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} | 1 | 2014-01-31T12:49:45Z | 2014-06-24T18:01:39Z | 2014-02-04T09:02:30Z | CONTRIBUTOR | null | Work on #6175 to specialize asserts for test_timeseries[_legacy].py. A work-in-progress.
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} | 0 | 2014-01-31T13:05:52Z | 2014-06-19T08:34:35Z | 2014-01-31T13:32:00Z | CONTRIBUTOR | null | closes #6169
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} | 23 | 2014-01-31T14:54:46Z | 2014-06-12T21:23:08Z | 2014-02-06T14:59:23Z | CONTRIBUTOR | null | This commit modifies the call to numpy.result_type to get around the
NPY_MAXARGS limit, which at the moment is 32. Instead of passing a
generator of all types involved in an expression, the type promotion
is done on a pair-wise basis with a call to reduce.
This fixes bugs for code such as the following:
```
from numpy.random import randn
from pandas import DataFrame
d = DataFrame(randn(10, 2), columns=list('ab'))
# Evaluates fine
print(d.eval('*'.join(['a'] * 32)))
# Fails to evaluate due to NumPy argument limits
print(d.eval('*'.join(['a'] * 33)))
```
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} | [] | closed | false | null | [] | null | 0 | 2014-01-31T15:28:19Z | 2014-07-16T08:51:15Z | 2014-01-31T16:11:38Z | MEMBER | null | Rediscovered an old branch: some clean-up of docstrings in series/generic.py.
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https://api.github.com/repos/pandas-dev/pandas/issues/6207 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6207/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6207/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6207/events | https://github.com/pandas-dev/pandas/pull/6207 | 26,691,271 | MDExOlB1bGxSZXF1ZXN0MTIwODM2MzM= | 6,207 | DOC: add links to other pages to contributing.rst | {
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} | [] | closed | false | null | [] | null | 2 | 2014-01-31T16:11:13Z | 2014-07-10T00:09:58Z | 2014-02-02T15:43:52Z | MEMBER | null | Awaiting for that the different developer pages could be reworked and added to this page, I thought to at least add some links here. So if people go from the docs to this page, they get redirected to the relevant pages.
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} | 1 | 2014-01-31T17:33:41Z | 2020-05-16T20:07:55Z | null | CONTRIBUTOR | null | This one is strange...
Here is a minimal example:
``` python
index = pd.MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['index', 'bar_name'])
df_mi = pd.DataFrame(np.random.randn(10, 3), index=index,
columns=['A', 'B', 'C'])
with pd.get_store('minimal_io.h5') as store:
store.put('df_mi', df_mi, format='table')
```
And the error backtrace:
``` python
--------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-22-bb85f423e84c> in <module>()
11
12 with pd.get_store('minimal_io.h5') as store:
---> 13 store.put('df_mi', df_mi, format='table')
/home/guillaume/python3/lib/python3.3/site-packages/pandas-0.13.0_496_ga2d5e53-py3.3-linux-x86_64.egg/pandas/io/pytables.py in put(self, key, value, format, append, **kwargs)
819 format = get_option("io.hdf.default_format") or 'fixed'
820 kwargs = self._validate_format(format, kwargs)
--> 821 self._write_to_group(key, value, append=append, **kwargs)
822
823 def remove(self, key, where=None, start=None, stop=None):
/home/guillaume/python3/lib/python3.3/site-packages/pandas-0.13.0_496_ga2d5e53-py3.3-linux-x86_64.egg/pandas/io/pytables.py in _write_to_group(self, key, value, format, index, append, complib, encoding, **kwargs)
1271
1272 # write the object
-> 1273 s.write(obj=value, append=append, complib=complib, **kwargs)
1274
1275 if s.is_table and index:
/home/guillaume/python3/lib/python3.3/site-packages/pandas-0.13.0_496_ga2d5e53-py3.3-linux-x86_64.egg/pandas/io/pytables.py in write(self, obj, data_columns, **kwargs)
3963 print(data_columns)
3964 return super(AppendableMultiFrameTable, self).write(
-> 3965 obj=obj, data_columns=data_columns, **kwargs)
3966
3967 def read(self, **kwargs):
/home/guillaume/python3/lib/python3.3/site-packages/pandas-0.13.0_496_ga2d5e53-py3.3-linux-x86_64.egg/pandas/io/pytables.py in write(self, obj, axes, append, complib, complevel, fletcher32, min_itemsize, chunksize, expectedrows, dropna, **kwargs)
3603
3604 # add the rows
-> 3605 self.write_data(chunksize, dropna=dropna)
3606
3607 def write_data(self, chunksize, dropna=True):
/home/guillaume/python3/lib/python3.3/site-packages/pandas-0.13.0_496_ga2d5e53-py3.3-linux-x86_64.egg/pandas/io/pytables.py in write_data(self, chunksize, dropna)
3661 for i, v in enumerate(values):
3662 new_shape = (nrows,) + self.dtype[names[nindexes + i]].shape
-> 3663 bvalues.append(values[i].ravel().reshape(new_shape))
3664
3665 # write the chunks
ValueError: total size of new array must be unchanged
> /home/guillaume/python3/lib/python3.3/site-packages/pandas-0.13.0_496_ga2d5e53-py3.3-linux-x86_64.egg/pandas/io/pytables.py(3663)write_data()
3662 new_shape = (nrows,) + self.dtype[names[nindexes + i]].shape
-> 3663 bvalues.append(values[i].ravel().reshape(new_shape))
3664
```
pandas version: '0.13.0-496-ga2d5e53'
Note that the bug is not there if the format is set to 'fixed', i.e. this works:
``` python
with pd.get_store('minimal_io.h5') as store:
store.put('df_mi', df_mi, format='fixed')
```
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} | 0 | 2014-01-31T19:15:57Z | 2014-01-31T21:30:11Z | 2014-01-31T21:30:11Z | CONTRIBUTOR | null | with `df = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])`.
From `df.plot(kind='bar', use_index=False)`:
From `df.plot(use_index=False)`:
Most of the time it doesn't make sense to do a bar plot and ignore the groups formed by the index, but I had an index with a bunch of long strings and just wanted to see the distribution (pretty much a histogram), so I didn't care about the index.
PR inbound.
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} | 3 | 2014-01-31T19:25:17Z | 2014-06-13T02:06:23Z | 2014-01-31T21:30:11Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/6209
Here's the fixed output for the same example from the issue: `df.plot(kind='bar', use_index=False)`:
I thew it in `.13.1`, can push to `.14` if need be.
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https://api.github.com/repos/pandas-dev/pandas/issues/6211 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6211/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6211/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6211/events | https://github.com/pandas-dev/pandas/pull/6211 | 26,709,697 | MDExOlB1bGxSZXF1ZXN0MTIwOTE4Nzk= | 6,211 | DOC: Fix typo in Melt section | {
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} | [] | closed | false | null | [] | null | 1 | 2014-01-31T19:40:05Z | 2014-06-14T10:23:30Z | 2014-01-31T20:07:50Z | NONE | null | Example in "Melt" documentation was referring to the wrong DataFrame.
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} | 2 | 2014-01-31T21:33:37Z | 2014-02-03T20:50:05Z | 2014-02-01T18:12:17Z | CONTRIBUTOR | null | This is a follow-up on #5530 and the general Python 3 compat for docs issue. It is the first episode of my journey into the unknown Python-3-compat-for-doc-examples-land.
While some errors should be rather easy to fix (e.g. `xrange`) I just stumbled upon this one (`doc/source/comparison_with_r.rst` line 54) which fails under Python 3:
``` python
import pandas as pd
import numpy as np
options.display.max_rows=15
from pandas import DataFrame
df = DataFrame({
'v1': [1,3,5,7,8,3,5,np.nan,4,5,7,9],
'v2': [11,33,55,77,88,33,55,np.nan,44,55,77,99],
'by1': ["red", "blue", 1, 2, np.nan, "big", 1, 2, "red", 1, np.nan, 12],
'by2': ["wet", "dry", 99, 95, np.nan, "damp", 95, 99, "red", 99, np.nan,
np.nan]
})
g = df.groupby(['by1','by2'])
g[['v1','v2']].mean()
```
``` python
TypeError: unorderable types: str() > int()
```
Is the usage wrong here or is it a bug?
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https://api.github.com/repos/pandas-dev/pandas/issues/6213 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6213/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6213/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6213/events | https://github.com/pandas-dev/pandas/issues/6213 | 26,727,260 | MDU6SXNzdWUyNjcyNzI2MA== | 6,213 | Next Step in documentation | {
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"description": "Long-Term Enhancement... | closed | false | null | [] | null | 4 | 2014-02-01T01:01:10Z | 2014-02-02T17:37:04Z | 2014-02-02T17:37:04Z | NONE | null | We've done lots of work on cleaning the the docs, improving our tools
and expanding our sections, led by @jorisvandenbossche.
A possible next step is to create a catalog of code examples by method,
with ipython_directive output, and making it easilyy accessible while working
in ipython.
It might be a systematic expansion of dosctring examples or it might be
a new section in the docs with links from docstrings or an `apropos`
command, we can figure that out later.
This is a huge undertaking, that will require lots of contributions from the
pandas community to really make it happen. If it does, it would really be
an amazing resource, and a great help to pandas newcomers.
Are there enough contributors interested in helping build such a beast?
raise your hand if you'd help.
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https://api.github.com/repos/pandas-dev/pandas/issues/6214 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6214/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6214/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6214/events | https://github.com/pandas-dev/pandas/issues/6214 | 26,728,367 | MDU6SXNzdWUyNjcyODM2Nw== | 6,214 | BUG: pandas 0.13 Series argmax/argmin returns index label instead of location (changed from 0.12) | {
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} | [] | closed | false | null | [] | null | 6 | 2014-02-01T01:41:38Z | 2016-11-16T13:31:49Z | 2014-02-01T04:58:56Z | NONE | null | ------------------------ pandas 0.12 --------
In [14]: pd.**version**
Out[14]: '0.12.0'
In [15]: a=pd.Series(range(3), index=('2','3','4'))
In [16]: a.argmax()
Out[16]: 2
In [17]: a.idxmax()
Out[17]: '4'
------------------------ pandas 0.13 --------
In [2]: pd.**version**
Out[2]: '0.13.0'
In [3]: a=pd.Series(range(3), index=('2','3','4'))
In [4]: a.argmax()
Out[4]: '4'
In [5]: a.idxmax()
Out[5]: '4'
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"color": "0052cc"... | closed | false | null | [] | null | 3 | 2014-02-01T03:14:49Z | 2014-03-22T21:30:29Z | 2014-03-22T21:30:22Z | NONE | null | s = pd.Series(['A', 'B', np.nan, 'C'])
print s
0 A
1 B
2 NaN
3 C
print pd.get_dummies(s)
A B C
0 1 0 0
1 0 1 0
2 0 0 1
3 0 0 1
In the third row,col C, the indicator should be 0 not 1.
Seems you'd either want to add a column for NaN values or fix the issue above
sorry for bad formatting...
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} | [] | closed | false | null | [] | null | 2 | 2014-02-01T04:20:49Z | 2014-07-21T13:38:13Z | 2014-02-17T20:42:35Z | CONTRIBUTOR | null | Fixes pep8 errors in test_common.py in rpy/tests.
Ran autopep8, then fixed remaining errors manually. Test still passes locally.
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} | 1 | 2014-02-01T07:36:21Z | 2014-07-16T08:51:22Z | 2014-02-04T08:51:51Z | NONE | null | Travis exposed a corner case in the handling of version strings since the `git-describe` shakeup.
Previously (0.10.1), version strings were of the form:
```
v0.10.1 # release
v0.10.1.dev # dev, no git available
v0.10.1.dev-abcd1234 # dev, git available
```
Now that we use git-describe, here's the case breakdown:
- release
- dev, no git
- dev, no git and pandas/version.py exists (installing from sdist) # *
- dev, git available
- dev, git but shallow clone (no tags) (travis) # *
the \* cases were hard to see coming (I didn't) and so the current logic does not handle
them gracefully, and weird version strings result.
Behavior summary following this PR:
```
v0.13.1 # release
v0.13.1.dev # dev, no git available, matches old behavior
v0.13.1.dev-abcd1234 # dev, git available but no tags (pip tarball), matches old behavior
foo # dev, no git available, pandas/version.py exists and contains foo
0.13.0-509-ga134eb6 # dev, git available, tags available (normal dev situation), normal new behavior.
```
Slightly dangerous to merge shortly before a release, but it would be worse
to keep this brokenness for months until 0.14 IMO.
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} | 8 | 2014-02-01T09:43:37Z | 2014-06-13T05:48:42Z | 2014-02-01T11:01:45Z | NONE | null | and some travis.yml cleanups after recent bug fixes by the travis guys.
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"des... | closed | false | null | [] | null | 4 | 2014-02-01T11:50:11Z | 2014-02-04T12:55:11Z | 2014-02-04T12:53:56Z | NONE | null | python uses 32 bytes for every int.
numpy uses 4 bytes for an int32 (with which you can index billion of keys).
We currently have a hardwired if preventing single-level multi-indexes,
which would allow us to take advantage of factorization to reduce memory
consumption by ~8x for indices which have lots of duplicates.
Indexes tend to be unique (by their nature). But the same principle goes
for string columns which are often highligh degenerate.
Basically, if we store catagorical data as catagorical, we can
reduce memory footprint drastically. If we do the factorization
at the stage where data is read in (perhaps in conjuction with the iterator based
reader planned for 0.14 https://github.com/pydata/pandas/issues/2193) we can drastically reduce the peak memory usage as well.
Update: https://github.com/pydata/pandas/issues/5313
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} | 1 | 2014-02-01T12:09:25Z | 2014-07-16T08:51:26Z | 2014-02-01T12:09:48Z | NONE | null | A couple more optimizations to get the times closer to what was advertised.
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} | 7 | 2014-02-01T15:14:40Z | 2014-06-27T15:12:54Z | 2014-02-16T18:23:26Z | MEMBER | null | closes #6317
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} | 0 | 2014-02-01T16:18:48Z | 2014-06-26T12:23:43Z | 2014-02-01T18:12:17Z | CONTRIBUTOR | null | closes #6212
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} | 0 | 2014-02-02T00:27:49Z | 2014-02-07T13:00:50Z | 2014-02-07T13:00:50Z | NONE | null | need to do `pd.read_clipboard(sep='\t')` and then things work fine.
Since it's for interactive use, it should be ok to change the default
without breaking existing code.
we did the same to `to_clipboard()` in #5070, I missed the fact that
`read_clipboard` only works out of the box for pure numerical data right now.
Why don't users report this stuff I don't know.
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Don't know what's the issue `pd.to_pickle` is certainly documented.
@JanSchulz?
```
Undocumented public functions
The following was found by http://nbviewer.ipython.org/urls/gist.github.com/JanSchulz/6874142/raw/fadef84066d89b3ceb7532e762ed06d87904f3c4/pandas_api.ipynb (and changing all context to pandas in api.rst)
```
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} | 3 | 2014-02-02T03:34:02Z | 2014-02-02T06:02:28Z | 2014-02-02T05:54:16Z | CONTRIBUTOR | null | ``` python
In [9]: type(detectors)
WARNING: Exception in text/html formatter: unbound method _repr_html_() must be called with DataFrame instance as first argument (got nothing instead)
Out[9]:
pandas.core.frame.DataFrame
In [10]: detectors.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1565757 entries, 2008-01-08 17:15:29.111000 to 2008-01-18 07:57:01.504000
Data columns (total 4 columns):
a6_11 1565757 non-null values
b1_11 1565757 non-null values
b2_11 1565757 non-null values
b3_11 1565757 non-null values
dtypes: int64(4)
In [11]:
pd.__version__
Out[11]:
'0.13.0rc1-59-g90990db'
```
I noticed another oddity: Why does the type-display create an output numbering (=Out[9]) but the display of detectors.info() does not? Something to do with IPython?
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} | 0 | 2014-02-02T07:05:23Z | 2014-06-13T00:04:07Z | 2014-02-04T08:57:33Z | NONE | null | #6223
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} | 1 | 2014-02-02T11:27:11Z | 2014-04-09T19:57:42Z | 2014-04-09T19:57:42Z | NONE | null | Series, frames and panels are all built on numpy arrays, for which
we can calcuate memory footprint.
For python objects as well we can determine footprint based on platform,
python ver/bits and object type.
We can use thse to generate a report of amount of memory used and where.
That would be useful and not difficult.
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} | 9 | 2014-02-02T11:45:19Z | 2016-12-29T13:57:13Z | 2014-02-05T16:17:23Z | CONTRIBUTOR | null | In a similar fashion as #6212, the following fails on Python 3 (`doc/source/reshaping.rst`):
``` python
import numpy as np
import pandas as pd
x = pd.Series(['A', 'A', np.nan, 'B', 3.14, np.inf])
np.unique(x, return_inverse=True)[::-1])
```
```
/Users/user/.virtualenvs/pandas-dev/lib/python3.3/site-packages/numpy/lib/arraysetops.py in unique(ar, return_index, return_inverse)
181 perm = ar.argsort(kind='mergesort')
182 else:
--> 183 perm = ar.argsort()
184 aux = ar[perm]
185 flag = np.concatenate(([True], aux[1:] != aux[:-1]))
TypeError: unorderable types: str() > float()
```
Although this error manifests in `numpy` code, I assume this has to do with `pd.Series`?
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} | 11 | 2014-02-02T14:57:14Z | 2014-06-15T16:36:13Z | 2014-02-04T09:23:23Z | CONTRIBUTOR | null | related is #6212
Fixes examples in documentation to be compatible with Python 3. Also tested it locally under Python 2.
- Turned occurrences of `(x)range()` to `range()` for iteration and to `list(range())` for list concat.
- Fixed leading zero argument(s) as in e.g. `datetime(2013, 01, 27)`.
- Removed explicit `StringIO` and `cStringIO` imports in examples (uses suppressed pandas.compat imports) and added a note on py2/3 library reorganization.
- Fixed `StringIO` for binary data to the more explicit `BytesIO`.
- Fixed `NameError` for missing `pd.` in `pd.Series`.
- Added note for failing `numpy.unique` example, see also #6229.
I am not sure about the `BytesIO` though. Maybe good to put a note saying this is Python 3 way to do it and users on 2.x should use the old `StringIO` instead. In general, should we favor Python 3 doc versions in such cases and add notes for Python 2.x users? What do you think?
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} | 0 | 2014-02-02T15:28:47Z | 2014-06-15T15:59:41Z | 2014-02-02T15:39:34Z | CONTRIBUTOR | null | tests for issues in #6212 / #6231
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} | 4 | 2014-02-02T16:15:07Z | 2015-04-03T00:41:09Z | 2015-04-03T00:41:09Z | MEMBER | null | There is a new page in the docs 'Contributing to pandas', with at the moment the docs on contributing the documentation. And in #6207 I added already some links to various pages.
At the moment, the information for contributors is a bit scattered:
- The developer pages on the website (http://pandas.pydata.org/developers.html)
- The new page on building the docs (http://pandas.pydata.org/pandas-docs/dev/contributing.html)
- Guidelines on bug reports and pull requests in CONTRIBUTING.md (https://github.com/pydata/pandas/blob/master/CONTRIBUTING.md)
- Some things in the wiki (which I see @y-p cleaned up, nice!):
- extra tips on using git (https://github.com/pydata/pandas/wiki/Using-Git)
- another contributing page (https://github.com/pydata/pandas/wiki/Contributing)
- and other .. (https://github.com/pydata/pandas/wiki)
So maybe we should make a choice where do we want to consolidate the docs:
- We could opt to put everything in the wiki and CONTRIBUTING.md, so further expanding the wiki.
- Or put everything in the main docs
- Or we could try to make a (clear) division:
- main docs: starting point for newcomers who want to contribute to pandas (how to submit an issue, guidelines for a pull request, basic info on git and building pandas, building the docs, ...)
- So this would mainly be to somehow merge CONTRIBUTING.md, developer pages on website and contributing page on wiki.
- wiki: more info for the core developers/regular contributors: some more advanced tips and tricks, release workflow, how to handle the python2/3 issues, which exceptions to raise, ...)
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https://api.github.com/repos/pandas-dev/pandas/issues/6233 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6233/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6233/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6233/events | https://github.com/pandas-dev/pandas/pull/6233 | 26,765,879 | MDExOlB1bGxSZXF1ZXN0MTIxMTY2ODQ= | 6,233 | BLD: Build and publish docs (sans API) as part of Travis jobs | {
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Now that travis is down to 5 minutes https://github.com/pydata/pandas/pull/6227, and
doc builds have been accelerated as well #6179, it's possible to include a doc build as part
of our CI process without incurring long build times.
This PR runs the build process in the background so that we parallelize with the I/O
heavy parts of the test suite, the final result is a build that's still much faster then
where we were last week. If no docs were modified by the commit, the doc build
is skipped.
Compiled Docs (without API reference or R section) are pushed immediately to
http://pandas-docs.github.io/pandas-docs-travis/
and can be seen there a few minutes after the CI job is finished following
a commit to master. The version string on the docs is broken,pending #6217.
Here's to shorter cycles :dancers: .
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https://api.github.com/repos/pandas-dev/pandas/issues/6234 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6234/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6234/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6234/events | https://github.com/pandas-dev/pandas/issues/6234 | 26,779,389 | MDU6SXNzdWUyNjc3OTM4OQ== | 6,234 | Perf regressions in 0.13.1 | {
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"creator": null,
"description": "2014-02-3: previous version released",
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} | 1 | 2014-02-03T03:38:23Z | 2014-04-28T00:47:00Z | 2014-04-28T00:47:00Z | NONE | null | Ive started work on the release [checklist](https://github.com/pydata/pandas/wiki/Release-Checklist), here's the vbench run against v0.13.0 (>30%):
```
indexing_dataframe_boolean_rows | 0.5086 | 0.3866 | 1.3155 |
groupby_transform | 265.5996 | 194.6437 | 1.3645 |
query_datetime_series | 52.5440 | 33.2900 | 1.5784 |
reshape_stack_simple | 4.7570 | 2.9666 | 1.6035 |
indexing_panel_subset | 1.3873 | 0.7103 | 1.9530 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Target [cef41bf] : potentialy 0.13.1
Base [a5410ed] : RLS: 0.13.0 final
```
A more detailed look, with summary stats over 10 runs for each vb to
supress noise, the quantiles suggest at least some of these are real.
```
./test_perf.sh -H -u 3 -S -n 5 -N 10 -c 3 -r 'indexing_panel_subset|reshape_stack_simple|query_datetime_series|groupby_transform|indexing_dataframe_boolean_rows' | tee log
Target [a5410ed] : RLS: 0.13.0 final
count mean std min 25% 50% 75% max
groupby_transform 10 202.459224 1.248072 201.371670 201.713940 201.888839 202.670515 205.056349
indexing_dataframe_boolean_rows 10 0.385046 0.001513 0.382344 0.384192 0.384967 0.385841 0.388066
indexing_dataframe_boolean_rows_object 10 0.633168 0.001114 0.631332 0.632445 0.633637 0.633697 0.634670
indexing_panel_subset 10 0.709184 0.001551 0.706275 0.708421 0.709136 0.710249 0.711600
reshape_stack_simple 10 2.804049 0.010563 2.785047 2.800802 2.803524 2.813021 2.818028
query_datetime_series 10 35.682742 0.926633 35.169999 35.275022 35.349131 35.618087 38.246711
Target [cef41bf] : potentialy 0.13.1
count mean std min 25% 50% 75% max
groupby_transform 10 271.738656 0.326605 271.227598 271.522979 271.702965 271.987200 272.217353
indexing_dataframe_boolean_rows 10 0.488933 0.002478 0.485341 0.488122 0.488679 0.489871 0.494321
indexing_dataframe_boolean_rows_object 10 0.739876 0.003478 0.735362 0.737846 0.739853 0.740846 0.747681
indexing_panel_subset 10 1.404691 0.005823 1.394351 1.401464 1.405795 1.408557 1.412312
reshape_stack_simple 10 4.802346 0.015722 4.780690 4.792313 4.796505 4.812539 4.831711
query_datetime_series 10 51.154764 0.140856 51.050027 51.059425 51.092466 51.194866 51.424026
```
https://github.com/pydata/pandas/issues/6103
cc @jreback
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} | 0 | 2014-02-03T04:39:18Z | 2014-06-26T19:23:40Z | 2014-02-03T04:39:46Z | NONE | null | - The git ref parsing didn't handle annotated tags well, returning a bad hash
if you tried `test_perf -t HEAD -b v0.13.0` and got a weird error, fixed. (git weirdness, fyi @cpcloud)
- try to Disable frame_expand_repr when printing stats, pasting results for comparison is much easier
that way.
- Added --temp-dir to name a specific (rather then randomized) scratch directory for vbench,
the randomized temp. dir was preventing ccache from doing it's job. Highly recommended
that you always invoke test_perf with a consistent `--temp-dir my-benching-dir`. This took down
the comparative runs from 5 minutes to less then 60 seconds on my box. cc @jreback, @yarikoptic.
xref https://github.com/pydata/pandas/issues/6199
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https://api.github.com/repos/pandas-dev/pandas/issues/6236 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6236/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6236/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6236/events | https://github.com/pandas-dev/pandas/issues/6236 | 26,804,425 | MDU6SXNzdWUyNjgwNDQyNQ== | 6,236 | foo | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6237 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6237/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6237/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6237/events | https://github.com/pandas-dev/pandas/issues/6237 | 26,807,292 | MDU6SXNzdWUyNjgwNzI5Mg== | 6,237 | Cannot load valid object form HDF5 | {
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} | [] | closed | false | null | [] | null | 6 | 2014-02-03T14:46:03Z | 2014-02-25T12:40:53Z | 2014-02-25T12:40:53Z | NONE | null | I have a rather weird error. I am essentially running the following code:
``` python
data = pd.read_hdf(filename.h5, pathstring)
```
I have also tried it in two steps
``` python
store = pd.HDFStore(filename.h5)
data = store.get(pathstring)
```
The problem is that when I am running the code on console/ipython it seems to work fine. but when I run it in a flask app feeding the string query parameters it breaks and raises KeyError saying the object doesn't exist. The path looks fine in HDFView as well.
I am using Continuum's Anaconda python distribution. All relevant packages (pandas, numpy, pytables) are up to date. I am on python 2.7.6 on OS X 10.9 x64
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https://api.github.com/repos/pandas-dev/pandas/issues/6238 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6238/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6238/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6238/events | https://github.com/pandas-dev/pandas/issues/6238 | 26,809,122 | MDU6SXNzdWUyNjgwOTEyMg== | 6,238 | Depracation warning on query() | {
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} | [] | closed | false | null | [] | null | 5 | 2014-02-03T15:05:03Z | 2014-03-04T03:18:19Z | 2014-02-03T16:40:36Z | CONTRIBUTOR | null | ```
anti_pda.query('label == "hd"')
```
```
home/ipython/envs/ipynb/local/lib/python2.7/site-packages/pandas/computation/ops.py:62: DeprecationWarning: object.__new__() takes no parameters
```
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} | 0 | 2014-02-03T17:48:31Z | 2014-02-17T00:31:22Z | 2014-02-17T00:31:22Z | CONTRIBUTOR | null | ```
In [21]: df = DataFrame({'A' : np.random.randn(5),
'B' : np.random.randn(5),'C' : np.random.randn(5),
'D':['a','b','c','d','e'] })
In [22]: df
Out[22]:
A B C D
0 -0.941264 0.272726 -0.547948 a
1 0.069432 1.398414 0.039093 b
2 -0.073638 -1.264676 -1.339994 c
3 1.520017 -0.562979 -0.739326 d
4 -0.395157 0.542807 0.766582 e
[5 rows x 4 columns]
In [23]: df.take([2,0,1,2,3], axis=1).drop('C',axis=1)
Out[23]:
A B D
0 -0.941264 0.272726 a
1 0.069432 1.398414 b
2 -0.073638 -1.264676 c
3 1.520017 -0.562979 d
4 -0.395157 0.542807 e
[5 rows x 3 columns]
In [24]: df.take([2,0,1,2,1], axis=1).drop('C',axis=1)
IndexError: index 3 is out of bounds for axis 0 with size 3
```
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} | 3 | 2014-02-03T18:19:59Z | 2014-06-24T20:53:44Z | 2014-02-03T20:30:58Z | CONTRIBUTOR | null | See http://bugs.python.org/issue20166
```
ERROR: Failure: ImportError (dynamic module does not define init function (PyInit_json))
----------------------------------------------------------------------
Traceback (most recent call last):
File "X:\Python34\lib\site-packages\nose\failure.py", line 39, in runTest
raise self.exc_val.with_traceback(self.tb)
File "X:\Python34\lib\site-packages\nose\loader.py", line 402, in loadTestsFromName
module = resolve_name(addr.module)
File "X:\Python34\lib\site-packages\nose\util.py", line 311, in resolve_name
module = __import__('.'.join(parts_copy))
File "X:\Python34\lib\site-packages\pandas\__init__.py", line 44, in <module>
from pandas.io.api import *
File "X:\Python34\lib\site-packages\pandas\io\api.py", line 7, in <module>
from pandas.io.excel import ExcelFile, ExcelWriter, read_excel
File "X:\Python34\lib\site-packages\pandas\io\excel.py", line 14, in <module>
from pandas import json
ImportError: dynamic module does not define init function (PyInit_json)
```
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"description": "Related ... | closed | false | null | [] | null | 10 | 2014-02-03T21:01:40Z | 2014-05-24T12:01:21Z | 2014-02-04T12:57:25Z | CONTRIBUTOR | null | As part of https://github.com/yhat/ggplot/issues/188 I'm trying to build a `factor` function which works the same way as the one in R (e.g. https://www.stat.berkeley.edu/classes/s133/factors.html) and which I could use to store factors in a dataframe.
What currently does not work is ordering:
```
mons = c("March","April","January","November","January",
+ "September","October","September","November","August",
+ "January","November","November","February","May","August",
+ "July","December","August","August","September","November",
+ "February","April")
factor(mons,levels=c("January","February","March",
+ "April","May","June","July","August","September",
+ "October","November","December"),ordered=TRUE)
```
Currently I see no way to make a change to the ordering of the levels to a predefined list.
``` python
In [69]: pd.Categorical(["b","a","c","a","c"], levels=["a","b","c"])
Out[69]: <repr(<pandas.core.categorical.Categorical at 0xb7f28d0>) failed: ValueError: invalid literal for long() with base 10: 'b'>
```
[The problem here seems to be that `pd.Categorial.__init__()` expects the arguments to be already transformed to a factor data structure (list of index positions + labels) and the above is actually a "labels + label ordering" structure]
`pd.Categorial.from_array()` only takes a `data` argument, but no ordering :-(
It seems that `pandas.core.algorithms.factorize()` was once intended to do that, but the `order` keyword is not used in the actual implementation (https://github.com/pydata/pandas/blob/master/pandas/core/algorithms.py#L116).
I'm not sure what I should do with `ordered =[True, False]`. What I think it should do is to remove some standard ordering operations in case of `ordered == False`?
Another problem is that i can't roundtrip factors into a dataframe:
```
>>> df = pd.DataFrame({"a":["b","a","c","a","c"]})
>>> df["factor"] = pd.Categorical.from_array(df.a)
>>> df
a factor
0 b b
1 a a
2 c c
3 a a
4 c c
[5 rows x 2 columns]
>>> df.factor
0 b
1 a
2 c
3 a
4 c
Name: factor, dtype: object
```
-> There is no level attribute anymore :-(
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"repos_url": "https://api.github.com/users/cpcloud/repos",
"site_admin": false,
"starred_url": "https://api.github.com/users/cpcloud/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/cpcloud/subscriptions",
"type": "User",
"url": "https://api.github.com/users/cpcloud"
} | [
{
"avatar_url": "https://avatars.githubusercontent.com/u/417981?v=4",
"events_url": "https://api.github.com/users/cpcloud/events{/privacy}",
"followers_url": "https://api.github.com/users/cpcloud/followers",
"following_url": "https://api.github.com/users/cpcloud/following{/other_user}",
"gists_u... | null | 8 | 2014-02-03T21:08:43Z | 2014-06-14T10:19:39Z | 2014-02-04T14:31:19Z | CONTRIBUTOR | null | Python 3.4 created a "NameConstant AST class to represent None, True, and False literals" (http://docs.python.org/3.4/whatsnew/changelog.html).
Pandas-0.13.1.win-amd64-py3.4 fails a few tests with `AttributeError: '...ExprVisitor' object has no attribute 'visit_NameConstant'`.
The suggested change fixes those test errors but I am not sure that it is the best/correct fix.
```
======================================================================
ERROR: test_scalar_unary (pandas.computation.tests.test_eval.TestEvalNumexprPandas)
----------------------------------------------------------------------
Traceback (most recent call last):
File "X:\Python34\lib\site-packages\pandas\computation\tests\test_eval.py", line 678, in test_scalar_unary
pd.eval('~True', parser=self.parser, engine=self.engine), ~True)
File "X:\Python34\lib\site-packages\pandas\computation\eval.py", line 207, in eval
truediv=truediv)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 762, in __init__
self.terms = self.parse()
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 781, in parse
return self._visitor.visit(self.expr)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 455, in visit_Module
return self.visit(expr, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 458, in visit_Expr
return self.visit(node.value, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 545, in visit_UnaryOp
operand = self.visit(node.operand)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 448, in visit
visitor = getattr(self, method)
AttributeError: 'PandasExprVisitor' object has no attribute 'visit_NameConstant'
======================================================================
ERROR: test_scalar_unary (pandas.computation.tests.test_eval.TestEvalNumexprPython)
----------------------------------------------------------------------
Traceback (most recent call last):
File "X:\Python34\lib\site-packages\pandas\computation\tests\test_eval.py", line 678, in test_scalar_unary
pd.eval('~True', parser=self.parser, engine=self.engine), ~True)
File "X:\Python34\lib\site-packages\pandas\computation\eval.py", line 207, in eval
truediv=truediv)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 762, in __init__
self.terms = self.parse()
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 781, in parse
return self._visitor.visit(self.expr)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 455, in visit_Module
return self.visit(expr, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 458, in visit_Expr
return self.visit(node.value, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 545, in visit_UnaryOp
operand = self.visit(node.operand)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 448, in visit
visitor = getattr(self, method)
AttributeError: 'PythonExprVisitor' object has no attribute 'visit_NameConstant'
======================================================================
ERROR: test_scalar_unary (pandas.computation.tests.test_eval.TestEvalPythonPandas)
----------------------------------------------------------------------
Traceback (most recent call last):
File "X:\Python34\lib\site-packages\pandas\computation\tests\test_eval.py", line 678, in test_scalar_unary
pd.eval('~True', parser=self.parser, engine=self.engine), ~True)
File "X:\Python34\lib\site-packages\pandas\computation\eval.py", line 207, in eval
truediv=truediv)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 762, in __init__
self.terms = self.parse()
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 781, in parse
return self._visitor.visit(self.expr)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 455, in visit_Module
return self.visit(expr, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 458, in visit_Expr
return self.visit(node.value, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 545, in visit_UnaryOp
operand = self.visit(node.operand)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 448, in visit
visitor = getattr(self, method)
AttributeError: 'PandasExprVisitor' object has no attribute 'visit_NameConstant'
======================================================================
ERROR: test_scalar_unary (pandas.computation.tests.test_eval.TestEvalPythonPython)
----------------------------------------------------------------------
Traceback (most recent call last):
File "X:\Python34\lib\site-packages\pandas\computation\tests\test_eval.py", line 678, in test_scalar_unary
pd.eval('~True', parser=self.parser, engine=self.engine), ~True)
File "X:\Python34\lib\site-packages\pandas\computation\eval.py", line 207, in eval
truediv=truediv)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 762, in __init__
self.terms = self.parse()
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 781, in parse
return self._visitor.visit(self.expr)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 455, in visit_Module
return self.visit(expr, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 458, in visit_Expr
return self.visit(node.value, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 545, in visit_UnaryOp
operand = self.visit(node.operand)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 448, in visit
visitor = getattr(self, method)
AttributeError: 'PythonExprVisitor' object has no attribute 'visit_NameConstant'
======================================================================
ERROR: test_bool_ops_with_constants (pandas.computation.tests.test_eval.TestOperationsNumExprPandas)
----------------------------------------------------------------------
Traceback (most recent call last):
File "X:\Python34\lib\site-packages\pandas\computation\tests\test_eval.py", line 1086, in test_bool_ops_with_constants
res = self.eval(ex)
File "X:\Python34\lib\site-packages\pandas\computation\tests\test_eval.py", line 1046, in eval
return pd.eval(*args, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\eval.py", line 207, in eval
truediv=truediv)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 762, in __init__
self.terms = self.parse()
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 781, in parse
return self._visitor.visit(self.expr)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 455, in visit_Module
return self.visit(expr, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 458, in visit_Expr
return self.visit(node.value, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 716, in visit_BoolOp
return reduce(visitor, operands)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 708, in visitor
lhs = self._try_visit_binop(x)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 704, in _try_visit_binop
return self.visit(bop)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 448, in visit
visitor = getattr(self, method)
AttributeError: 'PandasExprVisitor' object has no attribute 'visit_NameConstant'
======================================================================
ERROR: test_simple_bool_ops (pandas.computation.tests.test_eval.TestOperationsNumExprPandas)
----------------------------------------------------------------------
Traceback (most recent call last):
File "X:\Python34\lib\site-packages\pandas\computation\tests\test_eval.py", line 1078, in test_simple_bool_ops
res = self.eval(ex)
File "X:\Python34\lib\site-packages\pandas\computation\tests\test_eval.py", line 1046, in eval
return pd.eval(*args, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\eval.py", line 207, in eval
truediv=truediv)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 762, in __init__
self.terms = self.parse()
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 781, in parse
return self._visitor.visit(self.expr)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 455, in visit_Module
return self.visit(expr, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 458, in visit_Expr
return self.visit(node.value, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 716, in visit_BoolOp
return reduce(visitor, operands)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 708, in visitor
lhs = self._try_visit_binop(x)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 704, in _try_visit_binop
return self.visit(bop)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 448, in visit
visitor = getattr(self, method)
AttributeError: 'PandasExprVisitor' object has no attribute 'visit_NameConstant'
======================================================================
ERROR: test_bool_ops_with_constants (pandas.computation.tests.test_eval.TestOperationsNumExprPython)
----------------------------------------------------------------------
Traceback (most recent call last):
File "X:\Python34\lib\site-packages\pandas\computation\tests\test_eval.py", line 1389, in test_bool_ops_with_constants
res = self.eval(ex)
File "X:\Python34\lib\site-packages\pandas\computation\tests\test_eval.py", line 1046, in eval
return pd.eval(*args, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\eval.py", line 207, in eval
truediv=truediv)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 762, in __init__
self.terms = self.parse()
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 781, in parse
return self._visitor.visit(self.expr)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 455, in visit_Module
return self.visit(expr, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 458, in visit_Expr
return self.visit(node.value, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 536, in visit_BinOp
op, op_class, left, right = self._possibly_transform_eq_ne(node)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 492, in _possibly_transform_eq_ne
left = self.visit(node.left, side='left')
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 448, in visit
visitor = getattr(self, method)
AttributeError: 'PythonExprVisitor' object has no attribute 'visit_NameConstant'
======================================================================
ERROR: test_bool_ops_with_constants (pandas.computation.tests.test_eval.TestOperationsPythonPandas)
----------------------------------------------------------------------
Traceback (most recent call last):
File "X:\Python34\lib\site-packages\pandas\computation\tests\test_eval.py", line 1086, in test_bool_ops_with_constants
res = self.eval(ex)
File "X:\Python34\lib\site-packages\pandas\computation\tests\test_eval.py", line 1046, in eval
return pd.eval(*args, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\eval.py", line 207, in eval
truediv=truediv)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 762, in __init__
self.terms = self.parse()
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 781, in parse
return self._visitor.visit(self.expr)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 455, in visit_Module
return self.visit(expr, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 458, in visit_Expr
return self.visit(node.value, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 716, in visit_BoolOp
return reduce(visitor, operands)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 708, in visitor
lhs = self._try_visit_binop(x)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 704, in _try_visit_binop
return self.visit(bop)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 448, in visit
visitor = getattr(self, method)
AttributeError: 'PandasExprVisitor' object has no attribute 'visit_NameConstant'
======================================================================
ERROR: test_simple_bool_ops (pandas.computation.tests.test_eval.TestOperationsPythonPandas)
----------------------------------------------------------------------
Traceback (most recent call last):
File "X:\Python34\lib\site-packages\pandas\computation\tests\test_eval.py", line 1078, in test_simple_bool_ops
res = self.eval(ex)
File "X:\Python34\lib\site-packages\pandas\computation\tests\test_eval.py", line 1046, in eval
return pd.eval(*args, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\eval.py", line 207, in eval
truediv=truediv)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 762, in __init__
self.terms = self.parse()
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 781, in parse
return self._visitor.visit(self.expr)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 455, in visit_Module
return self.visit(expr, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 458, in visit_Expr
return self.visit(node.value, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 716, in visit_BoolOp
return reduce(visitor, operands)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 708, in visitor
lhs = self._try_visit_binop(x)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 704, in _try_visit_binop
return self.visit(bop)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 448, in visit
visitor = getattr(self, method)
AttributeError: 'PandasExprVisitor' object has no attribute 'visit_NameConstant'
======================================================================
ERROR: test_bool_ops_with_constants (pandas.computation.tests.test_eval.TestOperationsPythonPython)
----------------------------------------------------------------------
Traceback (most recent call last):
File "X:\Python34\lib\site-packages\pandas\computation\tests\test_eval.py", line 1389, in test_bool_ops_with_constants
res = self.eval(ex)
File "X:\Python34\lib\site-packages\pandas\computation\tests\test_eval.py", line 1046, in eval
return pd.eval(*args, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\eval.py", line 207, in eval
truediv=truediv)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 762, in __init__
self.terms = self.parse()
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 781, in parse
return self._visitor.visit(self.expr)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 455, in visit_Module
return self.visit(expr, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 458, in visit_Expr
return self.visit(node.value, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 536, in visit_BinOp
op, op_class, left, right = self._possibly_transform_eq_ne(node)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 492, in _possibly_transform_eq_ne
left = self.visit(node.left, side='left')
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 448, in visit
visitor = getattr(self, method)
AttributeError: 'PythonExprVisitor' object has no attribute 'visit_NameConstant'
======================================================================
ERROR: test_select_dtypes (pandas.io.tests.test_pytables.TestHDFStore)
----------------------------------------------------------------------
Traceback (most recent call last):
File "X:\Python34\lib\site-packages\pandas\io\tests\test_pytables.py", line 2939, in test_select_dtypes
result = store.select('df', Term('boolv == %s' % str(v)), columns = ['A','boolv'])
File "X:\Python34\lib\site-packages\pandas\io\pytables.py", line 664, in select
auto_close=auto_close).get_values()
File "X:\Python34\lib\site-packages\pandas\io\pytables.py", line 1338, in get_values
results = self.func(self.start, self.stop)
File "X:\Python34\lib\site-packages\pandas\io\pytables.py", line 653, in func
columns=columns, **kwargs)
File "X:\Python34\lib\site-packages\pandas\io\pytables.py", line 3788, in read
if not self.read_axes(where=where, **kwargs):
File "X:\Python34\lib\site-packages\pandas\io\pytables.py", line 3047, in read_axes
self.selection = Selection(self, where=where, **kwargs)
File "X:\Python34\lib\site-packages\pandas\io\pytables.py", line 4263, in __init__
self.terms = self.generate(where)
File "X:\Python34\lib\site-packages\pandas\io\pytables.py", line 4276, in generate
return Expr(where, queryables=q, encoding=self.table.encoding)
File "X:\Python34\lib\site-packages\pandas\computation\pytables.py", line 518, in __init__
self.terms = self.parse()
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 781, in parse
return self._visitor.visit(self.expr)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 455, in visit_Module
return self.visit(expr, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 458, in visit_Expr
return self.visit(node.value, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 689, in visit_Compare
return self.visit(binop)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 536, in visit_BinOp
op, op_class, left, right = self._possibly_transform_eq_ne(node)
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 494, in _possibly_transform_eq_ne
right = self.visit(node.right, side='right')
File "X:\Python34\lib\site-packages\pandas\computation\expr.py", line 448, in visit
visitor = getattr(self, method)
AttributeError: 'ExprVisitor' object has no attribute 'visit_NameConstant'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6244 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6244/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6244/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6244/events | https://github.com/pandas-dev/pandas/issues/6244 | 26,837,468 | MDU6SXNzdWUyNjgzNzQ2OA== | 6,244 | BLD: add python 3.4 build | {
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} | 13 | 2014-02-03T22:48:32Z | 2019-10-21T19:34:16Z | null | NONE | null | @jreback
I get this very strange error when writing very wide HDF5 tables. In this case a random float32 array with 1500 columns cant be written as an HDF5 table. However if rename the columns it seems to write fine...
Not sure whats going on.
-Gagi
``` python
In [1]: %paste
import numpy as np
import pandas as pd
print np.__version__
print pd.__version__
pd.options.display.large_repr = 'info'
df = pd.DataFrame(np.random.randn(1000,1500), dtype=np.float32)
#df.columns = df.columns.map(lambda x: 'A'+str(x))
df.to_hdf(r'Y:\table.h5', 'df', format='table', nan_rep='')
## -- End pasted text --
1.7.1
0.13.0
---------------------------------------------------------------------------
HDF5ExtError Traceback (most recent call last)
<ipython-input-1-ae7bcadf7834> in <module>()
9 df = pd.DataFrame(np.random.randn(1000,1500), dtype=np.float32)
10 #df.columns = df.columns.map(lambda x: 'A'+str(x))
---> 11 df.to_hdf(r'Y:\table.h5', 'df', format='table', nan_rep='')
D:\Python27\lib\site-packages\pandas\core\generic.pyc in to_hdf(self, path_or_buf, key, **kwargs)
860
861 from pandas.io import pytables
--> 862 return pytables.to_hdf(path_or_buf, key, self, **kwargs)
863
864 def to_msgpack(self, path_or_buf=None, **kwargs):
D:\Python27\lib\site-packages\pandas\io\pytables.pyc in to_hdf(path_or_buf, key, value, mode, complevel, complib, append, **kwargs)
269 with get_store(path_or_buf, mode=mode, complevel=complevel,
270 complib=complib) as store:
--> 271 f(store)
272 else:
273 f(path_or_buf)
D:\Python27\lib\site-packages\pandas\io\pytables.pyc in <lambda>(store)
264 f = lambda store: store.append(key, value, **kwargs)
265 else:
--> 266 f = lambda store: store.put(key, value, **kwargs)
267
268 if isinstance(path_or_buf, compat.string_types):
D:\Python27\lib\site-packages\pandas\io\pytables.pyc in put(self, key, value, format, append, **kwargs)
792 format = get_option("io.hdf.default_format") or 'fixed'
793 kwargs = self._validate_format(format, kwargs)
--> 794 self._write_to_group(key, value, append=append, **kwargs)
795
796 def remove(self, key, where=None, start=None, stop=None):
D:\Python27\lib\site-packages\pandas\io\pytables.pyc in _write_to_group(self, key, value, format, index, append, complib, encoding, **kwargs)
1244
1245 # write the object
-> 1246 s.write(obj=value, append=append, complib=complib, **kwargs)
1247
1248 if s.is_table and index:
D:\Python27\lib\site-packages\pandas\io\pytables.pyc in write(self, obj, axes, append, complib, complevel, fletcher32, min_itemsize, chunksize, expectedrows, dropna, **kwa
3548
3549 # set the table attributes
-> 3550 self.set_attrs()
3551
3552 # create the table
D:\Python27\lib\site-packages\pandas\io\pytables.pyc in set_attrs(self)
2858 self.attrs.index_cols = self.index_cols()
2859 self.attrs.values_cols = self.values_cols()
-> 2860 self.attrs.non_index_axes = self.non_index_axes
2861 self.attrs.data_columns = self.data_columns
2862 self.attrs.nan_rep = self.nan_rep
D:\Python27\lib\site-packages\tables\attributeset.pyc in __setattr__(self, name, value)
450
451 # Set the attribute.
--> 452 self._g__setattr(name, value)
453
454 # Log new attribute addition.
D:\Python27\lib\site-packages\tables\attributeset.pyc in _g__setattr(self, name, value)
393 value = stvalue[()]
394
--> 395 self._g_setattr(self._v_node, name, stvalue)
396
397 # New attribute or value. Introduce it into the local
D:\Python27\lib\site-packages\tables\hdf5extension.pyd in tables.hdf5extension.AttributeSet._g_setattr (tables\hdf5extension.c:6334)()
HDF5ExtError: HDF5 error back trace
File "..\..\hdf5-1.8.11\src\H5A.c", line 254, in H5Acreate2
unable to create attribute
File "..\..\hdf5-1.8.11\src\H5A.c", line 503, in H5A_create
unable to create attribute in object header
File "..\..\hdf5-1.8.11\src\H5Oattribute.c", line 347, in H5O_attr_create
unable to create new attribute in header
File "..\..\hdf5-1.8.11\src\H5Omessage.c", line 224, in H5O_msg_append_real
unable to create new message
File "..\..\hdf5-1.8.11\src\H5Omessage.c", line 1945, in H5O_msg_alloc
unable to allocate space for message
File "..\..\hdf5-1.8.11\src\H5Oalloc.c", line 1142, in H5O_alloc
object header message is too large
End of HDF5 error back trace
Can't set attribute 'non_index_axes' in node:
/df (Group) u''.
In [2]: df.columns = df.columns.map(lambda x: 'A'+str(x))
In [3]: df.to_hdf(r'Y:\tabl2e3.h5', 'df', format='table', nan_rep='')
```
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} | [] | closed | false | null | [] | null | 3 | 2014-02-04T02:57:48Z | 2014-02-04T03:35:33Z | 2014-02-04T03:35:33Z | NONE | null | Here are the steps I followed. I am using pandas (0.12.0) .
```
In [1]: import pandas as pd
In [4]: dataFrame = pd.read_csv('./test.csv')
In [7]: dataFrame
Out[7]:
r1 r2 r3 r4 r5
0 NaN 3.5 NaN NaN 5
1 4.5 NaN 4 NaN NaN
2 1.5 NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN
5 4.5 NaN 4 NaN NaN
6 NaN NaN NaN NaN NaN
In [8]: dataFrame['mean'] = dataFrame.mean(axis=1)
In [9]: dataFrame
Out[9]:
r1 r2 r3 r4 r5 mean
0 NaN 3.5 NaN NaN 5 4.25
1 4.5 NaN 4 NaN NaN 4.25
2 1.5 NaN NaN NaN NaN 1.50
3 NaN NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN NaN
5 4.5 NaN 4 NaN NaN 4.25
6 NaN NaN NaN NaN NaN NaN
In [10]: dataFrame.dtypes
Out[10]:
r1 float64
r2 float64
r3 float64
r4 float64
r5 float64
mean float64
dtype: object
In [11]: meanCenteredDataFrame = dataFrame.apply(lambda x: x -x['mean'])
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-11-e6cc746e933b> in <module>()
----> 1 meanCenteredDataFrame = dataFrame.apply(lambda x: x -x['mean'])
//anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in apply(self, func, axis, broadcast, raw, args, **kwds)
4414 return self._apply_raw(f, axis)
4415 else:
-> 4416 return self._apply_standard(f, axis)
4417 else:
4418 return self._apply_broadcast(f, axis)
//anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in _apply_standard(self, func, axis, ignore_failures)
4489 # no k defined yet
4490 pass
-> 4491 raise e
4492
4493
KeyError: ('mean', u'occurred at index r1')
In [12]: dataFrame.fillna(0,inplace=True)
In [13]: meanCenteredDataFrame = dataFrame.apply(lambda x: x -x['mean'])
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-13-e6cc746e933b> in <module>()
----> 1 meanCenteredDataFrame = dataFrame.apply(lambda x: x -x['mean'])
Type: DataFrame
String Form:
r1 r2 r3 r4 r5 mean
0 0.0 3.5 0 0 5 4.25
//anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in apply(self, func, axis, broadcast, raw, args, **kwds)
4414 return self._apply_raw(f, axis)
4415 else:
-> 4416 return self._apply_standard(f, axis)
4417 else:
4418 return self._apply_broadcast(f, axis)
//anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in _apply_standard(self, func, axis, ignore_failures)
4489 # no k defined yet
4490 pass
-> 4491 raise e
4492
4493
KeyError: ('mean', u'occurred at index r1')
```
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} | 1 | 2014-02-04T08:28:59Z | 2014-02-04T14:17:37Z | 2014-02-04T14:17:37Z | NONE | null | Caught while testing 185b3f11 on Win/x86_64 with PY3.4.0/64:
```
======================================================================
ERROR: test_select_dtypes (pandas.io.tests.test_pytables.TestHDFStore)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\python34-AMD64\lib\unittest\case.py", line 57, in testPartExecutor
yield
File "c:\python34-AMD64\lib\unittest\case.py", line 574, in run
testMethod()
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\34\pandas\io\tests\test_pytables.py", line 2939, in test_select_dtypes
result = store.select('df', Term('boolv == %s' % str(v)), columns = ['A','boolv'])
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\34\pandas\io\pytables.py", line 664, in select
auto_close=auto_close).get_values()
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\34\pandas\io\pytables.py", line 1338, in get_values
results = self.func(self.start, self.stop)
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\34\pandas\io\pytables.py", line 653, in func
columns=columns, **kwargs)
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\34\pandas\io\pytables.py", line 3788, in read
if not self.read_axes(where=where, **kwargs):
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\34\pandas\io\pytables.py", line 3047, in read_axes
self.selection = Selection(self, where=where, **kwargs)
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\34\pandas\io\pytables.py", line 4263, in __init__
self.terms = self.generate(where)
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\34\pandas\io\pytables.py", line 4276, in generate
return Expr(where, queryables=q, encoding=self.table.encoding)
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\34\pandas\computation\pytables.py", line 518, in __init__
self.terms = self.parse()
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\34\pandas\computation\expr.py", line 781, in parse
return self._visitor.visit(self.expr)
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\34\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\34\pandas\computation\expr.py", line 455, in visit_Module
return self.visit(expr, **kwargs)
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\34\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\34\pandas\computation\expr.py", line 458, in visit_Expr
return self.visit(node.value, **kwargs)
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\34\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\34\pandas\computation\expr.py", line 689, in visit_Compare
return self.visit(binop)
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\34\pandas\computation\expr.py", line 449, in visit
return visitor(node, **kwargs)
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\34\pandas\computation\expr.py", line 536, in visit_BinOp
op, op_class, left, right = self._possibly_transform_eq_ne(node)
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\34\pandas\computation\expr.py", line 494, in _possibly_transform_eq_ne
right = self.visit(node.right, side='right')
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\34\pandas\computation\expr.py", line 448, in visit
visitor = getattr(self, method)
AttributeError: 'ExprVisitor' object has no attribute 'visit_NameConstant'
```
xref https://github.com/pydata/pandas/pull/6243
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} | 23 | 2014-02-04T09:21:58Z | 2016-11-11T10:32:26Z | 2016-11-11T10:32:14Z | NONE | null | We've been stuck on how to handle PEP8 cleanups for a while.
On the one hand PEP8 storms break PRs and git blame, OTOH
the back and forth on code review about whitespace fixes drives some
maintainers (me) crazy.
But:
- Autopep8 accepts a line-range argument which tells it to only fix problems in a given area.
- git diff tells you which lines on which file were altered between two points in history.
So:
- write a script "pep8-radius.py" that takes a commit hash `h` as input, find altered lines
between HEAD and `h` and then invokes autopep8 on a given radius around those
altered lines to locally cleanup where things changed.
- Do one last pep8 storm.
- Get contributors to run the script and include/squash the cleanups in the PR.
This is a fun little hack which might become quite handy outside pandas.
would appreciate someone picking this idea up and making it happen as I'm
a little burned out on this sort of work.
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} | 50 | 2014-02-04T11:32:45Z | 2018-07-06T22:15:03Z | 2018-07-06T22:15:02Z | CONTRIBUTOR | null | after #6134 we can drop xs and implement directly. Thus we should deprecate it.
`.xs(key, level=n)`
is roughly equivalent to this
```
indexer = tuple([slice(None)]*(n-1) + key)]
axis_indexer = [ slice(None) ] * self.ndim
axis_indexer[axis] = indexer
self.loc[tuple(axis_indexer)]
```
roughly because needs to handle `.xs` full argument set
- drop_level (need to surround the indexer tuple with a list or not)
- n could be a level name
- n = 1 (needs a test)
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} | [] | closed | false | null | [] | null | 0 | 2014-02-04T11:35:35Z | 2014-06-13T02:06:51Z | 2014-02-04T11:35:39Z | NONE | null | Made py3 fixes to conf.py. Docs now compile on PY3 (good job @phaebz, @kermit666 and joris).
Also worked around recent breaking change to ipython https://github.com/ipython/ipython/pull/4504,
which somewhat deflates my theory that by having our own copy we can avoid having
other packages changes break our build (they can, if they change the packages it depends on).
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} | 10 | 2014-02-04T13:11:54Z | 2018-07-06T22:13:38Z | 2018-07-06T22:13:37Z | NONE | null | Pandas coverage is currently at 78%, not bad but could be better.
Open issue for first-time contributors: find some ill-tested corner
and bring it into the light.
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} | 0 | 2014-02-04T13:20:20Z | 2014-02-04T19:54:21Z | 2014-02-04T19:54:21Z | CONTRIBUTOR | null | from ML: https://groups.google.com/forum/#!topic/pydata/P3JFE3sob1U
empty indexing the frame
```
keys1 = ['@' + str(i) for i in range(1000)]
val1 = arange(1000)
keys2 = ['@' + str(i) for i in range(990)]
val2 = arange(990)
allKeys = list(set(keys1).union(keys2))
df = pandas.DataFrame(index = allKeys)
df['A'] = nan
df.loc[keys1, 'A'] = val1
df['B'] = nan
df.loc[keys2, 'B'] = val2
df.head()
## -- End pasted text --
<43>
A B
@679 679 679
@678 678 678
@337 337 337
@744 744 744
@673 673 673
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6253 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6253/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6253/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6253/events | https://github.com/pandas-dev/pandas/issues/6253 | 26,891,081 | MDU6SXNzdWUyNjg5MTA4MQ== | 6,253 | bug on DatatimeBlock._try_coerce_args() with pandas0.13.0 | {
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} | [] | closed | false | null | [] | null | 1 | 2014-02-04T15:48:49Z | 2014-02-04T15:53:19Z | 2014-02-04T15:53:19Z | NONE | null | When running the following example code, it throws error in DatatimeBlock._try_coerce_args()
> > T1 = pd.to_datetime(pd.Series([ np.nan, datetime(2012,1,1) ]))
> > T2 = pd.to_datetime(pd.Series([ datetime(2013,1,1), datetime(2012,1,1) ])
> > T1.where(~T1.isnull(), other=T2)
---
ValueError Traceback (most recent call last)
<ipython-input-21-2e937f1a9061> in <module>()
----> 1 T1.where( ~T1.isnull(), other = T2)
C:\Python64\lib\site-packages\pandas\core\generic.pyc in where(self, cond, other, inplace, axis, level, try_cast, raise_on_error)
2887 new_data = self._data.where(other, cond, align=axis is None,
2888 raise_on_error=raise_on_error,
-> 2889 try_cast=try_cast)
2890
2891 return self._constructor(new_data).__finalize__(self)
C:\Python64\lib\site-packages\pandas\core\internals.pyc in where(self, _args, *_kwargs)
2279
2280 def where(self, _args, *_kwargs):
-> 2281 return self.apply('where', _args, *_kwargs)
2282
2283 def eval(self, _args, *_kwargs):
C:\Python64\lib\site-packages\pandas\core\internals.pyc in apply(self, f, _args, *_kwargs)
2265
2266 else:
-> 2267 applied = getattr(blk, f)(_args, *_kwargs)
2268
2269 if isinstance(applied, list):
C:\Python64\lib\site-packages\pandas\core\internals.pyc in where(self, other, cond, align, raise_on_error, try_cast)
1076 # see if we can operate on the entire block, or need item-by-item
1077 # or if we are a single block (ndim == 1)
-> 1078 result = func(cond, values, other)
1079 if self._can_hold_na or self.ndim == 1:
1080
C:\Python64\lib\site-packages\pandas\core\internals.pyc in func(c, v, o)
1059 return v
1060
-> 1061 v, o = self._try_coerce_args(v, o)
1062 try:
1063 return self._try_coerce_result(
C:\Python64\lib\site-packages\pandas\core\internals.pyc in _try_coerce_args(self, values, other)
1518 values is always ndarra like, other may not be """
1519 values = values.view('i8')
-> 1520 if isnull(other) or (np.isscalar(other) and other == tslib.iNaT):
1521 other = tslib.iNaT
1522 elif isinstance(other, datetime):
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
The issue I found is the "if" condition. When input other is array-like, isnull return and the second "or" condition won't have the same size. A potential fix could be changing line 1520 to:
if np.isscalar(other) and (isnull(other) or other == tslib.iNaT):
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} | 7 | 2014-02-04T18:02:19Z | 2014-02-12T12:59:46Z | 2014-02-04T19:54:21Z | NONE | null | Using 0.13.1 (gohlke binaries, Win64, using numpy 1.8):
When using .loc, when passing unsorted labels, pandas doesn't assign the values in the designed labels' locations. Example:
`df = pandas.DataFrame(index=[3, 5, 4], columns=['A'])`
`df.loc[[4, 3, 5], 'A'] = [1, 2, 3]`
Result is:
```
Out[30]:
A
3 1
5 2
4 3
```
One would have expected:
```
Out[30]:
A
3 2
5 3
4 1
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6255 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6255/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6255/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6255/events | https://github.com/pandas-dev/pandas/pull/6255 | 26,903,999 | MDExOlB1bGxSZXF1ZXN0MTIxODk4NTI= | 6,255 | Update indexing.rst | {
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} | [] | closed | false | null | [] | null | 1 | 2014-02-04T18:29:18Z | 2014-06-24T22:55:00Z | 2014-02-04T18:47:26Z | CONTRIBUTOR | null | Just removing a typo: 'for' was written twice.
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https://api.github.com/repos/pandas-dev/pandas/issues/6256 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6256/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6256/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6256/events | https://github.com/pandas-dev/pandas/pull/6256 | 26,909,043 | MDExOlB1bGxSZXF1ZXN0MTIxOTI3ODI= | 6,256 | BUG: Indexing bugs with reordered indexes (GH6252, GH6254) | {
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} | 0 | 2014-02-04T19:36:49Z | 2014-07-16T08:51:48Z | 2014-02-04T19:54:21Z | CONTRIBUTOR | null | closes #6252
closes #6254
```
In [1]: df = DataFrame(index=[3, 5, 4], columns=['A'])
In [2]: df.loc[[4, 3, 5], 'A'] = [1, 2, 3]
In [3]: df
Out[3]:
A
3 2
5 3
4 1
[3 rows x 1 columns]
```
```
In [4]: keys1 = ['@' + str(i) for i in range(5)]
In [5]: val1 = np.arange(5)
In [6]: keys2 = ['@' + str(i) for i in range(4)]
In [7]: val2 = np.arange(4)
In [8]: index = list(set(keys1).union(keys2))
In [9]: df = DataFrame(index = index)
In [10]: df['A'] = nan
In [11]: df.loc[keys1, 'A'] = val1
In [12]: df['B'] = nan
In [13]: df.loc[keys2, 'B'] = val2
In [14]: df
Out[14]:
A B
@2 2 2
@3 3 3
@0 0 0
@1 1 1
@4 4 NaN
[5 rows x 2 columns]
```
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} | 5 | 2014-02-04T21:19:25Z | 2017-12-13T01:59:36Z | 2017-12-13T01:59:36Z | CONTRIBUTOR | null | Currently there is no documentation (other than the signature) for `pandas.core.groupby.GroupBy.transform`.
For example passing the string `"shift"` works and shifts the results.
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} | 7 | 2014-02-04T21:37:56Z | 2014-02-05T02:08:12Z | 2014-02-05T02:08:12Z | CONTRIBUTOR | null | This works:
```
In [141]:
dates = pd.to_datetime(['2013-09-03', '2013-09-04', '2013-09-05'])
pd.concat({'A':pd.Series([1,3,4], index=dates), 'B':pd.Series([1,3,4], index=dates)}).to_frame().xs(key='2013-09-03', level=1)
0
A 1
B 1
[2 rows x 1 columns]
```
but this does not:
```
In [142]:
dates = pd.to_datetime(['2013-09-03', '2013-09-04', '2013-09-05'])
pd.concat({'A':pd.Series([1,3,4], index=dates), 'B':pd.Series([1,3,4], index=dates)}).xs(key='2013-09-03', level=1)
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-142-06aba7d44961> in <module>()
1 dates = pd.to_datetime(['2013-09-03', '2013-09-04', '2013-09-05'])
----> 2 pd.concat({'A':pd.Series([1,3,4], index=dates), 'B':pd.Series([1,3,4], index=dates)}).xs(key='2013-09-03', level=1)
/usr/lib64/python2.7/site-packages/pandas/core/series.py in _xs(self, key, axis, level, copy)
437
438 def _xs(self, key, axis=0, level=None, copy=True):
--> 439 return self.__getitem__(key)
440
441 xs = _xs
/usr/lib64/python2.7/site-packages/pandas/core/series.py in __getitem__(self, key)
482 def __getitem__(self, key):
483 try:
--> 484 result = self.index.get_value(self, key)
485 if isinstance(result, np.ndarray):
486 return self._constructor(result,index=[key]*len(result)).__finalize__(self)
/usr/lib64/python2.7/site-packages/pandas/core/index.py in get_value(self, series, key)
2294 raise InvalidIndexError(key)
2295 else:
-> 2296 raise e1
2297 except Exception: # pragma: no cover
2298 raise e1
KeyError: '2013-09-03'
```
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} | 4 | 2014-02-04T23:03:45Z | 2014-06-16T02:27:27Z | 2014-02-05T02:08:12Z | CONTRIBUTOR | null | closes #6258
closes #5684
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} | 15 | 2014-02-05T01:50:13Z | 2014-06-21T12:50:41Z | 2014-06-21T12:49:33Z | CONTRIBUTOR | null | When saving an excel file from a DataFrame with the method to_excel, there is a misbehaviour with index_label: it is placed in the 2nd row.
For instance
``` python
>>> df = pd.DataFrame([[1,2,3],[4,5,6]], columns=['a', 'b', 'c'])
>>> df.index.name = 'n'
>>> df
a b c
n
0 1 2 3
1 4 5 6
[2 rows x 3 columns]
>>> df.to_excel(path, index_label='test')
```
Produces the following result within the xls file:
Even if I force index_label=None, the behaviour persists
``` python
>>> df.to_excel('/home/julio/Testes/cost-home/test.xls', index_label=None)
```
Which is written as
This is a problem because then I can't read the same DF after I write it:
``` python
>>> df2 = pd.read_excel('/home/julio/Testes/cost-home/test.xls', 0)
>>> df2
a b c
n NaN NaN NaN
0 1 2 3
1 4 5 6
[3 rows x 3 columns]
```
I'm using pandas 0.13.0. In version 0.12 the label index was inserted in the first row.
``` python
>>> show_versions()
INSTALLED VERSIONS
------------------
Python: 2.7.4.final.0
OS: Linux
Release: 3.8.0-19-generic
Processor: x86_64
byteorder: little
LC_ALL: None
LANG: pt_PT.UTF-8
pandas: 0.13.0
Cython: 0.19.1
Numpy: 1.8.0
Scipy: 0.13.2
statsmodels: 0.5.0
patsy: 0.2.1
scikits.timeseries: Not installed
dateutil: 2.2
pytz: 2013.9
bottleneck: 0.7.0
PyTables: 3.0.0
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: 1.6.2
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: 0.5.2
sqlalchemy: Not installed
lxml: 3.2.3
bs4: Not installed
html5lib: Not installed
bigquery: Not installed
apiclient: Not installed
```
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} | 2 | 2014-02-05T01:54:36Z | 2014-06-19T04:52:19Z | 2014-02-05T12:16:52Z | CONTRIBUTOR | null | Work on #6175 to specialize assert_'s. Tests still pass locally. A work-in-progress.
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https://api.github.com/repos/pandas-dev/pandas/issues/6262 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6262/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6262/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6262/events | https://github.com/pandas-dev/pandas/pull/6262 | 26,936,316 | MDExOlB1bGxSZXF1ZXN0MTIyMDg4OTA= | 6,262 | CLN: Work on converting assert_'s to specialized forms, in pandas/tests | {
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} | [] | closed | false | null | [] | null | 1 | 2014-02-05T03:55:11Z | 2014-06-19T04:48:27Z | 2014-02-05T12:18:04Z | CONTRIBUTOR | null | Work on #6175, in pandas/tests. Tests still pass locally. A work-in-progress, but--with #6261--removes many/most cases of assert_(x == y) in this directory
Replacements:
- assert_(x == y) ==> assertEqual(x, y)
- assert_(x == True) ==> assertTrue(x)
- assert_(x == False) ==> assertFalse(x)
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https://api.github.com/repos/pandas-dev/pandas/issues/6263 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6263/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6263/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6263/events | https://github.com/pandas-dev/pandas/issues/6263 | 26,938,743 | MDU6SXNzdWUyNjkzODc0Mw== | 6,263 | ValueError on groupby boxplot | {
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... | closed | false | null | [] | null | 5 | 2014-02-05T05:30:12Z | 2014-03-22T21:32:16Z | 2014-03-22T21:31:56Z | NONE | null | I have a pretty simple data frame of two columns, containing dates and velocities
```
velocity date
0 83.9 2007-08-04
1 89.6 2007-08-04
2 88.2 2007-08-04
3 88.9 2007-08-04
4 89.4 2007-08-04
```
Yet, when I try to make a box plot of velocities grouped by date, I get a ValueError:
```
pitches.boxplot(column='velocity', by='date')
ValueError: Wrong number of items passed 109, indices imply 108
```
I've confirmed that `velocity` are floats and `date` are np.datetime64.
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} | [] | closed | false | null | [] | null | 1 | 2014-02-05T06:00:15Z | 2014-02-06T15:49:01Z | 2014-02-06T15:49:01Z | CONTRIBUTOR | null | Observe:
```
In [62]: %timeit com.is_integer(13)
1000000 loops, best of 3: 965 ns per loop
In [63]: %timeit com.is_integer(np.int32(2))
100000 loops, best of 3: 2.56 µs per loop
In [64]: %timeit com.is_integer(np.int64(2))
1000000 loops, best of 3: 1.27 µs per loop
In [65]: %timeit com.is_integer(14L)
1000000 loops, best of 3: 970 ns per loop
In [76]: %timeit com.is_integer("1")
1000000 loops, best of 3: 1.66 µs per loop
```
The lower bound of this function would be:
```
In [66]: %timeit type(3) is int
10000000 loops, best of 3: 85.8 ns per loop
```
My first attempt is the following which pulls the tuple creation out and optimizes for the common case of calling with `int`. This seems to make all cases faster, especially the common, `int`, case:
```
_int_types = (int, numbers.Integral, np.integer)
def is_integer(obj):
return isinstance(obj, _int_types)
```
and results:
```
In [70]: %timeit is_integer(13)
10000000 loops, best of 3: 146 ns per loop
In [71]: %timeit is_integer(np.int32(2))
100000 loops, best of 3: 2.43 µs per loop
In [72]: %timeit is_integer(np.int64(2))
1000000 loops, best of 3: 375 ns per loop
In [73]: %timeit is_integer(14L)
1000000 loops, best of 3: 911 ns per loop
In [75]: %timeit is_integer("1")
1000000 loops, best of 3: 1.57 µs per loop
```
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} | 3 | 2014-02-05T10:58:34Z | 2014-03-05T21:58:54Z | 2014-03-05T21:58:54Z | NONE | null | Consider this very basic group by operation:
``` python
df = pd.DataFrame({'col1': ['a', 'b', 'a', 'c'], 'col2': [1, 3, 2, 5]})
df_grp = df.groupby('col1')['col2']
```
When aggregating using `mean`, `min`, ... (everything but `count`) the resulting Series has `col2` as its name:
``` python
df_grp.mean()
col1
a 1.5
b 3.0
c 5.0
Name: col2, dtype: float64
```
However, if I use `count` for aggregation, the name is not set:
``` python
df_grp.count()
col1
a 2
b 1
c 1
dtype: int64
```
Not a big problem for a simple case like that, but I stumbled over that while working with a MultiIndex that needed to be reindexed and led to a `KeyError` due to that missing column name.
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PERF: micro optimzations in inference
API/CLN: core.internals.apply now only takes kwargs; makes code a bit simpler
CLN: wrap core/common/isnull into more direct Block.isnull
PERF: fast path series block operations
closes #6264
original timings
```
In [11]: %timeit com.is_integer(13)
100000 loops, best of 3: 1.29 µs per loop
In [12]: %timeit com.is_integer(np.int32(2))
100000 loops, best of 3: 3.32 µs per loop
In [13]: %timeit com.is_integer(np.int64(2))
100000 loops, best of 3: 1.86 µs per loop
In [14]: %timeit com.is_integer(14L)
100000 loops, best of 3: 1.29 µs per loop
In [15]: %timeit com.is_integer("1")
100000 loops, best of 3: 2.2 µs per loop
```
withi this PR
```
In [2]: %timeit com.is_integer(13)
10000000 loops, best of 3: 72.5 ns per loop
In [3]: %timeit com.is_integer(np.int32(2))
1000000 loops, best of 3: 620 ns per loop
In [4]: %timeit com.is_integer(np.int64(2))
1000000 loops, best of 3: 315 ns per loop
In [5]: %timeit com.is_integer(14L)
10000000 loops, best of 3: 72.4 ns per loop
In [6]: %timeit com.is_integer("1")
10000000 loops, best of 3: 78.8 ns per loop
```
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} | 5 | 2014-02-05T13:22:33Z | 2014-06-19T15:12:16Z | 2014-02-07T13:46:21Z | CONTRIBUTOR | null | Work on #6175, in pandas/*/tests. Tests still pass locally. Finishes most (all?) of the remaining vanilla assert_(x == y) conversions
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closes #5861
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} | 1 | 2014-02-05T13:45:03Z | 2014-07-16T08:52:22Z | 2014-02-06T15:49:01Z | CONTRIBUTOR | null | PERF: enhance tseries getitem indexing
PERF: micro optimzations in inference
API/CLN: core.internals.apply now only takes kwargs; makes code a bit simpler
CLN: wrap core/common/isnull into more direct Block.isnull
PERF: fast path series block operations
closes #6264
original timings
```
In [11]: %timeit com.is_integer(13)
100000 loops, best of 3: 1.29 µs per loop
In [12]: %timeit com.is_integer(np.int32(2))
100000 loops, best of 3: 3.32 µs per loop
In [13]: %timeit com.is_integer(np.int64(2))
100000 loops, best of 3: 1.86 µs per loop
In [14]: %timeit com.is_integer(14L)
100000 loops, best of 3: 1.29 µs per loop
In [15]: %timeit com.is_integer("1")
100000 loops, best of 3: 2.2 µs per loop
```
with this PR
```
In [2]: %timeit com.is_integer(13)
10000000 loops, best of 3: 72.5 ns per loop
In [3]: %timeit com.is_integer(np.int32(2))
1000000 loops, best of 3: 620 ns per loop
In [4]: %timeit com.is_integer(np.int64(2))
1000000 loops, best of 3: 315 ns per loop
In [5]: %timeit com.is_integer(14L)
10000000 loops, best of 3: 72.4 ns per loop
In [6]: %timeit com.is_integer("1")
10000000 loops, best of 3: 78.8 ns per loop
```
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```
======================================================================
FAIL: test_cummethods_bool (pandas.tests.test_series.TestSeries)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.13.1/debian/tmp/usr/lib/python3/dist-packages/pandas/tests/test_series.py", line 5202, in test_cummethods_bool
assert_series_equal(res, expecteds[method])
File "/tmp/buildd/pandas-0.13.1/debian/tmp/usr/lib/python3/dist-packages/pandas/util/testing.py", line 458, in assert_series_equal
assert_almost_equal(left.values, right.values, check_less_precise)
File "testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2561)
File "testing.pyx", line 93, in pandas._testing.assert_almost_equal (pandas/src/testing.c:1803)
File "testing.pyx", line 135, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2342)
AssertionError: (very low values) expected 1.00000 but got 0.00000
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.2.3.final.0
python-bits: 32
OS: Linux
OS-release: 3.2.0-4-amd64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: C
pandas: 0.13.1
Cython: None
numpy: 1.6.1
scipy: 0.9.0
statsmodels: None
IPython: None
sphinx: 1.1.3
patsy: None
scikits.timeseries: None
dateutil: 2.0
pytz: 2012c
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
sqlalchemy: None
lxml: None
bs4: 4.0.2
html5lib: None
bq: None
apiclient: None
```
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I have just installed pandas 0.13.0 and was observing some strange results produced by old codes.
One major issue is that I found that when summing two columns of a DataFrame, one of which is a float64 and the other an object, the result includes just the float64 columns.
This would be not an issue per se, but no warning message is displayed.
I think a warning in this case is of paramount importance.
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```
yearID Krate teamID
37197 2006 0.115385 COL
37890 2007 0.235294 COL
38621 2008 0.242595 COL
39320 2009 0.264352 COL
40028 2010 0.279373 COL
40738 2011 0.276347 COL
40739 2011 0.275556 CLE
41463 2012 0.225908 CLE
42183 2013 0.307448 CLE
```
However, when I do so, the resulting plot does not put the proper values on the x-axis.
```
jimenez.groupby('teamID').plot(x='yearID', y='Krate', grid=False)
```
I have confirmed that `yearID` is `int64`.
Running Pandas '0.13.0-395-gef55e60' on OS X 10.9.1 (Python 2.7.5).
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} | 2 | 2014-02-05T17:27:17Z | 2014-02-05T18:31:59Z | 2014-02-05T18:31:59Z | CONTRIBUTOR | null | I'm fairly certain this worked before a recent upgrade. Too busy to check right now. Maybe it shouldn't work and it's just been fixed, but ... I have something like this in a lot of places in my code.
```
df = pd.DataFrame(np.random.rand(5,3))
df['date'] = ['1-1-1990', '2-1-1990', '3-1-1990', '4-1-1990', '5-1-1990']
pd.DatetimeIndex(df['date'], freq='MS')
```
Raises about Series not having an attribute inferred_freq. Why is it trying to infer a frequency when I passed freq? Is it a new check?
```
[22]: pd.version.version
[22]: '0.13.0-472-g9109812'
```
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"id": ... | closed | false | null | [] | null | 1 | 2014-02-05T17:32:52Z | 2014-02-05T18:31:59Z | 2014-02-05T18:31:59Z | CONTRIBUTOR | null | Somewhat related to #6273 . I'm fairly certain though not positive that this also used to work on 0.12.x.
```
df = pd.DataFrame(np.random.rand(5,3))
df['date'] = ['1-1-1990', '2-1-1990', '3-1-1990', '4-1-1990', '5-1-1990']
pd.infer_freq(df['date'])
```
Raises Series has no attribute `is_monotonic`. I guess `infer_freq` now expects either an Index or ....?
```
[22]: pd.version.version
[22]: '0.13.0-472-g9109812'
```
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} | 0 | 2014-02-05T18:03:20Z | 2014-06-14T23:51:41Z | 2014-02-05T18:31:59Z | CONTRIBUTOR | null | closes #6273
closes #6274
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} | 0 | 2014-02-05T18:36:17Z | 2014-02-14T03:33:24Z | 2014-02-14T03:33:24Z | CONTRIBUTOR | null | https://travis-ci.org/jreback/pandas/jobs/18289056
anytime try to install 2.3 this fails because it can't find numpy....weird?
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https://api.github.com/repos/pandas-dev/pandas/issues/6277 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6277/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6277/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6277/events | https://github.com/pandas-dev/pandas/issues/6277 | 26,987,809 | MDU6SXNzdWUyNjk4NzgwOQ== | 6,277 | Series.loc faulty behaviour | {
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} | [] | closed | false | null | [] | null | 2 | 2014-02-05T19:03:23Z | 2014-03-13T10:05:55Z | 2014-03-13T10:05:41Z | NONE | null | ### INSTALLED VERSIONS
Python: 2.7.6.final.0
OS: Darwin
Release: 13.0.0
Processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.13.0
Cython: 0.19.2
Numpy: 1.8.0
Scipy: 0.13.2
statsmodels: 0.5.0
patsy: 0.2.0
scikits.timeseries: 0.91.3
dateutil: 2.2
pytz: 2013.8
bottleneck: 0.7.0
PyTables: 2.4.0
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: 1.5.8
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: Not installed
sqlalchemy: 0.8.3
lxml: 3.2.3
bs4: Not installed
html5lib: 0.95-dev
bigquery: Not installed
apiclient: Not installed
### Explanation by use-case:
```
ipdb> !type(self.df)
<class 'pandas.core.frame.DataFrame'>
ipdb> !type(self.df[Column.TRR])
<class 'pandas.core.series.Series'>
ipdb> !type(events_time_axis)
<class 'pandas.core.index.Int64Index'>
ipdb> !type(self.df[Column.TRR].index)
<class 'pandas.core.index.MultiIndex'>
ipdb> !type(events_time_axis[0])
<type 'numpy.int64'>
ipdb> !events_time_axis[0]
120
ipdb> !self.df[Column.TRR].loc[events_time_axis[0]]
*** ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
ipdb> !self.df[Column.TRR].loc[np.int64(events_time_axis[0])]
*** ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
ipdb> !self.df[Column.TRR].loc[int(events_time_axis[0])]
EXTENT
53760 0
192000 1
783360 4
829440 4
...
```
As you see in the last line the workaround was to cast numpy.int64 retrieval from pandas.core.index.Int64Index to int.
```
ipdb> w
<ipython-input-222-a92c150d8481>(2)<module>()
1 simulator = FASTSimulator(df, 5, half_life=30 * 12) # ((30=h)*12)=12 hours)
----> 2 fast_flash_memory_hits, read_requests_at_all = simulator.execute()
> /Users/shkolyar/workspace/python/thesis/simulator.py(82)execute()
80 current_values_as_zeros = pd.Series(np.zeros_like(extents, np.int),
81 extents)
---> 82 forecast = self.df[Column.TRR].loc[events_time_axis[0]].reindex(
83 extents, fill_value=0)
84
/Users/shkolyar/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/indexing.py(1020)__getitem__()
1018 return self._getitem_tuple(key)
1019 else:
-> 1020 return self._getitem_axis(key, axis=0)
1021
1022 def _getitem_axis(self, key, axis=0):
/Users/shkolyar/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/indexing.py(1145)_getitem_axis()
1143 else:
1144 self._has_valid_type(key, axis)
-> 1145 return self._get_label(key, axis=axis)
1146
1147
/Users/shkolyar/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/indexing.py(68)_get_label()
66 return self.obj._xs(label, axis=axis, copy=False)
67 except Exception:
---> 68 return self.obj._xs(label, axis=axis, copy=True)
69
70 def _get_loc(self, key, axis=0):
/Users/shkolyar/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/series.py(439)_xs()
437
438 def _xs(self, key, axis=0, level=None, copy=True):
--> 439 return self.__getitem__(key)
440
441 xs = _xs
/Users/shkolyar/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/series.py(504)__getitem__()
502 if type(new_key) != type(key):
503 return self.__getitem__(new_key)
--> 504 raise
505
506 except Exception:
/Users/shkolyar/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/index.py(2319)get_value()
2317 pass
2318
-> 2319 raise InvalidIndexError(key)
2320
2321 def get_level_values(self, level):
/Users/shkolyar/workspace/HDF5/tmp/index.pyx(416)pandas.index._bin_search (pandas/index.c:7437)()
```
### Stacktrace:
```
82 forecast = self.df[Column.TRR].loc[events_time_axis[0]].reindex(
83 extents, fill_value=0)
84
/Users/shkolyar/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/indexing.pyc in __getitem__(self, key)
1018 return self._getitem_tuple(key)
1019 else:
-> 1020 return self._getitem_axis(key, axis=0)
1021
1022 def _getitem_axis(self, key, axis=0):
/Users/shkolyar/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/indexing.pyc in _getitem_axis(self, key, axis)
1143 else:
1144 self._has_valid_type(key, axis)
-> 1145 return self._get_label(key, axis=axis)
1146
1147
/Users/shkolyar/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/indexing.pyc in _get_label(self, label, axis)
66 return self.obj._xs(label, axis=axis, copy=False)
67 except Exception:
---> 68 return self.obj._xs(label, axis=axis, copy=True)
69
70 def _get_loc(self, key, axis=0):
/Users/shkolyar/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/series.pyc in _xs(self, key, axis, level, copy)
437
438 def _xs(self, key, axis=0, level=None, copy=True):
--> 439 return self.__getitem__(key)
440
441 xs = _xs
/Users/shkolyar/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/series.pyc in __getitem__(self, key)
482 def __getitem__(self, key):
483 try:
--> 484 result = self.index.get_value(self, key)
485 if isinstance(result, np.ndarray):
486 return self._constructor(result,index=[key]*len(result)).__finalize__(self)
/Users/shkolyar/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/index.pyc in get_value(self, series, key)
2278
2279 try:
-> 2280 return self._engine.get_value(s, k)
2281 except KeyError as e1:
2282 try:
/Users/shkolyar/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/index.so in pandas.index.IndexEngine.get_value (pandas/index.c:2890)()
/Users/shkolyar/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/index.so in pandas.index.IndexEngine.get_value (pandas/index.c:2702)()
/Users/shkolyar/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/index.so in pandas.index.IndexEngine.get_loc (pandas/index.c:3301)()
/Users/shkolyar/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/index.so in pandas.index._bin_search (pandas/index.c:7437)()
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
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} | 12 | 2014-02-06T04:57:30Z | 2020-06-22T15:30:55Z | 2020-06-22T15:30:55Z | CONTRIBUTOR | null | At least wherever possible.
``` python
In [2]: df = pd.DataFrame(np.random.randn(30, 2), columns=['A', 'B'])
In [3]: df['C'] = 15 * ['a'] + 15 * ['b']
In [4]: ax = df.groupby('C')['A'].hist()
```
Ideally the group keys would be the legend for the plot.
I can take this one.
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} | 1 | 2014-02-06T05:45:22Z | 2014-02-06T11:08:21Z | 2014-02-06T11:07:58Z | CONTRIBUTOR | null | Travis got the following on Python 2.7 `NOSE_ARGS="slow and not network" LOCALE_OVERRIDE="zh_CN.GB18030" FULL_DEPS=true JOB_TAG=_LOCALE JOB_NAME=27_slow_nnet_LOCALE`:
```
======================================================================
FAIL: test_partially_invalid_plot_data (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pandas/tests/test_graphics.py", line 952, in test_partially_invalid_plot_data
df.plot(kind=kind)
File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pandas/util/testing.py", line 1320, in __exit__
raise AssertionError("{0} not raised.".format(name))
AssertionError: TypeError not raised.
```
When I re-ran test, it passed.
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} | 13 | 2014-02-06T08:35:41Z | 2014-02-06T21:41:11Z | 2014-02-06T21:41:11Z | NONE | null | Hello, I recently stumbled across this:
``` python
df1 = pd.DataFrame({'a':[1.,2.,3.,4.,nan,6,7,8]}, dtype='<f4')
df1.a.interpolate(inplace=True, downcast=None)
---------------------------------------------------------------------------
UnboundLocalError Traceback (most recent call last)
<ipython-input-9-bf0994f72664> in <module>()
----> 1 df1.a.interpolate(inplace=True, downcast=None)
C:\Program Files\Python27\lib\site-packages\pandas\core\generic.pyc in interpolate(self, method, axis, limit, inplace, downcast, **kwargs)
2304 if axis == 1:
2305 res = res.T
-> 2306 return res
2307
2308 #----------------------------------------------------------------------
UnboundLocalError: local variable 'res' referenced before assignment
```
The problem arises with setting inplace to True regardless of 'downcast'.
But speaking of which: running interpolate() without any options, I would excpect to keep data types by default! I often chunk through datasets and glue them together with to_hdf. It took me a while to figure out that a float column in one chunk had just zeros in it so interpolate downcasted it to int => to_hdf raised on appending.
Thx in advance
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} | [] | closed | false | null | [] | null | 7 | 2014-02-06T11:26:41Z | 2014-02-06T12:56:15Z | 2014-02-06T12:48:30Z | NONE | null | Line 7 in pandas.com.rpy.common.py:
from pandas.compat import zip, range
causes "ImportError: cannot import name zip"
zip is not contained in compat.
Best regards
Marc
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} | 1 | 2014-02-06T13:03:12Z | 2014-02-14T01:14:02Z | 2014-02-14T01:14:02Z | CONTRIBUTOR | null | @y-p looks like 3.3 windows build does not install numexpr/tables....can you have these tested on 3.3 and 2.7 (or similar to travis)
except leave at least 1 build w/o a num expr at all if you can
thanks
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} | 2 | 2014-02-06T13:32:22Z | 2014-07-16T08:52:32Z | 2014-02-06T21:41:11Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/6281
I didn't have the non inplace return in an else block. And I clearly didn't test this :/
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} | 9 | 2014-02-06T15:09:29Z | 2018-07-06T20:21:03Z | 2017-07-10T21:09:59Z | CONTRIBUTOR | null | If you have a Panel with a MultiIndex, you can read but not write to it with `.ix` (same with `.loc`).
Before 0.13 I would normally do something like `pn[item].ix[a, b][stuff] = values`, surprisingly it used to work (now it spits out a `SettingWithCopyWarning` but still kinda works).
So what are the valid options in 0.13?
``` python
>>> df = pd.DataFrame([1] * 3, pd.MultiIndex.from_tuples(zip(range(3), range(3))))
>>> df
0
0 0 1
1 1 1
2 2 1
[3 rows x 1 columns]
>>> pn = pd.Panel(dict((k, df.copy() * k) for k in xrange(2)))
>>> pn
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 3 (major_axis) x 1 (minor_axis)
Items axis: 0 to 1
Major_axis axis: (0, 0) to (2, 2)
Minor_axis axis: 0 to 0
>>> pn.ix[1, (0, 0), 0]
1
>>> pn.ix[1, (0, 0), 0] = 1
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-64-f48c3a7f93c3> in <module>()
----> 1 pn.ix[1, (0, 0), 0] = 1
/home/smirnov/anaconda/lib/python2.7/site-packages/pandas-0.13.1-py2.7-linux-x86_64.egg/pandas/core/indexing.pyc in __setitem__(self, key, value)
96 indexer = self._convert_to_indexer(key, is_setter=True)
97
---> 98 self._setitem_with_indexer(indexer, value)
99
100 def _has_valid_type(self, k, axis):
/home/smirnov/anaconda/lib/python2.7/site-packages/pandas-0.13.1-py2.7-linux-x86_64.egg/pandas/core/indexing.pyc in _setitem_with_indexer(self, indexer, value)
299 obj = self.obj[item]
300 index = obj.index
--> 301 idx = indexer[:info_axis][0]
302 try:
303 if idx in index:
IndexError: tuple index out of range
> /home/smirnov/anaconda/lib/python2.7/site-packages/pandas-0.13.1-py2.7-linux-x86_64.egg/pandas/core/indexing.py(301)_setitem_with_indexer()
300 index = obj.index
--> 301 idx = indexer[:info_axis][0]
302 try:
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6287 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6287/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6287/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6287/events | https://github.com/pandas-dev/pandas/issues/6287 | 27,074,280 | MDU6SXNzdWUyNzA3NDI4MA== | 6,287 | idxmax and idxmin do not work with strings | {
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```
In [8]: pandas.DataFrame({'A':["A","B","C", "A"], 'B':list("ABCD")}).groupby("A").B.idxmax()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-8-6aa5f42cc358> in <module>()
----> 1 pandas.DataFrame({'A':["A","B","C", "A"], 'B':list("ABCD")}).groupby("A").B.idxmax()
/home/apprun/gitrepos/pandas/pandas/core/groupby.py in wrapper(*args, **kwargs)
341 return self.apply(curried_with_axis)
342 except Exception:
--> 343 return self.apply(curried)
344
345 return wrapper
/home/apprun/gitrepos/pandas/pandas/core/groupby.py in apply(self, func, *args, **kwargs)
422 return func(g, *args, **kwargs)
423
--> 424 return self._python_apply_general(f)
425
426 def _python_apply_general(self, f):
/home/apprun/gitrepos/pandas/pandas/core/groupby.py in _python_apply_general(self, f)
425
426 def _python_apply_general(self, f):
--> 427 keys, values, mutated = self.grouper.apply(f, self.obj, self.axis)
428
429 return self._wrap_applied_output(keys, values,
/home/apprun/gitrepos/pandas/pandas/core/groupby.py in apply(self, f, data, axis)
881 # group might be modified
882 group_axes = _get_axes(group)
--> 883 res = f(group)
884 if not _is_indexed_like(res, group_axes):
885 mutated = True
/home/apprun/gitrepos/pandas/pandas/core/groupby.py in f(g)
420 @wraps(func)
421 def f(g):
--> 422 return func(g, *args, **kwargs)
423
424 return self._python_apply_general(f)
/home/apprun/gitrepos/pandas/pandas/core/groupby.py in curried(x)
327
328 def curried(x):
--> 329 return f(x, *args, **kwargs)
330
331 # preserve the name so we can detect it when calling plot methods,
/home/apprun/gitrepos/pandas/pandas/core/series.py in idxmax(self, axis, out, skipna)
1284 DataFrame.idxmax
1285 """
-> 1286 i = nanops.nanargmax(_values_from_object(self), skipna=skipna)
1287 if i == -1:
1288 return pa.NA
/home/apprun/gitrepos/pandas/pandas/core/nanops.py in nanargmax(values, axis, skipna)
387 """
388 values, mask, dtype = _get_values(values, skipna, fill_value_typ='-inf',
--> 389 isfinite=True)
390 result = values.argmax(axis)
391 result = _maybe_arg_null_out(result, axis, mask, skipna)
/home/apprun/gitrepos/pandas/pandas/core/nanops.py in _get_values(values, skipna, fill_value, fill_value_typ, isfinite, copy)
139 values = _values_from_object(values)
140 if isfinite:
--> 141 mask = _isfinite(values)
142 else:
143 mask = isnull(values)
/home/apprun/gitrepos/pandas/pandas/core/nanops.py in _isfinite(values)
173 return isnull(values)
174 elif isinstance(values.dtype, object):
--> 175 return -np.isfinite(values.astype('float64'))
176
177 return -np.isfinite(values)
ValueError: could not convert string to float: D
```
but this works:
```
In [9]: pandas.DataFrame({'A':["A","B","C", "A"], 'B':list("ABCD")}).groupby("A").B.max()
Out[9]:
A
A D
B B
C C
Name: B, dtype: object
```
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} | 5 | 2014-02-06T19:10:53Z | 2014-02-15T19:43:51Z | 2014-02-15T19:42:56Z | CONTRIBUTOR | null | Hi,
TomAugspurger suggested me on [StackOverFlow](https://stackoverflow.com/questions/21584434/group-by-value-of-sum-of-columns-with-pandas) to suggest this example of `df.groupby()` here for being considered as relevant to add to the docs. The idea was to determine a way to regroup columns of a DataFrame according to their sum, and to sum the aggregated ones.
``` python
In [1]: import pandas as pd
In [2]: dat = {'a':[1,0,0], 'b':[0,1,0], 'c':[1,0,0], 'd':[2,3,4]}
In [3]: df = pd.DataFrame(dat)
In [4]: df
Out[4]:
a b c d
0 1 0 1 2
1 0 1 0 3
2 0 0 0 4
In [5]: df.groupby(df.sum(), axis=1).sum()
Out[5]:
1 9
0 2 2
1 1 3
2 0 4
```
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} | 7 | 2014-02-06T19:40:15Z | 2014-06-21T08:12:07Z | 2014-02-07T01:16:39Z | CONTRIBUTOR | null | Been bugging me lately. Simple enough fix I think. Example usage
```
def mean(x, const):
return np.mean(x) + const
df = pd.DataFrame(np.random.rand(20, 3))
pd.expanding_apply(df, mean, args=(20,)
pd.expanding_apply(df, mean, kwargs=dict(const=20))
```
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} | 5 | 2014-02-06T21:40:30Z | 2014-02-17T17:35:57Z | 2014-02-17T17:35:57Z | CONTRIBUTOR | null | Originally reported as a comment in #6281 Reposting the example from there:
If you start out with a DataFrame like
``` python
In [14]: df = DataFrame({'A': [1, 2, np.nan, 4, 5, np.nan, 7],
....: 'C': [1, 2, 3, 5, 8, 13, 21]})
In [15]: df.dtypes
Out[15]:
A float64
C int64
dtype: object
```
So one float, one int, with no NaNs in the int. We apply the interpolation method to each block, so it gets applied to the int block/column. The interpolation will generally return a float dtype.
``` python
In [18]: df.interpolate(method='cubic', downcast=None).dtypes
Out[18]:
A float64
C float64
dtype: object
```
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} | 14 | 2014-02-06T21:45:18Z | 2014-06-19T09:13:01Z | 2014-02-17T17:06:56Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/6290
@jreback is there a good way to update **part** of a BlockManager inplace? I have a new BlockManager from `interpolate` and the original BlockManager (or DataFrame) which contains some "all good" columns that didn't get interpolate on, and some bad columns that did get interpolated on.
For the non inplace version I'm calling `self._constructor` on the BlockManager returned from interpolating and concating that to the all good columns.
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} | 9 | 2014-02-06T22:29:18Z | 2014-07-15T20:34:33Z | 2014-05-17T00:04:45Z | MEMBER | null | #5950 is now merged! (Thanks to @mangecoeur)
But there were still some issue to flesh out/fix:
- [x] release notes / 0.14.0 whatsnew (Here is the section in whatsnew for when you are ready): http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0140-sql -> #7120
- [ ] figure out user API (#6300) and update api.rst/docs accordingly
- ~~functional api (read_table/read_sql)~~ (fixed in #6867)
- ~~other public functions? (execute, has_table)`~~ (see #6300 and #6987)
- OO API (PandasSQLAlchemy?)
- [x] get travis working (not skipping the tests)
MySQL was taken out of the 2.7 build, for some reason was failing the build process: https://github.com/pydata/pandas/commit/043d165290180c6d9e3a480c18e220548af56c53, and failure here (see install.sh): https://travis-ci.org/jreback/pandas/jobs/18477137 (note Joris: I think this is OK now with pymysql)
- [x] get MySQL working (failing tests, see https://github.com/pydata/pandas/pull/6316#issuecomment-34960181) -> fixed in #6613
- [x] handle `OperationsError` (e.g. have PyMySQL installed but server is not running) -> #6651
- lower bar on sql alchemy version -> moved to #6340, #7022
- [x] add postgresql tests (#6316) (and other flavors?)
- [x] move DataFrame.to_sql to generic and update it with new functionality -> fixed in #6598
- timezone issues (see also https://github.com/pydata/pandas/pull/5950#issuecomment-33630742) -> moved to #7139
- [x] check if relevant parts of old legacy tests pass with master -> #6987
- [x] check coverage of the test suite
- [ ] add vbench (esp. vs old code)
- [x] `columns` argument in `read_table` not working
- [x] use `itertuples` rather than `iterrows` #6509
- [x] error when having integer column names (fixed in #6902)
- [x] read and write a `MultiIndex` (so `index_col` should take a list as well as a scalar) (fixed in #6735)
- [x] Allow mapping as parameters for SQL DBAPI2 #6708 (fixed in #6709)
- [x] Conversion of invalid column names is broken (you get column with None's) #6796, fixed in #6902
- [x] Data written with to_sql legacy mode (sqlite/mysql) is not persistent (not written, only in memory) #6846, fixed in #6875
There is also a todo list in https://github.com/pydata/pandas/issues/4163, but I thought it is cleaner to start a new one (but maybe we should copy some over).
Complement the list if you think of others!
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} | [] | closed | false | null | [] | null | 4 | 2014-02-07T10:15:35Z | 2014-02-25T12:41:49Z | 2014-02-25T12:41:49Z | MEMBER | null | For example, link provided by google when searching for 'pandas dataframe set_index': http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.set_index.html.
This link does not work anymore, as it appears now the dev docs are hosted on http://pandas-docs.github.io/pandas-docs-travis/
@y-p Would it be possible to redirect or also put the build on the old place?
Maybe google on my computer is somewhat biased because of my browse history, but if I search for certain doc pages through google, it almost always returns the page on the dev docs.
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] | closed | false | null | [] | null | 2 | 2014-02-07T11:17:11Z | 2014-05-05T13:27:25Z | 2014-05-05T13:27:25Z | CONTRIBUTOR | null | Installing from source requires distribute (0.6). In 2013, setuptools and distribute were merged into setuptools 2.1+ : http://pythonhosted.org/setuptools/merge.html
Attemping to install with setuptools 2.1 results in error "global name pkg_resources not found"
Pandas should be installable with setuptools 2.1+
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} | 13 | 2014-02-07T16:21:46Z | 2015-05-09T21:51:59Z | 2015-05-09T21:51:59Z | CONTRIBUTOR | null | I haven't looked at the source yet, but do columns ever get pretty printed? It looks like indices do if it's a DatetimeIndex, but not otherwise.
I often find myself doing
```
df.columns.tolist()
```
otherwise it's not really readable.
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} | 4 | 2014-02-07T19:20:21Z | 2014-02-25T09:00:57Z | 2014-02-08T15:53:33Z | CONTRIBUTOR | null | IMO this should work
```
x = pd.DataFrame(np.random.rand(5,3))
x.iloc[:6]
```
Consider
```
np.arange(5)[:7]
```
And
```
range(5)[:7]
```
I'm on
```
[~/]
[14]: pd.version.version
[14]: '0.13.1-63-gcc6ee40'
```
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} | 2 | 2014-02-07T21:18:04Z | 2014-04-22T13:09:37Z | 2014-04-22T13:09:37Z | NONE | null | rolling_max on a TimeSeries with freq='D' appears to actually compute the rolling mean, and not the rolling max.
``` python
In [118]: import pandas
In [119]: indices = [datetime.datetime(1975, 1, i, 12, 0) for i in range(1, 6)]
In [120]: indices.append(datetime.datetime(1975, 1, 3, 6, 0)) # So that we can have 2 datapoints on one of the days
In [121]: series = pandas.Series(range(1, 7), index=indices)
In [122]: series = series.map(lambda x: float(x)) # Use floats instead of ints as values
In [123]: series = series.sort_index() # Sort chronologically
In [124]: expected_result = pandas.Series([1.0, 2.0, 6.0, 4.0, 5.0], index=[datetime.datetime(1975, 1, i, 12, 0) for i in range(1, 6)])
In [125]: actual_result = pandas.rolling_max(series, window=1, freq='D')
In [126]: assert((actual_result==expected_result).all())
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-126-cc436c4798a7> in <module>()
----> 1 assert((actual_result==expected_result).all())
AssertionError:
In [127]: expected_result
Out[127]:
1975-01-01 12:00:00 1
1975-01-02 12:00:00 2
1975-01-03 12:00:00 6
1975-01-04 12:00:00 4
1975-01-05 12:00:00 5
dtype: float64
In [128]: actual_result
Out[128]:
1975-01-01 1.0
1975-01-02 2.0
1975-01-03 4.5
1975-01-04 4.0
1975-01-05 5.0
Freq: D, dtype: float64
```
With a window of size 2 days, it still looks like the rolling mean:
``` python
In [130]: pandas.rolling_max(series, window=2, freq='D')
Out[130]:
1975-01-01 NaN
1975-01-02 2.0
1975-01-03 4.5
1975-01-04 4.5
1975-01-05 5.0
Freq: D, dtype: float64
```
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} | [] | closed | false | null | [] | null | 1 | 2014-02-07T21:35:24Z | 2014-06-26T12:27:21Z | 2014-02-09T22:40:32Z | MEMBER | null | @mangecoeur I don't think I changed some real content, just some adjustments for the numpy docstring standards. Only for has_table I corrected the parameters.
And the second commit should fix the doc builds (see http://pandas-docs.github.io/pandas-docs-travis/io.html#io-sql)
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} | 7 | 2014-02-07T22:02:01Z | 2014-06-16T19:22:59Z | 2014-02-08T15:53:33Z | CONTRIBUTOR | null | closes #6296
Here's the behavior
I left it raising an `IndexError` if a single indexer is given that is out-of-bounds (consistent with the way `ix/loc` would work)
```
In [1]: df = DataFrame(np.random.randn(5,2),columns=list('AB'))
In [2]: df
Out[2]:
A B
0 1.105982 0.309531
1 -0.619102 -0.002967
2 -0.817371 0.270714
3 1.569206 1.595204
4 0.276390 -1.316232
[5 rows x 2 columns]
In [3]: df.iloc[[4,5,6]]
Out[3]:
A B
4 0.27639 -1.316232
[1 rows x 2 columns]
In [4]: df.iloc[4:6]
Out[4]:
A B
4 0.27639 -1.316232
[1 rows x 2 columns]
In [5]: df.iloc[:,2:3]
Out[5]:
Empty DataFrame
Columns: []
Index: [0, 1, 2, 3, 4]
[5 rows x 0 columns]
In [6]: df.iloc[:,1:3]
Out[6]:
B
0 0.309531
1 -0.002967
2 0.270714
3 1.595204
4 -1.316232
[5 rows x 1 columns]
In [7]: df.iloc[10]
IndexError:
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6300 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6300/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6300/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6300/events | https://github.com/pandas-dev/pandas/issues/6300 | 27,180,503 | MDU6SXNzdWUyNzE4MDUwMw== | 6,300 | API: user api for new sql functionality | {
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} | 47 | 2014-02-07T22:44:41Z | 2014-08-08T09:20:26Z | 2014-08-08T09:20:04Z | MEMBER | null | Starting a new issue to discuss this. See merged PR #5950 and follow-up issue #6292.
Summary of situation:
**Current (0.13)**:
- top-level `read_sql`, `read_frame`, `write_frame` and `DataFrame.to_sql`
- in sql module: `uquery`, `tquery`, `has_table` (+ `read_sql`)
**New after #5950**
- top-level `read_sql` and `DataFrame.to_sql` (but not yet used in the docs)
- in sql module: `read_sql`, `read_table`, `to_sql`, `has_table`, `execute`
- `read_frame`, `write_frame`, `uquery`, `tquery` are deprecated
**Points to dicsuss**
- docs: use top-level functions `read_sql` and `DataFrame.to_sql` instead of functions from `sql`
- `sql.read_table` vs existing `pd.read_table`. Is this a problem?
- merge functionality of `read_table` and `read_sql` or not (see this comment of @mangecoeur to not do that: https://github.com/pydata/pandas/pull/5950#issuecomment-33174797)
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