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CLN: convert lambda to function
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CONTRIBUTOR
| null |
Minor cleanup: conversion to proper function.
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CLN: Index._format_with_header (remove kwargs etc.)
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CONTRIBUTOR
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Refactor of ``Index._format_with_header`` and subclass methods:
* remove ``kwargs`` for cleaner signature.
* Add return type
* Move functionality specific to ``CategoricalIndex`` to that class.
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REF: make ccalendar self-contained
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TYP, DOC, CLN:SeriesGroupBy._wrap_applied_output
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- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0NDQ0Mjc3NDkx
| 35,121
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TST add test case for drop_duplicates
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| 2020-07-08T15:40:09Z
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CONTRIBUTOR
| null |
Add a test case to drop_duplicates for inplace=True.
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|
CLN: remove kwargs in Index.format
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| 2020-07-04T13:06:52Z
| 2020-08-08T08:30:27Z
| 2020-07-14T22:32:48Z
|
CONTRIBUTOR
| null |
Removes kwargs in ``Index.format`` and subclasses. Also changes ``Index._format_with_header`` to have parameter ``na_rep`` not have a default value (in order to not have default value set in two locations).
Related to #35118.
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|
BUG: pandas does not build on AIX - when using xlc/xlC as compiler
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| 2020-07-04T14:02:46Z
| 2021-08-08T16:50:17Z
| null |
CONTRIBUTOR
| null |
- [x ] I have checked that this issue has not already been reported.
- [ x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
Panda build stops with:
```
"pandas/_libs/window/aggregations.cpp", line 5436.23: 1540-0063 (S) The text "(" is unexpected.
"pandas/_libs/window/aggregations.cpp", line 5523.23: 1540-0063 (S) The text "(" is unexpected.
"pandas/_libs/window/aggregations.cpp", line 6759.21: 1540-0063 (S) The text "(" is unexpected.
"pandas/_libs/window/aggregations.cpp", line 20023.21: 1540-0063 (S) The text "(" is unexpected.
```
On examining this I see that there are special statements - perhaps unsupported by xlc/xlC (v11).
```
+5436 __pyx_t_1 = (std::signbit(__pyx_v_val) != 0);
+5523 __pyx_t_1 = (std::signbit(__pyx_v_val) != 0);
+6759 __pyx_t_1 = (std::isnan(__pyx_v_val) != 0);
+20023 __pyx_t_1 = (std::isnan(__pyx_v_val) != 0);
```
There is a good chance this is an issue caused by cython. but I thought I would post/mention it here as well.
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BUG: Rounding on float16 type not working
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| 2020-07-07T18:54:31Z
| 2020-07-05T12:24:53Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Problem description
I'm trying to round a column to 4 decimal places to store them in the DB. I must use `np.float16` type to work with as I need the smallest possible precision, but it's not working (see *output* section), if I don't cast the values and leave them as `np.float64` type It'll work correctly.
I think it's a bug of Pandas as if I run:
```python
np.array([0.37324, 0.12321, 0.23]).astype(np.float16).round(4)
```
It works as expected, so It doesn't seem to be a problem for Numpy.
#### Code Sample
```python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.array([0.37324, 0.12321, 0.23]), columns=['A'])
df.astype(np.float16).round(4)
```
Running:
```python
pd.DataFrame(np.array([0.37324, 0.12321, 0.23]).astype(np.float16), columns=['A']).round({'A': 4})
```
Doesn't work either as it has the same output.
#### Current Output
```
A
0 0.373291
1 0.123230
2 0.229980
```
#### Expected Output
```
A
0 0.3732
1 0.1232
2 0.23
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-62-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : es_AR.UTF-8
LOCALE : es_AR.UTF-8
pandas : 1.0.5
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 18.1
setuptools : 40.8.0
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
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 : 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 : None
</details>
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TST: Add test for category equalness on applies (#21239)
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- [x] closes #21239
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
The test checks if equalness comparisons through `df.apply()` works correctly for the categorical `dtype`, especially with `np.NaN` and `None` values.
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BUG: unexpected cut() results
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| 2020-07-04T22:42:41Z
| 2021-08-08T16:51:20Z
| null |
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
nbins = 42
maxs = [96, 97, 98]
for max in maxs:
data = [-max, 0, max]
bins = pd.cut(data, nbins, labels=False)
print("Data: ", data)
print("Bins: ", bins, "\n")
```
#### Problem description
According to [documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.cut.html), I would expect the `0` value to go to the bin `20`. For `max == 96` and `max == 98` it does go to the bin `20`, however, when `max == 97` for some reason `0` goes to the bin `21`:
```python
Data: [-96, 0, 96]
Bins: [ 0 20 41]
Data: [-97, 0, 97]
Bins: [ 0 21 41]
Data: [-98, 0, 98]
Bins: [ 0 20 41]
```
Feels like a bug, or may be I'm missing something on the docs that explains such a `cut()` behavior. Would appreciate any help on this. Thanks.
#### Expected Output
```python
Data: [-96, 0, 96]
Bins: [ 0 20 41]
Data: [-97, 0, 97]
Bins: [ 0 20 41]
Data: [-98, 0, 98]
Bins: [ 0 20 41]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Darwin
OS-release : 19.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.16.1
pytz : 2018.9
dateutil : 2.8.0
pip : 20.0.2
setuptools : 40.6.3
Cython : None
pytest : 4.1.1
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.8.0
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 : 4.1.1
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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ENH: make is_list_like handle non iterable numpy-like arrays correctly
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CONTRIBUTOR
| null |
- [x] closes #35131
- [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: remove the circular import in NDFrame.dtypes
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MEMBER
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|
support binary file handles in to_csv
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| 2020-07-05T20:07:33Z
| 2020-08-07T22:45:53Z
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|
CONTRIBUTOR
| null |
- [x] fixes #19827, fixes #35058, fixes #23854 *, fixes #13068 *, and fixes #22555
- [x] 3 tests added
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
The first commit addresses https://github.com/pandas-dev/pandas/issues/35058#issuecomment-653844610: python's `open` cannot take an `encoding` argument when `mode` contains a `'b'` (opened in binary mode). This avoids an error when executing `df.to_csv("output.csv", mode="w+b")`.
The second commit fixes #35058, #19827, #23854 *, and #13068 *: `to_csv` supports file handles in binary mode if `mode` contains a `b` and it honors `encoding`. `to_csv` re-invented a lot that was already done in `get_handle`. Let `get_handle` do the heavy lifting and remove all special cases from `to_csv`.
The third commit fixes #22555: some compression algorithms did not set the mode to be writeable for file handles. Together with the re-factoring in the second commit, it is now possible to write to binary file handles with compression!
*requesting an encoding for a non-binary file handles through `to_csv` still doesn't work but imho also doesn't make sense: specify the encoding yourself when opening the file or use a binary file handle
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BUG: transform with nunique should have dtype int64
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MEMBER
| null |
- [x] closes #35109
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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ENH: Identify numpy-like zero-dimensional arrays as non-iterable
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| 2020-07-05T22:53:25Z
| 2020-07-09T18:40:36Z
| null |
CONTRIBUTOR
| null |
Note: I wasn't sure whether to label this as an enhancement or bug. I've put enhancement as I don't think the original implementation was actually incorrect, rather just limited in scope. I'm happy to change to bug if that's more appropriate.
#### Is your feature request related to a problem?
I'm one of the developers of [Pint-Pandas](https://github.com/hgrecco/pint-pandas), which provides an extension type for storing numeric data with units (and, once mature, may solve #10349). The problem we're having is that we can't pass all the extension type tests. In particular, we fail many tests due to Panda's current implementation of `is_list_like`.
As @hgrecco shows [here](https://github.com/hgrecco/pint-pandas/issues/33#issuecomment-647225205), pandas determines whether something `is_list_like` [here](https://github.com/pandas-dev/pandas/blob/4743cc91719146167eefb43865413fc0b0aef1d4/pandas/_libs/lib.pyx#L992) (code copied below).
```python
cdef inline bint c_is_list_like(object obj, bint allow_sets) except -1:
return (
isinstance(obj, abc.Iterable)
# we do not count strings/unicode/bytes as list-like
and not isinstance(obj, (str, bytes))
# exclude zero-dimensional numpy arrays, effectively scalars
and not (util.is_array(obj) and obj.ndim == 0)
# exclude sets if allow_sets is False
and not (allow_sets is False and isinstance(obj, abc.Set))
)
```
At the moment, all Pint `Quantity` instances have an `__iter__` method. It is designed to raise a `TypeError` if the quantity isn't actually iterable (code [here](https://github.com/hgrecco/pint/blob/4ab68ba0a00f96ed68fdf084233b61d68127c461/pint/quantity.py#L242)).
The problem is that the `__iter__` method is defined, regardless of whether the `Quantity` instance is actually iterable or not. This is a problem because it means that, in all cases, `isinstance(quantity_instance, abc.Iterable)` returns `True` (even if `quantity_instance` isn't actually iterable and would return a `TypeError` as soon as one tried to iterate with it).
A numpy array suffers from the same issue which is where the `not (util.is_array(obj) and obj.ndim == 0)` in Panda's `is_list_like` is important, because it excludes scalars. The problem is that `util.is_array(obj)` returns `False` if `obj` is anything other than `np.ndarray`. As a result, we can't just add an `ndim` attribute to `Quantity` because the `obj.ndim == 0` check will never be evaluated anyway.
#### Describe the solution you'd like
Update `is_list_like` so that it correctly identifies implementations of scalars in numpy-like arrays (I think these are called 'duck numpy arrays'?). I've put a draft in #35127 and would be grateful for feedback on the proposed solution there. If there's a better solution, I'm also happy to implement that instead.
#### API breaking implications
`is_list_like` would now return `False` for scalars of numpy-like array implementations. If we used the implementation in #35127 then any object that has an `ndim` attribute and has `obj.ndim == 0` would no longer be identified as list-like.
#### Describe alternatives you've considered
We've discussed how we could get the behaviour we want by changing Pint, rather than changing Pandas, but it seems to be impossible if we want to maintain the numpy-like implementation without separate scalar and array classes (see discussion [here](https://github.com/hgrecco/pint/issues/1128)).
#### Additional context
```python
>>> import numpy as np
>>> import pandas as pd
>>> import pint
>>> from collections import abc
>>> numpy_scalar = np.array(2)
>>> numpy_scalar
array(2)
# numpy 0d-array (i.e. scalar) quantities appear to be iterable even though they're not
>>> isinstance(numpy_scalar, abc.Iterable)
True
>>> numpy_scalar[0]
...
IndexError: too many indices for array: array is 0-dimensional, but 1 were indexed
# however, numpy 0d-array quantities are correctly handled by Pandas
>>> pd.core.dtypes.inference.is_list_like(numpy_scalar)
False
# compare this with Pint's numpy-like implementation
>>> ureg = pint.UnitRegistry()
>>> quantity_scalar = 3 * ureg.meter
>>> quantity_scalar
<Quantity(3, 'meter')>
# similar to a numpy array, scalar quantities appear to be iterable even though they're not
>>> isinstance(quantity_scalar, abc.Iterable)
True
>>> quantity_scalar[0]
...
TypeError: Neither Quantity object nor its magnitude (3)supports indexing
# however, Pandas identifies Pint's implementation as being iterable which is what is causing
# our problem
>>> pd.core.dtypes.inference.is_list_like(quantity_scalar)
True # we would like this to be False
```
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BUG: Passing `level=` to `DataFrame.reindex` fails when the index is flat
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| 2020-07-06T01:41:35Z
| 2020-09-04T14:54:03Z
| null |
NONE
| null |
- [ X] I have checked that this issue has not already been reported.
- [ X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(100,40), index=pd.Index(np.arange(100), name='foo'))
new_index = pd.Index(np.arange(50), name='foo')
df.reindex(new_index, level='foo')
```
#### Problem description
Currently the above raises `AttributeError: 'Int64Index' object has no attribute 'levels'`.
The behavior of `DataFrame.reindex` should be consistent between flat Indexes and MultiIndexes - as long the level is valid, I shouldn't have to check whether the index is a flat index or a MultiIndex before deciding whether to pass `level=`.
#### Expected Output
Same as `df.reindex(new_index)`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-53-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1.post20200622
Cython : 0.29.19
pytest : None
hypothesis : None
sphinx : 3.1.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : 3.4.4
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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CLN: unused imports in tslibs
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MDU6SXNzdWU2NTEzMjk0NDA=
| 35,134
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CI: Update to isort 5
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| 8
| 2020-07-06T07:57:02Z
| 2020-08-01T08:43:01Z
| 2020-08-01T08:43:01Z
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MEMBER
| null |
`isort` 5.x is out with some changes to how it sorts. We'll need to update the code and unpin in our environment file.
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MDU6SXNzdWU2NTE1MDUyNTY=
| 35,135
|
BUG: .corr() values higher than 1
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| 2020-08-22T16:49:47Z
| 2020-08-22T16:49:47Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
_Sorry for including an external dataset. I couldn't reproduce this bug with a smaller one._
```python
url = 'https://raw.githubusercontent.com/MislavSag/trademl/master/trademl/modeling/random_forest/X_TEST.csv'
df = pd.read_csv(url, sep=',')
df = X_TEST.loc[:,['RSI30','CMO30']]
df.corr() > 1
```
#### Problem description
When applying .corr() on the given dataset the output Pearson's correlation is slightly >1 (6.661338e-16). I'd assume it should be equal to 1.
#### Expected Output
The expected result would be correlation values <= 1.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.5
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.0.post20200616
Cython : None
pytest : None
hypothesis : None
sphinx : 3.1.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
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 : 0.49.1
</details>
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CI: pin isort version
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| 2020-07-06T13:04:58Z
| 2020-07-09T15:12:19Z
| 2020-07-06T14:19:44Z
|
MEMBER
| null |
- [X] xref #35134
- [X] 0 tests added / 0 passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry. __this is a CI PR. I don't think we touch whatsnew for those.__
### Scope
<s>Remove the `--recursive` keyword from `code_checks.sh`, so that the newest version of isort can run without errors (it does recursive sorting by default).</s>
Pin the version instead, because version 5 has changed the way isort sorts imports. Needs more work to switch safely.
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BUG:sort_values in groupby make some value lost
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|
NONE
| null |
- [ .] I have checked that this issue has not already been reported.
- [ .] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
- source data refer to [b.csv](https://raw.githubusercontent.com/leohazy/bug/master/b.csv)
```python
import pandas as pd
a=pd.read_csv('https://raw.githubusercontent.com/leohazy/bug/master/b.csv')
def holding0(x):
x["holding"] = x["shares"].sum()
print(x[['close_max','close_min']])
return x
def holding(x):
x["holding"] = x["shares"].sum()
x = x.sort_values(by=["hold_start"])
print(x[['close_max','close_min']])
return x
def holding1(x):
x["holding"] = x["shares"].sum()
x['hold_start']=x['hold_start'].apply(lambda x:pd.to_datetime(x,format="%Y-%m-%d"))
x = x.sort_values(by=["hold_start"])
print(x[['close_max','close_min']])
return x
def holding2(x):
x["holding"] = int(x["shares"].sum())
x = x.sort_values(by=["hold_start"])
print(x[['close_max','close_min']])
return x
def holding3(x):
x["holding"] = float(x["shares"].sum())
x = x.sort_values(by=["hold_start"])
print(x[['close_max','close_min']])
return x
def holding4(x):
x["holding"] = x["shares"].sum()
x.sort_values(by=["hold_start"],inplace=True)
print(x[['close_max','close_min']])
return x
def holding5(x):
x["holding"] = x["shares"].sum()
x=x.sort_values(by=["hold_start"]).copy()
print(x[['close_max','close_min']])
return x
b = a.groupby(by="companycode").apply(holding)
```
#### Problem description
- after sort in groupby, several columns got only repeated values, such as ratio,shares,close_max,close_min(**columns after 'ratio'**)
0. apply with hoding(x) function,the 'close_max' and 'close_min'
<details>
<summary>result</summary>
close_max close_min
33 16.9286 14.5889
43 16.9286 14.5889
13 16.8893 11.9444
23 16.8893 11.9444
53 16.8893 11.9444
close_max close_min
36 16.9286 14.5889
46 16.9286 14.5889
16 16.8893 11.9444
26 16.8893 11.9444
56 16.8893 11.9444
close_max close_min
6 16.8893 11.9444
close_max close_min
4 16.8893 11.9444
close_max close_min
39 16.9286 14.5889
49 16.9286 14.5889
19 16.8893 11.9444
29 16.8893 11.9444
59 16.8893 11.9444
close_max close_min
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48 16.9286 14.5889
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27 16.8893 11.9444
58 16.8893 11.9444
close_max close_min
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close_max close_min
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24 16.8893 11.9444
54 16.8893 11.9444
close_max close_min
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close_max close_min
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42 16.9286 14.5889
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52 16.8893 11.9444
close_max close_min
0 16.8893 11.9444
close_max close_min
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40 16.9286 14.5889
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50 16.8893 11.9444
close_max close_min
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41 16.9286 14.5889
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51 16.8893 11.9444
close_max close_min
35 16.9286 14.5889
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55 16.8893 11.9444
close_max close_min
3 16.8893 11.9444
close_max close_min
8 16.8893 11.9444
close_max close_min
9 16.8893 11.9444
close_max close_min
1 16.8893 11.9444
close_max close_min
37 16.9286 14.5889
47 16.9286 14.5889
18 16.8893 11.9444
28 16.8893 11.9444
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close_max close_min
2 16.8893 11.9444
</details>
1. change 'hold_start' into datetime,got expected output
<details>
<summary>result</summary>
close_max close_min
13 16.8893 11.9444
23 16.8893 11.9444
33 16.9286 14.5889
43 16.9286 14.5889
53 16.8893 11.9444
close_max close_min
16 32.56 24.55
26 32.56 24.55
36 30.45 25.90
46 30.45 25.90
56 32.56 24.55
close_max close_min
6 36.6211 24.5361
close_max close_min
4 76.3976 61.7438
close_max close_min
19 38.9398 28.2549
29 38.9398 28.2549
39 25.9433 16.4589
49 25.9433 16.4589
59 38.9398 28.2549
close_max close_min
17 22.4004 14.0188
27 22.4004 14.0188
38 14.8609 10.2045
48 14.8609 10.2045
58 22.4004 14.0188
close_max close_min
5 30.7038 23.6078
close_max close_min
14 120.50 80.3
24 120.50 80.3
34 93.12 70.0
44 93.12 70.0
54 120.50 80.3
close_max close_min
7 39.37 27.12
close_max close_min
12 77.2825 65.6768
22 77.2825 65.6768
32 79.5022 65.4191
42 79.5022 65.4191
52 77.2825 65.6768
close_max close_min
0 34.8514 25.0396
close_max close_min
11 135.105 108.1080
21 135.105 108.1080
30 113.939 98.7243
40 113.939 98.7243
50 135.105 108.1080
close_max close_min
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20 1157.48 984.503
31 1219.51 1105.090
41 1219.51 1105.090
51 1157.48 984.503
close_max close_min
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25 58.1580 43.2304
35 47.4252 39.7685
45 47.4252 39.7685
55 58.1580 43.2304
close_max close_min
3 113.34 91.48
close_max close_min
8 56.0291 40.9621
close_max close_min
9 52.1465 42.1393
close_max close_min
1 31.8664 21.9908
close_max close_min
18 61.8531 48.5440
28 61.8531 48.5440
37 61.8132 47.8151
47 61.8132 47.8151
57 61.8531 48.5440
close_max close_min
2 66.2883 50.5529
</details>
2. change x["shares"].sum() into int,got expected output
3. change x["shares"].sum() into float,got error
4. use inplace=true in sort ,got expected output
5. use copy in sort,got error
6. use parse_dates=['hold_start'] in read_csv ,got error(against case 1,function holding1(x))
#### Expected Output
got correct values after sort in groupby apply
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.0.5
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 46.4.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.14.0
pandas_datareader: 0.8.1
bs4 : None
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.1
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
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 : None
</details>
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MDU6SXNzdWU2NTE1NjI3NzA=
| 35,138
|
CI/DOC: autodoc failures for DataFrame.sparse methods with latest sphinx
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| 1
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| 2020-08-20T07:24:56Z
| 2020-08-20T07:24:26Z
|
MEMBER
| null |
On master and on PRs, there is a CI failure:
```
WARNING: autodoc: failed to import method 'sparse.from_spmatrix' from module 'DataFrame'; the following exception was raised:
No module named 'DataFrame'
WARNING: autodoc: failed to import method 'sparse.to_coo' from module 'DataFrame'; the following exception was raised:
No module named 'DataFrame'
WARNING: autodoc: failed to import method 'sparse.to_dense' from module 'DataFrame'; the following exception was raised:
No module named 'DataFrame'
WARNING: autodoc: failed to import method 'sparse.from_coo' from module 'Series'; the following exception was raised:
No module named 'Series'
WARNING: autodoc: failed to import method 'sparse.to_coo' from module 'Series'; the following exception was raised:
No module named 'Series'
```
Looking at the env of the failing ones and the last one that passed on master: sphinx upgraded from 3.1.1 to 3.1.2.
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MDExOlB1bGxSZXF1ZXN0NDQ0Nzk3MDY3
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CI: pin sphinx <= 3.1.1 for autodoc failure
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MEMBER
| null |
xref https://github.com/pandas-dev/pandas/issues/35138
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MDExOlB1bGxSZXF1ZXN0NDQ0ODExNjEy
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Fix regression on datetime in MultiIndex
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| 6
| 2020-07-06T14:27:12Z
| 2020-07-08T15:40:22Z
| 2020-07-08T12:43:19Z
|
CONTRIBUTOR
| null |
- [x] closes #35015
- [x] tests added / passed
- `tests.indexing.multiindex.test_datetime:test_multiindex_datetime_columns`
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
- Not needed - this was a regression
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DEPR: Deprecate n-dim indexing for Series
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| 1
| 2020-07-06T15:14:46Z
| 2020-07-06T23:25:32Z
| 2020-07-06T23:25:28Z
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CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/27837
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improved exception message
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| 2020-11-04T08:29:21Z
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NONE
| null |
for ValueError: cannot reindex from a duplicate axis
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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QST:What is the largest datetime in pandas to_datetime?
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| 2
| 2020-07-06T16:34:11Z
| 2020-07-06T16:46:36Z
| 2020-07-06T16:46:02Z
|
NONE
| null |
- [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [x] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, please read [this guide](
https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your question.
I am trying to read a date from excel. For validation, when I use 'pd.to_datetime' to compare against another datetime object, for date 9999-12-31, I am getting an exception: 'OutofBoundsDateTime', whereas I believe this should still be a valid MAXDATE as per standards. Here is a sample code where I am facing such issue:
```python
# Your code here, if applicable
import pandas as pd
df = pd.DataFrame(["9999-12-31"])
pd.to_datetime(df[0])
```
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BUG: pd.crosstab fails when passed multiple columns, margins True and normalize True
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| 6
| 2020-07-06T16:41:57Z
| 2020-11-16T16:48:16Z
| 2020-08-19T17:59:04Z
|
CONTRIBUTOR
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
"bar", "bar", "bar", "bar"],
"B": ["one", "one", "one", "two", "two",
"one", "one", "two", "two"],
"C": ["small", "large", "large", "small",
"small", "large", "small", "small",
"large"],
"D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
"E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
pd.crosstab(index=df.C,
columns=[df.A,df.B],
margins=True,
margins_name='Sub-Total',
normalize='all')
# returns ValueError: Sub-Total not in pivoted DataFrame
```
#### Problem description
#27663 found that `pd.crosstab` failed when `margins` and `normalize` were true. This continues to be the case when more than one column is passed.
This is not the case when a single column and multiple rows is passed.
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 47.1.1.post20200604
Cython : None
pytest : None
hypothesis : None
sphinx : 3.1.1
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.13.0
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 : 1.3.17
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : 1.2.9
numba : None
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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AttributeError: 'DataFrame' object has no attribute 'compare'
|
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[] | null | 2
| 2020-07-06T17:27:32Z
| 2020-07-06T17:42:08Z
| 2020-07-06T17:41:36Z
|
NONE
| null |
pandas .compare() from the [documentation ](https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.compare.html)returns:
```AttributeError: 'DataFrame' object has no attribute 'compare'```
```
Python 3.6.5 |Anaconda, Inc.| (default, Mar 29 2018, 13:32:41) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> import numpy as np
>>>
>>> print(pd.__name__, pd.__version__)
pandas 1.0.3
>>>
>>> df = pd.DataFrame(
... {
... "col1": ["a", "a", "b", "b", "a"],
... "col2": [1.0, 2.0, 3.0, np.nan, 5.0],
... "col3": [1.0, 2.0, 3.0, 4.0, 5.0]
... },
... columns=["col1", "col2", "col3"],
... )
>>> df
col1 col2 col3
0 a 1.0 1.0
1 a 2.0 2.0
2 b 3.0 3.0
3 b NaN 4.0
4 a 5.0 5.0
>>>
>>> df2 = df.copy()
>>> df2.loc[0, 'col1'] = 'c'
>>> df2.loc[2, 'col3'] = 4.0
>>> df2
col1 col2 col3
0 c 1.0 1.0
1 a 2.0 2.0
2 b 3.0 4.0
3 b NaN 4.0
4 a 5.0 5.0
>>>
>>> df.compare(df2)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\randy\Anaconda3\lib\site-packages\pandas\core\generic.py", line 5274, in __getattr__
return object.__getattribute__(self, name)
AttributeError: 'DataFrame' object has no attribute 'compare'
```
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MDExOlB1bGxSZXF1ZXN0NDQ0OTI1NjYx
| 35,146
|
Move mark registration
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| 2
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| 2020-10-05T11:22:40Z
| 2020-07-06T23:28:06Z
|
CONTRIBUTOR
| null |
Should avoid warnings like
```
D:\a\1\s\test_venv\lib\site-packages\pandas\tests\plotting\test_series.py:655: PytestUnknownMarkWarning: Unknown pytest.mark.slow - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/latest/mark.html
@pytest.mark.slow
```
in https://dev.azure.com/pandas-dev/pandas-wheels/_build/results?buildId=38672&view=logs&j=c0130b29-789d-5a3c-6978-10796a508a7f&t=e120bc6c-1f5e-5a41-8f0a-1d992cd2fbfb
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MDU6SXNzdWU2NTE3MjcxNTA=
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CI: MacPython failing TestPandasContainer.test_to_json_large_numbers
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| 4
| 2020-07-06T18:08:13Z
| 2020-07-15T13:43:45Z
| 2020-07-15T13:43:45Z
|
CONTRIBUTOR
| null |
```
_________ TestPandasContainer.test_to_json_large_numbers[2147483648] __________
[gw0] win32 -- Python 3.7.7 D:\a\1\s\test_venv\Scripts\python.exe
self = <pandas.tests.io.json.test_pandas.TestPandasContainer object at 0x2C6E2A70>
bigNum = 2147483648
@pytest.mark.parametrize("bigNum", [sys.maxsize + 1, -(sys.maxsize + 2)])
def test_to_json_large_numbers(self, bigNum):
# GH34473
series = Series(bigNum, dtype=object, index=["articleId"])
json = series.to_json()
expected = '{"articleId":' + str(bigNum) + "}"
assert json == expected
# GH 20599
with pytest.raises(ValueError):
json = StringIO(json)
result = read_json(json)
tm.assert_series_equal(series, result)
df = DataFrame(bigNum, dtype=object, index=["articleId"], columns=[0])
json = df.to_json()
expected = '{"0":{"articleId":' + str(bigNum) + "}}"
assert json == expected
# GH 20599
with pytest.raises(ValueError):
json = StringIO(json)
result = read_json(json)
> tm.assert_frame_equal(df, result)
E AssertionError: Attributes of DataFrame.iloc[:, 0] (column name="0") are different
E
E Attribute "dtype" are different
E [left]: object
E [right]: int64
```
https://dev.azure.com/pandas-dev/pandas-wheels/_build/results?buildId=38672&view=logs&j=c0130b29-789d-5a3c-6978-10796a508a7f&t=e120bc6c-1f5e-5a41-8f0a-1d992cd2fbfb
xref https://github.com/pandas-dev/pandas/pull/34473.
cc @arw2019.
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CI: MacPython failing test_consistency_with_window
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| 0
| 2020-07-06T18:09:41Z
| 2020-08-24T23:42:02Z
| 2020-08-24T23:42:02Z
|
CONTRIBUTOR
| null |
https://dev.azure.com/pandas-dev/pandas-wheels/_build/results?buildId=38672&view=logs&j=c0130b29-789d-5a3c-6978-10796a508a7f&t=e120bc6c-1f5e-5a41-8f0a-1d992cd2fbfb&l=1286
```python
def test_consistency_with_window():
# consistent return values with window
df = test_frame
index_array = self.index_array
indexer = self.rolling_indexer(
index_array=index_array, window_size=self.window_size,
)
start, end = indexer.get_window_bounds(
len(indicies), min_periods, center, closed
)
start = start.astype(np.int64)
end = end.astype(np.int64)
# Cannot use groupby_indicies as they might not be monotonic with the object
# we're rolling over
window_indicies = np.arange(
window_indicies_start, window_indicies_start + len(indicies),
)
window_indicies_start += len(indicies)
# Extend as we'll be slicing window like [start, end)
window_indicies = np.append(
window_indicies, [window_indicies[-1] + 1]
).astype(np.int64)
> start_arrays.append(window_indicies.take(start))
E TypeError: Cannot cast array data from dtype('int64') to dtype('int32') according to the rule 'safe'
test_venv\lib\site-packages\pandas\core\window\indexers.py:318: TypeError
```
cc @mroeschke if you know where to look / who to ping. I haven't looked yet.
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ASV: tslibs.fields
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MDExOlB1bGxSZXF1ZXN0NDQ0OTc3ODM2
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BUG: pd.crosstab fails when passed multiple columns, margins True and normalize True
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| 3
| 2020-07-06T20:09:58Z
| 2020-08-20T00:09:02Z
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|
MEMBER
| null |
- [x] closes #35144
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CI: move py36 slow to azure (#34776)
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MEMBER
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BUG: transform with nunique should have dtype int64
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| 3
| 2020-07-06T22:40:18Z
| 2020-07-10T22:47:43Z
| 2020-07-10T22:46:42Z
|
MEMBER
| null |
- [x] closes #35109
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
cc @WillAyd
Removes casting on transformations which go through `_transform_fast`. The result is a reduction that is broadcast to the original index, so casting isn't necessary once special care is taken for categoricals when `observed=False`
If this PR is accepted, I'll close #35130 which is for the same issue.
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BUG: DataFrame improperly storing ints as floats
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| 3
| 2020-07-07T01:25:17Z
| 2020-07-07T01:46:24Z
| 2020-07-07T01:35:43Z
|
NONE
| null |
- [ X ] I have checked that this issue has not already been reported.
- [ X ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
seed = [{'start': 0, 'end': 0, 'dist': 5900.22782978315}, {'start': 0, 'end': -1, 'dist': 0.0}, {'start': -1, 'end': 0, 'dist': 14258.70864551041}, {'start': -1, 'end': -1, 'dist': 8397.882580246285}]
df = pd.DataFrame(seed)
print('Original dtypes:')
print(df.dtypes)
df['start']=df['start'].astype(int)
df['end']=df['end'].astype(int)
print('\r\nNew column dtypes:')
print(df.dtypes)
print('\r\nValue dtypes:')
row = df.iloc[0]
print(f"A: {row['start'].dtype}")
print(f"B: {row['end'].dtype}")
```
#### Observed output
Original dtypes:
start int64
end int64
dist float64
dtype: object
New column dtypes:
start int64
end int64
dist float64
dtype: object
Value dtypes:
A: float64
B: float64
#### Problem description
It seems that under some circumstances (I'm not sure what's unique about this particular case), pandas is storing integer columns as floats even when there is not a missing/NaN value. The column type is listed as int64, doing an `astype(int)` yields an int64 column, but the actual value dtypes are float64. They should be int64.
#### Expected Output
Original dtypes:
start int64
end int64
dist float64
dtype: object
New column dtypes:
start int64
end int64
dist float64
dtype: object
Value dtypes:
A: **int64**
B: **int64**
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1.post20200622
Cython : 0.29.20
pytest : 5.4.3
hypothesis : 5.18.3
sphinx : 3.1.1
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.1
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.3
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.9
numba : 0.50.1
</details>
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MDExOlB1bGxSZXF1ZXN0NDQ1MDkzNjIz
| 35,154
|
REF: remove always-UTC arg from tz_convert, tz_convert_single
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| 1
| 2020-07-07T01:53:44Z
| 2020-07-09T22:04:13Z
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MEMBER
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MDU6SXNzdWU2NTIwMDczMDQ=
| 35,155
|
BUG: Dollar symbol appears in dataframe rows which have numerical values
|
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| 2020-07-07T05:16:20Z
| 2020-08-21T17:05:20Z
| 2020-07-07T05:45:45Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
pd.options.display.max_rows = 1000
pd.options.display.float_format = '${:,.2f}'.format
df = pd.DataFrame(columns=['topic', 'key', 'min', 'avg', 'max', 'current'])
list1 = [
['aggregator1', 'NA', 628, '14.26 K', '15.39 K', 14014.27],
['aggregator2', 'changelog', 0, '4 k', '8 k', 21.2],
['aggregator3', 'CacheData', 0, '2 k', '3 k', 3432.2],
['aggregator4', 'EntityData', 0, 200, 600, 456.21],
['aggregator5', 'internalData', 482, '10.79 K', '11.65 K', 10646.77],
['aggregator6', 'externalData', 145, '3.47 K', '3.74 K', 3366.96]
]
i = 0
for el in list1:
df.loc[i] = el
i = i + 1
print(df)
print(df.to_html())
print(df.to_markdown())
```
#### Problem description
Sometimes when a dataframe is converted to markdown or HTML with to_markdown() or to_html(), some numeric values automatically get converted to currency, like $40 instead of 40. There is no mention of currency in the code I Used
#### Expected Output
$ sign should be avoided if user doesn't choose it
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-1048-aws
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10
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 : 0.8.7
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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| 35,156
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BUG: Int64 data casted to float64 when using [<column_name>] in a DataFrame
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| 1
| 2020-07-07T07:24:04Z
| 2020-07-07T09:38:44Z
| 2020-07-07T09:38:44Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame([[0, 1.0], [2, 3.0]], columns=['intCol', 'floatCol'])
print(type(df.loc[0, 'intCol']))
print(type(df.loc[0]['intCol']))
```
#### Problem description
I have create a Dataframe with a int and a float column.
When accessing to a value of a row by .loc for the int column I have a numpy.int64.
But when accessing by a column selection I have a numpy.int64.
#### Expected Output
In the code sample the expected output is :
<class 'numpy.int64'>
<class 'numpy.int64'>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.0.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-109-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1.post20200622
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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July 2020 Developer Meeting
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| 2020-07-07T11:16:37Z
| 2020-08-04T16:15:52Z
| 2020-08-04T16:15:52Z
|
CONTRIBUTOR
| null |
The monthly dev meeting is Wednesday July 8th, at 18:00 UTC. Our calendar is at https://pandas.pydata.org/docs/development/meeting.html#calendar to check your local time.
Video Call: https://zoom.us/j/942410248
Minutes: https://docs.google.com/document/u/1/d/1tGbTiYORHiSPgVMXawiweGJlBw5dOkVJLY-licoBmBU/edit?ouid=102771015311436394588&usp=docs_home&ths=true
Please add items you'd like to see discussed to the agenda.
All are welcome to attend.
FYI, I might not be able to attend, or I might just be on the phone. I think the Zoom call is structured to start automatically without me. @jreback and @jorisvandenbossche should also have Zoom accounts through NumFOCUS that can be used.
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BUG: datetime total_seconds inaccurate
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| 2020-07-07T11:19:12Z
| 2020-07-09T13:56:33Z
| 2020-07-09T13:56:18Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({'x':['2020-07-07T10:33:14.971260986',
'2020-07-07T10:17:14.971260986', '2020-07-07T10:11:14.971260986',
'2020-07-07T09:59:13.971260986'],'y':['2020-07-07T10:35:14.971260986',
'2020-07-07T10:24:14.971260986', '2020-07-07T10:15:14.971260986',
'2020-07-07T10:04:14.971260986']})
df.x = pd.to_datetime(df.x)
df.y = pd.to_datetime(df.y)
print((df.y-df.x).dt.total_seconds()[0]) # 120.00000000000001
print((df.y-df.x).apply(lambda x: x.total_seconds())[0]) #120.0
```
#### Problem description
There seems to be a loss of precision when doing date substractions and using total_seconds method.
As a reference, using an apply with the standard library total_seconds yields the correct value.
#### Expected Output
Value should be 120.0
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.0.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.0.5
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.14.0
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 : 1.4.1
sqlalchemy : 1.3.11
tables : None
tabulate : 0.8.6
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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|
BUG: subtract `pd.offsets.BDay()` from `Timestamp` gives different results when same are expected
|
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| 2020-07-07T12:41:17Z
| 2020-08-21T17:05:21Z
| 2020-07-07T12:49:37Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
def close_prices_start_date(date, period):
start_date = date - period * pd.offsets.BDay()
print(
'date.tzinfo =', date.tzinfo,
'date.freqstr =', date.freqstr,
'date.tzname() =', date.tzname(),
'date =', date,
'period =', period,
'result =', start_date)
return start_date
```
#### Problem description
The above function gives different results when called with the same parameters. I can't see any reason for that so my guess is that this behavior is unwanted. The parameter `date` comes from different `DataFrames` and is read with the following function:
```python
def last_date_of_column(df, column_name):
return df[column_name].index[-1]
```
I mention this, because the same dataframes give the same results, so it is reproducible. I won't provide the dataframes here, since any relevant information about the parameters should be in the function's print out.
#### Expected Output
When calling the above code with the same parameters `date` and `period` i expect to get the same result
#### Output of ``pip freeze``
```console
$ pip freeze
numpy==1.19.0
pandas==1.0.5
python-dateutil==2.8.1
pytz==2020.1
quantitative==0.1
schedule==0.6.0
six==1.15.0
```
The print out in the function (result is on the very right and differs for any output for `AAPL US EQUITY`)
```
AAPL US EQUITY
date.tzinfo = None date.freqstr = None date.tzname() = None date = 2020-06-26 00:00:00 period = 20 result = 2020-05-29 00:00:00
9984 JT EQUITY
date.tzinfo = None date.freqstr = None date.tzname() = None date = 2020-06-29 00:00:00 period = 20 result = 2020-06-01 00:00:00
7203 JT EQUITY
date.tzinfo = None date.freqstr = None date.tzname() = None date = 2020-06-29 00:00:00 period = 20 result = 2020-06-01 00:00:00
6963 JP EQUITY
date.tzinfo = None date.freqstr = None date.tzname() = None date = 2020-06-29 00:00:00 period = 20 result = 2020-06-01 00:00:00
6861 JP EQUITY
date.tzinfo = None date.freqstr = None date.tzname() = None date = 2020-06-29 00:00:00 period = 20 result = 2020-06-01 00:00:00
AAPL US EQUITY
date.tzinfo = None date.freqstr = None date.tzname() = None date = 2020-06-26 00:00:00 period = 20 result = 2020-05-29 00:00:00
9984 JT EQUITY
date.tzinfo = None date.freqstr = None date.tzname() = None date = 2020-06-29 00:00:00 period = 20 result = 2020-06-01 00:00:00
7203 JT EQUITY
date.tzinfo = None date.freqstr = None date.tzname() = None date = 2020-06-29 00:00:00 period = 20 result = 2020-06-01 00:00:00
6963 JP EQUITY
date.tzinfo = None date.freqstr = None date.tzname() = None date = 2020-06-29 00:00:00 period = 20 result = 2020-06-01 00:00:00
6861 JP EQUITY
date.tzinfo = None date.freqstr = None date.tzname() = None date = 2020-06-29 00:00:00 period = 20 result = 2020-06-01 00:00:00
AAPL US EQUITY
date.tzinfo = None date.freqstr = None date.tzname() = None date = 2020-06-26 00:00:00 period = 20 result = 2020-05-29 00:00:00
9984 JT EQUITY
date.tzinfo = None date.freqstr = None date.tzname() = None date = 2020-06-29 00:00:00 period = 20 result = 2020-06-01 00:00:00
7203 JT EQUITY
date.tzinfo = None date.freqstr = None date.tzname() = None date = 2020-06-29 00:00:00 period = 20 result = 2020-06-01 00:00:00
6963 JP EQUITY
date.tzinfo = None date.freqstr = None date.tzname() = None date = 2020-06-29 00:00:00 period = 20 result = 2020-06-01 00:00:00
6861 JP EQUITY
date.tzinfo = None date.freqstr = None date.tzname() = None date = 2020-06-29 00:00:00 period = 20 result = 2020-06-01 00:00:00
AAPL US EQUITY
date.tzinfo = None date.freqstr = None date.tzname() = None date = 2020-06-26 00:00:00 period = 20 result = 2020-05-29 00:00:00
9984 JT EQUITY
date.tzinfo = None date.freqstr = None date.tzname() = None date = 2020-06-29 00:00:00 period = 20 result = 2020-06-01 00:00:00
7203 JT EQUITY
date.tzinfo = None date.freqstr = None date.tzname() = None date = 2020-06-29 00:00:00 period = 20 result = 2020-06-01 00:00:00
6963 JP EQUITY
date.tzinfo = None date.freqstr = None date.tzname() = None date = 2020-06-29 00:00:00 period = 20 result = 2020-06-01 00:00:00
6861 JP EQUITY
date.tzinfo = None date.freqstr = None date.tzname() = None date = 2020-06-29 00:00:00 period = 20 result = 2020-06-01 00:00:00
```
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CI: add sql tests back to py36
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| 0
| 2020-07-07T13:09:27Z
| 2020-10-07T02:24:20Z
| 2020-10-07T02:24:20Z
|
CONTRIBUTOR
| null |
xref https://github.com/pandas-dev/pandas/issues/34776
removed in the PR: https://github.com/pandas-dev/pandas/pull/35151, we should add them back.
cc @VirosaLi
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BUG: KeyError on MultiIndex when filtering by only two indexes
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| 2020-07-10T16:48:05Z
| 2020-07-09T18:53:30Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
#### Code
```python
import pandas as pd
df = pd.DataFrame({'col1':[1,2,1,2],'col2':['a','a','b','b'],'col3':['US','US','US','BR'],'values':[1.0,2.0,3.0,4.5]})
df.set_index(['col1','col2','col3'],inplace = True)
df.loc[([1],['a'])]
```
```python-traceback
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/fabio.rangel/projects/audience_topic_viewership/.direnv/python-3.7.7/lib/python3.7/site-packages/pandas/core/indexing.py", line 1762, in __getitem__
return self._getitem_tuple(key)
File "/home/fabio.rangel/projects/audience_topic_viewership/.direnv/python-3.7.7/lib/python3.7/site-packages/pandas/core/indexing.py", line 1272, in _getitem_tuple
return self._getitem_lowerdim(tup)
File "/home/fabio.rangel/projects/audience_topic_viewership/.direnv/python-3.7.7/lib/python3.7/site-packages/pandas/core/indexing.py", line 1373, in _getitem_lowerdim
return self._getitem_nested_tuple(tup)
File "/home/fabio.rangel/projects/audience_topic_viewership/.direnv/python-3.7.7/lib/python3.7/site-packages/pandas/core/indexing.py", line 1453, in _getitem_nested_tuple
obj = getattr(obj, self.name)._getitem_axis(key, axis=axis)
File "/home/fabio.rangel/projects/audience_topic_viewership/.direnv/python-3.7.7/lib/python3.7/site-packages/pandas/core/indexing.py", line 1954, in _getitem_axis
return self._getitem_iterable(key, axis=axis)
File "/home/fabio.rangel/projects/audience_topic_viewership/.direnv/python-3.7.7/lib/python3.7/site-packages/pandas/core/indexing.py", line 1595, in _getitem_iterable
keyarr, indexer = self._get_listlike_indexer(key, axis, raise_missing=False)
File "/home/fabio.rangel/projects/audience_topic_viewership/.direnv/python-3.7.7/lib/python3.7/site-packages/pandas/core/indexing.py", line 1553, in _get_listlike_indexer
keyarr, indexer, o._get_axis_number(axis), raise_missing=raise_missing
File "/home/fabio.rangel/projects/audience_topic_viewership/.direnv/python-3.7.7/lib/python3.7/site-packages/pandas/core/indexing.py", line 1640, in _validate_read_indexer
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['a'], dtype='object')] are in the [columns]"
```
#### Problem description
The problem occurs only when filtering using MultiIndex with a two-sized tuple. When using one, three or more it does not occurs. I tested using up to four indexes.
#### Expected Output
```sh
values
col1 col2 col3
1 a US 1.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-174-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : pt_BR.UTF-8
LOCALE : pt_BR.UTF-8
pandas : 1.0.5
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1
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 : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
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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ENH: should pandas.core.arrays.Categorical have a dropna=False option?
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| 6
| 2020-07-07T17:30:41Z
| 2020-09-05T10:50:58Z
| 2020-09-05T10:50:58Z
|
MEMBER
| null |
I ran into this while working on #35078. Here's a simple reproducer:
```
In [1]: from pandas.core.arrays import Categorical
In [2]: Categorical([1, 2, 3,4,4,4, None, None])
Out[2]:
[1, 2, 3, 4, 4, 4, NaN, NaN]
Categories (4, int64): [1, 2, 3, 4]
```
Do people think it's worth having a `dropna` argument for Categorical, so that one could do:
```
In [2]: Categorical([1, 2, 3,4,4,4, None, None], dropna=True)
Out[2]:
[1, 2, 3, 4, 4, 4, NaN, NaN]
Categories (4, int64): [1, 2, 3, 4, NaN]
```
If we set `dropna=False` by default presumably there shouldn't be backward compatibility issues.
I can see arguments either way! In case we do want to add this I'm happy work on it (and the solution in #35078 will be a lot cleaner)
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CI: update to isort 5 (#35134)
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|
MEMBER
| null |
- [x] closes #35134
- [x] 0 tests added / 0 passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NDQ1NTY5OTY0
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|
Infer compression if file extension is uppercase
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| 2020-07-07T18:06:03Z
| 2020-07-17T10:58:39Z
| 2020-07-17T10:58:30Z
|
CONTRIBUTOR
| null |
Inferring compression fails for files with uppercase extensions (e.g. `x.zip` works but `y.ZIP` does not)
- [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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MDExOlB1bGxSZXF1ZXN0NDQ1NTk1MzIy
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Fixed apply_index
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CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/34580
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|
Fixed Series.apply performance regression
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| 2020-07-07T19:38:58Z
| 2020-07-08T12:40:56Z
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|
CONTRIBUTOR
| null |
Set the option once, rather than in the loop.
Closes https://github.com/pandas-dev/pandas/issues/35047
```python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(1000, 3), columns=list("ABC"))
%timeit df.apply(lambda x: x["A"] + x["B"], axis=1)
```
```
# 1.0.4
22.5 ms ± 4.73 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
# PR
10.4 ms ± 714 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```
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CLN: remove unused freq kwarg in libparsing
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MDExOlB1bGxSZXF1ZXN0NDQ1NjUxODcy
| 35,168
|
REF: collect get_dst_info-using functions in tslibs.vectorized
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| 0
| 2020-07-07T20:22:31Z
| 2020-07-08T22:06:58Z
| 2020-07-08T21:50:17Z
|
MEMBER
| null |
I'm open to better names for the new module.
With these all in the same place, we can separate out helper functions and de-duplicate a whole bunch of verbose code.
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|
Plan for a native string dtype
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[] | null | 27
| 2020-07-07T21:40:35Z
| 2021-07-22T14:51:35Z
| 2021-07-22T14:51:35Z
|
CONTRIBUTOR
| null |
Apache Arrow has support for natively storing UTF-8 data. And [work is ongoing](https://github.com/apache/arrow/pull/7449)
adding kernels (e.g. `str.isupper()`) to operate on that data. This issue
is to discuss how we can expose the native string dtype to pandas' users.
There are several things to discuss:
1. How do users opt into this behavior?
2. A fallback mode for not implemented kernels.
### How do users opt into Arrow-backed StringArray?
The primary difficulty is the additional Arrow dependency. I'm assuming that we
are not ready to adopt it as a required dependency, so all of this will be
opt-in for now (though this point is open for discussion).
StringArray is marked as experimental, so our usual API-breaking restrictions
rules don't apply. But we want to do this in a way that's not too disruptive.
There are three was to get a `StringDtype`-dtype array today:
1. Infer: `pd.array(['a', 'b', None])`
2. Explicit `dtype=pd.StringDtype()`
3. String alias `dtype="string"`
My preference is for all of these to stay consistent. They all either give a
StringArray backed by an object-dtype ndarray or a StringArray backed by Arrow
memory.
I also have a preference for not keeping around our old implementation for too
long. So I don't think we want something like `pd.PythonStringDtype()` as a
way to get the StringArray backed by an object-dtype ndarray.
The easiest way to support this is, I think, an option.
```python
>>> pd.options.mode.use_arrow_string_dtype = True
```
Then all of those would create an Arrow-backed StringArray.
### Fallback Mode
It's likely that Arrow 1.0 will not implement all the string kernels we need.
So when someone does
```python
>>> Series(['a', 'b'], dtype="string").str.normalize() # no arrow kernel
```
we have a few options:
1. Raise, stating that there's no kernel for normalize.
2. PerformanceWarning, astype to object, do the operation, and convert back
I'm not sure which is best. My preference for now is probably to raise, but I could see doing either.
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MDExOlB1bGxSZXF1ZXN0NDQ1NzM1NTYw
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|
DOC: Add pint pandas ecosystem docs
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| 4
| 2020-07-07T22:41:49Z
| 2020-07-10T20:33:25Z
| 2020-07-10T20:33:18Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx (N/A)
- [ ] tests added / passed (N/A)
- [ ] passes `black pandas` (N/A)
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` (N/A)
- [ ] whatsnew entry
|
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PERF: periodarr_to_dt64arr
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MEMBER
| null |
- [x] closes #33919
- [ ] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Re-use `ensure_datetime64ns` for the subset of cases where that is applicable. I'm at a loss for why we get such a slowdown for the other cases.
```
before after ratio
[42a6d445] [a0ece778]
<master> <perf-periodarr_to_dt64arr>
+ 48.7±0.8ms 133±6ms 2.72 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1000000, 4000)
+ 48.0±1ms 131±0.8ms 2.72 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1000000, 5000)
+ 493±10μs 1.33±0.05ms 2.70 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(10000, 4000)
+ 47.8±0.4ms 125±3ms 2.62 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1000000, 4006)
+ 599±20μs 1.47±0.07ms 2.46 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(10000, 2000)
+ 60.6±0.9ms 149±7ms 2.45 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1000000, 3000)
+ 524±40μs 1.27±0.04ms 2.42 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(10000, 4006)
+ 587±10μs 1.42±0.04ms 2.41 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(10000, 1000)
+ 59.9±0.8ms 144±0.9ms 2.40 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1000000, 1000)
+ 526±40μs 1.26±0.05ms 2.39 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(10000, 5000)
+ 61.0±1ms 144±2ms 2.35 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1000000, 2011)
+ 60.8±0.5ms 143±0.9ms 2.35 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1000000, 2000)
+ 7.72±0.3μs 17.7±3μs 2.30 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(100, 4000)
+ 63.7±0.6ms 145±3ms 2.28 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1000000, 1011)
+ 3.04±0.06μs 6.94±1μs 2.28 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1, 6000)
+ 611±10μs 1.38±0.06ms 2.27 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(10000, 2011)
+ 653±20μs 1.44±0.03ms 2.20 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(10000, 1011)
+ 2.82±0.07μs 6.12±0.2μs 2.17 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(0, 6000)
+ 2.81±0.06μs 6.06±0.2μs 2.16 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1, 9000)
+ 2.99±0.06μs 6.16±0.2μs 2.06 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(0, 8000)
+ 2.80±0.06μs 5.75±0.2μs 2.05 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(0, 11000)
+ 2.95±0.09μs 6.02±0.4μs 2.04 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(0, 9000)
+ 2.94±0.06μs 5.97±0.2μs 2.03 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(0, 7000)
+ 701±50μs 1.41±0.01ms 2.01 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(10000, 3000)
+ 3.09±0.1μs 6.18±0.1μs 2.00 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1, 7000)
+ 3.02±0.2μs 6.02±0.3μs 1.99 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1, 8000)
+ 3.05±0.1μs 5.95±0.2μs 1.95 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1, 10000)
+ 7.98±0.3μs 15.4±0.3μs 1.93 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(100, 4006)
+ 9.01±0.4μs 17.3±0.8μs 1.92 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(100, 2011)
+ 2.99±0.1μs 5.66±0.2μs 1.89 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1, 11000)
+ 3.09±0.09μs 5.82±0.1μs 1.88 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(0, 10000)
+ 8.20±0.4μs 15.3±0.5μs 1.87 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(100, 5000)
+ 9.37±0.6μs 17.4±0.5μs 1.86 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(100, 2000)
+ 9.28±0.6μs 17.2±0.7μs 1.85 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(100, 3000)
+ 9.61±0.3μs 17.6±0.6μs 1.83 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(100, 1011)
+ 10.1±0.7μs 17.5±0.7μs 1.73 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(100, 1000)
- 2.93±0.1μs 2.48±0.1μs 0.85 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1, 4000)
- 2.90±0.04μs 2.44±0.08μs 0.84 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1, 2011)
- 2.92±0.06μs 2.44±0.1μs 0.84 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1, 1011)
- 3.08±0.1μs 2.56±0.1μs 0.83 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(0, 2011)
- 3.03±0.2μs 2.52±0.09μs 0.83 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1, 4006)
- 9.41±0.4μs 7.79±0.2μs 0.83 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(100, 8000)
- 2.97±0.1μs 2.42±0.1μs 0.81 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1, 2000)
- 2.99±0.1μs 2.40±0.07μs 0.80 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(0, 4006)
- 3.18±0.1μs 2.55±0.1μs 0.80 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(0, 1000)
- 3.21±0.2μs 2.48±0.03μs 0.77 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1, 5000)
- 3.07±0.2μs 2.34±0.05μs 0.76 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1, 3000)
- 3.08±0.1μs 2.34±0.04μs 0.76 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(0, 5000)
- 3.23±0.1μs 2.43±0.1μs 0.75 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1, 1000)
- 3.16±0.1μs 2.37±0.05μs 0.75 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(0, 2000)
- 3.28±0.3μs 2.33±0.08μs 0.71 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(0, 4000)
- 3.44±0.3μs 2.36±0.1μs 0.69 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(0, 1011)
- 398±20μs 242±7μs 0.61 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(10000, 6000)
- 39.0±1ms 23.7±0.4ms 0.61 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1000000, 6000)
- 62.4±0.5ms 28.9±0.5ms 0.46 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1000000, 11000)
- 64.4±0.2ms 28.6±0.4ms 0.44 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1000000, 10000)
- 657±20μs 290±10μs 0.44 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(10000, 11000)
- 64.7±0.4ms 27.9±0.4ms 0.43 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1000000, 9000)
- 659±20μs 279±8μs 0.42 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(10000, 7000)
- 64.1±0.6ms 27.1±0.4ms 0.42 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1000000, 8000)
- 64.6±0.9ms 26.7±0.3ms 0.41 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1000000, 7000)
- 661±20μs 272±8μs 0.41 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(10000, 8000)
- 661±10μs 269±10μs 0.41 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(10000, 9000)
- 698±50μs 282±7μs 0.40 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(10000, 10000)
- 3.02±0.1μs 344±10ns 0.11 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(0, 12000)
- 3.21±0.2μs 342±10ns 0.11 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1, 12000)
- 10.6±0.5μs 339±10ns 0.03 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(100, 12000)
- 661±20μs 346±5ns 0.00 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(10000, 12000)
- 62.3±0.6ms 368±10ns 0.00 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1000000, 12000)
```
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| 652,874,268
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MDU6SXNzdWU2NTI4NzQyNjg=
| 35,172
|
QST: I get an error when use to_sql func to save a dataframe to mysql for BLOB column.how to fix it ?
|
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closed
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| null |
[] | null | 1
| 2020-07-08T03:04:53Z
| 2020-08-21T17:05:21Z
| 2020-07-08T03:40:58Z
|
NONE
| null |
my package list is:
Package Version Note: you may need to restart the kernel to use updated packages.
--------------------------------- -------------------
-cikit-learn 0.22.2.post1
asn1crypto 0.24.0
attrs 19.3.0
automan 1.0.0
autopep8 1.5.2
backcall 0.1.0
bcrypt 3.1.4
bitarray 1.2.1
bleach 3.1.4
cacheout 0.11.1
certifi 2018.11.29
cffi 1.12.2
chardet 3.0.4
Click 7.0
cloudpickle 1.4.1
colorama 0.4.3
configobj 5.0.6
cryptography 2.3.1
cycler 0.10.0
Cython 0.29.19
DBUtils 1.3
decorator 4.4.2
defusedxml 0.6.0
entrypoints 0.3
et-xmlfile 1.0.1
Flask 1.0.2
future 0.18.2
hyperopt 0.2.3
idna 2.6
imbalanced-learn 0.6.2
imblearn 0.0
importlib-metadata 1.5.0
impyla 0.14.0
ipykernel 5.1.4
ipython 7.13.0
ipython-genutils 0.2.0
itsdangerous 1.1.0
jdcal 1.4.1
jedi 0.16.0
Jinja2 2.10
joblib 0.14.1
jsonschema 3.2.0
jupyter-client 6.1.2
jupyter-contrib-core 0.3.3
jupyter-contrib-nbextensions 0.5.1
jupyter-core 4.6.3
jupyter-highlight-selected-word 0.2.0
jupyter-latex-envs 1.4.6
jupyter-nbextensions-configurator 0.4.1
kafka-python 1.3.5
kiwisolver 1.2.0
lxml 4.5.0
MarkupSafe 1.1.1
matplotlib 3.2.1
mistune 0.8.4
mysqlclient 1.4.6
nbconvert 5.6.1
nbformat 5.0.4
networkx 2.2
notebook 6.0.3
numpy 1.18.2
opencv-python 4.2.0.34
openpyxl 3.0.3
pandas 0.25.3
pandocfilters 1.4.2
paramiko 2.4.2
parso 0.6.2
patsy 0.5.1
pickleshare 0.7.5
Pillow 7.1.2
pip 20.0.2
ply 3.11
prometheus-client 0.7.1
prompt-toolkit 3.0.4
pyasn1 0.4.5
pycodestyle 2.5.0
pycparser 2.19
pydotplus 2.0.2
Pygments 2.6.1
PyHDFS 0.2.1
PyMySQL 0.9.2
PyNaCl 1.3.0
pyparsing 2.4.7
pyrsistent 0.16.0
python-dateutil 2.8.1
pytz 2018.9
pywin32 227
pywinpty 0.5.7
PyYAML 5.3.1
pyzmq 18.1.1
redis 3.2.1
requests 2.18.4
sasl 0.2.1
scikit-learn 0.19.1
scipy 1.4.1
seaborn 0.10.0
Send2Trash 1.5.0
setuptools 46.1.3.post20200330
simplejson 3.16.0
six 1.12.0
sklearn-pandas 1.8.0
sklearn2pmml 0.49.0
SQLAlchemy 1.3.15
statsmodels 0.11.1
terminado 0.8.3
testpath 0.4.4
thrift 0.10.0
thrift-sasl 0.2.1
thriftpy 0.3.9
tornado 6.0.4
tqdm 4.46.1
traitlets 4.3.3
urllib3 1.22
wcwidth 0.1.9
webencodings 0.5.1
Werkzeug 0.14.1
wheel 0.34.2
wincertstore 0.2
xgboost 0.6
xlrd 1.2.0
XlsxWriter 1.2.9
zipp 2.2.0
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
from sqlalchemy.types import VARCHAR, Float, Integer, Date, Numeric,BLOB,CLOB
from sqlalchemy import create_engine
# db_conn_str = 'mysql+pymysql://user:pswd@localhost:3307/xxx?charset=utf8'
con=create_engine(db_conn_str)
# print(db_conn_str)
a = pd.DataFrame({'a':[1,2,3,],'b':['dddddd','3','5']})
a.to_sql(name='test_blob', con=con, if_exists='append', dtype={'b':BLOB},index=False)
```
#### Problem description
<details>
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
D:\Anaconda3\envs\py3-back\lib\site-packages\sqlalchemy\engine\base.py in _execute_context(self, dialect, constructor, statement, parameters, *args)
1174
-> 1175 context = constructor(dialect, self, conn, *args)
1176 except BaseException as e:
D:\Anaconda3\envs\py3-back\lib\site-packages\sqlalchemy\engine\default.py in _init_compiled(cls, dialect, connection, dbapi_connection, compiled, parameters)
833 )
--> 834 for key in compiled_params
835 )
D:\Anaconda3\envs\py3-back\lib\site-packages\sqlalchemy\engine\default.py in <genexpr>(.0)
833 )
--> 834 for key in compiled_params
835 )
D:\Anaconda3\envs\py3-back\lib\site-packages\sqlalchemy\sql\sqltypes.py in process(value)
912 if value is not None:
--> 913 return DBAPIBinary(value)
914 else:
D:\Anaconda3\envs\py3-back\lib\site-packages\pymysql\__init__.py in Binary(x)
84 else:
---> 85 return bytes(x)
86
TypeError: string argument without an encoding
The above exception was the direct cause of the following exception:
StatementError Traceback (most recent call last)
<ipython-input-66-e1bff449d290> in <module>
6 a = pd.DataFrame({'a':[1,2,3,],'b':['dddddd','3','5']})
7
----> 8 a.to_sql(name='test_blob', con=con, if_exists='append', dtype={'b':BLOB},index=False)
~\AppData\Roaming\Python\Python36\site-packages\pandas\core\generic.py in to_sql(self, name, con, schema, if_exists, index, index_label, chunksize, dtype, method)
2710 chunksize=chunksize,
2711 dtype=dtype,
-> 2712 method=method,
2713 )
2714
~\AppData\Roaming\Python\Python36\site-packages\pandas\io\sql.py in to_sql(frame, name, con, schema, if_exists, index, index_label, chunksize, dtype, method)
516 chunksize=chunksize,
517 dtype=dtype,
--> 518 method=method,
519 )
520
~\AppData\Roaming\Python\Python36\site-packages\pandas\io\sql.py in to_sql(self, frame, name, if_exists, index, index_label, schema, chunksize, dtype, method)
1318 )
1319 table.create()
-> 1320 table.insert(chunksize, method=method)
1321 if not name.isdigit() and not name.islower():
1322 # check for potentially case sensitivity issues (GH7815)
~\AppData\Roaming\Python\Python36\site-packages\pandas\io\sql.py in insert(self, chunksize, method)
754
755 chunk_iter = zip(*[arr[start_i:end_i] for arr in data_list])
--> 756 exec_insert(conn, keys, chunk_iter)
757
758 def _query_iterator(
~\AppData\Roaming\Python\Python36\site-packages\pandas\io\sql.py in _execute_insert(self, conn, keys, data_iter)
668 """
669 data = [dict(zip(keys, row)) for row in data_iter]
--> 670 conn.execute(self.table.insert(), data)
671
672 def _execute_insert_multi(self, conn, keys, data_iter):
D:\Anaconda3\envs\py3-back\lib\site-packages\sqlalchemy\engine\base.py in execute(self, object_, *multiparams, **params)
982 )
983 else:
--> 984 return meth(self, multiparams, params)
985
986 def _execute_function(self, func, multiparams, params):
D:\Anaconda3\envs\py3-back\lib\site-packages\sqlalchemy\sql\elements.py in _execute_on_connection(self, connection, multiparams, params)
291 def _execute_on_connection(self, connection, multiparams, params):
292 if self.supports_execution:
--> 293 return connection._execute_clauseelement(self, multiparams, params)
294 else:
295 raise exc.ObjectNotExecutableError(self)
D:\Anaconda3\envs\py3-back\lib\site-packages\sqlalchemy\engine\base.py in _execute_clauseelement(self, elem, multiparams, params)
1101 distilled_params,
1102 compiled_sql,
-> 1103 distilled_params,
1104 )
1105 if self._has_events or self.engine._has_events:
D:\Anaconda3\envs\py3-back\lib\site-packages\sqlalchemy\engine\base.py in _execute_context(self, dialect, constructor, statement, parameters, *args)
1176 except BaseException as e:
1177 self._handle_dbapi_exception(
-> 1178 e, util.text_type(statement), parameters, None, None
1179 )
1180
D:\Anaconda3\envs\py3-back\lib\site-packages\sqlalchemy\engine\base.py in _handle_dbapi_exception(self, e, statement, parameters, cursor, context)
1480 elif should_wrap:
1481 util.raise_(
-> 1482 sqlalchemy_exception, with_traceback=exc_info[2], from_=e
1483 )
1484 else:
D:\Anaconda3\envs\py3-back\lib\site-packages\sqlalchemy\util\compat.py in raise_(***failed resolving arguments***)
176
177 try:
--> 178 raise exception
179 finally:
180 # credit to
D:\Anaconda3\envs\py3-back\lib\site-packages\sqlalchemy\engine\base.py in _execute_context(self, dialect, constructor, statement, parameters, *args)
1173 conn = self._revalidate_connection()
1174
-> 1175 context = constructor(dialect, self, conn, *args)
1176 except BaseException as e:
1177 self._handle_dbapi_exception(
D:\Anaconda3\envs\py3-back\lib\site-packages\sqlalchemy\engine\default.py in _init_compiled(cls, dialect, connection, dbapi_connection, compiled, parameters)
832 else compiled_params[key],
833 )
--> 834 for key in compiled_params
835 )
836
D:\Anaconda3\envs\py3-back\lib\site-packages\sqlalchemy\engine\default.py in <genexpr>(.0)
832 else compiled_params[key],
833 )
--> 834 for key in compiled_params
835 )
836
D:\Anaconda3\envs\py3-back\lib\site-packages\sqlalchemy\sql\sqltypes.py in process(value)
911 def process(value):
912 if value is not None:
--> 913 return DBAPIBinary(value)
914 else:
915 return None
D:\Anaconda3\envs\py3-back\lib\site-packages\pymysql\__init__.py in Binary(x)
83 return bytearray(x)
84 else:
---> 85 return bytes(x)
86
87
StatementError: (builtins.TypeError) string argument without an encoding
[SQL: INSERT INTO test_blob (a, b) VALUES (%(a)s, %(b)s)]
[parameters: [{'a': 1, 'b': 'dddddd'}, {'a': 2, 'b': '3'}, {'a': 3, 'b': '5'}]]
</details>
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MDU6SXNzdWU2NTI5NzY3OTQ=
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|
BUG: groupby / resample / aggregate produces wrong results
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[] | null | 2
| 2020-07-08T05:25:04Z
| 2020-07-09T11:06:29Z
| 2020-07-09T11:06:15Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
data = """ date revenue name
0 2020-02-24 9 n_1
1 2020-05-12 8 n_2
2 2020-03-28 9 n_2
3 2020-01-14 2 n_0"""
df = pd.read_csv(StringIO(data), sep = "\s+", engine = "python")
df.date = pd.to_datetime(df.date)
gb = df.groupby("name").resample("M", on="date")
res1 = gb.sum()[['revenue']]
# ==>
# revenue
# name date
# n_0 2020-01-31 2
# n_1 2020-02-29 9
# n_2 2020-03-31 9
# 2020-04-30 0
# 2020-05-31 8
res2 = gb.aggregate({'revenue':'sum'})
# ==>
# revenue
# name date
# n_0 2020-05-31 2
# n_1 2020-01-31 9
# n_2 2020-02-29 8
# 2020-03-31 9
```
#### Problem description
As the code sample above shows, there are two issues:
* `agg(sum)` produces different results from just `sum`.
* The results of `agg(sum)` are plain wrong. The name "n_2" has no data in Feb, and yet - the aggregation shows that it does.
#### Expected Output
The correct output is the one produced by `sum`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.6.0
Cython : None
pytest : 5.4.3
hypothesis : 5.18.0
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.10.1
pandas_datareader: 0.8.1
bs4 : 4.9.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.3
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : None
tabulate : None
xarray : 0.15.1
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.9
numba : None
</details>
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MDU6SXNzdWU2NTI5ODc0ODY=
| 35,174
|
ENH: Inconsistency: .astype("string") should convert int-Series to string
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| 2020-07-08T05:49:26Z
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|
NONE
| null |
```python
pd.Series(['1', '2', '3'], dtype="string").astype("int")
```
Turns the String Series into an Integer Series and converts the values.
However, the inverse operation raises an Error:
```python
pd.Series([1, 2, 3], dtype="int").astype("string")
```
In order to be consistent, the inverse operation should also work.
Currently, I think that the best alternative solutions are something like this:
```python
pd.Series(pd.Series([1, 2, 3], dtype="int").apply(str), dtype="string")
```
or
```python
pd.Series([1, 2, 3], dtype="int").apply(str).convert_dtypes()
```
But those solutions are too convoluted and not consistent with the above API.
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DOC: fix code snippets for generic indexing
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CONTRIBUTOR
| null |
The code example for generic indexing is the same as positional/label indexing above.
I think generic indexing here intended to be referred to something like `df[0:3]` and `df['bar':'kar']`
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MDU6SXNzdWU2NTMyMDM0MjE=
| 35,176
|
ENH: ListDtype / ListArray
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| null |
CONTRIBUTOR
| null |
This issue is for adding a ListDtype. This might be useful on it's own, and will be useful for https://github.com/pandas-dev/pandas/issues/35169 when we have string operations that return a List of values per scalar element.
I think the primary points to discuss are around
1. How the `value_type` of the List, the `T` in `List[T]`, should be specified by the user
2. How, if at all, to switch between the `list_` and `large_list` types.
xref https://github.com/rapidsai/cudf/issues/5610, where cudf is implementing a ListDtype. Let's chime in over there if we have any thoughts.
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BUG: ImportError: cannot import name 'Collection' from 'typing'
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| 2020-08-19T12:21:16Z
|
NONE
| null |
- [ ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
data = pd.read_csv('Simulations Ãœbersicht_Labled_intrusion.csv')
print(data.head(5))
```
#### Problem description
base) ballal@i6.emi.fhg.de:/home-24/ballal/Skripte/Rule Extraction
$ python3 decisiontree.py
Traceback (most recent call last):
File "decisiontree.py", line 1, in <module>
import pandas as pd
File "/home/ballal/anaconda3/lib/python3.7/site-packages/pandas/__init__.py", line 54, in <module>
from pandas.core.api import (
File "/home/ballal/anaconda3/lib/python3.7/site-packages/pandas/core/api.py", line 6, in <module>
from pandas.core.dtypes.dtypes import (
File "/home/ballal/anaconda3/lib/python3.7/site-packages/pandas/core/dtypes/dtypes.py", line 10, in <module>
from pandas._typing import Ordered
File "/home/ballal/anaconda3/lib/python3.7/site-packages/pandas/_typing.py", line 2, in <module>
from typing import (
ImportError: cannot import name 'Collection' from 'typing' (/usr/lib/python3.4/site-packages/typing.py)
I cannot import pandas. I installed the latest version of pandas too.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
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MDU6SXNzdWU2NTMzNTg0OTk=
| 35,178
|
ENH: use EA.argmin/argmax in EA._reduce / Series.argmin/argmax
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| 1
| 2020-07-08T14:46:26Z
| 2020-07-08T15:10:00Z
| null |
MEMBER
| null |
Follow-up on https://github.com/pandas-dev/pandas/pull/27801: that PR added `ExtensionArray.argmin()/argmax()` methods, but this is not yet used in the equivalent Series methods (through `_reduce`).
When adding this, we will need to decide on the following issues as well:
* API/BUG: Series.argmin/max with all-NaN data returns -1 ? #33941
* API: argmin/argmax behaviour for nullable dtypes with skipna=False #33942
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MDU6SXNzdWU2NTMzNjcwMjM=
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|
TST verify inplace ops return None
|
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| 2
| 2020-07-08T14:56:54Z
| 2020-07-09T22:05:34Z
| 2020-07-09T22:05:34Z
|
CONTRIBUTOR
| null |
test if pandas methods, when called inplace, return None
drop_duplicates https://github.com/pandas-dev/pandas/pull/35121. **merged**
dropna https://github.com/pandas-dev/pandas/pull/35180
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MDExOlB1bGxSZXF1ZXN0NDQ2MzI0NzA2
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|
Tst verify dropna returns None inplace (#35179)
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| 2020-07-08T15:37:01Z
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|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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TST Verifiy that dropna returns none when called inplace (#35179)
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| 2
| 2020-07-08T17:09:01Z
| 2020-07-10T10:20:35Z
| 2020-07-09T22:05:35Z
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CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/35179
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ENH: Add compute.use_numba configuration for automatically using numba
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- [x] closes #33966
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Json Visual Clutter cleanup
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MEMBER
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Removed PRINTMARK symbols and ran `clang-format -sort-includes=0 -i -style="{IndentWidth: 4}" pandas/_libs/src/ujson/python/objToJSON.c`
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MEMBER
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- [x] closes #35147
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| 1
| 2020-07-08T18:49:58Z
| 2020-12-14T01:41:44Z
| 2020-12-14T01:41:44Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas
query = ...
connection = ...
parse_dates_dict = {'column_name': {'errors': 'coerce'}}
pandas.read_sql(query, parse_dates=parse_dates_dict)
```
#### Problem description
According to the api for [read_sql](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_sql.html), the `parse_date` argument should be able to contain a dict of dicts with kwargs per column to be passed along to `pandas.to_datetime()`, as shown in the example above.
However, this will result in the error:
`TypeError: to_datetime() got multiple values for keyword argument 'errors'`
This is because of [this line of code:](https://github.com/pandas-dev/pandas/blob/v1.0.5/pandas/io/sql.py#L81)
`return to_datetime(col, errors="ignore", **format)` - where `format` is our kwargs from that specific column.
Specifically, the hardcoded `errors` argument collides with the passed kwarg.
It seems like the `errors` argument cannot be overridden by kwargs in this implementation.
#### Expected Output
Expect `errors` arg in to be overridden by the kwargs argument, or that the documentation explicitly says that it cannot be overridden for some reason.
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MDExOlB1bGxSZXF1ZXN0NDQ2NDMzOTc4
| 35,186
|
Performance regression in stat_ops.FrameMultiIndexOps.time_op
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| 3
| 2020-07-08T19:15:05Z
| 2020-07-25T15:02:04Z
| 2020-07-09T09:26:01Z
|
MEMBER
| null |
- [ ] closes #35050
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
```
import pandas as pd
import numpy as np
levels = [np.arange(10), np.arange(100), np.arange(100)]
codes = [
np.arange(10).repeat(10000),
np.tile(np.arange(100).repeat(100), 10),
np.tile(np.tile(np.arange(100), 100), 10),
]
index = pd.MultiIndex(levels=levels, codes=codes)
df = pd.DataFrame(np.random.randn(len(index), 4), index=index)
%timeit df.std(level=1)
# 9.09 ms ± 59.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) -> master
# 7.39 ms ± 71.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) -> 0.25.3
# 7.21 ms ± 113 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) -> PR
```
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MDExOlB1bGxSZXF1ZXN0NDQ2NTc4MjI0
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Add date overflow message to tz_localize (#32967)
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| 2
| 2020-07-09T02:25:32Z
| 2020-07-16T01:51:29Z
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|
CONTRIBUTOR
| null |
- [x] closes #32967
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Added an error message to tz_localize when the timestamp overflows. I got a little confused by the history in #32979 as some changes were lost on the force pushes, but I tried to handle all review comments.
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|
Possible performance regression in Series.xs() and DataFrame.xs() with MultiIndex.
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[] | null | 1
| 2020-07-09T07:47:58Z
| 2021-06-29T01:31:34Z
| null |
NONE
| null |
Performance of `Series.xs()` when getting values at specified index and the **first level** of a large dataset seems to have regressed significantly, after pandas was upgraded to v1.0.5 from v0.25.3. So is `DataFrame.xs()`.
code:
```python
import numpy as np
import pandas as pd
n=1e4
data = pd.Series(np.arange(n**2), index=pd.MultiIndex.from_product([np.arange(n), -np.arange(n)]))
%timeit data.xs(100, level=0) #first level
%timeit data.xs(-100, level=1) #second level
```
outcome
```python
## 0.25.3
%timeit data.xs(100, level=0)
571 µs ± 1.79 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%timeit data.xs(-100, level=1)
193 ms ± 947 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
## 1.0.5
%timeit data.xs(100, level=0)
683 ms ± 3.35 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
%timeit data.xs(-100, level=1)
197 ms ± 2.42 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
```
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|
BUG: sorting a MultiIndex with pd.Period does not sort correctly when assigning new values inplace
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| 2020-07-09T07:52:26Z
| 2020-08-21T17:05:22Z
| 2020-07-09T09:20:30Z
|
NONE
| null |
This is a problem on pandas 1.0.5:
```python
import pandas as pd
s = pd.Series(
{
("A", pd.Period("2021")): 20,
("A", pd.Period("2020")): 10,
}
)
s.sort_index(level=1, sort_remaining=False)
# gives:
# A 2020 10
# 2021 20
# dtype: int64
# --> works!
s[("B", pd.Period("2019"))] = 15
s.sort_index(level=1, sort_remaining=False)
# gives:
# A 2020 10
# 2021 20
# B 2019 15
# dtype: int64
# --> wrong?
s.sort_index(level=1, sort_remaining=False, ascending=False)
# gives:
# B 2019 15
# A 2021 20
# 2020 10
# dtype: int64
# --> wrong?
```
#### Problem description
The problem seems to be that adding new periods is giving a wrong ordering of the periods in MultiIndex levels, so that sorting takes the wrong order to sort them.
If I do `s.index.levels[-1]` I can see the levels are ordered wrong - and when I do `s.index.levels.sort_values()` they are ordered correctly. So, it seems like internally the wrong ordering is somehow used?
Is this indeed a bug - are there ways I can help to fix it? If not, is there a work around for me to achieve what I need to achieve?
Thanks a lot,
Ron
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MDExOlB1bGxSZXF1ZXN0NDQ2NzU3MDUy
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Be specific about more indexes or values
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| 2020-07-14T17:21:47Z
| 2020-07-14T17:05:59Z
|
CONTRIBUTOR
| null |
The generic "are not equal" really frustrated me just now when someone was conveying their issue.
This specific two errors lets me know which way around, although I believe having more indexes than data shows a place in the api for a default value (possibly per-index) which would allow this to later be reduced to the case I feel I fully understand of more data than indexes, which I think probably is unsolvable generally.
I can see that both have problems, but I still think it's more important to be specific about which side is mismatched.
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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ENH: Print and manipulate rows
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| 2020-08-21T17:05:23Z
| 2020-07-10T05:24:20Z
|
NONE
| null |
#### Is your feature request related to a problem?
[this should provide a description of what the problem is, e.g. "I wish I could use pandas to do [...]"]
#### Describe the solution you'd like
[this should provide a description of the feature request, e.g. "`DataFrame.foo` should get a new parameter `bar` that [...]", try to write a docstring for the desired feature]
#### API breaking implications
[this should provide a description of how this feature will affect the API]
#### Describe alternatives you've considered
[this should provide a description of any alternative solutions or features you've considered]
#### Additional context
[add any other context, code examples, or references to existing implementations about the feature request here]
```python
# Your code here, if applicable
```
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Create sayan2
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|
NONE
| null |
code to extract row from database and replace values within row
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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Python3.6 FreeBSD pip3 install pandas fails with error 1
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|
NONE
| null |
- [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, please read [this guide](
https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your question.
```python
# Your code here, if applicable
```
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BUG: Adding two DataFrames together with duplicate column names in one results in infinite loop
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| 4
| 2020-07-09T19:18:44Z
| 2020-07-16T22:49:13Z
| 2020-07-16T22:49:13Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example
```python
df1 = pd.DataFrame(data=[[0]], columns=['second'])
df2 = pd.DataFrame(data=[[0, 0, 0]], columns=['first', 'second', 'second'])
```
<img width="191" alt="Screen Shot 2020-07-09 at 3 15 10 PM" src="https://user-images.githubusercontent.com/7226751/87081253-01d46100-c1f7-11ea-9767-5265010c62f7.png">
Adding them together never completes (infinite loop?)
```python
df1 + df2
```
#### Problem description
When adding two DataFrame where one has duplicate column names and the other has one (or more) of the duplicated columns, the operation never completes
#### Expected Output
Expected to follow rules of automatic alignment of the index.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.1.post20200529
Cython : None
pytest : 5.4.2
hypothesis : None
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.1
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.2
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDQ3MDY5Mjc1
| 35,195
|
PERF: MonthOffset.apply_index
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| 1
| 2020-07-09T20:10:39Z
| 2020-07-10T14:12:55Z
| 2020-07-10T12:13:21Z
|
MEMBER
| null |
- [x] closes #35048
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
```
import pandas as pd
import numpy as np
N = 10000
rng = pd.date_range(start="1/1/2000", periods=N, freq="T")
offset = pd.offsets.MonthBegin()
In [6]: %timeit offset + rng
463 µs ± 19 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- PR
508 µs ± 18.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- 1.0.4
736 µs ± 15 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- master
```
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PERF: lookups on Timestamp attributes
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| 2020-07-09T20:23:12Z
| 2020-07-09T21:37:32Z
| 2020-07-09T21:35:29Z
|
MEMBER
| null |
This is pretty ugly, but it avoids python-space attribute lookups e.g. `__Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_microsecond)`
cc @scoder is there a way to make this unnecessary? I'm imagining a cdef property that aliased these attributes appropriately.
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|
API: make DTA/DTI == string behavior match DTI.get_loc partial-string-matching?
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[] | null | 1
| 2020-07-09T21:16:13Z
| 2021-06-19T03:40:46Z
| 2021-06-19T03:40:46Z
|
MEMBER
| null |
In most cases, there is a relationship between `index.get_loc` and `index.__eq__`. `index.get_loc(key)` roughly matches `(index == key).nonzero()`
`DatetimeIndex.get_loc` has special treatment for strings that we could extend to DatetimeIndex (and DatetimeArray) `__eq__`.
```
>>> dti = pd.date_range("2016-01-01 20:00", periods=10, freq="H")
>>> s = "2016-01-01
>>> dti.get_loc(s)
slice(0, 4, None)
>>> dti == s
array([False, False, False, False, False, False, False, False, False, False])
```
Under this proposal, the last comparison here would give `array([True, True, True, True, False, False, False, False, False, False])`
The main benefit of this would be internal consistency. Possibly some code sharing, but I'm not ready to make any promises on that front.
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REF: remove libresolution
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| 0
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MEMBER
| null |
It only has one function left, and that fits fine in fields.pyx
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MDExOlB1bGxSZXF1ZXN0NDQ3MTI0OTQ5
| 35,199
|
REF: re-use get_firstbday, get_lastbday in fields.pyx
|
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| 1
| 2020-07-09T22:27:50Z
| 2020-07-09T23:46:48Z
| 2020-07-09T23:40:15Z
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MEMBER
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MDU6SXNzdWU2NTQ0MDI2NzI=
| 35,200
|
Strange dtype behavior for numeric strings when using to_csv and read_csv
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| 2020-07-09T23:13:21Z
| 2020-07-09T23:24:28Z
| 2020-07-09T23:24:14Z
|
NONE
| null |
Version 0.25.2
The following code writes out a column containing strings of the numerals 0 to N-1 plus a string 'None', and then reads it back in.
```python
for N in [10,100,1000,10000,100000,1000000]:
A = pd.DataFrame(list(range(N)) + list(range(N)), columns=['a'])
A['a'] = A['a'].map(str)
A.loc[N-1,'a'] = 'None'
# Test
A.to_csv('./test.csv', index=False)
B = pd.read_csv('./test.csv')
print(len(A['a'].unique()), len(B['a'].unique()))
```
However, the output is
```
11 11
101 101
1001 1001
10001 10001
100001 100001
1000001 1524288
```
_Only_ at the N=1000000 case, the values read back in get cast to a mix of int and string, and a warning is thrown
```
.../interactiveshell.py:3049: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.
interactivity=interactivity, compiler=compiler, result=result)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/35201
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| 654,406,546
|
MDExOlB1bGxSZXF1ZXN0NDQ3MTQxNjgw
| 35,201
|
BUG: GroupBy.count() and GroupBy.sum() incorreclty return NaN instead of 0 for missing categories
|
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[] | null | 1
| 2020-07-09T23:23:32Z
| 2020-07-15T01:33:42Z
| 2020-07-15T01:33:41Z
|
MEMBER
| null |
- [x] closes #31422
- [x] closes #35028
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
*Behavioural Changes*
Fixing two related bugs: when grouping on multiple categoricals, `.sum()` and `.count()` would return `NaN` for the missing categories, but they are expected to return `0` for the missing categories. Both these bugs are fixed.
*Tests*
Tests were added in PR #35022 when these bugs were discovered and the tests were marked with an `xfail`. For this PR the `xfails` are removed and the tests are passing normally. As well, a few other existing tests were expecting `sum()` to return `NaN`; these have been updated so that the tests now expect to get `0` (which is the desired behaviour).
*Pivot*
The change to `.sum()` also impacts the `df.pivot_table()` if it is called with `aggfunc=sum` and is pivoted on a Categorical column with `observed=False`. This is not explicitly mentioned in either of the bugs, but it does make the behaviour consistent (i.e. the sum of a missing category is zero, not `NaN`). One test on test_pivot.py was updated to reflect this change.
*Default Behaviour*
Because `df.groupby()` and `df.pivot_table()` have `observed=False` as the default, the default behaviour will change for a user calling `df.groupby().sum()` or `df.pivot_table(..., aggfunc='sum')` if they are grouping/pivoting on a categorical with missing categories. Previously the default would give them `NaN` for the missing categories, now the default will give them `0`.
What is the appropriate to highlight/document this change to the default behaviour?
|
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MDU6SXNzdWU2NTQ0MDg2NDU=
| 35,202
|
BUG: `len(df.groupby(..., dropna=False))` raises ValueError: Categorical categories cannot be null
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| 2
| 2020-07-09T23:29:10Z
| 2020-07-11T00:01:58Z
| null |
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
(only in current master; dropna=False didn't exist previously)
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
>>> df = pd.DataFrame({'a': [pd.to_datetime('2019-02-12'), pd.to_datetime('2019-02-12'), pd.to_datetime('2019-02-13'), pd.NaT], 'b': [1, 2, 3, 4]})
>>>
>>> dfg = df.groupby(['a'], dropna=False)
>>> len(dfg)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "./venv/pandas-1/lib64/python3.8/site-packages/pandas/core/groupby/groupby.py", line 539, in __len__
return len(self.groups)
File "./venv/pandas-1/lib64/python3.8/site-packages/pandas/core/groupby/groupby.py", line 558, in groups
return self.grouper.groups
File "pandas/_libs/properties.pyx", line 33, in pandas._libs.properties.CachedProperty.__get__
File "./venv/pandas-1/lib64/python3.8/site-packages/pandas/core/groupby/ops.py", line 257, in groups
return self.groupings[0].groups
File "pandas/_libs/properties.pyx", line 33, in pandas._libs.properties.CachedProperty.__get__
File "./venv/pandas-1/lib64/python3.8/site-packages/pandas/core/groupby/grouper.py", line 598, in groups
return self.index.groupby(Categorical.from_codes(self.codes, self.group_index))
File "./venv/pandas-1/lib64/python3.8/site-packages/pandas/core/arrays/categorical.py", line 606, in from_codes
dtype = CategoricalDtype._from_values_or_dtype(
File "./venv/pandas-1/lib64/python3.8/site-packages/pandas/core/dtypes/dtypes.py", line 365, in _from_values_or_dtype
dtype = CategoricalDtype(categories, ordered)
File "./venv/pandas-1/lib64/python3.8/site-packages/pandas/core/dtypes/dtypes.py", line 252, in __init__
self._finalize(categories, ordered, fastpath=False)
File "./venv/pandas-1/lib64/python3.8/site-packages/pandas/core/dtypes/dtypes.py", line 406, in _finalize
categories = self.validate_categories(categories, fastpath=fastpath)
File "./venv/pandas-1/lib64/python3.8/site-packages/pandas/core/dtypes/dtypes.py", line 579, in validate_categories
raise ValueError("Categorical categories cannot be null")
ValueError: Categorical categories cannot be null
```
#### Problem description
When using groupby with the option of `dropna=False`, it may happen that a `ValueError` is being raised when the result of `len(groupby(..., dropna=False))` is obtained. This works fine in older version of Pandas (0.25.x) which always dropped na rows by default. Even in the current master version (1.1.0.dev0+2054.gc15f08084) `len(...)` still works fine when `dropna=True` is used. The interesting bit (for me at least) is that categoricals are somehow used while there are no categoricals in the dataframe to begin with. Perhaps this is a code path that is not meant to be executed.
#### Expected Output
No exception being raised (there isn't any categorical after all) and `len(...)` isn't modifying anything so an exception is totally unexpected.
#### Output of ``pd.show_versions()``
<details>
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : c15f0808428b5cdb4eaea290fb26a71c3534630b
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.7.7-200.fc32.x86_64
Version : #1 SMP Wed Jul 1 19:53:01 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 1.1.0.dev0+2054.gc15f08084
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1
setuptools : 46.1.3
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
```
</details>
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MDU6SXNzdWU2NTQ0MTcxODE=
| 35,203
|
BUG: indexing with -1 doesn't work anymore in expanding function
|
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[] | null | 3
| 2020-07-09T23:55:48Z
| 2020-07-10T16:24:57Z
| 2020-07-10T15:46:25Z
|
MEMBER
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
import numpy as np
# setup
df = pd.DataFrame([5,7,4,12,3,4,1], columns=['Value'])
# calculate countif
df['Count'] = df.Value.expanding(1).apply(lambda x: np.sum(np.where(x > x[-1], 1, 0))).astype('int')
```
raises `Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/home/tobias/anaconda3/envs/py38/lib/python3.8/site-packages/pandas/core/window/expanding.py", line 152, in apply
return super().apply(func, raw=raw, args=args, kwargs=kwargs)
File "/home/tobias/anaconda3/envs/py38/lib/python3.8/site-packages/pandas/core/window/rolling.py", line 1300, in apply
return self._apply(
File "/home/tobias/anaconda3/envs/py38/lib/python3.8/site-packages/pandas/core/window/rolling.py", line 507, in _apply
result = calc(values)
File "/home/tobias/anaconda3/envs/py38/lib/python3.8/site-packages/pandas/core/window/rolling.py", line 495, in calc
return func(x, start, end, min_periods)
File "/home/tobias/anaconda3/envs/py38/lib/python3.8/site-packages/pandas/core/window/rolling.py", line 1326, in apply_func
return window_func(values, begin, end, min_periods)
File "pandas/_libs/window/aggregations.pyx", line 1375, in pandas._libs.window.aggregations.roll_generic_fixed
File "<stdin>", line 2, in <lambda>
File "/home/tobias/anaconda3/envs/py38/lib/python3.8/site-packages/pandas/core/series.py", line 871, in __getitem__
result = self.index.get_value(self, key)
File "/home/tobias/anaconda3/envs/py38/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 4405, in get_value
return self._engine.get_value(s, k, tz=getattr(series.dtype, "tz", None))
File "pandas/_libs/index.pyx", line 80, in pandas._libs.index.IndexEngine.get_value
File "pandas/_libs/index.pyx", line 90, in pandas._libs.index.IndexEngine.get_value
File "pandas/_libs/index.pyx", line 138, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 998, in pandas._libs.hashtable.Int64HashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1005, in pandas._libs.hashtable.Int64HashTable.get_item
KeyError: -1
`
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
On 0.25.2 this resulted in
```
Value Count
0 5 0
1 7 0
2 4 2
3 12 0
4 3 4
5 4 3
6 1 6
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.0.final.0
python-bits : 64
OS : Linux
OS-release : 4.10.0-35-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 1.0.5
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2.post20191203
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.10.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.3.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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QST: value assignment by using '.loc' with boolean and label indexing
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[] | null | 2
| 2020-07-10T02:21:36Z
| 2020-07-10T15:26:11Z
| 2020-07-10T15:25:52Z
|
NONE
| null |
- [Yes] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [No] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
To assign values from one column of a dataframe to another column with boolean and label indexing, I found the following code works. But I didn't find any document about it, so I am wondering if this method is proper/safe to use?
Thanks.
```python
df = pd.DataFrame({'A':np.arange(5),
'B':[0,1,0,1,0],
'C':-np.ones(5)})
print(df)
df.loc[df['B']==1, 'A'] = df['C']
# Value error if we use
# df.loc[df['B']==1, 'A'] = -np.ones(5)
print(df)
'''
Output:
A B C
0 0 0 -1.0
1 1 1 -1.0
2 2 0 -1.0
3 3 1 -1.0
4 4 0 -1.0
A B C
0 0.0 0 -1.0
1 -1.0 1 -1.0
2 2.0 0 -1.0
3 -1.0 1 -1.0
4 4.0 0 -1.0
'''
```
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CLN: tighten types to get_rule_month
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MEMBER
| null |
We're a bit inconsistent about whether we are extracting freq.freqstr vs freq.rule_code; I'd like to get DateOffsets out of libparsing altogether before long.
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| 6
| 2020-07-10T06:25:50Z
| 2021-08-08T16:53:56Z
| null |
NONE
| null |
#### Location of the documentation
[pandas.read_csv](https://dev.pandas.io/docs/reference/api/pandas.read_csv.html)
#### Documentation problem
I've just noticed that `s3fs` is required when you read an URL from s3. While it is documented that you can read from S3, the implication that you need to install an extra is not documented.
Also, it would be nice if this was a pandas extra in setup.py (e.g. `s3`).
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MDExOlB1bGxSZXF1ZXN0NDQ3MjgwNzgy
| 35,207
|
ENH: raise limit for completion number of columns and warn beyond
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| 2020-07-10T07:41:05Z
| 2020-11-22T04:57:46Z
| 2020-11-22T04:57:46Z
|
CONTRIBUTOR
| null |
Hi,
Currently the user-completion (ipython "\<tab\>") on a dataframe with more than 100 columns will silently ignore some columns, letting an unaware user confused on whether data disapeared.
This is actually documented, and due to an arbitrary limit set to workaround a performance issue ( See #18587 )
Dataframe with more than 100 columns are quite common, so this can potentially affect and suprise many users. Therefore, I suggest to increase that limit to, say, 1000. In any case, it would probably be good to warn the user hitting that limit.
The attached quickfix raises the limit to 1000 and adds a warning beyond.
(Note that I didn't experience any completion latency increasing with the axis size, so I'm not sure whether this limit is still relevant in the first place).
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| 35,208
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ROADMAP: add consistent missing values for all dtypes to the roadmap
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| 21
| 2020-07-10T09:34:16Z
| 2020-08-20T15:50:22Z
| 2020-08-20T14:52:53Z
|
MEMBER
| null |
The more detailed motivation is described in https://hackmd.io/@jorisvandenbossche/Sk0wMeAmB, and many aspects have already been discussed in https://github.com/pandas-dev/pandas/issues/28095 and linked issues.
(and there are probably still other details / practical aspects that can be further discussed in dedicated issues)
Last year when I made the `pd.NA` proposal (and which resulted in using that for the nullable integer, boolean and string dtypes), which described it as "can be used consistently across all data types", the implicit / aspirational end goal of this proposal for me always was to *actually* have this for all dtypes (and as the default, at some point).
I tried to discuss this goal more explicitly on the mailing list earlier this year (in the thread about pandas 2.0: https://mail.python.org/pipermail/pandas-dev/2020-February/001180.html). But we never really "officially" adopted this as a goal / roadmap item, or discussed about doing that.
Proposing to add a section about it to the roadmap is an attempt to do this (as that is actually how it's described to do it in our [roadmap](https://pandas.pydata.org/docs/dev/development/roadmap.html#roadmap-evolution)).
The aforementioned mailing list thread mostly resulted in a discussion about how to integrate the new semantics in the datetime-like dtypes (pd.NaT vs pd.NA, keep pd.NaT but change its behaviour, etc). This is still a technical discussion we further need to resolve, but note that I kept the text on that in the PR somewhat vague on purpose for this reason: *"..: all data types should support missing values and with the same behaviour"*
And the general disclaimer that is also in our roadmap: *An item being on the roadmap does not mean that it will* necessarily *happen, even with unlimited funding. During the implementation period we may discover issues preventing the adoption of the feature.*
cc @pandas-dev/pandas-core @pandas-dev/pandas-triage
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ArrowIOError: Invalid parquet file. Corrupt footer. When modifying files in S3 (Bump s3fs to 0.4.2)
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| 2020-07-10T09:37:37Z
| 2021-08-08T16:56:36Z
| null |
NONE
| null |
When loading a file that has been modified in S3 directly with pd.read_parquet() from **another terminal**, we get the ArrowIOError: Invalid parquet file. Corrupt footer. Error.
The code to replicate this error is:
```
import pandas as pd
pd.show_versions()
n = 5
df = pd.DataFrame()
for i in range(n):
df[str(i)] = [1, 2, 3]
bucket = 'XXXXXX'
file_name = f's3://{bucket}/dataframe.pqt'
df.to_parquet(file_name)
df = pd.read_parquet(file_name)
```
Then upload a modified DataFrame with the same name from another terminal:
```
import pandas as pd
n = 6
df = pd.DataFrame()
for i in range(n):
df[str(i)] = [1, 2, 3]
bucket = 'XXXXXX'
file_name = f's3://{bucket}/dataframe.pqt'
df.to_parquet(file_name)
```
And after trying to read it again from the first one:
```
df = pd.read_parquet(file_name)
```
We get an error:
```
----> 1 df = pd.read_parquet(file_name)
~/venv/lib/python3.6/site-packages/pandas/io/parquet.py in read_parquet(path, engine, columns, **kwargs)
313
314 impl = get_engine(engine)
--> 315 return impl.read(path, columns=columns, **kwargs)
~/venv/lib/python3.6/site-packages/pandas/io/parquet.py in read(self, path, columns, **kwargs)
129 def read(self, path, columns=None, **kwargs):
130 parquet_ds = self.api.parquet.ParquetDataset(
--> 131 path, filesystem=get_fs_for_path(path), **kwargs
132 )
133 kwargs["columns"] = columns
~/venv/lib/python3.6/site-packages/pyarrow/parquet.py in __init__(self, path_or_paths, filesystem, schema, metadata, split_row_groups, validate_schema, filters, metadata_nthreads, read_dictionary, memory_map, buffer_size)
1058
1059 if validate_schema:
-> 1060 self.validate_schemas()
1061
1062 def equals(self, other):
~/venv/lib/python3.6/site-packages/pyarrow/parquet.py in validate_schemas(self)
1090 self.schema = self.common_metadata.schema
1091 else:
-> 1092 self.schema = self.pieces[0].get_metadata().schema
1093 elif self.schema is None:
1094 self.schema = self.metadata.schema
~/venv/lib/python3.6/site-packages/pyarrow/parquet.py in get_metadata(self)
558 metadata : FileMetaData
559 """
--> 560 f = self.open()
561 return f.metadata
562
~/venv/lib/python3.6/site-packages/pyarrow/parquet.py in open(self)
565 Returns instance of ParquetFile
566 """
--> 567 reader = self.open_file_func(self.path)
568 if not isinstance(reader, ParquetFile):
569 reader = ParquetFile(reader, **self.file_options)
~/venv/lib/python3.6/site-packages/pyarrow/parquet.py in _open_dataset_file(dataset, path, meta)
940 read_dictionary=dataset.read_dictionary,
941 common_metadata=dataset.common_metadata,
--> 942 buffer_size=dataset.buffer_size
943 )
944
~/venv/lib/python3.6/site-packages/pyarrow/parquet.py in __init__(self, source, metadata, common_metadata, read_dictionary, memory_map, buffer_size)
135 self.reader.open(source, use_memory_map=memory_map,
136 buffer_size=buffer_size,
--> 137 read_dictionary=read_dictionary, metadata=metadata)
138 self.common_metadata = common_metadata
139 self._nested_paths_by_prefix = self._build_nested_paths()
~/venv/lib/python3.6/site-packages/pyarrow/_parquet.pyx in pyarrow._parquet.ParquetReader.open()
~/venv/lib/python3.6/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowIOError: Invalid parquet file. Corrupt footer.
```
The output from `pd.show_versions()` is:
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-61-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 20.1.1
setuptools : 41.4.0
Cython : None
pytest : 5.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.0
pytables : None
pytest : 5.2.1
pyxlsb : None
s3fs : 0.3.5
scipy : 1.4.1
sqlalchemy : 1.3.10
tables : None
tabulate : 0.8.5
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.46.0
```
In order to reproduce the error, the DataFrame in S3 must be modified from a different terminal, uploading the modified DataFrame and reading it again from the same terminal will not raise the error.
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Tst return none inplace series
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| 4
| 2020-07-10T10:13:39Z
| 2020-07-10T19:32:38Z
| 2020-07-10T19:32:34Z
|
CONTRIBUTOR
| null |
verify we return None for all inplace calls in /series
related: https://github.com/pandas-dev/pandas/pull/35181#event-3531612625
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MDU6SXNzdWU2NTQ3MTI0NTQ=
| 35,211
|
BUG: in read_excel for mangled columns only the original/first column dtype is correct, col.N is not parsed correctly
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| 7
| 2020-07-10T11:25:18Z
| 2021-05-12T13:55:50Z
| 2021-05-12T13:55:50Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
This requires setting up an excel file. Reproducible by setting 2 columns with equal headers like `a` and a row with values `1`. Schematic:
```
a a
1 1
```
Then using:
```python
df = pd.read_excel(file, dtype={'a': str})
assert type(df['a'][0]) == type(df['a.1'][0])
```
#### Problem description
The problem is that column `a` is parsed as a `str` and column `a.1` is parsed as a `float64`.
#### Expected Output
Both `a` and `a.1` should be parsed as `str`, such that the assertion is passed.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 40.8.0
Cython : 0.29.20
pytest : 5.3.5
hypothesis : None
sphinx : 3.0.4
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.1
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.15.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.1
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
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 : 1.2.9
numba : 0.50.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDQ3NDMzNTM4
| 35,212
|
Fix strange behavior when precision display option is zero (#20359)
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| 2
| 2020-07-10T13:15:20Z
| 2020-07-17T09:29:04Z
| 2020-07-10T19:33:14Z
|
CONTRIBUTOR
| null |
- [x] closes #20359
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Notes:
- [x] I added a unit test for the example given in #20359
- [x] As far as the context of this code is concerned, I replacde '.' with decimal string (passed from self when called) in case of non-standard decimal use
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|
Add xarray copyright notice to comply with reuse under Apache License
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| 1
| 2020-07-10T14:24:58Z
| 2020-07-10T14:55:40Z
| 2020-07-10T14:55:39Z
|
CONTRIBUTOR
| null |
In https://github.com/xarray-contrib/pint-xarray/pull/11, @keewis and I noticed that pandas's (pandas'?...possessive forms are weird) reuse with modification of xarray code added in https://github.com/pandas-dev/pandas/commit/eee83e23ba0c5b32e27db3faca931ddb4c9619aa did not include xarray's copyright notice as required by the Apache License, Version 2.0 (which is not included in the license text itself, unlike MIT or BSD). I'm not sure how big of a deal this is, but given that pint-xarray is going ahead and modeling its code citation practice after that of pandas, I wanted to see what the thoughts are here on it. This PR takes a simple fix and just adds the copyright notice to top of the `LICENSES/XARRAY_LICENSE` file.
Also, I'm not sure if this requires a whatsnew entry or not.
- ~~closes #xxxx~~
- ~~tests added / passed~~
- ~~passes `black pandas`~~
- ~~passes `git diff upstream/master -u -- "*.py" | flake8 --diff`~~
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NDQ3NDkxNTk4
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|
Drop Python 3.6 support
|
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| 16
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|
MEMBER
| null |
- [x] closes #34472
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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| 654,860,372
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MDU6SXNzdWU2NTQ4NjAzNzI=
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|
BUG: memory spike in DatetimeIndex after .loc based assignment
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[] | null | 4
| 2020-07-10T15:28:09Z
| 2020-09-03T18:20:12Z
| 2020-09-03T02:00:42Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
n = 1500000
idx = pd.date_range(end = pd.Timestamp.now().floor("1T"), freq='1T', periods=n)
srs1 = pd.Series(np.random.random(n), idx)
srs2 = pd.Series(np.random.random(n), idx)
print(sys.getsizeof(srs1.index)) # 12000032
print(sys.getsizeof(srs2.index)) # 12000032
filter_srs = srs1[srs1 > .9].index
srs2.loc[filter_srs] = .9
print(sys.getsizeof(srs2.index)) # 53943072
assert (srs1.index == srs2.index).all()
```
#### Problem description
hi pandas, after filtering a series on some index, theres a strange spike in memory usage of the index where it can grow in size by a factor of > 4. I know this is a strange use case but its a MWE of a version where the `srs2` index might be mutated. The `nbytes` attributes of the indexes stay the same, but the overall memory usage according to sys grows larger and stays large. Also, if i do something like `srs2.index = pd.DatetimeIndex(srs2.index)` the memory usage falls back to a normal level
#### Expected Output
I would expect all of them to be the same.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1.post20200622
Cython : 0.29.20
pytest : 5.4.3
hypothesis : 5.18.3
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.1
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.3
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.9
numba : 0.50.1
</details>
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| 654,890,795
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MDExOlB1bGxSZXF1ZXN0NDQ3NTI3OTU5
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|
TST add corner cases in test_constructors
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| 3
| 2020-07-10T16:18:31Z
| 2020-07-11T10:45:09Z
| 2020-07-11T10:44:57Z
|
CONTRIBUTOR
| null |
add two more corner cases to frame/test_constructors.
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