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} | 4 | 2020-01-22T18:38:45Z | 2020-02-01T20:33:13Z | 2020-01-22T20:11:24Z | CONTRIBUTOR | null | Updated min version for `pyxlsb` to 1.0.6 and removed the now unnecessary xfail for read from url. Turns out the other xfail is actually unnecessary so I've removed it.
Question: is my name supposed to show up in the contributors list?
- [X] closes #8540
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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- [x] closes #30512
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 1 | 2020-01-22T19:09:30Z | 2020-01-22T19:38:00Z | 2020-01-22T19:37:57Z | CONTRIBUTOR | null | The lack of newline below the ipython directive may be causing issues. | {
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} | 0 | 2020-01-22T19:46:25Z | 2020-07-10T13:58:36Z | null | MEMBER | null | DatetimeArray/TimedeltaArray `__setitem__` resets the array's freq to None, but this doesn't propagate to views:
```
dti = pd.date_range('2016-01-01', periods=5)
dta = dti._data
view = dta[:]
assert view.freq == dta.freq == "D"
dta[0] = pd.NaT
assert dta.freq is None
>>> view.freq
<Day>
```
`view.freq` is now incorrect.
It will be a hassle, but I think the thing to do is remove `freq` from DTA/TDA altogether. It has a different meaning from PeriodArray.freq and isn't necessary for the "vectorized Timestamp/Timedelta" abstraction. | {
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cc @jorisvandenbossche @TomAugspurger | {
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... | null | 5 | 2020-01-22T19:56:25Z | 2020-09-20T20:47:21Z | 2020-09-20T20:47:21Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
Index.sort_values places missing values at the start of the result with `ascending=False`
```python
In [4]: pd.Index([1, np.nan, 0]).sort_values(ascending=False)
Out[4]: Float64Index([nan, 1.0, 0.0], dtype='float64')
In [5]: pd.Series([1, np.nan, 0]).sort_values(ascending=False)
Out[5]:
0 1.0
2 0.0
1 NaN
dtype: float64
```
#### Problem description
This differs from Series.sort_values, which always leaves NA values at the end.
#### Expected Output
```python
Out[4]: Float64Index([1.0, 0.0, nan], dtype='float64')
``` | {
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} | 1 | 2020-01-22T20:11:35Z | 2020-01-23T09:40:20Z | 2020-01-22T21:08:55Z | NONE | null | Backport PR #31215: Follow-up: XLSB Support | {
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} | [] | closed | false | null | [] | null | 2 | 2020-01-22T21:57:38Z | 2020-01-23T09:52:06Z | 2020-01-23T09:52:05Z | NONE | null | #### Code Sample
```python
from pandas import DataFrame, Series, DatetimeIndex
from numpy import float64
df = DataFrame(index=DatetimeIndex([]), columns=["A", "B"]).astype({"A": float64})
df.loc[:, "A"] = Series(index=df.index, dtype=float64)
type(df.index)
```
#### Output
```python
pandas.core.indexes.range.RangeIndex
```
#### Problem description
The index of the dataframe gets converted from `DatetimeIndex` into `RangeIndex` even though the `Series` assigned to one of the columns has the exact same index as the dataframe.
We often want to check that the index is a `DatetimeIndex` when validating the data we receive as input. If this can change unexpectedly from a simple assignment operation when the `DataFrame` is empty, it becomes necessary to treat empty `DataFrame`s as special cases in our code.
This is at the very least unexpected behaviour:
- It doesn't happen if the index is not empty;
- It doesn't happen if we use `df["A"]` (instead of `.loc`);
- It doesn't happen if there's only one column;
- It doesn't happen if we omit `.astype({"A": float64})` (i.e. if both columns initially have dtype `object`).
#### Expected Output
```python
pandas.core.indexes.datetimes.DatetimeIndex
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-26-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2
Cython : None
pytest : 5.3.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/31223 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31223/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31223/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31223/events | https://github.com/pandas-dev/pandas/issues/31223 | 553,839,409 | MDU6SXNzdWU1NTM4Mzk0MDk= | 31,223 | GroupBy aggregation fails if DataFrame has CategoricalIndex | {
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} | 2 | 2020-01-22T22:47:16Z | 2020-01-28T01:57:19Z | 2020-01-28T01:57:19Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
ids = pd.Categorical([0, 1, 2])
df = pd.DataFrame({
'id': ids,
'groups': [1, 1, 2],
'value': [0, 1, 0]
}).set_index('id')
df.groupby('groups').agg({'value': pd.Series.nunique})
```
#### Problem description
The above works in v. `0.25.3` but not in `1.0.0rc0`. It fails with: `TypeError: Cannot convert Categorical to numpy.ndarray`
Full stack trace:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "~/.virtualenvs/3.6-test-pd-1.0.0/lib/python3.6/site-packages/pandas/core/groupby/generic.py", line 940, in aggregate
result, how = self._aggregate(func, *args, **kwargs)
File "~/.virtualenvs/3.6-test-pd-1.0.0/lib/python3.6/site-packages/pandas/core/base.py", line 430, in _aggregate
result = _agg(arg, _agg_1dim)
File "~/.virtualenvs/3.6-test-pd-1.0.0/lib/python3.6/site-packages/pandas/core/base.py", line 397, in _agg
result[fname] = func(fname, agg_how)
File "~/.virtualenvs/3.6-test-pd-1.0.0/lib/python3.6/site-packages/pandas/core/base.py", line 381, in _agg_1dim
return colg.aggregate(how)
File "~/.virtualenvs/3.6-test-pd-1.0.0/lib/python3.6/site-packages/pandas/core/groupby/generic.py", line 265, in aggregate
return self._python_agg_general(func, *args, **kwargs)
File "~/.virtualenvs/3.6-test-pd-1.0.0/lib/python3.6/site-packages/pandas/core/groupby/groupby.py", line 935, in _python_agg_general
result, counts = self.grouper.agg_series(obj, f)
File "~/.virtualenvs/3.6-test-pd-1.0.0/lib/python3.6/site-packages/pandas/core/groupby/ops.py", line 624, in agg_series
return self._aggregate_series_fast(obj, func)
File "~/.virtualenvs/3.6-test-pd-1.0.0/lib/python3.6/site-packages/pandas/core/groupby/ops.py", line 648, in _aggregate_series_fast
grouper = libreduction.SeriesGrouper(obj, func, group_index, ngroups, dummy)
File "pandas/_libs/reduction.pyx", line 329, in pandas._libs.reduction.SeriesGrouper.__init__
TypeError: Cannot convert Categorical to numpy.ndarray
```
#### Expected Output
The correct result applying the aggregation function to the DataFrameGroupBy
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0rc0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.1
setuptools : 45.1.0
Cython : None
pytest : 5.2.0
hypothesis : None
sphinx : 2.0.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.2.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : 0.3.2
gcsfs : None
lxml.etree : None
matplotlib : 3.0.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.2.0
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.47.0
</details>
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} | 2 | 2020-01-22T22:49:36Z | 2020-01-23T17:20:01Z | 2020-01-23T14:25:31Z | MEMBER | null | ATM we are mixing/matching pytest vs UnitTest idioms, which is confusing for me because I have to track down both inheritance and fixtures. So I'm trying to clean this up, leading to some yak shaving.
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"gi... | null | 1 | 2020-01-22T22:55:42Z | 2020-03-06T15:25:33Z | 2020-03-06T15:25:33Z | NONE | null | #### Problem description
The current documentation of str.replace says ```Replace occurrences of pattern/regex in the Series/Index with some other string. Equivalent to str.replace() or re.sub().```.
For the novice user this suggests that base Python's str.replace() and re.sub() provide equivalent functionality. My suggestion is to explicitly specify that this depends on the value of regex, making it clearer. Something like this:
```Replace occurrences of pattern/regex in the Series/Index with some other string. Equivalent to str.replace() or re.sub() **depending on the _regex_ value**.``` | {
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} | 1 | 2020-01-22T23:21:17Z | 2020-01-23T17:53:27Z | 2020-01-23T17:53:24Z | MEMBER | null | elsehwere we use `obj = self.obj` which is either a Series or DataFrame, better to call this `key` like we do elsewhere | {
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"d... | closed | false | null | [] | null | 4 | 2020-01-22T23:26:18Z | 2021-11-20T23:21:54Z | 2020-01-29T16:11:35Z | MEMBER | null | @WillAyd nearly all of the remaining test failures locally are in the json tests. can you take a look and suggest whether we just need to pass `check_names=False`
I expect if we do something like this we won't want to actually randomize; that part I guess is a proof of concept for now. | {
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} | 1 | 2020-01-23T00:03:11Z | 2020-01-24T12:19:57Z | 2020-01-23T15:55:56Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2020-01-23T00:14:58Z | 2020-01-24T12:18:37Z | 2020-01-23T00:49:55Z | MEMBER | null | - [x] closes #31149
- [x] tests added / passed
- [x] passes `black pandas`
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- [ ] whatsnew entry
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} | 0 | 2020-01-23T00:30:46Z | 2020-01-23T17:29:18Z | 2020-01-23T14:27:36Z | MEMBER | null | the wrong-dtype NaT is a bug, the DTI analogue is fixed in #31163.
The refactor to make get_value is get_loc is basically what we want all of them index subclasses to do (at which point we de-duplicate and possibly get rid of get_value altogether)
cc @jreback | {
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No whatnew since this is a regression.
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} | 1 | 2020-01-23T02:01:47Z | 2020-01-26T01:15:23Z | 2020-01-26T01:15:23Z | CONTRIBUTOR | null | In the [API reference for DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/frame.html), why is `DataFrame.info()` in the "Serialization / IO / conversion" section? It would make more sense to me in the "Attributes and underlying data" section, which has similar methods and attributes like `DataFrame.memory_usage()`, `DataFrame.dtypes`and `DataFrame.shape`.
Glad to make a pull request if this makes sense to others. | {
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} | 3 | 2020-01-23T02:15:21Z | 2020-01-24T17:49:58Z | 2020-01-24T17:49:58Z | CONTRIBUTOR | null | The [API Reference documentation for `Series`](https://pandas.pydata.org/pandas-docs/stable/reference/series.html) and [`DataFrame`](https://pandas.pydata.org/pandas-docs/stable/reference/frame.html) have truncated documentation strings for `Series.at_time()`, `Series.unstack()`, and `DataFrame.at_time()`. The bug appears to occur where the docstring contains ". " (that is, a period followed by a space). Presumably, this pattern is being used to find the end of the one-line function description. | {
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] | closed | false | null | [] | null | 1 | 2020-01-23T02:21:51Z | 2020-04-21T23:03:13Z | 2020-01-23T18:41:15Z | MEMBER | null | @TomAugspurger @jorisvandenbossche as you can see, the `__array_ufunc__` that this implements is an unholy mess. This all came from a much smaller goal: I wanted to add the "datetime-tz" key in tests.indexes.conftest.
That broke a test for `np.isfinite`, so I started implementing `array_ufunc`. Then that broke other ufuncs, and so on and so on.
I guess I could just xfail the one isfinite test. In retrospect that would have saved a lot of effort. Woops.
Anyhow, we probably want/need to implement this eventually anyway, so any ideas on how to do it but less awful?
xref #31219. | {
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- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Examples(all three passed)
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} | 7 | 2020-01-23T02:38:37Z | 2020-01-30T09:59:53Z | 2020-01-28T01:57:19Z | MEMBER | null | - [X] closes #31223
- [X] closes #31248
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
No whatsnew since this is a regression. | {
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 3 | 2020-01-23T05:36:36Z | 2020-01-24T01:07:21Z | 2020-01-24T00:59:33Z | MEMBER | null | Much easier alternative to #31236. | {
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} | 14 | 2020-01-23T10:51:32Z | 2020-11-29T16:15:15Z | 2020-11-29T16:10:19Z | MEMBER | null | xref #29752, #30929
Using some work I am doing in pyarrow (https://github.com/apache/arrow/pull/6189), we are able to provide an option in `read_parquet` to directly use new nullable dtypes instead of first using the default conversion (eg which gives floats for ints with nulls) and doing the conversion afterwards
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https://api.github.com/repos/pandas-dev/pandas/issues/31243 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31243/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31243/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31243/events | https://github.com/pandas-dev/pandas/issues/31243 | 554,089,655 | MDU6SXNzdWU1NTQwODk2NTU= | 31,243 | Potential bug in reading SAS files with CHAR (RLE) compression and many repeated characters | {
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"id"... | open | false | null | [] | null | 10 | 2020-01-23T11:08:19Z | 2020-12-17T06:11:56Z | null | NONE | null | Hi,
I think I ran into a bug in the RLE decompression implementation.
**Short description**:
String fields with more than 32 repeated consecutive characters are ~be~ cropped at 32 and next fields will spill over corrupting the whole dataframe.
**Example**:
example.csv with fields of length 50
```
long_string_field1,long_string_field2,long_string_field3
"00000000000000000000000000000000000000000000000000","11111111111111111111111111111111111111111111111111","aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
```
Create a CHAR compressed sas7bdat file (system encoding is set to latin1)
```sas
options compress=char;
proc import datafile="path\example.csv"
out=your_lib.example
dbms=csv
replace;
getnames=yes;
run;
```
```python
import pandas as pd
example = pd.read_sas("./example.sas7bdat", encoding="latin1")
```
This is what you get:
```python
>>> example['long_string_field1'].values[0]
'00000000000000000000000000000000 111111111111111111'
```
```python
>>> example['long_string_field2'].values[0]
'11111111111111 aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa'
```
```python
>>> example['long_string_field3'].values[0]
nan
```
There are a couple of interesting points:
1. Exactly 32 characters are read/written from each field. Originals had 50 characters.
2. The dataframe fields are filled up to the 50 limit (+ 2 additional spaces between the sources of the fields. In case of consecutive integers) with spillovers from next fields.
3. This only happens if one uses the CHAR compression. (RLE)
4. This only happens if you have repeated consecutive characters.
5. The `sas7bdat` package works fine.
#### DISCLAIMER
I would never use any of the things above out of my free will. Sadly, this is an actual case I keep running into when having to deal with SAS... 😢
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.5
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 45.1.0.post20200119
Cython : None
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 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
s3fs : None
scipy : None
sqlalchemy : 1.3.12
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
```
</details>
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} | 0 | 2020-01-23T12:35:26Z | 2020-01-23T15:56:21Z | 2020-01-23T13:25:57Z | NONE | null | Backport PR #31167: DOC: fix DataFrame.plot docs | {
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https://api.github.com/repos/pandas-dev/pandas/issues/31245 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31245/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31245/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31245/events | https://github.com/pandas-dev/pandas/issues/31245 | 554,155,669 | MDU6SXNzdWU1NTQxNTU2Njk= | 31,245 | Verbose parameter in pandas.Series.apply | {
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... | closed | false | null | [] | null | 6 | 2020-01-23T13:26:45Z | 2020-09-22T01:30:44Z | 2020-09-22T01:30:43Z | NONE | null | Hi,
I think it could be really useful to add a **Verbose** parameter in the **pandas.Series.apply** function. It can take a really long time to get an output so having some kind of verbosity with something like tqdm could be really useful.
Cheers, | {
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} | 0 | 2020-01-23T14:16:12Z | 2020-01-23T15:56:52Z | 2020-01-23T15:23:12Z | NONE | null | Backport PR #31232: REGR: Fix IntervalIndex.map when result is object dtype | {
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} | 4 | 2020-01-23T14:39:35Z | 2020-02-26T02:24:35Z | 2020-02-26T02:20:44Z | MEMBER | null | - [x] closes #5377
- [x] tests added / passed
- [x] passes `black pandas`
- [x] whatsnew entry
This is picking up where https://github.com/pandas-dev/pandas/pull/27350 left off because I think it'd be a nice feature to have. At least one thing that still needs to be done is implementing `bins` when we have only a single column in `subset`, in which case maybe we can just delegate to `Series.value_counts`. | {
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} | 4 | 2020-01-23T14:53:42Z | 2020-01-28T01:57:19Z | 2020-01-28T01:57:19Z | MEMBER | null | It looks like the `_index_data` attribute wasn't added to `CategoricalIndex` or `IntervalIndex`. This leads to some regressions with `GroupBy.apply`.
Not sure what the fix should be, as there isn't a singular array that directly backs the underlying values for `CategoricalIndex` or `IntervalIndex`, so the approach used to add `_index_data` for other types doesn't look like it will work here.
Example of broken behavior on `master`:
```python
In [1]: import pandas as pd; pd.__version__
Out[1]: '1.0.0rc0+146.g6f395ad42'
In [2]: index = pd.CategoricalIndex(list("abc"))
...: df = pd.DataFrame({"group": [0, 0, 1], "value": [1, 2, 3]}, index=index)
In [3]: df.groupby("group").apply(lambda x: x)
---------------------------------------------------------------------------
AttributeError: 'CategoricalIndex' object has no attribute '_index_data'
```
Same behavior worked on 0.25.3:
```python
In [1]: import pandas as pd; pd.__version__
Out[1]: '0.25.3'
In [2]: index = pd.CategoricalIndex(list("abc"))
...: df = pd.DataFrame({"group": [0, 0, 1], "value": [1, 2, 3]}, index=index)
In [3]: df.groupby("group").apply(lambda x: x)
Out[3]:
group value
a 0 1
b 0 2
c 1 3
```
Swapping `CategoricalIndex` with `IntervalIndex` results in the same behavior above. | {
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} | 2 | 2020-01-23T15:52:37Z | 2020-01-24T16:00:07Z | 2020-01-23T23:10:18Z | CONTRIBUTOR | null | - [x] closes #31175
- [x] tests added / passed
- tests/scalar/timestamp/test_timestamp.py
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
- Not needed for fixing a test
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https://api.github.com/repos/pandas-dev/pandas/issues/31250 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31250/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31250/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31250/events | https://github.com/pandas-dev/pandas/issues/31250 | 554,265,103 | MDU6SXNzdWU1NTQyNjUxMDM= | 31,250 | pd.DataFrame.duplicated, drop_duplicates does not support column operation | {
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} | [] | closed | false | null | [] | null | 1 | 2020-01-23T16:26:06Z | 2020-01-27T17:54:14Z | 2020-01-27T17:54:14Z | NONE | null | I was trying to use .duplicated to find identical columns, and possibly dropping them, but realized it doesn't do columnar operations.
It would be great if pd.DataFrame.duplicated or pd.DataFrame.drop_duplicates supports axis= argument.
Current version 0.25.3 not supporting axis=
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.duplicated.html
Some workaround by custom function
https://thispointer.com/how-to-find-drop-duplicate-columns-in-a-dataframe-python-pandas/
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} | 5 | 2020-01-23T16:41:05Z | 2020-03-17T00:03:12Z | 2020-03-17T00:03:12Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas as pd
import io
content = io.StringIO('''
time,val
212.23, 32
''')
date_cols = ['time']
df = pd.read_csv(
content,
sep=',',
usecols=['val'],
dtype= { 'val': int },
parse_dates=date_cols,
)
```
#### Problem description
triggers
```
Traceback (most recent call last):
File "test.py", line 16, in <module>
parse_dates=date_cols,
File "/nix/store/k4fd48jzsyafvcifa6wi6pk4vaprnw36-python3.7-pandas-0.25.3/lib/python3.7/site-packages/pandas/io/parsers.py",
line 685, in parser_f
return _read(filepath_or_buffer, kwds)
File "/nix/store/k4fd48jzsyafvcifa6wi6pk4vaprnw36-python3.7-pandas-0.25.3/lib/python3.7/site-packages/pandas/io/parsers.py",
line 463, in _read
data = parser.read(nrows)
File "/nix/store/k4fd48jzsyafvcifa6wi6pk4vaprnw36-python3.7-pandas-0.25.3/lib/python3.7/site-packages/pandas/io/parsers.py",
line 1154, in read
ret = self._engine.read(nrows)
File "/nix/store/k4fd48jzsyafvcifa6wi6pk4vaprnw36-python3.7-pandas-0.25.3/lib/python3.7/site-packages/pandas/io/parsers.py",
line 2134, in read
names, data = self._do_date_conversions(names, data)
File "/nix/store/k4fd48jzsyafvcifa6wi6pk4vaprnw36-python3.7-pandas-0.25.3/lib/python3.7/site-packages/pandas/io/parsers.py",
line 1885, in _do_date_conversions
keep_date_col=self.keep_date_col,
File "/nix/store/k4fd48jzsyafvcifa6wi6pk4vaprnw36-python3.7-pandas-0.25.3/lib/python3.7/site-packages/pandas/io/parsers.py",
line 3335, in _process_date_conversion
data_dict[colspec] = converter(data_dict[colspec])
KeyError: 'time'
```
i.e., if you use columns in parse_dates that dont appear in use_cols, then you are screwed.
Either parse_dates sould be added to use_cols or the documentation precise it. An assert could make the error more understandable too.
pandas 0.25.3 | {
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... | open | false | null | [] | null | 0 | 2020-01-23T17:38:24Z | 2020-06-07T00:39:07Z | null | CONTRIBUTOR | null | One of the provided (BaseMethods) Extension tests is `test_factorize_empty`. I don't have a way of passing this test given how `factorize` works for `ExtensionArray`.
```python
def test_factorize_empty(self, data):
codes, uniques = pd.factorize(data[:0])
expected_codes = np.array([], dtype=np.intp)
expected_uniques = type(data)._from_sequence([], dtype=data[:0].dtype)
tm.assert_numpy_array_equal(codes, expected_codes)
self.assert_extension_array_equal(uniques, expected_uniques)
```
#### Code Example
The relevant portion of my `GenotypeArray` class `__init__` method, when an empty array of values is passed.
```python
def __init__(self,
values: Union[List[Genotype], 'GenotypeArray', np.ndarray],
dtype: Optional[GenotypeDtype] = None,
copy: bool = False):
"""Initialize assuming values is a GenotypeArray or a numpy array with the correct underlying shape"""
# If the dtype is passed, ensure it is the correct type
if GenotypeDtype.is_dtype(dtype):
self._dtype = dtype
elif dtype is None:
self._dtype = None
else:
raise ValueError(f"The passed dtype '{dtype}' is not a GenotypeDtype")
# Load the values
# ---------------
if isinstance(values, np.ndarray) and (values.dtype == GenotypeDtype._record_type):
# Stored data format
self._data = values
elif len(values) == 0:
# Return an empty Genotype Array
if self._dtype is not None:
self._data = np.array(values, dtype=GenotypeDtype._record_type)
else:
raise ValueError("Cannot create a Genotype Array with neither values nor a dtype")
```
#### Problem description
The ValueError is raised during this test. I don't have another solution- the `GenotypeArray` has an associated `Variant` instance as part of the associated `GenotypeDtype` class. It is possible to access this value from an instance (if `values` isn't empty) or from the `dtype`, but a GenotypeArray without this is meaningless.
The actual problem occurs in `_reconstruct_data` defined in `core/algorithms.py`:
```python
def _reconstruct_data(values, dtype, original):
"""
reverse of _ensure_data
Parameters
----------
values : ndarray
dtype : pandas_dtype
original : ndarray-like
Returns
-------
Index for extension types, otherwise ndarray casted to dtype
"""
if is_extension_array_dtype(dtype):
values = dtype.construct_array_type()._from_sequence(values)
elif is_bool_dtype(dtype):
values = values.astype(dtype, copy=False)
# we only support object dtypes bool Index
if isinstance(original, ABCIndexClass):
values = values.astype(object, copy=False)
elif dtype is not None:
values = values.astype(dtype, copy=False)
return values
```
This would be fixed by passing the dtype to `_from_sequence`:
```python
if is_extension_array_dtype(dtype):
values = dtype.construct_array_type()._from_sequence(values, dtype)
```
I can't think of any reason this would be a problem.
#### Expected Output
Passing the test if the `factorize` method on the ExtensionArray class works correctly.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-18362-Microsoft
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0rc0
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.2.0
Cython : None
pytest : 5.3.2
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.2
s3fs : None
scipy : 1.3.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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- [ ] closes #31252
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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```python
>>> import pandas as pd
>>> ids = range(5)
>>> groups = pd.Series([0, 1, 1, 2, 2])
>>> values = pd.Categorical([0, 0, 0, 0, 1])
>>> df = pd.DataFrame({
'id': ids,
'groups': groups,
'value': values
}).set_index('id')
>>> df.groupby('groups').agg(pd.Series.nunique)
value
groups
0 1
1 1
2 NaN
>>> df.groupby('groups').agg(pd.Series.nunique)['value'].dtype
CategoricalDtype(categories=[0, 1], ordered=False)
>>> df.groupby('groups').agg(pd.Series.count)
value
groups
0 1
1 NaN
2 NaN
>>> df.groupby('groups').agg(pd.Series.mode)
value
groups
0 0
1 0 0
Name: value, dtype: category
Categories (2, int64): [0, 1]
2 0 0
1 1
Name: value, dtype: category
Categories (2, int64): [0, 1]
```
#### Problem description
There's some regression bugs in `1.0.0rc0` when applying groupby aggregations to Categorical columns. All of these examples work as expected in `0.25.3`.
Applying `pd.Series.nunique` and `pd.Series.count` both result in `NaN` for some groupings, the result has a Categorical dtype. The resulting dtype should be int64 and `NaN` should not be produced.
Applying `pd.Series.mode` returns the correct result for the first grouping but produces nonsensical output for subsequent groupings.
#### Expected Output
Aggregations should work as expected on Categorical columns as they do in `0.25.3`:
```python
>>> df.groupby('groups').agg(pd.Series.nunique)
value
groups
0 1
1 1
2 2
>>> df.groupby('groups').agg(pd.Series.nunique)['value'].dtype
dtype('int64')
>>> df.groupby('groups').agg(pd.Series.count)
value
groups
0 1
1 2
2 2
>>> df.groupby('groups').agg(pd.Series.mode)
value
groups
0 0 0
1 0 0
2 0 0
1 1
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0rc0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.1
setuptools : 45.1.0
Cython : None
pytest : 5.2.0
hypothesis : None
sphinx : 2.0.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.2.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : 0.3.2
gcsfs : None
lxml.etree : None
matplotlib : 3.0.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.2.0
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.47.0
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/31259 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31259/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31259/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31259/events | https://github.com/pandas-dev/pandas/issues/31259 | 554,386,953 | MDU6SXNzdWU1NTQzODY5NTM= | 31,259 | Please help me to understand TypeError: Expected tuple, got str | {
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} | [] | closed | false | null | [] | null | 2 | 2020-01-23T20:24:25Z | 2020-01-24T17:19:09Z | 2020-01-23T21:19:24Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Summarize data by schools
schools_group = complete_data_pd.groupby(["school_name"])
sum_students = schools_group["student_name"].value_counts()
budgets_df = schools_group["budget"].mean()
per_student = budgets_df / sum_students
math_mavens_group = complete_data_pd[complete_data_pd['math_score'] >= 70].groupby(["school_name"])
rad_readers_group = complete_data_pd[complete_data_pd['reading_score'] >= 70].groupby(["school_name"])
math_per_school = schools_group["math_score"].mean()
reading_per_school = schools_group["reading_score"].mean()
mavens_per_school = math_mavens_group.count()["student_name"] / sum_students
readers_per_school = rad_readers_group.count()["student_name"] / sum_students
rate_passing = ((mavens_per_school + readers_per_school) / 2)
# Create new dataframe
summary_df = pd.DataFrame({
"School Type" : type,
"Total Budget" : budgets_df,
"Budget Per Student" : per_student,
"Average Math Score" : math_per_school,
"Average Reading Score" : reading_per_school,
"% Passing Math" : mavens_per_school,
"% Passing Reading" : readers_per_school,
"% Overall Passing Rate" : rate_passing
})
```
#### Problem description
If this has been answered in an earlier issue, I couldn't translate it into anything I understand
The above code is my attempt to create a new dataframe by using a groupby object from an earlier dataframe. When I try to run the code, I get a whole lot of traceback that I do not understand, ending with the line, TypeError: Expected tuple, got str.
The error message suggests that the problem is in the line, "% Overall Passing Rate" : rate_passing; however, when I remove that line, the same error message throws, but now points to the new last line. Putting brackets around the variables did nothing. What exactly is going on here with tuples and strings? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/31260 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31260/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31260/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31260/events | https://github.com/pandas-dev/pandas/pull/31260 | 554,393,316 | MDExOlB1bGxSZXF1ZXN0MzY2NTU2Mzc5 | 31,260 | DOC fix truncated documentation for Series.unstack(), Series. | {
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} | 3 | 2020-01-23T20:37:29Z | 2020-01-24T19:30:59Z | 2020-01-24T17:49:59Z | CONTRIBUTOR | null | fixes https://github.com/pandas-dev/pandas/issues/31235
Changed "e.g. " to "e.g., "
Changed "a.k.a. " to "also known as"
This helps avoid the truncation caused in the docstring | {
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} | 3 | 2020-01-23T21:32:22Z | 2020-07-03T16:15:11Z | 2020-02-11T21:27:08Z | MEMBER | null | - [x] closes #30346
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Found out that not only `medianprops` info is lost, so is `whiskerprops`, `capprops` and `boxprops`, so I also added them alongside. Since this is a visualization PR, so add a screenshot to show what it looks like after the change
<img width="587" alt="Screen Shot 2020-01-24 at 4 16 26 PM" src="https://user-images.githubusercontent.com/9269816/73079678-eb468180-3ec4-11ea-9628-5ab13e73f15c.png">
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https://api.github.com/repos/pandas-dev/pandas/issues/31263 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31263/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31263/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31263/events | https://github.com/pandas-dev/pandas/pull/31263 | 554,445,329 | MDExOlB1bGxSZXF1ZXN0MzY2NTk5MTQ5 | 31,263 | BUG: na_logical_op with 2D inputs | {
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} | 1 | 2020-01-23T22:31:18Z | 2020-01-24T17:44:19Z | 2020-01-24T03:53:17Z | MEMBER | null | Broken off from a branch implementing block-wise ops for op(dataframe, dataframe) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/31264 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31264/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31264/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31264/events | https://github.com/pandas-dev/pandas/issues/31264 | 554,447,376 | MDU6SXNzdWU1NTQ0NDczNzY= | 31,264 | Cannot add pandas_profiling module into SQL Server | {
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} | [] | closed | false | null | [] | null | 1 | 2020-01-23T22:36:35Z | 2020-01-23T23:13:04Z | 2020-01-23T23:13:04Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import sqlmlutils
connection = sqlmlutils.ConnectionInfo(server="localhost", database="SQLAI")
sqlmlutils.SQLPackageManager(connection).install("pandas-profiling")
```
#### Problem description
I was trying to install some module (i.e. pandas_profiling) in SQL Server so I can run the script directly from there. I was utilizing a Microsoft tool called 'sqlmlutils' to achieve this, but it returned an error specifically when I tried to install pandas_profiling (in fact another library called 'text-tools' was successfully installed.
#### Expected Output (this is the output I got by running the scripts)
Traceback (most recent call last):
File "<ipython-input-2-e8db1d445900>", line 1, in <module>
runfile('C:/Users/BusinessUser/Desktop/abc.py', wdir='C:/Users/BusinessUser/Desktop')
File "C:\Users\BusinessUser\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 827, in runfile
execfile(filename, namespace)
File "C:\Users\BusinessUser\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 110, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/Users/BusinessUser/Desktop/abc.py", line 3, in <module>
sqlmlutils.SQLPackageManager(connection).install("pandas_profiling")
File "C:\Users\BusinessUser\Anaconda3\lib\site-packages\sqlmlutils\packagemanagement\sqlpackagemanager.py", line 74, in install
self._install_from_pypi(package, upgrade, version, install_dependencies, scope, out_file=out_file)
File "C:\Users\BusinessUser\Anaconda3\lib\site-packages\sqlmlutils\packagemanagement\sqlpackagemanager.py", line 151, in _install_from_pypi
self._install_from_file(target_package_file, scope, upgrade, out_file=out_file)
File "C:\Users\BusinessUser\Anaconda3\lib\site-packages\sqlmlutils\packagemanagement\sqlpackagemanager.py", line 171, in _install_from_file
required_installs = resolver.get_required_installs(target_package_requirements)
File "C:\Users\BusinessUser\Anaconda3\lib\site-packages\sqlmlutils\packagemanagement\dependencyresolver.py", line 34, in get_required_installs
self._server_packages, requirement.name, spec)
File "C:\Users\BusinessUser\Anaconda3\lib\site-packages\sqlmlutils\packagemanagement\dependencyresolver.py", line 60, in _check_if_installed_package_meets_spec
return getattr(operator, operator_map[op_str])(LooseVersion(installed_version), LooseVersion(req_version))
File "C:\Users\BusinessUser\Anaconda3\lib\distutils\version.py", line 46, in __eq__
c = self._cmp(other)
File "C:\Users\BusinessUser\Anaconda3\lib\distutils\version.py", line 337, in _cmp
if self.version < other.version:
TypeError: '<' not supported between instances of 'int' and 'str'
#### Output of ``pd.show_versions()``
<details>
Not sure what this mean but it was saying 'The version installed on the server is 0.23.4'
</details>
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} | 0 | 2020-01-23T23:10:32Z | 2020-01-23T23:52:52Z | 2020-01-23T23:52:52Z | NONE | null | Backport PR #31249: TST: Fix timestamp comparison test | {
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- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 13 | 2020-01-24T04:59:08Z | 2020-06-03T23:28:11Z | 2020-06-03T23:28:11Z | MEMBER | null | #### Summary
Currently on master, `groupby.transform` with ``func`` equals one of `('fillna', 'pad', 'backfill', 'ffill', etc.)` yields wrong results (See #30918).
With updates from PR (#31101), the incorrect outputs are fixed. However, when ``func`` is one of `('pad', 'backfill', 'cumcout')`, `groupby.transform` then raises ``AttributeError`` similar to what's reported in #27472.
#### Code Sample
```python
# On branch of PR31101
>>> pd.__version__
'1.0.0rc0+162.g56c70234e'
>> df = pd.DataFrame(
... {
... "A": ["foo", "foo", "foo", "foo", "bar", "bar", "baz"],
... "B": [1, 2, np.nan, 3, 3, np.nan, 4],
... }
... )
>>> df
A B
0 foo 1.0
1 foo 2.0
2 foo NaN
3 foo 3.0
4 bar 3.0
5 bar NaN
6 baz 4.0
>>> df.groupby("A").pad() # This is ok
B
0 1.0
1 2.0
2 2.0
3 3.0
4 3.0
5 3.0
6 4.0
>>> df.groupby("A").transform("pad") # This raises AttributeError
Traceback (most recent call last):
...
...
AttributeError: 'Series' object has no attribute 'pad'
>>> df.groupby("A").transform("cumcount")
Traceback (most recent call last):
...
...
AttributeError: 'Series' object has no attribute 'cumcount'
```
Ideally we want to allow all of the above 3 functions in `groupby.transform` | {
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} | 1 | 2020-01-24T05:08:26Z | 2020-03-12T04:47:13Z | 2020-03-12T04:47:13Z | MEMBER | null | #### Code Sample
```python
>>> import pandas as pd
>>> pd.__version__
'1.0.0rc0+183.g4da554f75'
>>> df = pd.DataFrame(
... {
... "A": [1, 1, 1, 1],
... "B": [0, 1, 2, 3]
... }
... )
>>> df
A B
0 1 0
1 1 1
2 1 2
3 1 3
>>> df.groupby("A").corrwith(df) # Clearly corrwith is a reduction rather than transformation
B A
A
1 1.0 NaN
```
On current master (`1.0.0rc0+183.g4da554f75`) we have "corrwith" in the list of ``transformation_kernels`` rather than ``reduction_kernels`` within `groupby.base`.
We probably have to move it and fix any impact it has. | {
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} | 3 | 2020-01-24T05:30:30Z | 2020-01-26T00:55:47Z | 2020-01-26T00:37:02Z | MEMBER | null | We're pretty close to being able to combine _combine_frame and _combine_match_index (and hopefully get rid of them both before long) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/31272 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31272/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31272/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31272/events | https://github.com/pandas-dev/pandas/issues/31272 | 554,564,283 | MDU6SXNzdWU1NTQ1NjQyODM= | 31,272 | Dropped columns still available for filtering | {
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} | [] | closed | false | null | [] | null | 1 | 2020-01-24T06:17:57Z | 2020-01-24T06:21:27Z | 2020-01-24T06:20:43Z | NONE | null | #### Code Sample
```python3
import pandas as pd
df = pd.DataFrame(data={'foo': [0, 1], 'bar': ["a", "b"]})
df.drop(columns=["bar"])[df["bar"] == "a"]
```
#### Problem description
The code sample above should result in an error like the following:
```
KeyError: 'bar'
```
However, it does not but instead works without error, as if the column "bar" would exist.
#### Output of ``pd.show_versions()``
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.13.0-38-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2018.5
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.6.0
Cython : 0.29.6
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.5 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : 7.5.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.0.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.2.10
tables : None
xarray : 0.14.1
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
```
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} | 1 | 2020-01-24T07:50:08Z | 2020-01-24T07:51:18Z | 2020-01-24T07:51:18Z | MEMBER | null | Our nullable, mask-based ExtensionArrays (currently integer and boolean, inheriting from MaskedArray) have a `_data` and `_mask` numpy arrays stored under the hood.
A relatively straight-forward optimization is probably to allow the `_mask` to be None, for the case that there are no missing values.
(to be checked how involved the ops code would become to handle this as optional) | {
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} | 3 | 2020-01-24T08:21:46Z | 2020-01-24T15:03:12Z | 2020-01-24T15:03:06Z | NONE | null | This issue was initially reported as #19622, which was then marked as a duplicate of #15217 and closed since then. However, this issue is still reproducible with the most recent version 0.25.3
To reproduce, copy-paste the following code and run:
```python
import pandas as pd
import numpy as np
import time
n_rows = 1000
n_categories = 250
df = pd.DataFrame({'cat1': np.random.choice(np.arange(0,n_categories), size=n_rows, replace=True),
'cat2': np.random.choice(np.arange(0,n_categories), size=n_rows, replace=True),
'cat3': np.random.choice(np.arange(0,n_categories), size=n_rows, replace=True),
'real_numbers': np.random.normal(size=n_rows)})
# Case 1: Pivot table without multi-index columns
now = time.time()
pd.pivot_table(df, values='real_numbers', index='cat1', columns=['cat2'])
print("Elapsed time Case 1:", time.time() - now)
# Case 2: Pivot table with multi-index columns
now = time.time()
pd.pivot_table(df, values='real_numbers', index='cat1', columns=['cat2', 'cat3'])
print("Elapsed time Case 2:", time.time() - now)
# Typecast a feature to categorical.
df_cast = df.astype({'cat2': 'category'})
# Case 3: Pivot table without multi-index columns,
＃ but with at least one categorical column (i.e., cat2)
# - still OK, but already show performance issue here.
now = time.time()
pd.pivot_table(df_cast, values='real_numbers', index=['cat1'], columns=['cat2'])
print("Elapsed time Case 3:", time.time() - now)
# Case 4: Pivot table with multi-index columns
# mixture of categorical (i.e., cat2) and non-categorical column (i.e., cat3)
# - strong increase in elapsed time/ memory footprint.
now = time.time()
pd.pivot_table(df_cast, values='real_numbers', index=['cat1'], columns=['cat2', 'cat3'])
print("Elapsed time Case 4:", time.time() - now)
```
#### Problem description
Elapsed time Case 1: 0.03320956230163574
Elapsed time Case 2: 0.017639636993408203
Elapsed time Case 3: 0.35034942626953125
Elapsed time Case 4: 119.43837356567383
This issue will eat all my memory if the dataframe is large.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-26-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : zh_CN.UTF-8
LOCALE : zh_CN.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2
Cython : None
pytest : None
hypothesis : None
sphinx : 2.2.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.2
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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} | 0 | 2020-01-24T08:32:43Z | 2020-01-24T09:06:39Z | 2020-01-24T09:06:31Z | MEMBER | null | https://github.com/pandas-dev/pandas/pull/31037 | {
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} | 2 | 2020-01-24T08:50:52Z | 2020-01-24T13:49:50Z | 2020-01-24T13:49:43Z | NONE | null | The following is my code:
```python
import pandas as pd
a = ['16', '66', '66', '42', '17', '55', '66b', '17', '48',
'66', '48', '75', '76', '6a', '80', '57', '66', '13',
'17', '31', '66', '6c', '17', '65b', '17', '39', '17',
'66.0', '80.0', '6.0', '66.0', '21.0', '31.0', '7.0', '8.0']
df = pd.DataFrame(a)
print(df[0].str.replace('.0', '').values)
'''
Output:
['16' '66' '66' '42' '17' '55' '66b' '17' '48' '66' '48' '75' '76' '6a' ''
'57' '66' '13' '17' '31' '66' '6c' '17' '65b' '17' '39' '17' '66' '' '6'
'66' '21' '31' '7' '8']
'''
print(df[0].apply(lambda x: x.replace('.0', '')).values)
'''
Output:
['16' '66' '66' '42' '17' '55' '66b' '17' '48' '66' '48' '75' '76' '6a'
'80' '57' '66' '13' '17' '31' '66' '6c' '17' '65b' '17' '39' '17' '66'
'80' '6' '66' '21' '31' '7' '8']
'''
```
#### Problem description
I was trying to replace `'.0'` with `''`, and when i use **pandas.Series.str.replace** `80` and `80.0` is replaced as `''`; whereas when I replace using **pandas.Series.apply(lambda x: x.replace('.0', ''))** it is working as expected.
Looks like an issue.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.5.final.0
python-bits : 64
OS : Windows
OS-release : 7
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.17.0
pytz : 2018.4
dateutil : 2.7.3
pip : 19.3.1
setuptools : 42.0.2
Cython : 0.28.2
pytest : 4.6.3
hypothesis : None
sphinx : 1.7.4
blosc : None
feather : None
xlsxwriter : 1.0.4
lxml.etree : 4.2.1
html5lib : 1.0.1
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10
IPython : 6.4.0
pandas_datareader: None
bs4 : 4.6.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.1
matplotlib : 3.1.1
numexpr : 2.6.5
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.3
sqlalchemy : 1.2.7
tables : 3.4.3
xarray : None
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : 1.0.4
</details>
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} | 0 | 2020-01-24T09:14:35Z | 2020-01-24T13:11:28Z | 2020-01-24T13:09:50Z | MEMBER | null | Just thought it would be useful to have a benchmark for `.array` (now the performance regression of https://github.com/pandas-dev/pandas/pull/31037 was caught indirectly through an indexing benchmark) | {
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} | 10 | 2020-01-24T12:08:57Z | 2020-03-26T12:49:31Z | 2020-03-26T12:45:39Z | MEMBER | null | Resurrection of #27762
I fixed the NameError in the test, and also changed the example given in the whatsnew entry (I couldn't see a difference between the outputs of 0.25.x and 1.0.0 in the original)
- [x] closes #27453
- [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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- [x] whatsnew entry
- moved whatsnew entry from 1.1 to 1.0
Per comment here from @jreback: https://github.com/pandas-dev/pandas/pull/30929#issuecomment-577989924
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} | 4 | 2020-01-24T14:00:44Z | 2020-01-25T15:37:59Z | 2020-01-25T15:37:59Z | CONTRIBUTOR | null | Email from Azure support
> On March 23, 2020, we’ll be removing the following Azure Pipelines hosted images:
>
> * Windows Server 2012R2 with Visual Studio 2015, a.k.a vs2015-win2012r2
> * macOS X High Sierra 10.13, a.k.a macOS-10.13
> * Windows Server Core 1803, a.k.a win1803
> If you use YAML pipelines, update your pipeline by editing its YAML file.
>
>
>
> jobs:
>
> - job: Windows
>
> pool:
>
> vmImage: 'vs2017-win2016'
>
> steps:
>
> - script: echo hello from Windows | {
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https://api.github.com/repos/pandas-dev/pandas/issues/31284 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31284/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31284/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31284/events | https://github.com/pandas-dev/pandas/issues/31284 | 554,799,955 | MDU6SXNzdWU1NTQ3OTk5NTU= | 31,284 | Can't replace tz-aware with None | {
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} | [] | closed | false | null | [] | null | 1 | 2020-01-24T15:27:30Z | 2020-01-24T15:29:31Z | 2020-01-24T15:29:31Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
value_a = pd.Timestamp("20130102", tz="US/Eastern")
value_b = pd.Timestamp("20130102", tz="US/Eastern")
df = pd.DataFrame({"A": [value_a], "B": [value_b]})
df.replace(to_replace={value_a: None}).dtypes.drop("A")
```
#### Problem description
Throws `*** TypeError: data type not understood`. On the other hand, this works:
```python
import pandas as pd
value_a = pd.Timestamp("20130102")
value_b = pd.Timestamp("20130102")
df = pd.DataFrame({"A": [value_a], "B": [value_b]})
df.replace(to_replace={value_a: None}).dtypes.drop("A")
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7adbc4d0516159cb21fa85c5a476fc67a28af917
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-74-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.0+1928.g7adbc4d05.dirty
numpy : 1.17.5
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 45.1.0.post20200119
Cython : 0.29.14
pytest : 5.3.4
hypothesis : 5.3.0
sphinx : 2.3.1
blosc : 1.8.3
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.3.4
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.14.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.47.0
</details>
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} | 6 | 2020-01-24T16:28:06Z | 2020-01-26T11:04:44Z | 2020-01-26T09:52:21Z | NONE | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I have added a shuffle method to pd.DataFrame, as writing df.sample(frac=1) is not intuitive. The method just calls df.sample with 1 line of code.
To test it, I have added a test, here is the code:
```# flake8: noqa
"""
Public testing utility functions.
"""
import pandas as pd
from pandas.util.testing import (
assert_frame_equal, assert_index_equal, assert_series_equal)
df = pd.DataFrame([[1, 2], [3, 4], [5, 6]], columns=['a', 'b'])
shuffled = df.shuffle(random_state=1)
expected = pd.DataFrame([[1, 2], [5, 6], [3, 4]], columns=['a', 'b'])
expected.set_index(pd.Series([0, 2, 1]), inplace=True)
assert_frame_equal(shuffled, expected)
```
This PR doesn't resolve any issue, I just thought a shuffle method would make a nice addition to pandas.
Also, I did not understand what you mean by 'whatsnew entry', could you please clarify? | {
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} | 5 | 2020-01-24T16:32:26Z | 2020-12-28T00:03:23Z | 2020-12-28T00:03:23Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
def f(x, y, window):
return x.rolling(window).corr(y)
def g(x, y, window):
return x.rolling(window).apply(lambda x: x.corr(y), raw=False)
if __name__ == "__main__":
N = 20
n = 10
x = pd.Series(np.random.randn(N))
y = 1.0 * x
x[0:n] = 0.
window = 4
print(f(x, y, window)[n + window - 1 :])
>>> 13 1.0
14 1.0
15 1.0
16 1.0
17 1.0
18 1.0
19 1.0
dtype: float64
print(g(x, y, window)[n + window - 1 :])
>>> 13 1.0
14 1.0
15 1.0
16 1.0
17 1.0
18 1.0
19 1.0
index = pd.date_range("2001-01-01", freq="D", periods=N)
x = pd.Series(np.random.randn(N), index=index)
y = 2.0 * x
x[0:n] = 0.
print(f(x, y, window)[n + window - 1 :])
>>> 2001-01-14 1.0
2001-01-15 1.0
2001-01-16 1.0
2001-01-17 1.0
2001-01-18 1.0
2001-01-19 1.0
2001-01-20 1.0
Freq: D, dtype: float64
print(g(x, y, window)[n + window - 1 :])
>>> 2001-01-14 1.0
2001-01-15 1.0
2001-01-16 1.0
2001-01-17 1.0
2001-01-18 1.0
2001-01-19 1.0
2001-01-20 1.0
Freq: D, dtype: float64
dt_window = pd.to_timedelta("4D")
print(f(x, y, dt_window)[n + window - 1 :])
>>> 2001-01-14 0.354308
2001-01-15 0.373106
2001-01-16 0.372752
2001-01-17 0.380531
2001-01-18 0.380298
2001-01-19 0.386142
2001-01-20 0.410147
Freq: D, dtype: float64
print(g(x, y, dt_window)[n + window - 1 :])
>>> 2001-01-14 1.0
2001-01-15 1.0
2001-01-16 1.0
2001-01-17 1.0
2001-01-18 1.0
2001-01-19 1.0
2001-01-20 1.0
Freq: D, dtype: float64
```
#### Problem description
Both functions `f` and `g` should return the same value for entries `13 - 19` in the resulting series.
Currently the result of `f` when `window = Timedelta(days=4)` is not the correlation between the values of `x` and `y` which should be equal to `1.0` for entries `13 - 19` in the result.
Computed values on a `DataFrame` are also affected, i.e.
```python
df = pd.DataFrame({"x": x, "y": y})
df.rolling(dt_window).corr()
```
does also compute unexpected values for the crosscorrelation.
If `.corr` is replaced with `.cov` in `f` and `g` both functions return identical results, so it is likely that it is caused by a difference in the normalisation in the correlation computation that is applied when using `f` and when using `g`.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
pandas : 0.25.3
numpy : 1.15.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0.post20191030
Cython : 0.29.6
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 2.2.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/31287 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31287/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31287/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31287/events | https://github.com/pandas-dev/pandas/issues/31287 | 554,852,757 | MDU6SXNzdWU1NTQ4NTI3NTc= | 31,287 | Allow .dt.month_name() and .dt.day_name() to return ordered categorical | {
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} | [] | closed | false | null | [] | null | 2 | 2020-01-24T17:06:52Z | 2020-01-24T21:42:15Z | 2020-01-24T18:38:37Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
# A data frame with a date column
dates = pd.date_range("2010-01-01", "2020-01-01", freq="1D").tolist()
groups = ["first", "second", "third"]
df = pd.DataFrame({
"date": dates * len(groups),
"group": np.repeat(groups, len(dates)).tolist(),
"value": np.random.normal(0.0, 1.0, len(groups) * len(dates))
})
# This pivot table has months alphabetically, which isn't helpful
df["month"] = df["date"].dt.month_name()
df.pivot_table("value", "month", "group")
## group first second third
## month
## April 0.100473 0.038078 -0.044501
## August 0.036322 0.092148 0.076523
## December 0.007469 -0.008177 -0.001926
## February -0.177943 -0.050950 0.079945
## January 0.034046 -0.019727 0.035488
## July -0.095040 0.123110 -0.079442
## June -0.023626 0.003913 -0.028143
## March -0.057740 0.050930 -0.059962
## May -0.110125 -0.053679 0.009097
## November -0.097954 -0.001033 0.002073
## October -0.023615 -0.008795 0.033985
## September -0.030819 -0.024159 -0.046538
# To get months chronologically, it's much more effort
month_names = ['January', 'February', 'March', 'April', 'May', 'June', 'July',
'August', 'September', 'October', 'November', 'December']
df["month2"] = pd.Categorical(
df["date"].dt.month_name(),
categories=month_names,
ordered=True)
df.pivot_table("value", "month2", "group")
## group first second third
## month2
## January 0.034046 -0.019727 0.035488
## February -0.177943 -0.050950 0.079945
## March -0.057740 0.050930 -0.059962
## April 0.100473 0.038078 -0.044501
## May -0.110125 -0.053679 0.009097
## June -0.023626 0.003913 -0.028143
## July -0.095040 0.123110 -0.079442
## August 0.036322 0.092148 0.076523
## September -0.030819 -0.024159 -0.046538
## October -0.023615 -0.008795 0.033985
## November -0.097954 -0.001033 0.002073
## December 0.007469 -0.008177 -0.001926
# What I wish I could have typed
df["month"] = df["date"].dt.month_name(ordered=True)
df.pivot_table("value", "month", "group")
```
#### Problem description
[`pandas.Series.dt.month_name()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.dt.month_name.html) returns the month names as strings, which means that they get sorted alphabetically. In most cases, that's less useful than them begin sorted from January to December. (See the pivot table generated by the code snippet as an example.)
It is possible to generate month names as an ordered categorical variable, but it feels like a lot of extra code for such a routine task.
I'd like to be able to pass an argument to `.dt.month_name()` to specify that I want ordered months, and have pandas do the work for me.
There are a few options for the user interface,; I'm not sure which is best. `ordered=True` is one possible interface. Another would be having a `sort_order` argument that could be `alphabetical` or `chronological`. A third possibility would would be `return_type` as `string` or `categorical`.
The situation is similar for [`pandas.Series.dt.day_name()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.dt.day_name.html), though that has the added complication that sometimes you want an order of Sunday -> Saturday, and sometimes you want an order of Monday -> Sunday.
#### Expected Output
Months are ordered alphabetically. This is expected, but having them ordered from January to December is more useful.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.1.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.13.3
pytz : 2017.2
dateutil : 2.7.3
pip : 19.2.3
setuptools : 44.0.0.post20200106
Cython : 0.25.2
pytest : 3.0.7
hypothesis : None
sphinx : 1.5.6
blosc : None
feather : None
xlsxwriter : 0.9.6
lxml.etree : 3.7.3
html5lib : 0.999
pymysql : 0.9.3
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 5.3.0
pandas_datareader: None
bs4 : 4.6.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 3.7.3
matplotlib : 2.0.2
numexpr : 2.6.2
odfpy : None
openpyxl : 2.4.7
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 0.19.1
sqlalchemy : 1.3.8
tables : 3.3.0
xarray : None
xlrd : 1.0.0
xlwt : 1.2.0
xlsxwriter : 0.9.6
</details>
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} | 2 | 2020-01-24T17:22:04Z | 2020-01-31T04:23:10Z | 2020-01-31T03:36:34Z | MEMBER | null | This puts us within striking distance of de-duplicating _flex_comp_method_FRAME and _comp_method_FRAME, with _arith_method_FRAME not far behind | {
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} | 1 | 2020-01-24T17:26:08Z | 2020-01-24T17:27:33Z | 2020-01-24T17:27:21Z | CONTRIBUTOR | null | If a user has configured warnings from NumPy to raise, and performs a `groupby.cov` when one or more groups has a single operation, then they'll get the wrong result.
```python
In [1]: import warnings
In [2]: warnings.filterwarnings("error", category=RuntimeWarning)
In [3]: import pandas as pd; import numpy as np
In [4]: data = np.random.randn(20, 3)
In [5]: key = [2, 2, 2, 2, 2, 0, 2, 0, 0, 0, 2, 0, 2, 2, 2, 2, 2, 1, 0, 2]
In [7]: df = pd.DataFrame(data).assign(key=key)
In [8]: df.groupby("key").cov()
Out[8]:
Empty DataFrame
Columns: []
Index: []
```
I would expect
```python
In [6]: df.groupby("key").cov()
Out[6]:
0 1 2
key
0 0 0.399063 0.187969 -0.081610
1 0.187969 0.912905 -0.037523
2 -0.081610 -0.037523 0.306056
1 0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
2 0 0.653468 -0.007848 -0.213145
1 -0.007848 0.460083 -0.153129
2 -0.213145 -0.153129 0.662283
```
We should probably be robust to the warning setting, or resurface the exception.
This is with pandas 0.25.3 | {
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} | 7 | 2020-01-24T18:18:11Z | 2020-01-28T12:41:00Z | 2020-01-28T01:52:09Z | CONTRIBUTOR | null | xref https://github.com/pandas-dev/pandas/issues/30944.
I think this doesn't close it, since only the pd.cut compoment
is fixed.
cc @jorisvandenbossche @jreback. The changes here attempt to be extremely conservative, since we're backporting stuff. I'm trying to not change behavior for anything other than IntegerArray. In particular, I'm not trying to support arbitrary EAs in `pd.cut`. This leads to some code that's fairly ugly & specific to IntegerArray. I think we should attempt to clean that up in 1.1. | {
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} | [] | closed | false | null | [] | null | 1 | 2020-01-24T18:20:56Z | 2020-01-26T01:38:02Z | 2020-01-26T01:38:02Z | MEMBER | null | The docstring and doctest say that lists are considered iterators, but `isiterator([1, 2, 3])` (one of the docstring examples) returns False (which I think is a more useful behavior.
Is this doctest not run? | {
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} | 4 | 2020-01-24T18:22:05Z | 2020-01-30T12:42:20Z | 2020-01-25T15:37:59Z | MEMBER | null | - [x] closes #31281
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"description": "np.nan, ... | open | false | null | [] | null | 4 | 2020-01-24T19:35:20Z | 2020-04-28T06:03:02Z | null | MEMBER | null | Currently, our nullable / masked extension arrays (boolean, integer, for now) are using a numpy boolean array as their `_mask` to keep track of missing values. A potential route for improving memory and performance would be using a bitarray instead of a boolean numpy array (which is a byte per value).
This should require some exploration: what are options how to implement this? (existing libraries, custom implementation) What is the performance impact? (some things like masking will also be slower, since we still rely on numpy for that, which needs boolean arrays) Is this worth it to do a custom implementation rather than using pyarrow for this? etc | {
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} | 3 | 2020-01-24T19:58:26Z | 2020-05-11T13:56:13Z | 2020-01-26T00:34:33Z | MEMBER | null | While working on #30349 I noticed that `is_scalar` is pretty slow for non-scalar args, turns out we can get a 9-19x improvement for listlike cases, 5-10x improvement for Decimal/Period/DateOffset/Interval, with small improvements in nearly every other case while we're at it (the fractions.Fraction object is the only one where i found a small decrease in perf, within the margin of error)
xref #31291, assuming this is the desired behavior for is_iterator, this closes that.
Setup:
```
import pandas as pd
from pandas.core.dtypes.common import *
import decimal, fractions
dec = decimal.Decimal(19.45678)
frac = fractions.Fraction(1)
i64 = np.int64(-1)
arr = np.arange(5)
ser = pd.Series(arr)
idx = pd.Index(arr)
interval = pd.Interval(0, 1)
per = pd.Period("2017")
offset = per - per
iterator = (x for x in [])
```
Non-Scalar Cases
```
In [6]: %timeit is_scalar([])
1.53 µs ± 36.4 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <-- master
79.7 ns ± 5.26 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- PR
In [7]: %timeit is_scalar(arr)
1.58 µs ± 43.8 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <-- master
77.6 ns ± 5.02 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- PR
In [8]: %timeit is_scalar(ser)
1.01 µs ± 51.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <-- master
92.3 ns ± 0.505 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- PR
In [9]: %timeit is_scalar(idx)
876 ns ± 4.86 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <-- master
92.7 ns ± 1.47 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- PR
In [10]: %timeit is_scalar(iterator)
2.08 µs ± 22.9 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <-- master
869 ns ± 20.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <-- PR
```
Non-Built-In Scalar Cases
```
In [10]: %timeit is_scalar(dec)
1.05 µs ± 9.75 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <-- master
79.5 ns ± 2.16 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- PR
In [12]: %timeit is_scalar(interval)
748 ns ± 22.6 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <-- master
139 ns ± 5.38 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- PR
In [13]: %timeit is_scalar(per)
706 ns ± 24.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <-- master
127 ns ± 14.2 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- PR
In [14]: %timeit is_scalar(offset)
930 ns ± 40.6 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <-- master
211 ns ± 2.45 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <-- PR
```
Built-In Scalar Cases
```
In [3]: %timeit is_scalar(4)
60.1 ns ± 2.39 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- master
54.9 ns ± 1.15 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- PR
In [4]: %timeit is_scalar(4.0)
53 ns ± 4.94 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- master
48.2 ns ± 0.613 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- PR
In [5]: %timeit is_scalar(i64)
58.5 ns ± 1.39 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- master
58.1 ns ± 5.88 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- PR
In [11]: %timeit is_scalar(frac)
93.5 ns ± 2.05 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- master
94.1 ns ± 6.4 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- PR
```
is_iterator check
```
In [16] %timeit is_iterator(iterator)
283 ns ± 5.35 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <-- master
58.9 ns ± 2.31 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- PR
In [17] %timeit is_iterator(arr)
228 ns ± 3.98 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <-- master
55 ns ± 3.53 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- PR
In [17] %timeit is_iterator("foo")
231 ns ± 15.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <-- master
49.4 ns ± 5.08 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- PR
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- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 7 | 2020-01-24T22:57:15Z | 2020-03-08T16:28:25Z | 2020-03-08T16:07:04Z | MEMBER | null | Also fixes the same bug as #31271 (with the same test ported), so if this is accepted that will be closeable.
~2000x speedups for very-wide mixed-dtype cases.
```
arr = np.arange(10**6).reshape(100, -1)
df = pd.DataFrame(arr)
df["C"] = 1.0
ser = df[0]
In [11]: %timeit df.eq(ser, axis=0)
1.58 s ± 20 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # <-- master
595 µs ± 79.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) <-- PR
In [12]: %timeit df.add(ser, axis=0)
2.06 s ± 52.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # <-- master
1.04 ms ± 5.07 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- PR
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} | 6 | 2020-01-24T23:11:03Z | 2020-02-23T17:13:37Z | 2020-02-23T16:18:50Z | MEMBER | null | This doesn't get all of the places where we fail to pass str_rep, still working out a couple of places where passing it breaks tests. | {
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"description": "Apply, Aggregate, Transf... | open | false | null | [] | null | 3 | 2020-01-25T01:10:47Z | 2020-09-05T19:11:16Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame( {"col_1" : [1, 2, 3, 4, 5],
"col_2" : [3, 5, 3, 4, 9],
"col_3" : ["cat", "dog", "dog", "cat", "cat"],
"col_4" : ["feline", "canine", "canine", "feline", "feline"]})
func_list = [lambda x : len(x),
lambda x : x.isnull().sum()]
num_col = df.select_dtypes(include="number").columns
cat_col = df.select_dtypes(include="object").columns
#works correctly for numeric columns
print(df[num_col].agg(func_list))
print(df[cat_col].agg(func_list))
```
#### Problem description
There is an issue with aggregating and applying a list of functions. These functions should be able to be applied to categorical or object columns but cannot currently.
#### Expected Output
I would expect the output for the categorical and numeric columns to be the same with these simple functions.
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 19.0.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.9
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
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} | 2 | 2020-01-25T01:52:15Z | 2020-02-09T17:08:54Z | 2020-02-09T17:08:54Z | MEMBER | null | One of the more convoluted parts of `Series.__getitem__` is for handling single-entry lists that contain a slice, where we end up just returning `self[key[0]]`. We have two tests that hit this path, but it isn't clear to me why this should be supported.
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} | 4 | 2020-01-25T02:01:57Z | 2020-01-25T16:28:10Z | 2020-01-25T16:20:21Z | MEMBER | null | This has a small perf bump, but more importantly is necessary for the blockwise frame-with-frame PR that is coming up.
```
import pandas as pd
from pandas.core.ops import *
arr = np.arange(10**6)
df = pd.DataFrame({"A": arr})
ser = df["A"]
%timeit result = df.add(df, fill_value=4)
7.77 ms ± 20.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- master
5.46 ms ± 36.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- PR
%timeit result = ser.add(ser, fill_value=1)
6.79 ms ± 56.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- master
4.65 ms ± 82.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- PR
```
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} | 4 | 2020-01-25T02:37:01Z | 2020-01-25T16:04:55Z | 2020-01-25T16:01:40Z | MEMBER | null | its not used anywhere, apparently legacy leftover | {
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} | 0 | 2020-01-25T02:41:42Z | 2020-10-02T20:35:11Z | 2020-10-02T20:35:11Z | MEMBER | null | Following on discussion happened in #30923, we may want to change the default behavior of `rolling.count` with regards to its parameter ``min_periods``, so it is consistent with all other similar APIs such as `rolling.mean` and `rolling.sum`.
#### Code Sample
With the updates from #30923
```python
>>> import numpy as np
>>> import pandas as pd
>>> s = pd.Series([1, 1, 1, np.nan, 1, 1, 1])
>>> s
0 1.0
1 1.0
2 1.0
3 NaN
4 1.0
5 1.0
6 1.0
dtype: float64
# rolling.mean and rolling.sum defaults min_periods to the same value as window size (3 in this case)
# notice that it requires not only the window size to be at least 3, but also the number of valid entries (not NaN) to be at least 3
>>> s.rolling(3).mean()
0 NaN
1 NaN
2 1.0
3 NaN
4 NaN
5 NaN
6 1.0
dtype: float64
>>> s.rolling(3).sum()
0 NaN
1 NaN
2 3.0
3 NaN
4 NaN
5 NaN
6 3.0
dtype: float64
# the default value of min_periods for rolling.count is 0
# we may want to change this behavior so it's consistent with other APIs
>>> s.rolling(3).count()
0 1.0
1 2.0
2 3.0
3 2.0
4 2.0
5 2.0
6 3.0
dtype: float64
# notice that rolling.count requires window size to be at least equal to min_periods to give a result
# it doesn't care about how many valid entries (not NaN) to determine if it should output NaN
# we should retain this behavior because this function is meant to count the number of valid entries
>>> s.rolling(3, min_periods=3).count()
0 NaN
1 NaN
2 3.0
3 2.0
4 2.0
5 2.0
6 3.0
dtype: float64
```
#### Expected Output
```python
>>> s.rolling(3).count()
0 NaN
1 NaN
2 3.0
3 2.0
4 2.0
5 2.0
6 3.0
dtype: float64
```
#### Problem description
With the updates from #30923, the ``min_periods`` argument of `rolling.count` is now respected (it used to be completely ignored). However, the default value remains 0 for backward compatibility purpose. In future updates we probably want to change this default behavior so it's consistent with other similar APIs.
@mroeschke previously mentioned we needed to start with a ``DeprecationWarning`` to inform users of future changes, then probably in the following release make the actual change.
@jreback @WillAyd
Let me know what you guys think!
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https://api.github.com/repos/pandas-dev/pandas/issues/31303 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31303/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31303/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31303/events | https://github.com/pandas-dev/pandas/issues/31303 | 555,032,684 | MDU6SXNzdWU1NTUwMzI2ODQ= | 31,303 | applymap fails in presence of nan values and function returning None | {
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] | closed | false | null | [] | null | 1 | 2020-01-25T02:41:59Z | 2020-01-26T03:44:45Z | 2020-01-26T02:58:40Z | NONE | null | Is this the proper behavior?
```python
df = pd.DataFrame([{'a': np.nan}, {'a': 3.5}])
def convert_cell(x):
if isinstance(x, float):
if math.isnan(x):
return None
else:
return round(x)
else:
return x
df.applymap(convert_cell)
# gives
# a
#0 NaN
#1 4.0
[ convert_cell(x) for x in [np.nan, 3.5] ]
# gives
# [None, 4]
```
If we replace `None` with say, -1, in `convert_cell` it works as expected. If all values in the column are nan, it works. If all values are non-nan floats it works. If `convert_cell = lambda x: None`, that works.
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-37-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_CA.UTF-8
LOCALE : en_CA.UTF-8
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 9.0.1
setuptools : 39.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.2
html5lib : 0.999999999
pymysql : 0.8.0
psycopg2 : None
jinja2 : 2.10
IPython : 7.3.0
pandas_datareader: None
bs4 : 4.6.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.2
matplotlib : 3.0.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.1.11
tables : None
xarray : 0.14.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : None
```
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} | 1 | 2020-01-25T04:36:10Z | 2020-01-26T00:46:49Z | 2020-01-26T00:40:31Z | MEMBER | null | Since ix has been removed, a bunch of _NDFrameIndexer methods are defined in now-weird places. This fixes the one that confused me today. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/31305 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31305/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31305/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31305/events | https://github.com/pandas-dev/pandas/pull/31305 | 555,042,558 | MDExOlB1bGxSZXF1ZXN0MzY3MDg3NDgz | 31,305 | POC: use typing.final | {
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} | 5 | 2020-01-25T04:49:23Z | 2020-02-28T00:18:52Z | 2020-01-31T01:02:02Z | MEMBER | null | When working on index/indexing code I frequently find myself checking whether a method is overriden in subclasses. `@typing.final` marks a method as not-overriden in a way that mypy can check (py38+). This imports typing.final in py38+ and makes a dummy otherwise.
We'd need to make sure the CI runs mypy in py38. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/31306 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31306/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31306/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31306/events | https://github.com/pandas-dev/pandas/issues/31306 | 555,053,100 | MDU6SXNzdWU1NTUwNTMxMDA= | 31,306 | NaT values cannot be compared with tz-aware datetime objects | {
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} | 11 | 2020-01-25T07:10:18Z | 2020-01-26T02:10:56Z | 2020-01-26T01:08:24Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas
import datetime
a = pandas.DataFrame({'b': [pandas.NaT]})
a.b > datetime.datetime.now(datetime.timezone.utc)
```
Output:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../pandas/core/ops/__init__.py", line 1179, in wrapper
res_values = dispatch_to_index_op(op, self, other, pd.DatetimeIndex)
File ".../pandas/core/ops/__init__.py", line 630, in dispatch_to_index_op
result = op(left_idx, right)
File ".../pandas/core/indexes/datetimelike.py", line 132, in wrapper
result = op(self._data, maybe_unwrap_index(other))
File ".../pandas/core/arrays/datetimes.py", line 168, in wrapper
self._assert_tzawareness_compat(other)
File ".../pandas/core/arrays/datetimes.py", line 789, in _assert_tzawareness_compat
"Cannot compare tz-naive and tz-aware " "datetime-like objects."
TypeError: Cannot compare tz-naive and tz-aware datetime-like objects.
```
#### Problem description
When parsing timestamps, I return `pandas.NaT` if value is not parsed correctly. Otherwise I return timezone-aware timestamps. I also want then to compare those values with a timezone-aware timestamp. But if there is any `pandas.NaT` value present comparison fails.
#### Expected Output
```
0 False
Name: b, dtype: bool
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-37-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.2
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.0.3
setuptools : 40.4.3
Cython : 0.29.10
pytest : 4.4.1
hypothesis : None
sphinx : 1.8.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.2.5
html5lib : 0.9999999
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : None
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.5
matplotlib : 3.0.0
numexpr : 2.6.9
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/31307 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31307/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31307/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31307/events | https://github.com/pandas-dev/pandas/pull/31307 | 555,081,140 | MDExOlB1bGxSZXF1ZXN0MzY3MTE0NjA4 | 31,307 | Add test for multiindex json | {
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} | 3 | 2020-01-25T12:42:39Z | 2020-01-31T09:03:28Z | 2020-01-25T16:19:06Z | CONTRIBUTOR | null | - [x] related to #31028 and adds test for #27618
- [x] tests added / passed
- [x] passes `black pandas`
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https://api.github.com/repos/pandas-dev/pandas/issues/31308 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31308/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31308/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31308/events | https://github.com/pandas-dev/pandas/issues/31308 | 555,089,802 | MDU6SXNzdWU1NTUwODk4MDI= | 31,308 | pd.concat inconsistent with non-unique index | {
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"description": "Concat, Merge/Join, Stac... | open | false | null | [] | null | 3 | 2020-01-25T14:10:14Z | 2021-07-27T05:19:13Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
Following works:
```py
df4 = pd.DataFrame([['bird', 'polly'], ['monkey', 'george'], ['a', 'b']],
columns=['animal', 'name'])
df1 = pd.DataFrame([['a', 1], ['b', 2]],
columns=['letter', 'number'])
df4.index = [0, 1, 1]
pd.concat([df1, df4], axis=1)
letter number animal name
0 a 1 bird polly
1 b 2 monkey george
1 b 2 a b
```
But if the order of index values is the opposite, it raises ValueError:
```py
df4 = pd.DataFrame([['bird', 'polly'], ['monkey', 'george'], ['a', 'b']],
columns=['animal', 'name'])
df1 = pd.DataFrame([['a', 1], ['b', 2]],
columns=['letter', 'number'])
df4.index = [1, 0, 0]
pd.concat([df1, df4], axis=1)
ValueError: Shape of passed values is (3, 4), indices imply (2, 4)
```
#### Problem description
As shown in the example, `concat` behaves inconsistently if index has duplicated value. It depends on the order of values if it succeeds or not. It is causing issues in geopandas (https://github.com/geopandas/geopandas/issues/1223). I know that duplicated index is not ideal thing to have, but this seems to be a bug.
#### Expected Output
Expected output would be concatenated gdf as in the example with sorted index.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.0.final.0
python-bits : 64
OS : Darwin
OS-release : 19.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2.post20191201
Cython : None
pytest : 5.3.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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} | 0 | 2020-01-25T16:17:07Z | 2020-01-26T00:26:04Z | 2020-01-26T00:26:04Z | CONTRIBUTOR | null | this is failing builds occasionally. not sure if its a deeper problem. can just xfail for now and investigate.
https://travis-ci.org/pandas-dev/pandas/jobs/641752516
```
=================================== FAILURES ===================================
__________________________ test_legacy_sparse_warning __________________________
[gw0] linux -- Python 3.6.10 /home/travis/miniconda3/envs/pandas-dev/bin/python
datapath = <function datapath.<locals>.deco at 0x7fd87cf76268>
def test_legacy_sparse_warning(datapath):
"""
Generated with
>>> df = pd.DataFrame({"A": [1, 2, 3, 4], "B": [0, 0, 1, 1]}).to_sparse()
>>> df.to_pickle("pandas/tests/io/data/pickle/sparseframe-0.20.3.pickle.gz",
... compression="gzip")
>>> s = df['B']
>>> s.to_pickle("pandas/tests/io/data/pickle/sparseseries-0.20.3.pickle.gz",
... compression="gzip")
"""
with tm.assert_produces_warning(FutureWarning):
simplefilter("ignore", DeprecationWarning) # from boto
pd.read_pickle(
datapath("io", "data", "pickle", "sparseseries-0.20.3.pickle.gz"),
> compression="gzip",
)
pandas/tests/io/test_pickle.py:218:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <contextlib._GeneratorContextManager object at 0x7fd87ceed6a0>
type = None, value = None, traceback = None
def __exit__(self, type, value, traceback):
if type is None:
try:
> next(self.gen)
E AssertionError: Caused unexpected warning(s): [('ResourceWarning', ResourceWarning("unclosed <socket.socket fd=18, family=AddressFamily.AF_INET, type=2049, proto=6, laddr=('10.20.0.67', 37224), raddr=('74.125.202.84', 443)>",), '/home/travis/miniconda3/envs/pandas-dev/lib/python3.6/inspect.py', 732), ('ResourceWarning', ResourceWarning("unclosed <socket.socket fd=19, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=6, laddr=('10.20.0.67', 46940), raddr=('172.217.214.95', 443)>",), '/home/travis/miniconda3/envs/pandas-dev/lib/python3.6/inspect.py', 732)]
../../../miniconda3/envs/pandas-dev/lib/python3.6/contextlib.py:88: AssertionError
=============================== warnings summary ===============================
/home/travis/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/fastparquet/dataframe.py:5
/home/travis/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/fastparquet/dataframe.py:5
/home/travis/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/fastparquet/dataframe.py:5: FutureWarning: pandas.core.index is deprecated and will be removed in a future version. The public classes are available in the top-level namespace.
from pandas.core.index import CategoricalIndex, RangeIndex, Index, MultiIndex
/home/travis/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/statsmodels/tools/_testing.py:19
/home/travis/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
import pandas.util.testing as tm
``` | {
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} | 1 | 2020-01-25T17:36:58Z | 2020-01-26T00:26:20Z | 2020-01-26T00:26:02Z | CONTRIBUTOR | null | xref #31310
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} | 7 | 2020-01-25T18:25:24Z | 2020-02-12T16:04:32Z | 2020-02-12T16:04:23Z | CONTRIBUTOR | null | - [x] closes #31325
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
The intersection of 2 `MultiIndex` with `sort=False` does not preserve the order, whereas `Index. intersection()` does. This behavior does not seem to be intentional since the tests for `difference` and `symmetric_difference` are testing for order preservation.
For example:
```python
import pandas as pd
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
tuples = list(zip(*arrays))
idx = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
left = idx[2::-1]
print(left)
#> MultiIndex([('baz', 'one'),
#> ('bar', 'two'),
#> ('bar', 'one')],
#> names=['first', 'second'])
right = idx[:5]
print(right)
#> MultiIndex([('bar', 'one'),
#> ('bar', 'two'),
#> ('baz', 'one'),
#> ('baz', 'two'),
#> ('foo', 'one')],
#> names=['first', 'second'])
# expected same order as left
intersect = left.intersection(right, sort=False)
print(intersect)
#> MultiIndex([('bar', 'two'),
#> ('baz', 'one'),
#> ('bar', 'one')],
#> names=['first', 'second'])
```
<sup>Created on 2020-01-25 by the [reprexpy package](https://github.com/crew102/reprexpy)</sup>
I verified that my PR does not decrease performances.
I also modified the test for `union`. The implementation was preserving the order with `sort=False` but the tests were not verifying it. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/31313 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31313/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31313/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31313/events | https://github.com/pandas-dev/pandas/issues/31313 | 555,145,864 | MDU6SXNzdWU1NTUxNDU4NjQ= | 31,313 | Series logical operations with nullable Boolean data type | {
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} | [] | closed | false | null | [] | null | 1 | 2020-01-25T22:14:51Z | 2020-01-25T22:26:00Z | 2020-01-25T22:26:00Z | MEMBER | null | Should the `Series` logical operations also follow three-valued logic when using the nullable boolean data type? For example I would expect the two cases below to return `pd.NA`:
```python
import pandas as pd
pd.Series([True, None], dtype="boolean").all()
# True
pd.Series([False, None], dtype="boolean").any()
# False
pd.__version__
# '1.0.0rc0+198.g5ecd94c72'
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/31314 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31314/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31314/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31314/events | https://github.com/pandas-dev/pandas/pull/31314 | 555,147,771 | MDExOlB1bGxSZXF1ZXN0MzY3MTYxMzk2 | 31,314 | REF: DatetimeIndex.get_value wrap DTI.get_loc | {
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} | 1 | 2020-01-25T22:33:42Z | 2020-01-26T00:38:36Z | 2020-01-26T00:23:57Z | MEMBER | null | After this, DTI.get_value is identical to TDI.get_value. Separate PR to get PeriodIndex to match too, then de-duplicate. | {
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