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REGR: read_excel does not work for most file handles
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- [x] closes #38788
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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CI,STYLE: GH38802 narrow down list of ignored words
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ENH: concat & merge: return True if left DataFrame is modified
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|
NONE
| null |
Hi,
This ticket is to propose 2 changes:
1st change: make available `concat` as method of DataFrame (similar to `merge`)
Concat could accept a list of DataFrame, similar to `pd.concat`.
2nd change: whichever `pandas.DataFrame.merge` or (newly implemented) `pandas.DataFrame.concat`
- modify left DataFrame in place
- return a boolean:
- `True`if left DataFrame has actually been modified
- `False`if left DataFrame has actually not been modified
Such a feature would prevent the user to make a check `pandas.DataFrame.equals()` to know if the left DataFrame has been modified or not.
Personnally, in my case, the left DataFrame can be a DataFrame I am just reading from a data file, and the right DataFrame, some 'new data' I am thinking it is new and needs to be merged/concatenated to the existing one.
If my merge/concat processing do not result in any change, I will not overwrite the same DataFrame on disk (useless: it has not been modified)
Could this be considered?
Thanks for your help.
Bests
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CLN: Simplify inspect_excel_format
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| 2020-12-30T19:21:48Z
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|
MEMBER
| null |
The function `io.excel._base.inspect_excel_format` does not need to take two parameters `path` and `content` since either is passed to get_handle. It should instead take a single parameter, `path_or_buf`.
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| 2021-01-08T21:39:23Z
| 2021-01-08T21:39:23Z
|
MEMBER
| null |
ref: #38819
The function `io.excel._base.inspect_excel_format` does not need to take two parameters `path` and `content` since either is passed to get_handle. It should instead take a single parameter, `path_or_buf`.
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MDExOlB1bGxSZXF1ZXN0NTQ3MDQwNTUy
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TST: add missing loc label indexing test
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| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NTQ3MDQxNTUw
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TST: GH30999 Change all pytest.raises in pandas/tests/indexing to tm.external_error_raised
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| 1
| 2020-12-30T18:18:50Z
| 2020-12-31T16:40:17Z
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|
MEMBER
| null |
Addresses xref #30999 for pandas/tests/indexing by changing three bare `pytest.raise` instances to `tm.external_error_raised`
This is the last of the simpler PRs for #30999 - after that it gets more complex.
- [ ] closes #xxxx
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0NTQ3MDQ0MTAw
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CLN: add typing to dtype arg in selection of files in core/arrays (GH38808)
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CONTRIBUTOR
| null |
Follow on PR for #38808
- [ ] closes #xxxx
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ENH: Expanded display of dataframe, akin to postgres \x
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| 2021-08-14T22:57:13Z
| 2021-08-14T22:57:13Z
|
NONE
| null |
#### Is your feature request related to a problem?
When viewing large dataframes in a terminal, it is often preferable to display the data in "expanded format" or "long format" (as opposed to "wide"), essentially the result of melting every column. This makes the entire dataframe less sensitive to the width of the terminal.
#### Describe the solution you'd like
Postgres handles this nicely with its expanded mode (see [-x or --expanded](https://www.postgresql.org/docs/current/app-psql.html)). To take a random postgres example, the following dataframe
```
id | time | humanize_time | value
----+-------+---------------------------------+-------
1 | 09:30 | Early Morning - (9.30 am) | 570
2 | 11:30 | Late Morning - (11.30 am) | 690
3 | 13:30 | Early Afternoon - (1.30pm) | 810
4 | 15:30 | Late Afternoon - (3.30 pm) | 930
(4 rows)
```
becomes printed as
```
-[ RECORD 1 ]-+---------------------------
id | 1
time | 09:30
humanize_time | Early Morning - (9.30 am)
value | 570
-[ RECORD 2 ]-+---------------------------
id | 2
time | 11:30
humanize_time | Late Morning - (11.30 am)
value | 690
-[ RECORD 3 ]-+---------------------------
id | 3
time | 13:30
humanize_time | Early Afternoon - (1.30pm)
value | 810
-[ RECORD 4 ]-+---------------------------
id | 4
time | 15:30
humanize_time | Late Afternoon - (3.30 pm)
value | 930
```
#### API breaking implications
I don't see there being any.
#### Additional context
My naive suggestion would be to place a global option that turns this on, such as
```python
pd.set_option('display.expanded_mode', True)
```
Under the hood, this can be as simple as melting the dataframe, and printing each record out with a separator between them. However I am not familiar with the intricacies of the display logic and leave this here for others' consideration.
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MDExOlB1bGxSZXF1ZXN0NTQ3MDQ3ODI3
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Backport PR #38789 on branch 1.2.x (BUG: Fix precise_xstrtod segfault on long exponent)
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| 0
| 2020-12-30T18:41:31Z
| 2020-12-30T20:28:25Z
| 2020-12-30T20:28:25Z
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NONE
| null |
Backport PR #38789: BUG: Fix precise_xstrtod segfault on long exponent
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Parametrized groupby allowlist test
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| 3
| 2020-12-30T19:52:09Z
| 2020-12-31T01:48:56Z
| 2020-12-31T01:48:53Z
|
MEMBER
| null |
Still exploring a few things to remove setting the .data pointer in Cython - this test is liable to fail but very tough to debug with current loop
@jbrockmendel
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BUG: Misaligned columns from read_csv on Ecobee CVS
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| 4
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| 2021-01-06T22:56:26Z
| 2021-01-06T22:56:26Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.read_csv("pdbug.csv", comment="#")
df.info()
print(df)
```
with `pdbug.csv` being this:
```csv
#,Thermostat,identifier,(REDACTED)
#,Thermostat,name,Downstairs
#,Start,date,2020-12-22
#,End,date,2020-12-30
Date,Time,System Setting,System Mode,Calendar Event,Program Mode,Cool Set Temp (F),Heat Set Temp (F),Current Temp (F),Current Humidity (%RH),Outdoor Temp (F),Wind Speed (km/h),Cool Stage 1 (sec),Heat Stage 1 (sec),Fan (sec),DM Offset,Thermostat Temperature (F),Thermostat Humidity (%RH),Study (F),Study2,Breakfast Nook (F),Breakfast Nook2
2020-12-22,02:55:00,heat,heatOff,,Sleep,85,61,67.4,40,30.8,0,0,0,0,,67.4,40,,,,,
2020-12-22,03:00:00,heat,heatOff,,Sleep,85,61,67.3,40,29.6,0,0,0,0,,67.3,40,,,,,
2020-12-22,03:05:00,heat,heatOff,,Sleep,85,61,67.2,40,29.6,0,0,0,180,,67.2,40,,,,,
2020-12-22,03:10:00,heat,heatOff,,Sleep,85,61,67.1,42,29.6,0,0,0,120,,67.1,42,,,,,
2020-12-22,03:15:00,heat,heatOff,,Sleep,85,61,67.1,40,29.6,0,0,0,0,,67.1,40,,,,,
2020-12-22,03:20:00,heat,heatOff,,Sleep,85,61,67,40,29.6,0,0,0,0,,67,40,,,,,
2020-12-22,03:25:00,heat,heatOff,,Sleep,85,61,67,40,29.6,0,0,0,0,,67,40,,,,,
2020-12-22,03:30:00,heat,heatOff,,Sleep,85,61,66.9,40,29,0,0,0,0,,66.9,40,,,,,
```
(a slight edit of a CSV file of HVAC system performance data downloaded from https://ecobee.com)
gives
```
<class 'pandas.core.frame.DataFrame'>
Index: 8 entries, 2020-12-22 to 2020-12-22
Data columns (total 22 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Date 8 non-null object
1 Time 8 non-null object
2 System Setting 8 non-null object
3 System Mode 0 non-null float64
4 Calendar Event 8 non-null object
5 Program Mode 8 non-null int64
6 Cool Set Temp (F) 8 non-null int64
7 Heat Set Temp (F) 8 non-null float64
8 Current Temp (F) 8 non-null int64
9 Current Humidity (%RH) 8 non-null float64
10 Outdoor Temp (F) 8 non-null int64
11 Wind Speed (km/h) 8 non-null int64
12 Cool Stage 1 (sec) 8 non-null int64
13 Heat Stage 1 (sec) 8 non-null int64
14 Fan (sec) 0 non-null float64
15 DM Offset 8 non-null float64
16 Thermostat Temperature (F) 8 non-null int64
17 Thermostat Humidity (%RH) 0 non-null float64
18 Study (F) 0 non-null float64
19 Study2 0 non-null float64
20 Breakfast Nook (F) 0 non-null float64
21 Breakfast Nook2 0 non-null float64
dtypes: float64(10), int64(8), object(4)
memory usage: 1.4+ KB
Date Time System Setting System Mode Calendar Event Program Mode Cool Set Temp (F) Heat Set Temp (F) Current Temp (F) Current Humidity (%RH) Outdoor Temp (F) Wind Speed (km/h) Cool Stage 1 (sec) Heat Stage 1 (sec) Fan (sec) DM Offset Thermostat Temperature (F) Thermostat Humidity (%RH) Study (F) Study2 Breakfast Nook (F) Breakfast Nook2
2020-12-22 02:55:00 heat heatOff NaN Sleep 85 61 67.4 40 30.8 0 0 0 0 NaN 67.4 40 NaN NaN NaN NaN NaN
2020-12-22 03:00:00 heat heatOff NaN Sleep 85 61 67.3 40 29.6 0 0 0 0 NaN 67.3 40 NaN NaN NaN NaN NaN
2020-12-22 03:05:00 heat heatOff NaN Sleep 85 61 67.2 40 29.6 0 0 0 180 NaN 67.2 40 NaN NaN NaN NaN NaN
2020-12-22 03:10:00 heat heatOff NaN Sleep 85 61 67.1 42 29.6 0 0 0 120 NaN 67.1 42 NaN NaN NaN NaN NaN
2020-12-22 03:15:00 heat heatOff NaN Sleep 85 61 67.1 40 29.6 0 0 0 0 NaN 67.1 40 NaN NaN NaN NaN NaN
2020-12-22 03:20:00 heat heatOff NaN Sleep 85 61 67.0 40 29.6 0 0 0 0 NaN 67.0 40 NaN NaN NaN NaN NaN
2020-12-22 03:25:00 heat heatOff NaN Sleep 85 61 67.0 40 29.6 0 0 0 0 NaN 67.0 40 NaN NaN NaN NaN NaN
2020-12-22 03:30:00 heat heatOff NaN Sleep 85 61 66.9 40 29.0 0 0 0 0 NaN 66.9 40 NaN NaN NaN NaN NaN
```
#### Problem description
Observe that the "Date" column absorbs both the date and time values provided, and that the "Time" column returend by read_csv contains data intended for the next column over ("System Setting"). The source CSV seems sane, but the `read_csv` output does not.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.9.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.9.0-5-amd64
Version : #1 SMP Debian 5.9.15-1 (2020-12-17)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 1.2.0
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.1
setuptools : 44.0.0
Cython : 0.29.21
pytest : 6.2.0
hypothesis : None
sphinx : 3.3.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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| 776,619,746
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MDU6SXNzdWU3NzY2MTk3NDY=
| 38,831
|
BUG: frame_length_0[key] = nonempty_frame raises ValueError
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| 0
| 2020-12-30T20:15:05Z
| 2021-01-06T00:35:38Z
| 2021-01-06T00:35:38Z
|
MEMBER
| null |
```
df = pd.DataFrame(columns=["A", "B"])
other = pd.DataFrame({"B": [1, 2]})
>>> df["B"] = other
ValueError: Cannot set a frame with no defined index and a value that cannot be converted to a Series
>>> df[["B"]] = other # <-- works as expected
```
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MDExOlB1bGxSZXF1ZXN0NTQ3MDg4OTcw
| 38,832
|
Backport PR #38819 on branch 1.2.x (REGR: read_excel does not work for most file handles)
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| 0
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NONE
| null |
Backport PR #38819: REGR: read_excel does not work for most file handles
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|
REF: move putmask internals in array_algos.putmask
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| 0
| 2020-12-30T22:25:53Z
| 2020-12-31T01:59:44Z
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|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NTQ3MTEwNzUy
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|
BUG: IntervalIndex.intersection returning duplicates
|
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| 3
| 2020-12-30T22:46:08Z
| 2021-01-01T21:33:37Z
| 2020-12-31T01:50:22Z
|
MEMBER
| null |
- [x] closes #38743
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Moved the wrong test code into a new test with adjusted behavior
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MDExOlB1bGxSZXF1ZXN0NTQ3MTE2MTA2
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|
BUG: astype_nansafe with copy=False
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| 2020-12-30T23:13:03Z
| 2020-12-31T01:57:55Z
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MEMBER
| null |
- [x] closes #34456
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Opens up possibility of addressing #23092
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MDExOlB1bGxSZXF1ZXN0NTQ3MTE4NDIw
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DEPR: try_cast kwarg in mask, where
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| 2020-12-30T23:23:04Z
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MEMBER
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- [ ] closes #xxxx
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It is silently ignored ATM.
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MDExOlB1bGxSZXF1ZXN0NTQ3MTIwNjE5
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DOC: create shared includes for comparison docs, take II
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| 2021-01-03T19:39:32Z
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|
MEMBER
| null |
This is a duplicate of https://github.com/pandas-dev/pandas/pull/38771, but instead of defining `:tips:` in a `:suppress:` block, it attempts to tell flake8-rst to ignore those warnings. In other words, only one of these pull requests should be merged; the other can be closed.
- [ ] ~~closes #xxxx~~
- [ ] tests added / passed
- [ ] ~~passes `black pandas`~~
- [ ] ~~passes `git diff upstream/master -u -- "*.py" | flake8 --diff`~~
- [ ] ~~whatsnew entry~~
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MDExOlB1bGxSZXF1ZXN0NTQ3MTIxMzAz
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|
Backport PR #38816 on branch 1.2.x (REGR: groupby.sem with nuisance columns)
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| 2020-12-30T23:39:23Z
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NONE
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Backport PR #38816: REGR: groupby.sem with nuisance columns
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MDExOlB1bGxSZXF1ZXN0NTQ3MTI4MjA3
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DOC: fix includes
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| 3
| 2020-12-31T00:10:27Z
| 2021-01-01T21:14:25Z
| 2020-12-31T16:39:16Z
|
MEMBER
| null |
- Added missing `.` to the `.. include`s
- Put the includes in a folder to exclude from Sphinx, to avoid `:orphan:` being written to the HTML
Before:
<img width="1156" alt="Screen_Shot_2020-12-30_at_7_08_52_PM" src="https://user-images.githubusercontent.com/86842/103387595-99de4380-4ad2-11eb-85bf-8ac8da85676a.png">
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---
- [ ] ~~closes #xxxx~~
- [ ] tests added / passed
- [ ] ~~passes `black pandas`~~
- [ ] ~~passes `git diff upstream/master -u -- "*.py" | flake8 --diff`~~
- [ ] ~~whatsnew entry~~
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Update conf.py to execute imports during pdf building
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| 6
| 2020-12-31T02:32:07Z
| 2021-01-06T04:38:01Z
| 2021-01-05T12:57:09Z
|
CONTRIBUTOR
| null |
closes #38451
According [https://ipython.readthedocs.io/en/stable/sphinxext.html](), it should be `ipython_execlines` not `ipython_exec_lines`. Should close issue #38451
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API/BUG: silently-ignored dtype in DataFrame constructor
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MEMBER
| null |
I would expect this to raise rather than silently ignore:
```
df = pd.DataFrame({"A": ["foo", "bar"]}, dtype="i8")
>>> df
A
0 foo
1 bar
>>> df.dtypes
A object
dtype: object
```
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| 2021-01-05T22:14:19Z
| 2021-01-01T23:03:25Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Because we dont have 2D EAs, we end up passing axis=0 to concat_compat from internals.concat. As a result we drop empty arrays only when EAs are present. This leads to dtypes being preserved in EA cases while being cast to object in non-EA cases.
One solution is just to support 2D EAs. But since I like the dropping-empties behavior better anyway, this is good too.
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| 776,747,844
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MDU6SXNzdWU3NzY3NDc4NDQ=
| 38,844
|
BUG: pandas.Series.round not yet implemented for FloatingArray
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| 4
| 2020-12-31T03:39:14Z
| 2021-08-04T18:38:14Z
| 2021-03-08T14:32:31Z
|
NONE
| null |
- [X ] I have checked that this issue has not already been reported.
- [ X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame([{'a':1.2},{'a':2.5}])
df = df.convert_dtypes()
df['a'] = df['a'].round()
```
#### Problem description
Getting an error when applying `round` to a Series with new FloatingArray.
```python
import pandas as pd
df = pd.DataFrame([{'a':1.2},{'a':2.5}])
df = df.convert_dtypes()
df['a'] = df['a'].round()
```
The error message is:
```
File ".../lib/python3.8/site-packages/pandas/core/series.py", line 2183, in round
result = self._values.round(decimals)
AttributeError: 'FloatingArray' object has no attribute 'round'
```
#### Expected Output
rounded floats
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 20.2.0
Version : Darwin Kernel Version 20.2.0: Wed Dec 2 20:39:59 PST 2020; root:xnu-7195.60.75~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3
setuptools : 50.3.2.post20201201
Cython : None
pytest : 6.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.4
fastparquet : None
gcsfs : 0.7.1
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.20
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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BUG: DataFrame([listlikes]) different shape if first listlike is Categorical
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| 13
| 2020-12-31T03:47:50Z
| 2021-05-21T16:46:34Z
| 2021-05-21T16:46:34Z
|
MEMBER
| null |
```
arrs = [np.arange(0, 40, 10), np.arange(0, 40, 10), np.arange(0, 40, 10)]
arrs2 = arrs.copy()
arrs2[0] = pd.Categorical(arrs2[0])
df = pd.DataFrame(arrs)
df2 = pd.DataFrame(arrs2)
df3 = pd.DataFrame(arrs2[::-1])
>>> df.shape
(3, 4)
>>> df2.shape
(4, 3)
>>> df3.shape
(3, 4)
```
It's specific to Categorical, as we check `isinstance(data[0], Categorical)` in nested_data_to_arrays
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MDExOlB1bGxSZXF1ZXN0NTQ3MTkzMTM4
| 38,846
|
remove docs from packages
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| 19
| 2020-12-31T04:15:58Z
| 2021-04-09T00:10:24Z
| 2021-04-09T00:04:51Z
|
CONTRIBUTOR
| null |
- ~closes #xxxx~
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
#30741 discusses removing documentation and tests from package distributions. This is valuable because it makes `pandas` easier to use in storage-sensitive environments such as AWS Lambda (https://github.com/pandas-dev/pandas/issues/30741#issuecomment-638158062, https://github.com/pandas-dev/pandas/issues/30741#issuecomment-752751976).
This PR does not close that issue, but it's a first step. This proposes removing the content of `doc/` from package distributions. This PR trims about 1MB (compressed) and 6MB (uncompressed) out of the `sdist` package.
| | `master` | this PR |
|:-----------------------:|:----------:|:--------:|
| sdist (compressed) | 5.7M | 4.5M |
| sdist (uncompressed) | 25M | 19M |
<details><summary>how I checked these sizes</summary>
```shell
rm -rf pandas.egg-info
rm -rf dist/
rm -rf __pycache__
echo ""
echo "building source distribution"
echo ""
python setup.py sdist > /dev/null
cp pandas.egg-info/SOURCES.txt ~/PANDAS-SOURCES.txt
pushd dist/
echo ""
echo "sdist compressed size"
echo ""
du -a -h .
tar -xf pandas*.tar.gz
rm pandas*.tar.gz
ls .
echo ""
echo "sdist uncompressed size"
echo ""
du -sh .
popd
```
</details>
To confirm that the documentation files were removed correctly by these changes, I ran the following
```shell
python setup.py sdist
cat pandas.egg-info/SOURCES.txt | grep -E "^doc"
```
Thanks for your time and consideration.
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CI,STYLE: narrow down ignore-words-list of codespell
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| 2021-01-04T01:13:47Z
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| null |
- [x] xref #38820
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
see Issue #38802, task4
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Backport PR #38806: DOC: fix sphinx directive error in 1.2.1 release notes
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| 2020-12-31T10:35:30Z
| 2020-12-31T12:00:42Z
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MEMBER
| null |
Backport PR #38806
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MDExOlB1bGxSZXF1ZXN0NTQ3MzQzMzg2
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TST: GH30999 address all bare pytest.raises in pandas/tests/arrays/boolean/test_arithmetic.py
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| 2020-12-31T12:14:36Z
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MEMBER
| null |
xref #30999 I thought I was done with the simple bare `pytest.raise` instances but I somehow missed this test module. Four instances, three fixed by adding `match` and one by using `tm.external_error_raised` because I believe the error comes from numpy.
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0NTQ3MzQ0MjM2
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DOC: sphinx error in 1.2.1 release notes
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REGR: fillna on datetime64[ns, UTC] column hits RecursionError
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| 2021-01-16T01:09:21Z
| 2021-01-16T01:09:21Z
|
NONE
| null |
#### Code Sample
```python
from datetime import datetime, timezone
import pandas as pd
df = pd.DataFrame(range(31))
df["dt"] = pd.date_range("2020/12/01", "2020/12/31", tz="UTC")
df["dt"].iloc[5] = pd.NaT
df["dt"] = df["dt"].fillna(datetime(1980, 1, 1, tzinfo=timezone.utc))
```
#### Problem description
pandas 1.2.0 is throwing a RecursionError while attempting to `fillna` on a `datetime64[ns, UTC]` column. Same code worked in prior releases.
```
raceback (most recent call last):
File "recursion.py", line 7, in <module>
df["dt"] = df["dt"].fillna(utc(1980, 1, 1, tzinfo=timezone.utc))
File "/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/series.py", line 4433, in fillna
return super().fillna(
File "/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/generic.py", line 6403, in fillna
new_data = self._mgr.fillna(
File "/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 621, in fillna
return self.apply(
File "/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 427, in apply
applied = getattr(b, f)(**kwargs)
File "/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/internals/blocks.py", line 461, in fillna
return self.split_and_operate(None, f, inplace)
File "/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/internals/blocks.py", line 516, in split_and_operate
nv = f(mask, new_values, None)
File "/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/internals/blocks.py", line 459, in f
return block.fillna(value, limit=limit, inplace=inplace, downcast=None)
File "/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/internals/blocks.py", line 461, in fillna
return self.split_and_operate(None, f, inplace)
File "/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/internals/blocks.py", line 516, in split_and_operate
nv = f(mask, new_values, None)
File "/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/internals/blocks.py", line 459, in f
return block.fillna(value, limit=limit, inplace=inplace, downcast=None)
...snipped...
File "/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/internals/blocks.py", line 441, in fillna
if self._can_hold_element(value):
File "/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/internals/blocks.py", line 2267, in _can_hold_element
tipo = maybe_infer_dtype_type(element)
File "/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/dtypes/cast.py", line 924, in maybe_infer_dtype_type
elif is_list_like(element):
File "pandas/_libs/lib.pyx", line 1033, in pandas._libs.lib.is_list_like
File "pandas/_libs/lib.pyx", line 1038, in pandas._libs.lib.c_is_list_like
File "/opt/miniconda3/envs/prod/lib/python3.8/abc.py", line 98, in __instancecheck__
return _abc_instancecheck(cls, instance)
RecursionError: maximum recursion depth exceeded in comparison
```
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
pd.show_versions()
/opt/miniconda3/envs/prod/lib/python3.8/site-packages/_distutils_hack/__init__.py:30: UserWarning: Setuptools is replacing distutils.
warnings.warn("Setuptools is replacing distutils.")
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.18.0-240.8.1.el8_3.x86_64
Version : #1 SMP Fri Dec 4 12:24:03 EST 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0
numpy : 1.19.4
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 50.0.0.post20200830
Cython : 0.29.21
pytest : 6.2.1
hypothesis : None
sphinx : 3.4.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.3
sqlalchemy : 1.3.22
tables : None
tabulate : None
xarray : 0.16.2
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.52.0
</details>
Thank you.
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MDExOlB1bGxSZXF1ZXN0NTQ3NDUwOTI2
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BLD: move metadata to setup.cfg
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| 32
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MEMBER
| null |
move metadata to setup.cfg to separate them from build logic.
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Backport PR #38850 on branch 1.2.x (DOC: sphinx error in 1.2.1 release notes)
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NONE
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Backport PR #38850: DOC: sphinx error in 1.2.1 release notes
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Fix broken link in docs of DataFrame.to_hdf
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| 1
| 2020-12-31T15:58:31Z
| 2021-01-01T12:14:28Z
| 2020-12-31T18:48:28Z
|
CONTRIBUTOR
| null |
The link in https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_hdf.html in "See also" seems to be broken. This should fix it.
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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TST: GH30999 address all bare pytest.raises in pandas/tests/series and add EMPTY_STRING_PATTERN to tm
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| 1
| 2020-12-31T16:04:09Z
| 2020-12-31T18:47:41Z
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|
MEMBER
| null |
This PR is to address xref #30999 in pandas/tests/series
Three of the four errors raised within `pytest.raises` were for `NotImplementedError` with the empty string as the error message. Rather than individually making a regular expression for the empty string, I added one as a constant to `pandas/_testing/__init__.py` . There are two more similar cases in other modules so I wanted to put it somewhere that all the testing modules import. But if you don't like it I can revert it and instead use an individual empty string pattern in each case.
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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BUG: Groupby + Fillna loses the group
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| 4
| 2020-12-31T16:39:55Z
| 2021-01-01T00:00:55Z
| 2021-01-01T00:00:55Z
|
NONE
| null |
Hello there and happy new year!
Perhaps one of the last bugs of 2020... Please consider this example where I fill forward missing values by groups.
```
pd.__version__
Out[33]: '1.2.0'
df = pd.DataFrame({'group' : ['a','a','a','b','b'],
'time' : [pd.to_datetime('2020-01-01 10:00:00'),
pd.to_datetime('2020-01-01 10:00:01'),
pd.to_datetime('2020-01-01 10:00:02'),
pd.to_datetime('2020-01-01 10:00:00'),
pd.to_datetime('2020-01-01 10:00:01')],
'values' : [np.NaN,2,np.NaN,1,np.NaN]})
df.set_index('time', inplace = True)
df
Out[32]:
group values
time
2020-01-01 10:00:00 a NaN
2020-01-01 10:00:01 a 2.00000000
2020-01-01 10:00:02 a NaN
2020-01-01 10:00:00 b 1.00000000
2020-01-01 10:00:01 b NaN
```
The folllowing used to work but now it loses the `group` var entirely!
```
df.groupby('group').fillna(method = 'ffill')
Out[34]:
values
time
2020-01-01 10:00:00 NaN
2020-01-01 10:00:01 2.00000000
2020-01-01 10:00:02 2.00000000
2020-01-01 10:00:00 1.00000000
2020-01-01 10:00:01 1.00000000
```
```
df.groupby('group', as_index = False).fillna(method = 'ffill')
Out[35]:
values
time
2020-01-01 10:00:00 NaN
2020-01-01 10:00:01 2.00000000
2020-01-01 10:00:02 2.00000000
2020-01-01 10:00:00 1.00000000
2020-01-01 10:00:01 1.00000000
```
Is this the expected behavior? I can still use `apply` as a workaround
```
df.groupby('group').apply(lambda x: x.fillna(method = 'ffill'))
Out[36]:
group values
time
2020-01-01 10:00:00 a NaN
2020-01-01 10:00:01 a 2.00000000
2020-01-01 10:00:02 a 2.00000000
2020-01-01 10:00:00 b 1.00000000
2020-01-01 10:00:01 b 1.00000000
```
Thanks!
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| 2020-12-31T17:01:44Z
| 2021-01-02T02:17:50Z
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|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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| 2
| 2020-12-31T17:28:16Z
| 2021-01-13T15:03:03Z
| 2021-01-13T14:30:21Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Fixes running docs via
python make.py --no-api
A few notes:
- The removed check `if os.path.exists(path):`; will incorrectly raise if docs are being rebuilt.
- The value `dirname` returned by `os.walk` is an absolute path, but the check was treating it as relative.
- If docs are built with api and then rebuilt with --no-api, we want to exclude everything in the `reference` directory; in particular `reference/api`.
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|
ENH: Implement '_period_dtype_code' attribute to 'pandas._libs.tslibs.offsets.MonthBegin'
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[] | null | 6
| 2020-12-31T17:37:55Z
| 2021-08-14T22:57:49Z
| null |
NONE
| null |
Code...
```python
index_offsets = pd.tseries.frequencies.to_offset('1MS')
time_marker = pd.Timestamp('2020/01/02 1:59')
period = pd.Period(time_marker, freq = index_offsets.freqstr)
```
... raises following error message:
```python
Traceback (most recent call last):
File "pandas/_libs/tslibs/period.pyx", line 2436, in pandas._libs.tslibs.period.freq_to_dtype_code
AttributeError: 'pandas._libs.tslibs.offsets.MonthBegin' object has no attribute '_period_dtype_code'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<ipython-input-63-c78e6c74b47c>", line 1, in <module>
period = pd.Period(time_marker, freq = index_offsets.freqstr)
File "pandas/_libs/tslibs/period.pyx", line 2422, in pandas._libs.tslibs.period.Period.__new__
File "pandas/_libs/tslibs/period.pyx", line 2438, in pandas._libs.tslibs.period.freq_to_dtype_code
ValueError: Invalid frequency: {0}
```
Please, could an appropriate '_period_dtype_code' attribute be provided to 'pandas._libs.tslibs.offsets.MonthBegin' object to solve this error?
Thanks for your amazing lib!
```python
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-58-generic
Version : #64-Ubuntu SMP Wed Dec 9 08:16:25 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.1.post20201107
Cython : 0.29.21
pytest : 6.1.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : 0.4.2
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
```
Not building from sources (using anaconda)
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TYP: add RandomState alias
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| 1
| 2020-12-31T17:47:15Z
| 2021-06-22T00:34:09Z
| 2021-06-22T00:34:09Z
|
CONTRIBUTOR
| null |
This is a follow up of #38594. When adding type hints to the `sample` method, I ran into some problems for `random_state`. The reason is that `numpy < 1.17.0` does not have the `random.Generator` class implemented. I tried to solve this with checking the numpy version with:
```python
if np_version_under1p17:
RandomState = Union[int, ArrayLike, np.random.RandomState]
else:
RandomState = Union[int, ArrayLike, np.random.Generator, np.random.RandomState]
```
But this resulted in:
```commandline
Cannot assign multiple types to name "RandomState" without an explicit "Type[...]" annotation [misc]
```
To solve this issue we have to add typing for `random_state`.
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BUG: additional keys in groupby indices when NAs are present
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| 2
| 2020-12-31T18:06:26Z
| 2021-01-02T20:52:46Z
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|
MEMBER
| null |
- [x] closes #9304
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
`lib.indices_fast` included logic to skip over null values (-1's), but did not include the case where the first index is null. Currently this function is only used in `sorting.get_indexer_dict` where the case of all null group_index is handled separately.
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CI speed-up @github-actions pre-commit by only running it on changed files
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| 2021-01-31T19:36:22Z
| 2021-01-31T19:36:22Z
|
MEMBER
| null |
From #38858 it seems the actions works fine, though is perhaps a bit slow as it has to run on all files
If we apply the diff
```diff
diff --git a/.github/workflows/comment_bot.yml b/.github/workflows/comment_bot.yml
index 98b8e10f6..dc396be75 100644
--- a/.github/workflows/comment_bot.yml
+++ b/.github/workflows/comment_bot.yml
@@ -29,7 +29,7 @@ jobs:
- name: Install-pre-commit
run: python -m pip install --upgrade pre-commit
- name: Run pre-commit
- run: pre-commit run --all-files || (exit 0)
+ run: pre-commit run --from-ref=origin/master --to-ref=HEAD --all-files || (exit 0)
- name: Commit results
run: |
git config user.name "$(git log -1 --pretty=format:%an)"
```
then it should run much faster
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MDU6SXNzdWU3NzcxMjE3MDY=
| 38,863
|
DOC: refactor CSV parser section in user guide
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| 1
| 2020-12-31T19:39:06Z
| 2021-01-07T07:30:01Z
| null |
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/31817#discussion_r509808971
We want to refactor that entire section of the user guide to provide a table-like comparison of all of the parsers.
This should wait until #38370 is merged and the `pyarrow` engine is available.
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|
TST: GH30999 add match=msg to 2 pytest.raises in pandas/tests/io/json/test_normalize.py
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|
MEMBER
| null |
xref #30999 for pandas/tests/io/json/test_normalize.py
And with this my work on #30999 is done. Will head over to the issue to explain why I am not fixing the remaining 15 instances of `pytest.raise`
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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REGR: Sorted pandas series is not plotted in the expected order in pandas 1.2.0
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| 7
| 2020-12-31T21:09:45Z
| 2021-02-02T01:42:42Z
| 2021-02-02T01:42:42Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
Note: The code was executed in Jupyter notebook.
```
import pandas as pd
print(pd.__version__)
d = {'col1': [4,2,3,1]}
df = pd.DataFrame(data=d)
df['col1'].sort_values().plot(kind='bar')
```
#### Problem description
In Pandas 1.2.0, the sorted values are not plotted in the correct order. Note that ```df['col1'].sort_values()``` does return sorted values.
#### Expected Output
In Pandas 1.1.5, this works as expected. This might be the case in older versions of pandas as well.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 20.2.0
Version : Darwin Kernel Version 20.2.0: Wed Dec 2 20:39:59 PST 2020; root:xnu-7195.60.75~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_CA.UTF-8
LOCALE : en_CA.UTF-8
pandas : 1.1.5
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.0.post20201006
Cython : 0.29.21
pytest : 6.1.1
hypothesis : 5.37.4
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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| 777,143,460
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MDExOlB1bGxSZXF1ZXN0NTQ3NTEyNzI4
| 38,866
|
ENH: implement FloatingArray.round()
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| 7
| 2020-12-31T21:43:06Z
| 2021-02-11T01:30:19Z
| 2021-02-11T01:30:19Z
|
MEMBER
| null |
- [x] closes #38844
- [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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|
REF: Move aggregation into apply
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| 1
| 2020-12-31T21:51:41Z
| 2021-01-28T23:02:00Z
| 2021-01-03T16:56:06Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This is a step toward #35725. We can't simply ban agg from not aggregating because it's used by apply for series, dataframe, groupby, resampler, and rolling. There are two other related issues. First, apply, agg, and transform are all doing similar things but with different implementations leading to unintended inconsistencies. Second, we would also like to ban mutation in apply (and presumably agg and transform for the same reasons).
It seems best to me to handle all of these by first combining the implementation into apply, cleaning up and sharing code between the implementations, and then we would be in a good place to ban agg from not aggregating.
This starts the process by moving aggregation.aggregate into apply, but it is only utilized for frames. Once this is done for the remaining objects (series, groupby, resampler, rolling), the rest of the functions in aggregation can be moved into apply as well.
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DOC: Methods of conditional selection
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[] | null | 2
| 2020-12-31T22:12:00Z
| 2021-08-14T22:58:25Z
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NONE
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html#setting
https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html#selection
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.where.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html
https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#query-python-versus-pandas-syntax-comparison
#### Documentation problem and suggested fixes
##### Selection with `df[df.A > 10]`
Since I started using Pandas, I have exclusively used syntax like `df[df.A > 10]` to select rows of a DataFrame, or elements therein (`df[df.A > 10].B`) matching some condition. (By the way, is there a name for this style of selection?) I probably picked up this technique from "10 minutes to pandas" (https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html#setting), specifically
```
A where operation with setting.
In [52]: df2 = df.copy()
In [53]: df2[df2 > 0] = -df2
```
While that section intends to describe how to set values, it also by chance shows selection with the `df[df.A > 10]` method. As far as I can tell, this is the only time this page shows how to perform conditional selection. Since selecting part of a DataFrame is such a common operation, I suggest adding a specific subsection under https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html#selection for `Conditional selection` or similar. As discussed below, it should illustrate both `df[df.A > 10]` and `query`.
##### How to select conditionally
From the "10 minutes" page, one might wish to learn more about how to select conditionally. The obvious place to look is https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.where.html, since the "10 minutes" page specifically mentions `where` just before `df2[df2 > 0]`. However, `where` is intended for replacement, not selection. I suggest adding a note to the `where` page that points to https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html, since `query` is the appropriate tool for conditional selection.
##### Dynamic construction of column names
I would also like to suggest an addition to https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#query-python-versus-pandas-syntax-comparison and the other places where `df[df.A > 10]` selection appears. Suppose one has a DataFrame with columns including `'A1', 'A2', 'A3', ..., B`, and that one wishes to run some process on subsets of each column. Rather than building the list of columns and iterating over them, like with `for col in ['A1', 'A2', 'A3']:`, I prefer to use
```
for col_suffix in [1,2,3]:
these_data = df[df[f'A{col_suffix}'] > 0]
x = these_data.B
y = these_data[f'A{col_suffix}']
```
I have not seen this particular idiom in the pandas docs. While the standard advice (https://stackoverflow.com/questions/2259224/python-inserting-a-variable-value-into-a-variable-name) is to avoid such constructions, DataFrames often come with collections of column names like `['A1', 'A2', 'A3']`, so I have found this "dynamic" approach quite useful—particularly when translating code that I wrote and tested on a single column name to operate on many columns.
##### Differences in the output of `df[df.A > 10]` and `query`
Finally, in selecting with `df[df.A > 10]`, I have encountered a warning that does not appear with `query`. I am running this code in a Jupyter notebook. When I perform the selection with
```
nonzero_data = df[df[f'A{col_suffix} %'] > 0]
```
and then plot two columns from `nonzero_data` against one another, I get the following error:
```
.../lib/python3.6/site-packages/matplotlib/cbook/__init__.py:2062: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.
x[:, None]
```
If I run the same code but replace the selection line with
```
nonzero_data = df.query(f"`A{col_suffix} %` > 0")
```
no error appears. I can see that matplotlib is throwing the error, but the fact that it depends on how I select the data in pandas suggests the latter is returning objects that differ in some way with each selection method. While I suspect this behavior is documented, I was not able to locate that information.
In both cases, the `x` and `y` values being plotted appear to be identical. Calling `display(<var>)` just before `plt.plot(x,y)`, I find that `x` is
```
datetime
2020-12-20 21:48:00 106.140
2020-12-20 22:29:00 108.690
...
Name: B, dtype: float64
```
and `y` is
```
datetime
2020-12-20 21:48:00 6.980
2020-12-20 22:29:00 7.325
...
Name: A1 %, dtype: float64
```
The error disappears, even selecting with `nonzero_data = df[df[f'A{col_suffix} %'] > 0]`, if I recast `x` and `y` as numpy arrays. What, then, is the difference in the DataFrames that `query` or `df[df[f'A{col_suffix} %'] > 0]` return?
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TST/REF: implement tests.frame.constructors
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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DEPS: bump pyarrow min to 0.17.0
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| 2020-12-31T23:58:06Z
| 2021-05-17T15:21:56Z
| 2021-05-17T15:21:56Z
|
CONTRIBUTOR
| null |
this would remove some complexity around parquet, csv engine & string array. this is an optional dep and so i don't think this a big deal.
keep in mind that we likley won't release 1.3 until june time frame anyhow
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Pyarrow CSV Reader Integration Tracker
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Issue tracking Pyarrow engine integration in read_csv (for after #38370 is merged)
**Current unsupported options**
- [ ] skipfooter
- [ ] float_precision
- [ ] chunksize
- [ ] comment
- [ ] nrows
- [ ] thousands
- [ ] memory map
- [ ] dialect
- [ ] warn_bad_lines
- [ ] error_bad_lines
- [ ] delim_whitespace
- [ ] quoting
- [ ] lineterminator (this is c-only)
- [ ] converters
- [ ] decimal
- [ ] iterator
- [ ] dayfirst
- [ ] infer_datetime_format
- [ ] verbose
- [ ] skipinitialspace
- [ ] low_memory (this is c-only)
**TODO**
- [ ] Revert skipping of test_parse_dates due to removal of np.asarray in date_converters in #38370
- [ ] Enable skiprows tests disabled in #38370
- [ ] Enable encoding tests.
**Proposed enhancements**
- [ ] ENH: Pass arguments to `to_pandas` in `read_csv(engine="pyarrow") #34823
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| 2021-01-01T08:35:16Z
| 2021-01-03T17:10:45Z
| 2021-01-03T17:10:42Z
|
CONTRIBUTOR
| null |
- [ ] closes #25744, closes #27338
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU3NzcyNDgzMjM=
| 38,874
|
REGR: incorrect results with std on rolling window since 1.2.0
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[] | null | 6
| 2021-01-01T09:08:54Z
| 2021-01-16T10:16:50Z
| 2021-01-16T01:23:13Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1e-8, -1.1e-8, 1.2e-8, -1.1e-8] * 5})
df['std'] = df['A'].rolling(10).std(ddof=0)
print(df)
```
#### Problem description
when i ran above code in 1.2.0, the output is:
```
A std
0 1.000000e-08 NaN
1 -1.100000e-08 NaN
2 1.200000e-08 NaN
3 -1.100000e-08 NaN
4 1.000000e-08 NaN
5 -1.100000e-08 NaN
6 1.200000e-08 NaN
7 -1.100000e-08 NaN
8 1.000000e-08 NaN
9 -1.100000e-08 0.0
10 1.200000e-08 0.0
11 -1.100000e-08 0.0
12 1.000000e-08 0.0
13 -1.100000e-08 0.0
14 1.200000e-08 0.0
15 -1.100000e-08 0.0
16 1.000000e-08 0.0
17 -1.100000e-08 0.0
18 1.200000e-08 0.0
19 -1.100000e-08 0.0
```
#### Expected Output
ran in 1.1.5:
```
A std
0 1.000000e-08 NaN
1 -1.100000e-08 NaN
2 1.200000e-08 NaN
3 -1.100000e-08 NaN
4 1.000000e-08 NaN
5 -1.100000e-08 NaN
6 1.200000e-08 NaN
7 -1.100000e-08 NaN
8 1.000000e-08 NaN
9 -1.100000e-08 1.092200e-08
10 1.200000e-08 1.112160e-08
11 -1.100000e-08 1.112160e-08
12 1.000000e-08 1.092200e-08
13 -1.100000e-08 1.092200e-08
14 1.200000e-08 1.112160e-08
15 -1.100000e-08 1.112160e-08
16 1.000000e-08 1.092200e-08
17 -1.100000e-08 1.092200e-08
18 1.200000e-08 1.112160e-08
19 -1.100000e-08 1.112160e-08
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.7.5.final.0
python-bits : 64
OS : Darwin
OS-release : 20.2.0
Version : Darwin Kernel Version 20.2.0: Wed Dec 2 20:39:59 PST 2020; root:xnu-7195.60.75~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : zh_CN.UTF-8
LOCALE : zh_CN.UTF-8
pandas : 1.2.0
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.3.3
setuptools : 41.2.0
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.2
html5lib : None
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.15
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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QST: function similarity
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[] | null | 1
| 2021-01-01T09:09:19Z
| 2021-01-01T09:15:40Z
| 2021-01-01T09:14:59Z
|
NONE
| null |
Does Pandas also support a function like what np.vstack or np.hstack does?
|
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TST: GH30999 TESTING PR do not merge or review
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MEMBER
| null |
There are 3 tests that don't run on my dev machine even with tweaks. They should fail in the CI and I can get the messages from there. So for now this is just a PR designed to run (and fail) the CI pipelines.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BUG: rolling does not accept MultiIndex name
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| 2021-01-01T09:50:55Z
| 2021-01-02T02:21:36Z
| 2021-01-02T02:21:36Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
If
```python
df = pandas.DataFrame()
df['date'] = pandas.date_range('2021-1-1', '2021-1-5', freq='h')
df.loc[df.date <= '2021-1-3', 'category'] = 'A'
df.loc[df.date > '2021-1-3', 'category'] = 'B'
df.set_index(['category','date'], inplace=True)
df['value'] = 1
```
then
```python
df.groupby('category').rolling('D', on='date')
```
breaks with
```python
ValueError: invalid on specified as date, must be a column (of DataFrame), an Index or None
```
#### Problem description
The [documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rolling.html?highlight=rolling#pandas.DataFrame.rolling) states that the `on` argument can be a `MultiIndex` level (and then confusingly adds "rather than the DataFrame's index").
#### Expected Output
`rolling` should accept a `MultiIndex` level. Interestingly,
```python
df.groupby('category').resample('D', level='date')
```
works, but it would be nice to unify the interfaces: `resample` accepts `level` and `on` for index and column respectively, while `rolling` accepts `on` for both.
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BUG: Unexpected typecast on datetime.date in groupby.key()
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| 4
| 2021-01-01T12:27:19Z
| 2021-01-08T14:17:27Z
| 2021-01-08T14:17:27Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
>>> import datetime as dt
>>> import pandas as pd
>>> df = pd.DataFrame([[dt.date(2021,1,1), 1, 'a'], [dt.date(2021,1,2), 2, 'b']])
>>> g = df.groupby([0, 1])
>>> g.groups.key()
... dict_keys([(Timestamp('2021-01-01 00:00:00'), 1), (Timestamp('2021-01-02 00:00:00'), 2)])
```
#### Problem description
When multiple columns pass to groupby, the `datetime.date` type columns cast o the `Timestamp` type.
Due to the unexpected type casting, following code raises a `KeyError`.
```python
>>> list(g.get_group(key) for key in g.groups.keys())
... KeyError: (Timestamp('2021-01-01 00:00:00'), 1)
```
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
This typecast will only be performed if multiple indexes are specified.
```python
>>> g = df.groupby([0])
>>> g.groups.key()
... dict_keys([datetime.date(2021, 1, 1), datetime.date(2021, 1, 2)])
```
#### Expected Output
```python
>>> g.groups.key()
... dict_keys([(datetime.date(2021, 1, 1), 1), (datetime.date(2021, 1, 2), 2)])
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.7.final.0
python-bits : 64
OS : Darwin
OS-release : 20.2.0
Version : Darwin Kernel Version 20.2.0: Wed Dec 2 20:39:59 PST 2020; root:xnu-7195.60.75~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : ja_JP.UTF-8
pandas : 1.2.0
numpy : 1.19.4
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.1.1
Cython : 0.29.21
pytest : 6.2.1
hypothesis : None
sphinx : 3.4.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : 2.7.2
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.52.0
</details>
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BUG: silently ignoring dtype kwarg in Index.__new__
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| 2
| 2021-01-01T18:37:36Z
| 2021-01-02T01:37:47Z
| 2021-01-01T23:02:18Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
ATM we handle object but not others, xref #21311
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TST: add missing loc label indexing test
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| 3
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| 2021-01-10T11:02:40Z
| 2021-01-03T17:17:59Z
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CONTRIBUTOR
| null |
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- [x] tests added / passed
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| 777,336,146
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MDExOlB1bGxSZXF1ZXN0NTQ3NjY0Njkw
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TST: strictly xfail mid-test
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| 2021-01-02T15:41:53Z
| 2021-01-02T15:41:46Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Using `pytest.xfail` when a test is running will immediately stop the test and will not xpass if test would otherwise succeed. It doesn't seem to be documented anywhere, but an alternative would be to use the request fixture. In this demo, I've added an xfail that runs successfully, making the test fail (xpass).
If this looks like a good idea, I can make a tracking issue to replace `pytest.xfail` with the method used here.
|
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CLN: Use signed integers in khash maps for signed integer keys
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| 3
| 2021-01-01T20:56:10Z
| 2021-01-03T16:56:28Z
| 2021-01-03T16:56:24Z
|
CONTRIBUTOR
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
The goal of this PR is to make this comment obsolete: https://github.com/pandas-dev/pandas/blob/1fc5efd76ea1f85979fa2364a292d85482e338aa/pandas/_libs/src/klib/khash.h#L619-L624
see also [this discussion]( https://github.com/pandas-dev/pandas/pull/37920#discussion_r527191378) for even more details.
The issue is:
- the current way (inherited from khash-original) is too clever (and suprising)
- it is wrong as the (unsigned) keys are converted back to signed values (which is implementation defined behavior) for example here: https://github.com/pandas-dev/pandas/blob/1fc5efd76ea1f85979fa2364a292d85482e338aa/pandas/_libs/hashtable_func_helper.pxi.in#L110
Using signed integer, one must take into account that hash-function with signed integers might have undefined/implementation defined behavior, thus we cast signed integers to unsigned counterpart in the (64bit-) hash function now.
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TST: Add test_groupby_std_on_nullable_column.
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NONE
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- [x] closes #35516
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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DEPR: Remove xlrd as being the default reader for xlsx
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| 2
| 2021-01-01T21:52:51Z
| 2021-02-16T23:08:52Z
| 2021-02-16T23:08:52Z
|
MEMBER
| null |
Currently the default reader for xlsx is openpyxl if installed, otherwise it is xlrd. While xlrd == 1.2.0 will work with xlsx files, xlrd >= 2.0 will not. In pandas 1.2, a FutureWarning is raised if xlrd is used on non-xls files. We should remove xlrd as being the default for xlsx files in pandas 2.0, but perhaps even 1.3 (if 1.3 != 2.0).
cc @jorisvandenbossche
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BUG: DatetimeIndex.to_period().to_timestamp() forgets freq value
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[] | null | 9
| 2021-01-01T22:12:36Z
| 2021-08-16T19:47:51Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
>>> pd.date_range(start='1/1/2018 00:00:00', end='2/1/2018 00:01:00', freq="MS")
DatetimeIndex(['2018-01-01', '2018-02-01'], dtype='datetime64[ns]', freq='MS')
>>> pd.date_range(start='1/1/2018 00:00:00', end='2/1/2018 00:01:00', freq="MS").to_period().to_timestamp()
DatetimeIndex(['2018-01-01', '2018-02-01'], dtype='datetime64[ns]', freq=None)
```
#### Problem description
Why is `freq=None` here in the end? Is it possible to make conversion between `DatetimeIndex` and `PeriodIndex` more reliable in any way? How can I know which `freq` values are not able to be converted from `PeriodIndex` into `DatetimeIndex`?
#### Expected Output:
I would expect to have the same `freq` value after doing `.to_period().to_timestamp()`.
```python
>>> pd.date_range(start='1/1/2018 00:00:00', end='2/1/2018 00:01:00', freq="MS").to_period().to_timestamp()
DatetimeIndex(['2018-01-01', '2018-02-01'], dtype='datetime64[ns]', freq="MS")
```
#### Output of ``pd.show_versions()``
``````
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.7.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : AMD64 Family 21 Model 112 Stepping 0, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.2.0
numpy : 1.19.4
pytz : 2019.3
dateutil : 2.8.1
pip : 20.3.1
setuptools : 51.0.0.post20201207
Cython : 0.29.17
pytest : 6.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.4
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.50.1
</details>
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CLN: add typing for dtype arg in core/arrays (GH38808)
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| 1
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| 2021-01-05T02:02:14Z
| 2021-01-05T02:02:08Z
|
CONTRIBUTOR
| null |
Follow on PR for #38808
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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DOC: create shared includes for comparison docs, take III
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MEMBER
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From original pull request (https://github.com/pandas-dev/pandas/pull/38771):
> This will help ensure consistency between the examples.
- [ ] ~~closes #xxxx~~
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- [ ] ~~passes `black pandas`~~
- [ ] ~~passes `git diff upstream/master -u -- "*.py" | flake8 --diff`~~
- [ ] ~~whatsnew entry~~
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BUG: import_optional_dependency doesn't check version for submodules
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| 2021-01-01T22:38:39Z
| 2021-01-04T00:14:16Z
| 2021-01-04T00:14:16Z
|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/38370#discussion_r550723991
We'd like `import_optional_dependency` to correctly handle submodule imports AND to be able to import a specified minimum version of the dependency (for when a particular feature requires higher than global minimum version).
cc @lithomas1
#### Expected Output
```
In [14]: import pandas as pd
...: from pandas.compat._optional import import_optional_dependency
...: import_optional_dependency("pyarrow.csv", min_version="3.0")
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-14-7cfc85634756> in <module>
1 import pandas as pd
2 from pandas.compat._optional import import_optional_dependency
----> 3 import_optional_dependency("pyarrow.csv", min_version="3.0")
~/repos/pandas/pandas/compat/_optional.py in import_optional_dependency(name, extra, raise_on_missing, on_version, min_version)
ImportError: Pandas requires version '3.0' or newer of 'pyarrow.csv' (version '2.0.0' currently installed).
```
```
In [16]: import_optional_dependency("pyarrow.csv", min_version="3.0", on_version="warn")
/Users/andrewwieteska/repos/pandas/pandas/compat/_optional.py:137: UserWarning: Pandas requires version '3.0' or newer of 'pyarrow.csv' (version '2.0.0' currently installed).
```
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MDU6SXNzdWU3NzczNjU3MTk=
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CLN/TST: Pyarrow CSV engine
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[] | null | 1
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MEMBER
| null |
This is a master tracker for the more cosmetic follow-ups to #38370: styling, testing, etc.
- [ ] #38888 correctly check version for optional dependency submodule imports (to handle `import pyarrow.csv`)
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CLN: add typing for dtype arg in directories core/indexes and core/strings (GH38808)
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| 1
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|
CONTRIBUTOR
| null |
Follow on PR for #38808
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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Backport PR #38471 on branch 1.2.x (DOC: fixes for assert_frame_equal check_freq argument)
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CONTRIBUTOR
| null |
Backport PR #38471
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ASV: Add asv for groupby.indices
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| 1
| 2021-01-01T23:56:57Z
| 2021-01-03T18:44:59Z
| 2021-01-03T18:44:22Z
|
MEMBER
| null |
- [x] closes #38495
Asv for the indices performance regression
|
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doc fix for testing.assert_series_equal check_freq arg
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| 2
| 2021-01-02T00:46:40Z
| 2021-01-04T16:50:08Z
| 2021-01-03T17:01:53Z
|
CONTRIBUTOR
| null |
This fixes `check_freq` arg docs for `assert_series_equal` like #38471 did for `assert_frame_equal`.
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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TST/CLN: deduplicate troublesome rank values
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| 3
| 2021-01-02T01:11:31Z
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|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/38681#discussion_r548554225
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| 6
| 2021-01-02T02:06:51Z
| 2021-06-14T20:11:38Z
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|
MEMBER
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Adds `engine ` and `engine_kwargs` argument to `mean`, `median`, `sum`, `min`, `max`
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API: setitem copy/view behavior ndarray vs Categorical vs other EA
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| null |
MEMBER
| null |
xref #33457 which is about similar issue but goes through different code paths.
In `Block.setitem` in cases where we are setting all the values for this block we have:
```
elif exact_match and is_categorical_dtype(arr_value.dtype):
# GH25495 - If the current dtype is not categorical, we need to create a new categorical block
values[indexer] = value
return self.make_block(Categorical(self.values, dtype=arr_value.dtype))
elif exact_match and is_ea_value:
# GH#32395 if we're going to replace the values entirely, just substitute in the new array
return self.make_block(arr_value)
elif exact_match:
# We are setting _all_ of the array's values, so can cast to new dtype
values[indexer] = value
values = values.astype(arr_value.dtype, copy=False)
```
So we overwrite the existing values for categorical `value` or non-EA `value`. Example:
```
df = pd.DataFrame({
"A": [.1, .2, .3],
"B": pd.array([1, 2, None], dtype="Int64"),
"C": ["a", "b", "c"]
})
orig_df = df[:]
arr_np = df["A"]._values
arr_ea = df["B"]._values
cat = pd.Categorical(df["C"])
# Note: there are many equivalent-looking ways of doing this setitem operation but few of them go through this code path.
df.loc[range(3), "A"] = arr_np[::-1]
df.loc[range(3), "B"] = arr_ea[::-1]
df.loc[range(3), "C"] = cat[::-1]
>>> df
A B C
0 0.3 <NA> c
1 0.2 2 b
2 0.1 1 a
>>> df.dtypes
A float64
B Int64
C category
dtype: object
>>> orig_df
A B C
0 0.3 1 a
1 0.2 2 b
2 0.1 <NA> c
>>> orig_df.dtypes
A float64
B Int64
C object
dtype: object
```
The categorical behavior we implemented in #23393 and AFAICT the over-writing behavior was not discussed/intentional. Similarly the other EA behavior was implemented in #32479 and I don't see anything about the overwrite-or-not. I haven't tracked down the origin of the non-EA behavior.
I think all three cases should have the same behavior. We should also have the same behavior for should-be-equivalent setters, e.g. if we used iloc instead of loc, or `[:, "A"]` instead of `[range(3), "A"]`.
I think I agree with @TomAugspurger's [comment](https://github.com/pandas-dev/pandas/issues/33457#issuecomment-660126628) that these should always be in-place, but not sure ATM if that can be done without breaking consistency elsewhere.
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| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Another precursor to #38370
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MDU6SXNzdWU3Nzc0Mjc1MTQ=
| 38,898
|
BUG: Categorical RollingGroupby will raise IndexError
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[] | null | 2
| 2021-01-02T08:42:24Z
| 2021-05-10T18:43:59Z
| 2021-01-02T08:51:57Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
```python
import pandas as pd
print(pd.__version__, '\n')
df = pd.DataFrame([1, 2, 3, 4, 5],
index=pd.CategoricalIndex(["a", "b", "b", "a", "c"]))
print(df)
print(df.iloc[:4].groupby(level=0).rolling(2).sum())
```
#### Problem description
When the index contains only part of the Categorical, RollingGroupby will raise an error:
```
pandas\core\window\indexers.py in get_window_bounds(self, num_values, min_periods, center, closed)
320 # Extend as we'll be slicing window like [start, end)
321 window_indicies = np.append(
--> 322 window_indicies, [window_indicies[-1] + 1]
323 ).astype(np.int64)
324 start_arrays.append(window_indicies.take(start))
IndexError: index -1 is out of bounds for axis 0 with size 0
```
This problem has appeared since 1.1.0, and 1.0.5 runs normally
#### Expected Output
```
1.0.5
0
a 1
b 2
b 3
a 4
c 5
0
a a NaN
a 5.0
b b NaN
b 5.0
```
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.0.0.post20201207
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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Validate axis args
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| 2
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|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This is a follow up PR on #37924 @jreback cheers
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| 5
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| 2021-01-09T22:05:13Z
| 2021-01-09T22:05:13Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
>>> import pandas as pd
>>> df = pd.DataFrame(['o',"o'doul's",'oad','oaf','oafish'])
>>> df = df.astype('|S')
>>> df
0
0 b'o'
1 b"o'doul's"
2 b'oad'
3 b'oaf'
4 b'oafish'
>>> import numpy as np
>>> df.replace({None:np.nan})
```
#### Problem description
This was working in 1.1.5
```
df[c].replace({None: np.NaN}, inplace=True)
File "/home/user/anaconda3/envs/uniqueness/lib/python3.8/site-packages/pandas/core/series.py", line 4479, in replace
return super().replace(
File "/home/user/anaconda3/envs/uniqueness/lib/python3.8/site-packages/pandas/core/generic.py", line 6840, in replace
return self.replace(
File "/home/user/anaconda3/envs/uniqueness/lib/python3.8/site-packages/pandas/core/series.py", line 4479, in replace
return super().replace(
File "/home/user/anaconda3/envs/uniqueness/lib/python3.8/site-packages/pandas/core/generic.py", line 6889, in replace
new_data = self._mgr.replace_list(
File "/home/user/anaconda3/envs/uniqueness/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 664, in replace_list
bm = self.apply(
File "/home/user/anaconda3/envs/uniqueness/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 427, in apply
applied = getattr(b, f)(**kwargs)
File "/home/user/anaconda3/envs/uniqueness/lib/python3.8/site-packages/pandas/core/internals/blocks.py", line 910, in _replace_list
[b.convert(numeric=False, copy=True) for b in result]
File "/home/user/anaconda3/envs/uniqueness/lib/python3.8/site-packages/pandas/core/internals/blocks.py", line 910, in <listcomp>
[b.convert(numeric=False, copy=True) for b in result]
File "/home/user/anaconda3/envs/uniqueness/lib/python3.8/site-packages/pandas/core/internals/blocks.py", line 2558, in convert
values = f(None, self.values.ravel(), None)
File "/home/user/anaconda3/envs/uniqueness/lib/python3.8/site-packages/pandas/core/internals/blocks.py", line 2542, in f
values = soft_convert_objects(
File "/home/user/anaconda3/envs/uniqueness/lib/python3.8/site-packages/pandas/core/dtypes/cast.py", line 1139, in soft_convert_objects
values = lib.maybe_convert_objects(values, convert_datetime=True)
File "pandas/_libs/lib.pyx", line 2125, in pandas._libs.lib.maybe_convert_objects
ValueError: Buffer dtype mismatch, expected 'Python object' but got a string
```
#### Expected Output
Nones are replaced with nans
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-128-generic
Version : #131-Ubuntu SMP Wed Dec 9 06:57:35 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.5
numpy : 1.19.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.2
Cython : 3.0a6
pytest : 6.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.2
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 3.0.0a1
IPython : 7.17.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.20
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.52.0
</details>
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MDExOlB1bGxSZXF1ZXN0NTQ3NzY3MDgx
| 38,901
|
TST: strictly xfail
|
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| 5
| 2021-01-02T15:40:36Z
| 2021-01-03T20:35:30Z
| 2021-01-03T19:43:59Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Continuation of #38881. This handles cases where replacing pytest.xfail is clear, opened #38902 for the handful of remaining cases.
In the case where `pytest.xfail` is used for a test which has been yet to be implemented, I've added ~~`assert False` to maintain the current behavior of counting it as an xfail. Maybe there is a better alternative here.~~ `raise NotImplementedError`.
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|
TST: Disallow pytest.xfail
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| 0
| 2021-01-02T15:40:50Z
| 2021-02-08T21:47:50Z
| 2021-02-08T21:47:50Z
|
MEMBER
| null |
pytest.xfail will immediately stop a test, not checking if it would have otherwise succeeded. The request fixture can used to run a test and only pass if the test does indeed fail:
request.node.add_marker(pytest.mark.xfail(reason="..."))
- [x] Remove all occurrences of `pytest.xfail`
- [ ] Update docs
- [ ] Add pre-commit hook
PR #38901 handles many of the current occurrences. The remaining ones are in:
- [x] indexes/test_common
- [x] pandas/conftest
- [x] indexes/test_setops
- [x] indexing/test_coercion
- [x] series/indexing/test_setitem
- [x] series/test_arithmetic
- [x] series/indexing/test_where
- [x] tseries/offsets
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MDExOlB1bGxSZXF1ZXN0NTQ3NzY4OTA4
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|
ENH: Use Kahan summation to calculate groupby.sum()
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| 3
| 2021-01-02T15:58:45Z
| 2021-01-03T23:29:09Z
| 2021-01-03T23:25:09Z
|
MEMBER
| null |
- [x] closes #38778
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This simplifies the op example.
I think
```
series = Series([1e16, 99, -5e15, -5e15])
series.sum()
```
is dispatched to numpy? The result is wrong too. ``100.0``
Mean doesn't work either. I will look through the functions to determine which need Kahan summation too and open an issue to track these.
|
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MDU6SXNzdWU3Nzc0OTIwMTg=
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|
TST: skips in extension.test_categorical
|
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| 2021-01-12T01:17:22Z
| 2021-01-12T01:17:22Z
|
MEMBER
| null |
ref: https://github.com/pandas-dev/pandas/pull/38901#discussion_r550899659
At least some of the tests that are skipped here should instead be xfailed.
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CI: enable parallel testing on arm64 build #36719
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MEMBER
| null |
- [x] closes #36719
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BUG: reindexing empty CategoricalIndex fails if target contains duplicates
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| 1
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| 2021-01-10T02:39:53Z
| 2021-01-10T02:39:53Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas. (Tested version 1.2.0)
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
This fails:
```python
pd.DataFrame(columns=pd.CategoricalIndex([]), index=['K']).reindex(columns=pd.CategoricalIndex(['A', 'A']))
```
But these succeed:
```python
pd.DataFrame(columns=pd.Index([]), index=['K']).reindex(columns=pd.CategoricalIndex(['A', 'A']))
pd.DataFrame(columns=pd.CategoricalIndex([]), index=['K']).reindex(columns=pd.CategoricalIndex(['A', 'B']))
pd.DataFrame(columns=pd.CategoricalIndex([]), index=['K']).reindex(columns=pd.CategoricalIndex([]))
```
The error is:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\mboling\Anaconda3\envs\pandastest\lib\site-packages\pandas\util\_decorators.py", line 312, in wrapper
return func(*args, **kwargs)
File "C:\Users\mboling\Anaconda3\envs\pandastest\lib\site-packages\pandas\core\frame.py", line 4173, in reindex
return super().reindex(**kwargs)
File "C:\Users\mboling\Anaconda3\envs\pandastest\lib\site-packages\pandas\core\generic.py", line 4806, in reindex
return self._reindex_axes(
File "C:\Users\mboling\Anaconda3\envs\pandastest\lib\site-packages\pandas\core\frame.py", line 4013, in _reindex_axes
frame = frame._reindex_columns(
File "C:\Users\mboling\Anaconda3\envs\pandastest\lib\site-packages\pandas\core\frame.py", line 4055, in _reindex_columns
new_columns, indexer = self.columns.reindex(
File "C:\Users\mboling\Anaconda3\envs\pandastest\lib\site-packages\pandas\core\indexes\category.py", line 448, in reindex
new_target, indexer, _ = result._reindex_non_unique(np.array(target))
File "C:\Users\mboling\Anaconda3\envs\pandastest\lib\site-packages\pandas\core\indexes\base.py", line 3589, in _reindex_non_unique
new_indexer = np.arange(len(self.take(indexer)))
File "C:\Users\mboling\Anaconda3\envs\pandastest\lib\site-packages\pandas\core\indexes\base.py", line 751, in take
taken = algos.take(
File "C:\Users\mboling\Anaconda3\envs\pandastest\lib\site-packages\pandas\core\algorithms.py", line 1657, in take
result = arr.take(indices, axis=axis)
IndexError: cannot do a non-empty take from an empty axes.
```
#### Problem description
It is unexpected that `CategoricalIndex` behaves differently than `Index` in this regard. A problem similar to this was already reported and solved in #16770, but it looks like there is a remaining bug in the edge case where the target index contains duplicates.
#### Expected Output
The failing code should return a dataframe with two columns and one row.
#### Output of ``pd.show_versions()``
<details>
pd.show_versions() fails because I dont' have numba installed in this environment and it's currently impossible to install it simultaneously with Pandas 1.2.0, so this is the output of "conda list" instead:
blas 1.0 mkl
bottleneck 1.3.2 py39h7cc1a96_1
ca-certificates 2020.12.8 haa95532_0
certifi 2020.12.5 py39haa95532_0
et_xmlfile 1.0.1 py_1001
icc_rt 2019.0.0 h0cc432a_1
intel-openmp 2020.3 h57928b3_311 conda-forge
jdcal 1.4.1 py_0
libblas 3.9.0 5_mkl conda-forge
libcblas 3.9.0 5_mkl conda-forge
liblapack 3.9.0 5_mkl conda-forge
mkl 2020.4 hb70f87d_311 conda-forge
mkl-service 2.3.0 py39h196d8e1_0
numpy 1.19.4 py39h6635163_1 conda-forge
openpyxl 3.0.5 py_0
openssl 1.1.1i h2bbff1b_0
pandas 1.2.0 py39h2e25243_0 conda-forge
pip 20.3.3 pyhd8ed1ab_0 conda-forge
python 3.9.1 h7840368_2_cpython conda-forge
python-dateutil 2.8.1 py_0 conda-forge
python_abi 3.9 1_cp39 conda-forge
pytz 2020.5 pyhd8ed1ab_0 conda-forge
scipy 1.5.2 py39h14eb087_0
setuptools 49.6.0 py39h467e6f4_2 conda-forge
six 1.15.0 pyh9f0ad1d_0 conda-forge
sqlite 3.34.0 h8ffe710_0 conda-forge
tzdata 2020f he74cb21_0 conda-forge
vc 14.2 hb210afc_2 conda-forge
vs2015_runtime 14.28.29325 h5e1d092_0 conda-forge
wheel 0.36.2 pyhd3deb0d_0 conda-forge
wincertstore 0.2 py39hde42818_1005 conda-forge
xlrd 2.0.1 pyhd3eb1b0_0
</details>
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- [ ] closes #xxxx
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Covers the case missed by #38843
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MDU6SXNzdWU3Nzc1MTk1MTY=
| 38,908
|
BUG: pandas 1.2.0 df.rolling().aggregate('skew') modified original data
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| 3
| 2021-01-02T20:22:25Z
| 2021-01-04T13:37:14Z
| 2021-01-04T13:37:14Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '1.2.0'
In [3]: import numpy as np
In [4]: df = pd.DataFrame({'a': [1., np.nan, 5.], 'b': [.1, .5, 1.0]})
In [5]: df
Out[5]:
a b
0 1.0 0.1
1 NaN 0.5
2 5.0 1.0
In [6]: df.rolling(4).aggregate('skew')
Out[6]:
a b
0 NaN NaN
1 NaN NaN
2 NaN NaN
In [7]: df
Out[7]:
a b
0 -2.0 -0.9
1 NaN -0.5
2 2.0 0.0
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
For pandas 1.2.0, `df.rolling().aggregate('skew')` modified original data. And I verified that `kurt` has the same problem.
#### Expected Output
Original data should not be modified, the behavior works well in pandas 1.1.5.
```
In [1]: import pandas as pd
In [3]: import numpy as np
In [4]: df = pd.DataFrame({'a': [1., np.nan, 5.], 'b': [.1, .5, 1.0]})
In [5]: pd.__version__
Out[5]: '1.1.5'
In [6]: df.rolling(4).aggregate('skew')
Out[6]:
a b
0 NaN NaN
1 NaN NaN
2 NaN NaN
In [7]: df
Out[7]:
a b
0 1.0 0.1
1 NaN 0.5
2 5.0 1.0
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
In [8]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Oct 29 22:56:45 PDT 2020; root:xnu-6153.141.2.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : zh_CN.UTF-8
LOCALE : zh_CN.UTF-8
pandas : 1.2.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : 0.29.16
pytest : 5.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : 2.7.2
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NTQ3ODA4MjE3
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|
BUG: Fixed regression in rolling.skew and rolling.kurt modifying object
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| 2
| 2021-01-02T23:07:19Z
| 2021-01-04T14:04:50Z
| 2021-01-04T13:37:14Z
|
MEMBER
| null |
- [x] closes #38908
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Alternative would be np.copy before releasing gil. In this case we would have to touch values_copy twice in case of ``min_val - mean_val > -1e5``
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REF: simplify Index.__new__
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| 1
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MEMBER
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Avoid recursing where possible.
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| 777,541,963
|
MDExOlB1bGxSZXF1ZXN0NTQ3ODA5Njgz
| 38,911
|
DOC: how to revert MultiIndex.to_flat_index
|
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| 1
| 2021-01-02T23:28:30Z
| 2021-01-04T01:12:59Z
| 2021-01-04T01:12:07Z
|
CONTRIBUTOR
| null |
Took me way too long to figure this out. Hopefully this benefits someone else!
|
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MDExOlB1bGxSZXF1ZXN0NTQ3ODE2NTM0
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|
CLN: re-use sanitize_index
|
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| 0
| 2021-01-03T01:02:09Z
| 2021-01-03T18:05:17Z
| 2021-01-03T17:11:39Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDU6SXNzdWU3Nzc1NjQyMTI=
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|
ENH: melt into categorical column
|
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[] | null | 2
| 2021-01-03T03:13:17Z
| 2021-08-14T23:10:16Z
| null |
NONE
| null |
Reference: https://stackoverflow.com/questions/64900604/categorical-column-after-melt-in-pandas
When the `value_vars` are being melted into `var_name`, can `var_name` be setup as a categorical dtype, either during or before the `melt`?
I'm hoping this would save a load of memory by having just a single copy of repetitive values...
|
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MDU6SXNzdWU3Nzc1NzAxMzY=
| 38,914
|
BUG: Timestamp.to_period() fails for freq="MS"
|
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| 2021-01-03T04:17:29Z
| 2021-08-14T23:00:24Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
idx = pd.date_range(start='1/1/2018', end='12/1/2018', freq="MS")
idx[-1].to_period()
```
#### Problem description
This results in the following exception:
```python
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
pandas\_libs\tslibs\period.pyx in pandas._libs.tslibs.period.freq_to_dtype_code()
AttributeError: 'pandas._libs.tslibs.offsets.MonthBegin' object has no attribute '_period_dtype_code'
The above exception was the direct cause of the following exception:
ValueError Traceback (most recent call last)
<ipython-input-9-8bcd22fce921> in <module>
1 idx = pd.date_range(start='1/1/2018', end='12/1/2018', freq="MS")
----> 2 idx[-1].to_period()
pandas\_libs\tslibs\timestamps.pyx in pandas._libs.tslibs.timestamps._Timestamp.to_period()
pandas\_libs\tslibs\period.pyx in pandas._libs.tslibs.period.Period.__new__()
pandas\_libs\tslibs\period.pyx in pandas._libs.tslibs.period.freq_to_dtype_code()
ValueError: Invalid frequency: {0}
```
`Timestamp.to_period()` does not work for `freq="MS"`, other `freq` values might also not work?
#### Expected Output
```python
Period('2018-01', 'M')
```
This can only be achieved by the following workaround:
```python
idx = pd.date_range(start='1/1/2018 00:00:00', end='1/5/2018 00:01:00', freq="MS")
cutoff = idx[-1]
date = pd.DatetimeIndex([cutoff], freq=cutoff.freq)
date.to_period()[0]
>>> Period('2018-01', 'M')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : AMD64 Family 21 Model 112 Stepping 0, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.1.5
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.1.post20201107
Cython : 0.29.21
pytest : 3.6.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.4
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.50.1
</details>
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MDExOlB1bGxSZXF1ZXN0NTQ3ODMzOTcx
| 38,915
|
REF: de-duplicate code in libparsing/libperiod
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MDExOlB1bGxSZXF1ZXN0NTQ3ODM2NzIx
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|
Remove Python2 numeric relics
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- [ ] closes #xxxx
- [x] tests added / passed
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- [ ] whatsnew entry
Modernize the code to Python 3 by removing Python 2 numeric hacks, such as:
- `a / b` of two integers now always returns a float, so no need to convert `a` and/or `b` to float (or to use float literals)
- `a // b` of two integers returns an integer, use it instead of `int(a / b)`
- `math.ceil`, `math.floor`, and `round` return integer, so no need to convert the result to integer
- `1e6` is a float, converting it to `int(1e6)` is slower than `10 ** 6` (or `1_000_000`)
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Passing DataFrame of values to fillna()
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| 6
| 2021-01-03T06:45:05Z
| 2021-02-20T18:25:19Z
| 2021-01-25T16:40:37Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
I'm not sure if this is a bug, a documentation issue, or just user error.
I found related issues #9750 #29048 that help me get the desired output but still don't explain why behavior doesn't match expectation based on my reading of the docs.
#### Code Sample
Following #9750 here's some code to reproduce the issue.
```python
df = pd.DataFrame([[1, 1, 1],[2, 1, 1],[2, 1, 1],
[np.nan, np.nan, np.nan]], columns=["a","b","c"])
df.fillna(df.mode())
```
The resulting DataFrame is just `df` itself, i.e. without any null values filled.
#### Problem description
I understand from the issues I mentioned (and numerous StackOverflow answers) that this is due to `df.mode()` returning a `DataFrame`, as opposed to a `Series`, (which is why e.g. `df.mean()` gives the expected results). To get the desired output `df.fillna(df.mode().iloc[0])` works in this case.
But I'm confused by [the docs](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.fillna.html) which describe the value parameter:
> value: scalar, dict, Series, or DataFrame
Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values
specifying which value to use for each index (for a Series) or column (for a
DataFrame). Values not in the dict/Series/DataFrame will not be filled. This value
cannot be a list.
From how I read this, you should be able to pass a DataFrame with the same column names and have values filled for those columns.
Just in case the passed in `DataFrame` needed to be the same shape, I tried
```python
df2 = pd.DataFrame(np.zeros((4, 3)))
df.fillna(df2)
```
But still got `df` back.
I read the source but the answer still eludes me.
#### Expected Output
The expected behavior is that the missing values will be filled with mode of each column.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.2
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.0.0.post20201207
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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ENH: change the category type to use unsigned int for the internal pointers
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"id": 219960758,
"name": "Needs Discussion",
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open
| false
| null |
[] | null | 5
| 2021-01-03T11:40:05Z
| 2021-08-14T23:11:20Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
It seems that the current implementation of the category type is using signed int for the pointers per item.
Thus, it will take 2 bytes (int16) per item as long as the number of unique items is less then 32K, then it goes up to 4 bytes per item.
Since there is no need for the sign here, we could use unsigned int (uint16) and have the pointers at 2 bytes for up to 64k unique items.
the same is true for moving from 1 byte to 2 bytes, or from 4 bytes to 8 bytes.
This can save a lot of memory for very large dataframes which use category types extensively.
#### Describe the solution you'd like
change the internal pointers to use unsigned int.
#### API breaking implications
n/a
#### Describe alternatives you've considered
n/a
#### Additional context
|
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https://api.github.com/repos/pandas-dev/pandas/issues/38918/timeline
| null | null | null |
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