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NONE
| null |
Per request of issue #13950, added documentation clarifying alignment of DataFrame Objects and assignment of subset of one DataFrame to subset indices of another Dataframe.
Main issue was that if indices did not match, behavior of Dataframe would render rows of non-matched indices as NaN. This could be corrected by assigning a non-Dataframe object of same appropriate dimension and value configuration to the proper Dataframe indices.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
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| 3 | 2017-02-25T22:20:06Z | 2017-02-25T22:26:47Z | 2017-02-25T22:26:07Z |
MEMBER
| null |
Per discussion in <a href="https://github.com/pandas-dev/pandas/pull/15414#discussion_r103089692">#15414 (comment) </a>.
@jorisvandenbossche
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PERF: faster unstacking
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| 2 | 2017-02-26T02:25:04Z | 2020-04-09T17:23:38Z | 2017-03-05T22:11:29Z |
CONTRIBUTOR
| null |
closes #15503
so on a non-masked unstack (IOW, a fully product multi-index for example), this is now just
a simple reshape. On a masked unstack, it now will have a much lower O constant, as its in cython, and with release the GIL.
0.19.2 / master
```
In [2]: m = 100
...: n = 1000
...:
...: levels = np.arange(m)
...: index = pd.MultiIndex.from_product([levels]*2)
...: columns = np.arange(n)
...: values = np.arange(m*m*n).reshape(m*m, n)
...: df = pd.DataFrame(values, index, columns)
...:
In [3]: %timeit df.unstack()
1 loop, best of 3: 285 ms per loop
In [4]: df2 = df.iloc[:-1]
In [5]: %timeit df2.unstack()
1 loop, best of 3: 306 ms per loop
```
PR
```
In [2]: %timeit df.unstack()
10 loops, best of 3: 70 ms per loop
In [3]: df2 = df.iloc[:-1]
# & releasing the GIL here.
In [4]: %timeit df2.unstack()
1 loop, best of 3: 191 ms per loop
```
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| 3 | 2017-02-26T03:30:35Z | 2017-03-04T21:41:59Z | 2017-03-04T21:41:53Z |
NONE
| null |
- [x] closes #15193
- [x] tests added / passed
- [] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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MDU6SXNzdWUyMTAzMDIwMjU=
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rolling.agg ignores NaNs
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| 3 | 2017-02-26T09:20:15Z | 2017-02-26T15:16:06Z | 2017-02-26T11:57:13Z |
NONE
| null |
```python
from pandas import DataFrame, Timestamp
from numpy import nan
b = DataFrame({u'average_rrc_connected': {Timestamp('2016-11-29 17:30:00', freq='15T'): nan, Timestamp('2016-11-29 16:00:00', freq='15T'): 0.0, Timestamp('2016-11-29 17:15:00', freq='15T'): nan, Timestamp('2016-11-29 17:00:00', freq='15T'): 0.0, Timestamp('2016-11-29 15:30:00', freq='15T'): nan, Timestamp('2016-11-29 15:45:00', freq='15T'): 0.0, Timestamp('2016-11-29 16:15:00', freq='15T'): 0.0, Timestamp('2016-11-29 16:30:00', freq='15T'): 0.0, Timestamp('2016-11-29 16:45:00', freq='15T'): 0.0}})
In [26]: b
Out[26]:
average_rrc_connected
2016-11-29 15:30:00 NaN
2016-11-29 15:45:00 0.0
2016-11-29 16:00:00 0.0
2016-11-29 16:15:00 0.0
2016-11-29 16:30:00 0.0
2016-11-29 16:45:00 0.0
2016-11-29 17:00:00 0.0
2016-11-29 17:15:00 NaN
2016-11-29 17:30:00 NaN
rb = b.rolling(window=2)
```
Show the rolling window:
```python
def show_x(x):
print x
return x[1]
In [28]: rs_b = rb.agg(show_x)
[ 0. 0.]
[ 0. 0.]
[ 0. 0.]
[ 0. 0.]
[ 0. 0.]
In [29]: rs_b
Out[29]:
average_rrc_connected
2016-11-29 15:30:00 NaN
2016-11-29 15:45:00 NaN
2016-11-29 16:00:00 0.0
2016-11-29 16:15:00 0.0
2016-11-29 16:30:00 0.0
2016-11-29 16:45:00 0.0
2016-11-29 17:00:00 0.0
2016-11-29 17:15:00 NaN
2016-11-29 17:30:00 NaN
```
#### Problem description
In this example, I expect the 15:45 slot to show 0.0 instead of NaN. This is to show windows that include NaNs are automatically aggregated as NaN, even if the aggregation function does something different.
What I'm actually trying to achieve is a kind of "rolling reshape", where "historical" data from the beginning of the window (including NaNs) is placed in columns alongside each other. Perhaps there's a better way to achieve this:
```python
def get_index(i):
f = lambda x: x[i]
f.__name__ = str(i)
return f
rs_b2 = rb.agg({col: {i - 1: get_index(i) for i in range(2)} for col in b.columns})
In [37]: rs_b2
Out[37]:
average_rrc_connected
0 -1
2016-11-29 15:30:00 NaN NaN
2016-11-29 15:45:00 NaN NaN
2016-11-29 16:00:00 0.0 0.0
2016-11-29 16:15:00 0.0 0.0
2016-11-29 16:30:00 0.0 0.0
2016-11-29 16:45:00 0.0 0.0
2016-11-29 17:00:00 0.0 0.0
2016-11-29 17:15:00 NaN NaN
2016-11-29 17:30:00 NaN NaN
```
Where the expected output would be for the 15:45 slot to have `-1: NaN, 0: 0.0`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Darwin
OS-release: 16.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 28.6.1
Cython: 0.25.2
numpy: 1.12.0
scipy: 0.18.1
statsmodels: 0.8.0
xarray: 0.9.1
IPython: 5.2.2
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.2
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.3
bs4: 4.5.3
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.9.5
boto: 2.45.0
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTA4MDE2OTQ3
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API: add top-level functions as method
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[] | null | 16 | 2017-02-26T19:03:52Z | 2017-02-27T16:32:29Z | 2017-02-27T16:31:45Z |
CONTRIBUTOR
| null |
- [ ] closes #12640
- [x] pd.crosstab (DataFrame)
- [x] pd.melt (DataFrame) (xref #14876)
- [x] pd.pivot_table (might need a new name as would be confusing with DataFrame.pivot)
- [x] pd.get_dummies (both)
- [x] pd.cut/qcut (only on Series)
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
|
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MDU6SXNzdWUyMTAzNjIyNzc=
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TST: a tiny bit more integration testing in pandas for gbq
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| 2 | 2017-02-27T00:25:15Z | 2021-08-26T18:24:23Z | 2021-08-26T18:24:23Z |
CONTRIBUTOR
| null |
after #15484
just need to have a full on write-table (which we do), and read (which we do), but need to do a proper comparison.
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| 7 | 2017-02-27T00:30:03Z | 2017-02-27T20:43:53Z | 2017-02-27T20:26:01Z |
CONTRIBUTOR
| null |
- [ ] closes #9351
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
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VIS: plot method cannot handle new 'C0'-like colors (matplotlib 2.0)
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| 4 | 2017-02-27T08:50:03Z | 2019-06-07T06:45:31Z | 2017-04-12T20:49:49Z |
MEMBER
| null |
matplotlib 2.0 introduced a few 2-character color abbreviations: C0, C1, C2, ... to refer to the colors of the current color cycle
(very handy! this way you can refer to a color of the color cycle without hardcoding it, and use it as a shortcut for `color='tab:blue'` if you want the new blue instead of the ugly `color='b'`)
But, it seems pandas cannot handle this at the moment, as it tries to interpret it as multiple colors:
```
df = pd.DataFrame(np.random.randn(10,3), columns=['a', 'b', 'c'])
df.plot(color='C0')
```
gives `ValueError: 'C0' can be parsed as both single color and color cycle. Specify each color using a list like ['C0'] or ['C', '0']`
I know the error message already gives the solution (using `color=['C0']`), but I think it would be nice to special case those to have to working by default.
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BUG: DataFrame.reindex sometimes promotes float16 / float32
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| 2 | 2017-02-27T10:32:47Z | 2018-01-12T11:08:47Z | 2018-01-12T11:08:47Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> x = pd.DataFrame([[0]], dtype='float32')
>>> [x.reindex(i, [1]).dtypes[1] for i in [[0], [1]]]
[dtype('float64'), dtype('float32')]
```
#### Problem description
When a float16 / float32 DataFrame gets reindexed, new columns of nans are sometimes created as float64, depending on the specific indices requested. I think the expected behaviour is to create nans at the largest type of existing columns? In any case, the type of an empty column probably shouldn't depend on which indices it couldn't find.
#### Expected Output
```python
[dtype('float32'), dtype('float32')]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.8-100.fc24.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: C
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.8.0
xarray: 0.9.1
IPython: 4.2.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.2
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTA4MTE4MTkz
| 15,518 |
Rank categorical perf
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| 9 | 2017-02-27T13:54:13Z | 2017-03-01T23:48:25Z | 2017-03-01T21:56:17Z |
CONTRIBUTOR
| null |
- [x] closes #15498
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew
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MDU6SXNzdWUyMTA1MDUwMDM=
| 15,519 |
TST: move pandas/tests/io/test_date_converters.py to pandas/tests/io/parsers/parse_dates.py
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| 2 | 2017-02-27T14:49:06Z | 2017-03-18T02:00:51Z | 2017-03-18T02:00:51Z |
CONTRIBUTOR
| null |
see https://github.com/pandas-dev/pandas/pull/15378#issuecomment-282740398
we have centralized ``parse_dates`` testsing with all parsers. Need to move these tests appropriately.
|
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| 210,531,101 |
MDU6SXNzdWUyMTA1MzExMDE=
| 15,520 |
BUG: invalid dtypes should raise
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| 5 | 2017-02-27T16:16:01Z | 2017-04-26T13:32:59Z | 2017-04-26T13:32:58Z |
CONTRIBUTOR
| null |
xref https://github.com/dask/dask/issues/1965
when an explict dtype is provided, we should raise if its not a valid ``numpy/pandas`` dtype.
```
In [1]: pd.Series([], name='time',dtype=pd.Timestamp)
Out[1]: Series([], Name: time, dtype: object)
```
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MDExOlB1bGxSZXF1ZXN0MTA4MTUzNTgw
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API: add top-level melt function as method
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| 20 | 2017-02-27T16:43:23Z | 2017-04-05T07:12:09Z | 2017-04-04T17:50:58Z |
CONTRIBUTOR
| null |
- [x] (progress on?) #12640
- [x] pd.melt (DataFrame) (xref #14876)
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
This is continued from [PR#15513](https://github.com/pandas-dev/pandas/pull/15513).
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DataFrame.fillna() working on row vector instead of column vector?
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| 6 | 2017-02-27T20:23:37Z | 2018-07-02T23:44:12Z | 2018-07-02T23:44:12Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> df.head(5)
time id bid bid_depth bid_depth_total \
0 2017-02-27 11:34:31+00:00 105 148.0 497.0 216589.0
1 2017-02-27 11:34:35+00:00 105 NaN NaN NaN
2 2017-02-27 11:34:38+00:00 105 NaN NaN NaN
3 2017-02-27 11:34:40+00:00 105 NaN NaN NaN
4 2017-02-27 11:34:41+00:00 105 NaN NaN NaN
bid_number offer offer_depth offer_depth_total offer_number open \
0 243.0 148.1 14192.0 530373.0 503.0 147.5
1 NaN NaN 14272.0 530453.0 504.0 NaN
2 NaN NaN 14192.0 530373.0 503.0 NaN
3 NaN NaN 14272.0 530453.0 504.0 NaN
4 NaN NaN 14492.0 530673.0 505.0 NaN
high low last change change_percent volume value trades
0 148.2 147.3 148.0 0.9 0.61 1286830.0 190224000.0 2112.0
1 NaN NaN NaN NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN NaN NaN NaN
>>> df.fillna(method='pad')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python3.6/site-packages/pandas/core/frame.py", line 2842, in fillna
downcast=downcast, **kwargs)
File "/usr/lib/python3.6/site-packages/pandas/core/generic.py", line 3250, in fillna
downcast=downcast)
File "/usr/lib/python3.6/site-packages/pandas/core/internals.py", line 3177, in interpolate
return self.apply('interpolate', **kwargs)
File "/usr/lib/python3.6/site-packages/pandas/core/internals.py", line 3056, in apply
applied = getattr(b, f)(**kwargs)
File "/usr/lib/python3.6/site-packages/pandas/core/internals.py", line 917, in interpolate
downcast=downcast, mgr=mgr)
File "/usr/lib/python3.6/site-packages/pandas/core/internals.py", line 956, in _interpolate_with_fill
values = self._try_coerce_result(values)
File "/usr/lib/python3.6/site-packages/pandas/core/internals.py", line 2448, in _try_coerce_result
result = result.reshape(len(result))
ValueError: cannot reshape array of size 24311 into shape (1,)
```
#### Problem description
msgpack of dataframe for replication:
https://www.dropbox.com/s/5skf6v8x2vg103o/dataframe?dl=0
I'm a beginner so I can only guess at what is wrong, but it seems to be working on rows instead of the columns. I can loop through df.columns and do it series by series to end up with the expected output so it doesn't seem to me as if it is a problem with any of the columns.
#### Expected Output
Fill the columns of NaN's with prior value in column.
#### Output of ``pd.show_versions()``
<details>
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.8-1-ARCH
machine: x86_64
processor:
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: 34.2.0
Cython: None
numpy: 1.12.0
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: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: None
boto: None
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTA4MjAxNjE1
| 15,523 |
DOC: Update contributing for test_fast, fix doc Windows build
|
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| 4 | 2017-02-27T21:08:29Z | 2017-02-28T15:43:49Z | 2017-02-27T21:43:02Z |
CONTRIBUTOR
| null |
- Add `test_fast.bat` for Windows developers.
- Update `contributing.rst` to refer to `pytest-xdist` and reference fast testing scripts
- Update `contributing.rst` to delete references to `pep8radius`
- Fix `doc\make.py` to not spit out error message about `rm` on Windows by using `os.remove()`
No code changes!
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MDU6SXNzdWUyMTA2MTcxMjU=
| 15,524 |
BUG: read_csv returns object dtype for dates in empty frame
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| 24 | 2017-02-27T21:48:25Z | 2021-05-16T23:31:36Z | null |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> import io
>>> import pandas as pd
>>> csv = lambda: io.StringIO('date')
>>> df = pd.read_csv(csv(), dtype=int)
>>> df.dtypes
date int64
dtype: object
>>> df = pd.read_csv(csv(), parse_dates=['date'])
>>> df.dtypes
date object
dtype: object
>>> df.date.dt.tz_localize('UTC')
Traceback (most recent call last):
File "<ipython-input-49-067c7ce27232>", line 1, in <module>
df.date = df.date.dt.tz_localize('UTC')
File "~/anaconda/lib/python3.5/site-packages/pandas/core/generic.py", line 2740, in __getattr__
return object.__getattribute__(self, name)
File "~/anaconda/lib/python3.5/site-packages/pandas/core/base.py", line 241, in __get__
return self.construct_accessor(instance)
File "~/anaconda/lib/python3.5/site-packages/pandas/core/series.py", line 2743, in _make_dt_accessor
raise AttributeError("Can only use .dt accessor with datetimelike "
AttributeError: Can only use .dt accessor with datetimelike values
>>> parser = lambda x: pd.to_datetime(x, utc=True)
>>> df = pd.read_csv(csv(), parse_dates=['date'], date_parser=parser)
>>> df.dtypes
date object
dtype: object
>>> df.date.dt.tz_convert('EST')
Traceback (most recent call last):
File "<ipython-input-129-3d458af8513c>", line 1, in <module>
df.date.dt.tz_convert('EST')
File "~/anaconda/lib/python3.5/site-packages/pandas/core/generic.py", line 2740, in __getattr__
return object.__getattribute__(self, name)
File "~/anaconda/lib/python3.5/site-packages/pandas/core/base.py", line 241, in __get__
return self.construct_accessor(instance)
File "~/anaconda/lib/python3.5/site-packages/pandas/core/series.py", line 2743, in _make_dt_accessor
raise AttributeError("Can only use .dt accessor with datetimelike "
AttributeError: Can only use .dt accessor with datetimelike values
```
#### Problem description
When reading CSVs with no data rows, `read_csv()` returns the dtype `object` for dates, which can raise errors on later manipulation. This is contrary to the general behaviour of `read_csv()`, which otherwise correctly sets dtypes for empty frames when those dtypes are explicitly passed.
I don't think it would be hard to return the correct dtype here? If `date_parser` is not set, we know the dtype is `datetime64[ns]`; otherwise, we can call the parser with empty data, and use the returned dtype.
Note that e.g. `read_csv(..., dtype='datetime64[ns]')` is not a solution, as this throws an error when the csv is non-empty.
#### Expected Output
```
date int64
dtype: object
date datetime64[ns]
dtype: object
date datetime64[ns, UTC]
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.9.1
IPython: 4.2.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
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: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.4
pymysql: None
psycopg2: None
jinja2: 2.8.1
boto: 2.45.0
pandas_datareader: None
</details>
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MDU6SXNzdWUyMTA2MjM2MDI=
| 15,525 |
BUG: concat with empty frame upcasts float32 -> float64
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| 5 | 2017-02-27T22:12:46Z | 2017-03-03T13:19:17Z | 2017-03-03T13:19:08Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> x = pd.DataFrame([[0]], dtype='float32')
>>> pd.concat([x, x.loc[0:]]).dtypes
0 float32
dtype: object
>>> pd.concat([x, x.loc[1:]]).dtypes
0 float64
dtype: object
```
#### Problem description
Calling `concat()` on two float32 DataFrames, one of which is empty, will upcast the data to float64, wasting memory.
#### Expected Output
```python
0 float32
dtype: object
0 float32
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.9.1
IPython: 4.2.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
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: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.4
pymysql: None
psycopg2: None
jinja2: 2.8.1
boto: 2.45.0
pandas_datareader: None
</details>
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.loc assignment of pd.Timestamp to Series of dtype object results in cast to Long
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| 7 | 2017-02-27T22:13:13Z | 2018-03-09T11:05:06Z | 2018-03-09T11:04:58Z |
NONE
| null |
xref #6942
xref #12499
xref #14179
Simple repro, casting to ``object``, but a datetimelike is cast to its underlying repr
```
In [1]: s = pd.Series([1])
In [2]: s
Out[2]:
0 1
dtype: int64
In [3]: s[1] = pd.Timestamp('20130101')
In [4]: s
Out[4]:
0 1
1 1356998400000000000
dtype: object
```
#### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> s = pd.Series()
>>> s.loc['date'] = pd.Timestamp.now()
>>> s
date 2017-02-27 15:04:32.357
dtype: datetime64[ns]
>>> s.loc['date2'] = pd.Timestamp.now()
>>> s
date 2017-02-27 15:04:32.357
date2 2017-02-27 15:04:41.724
dtype: datetime64[ns]
>>> s.loc['date3'] = 3
>>> s
date 2017-02-27 15:04:32.357000
date2 2017-02-27 15:04:41.724000
date3 3
dtype: object
>>> s.loc['date4'] = pd.Timestamp.now()
>>> s
date 2017-02-27 15:04:32.357000
date2 2017-02-27 15:04:41.724000
date3 3
date4 1488207907032000000
dtype: object
```
#### Problem description
When a series' dtype is object, .loc assignment of pd.Timestamp values results in being cast to a long, rather than remaining a datetime. Since the dtype is object, I would expect objects to be uncasted.
#### Expected Output
The expected behavior is to not be casted, i.e. to remain a datetime.
```python
>>> s = s.append(pd.Series(data = [pd.Timestamp.now()], index = ['date_x']))
>>> s
date 2017-02-27 15:04:32.357000
date2 2017-02-27 15:04:41.724000
date3 3
date4 1488207907032000000
date_x 2017-02-27 15:11:03.995000
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
27-Feb-17 15:08:27 DEBUG lzma module is not available
27-Feb-17 15:08:27 DEBUG Registered VCS backend: git
27-Feb-17 15:08:28 DEBUG Registered VCS backend: hg
27-Feb-17 15:08:28 DEBUG Config variable 'Py_DEBUG' is unset, Python ABI tag may be incorrect
27-Feb-17 15:08:28 DEBUG Config variable 'WITH_PYMALLOC' is unset, Python ABI tag may be incorrect
27-Feb-17 15:08:28 DEBUG Config variable 'Py_UNICODE_SIZE' is unset, Python ABI tag may be incorrect
27-Feb-17 15:08:28 DEBUG Config variable 'Py_DEBUG' is unset, Python ABI tag may be incorrect
27-Feb-17 15:08:28 DEBUG Config variable 'WITH_PYMALLOC' is unset, Python ABI tag may be incorrect
27-Feb-17 15:08:28 DEBUG Config variable 'Py_UNICODE_SIZE' is unset, Python ABI tag may be incorrect
27-Feb-17 15:08:28 DEBUG Registered VCS backend: svn
27-Feb-17 15:08:28 DEBUG Registered VCS backend: bzr
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 33.1.0.post20170122
Cython: 0.25.2
numpy: 1.10.4
scipy: 0.17.1
statsmodels: 0.8.0
xarray: 0.9.1
IPython: 5.2.2
sphinx: 1.5.2
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.45.0
pandas_datareader: 0.2.1
</details>
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MDExOlB1bGxSZXF1ZXN0MTA4MjI2ODc2
| 15,527 |
ENH: fill_value argument for shift
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[] | null | 10 | 2017-02-27T23:40:08Z | 2017-08-01T22:53:18Z | 2017-08-01T22:53:18Z |
CONTRIBUTOR
| null |
- [ ] closes #15486
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
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| 15,528 |
Bug:DataFrame index & column returned by corr & cov are the same (#14617)
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| 2 | 2017-02-28T05:45:18Z | 2017-12-20T02:01:24Z | 2017-02-28T14:28:38Z |
MEMBER
| null |
- [x] closes #14617
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
This picks up PR #14667. Ensures that the column and index returned by `cov()` and `corr()` are not the same object.
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Index Column duplication when adding rows in a grouped-by DataFrame
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| 2 | 2017-02-28T11:16:21Z | 2018-07-06T20:21:16Z | 2017-02-28T15:55:26Z |
NONE
| null |
#### Here's my code
```python
import numpy as np
import pandas as pd
from numpy.random import randn
az = pd.DataFrame({
'A' : ['a1', 'a1', 'a2', 'a3']
, 'B' : ['b1', 'b2', 'b3', 'b4']
, 'Vals' : randn(4)
}).groupby(['A', 'B']).sum()
def add_zero(row) :
name = row.name
row.loc[name,'b0'] = 0
return row
az['Vals'].groupby(level='A').apply(lambda s : add_zero(s))
>>> A A B
>>> a1 a1 b1 1.238985
>>> b2 0.686438
>>> b0 0.000000
>>> a2 a2 b3 -0.503039
>>> b0 0.000000
>>> a3 a3 b4 -0.083150
>>> b0 0.000000
>>> Name: Vals, dtype: float64
```
#### Expected Output
```python
>>> A B
>>> a1 b1 1.238985
>>> b2 0.686438
>>> b0 0.000000
>>> a2 b3 -0.503039
>>> b0 0.000000
>>> a3 b4 -0.083150
>>> b0 0.000000
>>> Name: Vals, dtype: float64
```
#### Problem description
I would like to add rows with new index in a grouped by Dataframe, it seems to work, yet it duplicates an index/level column, and I can't figure why it does.
Is there any way around ?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
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: 1.2.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: 0.10.3
apiclient: 1.6.2
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTA4MzA5MDkw
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ENH: support Styler in ExcelFormatter
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| 63 | 2017-02-28T11:17:19Z | 2017-04-28T00:17:04Z | 2017-04-20T01:02:13Z |
CONTRIBUTOR
| null |
Pandas is a nice way to export data from Python to Excel. It even has an internal representation of Excel cell styling. It also has a data-driven framework for styling Pandas dataframes with CSS.
This PR connects those features together in a rudimentary and experimental way, for advanced users only. It allows `ExcelFormatter` to accept a `Styler` in place of a `DataFrame` and to provide a `style_converter` callback for conversion from CSS styles to the styles understood by `ExcelWriter`.
While I acknowledge that I am extending a hidden feature (to make it more usable) and that high-coverage conversion of CSS to Excel styles is no small feat so ?all prospective usages will be a hack, I would enjoy using this feature and think others would too.
I wasn't sure how to test this as no tests directly instantiate `ExcelFormatter`. (I could of course alter the `to_excel` parameters to provide the new feature, but I thought it might best remain hidden. WDYT?)
A test follows:
```python
import pandas as pd
from pandas.formats.format import ExcelFormatter
def test_style_converter():
df = pd.DataFrame({'a': list(range(3))})
header_style = '!unset'
# default Styler provided
formatter = ExcelFormatter(df)
for cell in formatter.get_formatted_cells():
if cell.row < 1 or cell.col < 1:
if header_style == '!unset':
header_style = cell.style
assert header_style is not None
else:
assert cell.style == header_style
else:
assert cell.style is None
styler = df.style.background_gradient(cmap='Greys', low=.5, high=0)
expected = ['c6c6c6', '686868', '000000']
formatter = ExcelFormatter(styler)
for cell in formatter.get_formatted_cells():
if cell.row < 1 or cell.col < 1:
assert cell.style is header_style
else:
assert cell.style == {'fill': {'fgColor': expected[cell.row - 1],
'patternType': 'solid'}}
styler.to_excel('/tmp/out.xlsx', engine='openpyxl')
with open('/tmp/out.html', 'w') as f:
f.write(styler.render())
```
- [x] closes #1663
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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CLN/TST: split test_format.py
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| 1 | 2017-02-28T13:35:40Z | 2017-03-02T08:39:35Z | 2017-03-02T08:39:35Z |
MEMBER
| null |
`test_format.py` is still a huge file, so in our restructuring of the tests, I think splitting up `tests/formats/test_format.py` in multiple files would help.
For example, the tests regarding `to_html` and `to_latex` can get certainly their own class/file.
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BLD: rename some extensions
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| 2 | 2017-02-28T15:49:54Z | 2017-03-01T19:23:23Z | 2017-03-01T19:23:23Z |
CONTRIBUTOR
| null |
xref #12588
This is a move (and deprecation of json/parser) of ``pandas.json``, ``pandas.parser``,
also moves some extensions into the main structure as appropriate
renames extensions to follow the pattern "libxxx" for consistency (as well as some renaming of the .pyx files themselves). In effect locates the library code next to the calling code.
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ENH: standardize fill_value behavior across the API
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| 16 | 2017-02-28T21:00:17Z | 2020-05-07T03:45:59Z | null |
CONTRIBUTOR
| null |
#### Problem
In [the PR](https://github.com/pandas-dev/pandas/pull/15527#issuecomment-283038598) for #15486, I found that type validation for the `fill_value` parameters strewn across a large number of `pandas` API methods is done ad-hoc. This results in a wide variety of possible accepted inputs. I think it would be good to standardize this so that all of these methods use the same behavior, the one currently used by `fillna`.
#### Implementation Details
Partially the point of providing a `fill_value` is to avoid having to do a slow-down type conversion otherwise (using `.fillna().astype()`). However, specifying other formats is nevertheless a useful convenience to have. Implementation would roughly be:
Before executing the rest of the method body, check whether or not the `fill_value` is valid (using a centralized `maybe_fill` method). If it is not, throw a `ValueError`. If it is, check whether or not incorporating the `fill_value` would result in an upcast in the column `dtype.` If it would not, follow a code path where the column never gets type-converted. If it would, follow that same code path, then do something like a `filla` operation at the end before returning.
#### Target Implementation
The same as what `fillna` currently does. Which follows.
Invalid:
* `categorical` fill for a category not in the categories will raise a `ValueError`.
* `sparse` matrices refuse upcasting.
* Passing an object or list or other non-coercable "thing" as a fill.
Valid, upcast:
* `int` fill will promote `bool` dtypes to `int`.
* `float` fill will promote `int` and `bool` dtypes to `float` (this is what happens with `np.nan` already).
* `object` (`str`) fill would promote lesser dtypes to `object`.
* `int`, `float`, and `bool` fill to a `datetime` dtype will be treated as a UNIX-like timestamp and promoted to `datetime`.
* `object` fill will promote `datetime` dtype to `object`.
Valid, no-cast:
* Everything else.
#### Current Implementation
...is ad-hoc. The following are the methods which currently provide a `fill_value` input, as well as where they deviate from the model above.
* `Series.combine`, `DataFrame.combine`, `Series.to_sparse`: These are unique usages of `fill_value` which aren't compatible with the rest of them.
* `Series.unstack`, `DataFrame.unstack`: any `fill_value` is allowed. You can pass an object if you'd like, or even another `DataFrame` (yo dawg...).
* `DataFrame.align`: Any `fill_value` is allowed.
* `DataFrame.reindex_axis`: Lists and dicts are allowed, objects are not.
* `DataFrame.asfreq`, `Series.asfreq`: any `fill_value` is allowed.
* `pd.pivot_table`: ...
* `Series.add`, `DataFrame.add`: ...
* `Series.subtract`, `DataFrame.substract`: ...
* Probably others, there's a lot of these.
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MDU6SXNzdWUyMTA5MTQxMDY=
| 15,534 |
json_normalize should be able to accept an empty list
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| 1 | 2017-02-28T21:59:08Z | 2017-03-04T11:09:46Z | 2017-03-04T11:09:46Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python-traceback
In[21]: pandas.io.json.json_normalize([])
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-21-1bd834af8a9b> in <module>()
----> 1 pandas.io.json.json_normalize([])
/usr/local/lib/python3.6/site-packages/pandas/io/json.py in json_normalize(data, record_path, meta, meta_prefix, record_prefix)
791
792 if record_path is None:
--> 793 if any([isinstance(x, dict) for x in compat.itervalues(data[0])]):
794 # naive normalization, this is idempotent for flat records
795 # and potentially will inflate the data considerably for
IndexError: list index out of range
```
#### Problem description
This should probably return an empty dataframe when the list is empty, based on `len(data)` being 0.
#### Expected Output
```python
>>> pandas.io.json.json_normalize([])
Empty DataFrame
Columns: []
Index: []
```
Admittedly, this could be a series too -- I was using this across several collections and noticed I had to code around when some entries were empty.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Darwin
OS-release: 16.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 34.1.0
Cython: None
numpy: 1.12.0
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.2.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 2.0.0
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: 2.8.1
boto: None
pandas_datareader: None
```
</details>
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BUG: handle empty lists in json_normalize
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| 6 | 2017-03-01T00:31:51Z | 2017-03-04T16:02:42Z | 2017-03-04T11:09:46Z |
CONTRIBUTOR
| null |
- [x] closes #15534
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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Header option for to_latex
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| 2 | 2017-03-01T13:50:26Z | 2017-03-09T10:58:49Z | 2017-03-09T10:58:49Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
data = pd.DataFrame(np.random.rand(5,2), columns=['s', 't'])
print(data.rename(columns={'s' : '$s$ in m', 't' : '$t$ in s'}).to_latex(escape=False))
# would be cool to write instead
print(data.to_latex(header=['$s$ in m', '$t$ in s'], escape=False))
# this works as expected
print(data.to_csv(header=['$s$ in m', '$t$ in s'], escape=False))
```
#### Problem description
The header option of ``to_latex`` does not rename the columns as in other exporting functions and forces to rename them before.
I am not sure if this is a bug or intended.
But it is counterintuitive (at least for me) and makes code harder to read.
#### Expected Output
```python
# would be cool to write instead
print(data.to_latex(header=['$s$ in m', '$t$ in s'], escape=False))
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-64-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: 9.0.1
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.2
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
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: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
</details>
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BLD: consolidate remaining extensions
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| 17 | 2017-03-01T15:42:58Z | 2017-03-07T23:23:46Z | 2017-03-07T23:22:57Z |
CONTRIBUTOR
| null |
moves extensions to ``pandas/libs``, which holds the extension code and also the generated builds (as its importable). ``pandas/src`` is now almost an includes dir, holding low-frequency changing code.
This consolidates the import process making it more uniform and consistent throughout the codebase.
Finally this cleans up the remaining top-level namespace (with some deprecations in place for example ``pandas.lib``, ``pandas.tslib``, ``pandas.json``, ``pandas.parser``. I listed all of the changes in the whatsnew, but I don't think worthwhile deprecating anything else.
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DEPR: deprecate some top-level non-used functions
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| 6 | 2017-03-01T16:26:41Z | 2017-03-03T10:12:22Z | 2017-03-03T10:12:22Z |
CONTRIBUTOR
| null |
closes #13790
```
pd.pnow()
pd.groupby()
pd.match()
pd.Term
pd.Expr
```
remove info.py
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DOC: Use nbsphinx for notebook build
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| 0 | 2017-03-01T16:28:37Z | 2017-04-08T16:11:10Z | 2017-04-08T16:11:10Z |
CONTRIBUTOR
| null |
https://nbsphinx.readthedocs.io/en/0.2.13/index.html
This was just starting when I added the notebook for `DataFrame.style`. Seems to be pretty mature now. I'll explore adding a doc-dependency for it, and removing my hack in `doc/make.py` to execute and convert the notebook.
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MDExOlB1bGxSZXF1ZXN0MTA4NTk0MzE1
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BLD: fix 2.7_LOCALE build
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| 3 | 2017-03-01T17:59:02Z | 2017-03-01T21:48:41Z | 2017-03-01T19:17:54Z |
CONTRIBUTOR
| null |
simplify install_travis.sh, always using a clean miniconda install
bump matplotlib to 1.4.0 for _LOCALE, numpy to 1.8.2
So this removes our testing of mpl 1.2.1 with numpy 1.7.1. We *still* test
elsewhere 1.7.1, and mpl 1.3.1 (with 1.8.2), so I think this is sufficient.
something changed in conda meta data to make the installtion of numpy=1.7.1 / mpl=1.2.1 fail
https://travis-ci.org/jreback/pandas/jobs/206680630
The only question is to we want to 'officially' say that we require mpl >= 1.3.1 ?
Things will still work as long as the installation succeeds, but travis is being problematic.
cc @jorisvandenbossche
cc @tacaswell
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| 36 | 2017-03-01T19:13:01Z | 2017-04-03T21:53:07Z | 2017-04-03T21:52:14Z |
CONTRIBUTOR
| null |
closes #14800
this doesn't actually deprecate the existing (as we have discussed in #15181) but baby steps.
I also didn't add the 'hated'! ``SettingWithCopy*`` ones; these will be removed in pandas 2.0 anyhow, so no point in adding them.
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MDU6SXNzdWUyMTEyMDQ2OTg=
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pandas.DataFrame.sum() skipna behavior does not match documentation
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| 1 | 2017-03-01T20:52:36Z | 2017-03-01T21:09:09Z | 2017-03-01T21:09:09Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
`
import pandas as pd
df = pd.DataFrame([[0,0],[1,1],[np.NaN,0],[np.NaN,1],[np.NaN,np.NaN]], columns = ['a','b'])
df
Out[3]:
a b
0 0.0 0.0
1 1.0 1.0
2 NaN 0.0
3 NaN 1.0
4 NaN NaN
df.sum(axis=1, skipna=True)
Out[4]:
0 0.0
1 2.0
2 0.0
3 1.0
4 0.0
dtype: float64
`
#### Problem description
The [documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sum.html) for pandas.DataFrame.sum indicates that:
> skipna : boolean, default True
Exclude NA/null values. **_If an entire row/column is NA, the result will be NA_**
However, it appears that the actual behavior under the skipna option within this particular environment is for the summation routine to evaluate full rows of na values to 0 rather than the documented value of NA.
Currently this behavior only occurs on within my local windows environment, and NOT within the linux environment my organization's grid runs on. I have provided output for pd.versions for both environments below to help localize this issue.
#### Expected Output
df.sum(axis=1, skipna=True)
Out[6]:
0 0.0
1 2.0
2 0.0
3 1.0
4 NaN
dtype: float64
#### Output of ``pd.show_versions()``
<details>
_**Windows version in which issue is present**_
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 45 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
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: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
_**Linux version in which issue is NOT present**_
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-642.15.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: None
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: 2.4.1
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.2
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: 0.2.1
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DEPS: drop matplotlib < 1.4.3
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| 2 | 2017-03-01T22:59:12Z | 2017-08-22T16:13:12Z | 2017-08-22T09:50:58Z |
CONTRIBUTOR
| null |
xref https://github.com/pandas-dev/pandas/pull/15540
so we are technically still testing 1.3.1 (numpy 1.8.2).
But this should be reasonable to officially drop support for these older versions.
xref to #15206 (drop numpy < 1.10)
though mpl 1.4.3 is build on numpy 1.9
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BUG: syntax error in hdf query with ts
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| 5 | 2017-03-02T00:48:42Z | 2017-03-10T14:18:04Z | 2017-03-03T15:07:47Z |
CONTRIBUTOR
| null |
- [x] closes #15492
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0MTA4NjY1MDAy
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DOC: Styler.set_table_attributes docstring
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| 2 | 2017-03-02T01:04:17Z | 2017-03-02T02:22:10Z | 2017-03-02T02:22:09Z |
CONTRIBUTOR
| null |
- [x] tests not needed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew not needed
Fixes a copy-paste error.
|
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MDExOlB1bGxSZXF1ZXN0MTA4NjcxNDE5
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TST: split tests/format/test_format.py
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| 1 | 2017-03-02T02:03:46Z | 2017-03-02T08:39:35Z | 2017-03-02T08:39:35Z |
CONTRIBUTOR
| null |
closes #15531
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MDExOlB1bGxSZXF1ZXN0MTA4NzMwMDg2
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CLN: remove deprecated irow, icol, iget, iget_value (GH10711)
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| 1 | 2017-03-02T10:34:27Z | 2017-03-02T12:55:16Z | 2017-03-02T12:55:15Z |
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/issues/6581
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ENH: IO header formatting
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| 22 | 2017-03-02T13:51:56Z | 2017-03-09T10:59:12Z | 2017-03-09T10:58:49Z |
CONTRIBUTOR
| null |
- [x] closes #15536
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
In this branch I changed ``pd.formats.format.DataFrameFormatter._to_str_columns`` which
is used by ``to_latex`` and ``to_string`` to format output nicely.
They support now optional headers for the column titles.
See the appended [example jupyter notebook](https://github.com/pandas-dev/pandas/files/813954/Examples_IO_formatting_issue15536.txt).
``to_html`` does not use ``pd.formats.format.DataFrameFormatter._to_str_columns`` and remains an issue.
Test cases were created.
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| 211,446,819 |
MDU6SXNzdWUyMTE0NDY4MTk=
| 15,549 |
BUG: resample with tz-aware: Values falls after last bin
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| 2 | 2017-03-02T16:39:03Z | 2017-11-21T13:14:14Z | 2017-11-21T13:14:14Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
index = pd.DatetimeIndex([1450137600000000000, 1474059600000000000], tz='UTC').tz_convert('America/Chicago')
print(index)
df = pd.DataFrame([1, 2], index=index)
print(df.resample('12h', closed='right', label='right').last().ffill())
```
#### Problem description
resampling is not handling non-UTC index properly due to daylight saving time change
and the problem occurs in file https://github.com/pandas-dev/pandas/blob/master/pandas/tseries/resample.py
function: `_get_time_bins`
code: `binner = labels = DatetimeIndex(freq=self.freq,...`
this problem can be solved by converting ax tz to UTC before the resampling and applying the original tz after DatetimeIndex is created
so the code will look like this
tz = ax.tz
ax = ax.tz_convert('UTC')
if len(ax) == 0:
binner = labels = DatetimeIndex(
data=[], freq=self.freq, name=ax.name)
return binner, [], labels
first, last = ax.min(), ax.max()
first, last = _get_range_edges(first, last, self.freq,
closed=self.closed,
base=self.base)
# GH #12037
# use first/last directly instead of call replace() on them
# because replace() will swallow the nanosecond part
# thus last bin maybe slightly before the end if the end contains
# nanosecond part and lead to `Values falls after last bin` error
binner = labels = DatetimeIndex(freq=self.freq,
start=first,
end=last,
name=ax.name).tz_convert(tz)
this cause the bins to be always aligned by UTC times rather than original tz, but I think that it is adequate behaviour as well.
#### Expected Output
I expect the resampling to be successful regardless of the time range selected
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.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: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 34.0.2
Cython: None
numpy: 1.12.0
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
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: 0.7.9.None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
</details>
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MDU6SXNzdWUyMTE0NTM4NTI=
| 15,550 |
API: expose to_numeric/to_datetime/to_timedelta methods on Series
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[] | null | 4 | 2017-03-02T17:00:56Z | 2021-05-08T01:15:54Z | null |
CONTRIBUTOR
| null |
if we have a string Series, e.g. ``s = Series(['2013-01-02', '2013-1-03'])``
we normally just run this thru ``pd.to_datetime(s)``
it might be convenient to do these operations (``.to_numeric,.to_datetime,.to_timedelta``)
directly as methods.
- ``s.str.to_numeric(....)`` , IOW in the ``.str`` namespace
- ``s.to_numeric(...)``, in main Series namespace
- ``s.astype(.., dtype=...)`` but actually pass thru to using the ``.to_numeric/.to_datetime`` functions directly, so you could have something like
``s.astype('datetime', format='%Y-%m-%d')`` and it would do the right thing. This overloads things a bit though
right now we have a bit of confusion because we support ``.astype`` which is pretty much a pass thru to numpy, and the more dtype-aware (and functional) ``.to_*`` ones.
Furthermore I think we could update the ``.astype`` docs (and the ``.to_*``) ones to cross reference.
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| 15,551 |
Pandas str.findall conflict with NLTK?
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| 2 | 2017-03-02T17:17:35Z | 2017-03-03T13:21:13Z | 2017-03-03T13:21:05Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
df['url'] = df['text'].str.findall('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+')
df['namedAuthor'] = df['text'].str.findall('(?<=\s)@\w*') # get the named authors
df['hash'] = df['text'].str.findall('(?<=\s)#\w*') # get the hastags
df['textGood'] = df['text'].str.replace('RT','')
df['textGood'] = df['textGood'].str.replace(r'@\S+','')
df['textGood'] = df.apply (lambda row: re.sub(r"http\S+",'',row["textGood"]),axis=1)
df['textGood'] = df.apply (lambda row: re.sub(r"#",'',row["textGood"]),axis=1)
df['tokenized'] = df.apply (lambda row: nltk.word_tokenize(row["textGood"]),axis=1)
df['posTag'] = df.apply (lambda row: nltk.pos_tag(row["tokenized"]),axis=1)
df['chuncked'] = df.apply (lambda row: nltk.ne_chunk(row["posTag"], binary=True),axis=1)
df['Names'] = df.apply (lambda row: getNames(row["posTag"]),axis=1)
df['Adj'] = df.apply (lambda row: getAdj(row["posTag"]),axis=1)
```
#### Problem description
I have a Pandas dataframe containing a "text" column. Every text (tweets) contains valuable information such as url, named authors (@Blabla) and hashtags (#onioq) i'd like to save in separate columns (first three lines) before I clean the text for an nltk based analysis (last five lines). **My problem is**: my code works every time I use only one of the first 3 lines but bug when I use 2 or more. What am I missing
<details>
Traceback (most recent call last):
File "test.py", line 87, in <module>
df['chuncked'] = df.apply (lambda row: nltk.ne_chunk(row["posTag"], binary=True),axis=1)
File "/usr/local/lib/python3.5/dist-packages/pandas/core/frame.py", line 4152, in apply
return self._apply_standard(f, axis, reduce=reduce)
File "/usr/local/lib/python3.5/dist-packages/pandas/core/frame.py", line 4265, in _apply_standard
result = self._constructor(data=results, index=index)
File "/usr/local/lib/python3.5/dist-packages/pandas/core/frame.py", line 266, in __init__
mgr = self._init_dict(data, index, columns, dtype=dtype)
File "/usr/local/lib/python3.5/dist-packages/pandas/core/frame.py", line 402, in _init_dict
return _arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
File "/usr/local/lib/python3.5/dist-packages/pandas/core/frame.py", line 5408, in _arrays_to_mgr
return create_block_manager_from_arrays(arrays, arr_names, axes)
File "/usr/local/lib/python3.5/dist-packages/pandas/core/internals.py", line 4267, in create_block_manager_from_arrays
construction_error(len(arrays), arrays[0].shape, axes, e)
File "/usr/local/lib/python3.5/dist-packages/pandas/core/internals.py", line 4229, in construction_error
raise e
File "/usr/local/lib/python3.5/dist-packages/pandas/core/internals.py", line 4262, in create_block_manager_from_arrays
blocks = form_blocks(arrays, names, axes)
File "/usr/local/lib/python3.5/dist-packages/pandas/core/internals.py", line 4359, in form_blocks
object_blocks = _simple_blockify(object_items, np.object_)
File "/usr/local/lib/python3.5/dist-packages/pandas/core/internals.py", line 4389, in _simple_blockify
values, placement = _stack_arrays(tuples, dtype)
File "/usr/local/lib/python3.5/dist-packages/pandas/core/internals.py", line 4453, in _stack_arrays
stacked[i] = _asarray_compat(arr)
ValueError: could not broadcast input array from shape (28) into shape (15)
</details>
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MDU6SXNzdWUyMTE0NzM3Nzg=
| 15,552 |
BUG: tz-aware datetime with column-wise comparisions failing with np.minmum/maximum
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| 5 | 2017-03-02T18:15:46Z | 2019-07-17T11:48:30Z | 2019-07-17T11:48:30Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> import numpy as np
>>> import pandas as pd
>>> naive = pd.to_datetime(['now'])
>>> utc = naive.tz_localize('UTC')
>>> np.minimum(naive, naive)
>>> np.minimum(utc, utc)
>>> np.minimum(pd.Series(naive), pd.Series(naive))
>>> np.minimum(pd.Series(utc), pd.Series(utc))
File "bug.py", line 10, in <module>
np.minimum(pd.Series(utc), pd.Series(utc))
File "~/anaconda3/lib/python3.5/site-packages/pandas/core/series.py", line 498, in __array_prepare__
op=context[0].__name__))
TypeError: Series with dtype datetime64[ns, UTC] cannot perform the numpy op minimum
```
#### Problem description
When a tz-aware datetime is placed in a `Series`, the numpy operations `fmin`/`fmax`/`minimum`/`maximum` throw an error, even though these operations work fine on a tz-aware `DatetimeIndex`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.8-100.fc24.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: C
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.8.0
xarray: 0.9.1
IPython: 4.2.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.2
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: None
</details>
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BUG: Series.cumin/cummax fails with datetime64[ns, tz] dtype
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| 4 | 2017-03-02T18:25:09Z | 2019-12-25T23:27:30Z | 2019-12-25T23:27:30Z |
CONTRIBUTOR
| null |
- [x] ``.where``
- [x] ``.combine_first``
- [x] ``.update``
- [ ] ``.cummin/cummax``
works with naive dt, should work with tz-aware
```
In [40]: s = Series(pd.date_range('20130101', periods=2))
In [41]: s.where(s>s,s)
Out[41]:
0 2013-01-01
1 2013-01-02
dtype: datetime64[ns]
In [42]: s = Series(pd.date_range('20130101', periods=2, tz='US/Eastern'))
In [43]: s.where(s>s,s)
TypeError: invalid type promotion
TypeError: Could not operate [array(['2013-01-01T05:00:00.000000000', '2013-01-02T05:00:00.000000000'], dtype='datetime64[ns]')] with block values [invalid type promotion]
```
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if the logic of both queries is the same, why are the results different?
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[] | null | 3 | 2017-03-02T20:54:02Z | 2017-03-03T13:34:39Z | 2017-03-03T13:34:33Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
#query 1 (the second part of the query does not work accurately on the attached dataset)
contains = df.eq(3.2).any(axis=1)
not_contains = ~df.isin([0.1,1.2]).any(axis=1)
print(df[contains & not_contains])
#query 2 (works accurately)
a = df.values
df_out = df.iloc[(a == 3.2).any(1) & (((a!=0.1) & (a!=1.2)).all(1))]
```
#### Problem description
the above two pandas data slicing queries (query 1 & query 2) are suppose to produce same output (logically). However, for the given data they do not. Additionally, query 1 works properly on the subset of the data but not on the complete data without throwing any errors. On the other hand, query 2 works properly on the attached dataset. this issue was first raised here: http://stackoverflow.com/questions/42542790/pandas-slice-rows-based-on-joint-condition
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.12.0
scipy: 0.18.1
statsmodels: 0.8.0
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.6.2
matplotlib: 2.0.0
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: 3.7.2
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
[df.txt](https://github.com/pandas-dev/pandas/files/815046/df.txt)
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BUG: resample with tz-aware: Values falls after last bin
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[] | null | 12 | 2017-03-02T21:01:26Z | 2017-03-28T18:58:43Z | 2017-03-28T18:26:48Z |
CONTRIBUTOR
| null |
- [ ] closes https://github.com/pandas-dev/pandas/issues/15549
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry: resampling will now always produce bins alligned by UTC instead of custom time zone. so for example if you resampling by 12 hours frequency in UTC-5 timezone, you will get bins like
7:00 19:00 7:00 19:00
instead of
00:00 12:00 00:00 12:00
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| 15,556 |
DOC: more modern sphinx theme
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| 43 | 2017-03-02T21:34:27Z | 2019-10-13T07:41:13Z | 2019-10-13T07:41:06Z |
MEMBER
| null |
We currently use a slightly adapted version of the 'nature' theme (you can also see it in this list of built-in themes: http://www.sphinx-doc.org/en/stable/theming.html#builtin-themes).
I think this can use an update, for some reasons:
- a mobile friendly theme (more and more useful, and currently this completely fails on mobile)
- more powerful theme (allowing eg better nested structure of the docs, which could enable a reorganisation of the docs. Now we have a single flat table of contents on the left)
- refreshed look (but this is subjective of course :-))
(could go together with a refresh of the main website as well, but not necessarily I think)
http://www.writethedocs.org/guide/tools/sphinx-themes/
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Silent integer overflow not mentioned in Caveats and Gotchas
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[] | null | 1 | 2017-03-03T09:25:38Z | 2021-05-08T01:17:20Z | null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
my_ints = pd.Series([406, 372, 496, 41, 63, 118, 311, 271, 431, 95, 57, 52])
assert my_ints.prod() == 406 * 372 * 496 * 41 * 63 * 118 * 311 * 271 * 431 * 95 * 57 * 52
```
#### Problem description
The above code produces an `AssertionError`.
Pandas `prod` method seems to silently overflow. This is not mentioned in <http://pandas.pydata.org/pandas-docs/stable/gotchas.html>
#### Expected Output
I expect either an `OverflowError` (or something like that) or no error at all (i.e. correct product), or at least some warning about this in the "Caveats and Gotchas" page.
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-62-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 28.8.0
Cython: 0.25.2
numpy: 1.12.0
scipy: 0.18.1
statsmodels: 0.8.0
xarray: None
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 2.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.3
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.5
boto: None
pandas_datareader: None
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DOC/API: clarify usage of where, mask broadcast arguments
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| 5 | 2017-03-03T12:19:49Z | 2019-10-21T19:52:17Z | null |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> x = pd.Series(0, pd.MultiIndex.from_product([[0], [1, 2]]))
>>> y = pd.Series(True, [0])
>>> x.where(y, level=0)
Traceback (most recent call last):
File "bug.py", line 1, in <module>
x.where(y, level=0)
File "~/anaconda3/lib/python3.5/site-packages/pandas/core/generic.py", line 4813, in where
raise_on_error)
File "~/anaconda3/lib/python3.5/site-packages/pandas/core/generic.py", line 4557, in _where
cond, _ = cond.align(self, join='right', broadcast_axis=1)
File "~/anaconda3/lib/python3.5/site-packages/pandas/core/series.py", line 2350, in align
broadcast_axis=broadcast_axis)
File "~/anaconda3/lib/python3.5/site-packages/pandas/core/generic.py", line 4419, in align
fill_axis=fill_axis)
File "~/anaconda3/lib/python3.5/site-packages/pandas/core/generic.py", line 4488, in _align_series
return_indexers=True)
File "~/anaconda3/lib/python3.5/site-packages/pandas/indexes/base.py", line 2646, in join
return_indexers=return_indexers)
File "~/anaconda3/lib/python3.5/site-packages/pandas/indexes/base.py", line 2738, in _join_multi
raise ValueError("cannot join with no level specified and no "
ValueError: cannot join with no level specified and no overlapping names
>>> x = pd.DataFrame(0, pd.MultiIndex.from_product([[0], [1, 2]]), [3])
>>> y = pd.Series(True, [0])
>>> x.where(y, axis=0, level=0)
3
0 1 NaN
2 NaN
>>> y = pd.Series(True, [3])
>>> x.where(y, axis=1)
3
0 1 NaN
2 NaN
>>> y = pd.DataFrame(True, [0], [3])
>>> x.where(y, level=0)
Traceback (most recent call last):
File "bug.py", line 3, in <module>
x.where(y, level=0)
File "~/anaconda3/lib/python3.5/site-packages/pandas/core/generic.py", line 4813, in where
raise_on_error)
File "~/anaconda3/lib/python3.5/site-packages/pandas/core/generic.py", line 4557, in _where
cond, _ = cond.align(self, join='right', broadcast_axis=1)
File "~/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py", line 2816, in align
broadcast_axis=broadcast_axis)
File "~/anaconda3/lib/python3.5/site-packages/pandas/core/generic.py", line 4414, in align
fill_axis=fill_axis)
File "~/anaconda3/lib/python3.5/site-packages/pandas/core/generic.py", line 4436, in _align_frame
other.index, how=join, level=level, return_indexers=True)
File "~/anaconda3/lib/python3.5/site-packages/pandas/indexes/base.py", line 2646, in join
return_indexers=return_indexers)
File "~/anaconda3/lib/python3.5/site-packages/pandas/indexes/base.py", line 2738, in _join_multi
raise ValueError("cannot join with no level specified and no "
ValueError: cannot join with no level specified and no overlapping names
```
#### Problem description
`where()` and `mask()` ignore the broadcasting arguments `axis` and `level`
#### Expected output
```python
0 1 0
2 0
dtype: int64
3
0 1 0
2 0
3
0 1 0
2 0
3
0 1 0
2 0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.8-100.fc24.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: C
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.8.0
xarray: 0.9.1
IPython: 4.2.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.2
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: None
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DOC: doc build on travis timing out
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[] | null | 6 | 2017-03-03T14:23:16Z | 2018-10-28T06:44:06Z | 2018-10-28T06:44:06Z |
MEMBER
| null |
For some reason, the doc build on travis started timing out since a couple of commits. See eg https://travis-ci.org/pandas-dev/pandas/builds/206994038. It just takes so long it hits the "The job exceeded the maximum time limit for jobs, and has been terminated." (around 50 min), while before the doc build on travis took between 20 and 30 min.
It started with the FrozenList changes (https://github.com/pandas-dev/pandas/commit/37fe2c4edddbec2c08d561667897a1ef5a18771c), but I don't see how that could cause this.
I also don't directly see any changes in the environment that is created on travis to build the docs (eg both using sphinx 1.5.1, no change there).
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MDU6SXNzdWUyMTE3Mjc0MzY=
| 15,560 |
Timedeltas cast as datetime in DataFrames
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| 3 | 2017-03-03T15:57:38Z | 2017-03-03T16:10:30Z | 2017-03-03T16:10:13Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [10]: pandas.DataFrame([[datetime.datetime(2017, 1, 1)]]) - datetime.datetime(2001, 1, 1)
Out[10]:
0
0 1986-01-01
In [11]: pandas.Series([datetime.datetime(2017, 1, 1)]) - datetime.datetime(2001, 1, 1)
Out[11]:
0 5844 days
dtype: timedelta64[ns]
```
#### Problem description
For dataframe, the value "5844 days" is cast as a datetime, so it becomes "1970-01-01 + 5844 days = 1986-01-01"
#### Expected Output
In [10]: pandas.DataFrame([[datetime.datetime(2017, 1, 1)]]) - datetime.datetime(2001, 1, 1)
Out[10]:
0
0 5844 days
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: 211ecd5d829e6ff9019261680f1d4e6f1b193a13
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-59-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0+527.g211ecd5
pytest: 3.0.6
pip: 9.0.1
setuptools: 22.0.0
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.1
xarray: None
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
pandas_gbq: None
pandas_datareader: None
</details>
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BUG: cython version of groupby.cummax throws error on datetimes
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| 4 | 2017-03-03T18:11:23Z | 2017-03-05T02:02:41Z | 2017-03-05T02:02:41Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> x = pd.DataFrame(dict(a=[1], b=pd.to_datetime(['2001'])))
>>> x.groupby('a').b.cummax()
Traceback (most recent call last):
File "<ipython-input-9-316257648d5f>", line 1, in <module>
x.groupby('a').b.cummax()
File "~/anaconda3/lib/python3.5/site-packages/pandas/core/groupby.py", line 1454, in cummax
return self._cython_transform('cummax', **kwargs)
File "~/anaconda3/lib/python3.5/site-packages/pandas/core/groupby.py", line 806, in _cython_transform
raise DataError('No numeric types to aggregate')
DataError: No numeric types to aggregate
```
#### Problem description
The current github version of pandas has cython implementations of `groupby.cummin` and `groupby.cummax`, which throw an error if called on datetime columns. (See #15048, 0fe491d.)
#### Expected Output
```python
0 2001-01-01
Name: b, dtype: datetime64[ns]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.8-100.fc24.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: C
LOCALE: None.None
pandas: 0.19.0+531.g04e1168
pytest: 3.0.5
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
xarray: 0.9.1
IPython: 4.2.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.2
feather: None
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: 0.999
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.9.4
s3fs: None
pandas_gbq: None
pandas_datareader: None
</details>
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Failure in groupby-apply if aggregating timedelta and datetime columns
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| 10 | 2017-03-04T00:29:36Z | 2019-10-19T17:11:16Z | 2019-10-19T17:11:16Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
fname = 'foo.csv'
sample_data = '''clientid,datetime
A,2017-02-01 00:00:00
B,2017-02-01 00:00:00
C,2017-02-01 00:00:00'''
open(fname, 'w').write(sample_data)
df = pd.read_csv(fname)
df['datetime'] = pd.to_datetime(df.datetime)
df['time_delta_zero'] = df.datetime - df.datetime
# Next line fails w error message below
print df.groupby('clientid').apply(
lambda ddf: pd.Series(dict(
clientid_age = ddf.time_delta_zero.min(),
date = ddf.datetime.min()
))
)
```
#### Problem description
The current behavior is that the groupby-apply line fails, with the error message indicated below.
#### Expected Output
clientid date clientid_age
0 A 2017-02-01 00:00:00 0 days
1 B 2017-02-01 00:00:00 0 days
2 C 2017-02-01 00:00:00 0 days
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 16.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
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: 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: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/fieldc/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py", line 651, in apply
return self._python_apply_general(f)
File "/Users/fieldc/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py", line 660, in _python_apply_general
not_indexed_same=mutated or self.mutated)
File "/Users/fieldc/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py", line 3343, in _wrap_applied_output
axis=self.axis).unstack()
File "/Users/fieldc/anaconda/lib/python2.7/site-packages/pandas/core/series.py", line 2043, in unstack
return unstack(self, level, fill_value)
File "/Users/fieldc/anaconda/lib/python2.7/site-packages/pandas/core/reshape.py", line 408, in unstack
return unstacker.get_result()
File "/Users/fieldc/anaconda/lib/python2.7/site-packages/pandas/core/reshape.py", line 169, in get_result
return DataFrame(values, index=index, columns=columns)
File "/Users/fieldc/anaconda/lib/python2.7/site-packages/pandas/core/frame.py", line 255, in __init__
copy=copy)
File "/Users/fieldc/anaconda/lib/python2.7/site-packages/pandas/core/frame.py", line 432, in _init_ndarray
return create_block_manager_from_blocks([values], [columns, index])
File "/Users/fieldc/anaconda/lib/python2.7/site-packages/pandas/core/internals.py", line 3993, in create_block_manager_from_blocks
construction_error(tot_items, blocks[0].shape[1:], axes, e)
File "/Users/fieldc/anaconda/lib/python2.7/site-packages/pandas/core/internals.py", line 3967, in construction_error
if block_shape[0] == 0:
IndexError: tuple index out of range
</details>
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| 211,842,337 |
MDU6SXNzdWUyMTE4NDIzMzc=
| 15,563 |
Preserve subclass family on reshape operations
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| 0 | 2017-03-04T01:14:08Z | 2020-12-07T08:17:01Z | 2018-01-12T11:49:01Z |
NONE
| null |
Subclassed Series and DataFrames with custom `_constructor`, `_constructor_sliced`, and `_constructor_expanddim` methods return pandas Series and DataFrame objects on reshape operations:
```python
>>> import numpy as np, pandas as pd
>>> import pandas.util.testing as tm
>>> df = tm.SubclassedDataFrame(np.random.random((4,5)))
>>> df
0 1 2 3 4
0 0.151944 0.948561 0.639122 0.718071 0.296193
1 0.004606 0.978378 0.827614 0.479320 0.495945
2 0.158874 0.535476 0.173194 0.065292 0.365851
3 0.772392 0.105038 0.671064 0.164165 0.795803
>>> type(df)
<class 'pandas.util.testing.SubclassedDataFrame'>
```
Slicing operations use `_constructor_sliced`:
```python
>>> type(df[0])
<class 'pandas.util.testing.SubclassedSeries'>
```
Reshape operations return regular pandas objects:
```python
>>> type(df.stack())
<class 'pandas.core.series.Series'>
```
It would be great if this returned a `SublcassedSeries`. Same goes for unstack and pivot.
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Subclassed reshape
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[] | null | 7 | 2017-03-04T01:21:36Z | 2017-03-09T02:02:09Z | 2017-03-08T23:05:53Z |
NONE
| null |
- [x] closes #15563
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
Reshape operations in pandas/core/reshape.py modified to use `obj._constructor`, `obj._constructor_sliced`, and `obj._constructor_expanddim` for Series and DataFrame. This preserves a subclass family on `stack`, `unstack`, and `pivot` operations.
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pandas index does not give expected behaviour when raised to a power
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| 1 | 2017-03-04T02:46:31Z | 2017-03-04T07:34:19Z | 2017-03-04T07:33:43Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> test = list(range(5))
>>> a = pd.Series(test, index=test)
>>> a.index
Int64Index([0, 1, 2, 3, 4], dtype='int64')
>>> 0.5**a.index
Float64Index([0.0, 1.0, 1.41421356237, 1.73205080757, 2.0], dtype='float64')
```
#### Problem description
The index doesn't seem to behave properly when it is being raised to the power. It is not giving the correct values. It seems to work well with other operations like multiplication.
#### Expected Output
```python
Float64Index(array([ 1. , 0.5 , 0.25 , 0.125 , 0.0625]), dtype='float64')
```
#### 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 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.11.3
scipy: 0.18.1
statsmodels: None
xarray: 0.9.1
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: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: None
pandas_datareader: None
</details>
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Revert FrozenList changes (doc build slowdown, #15559)
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| 6 | 2017-03-04T11:33:00Z | 2017-03-04T15:38:43Z | 2017-03-04T15:38:43Z |
MEMBER
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See #15559. This temporarily reverts #15506, to see if this fixes the doc build slowdown.
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| 1 | 2017-03-04T11:33:23Z | 2017-03-04T14:14:37Z | 2017-03-04T14:14:37Z |
MEMBER
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#15098 removed the TimeSeries alias, but we also deprecated SparseTimeSeries (#10890), so this makes the removal complete.
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| 3 | 2017-03-04T18:26:52Z | 2017-12-20T02:01:17Z | 2017-03-05T16:26:59Z |
MEMBER
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- [x] closes #14440
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Employs @eoincondron's fix for float point inaccuracies when rounding by milliseconds for `DatetimeIndex.round` and `Timestamp.round`
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BUG: Groupby.cummin/max DataError on datetimes (#15561)
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| 3 | 2017-03-04T20:20:15Z | 2017-12-20T02:01:10Z | 2017-03-05T02:02:41Z |
MEMBER
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`numeric_only=False` set by default for `groupby.cummin/cummax` like `groupby.max/min` allows datetimes to work.
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| 2 | 2017-03-04T21:07:05Z | 2017-03-05T01:45:25Z | 2017-03-05T01:45:21Z |
CONTRIBUTOR
| null |
- [x] closes #15473
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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DOC: fix build_table_schema docs
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| 0 | 2017-03-04T21:56:54Z | 2017-03-04T21:58:33Z | 2017-03-04T21:58:33Z |
MEMBER
| null |
Small follow-up on #14904
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DOC: reset table_schema option after example
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| 1 | 2017-03-04T22:10:55Z | 2017-03-05T10:28:58Z | 2017-03-05T10:28:57Z |
MEMBER
| null |
@jreback I think this should fix the errors you noticed here https://github.com/pandas-dev/pandas/issues/15559#issuecomment-284166380 (let's see if travis agrees with me)
The table schema repr was activated due to the example (the ipython environment lives on between different documents; it would actually be a good idea to completely reset them for each rst file)
cc @TomAugspurger in any case an example that it's not yet ready to be default on True :-)
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ENH: Allow parameters method and min_periods in DataFrame.corrwith()
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[] | null | 4 | 2017-03-04T23:18:17Z | 2017-08-17T10:29:06Z | 2017-08-17T10:29:06Z |
NONE
| null |
Added new keyword parameters for `DataFrame.corrwith()`, which allows methods other than Pearson to be used. See #9490.
- [x] closes #9490
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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Added exception for invalid dtypes per issue #15520
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[] | null | 3 | 2017-03-05T04:15:30Z | 2017-03-10T12:08:46Z | 2017-03-10T12:08:46Z |
NONE
| null |
close #15520
- [ ] closes #xxxx
- [ ] tests added / passed
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Converting dtype of column from str to S not working on reassignment
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| 5 | 2017-03-05T12:21:58Z | 2017-03-05T17:00:54Z | 2017-03-05T14:42:51Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'a': ['Hello', 'World']})
df['a'] = df['a'].astype('S')
df['b'] = df['a'].astype('S')
print(df.dtypes)
```
Output is:
```
a object
b |S5
dtype: object
```
#### Problem description
The new column has the correct dtype of numpy byte string, the old column no contains
the new values but still has dtype `object`.
#### Expected Output
Both columns should have dtype 'S'
#### Output of ``pd.show_versions()``
<details>
import pandas as pd
df = pd.DataFrame({'a': ['Hello', 'World']})
df['a'] = df['a'].astype('S')
df['b'] = df['a'].astype('S')
print(df.dtypes)
</details>
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MDExOlB1bGxSZXF1ZXN0MTA5MTQ5NTA2
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Raising for invalid dtype issue #15520
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[] | null | 3 | 2017-03-05T14:06:12Z | 2017-03-10T12:08:16Z | 2017-03-10T12:08:16Z |
CONTRIBUTOR
| null |
closes #15520
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes git diff upstream/master | flake8 --diff
- [ ] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0MTA5MTUwODE4
| 15,577 |
Comment typo correction
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| 2 | 2017-03-05T14:45:38Z | 2017-03-05T16:21:17Z | 2017-03-05T16:21:15Z |
CONTRIBUTOR
| null |
- [ ] closes #xxxx
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- [ ] passes ``git diff upstream/master | flake8 --diff``
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MDU6SXNzdWUyMTE5NjQxMTc=
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Timestamp .round precision for ns
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| 1 | 2017-03-05T16:25:14Z | 2017-03-07T13:26:22Z | 2017-03-07T13:26:22Z |
CONTRIBUTOR
| null |
xref https://github.com/pandas-dev/pandas/pull/15568/files#r104304074
```
In [7]: pd.DatetimeIndex(['2016-10-17 12:00:00.0015']).round('ns')
Out[7]: DatetimeIndex(['2016-10-17 12:00:00.001499904'], dtype='datetime64[ns]', freq=None)
```
This is the unfortunate problem of converting from int64->float64->int64. I am not sure there is an easy way to fix this. We could:
- ignore (maybe doc it)
- raise/warn
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DOC: remove Panel4D from the API docs
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| 2 | 2017-03-05T16:38:48Z | 2017-03-07T07:42:47Z | 2017-03-07T07:42:47Z |
MEMBER
| null |
Panel4D is deprecated, so I think we can remove it from the API docs.
There is still a small mention of it in the data structures page: http://pandas-docs.github.io/pandas-docs-travis/dsintro.html#panel4d-and-panelnd-deprecated. I think that is sufficient, and still building the API docs (and all its methods) just adds a lot of time to building the docs.
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DOC: overall improvement of docstrings
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| 11 | 2017-03-05T17:11:26Z | 2021-11-06T18:19:26Z | null |
MEMBER
| null |
This is a general issue, to have as a starting point for (new) contributors to improve the docstrings (many docstrings have missing parts, and improvements on this front are always welcome).
In general, some attention points for docstrings in pandas. Each docstring should have:
- Clear explanation of the function/method/attribute's purpose and what it does
- Clear explanation of all parameter values
- State the return value and type (xref #17114)
- Some examples
- Note: examples should be runnable (thus include creation of the example DataFrame/Series, `import pandas as pd` not needed to include) (xref #3439)
- follow more the numpydoc docstring standard (stylistic, see also, all arguments)
There are quite some docstrings that are still lacking on some of these points (in particular examples), and PRs to improve this are always welcome.
**Approach**: pick a function, check the docstring, and if needed make a PR with updates!
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MDExOlB1bGxSZXF1ZXN0MTA5MTU4OTg0
| 15,581 |
DOC: Use nbsphinx
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| 29 | 2017-03-05T18:28:19Z | 2017-05-29T20:43:53Z | 2017-04-08T16:11:10Z |
CONTRIBUTOR
| null |
Update header levels for nbsphinx
Link to nb, nicer default table
Closes #15539
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MDU6SXNzdWUyMTE5NzIwNTQ=
| 15,582 |
Join result takes the index order of the other (right) DataFrame instead of the calling's (left) one
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| 11 | 2017-03-05T18:28:45Z | 2017-03-29T00:29:47Z | 2017-03-29T00:29:47Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
df1 = pd.DataFrame({'a': [0, 10, 20]})
df2 = pd.DataFrame({'b': [200, 100]}, index=[2,1])
print(df1.join(df2, how='inner'))
print(df2.join(df1, how='inner'))
print(df1.join(df2, how='inner', sort=True))
```
#### Problem description
Contrary to what is stated in the documentation of DataFrame.join(), when using the default sort=False, the return DataFrame preserves the index order of the other (right) DataFrame, instead of the index order of the calling (left) DataFrame.
Besides, the sort=True argument does not work.
#### Expected Output
The expected output is that the return DataFrame should preserve the index order of the calling (left) DataFrame.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-108-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: ca_ES.UTF-8
LOCALE: ca_ES.UTF-8
pandas: 0.19.2
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0.post20161106
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: 0.2.1
</details>
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MDExOlB1bGxSZXF1ZXN0MTA5MTY0OTkx
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BUG: Fix index order for Index.intersection()
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| 23 | 2017-03-05T20:57:02Z | 2017-03-29T08:06:09Z | 2017-03-29T00:29:47Z |
CONTRIBUTOR
| null |
- [x] closes #15582
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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MDU6SXNzdWUyMTE5ODUxMTA=
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rolling( window='10D') does not work for df with MultiIndex
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| 3 | 2017-03-05T21:17:48Z | 2019-10-22T01:35:37Z | 2019-10-22T01:35:37Z |
NONE
| null |
```python
# Your code here
tdf.rolling( '10D' ).mean()
```
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-49-707b0ff61efc> in <module>()
10 tdf = df.groupby(level=1).get_group( 64413 )
11
---> 12 tdf.rolling( '10D' ).mean()
13
14 tdf.reset_index().set_index('time').rolling( '10D' ).mean()
/home/firdaus/.conda/envs/tsconda/lib/python3.5/site-packages/pandas/core/generic.py in rolling(self, window, min_periods, freq, center, win_type, on, axis)
5502 min_periods=min_periods, freq=freq,
5503 center=center, win_type=win_type,
-> 5504 on=on, axis=axis)
5505
5506 cls.rolling = rolling
/home/firdaus/.conda/envs/tsconda/lib/python3.5/site-packages/pandas/core/window.py in rolling(obj, win_type, **kwds)
1797 return Window(obj, win_type=win_type, **kwds)
1798
-> 1799 return Rolling(obj, **kwds)
1800
1801
/home/firdaus/.conda/envs/tsconda/lib/python3.5/site-packages/pandas/core/window.py in __init__(self, obj, window, min_periods, freq, center, win_type, axis, on, **kwargs)
76 self.win_type = win_type
77 self.axis = obj._get_axis_number(axis) if axis is not None else None
---> 78 self.validate()
79
80 @property
/home/firdaus/.conda/envs/tsconda/lib/python3.5/site-packages/pandas/core/window.py in validate(self)
1055
1056 elif not is_integer(self.window):
-> 1057 raise ValueError("window must be an integer")
1058 elif self.window < 0:
1059 raise ValueError("window must be non-negative")
ValueError: window must be an integer
```
#### Problem description
The offset feature of specifying timelike windows in 'rolling' doesn't work if the dataframe has multindex with level_0 = 'time' and level_1 = something else.
#### Expected Output
``` python
tdf.reset_index().set_index('time').rolling( '10D' ).mean()
```
Works correctly.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-0.bpo.4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2.post+ts3
nose: None
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
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: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: None
xlsxwriter: 0.9.6
lxml: None
bs4: 4.5.3
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.4
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: None
pandas_datareader: None
</details>
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MDU6SXNzdWUyMTE5OTI3MTU=
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API: is_string_dtype is not strict
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| 5 | 2017-03-05T23:02:41Z | 2020-05-03T20:15:45Z | null |
CONTRIBUTOR
| null |
https://github.com/pandas-dev/pandas/issues/15533#issuecomment-284270591
``pandas.types.common.is_string_dtype`` is not strict, just checking if its a fixed-width numpy string/unicode type or ``object`` dtype. It could be made strict via something like this.
```
def is_string_dtype(arr_or_dtype):
dtype = _get_dtype(arr_or_dtype)
if isinstance(arr_or_dtype, np.ndarray):
if not lib.infer_dtype(arr_or_dtype) in ['string', 'unicode']:
return False
return dtype.kind in ('O', 'S', 'U') and not is_period_dtype(dtype)
```
this would need performance checking to see if its a problem. Further this then changes the API a tiny bit (as we allow an object OR a dtype to be passed in). Which is probably ok as long as its documented a bit.
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Join result takes on the calling from index
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[] | null | 3 | 2017-03-06T01:00:27Z | 2017-03-07T01:24:00Z | 2017-03-06T23:29:45Z |
NONE
| null |
https://github.com/pandas-dev/pandas/issues/15582
Fixes indexing referenced in 15582. Tests appear to work on my end, we'll see...
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ENH: standardize fill_value behavior across the API
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[] | null | 12 | 2017-03-06T04:32:39Z | 2017-03-11T17:48:07Z | 2017-03-11T17:38:27Z |
CONTRIBUTOR
| null |
- [ ] closes #15533
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
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| 3 | 2017-03-06T05:48:56Z | 2017-12-20T02:01:01Z | 2017-03-07T13:26:22Z |
MEMBER
| null |
- [x] closes #15578
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
Nice trick @jreback! `round()` patched for `DatetimeIndex` and `Timestamp` when rounding by ns.
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MDExOlB1bGxSZXF1ZXN0MTA5MjIwODQ3
| 15,589 |
API: return Index instead of array from DatetimeIndex field accessors (GH15022)
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| 27 | 2017-03-06T09:43:49Z | 2017-03-22T18:47:11Z | 2017-03-22T18:47:11Z |
MEMBER
| null |
- [x] closes #15022
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
This changes the datetime field accessors of a DatetimeIndex (and PeriodIndex, etc) to return an Index object instead of a plain array:
So for example:
```
# PR
In [1]: idx = pd.date_range("2015-01-01", periods=5, freq='10H')
In [2]: idx
Out[2]:
DatetimeIndex(['2015-01-01 00:00:00', '2015-01-01 10:00:00',
'2015-01-01 20:00:00', '2015-01-02 06:00:00',
'2015-01-02 16:00:00'],
dtype='datetime64[ns]', freq='10H')
In [3]: idx.hour
Out[3]: Int64Index([0, 10, 20, 6, 16], dtype='int64')
```
instead of
```
# master
In [1]: idx = pd.date_range("2015-01-01", periods=5, freq='10H')
In [2]: idx.hour
Out[2]: array([ 0, 10, 20, 6, 16], dtype=int32)
```
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MDU6SXNzdWUyMTIwODgzMjQ=
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DOC: clarify level argument of reindex
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| 4 | 2017-03-06T10:58:31Z | 2019-10-21T19:52:17Z | null |
MEMBER
| null |
The current [docstring explanation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.reindex.html) of the `level` argument:
> Broadcast across a level, matching Index values on the passed MultiIndex level
is not very clarifying (match what on what?).
So it would be good to update that description and also add an example.
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MDU6SXNzdWUyMTIwOTc1NDA=
| 15,591 |
DOC: enable parallel building of the docs
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| 8 | 2017-03-06T11:39:31Z | 2018-02-22T02:05:44Z | 2018-02-22T02:05:44Z |
MEMBER
| null |
Sphinx supports building the docs in parallel using multiple processes (with eg `-j 4`), see http://www.sphinx-doc.org/en/stable/invocation.html#cmdoption-sphinx-build-j
I quickly tested this, and that seems to reduce on my laptop (with 4 processes) build time from ca 17 min to 8 min.
This also takes a lot more memory, so not sure if we want to enable it by default, but it would be nice to at least give the possibility to specify this with `python make.py html`.
@jreback do you know if we can use multiple processes in parallel on travis?
What this needs:
- update the sphinxext (ipython directive and numpydoc) to allow parallel read (incorporate some upstream changes into our vendored versions)
- update `make.py` to be able to pass the `-j` argument
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Moved freeze_panes validation to io/excel.py (#15160)
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| 2 | 2017-03-06T14:51:06Z | 2017-03-07T13:22:10Z | 2017-03-07T13:22:10Z |
CONTRIBUTOR
| null |
- follow up to #15160
- [x] tests added / passed - Existing test suite passed
- [x] passes ``git diff upstream/master | flake8 --diff``
|
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| 212,236,957 |
MDU6SXNzdWUyMTIyMzY5NTc=
| 15,593 |
handle nan values in DataFrame.update when overwrite=False
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| 5 | 2017-03-06T20:16:29Z | 2017-06-16T14:04:29Z | 2017-05-24T23:15:32Z |
CONTRIBUTOR
| null |
#### Code Sample
```python
from pandas import DataFrame, date_range
df1 = DataFrame({'A': [1,None,3], 'B': date_range('2000', periods=3)})
df2 = DataFrame({'A': [None, 2, 3]})
df1.update(df2, overwrite=False)
df1
```
#### Problem description
I got *TypeError: invalid type promotion* error when updating a DF with a datetime column. The 2nd DF doesn't have this column. The error message is in the details (although bad formatted).
IMHO, the culpit is in the DataFrame.update. The block checking `mask.all` should be outside the `if` block and applies to the case `overwrite=False` as well.
```python
if overwrite:
mask = isnull(that)
# don't overwrite columns unecessarily
if mask.all():
continue
else:
mask = notnull(this)
```
<details>
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-70-23beb565ef70> in <module>()
1 df1 = DataFrame({'A': [1,None,3], 'B': date_range('2000', periods=3)})
2 df2 = DataFrame({'A': [None, 2, 3]})
----> 3 df1.update(df2, overwrite=False)
4 df1
5
C:\Users\pcluo\Anaconda3\lib\site-packages\pandas\core\frame.py in update(self, other, join, overwrite, filter_func, raise_conflict)
3845
3846 self[col] = expressions.where(mask, this, that,
-> 3847 raise_on_error=True)
3848
3849 # ----------------------------------------------------------------------
C:\Users\pcluo\Anaconda3\lib\site-packages\pandas\computation\expressions.py in where(cond, a, b, raise_on_error, use_numexpr)
228
229 if use_numexpr:
--> 230 return _where(cond, a, b, raise_on_error=raise_on_error)
231 return _where_standard(cond, a, b, raise_on_error=raise_on_error)
232
C:\Users\pcluo\Anaconda3\lib\site-packages\pandas\computation\expressions.py in _where_numexpr(cond, a, b, raise_on_error)
151
152 if result is None:
--> 153 result = _where_standard(cond, a, b, raise_on_error)
154
155 return result
C:\Users\pcluo\Anaconda3\lib\site-packages\pandas\computation\expressions.py in _where_standard(cond, a, b, raise_on_error)
126 def _where_standard(cond, a, b, raise_on_error=True):
127 return np.where(_values_from_object(cond), _values_from_object(a),
--> 128 _values_from_object(b))
129
130
TypeError: invalid type promotion
</details>
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MDExOlB1bGxSZXF1ZXN0MTA5MzUwMDA5
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BUG: upcasting on reshaping ops #13247
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| 11 | 2017-03-06T21:13:02Z | 2017-03-20T21:21:56Z | 2017-03-14T13:33:58Z |
CONTRIBUTOR
| null |
Only rebasing and fixing the merge conflicts
Original work done by: @jennolsen84
Original branch: https://github.com/jennolsen84/pandas/tree/concatnan
Original PR: #13337
- [x] closes #13247
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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The "no installation required" instructions refer to outdated wakari.io service
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| 1 | 2017-03-06T21:21:27Z | 2017-03-06T22:15:10Z | 2017-03-06T22:15:10Z |
CONTRIBUTOR
| null |
#### Problem description
The current [Trying out pandas, no installation required!](http://pandas.pydata.org/pandas-docs/stable/install.html#trying-out-pandas-no-installation-required) instructions point to wakari.io, a service that is now part of Anaconda Cloud. I do not think that the hosted notebooks are still part of the service.
To test this, I created an Anaconda Cloud account. I cannot find any instructions for a web notebook interface with `pandas` already installed. For example [these instructions](https://docs.continuum.io/anaconda-cloud/using.html#Notebooks) point to the need to install `pandas` locally.
#### Proposed solution
I propose to delete the following text in [install.rst](https://github.com/pandas-dev/pandas/blob/303541eba0797f30c6f10084acbd522220cbc56a/doc/source/install.rst). If this sounds good I'll submit a pull request.
```
Trying out pandas, no installation required!
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The easiest way to start experimenting with pandas doesn't involve installing
pandas at all.
`Wakari <https://wakari.io>`__ is a free service that provides a hosted
`IPython Notebook <http://ipython.org/notebook.html>`__ service in the cloud.
Simply create an account, and have access to pandas from within your brower via
an `IPython Notebook <http://ipython.org/notebook.html>`__ in a few minutes.
```
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DOC: remove wakari.io section
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| 4 | 2017-03-06T21:46:49Z | 2017-03-06T23:56:59Z | 2017-03-06T22:15:10Z |
CONTRIBUTOR
| null |
- [x] closes #15595
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
|
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Not sure how to fix/fill empty Data Frame in Python. Importing data from csv.
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| 1 | 2017-03-07T03:57:43Z | 2017-03-07T13:50:30Z | 2017-03-07T11:59:43Z |
NONE
| null |
Hi,
I am trying to group this one column that is for 1-11 regions as categorical variable and then print the mean for each group. My data column is 'Alcohol' and region column to groupby is 'REGION' but I keep getting error. Here is code. I need help as I am new to programming.
```
import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
import statsmodels.stats.multicomp as multi
data = pd.read_csv('gapminder2.csv', low_memory=False)
#setting variables you will be working with to numeric
data['ALCOHOL'] = data['alcconsumption'].convert_objects(convert_numeric=True)
data['REGION'] = data['country2'].convert_objects(convert_numeric=True)
ALCOHOL = data['ALCOHOL']
meanTest = pd.DataFrame(columns = ['ALCOHOL', 'REGION']).astype(float)
print ('mean per region')
mean = meanTest.groupby('REGION').mean()
print (mean)
```
Here is my error:
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
mean per region
Empty DataFrame
Columns: [ALCOHOL]
Index: []
C:/Users/blahblahblah/Documents/untitled1.py:15: FutureWarning: convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.
data['ALCOHOL'] = data['alcconsumption'].convert_objects(convert_numeric=True)
C:/Users/C5182508/OneDrive - SAP SE/Documents/untitled1.py:16: FutureWarning: convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.
data['REGION'] = data['country2'].convert_objects(convert_numeric=True)
Here is picture:
(https://cloud.githubusercontent.com/assets/26236052/23641223/7ad06c26-02af-11e7-8811-52cb3d1b8f2c.png)
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DOC: remove Panel4D from the API docs #15579
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| 2 | 2017-03-07T07:02:55Z | 2017-03-07T07:51:22Z | 2017-03-07T07:41:31Z |
CONTRIBUTOR
| null |
Hi this is my first PR to Pandas
I'm pretty sure this is what @jorisvandenbossche was getting at on ticket <a href='https://github.com/pandas-dev/pandas/issues/15579'>#15579<a/>.
I noticed that <a href='https://github.com/pandas-dev/pandas/blob/be4a63fe791e27c2f8a9ae4f3a419ccc255c1b5b/pandas/tests/test_expressions.py#L206'>tests</a> for panel4D also still exist, is that something that should remain?
Thanks!
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rolling casts int to float unnecessarily
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| 4 | 2017-03-07T13:51:18Z | 2018-08-05T19:54:48Z | 2017-03-07T19:51:55Z |
NONE
| null |
Take the following example:
```python
>>> df = pd.DataFrame(list(range(5)))
>>> df
0
0 0
1 1
2 2
3 3
4 4
>>> df.rolling(3, min_periods=1).sum()
0
0 0.0
1 1.0
2 3.0
3 6.0
4 9.0
```
There is no need to cast the ints to floats in this case since no NaNs have to be substituted for missing values when `min_periods=1`. I think it would make sense to treat the case `min_periods<=1` separately to prevent unnecessary type conversions like this one.
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Parallelize doc build
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| 1 | 2017-03-07T14:29:18Z | 2017-03-07T14:43:48Z | 2017-03-07T14:31:01Z |
CONTRIBUTOR
| null |
closes #15591
https://travis-ci.org/jreback/pandas/builds/208594369
a couple of minutes faster with -j 2.
fixes some deprecated use of ``pd.Term``.
Note there is another error in HDFStore build, though I can't repro it locally.
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MDExOlB1bGxSZXF1ZXN0MTA5NTE3MTM4
| 15,601 |
DEPR: Panel deprecated
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| 17 | 2017-03-07T16:27:43Z | 2018-10-28T08:20:26Z | 2017-04-07T19:10:25Z |
CONTRIBUTOR
| null |
closes #13563
going to be on top of #15677
Partially addresses https://github.com/pandas-dev/pandas/issues/14565#issuecomment-275746292
Note this is currently a ``FutureWarning`` mainly to fail the tests if I missed anything. Intention is to change to a ``DeprecationWarning``.
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BUG: DataFrame.hist() does not get along with matplotlib.pyplot.tight_layout()
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| 5 | 2017-03-07T17:01:17Z | 2017-03-13T13:29:48Z | 2017-03-12T23:51:15Z |
CONTRIBUTOR
| null |
- [ ] (re)closes #9351
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
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df.to_sql(...) crashes when attempting to make a new table
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| 7 | 2017-03-07T17:13:09Z | 2017-03-21T22:09:02Z | 2017-03-13T14:39:47Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import mysql.connector
from sqlalchemy import create_engine
import pandas as pd
# So I don't have to type my password
engine = create_engine('mysql://',
creator=lambda: mysql.connector.connect(
option_files='/Users/simnim/.mylogin_ue.cnf',
buffered=True,
charset='utf8'
)
)
# Code to make df ...
df.to_sql('my_table', engine, schema='my_db', index=False, if_exists='replace')
---------------------------------------------------------------------------
ProgrammingError Traceback (most recent call last)
<ipython-input-166-decc2798114f> in <module>()
----> 1 df.to_sql('my_table', engine, schema='my_db', index=False)
/usr/local/lib/python2.7/site-packages/pandas/core/generic.pyc in to_sql(self, name, con, flavor, schema, if_exists, index, index_label, chunksize, dtype)
1199 sql.to_sql(self, name, con, flavor=flavor, schema=schema,
1200 if_exists=if_exists, index=index, index_label=index_label,
-> 1201 chunksize=chunksize, dtype=dtype)
1202
1203 def to_pickle(self, path):
/usr/local/lib/python2.7/site-packages/pandas/io/sql.pyc in to_sql(frame, name, con, flavor, schema, if_exists, index, index_label, chunksize, dtype)
468 pandas_sql.to_sql(frame, name, if_exists=if_exists, index=index,
469 index_label=index_label, schema=schema,
--> 470 chunksize=chunksize, dtype=dtype)
471
472
/usr/local/lib/python2.7/site-packages/pandas/io/sql.pyc in to_sql(self, frame, name, if_exists, index, index_label, schema, chunksize, dtype)
1145 if_exists=if_exists, index_label=index_label,
1146 schema=schema, dtype=dtype)
-> 1147 table.create()
1148 table.insert(chunksize)
1149 if (not name.isdigit() and not name.islower()):
/usr/local/lib/python2.7/site-packages/pandas/io/sql.pyc in create(self)
582
583 def create(self):
--> 584 if self.exists():
585 if self.if_exists == 'fail':
586 raise ValueError("Table '%s' already exists." % self.name)
/usr/local/lib/python2.7/site-packages/pandas/io/sql.pyc in exists(self)
570
571 def exists(self):
--> 572 return self.pd_sql.has_table(self.name, self.schema)
573
574 def sql_schema(self):
/usr/local/lib/python2.7/site-packages/pandas/io/sql.pyc in has_table(self, name, schema)
1173 self.connectable.dialect.has_table,
1174 name,
-> 1175 schema or self.meta.schema,
1176 )
1177
/usr/local/lib/python2.7/site-packages/sqlalchemy/engine/base.pyc in run_callable(self, callable_, *args, **kwargs)
2034 """
2035 with self.contextual_connect() as conn:
-> 2036 return conn.run_callable(callable_, *args, **kwargs)
2037
2038 def execute(self, statement, *multiparams, **params):
/usr/local/lib/python2.7/site-packages/sqlalchemy/engine/base.pyc in run_callable(self, callable_, *args, **kwargs)
1523
1524 """
-> 1525 return callable_(self, *args, **kwargs)
1526
1527 def _run_visitor(self, visitorcallable, element, **kwargs):
/usr/local/lib/python2.7/site-packages/sqlalchemy/dialects/mysql/base.pyc in has_table(self, connection, table_name, schema)
1656 try:
1657 rs = connection.execution_options(
-> 1658 skip_user_error_events=True).execute(st)
1659 have = rs.fetchone() is not None
1660 rs.close()
/usr/local/lib/python2.7/site-packages/sqlalchemy/engine/base.pyc in execute(self, object, *multiparams, **params)
937 """
938 if isinstance(object, util.string_types[0]):
--> 939 return self._execute_text(object, multiparams, params)
940 try:
941 meth = object._execute_on_connection
/usr/local/lib/python2.7/site-packages/sqlalchemy/engine/base.pyc in _execute_text(self, statement, multiparams, params)
1095 statement,
1096 parameters,
-> 1097 statement, parameters
1098 )
1099 if self._has_events or self.engine._has_events:
/usr/local/lib/python2.7/site-packages/sqlalchemy/engine/base.pyc in _execute_context(self, dialect, constructor, statement, parameters, *args)
1187 parameters,
1188 cursor,
-> 1189 context)
1190
1191 if self._has_events or self.engine._has_events:
/usr/local/lib/python2.7/site-packages/sqlalchemy/engine/base.pyc in _handle_dbapi_exception(self, e, statement, parameters, cursor, context)
1394 )
1395 else:
-> 1396 util.reraise(*exc_info)
1397
1398 finally:
/usr/local/lib/python2.7/site-packages/sqlalchemy/engine/base.pyc in _execute_context(self, dialect, constructor, statement, parameters, *args)
1180 statement,
1181 parameters,
-> 1182 context)
1183 except BaseException as e:
1184 self._handle_dbapi_exception(
/usr/local/lib/python2.7/site-packages/sqlalchemy/engine/default.pyc in do_execute(self, cursor, statement, parameters, context)
468
469 def do_execute(self, cursor, statement, parameters, context=None):
--> 470 cursor.execute(statement, parameters)
471
472 def do_execute_no_params(self, cursor, statement, context=None):
/usr/local/lib/python2.7/site-packages/mysql/connector/cursor.pyc in execute(self, operation, params, multi)
513 else:
514 try:
--> 515 self._handle_result(self._connection.cmd_query(stmt))
516 except errors.InterfaceError:
517 if self._connection._have_next_result: # pylint: disable=W0212
/usr/local/lib/python2.7/site-packages/mysql/connector/connection.pyc in cmd_query(self, query, raw, buffered, raw_as_string)
486 if not isinstance(query, bytes):
487 query = query.encode('utf-8')
--> 488 result = self._handle_result(self._send_cmd(ServerCmd.QUERY, query))
489
490 if self._have_next_result:
/usr/local/lib/python2.7/site-packages/mysql/connector/connection.pyc in _handle_result(self, packet)
393 return self._handle_eof(packet)
394 elif packet[4] == 255:
--> 395 raise errors.get_exception(packet)
396
397 # We have a text result set
ProgrammingError: 1146 (42S02): Table 'my_db.my_table' doesn't exist
########### More minimal version ###########
sql_builder = pandas.io.sql.pandasSQL_builder(engine, schema='my_db')
# This one should return True
sql_builder.has_table('table_that_exists')
Out[169]:
True
# And it did.
# This one should return False
sql_builder.has_table('table_that_doesnt_exist')
### Similar trace as above (omitted for length)
# Nope, it breaks
```
#### Problem description
to_sql should create the table if it does not exist instead of raising an error.
has_table should return False instead of raising an error
#### Expected Output
False
#### Output of ``pd.show_versions()``
<details>
In [3]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Darwin
OS-release: 16.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 32.1.0
Cython: None
numpy: 1.12.0
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.2.1
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 2.0.0
openpyxl: None
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: None
lxml: None
bs4: 4.5.3
html5lib: 0.9999999
httplib2: 0.9.2
apiclient: 1.6.1
sqlalchemy: 1.1.5
pymysql: 0.7.10.None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.9.5
boto: 2.45.0
pandas_datareader: None
</details>
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| 212,519,882 |
MDU6SXNzdWUyMTI1MTk4ODI=
| 15,604 |
pandas.Series.sort_values(ascending=[False]) behaves as ascending=True
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| 7 | 2017-03-07T18:32:43Z | 2017-03-08T13:37:19Z | 2017-03-08T13:37:19Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas as pd
x = pd.Series([4,5,6,1,2,3])
print(x.sort_values(ascending=False))
print(x.sort_values(ascending=[False]))
```
#### Problem description
False and [False] behave differently. They should behave in the same way according to the documentation (and it makes sense that they do).
#### Expected Output
2 6
1 5
0 4
5 3
4 2
3 1
dtype: int64
2 6
1 5
0 4
5 3
4 2
3 1
dtype: int64
#### 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 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
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: 1.2.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: None
</details>
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MDU6SXNzdWUyMTI1MjMyMjQ=
| 15,605 |
Broken link in cookbook for read_csv
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| 4 | 2017-03-07T18:45:59Z | 2017-03-17T15:42:26Z | 2017-03-17T15:42:26Z |
NONE
| null |
The second link in the section http://pandas.pydata.org/pandas-docs/stable/cookbook.html#csv
is broken.
I.e. the link [read_csv in action](http://wesmckinney.com/blog/?p=635) is supposed to go to Wes McKinney's blog, but returns a 404 error.
A cursory look at said blog didn't result in anything jumping out at me as far as what *should* be the correct entry to link to.
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| 15,606 |
pd.Index ignores dtype for integer inputs (explicit or inferred), always returning int64
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[] | null | 5 | 2017-03-07T19:28:24Z | 2017-03-07T20:00:09Z | 2017-03-07T19:52:49Z |
NONE
| null |
#### Code example / reproduction
```python
### pd.Index will always coerce integer inputs to int64, regardless of original dtype
# or explicit dtype arg
# Default behavior assumes int64, as expected
pd.Index(np.arange(10)).dtype # dtype('int64')
# Input values are ignored, i.e. no inferring of dtype results
np.arange(10, dtype=np.int8).dtype # dtype('int8')
pd.Index(np.arange(10, dtype=np.int8)).dtype # dtype('int64')
# Explicit dtype argument doesn't affect behavior
pd.Index(np.arange(10, dtype=np.int8), dtype=np.int8).dtype # dtype('int64')
# Naturally, DataFrame yields the same, whether or not an index is provided
# Default behavior assumes int64, as expected
pd.DataFrame(np.arange(10)).index.dtype # dtype('int64')
# Implicit dtype of values provided to be used as index
np.arange(10, dtype=np.int8).dtype # dtype('int8')
pd.DataFrame(np.arange(10), index=np.arange(10, dtype=np.int8)).index.dtype # dtype('int64')
# As shown above, one cannot create an Index beforehand to pass to DataFrame(), because
# the Index instance itself is/was already coerced to Int64Index
```
#### Problem source
The problem traces down to `Index.__new__` in [indexes.base.py around line 240](https://github.com/pandas-dev/pandas/blob/master/pandas/indexes/base.py#L243-L245) wherein an `Int64Index` is always returned, regardless of the original values' `dtype`:
```python
## pandas-dev
if is_signed_integer_dtype(data.dtype):
from .numeric import Int64Index
return Int64Index(data, copy=copy, dtype=dtype, name=name)
## v19.2, lines 238-240:
if issubclass(data.dtype.type, np.integer):
from .numeric import Int64Index
return Int64Index(data, copy=copy, dtype=dtype, name=name)
```
Various previous steps take care to follow inferred `dtype` and/or that explicitly specified via `dtype` parameter, but this information is effectively discarded. If this is the preferred behavior, there might be a warning emitted or some other method of notifying that type coercion had taken place.
#### Suggested behavior / discussion
I would argue, though, that the preferred behavior would be to utilize the typing requested. This would more closely follow the expressed intent of `RangeIndex` in implementing a "memory-saving special case" but using the minimum memory required to represent the index values.
#### Example use case
To provide a legitimate use case, I'm currently working on a project involving simple calculations (resulting in various integer values, but all of which fall within the -127,128 int-8 range) on massive numbers of elements, for example:
```python
"{:.2f} MB".format(np.array([1,]*len(rulesdf)).nbytes / 1024**2)
# ^ '3814.70 MB', default int64
"{:.2f} MB".format(np.array([1,]*len(rulesdf), dtype=np.int8).nbytes / 1024**2)
# ^ '476.84 MB' using int8
## Above example uses ~500 million elements, but project needs are ~tens-of-billions
```
I've had to add steps to convert data from pandas DataFrame to numpy arrays in order to conserve memory by forcing all values to `np.int8` (or similarly `np.int16` in some cases), and only later returning to DataFrame and/or Series values once various transformations are done to reduce the numbers of elements involved (unfortunately, a lot of those transformations would be more easily done as a pandas DataFrame, to boot!).
From what I can find, it looks like that may require a different `NumericIndex` subclass, which could perhaps provide a base class for alternative `RangeIndex` and/or `Int__Index` classes. But I'm not particularly well versed on what else might exist, this is only what I've found so far.
#### Environment / Version Info
These examples were all done locally on a MacBook Pro, macOS 10.12.3. But the issue was originally come across on various AWS EC2 servers, all running Ubuntu. I've provided the `show_versions()` info for one EC2 instance, but found the same on c4.4xlarge & c4.8xlarge instances.
<details>
## Local MacBook
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.4.0
machine: x86_64
processor: i386
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: 28.8.0.post20161110
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: 1.1.4
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: None
boto: None
pandas_datareader: None
## AWS EC2 Micro instance
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-64-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: 28.8.0.post20161110
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: 1.1.4
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
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MDExOlB1bGxSZXF1ZXN0MTA5NTgyMzMy
| 15,607 |
BUG: make Series.sort_values(ascending=[False]) behave as ascending=False (#15604)
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| 8 | 2017-03-07T22:06:20Z | 2017-03-08T13:37:37Z | 2017-03-08T13:37:19Z |
NONE
| null |
- [X ] closes #15604
- [ X] tests added / passed
- [X] passes ``git diff upstream/master | flake8 --diff``
- [X] whatsnew entry
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