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https://api.github.com/repos/pandas-dev/pandas/issues/4801 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4801/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4801/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4801/events | https://github.com/pandas-dev/pandas/issues/4801 | 19,267,476 | MDU6SXNzdWUxOTI2NzQ3Ng== | 4,801 | docs: add shortcut page | {
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Instead of a cheatsheet, it shall just provides refrences preventing the reader from need to bookmark refrence pages or search through the documentation.
This is the idea which is waiting to be converted into a proper PR:
https://github.com/timmie/pandas/blob/9b0932368d6cf608f9e39d14a9532754f672d410/doc/source/shortcuts.rst
If you like to have such a page as well, you can assign me and I will finally prepare the PR.
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} | 6 | 2013-09-10T15:47:28Z | 2013-09-30T12:19:15Z | 2013-09-30T12:19:15Z | CONTRIBUTOR | null | This is the idea which is waiting to be converted into a proper PR:
https://github.com/timmie/pandas/blob/9b0932368d6cf608f9e39d14a9532754f672d410/doc/source/timeseries.rst
If you like to have such a section as well, you can assign me and I will finally prepare the PR.
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} | 12 | 2013-09-10T16:08:01Z | 2014-04-07T15:41:42Z | 2014-04-07T15:41:42Z | CONTRIBUTOR | null | it would be cool if a DataFrame could be displayed on a matplotlib plot, perhaps by calling the table() function.
( i am thinking about displaying the output of DataFrame.describe() or similar (small) amount of data)
related use of the table:
http://stackoverflow.com/questions/10194482/custom-matplotlib-plot-chess-board-like-table-with-colored-cells
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https://api.github.com/repos/pandas-dev/pandas/issues/4804 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4804/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4804/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4804/events | https://github.com/pandas-dev/pandas/issues/4804 | 19,276,159 | MDU6SXNzdWUxOTI3NjE1OQ== | 4,804 | BUG: seems that subtracting a YearBegin offset from an index loses the months/days | {
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} | 17 | 2013-09-10T18:24:43Z | 2018-07-06T21:55:51Z | 2018-07-06T21:55:50Z | CONTRIBUTOR | null | related is #4637 (on freq for WeekDay)
This might be expected, e.g. it conforms the values. or is this a bug?
```
In [48]: index = date_range('20140101 00:15:00',freq='D',periods=10)
```
```
In [44]: df.index.values
Out[44]:
array(['2013-12-31T19:15:00.000000000-0500',
'2014-01-01T19:15:00.000000000-0500',
'2014-01-02T19:15:00.000000000-0500',
'2014-01-03T19:15:00.000000000-0500',
'2014-01-04T19:15:00.000000000-0500',
'2014-01-05T19:15:00.000000000-0500',
'2014-01-06T19:15:00.000000000-0500',
'2014-01-07T19:15:00.000000000-0500',
'2014-01-08T19:15:00.000000000-0500',
'2014-01-09T19:15:00.000000000-0500'], dtype='datetime64[ns]')
In [46]: (df.index - pd.offsets.YearBegin(1)).values
Out[46]:
array(['2012-12-31T19:15:00.000000000-0500',
'2013-12-31T19:15:00.000000000-0500',
'2013-12-31T19:15:00.000000000-0500',
'2013-12-31T19:15:00.000000000-0500',
'2013-12-31T19:15:00.000000000-0500',
'2013-12-31T19:15:00.000000000-0500',
'2013-12-31T19:15:00.000000000-0500',
'2013-12-31T19:15:00.000000000-0500',
'2013-12-31T19:15:00.000000000-0500',
'2013-12-31T19:15:00.000000000-0500'], dtype='datetime64[ns]')
```
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} | 68 | 2013-09-10T18:58:31Z | 2014-06-19T23:22:31Z | 2014-04-09T23:32:01Z | CONTRIBUTOR | null | closes #3323
related #4187
related #1848
Hi,
I'd love for Series (and DataFrame) initialized with a dict to automatically create the MultiIndex. See below example.
Desired Result:
```
In [1]: pandas.Series({('a', 'b'): 1, ('a', 'a'): 0, ('a', 'c'): 2, ('b', 'a'): 3, ('b', 'b'): 4})
Out[1]:
a a 0
b 1
c 2
b a 3
b 4
dtype: int64
In [2]:
```
Current result:
```
In [1]: pandas.Series({('a', 'b'): 1, ('a', 'a'): 0, ('a', 'c'): 2, ('b', 'a'): 3, ('b', 'b'): 4})
Out[1]:
(a, a) 0
(a, b) 1
(a, c) 2
(b, a) 3
(b, b) 4
dtype: int64
In [2]:
```
Is this something I can go ahead and update tests for? It seems like we should have it.
Also, what are your thoughts about adding this to the DataFrame constructor as well?
Thanks,
Alex
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} | 0 | 2013-09-10T19:26:27Z | 2014-07-03T22:52:42Z | 2013-09-10T19:54:07Z | CONTRIBUTOR | null | closes #4686
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} | 6 | 2013-09-10T21:47:11Z | 2013-10-11T21:09:22Z | 2013-10-11T21:09:22Z | MEMBER | null | The example code seems not to run on the computer where the documentation is build: http://pandas.pydata.org/pandas-docs/dev/timeseries.html#custom-business-days-experimental
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} | 1 | 2013-09-10T23:05:10Z | 2015-03-03T15:12:42Z | 2015-03-03T01:49:36Z | CONTRIBUTOR | null | discussed in #4698
`convert_data` would return an appropriate Converter object to do various types of conversions. Mainly useful when you have to chunk both sides of these.
```
csv -> hdf
sql -> hdf
hdf -> csv
```
```
c = pd.convert_data('csv','hdf', chunksize=....)
c.read_csv(input_path. ......)
c.to_hdf(output_path, key.....)
c.execute()
```
pretty simple under the hood...
biggest issue is MAY need to read the input file twice (for read_csv) (if ints are detected in the first input chunk), because the dtype MAY change (e.g. you get a NaN later in later chunks)
useful?
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} | 0 | 2013-09-11T00:14:48Z | 2014-07-16T08:27:38Z | 2013-09-11T00:25:20Z | CONTRIBUTOR | null | CLN: removed need for Coordinates, instead return an Index of the coordinates
DOC: added section on select_as_coordinates
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} | 14 | 2013-09-11T13:00:53Z | 2018-07-06T21:52:50Z | 2018-07-06T21:52:50Z | NONE | null | I have been struggling a little to get my head around MultiIndexing. When playing with them in python notebook, I think it would have been much easier for me to pick things up if the `__repr__`was a little bit more informative:
For example:
``` python
print(mp_stats_unstack.__repr__())
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1848 entries, 1378842548 to 1378844396
Columns: 252 entries, (0/s, 0) to (MIS/s, 3)
dtypes: float64(252)
```
With the Column attribute, the following two things were not obvious to me:
- The Column is a MultiIndex
- That the Column MultiIndex has two levels named 'var' and 'CPU'
Of course, now that I know what to look for (If a column has tuple it is (or probably is) a multiindex, I can inspect the .columns.names attribute. But when trying to grok this I think a more verbose `__repr__()` might be helpful for both learning and convenience.
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} | 11 | 2013-09-11T13:46:01Z | 2013-09-20T18:07:36Z | 2013-09-20T18:07:36Z | NONE | null | With the following, I can plot work, but I can not plot nowork. I can't see any clear difference between the two resulting dataframes. I also get the issue when trying to slice use .loc and range of labels.
``` python
work = mp_stats_unstack.xs(0, level='CPU', axis=1).iloc[:,[x for x in range(48,63)]]
nowork = mp_stats_unstack.xs(0, level='CPU', axis=1).iloc[:,48:63]
nowork
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1848 entries, 1378842548 to 1378844396
Data columns (total 15 columns):
NMI/s 1848 non-null values
LOC/s 1848 non-null values
SPU/s 1848 non-null values
PMI/s 1848 non-null values
IWI/s 1848 non-null values
RTR/s 1848 non-null values
RES/s 1848 non-null values
CAL/s 1848 non-null values
TLB/s 1848 non-null values
TRM/s 1848 non-null values
THR/s 1848 non-null values
MCE/s 1848 non-null values
MCP/s 1848 non-null values
ERR/s 1848 non-null values
MIS/s 1848 non-null values
dtypes: float64(15)
work
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1848 entries, 1378842548 to 1378844396
Data columns (total 15 columns):
NMI/s 1848 non-null values
LOC/s 1848 non-null values
SPU/s 1848 non-null values
PMI/s 1848 non-null values
IWI/s 1848 non-null values
RTR/s 1848 non-null values
RES/s 1848 non-null values
CAL/s 1848 non-null values
TLB/s 1848 non-null values
TRM/s 1848 non-null values
THR/s 1848 non-null values
MCE/s 1848 non-null values
MCP/s 1848 non-null values
ERR/s 1848 non-null values
MIS/s 1848 non-null values
dtypes: float64(15)
nowork.plot()
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-482-b831b20208bb> in <module>()
----> 1 nowork.plot()
/usr/local/lib/python3.3/site-packages/pandas/tools/plotting.py in plot_frame(frame, x, y, subplots, sharex, sharey, use_index, figsize, grid, legend, rot, ax, style, title, xlim, ylim, logx, logy, xticks, yticks, kind, sort_columns, fontsize, secondary_y, **kwds)
1634 logy=logy, sort_columns=sort_columns,
1635 secondary_y=secondary_y, **kwds)
-> 1636 plot_obj.generate()
1637 plot_obj.draw()
1638 if subplots:
/usr/local/lib/python3.3/site-packages/pandas/tools/plotting.py in generate(self)
852 def generate(self):
853 self._args_adjust()
--> 854 self._compute_plot_data()
855 self._setup_subplots()
856 self._make_plot()
/usr/local/lib/python3.3/site-packages/pandas/tools/plotting.py in _compute_plot_data(self)
925 try:
926 # might be an ndframe
--> 927 numeric_data = self.data._get_numeric_data()
928 except AttributeError: # TODO: rm in 0.13 (series-inherit-ndframe)
929 numeric_data = self.data
/usr/local/lib/python3.3/site-packages/pandas/core/frame.py in _get_numeric_data(self)
5291
5292 def _get_numeric_data(self):
-> 5293 return self._constructor(self._data.get_numeric_data(), index=self.index, copy=False)
5294
5295 def _get_bool_data(self):
/usr/local/lib/python3.3/site-packages/pandas/core/internals.py in get_numeric_data(self, copy, type_list, as_blocks)
1398
1399 indexer = np.sort(np.concatenate([b.ref_locs for b in num_blocks]))
-> 1400 new_items = self.items.take(indexer)
1401
1402 new_blocks = []
/usr/local/lib/python3.3/site-packages/pandas/core/index.py in take(self, indexer, axis)
393 """
394 indexer = com._ensure_platform_int(indexer)
--> 395 taken = self.view(np.ndarray).take(indexer)
396 return self._constructor(taken, name=self.name)
397
IndexError: index 15 is out of bounds for axis 0 with size 15
```
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} | 0 | 2013-09-11T16:54:59Z | 2013-09-13T00:08:05Z | 2013-09-13T00:08:05Z | CONTRIBUTOR | null | ```
In [2]: df = DataFrame(randn(4,12),index=[2000,2000,2000,2000],columns=[ pd.Period(year=2000,month=i+1,freq='M') for i in range(12) ])
In [3]: df
Out[3]:
2000-01 2000-02 2000-03 2000-04 2000-05 2000-06 2000-07 2000-08 2000-09 2000-10 2000-11 2000-12
2000 -0.630327 0.698388 -0.678858 -1.289394 1.541255 1.828321 -0.157585 -0.362442 1.315526 0.783812 1.195390 -0.350228
2000 -0.213631 -0.759442 -0.418679 0.199200 0.449716 -1.936404 -1.040448 -1.867548 1.066477 -0.303183 1.982605 0.138027
2000 1.052005 1.291127 1.311332 -0.215721 -1.075875 0.209229 -0.159184 -0.662904 -1.812327 0.830689 -1.720358 1.323978
2000 -1.266226 -0.508003 -0.382924 -0.165822 -0.876964 -0.767572 -0.334778 -1.576188 0.378332 -0.617412 -1.053356 1.843076
In [4]: df.resample('Q',axis=1)
AttributeError: 'DataFrame' object has no attribute 'flags'
```
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} | 0 | 2013-09-11T17:19:26Z | 2014-06-19T21:26:34Z | 2013-09-13T00:08:05Z | CONTRIBUTOR | null | closes #4812
was already fixed!
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https://api.github.com/repos/pandas-dev/pandas/issues/4814 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4814/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4814/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4814/events | https://github.com/pandas-dev/pandas/issues/4814 | 19,338,855 | MDU6SXNzdWUxOTMzODg1NQ== | 4,814 | Column multi-index not restored correctly on load from HDF5 table | {
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data = pd.DataFrame(index=[1,2,3], columns=cols, dtype=float)
data.values.fill(0.)
s = store('/tmp/test', mode='a')
s.append('mi_frame', data)
reloaded = s['mi_frame'] # also applies to s.select...
print type(data.columns)
Out: pandas.core.index.MultiIndex
print type(reloaded.columns)
Out: pandas.core.index.Index
# It's easy enough to re-convert upon reload, but would be nice not to have to
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https://api.github.com/repos/pandas-dev/pandas/issues/4815 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4815/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4815/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4815/events | https://github.com/pandas-dev/pandas/issues/4815 | 19,338,914 | MDU6SXNzdWUxOTMzODkxNA== | 4,815 | Column multi-index not restored correctly on load from HDF5 table | {
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cols = pd.MultiIndex.from_tuples([(1,2), (3,4)])
data = pd.DataFrame(index=[1,2,3], columns=cols, dtype=float)
data.values.fill(0.)
s = store('/tmp/test', mode='a')
s.append('mi_frame', data)
reloaded = s['mi_frame'] # also applies to s.select...
print type(data.columns)
Out: pandas.core.index.MultiIndex
print type(reloaded.columns)
Out: pandas.core.index.Index
It's easy enough to re-convert upon reload, but would be nice not to have to
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data.values.fill(0.)
s = store('/tmp/test', mode='a')
s.append('mi_frame', data)
reloaded = s['mi_frame'] # also applies to s.select...
print type(data.columns)
Out: pandas.core.index.MultiIndex
print type(reloaded.columns)
Out: pandas.core.index.Index
It's easy enough to re-convert upon reload, but would be nice not to have to
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} | 9 | 2013-09-11T18:54:11Z | 2021-08-12T13:55:27Z | 2021-08-12T13:55:27Z | CONTRIBUTOR | null | Perhaps there should be some argument to pivot_table to include subtotals of MI columns and rows, i.e. not just margins.
http://stackoverflow.com/questions/15570099/pandas-pivot-tables-row-subtotals
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} | 19 | 2013-09-11T19:29:48Z | 2021-03-21T15:59:30Z | 2021-03-21T15:59:30Z | CONTRIBUTOR | null | Don't have time to implement this, but I wanted to float the idea and park it. It's pretty trivial and you can achieve the same thing with filter, but it might be nice if drop had a regex keyword. E.g., these would be equivalent
```
df = df.filter(regex="^(?!var_start)")
df = df.drop(regex="^var_start", axis=1)
```
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} | 11 | 2013-09-11T20:27:05Z | 2013-09-15T09:01:17Z | 2013-09-11T21:23:29Z | NONE | null | The `Series` class in 0.10 had a `real` and a `imag` attribute but in 0.12dev these attrs are missing. This probably happend during the refactoring of the Series class for 0.12.
``` python
In [1]: pd.__version__
Out[1]: '0.10.0'
In [2]: a = np.arange(5)
In [3]: b = pd.Series(a + 4j*a)
In [4]: b.real
Out[4]:
0 0
1 1
2 2
3 3
4 4
```
``` python
In [1]: pd.__version__
Out[1]: '0.12.0-381-g15d5f0a'
In [2]: a = np.arange(5)
In [3]: b = pd.Series(a + 4j*a)
In [4]: b.real
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-4-ec5ddb077f9e> in <module>()
----> 1 b.real
/usr/lib64/python2.7/site-packages/pandas-0.12.0_381_g15d5f0a-py2.7-linux-x86_64.egg/pandas/core/generic.pyc in __getattr__(self, name)
1287 return self[name]
1288 raise AttributeError("'%s' object has no attribute '%s'" %
-> 1289 (type(self).__name__, name))
1290
1291 def __setattr__(self, name, value):
AttributeError: 'Series' object has no attribute 'real'
```
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} | 0 | 2013-09-11T21:06:26Z | 2014-06-23T07:11:10Z | 2013-09-11T21:23:29Z | CONTRIBUTOR | null | closes #4819
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} | 9 | 2013-09-11T23:38:26Z | 2014-06-25T20:28:51Z | 2013-09-11T23:49:00Z | CONTRIBUTOR | null | Add `__abs__` method so that you can use the toplevel `abs()` with
PandasObjects. I note there aren't that many existing test cases for
abs. At some point we might want to think about that.
Also related #4819
cc @thisch
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} | 5 | 2013-09-12T00:05:21Z | 2014-07-02T22:36:46Z | 2013-09-12T16:05:09Z | CONTRIBUTOR | null | closes .#3577
realted #3009
- ENH: add top-level `to_timedelta` to convert string/integer to timedeltas
- TST: add pandas/tseries/tests/test_timedeltas.py
- API: add full timedelta parsing and conversion to np.timedelta64[ns]
- CLN: refactored locations of timedeltas to core/tseries/timedeltas (from a series of functions in core/common)
- ENH: support timedelta64[ns] as a serialization type in HDFStore for query and append (GH3577)
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} | 9 | 2013-09-12T10:56:03Z | 2017-07-09T02:38:47Z | 2017-07-09T02:38:40Z | CONTRIBUTOR | null | At the moment you have to use MI.from_arrays or MI.from_tuples
I think it would be nicer to be able to call MultiIndex directly (and have an orient argument, similar to some other functions). Perhaps even with a little guess work to decide what the user wants similar to other contructors.
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} | 29 | 2013-09-12T15:05:41Z | 2013-09-16T13:01:20Z | 2013-09-16T12:56:55Z | CONTRIBUTOR | null | The following raises the error
```
pandas.Series([0.1, 0.2], index=[1, 2]).loc[[2, 3, 2]]
```
whilst
```
pandas.Series([0.1, 0.2], index=[1, 2]).loc[[2, 2, 3]]
```
does not. Or is there something invalid with this code?
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} | 4 | 2013-09-12T17:07:14Z | 2014-06-26T11:55:44Z | 2013-09-12T18:36:28Z | CONTRIBUTOR | null | ```
In [1]: rng = date_range('1/1/2000', periods=10000, freq='D')
In [2]: strings = Series(rng.year*10000+rng.month*100+rng.day,dtype=np.int64).apply(str)
In [3]: %timeit pandas.tslib.array_strptime(strings.values,"%Y%m%d")
10 loops, best of 3: 42.9 ms per loop
In [4]: %timeit pd.to_datetime(strings,format="%Y%m%d")
100 loops, best of 3: 9.21 ms per loop
In [5]: (pd.to_datetime(strings,format="%Y%m%d") == pandas.tslib.array_strptime(strings.values,"%Y%m%d")).all()
Out[5]: True
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4827 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4827/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4827/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4827/events | https://github.com/pandas-dev/pandas/issues/4827 | 19,419,872 | MDU6SXNzdWUxOTQxOTg3Mg== | 4,827 | BUG: Fred tests failing - possible error with Fred data IO. | {
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} | 2 | 2013-09-12T22:41:57Z | 2013-09-20T08:01:33Z | 2013-09-20T08:01:33Z | CONTRIBUTOR | null | These two tests are failing (but they've been hidden for a while). They are (going to be) skipped on master for the moment, but need to be fixed/removed at some pont.
Is someone responsible/using the Fred data reader right now?
```
======================================================================
ERROR: test_fred_nan (pandas.io.tests.test_data.TestFred)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pandas-0.0.0-py2.7-linux-x86_64.egg/pandas/util/testing.py", line 842, in network_wrapper
return t(*args, **kwargs)
File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pandas-0.0.0-py2.7-linux-x86_64.egg/pandas/io/tests/test_data.py", line 387, in test_fred_nan
assert pd.isnull(df.ix['2010-01-01'])
File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pandas-0.0.0-py2.7-linux-x86_64.egg/pandas/core/generic.py", line 559, in __nonzero__
raise ValueError("The truth value of an array is ambiguous. Use a.empty, a.item(), a.any() or a.all().")
ValueError: The truth value of an array is ambiguous. Use a.empty, a.item(), a.any() or a.all().
-------------------- >> begin captured stdout << ---------------------
Failed: ValueError('The truth value of an array is ambiguous. Use a.empty, a.item(), a.any() or a.all().',)
--------------------- >> end captured stdout << ----------------------
```
```
======================================================================
ERROR: test_fred_parts (pandas.io.tests.test_data.TestFred)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pandas-0.0.0-py2.7-linux-x86_64.egg/pandas/util/testing.py", line 842, in network_wrapper
return t(*args, **kwargs)
File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pandas-0.0.0-py2.7-linux-x86_64.egg/pandas/io/tests/test_data.py", line 394, in test_fred_parts
self.assertEqual(df.ix['2010-05-01'], 217.23)
File "/usr/lib/python2.7/unittest/case.py", line 511, in assertEqual
assertion_func(first, second, msg=msg)
File "/usr/lib/python2.7/unittest/case.py", line 501, in _baseAssertEqual
if not first == second:
File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pandas-0.0.0-py2.7-linux-x86_64.egg/pandas/core/generic.py", line 559, in __nonzero__
raise ValueError("The truth value of an array is ambiguous. Use a.empty, a.item(), a.any() or a.all().")
ValueError: The truth value of an array is ambiguous. Use a.empty, a.item(), a.any() or a.all().
-------------------- >> begin captured stdout << ---------------------
Failed: ValueError('The truth value of an array is ambiguous. Use a.empty, a.item(), a.any() or a.all().',)
--------------------- >> end captured stdout << ----------------------
----------------------------------------------------------------------
```
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} | 0 | 2013-09-12T22:55:15Z | 2014-07-16T08:27:57Z | 2013-09-12T23:46:46Z | CONTRIBUTOR | null | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4829 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4829/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4829/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4829/events | https://github.com/pandas-dev/pandas/pull/4829 | 19,424,236 | MDExOlB1bGxSZXF1ZXN0ODI5ODgxNA== | 4,829 | ENH/API: allow DataFrame constructor to better accept list-like collections (GH3783,GH4297) | {
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} | 3 | 2013-09-13T00:51:20Z | 2014-06-21T08:22:50Z | 2013-09-13T22:54:42Z | CONTRIBUTOR | null | closes #3783
closes #4297
related to #2305 (as now accept GeneratorType), but just convert to list, don't incrementally create the frame
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Only copies index/columns with `deep=True` on `BlockManager`. Plus some
tests...yay!
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```
In [1]: ser = Series([0.1, 0.2], index=[1, 2])
In [3]: ser
Out[3]:
1 0.1
2 0.2
dtype: float64
In [4]: ser.loc[[3, 2, 3]]
Out[4]:
3 NaN
2 0.2
3 NaN
dtype: float64
In [5]: ser.loc[[3, 3, 3]]
Out[5]:
3 NaN
3 NaN
3 NaN
dtype: float64
In [6]: ser.loc[[2, 2, 3]]
Out[6]:
2 0.2
2 0.2
3 NaN
dtype: float64
In [7]: ser.iloc[[1,1,0,0]]
Out[7]:
2 0.2
2 0.2
1 0.1
1 0.1
dtype: float64
```
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} | 1 | 2013-09-13T16:01:26Z | 2020-04-30T05:34:52Z | null | CONTRIBUTOR | null | There is no way to pass keyword arguments down to the histogram call in `pandas.tools.plotting.scatter_matrix`. I understand that it's sitting on top of a few interfaces and so intelligently deciding which keyword arguments go where is complex.
A simple solution would be to add a `hist_kwargs={}` keyword argument to which users could pass values like `{'bins': 100}` and then use this in the `hist` call like so
```
ax.hist(values, **hist_kwargs)
```
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} | 0 | 2013-09-14T02:38:04Z | 2014-06-19T05:28:50Z | 2013-09-15T05:01:34Z | CONTRIBUTOR | null | Closes #4759
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Fixes #4730.
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} | 13 | 2013-09-14T15:27:56Z | 2016-10-12T23:04:11Z | 2013-09-17T05:40:40Z | NONE | null | ```
In [7]: d
Out[7]: {'one': [1.0, 2.0, 3.0, 4.0], 'two': [4.0, 3.0, 2.0, 1.0]}
In [11]: pd.DataFrame(d).sort_index(by=['two'], ascending=[0,])
Out[11]:
one two
3 4 1
2 3 2
1 2 3
0 1 4
In [12]: pd.DataFrame(d).sort_index(by=['two'], ascending=0)
Out[12]:
one two
0 1 4
1 2 3
2 3 2
3 4 1
```
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} | 1 | 2013-09-14T19:48:59Z | 2014-08-18T12:52:39Z | 2013-09-20T17:18:15Z | CONTRIBUTOR | null | This is now a PR based on discussions in #4802
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thanks @tavistmorph and @numpand
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} | 12 | 2013-09-15T04:01:28Z | 2013-10-03T22:07:25Z | 2013-10-03T22:07:22Z | CONTRIBUTOR | null | ```
>>> from pandas import MultiIndex
>>> mi = MultiIndex.from_tuples(list(zip(range(10), range(10))))
>>> mi.shape
(0,)
>>> mi.ndim
1
```
I'd expect ndim to return 2 and shape to be (10, 2).
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} | 1 | 2013-09-15T04:59:45Z | 2014-06-12T09:08:04Z | 2013-09-15T05:12:52Z | CONTRIBUTOR | null | 2.6 added ability to use property-decorated function as decorator for
setters and deleters, so pandas/util/rwproperty isn't necessary anymore.
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} | 21 | 2013-09-15T15:32:48Z | 2014-06-19T20:46:34Z | 2014-02-18T20:04:51Z | CONTRIBUTOR | null | Fixes #4471.
Only when it's greater than uint64 max (and not negative, etc.)
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} | 6 | 2013-09-15T16:52:08Z | 2014-06-29T21:06:36Z | 2013-09-17T05:40:41Z | CONTRIBUTOR | null | Fixes #4839.
now actually checks the first element of the list
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} | 11 | 2013-09-16T13:39:44Z | 2017-04-05T02:05:40Z | 2013-09-30T13:57:37Z | CONTRIBUTOR | null | I also fixed a few typos in a separate commit. I can remove that commit if they're going to cause conflicts.
Also did I get the syntax correct for this link?
> See :ref:`Boolean Indexing<indexing.boolean>` for more
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} | [] | closed | false | null | [] | null | 11 | 2013-09-16T14:47:04Z | 2013-09-16T16:34:56Z | 2013-09-16T16:22:31Z | NONE | null | Hi. I'm using Pandas 0.11.0. I have a table with some string None values which I both pickled and appended to an HDFStore (compressed with blosc), and then retrieved with pandas.load and HDFStore.get. In the unpickled table, string values are converted to the string "nan", which does not get detected by isnull. On the other hand, the HDFStore correctly retrieves NaN values.
I'm using natural language data and there is a slim but non-zero chance that "nan" (as a string) is a value representing actual data, and even worse, I didn't know that the unpickled table even _had_ None values until I tested out the HDFStore.
This is my first github bug report, and I don't know if this is a bug or intended behaviour, but it seems bug-like to me.
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} | 48 | 2013-09-16T14:48:21Z | 2014-07-05T04:36:06Z | 2013-09-25T20:08:12Z | CONTRIBUTOR | null | closes #236
- CLN: refactor all scalar/slicing code from core/indexing.py to core/index.py on a per-index basis
- BUG: provide better `.loc` semantics on floating indicies
- API: provide a implementation of `Float64Index`
- BUG: raise on indexing with float keys on a non-float index
provide better `loc` semantics on `Float64Index`
```
In [1]: s = Series(np.arange(5), index=np.arange(5) * 2.5)
In [9]: s.index
Out[9]: Float64Index([0.0, 2.5, 5.0, 7.5, 10.0], dtype=object)
In [2]: s
Out[2]:
0.0 0
2.5 1
5.0 2
7.5 3
10.0 4
dtype: int64
In [3]: # label based slicing
In [4]: s[1.0:3.0]
Out[4]:
2.5 1
dtype: int64
In [5]: s.ix[1.0:3.0]
Out[5]:
2.5 1
dtype: int64
In [6]: s.loc[1.0:3.0]
Out[6]:
2.5 1
dtype: int64
In [7]: # exact indexing when found
In [8]: s[5.0]
Out[8]: 2
In [9]: s.loc[5.0]
Out[9]: 2
In [10]: s.ix[5.0]
Out[10]: 2
In [11]: # non-fallback location based should raise this error (__getitem__,ix fallback here)
In [12]: s.loc[4.0]
KeyError: 'the label [4.0] is not in the [index]'
In [13]: s[4.0] == s[4]
Out[13]: True
In [14]: s[4] == s[4]
Out[14]: True
# [],ix,s.ix[2:5]
loc is clear about slicing on the values ONLY
In [11]: s[2:5]
Out[11]:
2.5 1
5.0 2
dtype: int64
In [12]: s.ix[2:5]
Out[12]:
2.5 1
5.0 2
dtype: int64
In [2]: s.loc[2:5]
Out[2]:
2.5 1
5.0 2
dtype: int64
In [15]: s.loc[2.0:5.0]
Out[15]:
2.5 1
5.0 2
dtype: int64
In [16]: s.loc[2.0:5]
Out[16]:
2.5 1
5.0 2
dtype: int64
In [17]: s.loc[2.1:5]
Out[17]:
2.5 1
5.0 2
dtype: int64
```
Float Slicing now raises when indexing by a non-integer slicer
```
In [1]: s = Series(np.arange(5))
In [2]: s
Out[2]:
0 0
1 1
2 2
3 3
4 4
dtype: int64
In [3]: s[3.5]
KeyError:
In [4]: s.loc[3.5]
KeyError: 'the label [3.5] is not in the [index]'
In [5]: s.ix[3.5]
KeyError: 'the label [3.5] is not in the [index]'
In [6]: s.iloc[3.5]
TypeError: the positional label [3.5] is not a proper indexer for this index type (Float64Index)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4851 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4851/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4851/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4851/events | https://github.com/pandas-dev/pandas/issues/4851 | 19,562,983 | MDU6SXNzdWUxOTU2Mjk4Mw== | 4,851 | List of 0-dim np.ndarrays no longer works for DataFrame constructor | {
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} | [] | closed | false | null | [] | null | 1 | 2013-09-16T16:57:47Z | 2013-09-16T17:36:54Z | 2013-09-16T17:36:54Z | CONTRIBUTOR | null | ``` python
data = [np.array(x) for x in range(10)]
pd.DataFrame(data)
4814 else:
4815 # last ditch effort
-> 4816 data = lmap(tuple, data)
4817 return _list_to_arrays(data, columns,
4818 coerce_float=coerce_float,
TypeError: iteration over a 0-d array
```
Previously this case would fall through to `_prep_ndarray` but since `common.is_list_like` doesn't check that whether the `np.ndarray` is 0-dim, it incorrectly fails in `to_arrays`.
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https://api.github.com/repos/pandas-dev/pandas/issues/4852 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4852/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4852/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4852/events | https://github.com/pandas-dev/pandas/pull/4852 | 19,563,936 | MDExOlB1bGxSZXF1ZXN0ODM1ODYzMQ== | 4,852 | BUG: (GH4851) path for 0-dim arrays in DataFrame construction were incorrect | {
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} | 0 | 2013-09-16T17:15:05Z | 2014-07-16T08:28:38Z | 2013-09-16T17:36:54Z | CONTRIBUTOR | null | closes #4851
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} | 3 | 2013-09-16T17:21:21Z | 2014-04-06T15:14:01Z | 2013-09-18T13:40:57Z | CONTRIBUTOR | null | `tshift` is broken for `Panel` because `Panel.shift` does not handle `freq`.
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} | [] | closed | false | null | [] | null | 10 | 2013-09-16T20:22:33Z | 2013-09-16T23:46:40Z | 2013-09-16T22:49:08Z | CONTRIBUTOR | null | On latest master `0.12.0-315-ga99b3be`, when I try to select a table from an HDFStore, I get a Unicode Error
```
In [4]: df = store.select('monthly/data/m1998_01')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-67c3fabe2905> in <module>()
----> 1 df = store.select('monthly/data/m1998_01')
/Users/tom/Envs/statsmodels/lib/python2.7/site-packages/pandas/pandas/io/pytables.py in select(self, key, where, start, stop, columns, iterator, chunksize, auto_close, **kwargs)
579 where = _ensure_term(where)
580 s = self._create_storer(group)
--> 581 s.infer_axes()
582
583 # what we are actually going to do for a chunk
/Users/tom/Envs/statsmodels/lib/python2.7/site-packages/pandas/pandas/io/pytables.py in infer_axes(self)
2001 if s is None:
2002 return False
-> 2003 self.get_attrs()
2004 return True
2005
/Users/tom/Envs/statsmodels/lib/python2.7/site-packages/pandas/pandas/io/pytables.py in get_attrs(self)
2740 t = self.table
2741 self.index_axes = [a.infer(t)
-> 2742 for a in self.indexables if a.is_an_indexable]
2743 self.values_axes = [a.infer(t)
2744 for a in self.indexables if not a.is_an_indexable]
/Users/tom/Envs/statsmodels/lib/python2.7/site-packages/pandas/pandas/io/pytables.py in indexables(self)
2793
2794 self._indexables.extend(
-> 2795 [f(i, c) for i, c in enumerate(self.attrs.values_cols)])
2796
2797 return self._indexables
/Users/tom/Envs/statsmodels/lib/python2.7/site-packages/pandas/pandas/io/pytables.py in f(i, c)
2790 if c in dc:
2791 klass = DataIndexableCol
-> 2792 return klass.create_for_block(i=i, name=c, pos=base_pos + i, version=self.version)
2793
2794 self._indexables.extend(
/Users/tom/Envs/statsmodels/lib/python2.7/site-packages/pandas/pandas/io/pytables.py in create_for_block(cls, i, name, cname, version, **kwargs)
1537 pass
1538
-> 1539 return cls(name=name, cname=cname, **kwargs)
1540
1541 def __init__(self, values=None, kind=None, typ=None,
/Users/tom/Envs/statsmodels/lib/python2.7/site-packages/pandas/pandas/io/pytables.py in __init__(self, values, kind, typ, cname, data, block, **kwargs)
1544 values=values, kind=kind, typ=typ, cname=cname, **kwargs)
1545 self.dtype = None
-> 1546 self.dtype_attr = u("%s_dtype" % self.name)
1547 self.set_data(data)
1548
/Users/tom/Envs/statsmodels/lib/python2.7/site-packages/pandas/pandas/compat/__init__.py in u(s)
189
190 def u(s):
--> 191 return unicode(s, "unicode_escape")
192
193
TypeError: decoding Unicode is not supported
```
I'm still trying to whittle down where this behavior changed, but at least as of `a99b3be`, it worked. I'm going to go through commits changing HDFStore, but maybe someone knows exactly where this might be from?
I'll also try to come up with a minimal example that reproduces. Simple ones aren't doing it which is a good thing I guess.
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} | 0 | 2013-09-17T03:24:43Z | 2013-09-22T22:17:09Z | 2013-09-22T22:17:09Z | CONTRIBUTOR | null | Related: #4039 (where this started to become an issue), #4202, #4830, #4628, #4718, #4628.
Very simple: both indices get an `_id` property that can be compared with `is` and is reset every time an object is created, so it only stays set when set explicitly (like in view(), but not in copy()). Adds an `is_` method that wraps this for external use and makes it so you don't need `hasattr` checks, etc.
``` python
>>> ind = Index(range(10))
>>> ind.is_(ind)
True
>>> ind.is_(Index(range(10)))
False
>>> ind.is_(ind.view().view().view().view())
True
>>> ind.is_(ind.copy())
False
>>> ind.is_(ind.copy(deep=False))
False
>>> ind.is_(ind[:])
False
>>> ind.is_(ind.view(np.ndarray).view(Index))
False
>>> ind.is_(np.array(range(10)))
False
>>> ind.is_(ind.view().base) # quasi-implementation dependent
True
>>> ind2 = ind.view()
>>> ind2.name = 'bob'
>>> ind.is_(ind2)
True
>>> ind2.is_(ind)
True
>>> # doesn't matter if Indices are *actually* views of underlying data, e.g.
>>> ind.is_(Index(ind.values))
False
>>> arr = np.array(range(1, 11))
>>> ind1 = Index(arr, copy=False)
>>> ind2 = Index(arr, copy=False)
>>> ind1.is_(ind2)
False
```
MultiIndex behavior (same behavior for set labels and set levels)
``` python
>>> mi = MultiIndex.from_tuples(zip(range(10), range(10)))
>>> mi.is_(mi)
True
>>> mi.is_(mi.view())
True
>>> mi.is_(mi.view().view().view().view())
True
>>> mi2 = mi.view()
>>> mi2.names = ["A", "B"]
>>> mi2.is_(mi)
True
>>> mi.is_(mi2)
True
>>> mi.is_(mi.set_names(["C", "D"]))
True
>>> mi3 = mi2.set_levels([range(10), range(10)])
>>> mi3.is_(mi2)
False
>>> mi4 = mi3.view()
>>> mi4.set_levels([[1 for _ in range(10)], range(10)], inplace=True)
>>> mi4.is_(mi3)
False
>>> mi5 = mi.view()
>>> mi5.set_levels(mi5.levels, inplace=True)
>>> mi5.is_(mi)
False
```
Could make it easier to resolve #4013.
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} | 2 | 2013-09-17T13:48:56Z | 2018-11-20T02:00:06Z | 2018-11-20T02:00:06Z | CONTRIBUTOR | null | Lookup in a PeriodIndex using a datetime works as expected (the period in which the timestamp falls will be returned). However, when the PeriodIndex is part of a hierarchy, this functionality fails in a non-obvious way:
```
>>> s = pd.Series([1,2,3,4,5], pd.MultiIndex.from_arrays([["a", "a", "a", "b", "b"], pd.period_range("2012-01", periods=5, freq="M")]))
>>> s.loc["a", datetime(2012,1,1)]
Traceback (most recent call last):
File "C:\VirtualEnvs\test\lib\site-packages\ipython-1.0.dev-py2.6.egg\IPython\core\interactiveshell.py", line 2837, in run_code
exec code_obj in self.user_global_ns, self.user_ns
File "<ipython-input-18-9e6cd34eee66>", line 1, in <module>
a.loc["a", datetime(2012,1,1)]
File "C:\VirtualEnvs\test\lib\site-packages\pandas-0.12.0-py2.6-win32.egg\pandas\core\indexing.py", line 697, in __getitem__
return self._getitem_tuple(key)
File "C:\VirtualEnvs\test\lib\site-packages\pandas-0.12.0-py2.6-win32.egg\pandas\core\indexing.py", line 258, in _getitem_tuple
self._has_valid_tuple(tup)
File "C:\VirtualEnvs\test\lib\site-packages\pandas-0.12.0-py2.6-win32.egg\pandas\core\indexing.py", line 691, in _has_valid_tuple
raise ValueError('Too many indexers')
ValueError: Too many indexers
```
Using a period works just fine:
```
>>> s.loc["a", pd.Period("2012-01")]
1
```
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} | 2 | 2013-09-17T16:32:44Z | 2015-01-26T01:29:07Z | 2015-01-26T01:29:07Z | NONE | null | {Python 2.6.6, pandas 0.12}
A DataFrame will fail to unstack() when one of the columns retained as an index has NaN values. The code below sets up a dataframe with NaN in some index entries, at which point calling unstack() will fail.
In the first failure, the exception message is that the index "has duplicate entries" which is patently false. In the second failure, where a given id only has one NaN, the error message becomes cannot convert float NaN to integer.
A final try, with NaN converted to a sentinel value of 42, shows proper behavior.
``` python
import pandas
from numpy import nan
df = pandas.DataFrame(
{'agent': {
17263: 'Hg',
17264: 'U',
17265: 'Pb',
17266: 'Sn',
17267: 'Ag',
17268: 'Hg'},
'change': {
17263: nan,
17264: 0.0,
17265: 7.070e-06,
17266: 2.3614e-05,
17267: 0.0,
17268: -0.00015},
'dosage': {
17263: nan,
17264: nan,
17265: nan,
17266: 0.0133,
17267: 0.0133,
17268: 0.0133},
's_id': {
17263: 680585148,
17264: 680585148,
17265: 680585148,
17266: 680607017,
17267: 680607017,
17268: 680607017}}
)
try:
dupe = df.copy().set_index(['s_id','dosage','agent'])
badDupe = dupe.unstack()
except Exception as e:
print( 'Error with all data was: %s'%(e,) )
try:
getnan = df.ix[17264:].copy().set_index(['s_id','dosage','agent'])
badNan = getnan.unstack()
except Exception as e:
print( 'Error dropping first entry was: %s'%(e,) )
df.dosage[:3]=42
willWork = df.copy().set_index(['s_id','dosage','agent'])
u = willWork.unstack()
print(u)
```
Overall output:
```
Error with all data was: Index contains duplicate entries, cannot reshape
Error dropping first entry was: cannot convert float NaN to integer
change
agent Ag Hg Pb Sn U
s_id dosage
680585148 42.0000 NaN NaN 0.000007 NaN 0
680607017 0.0133 0 -0.00015 NaN 0.000024 NaN
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4863 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4863/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4863/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4863/events | https://github.com/pandas-dev/pandas/pull/4863 | 19,636,136 | MDExOlB1bGxSZXF1ZXN0ODM5Njc3OQ== | 4,863 | BUG: Constrain date parsing from strings a little bit more #4601 | {
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} | 14 | 2013-09-17T19:23:39Z | 2014-06-22T11:10:12Z | 2013-09-20T22:34:04Z | CONTRIBUTOR | null | closes #4601
Currently dateutil will parse almost any string into a datetime. This
change adds a filter in front of dateutil that will prevent it from
parsing certain strings that don't look like datetimes:
1) Strings that parse to float values that are less than 1000
2) Certain special one character strings (this was already in there,
this just moves that code)
Additionally, this filters out datetimes that are out of range for the
datetime64[ns] type. Currently any out-of-range datetimes will just
overflow and be mapped to some random time within the bounds of
datetime64[ns].
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https://api.github.com/repos/pandas-dev/pandas/issues/4864 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4864/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4864/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4864/events | https://github.com/pandas-dev/pandas/pull/4864 | 19,644,903 | MDExOlB1bGxSZXF1ZXN0ODM5OTIzNg== | 4,864 | Panel tshift 4853 | {
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} | [] | closed | false | null | [] | null | 16 | 2013-09-17T20:43:41Z | 2014-07-16T08:28:52Z | 2013-09-18T13:40:29Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/4853
I think I'm doing this right. I moved the `tshift` into generic and had `shift` offload to `tshift` if `freq` is found.
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https://api.github.com/repos/pandas-dev/pandas/issues/4865 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4865/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4865/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4865/events | https://github.com/pandas-dev/pandas/issues/4865 | 19,657,500 | MDU6SXNzdWUxOTY1NzUwMA== | 4,865 | [BUG] NumPy timedelta64 order of magnitude not changed during date operations | {
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} | [] | closed | false | null | [] | null | 2 | 2013-09-18T01:28:44Z | 2013-10-14T11:08:21Z | 2013-09-18T02:03:25Z | NONE | null | It seems that whatever the unit of time used in timedelta64 (day, week, hour, year), when operating with a datetime64[ns] value, the timedelta integer is operated as nanosecond. Hence, if we intend to add or subtract one week (or day, or hour, or year), only one nanosecond is subtracted instead.
``` python
In [7]: import numpy as np
In [8]: import pandas as pd
In [9]: from pandas import Series
In [10]: a = np.timedelta64(1, 'W')
In [11]: a
Out[11]: numpy.timedelta64(1,'W')
In [12]: b = Series(pd.to_datetime('1/1/2010'))
In [13]: b
Out[13]:
0 2010-01-01 00:00:00
dtype: datetime64[ns]
In [14]: b - a
Out[14]:
0 2009-12-31 23:59:59.999999999
dtype: datetime64[ns]
In [15]: pd.__version__, np.__version__
Out[15]: ('0.12.0', '1.7.1')
```
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} | 27 | 2013-09-18T02:41:16Z | 2013-09-26T20:31:34Z | 2013-09-26T20:30:31Z | CONTRIBUTOR | null | Windows 7 - 64-bit
Enthought Python 2.7 - 32 bit
VS2008 compiled cython+pandas
```
======================================================================
FAIL: test_round_trip_frame (pandas.io.tests.test_clipboard.TestClipboard)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\io\tests\test_clipboard.py", line 50, in test
_round_trip_frame
self.check_round_trip_frame(dt)
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\io\tests\test_clipboard.py", line 46, in chec
k_round_trip_frame
tm.assert_frame_equal(data, result)
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\util\testing.py", line 295, in assert_frame_e
qual
check_less_precise=check_less_precise)
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\util\testing.py", line 244, in assert_series_
equal
assert_attr_equal('dtype', left, right)
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\util\testing.py", line 161, in assert_attr_eq
ual
assert_equal(left_attr,right_attr,"attr is not equal [{0}]" .format(attr))
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\util\testing.py", line 146, in assert_equal
assert a == b, "%s: %r != %r" % (msg.format(a,b), a, b)
AssertionError: attr is not equal [dtype]: dtype('int32') != dtype('int64')
======================================================================
FAIL: test_constructor_generator (pandas.tests.test_frame.TestDataFrame)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\tests\test_frame.py", line 2727, in test_cons
tructor_generator
assert_frame_equal(result, expected)
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\util\testing.py", line 295, in assert_frame_e
qual
check_less_precise=check_less_precise)
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\util\testing.py", line 244, in assert_series_
equal
assert_attr_equal('dtype', left, right)
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\util\testing.py", line 161, in assert_attr_eq
ual
assert_equal(left_attr,right_attr,"attr is not equal [{0}]" .format(attr))
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\util\testing.py", line 146, in assert_equal
assert a == b, "%s: %r != %r" % (msg.format(a,b), a, b)
AssertionError: attr is not equal [dtype]: dtype('int64') != dtype('int32')
======================================================================
FAIL: test_plot_xy (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\tests\test_graphics.py", line 454, in test_pl
ot_xy
== np.array((16., 8.))).all())
AssertionError: False is not true
======================================================================
FAIL: test_unsorted_index (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\tests\test_graphics.py", line 500, in test_un
sorted_index
tm.assert_series_equal(rs, df.y)
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\util\testing.py", line 244, in assert_series_
equal
assert_attr_equal('dtype', left, right)
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\util\testing.py", line 161, in assert_attr_eq
ual
assert_equal(left_attr,right_attr,"attr is not equal [{0}]" .format(attr))
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\util\testing.py", line 146, in assert_equal
assert a == b, "%s: %r != %r" % (msg.format(a,b), a, b)
AssertionError: attr is not equal [dtype]: dtype('int64') != dtype('int32')
======================================================================
FAIL: test_plot (pandas.tests.test_graphics.TestSeriesPlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\tests\test_graphics.py", line 80, in test_plo
t
== np.array((16., 8.))).all())
AssertionError: False is not true
======================================================================
FAIL: test_partial_setting (pandas.tests.test_indexing.TestIndexing)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\tests\test_indexing.py", line 1380, in test_p
artial_setting
assert_frame_equal(df,expected)
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\util\testing.py", line 295, in assert_frame_e
qual
check_less_precise=check_less_precise)
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\util\testing.py", line 244, in assert_series_
equal
assert_attr_equal('dtype', left, right)
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\util\testing.py", line 161, in assert_attr_eq
ual
assert_equal(left_attr,right_attr,"attr is not equal [{0}]" .format(attr))
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\util\testing.py", line 146, in assert_equal
assert a == b, "%s: %r != %r" % (msg.format(a,b), a, b)
AssertionError: attr is not equal [dtype]: dtype('int32') != dtype('int64')
======================================================================
FAIL: test_constructor_iterator (pandas.tests.test_series.TestSeries)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\tests\test_series.py", line 360, in test_cons
tructor_iterator
assert_series_equal(result, expected)
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\util\testing.py", line 244, in assert_series_
equal
assert_attr_equal('dtype', left, right)
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\util\testing.py", line 161, in assert_attr_eq
ual
assert_equal(left_attr,right_attr,"attr is not equal [{0}]" .format(attr))
File "C:\Users\kieran\Documents\GitHub\pandas\pandas\util\testing.py", line 146, in assert_equal
assert a == b, "%s: %r != %r" % (msg.format(a,b), a, b)
AssertionError: attr is not equal [dtype]: dtype('int32') != dtype('int64')
----------------------------------------------------------------------
Ran 3705 tests in 1360.689s
FAILED (SKIP=168, failures=7)
```
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} | 22 | 2013-09-18T04:10:11Z | 2014-07-16T08:28:56Z | 2013-09-27T03:50:38Z | CONTRIBUTOR | null | closes #4638
Before:
```
In [22]: WeekOfMonth(weekday=1,week=2)
Out[22]: <1 WeekOfMonth: week=2, kwds={'week': 2, 'weekday': 1}, weekday=1>
In [32]: QuarterEnd()
Out[32]: <1 QuarterEnd: startingMonth=3, offset=<3 MonthEnds>>
In [40]: BQuarterBegin()
Out[40]: <1 BusinessQuarterBegin: startingMonth=3, offset=<3 BusinessMonthBegins>>
In [41]: BQuarterBegin(startingMonth=3)
Out[41]: <1 BusinessQuarterBegin: startingMonth=3, kwds={'startingMonth': 3}, offset=<3 BusinessMonthBegins>>
```
after:
```
In [2]: WeekOfMonth(weekday=1,week=2)
Out[2]: <1 WeekOfMonth: week=2, weekday=1>
In [3]: QuarterEnd()
Out[3]: <1 QuarterEnd: startingMonth=3>
In [4]: BQuarterBegin()
Out[4]: <1 BusinessQuarterBegin: startingMonth=3>
In [5]: BQuarterBegin(startingMonth=3)
Out[5]: <1 BusinessQuarterBegin: startingMonth=3>
```
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In [2]: pd.json.dumps(pd.Timestamp('2013-01-01'), iso_dates=True)
Out[2]: '"699470488266606557-01-01T00:00:00.000Z"'
```
should be
``` python
In [4]: pd.json.dumps(pd.Timestamp('2013-01-01'), iso_dates=True)
Out[4]: '"2013-01-01T00:00:00.000Z"'
```
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} | 10 | 2013-09-18T13:00:03Z | 2014-03-19T00:41:38Z | 2014-03-19T00:41:38Z | CONTRIBUTOR | null | It seems that pandas.isnull returns different results with tuple and list arguments. I would expect to see identical results with tuple argument.
Here is an example of the issue
``` python
>>> pd.version.version
'0.12.0'
>>> pd.isnull((3,2))
False
>>> pd.isnull([3,2])
array([False, False], dtype=bool)
```
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} | 8 | 2013-09-18T14:12:09Z | 2013-10-07T12:27:05Z | 2013-10-07T12:27:05Z | NONE | null | `datetime.time` objects cause `to_json` to fail, presumably because they are attempting to be converted in the same way as `datetime.datetime` objects.
For example:
```
>>> pd.DataFrame(data=dict(a = [datetime.time()])).to_json()
...
OverflowError: Maximum recursion level reached
```
In contrast:
```
>>> pd.DataFrame(data=dict(a = [datetime.datetime.now()])).to_json()
'{"a":{"0":1379498870147}}'
```
Tested on 0.12 and HEAD (b039c1d33722bfdac8c665f9b1f3af44782e0e58)
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} | 31 | 2013-09-18T14:47:33Z | 2014-07-13T22:56:30Z | 2014-03-09T22:44:51Z | CONTRIBUTOR | null | closes #4867. First part of changing `Panel.shift` is to make `NDFrame.shift` work with any axis and number of dimensions.
Until Panel uses it, the only case of this is `DataFrame.shift(axis='columns')` which is added as a test.
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} | 3 | 2013-09-18T14:53:39Z | 2013-09-30T22:36:15Z | 2013-09-30T22:36:15Z | NONE | null | I haven't had issues previously deploying updates to my application; however, today I tried to deploy a branch of my code to a new environment and I keep receiving this error:
building 'pandas.algos' extension
gcc -pthread -fno-strict-aliasing -O2 -g -pipe -Wall -Wp,-D_FORTIFY_SOURCE=2 -fexceptions -fstack-protector --param=ssp-buffer-size=4 -m64 -mtune=generic -D_GNU_SOURCE -fPIC -fwrapv -DNDEBUG -O2 -g -pipe -Wall -Wp,-D_FORTIFY_SOURCE=2 -fexceptions -fstack-protector --param=ssp-buffer-size=4 -m64 -mtune=generic -D_GNU_SOURCE -fPIC -fwrapv -fPIC -Ipandas/src/klib -Ipandas/src -I/opt/python/run/venv/lib/python2.6/site-packages/numpy/core/include -I/usr/include/python2.6 -c pandas/algos.c -o build/temp.linux-x86_64-2.6/pandas/algos.o
In file included from /opt/python/run/venv/lib/python2.6/site-packages/numpy/core/include/numpy/ndarraytypes.h:1728,
from /opt/python/run/venv/lib/python2.6/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
from /opt/python/run/venv/lib/python2.6/site-packages/numpy/core/include/numpy/arrayobject.h:15,
from pandas/algos.c:314: /opt/python/run/venv/lib/python2.6/site-packages/numpy/core/include/numpy/npy_deprecated_api.h:11:2: warning: #warning "Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION"
pandas/algos.c: In function __pyx_pf_6pandas_5algos_450group_ohlc_float64:
.... (other warnings)
pandas/algos.c:118189: warning: __pyx_v_vclose may be used uninitialized in this function
{standard input}: Assembler messages:
{standard input}:2324793: Warning: end of file in string; '"' inserted
....
gcc: Internal error: Killed (program cc1)
error: command 'gcc' failed with exit status 1
Pandas 0.12.0 (and NumPy 1.7.1 etc.) are all included in my requirements.txt. Has something changed or am I doing something wrong here? Everything is working fine in my local environment.
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} | 0 | 2013-09-18T19:25:31Z | 2014-06-13T05:16:05Z | 2013-09-18T20:41:31Z | MEMBER | null | Corrects tests of the form
`self.assert_(x, y)` to `self.assertEqual(x, y)`. Also changes tests of the
form `self.assert_(x == y)` to `self.assertEqual(x, y)`.
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} | 3 | 2013-09-19T02:53:01Z | 2014-02-27T19:27:44Z | 2014-02-27T19:27:44Z | MEMBER | null | ```
In [57]: # float dtype
In [58]: s = Series(rand(1000000))
In [59]: timeit s.mean()
1000 loops, best of 3: 1.03 ms per loop
In [60]: # int dtype
In [61]: s = Series(randint(100, size=1000000))
In [62]: timeit s.mean()
1000 loops, best of 3: 1.33 ms per loop
In [63]: # bool dtype
In [64]: s = Series(rand(1000000) > 0.5)
In [65]: timeit s.mean()
10 loops, best of 3: 26.5 ms per loop
```
i'll submit an issue to `bottleneck`, but just wanted to bring it up here in case we want to default to the non-bottleneck impl for bool dtype.
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} | 18 | 2013-09-19T03:32:51Z | 2021-04-11T03:18:50Z | null | CONTRIBUTOR | null | Something seems wrong here:
```
print repr(pd.Period("2013-12", freq=pd.offsets.MonthEnd()))
```
correctly prints:
```
Period('2013-12', 'M')
```
but:
```
print pd.Period("2013-12", freq=pd.offsets.BusinessMonthEnd())
```
leads to:
```
Traceback (most recent call last):
File "/home/alex/git/pandas/pandas/tseries/tests/test_52.py", line 29, in testName
a = pd.Period("2013-12", freq=BusinessMonthEnd())
File "/home/alex/git/pandas/pandas/tseries/period.py", line 121, in __init__
base, mult = _gfc(freq)
File "/home/alex/git/pandas/pandas/tseries/frequencies.py", line 92, in get_freq_code
code = _period_str_to_code(freqstr[0])
File "/home/alex/git/pandas/pandas/tseries/frequencies.py", line 757, in _period_str_to_code
alias = _period_alias_dict[freqstr]
KeyError: 'BM'
```
Strangely, the following both work:
```
In [10]: pd.date_range("2013-01","2013-10",freq=pd.offsets.MonthEnd())
Out[10]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-31 00:00:00, ..., 2013-09-30 00:00:00]
Length: 9, Freq: M, Timezone: None
In [11]: pd.date_range("2013-01","2013-10",freq=pd.offsets.BusinessMonthEnd())
Out[11]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-31 00:00:00, ..., 2013-09-30 00:00:00]
Length: 9, Freq: BM, Timezone: None
```
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} | 1 | 2013-09-19T10:33:28Z | 2013-09-19T12:55:15Z | 2013-09-19T12:55:15Z | CONTRIBUTOR | null | Hit this `AssertionError: cannot create BlockManager._ref_locs ...` while processing a dataframe with multiple empty column names. Appears to fail with 0.12/master and pass with 0.11.
```
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.rand(3,4), columns=['', '', 'C', 'D'])
df[df.C > .5]
```
```
Traceback (most recent call last):
File "test_fail.py", line 5, in <module>
df[df.C > .5]
File "/home/gmd/.virtualenvs/pandas-master/local/lib/python2.7/site-packages/pandas-0.12.0_476_gb889fda-py2.7-linux-i686.egg/pandas/core/frame.py", line 1830, in __getitem__
return self._getitem_frame(key)
File "/home/gmd/.virtualenvs/pandas-master/local/lib/python2.7/site-packages/pandas-0.12.0_476_gb889fda-py2.7-linux-i686.egg/pandas/core/frame.py", line 1898, in _getitem_frame
return self.where(key)
File "/home/gmd/.virtualenvs/pandas-master/local/lib/python2.7/site-packages/pandas-0.12.0_476_gb889fda-py2.7-linux-i686.egg/pandas/core/generic.py", line 2372, in where
cond = cond.reindex(**self._construct_axes_dict())
File "/home/gmd/.virtualenvs/pandas-master/local/lib/python2.7/site-packages/pandas-0.12.0_476_gb889fda-py2.7-linux-i686.egg/pandas/core/generic.py", line 1118, in reindex
return self._reindex_axes(axes, level, limit, method, fill_value, copy, takeable=takeable)._propogate_attributes(self)
File "/home/gmd/.virtualenvs/pandas-master/local/lib/python2.7/site-packages/pandas-0.12.0_476_gb889fda-py2.7-linux-i686.egg/pandas/core/frame.py", line 2413, in _reindex_axes
fill_value, limit, takeable=takeable)
File "/home/gmd/.virtualenvs/pandas-master/local/lib/python2.7/site-packages/pandas-0.12.0_476_gb889fda-py2.7-linux-i686.egg/pandas/core/frame.py", line 2436, in _reindex_columns
copy=copy, fill_value=fill_value, allow_dups=takeable)
File "/home/gmd/.virtualenvs/pandas-master/local/lib/python2.7/site-packages/pandas-0.12.0_476_gb889fda-py2.7-linux-i686.egg/pandas/core/generic.py", line 1218, in _reindex_with_indexers
fill_value=fill_value, allow_dups=allow_dups)
File "/home/gmd/.virtualenvs/pandas-master/local/lib/python2.7/site-packages/pandas-0.12.0_476_gb889fda-py2.7-linux-i686.egg/pandas/core/internals.py", line 2840, in reindex_indexer
return self._reindex_indexer_items(new_axis, indexer, fill_value)
File "/home/gmd/.virtualenvs/pandas-master/local/lib/python2.7/site-packages/pandas-0.12.0_476_gb889fda-py2.7-linux-i686.egg/pandas/core/internals.py", line 2885, in _reindex_indexer_items
return self.__class__(new_blocks, new_axes)
File "/home/gmd/.virtualenvs/pandas-master/local/lib/python2.7/site-packages/pandas-0.12.0_476_gb889fda-py2.7-linux-i686.egg/pandas/core/internals.py", line 1735, in __init__
self._set_ref_locs(do_refs=True)
File "/home/gmd/.virtualenvs/pandas-master/local/lib/python2.7/site-packages/pandas-0.12.0_476_gb889fda-py2.7-linux-i686.egg/pandas/core/internals.py", line 1863, in _set_ref_locs
"does not have _ref_locs set" % (block, labels))
AssertionError: cannot create BlockManager._ref_locs because block [BoolBlock: [C], 1 x 3, dtype: bool] with duplicate items [Index([u'', u'', u'C', u'D'], dtype=object)] does not have _ref_locs set
```
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} | 4 | 2013-09-19T10:48:00Z | 2016-07-26T08:41:47Z | 2016-07-26T08:41:43Z | NONE | null | The `delim_whitespace` options no longer works when specifying a `skip_footer` other then zero. I can replicate the behavior in 0.12 with the following example:
```
import pandas as pd
from StringIO import StringIO
indata = StringIO("""1.2 5.6 8.5
4.5 6.7 6.4
""")
indata.seek(0)
df = pd.read_csv(indata, delim_whitespace=True, header=None, skip_footer=2)
```
Which returns:
```
0
0 1.2 5.6 8.5
1 4.5 6.7 6.4
```
Note how its only one column, instead of three. Changing the `skip_footer` to 0 makes it work as expected.
```
indata.seek(0)
df = pd.read_csv(indata, delim_whitespace=True, header=None, skip_footer=0)
```
Returns:
```
0 1 2
0 1.2 5.6 8.5
1 4.5 6.7 6.4
2 NaN NaN NaN
3 NaN NaN NaN
```
If the above example is used with something like `names=['a','b','c']` an (obvious) 'ValueError' exception occurs: `Expected 3 fields in line 1, saw 1`.
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} | 0 | 2013-09-19T12:23:50Z | 2014-06-20T21:01:35Z | 2013-09-19T12:50:15Z | CONTRIBUTOR | null | raising previously as this was not handled properly
closes #4879
```
In [1]: df = DataFrame(np.arange(12).reshape(3,4), columns=['A', 'B', 'C', 'D'],dtype='float64')
In [2]: df
Out[2]:
A B C D
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
In [3]: df[df.C>6]
Out[3]:
A B C D
2 8 9 10 11
In [4]: df = DataFrame(np.arange(12).reshape(3,4), columns=['A', 'A', 'C', 'D'],dtype='float64')
In [5]: df
Out[5]:
A A C D
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
In [6]: df[df.C>6]
Out[6]:
A A C D
2 8 9 10 11
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4882 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4882/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4882/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4882/events | https://github.com/pandas-dev/pandas/issues/4882 | 19,751,472 | MDU6SXNzdWUxOTc1MTQ3Mg== | 4,882 | pandas.core.groupby.DataFrameGroupBy to_csv method doesn't ouput csv file as expected | {
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} | 13 | 2013-09-19T12:27:39Z | 2013-09-30T12:36:58Z | 2013-09-30T12:36:58Z | NONE | null | ```
>>> df1 = pd.DataFrame( {
"Name" : ["Alice", "Bob", "Mallory", "Mallory", "Bob" , "Mallory"] ,
"City" : ["Seattle", "Seattle", "Portland", "Seattle", "Seattle", "Portland"] } )
>>> g1 = df1.groupby( [ "Name" ] )
>>> print g1.head()
City Name
Name
Alice 0 Seattle Alice
Bob 1 Seattle Bob
4 Seattle Bob
Mallory 2 Portland Mallory
3 Seattle Mallory
5 Portland Mallory
>>> g1.to_csv('out.csv')
g1.to_csv('out.csv')
Out[10]:
Name
Alice None
Bob None
Mallory None
dtype: object
```
(Why some data are output to ipython console ?)
```
>>> !cat out.csv
,City,Name
2,Portland,Mallory
3,Seattle,Mallory
5,Portland,Mallory
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4883 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4883/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4883/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4883/events | https://github.com/pandas-dev/pandas/issues/4883 | 19,751,846 | MDU6SXNzdWUxOTc1MTg0Ng== | 4,883 | pandas.core.groupby.DataFrameGroupBy should provide a method to be converted into a (MultiIndex) DataFrame | {
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>>> df1 = pd.DataFrame( {
"Name" : ["Alice", "Bob", "Mallory", "Mallory", "Bob" , "Mallory"] ,
"City" : ["Seattle", "Seattle", "Portland", "Seattle", "Seattle", "Portland"] } )
>>> g1 = df1.groupby( [ "Name" ] )
>>> print g1.head()
City Name
Name
Alice 0 Seattle Alice
Bob 1 Seattle Bob
4 Seattle Bob
Mallory 2 Portland Mallory
3 Seattle Mallory
5 Portland Mallory
>>>> pd.DataFrame(g1)
```
raise exception
AttributeError: 'numpy.ndarray' object has no attribute 'index'
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} | 3 | 2013-09-19T13:46:12Z | 2014-03-28T23:44:25Z | 2014-03-28T23:44:25Z | NONE | null | { python 2.6.6, pandas 0.13, numpy 1.6.2 }
A pandas DataFrame containing a Timestamp-type column will display itself nicely in HTML if formatters are unused. However, if the user employs the `formatters` argument to set column-wise formatting, then the timestamp objects are somehow munged before reaching the given format function.
This is most easily investigated in an iPython notebook:
```
from IPython.display import HTML
df=pandas.DataFrame( {'a':range(3),
'b':[
pandas.tslib.Timestamp('2013-08-01 09:03:35.013748', tz=None),
pandas.tslib.Timestamp('2013-08-01 10:03:35.013748', tz=None),
pandas.tslib.Timestamp('2013-08-01 11:03:35.013748', tz=None),
]
} )
from numbers import Number
def fmt(x):
if not isinstance(x, Number):
return str(x)+': '+x.__class__.__name__
else:
return '%.1f'%x
HTML(df.to_html(formatters=[fmt,fmt]))
```
The expected output would show a table whose second column includes string versions of datetimes in 2013. Instead we see nonsensical times from 1970.
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} | 17 | 2013-09-19T17:20:01Z | 2014-04-23T11:20:50Z | 2013-11-26T11:07:01Z | CONTRIBUTOR | null | Helping out with a moderately beginner class recently, I noticed several people having problems, because they could easily display a table view of a small DataFrame, but the representation looked completely different when it exceeded a certain size. People thought that they had a different type of object, or that the detailed information was some kind of an error message. There's no obvious way to get the HTML repr for larger DataFrames.
Suggestions:
- Increase the size limit for displaying the full HTML repr. When we did force larger dataframes to display as HTML tables, the IPython notebook can easily handle substantially larger tables than the current cutoff.
- When the DataFrame is too large to display whole, produce a truncated HTML table rather than switching to a completely different kind of repr.
I'll try to work on this soon if no-one objects or beats me to it.
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I'm using a recent anaconda build on Windows, which includes v 0.12.0 pandas. I'm attempting to use a multi-index header, write it out to a json file, import it and get the same formatted dataframe. The column headers come out stacked vertically instead.
import numpy as np
import pandas as pd
pd.set_option('html',False)
index = pd.MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['foo', 'bar'])
df = pd.DataFrame(np.random.randn(10, 3), index=index,
columns=['A', 'B', 'C'])
dft = df.transpose()
dft
dft.to_json('D:\xxmt.json',orient='split')
dftj=pd.read_json('D:\xxmt.json',orient='split')
dftj
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} | [] | closed | false | null | [] | null | 3 | 2013-09-20T04:02:15Z | 2014-07-01T18:53:21Z | 2013-09-20T04:29:12Z | CONTRIBUTOR | null | Fixes #4827
Assertions were being made about arrays (of one item) equaling
a scaler.
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Fixes #4827
Assertions were being made about arrays (of one item) equaling
a scaler.
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} | 0 | 2013-09-20T12:19:28Z | 2014-04-11T22:33:35Z | 2014-04-11T22:33:35Z | CONTRIBUTOR | null | see #4850
`Series(range(5))[3.0]`
is accepted in 0.13, but ought to be deprecated (and warned)
ideally raise a `TypeError` after some transition period
note this does not really affect positional indexing fallback per se,
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} | 11 | 2013-09-20T14:39:31Z | 2016-10-12T23:04:12Z | 2015-09-05T22:26:10Z | NONE | null | df = P.DataFrame({'a':[1,1,2,2,3,3]})
df.eval("a.mean()")
raise:
NotImplementedError: 'Call' nodes are not implemented
i hope this will be possible
thank's
Gla
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} | 6 | 2013-09-20T14:44:16Z | 2014-05-29T07:43:47Z | 2014-05-29T07:43:47Z | NONE | null | df = P.DataFrame({'a':[1,1,2,2,3,3], 'b':range(6)})
How about a function applyeval
df.groupby('a').applyeval( "b/b.mean()" )
instead of:
df.groupby('a').apply( lambda x: x.eval( "b/b.mean()" ))
Thank you
Gla
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} | 0 | 2013-09-20T15:04:41Z | 2020-04-03T05:20:21Z | null | CONTRIBUTOR | null | `astype/convert_objects` should be able to do this type of coercion from string complex looking
```
In [45]: Series(['1.0+5j','1.5-3j']).apply(lambda x: np.complex(x))
Out[45]:
0 (1+5j)
1 (1.5-3j)
dtype: complex128
```
http://stackoverflow.com/questions/18919699/python-pandas-complex-number/18919965#18919965
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} | 1 | 2013-09-20T15:28:18Z | 2016-03-03T02:24:25Z | 2016-03-03T02:24:25Z | NONE | null | Dropping the index column with index=False normally works OK, but if the column headings are from a MultiIndex, it fails to drop the empty `<th></th>` spacers at the start of the header, so data rows are not aligned properly:
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df.to_html(index=False)
```
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} | 0 | 2013-09-20T15:34:23Z | 2013-09-27T19:34:46Z | 2013-09-27T19:34:46Z | MEMBER | null | doesn't work correctly with `NaT` values
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} | 5 | 2013-09-20T16:55:30Z | 2019-09-02T12:17:37Z | null | CONTRIBUTOR | null | This is not a Pandas issue but a [numpy one](https://github.com/numpy/numpy/issues/2522).
see also #5230
However, it is highly confusing and it could be appropriate to fix in Pandas by promoting `uint(2^x) dtypes to int(2^(x+1))` in `Series.diff()`.
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} | 0 | 2013-09-20T17:38:03Z | 2013-09-22T02:52:23Z | 2013-09-22T02:52:23Z | MEMBER | null | old(ish) SO question: http://stackoverflow.com/questions/12590131/how-to-slice-multindex-columns-in-pandas-dataframes
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