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} | 2 | 2014-07-09T07:11:32Z | 2016-10-12T23:05:00Z | 2014-07-10T15:15:02Z | MEMBER | null | Example from Wes' book:
```
In [1]: data = pd.DataFrame({'year':[1959,1959,1959,1959,1960,1960,1960,1960], '
quarter':[1,2,3,4,1,2,3,4]})
In [2]: data
Out[2]:
quarter year
0 1 1959
1 2 1959
2 3 1959
3 4 1959
4 1 1960
5 2 1960
6 3 1960
7 4 1960
In [3]: pd.PeriodIndex(year=data.year, quarter=data.quarter)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
...
C:\Anaconda\envs\devel\lib\site-packages\numpy\core\fromnumeric.pyc in repeat(a,
repeats, axis)
342 except AttributeError:
343 return _wrapit(a, 'repeat', repeats, axis)
--> 344 return repeat(repeats, axis)
345
346
TypeError: repeat() takes exactly 2 arguments (3 given)
```
The docstring of PeriodIndex says `int or array` for the `year` and `quarter` keywords, and indeed with arrays it does work:
```
In [4]: pd.PeriodIndex(year=data.year.values, quarter=data.quarter.values)
Out[4]:
<class 'pandas.tseries.period.PeriodIndex'>
[1959Q1, ..., 1960Q4]
Length: 8, Freq: Q
```
But it previously also worked for Series (I suppose when it was still a subclass?). So, should we keep the current behaviour or make it also work for Series?
In any case, a very strange error message that should be improved.
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} | 5 | 2014-07-09T08:49:59Z | 2014-07-09T17:09:30Z | 2014-07-09T17:00:54Z | CONTRIBUTOR | null | closes #7683
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} | 3 | 2014-07-09T11:29:32Z | 2018-07-06T22:01:49Z | 2018-07-06T21:59:47Z | NONE | null | I think [this StackOverflow question](http://stackoverflow.com/q/24650117/1156707) illustrates both the premise and the problem quite well. In short, it would be nice to have something like `df.window(df.somecolumn == 'ping', winsize=5)` which would return a subset of `df` such that for each row where `df.somecolumn=='ping'`, we select the 5 previous lines and five following lines.
This would be particularly useful for time-locking epochs to certain values (e.g. TTLs from some hardware).
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} | 0 | 2014-07-09T12:19:05Z | 2014-09-19T20:05:55Z | 2014-09-19T20:05:55Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/24650117/how-can-i-select-all-dataframe-rows-that-are-within-a-certain-distance-of-a-give/24653406#24653406
```
In [32]: df = pd.DataFrame(np.random.rand(30, 3), columns=tuple('ABC'))
In [33]: df['event'] = np.nan
In [34]: df.dtypes
Out[34]:
A float64
B float64
C float64
event float64
dtype: object
In [35]: df.loc[10,'event'] = 'foo'
In [37]: df.dtypes
Out[37]:
A object
B object
C object
event object
dtype: object
```
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} | 5 | 2014-07-09T14:10:24Z | 2014-07-10T11:01:35Z | 2014-07-09T15:48:51Z | MEMBER | null | Closes problem found in #7375.
@jreback Is this for 0.15 (needs release note)?
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} | 5 | 2014-07-09T14:17:08Z | 2020-06-28T17:38:27Z | 2020-06-28T17:38:26Z | MEMBER | null | From https://github.com/pydata/pandas/pull/7375#issuecomment-48467303:
related: https://github.com/pydata/pandas/issues/4804
xref: #7705 (MonthOffset)
```
In [55]: offsets.YearOffset()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-55-b53e59b753d1> in <module>()
----> 1 offsets.YearOffset()
c:\users\vdbosscj\scipy\pandas-joris\pandas\tseries\offsets.pyc in __init__(self
, n, normalize, **kwds)
1331
1332 def __init__(self, n=1, normalize=False, **kwds):
-> 1333 self.month = kwds.get('month', self._default_month)
1334
1335 if self.month < 1 or self.month > 12:
AttributeError: 'YearOffset' object has no attribute '_default_month'
```
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```
In [67]: from pandas.tseries import offsets
In [69]: d = pd.Timestamp('2014-01-01 09:00:00')
In [70]: d + offsets.MonthOffset()
Out[70]: Timestamp('2014-01-02 09:00:00')
In [71]: offsets.MonthOffset().apply(d)
Out[71]: Timestamp('2014-01-02 09:00:00')
In [72]: d + offsets.Day()
Out[72]: Timestamp('2014-01-02 09:00:00')
```
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} | 1 | 2014-07-09T15:14:46Z | 2014-07-23T11:09:22Z | 2014-07-21T11:49:58Z | MEMBER | null | Add `resolution` to `PeriodIndex` as `DatetimeIndex` has.
NOTE: Going to use this to calculate common freq in #7670.
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} | 13 | 2014-07-09T17:00:31Z | 2014-07-10T15:32:51Z | 2014-07-10T15:32:51Z | CONTRIBUTOR | null | closes #7683
Move `__iter__` from `DatatimeIndex/PeriodIndex` to `DatetimeIndexOpsMixin`
Adding perf tests for change
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
timeseries_iter_datetimeindex_preexit | 35.6683 | 2546.9604 | 0.0140 |
timeseries_iter_periodindex | 5051.6427 | 4353.0200 | 1.1605 |
timeseries_iter_periodindex_preexit | 50.9930 | 43.8580 | 1.1627 |
timeseries_iter_datetimeindex | 3463.0601 | 2596.2270 | 1.3339 |
```
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} | 7 | 2014-07-09T19:48:43Z | 2016-09-01T07:28:07Z | 2016-09-01T07:28:07Z | NONE | null | ```
In [1]: import pandas as pd
In [2]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.7.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 30 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.14.0
nose: 1.3.3
Cython: 0.20.2
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.1.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2014.4
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.5
lxml: 3.3.5
bs4: 4.3.1
html5lib: 0.999
bq: None
apiclient: None
rpy2: 2.3.9
sqlalchemy: 0.9.4
pymysql: None
psycopg2: 2.5.3 (dt dec pq3 ext)
In [3]: s = pd.TimeSeries(1, [pd.Period('2002-01-01',freq='D')])
In [4]: s[[0]]
Out[4]:
2002-01-01 1
Freq: D, dtype: int64
In [5]: s[[pd.Period('2002-01-01',freq='D')]]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-5-0e636383b52c> in <module>()
----> 1 s[[pd.Period('2002-01-01',freq='D')]]
C:\Python27\lib\site-packages\pandas\core\series.pyc in __getitem__(self, key)
517 key = _check_bool_indexer(self.index, key)
518
--> 519 return self._get_with(key)
520
521 def _get_with(self, key):
C:\Python27\lib\site-packages\pandas\core\series.pyc in _get_with(self, key)
555 # handle the dup indexing case (GH 4246)
556 if isinstance(key, (list, tuple)):
--> 557 return self.ix[key]
558
559 return self.reindex(key)
C:\Python27\lib\site-packages\pandas\core\indexing.pyc in __getitem__(self, key)
68 return self._getitem_tuple(key)
69 else:
---> 70 return self._getitem_axis(key, axis=0)
71
72 def _get_label(self, label, axis=0):
C:\Python27\lib\site-packages\pandas\core\indexing.pyc in _getitem_axis(self, key, axis, validate_iterable)
849 raise ValueError('Cannot index with multidimensional key')
850
--> 851 return self._getitem_iterable(key, axis=axis)
852 else:
853 if com.is_integer(key):
C:\Python27\lib\site-packages\pandas\core\indexing.pyc in _getitem_iterable(self, key, axis)
905 # existing labels are unique and indexer is unique
906 if labels.is_unique and keyarr_is_unique:
--> 907 return _reindex(keyarr, level=level)
908
909 else:
C:\Python27\lib\site-packages\pandas\core\indexing.pyc in _reindex(keys, level)
875 if axis != 0:
876 raise AssertionError('axis must be 0')
--> 877 return self.obj.reindex(keys, level=level)
878
879 if com._is_bool_indexer(key):
C:\Python27\lib\site-packages\pandas\core\series.pyc in reindex(self, index, **kwargs)
2026 @Appender(generic._shared_docs['reindex'] % _shared_doc_kwargs)
2027 def reindex(self, index=None, **kwargs):
-> 2028 return super(Series, self).reindex(index=index, **kwargs)
2029
2030 def reindex_axis(self, labels, axis=0, **kwargs):
C:\Python27\lib\site-packages\pandas\core\generic.pyc in reindex(self, *args, **kwargs)
1608 # if indicated must have index names equal here as well as values
1609 if all([self._get_axis(axis).identical(ax)
-> 1610 for axis, ax in axes.items() if ax is not None]):
1611 if copy:
1612 return self.copy()
C:\Python27\lib\site-packages\pandas\core\index.pyc in identical(self, other)
849 also equal
850 """
--> 851 return (self.equals(other) and
852 all((getattr(self, c, None) == getattr(other, c, None)
853 for c in self._comparables)) and
C:\Python27\lib\site-packages\pandas\tseries\period.pyc in equals(self, other)
822 return True
823
--> 824 return np.array_equal(self.asi8, other.asi8)
825
826 def tolist(self):
AttributeError: 'numpy.ndarray' object has no attribute 'asi8'
```
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} | 33 | 2014-07-09T19:58:29Z | 2014-08-12T22:03:26Z | 2014-08-12T21:59:20Z | MEMBER | null | ## NumPy 1.6
NumPy 1.6 introduced numerous bugs around datetimes, forcing a plethora of hacks, and causing numerous stress-induced ulcers for an uncountably infinite number of people.
The time for pandas to lose this vestigial organ has come. The main reason for this is that datetime support is just broken, in nearly every sense of the word. Take a look at some of the conversion code in `pandas/core/common.py` and you'll see what I'm talking about.
I think we've probably reached a point where supporting numpy 1.6 is just going to cause more pain than it's worth. To me it makes _no_ sense to want to keep upgrading pandas while keeping `numpy <= 1.6.1`. If you want modern pandas, you need to use modern-ish versions of its dependencies.
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https://api.github.com/repos/pandas-dev/pandas/issues/7713 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7713/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7713/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7713/events | https://github.com/pandas-dev/pandas/pull/7713 | 37,507,936 | MDExOlB1bGxSZXF1ZXN0MTgxODYzODY= | 7,713 | DOC: fix doc build warnings | {
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| null | 1 | 2014-07-09T21:01:45Z | 2014-07-10T07:17:55Z | 2014-07-10T07:17:53Z | MEMBER | null | This should fix all doc build errors/warnings (apart from the known ones).
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} | 29 | 2014-07-10T07:57:40Z | 2014-07-11T20:38:28Z | 2014-07-10T23:08:37Z | MEMBER | null | - removed the sections with no entries
- removed whitespace
- moved some entries from API-changes to Enhancements/Big fixes when it was not really a backwards incompatible change or if it was not clear what the relevant change was for users (and for the offsets normalize issue I rewrote it)
Highlights should still be written to include here and in the release.rst
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} | 3 | 2014-07-10T08:43:53Z | 2014-09-26T22:06:18Z | 2014-09-26T22:06:18Z | CONTRIBUTOR | null | At the moment `read_hdf` is implemented such way that it opens HDFStore connection immediately when it is called. If the file does not exist, then it creates an HDF file and then throws `KeyError`. There are two side effects:
- You can not distinguish situation when the file does not exist from situation when the key does not exist in the file
- If you have a typo in the filename, empty file is created -> polluting directory
I suggest to check if the file exist before and throw `IOError` if it does not.
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| null | 3 | 2014-07-10T12:30:41Z | 2015-10-20T22:29:05Z | 2015-10-20T22:29:05Z | CONTRIBUTOR | null | Say I have a dataframe `df` where `df.index` consists of `datetime` objects, e.g.
```
> df.index[0]
datetime.date(2014, 5, 5)
```
If I plot it, Pandas nicely preserves the `datetime` type in the plot, which allows the user to change the time-series sampling as well formatting options of the plot:
```
# Plot the dataframe:
f = plt.figure(figsize=(8,8))
ax = f.add_subplot(1,1,1)
lines = df.plot(ax=ax)
# Choose the sampling rate in terms of dates:
ax.xaxis.set_major_locator(matplotlib.dates.WeekdayLocator(byweekday=(0,1,2,3,4,5,6),
interval=1))
# We can also re-sample the X axis numerically if we want (e.g. every 4 steps):
N = 4
ticks = ax.xaxis.get_ticklocs()
ticklabels = [l.get_text() for l in ax.xaxis.get_ticklabels()]
ax.xaxis.set_ticks(ticks[-1::-N][::-1])
ax.xaxis.set_ticklabels(ticklabels[-1::-N][::-1])
# Choose a date formatter using a date-friendly syntax:
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%b\n%d'))
plt.show()
```
However, the above does **not** work for a `boxplot` (the tick labels for the x axis are rendered empty):
```
df2.boxplot(column='A', by='created_dt',ax=ax, sym="k.")
# same code as above ...
```
It looks like in the last example Pandas converts the x-axis labels into **string** type, so the formatter and locators don't work anymore.
This post re-uses solutions from the following threads:
1. Accepted answer to [Pandas timeseries plot setting x-axis major and minor ticks and labels](http://stackoverflow.com/a/13674286/283296)
2. Accepted answer to http://stackoverflow.com/questions/19143857
Why? How can I use `boxplot` in a way that allows me to use `matplotlib` date locators and formatters?
The original post in SO is [here](http://stackoverflow.com/questions/24665990/time-series-plotting-inconsistencies-in-pandas)
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} | 3 | 2014-07-10T13:02:54Z | 2014-07-23T11:08:48Z | 2014-07-21T13:14:05Z | MEMBER | null | Related to #7670. Made `LinePlot` to use single plotting flow in `x_compat` and `tsplot`.
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} | 8 | 2014-07-10T14:01:11Z | 2016-01-08T14:07:14Z | 2016-01-08T14:07:14Z | MEMBER | null | will close #8760 at same time
The slowest of the slow of Linux distributions (RHEL) has upgraded their version of Python to 2.7. I think if they're happy with not supporting 2.6, then we can be too. Supporting it is not a _huge_ deal, but it would be nice to really encourage people to move forward with upgrading to later versions of Python which would provide the community with momentum towards Python 3. While we can't force everyone to use Python 3, I think dropping support for Python 2.6 is reasonable. The last binary release of Python 2.6 was 2.6.6 and that was in 2010. By now, most people are using python 2.7.
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} | 15 | 2014-07-10T14:24:58Z | 2014-07-10T22:35:31Z | 2014-07-10T22:34:56Z | MEMBER | null | From discussion here: https://github.com/pydata/pandas/pull/7634#issuecomment-48111148
Due to switching from Integer to BigInteger (to support int64 on some database systems), reading a table from sqlite with integers leads to a warning when you have an slqalchemy version of below 0.8.2.
I know it is very very late and goes against all reservations of putting in new stuff just before a release, but after some more consideration, I think we should include this (or at least something that fixes it, and I think this does). @jreback, you said not to worry about it (it is just a warning), but the sqlalchemy release that fixes it is only just a year old and this is something most users will try as the first thing I think when using the sqlalchemy functions (writing/reading simple dataframe with some numbers with sqlite), so should not get a warning they don't understand.
I tested it locally with sqlalchemy 0.7.8 (on Windows), and on travis it is tested with 0.7.1 (the py2.6 build) and there also the warnings disappeared.
What do you think?
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} | 2 | 2014-07-10T14:50:32Z | 2014-07-16T00:30:13Z | 2014-07-16T00:29:51Z | CONTRIBUTOR | null | closes #7683
`PeriodIndex` creation is still in python space, not much help
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
timeseries_iter_datetimeindex_preexit | 12.7254 | 3657.9890 | 0.0035 |
timeseries_iter_datetimeindex | 679.8913 | 3726.6284 | 0.1824 |
timeseries_iter_periodindex_preexit | 69.0370 | 62.8881 | 1.0978 |
timeseries_iter_periodindex | 6941.9633 | 6024.2947 | 1.1523 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [142718e] : PERF: DatetimeIndex.__iter__ now uses ints_to_pydatetime with boxing
Base [f9493ea] : Merge pull request #7713 from jorisvandenbossche/doc-fixes3
DOC: fix doc build warnings
```
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} | 1 | 2014-07-10T18:23:57Z | 2014-09-03T19:50:12Z | 2014-09-03T19:50:12Z | CONTRIBUTOR | null | related #7570
`agg(min/max)` are NOT calling `.min()/max()`
https://groups.google.com/forum/#!topic/pydata/xinuPFPVakY
need to check:
`sum/mean` look ok
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| null | 3 | 2014-07-10T18:37:25Z | 2014-07-10T18:54:20Z | 2014-07-10T18:47:20Z | NONE | null | > > import pandas
> > ts1 = pandas.Series({'b':0., 'a':2.})
> > ts2 = pandas.Series({'b':2., 'a':0.}).reindex(['b', 'a'])
> > print ts1.dot(ts2)
4.0
the dot product should be 0.0 not 4.0.
vishy
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} | 17 | 2014-07-10T20:05:04Z | 2014-11-21T23:20:24Z | 2014-11-21T23:20:24Z | NONE | null | I am using `pandas.__version__==0.14.0`.
This is my first bug report in github so excuse my possibly poorly formatted submission.
This bug report might be related to, or duplicate:
1. https://github.com/pydata/pandas/issues/2646
2. https://github.com/pydata/pandas/issues/6501
It was explained to me by [http://stackoverflow.com/users/1427416/brenbarn] on the following StackOverflow question, http://stackoverflow.com/questions/24683023/having-issue-with-hierarchical-index-set-behavior/24684844#24684844
Essentially, the following behavior is not desirable:
```
print pd.__version__
WeirdIdx = pd.MultiIndex(levels=[[0], [1]],labels=[[0, 0], [0,0]],names=[u'X', u'Y'])
print WeirdIdx
print (0, 0) in WeirdIdx
print (1, 0) in WeirdIdx
print (100, 0) in WeirdIdx
print (100, 100) in WeirdIdx
```
since it prints:
0.14.0
X Y
0 1
1
True
True
True
True
despite the fact that `(100,0)` and `(100,100)` are unambiguously not part of the index.
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} | 2 | 2014-07-10T21:09:41Z | 2014-09-08T14:13:22Z | 2014-09-08T14:13:22Z | CONTRIBUTOR | null | `Series` have a `get` method, but using the default value doesn't work for certain type combinations:
```
>>> s = pd.Series([1,2,3], index=["a","b","c"])
>>> s
a 1
b 2
c 3
dtype: int64
>>> s.get("d", 0)
0
>>> s.get(10, 0)
Traceback (most recent call last):
File "<ipython-input-18-26d73ac73179>", line 1, in <module>
s.get(10, 0)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.14.0_421_g20dfc6b-py2.7-linux-x86_64.egg/pandas/core/generic.py", line 1040, in get
return self[key]
File "/usr/local/lib/python2.7/dist-packages/pandas-0.14.0_421_g20dfc6b-py2.7-linux-x86_64.egg/pandas/core/series.py", line 484, in __getitem__
result = self.index.get_value(self, key)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.14.0_421_g20dfc6b-py2.7-linux-x86_64.egg/pandas/core/index.py", line 1202, in get_value
return tslib.get_value_box(s, key)
File "tslib.pyx", line 540, in pandas.tslib.get_value_box (pandas/tslib.c:11831)
File "tslib.pyx", line 555, in pandas.tslib.get_value_box (pandas/tslib.c:11678)
IndexError: index out of bounds
```
I'm not sure whether it makes the most sense just to teach `.get` to catch IndexErrors as well as KeyErrors and ValueErrors (which is what it does now), or whether a deeper change is warranted.
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} | 6 | 2014-07-10T21:36:07Z | 2014-07-16T18:59:42Z | 2014-07-16T00:36:36Z | CONTRIBUTOR | null | Added support for uint (uint8, uint16 and uint32, but not uint64) and bool
datatypes in to_stata.
closes #7097
closes #7365
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| null | 4 | 2014-07-11T01:04:52Z | 2014-07-11T13:41:56Z | 2014-07-11T12:37:21Z | CONTRIBUTOR | null | http://pandas.pydata.org/community.html still lists "0.13.1 - February 2014" the Official Release, and "0.14 - May 2014" as the Development release, whereas the main page http://pandas.pydata.org/ correctly lists "0.14.0 - May 2014" as the Official Release, and "0.14.1 - June 2014" [should be July at this point] as the Development release.
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} | 5 | 2014-07-11T01:17:23Z | 2014-09-08T14:13:33Z | 2014-09-08T14:13:22Z | CONTRIBUTOR | null | Fixes #7725 by adding IndexError to the tuple of caught exceptions.
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} | 2 | 2014-07-11T14:56:31Z | 2014-09-17T20:46:01Z | 2014-09-17T20:46:01Z | NONE | null | These return object of type class `pandas.tslib.Timestamp`:
```
print type(pd.Series(['2014-01-01', '2014-02-02'], dtype='datetime64[ns]')[0])
print type(pd.Series(['2014-01-01', '2014-02-02'], dtype='datetime64[ns]').min())
```
but using `iat[]` returns object of type type `numpy.datetime64`:
```
print type(pd.Series(['2014-01-01', '2014-02-02'], dtype='datetime64[ns]').iat[0])
```
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} | 3 | 2014-07-11T17:10:31Z | 2014-09-09T07:51:12Z | 2014-09-09T07:51:12Z | NONE | null | Consider the following code.
```
import pandas as pd
import psycopg2
bad_query = 'select bad_column from my_table;'
conn = psycopg2.connect(...)
pd.read_sql(bad_query, conn)
```
Running the above in my environment produces a DatabaseError exception with the following message:
"Execution failed on sql: select bad_column from my_table;"
However, when I run the below code to query the database:
```
import psycopg2
bad_query = 'select bad_column from my_table;'
conn = psycopg2.connect(...)
conn.cursor().execute(bad_query)
```
The following ProgrammingError message is returned:
"column "bad_column" does not exist in my_table"
This information is helpful for debugging, especially as queries become more complicated, and I believe it should be propagated through to the user.
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https://api.github.com/repos/pandas-dev/pandas/issues/7731 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7731/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7731/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7731/events | https://github.com/pandas-dev/pandas/issues/7731 | 37,703,983 | MDU6SXNzdWUzNzcwMzk4Mw== | 7,731 | Import error in pkg_resources.py | {
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| null | 2 | 2014-07-11T22:46:10Z | 2014-07-14T18:23:25Z | 2014-07-14T18:23:25Z | NONE | null | Pandas: 0.14
Python: 3.4
OS: Mac (installed through macports.)
```
import pyqtgraph as pg
import pandas as pd
```
running the above gives the following error (ImportError on cStringIO).
```
File "/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/pkg_resources.py", line 50, in <module>
from cStringIO import StringIO as BytesIO
```
To overcome this the following change (in line 51 pkg_resources.py) seems to work
```
try:
basestring
next = lambda o: o.next()
from cStringIO import StringIO as BytesIO
except (NameError,ImportError):
```
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| null | 1 | 2014-07-11T22:46:19Z | 2014-07-11T23:31:34Z | 2014-07-11T23:31:34Z | NONE | null | FYI .. the following code gives an index error. I was expecting it to plot. The error doesn't occur if nrows=ncols. I found a work around to this.
>>> import pandas as pd
>>> import pandas.io.data as web
>>> import matplotlib.pyplot as plt
>>> import datetime
>>>df1=web.get_data_yahoo('GE',start=datetime.datetime.today()-datetime.timedelta(days=180), end=datetime.datetime.today())
>>>df2=web.get_data_yahoo('AAPL',start=datetime.datetime.today()-datetime.timedelta(days=180), end=datetime.datetime.today())
>>> fig,axes=plt.subplots(nrows=2,ncols=1)
>>> df1['Close'].plot(ax=axes[0,0])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
IndexError: too many indices
>>>
## INSTALLED VERSIONS
commit: None
python: 3.3.5.final.0
python-bits: 64
OS: Darwin
OS-release: 13.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.0
nose: None
Cython: 0.20.2
numpy: 1.8.1
scipy: None
statsmodels: None
IPython: 2.1.0
sphinx: 1.2.1
patsy: None
scikits.timeseries: None
dateutil: 2.0
pytz: 2013.7
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.3.1
openpyxl: 1.6.1
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
bq: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
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} | 2 | 2014-07-12T00:14:34Z | 2014-07-25T20:44:05Z | 2014-07-24T12:59:45Z | MEMBER | null | Must be revisited after #7717.
Repeated line and area plot may result incorrect if it requires resampling.
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(2, 2, figsize=(7, 5))
np.random.seed(1)
df1 = pd.DataFrame(np.random.rand(5, 2), pd.date_range('2011-01-01', periods=5, freq='D'))
df2 = pd.DataFrame(np.random.rand(2, 2), pd.date_range('2011-01-01', periods=2, freq='M'))
df1.plot(kind='line', ax=axes[0][0], legend=False)
df2.plot(kind='area', ax=axes[0][0], legend=False)
df1.plot(kind='area', ax=axes[1][0], legend=False)
df2.plot(kind='line', ax=axes[1][0], legend=False)
df2.plot(kind='line', ax=axes[0][1], legend=False)
df1.plot(kind='area', ax=axes[0][1], legend=False)
# ValueError: Argument dimensions are incompatible
df2.plot(kind='area', ax=axes[1][1], legend=False)
df1.plot(kind='line', ax=axes[1][1], legend=False)
```
### Result using current master
- line with low freq -> area with high freq results in `ValueError` (top-right axes)
- area with low freq -> line with high freq results in all lines, not area (bottom-right axes)
### Result after fix
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} | 9 | 2014-07-12T11:54:36Z | 2014-07-26T13:27:25Z | 2014-07-25T21:04:51Z | MEMBER | null | Found 2 problems related to `value_counts`.
- `DatetimeIndex.value_counts` loses tz.
```
didx = pd.date_range('2011-01-01 09:00', freq='H', periods=3, tz='Asia/Tokyo')
print(didx.value_counts())
#2011-01-01 00:00:00 1
#2011-01-01 01:00:00 1
#2011-01-01 02:00:00 1
# dtype: int64
```
- `PeriodIndex.value_counts` results in `Int64Index`, and unable to drop `NaT`.
```
pidx = pd.PeriodIndex(['2011-01-01 09:00', '2011-01-01 10:00', pd.NaT], freq='H')
print(pidx.value_counts())
# 359410 1
# 359409 1
# -9223372036854775808 1
# dtype: int64
```
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} | 25 | 2014-07-12T12:51:02Z | 2014-09-10T12:10:57Z | 2014-08-19T17:09:15Z | MEMBER | null | - Added `layout` keyword to `plot_frame` (Closes #6667)
- Allow to pass multiple axes to `plot_frame`, `hist` and `boxplot` (Closes #5353, Closes #6970, Closes #7069)
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pandas.util.testing as tm
n = 20
gender = tm.choice(['Male', 'Female'], size=n)
classroom = tm.choice(['A', 'B', 'C'], size=n)
df = pd.DataFrame({'gender': gender,
'classroom': classroom,
'height': np.random.normal(66, 4, size=n),
'weight': np.random.normal(161, 32, size=n),
'category': np.random.randint(4, size=n)})
fig, axes = plt.subplots(6, 3, figsize=(6, 7))
df.boxplot(by='category', column=['height', 'weight', 'category'], ax=axes[0])
df.groupby('classroom').boxplot(column=['height', 'weight', 'category'], ax=axes[1])
df.hist(column=['height', 'weight', 'category'], ax=axes[2])
df.hist(by='classroom', ax=axes[3])
df.plot(subplots=True, ax=axes[4], legend=False)
df.plot(kind='pie', subplots=True, ax=axes[5], legend=False)
plt.subplots_adjust(hspace=1, bottom=0.05)
```
### Result
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} | 8 | 2014-07-12T16:53:33Z | 2014-08-11T13:57:14Z | 2014-08-11T13:04:16Z | CONTRIBUTOR | null | Use the _constructor property when creating the merge result to
preserve the output type.
If a [GeoPandas](http://github.com/geopandas/geopandas) `GeoDataFrame` is merged with a `DataFrame`, the result is hard-coded to always be `DataFrame` [GeoPandas Issue #118](https://github.com/geopandas/geopandas/issues/118). We'd like it to return `GeoDataFrame` in these cases
```
>>> import geopandas as gpd
>>> import pandas as pd
>>> gdf = gpd.GeoDataFrame(...)
>>> df = pd.DataFrame(...)
>>> merged = pd.merge(gdf, df, on='column')
>>> type(merged)
GeoDataFrame
```
This PR uses `left._constructor` to generate the result type for merge operations.
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} | 14 | 2014-07-12T18:57:33Z | 2014-09-10T00:12:39Z | 2014-07-25T14:34:07Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/7542.
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} | 17 | 2014-07-12T20:58:54Z | 2019-01-26T15:19:31Z | 2014-07-12T21:02:22Z | NONE | null | This bug is easy to reproduce:
```
def printA(group):
print group.A.values[0]
return 0
pandas.DataFrame({'A': [0,0,1,1], 'B': [0,1,0,1]}).groupby('A').apply(printA)
```
This should print
```
0
1
```
and return a `Series` containing two zeroes. Although it does return the expected `Series`, it prints
```
0
0
1
```
So, the function `printA` is being applied to the first group twice. This doesn't seem to affect the eventual returned result of apply, but it duplicates any side effects, and if `printA` performed some expensive computation, it would increased the time to completion.
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} | 1 | 2014-07-12T23:17:49Z | 2014-08-10T14:53:57Z | 2014-08-10T14:53:57Z | MEMBER | null | `Period` + `np.timedelta64` doesn't raise `TypeError(NotImplemented)` (like `offsets` does), and results in erratic internal representation.
```
import pandas as pd
import numpy as np
p = pd.Period('2011-01-01', freq='D')
p = p + np.timedelta64(2, 'h')
# This raises no error
print(p)
# TypeError: don't know how to convert scalar number to int
p.ordinal
#14977 hours
```
`PeriodIndex` + `np.timedelta64` also doesn't raise `TypeError(NotImplemented)`, and are added ignoring `PeriodIndex.freq`.
```
pidx = pd.period_range('2011-01-01', freq='D', periods=5)
pidx = pidx + np.timedelta64(2, 'h')
pidx
# <class 'pandas.tseries.period.PeriodIndex'>
# [2011-01-03, ..., 2011-01-07]
# Length: 5, Freq: D
```
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} | 2 | 2014-07-13T00:14:25Z | 2014-07-25T20:42:29Z | 2014-07-23T21:28:50Z | MEMBER | null | Fixes 2 issues related to `DetetimeIndex` and `PeriodIndex` ops.
- Addition / subtraction between `PeriodIndex` raise `TypeError` (Closes #3367).
```
pidx + pidx
# TypeError: unsupported operand type(s) for +: 'PeriodIndex' and 'PeriodIndex'
```
- In-place addition / subtraction doesn't return the same result as normal addition / subtraction. Specifically, `PeriodIndex` in-place operation results in `Int64Index` (Closes #6527)
```
didx = pd.date_range('2011-01-01', freq='D', periods=5)
# This results shift (expected)
didx + 1
# <class 'pandas.tseries.index.DatetimeIndex'>
# [2011-01-02, ..., 2011-01-06]
# Length: 5, Freq: D, Timezone: None
# This result adding 1 unit (nano second) (NG)
didx += 1
didx
# <class 'pandas.tseries.index.DatetimeIndex'>
# [2011-01-01 00:00:00.000000001, ..., 2011-01-05 00:00:00.000000001]
# Length: 5, Freq: None, Timezone: None
```
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| null | 4 | 2014-07-13T09:07:45Z | 2014-08-05T17:10:19Z | 2014-08-05T17:10:19Z | CONTRIBUTOR | null | Force violate array to be of type int. When no nan values are found, the array defaults to an empty array of type float, so you get an error: **\* IndexError: arrays used as indices must be of integer (or boolean) type
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} | 1 | 2014-07-13T09:29:02Z | 2021-04-11T04:52:30Z | null | MEMBER | null | Previously, for resampling with PeriodIndex, you had two conventions: `'start'` (start -> start) and `'end'` (end -> end). This would give something like this (note: the following is **not current real code output**, but from Wes' book):
```
In [25]: s = pd.Series(np.arange(2), index=pd.period_range('2000-1', periods=2, freq='A'))
In [26]: s
Out[26]:
2000 0
2001 1
Freq: A-DEC, dtype: int32
In [27]: s.resample('Q-DEC', fill_method='ffill', convention='start')
Out[27]:
2000Q1 0
2000Q2 0
2000Q3 0
2000Q4 0
2001Q1 1
Freq: Q-DEC, dtype: int32
In [28]: s.resample('Q-DEC', fill_method='ffill', convention='end')
Out[27]:
2000Q4 0
2001Q1 1
2001Q2 1
2001Q3 1
2001Q4 1
Freq: Q-DEC, dtype: int32
```
Following Wes' book, the default argument was 'end'. However, the current behaviour is like this (this is real output):
```
In [27]: s.resample('Q-DEC', fill_method='ffill')
Out[27]:
2000Q1 0
2000Q2 0
2000Q3 0
2000Q4 0
2001Q1 1
2001Q2 1
2001Q3 1
2001Q4 1
Freq: Q-DEC, dtype: int32
```
So in fact this is a third option `'span'` (start -> end). This option is mentioned in #1635, but from the issue it seems it was never implemented ([the commit](https://github.com/changhiskhan/pandas/commit/cb81f9ad5607419a5d11ceafd2225c2fdf0e6632) was never merged. There was a test added in comments at that time, but this is still in comments: https://github.com/pydata/pandas/blob/master/pandas/tseries/tests/test_resample.py#L1134).
In practice, however, this is the case (the default behaviour is this mentioned 'span' behaviour). But also the option `'start'` has changed:
```
In [28]: s.resample('Q-DEC', fill_method='ffill', convention='start')
Out[28]:
2000Q1 0
2000Q2 0
2000Q3 0
2000Q4 0
2001Q1 1
2001Q2 1
2001Q3 1
2001Q4 1
Freq: Q-DEC, dtype: int32
```
This gives the same as the default (only for `'end'` it is the same as before).
Some issues/questions:
- what is the default value for `convention`? It is nowhere in the [docs](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#up-and-downsampling), and also not in the [docstring](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.resample.html) (apart from the signature, which says 'start').
- I don't find the issue/PR/release note where it says that the default for period resample (upsampling) has changed
- the default now is a 'spanning' behaviour, but this is the same as 'start'. Shouldn't be this something else? So that the 'start' option has another behaviour (start -> start) than the default spanning behaviour ('start' -> 'end')?
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} | 0 | 2014-07-13T12:36:14Z | 2014-07-19T13:45:50Z | 2014-07-19T13:39:59Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/24718730/is-there-any-way-to-keep-a-periodindex-as-a-series-of-periods-with-a-reset-index
related: #7791, #7792 (and I think this causes both)
duped by #7793
```
In [10]: df = DataFrame(np.arange(9).reshape(-1,1),index=pd.MultiIndex.from_product([period_range('20130101',periods=3,freq='M'),['a','b','c']],names=['month','feature']),columns=['value'])
In [11]: df
Out[11]:
value
month feature
2013-01 a 0
b 1
c 2
2013-02 a 3
b 4
c 5
2013-03 a 6
b 7
c 8
In [12]: df.reset_index()
ValueError: Length of values does not match length of index
```
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| null | 1 | 2014-07-14T04:53:52Z | 2014-07-14T07:39:24Z | 2014-07-14T07:39:19Z | CONTRIBUTOR | null | null | {
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} | 11 | 2014-07-14T13:10:54Z | 2014-07-25T16:57:51Z | 2014-07-19T12:10:05Z | NONE | null | Hi All,
I have a strange bug when I want to mask values of a time series (that I get from a pickled Panel).
I have the following Series:
``` python
In[30]: ts=pd.read_pickle('df_issue.pkl')['wind_speed']
In [31]:ts
Out[31]:
2013-12-31 16:00:00 NaN
2013-12-31 17:00:00 NaN
2013-12-31 18:00:00 9.845031
2013-12-31 19:00:00 NaN
2013-12-31 20:00:00 NaN
2013-12-31 21:00:00 NaN
2013-12-31 22:00:00 NaN
2013-12-31 23:00:00 NaN
Freq: H, Name: wind_speed, dtype: float64
```
And I have an exception when I try to mask it:
``` python
In [32]: ts<0.
Traceback (most recent call last):
File "<ipython-input-32-534147d368f7>", line 1, in <module>
ts<0.
File "/usr/local/lib/python2.7/dist-packages/pandas/core/ops.py", line 585, in wrapper
res[mask] = masker
File "/usr/local/lib/python2.7/dist-packages/pandas/core/series.py", line 637, in __setitem__
self.where(~key, value, inplace=True)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/generic.py", line 3238, in where
inplace=True)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/internals.py", line 2219, in putmask
return self.apply('putmask', **kwargs)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/internals.py", line 2185, in apply
b_items = self.items[b.mgr_locs.indexer]
File "/usr/local/lib/python2.7/dist-packages/pandas/tseries/index.py", line 1387, in __getitem__
new_offset = key.step * self.offset
File "/usr/local/lib/python2.7/dist-packages/pandas/tseries/offsets.py", line 265, in __rmul__
return self.__mul__(someInt)
File "/usr/local/lib/python2.7/dist-packages/pandas/tseries/offsets.py", line 262, in __mul__
return self.__class__(n=someInt * self.n, normalize=self.normalize, **self.kwds)
AttributeError: 'Hour' object has no attribute 'normalize'
```
If I make the same Series manually, it is working:
``` python
ts1=pd.Series(data=[np.nan,np.nan,9.845031]+[np.nan]*5,index=pd.date_range('2013-12-31 16:00:00',periods=8,freq='H'))
In [36]: ts1
Out[36]:
2013-12-31 16:00:00 NaN
2013-12-31 17:00:00 NaN
2013-12-31 18:00:00 9.845031
2013-12-31 19:00:00 NaN
2013-12-31 20:00:00 NaN
2013-12-31 21:00:00 NaN
2013-12-31 22:00:00 NaN
2013-12-31 23:00:00 NaN
Freq: H, dtype: float64
In [37]: ts1<0.
Out[37]:
2013-12-31 16:00:00 False
2013-12-31 17:00:00 False
2013-12-31 18:00:00 False
2013-12-31 19:00:00 False
2013-12-31 20:00:00 False
2013-12-31 21:00:00 False
2013-12-31 22:00:00 False
2013-12-31 23:00:00 False
Freq: H, dtype: bool
```
I really don't understand why...
You can find the pickle file here: http://we.tl/lrsFvmanVl
Thanks,
Greg
Here is the show_version output:
## INSTALLED VERSIONS
commit: None
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.8.0-37-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.1
nose: 1.3.0
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.13.3
statsmodels: 0.6.0.dev-Unknown
IPython: 1.1.0
sphinx: 1.1.3
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.4
bottleneck: 0.6.0
tables: 3.1.1
numexpr: 2.0.1
matplotlib: 1.3.1
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.2
xlsxwriter: None
lxml: 3.0.0
bs4: 4.3.2
html5lib: None
httplib2: 0.7.2
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
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| null | 1 | 2014-07-14T17:26:58Z | 2014-07-14T17:32:52Z | 2014-07-14T17:32:46Z | NONE | null | https://github.com/pydata/pandas/blob/master/RELEASE.md
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| null | 20 | 2014-07-14T18:00:37Z | 2014-07-20T16:43:34Z | 2014-07-14T18:14:41Z | NONE | null | #6848 is no where mentioned in the release notes. There's a single item on `.order`
changing to quicksort, but it doesn't explicitly warn that the sort was previously stable
and now isn't. There's also no warning about `series.sort` changing as well.
That API has been around forever and afaict nothing was gained by breaking it.
This was a fairly reckless change in my opinion, I do wish the current maintainers
cared more about backwards-compatibility then they seem to in the latest releases.
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} | 21 | 2014-07-14T18:47:54Z | 2016-12-30T19:02:01Z | 2016-12-30T19:02:01Z | NONE | null | xref #8309
for example:
``` python
pd.qcut([1,1,1,1,1,1,1,1,1,1,1,1,1,5,5,5], [0.00001, 0.5])
```
will raise "ValueError: Bin edges must be unique: array([ 1., 1.])" exception
Fix suggestion - add one new line:
``` python
def qcut(x, q, labels=None, retbins=False, precision=3):
if com.is_integer(q):
quantiles = np.linspace(0, 1, q + 1)
else:
quantiles = q
bins = algos.quantile(x, quantiles)
--->bins = np.unique(bins)
return _bins_to_cuts(x, bins, labels=labels, retbins=retbins,
precision=precision, include_lowest=True)
```
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| null | 22 | 2014-07-14T18:55:41Z | 2015-03-08T16:57:00Z | 2015-03-08T16:57:00Z | NONE | null | so I have a web application where users need to be able to upload csv files. If I use the c engine the whole thing hangs indifferently. I was able to read_csv hanging.
code:
data = smart_unicode(data) #convert to unicode properly
frames = {"default":pandas.read_csv(io.StringIO(data))}
where 'data' is a Unicode string output by Django's 'smart_unicode'. The same issue occurs if you use data = data.decode('utf-8'). The issue does not occur if I use the python engine however.
I am running an apache 2 server with django 1.6 and mod_wsgi. I have no clue what is causing this however.
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} | 14 | 2014-07-14T19:02:09Z | 2015-03-08T16:58:52Z | 2015-03-08T16:58:52Z | NONE | null | code:
pandas.read_csv(io.StringIO(data), engine="python")
if 'data' contains a non-ascii character this code will throw an exception
stack trace:
File "/home/jake/Desktop/WiderNSF/physics/service/ExcelTable.py", line 63, in **init**
frames = {"default":pandas.read_csv(io.StringIO(data), engine="python")}
File "/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 400, in parser_f
return _read(filepath_or_buffer, kwds)
File "/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 198, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 479, in __init__
self._make_engine(self.engine)
File "/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 592, in _make_engine
self._engine = klass(self.f, **self.options)
File "/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 1240, in __init__
self.columns = self._infer_columns()
File "/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 1406, in _infer_columns
line = self._next_line()
File "/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 1473, in _next_line
line = next(self.data)
UnicodeEncodeError: 'ascii' codec can't encode character u'\u03bb' in position 20: ordinal not in range(128)
I have experienced this issue on multipule systems
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} | 3 | 2014-07-14T19:23:14Z | 2015-06-02T19:25:19Z | 2015-05-12T13:06:54Z | NONE | null | example test:
``` python
intSeries = pd.Series(5, pd.date_range(start='2000-01-01', end='2000-01-05', freq='555000U'), dtype='int64')
timeSeries = intSeries.astype('datetime64[ns]')
time0 = time.time()
intSeries.resample('1S', how='last')
print 'resampling the int series took ', str(timedelta(seconds = time.time()-time0)), '(H:M:S)'
time0 = time.time()
timeSeries.resample('1S', how='last')
print 'resampling the datetime64[ns] series took ', str(timedelta(seconds = time.time()-time0)), '(H:M:S)'
```
output shows 14sec vs 0.5sec:
resampling the int series took **0:00:00.054000** (H:M:S)
resampling the datetime64[ns] series took **0:00:14.350000** (H:M:S)
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| null | 2 | 2014-07-14T20:34:59Z | 2015-10-20T22:41:12Z | 2015-10-20T22:41:12Z | NONE | null | I'm not sure about the exact circumstances and am unable to create a minimal example, but when trying to run an application importing pandas in debug mode through Pycharm, it freezes. I followed the call stack to:
```
try:
import numexpr as ne
_NUMEXPR_INSTALLED = ne.__version__ >= LooseVersion('2.0')
except ImportError: # pragma: no cover
_NUMEXPR_INSTALLED = False
```
in `expressions.py` (the actual import). I continued to trace the call stack a bit more into `numepxr` to `cpuinfo.py` at `cpu = cpuinfo()` (`DarwinCPUInfo` where it calls `command_info` which eventually forks a new process ("machine") and never returns).
I'm running Mac OS X Maverick, Python 2.7.5, pandas 0.14.0 and numexpr 2.4. This problem doesn't occur when running the application in Pycharm (as opposed to running in debug mode and setting breakpoints). It also doesn't happen when I import pandas right at the beginning of the application (my failed attempt to construct a minimal example).
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} | 2 | 2014-07-15T02:31:45Z | 2014-07-15T12:57:21Z | 2014-07-15T12:57:21Z | CONTRIBUTOR | null | Let's actually talk about what the current behaviour is, not what the
behaviour used to be.
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} | 11 | 2014-07-15T02:55:22Z | 2017-08-03T16:37:27Z | 2017-08-03T16:37:27Z | CONTRIBUTOR | null | `read_csv` with a single-row header either breaks any names that might be on the index, or reads all data as `NaN`. This problem might exist because `pd.read_csv` hasn't caught up to #7589.
``` python
In [1]: import pandas as pd
In [2]: from StringIO import StringIO
In [3]: pd.read_csv(StringIO('''\
alpha,,A,B
foo,bar,,
this,x,1,2
this,y,5,6
that,x,3,4
that,y,7,8'''), header=0, index_col=[0,1])
Out[3]:
A B
alpha Unnamed: 1
foo bar NaN NaN
this x 1 2
y 5 6
that x 3 4
y 7 8
In [4]: pd.read_csv(StringIO('''\
alpha,,A,B
foo,bar,,
this,x,1,2
this,y,5,6
that,x,3,4
that,y,7,8'''), header=[0], index_col=[0,1])
Out[4]:
alpha A B
foo bar
this x NaN NaN
y NaN NaN
that x NaN NaN
y NaN NaN
In [5]: pd.read_csv(StringIO('''\
alpha,,A,B
beta,,Y,Z
foo,bar,,
this,x,1,2
this,y,5,6
that,x,3,4
that,y,7,8'''), header=[0,1], index_col=[0,1])
Out[5]:
alpha A B
beta Y Z
foo bar
this x 1 2
y 5 6
that x 3 4
y 7 8
```
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} | 11 | 2014-07-15T12:21:22Z | 2014-07-16T11:57:13Z | 2014-07-16T11:57:13Z | CONTRIBUTOR | null | astype unicode seems to call str, so that the following code throws
```
import pandas
df = pandas.DataFrame({"somecol": [u"適当"]})
df["somecol"].astype("unicode")
```
raises :
```
UnicodeEncodeError: 'ascii' codec can't encode ch
aracters in position 0-1: ordinal not in range(12
8)
```
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| null | 5 | 2014-07-15T12:25:14Z | 2017-01-15T22:56:50Z | 2014-07-15T12:53:10Z | CONTRIBUTOR | null | Basically
```
pandas.DataFrame({
"a": [1,1,1,2,2,2]
}).replace({
"a": {
1: 2,
2: 3
}
})
```
returns (I was suprised on that one)
```
a
0 2
1 3
2 2
3 3
4 2
5 3
```
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} | 1 | 2014-07-15T13:06:55Z | 2014-07-19T14:11:35Z | 2014-07-19T14:11:35Z | NONE | null | So i have two dataframes with simmilar data but, indexed by datetime64
dfA : http://pastebin.com/HenYJxdV
dfB : http://pastebin.com/qAsEqSxf
so i have following problem when i try to plot them this way:
```
In [107]:
seriesA=dfA["more"]
seriesB=dfB["less"]
In [108]:
plt.figure()
plt.plot(seriesA.index.tolist(),seriesA.tolist())
plt.plot(seriesB.index.tolist(),seriesB.tolist())
```
it's ok and both series have been plotted on same figure
but when i'll try this:
```
plt.figure()
seriesA.plot()
seriesB.plot()
```
it wrecks and i get following output
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-109-5054484e1e31> in <module>()
1 plt.figure()
2 seriesA.plot()
----> 3 seriesB.plot()
4
C:\winpy\WinPython-64bit-2.7.6.4\python-2.7.6.amd64\lib\site-packages\pandas\tools\plotting.pyc in plot_series(series, label, kind, use_index, rot, xticks, yticks, xlim, ylim, ax, style, grid, legend, logx, logy, secondary_y, **kwds)
2254 secondary_y=secondary_y, **kwds)
2255
-> 2256 plot_obj.generate()
2257 plot_obj.draw()
2258
C:\winpy\WinPython-64bit-2.7.6.4\python-2.7.6.amd64\lib\site-packages\pandas\tools\plotting.pyc in generate(self)
900 self._compute_plot_data()
901 self._setup_subplots()
--> 902 self._make_plot()
903 self._add_table()
904 self._make_legend()
C:\winpy\WinPython-64bit-2.7.6.4\python-2.7.6.amd64\lib\site-packages\pandas\tools\plotting.pyc in _make_plot(self)
1549 if self._is_ts_plot():
1550 data = self._maybe_convert_index(self.data)
-> 1551 self._make_ts_plot(data)
1552 else:
1553 from pandas.core.frame import DataFrame
C:\winpy\WinPython-64bit-2.7.6.4\python-2.7.6.amd64\lib\site-packages\pandas\tools\plotting.pyc in _make_ts_plot(self, data, **kwargs)
1646 y_values = self._get_stacked_values(y, label)
1647
-> 1648 newlines = plotf(y_values, ax, label, style, **kwds)
1649 self._add_legend_handle(newlines[0], label, index=i)
1650
C:\winpy\WinPython-64bit-2.7.6.4\python-2.7.6.amd64\lib\site-packages\pandas\tools\plotting.pyc in _plot(data, ax, label, style, **kwds)
1623 else:
1624 lines = tsplot(data, plotf, ax=ax, label=label,
-> 1625 style=style, **kwds)
1626 return lines
1627 return _plot
C:\winpy\WinPython-64bit-2.7.6.4\python-2.7.6.amd64\lib\site-packages\pandas\tseries\plotting.pyc in tsplot(series, plotf, **kwargs)
57 series = series.to_period(freq=freq)
58 freq, ax_freq, series = _maybe_resample(series, ax, freq, plotf,
---> 59 kwargs)
60
61 # Set ax with freq info
C:\winpy\WinPython-64bit-2.7.6.4\python-2.7.6.amd64\lib\site-packages\pandas\tseries\plotting.pyc in _maybe_resample(series, ax, freq, plotf, kwargs)
107 ax_freq = freq
108 else: # pragma: no cover
--> 109 raise ValueError('Incompatible frequency conversion')
110 return freq, ax_freq, series
111
ValueError: Incompatible frequency conversion
```
Which is extremely weird because before, i plotted similar but yet slightly different data and it plotted correctly.
P.S. pd.show_versions():
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 37 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.14.0
nose: 1.3.1
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 2.0.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2013.9
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: None
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
bq: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
```
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} | 5 | 2014-07-15T15:05:57Z | 2014-07-21T11:39:29Z | 2014-07-21T11:39:29Z | CONTRIBUTOR | null | This leads to an error on read, writing is possible however. Since the format has a fundamental problem with this case if there are columns with the same name but different data types this is not fixable.
However, `df.to_hdf` should just not write the respective dataframe if the column index is not unique.
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} | 4 | 2014-07-15T17:08:10Z | 2016-10-12T23:05:01Z | 2014-07-28T13:24:39Z | NONE | null | I assumed reading wikipedia html tables should work for `read_html`, but it returned a lot of garbage :frowning:
Example:
```
pd.io.html.read_html("https://en.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_area", "Arizona")
```
I've seen several related issues, maybe this is an useful test case.
Versions:
```
pandas: 0.14.0
Cython: 0.19.2
IPython: 2.1.0
bs4: 4.3.1
html5lib: 0.95-dev
lxml: 3.3.5
dateutil: 2.2
```
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} | 7 | 2014-07-15T17:29:04Z | 2014-09-26T23:36:40Z | 2014-09-26T23:36:40Z | NONE | null | I am updating a codebase from an OLD version of pandas (0.7.3) to current version (0.14.1).
```
pandas: 0.14.1
numpy: 1.8.0
```
The following code has stopped working correctly (using .ix[] or .loc[]):
``` python
import numpy as np
import pandas as ps
np.random.seed(0)
index=range(3)
columns = list('abc')
panel = ps.Panel({'A' : ps.DataFrame(np.random.randn(3, 3), index=index, columns=columns),
'B' : ps.DataFrame(np.random.randn(3, 3), index=index, columns=columns),
'C' : ps.DataFrame(np.random.randn(3, 3), index=index, columns=columns)
})
replace = ps.DataFrame(np.eye(3,3), index=range(3), columns=columns)
print panel['A']
for idx in list('ABC'):
panel.loc[idx,:,:] = replace
print panel['A']
```
Output:
```
a b c
0 1.764052 0.400157 0.978738
1 2.240893 1.867558 -0.977278
2 0.950088 -0.151357 -0.103219
a b c
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
```
However, if I simply do this:
``` python
for idx in list('ABC'):
panel[idx] = replace
print panel['A']
```
It behaves as expected:
```
a b c
0 1 0 0
1 0 1 0
2 0 0 1
```
Also, if I assign more than just the item, it works fine:
``` python
panel.loc['A', 0, 'a'] = 1.0
print panel['A']
```
```
a b c
0 1.000000 0.400157 0.978738
1 2.240893 1.867558 -0.977278
2 0.950088 -0.151357 -0.103219
```
Thanks.
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} | 11 | 2014-07-15T19:04:18Z | 2014-07-23T12:17:17Z | 2014-07-23T12:17:17Z | NONE | null | I am updating a codebase from an OLD version of pandas (0.7.3) to current version (0.14.1) in python 2.7.
```
pandas: 0.14.1
numpy: 1.8.0
```
Not sure if this is properly classified as a bug, or if it is an expected code change. However, it seems rolling_cov / rolling_corr are specifying the window as min(window given, max(len(arg1), len(arg2)), and then erring out if arg1 AND arg2 are shorter than min_periods. Before, we would just get NaNs back.
Here is some test code:
``` python
import numpy as np
import pandas as pd
np.random.seed(0)
s1 = pd.Series(np.random.randn(4))
s2 = pd.Series(np.random.randn(4))
print '\nrolling_corr:'
try:
print pd.rolling_corr(s1, s2, window=10, min_periods=5)
except Exception as inst:
print type(inst)
print inst
print '\nrolling_cov:'
try:
print pd.rolling_cov(s1, s2, window=10, min_periods=5)
except Exception as inst:
print type(inst)
print inst
```
In **0.14.1**:
```
rolling_corr:
<type 'exceptions.ValueError'>
min_periods (5) must be <= window (4)
rolling_cov:
<type 'exceptions.ValueError'>
min_periods (5) must be <= window (4)
```
In **0.7.3**:
```
rolling_corr:
0 NaN
1 NaN
2 NaN
3 NaN
rolling_cov:
0 NaN
1 NaN
2 NaN
3 NaN
```
Note that changing either s1 OR s2 to be 6, the code doesn't error out, and instead outputs:
```
rolling_corr:
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
5 NaN
dtype: float64
rolling_cov:
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
5 NaN
dtype: float64
```
Meanwhile, in 0.14.1, other rolling functions continue to behave as before:
``` python
s1 = pd.Series(np.random.randn(4))
print '\rolling_std:'
print pd.rolling_std(s1, window=10, min_periods=5)
print '\nrolling_max:'
print pd.rolling_max(s1, window=10, min_periods=5)
```
Outputs:
```
rolling_std:
0 NaN
1 NaN
2 NaN
3 NaN
dtype: float64
rolling_max:
0 NaN
1 NaN
2 NaN
3 NaN
dtype: float64
```
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} | 4 | 2014-07-15T21:42:06Z | 2014-07-16T21:49:37Z | 2014-07-16T11:57:48Z | CONTRIBUTOR | null | I didn't have to rely on `infer_dtype` as suggested by @jreback in #7758
as I delegated to numpy to do all the dirty job :
Just calls numpy.unicode on all the values.
Please have a critical look at this pull request before merging :
as I am unfamiliar with both pandas and cython; and may have
misunderstood the way pandas is working.
Unit tests are attached and seem to work alright on `python 2.7.2` and `python 3.3.0`
closes #7758
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} | 22 | 2014-07-16T01:29:45Z | 2014-09-10T00:12:30Z | 2014-07-23T12:17:01Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/7764.
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} | 49 | 2014-07-16T18:42:14Z | 2014-08-12T23:45:39Z | 2014-08-12T16:18:56Z | CONTRIBUTOR | null | Some fixups for Categoricals.
Fixes: #3678
- [x] Maybe change Series.cat after the discussion in https://github.com/pydata/pandas/issues/7207 ?
- [ ] remove Series.cat from tab completition if Series is not of dtype category
- [x] fix for the "FIXME" in unittests
- [x] Look at problems in docs (-> hdf support)
- [x] Fixup Comparison thingies...
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} | 5 | 2014-07-16T19:03:36Z | 2014-08-20T15:32:51Z | 2014-07-28T20:41:48Z | CONTRIBUTOR | null | Change log is missing some key words on changes to StataReader and StataWriter.
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} | 11 | 2014-07-16T23:51:25Z | 2014-07-21T11:43:11Z | 2014-07-21T11:42:51Z | CONTRIBUTOR | null | closes #7660
The specific case that came up in #7660 (and originally in #7653) seems easy enough to fix, so I've covered that in the PR. I feel like this raises a few other potential problems though, e.g.:
- If the list of level numbers is not in order, is there any sensible way to deal with them? I've sorted the level list in my fix, as that seems like the most straightforward way of making sure that when you do each stack, it's only the level numbers higher than the current that are affected. This might produce undesired results though, so maybe we should just raise a `ValueError` if the level numbers aren't sorted?
- I'm not sure how to extend this to deal with negative level numbers.
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} | 17 | 2014-07-17T02:52:42Z | 2014-12-23T03:25:29Z | 2014-12-23T03:25:29Z | CONTRIBUTOR | null | This is biting me when applying nunique on some groupby operations.
In one benchmark, which I'll shortly share, nunique() is slower until we get to a length of 3000.
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} | 2 | 2014-07-17T08:23:20Z | 2014-07-19T17:27:14Z | 2014-07-19T14:11:35Z | NONE | null | IO am opening new issue as i solved what is wrong with https://github.com/pydata/pandas/issues/7760 .
So, i have two sets of data one with frequency 1 second:
http://pastebin.com/HenYJxdV
And other with frequency 0.1 second (100 miliseconds):
http://pastebin.com/HenYJxdV
I wanted to plot them on same axe so i did:
```
seriesA=dfA["more"]
seriesB=dfB["less"]
plt.figure()
seriesA.plot()
seriesB.plot()
```
which yielded error seen in linked issue.
But having simmilar data, from the same source, of the same type it often was plotted corectly.
What caused this, and what was the difference since data was from the same source ?
Two things:
1.First:
line 1515 in tools\plotting.py
```
return Period(x[0], freq).to_timestamp(tz=x.tz) == x[0]
```
This line caused different behaviour of plotting function for different set of data, but only for datas with milisecond frequency (seriesB.plot())
How different ?
When the x[0] is
```
Timestamp: 2001-01-01 09:12:13.200000
```
then Period(x[0], freq).to_timestamp(tz=x.tz) is:
```
Timestamp: 2001-01-01 09:12:13.200000002
```
which causes the comparison to return False value, but having x[0]:
```
Timestamp: 2001-01-01 09:03:11.500000
```
we have expression Period(x[0], freq).to_timestamp(tz=x.tz) being set to:
```
Timestamp: 2001-01-01 09:03:11.500000
```
Which returns True.
Suprisingly it plotted correctly in the first case, when that line returns false (ex. Timestamp: 2001-01-01 09:12:13.200000002)
Because call stack goes way back to line 1539 same file:
```
if self._is_ts_plot():
```
and caused self._is_ts_plot() to be False, which cause pandas not to try detect frequency and try to convert it but simply plot it.
2.Second:
line 92 of tseries\plotting.py
```
def _maybe_resample(series, ax, freq, plotf, kwargs):
ax_freq = _get_ax_freq(ax)
if ax_freq is not None and freq != ax_freq:
if frequencies.is_superperiod(freq, ax_freq): # upsample input
series = series.copy()
series.index = series.index.asfreq(ax_freq, how='s')
freq = ax_freq
elif _is_sup(freq, ax_freq): # one is weekly
how = kwargs.pop('how', 'last')
series = series.resample('D', how=how).dropna()
series = series.resample(ax_freq, how=how).dropna()
freq = ax_freq
elif frequencies.is_subperiod(freq, ax_freq) or _is_sub(freq, ax_freq):
_upsample_others(ax, freq, plotf, kwargs)
ax_freq = freq
else: # pragma: no cover
raise ValueError('Incompatible frequency conversion')
return freq, ax_freq, series
```
this function for the set of data which does not plot (the one with Timestamp: 2001-01-01 09:03:11.500000) goes through all the conditions and goes to the last "else"
with
```
ax_freq = "S"
freq = "L"
```
None of the functions handles the frequency "L".
I presume that it should be handled in
```
frequencies.is_subperiod(freq, ax_freq)
```
but as you can see here https://github.com/pydata/pandas/blob/790d6464130fe9448739e48678f466d5452992ca/pandas/tseries/frequencies.py#L904
it is not done.
Anyway, as i presume fixing problem with sampling may be tricky, i'd like to suggest an option (if it does not exist) which could allow pandas not to look for periods, or treat it as timeseries, but simply plot it.
Something like
```
seriesB.plot(please_oh_please_make_this_thing_plot=True)
```
which would cause to not treat it as timeseries and forced
`self._is_ts_plot():` to be false.
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} | 7 | 2014-07-17T09:34:07Z | 2016-04-18T15:51:26Z | 2016-04-18T15:51:26Z | NONE | null | I'm trying to read a bunch of csv files, but some of them have a header line without any data. I expect to get empty data but Pandas raises an exception on these files:
```
Traceback (most recent call last):
File "/usr/bin/positionneur_rng", line 9, in <module>
load_entry_point('pymela==0.1.0', 'console_scripts', 'positionneur_rng')()
File "/data/libs/python/pymela_scripts/positionneur_rng.py", line 203, in main
= importer_csv_pandas(fichier)
File "/data/libs/python/pymela_scripts/positionneur_rng.py", line 179, in importer_csv_pandas
engine='python')
File "/usr/lib/python3.4/site-packages/pandas/io/parsers.py", line 443, in parser_f
return _read(filepath_or_buffer, kwds)
File "/usr/lib/python3.4/site-packages/pandas/io/parsers.py", line 235, in _read
return parser.read()
File "/usr/lib/python3.4/site-packages/pandas/io/parsers.py", line 686, in read
ret = self._engine.read(nrows)
File "/usr/lib/python3.4/site-packages/pandas/io/parsers.py", line 1506, in read
self.index_names)
File "/usr/lib/python3.4/site-packages/pandas/io/parsers.py", line 2129, in _get_empty_meta
index = MultiIndex.from_arrays([[]] * len(index_col),
TypeError: object of type 'bool' has no len()
```
I used this command: `pd.read_csv(fichier_data, sep=None, index_col=False, engine='python')`
The file content is a single line:
```
TEMPS;MODE;STATUS;LATITUDE;LONGITUDE;ALTITUDE;VNORD;VEST;VHAUT;ROLL;PITCH;YAW;DELTA YAW;RATEX;RATEY;RATEZ;ACCX;ACCY;ACCZ;GISEMENT;SITE;CONAZ;CONEL;POSAZ;POSEL;LATCIBLE;LONGICIBLE;ALTICIBLE;DISTANCE;
```
```
>>> pandas.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.1.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.43-1-MANJARO
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
pandas: 0.14.0
nose: 1.3.3
Cython: 0.20.2
numpy: 1.8.1
scipy: 0.14.0
statsmodels: None
IPython: 2.1.0
sphinx: 1.2.2
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.4
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.5.5
lxml: 3.3.5
bs4: 4.3.2
html5lib: 0.999
bq: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.6
pymysql: None
psycopg2: None
```
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} | 14 | 2014-07-17T12:28:40Z | 2014-10-05T14:05:18Z | 2014-10-05T14:05:18Z | CONTRIBUTOR | null | regression from 0.14.0 (which return `Index`, which is also not correct).
`iloc` DOES work correctly though
```
In [9]: df = tm.makeTimeDataFrame()
In [10]: df.index
Out[10]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2000-01-03, ..., 2000-02-11]
Length: 30, Freq: B, Timezone: None
In [11]: df.loc[[]].index
Out[11]: Float64Index([], dtype='float64')
# this is the correct result
In [4]: df.iloc[[]].index
Out[4]:
<class 'pandas.tseries.index.DatetimeIndex'>
Length: 0, Freq: None, Timezone: None
```
```
In [7]: p = tm.makePanel()
In [8]: p.major_axis
Out[8]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2000-01-03, ..., 2000-02-11]
Length: 30, Freq: B, Timezone: None
In [9]: p.loc[:,[],:].major_axis
Out[9]: Float64Index([], dtype='float64')
```
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} | 1 | 2014-07-17T13:54:41Z | 2019-07-01T11:41:24Z | 2019-07-01T11:41:24Z | CONTRIBUTOR | null | xref: #8165
http://stackoverflow.com/questions/24805307/losing-timezone-awareness-when-saving-hyerarchical-pandas-datetimeindex-to-hdf5/24805506#2480550
should raise NotImplementedError for now, or actually implement
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| null | 1 | 2014-07-17T21:24:01Z | 2014-07-17T22:36:56Z | 2014-07-17T22:36:48Z | NONE | null | ``` python
import pandas as pd
import numpy as np
from decimal import *
def make_currency(x):
try:
return Decimal(x).quantize(Decimal('.01'))
except (InvalidOperation, TypeError) as e:
return Decimal(0).quantize(Decimal('.01'))
cols = {"c1": [1.23,2.34,3.45], "c2": [4,5,6], "c3": [1,2,3]}
index = [1, 1, 1]
df = pd.DataFrame(cols, index)
df['c1'] = df['c1'].apply(make_currency)
```
**Test Case 1**
Expected - _Return all 3 columns with sum aggregation applied_
Observations - _Passed_
```
In [2]: df.sum()
Out[2]:
c1 7.02
c2 15.00
c3 6.00
dtype: float64
```
**Test Case 2**
Expected - _Return all 3 columns with sum aggregation applied_
Observations - _Column 'c1' has not been returned by the sum function, which in this case is of Decimal datatype_
```
In [8]: df.sum(level=0)
Out[8]:
c2 c3
1 15 6
In [10]: df.groupby(level=0).sum()
Out[10]:
c2 c3
1 15 6
```
**Workaround**
Using numpy.sum in-conjunction with pandas.DataFrame.apply function appears to give the expected result one would expect from Test Case 2:
```
In [9]: df.groupby(level=0).apply(lambda x: np.sum(x))
Out[9]:
c1 c2 c3
1 7.02 15 6
```
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} | 7 | 2014-07-17T22:05:17Z | 2014-09-03T13:57:26Z | 2014-07-22T15:24:22Z | CONTRIBUTOR | null | I was having issues with lost tz-info when retrieving a DatetimeIndex from an HDF store using `store.select_column('data', 'index')`. I was able to track down the issue to `tseries/index.py` in the `Index._to_embed` method. The issue is
``` python
def _to_embed(self, keep_tz=False):
""" return an array repr of this object, potentially casting to object """
if keep_tz and self.tz is not None and str(self.tz) != 'UTC':
return self.asobject.values
return self.values
```
It looks like it explicitly rejects UTC timezones. Is there a good reason for this?
The below code reproduces the problem for me.
``` python
import pandas as pd
drange = pd.date_range('2014-07-07 00:00:00', '2014-07-07 03:00:00', freq='1h')
drange_utc = drange.tz_localize('UTC')
drange_mst = drange.tz_localize('MST')
print drange._to_embed(keep_tz=True)
print drange_utc._to_embed(keep_tz=True)
print drange_mst._to_embed(keep_tz=True)
```
I'm using python 2.7.6 with the following packages:
```
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.17.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.utf8
pandas: 0.14.1
nose: 1.3.3
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.0.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.2
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: 1.8.6
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.3.5
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
```
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} | 7 | 2014-07-17T22:05:20Z | 2014-07-18T01:00:41Z | 2014-07-18T01:00:30Z | NONE | null | I think there may be a bug with the row-wise handling of `numpy.timedelta64` data types when using `DataFrame.apply`. As a check, the problem does not appear when using `DataFrame.applymap`. The problem may be related to #4532, but I'm unsure. I've included an example below.
This is only a minor problem for my use-case, which is cross-checking timestamps from a counter/timer card. I can easily work around the issue with `DataFrame.itertuples` etc.
Thank you for your time and for making such a useful package!
#### Example
##### Version
Import and check versions.
```
$ date
Thu Jul 17 16:28:38 CDT 2014
$ conda update pandas
Fetching package metadata: ..
# All requested packages already installed.
# packages in environment at /Users/harrold/anaconda:
#
pandas 0.14.1 np18py27_0
$ ipython
Python 2.7.8 |Anaconda 2.0.1 (x86_64)| (default, Jul 2 2014, 15:36:00)
Type "copyright", "credits" or "license" for more information.
IPython 2.1.0 -- An enhanced Interactive Python.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://binstar.org
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
In [1]: from __future__ import print_function
In [2]: import numpy as np
In [3]: import pandas as pd
In [4]: pd.util.print_versions.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Darwin
OS-release: 11.4.2
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.1
nose: 1.3.3
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.1.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2014.4
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.5
lxml: 3.3.5
bs4: 4.3.1
html5lib: 0.999
httplib2: 0.8
apiclient: 1.2
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
```
##### Create test data
Using subset of original raw data as example.
```
In [5]: datetime_start = np.datetime64(u'2014-05-31T01:23:19.9600345Z')
In [6]: timedeltas_elapsed = [30053400, 40053249, 50053098]
```
Compute datetimes from elapsed timedeltas, then create differential timedeltas from datetimes. All elements are either type `numpy.datetime64` or `numpy.timedelta64`.
```
In [7]: df = pd.DataFrame(dict(datetimes = timedeltas_elapsed))
In [8]: df = df.applymap(lambda elt: np.timedelta64(elt, 'us'))
In [9]: df = df.applymap(lambda elt: np.datetime64(datetime_start + elt))
In [10]: df['differential_timedeltas'] = df['datetimes'] - df['datetimes'].shift()
In [11]: print(df)
datetimes differential_timedeltas
0 2014-05-31 01:23:50.013434500 NaT
1 2014-05-31 01:24:00.013283500 00:00:09.999849
2 2014-05-31 01:24:10.013132500 00:00:09.999849
```
##### Expected behavior
With element-wise handling using `DataFrame.applymap`, all elements are correctly identified as datetimes (timestamps) or timedeltas.
```
In [12]: print(df.applymap(lambda elt: type(elt)))
datetimes differential_timedeltas
0 <class 'pandas.tslib.Timestamp'> <type 'numpy.timedelta64'>
1 <class 'pandas.tslib.Timestamp'> <type 'numpy.timedelta64'>
2 <class 'pandas.tslib.Timestamp'> <type 'numpy.timedelta64'>
```
##### Bug
With row-wise handling using `DataFrame.apply`, all elements are type `pandas.tslib.Timestamp`. I expected 'differential_timedeltas' to be type `numpy.timedelta64` or another type of timedelta, not a type of datetime (timestamp).
```
In [13]: # For 'datetimes':
In [14]: print(df.apply(lambda row: type(row['datetimes']), axis=1))
0 <class 'pandas.tslib.Timestamp'>
1 <class 'pandas.tslib.Timestamp'>
2 <class 'pandas.tslib.Timestamp'>
dtype: object
In [15]: # For 'differential_timedeltas':
In [16]: print(df.apply(lambda row: type(row['differential_timedeltas']), axis=1))
0 <class 'pandas.tslib.NaTType'>
1 <class 'pandas.tslib.Timestamp'>
2 <class 'pandas.tslib.Timestamp'>
dtype: object
```
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} | 6 | 2014-07-17T23:34:17Z | 2014-07-18T01:00:30Z | 2014-07-18T01:00:30Z | CONTRIBUTOR | null | closes #7778
TST: tests for internals/as_matrix() for all dtypes (including categoricals)
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} | 38 | 2014-07-18T05:52:42Z | 2014-09-17T22:27:52Z | 2014-09-11T19:48:42Z | MEMBER | null | `matplotlib.pyplot.scatter` supports the argument `c` for setting the color of
each point. This patch lets you easily set it by giving a column name (currently
you need to supply an ndarray to make it work, since pandas isn't aware of it):
```
df.plot('x', 'y', c='z', kind='scatter')
```
vs
```
df.plot('x', 'y', c=df['z'].values, kind='scatter')
```
While I was at it, I noticed that `kind='scatter'` did not support the `colormap`
argument that some of the other methods support (notably `kind='hexbin'`). So
I added it, too.
This change should be almost entirely backwards compatible, unless folks are naming
columns in their data frame valid matplotlib colors and using the same color name
for the `c` argument.
A colorbar will also be added automatically if relevant.
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} | 8 | 2014-07-18T11:11:57Z | 2014-08-03T21:26:08Z | 2014-08-03T21:26:08Z | MEMBER | null | Apply `tz_convert_single` to the same time representation with different tz should be all `True`?
```
import pandas as pd
import numpy as np
import datetime
import pytz
for i in range(9):
utc_dt = datetime.datetime(2014, 3, 9, i, 0, tzinfo=pytz.timezone('UTC'))
tz_dt = datetime.datetime(2014, 3, 9, i, 0, tzinfo=pytz.timezone('US/Eastern'))
utc_ts = pd.Timestamp(utc_dt)
tz_ts = pd.Timestamp(tz_dt)
print(utc_ts, tz_ts)
print(utc_ts.value == pd.tslib.tz_convert_single(tz_ts.value, pytz.timezone('UTC'),
pytz.timezone('US/Eastern')))
print(tz_ts.value == pd.tslib.tz_convert_single(utc_ts.value, pytz.timezone('US/Eastern'),
pytz.timezone('UTC')))
```
## Current Result
```
(Timestamp('2014-03-09 00:00:00+0000', tz='UTC'), Timestamp('2014-03-09 00:00:00-0500', tz='US/Eastern'))
True
True
(Timestamp('2014-03-09 01:00:00+0000', tz='UTC'), Timestamp('2014-03-09 01:00:00-0500', tz='US/Eastern'))
True
True
(Timestamp('2014-03-09 02:00:00+0000', tz='UTC'), Timestamp('2014-03-09 02:00:00-0500', tz='US/Eastern'))
True
True
(Timestamp('2014-03-09 03:00:00+0000', tz='UTC'), Timestamp('2014-03-09 03:00:00-0500', tz='US/Eastern'))
False
True
(Timestamp('2014-03-09 04:00:00+0000', tz='UTC'), Timestamp('2014-03-09 04:00:00-0500', tz='US/Eastern'))
False
True
(Timestamp('2014-03-09 05:00:00+0000', tz='UTC'), Timestamp('2014-03-09 05:00:00-0500', tz='US/Eastern'))
False
True
(Timestamp('2014-03-09 06:00:00+0000', tz='UTC'), Timestamp('2014-03-09 06:00:00-0500', tz='US/Eastern'))
False
True
(Timestamp('2014-03-09 07:00:00+0000', tz='UTC'), Timestamp('2014-03-09 07:00:00-0500', tz='US/Eastern'))
False
True
(Timestamp('2014-03-09 08:00:00+0000', tz='UTC'), Timestamp('2014-03-09 08:00:00-0500', tz='US/Eastern'))
False
False
```
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| null | 1 | 2014-07-18T11:51:53Z | 2014-07-18T12:02:33Z | 2014-07-18T12:02:30Z | CONTRIBUTOR | null | {
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} | 3 | 2014-07-18T13:28:24Z | 2014-07-18T14:32:38Z | 2014-07-18T14:32:28Z | NONE | null | # problem when denominator is interger type and has 0 element
# no such problem with pandas <= 0.13.1
print pd.**version**
print np.**version**
testdata=pd.DataFrame({'TL': (1,0), 'PI': (-0.01,-0.02)})
print "--- direct pd.Series division (wrong) : \n", (testdata.PI)/testdata.TL
print "--- np.where filtered (still wrong!!): ",np.where(testdata.TL>0.001,testdata.PI/testdata.TL,0 )
print "--- direct pd.Series division (OK for float type) : \n", testdata.PI/(testdata.TL.astype(float))
print "--- values division (correct)", testdata.PI.values/testdata.TL.values
-------------- Output ---------
0.14.0
1.7.1
--- direct pd.Series division (wrong) :
0 -inf
1 -inf
dtype: float64
--- np.where filtered (still wrong!!): [-inf 0.]
--- direct pd.Series division (OK for float type) :
0 -0.010000
1 -inf
dtype: float64
--- values division (correct) [-0.01 -inf]
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} | 0 | 2014-07-18T14:01:14Z | 2014-07-18T14:32:29Z | 2014-07-18T14:32:28Z | CONTRIBUTOR | null | closes #7785
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} | 11 | 2014-07-18T14:23:04Z | 2021-04-11T04:55:05Z | 2021-04-11T04:55:05Z | NONE | null | One can construct DataFrame with frozensets in the index:
```
>>> import pandas
>>> a = frozenset([1])
>>> b = frozenset([1, 2])
>>> c = frozenset([1, 2, 3])
>>>
>>> frame = pandas.DataFrame(index=[a, b], columns=[c])
>>> frame
(1, 2, 3)
(1) NaN
(1, 2) NaN
```
Setting new values for existing items works:
```
>>> frame.loc[a] = 0
>>> frame
(1, 2, 3)
(1) 0
(1, 2) NaN
>>> frame[c] = 1
>>> frame
(1, 2, 3)
(1) 1
(1, 2) 1
>>> frame[frozenset([4])] = 1
>>> frame
(1, 2, 3) (4)
(1) 1 1
(1, 2) 1 1
```
But new items cannot be created:
```
>>> frame.loc[frozenset([3])] = 1
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "venv/lib/python2.7/site-packages/pandas/core/indexing.py", line 118, in __setitem__
indexer = self._convert_to_indexer(key, is_setter=True)
File "venv/lib/python2.7/site-packages/pandas/core/indexing.py", line 1085, in _convert_to_indexer
raise KeyError('%s not in index' % objarr[mask])
KeyError: '[3] not in index'
```
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when fetching a DatetimeIndex column with tz=UTC
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} | 7 | 2014-07-18T16:42:38Z | 2015-11-11T03:20:28Z | 2015-11-11T03:20:28Z | NONE | null | Hi all,
I'm running pandas 0.14.1:
## INSTALLED VERSIONS
commit: None
python: 3.4.1.final.0
python-bits: 64
OS: Windows
OS-release: 8
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: DE
pandas: 0.14.1
nose: 1.3.3
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.14.0
statsmodels: None
IPython: 2.1.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.1
pytz: 2014.4
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.5.5
lxml: 3.3.5
bs4: 4.3.1
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
I'm experiencing a bug which might be related to https://github.com/pydata/pandas/issues/7622
How to reproduce the bug:
At first, a new DataFrame is created
```
dt_rng = pd.date_range(start='2014-01-01 00:00', periods = 1000, freq='1s')
df = pd.DataFrame({'a':np.random.randn(1000), 'b': np.random.randn(1000)},index = dt_rng)
df['b'] = df['b'].round()
```
Then, this DataFrame is exported as csv with a custom date_format:
```
df.to_csv(date_format='%Y demo')
```
This does work: ',a,b\n2014 demo,-0.5582228932333034,1.0\n2014 demo,[...]'
Now, the other scenario. Same DataFrame but a groupby('b') and a resample:
```
df.groupby(df['b']).resample('1min').to_csv(date_format='%y demo')
```
Here, the custom date_format is not used:
'b,,a\n-4.0,2014-01-01 00:12:00,1.571130069273494[...]'
Thanks and best regards
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} | 3 | 2014-07-18T16:51:14Z | 2014-07-30T22:13:57Z | 2014-07-30T22:13:57Z | NONE | null | e.g.
```
set_names(self, names, inplace=False, level=None)
set_levels(self, levels, level=None, copy=False, validate=True, verify_integrity=False
```
if `level` is not None then treat `levels/names` as a list (or list-of-lists if level is a list) and just set those levels.
e.g.
```
set_names('foo',level=1)
set_names(['foo','bar'],level=[1,2])
set_levels(['a','b','c'],level=1)
set_levels([['a','b','c'],[1,2,3]],level=[1,2])
```
Hi all,
first of all: I'm not sure whether this is a bug or if there is just no nice way to do this. I'd like to use tz_convert in a MultiIndex DataFrame in pandas 0.14.1.
```
dt_rng = pd.date_range(start='2014-01-01 00:00', periods = 1000, freq='1s', tz='Europe/Berlin')
df = pd.DataFrame({'a':np.random.randn(1000), 'b': np.random.randn(1000)},index = dt_rng)
df['b'] = df['b'].round()
df = df.groupby('b').resample('1h')
df.index.levels[1] = df.index.levels[1].tz_convert('UTC')
```
This does not work: 'FrozenList' does not support mutable operations.
What I currently do:
```
dt_rng = pd.date_range(start='2014-01-01 00:00', periods = 1000, freq='1s', tz='Europe/Berlin')
df = pd.DataFrame({'a':np.random.randn(1000), 'b': np.random.randn(1000)},index = dt_rng)
df['b'] = df['b'].round()
df = df.groupby('b').resample('1h')
df.index.set_levels([
df.index.levels[0],
df.index.levels[1].tz_convert('UTC')
],inplace=True)
```
This does work as expected, but is it really the way to do it?!
Thanks and best regards!
## PS: INSTALLED VERSIONS
commit: None
python: 3.4.1.final.0
python-bits: 64
OS: Windows
OS-release: 8
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: DE
pandas: 0.14.1
nose: 1.3.3
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.14.0
statsmodels: None
IPython: 2.1.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.1
pytz: 2014.4
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.5.5
lxml: 3.3.5
bs4: 4.3.1
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
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} | 1 | 2014-07-18T17:16:05Z | 2014-07-19T09:10:42Z | 2014-07-18T19:06:27Z | CONTRIBUTOR | null | related: #7791, #7792 (and I think this causes both)
this is a dupe of #7746
```
In [8]: df = DataFrame(np.arange(6).reshape(6,1),index=MultiIndex.from_product([['a','b'],date_range('20130101',periods=3,tz='Europe/Berlin')]))
In [9]: df
Out[9]:
0
a 2013-01-01 00:00:00+01:00 0
2013-01-02 00:00:00+01:00 1
2013-01-03 00:00:00+01:00 2
b 2013-01-01 00:00:00+01:00 3
2013-01-02 00:00:00+01:00 4
2013-01-03 00:00:00+01:00 5
In [10]: df.reset_index()
ValueError: Length of values does not match length of index
```
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} | 7 | 2014-07-18T17:45:02Z | 2014-07-29T10:04:36Z | 2014-07-21T11:42:08Z | CONTRIBUTOR | null | Series, frames and panels that contain only one block can be unpickled
under the assumption that block items correspond to manager items 1-to-1
(as pointed out in #7329).
I still don't have any of darwin/win/32bit platforms at hand, so I
cannot post those pickles. Also, my linux has a fresher python than
indicated by filenames of existing pickle test data. I did generate
some data to test the fix, the question is do you want me to upload it?
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} | 6 | 2014-07-18T18:02:58Z | 2016-11-11T18:31:21Z | 2016-09-03T09:20:29Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/24830952/changing-timezone-on-pandas-datetimeindex-when-concatenating-dataframes-in-pytho
```
ix1 = pd.DatetimeIndex(start=pd.Timestamp('20140715', tz='EST5EDT'), end=pd.Timestamp('20140717', tz='EST5EDT'), freq='D', tz='EST5EDT')
ix2 = pd.DatetimeIndex([pd.Timestamp('2014-07-11 00:00:00', tz='EST5EDT'), pd.Timestamp('2014-07-21 00:00:00', tz='EST5EDT')])
df1 = pd.DataFrame(0, index=ix1, columns=['A', 'B'])
df2 = pd.DataFrame(0, index=ix2, columns=['A', 'B'])
```
I think the because the 2nd index is only length 2 its freq is not inferred causing a reset to UTC.
```
In [46]: concat([df1,df2])
Out[46]:
A B
2014-07-15 04:00:00+00:00 0 0
2014-07-16 04:00:00+00:00 0 0
2014-07-17 04:00:00+00:00 0 0
2014-07-11 04:00:00+00:00 0 0
2014-07-21 04:00:00+00:00 0 0
In [47]: concat([df1,df2]).index
Out[47]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2014-07-15 04:00:00+00:00, ..., 2014-07-21 04:00:00+00:00]
Length: 5, Freq: None, Timezone: UTC
```
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} | 13 | 2014-07-18T18:42:18Z | 2021-11-06T19:44:16Z | null | NONE | null | Works for 'fixed'
buggy for 'table'
need explicit test for both
This may be a MultiIndex issue.
```
In [7]: weeklyPrices.head()
Out[7]:
price
week id
2013-07-28/2013-08-03 0002189585 15.26
2013-08-04/2013-08-10 0002189585 15.25
2013-08-11/2013-08-17 0002189585 15.25
2013-08-18/2013-08-24 0002189585 14.83
2013-09-01/2013-09-07 0002189585 14.83
In [8]: pd.read_hdf('weeklyPrices.h5','weeklyPrices').head()
Out[8]:
price
week id
2274 0002189585 15.26
2275 0002189585 15.25
2276 0002189585 15.25
2277 0002189585 14.83
2279 0002189585 14.83
```
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| null | 8 | 2014-07-18T20:23:07Z | 2014-07-19T12:10:11Z | 2014-07-19T12:10:11Z | CONTRIBUTOR | null | Is there any way to downsample(i.e.) go from minutes to hours, hours to minute when performing an operation between to dataframes?
In finance, there are questions like what is my overnight return right now so you may want to compute all returns at a time period using the reference point yesterday's price at 16:00
My solution has always looked liked this:
df_daily = df[df["dt"].hour == 16].copy().shift(1)
df = df.join(df_daily, rsuffix="closing_").fillna(method="ffill")
Is there any approach that I am missing or that I can contribute to cython that would solve this?
Also, here is an example question from stack overflow where it came up today
http://stackoverflow.com/q/24826483/1064197
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} | 13 | 2014-07-18T22:36:18Z | 2014-08-04T13:18:25Z | 2014-08-03T01:41:58Z | MEMBER | null | These functions may return different result in case of DST. There seems to be 2 problems:
- `tslib.tz_convert` checks DST and change `deltas` by adjusting `pos` by 1. If input has time-gaps more than 2 DST spans, result will be incorrect.
- `tslib.tz_convert_single` results incorrect if input is just on DST edge.
```
import pandas as pd
import numpy as np
import datetime
import pytz
idx = pd.date_range('2014-03-01', '2015-01-10', freq='H')
f = lambda x: pd.tslib.tz_convert_single(x, pd.tslib.maybe_get_tz('US/Eastern'), 'UTC')
result = pd.tslib.tz_convert(idx.asi8, pd.tslib.maybe_get_tz('US/Eastern'), 'UTC')
result_single = np.vectorize(f)(idx.asi8)
result[result != result_single]
# [1394370000000000000 1394373600000000000 1394377200000000000 ...,
# 1414918800000000000 1414922400000000000 1414926000000000000]
```
#### Note
Additionally, it was modifed to close #7880 also.
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| null | 0 | 2014-07-19T00:03:14Z | 2014-07-19T00:04:05Z | 2014-07-19T00:04:05Z | CONTRIBUTOR | null | ref pydata/pandas#6194
ref pydata/pandas#7784
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| null | 1 | 2014-07-19T07:55:11Z | 2014-07-19T12:07:41Z | 2014-07-19T12:07:37Z | CONTRIBUTOR | null | {
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