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Backport PR #35473 on branch 1.1.x (REGR: Fix conversion of mixed dtype DataFrame to numpy str)
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Backport PR #35473: REGR: Fix conversion of mixed dtype DataFrame to numpy str
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tp_print is deprecated error
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NONE
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# tp_print is deprecated
I'm getting ```tp_print is deprecated``` whenever I try to install Pandas on Termux.
Here's few lines of error I'm getting
```
/data/data/com.termux/files/usr/include/python3.8/cpython/object.h:260:5: note: 'tp_print' has been explicitly marked deprecated he
Py_DEPRECATED(3.8) int (*tp_print)(PyObject *, FILE *, int);
^
/data/data/com.termux/files/usr/include/python3.8/pyport.h:515:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
pandas/io/sas/sas.c:20655:3: error: 'tp_print' is deprecated [-Werror,-Wdeprecated-declarations]
0, /*tp_print*/
^
/data/data/com.termux/files/usr/include/python3.8/cpython/object.h:260:5: note: 'tp_print' has been explicitly marked deprecated he
Py_DEPRECATED(3.8) int (*tp_print)(PyObject *, FILE *, int);
^
/data/data/com.termux/files/usr/include/python3.8/pyport.h:515:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
5 errors generated.
building 'pandas._libs.json' extension
creating build/temp.linux-aarch64-3.8/pandas/_libs/src/ujson
creating build/temp.linux-aarch64-3.8/pandas/_libs/src/ujson/python
creating build/temp.linux-aarch64-3.8/pandas/_libs/src/ujson/lib
aarch64-linux-android-clang -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fstack-protector-st
aarch64-linux-android-clang -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fstack-protector-st
aarch64-linux-android-clang -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fstack-protector-st
aarch64-linux-android-clang -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fstack-protector-st
aarch64-linux-android-clang -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fstack-protector-st
aarch64-linux-android-clang -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fstack-protector-st
aarch64-linux-android-clang -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fstack-protector-st
aarch64-linux-android-clang -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fstack-protector-st
aarch64-linux-android-clang -shared -L/data/data/com.termux/files/usr/lib -Wl,-rpath=/data/data/com.termux/files/usr/lib -Wl,--enab
error: command 'aarch64-linux-android-clang' failed with exit status 1
----------------------------------------
ERROR: Failed building wheel for pandas
Failed to build pandas
```
However rolling back cython didn't work for me, I was still getting error. So I tried to rollback Pandas to 1.0.5 and it worked. So I think there's some bug in Pandas package from your side which results above error.
```
pip list
Package Version
------------------ ---------
appdirs 1.4.4
argon2-cffi 20.1.0
asv 0.4.2
attrs 19.3.0
backcall 0.2.0
black 19.10b0
bleach 3.1.5
certifi 2020.6.20
cffi 1.14.1
chardet 3.0.4
click 7.1.2
Cython 0.29.21
decorator 4.4.2
defusedxml 0.6.0
entrypoints 0.3
idna 2.10
ipykernel 5.3.4
ipython 7.17.0
ipython-genutils 0.2.0
ipywidgets 7.5.1
jedi 0.17.2
Jinja2 2.11.2
jsonschema 3.2.0
jupyter 1.0.0
jupyter-client 6.1.6
jupyter-console 6.1.0
jupyter-core 4.6.3
lxml 4.5.2
MarkupSafe 1.1.1
mistune 0.8.4
nbconvert 5.6.1
nbformat 5.0.7
notebook 6.1.1
numpy 1.19.1
packaging 20.4
pandas 1.0.5
pandocfilters 1.4.2
parso 0.7.1
pathspec 0.8.0
pexpect 4.8.0
pickleshare 0.7.5
pip 20.2.1
prometheus-client 0.8.0
prompt-toolkit 3.0.5
ptyprocess 0.6.0
pycparser 2.20
Pygments 2.6.1
pymongo 3.11.0
pyparsing 2.4.7
pyrsistent 0.16.0
python-dateutil 2.8.1
pytz 2020.1
pyzmq 19.0.2
qtconsole 4.7.5
QtPy 1.9.0
regex 2020.7.14
requests 2.24.0
Send2Trash 1.5.0
setuptools 49.2.1
six 1.15.0
terminado 0.8.3
testpath 0.4.4
toml 0.10.1
tornado 6.0.4
traitlets 4.3.3
typed-ast 1.4.1
urllib3 1.25.10
wcwidth 0.2.5
webencodings 0.5.1
wheel 0.34.2
widgetsnbextension 3.5.1
```
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Backport PR #35588: BUG: fix styler cell_ids arg so that blank style is ignored on False
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| 1
| 2020-08-08T09:01:27Z
| 2020-08-19T17:42:48Z
| 2020-08-19T17:42:48Z
|
MEMBER
| null |
tests using `async_mark` are failing cc @alimcmaster1
```
=========================== short test summary info ============================
ERROR pandas/tests/arrays/categorical/test_warnings.py::TestCategoricalWarnings
ERROR pandas/tests/arrays/categorical/test_warnings.py::TestCategoricalWarnings
ERROR pandas/tests/frame/test_api.py::TestDataFrameMisc - pytest.PytestDeprec...
ERROR pandas/tests/frame/test_api.py::TestDataFrameMisc - pytest.PytestDeprec...
ERROR pandas/tests/indexes/test_base.py::TestIndex - pytest.PytestDeprecation...
ERROR pandas/tests/indexes/test_base.py::TestIndex - pytest.PytestDeprecation...
ERROR pandas/tests/resample/test_resampler_grouper.py - pytest.PytestDeprecat...
ERROR pandas/tests/resample/test_resampler_grouper.py - pytest.PytestDeprecat...
ERROR pandas/tests/series/test_api.py::TestSeriesMisc - pytest.PytestDeprecat...
ERROR pandas/tests/series/test_api.py::TestSeriesMisc - pytest.PytestDeprecat...
= 71242 passed, 1936 skipped, 1024 xfailed, 156 warnings, 10 errors in 655.85s (0:10:55) =
```
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| 2
| 2020-08-08T09:11:12Z
| 2020-08-08T09:50:57Z
| 2020-08-08T09:47:44Z
|
MEMBER
| null |
xref #35620
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BUG: error: may be used uninitialized. In alpine
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| 8
| 2020-08-08T09:35:02Z
| 2020-11-18T01:16:38Z
| 2020-08-17T18:27:45Z
|
NONE
| null |
- [ x ] I have checked that this issue has not already been reported. (Not as a separate issue)
- [ 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.
---
#### Problem description
In an alpine contarner, running `pip3 install pandas=1.1.0`,
results in error:
```
'__pyx_v_year' may be used uninitialized in this function`
7787 | __pyx_v_year = (__pyx_v_year - 1);
| ~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~
cc1: all warnings being treated as errors
error: command 'gcc' failed with exit status 1
```
#### Expected Output
Successful install
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Backport PR #35621: CI: Linux py36_locale failures with pytest DeprecationWarning
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| 1
| 2020-08-08T10:14:59Z
| 2020-08-24T08:07:17Z
| 2020-08-22T16:03:21Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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BUG: Groupby rolling count slower than rolling mean & sum (v1.1.0)
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| 2
| 2020-08-08T12:13:17Z
| 2020-11-08T17:15:24Z
| 2020-10-05T13:01:37Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
# Generate sample df
df = pd.DataFrame({'column1': range(600), 'group': 5*['l'+str(i) for i in range(120)]})
# sort by group for easy/efficient joining of new columns to df
df=df.sort_values('group',kind='mergesort').reset_index(drop=True)
# timing of groupby rolling count, sum and mean
%timeit df['mean']=df.groupby('group').rolling(3,min_periods=1)['column1'].mean().values
%timeit df['sum']=df.groupby('group').rolling(3,min_periods=1)['column1'].sum().values
%timeit df['count']=df.groupby('group').rolling(3,min_periods=1)['column1'].count().values
### Output
6.14 ms ± 812 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
5.61 ms ± 179 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
76.1 ms ± 4.78 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
```
#### Problem description
I am running a groupby rolling count, sum & mean using Pandas v1.1.0 and I notice that the rolling count is considerably slower than the rolling mean & sum. This seems counter intuitive as we can derive the count from the mean and sum and save time.
#### Expected Output
Expecting more efficient computation of groupby rolling count
```
### df Output for illustration
print(df.head(10))
column1 group mean sum count
0 0 l0 0.0 0.0 1.0
1 120 l0 60.0 120.0 2.0
2 240 l0 120.0 360.0 3.0
3 360 l0 240.0 720.0 3.0
4 480 l0 360.0 1080.0 3.0
5 1 l1 1.0 1.0 1.0
6 121 l1 61.0 122.0 2.0
7 241 l1 121.0 363.0 3.0
8 361 l1 241.0 723.0 3.0
9 481 l1 361.0 1083.0 3.0
```
#### Output of ``pd.show_versions()``
<details>
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 11, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.1.0
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.2.1.post20200802
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : 0.8.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDY1MDI1MDI0
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|
DEPR: Deprecate inplace param in MultiIndex.set_codes and MultiIndex.set_levels
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| 2
| 2020-08-08T12:13:30Z
| 2020-08-10T17:52:24Z
| 2020-08-10T13:18:54Z
|
CONTRIBUTOR
| null |
Deprecates ``inplace`` parameter in ``MultiIndex.set_codes`` and ``MultiIndex.set_levels``.
Allowing inplace ops in indexes makes them more complicated wtr. caching etc. After ``inplace`` is removed, ``MultiIndex`` will be immutable, except the ``names`` attribute, similarly to other index classes.
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|
ENH: change to_sql default from Text to UnicodeText
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| 2020-08-08T13:43:57Z
| 2020-09-02T23:05:32Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
Yes. Databases that differentiate between Unicode and SBCS columns (notably Microsoft SQL Server `nvarchar` vs. `varchar`) can fail simple round-trips when using `to_sql` to write columns with Unicode strings.
#### Describe the solution you'd like
Change the default column `dtype` from `Text` ...
https://github.com/pandas-dev/pandas/blob/a9cb64ad105f8f7051f9d77b66e1bfe7c09d386f/pandas/io/sql.py#L1080
... to `UnicodeText`.
#### API breaking implications
None that I can see.
#### Describe alternatives you've considered
An alternative would be to modify the behaviour of `Text` in SQLAlchemy's mssql dialect, but SQLA already has `UnicodeText` and it solves the problem.
#### Additional context
```python
import pandas as pd
import sqlalchemy as sa
engine = sa.create_engine("mssql+pyodbc://@mssqlLocal")
df = pd.DataFrame(
[("English", "CHEERS!"), ("Greek", "ΟΠΑ!")], columns=["language", "toast"]
)
df.to_sql("pd_test", engine, index=False, if_exists="replace")
print(pd.read_sql_query("SELECT * FROM pd_test", engine))
"""console output:
language toast
0 English CHEERS!
1 Greek ???!
"""
```
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MDExOlB1bGxSZXF1ZXN0NDY1MDMzNjgw
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|
TYP: update setup.cfg
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MDExOlB1bGxSZXF1ZXN0NDY1MDM1MTk2
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DOC: docstrings for __array_wrap__
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MEMBER
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non-code changes broken-off #35334
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TST: add test for agg on ordered categorical cols
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| 2
| 2020-08-08T14:44:51Z
| 2020-08-21T22:34:57Z
| 2020-08-21T22:34:52Z
|
CONTRIBUTOR
| null |
- [x] closes #27800
- [x] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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|
skiprows function adding extra column in pandas
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| 2020-08-08T15:12:20Z
| 2020-08-21T17:04:44Z
| 2020-08-08T15:24:19Z
|
NONE
| null |
I am new to python. I am trying to read excel files and merge into master excel.
I want to skip first 3 rows in my excel which contains heading and just take rest of rows.
i am using skiprows=3 in pd.read_excel() method, it is skipping first 3 rows but the problem is, it is including one extra column with name 2 in my excel.
if i mention skiprows=2 then that extra column (2) is not coming. but empty row in excel is getting added with "Unnamed". if i mention skiprows=3 it is not adding "Unamed" row but adding extra column with name 2.
can someone please help me out in this. below is my code.
```
import os
import pandas as pd
if os.path.isfile('total_sales.xlsx'):
os.remove('total_sales.xlsx')
cwd = os.path.abspath('')
files = os.listdir(cwd)
df = pd.DataFrame() #create an empty dataframe df for storing the data for master spreadsheet
cols = [0,1,2,3,4,6,7,8,9,10,11]
for file in files:
if file.endswith('.xlsx'):
df=df.append(pd.read_excel(file,skiprows=3,usecols=cols))
print(cwd)
print(df.head())
print(df.shape)
df.to_excel('total_sales.xlsx')
```
please find below master excel with output.
[![enter image description here][1]][1]
[1]: https://i.stack.imgur.com/Y3MNB.png
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MDExOlB1bGxSZXF1ZXN0NDY1MDU2ODg0
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REF: _cython_agg_blocks follow patterns similar to _apply_blockwise
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| 1
| 2020-08-08T17:48:52Z
| 2020-08-12T22:49:04Z
| 2020-08-12T22:15:36Z
|
MEMBER
| null |
Follows #35535
This isn't yet identical to apply_blockwise, because both this and apply_blockwise have some odd behaviors that need to get standardized before they get combined into a BlockManager method, but this is a move in that direction.
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BUG: DataFrame.apply with func altering row in-place
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| 2020-08-08T17:55:50Z
| 2020-08-11T08:40:19Z
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|
MEMBER
| null |
closes #35462
closes #35634
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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|
BUG: Pandas 1.1.0 apply function with axis=1 seems to mishandle the rows
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| 1
| 2020-08-08T18:08:39Z
| 2020-08-10T13:21:13Z
| 2020-08-10T13:13:23Z
|
NONE
| null |
### The functionality of the **apply(func, axis=1)** in the newly released pandas==1.1.0 is not working as expected.
Pandas seems to be overwriting all the rows in the data frame with the 1st row present. This is happening specifically when introducing a new column in the data frame when running **func** method on each of the rows.
This working in pandas==1.0.5, but seems to be a bug in pandas=1.10.
I am attaching a sample script and the logs captured for pandas==1.0.5 and pandas==1.10.
### Attachments:
1. sample script to reproduce the issue (rename to .py before running) --> [script.txt](https://github.com/pandas-dev/pandas/files/5045977/script.txt)
2. output 1 (pandas==1.0.5) - working as expected --> [out_pandas_1.0.5.log](https://github.com/pandas-dev/pandas/files/5045978/out_pandas_1.0.5.log)
3. output 2 (pandas==1.1.0) - **buggy** --> [out_pandas_1.1.0.log](https://github.com/pandas-dev/pandas/files/5045980/out_pandas_1.1.0.log)
As you can see in the *out_pandas_1.1.0.log* log, after preprocessing the data frame using ``` df = df.apply(process_text, axis=1) ``` all the rows in the data frame have been overwritten with the 1st row.
This was not the case with pandas==1.0.5, check the **out_pandas_1.0.5.log** log.
### Environment
- OS: Ubuntu 20.04
- Python: 3.7.7 (anaconda env)
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MDU6SXNzdWU2NzU1NzYxMTc=
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|
ENH: to_sql support DB-API other than sqlite3/sqlalchemy (eg peewee ORM)
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| 2020-09-04T16:37:29Z
| 2020-09-04T16:37:29Z
|
NONE
| null |
#### Is your feature request related to a problem?
I can't use, to_sql, api to insert data to a database table that complies with DB-API (pep-249) v2.
#### Describe the solution you'd like
I would like to use, to_sql, to insert data to a database table that complis with DB-API (pep-249) v2.
Example, I use peewee ORM (not sqlalchemy / sqlite3 connections) to interact with a database that has a DB-API v2 driver.
#### Describe alternatives you've considered
I'm pulling each row of the dataframe to do inserts using threading. But I'd like this api to support connections other than sqlalchemy and sqlite3.
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MDExOlB1bGxSZXF1ZXN0NDY1MDY2MTIx
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CLN: consistent signatures for equals methods
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ENH: Make explode work for sets
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DOC: Mention NA for missing data in README
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MEMBER
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DOC: Add specific Visual Studio Installer instructions
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CONTRIBUTOR
| null |
- [ ] further improves #28316 - Currently the 'Contributing to pandas', 'Installing a C compiler' section advises to install Build Tools for Visual Studio 2017 but doesn't specify which components to install.
Notes:
- The first time I installed the Build Tools it didn't seem to get everything needed, and I had to search around for a while before I could figure out what I'd missed.
- The advice at [python.org](https://wiki.python.org/moin/WindowsCompilers#Microsoft_Visual_C.2B-.2B-_14.2_standalone:_Build_Tools_for_Visual_Studio_2019_.28x86.2C_x64.2C_ARM.2C_ARM64.29) is more specific, so I have added that in here. I'm fairly sure that's the minimum required, correct?
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| 2020-08-10T23:00:12Z
| 2020-08-10T22:52:08Z
|
CONTRIBUTOR
| null |
Currently, the heavy-to-calculate ``MultiIndex.values`` attribute is cached in ``MultiIndex._tuples``. It would be more dogmatic to store it in ``MultiIndex._cache`` IMO, which is what this PR does.
This has the added benefit of ``._values`` getting copied over to new copies of the MultiIndex, so also gives a performance boost in cases where copying is needed:
```python
>>> n = 100_000;
>>> df = pd.DataFrame({'a': ['a', 'b'] * int(n / 2), 'b': range(n), 'c': range(20, n + 20)})
>>> mi = pd.MultiIndex.from_frame(df)
>>> mi.values # also caches mi._values in mi._cache
array([('a', 0, 20), ('b', 1, 21), ('a', 2, 22), ...,
('b', 99997, 100017), ('a', 99998, 100018), ('b', 99999, 100019)],
dtype=object)
>>> %timeit mi._shallow_copy().values
22.8 ms ± 3.43 ms per loop # master
34.6 µs ± 997 ns per loop # this PR
```
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BUG:Resample with groupby & agg()
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| 2020-08-11T11:08:40Z
| 2020-08-11T11:08:24Z
|
NONE
| null |
- [ X] I have checked that this issue has not already been reported.
- [ all versions] 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 = pd.DataFrame({
'cat': ['cat_1', 'cat_1', 'cat_2', 'cat_1', 'cat_2', 'cat_1', 'cat_2', 'cat_1'],
'num': [5,20,22,3,4,30,10,50],
'date': ['2019-2-1', '2018-02-03','2020-3-11','2019-2-2', '2019-2-2', '2018-12-4','2020-3-11', '2020-12-12']
})
data['date'] = pd.to_datetime(data['date'])
aggreg = data.groupby('cat').resample('Y', on='date')
summ_ = aggreg.sum()
agg_summ_ = aggreg.agg({'num': 'sum'})
summ_
agg_summ_
```
#### Problem description
When I want aggregate all columns by sum, example with summ_ calculates normal, but if I do example with agg it calculates incorrect.
#### Expected Output
summ_
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Sun Jul 5 00:43:10 PDT 2020; root:xnu-6153.141.1~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : ru_RU.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.18.4
pytz : 2019.3
dateutil : 2.8.0
pip : 20.0.2
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fsspec : 0.5.2
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.9
tables : 3.5.2
tabulate : 0.8.7
xarray : 0.16.0
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.45.1
</details>
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|
ENH: Styler tooltips feature
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| 14
| 2020-08-09T12:41:11Z
| 2021-01-21T06:46:55Z
| 2021-01-17T15:46:56Z
|
CONTRIBUTOR
| null |
- [x] closes #21266
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This is a draft PR to address 21266: Tooltips for DataFrames in Styler.
This PR has the following objectives:
1) Add functionality whilst being completely (almost) decoupled from the rest of the HTML rendering process
2) Be fully backwards compatible and have no impact at all on previous Styler's that have never used tooltips.
3) Use pseudo CSS class rendering via `table_styles` to control the visualisation of the tooltips.
To address 1) the architecture was simple:
- If `tooltips` are detected then an additional HTML element is added to every data cell: `<span class="pd-t"></span>`. If `tooltips` are not detected (by default) nothing is done.
- Add generic table level CSS for the tooltip class, hiding tooltips by default but positioning, sizing, and coloring them.
- Loop through a tooltips DataFrame and based on the row and col index add additional content to the <span> element using the ::after pseudo-element targeting the exact cell id. This is table level also. Rendering is performed as the last step.
To address 2) default values ensure the functions make no changes unless `set_tooltips` has been called.
## Extensions
The very simple architecture requires a DataFrame of tooltips to be supplied. This requires the user to have constructed it privately (possibly using the regular DataFrame.apply and DataFrame.applymap) methods. These could be incorporated.
It would also be better to only add extra HTML <span> elements to data cells that have tooltips. This requires conditional looping of the render dict, which I haven't included here to save on initial complexity. For small dataframes it is also irrelevant.
However, given my time constraints I would rather leave this to a more available and more competent developer!!
Comments welcome..
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BUG: `Series.sum` & `Series.mean` are inconsitent for `timedelta64` dtype
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| 2020-09-02T13:06:44Z
| 2020-09-01T23:26:25Z
|
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.__version__
'1.1.0'
>>> s = pd.Series([None, None], dtype='timedelta64[ns]')
>>> s
0 NaT
1 NaT
dtype: timedelta64[ns]
>>> s.sum()
Timedelta('0 days 00:00:00')
>>> s.mean()
NaT
>>> s.sum() / 2 # Mean
Timedelta('0 days 00:00:00')
```
#### Problem description
When there are `NaT` values in the series, the sum in the above case to returns `0`, which will result in incorrect/inconsistent calculations if other calculations rely on the result of `sum`, for instance if we try calculating a mean from the result of sum, we get `0` instead of correct result `NaT`(as returned by `Series.mean`).
#### Expected Output
When there are `NaT` values in the series, I'd expect the sum in the above case to return `NaT`, as `Series.mean` returns `NaT` as expected.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Sun Jul 5 00:43:10 PDT 2020; root:xnu-6153.141.1~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDY1MTc3NDMw
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BUG/ENH: consistent gzip compression arguments
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| 8
| 2020-08-09T17:11:31Z
| 2020-08-13T22:04:56Z
| 2020-08-13T22:04:50Z
|
CONTRIBUTOR
| null |
- [x] closes #28103
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
`to_csv` let's the user set all keyword arguments for gzip. Depending on whether the user provides a filename or a file object different keyword arguments can be set (`gzip.open` vs `gzip.GzipFile`).
This PR always uses `gzip.GzipFile`. The additional keyword arguments valid for `gzip.open` but not valid for `gzip.GzipFile` (`encoding`, `errors`, and ~~`newline`~~) are still accessible:
https://github.com/pandas-dev/pandas/blob/aefae55e1960a718561ae0369e83605e3038f292/pandas/io/common.py#L512
Using `gzip.GzipFile`, also allows us to set `mtime` to create reproducible gzip archives.
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BUG: groupby(..., dropna=False).indices with single group key does not include nan group
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| 2
| 2020-08-10T02:31:36Z
| 2020-11-04T02:59:03Z
| 2020-11-04T02:59:03Z
|
MEMBER
| 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.
---
#### Code Sample, a copy-pastable example
```python
# Your code here
In [9]: data = {'group':['g1', 'g1', 'g1', np.nan, 'g1', 'g1', 'g2', 'g2', 'g2', 'g2', np.nan],
...: 'A':[3, 1, 8, 2, 6, -1, 0, 13, -4, 0, 1],
...: 'B':[5, 2, 3, 7, 11, -1, 4,-1, 1, 0, 2]}
...: df = pd.DataFrame(data)
...: df.groupby('group',dropna=True).indices
Out[9]: {'g1': array([0, 1, 2, 4, 5]), 'g2': array([6, 7, 8, 9])}
In [10]: df.groupby('group',dropna=False).indices
Out[10]: {'g1': array([0, 1, 2, 4, 5]), 'g2': array([6, 7, 8, 9])}
In [11]: pd.__version__
Out[11]: '1.2.0.dev0+67.gaefae55e1'
```
#### Problem description
The grouping codes + indices are determined for a single group by key here
https://github.com/pandas-dev/pandas/blob/aefae55e1960a718561ae0369e83605e3038f292/pandas/core/groupby/grouper.py#L559
And `Categorical` does not support `nan` as a label (only a missing -1 code)
This works correctly if multiple group keys are passed
Once this issue is addressed, https://github.com/pandas-dev/pandas/issues/35542 will be fixed
#### Expected Output
```
In [10]: df.groupby('group',dropna=False).indices
Out[10]: {'g1': array([0, 1, 2, 4, 5]), 'g2': array([6, 7, 8, 9]), np.nan: array([3, 10]}
```
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BUG: Support custom BaseIndexers in groupby.rolling
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| 2
| 2020-08-10T07:01:01Z
| 2020-08-13T06:14:05Z
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|
MEMBER
| null |
- [x] closes #35557
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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BUG: Reading files fails when option use_inf_as_na is set to True
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| 2020-08-10T07:05:20Z
| 2020-08-10T08:44:01Z
| 2020-08-10T08:43:51Z
|
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.set_option("use_inf_as_na", True)
pd.read_csv(some_csv_file)
```
#### Problem description
When the option `use_inf_as_na` is set to `True` reading any csv-file fails with this error:
```
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\io\parsers.py", line 686, in read_csv
return _read(filepath_or_buffer, kwds)
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\io\parsers.py", line 458, in _read
data = parser.read(nrows)
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\io\parsers.py", line 1201, in read
df = DataFrame(col_dict, columns=columns, index=index)
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\frame.py", line 467, in __init__
mgr = init_dict(data, index, columns, dtype=dtype)
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\internals\construction.py", line 250, in init_dict
missing = arrays.isna()
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\series.py", line 4795, in isna
return super().isna()
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\generic.py", line 7109, in isna
return isna(self).__finalize__(self, method="isna")
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\dtypes\missing.py", line 124, in isna
return _isna(obj)
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\dtypes\missing.py", line 157, in _isna
return _isna_ndarraylike(obj, inf_as_na=inf_as_na)
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\dtypes\missing.py", line 218, in _isna_ndarraylike
result = _isna_string_dtype(values, dtype, inf_as_na=inf_as_na)
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\dtypes\missing.py", line 246, in _isna_string_dtype
vec = libmissing.isnaobj_old(values.ravel())
File "pandas\_libs\missing.pyx", line 160, in pandas._libs.missing.isnaobj_old
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
EDIT 1: Error also happens with `read_table()` and when feeding a `StringIO` object to `read_csv()`.
EDIT 2: `read_excel()` fails as well.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.7.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 79 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.2.1.post20200802
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : 1.3.1
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : 0.8.0
fastparquet : 0.4.1
gcsfs : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 1.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.50.1
</details>
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MDExOlB1bGxSZXF1ZXN0NDY1MzI2NTIx
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|
Refactor tables latex
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| 3
| 2020-08-10T07:21:07Z
| 2020-08-14T06:13:01Z
| 2020-08-13T18:17:39Z
|
CONTRIBUTOR
| null |
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
As suggested [here](https://github.com/pandas-dev/pandas/pull/35284#issuecomment-665273834), utils fonctions which begin or end tables / tabulars / longtables environments could be merged.
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BUG: pd.factorize with read-only datetime64 numpy array raises ValueError
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|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
In [1]: pandas.__version__
[PYFLYBY] import pandas
Out[1]: u'0.24.2'
In [2]: arr = numpy.array([numpy.datetime64('2015-11-20T15:06:58.000')])
In [3]: arr.dtype
Out[3]: dtype('<M8[ms]')
In [4]: arr.flags.writeable = False
[PYFLYBY] import pandas as pd
In [5]: pd.factorize(arr)
```
#### Problem description
[Construction with non-mutable datetime64 strings]
#### Expected Output
(array([0]), array(['2015-11-20T15:06:58.000000000'], dtype='datetime64[ns]'))
#### Output of ``pd.show_versions()``
<details>
pandas/_libs/tslibs/conversion.pyx in pandas._libs.tslibs.conversion.ensure_datetime64ns()
/usr/local/python/python-2.7/std/lib/python2.7/site-packages/pandas/_libs/tslibs/conversion.so in View.MemoryView.memoryview_cwrapper()
/usr/local/python/python-2.7/std/lib/python2.7/site-packages/pandas/_libs/tslibs/conversion.so in View.MemoryView.memoryview.__cinit__()
ValueError: buffer source array is read-only
</details>
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Backport PR #35639 on branch 1.1.x (BUG: RollingGroupby with closed and column selection no longer raises ValueError)
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NONE
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Backport PR #35639: BUG: RollingGroupby with closed and column selection no longer raises ValueError
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Backport PR #35522 on branch 1.1.x (BUG: Fix assert_equal when check_exact=True for non-numeric dtypes #3…)
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| 2020-08-10T13:32:51Z
| 2020-08-10T14:54:15Z
| 2020-08-10T14:53:32Z
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NONE
| null |
Backport PR #35522: BUG: Fix assert_equal when check_exact=True for non-numeric dtypes #3…
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QST: How to select the week data from a given DataFrame?
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| 1
| 2020-08-10T14:59:18Z
| 2020-08-10T16:40:57Z
| 2020-08-10T16:40:57Z
|
NONE
| null |
Question about pandas
For the given DataFrame How do I select data between two weeks?
Date Open High Low Close
0 01-08-2019 97.85 98.45 96.40 97.25
1 02-08-2019 97.15 98.95 96.75 98.15
2 05-08-2019 98.30 98.70 94.30 95.65
3 06-08-2019 95.75 97.75 95.20 97.05
4 07-08-2019 96.80 97.70 96.05 96.90
5 08-08-2019 97.40 98.90 96.55 97.40
6 09-08-2019 97.20 98.10 96.65 97.30
7 12-08-2019 97.20 97.25 93.40 93.75
8 13-08-2019 93.70 96.60 93.15 96.35
9 14-08-2019 95.85 96.40 94.00 94.45
August 01-08-2019 ,02-08-2019 is one week.
August 05-08-2019, 06-08-2019, 07-08-2019, 08-08-2019, 09-08-2019 is second week
I want data in dataframe should be grouped according to week.
I want to group the week data for whole year?
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BUG: GH-35558 merge_asof tolerance error
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| 3
| 2020-08-10T15:07:53Z
| 2020-08-13T10:17:17Z
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CONTRIBUTOR
| null |
- [x] closes #35558
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Moto server
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| 8
| 2020-08-10T15:40:01Z
| 2020-08-22T19:26:28Z
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CONTRIBUTOR
| null |
Changes moto for s3 tests from monkeypatched/mocking to server mode. This allows aiobotocore exceptions to raise correctly, needed for tests to pass; the change is required by the upcoming async release of s3fs, but also works for old sync (botocore) version. A release of s3fs without this change would break pandas tests.
Note: since I now need to pass storage_options to the various s3 calls in the tests to make them pass (giving the endpoint of the moto s3 server), I needed to also plumb storage_options through the excel IO, which was previously missing, and update the excel tests.
- [ ] closes #xxxx
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ENH: Uniformised the boolean results in Pandas
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[] | null | 5
| 2020-08-10T15:56:08Z
| 2021-08-08T19:57:10Z
| null |
NONE
| null |
Hello everybody,
I'm not completely sure this issue has not been submitted earlier, please accept my apologies if so. It is also possible that what I saw results from an incorrect use of the Pandas package.
It seems that boolean answers are not always rigourosly the same in Pandas, which is really disturbing, and can involve errors when developping scripts using Pandas.
Here are examples of the problem (realized with Python 3.8.5 and Pandas 1.0.5). Let's imagine we have a DataFrame named "data", with a column "col1". We are first using the all() function:
>>>data["col1"].all()
Out: True
>>>data["col1"].all() == True
Out: True
>>>data["col1"].all() is True
False
In this case, the checking of the value using the PEP recommended "is" return a wrong answer.
Same idea with the "empty" function :
>>>data["col1].empty
Out: False
>>>data["col1"].empty == False
Out: True
>>>data["col1"].empty is False
Out: True
This time, checking the value using the "is" statement works well.
From what I understand, some boolean answers are of different "types" (Numpy logical booleans, ...).
Maybe a uniformisation of the boolean answer would avoid these kinds of problems and could be an improvment for future Pandas versions ?
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MDU6SXNzdWU2NzYzNDY3Mzc=
| 35,657
|
BUG: ValueError: cannot convert float NaN to integer - on dataframe.reset_index()
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| 4
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| 2020-08-14T01:50:52Z
| 2020-08-14T01:50:52Z
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NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
import pandas as pd
df = pd.DataFrame(
dict(c1=[10.], c2=['a'], c3=pd.to_datetime('2020-01-01')))
# Triggering conditions: multiindex with date, empty dataframe
# Multiindex without date works
df.set_index(['c1', 'c2']).head(0).reset_index()
# Regular index with date also works
df.set_index(['c3']).head(0).reset_index()
# Multiindex with date crashes...
df.set_index(['c2', 'c3']).head(0).reset_index()
# >> ValueError: cannot convert float NaN to integer
# This used to work on pandas 1.0.3, but breaks on pandas 1.1.0
# Though the error doesn't trigger if the dataframe is empty before
# calling set_index()
df.head(0).set_index(['c2', 'c3']).reset_index()
# I originally observed the bug in a groupby call
df.head(0).groupby(['c2', 'c3'])[['c1']].sum().reset_index()
# >> ValueError: cannot convert float NaN to integer
# This used to work on pandas 1.0.3, but breaks on pandas 1.1.0
```
#### Problem description
On pandas 1.1.0, I'm getting a ValueError exception when calling dataframe.reset_index() under the following conditions:
- Input dataframe is empty
- Multiindex from multiple columns, at least one of which is a datetime
The exception message is `ValueError: cannot convert float NaN to integer`.
Error trace:
```
Error
Traceback (most recent call last):
df_out.reset_index()
File "/Users/pec21/PycharmProjects/anp_voice_report/virtual/lib/python3.6/site-packages/pandas/core/frame.py", line 4848, in reset_index
level_values = _maybe_casted_values(lev, lab)
File "/Users/pec21/PycharmProjects/anp_voice_report/virtual/lib/python3.6/site-packages/pandas/core/frame.py", line 4782, in _maybe_casted_values
fill_value, len(mask), dtype
File "/Users/pec21/PycharmProjects/anp_voice_report/virtual/lib/python3.6/site-packages/pandas/core/dtypes/cast.py", line 1554, in construct_1d_arraylike_from_scalar
subarr.fill(value)
ValueError: cannot convert float NaN to integer
```
This error didn't happen on pandas 1.0.3 and earlier. I haven't tested any intermediate releases, nor the master branch.
#### Expected Output
No exception is raised, returns an empty dataframe.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.6.6.final.0
python-bits : 64
OS : Darwin
OS-release : 18.6.0
Version : Darwin Kernel Version 18.6.0: Thu Apr 25 23:16:27 PDT 2019; root:xnu-4903.261.4~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_GB.UTF-8
pandas : 1.1.0
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 49.1.0
Cython : None
pytest : 5.3.4
hypothesis : None
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : None
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.3.3
sqlalchemy : 1.3.11
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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BUG: IntervalIndex cannot be round-tripped pickled
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| 2
| 2020-08-10T21:34:50Z
| 2020-09-04T15:17:10Z
| 2020-09-04T15:17:10Z
|
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
import pickle as pkl
interval = pd.IntervalIndex.from_breaks(np.linspace(0, 100, 5+1), closed="left")
print("original:", interval)
print("pickle", pkl.loads(pkl.dumps(interval)))
```
```output
original: IntervalIndex([[0.0, 20.0), [20.0, 40.0), [40.0, 60.0), [60.0, 80.0), [80.0, 100.0)],
closed='left',
dtype='interval[float64]')
pickle IntervalIndex([(0.0, 20.0], (20.0, 40.0], (40.0, 60.0], (60.0, 80.0], (80.0, 100.0]],
closed='right',
dtype='interval[float64]')
```
#### Problem description
Having recently updated to v1.1.0 from v1.0.5, it seems IntervalIndex is no longer preserving the closure of the intervals as being left or right. When unpickling the IntervalIndex, the closure is reset to the default value of "right". This seems similar to the serialisation issues of #32037 and #35420, but those seem to be for older versions of the master branch.
#### Expected Output
```output
original: [IntervalIndex([[0.0, 20.0), [20.0, 40.0), [40.0, 60.0), [60.0, 80.0), [80.0, 100.0)],
closed='left',
dtype='interval[float64]')]
pickle [IntervalIndex([[0.0, 20.0), [20.0, 40.0), [40.0, 60.0), [60.0, 80.0), [80.0, 100.0)],
closed='left',
dtype='interval[float64]')]
```
#### Output of ``pd.show_versions()``
<details>
python -c "import pandas as pd; pd.show_versions()"
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1062.7.1.el7.x86_64
Version : #1 SMP Mon Dec 2 17:33:29 UTC 2019
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 19.3.1
setuptools : 45.2.0
Cython : None
pytest : 4.3.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.48.0
</details>
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ENH: Making Pandas dataframe slicing syntax match R dataframe syntax.
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| 3
| 2020-08-10T22:52:14Z
| 2020-08-11T17:22:11Z
| 2020-08-10T23:58:27Z
|
NONE
| null |
I imagine the following request is popular enough that it has probably been asked before. I just couldn't find it. Feel free to close if the discussion has already occurred.
Consider the following R example:
```r
# R code
> df <- data.frame(A=c(0:4), B=c("str0","str1","str2","str3","str4"), C=(1:5))
> df
A B C
1 0 str0 1
2 1 str1 2
3 2 str2 3
4 3 str3 4
5 4 str4 5
```
The equivalent Pandas syntax would be:
```python
# python code
> df = pd.DataFrame({ "A":range(0,5), "B":["str"+str(x) for x in range(0,5)], "C":range(1,6) })
> df
A B C
0 0 str0 1
1 1 str1 2
2 2 str2 3
3 3 str3 4
4 4 str4 5
```
Now when trying to slice a R dataframe, the syntax allows direct access with the brackets operator:
```r
# R code
> df[1,]
A B C
1 0 str0 1
```
The pandas equivalent would be:
```python
# python code
> df.iloc[0,]
A 0
B str0
C 1
Name: 0, dtype: object
```
Disregarding the 0-index vs 1-index between the languages, I would like to propose adding a new slicing operation to the Pandas dataframe [`getitem()`](https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L2904) that overloads the brackets operator and matches the behavior of R-style syntax. It would allow for cleaner and less-verbose code (especially when chaining multiple slice operations).
#### API breaking implications
The API change would be a positive addition. No removal of current slicing operations (`iloc`, `loc`, etc.)
#### Additional context
The following slicing examples will error out in Pandas whereas it would be valid slicing in R:
```python
# python code
> df[0]
> df[0, ]
> df[0,0]
> df[0, 1:2]
```
Note that calling `iloc` with the above commands would all succeed. What I'm suggesting is that the logic for `iloc` be copied/moved into Pandas dataframe `__getitem__()` function.
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BUG/CI: unreliable tests in `io\parser\test_common.py`
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| 0
| 2020-08-11T01:54:53Z
| 2020-08-17T20:50:14Z
| 2020-08-17T20:50:14Z
|
MEMBER
| null |
Those two tests
```bash
pandas\tests\io\parser\test_common.py:test_chunks_have_consistent_numerical_type
pandas\tests\io\parser\test_common.py:test_warn_if_chunks_have_mismatched_type
```
have been producing ResourceWarning occasionally on Windows-py37-np16 and travis-37. I think they might happen on other builds as well. I just haven't seen them yet.
```bash
all_parsers = <pandas.tests.io.parser.conftest.PythonParser object at 0x0000016ADEAFB808>
def test_chunks_have_consistent_numerical_type(all_parsers):
parser = all_parsers
integers = [str(i) for i in range(499999)]
data = "a\n" + "\n".join(integers + ["1.0", "2.0"] + integers)
# Coercions should work without warnings.
with tm.assert_produces_warning(None):
> result = parser.read_csv(StringIO(data))
pandas\tests\io\parser\test_common.py:1148:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <contextlib._GeneratorContextManager object at 0x0000016AA37A05C8>
type = None, value = None, traceback = None
def __exit__(self, type, value, traceback):
if type is None:
try:
> next(self.gen)
E AssertionError: Caused unexpected warning(s): [('ResourceWarning', ResourceWarning("unclosed <socket.socket fd=2972, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=0, laddr=('10.1.0.4', 1650), raddr=('169.254.169.254', 80)>"), 'D:\\a\\1\\s\\pandas\\io\\parsers.py', 3007)]
C:\Miniconda\envs\pandas-dev\lib\contextlib.py:119: AssertionError
```
```bash
all_parsers = <pandas.tests.io.parser.conftest.PythonParser object at 0x000001F32E4A2748>
def test_warn_if_chunks_have_mismatched_type(all_parsers):
warning_type = None
parser = all_parsers
integers = [str(i) for i in range(499999)]
data = "a\n" + "\n".join(integers + ["a", "b"] + integers)
# see gh-3866: if chunks are different types and can't
# be coerced using numerical types, then issue warning.
if parser.engine == "c" and parser.low_memory:
warning_type = DtypeWarning
with tm.assert_produces_warning(warning_type):
> df = parser.read_csv(StringIO(data))
pandas\tests\io\parser\test_common.py:1167:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <contextlib._GeneratorContextManager object at 0x000001F3076E7948>
type = None, value = None, traceback = None
def __exit__(self, type, value, traceback):
if type is None:
try:
> next(self.gen)
E AssertionError: Caused unexpected warning(s): [('ResourceWarning', ResourceWarning("unclosed <socket.socket fd=6620, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=0, laddr=('10.1.0.4', 1656), raddr=('169.254.169.254', 80)>"), 'D:\\a\\1\\s\\pandas\\io\\parsers.py', 3007)]
C:\Miniconda\envs\pandas-dev\lib\contextlib.py:119: AssertionError
```
Those two tests don't fail everytime. And they are skipped for now.
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CI: DeprecationWarning in `tests/io/json/test_json_table_schema.py` and `tests/io/json/test_pandas.py`
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[] | null | 3
| 2020-08-11T01:59:02Z
| 2021-06-11T12:59:13Z
| null |
MEMBER
| null |
On the NumPy dev build. The following tests:
```bash
FAILED pandas/tests/io/json/test_json_table_schema.py::TestTableOrient::test_to_json
FAILED pandas/tests/io/json/test_json_table_schema.py::TestTableOrient::test_date_format_raises
FAILED pandas/tests/io/json/test_pandas.py::TestPandasContainer::test_datetime_tz
FAILED pandas/tests/io/json/test_pandas.py::TestPandasContainer::test_tz_is_utc[ts0]
FAILED pandas/tests/io/json/test_pandas.py::TestPandasContainer::test_tz_is_utc[ts1]
FAILED pandas/tests/io/json/test_pandas.py::TestPandasContainer::test_tz_is_utc[ts2]
FAILED pandas/tests/io/json/test_pandas.py::TestPandasContainer::test_tz_range_is_utc[tz_range0]
FAILED pandas/tests/io/json/test_pandas.py::TestPandasContainer::test_tz_range_is_utc[tz_range1]
FAILED pandas/tests/io/json/test_pandas.py::TestPandasContainer::test_tz_range_is_utc[tz_range2]
```
raise `DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.`
I filtered this warning for now.
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|
CI: troubleshoot ResourceWarnings
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| 2020-08-11T04:06:37Z
| 2020-08-12T18:01:36Z
| 2020-08-12T18:01:16Z
|
MEMBER
| null |
There's a file being left open somewhere, trying to track it down.
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BUG: Styler `cells_ids` fails on second render
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| 0
| 2020-08-11T06:39:47Z
| 2020-08-21T17:04:45Z
| 2020-08-14T12:36:10Z
|
CONTRIBUTOR
| null |
issue #35588 addressed the issue of `cell_ids` failing.
This bug reports a failure on second render(), due to `ctx` object, as a `defaultdict`, having a key inserted when it is queried, which then impacts the programming logic the second time it is run.
The solution is to move the query within conditional a few lines above.
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MDExOlB1bGxSZXF1ZXN0NDY1OTAzNjky
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BUG: Styler cell_ids fails on multiple renders
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| 2020-08-11T06:44:45Z
| 2020-08-15T16:28:19Z
| 2020-08-14T12:36:10Z
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CONTRIBUTOR
| null |
- [x] closes #35663
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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| 2021-08-09T11:44:18Z
| 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
foo = pd.DataFrame({'some_string': ['612092d7-071f-467e832d-dd53e0f2b590-0006'], 'time': [pd.NaT]})
for _, row in foo.iterrows():
pass
```
#### Problem description
The error `OverflowError: Python int too large to convert to C long`. I would expect the snippet to iterate just fine without any issue.
#### Expected Output
The snippet is not expected to show any output.
#### Output of ``pd.show_versions()``
```
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
Version : Darwin Kernel Version 19.5.0: Tue May 26 20:41:44 PDT 2020; root:xnu-6153.121.2~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2
setuptools : 49.2.0.post20200712
Cython : 0.29.21
pytest : 6.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
<details>
```
---------------------------------------------------------------------------
OverflowError Traceback (most recent call last)
<ipython-input-7-d71373909082> in <module>
1 foo = pd.DataFrame({'some_string': ['612092d7-071f-467e832d-dd53e0f2b590-0006'], 'time': [pd.NaT]})
----> 2 for _, row in foo.iterrows():
3 pass
~/env/miniconda3/envs/data/lib/python3.8/site-packages/pandas/core/frame.py in iterrows(self)
1011 klass = self._constructor_sliced
1012 for k, v in zip(self.index, self.values):
-> 1013 s = klass(v, index=columns, name=k)
1014 yield k, s
1015
~/env/miniconda3/envs/data/lib/python3.8/site-packages/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
325 data = data.copy()
326 else:
--> 327 data = sanitize_array(data, index, dtype, copy, raise_cast_failure=True)
328
329 data = SingleBlockManager.from_array(data, index)
~/env/miniconda3/envs/data/lib/python3.8/site-packages/pandas/core/construction.py in sanitize_array(data, index, dtype, copy, raise_cast_failure)
425 else:
426 # we will try to copy be-definition here
--> 427 subarr = _try_cast(data, dtype, copy, raise_cast_failure)
428
429 elif isinstance(data, ABCExtensionArray):
~/env/miniconda3/envs/data/lib/python3.8/site-packages/pandas/core/construction.py in _try_cast(arr, dtype, copy, raise_cast_failure)
551 subarr = arr
552 else:
--> 553 subarr = maybe_cast_to_datetime(arr, dtype)
554
555 # Take care in creating object arrays (but iterators are not
~/env/miniconda3/envs/data/lib/python3.8/site-packages/pandas/core/dtypes/cast.py in maybe_cast_to_datetime(value, dtype, errors)
1430 )
1431 ):
-> 1432 value = maybe_infer_to_datetimelike(value)
1433
1434 return value
~/env/miniconda3/envs/data/lib/python3.8/site-packages/pandas/core/dtypes/cast.py in maybe_infer_to_datetimelike(value, convert_dates)
1290 # try timedelta first to avoid spurious datetime conversions
1291 # e.g. '00:00:01' is a timedelta but technically is also a datetime
-> 1292 value = try_timedelta(v)
1293 if lib.infer_dtype(value, skipna=False) in ["mixed"]:
1294 # cannot skip missing values, as NaT implies that the string
~/env/miniconda3/envs/data/lib/python3.8/site-packages/pandas/core/dtypes/cast.py in try_timedelta(v)
1266
1267 try:
-> 1268 td_values = to_timedelta(v)
1269 except ValueError:
1270 return v.reshape(shape)
~/env/miniconda3/envs/data/lib/python3.8/site-packages/pandas/core/tools/timedeltas.py in to_timedelta(arg, unit, errors)
110 arg = arg.item()
111 elif is_list_like(arg) and getattr(arg, "ndim", 1) == 1:
--> 112 return _convert_listlike(arg, unit=unit, errors=errors)
113 elif getattr(arg, "ndim", 1) > 1:
114 raise TypeError(
~/env/miniconda3/envs/data/lib/python3.8/site-packages/pandas/core/tools/timedeltas.py in _convert_listlike(arg, unit, errors, name)
149
150 try:
--> 151 value = sequence_to_td64ns(arg, unit=unit, errors=errors, copy=False)[0]
152 except ValueError:
153 if errors == "ignore":
~/env/miniconda3/envs/data/lib/python3.8/site-packages/pandas/core/arrays/timedeltas.py in sequence_to_td64ns(data, copy, unit, errors)
926 if is_object_dtype(data.dtype) or is_string_dtype(data.dtype):
927 # no need to make a copy, need to convert if string-dtyped
--> 928 data = objects_to_td64ns(data, unit=unit, errors=errors)
929 copy = False
930
~/env/miniconda3/envs/data/lib/python3.8/site-packages/pandas/core/arrays/timedeltas.py in objects_to_td64ns(data, unit, errors)
1036 values = np.array(data, dtype=np.object_, copy=False)
1037
-> 1038 result = array_to_timedelta64(values, unit=unit, errors=errors)
1039 return result.view("timedelta64[ns]")
1040
pandas/_libs/tslibs/timedeltas.pyx in pandas._libs.tslibs.timedeltas.array_to_timedelta64()
pandas/_libs/tslibs/timedeltas.pyx in pandas._libs.tslibs.timedeltas.parse_timedelta_string()
pandas/_libs/tslibs/timedeltas.pyx in pandas._libs.tslibs.timedeltas.timedelta_from_spec()
pandas/_libs/tslibs/conversion.pyx in pandas._libs.tslibs.conversion.cast_from_unit()
OverflowError: Python int too large to convert to C long
```
</details>
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Backport PR #35633 on branch 1.1.x (BUG: DataFrame.apply with func altering row in-place)
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| 1
| 2020-08-11T08:40:57Z
| 2020-08-11T09:41:03Z
| 2020-08-11T09:41:03Z
|
NONE
| null |
Backport PR #35633: BUG: DataFrame.apply with func altering row in-place
|
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MDU6SXNzdWU2NzY3MTczNTk=
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DOC: document dropna kwarg of pd.factorize
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| 11
| 2020-08-11T09:18:43Z
| 2020-09-02T15:00:50Z
| 2020-09-02T15:00:50Z
|
NONE
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.factorize.html
#### Documentation problem
The docs show the existence of a kwarg "dropna" which does not exist
#### Suggested fix for documentation
Delete the kwarg "dropna"
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ENH: Enable short_caption in to_latex
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| 10
| 2020-08-11T11:51:27Z
| 2020-11-06T15:34:10Z
| 2020-10-17T16:23:33Z
|
MEMBER
| null |
- [x] closes #36267
- [x] tests added / passed
- [x] passes black pandas
- [x] passes git diff upstream/master -u -- "*.py" | flake8 --diff
- [x] whatsnew entry
Enable short_caption for ``DataFrame.to_latex`` by expanding the meaning of kwarg ``caption``.
Optionally ``caption`` can be a ``Tuple[str, str] = full_caption, short_caption``.
The final caption macro would look like this:
```
\caption[short_caption]{full_caption}
```
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BUG: Groupby - apply - reset_index on a one row dataframe and with one categorical grouping column does not return the categorical column
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| 3
| 2020-08-11T14:29:03Z
| 2020-08-22T16:47:12Z
| 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](as) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
def apply_func(group):
return pd.DataFrame([{'cnt': group.b.sum()}])
def func(df):
return df.groupby(['a']).apply(apply_func).reset_index(level=0)
# OK : Apply the method to a one row dataframe with no category types
func(pd.DataFrame([{'a': 1, 'b': 2}]))
# As expected, output has column 'a'
# KO : Apply method to a one row dataframe where grouping column is of category type
df = pd.DataFrame([{'a': 1, 'b': 2}])
df.a = df.a.astype('category')
func(df)
# I expected column 'a' instead of column 'index'
```
#### Problem description
As soon as the input dataframe is not empty, the method `func` should return a dataframe with column 'a'. This seems to happen in all the cases, except if the dataframe is only one row and the grouping column is categorical.
*NB: this does not seem to happen when not using `apply` *
#### Expected Output
As shown, the result we expect for both method calls is the one we obtain when grouping column is not categorical:
```python
# a cnt
# 0 1 2
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.6.8.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
Version : Darwin Kernel Version 17.7.0: Thu Jun 18 21:21:34 PDT 2020; root:xnu-4570.71.82.5~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 20.2
setuptools : 40.6.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.9.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 2.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.0.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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|
BUG: surprising and possibly erroneous behavior of GroupBy.apply with an indexed series (index winds up duplicated)
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[] | null | 2
| 2020-08-11T15:51:35Z
| 2020-09-04T18:04:10Z
| 2020-09-04T18:04:10Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- I can't find anything in the bug tracker that matches the symptoms I'm reporting here, although it's a bit difficult to search (I'm not totally sure how to describe it)
- [x] I have confirmed this bug exists on the latest version of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
data = [{"label": l, "x" : x, "y": x + 1} for l in ("foo", "bar") for x in range(5)]
df = pd.DataFrame(data)
df = df.set_index(["label", "x"])
series = df["y"]
series2 = series.groupby(["label"]).apply(lambda s: s[2:])
print(series2.index)
# Output:
MultiIndex([('bar', 'bar', 2),
('bar', 'bar', 3),
('bar', 'bar', 4),
('foo', 'foo', 2),
('foo', 'foo', 3),
('foo', 'foo', 4)],
names=['label', 'label', 'x'])
```
#### Problem description
The "label" field is duplicated in the index of the result
#### Expected Output
I expect the index after the apply to be the same as before, ie to only contain "label" once
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.8.0.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-42-generic
Version : #46-Ubuntu SMP Fri Jul 10 00:24:02 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.1
Cython : 0.29.15
pytest : 5.4.0
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : 0.8.6
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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| 35,671
|
df[col].astype('interval') no longer works for interval string
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| 2020-08-11T16:11:17Z
| 2020-08-15T15:33:40Z
| 2020-08-15T15:33:40Z
|
NONE
| null |
We used the below code to roundtrip pd.Interval values in 0.25.3, but it throws TypeError in 1.0.5:
```
df = pd.DataFrame(data={'i': [pd.Interval(0, 2, closed='left')]})
df['i'] = df['i'].astype(str)
df['i'].astype('interval')
# throws: TypeError: type <class 'str'> with value [0, 2) is not an interval
```
We didn't use to_json/read_json because it is also not parsing pd.Interval back properly:
```
df = pd.DataFrame(data={'i': [pd.Interval(0, 2, closed='left')]})
value_json = df.to_json(orient='values')
pd.read_json(value_json, orient='values').values
# returns: array([[{'closed': 'left', 'closed_right': False, 'left': 0, 'mid': 1.0, 'open_right': True, 'right': 2}]],
dtype=object)
```
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CI/TST: change skip to xfail #35660
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Followup of #35660
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REGR: Dataframe.reset_index() on empty DataFrame with MI and datatime level
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|
MEMBER
| null |
- [ ] closes #35606
- [ ] closes #35657
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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| 6
| 2020-08-11T16:25:43Z
| 2020-08-22T03:08:29Z
| 2020-08-22T03:08:19Z
|
CONTRIBUTOR
| null |
A minor change to avoid 301 redirect on link.
|
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MDExOlB1bGxSZXF1ZXN0NDY2MjUzMzIw
| 35,675
|
Fix cython3
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| 2020-08-11T17:40:20Z
| 2020-09-09T22:57:57Z
| 2020-09-09T22:57:26Z
|
CONTRIBUTOR
| null |
I'm not sure if this actually works, but it gets cython to compile with py310.
- [x] closes #34014
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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PERF: make RangeIndex iterate over ._range
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| 2020-08-11T18:35:41Z
| 2020-08-13T18:27:18Z
| 2020-08-13T18:06:36Z
|
CONTRIBUTOR
| null |
Minor performance issue.
By adding a custom ``__iter__`` method to ``RangeIndex``, we partly avoid needing to create/cache the expensive ``_data`` attribute and partly it's just faster to iterate over a ``range`` than a ``ndarray``:
```python
>>> idx = pd.RangeIndex(100_000)
>>> %%timeit
... for _ in idx:
... pass
10.9 ms ± 74.7 µs per loop # master
6.11 ms ± 48.8 µs per loop # this PR
>>> "_data" in idx._cache
True # master
False # this PR
```
xref #35432, #26565.
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to_csv to Google Cloud Storage ignores compression
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| 2020-08-11T18:57:54Z
| 2020-09-03T03:06:18Z
| 2020-09-03T03:06:18Z
|
NONE
| null |
#### Code Sample, a copy-pastable example
```python
dtf.to_csv('gs://mybucket/test1.csv.gz')
dtf.to_csv('gs://mybucket/test2.csv.gz', compression='gzip')
```
#### Problem description
Compression is not working for a file located on gs://
#### Expected Output
Working as a local version.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.8.1.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Sun Jul 5 00:43:10 PDT 2020; root:xnu-6153.141.1~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.18.5
pytz : 2019.3
dateutil : 2.8.1
pip : 20.2.1
setuptools : 41.2.0
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.13.0
pandas_datareader: 0.9.0
bs4 : None
bottleneck : None
fsspec : 0.8.0
fastparquet : None
gcsfs : 0.6.2
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 0.7.10
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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August 2020 Developer Meeting (August 12th)
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| 2020-08-11T20:33:00Z
| 2020-08-14T07:30:17Z
| 2020-08-14T07:30:17Z
|
MEMBER
| null |
The monthly dev meeting is Wednesday August 12th, at 18:00 UTC. Our calendar is at https://pandas.pydata.org/docs/development/meeting.html#calendar to check your local time.
Video Call: https://zoom.us/j/942410248
Minutes: https://docs.google.com/document/u/1/d/1tGbTiYORHiSPgVMXawiweGJlBw5dOkVJLY-licoBmBU/edit?ouid=102771015311436394588&usp=docs_home&ths=true
Please add items you'd like to see discussed to the agenda.
All are welcome to attend.
cc @pandas-dev/pandas-core @pandas-dev/pandas-triage
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BUG: DataFrame.to_pickle(bytes_io_buffer) is automatically closed internally
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| 2
| 2020-08-11T21:25:40Z
| 2020-08-12T22:23:26Z
| 2020-08-12T22:23:26Z
|
NONE
| null |
#### Code Sample
```python
import pandas as pd
import io
# create example DataFrame
d = {'col1': [1, 2], 'col2': [3, 4]}
df = pd.DataFrame(data=d)
# load dataframe into bytesIO
buffer = io.BytesIO()
df.to_pickle(path=buffer)
# make sure buffer is still open
assert not buffer.closed
```
#### Problem description
Instead of dumping the binarized DataFrame on disk as e.g., by passing a file path or a file handle, I want to store its byte stream into an in memory bytesIO buffer, as shown above. Unfortunately, inside of `to_pickle` the bytesIO stream is already closed, thus rendering it useless. As far as I know the Python io API does not let you reopen a stream once it was closed.
#### Expected Output
In my opinion it makes more sense to leave it to the user, when to close the buffer, e.g., by using a context manager:
```python
with io.BytesIO() as f:
df.to_pickle(path=buffer)
# do something with f
```
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MDU6SXNzdWU2NzcyNjAwMDk=
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|
BUG: Different results from Series.replace with compiled regex
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| 2
| 2020-08-11T23:18:28Z
| 2020-08-17T11:23:54Z
| 2020-08-17T10:59:09Z
|
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.
- [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
import re
import pandas as pd
print(pd.__version__)
s = pd.Series(['a', 'aa', 'b'])
regex = {'^a$': 1, '^b$': 2, '^aa$': 3}
print(s.replace(regex, regex=True))
print(s.replace({re.compile(k): v for k,v in regex.items()}, regex=True))
```
#### Problem description
These two `.replace` calls are equivalent on pandas `1.0.5`. They are different on `1.1.0` and `master`.
```
1.2.0.dev0+79.g3c87b019b
0 1
1 3
2 2
dtype: int64
0 a
1 aa
2 b
dtype: object
1.0.5
0 1
1 3
2 2
dtype: int64
0 1
1 3
2 2
dtype: int64
```
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.5
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.3.1.post20200810
Cython : 0.29.21
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
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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BUG/ENH: compression for google cloud storage in to_csv
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| 6
| 2020-08-12T01:38:04Z
| 2020-09-03T15:56:27Z
| 2020-09-03T03:06:19Z
|
CONTRIBUTOR
| null |
- [x] closes #35677, closes #26124, and closes #32392
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
By inferring the compression before converting the path to a file object `df.to_csv("gs://mybucket/test2.csv.gz", compression="infer", mode="wb")` works. By wrapping fsspec file-objects in a `TextIOWrapper` `df.to_csv("gs://mybucket/test2.csv", mode="wb")` works as well. Path-like objects that are internally converted to file-like objects (in `get_filepath_or_buffer`) are now always opened in binary mode (unless text mode is explicitly requested) and the potentially changed mode is returned (no need to specify `mode="wb"` for google cloud files). As long as the google file is opened in binary mode (which is now always the case), we also honor the requested `encoding`.
This PR also fixes Zip compression for file objects not having a name.
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|
QST: Such a behavior of DataFrameGroupBy.cumcount() is intended ??
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| 2020-08-12T03:58:20Z
| 2020-08-17T14:54:06Z
| 2020-08-13T07:56:46Z
|
NONE
| null |
#### Question about pandas
Let's say we have a `DataFrame` named by `pdf` like the below.
```python
>>> pdf = pd.DataFrame([['a'], ['a'], ['a'], ['b'], ['b'], ['a']], columns=list('A'))
>>> pdf
A
0 a
1 a
2 a
3 b
4 b
5 a
```
For above `pdf`, `GroupBy.cum~` functions seems not work, but raise `DataError`
```python
# GroupBy.cummax
>>> pdf.groupby("A").cummax()
Traceback (most recent call last):
...
pandas.core.base.DataError: No numeric types to aggregate
# GroupBy.cummin
>>> pdf.groupby("A").cummin()
Traceback (most recent call last):
...
pandas.core.base.DataError: No numeric types to aggregate
# GroupBy.cumprod
>>> pdf.groupby("A").cumprod()
Traceback (most recent call last):
...
pandas.core.base.DataError: No numeric types to aggregate
# GroupBy.cumsum
>>> pdf.groupby("A").cumsum()
Traceback (most recent call last):
...
pandas.core.base.DataError: No numeric types to aggregate
```
And I thought that `GroupBy.cumcount()` also wouldn't work, but It's actually working like the below.
```python
>>> pdf.groupby("A").cumcount()
0 0
1 1
2 2
3 0
4 1
5 3
dtype: int64
```
Would someone let me know if Is it intended or unexpected behavior ?
Thanks.
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BUG: DataFrame.apply returns inconsistent index depending on applied function's return type
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| 12
| 2020-08-12T04:10:24Z
| 2020-12-20T11:09:06Z
| 2020-12-20T11:09:06Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- Possibly related to [this issue](https://github.com/pandas-dev/pandas/issues/35670), but from what I can see, it's not quite the same thing (might be different symptoms of the same problem)
- [x] I have confirmed this bug exists on the latest version of pandas.
---
#### Code Sample
```python
import pandas as pd
def func(col, _type):
return _type()
df = pd.DataFrame([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]], columns=['a', 'b'])
for _type in [int, bool, float, str, list, tuple, dict, set]:
type_lambda = lambda col: func(col, _type)
print(f'Returning values of type {_type}:', df.apply(type_lambda).index)
```
With pandas 1.1.0, this outputs:
```
Returning values of type <class 'int'>: Index(['a', 'b'], dtype='object')
Returning values of type <class 'bool'>: Index(['a', 'b'], dtype='object')
Returning values of type <class 'float'>: Index(['a', 'b'], dtype='object')
Returning values of type <class 'str'>: Index(['a', 'b'], dtype='object')
Returning values of type <class 'list'>: RangeIndex(start=0, stop=0, step=1)
Returning values of type <class 'tuple'>: RangeIndex(start=0, stop=0, step=1)
Returning values of type <class 'dict'>: Int64Index([0, 1], dtype='int64')
Traceback (most recent call last):
File "test.py", line 12, in <module>
print(f'Returning values of type {_type}:', df.apply(type_lambda).index)
File "/home/louis/venvs/test/lib/python3.7/site-packages/pandas/core/frame.py", line 7541, in apply
return op.get_result()
File "/home/louis/venvs/test/lib/python3.7/site-packages/pandas/core/apply.py", line 180, in get_result
return self.apply_standard()
File "/home/louis/venvs/test/lib/python3.7/site-packages/pandas/core/apply.py", line 258, in apply_standard
return self.wrap_results(results, res_index)
File "/home/louis/venvs/test/lib/python3.7/site-packages/pandas/core/apply.py", line 299, in wrap_results
return self.wrap_results_for_axis(results, res_index)
File "/home/louis/venvs/test/lib/python3.7/site-packages/pandas/core/apply.py", line 352, in wrap_results_for_axis
result = self.obj._constructor(data=results)
File "/home/louis/venvs/test/lib/python3.7/site-packages/pandas/core/frame.py", line 467, in __init__
mgr = init_dict(data, index, columns, dtype=dtype)
File "/home/louis/venvs/test/lib/python3.7/site-packages/pandas/core/internals/construction.py", line 283, in init_dict
return arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
File "/home/louis/venvs/test/lib/python3.7/site-packages/pandas/core/internals/construction.py", line 83, in arrays_to_mgr
arrays = _homogenize(arrays, index, dtype)
File "/home/louis/venvs/test/lib/python3.7/site-packages/pandas/core/internals/construction.py", line 352, in _homogenize
val, index, dtype=dtype, copy=False, raise_cast_failure=False
File "/home/louis/venvs/test/lib/python3.7/site-packages/pandas/core/construction.py", line 452, in sanitize_array
raise TypeError("Set type is unordered")
TypeError: Set type is unordered
```
#### Problem description
As shown above, when different data types are turned by the applied function, the index of the result is changed. From what I have seen, this is neither documented behaviour, nor is it consistent with itself. Additionally, in previous versions of pandas, `apply` worked differently, as shown below.
#### Expected Output
This is the output when running with pandas 1.0.5 and pandas 1.0.4, and is what I would expect:
```
Returning values of type <class 'int'>: Index(['a', 'b'], dtype='object')
Returning values of type <class 'bool'>: Index(['a', 'b'], dtype='object')
Returning values of type <class 'float'>: Index(['a', 'b'], dtype='object')
Returning values of type <class 'str'>: Index(['a', 'b'], dtype='object')
Returning values of type <class 'list'>: Index(['a', 'b'], dtype='object')
Returning values of type <class 'tuple'>: Index(['a', 'b'], dtype='object')
Returning values of type <class 'dict'>: Index(['a', 'b'], dtype='object')
Returning values of type <class 'set'>: Index(['a', 'b'], dtype='object')
```
#### Output of ``pd.show_versions()``
Environment with pandas 1.1.0:
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-42-generic
Version : #46~18.04.1-Ubuntu SMP Fri Jul 10 07:21:24 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_NZ.UTF-8
LOCALE : en_NZ.UTF-8
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.1
setuptools : 39.0.1
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 : 0.8.0
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
Environment with pandas 1.0.5:
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-42-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_NZ.UTF-8
LOCALE : en_NZ.UTF-8
pandas : 1.0.5
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.1
setuptools : 39.0.1
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
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : 0.4.2
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU2Nzc1ODU5ODM=
| 35,684
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BUG: get UserWarning about FixedFormatter/FixedLocator in series.plot()
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| 19
| 2020-08-12T10:39:47Z
| 2020-11-08T01:25:45Z
| 2020-09-05T19:50:44Z
|
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.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
#### Code Sample, a copy-pastable example
```python
pd.Series({"20190102": 10, "20190103": 10}).plot()
```
#### Problem description
Got a UserWarning:
```
/home/.../miniconda3/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:1192: UserWarning: FixedFormatter should only be used together with FixedLocator
```
And to catch the Warning:
```python
import warnings
with warnings.catch_warnings():
warnings.filterwarnings("error")
pd.Series({"20190102": 10, "20190103": 10}).plot()
```
Got
```
---------------------------------------------------------------------------
UserWarning Traceback (most recent call last)
<ipython-input-5-4339a8efd56e> in <module>
2 with warnings.catch_warnings():
3 warnings.filterwarnings("error")
----> 4 pd.Series({"20190102": 10, "20190103": 10}).plot()
~/miniconda3/lib/python3.8/site-packages/pandas/plotting/_core.py in __call__(self, *args, **kwargs)
845 data.columns = label_name
846
--> 847 return plot_backend.plot(data, kind=kind, **kwargs)
848
849 __call__.__doc__ = __doc__
~/miniconda3/lib/python3.8/site-packages/pandas/plotting/_matplotlib/__init__.py in plot(data, kind, **kwargs)
59 kwargs["ax"] = getattr(ax, "left_ax", ax)
60 plot_obj = PLOT_CLASSES[kind](data, **kwargs)
---> 61 plot_obj.generate()
62 plot_obj.draw()
63 return plot_obj.result
~/miniconda3/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py in generate(self)
268 for ax in self.axes:
269 self._post_plot_logic_common(ax, self.data)
--> 270 self._post_plot_logic(ax, self.data)
271
272 def _args_adjust(self):
~/miniconda3/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py in _post_plot_logic(self, ax, data)
1190 xticks = ax.get_xticks()
1191 xticklabels = [get_label(x) for x in xticks]
-> 1192 ax.set_xticklabels(xticklabels)
1193 ax.xaxis.set_major_locator(FixedLocator(xticks))
1194
~/miniconda3/lib/python3.8/site-packages/matplotlib/axes/_base.py in wrapper(self, *args, **kwargs)
61
62 def wrapper(self, *args, **kwargs):
---> 63 return get_method(self)(*args, **kwargs)
64
65 wrapper.__module__ = owner.__module__
~/miniconda3/lib/python3.8/site-packages/matplotlib/cbook/deprecation.py in wrapper(*args, **kwargs)
449 "parameter will become keyword-only %(removal)s.",
450 name=name, obj_type=f"parameter of {func.__name__}()")
--> 451 return func(*args, **kwargs)
452
453 return wrapper
~/miniconda3/lib/python3.8/site-packages/matplotlib/axis.py in _set_ticklabels(self, labels, fontdict, minor, **kwargs)
1791 if fontdict is not None:
1792 kwargs.update(fontdict)
-> 1793 return self.set_ticklabels(labels, minor=minor, **kwargs)
1794
1795 @cbook._make_keyword_only("3.2", "minor")
~/miniconda3/lib/python3.8/site-packages/matplotlib/axis.py in set_ticklabels(self, ticklabels, minor, **kwargs)
1728 ticks = self.get_minor_ticks(len(locs))
1729 else:
-> 1730 self.set_major_formatter(formatter)
1731 locs = self.get_majorticklocs()
1732 ticks = self.get_major_ticks(len(locs))
~/miniconda3/lib/python3.8/site-packages/matplotlib/axis.py in set_major_formatter(self, formatter)
1589 formatter : `~matplotlib.ticker.Formatter`, ``str``, or function
1590 """
-> 1591 self._set_formatter(formatter, self.major)
1592
1593 def set_minor_formatter(self, formatter):
~/miniconda3/lib/python3.8/site-packages/matplotlib/axis.py in _set_formatter(self, formatter, level)
1619 and len(formatter.seq) > 0
1620 and not isinstance(level.locator, mticker.FixedLocator)):
-> 1621 cbook._warn_external('FixedFormatter should only be used together '
1622 'with FixedLocator')
1623
~/miniconda3/lib/python3.8/site-packages/matplotlib/cbook/__init__.py in _warn_external(message, category)
2088 break
2089 frame = frame.f_back
-> 2090 warnings.warn(message, category, stacklevel)
2091
2092
UserWarning: FixedFormatter should only be used together with FixedLocator
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.104-microsoft-standard
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.3.1.post20200810
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.2
html5lib : None
pymysql : 0.10.0
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.2
matplotlib : 3.3.0
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 1.0.0
pytables : None
pytest : 6.0.1
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU2Nzc2MDcxOTc=
| 35,685
|
DOC/DEV: document approach to bisect regressions
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[] | null | 7
| 2020-08-12T11:17:29Z
| 2021-08-08T20:00:23Z
| null |
MEMBER
| null |
@simonjayhawkins you have been doing great work on finding the causes of bugs/regressions!
To make it easier for other contributors to also do this, I think it would be nice to document this is the contributing docs (even though it is relying on git, using it with pandas (which needs to be rebuilt for each commit) requires a bit of custom setup I think?)
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BUG: to_pickle/read_pickle do not close user-provided file objects
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CONTRIBUTOR
| null |
- [x] closes #35679
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Do not close user-provided file-objects in `to_pickle` and `read_pickle`.
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ENH: Timedelta cannot span more than 293 years => implementation limitation
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[] | null | 6
| 2020-08-12T12:53:20Z
| 2021-02-22T23:21:36Z
| null |
NONE
| null |
The Pandas datetime arithmetic module is indeed very useful and it can be used for archeological research studies as well. Unfortunately, the limitation that Timedelta object cannot span more than 293 years puts a huge shame on this wonderful piece of data science library. With this limitation, it is not possible to go back for thousands or even more than 300 years in history.
```
>>> pd.to_timedelta('1Y')*5
Timedelta('1826 days 05:06:00')
>>> pd.to_timedelta('1Y')*292
Timedelta('106650 days 19:26:24')
>>> pd.to_timedelta('1Y')*293
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/_libs/tslibs/timedeltas.pyx", line 1347, in pandas._libs.tslibs.timedeltas.Timedelta.__mul__
File "pandas/_libs/tslibs/timedeltas.pyx", line 1230, in pandas._libs.tslibs.timedeltas.Timedelta.__new__
File "pandas/_libs/tslibs/timedeltas.pyx", line 180, in pandas._libs.tslibs.timedeltas.convert_to_timedelta64
File "pandas/_libs/tslibs/timedeltas.pyx", line 308, in pandas._libs.tslibs.timedeltas.cast_from_unit
OverflowError: Python int too large to convert to C long
```
In terms of practicality, time resolution and time span are always contradicting with each other. Nowadays, quantum physics often deals with time objects at a scale of nano (10**-9), pico (10**-12), or even femto(10**-15)-second. While archeology often deals with time objects at a span of thousands, millions, or even billions of years. If I remember correctly, pandas set the time counter base unit at nano-seconds, thus, the span will be short. The solution to cater for both high resolution and large span is to use floating point rather than a large integer, as the time counter base unit. The speed will be slightly slower for floating point. But if you take a look at Intel Architecture, on modern CPU, floating point arithmetic is almost the same as integer especially when SIMD is used.
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Fix GH-29442 DataFrame.groupby doesn't preserve _metadata
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CONTRIBUTOR
| null |
This bug is a regression in v1.1.0 and was introduced by the fix for GH-34214 in commit [6f065b].
Underlying cause is that the `*Splitter` classes do not use the `._constructor` property and do not call `__finalize__`.
Please note that the method name used for `__finalize__` calls was my best guess since documentation for the value has been hard to find.
[6f065b]: https://github.com/pandas-dev/pandas/commit/6f065b6d423ea211d803e8be93c27f547541c372
- [x] closes #29442
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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ENH: Support PostgreSQL server-side cursors to prevent memory hog on large datasets
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| 2020-08-12T15:11:27Z
| 2021-04-05T18:51:37Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
`pandas.read_sql_query` supports Python "generator" pattern when providing `chunksize` argument. It's not very helpful when working with large datasets, since the whole data is initially retrieved from DB into client-side memory and later chunked into separate frames based on `chunksize`. Large datasets will easily run into out-of-memory problems with this approach.
#### Describe the solution you'd like
Postgres/psycopg2 are addressing this problem with [server-side cursors](https://www.psycopg.org/docs/usage.html#server-side-cursors). But Pandas does not support it.
#### API breaking implications
`is_cursor` argument of `SQLDatabase` or `SQLiteDatabase` is not exposed in `pandas.read_sql_query` or `pandas.read_sql_table` without any reason. It should be exposed, this way named (server-side) cursor could be provided.
#### Describe alternatives you've considered
Instead of doing:
```
iter = sql.read_sql_query(sql,
conn,
index_col='col1',
chunksize=chunksize)
```
I tried reimplementing it like this:
```
from pandas.io.sql import SQLiteDatabase
curs = conn.cursor(name='cur_name') # server side cursor creation
curs.itersize = chunksize
pandas_sql = SQLiteDatabase(curs, is_cursor=True)
iter = pandas_sql.read_query(
sql,
index_col='col1',
chunksize=chunksize)
```
but it fails because `SQLiteDatabase` tries to access `cursor.description`, which is NULL for some reason with server-side cursors (and idea why?).
#### Additional references
* https://github.com/pandas-dev/pandas/issues/10693
* https://github.com/pandas-dev/pandas/issues/13168
* https://stackoverflow.com/posts/63378600/
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DatetimeIndex.get_slice_bound(...) raises TypeErrors for unexpected YYYY-MM-DD/datetime.date/Timestamp combinations
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| 7
| 2020-08-12T16:12:54Z
| 2020-09-02T21:36:38Z
| 2020-09-02T21:36:38Z
|
NONE
| null |
Below find a handful of reproducible observations in Pandas 1.0.5 re: `DatetimeIndex.get_slice_bound(...)` where I'm genuinely not sure what the correct behaviors should all be. Some of these appear related to #34077 in that `slice_locs(...)` uses `get_slice_bound(...)` under the covers.
Observations inlined and expected behaviors discussed afterwards:
```
########################################
OBSERVATIONS 1 using a UTC DatetimeIndex
########################################
import datetime
import pandas as pd
import pandas.util.testing as put
# Generate a UTC-localized DatetimeIndex
df = put.makeTimeDataFrame()
df = df.tz_localize("utc")
index = df.index
# Show the generated DatetimeIndex, which should look like:
# DatetimeIndex(['2000-01-03 00:00:00+00:00', '2000-01-04 00:00:00+00:00',
# ...
# '2000-02-10 00:00:00+00:00', '2000-02-11 00:00:00+00:00'],
# dtype='datetime64[ns, UTC]', freq='B')
print(index)
# (A) When the date is inside the DatetimeIndex, this call completes.
index.get_slice_bound(datetime.date(2000, 1, 7), kind="ix", side="left")
# (B) Notice date before start of index
# TypeError: searchsorted requires compatible dtype or scalar, not date
index.get_slice_bound(datetime.date(2000, 1, 1), kind="ix", side="left")
# (C) Notice date after end of index
# TypeError: searchsorted requires compatible dtype or scalar, not date
index.get_slice_bound(datetime.date(2020, 1, 1), kind="ix", side="left")
# (D) When the Timestamp is inside the DatetimeIndex, this call completes
index.get_slice_bound(pd.Timestamp("2000-01-07"), kind="ix", side="left")
# (E) Notice Timestamp before start of index
# TypeError: Cannot compare tz-naive and tz-aware datetime-like objects
index.get_slice_bound(pd.Timestamp("2000-01-01"), kind="ix", side="left")
# (F) Notice Timestamp after end of index
# TypeError: Cannot compare tz-naive and tz-aware datetime-like objects
index.get_slice_bound(pd.Timestamp("2020-01-01"), kind="ix", side="left")
```
Discussion:
1. Above, I do not know if (A)-(C) should behave as (E)-(F) or not.
2. Above, I suspect (D) should raise as (E)-(F) do.
3. I can see arguments where datetime.date's lacking tzinfo could be inferred to be the DatetimeIndex.tzinfo. Then (A)-(C) would not raise.
4. I can see arguments where Timestamps lacking tzinfo could be inferred to be the DatetimeIndex.tzinfo. Then (D)-(F) would not raise.
5. I don't expect any data-dependence, meaning that the specific YYYY-MM-DD should not impact if something raises.
6. I have not tested datetime.datetime under these circumstances.
7. I believe some of these behaviors may have changed since the 0.2-series.
Again, observations inlined and expected behaviors discussed afterwards:
```
########################################################
OBSERVATIONS 2 using a DatetimeIndex with tzinfo is None
########################################################
import datetime
import pandas as pd
import pandas.util.testing as put
# Generate a non-localized DatetimeIndex
df = put.makeTimeDataFrame()
index = df.index
assert index.tzinfo is None
# Show the generated DatetimeIndex, which should look like:
# DatetimeIndex(['2000-01-03', '2000-01-04', '2000-01-05', '2000-01-06',
# ...
# '2000-02-10', '2000-02-11'],
# dtype='datetime64[ns]', freq='B')
print(index)
# (G) When the date is inside the DatetimeIndex, this call completes.
index.get_slice_bound(datetime.date(2000, 1, 7), kind="ix", side="left")
# (H) Notice date before start of index
# TypeError: searchsorted requires compatible dtype or scalar, not date
index.get_slice_bound(datetime.date(2000, 1, 1), kind="ix", side="left")
# (I) Notice date after end of index
# TypeError: searchsorted requires compatible dtype or scalar, not date
index.get_slice_bound(datetime.date(2020, 1, 1), kind="ix", side="left")
# (J) When the Timestamp is inside the DatetimeIndex, this call completes
index.get_slice_bound(pd.Timestamp("2000-01-07"), kind="ix", side="left")
# (K) Call completes for Timestamp before start of index
index.get_slice_bound(pd.Timestamp("2000-01-01"), kind="ix", side="left")
# (L) Call completes for Timestamp after end of index
index.get_slice_bound(pd.Timestamp("2020-01-01"), kind="ix", side="left")
```
Discussion:
1. Above, I expect (H)-(I) to behave as (G).
2. Above, (J)-(K) appear correct to me.
3. I don't expect any data-dependence, meaning that the specific YYYY-MM-DD should not impact if something raises.
4. I have not tested datetime.datetime under these circumstances.
5. I believe some of these behaviors may have changed since the 0.2-series.
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.67-ts1
machine : x86_64
processor :
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.utf8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.16.6
pytz : 2019.2
dateutil : 2.8.0
pip : 19.0.3
setuptools : 40.8.0
Cython : 0.29.20
pytest : 5.3.1
hypothesis : 3.57.0
sphinx : 1.8.5
blosc : 1.5.1
feather : None
xlsxwriter : 1.0.2
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.2.1
fastparquet : 0.3.3
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.0.3
numexpr : 2.6.4
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.0
pytables : None
pytest : 5.3.1
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.1
sqlalchemy : 1.2.1
tables : 3.5.2
tabulate : 0.8.3
xarray : 0.10.0
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : 1.0.2
numba : 0.50.1
```
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BUG: DatetimeIndex.get_loc failing when key is int (introduced in v1.1.0)
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| 2
| 2020-08-12T16:49:14Z
| 2020-08-12T22:00:19Z
| 2020-08-12T22:00:19Z
|
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.
In version 1.0.5 the below was running successfully, but in 1.1.0, this is now broken as `int` keys are not accepted anymore (a `KeyError` is raised because the type of the key is now unexpected).
```
import numpy as np
from numpy import datetime64
from pandas import DatetimeIndex
dt = datetime64("2020-08-12T00:00:00.000000000")
dt_item = np.array(dt).item()
index = DatetimeIndex(data=[dt])
print(index.get_loc(dt))
print(index.get_loc(dt_item))
```
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ENH: GH-35611 Tests for top-level Pandas functions serializable
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| 1
| 2020-08-12T16:52:24Z
| 2020-08-19T18:03:00Z
| 2020-08-19T18:02:56Z
|
CONTRIBUTOR
| null |
- [x] closes #35611
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
|
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CI: avoid file leaks in sas_xport tests
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| 1
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MEMBER
| null |
Introduces a contextmanager version of td.check_file_leaks so we can do more targeted versions of those checks for debugging
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Converting DataFrame with nullable types to DataFrame with non-nullable types?
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| null |
CONTRIBUTOR
| null |
Is there a way to convert DataFrames containing nullable types to DataFrames containing the equivalent non-nullable types?
For example:
```python
>>> import pandas as pd
>>> a = pd.DataFrame()
>>> a['a'] = pd.Series([1, None, 3], dtype="Int32")
>>> a['b'] = pd.Series([True, False, None], dtype="boolean")
>>> a
a b
0 1 True
1 <NA> False
2 3 <NA>
>>> a.dtypes
a Int32
b boolean
dtype: object
```
Is there a way to convert this to a DataFrame with `int32` and `bool` dtypes, assuming the user can specify `na_values` for those? Ideally, if there are no nulls in the input, then the user wouldn't even have to specify `na_value`.
### Context:
cuDF is [moving in the direction](https://github.com/rapidsai/cudf/issues/5754) of returning nullable Pandas objects when the `.to_pandas()` method is called on cuDF objects. Our hope is that this is a step in the right direction of the community adopting arrow-like nullable types. In the near term, however, this will break user workflows (e.g., https://github.com/rapidsai/cudf/issues/5928) and it would be fantastic if we could point users to a convenient way to recover "classical" Pandas objects from what we return.
cc: @brandon-b-miller @kkraus14
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BUG: df.query raises TypeError when a column is named datetime
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| 2
| 2020-08-12T21:48:39Z
| 2021-08-10T04:22:20Z
| null |
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
col_name = "datetime"
col_data = [0.1, 0.3, 0.5, 1]
df = pd.DataFrame({col_name: col_data})
min_bound, max_bound = 0.25, 0.75
query = f"{min_bound} < {col_name} < {max_bound}"
df.query(query)
```
#### Problem description
I know this is a bit of an unfair issue but running the code above raises a TypeError (`TypeError: '<' not supported between instances of 'float' and 'type'`). Full error bellow:
<details>
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-1-fe357c1c3e3c> in <module>
7 min_bound, max_bound = 0.25, 0.75
8 query = f"{min_bound} < {col_name} < {max_bound}"
----> 9 df.query(query)
~/personal/hackatlon/pandas/pandas/core/frame.py in query(self, expr, inplace, **kwargs)
3368 kwargs["level"] = kwargs.pop("level", 0) + 1
3369 kwargs["target"] = None
-> 3370 res = self.eval(expr, **kwargs)
3371
3372 try:
~/personal/hackatlon/pandas/pandas/core/frame.py in eval(self, expr, inplace, **kwargs)
3498 kwargs["resolvers"] = kwargs.get("resolvers", ()) + tuple(resolvers)
3499
-> 3500 return _eval(expr, inplace=inplace, **kwargs)
3501
3502 def select_dtypes(self, include=None, exclude=None) -> "DataFrame":
~/personal/hackatlon/pandas/pandas/core/computation/eval.py in eval(expr, parser, engine, truediv, local_dict, global_dict, resolvers, level, target, inplace)
344 eng = _engines[engine]
345 eng_inst = eng(parsed_expr)
--> 346 ret = eng_inst.evaluate()
347
348 if parsed_expr.assigner is None:
~/personal/hackatlon/pandas/pandas/core/computation/engines.py in evaluate(self)
125
126 def evaluate(self):
--> 127 return self.expr()
128
129 def _evaluate(self) -> None:
~/personal/hackatlon/pandas/pandas/core/computation/expr.py in __call__(self)
792
793 def __call__(self):
--> 794 return self.terms(self.env)
795
796 def __repr__(self) -> str:
~/personal/hackatlon/pandas/pandas/core/computation/ops.py in __call__(self, env)
394 """
395 # recurse over the left/right nodes
--> 396 left = self.lhs(env)
397 right = self.rhs(env)
398
~/personal/hackatlon/pandas/pandas/core/computation/ops.py in __call__(self, env)
397 right = self.rhs(env)
398
--> 399 return self.func(left, right)
400
401 def evaluate(self, env, engine: str, parser, term_type, eval_in_python):
TypeError: '<' not supported between instances of 'float' and 'type'
```
</details>
I tried changing `col_name` to every value from the list bellow and only `datetime` raises an error.
```
["float", "float64", "int", "int64", "category", "string", "object", "datetime"]
```
I get the same behaviour on pandas 1.1.0 and on the latest dev version 1.2.0.dev0+80.g59ffa2512.
#### Expected Output
```
datetime
1 0.3
2 0.5
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 59ffa251269ddb8b561d8d88f631a5db21f660dc
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.3.0
Version : Darwin Kernel Version 19.3.0: Thu Jan 9 20:58:23 PST 2020; root:xnu-6153.81.5~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.2.0.dev0+80.g59ffa2512
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.3.2
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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REF: implement reset_dropped_locs
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MEMBER
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We get to get rid of the comment `# really should be done in internals :<`
Making the window.rolling usage use this helper method is the next step.
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REGR: Don't ignore compiled patterns in replace
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| 2020-08-13T02:19:55Z
| 2020-08-17T13:03:21Z
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MEMBER
| null |
- [x] closes #35680
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NDY3MTA5MDQ2
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CLN: replace PyObject_Str with str #34213
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|
MEMBER
| null |
Part of #34213
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Backport PR #35647 on branch 1.1.x (BUG: Support custom BaseIndexers in groupby.rolling)
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| 1
| 2020-08-13T06:14:44Z
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NONE
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Backport PR #35647: BUG: Support custom BaseIndexers in groupby.rolling
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BUG: pandas 1.1.0 introduces string comparison bug for larger datasets
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| 6
| 2020-08-13T07:22:54Z
| 2020-09-18T21:55:06Z
| 2020-09-18T21:55:06Z
|
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
# This works fine
df1 = pd.DataFrame({"a": [1,2,3], "b": ["x","y","z"]})
print(df1 == "x")
# Loading in some bigger data from Kaggle https://www.kaggle.com/dgomonov/new-york-city-airbnb-open-data
# data and code file included in zip to make it easy
df2 = pd.read_csv("AB_NYC_2019.csv")
# On pandas v1.1.0 throws a ValueError: unknown type str32
print(df2 == "x")
# On pandas v1.1.0 throws a ValueError: unknown type str128
print(df2 == "John")
```
[reproduce.zip](https://github.com/pandas-dev/pandas/files/5067660/reproduce.zip)
#### Problem description
Previously comparing the dataframe to a string value would give a boolean mask as expected. It is now erroring. Confirmed that on v1.0.5 this works as expected, only on 1.1.0 does this error.
#### Expected Output
A boolean mask of true or false values with the same shape as the dataframe.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.3.1.post20200810
Cython : 0.29.21
pytest : 6.0.1
hypothesis : 5.24.0
sphinx : 3.2.0
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.0
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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MDU6SXNzdWU2NzgyNDcwODQ=
| 35,701
|
BUG: Load data frame pickled under pandas 1.0.5 using pandas 1.1.0
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| 5
| 2020-08-13T08:17:23Z
| 2020-12-15T13:28:14Z
| 2020-08-13T10:53:49Z
|
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
'''
Have pandas 1.0.5 installed.
'''
#%% import libs;
import numpy as np
import pandas as pd
import time
import datetime
import pickle
#%% do;
df0 = pd.DataFrame(
{
'sdtr3mm': [datetime.datetime.now()],
'count1': [1]
}
)
df0['month_year'] = df0['sdtr3mm'].dt.to_period('M')
df0['year'] = df0['sdtr3mm'].dt.to_period('Y')
with open("Z:/test.data", "wb") as filehandle:
pickle.dump(df0, filehandle)
'''
Update to pandas : 1.1.0
'''
with open("Z:/test.data", "rb") as filehandle:
df1 = pickle.load(filehandle)
```
[test.zip](https://github.com/pandas-dev/pandas/files/5067895/test.zip) (zipped data frame, pickled under pandas 1.0.5)
#### Problem description
I pickled a dataframe, containing a datetime variable, an integer, a pandas-month-year, panda-year variable, under pandas 1.0.5 to my disk.
After the update to pandas 1.1.0, I can no longer load it, receiving an "TypeError: Expected tuple, got dict",
with:
```
Traceback (most recent call last):
File "<ipython-input-5-fa4170ff1942>", line 2, in <module>
df1 = pickle.load(filehandle)
File "stringsource", line 17, in pandas._libs.tslibs.period.PeriodMixin.__setstate_cython__
```
Creating a data frame as above under pandas 1.1.0 and trying to load it in under pandas 1.0.5 does not work either.
*** Update:
Also does not work using pd.read_pickle either.
I am sorry if this is the wrong place and/or the wrong way to post my issue.
#### Expected Output
Load in the data frame created as in the code above.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 1.0.5
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.2.1.post20200807
Cython : None
pytest : None
hypothesis : None
sphinx : 3.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : None
numba : None
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Backport PR #35654 on branch 1.1.x (BUG: GH-35558 merge_asof tolerance error)
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| 1
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| 2020-08-13T11:04:51Z
| 2020-08-13T11:04:50Z
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NONE
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Backport PR #35654: BUG: GH-35558 merge_asof tolerance error
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CI / Checks (pull_request) on Github Actions failing with isort 5.3.2 -> 5.4.0
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| 2020-08-15T03:42:53Z
| 2020-08-14T10:09:29Z
|
MEMBER
| null |
```
2020-08-13T10:30:51.9470350Z isort --version-number
2020-08-13T10:30:52.2249999Z 5.4.0
2020-08-13T10:30:52.2443133Z Check import format using isort
2020-08-13T10:30:56.4886273Z ##[error]ERROR: /home/runner/work/pandas/pandas/pandas/_testing.py Imports are incorrectly sorted and/or formatted.
2020-08-13T10:30:56.4892079Z ##[error]ERROR: /home/runner/work/pandas/pandas/pandas/_typing.py Imports are incorrectly sorted and/or formatted.
2020-08-13T10:30:56.4892907Z ##[error]ERROR: /home/runner/work/pandas/pandas/pandas/tests/frame/test_analytics.py Imports are incorrectly sorted and/or formatted.
2020-08-13T10:30:56.4893331Z ##[error]ERROR: /home/runner/work/pandas/pandas/pandas/tests/extension/base/__init__.py Imports are incorrectly sorted and/or formatted.
2020-08-13T10:30:56.4893737Z ##[error]ERROR: /home/runner/work/pandas/pandas/pandas/tseries/frequencies.py Imports are incorrectly sorted and/or formatted.
2020-08-13T10:30:56.4894183Z ##[error]ERROR: /home/runner/work/pandas/pandas/pandas/tseries/offsets.py Imports are incorrectly sorted and/or formatted.
2020-08-13T10:30:56.4894928Z ##[error]ERROR: /home/runner/work/pandas/pandas/pandas/tseries/holiday.py Imports are incorrectly sorted and/or formatted.
2020-08-13T10:30:56.4895326Z ##[error]ERROR: /home/runner/work/pandas/pandas/pandas/plotting/_matplotlib/timeseries.py Imports are incorrectly sorted and/or formatted.
2020-08-13T10:30:56.4895747Z ##[error]ERROR: /home/runner/work/pandas/pandas/pandas/core/index.py Imports are incorrectly sorted and/or formatted.
2020-08-13T10:30:56.4896139Z ##[error]ERROR: /home/runner/work/pandas/pandas/pandas/core/common.py Imports are incorrectly sorted and/or formatted.
2020-08-13T10:30:56.4896552Z ##[error]ERROR: /home/runner/work/pandas/pandas/pandas/core/ops/__init__.py Imports are incorrectly sorted and/or formatted.
2020-08-13T10:30:56.4897157Z ##[error]ERROR: /home/runner/work/pandas/pandas/pandas/core/window/__init__.py Imports are incorrectly sorted and/or formatted.
2020-08-13T10:30:56.4897613Z ##[error]ERROR: /home/runner/work/pandas/pandas/pandas/core/dtypes/dtypes.py Imports are incorrectly sorted and/or formatted.
2020-08-13T10:30:56.4898032Z ##[error]ERROR: /home/runner/work/pandas/pandas/pandas/core/dtypes/common.py Imports are incorrectly sorted and/or formatted.
2020-08-13T10:30:56.4898422Z ##[error]ERROR: /home/runner/work/pandas/pandas/pandas/core/reshape/melt.py Imports are incorrectly sorted and/or formatted.
2020-08-13T10:30:56.4898832Z ##[error]ERROR: /home/runner/work/pandas/pandas/pandas/core/reshape/merge.py Imports are incorrectly sorted and/or formatted.
2020-08-13T10:30:56.4899228Z ##[error]ERROR: /home/runner/work/pandas/pandas/pandas/core/internals/__init__.py Imports are incorrectly sorted and/or formatted.
2020-08-13T10:30:56.4899642Z ##[error]ERROR: /home/runner/work/pandas/pandas/pandas/core/tools/datetimes.py Imports are incorrectly sorted and/or formatted.
2020-08-13T10:30:56.4900046Z ##[error]ERROR: /home/runner/work/pandas/pandas/pandas/api/__init__.py Imports are incorrectly sorted and/or formatted.
2020-08-13T10:30:56.4900461Z ##[error]ERROR: /home/runner/work/pandas/pandas/pandas/util/__init__.py Imports are incorrectly sorted and/or formatted.
2020-08-13T10:30:56.4900859Z ##[error]ERROR: /home/runner/work/pandas/pandas/pandas/io/pytables.py Imports are incorrectly sorted and/or formatted.
2020-08-13T10:30:56.4901145Z Check import format using isort DONE
```
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MDExOlB1bGxSZXF1ZXN0NDY3MzE2NjMy
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ENH add na_action to DataFrame.applymap
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| 5
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| 2020-09-11T17:40:18Z
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|
CONTRIBUTOR
| null |
For symmetry with Series.map
- [x] closes #23803
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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CI: pin isort < 5.4.0
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|
MEMBER
| null |
xref #35703
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MDU6SXNzdWU2Nzg0NDE1ODQ=
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|
BUG: pandas dataframe style render produces incorrect html with multiindex
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| 2020-08-13T13:34:04Z
| 2020-09-08T23:56:47Z
| 2020-09-08T23:56:47Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
import datetime
df = pd.DataFrame(
data={
('level0', 'level1a',): [1234565.23424],
('level0', 'level1b',): [1234565.23424],
('level0', 'level1c',): [1234565.23424],
('level0', 'level1d',): [1234565.23424],
('level0', 'level1e',): [1234565.23424],
},
columns=pd.MultiIndex.from_tuples(
[
('level0', 'level1a',),
('level0', 'level1b',),
('level0', 'level1c',),
('level0', 'level1d',),
('level0', 'level1e',),
]
),
index=['test']
)
df.style.format('{:,.2f}').render()
```
#### Problem description
The render() of the styler produces incorrect html in case the columns are a MultiIndex.
#### Expected Output
re.sub(r'colspan=(\d)', r'colspan="\1"', df.style.format('{:,.2f}').render())
The current implementation returns something like
<th class="col_heading level0 col0" colspan=5> while it should be
<th class="col_heading level0 col0" colspan="5">
See also https://www.w3schools.com/tags/att_td_colspan.asp
Same goes for MultiIndex indices where it concerns rowspan instead of colspan.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.6.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.111-1.el7.centos.x86_64
Version : #1 SMP Wed Apr 17 17:45:41 CEST 2019
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 9.0.1
setuptools : 28.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.14.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDY3Mzk4NTgy
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REGR: fix DataFrame.diff with read-only data
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| 2020-08-14T14:42:58Z
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|
MEMBER
| null |
Closes #35559
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Reorganize imports to be compliant with isort (and conventional)
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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add web/ directory to isort checks
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDU6SXNzdWU2Nzg1NzExNjU=
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ENH: DataFrame.append is slow when the DataFrame is huge
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| 2
| 2020-08-13T16:30:54Z
| 2020-08-13T16:54:22Z
| 2020-08-13T16:54:22Z
|
CONTRIBUTOR
| null |
#### Is your feature request related to a problem?
pandas.DataFrame.append is slow.
Users use DataFrame.append multiple times. And it's more pronounced when the data is huge.
That would take a long time to combine the data.
So some users are using DataFrame.from_dict which is faster than DataFrame.append. (check additional reference)
```python
N = 10000
for i in range(N):
df = df.append(other, ignore_index=True)
```
#### Describe the solution you'd like
I guess DataFrame.append is slow because there's a copy operation of DataFrame in somewhere.
But I haven't found it yet.
The solution will be to rewrite the process to be more efficient by not making copies.
#### Additional reference
https://stackoverflow.com/questions/27929472/improve-row-append-performance-on-pandas-dataframes
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CI: Check for file leaks in _all_ tests
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MEMBER
| null |
Sits on top of #35693
Still have two tests failing locally, but I'm not seeing any socket leaks that I occasionally see in the CI logs.
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MDExOlB1bGxSZXF1ZXN0NDY3NTY3MDM1
| 35,712
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PERF: RangeIndex.format performance
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| 13
| 2020-08-13T18:39:42Z
| 2020-08-26T13:29:51Z
| 2020-08-26T11:12:56Z
|
CONTRIBUTOR
| null |
#35440 dropped ``RangeIndex._format_with_header``, which was functionally not needed, but needed to avoid creating an internal ndarray.
This rectifies that + gives some perf. improvements.
```python
>>> idx = pd.RangeIndex(1_000_000)
>>> %timeit idx.format()
4.6 s ± 102 ms per loop # pandas v1.1.0
1.67 s ± 19.6 ms per loop # master
595 ms ± 2.35 ms per loop # this PR
```
Also, now the ``_data`` attribute isn't called, so this PR gives a perf. & memory improvement in some use cases compared to master:
```python
>>> idx = pd.RangeIndex(1_000_000)
>>> idx.format()
>>> "_data" in idx._cache
False # pandas v.1.1.0
True # master
False # this PR
```
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BUG: read_csv converts quoted strings to floats with quoting=csv.QUOTE_NONNUMERIC
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| 3
| 2020-08-14T01:30:04Z
| 2020-08-19T08:03:23Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import csv
import io
import pandas as pd
source = io.StringIO('"col"\n"1"')
df = pd.read_csv(source, quoting=csv.QUOTE_NONNUMERIC)
# Expect a string, but it parsed into a float.
assert df['col'][0] == '1'
```
#### Problem description
In the [documentation](https://docs.python.org/3/library/csv.html#csv.QUOTE_NONNUMERIC) of `csv.QUOTE_NONNUMERIC`, it says:
> Instructs the reader to convert all **non-quoted** fields to type float.
Yet `pandas.read_csv` is converting my **quoted** value into a float. The above test case fails.
#### Expected Output
The code sample should pass the assertion.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.8.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Mon Feb 10 21:08:45 PST 2020; root:xnu-4903.278.28~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 46.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU2Nzg5NjY4NDk=
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|
BUG: Negative RangeIndex can have positive step
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| 2020-08-14T07:39:16Z
| 2020-08-17T14:54:30Z
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|
CONTRIBUTOR
| null |
Python allows positve steps in ranges, e.g.
```python
>>> rng = range(2, -2)
>>> rng.step
1 # positive step in a negative range
list(rng)
[] # empty
```
which surprises me and surely must be a bug in Python. This affects pandas' RangeIndex:
```python
>>> idx = pd.RangeIndex(2, -2)
>>> df = pd.DataFrame(idx, index=idx)
>>> df.shape
(0, 1) # 0 length
>>> df.index
RangeIndex(start=2, stop=-2, step=1) # positive step!
>>> df # diplays as empty, even though isn't empty
Empty DataFrame
Columns: [0]
Index: []
>>> df.sum() # result in master, wrong
0 0
dtype: int64
>>> df.sum() # Correct result, not in master
0 2
dtype: int64
```
We see that the dataframe displays as empty, even though it is not actually empty and aggregations give wrong results.
Do people agree that in cases of negative ranges, we should supply a default step of -1?
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Attribute "dtype" are different error with testing.assert_frame_equal
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| 2
| 2020-08-14T09:59:34Z
| 2020-08-17T18:20:20Z
| 2020-08-17T18:20:20Z
|
NONE
| null |
Hi,
When trying testing.assert_frame_equal, I get an assert error.
E Attribute "dtype" are different
E [left]: Int64
E [right]: Int16
However, for assert_frame_equal, I do set the argument `check_dtype=False`.
From traceback, I can see that `assert_extension_array_equal` is being called with `check_dtype=True` which is causing the issue.
I do not have a sample at hand and I will update when I can.
Affected version: 1.1.0
The test cases were / are running fine with 1.0.5
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Backport PR #35673 on branch 1.1.x (REGR: Dataframe.reset_index() on empty DataFrame with MI and datatime level)
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Backport PR #35673: REGR: Dataframe.reset_index() on empty DataFrame with MI and datatime level
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