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} | 1 | "2021-07-20T04:40:51Z" | "2021-07-26T02:57:10Z" | "2021-07-22T04:23:41Z" | CONTRIBUTOR | null | read_clipboard passes the text to read_csv not read_table. See here for example: https://pandas.pydata.org/docs/dev/reference/api/pandas.read_clipboard.html
also replace "OS X" -> macOS
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 4 | "2021-07-20T10:28:28Z" | "2021-07-28T17:24:34Z" | "2021-07-23T23:25:52Z" | CONTRIBUTOR | null | This addresses part of the bug identified in #42537, where column/index objects are not `MultiIndexes`.
Will need a follow-on to fully correct for the `MultiIndex` case.
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
pd.to_datetime(1, unit='s')
# Timestamp('1970-01-01 00:00:01')
pd.to_datetime(1, unit='s', origin=1)
# Current --> Timestamp('1970-01-01 00:00:01')
# Expected -> Timestamp('1970-01-01 00:00:02')
pd.to_datetime(1, unit='s', origin=1000000000)
# Current --> Timestamp('1970-01-01 00:00:02')
# Expected -> Timestamp('2001-09-09 01:46:41')
```
#### Problem description
The numeric values of origin should be parsed as number of units defined by unit
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.9.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19042
machine : AMD64
processor : Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.950
pandas : 1.3.0
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 57.0.0
Cython : 0.29.23
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.25.0
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : 2021.06.1
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : None
tables : None
tabulate : 0.8.9
xarray : None
xlrd : 2.0.1
xlwt : None
numba : 0.53.1
</details>
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- [Y] I have asked my usage related question on [StackOverflow](https://stackoverflow.com/questions/68456672/group-by-rolling-slow-on-one-instance-but-faster-on-another-is-it-possible-to-o).
---
#### Question about pandas
I wanted to perform group by rolling sum, but I see quite different performance in two types of cases. Consider this two case:
Case 1, "time" and "id" has roughly the same size of 3000.
import pandas as pd
import numpy as np
size_now = 1e6
df = pd.DataFrame({"time":np.random.randint(3000,size=int(size_now)),
"id":np.random.randint(3000,size=int(size_now)),
"data":np.random.randint(1000,size=int(size_now))})
df.time = pd.to_datetime(df.time,unit="s")
df = df.groupby(["id","time"]).sum().reset_index()
then
%time df.sort_values(["id","time"]).groupby("id").rolling("1001ms",on="time")[["data"]].sum().sort_index()
executed in 692ms, finished 10:25:20 2021-07-20
CPU times: user 568 ms, sys: 41.6 ms, total: 610 ms
Wall time: 671 ms
It took 700ms to complete the calculations.
Case 2, where `id` varies significantly more than `time`:
import pandas as pd
import numpy as np
size_now = 1e6
df = pd.DataFrame({"time":np.random.randint(162,size=int(size_now)),
"id":np.random.randint(70000,size=int(size_now)),
"data":np.random.randint(1000,size=int(size_now))})
df.time = pd.to_datetime(df.time,unit="s")
df = df.groupby(["id","time"]).sum().reset_index()
Then
%time df.sort_values(["id","time"]).groupby("id").rolling("1001ms",on="time")[["data"]].sum().sort_index()
executed in 2.90s, finished 10:28:39 2021-07-20
CPU times: user 2.84 s, sys: 45.7 ms, total: 2.89 s
Wall time: 2.88 s
speed is much slower now... is it possible to optimize Case 2 to have similar performance as Case 1? | {
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} | 2 | "2021-07-20T15:14:10Z" | "2021-08-10T20:03:34Z" | "2021-08-10T20:03:34Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
pd.Series([1, 2, 3], dtype="Int64").quantile([0.75])
```
The result of the above quantile call should be `2.5`, and the following error is thrown any time the result of the quantile call is a float: `TypeError: cannot safely cast non-equivalent object to int64`. Below, when we only calculate the `0.5` quantile (which is 2), no error is thrown:
```python
pd.Series([1, 2, 3], dtype="Int64").quantile([0.5])
```
#### Problem description
In 1.2.5, this call works for Int64Dtype with the resulting dtype being `object`, and in 1.3.0, this call works for the non nullable `int64` with dtype `float64`.
#### Expected Output
If we want to go with the 1.2.5 output, the expected output would be to not error and result in a Series with dtype `object`. It could also make sense for the resulting series to have `float64` dtype.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
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 : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 41.2.0
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.7
fastparquet : 0.5.0
gcsfs : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.1
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/42627 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42627/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42627/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42627/events | https://github.com/pandas-dev/pandas/issues/42627 | 948,764,022 | MDU6SXNzdWU5NDg3NjQwMjI= | 42,627 | BUG: conda package for pandas and python37 on linux64 install wrong pandas-1.3.0-py39 | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
On linux (debian)
```bash
conda create -n toto python=3.7 pandas
```
#### Problem description
Once executed the command above will install python37 but pandas for python39
```bash
root@86dbda50e2d0:~# conda create -n toto python=3.7 pandas
Collecting package metadata (current_repodata.json): done
Solving environment: done
## Package Plan ##
environment location: /root/miniconda/envs/toto
added / updated specs:
- pandas
- python=3.7
The following packages will be downloaded:
package | build
---------------------------|-----------------
bottleneck-1.3.2 | py37heb32a55_1 125 KB
numexpr-2.7.3 | py37h22e1b3c_1 188 KB
pandas-1.3.0 | py39h295c915_0 9.6 MB
pip-21.1.3 | py37h06a4308_0 1.8 MB
python-3.7.10 | h12debd9_4 44.0 MB
------------------------------------------------------------
Total: 55.6 MB
The following NEW packages will be INSTALLED:
_libgcc_mutex pkgs/main/linux-64::_libgcc_mutex-0.1-main
_openmp_mutex pkgs/main/linux-64::_openmp_mutex-4.5-1_gnu
blas pkgs/main/linux-64::blas-1.0-mkl
bottleneck pkgs/main/linux-64::bottleneck-1.3.2-py37heb32a55_1
ca-certificates pkgs/main/linux-64::ca-certificates-2021.7.5-h06a4308_1
certifi pkgs/main/linux-64::certifi-2021.5.30-py37h06a4308_0
intel-openmp pkgs/main/linux-64::intel-openmp-2021.3.0-h06a4308_3350
ld_impl_linux-64 pkgs/main/linux-64::ld_impl_linux-64-2.35.1-h7274673_9
libffi pkgs/main/linux-64::libffi-3.3-he6710b0_2
libgcc-ng pkgs/main/linux-64::libgcc-ng-9.3.0-h5101ec6_17
libgomp pkgs/main/linux-64::libgomp-9.3.0-h5101ec6_17
libstdcxx-ng pkgs/main/linux-64::libstdcxx-ng-9.3.0-hd4cf53a_17
mkl pkgs/main/linux-64::mkl-2021.3.0-h06a4308_520
mkl-service pkgs/main/linux-64::mkl-service-2.4.0-py37h7f8727e_0
mkl_fft pkgs/main/linux-64::mkl_fft-1.3.0-py37h42c9631_2
mkl_random pkgs/main/linux-64::mkl_random-1.2.2-py37h51133e4_0
ncurses pkgs/main/linux-64::ncurses-6.2-he6710b0_1
numexpr pkgs/main/linux-64::numexpr-2.7.3-py37h22e1b3c_1
numpy pkgs/main/linux-64::numpy-1.20.3-py37hf144106_0
numpy-base pkgs/main/linux-64::numpy-base-1.20.3-py37h74d4b33_0
openssl pkgs/main/linux-64::openssl-1.1.1k-h27cfd23_0
pandas pkgs/main/linux-64::pandas-1.3.0-py39h295c915_0
pip pkgs/main/linux-64::pip-21.1.3-py37h06a4308_0
python pkgs/main/linux-64::python-3.7.10-h12debd9_4
python-dateutil pkgs/main/noarch::python-dateutil-2.8.2-pyhd3eb1b0_0
pytz pkgs/main/noarch::pytz-2021.1-pyhd3eb1b0_0
readline pkgs/main/linux-64::readline-8.1-h27cfd23_0
setuptools pkgs/main/linux-64::setuptools-52.0.0-py37h06a4308_0
six pkgs/main/noarch::six-1.16.0-pyhd3eb1b0_0
sqlite pkgs/main/linux-64::sqlite-3.36.0-hc218d9a_0
tk pkgs/main/linux-64::tk-8.6.10-hbc83047_0
wheel pkgs/main/noarch::wheel-0.36.2-pyhd3eb1b0_0
xz pkgs/main/linux-64::xz-5.2.5-h7b6447c_0
zlib pkgs/main/linux-64::zlib-1.2.11-h7b6447c_3
Proceed ([y]/n)?
```
#### Expected Output
Install pandas-1.3.0-py37hxxxx instead
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/42629 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42629/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42629/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42629/events | https://github.com/pandas-dev/pandas/issues/42629 | 948,781,040 | MDU6SXNzdWU5NDg3ODEwNDA= | 42,629 | pandas.DataFrame.pivot_table cannot generate a correct Pivot Table for financial data of `General Ledger`. | {
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] | closed | false | null | [] | null | 7 | "2021-07-20T15:36:53Z" | "2021-08-21T19:13:53Z" | "2021-08-21T19:13:53Z" | NONE | null | ## description
As I have to cope with financial data, like an accountant, pivot table is a powerful tool for me.Yet recently, calling method `pandas.DataFrame.pivot_table`, I encountered a problem.
With data of `General Ledgers` (17 columns and over 7000 rows) I had expected to get a pivot table with values = `debit amount minus credit amount` for each `journal ID`, which identifies each `journal entry`, columns = `account name`, and index = `journal ID`. According to the `primary accounting equation`, for each `journal entry` we have `sum of debit amount` equal `sum of credit amount`. Thus, sum of all data of a big pivot table, whose index is `journal ID`, whose columns is `account name` and whose values are `debit minus credit`, must be zero.
However, when having called `pandas.DataFrame.pivot_table`, I found that sum of all data of that pivot table, did not equals zero. To be carefully, I generated a pivot table with `Microsoft Excel`, and it was no doubt that sum of whole pivot table equaled zero.
## code
Talk is cheap, here is the code.
```python
#!/usr/bin/env python
from pandas import read_excel
d=read_excel(excel_file_of_gl,sheet_name='Sheet1',header=0,engine='openpyxl')
d.pivot_table(values=['drcr'],index=['jid'],columns=['accna']).to_excel(./data.xlsx')
# 'drcr' means 'debit minus credit','jid' means 'journal id', and 'accna' means 'account name'.
# open the output file, sum of all the 'drcr' value did not equaled zero, which was weird.
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} | 2 | "2021-07-20T16:16:18Z" | "2021-08-21T19:15:22Z" | null | NONE | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
data = {
"A": [15, np.nan, 5, 4],
"B": [15, 5, np.nan, 4],
}
df = pd.DataFrame(data)
df = df.astype({"A": "Int64", "B": "float64"})
df["C"] = df["A"] / df["B"]
print(df)
print(df.dtypes)
print(df.dropna(subset=["C"]))
```
This example outputs:
```
A B C
0 15 15.0 1.0
1 <NA> 5.0 <NA>
2 5 NaN NaN
3 4 4.0 1.0
A Int64
B float64
C Float64
dtype: object
A B C
0 15 15.0 1.0
2 5 NaN NaN
3 4 4.0 1.0
```
#### Problem description
Dividing an ``Int64`` column by a ``float64`` column results in a ``Float64`` column that can contain both ``<NA>`` and ``NaN`` values. However, only ``<NA>`` values will be removed by the ``dropna`` function. I would expect only ``<NA>`` values in the new column and I would also expect the ``dropna`` function to remove all NA variants (both ``<NA>`` and ``NaN``).
#### Expected Output
The row with index 2 should be dropped.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.9.4.final.0
python-bits : 64
OS : Darwin
OS-release : 20.5.0
Version : Darwin Kernel Version 20.5.0: Sat May 8 05:10:33 PDT 2021; root:xnu-7195.121.3~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.3.0
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 49.2.1
Cython : 0.29.23
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.23.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 2021.06.0
fastparquet : 0.6.3
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 4.0.1
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : 1.4.17
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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} | 4 | "2021-07-20T16:26:31Z" | "2021-07-24T04:25:45Z" | "2021-07-23T23:22:25Z" | MEMBER | null | ```
In [1]: from asv_bench.benchmarks.frame_ctor import *
In [2]: cls = FromArrays
In [3]: self = cls()
In [4]: self.setup()
In [5]: %timeit self.time_frame_from_arrays_int()
6.21 ms ± 184 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- master
3.27 ms ± 182 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- PR
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} | 1 | "2021-07-20T17:11:19Z" | "2021-07-20T19:26:25Z" | "2021-07-20T19:25:25Z" | MEMBER | null | Backport PR #42612 | {
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} | 1 | "2021-07-20T17:55:13Z" | "2021-07-22T22:54:19Z" | "2021-07-22T22:54:16Z" | MEMBER | null | - [x] closes #42570
- [x] tests added / passed
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Is there a better way to check the stacklevel here?
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} | 3 | "2021-07-20T18:04:29Z" | "2021-11-11T01:38:31Z" | "2021-07-25T14:15:41Z" | MEMBER | null | - [ ] closes #xxxx
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Don't re-run ArrayManager tests if we are already in ArrayManager mode.
P.S. I'm not sure what the arraymanager marker is supposed to be for. If it only runs the frame tests, wouldn't it be better to just specify that directly in the ``PYTEST_AM_CMD``.
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} | 1 | "2021-07-20T18:46:56Z" | "2021-07-25T14:07:06Z" | "2021-07-25T14:07:02Z" | CONTRIBUTOR | null | - [x] closes #28238
- [x] tests added / passed
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Test added to test_to_datetime.py to test the conversion to datetime for a series with a float index. | {
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- [x] tests added / passed
- [x] whatsnew entry 1.4.0
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https://api.github.com/repos/pandas-dev/pandas/issues/42638 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42638/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42638/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42638/events | https://github.com/pandas-dev/pandas/issues/42638 | 949,159,628 | MDU6SXNzdWU5NDkxNTk2Mjg= | 42,638 | date_range bug for | {
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When I use this code :
debut = df_idmatch.date.min().replace(tzinfo=None)
fin = df_idmatch.date.max().replace(tzinfo=None)
print(debut, fin)
dates=pd.date_range(start=debut,end=fin,freq='W')
date_index=dates.year.astype(str)+"-"+dates.weekofyear.astype(str).str.zfill(2)
date_index.tolist()
There is a trouble when you pass from the end of the year to next year. If you print date_index, it will print you :
Index(['2020-38', '2020-39', '2020-40', '2020-41', '2020-42', '2020-43',
'2020-44', '2020-45', '2020-46', '2020-47', '2020-48', '2020-49',
'2020-50', '2020-51', '2020-52', '**2021-53**', '2021-01', '2021-02',
'2021-03', '2021-04', '2021-05', '2021-06', '2021-07', '2021-08',
'2021-09', '2021-10', '2021-11', '2021-12', '2021-13', '2021-14',
'2021-15', '2021-16', '2021-17', '2021-18', '2021-19', '2021-20',
'2021-21', '2021-22', '2021-23', '2021-24', '2021-25', '2021-26',
'2021-27', '2021-28', '2021-29', '2021-30'],
dtype='object')
in fact, 2021-53 should be 2020-53
is it a problem with the date_range fonction or is it inside my own code ?
Regards,
Thibaud | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
# 51 rows results in missingness
df = pd.DataFrame({'date':['07.30.2020'] + ['30.07.2020']*50})
pd.to_datetime(df.date, dayfirst=True).isna().sum()
# 50 rows results in no missingness
pd.to_datetime(df.date[0:50], dayfirst=True).isna().sum()
# 51 rows with cache=False results in no missingness
pd.to_datetime(df.date, dayfirst=True, cache=False).isna().sum()
```
#### Problem description
Starting with v1.3.0 we began running into issues where pd.to_datetime would not convert all dates as expected if there are conflicting formats. It appears that the default behavior of `cache=True` behaves differently between the two versions. In v1.3.0 it seems that `cache=False` fixes the problem but that slows things down.
#### Expected Output
We would expect there to be no missingness with the default of `cache=True`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.7.10.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-957.12.2.el7.x86_64
Version : #1 SMP Tue May 14 21:24:32 UTC 2019
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.2
pip : 21.1.3
setuptools : 52.0.0.post20210125
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 0 | "2021-07-20T22:58:32Z" | "2021-07-24T03:29:23Z" | "2021-07-24T03:29:23Z" | CONTRIBUTOR | null | - [x] closes #42618
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Was introduced in v1.3.0 [here](https://github.com/pandas-dev/pandas/pull/39326). I couldn't find a reason why `len(lchanges)` should be used and the unit tests still passed with `len(val)` instead. | {
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```
>>> df = pd.DataFrame([{'x': 60}, {'x': 70}, {'x': 1514}, {'x': 1514}, {'x': 1514}, {'x': 1019}, {'x': 60}, {'x': 77}, {'x': 60}, {'x': 60}, {'x': 60}], dtype=np.uint32)
>>> df['x'].quantile(0.75)
1266.5
```
Now Pandas:
```
>>> df = pd.DataFrame([{'x': 60}, {'x': 70}, {'x': 1514}, {'x': 1514}, {'x': 1514}, {'x': 1019}, {'x': 60}, {'x': 77}, {'x': 60}, {'x': 60}, {'x': 60}], dtype=pd.UInt32Dtype())
>>> df['x'].quantile(0.75)
Traceback (most recent call last):
File "/usr/local/lib/python3.9/site-packages/pandas/core/arrays/integer.py", line 126, in safe_cast
return values.astype(dtype, casting="safe", copy=copy)
TypeError: Cannot cast array data from dtype('O') to dtype('uint32') according to the rule 'safe'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.9/site-packages/pandas/core/series.py", line 2447, in quantile
result = df.quantile(q=q, interpolation=interpolation, numeric_only=False)
File "/usr/local/lib/python3.9/site-packages/pandas/core/frame.py", line 10290, in quantile
res = self.quantile(
File "/usr/local/lib/python3.9/site-packages/pandas/core/frame.py", line 10309, in quantile
res = data._mgr.quantile(qs=q, axis=1, interpolation=interpolation)
File "/usr/local/lib/python3.9/site-packages/pandas/core/internals/managers.py", line 1338, in quantile
blocks = [
File "/usr/local/lib/python3.9/site-packages/pandas/core/internals/managers.py", line 1339, in <listcomp>
blk.quantile(axis=axis, qs=qs, interpolation=interpolation)
File "/usr/local/lib/python3.9/site-packages/pandas/core/internals/blocks.py", line 1324, in quantile
result = quantile_compat(self.values, np.asarray(qs._values), interpolation)
File "/usr/local/lib/python3.9/site-packages/pandas/core/array_algos/quantile.py", line 46, in quantile_compat
out = _quantile_ea_fallback(values, qs, interpolation)
File "/usr/local/lib/python3.9/site-packages/pandas/core/array_algos/quantile.py", line 184, in _quantile_ea_fallback
out = type(values)._from_sequence(res, dtype=values.dtype)
File "/usr/local/lib/python3.9/site-packages/pandas/core/arrays/integer.py", line 323, in _from_sequence
values, mask = coerce_to_array(scalars, dtype=dtype, copy=copy)
File "/usr/local/lib/python3.9/site-packages/pandas/core/arrays/integer.py", line 231, in coerce_to_array
values = safe_cast(values, dtype, copy=False)
File "/usr/local/lib/python3.9/site-packages/pandas/core/arrays/integer.py", line 133, in safe_cast
raise TypeError(
TypeError: cannot safely cast non-equivalent object to uint32
```
```
root@572eb14c4d4f:/tmp# pip3 show numpy
Name: numpy
Version: 1.21.0
Summary: NumPy is the fundamental package for array computing with Python.
Home-page: https://www.numpy.org
Author: Travis E. Oliphant et al.
Author-email: None
License: BSD
Location: /usr/local/lib/python3.9/site-packages
Requires:
Required-by: scipy, scikit-learn, pandas
root@572eb14c4d4f:/tmp# pip3 show pandas
Name: pandas
Version: 1.3.0
Summary: Powerful data structures for data analysis, time series, and statistics
Home-page: https://pandas.pydata.org
Author: The Pandas Development Team
Author-email: [email protected]
License: BSD-3-Clause
Location: /usr/local/lib/python3.9/site-packages
Requires: pytz, numpy, python-dateutil
root@572eb14c4d4f:/tmp# python3 --version
Python 3.9.6
```
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Pass the provided copy argument through to the DataFrame constructor when constructing a DataFrame from a Series iterable, as is done with the other DataFrame construction call in the get_result method. | {
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- [x] closes #42530
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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] | open | false | null | [] | null | 2 | "2021-07-21T14:43:02Z" | "2021-07-31T06:09:16Z" | null | NONE | null | #### Is your feature request related to a problem?
I would like to be able to query a table immediately after creating it using `df.to_sql()`, in the SQLAlchemy interface.
#### Describe the solution you'd like
Currently `to_sql()` has no return value. I suggest simply returning the new table object, which is of type `sqlalchemy.Table`.
#### API breaking implications
None, as `to_sql()` currently has no return value.
#### Describe alternatives you've considered
* You can query the table immediately using raw SQL as shown in the documentation for this function, but this is relatively low-level
* You could obtain a `Table` object from the [`automap`](https://docs.sqlalchemy.org/en/14/orm/extensions/automap.html) extension. However this is redundant as Pandas at some point generated the table metadata so this would involve throwing it away and then trying to guess what it used to be from the state of the database.
#### Additional context
This conflicts with #23998, which suggests that we return the `ResultProxy` instead. Perhaps we return both, as a tuple? Both of these objects would be useful.
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} | 8 | "2021-07-21T18:14:18Z" | "2021-08-04T04:31:55Z" | "2021-08-04T04:23:27Z" | CONTRIBUTOR | null | - [x] closes #42452
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 1 | "2021-07-21T18:17:16Z" | "2021-07-26T08:20:30Z" | "2021-07-25T14:18:38Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pickle
import pandas as pd
index = [1, 1, 3, 4, 5] # note the non-unique index!!
data = [1, 2, 3, 4, 5]
df = pd.DataFrame(data=data, index=index)
# prepent an index-column using concat
df = pd.concat([df],
keys=["A"],
names=["ID", "date"])
dump = pickle.dumps(df)
load = pickle.loads(dump)
```
```python
Traceback (most recent call last):
File "<ipython-input-71-cb78b67666e1>", line 9, in <module>
load = pickle.loads(dump)
File "C:\Users\rquast\Miniconda3\envs\rt1\lib\site-packages\pandas\core\indexes\base.py", line 242, in _new_Index
return cls.__new__(cls, **d)
File "C:\Users\rquast\Miniconda3\envs\rt1\lib\site-packages\pandas\core\indexes\multi.py", line 339, in __new__
new_codes = result._verify_integrity()
File "C:\Users\rquast\Miniconda3\envs\rt1\lib\site-packages\pandas\core\indexes\multi.py", line 413, in _verify_integrity
f"Level values must be unique: {list(level)} on level {i}"
ValueError: Level values must be unique: [1, 1, 3, 4, 5] on level 1
```
... doing the very same thing manually works just fine
```python
multiindex = pd.MultiIndex.from_product([["A"], index], names=["ID", "date"])
df2 = pd.DataFrame(data=data,
index=multiindex,
)
dump = pickle.dumps(df2)
load = pickle.loads(dump)
load.equals(df)
>>> True
```
#### Problem description
I guess there's not much problem-description needed...
I stumbled upon this really puzzling issue while working on a multiprocessing-routine that puts pandas-dataframes in a queue...
... it took a while until i realized where the actual problem was coming from... I still don't get what's happening here though...
just to show what I mean... this is where the error originally occured:
```python
import multiprocessing as mp
manager = mp.Manager()
queue = manager.Queue()
queue.put(df)
```
#### Expected Output
I'd like pd.concat to output a nicely pickleable DataFrame
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.7.10.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17763
machine : AMD64
processor : AMD64 Family 25 Model 33 Stepping 0, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 1.3.0
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.1.3
setuptools : 49.6.0.post20210108
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : 4.1.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.25.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : 0.18.2
xlrd : None
xlwt : None
numba : None
</details>
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to_excel could be faster
#### Describe the solution you'd like
openpyxl's write only workbook is very fast and should be used
https://openpyxl.readthedocs.io/en/stable/optimized.html
```python
# Your code here, if applicable
def to_excel_fast(df,name):
wb = openpyxl.Workbook(write_only = True)
ws=wb.create_sheet()
ws.title = "df"
#Set up header row
col_row = []
BOLD_FONT = openpyxl.styles.Font(bold=True)
BOTTOM_LEFT_RIGHT_BORDER = openpyxl.styles.Border(bottom=openpyxl.styles.Side(style='thin'),left=openpyxl.styles.Side(style='thin'),right=openpyxl.styles.Side(style='thin'))
CENTER_ALIGNMENT_NO_WRAP = openpyxl.styles.Alignment(horizontal='center')
for column in df.columns:
newCell = openpyxl.cell.WriteOnlyCell(ws,value=column)
newCell.font = BOLD_FONT
newCell.border = BOTTOM_LEFT_RIGHT_BORDER
newCell.alignment = CENTER_ALIGNMENT_NO_WRAP
col_row.append(newCell)
ws.append(col_row)
#Add data
for i in range(len(df)):
ws.append(tuple(df.iloc[i]))
wb.save(name)
```
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] | closed | false | null | [] | null | 6 | "2021-07-21T21:39:02Z" | "2021-07-22T21:21:07Z" | "2021-07-22T21:21:07Z" | NONE | null | - [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [x] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
In rolling aggregation, `min_periods` dictates that if there's `N` valid observations, and that `N >= min_periods`, the aggregation should be computed with all valid observations in the window. However, for `std` and `var` aggregation, the above guideline is not observed:
```python
# Your code here, if applicable
s = pd.Series([1.0, 1.0, 1.0, np.nan, 1.0, 2.0])
s.rolling(2, min_periods=1).std()
0 NaN
1 0.000000
2 0.000000
3 NaN
4 NaN
5 0.707107
dtype: float64
```
The question is: should `min_periods` have effects on `std` aggregation? | {
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} | 7 | "2021-07-22T08:29:22Z" | "2021-10-26T15:31:48Z" | null | NONE | null | - [y] I have checked that this issue has not already been reported.
- [y] 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
this is a tiny example
```python
tmp = pd.DataFrame({'id': ['1624477460271-3908654213', '1624477460271-3908654213'], 'dt':[np.nan, pd.to_timedelta('0:0:2')]})
tmp
Out[6]:
id dt
0 1624477460271-3908654213 NaT
1 1624477460271-3908654213 0 days 00:00:02
```
#### Problem description
dataframe.groupby('some_column').timedelta.sum() results wrong when timedelta contains NaT
```python
tmp.groupby('id').dt.sum()
Out[7]:
id
1624477460271-3908654213 -106752 days +00:12:45.145224192
Name: dt, dtype: timedelta64[ns]
```
#### Expected Output
```python
tmp.dt.sum()
Out[8]: Timedelta('0 days 00:00:02')
```
#### Output of ``pd.show_versions()``
<details>
[pandas 1.3.0]
</details>
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} | 2 | "2021-07-22T12:36:29Z" | "2021-07-22T20:24:49Z" | "2021-07-22T20:24:45Z" | MEMBER | null |
Backport PR #42261 | {
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} | 1 | "2021-07-22T12:41:33Z" | "2021-07-22T20:26:43Z" | "2021-07-22T20:25:05Z" | MEMBER | null | xref #42660, https://github.com/pandas-dev/pandas/issues/42259#issuecomment-884870958
release note is moved in backport #42660, so this PR does not need backport. | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**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
>>> df = pd.DataFrame([[4, 9]]* 3 + [[3, 8]] * 2, columns=['A', 'B'])
>>> df.groupby('A').apply(max)
A B
A
3 3 8
4 4 9
>>> df.groupby('A').apply(max, raw=True)
Traceback (most recent call last):
File "C:\Program Files\Python39\lib\site-packages\pandas\core\groupby\groupby.py", line 1253, in apply
result = self._python_apply_general(f, self._selected_obj)
File "C:\Program Files\Python39\lib\site-packages\pandas\core\groupby\groupby.py", line 1287, in _python_apply_general
keys, values, mutated = self.grouper.apply(f, data, self.axis)
File "C:\Program Files\Python39\lib\site-packages\pandas\core\groupby\ops.py", line 783, in apply
result_values, mutated = splitter.fast_apply(f, sdata, group_keys)
File "C:\Program Files\Python39\lib\site-packages\pandas\core\groupby\ops.py", line 1328, in fast_apply
return libreduction.apply_frame_axis0(sdata, f, names, starts, ends)
File "pandas\_libs\reduction.pyx", line 381, in pandas._libs.reduction.apply_frame_axis0
File "C:\Program Files\Python39\lib\site-packages\pandas\core\groupby\groupby.py", line 1237, in f
return func(g, *args, **kwargs)
File "<__array_function__ internals>", line 4, in amax
TypeError: _amax_dispatcher() got an unexpected keyword argument 'raw'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Program Files\Python39\lib\site-packages\pandas\core\groupby\groupby.py", line 1264, in apply
return self._python_apply_general(f, self._selected_obj)
File "C:\Program Files\Python39\lib\site-packages\pandas\core\groupby\groupby.py", line 1287, in _python_apply_general
keys, values, mutated = self.grouper.apply(f, data, self.axis)
File "C:\Program Files\Python39\lib\site-packages\pandas\core\groupby\ops.py", line 783, in apply
result_values, mutated = splitter.fast_apply(f, sdata, group_keys)
File "C:\Program Files\Python39\lib\site-packages\pandas\core\groupby\ops.py", line 1328, in fast_apply
return libreduction.apply_frame_axis0(sdata, f, names, starts, ends)
File "pandas\_libs\reduction.pyx", line 381, in pandas._libs.reduction.apply_frame_axis0
File "C:\Program Files\Python39\lib\site-packages\pandas\core\groupby\groupby.py", line 1237, in f
return func(g, *args, **kwargs)
File "<__array_function__ internals>", line 4, in amax
TypeError: _amax_dispatcher() got an unexpected keyword argument 'raw'
```
#### Problem description
When calling apply after a groupby and using additional parameters such as "raw", or "result_type" it raises a
```TypeError: _amax_dispatcher() got an unexpected keyword argument 'raw'``` as if the parameter was passed to "func" instead of being passed to ```apply```. When used without a groupby or with a groupby and without any additional parameter, it works fine. A similar issue has been raised in SOF https://stackoverflow.com/questions/39125895/how-to-use-keyword-raw-in-apply-to-a-groupby-object but the answers do not really help soliving the problem.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
pandas : 1.3.0
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 56.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.24.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.1
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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I wish I could use pandas.read_csv() to include the original line number of csv file. It can be useful for error report in case of invalid entry (invalid from an application point of view not from csv's one). Given that users can skip few starting lines, blank lines or commented ones, the df index may not correspond to the original line number from csv file.
#### Describe the solution you'd like
`read_csv()` should include an optional parameter, let say `append_line_numbers=None`. If its value is None, nothing happens, i.e. the classical way of functioning is kept. If it is a string, its value is used to name a column in a returned data frame that will contain line numbers
#### API breaking implications
No API breaking is expected
#### Describe alternatives you've considered
read the file without skipping any line then remove headers, blank lines, etc. "manually" in the users's application | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
a = pd.DataFrame({"A": pd.array(['a', 'b'], dtype=pd.StringDtype("pyarrow"))})
a.to_parquet("test.parquet")
b = pd.read_parquet("test.parquet")
pd.testing.assert_frame_equal(a, b)
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
/tmp/ipykernel_493/3001616580.py in <module>
3 a.to_parquet("test.parquet")
4 b = pd.read_parquet("test.parquet")
----> 5 pd.testing.assert_frame_equal(a, b)
[... skipping hidden 3 frame]
/srv/conda/envs/notebook/lib/python3.8/site-packages/pandas/_testing/asserters.py in raise_assert_detail(obj, message, left, right, diff, index_values)
663 msg += f"\n[diff]: {diff}"
664
--> 665 raise AssertionError(msg)
666
667
AssertionError: Attributes of DataFrame.iloc[:, 0] (column name="A") are different
Attribute "dtype" are different
[left]: string[pyarrow]
[right]: string[python]
```
#### Problem description
read_parquet currently loads all string dtype as `string[python]`. We'd ideally match what was written.
#### Expected Output
A DataFrame with `string[pyarrow]` rather than `string[python]`
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-1040-azure
Version : #42~18.04.1-Ubuntu SMP Mon Feb 8 19:05:32 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : C.UTF-8
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.7.5
pip : 20.3.4
setuptools : 49.6.0.post20210108
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : 1.10.2
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.9.1 (dt dec pq3 ext lo64)
jinja2 : 3.0.1
IPython : 7.25.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.06.1
fastparquet : None
gcsfs : 2021.06.1
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.0
pyxlsb : None
s3fs : 2021.06.1
scipy : 1.7.0
sqlalchemy : 1.4.20
tables : None
tabulate : 0.8.9
xarray : 0.18.2
xlrd : None
xlwt : None
numba : 0.53.1
```
</details>
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
added test to pandas/tests/indexing/multi/test_loc.py | {
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 3 | "2021-07-22T16:56:28Z" | "2021-07-28T01:23:37Z" | "2021-07-28T01:23:33Z" | CONTRIBUTOR | null | - [x] closes #32556
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 2 | "2021-07-22T18:14:53Z" | "2021-08-09T17:06:55Z" | "2021-08-04T23:46:18Z" | MEMBER | null | - [ ] closes #39383
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Precursor for pyarrow csv reader integration.
@jorisvandenbossche Do you know if there is any workaround I can use to read StringIO's on the arrow side? Adding this wrapper feels very hacky. | {
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Fix the next doctest error:
```
___________________________________________________________________ [doctest] pandas.io.stata.read_stata ____________________________________________________________________
2044 Categorical variables read through an iterator may not have the same
2045 categories and dtype. This occurs when a variable stored in a DTA
2046 file is associated to an incomplete set of value labels that only
2047 label a strict subset of the values.
2048
2049 Examples
2050 --------
2051 Read a Stata dta file:
2052
2053 >>> df = pd.read_stata('filename.dta')
UNEXPECTED EXCEPTION: FileNotFoundError(2, 'No such file or directory')
Traceback (most recent call last):
File "/home/mgarcia/miniconda3/envs/pandas-dev/lib/python3.8/doctest.py", line 1336, in __run
exec(compile(example.source, filename, "single",
File "<doctest pandas.io.stata.read_stata[0]>", line 1, in <module>
File "/home/mgarcia/src/pandas/pandas/io/stata.py", line 1946, in read_stata
reader = StataReader(
File "/home/mgarcia/src/pandas/pandas/io/stata.py", line 1091, in __init__
with get_handle(
File "/home/mgarcia/src/pandas/pandas/io/common.py", line 710, in get_handle
handle = open(handle, ioargs.mode)
FileNotFoundError: [Errno 2] No such file or directory: 'filename.dta'
/home/mgarcia/src/pandas/pandas/io/stata.py:2053: UnexpectedException
```
# Detailed instructions
Python allows to have example code in the documentation, like in:
```python
def add(num1, num2):
"""
Computes the sum of the two numbers.
Examples
--------
>>> add(2, 2)
4
"""
return num1 + num2
```
In pandas, we use this to document most elements. And there are tools, like pytest,
that can run the examples, and make sure everything is correct.
For historical reasons, we have many examples where the code fails to run, or the
actual output is different from the expected output. For example, check the next
incorrect examples:
```python
def add(num1, num2):
"""
Computes the sum of the two numbers.
Examples
--------
>>> add(2, 2)
5
>>> add(2, 2
4
>>> add(2, number)
4
...
"""
return num1 + num2
```
All them will fail for different reasons. To test the docstring of an object,
the next command can be run:
```
python -m pytest --doctest-modules pandas/core/frame.py::pandas.core.frame.DataFrame.info
```
Where `pandas/core/frame.py` is the file where the docstring is defined, and
`pandas.core.frame.DataFrame.info` is the object. A whole file can also be tested
by removing the `::` and the object from the command above.
In general, the errors in the examples can be fixed with things like:
- Fixing a typo (a missing comma, an mispelled variable name...)
- Adding an object that hasn't been defined (like, if `df` is used, but
no sample dataset `df` has been first defined)
- Fixing the expected output, when it's wrong
- In exceptional cases, examples shouldn't run, since they can't work.
For example, a function that connects to a private webservice. In
such cases, we can add `# doctest: +SKIP` at the end of the lines
that should not run
To be able to properly fix an example for the first time, the next steps
are needed:
- Install a pandas development environment in your computer. There are
simplified instructions [in this page](https://python-sprints.github.io/pandas/guide/pandas_setup.html),
and more detailed information in [pandas official contributing page](https://pandas.pydata.org/docs/development/index.html).
- Run the doctests for the object of interest (the one in this issue),
and make sure the examples are still broken in the `master` branch of
pandas
- Fix the file locally, and run the doctests again, to make sure the
fix is working as expected
- Optionally have a look and make sure that the code in the examples
follow PEP-8, and fix the style if it doesn't
- Commit your changes, push your branch to a fork, and open a pull
request. Make sure you edit the line `Closes #XXXX` with the issue
number you are addressing, so the issue is automatically closed,
when the pull request is merged
- Make sure the continuous integration of your pull request finishes
in green. If it doesn't, check if the problem is in your changes
(sometimes things break in master for technical problems, and in
that case you just need to wait for a core developer to fix the
problem)
- Address any comment from the reviewers (just make changes locally,
commit, and push to your branch, no need to open new pull requests) | {
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} | 4 | "2021-07-22T20:40:34Z" | "2021-07-30T15:51:30Z" | "2021-07-30T15:51:30Z" | MEMBER | null | # tl;dr
Fix the next doctest error:
```
_________________________________________________________ [doctest] pandas.io.formats.format.EngFormatter.__call__ __________________________________________________________
1956
1957 Formats a number in engineering notation, appending a letter
1958 representing the power of 1000 of the original number. Some examples:
1959
1960 >>> format_eng(0) # for self.accuracy = 0
UNEXPECTED EXCEPTION: NameError("name 'format_eng' is not defined")
Traceback (most recent call last):
File "/home/mgarcia/miniconda3/envs/pandas-dev/lib/python3.8/doctest.py", line 1336, in __run
exec(compile(example.source, filename, "single",
File "<doctest pandas.io.formats.format.EngFormatter.__call__[0]>", line 1, in <module>
NameError: name 'format_eng' is not defined
/home/mgarcia/src/pandas/pandas/io/formats/format.py:1960: UnexpectedException
```
# Detailed instructions
Python allows to have example code in the documentation, like in:
```python
def add(num1, num2):
"""
Computes the sum of the two numbers.
Examples
--------
>>> add(2, 2)
4
"""
return num1 + num2
```
In pandas, we use this to document most elements. And there are tools, like pytest,
that can run the examples, and make sure everything is correct.
For historical reasons, we have many examples where the code fails to run, or the
actual output is different from the expected output. For example, check the next
incorrect examples:
```python
def add(num1, num2):
"""
Computes the sum of the two numbers.
Examples
--------
>>> add(2, 2)
5
>>> add(2, 2
4
>>> add(2, number)
4
...
"""
return num1 + num2
```
All them will fail for different reasons. To test the docstring of an object,
the next command can be run:
```
python -m pytest --doctest-modules pandas/core/frame.py::pandas.core.frame.DataFrame.info
```
Where `pandas/core/frame.py` is the file where the docstring is defined, and
`pandas.core.frame.DataFrame.info` is the object. A whole file can also be tested
by removing the `::` and the object from the command above.
In general, the errors in the examples can be fixed with things like:
- Fixing a typo (a missing comma, an mispelled variable name...)
- Adding an object that hasn't been defined (like, if `df` is used, but
no sample dataset `df` has been first defined)
- Fixing the expected output, when it's wrong
- In exceptional cases, examples shouldn't run, since they can't work.
For example, a function that connects to a private webservice. In
such cases, we can add `# doctest: +SKIP` at the end of the lines
that should not run
To be able to properly fix an example for the first time, the next steps
are needed:
- Install a pandas development environment in your computer. There are
simplified instructions [in this page](https://python-sprints.github.io/pandas/guide/pandas_setup.html),
and more detailed information in [pandas official contributing page](https://pandas.pydata.org/docs/development/index.html).
- Run the doctests for the object of interest (the one in this issue),
and make sure the examples are still broken in the `master` branch of
pandas
- Fix the file locally, and run the doctests again, to make sure the
fix is working as expected
- Optionally have a look and make sure that the code in the examples
follow PEP-8, and fix the style if it doesn't
- Commit your changes, push your branch to a fork, and open a pull
request. Make sure you edit the line `Closes #XXXX` with the issue
number you are addressing, so the issue is automatically closed,
when the pull request is merged
- Make sure the continuous integration of your pull request finishes
in green. If it doesn't, check if the problem is in your changes
(sometimes things break in master for technical problems, and in
that case you just need to wait for a core developer to fix the
problem)
- Address any comment from the reviewers (just make changes locally,
commit, and push to your branch, no need to open new pull requests) | {
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} | 2 | "2021-07-22T20:41:46Z" | "2021-07-28T00:05:23Z" | "2021-07-28T00:05:23Z" | MEMBER | null | # tl;dr
Fix the next doctest error:
```
_____________________________________________________________ [doctest] pandas.io.formats.style.Styler.to_latex _____________________________________________________________
530 should be replaced by a LaTeX
531 ``(<command>, <options>)`` approach. Each cell will be styled individually
532 using nested LaTeX commands with their accompanied options.
533
534 For example the following code will highlight and bold a cell in HTML-CSS:
535
536 >>> df = pd.DataFrame([[1,2], [3,4]])
537 >>> s = df.style.highlight_max(axis=None,
538 ... props='background-color:red; font-weight:bold;')
539 >>> s.render()
Expected nothing
Got:
'<style type="text/css">\n#T_d86c0_row1_col1 {\n background-color: red;\n font-weight: bold;\n}\n</style>\n<table id="T_d86c0_">\n <thead>\n <tr>\n <th class="blank level0" > </th>\n <th class="col_heading level0 col0" >0</th>\n <th class="col_heading level0 col1" >1</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th id="T_d86c0_level0_row0" class="row_heading level0 row0" >0</th>\n <td id="T_d86c0_row0_col0" class="data row0 col0" >1</td>\n <td id="T_d86c0_row0_col1" class="data row0 col1" >2</td>\n </tr>\n <tr>\n <th id="T_d86c0_level0_row1" class="row_heading level0 row1" >1</th>\n <td id="T_d86c0_row1_col0" class="data row1 col0" >3</td>\n <td id="T_d86c0_row1_col1" class="data row1 col1" >4</td>\n </tr>\n </tbody>\n</table>\n'
/home/mgarcia/src/pandas/pandas/io/formats/style.py:539: DocTestFailure
```
# Detailed instructions
Python allows to have example code in the documentation, like in:
```python
def add(num1, num2):
"""
Computes the sum of the two numbers.
Examples
--------
>>> add(2, 2)
4
"""
return num1 + num2
```
In pandas, we use this to document most elements. And there are tools, like pytest,
that can run the examples, and make sure everything is correct.
For historical reasons, we have many examples where the code fails to run, or the
actual output is different from the expected output. For example, check the next
incorrect examples:
```python
def add(num1, num2):
"""
Computes the sum of the two numbers.
Examples
--------
>>> add(2, 2)
5
>>> add(2, 2
4
>>> add(2, number)
4
...
"""
return num1 + num2
```
All them will fail for different reasons. To test the docstring of an object,
the next command can be run:
```
python -m pytest --doctest-modules pandas/core/frame.py::pandas.core.frame.DataFrame.info
```
Where `pandas/core/frame.py` is the file where the docstring is defined, and
`pandas.core.frame.DataFrame.info` is the object. A whole file can also be tested
by removing the `::` and the object from the command above.
In general, the errors in the examples can be fixed with things like:
- Fixing a typo (a missing comma, an mispelled variable name...)
- Adding an object that hasn't been defined (like, if `df` is used, but
no sample dataset `df` has been first defined)
- Fixing the expected output, when it's wrong
- In exceptional cases, examples shouldn't run, since they can't work.
For example, a function that connects to a private webservice. In
such cases, we can add `# doctest: +SKIP` at the end of the lines
that should not run
To be able to properly fix an example for the first time, the next steps
are needed:
- Install a pandas development environment in your computer. There are
simplified instructions [in this page](https://python-sprints.github.io/pandas/guide/pandas_setup.html),
and more detailed information in [pandas official contributing page](https://pandas.pydata.org/docs/development/index.html).
- Run the doctests for the object of interest (the one in this issue),
and make sure the examples are still broken in the `master` branch of
pandas
- Fix the file locally, and run the doctests again, to make sure the
fix is working as expected
- Optionally have a look and make sure that the code in the examples
follow PEP-8, and fix the style if it doesn't
- Commit your changes, push your branch to a fork, and open a pull
request. Make sure you edit the line `Closes #XXXX` with the issue
number you are addressing, so the issue is automatically closed,
when the pull request is merged
- Make sure the continuous integration of your pull request finishes
in green. If it doesn't, check if the problem is in your changes
(sometimes things break in master for technical problems, and in
that case you just need to wait for a core developer to fix the
problem)
- Address any comment from the reviewers (just make changes locally,
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Fix the next doctest error:
```
_____________________________________________________ [doctest] pandas.io.formats.style_render._parse_latex_header_span _____________________________________________________
1329 ``wrap`` is used to enclose the `display_value` in braces which is needed for
1330 column headers using an siunitx package.
1331
1332 Requires the package {multirow}, whereas multicol support is usually built in
1333 to the {tabular} environment.
1334
1335 Examples
1336 --------
1337 >>> cell = {'display_vale':'text', 'attributes': 'colspan="3"'}
1338 >>> _parse_latex_header_span(cell, 't', 'c')
UNEXPECTED EXCEPTION: KeyError('display_value')
Traceback (most recent call last):
File "/home/mgarcia/miniconda3/envs/pandas-dev/lib/python3.8/doctest.py", line 1336, in __run
exec(compile(example.source, filename, "single",
File "<doctest pandas.io.formats.style_render._parse_latex_header_span[1]>", line 1, in <module>
File "/home/mgarcia/src/pandas/pandas/io/formats/style_render.py", line 1347, in _parse_latex_header_span
f"\\multicolumn{{{colspan}}}{{{multicol_align}}}"
KeyError: 'display_value'
/home/mgarcia/src/pandas/pandas/io/formats/style_render.py:1338: UnexpectedException
```
# Detailed instructions
Python allows to have example code in the documentation, like in:
```python
def add(num1, num2):
"""
Computes the sum of the two numbers.
Examples
--------
>>> add(2, 2)
4
"""
return num1 + num2
```
In pandas, we use this to document most elements. And there are tools, like pytest,
that can run the examples, and make sure everything is correct.
For historical reasons, we have many examples where the code fails to run, or the
actual output is different from the expected output. For example, check the next
incorrect examples:
```python
def add(num1, num2):
"""
Computes the sum of the two numbers.
Examples
--------
>>> add(2, 2)
5
>>> add(2, 2
4
>>> add(2, number)
4
...
"""
return num1 + num2
```
All them will fail for different reasons. To test the docstring of an object,
the next command can be run:
```
python -m pytest --doctest-modules pandas/core/frame.py::pandas.core.frame.DataFrame.info
```
Where `pandas/core/frame.py` is the file where the docstring is defined, and
`pandas.core.frame.DataFrame.info` is the object. A whole file can also be tested
by removing the `::` and the object from the command above.
In general, the errors in the examples can be fixed with things like:
- Fixing a typo (a missing comma, an mispelled variable name...)
- Adding an object that hasn't been defined (like, if `df` is used, but
no sample dataset `df` has been first defined)
- Fixing the expected output, when it's wrong
- In exceptional cases, examples shouldn't run, since they can't work.
For example, a function that connects to a private webservice. In
such cases, we can add `# doctest: +SKIP` at the end of the lines
that should not run
To be able to properly fix an example for the first time, the next steps
are needed:
- Install a pandas development environment in your computer. There are
simplified instructions [in this page](https://python-sprints.github.io/pandas/guide/pandas_setup.html),
and more detailed information in [pandas official contributing page](https://pandas.pydata.org/docs/development/index.html).
- Run the doctests for the object of interest (the one in this issue),
and make sure the examples are still broken in the `master` branch of
pandas
- Fix the file locally, and run the doctests again, to make sure the
fix is working as expected
- Optionally have a look and make sure that the code in the examples
follow PEP-8, and fix the style if it doesn't
- Commit your changes, push your branch to a fork, and open a pull
request. Make sure you edit the line `Closes #XXXX` with the issue
number you are addressing, so the issue is automatically closed,
when the pull request is merged
- Make sure the continuous integration of your pull request finishes
in green. If it doesn't, check if the problem is in your changes
(sometimes things break in master for technical problems, and in
that case you just need to wait for a core developer to fix the
problem)
- Address any comment from the reviewers (just make changes locally,
commit, and push to your branch, no need to open new pull requests) | {
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} | 12 | "2021-07-22T20:44:27Z" | "2021-07-28T01:09:32Z" | "2021-07-28T01:09:32Z" | MEMBER | null | # tl;dr
Fix the next doctest error:
```
____________________________________________________ [doctest] pandas.io.formats.style_render._parse_latex_table_styles _____________________________________________________
1249
1250 Return the first 'props' 'value' from ``tables_styles`` identified by ``selector``.
1251
1252 Examples
1253 --------
1254 >>> table_styles = [{'selector': 'foo', 'props': [('attr','value')],
UNEXPECTED EXCEPTION: SyntaxError('invalid syntax', ('<doctest pandas.io.formats.style_render._parse_latex_table_styles[0]>', 2, 72, " {'selector': 'bar', 'props': [('attr', 'overwritten')]},\n"))
Traceback (most recent call last):
File "/home/mgarcia/miniconda3/envs/pandas-dev/lib/python3.8/doctest.py", line 1336, in __run
exec(compile(example.source, filename, "single",
File "<doctest pandas.io.formats.style_render._parse_latex_table_styles[0]>", line 2
{'selector': 'bar', 'props': [('attr', 'overwritten')]},
^
SyntaxError: invalid syntax
/home/mgarcia/src/pandas/pandas/io/formats/style_render.py:1254: UnexpectedException
```
# Detailed instructions
Python allows to have example code in the documentation, like in:
```python
def add(num1, num2):
"""
Computes the sum of the two numbers.
Examples
--------
>>> add(2, 2)
4
"""
return num1 + num2
```
In pandas, we use this to document most elements. And there are tools, like pytest,
that can run the examples, and make sure everything is correct.
For historical reasons, we have many examples where the code fails to run, or the
actual output is different from the expected output. For example, check the next
incorrect examples:
```python
def add(num1, num2):
"""
Computes the sum of the two numbers.
Examples
--------
>>> add(2, 2)
5
>>> add(2, 2
4
>>> add(2, number)
4
...
"""
return num1 + num2
```
All them will fail for different reasons. To test the docstring of an object,
the next command can be run:
```
python -m pytest --doctest-modules pandas/core/frame.py::pandas.core.frame.DataFrame.info
```
Where `pandas/core/frame.py` is the file where the docstring is defined, and
`pandas.core.frame.DataFrame.info` is the object. A whole file can also be tested
by removing the `::` and the object from the command above.
In general, the errors in the examples can be fixed with things like:
- Fixing a typo (a missing comma, an mispelled variable name...)
- Adding an object that hasn't been defined (like, if `df` is used, but
no sample dataset `df` has been first defined)
- Fixing the expected output, when it's wrong
- In exceptional cases, examples shouldn't run, since they can't work.
For example, a function that connects to a private webservice. In
such cases, we can add `# doctest: +SKIP` at the end of the lines
that should not run
To be able to properly fix an example for the first time, the next steps
are needed:
- Install a pandas development environment in your computer. There are
simplified instructions [in this page](https://python-sprints.github.io/pandas/guide/pandas_setup.html),
and more detailed information in [pandas official contributing page](https://pandas.pydata.org/docs/development/index.html).
- Run the doctests for the object of interest (the one in this issue),
and make sure the examples are still broken in the `master` branch of
pandas
- Fix the file locally, and run the doctests again, to make sure the
fix is working as expected
- Optionally have a look and make sure that the code in the examples
follow PEP-8, and fix the style if it doesn't
- Commit your changes, push your branch to a fork, and open a pull
request. Make sure you edit the line `Closes #XXXX` with the issue
number you are addressing, so the issue is automatically closed,
when the pull request is merged
- Make sure the continuous integration of your pull request finishes
in green. If it doesn't, check if the problem is in your changes
(sometimes things break in master for technical problems, and in
that case you just need to wait for a core developer to fix the
problem)
- Address any comment from the reviewers (just make changes locally,
commit, and push to your branch, no need to open new pull requests) | {
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} | 2 | "2021-07-22T20:44:51Z" | "2021-07-25T15:35:16Z" | "2021-07-25T14:18:38Z" | MEMBER | null | - [x] closes #42651
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
I am checking the levels here to make it clear, what caused the issue previously. | {
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} | 1 | "2021-07-22T22:42:21Z" | "2021-07-22T23:05:31Z" | "2021-07-22T23:05:19Z" | NONE | null | I tried to install Pandas via `conda install`. When I run the installation, Pandas forced a downgrade of a bunch of other packages not related to the core competency of Pandas. It eventually installed Pandas 1.2.4.
```
> conda install pandas
The following packages will be downloaded:
package | build
---------------------------|-----------------
h5py-2.10.0 | py39hec9cf62_0 1.1 MB
intel-openmp-2020.2 | 254 947 KB
mkl-2020.2 | 256 213.9 MB
mkl-service-2.3.0 | py39he8ac12f_0 58 KB
mkl_fft-1.3.0 | py39h54f3939_0 195 KB
mkl_random-1.0.2 | py39h63df603_0 379 KB
numpy-1.19.2 | py39h89c1606_0 21 KB
numpy-base-1.19.2 | py39h2ae0177_0 5.3 MB
pandas-1.2.4 | py39h2531618_0 11.4 MB
python-dateutil-2.8.2 | pyhd3eb1b0_0 241 KB
pytz-2021.1 | pyhd3eb1b0_0 244 KB
scipy-1.6.2 | py39h91f5cce_0 20.6 MB
tensorflow-2.4.1 |mkl_py39h4683426_0 3 KB
tensorflow-base-2.4.1 |mkl_py39h43e0292_0 125.5 MB
------------------------------------------------------------
Total: 379.7 MB
The following NEW packages will be INSTALLED:
pandas pkgs/main/linux-64::pandas-1.2.4-py39h2531618_0
python-dateutil pkgs/main/noarch::python-dateutil-2.8.2-pyhd3eb1b0_0
pytz pkgs/main/noarch::pytz-2021.1-pyhd3eb1b0_0
The following packages will be DOWNGRADED:
h5py 3.2.1-py39h6c542dc_0 --> 2.10.0-py39hec9cf62_0
intel-openmp 2021.3.0-h06a4308_3350 --> 2020.2-254
mkl 2021.3.0-h06a4308_520 --> 2020.2-256
mkl-service 2.4.0-py39h7f8727e_0 --> 2.3.0-py39he8ac12f_0
mkl_fft 1.3.0-py39h42c9631_2 --> 1.3.0-py39h54f3939_0
mkl_random 1.2.2-py39h51133e4_0 --> 1.0.2-py39h63df603_0
numpy 1.20.3-py39hf144106_0 --> 1.19.2-py39h89c1606_0
numpy-base 1.20.3-py39h74d4b33_0 --> 1.19.2-py39h2ae0177_0
scipy 1.6.2-py39had2a1c9_1 --> 1.6.2-py39h91f5cce_0
tensorflow 2.5.0-mkl_py39h4a0693c_0 --> 2.4.1-mkl_py39h4683426_0
tensorflow-base 2.5.0-mkl_py39h35b2a3d_0 --> 2.4.1-mkl_py39h43e0292_0
Proceed ([y]/n)?
```
There is absolutely no reason why Pandas needs to touch TensorFlow or Numpy. Please fix.
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} | 0 | "2021-07-23T01:02:36Z" | "2021-07-23T08:05:33Z" | "2021-07-23T08:05:33Z" | NONE | null | Backport PR #42579: BUG: DataFrame.copy not consolidating | {
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] | closed | false | null | [] | null | 3 | "2021-07-23T01:03:20Z" | "2021-07-23T15:36:56Z" | "2021-07-23T15:36:43Z" | NONE | null | In pandas 1.3.0 pd.to_datetime will produce NaT for valid datetime strings when processing lists or Series of certain size and content. Here's the actual reproducible example:
```
import pandas as pd
pd.__version__
ld = ['2021-09-24T00:00:00Z',
'2021-09-24T00:00:00Z',
'2021-09-24T00:00:00Z',
'2021-09-24T00:00:00Z',
'2021-09-24T00:00:00Z',
'2021-07-30T00:00:00Z',
'2021-07-30T00:00:00Z',
'2021-07-30T00:00:00Z',
'2021-07-30T00:00:00Z',
'2021-07-30T00:00:00Z',
'2021-08-16T00:00:00Z',
'2021-08-16T00:00:00Z',
'2021-08-16T00:00:00Z',
'2021-08-16T00:00:00Z',
'2021-08-16T00:00:00Z',
'2021-08-16T00:00:00Z',
'2021-08-16T00:00:00Z',
'2021-08-16T00:00:00Z',
'2021-08-16T00:00:00Z',
'2021-08-16T00:00:00Z',
'2021-08-16T00:00:00Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2024-12-17T00:00:00.0000000Z',
'2024-12-17T00:00:00.0000000Z',
'2024-12-17T00:00:00.0000000Z',
'2024-12-17T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z',
'2021-08-16T00:00:00.0000000Z']
pd.to_datetime(ld)
```
And proof:
```
(p130) C:\Users\volex>python
Python 3.9.5 (default, May 18 2021, 14:42:02) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> pd.__version__
'1.3.0'
>>> ld = ['2021-09-24T00:00:00Z',
... '2021-09-24T00:00:00Z',
... '2021-09-24T00:00:00Z',
... '2021-09-24T00:00:00Z',
... '2021-09-24T00:00:00Z',
... '2021-07-30T00:00:00Z',
... '2021-07-30T00:00:00Z',
... '2021-07-30T00:00:00Z',
... '2021-07-30T00:00:00Z',
... '2021-07-30T00:00:00Z',
... '2021-08-16T00:00:00Z',
... '2021-08-16T00:00:00Z',
... '2021-08-16T00:00:00Z',
... '2021-08-16T00:00:00Z',
... '2021-08-16T00:00:00Z',
... '2021-08-16T00:00:00Z',
... '2021-08-16T00:00:00Z',
... '2021-08-16T00:00:00Z',
... '2021-08-16T00:00:00Z',
... '2021-08-16T00:00:00Z',
... '2021-08-16T00:00:00Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2024-12-17T00:00:00.0000000Z',
... '2024-12-17T00:00:00.0000000Z',
... '2024-12-17T00:00:00.0000000Z',
... '2024-12-17T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z',
... '2021-08-16T00:00:00.0000000Z']
>>> pd.to_datetime(ld)
DatetimeIndex(['2021-09-24 00:00:00+00:00', '2021-09-24 00:00:00+00:00',
'2021-09-24 00:00:00+00:00', '2021-09-24 00:00:00+00:00',
'2021-09-24 00:00:00+00:00', '2021-07-30 00:00:00+00:00',
'2021-07-30 00:00:00+00:00', '2021-07-30 00:00:00+00:00',
'2021-07-30 00:00:00+00:00', '2021-07-30 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', '2021-08-16 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', '2021-08-16 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', '2021-08-16 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', '2021-08-16 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', '2021-08-16 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', 'NaT',
'NaT', 'NaT',
'NaT', 'NaT',
'NaT', 'NaT',
'NaT', 'NaT',
'NaT', 'NaT',
'NaT', 'NaT',
'NaT', '2024-12-17 00:00:00+00:00',
'2024-12-17 00:00:00+00:00', '2024-12-17 00:00:00+00:00',
'2024-12-17 00:00:00+00:00', 'NaT',
'NaT', 'NaT',
'NaT', 'NaT',
'NaT', 'NaT',
'NaT', 'NaT',
'NaT', 'NaT',
'NaT'],
dtype='datetime64[ns, UTC]', freq=None)
>>>
```
Interestingly when the list is of size 50, it will work fine:
```
>>> pd.to_datetime(ld[1:])
DatetimeIndex(['2021-09-24 00:00:00+00:00', '2021-09-24 00:00:00+00:00',
'2021-09-24 00:00:00+00:00', '2021-09-24 00:00:00+00:00',
'2021-07-30 00:00:00+00:00', '2021-07-30 00:00:00+00:00',
'2021-07-30 00:00:00+00:00', '2021-07-30 00:00:00+00:00',
'2021-07-30 00:00:00+00:00', '2021-08-16 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', '2021-08-16 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', '2021-08-16 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', '2021-08-16 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', '2021-08-16 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', '2021-08-16 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', '2021-08-16 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', '2021-08-16 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', '2021-08-16 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', '2021-08-16 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', '2021-08-16 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', '2021-08-16 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', '2021-08-16 00:00:00+00:00',
'2024-12-17 00:00:00+00:00', '2024-12-17 00:00:00+00:00',
'2024-12-17 00:00:00+00:00', '2024-12-17 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', '2021-08-16 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', '2021-08-16 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', '2021-08-16 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', '2021-08-16 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', '2021-08-16 00:00:00+00:00',
'2021-08-16 00:00:00+00:00', '2021-08-16 00:00:00+00:00'],
dtype='datetime64[ns, UTC]', freq=None)
```
No such anomaly observed in pandas 1.2.x.
This is on Windows 10 64 bit using Anaconda.
```
(p130) C:\Users\volex>conda list
# packages in environment at C:\Users\volex\AppData\Local\Continuum\anaconda3\envs\p130:
#
# Name Version Build Channel
blas 1.0 mkl
bottleneck 1.3.2 py39h7cc1a96_1
ca-certificates 2021.7.5 haa95532_1
certifi 2021.5.30 py39haa95532_0
intel-openmp 2021.3.0 haa95532_3372
mkl 2021.3.0 haa95532_524
mkl-service 2.4.0 py39h2bbff1b_0
mkl_fft 1.3.0 py39h277e83a_2
mkl_random 1.2.2 py39hf11a4ad_0
numexpr 2.7.3 py39hb80d3ca_1
numpy 1.20.3 py39ha4e8547_0
numpy-base 1.20.3 py39hc2deb75_0
openssl 1.1.1k h2bbff1b_0
pandas 1.3.0 py39hd77b12b_0
pip 21.1.3 py39haa95532_0
python 3.9.5 h6244533_3
python-dateutil 2.8.2 pyhd3eb1b0_0
pytz 2021.1 pyhd3eb1b0_0
setuptools 52.0.0 py39haa95532_0
six 1.16.0 pyhd3eb1b0_0
sqlite 3.36.0 h2bbff1b_0
tzdata 2021a h52ac0ba_0
vc 14.2 h21ff451_1
vs2015_runtime 14.27.29016 h5e58377_2
wheel 0.36.2 pyhd3eb1b0_0
wincertstore 0.2 py39h2bbff1b_0
``` | {
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[pandas-bug.zip](https://github.com/pandas-dev/pandas/files/6866965/pandas-bug.zip)
KeyError: 'x'
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
~\AppData\Roaming\Python\Python39\site-packages\IPython\core\formatters.py in __call__(self, obj)
700 type_pprinters=self.type_printers,
701 deferred_ppr | {
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] | open | false | null | [] | null | 1 | "2021-07-23T07:58:47Z" | "2021-08-21T19:59:30Z" | null | NONE | null | - I have checked that this issue has not already been reported.
- I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note** This issue is very similar to [Issue #8902](https://github.com/pandas-dev/pandas/issues/8902), which is fixed in `pandas==1.3.0`, but in cases where the DataFrame to concat has different columns settings as stated below, `pd.concat` is still acting weird?
####
```python
import pandas as pd
# pd.concat with empty DataFrame won't coerce type now
df = pd.DataFrame([{"col1": 1}, {"col1":2}])
df2 = pd.DataFrame({})
df_new = pd.concat([df,df2])
df_new.col1.dtype # dtype('int64')
# however, pd.concat with empty DataFrame with column different than the original one will still coerce type
df = pd.DataFrame([{"col1": 1}, {"col1":2}])
df2 = pd.DataFrame({}, columns=["whatever"])
df_new = pd.concat([df,df2])
df_new.col1.dtype # dtype('float64')
# pd.concat with empty DataFrame with same columns will still coerce type to `object`
df = pd.DataFrame([{"col1": 1}, {"col1":2}])
df2 = pd.DataFrame({}, columns=["col1"])
df_new = pd.concat([df,df2])
df_new.col1.dtype # dtype('object')
```
#### Problem description
`pd.concat` is acting inconsistently when concatenating empty DataFrame:
If the empty DataFrame is present with same column, that column is changed to `dtype('O')`;
If the empty DataFrame is present with columns different than target, the target DataFrame has its original column values of type `int64` coerced to `float64`;
If the empty DataFrame is present without any column, the target DataFrame keeps its dtype;
#### Expected Output
I suggest just not coerce values when `pd.concat` an empty DataFrame...
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.9.2.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.81.bm.15-amd64
Version : #1 SMP Debian 4.14.81.bm.15 Sun Sep 8 05:02:31 UTC 2019
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.19.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : 0.29.23
pytest : 6.2.4
hypothesis : None
sphinx : 3.5.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 0.9.2
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.4.20
tables : None
tabulate : 0.8.9
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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] | open | false | null | [] | null | 1 | "2021-07-23T09:19:57Z" | "2021-08-05T02:12:57Z" | null | NONE | null | #### Is your feature request related to a problem?
Very often we merge very data sets(100Gb) with 'left'/'right'/'outer' which introduces NAs.
Basically, if we have integer field, we have two options:
1. Allow pandas autoconvert it to float which could be nan (default behaviour)
2. Use Int64 type, which is actually quite slow to use it in large datasets spending ages in _PyEval_EvalFrameDefault
#### Describe the solution you'd like
The obvious option is to use fill value, like -1 for NaN, it currently could be done after 1st or 2nd step, with fillna and conversion to integer type back. It will be quite simple to introduce new parameter inmerge fillna={ "column1":-1, "column2":-1} which do the same task without addition computations.
#### API breaking implications
By default fillna should be None, which will keep current behaviour.
p.s. the same thing is reasonable for pd.concat
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} | 5 | "2021-07-23T10:46:25Z" | "2021-08-10T08:26:48Z" | "2021-07-30T22:15:04Z" | CONTRIBUTOR | null | - [x] closes #41634
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 1 | "2021-07-23T11:09:46Z" | "2021-07-25T12:30:58Z" | "2021-07-25T12:29:55Z" | MEMBER | null | will fail till v1.3.1 tag exists
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} | 5 | "2021-07-23T12:43:48Z" | "2021-11-12T14:57:14Z" | "2021-11-12T14:57:14Z" | 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.
---
#### Code Sample
```python
s = pd.Series([None, np.nan, 'Y'])
s.nunique(dropna=False)
s.value_counts(dropna=False)
```
#### Problem description
nunique returns 3, but value_counts only has 2 rows. This may be related to Issue https://github.com/pandas-dev/pandas/issues/37566
#### Expected Output
I would expect these to be consistent.
#### Output of ``pd.show_versions()``
<details>
pandas : 1.3.0
numpy : 1.19.5
pytz : 2019.3
dateutil : 2.8.1
pip : 21.1.2
setuptools : 56.0.0
Cython : 0.29.23
pytest : 6.2.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.24.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.3.23
tables : None
tabulate : None
xarray : 0.17.0
xlrd : 2.0.1
xlwt : None
numba : 0.52.0
</details>
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} | 1 | "2021-07-23T17:42:26Z" | "2021-07-23T23:25:17Z" | "2021-07-23T23:24:43Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
This is also going to be necessary in order to allow round-tripping DataFrame(arr).values as a view on arr | {
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} | 2 | "2021-07-23T19:45:48Z" | "2021-07-30T14:30:49Z" | "2021-07-30T08:07:43Z" | MEMBER | null | - [ ] closes #42387
- [ ] tests added / passed
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I messed up my acces token and accidentially pushed to master. Not sure how this could happen :(
If someone has a better idea than simply reverting we can close this
cc @pandas-dev/pandas-core
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} | 3 | "2021-07-23T20:27:39Z" | "2021-07-24T09:38:28Z" | "2021-07-23T23:14:49Z" | MEMBER | null | - [x] xref #34488
- [x] closes https://github.com/pandas-dev/pandas/issues/29130
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
This time correctly. This should cover the index case missed in the past | {
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} | 1 | "2021-07-23T21:01:36Z" | "2021-07-24T08:51:17Z" | "2021-07-23T23:23:34Z" | CONTRIBUTOR | null | A small clean-up, seperated out from #41153.
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} | 8 | "2021-07-24T05:44:10Z" | "2021-11-14T02:23:35Z" | "2021-11-14T02:23:35Z" | NONE | null | #### Location of the documentation
https://pandas.pydata.org/docs/dev/reference/series.html
https://pandas.pydata.org/docs/dev/reference/api/pandas.Series.empty.html
#### Documentation problem
The description about Series.empty are referencing DataFrames instead of Series:
https://pandas.pydata.org/docs/dev/reference/series.html
> Series.empty | Indicator whether DataFrame is empty.
https://pandas.pydata.org/docs/dev/reference/api/pandas.Series.empty.html
> True if DataFrame is entirely empty (no items), meaning any of the axes are of length 0.
(And many more references to "DataFrame" instead of "Series" on that page.)
#### Suggested fix for documentation
Replace all mentions of "DataFrame" with "Series". | {
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} | 1 | "2021-07-24T09:42:29Z" | "2021-07-24T15:57:08Z" | "2021-07-24T10:57:39Z" | MEMBER | null | Reverts pandas-dev/pandas#42631
reverting to get ci to green quickly. @jbrockmendel can you re-open original PR | {
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} | 0 | "2021-07-24T11:19:30Z" | "2021-07-24T12:15:55Z" | "2021-07-24T12:15:55Z" | NONE | null | Backport PR #42686: DOC: 1.3.1 release date | {
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} | 7 | "2021-07-24T13:18:05Z" | "2021-07-26T22:39:14Z" | "2021-07-26T22:39:09Z" | CONTRIBUTOR | null | The following commit fixes the doctext in _parse_latex_header_span. Solves issue #42673
- [X] closes #42673
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] example docstring code fixed for read_strata
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} | 2 | "2021-07-24T16:06:21Z" | "2021-07-26T22:36:35Z" | "2021-07-26T22:35:04Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Re-implement #42631 without the bug-causing caching of Block.values | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
---
#### Code Sample, a copy-pastable example
``` python
import pandas as pd
# Create mock dataframe
df = pd.DataFrame({
'a': [1, 2, 3, 4]
})
m = df['a'].eq(2)
new_df1 = df[m]
# <weakref at 0x00000213E01C5090; to 'DataFrame' at 0x00000213DE9C6FD0>
print(new_df1._is_copy)
# SettingWithCopyWarning (expected)
new_df1['b'] = 1
new_df2 = df.loc[m]
# <weakref at 0x00000213E01C5090; to 'DataFrame' at 0x00000213DE9C6FD0>
print(new_df2._is_copy)
# SettingWithCopyWarning (unexpected)
new_df2['c'] = 1
new_df3 = df.loc[m, :]
# <weakref at 0x00000213E01C5090; to 'DataFrame' at 0x00000213DE9C6FD0>
print(new_df3._is_copy)
# SettingWithCopyWarning (unexpected)
new_df3['d'] = 1
new_df4 = df.loc[m, df.columns]
# None
print(new_df4._is_copy)
# No SettingWithCopyWarning (expected?)
new_df4['e'] = 1
```
#### Problem description
I expect that `df[m]` would produce a weakref, but I don't understand why I'm getting a weakref for the `loc` options where columns are not explicitly defined.
The issue being that:
``` python
new_df2 = df.loc[m]
new_df2['b'] = 1
```
and
``` python
new_df3 = df.loc[m, :]
new_df3['c'] = 1
```
Both warn:
``` python-traceback
SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
self._set_item(key, value)
```
I couldn't find any documentation saying that columns must be explicitly defined when using `loc` to not produce a copy, although all of the examples in the docs on [Why does assignment fail when using chained indexing?](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#why-does-assignment-fail-when-using-chained-indexing) _do_ explicitly declare the column or columns.
#### Expected Output
I would expect not to get a `SettingWithCopyWarning` especially since `:` works when slicing the Index:
``` python
filtered_df = df.loc[:, ['a']]
print(filtered_df._is_copy) # None
filtered_df['b'] = 1 # No Warning
```
#### Output of ``pd.show_versions()``
Confirmed on Windows
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.9.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19042
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.3.0
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.1
pip : None
setuptools : None
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.22.0
pandas_datareader: 0.9.0
bs4 : 4.9.3
bottleneck : None
fsspec : 2021.05.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
Confirmed On Mac
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.9.2.final.0
python-bits : 64
OS : Darwin
OS-release : 20.5.0
Version : Darwin Kernel Version 20.5.0: Sat May 8 05:10:31 PDT 2021; root:xnu-7195.121.3~9/RELEASE_ARM64_T8101
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 54.2.0
Cython : 0.29.22
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 : 3.4.2
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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} | 1 | "2021-07-25T01:47:47Z" | "2021-07-26T16:12:55Z" | "2021-07-26T12:43:36Z" | MEMBER | null | ```
from asv_bench.benchmarks.reshape import *
self = ReshapeExtensionDtype()
self.setup("Period[s]")
%timeit self.time_unstack_fast("Period[s]")
23.2 ms ± 1.15 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- master
9.37 ms ± 128 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- PR
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} | 25 | "2021-07-25T09:01:34Z" | "2021-07-30T15:57:14Z" | "2021-07-30T15:51:31Z" | CONTRIBUTOR | null | - [x] closes #42671
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] fixed the doctest error in the docs for format.py
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} | 3 | "2021-07-25T09:03:51Z" | "2021-08-13T16:32:24Z" | "2021-08-13T16:23:56Z" | CONTRIBUTOR | null | - [x] closes #41272
Docs etc. for the newly added NumericIndex (#41153) + adding NumericIndex to the public namespace. | {
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} | 5 | "2021-07-25T13:07:34Z" | "2021-08-28T18:34:28Z" | "2021-08-28T18:34:27Z" | MEMBER | null | Tracking issue for the 1.3.2 release. Currently scheduled for August 15, 2021.
https://github.com/pandas-dev/pandas/milestone/88
List of open regressions: https://github.com/pandas-dev/pandas/issues?q=is%3Aopen+is%3Aissue+label%3ARegression
performance regressions between 1.2.5 and 1.3.x https://simonjayhawkins.github.io/fantastic-dollop/#regressions?sort=3&dir=desc&branch=1.3.x
cc @pandas-dev/pandas-core @pandas-dev/pandas-triage | {
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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- [ ] tests added / passed
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} | 1 | "2021-07-25T18:09:31Z" | "2021-07-26T12:41:35Z" | "2021-07-26T12:37:52Z" | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
As discussed a few days ago
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} | 3 | "2021-07-25T19:08:47Z" | "2021-07-26T14:35:56Z" | "2021-07-26T14:34:32Z" | MEMBER | null | xref #42708
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Fixes `isin` slowdowns mentioned in https://simonjayhawkins.github.io/fantastic-dollop/#regressions?sort=3&dir=desc&branch=1.3.x
Benchmarks:
<details>
```
- 550±7ms 495±10ms 0.90 algos.isin.IsInLongSeriesValuesDominate.time_isin('Float64', 'random')
- 514±8ms 449±6ms 0.87 algos.isin.IsInLongSeriesValuesDominate.time_isin('Float64', 'monotone')
- 2.22±0.07ms 1.68±0.05ms 0.76 algos.isin.IsInFloat64.time_isin('Float64', 'many_different_values')
- 208±9ms 147±8ms 0.71 algos.isin.IsInLongSeriesValuesDominate.time_isin('Int64', 'monotone')
- 395±20μs 279±4μs 0.71 algos.isin.IsIn.time_isin_categorical('boolean')
- 304±10μs 200±2μs 0.66 algos.isin.IsIn.time_isin_empty('boolean')
- 341±10μs 218±10μs 0.64 algos.isin.IsIn.time_isin('Int64')
- 339±10μs 212±3μs 0.63 algos.isin.IsIn.time_isin_categorical('Int64')
- 258±10μs 147±3μs 0.57 algos.isin.IsIn.time_isin_empty('Int64')
- 221±5μs 104±3μs 0.47 algos.isin.IsIn.time_isin('boolean')
- 155±5μs 63.4±1μs 0.41 algos.isin.IsIn.time_isin_mismatched_dtype('boolean')
- 166±7μs 63.4±1μs 0.38 algos.isin.IsIn.time_isin_mismatched_dtype('Int64')
- 499±6ms 176±2ms 0.35 algos.isin.IsInLongSeriesLookUpDominates.time_isin('Float64', 1000, 'random_misses')
- 500±8ms 173±2ms 0.35 algos.isin.IsInLongSeriesLookUpDominates.time_isin('Float64', 1000, 'monotone_misses')
- 125±1ms 33.2±0.8ms 0.27 algos.isin.IsInLongSeriesLookUpDominates.time_isin('Int64', 5, 'random_hits')
- 437±6ms 116±1ms 0.26 algos.isin.IsInLongSeriesLookUpDominates.time_isin('Float64', 1000, 'random_hits')
- 125±1ms 32.9±0.8ms 0.26 algos.isin.IsInLongSeriesLookUpDominates.time_isin('Int64', 5, 'random_misses')
- 131±0.9ms 33.3±2ms 0.25 algos.isin.IsInLongSeriesLookUpDominates.time_isin('Int64', 5, 'monotone_hits')
- 443±10ms 109±0.6ms 0.25 algos.isin.IsInLongSeriesLookUpDominates.time_isin('Int64', 1000, 'monotone_misses')
- 417±10ms 81.0±0.6ms 0.19 algos.isin.IsInLongSeriesLookUpDominates.time_isin('Float64', 1000, 'monotone_hits')
- 298±8ms 41.2±0.1ms 0.14 algos.isin.IsInLongSeriesLookUpDominates.time_isin('Int64', 1000, 'monotone_hits')
- 343±10ms 41.0±0.3ms 0.12 algos.isin.IsInLongSeriesLookUpDominates.time_isin('Int64', 1000, 'random_hits')
- 370±7ms 39.3±0.2ms 0.11 algos.isin.IsInLongSeriesLookUpDominates.time_isin('Int64', 1000, 'random_misses')
- 371±10ms 36.6±0.3ms 0.10 algos.isin.IsInLongSeriesLookUpDominates.time_isin('Int64', 5, 'monotone_misses')
- 350±10ms 33.8±0.5ms 0.10 algos.isin.IsInLongSeriesLookUpDominates.time_isin('Float64', 5, 'monotone_misses')
- 351±10ms 32.7±0.7ms 0.09 algos.isin.IsInLongSeriesLookUpDominates.time_isin('Float64', 5, 'monotone_hits')
- 347±10ms 32.0±0.8ms 0.09 algos.isin.IsInLongSeriesLookUpDominates.time_isin('Float64', 5, 'random_misses')
- 357±10ms 31.7±0.5ms 0.09 algos.isin.IsInLongSeriesLookUpDominates.time_isin('Float64', 5, 'random_hits')
```
</details>
Not sure if we do this anywhere else, but based on this slowdown seems like `empty/zeros_like(extensionarr)` should be avoided (this call has extra overhead through dispatching, but also triggers an `astype` somewhere, which is the main perf difference).
Also as a sidenote seems like `empty_like/zeros_like` should be avoided in general in favor of `zeros/empty`, see https://stackoverflow.com/questions/27464039/why-the-performance-difference-between-numpy-zeros-and-numpy-zeros-like
But `zeros_like` -> `zeros` makes a much smaller difference than `zeros_like(self)` -> `zeros_like(self._data)` | {
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} | 1 | "2021-07-25T19:27:06Z" | "2021-07-26T12:40:50Z" | "2021-07-26T09:26:40Z" | MEMBER | null | - [x] xref #42716
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Pinning until fixed | {
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} | 3 | "2021-07-25T19:28:26Z" | "2021-11-18T22:46:59Z" | null | MEMBER | null | Xarray was recently upgrade from 0.18.2 to 0.19 in our ci. This broke the checks pipeline
Passing:
https://github.com/pandas-dev/pandas/runs/3155104841
Failing:
https://github.com/pandas-dev/pandas/pull/42713/checks?check_run_id=3155841275 | {
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} | 0 | "2021-07-25T20:09:03Z" | "2021-10-22T09:00:33Z" | null | CONTRIBUTOR | null | In Pandas 2.0 NumericIndex should be the default numerical index and `Int64Index`, `UInt64Index` and `Float64Index` should be removed. See #41153 & #41272 for more discussion.
#41153 adds an internal attribute `_is_backward_compat_public_numeric_index` to the `Index` class (and its numeric child classes). This is only used to keep track of whether an index is a `NumericIndex` or one of its child indexes (`Int64Index`, `UInt64Index` and `Float64Index`) in order to maintain backwards compatability in Pandas 1.x. It will not be needed in pandas 2.0 after the child classes are removed and should removed in pandas 2.0 also. | {
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} | 5 | "2021-07-25T20:38:18Z" | "2021-07-28T00:05:29Z" | "2021-07-28T00:05:24Z" | CONTRIBUTOR | null | - [x] closes #42672
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 4 | "2021-07-26T01:58:36Z" | "2021-07-27T19:58:52Z" | "2021-07-27T19:58:52Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
npa = np.random.RandomState(0).randint(1000, size=(20, 8))
df = pd.DataFrame(npa, columns=[f'c{i}' for i in range(8)])
pd.concat([df.iloc[:6, 3:5], df.iloc[:6, 6:8]], axis=1).shift(periods=2, axis=1, freq=None, fill_value=0)
```
```
Traceback (most recent call last):
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3417, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-4-9916dba4e800>", line 1, in <module>
pd.concat([df.iloc[:6, 3:5], df.iloc[:6, 6:8]], axis=1).shift(periods=2, axis=1, freq=None, fill_value=0)
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/frame.py", line 5313, in shift
return super().shift(
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/generic.py", line 9179, in shift
new_data = self._mgr.shift(
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 393, in shift
result = self.reindex_indexer(
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 669, in reindex_indexer
new_blocks = self._slice_take_blocks_ax0(
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 714, in _slice_take_blocks_ax0
sl_type, slobj, sllen = _preprocess_slice_or_indexer(
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 2033, in _preprocess_slice_or_indexer
raise TypeError(type(slice_or_indexer), dtype)
TypeError: (<class 'list'>, None)
```
#### Problem description
`shift` should work without exception. When `df._data._consolidate_inplace()` is called before shift, it works well.
I've submitted an issue at #42401 once v1.3.0 is released, but no one responds till v1.3.1 is released today.
#### Expected Output
```
c3 c4 c6 c7
0 0 0 192 835
1 0 0 754 804
2 0 0 705 486
3 0 0 537 845
4 0 0 709 847
5 0 0 755 797
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : daec2e73b48fe34086dcdeeb0858d29536b6ca6a
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 20.6.0
Version : Darwin Kernel Version 20.6.0: Wed Jun 23 00:26:31 PDT 2021; root:xnu-7195.141.2~5/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : zh_CN.UTF-8
LOCALE : zh_CN.UTF-8
pandas : 1.4.0.dev0+292.gdaec2e73b4
numpy : 1.19.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 52.0.0.post20210125
Cython : 0.29.24
pytest : 6.2.2
hypothesis : 6.3.0
sphinx : 3.5.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
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 : 4.0.1
pyxlsb : None
s3fs : None
scipy : 1.6.1
sqlalchemy : 1.4.7
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.51.2
</details>
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