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Uniform file IO API and consolidated codebase
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| 7 | 2016-12-29T14:26:48Z | 2021-05-08T00:14:25Z | null |
CONTRIBUTOR
| null |
There are at least three things that many of the IO methods must deal with: reading from URL, reading/writing to a compressed format, and different text encodings. It would be great if all io functions where these factors were relevant could use the same code (consolidated codebase) and expose the same options (uniform API).
In https://github.com/pandas-dev/pandas/pull/14576, we consolidated the codebase but more consolidation is possible. In [`io.common.py`](https://github.com/pandas-dev/pandas/blob/dc4b0708f36b971f71890bfdf830d9a5dc019c7b/pandas/io/common.py), there are three functions that must be sequentially called to get a file-like object: `get_filepath_or_buffer`, `_infer_compression`, and `_get_handle`. This should be consolidated into a single function, which can then delegate to sub functions.
Currently, pandas supports the [following io methods](http://pandas-docs.github.io/pandas-docs-travis/io.html). First for reading:
- [ ] read_csv
- [ ] read_excel
- [ ] read_hdf
- [ ] read_feather
- [ ] read_sql
- [ ] read_json
- [ ] read_msgpack (experimental)
- [ ] read_html
- [ ] read_gbq (experimental)
- [ ] read_stata
- [ ] read_sas
- [ ] read_clipboard
- [ ] read_pickle
And then for writing:
- [ ] to_csv
- [ ] to_excel
- [ ] to_hdf
- [ ] to_feather
- [ ] to_sql
- [ ] to_json
- [ ] to_msgpack (experimental)
- [ ] to_html
- [ ] to_gbq (experimental)
- [ ] to_stata
- [ ] to_clipboard
- [ ] to_pickle
Some of these should definitely use the consilidated/uniform API, such as `read_csv`, `read_html`, `read_pickle`, `read_excel`.
Some functions perhaps should be kept separate, such as `read_feather` or `read_clipboard`.
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Fix gbq integration tests so that unique datasets/table names are used
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| 2 | 2016-12-29T14:28:40Z | 2016-12-30T18:45:29Z | 2016-12-30T18:45:21Z |
CONTRIBUTOR
| null |
This PR will resolve an issue where a gbq integration test was failing. The same dataset/table name was used twice in `test_gbq.py` which could result in a schema conflict if the previous table schema is cached and the new schema has not successfully propagated through Google's backend.
See https://github.com/pandas-dev/pandas/commit/45910ae646eba417120dd2bd4ada68a018c97b32
All gbq units tests passed on Travis on my fork : https://travis-ci.org/parthea/pandas/builds/187463043
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BUG/PY3? astype("string") fails for python 3
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| 4 | 2016-12-29T14:38:29Z | 2018-06-29T02:33:26Z | 2018-06-29T02:33:20Z |
MEMBER
| null |
From the categorical example docs, with python 2.7:
```python
In [1]: s = pd.Series(["a","b","c","a"])
In [2]: s2 = s.astype('category')
In [3]: s2.astype('string')
Out[3]:
0 a
1 b
2 c
3 a
dtype: object
```
However, with python 3, this fails:
```
In [74]: s = pd.Series(["a","b","c","a"])
In [75]: s2 = s.astype('category')
In [76]: s2.astype('string')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-76-70b55f934dfe> in <module>()
----> 1 s2.astype('string')
/home/joris/scipy/pandas/pandas/core/generic.py in astype(self, dtype, copy, raise_on_error, **kwargs)
3179 conversion, with unconvertible values becoming NaT.
3180 convert_numeric : boolean, default False
-> 3181 If True, attempt to coerce to numbers (including strings), with
3182 unconvertible values becoming NaN.
3183 convert_timedeltas : boolean, default True
/home/joris/scipy/pandas/pandas/core/internals.py in astype(self, dtype, **kwargs)
3188
3189 def astype(self, dtype, **kwargs):
-> 3190 return self.apply('astype', dtype=dtype, **kwargs)
3191
3192 def convert(self, **kwargs):
/home/joris/scipy/pandas/pandas/core/internals.py in apply(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)
3055
3056 kwargs['mgr'] = self
-> 3057 applied = getattr(b, f)(**kwargs)
3058 result_blocks = _extend_blocks(applied, result_blocks)
3059
/home/joris/scipy/pandas/pandas/core/internals.py in astype(self, dtype, copy, raise_on_error, values, **kwargs)
459 **kwargs):
460 return self._astype(dtype, copy=copy, raise_on_error=raise_on_error,
--> 461 values=values, **kwargs)
462
463 def _astype(self, dtype, copy=False, raise_on_error=True, values=None,
/home/joris/scipy/pandas/pandas/core/internals.py in _astype(self, dtype, copy, raise_on_error, values, klass, mgr)
2158 values = self.values
2159 else:
-> 2160 values = np.asarray(self.values).astype(dtype, copy=False)
2161
2162 if copy:
TypeError: data type "string" not understood
```
Should this also work? (or should be just update the docs) In any case, consistency would be nice here.
The root cause is probably that this difference also exists in numpy's astype.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-53-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0+270.gc72f297
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.5
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.0.0
tables: 3.3.0
numexpr: 2.6.1
feather: 0.3.1
matplotlib: 2.0.0rc2
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: None
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
s3fs: 0.0.7
pandas_datareader: None
```
</details>
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DOC: python 3 compatibility in code examples
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| 1 | 2016-12-29T15:01:28Z | 2016-12-29T21:57:16Z | 2016-12-29T21:57:16Z |
MEMBER
| null |
Some changes to our code examples needed to make them all run with python 3.
(there are still problems with our accessor extensions to have doc building on py3 fully working)
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DOC/BLD: doc building with python 3
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| 3 | 2016-12-29T22:23:39Z | 2017-03-05T11:48:08Z | 2016-12-30T14:46:18Z |
MEMBER
| null |
Makes our doc building machinery (specific accessor stuff) python 3 compat + switches travis doc building to python 3.5 + preventing some other warnings (see individual commits)
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TST: Series Division with zeros numpy array (#8674)
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| 2 | 2016-12-30T07:35:45Z | 2017-12-20T02:04:02Z | 2016-12-30T09:39:39Z |
MEMBER
| null |
- [x] closes #8674
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
This issue operates normally on master and relates to Python's 2.x division. Doesn't appear that any PRs in 0.20 addressed this issue.
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read_html incorrectly parses a table from Wikipedia
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| 1 | 2016-12-30T14:36:56Z | 2016-12-30T17:05:34Z | 2016-12-30T17:05:24Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import html5lib
import requests
from lxml import html
from lxml import etree
url = "https://en.wikipedia.org/wiki/Comparison_of_single-board_computers"
xpath = "//*[@id=\"mw-content-text\"]/table"
res = requests.get(url)
tree = html.fromstring(res.content)
tables = tree.xpath(xpath)
# for index, t in enumerate(tables):
# print("Table index [%s]" % index)
# print(t.xpath('descendant-or-self::th/text()'))
# print
headers = ['Name', 'PCIe',
'USB 2.0', 'USB 3.0', 'USB devices',
'Storage on-board', 'Storage flash slots', 'Storage SATA',
'Networking ETH', 'Networking WiFi',
'Communication bluetooth', 'Communication I2C', 'Communication SPI',
'Generic I/O GPIO', 'Generic I/O Analog',
'Other interfaces']
# at the beginning and at the end there are 2 unnecessary rows,
# because the original header was to complex for the parser, so it's removed
dta = pd.read_html(html.tostring(tables[2]), skiprows=2)[0][:-2]
dta.columns= headers
dta.sort_values(by=['Name'])[61:]
```
#### Problem description
Roughly, starting from id=69 everything seems to be mixed. Either it's because of the parsing library or the specific of the table from Wiki. Values looks like they are shifted to the left.
#### Expected output
e.g. "Orange Pi Lite" should have USB3.0 == "No", instead there is "b/g/n (RTL8189FTV)", and that value should be shifted 5 columns to the right
#### Output of ``pd.show_versions()``
html5lib installed using pip
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-28-generic
machine: x86_64
processor:
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 32.3.1
Cython: 0.23.5
numpy: 1.10.4
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: 3.7.1
bs4: 4.5.1
html5lib: 0.999999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: 0.2.1
</details>
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MDU6SXNzdWUxOTgxNzAxODI=
| 15,015 |
API: handle exceptions in particular columns when .agg
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| 0 | 2016-12-30T16:57:40Z | 2017-04-14T15:37:14Z | 2017-04-14T15:37:14Z |
CONTRIBUTOR
| null |
followup to #14668
When you have an exception in *some* aggregation columns, need to be more friendly. We exclude them in .groupby aggs so need to do that here.
```
In [1]: df = tm.makeMixedDataFrame()
In [2]: df
Out[2]:
A B C D
0 0.0 0.0 foo1 2009-01-01
1 1.0 1.0 foo2 2009-01-02
2 2.0 0.0 foo3 2009-01-05
3 3.0 1.0 foo4 2009-01-06
4 4.0 0.0 foo5 2009-01-07
In [3]: df.agg('sum')
Out[3]:
A 10.0
B 2.0
dtype: float64
In [4]: df.agg(['sum'])
ValueError: no results
In [6]: df.agg(['min'])
Out[6]:
A B C D
min 0.0 0.0 foo1 2009-01-01
```
so [4] should prob not happen
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MDU6SXNzdWUxOTgxNzAzNzk=
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Using to_json() while reading from a specific csv file causes Django to not store the variable in the session
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| 2 | 2016-12-30T16:59:04Z | 2016-12-30T17:03:56Z | 2016-12-30T17:03:13Z |
NONE
| null |
Inside of a Django view:
```python
my_names = ['Date','Open','High','Low','Close','Volume','B0','B1','B2','B3','B4','B5','B6','B7']
request.session['df'] = pd.read_csv( request.FILES.get('data'), names=my_names ).to_json()
if request.session.test_cookie_worked():
print('Test cookie worked! Deleting... ')
request.session.delete_test_cookie()
else:
request.session.set_test_cookie()
print('set test cookie')
```
#### Problem description
If I comment out the line "request.session['df'] = pd.read_csv( request.FILES.get('data'), names=my_names ).to_json()", then the first page load will print "set test cookie" on the first page load. On the second page load, the link 'Test cookie worked! Deleting..." will appear.
When I run the code as-is, the Terminal prints set test cookie on every single page load.
_GitHub won't let me upload the csv file even though it's zipped. Let me know how I can send that to you._
#### Expected Output
The terminal output should alternate between 'set test cookie' and 'Test cookie worked!' on every page load.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-51-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 20.7.0
Cython: None
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.7.3
lxml: None
bs4: 4.4.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
None
</details>
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| 198,193,979 |
MDU6SXNzdWUxOTgxOTM5Nzk=
| 15,017 |
error in Cheat Sheet "pd." is "Pd."
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[] | null | 1 | 2016-12-30T20:59:19Z | 2017-01-01T18:50:48Z | 2017-01-01T18:50:48Z |
CONTRIBUTOR
| null |
The [cheat sheet](https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf) refers to the panda module as "pd" (as in import pandas as pd), except in the table titled "Logic in Python (and pandas)", where it says "Pd.notnull(obj)".
It probably got capitalized automatically.
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| 198,204,619 |
MDExOlB1bGxSZXF1ZXN0OTk3ODk3ODk=
| 15,018 |
MAINT: Refactor logic in maybe_convert_*
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| 16 | 2016-12-30T23:14:36Z | 2017-01-03T11:25:00Z | 2017-01-03T11:24:59Z |
MEMBER
| null |
Creates a Seen class to abstract objects encountered when doing type conversions.
Follow-up to #15005.
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BUG: to_html misses truncation indicators (...) when index=False
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| 2 | 2016-12-31T10:05:24Z | 2018-11-15T13:49:17Z | 2018-11-15T13:49:17Z |
MEMBER
| null |
When the html output is truncated (by using `mmax_rows=..`), you get `...` to indicate this, but not when using ``index=False``:
```python
df = pd.DataFrame(np.random.randn(5,2))
df.to_html(max_rows=4, index=False)
```
cc @TomAugspurger follow-up from #14999
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BUG: Parse uint64 in read_csv
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| 7 | 2016-12-31T10:11:45Z | 2017-01-02T19:53:09Z | 2017-01-02T19:50:02Z |
MEMBER
| null |
Adds behavior to allow for parsing of `uint64` data in `read_csv`. Also ensures
that they are properly handled along with `NaN` and negative values.
Closes #14983.
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ENH: enable 'on' keyword for groupby(..).resample()
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| 0 | 2016-12-31T10:14:32Z | 2017-02-07T14:00:00Z | 2017-02-07T13:59:45Z |
MEMBER
| null |
#### Problem description
`resample` gained a new keyword `on` to be able to resample based on a column of datetime values instead of the index (which can prevent some use of `set_index`/`reset_index`). This feature is however not yet available for resample after a groupby.
#### Code Sample
```
In [8]: df = pd.DataFrame({'key': ['A', 'B']*5, 'dates': pd.date_range('2016-01-01', periods=10), 'values': np.random.randn(10)})
In [9]: df
Out[9]:
dates key values
0 2016-01-01 A -1.291535
1 2016-01-02 B 0.922945
2 2016-01-03 A -1.039163
3 2016-01-04 B -1.102022
4 2016-01-05 A 1.220347
5 2016-01-06 B 0.265720
6 2016-01-07 A 0.888080
7 2016-01-08 B -0.250236
8 2016-01-09 A 0.485518
9 2016-01-10 B -0.188880
In [11]: df.set_index('dates').groupby('key').resample('D').mean()
Out[11]:
values
key dates
A 2016-01-01 -1.291535
2016-01-02 NaN
2016-01-03 -1.039163
2016-01-04 NaN
2016-01-05 1.220347
2016-01-06 NaN
2016-01-07 0.888080
2016-01-08 NaN
2016-01-09 0.485518
B 2016-01-02 0.922945
2016-01-03 NaN
2016-01-04 -1.102022
2016-01-05 NaN
2016-01-06 0.265720
2016-01-07 NaN
2016-01-08 -0.250236
2016-01-09 NaN
2016-01-10 -0.188880
In [12]: df.groupby('key').resample('D', on='dates').mean()
...
TypeError: __init__() got an unexpected keyword argument 'on'
```
Using pandas master, 0.19.0+289.g1bf94c8
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| 15,022 |
API: let DatetimeIndex date/time components return a new Index instead of array
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| 1 | 2016-12-31T10:26:53Z | 2017-03-22T18:47:11Z | 2017-03-22T18:47:11Z |
MEMBER
| null |
#### Problem description
Currently the date and time components on a DatetimeIndex return a numpy array. I would propose to let them return a new Index object (and in this way retaining all the interesting added functionality of an Index object).
Related to https://github.com/pandas-dev/pandas/pull/14506 that changed `Index.map` to return an Index instead of an array.
#### Code Sample
```
In [14]: ts = pd.Series(range(10), index=pd.date_range('2016-01-01', periods=10))
In [15]: ts
Out[15]:
2016-01-01 0
2016-01-02 1
2016-01-03 2
2016-01-04 3
2016-01-05 4
2016-01-06 5
2016-01-07 6
2016-01-08 7
2016-01-09 8
2016-01-10 9
Freq: D, dtype: int64
In [16]: ts.index.dayofweek
Out[16]: array([4, 5, 6, 0, 1, 2, 3, 4, 5, 6], dtype=int32)
```
When the above would return an index, something that would become possible is this:
```
ts[ts.index.dayofweek.isin([5,6])]
```
which is now not possible as an array has no isin method.
Using pandas master, 0.19.0+289.g1bf94c8
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| 198,227,451 |
MDU6SXNzdWUxOTgyMjc0NTE=
| 15,023 |
ENH: add quantile method to resample
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| 5 | 2016-12-31T10:35:27Z | 2018-08-22T12:27:03Z | 2018-08-22T12:27:03Z |
MEMBER
| null |
#### Problem description
The `quantile` method is currently not supported on `resample`. While `resample(...).agg(lambda x: x.quantile(..))` works fine, it would be nice to add the `resample(..).quantile(..)` shortcut.
#### Code Sample
```
In [19]: ts = pd.Series(range(20), index=pd.date_range('2016-01-01', periods=20))
In [21]: ts.resample('W').agg(lambda x: x.quantile(0.75))
Out[21]:
2016-01-03 1.5
2016-01-10 7.5
2016-01-17 14.5
2016-01-24 18.5
Freq: W-SUN, dtype: float64
```
So it would be nice the following would give the same result:
```
In [22]: ts.resample('W').quantile(0.75)
/home/joris/miniconda3/envs/dev/bin/ipython:1: FutureWarning:
.resample() is now a deferred operation
You called quantile(...) on this deferred object which materialized it into a series
by implicitly taking the mean. Use .resample(...).mean() instead
#!/home/joris/miniconda3/envs/dev/bin/python
Out[22]: 14.25
```
The fact that this currently implicitly takes the mean before calculating the quantile (`ts.resample('W').mean().quantile(0.75)`) would make this change slightly API breaking.
Using pandas master, 0.19.0+289.g1bf94c8
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| 15,024 |
BUG: Patch float and uint handling in to_numeric
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| 2 | 2016-12-31T10:54:57Z | 2016-12-31T15:45:31Z | 2016-12-31T15:45:30Z |
MEMBER
| null |
1) Patches `float` handling by reducing the "closeness" level when checking conversions.
2) Patches `uint` handling by allowing casts to `uint` dtypes of equal or lesser size to `int64` (when values are less than `INT64_MAX`
Closes #14941.
Follow-up to #15005.
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VIS/BUG: subplots=True gives rotated tick labels
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| 1 | 2016-12-31T11:03:37Z | 2017-01-04T01:54:59Z | null |
MEMBER
| null |
#### Problem description
When using the normal `plot()` method vs `plot(subplots=True)`, you get a different rotation for the ticklabels. I don't think this inconsistency is needed, and would use non-rotated ticklabels for `subplots=True` as well.
#### Code Sample
```
In [6]: df = pd.DataFrame(np.random.randn(20,2), columns=['A', 'B'], index=pd.date_range("2016-01-01", periods=20))
In [8]: df.plot()
Out[8]: <matplotlib.axes._subplots.AxesSubplot at 0x7f00389f0198>
```
```
In [9]: df.plot(subplots=True)
Out[9]:
array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f00333381d0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7f00332ddf98>], dtype=object)
```
The above example is with a time series, but you actually get exactly the same when eg just using the default integer index.
Using pandas master, 0.19.0+289.g1bf94c8
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docs cheat sheet pandas Pd to pd
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| 2 | 2016-12-31T23:13:06Z | 2017-01-01T18:50:55Z | 2017-01-01T18:50:48Z |
CONTRIBUTOR
| null |
Change pandas syntax from Pd to pd
Closes [issue 15017](https://github.com/pandas-dev/pandas/issues/15017#issuecomment-269871137)
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groupby casting to int64
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| 2 | 2016-12-31T23:58:41Z | 2018-07-06T20:21:14Z | 2017-01-01T17:29:46Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
from StringIO import StringIO
import pandas as pd
import numpy as np
data = "some_id,other_id,some_int\n1,234,0\n2,324,1"
dtypes = {'some_id': np.uint32, 'other_id': np.uint32, 'some_int': np.int8}
train = pd.read_csv(StringIO(data), dtype=dtypes)
print(train.dtypes) # dtypes is respected
grouped = train.groupby('some_id').some_int.agg(['sum'])
print(grouped.index.dtype) # dtype('int64')
```
#### Problem description
I am explicitly asking for uint32 but pandas coerces "some_id" column to int64 after using groupby. Is this intended? Possible duplicate of #14423 ?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-57-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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DOC: Added examples to pandas.concat documentation
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| 5 | 2017-01-01T02:20:26Z | 2017-01-04T13:51:46Z | 2017-01-04T13:51:35Z |
CONTRIBUTOR
| null |
I've added two simple examples to the ``pd.concat`` documentation. I've also done a very small amount of clean up on the rest of the docstring.
- [x] passes ``git diff upstream/master | flake8 --diff``
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API: allow list-like to DataFrame rename
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CONTRIBUTOR
| null |
- [x] closes #14829
- [ ] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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| 1 | 2017-01-01T17:53:14Z | 2017-01-01T18:51:24Z | 2017-01-01T18:51:24Z |
CONTRIBUTOR
| null |
compat in Timestamp.replace when passing longs (and not ints)
xref https://github.com/statsmodels/statsmodels/issues/3349
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WIP/ENH: Weightby
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[] | null | 6 | 2017-01-01T19:01:43Z | 2017-01-02T18:36:33Z | 2017-01-02T18:36:33Z |
CONTRIBUTOR
| null |
closes #10030
this is on top of #14483
provides a groupby-like API to weighted calculations. The weights are lazily calculated and cached.
Deprecates the ``weights`` parameter to ``.sample()``, and implements all of this logic inside ``.weightby``.
TODO:
- only sum/mean are implemented ATM, but logic for other ops (std, var, kurt, skew) are straightforward.
- no logic ATM for a ``.groupby(...).weightby(...)`` or ``.groupby(...).rolling(...)``, but should be a straightforward enhancement.
- easy enhancement to add 'other' weight calculations at some point (just adding args to the ``.weightby`` constructor), see [here](https://github.com/pandas-dev/pandas/issues/10030#issuecomment-238324993)
```
In [1]: df = DataFrame({'A': [1, 2, 3, 4],
...: 'B': [1, 2, 3, 4]})
...:
In [2]: df
Out[2]:
A B
0 1 1
1 2 2
2 3 3
3 4 4
In [3]: df.weightby('A').B.sum()
Out[3]: 3.0
In [4]: df.weightby('A').sum()
Out[4]:
B 3.0
dtype: float64
In [5]: df.weightby('A').sample(n=2)
Out[5]:
B
3 4
1 2
In [7]: w = df.weightby('A')
In [8]: w.mean()
Out[8]:
B 0.75
dtype: float64
In [9]: w._weights
Out[9]: array([ 0.1, 0.2, 0.3, 0.4])
```
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[WIP]: Add quantile shortcut
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[] | null | 5 | 2017-01-01T19:15:46Z | 2017-04-28T16:10:39Z | 2017-03-28T18:01:45Z |
CONTRIBUTOR
| null |
- [x] closes #15023
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
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Enhancement: add "median" to series.describe()
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[] | null | 1 | 2017-01-01T19:57:27Z | 2017-01-01T20:00:52Z | 2017-01-01T20:00:52Z |
NONE
| null |
Series.describe() already has "mean", but it would be very useful to also have "median" as well. Can we add "median" as one of the default values returned by series.describe()?
|
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UnicodeDecodeError: 'utf-8' codec can't decode byte 0xbb with read_excel function
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| 5 | 2017-01-02T10:21:27Z | 2017-01-03T10:16:33Z | 2017-01-02T10:37:04Z |
NONE
| null |
```python
import pandas as pd
data = pd.read_excel(open("2016-07-12_Zuwendungsbericht_2015_OpenData.xlsx"), sheetname="Zuwendungsbericht", encoding="utf-8")
```
I'm getting this error when I try to read this excel file: http://transparenz.bremen.de/sixcms/media.php/13/2016-07-12_Zuwendungsbericht_2015_OpenData.xlsx .
`UnicodeDecodeError: 'utf-8' codec can't decode byte 0xbb in position 14: invalid start byte`
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-57-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
LOCALE: de_DE.UTF-8
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 28.8.0
Cython: None
numpy: 1.11.3
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
None
</details>
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TST: Run ASV on Travis?
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| 7 | 2017-01-02T14:58:58Z | 2018-02-14T15:56:53Z | 2018-02-14T15:56:52Z |
CONTRIBUTOR
| null |
Running ASV locally entrusts it to the pull requester, which means it gets run only in occasional circumstances (and is a bit of a burden).
Is there a way to run it on Travis, without significantly slowing down the builds? I know CircleCI has the ability to skip tests depending on the commit message - is there something similar we could do for Travis, so it only runs with an `#run_asv` string in the commit message (or similar)?
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MDU6SXNzdWUxOTgzNTQ3OTY=
| 15,036 |
ERR: all nan grouping provides unfriendly error message
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| 4 | 2017-01-02T16:11:58Z | 2021-05-19T12:53:58Z | 2021-05-19T12:53:58Z |
CONTRIBUTOR
| null |
https://github.com/pandas-dev/pandas/pull/14649/files#r94330188
This seems unfriendly, should instead b a ``DataError``
```
In [8]: s = Series([1,2,3],[np.nan,np.nan,np.nan])
In [9]: s.groupby(s.index).sum()
TypeError: unhashable type: 'Float64Index'
```
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Typing error in "ExcelFile class" example
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| 2 | 2017-01-02T16:47:04Z | 2017-01-02T19:42:06Z | 2017-01-02T19:42:06Z |
CONTRIBUTOR
| null |
Current example ExcelFile class [(see ExcelFile class pandas 0.19.2 documentation)](http://pandas.pydata.org/pandas-docs/stable/io.html#excelfile-class) :
> xlsx = pd.ExcelFile('path_to_file.xls)
> df = pd.read_excel(xlsx, 'Sheet1')
#### Problem description
The closing apostrophe is missing for 'path_to_file.xls'
#### Expected Output
xlsx = pd.ExcelFile('path_to_file.xls')
df = pd.read_excel(xlsx, 'Sheet1')
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
pandas: 0.18.1
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.3
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.5
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.10
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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MDU6SXNzdWUxOTgzNjU0MzY=
| 15,038 |
drop_duplicates() returns with error if column datatype is list
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| 1 | 2017-01-02T17:56:25Z | 2018-07-06T20:21:14Z | 2017-01-02T18:08:17Z |
NONE
| null |
Can it be possible to improve `drop_duplicates()` method to handle `list` column datatpyes? Now if I have a column with `list` elements then `drop_duplicates()` throws the following exception:
> TypeError: type object argument after * must be a sequence, not map
Example:
```
df = pd.DataFrame({'a':[10,10,10,11,12], 'b':[['a', 'b'], ['a', 'b'], ['x', 'y'], ['a', 'b'], ['x', 'y']]})
df.drop_duplicates()
```
Now I have to create a new column that contains the original `list` elements as strings and I have to use this new column at `drop_duplicates()`.
Thank you!
|
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| 15,039 |
WIP/ENH: add weights kw to numeric aggregation functions
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[] | null | 10 | 2017-01-02T18:36:13Z | 2019-12-11T19:04:18Z | 2017-02-27T16:03:21Z |
CONTRIBUTOR
| null |
closes #10030
alt to #15031
```
In [5]: df = DataFrame({'A': [1, 1, 2, 2],
...: 'B': [1, 2, 3, 4]})
In [6]: df
Out[6]:
A B
0 1 1
1 1 2
2 2 3
3 2 4
In [7]: df.mean()
Out[7]:
A 1.5
B 2.5
dtype: float64
In [8]: df.mean(weights='A')
Out[8]:
A 0.416667
B 0.708333
dtype: float64
```
New signatures
```
In [9]: Series.mean?
Signature: Series.mean(self, axis=None, skipna=None, level=None, weights=None, numeric_only=None, **kwargs)
Docstring:
Return the mean of the values for the requested axis
Parameters
----------
axis : {index (0)}
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA
level : int or level name, default None
If the axis is a MultiIndex (hierarchical), count along a
particular level, collapsing into a scalar
weights : str or ndarray-like, optional
Default 'None' results in equal probability weighting.
If passed a Series, will align with target object on index.
Index values in weights not found in the target object
will be ignored and index values in the target object
not in weights will be assigned weights of zero.
If called on a DataFrame, will accept the name of a column
when axis = 0.
Unless weights are a Series, weights must be same length
as axis of the target object.
If weights do not sum to 1, they will be normalized to sum to 1.
Missing values in the weights column will be treated as zero.
inf and -inf values not allowed.
numeric_only : boolean, default None
Include only float, int, boolean columns. If None, will attempt to use
everything, then use only numeric data. Not implemented for Series.
Returns
-------
mean : scalar or Series (if level specified)
File: ~/pandas/pandas/core/generic.py
Type: function
```
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DOC: Typing error in ExcelFile class example
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| 1 | 2017-01-02T19:32:57Z | 2017-01-02T19:42:11Z | 2017-01-02T19:42:06Z |
CONTRIBUTOR
| null |
- [x] closes #15037
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
- Tests with local build of documentation passed
- Flake8 seems to be relevant for source code changes, only
- Thought that a whatsnew entry is not relevant in this case
|
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BUG: Indexing MultiIndex with Series failed.
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| 2 | 2017-01-03T01:38:35Z | 2017-01-04T00:00:28Z | 2017-01-04T00:00:09Z |
CONTRIBUTOR
| null |
Previously, accessing elements of a MultiIndex-indexed DataFrame with a Series
failed. This changes that behavior so that it is possible to use a Series to
access elements from a MultiIndex-indexed DataFrame, just as one would use
a list.
- [x] closes #14730
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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BUG:MultiIndex.from_arrays gives wrong lable on level=NaN
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| 3 | 2017-01-03T03:28:42Z | 2017-01-09T15:31:41Z | 2017-01-03T11:05:25Z |
CONTRIBUTOR
| null |
When I tried to generate multiindex with `np.nan` or `None` from array, I got:
```python
import pandas as pd
import numpy as np
from pandas import MultiIndex as MultiIndex
from pandas import Index as Index
m = MultiIndex.from_arrays([[np.nan, np.nan, 'one', 'one', 'two', 'two', 'All'],
['dull', 'shiny', 'dull', 'shiny', 'dull', 'shiny', '']],
names=['b', 'c'])
print m
[Output]
MultiIndex(levels=[[u'All', u'one', u'two'], [u'', u'dull', u'shiny']],
labels=[[-1, -1, 1, 1, 2, 2, 0], [1, 2, 1, 2, 1, 2, 0]],
names=[u'b', u'c'])
```
While it should be (and can be achieved from MultiIndex constructor):
```python
m = MultiIndex(levels=[Index(['All', np.nan, 'one', 'two']),
Index(['', 'dull', 'shiny'])],
labels=[[1, 1, 2, 2, 3, 3, 0],
[1, 2, 1, 2, 1, 2, 0]], names=['b', 'c'])
print m
[Output]
MultiIndex(levels=[[u'All', nan, u'one', u'two'], [u'', u'dull', u'shiny']],
labels=[[1, 1, 2, 2, 3, 3, 0], [1, 2, 1, 2, 1, 2, 0]],
names=[u'b', u'c'])
```
The problems seems to be [here](https://github.com/pandas-dev/pandas/blob/master/pandas/indexes/multi.py#L936)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: 74e20a0c4146919a82312c1027dcb57b48b613e3
python: 2.7.13.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0+300.g74e20a0c4
nose: 1.3.3
pip: 9.0.1
setuptools: 32.1.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.14.0
statsmodels: 0.5.0
xarray: 0.7.2
IPython: 5.1.0
sphinx: 1.4.1
patsy: 0.2.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 1.5.3
openpyxl: 2.3.5
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: None
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0OTk5MjAwMzU=
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TST: Test empty method and simplify logic
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[] | null | 3 | 2017-01-03T07:59:46Z | 2017-12-20T02:04:03Z | 2017-01-04T14:36:58Z |
MEMBER
| null |
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
Since the docstring of `.empty` states
> True if NDFrame is entirely empty [no items], meaning any of the axes are of length 0.
I was wondering if the logic could be changed accordingly for code clarity. Unless it was set as `not all(len(self._get_axis(a)) > 0 for a in self._AXIS_ORDERS)` for a particular reason.
Also this method did not appear to have any test.
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| 198,521,568 |
MDExOlB1bGxSZXF1ZXN0OTk5ODc3NTA=
| 15,044 |
BF: define expected Series of correct for arch (eg i386) int
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[] | null | 3 | 2017-01-03T17:11:11Z | 2017-04-03T15:22:35Z | 2017-04-03T15:22:26Z |
CONTRIBUTOR
| null |
- [x] Closes #14866
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry (I believe it is nothing new ;) )
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stack() method of SparseDataFrame should return a SparseSeries and optimize memory usage
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| 7 | 2017-01-03T17:37:57Z | 2017-09-26T19:01:40Z | 2017-09-26T19:01:40Z |
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
df = pd.DataFrame({'a': [np.nan, np.nan, np.nan, np.nan, np.nan, 1., np.nan],
'b': [1., np.nan, np.nan, 1., np.nan, np.nan, np.nan]}).to_sparse()
print(type(df))
print(type(df.stack()))
```
> <class 'pandas.sparse.frame.SparseDataFrame'>
> <class 'pandas.core.series.Series'>
#### Problem description
I'm trying to convert a SparseDataFrame (obtained it from pd.get_dummies()) into a scipy sparse matrix, by using the experimental .to_coo(). As this method accepts a MultiIndex Series, instead of a DataFrame, i call the .stack() method of this SparseDataFrame.
The problem is that it looks like the .stack() method doesn't process the SparseDataFrame as sparse, and instead stacks it as dense, consuming too much memory, and returning a (dense) Series.
Returning a dense Series could be all right, as np.nan values are drop by default with the dropna parameters, but the memory consumption is a problem.
I'm aware the whole sparse functionality is not yet mature. And I saw the function pd.sparse.frame.stack_sparse_frame which I guess it's a step to fix this problem (which doesn't work for me). But as I couldn't find a specific issue for this problem, I thought it was worth opening it.
#### Expected Output
> <class 'pandas.sparse.frame.SparseDataFrame'>
> <class 'pandas.sparse.series.SparseSeries'>
#### Output of ``pd.show_versions()``
<details>
> commit: None
> python: 3.5.2.final.0
> python-bits: 64
> OS: Linux
> OS-release: 4.7.5-100.fc23.x86_64
> machine: x86_64
> processor: x86_64
> byteorder: little
> LC_ALL: None
> LANG: en_US.utf8
> LOCALE: en_US.UTF-8
>
> pandas: 0.19.2+0.g825876c.dirty
> nose: 1.3.7
> pip: 9.0.1
> setuptools: 23.0.0
> Cython: 0.24
> numpy: 1.11.1
> scipy: 0.17.1
> statsmodels: 0.6.1
> xarray: None
> IPython: 4.2.0
> sphinx: 1.4.1
> patsy: 0.4.1
> dateutil: 2.5.3
> pytz: 2016.4
> blosc: None
> bottleneck: 1.1.0
> tables: 3.2.3.1
> numexpr: 2.6.0
> matplotlib: 1.5.1
> openpyxl: 2.3.2
> xlrd: 1.0.0
> xlwt: 1.1.2
> xlsxwriter: 0.9.2
> lxml: 3.6.0
> bs4: 4.4.1
> html5lib: None
> httplib2: None
> apiclient: None
> sqlalchemy: 1.0.13
> pymysql: None
> psycopg2: 2.6.1 (dt dec pq3 ext)
> jinja2: 2.8
> boto: 2.40.0
> pandas_datareader: None
>
</details>
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difference in overflows handling while doing pow on large integers
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| 4 | 2017-01-03T17:47:57Z | 2019-10-07T01:38:55Z | 2019-10-07T01:38:55Z |
CONTRIBUTOR
| null |
#### Output from running tests and troubleshooting
probably related to having numpy beta release or freshish numexpr...
```
$> nosetests -s -v --pdb pandas/tests/test_expressions.py
...
test_integer_arithmetic (pandas.tests.test_expressions.TestExpressions) ... > /home/yoh/deb/gits/pkg-exppsy/pandas/pandas/util/testing.py(1018)raise_assert_detail()
-> raise AssertionError(msg)
*(Pdb) print msg
DataFrame.iloc[:, 0] are different
DataFrame.iloc[:, 0] values are different (85.16148 %)
[left]: [2565992168703393792, 2110287360296998863, 0, 0, 2644539488379390509, -5278486589563110205, -4689350456247753643, 8450172506621111015, -2101438300051996672, 8028209804145959321, 302875106592253, -5278486589563110205, -4053175462519618527, -4071272324936764365, 0, 0, 0, 256, 8450172506621111015, 0, -145357957422043229, 3030756035345000135, 256, 0, -4071272324936764365, -5278486589563110205, -2863221430593058543, 0, 0, 0, 4951460147845608313, 0, -1595931050845505211, -5821812116355782473, -6284992600390200471, 2435553365352800019, -1595931050845505211, -4053175462519618527, 0, 3792227444683241861, -6776596920136667815, -5821812116355782473, 0, -8795647696940200745, 4593512421086651489, 256, 0, 0, 2644539488379390509, -5821812116355782473, -7924192819420782515, 1826852818786091687, 0, 2110287360296998863, -2559158286230232783, -4689350456247753643, -6646187150092009472, 8028209804145959321, 0, 437893890380859375, 0, -5278486589563110205, 0, 2467972595799031808, 2604998672350111773, -4689350456247753643, 7437354637604294721, 0, 285311670611, 5529354540715494413, 2435553365352800019, -1595931050845505211, 256, 823543, -2559158286230232783, 0, -3660930864499998817, 0, -4642015662142636065, -4071272324936764365, 4611686018427387904, 285311670611, 1, 0, 2565992168703393792, 8028209804145959321, -145357957422043229, 8450172506621111015, -5821812116355782473, 0, -4675932718989443137, 8037740809528595931, -4071272324936764365, 0, 6353754964178307979, 0, -1595931050845505211, -1595931050845505211, -3264474685660004015, 2823291043684304491, ...]
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(Pdb) up
> /home/yoh/deb/gits/pkg-exppsy/pandas/pandas/src/testing.pyx(173)pandas._testing.assert_almost_equal (pandas/src/testing.c:3274)()
-> raise_assert_detail(obj, msg, lobj, robj)
(Pdb)
> /home/yoh/deb/gits/pkg-exppsy/pandas/pandas/src/testing.pyx(59)pandas._testing.assert_almost_equal (pandas/src/testing.c:4156)()
-> cpdef assert_almost_equal(a, b,
(Pdb)
> /home/yoh/deb/gits/pkg-exppsy/pandas/pandas/util/testing.py(1181)assert_series_equal()
-> obj='{0}'.format(obj))
(Pdb)
> /home/yoh/deb/gits/pkg-exppsy/pandas/pandas/util/testing.py(1313)assert_frame_equal()
-> obj='DataFrame.iloc[:, {0}]'.format(i))
(Pdb)
> /home/yoh/deb/gits/pkg-exppsy/pandas/pandas/tests/test_expressions.py(100)run_arithmetic_test()
-> assert_func(expected, result)
(Pdb) p df
A B C D
0 30 89 97 21
1 47 22 16 80
2 78 23 84 33
3 88 25 61 59
4 61 1 41 96
5 27 67 76 98
6 37 13 13 82
7 23 60 93 1
8 20 6 18 90
9 89 97 27 73
10 13 95 37 38
11 27 53 70 77
12 33 51 44 39
13 43 89 95 5
14 36 84 51 97
15 44 8 82 31
16 84 18 50 35
17 4 52 74 37
18 23 52 73 42
19 76 99 11 67
20 59 95 87 68
21 55 18 89 73
22 4 26 64 19
23 94 59 47 36
24 43 79 37 26
25 27 42 41 84
26 17 30 4 3
27 24 37 63 88
28 66 14 92 86
29 52 38 27 97
... .. .. .. ..
9971 25 13 53 68
9972 9 46 89 93
9973 70 53 47 33
9974 10 30 47 3
9975 98 15 98 43
9976 34 5 53 76
9977 21 29 65 95
9978 30 83 67 82
9979 52 1 58 17
9980 4 84 38 79
9981 83 96 57 12
9982 13 38 93 72
9983 58 64 24 13
9984 18 46 5 43
9985 70 84 88 45
9986 79 94 57 29
9987 18 31 63 57
9988 58 90 99 5
9989 8 88 77 55
9990 46 12 61 61
9991 69 70 96 45
9992 71 67 48 25
9993 77 68 46 16
9994 88 55 80 37
9995 6 93 76 73
9996 11 68 45 52
9997 57 79 3 69
9998 63 48 79 8
9999 11 5 70 16
10000 10 32 32 88
[10001 rows x 4 columns]
(Pdb) p op
<function pow at 0x7efedaaad758>
```
#### Problem description
Not sure what to blame here -- numexpr or numpy or ... What would you expect while trying to get e.g. `30**30`? max for int of original pow base type?
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
$> ./ci/print_versions.py
INSTALLED VERSIONS
------------------
commit: ad13f08e305951204e1beee0281725c3e0538024
python: 2.7.13.candidate.1
python-bits: 64
OS: Linux
OS-release: 4.8.0-1-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0+git14-ga40e185
nose: 1.3.7
pip: 8.1.2
setuptools: 28.7.1
Cython: 0.25.2b0
numpy: 1.12.0b1
scipy: 0.18.1
statsmodels: 0.8.0rc1
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: 0.4.1+dev
dateutil: 2.5.3
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
feather: None
matplotlib: 1.5.3
openpyxl: 2.3.0
xlrd: 1.0.0
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.6.4
bs4: 4.5.1
html5lib: 0.999
httplib2: 0.9.2
apiclient: 1.5.2
sqlalchemy: 1.0.15
pymysql: None
psycopg2: 2.6.2 (dt dec mx pq3 ext lo64)
jinja2: 2.8
s3fs: None
pandas_datareader: None
</details>
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| 198,583,620 |
MDU6SXNzdWUxOTg1ODM2MjA=
| 15,047 |
Treat Series as 1-column dataframe in concat with dataframe
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| 4 | 2017-01-03T22:17:26Z | 2019-10-21T19:52:17Z | null |
CONTRIBUTOR
| null |
When concatenating a dataframe and series along axis 1, [the docs](http://pandas.pydata.org/pandas-docs/stable/merging.html#concatenating-with-mixed-ndims) indicate that the series will be treated as a series with a single column matching the column name. Based on this, I'd expect similar behavior (or an error) when concatenating a named series with a dataframe along axis 0. However, this seems to result in a new numeric column for the named series instead of converting the series to a dataframe:
```python
In [10]: pd.__version__
Out[10]: '0.19.1'
In [11]: df = pd.DataFrame({'x': range(5), 'y': range(5), 'z': range(5)})
In [12]: pd.concat([df, df.x])
Out[12]:
x y z 0
0 0.0 0.0 0.0 NaN
1 1.0 1.0 1.0 NaN
2 2.0 2.0 2.0 NaN
3 3.0 3.0 3.0 NaN
4 4.0 4.0 4.0 NaN
0 NaN NaN NaN 0.0
1 NaN NaN NaN 1.0
2 NaN NaN NaN 2.0
3 NaN NaN NaN 3.0
4 NaN NaN NaN 4.0
In [13]: pd.concat([df, df.x.to_frame()])
Out[13]:
x y z
0 0 0.0 0.0
1 1 1.0 1.0
2 2 2.0 2.0
3 3 3.0 3.0
4 4 4.0 4.0
0 0 NaN NaN
1 1 NaN NaN
2 2 NaN NaN
3 3 NaN NaN
4 4 NaN NaN
```
I'd expect either an error, or the behavior in out[13].
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PERF: groupby-cummax,cummin
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| 0 | 2017-01-03T22:21:33Z | 2017-01-11T13:22:16Z | 2017-01-11T13:22:03Z |
CONTRIBUTOR
| null |
these could be implemented in cython, should be very straightforward as we already have much templated code
```
In [1]: np.random.seed(1234)
In [4]: G = 1000
In [5]: N = 10000
In [6]: df = pd.DataFrame({'A':np.random.randint(0,G,size=N),'B':np.random.randn(N)})
In [7]: %timeit df.groupby('A').cumsum()
1000 loops, best of 3: 1.27 ms per loop
In [8]: %timeit df.groupby('A').cummax()
1 loop, best of 3: 799 ms per loop
In [9]: %timeit df.groupby('A').cummin()
1 loop, best of 3: 796 ms per loop
In [10]: %timeit df.groupby('A').cumprod()
1000 loops, best of 3: 1.26 ms per loop
```
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name property changes unexpectedly after Series.append
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| 5 | 2017-01-04T06:09:00Z | 2017-05-30T11:17:32Z | 2017-01-05T11:53:22Z |
NONE
| null |
Hi, I have tried Series.append but the name property changes unexpectedly.
```python
a = pd.Series(name='a')
b = pd.Series(name='b')
c = pd.Series()
# a.name turns to be None if a.name is different with b.name
a.append(b) # a.name is None after append
# a.name turns to be None if c dose not have a name
a.append(c) # a.name is None after append
# a.name remains unchanged
a.append(pd.Series(name=a.name)) # a.name dose not change
```
#### Problem description
As a new series is appended to an old one, the name of the old series should be unchanged.
This should not be satisfied only when the name of the new series is explicitly specified.
However, the above problem happens.
#### Expected Output
Whenever a.append(b) or a.append(c) is done, a.name should remain unchanged, which is 'a'.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 8.1.2
setuptools: 19.2
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.1
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
</details>
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| 15,050 |
ERR: Disallow multi-char quotechar for C engine
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| 3 | 2017-01-04T06:22:54Z | 2017-01-05T22:20:25Z | 2017-01-05T22:20:25Z |
MEMBER
| null |
Raise `ValueError` or issue `ParserWarning` (as we do with other unsupported features) when a multi-char `quotechar` is passed in, and the C engine is used.
Closes #11592.
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TST: Add new test for flaky usecols
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| 1 | 2017-01-04T06:51:20Z | 2017-01-04T08:24:51Z | 2017-01-04T08:24:51Z |
MEMBER
| null |
Title is self-explanatory.
xref #14984.
Closes #13604.
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DOC: Add example of skipcols in read_csv
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| 2 | 2017-01-04T07:01:45Z | 2017-01-04T22:28:20Z | 2017-01-04T22:28:13Z |
MEMBER
| null |
Illustrate how we can use the `usecols` argument to skip particular columns.
Closes #10882.
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PERF: Cythonize Groupby.cummin/cummax (#15048)
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| 18 | 2017-01-04T08:25:44Z | 2017-12-20T02:04:03Z | 2017-01-11T13:22:03Z |
MEMBER
| null |
- [x] closes #15048
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
```
before after ratio
[6eb705f5] [e8115142]
- 1.08s 1.74ms 0.00 groupby.groupby_cummax_cummin.time_cummax
- 1.09s 1.73ms 0.00 groupby.groupby_cummax_cummin.time_cummin
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
```
Still need to add some tests to see if this is equivalent to the old implimentation (numpy.minimum.accumulate?). Any early feedback appreciated.
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MDExOlB1bGxSZXF1ZXN0MTAwMTEwMjkw
| 15,054 |
BUG: make sure that we are passing thru kwargs to groupby
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| 3 | 2017-01-04T12:51:12Z | 2017-01-18T16:14:36Z | 2017-01-18T16:14:35Z |
CONTRIBUTOR
| null |
BUG: allow timedelta64 to work in groupby with numeric_only=False
closes #5724
```
In [6]: df = pd.DataFrame(
...: {'group': [1, 1, 2],
...: 'int': [1, 2, 3],
...: 'float': [4., 5., 6.],
...: 'string': list('abc'),
...: 'category_string': pd.Series(list('abc')).astype('category'),
...: 'category_int': [7, 8, 9],
...: 'datetime': pd.date_range('20130101', periods=3),
...: 'datetimetz': pd.date_range('20130101',
...: periods=3,
...: tz='US/Eastern'),
...: 'timedelta': pd.timedelta_range('1 s', periods=3, freq='s')},
...: columns=['group', 'int', 'float', 'string',
...: 'category_string', 'category_int',
...: 'datetime', 'datetimetz',
...: 'timedelta'])
In [7]: df
Out[7]:
group int float string category_string category_int datetime datetimetz timedelta
0 1 1 4.0 a a 7 2013-01-01 2013-01-01 00:00:00-05:00 00:00:01
1 1 2 5.0 b b 8 2013-01-02 2013-01-02 00:00:00-05:00 00:00:02
2 2 3 6.0 c c 9 2013-01-03 2013-01-03 00:00:00-05:00 00:00:03
# same as in master
In [8]: df.groupby('group').mean()
Out[8]:
int float category_int
group
1 1.5 4.5 7.5
2 3.0 6.0 9.0
# works
In [9]: df.groupby('group').mean(numeric_only=False)
Out[9]:
int float category_int datetime datetimetz timedelta
group
1 1.5 4.5 7.5 2013-01-01 12:00:00 2013-01-01 12:00:00-05:00 00:00:01.500000
2 3.0 6.0 9.0 2013-01-03 00:00:00 2013-01-03 00:00:00-05:00 00:00:03
# same as in master
In [10]: df.groupby('group').sum()
Out[10]:
int float category_int
group
1 3 9.0 15
2 3 6.0 9
# works
In [11]: df.groupby('group').sum(numeric_only=False)
Out[11]:
int float string category_int timedelta
group
1 3 9.0 ab 15 00:00:03
2 3 6.0 c 9 00:00:03
# same as in master
In [13]: df.groupby('group').min()
Out[13]:
int float string category_int datetime datetimetz timedelta
group
1 1 4.0 a 7 2013-01-01 2013-01-01 00:00:00-05:00 00:00:01
2 3 6.0 c 9 2013-01-03 2013-01-03 00:00:00-05:00 00:00:03
```
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.str.replace does not accept a repl function
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| 1 | 2017-01-04T14:18:48Z | 2017-01-23T18:11:50Z | 2017-01-23T18:11:50Z |
CONTRIBUTOR
| null |
I wanted to pass a function as `repl` to `.str.replace` so it was used by `re.sub`. But it seems that a bug fix for #13438 prevented that to happen. What are your thoughts about this?
#### Code Sample
```python
s = pd.Series(['foo', 'bar', 'baz'])
s.str.replace(r'[aeiou]', lambda m: m.group().upper())
```
#### Problem description
```pytb
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-26-bc58c5c60bd2> in <module>()
----> 1 s.str.replace(r'[aeiou]', lambda m: m.group().upper())
/anaconda3/lib/python3.4/site-packages/pandas/core/strings.py in replace(self, pat, repl, n, case, flags)
1504 def replace(self, pat, repl, n=-1, case=True, flags=0):
1505 result = str_replace(self._data, pat, repl, n=n, case=case,
-> 1506 flags=flags)
1507 return self._wrap_result(result)
1508
/anaconda3/lib/python3.4/site-packages/pandas/core/strings.py in str_replace(arr, pat, repl, n, case, flags)
320 # Check whether repl is valid (GH 13438)
321 if not is_string_like(repl):
--> 322 raise TypeError("repl must be a string")
323 use_re = not case or len(pat) > 1 or flags
324
TypeError: repl must be a string
```
#### Expected Output
0 fOO
1 bAr
2 bAz
dtype: object
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.5.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-573.3.1.el6.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: 0.19.0
nose: 1.3.7
pip: 9.0.1
setuptools: 18.1
Cython: 0.25.1
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 5.0.0
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.0
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.14
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.8
boto: 2.38.0
pandas_datareader: None
</details>
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BUG: Fix for .str.replace with repl function
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| 15 | 2017-01-04T15:30:09Z | 2017-01-24T08:04:48Z | 2017-01-23T18:11:50Z |
CONTRIBUTOR
| null |
.str.replace now accepts a callable (function) as replacement string. It now raises a TypeError when repl is not string like nor a callable. Docstring updated accordingly.
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
- [x] closes #15055
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DOC: Small corrections to describe docstring
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| 1 | 2017-01-04T16:31:18Z | 2017-01-05T18:20:52Z | 2017-01-05T09:44:24Z |
CONTRIBUTOR
| null |
I've made a number of small changes to an earlier pull request revising the describe documentation to bring it closer to pandas official style. I learned this rules as part of a subsequent request expanding the concat documentation and it seemed right to loop back here.
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When running set_index on a categorical to a MultiIndex, it gets coerced to a string.
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| 15 | 2017-01-04T17:39:51Z | 2020-01-21T01:06:11Z | 2020-01-21T01:06:11Z |
CONTRIBUTOR
| null |
Hello!
I apologize if this expected behavior. This is relatively similar to [this StackOverflow question](http://stackoverflow.com/questions/30574740/pandas-multiindex-levels-custom-sort).
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
x = pd.Categorical(['apples', 'dairy', 'chicken', 'beef', 'apples', 'dairy', 'chicken'], categories=['apples', 'dairy', 'beef', 'chicken'])
y = pd.Series([1, 2, 1, 2, 1, 2, 1])
z = pd.Series([3, 4, 2, 1, 3, 2, 1])
df = pd.DataFrame({'z': z, 'x': x, 'y':y})
df.set_index(['x', 'y']).sort_index()
df.sort_values('x')
```
#### Problem description
I would like to sort and group-by a column in a custom way. In the example above, I've ordered a categorical (it could be a string) in a way that makes intuitive sense. In this example, I want fruits first, followed by dairy, followed by meats.
#### Expected Output
When the categorical is in a MultiIndex, `set_index` seems to coerce the categorical to a string before adding it to the index. It would be nicer if pandas kept the categorical ordering for the index.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: 0.8.0.dev0+7e6b94b
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: 0.7.9.None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTAwMTY1Mzk5
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ENH: Accept callable for skiprows in read_csv
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| 12 | 2017-01-04T18:36:32Z | 2017-01-14T17:05:59Z | 2017-01-14T17:05:52Z |
MEMBER
| null |
Title is self-explanatory.
xref #10882.
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MDU6SXNzdWUxOTg3OTI0Njk=
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ENH: allow pd.merge / join to create a MultiIndex result for columns
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| 7 | 2017-01-04T19:39:19Z | 2021-05-02T01:07:18Z | null |
CONTRIBUTOR
| null |
If no ``suffixes`` are specified, you get '_x', '_y' behavior.
```
In [76]: a = pd.DataFrame(dict(a=np.arange(5), b=np.arange(5), c=np.arange(5)))
In [77]: b = pd.DataFrame(dict(a=np.arange(6), b=[0,1,2,3,5,6]))
In [78]: a
Out[78]:
a b c
0 0 0 0
1 1 1 1
2 2 2 2
3 3 3 3
4 4 4 4
In [79]: b
Out[79]:
a b
0 0 0
1 1 1
2 2 2
3 3 3
4 4 5
5 5 6
In [80]: res = pd.merge(a, b, left_index=True, right_index=True)
In [81]: res
Out[81]:
a_x b_x c a_y b_y
0 0 0 0 0 0
1 1 1 1 1 1
2 2 2 2 2 2
3 3 3 3 3 3
4 4 4 4 4 5
```
If can be useful to allow ``suffix='expand'`` (or something like this). to enable an automatic
creation of a column multiindex (this is analagous to ``keys=`` arg in ``pd.concat``)
```
In [82]: res.columns = res.columns.str.split('_', expand=True)
In [83]: res = res.sort_index(axis=1)
In [84]: res
Out[84]:
a b c
x y x y NaN
0 0 0 0 0 0
1 1 1 1 1 1
2 2 2 2 2 2
3 3 3 3 3 3
4 4 4 4 5 4
```
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MDU6SXNzdWUxOTg4MTM1ODQ=
| 15,061 |
weird Pandas version name after update with anaconda?
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| 2 | 2017-01-04T21:20:03Z | 2017-01-06T02:34:34Z | 2017-01-05T08:58:32Z |
NONE
| null |
Hello there,
Happy new year! Just updated my `pandas` package using `anaconda` (`conda update --all`) and `pd.__version__ ` gives:
`u'0.19.2+0.g825876c.dirty'`
```
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None
pandas: 0.19.2+0.g825876c.dirty
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.6
lxml: 3.7.0
bs4: 4.5.3
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.4
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.45.0
pandas_datareader: None
```
Is that expected/normal?
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add the 'name' attribute to dataframes that go through apply_frame_axis0
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| 12 | 2017-01-05T00:22:46Z | 2017-03-25T16:15:40Z | 2017-03-25T16:15:15Z |
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
Previously, if you did `group.name` in the applied function, it would fail and fall back to the slower path because the attribute did not exist.
`shape_before` was unused.
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Constructing a Period having a multiple of the freq ignores the freq multiplier
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[] | null | 9 | 2017-01-05T00:22:54Z | 2020-05-11T02:15:20Z | null |
CONTRIBUTOR
| null |
When using a multiple of the freq, the `period_range` function behaves as expected - e.g. for a half-yearly frequency (with a `DEC` anchor) it constructs a `PeriodIndex` with periods starting on the 1st and 3rd quarters:
```python
In [27]: pd.period_range('01-Jan-2017', '01-Jan-2019', freq='2Q-DEC')[:-1]
Out[27]: PeriodIndex(['2017Q1', '2017Q3', '2018Q1', '2018Q3'], dtype='period[2Q-DEC]', freq='2Q-DEC')
```
I would expect that when constructing a `Period` with a freq of `2Q-DEC` that you should only be able to get periods starting on the 1st and 3rd quarters - i.e. any date in[Q1, Q3) would give a period starting on Q1 and any date in [Q3, Q1) would give a period starting on Q3. What currently happens is that it appears to ignore the frequency multiplier and treat the periods if they were simply straight quarters:
```python
In [31]: [pd.Period('01-Feb-2017', freq='2Q'),
...: pd.Period('01-May-2017', freq='2Q'),
...: pd.Period('01-Aug-2017', freq='2Q'),
...: pd.Period('01-Nov-2017', freq='2Q')]
Out[31]:
[Period('2017Q1', '2Q-DEC'),
Period('2017Q2', '2Q-DEC'),
Period('2017Q3', '2Q-DEC'),
Period('2017Q4', '2Q-DEC')]
```
i.e. I would expect instead:
```python
[Period('2017Q1', '2Q-DEC'),
Period('2017Q1', '2Q-DEC'),
Period('2017Q3', '2Q-DEC'),
Period('2017Q3', '2Q-DEC')]
```
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pathlib support in pytables
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[] | null | 3 | 2017-01-05T00:46:41Z | 2017-03-29T09:32:42Z | 2017-03-28T18:03:08Z |
CONTRIBUTOR
| null |
- [ ] closes #14705
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
As we talked about on gitter, I added the line (https://github.com/pandas-dev/pandas/blob/master/pandas/io/pytables.py#L266) to the HDFStore.open function. Let me know if I misunderstood or should make further edits, thank you!
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| 198,854,136 |
MDExOlB1bGxSZXF1ZXN0MTAwMjE5NjQ4
| 15,065 |
testing another pytest commit
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[] | null | 1 | 2017-01-05T01:26:00Z | 2017-04-05T02:07:16Z | 2017-01-05T14:02:39Z |
CONTRIBUTOR
| null |
Wondering if there's some strange caching on my other commit because [this](https://travis-ci.org/pandas-dev/pandas/jobs/189014114) failure doesn't make sense. Even nose is failing on those tests :/
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BUG: Patch missing data handling with usecols
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| 13 | 2017-01-05T05:05:57Z | 2017-01-11T07:57:34Z | 2017-01-11T07:57:21Z |
MEMBER
| null |
Patch handling of cases when the first row of a CSV is incomplete and `usecols` is specified.
Closes #6710.
Closes #8985.
xref #14782.
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Adds custom plot formatting for TimedeltaIndex.
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| 20 | 2017-01-05T08:20:50Z | 2018-09-06T13:25:02Z | 2017-02-22T16:06:33Z |
CONTRIBUTOR
| null |
Instead of hacking the PeriodIndex plotting code to work for TimedeltaIndex, this just creates a simple formatter unique to TimedeltaIndex. It does fine for regular intervals up to a day, then the labels and run into eachothter. If this happens, you can use plt.autofmt_xdate() to fix it.
- [x] closes #8711
- [ ] tests passed, none added
- [x] passes ``git diff upstream/master | flake8 --diff``
```python
d.t_sensor_gyro_0__f_x.plot()
plt.grid()
```
```python
index = pd.timedelta_range('1 day 2 hr 30 min 10 s', periods=10, freq='1 d')
s = pd.Series(np.random.randn(len(index)), index)
s.plot()
plt.gcf().autofmt_xdate()
plt.grid()
```
```python
index = pd.timedelta_range('1 day 2 hr 30 min 10 s', periods=10, freq='1 ns')
s = pd.Series(np.random.randn(len(index)), index)
s.plot()
plt.gcf().autofmt_xdate()
plt.grid()
```
```python
index = pd.timedelta_range('0s', periods=10, freq='100000 ns')
s = pd.Series(np.random.randn(len(index)), index)
s.plot()
plt.gcf().autofmt_xdate()
plt.grid()
plt.xlabel('time')
plt.ylabel('y')
```
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| 15,068 |
Pre-compute the size of an outer merge and raise MemoryError if merge will be too large
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[] | null | 8 | 2017-01-05T11:19:47Z | 2021-05-08T00:16:06Z | null |
NONE
| null |
When performing an outer merge on large dataframes, `pandas` tries to compute the merge and only when the system has run out of memory will a MemoryError be thrown, or, in more extreme cases, the kernel will simply take over and kill the application.
In the more extreme cases, this makes it impossible to handle the error and / or switch to an alternate approach.
#### Testing:
```python
import math
import pandas as pd
import numpy as np
def create_table(x, y, name_len=2):
alphabet = [a for a in 'abcdefghijklmnopqrstuvwxyz']
# initial table
table = pd.DataFrame(np.random.rand(x, (y - 1)))
table = table.assign(name=[np.random.choice(alphabet) for x in range(len(table))])
# extra rows which can be unique to each frame:
l = math.ceil(len(table) / 12)
c = pd.DataFrame(np.random.rand(l, (y - 1)))
col = []
for x in range(l):
ch = np.random.choice(alphabet)
col.append(''.join([ch for x in range(name_len)]))
table = table.append(c.assign(name=col))
return table.reset_index(drop=True)
df1 = create_table(13000, 4)
df2 = create_table(370000, 4, name_len=3)
df1.merge(df2, on='name', how='outer') # Killed
```
#### Problem description
The real issue at hand here is the use of memory to determine if a merge can be carried out. Because merges are computationally heavy solutions, systems can quickly become unstable as the merge takes place and memory is not left available for other processes.
Secondly, because a MemoryError is not raised until the system has already run out of memory, it is not always possible to handle this within the application.
By first looking at the intersection of groups within the dataframes being merged, it is possible to predict how many rows will be contained within the final merged dataframe. This can be achieved with:
```python
def merge_size(left_frame, right_frame, group_by):
left_groups = left_frame.groupby(group_by).size()
right_groups = right_frame.groupby(group_by).size()
left_keys = set(left_groups.index)
right_keys = set(right_groups.index)
intersection = right_keys & left_keys
left_diff = left_keys - intersection
right_diff = right_keys - intersection
# if the joining column contains np.nan values, these get missed by the intersections
# but are present in the merge. These need to be added separately.
left_nan = len(left_frame.query('{0} != {0}'.format(group_by)))
right_nan = len(right_frame.query('{0} != {0}'.format(group_by)))
left_nan = 1 if left_nan == 0 and right_nan != 0 else left_nan
right_nan = 1 if right_nan == 0 and left_nan != 0 else right_nan
sizes = [(left_groups[group_name] * right_groups[group_name]) for group_name in intersection]
sizes += [left_groups[group_name] for group_name in left_diff]
sizes += [right_groups[group_name] for group_name in right_diff]
sizes += [left_nan * right_nan]
return sum(sizes)
```
Using this method, it is then possible to calculate how much memory will be required by the final merge, such that:
```python
rows = merge_size(df1, df2, 'unique_key') # 14084 * 400834 = 185030090 rows
cols = len(df1.columns) + (len(df1.columns) - 1) # 7
required_memory = (rows * cols) * np.dtype(np.float64).itemsize # 5920962904 bytes
```
At this point it would already be clear that the amount of memory required for the merge far outweighs the amount available to the system and a memory error can be raised early without causing instability within the application or surrounding system.
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-514.2.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.19.0
nose: 1.3.7
pip: 9.0.1
setuptools: 20.10.1
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
</details>
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| 15,069 |
qcut can fail for highly discontinuous data distributions
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[] | null | 6 | 2017-01-05T20:16:07Z | 2020-04-05T02:01:33Z | null |
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
This code fails for any `K`:
```python
# Your code here
K = 100
pd.qcut([0] * K + [1] * (K + 1), 2)
```
#### Problem description
With pandas 0.19.2, I have:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-8-782385490865> in <module>()
----> 1 pd.qcut([0] * K + [1] * (K + 1), 2)
pandas/tools/tile.py in qcut(x, q, labels, retbins, precision)
173 bins = algos.quantile(x, quantiles)
174 return _bins_to_cuts(x, bins, labels=labels, retbins=retbins,
--> 175 precision=precision, include_lowest=True)
176
177
pandas/tools/tile.py in _bins_to_cuts(x, bins, right, labels, retbins, precision, name, include_lowest)
192
193 if len(algos.unique(bins)) < len(bins):
--> 194 raise ValueError('Bin edges must be unique: %s' % repr(bins))
195
196 if include_lowest:
ValueError: Bin edges must be unique: array([0, 1, 1])
```
#### Expected Output
We need some kind of option to decide how to assign values to a quantile bucket in the event that two quantiles have the same value prior to the `searchsorted` call. In this case, the appropriate behavior may be to assign all `1` values to the 50% quantile bucket.
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pd.DataFrame.and_ is not exposed
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[] | null | 5 | 2017-01-05T21:57:28Z | 2021-05-08T00:17:08Z | null |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
data = pd.DataFrame.from_dict({'a': [1, 0, 0, 1], 'b': [0, 0, 1, 1]}, dtype=bool)
flags = pd.Series([1, 0, 0, 1], dtype=bool)
# This raises a warning
data.mul(flags, axis=0)
#/home/joncrall/venv2/local/lib/python2.7/site-packages/pandas/computation/expressions.py:182: #UserWarning: evaluating in Python space because the '*' operator is not supported by numexpr for the #bool dtype, use '&' instead
# But we can't do data & mask because we need to specify axis=0, instead we have to do
data.__and__(mask, axis=0)
# Why is there no data.and_(mask, axis=0)?
```
#### Problem description
I have a use case where I have a data frame of where rows are samples and columns are
sample properties. I also have a Series with associated flags that indicate which rows should have
all values set to false.
At first I thought I could just the `&` operator, but I ran into a broadcasting problem `ValueError: operands could not be broadcast together with shapes (16,) (4,) `. So, I started to look for another way. I don't want to do this inplace so `data[flags] = False` wont work unless I use copy (but that's ugly).
Instead I want to use an `and` function, but I couldn't find one (I'm still a bit new to the pandas API). So, I settled for using multiplication. However, I got a warning when I used `data.mul`. It told me to use `&` which tipped me off that there was some way to call a `and` function with the kwargs `axis=0`. I found that using `data.__and__(flags, axis=0)` will work.
I'm wondering why `data.mul` is exposed as a public version of `data.__mul__` but why is there no `data.and_` version of `data.__and__`?
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-106-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.7
pip: 9.0.1
setuptools: 25.2.0
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.0
statsmodels: 0.8.0rc1
xarray: None
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.7.1
boto: 2.42.0
pandas_datareader: None
</details>
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| 15,071 |
API/CLN: timeseries plotting
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[] | null | 2 | 2017-01-05T23:09:09Z | 2021-05-08T00:17:51Z | null |
MEMBER
| null |
Inspired by the timedelta plotting issue, I thought to look again at our timeseries plotting machinery. We know it is quite complex, and due to that several bugs, inconsistencies or unexpected behaviours exist (eg different results depending on order of plotting several serieses, wrong results when combining different types of time series, among others https://github.com/pandas-dev/pandas/issues/9053, https://github.com/pandas-dev/pandas/issues/6608, https://github.com/pandas-dev/pandas/issues/14322, ..).
There has been some discussion related to this on the tsplot refactor PR of @sinhrks https://github.com/pandas-dev/pandas/pull/7670 (not merged).
One of the reasons of the complexities is the distinction between 'irregular' and 'regular' time series (see eg https://github.com/pandas-dev/pandas/pull/7670#issuecomment-149235874):
- 'regular' time series plotting is based on Periods, and is used for timeseries with a freq or inferred_freq (and also for periods)
- 'irregular' time series plotting is based on the default matplotlib's handling of dates, i.e. converting to 'numerical values' (floats representing time in days since 0001-01-01, http://matplotlib.org/api/dates_api.html). You can always get this also for regular timeseries by passing `x_compat=True`.
So part of the problems and confusions comes from the differences between both (eg different label formatting) and from combining those two. Leading to the question:
### Do we need both types of timeseries plotting?
The question is what the reason is that we convert DatetimeIndex to periods for plotting. The reasons I can think of:
- **Performance**. Currently, the regular plotting is faster (so for a regular series `ts.plot()`is faster as `ts.plot(x_compat=True)`). However, I think this could be solved as most of the time is spent in converting the datetimes to floats (which should be vectorizable).
- **Nicer tick label locations and formatting**. This is a clear plus, our (convoluted) ticklocators and formatters give much nicer results as the default matplotlib (IMO)
Others reasons that I am missing?
But, there are also clear drawbacks. Apart from the things mentioned above, you sometimes get clearly wrong behaviour: see eg the plot in https://github.com/pandas-dev/pandas/pull/7670#issuecomment-57410361. In this case, the dates somewhere within a month, are snapped to the month edges when first a regular series is plotted with monthy frequency.
Another example of 'wrong' plotting is a yearly series (bug with freq 'A-dec', so end of year) plotted in the beginning of a year. See http://nbviewer.jupyter.org/gist/jorisvandenbossche/c0c68dce2fa02f1dfc4a8c343ec88cb6. But of course, in many cases, this behaviour is can also be the desired behaviour.
But do we need both? Would we want, if possible, to unify into one approach?
### Can we unify both approaches?
Can we just use the matplotlib floats for timeseries plotting? Or always use the period-based machinery?
* **Using matplotlib's float-based plotting**
* Do we want this? It will give slightly different behaviour for certain 'regular' cases.
* Assuming we can implement a similar tick locator/formatter comparable to period-based one. But, this may be impossible and the reason we have the current situation?
* But we could keep the PeriodConverter for purely plotting actual Periods
* Problem: float64 representing days can only give a precision of \~5µs, not up to 1ns (note: the period-based plotting can also not handle ns, but can handle 1µs precision).
* **Using period-based plotting for all timeseries**
* Do we want this? (deviates more from matplotlib -> larger difference in plotting dates with and without importing pandas)
* What prevents us from converting an irregular timeseries to Periods? I would think we can find some common freq in almost all cases? (just a high-precision freq if needed)
* **Or create a new converter based on datetime64[ns] (so int64)**?
* Instead of using matplotlibs floats, and instead of varying freq Periods (at least for DatetimeIndex)
* Again, assuming we can have nice tick label locator/formatting for this
cc @pandas-dev/pandas-core (especially @TomAugspurger and @sinhrks, I think you haven been most involved in plotting code recently, or @wesm for historical viewpoint)
I know it's a long issue, but if you could give it a read and give your thoughts on this, very welcome!
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| 2 | 2017-01-06T10:08:49Z | 2018-09-26T10:35:54Z | 2018-09-26T10:35:54Z |
NONE
| null |
#### GroupBy.rolling.agg failed with Column(s) low already selected
```python
data = pd.DataFrame({"stock": [1, 1, 1, 2, 2, 2, 2], "low": [10, 20, 30, 10, 30, 40, 80]})
data.set_index("stock", inplace=True)
# failed
data.groupby(level="stock").rolling(2).agg({"low": {"mean": "mean", "max": "max"}})
# works
data.rolling(2).agg({"low": {"mean": "mean", "max": "max"}})
```
This also hits `groupby().resample.agg`
```python
In [13]: df = pd.DataFrame({"A": pd.to_datetime(['2015', '2017']), "B": [1, 1]})
In [14]: df
Out[14]:
A B
0 2015-01-01 1
1 2017-01-01 1
In [15]: df.set_index("A").groupby([0, 0]).resample("AS")
Out[15]: DatetimeIndexResamplerGroupby [freq=<YearBegin: month=1>, axis=0, closed=left, label=left, convention=e, base=0]
In [16]: df.set_index("A").groupby([0, 0]).resample("AS").agg(['sum', 'count'])
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-16-5f1c18a8d4ac> in <module>()
----> 1 df.set_index("A").groupby([0, 0]).resample("AS").agg(['sum', 'count'])
~/Envs/pandas-dev/lib/python3.6/site-packages/pandas/pandas/core/resample.py in aggregate(self, arg, *args, **kwargs)
339
340 self._set_binner()
--> 341 result, how = self._aggregate(arg, *args, **kwargs)
342 if result is None:
343 result = self._groupby_and_aggregate(arg,
~/Envs/pandas-dev/lib/python3.6/site-packages/pandas/pandas/core/base.py in _aggregate(self, arg, *args, **kwargs)
538 return self._aggregate_multiple_funcs(arg,
539 _level=_level,
--> 540 _axis=_axis), None
541 else:
542 result = None
~/Envs/pandas-dev/lib/python3.6/site-packages/pandas/pandas/core/base.py in _aggregate_multiple_funcs(self, arg, _level, _axis)
583 try:
584 colg = self._gotitem(col, ndim=1, subset=obj[col])
--> 585 results.append(colg.aggregate(arg))
586 keys.append(col)
587 except (TypeError, DataError):
~/Envs/pandas-dev/lib/python3.6/site-packages/pandas/pandas/core/resample.py in aggregate(self, arg, *args, **kwargs)
339
340 self._set_binner()
--> 341 result, how = self._aggregate(arg, *args, **kwargs)
342 if result is None:
343 result = self._groupby_and_aggregate(arg,
~/Envs/pandas-dev/lib/python3.6/site-packages/pandas/pandas/core/base.py in _aggregate(self, arg, *args, **kwargs)
538 return self._aggregate_multiple_funcs(arg,
539 _level=_level,
--> 540 _axis=_axis), None
541 else:
542 result = None
~/Envs/pandas-dev/lib/python3.6/site-packages/pandas/pandas/core/base.py in _aggregate_multiple_funcs(self, arg, _level, _axis)
582 for col in obj:
583 try:
--> 584 colg = self._gotitem(col, ndim=1, subset=obj[col])
585 results.append(colg.aggregate(arg))
586 keys.append(col)
~/Envs/pandas-dev/lib/python3.6/site-packages/pandas/pandas/core/base.py in _gotitem(self, key, ndim, subset)
675 for attr in self._attributes])
676 self = self.__class__(subset,
--> 677 groupby=self._groupby[key],
678 parent=self,
679 **kwargs)
~/Envs/pandas-dev/lib/python3.6/site-packages/pandas/pandas/core/base.py in __getitem__(self, key)
241 if self._selection is not None:
242 raise Exception('Column(s) {selection} already selected'
--> 243 .format(selection=self._selection))
244
245 if isinstance(key, (list, tuple, ABCSeries, ABCIndexClass,
Exception: Column(s) B already selected
```
#### Problem description
It's not work as expected
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-53-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2+0.g825876c.dirty
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: 0.7.9.None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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| 199,180,269 |
MDExOlB1bGxSZXF1ZXN0MTAwNDQ4MzE0
| 15,073 |
PERF: improve perf of float-based timeseries plotting
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| 3 | 2017-01-06T11:45:05Z | 2017-01-10T00:35:45Z | 2017-01-09T19:03:48Z |
MEMBER
| null |
xref #15071
`_dt_to_float_ordinal` was already vectorized, so no need to map it on the index.
So now is 'irregular' plotting even faster as period-based plotting:
```
$ asv continuous upstream/master HEAD -b TimeseriesPlotting
...
· Running 4 total benchmarks (2 commits * 1 environments * 2 benchmarks)
[ 0.00%] · For pandas commit hash 4fc0dca6:
[ 0.00%] ·· Benchmarking conda-py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 25.00%] ··· Running plotting.TimeseriesPlotting.time_plot_regular 126.57ms
[ 50.00%] ··· Running plotting.TimeseriesPlotting.time_plot_regular_compat 87.57ms
[ 50.00%] · For pandas commit hash de09c988:
[ 50.00%] ·· Benchmarking conda-py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 75.00%] ··· Running plotting.TimeseriesPlotting.time_plot_regular 126.01ms
[100.00%] ··· Running plotting.TimeseriesPlotting.time_plot_regular_compat 305.58ms
before after ratio
[de09c988] [4fc0dca6]
- 305.58ms 87.57ms 0.29 plotting.TimeseriesPlotting.time_plot_regular_compat
```
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MDU6SXNzdWUxOTkxOTEwMTg=
| 15,074 |
Inconsistent plotting and crashing python on plotting with irregular time series
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[] | null | 1 | 2017-01-06T12:54:11Z | 2021-05-08T00:18:21Z | null |
CONTRIBUTOR
| null |
I believe it relates to the discussion in #15071, but I still think that reporting a test-case would be useful.
Synthetic example of two series
```python
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
d1 = ['2015-01-05', '2015-01-06', '2015-01-07', '2015-01-08',
'2015-01-09', '2015-01-12', '2015-01-13', '2015-01-14',
'2015-01-15', '2015-01-16', '2015-01-19', '2015-01-20',
'2015-01-21', '2015-01-22', '2015-01-23', '2015-01-26',
'2015-01-27', '2015-01-28', '2015-01-29', '2015-01-30',
'2015-02-02', '2015-02-03', '2015-02-04', '2015-02-05',
'2015-02-06', '2015-02-09', '2015-02-10', '2015-02-11',
'2015-02-12', '2015-02-13', '2015-02-16', '2015-02-17',
'2015-02-25', '2015-02-26', '2015-02-27', '2015-03-02',
'2015-03-03', '2015-03-04', '2015-03-05', '2015-03-06',
'2015-03-09', '2015-03-10', '2015-03-11']
d2 = ['2015-01-31', '2015-02-28', '2015-03-31', '2015-04-30']
t1 = pd.Series(np.random.randn(len(d1)), index=pd.to_datetime(d1)).cumsum()+10
t2 = pd.Series(np.random.randn(len(d2)), index=pd.to_datetime(d2)).cumsum()+1
```
#### Problem description
Plotting it this way is done correctly:
```python
t1.plot()
t2.plot()
```
If plotting in opposite order, it gives incorrect result:
```python
t2.plot()
t1.plot()
```
Same problem if using `secondary_y`:
```python
t2.plot()
t1.plot(secondary_y=True)
```
And if I try to plot it in the first order using `secondary_y` - it **crashes the python** (in IPython notebook - on the cell evaluation, in console - on `plt.show()`):
```python
t1.plot()
t2.plot(secondary_y=True)
```
P.s. using `plot(..., x_compat=True)` resolves these issues.
#### Output of ``pd.show_versions()``
The pandas version is `0.19.2+0.g825876c.dirty`, which was installed as a default option by anaconda.
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 0, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2+0.g825876c.dirty
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.0
bs4: 4.5.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.1.3
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.43.0
pandas_datareader: 0.2.0
</details>
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MDU6SXNzdWUxOTkxOTYzODA=
| 15,075 |
Determine datetime_format from more than one element
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| 6 | 2017-01-06T13:28:16Z | 2017-01-06T15:13:24Z | 2017-01-06T13:33:31Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
series_1 = pd.Series(['13/03/2000', '01/03/2000', '02/03/2000']) # dates in European format
series_2 = pd.Series(['01/03/2000', '13/03/2000', '02/03/2000']) # same dates, reordered
converted_1 = pd.to_datetime(series_1, infer_datetime_format=True)
converted_2 = pd.to_datetime(series_2, infer_datetime_format=True)
print sorted(series_1) == sorted(series_2) # True
print sorted(converted_1) == sorted(converted_2) # False
```
#### Problem description
One would hope to obtain the same result when applying `pd.to_datetime` to the same series twice, but shuffled.
#### Expected Output
My understanding is that the format determined when setting `infer_datetime_format=True` is obtained from the first non-null value of the Series (see function `_guess_datetime_format_for_array` in `tseries.tools`), which explains the result above. I understand this logic in terms of optimizing the operation, and it does work as expected most of the time.
However, I feel like the example provided above is fairly generic. Ideally, the function would find the best `datetime_format` for the entire Series. Any ideas on how to implement this ?
#### Output of ``pd.show_versions()``
<details>INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-57-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.utf8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 28.8.0
Cython: None
numpy: 1.11.3
scipy: 0.18.0
statsmodels: None
xarray: None
IPython: 2.4.1
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
</details>
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Bug : HDFStore is still locked after closing the file
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| 1 | 2017-01-06T13:34:20Z | 2017-01-06T13:37:27Z | 2017-01-06T13:37:06Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
After running this code :
```
store.append(tablename, df_i, format=format, complib=complib)
store.close(); del store
```
#### Problem description
I cannot delete/move the windows h5 store file, it is still locked by Python.exe
#### Expected Output
We expect the file being release.
#### Output of ``pd.show_versions()``
pandas: 0.18.1
<details>
pandas: 0.18.1
</details>
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Comparisons result in different dtypes for empty DataFrames
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| 1 | 2017-01-07T00:08:39Z | 2017-01-21T15:32:28Z | 2017-01-21T15:32:28Z |
CONTRIBUTOR
| null |
The comparison methods (`lt`, `gt`, etc...) return incorrect dtypes for empty dataframes. Interestingly, using the operators instead results in correct dtypes. Correct dtypes are also returned for empty series.
```python
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({'x': [1, 2, 3], 'y': [1., 2., 3.]})
In [3]: empty = df.iloc[:0]
In [4]: df.lt(2).dtypes
Out[4]:
x bool
y bool
dtype: object
In [5]: empty.lt(2).dtypes # Should be all bool, but isn't
Out[5]:
x int64
y float64
dtype: object
In [6]: (df < 2).dtypes
Out[6]:
x bool
y bool
dtype: object
In [7]: (empty < 2).dtypes # Things do work if you use the operator though
Out[7]:
x bool
y bool
dtype: object
In [8]: df.x.lt(2).dtype
Out[8]: dtype('bool')
In [9]: empty.x.lt(2).dtype # Correct dtype for empty series
Out[9]: dtype('bool')
In [10]: pd.__version__
Out[10]: '0.19.2'
```
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BUG: astype(CategoricalDtype) has no effect
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| 9 | 2017-01-07T05:58:58Z | 2017-09-23T18:56:12Z | 2017-09-23T18:56:12Z |
NONE
| null |
The following fails silently to convert the dtype:
```
In [14]: s = pd.Series(['a', 'b', 'c'])
In [15]: s
Out[15]:
0 a
1 b
2 c
dtype: object
In [16]: s.astype(pd.types.dtypes.CategoricalDtype)
Out[16]:
0 a
1 b
2 c
```
I would think this either to work or either to raise an error that the dtype is not undertood.
----
#### Code Sample
When using dictionnary of dtype, it does not convert the dataframe :
Types are not modified whereas df['col']= df['col].astype(type1) works....
```
dtype0= {'brand': np.dtype('int64'),
'category': np.dtype('int64'),
'chain': np.dtype('int64'),
'company': np.dtype('int64'),
'date': np.dtype('O'),
'dept': pandas.types.dtypes.CategoricalDtype,
'id': np.dtype('int64')}
df= df.astype(dtype0)
df.dtypes
```
#### Problem description
When using dictionnary of dtype, it does not convert the dataframe :
Types are not modified whereas df['col']= df['col].astype(type1) works....
#### Expected Output
columns are converted into the desire types.
#### Output of ``pd.show_versions()``
Pandas 0.19.2
<details>
# Paste the output here pd.show_versions() here
</details>
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| 15,079 |
DataFrame.plot.box ignores by argument
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| 7 | 2017-01-07T13:21:43Z | 2021-07-12T13:10:44Z | 2021-07-12T13:10:44Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({'a': ['one', 'one', 'two', 'one', 'two', 'two'],
'b': [1, 2, -1, 2, -2, -3]})
df.boxplot(by='a')
# two boxplots, one for 'one' and the other for 'two'
# this is an expected behaviour
```
```python
df.plot(kind='box', by='a')
# one boxplot, grouping ignored
```
```python
df.plot.box(by='a')
# one boxplot, grouping ignored
```
#### Problem description
It seems that `by` argument is silently ignored and no grouping occurs. Probably, it is related to #9274.
#### Expected Output
I expect that three figures will be the same as the first one.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0+311.gb895968
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.1
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTAwNTc5MzQ5
| 15,080 |
Pypy fixes
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| 7 | 2017-01-07T19:25:36Z | 2017-01-10T20:46:50Z | 2017-01-10T20:32:31Z |
CONTRIBUTOR
| null |
benign changes visa-vis CPython needed to build pandas for PyPy 2.7
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| 15,081 |
Allow indices to be mapped through through dictionaries or series
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| 22 | 2017-01-08T00:17:29Z | 2017-11-25T18:22:47Z | 2017-11-25T14:24:16Z |
CONTRIBUTOR
| null |
- [X] closes #12756
- [X] tests added / passed
- [X] passes ``git diff upstream/master | flake8 --diff``
- [X] whatsnew entry
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Unclear ValueError on core.groupby
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| 6 | 2017-01-08T08:30:27Z | 2017-01-13T14:27:16Z | 2017-01-11T08:08:27Z |
NONE
| null |
#### Code Sample
```python
def test_groupby_aggregate_item_by_item(self):
def test_df():
s = pd.DataFrame(np.array([[13, 14, 15, 16]]),
index=[0],
columns=['b', 'c', 'd', 'e'])
num = np.array([[s, s, s, datetime.strptime('2016-12-28', "%Y-%m-%d"), 'asdf', 24],
[s, s, s, datetime.strptime('2016-12-28', "%Y-%m-%d"), 'asdf', 6]])
columns = ['a', 'b', 'c', 'd', 'e', 'f']
idx = [x for x in xrange(0, len(num))]
return pd.DataFrame(num, index=idx, columns=columns)
c = [test_df().sort_values(['d', 'e', 'f']),
test_df().sort_values(['d', 'e', 'f'])]
df = pd.concat(c)
df = df[["e", "a"]].copy().reset_index(drop=True)
df["e_idx"] = df["e"]
what = [0, 0.5, 0.5, 1]
def x():
df.groupby(["e_idx", "e"])["a"].quantile(what)
self.assertRaisesRegexp(ValueError,
"'SeriesGroupBy' object has no attribute '_aggregate_item_by_item'",
x)
```
#### Problem description
The return message from the ValueError in _GroupBy._aggregate_item_by_item is vague.
```
except (AttributeError):
> raise ValueError
E ValueError
core/groupby.py:592: ValueError
```
The proposed change raises the error message for the user to see.
#### Expected Output
```
except (AttributeError) as e:
> raise ValueError(e)
E ValueError: 'SeriesGroupBy' object has no attribute '_aggregate_item_by_item'
core/groupby.py:592: ValueError
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: b895968a90c918709b01b49db427f9b5ab28c1fe
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0+311.gb895968.dirty
nose: 1.3.7
pip: 9.0.1
setuptools: 32.3.1
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
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lxml: None
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html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
pandas_datareader: None
</details>
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| 15,083 |
BUG: Value error aggregate item by item
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[] | null | 1 | 2017-01-08T08:51:44Z | 2017-01-09T18:22:22Z | 2017-01-09T18:22:22Z |
NONE
| null |
- [x] closes #15082
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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read_html not considering thousands and decimal correctly
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| 1 | 2017-01-09T12:34:24Z | 2017-01-09T19:56:44Z | 2017-01-09T13:05:49Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
def baselink():
return 'http://www2.aneel.gov.br/aplicacoes/capacidadebrasil/GeracaoTipoFase.asp'
formdata={
'tipo':'0',
'fase':'3'
}
import requests
session = requests.Session()
r=session.post(baselink(),data=formdata)
a=pd.read_html(r.text,thousands='.',decimal=',',na_values=['-'],flavor='html5lib')
test=a[1]
```
#### Problem description
The code above will interpret a cell value of "1.401,40" as a numeric type (float) 140140.0, when it should be 1401.40. I am using pandas 0.19.0. I tried with both flavor='html5lib' and flavor=None.
#### Expected Output
The string "1.401,40" should be interpreted as (float) 1401.40.
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: None
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.1.4
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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ERR: Rolling().Apply() on Object-Series should raise internally caught errors
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| 3 | 2017-01-09T12:36:13Z | 2019-10-14T00:31:15Z | 2019-10-14T00:31:15Z |
NONE
| null |
```python
import pandas as pd
s = pd.Series([set([i]) for i in range(10)])
s.rolling(2).apply(lambda x: x[0] | x[1]) # just returns original series - lambda function gets never called)
```
.rolling().apply() on a object-series seems to do nothing. One can even write:
```python
s.rolling(2).apply(lambda x: _non_existing_function_)
```
without getting an exception.
It would be great to get an exception if rolling().apply() can not be used with object-series or even better: A proper working rolling().apply() for object-series :)
The expected output from the rolling().apply() from above would be sth like:
NaN
{1,2}
{2,3}
{3,4}
...
or, as said before, at least an exception.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.39-moby
machine: x86_64
processor:
byteorder: little
LC_ALL: C.UTF-8
LANG: C.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.8.2
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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OSError when reading file with accents in file path
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| 27 | 2017-01-09T14:16:22Z | 2020-10-19T13:39:30Z | 2019-01-14T13:36:12Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
`test.txt` and `test_é.txt` are the same file, only the name change:
```python
pd.read_csv('test.txt')
Out[3]:
1 1 1
0 1 1 1
1 1 1 1
pd.read_csv('test_é.txt')
Traceback (most recent call last):
File "<ipython-input-4-fd67679d1d17>", line 1, in <module>
pd.read_csv('test_é.txt')
File "d:\app\python36\lib\site-packages\pandas\io\parsers.py", line 646, in parser_f
return _read(filepath_or_buffer, kwds)
File "d:\app\python36\lib\site-packages\pandas\io\parsers.py", line 389, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "d:\app\python36\lib\site-packages\pandas\io\parsers.py", line 730, in __init__
self._make_engine(self.engine)
File "d:\app\python36\lib\site-packages\pandas\io\parsers.py", line 923, in _make_engine
self._engine = CParserWrapper(self.f, **self.options)
File "d:\app\python36\lib\site-packages\pandas\io\parsers.py", line 1390, in __init__
self._reader = _parser.TextReader(src, **kwds)
File "pandas\parser.pyx", line 373, in pandas.parser.TextReader.__cinit__ (pandas\parser.c:4184)
File "pandas\parser.pyx", line 669, in pandas.parser.TextReader._setup_parser_source (pandas\parser.c:8471)
OSError: Initializing from file failed
```
#### Problem description
Pandas return OSError when trying to read a file with accents in file path.
The problem is new (Since I upgraded to Python 3.6 and Pandas 0.19.2)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.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: fr
LOCALE: None.None
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 32.3.1
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: None
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999999999
httplib2: None
apiclient: None
sqlalchemy: 1.1.4
pymysql: None
psycopg2: None
jinja2: 2.9.3
boto: None
pandas_datareader: None
</details>
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MDU6SXNzdWUxOTk1OTczMzI=
| 15,087 |
MultiIndex with ordered Categorical level should (maybe) respect ordered
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| 2 | 2017-01-09T16:29:47Z | 2017-01-10T00:46:40Z | 2017-01-10T00:46:40Z |
CONTRIBUTOR
| null |
Currently we lexsort. We should maybe follow the Categorical.ordered attribute. This is potentially a can of worms though
```python
In [30]: import pandas as pd
...:
...: x = pd.Categorical(['apples', 'dairy', 'chicken', 'beef', 'apples', 'dairy', 'chicken'], categories=['apples', 'dairy', 'beef', 'chicken'], ordered=True)
...: y = pd.Series([1, 2, 1, 2, 1, 2, 1])
...: z = pd.Series([3, 4, 2, 1, 3, 2, 1])
...:
...: df = pd.DataFrame({'z': z, 'x': x, 'y':y})
...: df.set_index(['x', 'y']).sort_index()
Out[30]:
z
x y
apples 1 3
1 3
beef 2 1
chicken 1 2
1 1
dairy 2 4
2 2
```
The index is lex-sorted. If it follows the categorical ordering, it'd be
```python
z
x y
apples 1 3
1 3
dairy 2 4
2 2
beef 2 1
chicken 1 2
1 1
```
cc @ thequackdaddy
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TST/BLD: test 3.6 on appveyor
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| 1 | 2017-01-09T17:52:17Z | 2017-01-09T19:52:50Z | 2017-01-09T17:52:23Z |
CONTRIBUTOR
| null |
CLN: clean up appveyor.yml a bit
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| 0 | 2017-01-09T18:48:48Z | 2017-01-09T18:48:54Z | 2017-01-09T18:48:53Z |
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COMPAT: openpyxl deprecation warnings
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| 0 | 2017-01-09T18:57:14Z | 2017-01-21T16:49:22Z | 2017-01-21T16:49:22Z |
CONTRIBUTOR
| null |
other issues about warnings
xref #14519
xref #14539
tons of warnings on openpyxl 2.4.0 (this is under 3.5), but we have the latest on lots of travis builds.
so need to fix these based on the version, IOW. have a function that introspects the version then does a) if < 2.4.0 (or whatever version triggers these), else does the the new thing.
```
(pandas) bash-3.2$ nosetests pandas/io/tests/test_excel.py -v
test_ExcelWriter_dispatch (pandas.io.tests.test_excel.ExcelWriterEngineTests) ... /Users/jreback/miniconda3/envs/pandas/lib/python3.5/site-packages/xlwt/ExcelFormulaLexer.py:53: DeprecationWarning: LOCALE flag with a str pattern is deprecated. Will be an error in 3.6
VERBOSE+LOCALE+IGNORECASE)
ok
test_register_writer (pandas.io.tests.test_excel.ExcelWriterEngineTests) ... ok
SKIP: openpyxl 2.4.0 >= 2.2
test_basics_with_nan (pandas.io.tests.test_excel.Openpyxl22Tests) ... /Users/jreback/miniconda3/envs/pandas/lib/python3.5/site-packages/openpyxl/workbook/workbook.py:179: DeprecationWarning: Call to deprecated function or class remove_sheet (Use wb.remove(worksheet) or del wb[sheetname]).
def remove_sheet(self, worksheet):
/Users/jreback/miniconda3/envs/pandas/lib/python3.5/site-packages/openpyxl/workbook/workbook.py:179: DeprecationWarning: Call to deprecated function or class remove_sheet (Use wb.remove(worksheet) or del wb[sheetname]).
def remove_sheet(self, worksheet):
/Users/jreback/miniconda3/envs/pandas/lib/python3.5/site-packages/openpyxl/workbook/workbook.py:179: DeprecationWarning: Call to deprecated function or class remove_sheet (Use wb.remove(worksheet) or del wb[sheetname]).
def remove_sheet(self, worksheet):
/Users/jreback/miniconda3/envs/pandas/lib/python3.5/site-packages/openpyxl/workbook/workbook.py:179: DeprecationWarning: Call to deprecated function or class remove_sheet (Use wb.remove(worksheet) or del wb[sheetname]).
def remove_sheet(self, worksheet):
ok
test_bool_types (pandas.io.tests.test_excel.Openpyxl22Tests) ... /Users/jreback/miniconda3/envs/pandas/lib/python3.5/site-packages/openpyxl/workbook/workbook.py:179: DeprecationWarning: Call to deprecated function or class remove_sheet (Use wb.remove(worksheet) or del wb[sheetname]).
def remove_sheet(self, worksheet):
/Users/jreback/miniconda3/envs/pandas/lib/python3.5/site-packages/openpyxl/workbook/workbook.py:179: DeprecationWarning: Call to deprecated function or class remove_sheet (Use wb.remove(worksheet) or del wb[sheetname]).
def remove_sheet(self, worksheet):
ok
test_bytes_io (pandas.io.tests.test_excel.Openpyxl22Tests) ... /Users/jreback/miniconda3/envs/pandas/lib/python3.5/site-packages/openpyxl/workbook/workbook.py:179: DeprecationWarning: Call to deprecated function or class remove_sheet (Use wb.remove(worksheet) or del wb[sheetname]).
def remove_sheet(self, worksheet):
ok
```
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pivot_table: precision lost
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| 14 | 2017-01-09T19:52:10Z | 2017-01-10T20:45:17Z | 2017-01-09T20:09:16Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [29]: df = pd.DataFrame([[1, 2, 11111111111111111]], columns=['index', 'type', 'value'])
In [30]: df.dtypes
Out[30]:
index int64
type int64
value int64
dtype: object
In [31]: df.pivot_table(index='index', columns='type', values='value')
Out[31]:
type 2
index
1 11111111111111112
```
#### Problem description
Since `value` is a 64-bit integer, we should be able to present it with zero precision loss.
#### Expected Output
```python
In [31]: df.pivot_table(index='index', columns='type', values='value')
Out[31]:
type 2
index
1 11111111111111111
```
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-514.2.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 9.0.1
setuptools: 28.8.0.post20161110
Cython: 0.23.4
numpy: 1.11.2
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.6.0
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.2
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
</details>
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BUG: encode filepaths on windows in 3.6
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[] | null | 17 | 2017-01-09T20:07:07Z | 2017-02-07T17:16:08Z | 2017-01-31T18:23:21Z |
NONE
| null |
Not for Merge, just a small test for #15086 with AppVeyor.
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BUG: Inconsistent return type for downsampling on resample of empty DataFrame
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| 12 | 2017-01-09T21:58:41Z | 2017-06-13T23:16:07Z | 2017-06-13T23:16:03Z |
CONTRIBUTOR
| null |
- [x] closes #14962
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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| 11 | 2017-01-10T00:42:26Z | 2017-01-12T23:33:39Z | 2017-01-12T23:33:39Z |
CONTRIBUTOR
| null |
xref #15073
https://travis-ci.org/pandas-dev/pandas/jobs/190356982
was failing on tz-aware dtypes. these routines are the same in that they require datetime64ns (but any *also* accepts tz-aware).
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API: Table-wise rolling / expanding / EWM function application
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| 11 | 2017-01-10T01:29:46Z | 2020-12-27T23:02:31Z | 2020-12-27T23:02:31Z |
CONTRIBUTOR
| null |
In https://github.com/pandas-dev/pandas/pull/11603#issuecomment-162113949 (the main PR implementing the deferred API for rolling / expanding / ewm), we discussed how to specify table-wise `apply`s. `Groupby.apply(f)` feeds the entire group (all columns) to `f`. For backwards-compatibility, `.rolling(n).apply(f)` needed to be column-wise.
https://github.com/pandas-dev/pandas/pull/11603#issuecomment-162116556 mentions a possible API like what I added for `.style`
- `axis=0`: apply to each column independently
- `axis=1`: apply to each row independently
- `axis=None`: apply the supplied function to the entire table
So it'd be `df.rolling(n).apply(f, axis=None)`.
Do people like the axis=0 / 1 / None idiom? Is it obvious enough?
This is prompted by @josef-pkt's [post on the mailinglist](https://groups.google.com/forum/#!topic/pydata/FcAT8LBPmlg). Needing a rolling OLS.
An example:
```python
In [2]: import numpy as np
...: import pandas as pd
...:
...: np.random.seed(0)
...: df = pd.DataFrame(np.random.randint(0, 10, size=(10, 2)), columns=["A", "B"])
...: df
...:
Out[2]:
A B
0 5 0
1 3 3
2 7 9
3 3 5
4 2 4
5 7 6
6 8 8
7 1 6
8 7 7
9 8 1
```
For a concrete example, get the table-wise max (this is equivalent to `df.rolling(4).max().max(1)`)
```python
In [10]: df.rolling(4).apply(np.max, axis=None)
Out[10]:
0 NaN
1 NaN
2 NaN
3 9.0
4 9.0
5 9.0
6 8.0
7 8.0
8 8.0
9 8.0
dtype: float64
```
A real example is something like a rolling OLS:
```python
import statsmodels.api as sm
f = lambda x: sm.OLS.from_formula('A ~ B', data=x).fit() # wrong, but w/e
df.rolling(5).apply(f, axis=None)
```
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MDU6SXNzdWUxOTk3NDAyNjE=
| 15,096 |
to_json() line separation broken by backslash in content
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| 1 | 2017-01-10T06:09:11Z | 2017-01-13T20:10:08Z | 2017-01-13T20:10:08Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
>>> df = pd.read_json("file://localhost/home/ubuntu/test.json", lines=True)
>>> df.to_json("test-out.json", orient="records", lines=True)
>>> df = pd.read_json("file://localhost/home/ubuntu/test2.json", lines=True)
>>> df.to_json("test2-out.json", orient="records", lines=True)
```
#### Problem description
I noticed this issue when I saw that a ~8500 row dframe became a ~3400 line file, even though in theory "lines=True" should mean that the number of rows and number of lines are equal. I dug into the code a bit, and it turns out that convert_json_to_lines() does not correctly insert newlines if the json contains a backslash before a double quote, even if the backslash itself is escaped.
(https://github.com/pandas-dev/pandas/blob/v0.19.2/pandas/lib.pyx#L1114)
Here are the test files I used with the code sample above, and the outputs I got:
```
ubuntu@ip:~$ cat test.json
{"test": "qwer"}
{"test": "asdf \\"}
{"test": "zxcv"}
ubuntu@ip:~$ cat test-out.json
{"test":"qwer"}
{"test":"asdf \\"},{"test":"zxcv"}ubuntu@ip:~$
ubuntu@ip:~$ cat test2.json
{"test": "123"}
{"test": "234 \\"}
{"test": "345"}
{"test": "456 \\"}
{"test": "567"}
ubuntu@ip:~$ cat test2-out.json
{"test":"123"}
{"test":"234 \\"},{"test":"345"},{"test":"456 \\"}
{"test":"567"}ubuntu@ip:~$
```
#### Expected Output
"-out" files should be identical to originals.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-68-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: 2.43.0
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTAwODk0NzM5
| 15,097 |
fix misspelled word
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| 1 | 2017-01-10T15:28:36Z | 2017-01-10T15:37:18Z | 2017-01-10T15:37:15Z |
CONTRIBUTOR
| null |
`colunwise` should be `columnwise`
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MDExOlB1bGxSZXF1ZXN0MTAwODk1Njcw
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DEPR: remove pd.TimeSeries & Series.is_time_series
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| 19 | 2017-01-10T15:33:20Z | 2017-02-28T14:17:54Z | 2017-02-28T14:17:54Z |
CONTRIBUTOR
| null |
xref #10890
|
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DEPR: deprecate sortlevel in favor of sort_index
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| 2 | 2017-01-10T15:38:41Z | 2017-01-11T08:02:34Z | 2017-01-11T08:02:34Z |
CONTRIBUTOR
| null |
closes #14279
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| 0 | 2017-01-10T18:15:51Z | 2017-01-10T21:10:05Z | 2017-01-10T21:10:05Z |
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MDExOlB1bGxSZXF1ZXN0MTAwOTI2Mjc2
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CLN: check NULL and have similar __dealloc__ code for all hashtables
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TST: add network decorator on parser network tests for compressed url in parser
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| 0 | 2017-01-10T21:09:15Z | 2017-01-10T21:46:55Z | 2017-01-10T21:46:55Z |
CONTRIBUTOR
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i have a problem with installing some packages on my pycharm
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| 13 | 2017-01-11T00:50:58Z | 2020-04-09T17:48:25Z | 2017-01-11T01:24:24Z |
NONE
| null |
#
```python
###
```
#### Problem description
Hi.
i have a problem with installing some packages on my pycharm.
when i try to install a package for example "Pandas",on my pycharm,i got this error:
_RuntimeError: Broken toolchain: cannot link a simple C program_
i'm an amateur with programming,and i couldn't fix it for a week!
can you please help me and tell me what should i do?
i appreciate that,i search all internet for solution,but i can't find it.
thank you and eexcuse me for my bad english.
and i should say my pip is updated i guess!
i upload the screen shot of error:
</details>
proposed solution from program:
Try to run this command from the system terminal. Make sure that you use the correct version of 'pip' installed for your Python interpreter located at 'C:\Users\Arien Lupin\AppData\Local\Programs\Python\Python36-32\python.exe'.
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API/ENH: relabel method
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| 15 | 2017-01-11T01:22:19Z | 2017-03-20T15:02:51Z | 2017-03-20T15:02:34Z |
CONTRIBUTOR
| null |
Alt to #15029
- [x] closes #14829
- [ ] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
small example
```python
In [7]: df = pd.DataFrame({'a': [1,2], 'b': [3, 4]})
In [8]: df.relabel(columns=['J', 'K'])
Out[8]:
J K
0 1 3
1 2 4
```
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`sort=False` option to stack/unstack/pivot
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| 11 | 2017-01-11T06:24:45Z | 2021-05-08T00:19:44Z | null |
NONE
| null |
It would be really nice if there was a `sort=False` option on stack/unstack and pivot. (Preferably the default)
It is reasonably common to have data in non-standard order that actually provides information (in my case, I have model names, and the order of the names denotes complexity of the models). Stacking or unstacking currently loses all of this information, with no way to retrieve it. That does not seem like a sensible default to me.
It would be relatively easy to work around a non-sorted stack/unstack method (using .sort_index). To go the other way is less trivial, requiring the user to store a list of the values in the necessary order.
I actually find it hard to think of a situation where a sort on unstack would be more useful...
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Groupby + sum by multiple columns on an empty DataFrame drops list of columns
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| 9 | 2017-01-11T09:54:13Z | 2021-05-19T12:53:58Z | 2021-05-19T12:53:58Z |
NONE
| null |
#### Code Sample
```python
import pandas as pd
print pd.DataFrame(data=[[1,2,3]], columns=['A', 'B', 'C'])\
.groupby(['A', 'B'])\
.sum()\
.reset_index()\
.columns\
.tolist()
# ['A', 'B', 'C']
print pd.DataFrame(data=[], columns=['A', 'B', 'C'])\
.groupby(['A'])\
.sum()\
.reset_index()\
.columns\
.tolist()
# ['A', 'B', 'C']
print pd.DataFrame(data=[], columns=['A', 'B', 'C'])\
.groupby(['A', 'B'])\
.sum()\
.reset_index()\
.columns\
.tolist()
# ['index']
```
#### Problem description
As the original list of columns is lost in the second case, I have to handle empty data frames differently, or add columns back by myself, both of which are inconvenient.
#### Expected Output
The list of columns is expected to be equal to the original one from data frame
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-57-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 9.0.1
setuptools: 28.8.0
Cython: None
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.1.4
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/15107
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|
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| 200,053,598 |
MDU6SXNzdWUyMDAwNTM1OTg=
| 15,107 |
unexpected behavior with NaN in multiIndex
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"default": false,
"description": "Related to indexing on series/frames, not to indexes themselves",
"id": 2822098,
"name": "Indexing",
"node_id": "MDU6TGFiZWwyODIyMDk4",
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"url": "https://api.github.com/repos/pandas-dev/pandas/labels/MultiIndex"
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] |
closed
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"description": "Changes that would be nice to have in the next release. These issues are not blocking. They will be pushed to the next release if no one has time to fix them.",
"due_on": null,
"html_url": "https://github.com/pandas-dev/pandas/milestone/32",
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"open_issues": 1053,
"state": "open",
"title": "Contributions Welcome",
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}
| 5 | 2017-01-11T10:19:05Z | 2019-06-28T19:16:38Z | 2019-06-28T19:16:38Z |
NONE
| null |
```
df = pd.DataFrame({'col':[1, 2, 3, 4, 5], 'ind1':['a','b','c','d',np.nan], 'ind2':[1,2,3,4,5] })
df.set_index(['ind1','ind2'],inplace=True)
df.loc['a']
# col
# ind2
# 1 1
df.loc[['a']]
# col
# ind1 ind2
# a 1 1
# 5 5
df.loc[['a']].reset_index()
# ind1 ind2 col
# 0 a 1 1
# 1 a 5 5
```
#### Problem description
Is it normal replacing np.nan with 'a' ?
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-327.28.3.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: 0.19.2
nose: None
pip: 9.0.1
setuptools: 32.0.0
Cython: None
numpy: 1.11.3
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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