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https://api.github.com/repos/pandas-dev/pandas/issues/29414 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29414/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29414/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29414/events | https://github.com/pandas-dev/pandas/issues/29414 | 517,893,297 | MDU6SXNzdWU1MTc4OTMyOTc= | 29,414 | ENH: Allow sorting of a dataframe on a boolean/function? | {
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"description": "Non-arit... | closed | false | null | [] | null | 4 | 2019-11-05T16:49:03Z | 2021-07-22T06:01:03Z | 2021-07-22T06:01:03Z | NONE | null | From searching variants of the phrase "sort pandas on boolean," I don't think the feature I am describing is doable in an easy way at this point, so I think it's a good candidate for a feature. It might not be possible in the way I'm envisioning, but I'm interested in whether or not the base feature can be done.
Forgive me if there is a way to do what I'm getting at, and I just haven't found it- I don't mean to use this as a stack overflow page, but I genuinely think there is no feature like I'm describing.
So here's an example of a dataframe, which has two string fields that I want to sort on. The issue is that one of the string fields is generated partially from user entry, so I have little control over what is in the field. It looks like:
``` Python
>>> df = pd.DataFrame({
>>> "NBR": ['B3', 'A1', 'C3', 'D5', 'A2', 'B1'],
>>> "DESCR": [
>>> 'GADGET',
>>> 'GIZMO THAT IS BLUE',
>>> 'GIZMO THAT WHIRS',
>>> 'GADGET WITH CHEESE',
>>> 'GADGET THAT IS BIG',
>>> 'GIZMO THAT IS ROUND'
>>> ]
>>> })
>>> df
NBR DESCR
0 B3 GADGET
1 A1 GIZMO THAT IS BLUE
2 C3 GIZMO THAT WHIRS
3 D5 GADGET WITH CHEESE
4 A2 GADGET THAT IS BIG
5 B1 GIZMO THAT IS ROUND
```
Every row is either a gizmo or a gadget, so I want to sort on that category. Then, I want to sort on the number of the part.
Sorting on DESCR doesn't give the desired result- in this example, since B3 has a description that is alphabetically before A1, it would be sorted first. However, I want them to be sorted based on the category of gizmo or gadget.
That is:
``` Python
>>># not desired result
>>>df.sort_values(['DESCR', 'NBR'])
NBR DESCR
0 B3 GADGET
4 A2 GADGET THAT IS BIG
3 D5 GADGET WITH CHEESE
1 A1 GIZMO THAT IS BLUE
5 B1 GIZMO THAT IS ROUND
2 C3 GIZMO THAT WHIRS
>>># desired result requires a column/intermediary category or series
>>># example of intermediary method
>>>df['TYPE'] = df.DESCR.apply(lambda s: "GIZMO" if s.startswith('GIZMO') else 'GADGET')
>>>df
NBR DESCR TYPE
0 B3 GADGET GADGET
1 A1 GIZMO THAT IS BLUE GIZMO
2 C3 GIZMO THAT WHIRS GIZMO
3 D5 GADGET WITH CHEESE GADGET
4 A2 GADGET THAT IS BIG GADGET
5 B1 GIZMO THAT IS ROUND GIZMO
>>>df.sort_values(['TYPE', 'NBR'])
NBR DESCR TYPE
4 A2 GADGET THAT IS BIG GADGET
0 B3 GADGET GADGET
3 D5 GADGET WITH CHEESE GADGET
1 A1 GIZMO THAT IS BLUE GIZMO
5 B1 GIZMO THAT IS ROUND GIZMO
2 C3 GIZMO THAT WHIRS GIZMO
>>># finally, the desired result
>>>df = df.sort_values(['TYPE', 'NBR']).drop(columns='TYPE')
>>>df
NBR DESCR
4 A2 GADGET THAT IS BIG
0 B3 GADGET
3 D5 GADGET WITH CHEESE
1 A1 GIZMO THAT IS BLUE
5 B1 GIZMO THAT IS ROUND
2 C3 GIZMO THAT WHIRS
>>># suggested syntax
>>>df.sort_values([lambda x: x.startswith('GIZMO'), 'NBR']
```
The issue I have with this is that there are multiple conditional sorts that I'm performing before the normal, column based sort, and the dataframe is large enough where creating the extra column is not desired.
I'm open to the possibility that this is just impractical to implement, or a bad idea for some reason, and the intermediary is the way to go.
I'd love to work on this myself, but I'm not very familiar with pandas source code yet. I'll keep playing and see if I can figure out an implementation :) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29415 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29415/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29415/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29415/events | https://github.com/pandas-dev/pandas/issues/29415 | 517,895,425 | MDU6SXNzdWU1MTc4OTU0MjU= | 29,415 | Add GroupBy.gmean() and GroupBy.gstd() | {
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} | [] | closed | false | null | [] | null | 2 | 2019-11-05T16:52:57Z | 2019-11-06T07:27:08Z | 2019-11-06T04:57:27Z | NONE | null | #### Code Sample, a copy-pastable example if possible
Calculating the arithmetic mean and arithmetic standard deviation using `GroupBy` objects can e.g. be done like
```
df = pd.DataFrame(
columns=[0.0, 0.1, 0.2, 0.3, 0.4],
data=[[0, 1, np.nan, 1, 9],
[np.nan, np.nan, np.nan, np.nan, 9]]
)
bins = pd.IntervalIndex.from_tuples([(0.0, 0.2), (0.2, 0.4)])
def mean_and_std(df, bins):
cuts = pd.cut(df.columns, bins, include_lowest=True)
return df.T.groupby(cuts).agg(['mean', 'std']).T
mean_and_std(df, bins)
```
which gives
```
(0.0, 0.2] (0.2, 0.4]
0 mean 1.0 5.000000
std NaN 5.656854
1 mean NaN 9.000000
std NaN NaN
```
.
#### Problem description
Using `GroupBy` objects is a common option during data binning operations. It's particularly important if dataframes to be processed are big which can be handled with parallel proccessing. E.g. seems like [Dask supports GroupBy objects](https://examples.dask.org/dataframes/02-groupby.html). The geometric mean and the geometric standard deviation are quite common statistic quantities. Unfortunatelly [`GroupyBy`](https://pandas.pydata.org/pandas-docs/stable/reference/groupby.html#computations-descriptive-stats) does not support `gmean()` and `gstd()` (yet). Implementing a performant workaround with e.g. [scipy.stats.mstats.gmean()](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.mstats.gmean.html) and [scipy.stats.gstd](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gstd.html()) from scratch feels boilerplate and does not support bultin support for parallel processing. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29416 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29416/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29416/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29416/events | https://github.com/pandas-dev/pandas/pull/29416 | 517,927,753 | MDExOlB1bGxSZXF1ZXN0MzM2OTA0OTk2 | 29,416 | maybe_promote: Restrict fill_value to scalar for non-object dtype | {
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} | 1 | 2019-11-05T17:45:37Z | 2020-04-05T17:44:46Z | 2019-11-06T19:29:03Z | MEMBER | null | Partially reverts #29362 by allowing non-scalar fill_value for _object_ dtypes. i.e. in 0.25.3 `pd.Series(range(3), dtype=object).shift(1, fill_value={})` would work, #29362 broke that, and this restores it. Added `test_shift_object_non_scalar_fill` for this.
With the new restriction on `maybe_promote` in place, we can get rid of all the `box` tests and simplify test_promote a _ton_. This removes about 2500 tests. This also uncovers the fact that we were failing to run some of the non-box cases, which are now xfailed. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29417 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29417/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29417/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29417/events | https://github.com/pandas-dev/pandas/issues/29417 | 517,940,238 | MDU6SXNzdWU1MTc5NDAyMzg= | 29,417 | DataFrame shift along columns not respecting position when dtypes are mixed | {
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"description": "Non-arithmetic algos: va... | closed | false | null | [] | null | 9 | 2019-11-05T18:07:25Z | 2020-09-26T22:58:30Z | 2020-09-26T22:58:29Z | NONE | null | #### Setup
```python
import pandas as pd
df = pd.DataFrame(dict(
A=[1, 2], B=[3., 4.], C=['X', 'Y'],
D=[5., 6.], E=[7, 8], F=['W', 'Z']
))
df
df.shift(axis=1)
```
Outputs
```python
A B C D E F
0 1 3.0 X 5.0 7 W
1 2 4.0 Y 6.0 8 Z
A B C D E F
0 NaN NaN NaN 3.0 1.0 X
1 NaN NaN NaN 4.0 2.0 Y
```
#### Problem description
[See Associated Stackoverflow question](https://stackoverflow.com/q/58715801/2336654)
The shift places a column's values into the next column that shares the same dtype. The expectation is that the column's values are placed into the adjacent column.
#### Expected Output
```python
dtypes = df.dtypes.shift(fill_value=object)
df_shifted = df.astype(object).shift(1, axis=1).astype(dtypes)
df_shifted
A B C D E F
0 NaN 1 3.0 X 5.0 7
1 NaN 2 4.0 Y 6.0 8
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1062.4.1.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 : 0.4.0
xlsxwriter : None
lxml.etree : 4.3.4
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.6.1
pandas_datareader: 0.7.4
bs4 : 4.7.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.11.1
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.5
tables : 3.5.2
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29418 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29418/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29418/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29418/events | https://github.com/pandas-dev/pandas/issues/29418 | 517,965,976 | MDU6SXNzdWU1MTc5NjU5NzY= | 29,418 | ENH: Group By Grouping set /Cube/ Rollup | {
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} | 10 | 2019-11-05T19:00:35Z | 2021-07-22T06:02:21Z | null | NONE | null | #### Problem description
Basically these are performance tools in SQL to get analysis in multiple dimensions and they are missing in Pandas out of the box. Some of these can be achieved by a pivot table and melt/stack functions but being tools for analysis these functions should be a must and it also decreases the number of lines of code.
Group by Grouping set will help to rewrite the query with multiple groups by clauses combined with union statements into a single query. Cube is shorthand notation of grouping sets if the user chooses all the combinations of the fields listed in the cube clause
```SQL Code
SELECT
column1,
column2,
aggregate_function (column3)
FROM
table_name
GROUP BY
GROUPING SETS (
(column1, column2),
(column1),
(column2),
()
);
Select column1,
column2,
column3,
column4,
aggregate_function (column5)
from table
group by column1, column2, cube (column3,column4)```
Current way
```pseudo code
a= <pandas dataframe>
a1 = a.groupby([column1]).sum(column5)
a2 = a.groupby([column1,column2]).sum(column5)
...
an = a.groupby([column1,...,columnn]).sum(column5)
result= union(a1,a2,......an)
```
Expected way
```pseudo code
a= <pandas dataframe>
gropby_cube1 = a.gropby([column1,column2]).cube([column3,.....,columnn]).sum(column5)
gropby_cube2 = a.gropby.cube([column1,column2,.....,columnn]).sum(column5)
gropby_sets1 = a.gropby.sets( {column1,column2} ,{column1,column2,column3} ,{}).sum(column5)
gropby_sets2 = a.gropby([column1,column2).sets({column1,column2,column3} ,{} ).sum(column5)
gropby_rollup1 = a.gropby.rollup({column1,column2,column3}).sum(column5)
gropby_rollup2 = a.gropby([column1,column2).rollup({column3} ).sum(column5)
```
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} | 2 | 2019-11-05T19:46:33Z | 2019-11-06T18:19:32Z | 2019-11-06T18:11:04Z | MEMBER | null | diff_2d no longer needs to be in the pxi.in file, so moved it to the pyx
A couple of recently-identified bugs in the groupby code are caused by passing incorrect types, so im getting more motivated to add annotations in/around the affected code. | {
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} | 8 | 2019-11-05T21:28:23Z | 2019-11-28T12:21:18Z | 2019-11-27T20:47:44Z | CONTRIBUTOR | null | - [x] closes #28023 and closes #28279
- [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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} | 1 | 2019-11-05T22:14:11Z | 2019-11-06T20:03:02Z | 2019-11-06T19:27:15Z | MEMBER | null | - [x] closes #26877
The file is auto-generated, not something for us to worry about | {
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} | 4 | 2019-11-05T22:29:45Z | 2019-11-13T02:13:06Z | 2019-11-13T02:13:06Z | MEMBER | null | related #28201
I find out the result gotten from pandas master is different from using a specific version recently
Also, minimum reproducible example:
```python
df = pd.DataFrame({"group": ['a', 'a', 'b', 'b'], "A": [0, 1, 2, 3], "B": [5, 6, 7, 8]})
df.columns = pd.MultiIndex.from_tuples([('x', 'group'), ('y', 'A'), ('y', 'B')])
```
if running the code below on pandas master:
```python
df.groupby(('x', 'group')).agg(a_max=(('y', 'A'), "max"))
```
But if running the same code in `pandas==0.25.3`, then will get the error:
```
ValueError: operands could not be broadcast together with shapes (1,2) (3,) (1,2)
```
I didn't find any PRs since 0.25.3 was released related to this issue, so curious why this was caused.
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} | 1 | 2019-11-05T22:36:02Z | 2019-11-06T19:28:04Z | 2019-11-06T19:10:35Z | MEMBER | null | Closes https://github.com/pandas-dev/pandas/issues/21411 | {
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} | 2 | 2019-11-05T23:10:16Z | 2019-11-06T21:43:39Z | 2019-11-06T21:11:44Z | MEMBER | null | Closes https://github.com/pandas-dev/pandas/issues/21131 | {
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} | 1 | 2019-11-06T01:24:04Z | 2019-11-06T21:33:09Z | 2019-11-06T21:25:07Z | MEMBER | null | cc @jreback @WillAyd
There are a few ways in which we incorrectly raise TypeError within _python_agg_general that this fixes.
A lot of the complexity in this code comes from the fact that we drop columns on which a function is invalid instead of requiring the user to subset columns. | {
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} | 1 | 2019-11-06T02:00:21Z | 2019-11-06T11:15:27Z | 2019-11-06T11:15:03Z | NONE | null | #### Code Sample (from pandas source)
```python
# pandas/core/indexes/base.py:342
# extension dtype
elif is_extension_array_dtype(data) or is_extension_array_dtype(dtype):
data = np.asarray(data)
if not (dtype is None or is_object_dtype(dtype)):
# coerce to the provided dtype
ea_cls = dtype.construct_array_type()
data = ea_cls._from_sequence(data, dtype=dtype, copy=False)
# coerce to the object dtype
data = data.astype(object)
return Index(data, dtype=object, copy=copy, name=name, **kwargs)
```
#### Problem description
Right now, when you create an index from an ExtensionArray, it causes a coercion to a numpy array of Python objects. See above. For many use cases, this is an expensive operation - many people switch to ExtensionArrays to avoid having numpy lists of objects to begin with! Ideally, it would be possible to store ExtensionArrays inside indices (or provide an interface for ExtensionArrays to implement) so that a conversion to object types would not be necessary.
For my use case, I have many 8-byte python objects that I want to do fast operations on. I use ExtensionArrays to store a contiguous np.uint64 array that has the proper repr inside a Series, and that get lazily converted on access. I want to be able to use them inside indices without a massive performance degradation!
#### Expected Output
A version of pandas where ExtensionArrays are stored directly inside indices, as opposed to coercing to object.
#### Output of ``pd.show_versions()``
<details>
Current master branch.
</details>
##### A big thank you to all of the pandas maintainers. Without your work, I wouldn't be here in the first place! Thanks for considering this issue. | {
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} | 1 | 2019-11-06T03:11:19Z | 2019-11-06T19:24:46Z | 2019-11-06T19:11:31Z | MEMBER | null | - [x] closes #28938
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Stops catching DataError in the 1D case for _aggregate_multiple_funcs. This change is mostly unrelated, but shares the process of reasoning about what cases need _shallow_copy/DataError. | {
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} | 11 | 2019-11-06T07:30:05Z | 2019-11-26T14:22:18Z | 2019-11-21T12:59:31Z | MEMBER | null | - [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Pre-req for https://github.com/pandas-dev/pandas/issues/28987
Currently many of the aggregation functions in `window.pyx` follow the form:
```
def roll_func(values, window, minp, N, closed):
# calculate window bounds _and_ validate arguments
start, end, ... = get_window_bounds(values, window, minp, N, ...)
for i in range(values):
s = start[i]
....
```
This PR refactors out the window bound calculation into `window_indexer.pyx` and validation so the aggregation functions can be of the form:
```
def roll_func(values, start, end, minp):
for i in range(values):
s = start[i]
....
```
The methods therefore in `rolling.py` now have the following pattern:
1. Fetch the correct cython aggregation function (whether the window is fixed or variable), and prep it with kwargs if needed
2. Compute the `start` and `end` window bounds from functionality in `window_indexer.pyx`
3. Pass in the `values`, `start`, `end`, `min periods` into the aggregation function. | {
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} | 0 | 2019-11-06T09:13:30Z | 2019-11-06T16:45:18Z | 2019-11-06T16:45:13Z | CONTRIBUTOR | null | Just missing the 'user_guide' part => one line change.
- [ ] closes #xxxx (NOT THAT I KNOW)
- [ ] tests added / passed (NO CODE CHANGE)
- [ ] passes `black pandas` (NO CODE CHANGE)
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` (NO CODE CHANGE)
- [ ] whatsnew entry (NO CHANGE)
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- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
P.S
**Need code review** | {
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} | 1 | 2019-11-06T16:09:15Z | 2019-11-06T18:13:05Z | 2019-11-06T18:12:52Z | CONTRIBUTOR | null | Closes #29330
This was most likely due to inconsistent constraints between conda-forge & defaults.
Also, pinning to 3.7 for now until the 3.8 buildout is done to make the solver's life a bit easier. | {
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} | 1 | 2019-11-06T16:23:11Z | 2019-11-25T23:00:28Z | 2019-11-25T23:00:28Z | CONTRIBUTOR | null | I can reproduce with numpy master
```
==================================== ERRORS ====================================
__________ ERROR collecting pandas/tests/arrays/sparse/test_array.py ___________
pandas/tests/arrays/sparse/test_array.py:22: in <module>
class TestSparseArray:
pandas/tests/arrays/sparse/test_array.py:511: in TestSparseArray
dtype=SparseDtype("datetime64[ns]", pd.Timestamp("1970")),
pandas/core/arrays/sparse/array.py:364: in __init__
data, kind=kind, fill_value=fill_value, dtype=dtype
pandas/core/arrays/sparse/array.py:1584: in make_sparse
sparsified_values = arr[mask]
E IndexError: arrays used as indices must be of integer (or boolean) type
--------- generated xml file: /home/vsts/work/1/s/test-data-single.xml ---------
========================== slowest 10 test durations ===========================
```
```python
(Pdb) pp arr
array(['1970-01-01T00:00:00.000000000', '1970-01-01T00:00:00.000000001'],
dtype='datetime64[ns]')
(Pdb) pp fill_value
Timestamp('1970-01-01 00:00:00')
(Pdb) pp arr != fill_value
array([True, True], dtype=object)
```
Previously, that was bool dtype
```python
In [6]: np.array(['1970-01-01T00:00:00.000000000', '1970-01-01T00:00:00.000000001'],
...: dtype='datetime64[ns]') == pd.Timestamp('1970-01-01 00:00:00')
Out[6]: array([False, False])
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} | 3 | 2019-11-06T16:31:35Z | 2019-11-06T21:21:01Z | 2019-11-06T19:10:04Z | CONTRIBUTOR | null | We don't want this long-term. But there's no easy way
to skip this for numpydev, since it errors in setup.
xref #29432 (keep open till numpydev is fixed) | {
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} | 7 | 2019-11-06T17:03:42Z | 2020-01-06T16:47:00Z | 2019-11-06T20:36:34Z | CONTRIBUTOR | null | part of #28602 | {
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] | closed | false | null | [] | null | 2 | 2019-11-06T17:31:14Z | 2019-11-07T00:33:44Z | 2019-11-06T19:03:12Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
list_of_columns = ["name", "weight"]
for column in list_of_columns:
if column in other_df.columns:
df = df.assign(column = other_df[column])
```
#### Problem description
df.assign creates a new column called "column" and then overwrites it with another new column called "column" instead of adding the collumns "name" and "weight". i think its because of the use of an equal sign instead of colon or comma.
i would like to have an "assign" function, that i can use in a for loop.
im sorry if im wrong or if this is not the right place for this issue. im quite new to programming.
regards
Florian | {
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Largely using annotations as an excuse to put eyeballs on parts of the code that would otherwise be left alone. | {
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} | 4 | 2019-11-06T19:06:28Z | 2019-11-06T21:30:26Z | 2019-11-06T21:22:46Z | MEMBER | null | We raise a ValueError and immediately ignore it. | {
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} | 1 | 2019-11-06T19:33:12Z | 2019-11-06T21:23:33Z | 2019-11-06T21:23:27Z | CONTRIBUTOR | null | - [x] closes #19351
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This is a unit test for an already fixed problem.
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} | 4 | 2019-11-06T20:17:45Z | 2019-12-21T10:47:08Z | 2019-12-21T10:47:08Z | CONTRIBUTOR | null | we should group the test files in pandas/tests/io/data into sub directories, e.g. for stata, excel, html, csv etc. | {
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} | 1 | 2019-11-06T20:19:38Z | 2021-07-22T06:02:44Z | null | CONTRIBUTOR | null | we have a mix of stack / unstack based tests in various places: pandas/tests/frame/test_reshape.py and pandas/tests/test_multilevel.py
would be nice to consolidate these into pandas/tests/reshape | {
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} | 4 | 2019-11-06T20:27:23Z | 2019-11-18T01:40:59Z | 2019-11-18T01:40:48Z | CONTRIBUTOR | null | Relates to #19159
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 9 | 2019-11-06T20:36:45Z | 2020-10-14T18:37:51Z | 2020-10-14T18:37:51Z | NONE | null | I'm following the 0.25.3 [documentation](https://pandas.pydata.org/pandas-docs/stable/development/extending.html#subclassing-pandas-data-structures) on using _metadata.
```python
sdf = SubclassedDataFrame2(my_data_as_dictionary)
sdf.added_property = 'hello pandas'
df = sdf.groupby('mycategorical')[['myfloat1', 'myfloat2']].sum()
print(df.added_property)
```
The above output produces the result:
AttributeError: 'DataFrame' object has no attribute 'added_property'
**added_property** is _not_ being "passed to manipulation results" as described in the documentation. | {
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} | 1 | 2019-11-06T20:36:47Z | 2019-11-07T14:28:17Z | 2019-11-07T14:28:17Z | MEMBER | null | While running the command on a fresh virtual env (using pip), I got the error messages:
```
ERROR: Could not find a version that satisfies the requirement python==3.7 (from -r requirements-dev.txt (line 2)) (from versions: none)
ERROR: No matching distribution found for python==3.7 (from -r requirements-dev.txt (line 2))
```
I can't run ```pd.show_versions()```.
So here's what I've got:
- pip -19.3.1
- setuptools - 41.6.0
SS of the error:
Full text:
```
(venv-pandas) [bummy@null pandas-MomIsBestFriend]$ python -m pip install -r requirements-dev.txt
Collecting git+https://github.com/pandas-dev/pandas-sphinx-theme.git@master (from -r requirements-dev.txt (line 68))
Cloning https://github.com/pandas-dev/pandas-sphinx-theme.git (to revision master) to /tmp/pip-req-build-_jc5c_b6
Running command git clone -q https://github.com/pandas-dev/pandas-sphinx-theme.git /tmp/pip-req-build-_jc5c_b6
Collecting numpy>=1.15
Using cached https://files.pythonhosted.org/packages/00/4a/e34fce8f18c0e052c2b49f1b3713469d855f7662d58ae2b82a88341e865b/numpy-1.17.3-cp37-cp37m-manylinux1_x86_64.whl
ERROR: Could not find a version that satisfies the requirement python==3.7 (from -r requirements-dev.txt (line 2)) (from versions: none)
ERROR: No matching distribution found for python==3.7 (from -r requirements-dev.txt (line 2))
(venv-pandas) [bummy@null pandas-MomIsBestFriend]$
```
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} | 9 | 2019-11-06T20:41:21Z | 2019-11-08T01:03:38Z | 2019-11-08T01:03:24Z | CONTRIBUTOR | null | …essage in case replacement is set to False
- [X] closes #27451
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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cc @MomIsBestFriend | {
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} | 2 | 2019-11-06T21:11:17Z | 2019-11-07T16:31:52Z | 2019-11-07T16:31:50Z | CONTRIBUTOR | null | Pandas Sprint at PyData NYC :-)
Fixed at the direction of @datapythonista
The only file touched is a markdown file.
- [ ] closes #xxxx
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} | 11 | 2019-11-06T21:54:14Z | 2019-12-23T20:57:03Z | 2019-12-11T08:11:52Z | CONTRIBUTOR | null | - [x] closes #25229
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Added an ORC reader following the `read_parquet` API. Still need to give some additional love to the docstrings but this is at least ready for some discussion and eyes on it. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29450 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29450/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29450/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29450/events | https://github.com/pandas-dev/pandas/pull/29450 | 518,875,758 | MDExOlB1bGxSZXF1ZXN0MzM3NzEyNTI3 | 29,450 | CLN: remove is_stringlike | {
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} | 1 | 2019-11-06T22:23:54Z | 2019-11-07T17:18:30Z | 2019-11-07T07:34:28Z | MEMBER | null | its now just an alias for `isinstance(obj, str)` | {
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... | open | false | null | [] | null | 2 | 2019-11-07T00:21:43Z | 2021-07-22T06:03:03Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame()
df['x'] = range(0,100,5)
df['y'] = range(0,1000,50)
df = df.set_index('x')
# line plot
df.plot.line()
plt.savefig('line')
# stepped line plot
df.plot.line(drawstyle="steps")
plt.savefig('line-steps')
# area plot
df.plot.area()
plt.savefig('area')
# Feature Request: stepped area plot
df.plot.area(drawstyle="steps-mid")
plt.savefig('area-steps')
# Alternative solution, use bar plots.
df.plot.bar(width=1.0)
plt.savefig('bar')
```
#### Problem description
It would be nice to be able to create a "stepped" area plot. Similar to a line(drawstyle="steps"), but with the area under the line filled in:
**df.plot.line(drawstyle="steps")**
As a work around, I have been plotting a `bar` plot with `width=1.0`:
**df.plot.bar(width=1.0)**
however this has issues of its own:
1. It is much slower with lots of data
2. Will have to limit the xaxis labels, by default it will generate a label per bar.
3. It doesn't interact well with line plots on a secondary y-axis
Alternatively, I think could manually resample the data, but it would be really nice to have similar `drawstyle='steps'`
#### Expected Output
I would expect a plot similar to this:
**df.plot.area(drawstyle="steps")**
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} | 2 | 2019-11-07T00:28:51Z | 2019-11-07T21:56:05Z | 2019-11-07T21:23:32Z | MEMBER | null | - [x] closes #23914
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
AFAICT this is strictly dominated by needs_i8_conversion | {
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] | closed | false | null | [] | null | 1 | 2019-11-07T01:39:28Z | 2019-11-07T13:05:10Z | 2019-11-07T01:41:22Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
import numpy as np
# Pandas is useful to read in Excel-files.
import pandas as pd
# matplotlib.pyplot as plotting tool
import matplotlib.pyplot as plt
# import sympy for functions and monte-carlo analysis.
from sympy import *
# Import sys and os to manipulate directories and file-names.
import sys, os
# Mathematical functions
import math
import cmath
"""
Following convention is used in the program:
- Names with small-letters without extentions is used for sympy-variables
- Capital letters are used for panda-dataframes.
- Smalle-letters followed by "_np" are used for numpy-arrays.
Next functions are used within the program.
The target of using functions:
- when parts of the program are used several times
- to keep the main program simpler
- to use the same function within different versions of the program
Folowing functions are used:
- read_data(): makes use of pandas to read excel-files
- plot_results(): plot the results in x-y diagram + required formatting
- plot_results_lin(): plot the results of linear regressing + required formatting
- tacho_sync(): function to calibrate tacho
#########################
# Functions #
#########################
"""
def read_data(full_path, sheet_name, rows, cols):
"""
Function to read part of the excel-file and to convert sheet in to dataframe.
---
full_path = name of the excel_file that contains the measurements
sheet_name = name of the excel_sheet in the excel_file
---
Column 0 contains headers
cols = nummer of columns
---
Row 0 contains names for the rows
rows = nummer of rows
The information about this function can be called through the command help(read_data)
"""
with open(full_path) as f:
Data = pd.read_excel(full_path, sheet_name, header = 0, index_col = 0, parse_cols = cols)[0:rows]
f.closed
return Data
def plot_results(x, y, Label="nolabel", xlabel="x", ylabel="y", title="Title"):
"""
Function to plot data and to format the plot
---
x = x-coordinates
y = y-coordinates
Label = legend of the plot
xlabel = label for x-coordinates
ylabel = label for x-coordinates
title = title for the plot
"""
plt.figure(1)
plt.plot(x, y, "ro-", linewidth = 2, markersize = 12, label= Label)
# Format plot
plt.legend(loc = "upper left")
#plt.axis([15, 40, 0.9, 1]) #plt.axis([xmin, xmax, ymin, ymax])
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
plt.grid()
plt.savefig(date + " Resistor-to-temp" + ".pdf")
plt.show()
def plot_results_lin(x, y, y_reg, y_min, y_max, xlabel="x", ylabel="y", title="Title"):
"""
Function to plot the results of linear regression
----
x = x-coordinates
y = y-coordinates of original data
y_reg = y-coordinates of fitted data
y_min = y-coordinates of minimum values for fitted data
y_max = y-coordinates of maximum values for fitted data
xlabel = label for x-coordinates
ylabel = label for x-coordinates
title = title for the plot
"""
plt.figure(2)
plt.plot(x, y, 'o-', label='Original data', markersize=10)
plt.plot(x, y_reg, 'r', label='Fitted line')
plt.plot(x, y_max,'b-', label='max')
plt.plot(x, y_min,'b-', label='min')
# Format plot
plt.legend(loc = "upper left")
#plt.axis([15, 40, 0.9, 1]) #plt.axis([xmin, xmax, ymin, ymax])
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
plt.grid()
plt.savefig(date + " Linear regression" + ".pdf")
plt.show()
def tacho_sync():
"""
Line regression for pmech i.f.v. slip
Ijking van tacho zodat slip = 0 voor pmech = 0
Results in IPython console shows the optimal value
"""
for x in np.linspace(53.6,53.7,10):
ut_syn = x
slip_bp = (ut_syn - ut_np)/ut_syn
pjr_bp = plu_bp_np*slip_bp
# Mechanisch vermogen
pmech_bp = (1-slip_bp)*plu_bp_np
# Linear regression pmech_bp in function of slip_bp
# return z contains slope and intercept
z = np.polyfit(slip_bp, pmech_bp, deg=1)
intercept = z[1]
# intercept should be 0 "no pmech at slip = 0"
# print result
print(ut_syn, intercept)
def plot_losses(p_bp_np, pwv_np, pfe_np, pjs_np, psu_np, pjr_np):
"""
Plot of losses of induction motor
"""
# plot results
plt.figure(3)
plt.plot(p_bp_np, pwv_np, "ro-", label = "wrijvings en ventilatie")
plt.plot(p_bp_np, pfe_np, "bo-", label = "ijzerverliezen")
plt.plot(p_bp_np, pjs_np, "go-", label = "joule-verliezen stator")
plt.plot(p_bp_np, psu_np, "cs-", label = "supplementaire-verliezen")
plt.plot(p_bp_np, pjr_np, "m^-", label = "joule-verliezen rotor")
plt.legend(loc = "upper left")
plt.savefig(date + " Verliezen_absoluut" + ".pdf")
plt.grid()
plt.show()
# Plot bar-chart with losses
tot_loss = pjs_np + pfe_np + psu_np + pjr_np + pwv_np
p_tot_loss = tot_loss/p_bp_np
p_pjs = pjs_np/tot_loss
p_pfe = pfe_np/tot_loss
p_psu = psu_np/tot_loss
p_pjr = pjr_np/tot_loss
p_pwv = pwv_np/tot_loss
ylabel = "procent loss"
title = "Loss measured for different loads"
#for x in range(5):
# plt.bar(range(5),y[:,x], 1/1.5, color="blue")
# plt.show()
ind = np.arange(rows) # the x locations for the measurements
width = 0.35 # the width of the bars: can also be len(x) sequence
plt.figure(4)
p1 = plt.bar(ind, p_pjs, width, color='r')
p2 = plt.bar(ind, p_pfe, width, color='m', bottom = p_pjs)
p3 = plt.bar(ind, p_psu, width, color='g', bottom = p_pjs+p_pfe)
p4 = plt.bar(ind, p_pjr, width, color='c', bottom = p_pjs+p_pfe+p_psu)
p5 = plt.bar(ind, p_pwv, width, color='b', bottom = p_pjs+p_pfe+p_psu+p_pjr)
plt.ylabel("procent loss")
plt.title("Loss measured for different loads")
plt.xticks(ind, ('M1', 'M2', 'M3', 'M4', 'M5', 'M6'))
plt.yticks(np.arange(0, 1.1, 0.1))
plt.legend((p1[0], p2[0], p3[0], p4[0], p5[0]), ('pjs','pfe', 'psu', 'pjr', 'pwv'), loc = 'center left')
plt.savefig(date + " Verliezen_procentueel_bar_chart" + ".pdf")
plt.grid()
plt.show()
plt.figure(5)
p1 = plt.bar(ind, pjs_np, width, color='r')
p2 = plt.bar(ind, pfe_np, width, color='m', bottom = pjs_np)
p3 = plt.bar(ind, psu_np, width, color='g', bottom = pjs_np+pfe_np)
p4 = plt.bar(ind, pjr_np, width, color='c', bottom = pjs_np+pfe_np+psu_np)
p5 = plt.bar(ind, pwv_np, width, color='b', bottom = pjs_np+pfe_np+psu_np+pjr_np)
plt.ylabel("Loss in Watt")
plt.title("Loss measured for different loads")
plt.xticks(ind, ('M1', 'M2', 'M3', 'M4', 'M5', 'M6'))
plt.yticks(np.arange(0, 1000, 100))
plt.legend((p1[0], p2[0], p3[0], p4[0], p5[0]), ('pjs','pfe', 'psu', 'pjr', 'pwv'), loc = 'upper left')
plt.savefig(date + " Verliezen_absoluut_bar_chart" + ".pdf")
plt.grid()
plt.show()
"""
#################
# Main program #
#################
#############################################################
# 1.0 Calculation of temperature based on resistor measurements
#############################################################
# 1.1 Read data from excel-file
#--------------------------
"""
# folder path of current script
# Function to determine the working_directory
folder = os.path.dirname(sys.argv[0])
# Filename of excel file with the measured data
file_name = r"/Rendementsmeting.xls"
# Full path of excel file
full_path = folder + file_name
# Date for output files
date = "8-sept-2019"
# Excel-sheet with resistor-values
sheet_name = "Stator R"
rows = 5
cols = 3
# R_ST is a panda-Dataframe
R_ST = read_data(full_path, sheet_name, rows, cols)
# Extract the numpy-arrays from the panda dataframe
# ur_np numpy variable
ur_np = R_ST["U"].values
# ir_np numpy variable
ir_np = R_ST["I"].values
"""
# 1.2 Calculate the stator resistor: 3 resistors are measured in serie
#----------------------------------------------------------
"""
r_DR_np = ur_np/ir_np/3 # r_DR_np is resistor in triangle-connection
# Triangle to star transformation
rst_np = r_DR_np/3 # rst_np is resistor in star-connection
# Calculate temperature
#-----------------------------
# First measurment "t1" is at 20°C (room-temperature)
# Other measurements are performed at t2 = Rt2/Rt1*(t1+k) - k
# k for copper = 234,5 °C
t_20 = 20
k = 234.5
rst_20 = rst_np[0] # First measurment is at 20 °C
# calculate temperature corresponding with different resistor values
temp_np = rst_np / rst_20 * (t_20 + k) - k
"""
# 1.3 Plot temp
#-----------
"""
xlabel = "Temp [°C]"
ylabel = "rst [Ohm]"
title = "Measurement of stator resistor"
Label = R_ST.comment # Column of the Excel-file
plot_results(temp_np, rst_np, Label, xlabel, ylabel, title)
"""
# 1.4 Define function for rst (temp)
# ----------------------------------
"""
# Function to calculate the stator-resistor at other temperature then
# the temperature used in the measurements.
# Define the function in sympy for the sympy-variable temp
temp = symbols("temp")
rst = rst_20 * (temp + k)/(20 + k)
# convert the function for numpy-arrays
f_rst = lambdify(temp, rst, "numpy") # function for numpy-calculations
"""
##############################
# 2.0 Zero-load condition #
##############################
# 2.1 Read excel-file
#--------------------
"""
sheet_name = "Zero-load"
rows = 6
cols = 4
NL = read_data(full_path, sheet_name, rows, cols)
# Read data from dataframe
# Line-voltage
ul_nl_np=np.array(NL["U"].values,dtype=np.float32)
#ul_nl_np = NL["U"].values
# Phase-voltage (Star connection)
up_nl_np = ul_nl_np / 3**0.5
# Line current
i_nl_np = np.array(NL["I"].values,dtype=np.float32)
wnl1_np = np.array(NL["W1"].values,dtype=np.float32)
wnl2_np = np.array(NL["W2"].values,dtype=np.float32)
pel_nl_np = wnl1_np + wnl2_np
"""
# 2.2 Calculate losses for zero-load conditions
# ---------------------------------------------
"""
# Joule-verliezen in stator_weerstand
pjs_nl_np = 3 * f_rst(36) * i_nl_np**2
# Verliezen = gemeten vermogen - Joule-verliezen
pve_nl_np = pel_nl_np - pjs_nl_np
"""
# 2.3 Linear regression verliezen in functie van U**2
#------------------------------------------------
"""
# Voltage at de air-gap is smaller then the applied voltage
cosp_nl_np = (pel_nl_np / 3) / (up_nl_np * i_nl_np)
phi_nl_np = np.arccos(cosp_nl_np) # Angle is given in radians
phi_deg_nl_np = phi_nl_np / math.pi * 180 # in degrees
e1_nl_np = up_nl_np - f_rst(36) * i_nl_np * cosp_nl_np
x = e1_nl_np**2
y = pve_nl_np
z = np.polyfit(x,y,deg=1)
# z contains the slope and the intercept
slope_lg = z[0]
pwv_lg = z[1]
# intercept at zero volt is the friction loss and ventilation loss
"""
Remark the value of pwv_lg must be devided by 2
The measured value is due to the motor and the load on the same shaft.
"""
# Helft van verliezen te wijten aan DC-motor
pwv_cst = pwv_lg/2
"""
# 2.4 Plot results of linear regression
#--------------------------------------
"""
# Define function based on the result of the linear regression stored in z
# pverlies = slope * x + intercept
p_verlies = np.poly1d(z)
err_lg_np = pve_nl_np - p_verlies(e1_nl_np**2)
mean_err_nl = np.mean(err_lg_np) # should be very small
print("Mean of Error of linear regression = ", mean_err_nl)
std_err_nl = np.std(err_lg_np) # should be very small
print("Standard deviation of Error of linear regression = ", std_err_nl)
reg_line_np = p_verlies(e1_nl_np**2)
max_line_np = p_verlies(e1_nl_np**2) + 3 * std_err_nl # array in numpy
min_line_np = p_verlies(e1_nl_np**2) - 3 * std_err_nl # array in numpy
xlabel = "sqrt U [V**2]"
ylabel = "nullast verlies [W]"
title = "Nullast verlies"
plot_results_lin(x, y, reg_line_np, max_line_np, min_line_np, xlabel, ylabel, title)
"""
# 2.5 Components of equivalent schematic
#-------------------------------------------
"""
r_mag_np = e1_nl_np**2 / (pve_nl_np / 3)
sve_nl_np = 3 * e1_nl_np * i_nl_np # Apparant power
qve_nl_np = (sve_nl_np**2 - pve_nl_np**2)**0.5 # Reactive power
x_mag_np = e1_nl_np**2 / qve_nl_np
# Components of equivalent schematic based on complex numbers
# cos-phi at the input
# complex current referred to the voltage
i_nl_cn = i_nl_np * np.cos(phi_nl_np) - i_nl_np * np.sin(phi_nl_np) * 1j
# complex voltage after the stator resistor
e1_nl_cn = up_nl_np - f_rst(36) * i_nl_cn
# Admitance
y_cn = i_nl_cn / e1_nl_cn
rm_np = 1 / np.real(y_cn)
# Resistor includes friction and ventilation losses.
xm_np = - 1 / np.imag(y_cn)
# Inductance includes leakage inductance of the stator
"""
# 2.6 Sympy functions for the Monte-Carlo analyses
#-------------------------------------------------
We will build some functions in sympy for the Monte-Carlo analysis.
Following steps:
a. Define the basic variables = measurements variables.
b. Define functions in Sympy to calculate the losses.
(Sympy will determin a global function out of the different sub-functions.)
c. Convert the global function to numpy with the command lambdify.
d. Define a numpy array (random errors) for all basic variables.
e. Monte-Carlo: Apply the functions on all numpy arrays.
"""
"""
# a. Define the basic variables = measurements variables.
"""
ul_nl, i_nl, wnl1, wnl2 = symbols("ul_nl i_nl wnl1 wnl2")
"""
# b. Define functions in Sympy to calculate the losses.
"""
pel_nl = wnl1 + wnl2
# Function for rst is defined under resistor measuremnts
# rst = rst_20 * (temp + k)/(20 + k) with temp as variable
pve_nl = pel_nl - 3 * i_nl**2 * rst
# Sympy-function for pwv_nl
up_nl = ul_nl / 3**0.5
cosp_nl = (pel_nl / 3) / (up_nl * i_nl)
e1_nl = up_nl - rst * i_nl * cosp_nl
pwv_nl = pve_nl - slope_lg * e1_nl**2
# Sympy-function for pfe_nl
pfe_nl = pve_nl - pwv_nl
"""
# c. Convert the global function to numpy with the command lambdify.
"""
f_pwv = lambdify((ul_nl, i_nl, wnl1, wnl2, temp), pwv_nl, "numpy")
f_pfe = lambdify((ul_nl, i_nl, wnl1, wnl2, temp), pfe_nl, "numpy")
"""
# d. Define a numpy array (random errors) for all basic variables.
"""
N = 1000 # Defines the size of the numpy-arrays used for MC.
m_ul_nl = ul_nl_np[-1] # Mean value = Last voltage of the numpy-array ul_nl_np
st_ul_nl = 0.005 * m_ul_nl # Standard deviation = total-accuracy = 2.6 * 0.01
ul_nl_mc = np.random.normal(loc = m_ul_nl, scale = st_ul_nl, size = N)
m_i_nl = i_nl_np[-1] # Mean value = Last voltage of the numpy-array i_nl_np
st_i_nl = 0.005 * m_i_nl # Standard deviation = total-accuracy = 2.6 * 0.01
i_nl_mc = np.random.normal(loc = m_i_nl, scale = st_i_nl, size = N)
m_wnl1 = wnl1_np[-1] # Mean value = Last voltage of the numpy-array i_nl_np
st_wnl1 = 0.01 * m_wnl1 # Standard deviation = total-accuracy = 2.6 * 0.01
wnl1_mc = np.random.normal(loc = m_wnl1, scale = st_wnl1, size = N)
m_wnl2 = wnl2_np[-1] # Mean value = Last voltage of the numpy-array i_nl_np
st_wnl2 = 0.01 * abs(m_wnl2) # Standard deviation = total-accuracy = 2.6 * 0.01
wnl2_mc = np.random.normal(loc = m_wnl2, scale = st_wnl2, size = N)
d_temp = (temp_np[1] - temp_np[2]) / 2 # Half of the delta_t between zero load and warm
m_temp = temp_np[1] + d_temp # Mean value = temperature measured after the zero load.
temp_mc = np.random.uniform(low = m_temp - d_temp, high = m_temp + d_temp, size = N)
"""
# e. Apply the function on all numpy arrays.
"""
pwv_mc = f_pwv(ul_nl_mc, i_nl_mc, wnl1_mc, wnl2_mc, temp_mc)
pfe_mc = f_pfe(ul_nl_mc, i_nl_mc, wnl1_mc, wnl2_mc, temp_mc)
"""
# 2.6 Plot the normal distribution and calculate mean and standard deviation.
#----------------------------------------------------------------------------
"""
m_pwv = np.mean(pwv_mc)
st_pwv = np.std(pwv_mc)
plt.figure(6)
n, bins, _ = plt.hist(pwv_mc,
bins = np.linspace(m_pwv-3*st_pwv, m_pwv+3*st_pwv, int(N/10)),
normed=True, histtype=u'step')
plt.savefig(date + "monte_carlo pwv" + ".pdf")
plt.grid()
plt.show()
print("Mean value for pwv = ", m_pwv)
print("Standard deviation for pmv = ", st_pwv)
m_pfe = np.mean(pfe_mc)
st_pfe = np.std(pfe_mc)
plt.figure(7)
n, bins, _ = plt.hist(pfe_mc,
bins = np.linspace(m_pfe-3*st_pfe, m_pfe+3*st_pfe, int(N/10)),
normed=True, histtype=u'step')
plt.savefig(date + " monte_carlo pfe" + ".pdf")
plt.grid()
plt.show()
print("Mean value for pfe = ", m_pfe)
print("Standard deviation for pfe = ", st_pfe)
"""
#### Problem description
It should create pdf files with graphs but it returns an error
#### Expected Output
graph output of monte carlo analysis
#### Output of ``pd.show_versions()``
File "C:\Users\User\Anaconda3\lib\site-packages\pandas\io\excel\_base.py", line 306, in read_excel
"read_excel() got an unexpected keyword argument " "`{}`".format(arg)
TypeError: read_excel() got an unexpected keyword argument `parse_cols`
[paste the output of ``pd.show_versions()`` here below this line]
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} | 5 | 2019-11-07T01:45:46Z | 2019-11-08T19:57:37Z | 2019-11-08T19:57:37Z | CONTRIBUTOR | null | - [x] xref #16979
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2019-11-07T02:12:32Z | 2019-11-08T04:17:35Z | 2019-11-08T04:17:29Z | CONTRIBUTOR | null | - [x] closes #20888
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 8 | 2019-11-07T05:18:19Z | 2019-11-13T12:12:10Z | 2019-11-13T00:39:19Z | MEMBER | null | @simonjayhawkins mypy is still giving a couple of complaints I could use your help sorting out:
```
pandas/core/groupby/ops.py:791: error: Signature of "groupings" incompatible with supertype "BaseGrouper"
pandas/core/groupby/ops.py:872: error: Argument 1 of "_chop" is incompatible with supertype "DataSplitter"; supertype defines the argument type as "NDFrame"
pandas/core/groupby/ops.py:884: error: Argument 1 of "_chop" is incompatible with supertype "DataSplitter"; supertype defines the argument type as "NDFrame"
```
For the groupings complaint, AFAICT the attribute has the same annotation, but in the subclass its a property instead of defined in `__init__`. For the other two, I annotated an argument with `NDFrame` in the base class and overrode with `Series` and `DataFrame` in the subclasses. What is the preferred idiom for this pattern? | {
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} | 11 | 2019-11-07T05:24:40Z | 2019-11-08T15:19:15Z | 2019-11-08T14:37:45Z | MEMBER | null | It is mostly redundant with `is_extension_array_dtype`, and having both is confusing.
xref #23179. | {
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} | 1 | 2019-11-07T07:33:28Z | 2019-11-08T14:58:58Z | 2019-11-08T08:54:52Z | CONTRIBUTOR | null | Add types + make some minor cleanups. | {
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... | open | false | null | [] | null | 3 | 2019-11-07T10:01:36Z | 2021-07-22T06:03:47Z | null | NONE | null | #### Code Sample
```python
import pandas as pd
import matplotlib.pyplot as plt
from io import StringIO
csv_string = """x,y1,y2
0, 1, 1
1, 1, 2
2, 1, 3
3, 4, 2
4, 2, 4
5, 3, 3
"""
data = pd.read_csv(StringIO(csv_string))
dataframe = pd.DataFrame(data)
dataframe.plot(x="x",
y=["y1", "y2"],
kind="line",
subplots=True,
sharex=True,
marker=",",
linewidth=0.5,
title="Title"
)
plt.show()
```
#### Problem description
The title overlaps the subplot:
Also the xticks should not be rotated, but I will create a new issue for that.
#### Expected Output
The title shouldn't overlap the subplot.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.2.final.0
python-bits : 32
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0
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 : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
import sys; print('Python %s on %s' % (sys.version, sys.platform))
Python 3.7.2 (tags/v3.7.2:9a3ffc0492, Dec 23 2018, 22:20:52) [MSC v.1916 32 bit (Intel)] on win32
</details>
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} | 1 | 2019-11-07T10:04:50Z | 2020-09-13T20:46:09Z | 2020-09-13T20:46:09Z | NONE | null | #### Code Sample
```python
import pandas as pd
import matplotlib.pyplot as plt
from io import StringIO
csv_string = """x,y1,y2
0, 1, 1
1, 1, 2
2, 1, 3
3, 4, 2
4, 2, 4
5, 3, 3
"""
data = pd.read_csv(StringIO(csv_string))
dataframe = pd.DataFrame(data)
dataframe.plot(x="x",
y=["y1", "y2"],
kind="line",
subplots=True,
sharex=True,
marker=",",
linewidth=0.5,
title="Title"
)
plt.show()
```
#### Problem description
The xticks are unnecessarily rotated:
Also the title overlaps the subplot, but I will create a new issue for that.
#### Expected Output
The xticks shouldn't be rotated.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.2.final.0
python-bits : 32
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0
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 : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
import sys; print('Python %s on %s' % (sys.version, sys.platform))
Python 3.7.2 (tags/v3.7.2:9a3ffc0492, Dec 23 2018, 22:20:52) [MSC v.1916 32 bit (Intel)] on win32
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29461 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29461/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29461/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29461/events | https://github.com/pandas-dev/pandas/issues/29461 | 519,286,505 | MDU6SXNzdWU1MTkyODY1MDU= | 29,461 | Timestamp.strftime(): missing support for nanoseconds | {
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... | open | false | null | [] | null | 4 | 2019-11-07T14:04:55Z | 2020-11-03T20:28:04Z | null | CONTRIBUTOR | null | When parsing text into a `Timestamp` object we can specify a format string. Currently `%f` is documented with
> note that "%f" will parse all the way up to nanoseconds
See https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html, in particular the description of the `format` parameter. The note about `%f` was added in this patch: https://github.com/pandas-dev/pandas/pull/8904
The fact that we can parse text using nanosecond precision is great, and here I make use of that behavior (showing two methods yielding the same result):
```
# Implicit format:
>>> t = pd.to_datetime('2019-10-03T09:30:12.133333337')
>>> t
Timestamp('2019-10-03 09:30:12.133333337')
# Explicit format using %f:
>>> t = pd.to_datetime('2019-10-03T09:30:12.133333337', format='%Y-%m-%dT%H:%M:%S.%f')
>>> t
Timestamp('2019-10-03 09:30:12.133333337')
````
But when I now want to invert that process using `strftime()` then the fractional part is truncated to microsecond precision:
```
>>> t.strftime('%Y-%m-%dT%H:%M:%S.%f')
'2019-10-03T09:30:12.133333'
```
On the one hand this is inconsistent with the meaning of `%f` while parsing. On the other hand it corresponds to what's documented in Python's stdlib documentation (which says that `%f` means "Microsecond as a decimal number, zero-padded on the left.").
In any case, I think it would make sense to have a format string specifier that allows us to turn the timestamp into a string with nanosecond precision.
If I am not mistaken, we otherwise have to work around the absence of that format specifier by using the `nanosecond` property:
```
>>> t.nanosecond
337
>>> t.strftime('%Y-%m-%dT%H:%M:%S.%f') + str(t.nanosecond)
'2019-10-03T09:30:12.133333337'
```
Do you agree that we should have a format specifier for that? Or do we have one, and it's just not documented?
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.7-200.fc30.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.2
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 19.0.3
setuptools : 40.8.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : 4.41.3
sphinx : 2.2.0
blosc : 1.8.1
feather : None
xlsxwriter : 1.2.2
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.1
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 : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : 0.3.5
scipy : 1.3.1
sqlalchemy : 1.3.10
tables : 3.6.0
xarray : 0.14.0
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.2
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... | open | false | null | [] | null | 2 | 2019-11-07T14:52:03Z | 2021-07-23T03:58:19Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
test_json = [{"_id": 'a', 'date': datetime.now()}, {"_id": 'b', 'date': datetime.now()}]
test_df = pd.DataFrame(test_json)
new_df = test_df.copy()
new_df["date"] = None
new_df.update(test_df)
print(test_df.head())
print(new_df.head())
```
#### Problem description
When using update function with datetime data, it is automatically converted to timestamp, which for me it seems like an abnormal behaviour. Code from above would output
_id date
0 a 2019-11-07 15:50:06.072158
1 b 2019-11-07 15:50:06.072158
_id date
0 a 1573141806072158000
1 b 1573141806072158000
#### Expected Output
_id date
0 a 2019-11-07 15:50:06.072158
1 b 2019-11-07 15:50:06.072158
_id date
0 a 2019-11-07 15:50:06.072158
1 b 2019-11-07 15:50:06.072158
#### 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 : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.13
pytest : None
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
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 : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
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} | 6 | 2019-11-07T14:54:00Z | 2020-05-20T05:40:36Z | 2020-05-20T05:40:36Z | NONE | null | ## Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({
'val': range(10),
'time': pd.date_range(start='2019-01-01', freq='1d', periods=10, tz='UTC')
})
# This works.
df.query('"2019-01-03 00:00:00+00" < time')
# This raises an exception.
df.set_index('time').query('"2019-01-03 00:00:00+00" < time')
# Perhaps interestingly, this works.
df.set_index('time').reset_index().query('"2019-01-03 00:00:00+00" < time')
```
#### Problem description
query shouldn't behave differently when the thing being queried is an index vs a column. Also, the error just seems objectively incorrect, as both values are tz-aware to start with (perhaps it's getting lost along the way).
May be related to #26236 , but the repro is different.
#### Expected Output
Not an exception.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.3.5-smp-821.23.0.0
machine: x86_64
processor:
byteorder: little
LC_ALL: en_US.UTF-8
LANG: None
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: None
pip: None
setuptools: unknown
Cython: None
numpy: 1.16.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 2.0.0
sphinx: None
patsy: 0.4.1
dateutil: 2.8.0
pytz: 2019.3
blosc: None
bottleneck: None
tables: 3.5.2
numexpr: 2.6.10dev0
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.3
s3fs: None
fastparquet: None
pandas_gbq: 0+unknown
pandas_datareader: None
gcsfs: None
</details>
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... | closed | false | null | [] | null | 5 | 2019-11-07T15:29:45Z | 2019-12-01T01:02:30Z | 2019-12-01T01:02:29Z | NONE | null | botocore has pinned dateutil to <=2.8.0, which conflicts with our dateutil here and leads to
```
ERROR: botocore 1.13.12 has requirement python-dateutil<2.8.1,>=2.1; python_version >= "2.7", but you'll have python-dateutil 2.8.1 which is incompatible.
```
See https://github.com/boto/botocore/commit/e87e7a745fd972815b235a9ee685232745aa94f9
Closes #29465 | {
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] | closed | false | null | [] | null | 1 | 2019-11-07T15:32:51Z | 2019-11-07T15:45:16Z | 2019-11-07T15:45:16Z | NONE | null | #### Problem description
Trying to install `requirements-dev.txt` fails with
```
ERROR: botocore 1.13.12 has requirement python-dateutil<2.8.1,>=2.1; python_version >= "2.7", but you'll have python-dateutil 2.8.1 which is incompatible.
```
because of https://github.com/boto/botocore/commit/e87e7a745fd972815b235a9ee685232745aa94f9
I suspect CI/CD pipelines are still okay because it caches dependencies.
#### Expected Output
Installation successful | {
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} | 12 | 2019-11-07T15:58:25Z | 2020-01-06T16:47:00Z | 2019-11-08T15:49:14Z | CONTRIBUTOR | null | chunk of #28602 | {
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} | 1 | 2019-11-07T18:16:09Z | 2019-11-07T21:07:21Z | 2019-11-07T21:01:52Z | MEMBER | null | Operation: Get This Over With | {
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} | [] | closed | false | null | [] | null | 0 | 2019-11-07T18:24:54Z | 2019-11-09T09:27:45Z | 2019-11-09T09:27:45Z | MEMBER | null | In the panda's [docstring style guide](https://dev.pandas.io/docs/development/contributing_docstring.html#docstring), there's no reference about the "Raises" section.
I do see the "Raises" section appear hear and there in the codebase, and wondered if it appeared in the right order(i.g "Returns" before "Parameters"), when came to realize that there's no explanation about it.
Just want to hear feedback about the idea. | {
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```
In [4]: mi_med = pd.MultiIndex.from_product(
...: [np.arange(1000), np.arange(10), list("A")], names=["one", "two", "three"]
...: )
In [5]: %timeit mi_med.get_loc((999, 9, "A"))
master --> 42.6 µs ± 411 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
PR --> 11.4 µs ± 120 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
```
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} | 3 | 2019-11-07T19:26:23Z | 2019-11-08T04:41:11Z | 2019-11-08T04:41:07Z | CONTRIBUTOR | null | Rename ``BaseGrouper.recons_codes`` to ``BaseGrouper.reconstructed_codes``. This is an internal attribute, so ok to rename.
Also adds some typing. | {
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I'm thinking on giving it a try for the documentation build, and see if we can get an improvement on current CI problems:
- Too many steps from the PR page to the logs (#26895)
- Notify with a comment on the PR when there are problems (#26930)
- Publish the documentation for a PR so it can be seen
Any objection? Depending on how things go with the doc build, I think it may make sense to give other builds a try to. | {
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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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} | 6 | 2019-11-07T23:50:22Z | 2019-11-08T15:18:41Z | 2019-11-08T14:39:32Z | MEMBER | null | The class hierarchy of in/around groupby is really tough, this simplifies it a little bit | {
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} | 7 | 2019-11-08T03:10:04Z | 2020-05-30T16:32:52Z | 2020-05-30T16:32:52Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
Should to_parquet without file behave the same way than to_json in which if no file is specified we return the buffer? | {
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} | 2 | 2019-11-08T04:21:41Z | 2019-11-13T00:02:06Z | 2019-11-12T23:44:19Z | MEMBER | null | make `apply_broadcast` have consistent signature.
After this I plan to do a pass to make these classes much less stateful | {
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} | 3 | 2019-11-08T06:53:28Z | 2019-11-29T17:51:05Z | 2019-11-29T17:51:05Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas as pd
from datetime import datetime
if __name__ == '__main__':
s = pd.Series([datetime.now()])
with pd.option_context('use_inf_as_na', True):
s.count()
```
#### Problem description
Previously with pandas v0.24.1, the above code works well,
however, after I upgrade the version to v0.25.3,
the above code raises an exception:
`AttributeError: 'DatetimeArray' object has no attribute '_constructor'`
It seems that something goes wrong when passing a `DatetimeArray` to `_isna_old`.
I think it is a bug introduced in some recent update.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 18.1
setuptools : 40.6.2
Cython : None
pytest : 4.3.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
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 : 0.12.1
pytables : None
s3fs : None
scipy : 1.2.1
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/29479 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29479/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29479/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29479/events | https://github.com/pandas-dev/pandas/issues/29479 | 519,875,180 | MDU6SXNzdWU1MTk4NzUxODA= | 29,479 | df.explode is required for reset_index | {
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} | [] | closed | false | null | [] | null | 2 | 2019-11-08T08:19:01Z | 2019-11-08T09:17:52Z | 2019-11-08T09:17:52Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
A = pd.DataFrame({'x': [['hoge', 'piyo'], ['fuga']], 'y': ['1', '2']})
B = pd.DataFrame({'x': [['hoge2', 'piyo2'], ['fuga2'], ['meta2']], 'y': ['1', '2', '3']})
A = A.append(B)
```
```
# >>> print(A)
x y
0 [hoge, piyo] 1
1 [fuga] 2
0 [hoge2, piyo2] 1
1 [fuga2] 2
2 [meta2] 3
```
```
# >>> A.explode('x')
x y
0 hoge 1
0 piyo 1
0 hoge2 1
0 piyo2 1
0 hoge 1
0 piyo 1
0 hoge2 1
0 piyo2 1
1 fuga 2
1 fuga2 2
1 fuga 2
1 fuga2 2
2 meta2 3
```
#### Problem description
The ideal of `A.explode('x')` output is following.
```
x y
hoge 1
piyo 1
fuga 2
hoge2 1
piyo2 1
fuga2 2
meta2 3
```
But, The actual output is enlarged because the index of `DataFrame A`.
This will cause mistakes for users of explode method.
#### Expected Output
The solution is to do a `reset_index` before the `explode`.
```
# >>> A.reset_index(drop=True).explode('x')
x y
0 hoge 1
0 piyo 1
1 fuga 2
2 hoge2 1
2 piyo2 1
3 fuga2 2
4 meta2 3
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : ja_JP.UTF-8
LOCALE : ja_JP.UTF-8
pandas : 0.25.3
numpy : 1.16.4
pytz : 2018.9
dateutil : 2.7.5
pip : 19.2.3
setuptools : 41.0.1
Cython : 0.29.4
pytest : 4.4.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.3
html5lib : None
pymysql : None
psycopg2 : 2.8.1 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.4.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fastparquet : None
gcsfs : 0.3.0+7.g59da8cd
lxml.etree : 4.3.3
matplotlib : 3.0.3
numexpr : None
odfpy : None
openpyxl : 2.6.0
pandas_gbq : 0.10.0
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.4
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
Sorry for ugly English. Thanks.
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] | closed | false | null | [] | null | 7 | 2019-11-08T10:24:34Z | 2019-12-02T14:20:23Z | 2019-12-02T14:20:22Z | MEMBER | null | @WillAyd
I think we should discuss reverting #28173 and only use `TypeVar("FrameOrSeries", bound="NDFrame")` in `core.generic`. perhaps call it `_NDFrameT` to avoid confusion.
This PR is to show the changes required to keep mypy green if we wanted to revert.
It also reverts the change in #29458, see https://github.com/pandas-dev/pandas/pull/29458#discussion_r343733263
`_GroupBy` is defined as a generic class, but not sure about `Grouping`
I've created it as a draft PR, since I think we should wait for more issues to surface going forward to help decide. The number of changes needed at this stage is not significant enough to warrant a hasty decision. | {
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} | 14 | 2019-11-08T12:13:18Z | 2020-11-20T13:05:48Z | 2020-11-20T13:00:32Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame(index=np.arange(10), columns=np.arange(5), dtype=float)
df =
0 1 2 3 4
0 NaN NaN NaN NaN NaN
1 NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN
5 NaN NaN NaN NaN NaN
6 NaN NaN NaN NaN NaN
7 NaN NaN NaN NaN NaN
8 NaN NaN NaN NaN NaN
9 NaN NaN NaN NaN NaN
df.groupby(pd.Series(['a', 'a', 'b', 'b', 'b']), axis=1).agg('sum', skipna=True)
```
#### Problem description
The above call to `agg` gives
```python
KeyError: 'a'
```
This is, because here:
https://github.com/pandas-dev/pandas/blob/67ee16a283b826b4adb07e5ca64a5206d242acaf/pandas/core/groupby/groupby.py#L1376
we are trying to access a new column name (`'a'`) in the original DataFrame.
It only occurs, when no `_cython_agg_general` is possible, e.g., when keyword argument `skipna` is given to `agg`. Without `skipna` argument the expected output below will be produced.
#### Expected Output
```
df =
a b
0 0.0 0.0
1 0.0 0.0
2 0.0 0.0
3 0.0 0.0
4 0.0 0.0
5 0.0 0.0
6 0.0 0.0
7 0.0 0.0
8 0.0 0.0
9 0.0 0.0
```
#### Output of ``pd.show_versions()``
<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 42 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.4
pytz : 2018.9
dateutil : 2.8.0
pip : 19.2.3
setuptools : 40.8.0
Cython : 0.29.7
pytest : 4.3.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.1
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.1
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
```
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29482 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29482/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29482/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29482/events | https://github.com/pandas-dev/pandas/issues/29482 | 520,002,936 | MDU6SXNzdWU1MjAwMDI5MzY= | 29,482 | Dataframe assignment breaks Timestamp series (possibly due to rounding) | {
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] | closed | false | null | [] | null | 3 | 2019-11-08T13:02:10Z | 2021-01-18T17:06:08Z | 2021-01-18T17:06:07Z | NONE | null | #### Code Sample
```python
import pandas as pd
print(pd.__version__)
df = pd.DataFrame({
't': [pd.Timestamp('2017-05-22 22:28:35.489689'),
pd.Timestamp('2017-05-22 22:28:36.086933'),
pd.Timestamp('2017-05-22 22:28:37.000001')]
})
print(df)
print()
mask = [True, True, True]
t = df['t'].values
print(t)
print()
df.loc[mask, 't'] = t
print(df)
print()
```
#### Problem description
Upgrade from Pandas 0.24.1 to 0.25.3 causes a bug in how a Timestamp series/ndarray is handled. The bug seems to be present at least since 0.25.1.
The code above takes a subset of dataframe column and stores it as separate series or ndarray. After the assignment back to the dataframe, nanosecond precision is broken as shows the script output.
```
0.25.3
t
0 2017-05-22 22:28:35.489689
1 2017-05-22 22:28:36.086933
2 2017-05-22 22:28:37.000001
['2017-05-22T22:28:35.489689000' '2017-05-22T22:28:36.086933000'
'2017-05-22T22:28:37.000001000']
t
0 2017-05-22 22:28:35.489689088
1 2017-05-22 22:28:36.086932992
2 2017-05-22 22:28:37.000001024
```
#### Expected Output
The "made up" nanoseconds are not random, hence it seems to be some kind of rounding. I have tried to provide as-simple-as-possible case based on our code that combines further data handling.
We do use nanoseconds, but sometimes Timestamp is rounded to microseconds and we expect Pandas to keep it so.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.113-el6.x86_64.lime.1
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.14.1
pytz : 2019.3
dateutil : 2.8.0
pip : 18.1
setuptools : 39.2.0
Cython : 0.29.6
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.2.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.4.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.2.1
matplotlib : 2.2.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 5 | 2019-11-08T13:42:47Z | 2019-11-19T15:19:03Z | 2019-11-19T15:17:45Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/issues/20612
This implements the `__from_arrow__` method for integer/string extension types, so roundtripping to arrow/parquet fully works. | {
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} | 2 | 2019-11-08T13:54:48Z | 2021-07-23T04:15:39Z | null | NONE | null | Hi,
Im having a problem using pandas.plot.hist with the bins argument set to 'auto'.
I'm on os x, with the following package versions installed:
pandas: '0.25.3'
numpy: '1.17.2'
matplotlib: '3.1.1'
This error happens if I'm in jupyterlab, notebook, python or ipython.
#### Here's what produces the error:
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
test_data = np.random.randint(0, 100, 10000)
dataset = pd.DataFrame({'test': test_data})
dataset.plot.hist(bins='auto')
```
#### Output of the above code:
<details>
ValueError Traceback (most recent call last)
<ipython-input-16-520a066d2367> in <module>
6 dataset = pd.DataFrame({'test': test_data})
7
----> 8 dataset.plot.hist(bins='auto')
~/anaconda3/envs/default/lib/python3.7/site-packages/pandas/plotting/_core.py in hist(self, by, bins, **kwargs)
1123 >>> ax = df.plot.hist(bins=12, alpha=0.5)
1124 """
-> 1125 return self(kind="hist", by=by, bins=bins, **kwargs)
1126
1127 def kde(self, bw_method=None, ind=None, **kwargs):
~/anaconda3/envs/default/lib/python3.7/site-packages/pandas/plotting/_core.py in __call__(self, *args, **kwargs)
792 data.columns = label_name
793
--> 794 return plot_backend.plot(data, kind=kind, **kwargs)
795
796 def line(self, x=None, y=None, **kwargs):
~/anaconda3/envs/default/lib/python3.7/site-packages/pandas/plotting/_matplotlib/__init__.py in plot(data, kind, **kwargs)
60 kwargs["ax"] = getattr(ax, "left_ax", ax)
61 plot_obj = PLOT_CLASSES[kind](data, **kwargs)
---> 62 plot_obj.generate()
63 plot_obj.draw()
64 return plot_obj.result
~/anaconda3/envs/default/lib/python3.7/site-packages/pandas/plotting/_matplotlib/core.py in generate(self)
279 self._compute_plot_data()
280 self._setup_subplots()
--> 281 self._make_plot()
282 self._add_table()
283 self._make_legend()
~/anaconda3/envs/default/lib/python3.7/site-packages/pandas/plotting/_matplotlib/hist.py in _make_plot(self)
81
82 kwds = self._make_plot_keywords(kwds, y)
---> 83 artists = self._plot(ax, y, column_num=i, stacking_id=stacking_id, **kwds)
84 self._add_legend_handle(artists[0], label, index=i)
85
~/anaconda3/envs/default/lib/python3.7/site-packages/pandas/plotting/_matplotlib/hist.py in _plot(cls, ax, y, style, bins, bottom, column_num, stacking_id, **kwds)
60 bottom = bottom + cls._get_stacked_values(ax, stacking_id, base, kwds["label"])
61 # ignore style
---> 62 n, bins, patches = ax.hist(y, bins=bins, bottom=bottom, **kwds)
63 cls._update_stacker(ax, stacking_id, n)
64 return patches
~/anaconda3/envs/default/lib/python3.7/site-packages/matplotlib/__init__.py in inner(ax, data, *args, **kwargs)
1599 def inner(ax, *args, data=None, **kwargs):
1600 if data is None:
-> 1601 return func(ax, *map(sanitize_sequence, args), **kwargs)
1602
1603 bound = new_sig.bind(ax, *args, **kwargs)
~/anaconda3/envs/default/lib/python3.7/site-packages/matplotlib/axes/_axes.py in hist(self, x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, normed, **kwargs)
6841 patch = _barfunc(bins[:-1]+boffset, height, width,
6842 align='center', log=log,
-> 6843 color=c, **{bottom_kwarg: bottom})
6844 patches.append(patch)
6845 if stacked:
~/anaconda3/envs/default/lib/python3.7/site-packages/matplotlib/__init__.py in inner(ax, data, *args, **kwargs)
1599 def inner(ax, *args, data=None, **kwargs):
1600 if data is None:
-> 1601 return func(ax, *map(sanitize_sequence, args), **kwargs)
1602
1603 bound = new_sig.bind(ax, *args, **kwargs)
~/anaconda3/envs/default/lib/python3.7/site-packages/matplotlib/axes/_axes.py in bar(self, x, height, width, bottom, align, **kwargs)
2373 x, height, width, y, linewidth = np.broadcast_arrays(
2374 # Make args iterable too.
-> 2375 np.atleast_1d(x), height, width, y, linewidth)
2376
2377 # Now that units have been converted, set the tick locations.
<__array_function__ internals> in broadcast_arrays(*args, **kwargs)
~/anaconda3/envs/default/lib/python3.7/site-packages/numpy/lib/stride_tricks.py in broadcast_arrays(*args, **kwargs)
262 args = [np.array(_m, copy=False, subok=subok) for _m in args]
263
--> 264 shape = _broadcast_shape(*args)
265
266 if all(array.shape == shape for array in args):
~/anaconda3/envs/default/lib/python3.7/site-packages/numpy/lib/stride_tricks.py in _broadcast_shape(*args)
189 # use the old-iterator because np.nditer does not handle size 0 arrays
190 # consistently
--> 191 b = np.broadcast(*args[:32])
192 # unfortunately, it cannot handle 32 or more arguments directly
193 for pos in range(32, len(args), 31):
ValueError: shape mismatch: objects cannot be broadcast to a single shape
</details>
Thanks for any help with this. I think it may be specific to os x, I didn't used to see this error in windows/linux (although I don't have access to test this out on other systems currently to confirm this) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29485 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29485/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29485/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29485/events | https://github.com/pandas-dev/pandas/issues/29485 | 520,047,615 | MDU6SXNzdWU1MjAwNDc2MTU= | 29,485 | groupby.quantile() fails on Timedelta columns | {
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} | 2 | 2019-11-08T14:33:54Z | 2020-10-31T18:57:02Z | 2020-10-31T18:57:02Z | NONE | null | #### Code Sample, groupby quantile example
```python
# groupby quantile example
import pandas as pd
print(f"Pandas {pd.__version__}")
df = pd.DataFrame({
'idx': [1, 1, 2],
'start_date': ['2019-10-02', '2019-10-04', '2019-10-05'],
'arrival_date': ['2019-11-02', '2019-10-14', '2019-10-15']})
for c in ['start_date', 'arrival_date']:
df[c] = pd.to_datetime(df[c])
df['delta'] = df['arrival_date'] - df['start_date']
print(df.groupby('idx')[['delta']].quantile(.9))
```
#### Problem description
The code example used to work in 0.21.1.
In 0.25 I obtain a "TypeError: No matching signature found"
#### Expected Output
Pandas 0.21.1
delta
idx
1 28 days 21:36:00
2 10 days 00:00:00
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-693.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 : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 18.1
setuptools : 40.8.0.post20191016
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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] | closed | false | null | [] | null | 2 | 2019-11-08T16:17:47Z | 2019-11-10T00:44:19Z | 2019-11-10T00:44:19Z | MEMBER | null | Follow up to #29212 we should bump Numpy to 1.15
**Optional**: we hit CI failures in the PR above on Python 3.6.0 related to Numpy. We bumped Python to 3.6.1 but if we bump numpy instead 3.6.0 might work, if we even care | {
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"description": "np.nan, pd.NaT, pd.NA, d... | open | false | null | [] | null | 11 | 2019-11-08T16:43:08Z | 2021-07-23T04:16:05Z | null | NONE | null | ```python
>>> pd.Series([True, np.float64(5)>0.2, np.nan]).sum()
1
>>> pd.Series([np.nan, np.float64(5)>0.2, True]).sum()
2
```
#### Problem description
The only thing different between the two cases above is the order of the arguments, which should not affect the result.
.mean() is also affected, and the same result occurs when using the series as a column in a dataframe.
#### Expected Output
I would expect both of the above to return 2, as there are two True values in the list
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Darwin
OS-release : 18.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0.post20191030
Cython : 0.29.14
pytest : 5.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.9.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : 0.11.0
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : 3.6.1
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29488 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29488/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29488/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29488/events | https://github.com/pandas-dev/pandas/issues/29488 | 520,119,891 | MDU6SXNzdWU1MjAxMTk4OTE= | 29,488 | Cannot convert string to Int64 | {
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] | closed | false | null | [] | null | 1 | 2019-11-08T16:49:19Z | 2019-11-08T18:12:17Z | 2019-11-08T18:12:10Z | NONE | null | ```python
pd.Series(['1']).astype(float).astype('Int64') # succeeds
pd.Series(['1']).astype('Int64') # fails with:
# TypeError: object cannot be converted to an IntegerDtype
```
#### Problem description
Unlike with the other numeric data types, It doesn't seem to be possible to parse a string as `Int64`. A decent workaround seems to be converting first to float, but that's rather awkward.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-1052-aws
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 : 0.25.1
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.1
setuptools : 41.0.1
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 : None
jinja2 : 2.10.1
IPython : 7.8.0
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 : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.8
tables : None
xarray : 0.14.0
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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} | 6 | 2019-11-08T17:01:59Z | 2020-01-23T12:35:15Z | 2020-01-23T12:35:15Z | MEMBER | null | Not showing anything useful currently. I think the regression came between 0.24 and 0.25
0.25.0 docs:
https://pandas.pydata.org/pandas-docs/version/0.25.0/reference/api/pandas.DataFrame.plot.html
0.24.2 docs:
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} | 2 | 2019-11-08T17:06:10Z | 2019-11-13T14:29:10Z | 2019-11-12T23:43:05Z | MEMBER | null | This will need multiple passes, trying to keep a moderately-sized diff. | {
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CC: @jreback | {
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} | 13 | 2019-11-08T20:40:00Z | 2020-10-04T08:09:58Z | 2019-12-19T13:37:08Z | MEMBER | null | - [ ] closes #27568
I'll open a new issue for resolution of mypy errors per module, see https://gitter.im/pydata/pandas?at=5dc5cdf2091dd14a0e86103a and #28926 for process used for test modules.
cc @WillAyd @jbrockmendel | {
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df[df['Delivery'].notnull()]['Delivery'].astype(int).to_clipboard(excel=True, sep=',',index=False)
```
#### Problem description
Works fine when I run the program in spyder or jupyter but after taking the build using Pyinstaller the the data from clipboard is not pasting to respective screen.
Is there anything needs to added or removed before taking the build.
Thanks in advance.
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Main differences can be found on the 19.10 changelog:
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This is an internal name change, so is ok from an API stability perspective
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} | 1 | 2019-11-09T14:27:41Z | 2019-11-09T19:43:49Z | 2019-11-09T19:43:42Z | CONTRIBUTOR | null | - [x] closes #15853
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 9 | 2019-11-09T14:47:26Z | 2019-12-01T21:44:48Z | 2019-11-12T23:35:53Z | CONTRIBUTOR | null | Remove kwargs from ``_GroupBy`.__init__`` and adds an explicit ``mutated`` parameter name instead. Also makes related changes.
Avoiding kwargs when possible is better for typing ergonomics. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29512 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29512/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29512/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29512/events | https://github.com/pandas-dev/pandas/issues/29512 | 520,461,464 | MDU6SXNzdWU1MjA0NjE0NjQ= | 29,512 | DEPS: Minimum numpy version for pandas 1.0 | {
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"description": "Required and... | closed | false | null | [] | null | 4 | 2019-11-09T15:43:01Z | 2020-06-10T01:43:29Z | 2020-06-10T01:43:29Z | MEMBER | null | There has been some discussion about dropping numpy 1.13, and require 1.15 for pandas 1.0. See https://github.com/pandas-dev/pandas/pull/29212#issuecomment-551856248
I think that'd be nice to reduce our matrix of builds for the CI, other than that I'm not sure what advantages this will have in our code base.
Any opinions @pandas-dev/pandas-core ? | {
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} | 6 | 2019-11-09T16:03:36Z | 2019-12-25T20:36:12Z | 2019-11-13T16:58:08Z | CONTRIBUTOR | null | - [x] closes https://github.com/pandas-dev/pandas/issues/29439
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
A few of the "network" tests are failing locally as they require the data on master:
e.g
```
@tm.network
def test_spam_url(self):
url = (
"https://raw.githubusercontent.com/pandas-dev/pandas/master/"
"pandas/tests/io/data/html/spam.html"
)
df1 = self.read_html(url, ".*Water.*")
df2 = self.read_html(url, "Unit")
assert_framelist_equal(df1, df2)
```
^ Will change to this branch to get the test passing on this PR. Then in a follow up PR will revert to master unless any core devs have a different idea
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