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} | 0 | 2013-09-20T18:46:55Z | 2013-09-21T12:03:26Z | 2013-09-21T12:03:26Z | CONTRIBUTOR | null | `_ensure_numeric` from `pandas.core.nanops` does not include a check for complex numbers, just converts them to floats.
Relevant StackOverflow question that found the bug: [python pandas complex number](http://stackoverflow.com/questions/18919699/python-pandas-complex-number/)
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} | 0 | 2013-09-20T18:47:18Z | 2016-07-21T16:03:04Z | 2015-10-16T07:53:21Z | CONTRIBUTOR | null | related/dup #4340
Was building on [this example on SO](http://stackoverflow.com/questions/15555005/get-inferred-dataframe-types-iteratively-using-chunksize), and found a bug in `ExcelFile().parse` option `skiprows`, when passed an index.
``` python
In [2]: xls = pd.ExcelFile('example.xlsx')
In [3]: df = xls.parse(xls.sheet_names[0])
In [4]: print df
Bill Date Meter # Type SIC Code Billing Days Rate Code
0 2011-10-01 00:00:00 1892213 E 8111 29 ALTOU
1 2011-11-01 00:00:00 1892213 E 8111 29 ALTOU
2 NaN NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN NaN
4 Bill Date Meter # Type SIC Code Billing Days Rate Code
5 2011-10-01 00:00:00 553961 G 8111 29 GN3
6 2011-11-01 00:00:00 553961 G 8111 29 GN3
7 NaN NaN NaN NaN NaN NaN
8 Bill Date Meter # Type SIC Code Billing Days Rate Code
9 2011-10-01 00:00:00 6322158 E 29 A
10 2011-11-01 00:00:00 6322158 E 29 A
```
Wanting to avoid the repeated headers and empty rows, I parse with `skiprows`, but no dice (no difference with previously parsed sheet):
``` python
In [5]: skip_idx = np.array([2,3,4,7,8])
In [6]: df = xls.parse(xls.sheet_names[0], skiprows=skip_idx+1)
In [8]: df
Out[8]:
Bill Date Meter # Type SIC Code Billing Days Rate Code
0 2011-10-01 00:00:00 1892213 E 8111 29 ALTOU
1 2011-11-01 00:00:00 1892213 E 8111 29 ALTOU
2 NaN NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN NaN
4 Bill Date Meter # Type SIC Code Billing Days Rate Code
5 2011-10-01 00:00:00 553961 G 8111 29 GN3
6 2011-11-01 00:00:00 553961 G 8111 29 GN3
7 NaN NaN NaN NaN NaN NaN
8 Bill Date Meter # Type SIC Code Billing Days Rate Code
9 2011-10-01 00:00:00 6322158 E 29 A
10 2011-11-01 00:00:00 6322158 E 29 A
```
Meanwhile, our little friend `pd.read_csv()`, doesn't seem to have the same hangups. Creating a csv directly from the original `.xlsx` and performing the same operations:
``` python
In [9]: df = pd.read_csv('example.csv')
In [10]: df
Out[10]:
Bill Date Meter # Type SIC Code Billing Days Rate Code
0 Oct-11 1892213 E 8111 29 ALTOU
1 Nov-11 1892213 E 8111 29 ALTOU
2 NaN NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN NaN
4 Bill Date Meter # Type SIC Code Billing Days Rate Code
5 Oct-11 553961 G 8111 29 GN3
6 Nov-11 553961 G 8111 29 GN3
7 NaN NaN NaN NaN NaN NaN
8 Bill Date Meter # Type SIC Code Billing Days Rate Code
9 Oct-11 6322158 E 29 A
10 Nov-11 6322158 E 29 A
```
Looks fine, and now passing the `skiprows` option, and I get a correctly parsed `df` with all the guilty lines missing:
``` python
In [11]: df = pd.read_csv('example.csv', skiprows=skip_id+1)
In [12]: df
Out[12]:
Bill Date Meter # Type SIC Code Billing Days Rate Code
0 Oct-11 1892213 E 8111 29 ALTOU
1 Nov-11 1892213 E 8111 29 ALTOU
2 Oct-11 553961 G 8111 29 GN3
3 Nov-11 553961 G 8111 29 GN3
4 Oct-11 6322158 E 29 A
5 Nov-11 6322158 E 29 A
```
Started tracing for troubleshooting, but after the 5th handoff of the `skiprows` parameter all over in `.io`, I gave up. :(
Thoughts?
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} | 23 | 2013-09-20T18:58:30Z | 2014-08-15T20:01:48Z | 2013-09-21T12:03:26Z | CONTRIBUTOR | null | closes #4902, Added a check for complex numbers.
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} | 2 | 2013-09-20T20:10:03Z | 2018-07-06T20:21:01Z | 2017-07-10T21:08:44Z | CONTRIBUTOR | null | ``` python
In [1]: import pandas as pd
In [2]: panel = pd.util.testing.makePa
pd.util.testing.makePanel pd.util.testing.makePanel4D
In [2]: panel = pd.util.testing.makePanel()
In [3]: panel.resample("W")
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-b71dd2bc83c6> in <module>()
----> 1 panel.resample("W")
/Users/datacaliber/Dropbox/Projects/trading/python/externals/pandas/pandas/core/generic.py in resample(self, rule, how, axis, fill_method, closed, label, convention, kind, loffset, limit, base)
2138 fill_method=fill_method, convention=convention,
2139 limit=limit, base=base)
-> 2140 return sampler.resample(self)
2141
2142 def first(self, offset):
/Users/datacaliber/Dropbox/Projects/trading/python/externals/pandas/pandas/tseries/resample.pyc in resample(self, obj)
99 return obj
100 else: # pragma: no cover
--> 101 raise TypeError('Only valid with DatetimeIndex or PeriodIndex')
102
103 rs_axis = rs._get_axis(self.axis)
TypeError: Only valid with DatetimeIndex or PeriodIndex
```
Would expect this to default to the major axis.
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} | 37 | 2013-09-20T22:41:00Z | 2021-04-11T03:22:04Z | null | CONTRIBUTOR | null | Just using the existing configuration framework, but with a file format like matplotlib uses... See how they do it here: http://matplotlib.org/users/customizing.html.
Plus, we can document all the config options in a single file.
related #2452, #3046
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} | 6 | 2013-09-20T22:46:34Z | 2013-09-27T12:31:53Z | 2013-09-27T12:31:26Z | MEMBER | null | dummy issue to track this one:
https://code.google.com/p/numexpr/issues/detail?id=115
https://github.com/PyTables/PyTables/issues/283
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} | 17 | 2013-09-21T00:23:39Z | 2014-06-13T23:57:44Z | 2013-09-22T22:17:09Z | CONTRIBUTOR | null | Closes #4859.
Adds a method that can quickly tell whether something is a view or not
(in the way that actually matters). Also changes checks in `equals()` to
use `is_` rather than `is`.
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} | 0 | 2013-09-21T00:46:00Z | 2014-03-25T00:35:08Z | 2013-10-04T20:19:07Z | CONTRIBUTOR | null | With 0.12 and development version it's not possible fo feed a generator to Dataframe.from_records if nrows = None, it leds to a TypeError exception see: http://stackoverflow.com/questions/18915941/create-a-pandas-dataframe-from-generator
It's no clear whats nrows do, it's not documented.
I've create a PR with a simple fix without touch nrows, and two tests for generator data input.
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} | 14 | 2013-09-21T00:56:24Z | 2014-06-20T19:30:09Z | 2013-10-04T20:19:07Z | CONTRIBUTOR | null | closes #4910
- nrows implementation doesn't allow unknown size iterator like
generators, if nrows = none ends with a TypeError.
- To allow generators if nrows=None consume it into a list.
- Add two tests of generators input.
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} | 5 | 2013-09-21T02:28:14Z | 2014-07-16T08:29:28Z | 2013-09-21T18:26:06Z | MEMBER | null | Just a couple of cleanups here and there
Adds a new decorator to skip matplotlib in a class method
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} | 13 | 2013-09-21T12:41:29Z | 2013-10-03T16:18:30Z | 2013-10-03T16:18:30Z | NONE | null | We use Pandas to analyse flight data. Many of the recorded measurements are 3D vectors of double, e.g. position, velocity, acceleration. Currently I can only store scalars or objects in a time series. Scalars make the dataset very large, processing it is not very convenient.
I would like to be able to do:
df.velocity.norm().plot()
to plot the norm of the velocity vector that is stored in the data-frame.
Currently I have to type:
pd.Series(np.sqrt(df.velocity_x**2 + df.velocity_y**2 + df.velocity_z**2)).plot()
which is not very convenient.
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} | 3 | 2013-09-21T13:36:05Z | 2014-11-04T22:50:04Z | 2013-12-15T21:00:17Z | MEMBER | null | Here's a notebook:
http://nbviewer.ipython.org/6650724
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} | 97 | 2013-09-21T13:52:05Z | 2014-06-12T22:49:52Z | 2013-10-09T20:30:04Z | CONTRIBUTOR | null | closes #4434
closes #1892
I've basically just hacked out the Series interpolate and stuffed it under generic under a big `if` statement. Gonna make that much cleaner. Moved the interpolation and fillna specific tests in `test_series.py` to `test_generic.py`.
API question for you all. The interpolation procedures in Scipy take an array of x-values and an array of y-values that form the basis for the interpolation object. The interpolation object can then be evaluated wherever, but it maps X -> Y; f(x-values) == y-values. So we have 3 arrays to deal with:
1. x-values
2. y-values
3. new values
Preferences for names? The other issue is for defaults. Right now I'm thinking
1. x-values: index if numeric.
2. y-values: the Series.values if Series. Each numeric column of DataFrame if DataFrame?
3. new values: Fill NaNs by default. Interpolate at `new values` if an array is given.
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} | 16 | 2013-09-21T15:14:44Z | 2021-04-11T03:24:35Z | 2021-04-11T03:24:34Z | CONTRIBUTOR | null | Decide what to do with each of these
- [x] deprecate `from_csv` ~~/ change to be exactly like `read_csv`~~ -> https://github.com/pandas-dev/pandas/pull/17812
- [ ] from_dict
- [ ] from_records (see #8161)
- [x] from_items (#18529)
This is just a suggestion: `DataFrame` constructor already supports the things that `from_dict` and `from_records` do (`from_items` is not supported by the constructor, but maybe it should be?). Also `from_csv` does the same thing as `pd.read_csv`.
Since `There should be one-- and preferably only one --obvious way to do it.` maybe these methods should be removed/deprecated?
I know that this would cause backward incompatibility, so maybe just a deprecation warning could be displayed until a future major release that could remove them.
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```
url = "http://cameron.econ.ucdavis.edu/mmabook/patr7079.asc"
names = ["CUSIP", "ARDSSIC", "SCISECT", "LOGK", "SUMPAT", "LOGR70",
"LOGR71", "LOGR72", "LOGR73", "", "LOGR74", "LOGR75", "LOGR76",
"LOGR77", "LOGR78", "LOGR79", "PAT70", "PAT71", "PAT72", "PAT73",
"PAT74", "PAT75", "PAT76", "PAT77", "PAT78", "PAT79"]
dta = pd.read_table(url, sep=r" *(?=\n)?", names=names)
```
[1] http://cameron.econ.ucdavis.edu/mmabook/patr7079.asc
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} | 37 | 2013-09-21T17:46:25Z | 2016-10-12T23:04:12Z | 2013-10-09T18:47:42Z | NONE | null | Hi, I'm testing the current pandas master on debian sid with updated numexpr and PyTables from experimental and I'm experimenting some test failures:
```
$ env PYTHONPATH=. nosetests pandas
.................................EES.FSSS......................................................S.............F..........................SSSS...SS.......................S........................................................................................................................................................................................................................................................................................................................................................S.......................................................................................................................SSSS..........................S.............................................................SSSSSSSSSS....................SS...................................................................................................................................................S...............................S.......................SSSSSSS.........................................F..............................................S....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................can't invoke "event" command: application has been destroyed
while executing
"event generate $w <<ThemeChanged>>"
(procedure "ttk::ThemeChanged" line 6)
invoked from within
"ttk::ThemeChanged"
E...................................................................................................................................................................................................................................................S........................................................................................................................................................................S..................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................S.................................................................................S.S...............S.S............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................S.............................................................................................S..............................................................................................................................................................................S...S............SSS.........S............SS............SS.....SSSS.................................................................................................................................................................................................................S.......................................................................................................................................................................................................................
======================================================================
ERROR: test_get_goog_volume (pandas.io.tests.test_data.TestGoogle)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/antonio/pandas/pandas/util/testing.py", line 857, in network_wrapper
return t(*args, **kwargs)
File "/home/antonio/pandas/pandas/io/tests/test_data.py", line 55, in test_get_goog_volume
self.assertEqual(df.Volume.ix['OCT-08-2010'], 2863473)
File "/home/antonio/pandas/pandas/core/indexing.py", line 54, in __getitem__
return self._getitem_axis(key, axis=0)
File "/home/antonio/pandas/pandas/core/indexing.py", line 631, in _getitem_axis
return self._get_label(key, axis=axis)
File "/home/antonio/pandas/pandas/core/indexing.py", line 1273, in _get_label
return self.obj[key]
File "/home/antonio/pandas/pandas/core/series.py", line 920, in __getitem__
return self.index.get_value(self, key)
File "/home/antonio/pandas/pandas/tseries/index.py", line 1184, in get_value
raise KeyError(key)
KeyError: 'OCT-08-2010'
-------------------- >> begin captured stdout << ---------------------
Failed: KeyError('OCT-08-2010',)
--------------------- >> end captured stdout << ----------------------
======================================================================
ERROR: test_get_multi1 (pandas.io.tests.test_data.TestGoogle)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/antonio/pandas/pandas/util/testing.py", line 857, in network_wrapper
return t(*args, **kwargs)
File "/home/antonio/pandas/pandas/io/tests/test_data.py", line 68, in test_get_multi1
testit()
File "/home/antonio/pandas/pandas/io/tests/test_data.py", line 64, in testit
self.assertEquals(ts[0].dayofyear, 96)
File "/home/antonio/pandas/pandas/tseries/index.py", line 1298, in __getitem__
val = arr_idx[key]
IndexError: index 0 is out of bounds for axis 0 with size 0
-------------------- >> begin captured stdout << ---------------------
Failed: IndexError('index 0 is out of bounds for axis 0 with size 0',)
--------------------- >> end captured stdout << ----------------------
======================================================================
ERROR: test_axis_limits (pandas.tseries.tests.test_plotting.TestTSPlot)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/antonio/pandas/pandas/tseries/tests/test_plotting.py", line 344, in test_axis_limits
_test(ax)
File "/home/antonio/pandas/pandas/tseries/tests/test_plotting.py", line 308, in _test
ax.get_figure().canvas.draw()
File "/usr/lib/pymodules/python2.7/matplotlib/backends/backend_tkagg.py", line 349, in draw
tkagg.blit(self._tkphoto, self.renderer._renderer, colormode=2)
File "/usr/lib/pymodules/python2.7/matplotlib/backends/tkagg.py", line 20, in blit
tk.call("PyAggImagePhoto", photoimage, id(aggimage), colormode, id(bbox_array))
TclError: this isn't a Tk application
======================================================================
FAIL: test_google (pandas.io.tests.test_data.TestGoogle)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/antonio/pandas/pandas/util/testing.py", line 857, in network_wrapper
return t(*args, **kwargs)
File "/home/antonio/pandas/pandas/io/tests/test_data.py", line 42, in test_google
13.68)
AssertionError: 17.66 != 13.68
-------------------- >> begin captured stdout << ---------------------
Failed: AssertionError('17.66 != 13.68',)
--------------------- >> end captured stdout << ----------------------
======================================================================
FAIL: test_invalid_url (pandas.io.tests.test_html.TestReadHtmlBase)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/antonio/pandas/pandas/util/testing.py", line 857, in network_wrapper
return t(*args, **kwargs)
File "/home/antonio/pandas/pandas/io/tests/test_html.py", line 299, in test_invalid_url
'http://www.a23950sdfa908sd.com')
AssertionError: No tables found
-------------------- >> begin captured stdout << ---------------------
Failed: AssertionError('No tables found',)
--------------------- >> end captured stdout << ----------------------
======================================================================
FAIL: testWLS (pandas.stats.tests.test_ols.TestOLS)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/antonio/pandas/pandas/stats/tests/test_ols.py", line 111, in testWLS
self._check_wls(X, Y, weights)
File "/home/antonio/pandas/pandas/stats/tests/test_ols.py", line 134, in _check_wls
self.checkMovingOLS('rolling', x, y, weights=weights)
File "/home/antonio/pandas/pandas/stats/tests/test_ols.py", line 208, in checkMovingOLS
result_index=n)
File "/home/antonio/pandas/pandas/stats/tests/test_ols.py", line 247, in compare
assert_almost_equal(ref, res)
File "/home/antonio/pandas/pandas/util/testing.py", line 198, in assert_almost_equal
assert_almost_equal(a[i], b[i], check_less_precise)
File "/home/antonio/pandas/pandas/util/testing.py", line 227, in assert_almost_equal
1, a / b, decimal=decimal, err_msg=err_msg(a, b), verbose=False)
File "/usr/lib/pymodules/python2.7/numpy/testing/utils.py", line 468, in assert_almost_equal
raise AssertionError(msg)
AssertionError:
Arrays are not almost equal to 5 decimals expected 6.61194 but got 7.66003
-------------------- >> begin captured stdout << ---------------------
Make sure you're using statsmodels 0.5.0.dev-cec4f26 or later.
--------------------- >> end captured stdout << ----------------------
----------------------------------------------------------------------
Ran 3933 tests in 407.389s
FAILED (SKIP=64, errors=3, failures=3)
```
Some cfg info:
```
PyTables version: 3.0.0
HDF5 version: 1.8.11
NumPy version: 1.7.1
Numexpr version: 2.2 (not using Intel's VML/MKL)
Python version: 2.7.5+ (default, Sep 17 2013, 15:31:50) [GCC 4.8.1]
Platform: linux2-x86_64
```
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} | 4 | 2013-09-21T17:59:40Z | 2015-10-14T11:45:06Z | 2015-10-14T11:45:06Z | CONTRIBUTOR | null | Right now the Google Analytics functionality is too specific to the Core Reporting API. It should be generalized to handle MultiChannel Attribution as well.
For example,
```
def _get_col_names(header_info):
return [x['name'][3:] for x in header_info]
```
Would break because headers in are prepended with `mcf:` in the MultiChannel Attribution API.
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} | 5 | 2013-09-21T18:49:44Z | 2013-12-07T14:15:52Z | 2013-12-07T14:15:52Z | CONTRIBUTOR | null | This has been a pain point for me. The use case is pretty specific. Cf. [reshape long](http://www.stata.com/help.cgi?reshape) in stata.
https://gist.github.com/jseabold/6653108
Can add tests and make a PR, if desired.
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} | 1 | 2013-09-21T20:53:20Z | 2014-06-24T23:41:01Z | 2013-09-21T21:29:28Z | CONTRIBUTOR | null | It's unnecessary - get file handle wraps this instead.
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} | 14 | 2013-09-21T21:47:41Z | 2014-06-20T02:58:26Z | 2013-09-27T19:34:46Z | MEMBER | null | closes #4897
Also adds `DatetimeIndex` comparisons in query expressions and a corresponding vbench for a simple `Timestamp` vs either `Series` or `DatetimeIndex`.
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} | 11 | 2013-09-21T21:52:36Z | 2014-03-17T17:58:22Z | 2014-02-24T20:20:54Z | MEMBER | null | right now it "works", but it's not tested:
```
In [34]: df = DataFrame({'gender': tm.choice(['Male', 'Female'], size=n), 'height': random.normal(66, 4, size=n)})
In [35]: df
Out[35]:
gender height
0 Female 75.295
1 Male 64.490
2 Male 68.218
3 Male 62.713
4 Male 63.967
5 Female 69.106
6 Male 61.441
7 Male 69.658
8 Male 67.151
9 Female 64.251
In [36]: df.groupby('gender').query('61 < height < 65')
Out[36]:
gender height
gender
Female 9 Female 64.251
Male 1 Male 64.490
3 Male 62.713
4 Male 63.967
6 Male 61.441
```
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} | 3 | 2013-09-21T21:57:03Z | 2014-06-21T19:09:49Z | 2013-10-07T23:00:26Z | CONTRIBUTOR | null | closes #4065
To fix the bug, this change adds bounds checking to
_get_datetime64_nanos() for numpy datetimes that aren't already in [ns]
units.
Additionally, it updates _check_dts_bounds() to do the bound check just
based off the pandas_datetimestruct, by comparing to the minimum and
maximum valid pandas_datetimestructs for datetime64[ns]. It is simpler
and more accurate than the previous system.
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} | 5 | 2013-09-22T01:24:34Z | 2016-10-12T23:04:12Z | 2013-09-23T03:45:56Z | NONE | null | Webservers are currently rejecting calls from the `read_html()` function because of unrecognized headers.
e.g.,
``` python
import pandas as pd
url = r'http://www.transfermarkt.co.uk/en/premier-league/gegentorminuten/wettbewerb_GB1.html'
df = pd.read_html(url)
```
return an "ValueError: Invalid URL"
The same seems to be true for the default parameters for the urllib2.urlopen() function i.e. urllib2.urlopen(url)
However if the user-agent is modified, calls are successful.
e.g.
``` python
import urllib2
opener = urllib2.build_opener()
opener.addheaders = [('User-agent', 'Mozilla/5.0')]
url = r'http://www.transfermarkt.co.uk/en/premier-league/gegentorminuten/wettbewerb_GB1.html'
response = opener.open(url)
html = response.read()
```
Can we modify the code to include a browser_agent variable to be passed for the read_html function?
This case has been raised as per the advice at Stack-overflow - http://stackoverflow.com/questions/18939133/how-to-modify-pandass-read-html-user-agent
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} | 6 | 2013-09-22T01:30:56Z | 2013-10-08T14:30:56Z | 2013-10-08T14:30:56Z | MEMBER | null | cc @danbirken
```
In [2]: to_datetime([1,'1'])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-2-1d4cd9e078aa> in <module>()
----> 1 to_datetime([1,'1'])
/home/phillip/Documents/code/py/pandas/pandas/tseries/tools.pyc in to_datetime(arg, errors, dayfirst, utc, box, format, coerce, unit)
137 return Series(values, index=arg.index, name=arg.name)
138 elif com.is_list_like(arg):
--> 139 return _convert_listlike(arg, box=box)
140
141 return _convert_listlike(np.array([ arg ]), box=box)[0]
/home/phillip/Documents/code/py/pandas/pandas/tseries/tools.pyc in _convert_listlike(arg, box)
117 result = tslib.array_to_datetime(arg, raise_=errors == 'raise',
118 utc=utc, dayfirst=dayfirst,
--> 119 coerce=coerce, unit=unit)
120 if com.is_datetime64_dtype(result) and box:
121 result = DatetimeIndex(result, tz='utc' if utc else None)
/home/phillip/Documents/code/py/pandas/pandas/tslib.so in pandas.tslib.array_to_datetime (pandas/tslib.c:16487)()
TypeError: object of type 'int' has no len()
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4929 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4929/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4929/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4929/events | https://github.com/pandas-dev/pandas/pull/4929 | 19,866,237 | MDExOlB1bGxSZXF1ZXN0ODUwNzczMw== | 4,929 | BUG: Fix FrozenNDArray & FrozenList string methods | {
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} | 0 | 2013-09-22T01:35:30Z | 2014-06-13T23:41:37Z | 2013-09-23T01:24:51Z | CONTRIBUTOR | null | Plus basic tests (i.e., "Hey! If you pass me unicode I don't fail - yay!")
Previously were showing bad object reprs, now do this:
```
In [4]: mi = MultiIndex.from_arrays([range(10), range(10)])
In [5]: mi
Out[5]:
MultiIndex
[(0, 0), (1, 1), (2, 2), (3, 3), (4, 4), (5, 5), (6, 6), (7, 7), (8, 8), (9, 9)]
In [6]: mi.levels
Out[6]: FrozenList([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]])
In [7]: mi.levels[0]
Out[7]: Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64)
In [8]: mi.labels[0]
Out[8]: FrozenNDArray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64)
In [9]: mi.labels[1]
Out[9]: FrozenNDArray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64)
In [10]: mi = MultiIndex.from
MultiIndex.from_arrays MultiIndex.from_tuples
In [10]: mi = MultiIndex.from_arrays([range(5), list('abcde')])
In [11]: mi.levels
Out[11]: FrozenList([[0, 1, 2, 3, 4], [u'a', u'b', u'c', u'd', u'e']])
In [12]: mi.labels[0]
Out[12]: FrozenNDArray([0, 1, 2, 3, 4], dtype=int64)
In [13]: mi.labels[1]
Out[13]: FrozenNDArray([0, 1, 2, 3, 4], dtype=int64)
In [14]: mi.levels[0]
Out[14]: Int64Index([0, 1, 2, 3, 4], dtype=int64)
In [15]: mi.levels[1]
Out[15]: Index([u'a', u'b', u'c', u'd', u'e'], dtype=object)
```
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} | 10 | 2013-09-22T02:28:22Z | 2014-06-24T19:51:24Z | 2013-09-23T03:29:44Z | MEMBER | null | closes #4927
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} | 4 | 2013-09-22T02:40:11Z | 2015-03-03T01:45:05Z | 2015-03-03T01:45:05Z | CONTRIBUTOR | null | 0.12 and current master have this problem (problem is that we were only testing frames with floats):
```
>>> df = DataFrame(range(10))
>>> df.dtypes
0 int64
dtype: object
>>> df.index
Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64)
>>> df.to_excel('test1.xlsx')
>>> new_df = read_excel('test1.xlsx', 'sheet1')
>>> new_df.index
Index([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0], dtype=object)
>>> new_df.dtypes
0 float64
dtype: object
```
Can we do some type inference here or something? `read_csv` handles it better:
```
>>> df.to_csv('out.csv')
>>> new_df = read_csv('out.csv')
>>> new_df.index
Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64)
>>> new_df.dtypes
Unnamed: 0 int64
0 int64
dtype: object
```
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} | 3 | 2013-09-22T02:47:02Z | 2014-06-16T04:28:54Z | 2013-09-23T04:15:21Z | CONTRIBUTOR | null | Fixes #3441.
both can be used in with statements now. Makes it easier to use with multiple
writers, etc.
Plus touch up the docs on writers and such.
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} | 12 | 2013-09-22T05:21:27Z | 2014-04-09T12:17:29Z | 2014-04-09T12:17:29Z | CONTRIBUTOR | null | ``` python
import pandas as pd
import numpy as np
N = 1000000
bools = np.random.randn(N, 1) > 0
df = pd.DataFrame(bools)
df * df
```
```
/python2.7/site-packages/numexpr/necompiler.pyc in typeCompileAst(ast)
147 raise NotImplementedError(
148 "couldn't find matching opcode for '%s'"
--> 149 % (ast.value + '_' + retsig+basesig))
150 # First just cast constants, then cast variables if necessary:
151 for i, (have, want) in enumerate(zip(basesig, sig)):
NotImplementedError: couldn't find matching opcode for 'mul_bbb'
```
I'm guessing there's a size trigger that throws ops to `numexpr` since this doesn't fail for smaller frames.
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} | 2 | 2013-09-22T05:28:08Z | 2014-06-20T15:58:33Z | 2013-09-27T00:30:19Z | CONTRIBUTOR | null | Fixes #3347.
All interested parties are invited to submit test cases. cc @y-p @hayd
---
Examples:
```
In [1]: import pandas as pd
In [2]: pd.MultiIndex.from_arrays([[1, 1, 1, 1], [1, 3, 5, 7], [9, 9, 1, 1]])
Out[2]:
MultiIndex(levels=[[1], [1, 3, 5, 7], [1, 9]]
labels=[[0, 0, 0, 0], [0, 1, 2, 3], [1, 1, 0, 0]])
In [3]: mi = _
In [4]: mi.names = list('abc')
In [5]: print mi
a b c
1 1 9
3 9
5 1
7 1
```
And with too many rows:
```
In [10]: pd.set_option('display.max_rows', 15)
In [11]: lst1 = [1] * 3 + [2] * 5 + [3] * 2
In [12]: lst1
Out[12]: [1, 1, 1, 2, 2, 2, 2, 2, 3, 3]
In [13]: lst2 = ['a'] * 6 + ['b'] * 3 + ['c'] * 1
In [14]: lst2
Out[14]: ['a', 'a', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'c']
In [15]: mi = pd.MultiIndex.from_arrays([lst1 * 10, lst2 * 10, range(100)]); mi
Out[15]:
MultiIndex(levels=[[1, 2, 3], [u'a', u'b', u'c'], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]]
labels=[[0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2], [0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])
In [16]: mi = _
In [17]: print mi
1 a 0
1
2
2 a 3
4
5
...
2 a 93
94
95
b 96
97
3 b 98
c 99
```
Not sure how to handle too wide -- wrap? format() doesn't try to sparsify after a column with values everywhere.
```
In [19]: mi = pd.MultiIndex.from_arrays([lst1 * 10, lst2 * 10, range(100)] * 10)
In [20]: print mi
1 a 0 1 a 0 1 a 0 1 a 0 1 a 0 1 a 0 1 a 0 1 a 0 1 a 0 1 a 0
1 1 a 1 1 a 1 1 a 1 1 a 1 1 a 1 1 a 1 1 a 1 1 a 1 1 a 1
2 1 a 2 1 a 2 1 a 2 1 a 2 1 a 2 1 a 2 1 a 2 1 a 2 1 a 2
2 a 3 2 a 3 2 a 3 2 a 3 2 a 3 2 a 3 2 a 3 2 a 3 2 a 3 2 a 3
4 2 a 4 2 a 4 2 a 4 2 a 4 2 a 4 2 a 4 2 a 4 2 a 4 2 a 4
5 2 a 5 2 a 5 2 a 5 2 a 5 2 a 5 2 a 5 2 a 5 2 a 5 2 a 5
...
2 a 93 2 a 93 2 a 93 2 a 93 2 a 93 2 a 93 2 a 93 2 a 93 2 a 93 2 a 93
94 2 a 94 2 a 94 2 a 94 2 a 94 2 a 94 2 a 94 2 a 94 2 a 94 2 a 94
95 2 a 95 2 a 95 2 a 95 2 a 95 2 a 95 2 a 95 2 a 95 2 a 95 2 a 95
b 96 2 b 96 2 b 96 2 b 96 2 b 96 2 b 96 2 b 96 2 b 96 2 b 96 2 b 96
97 2 b 97 2 b 97 2 b 97 2 b 97 2 b 97 2 b 97 2 b 97 2 b 97 2 b 97
3 b 98 3 b 98 3 b 98 3 b 98 3 b 98 3 b 98 3 b 98 3 b 98 3 b 98 3 b 98
c 99 3 c 99 3 c 99 3 c 99 3 c 99 3 c 99 3 c 99 3 c 99 3 c 99 3 c 99
```
```
In [9]: labels = [('foo', '2012-07-26T00:00:00', 'b5c2700'),
...: ('foo', '2012-08-06T00:00:00', '900b2ca'),
...: ('foo', '2012-08-15T00:00:00', '07f1ce0'),
...: ('foo', '2012-09-25T00:00:00', '5c93e83'),
...: ('foo', '2012-09-25T00:00:00', '9345bba')]
In [10]: print pd.MultiIndex.from_tuples(labels)
foo 2012-07-26T00:00:00 b5c2700
2012-08-06T00:00:00 900b2ca
2012-08-15T00:00:00 07f1ce0
2012-09-25T00:00:00 5c93e83
9345bba
```
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} | 7 | 2013-09-22T16:11:12Z | 2014-06-12T14:29:29Z | 2013-09-30T13:44:54Z | CONTRIBUTOR | null | `to_json` has a records type that turns a DataFrame into a
```
[{'a': 1, 'b': 3}, {'a': 2, 'b': 4}]
```
records json string. It'd be nice if you could do a similar thing but not have to go `json.loads`.
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} | 0 | 2013-09-22T17:22:12Z | 2014-03-20T12:34:07Z | 2013-09-22T19:28:16Z | CONTRIBUTOR | null | ```
In [40]: x = Panel(dict(A = dict(a = ['1','1.0'])))
In [41]: x
Out[41]:
<class 'pandas.core.panel.Panel'>
Dimensions: 1 (items) x 2 (major_axis) x 1 (minor_axis)
Items axis: A to A
Major_axis axis: 0 to 1
Minor_axis axis: a to a
In [42]: x.values
Out[42]:
array([[['1'],
['1.0']]], dtype=object)
In [43]: x.convert_objects(convert_numeric='force')
ValueError: Buffer has wrong number of dimensions (expected 1, got 2)
```
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} | 0 | 2013-09-22T18:46:31Z | 2014-06-22T04:28:57Z | 2013-09-22T19:28:16Z | CONTRIBUTOR | null | closes #4937
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} | 2 | 2013-09-22T22:41:13Z | 2013-09-23T00:25:20Z | 2013-09-23T00:24:59Z | CONTRIBUTOR | null | When doing setting with enlargement like:
``` python
import pandas as pd
panel = pd.util.testing.makePanel()
pd.ix[0] # get first item into cache
panel.ix[:, :, 'A+1'] = panel.ix[:, :, 'A'] + 1
assert "A+1" in panel.ix[0].columns # fails due to stale item cache
assert "A+1" in panel.ix[1].columns # succeeds
```
The same thing happens with DataFrames.
``` python
df = pd.util.testing.makeDataFrame()
df['A'] # cache series
df.ix["Hello Friend"] = df.ix[0]
assert "Hello Friend" in df['A'].index # fails
assert "Hello Friend" in df['B'].index # success
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4940 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4940/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4940/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4940/events | https://github.com/pandas-dev/pandas/issues/4940 | 19,880,937 | MDU6SXNzdWUxOTg4MDkzNw== | 4,940 | Adding new row on TimeDataFrame silently does nothing for integers. | {
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] | closed | false | null | [] | null | 4 | 2013-09-22T22:45:42Z | 2014-01-02T07:37:14Z | 2013-09-23T11:38:40Z | CONTRIBUTOR | null | ``` python
df = pd.util.testing.makeTimeDataFrame()
df.ix[100,:] = df.ix[0] # does nothing
df.ix[100.0, :] = df.ix[0] # adds a new row
```
Not sure why someone would do such lunacy, but figured I'd report it :P
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} | 5 | 2013-09-22T23:37:57Z | 2014-08-13T07:00:34Z | 2013-10-01T00:05:03Z | CONTRIBUTOR | null | closes #4717
```
In [2]: pd.DataFrame.reindex?
Type: instancemethod
String Form:<unbound method DataFrame.reindex>
File: /mnt/home/jreback/pandas/pandas/core/generic.py
Definition: pd.DataFrame.reindex(self, *args, **kwargs)
Docstring:
Conform DataFrame to new index with optional filling logic, placing
NA/NaN in locations having no value in the previous index. A new object
is produced unless the new index is equivalent to the current one and
copy=False
Parameters
----------
index : array-like, optional
New labels / index to conform to. Preferably an Index object to
avoid duplicating data
columns : array-like, optional
New labels / index to conform to. Preferably an Index object to
avoid duplicating data
method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
Method to use for filling holes in reindexed DataFrame
pad / ffill: propagate last valid observation forward to next valid
backfill / bfill: use NEXT valid observation to fill gap
copy : boolean, default True
Return a new object, even if the passed indexes are the same
level : int or name
Broadcast across a level, matching Index values on the
passed MultiIndex level
fill_value : scalar, default np.NaN
Value to use for missing values. Defaults to NaN, but can be any
"compatible" value
limit : int, default None
Maximum size gap to forward or backward fill
takeable : boolean, default False
treat the passed as positional values
Examples
--------
>>> df.reindex(index=[date1, date2, date3], columns=['A', 'B', 'C'])
Returns
-------
reindexed : DataFrame
```
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} | 17 | 2013-09-23T03:43:26Z | 2014-06-24T14:30:24Z | 2013-09-26T01:03:38Z | CONTRIBUTOR | null | closes #4678
closes #4322
I've fixed the C parser portion. The issue there was that it did not handle the case where parse_dates is a dict.
Python parser fix yet to come. That test still fails.
Example:
s = '06-02-2013;13:00;1-000,215;0,215;0,185;0,205;0,00'
d = pd.read_csv(StringIO(s), header=None, parse_dates={'Date': [0, 1]}, thousands='-', sep=';', decimal=',')
Then d should have a column 'Date' with value "2013-06-02 13:00:00"
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} | 1 | 2013-09-23T03:55:01Z | 2013-09-27T17:04:48Z | 2013-09-27T17:04:48Z | MEMBER | null | 0.4.2 should work (read: be tested) with pandas.
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} | 0 | 2013-09-23T06:05:05Z | 2013-10-01T13:28:40Z | 2013-10-01T13:28:40Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/18952544/in-python-pandas-boolean-operation
```
In [1]: a = pd.Series([True, False, True], list('bca'))
In [2]: b = pd.Series([False, True, False], list('abc'))
In [3]: a & b
Out[3]:
b False
c False
a False
dtype: bool
```
Expected to use labels:
```
In [6]: index = a.index | b.index
In [7]: a.reindex(index) & b.reindex(index)
Out[7]:
a False
b True
c False
dtype: bool
```
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} | 9 | 2013-09-23T07:16:55Z | 2013-10-14T01:38:51Z | 2013-10-14T01:38:51Z | NONE | null | The download link for ver 0.11 on the pandas.pydata.org (in the right panel, Previous Releases ) links to 0.10
The correct URL would be:
https://pypi.python.org/pypi/pandas/0.11.0/#downloads
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} | [] | closed | false | null | [] | null | 2 | 2013-09-23T07:17:20Z | 2013-09-23T22:30:39Z | 2013-09-23T10:14:17Z | CONTRIBUTOR | null | In https://github.com/pydata/pandas/commit/0bc568fb5e19208b30255d771dbf08552f47a4ba#diff-04c6e90faac2675aa89e2176d2eec7d8
you changed to markdown.
Why?
It had stopped to daily Ubuntu builds. So I am just interested in the reason.
After a short explanation, you may close here.
Thanks.
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} | 29 | 2013-09-23T12:23:36Z | 2014-06-12T17:47:11Z | 2014-03-28T14:47:49Z | CONTRIBUTOR | null | I added the functions rolling_cov_pairwise(), expanding_cov_pairwise() and ewmcov_pairwise(). I also modified the behaviour of rolling_corr_pairwise(), expanding_corr_pairwise() and ewmcorr_pairwise() slightly so that now these can be computed between different DataFrames and the resulting matrices at each time slice will be rectangular rather than square.
I think this is what was asked for in the original discussion that gave rise to issue #1853.
Fixes #1853
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} | 35 | 2013-09-23T18:15:50Z | 2014-06-26T17:16:32Z | 2013-10-01T13:28:40Z | CONTRIBUTOR | null | closes #4947
You ask for a boolean comparsion, that is what you get
```
In [1]: a = Series([True, False, True], list('bca'))
In [2]: b = Series([False, True, False], list('abc'))
In [19]: a
Out[19]:
b True
c False
a True
dtype: bool
In [20]: b
Out[20]:
a False
b True
c False
dtype: bool
In [21]: a & b
Out[21]:
b True
c False
a False
dtype: bool
In [22]: a | b
Out[22]:
b True
c False
a True
dtype: bool
In [23]: a ^ b
Out[23]:
b False
c False
a True
dtype: bool
In [24]: a & Series([])
Out[24]:
b False
c False
a False
dtype: bool
In [25]: a | Series([])
Out[25]:
b True
c False
a True
dtype: bool
```
these ok?
```
In [9]: Series([np.nan,np.nan])&Series([np.nan,np.nan])
Out[9]:
0 False
1 False
dtype: bool
In [10]: Series([np.nan,np.nan])|Series([np.nan,np.nan])
Out[10]:
0 False
1 False
dtype: bool
```
scalars
```
In [30]: a | True
Out[30]:
b True
c True
a True
dtype: bool
In [31]: a | False
Out[31]:
b True
c False
a True
dtype: bool
In [32]: a & True
Out[32]:
b True
c False
a True
dtype: bool
In [33]: a & False
Out[33]:
b False
c False
a False
dtype: bool
```
Invalid scalars
```
In [28]: a | 'foo'
TypeError: cannot compare a dtyped [bool] array with a scalar of type [bool]
In [29]: a | np.nan
TypeError: cannot compare a dtyped [bool] array with a scalar of type [float]
```
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} | 9 | 2013-09-23T20:04:51Z | 2014-06-19T23:59:23Z | 2013-09-25T15:10:31Z | CONTRIBUTOR | null | Fixes issue https://github.com/pydata/pandas/issues/3415.
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} | 37 | 2013-09-23T22:52:12Z | 2014-06-13T19:04:55Z | 2013-09-30T10:18:12Z | CONTRIBUTOR | null | closes #4488
Implemented an algorithm that uses a bitmask to detect the gaps between the columns.
Also the reader buffers the lines used for detection in case it's input is not seek-able.
Added tests.
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} | 2 | 2013-09-24T01:52:16Z | 2014-06-12T14:21:17Z | 2013-09-24T02:20:40Z | MEMBER | null | TypeError was being raised when a ValueError was raised because the
ValueError's message wasn't being converted to a string.
closes #4956
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} | 2 | 2013-09-24T02:23:45Z | 2014-01-29T05:54:33Z | 2014-01-29T05:54:33Z | CONTRIBUTOR | null | Still don't have down the code to dispatch appropriately (though I'm not
even clear why the existing code has individual flavors, given that it
appears to only accept SQLAlchemy objects anyways). Also would be
helpful to cover additional cases where we get connections and want to
convert to cursors.
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See: https://github.com/pydata/pandas/blob/master/pandas/io/excel.py#L110
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} | 6 | 2013-09-24T03:57:24Z | 2013-10-03T21:14:18Z | 2013-10-03T21:14:18Z | CONTRIBUTOR | null | ``` python
import pandas as pd
import numpy as np
data = np.random.randn(5, 100, 5)
panel = pd.Panel(data, items=list("AACDE"))
panel.iloc[0] # ValueError: Wrong number of dimensions
fr = panel.loc["E"]
fr.values.shape # (5, 100, 5) wrong
```
So this is another contrived example that I got from just running a battery of tests on random objects. Basically having a duplicate item key will bork the `Panel`.
I'm actually not sure whether Panel is meant to be a collection of uniquely identified DataFrames, or more like a generic 3d container that has convenient semantics for its axes. I think at one point it was the former, but as more things pull from generic it's becoming the later?
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https://api.github.com/repos/pandas-dev/pandas/issues/4961 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4961/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4961/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4961/events | https://github.com/pandas-dev/pandas/issues/4961 | 19,956,653 | MDU6SXNzdWUxOTk1NjY1Mw== | 4,961 | Change ExcelFile to accept a workbook for the path_or_buf argument | {
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} | [] | closed | false | null | [] | null | 0 | 2013-09-24T05:01:24Z | 2013-09-28T20:43:47Z | 2013-09-27T04:39:58Z | CONTRIBUTOR | null | Change ExcelFile to accept a workbook for the path_or_buf argument. That will also mean that read_excel accepts workbooks too.
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https://api.github.com/repos/pandas-dev/pandas/issues/4962 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4962/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4962/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4962/events | https://github.com/pandas-dev/pandas/pull/4962 | 19,956,720 | MDExOlB1bGxSZXF1ZXN0ODU0NzY1NA== | 4,962 | ENH Change ExcelFile to accept a workbook for the path_or_buf argument. | {
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} | [] | closed | false | null | [] | null | 12 | 2013-09-24T05:04:38Z | 2014-06-26T19:32:20Z | 2013-09-27T04:39:58Z | CONTRIBUTOR | null | That will also mean that read_excel accepts workbooks too. (closes #4961 and #4959)
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https://api.github.com/repos/pandas-dev/pandas/issues/4963 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4963/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4963/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4963/events | https://github.com/pandas-dev/pandas/issues/4963 | 19,982,621 | MDU6SXNzdWUxOTk4MjYyMQ== | 4,963 | BUG: unicode(x) causes stack overflow when x is a 0-d index | {
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] | closed | false | null | [] | null | 3 | 2013-09-24T14:47:46Z | 2013-09-24T15:02:11Z | 2013-09-24T14:52:32Z | CONTRIBUTOR | null | The issue can be reproduced at current 'master' as follows:
``` python
$ python
Python 2.7.3 (default, Jul 5 2013, 08:39:51)
[GCC 4.6.3] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas
>>> pandas.__version__
'0.12.0-21-g3dab215'
>>> unicode(pandas.Index([0]).max())
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/core/index.py", line 151, in __unicode__
prepr = com.pprint_thing(self, escape_chars=('\t', '\r', '\n'),quote_strings=True)
File "pandas/core/common.py", line 2067, in pprint_thing
result = as_escaped_unicode(thing)
File "pandas/core/common.py", line 2028, in as_escaped_unicode
result = unicode(thing) # we should try this first
File "pandas/core/index.py", line 151, in __unicode__
prepr = com.pprint_thing(self, escape_chars=('\t', '\r', '\n'),quote_strings=True)
File "pandas/core/common.py", line 2067, in pprint_thing
result = as_escaped_unicode(thing)
File "pandas/core/common.py", line 2028, in as_escaped_unicode
result = unicode(thing) # we should try this first
File "pandas/core/index.py", line 151, in __unicode__
prepr = com.pprint_thing(self, escape_chars=('\t', '\r', '\n'),quote_strings=True)
File "pandas/core/common.py", line 2067, in pprint_thing
result = as_escaped_unicode(thing)
File "pandas/core/common.py", line 2028, in as_escaped_unicode
result = unicode(thing) # we should try this first
...
<snipped a lot of repeating backtrace entries>
...
File "pandas/core/index.py", line 151, in __unicode__
prepr = com.pprint_thing(self, escape_chars=('\t', '\r', '\n'),quote_strings=True)
File "pandas/core/common.py", line 2067, in pprint_thing
result = as_escaped_unicode(thing)
File "pandas/core/common.py", line 2028, in as_escaped_unicode
result = unicode(thing) # we should try this first
RuntimeError: maximum recursion depth exceeded
```
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} | 12 | 2013-09-24T15:40:42Z | 2018-03-21T15:42:42Z | 2016-03-06T00:04:08Z | NONE | null | Hi everybody,
I discovered that the rolling_apply function is only applicable to numeric columns. I think this should be changed as this seems too limited to me. Let's take the following example,
```
import datetime as DT
df = pd.DataFrame({
'Buyer': 'Carl Mark Carl Joe Joe Carl'.split(),
'Quantity': [1,3,5,8,9,3],
'Date' : [
DT.datetime(2013,9,1,13,0),
DT.datetime(2013,9,1,13,5),
DT.datetime(2013,10,1,20,0),
DT.datetime(2013,10,3,10,0),
DT.datetime(2013,12,2,12,0),
DT.datetime(2013,12,2,14,0),
]}).set_index('Date')
```
Now I want to count all new customers each 10 days.
```
buyers = []
def novices(x):
new = [n for n in x if n not in buyers]
if (len(new) > 0): buyers.extend(new)
return len(new)
pd.rolling_apply(df['Buyer'], 10, novices)
```
throws an exception "ValueError: could not convert string to float: Carl"
However, although meaningless a call with a numeric column such as:
```
pd.rolling_apply(df['Quantity'], 2, novices)
```
works.
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} | 9 | 2013-09-24T18:43:46Z | 2013-09-24T21:00:51Z | 2013-09-24T20:35:53Z | CONTRIBUTOR | null | Suggestion: Changing `print open('bar.csv').read()` statements to `print(open('bar.csv').read())` in documentation would make the docs more compatible with Python3.X and still keep it compatible with Python2.X.
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} | 3 | 2013-09-24T18:55:32Z | 2013-09-24T21:09:59Z | 2013-09-24T20:28:02Z | MEMBER | null | works
```
In [1]: df = DataFrame(randint(10, size=(10, 1)), columns=['a'])
In [2]: df['dates'] = date_range('20010101', periods=len(df))
In [3]: df.a == df.dates
Out[3]:
0 False
1 False
2 False
3 False
4 False
5 False
6 False
7 False
8 False
9 False
dtype: bool
```
doesn't work:
```
In [4]: df.a <= df.dates
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-74e89260dea7> in <module>()
----> 1 df.a <= df.dates
/home/phillip/Documents/code/py/pandas/pandas/core/series.pyc in wrapper(self, other)
336 if len(self) != len(other):
337 raise ValueError('Series lengths must match to compare')
--> 338 return self._constructor(na_op(self.values, other.values),
339 index=self.index, name=name)
340 elif isinstance(other, DataFrame): # pragma: no cover
/home/phillip/Documents/code/py/pandas/pandas/core/series.pyc in na_op(x, y)
325 result = lib.scalar_compare(x, y, op)
326 else:
--> 327 result = op(x, y)
328
329 return result
TypeError: invalid type promotion
```
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} | 5 | 2013-09-24T20:04:27Z | 2018-07-06T20:21:01Z | 2017-07-10T21:08:52Z | CONTRIBUTOR | null | Taken from #4960
Currently Panel arith methods only work with scalars.
https://github.com/jreback/pandas/commit/adf9d48bf2cca5f25c00f780825c36feafa05bf1
removes the explicit check, but need to make sure it works with tests.
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`print` statements refactoring
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} | 7 | 2013-09-24T22:27:33Z | 2014-07-16T08:31:00Z | 2013-09-25T01:28:32Z | MEMBER | null | Current running time of the `eval` test suite (not including `query`)
```
$ nosetests pandas/computation/tests/test_eval.py 1 ↵
......../home/phillip/Documents/code/py/pandas/pandas/core/frame.py:3088: FutureWarning: TimeSeries broadcasting along DataFrame index by default is deprecated. Please use DataFrame.<op> to explicitly broadcast arithmetic operations along the index
FutureWarning)
.........................................................................................S.......................................................................................................................
----------------------------------------------------------------------
Ran 217 tests in 190.685s
OK (SKIP=1)
```
New running time :speedboat: :
```
$ nosetests pandas/computation/tests/test_eval.py
......../home/phillip/Documents/code/py/pandas/pandas/core/frame.py:3088: FutureWarning: TimeSeries broadcasting along DataFrame index by default is deprecated. Please use DataFrame.<op> to explicitly broadcast arithmetic operations along the index
FutureWarning)
.........................................................................................S.......................................................................................................................
----------------------------------------------------------------------
Ran 217 tests in 39.299s
OK (SKIP=1)
```
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} | 3 | 2013-09-25T00:06:26Z | 2013-09-26T00:21:08Z | 2013-09-26T00:21:08Z | CONTRIBUTOR | null | Trying to merge a bunch of files in a loop. I realize there's a better way to do this and a workaround, but it took me a second to realize what was going on here.
```
w = pd.DataFrame(np.random.random((4,2)), columns=["x", "y"])
x = pd.DataFrame(np.random.random((4,2)), columns=["x", "y"])
y = pd.DataFrame(np.random.random((4,2)), columns=["x", "y"])
z = pd.DataFrame(np.random.random((4,2)), columns=["x", "y"])
dta = x.merge(y, left_index=True, right_index=True).merge(z, left_index=True, right_index=True, how="outer")
dta.merge(w, left_index=True, right_index=True)
```
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Tested on Linux and Windows.
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} | 10 | 2013-09-25T03:11:01Z | 2016-10-12T23:04:13Z | 2014-01-29T04:10:38Z | NONE | null | read_html can not parse financial statements on WSJ.com
```
import pandas as pd
url = 'http://quotes.wsj.com/CN/XSHG/600000/financials/annual/balance-sheet'
tb = pd.read_html(url)
print(tb[0])
```
Check the contents. Row and column names are parsed correctly, but it seems that all numbers including ',' or in parentheses are ignored.
I'm using Python 3.3.2 and pandas 0.12.0
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} | 2 | 2013-09-25T04:35:06Z | 2014-01-29T05:50:41Z | 2014-01-24T14:16:00Z | CONTRIBUTOR | null | Some Excel Files are stored in MHT / MHTML / Single File Web Page/ Web Archive files.
Currently Pandas cannot handle these files, but rather produce:
```
XLRDError: Unsupported format, or corrupt file: Expected BOF record; found '\xef\xbb\xbfMIME-'
```
For example see: http://www.sec.gov/Archives/edgar/data/1490281/000149028113000033/Financial_Report.xls
I wonder if something like https://github.com/Modified/MHTifier can be used to exctract the content before passing to the xlrd library.
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} | 8 | 2013-09-25T11:50:09Z | 2014-05-12T20:16:36Z | 2014-02-18T22:18:15Z | NONE | null | related #4971
Currently Panel will allow us to expand along the _items_ dimension and will align cells in added DataFrames to the current _major_ and _minor_ indexes. However, if our DataFrame has a _major_ or _minor_ indexes not present in the original Panel, our append will silently fail (NaN s will populate the whole item)
I feel that a more natural behavior is for the Panel to expand along its major and minor axes to accomodate the new data.
I have to add here that I am really enjoying Panel and I find the logical progression from Series -> DataFrame -> Panel very well thought out.
Thanks.
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} | 5 | 2013-09-25T14:41:37Z | 2021-11-15T13:55:26Z | 2021-11-15T13:55:25Z | NONE | null | When specifying a dictionary map for renaming the index of a DataFrame, the renaming ends up including column values as well, even when they are not of the same type. For example, in the screen shot below, I have demographic information on race coded as integers 1-7 that I am remapping to string representations, and boolean values for the column headers specifying case status. When I specify a dictionary of mappings from integers to strings as the `index` argument, it treats the `True` value in the column header as a 1 and substitutes it.
Even if I specify value for `columns` it does not change the behavior.
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} | 2 | 2013-09-25T16:02:41Z | 2014-08-18T17:03:33Z | 2013-09-27T15:53:26Z | MEMBER | null | ```
In [2]: df = DataFrame({'dates': date_range('20010101', periods=10)})
In [3]: Timestamp('20010109') < df.dates
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-713f2b228d0e> in <module>()
----> 1 Timestamp('20010109') < df.dates
/home/phillip/Documents/code/py/pandas/pandas/tslib.so in pandas.tslib._Timestamp.__richcmp__ (pandas/tslib.c:9555)()
TypeError: Cannot compare Timestamp with 'Series'
```
but when a `Series` is on the left hand side, things go swimmingly:
```
In [4]: df.dates < Timestamp('20010109')
Out[4]:
0 True
1 True
2 True
3 True
4 True
5 True
6 True
7 True
8 False
9 False
Name: dates, dtype: bool
```
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} | 6 | 2013-09-25T17:48:38Z | 2013-09-25T20:07:40Z | 2013-09-25T20:07:40Z | NONE | null | If I have a Series of `timedelta64[ns]` (the result of a `diff()` on a Timestamp Series in my case), doing mean, median, and quantile operations on that series returns inconsistent values. Example:
```
In [68]: s
Out[68]:
0 00:00:14.020705
1 00:00:14.020705
2 00:00:14.020705
3 00:00:28.041410
4 00:00:28.041410
5 00:00:42.062116
6 00:00:42.062116
7 00:00:42.062116
8 00:00:56.082821
9 00:02:20.207052
dtype: timedelta64[ns]
In [69]: s.mean()
Out[69]: 42062115618.0
In [70]: type(s.mean())
Out[70]: numpy.float64
In [71]: s.median()
Out[71]: 35051763015.0
In [72]: type(s.median())
Out[72]: float
In [73]: s.quantile(.95)
Out[73]: numpy.timedelta64(102351148003,'ns')
In [74]: type(s.quantile(.95))
Out[74]: numpy.timedelta64
```
As you can see, `mean` and `median` return a float (although the printed Out is a little different), but `quantile` returns a single `timedelta64[ns]`.
This is with versions pandas==0.12.0 and numpy==1.7.1
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} | 3 | 2013-09-25T19:10:51Z | 2014-06-27T14:49:50Z | 2013-09-25T20:07:40Z | CONTRIBUTOR | null | closes #4984
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} | 2 | 2013-09-25T20:17:56Z | 2014-02-15T21:16:56Z | 2014-02-15T21:16:56Z | CONTRIBUTOR | null | related #3551
``` python
import pandas as pd
pd.isnull(pd.DataFrame()) # works
pd.isnull(pd.Panel()) # IndexError: index 0 is out of bounds for axis 0 with size 0
```
While the above is contrived. I hit the error when building out a `slice(0,0)` programmatically like the following:
``` python
panel = pd.util.testing.makePanel()
np.all(pd.isnull(panel.iloc[0:0])) # IndexError: index 0 is out of bounds for axis 0 with size 0
```
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} | 4 | 2013-09-25T21:27:55Z | 2015-03-06T23:06:43Z | 2015-03-06T23:06:43Z | CONTRIBUTOR | null | I know that the whitespace doesn't meter in HTML but this is really a strange behaviour:
``` python
>>> df = pd.read_html(
'http://en.wikipedia.org/wiki/Vancouver',
match='Municipality', header=0
)[0]
>>> df
0 Country Municipality NaN
1 Ukraine Odessa 1944
2 Japan Yokohama 1965
3 United Kingdom Edinburgh[198][199] 1978
4 China Guangzhou[200] 1985
5 United States Los Angeles 1986
6 South Korea Seoul 2007
>>> df.iat[3, 0]
'China'
>>> df.iat[4, 0]
'United States'
>>> df.iat[5, 1]
'Seoul'
```
Which is all fine, but if i try to convert it to HTML I get a really funky looking data :)
``` python
>>> print(df.to_html(index=False))
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>Country</th>
<th>Municipality</th>
<th>nan</th>
</tr>
</thead>
<tbody>
<tr>
<td> Ukraine</td>
<td> Odessa</td>
<td> 1944</td>
</tr>
<tr>
<td> Japan</td>
<td> Yokohama</td>
<td> 1965</td>
</tr>
<tr>
<td> United Kingdom</td>
<td> Edinburgh[198][199]</td>
<td> 1978</td>
</tr>
<tr>
<td> China</td>
<td> Guangzhou[200]</td>
<td> 1985</td>
</tr>
<tr>
<td> United States</td>
<td> Los Angeles</td>
<td> 1986</td>
</tr>
<tr>
<td> South Korea</td>
<td> Seoul</td>
<td> 2007</td>
</tr>
</tbody>
</table>
```
And also, if I specify explicitly the `classes` why does it always adds the `dataframe` class? Shouldn't the `classes` override the default `dataframe` class?
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} | 12 | 2013-09-25T22:12:05Z | 2017-10-04T23:57:16Z | 2017-10-04T23:56:21Z | CONTRIBUTOR | null | `read_csv` uses the `usecols` parameter, and the `read_excel` function uses `parse_cols` parameter. Both parameter do basically the same thing:
```
usecols : array-like
Return a subset of the columns.
Results in much faster parsing time and lower memory usage.
```
```
parse_cols : int or list, default None
* If None then parse all columns,
* If int then indicates last column to be parsed
* If list of ints then indicates list of column numbers to be parsed
* If string then indicates comma separated list of column names and
column ranges (e.g. "A:E" or "A,C,E:F")
```
Also maybe supporting the same thing that `parse_cols` does in `usecol` wouldn't be so bad?
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} | 1 | 2013-09-25T23:53:33Z | 2014-06-13T14:32:17Z | 2013-09-26T00:21:08Z | CONTRIBUTOR | null | partially fixed in #4771
closes #4975
```
In [1]: w = DataFrame(np.random.randn(4,2), columns=["x", "y"])
In [2]: x = DataFrame(np.random.randn(4,2), columns=["x", "y"])
In [3]: y = DataFrame(np.random.randn(4,2), columns=["x", "y"])
In [4]: z = DataFrame(np.random.randn(4,2), columns=["x", "y"])
In [5]: dta = x.merge(y, left_index=True, right_index=True).merge(z, left_index=True, right_index=True, how="outer")
In [6]: dta = dta.merge(w, left_index=True, right_index=True)
In [7]: dta
Out[7]:
x_x y_x x_y y_y x_x y_x x_y y_y
0 0.393625 -0.340291 0.035043 -0.195235 -0.892856 -0.357269 0.820424 0.142803
1 -1.600176 0.737261 -0.571140 1.352393 0.201634 1.403633 0.590919 -1.003057
2 -1.046113 2.148139 2.406527 -1.460300 0.881712 0.949246 -0.061758 -0.386265
3 -0.472761 -0.055612 -0.449152 -0.209876 1.076689 0.294275 -0.684433 0.925683
```
Via direct concat
```
In [11]: concat([x,y,z,w],axis=1)
Out[11]:
x y x y x y x y
0 0.393625 -0.340291 0.035043 -0.195235 -0.892856 -0.357269 0.820424 0.142803
1 -1.600176 0.737261 -0.571140 1.352393 0.201634 1.403633 0.590919 -1.003057
2 -1.046113 2.148139 2.406527 -1.460300 0.881712 0.949246 -0.061758 -0.386265
3 -0.472761 -0.055612 -0.449152 -0.209876 1.076689 0.294275 -0.684433 0.925683
In [8]: expected = concat([x,y,z,w],axis=1)
In [9]: expected.columns=['x_x','y_x','x_y','y_y','x_x','y_x','x_y','y_y']
In [10]: expected
Out[10]:
x_x y_x x_y y_y x_x y_x x_y y_y
0 0.393625 -0.340291 0.035043 -0.195235 -0.892856 -0.357269 0.820424 0.142803
1 -1.600176 0.737261 -0.571140 1.352393 0.201634 1.403633 0.590919 -1.003057
2 -1.046113 2.148139 2.406527 -1.460300 0.881712 0.949246 -0.061758 -0.386265
3 -0.472761 -0.055612 -0.449152 -0.209876 1.076689 0.294275 -0.684433 0.925683
```
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} | [] | closed | false | null | [] | null | 1 | 2013-09-26T06:28:00Z | 2014-07-16T08:31:19Z | 2013-09-26T10:49:39Z | CONTRIBUTOR | null | Fixes new / remaining test failure in #4866
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} | 1 | 2013-09-26T10:41:38Z | 2013-09-26T18:31:38Z | 2013-09-26T18:31:38Z | NONE | null | Hello,
Here is a issue I discovered in pandas version '0.12.0' (already present in preview version I think)
Issue:
When appending a DataFrame (with a new datetime64 column) to an existing one, the default value for missing values should be a pandas.tslib.NaT.
Example:
``` python
import pandas as pd
import datetime as dt
from pandas.tslib import NaT
df1 = pd.DataFrame(index=[1,2],\
data=[dt.datetime(2013,1,1,0,0),dt.datetime(2013,1,2,0,0)],\
columns=['start_time'])
```
``` python
df2 = pd.DataFrame(index=[4,5],\
data=[[dt.datetime(2013,1,3,0,0),dt.datetime(2013,1,3,6,10)],[dt.datetime(2013,1,4,0,0),dt.datetime(2013,1,4,7,10)]],\
columns=['start_time','end_time'])
```
``` python
df3=df1.append(df2,ignore_index=True)
```
While in reallity we want:
A simple work around is:
``` python
df3['end_time']=df3['end_time'].apply(pd.to_datetime)
```
Could be nice if, be default, when a new "datetime64" column is added, the default for missing values is NaT. Otherwise this creates problem when, for example, saving as HDF5 using pytable which does not accept mixed types per column.
Have a nice day,
Patrick
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} | 20 | 2013-09-26T13:46:43Z | 2014-06-26T17:16:33Z | 2014-02-18T20:06:02Z | CONTRIBUTOR | null | The internal axis slicer. I overloaded `indexer` to be `start` when `stop` is passed in instead of having a separate indexer kwargs.
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} | 1 | 2013-09-26T13:59:00Z | 2014-06-24T01:41:21Z | 2013-09-26T18:31:38Z | CONTRIBUTOR | null | closes #4993
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} | 27 | 2013-09-26T16:27:34Z | 2013-10-02T21:16:57Z | 2013-10-02T21:16:57Z | CONTRIBUTOR | null | ```
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: np.random.seed(10)
In [4]: a = np.random.randint(0, 20, (6, 5))
In [5]: df = pd.DataFrame(a, columns=list('abcde'))
In [6]: df
Out[6]:
a b c d e
0 9 4 15 0 17
1 16 17 8 9 0
2 10 8 4 19 16
3 4 15 11 11 1
4 8 4 14 17 19
5 13 5 13 19 13
```
Changing columns with `+` is fine
```
In [7]: df.columns = df.columns + '_x'
In [8]: df
Out[8]:
a_x b_x c_x d_x e_x
0 9 4 15 0 17
1 16 17 8 9 0
2 10 8 4 19 16
3 4 15 11 11 1
4 8 4 14 17 19
5 13 5 13 19 13
```
The `+=` operator on the other hand correctly changes the columns,
```
In [9]: df.columns += '_y'
In [10]: df.columns
Out[10]: Index([u'a_x_y', u'b_x_y', u'c_x_y', u'd_x_y', u'e_x_y'], dtype=object)
```
but gives an error when trying to display the whole dataframe:
```
In [11]: df
Out[11]: ---------------------------------------------------------------------------
UnboundLocalError Traceback (most recent call last)
```
Traceback is [here](https://gist.github.com/d10genes/6716510). Not sure if it's an ipython or pandas issue. `pd.__version__ == '0.12.0-371-g1434776'`
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The half life is basically just the number of periods in which the exponential weight drops to one half, i.e.,
(1 - \alpha)^h = 0.5, h: half life
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} | 5 | 2013-09-26T19:47:13Z | 2015-04-25T23:32:49Z | 2013-09-29T17:51:40Z | CONTRIBUTOR | null | Currently for the exponentially weighted moving functions (ewma, ewmstd, ewmvol, ewmvar, ewmcov) there are two ways (span, center of mass) to specify how fast the exponential decay is. It would be nice to support a "half life" option as well.
The half life is basically just the number of periods in which the exponential weight drops to one half, i.e.,
(1 - \alpha)^h = 0.5, h: half life
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} | 3 | 2013-09-26T20:31:34Z | 2014-02-18T18:49:30Z | 2014-02-18T18:49:30Z | MEMBER | null | Original mention: https://github.com/pydata/pandas/issues/4866#issuecomment-25157972
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} | 0 | 2013-09-26T21:21:26Z | 2014-06-14T18:21:49Z | 2013-09-26T21:22:21Z | CONTRIBUTOR | null | closes #3650
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