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https://api.github.com/repos/pandas-dev/pandas/issues/32416
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MDExOlB1bGxSZXF1ZXN0MzgzMTAwMTIx
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`_ensure_type` should use `issubclass`
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| 2020-03-13T17:37:26Z
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NONE
| null |
Commit pandas-dev/pandas@6fd326d5a249967f9b6be60fc3c5f7366d914684 in pull request pandas-dev/pandas#30613 added `_ensure_type`, which utilizes `isinstance`. However, it is reasonable to assume that someone may want to create a DataFrame subclass. Therefore, `_ensure_type` should use `issubclass`.
- [x] closes #31925
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
whatsnew entry isn't necessary?
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MDU6SXNzdWU1NzQ4NzI2OTQ=
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BLD: Clang error while installing snappy in requirements-dev.txt
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| 2020-03-03T18:57:07Z
| 2021-07-29T06:25:23Z
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NONE
| null |
### Problem description
I was going through the instructions in setting up the dev environment on Mac here:
[Contributing to pandas](https://github.com/pandas-dev/pandas/blob/master/doc/source/development/contributing.rst#creating-a-python-environment-pip)
And while I was trying to execute the below code:
```
# Install the build dependencies
python -m pip install -r requirements-dev.txt
```
I got the below clang error (redacted):
```
Running setup.py install for python-snappy ... error
ERROR: Command errored out with exit status 1:
…
clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.15.sdk -I/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.15.sdk/usr/include -I/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/xxx/virtualenvs/pandas-dev/include -I/usr/local/Cellar/python/3.7.6_1/Frameworks/Python.framework/Versions/3.7/include/python3.7m -c snappy/snappymodule.cc -o build/temp.macosx-10.15-x86_64-3.7/snappy/snappymodule.o
snappy/snappymodule.cc:31:10: fatal error: 'snappy-c.h' file not found
#include <snappy-c.h>
^~~~~~~~~~~~
1 error generated.
error: command 'clang' failed with exit status 1
----------------------------------------
ERROR: Command errored out with exit status 1: …
```
I went through the rest of the virtual environment set up fine besides this issue.
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MDExOlB1bGxSZXF1ZXN0MzgzMTQyMjgw
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CLN: move away from .values, _ndarray_values
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MEMBER
| null |
Also _values_from_object and _internal_get_values, though none of those made it into this PR.
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ENH: Categorical.fillna allow Categorical/ndarray
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MEMBER
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- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
xref #32414.
cc @TomAugspurger ATM the new test implemented in tests.arrays.categorical.test_missing is failing on the ndarray case for reasons that I dont fully grok. Are you familiar with this?
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TYP: enforce annotation on SingleBlockManager.__init__
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| 2
| 2020-03-03T20:18:58Z
| 2020-03-11T14:07:31Z
| 2020-03-11T02:32:11Z
|
MEMBER
| null |
cc @simonjayhawkins my impression from yesterday's threads was that mypy should be catching the wrong-type being passed in `SingleBlockManager.__init__`, did I misunderstand something?
Also (and I know ive asked before) is there a nice way to annotate the return type as "same type as self"? I tried `pandas._typing.T` but that didnt do it.
Parts of this are repeated in several BlockManager methods, should probably be made into a helper function, pending discussion on above.
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MDExOlB1bGxSZXF1ZXN0MzgzMTg1NDky
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CLN: avoid values_from_object in NDFrame
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| 1
| 2020-03-03T21:20:54Z
| 2020-04-05T17:45:35Z
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|
MEMBER
| null |
xref #32419
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MDU6SXNzdWU1NzQ5NzU3NDM=
| 32,423
|
BUG: df.apply disobeys raw=True
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| 3
| 2020-03-03T21:56:01Z
| 2020-03-16T10:08:28Z
| 2020-03-16T10:08:27Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> df = pd.DataFrame(dict(ints=np.arange(3),
... floats=np.arange(3, dtype=float)))
# df.apply callbacks with Series even though raw requested
>>> df.apply(type, raw=True)
ints <class 'pandas.core.series.Series'>
floats <class 'pandas.core.series.Series'>
dtype: object
# Works for single dtype dfs
>>> df.astype(int).apply(type, raw=True)
ints <class 'numpy.ndarray'>
floats <class 'numpy.ndarray'>
dtype: object
```
#### Problem description
When `df.apply(..., raw=True)`, the callback should _always_ be passed a numpy array (as documented) for reasons of performance and _convenience_ (arrays index much differently than Series).
This is not a recent regression; 0.25.3 exhibits the same behavior.
#### Expected Output
```py
>>> df.apply(type, raw=True, axis=1)
0 <class 'numpy.ndarray'>
1 <class 'numpy.ndarray'>
2 <class 'numpy.ndarray'>
dtype: object
```
#### Output of ``pd.show_versions()``
pandas 1.1.0.dev0+679.gd33b0025d
pandas 1.0.1
pandas 0.25.3
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MDExOlB1bGxSZXF1ZXN0MzgzMjIwMTQ3
| 32,424
|
Fix BUG: overflow on pd.Timedelta(nanoseconds=) constructor
|
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| 2
| 2020-03-03T22:24:14Z
| 2020-03-04T15:43:52Z
| 2020-03-04T14:22:36Z
|
CONTRIBUTOR
| null |
Add regression test
- [x] closes #32402
- [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: Fix DataFrame.apply(..., raw=True) not calling with raw array
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| 2020-03-16T09:25:35Z
| 2020-03-14T20:21:12Z
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- [x] closes https://github.com/pandas-dev/pandas/issues/32423
- [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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| 575,063,674
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MDExOlB1bGxSZXF1ZXN0MzgzMjgwMjc3
| 32,426
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CLN: avoid values_from_object in Series
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| 2020-04-24T14:59:13Z
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MEMBER
| null |
xref #32422, #32419
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CLN: avoid _internal_get_values in groupby.generic
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MEMBER
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xref #27165, #27167
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MDExOlB1bGxSZXF1ZXN0MzgzNDEwNzM0
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WIP: ENH: Add engine keyword argument to groupby.apply to leverage Numba
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MEMBER
| null |
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- [ ] tests added / passed
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MDExOlB1bGxSZXF1ZXN0MzgzNDM4NTA3
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DOC: Fix EX02 in pandas.Index.get_loc
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CONTRIBUTOR
| null |
- [ ] closes #xxxx
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Related to #27977.
```
################################################################################
################################## Validation ##################################
################################################################################
5 Errors found:
No extended summary found
Parameter "key" has no description
The first line of the Returns section should contain only the type, unless multiple values are being returned
Return value has no description
See Also section not found
```
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MDU6SXNzdWU1NzUyNjA0MjQ=
| 32,430
|
ENH: support nullable dtypes in get_dummies
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| 1
| 2020-03-04T09:39:33Z
| 2020-03-10T14:14:56Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
series = pd.Series(list('AA'))
col = pd.get_dummies(series, dtype='UInt8').A
assert col.dtype.name == 'UInt8' # Actual: np.uint8
```
#### Problem description
In `get_dummies`, dtype strings of "Int64", etc., are ignored in favor of their non-nullable Numpy equivalents.
There's a few other places that *could* cause this bug, but I haven't checked public API:
- `.core.reshape.reshape._Unstacker.get_new_values`
- `.core.dtypes.cast.maybe_downcast_to_dtype`
- `.core.dtypes.cast.maybe_downcast_numeric`
#### Expected Output
The dtype of such a call should be the Nullable type.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
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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MDU6SXNzdWU1NzUyODEzNzc=
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TypeError: cannot do label indexing on <class 'pandas.core.indexes.base.Index'> with these indexers [nan] of <class 'float'>
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| 8
| 2020-03-04T10:11:51Z
| 2020-11-18T13:53:17Z
| 2020-11-18T13:53:17Z
|
NONE
| null |
```py
import pandas as pd
import numpy as np
df = pd.Series([1, '{1,2}', 1, None])
vc = df.value_counts(dropna=False)
print(vc.loc[np.nan])
print(vc[np.nan])
```
```
TypeError: cannot do label indexing on <class 'pandas.core.indexes.base.Index'> with these indexers [nan] of <class 'float'>
```
Hello, now I have trouble getting null value count, help.
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BUG: pandas.DataFrame.any bool_only parameter not working since v0.24.0
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| 3
| 2020-03-04T12:35:02Z
| 2020-09-11T01:11:44Z
| 2020-09-11T01:11:44Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
This is the code that I run to test the DataFrame.any function in different pandas versions
```python
import pandas as pd
pd.__version__
df = pd.DataFrame({"A": [True, False], "B": [1, 2]})
df
df.dtypes
df.any(axis=1)
df.any(axis=1, bool_only=True)
```
#### Problem description
In pandas version 0.23.4, the output is expected when `` bool_only=True`` is specified
```python
>>> import pandas as pd
>>> pd.__version__
'0.23.4'
>>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]})
>>> df
A B
0 True 1
1 False 2
>>> df.dtypes
A bool
B int64
>>> df.any(axis=1)
0 True
1 True
dtype: bool
>>> df.any(axis=1, bool_only=True)
0 True
1 False
dtype: bool
```
However in pandas version 0.24.0, the final output is wrong for ``bool_only=True``
```python
>>> import pandas as pd
>>> pd.__version__
'0.24.0'
>>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]})
>>> df
A B
0 True 1
1 False 2
>>> df.dtypes
A bool
B int64
>>> df.any(axis=1)
0 True
1 True
dtype: bool
>>> df.any(axis=1, bool_only=True)
0 True
1 True
dtype: bool
```
#### Expected Output
```python
>>> df.any(axis=1, bool_only=True)
0 True
1 False
dtype: bool
```
#### Output of ``pd.show_versions()``
I can only reproduce this bug for pandas version v0.24.0 and above so I'll include details for v0.24.0.
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.4.final.0
python-bits: 64
OS: Darwin
OS-release: 18.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.0
pytest: 5.0.1
pip: 20.0.2
setuptools: 40.8.0
Cython: None
numpy: 1.17.0
scipy: None
pyarrow: 0.14.1
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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Memory leaks if an object is saved in a DataFrame and references the DataFrame
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[] | null | 2
| 2020-03-04T14:05:16Z
| 2021-07-29T06:27:32Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
class MemoryLeakDemoDF:
"""
Dummy class to demonstrate memory leaks
"""
def __init__(self, df):
self.df = df
# if "self" is saved in a DataFrame, it causes a memory leak
df.loc[0, 'object'] = self
num = 3
for _ in range(num):
df = pd.DataFrame()
MemoryLeakDemoDF(df)
```
#### Problem description
In the example above, the objects of MemoryLeakDemoDF are floating in memory and not removed by garbage collector. A similar example with a dict instead of a DataFrame does not lead to this problem. This behavior can cause massive memory leaks in a program that uses DataFrames that have references to other objects.
#### Expected Output
It is expected that there are no memory leaks in a code example above.
What is the reason for the memory leak in this case? What is the root cause of the garbage collector not being able to remove the objects from memory?
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2.post20191201
Cython : 0.29.14
pytest : 5.3.2
hypothesis : None
sphinx : 2.3.0
blosc : None
feather : None
xlsxwriter : 1.2.6
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.2
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.6
</details>
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MDU6SXNzdWU1NzU1MTI0OTY=
| 32,434
|
Rounding issue when using pd.read_excel() followed by pd.to_sql()
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| 2020-03-04T15:39:10Z
| 2020-10-21T01:43:03Z
| 2020-10-21T01:43:03Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
Single column Excel(.xls) file with the following data:
Document Summary
All
Total Sales
1479.9
39002.33
11295.76
```python
import pandas as pd
import sqlalchemy
db_user = "user"
db_pass = "pass"
server = "server_name"
db = "database"
sql_engine = sqlalchemy.create_engine(
f"mssql+pyodbc://{db_user}:{db_pass}@{server}/{db}"
"?driver=ODBC+Driver+17+for+SQL+Server"
)
# single column Excel file containing mixed string and float data types
file_path = (
r"doc_summary.xls"
)
df = pd.read_excel(io=file_path, sheet_name=0, header=None)
# check our values once loaded into pandas dataframe
print(df[0].values)
# prints ['Document Summary' 'All' 'Total Sales' 1479.9 39002.32999999997 11295.76000000001]
table_name = "stopRounding"
# load dataframe into sql table
df.to_sql(table_name, sql_engine, if_exists="replace")
# !! this is where floating point values above are being rounded
# retrieve data from table created from dataframe
sql_val = pd.read_sql(f"SELECT * FROM {table_name}", sql_engine)
print(sql_val.values)
# prints [[0 'Document Summary']
# [1 'All']
# [2 'Total Sales']
# [3 '1479.9']
# [4 '39002.3']
# [5 '11295.8']]
```
#### Problem description
When using pandas.read_excel() to load the data into a dataframe followed by pandas.to_sql(), the float precision is being rounded up to the nearest tenth decimal place.
I read over the pandas documentation and do not see any arguments that can be passed to maintain the float precision.
I've also tried the same thing with pandas.read_csv() followed by pandas.to_sql() using the same data, and the float precision is maintained! So the issue seems to be with read_excel() only.
#### Expected Output
Document Summary
All
Total Sales
1479.9
39002.33
11295.76
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.0
pip : 20.0.2
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.13
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.8
numba : None
</details>
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MDU6SXNzdWU1NzU1MTMwMzY=
| 32,435
|
IntegerArray.astype("datetime64[ns]") fails with NAs
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| 0
| 2020-03-04T15:39:43Z
| 2020-03-14T03:37:22Z
| 2020-03-14T03:37:22Z
|
MEMBER
| null |
tools.datetimes has to dig into IntegerArray internals in a way that in principle should be handled by IntegerArray.astype
```
ser = pd.Series(range(2), dtype="Int64")
arr = ser.array
>>> arr.astype("datetime64[ns]")
array(['1970-01-01T00:00:00.000000000', '1970-01-01T00:00:00.000000001'],
dtype='datetime64[ns]')
arr[-1] = pd.NA
>>> arr.astype("datetime64[ns]")
ValueError: cannot convert to 'datetime64[ns]'-dtype NumPy array with missing values. Specify an appropriate 'na_value' for this dtype.
```
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MDExOlB1bGxSZXF1ZXN0MzgzNjkxMzYw
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DOC: correct issue number for PR #32424
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MEMBER
| null |
xref #32424
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Include tests for pd.NA and pd.NaT in tests/base/test_ops.py
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CONTRIBUTOR
| null |
This is a follow-up from #32281, specifically on [this comment](https://github.com/pandas-dev/pandas/pull/32281/files#r387677448) from jreback.
`test_unique_null`, `test_nunique_null` and `test_value_counts_null` should be tested for `pd.NA` and `pd.NaT` as well, ideally using the `nulls_fixture` from `pandas/conftest.py`.
As discovered/discussed in the mentioned PR, they would xfail for now. It's something that should be tested and ideally fixed though.
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CI: mypy fixup for #32261
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https://github.com/pandas-dev/pandas/runs/485121000
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DOC: Fix capitalization of the word pandas in the docs
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| 2020-03-04T17:09:20Z
| 2020-03-12T04:49:31Z
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|
CONTRIBUTOR
| null |
- [x] closes #32316
- [x] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
As stated in the referred issue, _pandas_ reference in the docs should be standardised to pandas, not *pandas* or Pandas.
I've only changed the references in `doc/source/development/*.rst` for now, to see if the changes are okay or not
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CI Failing: Linux py37_locale TestTSPlot.test_matplotlib_scatter_datetime64
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matplotlib 3.2.0
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`DataFrame.diff` on `datetime64` returns result inconsistent with `Series.diff`
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| 6
| 2020-03-04T17:27:15Z
| 2020-10-10T17:40:57Z
| 2020-10-10T17:40:56Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> pd.Series(['NaT', '2019-01-01', '2019-01-02'], dtype='datetime64[ns]').diff()
0 NaT
1 NaT
2 1 days
dtype: timedelta64[ns]
>>> pd.Series(['NaT', '2019-01-01', '2019-01-02'], dtype='datetime64[ns]').to_frame().diff()
0
0 NaT
1 -88855 days +00:12:43.145224
2 1 days 00:00:00
```
#### Problem description
Doing `.diff()` on a `DataFrame` of `datetime64` should yield exactly time deltas in exactly the same way as on the `Series` i.e. index `(1,0)` in the `DataFrame` above should be `NaT`, not an overflown timedelta64.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.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 : English_United Kingdom.936
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
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 : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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CI: fix test_matplotlib_scatter_datetime64
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| 2
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| 2020-03-04T19:14:42Z
| 2020-03-04T19:11:38Z
|
MEMBER
| null |
- [ ] closes #32440
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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|
incosistencies in dataframe.dtype.isin() - I am getting inconsistent with different runs on same code.
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| 2020-03-04T17:40:32Z
| 2020-12-16T02:59:10Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```
import numpy as np
import pandas
data = [[2, 'tom', 10], [3, 'nick', 15], [4, 'juli', 14]]
myDf = pandas.DataFrame(data, columns=['a','b','c'])
print (myDf.dtypes)
print((myDf.dtypes.isin(['int64'])))
```
#### Problem description
Problem is that I am getting inconsistent results based on running the same code:
So first run gives me:
```
a False
b False
c False
```
and second (sometimes it takes up to 10 attempts) run gives me:
```
a True
b False
c True
```
the second case is the truth and matches the myDf.dtypes:
```
a int64
b object
c int64
```
.
Again to reiterate, I am not changing the code but when I keep running the same code I sometime get the first output and sometime get the second output. This is while I do not change the input, code or interpreter. I tried it on different machines and interpreters and still getting the same inconsistent result
the version of Python 3.7
the version of Pandas: 1.0
#### Expected Output
Consistent result (as it sometimes matches with the current output).
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 19.3.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
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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CI: ax.rowNum and ax.colNum attributes deprecated in Matplotlib 3.2
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| 12
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Backport PR #32442 on branch 1.0.x (CI: fix test_matplotlib_scatter_datetime64)
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| 1
| 2020-03-04T19:12:19Z
| 2020-03-04T20:03:13Z
| 2020-03-04T20:03:13Z
|
NONE
| null |
Backport PR #32442: CI: fix test_matplotlib_scatter_datetime64
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| 32,446
|
BLD: Remove snappy from requirements-dev.txt
|
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| 2020-03-04T19:35:36Z
| 2020-03-04T21:31:59Z
| 2020-03-04T21:31:58Z
|
NONE
| null |
#### Problem description
As raised in issues #32417 and #32327, `snappy` is causing issues while setting up a dev environment `requirements-dev.txt`.
#### Proposed change
I suggest removing `snappy` from `requirements-dev.txt` to allow fewer issues when setting up the environment.
#### Acknowledgements
@WillAyd for suggesting to raise this issue.
Related Issues: #32417, #32327
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|
how to keep nanosecond timestamps?
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| 2020-03-04T20:02:28Z
| 2020-04-19T01:49:29Z
| 2020-04-19T01:49:28Z
|
NONE
| null |
Hello there,
I apologize if this a very simple question but what is the best way to store nanoseconds timestamps with `pandas` and `pyarrow`?
Right now it seems that using the `flavor = 'spark'` keeps the nanosecond precision while the other flavors do not.
Is that the right way to see things?
Consider this:
```
import pyarrow as pa
import pandas as pd
import numpy as np
pa.__version__
pa.__version__
Out[131]: '0.13.0'
pd.__version__
pd.__version__
Out[128]: '0.24.2'
mydf = pd.DataFrame({'mytime' : [pd.to_datetime('2020-01-01 10:10:10.123456'),
pd.to_datetime('2020-01-01 10:10:10.234567')],
'value' : [1,2]})
mydf.to_parquet('testfile_spark.pq', engine = 'pyarrow', flavor = 'spark')
mydf.to_parquet('testfile_regular.pq', engine = 'pyarrow', allow_truncated_timestamps = True)
one = pd.read_parquet('testfile_spark.pq')
one.head()
Out[134]:
mytime value
0 2020-01-01 10:10:10.123456 1
1 2020-01-01 10:10:10.234567 2
two = pd.read_parquet('testfile_regular.pq')
two.head()
Out[136]:
mytime value
0 2020-01-01 10:10:10.123 1
1 2020-01-01 10:10:10.234 2
```
EDIT
@wesm asked me to post here from https://github.com/apache/arrow/issues/6536
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|
Series.convert_dtypes is not idempotent with small strings
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| 2020-03-04T20:02:59Z
| 2020-03-06T07:17:19Z
| 2020-03-06T07:17:19Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
s = pd.Series(['x', 'y', 'z'])
print(s.dtype, s.convert_dtypes().dtype, s.convert_dtypes().convert_dtypes().dtype, sep='\n')
```
Output:
```
object
string
|S1
```
#### Problem description
It is reasonably expected that the output of `convert_dtypes` will always be stable such that if it's applied multiple times to the same Series, the subsequent calls after the first one will be redundant and will result in no change. This reasonable assumption doesn't however hold; it's violated in different ways by the current implementation. In the pasted example, it converts `object` to `string` and then to `|S1` which is a clear violation of stability. In other cases I've seen it convert `object` to `string` and then back to `object`, with this making no sense.
Also, the word "best" should be removed from the documentation. In what formal way exactly is its output the best? In some cases I've seen this method change the dtype to dramatically increase the deep memory usage of a Series. Given the seemingly whimsical implementation, the term seems to carry no meaning. I think this entire method, as it stands, is a hack job, and has no place in the package.
#### Expected Output
```
object
|S1
|S1
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
/usr/local/lib/python3.6/dist-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use "pip install psycopg2-binary" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.
""")
/usr/local/lib/python3.6/dist-packages/pandas_datareader/compat/__init__.py:18: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
from pandas.util.testing import assert_frame_equal
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.137+
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.17.5
pytz : 2018.9
dateutil : 2.6.1
pip : 19.3.1
setuptools : 45.1.0
Cython : 0.29.15
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 5.5.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.6
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.11.0
pyarrow : 0.14.1
pytables : None
pytest : 3.6.4
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.4.4
tabulate : 0.8.6
xarray : 0.14.1
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : None
numba : 0.47.0
</details>
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CI: Fix flaky test_value_counts_null
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| 5
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| 2020-03-09T19:32:12Z
| 2020-03-07T09:40:10Z
|
CONTRIBUTOR
| null |
As mentioned by @simonjayhawkins in #32438
Trace of the failing test:
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=29965&view=logs&j=077026cf-93c0-54aa-45e0-9996ba75f6f7&t=e95cf409-86ae-5b4d-6c5f-79395ef75e8f
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Converting a StringDtype series to an Inte64Dtype not working as expected
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| 3
| 2020-03-04T22:01:12Z
| 2020-03-27T14:59:58Z
| 2020-03-27T14:59:58Z
|
NONE
| null |
I am interested in converting a StringDtype series to an Inte64Dtype. The following code produces a TypeError:
```
x = pd.Series(['1', pd.NA, '3'], dtype=pd.StringDtype())
x.astype('Int64')
...
TypeError: data type not understood
```
If I rewrite it as follows, I get a different TypeError:
```
x = pd.Series(['1', pd.NA, '3'], dtype=pd.StringDtype())
x.astype(int)
...
TypeError: int() argument must be a string, a bytes-like object or a number, not 'NAType
```
The only way I have been able to convert from StringDtype is:
```
x = pd.Series(['1', pd.NA, '3'], dtype=pd.StringDtype())
pd.to_numeric(x, errors='coerce').convert_dtypes()
...
0 1
1 <NA>
2 3
dtype: Int64
```
This works fine, but is inelegant. I would have expect astype to be able to do the conversion directly. Is there a recommended way to convert between these types?
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MDExOlB1bGxSZXF1ZXN0MzgzOTYyODk4
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TST/VIZ: add test for legend colors for DataFrame with duplicate column labels #11136
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| 3
| 2020-03-04T23:16:33Z
| 2020-03-12T04:52:36Z
| 2020-03-12T04:52:30Z
|
CONTRIBUTOR
| null |
- [X] closes #11136
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CLN: avoid _ndarray_values, values in MultiIndex
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| 1
| 2020-03-05T00:36:55Z
| 2020-04-05T17:45:35Z
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|
MEMBER
| null |
There are a couple of places in MultiIndex left after this that are a little trickier, will do in a separate pass.
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|
Nullable integer data type bug in read_csv
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| 1
| 2020-03-05T07:45:30Z
| 2020-03-10T14:07:50Z
| 2020-03-10T14:07:40Z
|
NONE
| null |
When reading csv file column with NA fields loose precision. I think the reason of the problem is the cast to float somewhere under hood.
`/tmp/1.csv:`
```csv
field1,field2
,1577134800018164904
1577134800018226901,1577134800018267738
```
```python
A = pd.read_csv('/tmp/1.csv', dtype='Int64')
A
```
| field1 | field2
-- | -- | --
0| <NA> | 1577134800018164904
1| **1577134800018226944** | 1577134800018267738
```python
int(float(1577134800018226901))
1577134800018226944
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.1.final.0
python-bits : 64
OS : Linux
OS-release : 3.16.0-77-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 28.8.0
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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DEPS: Removing snappy from local/docs dependencies
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MEMBER
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- [X] xref #32417
The docs build should fail if snappy is required to build the docs, and should point out where it's being used. If it's not failed, I think this can be merged and we can remove snappy from the local and docs dependencies.
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VIZ: Fixing Matplotlib depecation warnings for ax.rowNum and ax.colNum
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MEMBER
| null |
- [X] xref #32444
Fixing the deprecation warning in the docs (testing if we really need to be compatible with the old syntax)
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Backport PR #32444 on branch 1.0.x (CI: ax.rowNum and ax.colNum attributes deprecated in Matplotlib 3.2)
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NONE
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Backport PR #32444: CI: ax.rowNum and ax.colNum attributes deprecated in Matplotlib 3.2
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commit history attributes commit to committer instead of author
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MEMBER
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https://github.com/pandas-dev/pandas/commits/master since 690e382d2e93198c3629943607cef9fc3012dfd4
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CLN: Removed unused variable
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|
MEMBER
| null |
I can't see the reason for ```res_view``` to exist, the CI should tell me if I am wrong.
Also this is removing one warning when compiling ```pandas/_libs/internals.pyx```
This is the warning that is removing:
```
pandas/_libs/internals.c:8952:36: warning: ‘__pyx_t_23’ may be used uninitialized in this function [-Wmaybe-uninitialized]
8952 | __pyx_cur_scope->__pyx_t_9 = __pyx_t_23;
```
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CLN: Using clearer imports
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0Mzg0MjM4MTg2
| 32,460
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Disallow .__call__() as workaround for non-named functions
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| 5
| 2020-03-05T12:07:04Z
| 2020-05-13T15:53:48Z
| 2020-05-13T15:53:45Z
|
CONTRIBUTOR
| null |
Currently this script:
```python
import pandas as pd
funcs = [lambda: 1]
pd.eval("funcs[0]()")
```
Fails with:
TypeError: Only named functions are supported
however this can easily be worked around by adding `.__call__`:
```python
pd.eval("funcs[0].__call__()")
```
I'm assuming we don't want to allow this workaround. This PR ensures that it will fail with the same error.
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MDU6SXNzdWU1NzYyMTg2MzE=
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|
DataFrame output too wide / not truncated properly
|
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[] | null | 0
| 2020-03-05T12:29:15Z
| 2020-05-16T21:15:30Z
| null |
NONE
| null |
#### Problem description
It seems to me, that DataFrames are not always correctly trunctaed to fit the terminal width (using pandas 1.0.1).
My pandas config is set such, that it auto detects terminal width and the representation of the DataFrame should fit in.
<details>
<summary>Relevant pandas config settings</summary>
<p>
```python
display.width : int
Width of the display in characters. In case python/IPython is running in
a terminal this can be set to None and pandas will correctly auto-detect
the width.
Note that the IPython notebook, IPython qtconsole, or IDLE do not run in a
terminal and hence it is not possible to correctly detect the width.
[default: 80] [currently: 80]
display.max_columns : int
If max_cols is exceeded, switch to truncate view. Depending on
`large_repr`, objects are either centrally truncated or printed as
a summary view. 'None' value means unlimited.
In case python/IPython is running in a terminal and `large_repr`
equals 'truncate' this can be set to 0 and pandas will auto-detect
the width of the terminal and print a truncated object which fits
the screen width. The IPython notebook, IPython qtconsole, or IDLE
do not run in a terminal and hence it is not possible to do
correct auto-detection.
[default: 0] [currently: 0]
display.max_colwidth : int or None
The maximum width in characters of a column in the repr of
a pandas data structure. When the column overflows, a "..."
placeholder is embedded in the output. A 'None' value means unlimited.
[default: 50] [currently: 50]
display.expand_frame_repr : boolean
Whether to print out the full DataFrame repr for wide DataFrames across
multiple lines, `max_columns` is still respected, but the output will
wrap-around across multiple "pages" if its width exceeds `display.width`.
[default: True] [currently: True]
display.large_repr : 'truncate'/'info'
For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can
show a truncated table (the default from 0.13), or switch to the view from
df.info() (the behaviour in earlier versions of pandas).
[default: truncate] [currently: truncate]
display.column_space No description available.
[default: 12] [currently: 12]
```
</p></details>
#### Code Sample
Please resize terminal to have width 127 to reproduce
Check with shutil.get_terminal_size()
```python
import pandas as pd
from io import StringIO
import shutil
# terminal width (in my case: columns=127, lines=40)
print('terminal width: {} characters'.format(shutil.get_terminal_size()[0]))
s = 'id,tstamp,00aaaaa,01a,02aaaaa,03a,04aaaaa,05,06aaaa,07aaaaaaaaaaaaa,08aaaaaa,09aaaaaaa,10aa,11aaaaaa,12aaaaaaaaa,13aaaaaaaa,14aaaaaaa,15aaaaaaaa,16a,17aaaaa,18aa,19aaaaaa,20,21,22aaaaaa,23aaaaaa,24aaaaa,25aa,26aaaaaa,27a,28aaaaa,29aaaa\r\n779491690,2019-02-01 00:00:02+00:00,,161.38538188324176,297.461393148902,,,,0.466667,False,False,3,,0.007,53.8849,,0.0323102,-0.4,,0.008,,0.0,,,,17.1,-1e-06,,0.024,,0.045,159.72512756708358\r\n779491691,2019-02-01 00:05:02+00:00,,162.2999981618803,299.5123814547798,,,,0.553571,True,False,3,,0.007,-85.1749,,0.0969305,-0.5,,0.008,,0.0,,,,17.0,-3e-06,,0.031,,0.049,160.6961983114413\r\n779491692,2019-02-01 00:10:02+00:00,,163.1754248277306,301.7498948568431,,,,0.530612,False,False,3,,0.007,,,-0.0646204,-0.4,,0.007,,0.0,,,,17.0,2e-06,,0.026,,0.049,161.6468595698913\r\n779491693,2019-02-01 00:15:02+00:00,,164.00520705009447,304.19960616946184,,,,0.466667,False,False,3,,0.007,,,-0.0323102,-0.4,,0.008,,0.0,,,,17.0,1e-06,,0.024,,0.045,162.5736942291059\r\n779491694,2019-02-01 00:20:02+00:00,,164.78185830034352,306.8905124087089,,,,0.511792,True,False,3,,0.007,128.438,,-0.0323102,-0.4,,0.008,,0.0,,,,17.0,1e-06,,0.031,,0.053,163.47261824257413\r\n'
f = StringIO()
f.write(s)
f.seek(0)
df = pd.read_csv(f, index_col=[0, 1])
print(df)
```
The lines of the string representation of the DataFrame are too long, therefore each line spans across two lines (depending on the terminal width; with the given settings it needs 129 characters instead of the available 127). To me this looks like a bug in the DataFrameFormatter. As I understand probably in the write_result method line 839ff
Could that be?
By the way, I also don't quite understand the display.expand_frame_repr setting. It is now set to True. When I set it to False the DataFrame does not get truncated but the full represenation is printed across multiple lines. Shouldn't that be excactly the other way round?
The following issue seems to be related, but I did not find the exact same problem. Hope I didn't miss anything: https://github.com/pandas-dev/pandas/issues/16911
#### Expected Output
A DataFrame representation truncated such, that each line fits the terminal width.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.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 : None
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200209
Cython : 0.29.15
pytest : None
hypothesis : None
sphinx : 2.4.3
blosc : None
feather : 0.4.0
xlsxwriter : 1.2.8
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fastparquet : 0.3.3
gcsfs : None
lxml.etree : None
matplotlib : 3.2.0
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : 0.15.0
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.48.0
</details>
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MDU6SXNzdWU1NzYyNzcxNzk=
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|
Inconsistent result with cumsum columns
|
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[] | null | 1
| 2020-03-05T14:05:04Z
| 2021-07-29T06:30:25Z
| null |
NONE
| null |
I have problem with cumsum and multiple columns
```python
>>> df1 = pd.DataFrame({"A": [2, 1, np.nan, 1, 2, 2, 1],"B": [-8, 2, 3, 1, 5, 6, 7],"C": [3, 5, 6, 5, 4, 4, 3]})
>>> df1.groupby("A").cumsum()
B C
0 -8 3
1 2 5
2 -1 6
3 3 10
4 -3 7
5 3 11
6 10 13
>>> df1.groupby("A").cumsum()
B C
0 -8 3
1 2 5
2 -1 -1
3 3 10
4 -3 7
5 3 11
6 10 13
```
The cumsum is computed only on the first column B and the column C is left unchanged. Worse, when recomputing then I get the result I would expect. I could accept either behavior but inconsistent result when iterating seems wrong to me.
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|
Difference between count and nunique formatting
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[] | null | 1
| 2020-03-05T14:13:53Z
| 2020-10-19T06:44:53Z
| 2020-10-19T06:44:53Z
|
NONE
| null |
The count with groupby gives the number of entries while nunique gives the number of unique entries. The problem is that we get an additional column
```python
>>> df = pd.DataFrame({"A": [0,0,1,1,0], "B": [1,2,3,4,5]})
>>> df.groupby("A").count()
B
A
0 3
1 2
>>> df.groupby("A").nunique()
A B
A
0 1 3
1 1 2
```
Why is the A column being added in nunique output?
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Grouping by all columns of an empty DataFrame should produce MultiIndex, but doesn't
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| 2
| 2020-03-05T15:48:21Z
| 2021-10-16T14:33:36Z
| null |
CONTRIBUTOR
| null |
Greetings!
This comes up in [cuDF](https://github.com/rapidsai/cudf) in our tests that check for compatibility with Pandas:
```
In [21]: a = pd.DataFrame({'a': [], 'b': [], 'c': []})
In [22]: a.groupby(['a', 'b']).sum().index
Out[22]: MultiIndex([], names=['a', 'b'])
In [23]: a.groupby(['a', 'b', 'c']).sum().index
Out[23]: Index([], dtype='object')
```
Would it be more consistent for grouping by multiple columns to always result in a MultiIndex?
Edit: Checked with Pandas 1.0.1 as well as 0.25.3 -- same behaviour.
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| 576,364,658
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MDExOlB1bGxSZXF1ZXN0Mzg0MzY1NDU2
| 32,465
|
TST: Fixed xfail for tests in pandas/tests/tseries/offsets/test_offsets_properties.py
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| 2020-03-05T16:08:18Z
| 2021-05-03T12:29:04Z
| 2020-06-14T17:27:09Z
|
MEMBER
| null |
- [x] ref https://github.com/pandas-dev/pandas/pull/32459#issuecomment-595277125
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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| 576,380,396
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MDU6SXNzdWU1NzYzODAzOTY=
| 32,466
|
Should Whitespaces be placed at the begging of a line
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| 2020-03-05T16:31:41Z
| 2020-04-28T14:48:42Z
| 2020-04-28T14:48:42Z
|
MEMBER
| null |
As of our current [code style guide](https://dev.pandas.io/docs/development/code_style.html) we are placing whitespaces at the begging of the lines, [Black](https://github.com/psf/black) had a discussion at https://github.com/psf/black/issues/182 (https://github.com/psf/black/pull/1132#issuecomment-594062628 sums all of that discussion) where they decided that when "Black" wraps long strings it will place the whitespace at the begging of the line.
I think that we should reconsider/open for discussion the placement of whitespaces over long strings.
|
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| 576,449,538
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MDExOlB1bGxSZXF1ZXN0Mzg0NDMzNjU3
| 32,467
|
CLN: use _values_for_argsort for join_non_unique, join_monotonic
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| 7
| 2020-03-05T18:29:29Z
| 2020-03-13T15:53:19Z
| 2020-03-11T02:13:24Z
|
MEMBER
| null |
With the `.copy()` removed from `Categorical._values_for_argsort`, `ea_backed_index._data._values_for_argsort()` matches `ea_backed_index._ndarray_values` in all extant cases.
cc @jorisvandenbossche @TomAugspurger need to confirm
a) this is an intended-adjacent use of _values_for_argsort, and not just a coincidence that it matches extant behavior
b) the `.copy()` this removes from `Categorical._values_for_argsort` is not important for some un-tested reason
xref #32452, #32426
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MDU6SXNzdWU1NzY0NTgwNjQ=
| 32,468
|
Should Groupby.sum modify _selected_obj?
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[] | null | 2
| 2020-03-05T18:45:36Z
| 2021-02-14T15:35:29Z
| 2021-02-14T15:35:29Z
|
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({"A": ["foo"] * 3 + ["bar"] * 3, "B": [1] * 6})
In [3]: g = df.groupby("A")
In [4]: g._selected_obj
Out[4]:
A B
0 foo 1
1 foo 1
2 foo 1
3 bar 1
4 bar 1
5 bar 1
In [5]: g.sum()
Out[5]:
B
A
bar 3
foo 3
In [6]: g._selected_obj
Out[6]:
B
0 1
1 1
2 1
3 1
4 1
5 1
```
#### Problem description
Noticed this while working on #32332
#### Expected Output
I wasn't expecting this to have side-effects
#### Output of ``pd.show_versions()``
<details>
In [7]: pd.show_versions()
/home/SERILOCAL/m.gorelli/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/fastparquet/dataframe.py:5: FutureWarning: pandas.core.index is deprecated and will be removed in a future version. The public classes are available in the top-level namespace.
from pandas.core.index import CategoricalIndex, RangeIndex, Index, MultiIndex
INSTALLED VERSIONS
------------------
commit : f7bed0583556f66f18f6391eddf3e0802c4933f3
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-88-generic
Version : #88-Ubuntu SMP Tue Feb 11 20:11:34 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.0.dev0+694.gf7bed0583
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 45.1.0.post20200119
Cython : 0.29.14
pytest : 5.3.4
hypothesis : 5.3.0
sphinx : 2.3.1
blosc : 1.8.3
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.14.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.47.0
</details>
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MDU6SXNzdWU1NzY0NjE1NjU=
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|
DOC: fix styling (css) of getting started tutorials
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| 0
| 2020-03-05T18:52:26Z
| 2020-03-10T07:24:41Z
| 2020-03-10T07:18:29Z
|
MEMBER
| null |
The sphinx theme got some styling updates, but this seems to have broken the custom styling of the getting started guide a bit.
See eg https://pandas.io/docs/getting_started/intro_tutorials/03_subset_data.html : the question marks are not visible.
And there is also some strange green around the code block in the box of installation at https://pandas.io/docs/getting_started/index.html
cc @stijnvanhoey
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|
Mishandling exception when trying to access inexistent files at private S3 bucket using several pandas.read_ methods
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| 4
| 2020-03-05T21:06:26Z
| 2020-04-21T23:08:35Z
| 2020-04-21T20:40:17Z
|
NONE
| null |
#### Problem description
When trying to access a non-existing file on a private S3 bucket, using appropriated credentials, pandas will throw a ConnectionTimeoutError instead of a FileNotFoundError.
I tested the following read_ methods and got same behavior in all: **read_csv, read_excel, read_fwf, read_json, read_msgpack, read_parquet,**
You got to have right credentials and try to access a private bucket to reproduce it. The s3fs library implicitly reads credentials from environment variables if you have it named properly (AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY).
#### Code to reproduce the issue
```python
import pandas
import s3fs
fs = s3fs.S3FileSystem()
file_name = 's3://<my_bucket>/<my_file>'
pandas.read_csv(file_name)
```
#### Root Cause
I checked source files and found the following excerpt from pandas/io/s3.py
```python
fs = s3fs.S3FileSystem(anon=False)
try:
filepath_or_buffer = fs.open(_strip_schema(filepath_or_buffer), mode)
except (compat.FileNotFoundError, NoCredentialsError):
# boto3 has troubles when trying to access a public file
# when credentialed...
# An OSError is raised if you have credentials, but they
# aren't valid for that bucket.
# A NoCredentialsError is raised if you don't have creds
# for that bucket.
fs = s3fs.S3FileSystem(anon=True)
filepath_or_buffer = fs.open(_strip_schema(filepath_or_buffer), mode)
return filepath_or_buffer, None, compression, True
```
Pandas is trying to workaround an issue of boto3 and when receiving FileNotFoundError or NoCredentialsError it chances connection credentials to anonymous (see the anon=True argument while creating object fs from class S3FileSystem) and try it again.
This help accessing public buckets/files while using S3 credentials, as comment states. However, when trying to access a non-existing file in a private bucket S3 will not return a file not found it will let connection timeout. They probably do that for security reasons, not disclosure private file existence without proper credentials.
#### Why this is an issue
Instead of quiclky throwns a FileNotFoundError it takes a while and then throwns a ConnectionTimeOutError.
#### Double check the cause
Just to make sure this is the cause I tried the code below. However, I don't really understand all implications to check if it is a acceptable solution.
```python
fs = s3fs.S3FileSystem(anon=False)
try:
filepath_or_buffer = fs.open(_strip_schema(filepath_or_buffer), mode)
except (compat.FileNotFoundError, NoCredentialsError):
# boto3 has troubles when trying to access a public file
# when credentialed...
# An OSError is raised if you have credentials, but they
# aren't valid for that bucket.
# A NoCredentialsError is raised if you don't have creds
# for that bucket.
if fs.exists(filepath_or_buffer[0:filepath_or_buffer.rfind('/')]):
raise FileNotFoundError(filepath_or_buffer, 'not found')
else:
fs = s3fs.S3FileSystem(anon=True)
filepath_or_buffer = fs.open(_strip_schema(filepath_or_buffer), mode)
return filepath_or_buffer, None, compression, True
```
I just test if credentials can access up directory. If yes It conclude this is not a credentials issue and thrown a FileNotFoundError.
#### Expected Output
Pandas read methods thrown FileNotFoundError when file doesn't exist on private S3 buckets.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
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 : 0.16.0
pytables : None
pytest : None
pyxlsb : None
s3fs : 0.4.0
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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| 32,471
|
Dataframe Groupby value_counts with bins parameter
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| 2
| 2020-03-05T21:08:02Z
| 2021-07-29T06:32:08Z
| null |
NONE
| null |
Found on Stack Overflow post [here](https://stackoverflow.com/q/60553500/6361531)
```python
df=pd.DataFrame([[0,0],[1,100],[0,100],[2,0],[3,100],[4,100],[4,100],[4,100],[1,100],[3,100]],columns=['key','score'])
df.groupby('key')['score'].value_counts(bins=[0,20,40,60,80,100])
```
#### Problem description
Outputs counts at the with wrong index labels. **_Note:_** data only occurs in 0-20 and 80-100.
```
key score
0 (-0.001, 20.0] 1
(20.0, 40.0] 1
(40.0, 60.0] 0
(60.0, 80.0] 0
(80.0, 100.0] 0
1 (20.0, 40.0] 2
(-0.001, 20.0] 0
(40.0, 60.0] 0
(60.0, 80.0] 0
(80.0, 100.0] 0
2 (-0.001, 20.0] 1
(20.0, 40.0] 0
(40.0, 60.0] 0
(60.0, 80.0] 0
(80.0, 100.0] 0
3 (20.0, 40.0] 2
(-0.001, 20.0] 0
(40.0, 60.0] 0
(60.0, 80.0] 0
(80.0, 100.0] 0
4 (20.0, 40.0] 3
(-0.001, 20.0] 0
(40.0, 60.0] 0
(60.0, 80.0] 0
(80.0, 100.0] 0
Name: score, dtype: int64
```
#### Expected Output
`df.groupby('key')['score'].apply(pd.Series.value_counts, bins=[0,20,40,60,80,100])`
```
key
0 (80.0, 100.0] 1
(-0.001, 20.0] 1
(60.0, 80.0] 0
(40.0, 60.0] 0
(20.0, 40.0] 0
1 (80.0, 100.0] 2
(60.0, 80.0] 0
(40.0, 60.0] 0
(20.0, 40.0] 0
(-0.001, 20.0] 0
2 (-0.001, 20.0] 1
(80.0, 100.0] 0
(60.0, 80.0] 0
(40.0, 60.0] 0
(20.0, 40.0] 0
3 (80.0, 100.0] 2
(60.0, 80.0] 0
(40.0, 60.0] 0
(20.0, 40.0] 0
(-0.001, 20.0] 0
4 (80.0, 100.0] 3
(60.0, 80.0] 0
(40.0, 60.0] 0
(20.0, 40.0] 0
(-0.001, 20.0] 0
Name: score, dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.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 : 1.0.1
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.12
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.0.1
pyxlsb : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.5
tables : 3.5.2
tabulate : 0.8.6
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
numba : 0.45.0
</details>
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MDExOlB1bGxSZXF1ZXN0Mzg0NTA0NDY3
| 32,472
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CLN: remove unreachable _internal_get_values in blocks
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| 1
| 2020-03-05T21:16:25Z
| 2020-03-07T15:14:48Z
| 2020-03-07T11:02:48Z
|
MEMBER
| null |
_putmask_smart is only called from Block.putmask, which ExtensionBlock overrides.
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ENH: Feature request, date type
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| 2021-10-14T14:39:06Z
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CONTRIBUTOR
| null |
There currently isn’t any native date dtype in pandas, which makes it impossible to integrate this type into file formats like parquet, where schema is defined
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pandas documentation error
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pandas documentation
Date: Feb 05, 2020 Version: 1.0.1
Download documentation: PDF Version
Why this PDF Version have no contents>
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CLN: assorted cleanups, annotations
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ENH: implement ExtensionIndex.insert
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Sort of.
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MDExOlB1bGxSZXF1ZXN0Mzg0NTcwMjQ4
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BUG: iloc.__setitem__ with duplicate columns
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MEMBER
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- [x] closes #15686
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Made apt changes to pandas.Series.str.replace()
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CONTRIBUTOR
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- [x] closes #31225
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MDExOlB1bGxSZXF1ZXN0Mzg0NjEwODIz
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BUG: Fix issue with datetime[ns, tz] input in Block.setitem GH32395
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| 2020-05-25T19:29:50Z
| 2020-05-25T17:39:42Z
|
CONTRIBUTOR
| null |
- [x] closes #32395
- [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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MDU6SXNzdWU1NzY2NzExODg=
| 32,480
|
json_normalize errors if meta fields are integer
|
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| 2020-03-10T20:24:51Z
| 2020-03-10T20:24:14Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
data = [{"person_id": "test",
"test_field": 1,
"access": [{"step": 1,
"phase":"starting"},
{"step":2,
"phase":"ending"}
]
}]
df = pd.json_normalize(data,"access",meta=["person_id","test_field"])
print(df)
```
#### Problem description
If meta fields are integer we get the below error. json_normalize works fine for string datatype
Traceback (most recent call last):
File "C:/Users/HOME/PycharmProjects/pandas_test/pandas_test.py", line 12, in <module>
df = pd.json_normalize(data,"access",meta=["person_id","test_field"])
File "C:\Users\HOME\Anaconda3\envs\pandas_test\lib\site-packages\pandas\io\json\_normalize.py", line 327, in _json_normalize
_recursive_extract(data, record_path, {}, level=0)
File "C:\Users\HOME\Anaconda3\envs\pandas_test\lib\site-packages\pandas\io\json\_normalize.py", line 314, in _recursive_extract
meta_val = _pull_field(obj, val[level:])
File "C:\Users\HOME\Anaconda3\envs\pandas_test\lib\site-packages\pandas\io\json\_normalize.py", line 246, in _pull_field
f"{js} has non iterable value {result} for path {spec}. "
TypeError: {'person_id': 'test', 'test_field': 1, 'access': [{'step': 1, 'phase': 'starting'}, {'step': 2, 'phase': 'ending'}]} has non iterable value 1 for path ['test_field']. Must be iterable or null.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : AMD64 Family 16 Model 10 Stepping 0, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
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 : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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CLN: imports in pandas/io/excel/_base.py
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|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU1NzY3OTMwMjk=
| 32,482
|
Plot bar in Pandas 0.25.3 not changing the colors for each of the bars
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| 2020-03-06T09:08:21Z
| 2020-03-06T09:43:24Z
| 2020-03-06T09:42:55Z
|
NONE
| null |
```python
# Code
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
df = pd.DataFrame({'category': ['A', 'B', 'C'],
'value': [-1432.32, 10.43, 30000.00],
'db-id': [1234, 2424, 251]
})
df.category.value_counts().plot.bar(color = plt.cm.Paired.colors)
```
#### Problem description
When using the above code on pandas '0.25.3', all bars are plotted with the same color. I would like to have them plotted each with its own color. It used to work before but all of a sudden it doesnt. Possibly due to the pandas version.
Any clue?
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CLN: Split and fixturized test_fillna in tests/base/test_ops.py
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| 2
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CONTRIBUTOR
| null |
part of #23877
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0Mzg0NzI3MTk2
| 32,484
|
CLN: Remove redundant index test from tests/base/test_ops.py
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CONTRIBUTOR
| null |
part of #23877
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Boolean indexing is already thoroughly tested in `test_series_mask_boolean` in `tests/indexes/test_na_indexing.py` - [see here](https://github.com/pandas-dev/pandas/blob/master/pandas/tests/indexing/test_na_indexing.py#L7-L61)
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CLN: Replace isinstance(foo, Class) with isinstance(foo, ABCClass)
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MEMBER
| null |
- [x] ref #27353
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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to_csv fails silently with s3fs
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| 6
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| 2021-08-03T10:35:42Z
| 2020-04-21T12:39:06Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))
df.to_csv("s3://super_dumb_s3_bucket/not_real.csv")
```
#### Problem description
If you provide an invalid s3 path (e.g. a bucket that you don't have access to), this command fails silently.
#### Expected Output
Raise some exception at least.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.2.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-88-generic
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.0.2
setuptools : 40.8.0
Cython : None
pytest : 3.10.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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CLN: Remove redundant tests for .duplicated and .drop_duplicates in tests/base
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| 7
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| 2020-03-15T00:51:49Z
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CONTRIBUTOR
| null |
part of #23877
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
duplicated and drop_duplicates are already thoroughly tested in `tests/indexes` and `tests/series`. I added comments to highlight the redundancy and extended the existing test cases where needed.
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Problem with docker build to contribute on Mac
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| 2020-03-06T10:56:50Z
| 2020-05-16T16:51:19Z
| null |
NONE
| null |
Hi. I'm not sure if this is the right place to post this but I would love to contribute so I'm asking here. I apologise if this is the wrong place to post it. If it is, could someone redirect me to a better person or place to ask?
I use:
- Mac OS version 10.15.1
- I used brew cask install anaconda to install anaconda and I have `export PATH=~/opt/anaconda3/bin:$PATH` in my .zshrc file. conda --version outputs `conda 4.7.12`
I'm having trouble using docker with this project. This is what happens when I follow [VS code remote containers](https://code.visualstudio.com/docs/remote/containers) and run `Remote-Containers: Reopen in Container`. My error is below (I've removed the bits that were successful).
[2020-03-06T10:49:02.538Z] [PID 15817] [29814 ms] Step 8/9 : RUN conda env update -n base -f "$pandas_home/environment.yml"
[2020-03-06T10:49:02.538Z] [PID 15817] [29814 ms]
[2020-03-06T10:49:05.305Z] [PID 15817] [32581 ms] ---> Running in d123b4efef44
[2020-03-06T10:49:05.960Z] [PID 15817] [33236 ms] Traceback (most recent call last):
File "/opt/conda/bin/conda", line 12, in <module>
from conda.cli import main
ModuleNotFoundError: No module named 'conda'
[2020-03-06T10:49:06.502Z] [PID 15817] [33778 ms] The command '/bin/sh -c conda env update -n base -f "$pandas_home/environment.yml"' returned a non-zero code: 1
[2020-03-06T10:49:06.505Z] [PID 15817] [33781 ms] Failed: Building an image from the Dockerfile (this can take a while).
[2020-03-06T10:49:06.508Z] [PID 15817] [33784 ms] Command failed: docker build -f /Users/mxchaeltrxn/Documents/10-19_learning/14_open_source/14.03_pandas/Dockerfile -t vsc-14.03_pandas-a5707944db87809e2912a2e16627e33b /Users/mxchaeltrxn/Documents/10-19_learning/14_open_source/14.03_pandas
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CLN: trying isort-dev
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| 2
| 2020-03-06T11:44:45Z
| 2020-03-24T13:41:18Z
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|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
---
Trying ```isort``` 5.0.0 which is still under development.
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MDExOlB1bGxSZXF1ZXN0Mzg0Nzk4NDE1
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BUG: Fix bug, where BooleanDtype columns are converted to Int64
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| 12
| 2020-03-06T12:34:41Z
| 2020-03-26T14:28:41Z
| 2020-03-11T01:48:35Z
|
CONTRIBUTOR
| null |
- [x] closes #32287
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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|
DOC indexing: Google search for 'pandas greater than' returns `pandas.DataFrame.ge` as #1, and `pandas.DataFrame.gt`/`pandas.Series.gt` aren't even found
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| 2020-03-06T12:39:19Z
| 2020-09-01T22:50:31Z
| 2020-09-01T22:50:31Z
|
NONE
| null |
#### Problem description
(I'm aware that Google SEO is not under pandas' control. However this one's quite important, and nowhere in the pandas doc does it actually tell us where/how to report SEO fails, if at all. So please tell us how and where to report things like this. Are all pandas doc pages automatically submitted to be crawled by Google?)
The indexing of pandas DataFrame/Series operators seems to have some holes:
A Google search for *'pandas greater than'* returns ['pandas.DataFrame.ge'](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.ge.html) as #1, and `pandas.DataFrame.gt`/`pandas.Series.gt` aren't even found, let alone in the top 10.
1. The #1 hit should be `pandas.DataFrame.gt`
1. `pandas.Series.gt` should also be found, #2, but they're not. Similarly #3 `pandas.Series.between`, `pandas.Series.ge` etc.
A Google search for *'pandas less than'* is slightly better:
1. #1 hit is `pandas.DataFrame.le` (pandas 1.0.1 doc)
1. #2 hit is `pandas.Series.le` (pandas 1.0.1 doc)
1. #3 hit is `pandas.Series.between` (pandas 0.23.1 doc(?), not 1.0.x)
1. The #1 and #2 hits should be `pandas.DataFrame.lt`/`pandas.Series.lt`
#### Expected Output
as above
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dataframe.groupby incorrect with multiindex and None value
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| 2020-03-06T12:51:08Z
| 2021-07-29T06:35:22Z
| null |
NONE
| null |
#### Minimally reproducible example
```python
# pandas==0.24.2
df = pd.DataFrame(data={
'A': ['a1','a2',None],
'B': ['b1','b2','b1'],
'val': [1,2,3],
})
print(df)
print('\n')
grps = df.groupby(['A', 'B'])
print(grps.get_group(('a2','b1')))
```
#### Problem description
Selecting a group which doesn't appear, I obtain a group which should not exist.
#### Expected Output
KeyError
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 2.0.1
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: 1.1.7
lxml.etree: 4.3.3
bs4: None
html5lib: 1.0.1
sqlalchemy: 1.3.8
pymysql: 0.9.3
psycopg2: 2.8.3 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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| 576,927,563
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MDU6SXNzdWU1NzY5Mjc1NjM=
| 32,493
|
to_numeric fails with empty data and downcast="unsigned"
|
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| 4
| 2020-03-06T13:21:28Z
| 2020-03-16T16:00:40Z
| 2020-03-16T02:30:10Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
pd.to_numeric([], downcast="unsigned")
```
#### Problem description
The code currently calls `np.min()` on the data which fails when the data is empty. This does not happen for any other downcast.
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../lib/python3.7/site-packages/pandas/core/tools/numeric.py", line 163, in to_numeric
elif downcast == "unsigned" and np.min(values) >= 0:
File "<__array_function__ internals>", line 6, in amin
File "/.../lib/python3.7/site-packages/numpy/core/fromnumeric.py", line 2793, in amin
keepdims=keepdims, initial=initial, where=where)
File "/.../lib/python3.7/site-packages/numpy/core/fromnumeric.py", line 90, in _wrapreduction
return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
ValueError: zero-size array to reduction operation minimum which has no identity
```
#### Expected Output
array([], dtype=uint8)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.3.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 41.2.0
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : 0.6.0
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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MDU6SXNzdWU1NzY5NjcwNTg=
| 32,494
|
Dataframe.groupby aggregations with categorical columns lead to incorrect results.
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| 2
| 2020-03-06T14:28:25Z
| 2020-06-14T15:02:05Z
| 2020-06-14T15:02:05Z
|
CONTRIBUTOR
| null |
#### Code Sample
```python
# In[2]:
import pandas as pd
def create_df():
df = pd.DataFrame(
{
'major_id': [1, 2, 1, 2, 2],
'minor_id': ['a', 'b', 'c', 'd', 'e'],
'values': [1, 2, 3, 4, 5]
}
)
return df
def groupby(df):
df['max_value'] = (
df
.groupby(['major_id', 'minor_id'])
['values']
.transform('max')
)
return df
# In[3]:
# correct result
df = create_df()
groupby(df)
# Out[3]
" major_id minor_id values max_value\n",
"0 1 a 1 1\n",
"1 2 b 2 2\n",
"2 1 c 3 3\n",
"3 2 d 4 4\n",
"4 2 e 5 5"
# In[4]:
# incorrect result: groupby with one non-categorical column and one categorical column
df = create_df()
df = df.astype({'minor_id': 'category'})
groupby(df)
# Out[4]
" major_id minor_id values max_value\n",
"0 1 a 1 1.0\n",
"1 2 b 2 3.0\n",
"2 1 c 3 NaN\n",
"3 2 d 4 NaN\n",
"4 2 e 5 NaN"
```
#### Problem description
`groupby` with one non-categorical column and one categorical column leads to incorrect aggregations (wrong values, or `NAN`s).
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.1
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : None
pytest : 5.2.1
hypothesis : 5.5.4
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : 0.3.3
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.2.1
pyxlsb : None
s3fs : 0.4.0
scipy : 1.3.1
sqlalchemy : 1.3.10
tables : None
tabulate : 0.8.5
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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MDU6SXNzdWU1NzY5OTk2NjM=
| 32,495
|
read_csv option 'skipfooter' crashes with lines starting with "
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[] | null | 2
| 2020-03-06T15:21:02Z
| 2020-03-07T19:17:21Z
| 2020-03-07T19:17:21Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
# Generate the test-file
data_file = 'test.txt'
txt = """yyyy
1991
2002
"a
"""
with open(data_file, 'w') as fh:
fh.write(txt)
# Parsing working with:
df_ok = pd.read_csv(data_file, nrows=2)
print(df_ok)
# ... and with
df_ok2 = pd.read_csv(data_file, skipfooter=1, delimiter='[,]', engine='python')
print(df_ok2)
# ... but crashes with
df_crash1 = pd.read_csv(data_file, skipfooter=1, delimiter=',', engine='python')
print(df_crash1)
# ... and with
df_crash2 = pd.read_csv(data_file, skipfooter=1)
print(df_crash2)
# in order to reach those last 2 lines, you have to comment out lines 24-25
# with the error message:
# ------------------------
# pandas.errors.ParserError: unexpected end of data. Error could possibly be
# due to parsing errors in the skipped footer rows (the skipfooter keyword is
# only applied after Python's csv library has parsed all rows).
# Process terminated with an exit code of 1
```
#### Problem description
Lines in txt-files that start with
"
can lead to crashes when these lines should be skipped with the option 'skipfooter'.
Funnily, this does NOT happen then the option
delimiter='[,]', engine='python'
is set, but DOES crash when the same option is set with
delimiter=',', engine='python'
The same data are read in correctly when the number of rows is limited with the option 'nrows'.
#### Expected Output
yyyy
0 1991
1 2002
yyyy
0 1991
1 2002
yyyy
0 1991
1 2002
yyyy
0 1991
1 2002
#### Output of above code sample:
<details>
Traceback (most recent call last):
File "c:\Programs\WPy64-3740\python-3.7.4.amd64\lib\site-packages\pandas\io\parsers.py", line 2854, in _next_iter_line
return next(self.data)
_csv.Error: unexpected end of data
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "d:\Users\thomas\Data\CloudStation\Teaching\Stats\Coding\error_report.py", line 24, in <module>
df_crash1 = pd.read_csv(data_file, skipfooter=1, delimiter=',', engine='python')
File "c:\Programs\WPy64-3740\python-3.7.4.amd64\lib\site-packages\pandas\io\parsers.py", line 702, in parser_f
return _read(filepath_or_buffer, kwds)
File "c:\Programs\WPy64-3740\python-3.7.4.amd64\lib\site-packages\pandas\io\parsers.py", line 435, in _read
data = parser.read(nrows)
File "c:\Programs\WPy64-3740\python-3.7.4.amd64\lib\site-packages\pandas\io\parsers.py", line 1139, in read
ret = self._engine.read(nrows)
File "c:\Programs\WPy64-3740\python-3.7.4.amd64\lib\site-packages\pandas\io\parsers.py", line 2388, in read
content = self._get_lines(rows)
File "c:\Programs\WPy64-3740\python-3.7.4.amd64\lib\site-packages\pandas\io\parsers.py", line 3128, in _get_lines
row_num=self.pos + rows + 1)
File "c:\Programs\WPy64-3740\python-3.7.4.amd64\lib\site-packages\pandas\io\parsers.py", line 2873, in _next_iter_line
self._alert_malformed(msg, row_num)
File "c:\Programs\WPy64-3740\python-3.7.4.amd64\lib\site-packages\pandas\io\parsers.py", line 2835, in _alert_malformed
raise ParserError(msg)
pandas.errors.ParserError: unexpected end of data. Error could possibly be due to parsing errors in the skipped footer rows (the skipfooter keyword is only applied after Python's csv library has parsed all rows).
Process terminated with an exit code of 1
</details>
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MDU6SXNzdWU1NzcwMzEzODY=
| 32,496
|
dtype passed into read_csv as an arg not passed to the dataframe when being created
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| 2020-03-06T16:13:29Z
| 2020-03-11T14:01:24Z
| 2020-03-11T14:01:24Z
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NONE
| null |
https://github.com/pandas-dev/pandas/blob/015c1c154b9bc70d3b1981ba1dc31c09676065bf/pandas/io/parsers.py#L1143
Is this the desired behaviour? Maybe I am missing something but when we pass dtype to read_csv, the dtype is not used anywhere. Passing it to the dataframe when creating it seems like a good place for that.
|
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| 32,497
|
[Regression] DataFrame.replace throws TypeError when to_replace is list and DF has categorical
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| 2020-03-06T16:15:43Z
| 2020-03-07T16:21:15Z
| 2020-03-06T21:54:26Z
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NONE
| null |
#### Code Sample
```python
df = pd.DataFrame({
'x': pd.Series([-np.inf, np.inf]),
'y': pd.Series(["a", "b"], dtype="category")
})
df.replace([-np.inf, np.inf], np.nan)
```
results in
```
...
~/anaconda/envs/grapy/lib/python3.7/site-packages/pandas/core/indexes/base.py in __contains__(self, key)
3898 @Appender(_index_shared_docs["contains"] % _index_doc_kwargs)
3899 def __contains__(self, key) -> bool:
-> 3900 hash(key)
3901 try:
3902 return key in self._engine
TypeError: unhashable type: 'list'
```
#### Problem description
When a DF contains a categorical column, and when `to_replace` argument is a list, `replace()` throws `TypeError: unhashable type: 'list'`.
#### Expected Output
When the `to_replace` argument is a tuple rather than a list (which is not a documented option), all seems good:
```python
df.replace((-np.inf, np.inf), np.nan)
```
```
x y
0 NaN a
1 NaN b
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.0
pip : 20.0.2
setuptools : 45.2.0.post20200209
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : 2.4.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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Local test failure pandas.tests.frame.test_analytics
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| 2021-07-29T06:36:06Z
| 2021-07-29T06:36:05Z
|
MEMBER
| null |
Running
```
pytest -q --cache-clear pandas/tests/frame/test_analytics.py
```
Yield the following error:
<details>
```
___________________________ TestDataFrameAnalytics.test_mean_mixed_string_decimal ____________________________
TypeError: unsupported operand type(s) for +: 'int' and 'str'
The above exception was the direct cause of the following exception:
self = Decimal('3351.00'), other = Decimal('572.00'), context = None
def __add__(self, other, context=None):
"""Returns self + other.
-INF + INF (or the reverse) cause InvalidOperation errors.
"""
> other = _convert_other(other)
/usr/lib/python3.8/_pydecimal.py:1158:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
other = Decimal('572.00'), raiseit = False, allow_float = False
def _convert_other(other, raiseit=False, allow_float=False):
"""Convert other to Decimal.
Verifies that it's ok to use in an implicit construction.
If allow_float is true, allow conversion from float; this
is used in the comparison methods (__eq__ and friends).
"""
> if isinstance(other, Decimal):
E SystemError: <built-in function isinstance> returned a result with an error set
/usr/lib/python3.8/_pydecimal.py:6017: SystemError
The above exception was the direct cause of the following exception:
self = Decimal('3351.00'), other = Decimal('609.00'), context = None
def __add__(self, other, context=None):
"""Returns self + other.
-INF + INF (or the reverse) cause InvalidOperation errors.
"""
> other = _convert_other(other)
/usr/lib/python3.8/_pydecimal.py:1158:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
other = Decimal('609.00'), raiseit = False, allow_float = False
def _convert_other(other, raiseit=False, allow_float=False):
"""Convert other to Decimal.
Verifies that it's ok to use in an implicit construction.
If allow_float is true, allow conversion from float; this
is used in the comparison methods (__eq__ and friends).
"""
> if isinstance(other, Decimal):
E SystemError: <built-in function isinstance> returned a result with an error set
/usr/lib/python3.8/_pydecimal.py:6017: SystemError
The above exception was the direct cause of the following exception:
self = Decimal('3351.00'), other = Decimal('820.00'), context = None
def __add__(self, other, context=None):
"""Returns self + other.
-INF + INF (or the reverse) cause InvalidOperation errors.
"""
> other = _convert_other(other)
/usr/lib/python3.8/_pydecimal.py:1158:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
other = Decimal('820.00'), raiseit = False, allow_float = False
def _convert_other(other, raiseit=False, allow_float=False):
"""Convert other to Decimal.
Verifies that it's ok to use in an implicit construction.
If allow_float is true, allow conversion from float; this
is used in the comparison methods (__eq__ and friends).
"""
> if isinstance(other, Decimal):
E SystemError: <built-in function isinstance> returned a result with an error set
/usr/lib/python3.8/_pydecimal.py:6017: SystemError
The above exception was the direct cause of the following exception:
self = <pandas.tests.frame.test_analytics.TestDataFrameAnalytics object at 0x612b1fcb3700>
def test_mean_mixed_string_decimal(self):
# GH 11670
# possible bug when calculating mean of DataFrame?
d = [
{"A": 2, "B": None, "C": Decimal("628.00")},
{"A": 1, "B": None, "C": Decimal("383.00")},
{"A": 3, "B": None, "C": Decimal("651.00")},
{"A": 2, "B": None, "C": Decimal("575.00")},
{"A": 4, "B": None, "C": Decimal("1114.00")},
{"A": 1, "B": "TEST", "C": Decimal("241.00")},
{"A": 2, "B": None, "C": Decimal("572.00")},
{"A": 4, "B": None, "C": Decimal("609.00")},
{"A": 3, "B": None, "C": Decimal("820.00")},
{"A": 5, "B": None, "C": Decimal("1223.00")},
]
df = pd.DataFrame(d)
> result = df.mean()
pandas/tests/frame/test_analytics.py:499:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/core/generic.py:11039: in stat_func
return self._reduce(
pandas/core/frame.py:7876: in _reduce
result = f(values)
pandas/core/frame.py:7828: in f
return op(x, axis=axis, skipna=skipna, **kwds)
pandas/core/nanops.py:127: in f
result = alt(values, axis=axis, skipna=skipna, **kwds)
pandas/core/nanops.py:560: in nanmean
the_sum = _ensure_numeric(values.sum(axis, dtype=dtype_sum))
../../Venvs/venv-pandas/lib/python3.8/site-packages/numpy/core/_methods.py:38: in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
/usr/lib/python3.8/_pydecimal.py:1158: in __add__
other = _convert_other(other)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
other = Decimal('1223.00'), raiseit = False, allow_float = False
def _convert_other(other, raiseit=False, allow_float=False):
"""Convert other to Decimal.
Verifies that it's ok to use in an implicit construction.
If allow_float is true, allow conversion from float; this
is used in the comparison methods (__eq__ and friends).
"""
> if isinstance(other, Decimal):
E SystemError: <built-in function isinstance> returned a result with an error set
/usr/lib/python3.8/_pydecimal.py:6017: SystemError
1 failed, 90 passed, 1 xfailed in 1.89s
```
</details>
---
Output of ```pd.show_versions()```:
<details>
INSTALLED VERSIONS
------------------
commit : f6b3e82b5514da97b48918c99bd28a07b754291b
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.23.a-1-hardened
Version : #1 SMP PREEMPT Sun, 01 Mar 2020 18:23:23 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0.dev0+696.gf6b3e82b5
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0
Cython : 0.29.15
pytest : 5.3.5
hypothesis : 5.6.0
sphinx : 2.4.4
blosc : 1.8.3
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : 0.3.3
gcsfs : None
matplotlib : 3.2.0
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.15.0
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
</details>
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|
Better error message for OOB result
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| 3
| 2020-03-06T17:10:53Z
| 2020-03-12T12:22:19Z
| 2020-03-11T01:45:54Z
|
CONTRIBUTOR
| null |
Closes #31774
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| 577,074,681
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MDU6SXNzdWU1NzcwNzQ2ODE=
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[Regression] to_json doesn't export Int64 dtype correctly
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| 2
| 2020-03-06T17:29:12Z
| 2020-05-06T15:43:03Z
| 2020-03-06T20:35:48Z
|
NONE
| null |
#### Code Sample
In 1.0.1:
```python
pd.Series([0,1,None,3], dtype="Int64").to_json()
>> '{"0":0,"1":1,"2":{},"3":3}'
```
#### Problem description
Not sure if that's the desired result, notice the object instead of null value.
#### Expected Output
0.25.3 used to produce this:
```python
pd.Series([0,1,None,3], dtype="Int64").to_json()
>> # '{"0":0,"1":1,"2":null,"3":3}'
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.0
pip : 20.0.2
setuptools : 45.2.0.post20200209
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : 2.4.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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| 577,114,985
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MDExOlB1bGxSZXF1ZXN0Mzg0OTcxNzUw
| 32,501
|
Disallow lossy SparseArray conversion
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| 3
| 2020-03-06T18:58:19Z
| 2020-03-14T16:47:12Z
| 2020-03-14T16:33:20Z
|
MEMBER
| null |
cc @TomAugspurger
|
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| 577,139,626
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MDU6SXNzdWU1NzcxMzk2MjY=
| 32,502
|
DOC: `ref` issues in the contributing.rst file
|
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| 2020-03-06T19:46:26Z
| 2020-03-06T20:38:15Z
| 2020-03-06T20:38:15Z
|
NONE
| null |
#### Problem description
Anytime I click on an internal reference link on the [Contributing.rst](https://github.com/pandas-dev/pandas/blob/master/doc/source/development/contributing.rst) file, I expect to be directed somewhere in the internal documentation that explains something about the internal link.
While it generates a `#id39` tag at the end of the link, it doesn't direct me to the page.
I suspect that this is not typical behavior. I just want to make sure this is the case before I post a PR on it.
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MDU6SXNzdWU1NzcxNjc4MTk=
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core.common.random_state is too strict for state input
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| 1
| 2020-03-06T20:48:17Z
| 2020-03-17T02:38:04Z
| 2020-03-17T02:38:04Z
|
CONTRIBUTOR
| null |
The numpy RandomState seed can be an int, array-like, or BitGenerator
```python
seed : {None, int, array_like, BitGenerator}, optional
Random seed used to initialize the pseudo-random number generator or
an instantized BitGenerator. If an integer or array, used as a seed for
the MT19937 BitGenerator. Values can be any integer between 0 and
2**32 - 1 inclusive, an array (or other sequence) of such integers,
or ``None`` (the default). If `seed` is ``None``, then the `MT19937`
BitGenerator is initialized by reading data from ``/dev/urandom``
(or the Windows analogue) if available or seed from the clock
otherwise.
```
Right now, we only pass through integers. Arrays (and BitGenerators if possible) should be passed through to RandomState at https://github.com/pandas-dev/pandas/blob/54c5e9e6e0819f848a10045d1cbc8ee936ffd075/pandas/core/common.py#L409-L410.
## Current Behavior
```pytb
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: state_data = np.random.randint(0, 2**31, size=624, dtype='uint32')
In [4]: pd.core.common.random_state(state_data)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-4-4f97e41a5899> in <module>
----> 1 pd.core.common.random_state(state_data)
~/sandbox/pandas/pandas/core/common.py in random_state(state)
415 else:
416 raise ValueError(
--> 417 "random_state must be an integer, a numpy RandomState, or None"
418 )
419
ValueError: random_state must be an integer, a numpy RandomState, or None
```
## Expected Output
```python
Out[4]: RandomState(MT19937) at 0x115B16050
```
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MDExOlB1bGxSZXF1ZXN0Mzg1MDIyNjc3
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CLN: avoid values_from_object in construction
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(wip) BUG: groupby with sort=False create buggy multiindex
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| 2020-03-06T22:11:55Z
| 2020-10-10T14:14:56Z
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|
MEMBER
| null |
- [ ] closes #32259
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Still messy, just running this through the test suite as my laptop's not great (but please let me know if such behaviour is unwelcome and I won't do it again)
pandas/tests/groupby/test_timegrouper.py
pandas/tests/groupby/test_grouping.py
pandas/tests/groupby/test_categorical.py
pandas/tests/groupby/test_groupby.py
pandas/tests/test_multilevel.py
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| 2020-03-06T23:31:35Z
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CONTRIBUTOR
| null |
* [x] ref #30999
* [x] tests added / passed
* [x] passes `black pandas`
* [x] passes `git diff origin/master -u -- "*.py" | flake8 --diff`
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- [ ] closes #xxxx
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CLN: unused code in reshape.merge
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MEMBER
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check_duplicates is always passed as False, so we can trim some code
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MDExOlB1bGxSZXF1ZXN0Mzg1MDk0OTgy
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[BUG] Loosen random_state input restriction
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CONTRIBUTOR
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Alllow for array-like as well as BitGenerator inputs
Addresses: GH32503
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MDExOlB1bGxSZXF1ZXN0Mzg1MTAxMTIw
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[BUG] Add int-array-like into random state
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MDExOlB1bGxSZXF1ZXN0Mzg1MTA0MDQ4
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[BUG] add consistency to_numeric on empty list
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| 1
| 2020-03-07T04:52:51Z
| 2020-03-20T02:04:17Z
| 2020-03-16T02:30:10Z
|
CONTRIBUTOR
| null |
to_numeric should work similarly on empty lists for
downcast=unsigned/float/integer
Addresses: GH32493
- [x] closes #32493
- [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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| 577,278,286
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MDExOlB1bGxSZXF1ZXN0Mzg1MTA0MDUw
| 32,513
|
CLN: remove check_series_type
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| 9
| 2020-03-07T04:52:54Z
| 2020-03-09T21:13:50Z
| 2020-03-08T16:13:46Z
|
MEMBER
| null |
Trying to clean up assert_series_equal and avoid usage of _internal_get_values; I'm finding that these functions are _weird_, and in particular categorical dtype handling is opaque
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MDU6SXNzdWU1NzczMDgwNTI=
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TST: Seemingly non-deterministic order of value_counts
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| 2020-03-07T09:33:03Z
| 2020-05-22T03:42:29Z
| null |
CONTRIBUTOR
| null |
We had a flaky test for `test_value_counts` in `pandas/tests/base/test_ops.py` which was related to the sort order of entries with the same count. A hotfix was introduced in #32449 and resolves the CI issue.
The underlying issue still isn't fixed/confirmed. The test assumes that in case of same counts the order of the original index/series object is preserved. This assumption breaks on CI though. Please refer to [my comment](https://github.com/pandas-dev/pandas/pull/32449#discussion_r388388353) (and the following ones) for all related details I have found so far.
It doesn't necessarily seem to be related to the narrow dtypes. It's probably just more likely that values are duplicated the narrower the dtype is.
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df.to_dict() accepts orient which are not in the list of options
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| 2020-03-07T12:07:45Z
| 2020-12-12T17:59:19Z
| 2020-03-14T21:33:06Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame(
{'name': ['alice', 'bob'],
'age': [30, 28]})
df.to_dict(orient='racoon')
```
returns:
```python
[{'age': 30, 'name': 'alice'}, {'age': 28, 'name': 'bob'}]
```
instead of `ValueError`
#### Problem description
The current version of pandas accepts orient= any word which starts with r, l, s, sp, i and d instead of just the actual options in the documentation.
The function should be returning `ValueError` if the orient option is not any of the following: `{'dict', 'list', 'series', 'split', 'records', 'index'}`.
Not fixing this might lead to mistakes by using an orient which might point to a wrong output. I would not consider this a bug but this should be consistent with other methods such as `df.to_json()`. The part of the code which needs to be fixed in master is:
```python
if orient.lower().startswith("d"):
return into_c((k, v.to_dict(into)) for k, v in self.items())
elif orient.lower().startswith("l"):
return into_c((k, v.tolist()) for k, v in self.items())
elif orient.lower().startswith("sp"):
return into_c(
(
("index", self.index.tolist()),
("columns", self.columns.tolist()),
(
"data",
[
list(map(com.maybe_box_datetimelike, t))
for t in self.itertuples(index=False, name=None)
],
),
)
)
elif orient.lower().startswith("s"):
return into_c((k, com.maybe_box_datetimelike(v)) for k, v in self.items())
elif orient.lower().startswith("r"):
columns = self.columns.tolist()
rows = (
dict(zip(columns, row))
for row in self.itertuples(index=False, name=None)
)
return [
into_c((k, com.maybe_box_datetimelike(v)) for k, v in row.items())
for row in rows
]
elif orient.lower().startswith("i"):
if not self.index.is_unique:
raise ValueError("DataFrame index must be unique for orient='index'.")
return into_c(
(t[0], dict(zip(self.columns, t[1:])))
for t in self.itertuples(name=None)
)
else:
raise ValueError(f"orient '{orient}' not understood")
```
by replacing the `.lower().startswith()` with ` ==` and the corresponding string
I with now open a PR to fix the issue. *THE BUG IS STILL IN MASTER*
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-88-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.3.1
setuptools: 41.6.0
Cython: None
numpy: 1.17.4
scipy: None
pyarrow: None
xarray: None
IPython: 7.9.0
sphinx: None
patsy: None
dateutil: 2.8.1
pytz: 2019.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.3.11
pymysql: 0.9.3
psycopg2: None
jinja2: 2.10.3
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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MDExOlB1bGxSZXF1ZXN0Mzg1MTQyMDAz
| 32,516
|
Deprecate Aliases as orient Argument in DataFrame.to_dict
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| 11
| 2020-03-07T12:53:26Z
| 2020-06-01T14:52:06Z
| 2020-03-14T21:33:06Z
|
CONTRIBUTOR
| null |
- [ x] closes #32515
- [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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