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} | 1 | 2021-03-13T16:43:50Z | 2021-03-15T05:22:19Z | 2021-03-14T23:43:56Z | CONTRIBUTOR | null | - [ ] closes #40375
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
A new suggestion was made to include code styling.
Beginner's question: What is the procedure when my pull request (in this case #40400) is merged, which closes an issue after which a new suggestion for further improvement is made in the closed issue. Can I just implement the new suggestion and make a new pull request like this? Or is the procedure different?
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} | 7 | 2021-03-13T16:43:58Z | 2021-04-23T14:58:07Z | 2021-04-23T14:58:07Z | NONE | null | ```
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '1.2.3'
In [3]: tmp = pd.DataFrame({'f': [1,2]})
In [4]: import numpy as np
In [5]: tmp['h'] = np.nan
#This is the expected behaviour
In [6]: tmp['f'].clip(0, np.inf)
Out[6]:
0 1
1 2
Name: f, dtype: int64
# This seems wrong:
In [7]: tmp['f'].clip(0, tmp['h'])
Out[7]:
0 NaN
1 NaN
Name: f, dtype: float64
```
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Per discussion with @simonjayhawkins https://github.com/pandas-dev/pandas/pull/39501#issuecomment-798617105
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} | 13 | 2021-03-13T20:49:09Z | 2021-05-26T20:02:47Z | 2021-05-24T13:44:07Z | CONTRIBUTOR | null | ## Enhancing Styler to allow LaTeX Input
- [x] closes #21673
also indirectly (by providing an alternative to DataFrame.to_latex):
- [x] closes #38328 (enh: adding highlighting options to to_latex function)
- [x] closes #28291 (enh: add option to format \hline in to_latex)
- [x] closes #26278 (easier digit format in to_latex) (although this seems to have been separately addressed)
- [x] closes #26111 (to_latex adds extra empty header row when index has a name even if index=False)
- [x] closes #20008 (to_latex shifts column names if MultiIndex contains empty label)
- [x] closes #13203 (enhancement: add optional row/col paramater to to_latex formatter)
- [x] closes #41388 (text truncates in to_latex)
- [x] closes #6491 (text truncates in to_latex)
- [x] closes #41636 (bug in latex formatting decimal of complex numbers)
This PR leverages the conditional rendering mechanics and unit tests in `Styler` to create a pure LaTeX version, i.e. a Styler that instead of CSS (attribute, value) tuples has LaTeX (command, options) tuples and renders directly to LaTeX with nested cell
An extension to this is provided in another PR which converts an HTML-CSS Styler to a LaTeX Styler, and then renders in LaTeX.
### How is this achieved?
#### Internally:
- [x] a new jinja2 template is created for generating `latex` output.
- [x] a new `Styler._render_latex()` method is added to invoke the new template.
- [x] a `Styler._translate_latex` method is added to make structural changes to the usual render dict `d` to make it suitable for latex template.
- [x] parsing functions are added which do simple tasks to facilitate the jinja2 template's operation, including sparsifying multiindexes
#### For Users:
- [x] a new `Styler.to_latex()` method is introduced to give the user control.
## Comparison with DataFrame.to_latex()
### Input Arguments
- Replicating the **io** arguments: ``buf``, ``encoding``,
- Replicating the **LaTeX** arguments: ``position``, ``caption``, ``label``, ``sparsify``, ``column_format``
- Adding additional **LaTeX** arguments: ``position_float``, ``hrules``
- Removing all of the **formatting** arguments: ``na_rep``, ``formatters``, ``float_format``, ``escape``, ``decimal``
- The pattern ``styler.format(...).to_latex(...)`` fully replicates the functionality, and can be intermediately viewed in a Notebook.
- The pattern ``styler.format(..)...format(..).to_latex(...)`` performs better formatting than a single ``to_latex(..)`` with formatting options could replicate.
- For code maintenance it is better to only maintain the ``.format`` method rather than ``.to_latex`` as downstream.
- Removing the pseudo-formatting arguments: ``columns``, ``index``, ``index_names``, ``header``,
- The pattern ``styler.hide_index().hide_columns()`` can replicate the first three, the last seemed a bit unnecessary.
- Modifying the multiindex args:
- Now we have <s>``sparsify {bool}``</s> ``sparse_index`` and ``sparse_columns`` and ``multicol_align {str}`` and ``multirow_align {str}``.
- Previously the options were ``sparsify {bool}``, ``multicolumn {bool}``, ``multirow {bool}`` and ``multicolumn_format {str}``
- Additional functionality not coded (yet?): ``bold_rows`` (maybe ``bold_labels``), and ``longtable``.
### Performance
Suppose a user will never want to create a LaTeX bigger than (200,20) so we test that size:
| Method | Time |
|---------|------|
| df.to_latex() | 151ms |
| df.style.to_latex() | 43ms |
| df.to_latex(float_format="{:.2f}".format) | 65ms |
| <s>df.style.format(":.2f".format).to_latex()</s> | <s>61ms</s> |
| df.style.format(":.2f".format).to_latex() | 45ms |
(this was improved by #41269)
Styler is broadly unaffected here by adding formatting methods, although `DataFrame.to_latex` is oddly much faster. Anyway the conclusion seems to be for this size frame and smaller performance is broadly the same, if not a bit better in Styler.
### Docs
Here are the rendered versions of the to_latex documentation
[docs_to_latex.zip](https://github.com/pandas-dev/pandas/files/6292495/docs_to_latex.zip)
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} | 0 | 2021-03-14T00:28:27Z | 2021-03-14T16:24:11Z | 2021-03-14T09:58:33Z | MEMBER | null | Also added some missing annotations I saw along the way. | {
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} | 0 | 2021-03-14T05:19:36Z | 2021-03-18T16:40:07Z | 2021-03-15T16:59:52Z | MEMBER | null | follow up #38344
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} | 1 | 2021-03-14T09:12:31Z | 2021-03-15T14:27:55Z | 2021-03-14T23:50:51Z | CONTRIBUTOR | null | This PR is a minor fix/improvement to a recent PR which deprecated `set_na_rep` and `set_precision`. #40134
For a dataframe with 100,000 floats the current Styler default formatter will make 100,000 `get_option` lookups for the pandas precision.
Doing this calculation once prevents all these lookups and saves just over 10% render time for larger tables.
```
rows THIS PR
------ -------------------------
cols 12 120
====== ============ ============
12 11.8±0.2ms 40.8±0.8ms
24 22.5±0.3ms 69.2±2ms
36 32.3±0.6ms 96.9±1ms
====== ============ ============
```
```
rows MASTER
------ -------------------------
cols 12 120
====== ============ ============
12 12.9±0.4ms 43.4±1ms
24 22.3±0.1ms 75.2±0.7ms
36 32.3±0.3ms 105±0.5ms
====== ============ ============
```
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- Combined test_series_agg_nested_renamer and test_series_nested_renamer via parametrization
- test_transform_nested_renamer is already a part of test_nested_renamer
- test_transform_missing_columns is essentially a duplicate of test_missing_column | {
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.drop and .rename have similar signatures to some extent, and implement basic dataframe functionalities. When drop is called and the label is not in the index, it fails by default (due to the errors='raise' default parameter). However, when rename is called with a label not in the specified index, it passes by silently by default. I think it would be a useful improvement to get the parameterization and default behavior of these functions aligned, possibly by enriching rename. I know it might be incompatible with previous versions if it would start failing if a label is not in the index, but I am also concerned if it is an intentional and valid usecase to rename columns which might not be present in the dataframe.
#### Describe the solution you'd like
rename should be extended with the same parameters as drop, possibly with the default setting of raising errors.
#### API breaking implications
If the raising of errors is not made default, it should not break anything. If the raising of errors is default (making it similar to drop), then those codes attempt to rename labels not present in the index would fail. Possibly it could be a 2 stage improvement, first raising a warning and in a later release raising an error.
#### Describe alternatives you've considered
One can do workarounds in the host code, but I don't see alternatives solutions.
#### Additional context
I came across this issue when working with data as part of my job. Columns which I expected at some point of the code were not there, the reason was that I did not specify the index='columns' flag in rename, which passed by silently trying to rename entries in the row index. If rename would have failed as drop would have, it would have been easier to figure out where is the issue.
Then I was pondering if it is a valid use case to pass by silently if there is no label to be renamed, and decided to write this request.
```python
>>> pandas.__version__
'1.3.0.dev0+1040.g454194e76a'
>>> tmp= pandas.DataFrame({'a': [1, 2, 3], 'b': [3, 2, 1]}, index=['b', 'c', 'd'])
>>> tmp
a b
b 1 3
c 2 2
d 3 1
>>> tmp.drop(['e'], axis='columns')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/pandas/pandas/core/frame.py", line 4776, in drop
return super().drop(
File "/home/pandas/pandas/core/generic.py", line 4211, in drop
obj = obj._drop_axis(labels, axis, level=level, errors=errors)
File "/home/pandas/pandas/core/generic.py", line 4246, in _drop_axis
new_axis = axis.drop(labels, errors=errors)
File "/home/pandas/pandas/core/indexes/base.py", line 5925, in drop
raise KeyError(f"{labels[mask]} not found in axis")
KeyError: "['e'] not found in axis"
>>> tmp
a b
b 1 3
c 2 2
d 3 1
>>> tmp.rename({'e': 'g'}, axis='columns')
a b
b 1 3
c 2 2
d 3 1
>>> tmp
a b
b 1 3
c 2 2
d 3 1
>>>
```
Any comments are welcome! | {
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 5 | 2021-03-14T11:42:24Z | 2021-08-19T02:04:26Z | null | NONE | null | Summary: if `from_records` is called upon a `Series` with index that doesn't start with `0`, it fails.
```python
import pandas as pd
print(f'Pandas version is {pd.__version__}')
data = pd.Series([{'a': 1, 'b': 2}, {'a': 11, 'b': 22}], index=[10, 20])
pd.DataFrame.from_records(data.values) # works
pd.DataFrame.from_records(data) # fails
```
Output
```text
Pandas version is 1.2.1
Traceback (most recent call last):
File "/Users/boris/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3080, in get_loc
return self._engine.get_loc(casted_key)
File "pandas/_libs/index.pyx", line 70, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 101, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1625, in pandas._libs.hashtable.Int64HashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1632, in pandas._libs.hashtable.Int64HashTable.get_item
KeyError: 0
....
```
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} | 2 | 2021-03-14T14:17:50Z | 2021-03-17T23:35:16Z | 2021-03-17T21:17:56Z | MEMBER | null | - [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Currently ExcelWriter has **engine_kwargs to pass through to the engine, [although this is undocumented](https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.ExcelWriter.html). All other places in pandas where engine_kwargs is used, they are a regular argument.
This PR
- Renames **engine_kwargs to **kwargs (not an API change)
- Adds **kwargs to the docstring of ExcelWriter
- Deprecates **kwargs
- Introduces engine_kwargs as a regular argument for their replacement
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} | 3 | 2021-03-14T15:50:33Z | 2021-03-15T01:08:54Z | 2021-03-15T01:08:49Z | CONTRIBUTOR | null | The removed code has never been used since #36997. `TextReader` is only ever called with file handles. | {
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] | open | false | null | [] | null | 0 | 2021-03-14T17:32:03Z | 2021-08-19T02:04:56Z | null | NONE | null | #### Is your feature request related to a problem?
Not really. I simply wish I could use pandas to [protect](https://stackoverflow.com/questions/66611187/protect-a-nested-object-against-flattening-when-using-pandas-json-normalize) a nested structure / object / dict against flattening when using [pd.json_normalize()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.json_normalize.html) on a JSON object, for example an API response.
#### Describe the solution you'd like
Something like this (see below for a complete example):
```python
df = pd.DataFrame(pd.json_normalize(data, protect="foo.bar.baz"))
# or this, if there isn't more than 1 object with the same name
# (or even if there are more than one object with the same name, it should apply to all of them) :
df = pd.DataFrame(pd.json_normalize(data, protect="baz"))
```
#### API breaking implications
I have no detailed idea what this could/would break.
#### Describe alternatives you've considered
My current workaround is to duplicate the DataFrame; building one without normalizing, the other with. Keeping only the protected column(s) of the one which hasn't been normalized and concatenating them to the other. Remove redundant columns on the final DataFrame.
#### Additional context
Here's a dummy example:
```python
import pandas as pd
response = '''
{
"results": [
{
"geometry": {
"type": "Polygon",
"crs": 4326,
"coordinates":
[[
[6.0, 49.0],
[6.0, 40.0],
[7.0, 40.0],
[7.0, 49.0],
[6.0, 49.0]
]]
},
"attribute": "layer.metadata",
"bbox": [6, 40, 7, 49],
"featureName": "Coniferous_Trees",
"layerName": "State_Forests",
"type": "Feature",
"id": "17",
"properties": {
"resolution": "100",
"Year": "2020",
"label": "Coniferous"
}
}
]
}
'''
data = json.loads(response)['results']
df = pd.DataFrame(pd.json_normalize(data))
```
Then:
```python
>>> print(df.columns)
Index(['attribute', 'bbox', 'featureName', 'layerName', 'type', 'id',
'geometry.type', 'geometry.crs', 'geometry.coordinates', # <-- the geometry has been flattened along all the other objects
'properties.resolution', 'properties.Year', 'properties.label'],
dtype='object')
```
Desired behaviour:
```python
df = pd.DataFrame(pd.json_normalize(data, protect="results.geometry"))
```
which would lead to:
```
>>> print(df.columns)
Index(['attribute', 'bbox', 'featureName', 'layerName', 'type', 'id',
'geometry', # <-- the geometry element has been protected, it stays as a nested JSON structure in its own column in the DataFrame.
'properties.resolution', 'properties.Year', 'properties.label'],
dtype='object')
```
Thanks for reading.
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} | 2 | 2021-03-14T18:17:06Z | 2021-03-16T14:26:50Z | 2021-03-16T13:18:21Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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- [Y ] I have confirmed this bug exists on the latest version of pandas.
- [ Y] (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
from datetime import datetime, timedelta
import dateutil
import psycopg2
#### Same time zone but using different tz classes though with same offset
IST_tz_offset = dateutil.tz.tz.tzoffset(None, 19800) # offset in seconds, 19800s equals 330 mins
IST_tz_postgres_offset = psycopg2.tz.FixedOffsetTimezone(offset=330, name=None) # it is in mins
# create list of lists
data1 = [[pd.Timestamp('2021-03-14 06:00:00', tz = IST_tz_offset), 15], [pd.Timestamp('2021-03-14 06:00:00', tz = IST_tz_offset) - timedelta(hours=1), 14], [pd.Timestamp('2021-03-14 06:00:00', tz = IST_tz_offset) - timedelta(hours=2), 12]]
# Create the pandas DataFrame
df1 = pd.DataFrame(data1, columns = ['datetime', 'value'])
df1.set_index('datetime', inplace = True)
# initialize list of lists
data2 = [[pd.Timestamp('2021-03-14 06:00:00', tz = IST_tz_postgres_offset) - timedelta(days=1), 10], [pd.Timestamp('2021-03-14 06:00:00', tz = IST_tz_postgres_offset) - timedelta(days = 1, hours=1), 9], [pd.Timestamp('2021-03-14 06:00:00', tz = IST_tz_postgres_offset) - timedelta(days = 1, hours=2), 8]]
# Create the pandas DataFrame
df2 = pd.DataFrame(data2, columns = ['datetime', 'value'])
df2.set_index('datetime', inplace = True)
print ("Same timezones but different tz classes: df1.index[0] = {}, df2.index[0] = {}".format(df1.index[0], df2.index[0]))
print ("Same timezones but different tz classes: type(df1.index) = {}, type(df2.index) = {}".format(type(df1.index), type(df2.index)))
appended_df = df2.append(df1)
concatenated_df = pd.concat([df2, df1])
print ("Same timezones but different tz classes: type(appended_df.index) = {}, type(concatenated_df.index) = {}\n".format(type(appended_df.index), type(concatenated_df.index)))
```
#### Problem description
Have hit this issue when trying to append/concat dataframes with DatetimeIndex index types, where the data comes from different sources i.e. in this case, from postgres db and a data providers, and though the data has the same timezone (Asia/Kolkata), as can be seen from sample data printed in code, post append/concat the index type gets converted to "Index" in place of DatetimeIndex. This further caused an issue at our end, for things like resample() which in turn required DatetimeIndex type. The only different in the Timestamp data that we found was that their tz datatype was different even though both had the same offset value (19800 seconds or 330 mins, both being equal).
Sample data when printed:
Same timezones but different tz classes: df1.index[0] = 2021-03-14 06:00:00+05:30, df2.index[0] = 2021-03-13 06:00:00+05:30
#### Expected Output
In this case, since the timezone was essentially the same for the data, would have expected that the datatype for the index should not have changed after append or concat. It should have remained as DatetimeIndex type.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.2.2
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.23
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 0 | 2021-03-14T22:37:57Z | 2021-03-30T16:06:08Z | 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 Sample, a copy-pastable example
```python
In [3]: import pandas as pd
In [4]: pd.__version__
Out[4]: '1.2.3'
In [5]: import numpy as np
In [7]: x = np.random.choice(["foo", "bar", "baz"], size=10, replace=True)
In [8]: df = pd.DataFrame({"x": x})
In [9]: df
Out[9]:
x
0 bar
1 baz
2 foo
3 baz
4 bar
5 baz
6 baz
7 bar
8 foo
9 foo
In [10]: df.query("x == 'bar'")
Out[10]:
x
0 bar
4 bar
7 bar
In [11]: df.query("x == 'bar'", parser="python")
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
...
~/malariagen/binder/conda/envs/xarray-tests/lib/python3.8/site-packages/pandas/core/computation/expr.py in f(self, *args, **kwargs)
251
252 def f(self, *args, **kwargs):
--> 253 raise NotImplementedError(f"'{node_name}' nodes are not implemented")
254
255 return f
NotImplementedError: 'In' nodes are not implemented
In [12]: df.query("x != 'foo'")
Out[12]:
x
0 bar
1 baz
3 baz
4 bar
5 baz
6 baz
7 bar
In [13]: df.query("x != 'foo'", parser="python")
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
...
~/malariagen/binder/conda/envs/xarray-tests/lib/python3.8/site-packages/pandas/core/computation/expr.py in f(self, *args, **kwargs)
251
252 def f(self, *args, **kwargs):
--> 253 raise NotImplementedError(f"'{node_name}' nodes are not implemented")
254
255 return f
NotImplementedError: 'NotIn' nodes are not implemented
```
#### Problem description
If the query expression has an equals or not equals comparison with a string, and the "python" parser is selected, then a `NotImplemented` error is raised. The error messages however reports that "In" nodes are not implemented, or "NotIn" nodes are not implemented, which doesn't fit with the expression, suggesting a bug in how the query expression is being parsed.
#### Expected Output
Expected output should match what you get with the "pandas" parser as semantics for this type of query expression should be the same for either parser.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-66-generic
Version : #74~18.04.2-Ubuntu SMP Fri Feb 5 11:17:31 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.2.3
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 49.6.0.post20210108
Cython : None
pytest : 6.2.2
hypothesis : 6.8.0
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.7
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.17.1.dev3+g48378c4b
xlrd : None
xlwt : None
numba : 0.52.0
</details>
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} | 5 | 2021-03-14T22:42:47Z | 2021-03-23T17:32:29Z | 2021-03-23T17:03:14Z | CONTRIBUTOR | null | - [x] closes #40388
Adds an HTML `escape` parameter to `Styler.format`, which acts to escape the data value **before** it is passed to a formatter.
For example, `Styler.format('<a href="a.com/{0}">{0}</a>', escape=True)` will not escape the HTML as part of the formatting function, it will only escape the value `{0}` going into the formatter. | {
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} | 3 | 2021-03-15T07:24:18Z | 2021-03-16T14:28:45Z | 2021-03-15T08:36:28Z | CONTRIBUTOR | null | <!-- START pr-commits -->
<!-- END pr-commits -->
## Base PullRequest
default branch (https://github.com/pandas-dev/pandas/tree/master)
## Command results
<details>
<summary>Details: </summary>
<details>
<summary><em>add path</em></summary>
```Shell
/home/runner/work/_actions/technote-space/create-pr-action/v2/node_modules/npm-check-updates/bin
```
</details>
<details>
<summary><em>pip install pre-commit</em></summary>
```Shell
Collecting pre-commit
Downloading pre_commit-2.11.1-py2.py3-none-any.whl (187 kB)
Collecting identify>=1.0.0
Downloading identify-2.1.3-py2.py3-none-any.whl (98 kB)
Collecting toml
Using cached toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting pyyaml>=5.1
Downloading PyYAML-5.4.1-cp39-cp39-manylinux1_x86_64.whl (630 kB)
Collecting cfgv>=2.0.0
Using cached cfgv-3.2.0-py2.py3-none-any.whl (7.3 kB)
Collecting nodeenv>=0.11.1
Using cached nodeenv-1.5.0-py2.py3-none-any.whl (21 kB)
Collecting virtualenv>=20.0.8
Downloading virtualenv-20.4.2-py2.py3-none-any.whl (7.2 MB)
Collecting distlib<1,>=0.3.1
Using cached distlib-0.3.1-py2.py3-none-any.whl (335 kB)
Collecting six<2,>=1.9.0
Using cached six-1.15.0-py2.py3-none-any.whl (10 kB)
Collecting filelock<4,>=3.0.0
Using cached filelock-3.0.12-py3-none-any.whl (7.6 kB)
Collecting appdirs<2,>=1.4.3
Using cached appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)
Installing collected packages: six, filelock, distlib, appdirs, virtualenv, toml, pyyaml, nodeenv, identify, cfgv, pre-commit
Successfully installed appdirs-1.4.4 cfgv-3.2.0 distlib-0.3.1 filelock-3.0.12 identify-2.1.3 nodeenv-1.5.0 pre-commit-2.11.1 pyyaml-5.4.1 six-1.15.0 toml-0.10.2 virtualenv-20.4.2
```
</details>
<details>
<summary><em>pre-commit autoupdate || (exit 0);</em></summary>
```Shell
Updating https://github.com/MarcoGorelli/absolufy-imports ... [INFO] Initializing environment for https://github.com/MarcoGorelli/absolufy-imports.
updating v0.2.2 -> v0.3.0.
Updating https://github.com/python/black ... already up to date.
Updating https://github.com/codespell-project/codespell ... [INFO] Initializing environment for https://github.com/codespell-project/codespell.
already up to date.
Updating https://github.com/pre-commit/pre-commit-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pre-commit-hooks.
already up to date.
Updating https://github.com/cpplint/cpplint ... [INFO] Initializing environment for https://github.com/cpplint/cpplint.
=====> /home/runner/.cache/pre-commit/repoj060n6sd/.pre-commit-hooks.yaml does not exist
Updating https://gitlab.com/pycqa/flake8 ... [INFO] Initializing environment for https://gitlab.com/pycqa/flake8.
updating 3.8.4 -> 3.9.0.
Updating https://github.com/PyCQA/isort ... [INFO] Initializing environment for https://github.com/PyCQA/isort.
already up to date.
Updating https://github.com/MarcoGorelli/no-string-hints ... [INFO] Initializing environment for https://github.com/MarcoGorelli/no-string-hints.
Cannot update because the update target is missing these hooks:
no-string-hints
Updating https://github.com/asottile/pyupgrade ... [INFO] Initializing environment for https://github.com/asottile/pyupgrade.
already up to date.
Updating https://github.com/pre-commit/pygrep-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pygrep-hooks.
already up to date.
Updating https://github.com/asottile/yesqa ... already up to date.
```
</details>
<details>
<summary><em>pre-commit run -a || (exit 0);</em></summary>
```Shell
[INFO] Initializing environment for https://github.com/cpplint/cpplint.
[INFO] Initializing environment for https://gitlab.com/pycqa/flake8:flake8-comprehensions>=3.1.0.
[INFO] Initializing environment for https://github.com/MarcoGorelli/no-string-hints.
[INFO] Installing environment for https://github.com/MarcoGorelli/absolufy-imports.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/python/black.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/codespell-project/codespell.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/pre-commit/pre-commit-hooks.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/cpplint/cpplint.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/PyCQA/isort.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/MarcoGorelli/no-string-hints.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/pyupgrade.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/yesqa.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
absolufy-imports.......................................................................................Passed
black..................................................................................................Passed
codespell..............................................................................................Passed
Fix End of Files.......................................................................................Passed
Trim Trailing Whitespace...............................................................................Passed
cpplint................................................................................................Passed
flake8.................................................................................................Passed
flake8 (cython)........................................................................................Passed
flake8 (cython template)...............................................................................Passed
isort..................................................................................................Passed
no-string-hints........................................................................................Passed
pyupgrade..............................................................................................Passed
rst ``code`` is two backticks..........................................................................Passed
rst directives end with two colons.....................................................................Passed
rst ``inline code`` next to normal text................................................................Passed
Strip unnecessary `# noqa`s............................................................................Failed
- hook id: yesqa
- exit code: 1
- files were modified by this hook
Rewriting pandas/_typing.py
flake8-rst.............................................................................................Passed
Check for use of Union[Series, DataFrame] instead of FrameOrSeriesUnion alias..........................Passed
Check for inconsistent use of pandas namespace in tests................................................Passed
Check for incorrect code block or IPython directives...................................................Passed
Check code for instances of os.remove..................................................................Passed
Check code for instances of pd.api.types...............................................................Passed
Check for non-standard imports.........................................................................Passed
Check for non-standard imports in test suite...........................................................Passed
Check for non-standard numpy.random-related imports excluding pandas/_testing.py.......................Passed
Check for use of np.bool instead of np.bool_...........................................................Passed
Check for use of np.object instead of np.object_.......................................................Passed
Generate pip dependency from conda.....................................................................Passed
Validate correct capitalization among titles in documentation..........................................Passed
Check for use of foo.__class__ instead of type(foo)....................................................Passed
Check for use of bare pytest raises....................................................................Passed
Check for use of private functions across modules......................................................Passed
Check for import of private attributes across modules..................................................Passed
Check for use of pytest.xfail..........................................................................Passed
Check for use of not concatenated strings..............................................................Passed
Check for strings with wrong placed spaces.............................................................Passed
Check for outdated annotation syntax and missing error codes...........................................Passed
```
</details>
</details>
## Changed files
<details>
<summary>Changed 2 files: </summary>
- .pre-commit-config.yaml
- pandas/_typing.py
</details>
<hr>
[:octocat: Repo](https://github.com/technote-space/create-pr-action) | [:memo: Issues](https://github.com/technote-space/create-pr-action/issues) | [:department_store: Marketplace](https://github.com/marketplace/actions/create-pr-action) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/40440 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40440/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40440/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40440/events | https://github.com/pandas-dev/pandas/issues/40440 | 831,560,477 | MDU6SXNzdWU4MzE1NjA0Nzc= | 40,440 | BUG: Unexpected behavior of setting item to DataFrame slice | {
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} | 5 | 2021-03-15T08:30:16Z | 2021-05-02T23:47:54Z | 2021-05-02T23:47:54Z | 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
df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4], 'col3': [6, 7]})
df[1:] = [1, 2, 3]
```
#### Problem description
I get the `ValueError: cannot set using a slice indexer with a different length than the value`, when running the above code snippet. However, when running the following one, the error doesn't appear:
```python
df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4], 'col3': [6, 7]})
df[1:] = ['a', 'b', 'c']
df[1:] = [1, 2, 3]
```
Looks like assigning value to slice have different behavior depending on type on the value, which doesn't seem to be intended and is not documented.
Another example is:
```python
df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4], 'e': [6, 7]})
df[1:] = ['a', 'b', 'c']
df[pd.DataFrame({'col1': [True, True], 'col2': [False, True]})] = [2, 3, 4]
```
raises `ValueError: cannot assign mismatch length to masked array` for line 3, but this code doesn't:
```python
df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4], 'e': [6, 7]})
df[pd.DataFrame({'col1': [True, True], 'col2': [False, True]})] = [2, 3, 4]
```
#### Expected Output
Exceptions thrown should be the same regardless of the previous calls on the given data frame. If assigning value of array of strings have some special meaning, it should be documented. String array assignment shouldn't change behavior of succeeding calls.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.6.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : pl_PL.UTF-8
pandas : 1.2.3
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0
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
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/40441 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40441/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40441/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40441/events | https://github.com/pandas-dev/pandas/pull/40441 | 831,615,383 | MDExOlB1bGxSZXF1ZXN0NTkyOTA5NzIz | 40,441 | [ArrayManager] DataFrame constructor from ndarray | {
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} | 1 | 2021-03-15T09:30:56Z | 2021-04-26T17:42:54Z | 2021-04-26T09:34:19Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/issues/39146
Currently, the `DataFrame(ndarray)` construction for ArrayManager still went through BlockManager and then `mgr_to_mgr` conversion, which is less efficient.
This PR adds a check in `ndarray_to_mgr` for directly creating an ArrayManager.
~It's still WIP, because there are still some validation steps that are now done inside BlockManager/Block inits, that need to be factored out / shared / added to ArrayManager path.~ | {
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} | 3 | 2021-03-15T10:08:55Z | 2021-04-23T00:07:08Z | 2021-04-23T00:07:08Z | 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
from pathlib import Path
import pandas as pd
excel_file = Path("test_excel_file.xlsx")
table = pd.read_excel(excel_file, 'Taul1', header=[0, 1])
```
#### Problem description
Given table
| A column | B column | C column |
|-|-|-|
| **part 1** | **part 2** | **part 3** |
| [empty] | [empty] | [empty] |
| [empty] | [empty] | [empty] |
| Row 1 Col A | Row 1 Col B | Row 1 Col C |
| Row 2 Col A | Row 2 Col B | Row 2 Col C |
pd.read_excel with the new openpyxl-backend fails with exception
Traceback (most recent call last):
File "pandas_openpyxl_bug\test_pandas_openpyxl.py", line 12, in <module>
table = pd.read_excel(excel_file, 'Taul1', header=[0, 1])
File ".virtualenvs\pandas_openpyxl_bug-3rzOJb6e\lib\site-packages\pandas\util\_decorators.py", line 299, in wrapper
return func(*args, **kwargs)
File "virtualenvs\pandas_openpyxl_bug-3rzOJb6e\lib\site-packages\pandas\io\excel\_base.py", line 344, in read_excel
data = io.parse(
File ".virtualenvs\pandas_openpyxl_bug-3rzOJb6e\lib\site-packages\pandas\io\excel\_base.py", line 1170, in parse
return self._reader.parse(
File ".virtualenvs\pandas_openpyxl_bug-3rzOJb6e\lib\site-packages\pandas\io\excel\_base.py", line 566, in parse
output[asheetname] = parser.read(nrows=nrows)
File ".virtualenvs\pandas_openpyxl_bug-3rzOJb6e\lib\site-packages\pandas\io\parsers.py", line 1057, in read
index, columns, col_dict = self._engine.read(nrows)
File ".virtualenvs\pandas_openpyxl_bug-3rzOJb6e\lib\site-packages\pandas\io\parsers.py", line 2503, in read
index, columns = self._make_index(data, alldata, columns, indexnamerow)
File ".virtualenvs\pandas_openpyxl_bug-3rzOJb6e\lib\site-packages\pandas\io\parsers.py", line 1566, in _make_index
index = index.set_names(indexnamerow[:coffset]) # type: ignore[union-attr]
AttributeError: 'NoneType' object has no attribute 'set_names'
#### Expected Output
A dataframe without a crash
#### Output of ``pd.show_versions()``
<details>
[pandas_openpyxl_bug.zip](https://github.com/pandas-dev/pandas/files/6140468/pandas_openpyxl_bug.zip)
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.9.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : AMD64 Family 23 Model 8 Stepping 2, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Finnish_Finland.1252
pandas : 1.2.3
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 53.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [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.
-------------
Hello,
While using pandas in my project, I saw the memory usage of my process raising. After some digging, it looks like there is a memory leak in the JSON encoding code.
This simple test should be able to reproduce the issue:
```python
import pandas as pd
if __name__ == "__main__":
df = pd.DataFrame([[1,2], [1,2], [3,4], [3,4], [3,6], [5,6], [7,8], [7,8]], columns=["a", "b"], index = pd.date_range('1/1/2000', periods=8, freq='T'))
for i in range(10000):
str = df.to_json(orient="table")
df = pd.read_json(str, orient="table")
```
Which ran using Valgrind should show that kind of result:
```
$ PYTHONMALLOC=malloc valgrind --leak-check=yes --track-origins=yes --log-file=valgrind-log.txt python test.py
...
==214631== 3,358,152 bytes in 79,956 blocks are definitely lost in loss record 15,015 of 15,015
==214631== at 0x483E77F: malloc (vg_replace_malloc.c:307)
==214631== by 0x4F811482: int64ToIso (in /lib/python3.7/site-packages/pandas/_libs/json.cpython-37m-x86_64-linux-gnu.so)
==214631== by 0x4F81364B: encode (in /lib/python3.7/site-packages/pandas/_libs/json.cpython-37m-x86_64-linux-gnu.so)
==214631== by 0x4F813514: encode (in /lib/python3.7/site-packages/pandas/_libs/json.cpython-37m-x86_64-linux-gnu.so)
==214631== by 0x4F8135F7: encode (in /lib/python3.7/site-packages/pandas/_libs/json.cpython-37m-x86_64-linux-gnu.so)
==214631== by 0x4F813514: encode (in /lib/python3.7/site-packages/pandas/_libs/json.cpython-37m-x86_64-linux-gnu.so)
==214631== by 0x4F813A80: JSON_EncodeObject (in /lib/python3.7/site-packages/pandas/_libs/json.cpython-37m-x86_64-linux-gnu.so)
==214631== by 0x4F811119: objToJSON (in /lib/python3.7/site-packages/pandas/_libs/json.cpython-37m-x86_64-linux-gnu.so)
==214631== by 0x4993B18: _PyMethodDef_RawFastCallKeywords (in /usr/lib/libpython3.7m.so.1.0)
==214631== by 0x4993713: _PyCFunction_FastCallKeywords (in /usr/lib/libpython3.7m.so.1.0)
==214631== by 0x499364C: ??? (in /usr/lib/libpython3.7m.so.1.0)
==214631== by 0x498E0CD: _PyEval_EvalFrameDefault (in /usr/lib/libpython3.7m.so.1.0)
...
```
Which points to the `int64ToIso()` function in this case but mostly any function used in the `getStringValue()` function is allocating memory and this memory appears to not be freed after that (if I'm not missing something).
https://github.com/pandas-dev/pandas/blob/2d51ebb77f5dc7f7824fd0b7b7edd538f2eaa819/pandas/_libs/src/ujson/lib/ultrajsonenc.c#L1082
It would be great if someone can confirm my deduction. If I'm right, I will try to submit a PR.
Thanks.
The environment I used:
```
pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.7.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.2-1-MANJARO
Version : #1 SMP PREEMPT Fri Feb 26 12:17:53 UTC 2021
machine : x86_64
processor :
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.2
numpy : 1.19.5
``` | {
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} | 8 | 2021-03-15T10:40:05Z | 2021-03-24T07:26:28Z | 2021-03-23T16:47:38Z | MEMBER | null | This PR does 2 things:
- change `pandas.core.ops.roperator` imports to all import the module and use `roperator.<rop>` instead of importing the actual functions (this makes the usage similar as for the non-reversed operators with `import operator`)
- move `roperator.py` from pandas/core/ops to pandas/core, so it can be imported in `pandas.core.computation.expressions` without relying on the `pandas.core.ops` module, which in its turn means we can move the inline import of `pandas.core.computation.expressions` in `pandas.core.ops.array_ops` to a top-level import (better for performance for a function that is called repeatedly)
I think the first change is useful anyway (can also do it as a separate PR), for the second point, a possible alternative would be to move `pandas.core.computation.expressions` from the computation/ module to the ops/ module
One consequence of this, though: by always importing this in `array_ops`, it gets imported on `import pandas` time, and thus `numexpr` always gets imported at that point (while now it's only a delayed import when doing a first operation).
Now, I checked with `python -X importtime -c "import numpy, pandas, numexpr"`, and after already having import numpy (and pandas), also import numexpr is only a small addition (most of the import of numexpr is import numpy).
Based on running that a few times, it seems to adds 1.5-2 % to the total pandas import time. | {
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} | 3 | 2021-03-15T11:12:27Z | 2021-03-23T13:59:52Z | 2021-03-23T13:46:02Z | MEMBER | null | In practice, this case is only covered by specific tests using DataFrame in `test_expressions.py`, not from actual usage through ops (so I just updated the failing tests to not use any DataFrame as input to those checks).
Although our test suite might not be the best suited to check that, since many tests are small and thus don't use the numexpr path... (fixing that in https://github.com/pandas-dev/pandas/pull/40463) | {
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} | 0 | 2021-03-15T13:12:32Z | 2021-08-12T16:21:47Z | 2021-08-12T16:21:47Z | MEMBER | null | From https://github.com/pandas-dev/pandas/issues/39146#issuecomment-799397428 (discovered while investigating a benchmark difference). It seems that in groupby/ops.py, the `fast_apply` (using libreduction) vs the generic python apply gives a different result in case of same-indexed output of the function.
Using a small example dataframe and a function to be applied which simply copies the input:
```python
N = 10
df = pd.DataFrame(
{
"key": np.random.randint(0, 3, size=N),
"value1": np.random.randn(N),
"value2": ["foo", "bar"] * (N // 2),
}
)
def df_copy_function(g):
# ensure that the group name is available (see GH #15062)
g.name
return g.copy()
```
By default you get this result:
```
In [3]: df.groupby("key").apply(df_copy_function)
Out[3]:
key value1 value2
key
0 8 0 -0.149534 foo
9 0 -0.391135 bar
1 1 1 -0.581107 bar
2 1 -0.338278 foo
3 1 0.768924 bar
6 1 -0.778718 foo
2 0 2 0.196477 foo
4 2 -0.364822 foo
5 2 -0.976079 bar
7 2 -2.671668 bar
```
But if I trigger to *not* take the fast apply path (in this case by making one column an extension dtype), we get a different result:
```
In [4]: df['value2'] = df["value2"].astype("string")
In [5]: df.groupby("key").apply(df_copy_function)
Out[5]:
key value1 value2
0 2 0.196477 foo
1 1 -0.581107 bar
2 1 -0.338278 foo
3 1 0.768924 bar
4 2 -0.364822 foo
5 2 -0.976079 bar
6 1 -0.778718 foo
7 2 -2.671668 bar
8 0 -0.149534 foo
9 0 -0.391135 bar
```
This might be another manifestation of https://github.com/pandas-dev/pandas/pull/34998 and the issues linked from that PR. | {
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} | 1 | 2021-03-15T16:21:19Z | 2021-03-16T16:22:52Z | 2021-03-16T16:16:10Z | NONE | null | #### Is your feature request related to a problem?
As a part of a research project, me and two co-authors have been mining GitHub for Jupyter notebooks and studied a number of frequent function calls in the notebooks. It appears that users sometimes make calls to `read_csv` where they specify that non-date data should be parsed as dates. The most common case is that `parse_date` is set to `True` while the index cannot be parsed as dates. We have also seen cases where columns whose data cannot be parsed as dates are specified, by name (e.g. `parse_dates=["REQUEST_DATE", "AUTHORIZATION_DATE"]`) or column index (e.g. `parse_dates=[1]`). As a result, data will be stored as objects while the user thinks that it is stored on datetime format. Then results of e.g. filtering or merging on the column (or index) in question may be incorrect. I think that `read_csv` should issue a warning in these cases.
Moreover, the fact that the user tries to parse a non-date column as dates may be an indication that the input data does not look as the user expects, which is also worth a warning!
#### Describe the solution you'd like
The function `read_csv` should warn when the value of the parameter `parse_dates` specifies that a column (or index) should be parsed as dates but the column (or index) in question is on non-date format.
#### API breaking implications
If only a warning is issued, I don't see how this could break anything.
#### Describe alternatives you've considered
An alternative could be to issue an error instead of a warning. | {
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] | closed | false | null | [] | null | 2 | 2021-03-15T16:39:33Z | 2021-03-16T04:05:25Z | 2021-03-16T04:05:25Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
s = pd.Series([5, 5, 6, 7, 5, 2, 5])
print("## Working sample:")
print(s.rolling(3).std())
s = pd.Series([5, 5, 6, 7, 5, 2, 5]) * 1e-8
print("## Doesn't work for small data:")
std = s.rolling(3).std()
print(std)
assert std.iloc[-2] == 0
data = list(s.rolling(3))[-2]
std_manual = data.std()
print("## Manually calculated std:", std_manual)
assert std.iloc[-2] == std_manual, "Rolling std and manually calculated rolling std do not match"
```
#### Problem description
I found that when the series consists of small floating numbers (about the order of 1e-8) the rolling standard deviation will be zero. If I use the same window and compute the standard deviation directly the output is non-zero. Is there an internal precision setting for the rolling `std` function?
#### Expected Output
```python
s = pd.Series([5, 5, 6, 7, 5, 2, 5]) * 1e-8
std = s.rolling(3).std()
```
`std` should be nonzero for the last few elements.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.2.3
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : None
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : 0.52.0
</details>
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} | 3 | 2021-03-15T17:05:11Z | 2021-03-16T16:01:20Z | 2021-03-16T13:45:28Z | NONE | null | #### Is your feature request related to a problem?
When you call `read_csv` and the length of `names` is larger than the number of columns in the data, extra column(s) containing only `NaN` will be added to the DataFrame created. I think that, generally, the extra name(s) is/are a sign of a bug in the program and/or input data that does not look as the user expects. In both cases, I think it could be helpful to issue a warning to make the user aware of the issue. Moreover, a warning may help avoiding future errors, for example if the user filters or merges data on the column containing only `NaN`s, thinking that it contains actual data.
#### Describe the solution you'd like
I think it would be helpful if `read_csv` issued a warning when the number of elements in `names` is larger than the number of columns in the data.
#### API breaking implications
If only a warning is issued, I don't see how this could break anything.
#### Describe alternatives you've considered
An alternative could be to issue an error instead of a warning. | {
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} | 7 | 2021-03-15T18:19:50Z | 2021-04-02T19:10:02Z | 2021-04-02T19:09:57Z | CONTRIBUTOR | null | - [X] closes #40351
- [X] tests added / passed
- [X] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
This was failing due to `elif np.issubdtype(dtype, np.floating)` [here](https://github.com/pandas-dev/pandas/blob/master/pandas/core/arrays/string_.py#L330), which fails for the pandas dtypes.
StringArrays are now being cast to Categorical just as they were before https://github.com/pandas-dev/pandas/pull/38530, when the casting still happened inside `Block`. | {
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
This makes us slightly more aggressive in casting to dt64 in `Index.__new__` (see the added test_index_new test, it contains all of the cases that are reached in the existing tests where the current behavior differs from the PR behavior)
Technically an API change, but I think its worth it to match the Series behavior.
Found+fixed a `pd.array` bug along the way. | {
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Also removing redundant code `cell_context.get("col_headings", {})` would **never** return anything other `{}` | {
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works fine on Ubuntu.
Might be a 3.9 thing; until fairly recently i couldnt install numba on 3.9
cc @mroeschke | {
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} | 2 | 2021-03-16T01:19:45Z | 2021-03-30T15:08:57Z | 2021-03-30T12:51:50Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Broken off from #40149. Like #40149, this backs DTBlock/TDBlock by (sometimes 2D) DTA/TDA. Unlike #40149, this does _not_ make the DTA backing DatetimeTZBlock sometimes-2D. | {
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 2 | 2021-03-16T03:40:50Z | 2021-03-16T21:45:30Z | 2021-03-16T20:31:41Z | CONTRIBUTOR | null | xref #14782
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} | 2 | 2021-03-16T08:16:44Z | 2021-03-21T15:24:17Z | 2021-03-21T15:24:11Z | NONE | null | #### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.replace.html
#### Documentation problem
The documentation has DataFrame examples and not those related to Series
#### Suggested fix for documentation
Give examples related to Series.
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https://api.github.com/repos/pandas-dev/pandas/issues/40460 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40460/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40460/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40460/events | https://github.com/pandas-dev/pandas/issues/40460 | 832,546,863 | MDU6SXNzdWU4MzI1NDY4NjM= | 40,460 | QST: Support of SQLAlchemy 2.0 in Pandas | {
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] | closed | false | null | [] | null | 10 | 2021-03-16T08:30:01Z | 2021-03-28T04:45:16Z | 2021-03-28T04:44:34Z | NONE | null | After search in Stackoverflow and in your open and closed issues, I can't find any information about the support of SQL Alchemy 2.0 using `pd.to_sql()`
---
#### Question about pandas
An exemple is better than a talk.
```python
from sqlalchemy import create_engine, MetaData, Table, Column, String, Integer
import pandas as pd
# Table creation
engine = create_engine(connection_string, echo=True, future=True) # This uses SQL Alchemy 2.0 API
metadata = MetaData()
category = Table(
'category',
metadata,
Column('id', Integer, primary_key = True),
Column('name', String(128), nullable = False),
Column('slug', String(128), nullable = False)
)
category.create(engine, checkfirst=True)
## Insert using pandas => FAIL
categories = pd.DataFrame([{'id' : 1, 'slug': 'test', 'name': 'Test'}])
categories.to_sql('category', engine, index=False)
```
This is the error I get from the CLI output :
```
File "e:\Projects\test\database.py", line 127, in populate_category_table
categories.to_sql('category', engine, index=False)
File "E:\Projects\test\env\lib\site-packages\pandas\core\generic.py",
line 2779, in to_sql
sql.to_sql(
File "E:\Projects\test\env\lib\site-packages\pandas\io\sql.py", line 590, in to_sql
pandas_sql.to_sql(
File "E:\Projects\test\env\lib\site-packages\pandas\io\sql.py", line 1392, in to_sql
table.create()
File "E:\Projects\test\env\lib\site-packages\pandas\io\sql.py", line 722, in create
if self.exists():
File "E:\Projects\test\env\lib\site-packages\pandas\io\sql.py", line 709, in exists
return self.pd_sql.has_table(self.name, self.schema)
File "E:\Projects\test\env\lib\site-packages\pandas\io\sql.py", line 1429, in has_table
return self.connectable.run_callable(
File "E:\Projects\test\env\lib\site-packages\sqlalchemy\future\engine.py", line 335, in _not_implemented
raise NotImplementedError(
NotImplementedError: This method is not implemented for SQLAlchemy 2.0.
```
For information about the version of every dependency :
````
$ ./env/Scripts/pip list
Package Version
--------------- ---------
beautifulsoup4 4.9.3
certifi 2020.12.5
chardet 4.0.0
esprima 4.0.1
greenlet 1.0.0
idna 2.10
mysqlclient 2.0.3
numpy 1.20.1
pandas 1.2.3
patreon 0.5.0
pip 21.0.1
python-dateutil 2.8.1
pytz 2021.1
requests 2.25.1
setuptools 49.2.1
six 1.15.0
soupsieve 2.2
SQLAlchemy 1.4.0
urllib3 1.26.3
````
Is there a workaround right now or should I avoid using pandas to insert the data into my database ?
Regards,
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https://api.github.com/repos/pandas-dev/pandas/issues/40461 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40461/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40461/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40461/events | https://github.com/pandas-dev/pandas/issues/40461 | 832,557,474 | MDU6SXNzdWU4MzI1NTc0NzQ= | 40,461 | BUG: inconsistency in division for scalar Offset vs series/array of Offsets | {
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] | open | false | null | [] | null | 2 | 2021-03-16T08:44:52Z | 2021-03-19T17:08:27Z | null | MEMBER | null | When dividing a scalar Offset with a numeric series, we get a timedelta64 series. But when dividing a Series of Offsets, we preserve the offsets.
```
In [26]: td = pd.offsets.Day(3)
In [27]: td / pd.Series([1, 2])
Out[27]:
0 3 days 00:00:00
1 1 days 12:00:00
dtype: timedelta64[ns]
In [28]: pd.Series([td, td]) / pd.Series([1, 2])
Out[28]:
0 <3 * Days>
1 <36 * Hours>
dtype: object
```
Also "scalar / scalar" operation gives an Offset.
cc @jbrockmendel | {
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} | 1 | 2021-03-16T08:45:19Z | 2021-04-26T21:57:35Z | 2021-04-26T21:57:35Z | MEMBER | null | When surpassing the minimum number of elements threshold to use numexpr, the following operation starts to fail:
```
In [19]: td = pd.offsets.Day(3)
In [20]: td / pd.Series([1, 2]*5000)
Out[20]:
0 3 days 00:00:00
1 1 days 12:00:00
...
9998 3 days 00:00:00
9999 1 days 12:00:00
Length: 10000, dtype: timedelta64[ns]
In [21]: td / pd.Series([1, 2]*5001)
...
~/miniconda3/envs/dev/lib/python3.7/site-packages/numexpr/necompiler.py in getType(a)
701 if kind == 'S':
702 return bytes
--> 703 raise ValueError("unknown type %s" % a.dtype.name)
704
705
ValueError: unknown type object
```
This doesn't happen for a Timedelta object, I assume because of this check:
https://github.com/pandas-dev/pandas/blob/7af47c999dc3d2782caf63fe8c42f0858937867b/pandas/core/ops/array_ops.py#L202-L203
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} | 5 | 2021-03-16T10:40:58Z | 2021-04-27T06:38:45Z | 2021-04-26T21:57:35Z | MEMBER | null | Closes #40462
This PR adds a auto-used fixture for `pandas/tests/arithmetic` that sets the `expressions._MIN_ELEMENTS` to 0, to force taking the numexpr path, even if our test data are small (which would otherwise never excercise the numexpr code path)
Further, it makes some fixes to get the tests passing with this better coverage.
The fixes are a bit quick-and-dirty, but at least already show what is needed to get things working. | {
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] | closed | false | null | [] | null | 2 | 2021-03-16T14:10:50Z | 2021-03-22T16:42:11Z | 2021-03-22T16:42:11Z | 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
print(pd.__version__)
df = pd.DataFrame({'col1': [1,2,3,4,5], 'col2': ['s','p','d','q','r']})
# check data types
print(df.dtypes) # <-- col1 dtype: int64
# convert col1 data type from int64 to object
df = df.astype({'col1': 'object'})
print(df.dtypes) # <-- col1 dtype: object
# write it to a parquet file
df.to_parquet('./test.parquet', compression='gzip')
re-load the dataset
df1 = pd.read_parquet('./test.parquet')
print(df1.dtypes) # <-- col1 dtype: int64
```
#### Problem description
In the above code, col1 in df is converted to object before writing it to parquet file. Surprisingly, it is converted to integer after re-load. Is that an expected behaviour? I think it should preserve the data types of each column.
I have seen same behaviour for float values as well.
#### Expected Output
col1 object
col2 object
dtype: object
#### Output of ``pd.show_versions()``
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.7.10.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.2.3
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : 0.29.22
pytest : 6.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.52.0
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} | 1 | 2021-03-16T14:43:33Z | 2021-03-17T14:08:53Z | 2021-03-17T10:39:10Z | MEMBER | null | and more consistent naming/args for MultiIndex engine.
Preliminary before making index.pyi | {
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frequency = 'monthly'
start = '1969-12-31'
end = '2020-12-31'
data = quandl.get(["ISM/MAN_PMI"], trim_start=start, trim_end=end)
data.head()
```
ISM/MAN_PMI - PMI Date
1970-01-01 48.7 1970-02-01 47.4 1970-03-01 46.9 1970-04-01 45.0 1970-05-01 47.2
```
df_log = numpy.log(df / df.shift(1))
df_log.head()
```
Adjusted Close Date
1969-12-31 NaN 1970-01-30 -0.079554 1970-02-27 0.051352 1970-03-31 0.001451 1970-04-30 -0.094842
`pd.merge(df, data)`
Error: MergeError: No common columns to perform merge on. Merge options: left_on=None, right_on=None, left_index=False, right_index=False
How chan I merge the two tables and harmonize them to:
1969-12
1970-1
1970-2
1970-3
..
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#### Code Sample, a copy-pastable example
I've not yet been able to isolate the code path where `run_callable` is used
https://github.com/pandas-dev/pandas/blob/ac3e3f71129bc9ea9ed0a27f3adcd89e367fa9af/pandas/io/sql.py#L1615-L1618
However, it looks like several tests, for example `pandas/tests/io/test_sql.py::TestSQLApi.test_timedelta`, will trigger this warning. For instance [here's a recent CI build](https://github.com/pandas-dev/pandas/runs/2122982478#step:7:78) where a deprecation warning is being raised and actually causes the test to fail.
#### Problem description
[`run_callable` is deprecated in sqlalchemy 1.4](https://docs.sqlalchemy.org/en/14/core/connections.html#sqlalchemy.engine.Connection.run_callable) -- `Connection.run_callable()` should be used instead
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.8.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
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 : 7.21.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.4.0
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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} | 5 | 2021-03-16T17:01:16Z | 2021-03-16T20:45:01Z | 2021-03-16T20:30:09Z | MEMBER | null | Fixes 2/3 of the failures on master that im aware of. sqlalchemy is the one left out. | {
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I read an excel file with pandas via openpyxl package. It converts the number in the `'ATC'` column to a weird bitwise string. Previously I was using `xlrd(1.2.0)` instead of `openpyxl` and it was working fine:
```
# with openpyxl 3.0.7
df = pd.read_excel('excel_file.xlsx')
>>>df['ATC']
0 _x0033_40.00
Name: ATC, dtype: object
>>>df['ATC'][0]
'_x0033_40.00'
```
```
# with xlrd 1.2.0
df = pd.read_excel('excel_file.xlsx')
>>>df['ATC']
0 340.0
Name: ATC, dtype: float64
>>>df['ATC'][0]
340.0
```
Here is the excel file if you like to check:
[excel_file.xlsx](https://github.com/pandas-dev/pandas/files/6151138/excel_file.xlsx)
Any work arounds? or I should keep using `xlrd`? | {
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} | 3 | 2021-03-16T18:13:29Z | 2021-06-16T02:38:11Z | 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.
---
I have two datasets from different sources. They span more or less the same time, contain different data, sampled at different rate.
I am trying to plot these on two subplots with a shared x axis.
Making sample data:
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
time1 = pd.date_range(
'2020-11-08T09:31:06', '2020-12-01T22:59:59', freq='S', tz='UTC'
)
df1 = pd.DataFrame(
np.sin(2 * np.pi * 1e-5 * np.arange(time1.size)),
index=time1,
columns=['x1']
)
time2 = pd.date_range(
'2020-11-08T09:32:00', '2020-12-01T22:58:00', freq='D', tz='UTC'
)
df2 = pd.DataFrame(
np.sin(2 * np.pi * 1e-1 * np.arange(time2.size)),
index=time2,
columns=['x2']
)
```
Minimal plotting example:
```python
fig, (ax1, ax2) = plt.subplots(2, sharex=True)
fig.subplots_adjust(0, 0, 1, 1, hspace=0.15)
df1.x1.plot(ax=ax1, legend=False)
df2.x2.plot(ax=ax2, legend=False)
```
I get `OverflowError` and a very long traceback but I think the problem is how the `sharex=True` is handled. When I set `sharex=False`, the error goes away and it plots fine but unaligned.
#### Output of ``pd.show_versions()``
I get `ImportError: Can't determine version for pip`
But:
Pandas: 1.2.3
Matplotlib: 3.3.2
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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} | 5 | 2021-03-16T18:50:39Z | 2021-03-17T11:58:13Z | 2021-03-16T20:30:53Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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] | open | false | null | [] | null | 6 | 2021-03-16T20:03:06Z | 2021-08-19T02:37:27Z | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
#good
good = pd.Series({1: 'a', 2: 'b'}).astype('category').replace('a', 'c')
#bad
bad = pd.Series({1: np.nan, 2: 'b'}).astype('category').replace(np.nan, 'c')
# does not replace and bad is instead:
# 1 NaN
# 2 b
# dtype: category
# Categories (1, object): [b]
```
#### Problem description
When replacing np.nan on a categorical series, the values are not modified.
This is a breaking change introduced in 1.0 (it worked fine in 0.25.3).
My guess is that this was introduced by https://github.com/pandas-dev/pandas/pull/27026/files
which does nothing when "[to_replace in cat.categories](https://github.com/JustinZhengBC/pandas/blob/c4d3f8489e467c072e4d96a4e515b76f7243e7d8/pandas/core/arrays/categorical.py#L2503)" evaluates to False.
#### Expected Output
`pd.Series({1: 'c', 2: 'b'}).astype('category')`
displaying like
```
# 1 c
# 2 b
# dtype: category
# Categories (2, object): [c, b]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.8.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Oct 29 22:56:45 PDT 2020; root:xnu-6153.141.2.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.2.3
numpy : 1.19.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : 0.29.22
pytest : 4.5.0
hypothesis : None
sphinx : 3.5.2
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.1
sqlalchemy : 1.3.23
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.0
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} | 1 | 2021-03-16T20:37:52Z | 2021-03-18T14:12:59Z | 2021-03-18T09:01:29Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 4 | 2021-03-16T22:18:14Z | 2021-03-20T03:05:32Z | 2021-03-20T02:34:22Z | MEMBER | null | The places where this will need updating proliferate, so right now im just looking to see how the 32bit builds handle this. | {
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} | 4 | 2021-03-16T23:51:56Z | 2021-03-20T01:38:19Z | 2021-03-20T01:09:25Z | MEMBER | null | nailing down all the "np.intp"s bit by little bit.
AFAICT we're not going to be able to annotate ndarrays with ndim/dtype any time soon. Might be worth looking into https://pypi.org/project/nptyping/ | {
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} | 4 | 2021-03-17T03:37:05Z | 2021-03-23T23:10:42Z | 2021-03-23T22:50:18Z | MEMBER | null | Started with sharing some code, unearthed a bug along the way.
Nothing user-facing since it is only in ArrayManager. | {
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} | 3 | 2021-03-17T09:14:18Z | 2021-03-22T09:44:56Z | 2021-03-21T18:10:29Z | 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
from pandas.core.dtypes.common import is_extension_array_dtype
from pandas.core.base import ExtensionArray
dt = DatetimeArray(np.array([], dtype='datetime64[ns]'))
assert isinstance(dt, ExtensionArray) # passes
assert is_extension_array_dtype(dt) # fails
assert is_extension_array_dtype(dt.dtype) # fails
inter = pd.Interval(0, 5)
assert com.is_extension_array_dtype(inter) # fails
```
Similar problems for `Period` and `timedelta64[ns]`.
#### Problem description
Some of it (example: DatetimeArray) is due to this commit: https://github.com/pandas-dev/pandas/commit/be6885074de3ac333ab0af3271d45bc4eccb307c#diff-e279f0dcc8af317412f2a0db83d50c2ffedcd0743af7da8545ff272ee6ebe2f3R1529
Where DatetimeArray implements ExtensionArray, but `datetime64[ns]` is a np dtype and doesn't (yet) implement ExtensionDtype.
#### Expected Output
Function should return `true` for all dtypes whose Array implements `ExtensionArray`.
I need this for #40450.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : a75152784c17a73627a414319f6c89324cfa2840
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-44-generic
Version : #50-Ubuntu SMP Tue Feb 9 06:29:41 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : C.UTF-8
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0.dev0+1084.ga75152784c.dirty
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 20.3.3
setuptools : 49.6.0.post20201009
Cython : 0.29.22
pytest : 6.2.2
hypothesis : 6.7.0
sphinx : 3.5.2
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.7
fastparquet : 0.5.0
gcsfs : 0.7.2
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.6.0
sqlalchemy : 1.3.23
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.17.0
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.52.0
</details>
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} | 7 | 2021-03-17T09:48:45Z | 2021-04-28T14:50:46Z | 2021-04-28T14:49:50Z | MEMBER | null | This moves the potential upcasting of python/numpy scalars to pandas scalars out of the actual array op (`arithmetic_op`), but moves it up to the Series / DataFrame level before calling the array op. This avoids calling it repeatedly on the same scalar for each column. | {
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} | 1 | 2021-03-17T10:00:06Z | 2021-04-13T23:19:56Z | 2021-04-13T23:19:56Z | MEMBER | null | ```
In [2]: df = DataFrame(index=[3, 5, 4], columns=["A", "B"], dtype=float)
...: df["B"] = "string"
...: df.loc[[4, 3, 5], "A"] = np.array([1, 2, 3], dtype="int64")
In [3]: df
Out[3]:
A B
3 1 string
5 2 string
4 3 string
```
In the above (using master), the values [1, 2, 3] were set in that order, while the indexer used a different order. In released versions of pandas, we correctly get [2, 3, 1]:
```
In [3]: df
Out[3]:
A B
3 2 string
5 3 string
4 1 string
In [4]: pd.__version__
Out[4]: '1.2.3'
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Currently, we broadcast a Series to a DataFrame for elementwise operations in the "op(df, series)" case. This is done (I think) to be able to perform the operation block-wise (which is only implemented for block/block case, and not for block/array).
But, when using the ArrayManager, this broadcasting is 1) not necessary (since we perform column-by-column anyway) and 2) costly. | {
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] | closed | false | null | [] | null | 2 | 2021-03-17T15:09:10Z | 2021-03-22T12:53:01Z | 2021-03-22T12:53:01Z | NONE | null | i receive an excel attachment via outlook, after saving the file and running the python script the following warning is returned.
"UserWarning: Workbook contains no default style, apply openpyxl's default" if the warning is ignored then read_excel report 2 header records when there are 7 header records.
if I open the file via Excel and save the file with no changes, some dialog is shown but it is display so quickly I am unable to determine what excel is doing.
when I re-run the python script no exceptions occur and everything works as expected.
I have upgraded pandas from 1.2.1 to 1.2.3
i have upgraded openpyxl from 3.0.6 to 3.0.7
issue remains
ry:
df = pd.read_excel(Filename, header = 7)
print('no issues reading excel file')
except Exception as e:
print('Excel read error: ', e.args[0])
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} | 8 | 2021-03-17T18:11:58Z | 2021-04-02T17:20:24Z | 2021-04-02T17:20:24Z | CONTRIBUTOR | null | Currently `Styler.clear()` will clear the styles built up with the `apply` `aaplymap` and `where` method. It will clear class from the `set_td_classes` method and it will clear tooltips set with `set_tooltips`.
It does not clear table styles, and it does not clear formats. It does not reset hidden index or hidden columns.
Thoughts and PR into what this should do and how it should it be documented, as well consistency with `Styler.copy` are welcome.. | {
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} | 3 | 2021-03-17T18:29:32Z | 2021-04-04T19:57:20Z | 2021-04-04T19:57:20Z | CONTRIBUTOR | null | #### Location of the documentation
[URL of the documentation](https://pandas.pydata.org/docs/development/contributing.html#creating-a-python-environment-pip)
**Note**: You can check the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/).
(Problem exist in the latest version: [here](https://pandas.pydata.org/docs/dev/development/contributing.html?highlight=contributing#creating-a-python-environment-pip))
#### Documentation problem
In the documents, it says `at least Python 3.6.1 installed` to create Python virtual environment to contribute Pandas. However, to install some development requirements (such as NumPy) Python version must be >=3.7. See [here](https://pypi.org/project/numpy/) for NumPy minimum requirement.
#### Suggested fix for documentation
We should update the relevant document so contributors can install an appropriate virtual environment for contribution.
Suggested edit:
`You’ll need to have at least Python 3.7 installed on your system.` | {
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} | 2 | 2021-03-17T19:19:52Z | 2021-03-30T21:56:43Z | 2021-03-30T21:21:28Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 2 | 2021-03-17T21:16:48Z | 2021-04-02T22:35:24Z | 2021-04-02T22:35:24Z | 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.
---
```python
>>> s = pd.Series([1, 2], index=['a', 'b'])
>>> s.explode(ignore_index=True)
a 1
b 2
dtype: int64
```
#### Problem description
Even though exploding a non-nested array results in a no-op. `ignore_index` should still be considered to align with behavior of its dataframe sibling.
#### Expected Output
```python
>>> s.explode(ignore_index=True)
0 1
1 2
dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.7.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
Version : #86-Ubuntu SMP Fri Jan 17 17:24:28 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.2.0
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : 0.29.22
pytest : 6.2.2
hypothesis : 6.8.0
sphinx : 3.5.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.7
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.52.0
</details>
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame({'a': [1,1,2], 'b': [[10],[11],[12]]})
# ok:
>>> df['b'].cumsum()
0 [10]
1 [10, 11]
2 [10, 11, 12]
Name: b, dtype: object
# error:
>>> df.groupby('a')['b'].cumsum()
DataError: No numeric types to aggregate
```
#### Problem description
Given that taking the cumsum of a column of list-likes is allowed, it should similarly be allowed when doing the same to a groupby object.
#### Expected Output
```python
a
0 [10]
1 [10, 11]
2 [12]
Name: b, dtype: object
```
#### Workaround
This is only a partial workaround because it loses the index name, which is normally kept when doing a groupby.
```python
df.groupby('a')['b'].apply(lambda col: col.cumsum())
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.8.7.final.0
python-bits : 64
OS : Darwin
OS-release : 20.4.0
Version : Darwin Kernel Version 20.4.0: Fri Mar 5 03:57:04 PST 2021; root:xnu-7195.101.1~4/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.4
pytz : 2020.5
dateutil : 2.8.1
pip : None
setuptools : 50.3.1.post0
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 : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : None
```
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} | 6 | 2021-03-17T22:42:46Z | 2021-08-08T23:34:29Z | 2021-08-08T23:34:29Z | MEMBER | null | `Index.__new__` does inference on _numeric_ data more aggressively than `Series.__new__`. It would be nice if these behaviors matched. (xref #40451 also about differenced between Series vs Index inference, though in that case Series is more aggressive about inference)
```
data = np.array([np.nan, np.nan, 2.0], dtype=object)
>>> pd.Series(data).dtype
dtype('O')
>>> pd.Index(data).dtype
dtype('float64')
>>> pd.array(data).dtype
Float64Dtype()
```
(if we passed data[:-1] to pd.array we'd get back a PandasArray[object] bc it passes skipna=True to `lib.infer_dtype`)
Changing the Series behavior to match Index breaks 207 tests (140 of which are for the str accessor; i expect some others are false-positives), so this would be a non-trivial API change. | {
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} | 1 | 2021-03-18T00:42:26Z | 2021-03-20T01:47:46Z | 2021-03-20T01:18:39Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | [] | closed | false | null | [] | null | 2 | 2021-03-18T03:04:34Z | 2021-04-11T00:32:25Z | 2021-04-11T00:32:24Z | CONTRIBUTOR | null | I fixed the missing space that made the documentation render "isthe" instead of "is the".
Before the change:
- This **isthe** equivalent of the numpy.ndarray method argmax.
- This **isthe** equivalent of the numpy.ndarray method argmin.
After the change:
- This **is the** equivalent of the numpy.ndarray method argmax.
- This **is the** equivalent of the numpy.ndarray method argmin.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 0 | 2021-03-18T14:30:13Z | 2021-08-19T02:37:51Z | null | MEMBER | null | The [`test_expressions.py` test file](https://github.com/pandas-dev/pandas/blob/master/pandas/tests/test_expressions.py) could be updated to use some more pytest features (eg using fixtures for the test data, using fixture for the setup/teardown to set `_MIN_ELEMENTS`) | {
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} | 5 | 2021-03-18T14:50:41Z | 2021-10-26T19:54:58Z | 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 (1.0.5)
`series = pd.Series({'1':1,'2':None,'3':np.nan, '4':''}) `
will create a series which looks like:
1 1
2 None
3 NaN
4
dtype: object
When using the fillna method on this series on index 3 it changes also the entry in index 2!
series.fillna({'3':0})
This will result in a output as follows:
1 1
2 NaN
3 0
4
dtype: object
**Problem:**
The value at index 2 changed from None to NaN.
**Expected Behaviour:**
The value at index 2 should never be touched when using fillna and specifying the indices you want to alter.
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https://api.github.com/repos/pandas-dev/pandas/issues/40499 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40499/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40499/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40499/events | https://github.com/pandas-dev/pandas/issues/40499 | 834,939,044 | MDU6SXNzdWU4MzQ5MzkwNDQ= | 40,499 | BUG: pandas cannot open xlsx with openpyxl engine | {
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- [ x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
[NXTS122ND8S10.xlsx](https://github.com/pandas-dev/pandas/files/6165254/NXTS122ND8S10.xlsx)
I have this trouble that pandas cannot open any xlsx files. All of the xlsx files can be open and read by excel.
Current packages and versions in use:
pandas: v1.2.3
openpyxl: v 3.0.7
python : v 3.8.5
method 1:
excel = pd.read_excel('NXTS122ND8S10.xlsx',sheet_name='Run results',engine='openpyxl')
print(excel)
method 2:
excel = pd.read_excel(open('NXTS122ND8S10.xlsx','rb'), sheet_name ='Run results')
print(excel)
method 3:
wb = openpyxl.load_workbook('NXTS122ND8S10.xlsx')
sheet = wb.worksheets[0]
excel = pd.dataframe(sheet.values)
print(excel)
method 4:
excel = pd.read_excel('NXTS122ND8S10.xlsx')
print(excel)
same error code:
Traceback (most recent call last):
File "C:\Users\XXXXX\AppData\Local\Programs\Python\Python38-32\lib\site-packages\openpyxl\descriptors\base.py", line 55, in _convert
value = expected_type(value)
TypeError: Fill() takes no arguments
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\XXXXX\eclipse-workspace\Work Related Projects\transpose_3.py", line 107, in
excel = pd.read_excel(open(directory+''+file,'rb'), sheet_name ='Run results')
File "C:\Users\XXXXX\AppData\Local\Programs\Python\Python38-32\lib\site-packages\pandas\util_decorators.py", line 299, in wrapper
return func(*args, **kwargs)
File "C:\Users\XXXXX\AppData\Local\Programs\Python\Python38-32\lib\site-packages\pandas\io\excel_base.py", line 336, in read_excel
io = ExcelFile(io, storage_options=storage_options, engine=engine)
File "C:\Users\XXXXX\AppData\Local\Programs\Python\Python38-32\lib\site-packages\pandas\io\excel_base.py", line 1131, in init
self._reader = self._engines[engine](self._io, storage_options=storage_options)
File "C:\Users\XXXXX\AppData\Local\Programs\Python\Python38-32\lib\site-packages\pandas\io\excel_openpyxl.py", line 475, in init
super().init(filepath_or_buffer, storage_options=storage_options)
File "C:\Users\XXXXX\AppData\Local\Programs\Python\Python38-32\lib\site-packages\pandas\io\excel_base.py", line 391, in init
self.book = self.load_workbook(self.handles.handle)
File "C:\Users\XXXXX\AppData\Local\Programs\Python\Python38-32\lib\site-packages\pandas\io\excel_openpyxl.py", line 486, in load_workbook
return load_workbook(
File "C:\Users\XXXXX\AppData\Local\Programs\Python\Python38-32\lib\site-packages\openpyxl\reader\excel.py", line 317, in load_workbook
reader.read()
File "C:\Users\XXXXX\AppData\Local\Programs\Python\Python38-32\lib\site-packages\openpyxl\reader\excel.py", line 281, in read
apply_stylesheet(self.archive, self.wb)
File "C:\Users\XXXXX\AppData\Local\Programs\Python\Python38-32\lib\site-packages\openpyxl\styles\stylesheet.py", line 198, in apply_stylesheet
stylesheet = Stylesheet.from_tree(node)
File "C:\Users\XXXXX\AppData\Local\Programs\Python\Python38-32\lib\site-packages\openpyxl\styles\stylesheet.py", line 103, in from_tree
return super(Stylesheet, cls).from_tree(node)
File "C:\Users\XXXXX\AppData\Local\Programs\Python\Python38-32\lib\site-packages\openpyxl\descriptors\serialisable.py", line 103, in from_tree
return cls(**attrib)
File "C:\Users\XXXXX\AppData\Local\Programs\Python\Python38-32\lib\site-packages\openpyxl\styles\stylesheet.py", line 74, in init
self.fills = fills
File "C:\Users\XXXXX\AppData\Local\Programs\Python\Python38-32\lib\site-packages\openpyxl\descriptors\sequence.py", line 26, in set
seq = [_convert(self.expected_type, value) for value in seq]
File "C:\Users\XXXXX\AppData\Local\Programs\Python\Python38-32\lib\site-packages\openpyxl\descriptors\sequence.py", line 26, in
seq = [_convert(self.expected_type, value) for value in seq]
File "C:\Users\XXXXX\AppData\Local\Programs\Python\Python38-32\lib\site-packages\openpyxl\descriptors\base.py", line 57, in _convert
raise TypeError('expected ' + str(expected_type))
TypeError: expected <class 'openpyxl.styles.fills.Fill'>
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
pandas: v1.2.3
openpyxl: v 3.0.7
python : v 3.8.5
</details>
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} | 10 | 2021-03-18T16:07:50Z | 2021-03-24T14:08:52Z | 2021-03-24T14:08:52Z | MEMBER | null | Currently we have a MIN_ELEMENTS set at 10,000:
https://github.com/pandas-dev/pandas/blob/00a622401e06bd7afaaa508707a46f3dcc494fe4/pandas/core/computation/expressions.py#L42-L43
However, I have been noticing while running lots of performance comparisons recently, that numexpr still seems to show some overhead at that array size compared to numpy.
I did a few specific timings for a few ops comparing numpy and numexpr for a set of different array sizes:
<details>
<summary>Code used to create the plot</summary>
```python
import operator
import numpy as np
import pandas as pd
import numexpr as ne
import seaborn as sns
results = []
for s in [10**3, 10**4, 10**5, 10**6, 10**7, 10**8]:
arr1 = np.random.randn(s)
arr2 = np.random.randn(s)
for op_str, op in [("+", operator.add), ("*", operator.mul), ("==", operator.eq), ("<=", operator.le)]:
res_ne = %timeit -o ne.evaluate(f"a {op_str} b", local_dict={"a": arr1, "b": arr2}, casting="safe")
res_np = %timeit -o op(arr1, arr2)
results.append({"size": s, "op": op_str, "engine": "numepxr", "timing": res_ne.average, "timing_stdev": res_np.stdev})
results.append({"size": s, "op": op_str, "engine": "numpy", "timing": res_np.average, "timing_stdev": res_np.stdev})
df = pd.DataFrame(results)
fig = sns.relplot(data=df, x="size", y="timing", hue="engine", col="op", kind="line", col_wrap=2)
fig.set(xscale='log', yscale='log')
```
</details>
So in general, numexpr is not *that* much faster for the large arrays. But specifically, it still has a significant overhead compared to numpy up to 1e5 - 1e6, while the current minimum number of elements is 1e4.
Further, this might depend on your specific hardware and versions etc (this was run on my linux laptop with 8 cores, using latest versions of numpy and numexpr). So it is always hard to give a default suitable for all.
But based on the analysis above, I would propose raising the minimum from 1e4 to 1e5 (or maybe even 1e6). | {
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] | closed | false | null | [] | null | 5 | 2021-03-18T16:17:55Z | 2021-03-19T16:51:32Z | 2021-03-19T16:51:29Z | MEMBER | null | In some of the arithmetic benchmarks (xref https://github.com/pandas-dev/pandas/issues/39146#issuecomment-799482699), just this `is_numeric_v_string_like` check takes up 15-35% of the overall time.
This improves the performance of this check by using some ndarray-specialized dtype checks (checking the kind instead of the generic functions):
```
In [1]: from pandas.core.dtypes.common import is_numeric_v_string_like
In [2]: arr = np.array([1, 2, 3])
In [3]: %timeit is_numeric_v_string_like(arr, 2.0)
2.3 µs ± 46.9 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <-- master
482 ns ± 41.8 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <-- PR
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} | 1 | 2021-03-18T18:27:03Z | 2021-03-22T14:30:26Z | 2021-03-22T12:43:14Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 2 | 2021-03-18T18:47:09Z | 2021-03-20T00:22:58Z | 2021-03-19T03:09:08Z | 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).
I have a dataframe `season_statistics_T1`, its related information is given as follows. Each operation looks normal except `season_statistics_T1.head().to_dict()`, which gives the **keyerror: 'Season' message**. I am not sure why. At the same time, `season_statistics_T1.head().to_dict('records')` works fine.
---
#### 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.
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} | 8 | 2021-03-19T01:39:03Z | 2021-04-06T19:19:10Z | 2021-03-22T14:32:36Z | MEMBER | null | And in a few places where we know allow_fill=False, just call the numpy version directly. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/40511 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40511/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40511/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40511/events | https://github.com/pandas-dev/pandas/pull/40511 | 835,529,393 | MDExOlB1bGxSZXF1ZXN0NTk2MTc1NzY2 | 40,511 | CI: move 32-bit build to container job | {
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https://api.github.com/repos/pandas-dev/pandas/issues/40512 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40512/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40512/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40512/events | https://github.com/pandas-dev/pandas/issues/40512 | 835,550,596 | MDU6SXNzdWU4MzU1NTA1OTY= | 40,512 | Pandas multiindex unstack fillna not working | {
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} | [] | closed | false | null | [] | null | 4 | 2021-03-19T04:32:11Z | 2021-03-20T03:45:14Z | 2021-03-20T03:45:13Z | NONE | null | When we unstacking the multi-indexed pandas `dataframe`, the method `fillna` does not work.
Here is an example.
```python
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randint(0,10,(5,4)),
columns=['c1','c2','c3','c4'])
df.iloc[1,2] = np.nan
df.iloc[0,0] = np.nan
df['ind1'] = ['a','a','b','b','c']
df['ind2'] = [1,2,1,2,1]
df = df.set_index(['ind1','ind2'])
print(df)
```
Now we have `df` with some `NaN` values.
```
c1 c2 c3 c4
ind1 ind2
a 1 NaN 2 1.0 9
2 9.0 5 NaN 7
b 1 1.0 7 2.0 8
2 3.0 6 0.0 2
c 1 9.0 6 6.0 6
```
Then fillna to the unstacked `df` does not work.
```python
print(df.unstack().fillna(0))
```
```
c1 c2 c3 c4
ind2 1 2 1 2 1 2 1 2
ind1
a 0.0 9.0 2.0 5.0 1.0 0.0 9.0 7.0
b 1.0 3.0 7.0 6.0 2.0 0.0 8.0 2.0
c 9.0 0.0 6.0 NaN 6.0 0.0 6.0 NaN
```
I raised this issue at [stack overflow](https://stackoverflow.com/questions/66692036/pandas-multiindex-unstack-fillna-not-working), and I suspect this is a bug.
I would appreciate to fix this problem.
Thanks,
> **Note**
> The `pandas` version is `1.1.3` (current version in Anaconda), and there is no issue with the version `==1.2.1`. | {
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] | open | false | null | [] | null | 0 | 2021-03-19T07:31:17Z | 2021-08-19T02:39:18Z | null | NONE | null | - [X ] I have checked that this issue has not already been reported.
- [ X] I have confirmed this bug exists on the latest version of pandas.
- [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
# dummy data
test_data = {'a': 1,
'b': 2,
'c': {'cc': [333, 444]}, # records
'd': {'dd': 'meta'} # meta
}
# this works fine
basic_meta = pd.json_normalize(test_data, ['c', 'cc'], meta=['a'])
# this too
basic_meta = pd.json_normalize(test_data, ['c', 'cc'], meta=['a', ['d']])
# this does not
# exception: KeyError: "Try running with errors='ignore' as key 'dd' is not always present"
nested_meta = pd.json_normalize(test_data, ['c', 'cc'], meta=['a', ['d', 'dd']])
```
#### Problem description
See related https://github.com/pandas-dev/pandas/issues/27220
I don't think this behaviour is expected or documented.
I think the expected behaviour would be to produce a 'd.dd' column containing 'meta' strings. Mostly because this would be the result if the `record_path` were not nested.
Having a shot at debugging the code in `pandas/io/json/_normalize.py`, I suspect I know where the problem is.
In the function `_recursive_extract()`, at the top, we have:
```python
def _recursive_extract(data, path, seen_meta, level=0):
if isinstance(data, dict):
data = [data]
if len(path) > 1:
for obj in data:
for val, key in zip(_meta, meta_keys):
if level + 1 == len(val):
seen_meta[key] = _pull_field(obj, val[-1])
# problematic line V--- object is recursed into, which cuts off remaining metadata that also needs to be recursed into
_recursive_extract(obj[path[0]], path[1:], seen_meta, level=level + 1)
```
In the line at the bottom of the extract above, the data object is recursed into, because the `record_path` (the `path` variable here) is nested.
But as a result the next iteration only has access to the next level of the `record_path` and nothing else including the whole of the meta data that also needs to be recursed into. The remaining code in `_recursive_extract` tries to perform just this recursion into the remaining metadata, but can't find *any* metadata and so fails.
**This can be more or less proven by an absurd example that doesn't fail.**
Here, the `meta` path will be partially (but not completely) duplicated within the `record_path`.
```python
test_data = {'a': 1,
'b': 2,
'c': {'cc': [333, 444], 'dd': 'wrong_meta'}, # 'dd' is a duplication of the second level of the meta path
'd': {'dd': 'meta'} # original meta_path
}
```
And now the code from above will not fail, but produce the wrong but different output again.
```python
bad_meta = pd.json_normalize(test_data, ['c', 'cc'], meta=['a', ['d', 'dd']])
```
This produces the table below where the `'dd'` key within the dict at `'c'` has been taken up as the metadata because it does survive the recursion process.
| | 0 | a | d.dd |
|---:|----:|----:|:-----------|
| 0 | 333 | 1 | wrong_meta |
| 1 | 444 | 1 | wrong_meta |
Though I'm not on top of this code base, the solution could be adjusting `_recursive_extract` to take a separate metadata argument so that it can be separately recursed into, or, somehow separating the treatment of records and metadata at the appropriate node in the tree of the data.
#### Expected Output
To produce a 'd.dd' column containing 'meta' strings.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.186-146.268.amzn2.x86_64
Version : #1 SMP Tue Jul 14 18:16:52 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200712
Cython : 0.29.21
pytest : 6.2.1
hypothesis : 6.0.2
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.7.4
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 0.17.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.2
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
</details>
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} | 1 | 2021-03-19T08:35:33Z | 2021-03-19T17:15:29Z | 2021-03-19T17:15:26Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/issues/39146
In some of the `FrameWithFrameWide` benchmarks, this takes up around 15% of the time.
While I don't think this should be needed (but let's see if warnings appear in the CI builds). | {
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} | 1 | 2021-03-19T08:46:09Z | 2021-03-19T15:01:36Z | 2021-03-19T14:47:39Z | MEMBER | null | I got a PR into [pyupgrade](https://github.com/asottile/pyupgrade/blob/c90a455fceacdecc7484dab8519688353b6b2931/pyupgrade/_plugins/typing_pep563.py) to do what no-string-hints did (and in a much better way, their maintainer is amazing), so my little tool can be archived / removed from here :tada:
Also combining some other hooks while I'm here | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
s = pd.Series(0.0, pd.date_range('2001-10-14 02:00', periods=2, freq='1H', tz='America/Santiago'))
s.resample('30T').max() # raises error for '2001-10-14 00:00', which is not in the index
```
#### Problem description
This shouldn't raise an error, as there is no ambiguity on how the resampling should be made. Also it isn't clear why it raises an error for the '2001-10-14 00:00' timestamp, which is not in the index.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.8.final.0
python-bits : 64
OS : Darwin
OS-release : 20.2.0
Version : Darwin Kernel Version 20.2.0: Wed Dec 2 20:39:59 PST 2020; root:xnu-7195.60.75~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.2.3
numpy : 1.19.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : 0.29.22
pytest : 6.2.2
hypothesis : None
sphinx : 3.5.2
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.1
sqlalchemy : 1.3.23
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.0
</details>
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} | 1 | 2021-03-19T12:27:08Z | 2021-04-01T15:54:05Z | 2021-04-01T15:54:05Z | NONE | null | - [x] I have checked that this issue has not already been reported. There was a [previous issue](https://github.com/pandas-dev/pandas/issues/15630) about `rank(pct=True, method="dense")` but it was about applying it to `pd.Series` and has been resolved.
- [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
import numpy as np
data = [
[1, 3, 1],
[1, 3, -5],
[1, 3, 3],
[1, 3, 3],
[2, 3, np.nan],
[1, 3, np.nan],
]
df = pd.DataFrame(data, columns=["gr1", "gr2", "val"])
df["r"] = df.groupby(["gr1", "gr2"])["val"].rank(
method="dense", pct=True
)
print(df)
```
##### Output:
```
gr1 gr2 val r
0 1 3 1.0 1.0
1 1 3 -5.0 0.5
2 1 3 3.0 1.5
3 1 3 3.0 1.5
4 2 3 NaN NaN
5 1 3 NaN NaN
```
#### Problem description
The `pct=True` option should always return values in `[0,1]`. If we remove the row with `gr1==2` there is no issue. Somehow the rank function miscounts the number of elements in each group which should be used to turn the ranked series to `[0,1]` ranked series. Note that `pd.Series([1,-5,3,3,np.nan]).rank(pct=True, method='dense').values` returns the desired output: `array([0.66666667, 0.33333333, 1. , 1. , nan])` so the issue is related to `groupby`. Also, trying out all the options of `na_option` gives undesired result.
#### Expected Output
```
gr1 gr2 val r
0 1 3 1.0 0.666667
1 1 3 -5.0 0.333333
2 1 3 3.0 1.000000
3 1 3 3.0 1.000000
4 2 3 NaN NaN
5 1 3 NaN NaN
```
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.9.2.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:06:51 PST 2021; root:xnu-7195.81.3~1/RELEASE_ARM64_T8101
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.2.3
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 54.1.2
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
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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