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MDExOlB1bGxSZXF1ZXN0NDUwNjY5MjE2
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DOC: Rst Formatting, make sure continuation prompt are used.
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| 2020-09-10T18:53:46Z
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|
CONTRIBUTOR
| null |
Without the `...` for some tools it may look like
`with ExcelWriter('path_to_file.xlsx',` is an input and
`date...` is the output.
I initially thought that I could use the hanging indent to separate
code continuation from output but `pandas/core/indexing.py`
proved me wrong as the example just after the one I fixed have
the first line of the output be indented.
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Update multi.py
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| 2020-07-17T04:03:11Z
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NONE
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fix bugs be metioned on issue #35301
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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Update multi.py
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| 2020-07-17T07:19:33Z
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|
NONE
| null |
fix bugs be metioned on issue #35301
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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BUG:New epoch in matplotlib 3.3 causes plotting issues
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| 2020-08-21T17:04:38Z
| 2020-07-23T15:42:09Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import matplotlib.pyplot as plt
ndx = pd.to_datetime(['2020-07-1 04:01:34', '2020-07-2 04:03:33', '2020-07-3 04:05:33',
'2020-07-4 04:07:35', '2020-07-5 04:09:35'])
df = pd.DataFrame(index=ndx, data={'Vsw': [389.4, 388.5, 384.5, 384.7, 386.6]})
print(df.info())
ax1 = df['Vsw'].plot(xlim=['2020-7-1', '2020-7-6'])
print(ax1.get_xlim(), '\n\n\n')
plt.show()
# plot without specifying x limits
ax2 = df['Vsw'].plot()
print(ax2.get_xlim(), '\n\n\n')
plt.show()
# plot from a DataFrame with a pd.date_range index
df = pd.DataFrame(index=pd.date_range('2020-7-1', '2020-7-5', freq='D'),
data={'Vsw': [389.4, 388.5, 384.5, 384.7, 386.6]})
print(df.info())
ax3 = df['Vsw'].plot(xlim=['2020-7-1', '2020-7-6'])
print(ax3.get_xlim())
plt.show()
```
#### Problem description
See https://matplotlib.org/3.3.0/users/whats_new.html#dates-use-a-modern-epoch
The first plot is blank. get_xlim returns (18444.0, 18449.0) The second plot contains the values being plotted and get_xlim returns (737606.9674762732, 737611.3736001157). The third plot shows the plotted values and get_xlim returns (18444.0, 18449.0).
If the epoch is set to the old epoch value with
import matplotlib.dates as mpld
mpld.set_epoch('0000-12-31T00:00:00')
all three plots show the plotted values. get_xlim returns (737607.0, 737612.0) for the first plot, (737606.9674762732, 737611.3736001157) for the second and (18444.0, 18449.0) for the third.
#### Expected Output
The three plots should show the values being plotted whether or not xlim is specified in the plot() command or the DatetimeIndex has a Freq value
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 65090286608602ba6d67ca4a29cf0535156cd509
python : 3.8.4.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Sun Jul 5 00:43:10 PDT 2020; root:xnu-6153.141.1~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0.dev0+2131.g650902866
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1
Cython : 0.29.20
pytest : None
hypothesis : None
sphinx : 3.1.1
blosc : None
feather : None
xlsxwriter : 1.2.9
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 : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.50.1
</details>
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BUG: clip does not accept numpy's arguments
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| 8
| 2020-07-17T10:19:37Z
| 2020-07-18T17:40:06Z
| 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.
---
#### Code Sample, a copy-pastable example
```python
x = pd.Series(np.random.randn(100))
x.clip(lower=0) # Works
x.clip(lower=0, min=0) # TypeError: clip() got an unexpected keyword argument 'min'
```
#### Problem description
Documentation of `clip` ([dataframe](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.clip.html), [series](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.clip.html)) states the method signature:
```python
Series.clip(self: ~ FrameOrSeries, lower=None, upper=None, axis=None, inplace: bool = False, *args, **kwargs)
```
and explains about `**kwargs`:
```
*args, **kwargs
Additional keywords have no effect but might be accepted for compatibility with numpy.
```
However `min` or `max` arguments of numpy are not accepted and raised `TypeError`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.1.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
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 : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.1.1
setuptools : 42.0.1.post20191125
Cython : None
pytest : 5.3.0
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : 0.9.3
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : None
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.3
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pytest : 5.3.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.1
sqlalchemy : 1.3.13
tables : 3.4.4
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.1.0
xlwt : None
xlsxwriter : None
numba : 0.46.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDUxMDk1NzI2
| 35,322
|
DOC: add contributors
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| 2020-07-17T12:39:05Z
| 2020-11-28T14:56:39Z
| 2020-07-17T17:16:28Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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| 659,240,714
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MDExOlB1bGxSZXF1ZXN0NDUxMTExMjY3
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|
Remove deprecated warn kwarg for matplotlib use
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| 3
| 2020-07-17T13:04:46Z
| 2020-08-17T22:04:24Z
| 2020-07-17T16:24:15Z
|
CONTRIBUTOR
| null |
The warn param has been deprecated in 3.2.1: https://matplotlib.org/3.2.1/api/prev_api_changes/api_changes_3.2.0.html#matplotlib-use
On the most recent matplotlib, this is failing for me with
```
TypeError: use() got an unexpected keyword argument 'warn'
```
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Fix AttributeError when groupby as_index=False on empty DataFrame
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| 2
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| 2020-07-17T18:49:55Z
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|
CONTRIBUTOR
| null |
- [x] closes #35246
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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|
BUG: Combination of groupby.resample.interpolate() fails
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| 3
| 2020-07-17T14:35:14Z
| 2020-08-19T20:22:59Z
| 2020-08-19T20:22:59Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
d = {'price': [10, 11, 9, 13, 14, 18, 17, 19],
'volume': [50, 60, 40, 100, 50, 100, 40, 50]}
df = pd.DataFrame(d)
df['week_starting'] = pd.date_range('01/01/2018', periods=8, freq='W')
df \
.set_index("week_starting") \
.groupby("volume") \
.resample("1D") \
.interpolate(method="linear")
```
Error:
```
TypeError Traceback (most recent call last)
~/.cache/pypoetry/virtualenvs/lagps-6BuXYM4Y-py3.8/lib/python3.8/site-packages/pandas/core/groupby/groupby.py in apply(self, func, *args, **kwargs)
735 try:
--> 736 result = self._python_apply_general(f)
737 except TypeError:
~/.cache/pypoetry/virtualenvs/lagps-6BuXYM4Y-py3.8/lib/python3.8/site-packages/pandas/core/groupby/groupby.py in _python_apply_general(self, f)
751 def _python_apply_general(self, f):
--> 752 keys, values, mutated = self.grouper.apply(f, self._selected_obj, self.axis)
753
~/.cache/pypoetry/virtualenvs/lagps-6BuXYM4Y-py3.8/lib/python3.8/site-packages/pandas/core/groupby/ops.py in apply(self, f, data, axis)
205 group_axes = group.axes
--> 206 res = f(group)
207 if not _is_indexed_like(res, group_axes):
~/.cache/pypoetry/virtualenvs/lagps-6BuXYM4Y-py3.8/lib/python3.8/site-packages/pandas/core/resample.py in func(x)
988
--> 989 return x.apply(f, *args, **kwargs)
990
~/.cache/pypoetry/virtualenvs/lagps-6BuXYM4Y-py3.8/lib/python3.8/site-packages/pandas/core/resample.py in aggregate(self, func, *args, **kwargs)
284 grouper = None
--> 285 result = self._groupby_and_aggregate(how, grouper, *args, **kwargs)
286
~/.cache/pypoetry/virtualenvs/lagps-6BuXYM4Y-py3.8/lib/python3.8/site-packages/pandas/core/resample.py in _groupby_and_aggregate(self, how, grouper, *args, **kwargs)
360 else:
--> 361 result = grouped.aggregate(how, *args, **kwargs)
362 except DataError:
~/.cache/pypoetry/virtualenvs/lagps-6BuXYM4Y-py3.8/lib/python3.8/site-packages/pandas/core/groupby/generic.py in aggregate(self, func, *args, **kwargs)
923 # nicer error message
--> 924 raise TypeError("Must provide 'func' or tuples of '(column, aggfunc).")
925
TypeError: Must provide 'func' or tuples of '(column, aggfunc).
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-15-5d08bff36e4d> in <module>
----> 1 df = df \
2 .set_index("week_starting") \
3 .groupby("volume") \
4 .resample("1D") \
5 .interpolate(method="linear")
~/.cache/pypoetry/virtualenvs/lagps-6BuXYM4Y-py3.8/lib/python3.8/site-packages/pandas/core/resample.py in interpolate(self, method, axis, limit, inplace, limit_direction, limit_area, downcast, **kwargs)
797 Interpolate values according to different methods.
798 """
--> 799 result = self._upsample(None)
800 return result.interpolate(
801 method=method,
~/.cache/pypoetry/virtualenvs/lagps-6BuXYM4Y-py3.8/lib/python3.8/site-packages/pandas/core/resample.py in _apply(self, f, grouper, *args, **kwargs)
989 return x.apply(f, *args, **kwargs)
990
--> 991 result = self._groupby.apply(func)
992 return self._wrap_result(result)
993
~/.cache/pypoetry/virtualenvs/lagps-6BuXYM4Y-py3.8/lib/python3.8/site-packages/pandas/core/groupby/groupby.py in apply(self, func, *args, **kwargs)
745
746 with _group_selection_context(self):
--> 747 return self._python_apply_general(f)
748
749 return result
~/.cache/pypoetry/virtualenvs/lagps-6BuXYM4Y-py3.8/lib/python3.8/site-packages/pandas/core/groupby/groupby.py in _python_apply_general(self, f)
750
751 def _python_apply_general(self, f):
--> 752 keys, values, mutated = self.grouper.apply(f, self._selected_obj, self.axis)
753
754 return self._wrap_applied_output(
~/.cache/pypoetry/virtualenvs/lagps-6BuXYM4Y-py3.8/lib/python3.8/site-packages/pandas/core/groupby/ops.py in apply(self, f, data, axis)
204 # group might be modified
205 group_axes = group.axes
--> 206 res = f(group)
207 if not _is_indexed_like(res, group_axes):
208 mutated = True
~/.cache/pypoetry/virtualenvs/lagps-6BuXYM4Y-py3.8/lib/python3.8/site-packages/pandas/core/resample.py in func(x)
987 return getattr(x, f)(**kwargs)
988
--> 989 return x.apply(f, *args, **kwargs)
990
991 result = self._groupby.apply(func)
~/.cache/pypoetry/virtualenvs/lagps-6BuXYM4Y-py3.8/lib/python3.8/site-packages/pandas/core/resample.py in aggregate(self, func, *args, **kwargs)
283 how = func
284 grouper = None
--> 285 result = self._groupby_and_aggregate(how, grouper, *args, **kwargs)
286
287 result = self._apply_loffset(result)
~/.cache/pypoetry/virtualenvs/lagps-6BuXYM4Y-py3.8/lib/python3.8/site-packages/pandas/core/resample.py in _groupby_and_aggregate(self, how, grouper, *args, **kwargs)
359 result = grouped._aggregate_item_by_item(how, *args, **kwargs)
360 else:
--> 361 result = grouped.aggregate(how, *args, **kwargs)
362 except DataError:
363 # we have a non-reducing function; try to evaluate
~/.cache/pypoetry/virtualenvs/lagps-6BuXYM4Y-py3.8/lib/python3.8/site-packages/pandas/core/groupby/generic.py in aggregate(self, func, *args, **kwargs)
922 elif func is None:
923 # nicer error message
--> 924 raise TypeError("Must provide 'func' or tuples of '(column, aggfunc).")
925
926 func = _maybe_mangle_lambdas(func)
TypeError: Must provide 'func' or tuples of '(column, aggfunc).
```
On master the error is raised in line 86 of the same file.
#### Problem description
The combination of `groupby`, `resample`, and `interpolate` leads to an `TypeError: Must provide 'func' or tuples of '(column, aggfunc).` [0].
Other functions like `ffill`, or `bfill` work without issues.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-18362-Microsoft
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 : 20.0.2
setuptools : 44.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
[0], btw, there is a quote missing in the error message.
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|
BUG: multiindex reindex doesn't work properly
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[] | null | 1
| 2020-07-17T14:45:50Z
| 2020-07-24T11:09:24Z
| 2020-07-24T11:09:00Z
|
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
a = pd.DataFrame([[1,2,3,4],[5,6,7,8]], index=[['a','a'], ['x', 'y']])
a.reindex(['x','z'], level=1)
# 0 1 2 3
#a x 1 2 3 4
```
#### Problem description
pandas doesn't work properly for multiindex reindexing. It drops the index that didn't appear in the original dataset directly.
#### Expected Output
we expect pandas will reindex the second level of the multiindex to ['x' , 'z']. But it drops 'z' directly.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.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 : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.1.1
setuptools : 45.2.0.post20200210
Cython : 0.29.15
pytest : 5.3.5
hypothesis : 5.5.4
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
</details>
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Doc fixups
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Optimize array_equivalent for NDFrame.equals
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CONTRIBUTOR
| null |
Closes #35249
```python
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: N = 10**3
In [4]: float_df = pd.DataFrame(np.random.randn(N, N))
# 1.0.5
1.52 ms ± 94 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
# master
83.6 ms ± 1.15 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
# branch
31.2 ms ± 2.06 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
```
And proof of this being better for short-circuiting:
```python
In [4]: N = 10**3
In [5]: a = pd.DataFrame(np.random.randn(N, N))
In [6]: b = pd.DataFrame(np.random.randn(N, N))
In [7]: %timeit a.equals(b)
# 1.0.5
1.97 ms ± 127 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [10]: %timeit a.equals(b)
56.4 µs ± 1.67 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```
Discussion: the key thing here is that `BlockManager.equals` requires that the dtypes of `left` and `right` match. If they don't then we've already returned False. That lets us do a few things
1. Avoid many `is_foo_dtype` checks.
2. Use optimized checks like `np.isnan`
3. Avoid the slow case of considering `np.array([1, 2])` equivalent to `np.array([1.0, 2.0])`.
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MDExOlB1bGxSZXF1ZXN0NDUxMjU2ODk3
| 35,329
|
More whatsnew cleanup.
|
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CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/35276
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MDExOlB1bGxSZXF1ZXN0NDUxMjcxNDA2
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REF: remove special casing from Index.equals (always dispatch to subclass)
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MEMBER
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MDU6SXNzdWU2NTk2NTI2MTk=
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BUG: np.argwhere on pandas series
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[] | null | 2
| 2020-07-17T21:28:32Z
| 2021-07-16T16:04:25Z
| 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.
---
numpy/numpy#15555 reports an issue with `np.argwhere` on pandas Series. Reporting here for visibility.
MRE:
```python
>>> import numpy as np
>>> import pandas as pd
>>> s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])
>>> np.argwhere(s < 0)
```
which, with `numpy.__version__ ==1.20.0.dev0+046a736` gives:
**pd.__version__ == 0.25.3:**
```
FutureWarning: Series.nonzero() is deprecated and will be removed in a future version.Use Series.to_numpy().nonzero() instead
array([[3]])
```
**pd.__version__ == 1.0.5:**
```
ValueError: Length of passed values is 1, index implies 5.
```
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DOC: [Errno 2] No such file or directory
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[] | null | 2
| 2020-07-17T21:50:47Z
| 2020-09-04T15:21:48Z
| 2020-09-04T15:21:47Z
|
NONE
| null |
#### Location of the documentation
inline in the code
#### Documentation problem
the following is due to filename length, not _"No such file or directory"_
> FileNotFoundError: [Errno 2] No such file or directory: 'Queried Data - search_index__foo_sourcetype__foo_token_typetoken_typetoken_typetoken_typetotoken_typetoken_typetoken_token_typetytoken_typepetoken_typepetoken_SDSDDDypepeX_from_02-41-PM_July_17.csv'
#### Suggested fix for documentation
It appears to me there should be a `if len(myString) > 177, throw FileNameTooLong`
here is a example
```
file_description = "search index = foo sourcetype = foo token_typetoken_typetoken_typetoken_typetotoken_typetoken_typetoken_token_typetytoken_typepetoken_typepetoken_SDSDDDypepe"
file_description += " from " + datetime.datetime.now().strftime("%I-%M-%p %B %d")
file_description = str(file_description).strip().replace(' ', '_')
file_description = re.sub(r'(?u)[^-\w.]', '', file_description)
file_description
len(file_description)
X_train.to_csv("Queried Data - " + file_description + ".csv")
```
> search_index__foo_sourcetype__foo_token_typetoken_typetoken_typetoken_typetotoken_typetoken_typetoken_token_typetytoken_typepetoken_typepetoken_SDSDDDypepe_from_02-41-PM_July_17'
> 177
*Works as intended*
---
if you add a letter to the end of `file_description`, length will be `178`.
we get the following
```text
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-176-8fb818e0c708> in <module>
7
8 len(file_description)
----> 9 X_train.to_csv("Queried Data - " + file_description + ".csv")
~\Anaconda3\lib\site-packages\pandas\core\generic.py in to_csv(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, line_terminator, chunksize, date_format, doublequote, escapechar, decimal)
3202 decimal=decimal,
3203 )
-> 3204 formatter.save()
3205
3206 if path_or_buf is None:
~\Anaconda3\lib\site-packages\pandas\io\formats\csvs.py in save(self)
186 self.mode,
187 encoding=self.encoding,
--> 188 compression=dict(self.compression_args, method=self.compression),
189 )
190 close = True
~\Anaconda3\lib\site-packages\pandas\io\common.py in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text)
426 if encoding:
427 # Encoding
--> 428 f = open(path_or_buf, mode, encoding=encoding, newline="")
429 elif is_text:
430 # No explicit encoding
FileNotFoundError: [Errno 2] No such file or directory: 'Queried Data - search_index__foo_sourcetype__foo_token_typetoken_typetoken_typetoken_typetotoken_typetoken_typetoken_token_typetytoken_typepetoken_typepetoken_SDSDDDypepeX_from_02-41-PM_July_17.csv'
```
## outtake
I suggest a better error message to be used the file name is too long
getting a `No such file or directory` caused me to waste time, as it was wrong. The file name was too long
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MDExOlB1bGxSZXF1ZXN0NDUxNzAzOTU5
| 35,333
|
Fix MPL dt conversion (epoch2num deprecation) #34850
|
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| 2020-07-18T04:52:53Z
| 2020-07-22T02:30:02Z
| 2020-07-22T02:30:02Z
|
NONE
| null |
Fix datetime conversion after matplotlib (3.3.0 update) deprecating epoch2num.
- [ ] closes #34850
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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REGR: np.argwhere on pd.Series raises ValueError
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| 2020-07-18T10:47:19Z
| 2020-07-29T15:48:31Z
| 2020-07-29T15:48:30Z
|
MEMBER
| null |
- [ ] closes #35331
not sure what behaviour we want (will add tests after discussion). We should be thinking of removing \_\_array_wrap__ in favour of \_\_array_ufunc__ and \_\_array_function__ anyhow.
from https://numpy.org/doc/stable/user/basics.subclassing.html?highlight=__array_wrap__#array-wrap-for-ufuncs-and-other-functions
> It is hoped to eventually deprecate these, but __array_wrap__ is also used by other numpy functions and methods, such as squeeze, so at the present time is still needed for full functionality.
in 0.25.3 we returned the NumPy array (albeit with a warning)
```
>>> pd.__version__
'0.25.3'
>>>
>>> s = pd.Series(np.random.randn(5), index=["a", "b", "c", "d", "e"])
>>> s
a 1.640847
b 0.316918
c -0.193584
d 1.473205
e 0.357223
dtype: float64
>>>
>>> np.argwhere(s < 0)
C:\Users\simon\Anaconda3\lib\site-packages\numpy\core\fromnumeric.py:61: FutureWarning: Series.nonzero() is deprecated and will be removed in a
future version.Use Series.to_numpy().nonzero() instead
return bound(*args, **kwds)
array([[2]], dtype=int64)
>>>
```
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|
BUG: Inconsistent resampling behaviour
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| 2020-07-18T14:48:37Z
| 2020-07-23T16:36:07Z
| 2020-07-23T16:36:07Z
|
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.
---
Attached below is my code for extracting processing financial tick data from the MetaTrader5 application:
```python
def CollectTicks(Year, Month, Day, Hour, Minute, CurrencyPair, Digits, ResampleOffset = None):
StartTime = datetime.datetime(Year, Month, Day, Hour, Minute).timestamp() #Convert the start time to epoch time
EndTime = StartTime + 604800 #Add 1 week to the start time and set the result as the end time
Ticks = mt5.copy_ticks_range(CurrencyPair, StartTime, EndTime, mt5.COPY_TICKS_ALL) #Retrieve the ticks from the start time until the end time
BreakFlag = False #Set the break flag to false
while(True):
StartTime = EndTime #Set the prevous end time as the new start time
EndTime = StartTime + 604800 #Add 1 week to the new start time and set the result as the new end time
if(EndTime > time.time()): #If the end time is more than the current time
EndTime = time.time() #Set the end time as the current time
BreakFlag = True #Set the break flag to true
NewTicks = mt5.copy_ticks_range(CurrencyPair, StartTime, EndTime, mt5.COPY_TICKS_ALL) #Retrive the new ticks with the new start time and new end time
Ticks = np.hstack((Ticks, NewTicks)) #Cocatenate the newly retrieved ticks with the previously retrieved ticks
if(BreakFlag == True): #If the break flag is set to true
break #Exit the loop
TickDataframe = pd.DataFrame(Ticks) #Covert the ticks array to a dataframe
TickDataframe.drop(["time", "last", "volume", "flags", "volume_real"], axis = 1, inplace = True) #Drop the unwanted columns from the dataframe
TickDataframe["time_msc"] = pd.to_datetime(TickDataframe["time_msc"], unit = "ms") #Convert the milisecond timestap to the epoch date time
TickDataframe.set_index("time_msc", inplace = True) #Set the milisecond time column as the dataframe's index
TickDataframe.round(Digits) #Round all values in the dataframe to the desired number of digits
TickDataframe["mid"] = round(((TickDataframe["bid"] + TickDataframe["ask"]) / 2.0), Digits) #Generate the mid dataframe and round it to (Digits) figures
if(ResampleOffset != None): #If the ResampleOffset input is not None
CandleCloses = {} #Instantiate a dictionary
CandleCloses["Bid Closes"] = TickDataframe["bid"].resample(ResampleOffset).ohlc().close.dropna().to_numpy() #Resample the bid ticks to create bid candles based on the resample offset and convert it into a numpy array
CandleCloses["Ask Closes"] = TickDataframe["ask"].resample(ResampleOffset).ohlc().close.dropna().to_numpy() #Resample the ask ticks to create ask candles based on the resample offset and convert it into a numpy array
CandleCloses["Mid Closes"] = TickDataframe["mid"].resample(ResampleOffset).ohlc().close.dropna().to_numpy() #Resample the mid ticks to create mid candles based on the resample offset and convert it into a numpy array
return CandleCloses #Return the dictionary containing candle closes
else: #If the ResampleOffset input is None
return TickDataframe #Return the dataframe of ticks
```
#### Problem description
After resampling the collected tick data with the "1Min" offset, the resampled data for 2 different instruments have a different number of candles even when their time indexes cover the same date range.
#### Additional Observations
The other offsets do not have this inconsistent behaviour, the issue only happens with the "1Min" resample offset.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : English_Singapore.1252
pandas : 1.0.5
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200714
Cython : None
pytest : None
hypothesis : None
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
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 : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.50.1
</details>
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MDU6SXNzdWU2NjAyMDIyNDc=
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|
DOC: Unable to install pre-release
|
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| 2020-07-19T14:32:23Z
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|
NONE
| null |
#### Location of the documentation
["https://github.com/pandas-dev/pandas/releases/tag/v1.1.0rc0"]
#### Documentation problem
It seems that I am not able to install the v1.1.0 version of the Pandas package using the following commands:
```
python -m pip install --upgrade --pre pandas==1.1.0rc0
conda install -c conda-forge/label/rc pandas==1.1.0rc0
```
It says that:
PackagesNotFoundError: The following packages are not available from current channels:
- pandas==1.1.0rc0
Current channels:
```
- https://conda.anaconda.org/conda-forge/label/rc/osx-64
- https://conda.anaconda.org/conda-forge/label/rc/noarch
- https://repo.anaconda.com/pkgs/main/osx-64
- https://repo.anaconda.com/pkgs/main/noarch
- https://repo.anaconda.com/pkgs/r/osx-64
- https://repo.anaconda.com/pkgs/r/noarch
```
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DOC: Improve DataFrame.dropna subset example
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| 1
| 2020-07-18T19:25:08Z
| 2020-07-31T15:58:44Z
| 2020-07-29T09:13:53Z
|
CONTRIBUTOR
| null |
In the examples for [DataFrame.dropna](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.dropna.html), which begin like so:
```pycon
>>> df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
... "toy": [np.nan, 'Batmobile', 'Bullwhip'],
... "born": [pd.NaT, pd.Timestamp("1940-04-25"),
... pd.NaT]})
>>> df
name toy born
0 Alfred NaN NaT
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip NaT
```
the example that uses the `subset` parameter,
```pycon
>>> df.dropna(subset=['name', 'born'])
name toy born
1 Batman Batmobile 1940-04-25
```
yields the same output as the example that doesn't pass in any arguments,
```pycon
>>> df.dropna()
name toy born
1 Batman Batmobile 1940-04-25
```
I think changing the `subset` example to
```pycon
>>> df.dropna(subset=['name', 'toy'])
name toy born
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip NaT
```
would better illustrate the concept, as its result contains a row with one missing value, just in a column that wasn't specified in `subset`.
-----
I'm not able to test the docstring locally at the moment, but I ran the code in my console up to the new example, and it worked as expected.
```pycon
>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
... "toy": [np.nan, 'Batmobile', 'Bullwhip'],
... "born": [pd.NaT, pd.Timestamp("1940-04-25"),
... pd.NaT]})
>>> df
name toy born
0 Alfred NaN NaT
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip NaT
>>> df.dropna()
name toy born
1 Batman Batmobile 1940-04-25
>>> df.dropna(axis='columns')
name
0 Alfred
1 Batman
2 Catwoman
>>> df.dropna(how='all')
name toy born
0 Alfred NaN NaT
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip NaT
>>> df.dropna(thresh=2)
name toy born
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip NaT
>>> df.dropna(subset=['name', 'toy'])
name toy born
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip NaT
```
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BUG: assign consensus name to index union in array case GH13475
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CONTRIBUTOR
| null |
- [x] closes #13475
- [x] tests added / passed (except known datetime64 issue in #35080)
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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CLN: resolve isort mypy import confilict test_algos
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| 1
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MEMBER
| null |
Part of #35134
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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ENH: Select numeric ExtensionDtypes with DataFrame.select_dtypes
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| 0
| 2020-07-19T10:51:22Z
| 2020-12-14T17:58:22Z
| 2020-12-14T17:58:22Z
|
NONE
| null |
#### Is your feature request related to a problem?
I would like to plot ExtensionArrays. DataFrame.select_dtypes(np.number) is called when plotting, which filters out ExtensionArrays.
#### Describe the solution you'd like
DataFrame.select_dtypes(np.number) should select columns where ExtensionDtype._is_numeric is True, suggested here https://github.com/pandas-dev/pandas/issues/26173#issuecomment-503761441
#### API breaking implications
Not considered.
#### Describe alternatives you've considered
Not considered.
#### Additional context
N/A
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NONE
| null |
- [x] closes #35340
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDU6SXNzdWU2NjA4MjU5NDE=
| 35,342
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BUG: date_range returns wrong output in some cases
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[] | null | 5
| 2020-07-19T13:05:10Z
| 2021-02-11T18:00:00Z
| 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 pandas as pd
print(pd.date_range('2020-02-01 15:00', '2020-05-12 13:00', freq='MS')) # wrong output
print(pd.date_range('2020-02-01 15:00', '2020-05-12 15:00', freq='MS')) # correct output
print(pd.date_range('2020-02-01 15:00', '2020-05-12 16:00', freq='MS')) # correct output
print(pd.date_range('2020-02-01 15:00', '2020-05-01 01:00', freq='MS')) # wrong output
print(pd.date_range('2020-02-01 15:00', '2020-05-01 23:00', freq='MS')) # correct output
print(pd.date_range('2020-01-21 15:00', '2020-05-12 13:00', freq='MS')) # correct output
```
Currently outputs:
```
DatetimeIndex(['2020-02-01 15:00:00', '2020-03-01 15:00:00',
'2020-04-01 15:00:00'],
dtype='datetime64[ns]', freq='MS')
DatetimeIndex(['2020-02-01 15:00:00', '2020-03-01 15:00:00',
'2020-04-01 15:00:00', '2020-05-01 15:00:00'],
dtype='datetime64[ns]', freq='MS')
DatetimeIndex(['2020-02-01 15:00:00', '2020-03-01 15:00:00',
'2020-04-01 15:00:00', '2020-05-01 15:00:00'],
dtype='datetime64[ns]', freq='MS')
DatetimeIndex(['2020-02-01 15:00:00', '2020-03-01 15:00:00',
'2020-04-01 15:00:00'],
dtype='datetime64[ns]', freq='MS')
DatetimeIndex(['2020-02-01 15:00:00', '2020-03-01 15:00:00',
'2020-04-01 15:00:00', '2020-05-01 15:00:00'],
dtype='datetime64[ns]', freq='MS')
DatetimeIndex(['2020-02-01 15:00:00', '2020-03-01 15:00:00',
'2020-04-01 15:00:00', '2020-05-01 15:00:00'],
dtype='datetime64[ns]', freq='MS')
```
#### Problem description
Seems that if `freq`='MS', the date of `start` is the first day of a month, and the time of `start` is after the time of `end`, then the last month is missing from the output.
The same problems seems to also occur if `freq`='M' and the date of `start` is the last day of a month (and the time of `start` is after the time of `end`), as demonstrated below.
(Perhaps the problem could occur also with other values of the `freq` parameter. However, I only tested with 'MS' and 'M'.)
#### Expected Output
I would expect every code line above to produce the following output:
```
DatetimeIndex(['2020-02-01 15:00:00', '2020-03-01 15:00:00',
'2020-04-01 15:00:00', '2020-05-01 15:00:00'],
dtype='datetime64[ns]', freq='MS')
```
#### Another code Sample, a copy-pastable example
Here is an example with `freq`='M'.
```python
import pandas as pd
print(pd.date_range('2020-01-31 15:00', '2020-05-12 13:00', freq='M')) # wrong output
print(pd.date_range('2020-01-31 17:00', '2020-05-12 17:00', freq='M')) # correct output
print(pd.date_range('2020-01-30 15:00', '2020-05-12 13:00', freq='M')) # correct output
```
#### Expected Output
Here the expect output of each code line above is:
```
DatetimeIndex(['2020-01-31 17:00:00', '2020-02-29 17:00:00',
'2020-03-31 17:00:00', '2020-04-30 17:00:00'],
dtype='datetime64[ns]', freq='M')
```
#### Output of ``pd.show_versions()``
<details>
(While the below output reports pandas version 1.0.3, i have also tested with version 1.0.5 with same results.)
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.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 : fi_FI.UTF-8
LOCALE : fi_FI.UTF-8
pandas : 1.0.3
numpy : 1.19.0
pytz : 2019.3
dateutil : 2.8.1
pip : 9.0.1
setuptools : 46.1.3
Cython : 0.29.20
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 0.9.6
lxml.etree : 4.2.1
html5lib : 0.999999999
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 5.5.0
pandas_datareader: None
bs4 : 4.6.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.2.1
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.5.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.1.0
xlwt : None
xlsxwriter : 0.9.6
numba : None
</details>
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MDU6SXNzdWU2NjA4NTgxMjQ=
| 35,343
|
ENH: change sort behavior in stack() so it's user-directed
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[] | null | 11
| 2020-07-19T14:09:36Z
| 2020-07-20T12:23:56Z
| 2020-07-19T18:20:07Z
|
NONE
| null |
https://github.com/pandas-dev/pandas/blob/bfac13628394ac7317bb94833319bd6bf87603af/pandas/core/reshape/reshape.py#L623
**Is your feature request related to a problem?**
I need a multiindex dataframe to stack in a specific order. I also need the columns to fail the `this.columns.is_lexsorted()` test (the duplicated column names are how I'm merging the data while stacking it, as a workaround to not being able to get a `linspace` result out of the `rolling` method). Currently, the `sort_index` method is causing the dataframe to reorder alphabetically, which is *not* the order I need it to stack in.
**Describe the solution you'd like**
One of the following (preferably not the last):
1. be able to choose the index/column order by passing in a list or set of list
2. be able to choose to keep the input order of the stacking indexes/columns (or to have it default to keeping the input order; this seems the easiest)
3. receive a warning or be provided with documentation that indicates all indexes/columns will be reordered alphabetically if they have any duplicated labels, and to name their secondary sorting indexes/columns accordingly.
**API breaking implications**
Unsure about option 1, but the default suggested in option 2 should be fine.
**Describe alternatives you've considered**
This is already a workaround for the `rolling` method not working with non-scalar outputs. I'll be renaming the columns in the short term, but this seems hacky.
**Additional context**
This issues is closely related to the functionality of `rolling`. The `stack` method is being used/suggested as a workaround for the inability to use `rolling` to output linear results. `MultiIndex` is the pandas way of dealing with additional dimensionality of data; when the rolling method doesn't play nice with adding a dimension to the data set, the user then ends up trying to recreate a `rolling` method equivalent via `stack` (and other means); in order for `stack` and other methods to work well in this context, sorting behavior has to be explicit.
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TYP: remove # type: ignore for unpacking compression_args
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| 2
| 2020-07-19T14:43:11Z
| 2020-07-29T10:42:52Z
| 2020-07-29T09:16:25Z
|
MEMBER
| null |
eg. compresslevel for GzipFile is an int and compression in get_handle is Optional[Union[str, Mapping[str, Any]]] = None
once we adopt TypedDict, then maybe we could be more explicit here, but for now Any is appropriate.
the ignored message for GZipFile is `error: Argument 2 to "GzipFile" has incompatible type **Dict[str, str]"; expected "int"`
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DOC: expand "using a Docker container" section
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| 2
| 2020-07-19T19:38:12Z
| 2020-08-01T13:11:02Z
| 2020-08-01T13:10:24Z
|
MEMBER
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#using-a-docker-container
#### Documentation problem
Someone unfamiliar with Docker would be confused reading this.
#### Suggested fix for documentation
The expansion needed is quite small, just:
1. link to the Docker installation instructions
2. link to how to develop in a container using PyCharm (current docs only link to vs code instructions)
3. mention that you might still need to rebuild the C extensions if/when you merge with upstream/master with
```
python setup.py build_ext --inplace -j 4
```
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MDU6SXNzdWU2NjEwNzI0OTU=
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ENH: `.to_timedelta` method for `PeriodIndex` and `Period` objects
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[] | null | 3
| 2020-07-19T21:11:18Z
| 2021-08-08T17:10:04Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
I oftentimes find myself needing the duration of a `Period` or `PeriodIndex`, which I calculate with
```
timedelta = (p+1).start_time - p.start_time
```
It seems useful and commonly used enough to be included as a standard method.
#### Describe the solution you'd like
Additional `.to_timedelta` method, just as there exists a `.to_timestamp` method on these objects.
#### API breaking implications
None afaics.
#### Describe alternatives you've considered
Use a `.duration` attribute instead, which does the conversion from timedelta to seconds `((p+1).start_time - p.start_time).total_seconds()`, but that makes little sense.
#### Additional context
Sample:
```python
pi = pd.period_range('2019', periods=3, freq='Y')
pi
PeriodIndex(['2019', '2020', '2021'], dtype='period[A-DEC]', freq='A-DEC')
tdi = ((pi+1).start_time - pi.start_time) #now
tdi = pi.to_timedelta() #as suggested
tdi
TimedeltaIndex(['365 days', '366 days', '365 days'], dtype='timedelta64[ns]', freq=None)
# finding the middle of a period
mid_ts = pi.start_time + 0.5*((pi+1).start_time - pi.start_time) #now
mid_ts = pi.start_time + 0.5*pi.to_timedelta() #as suggested
mid_ts
DatetimeIndex(['2019-07-02 12:00:00', '2020-07-02 00:00:00', '2021-07-02 12:00:00'],
dtype='datetime64[ns]', freq='8772H')
```
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BUG/FEATURE: to_sql data types not reflecting types accurately
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[] | null | 2
| 2020-07-19T23:31:24Z
| 2021-06-24T15:46:39Z
| null |
NONE
| null |
I'm not sure whether or not this falls into a feature/bug or perhaps an "unfulfilled/unrealistic assumption". Further, my experience here might be "the tip of the iceberg" on a bigger underlying issue with to_sql() (or it could be a red herring that is only a problem for me :grin:)
### Conditions
DataFrame with multiple dtypes - my example has 200+ columns with dtypes of: ```{dtype('O'), dtype('int16'), dtype('uint8'), dtype('uint16'), dtype('uint32'), dtype('float64')}```. My test case is using Postgres 12 as the destination DB.
### Current Behavior
```DataFrame.to_sql(...)``` *does* work to Postgres. **However**, from the various dtypes listed above, only 3 Postgres column types are created:
1. bigint (for all integers)
* equivalent to int64 (64-bit / 8-byte precision integer)
2. double precision (for all floats)
* techncially not directly equivalent (I'm not having an issue, but others might)
3. text (for all objects)
See: [Postgres Numeric Data Types](https://www.postgresql.org/docs/12/datatype-numeric.html#DATATYPE-NUMERIC-DECIMAL) for reference.
### The Issue/Effects
The ultimate issue is that the created table, although it appears to work, does not really conform to the data types in the DataFrame. Specifically, with regard to integer types. Postgres (unlike MySQL) doesn't have unsigned integer types but does have (generally) 3 integer types:
* smallint (2-bytes / 16-bit integer)
* integer (4-bytes / 32-bit integer)
* bigint (8-bytes / 64-bit integer)
I'm still reading through the to_sql() code but if the docstring (quoted below) from SQLTable() is accurate, it would appear that Pandas is relying on SQLAlchemy to handle the conversion? If so, the assumption that SQLAlchemy is doing the conversion correctly/well looks to be unfounded.
https://github.com/pandas-dev/pandas/blob/bfac13628394ac7317bb94833319bd6bf87603af/pandas/io/sql.py#L663-L669
### Some of the Impacts
Bigger tables. Presumably longer insert times. Potentially conversion issues (I haven't tried feeding to_sql() a uint64 .... )
### Thoughts
If my understanding is correct that Pandas is relying on SQLAlchemy to do the type conversions, I guess this could be seen as either a Pandas issue or an SQLAlchemy issue:
A. As an SQLAlchemy Issue
* Pandas is feeding the data to SQLAlchemy with the assumption that it will properly convert the datatypes into various dialects (e.g. PGSQL, MySQL, SQLITE, etc.) correctly
* Therefore, SQLAlchemy needs to do a better job of converting the input data
B. As a Pandas issue
* AFAIK, SQLAlchemy was never designed with the plethora of "fully functional" data types provided by Numpy and which Pandas uses - it was designed around Python data types which are much simpler. So, SQLAlchemy can't be expected to make intelligent/informed decisions based on Numpy data types.
* Therefore, Pandas needs to either provide SQLAlchemy with more metadata (I'm not sure this is possible) and/or not assume that SQLAlchemy is going to do the conversion properly
### Potential Further Impacts
I'm really not well versed enough in the Pandas/Numpy/SQLAlchemy code base to understand the potential impacts. However, I'm especially curious about the handling of float values and also about the interaction with other database engines. (For example, something that seems absolutely crazy to me about SQLite3: column data types aren't strict, [they are more of a suggestion](https://www.sqlite.org/datatype3.html) ... )
### Summary/The Bottom Line/TL;DR;
I'd like to see to_sql() create a table with the *best* (e.g. smallest/most appropriate) data type - preferably a close match to the DataFrame.dtype. Currently, at least in the case of Postgres, it does not.
Finally, perhaps someone with more knowledge than I could double check to see if the assumption that SQLAlchemy *is actually* reliably converting *all* datatypes is correct?
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fix generate_range function in datetimes.py file
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NONE
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change some issues be metioned at #35342
- [ ] 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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Non-intuitive behavior when multi-indexing a Series
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| 2
| 2020-07-20T07:37:09Z
| 2021-01-28T00:59:52Z
| 2021-01-28T00:59:52Z
|
NONE
| null |
Most likely that is not a bug but intended behavior but I find it quite confusing.
Consider the following example
```
idx = pd.IndexSlice
s = pd.Series(index=pd.MultiIndex.from_tuples([('A', '0'), ('A', '1'), ('B', '0')]),
data=[21, 22, 23])
s.loc[idx['A', :], :]
```
which gives
```
A 0 21
1 22
B 0 23
dtype: int64
```
while intuitively, one would expect
```
A 0 21
1 22
dtype: int64
```
I would expect the latter because that's what you would get with a `pd.DataFrame` where the second `:` is referring to the column index. Not paying attention that the object is a series, gives you a completely different result than if you would have a data frame.
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MDU6SXNzdWU2NjE3NzcyMTI=
| 35,350
|
BUG: register_matplotlib_converters leads to wrong datetime interpretation with matplotlib 3.3
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| 7
| 2020-07-20T12:42:00Z
| 2020-07-23T22:06:04Z
| 2020-07-23T22:06:04Z
|
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 datetime
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
t = [
datetime.datetime(2022, 12, 31, 0, 0),
datetime.datetime(2022, 11, 30, 0, 0),
datetime.datetime(2022, 10, 31, 0, 0),
]
s = [0.0, 0.1, 0.2]
fig, ax = plt.subplots()
ax.plot_date(x=t, y=s)
ax.xaxis.set_major_formatter(DateFormatter("%b '%y"))
plt.xticks(rotation=90)
plt.show()
```
#### Problem description
The years in the plot are now 91 and 92.
**Note**: With `matplotlib 3.2.2` (the last stable release) the error is not there. So I guess this is a bug due to an api change inside `matplotlib 3.3.0`.
#### Expected Output
I expects the year 22 and 23 like in this image:
The second image can be produced by deleting the call to `register_matplotlib_converters()`
#### Output of ``pd.show_versions()``
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.5
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.3.0
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
```
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BUG: multiindex slice 1.1.0rc0
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| 5
| 2020-07-20T14:26:22Z
| 2020-07-21T11:20:01Z
| 2020-07-21T11:20:01Z
|
NONE
| null |
mulitindex slicing returns different (from pandas<=1.0.x) results in pandas 1.1.0rc0
```
df = pd.DataFrame( data=[[0,1,'a',1],[1,0,'a',0],[1,1,'b',1],[1,1,'c',2]], columns=['index','date','author','price'] )
# to keep the most recent date for every index (could happen in practice)
idx = df.drop_duplicates('index',keep='last').set_index(['index','date']).index
df.set_index(['index','date']).loc[idx,:].reset_index()
```
df
index | date | author | price
-- | -- | -- | --
0|1|a | 1
1|0|a | 0
1|1|b | 1
1|1|c | 2
Result
| | author | price
-- | -- | --
(0, 1) | a | 1
(1, 1) | b | 1
(1, 1) | c | 2
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MDU6SXNzdWU2NjE5NDAyOTY=
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|
to_sql functionality not working on python
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| 2020-07-20T15:15:36Z
| 2020-08-09T11:09:39Z
| 2020-08-09T11:09:39Z
|
NONE
| null |
Whenever I try to insert a pandas DF into my database, I get this error:
<img width="844" alt="Screen Shot 2020-07-20 at 11 13 28 AM" src="https://user-images.githubusercontent.com/48495622/87954278-255e9d80-ca7a-11ea-90aa-12316ef8b105.png">
It happens with any dataframe I try, even dataframes I had successfully inserted before. The specific test dataframe I was trying to insert is this:
<img width="208" alt="Screen Shot 2020-07-20 at 11 14 49 AM" src="https://user-images.githubusercontent.com/48495622/87954346-41623f00-ca7a-11ea-9ba6-04b1e3709c1e.png">
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REGR: MultiIndex Indexing
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| 2020-07-21T11:30:10Z
| 2020-07-21T11:20:02Z
|
MEMBER
| null |
- [ ] closes #35351
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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QST: How to provide regular expression in na_values ?
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[] | null | 6
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| 2021-03-13T13:17:59Z
| 2021-03-13T13:17:58Z
|
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.
```python
# Your code here, if applicable
```
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DOC: Fix small spelling mistake in style docs
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|
CONTRIBUTOR
| null |
This pull request fixes a small spelling mistake in the pandas style user guide.
I believe this change does not justify a whatsnew entry, since it is extremely small. It does not refer to a particular issue on GitHub. I have not run `black` or `flake8`, since I believe the change is out of scope for these tools.
- [ ] ~~closes #xxxx~~
- [ ] ~~tests added / passed~~
- [ ] ~~passes `black pandas`~~
- [ ] ~~passes `git diff upstream/master -u -- "*.py" | flake8 --diff`~~
- [ ] ~~whatsnew entry~~
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REF: Avoid post-processing in blockwise op
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| 2020-07-20T19:04:37Z
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|
MEMBER
| null |
Small step towards #34714
cc @TomAugspurger IIRC you implemented split_frames/split_items, which this removes
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MEMBER
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| 1
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| 2020-07-21T11:16:24Z
| 2020-07-20T22:17:51Z
|
CONTRIBUTOR
| null |
Should fix the doc build. We have https://github.com/pandas-dev/pandas/issues/34850 to fix this.
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REF: implement _apply_blockwise
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| 4
| 2020-07-20T21:35:19Z
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MEMBER
| null |
Preliminary pushing towards #34714
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| 35,360
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fix bug when combining groupby with resample and interpolate with dat…
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| 3
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| 2020-08-20T00:09:38Z
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MEMBER
| null |
…etime-index (GH 35325)
- [x] closes #35325
- [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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FIX: remove dependence on matplotlib.dates.epoch2num
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| 2020-07-20T22:14:23Z
| 2020-07-23T15:54:32Z
| 2020-07-23T15:44:57Z
|
CONTRIBUTOR
| null |
- [ ] closes #34850
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
`converter._dt_to_float_ordinal(dt)` special cased `datetime64` objects, and used `matplotlib.dates.epoch2num`. However, matplotlib has supported `datetime64` natively since Matplotlib 2.2 (https://github.com/matplotlib/matplotlib/pull/9779), so this special casing shouldn't be necessary (unless there is a subtlety I'm missing). Matplotlib would also prefer to deprecate `epoch2num`, so this would help us streamline.
I didn't check your support policy - if you need to support pre MPL 2.2, then I guess some try/except logic will need to go in here.
ping @TomAugspurger @dstansby
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Pandas is the most difficult data processing tool in Python, which is very inhumane and difficult to use
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NONE
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Pandas is the most difficult data processing tool in Python, which is very inhumane and difficult to use
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API: quantile function over columns transposes dataframe
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| 3
| 2020-07-21T07:35:14Z
| 2021-08-08T17:11:08Z
| 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.
---
#### Code snippet
```
import pandas as pd
import numpy as np
df = pd.DataFrame(data={'a': np.arange(100), 'b': np.arange(100),})
df = df.quantile([0.2, 0.5, 0.8], axis=1)
```
#### Problem description
Using `DataFrame.quantile` over axis=1 with multiple values for q transposes the dataframe. I would expect the quantiles to appear over the specified axis, so along the columns axis when axis = 1, and along rows when axis = 0. Ofcourse, an easy fix is a call to `DataFrame.transpose`. Ideally this should not be necessary in my opinion.
#### Expected Output
Quantile values over the specified axis without transposing the dataframe.
#### Output of ``pd.show_versions()``
<details>
C:\Anaconda64\lib\site-packages\setuptools\distutils_patch.py:26: UserWarning: Distutils was imported before Setuptools. This usage is discouraged and may exhibit undesirable behaviors or errors. Please use Setuptools' objects directly or at least import Setuptools first.
"Distutils was imported before Setuptools. This usage is discouraged "
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : nl_NL.UTF-8
LOCALE : None.None
pandas : 1.0.5
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200712
Cython : 0.29.21
pytest : 5.4.3
hypothesis : 5.19.3
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.2
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 : 4.5.2
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pyarrow : None
pytables : None
pytest : 5.4.3
pyxlsb : None
s3fs : None
scipy : 1.3.1
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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MDU6SXNzdWU2NjI3NDQ4ODI=
| 35,364
|
DOC: `to_pickle` arguments for `compression` have a mistake (easy-fix)
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| 2
| 2020-07-21T08:30:09Z
| 2020-12-02T17:05:38Z
| 2020-12-02T03:00:11Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_pickle.html
#### Documentation problem
`compression{‘infer’, ‘gzip’, ‘bz2’, ‘zip’, ‘xz’, None}, default ‘infer’` notes that `gzip` is a valid extension, this is wrong, the extension checked for is `gz`. The documentation for `to_csv` is correct with `... detect compression mode from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’ or ‘.xz’` and should act as a guide: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_csv.html
#### Suggested fix for documentation
`compression{‘infer’, ‘gz’, ‘bz2’, ‘zip’, ‘xz’, None}, default ‘infer’` to match the code implementation: https://github.com/pandas-dev/pandas/blob/v1.0.5/pandas/io/common.py#L223
There _may_ be an argument to extending the `_compression_to_extension` code to accept both `gzip` and `gz`for gzip compression, whoever reads this might decide this is worth escalating. For me making sure we fix the docs in line with the code is the priority.
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|
BUG: read_csv fails with float_precision="round_trip" and decimal=","
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| 1
| 2020-07-21T11:27:45Z
| 2020-08-11T00:36:27Z
| 2020-08-11T00:36:27Z
|
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.
---
#### Code Sample, a copy-pastable example
```python
import io
import pandas as pd
content = """\
6,5;3,3
3,4;1,0
"""
pd.read_csv(io.StringIO(content), delimiter=";", header=None,
dtype=float, decimal=",", float_precision="round_trip")
```
#### Problem description
read_csv fails if using `float_precision="round_trip"` and `decimal=","` (or any other decimal and thousands separator).
It raises an
```
Traceback (most recent call last):
File "pandas/_libs/parsers.pyx", line 1152, in pandas._libs.parsers.TextReader._convert_tokens
TypeError: Cannot cast array from dtype('O') to dtype('float64') according to the rule 'safe'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 10, in <module>
File "/Users/aleserjavec/virtual/py37/lib/python3.7/site-packages/pandas/io/parsers.py", line 676, in parser_f
return _read(filepath_or_buffer, kwds)
File "/Users/aleserjavec/virtual/py37/lib/python3.7/site-packages/pandas/io/parsers.py", line 454, in _read
data = parser.read(nrows)
File "/Users/aleserjavec/virtual/py37/lib/python3.7/site-packages/pandas/io/parsers.py", line 1133, in read
ret = self._engine.read(nrows)
File "/Users/aleserjavec/virtual/py37/lib/python3.7/site-packages/pandas/io/parsers.py", line 2037, in read
data = self._reader.read(nrows)
File "pandas/_libs/parsers.pyx", line 860, in pandas._libs.parsers.TextReader.read
File "pandas/_libs/parsers.pyx", line 875, in pandas._libs.parsers.TextReader._read_low_memory
File "pandas/_libs/parsers.pyx", line 952, in pandas._libs.parsers.TextReader._read_rows
File "pandas/_libs/parsers.pyx", line 1084, in pandas._libs.parsers.TextReader._convert_column_data
File "pandas/_libs/parsers.pyx", line 1158, in pandas._libs.parsers.TextReader._convert_tokens
ValueError: could not convert string to float: '6,5'
```
#### Expected Output
```
0 1
0 6.5 3.3
1 3.4 1.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.16.2
pytz : 2018.3
dateutil : 2.6.1
pip : 20.1.1
setuptools : 39.0.1
Cython : 0.29.13
pytest : 3.7.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.1.5
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 6.2.1
pandas_datareader: None
bs4 : 4.6.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.3.0
numexpr : 2.6.6
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 3.7.1
pyxlsb : None
s3fs : None
scipy : 1.1.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.1.0
xlwt : None
xlsxwriter : 1.1.5
numba : 0.41.0
</details>
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ENH: Allow to group by an empty list
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[] | null | 11
| 2020-07-21T14:29:35Z
| 2021-01-14T11:41:16Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
Yes, I cannot group by an empty list.
#### Describe the solution you'd like
I would like to be able to group by an empty list of columns. At the moment I get an error.
```
>>> DataFrame([{"a":1, "b":2}, {"a":3, "b":4}]).groupby([]).agg(aagg=('a', 'sum'), bagg=('b', 'sum'))
Traceback (most recent call last):
...
ValueError: No group keys passed!
```
I would prefer to see
```
>>> DataFrame([{"a":1, "b":2}, {"a":3, "b":4}]).groupby([]).agg(aagg=('a', 'sum'), bagg=('b', 'sum'))
aagg bagg
4 6
```
which would generalise grouping by a non-empty list, which works fine.
```
>>> DataFrame([{"a":1, "b":2}, {"a":3, "b":4}]).groupby(["a", "b"]).agg(aagg=('a', 'sum'), bagg=('b', 'sum'))
aagg bagg
a b
1 2 1 2
3 4 3 4
```
```
>>> DataFrame([{"a":1, "b":2}, {"a":3, "b":4}]).groupby(["a"]).agg(aagg=('a', 'sum'), bagg=('b', 'sum'))
aagg bagg
a
1 1 2
3 3 4
```
It seems to me this would be reasonable behaviour. Is there a particular reason it is not supported?
#### API breaking implications
None that I know of.
#### Describe alternatives you've considered
A partial workaround is to cook up a grouping function for simulating the behaviour I want, but this lacks uniformity.
```
>>> DataFrame([{"a":1, "b":2}, {"a":3, "b":4}]).groupby(lambda _: "Only row").agg(aagg=('a', 'sum'), bagg=('b', 'sum'))
aagg bagg
Only row 4 6
```
But I don't really want `"Only row"` to be there. I just want
```
>>> DataFrame([{"a":1, "b":2}, {"a":3, "b":4}]).groupby([]).agg(aagg=('a', 'sum'), bagg=('b', 'sum'))
aagg bagg
4 6
```
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MDU6SXNzdWU2NjMxMzE0OTY=
| 35,367
|
DOC: Rethink recommending eval/query in "Enhancing performance" section of user guide
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[] | null | 3
| 2020-07-21T16:02:48Z
| 2020-09-02T23:56:11Z
| null |
NONE
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/user_guide/enhancingperf.html#expression-evaluation-via-eval
#### Documentation problem
It seems that `eval` and `query` are not faster than boolean indexing in latest versions of Pandas. See [here](https://stackoverflow.com/questions/49936557/pandas-dataframe-loc-vs-query-performance) for example and [my own experience](https://groups.google.com/forum/#!topic/pydata/Bwj5Vd00yiI) is the same.
#### Suggested fix for documentation
Remove this section from "Ehnancing perfromance".
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DOC: Fix heading capitalization in doc/source/whatsnew - part6 (#32550) (open PR again due to branch removed)
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| 2
| 2020-07-21T20:22:21Z
| 2020-08-07T09:35:56Z
| 2020-08-07T09:35:40Z
|
CONTRIBUTOR
| null |
- [ ] Modify files v0.22.0.rst, v0.23.1.rst, v0.19.0.rst, v0.24.0.rst, v0.24.2.rst
-[ ] Add exceptions in 'validate_rst_title_capitalization.py'
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BUG: df reassignment following reorder_categories changed behavior in 1.1.0rc0
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| 2
| 2020-07-21T20:48:11Z
| 2020-07-27T14:12:36Z
| 2020-07-27T14:12:35Z
|
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
h = pd.Series(list("mn")).astype("category")
df = pd.DataFrame({"h":h})
df.h = df.h.cat.reorder_categories(["n", "m"])
print(df.h.cat.categories)
h = h.cat.reorder_categories(["n","m"])
print(h.cat.categories)
```
#### Problem description
Output with 1.1.0rc0:
```
Index(['m', 'n'], dtype='object')
Index(['n', 'm'], dtype='object')
```
The assignment of the reordered series back into the dataframe seems to remove the new order. This has changed from 1.0.5 but I could not find a note on this under the release notes.
#### Expected Output
As with 1.0.5:
```
Index(['n', 'm'], dtype='object')
Index(['n', 'm'], dtype='object')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : bfac13628394ac7317bb94833319bd6bf87603af
python : 3.8.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
Version : Darwin Kernel Version 19.5.0: Tue May 26 20:41:44 PDT 2020; root:xnu-6153.121.2~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.1.0rc0
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200712
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : 2.3.1
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
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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BUG: Categorical data is not correctly plotted with its associated value when using plt.plot.
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| 2020-07-21T21:28:57Z
| 2021-08-08T18:51:49Z
| 2021-08-08T18:51:49Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
### Bug report
**Bug summary**
- If a column is set as categorical with [`pd.Categorical`][1], the categorical column is not correctly plotted with the associated value, when using `plt.plot`, as shown below.
- This issue was reported here [Why is a column of categorical data not correctly plotted with its associated values?][2]
- I tweeted the issue @matplotlib and the response was _Ack, please open an issue! This looks like it might be a bug in our **units/categorical machinery** Confused face_
- A workaround is to sort the `label` column with `df.sort_values('label', inplace=True)`, which leads to a correct plot when using `plt.plot(df.label, df.value, "o")`.
- The bug was originally posted to [matplotlib][3].
**Code for reproduction**
```python
import pandas as pd
import matplotlib.pyplot as plt
labels = ["b", "b", "a", "b", "a"]
values = [10, 11, 1, 12, 2]
df = pd.DataFrame([{"label": label, "value": v} for label, v in zip(labels, values)])
df.label = pd.Categorical(df.label, categories=["a", "b"], ordered=True)
# display(df)
label value
0 b 10
1 b 11
2 a 1
3 b 12
4 a 2
# plot
plt.plot(df.label, df.value, "o")
```
**Actual outcome**
**Expected outcome**
```python
plt.scatter(df.label, df.value)
```
**Matplotlib version**
<!--Please specify your platform and versions of the relevant libraries you are using:-->
* Operating system: Windows 10 64-bit
* Matplotlib version: 3.2.2
* Matplotlib backend (`print(matplotlib.get_backend())`): `module://ipykernel.pylab.backend_inline`
* Python version: 3.7.7
* Jupyter Lab version (if applicable): 2.1.5
- matplotlib was installed with Anaconda and updated way `conda update conda` & `conda update --all`
- I don't know these details for the person who reported the issue on stack overflow
[1]: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Categorical.html#pandas-categorical
[2]: https://stackoverflow.com/questions/63013321
[3]: https://github.com/matplotlib/matplotlib/issues/18005
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MDU6SXNzdWU2NjMzNzg2MDk=
| 35,371
|
BUG: to_parquet doesn't reliably support file:// URI
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[] | null | 2
| 2020-07-21T23:55:12Z
| 2020-07-23T10:55:31Z
| 2020-07-22T14:53:55Z
|
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.__version__
'1.0.5'
df = pd.DataFrame() # Any dataframe is fine for this issue.
df.to_parquet('file:///tmp/df.parquet') # DOESN'T WORK! Raises urllib.error.URLError: <urlopen error [Errno 2] No such file or directory: '/tmp/df.parquet'>
df.to_parquet('/tmp/df.parquet') # Works fine.
df.to_parquet('file:///tmp/df.parquet') # Works given that the file now exists.
```
#### Problem description
It is important to correctly support file URIs so users can interchangeably write to S3 or local files, both of which I represent as a URI. With Pandas 1.0.5, `to_parquet` works with a file URI only if the file already exists.
#### Expected Output
`to_parquet` should work with a file URI independent of whether the file already exists.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.4.final.0
python-bits : 64
OS : Darwin
OS-release : 19.3.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1.post20200622
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fastparquet : 0.4.1
gcsfs : None
lxml.etree : 4.5.1
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pytest : 5.4.3
pyxlsb : None
s3fs : 0.4.2
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.50.1
</details>
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MDU6SXNzdWU2NjMzOTM3NTk=
| 35,372
|
BUG: Pandas Read_html can't get values from table td data-sort-values
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| 2020-07-22T00:45:13Z
| 2020-09-04T15:29:03Z
| 2020-09-04T15:29:03Z
|
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.
---
#### URL Example
[https://en.wikipedia.org/wiki/Comparison_of_text_editors](https://en.wikipedia.org/wiki/Comparison_of_text_editors)
```python
df = pd.read_html(url, flavor='bs4')
```
#### Problem description
I'm trying to scrape first table from WIkipedia, the column 'Open Source' and 'CLI available' are displaying the data with ✔ images to refer 'Yes' and ❌ for 'No'. if we inspect the HTML code.
```html
<td data-sort-value="Yes" style="background: #D2FFD2; color: black; vertical-align: middle; text-align: center;" class="table-yes2"><img alt="Yes" src="//upload.wikimedia.org/wikipedia/commons/thumb/0/03/Green_check.svg/13px-Green_check.svg.png" decoding="async" title="Yes" width="13" height="13" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/0/03/Green_check.svg/20px-Green_check.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/0/03/Green_check.svg/26px-Green_check.svg.png 2x" data-file-width="600" data-file-height="600">
</td>
```
The value is embedded in `data-sort-value` but `pandas.read_html` maybe read the column value as image and return NaN
#### Output of ``pd.show_versions()``
<details>
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_Indonesia.1252
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 : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.2
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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ENH: Add validation checks for non-unique merge keys
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| 2020-07-22T03:44:53Z
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| 2020-09-26T15:44:19Z
|
NONE
| null |
- [x] closes #27430
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
After implementing the suggestion in #27430 I realized it should also apply to `1:m` and `m:1` merges. When any type of `many` merge is specified, an error will be raised if the `many` side is actually unique.
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Change defaults for rolling/expanding.apply engine kwargs to None
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MEMBER
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This change was supposed to be apart of https://github.com/pandas-dev/pandas/pull/35182 but was overlooked. Since 1.1 isn't out, this doesn't need to be backported correct?
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MDU6SXNzdWU2NjM1Mzc5NTQ=
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|
ENH: Collation argument to to_sql
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| 2
| 2020-07-22T07:23:23Z
| 2020-07-28T15:35:46Z
| 2020-07-28T14:22:14Z
|
NONE
| null |
#### Is your feature request related to a problem?
I would like to be able to set Collation when sending the data to sql table.
#### Describe the solution you'd like
Now when I use pandas function `to_sql` to send data to mysql I would use `df.to_sql(table_name, engine, if_exists='replace', index=False, chunksize=100)` where engine is pymsql engine. But this doesn't work if my data contains utf-8 (Croatian characters). It converts č and ć characters to ?.
#### API breaking implications
It wouldn't affect API.
#### Describe alternatives you've considered
I tried to change collation manually.
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BUG: inconsistent replace
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| 21
| 2020-07-22T09:37:32Z
| 2020-09-26T01:25:20Z
| 2020-09-26T01:25:20Z
|
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.
---
#### Problem description
```python
>>> pd.DataFrame([[1,1.0],[2,2.0]]).replace(1.0, 5)
0 1
0 1 5.0
1 2 2.0
>>> pd.DataFrame([[1,1.0],[2,2.0]]).replace(1, 5)
0 1
0 5 5.0
1 2 2.0
```
#### Problem description
Maybe I don't understand somethink or this is just non-sens
#### Expected Output
```python
>>> pd.DataFrame([[1,1.0],[2,2.0]]).replace(1.0, 5)
0 1
0 1 5.0
1 2 2.0
>>> pd.DataFrame([[1,1.0],[2,2.0]]).replace(1, 5)
0 1
0 5 1.0
1 2 2.0
```
Or
```python
>>> pd.DataFrame([[1,1.0],[2,2.0]]).replace(1.0, 5)
0 1
0 5 5.0
1 2 2.0
>>> pd.DataFrame([[1,1.0],[2,2.0]]).replace(1, 5)
0 1
0 5 5.0
1 2 2.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.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 : 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 : 49.2.0
Cython : None
pytest : 5.4.3
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 : 0.6.2
lxml.etree : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pytest : 5.4.3
pyxlsb : None
s3fs : None
scipy : 1.5.1
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.50.1
</details>
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[FIX] Handle decimal and thousand separator in 'round_trip' converer
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| 5
| 2020-07-22T11:32:33Z
| 2020-08-11T00:36:31Z
| 2020-08-11T00:36:27Z
|
CONTRIBUTOR
| null |
- [x] closes #35365
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
In case of non c-locale decimal and tsep, copy and fixup the source string before passing it to PyOS_string_to_double
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DOC: Data Editing Samples/Guide
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| 4
| 2020-07-22T11:38:02Z
| 2020-08-03T09:29:32Z
| null |
NONE
| null |
#### Location of the documentation
N/A
#### Documentation problem
There seems to be general community confusion over when the use `iloc`, `loc`, `at`, `iat` or the other methods that allow you to update rows and columns. What is the best way to add a single new row? Multiple rows `pd.concat` something else?
It would be nice if a guide or doc was created to point to the best practices for data editing on a DataFrame.
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DOC: Expanded Using a Docker Container section
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| null |
- [x] closes #35345
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Added Docker installation link, Docker commands, and PyCharm sections.
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CLN: resolve isort mypy import confilict test_join
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| 1
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MEMBER
| null |
Part of #35134
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Storage options
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- [x] closes #xxxx
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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BUG: Inconsistent ordering of rows when merging with how=left and how=right
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| 1
| 2020-07-22T15:51:50Z
| 2020-11-21T22:23:27Z
| 2020-11-21T22:23:27Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({'a': [1, 0, 1]})
df.merge(df, on='a', how='left', sort=False)
a
0 1
1 1
2 0
3 1
4 1
df.merge(df, on='a', how='right', sort=False)
a
0 1
1 1
2 1
3 1
4 0
```
#### Problem description
Order of output rows should be the same for how=left and how=right in this case.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.181-140.257.amzn2.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 1.0.5
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
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 : 5.4.3
pyxlsb : None
s3fs : 0.4.2
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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| 663,871,050
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MDU6SXNzdWU2NjM4NzEwNTA=
| 35,383
|
Drop similar rows of dataframe except for one column
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[] | null | 3
| 2020-07-22T15:56:12Z
| 2020-08-21T17:04:40Z
| 2020-07-23T16:30:50Z
|
NONE
| null |
It seems a trivial question, but I don't find anything on the web. I have a dataframe with **inconsistent rows**. In the example below `primary_key` is the primary key so it has to be once in dataset, while `info` is an information of `primary_key`. While for **exactly repeated rows** I want to keep only one of them using a function like `df.drop_duplicates(keep = 'first')`, I don't know which keep in the case in which primary keys (`primary_key`) are the same so refer to the same item, but `info` is different, because they are inconsistent information. In this case I'd like to remove all of these rows with a function like `df.drop_duplicates(subset = "primary_key" , keep = False)`. I don't know how to do this kind of filter because I want to keep 1 row if one case and 0 rows in the other, depending on the values of a specific column within groups of _similar rows_.
### Desired behaviour
In:
```bash
primary_key info
0 a 1
1 a 1 #duplicated row of index 0
2 a 2 #similar row of indexes 0 and 1 but inconsistent with info field
3 b 1
4 b 1 #duplicated row of index 3
```
Out:
```bash
primary_key info
1 b 1 #the only primary key with the same value on info field
```
Thank you
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ENH: The parameter merge_cells parameter in function to_excel should take a 2-tuple one for index and one for columns
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| 1
| 2020-07-22T18:03:22Z
| 2020-07-22T20:09:55Z
| null |
NONE
| null |
Currently pandas `to_excel` function has one parameter named `merge_cells`: if `True` it merges all cells in the excel sheet corresponding to the index and columns. However, if there are many levels of the index and many values for the higher levels, then as we scroll on the sheet, values of the higher levels in index become invisible. One way to keep the repeated values is to set `merge_cells = False`. However, in that case the column levels are also affected.
It would be great to have a way to control this aspect of index and columns separately. One way would be to let `merge_cells` to take a 2-tuple of which one value is for the index and the other for the columns.
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BUG: ValueError: Cannot convert non-finite values (NA or inf) to integer only when DF exceed certain size
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|
NONE
| null |
- [ ] closes #35227
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CLN: resolve isort mypy import confilict test_transform
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| 3
| 2020-07-22T21:37:04Z
| 2020-07-29T16:46:03Z
| 2020-07-29T14:33:03Z
|
MEMBER
| null |
Part of #35134
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Display dataframe name or title when using display()
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| 2020-07-22T23:12:58Z
| 2020-08-21T17:04:40Z
| 2020-07-27T16:10:07Z
|
NONE
| null |
Requesting ability to display name of dataframe above dataframe when using display(df)
I have tried df.name = "Name"
and display(df, display_id = "Name")
Neither results in any kind of identifier for the dataframe when using the display() function in Jupyter Notebook.
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MDU6SXNzdWU2NjQxNTYxNTg=
| 35,388
|
BUG: pd.date_range with DST-crossing has incorrect freq attribute
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[] | null | 3
| 2020-07-23T02:07:01Z
| 2021-08-08T18:53:23Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
i = pd.date_range('2020-03-28', periods=4, freq='D', tz='Europe/Berlin')
i # DatetimeIndex(['2020-03-28 00:00:00+01:00', '2020-03-29 00:00:00+01:00', '2020-03-30 00:00:00+02:00', '2020-03-31 00:00:00+02:00'], dtype='datetime64[ns, Europe/Berlin]', freq='D')
i[0] + i.freq == i[1] #True
i[2] + i.freq == i[3] #True
i[1] + i.freq == i[2] #False (!)
```
#### Problem description
Variably-spaced timestamps are not handled well, if the variation is caused by DST. The result of `i[1] + i.freq` is
`Timestamp('2020-03-30 01:00:00+0200', tz='Europe/Berlin', freq='D')`, whereas
`Timestamp('2020-03-30 00:00:00+0200', tz='Europe/Berlin', freq='D')` was expected.
This is in contrast to timestamps where the variation is caused by e.g. months being different lengths, which *are* handled correctly.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : de_DE.cp1252
pandas : 1.0.5
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200714
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
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 : None
matplotlib : 3.2.2
numexpr : None
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 : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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ENH: Request for `Index.to_string` & `MultiIndex.to_string`
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| 1
| 2020-07-23T08:16:13Z
| 2021-08-08T18:53:51Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
I'd like to request `to_string` API support in `Index` & `MultiIndex`, functionality being similar to the existing `DataFrame.to_string` & `Series.to_string`.
#### Describe the solution you'd like
`to_string` provides flexibility to modify/control the string representation of pandas objects, we can do this currently with `DataFrame` & `Series` but the API support is not yet there for `Index` & `MultiIndex`.
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_string.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.to_string.html
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QST: DatetimeIndex changes
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| 3
| 2020-07-23T11:44:45Z
| 2020-07-23T12:38:53Z
| 2020-07-23T12:38:15Z
|
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
In version 1.0.5, DatetimeIndex seems greatly changed from earlier versions
Was this intentional and documented in the version changes?
Many statsmodels forecasting steps now break
For example, the code below no longer works
dx = pd.DatetimeIndex(start ='2020-01-01 00:00', freq ='M', periods = 12)
I get
TypeError: __new__() got an unexpected keyword argument 'start'
The documentation has these arguments
class pandas.DatetimeIndex(data=None, freq=None, tz=None, normalize=False, closed=None, ambiguous='raise', dayfirst=False, yearfirst=False, dtype=None, copy=False, name=None)[source]
with little explanation or usefulness IMHO
I see old Pandas has
def __new__(cls, data=None,
freq=None, start=None, end=None, periods=None, tz=None,
normalize=False, closed=None, ambiguous='raise',
dayfirst=False, yearfirst=False, dtype=None,
copy=False, name=None, verify_integrity=True):
```python
# Your code here, if applicable
# Create the DatetimeIndex
dx = pd.DatetimeIndex(start ='2020-01-01 00:00', freq ='M', periods = 12)
## expect TypeError: __new__() got an unexpected keyword argument 'start'
```
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BUG: Recursion error when querying empty df
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| 2020-07-23T12:52:50Z
| 2020-07-23T13:38:34Z
| 2020-07-23T13:38:15Z
|
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.
---
**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(columns=["a", "b"])
df.query("(a+b)==1")
```
**NOTE**: Under some circumstances the same might happen when the colums only contain ``np.nan``s but I couldn't get a MWE yet.
#### Problem description
Throws recursion error.
#### Expected Output
Empty df.
#### 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 : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.18.1
pytz : 2019.2
dateutil : 2.7.3
pip : 18.1
setuptools : 41.1.0
Cython : None
pytest : 5.3.2
hypothesis : None
sphinx : 3.0.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 5.8.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.2
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU2NjQ0OTM3MzQ=
| 35,392
|
BUG: Index.get_indexer_non_unique misbehaves when index contains multiple nan
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| 2021-08-05T11:51:18Z
| 2021-08-05T11:51:18Z
|
NONE
| null |
#### Code Sample, a copy-pastable example
```python
axis = pd.Index([np.nan, 'var1', np.nan])
axis.get_indexer_for([np.nan])
```
#### Current Output
`array([-1], dtype=int64)`
Meaning that `np.nan` is not in axis which is incorrect and makes `df.drop(columns=[np.nan])` to fail when columns contains multiple `nan`.
#### Expected Output
`array([0, 2], dtype=int64)`
#### 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 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 1.0.5
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200714
Cython : 0.29.21
pytest : 5.4.3
hypothesis : 5.20.3
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
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 : 4.5.2
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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MDExOlB1bGxSZXF1ZXN0NDU1NzczNDY2
| 35,393
|
Matplotlib 3.3 compatibility fixups
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| 4
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|
CONTRIBUTOR
| null |
Closes #34850
Closes https://github.com/pandas-dev/pandas/issues/35350
Haven't tested with matplotlib's older than 3.2 yet. We'll see what CI says.
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|
BUG: UnboundLocalError on `df.to_string(max_rows=0)`
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| 4
| 2020-07-23T16:13:12Z
| 2021-09-01T18:08:25Z
| 2021-09-01T18:08:25Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**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
d = {'col1': [1, 2], 'col2': [3, 4]}
df = pd.DataFrame(data=d)
df.to_string(max_rows=0)
```
example running it:
```python
>>> import pandas as pd
>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df.to_string()
' col1 col2\n0 1 3\n1 2 4'
>>> df.to_string(max_rows=1)
' col1 col2\n0 1 3\n.. ... ...'
>>> df.to_string(max_rows=0)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.7/site-packages/pandas/core/frame.py", line 818, in to_string
line_width=line_width,
File "/usr/local/lib/python3.7/site-packages/pandas/io/formats/format.py", line 612, in __init__
self._chk_truncate()
File "/usr/local/lib/python3.7/site-packages/pandas/io/formats/format.py", line 640, in _chk_truncate
n_add_rows = self.header + dot_row + show_dimension_rows + prompt_row
UnboundLocalError: local variable 'show_dimension_rows' referenced before assignment
```
#### Problem description
Throwing an unchecked exception on a valid input.
The variable `show_dimension_rows` is only conditionally defined here:
https://github.com/pandas-dev/pandas/blob/v1.0.5/pandas/io/formats/format.py#L636
A simple fix could be to include an else block and set it to zero.
#### Expected Output
Ideally it would output no data rows (if the df did have some rows):
```python
>>> df.to_string(max_rows=0)
' col1 col2\n.. ... ...'
```
or the standard empty data frame message if it had zero rows:
```python
>>> d = {'col1': [], 'col2': []}
>>> df = pd.DataFrame(data=d)
>>> df.to_string(max_rows=0)
'Empty DataFrame\nColumns: [col1, col2]\nIndex: []'
```
In the simple fix case, it would assume that it was in the terminal and output as if max_rows had not been set, which would probably be better than the exception.
```python
>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df.to_string(max_rows=0)
' col1 col2\n0 1 3\n1 2 4'
>>> df.to_string()
' col1 col2\n0 1 3\n1 2 4'
```
#### Output of ``pd.show_versions()``
<details>
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
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 : 1.19.1
pytz : 2020.1
dateutil : 2.8.0
pip : 19.3.1
setuptools : 42.0.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 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
```
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Random Sample Not Working
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[] | null | 1
| 2020-07-23T16:36:29Z
| 2020-08-21T17:04:40Z
| 2020-07-23T20:32:09Z
|
NONE
| null |
I think there's an issue with the sample function. It displays the randomized dataframe with a different heading, but when you actually get the value of the dataframe at a certain index, it displays the same value as before the random sample.
<img width="651" alt="Screen Shot 2020-07-23 at 9 30 38 AM" src="https://user-images.githubusercontent.com/47517229/88312776-68fa1700-ccc7-11ea-94fc-00087cc743e7.png">
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How can I fix this?
|
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PKG: Set max pin for Cython
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| 2020-07-23T17:56:06Z
| 2020-07-23T19:41:13Z
| 2020-07-23T19:10:34Z
|
CONTRIBUTOR
| null |
We know that pandas doesn't work with Cython 3.0
(https://github.com/pandas-dev/pandas/issues/34213,
https://github.com/pandas-dev/pandas/issues/34014)
This sets the maximum supported version of Cython in our pyproject.toml
to ensure that pandas 1.1.0 can continue to be built from source without
Cython pre-installed after Cython 3.0 is released.
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CLN: Remove leftover partial-result code from apply
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CONTRIBUTOR
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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DOC: Fixed formatting and errors in whatsnew v1.1.0
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| 2
| 2020-07-23T22:35:25Z
| 2020-07-25T14:49:01Z
| 2020-07-24T13:06:26Z
|
MEMBER
| null |
cc @TomAugspurger
Minor fixes for formatting, spelling, and grammar. Please let me know if any of this is too nit-picky, I can revert to just the definite spelling/syntax errors.
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API: read_csv, to_csv line_terminator keyword inconsistency
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| 2020-09-12T17:52:26Z
| 2020-09-10T19:06:39Z
|
MEMBER
| null |
- [x] closes #9568
- [x] tests added / passed
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- [ ] whatsnew entry (pending 1.2)
I followed discussion in #9568. The aim of this PR is to allow both `line_terminator` and `lineterminator` keyword args to `read_csv` and `to_csv` but only document `line_terminator`. As per @jorisvandenbossche's suggestion we preserve `lineterminator` to stay compatible with `csv` dialects.
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MDU6SXNzdWU2NjQ5NTU3NzU=
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|
BUG: Groupby Is Changing Date Format
|
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[] | null | 0
| 2020-07-24T06:49:08Z
| 2020-08-21T17:04:41Z
| 2020-07-24T09:52:39Z
|
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
```
df = pd.read_csv("Uk-Orders.csv",parse_dates=['WO Created'])
df=df.rename(columns={"WO Created":"WO-Created","Sale Price":"Sale-Price"})
df['Sale-Price']=df['Sale-Price'].str.replace(',', '').astype('float64')
df['WO-Created'] = pd.to_datetime(df['WO-Created']).dt.normalize()
df=df.groupby('WO-Created',sort=False)['Sale-Price'].sum().reset_index()
```
#### Problem description
I have a dataset that has two columns. WO created as starting on 04-01-2016 and are populated until 06-29-2020.
After running df=df.groupby('WO-Created',sort=False)['Sale-Price'].sum().reset_index(), the last row is giving me dates like 2020-12-06, which is incorrect, because there is no such date range in my report (no December in my dataset). I suppose it has something to do with date formatting, but I am not sure how to fix it.
#### Expected Output
I would expect dates to be summarized correctly.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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MDExOlB1bGxSZXF1ZXN0NDU2MjUzMDUw
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use a try block in _expand_user instead of a typing condition
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| 2020-07-24T12:13:11Z
| 2020-09-19T07:22:39Z
| 2020-09-19T07:22:39Z
|
CONTRIBUTOR
| null |
Noticed that `_expand_user`'s docstrings stated that expansion was performed "if possible", while the actual implementation was only applying expansion to str objects.
With a try block it should work with str, os.Pathlike and bytes objects (and everything else supported by `os.path.expanduser` in the future).
I except this change may be undesirable since it will force the output type to `str`. In this case I can replace this with a docstring update.
I'll complete the checklist with tests and whatsnew entry if the change is deemed desirable.
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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|
BUG: DateTime Index format bug when importing from .csv file
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| 2020-07-24T12:28:23Z
| 2020-07-27T05:29:12Z
| 2020-07-24T14:16:23Z
|
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
data = pd.read_csv(file_path, engine='python', sep=';')
data = data.set_index('timestamp_utc')
data.index = pd.to_datetime(data.index, utc=True)
```
#### Problem description
I am sorry but I cannot provide a mwe because time is against me, but I still want to report the problem.
I have loaded a .csv file which has a column called "timestamp_utc" containing the full timestamp, date+time.
I have then set the timestamp_utc column as my index, and converted it to datetime object.
My code was not working because some dates were apparently missing while, by hand inspectyion of the .csv file, they were present.
The .csv file has all dates in dd/mm/yyyy format, from 02/12/2019 to 03/06/2020, but after running the code above and checking the index in the dataframe, this was shown (see screenshot):
so the index jumps from 2019/12/02 (which is yyyy/dd/mm format) to 2019/02/13 (which is yyyy/mm/dd format)!
#### Expected Output
I do not mind much if Pandas choses to import something as yyyy/dd/mm or yyyy/mm/dd, I know that I can use the "dayfirst" option in case, but at least Pandas should be consistent. This simply breaks the logic of indexes, which should always be increasing values. I find it quite surprising that no warning was given during the set_index operation.
These kind of issues happen on a daily basis when working with Pandas and in my group several tens of hours are spent debugging what should be a straightforward thing. My suggestion to avoid these problems in the future is that Pandas should expect and treat dates as if they were dd/mm/yyyy or yyyy/mm/dd format, throwing (at least) a warning when a date in mm/dd/yyyy format is found, so that the user can correct the import.
#### 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 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : None.None
pandas : 1.0.5
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200714
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 : 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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MDU6SXNzdWU2NjUxNjY1NjU=
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|
ENH: Please provide `register_groupBy_accessor`
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[] | null | 2
| 2020-07-24T13:24:01Z
| 2021-08-08T18:56:02Z
| null |
NONE
| null |
Thanks for providing the option to create custom accessors via [pandas.api.extensions](https://pandas.pydata.org/pandas-docs/stable/development/extending.html). This is prefereable method to extend pandas dataframes over inheritance/sub-classing. Currently, this supports these: dataframe, series, index.
Please extend this support for [groupBy](https://pandas.pydata.org/pandas-docs/stable/reference/groupby.html) objects.
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QST:module 'pandas.core.indexing' has no attribute 'get_indexers_list'
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| null |
[] | null | 1
| 2020-07-24T15:35:29Z
| 2020-08-04T11:23:20Z
| 2020-07-24T16:11:34Z
|
NONE
| null |
- [ ] 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
```
n [1]: runfile('D:/zwPython/demo/zw005xver.py', wdir='D:/zwPython/demo')
Traceback (most recent call last):
File "D:\zwPython\demo\zw005xver.py", line 29, in <module>
import zipline
File "D:\zwPython\py3\python-3.8.3.amd64\lib\site-packages\zipline\__init__.py", line 24, in <module>
from . import finance
File "D:\zwPython\py3\python-3.8.3.amd64\lib\site-packages\zipline\finance\__init__.py", line 16, in <module>
from . import execution, trading
File "D:\zwPython\py3\python-3.8.3.amd64\lib\site-packages\zipline\finance\trading.py", line 20, in <module>
from zipline.utils.pandas_utils import normalize_date
File "D:\zwPython\py3\python-3.8.3.amd64\lib\site-packages\zipline\utils\pandas_utils.py", line 223, in <module>
'_' + name for (name, _) in pd.core.indexing.get_indexers_list()
AttributeError: module 'pandas.core.indexing' has no attribute 'get_indexers_list'
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MDExOlB1bGxSZXF1ZXN0NDU2MzcyODM0
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DOC: Add compose ecosystem docs
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| 1
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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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BUG: Creating a Series from a Series results in all NaN values
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| 4
| 2020-07-24T16:17:08Z
| 2020-09-05T14:44:27Z
| 2020-09-05T14:44:27Z
|
CONTRIBUTOR
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
pd.Series(pd.Series(np.random.randn(100)),index=pd.date_range("1980-1-1",periods=100,freq="D"))
```
output
```
1980-01-01 NaN
1980-01-02 NaN
1980-01-03 NaN
...
```
#### Problem description
Series accepts array-like, and Series is array-like.
#### Expected Output
A Series with the same values and my new index.
#### 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 : AMD64 Family 23 Model 113 Stepping 0, AuthenticAMD
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 : 49.2.0.post20200714
Cython : 0.29.21
pytest : 5.4.3
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: v0.10.0dev0+6.g642bd7b
bs4 : 4.9.1
bottleneck : None
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 : None
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : None
numba : 0.50.1
</details>
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|
Deprecate Series / DataFrame.append
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| 2021-11-19T12:02:08Z
| null |
CONTRIBUTOR
| null |
I think that we should deprecate `Series.append` and `DataFrame.append`. They're making an analogy to `list.append`, but it's a poor analogy since the behavior isn't (and can't be) in place. The data for the index and values needs to be copied to create the result.
These are also apparently popular methods. DataFrame.append is around the 10th most visited page in our API docs.
Unless I'm mistaken, users are always better off building up a list of values and passing them to the constructor, or building up a list of NDFrames followed by a single `concat`.
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DOC: update DataFrame.to_feather docstring
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| 3
| 2020-07-25T05:27:28Z
| 2020-09-13T23:11:01Z
| 2020-09-13T23:10:57Z
|
MEMBER
| null |
- [x] closes #29532
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
The issue was that `DataFrame.to_feather` had an `fname` argument which in fact could be either a filename or a buffer. Most of the work to close #29532 was done in #30338 where `fname` argument was deprecated in favor of `path`. Here I update the docstring to let users know that they can use a buffer.
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DOC: Inconsistent return type documentation for functions with "inplace" option
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| 10
| 2020-07-25T10:17:59Z
| 2020-10-10T19:41:57Z
| 2020-10-10T19:41:57Z
|
NONE
| null |
#### Location of the documentation
This proposal concerns all methods with the `inplace` keyword argument. Three examples:
1 (Return type described as "blah-blah-type or `None` if `inplace=True`) https://pandas.pydata.org/docs/dev/reference/api/pandas.CategoricalIndex.add_categories.html?highlight=inplace
2 (Return type specified, but doesn't mention `None`) https://pandas.pydata.org/docs/dev/reference/api/pandas.Series.interpolate.html?highlight=inplace
#### Documentation problem
Many methods in pandas have keyword argument `inplace`. When `inplace=True` is supplied, the method updates `self` and returns `None`. When `inplace=False` is supplied, the method does not affect `self` and returns a changed copy of `self`.
Some of these functions' return types are documented as "Returns blah-type if inplace=True". Others just say "Returns blah-type". This is confusing because it suggests inconsistencies in *behavior* that are not really there.
#### Suggested fix for documentation
Find all methods with the `inplace` keyword argument. Change the "returns" section of their docstrings to this form:
```
Returns
-------
Series or DataFrame
Returns the same object type as the caller, interpolated at
some or all ``NaN`` values. (Or return `None` if
`inplace=True`.)
```
Also change all the formal return type signatures to the form "--> Optional[Foo]" if they aren't all already like that.
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BUG: Wrong Custom Formatters applied when displaying trancated frames
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| 3
| 2020-07-25T13:48:04Z
| 2020-08-16T18:40:33Z
| null |
NONE
| null |
#### Problem description
I am providing custom formatters for specific columns as dict.
If frame is large enough and some columns are truncated - then wrong formatters are applied to the columns.
(In my case that leads to crushes as wrong data type is received by the formatter).
Please notice, that behavior changes depending on the width of the console window as different columns are displayed.
#### Problem investigation
I have examined the code of my version of panda (1.0.5) and compared with the last version in GitHub - the bug seems to be still there.
The source of the problem starts with this method (`DataFrameFormatter._to_str_columns`), when
frame is set to truncated ` frame = self.tr_frame` and then `self._format_col(i)` is called with __index of the column in the TRUNCATED frame__:
```python
def _to_str_columns(self) -> List[List[str]]:
"""
Render a DataFrame to a list of columns (as lists of strings).
"""
# this method is not used by to_html where self.col_space
# could be a string so safe to cast
self.col_space = cast(int, self.col_space)
frame = self.tr_frame
# may include levels names also
str_index = self._get_formatted_index(frame)
if not is_list_like(self.header) and not self.header:
stringified = []
for i, c in enumerate(frame):
fmt_values = self._format_col(i)
```
Then this "truncated" column index is passed to `self._get_formatter`:
```python
def _format_col(self, i: int) -> List[str]:
frame = self.tr_frame
formatter = self._get_formatter(i) # the problem is HERE? _get_formatter(frame.columns[i]) ?
```
which uses __full frame columns__ to retrieve formatter __using index `i` which corresponds to the columns of the truncated frame__:
```python
# ...
else:
if is_integer(i) and i not in self.columns:
i = self.columns[i]
return self.formatters.get(i, None)
```
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-37-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 : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0.post20200309
Cython : 0.29.15
pytest : 5.4.1
hypothesis : 5.19.3
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : 3.4.4
tabulate : 0.8.3
xarray : 0.15.0
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.50.1
</details>
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MDExOlB1bGxSZXF1ZXN0NDU2NjIwOTMw
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CLN: clarify TypeError for IndexSlice argument to pd.xs
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| 2
| 2020-07-25T14:20:48Z
| 2020-08-07T17:19:12Z
| 2020-08-07T16:58:25Z
|
MEMBER
| null |
- [x] closes #35301
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
As per discussion in #35301 we do not support `IndexSlice` arguments to `xs`. This PR aims to clarify the `TypeError` thrown when that happens.
I created a separate issue to house the discussion re: functionality of `loc` vs `xs` (#35418).
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CLN: _wrap_applied_output
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[] | null | 2
| 2020-07-25T20:03:20Z
| 2020-09-10T00:07:45Z
| 2020-08-18T21:47:16Z
|
MEMBER
| null |
The majority of this change is the result of three operations:
1. Move code that returns up top, to avoid the heavily nested structure.
2. Move variable creation as close as possible to where they are used. Previously, in certain cases computations were being done and then going unused.
3. Combine duplicated code.
There are two sections that I was able to entirely remove:
````
ping = self.grouper.groupings[0]
if len(keys) == ping.ngroups:
key_index = ping.group_index
key_index.name = key_names[0]
key_lookup = Index(keys)
indexer = key_lookup.get_indexer(key_index)
# reorder the values
values = [values[i] for i in indexer]
# update due to the potential reorder
first_not_none = next(com.not_none(*values), None)
````
and
````
# GH5788 instead of stacking; concat gets the
# dtypes correct
from pandas.core.reshape.concat import concat
result = concat(
values,
keys=key_index,
names=key_index.names,
axis=self.axis,
).unstack()
result.columns = index
````
For the single test that I touched, it was checking that the categorical dtype of an index was being dropped after a groupby. I don't believe that is the correct behavior - that the categorical dtype should remain. I checked groupby/categorical bugs and didn't find any issues this closes.
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BUG: When plotting a pandas dataframe year is wrong
|
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[] | null | 6
| 2020-07-25T21:55:56Z
| 2020-08-21T17:04:41Z
| 2020-07-29T13:10:01Z
|
NONE
| null |
- [ x] I have checked that this issue has not already been reported.
- [x ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import matplotlib.pyplot as plt
# Save the attached file somewhere and open it as follows
file_name = 'your_path'
df = pd.read_csv(file_name, index_col = 'date', parse_dates = True)
df.plot()
plt.show()
```
#### Problem description
The year is displayed as in 3900s. See picture:
I have noticed a few things:
- Generating random data and keeping the index the same does not resolve the issue
- Trying to recreate the index from scratch but using the same date does not resolve the issue
- Plotting using plotly backend resolves the issue
- Doing df.to_period('D').plot() also seems to resolve the issue
#### Expected Output
Year should not be in 3900s.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.104-microsoft-standard
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 44.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.16.1
pandas_datareader: 0.9.0
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.2
matplotlib : 3.3.0
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : 0.16.0
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU2NjU4MDI4MTY=
| 35,414
|
BUG: `freq` attribute sometimes lost when concatenating series that are tz-aware
|
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| 2020-07-26T15:27:09Z
| 2020-09-04T15:31:53Z
| 2020-09-04T15:31:53Z
|
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.
---
**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
s1 = pd.Series([1, 2, 3], index=pd.date_range('2020-03-29 00:00:00', periods=3, freq='D', tz='Europe/Berlin'))
s2 = pd.Series([4, 5], pd.date_range(s1.index[-1]+s1.index.freq, periods=2, freq=s1.index.freq))
r = pd.concat([s1, s2])
s1.index, s2.index, r.index
DatetimeIndex(['2020-03-29 00:00:00+01:00', '2020-03-30 00:00:00+02:00',
'2020-03-31 00:00:00+02:00'],
dtype='datetime64[ns, Europe/Berlin]', freq='D'),
DatetimeIndex(['2020-04-01 00:00:00+02:00', '2020-04-02 00:00:00+02:00'], dtype='datetime64[ns, Europe/Berlin]', freq='D'),
DatetimeIndex(['2020-03-29 00:00:00+01:00', '2020-03-30 00:00:00+02:00',
'2020-03-31 00:00:00+02:00', '2020-04-01 00:00:00+02:00',
'2020-04-02 00:00:00+02:00'],
dtype='datetime64[ns, Europe/Berlin]', freq=None)
```
#### Problem description
s1 and s2 have indices are in the same time zone with the same fixed frequency. This also hold when they are concatenated, however, the freq attribute is lost in the result, see above. Expected would be, to have `freq='D'`.
The problem does not always occur.
* When the DST change is not included, it seems to work:
```
#Same code, change 2020-03-29 to 2020-03-01
DatetimeIndex(['2020-03-01 00:00:00+01:00', '2020-03-02 00:00:00+01:00',
'2020-03-03 00:00:00+01:00'],
dtype='datetime64[ns, Europe/Berlin]', freq='D'),
DatetimeIndex(['2020-03-04 00:00:00+01:00', '2020-03-05 00:00:00+01:00'], dtype='datetime64[ns, Europe/Berlin]', freq='D'),
DatetimeIndex(['2020-03-01 00:00:00+01:00', '2020-03-02 00:00:00+01:00',
'2020-03-03 00:00:00+01:00', '2020-03-04 00:00:00+01:00',
'2020-03-05 00:00:00+01:00'],
dtype='datetime64[ns, Europe/Berlin]', freq='D')
```
* When DST is included incorrectly (see [here](https://github.com/pandas-dev/pandas/issues/35388)), and all timestamps are 24h apart, it also works:
```
#Same code, change 2020-03-29 to 2020-03-27
DatetimeIndex(['2020-03-27 00:00:00+01:00', '2020-03-28 00:00:00+01:00',
'2020-03-29 00:00:00+01:00'],
dtype='datetime64[ns, Europe/Berlin]', freq='D'),
DatetimeIndex(['2020-03-30 01:00:00+02:00', '2020-03-31 01:00:00+02:00'], dtype='datetime64[ns, Europe/Berlin]', freq='D'),
DatetimeIndex(['2020-03-27 00:00:00+01:00', '2020-03-28 00:00:00+01:00',
'2020-03-29 00:00:00+01:00', '2020-03-30 01:00:00+02:00',
'2020-03-31 01:00:00+02:00'],
dtype='datetime64[ns, Europe/Berlin]', freq='D')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : de_DE.cp1252
pandas : 1.0.5
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200714
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
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 : None
matplotlib : 3.2.2
numexpr : None
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 : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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QST: Subclassing and concat
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[] | null | 4
| 2020-07-26T18:04:17Z
| 2021-10-22T22:25:04Z
| null |
CONTRIBUTOR
| null |
I was looking into `df.astype` not respecting subclassed objects and noticed the issue had been resolved by modifying `pd.concat` to respect subclassed objects using the following code:
```python
cons = self.objs[0]._constructor
```
[Line 464](https://github.com/pandas-dev/pandas/blob/04e9e0afd476b1b8bed930e47bf60ee20fa78f38/pandas/core/reshape/concat.py#L464) PR #29627
```python
cons = self.objs[0]._constructor_expanddim
```
[Line 477](https://github.com/pandas-dev/pandas/blob/04e9e0afd476b1b8bed930e47bf60ee20fa78f38/pandas/core/reshape/concat.py#L477) PR #33884 (Set to be released on 1.1.0)
This assumes homogeneous object types, or at the very least that the first object is representative. My assumption was that functions on the pandas namespace did not respect subclassing and only methods on the objects did. Leaving developers to implement their own special use cases for subclassing. This seemed like a good delimitation to me, both logically and programmatically. But, now diving deeper into functions I don't regularly use I see I was mistaken as `merge` and `merge_ordered` both respect the `left` arg's subclassing.
With this in mind, I have some questions.
1. Should there be more documentation around subclassing? This would include how `concat`, `merge`, `merge_ordered` etc handle subclassing.
2. If functions on the pandas names space are going to implement varying degrees of support for subclassing then, should there be a `constructor` kwarg added to functions like `read_sql` and `read_csv`?
3. Does `to_hdf` not working with subclassing need to be fixed?
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ENH: Auto Detect engine in read_excel
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| 8
| 2020-07-26T21:26:31Z
| 2021-01-03T16:48:19Z
| 2021-01-03T16:48:19Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
This works:
```python
import pandas as pd
df = pd.read_excel('Book2.xlsb', engine='pyxlsb')
```
This doesn't:
```python
import pandas as pd
df = pd.read_excel('Book2.xlsb')
```
#### Problem description
All supported Excel formats (`xls`, `xlsx`, `xlsm`) have a default engine based on the extension, so you can simply do:
```
df = pd.read_excel('Book2.xlsx')
```
For `xlsb`, when you do `df = pd.read_excel('Book2.xlsb')`, you get this error:
```
XLRDError: Excel 2007 xlsb file; not supported
```
#### Expected Output
No error, i.e. it should figure out that for `xlsb` extensions, `pyxlsb` is the default engine.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
pandas : 1.0.5
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 49.2.0.post20200714
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pytest : 5.4.3
pyxlsb : 1.0.6
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : None
</details>
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