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} | 16 | 2015-04-03T14:39:25Z | 2015-04-22T13:55:12Z | 2015-04-18T23:18:34Z | MEMBER | null | Closes #9762. Does this needs release note?
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} | 6 | 2015-04-03T14:40:44Z | 2015-09-19T00:38:25Z | 2015-04-28T12:00:28Z | CONTRIBUTOR | null | Examples for `interpolate` and `groupby` weren't being formatted correctly. See http://pandas.pydata.org/pandas-docs/version/0.16.0/generated/pandas.Series.interpolate.html and http://pandas.pydata.org/pandas-docs/version/0.16.0/generated/pandas.Series.groupby.html
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} | 1 | 2015-04-03T17:18:07Z | 2015-09-19T00:38:19Z | 2015-04-14T15:24:45Z | CONTRIBUTOR | null | There are a few weird behaviors that this fixes:
1. If `other` is an `int` or `float`, then `where` throws an Exception while trying to call `other.view`
2. If `other` is an np.int64, it works fine
3. If `other` is an np.float64, there is no error, but the result is bizarre (it reinterprets the bits of the float as an integer, rather than casting it)
4. If `other` is list-like and has a numerical dtype, then it throws an exception while try to call `all` on the value False (for some reason, comparing an ndarray with dtype datetime64 and an ndarray with an integer dtype just returns a scalar False rather than a boolean ndarray)
```
>>> s = Series(date_range('20130102', periods=2))
>>> s.where(s.isnull(), 0)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/evanpw/Workspace/pandas/pandas/core/generic.py", line 3399, in where
try_cast=try_cast)
File "/home/evanpw/Workspace/pandas/pandas/core/internals.py", line 2469, in where
return self.apply('where', **kwargs)
File "/home/evanpw/Workspace/pandas/pandas/core/internals.py", line 2451, in apply
applied = getattr(b, f)(**kwargs)
File "/home/evanpw/Workspace/pandas/pandas/core/internals.py", line 1080, in where
result = func(cond, values, other)
File "/home/evanpw/Workspace/pandas/pandas/core/internals.py", line 1063, in func
v, o = self._try_coerce_args(v, o)
File "/home/evanpw/Workspace/pandas/pandas/core/internals.py", line 1819, in _try_coerce_args
other = other.view('i8')
AttributeError: 'int' object has no attribute 'view'
>>> s.where(s.isnull(), np.int64(10))
0 1970-01-01 00:00:00.000000010
1 1970-01-01 00:00:00.000000010
dtype: datetime64[ns]
>>> s.where(s.isnull(), np.float64(10))
0 2116-06-17 06:38:37.588971520
1 2116-06-17 06:38:37.588971520
dtype: datetime64[ns]
>>> s.where(s.isnull(), [0, 1])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/evanpw/Workspace/pandas/pandas/core/generic.py", line 3326, in where
if not (new_other == np.array(other)).all():
AttributeError: 'bool' object has no attribute 'all'
```
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} | 5 | 2015-04-03T18:19:26Z | 2015-05-06T02:50:31Z | 2015-04-03T22:41:42Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/9781
on master:
```
>>> df = DataFrame(columns=['jim', 'joe'])
>>> df
Empty DataFrame
Columns: [jim, joe]
Index: []
>>> df.to_json()
'{}'
```
mixed type will segfault:
```
>>> df['joe'] = df['joe'].astype('i8')
>>> df.to_json()
Segmentation fault (core dumped)
```
on branch:
```
>>> df.to_json()
'{"jim":{},"joe":{}}'
>>> df['joe'] = df['joe'].astype('i8')
>>> read_json(df.to_json())
Empty DataFrame
Columns: [jim, joe]
Index: []
```
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} | 7 | 2015-04-03T20:35:07Z | 2015-04-06T00:18:43Z | 2015-04-06T00:18:30Z | CONTRIBUTOR | null | closes #9764
In order to display a number from range (0,1) in decimal format with N significant digits (and not in scientific format), the number needs to be greater than or equal to 1e(-N+1), not 1e-N.
Did not test for range of display.precision, as this does not seem to current practice.
Did not include tests as they would imply an output format which might change in the future.
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} | 0 | 2015-04-04T02:05:10Z | 2015-04-14T13:19:45Z | 2015-04-14T13:19:45Z | CONTRIBUTOR | null | ```
In [11]: df = pd.DataFrame([[1, 3], [2, 3]])
In [12]: df.groupby(1).transform('mean')
Out[12]:
0
0 1
1 1
In [13]: df.groupby(1).mean() # converts when it's a normal groupby mean
Out[13]:
0
1
3 1.5
```
http://stackoverflow.com/a/29437332/1240268
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} | 1 | 2015-04-04T12:57:09Z | 2015-04-04T21:27:58Z | 2015-04-04T18:28:30Z | MEMBER | null | Closes #8801.
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} | 5 | 2015-04-04T17:32:17Z | 2017-01-25T23:48:10Z | 2017-01-25T23:48:06Z | CONTRIBUTOR | null | xref #9802
- I think making it FAQ would most appropriate
- want to think about the orderings
- need a release note to indicate that these have been merged
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} | 4 | 2015-04-04T18:13:24Z | 2015-04-06T12:54:31Z | 2015-04-06T12:18:51Z | CONTRIBUTOR | null | - provides instructions for creating a development on any platform with conda
- cleans up the install.rst a bit
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} | 2 | 2015-04-04T18:22:01Z | 2015-04-28T00:49:50Z | 2015-04-28T00:49:50Z | CONTRIBUTOR | null | I am going to be hosting a 2-day _pandas_ sprint at `PyCon 2015`, on April 13-14, see [here](https://us.pycon.org/2015/). Thanks for choosing to partcipate!
We are in room 513D.
This issue will be the landing page for contributors.
[pandas](http://pandas.pydata.org) is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
See our shiny new [Contributing Guidelines & Instructions](http://pandas-docs.github.io/pandas-docs-travis/contributing.html)
Here are various issues that are open and would like your attention:
- [Difficulty Novice](https://github.com/pydata/pandas/issues?q=is%3Aopen+is%3Aissue+label%3A%22Difficulty+Novice%22)
- [Difficulty Intermediate](https://github.com/pydata/pandas/issues?q=is%3Aopen+is%3Aissue+label%3A%22Difficulty+Intermediate%22)
- [Difficulty Advanced](https://github.com/pydata/pandas/issues?q=is%3Aopen+is%3Aissue+label%3A%22Difficulty+Advanced%22)
List of issues that would be nice to fix:
- https://github.com/pydata/pandas/issues/5724
- https://github.com/pydata/pandas/issues/5677
- https://github.com/pydata/pandas/issues/7820
- https://github.com/pydata/pandas/pull/7872
- https://github.com/pydata/pandas/issues/9847
- https://github.com/pydata/pandas/issues/9862
- https://github.com/pydata/pandas/issues/9602
Please comment on an issue if you are working on it.
Here is a [link](https://gitter.im/pydata/pandas) to the gitter chatroom.
For people who would like to participate remotely, I will post a link to a google hangout.
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} | 3 | 2015-04-05T00:55:47Z | 2015-04-11T23:35:42Z | 2015-04-05T13:52:22Z | MEMBER | null | Closes #9610, Closes #9779.
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| null | 8 | 2015-04-05T09:41:23Z | 2015-07-15T14:33:52Z | 2015-07-15T14:31:09Z | MEMBER | null | Because `test_graphics.py` gets little longer, I've splitted it to 2 files. Test methods itself are not changed.
- `test_graphics.py` is for `.plot()` methods.
- `test_graphics_others.py` is for other plots, such as `.hist()`, `.boxplot` and miscs.
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} | 10 | 2015-04-05T09:55:45Z | 2015-08-03T13:23:33Z | 2015-07-29T14:05:50Z | MEMBER | null | Closes #9003, Closes #9307.
- Not to clutter plot functions outside of classes.
- Changed to use classmethods rather than closures for plot funcs.
- Not cache plot function itself on `ax._plot_data`. Changed to store `str` indicates plot kind.
- Moved `tsplot` related codes to `tseries.plotting.py`.
Because the change is little big, I think it is better to wait until 0.17. Further simplification seems to be possible if we can remove `tsplot` as not public function.
CC: @qwhelan, @TomAugspurger
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} | 0 | 2015-04-05T18:05:26Z | 2016-04-27T14:01:13Z | 2016-04-27T14:01:13Z | CONTRIBUTOR | null | might be worth expanding the [comparison with R section](http://pandas.pydata.org/pandas-docs/stable/comparison_with_r.html); maybe have a basics section?
with some of the verbs from [here](http://nbviewer.ipython.org/urls/gist.githubusercontent.com/TomAugspurger/6e052140eaa5fdb6e8c0/raw/811585624e843f3f80b9b6fe89e18119d7d2d73c/dplyr_pandas.ipynb)
@TomAugspurger
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} | 7 | 2015-04-05T19:42:33Z | 2015-08-21T14:02:20Z | 2015-08-21T14:02:20Z | CONTRIBUTOR | null | It would make Pandas easier to teach, easier to learn, and easier to use if the sorting behavior were the same between series and dataframes. But the existing `order()` and `sort()` methods are locked into their old behaviors by all of the code that already depends on them.
But a new `sorted()` method could bring symmetry between series and dataframes for code written from now on:
```
Series.sorted() => same as existing Series.order()
DataFrame.sorted() => same as existing DataFrame.sort()
```
Having this new pair of methods with identical conventions, where possible, would solve several different problems that learners have with Pandas today:
- In Pandas, nearly all methods return a new object by default instead of doing modification in-place, but learners discover that `Series.sort()` is a special case.
- In Python, a `sort()` method traditionally returns `None` and does an in-place sort, but learners have to discover that `DataFrame.sort()` violates this convention in order to match the behavior of the rest of Pandas.
- The new-object sorter for series objects is `Series.order()` which is very difficult to discover, as nothing else in the Python ecosystem is named `order()`, and since one would normally expect an `order()` method to _tell_ you the order (ascending? descending? none?) instead of imposing a new order.
- The standard Python name for a sort that returns a new object is `sorted()`, per the universally loved Python built-in, but learners cannot transfer this knowledge to Pandas, where that concept exists but under the two different names `Series.order()` and `DataFrame.sort()`.
Yes, the `ed` at the end of `sorted()` would be one character longer than `order()` and two characters longer than the current practice of `df.sort()`. But, on balance, I think that most programmers would happily cede two characters in order to be able to use the same method name when they are flipping code between handling series and handling dataframes, and happy to have the option of using the standard Python name for the concept of a non-in-place sort.
I suspect that deprecating the old names would be overly disruptive at this point, and they could probably live alongside the new `sorted()` methods without much trouble — new documentation could adopt the new, consistent terminology where possible, if the Pandas developers did not want to disrupt current users of the old inconsistent names.
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} | 7 | 2015-04-05T23:26:48Z | 2021-04-18T06:12:16Z | null | NONE | null | When trying to intersect two Index objects containing floats I get the following unexpected behavior:
``` python
>>> new_index = pd.Index(np.arange(0.0,1.0,0.1),dtype='float64')
>>> new_index2 = pd.Index(np.arange(0.5,1.0,0.1),dtype='float64')
>>> intersection = new_index.intersection(new_index2)
Float64Index([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], dtype='float64')
Float64Index([0.5, 0.6, 0.7, 0.8, 0.9], dtype='float64')
Float64Index([0.5], dtype='float64')
```
Where I would expect the intersection to equal index2.
Pandas version string below:
## INSTALLED VERSIONS
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.0
nose: 1.3.4
Cython: 0.21.2
numpy: 1.9.2
scipy: 0.15.1
statsmodels: None
IPython: 2.4.1
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.2
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
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} | 8 | 2015-04-06T00:25:16Z | 2015-04-07T22:37:18Z | 2015-04-07T22:36:59Z | CONTRIBUTOR | null | Closes #9777
Previously the order of new columns from `.assign` was arbitrary. For predictability, we'll sort before inserting.
We need to be comfortable with this change since we can't change behavior later with a keyword arg.
Technically we _could_ allow referencing the a column defined within the same call to `assign` as long as they are sorted. e.g. `df.assign(C=df.A + df.B, D=df.C**2)` would work, but not `df.assign(df.D=df.A +df.B, C=df.D**2)`. But I don't think we should.
cc @mrocklin
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} | 2 | 2015-04-06T13:38:14Z | 2015-04-06T15:32:16Z | 2015-04-06T15:32:08Z | NONE | null | ```
tz = pytz.timezone('America/New_York')
tz.localize(datetime.datetime(2007,3,12)) - DateOffset(days=1)
# Timestamp('2007-03-11 00:00:00-0400', tz='America/New_York')
```
Shouldn't this be?
```
Timestamp('2007-03-11 00:00:00-0500', tz='America/New_York')
```
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} | 11 | 2015-04-06T18:39:49Z | 2016-07-21T15:46:03Z | 2015-10-21T07:18:03Z | NONE | null | Currently, when IPython notebooks are converted to latex, pandas `DataFrame`s are appear as raw (verbatim) text. One simple solution is to include a `_repl_latex_` function. I have tried this solution and works. See https://github.com/ipython/ipython/issues/8261
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| null | 7 | 2015-04-06T22:36:26Z | 2021-04-18T06:13:04Z | null | NONE | null | Is it possible in pandas to interpolate for missing values in multiindex dataframe? All data is numeric.
see the link for more details:
http://stackoverflow.com/questions/29465398
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| null | 6 | 2015-04-06T22:46:50Z | 2020-05-13T02:10:37Z | null | NONE | null | It would be great if `corrwith` could calculate correlations between dataframes that have different column names, but the same index.
For example, take the two (10, 5) `df1` and `df2` dataframes below.
```
import pandas as pd
import numpy as np
import string
nrow = 10
ncol = 5
axis = 0
index = list(string.ascii_lowercase[:nrow])
columns = list(string.ascii_uppercase[:ncol])
df1 = pd.DataFrame(np.random.randn(nrow, ncol), index=index, columns=columns)
df2 = pd.DataFrame(np.random.randn(nrow, ncol), index=index)
```
`df1` and `df2` have different columns, and I'd like to create a 5x5 matrix of the correlations of their columns, on the values in each row.
I've implemented a stopgap measure here: https://github.com/YeoLab/flotilla/blob/d9e53c219320c5d5dbbbfa41769abb2ab6f25574/flotilla/compute/generic.py#L429
Is this a planned feature for future releases?
Probably also related to the `method` issue: https://github.com/pydata/pandas/issues/9490
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} | 7 | 2015-04-07T00:44:21Z | 2015-04-08T14:27:32Z | 2015-04-08T14:00:34Z | CONTRIBUTOR | null | closes #9825
Previously, passing tz-aware inputs to Holiday.dates would always result in an error because the the input start_date was converted in a way that destroyed timezone information. This PR makes that conversion correctly preserve tz info, and converts several of the existing tests to run with both tz-aware and tz-naive inputs.
Noticed while working on a new trading calendar implementation here: https://github.com/quantopian/zipline/pull/556.
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} | 1 | 2015-04-07T00:46:43Z | 2015-04-08T14:27:18Z | 2015-04-08T14:27:18Z | CONTRIBUTOR | null | The conversion from `start_date` to `base_date` on https://github.com/pydata/pandas/blob/master/pandas/tseries/holiday.py#L206 drops tz information, which causes the `DatetimeIndex` constructor to barf if end_date is tz-aware. Fixed by https://github.com/pydata/pandas/pull/9824.
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} | 37 | 2015-04-07T01:16:54Z | 2020-04-08T23:50:39Z | 2015-10-18T14:02:24Z | NONE | null | Add [Biweight Midcorrelation](http://download-v2.springer.com/static/pdf/95/chp%253A10.1007%252F978-3-642-39678-6_14.pdf?token2=exp=1428360548~acl=%2Fstatic%2Fpdf%2F95%2Fchp%25253A10.1007%25252F978-3-642-39678-6_14.pdf*~hmac=980c2edddba12bbb73ebbadaa682092cde9d534bc8738d65785647402b683c21), an alternative correlation method to Pearson that is robust to outliers, to the series correlation method repertoire.
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} | 9 | 2015-04-07T04:13:35Z | 2015-04-14T15:32:17Z | 2015-04-14T15:32:17Z | NONE | null | When I tried to pip install pandas on a system with cython 0.15.1, it failed (I'm guessing that's not surprising). After pip installing cython (to 0.22), pandas installs fine. The error messages talked about missing lib.pxd and tslib.pxd files - to my untrained eye, Cython wasn't an obvious culprit!
My point is that, unless there's a good reason not to, the minimum cython version for compiling pandas should be a dependency in pip.
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| null | 3 | 2015-04-07T08:21:26Z | 2015-05-09T19:46:26Z | 2015-05-09T19:46:26Z | NONE | null | Preserve complex dtypes when a stat_function on DataFrame is called with the optional level argument.
Minimal example:
```
import pandas as pd
frame = pd.DataFrame({'z': [1+2j]})
frame.sum(level=0)
```
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| null | 8 | 2015-04-07T21:48:15Z | 2015-04-09T11:55:13Z | 2015-04-08T13:49:32Z | CONTRIBUTOR | null | Canonical/Ubuntu's launchpad was importing this repo on daily basis and creating packages for Ubuntu based distros
https://code.launchpad.net/~pythonxy/+archive/ubuntu/pythonxy-devel/+packages?field.name_filter=pandas
This run well for many years until recently some changes in the repo broke the chain:
> > The problem is that the Git repository was imported from Mercurial, and recent versions of hg-git >>add some >non-standard commit headers that bzr-git doesn't support, and it isn't clear how they >>should be supported.
>
> They must have recently merged a branch that was created by a new version of hg-git.
Please have a look to a discussion with a Launchpad developer here:
https://answers.launchpad.net/launchpad/+question/264432
Is there anything the maintainers could do to fix this issue?
Else, we would have to wait until Launchpad supports git directly.
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} | 5 | 2015-04-08T01:45:17Z | 2015-04-09T14:45:33Z | 2015-04-09T14:45:33Z | CONTRIBUTOR | null | ```
# passes for 0.15.0
# passes for 0.15.1
# fails for 0.15.2
# fails for 0.16.0
# This is for a "csv" file where there are a number of initial rows to be skipped at file start.
import pandas as pd
import urllib
test_data_url = 'http://www.bom.gov.au/fwo/IDV60901/IDV60901.95936.axf'
test_file = 'test.csv'
ROWS_TO_SKIP_AT_THE_START = 19
def main():
urllib.urlretrieve(test_data_url, test_file)
print('pandas version: {}'.format(pd.version.version))
data = pd.read_csv(test_file, skiprows=ROWS_TO_SKIP_AT_THE_START)
assert len(data) == 145
assert 'sort_order' in data
print('file successfully read by read_csv()')
if __name__ == '__main__':
main()
```
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| null | 2 | 2015-04-08T05:58:13Z | 2015-04-08T12:06:32Z | 2015-04-08T12:06:32Z | NONE | null | If using python 3.4 there will always be a TypeError thrown in the data.py
lines 330-332
```
if len(stocks) > 0 and len(failed) > 0:
df_na = stocks.values()[0].copy()
df_na[:] = np.nan
```
For python 3 users the stock.values needs to be wrapped because it's returning a view not a list
```
df_na = list(stocks.values())[0].copy()
```
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} | 2 | 2015-04-08T12:54:05Z | 2015-09-19T00:38:21Z | 2015-04-08T18:05:47Z | CONTRIBUTOR | null | Fixes GH #9832
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} | 4 | 2015-04-08T13:58:47Z | 2015-04-09T15:03:53Z | 2015-04-09T15:03:48Z | NONE | null | closes #9798
Add a check to see whether the `index_col` is `True`.
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} | 51 | 2015-04-08T14:40:25Z | 2015-04-15T23:04:01Z | 2015-04-11T20:07:12Z | NONE | null | When subsetting an ordered `pd.Categorical` object using less than/greater than on the ordered values, the less/than greater than follow lexicographical order, not categorical order.
If you create a dataframe and assign categories, you can subset:
```
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: df = pd.DataFrame({'x':np.arange(10), 'y':list('AAAABBBCCC')})
In [4]: df.y = df.y.astype('category')
In [5]: df.ix[(df.y >= "A") & (df.y <= "B")]
Out[5]:
x y
0 0 A
1 1 A
2 2 A
3 3 A
4 4 B
5 5 B
6 6 B
```
But if you try to subset on an ordered category, it does the lexicographical order instead:
```
In [6]: df = pd.DataFrame({'x':np.arange(10), 'y':list('AAAABBBCCC')})
In [7]: df.y = pd.Categorical(df.y, categories=['A', 'C', 'B'], ordered=True)
In [8]: df.ix[(df.y >= "A") & (df.y <= "C")]
Out[8]:
x y
0 0 A
1 1 A
2 2 A
3 3 A
4 4 B
5 5 B
6 6 B
7 7 C
8 8 C
9 9 C
```
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The problem was that at the `Block` level, `where` and `putmask` did a lot of guessing about how the block and the other parameters needed to be transposed in order to align correctly. It guessed wrongly when the DataFrame was symmetric because the shapes match up in both directions. This change tries to make everything explicit and remove the guessing.
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This needs to be merged before I can fix #9744.
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| null | 4 | 2015-04-09T14:00:53Z | 2015-04-28T01:17:41Z | 2015-04-28T01:17:41Z | NONE | null | It would be nice if pandas had an exclude_time function which gives you the opposite of between_time - this can be used to filter out periods of time where you don't want to consider the data, for example. Is this reasonable? I can submit a PR if so
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} | 0 | 2015-04-09T14:38:54Z | 2020-03-31T04:42:34Z | null | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/29502328/python-pandas-time-series-compare-specific-times/29541467#29541467)
using `transform` to broadcast the result works fine if the index is an `Int64Index` because the input and output are the same. However, if you actually need the index (e.g. in this example we are doing an `.at_time(...)` to lookup a particular value, then broadcasting fails.
I think the way to do this is to define `.at_time` as a transform function to handle this case.
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} | 8 | 2015-04-09T14:54:56Z | 2015-05-04T02:56:30Z | 2015-05-01T16:56:23Z | MEMBER | null | Derived from #9773, #9667. Fix docstrings to meet what current `.str` accessors does.
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| null | 0 | 2015-04-09T18:08:24Z | 2015-04-09T18:24:50Z | 2015-04-09T18:24:50Z | NONE | null | Is it possible to get updates from csv file using pandas.read_csv() function?
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} | 3 | 2015-04-10T02:41:56Z | 2015-04-12T13:38:26Z | 2015-04-12T13:38:22Z | CONTRIBUTOR | null | Fixes #9744
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} | 3 | 2015-04-10T02:41:58Z | 2015-04-12T13:57:14Z | 2015-04-12T13:37:27Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/5664
the added test covers the original issue, though I cannot reproduce that on master; [the one mentioned in the comments](https://github.com/pydata/pandas/issues/5664#issuecomment-49332533) still segfaults on master (if repeated few times) and is fixed by this pr.
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} | 4 | 2015-04-10T14:04:26Z | 2015-05-09T15:31:25Z | 2015-05-09T15:31:25Z | MEMBER | null | xref #9667 Now we also have string methods available on the index, the possible options for `return_type` ('series', 'frame') are a bit confusing I think: it will be confusing for users to get a Index back even if he/she supplies `return_type='series'` on a Index.str.split, or to get a series back with `Series.str.split(.., return_type='index')`?
Possible ways to make this a better API:
1) An `expand` keyword (or another name), that indicates for `False`: give same dimension back (so for series/index keep it a series/index), and for `True`: expand series to dataframe.
This would then be a duplicate for `return_type` of course. But the `return_type` was only introduced in 0.15.1 (and for `partition` it is still in a PR), so if we want to change this: better now than later. Or has this ship sailed?
2) easier solution of @jreback: `return_type='same'|'expand'` to satisfy this need? (and can be easily back-compat) -> so no need to change the name of the keyword, only the arguments.
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} | 27 | 2015-04-10T14:30:38Z | 2015-05-11T22:21:16Z | 2015-04-11T20:06:52Z | CONTRIBUTOR | null | Fixes: #9836
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| null | 1 | 2015-04-10T15:49:50Z | 2015-04-11T15:21:46Z | 2015-04-11T15:20:43Z | NONE | null | Hello,
I have a CSV like this :
``` csv
POSTE;DATE;RR;TN;TX;SIGMA
06088001;20150316;8,1;7,4;14,1;11
06088001;20150317;0,6;9,8;15,6;0
```
First column designate a 8 character code which should be handled as a string.
Second column is a datetime. The two columns are designated as index column with index_col argument :
``` python
>>>df = read_csv(fileame, sep=";",
header=0,
parse_dates=[1],
decimal=",",
index_col=[0,1],
dtype={"POSTE":str})
>>>df
RR TN TX SIGMA
POSTE DATE
6088001 2015-03-16 8.1 7.4 14.1 11
2015-03-17 0.6 9.8 15.6 0
```
As you can see, there is a missing "0" for POSTE column because it has been casted to integer, despite of `dtype={"POSTE":str}` argument. I also tried with `dtype={"POSTE":object}` instruction as indicated on forums it still gives the same result.
Apparently this only occurs only if column is used in an index column.
Column is not converted if I only use DATE for the index :
``` python
>>> df = read_csv(fileame, sep=";",
header=0,
parse_dates=[1],
decimal=",",
index_col=[1],
dtype={"POSTE":str})
>>> df
POSTE RR TN TX SIGMA
DATE
2015-03-16 06088001 8.1 7.4 14.1 11
2015-03-17 06088001 0.6 9.8 15.6 0
```
I'm using Pandas v 0.14.1 so it may have been fixed recently.
Do you confirm this is not the correct behaviour ?
Regards
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} | 1 | 2015-04-11T02:20:38Z | 2016-04-09T18:01:56Z | 2016-04-09T18:01:56Z | MEMBER | null | Related to #9802. I think these should return sparse.
```
import pandas as pd
ss = pd.SparseSeries([1, 0, 1])
type(ss.to_frame())
# <class 'pandas.core.frame.DataFrame'>
index = pd.MultiIndex.from_tuples([(0, 0), (0, 1), (1, 1)])
sdf = pd.SparseDataFrame([[1, 0, 1], [0, 0, 0], [1, 0, 0]], index=index)
type(sdf.to_panel())
# <class 'pandas.core.panel.Panel'>
```
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| null | 1 | 2015-04-11T05:08:05Z | 2015-04-11T13:06:48Z | 2015-04-11T13:06:10Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/29573441/calculate-difference-between-times-rows-in-dataframe-pandas#comment47297436_29573471
```
In [11]: pd.to_timedelta("06:40:00")
Out[11]: Timedelta('0 days 06:40:00')
In [12]: pd.to_timedelta("6:40:00")
ValueError: cannot create timedelta string converter for [6:40:00]
```
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} | 2 | 2015-04-11T12:18:15Z | 2015-04-18T14:26:06Z | 2015-04-12T13:39:44Z | MEMBER | null | This repeated time-series plotting works:
```
import pandas.util.testing as tm
s1 = tm.makeTimeSeries()
s2 = s1[[0, 5, 10, 11, 12, 13, 14, 15]]
ax = s1.plot()
ax2 = s2.plot(style='g')
```
But if converted to `DateFrame`, it doesn't:
```
s1 = s1.to_frame()
s2 = s1.iloc[[0, 5, 10, 11, 12, 13, 14, 15]]
print(s2.index.freq)
ax = s1.plot()
ax2 = s2.plot(style='g', ax=ax)
# TypeError: expected string or buffer
```
Fixed the problem, and cleaned up the code to merge `Series` and `DataFrame` flows.
#### After the Fix:
```
import pandas.util.testing as tm
fig, axes = plt.subplots(2, 1)
s1 = tm.makeTimeSeries()
s2 = s1[[0, 5, 10, 11, 12, 13, 14, 15]]
ax = s1.plot(ax=axes[0])
ax2 = s2.plot(style='g', ax=axes[0])
s1 = s1.to_frame(name='x')
s2 = s1.iloc[[0, 5, 10, 11, 12, 13, 14, 15]]
ax = s1.plot(ax=axes[1])
ax2 = s2.plot(style='g', ax=axes[1])
```
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} | 0 | 2015-04-11T12:24:14Z | 2015-04-11T15:58:20Z | 2015-04-11T15:18:00Z | MEMBER | null | ```
df = pd.DataFrame(np.random.rand(20, 5), columns=['A', 'B', 'C', 'D', 'E'])
df['C'] = ['A', 'B', 'C', 'D'] * 5
df['D'] = df['D'] * 100
df.plot(kind='hist')
# TypeError: cannot concatenate 'str' and 'float' objects
```
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} | 1 | 2015-04-11T16:50:54Z | 2015-04-14T13:56:16Z | 2015-04-14T13:56:16Z | CONTRIBUTOR | null | Here's a simple repro
```
In [19]: df = DataFrame(range(3),columns=['foo'],index=pd.date_range('20130101',periods=3,name='bar'))
In [20]: df
Out[20]:
foo
bar
2013-01-01 0
2013-01-02 1
2013-01-03 2
In [21]: df.index.name
Out[21]: 'bar'
In [22]: df.asfreq('10D')
Out[22]:
foo
2013-01-01 0
In [24]: df.asfreq('10D').index.name
```
---
This bug was originally reported on StackOverflow [here](http://stackoverflow.com/questions/29356412/efficiently-re-indexing-one-level-with-forward-fill-in-a-multi-index-dataframe/29404203?noredirect=1#comment47161025_29404203).
### Original question
The question was asking how to efficiently re-index one level of a multi-index DataFrame with “forward-fill” using the following input DataFrame as an example:
```
value
item_uid created_at
0S0099v8iI 2015-03-25 10652.79
0F01ddgkRa 2015-03-25 1414.71
0F02BZeTr6 2015-03-20 51505.22
2015-03-23 51837.97
2015-03-24 51578.63
2015-03-25 NaN
2015-03-26 NaN
2015-03-27 50893.42
0F02BcIzNo 2015-03-17 1230.00
2015-03-23 1130.00
0F02F4gAMs 2015-03-25 1855.96
0F02Vwd6Ou 2015-03-19 5709.33
0F04OlAs0R 2015-03-18 321.44
0F05GInfPa 2015-03-16 664.68
0F05PQARFJ 2015-03-18 1074.31
2015-03-26 1098.31
0F06LFhBCK 2015-03-18 211.49
0F06ryso80 2015-03-16 13.73
2015-03-20 12.00
0F07gg7Oth 2015-03-19 2325.70
```
### Andy's answer:
> You have a couple of options, the easiest IMO is to simply unstack the first level and then ffill. I think this make it much clearer about what's going on than a groupby/resample solution (I suspect it will also be faster, depending on the data):
```
In [11]: df1['value'].unstack(0)
Out[11]:
item_uid 0F01ddgkRa 0F02BZeTr6 0F02BcIzNo 0F02F4gAMs 0F02Vwd6Ou 0F04OlAs0R 0F05GInfPa 0F05PQARFJ 0F06LFhBCK 0F06ryso80 0F07gg7Oth 0S0099v8iI
created_at
2015-03-16 NaN NaN NaN NaN NaN NaN 664.68 NaN NaN 13.73 NaN NaN
2015-03-17 NaN NaN 1230 NaN NaN NaN NaN NaN NaN NaN NaN NaN
2015-03-18 NaN NaN NaN NaN NaN 321.44 NaN 1074.31 211.49 NaN NaN NaN
2015-03-19 NaN NaN NaN NaN 5709.33 NaN NaN NaN NaN NaN 2325.7 NaN
2015-03-20 NaN 51505.22 NaN NaN NaN NaN NaN NaN NaN 12.00 NaN NaN
2015-03-23 NaN 51837.97 1130 NaN NaN NaN NaN NaN NaN NaN NaN NaN
2015-03-24 NaN 51578.63 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2015-03-25 1414.71 NaN NaN 1855.96 NaN NaN NaN NaN NaN NaN NaN 10652.79
2015-03-26 NaN NaN NaN NaN NaN NaN NaN 1098.31 NaN NaN NaN NaN
2015-03-27 NaN 50893.42 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
```
> If you're missing some dates you have to reindex (assuming the start and end are present, otherwise you can do this manually e.g. with `pd.date_range`):
```
In [12]: df1['value'].unstack(0).asfreq('D')
Out[12]:
item_uid 0F01ddgkRa 0F02BZeTr6 0F02BcIzNo 0F02F4gAMs 0F02Vwd6Ou 0F04OlAs0R 0F05GInfPa 0F05PQARFJ 0F06LFhBCK 0F06ryso80 0F07gg7Oth 0S0099v8iI
2015-03-16 NaN NaN NaN NaN NaN NaN 664.68 NaN NaN 13.73 NaN NaN
2015-03-17 NaN NaN 1230 NaN NaN NaN NaN NaN NaN NaN NaN NaN
2015-03-18 NaN NaN NaN NaN NaN 321.44 NaN 1074.31 211.49 NaN NaN NaN
2015-03-19 NaN NaN NaN NaN 5709.33 NaN NaN NaN NaN NaN 2325.7 NaN
2015-03-20 NaN 51505.22 NaN NaN NaN NaN NaN NaN NaN 12.00 NaN NaN
2015-03-21 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2015-03-22 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2015-03-23 NaN 51837.97 1130 NaN NaN NaN NaN NaN NaN NaN NaN NaN
2015-03-24 NaN 51578.63 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2015-03-25 1414.71 NaN NaN 1855.96 NaN NaN NaN NaN NaN NaN NaN 10652.79
2015-03-26 NaN NaN NaN NaN NaN NaN NaN 1098.31 NaN NaN NaN NaN
2015-03-27 NaN 50893.42 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
```
**Note: `asfreq` drops the name of the index (which is most likely a bug!)**
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| null | 3 | 2015-04-11T18:14:58Z | 2015-04-12T13:27:58Z | 2015-04-12T13:27:58Z | NONE | null | One feature that I am missing from dataframes is to sort with a custom `key` function, similar to the builtin `sorted` or `list.sort`. I see there is some effort to unify sorting in issue #8239, so it might be a nice to add this powerful functionality to `pandas` while the sorting API is being overhauled.
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} | 1 | 2015-04-11T18:29:13Z | 2015-04-14T12:59:48Z | 2015-04-14T12:59:48Z | CONTRIBUTOR | null | xref #9650
```
In [62]: i = pd.MultiIndex(
levels=[[u'foo', u'bar'], [u'one', u'two'], [u'a', u'b']],
labels=[[0, 0, 1, 1], [0, 1, 0, 1], [1, 0, 1, 0]],
names=[u'first', u'second', u'third'])
In [63]: s = pd.Series(0, index=i)
In [64]: s.unstack([1,2]).stack(0)
KeyError: 'Level t not found'
```
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} | 3 | 2015-04-11T20:54:21Z | 2015-04-14T13:14:14Z | 2015-04-14T13:14:14Z | NONE | null | ```
df = pd.DataFrame({'x': [1,2,6], 'y': [2,2,8], 'z':[-5,0,5]})
df = df.set_index('z')
```
Then `df.index.name` is `'z'`. After modifying an existing row
```
df.loc[5] = {'x': 9, 'y': 99}
```
it's still fine. But once we add a new index:
```
df.loc[10] = {'x': 7, 'y': 77}
```
`df.index.name` is `None`.
I use Pandas 0.16.0 on Python 3.4.
It might be related to another issue with disappearing index name: https://github.com/pydata/pandas/issues/9854
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| null | 3 | 2015-04-11T21:11:03Z | 2015-04-13T19:07:10Z | 2015-04-12T08:01:34Z | CONTRIBUTOR | null | as discussed in https://github.com/pydata/pandas/pull/9667
@jorisvandenbossche please take a look, thanks
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} | 6 | 2015-04-11T21:32:42Z | 2018-10-25T10:13:00Z | 2018-10-25T10:13:00Z | MEMBER | null | See discussion here: https://github.com/pydata/pandas/pull/9848#discussion_r28181084
This would likely help clean up some of the Series/DataFrame discrepancies in arithmetic operations (mostly for datetime types).
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} | 3 | 2015-04-11T23:27:43Z | 2015-04-14T14:28:42Z | 2015-04-14T14:25:56Z | MEMBER | null | When `secondary_y` is enabled, twin axes are created as below:
- Assume current axes is `ax` (left ax)
- create `new_ax` (right ax) via `ax.twinx`
- Let `ax.right_ax` to specify `new_ax` (right ax)
- Let `new_ax.left_ax` to specify `ax` (left ax)
- Let `new_ax.right_ax` to specify `new_ax` (incorrect)
The last one is incorrect and confuses other logics such as `_get_all_lines`. I think it should be removed.
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| null | 9 | 2015-04-12T13:11:21Z | 2018-11-06T18:21:48Z | 2018-11-06T18:21:48Z | CONTRIBUTOR | null | cases where index names are lost in various operations, most of these involve an issue in `concat`
## Index.name
- [ ] `Panel.fillna`, #3570
- [ ] groupby on empty, #8093
- [x] `.append`, https://github.com/pydata/pandas/issues/9857 (closed by #9876)
- [x] groupby `.size`, #9925
- [x] timedelta ops, #9926
- [ ] `.intersection/.union`, #9943
- [x] `drop_duplicates`, #10116
- [x] `to_datetime/to_timedelta` #10875 (closed by #10945)
- [ ] `Index.__new__` #11193
## Series.name
- [x] rolling_\* funcions #10659
- [x] dt accessor funcions #10712
- [x] quantile #10881
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} | 1 | 2015-04-12T18:54:33Z | 2015-04-13T19:06:46Z | 2015-04-12T21:08:15Z | CONTRIBUTOR | null | {
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} | 4 | 2015-04-12T21:45:29Z | 2015-04-13T13:40:24Z | 2015-04-13T13:40:24Z | CONTRIBUTOR | null | Up to now, a comparison of categorical data and a scalar, which
is not in the categories would return `False` for all elements when
it should raise a `TypeError`, which it now does.
Also fix that `!=` comparisons would return `False` for all elements
when the more logical choice would be `True`.
Fixes the raised issue in https://github.com/pydata/pandas/issues/9836#issuecomment-92123057
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} | 6 | 2015-04-12T21:58:49Z | 2015-04-15T20:10:36Z | 2015-04-15T20:10:36Z | CONTRIBUTOR | null | I'm not sure if it is an issue of DOCs or code.
[Docs](http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.tz_convert.html) say that `tz_convert` "Convert the axis to target time zone. _If it is time zone naive, it will be localized to the passed time zone_."
But the code in contrast raises an [exception](https://github.com/pydata/pandas/blob/bb9d35c27abdd68f349950c404292426f730759d/pandas/tseries/index.py#L1597):
```
tmp = pd.Series([0], index=[pd.Timestamp('2008-12-16 13:10:00')])
tmp.tz_convert('UTC')
```
Results in `TypeError: Cannot convert tz-naive timestamps, use tz_localize to localize`
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} | 1 | 2015-04-13T02:46:35Z | 2015-04-13T19:04:07Z | 2015-04-13T07:28:03Z | CONTRIBUTOR | null | The boolean indexing example http://pandas.pydata.org/pandas-docs/version/0.16.0/indexing.html#boolean-indexing seems to be broken for a while. The variable `s` is being set in a much earlier part of the page and is not suitable for this example.
Also cleaned up a few minor typos.
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} | 10 | 2015-04-13T03:30:53Z | 2019-03-01T04:01:07Z | 2016-05-09T20:37:28Z | NONE | null | I found It's difficult to predict what `DataFrame.groupby().apply()` will return. the result depends on the type of the return object and the index of the return object. for example:
```
import pandas as pd
df = pd.DataFrame({"a":[1, 2, 1, 2], "b":[1, 2, 3, 4], "c":[5, 6, 7, 8]})
```
When the argument and the return object is `DataFrame` and has the same index object, there are not group keys in the result:
```
print df.groupby("a").apply(lambda x:x)
```
the output is:
```
a b c
0 1 1 5
1 2 2 6
2 1 3 7
3 2 4 8
```
if the index is not the same object, there are group keys, even the index values are the same:
```
print df.groupby("a").apply(lambda x:x[:])
```
the output is:
```
a b c
a
1 0 1 1 5
2 1 3 7
2 1 2 2 6
3 2 4 8
```
if the function returns `Series` object and the index of these `Series` objects are not he same values, the index of the result is a `MultiIndex`:
```
print df.groupby("a").apply(lambda x:x.b + x.c)
```
the output:
```
a
1 0 6
2 10
2 1 8
3 12
dtype: int64
```
If all the `Series` objects have the same index values, the `Series` objects are the rows of the result:
```
print df.groupby("a").apply(lambda x:(x.b + x.c).reset_index(drop=True))
```
the output:
```
0 1
a
1 6 10
2 8 12
```
Here are more exampes:
Because the index is the same object:
```
print df.groupby("a").apply(lambda x:(x.b + x.c).to_frame())
```
not group keys in the output:
```
0
0 6
1 8
2 10
3 12
```
If we copy the return value, the index is not the same object:
```
print df.groupby("a").apply(lambda x:(x.b + x.c).to_frame()[:])
```
the output contains group keys:
```
0
a
1 0 6
2 10
2 1 8
3 12
```
```
print df.groupby("a").apply(lambda x:x[["b", "c"]])
```
no group keys because the index object is the same (but use `x[:]` will get the group keys):
```
b c
0 1 5
1 2 6
2 3 7
3 4 8
```
It seems that there is no document about this.
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} | 7 | 2015-04-13T14:26:00Z | 2015-04-13T16:34:46Z | 2015-04-13T16:34:46Z | CONTRIBUTOR | null | See https://github.com/pydata/pandas/issues/9570
This commit allows for the `Timedelta` class to take a construction like `Timedelta('0:00:00')` or `Timedelta('00:00:00')`.
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} | 5 | 2015-04-13T15:28:38Z | 2015-04-14T14:26:45Z | 2015-04-14T14:26:45Z | CONTRIBUTOR | null | Added support for `datetime64` value for Period. Fixes #9054.
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} | 2 | 2015-04-13T16:20:01Z | 2015-04-14T13:27:50Z | 2015-04-14T13:27:50Z | CONTRIBUTOR | null | closes #9787
Decimal objects don't support dtype, so we exclude all objects from fill_zeros that do not support .dtype or .type and are not scalar.
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object. This allows the output dtype to differ from the input. Fixes #9807.
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} | 4 | 2015-04-13T16:59:52Z | 2015-04-14T14:15:14Z | 2015-04-14T14:15:14Z | MEMBER | null | uses the example DataFrame from #9857.
``` python
In [3]: df = pd.DataFrame({'x': [1,2,6], 'y': [2,2,8], 'z':[-5,0,5]})
In [4]: df = df.set_index('z')
In [5]: rhs = {'x': 9, 'y': 99}
In [6]: df.loc[5]
Out[6]:
x 6
y 8
Name: 5, dtype: int64
In [7]: df.loc[5] = rhs
In [8]: df
Out[8]:
x y
z
-5 1 2
0 2 2
5 {u'y': 99, u'x': 9} {u'y': 99, u'x': 9}
```
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} | 2 | 2015-04-13T17:04:10Z | 2015-04-14T12:59:54Z | 2015-04-14T12:59:48Z | CONTRIBUTOR | null | This PR fixes #9856
[PYCON 2015 Sprints]
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} | 1 | 2015-04-13T19:39:18Z | 2015-04-14T13:14:03Z | 2015-04-14T13:14:03Z | CONTRIBUTOR | null | closes issue #9857
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} | 5 | 2015-04-13T19:40:07Z | 2015-05-27T20:26:50Z | 2015-04-14T14:15:14Z | MEMBER | null | closes #9874
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} | 2 | 2015-04-13T19:41:07Z | 2015-04-21T23:41:18Z | 2015-04-21T23:41:18Z | CONTRIBUTOR | null | As a follow-on to #9827, add a test for packaging pandas and importing that package into a venv that does not contain cython via https://github.com/pydata/pandas/wiki/Release-Checklist:
- python setup.py sdist --formats=zip,gztar
- create virtualenv with pandas dependencies (requirements_dev.txt), minus cython
- pip install <path_to_dist_tar.gz>
- run some subset of nosetests once installed
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} | 1 | 2015-04-13T19:51:27Z | 2015-06-02T19:30:35Z | 2015-05-09T19:46:13Z | NONE | null | closes #7751
Created a more informative error message.
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| null | 8 | 2015-04-13T19:52:57Z | 2015-11-02T12:09:29Z | 2015-11-02T12:09:29Z | NONE | null | Closes #2679
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} | 5 | 2015-04-13T19:57:11Z | 2015-04-15T06:40:06Z | 2015-04-14T15:32:02Z | CONTRIBUTOR | null | closes #9827
Added a cython version check at the top of setup.py; minimum version of 0.19.1. If the minimum isn't met, _CYTHON_INSTALLED sets to false, so the installation can proceed if cython is not needed, but will fail if cython is needed (with an appropriate error message).
Two new travis jobs added to test install_conda and install_pydata flows for the old cython revision, and ensure that pandas setup fails with appropriate error messages. See https://travis-ci.org/ajamian/pandas/builds/58315862 for regression.
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} | 6 | 2015-04-13T19:59:20Z | 2015-07-23T11:37:58Z | 2015-07-23T11:37:58Z | CONTRIBUTOR | null | Hello, I'm just leaving this here, might try to pick this up as a weekend project soon.
The beautiful people at stata have released documentation for the format here: http://www.stata.com/help.cgi?dta
And the old format is here:
http://www.stata.com/help.cgi?dta_117
The docs say that it'll highlight changes between 118 and 117 but I couldn't find any such references. One thing I noticed is that they now have utf-8 support! Yay!
As a workaround for now, stata users who want to use stata 14 can use "saveold" which creates stata 13 files, which pandas supports.
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} | 1 | 2015-04-13T20:12:28Z | 2015-04-14T13:56:34Z | 2015-04-14T13:56:34Z | CONTRIBUTOR | null | it seems that it is open discussion whether reindex should preserve the name of the index.
(Towards the end of https://github.com/pydata/pandas/issues/6552)
My proposal for now is to set the name of the new index created by the asfreq function.
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} | 3 | 2015-04-13T20:21:25Z | 2021-04-18T06:15:41Z | null | CONTRIBUTOR | null | As mentioned in #6552 there is an open question about whether reindex should keep the index name if the new index has no name.
Current Behavior:
- reindex keeps index name if the argument is a list or an ndarray
- replaces the index name if the argument is an index, even if the new index does not have a new name.
Related Issues:
#9854
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} | 13 | 2015-04-13T20:40:45Z | 2021-04-18T06:21:20Z | 2021-04-18T06:21:20Z | MEMBER | null | Throughout the docs, many different imports are used, not always visible to the reader. And ideally, I think this would be a bit more uniform. I think we need:
- consistent imports
- visible imports
So the code snippets can be ran without having to know what should be imported, apart from the default imports.
But in that case, of course, we have to decide/agree on which imports to use.
---
I think `import pandas as pd` is regarded as recommended? But still, it is not used that much in the docs. So I was wondering, does everybody agree to use that in all docs (and is it just a case of "it is the intention, but no one did it")?
So everywhere `pd.DataFrame` and `pd.Series`, or do we still want the convenience of using `DataFrame(..)/Series(..)` without the `pd` (so to only `from pandas import DataFrame, Series`, and use `pd.` for all the rest)?
So: **Question 1: do we use `import pandas as pd` for everything or not?**
---
Then we have the often used imports from other packages. I think this is standard, and should not need discussion:
```
import numpy as np
import matplotlib.pyplot as plt
```
But for the datetime package, the imports are not really uniform. The following two imports are both used mixed in the docs, but are not compatible with each other:
```
import datetime
from datetime import datetime
```
So: **Question 2: How do we import datetime?**
Further, there are some other imports used a lot, like `from numpy.random import randn` or `from numpy import nan`.
For other non-pandas imports, I propose that they should always use the standard import (eg `np`) or be done explicitely where used, and never hidden (as it is now the case sometimes, eg `from dateutil.relativedelta import relativedelta`)
So: **Question 3: Do we agree that all other non-pandas imports should be done explicitely?**
This means that imports in the suppressed code block like the following will be removed:
```
from numpy import nan
randn = np.random.randn
randint = np.random.randint
from dateutil.relativedelta import relativedelta
import random
import os
import csv
```
---
Third issue: there are also some pandas imports of non top-level things.
For pandas submodules, imports like this appear in the docs:
```
from pandas.tseries.api import *
from pandas.tseries.offsets import *
from pandas.core.reshape import *
from pandas.tools.tile import *
```
happen in the docs (and often not visible to the users), what I think is a bad idea. First, a lot of these imports should never been done as the functions used from there are also in the top-level pandas namespace.
If it is for functions that are used from the submodules, that we have to decide how to import them. At least, the imports should happen _explicitely in the visible docs_. But to do that, there are some different forms possible:
```
from pandas.tseries.offsets import *
... BMonthEnd()
... Day()
from pandas.tseries.offsets import BMonthEnd, Day
... BMonthEnd()
... Day()
import pandas.tseries.offsets as offsets (or from pandas.tseries import offsets)
... offsets.BMonthEnd()
... offsets.Day()
```
So: **Question 4: How do we import from pandas submodules?**
TO DO:
- [ ] Decide on the imports (see questions above)
- [ ] Clearly document this in the contributor guidelines
- [ ] Adapt this in the documentation
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} | 2 | 2015-04-13T20:40:55Z | 2015-04-14T16:12:52Z | 2015-04-14T14:20:52Z | CONTRIBUTOR | null | This issue fixes #7872 by either describing the reason why a NotImplementedError is raised,
or by throwing an AbstractMethodError in the case of abstract methods (eg: Mixins) that
should be overwritten by the inheriting classes.
This issue is based on discussion of PR #7899 which was never merged.
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} | 5 | 2015-04-13T21:10:19Z | 2015-04-17T12:35:45Z | 2015-04-17T12:35:41Z | CONTRIBUTOR | null | Raise a NotImplementedError until fixed.
closes #9794
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} | 5 | 2015-04-14T03:05:52Z | 2015-04-15T18:38:52Z | 2015-04-15T17:33:15Z | CONTRIBUTOR | null | I'm not sure why `Panel.shift` currently raises with `axis='items'`, while `axis=0` works.
This PR enables `axis='items'` and also updates the docstring to show the parameter `periods` instead of the deprecated `lags`
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} | 3 | 2015-04-14T03:59:04Z | 2015-07-12T14:58:44Z | 2015-07-12T14:58:44Z | CONTRIBUTOR | null | Modifies the horizontal bar plot behavior to plot series in order from top-to-bottom.
Plotting a regular bar graph from a DataFrame object will plot each series of data from left-to-right in the same order as presented in the legend from top-to-bottom. When plotting a horizontal bar graph the series of data are plotted from bottom-to-top, which is the reverse of the legend and unintuitive.
Pandas allows a `legend='reverse'` option which reverses the order of the legend and preserves series colors, but does not fix the unintuitive bottom-to-top ordering of series.
###### Pandas Standard `barh` Behavior:
###### Desired `barh` Behavior:
###### Pandas Standard `barh` `legend='reverse'` Behavior:
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} | 6 | 2015-04-14T12:41:23Z | 2021-06-29T16:31:22Z | 2020-01-03T23:23:44Z | MEMBER | null | From [SO](http://stackoverflow.com/questions/29627143/setting-value-in-pandas-mulitindex-fails/29627444#29627444):
```
import pandas as pd
import itertools
a = pd.period_range('2013Q1','2013Q4', freq='Q')
i = (1111, 2222, 3333)
idx = pd.MultiIndex.from_tuples(list(itertools.product(a, i)),
names=('Periode', 'CVR'))
df = pd.DataFrame(index=idx,
columns=('OMS', 'OMK','RES','DRIFT_IND','OEVRIG_IND','FIN_IND','VARE_UD','LOEN_UD','FIN_UD'))
```
Trying to select with a string gives a ValueError:
```
In [15]: df.loc[('2013Q1',1111),'OMS']
---------------------------------------------------------------------------
KeyError: ('2013Q1', 1111)
```
while it works with an explicit Period:
```
In [16]: df.loc[(pd.Period('2013Q1'),1111),'OMS']
Out[16]: nan
```
and it also works when not using multi-index notation (only accessing the first level): `df.loc['2013Q1', 'OMS']`
and this **did work in 0.14.1**.
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| null | 4 | 2015-04-14T13:41:03Z | 2021-04-18T06:25:07Z | 2021-04-18T06:25:07Z | MEMBER | null | I think it is useful to simply add one row specifying a loc.
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} | 27 | 2015-04-14T13:58:13Z | 2015-07-22T10:49:08Z | 2015-07-22T10:49:05Z | MEMBER | null | Specifying `subplots=True` in line and kde plot draws all lines in black, and may not accept colors and styles keyword. Summarized a table to compare current / expected (fixed) behaviour.
Sample data:
```
df = pd.DataFrame(np.random.randn(10, 3))
```
| Command | Current Behavior | Expected Behavior |
| --- | --- | --- |
| `df.plot(subplots=True)` | All 3 lines are in black (OK) | 3 lines are in default color cycle. |
| `df.plot(style='r', subplots=True)` | All 3 lines are in red (OK) | No change |
| `df.plot(color='r', subplots=True)` | All 3 lines are in red (OK) | No change |
| `df.plot(color='red', subplots=True)` | All 3 lines are in red (OK) (thanks to #10387) | No change |
| `df.plot(color=['r', 'g', 'b'], subplots=True)` | ValueError: to_rgba: Invalid rgba arg "['r', 'g', 'b']" (NG) | 3 lines are drawn in red, green, blue |
| `df.plot(style=['r', 'g', 'b'], subplots=True)` | 3 lines are drawn in red, green, blue (OK) | No change |
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} | 10 | 2015-04-14T14:23:09Z | 2017-04-16T14:05:18Z | 2017-04-16T14:05:18Z | MEMBER | null | A lot of other projects that use pandas will (like to) use pandas testing functionality like `assert_frame_equal` in their test suite. Although the pandas testing functions are available in the namespace (#6188), they are not really 'officially' labeled as public API that other projects can use (and rely upon).
Numpy has a similar submodule `numpy.testing` (http://docs.scipy.org/doc/numpy/reference/routines.testing.html)
Some things we could do:
- make a selection of the functions in `util.testing` that we want to label as public
- add this list somewhere to the docs
- write docstrings for these public ones (the other could use that as well of course ..)
- add some tests for the public API
- I would also import them into a `pandas.testing` module, so it is this one we can publicly advertise (and users are less tempted to use other non-public functions in the `pandas.util.testing` namespace)
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} | 1 | 2015-04-14T15:08:03Z | 2015-04-14T18:51:28Z | 2015-04-14T18:51:28Z | CONTRIBUTOR | null | Let me know if that is not good enough, I played around with indenting it but it doesn't look good with big indexes.
This, current:
vs with indention
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} | 10 | 2015-04-14T15:50:50Z | 2015-06-09T22:06:10Z | 2015-06-09T22:05:05Z | CONTRIBUTOR | null | The former also is known to the 'pip install' command, and still works with conda install. Making requirements files compatible with 'pip install' is more relevant now that packages like numpy have a (binary) wheel available.
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} | 17 | 2015-04-14T16:00:36Z | 2015-05-14T16:17:09Z | 2015-05-08T07:45:25Z | MEMBER | null | Closes #9301. Though further table size adjustment / plotter module refactoring is required, I'd like to ask any feedbacks for current output.
I've prepared table plotter under utils.doctools, but is there anywhere more appropriate?
cc @JanSchulz
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| null | 2 | 2015-04-14T16:22:38Z | 2015-04-14T16:41:40Z | 2015-04-14T16:41:35Z | CONTRIBUTOR | null | I'm running all the tests for pandas, and I get the following failure:
```
ERROR: test_read_google (pandas.io.tests.test_data.TestDataReader)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/chrish/Code/pandas/pandas/util/testing.py", line 1461, in wrapper
return t(*args, **kwargs)
File "/Users/chrish/Code/pandas/pandas/io/tests/test_data.py", line 470, in test_read_google
gs = DataReader("GS", "google")
File "/Users/chrish/Code/pandas/pandas/io/data.py", line 81, in DataReader
retry_count=retry_count, pause=pause)
File "/Users/chrish/Code/pandas/pandas/io/data.py", line 458, in get_data_google
adjust_price, ret_index, chunksize, 'google')
File "/Users/chrish/Code/pandas/pandas/io/data.py", line 365, in _get_data_from
hist_data = src_fn(symbols, start, end, interval, retry_count, pause)
File "/Users/chrish/Code/pandas/pandas/io/data.py", line 229, in _get_hist_google
return _retry_read_url(url, retry_count, pause, 'Google')
File "/Users/chrish/Code/pandas/pandas/io/data.py", line 185, in _retry_read_url
"return a 200 for url %r" % (retry_count, name, url))
IOError: after 3 tries, Google did not return a 200 for url 'http://www.google.com/finance/historical?q=GS&startdate=Jan+01%2C+2010&enddate=Apr+14%2C+2015&output=csv'
```
Going to the URL in a browser returns a 404 page, with message "The requested URL was not found on this server.", so I don't think this test will be passing as is in the future...
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} | 61 | 2015-04-14T18:51:17Z | 2015-05-09T21:51:59Z | 2015-05-09T21:51:59Z | CONTRIBUTOR | null | With this PR, makes repr of all Index types consistent.
closes #6482
closes #6295
replaces #9897
Previous Behavior
```
In [1]: pd.get_option('max_seq_items')
Out[1]: 100
In [2]: pd.Index(range(4),name='foo')
Out[2]: Int64Index([0, 1, 2, 3], dtype='int64')
In [3]: pd.Index(range(104),name='foo')
Out[3]: Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...], dtype='int64')
In [4]: pd.date_range('20130101',periods=4,name='foo',tz='US/Eastern')
Out[4]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-01 00:00:00-05:00, ..., 2013-01-04 00:00:00-05:00]
Length: 4, Freq: D, Timezone: US/Eastern
In [5]: pd.date_range('20130101',periods=104,name='foo',tz='US/Eastern')
Out[5]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-01 00:00:00-05:00, ..., 2013-04-14 00:00:00-04:00]
Length: 104, Freq: D, Timezone: US/Eastern
```
New Behavior
```
# this is here just to have a consisten display here, it will normally get the value from the console width
In [1]: pd.set_option('display.width',100)
In [2]: pd.Index(range(4),name='foo')
Out[2]: Int64Index([0, 1, 2, 3], dtype='int64', name=u'foo')
In [3]: pd.Index(range(25),name='foo')
Out[3]:
Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
24],
dtype='int64', name=u'foo')
In [4]: pd.Index(range(104),name='foo')
Out[4]:
Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
...
94, 95, 96, 97, 98, 99, 100, 101, 102, 103],
dtype='int64', name=u'foo', length=104)
In [5]: pd.Index(['datetime', 'sA', 'sB', 'sC', 'flow', 'error', 'temp', 'ref', 'a_bit_a_longer_one']*2)
Out[5]:
Index([u'datetime', u'sA', u'sB', u'sC', u'flow', u'error', u'temp', u'ref', u'a_bit_a_longer_one',
u'datetime', u'sA', u'sB', u'sC', u'flow', u'error', u'temp', u'ref',
u'a_bit_a_longer_one'],
dtype='object')
In [6]: pd.CategoricalIndex(['a','bb','ccc','dddd'],ordered=True,name='foobar')
Out[6]: CategoricalIndex([u'a', u'bb', u'ccc', u'dddd'], categories=[u'a', u'bb', u'ccc', u'dddd'], ordered=True, name=u'foobar', dtype='category')
In [7]: pd.CategoricalIndex(['a','bb','ccc','dddd']*10,ordered=True,name='foobar')
Out[7]:
CategoricalIndex([u'a', u'bb', u'ccc', u'dddd', u'a', u'bb', u'ccc', u'dddd', u'a', u'bb', u'ccc',
u'dddd', u'a', u'bb', u'ccc', u'dddd', u'a', u'bb', u'ccc', u'dddd', u'a', u'bb',
u'ccc', u'dddd', u'a', u'bb', u'ccc', u'dddd', u'a', u'bb', u'ccc', u'dddd',
u'a', u'bb', u'ccc', u'dddd', u'a', u'bb', u'ccc', u'dddd'],
categories=[u'a', u'bb', u'ccc', u'dddd'], ordered=True, name=u'foobar', dtype='category')
In [8]: pd.CategoricalIndex(['a','bb','ccc','dddd']*100,ordered=True,name='foobar')
Out[8]:
CategoricalIndex([u'a', u'bb', u'ccc', u'dddd', u'a', u'bb', u'ccc', u'dddd', u'a', u'bb',
...
u'ccc', u'dddd', u'a', u'bb', u'ccc', u'dddd', u'a', u'bb', u'ccc', u'dddd'],
categories=[u'a', u'bb', u'ccc', u'dddd'], ordered=True, name=u'foobar', dtype='category', length=400)
In [9]: pd.CategoricalIndex(np.arange(1000),ordered=True,name='foobar')
Out[9]:
CategoricalIndex([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
...
990, 991, 992, 993, 994, 995, 996, 997, 998, 999],
categories=[0, 1, 2, 3, 4, 5, 6, 7, ...], ordered=True, name=u'foobar', dtype='category', length=1000)
In [10]: pd.date_range('20130101',periods=4,name='foo',tz='US/Eastern')
Out[10]:
DatetimeIndex(['2013-01-01 00:00:00-05:00', '2013-01-02 00:00:00-05:00',
'2013-01-03 00:00:00-05:00', '2013-01-04 00:00:00-05:00'],
dtype='datetime64[ns]', name=u'foo', freq='D', tz='US/Eastern')
In [11]: pd.date_range('20130101',periods=25,name='foo',tz='US/Eastern')
Out[11]:
DatetimeIndex(['2013-01-01 00:00:00-05:00', '2013-01-02 00:00:00-05:00',
'2013-01-03 00:00:00-05:00', '2013-01-04 00:00:00-05:00',
'2013-01-05 00:00:00-05:00', '2013-01-06 00:00:00-05:00',
'2013-01-07 00:00:00-05:00', '2013-01-08 00:00:00-05:00',
'2013-01-09 00:00:00-05:00', '2013-01-10 00:00:00-05:00',
'2013-01-11 00:00:00-05:00', '2013-01-12 00:00:00-05:00',
'2013-01-13 00:00:00-05:00', '2013-01-14 00:00:00-05:00',
'2013-01-15 00:00:00-05:00', '2013-01-16 00:00:00-05:00',
'2013-01-17 00:00:00-05:00', '2013-01-18 00:00:00-05:00',
'2013-01-19 00:00:00-05:00', '2013-01-20 00:00:00-05:00',
'2013-01-21 00:00:00-05:00', '2013-01-22 00:00:00-05:00',
'2013-01-23 00:00:00-05:00', '2013-01-24 00:00:00-05:00',
'2013-01-25 00:00:00-05:00'],
dtype='datetime64[ns]', name=u'foo', freq='D', tz='US/Eastern')
In [12]: pd.date_range('20130101',periods=104,name='foo',tz='US/Eastern')
Out[12]:
DatetimeIndex(['2013-01-01 00:00:00-05:00', '2013-01-02 00:00:00-05:00',
'2013-01-03 00:00:00-05:00', '2013-01-04 00:00:00-05:00',
'2013-01-05 00:00:00-05:00', '2013-01-06 00:00:00-05:00',
'2013-01-07 00:00:00-05:00', '2013-01-08 00:00:00-05:00',
'2013-01-09 00:00:00-05:00', '2013-01-10 00:00:00-05:00',
...
'2013-04-05 00:00:00-04:00', '2013-04-06 00:00:00-04:00',
'2013-04-07 00:00:00-04:00', '2013-04-08 00:00:00-04:00',
'2013-04-09 00:00:00-04:00', '2013-04-10 00:00:00-04:00',
'2013-04-11 00:00:00-04:00', '2013-04-12 00:00:00-04:00',
'2013-04-13 00:00:00-04:00', '2013-04-14 00:00:00-04:00'],
dtype='datetime64[ns]', name=u'foo', length=104, freq='D', tz='US/Eastern')
```
Note that `MultiIndex` are multi-line repr and do no truncate sequences (of e.g. labels), this is consistent with previous versions. (easy to change this though)
```
In [1]: MultiIndex.from_product([list('abcdefg'),range(10)],names=['first','second'])
Out[1]:
MultiIndex(levels=[[u'a', u'b', u'c', u'd', u'e', u'f', u'g'], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],
labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],
names=[u'first', u'second'])
```
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} | 8 | 2015-04-14T20:06:43Z | 2015-06-02T19:29:58Z | 2015-05-11T05:10:00Z | CONTRIBUTOR | null | closes #9057
Converted column names to strings before creating pytables metadata object
This brute force `str()`ing method _might_ not be the best approach, but AFAIK it hasn't broken any of the tests.
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"url": "https://api.github.com/repos/pandas-dev/pandas/pulls/9902"
} |
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