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} | 0 | 2014-01-19T14:57:34Z | 2014-07-16T08:47:36Z | 2014-01-19T14:57:44Z | NONE | null | cc @paulgb. awesome stuff.
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} | 4 | 2014-01-19T15:10:00Z | 2015-11-01T21:09:58Z | 2015-11-01T21:09:58Z | MEMBER | null | The last example in the "Enhancing performance" docs on cython (http://pandas.pydata.org/pandas-docs/dev/enhancingperf.html#more-advanced-techniques) is failing on Windows.
When I run the example I get a `ValueError: Buffer dtype mismatch, expected 'Py_ssize_t' but got 'long'` error:
```
In [18]: %timeit apply_integrate_f_wrap(df['a'].values, df['b'].values, df['N'].values)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-18-1222be30cf93> in <module>()
----> 1 get_ipython().magic(u"timeit apply_integrate_f_wrap(df['a'].values, df['b'].values, df['N'].values)")
c:\users\vdbosscj\scipy\ipython\IPython\core\interactiveshell.pyc in magic(self, arg_s)
2184 magic_name, _, magic_arg_s = arg_s.partition(' ')
2185 magic_name = magic_name.lstrip(prefilter.ESC_MAGIC)
-> 2186 return self.run_line_magic(magic_name, magic_arg_s)
2187
2188 #-------------------------------------------------------------------------
c:\users\vdbosscj\scipy\ipython\IPython\core\interactiveshell.pyc in run_line_magic(self, magic_name, line)
2105 kwargs['local_ns'] = sys._getframe(stack_depth).f_locals
2106 with self.builtin_trap:
-> 2107 result = fn(*args,**kwargs)
2108 return result
2109
c:\users\vdbosscj\scipy\ipython\IPython\core\magics\execution.pyc in timeit(self, line, cell)
c:\users\vdbosscj\scipy\ipython\IPython\core\magic.pyc in <lambda>(f, *a, **k)
191 # but it's overkill for just that one bit of state.
192 def magic_deco(arg):
--> 193 call = lambda f, *a, **k: f(*a, **k)
194
195 if callable(arg):
c:\users\vdbosscj\scipy\ipython\IPython\core\magics\execution.pyc in timeit(self, line, cell)
1011 number = 1
1012 for _ in range(1, 10):
-> 1013 if timer.timeit(number) >= 0.2:
1014 break
1015 number *= 10
C:\Anaconda\envs\devel\lib\timeit.pyc in timeit(self, number)
193 gc.disable()
194 try:
--> 195 timing = self.inner(it, self.timer)
196 finally:
197 if gcold:
<magic-timeit> in inner(_it, _timer)
C:\Users\vdbosscj\.ipython\cython\_cython_magic_fa1038e4eeefba896480cf749d6fc60d.pyd in _cython_magic_fa1038e4eeefba896480cf749d6fc60d.apply_integrate_f_wrap (C:\Users\vdbosscj\.ipython\cython\_cython_magic_fa1038e4eeefba896480cf749d6fc60d.c:1762)()
ValueError: Buffer dtype mismatch, expected 'Py_ssize_t' but got 'long'
```
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} | [] | closed | false | null | [] | null | 7 | 2014-01-19T15:44:09Z | 2014-06-15T14:16:41Z | 2014-01-24T16:50:52Z | MEMBER | null | This PR updates our local copy of numpydoc to latest upstream version (I just fully copied over that version, only left out setup.py and manifest.in). This would be a solution to the numpydoc part of issue #5221.
The intent would then be, from now on, to not touch this code, apart of pulling an updated version from numpydoc upstream from time to time. If we want to make a change, also send it upstreams to not let the two versions diverge.
**Other options** to solve this issue would be to just add numpydoc as a requirement for the doc building and removing out local option, or to add it as a git submodule. I am slightly in favor of removing our local copy, but I understood from previous comments it would be better to not have more build dependencies (and it would also tie us to a specific version).
I did build the docs with this and all seems OK. From looking at the history, I think only py3compat changes are done to these files after the initial import from Wes (@jtratner, @cpcloud you are the ones who touched these files, do you remember if you did something else than py3compat issues?)
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} | 0 | 2014-01-19T18:44:36Z | 2014-07-16T08:47:39Z | 2014-01-19T19:02:13Z | CONTRIBUTOR | null | closes #5999
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} | 2 | 2014-01-20T03:38:37Z | 2014-01-20T22:19:02Z | 2014-01-20T22:19:02Z | MEMBER | null | https://travis-ci.org/pydata/pandas/jobs/17252239
But why doesn't this show up on py3.x
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} | 30 | 2014-01-20T03:48:55Z | 2014-06-14T13:07:38Z | 2014-01-26T15:31:13Z | CONTRIBUTOR | null | Add `is_reduction` argument to `DataFrame.apply` to avoid undefined behavior when `.apply` is called on an empty DataFrame when function being applied is only defined for valid inputs.
Currently, if the DataFrame is empty, a guess is made at the correct return value (either a `DataFrame` or a `Series`) by calling the function being applied with an empty Series as an argument:
``` python
if not all(self.shape):
# How to determine this better?
is_reduction = False
try:
is_reduction = not isinstance(f(_EMPTY_SERIES), Series)
except Exception:
pass
if is_reduction:
return Series(NA, index=self._get_agg_axis(axis))
else:
return self.copy()
```
For reduction functions which produce undefined results on unexpected input (ex, a function which doesn't expect an empty argument), this means that the the result of `apply` is also undefined.
This pull request adds an explicit `is_reduction` argument so that it's possible to explicitly control this otherwise undefined behavior.
**Update**: there has been the suggestion that the existing `reduce` argument should be used. Is this reasonable? The PR would be updated as follows:
- Remove the `is_reduction` argument
- Change the default value of `reduce` from `True` to `None` (to preserve the current behavior of checking the return value of the function being applied)
- In the case of an empty DataFrame, treat `reduce` in the same way that I'm currently treating `is_reduction`
- Otherwise treat `reduce` as normal
Ref:
- http://stackoverflow.com/q/21225608/71522
- http://stackoverflow.com/q/21225552/71522
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} | 26 | 2014-01-20T06:04:15Z | 2014-06-16T16:32:48Z | 2014-01-25T19:27:51Z | CONTRIBUTOR | null | Only 50 countries show up for the get_countries() call in io.wb, rather than the 256 that actually exist. Looks like a typo in the [World Bank Documentation](http://data.worldbank.org/node/18). The workaround is to request 1000 countries. Running this change by @vincentarelbundock.
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} | [] | closed | false | null | [] | null | 1 | 2014-01-20T14:09:35Z | 2014-01-20T14:48:56Z | 2014-01-20T14:47:05Z | NONE | null | In Excel, it's very common to add conditional formatting to a range of data (either a single column, or a 2-d table). In newer versions of Excel, there are handy buttons to apply a full gradient as a background color to a range of data to highlight high and low values.
I wonder if we can easily provide a similar kind of functionality for Pandas. Two general strategies immediately came to mind for me, and I'm sure there will be other suggestions, too. But just to get the ball rolling:
1) We could provide a DataFrame method (maybe one for Series, too) that produces a Matplotlib heatmap with overlaid values as text. See:
http://stackoverflow.com/a/11918066/2501018
http://stackoverflow.com/a/21167108/2501018
2) (I really have no idea if this is possible, but just to throw it out there...) We could augment the HTML representation of a DataFrame to include the ability to use background colors for certain cells of the table.
It seems like either way there would be some general questions to answer/functionalities to provide:
-Color all cells, or just certain columns/rows?
-Color based on value or based on an arbitrary function of value (for example abs)
-Color cells in relation to their own column/row or in relation to all values in the table
What do you guys think? Is this a general enough problem to merit a solution within Pandas? Or is this perhaps something better suited to Matplotlib? Or should I just write functions that accept DataFrame input and create Matplotlib output in line with the SO posts above?
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} | 1 | 2014-01-20T15:35:48Z | 2014-02-15T20:57:10Z | 2014-02-15T20:57:10Z | NONE | null | Various places in pandas convert a series of datetime strings to datetime64/timestamps/etc' objects. read_csv, to_datetime, upcoming
sql improvements and probably elsewhere.
The code paths each takes are seperate so that fast paths aren't consistently available, and it's difficult to apply improvements such as #6021 in one place
and have them apply everywhere. and there's room for internal incosnistencies in parsing behaviour (#5958).
https://github.com/pydata/pandas/pull/5950/files#r9007497
https://github.com/pydata/pandas/pull/4826
https://github.com/pydata/pandas/pull/5993
Do something about it.
cc @jreback , @danbirken
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} | [] | closed | false | null | [] | null | 22 | 2014-01-20T16:50:43Z | 2014-07-19T14:26:36Z | 2014-01-23T17:31:35Z | NONE | null | Using the latest Pandas "pandas (0.13.0-246-g1e1907c)", one critical bug is still existed.
For instance, if tuples has some results from SQL and is converted to Dataframe, the first row of the tuples is added "twice" times into Dataframe as below.
qr = <tuples from SQL Select result> # number of records : 100
df = pd.DataFrame.from_records(qr) # number of records : 101
In the log, you can find the first and second row have been duplicated.
_redacted_
As a result, diggling down the source code "frame.py", some parts caused this issue.
Based on the latest version "frame.py", the line number is around 756.
Whatever happened, the list "values" has [first_row] around 756 line, and then the whole "data" is added into "value" list again around 760 ~ 769 line.
It make the first and second row duplicated.
Could you please fix this issue and then update the master branch as well ?
751 dtype = None
752 if hasattr(first_row, 'dtype') and first_row.dtype.names:
753 print("hasattr dtype")
754 dtype = first_row.dtype
755
756 #values = [first_row] ## caused the duplicated first and second row
757 values = [] ## should be fixed
758
759 # if unknown length iterable (generator)
760 if nrows is None:
761 # consume whole generator
762 values += list(data)
763 else:
764 #i = 1 ## caused the duplicated first and second row
i = 0 ## should be fixed
765 for row in data:
766 values.append(row)
767 i += 1
768 if i >= nrows:
769 break
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} | 6 | 2014-01-20T16:58:10Z | 2014-10-01T12:09:00Z | 2014-10-01T12:09:00Z | NONE | null | looked at this this morning some. In the _get_option_data function, when building the url for yahoo, if I try to get current month but forward year, I get the +Options url which returns nearest expiry. The way I quickly fixed it, but i'm not sure its the best way
```
if year != CUR_YEAR:
url += '&m={year}-{m1}'.format(year=year, m1=m1)
else:
if m1 != CUR_MONTH and m2 != CUR_MONTH:
url += '&m={year}-{m1}'.format(year=year, m1=m1)
else:
url += '+Options'
else: # Default to current month
url += '+Options'
```
I apologize in advance for any bad formatting or etiquette as I'm only just starting to use github.
Thanks
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} | 1 | 2014-01-20T16:59:54Z | 2014-05-18T19:01:23Z | 2014-01-21T21:44:21Z | NONE | null | On this page:
http://pandas.pydata.org/pandas-docs/stable/enhancingperf.html#enhancingperf
Right after In[11]: displays timing results, the text suggests we have seen a 10x speedup compared with the original code. It looks like the original code runs in 336ms/loop. And the code executed in In[11] executes in 105ms/loop.
So only 3x speedup, right?
(Edit) And then later on, after In[14], the text suggests a 3x speedup but that run seems to take execution from 105ms/loop down to 2.5ms/loop = 40x speedup...
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#The legend is sorted 'A, B, C', but the bar segments are sorted 'C, B, A'.
d = {'A': [2,3], 'B': [4,5], 'C': [6,7]}
df = pd.DataFrame(data=d)
df.plot(kind='bar', stacked=True)
```
Thanks, and here's my full version string:
## INSTALLED VERSIONS
Python: 2.7.6.final.0
OS: Darwin
Release: 13.0.0
Processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.0
Cython: Not installed
Numpy: 1.8.0
Scipy: 0.13.1
statsmodels: Not installed
patsy: Not installed
scikits.timeseries: Not installed
dateutil: 2.2
pytz: 2013.9
bottleneck: Not installed
PyTables: Not Installed
numexpr: Not Installed
matplotlib: 1.3.1
openpyxl: Not installed
xlrd: Not installed
xlwt: Not installed
xlsxwriter: Not installed
sqlalchemy: Not installed
lxml: Not installed
bs4: Not installed
html5lib: Not installed
bigquery: Not installed
apiclient: Not installed
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} | 2 | 2014-01-20T17:53:11Z | 2014-03-06T01:26:16Z | 2014-02-07T12:01:27Z | NONE | null | Additionally to the broken replace option ( https://github.com/pydata/pandas/issues/4110 ) the sql.write_frame function does not work anymore for me with 0.13 when trying to write a Dataframe into a SQLite DB:
```
sql.write_frame(frame, name=name, con=conn, if_exists='replace')
```
results in :
``` python
File "/usr/local/lib/python2.7/dist-packages/pandas/io/sql.py", line 228, in write_frame
func(frame, name, safe_names, cur)
File "/usr/local/lib/python2.7/dist-packages/pandas/io/sql.py", line 244, in _write_sqlite
cur.executemany(insert_query, data)
sqlite3.InterfaceError: Error binding parameter 0 - probably unsupported type.
```
The same code did work before without issues.
EDIT: I solved the problem by adding:
``` python
frame['index']=frame['index'].astype(long)
```
The index column was stored as datetime64 dtype before. Still don't know why my code broke, before it worked without type conversion.
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} | 17 | 2014-01-20T20:05:35Z | 2017-05-15T21:15:51Z | 2014-02-17T17:46:58Z | CONTRIBUTOR | null | Close https://github.com/pydata/pandas/issues/5543
If there's any interest for this in 0.14, here's the code.
Thoughts on the method name? A regular difference is `diff` so I want to be consistent with that. I figured it would be a bit weird to truncate `difference` to `diff` but not truncate `symmetric` to `sym` (or `symm`) so I went with `sym_diff` (python sets use `symmetric_difference`).
Also when writing the tests, I discovered that NaNs are _even_ weirder than I thought:
``` python
In [20]: idx = pd.Index([np.nan, 1, np.nan])
In [21]: sorted(set(idx))
Out[21]: [nan, 1.0, nan]
```
Seems like neither `set` nor `sorted` did anything. Anyway my PR should be consistent with this weirdness.
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} | 4 | 2014-01-20T22:00:24Z | 2014-07-16T08:47:48Z | 2014-01-20T22:19:02Z | NONE | null | No idea why all the travis builds didn't break sooner, only numpy < 1.8
apperently forgives missing atlas at compilation time? don't know.
six errors are a red herring.
closes #6006
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} | 1 | 2014-01-20T22:18:16Z | 2014-01-21T14:42:07Z | 2014-01-21T14:42:07Z | CONTRIBUTOR | null | Slicing in Series with MultiIndex does not seem to work on master with .ix or .loc. This is not comparable with previous versions.
``` python
In [54]:
s = pd.Series([1,2,3])
s.index = pd.MultiIndex.from_tuples([(0,0),(1,1), (2,1)])
s[:,0]
Out[55]:
1 2
2 3
In [56]:
s.ix[:,1]
IndexingError
Traceback (most recent call last)
<ipython-input-56-3163789d3245> in <module>()
----> 1 s.ix[:,1]
/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_120_gdd89ce4-py2.7-linux-x86_64.egg/pandas/core/indexing.pyc in __getitem__(self, key)
52 pass
53
---> 54 return self._getitem_tuple(key)
55 else:
56 return self._getitem_axis(key, axis=0)
/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_120_gdd89ce4-py2.7-linux-x86_64.egg/pandas/core/indexing.pyc in _getitem_tuple(self, tup)
593
594 # no multi-index, so validate all of the indexers
--> 595 self._has_valid_tuple(tup)
596
597 # ugly hack for GH #836
/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_120_gdd89ce4-py2.7-linux-x86_64.egg/pandas/core/indexing.pyc in _has_valid_tuple(self, key)
103 for i, k in enumerate(key):
104 if i >= self.obj.ndim:
--> 105 raise IndexingError('Too many indexers')
106 if not self._has_valid_type(k, i):
107 raise ValueError("Location based indexing can only have [%s] "
IndexingError: Too many indexers
```
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} | 2 | 2014-01-20T22:31:01Z | 2018-03-29T10:29:26Z | 2018-03-29T10:29:26Z | NONE | null | When attempting to use pandas.read_hdf() to read a link from an h5py created data file with an ExternalLink to an HDFStore() created entry, the following backtrace is received:
/Users/xxx/.pyenv/versions/anaconda/lib/python2.7/site-packages/pandas/io/pytables.pyc in select(self, key, where, start, stop, columns, iterator, chunksize, auto_close, **kwargs)
618 # create the storer and axes
619 where = _ensure_term(where)
--> 620 s = self._create_storer(group)
621 s.infer_axes()
622
/Users/xxx/.pyenv/versions/anaconda/lib/python2.7/site-packages/pandas/io/pytables.pyc in _create_storer(self, group, format, value, append, **kwargs)
1119 )
1120
-> 1121 pt = _ensure_decoded(getattr(group._v_attrs, 'pandas_type', None))
1122 tt = _ensure_decoded(getattr(group._v_attrs, 'table_type', None))
1123
/Users/xxx/.pyenv/versions/anaconda/lib/python2.7/site-packages/tables/link.pyc in **getattr**(self, name)
77 def **getattr**(self, name):
78 raise KeyError("you cannot get attributes from this "
---> 79 "`%s` instance" % self.**class**.**name**)
80
81 def **setattr**(self, name, value):
KeyError: 'you cannot get attributes from this `NoAttrs` instance'
In this example, 'store.h5' was created with HDFStore() and a single Series was stored in it at the location '/banana'. h5py.File was then used to create 'external.h5' with a single entry for /external that pointed to store.h5:/banana using h5py.ExternalLink.
In my use case, I had written code to parse and store some complex data and associated metadata in hdf5 using h5py. My intention was then to read that raw data in using pandas and then re-store the cooked data using HDFStore.
Unfortunately, pandas HDFStore() did not like the metadata in my h5py written file.
--snip--
/Users/xxx/.pyenv/versions/anaconda/lib/python2.7/site-packages/tables/attributeset.py:294: DataTypeWarning: Unsupported type for attribute 'is_key' in node 'some_node'. Offending HDF5 class: 8
value = self._g_getattr(self._v_node, name)
--snip--
I expected that I could work around this by simply storing the pandas content in a native HDFStore() written file and use ExternalLinks in the parent file. Unfortunately, that did not work properly either.
This is with pandas '0.13.0'.
Thanks!
-jbl
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} | 14 | 2014-01-20T23:59:14Z | 2014-06-15T00:41:51Z | 2014-01-20T23:59:21Z | NONE | null | closes #1876
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} | 28 | 2014-01-21T02:13:15Z | 2014-06-12T16:18:50Z | 2014-01-24T21:49:48Z | CONTRIBUTOR | null | closes #5490
Basically this attempts to figure out the the datetime format if given a list of datetime strings. If the format can be guessed successfully, then to_datetime() attempts to use a faster way of processing the datetime strings.
Here is a gist that shows some timings of this function: https://gist.github.com/danbirken/8533199
If the format can be guessed, this generally speeds up the processing by 10x. In the case where the format string is guessed to be an iso8601 string or subset of that, then the format is _not_ used, because the iso8601 fast-path is much faster than this (about 30-40x faster).
I just did a pretty basic way of guessing the format using only dateutil.parse, a simple regex and some simple logic. This doesn't use any private methods of dateutil. It certainly can be improved, but I wanted to get something started first before I went crazy having to handle more advanced cases.
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} | 0 | 2014-01-21T14:11:10Z | 2014-06-24T12:46:16Z | 2014-01-21T14:42:07Z | CONTRIBUTOR | null | closes #6018
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"updated_at": "2016-12-29T13:57:13Z",
"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/24"
} | 8 | 2014-01-21T15:04:47Z | 2014-03-18T14:59:47Z | 2014-03-18T14:59:47Z | MEMBER | null | Try the following:
```
In [1]: import pandas
In [2]: df = pandas.read_csv('http://pietrobattiston.it/c/test.csv')
In [3]: df_n = df[(df.cod >= 6001) & (df.cod <= 6012)]
In [4]: df_n['mese'] = ((df_n.cod - 6000) % 12) + 1
In [5]: ind = ['chi', 'anno', 'loc']
In [6]: df_n['cassa_t'] = 0
In [7]: df_ind = df.set_index(ind)
In [8]: df_n_ind = df_n.set_index(ind)
In [9]: df_n_ind['cassa_t'][df_n_ind.mese == 5] += df_ind[df_ind.cod == 6031]['quanto']
In [10]: df_n_ind['cassa_t'][df_n_ind.mese == 8] += df_ind[df_ind.cod == 6032]['quanto']
In [11]: df_n_ind[df_n_ind.cassa_t > 10].head()
Out[11]:
Unnamed: 0 cosa cod quanto mese cassa_t
chi anno loc
1edcd 2009 3b049 720 b4087 6004 98.765671 5 0
2010 3b049 721 b4087 6004 88.157426 5 0
2011 3b049 722 b4087 6004 87.671619 5 0
2012 3b049 723 b4087 6004 48.458905 5 0
2013 3b049 724 b4087 6004 40.620916 5 0
[5 rows x 6 columns]
```
Notice the filtering criterion!
Everything works smoothly instead if at step 6 you replace "0" with "float(0)". So I do know where the problem comes from: the sliced assignment between different types at steps 9 and 10 (nothing changes if you replace "+=" with "="). Still, I think this is a quite unexpected behaviour.
I'm currently not able to reproduce, but at some time I also got some very strange numbers after similar assignments, like if the memory representation of floats had been considered as an integer representation, or vice-versa.
Tested with updated git (commit ae2dd935dd673f1a72a7ab580a97fbe8f0e5fd81 ).
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https://api.github.com/repos/pandas-dev/pandas/issues/6024 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6024/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6024/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6024/events | https://github.com/pandas-dev/pandas/pull/6024 | 26,007,508 | MDExOlB1bGxSZXF1ZXN0MTE3MTYyMDE= | 6,024 | PERF: apply perf enhancements | {
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} | 0 | 2014-01-21T15:16:05Z | 2014-06-26T18:44:46Z | 2014-01-21T21:44:21Z | CONTRIBUTOR | null | added vbenches and make most apply operations a fair bit faster
closes #6013
I can't put this one in the vbench as its cython based, but previously was about 100ms (on my machine)
0.12 clocks in at 15ms (the remaining difference in time is due to the sub-indexing, e.g. x['a'] which in theory could also be speeded but a bit tricky and not a lot more to win)
```
In [6]: %timeit df.apply(lambda x: integrate_f_typed(x['a'], x['b'], x['N']), axis=1)
10 loops, best of 3: 26.2 ms per loop
```
Here's a related benchmark from #5654 (a version of which is in the vbenches):
```
In [16]: s = pd.Series(np.arange(4096.))
In [17]: df = pd.DataFrame({i:s for i in range(4096)})
```
0.12
```
In [18]: %timeit df.apply((lambda x: np.corrcoef(x, s)[0, 1]), axis=1)
1 loops, best of 3: 1.11 s per loop
```
with this PR
```
In [3]: In [18]: %timeit df.apply((lambda x: np.corrcoef(x, s)[0, 1]), axis=1)
1 loops, best of 3: 1.24 s per loop
```
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
frame_apply_ref_by_name | 15.4320 | 32.2250 | 0.4789 | like the cython example above
frame_apply_user_func | 166.7013 | 232.7064 | 0.7164 | bench for example right above
frame_apply_axis_1 | 108.5210 | 109.1857 | 0.9939 |
frame_apply_pass_thru | 6.5626 | 6.4860 | 1.0118 |
frame_apply_np_mean | 3.9033 | 0.9290 | 4.2018 | this suffers from some construction overhead, not much I can do
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6025 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6025/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6025/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6025/events | https://github.com/pandas-dev/pandas/issues/6025 | 26,013,727 | MDU6SXNzdWUyNjAxMzcyNw== | 6,025 | SettingWithCopyWarning for string manipulation in dataframes with a non-monotonic index | {
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} | 2 | 2014-01-21T16:36:28Z | 2014-01-23T08:49:55Z | 2014-01-23T02:24:57Z | NONE | null | Hi,
I get a SettingWithCopyWarning for string manipulation in dataframes with a non-monotonic index.
```
SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_index,col_indexer] = value instead
```
This only happens if the integer index skips a value. The code to reproduce:
```
from pandas import DataFrame
# create example dataframe:
df = DataFrame ({'column1':['a', 'a', 'a'], 'column2': [4,8,9] })
df
# assign string to 'column1':
df['column1'] = df['column1'] + 'b'
df
# it works just fine - no warnings
#now remove one line from dataframe df:
df = df [df['column2']!=8]
df
# adding string to 'column1' gives warning:
df['column1'] = df['column1'] + 'c'
df
```
NB: this only happens after a fresh login into python; if you run it again, it will not appear any more (!)
System:
- Pandas 0.13
- Python 2.7.2
- Ubuntu 12.04LTS 64bit
Is this is an actual issue or just a nonsensical warning?
Kind regards,
Egle
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https://api.github.com/repos/pandas-dev/pandas/issues/6026 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6026/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6026/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6026/events | https://github.com/pandas-dev/pandas/issues/6026 | 26,016,253 | MDU6SXNzdWUyNjAxNjI1Mw== | 6,026 | Segfault when modifying pandas.DataFrame in-place after creating from numpy recarray | {
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} | 6 | 2014-01-21T17:09:39Z | 2014-01-24T12:57:56Z | 2014-01-21T23:06:39Z | CONTRIBUTOR | null | The following code generates a segfault when `use_records` is True. This segfault only occurs when the DataFrame is generated from a record array and then I attempt to modify the series in-place. This was not an issue in the previous release (v0.12) of Pandas. Tested this code against 0.13.0-268-g08c1302.
```
import numpy as np
import pandas
data = [('right', 'left', 'left', 'left', 'right', 'left', 'timeout')]
use_records = True
if use_records:
recarray = np.rec.fromarrays(data, names=['response'])
df = pandas.DataFrame(recarray)
else:
df = pandas.DataFrame({'response': data[0]})
mask = df.response == 'timeout'
df.response[mask] = 'none'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6027 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6027/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6027/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6027/events | https://github.com/pandas-dev/pandas/issues/6027 | 26,021,325 | MDU6SXNzdWUyNjAyMTMyNQ== | 6,027 | BUG: strange behaviour of .index.min() | {
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} | [] | closed | false | null | [] | null | 1 | 2014-01-21T18:19:17Z | 2014-01-21T18:46:10Z | 2014-01-21T18:45:54Z | CONTRIBUTOR | null | I was expecting `df.index.min()` here to be equal to `min(df.index)`, and I guess it is; but I wasn't expecting this:
```
>>> import pandas as pd
>>> pd.__version__
'0.13.0-268-g08c1302'
>>> df = pd.DataFrame({"A": [1]})
>>> df.index
Int64Index([0], dtype='int64')
>>> min(df.index)
0
>>> type(min(df.index))
<type 'numpy.int64'>
>>> m = df.index.min()
>>> m == 0
True
>>> type(m)
<class 'pandas.core.index.Int64Index'>
>>> m
[...]
File "/usr/local/lib/python2.7/dist-packages/pandas-0.13.0_268_g08c1302-py2.7-linux-i686.egg/pandas/core/common.py", line 2570, in as_escaped_unicode
result = compat.text_type(thing) # we should try this first
File "/usr/local/lib/python2.7/dist-packages/pandas-0.13.0_268_g08c1302-py2.7-linux-i686.egg/pandas/core/base.py", line 201, in __unicode__
quote_strings=True)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.13.0_268_g08c1302-py2.7-linux-i686.egg/pandas/core/common.py", line 2597, in pprint_thing
elif _is_sequence(thing) and _nest_lvl < \
File "/usr/local/lib/python2.7/dist-packages/pandas-0.13.0_268_g08c1302-py2.7-linux-i686.egg/pandas/core/common.py", line 2100, in _is_sequence
iter(x)
RuntimeError: maximum recursion depth exceeded while calling a Python object
```
The same thing applies to `.max()`.
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https://api.github.com/repos/pandas-dev/pandas/issues/6028 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6028/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6028/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6028/events | https://github.com/pandas-dev/pandas/pull/6028 | 26,023,220 | MDExOlB1bGxSZXF1ZXN0MTE3MjU0NzE= | 6,028 | BUG: Bug in Series construction of mixed type with datelike and an integer | {
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} | 0 | 2014-01-21T18:43:34Z | 2014-06-19T14:23:55Z | 2014-01-21T19:26:52Z | CONTRIBUTOR | null | should result in object type and not automatic conversion
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https://api.github.com/repos/pandas-dev/pandas/issues/6029 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6029/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6029/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6029/events | https://github.com/pandas-dev/pandas/issues/6029 | 26,049,535 | MDU6SXNzdWUyNjA0OTUzNQ== | 6,029 | ENH: read_html has no timeout | {
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} | 16 | 2014-01-21T22:15:37Z | 2014-01-27T01:50:11Z | 2014-01-27T01:50:11Z | CONTRIBUTOR | null | Is there any reason why a user cannot pass in a timeout to read_html? I have a bunch of scripts that parse some tables on the web and was having an issue with a site when I noticed it.
If I add a pr for timeout, how would I test it?
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https://api.github.com/repos/pandas-dev/pandas/issues/6030 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6030/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6030/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6030/events | https://github.com/pandas-dev/pandas/issues/6030 | 26,049,815 | MDU6SXNzdWUyNjA0OTgxNQ== | 6,030 | DOC: autosummary table summary lines (sphinx 1.2.1) | {
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] | closed | false | null | [] | null | 7 | 2014-01-21T22:19:40Z | 2014-11-09T14:48:09Z | 2014-11-09T14:37:13Z | MEMBER | null | Related to #5899.
With the release of Sphinx 1.2.1, there is also a change in the behaviour of `sphinx.ext.autosummary` in how to define the text shown in the autosummary table (the tables you see here: http://pandas.pydata.org/pandas-docs/dev/api.html).
See:
- sphinx issue: https://bitbucket.org/birkenfeld/sphinx/issue/943/autosummary-doesnt-always-get-first
- http://stackoverflow.com/questions/10718846/use-first-paragraph-instead-of-first-line-in-sphinx-autosummary
**Summary**: the regex to define the part (idea: the first sentence) of the docstring that is used as the summary line in the tables has changed to allow the first sentence to be longer than the first line.
**Consequence for pandas**: The new regex searches for the end of the first sentence on all lines. In case of pandas docstrings, which are not that cleaned up to always have a period `.` at the end of the first line, this will sometimes give a first sentence including part of the parameter explanations.
Result: messy table in api.html
**To do**: go over all docstrings of public functions to adapt first sentence.
They should look like this:
```
Short summary line.
Optionally longer explanation.
bla bla
Parameters
----------
...
```
where especially the period `.` after the first short line is important.
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https://api.github.com/repos/pandas-dev/pandas/issues/6031 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6031/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6031/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6031/events | https://github.com/pandas-dev/pandas/pull/6031 | 26,050,929 | MDExOlB1bGxSZXF1ZXN0MTE3MzUxMDU= | 6,031 | BUG: Possible segfault when chained indexing with an object array under numpy 1.7.1 (GH6016) | {
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} | 1 | 2014-01-21T22:36:12Z | 2014-06-23T22:12:48Z | 2014-01-21T23:06:39Z | CONTRIBUTOR | null | closes #6026
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https://api.github.com/repos/pandas-dev/pandas/issues/6032 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6032/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6032/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6032/events | https://github.com/pandas-dev/pandas/issues/6032 | 26,062,243 | MDU6SXNzdWUyNjA2MjI0Mw== | 6,032 | Creating Series from DatetimeIndex w/ tz loses tz info | {
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} | 29 | 2014-01-22T02:22:44Z | 2014-02-20T14:23:08Z | 2014-02-19T14:30:24Z | CONTRIBUTOR | null | The original `DatetimeIndex` has the `tz`:
```
In [1]: pd.DatetimeIndex(
...: pd.tseries.tools.to_datetime(['2013-1-1 13:00'], errors="raise")).tz_localize('US/Pacific')[0]
Out[1]: Timestamp('2013-01-01 13:00:00-0800', tz='US/Pacific')
```
But when converted to a `Series`, the `tz` info is lost:
```
In [2]: pd.Series(pd.DatetimeIndex(
...: pd.tseries.tools.to_datetime(['2013-1-1 13:00'], errors="raise")).tz_localize('US/Pacific'))[0]
Out[2]: Timestamp('2013-01-01 21:00:00', tz=None)
```
If this is by design, then a warning would be nice.
This issue came up when I tried to add a list of timezone aware dates to a DataFrame.
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} | 2 | 2014-01-22T05:12:49Z | 2014-01-23T03:55:50Z | 2014-01-23T03:55:50Z | CONTRIBUTOR | null | ```
In [694]:
series = pd.Series([pd.NaT, '1'])
series[~pd.isnull(series)]
Out[694]:
0 NaN
1 1
dtype: object
```
Possibly related: https://github.com/pydata/pandas/issues/5967#issuecomment-32468477
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} | 7 | 2014-01-22T08:54:47Z | 2014-06-23T14:29:47Z | 2014-01-31T07:46:11Z | CONTRIBUTOR | null | Existing `pandas.core.format.CSVFormatter` param `quotechar` can now be
specified as keyword arg to `DataFrame.to_csv`
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} | 10 | 2014-01-22T09:55:58Z | 2016-08-01T10:16:55Z | 2016-08-01T10:16:54Z | CONTRIBUTOR | null | I am using anaconda python 2.7, pandas 0.12.0, with `from __future__ import unicode_literals`
#### The bug
When I call `dataframe.to_csv(fname, sep=sep, index=index, float_format=float_format, **csvkwargs)` and pass in `sep=','`, pandas throws an exception:
```
File "C:\Anaconda\lib\site-packages\pandas\core\frame.py", line 1410, in to_csv
formatter.save()
File "C:\Anaconda\lib\site-packages\pandas\core\format.py", line 959, in save
quoting=self.quoting)
File "C:\Anaconda\lib\site-packages\pandas\core\common.py", line 1770, in __init__
self.writer = csv.writer(self.queue, dialect=dialect, **kwds)
TypeError: "delimiter" must be an 1-character string
```
#### The workaround
---
If I change this to:
`dataframe.to_csv(fname, sep=str(sep), index=index, float_format=float_format, **csvkwargs)` everything works as you might expect. (I have observerved this error both when I pass in no encoding and when I pass in `encoding='Windows 1252'`)
#### What I would expect
---
When passed unicode, pandas should:
- If encoding is set, and the unicode `sep` is one character long i.e. `len(sep)==1` (is it really necessary to have this one character restriction?), use the `sep` character passed in (encoded appropriately)
- If encoding is not set, then check that the default encoding (I don't know whether this is ascii or platform dependent) of `sep` is only one character long
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} | [] | closed | false | null | [] | null | 9 | 2014-01-22T10:59:35Z | 2014-01-23T04:56:45Z | 2014-01-22T13:40:36Z | CONTRIBUTOR | null | Nosetests results in the segmentation fault error:
```
$ nosetests pandas
................/Users/v/.virtual_envs/system/lib/python2.7/site-packages/pandas/core/index.py:910: RuntimeWarning: tp_compare didn't return -1 or -2 for exception
result.sort()
................../Users/v/.virtual_envs/system/lib/python2.7/site-packages/numpy/core/fromnumeric.py:788: RuntimeWarning: tp_compare didn't return -1 or -2 for exception
a.sort(axis, kind, order)
..................................................................................................................................................................................................................................F....................................S...S.....S..........................................SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS..................................................................................................................................................................................................................................................................................................................................................................S....................................................................................................................................S..........S......................................................................................................................................SSSS............................S................................................................SSSSSSSSSS......................SS.....................................................................................................................................................S...Segmentation fault: 11
```
It crashes on the following test:
```
...
test_binop_maybe_preserve_name (pandas.sparse.tests.test_sparse.TestSparseSeries) ... ok
test_combine_first (pandas.sparse.tests.test_sparse.TestSparseSeries) ... ok
test_combine_first_dt64 (pandas.sparse.tests.test_sparse.TestSparseSeries) ... ok
test_combine_first_name (pandas.sparse.tests.test_sparse.TestSparseSeries) ... Segmentation fault: 11
```
Operating system: OSX 10.9.1 Mavericks. Library versions:
```
>>> show_versions()
INSTALLED VERSIONS
------------------
Python: 2.7.6.final.0
OS: Darwin
Release: 13.0.0
Processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.13.0
Cython: 0.20
Numpy: 1.9.0.dev-783b96d
Scipy: 0.13.2
statsmodels: 0.5.0
patsy: 0.2.1
scikits.timeseries: Not installed
dateutil: 2.1
pytz: 2013.9
bottleneck: 0.8.0
PyTables: 3.0.0
numexpr: 2.2.2
matplotlib: 1.1.1
openpyxl: 1.8.2
xlrd: 0.9.2
xlwt: Not installed
xlsxwriter: Not installed
sqlalchemy: 0.9.1
lxml: 3.2.5
bs4: 4.3.2
html5lib: 0.999
bigquery: Not installed
apiclient: Not installed
```
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} | [] | closed | false | null | [] | null | 2 | 2014-01-22T15:25:01Z | 2014-01-22T15:36:31Z | 2014-01-22T15:36:31Z | NONE | null | This minimal code crashes my Python. (Setting: pandas 0.13.0, python 2.7.3 AMD64, Win7.)
``` python
import pandas as pd
input_file = r"c3.csv"
input_df = pd.read_csv(input_file)
for col in input_df.columns: # strip whitespaces from string values
if input_df[col].dtype == object:
input_df[col] = input_df[col].apply(lambda x: x.strip())
print 'start'
for idx in range(len(input_df)):
input_df['LL'].iloc[idx] = 3
print idx
print 'finished'
```
Output:
```
start
0
Process finished with exit code -1073741819
```
What prevents the crash:
1. Removing lines from c3.csv.
2. Removing `.strip()` from the code.
3. Changing c3.csv changes the amount of `for` iterations until the crash in unexpected ways.
Contents of c3.csv:
<code>
Size , B/S , Symbol , Type , BN , Duration , VR , Time , SR ,LL,
0, xxxx , xxxx0 , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
0, xxxx , xxxxx , ,, xxx , 00000 , 00:00:00 , 000000000 , 00:00:00 ,
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https://api.github.com/repos/pandas-dev/pandas/issues/6038 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6038/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6038/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6038/events | https://github.com/pandas-dev/pandas/pull/6038 | 26,107,191 | MDExOlB1bGxSZXF1ZXN0MTE3NjY0MjE= | 6,038 | Make -NaN and -nan default NA values | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6039 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6039/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6039/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6039/events | https://github.com/pandas-dev/pandas/issues/6039 | 26,116,537 | MDU6SXNzdWUyNjExNjUzNw== | 6,039 | Boolean indexer assginment error | {
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} | [] | closed | false | null | [] | null | 5 | 2014-01-22T19:39:34Z | 2014-01-22T22:46:53Z | 2014-01-22T19:58:33Z | NONE | null | I'm new to Python and Pandas. Can someone help me understand the following. After installing pandas 0.13.0, the following code will crash python. I tested it in pandas 0.12.0, and it's OK.
Thank you!
``` python
import pandas as pd
df = pd.DataFrame(range(100), columns=['a'])
msk = df['a']<5
df['a']=df['a'].map(str)
df['a'][msk]='1'
print msk
```
Here're the current packages versions:
``` python
from pandas.util.print_versions import show_versions
show_versions()
INSTALLED VERSIONS
------------------
Python: 2.7.6.final.0
OS: Linux
Release: 2.6.18-308.4.1.el5
Processor: x86_64
byteorder: little
LC_ALL: C
LANG: en_US.UTF-8
pandas: 0.13.0
Cython: 0.19.2
Numpy: 1.7.1
Scipy: 0.13.2
statsmodels: 0.5.0
patsy: 0.2.1
scikits.timeseries: Not installed
dateutil: 1.5
pytz: 2013b
bottleneck: Not installed
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: Not installed
sqlalchemy: 0.8.3
lxml: 3.2.3
bs4: 4.3.1
html5lib: Not installed
bigquery: Not installed
apiclient: Not installed
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6040 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6040/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6040/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6040/events | https://github.com/pandas-dev/pandas/pull/6040 | 26,128,221 | MDExOlB1bGxSZXF1ZXN0MTE3Nzg5NTg= | 6,040 | TST: fix test that repr returns str | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6041 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6041/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6041/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6041/events | https://github.com/pandas-dev/pandas/pull/6041 | 26,131,371 | MDExOlB1bGxSZXF1ZXN0MTE3ODA4NDg= | 6,041 | ENH add show_dimensions display config | {
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} | 14 | 2014-01-22T23:09:30Z | 2014-06-21T02:57:57Z | 2014-01-26T12:47:58Z | CONTRIBUTOR | null | see http://stackoverflow.com/questions/21293983/disabling-dataframe-dimensions-printing
If you've already done this @DSM054 apologies, just saw your comment... :s
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} | 2 | 2014-01-22T23:20:22Z | 2014-06-19T06:37:29Z | 2014-01-23T02:24:57Z | CONTRIBUTOR | null | closes #6025
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} | 2 | 2014-01-23T00:19:54Z | 2014-01-23T08:20:10Z | 2014-01-23T01:30:42Z | CONTRIBUTOR | null | I find it convenient to store lists or series as elements in a dataframe. However, it seems panda gets confused if the iterable is the same length as the number of rows in the dataframe. Here's a minimal example:
``` python
matchmat_test = pd.DataFrame(index=[0,1],
columns=[1], dtype=list)
matchmat_test.ix[0,0] = [1,2,3]
```
Returns:
```
0
0 [1, 2, 3]
1 NaN
```
If we then assign to the same element again:
``` python
matchmat_test.ix[0,0] = [1,2]
```
we get:
```
0
0 1
1 2
```
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} | 0 | 2014-01-23T01:15:41Z | 2014-06-15T17:31:01Z | 2014-01-23T01:30:42Z | CONTRIBUTOR | null | closes #6043
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} | 0 | 2014-01-23T15:47:32Z | 2014-07-16T08:22:53Z | 2014-01-23T15:48:05Z | NONE | null | ipython is still deciding what the proper convention is for supressing the warning.
turns out None has worked for previous versions as well, so let's just do that.
closes #5922
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``` python
import pandas as pd
import psutil
import gc
print('Before', psutil.phymem_usage())
df = pd.DataFrame({'a': range(int(4e7))})
print('After', psutil.phymem_usage())
del df
gc.collect()
print('After deleting', psutil.phymem_usage())
```
and got these results
```
('Before', usage(total=6132502528L, used=2558156800L, free=3574345728L, percent=26.5))
('After', usage(total=6132502528L, used=4144177152L, free=1988325376L, percent=52.9))
('After deleting', usage(total=6132502528L, used=3503177728L, free=2629324800L, percent=42.5))
```
Is it a memory leak or am I doing something wrong?
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} | 0 | 2014-01-23T15:55:50Z | 2014-01-26T16:11:50Z | 2014-01-26T16:11:50Z | CONTRIBUTOR | null | rc1 revealed some bugs/API changes; hopefully this will not cause a need for an API change w.r.t 3.1
This may be a blocker for 0.13.1 (as may need to put in some checks for this)
https://github.com/PyTables/PyTables/issues/326
https://github.com/PyTables/PyTables/issues/327
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https://api.github.com/repos/pandas-dev/pandas/issues/6048 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6048/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6048/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6048/events | https://github.com/pandas-dev/pandas/issues/6048 | 26,176,968 | MDU6SXNzdWUyNjE3Njk2OA== | 6,048 | Unreliable test test_clipboard:test_round_trip_frame | {
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} | 5 | 2014-01-23T16:06:45Z | 2016-10-12T23:04:32Z | 2014-02-03T17:16:00Z | NONE | null | ```
=============================================================================
FAILURE: test_round_trip_frame (pandas.io.tests.test_clipboard.TestClipboard)
-----------------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Python33-AMD64\Lib\unittest\case.py", line 384, in _executeTestPart
function()
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\33\pandas\io\tests\test_clipboard.py", line 67, in test_round_trip_frame
self.check_round_trip_frame(dt)
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\33\pandas\io\tests\test_clipboard.py", line 55, in check_round_trip_frame
tm.assert_frame_equal(data, result, check_dtype=False)
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\33\pandas\util\testing.py", line 477, in assert_frame_equal
assert_index_equal(left.columns, right.columns)
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\33\pandas\util\testing.py", line 423, in assert_index_equal
right.dtype))
AssertionError: [index] left [object Index(['TeN9hs1zZI', 'gvGSK2jSbR', 'AvE1VqQppQ'], dtype='object')], right [Index(['Y3XElgd5QX', 'Xp1Xv4NQF2', 'SoKJHMsx3C'], dtype='object') object]
```
Many of the spurious failures on the CI status page come from this test
sporadically failing on windows.
Figure out if it's a real issue or not.
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https://api.github.com/repos/pandas-dev/pandas/issues/6049 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6049/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6049/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6049/events | https://github.com/pandas-dev/pandas/issues/6049 | 26,188,333 | MDU6SXNzdWUyNjE4ODMzMw== | 6,049 | df.info() pd.options.display.max_info_rows == 1690785 | {
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} | [] | closed | false | null | [] | null | 2 | 2014-01-23T18:44:58Z | 2014-01-23T18:56:42Z | 2014-01-23T18:56:42Z | NONE | null | Just messing around with 0.13.0 and I noticed that the usual truncation of long data frames when you call df repr on the entire df now does not produce the compact df.info() view.
Looks like pd.options.display.max_info_rows may be the culprit :smile:
Its strange since pd.options.display.max_rows == 60
``` python
In [1]: import pandas as pd
In [2]: pd.options.display.max
pd.options.display.max_columns pd.options.display.max_colwidth p
In [2]: pd.options.display.max_in
pd.options.display.max_info_columns pd.options.display.max_info_rows
In [2]: pd.options.display.max_info_rows
Out[2]: 1690785
In [3]: pd.options.display.max_info_columns
Out[3]: 100
In [4]: pd.__version__
Out[4]: '0.13.0'
In [5]: pd.options.display.max_rows
Out[5]: 60
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6050 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6050/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6050/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6050/events | https://github.com/pandas-dev/pandas/issues/6050 | 26,194,300 | MDU6SXNzdWUyNjE5NDMwMA== | 6,050 | divide does not work for Series of timedelta64[ns] | {
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} | [] | closed | false | null | [] | null | 3 | 2014-01-23T20:08:17Z | 2014-01-23T20:16:35Z | 2014-01-23T20:10:09Z | CONTRIBUTOR | null | Using this Series:
```
In [38]: pd.Series([timedelta(1)])
Out[38]:
0 1 days, 00:00:00
dtype: timedelta64[ns]
```
This works:
```
In [36]: pd.Series([timedelta(1)])[0]/ np.timedelta64(1,'D')
Out[36]:
1.0
```
But this does not:
```
In [39]:
pd.Series([timedelta(1)])/ np.timedelta64(1,'D')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-39-753422ac6b07> in <module>()
----> 1 pd.Series([timedelta(1)])/ np.timedelta64(1,'D')
/usr/lib64/python2.7/site-packages/pandas/core/series.py in wrapper(self, other, name)
128 elif is_timedelta_lhs and name not in ['__add__','__sub__']:
129 raise TypeError("can only operate on a timedeltas for "
--> 130 "addition and subtraction, but the operator [%s] was passed" % name)
131
132 dtype = 'timedelta64[ns]'
TypeError: can only operate on a timedeltas for addition and subtraction, but the operator [__div__] was passed
```
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} | 21 | 2014-01-23T20:57:37Z | 2019-12-24T14:24:25Z | 2019-12-24T14:24:25Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/21318865/read-multi-index-on-the-columns-from-csv-file
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} | 5 | 2014-01-23T21:11:30Z | 2021-04-11T04:09:44Z | null | MEMBER | null | It appears that neither the `formatters` nor `float_format` arguments to `DataFrame.to_latex` work if changed from their default values. Both raise the same exception: `AttributeError: 'numpy.float64' object has no attribute 'decode'`.
Note: neither of these arguments has test coverage in `pandas/tests/test_format.py`
My test script:
``` python
In [1]: from pandas.util.print_versions import show_versions
In [2]: show_versions()
INSTALLED VERSIONS
------------------
Python: 2.7.5.final.0
OS: Darwin
Release: 13.0.0
Processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.0
Cython: 0.19.2
Numpy: 1.8.0
Scipy: 0.13.1
statsmodels: Not installed
patsy: 0.2.1
scikits.timeseries: Not installed
dateutil: 2.2
pytz: 2013.9
bottleneck: 0.7.0
PyTables: Not Installed
numexpr: 2.1
matplotlib: 1.3.1
openpyxl: Not installed
xlrd: Not installed
xlwt: Not installed
xlsxwriter: Not installed
sqlalchemy: 0.7.5
lxml: Not installed
bs4: Not installed
html5lib: Not installed
bigquery: Not installed
apiclient: 1.0c1
In [3]: import pandas as pd
In [4]: df = pd.DataFrame({'a': [1.0, 2.0]})
In [5]: df.to_latex()
Out[5]: u'\\begin{tabular}{lr}\n\\toprule\n{} & a \\\\\n\\midrule\n0 & 1 \\\\\n1 & 2 \\\\\n\\bottomrule\n\\end{tabular}\n'
In [6]: df.to_latex(float_format=lambda x: x)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c932ce2a22b5> in <module>()
----> 1 df.to_latex(float_format=lambda x: x)
/Users/shoyer/dev/climatology-research/library/python/target/climatology/lib/python2.7/site-packages/pandas/core/frame.pyc in to_latex(self, buf, columns, col_space, colSpace, header, index, na_rep, formatters, float_format, sparsify, index_names, bold_rows, force_unicode)
1381 sparsify=sparsify,
1382 index_names=index_names)
-> 1383 formatter.to_latex()
1384
1385 if buf is None:
/Users/shoyer/dev/climatology-research/library/python/target/climatology/lib/python2.7/site-packages/pandas/core/format.pyc in to_latex(self, force_unicode, column_format)
446 strcols = [[info_line]]
447 else:
--> 448 strcols = self._to_str_columns()
449
450 if column_format is None:
/Users/shoyer/dev/climatology-research/library/python/target/climatology/lib/python2.7/site-packages/pandas/core/format.pyc in _to_str_columns(self)
325 stringified = []
326 for i, c in enumerate(cols_to_show):
--> 327 fmt_values = self._format_col(i)
328 cheader = str_columns[i]
329
/Users/shoyer/dev/climatology-research/library/python/target/climatology/lib/python2.7/site-packages/pandas/core/format.pyc in _format_col(self, i)
490 (self.frame.iloc[:self.max_rows_displayed, i]).get_values(),
491 formatter, float_format=self.float_format, na_rep=self.na_rep,
--> 492 space=self.col_space
493 )
494
/Users/shoyer/dev/climatology-research/library/python/target/climatology/lib/python2.7/site-packages/pandas/core/format.pyc in format_array(values, formatter, float_format, na_rep, digits, space, justify)
1615 justify=justify)
1616
-> 1617 return fmt_obj.get_result()
1618
1619
/Users/shoyer/dev/climatology-research/library/python/target/climatology/lib/python2.7/site-packages/pandas/core/format.pyc in get_result(self)
1733 fmt_values = self._format_with(fmt_str)
1734
-> 1735 return _make_fixed_width(fmt_values, self.justify)
1736
1737
/Users/shoyer/dev/climatology-research/library/python/target/climatology/lib/python2.7/site-packages/pandas/core/format.pyc in _make_fixed_width(strings, justify, minimum, truncated)
1795 _strlen = _strlen_func()
1796
-> 1797 max_len = np.max([_strlen(x) for x in strings])
1798
1799 if minimum is not None:
/Users/shoyer/dev/climatology-research/library/python/target/climatology/lib/python2.7/site-packages/pandas/core/format.pyc in _strlen(x)
227 def _strlen(x):
228 try:
--> 229 return len(x.decode(encoding))
230 except UnicodeError:
231 return len(x)
AttributeError: 'numpy.float64' object has no attribute 'decode'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6053 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6053/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6053/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6053/events | https://github.com/pandas-dev/pandas/pull/6053 | 26,199,914 | MDExOlB1bGxSZXF1ZXN0MTE4MjA5OTk= | 6,053 | BUG: Regression in .get(None) from 0.12 (GH5652) | {
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} | 3 | 2014-01-23T21:29:36Z | 2014-06-24T00:49:34Z | 2014-01-23T22:03:09Z | CONTRIBUTOR | null | closes #5652
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} | 2 | 2014-01-23T21:41:08Z | 2015-09-11T05:19:16Z | 2014-02-14T17:38:07Z | MEMBER | null | I am not 100% sure that string formatters must really return _unicode_ strings
(non-unicode strings seem to work, too), but I am leaving that because it's
what is already documented.
related #6052
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} | 13 | 2014-01-24T03:00:50Z | 2021-09-04T21:18:45Z | 2014-01-25T23:21:53Z | CONTRIBUTOR | null | Following up on this [StackOverflow post](http://stackoverflow.com/questions/21316628/make-a-pandas-multiindex-from-a-product-of-iterables).
This is a convenience function for when you have a set of iterables and you want to make a MultiIndex with each unique paring of the values in those iterables.
~~I'm testing it against `itertools.product` since it uses the internal `cartesian_product` function; please let me know if there are additional tests that would make sense.~~
Testing against MultiIndex.from_tuples instead.
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https://api.github.com/repos/pandas-dev/pandas/issues/6056 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6056/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6056/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6056/events | https://github.com/pandas-dev/pandas/issues/6056 | 26,232,487 | MDU6SXNzdWUyNjIzMjQ4Nw== | 6,056 | BUG: SEGFAULT when assigning nan via iloc | {
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} | [] | closed | false | null | [] | null | 9 | 2014-01-24T10:45:33Z | 2014-01-28T10:19:13Z | 2014-01-24T11:31:19Z | CONTRIBUTOR | null | I have a DataFrame that looks like this:
``` python
>>> df.dtypes
ask float64
bbg object
bid float64
last float64
quotetime object
size float64
snap object
snaptime float64
tradedate datetime64[ns]
tradetime object
sec_type object
dtype: object
>>> df.shape
(164618, 11)
df.sec_type.head()
0 Equity
1 Equity
2 Equity
3 Equity
4 Equity
Name: sec_type, dtype: object
```
Now, this segfaults:
``` python
>>> df.sec_type.iloc[0] = np.nan
fish: Job 1, 'ipython --pdb' terminated by signal SIGSEGV (Address boundary error)
```
This also segfaults, but not right away:
``` python
>>> df.sec_type.iloc[0] = None
>>> df.iloc[:10]
fish: Job 1, 'ipython --pdb' terminated by signal SIGSEGV (Address boundary error)
```
And this:
``` python
>>> df.sec_type.ix[0] = np.nan
>>> df.sec_type.head()
fish: Job 1, 'ipython --pdb' terminated by signal SIGSEGV (Address boundary error)
```
However this doesn't:
``` python
s = df.sec_type.copy()
s.iloc[0] = np.nan
```
I tried recreating the error with a minimal case but couldn't since small trivial examples don't seem to segfault; debugging is also kinda hard cause it goes deep into pandas guts..
This happened right after I updated to 0.13, never happened before.
Any ideas?
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https://api.github.com/repos/pandas-dev/pandas/issues/6057 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6057/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6057/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6057/events | https://github.com/pandas-dev/pandas/issues/6057 | 26,238,065 | MDU6SXNzdWUyNjIzODA2NQ== | 6,057 | tail() fails on empty DataFrames | {
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} | [] | closed | false | null | [] | null | 3 | 2014-01-24T12:38:39Z | 2014-01-24T12:54:05Z | 2014-01-24T12:40:21Z | CONTRIBUTOR | null | `tail()` method being called on empty DataFrame results in IndexError exception. At the same time `head()` works fine:
```
>>> df = pd.DataFrame()
>>> print df.head()
Empty DataFrame
Columns: []
Index: []
[0 rows x 0 columns]
>>> print df.tail()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/v/.virtual_envs/system/lib/python2.7/site-packages/pandas/core/generic.py", line 1572, in tail
return self.iloc[-n:]
File "/Users/v/.virtual_envs/system/lib/python2.7/site-packages/pandas/core/indexing.py", line 1020, in __getitem__
return self._getitem_axis(key, axis=0)
File "/Users/v/.virtual_envs/system/lib/python2.7/site-packages/pandas/core/indexing.py", line 1210, in _getitem_axis
return self._get_slice_axis(key, axis=axis)
File "/Users/v/.virtual_envs/system/lib/python2.7/site-packages/pandas/core/indexing.py", line 1202, in _get_slice_axis
typ='iloc')
File "/Users/v/.virtual_envs/system/lib/python2.7/site-packages/pandas/core/indexing.py", line 75, in _slice
typ=typ)
File "/Users/v/.virtual_envs/system/lib/python2.7/site-packages/pandas/core/frame.py", line 1836, in _slice
slobj, axis=axis, raise_on_error=raise_on_error)
File "/Users/v/.virtual_envs/system/lib/python2.7/site-packages/pandas/core/internals.py", line 2516, in get_slice
_check_slice_bounds(slobj, new_axes[axis])
File "/Users/v/.virtual_envs/system/lib/python2.7/site-packages/pandas/core/indexing.py", line 1523, in _check_slice_bounds
raise IndexError("out-of-bounds on slice (start)")
IndexError: out-of-bounds on slice (start)
```
Version of pandas: 0.13.0
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} | 0 | 2014-01-24T14:12:23Z | 2014-06-19T09:30:48Z | 2014-01-24T15:39:15Z | CONTRIBUTOR | null | related #6056
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} | 30 | 2014-01-24T14:19:50Z | 2014-01-25T01:10:54Z | 2014-01-24T16:38:08Z | CONTRIBUTOR | null | - [x] add sphinx/ipython to print_versions.py #6064
- ~~[ ] drop support scikit.timeseries in docs (and remove from print_versions.py)~~
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} | 2 | 2014-01-24T14:51:30Z | 2014-03-14T17:39:42Z | 2014-03-14T17:39:42Z | CONTRIBUTOR | null | I'd expect the output of
```
pd.DataFrame(columns=["asdf", "bsdf"]).to_html()
```
to be essentially `<th>asdf</th><th>bsdf</th>` as an html table. This would be a lot more homogeneous. The current output is pretty useless, being basically `repr(df)`.
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E.g. `.ix[row][col_name] = x` used to work but doesn't. Sometimes `.ix[row, col_name] = x` also doesn't work and I have to use `.loc[row, col_name] = x` instead although the docs state that .ix[] just falls back to either .loc or .iloc.
I'm changing most of my code but I guess I'm just wondering whether that was made explicit as I can't find it in the release notes or if unittests failed to catch it?
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} | 0 | 2014-01-24T18:58:11Z | 2014-06-13T04:25:18Z | 2014-01-24T21:49:17Z | CONTRIBUTOR | null | surfaced in #6059
very subtle iloc bug that requires both axis slicing then trying dtypes
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} | [] | closed | false | null | [] | null | 26 | 2014-01-24T18:59:19Z | 2014-01-26T19:12:53Z | 2014-01-24T19:05:42Z | NONE | null | In pandas 0.12.0 and below I was always able to get at the first row of any data frame (returned as a wide one row DataFrame, not a series) by using df[0:1], regardless if my index was a float or int or something else.
Currently in 0.13.0 trying df[0:1] on a dataframe with a Float64Index fails.
Is there a better way in 0.13.0 to return the i-th row of a data frame as a wide one-row data frame?
currently df2.iloc[0,:] returns a series cross section which I dont want.
Thanks!
``` python
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: df = pd.DataFrame(np.random.randn(10,10))
In [4]: df.index = df.index*100+3
In [5]: df
Out[5]:
0 1 2 3 4 5 6 \
3 0.961295 0.150850 -1.357989 -1.981159 1.409971 1.165354 0.412334
103 -0.357906 0.861821 -0.911108 0.551928 1.402282 -0.409175 0.833893
203 -0.892805 0.312257 -0.829338 -0.290734 0.065603 -0.159384 1.160495
303 0.155798 -0.385063 -0.689650 -0.197156 -1.107738 -1.112956 -1.856549
403 -1.223597 0.039183 -1.810251 1.234489 0.446190 0.029870 -1.586065
503 0.113397 0.982866 -1.984421 0.574137 0.704638 -1.593602 0.815498
603 1.154750 -0.018192 -1.806763 0.746731 -0.181660 -0.608605 0.327122
703 1.536969 2.054841 0.168666 -0.996030 -0.238698 -0.415467 0.560634
803 1.083026 1.624360 0.836893 0.593568 0.328877 -0.000506 0.185452
903 0.239799 -0.552065 0.745946 -0.544161 -1.241497 0.771948 -0.709924
7 8 9
3 -0.766668 -0.089740 0.731273
103 2.599487 -0.612015 -0.410110
203 0.737393 -1.406171 -1.676148
303 -0.574330 -0.031489 -0.991040
403 -0.366256 -0.853061 -0.093385
503 2.383536 -0.188622 0.030401
603 -1.463239 2.020704 -0.840727
703 -0.316375 -0.703592 1.359039
803 -2.133368 1.771383 -0.447949
903 -1.599941 -2.384423 0.178400
[10 rows x 10 columns]
In [6]: df[0:1]
Out[6]:
0 1 2 3 4 5 6 \
3 0.961295 0.15085 -1.357989 -1.981159 1.409971 1.165354 0.412334
7 8 9
3 -0.766668 -0.08974 0.731273
[1 rows x 10 columns]
In [7]: df2 = df.set_index(0)
In [8]: df2
Out[8]:
1 2 3 4 5 6 \
0
0.961295 0.150850 -1.357989 -1.981159 1.409971 1.165354 0.412334
-0.357906 0.861821 -0.911108 0.551928 1.402282 -0.409175 0.833893
-0.892805 0.312257 -0.829338 -0.290734 0.065603 -0.159384 1.160495
0.155798 -0.385063 -0.689650 -0.197156 -1.107738 -1.112956 -1.856549
-1.223597 0.039183 -1.810251 1.234489 0.446190 0.029870 -1.586065
0.113397 0.982866 -1.984421 0.574137 0.704638 -1.593602 0.815498
1.154750 -0.018192 -1.806763 0.746731 -0.181660 -0.608605 0.327122
1.536969 2.054841 0.168666 -0.996030 -0.238698 -0.415467 0.560634
1.083026 1.624360 0.836893 0.593568 0.328877 -0.000506 0.185452
0.239799 -0.552065 0.745946 -0.544161 -1.241497 0.771948 -0.709924
7 8 9
0
0.961295 -0.766668 -0.089740 0.731273
-0.357906 2.599487 -0.612015 -0.410110
-0.892805 0.737393 -1.406171 -1.676148
0.155798 -0.574330 -0.031489 -0.991040
-1.223597 -0.366256 -0.853061 -0.093385
0.113397 2.383536 -0.188622 0.030401
1.154750 -1.463239 2.020704 -0.840727
1.536969 -0.316375 -0.703592 1.359039
1.083026 -2.133368 1.771383 -0.447949
0.239799 -1.599941 -2.384423 0.178400
[10 rows x 9 columns]
In [9]: df2[0:1]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-9-f015a308451a> in <module>()
----> 1 df2[0:1]
D:\Python27\lib\site-packages\pandas\core\frame.pyc in __getitem__(self, key)
1621
1622 # see if we can slice the rows
-> 1623 indexer = _convert_to_index_sliceable(self, key)
1624 if indexer is not None:
1625 return self._getitem_slice(indexer)
D:\Python27\lib\site-packages\pandas\core\indexing.pyc in _convert_to_index_sliceable(obj, key)
1329 idx = obj.index
1330 if isinstance(key, slice):
-> 1331 return idx._convert_slice_indexer(key, typ='getitem')
1332
1333 elif isinstance(key, compat.string_types):
D:\Python27\lib\site-packages\pandas\core\index.pyc in _convert_slice_indexer(self, key, typ)
1809
1810 # translate to locations
-> 1811 return self.slice_indexer(key.start, key.stop, key.step)
1812
1813 def get_value(self, series, key):
D:\Python27\lib\site-packages\pandas\core\index.pyc in slice_indexer(self, start, end, step)
1491 This function assumes that the data is sorted, so use at your own peril
1492 """
-> 1493 start_slice, end_slice = self.slice_locs(start, end)
1494
1495 # return a slice
D:\Python27\lib\site-packages\pandas\core\index.pyc in slice_locs(self, start, end)
1530 else:
1531 try:
-> 1532 start_slice = self.get_loc(start)
1533
1534 if not is_unique:
D:\Python27\lib\site-packages\pandas\core\index.pyc in get_loc(self, key)
1015 loc : int if unique index, possibly slice or mask if not
1016 """
-> 1017 return self._engine.get_loc(_values_from_object(key))
1018
1019 def get_value(self, series, key):
D:\Python27\lib\site-packages\pandas\index.pyd in pandas.index.IndexEngine.get_loc (pandas\index.c:3326)()
D:\Python27\lib\site-packages\pandas\index.pyd in pandas.index.IndexEngine.get_loc (pandas\index.c:3206)()
D:\Python27\lib\site-packages\pandas\hashtable.pyd in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:10591)()
D:\Python27\lib\site-packages\pandas\hashtable.pyd in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:10544)()
KeyError: 0
```
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} | 3 | 2014-01-24T19:05:11Z | 2014-01-26T03:33:33Z | 2014-01-26T03:33:33Z | CONTRIBUTOR | null | Is there some reason that `test_ujson.py` starts with a UTF-8 BOM, or was it introduced accidentally? Can't think of why you'd want one there, but maybe I'm missing something.
```
>>> open("test_ujson.py").readline()
'\xef\xbb\xbffrom unittest import TestCase\n'
```
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} | 18 | 2014-01-24T19:10:46Z | 2014-07-06T10:27:46Z | 2014-03-05T21:58:54Z | CONTRIBUTOR | null | closes #6124
closes #6265
## Use case
This will facilitate DataFrame group/apply transformations when using a function that returns a Series. Right now, if we perform the following:
```
import pandas
df = pandas.DataFrame(
{'a': [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2],
'b': [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1],
'c': [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
'd': [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],
})
def count_values(df):
return pandas.Series({'count': df['b'].sum(), 'mean': df['c'].mean()}, name='metrics')
result = df.groupby('a').apply(count_values)
print result.stack().reset_index()
```
We get the following output:
```
a level_1 0
0 0 count 2.0
1 0 mean 0.5
2 1 count 2.0
3 1 mean 0.5
4 2 count 2.0
5 2 mean 0.5
[6 rows x 3 columns]
```
Ideally, the series name should be preserved and propagated through these operations such that we get the following output:
```
a metrics 0
0 0 count 2.0
1 0 mean 0.5
2 1 count 2.0
3 1 mean 0.5
4 2 count 2.0
5 2 mean 0.5
[6 rows x 3 columns]
```
The only way to achieve this (currently) is:
```
result = df.groupby('a').apply(count_values)
result.columns.name = 'metrics'
print result.stack().reset_index()
```
However, the key issue here is 1) this adds an extra line of code and 2) the name of the series created in the applied function may not be known in the outside block (so we can't properly fix the result.columns.name attribute).
The other work-around is to name the index of the series:
```
def count_values(df):
series = pandas.Series({'count': df['b'].sum(), 'mean': df['c'].mean()})
series.index.name = 'metrics'
return series
```
During the group/apply operation, this pull request will check to see whether series.index has the name attribute set. If the name attribute is not set, it will set the index.name attribute to the name of the series (thus ensuring the name propagates).
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} | 6 | 2014-01-24T19:37:01Z | 2014-01-30T04:18:45Z | 2014-01-30T04:18:45Z | CONTRIBUTOR | null | There are two strange things in `computation/tests/test_eval.py`.
First, in `check_complex_cmp_op`:
```
elif _bool_and_frame(lhs, rhs):
self.assertRaises(TypeError, _eval_single_bin, lhs_new, '&',
rhs_new, self.engine)
self.assertRaises(TypeError, pd.eval, ex,
local_dict={'lhs': lhs, 'rhs': rhs},
engine=self.engine, parser=self.parser)
```
neither `lhs_new` nor `rhs_new` are defined (they're defined later on another branch). Probably they're meant to be simply `lhs` and `rhs`.
Second, in the subfunction `check_operands`:
```
with assertRaises(NotImplementedError):
pd.eval(ex, engine=self.engine, parser=self.parser)
```
`assertRaises` isn't defined. This should probably be `tm.assertRaises`, as in the previous
```
with tm.assertRaises(ValueError):
pd.eval(ex, engine=self.engine, parser=self.parser)
```
I guess neither of these code paths are being exercised at the moment, which is why it hasn't been an issue.
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is it official or not?
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} | 3 | 2014-01-24T21:48:40Z | 2014-02-22T13:40:44Z | 2014-02-22T13:40:44Z | NONE | null | In [1]: import pandas as pd
...: import numpy as np
In [2]: mat = np.random.randn(5,6)
...: q = pd.DataFrame(mat, columns=['a','b','c','d','e','f'])
In [3]: a = q.corrwith(q['a'])
...: print(a)
a 1.000000
b 0.003617
c -0.025690
d 0.586906
e 0.654205
f -0.159210
dtype: float64
In [4]: a = q[['a','b','c','d','e']].corrwith(q['a'])
...: print(a)
a NaN
b NaN
c NaN
d NaN
e NaN
dtype: float64
In [5]: a = q[['a','b','c','d']].corrwith(q['a'])
...: print(a)
a 1.000000
b 0.003617
c -0.025690
d 0.586906
dtype: float64
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} | 0 | 2014-01-24T23:19:54Z | 2014-01-25T01:00:47Z | 2014-01-25T01:00:47Z | NONE | null | @jorisvandenbossche pointed out some doc issues in #6021
which cause exceptions when building the docs.
Putting this here to remind us to ensure it's fixed before tagging the release
@danbirken will probably have a PR ready soon.
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} | 13 | 2014-01-24T23:26:12Z | 2014-06-21T03:05:17Z | 2014-01-25T00:36:16Z | CONTRIBUTOR | null | Fixes typos found in #GH 6069. Does not change the tests to ensure those branches are exercised.
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} | 2 | 2014-01-24T23:46:33Z | 2014-07-16T08:48:48Z | 2014-01-24T23:47:00Z | NONE | null | ```
Running Sphinx v1.1.3
loading pickled environment... not yet created
loading intersphinx inventory from http://statsmodels.sourceforge.net/devel/objects.inv...
loading intersphinx inventory from http://docs.python.org/objects.inv...
building [html]: targets for 2 source files that are out of date
updating environment: 2 added, 0 changed, 0 removed
reading sources... [ 50%] dsintro
>>>-------------------------------------------------------------------------
Exception in /home/user1/src/pandas/doc/source/dsintro.rst at line 33: # <<<< This is new
---------------------------------------------------------------------------
ZeroDivisionError Traceback (most recent call last)
<ipython-input-4-05c9758a9c21> in <module>()
----> 1 1/0
ZeroDivisionError: integer division or modulo by zero
<<<-------------------------------------------------------------------------
```
No more excruciating "guess where" on errors.
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} | 0 | 2014-01-24T23:58:22Z | 2014-01-24T23:59:39Z | 2014-01-24T23:59:16Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/21328945/pandas-accessing-rows-of-a-sparsedataframe
```
import numpy as np
import pandas as pd
df = pd.DataFrame(np.arange(15).reshape(5,3), index=list('abcde'))
df.loc['b',1] = np.nan # for good measure...
sparse = df.to_sparse()
sparse[1] # This is OK.
df.loc['b'] # This is also OK.
sparse.loc['b'] # This blows up
```
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} | 16 | 2014-01-25T00:42:13Z | 2016-03-23T20:25:50Z | 2015-12-26T13:08:42Z | CONTRIBUTOR | null | related #5884
- [ ] OLS https://github.com/statsmodels/statsmodels/blob/master/statsmodels/regression/linear_model.py
- [ ] plm (regression for panels)
- [ ] fama_macbeth
- [ ] VAR https://github.com/statsmodels/statsmodels/tree/master/statsmodels/tsa/vector_ar
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Fixes #6073
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} | 1 | 2014-01-25T00:47:18Z | 2014-01-25T22:04:34Z | 2014-01-25T22:04:34Z | CONTRIBUTOR | null | related: https://github.com/pydata/pandas/pull/5995
http://nipy.bic.berkeley.edu/tgrid?category=pandas
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} | 6 | 2014-01-25T01:15:50Z | 2014-06-13T04:07:33Z | 2014-01-28T11:32:07Z | CONTRIBUTOR | null | `BigqueryError` wasn't imported directly, and so has to be referred to via the module.
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https://api.github.com/repos/pandas-dev/pandas/issues/6081 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6081/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6081/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6081/events | https://github.com/pandas-dev/pandas/issues/6081 | 26,283,707 | MDU6SXNzdWUyNjI4MzcwNw== | 6,081 | TST: test_reshape.py issues | {
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} | 3 | 2014-01-25T01:41:18Z | 2014-01-31T14:53:15Z | 2014-01-31T14:53:15Z | CONTRIBUTOR | null | `test_custom_var_and_value_name` refers to several variables which aren't defined, namely `df`, `var_name`, and `value_name`, all of which should probably have `self.` in front of them:
```
result18 = melt(df, id_vars=['id1', 'id2'],
value_vars='A', var_name=self.var_name, value_name=self.value_name)
self.assertEqual(result18.columns.tolist(), ['id1', 'id2', 'var', 'val'])
result19 = melt(self.df, id_vars=['id1', 'id2'],
value_vars=['A', 'B'], var_name=self.var_name, value_name=self.value_name)
expected19 = DataFrame({'id1': self.df['id1'].tolist() * 2,
'id2': self.df['id2'].tolist() * 2,
var_name: ['A']*10 + ['B']*10,
value_name: self.df['A'].tolist() + self.df['B'].tolist()},
```
But I was a little surprised that this wasn't causing an exception, and it turns out to be because `test_reshape.py` contains
```
def test_custom_var_and_value_name(self):
[code here]
def test_custom_var_and_value_name(self):
self.df.columns.name = 'foo'
result20 = melt(self.df)
self.assertEqual(result20.columns.tolist(), ['foo', 'value'])
```
and so the original test is getting clobbered. I'm not sure if they should be merged or if since the second one changes `self.df` it needs to be kept on its own or not.
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https://api.github.com/repos/pandas-dev/pandas/issues/6082 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6082/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6082/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6082/events | https://github.com/pandas-dev/pandas/pull/6082 | 26,283,892 | MDExOlB1bGxSZXF1ZXN0MTE4Njk3OTA= | 6,082 | BLD/DOC: report context on warnings and add :okwarning: to ipython_directive | {
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} | 2 | 2014-01-25T01:48:20Z | 2014-07-16T08:48:53Z | 2014-01-25T01:48:26Z | NONE | null | I plan to get this upstream soon, unless joris find bugs.
Reports context on warnings just like recently added context for raised exceptions.
adds :okwarning: option to ipython directive to suppress them.
```
reading sources... [ 6%] basics
>>>-------------------------------------------------------------------------
Warning raised in /home/user1/src/pandas/doc/source/basics.rst at line 734:
----------------------------------------------------------------------------
/home/user1/src/pandas/pandas/core/frame.py:2805: FutureWarning: TimeSeries broadcasting along DataFrame index by default is deprecated. Please use DataFrame.<op> to explicitly broadcast arithmetic operations along the index
FutureWarning)
<<<-------------------------------------------------------------------------
```
Let's get rid of the noise.
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} | 8 | 2014-01-25T02:07:49Z | 2014-06-18T17:57:32Z | 2014-01-25T22:03:54Z | CONTRIBUTOR | null | partial fix of #6079
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} | [] | closed | false | null | [] | null | 1 | 2014-01-25T04:57:41Z | 2014-07-12T12:36:41Z | 2014-01-25T08:30:47Z | CONTRIBUTOR | null | Added "THIS IS AN EXPERIMENTAL LIBRARY" to docstrings for read_gbq() and to_gbq() methods per:
closes https://github.com/pydata/pandas/issues/5956
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} | 6 | 2014-01-25T12:40:21Z | 2018-07-06T22:27:32Z | 2018-07-06T22:27:32Z | NONE | null | related: #4197, #5694
I'm encoutering a bug in pandas 0.13.0:
``` python
df = pandas.read_hdf('d:\\dropbox\\s.h5', 'data')
df.groupby(TimeGrouper(freq='48min', closed='right'))
```
```
Traceback (most recent call last):
File "D:/Dropbox/hf/tests/hdf.py", line 94, in <module>
main()
File "D:/Dropbox/hf/tests/hdf.py", line 74, in load
df.groupby(TimeGrouper(freq='48min', closed='right'))
File "C:\Anaconda\lib\site-packages\pandas\core\generic.py", line 2434, in groupby
sort=sort, group_keys=group_keys, squeeze=squeeze)
File "C:\Anaconda\lib\site-packages\pandas\core\groupby.py", line 789, in groupby
return klass(obj, by, **kwds)
File "C:\Anaconda\lib\site-packages\pandas\core\groupby.py", line 238, in __init__
level=level, sort=sort)
File "C:\Anaconda\lib\site-packages\pandas\core\groupby.py", line 1563, in _get_grouper
gpr = key.get_grouper(obj)
File "C:\Anaconda\lib\site-packages\pandas\tseries\resample.py", line 109, in get_grouper
return self._get_time_grouper(obj)[1]
File "C:\Anaconda\lib\site-packages\pandas\tseries\resample.py", line 115, in _get_time_grouper
binner, bins, binlabels = self._get_time_bins(axis)
File "C:\Anaconda\lib\site-packages\pandas\tseries\resample.py", line 150, in _get_time_bins
bins = lib.generate_bins_dt64(ax_values, bin_edges, self.closed)
File "lib.pyx", line 935, in pandas.lib.generate_bins_dt64 (pandas\lib.c:14968)
ValueError: Values falls after last bin
```
This is the max length when it works fine:
``` python
df = pandas.read_hdf('d:\\dropbox\\s.h5', 'data')
df = df[:23748]
df.groupby(TimeGrouper(freq='48min', closed='right'))
```
Also it works when I group it with TimeGrouper(freq='48min')
Here is data frame:
https://www.dropbox.com/s/ezrjralhb2yordm/s.h5
```
INSTALLED VERSIONS
------------------
Python: 2.7.5.final.0
OS: Windows
Release: 8
Processor: Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.13.0
Cython: 0.19.2
Numpy: 1.7.1
Scipy: 0.13.2
statsmodels: 0.5.0
patsy: 0.2.1
scikits.timeseries: Not installed
dateutil: 1.5
pytz: 2013b
bottleneck: Not installed
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: Not installed
sqlalchemy: 0.8.3
lxml: 3.2.3
bs4: 4.3.1
html5lib: Not installed
bigquery: Not installed
apiclient: Not installed
```
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} | [] | closed | false | null | [] | null | 0 | 2014-01-25T13:08:01Z | 2014-06-24T21:27:07Z | 2014-01-25T14:43:25Z | MEMBER | null | Using the new functionality of @y-p from #6082, suppressing some warnings in the older whatsnew sections.
Some of them are deprecation warnings, so from older functinality. We could also change the code example, but since it's from whatsnew I think it's more logical to leave it as it is (when they start to generate errors, then of course we have to do something else)
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} | 0 | 2014-01-25T13:15:16Z | 2014-01-25T17:36:09Z | 2014-01-25T17:36:09Z | MEMBER | null | @jreback related to #5850
There are two warnings remaining in the doc building, both coming from new `panel.apply` functionality:
```
>>>-------------------------------------------------------------------------
Warning in source/v0.13.1.txt at block ending on line 142
----------------------------------------------------------------------------
/home/joris/scipy/pandas/pandas/core/frame.py:2805: FutureWarning: TimeSeries
broadcasting along DataFrame index by default is deprecated. Please use
DataFrame.<op> to explicitly broadcast arithmetic operations along the index
FutureWarning)
<<<-------------------------------------------------------------------------
```
```
>>>-------------------------------------------------------------------------
Warning in /home/joris/scipy/pandas/doc/source/basics.rst at block ending on line 735
----------------------------------------------------------------------------
/home/joris/scipy/pandas/pandas/core/frame.py:2805: FutureWarning: TimeSeries
broadcasting along DataFrame index by default is deprecated. Please use
DataFrame.<op> to explicitly broadcast arithmetic operations along the index
FutureWarning)
<<<-------------------------------------------------------------------------
```
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} | [] | closed | false | null | [] | null | 1 | 2014-01-25T13:16:31Z | 2014-07-16T08:49:02Z | 2014-01-25T13:30:57Z | CONTRIBUTOR | null | The examples in the docstring showed lists being passed to array_equivalent. `array_equivalent` expects `ndarrays` only. (It raises an `AttributeError` when passed a list.)
Also, the use of `pd.isnull` can be removed from `array_equivalent`. My thinking here was muddled. `pd.isnull` is only needed if `array_equivalent` were to accept objects other than ndarrays. `np.array_equal` works as desired for all `ndarrays` except those of float or complex dtype.
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} | 3 | 2014-01-25T14:04:19Z | 2015-11-20T20:23:42Z | 2014-02-17T00:14:35Z | CONTRIBUTOR | null | https://github.com/pydata/pandas/issues/6087
I have to investigate more fully, but this warning
`/home/joris/scipy/pandas/pandas/core/frame.py:2805: FutureWarning: TimeSeries
broadcasting along DataFrame index by default is deprecated. Please use
DataFrame.<op> to explicitly broadcast arithmetic operations along the index
FutureWarning)`
can be removed for cases where the index are equal (e.g. no broadcasting)
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} | 4 | 2014-01-25T14:15:18Z | 2016-09-12T14:15:38Z | 2014-02-16T23:10:44Z | CONTRIBUTOR | null | disable here:
https://github.com/jreback/pandas/commit/928402e2bd69514a115a0274c06bde30f4057759
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```
def _count_generic(values, table_type, type_caster):
from pandas.core.series import Series
values = type_caster(values)
table = table_type(min(len(values), 1000000))
uniques, labels = table.factorize(values)
return Series(counts, index=uniques)
```
but this function doesn't seem to be used anywhere. The only reference I can find is in `algorithms.count`, or what would be `algorithms.count`, but that's commented out:
```
# def count(values, uniques=None):
# f = lambda htype, caster: _count_generic(values, htype, caster)
# if uniques is not None:
# raise NotImplementedError
# else:
# return _hashtable_algo(f, values.dtype)
```
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} | 3 | 2014-01-25T15:44:04Z | 2014-01-25T18:03:52Z | 2014-01-25T18:03:52Z | NONE | null | ```
======================================================================
FAIL: test_append_with_data_columns (pandas.io.tests.test_pytables.TestHDFStore)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/virtualenv/python3.2_with_system_site_packages/lib/python3.2/site-packages/pandas/io/tests/test_pytables.py", line 1330, in test_append_with_data_columns
tm.assert_frame_equal(result, expected)
File "/home/travis/virtualenv/python3.2_with_system_site_packages/lib/python3.2/site-packages/pandas/util/testing.py", line 479, in assert_frame_equal
assert_index_equal(left.index, right.index)
File "/home/travis/virtualenv/python3.2_with_system_site_packages/lib/python3.2/site-packages/pandas/util/testing.py", line 423, in assert_index_equal
right.dtype))
AssertionError: [index] left [datetime64[ns] <class 'pandas.tseries.index.DatetimeIndex'>
[2000-01-24, ..., 2000-01-28]
Length: 5, Freq: B, Timezone: None], right [<class 'pandas.tseries.index.DatetimeIndex'>
[2000-01-24, ..., 2000-01-28]
Length: 3, Freq: None, Timezone: None datetime64[ns]]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6094 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6094/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6094/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6094/events | https://github.com/pandas-dev/pandas/pull/6094 | 26,295,228 | MDExOlB1bGxSZXF1ZXN0MTE4NzQ3ODU= | 6,094 | BUG: ensure plt is imported before it's used in hist_frame | {
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} | [] | closed | false | null | [] | null | 1 | 2014-01-25T16:00:31Z | 2014-07-16T08:49:06Z | 2014-01-25T16:51:38Z | CONTRIBUTOR | null | If the `by` argument to `hist_frame` isn't `None`, then `plt.setp` can be called be called before `plt` is imported. This moves the import to the start of the function to avoid this.
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https://api.github.com/repos/pandas-dev/pandas/issues/6095 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6095/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6095/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6095/events | https://github.com/pandas-dev/pandas/pull/6095 | 26,296,713 | MDExOlB1bGxSZXF1ZXN0MTE4NzUzNDI= | 6,095 | DOC: beef up tutorial section. GH5837 | {
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} | 1 | 2014-01-25T17:13:27Z | 2014-07-16T08:49:07Z | 2014-01-25T17:13:33Z | NONE | null | closes #5837.
For the record, I'm somewhat skeptical of this google search results type documentation,
but then I said the same thing about the cookbook at the time.
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} | 3 | 2014-01-25T17:25:44Z | 2014-06-30T19:30:22Z | 2014-06-30T19:30:22Z | NONE | null | https://github.com/pydata/pandas/pull/5179 introduced the following warning
```
As of 10/10/13, there is a bug in Google’s API preventing result sets from being
larger than 100,000 rows. A patch is scheduled for the week of 10/14/13.
```
@jacobschaer, is this still valid?
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} | 0 | 2014-01-25T17:29:33Z | 2014-06-16T23:53:26Z | 2014-01-25T18:03:52Z | CONTRIBUTOR | null | closes #6093
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} | 1 | 2014-01-25T21:26:44Z | 2014-01-27T03:13:01Z | 2014-01-27T02:36:53Z | NONE | null | ## IPython snippet reproducing the problem
``` python
import pandas as pd
import numpy as np
df = pd.DataFrame({u'clé1': [u'a', u'a', u'b', u'b', u'a'],
u'clé2': [u'1er', u'2ème', u'1er', u'2ème', u'1er'],
'données1': np.random.randn(5),
'données2': np.random.randn(5)})
df.pivot_table(rows=[u'clé1'], cols=[u'clé2'])
```
## INSTALLED VERSIONS
Python: 2.7.5.final.0
OS: Linux
Release: 2.6.32-358.14.1.el6.x86_64
Processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.0
Cython: 0.19.2
Numpy: 1.8.0
Scipy: Not installed
statsmodels: Not installed
patsy: Not installed
scikits.timeseries: Not installed
dateutil: 2.2
pytz: 2013.9
bottleneck: Not installed
PyTables: Not Installed
numexpr: Not Installed
matplotlib: Not installed
openpyxl: Not installed
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: Not installed
sqlalchemy: Not installed
lxml: 3.2.5
bs4: Not installed
html5lib: Not installed
bigquery: Not installed
apiclient: Not installed
## Expected behavior
HTML formatted table.
## Seen instead
Warning message in IPython:
```
WARNING: Exception in text/html formatter: 'ascii' codec can't encode character u'\xe9' in position 2: ordinal not in range(128)
```
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} | 2 | 2014-01-25T21:31:53Z | 2014-06-24T23:13:28Z | 2014-01-27T03:13:14Z | NONE | null | Issue #6098
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} | 11 | 2014-01-25T22:11:01Z | 2015-10-07T21:42:44Z | 2015-10-07T21:42:44Z | MEMBER | null | I already mentioned it here: https://github.com/pydata/pandas/issues/5899#issuecomment-32967854, but then this did not appear on the dev doc builds on the website and only locally on my computer. But now I also see it there. See eg http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.html#pandas.DataFrame in the list of attributes, the `is_copy` is no link anymore.
So I get the following warning: `DataFrame.is_copy.rst:: WARNING: document isn't included in any toctree` for 8 methods:
```
pandas.DataFrame.is_copy
pandas.DatetimeIndex.name
pandas.DatetimeIndex.offset
pandas.Index.asi8
pandas.Index.name
pandas.Panel.is_copy
pandas.Panel4D.is_copy
pandas.Series.is_copy
```
Strange thing is, there have not been recent changes to these functions (I think), but the errors only appeared recently.
@y-p You did the most recent doc build on the website? (does this not happen automatically anymore?)
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