complain bc subclasses just pin the tuple as a class attribute instead of a property (easy to make a property, but much more verbose which im not wild about)
The stata unused type:ignore becomes used once we remove stata from the setup.cfg exclusion. The only working way I've found to make mypy accept `TYPE_MAP: List[Union[int, str]] = list(range(251)) + list("abcd")` is with
```
TYPE_MAP: List[Union[int, str]] = cast(List[Union[int, str]], list(range(251))) + list("abcd")
```
which i find much less clear than the `# type:ignore`d version. | {
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} | 0 | 2020-10-11T21:58:55Z | 2020-10-12T14:26:17Z | 2020-10-12T13:45:07Z | MEMBER | null | - [ ] closes #xxxx
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its already mixed in, this just removes the leftover _create_arithmetic_method usage.
This is the last one outside of tests, for which we still have DecimalArray to do. | {
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} | 3 | 2020-10-11T22:01:00Z | 2020-12-11T23:04:19Z | 2020-12-11T23:04:19Z | CONTRIBUTOR | null | create a mi
```
In [13]: mi = pd.MultiIndex.from_product([[1,2,3], list('abc'), pd.date_range('20200101', periods=2, tz='UTC')], names=['int', 'string', 'dt'])
```
You can already get .dtypes, but is slightly cumbersome. I would propose adding `Multidex.dtypes` (we *already* have MultiIndex.dtype but its *always* object). I think this is worth the convenience api.
```
In [14]: mi.to_frame().dtypes
Out[14]:
int int64
string object
dt datetime64[ns, UTC]
dtype: object
# we would actually implement like this.
In [15]: pd.Series({l.name: l.dtype for l in mi.levels})
Out[15]:
int int64
string object
dt datetime64[ns, UTC]
dtype: object
``` | {
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```
before after ratio
[89cc5bf9] [6af880e7]
<master> <feature/groupby_expanding_indexer>
- 45.5±0.6ms 3.78±0.04ms 0.08 rolling.ExpandingMethods.time_expanding_groupby('Series', 'int', 'max')
- 46.5±0.5ms 3.86±0.06ms 0.08 rolling.ExpandingMethods.time_expanding_groupby('Series', 'int', 'mean')
- 45.4±0.4ms 3.74±0.05ms 0.08 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'int', 'min')
- 45.5±0.4ms 3.73±0.06ms 0.08 rolling.ExpandingMethods.time_expanding_groupby('Series', 'int', 'min')
- 46.6±0.3ms 3.80±0.01ms 0.08 rolling.ExpandingMethods.time_expanding_groupby('Series', 'int', 'median')
- 46.7±1ms 3.77±0.05ms 0.08 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'int', 'max')
- 46.3±0.6ms 3.73±0.02ms 0.08 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'int', 'sum')
- 46.8±0.3ms 3.76±0.1ms 0.08 rolling.ExpandingMethods.time_expanding_groupby('Series', 'int', 'sum')
- 46.7±0.5ms 3.75±0.02ms 0.08 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'int', 'mean')
- 47.2±0.3ms 3.78±0.04ms 0.08 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'int', 'median')
- 51.2±0.7ms 3.89±0.1ms 0.08 rolling.ExpandingMethods.time_expanding_groupby('Series', 'int', 'std')
- 51.3±0.3ms 3.78±0.02ms 0.07 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'int', 'std')
- 62.6±0.6ms 3.98±0.02ms 0.06 rolling.ExpandingMethods.time_expanding_groupby('Series', 'int', 'count')
- 62.2±0.7ms 3.91±0.03ms 0.06 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'int', 'count')
- 64.3±0.7ms 3.98±0.05ms 0.06 rolling.ExpandingMethods.time_expanding_groupby('Series', 'float', 'sum')
- 63.5±0.7ms 3.92±0.01ms 0.06 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'float', 'max')
- 63.6±0.6ms 3.91±0.03ms 0.06 rolling.ExpandingMethods.time_expanding_groupby('Series', 'float', 'min')
- 63.2±0.7ms 3.86±0.02ms 0.06 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'float', 'min')
- 64.2±1ms 3.89±0.04ms 0.06 rolling.ExpandingMethods.time_expanding_groupby('Series', 'float', 'max')
- 64.8±0.8ms 3.91±0.06ms 0.06 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'float', 'sum')
- 65.2±0.6ms 3.92±0.05ms 0.06 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'float', 'mean')
- 65.2±0.9ms 3.90±0.05ms 0.06 rolling.ExpandingMethods.time_expanding_groupby('Series', 'float', 'median')
- 65.5±0.5ms 3.91±0.02ms 0.06 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'float', 'median')
- 65.3±0.2ms 3.86±0.01ms 0.06 rolling.ExpandingMethods.time_expanding_groupby('Series', 'float', 'mean')
- 69.4±0.8ms 4.01±0.07ms 0.06 rolling.ExpandingMethods.time_expanding_groupby('Series', 'float', 'std')
- 69.8±0.6ms 3.93±0.01ms 0.06 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'float', 'std')
- 80.1±0.2ms 4.24±0.1ms 0.05 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'float', 'count')
- 80.5±0.9ms 4.14±0.02ms 0.05 rolling.ExpandingMethods.time_expanding_groupby('Series', 'float', 'count')
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
```
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"gists_... | null | 5 | 2020-10-12T03:44:39Z | 2021-01-21T14:51:21Z | 2021-01-21T14:51:21Z | MEMBER | null | The xfail test `test_s3_roundtrip_for_dir` in `io/test_parquet` has passed on my machine. But I don't see it on our CI.
<details>
INSTALLED VERSIONS
------------------
commit : 4d3b197c98913e894bcea9edca9ee7bad2964d84
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-48-generic
Version : #52-Ubuntu SMP Thu Sep 10 10:58:49 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0.dev0+745.g4d3b197c9
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20201009
Cython : 0.29.21
pytest : 6.1.1
hypothesis : 5.37.1
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.6
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : 0.4.1
gcsfs : 0.7.1
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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and some unrelated cleanup in internals | {
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Will handle series case in follow-on | {
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] | closed | false | null | [] | null | 4 | 2020-10-12T05:27:32Z | 2020-10-13T06:36:34Z | 2020-10-13T04:49:52Z | 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 pandas as pd
>>> pd.Timestamp("1-01-01 00:00:00")
Timestamp('2001-01-01 00:00:00')
```
#### Problem description
Passing "1-01-01 00:00:00" to "2001-01-01 00:00:00" is clearly wrong, this should raise an out of bounds datetime error.
#### Expected Output
```python
>>> import pandas as pd
>>> pd.Timestamp("1-01-01 00:00:00")
OutOfBoundsDatetime
```
However, the problem here appears to be deeper than pandas, specifically
```python
>>> from dateutil import parser
>>> parser.parse("1-01-01 00:00:00")
datetime.datetime(2001, 1, 1, 0, 0)
```
If it's helpful, I'm happy to raise an issue in https://github.com/dateutil/dateutil instead. It's not super clear to me where the error really is (maybe dateutil have a very good reason for parsing "1-01-01 00:00:00" as "2001-01-01 00:00:00").
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : cd88ee2dddbb25f15742cf8c2d6141da5d6fa342
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Mon Aug 31 20:53:32 PDT 2020; root:xnu-4903.278.44~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 1.2.0.dev0+746.gcd88ee2dd
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.1.0.post20200704
Cython : 0.29.21
pytest : 6.1.1
hypothesis : 5.37.1
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.6
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : 0.4.0
gcsfs : 0.7.1
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.1
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
</details>
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} | 4 | 2020-10-12T06:21:28Z | 2020-11-01T02:24:22Z | 2020-10-31T15:00:03Z | MEMBER | null | - [x] closes #20594 (coupled with #37069 for empty tz-aware dataframes)
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
xref #20595 (stale PR that looked into this) | {
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} | 7 | 2020-10-12T09:42:47Z | 2020-12-11T23:04:25Z | 2020-12-11T23:04:19Z | CONTRIBUTOR | null | - [x] closes #37062
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Adds the .dtypes property to MultiIndex using the suggested implementation | {
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- [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
d = '{"columns":["col 1","col 2"], "index":["row 1","row 2"], "data":[["random s3://xyz/abc","wherever https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_json.html"],["c","d"]]}'
pd.read_json(d, orient='split')
```
#### Problem description
This leads to an error in pandas 1.1.x and works fine in <=1.0.5.
#### Expected Output
```
col 1 col 2
row 1 random s3://xyz/abc wherever https://pandas.pydata.org/pandas-docs...
row 2 c d
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.4.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 : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : 5.10.4
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.0.3
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.5.2
tabulate : 0.8.6
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.45.1
</details>
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} | 3 | 2020-10-12T11:11:05Z | 2020-10-13T07:33:56Z | 2020-10-13T07:33:56Z | NONE | null | #### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.cumsum.html
[this should provide the location of the documentation, e.g. "pandas.read_csv" or the URL of the documentation, e.g. "https://dev.pandas.io/docs/reference/api/pandas.read_csv.html"]
**Note**: You can check the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/).
#### Documentation problem
[this should provide a description of what documentation you believe needs to be fixed/improved]
#### Suggested fix for documentation
in the example shows how pandas.DataFrame.cumsum works, i think add one more column in the example df.cumsum(axis=1) will make thing more clear. Just like df.cumsum(axis=0),which have 3 row in the example
[this should explain the suggested fix and **why** it's better than the existing documentation]
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} | 1 | 2020-10-12T14:00:58Z | 2020-10-12T16:47:42Z | 2020-10-12T16:47:42Z | NONE | null | Backport PR #37034: REGR: Fix casting of None to str during astype | {
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] | closed | false | null | [] | null | 2 | 2020-10-12T14:06:35Z | 2020-10-14T19:42:10Z | 2020-10-14T19:42:10Z | CONTRIBUTOR | null | The monthly dev meeting is Wednesday October 14th, at 18:00 UTC. Our calendar is at https://pandas.pydata.org/docs/development/meeting.html#calendar to check your local time.
Video Call: https://zoom.us/j/942410248?pwd=T2l2Qi9vaC82Z294ZEtFczYxMVM2dz09
Minutes: https://docs.google.com/document/u/1/d/1tGbTiYORHiSPgVMXawiweGJlBw5dOkVJLY-licoBmBU/edit?ouid=102771015311436394588&usp=docs_home&ths=true
**Note: the call URL is different than previous meetings. You may need to download the meeting calendar again**. Zoom changed some things w.r.t. authentication and waiting rooms.
Please add items you'd like to see discussed to the agenda. All are welcome to attend. | {
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} | 14 | 2020-10-12T14:49:31Z | 2020-11-08T03:01:28Z | 2020-11-08T03:01:28Z | MEMBER | null | From https://pandas.pydata.org/speed/pandas/#stat_ops.FrameOps.time_op?Cython=0.29.21&Cython=0.29.16&p-op='sum'&p-dtype='int'&p-axis=0&commits=3a043f2d-4c03d07b&x-axis-scale=date
Reproducer:
```
values = np.random.randn(100000, 4)
df = pd.DataFrame(values).astype("int")
%timeit df.sum()
```
increased with a factor 2 to 3 x somewhere the last days. | {
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} | 4 | 2020-10-12T15:22:22Z | 2020-10-21T12:36:19Z | 2020-10-14T19:24:03Z | MEMBER | null | partially reverts pandas-dev/pandas#35537, xref https://github.com/pandas-dev/pandas/issues/35481#issuecomment-705144719 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/37083 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/37083/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/37083/comments | https://api.github.com/repos/pandas-dev/pandas/issues/37083/events | https://github.com/pandas-dev/pandas/issues/37083 | 719,488,166 | MDU6SXNzdWU3MTk0ODgxNjY= | 37,083 | BUG: Empty object column writes to parquet as INT32 instead of BINARY L:STRING | {
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"description": "parquet, feather",
"... | open | false | null | [] | null | 3 | 2020-10-12T15:57:47Z | 2021-06-09T20:53:44Z | 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
pd.DataFrame({"nullable_col": ["some_val"]}, dtype="object").to_parquet("non-null-obj.parquet")
pd.DataFrame({"nullable_col": []}, dtype="object").to_parquet("null-obj.parquet")
```
Once that runs, `parquet-tools` illustrates the issue. I would expect a datatype such as `OPTIONAL BINARY L:STRING`, as in:
```
$ parquet-tools meta non-null-obj.parquet | grep nullable_col
extra: pandas = {"index_columns": [{"kind": "range", "name": null, "start": 0, "stop": 1, "step": 1}], "column_indexes": [{"name": null, "field_name": null, "pandas_type": "unicode", "numpy_type": "object", "metadata": {"encoding": "UTF-8"}}], "columns": [{"name": "nullable_col", "field_name": "nullable_col", "pandas_type": "unicode", "numpy_type": "object", "metadata": null}], "creator": {"library": "pyarrow", "version": "1.0.1"}, "pandas_version": "1.1.3"}
nullable_col: OPTIONAL BINARY L:STRING R:0 D:1
nullable_col: BINARY SNAPPY DO:4 FPO:32 SZ:80/76/0.95 VC:1 ENC:PLAIN,PLAIN_DICTIONARY,RLE ST:[min: some_val, max: some_val, num_nulls: 0]
```
But instead got a datatype of `OPTIONAL INT32`, as in:
```
$ parquet-tools meta null-obj.parquet | grep nullable_col
extra: pandas = {"index_columns": [{"kind": "range", "name": null, "start": 0, "stop": 0, "step": 1}], "column_indexes": [{"name": null, "field_name": null, "pandas_type": "unicode", "numpy_type": "object", "metadata": {"encoding": "UTF-8"}}], "columns": [{"name": "nullable_col", "field_name": "nullable_col", "pandas_type": "empty", "numpy_type": "object", "metadata": null}], "creator": {"library": "pyarrow", "version": "1.0.1"}, "pandas_version": "1.1.3"}
nullable_col: OPTIONAL INT32 R:0 D:1
nullable_col: INT32 SNAPPY DO:4 FPO:0 SZ:15/14/0.93 VC:0 ENC:PLAIN,RLE,PLAIN_DICTIONARY ST:[no stats for this column]
```
#### Problem description
Writing out a column with pandas type `object` with no values appears to create a parquet type of `INT32` when I would expect it to be `BINARY L:STRING` or similar. I have a daily process that outputs a set of records to parquet and on days where there are no values in an object column the parquet datatype changes to `INT32`, thus breaking my process as the schemas has changed relative to previous days.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.2.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 : None
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0
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 : 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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"... | closed | false | null | [] | null | 5 | 2020-10-12T18:01:22Z | 2021-03-27T01:00:11Z | 2021-03-27T01:00:11Z | NONE | null | - [ ] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, 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 question.
I'm a little confused about the error I'm getting. Any pointers will be much appreciated.
```python
# Your code here, if applicable
df.to_csv("education_salary.csv")
```
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dti = pd.date_range("2016-01-01", periods=2)
dtype = dti.tz_localize("US/Pacific").dtype
dta = dti._data
ser = pd.Series(dta)
>>> dta.astype(dtype) # matches dti.astype(dtype)
<DatetimeArray>
['2016-01-01 00:00:00-08:00', '2016-01-02 00:00:00-08:00']
Length: 2, dtype: datetime64[ns, US/Pacific]
>>> ser.astype(dtype)._values
<DatetimeArray>
['2015-12-31 16:00:00-08:00', '2016-01-01 16:00:00-08:00']
Length: 2, dtype: datetime64[ns, US/Pacific]
```
`Series.astype` behavior is defined in `DatetimeBlock.astype`, is equivalent to `dta.tz_localize("UTC").tz_convert(dtype.tz)` | {
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"c... | open | false | null | [] | null | 0 | 2020-10-12T21:48:51Z | 2021-06-06T17:52:05Z | null | MEMBER | null | xref #27911 API: when to cast list-like to ndarray, check len match
xref #13637 API: Index/Series/DataFrame op 1-d list-like coercion
Related bugs:
#21517 Comparison of MultiIndex to tuple interprets tuple as collection
One way to ensure consistent behavior is to enshrine it in `ops.unpack_zerodim_and_defer`, which just about all of our ops now use. The following is an implementation that matches our current behavior (more specifically: passes current tests) when tacked on to unpack_zerodim_and_defer, with one exception, described below:
```
if is_list_like(other) and len(other) != len(self):
if isinstance(self, ABCDataFrame):
# DataFrame method does this in align_method_FRAME
pass
elif isinstance(self, ABCSeries) and isinstance(other, ABCSeries):
# We have _align_foo methods that will handle this
pass
elif self.dtype.kind == "O" and not hasattr(other, "dtype"):
# With e.g. list or tuple and object dtype, it is ambiguous
# whether this is intended to be treated as a scalar.
pass
else:
raise ValueError("Lengths must match")
```
The sticking point is what we do when we have a listlike that is not array-like, generally list or tuple. In this implementation, we would treat it as arraylike whenever we are not object-dtype, and keep it as-is otherwise.
The test that fails with this has a length-1 list:
```
s_0123 = Series(range(4), dtype="int64")
expected = Series([False] * 4)
result = s_0123 & [False]
```
ATM in` array_ops.logical_op` we cast list (only list, not tuple) to `ndarray[object]`. The implementation above would make this raise ValueError.
Outstanding questions:
1) Allow length-1 others to broadcast like numpy?
2) Allow length-1 self to broadcast like numpy?
3) DataFrame always casts listlike to arraylike; should that be changed?
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} | 1 | 2020-10-12T22:15:38Z | 2020-10-14T13:30:01Z | 2020-10-14T12:26:46Z | CONTRIBUTOR | null | Minor cleanup. | {
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"description": "SAS: rea... | open | false | null | [] | null | 8 | 2020-10-12T22:22:20Z | 2020-12-09T11:00:50Z | null | NONE | null | #### Is your feature request related to a problem?
Reading a large data file can exhaust system memory. However, most of the time, we don't need all data from that file. It will be convenient to be able to read only the data needed.
#### Describe the solution you'd like
Reading only the data needed through iteration.
#### API breaking implications
One more function for each type of files
#### Describe alternatives you've considered
May add conditions as well, which will be sweet
#### Additional context
[add any other context, code examples, or references to existing implementations about the feature request here]
```python
def read_sas_by_columns(file_path, keys, chunksize=100, charset = 'utf-8'):
data = pd.DataFrame()
for df in pd.read_sas(file_path,iterator=True, chunksize=chunksize):
data = data.append(df[keys])
for c in data.select_dtypes(include=['object']).columns:
data[c] = data[c].str.decode(charset)
return data
```
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} | 2 | 2020-10-13T07:09:16Z | 2021-01-21T05:05:23Z | 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
df = pd.DataFrame({"X": [1.0]})
df.groupby(pd.Series(["group"]), as_index=False).transform("sum")
```
#### Problem description
With version 1.1.3 this produces a dataframe containing NaN.
#### Expected Output
Expected is 1.0 and this is also what older versions produce (1.0.5).
It seems the following conditions have to be satisfied to trigger this bug:
- grouping by a series which does not contain indices corresponding to the original dataframe (grouping by `pd.Series([0])` works though it seems accidental)
- using `as_index=False`
- using `transform`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.6.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.76-linuxkit
Version : #1 SMP Tue May 26 11:42:35 UTC 2020
machine : x86_64
processor :
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : 0.29.15
pytest : 5.4.3
hypothesis : 5.6.0
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.6.2
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1.post2
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.15
tables : None
tabulate : 0.8.6
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.48.0
</details>
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} | 7 | 2020-10-13T07:22:20Z | 2020-10-30T10:51:28Z | 2020-10-30T10:51:28Z | 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
ROWS = 1000001 # <--------- with 1000000, it works
with open('out.dat', 'w') as fd:
for i in range(ROWS):
fd.write('%d\n' % i)
df = pd.read_csv('out.dat', names=['a'], dtype={'a': np.float64}, index_col=['a'])
```
#### Problem description
When `ROWS = 1000001`, I get the following traceback:
```
Traceback (most recent call last):
File "try.py", line 10, in <module>
df = pd.read_csv('out.dat', names=['a'], dtype={'a': np.float64}, index_col=['a'])
File "/tmp/new_pandas/lib64/python3.6/site-packages/pandas/io/parsers.py", line 686, in read_csv
return _read(filepath_or_buffer, kwds)
File "/tmp/new_pandas/lib64/python3.6/site-packages/pandas/io/parsers.py", line 458, in _read
data = parser.read(nrows)
File "/tmp/new_pandas/lib64/python3.6/site-packages/pandas/io/parsers.py", line 1196, in read
ret = self._engine.read(nrows)
File "/tmp/new_pandas/lib64/python3.6/site-packages/pandas/io/parsers.py", line 2231, in read
index, names = self._make_index(data, alldata, names)
File "/tmp/new_pandas/lib64/python3.6/site-packages/pandas/io/parsers.py", line 1677, in _make_index
index = self._agg_index(index)
File "/tmp/new_pandas/lib64/python3.6/site-packages/pandas/io/parsers.py", line 1770, in _agg_index
arr, _ = self._infer_types(arr, col_na_values | col_na_fvalues)
File "/tmp/new_pandas/lib64/python3.6/site-packages/pandas/io/parsers.py", line 1871, in _infer_types
mask = algorithms.isin(values, list(na_values))
File "/tmp/new_pandas/lib64/python3.6/site-packages/pandas/core/algorithms.py", line 443, in isin
if np.isnan(values).any():
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
```
#### Expected Output
With pandas 1.1.2, or ROWS = 1000000, it works fine.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.6.3.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-957.38.3.el7.x86_64
Version : #1 SMP Mon Nov 11 12:01:33 EST 2019
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 : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0
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 : 7.16.1
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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} | 0 | 2020-10-13T07:30:58Z | 2020-10-16T01:30:55Z | 2020-10-16T01:30:55Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/34385#pullrequestreview-506705288
This could also be a pre-commit hook
Opening this issue as a placeholder so we don't forget | {
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} | 3 | 2020-10-13T09:51:07Z | 2021-08-19T01:54:36Z | 2020-11-04T01:55:12Z | CONTRIBUTOR | null | - [x] closes #36562
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/37097 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/37097/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/37097/comments | https://api.github.com/repos/pandas-dev/pandas/issues/37097/events | https://github.com/pandas-dev/pandas/pull/37097 | 720,107,944 | MDExOlB1bGxSZXF1ZXN0NTAyMTg2NjQ5 | 37,097 | Update code_checks.sh to check for instances of os.remove | {
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"description": ... | closed | false | null | [] | null | 3 | 2020-10-13T10:42:01Z | 2020-10-16T09:13:58Z | 2020-10-16T01:30:56Z | CONTRIBUTOR | null | I have included a check for instances of os.remove in the tests folder. This is in line with issue #37095 whereby ensure_clean should be used rather than os.remove.
A few outstanding questions remain:
- Should this be expanded to search for cases of os.remove elsewhere?
- ~~Cases of `os.remove ` still exist in common.py, test_writers.py, and test_store.py. For some reason, the case in test_store.py is not picked up by this check? Any idea why?~~ _(solved by passing arguments separately rather than as a list)_
- ~~Advice on how to implement this as a pre-commit hook also welcome.~~ _(to be implemented in separate PR)_
- [x] closes #37095
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/37098 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/37098/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/37098/comments | https://api.github.com/repos/pandas-dev/pandas/issues/37098/events | https://github.com/pandas-dev/pandas/issues/37098 | 720,241,216 | MDU6SXNzdWU3MjAyNDEyMTY= | 37,098 | ENH: Support for loading pickles from OS X/macOS zip files containing extraneous "__MACOSX" folder and ".DS_STORE" file | {
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"description": "Issue th... | closed | false | null | [] | null | 1 | 2020-10-13T13:19:54Z | 2021-08-13T04:33:25Z | 2021-08-13T04:33:25Z | NONE | null | #### Is your feature request related to a problem?
I am trying to load some zipped data from my collaborator into pandas, but the zip was created under a version of OS X that adds an extraneous `__MACOSX` folder in the created zipfile, which causes pandas to error when loading the file.
The zipfile itself can obviously be easily fixed with e.g. `zip -d filename.zip __MACOSX/\*`, but it may cause a headache for a less experienced user. There is a similary problem involving the hidden file `.DS_STORE` on macOS (see [Wikipedia](https://en.wikipedia.org/wiki/.DS_Store) for more on this), which stores metadata on the user's icon preferences for the contents of the zip...
#### Describe the solution you'd like
I have made the following one line change locally:
https://github.com/pandas-dev/pandas/blob/9cb372376fc78aa66e2559de919592007b74cfaa/pandas/io/common.py#L567
becomes
```python
zip_names = [_ for _ in zf.namelist if not (_.startswith("__MACOSX/") or _.startswith(".DS_STORE"))]
```
Unfortunately this approach will not work for `read_csv(...)` as the compression is handled in the C code.
I can make a PR following the submission of this issue.
#### API breaking implications
Not that I can foresee, the only code path that this effects is changing from a `ValueError` to a correctly loaded the zip file.
#### Describe alternatives you've considered
As mentioned, this could be fixed directly in code before invoking pandas, but I think my suggestion is more convenient and doesn't not require any temporary files or extra disk space.
#### Additional context
Additional context: a lot of scientific data on e.g. figshare suffers from this issue. There may be other hidden files added by other operating systems that could be treated in a similar way. | {
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"description": "_... | open | false | null | [] | null | 3 | 2020-10-13T14:05:12Z | 2021-08-13T04:40:06Z | null | MEMBER | null | Pandas provides the mechanism for subclasses to customize how metadata propagation is handled through `_metadata` and `__finalize__`. However, this got more complicated with the introduction of `attrs` and `flags`, which are *also* handled in `__finalize__`.
Concrete example: GeoPandas implements a custom `GeoDataFrame.__finalize__` to be able to have custom logic about how metadata is propagated in methods.
See here for the current implementation: https://github.com/geopandas/geopandas/blob/7044aa479dbae72c14dfb647a5a782a01cff81d1/geopandas/geodataframe.py#L1148-L1162 (it specifically checks for `method="merge"|"concat"` to ensure we pass through `_metadata` of the first (concat) or left (merge) object).
For a long time, pandas only add a very simple `__finalize__` implementation (which just propagated `_metadata` unconditionally):
```python
def __finalize__(self, other, method=None, **kwargs):
"""
Propagate metadata from other to self.
Parameters
----------
other : the object from which to get the attributes that we are going
to propagate
method : optional, a passed method name ; possibly to take different
types of propagation actions based on this
"""
if isinstance(other, NDFrame):
for name in self._metadata:
object.__setattr__(self, name, getattr(other, name, None))
return self
```
However, with the recent changes, the default `__finalize__` now also does ([code link to current finalize impl](https://github.com/pandas-dev/pandas/blob/9cb372376fc78aa66e2559de919592007b74cfaa/pandas/core/generic.py#L5324-L5359)):
- propagate `attrs`
- propagate `flags.allows_duplicate_labels` and have custom logic for `method="concat"` for this
As a result of GeoPandas overriding `__finalize__`, the `attrs` and `flags` features currently don't work for GeoDataFrames. See https://github.com/geopandas/geopandas/issues/1654 for the bug report on the GeoPandas side (cc @martinfleis)
While we certainly want to enable those features for GeoDataFrames as well.
---
So the main question is: given this additional logic in the default `__finalize__`, how can we enable that subclasses can still override a *part* of that logic (eg only `_metadata`).
GeoPandas could in principle copy the implementation of pandas (and adds its own customizations to it) and keep up to date with it. But, that doesn't feel like the ideal solution, certainly if pandas is going to add more functionality to it (eg more flags).
cc @TomAugspurger
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"description": "read_json, to_json, j... | open | false | null | [] | null | 4 | 2020-10-13T14:17:22Z | 2021-08-13T04:44:41Z | 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
>>> import pandas as pd
>>> ser = pd.Series([1, 2, 3, 4])
>>> ser.to_json()
'{"0":1,"1":2,"2":3,"3":4}'
>>> ser.to_json(orient='records', lines=True)
'1\n2\n3\n4'
```
```
#### Problem description
When passing `orient=records` and `lines=True` with `Series.to_json`, the result is not in a valid json format.
#### Expected Output
This doesn't appear to be a valid json, which would break when other json readers are trying to retrieve the data from the file.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.1.3
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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} | [] | closed | false | null | [] | null | 10 | 2020-10-13T14:53:40Z | 2020-11-23T13:18:38Z | 2020-11-23T13:18:38Z | NONE | null | - [x] closes #37098
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This PR allows for `.zip` files created by OS X and macOS that contain `__MACOSX` and `.DS_STORE` metadata folders to be loaded by `read_pickle/read_csv/read_table/read_json` without error.
~It does **not** work with `pd.read_csv(...)` as in that case the compression is handled in the C code. If this enhancement is deemed desirable then I'm willing to have a go at writing it (and the test is already written).~
(No longer true as of https://github.com/pandas-dev/pandas/pull/36997)
Other similar folders may exist from other operating systems, in which case the list could be pulled out as a constant which could be used in the tests and in the module itself. | {
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} | 9 | 2020-10-13T15:44:23Z | 2020-10-15T08:02:43Z | 2020-10-14T17:54:11Z | CONTRIBUTOR | null | - [x] closes #37001
- [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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] | closed | false | null | [] | null | 6 | 2020-10-13T17:05:26Z | 2020-10-29T18:49:45Z | 2020-10-29T18:31:11Z | CONTRIBUTOR | null | # latest update
all files are moved to #37492
***
- [x] closes #37053 ~~(keep this issue open until both #37103 and #37117 are completed)~~
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
A separate PR(CI) will be opened to add related code_check(#37117).
~~PS: Should a whatsnew note be added?~~
# update
The whole project is reformatted except `pandas/_testing.py` for there are quite a few `tm.randn` used in testing files.
below is the script to do the whole clean-up
```
import os
import re
class NumpyRandomRelatedScript:
def __init__(self, base_dir: str = "/dir/of/pandas/pandas") -> None:
self.base_dir = base_dir
self.py_files = []
self.backup_files = []
self.p1 = re.compile("import numpy as np")
self.p2 = re.compile("from numpy.random import[ ,a-zA-Z]*[\s]")
self.p3 = re.compile("from numpy import random[\s]")
def search_py_files(self, pandas_file_dir: str) -> None:
if os.path.isfile(pandas_file_dir):
if pandas_file_dir[-3:] == ".py":
if pandas_file_dir != os.path.join(self.base_dir, "_testing.py"):
self.py_files.append(pandas_file_dir)
elif os.path.isdir(pandas_file_dir):
for d in os.listdir(pandas_file_dir):
self.search_py_files(os.path.join(pandas_file_dir, d))
def do_the_clean_up(self, file_dir: str) -> None:
with open(file_dir, "r") as file:
data = file.read()
m1, m2, m3 = (
re.search(self.p1, data),
re.search(self.p2, data),
re.search(self.p3, data),
)
if not (m2 or m3):
# print("There's no need to change, please recheck!")
return
backup_dir = file_dir + ".issue37053_backup"
self.backup_files.append(backup_dir)
print("Backup: " + backup_dir)
with open(backup_dir, "w+") as file:
file.write(data)
with open(file_dir, "w+") as file:
if m2:
if not m1:
data = re.sub(self.p2, "import numpy as np\n", data)
m1 = True
else:
data = re.sub(self.p2, "", data)
methods = (
m2.group(0)
.replace("from numpy.random import", "")
.replace(" ", "")
.replace("\n", "")
.split(",")
)
if isinstance(methods, str):
methods = [methods]
for method in methods:
data = re.sub(
r"[\s]{meth}[^a-z]".format(meth=method),
" np.random.{meth}(".format(meth=method),
data,
)
data = re.sub(
r"[\s]-{meth}[^a-z]".format(meth=method),
" -np.random.{meth}(".format(meth=method),
data,
)
data = re.sub(
r"[\(]{meth}[^a-z]".format(meth=method),
"(np.random.{meth}(".format(meth=method),
data,
)
if m3:
if not m1:
data = re.sub(self.p3, "import numpy as np\n", data)
else:
data = re.sub(self.p3, "", data)
data = re.sub(r"[\s]random.{1}", " np.random.", data)
data = re.sub(r"[\(]random.{1}", "(np.random.", data)
file.write(data)
print("Clean: " + file_dir)
def remove_backup(self) -> None:
for file in self.backup_files:
print("Remove: " + file)
os.remove(file)
if __name__ == "__main__":
script = NumpyRandomRelatedScript()
script.search_py_files(script.base_dir)
for f in script.py_files:
script.do_the_clean_up(f)
script.remove_backup()
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} | 4 | 2020-10-13T17:19:20Z | 2021-01-10T09:38:04Z | 2021-01-10T09:38:04Z | MEMBER | null | ```
mypy pandas
pandas/core/computation/parsing.py:41: error: unused 'type: ignore' comment
pandas/io/common.py:452: error: Unsupported operand types for + ("List[Optional[str]]" and "List[str]") [operator]
pandas/core/aggregation.py:483: error: Value of type variable "_LT" of "sorted" cannot be "Optional[Hashable]" [type-var]
pandas/core/aggregation.py:606: error: Value of type variable "_LT" of "sorted" cannot be "Optional[Hashable]" [type-var]
pandas/core/groupby/ops.py:270: error: Incompatible types in assignment (expression has type "Index", variable has type "Iterator[Any]") [assignment]
pandas/core/groupby/ops.py:270: note: 'Index' is missing following 'Iterator' protocol member:
pandas/core/groupby/ops.py:270: note: __next__
Found 5 errors in 4 files (checked 1057 source files)
``` | {
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} | 2 | 2020-10-13T18:33:08Z | 2020-10-21T19:14:04Z | 2020-10-21T19:14:04Z | NONE | null | #### Is your feature request related to a problem?
I find it useful to write a parquet to a `bytes` object for some unit tests. The code that I currently use to do this is quite verbose.
To provide some background, `df.to_csv()` (w/o args) just works. It returns a `str` object as is expected. In the same vein, **`df.to_parquet()` (w/o args) should return a `bytes` object.**
More precisely, the current behavior is:
```python
>>> df = pd.DataFrame()
>>> type(df.to_csv()) # This works
<class 'str'>
>>> df.to_parquet() # This should be made to work
Traceback (most recent call last):
File "<input>", line 1, in <module>
TypeError: to_parquet() missing 1 required positional argument: 'path'
```
#### Describe the solution you'd like
The requested behavior is:
```python
>>> df = pd.DataFrame()
>>> type(df.to_parquet())
<class 'bytes'>
```
Other uses of `df.to_parquet` should obviously remain unaffected.
#### API breaking implications
It won't break the documented API.
#### Describe alternatives you've considered
I currently use this verbose code to get what I want:
```python
import io
import pandas as pd
df = pd.DataFrame()
pq_file = io.BytesIO()
df.to_parquet(pq_file)
pq_bytes = pq_file.getvalue()
```
This workaround is too effortful. | {
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} | 1 | 2020-10-13T18:37:02Z | 2020-10-14T14:42:29Z | 2020-10-14T12:21:59Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
since DatetimeLike and Categorical no longer special-case these, we can inline them. | {
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I believe that the @TomAugspurger in #35393 found out that, when `sharex` or `sharey` are enabled, matplotlib counts the number of ticks of an axis in a subplot as the total number of ticks of the shared axis in all subplots. Since matplotlib became stricter with `set_xticks`, the proposed workaround was replicating the labels n times where n is "total number of ticks"/"number of ticks of this subplot"="number of subplots".
However, that fix was implemented only for vertical boxplots and not for horizontal boxplots, hence why the issue.
Also, that fix causes the labels of the shared axis to be drawn n times (where n is the number of subplots) in some cases, see below (matplotlib 3.3.2, code adapted from the one posted by the author of issue 36918)
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
df1 = pd.DataFrame(np.random.randint(0, 100, size=(100, 4)), columns=list('ABCD'))
df2 = pd.DataFrame(np.random.randint(0, 100, size=(100, 4)), columns=list('ABCD'))
f, axs = plt.subplots(nrows=2, ncols=1, figsize=(10,7), sharex=True)
df1.boxplot(ax=axs[0], vert=True)
df2.boxplot(ax=axs[1], vert=True)
plt.show()
```
In this fix I used `set_major_formatter` with a `FixedFormatter` which fixes the issue and does not overdraw the labels:
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} | 5 | 2020-10-13T20:31:00Z | 2021-01-10T15:29:51Z | 2021-01-10T09:38:05Z | MEMBER | null | - [x] closes #37104
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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"default"... | closed | false | null | [] | null | 10 | 2020-10-13T21:59:33Z | 2021-01-01T22:19:25Z | 2021-01-01T22:19:19Z | CONTRIBUTOR | null | Examples:
```python
>>> rng = pd.RangeIndex(100_000)
>>> %timeit rng == rng
429 µs ± 96.9 µs per loop # master
14.1 µs ± 93.8 ns per loop # this PR
>>> idx = rng.astype(int)
>>> %timeit idx == idx
347 µs ± 1.42 µs per loop #master
14.1 µs ± 91.4 ns per loop # this PR
>>> idx = rng.astype(object)
>>> %timeit idx == idx
5.97 ms ± 12.7 µs per loop # master
3.72 ms ± 28.4 µs per loop # this PR
```
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- [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
from requests import get
import numpy as np
import pandas as pd
# This is just to get the exact data I used
url = ('https://bitcoincharts.com/charts/chart.json?'
'm=bitstampUSD&SubmitButton=Draw&r=180&i=Hourly&c=0&s=&e=&Prev=&Next=&'
't=S&b=&a1=&m1=10&a2=&m2=25&x=0&i1=&i2=&i3=&i4=&v=1&cv=0&ps=0&l=0&p=0&')
res = get(url)
data = res.json()
df = pd.DataFrame(data)
# Using .rolling(20).mean() on all the records (4309 rows) will produce infs on almost all columns
print((df.rolling(20).mean() == np.inf).any())
# However, using only the last 100 records this doesn't happen
print((df.tail(100).rolling(20).mean() == np.inf).any())
# Exactly the same when using only a series. Here I include the count of values.
# We can see that ~95% of the rows turn infinite.
print((df[3].rolling(20).mean() == np.inf).value_counts())
```
#### Problem description
When applying the `.rolling(20).mean()` functions on either a `DataFrame` or a `Series` with ~4k rows approximately 95% of the results are `inf`. However, trimming down the data to only 100 rows, this works fine.
#### Expected Output
This should be the expected output of the last 10 rows:
<img width="750" alt="Captura de Pantalla 2020-10-14 a la(s) 09 24 51" src="https://user-images.githubusercontent.com/12915138/95988378-33188a80-0dff-11eb-9ba6-476c09264de1.png">
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0.post20201006
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
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 : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 2 | 2020-10-14T12:31:40Z | 2020-10-14T18:07:12Z | 2020-10-14T18:06:55Z | CONTRIBUTOR | null | Reference https://github.com/pandas-dev/pandas/issues/30999
- [x] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Hello! This is my first PR here. I followed the steps in the contributing guidelines [here](https://github.com/pandas-dev/pandas/blob/master/doc/source/development/contributing.rst#contributing-your-changes-to-pandas), but I didn't see anything about `git diff upstream/master -u -- "*.py" | flake8 --diff`.
When I run the command locally, with `origin/master`, it returns no output. I get fatal with `upstream/master`. Is that expected, and if it isn't, could anyone advise on how to resolve the issue please?
Thank you! | {
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} | 4 | 2020-10-14T13:59:55Z | 2020-12-02T23:30:04Z | 2020-12-02T23:30:04Z | 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.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
df1 = pd.DataFrame({
'a': ['A', 'A', 'B'],
'b': ['ca', 'cb', 'cb'],
'v': [10] * 3,
})
df1 = df1.set_index(['a', 'b'])
# int column
df1['is_'] = 1
df1 = df1.unstack('b')
# Will not work, keeping NaN in the value
df1[('is_', 'ca')] = df1[('is_', 'ca')].fillna(0)
# Will raise ValueError: Cannot convert non-finite values (NA or inf) to integer
df1[('is_', 'ca')] = df1[('is_', 'ca')].astype('uint8')
```
#### Problem description
`df.unstack` is creating columns from a int column, but adds some NaNs as expected.
Trying to get rid of the NaN is not possible as `fillna` will silently fail and leave the column values as is.
#### Potential Source of the Problem
It looks like with this configuration, the Block for this `df1[('is_', 'ca')]` Series in the BlockManager is actually `IntBlock`, but holds a float dtype `np.ndarray` (!?).
From what I understand, as soon as the BlockManager operates a block consolidation (with the _consolidate function), the Block is converted to a `FloatBlock` and everything is OK.
e.g. a call to `cat2._is_mixed_type` will trigger a `_consolidate` and thus clear the issue.
#### Potential Solution Suggestion
Should the unstack function fires a consolidation of blocks, especially for column types that do not accept NaN values (like 'int' and 'bool' from numpy)?
#### Some potential workarounds identified so far
* Trigger a Block consolidate:
* Add a column: the bug appeared if I use `fillna` before adding a column, but not after
* Call `cat2._is_mixed_type`
* Call `df1.infos()`: this was the most surprising one (it is because if triggers `df.count()` and thus consolidation)
* Use a float value: `df1['is_'] = 1.0`
#### Expected Output
```
v is_
b ca cb ca cb
a
A 10.0 10.0 1 1.0
B NaN 10.0 0 1.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.cp1252
pandas : 1.1.3
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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} | 2 | 2020-10-14T15:44:15Z | 2020-10-14T16:41:43Z | 2020-10-14T16:41:36Z | MEMBER | null | Reverts pandas-dev/pandas#37050 | {
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} | 4 | 2020-10-14T17:37:22Z | 2020-10-31T15:11:51Z | 2020-10-31T14:50:32Z | CONTRIBUTOR | null | ~~- [x] closes #37053 (keep this issue open until both #37492 and #37117 are completed)~~
- [x] tests added / passed ~~(once #37492is merged)~~
# update 2020-10-31
now use a pre-commit check instead of the previous code_check
***
~~code_check.sh will be used to locate where numpy.random-related imports are.~~ See the separate script to do the whole clean-up in https://github.com/pandas-dev/pandas/pull/37103#issue-502582592. | {
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} | 4 | 2020-10-14T18:22:06Z | 2020-10-17T13:50:01Z | 2020-10-17T13:49:55Z | CONTRIBUTOR | null | - [x] closes #37081
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Made the change proposed by @jorisvandenbossche in https://github.com/pandas-dev/pandas/pull/35881#discussion_r504035006
Did a very quick comparison :
With `self.dtypes` (old version) :
```
In [8]: values = np.random.randn(100000, 4)
...: df = pd.DataFrame(values).astype("int")
...: %timeit df.sum()
714 µs ± 21 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
With `self._iter_column_arrays()` (new version) :
```
In [4]: values = np.random.randn(100000, 4)
...: df = pd.DataFrame(values).astype("int")
...: %timeit df.sum()
477 µs ± 8.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
``` | {
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