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https://api.github.com/repos/pandas-dev/pandas/issues/28013 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28013/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28013/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28013/events | https://github.com/pandas-dev/pandas/issues/28013 | 482,329,552 | MDU6SXNzdWU0ODIzMjk1NTI= | 28,013 | Add Styler.applyformat to set values instead of styles | {
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"description": "conditio... | closed | false | null | [] | null | 10 | 2019-08-19T13:59:35Z | 2021-02-10T21:54:52Z | 2021-02-10T20:19:10Z | NONE | null | I have a DataFrame that has multiple pairs of columns that are are title and URL. I would like to be able to render this table by converting the title to an anchor: `<a href=URL>title</a>`.
Styler.format doesn't support this because the function only provides one value for a column and the format spec string doesn't support URL links.
Here's what I'd like to be able to do:
```
def to_href(title, url):
return pd.Series([f"<a href={url[i]}>{title[i]}</a>" for i in range(len(title))])
ewm = client.executeSparql(query) # a DataFrame from a SPARQL result set
ewm.columns = ['Capability', 'Capability URL', 'Capability Status', 'Plan Item', 'Plan Item URL', 'Plan Item Status', 'Risk', 'Work Item', 'Work Item URL', 'Work Item Status']
ewmStyler = ewm.style
ewmStyler.applyformat(to_href, axis=0, url=ewm['Capability URL'], subset='Capability')
ewmStyler.hide_columns(['Capability URL','Plan Item URL', 'Work Item URL'])
HTML(ewmStyler.render())
```
That is, applyformat works just like apply, but set the values, not the style.
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It's still pending that the designer polish the styles. And also the content can surely be improved with everybody's feedback. But I think will be useful to merge this once we iterate a bit on people's feedback, so we can start working on integrating the docs.
I'm coordinating with the devs of numpy/scipy, scikit-learn and matplotlib to see if we can reshare things implemented here. Ideally I'd like to have the `pysuerga.py` script in a separate repo (that's why it's not named pandas_web.py`), and also the layout and pages that can be shared (the code of conduct, the donate, the blog,...). For now I add everything here for simplicity, and will create a new project and have it as a dependency here if they are interested.
CC: @pandas-dev/pandas-core | {
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} | 4 | 2019-08-19T17:07:23Z | 2019-08-22T19:52:55Z | 2019-08-22T19:52:55Z | CONTRIBUTOR | null | Sometime this week, though we have a few outstanding issues I think: https://github.com/pandas-dev/pandas/issues?q=is%3Aopen+is%3Aissue+milestone%3A0.25.1
I've tentatively targeted Wednesday.
Does anyone want to do this actual release work? Otherwise, I can. | {
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} | 1 | 2019-08-19T17:13:13Z | 2019-08-19T18:41:50Z | 2019-08-19T18:29:07Z | MEMBER | null | xfail in more specific circumstance
- [x] closes #28011
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 0 | 2019-08-19T17:20:59Z | 2019-08-20T06:45:31Z | 2019-08-20T06:45:31Z | NONE | null | Backport PR #27773: BUG: _can_use_numexpr fails when passed large Series | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28018 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28018/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28018/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28018/events | https://github.com/pandas-dev/pandas/pull/28018 | 482,458,730 | MDExOlB1bGxSZXF1ZXN0MzA4NzI3NTg0 | 28,018 | DOC/TST: Update the parquet (pyarrow >= 0.15) docs and tests regarding Categorical support | {
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} | 12 | 2019-08-19T18:22:38Z | 2019-10-05T00:23:59Z | 2019-10-04T19:48:04Z | CONTRIBUTOR | null | - [x] closes #27955
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/28019 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28019/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28019/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28019/events | https://github.com/pandas-dev/pandas/pull/28019 | 482,461,664 | MDExOlB1bGxSZXF1ZXN0MzA4NzI5OTAw | 28,019 | Backport PR #28016 on branch 0.25.x (TST: fix flaky xfail) | {
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} | 0 | 2019-08-19T18:29:18Z | 2019-08-20T06:45:49Z | 2019-08-20T06:45:49Z | NONE | null | Backport PR #28016: TST: fix flaky xfail | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28020 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28020/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28020/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28020/events | https://github.com/pandas-dev/pandas/pull/28020 | 482,514,830 | MDExOlB1bGxSZXF1ZXN0MzA4NzcyMzAz | 28,020 | CLN: timeseries in plotting clean up | {
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} | 6 | 2019-08-19T20:35:31Z | 2019-09-11T01:44:01Z | 2019-09-11T01:43:55Z | MEMBER | null | `format_timedelta_ticks` appears both in `timeseries.py` and `matplotlib.converter.py`, and there is no test. Then i look through the code base, it is only used in `matplotlib.converter.py` for `TimeSeries_TimedeltaFormatter`. So I remove the one in `timeseries.py` and add test for it.
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/28021 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28021/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28021/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28021/events | https://github.com/pandas-dev/pandas/issues/28021 | 482,535,864 | MDU6SXNzdWU0ODI1MzU4NjQ= | 28,021 | Create correct xlim for ts plot and update datetimelike tests | {
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... | open | false | null | [] | null | 7 | 2019-08-19T21:25:11Z | 2021-07-11T05:53:42Z | null | MEMBER | null | xref: #27993
The view limit for non-datetime plotting is solved. and behavior will be the same as 0.23.4
e.g.
HOWEVER, issue in datetime-like still exists:
The issue with datetime-like plotting, xlim will be changed, and the view limit is set to data limit, so there is no margin on xaxis. Plots are looking like:
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https://api.github.com/repos/pandas-dev/pandas/issues/28022 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28022/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28022/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28022/events | https://github.com/pandas-dev/pandas/issues/28022 | 482,564,544 | MDU6SXNzdWU0ODI1NjQ1NDQ= | 28,022 | [Feature Request] pd.DataFrame.schema iterable | {
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"description": "Requires... | open | false | null | [] | null | 7 | 2019-08-19T22:52:30Z | 2021-07-11T05:56:11Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# feature request
schema = df.schema # include all the index columns and the df.columns
for column in schema:
print(column.name, column.dtype) # plus other attributes?
```
Currently this is only available by using a pyarrow table, e.g.
```python
import pyarrow as pa
table = pa.Table.from_pandas(df)
schema = table.schema
schema_types = [[s.name, str(s.type).upper()] for s in schema]
```
It requires extra code to distinguish between index columns and other columns.
```python
# assuming schema from above pyarrow table
schema_dict = dict([[s.name, str(s.type).upper()] for s in schema])
# then iterate on df.index.names and df.columns to filter the schema_dict, e.g.
schema_index = dict([[name, schema_dict[name]] for name in df.index.names])
```
#### Problem description
- the df.info() is not iterable, it just prints information
- also, it has no dtypes for the index columns
- the df.dtypes is a Series but it's not all columns, with index columns, e.g.
```python
types = df.dtypes
type(types)
# <class 'pandas.core.series.Series'>
types.index # df.columns but not df.index columns
for idx in types.index:
print(idx, types[idx])
```
- feature request for an iterable schema data structure
```python
schema = df.schema # include all the index columns and the df.columns
for column in schema:
print(column.name, column.dtype)
# the column object may have other attributes too:
column.name # str
column.dtype # numpy.dtype ?
column.is_index # boolean
df.schema.to_dict() # column.name as keys, column object as value
df.schema.to_sql() # just DDL output
```
Similar schema are available if the DataFrame is serialized to parquet and read with pyarrow, but the pyarrow table schema is not iterable in the manner requested here.
Also note that `MultiIndex` has no dtypes, this feature request implicates new features for an index object. | {
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} | 2 | 2019-08-19T22:59:39Z | 2019-11-27T20:47:44Z | 2019-11-27T20:47:44Z | NONE | null | ```python
bug = pd.Series([0, 1, 2, 3, 4], index=[7606741985629028552, 17876870360202815256, 13106359306506049338, 8991270399732411471, 8991270399732411471])
assert bug.loc[7606741985629028552]==0
assert bug.loc[17876870360202815256]==1
# throws KeyError: 'None of [[7606741985629028552, 17876870360202815256]] are in the [index]'
bug.loc[[7606741985629028552, 17876870360202815256]]
```
#### Problem description
This code throws KeyError even though clearly both keys are present in the index, as the asserts demonstrate. It is essential for the bug that there is a repeated element in the index (not the one being extracted). The bug disappears if the values are smaller; perhaps it's some kind of a type cast thing that makes the values not found?
#### Expected Output
7.606742e+18 0
1.787687e+19 1
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Linux
OS-release: 4.14.72-68.55.amzn1.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.4
pytest: 3.7.3
pip: 18.0
setuptools: 40.2.0
Cython: 0.28.5
numpy: 1.15.0
scipy: 1.1.0
pyarrow: None
xarray: 0.10.8
IPython: 6.5.0
sphinx: 1.7.5
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.6
feather: None
matplotlib: 2.2.3
openpyxl: 2.5.5
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.7
lxml: 4.2.4
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.11
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28024 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28024/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28024/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28024/events | https://github.com/pandas-dev/pandas/pull/28024 | 482,594,648 | MDExOlB1bGxSZXF1ZXN0MzA4ODM3MTQw | 28,024 | BUG: rfloordiv with fill_value, closes#27464 | {
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} | 2 | 2019-08-20T01:04:19Z | 2019-08-20T18:42:02Z | 2019-08-20T17:45:06Z | MEMBER | null | - [x] closes #27464, closes #26793
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/28025 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28025/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28025/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28025/events | https://github.com/pandas-dev/pandas/issues/28025 | 482,603,853 | MDU6SXNzdWU0ODI2MDM4NTM= | 28,025 | DES: Q about SparseDataFrame._combine_match_columns behavior | {
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] | closed | false | null | [] | null | 4 | 2019-08-20T01:49:52Z | 2019-09-18T14:02:12Z | 2019-09-18T14:02:12Z | MEMBER | null | There is some idiosyncratic behavior that I think is non-intentional, need to double check: cc @jreback introduced the relevant code 8ee0a898a74df38318dfd01b6f9faa7957b0543c
SparseDataFrame._combine_match_index and SparseDataFrame._combine_match_columns are both for arithmetic ops with a Series object, but with different alignment treatment. When calling the constructor, combine_match_index passes `default_fill_value = self._get_op_result_fill_value(other, func)`, which matches `_combine_frame` behavior. _combine_match_columns just passes `default_fill_value = self.default_fill_value`.
I think that `_get_op_result_fill_value` should be called in both cases. Can anyone confirm?
(BTW this is relevant even though the class is deprecated because consolidating the behavior will allow us to streamline code in ops)
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https://api.github.com/repos/pandas-dev/pandas/issues/28026 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28026/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28026/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28026/events | https://github.com/pandas-dev/pandas/issues/28026 | 482,615,559 | MDU6SXNzdWU0ODI2MTU1NTk= | 28,026 | DOC: Fix python code error in enhancingperf | {
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} | 0 | 2019-08-20T02:44:50Z | 2019-08-21T06:43:03Z | 2019-08-21T06:43:03Z | CONTRIBUTOR | null | #### Problem description
This screenshot is in `pandas 0.25.0 documentation`, https://pandas.pydata.org/pandas-docs/stable/user_guide/enhancingperf.html#using-ndarray
Current code is
```python3
.. ipython:: python
%prun -l 4 apply_integrate_f(df['a'].to_numpy(),
df['b'].to_numpy(),
df['N'].to_numpy())
```
`%prun` takes only single line, so it can be
```python3
.. ipython:: python
%prun -l 4 apply_integrate_f(df['a'].to_numpy(), df['b'].to_numpy(), df['N'].to_numpy())
```
or use `%%prun`
```python3
.. ipython:: python
%%prun -l 4 apply_integrate_f(df['a'].to_numpy(),
df['b'].to_numpy(),
df['N'].to_numpy())
```
#### Expected Output
#### Output of ``pd.show_versions()``
In [1]: import pandas as pd;pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 5b0a2a6e361f75c8cbb715cd33ff55dd61c631c9
python : 3.6.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-54-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0+191.g5b0a2a6e3.dirty
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.1.0
Cython : 0.29.13
pytest : 5.1.0
hypothesis : 4.32.3
sphinx : 1.8.5
blosc : 1.8.1
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : 3.5.2
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 25 | 2019-08-20T03:02:38Z | 2019-09-17T14:03:04Z | 2019-09-17T12:40:44Z | MEMBER | null | I _think_ that after this we're not far from being able to use the base class versions of _combine_frame, _combine_match_index, _combine_match_columns, and _combine_const. That'll be a good day.
The only actual behavior changed is in SparseDataFrame._combine_match_columns, which is changed to match the other methods. See #28025 | {
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} | 3 | 2019-08-20T06:51:22Z | 2019-08-21T06:43:26Z | 2019-08-21T06:43:04Z | CONTRIBUTOR | null | - [x] closes #28026
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/28030 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28030/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28030/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28030/events | https://github.com/pandas-dev/pandas/pull/28030 | 482,680,502 | MDExOlB1bGxSZXF1ZXN0MzA4OTA0MDM4 | 28,030 | Backport PR #27916 on branch 0.25.x (BUG: fix to_timestamp out_of_bounds) | {
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] | closed | false | null | [] | null | 1 | 2019-08-20T10:02:38Z | 2019-08-23T09:03:11Z | 2019-08-23T09:03:01Z | CONTRIBUTOR | null | - [ ] xref #27977
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Similar to issue #27979, and as pointed out by @datapythonista, the summaries of some dosctrings don’t end with a period.
I added the period to the following cases:
```
• pandas.IndexSlice
• pandas.MultiIndex.names
• pandas.MultiIndex.is_lexsorted
• pandas.MultiIndex.reorder_levels
• pandas.DatetimeIndex.snap
• pandas.DatetimeIndex.to_perioddelta
• pandas.DatetimeIndex.to_pydatetime
• pandas.DatetimeIndex.to_series
• pandas.TimedeltaIndex
• pandas.PeriodIndex.is_leap_year
• pandas.api.extensions.ExtensionArray._concat_same_type
• pandas.api.extensions.ExtensionArray.dropna
```
These are the outputs of validate_docstrings.py when evaluating each case:
**pandas.IndexSlice**
```
4 Errors found:
Examples do not pass tests
flake8 error: E231 missing whitespace after ',' (4 times)
flake8 error: E902 TokenError: EOF in multi-line statement
flake8 error: E999 SyntaxError: invalid syntax
1 Warnings found:
No extended summary found
```
**pandas.MultiIndex.names**
```
3 Warnings found:
No extended summary found
See Also section not found
No examples section found
```
**pandas.MultiIndex.is_lexsorted**
```
1 Errors found:
Return value has no description
3 Warnings found:
No extended summary found
See Also section not found
No examples section found
```
**pandas.MultiIndex.reorder_levels**
```
2 Errors found:
Parameters {order} not documented
Return value has no description
3 Warnings found:
No extended summary found
See Also section not found
No examples section found
```
**pandas.DatetimeIndex.snap**
```
2 Errors found:
Parameters {freq} not documented
Return value has no description
3 Warnings found:
No extended summary found
See Also section not found
No examples section found
```
**pandas.DatetimeIndex.to_perioddelta**
```
5 Errors found:
Summary should fit in a single line
Parameters {**kwargs, *args} not documented
Unknown parameters {freq}
Parameter "freq" has no description
Return value has no description
2 Warnings found:
See Also section not found
No examples section found
```
**pandas.DatetimeIndex.to_pydatetime**
```
4 Errors found:
Summary should fit in a single line
Parameters {**kwargs, *args} not documented
The first line of the Returns section should contain only the type, unless multiple values are being returned
Return value has no description
2 Warnings found:
See Also section not found
No examples section found
```
**pandas.DatetimeIndex.to_series**
```
2 Errors found:
Summary should fit in a single line
Return value has no description
2 Warnings found:
See Also section not found
No examples section found
```
**pandas.TimedeltaIndex**
```
3 Errors found:
Summary should fit in a single line
Parameters {dtype, verify_integrity, periods, copy, data} not documented
Unknown parameters {periods , copy , data }
1 Warnings found:
No examples section found
```
**pandas.PeriodIndex.is_leap_year**
```
3 Warnings found:
No extended summary found
See Also section not found
No examples section found
```
**pandas.api.extensions.ExtensionArray._concat_same_type**
```
2 Errors found:
Parameter "to_concat" has no description
Return value has no description
3 Warnings found:
No extended summary found
See Also section not found
No examples section found
```
**pandas.api.extensions.ExtensionArray.dropna**
```
2 Errors found:
The first line of the Returns section should contain only the type, unless multiple values are being returned
Return value has no description
3 Warnings found:
No extended summary found
See Also section not found
No examples section found
```
I would be happy to also work on the other errors/warnings in this PR on another. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28032 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28032/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28032/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28032/events | https://github.com/pandas-dev/pandas/pull/28032 | 482,799,380 | MDExOlB1bGxSZXF1ZXN0MzA5MDAwMDY0 | 28,032 | Add support to names keyword in Index | {
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/28033 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28033/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28033/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28033/events | https://github.com/pandas-dev/pandas/issues/28033 | 482,832,282 | MDU6SXNzdWU0ODI4MzIyODI= | 28,033 | warning in pandas code | {
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] | closed | false | null | [] | null | 2 | 2019-08-20T12:32:06Z | 2019-08-20T13:49:09Z | 2019-08-20T13:42:39Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df.loc[:, 'column'] = value
```
#### Problem description
`SettingWithCopyWarning` in pandas code
#### Output
```
/home/matheus/dev/my-project/venv/lib/python3.6/site-packages/pandas/core/indexing.py:362: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
self.obj[key] = _infer_fill_value(value)
/home/matheus/dev/my-project/venv/lib/python3.6/site-packages/pandas/core/indexing.py:480: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
self.obj[item] = s
```
#### Output of ``pd.show_versions()``
<details>
In [2]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-58-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 40.6.2
Cython : None
pytest : 4.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.0.5
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : None
IPython : 7.5.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : 3.4.3
xarray : None
xlrd : None
xlwt : None
xlsxwriter : 1.0.5
</details>
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"des... | closed | false | null | [] | null | 15 | 2019-08-20T13:36:54Z | 2020-03-30T02:52:56Z | 2020-03-30T02:52:55Z | NONE | null | Here's a pdb session showing the issue:
```python
ipdb> basei
DatetimeIndex(['2017-10-28 23:00:00+00:00', '2017-10-29 00:00:00+00:00',
'2017-10-29 01:00:00+00:00', '2017-10-29 02:00:00+00:00'],
dtype='datetime64[ns, UTC]', freq=None)
ipdb> diffi
DatetimeIndex(['2017-10-29 02:00:00+01:00', '2017-10-29 03:00:00+01:00',
'2017-10-29 04:00:00+01:00', '2017-10-29 05:00:00+01:00'],
dtype='datetime64[ns, Europe/Paris]', freq=None)
ipdb> basei.union(diffi)
Index([2017-10-28 23:00:00+00:00, 2017-10-29 00:00:00+00:00,
2017-10-29 01:00:00+00:00, 2017-10-29 02:00:00+00:00,
2017-10-29 04:00:00+01:00, 2017-10-29 05:00:00+01:00],
dtype='object')
```
With pandas 0.24 I got back an index with a `dtype='datetime64[ns, UTC]'`
This looks a bit like a cousin of https://github.com/pandas-dev/pandas/issues/26778
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} | 5 | 2019-08-20T14:42:49Z | 2019-08-27T00:19:49Z | 2019-08-26T23:39:13Z | MEMBER | null | @jorisvandenbossche you've mentioned some ops optimizations; any suggestions for things to add here? | {
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} | 17 | 2019-08-20T15:12:34Z | 2019-11-14T16:36:00Z | 2019-11-14T16:31:55Z | MEMBER | null | Progress towards #23853, with more progress available pending resolution of #27911.
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https://api.github.com/repos/pandas-dev/pandas/issues/28038 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28038/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28038/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28038/events | https://github.com/pandas-dev/pandas/issues/28038 | 482,938,906 | MDU6SXNzdWU0ODI5Mzg5MDY= | 28,038 | DOC: Remove docs code header and use explicit imports in the user guide | {
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] | open | false | null | [] | null | 22 | 2019-08-20T15:41:15Z | 2020-05-21T09:33:31Z | null | MEMBER | null | I've been discussing with the Binder team, and looks like it'll be possible soon to make the pandas examples in the docs runnable directly from the docs.
I think this will be amazing, but I think it's one more reason to start being explicit and self-contained with the docs. One option is of course that the trickiness we have now on importing things translate to Binder, and we automatically inject code for imports (and may be seeds...).
But my opinion is that the right thing is to start being explicit on what we run in the examples. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28039 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28039/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28039/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28039/events | https://github.com/pandas-dev/pandas/issues/28039 | 482,951,143 | MDU6SXNzdWU0ODI5NTExNDM= | 28,039 | Bug: Resample removes timezone localization from Period.start_time | {
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"description": "Incorrect or improved er... | open | false | null | [] | null | 3 | 2019-08-20T16:04:39Z | 2021-07-11T06:01:36Z | null | NONE | null | #### Code Sample
```python
from pytz import timezone
index = DatetimeIndex(['2019-01-01 06:00', '2019-01-01 07:00', '2019-01-01 08:00'], tz=timezone('Europe/Berlin'))
s = Series(index=index, data=[1, 2, 3])
print(s.index.tz)
rs = s.resample("3h", kind="period").sum()
for x in rs.index:
print(x.start_time.tz)
print(x.end_time.tz)
```
#### Problem description
All the Periods in the resampled PeriodIndex should have their start_time and end_time TimeStamps set with the correct Timezone.
Since the the DateTimeIndex was tz aware the attributes in the PeriodIndex should be too.
This is probably related to #13238 and #15777
#### Expected Output
Europe/Berlin
Europe/Berlin
Europe/Berlin
#### Tested Version
pandas 0.25.0 | {
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} | 0 | 2019-08-20T18:12:43Z | 2019-08-20T19:16:57Z | 2019-08-20T19:16:53Z | CONTRIBUTOR | null | Manual backport of #27664 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28042 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28042/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28042/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28042/events | https://github.com/pandas-dev/pandas/pull/28042 | 483,029,731 | MDExOlB1bGxSZXF1ZXN0MzA5MTg3NTM4 | 28,042 | Backport PR #26419 on branch 0.25.x (Fix GroupBy nth Handling with Observed=False) | {
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} | 2 | 2019-08-20T19:14:21Z | 2019-08-21T06:28:42Z | 2019-08-21T06:28:33Z | CONTRIBUTOR | null | - [ ] xref #27979
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Added missing period to IntervalArray docstrings as suggested by @datapythonista for the following attributes and parameters:
```
pandas.arrays.IntervalArray.left
pandas.arrays.IntervalArray.right
pandas.arrays.IntervalArray.closed
pandas.arrays.IntervalArray.mid
pandas.arrays.IntervalArray.length
pandas.arrays.IntervalArray.is_non_overlapping_monotonic
pandas.arrays.IntervalArray.from_tuples
pandas.arrays.IntervalArray.set_closed
pandas.arrays.IntervalArray.to_tuples
``` | {
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} | 3 | 2019-08-20T19:24:57Z | 2019-08-21T17:48:47Z | 2019-08-21T17:48:47Z | NONE | null | Backport PR #27991: DataFrame html repr: also follow min_rows setting | {
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... | open | false | null | [] | null | 2 | 2019-08-20T19:35:51Z | 2019-11-17T23:06:22Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```
>>> s = pd.Series(['9/22/1677', '4/11/2262'], dtype="U")
>>> s
0 9/22/1677
1 4/11/2262
dtype: object
>>> s.astype('datetime64')
0 1677-09-22
1 2262-04-11
dtype: datetime64[ns]
>>> s.astype('datetime64[s]')
0 1677-09-22
1 2262-04-11
dtype: datetime64[ns]
>>> s.astype('datetime64[m]')
0 1677-09-22
1 2262-04-11
dtype: datetime64[ns]
>>> s.astype('datetime64[h]')
0 1677-09-22
1 2262-04-11
dtype: datetime64[ns]
>>> s.astype('datetime64[D]')
0 2262-04-11 # <<<< wait, what?
1 2262-04-11
dtype: datetime64[ns]
```
#### Problem description
It appears that `astype('datetime64[D]')` breaks on the earliest representable full day -- it wraps around to the latest representable date.
While I know this is super edge case-y and highly unlikely to be worth a lot of time, the fact that it wraps around is indicative of a potential for integer overflow somewhere that may or may not bite us in the future.
#### Expected Output
`.astype('datetime64[D]')` should give 1677-09-22 instead of 2262-04-11.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.1.0
Cython : 0.29.13
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
s3fs : 0.3.3
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28046 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28046/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28046/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28046/events | https://github.com/pandas-dev/pandas/issues/28046 | 483,096,398 | MDU6SXNzdWU0ODMwOTYzOTg= | 28,046 | DOC: DataFrame.plot is missing most of the docstring | {
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"description": "Duplicate issue or pu... | closed | false | null | [] | null | 3 | 2019-08-20T21:39:56Z | 2019-08-30T21:51:01Z | 2019-08-30T21:50:43Z | NONE | null | In the pandas 0.25.0 docs, at
[pandas.DataFrame.plot.html](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html)
#### Problem description
The page is essentially missing. The only content is
```
DataFrame.plot(self, *args, **kwargs)[source]
Call self as a function.
```
#### Expected Output
It should look like [0.23.4 DataFrame.plot.html](https://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.DataFrame.plot.html)
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- [x] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x ] whatsnew entry
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"gists_url":... | null | 2 | 2019-08-21T01:39:18Z | 2021-08-22T18:03:11Z | 2021-08-22T18:03:11Z | MEMBER | null | TL;DR: our comparisons with complex dtypes are not internally consistent:
```
arr = np.arange(5).astype(np.complex128)
ser = pd.Series(arr)
df = ser.to_frame()
>>> df < df.astype(object)
[...]
TypeError: '<' not supported between instances of 'complex' and 'complex'
>>> df.lt(df.astype(object))
0
0 False
1 False
2 False
3 False
4 False
```
----
Digging into this...
numpy has some surprising behavior for comparing complex dtypes:
```
arr = np.arange(5)
arr = arr + arr*1j
arr2 = arr*2 - 1
>>> arr < arr2
array([False, True, True, True, True])
```
I would expect these inequalities to be undefined, and in fact, if we cast to object we get a TypeError:
```
>>> arr < arr2.astype(object)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: '<' not supported between instances of 'complex' and 'complex'
```
That's the behavior I expected in the first place, but then trying it on just the scalars:
```
>>> arr[0] < arr2[0]
False
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} | 2 | 2019-08-21T02:46:32Z | 2019-08-23T18:22:18Z | 2019-08-23T18:11:50Z | MEMBER | null | sys.executable is the pattern we use elsewhere in subprocess tests. | {
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} | 15 | 2019-08-21T03:10:21Z | 2019-09-16T04:40:14Z | 2019-09-16T02:33:40Z | CONTRIBUTOR | null | I modeled this off of https://github.com/pandas-dev/pandas/pull/24841. Some alternatives I considered:
* Instead of setting the option_context here, we could wind the param into the depths of the formatter. I tried this, actually, and started finding a number of edge cases and bugs. I realized that the issue only occurs in a pretty narrow case - if the user is explicitly calling to_string - because most of the time, when representing a DataFrame, the user *will* want long strings truncated for readability. So I think the safest way is to do it at the top level without interfering with lower-level formatters.
* Series.to_string() could arguably benefit from the same treatment, although that wasn't mentioned in the original issue (and I have never found the need to use it personally) so I didn't bring that in.
Here's an example on a real dataset showing long columns preserved in a text file produced by to_string():
Additional manual testing:
* Main use case- by default, no limits and ignores the display options, but can still override:
```
>>> print(df.to_string())
A B
0 NaN NaN
1 -1.0000 foo
2 -2.1234 foooo
3 3.0000 fooooo
4 4.0000 bar
>>> with option_context('display.max_colwidth', 5):
... print(df.to_string())
...
A B
0 NaN NaN
1 -1.0000 foo
2 -2.1234 foooo
3 3.0000 fooooo
4 4.0000 bar
>>> print(df.to_string(max_colwidth=5))
A B
0 NaN NaN
1 -1... foo
2 -2... f...
3 3... f...
4 4... bar
```
* The string representation of DataFrame **does** still use the display options (so it's only the explicit ```to_string``` that doesn't:
```
>>> with option_context('display.max_colwidth', 5):
... print(str(df))
...
A B
0 NaN NaN
1 -1... foo
2 -2... f...
3 3... f...
4 4... bar
```
* The new parameter validates for None and positive ints, but rejects anything else:
```
>>> print(df.to_string(max_colwidth=-5))
...
raise ValueError(msg)
ValueError: Value must be a nonnegative integer or None
```
- [ ] closes #9784
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 5 | 2019-08-21T05:16:45Z | 2019-09-28T07:39:09Z | 2019-09-28T07:39:08Z | CONTRIBUTOR | null | - [ ] xref #27977
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Add periods to Timestamp and Timedelta doctrings for the following attributes and methods:
```
pandas.Timestamp.resolution
pandas.Timestamp.tz
pandas.Timestamp.ceil
pandas.Timestamp.combine
pandas.Timestamp.floor
pandas.Timestamp.fromordinal
pandas.Timestamp.replace
pandas.Timestamp.round
pandas.Timestamp.weekday
pandas.Timedelta.ceil
pandas.Timedelta.floor
pandas.Timedelta.isoformat
pandas.Timedelta.round
```
I did not find the method for `pandas.Timestamp.isoweekday` this in the Timestamp source code. Was this deprecated ? @datapythonista | {
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} | 5 | 2019-08-21T07:15:42Z | 2019-08-21T20:54:42Z | 2019-08-21T20:54:42Z | NONE | null | #### Code Sample
```python
# Your code here
import pandas as pd
import numpy as np
times = np.array([np.datetime64('2012-02-15T12:00:00.000000000')])
times.setflags(write=False)
[d for d in pd.to_datetime(times)]
```
#### Problem description
The above code raises a `ValueError: buffer source array is read-only` during the iteration over the `DatetimeIndex`.
**Note**: This code snippet is just for demonstration. Read-only arrays are generated e.g. from an xarray `broadcast`.
This did not happen for pandas<=0.25.0
#### Expected Output
```python
[Timestamp('2012-02-15 12:00:00')]
```
#### Output of ``pd.show_versions()``
```shell
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.0-5-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.7.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/28056 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28056/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28056/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28056/events | https://github.com/pandas-dev/pandas/issues/28056 | 483,264,372 | MDU6SXNzdWU0ODMyNjQzNzI= | 28,056 | (df==0) vs. (df.values==0) performance penalty | {
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} | [] | closed | false | null | [] | null | 5 | 2019-08-21T08:09:12Z | 2019-08-21T20:15:54Z | 2019-08-21T16:15:07Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
T = 5000
S = 1000
X = np.random.randint(0, 10, size=(T, S))
df = pd.DataFrame(X, index=pd.date_range("2001-01-01", periods=T, freq="D"))
%timeit (df == 0)
>>> 327 ms ± 4.99 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
%timeit (df.values == 0)
2.85 ms ± 36.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```
#### Problem description
I am using the boolean frame `df == 0` to find and set zeros in a given DataFrame `df`, i.e.
df[df == 0] = FOO, where FOO is some nonzero integer.
I found that using the expression df[df.values == 0] = FOO gives a significant speed-up.
I am wondering if the large overhead of (df == 0) compared to (df.values == 0) is to be expected. If so, then in which situation should I prefer the expression `df[df == 0]` over the much faster `df[df.values == 0]`?
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.6
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.6.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.3
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28057 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28057/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28057/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28057/events | https://github.com/pandas-dev/pandas/issues/28057 | 483,316,281 | MDU6SXNzdWU0ODMzMTYyODE= | 28,057 | Does read_hdf() in v0.25 really support "file-like object" as it claims? | {
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} | [] | closed | false | null | [] | null | 1 | 2019-08-21T09:47:16Z | 2019-08-21T12:06:07Z | 2019-08-21T12:06:07Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas as pd
pd.read_hdf('housing.h5')
f=open('housing.h5', 'r')
pd.read_hdf(f)
```
#### Problem description
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_hdf.html says:
> path_or_buf : str, path object, pandas.HDFStore or file-like object
By file-like object, we refer to objects with a read() method, such as a file handler (e.g. via builtin open function) or StringIO.
>
#### Somehow when I was trying to use a 'file-like object', just as in pd.read_csv(), I encountered error below:
NotImplementedError Traceback (most recent call last)
<ipython-input-5-25a431458ef6> in \<module\>()
----> 1 pd.read_hdf(f)
~/anaconda3/lib/python3.6/site-packages/pandas/io/pytables.py in read_hdf(path_or_buf, key, mode, **kwargs)
368 if not isinstance(path_or_buf, str):
369 raise NotImplementedError(
--> 370 "Support for generic buffers has not " "been implemented."
371 )
372 try:
NotImplementedError: Support for generic buffers has not been implemented.
#### Expected Output
Out[3]:
MedInc HouseAge AveRooms AveBedrms Population AveOccup Latitude Longitude Price
0 3.1327 24.0 4.665122 1.020857 2264.0 2.623407 36.93 -121.75 2.181
1 3.6000 48.0 4.988920 0.977839 1071.0 2.966759 36.92 -121.75 1.945
2 2.4615 46.0 4.465574 1.052459 1068.0 3.501639 36.92 -121.75 1.778
3 3.2216 36.0 5.319797 1.038071 1454.0 3.690355 36.92 -121.76 2.383
4 3.4125 46.0 3.587121 0.973485 1120.0 4.242424 36.92 -121.76 1.607
... ... ... ... ... ... ... ... ... ...
2058 4.1250 37.0 7.285714 1.214286 29.0 2.071429 39.12 -121.52 0.720
2059 2.1667 36.0 6.573099 1.076023 504.0 2.947368 39.18 -121.43 0.938
2060 3.0000 5.0 6.067797 1.101695 169.0 2.864407 39.13 -121.32 1.625
2061 2.5952 19.0 5.238462 1.079487 1018.0 2.610256 39.10 -121.48 0.924
2062 2.0943 28.0 5.519802 1.020902 6912.0 3.801980 39.12 -121.39 1.083
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.5.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-957.5.1.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2018.4
dateutil : 2.7.3
pip : 19.2.1
setuptools : 41.0.1
Cython : 0.28.5
pytest : 3.5.1
hypothesis : None
sphinx : 1.7.4
blosc : None
feather : None
xlsxwriter : 1.0.4
lxml.etree : 4.2.5
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 6.4.0
pandas_datareader: 0.7.4
bs4 : 4.6.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.5
matplotlib : 2.2.2
numexpr : 2.6.5
odfpy : None
openpyxl : 2.5.3
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.2.7
tables : 3.4.3
xarray : None
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : 1.0.4
</details>
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} | 1 | 2019-08-21T12:40:45Z | 2019-08-21T14:23:49Z | 2019-08-21T14:23:42Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
pd.Series(["s", "s", "dfffdd", "d"]).nbytes
```
#### Problem description
I always get the same number of bytes, no matter how long the strings are
#### Expected Output
96 # or 36
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.13
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.0
numexpr : 2.7.0
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.7
tables : 3.4.4
xarray : None
xlrd : 1.2.0
xlwt : 1.2.0
xlsxwriter : 1.1.8
</details>
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} | 9 | 2019-08-21T12:42:37Z | 2019-08-21T15:18:11Z | 2019-08-21T15:18:10Z | MEMBER | null | - [ ] xref: #28021 #27993
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/28060 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28060/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28060/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28060/events | https://github.com/pandas-dev/pandas/pull/28060 | 483,491,389 | MDExOlB1bGxSZXF1ZXN0MzA5NTU3Nzc5 | 28,060 | Update groupby | {
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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/28061 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28061/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28061/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28061/events | https://github.com/pandas-dev/pandas/pull/28061 | 483,508,799 | MDExOlB1bGxSZXF1ZXN0MzA5NTcxMTY3 | 28,061 | PERF: cython-optimized datetime constructor | {
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} | [] | closed | false | null | [] | null | 1 | 2019-08-21T15:57:00Z | 2020-04-05T17:35:53Z | 2019-08-22T01:05:04Z | MEMBER | null | Calling the datetime constructor goes through python space, so the C code generated by `dt = datetime(obj.dts.year, obj.dts.month, obj.dts.day, obj.dts.hour, obj.dts.min, obj.dts.sec, obj.dts.us, obj.tzinfo)` in master is:
```
__pyx_t_1 = __Pyx_PyInt_From_npy_int64(__pyx_v_obj->dts.year); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 435, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_1);
__pyx_t_3 = __Pyx_PyInt_From_npy_int32(__pyx_v_obj->dts.month); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 435, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_3);
__pyx_t_7 = __Pyx_PyInt_From_npy_int32(__pyx_v_obj->dts.day); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 435, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_7);
__pyx_t_4 = __Pyx_PyInt_From_npy_int32(__pyx_v_obj->dts.hour); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 436, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_4);
__pyx_t_2 = __Pyx_PyInt_From_npy_int32(__pyx_v_obj->dts.min); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 436, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_2);
__pyx_t_5 = __Pyx_PyInt_From_npy_int32(__pyx_v_obj->dts.sec); if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 436, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_5);
__pyx_t_11 = __Pyx_PyInt_From_npy_int32(__pyx_v_obj->dts.us); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 437, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_11);
__pyx_t_12 = PyTuple_New(8); if (unlikely(!__pyx_t_12)) __PYX_ERR(0, 435, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_12);
__Pyx_GIVEREF(__pyx_t_1);
PyTuple_SET_ITEM(__pyx_t_12, 0, __pyx_t_1);
__Pyx_GIVEREF(__pyx_t_3);
PyTuple_SET_ITEM(__pyx_t_12, 1, __pyx_t_3);
__Pyx_GIVEREF(__pyx_t_7);
PyTuple_SET_ITEM(__pyx_t_12, 2, __pyx_t_7);
__Pyx_GIVEREF(__pyx_t_4);
PyTuple_SET_ITEM(__pyx_t_12, 3, __pyx_t_4);
__Pyx_GIVEREF(__pyx_t_2);
PyTuple_SET_ITEM(__pyx_t_12, 4, __pyx_t_2);
__Pyx_GIVEREF(__pyx_t_5);
PyTuple_SET_ITEM(__pyx_t_12, 5, __pyx_t_5);
__Pyx_GIVEREF(__pyx_t_11);
PyTuple_SET_ITEM(__pyx_t_12, 6, __pyx_t_11);
__Pyx_INCREF(__pyx_v_obj->tzinfo);
__Pyx_GIVEREF(__pyx_v_obj->tzinfo);
PyTuple_SET_ITEM(__pyx_t_12, 7, __pyx_v_obj->tzinfo);
__pyx_t_1 = 0;
__pyx_t_3 = 0;
__pyx_t_7 = 0;
__pyx_t_4 = 0;
__pyx_t_2 = 0;
__pyx_t_5 = 0;
__pyx_t_11 = 0;
__pyx_t_11 = __Pyx_PyObject_Call(((PyObject *)__pyx_ptype_7cpython_8datetime_datetime), __pyx_t_12, NULL); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 435, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_11);
__Pyx_DECREF(__pyx_t_12); __pyx_t_12 = 0;
__pyx_v_dt = ((PyDateTime_DateTime *)__pyx_t_11);
__pyx_t_11 = 0;
```
Using `datetime_new` we instead get:
```
__pyx_t_1 = __pyx_v_obj->tzinfo;
__Pyx_INCREF(__pyx_t_1);
__pyx_t_3 = __pyx_f_7cpython_8datetime_datetime_new(__pyx_v_obj->dts.year, __pyx_v_obj->dts.month, __pyx_v_obj->dts.day, __pyx_v_obj->dts.hour, __pyx_v_obj->dts.min, __pyx_v_obj->dts.sec, __pyx_v_obj->dts.us, __pyx_t_1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 437, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_3);
__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_ptype_7cpython_8datetime_datetime))))) __PYX_ERR(0, 437, __pyx_L1_error)
__pyx_v_dt = ((PyDateTime_DateTime *)__pyx_t_3);
__pyx_t_3 = 0;
```
Still need to run asv to see if this actually matters. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28062 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28062/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28062/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28062/events | https://github.com/pandas-dev/pandas/issues/28062 | 483,511,956 | MDU6SXNzdWU0ODM1MTE5NTY= | 28,062 | CI: Enable azure pipelines build on backport PRs | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28063 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28063/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28063/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28063/events | https://github.com/pandas-dev/pandas/pull/28063 | 483,513,703 | MDExOlB1bGxSZXF1ZXN0MzA5NTc0ODk2 | 28,063 | CI: Test azure | {
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} | [] | closed | false | null | [] | null | 1 | 2019-08-21T16:06:11Z | 2019-08-21T16:07:13Z | 2019-08-21T16:06:36Z | CONTRIBUTOR | null | In the azure UI we had set up a branch filter to only build on master. This is testing that we still build for PRs against master. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28064 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28064/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28064/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28064/events | https://github.com/pandas-dev/pandas/pull/28064 | 483,514,991 | MDExOlB1bGxSZXF1ZXN0MzA5NTc1ODYx | 28,064 | CI: Test azure | {
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} | [] | closed | false | null | [] | null | 2 | 2019-08-21T16:08:39Z | 2019-08-21T18:41:16Z | 2019-08-21T17:09:15Z | CONTRIBUTOR | null | Testing that an azure CI build is triggered (and passes) for 0.25.x | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28065 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28065/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28065/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28065/events | https://github.com/pandas-dev/pandas/pull/28065 | 483,536,532 | MDExOlB1bGxSZXF1ZXN0MzA5NTkyNzAw | 28,065 | CI: disable codecov | {
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This also simplifies the NullFrequencyError handling nicely. | {
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} | 4 | 2019-08-21T19:07:05Z | 2020-03-16T02:21:29Z | 2020-03-16T02:21:29Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd # tested with pandas 25.0 using Python 3.6.8
pd.read_csv('my_csv.csv', delimiter='|', header=4, nrows=1, skip_blank_lines=False) # this makes interpreter exit without any error message
pd.read_csv('my_csv.csv', delimiter='|', header=4, nrows=2, skip_blank_lines=False) # this is fine producing output below
int_id_1_1_1 date_2019-01-01_2019-12-31_2 ascii_str_8_8_3 double_-1.0_1.0_4 integer_-1000_1000_5
0 NaN NaN NaN NaN NaN
1 NaN NaN NaN NaN NaN
```
#### Problem description
I have been trying to load a test CSV file ("my_csv.txt", attached), which is structured in a way that there's an information text on the second row; row header line on the fifth row; and the data starts at the ninth row. As you can see in the Python code above, `read_csv` fails when `nrows=1` , but doesn't when `nrows>1`.
I think there's some uncaught bug in Pandas' `read_csv` when CSV file has blank lines between header and the start of the data rows. Thank you for your hard work maintaining and extending this very useful library.
[my_csv.txt](https://github.com/pandas-dev/pandas/files/3526892/my_csv.txt)
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} | 2 | 2019-08-21T19:31:38Z | 2019-08-21T20:53:13Z | 2019-08-21T20:53:10Z | CONTRIBUTOR | null | This was XPASSing on the MacPython builds:
https://travis-ci.org/MacPython/pandas-wheels/jobs/574706922 | {
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} | 3 | 2019-08-21T20:20:23Z | 2019-08-21T21:35:23Z | 2019-08-21T20:54:42Z | MEMBER | null | @TomAugspurger wanted to get this in under the wire?
- [x] closes #28055
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 7 | 2019-08-21T20:30:56Z | 2019-09-28T09:14:07Z | 2019-09-28T09:14:07Z | CONTRIBUTOR | null | A web search for "DataFrame from CSV" leads to
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.from_csv.html
which is currently a 404, since DataFrame.from_csv was removed. Redirect
it to read_csv.
Normally we shouldn't worry about this, but I suspect this is a common search term. | {
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} | 4 | 2019-08-21T22:14:21Z | 2019-09-26T12:25:29Z | 2019-09-26T12:25:29Z | MEMBER | null | In tests.frame.test_arithmetic we test the DataFrame part of the following, but we do not test the Series behavior (at least not in that file)
```
arr = np.array([np.nan, 1, 6, np.nan])
arr2 = np.array([2j, np.nan, 7, None])
ser = pd.Series(arr)
ser2 = pd.Series(arr2)
df = pd.DataFrame(ser)
df2 = pd.DataFrame(ser2)
ser < ser2 # raises TypeError, makes sense
df < df2 # raises TypeError, makes sense
ser.lt(ser2) # raises TypeError, makes sense
>>> df.lt(df2)
0
0 False
1 False
2 True
3 False
```
The `df.lt(df2)` version (the "flex" op) masks positions where either `df` or `df2` is null. That is fine, but why doesn't the `Series` version do the same thing? | {
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} | 0 | 2019-08-21T23:00:20Z | 2019-09-10T11:40:19Z | 2019-09-08T17:09:55Z | MEMBER | null | identified [here](https://github.com/pandas-dev/pandas/commit/a6c0d02c99cfaf90119d1316dd897019326472c1#r34783276). We handle this correctly for timedelta64, but apparently not datetime64
```
iser = pd.Series(range(3))
dt = np.datetime64(3, "D")
>>> iser + dt
0 1970-01-04
1 1970-01-05
2 1970-01-06
```
Should behave like `iser + pd.Timestamp(dt)`:
```
>>> iser + pd.Timestamp(dt)
[...]
ValueError: Cannot add integral value to Timestamp without freq.
``` | {
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} | 2 | 2019-08-22T03:09:37Z | 2019-09-02T22:56:03Z | 2019-09-02T21:06:27Z | MEMBER | null | Working on logical ops I'm finding a bunch of internal inconsistencies that will need to be addressed. Since those are liable to have large diffs, this starts off with refactor that leaves behavior unchanged: simplify the method itself, and split a giant test into well-scoped ones.
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Supersedes #28068 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28083 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28083/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28083/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28083/events | https://github.com/pandas-dev/pandas/issues/28083 | 483,754,978 | MDU6SXNzdWU0ODM3NTQ5Nzg= | 28,083 | `The default of the 'keep_tz' keyword in DatetimeIndex.to_series will change to True in a future release.` | {
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] | closed | false | null | [] | null | 2 | 2019-08-22T03:52:12Z | 2019-08-23T18:10:49Z | 2019-08-23T18:10:49Z | NONE | null | #### Code Sample, a copy-pastable example if possible
How am I supposed to turn this warning down without patching pandas ?
```
DEBUG:mptcpanalyzer.connection:Filling mptcp destinations
Traceback (most recent call last):
File "/nix/store/a5sg76c2q39jk0lgn5bxi4vfm0j36rrx-python3.7-cmd2-0.9.17/lib/python3.7/site-packages/cmd2/cmd2.py", line 1764, in onecmd_plus_hooks
stop = self.onecmd(statement, add_to_history=add_to_history)
File "/nix/store/a5sg76c2q39jk0lgn5bxi4vfm0j36rrx-python3.7-cmd2-0.9.17/lib/python3.7/site-packages/cmd2/cmd2.py", line 2159, in onecmd
stop = func(statement)
File "/nix/store/a5sg76c2q39jk0lgn5bxi4vfm0j36rrx-python3.7-cmd2-0.9.17/lib/python3.7/site-packages/cmd2/cmd2.py", line 217, in cmd_wrapper
return func(cmd2_app, args, unknown)
File "/home/teto/mptcpanalyzer/mptcpanalyzer/cli.py", line 1158, in do_plot
result = plotter.run(**dataframes, **dargs)
File "/home/teto/mptcpanalyzer/mptcpanalyzer/plot.py", line 269, in run
fig = self.plot(*pargs, styles=styles, **kwargs)
File "/home/teto/mptcpanalyzer/mptcpanalyzer/plots/dss.py", line 113, in plot
df = con.fill_dest(rawdf)
File "/home/teto/mptcpanalyzer/mptcpanalyzer/connection.py", line 336, in fill_dest
df_dest = df.query(q, engine="python")
File "/nix/store/mczyj76z8r6mnlkp3q71nmy2qv3mv3db-python3.7-pandas-0.25.0/lib/python3.7/site-packages/pandas/core/frame.py", line 3179, in query
res = self.eval(expr, **kwargs)
File "/nix/store/mczyj76z8r6mnlkp3q71nmy2qv3mv3db-python3.7-pandas-0.25.0/lib/python3.7/site-packages/pandas/core/frame.py", line 3289, in eval
index_resolvers = self._get_index_resolvers()
File "/nix/store/mczyj76z8r6mnlkp3q71nmy2qv3mv3db-python3.7-pandas-0.25.0/lib/python3.7/site-packages/pandas/core/generic.py", line 473, in _get_index_resolvers
d.update(self._get_axis_resolvers(axis_name))
File "/nix/store/mczyj76z8r6mnlkp3q71nmy2qv3mv3db-python3.7-pandas-0.25.0/lib/python3.7/site-packages/pandas/core/generic.py", line 457, in _get_axis_resolvers
s = level_values.to_series()
File "/nix/store/mczyj76z8r6mnlkp3q71nmy2qv3mv3db-python3.7-pandas-0.25.0/lib/python3.7/site-packages/pandas/core/indexes/datetimes.py", line 721, in to_series
stacklevel=2,
FutureWarning: The default of the 'keep_tz' keyword in DatetimeIndex.to_series will change to True in a future release. You can set 'keep_tz=True' to obtain the future behaviour and silence this warning.
EXCEPTION of type 'FutureWarning' occurred with message: 'The default of the 'keep_tz' keyword in DatetimeIndex.to_series will change to True in a future release. You can set 'keep_tz=True' to obtain the future behaviour and silence this warning.'
```
...since the `level_values.to_series()` call is in pandas.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.1.0
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.3
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 3.1.1
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.2.14
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : None
</details>
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} | [] | closed | false | null | [] | null | 1 | 2019-08-22T10:28:54Z | 2019-08-26T20:14:07Z | 2019-08-26T20:14:07Z | NONE | null | Reproduced issue in part without n2 and n3 and with n2 n3 see prior issue:
https://github.com/pandas-dev/pandas/issues/12257
The replace runs 10x slower when encountering int64 columns
WITHOUT n2,n3, **1.73 seconds**
```
a1=np.zeros((40000000,1))
a1[:,:]=np.inf
df=pd.DataFrame(a1)
df['a1']=''
df['n1']=0.0
# df['n2']=0.0
# df['n2']=df['n2'].astype(np.int64)
# df['n3']=0.0
# df['n3']=df['n3'].astype(np.int64)
df['n1'].astype('datetime64[ns]')
df['a1']=df['a1'].astype('category')
%time df.replace([np.inf, -np.inf], np.nan)
```
WITH n2, n3 **`21.4 seconds`**
```
a1=np.zeros((40000000,1))
a1[:,:]=np.inf
df=pd.DataFrame(a1)
df['a1']=''
df['n1']=0.0
df['n2']=0.0
df['n2']=df['n2'].astype(np.int64)
df['n3']=0.0
df['n3']=df['n3'].astype(np.int64)
df['n1'].astype('datetime64[ns]')
df['a1']=df['a1'].astype('category')
%time df.replace([np.inf, -np.inf], np.nan)
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} | 4 | 2019-08-22T11:29:00Z | 2019-08-23T13:30:32Z | 2019-08-22T12:17:29Z | NONE | null | Backport PR #27827: BUG: Fixed groupby quantile for listlike q | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28086 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28086/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28086/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28086/events | https://github.com/pandas-dev/pandas/issues/28086 | 483,944,591 | MDU6SXNzdWU0ODM5NDQ1OTE= | 28,086 | Index not iterable in Pandas 0.25.0 | {
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} | [] | closed | false | null | [] | null | 3 | 2019-08-22T11:45:15Z | 2019-08-22T14:02:31Z | 2019-08-22T14:02:31Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import matplotlib.pyplot as plt
# Get graphs in the notebook:
%matplotlib inline
pd.show_versions()
df = pd.DataFrame({'A': [1, 2, 3, 4, 15, 20, 99]})
data = df['A']
plt.plot(data, (data.index.values + 1) / len(data))
plt.show()
plt.plot(data, (data.index + 1) / len(data))
plt.show()
```
#### Problem description
In Pandas 0.23.4 the percentiles in CDF graphs can be expressed as (data.index + 1) / len(data), but in Pandas 0.25.0 this has to be changed to (data.index.values + 1) / len(data). No distaster but lots of work to change in many scripts.
#### Expected Output
[two nice CDF plots]
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-47-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.16.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.2
pytest : 4.0.2
hypothesis : None
sphinx : 1.8.2
blosc : None
feather : None
xlsxwriter : 1.1.2
lxml.etree : 4.2.5
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.2.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.5
matplotlib : 3.0.2
numexpr : 2.6.8
odfpy : None
openpyxl : 2.5.12
pandas_gbq : None
pyarrow : 0.11.1
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.2.15
tables : 3.4.4
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.2
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-5-4663fc04bafd> in <module>
14 plt.show()
15
---> 16 plt.plot(data, (data.index + 1) / len(data))
17 plt.show()
/opt/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/pyplot.py in plot(scalex, scaley, data, *args, **kwargs)
2811 return gca().plot(
2812 *args, scalex=scalex, scaley=scaley, **({"data": data} if data
-> 2813 is not None else {}), **kwargs)
2814
2815
/opt/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/__init__.py in inner(ax, data, *args, **kwargs)
1808 "the Matplotlib list!)" % (label_namer, func.__name__),
1809 RuntimeWarning, stacklevel=2)
-> 1810 return func(ax, *args, **kwargs)
1811
1812 inner.__doc__ = _add_data_doc(inner.__doc__,
/opt/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py in plot(self, scalex, scaley, *args, **kwargs)
1609 kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D._alias_map)
1610
-> 1611 for line in self._get_lines(*args, **kwargs):
1612 self.add_line(line)
1613 lines.append(line)
/opt/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py in _grab_next_args(self, *args, **kwargs)
391 this += args[0],
392 args = args[1:]
--> 393 yield from self._plot_args(this, kwargs)
394
395
/opt/anaconda/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py in _plot_args(self, tup, kwargs)
376 func = self._makefill
377
--> 378 ncx, ncy = x.shape[1], y.shape[1]
379 if ncx > 1 and ncy > 1 and ncx != ncy:
380 cbook.warn_deprecated("2.2", "cycling among columns of inputs "
IndexError: tuple index out of range
</details>
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} | 0 | 2019-08-22T13:07:58Z | 2019-08-22T13:47:33Z | 2019-08-22T13:47:33Z | NONE | null | Backport PR #27814: BUG: Series.rename raises error on values accepted by Series construc… | {
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} | 0 | 2019-08-22T13:10:13Z | 2019-08-22T14:29:30Z | 2019-08-22T14:29:29Z | NONE | null | Backport PR #27650: issue #27642 - timedelta merge asof with tolerance | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28089 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28089/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28089/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28089/events | https://github.com/pandas-dev/pandas/pull/28089 | 483,993,336 | MDExOlB1bGxSZXF1ZXN0MzA5OTYwMDQ2 | 28,089 | DOC: Make explicit in pandas IO doc the imports and options | {
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} | 24 | 2019-08-22T13:25:11Z | 2019-11-07T21:14:32Z | 2019-11-07T21:14:26Z | CONTRIBUTOR | null | - [x] works on #28038
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] Removes code header from IO doc page.
- [x] Uses explicit imports in the IO doc page of user guide on first occurrence of requirement.
Reference @datapythonista - [149](https://github.com/python-sprints/pandas-mentoring/issues/149) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28090 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28090/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28090/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28090/events | https://github.com/pandas-dev/pandas/issues/28090 | 484,019,552 | MDU6SXNzdWU0ODQwMTk1NTI= | 28,090 | Parquet, boolean dataframes & reindex | {
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} | [] | closed | false | null | [] | null | 5 | 2019-08-22T14:12:04Z | 2019-08-22T18:21:27Z | 2019-08-22T18:21:20Z | CONTRIBUTOR | null | Hello
#### Code Sample, a copy-pastable example if possible
```python
# pandas 0.25.0
# pyarrow 0.14.1
import pandas as pd
df = pd.DataFrame(dtype=bool, index=range(100))
df['A'] = True
df['B'] = False
df['C'] = True
df['D'] = False
df['E'] = True
df['F'] = False
index = [0, 2, 3, 4, 5, 6, 7, 8, 10, 11]
columns = ['A', 'C', 'D', 'F']
dk = df.reindex(columns=columns, index=index)
dk.to_parquet('/tmp/test.parquet')
dg = pd.read_parquet('/tmp/test.parquet')
assert dg.equals(dk) # ==> assert error
```
The saved dataframe is totally corrupted with most values now being False.
#### Problem description
This issue only appears when using boolean.
The issue is not there when reindexing only columns or only index or when doing both in two steps ie `df.reindex(columns=columns).reindex(index=index)`. `df.loc[index, columns]` is also fine.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.128-112.105.amzn2.x86_64
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 : 0.25.0
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.2.1
setuptools : 41.0.1
Cython : 0.29.13
pytest : 5.0.1
hypothesis : 4.32.2
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.0
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.0
matplotlib : 3.1.1
numexpr : 2.6.9
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
s3fs : 0.3.1
scipy : 1.3.0
sqlalchemy : 1.3.6
tables : 3.5.2
xarray : 0.12.3
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28091 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28091/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28091/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28091/events | https://github.com/pandas-dev/pandas/pull/28091 | 484,031,599 | MDExOlB1bGxSZXF1ZXN0MzA5OTkxODEw | 28,091 | Repeat headings every n-th row in Jupyter-lab notebook | {
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{
"color": "ededed",
"default": false,
"description": "__repr__ of pandas objects, to_string",
"id": 13101118,
"name": "Output-Formatting",
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"color": "AD7FA... | closed | false | null | [] | null | 6 | 2019-08-22T14:33:12Z | 2019-08-26T14:39:47Z | 2019-08-26T14:39:47Z | NONE | null | - [ ] closes #27563
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Adds a new option in `pd.set_option()` method.
Usage:
```python
# n is an int that represents the interval of rows which the headers are repeated (default = 0, no repetition)
pd.set_option("display.repeating_row_index", n)
```
Before | After (n = 3)
----------- | -------------
 | 
@leostayner | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28092 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28092/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28092/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28092/events | https://github.com/pandas-dev/pandas/issues/28092 | 484,041,331 | MDU6SXNzdWU0ODQwNDEzMzE= | 28,092 | Cannot map Timestamp.isocalendar on tz-aware timestamp series, gives ValueError: MultiIndex has no single backing array | {
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"description": "Extending pandas with cu... | closed | false | null | [] | null | 2 | 2019-08-22T14:49:55Z | 2019-08-23T05:18:53Z | 2019-08-23T05:18:52Z | CONTRIBUTOR | null | ### Problem description
While:
pd.to_datetime(dates).map(pd.Timestamp.isocalendar)
works as expected,
pd.to_datetime(dates, utc=True).map(pd.Timestamp.isocalendar)
(i.e. just adding `utc=True`)
or any variation of it using `map` fails with the below error. Only a list comprehension works, so it doesn't seem possible to vectorize the operation. The error message is also confusing.
### Example code
```python
import pandas as pd
pd.show_versions()
dates = pd.Series(pd.date_range(start='20180101', end='20180102', periods=2))
print(dates)
print(pd.to_datetime(dates).map(pd.Timestamp.isocalendar))
print([x.isocalendar() for x in pd.to_datetime(dates, utc=True)])
# Error here
print(pd.to_datetime(dates, utc=True).map(lambda x:x.isocalendar()))
```
Try it here: https://repl.it/repls/RawClosedPorts
Output:
```
0 2018-01-01
1 2018-01-02
dtype: datetime64[ns]
0 (2018, 1, 1)
1 (2018, 1, 2)
dtype: object
[(2018, 1, 1), (2018, 1, 2)]
Traceback (most recent call last):
File "main.py", line 9, in <module>
print(pd.to_datetime(dates, utc=True).map(lambda x:x.isocalendar()))
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/series.py", line 3821, in map
new_values = super()._map_values(arg, na_action=na_action)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/base.py", line 1300, in _map_values
new_values = map_f(values, mapper)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/base.py", line 1287, in <lambda>
map_f = lambda values, f: values.map(f)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/arrays/datetimelike.py", line 722, in map
return Index(self).map(mapper).array
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/indexes/multi.py", line 633, in array
raise ValueError(msg)
ValueError: MultiIndex has no single backing array. Use 'MultiIndex.to_numpy()' to get a NumPy array of tuples.
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-1036-gcp
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
```
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28093 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28093/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28093/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28093/events | https://github.com/pandas-dev/pandas/issues/28093 | 484,066,233 | MDU6SXNzdWU0ODQwNjYyMzM= | 28,093 | Circular reference warning after subsetting data frame | {
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import pandas
import numpy
df1 = pandas.DataFrame(numpy.array([[1, '123', 3], [4, 'dE1', 6], [7, 'gH2', 9]]), columns=['a', 'b', 'c'])
df1['b']=df1['b'].apply(str.lower) #.copy(deep=True)#.copy(deep=False)
df1
# WARNING
import pandas
import numpy
df1 = pandas.DataFrame(numpy.array([[1, '123', 3], [4, 'dE1', 6], [7, 'gH2', 9]]), columns=['a', 'b', 'c'])
df2 = df1[['a', 'b']]
df2['b']=df2['b'].apply(str.lower) #.copy(deep=True) #.copy(deep=False)
df2
no matter if I use copy or not I always get the same warning:
/home/owner/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
#### Output of ``pd.show_versions()``
<details>
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-26-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.13
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.4.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.0
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.6
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28094 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28094/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28094/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28094/events | https://github.com/pandas-dev/pandas/issues/28094 | 484,083,545 | MDU6SXNzdWU0ODQwODM1NDU= | 28,094 | Circular reference warning after subsetting data frame AND AFTER COPY | {
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} | [] | closed | false | null | [] | null | 3 | 2019-08-22T16:00:54Z | 2019-08-22T18:19:30Z | 2019-08-22T16:24:20Z | NONE | null | # PLEASE READ AND RUN BEFORE CLOSING
it is after subsetting that the warning appears no meter if copy is used
# WARNING AFTER SUBSETTING no matter if COPY was used
import pandas
import numpy
df1 = pandas.DataFrame(numpy.array([[1, '123', 3], [4, 'dE1', 6], [7, 'gH2', 9]]), columns=['a', 'b', 'c'])
df2 = df1[['a', 'b']]
df2['b']=df2['b'].apply(str.lower) .copy(deep=True)
df2
# or
df1 = pandas.DataFrame(numpy.array([[1, '123', 3], [4, 'dE1', 6], [7, 'gH2', 9]]), columns=['a', 'b', 'c'])
df2 = df1[['a', 'b']]
df2['b']=df2['b'].apply(str.lower).copy(deep=False)
df2
# or
df1 = pandas.DataFrame(numpy.array([[1, '123', 3], [4, 'dE1', 6], [7, 'gH2', 9]]), columns=['a', 'b', 'c'])
df2 = df1[['a', 'b']]
df2['b']=df2['b'].apply(str.lower)
df2
# COMPARE: NO WARNING:
import pandas
import numpy
df1 = pandas.DataFrame(numpy.array([[1, '123', 3], [4, 'dE1', 6], [7, 'gH2', 9]]), columns=['a', 'b', 'c'])
df1['b']=df1['b'].apply(str.lower).copy(deep=True)
df1
# or
df1 = pandas.DataFrame(numpy.array([[1, '123', 3], [4, 'dE1', 6], [7, 'gH2', 9]]), columns=['a', 'b', 'c'])
df1['b']=df1['b'].apply(str.lower) .copy(deep=False)
df1
# or
df1 = pandas.DataFrame(numpy.array([[1, '123', 3], [4, 'dE1', 6], [7, 'gH2', 9]]), columns=['a', 'b', 'c'])
df1['b']=df1['b'].apply(str.lower)
df1
no matter if I use copy or not I always get the same warning:
/home/owner/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
#### Output of ``pd.show_versions()``
<details>
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-26-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.13
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.4.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.0
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.6
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28095 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28095/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28095/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28095/events | https://github.com/pandas-dev/pandas/issues/28095 | 484,088,146 | MDU6SXNzdWU0ODQwODgxNDY= | 28,095 | ROADMAP: Consistent missing value handling with new NA scalar | {
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"color": "AD7FA8"... | open | false | null | [] | null | 63 | 2019-08-22T16:10:31Z | 2019-11-13T14:54:00Z | null | MEMBER | null | I cleaned up my initial write up on the consistent missing values proposal (https://github.com/pandas-dev/pandas/issues/27825#issuecomment-520583911), and incorporated the items brought up in the last video chat. So I think it is ready for some more detailed discussion.
The last version of the full proposal can be found here: https://hackmd.io/@jorisvandenbossche/Sk0wMeAmB
TL;DR:
- I propose to introduce a new scalar (singleton) `pd.NA` that can be used as the missing value indicator (when accessing a single value, not necessarily how it is stored under the hood).
- This can be used instead of `np.nan` or `pd.NaT` in new data types (eg nullable integers, potential string dtype)
- Long term, we can see if there is a migration possible to use this consistently for all data types.
cc @pandas-dev/pandas-core | {
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} | 4 | 2019-08-22T17:12:54Z | 2019-08-25T20:11:07Z | 2019-08-25T20:11:01Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 6 | 2019-08-22T17:34:29Z | 2019-10-27T10:23:18Z | 2019-10-25T16:34:52Z | CONTRIBUTOR | null | - [x] closes #20519
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
`.groupby` intialize "grouper" by _get_grouper_for_level. Base on "code", "level_index" fills with non-NA value if there is NA value. Problem is if there are only NA values, "level_index" can't fill with values.
So, check "level_index" whether all "level_index" values is NA. then "level_index" are all NA values, fill with NaN
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"de... | closed | false | null | [] | null | 0 | 2019-08-22T18:43:35Z | 2019-09-25T15:50:32Z | 2019-09-25T15:50:32Z | CONTRIBUTOR | null | #### Code Sample
```
import pandas as pd
import numpy as np
from datetime import timedelta
left = pd.DataFrame(
list(zip([0, 5, 10, 15, 20, 25], [0, 1, 2, 3, 4, 5])),
columns=["time", "left"],
)
left["time"] = pd.to_timedelta(left["time"], "ms")
right = pd.DataFrame(
list(zip([0, 3, 9, 12, 15, 18], [0, 1, 2, 3, 4, 5])),
columns=["time", "right"],
)
right["time"] = pd.to_timedelta(right["time"], "ms")
expected = pd.DataFrame(
list(
zip(
[0, 5, 10, 15, 20, 25],
[0, 1, 2, 3, 4, 5],
[0, np.nan, 2, 4, np.nan, np.nan],
)
),
columns=["time", "left", "right"],
)
expected["time"] = pd.to_timedelta(expected["time"], "ms")
print(expected)
# this throws MergeError: incompatible tolerance <class 'datetime.timedelta'>, must be compat with type dtype('<m8[ns]')
result = pd.merge_asof(left, right, on="time", tolerance=timedelta(microseconds=1000), direction="nearest")
# this works!
# result = pd.merge_asof(left, right, on="time", tolerance=pd.Timedelta("1ms"), direction="nearest")
print(result)
```
#### Problem description
I can use pd.Timedelta as tolerance, but not datetime.timedelta.
#### Expected Output
No error
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.0-5-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0+222.gdef01cf7b
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.2.0
Cython : 0.29.13
pytest : 5.1.1
hypothesis : 4.33.1
sphinx : 1.8.5
blosc : 1.8.1
feather : None
xlsxwriter : 1.1.9
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 2.6.3
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
s3fs : 0.3.3
scipy : 1.3.1
sqlalchemy : 1.3.7
tables : 3.5.2
xarray : 0.12.3
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.9
</details>
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- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
based on the OP's example:
```
In [2]: df = pd.DataFrame({"A": 0, "B": 0}, index=range(4*10**7))
In [3]: %timeit df.replace([np.inf, -np.inf], np.nan)
5.18 s ± 423 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # <-- master
414 ms ± 7.19 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # <-- PR
In [4]: %timeit df.replace([np.inf, -np.inf], np.nan, inplace=True)
2.89 s ± 111 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # <-- master
69.6 µs ± 4 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- PR
In [5]: %timeit df.replace([np.inf, -np.inf, 1], np.nan)
4.88 s ± 228 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # <-- master
466 ms ± 11.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # <-- PR
```
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] | closed | false | null | [] | null | 6 | 2019-08-22T19:15:06Z | 2019-08-31T02:39:22Z | 2019-08-27T22:07:51Z | CONTRIBUTOR | null | - [x] Fixes issue #28098
- [x] tests added / passed [1/0]
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 9 | 2019-08-22T19:49:26Z | 2019-08-26T14:22:35Z | 2019-08-26T14:22:31Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/27261
xref https://bugs.python.org/issue32892
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} | 25 | 2019-08-22T20:37:07Z | 2020-11-08T03:03:16Z | 2020-11-08T03:03:11Z | MEMBER | null | - [x] closes #16748
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 6 | 2019-08-22T21:04:28Z | 2021-08-13T20:46:31Z | 2020-08-13T22:04:50Z | CONTRIBUTOR | null | GZ-compression writes the filename and timestamp into compressed data's header. This means that compressing the same data at different times will produce outputs that are not byte-for-byte identical.
In the past, this has presented [problems for OS distros](https://wiki.debian.org/ReproducibleBuilds/TimestampsInGzipHeaders). It now prtesents problems for data science. Specifically, I frequently track compressed files using Git LFS which detects whether a file has changed by its hash. Therefore, if I run a pipeline to create gzip-compressed dataframes exported from Pandas, the `.gz` outputs will differ every time.
Currently, user's can use [this hack](https://stackoverflow.com/a/264303/4651668) which globally sets `gzip.time` to a fake time to create deterministic gzip compression from `pandas.DataFrame.to_csv`. I propose either of the following approaches that would be much cleaner:
1. changing Pandas' default behavior to set gzip's [`mtime`](https://docs.python.org/3/library/gzip.html#gzip.GzipFile.mtime) to a constant but erroneous time. Whatever `gzip --no-name` sets would probably be best.
2. Having some module or function level setting that users could activate for deterministic gzip compression.
Personally, I don't see much benefit to gzip's timestamp, and therefore prefer solution 1 to 2. It's pretty confusing to users to see gzip outputs change and have to figure out that it's the changing timestamp.
In either case, we should look into the other supported compression methods and check their determinism. We can check time-dependent output with:
```python
import gzip, time
data = b'data to compress'
output_1 = gzip.compress(data)
time.sleep(2)
output_2 = gzip.compress(data)
output_1 == output_2
```
[Here's](https://www.systutorials.com/docs/linux/man/1-gzip/) the docs for `gzip --no-name`
> -n --no-name
When compressing, do not save the original file name and time stamp by default. (The original name is always saved if the name had to be truncated.) When decompressing, do not restore the original file name if present (remove only the gzip suffix from the compressed file name) and do not restore the original time stamp if present (copy it from the compressed file). This option is the default when decompressing. | {
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} | 12 | 2019-08-22T21:20:46Z | 2020-04-02T05:03:14Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
g = pd.Period(year=2262, month=4, day=11, freq='D')
print(g.start_time)
g2 = pd.Period(year=2262, month=4, day=12, freq='D')
print(g2.start_time)
```
Output: (with pandas 0.25.0)
2262-04-11 00:00:00
1677-09-21 00:25:26.290448384
with pandas 0.25.0+216.g1aca08aa6
I get an OutOfBoundsDatetime error
>>> print(g2.start_time)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/_libs/tslibs/period.pyx", line 1768, in pandas._libs.tslibs.period._Period.start_time.__get__
File "pandas/_libs/tslibs/period.pyx", line 1811, in pandas._libs.tslibs.period._Period.to_timestamp
File "pandas/_libs/tslibs/period.pyx", line 1190, in pandas._libs.tslibs.period.period_ordinal_to_dt64
File "pandas/_libs/tslibs/np_datetime.pyx", line 118, in pandas._libs.tslibs.np_datetime.check_dts_bounds
pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 2262-04-12 00:00:00
#### Problem description
Overflow means that the Period is incorrect, and also hinders use of Period for date/times outside the limits of pd.Timestamp
#### Expected Output
Output:
2262-04-11 00:00:00
2262-04-12 00:00:00
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.6.final.0
python-bits : 64
OS : Windows
OS-release : 7
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.0
numpy : 1.16.3
pytz : 2018.9
dateutil : 2.7.5
pip : 19.1.1
setuptools : 40.8.0
Cython : None
pytest : 4.2.0
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.2
lxml.etree : 4.3.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : None
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.3
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : 1.3.1
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.1.2
</details>
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"description": "Arithmetic, Comparison, ... | open | false | null | [] | null | 3 | 2019-08-22T21:34:01Z | 2019-11-02T22:02:49Z | null | NONE | null | #### Code Sample
```python
>>> one = pd.Series([12, 13, 14], index=[3, 3, 3])
>>> other = pd.Series([-20, 20], index=[3, 3])
>>> another = pd.Series([-20, 20, 40], index=[3, 3, 3])
>>> print(one.subtract(other))
3 32
3 -8
3 33
3 -7
3 34
3 -6
dtype: int64
>>> print(one.subtract(another))
3 32
3 -7
3 -26
dtype: int64
```
#### Problem description
This behavior leads to output series length that depends on the number of duplicate index values. In data where this value is not known, the number of rows in the output is unpredictable.
#### Expected Output
I would expect there to be an option in `pd.Series.subtract` allowing the user to determine the operation's behavior with respect to repeated indices, something like the following:
```python
>>> one.subtract(another, repeat=True)
3 32
3 -8
3 -28
3 33
3 -7
3 -27
3 34
3 -6
3 -26
dtype: int64
>>> one.subtract(another, repeat=False)
3 32
3 -7
3 -26
dtype: int64
>>> # when the number of rows with index 3 is the same in both Series:
... one.subtract(other, repeat=True)
3 32
3 -8
3 33
3 -7
3 34
3 -6
dtype: int64
>>> one.subtract(other, repeat=False)
3 32
3 -7
3 NaN
dtype: int64
```
If this is what `level` is for, I have no idea what the documentation is saying.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-693.21.1.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.12
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.5
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
```
</details>
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} | 6 | 2019-08-22T22:38:35Z | 2019-08-24T06:00:20Z | 2019-08-24T06:00:16Z | NONE | null | ```python
import pandas
df = pandas.DataFrame([[1,2,3],[4,5,6]])
with pd.ExcelWriter('input_excel.xlsx', mode='a') as writer:
df.to_excel(writer, sheet_name='PandasTestSheet')
```
#### Problem description
'input_excel.xlsx' contains one sheet with some data in cells and an image added via excel's insert picture from file function. When I attempt to append a dataframe to a second new sheet, the resulting excel file no longer has the image in the first sheet.
#### Expected Output
The first sheet should be untouched with the image still there. The second sheet should contain the contents of the dataframe.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 2.5.14
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 2 | 2019-08-23T00:57:56Z | 2019-09-02T22:57:04Z | 2019-09-02T21:04:12Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/28108 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28108/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28108/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28108/events | https://github.com/pandas-dev/pandas/issues/28108 | 484,308,907 | MDU6SXNzdWU0ODQzMDg5MDc= | 28,108 | DataFrame.mean() ignores datetime series | {
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"following_url": "https://api.github.com/users/jbrockmendel/following{/other_user... | null | 4 | 2019-08-23T03:16:57Z | 2020-09-21T22:10:31Z | 2020-09-21T22:10:30Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
In [1]: import pandas as pd
In [2]: from datetime import datetime
In [3]: s = pd.Series([datetime(2014, 7, 9),
...: datetime(2014, 7, 10),
...: datetime(2014, 7, 11)])
In [4]: df = pd.DataFrame({'numeric':[1,2,3],
...: 'datetime':s})
In [5]: df.mean()
Out[5]:
numeric 2.0
dtype: float64
In [6]: s.mean()
Out[6]: Timestamp('2014-07-10 00:00:00')
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
As of pandas 0.25 it is possible to apply mean() to a datetime series. However, DataFrame.mean() ignores datetimes series columns rather than returning the mean of the datetime series as one might expect.
#### Expected Output
When axis=0, output could be a dataframe with dtype as the first row, and the value as the second row.
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```python
# factor_data.groupby(grouper)['factor'].rank()
```
#### Problem description
**when I use groupby.rank method , TypeError occur in "pandas\core\groupby\ops.py" file.**
File "<ipython-input-3-fdfa537b4972>", line 1, in <module>
runfile('E:/myStra/factor/funfile/analysis/factorAnaCNE6.py', wdir='E:/myStra/factor/funfile/analysis')
File "C:\ProgramData\Anaconda2\envs\py_36\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 786, in runfile
execfile(filename, namespace)
File "C:\ProgramData\Anaconda2\envs\py_36\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 110, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "E:/myStra/factor/funfile/analysis/factorAnaCNE6.py", line 21, in <module>
factor_data.groupby(grouper)['factor'].rank()
File "C:\ProgramData\Anaconda2\envs\py_36\lib\site-packages\pandas\core\groupby\groupby.py", line 1846, in rank
na_option=na_option, pct=pct, axis=axis)
File "C:\ProgramData\Anaconda2\envs\py_36\lib\site-packages\pandas\core\groupby\groupby.py", line 807, in _cython_transform
**kwargs)
File "C:\ProgramData\Anaconda2\envs\py_36\lib\site-packages\pandas\core\groupby\ops.py", line 551, in transform
return self._cython_operation('transform', values, how, axis, **kwargs)
File "C:\ProgramData\Anaconda2\envs\py_36\lib\site-packages\pandas\core\groupby\ops.py", line 511, in _cython_operation
**kwargs)
File "C:\ProgramData\Anaconda2\envs\py_36\lib\site-packages\pandas\core\groupby\ops.py", line 584, in _transform
transform_func(result, values, comp_ids, is_datetimelike, **kwargs)
File "C:\ProgramData\Anaconda2\envs\py_36\lib\site-packages\pandas\core\groupby\ops.py", line 397, in wrapper
return f(afunc, *args, **kwargs)
File "C:\ProgramData\Anaconda2\envs\py_36\lib\site-packages\pandas\core\groupby\ops.py", line 348, in <lambda>
kwargs.get('na_option', 'keep')
TypeError: 'NoneType' object is not callable
**After debugging the file I find the Error breakout in ops.py >_get_cython_function > func = afunc = get_func(ftype['name']) (line 390), return fun get a None Object. if I use "getattr(libgroupby, 'group_rank')(line 376)" I find that "AttributeError: module 'pandas._libs.groupby' has no attribute 'group_rank'"**
#### Expected Output
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None
pandas: 0.24.2
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.3.0
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: 0.9.3
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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Python 3.8 is due on October 21, which will likely beat 1.0.
We should release a 0.25.2 around then that's just 0.25.1 + 3.8 compatibility and regressions from 0.24.x. | {
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