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```
mask = (df['Quradate'] >= date1) & (df['Quradate'] < date2)
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https://github.com/pydata/pandas/blob/datetime64/pandas/src/timeseries/c_dates.c
stuff like week, day_of_year, etc.
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} | 4 | "2012-02-21T04:11:10Z" | "2014-06-19T09:19:45Z" | "2012-02-23T19:46:40Z" | CONTRIBUTOR | null | {
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https://api.github.com/repos/pandas-dev/pandas/issues/808 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/808/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/808/comments | https://api.github.com/repos/pandas-dev/pandas/issues/808/events | https://github.com/pandas-dev/pandas/pull/808 | 3,306,589 | MDExOlB1bGxSZXF1ZXN0ODU4NTA4 | 808 | New offsets | {
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} | [] | closed | false | null | [] | null | 2 | "2012-02-21T05:55:35Z" | "2012-02-22T17:21:49Z" | "2012-02-22T17:22:57Z" | CONTRIBUTOR | null | These are the new offsets and frequency strings posted on the wiki.
I haven't included any deprecations because I'm not clear how this should work yet.
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https://api.github.com/repos/pandas-dev/pandas/issues/809 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/809/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/809/comments | https://api.github.com/repos/pandas-dev/pandas/issues/809/events | https://github.com/pandas-dev/pandas/issues/809 | 3,312,180 | MDU6SXNzdWUzMzEyMTgw | 809 | Problem with datetime and HDFStore | {
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} | 8 | "2012-02-21T14:43:49Z" | "2012-05-13T04:38:55Z" | "2012-05-13T04:38:29Z" | NONE | null | When storing a DataFrame using HDFStore the datetime information is altered. My guess is that there is some problem with daylight saving and time zones when the DataFrame is loaded from the h5 file. An example:
```
In [1]: from pandas import *
In [2]: df = DataFrame([0,1], [datetime(2011, 3, 27, 2, 2, 2),datetime(2011, 3, 27, 3, 2, 2)])
In [3]: s = HDFStore("test.h5")
In [4]: s["test"] = df
In [5]: df
Out[5]:
0
2011-03-27 02:02:02 0
2011-03-27 03:02:02 1
In [6]: s["test"]
Out[6]:
0
2011-03-27 03:02:02 0
2011-03-27 03:02:02 1
```
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https://api.github.com/repos/pandas-dev/pandas/issues/810 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/810/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/810/comments | https://api.github.com/repos/pandas-dev/pandas/issues/810/events | https://github.com/pandas-dev/pandas/issues/810 | 3,330,286 | MDU6SXNzdWUzMzMwMjg2 | 810 | nosetests errors/failures --> install issue? | {
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} | [] | closed | false | null | [] | null | 5 | "2012-02-22T10:00:26Z" | "2012-02-23T20:30:48Z" | "2012-02-22T15:53:28Z" | CONTRIBUTOR | null | Pulled in master and installed. Running nosetests gave 47 errors, 5 failures.
Removed build directory, all *.so files and site_packages/pandas dir/files and tried again -- same.
Listing errors (many occured in several tests):
``` python
lib/python2.7/site-packages/pandas-0.8.0.dev_4e4b2f5-py2.7-linux-x86_64.egg/pandas/core/series.py", line 98, in na_op result = lib.scalar_compare(x, y, op)
AttributeError: 'module' object has no attribute 'scalar_compare'
lib/python2.7/site-packages/pandas-0.8.0.dev_4e4b2f5-py2.7-linux-x86_64.egg/pandas/core/index.py", line 283, in format
values = lib.maybe_convert_objects(values, safe=1)
File "inference.pyx", line 230, in pandas._tseries.maybe_convert_objects (pandas/src/tseries.c:64416)
TypeError: maybe_convert_objects() got an unexpected keyword argument 'safe'
InvalidOperation: comparison involving NaN
lib/python2.7/site-packages/pandas-0.8.0.dev_4e4b2f5-py2.7-linux-x86_64.egg/pandas/core/series.py", line 98, in na_op
result = lib.scalar_compare(x, y, op)
AttributeError: 'module' object has no attribute 'scalar_compare'
lib/python2.7/site-packages/pandas-0.8.0.dev_4e4b2f5-py2.7-linux-x86_64.egg/pandas/core/internals.py", line 227, in _pad
_method = lib.pad_2d_inplace_float64
AttributeError: 'module' object has no attribute 'pad_2d_inplace_float64
lib/python2.7/site-packages/pandas-0.8.0.dev_4e4b2f5-py2.7-linux-x86_64.egg/pandas/core/internals.py", line 237, in _backfill
_method = lib.backfill_2d_inplace_float64
AttributeError: 'module' object has no attribute 'backfill_2d_inplace_float64'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/811 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/811/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/811/comments | https://api.github.com/repos/pandas-dev/pandas/issues/811/events | https://github.com/pandas-dev/pandas/issues/811 | 3,340,762 | MDU6SXNzdWUzMzQwNzYy | 811 | DataFrame.join can not have datetime as column names | {
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} | 4 | "2012-02-22T19:06:53Z" | "2012-02-24T22:40:46Z" | "2012-02-24T22:40:46Z" | NONE | null | Issue:
For DataFrameA.join( DataFrameB , on = 'somecol' ), DataFrameB can not have datetime objects as column labels
Example:
from datetime import datetime
from pandas import DataFrame
str_dates = [ '20120209' , '20120222' ]
dt_dates = [ datetime(2012,2,9) , datetime(2012,2,22)]
A = DataFrame(str_dates , index = range(2) , columns = ['aa'] )
B = DataFrame([[1,2],[3,4]] , index = str_dates , columns = str_dates)
C = DataFrame([[1,2],[3,4]] , index = str_dates , columns = dt_dates )
works = A.join( B , on = 'aa' ) # works -- extra column labels are string
fails = A.join( C , on = 'aa' ) # fails -- extra column labels are datetime
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https://api.github.com/repos/pandas-dev/pandas/issues/812 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/812/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/812/comments | https://api.github.com/repos/pandas-dev/pandas/issues/812/events | https://github.com/pandas-dev/pandas/issues/812 | 3,347,341 | MDU6SXNzdWUzMzQ3MzQx | 812 | Reindex with Series with name field drops name | {
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} | 0 | "2012-02-22T22:05:00Z" | "2016-10-12T23:02:49Z" | "2012-02-24T18:56:22Z" | MEMBER | null | ```
In [34]: df2.pivot('Date', 'Individual')
Out[34]:
Exercises
Individual 1 2
Date
1/1/2001 10 NaN
1/3/2001 NaN 5
In [35]: df2.pivot('Date', 'Individual').reindex(df['Date'])
Out[35]:
Exercises
Individual 1 2
1/1/2001 10 NaN
1/2/2001 NaN NaN
1/3/2001 NaN 5
```
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} | 1 | "2012-02-22T22:43:28Z" | "2012-05-15T19:19:43Z" | "2012-05-15T19:18:20Z" | MEMBER | null | Something like this:
```
In [87]: df
Out[87]:
Date Px
0 1/1/2001 21.2
1 1/2/2001 22.0
2 1/3/2001 25.0
In [88]: df2
Out[88]:
Individual Date Exercises
0 1 1/1/2001 10
1 2 1/3/2001 5
In [89]: f = lambda x: merge(x, df, how='outer')
In [90]: concat([f(y) for x, y in df2.groupby('Individual')], ignore_index=False)
Out[90]:
Individual Date Exercises Px
0 1 1/1/2001 10 21.2
1 NaN 1/2/2001 NaN 22.0
2 NaN 1/3/2001 NaN 25.0
0 NaN 1/1/2001 NaN 21.2
1 NaN 1/2/2001 NaN 22.0
2 2 1/3/2001 5 25.0
```
but should be able to propagate certain fields and allow others to be NA
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https://api.github.com/repos/pandas-dev/pandas/issues/814 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/814/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/814/comments | https://api.github.com/repos/pandas-dev/pandas/issues/814/events | https://github.com/pandas-dev/pandas/issues/814 | 3,351,401 | MDU6SXNzdWUzMzUxNDAx | 814 | GroupBy case results in malformed DataFrame | {
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} | 0 | "2012-02-23T04:25:57Z" | "2012-02-23T04:57:24Z" | "2012-02-23T04:57:24Z" | MEMBER | null | ```
In [19]: d
Out[19]:
group zeros ones label
0 g1 0 1 l1
1 g2 0 1 l2
In [20]: d.groupby(['group']).mean()
Out[20]:
ones zeros
group
g1 1 0
g2 1 0
In [21]: d.groupby(['group']).mean().values
Out[21]:
array([[ 0., 1.],
[ 0., 1.]])
```
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} | 0 | "2012-02-23T04:56:54Z" | "2012-05-19T19:18:37Z" | "2012-05-19T19:18:37Z" | MEMBER | null | see #814
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In [13]: s = Series(np.arange(10))
In [14]: np.diff(s)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/wesm/code/pandas/<ipython-input-14-b5f9fe77ab7c> in <module>()
----> 1 np.diff(s)
/usr/lib/epd-7.1/lib/python2.7/site-packages/numpy/lib/function_base.pyc in diff(a, n, axis)
975 return diff(a[slice1]-a[slice2], n-1, axis=axis)
976 else:
--> 977 return a[slice1]-a[slice2]
978
979 def interp(x, xp, fp, left=None, right=None):
/home/wesm/code/pandas/pandas/core/series.pyc in __getitem__(self, key)
392 key = np.asarray(key, dtype=bool)
393
--> 394 return self._get_with(key)
395
396 def _get_with(self, key):
/home/wesm/code/pandas/pandas/core/series.pyc in _get_with(self, key)
406 else:
407 if isinstance(key, tuple):
--> 408 return self._get_values_tuple(key)
409
410 if not isinstance(key, (list, np.ndarray)): # pragma: no cover
/home/wesm/code/pandas/pandas/core/series.pyc in _get_values_tuple(self, key)
437
438 if not isinstance(self.index, MultiIndex):
--> 439 raise ValueError('Can only tuple-index with a MultiIndex')
440
441 # If key is contained, would have returned by now
ValueError: Can only tuple-index with a MultiIndex
```
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} | 0 | "2012-02-23T15:54:34Z" | "2012-02-24T20:50:26Z" | "2012-02-24T20:50:26Z" | CONTRIBUTOR | null | From mailing list
serie = pandas.io.parsers.read_csv(f, parse_dates=True,
date_parser=dateParser, index_col=0)
dateRange = pandas.DateRange(start, end, offset=5 *
pandas.datetools.Minute())
grouped = serie.groupby(dateRange.asof)
# version 1
t = time()
for date in serie.index:
k = grouped.grouper(date) # returns the key of the group where
date belongs
g = grouped.get_group(k)
print time()-t
# version 2
t = time()
for date in serie.index:
k = grouped.grouper(date)
g = serie.ix[grouped.groups[k]]
print time()-t
serie is something looking like this (financial data indexed with
datetime)
<class 'pandas.core.frame.DataFrame'>
Index: 476640 entries, 2011-01-03 00:00:00 to 2011-11-29 23:59:00
Data columns:
Open 476640 non-null values
High 476640 non-null values
Low 476640 non-null values
Close 476640 non-null values
Volume 476640 non-null values
dtypes: float64(5)
For 100.000 elts, version 1 performs in 480 secs, while version 2
takes only 25 secs
For the full 460.000 elts, we then get about 40 mins and 135 secs
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} | 0 | "2012-02-23T16:03:18Z" | "2012-02-24T17:59:57Z" | "2012-02-24T17:59:57Z" | CONTRIBUTOR | null | From mailing list:
Out of curiosity, is there a reason returning an izip iterator of the
index + cols isn't DataFrame method? I've got the following in my
toolbox now:
def iziprows(df):
series = [df[col] for col in df.columns]
series.insert(0,
df.index)
return izip(*series)
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} | 1 | "2012-02-23T16:28:07Z" | "2012-02-24T19:01:43Z" | "2012-02-24T19:01:40Z" | CONTRIBUTOR | null | Recreation:
```
In [2]: paste
In [357]: df = DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
.....: 'foo', 'bar', 'foo', 'foo'],
.....: 'B' : ['one', 'one', 'two', 'three',
.....: 'two', 'two', 'one', 'three'],
.....: 'C' : randn(8), 'D' : randn(8)})
## -- End pasted text --
In [3]: grouped = df.groupby(['A', 'B'], as_index=False)
In [4]: x = grouped['C'].agg({ 'Q' : np.sum})
```
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https://api.github.com/repos/pandas-dev/pandas/issues/820 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/820/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/820/comments | https://api.github.com/repos/pandas-dev/pandas/issues/820/events | https://github.com/pandas-dev/pandas/issues/820 | 3,358,752 | MDU6SXNzdWUzMzU4NzUy | 820 | dtype of result of string comparison wrong | {
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} | 0 | "2012-02-23T16:34:54Z" | "2012-02-23T17:03:50Z" | "2012-02-23T17:03:50Z" | CONTRIBUTOR | null | Recreation from mailing list, works in 0.7 but not in master:
```
from pandas import *
df = DataFrame([{ "a" : 1, "b" : "foo" }, {"a" : 2, "b" : "bar"}])
print df
mask_a = df.a > 1
print "Mask A"
print mask_a
print mask_a.dtype
print df[mask_a]
print df[-mask_a]
mask_b = df.b == "foo"
print "Mask B"
print mask_b
print mask_b.values.dtype
print df[mask_b]
print df[-mask_b]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/821 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/821/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/821/comments | https://api.github.com/repos/pandas-dev/pandas/issues/821/events | https://github.com/pandas-dev/pandas/issues/821 | 3,361,300 | MDU6SXNzdWUzMzYxMzAw | 821 | Implement fast C, C++, or Cython file parser | {
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} | 0 | "2012-02-23T19:14:24Z" | "2012-11-02T14:13:22Z" | "2012-11-02T14:13:22Z" | MEMBER | null | Goal: reduced memory footprint, better speed, more flexibility about dtypes of output arrays. should be able to output either structured arrays or DataFrame. Maybe contribute portions to NumPy once done but that's somewhat secondary
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https://api.github.com/repos/pandas-dev/pandas/issues/822 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/822/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/822/comments | https://api.github.com/repos/pandas-dev/pandas/issues/822/events | https://github.com/pandas-dev/pandas/issues/822 | 3,363,879 | MDU6SXNzdWUzMzYzODc5 | 822 | Non-string columns should get casted to string in DataFrame.to_records | {
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} | 0 | "2012-02-23T21:59:08Z" | "2016-10-12T23:02:49Z" | "2012-02-24T20:58:23Z" | MEMBER | null | data like this
```
In [36]: df
Out[36]:
0 1 2 3 4 5 6 7 8 9
0 2.1670 1.7100 0.61340 3.0150 2.5060 0.6787 1.6970 4.0460 1.2010 0.6134
1 1.2920 0.6526 0.33930 0.9789 1.9320 0.3915 0.6526 1.9710 1.0310 0.5221
2 0.4176 0.3002 0.01305 0.3132 0.5351 0.1566 0.2088 0.5612 0.1436 0.2349
3 3.2760 1.0700 0.52210 1.2140 1.8010 0.6917 1.7620 2.3360 0.7961 0.6134
4 2.2840 2.4800 0.41760 1.4100 1.8010 0.5221 0.8092 3.8370 1.1490 0.5743
5 0.4699 0.2349 0.26100 0.3393 0.4699 0.1044 0.2088 0.6526 0.3915 0.1175
6 1.6310 0.8222 0.30020 1.2920 1.1090 0.1697 0.4960 1.1350 0.4568 0.5743
7 3.8760 2.0100 0.53510 2.2840 3.5110 0.8875 1.2010 2.3100 1.2790 0.8222
8 0.7831 0.9397 0.18270 0.4960 0.6265 0.4046 0.3524 0.7178 0.3132 0.1436
9 0.8353 0.4829 0.28710 0.6265 0.7570 0.2219 0.5351 0.6787 0.3002 0.1044
```
and
```
In [35]: df.to_records()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/home/wesm/Dropbox/book/svn/<ipython-input-35-6d3142e97d2d> in <module>()
----> 1 df.to_records()
/home/wesm/code/pandas/pandas/core/frame.pyc in to_records(self, index)
716 names = list(self.columns)
717
--> 718 return np.rec.fromarrays(arrays, names=names)
719
720 @classmethod
/home/wesm/epd/lib/python2.7/site-packages/numpy/core/records.pyc in fromarrays(arrayList, dtype, shape, formats, names, titles, aligned, byteorder)
540 _names = descr.names
541 else:
--> 542 parsed = format_parser(formats, names, titles, aligned, byteorder)
543 _names = parsed._names
544 descr = parsed._descr
/home/wesm/epd/lib/python2.7/site-packages/numpy/core/records.pyc in __init__(self, formats, names, titles, aligned, byteorder)
142 def __init__(self, formats, names, titles, aligned=False, byteorder=None):
143 self._parseFormats(formats, aligned)
--> 144 self._setfieldnames(names, titles)
145 self._createdescr(byteorder)
146 self.dtype = self._descr
/home/wesm/epd/lib/python2.7/site-packages/numpy/core/records.pyc in _setfieldnames(self, names, titles)
177 raise NameError, "illegal input names %s" % `names`
178
--> 179 self._names = [n.strip() for n in names[:self._nfields]]
180 else:
181 self._names = []
AttributeError: 'numpy.int64' object has no attribute 'strip'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/823 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/823/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/823/comments | https://api.github.com/repos/pandas-dev/pandas/issues/823/events | https://github.com/pandas-dev/pandas/issues/823 | 3,365,749 | MDU6SXNzdWUzMzY1NzQ5 | 823 | Handling of dict return values in groupby.apply | {
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} | 0 | "2012-02-24T00:03:42Z" | "2012-05-12T16:53:13Z" | "2012-05-12T16:53:13Z" | MEMBER | null | Should be treated the same as returning Series
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https://api.github.com/repos/pandas-dev/pandas/issues/824 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/824/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/824/comments | https://api.github.com/repos/pandas-dev/pandas/issues/824/events | https://github.com/pandas-dev/pandas/issues/824 | 3,373,553 | MDU6SXNzdWUzMzczNTUz | 824 | provide rebuild option in appending to an HDFStore | {
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] | closed | false | null | [] | null | 1 | "2012-02-24T14:58:59Z" | "2012-11-24T22:22:34Z" | "2012-11-24T22:22:34Z" | CONTRIBUTOR | null | currently if you try to append to an existing table in an HDFStore and the fields are different from the existing fields,
pandas raises an Exception("append items do not match existing")
the reason for this the current structure of an HDFStore table is: index (time column), column (a string), values (a float array)
the values are in the order of the fields propertery in _v_attrs; so appending a different order (or different columns) invalidates the read-back mechanism, so we don't allow this
rebuilding is simple: read in the existing idea, concatenate with new data (which automatically reindexes), rename the existing file, write back the data file (could also write to a new file, then rename existing file, and rename new file to original file name - more atomic this way)
here is code that does this externally to HDFStore:
self.store is the HDFStore object (open,close methods are pretty simple so didn't show them)
```
def append(self, data, is_verbose = False, is_rebuild = True):
""" store a data object to a file """
if data is not None:
if not self.open(is_writer = True): return None
try:
self.store.append(self.label,data)
except (Exception), detail:
if is_rebuild and str(detail).startswith("appended items do not match existing items"):
self.rebuild(data)
else:
raise
finally:
self.close()
if self.is_verbose or is_verbose:
self.show_pretty('append', data = data)
def rebuild(self, data):
""" append to existiing data with a rebuild """
self.lo("rebuilding datafile -> [file->%s]" % self.file)
try:
# read it in, index and append
self.close()
new_data = apandas.Panel.append_many([self.select(), data])
# rename it
import os
os.rename(self.file, self.file + '.hdf_append_bak')
# write again
self.open(is_writer = True)
self.store.append(self.label,new_data)
self.lo("successful in append adjustment-> [file->%s]" % self.file)
except (Exception), detail:
self.lo("error in rebuild -> [file->%s] %s -> bailing out!" % (self.file,detail))
raise
```
as you can see - right now I have to catch and match a specific error, so this ideally would be done internally in HDFStore._write_table
one thing I don't handle is recompressing the original file to the filters that are currently present
Jeff
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https://api.github.com/repos/pandas-dev/pandas/issues/825 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/825/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/825/comments | https://api.github.com/repos/pandas-dev/pandas/issues/825/events | https://github.com/pandas-dev/pandas/issues/825 | 3,374,850 | MDU6SXNzdWUzMzc0ODUw | 825 | Maybe add limit option to fillna | {
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} | 0 | "2012-02-24T16:18:57Z" | "2012-04-04T22:39:32Z" | "2012-04-04T22:39:32Z" | MEMBER | null | For padding / backfilling, to limit extent
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} | 2 | "2012-02-24T20:18:45Z" | "2012-06-11T17:54:35Z" | "2012-06-11T17:53:35Z" | MEMBER | null | randomly get this crap
```
import pandas
import pandas.io.data as pd
def get_close(ticker, start, end):
stock = pd.DataReader(ticker, 'yahoo', start, end)
adj_close = stock['Adj Close']
return adj_close[-1]
start = datetime.datetime(2010,1,1)
end = datetime.datetime(2012,1,24)
ticker = ['TLT', 'IEF', 'IYR', 'XLY', 'XLK', 'XLU', 'XLV', 'XLP', 'XLI', 'XLF', 'XLB', 'XLE', 'SPY', 'EFA', 'EEM', 'EWJ', 'IWM', 'QQQ', 'LQD', 'JNK']
for t in ticker:
result = get_close(t, start, end)
print t,result
## -- End pasted text --
TLT 116.17
IEF 104.15
IYR 59.52
XLY 41.25
XLK 26.87
XLU 34.47
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
/home/wesm/code/pandas/<ipython-input-7-eb9190ce71e6> in <module>()
17
18 for t in ticker:
---> 19 result = get_close(t, start, end)
20 print t,result
/home/wesm/code/pandas/<ipython-input-7-eb9190ce71e6> in get_close(ticker, start, end)
6 def get_close(ticker, start, end):
7
----> 8 stock = pd.DataReader(ticker, 'yahoo', start, end)
9 adj_close = stock['Adj Close']
10
/home/wesm/code/pandas/pandas/io/data.pyc in DataReader(name, data_source, start, end)
51
52 if(data_source == "yahoo"):
---> 53 return get_data_yahoo(name=name, start=start, end=end)
54 elif(data_source == "fred"):
55 return get_data_fred(name=name, start=start, end=end)
/home/wesm/code/pandas/pandas/io/data.pyc in get_data_yahoo(name, start, end)
133
134 lines = urllib.urlopen(url).read()
--> 135 return read_csv(StringIO(lines), index_col=0, parse_dates=True)[::-1]
136
137 def get_data_fred(name=None, start=dt.datetime(2010, 1, 1),
/home/wesm/code/pandas/pandas/io/parsers.pyc in read_csv(filepath_or_buffer, sep, header, index_col, names, skiprows, na_values, parse_dates, date_parser, nrows, iterator, chunksize, skip_footer, converters, verbose, delimiter, encoding)
125 return parser
126
--> 127 return parser.get_chunk()
128
129 @Appender(_read_table_doc)
/home/wesm/code/pandas/pandas/io/parsers.pyc in get_chunk(self, rows)
483
484 if len(self.columns) != len(zipped_content):
--> 485 raise Exception('wrong number of columns')
486
487 data = dict((k, v) for k, v in izip(self.columns, zipped_content))
Exception: wrong number of columns
In [8]: %debug
> /home/wesm/code/pandas/pandas/io/parsers.py(485)get_chunk()
484 if len(self.columns) != len(zipped_content):
--> 485 raise Exception('wrong number of columns')
486
ipdb> u
> /home/wesm/code/pandas/pandas/io/parsers.py(127)read_csv()
126
--> 127 return parser.get_chunk()
128
ipdb>
> /home/wesm/code/pandas/pandas/io/data.py(135)get_data_yahoo()
134 lines = urllib.urlopen(url).read()
--> 135 return read_csv(StringIO(lines), index_col=0, parse_dates=True)[::-1]
136
ipdb> lines
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https://api.github.com/repos/pandas-dev/pandas/issues/828 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/828/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/828/comments | https://api.github.com/repos/pandas-dev/pandas/issues/828/events | https://github.com/pandas-dev/pandas/pull/828 | 3,379,498 | MDExOlB1bGxSZXF1ZXN0ODgzMzk5 | 828 | attempting to deflect more issues to the github tracker via the docs | {
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https://api.github.com/repos/pandas-dev/pandas/issues/829 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/829/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/829/comments | https://api.github.com/repos/pandas-dev/pandas/issues/829/events | https://github.com/pandas-dev/pandas/pull/829 | 3,379,780 | MDExOlB1bGxSZXF1ZXN0ODgzNTQw | 829 | DataFrame_repr_html changes according to #772 discussion. | {
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Implemented changes as discussed in #772.
Not so crazy on the scrollable div. For now i left the div max sizes as they were, could use suggestion on _good_ values.
I don't use the notebook often, what i noticed just now is that in combination with the IPython notebook `get_terminal_size()` returns the size of the terminal where the notebook was launched, not the size of the notebook display area. Don't know if there is a way to get the notebook size.
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https://api.github.com/repos/pandas-dev/pandas/issues/830 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/830/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/830/comments | https://api.github.com/repos/pandas-dev/pandas/issues/830/events | https://github.com/pandas-dev/pandas/pull/830 | 3,380,103 | MDExOlB1bGxSZXF1ZXN0ODgzNzAz | 830 | BUG: close #811, fix index.intersection where indices are incomparable | {
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} | 2 | "2012-02-26T16:32:24Z" | "2016-10-12T23:02:49Z" | "2012-05-07T19:31:31Z" | MEMBER | null | Make it easier to create a hierarchical index from a list of arrays in the Series and DataFrame constructors
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} | 9 | "2012-02-26T22:35:48Z" | "2018-07-06T19:40:06Z" | "2018-07-06T19:40:00Z" | CONTRIBUTOR | null | For example, want easy to and from timeseries for following data.
```
Out[179]:
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1949 112 118 132 129 121 135 148 148 136 119 104 118
1950 115 126 141 135 125 149 170 170 158 133 114 140
1951 145 150 178 163 172 178 199 199 184 162 146 166
1952 171 180 193 181 183 218 230 242 209 191 172 194
1953 196 196 236 235 229 243 264 272 237 211 180 201
1954 204 188 235 227 234 264 302 293 259 229 203 229
1955 242 233 267 269 270 315 364 347 312 274 237 278
1956 284 277 317 313 318 374 413 405 355 306 271 306
1957 315 301 356 348 355 422 465 467 404 347 305 336
1958 340 318 362 348 363 435 491 505 404 359 310 337
1959 360 342 406 396 420 472 548 559 463 407 362 405
1960 417 391 419 461 472 535 622 606 508 461 390 432
```
One idea is something like
```
df.stack().reindex_periods(year=0, month=1, convention='begin')
```
Or maybe API functions like:
```
consolidate_time_index(df.stack(), year=0, month=1, convention='begin')
```
And the reverse operation:
```
expand_time_index(series, year=0, month=1)
```
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} | 3 | "2012-02-27T05:37:36Z" | "2014-08-18T07:04:37Z" | "2012-02-28T17:35:05Z" | CONTRIBUTOR | null | PyTables Time64Col is in fact a float64 alias so adding fractional seconds and using the correct dtype fixes this issue
#513
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} | 6 | "2012-02-27T13:20:57Z" | "2015-08-10T09:01:35Z" | "2012-02-28T18:50:18Z" | NONE | null | Hi Wes,
Just a minor bug submisson:
When parsing a CSV file without an index, if the list with columns names is too short or too long, one gets a "Index contains duplicate entries" exception.
E.g.:
The file test.csv contains:
abc,1,a
abc,2,b
def,3,c
Running:
`table = pandas.read_csv('reports/9/test.csv', header=None, names=['a', 'b'],index_col=None)`
## Results in:
``` python
Exception Traceback (most recent call last)
/home/marius/Code/otsquant/analysis/ccapital/<ipython-input-70-7f388b2776b2> in <module>()
----> 1 table = pandas.read_csv('reports/9/test.csv', header=None, names=['a', 'b'],index_col=None)
/usr/lib64/python2.7/site-packages/pandas/io/parsers.pyc in read_csv(filepath_or_buffer, sep, header, index_col, names, skiprows, na_values, parse_dates, date_parser, nrows, iterator, chunksize, skip_footer, converters, verbose, delimiter, encoding)
125 return parser
126
--> 127 return parser.get_chunk()
128
129 @Appender(_read_table_doc)
/usr/lib64/python2.7/site-packages/pandas/io/parsers.pyc in get_chunk(self, rows)
467 if not index._verify_integrity():
468 dups = index.get_duplicates()
--> 469 raise Exception('Index has duplicates: %s' % str(dups))
470
471 if len(self.columns) != len(zipped_content):
Exception: Index has duplicates: ['abc']
```
Many thanks,
Marius
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https://api.github.com/repos/pandas-dev/pandas/issues/837 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/837/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/837/comments | https://api.github.com/repos/pandas-dev/pandas/issues/837/events | https://github.com/pandas-dev/pandas/issues/837 | 3,415,391 | MDU6SXNzdWUzNDE1Mzkx | 837 | ExcelFile throws an exception if parsing a file with only two lines | {
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} | 0 | "2012-02-28T09:20:30Z" | "2012-02-28T18:22:10Z" | "2012-02-28T18:22:09Z" | CONTRIBUTOR | null | Example (paste this in an Excel file):
Test Test1
aaaa bbbbb
``` python
excel_data = pandas.ExcelFile("test.xls")
parsed = pandas.parse("Sheet1")
/usr/lib64/python2.7/site-packages/pandas/io/parsers.pyc in parse(self, sheetname, header, skiprows, index_col, parse_dates, date_parser, na_values, chunksize)
629 parse_dates=parse_dates,
630 date_parser=date_parser,
--> 631 na_values=na_values, chunksize=chunksize)
632
633 def _parse_xlsx(self, sheetname, header=0, skiprows=None, index_col=None,
/usr/lib64/python2.7/site-packages/pandas/io/parsers.pyc in _parse_xls(self, sheetname, header, skiprows, index_col, parse_dates, date_parser, na_values, chunksize)
684 date_parser=date_parser,
685 skiprows=skiprows,
--> 686 chunksize=chunksize)
687
688 return parser.get_chunk()
/usr/lib64/python2.7/site-packages/pandas/io/parsers.pyc in __init__(self, f, delimiter, names, header, index_col, na_values, parse_dates, date_parser, chunksize, skiprows, skip_footer, converters, verbose, encoding)
262 self.data = f
263 self.columns = self._infer_columns()
--> 264 self.index_name = self._get_index_name()
265 self._first_chunk = True
266
/usr/lib64/python2.7/site-packages/pandas/io/parsers.pyc in _get_index_name(self)
381
382 try:
--> 383 next_line = self._next_line()
384 except StopIteration:
385 next_line = None
/usr/lib64/python2.7/site-packages/pandas/io/parsers.pyc in _next_line(self)
351 self.pos += 1
352
--> 353 line = self.data[self.pos]
354 else:
355 while self.pos in self.skiprows:
IndexError: list index out of range
```
Works correctly if the number of lines is at least 3. Pandas is from yesterday's git (path names have been slightly trimmed to improve readability).
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https://api.github.com/repos/pandas-dev/pandas/issues/838 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/838/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/838/comments | https://api.github.com/repos/pandas-dev/pandas/issues/838/events | https://github.com/pandas-dev/pandas/issues/838 | 3,419,580 | MDU6SXNzdWUzNDE5NTgw | 838 | DateRange - minute vs. minutes | {
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} | 3 | "2012-02-28T14:45:23Z" | "2012-02-28T19:32:58Z" | "2012-02-28T19:32:58Z" | NONE | null | I tried to create a DateRange in five minute steps.
`In [40]: DateRange(datetime(2011,11,11), datetime(2011,11,12), offset=DateOffset(minute=5))`
The %CPU is 100 and it consumes a lot of memory but nothing happens, no result and no error. I continued to experiment and realized that i wrote 'minute' instead of 'minutes'
`In [41]: DateRange(datetime(2011,11,11), datetime(2011,11,12), offset=DateOffset(minutes=5))`
My suggestion is to maybe add some kind of argument check.
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} | 3 | "2012-02-29T00:19:35Z" | "2012-02-29T18:04:21Z" | "2012-02-29T18:04:21Z" | CONTRIBUTOR | null | I think a bug:
In [1]: s = Series(arange(10))
In [2]: s
Out[2]:
0 0
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
In [3]: s[::2] = np.nan
In [4]: s
Out[4]:
0 -9223372036854775808
1 1
2 -9223372036854775808
3 3
4 -9223372036854775808
5 5
6 -9223372036854775808
7 7
8 -9223372036854775808
9 9
In [5]: s.dtype
Out[5]: dtype('int64')
Dataframe seems fine:
In [12]: df = DataFrame(np.random.random_integers(5, size=(5,5)))
In [13]: df
Out[13]:
0 1 2 3 4
0 5 5 3 1 2
1 1 3 3 3 2
2 1 5 2 2 3
3 5 3 1 1 4
4 2 5 2 3 2
In [14]: df[2] = np.nan
In [15]: df
Out[15]:
0 1 2 3 4
0 5 5 NaN 1 2
1 1 3 NaN 3 2
2 1 5 NaN 2 3
3 5 3 NaN 1 4
4 2 5 NaN 3 2
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```
In [37]: a = pandas.DataFrame(rand(4, 2), columns = list('ab'))
In [38]: print a
a b
0 0.372630 0.680347
1 0.557162 0.815903
2 0.058282 0.371265
3 0.772931 0.925008
In [39]: for l in a.ix:
....: print l
....:
a 0.372630
b 0.680347
a 0.557162
b 0.815903
Name: 1
a 0.058282
b 0.371265
Name: 2
a 0.772931
b 0.925008
Name: 3
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Q:\Groups\Bioinformatics\IgG assay optimization 2011\script development\scriptDevelopment\src\<ipython-input-39-50ae06db9052> in <module>()
----> 1 for l in a.ix:
2 print l
3
c:\python27\lib\site-packages\pandas\core\indexing.pyc in __getitem__(self, key)
30 return self._getitem_tuple(key)
31 else:
---> 32 return self._getitem_axis(key, axis=0)
33
34 def _get_label(self, label, axis=0):
c:\python27\lib\site-packages\pandas\core\indexing.pyc in _getitem_axis(self, key, axis)
182 idx = labels[key]
183
--> 184 return self._get_label(idx, axis=0)
185 else:
186 labels = self.obj._get_axis(axis)
c:\python27\lib\site-packages\pandas\core\indexing.pyc in _get_label(self, label, axis)
36 return self.obj.xs(label, axis=axis, copy=False)
37 except Exception:
---> 38 return self.obj.xs(label, axis=axis, copy=True)
39
40 def _slice(self, obj, axis=0):
c:\python27\lib\site-packages\pandas\core\frame.pyc in xs(self, key, axis, level, copy)
1592 loc, new_index = self.index.get_loc_level(key)
1593 else:
-> 1594 loc = self.index.get_loc(key)
1595
1596 if np.isscalar(loc):
c:\python27\lib\site-packages\pandas\core\index.pyc in get_loc(self, key)
493 loc : int
494 """
--> 495 return self._engine.get_loc(key)
496
497 def get_value(self, series, key):
c:\python27\lib\site-packages\pandas\_engines.pyd in pandas._engines.DictIndexEngine.get_loc (pandas\src\engines.c:2498)()
c:\python27\lib\site-packages\pandas\_engines.pyd in pandas._engines.DictIndexEngine.get_loc (pandas\src\engines.c:2460)()
KeyError: 4
```
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} | 1 | "2012-02-29T17:40:30Z" | "2014-05-23T22:43:06Z" | "2014-05-23T22:43:06Z" | CONTRIBUTOR | null | From the mailing list:
It would be nice to have a general, useful algorithm for doing
"approximate" reindexing like you describe. I'm thinking:
def approx_resample(obj, base, multiple, tolerance, offset=0, mode='nearest'):
...
then you would do:
result = approx_resample(df, 'min', 15, 5)
obviously
'min' <=> Minute()
offset would enable you to do like 1, 16, 31, 46 (offset=1) instead of
0, 15, 30, 45
mode parameter would allow you to do other things-- nearest, first,
last, mean of values, or some kind of interpolation method, etc.
to reflect the fact that you don't have some times you would just
dropna in the result-- could make the algo a bit smarter and only
generate dates that are within the tolerance of observed dates
(according to the frequency multiple)
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[[[ - Add `to_clipboard` function to pandas namespace for writing objects to
the system clipboard (`#774 <http://github.com/pydata/pandas/issues/774>`_) ]]]
If it won't mess up the docs, I can add these links to the rst. But, I wanted to check first to see if this anyone knew of a simpler method or whether this would cause problems with the Sphinx stuff.
Ideas?
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} | 2 | "2012-03-01T23:11:21Z" | "2012-03-15T20:36:07Z" | "2012-03-15T20:35:43Z" | NONE | null | ``` python
In [1]: import pandas
In [2]: pandas.__version__
Out[2]: '0.7.1'
In [3]: empty = pandas.Series([], index=[])
In [4]: import numpy as np
In [5]: np.__version__
Out[5]: '1.6.1'
In [6]: np.sum(empty)
Out[6]: nan
In [7]: np.sum(empty.values)
Out[7]: 0.0
```
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In [5]: df = DataFrame([[np.nan, 1], [1, 0]], dtype=np.int64)
In [6]: df
Out[6]:
0 1
0 -9223372036854775808 1
1 1 0
```
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} | 4 | "2012-03-03T22:44:32Z" | "2014-01-20T22:50:06Z" | "2014-01-20T22:50:06Z" | NONE | null | I'm an R user & am new to pandas, so hopefully this question isn't totally out of ignorance.
Are there any plans on creating an object similar to R/Bioconductor's AnnotatedDataFrame ( http://rss.acs.unt.edu/Rdoc/library/Biobase/html/class.AnnotatedDataFrame.html )? It wouldn't seem sensible to carry over the aspects of the class that are based on gene expression data sets, but it would be quite nice to allow annotations of labels to be accessed via a method like varMetadata.
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} | 2 | "2012-03-03T22:56:23Z" | "2012-09-08T22:22:04Z" | "2012-03-03T22:56:37Z" | CONTRIBUTOR | null | analogs to all the 'rolling_<stat>' functions
will be nearly identical setup with some Cython code to do _expanding_moment
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} | 0 | "2012-03-04T16:55:17Z" | "2012-04-15T01:36:26Z" | "2012-04-15T01:36:26Z" | CONTRIBUTOR | null | From mailing list:
The argument "parse_dates" will try to parse every column in the index.
Most of the times I have a dataset which looks like below and the first n columns will be used to set the multi-index.
Notice that my first column "date" is a date while the column "unitcode" is a integer.
If you use "parse_dates=True" than everything will be parsed.
Shouldn't it be me more logical that for parsing dates one has the option to explicitely parse certain columns like the index_col argument?
For the moment I uses read_csv without parsing, use the converters argument and then I use the set_index method. This works fine.
import pandas as pd
from StringIO import StringIO
data = '''date;destination;ventilationcode;unitcode;units
01/01/2010;P;P;50;1
01/01/2010;P;R;50;1
15/01/2010;P;P;50;1
01/05/2010;P;P;50;1'''
df = pd.read_csv(StringIO(data),sep=";",index_col = range(4),parse_dates=True)
print df.to_string()
# patch
df = pd.read_csv(StringIO(data),sep=";",converters={'date':pd.datetools.dateutil.parser.parse})
df = df.set_index(col_or_cols=['date','destination','ventilationcode','unitcode'])
print df.to_string()
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} | 7 | "2012-03-04T16:57:02Z" | "2012-06-03T21:16:53Z" | "2012-04-15T01:36:26Z" | CONTRIBUTOR | null | Again from mailing list:
Example:
data1 = '''date;value
01/05/2010;1
15/05/2010;2
31/05/2010;3
'''
df = pd.read_csv(StringIO(data1),sep=";",converters={'date':pd.datetools.dateutil.parser.parse})
print df.to_string()
Returns the first row wrong:
date value
0 2010-01-05 00:00:00 1
1 2010-05-15 00:00:00 2
2 2010-05-31 00:00:00 3
df = pd.read_csv(StringIO(data1),sep=";",converters={'date':lambda x: pd.datetools.dateutil.parser.parse(x, dayfirst=True)})
print df.to_string()
Returns the correct output
date value
0 2010-05-01 00:00:00 1
1 2010-05-15 00:00:00 2
2 2010-05-31 00:00:00 3
When using index_col and parse_dates, you can get also some of the dates that are wrong, some of them correct.
df = pd.read_csv(StringIO(data1),sep=";",index_col=[0],parse_dates=True)
print df.to_string()
Returns the first line also wrong:
value
date
2010-01-05 1
2010-05-15 2
2010-05-31 3
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} | 1 | "2012-03-04T16:57:52Z" | "2012-05-09T18:57:59Z" | "2012-05-09T18:57:59Z" | MEMBER | null | Inevitable to cause problems eventually when data sizes continue to increase. Want to insert checks in a few key places to check for int64 overflows-- only a couple places that are likely to happen and don't need to worry about the other places since there are no machines (?) which have over 2^63 bytes of RAM right now.
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} | 5 | "2012-03-04T17:14:30Z" | "2016-10-12T23:02:49Z" | "2012-03-04T20:15:21Z" | MEMBER | null | Users are confused by this:
```
if not index._verify_integrity():
dups = index.get_duplicates()
err_msg = 'Tried columns 1-X as index but found duplicates %s'
raise Exception(err_msg % str(dups))
```
this was added as I recall to give a somewhat better error message when INFERENCE (fewer column labels than columns of data) but this error message is also getting raised when there are duplicates in an explicitly specified `index_col`
see: http://stackoverflow.com/questions/9556892/pandas-dataframe-desired-index-has-duplicate-values
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} | 3 | "2012-03-04T17:35:54Z" | "2012-03-06T22:33:56Z" | "2012-03-06T22:33:56Z" | CONTRIBUTOR | null | In [57]: df = DataFrame(np.random.rand(5,5))
In [58]: df[df > 0.5]
ERROR: An unexpected error occurred while tokenizing input
The following traceback may be corrupted or invalid
## The error message is: ('EOF in multi-line statement', (63, 0))
KeyError Traceback (most recent call last)
/Volumes/HD2/adam/Documents/Code/python/rapidquant/<ipython-input-58-8ec7cde9a766> in <module>()
----> 1 df[df > 0.5]
/Volumes/HD2/adam/Documents/Code/python/pandas/pandas/core/frame.pyc in **getitem**(self, key)
1419 return self._getitem_multilevel(key)
1420 else:
-> 1421 return self._get_item_cache(key)
1422
1423 def _getitem_array(self, key):
/Volumes/HD2/adam/Documents/Code/python/pandas/pandas/core/generic.pyc in _get_item_cache(self, item)
280 return cache[item]
281 except Exception:
--> 282 values = self._data.get(item)
283 res = self._box_item_values(item, values)
284 cache[item] = res
/Volumes/HD2/adam/Documents/Code/python/pandas/pandas/core/internals.pyc in get(self, item)
612
613 def get(self, item):
--> 614 _, block = self._find_block(item)
615 return block.get(item)
616
/Volumes/HD2/adam/Documents/Code/python/pandas/pandas/core/internals.pyc in _find_block(self, item)
702
703 def _find_block(self, item):
--> 704 self._check_have(item)
705 for i, block in enumerate(self.blocks):
706 if item in block:
/Volumes/HD2/adam/Documents/Code/python/pandas/pandas/core/internals.pyc in _check_have(self, item)
709 def _check_have(self, item):
710 if item not in self.items:
--> 711 raise KeyError('no item named %s' % str(item))
712
713 def reindex_axis(self, new_axis, method=None, axis=0, copy=True):
KeyError: 'no item named 0 1 2 3 4\n0 False True False True False\n1 True True False True True\n2 True True True True False\n3 False True False False True\n4 False True False False True'
And then, this actually silently fails:
df.ix[df > 0.5]
Ie, it returns the wrong data
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} | 0 | "2012-03-04T18:12:04Z" | "2012-03-04T19:51:38Z" | "2012-03-04T19:51:38Z" | MEMBER | null | Very slow in some cases due to tuple creation
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} | 1 | "2012-03-04T19:50:49Z" | "2012-03-04T20:44:33Z" | "2012-03-04T20:44:33Z" | MEMBER | null | Get better performance that way. Also optimize for case of known range (a la groupby)
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} | 1 | "2012-03-05T01:53:56Z" | "2012-05-03T22:42:26Z" | "2012-05-03T22:42:26Z" | CONTRIBUTOR | null | Incorporate scikits.timeseries style interval indexing into datetime64 codebase
How much can be just plugged in, with datetime64 <-> interval conversions only where necessary?
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} | 1 | "2012-03-05T17:16:08Z" | "2012-04-23T17:15:53Z" | "2012-04-23T17:14:51Z" | CONTRIBUTOR | null | Like xts has.
eg, something like
ts.first("5W") # first five weeks of data
ts.first("7D") # first seven days
ts.last("3W").first("3B") # first 3 biz days of last three weeks
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} | 1 | "2012-03-05T17:26:58Z" | "2012-05-10T05:40:08Z" | "2012-05-10T05:40:08Z" | CONTRIBUTOR | null | cross-linking to statsmodel issue
https://github.com/statsmodels/statsmodels/issues/168
We may have to change some of the forecast dates code when pandas switches over to the ordinal timestamp representation. Right now, our current implementation is broken with the new datetime branch installed. To replicate, install pandas datetime branch and run examples/tsa/ex_dates.py. Make sure to write tests for this whenever the pandas code settles down.
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Have there been any thoughts on creating a data structure in pandas that can hold such metadata?
Thanks.
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https://api.github.com/repos/pandas-dev/pandas/issues/870 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/870/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/870/comments | https://api.github.com/repos/pandas-dev/pandas/issues/870/events | https://github.com/pandas-dev/pandas/issues/870 | 3,517,464 | MDU6SXNzdWUzNTE3NDY0 | 870 | nosetest failures in reshape | {
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} | 1 | "2012-03-06T01:22:25Z" | "2012-03-06T16:56:02Z" | "2012-03-06T16:56:02Z" | NONE | null | I've just done a git clone/install of pandas on Mac OSX 10.6.8 after a fresh install of EPD_free-7.2 to get the dependencies. The build was successful, but in running nostests I get 27 failures, each bottoming out at:
File "/Users/welch/src/pandas/pandas/core/reshape.py", line 95, in _make_selectors
group_mask.put(group_index, True)
TypeError: array cannot be safely cast to required type
here are the failed tests:
test_crosstab_margins (pandas.tools.tests.test_pivot.TestCrosstab)
test_crosstab_multiple (pandas.tools.tests.test_pivot.TestCrosstab)
test_crosstab_ndarray (pandas.tools.tests.test_pivot.TestCrosstab)
test_crosstab_pass_values (pandas.tools.tests.test_pivot.TestCrosstab)
test_crosstab_single (pandas.tools.tests.test_pivot.TestCrosstab)
test_margins (pandas.tools.tests.test_pivot.TestPivotTable)
test_pivot_integer_columns (pandas.tools.tests.test_pivot.TestPivotTable)
test_pivot_multi_functions (pandas.tools.tests.test_pivot.TestPivotTable)
test_pivot_multi_values (pandas.tools.tests.test_pivot.TestPivotTable)
test_pivot_table (pandas.tools.tests.test_pivot.TestPivotTable)
test_pivot_table_multiple (pandas.tools.tests.test_pivot.TestPivotTable)
test_pivot (pandas.tests.test_frame.TestDataFrame)
test_pivot_empty (pandas.tests.test_frame.TestDataFrame)
test_stack_unstack (pandas.tests.test_frame.TestDataFrame)
test_unstack_to_series (pandas.tests.test_frame.TestDataFrame)
test_frame_describe_multikey (pandas.tests.test_groupby.TestGroupBy)
test_groupby_multiple_columns (pandas.tests.test_groupby.TestGroupBy)
test_stack (pandas.tests.test_multilevel.TestMultiLevel)
test_stack_level_name (pandas.tests.test_multilevel.TestMultiLevel)
test_stack_unstack_multiple (pandas.tests.test_multilevel.TestMultiLevel)
test_stack_unstack_preserve_names (pandas.tests.test_multilevel.TestMultiLevel)
test_unstack (pandas.tests.test_multilevel.TestMultiLevel)
test_unstack_bug (pandas.tests.test_multilevel.TestMultiLevel)
test_unstack_level_name (pandas.tests.test_multilevel.TestMultiLevel)
test_unstack_preserve_types (pandas.tests.test_multilevel.TestMultiLevel)
test_pivot (pandas.tests.test_panel.TestLongPanel)
test_unstack (pandas.tests.test_series.TestSeries)
Any advice for me?
(And is this the proper place for such a question?)
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https://api.github.com/repos/pandas-dev/pandas/issues/871 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/871/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/871/comments | https://api.github.com/repos/pandas-dev/pandas/issues/871/events | https://github.com/pandas-dev/pandas/issues/871 | 3,521,384 | MDU6SXNzdWUzNTIxMzg0 | 871 | concat on axis=1 and ignore_index=True raises TypeError | {
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} | 1 | "2012-03-06T09:33:27Z" | "2012-03-06T21:34:15Z" | "2012-03-06T21:34:14Z" | CONTRIBUTOR | null | Test case:
``` python
>>> frame1 = pandas.DataFrame({"test1":["a", "b", "c"], "test2":[1,2,3], "test3": [4.5, 3.2, 1.2]})
>>> frame2 = pandas.DataFrame({"test4": [5.2, 2.2, 4.3]})
>>> pandas.concat([frame1, frame2], ignore_index=True, axis=1)
/usr/lib64/python2.7/site-packages/pandas/tools/merge.pyc in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names,
689 keys=keys, levels=levels, names=names,
690 verify_integrity=verify_integrity)
--> 691 return op.get_result()
692
693
/usr/lib64/python2.7/site-packages//pandas/tools/merge.pyc in get_result(self)
759 columns=self.new_axes[1])
760 else:
--> 761 new_data = self._get_concatenated_data()
762 return self.objs[0]._from_axes(new_data, self.new_axes)
763
/usr/lib64/python2.7/site-packages/pandas/tools/merge.pyc in _get_concatenated_data(self)
776 for kind in kinds:
777 klass_blocks = [mapping.get(kind) for mapping in blockmaps]
--> 778 stacked_block = self._concat_blocks(klass_blocks)
779 new_blocks.append(stacked_block)
780 new_data = BlockManager(new_blocks, self.new_axes)
/usr/lib64/python2.7/site-packages/pandas/tools/merge.pyc in _concat_blocks(self, blocks)
832 concat_items = _concat_indexes(all_items)
833
--> 834 return make_block(concat_values, concat_items, self.new_axes[0])
835
836 def _concat_single_item(self, item):
/usr/lib64/python2.7/site-packages/pandas/core/internals.pyc in make_block(values, items, ref_items, do_integrity_check)
287
288 return klass(values, items, ref_items, ndim=values.ndim,
--> 289 do_integrity_check=do_integrity_check)
290
291 # TODO: flexible with index=None and/or items=None
/usr/lib64/python2.7/site-packages/pandas/core/internals.pyc in __init__(self, values, items, ref_items, ndim, do_integrity_check)
28 self.ndim = ndim
29 self.items = _ensure_index(items)
---> 30 self.ref_items = _ensure_index(ref_items)
31
32 if do_integrity_check:
/usr/lib64/python2.7/site-packages/pandas/core/index.pyc in _ensure_index(index_like)
2152 if hasattr(index_like, 'name'):
2153 return Index(index_like, name=index_like.name)
-> 2154 return Index(index_like)
2155
2156 def _validate_join_method(method):
/usr/lib64/python2.7/site-packages/pandas/core/index.pyc in __new__(cls, data, dtype, copy, name)
70 else:
71 # other iterable of some kind
---> 72 subarr = com._asarray_tuplesafe(data, dtype=object)
73
74 if lib.is_integer_array(subarr) and dtype is None:
/usr/lib64/python2.7/site-packages/pandas/core/common.pyc in _asarray_tuplesafe(values, dtype)
493 def _asarray_tuplesafe(values, dtype=None):
494 if not isinstance(values, (list, tuple, np.ndarray)):
--> 495 values = list(values)
496
497 if isinstance(values, list) and dtype in [np.object_, object]:
TypeError: 'NoneType' object is not iterable
```
From today's git.
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https://api.github.com/repos/pandas-dev/pandas/issues/872 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/872/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/872/comments | https://api.github.com/repos/pandas-dev/pandas/issues/872/events | https://github.com/pandas-dev/pandas/issues/872 | 3,526,390 | MDU6SXNzdWUzNTI2Mzkw | 872 | DataFrame.to_string() does not always use formatters argument | {
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In [28]: myformatter = lambda x: '[%4.1f]' % x
In [29]: df = pandas.DataFrame(np.random.randn(2,2), columns=['A', 'B'])
In [30]: df
Out[30]:
A B
0 -0.478634 1.143561
1 0.043301 1.006389
In [31]: print df.to_string(formatters={'A': myformatter})
A B
0 [-0.5] 1.143561
1 [ 0.0] 1.006389
In [32]: df = pandas.DataFrame(np.random.randint(0, 10, (2, 2)), columns=['A', 'B'])
In [33]: df
Out[33]:
A B
0 4 3
1 0 4
In [34]: print df.to_string(formatters={'A': myformatter})
A B
0 4 3
1 0 4
In [35]:
```
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} | 2 | "2012-03-06T18:43:52Z" | "2016-10-12T23:02:50Z" | "2012-07-13T04:54:53Z" | NONE | null | Rather than parsing an entire worksheet, parse a slice of the columns. I don't know how this would work for _parse_xlsx but for _parse_xls the code could be changed, line 671 of parsers.py, from:
```
for value, typ in izip(sheet.row_values(i), sheet.row_types(i)):
```
To:
```
for value, typ in izip(sheet.row_values(i, index_col, None), sheet.row_types(i, index_col, None)):
```
So, there would be a new parameter to parse(), 'end_col' or 'index_col_offset' or something, passing the column to stop parsing at. The default would be 'None' get all the data in the worksheet. If index_col==None it would need to be zero in the sheet.row_values function.
Just a thought, as it would be nice to pull in just the data you want from a worksheet and not everything in the worksheet. Would be really nice to be able to specify specific columns of data, but I don't know how that would work, it is above my knowledge level.
Thanks, Bob
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} | 1 | "2012-03-07T02:10:41Z" | "2012-03-12T18:32:58Z" | "2012-03-12T18:32:58Z" | CONTRIBUTOR | null | instead of using the average rank for ties, use the smallest rank.
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} | 0 | "2012-03-07T02:11:15Z" | "2012-03-13T01:25:04Z" | "2012-03-13T01:25:04Z" | CONTRIBUTOR | null | true by default, rank descending otherwise
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} | 4 | "2012-03-07T15:52:40Z" | "2014-06-03T01:19:49Z" | "2014-06-03T01:19:49Z" | CONTRIBUTOR | null | Sometimes, ASCII data can contain strange fragments such as "/", "-", "%", "`", "#", "~" in the numerical data columns.
These charaters could either be inserted by a mal-functioning data logger (obeservation data), differencing conventions on NoData or wrong exports from other programs.
Before processing such data into a array/dataframe, there need to be sanity checks of the data.
- Such a checker would go through the data if ascii read-in fails and show on which line such a read error occurs.
Optionally, such das checker tools would offer the to replace data by a NaN or a defined value just like "fill_value".
- onther functionality would be to return the faulty lines and columns in order to check manually what's wrong in the input data
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from pandas import *
import pandas
import numpy as np
df = DataFrame({'a': np.random.randn(6),
'b': ['foo', 'bar'] * 3,
'c': np.random.randn(6)})
df.ix[2:4, ['b','c']]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/sandbox/PandasTutorial/<ipython-input-7-be5ec57dcb20> in <module>()
----> 1 df.ix[2:4, ['b','c']]
/software/pandas/pandas/core/indexing.pyc in __getitem__(self, key)
31 pass
32
---> 33 return self._getitem_tuple(key)
34 else:
35 return self._getitem_axis(key, axis=0)
/software/pandas/pandas/core/indexing.pyc in _getitem_tuple(self, tup)
113 continue
114
--> 115 retval = retval.ix._getitem_axis(key, axis=i)
116
117 return retval
/software/pandas/pandas/core/indexing.pyc in _getitem_axis(self, key, axis)
172 raise ValueError('Cannot index with multidimensional key')
173
--> 174 return self._getitem_iterable(key, axis=axis)
175 elif axis == 0:
176 is_int_index = _is_integer_index(labels)
/software/pandas/pandas/core/indexing.pyc in _getitem_iterable(self, key, axis)
213 keyarr = labels.take(keyarr)
214
--> 215 return self.obj.reindex(**{axis_name: keyarr})
216
217 def _convert_to_indexer(self, obj, axis=0):
/software/pandas/pandas/core/frame.pyc in reindex(self, index, columns, method, level, fill_value, copy)
1844
1845 if columns is not None:
-> 1846 frame = frame._reindex_columns(columns, copy, level, fill_value)
1847
1848 return frame
/software/pandas/pandas/core/frame.pyc in _reindex_columns(self, new_columns, copy, level, fill_value)
1907 new_columns, indexer = self.columns.reindex(new_columns, level=level)
1908 return self._reindex_with_indexers(None, None, new_columns, indexer,
-> 1909 copy, fill_value)
1910
1911 def _reindex_with_indexers(self, index, row_indexer, columns, col_indexer,
/software/pandas/pandas/core/frame.pyc in _reindex_with_indexers(self, index, row_indexer, columns, col_indexer, copy, fill_value)
1922 # TODO: speed up on homogeneous DataFrame objects
1923 new_data = new_data.reindex_indexer(columns, col_indexer, axis=0,
-> 1924 fill_value=fill_value)
1925 elif columns is not None and columns is not new_data.axes[0]:
1926 new_data = new_data.reindex_items(columns, copy=copy,
/software/pandas/pandas/core/internals.pyc in reindex_indexer(self, new_axis, indexer, axis, fill_value)
735 """
736 if axis == 0:
--> 737 return self._reindex_indexer_items(new_axis, indexer, fill_value)
738
739 mask = indexer == -1
/software/pandas/pandas/core/internals.pyc in _reindex_indexer_items(self, new_items, indexer, fill_value)
755 # TODO: less efficient than I'd like
756
--> 757 item_order = com.take_1d(self.items.values, indexer)
758
759 # keep track of what items aren't found anywhere
/software/pandas/pandas/core/common.pyc in take_1d(arr, indexer, out, fill_value)
186 out = np.empty(n, dtype=arr.dtype)
187 take_f = _take1d_dict[dtype_str]
--> 188 take_f(arr, indexer, out=out, fill_value=fill_value)
189 else:
190 out = arr.take(indexer, out=out)
/software/pandas/pandas/_tseries.so in pandas._tseries.take_1d_object (pandas/src/tseries.c:43051)()
TypeError: take_1d_object() got an unexpected keyword argument 'fill_value'
```
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} | [] | closed | false | null | [] | null | 0 | "2012-03-08T09:37:18Z" | "2014-07-31T08:51:13Z" | "2012-03-16T19:07:30Z" | CONTRIBUTOR | null | I've added simple editing feature in qtpandas.py. Could you merge in the master repository?
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} | 1 | "2012-03-08T16:56:48Z" | "2012-03-08T18:15:50Z" | "2012-03-08T18:15:48Z" | CONTRIBUTOR | null | Example:
import pandas as pn
ind = pn.MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1), ('b',2)], names=['fist', 'second'])
wp = pn.Panel(np.random.random((4,5,5)), items=ind,
major_axis=np.arange(5), minor_axis=np.arange(5))
In [10]: wp['a']
...
KeyError: 'no item named a'
In [11]: wp.ix['a']
...
KeyError: 'no item named a'
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} | 0 | "2012-03-08T17:05:02Z" | "2012-03-08T17:41:16Z" | "2012-03-08T17:41:16Z" | CONTRIBUTOR | null | ```
In [27]: df
Out[27]:
a b c d
0 2 0.103795 -0.066523 NaN
1 3 -0.939895 -0.208760 NaN
2 9 1.968456 0.419374 NaN
3 6 -0.105170 0.162064 NaN
4 7 0.381707 2.126133 NaN
In [28]: df[df['a']==3]
Out[28]:
a b c d
1 3 -0.939895 -0.20876 NaN
In [29]: df[df['a']==2]
Out[29]:
a b c d
0 2 0.103795 -0.066523 NaN
In [30]: df[df['a']==2] = 100
---------------------------------------------------------------------------
IndexingError Traceback (most recent call last)
/home/adam/code/pandas/<ipython-input-30-f7da2453a9f6> in <module>()
----> 1 df[df['a']==2] = 100
/home/adam/code/pandas/pandas/core/frame.py in __setitem__(self, key, value)
1491 self._boolean_set(key, value)
1492 elif isinstance(key, (np.ndarray, list)):
-> 1493 return self._set_item_multiple(key, value)
1494 else:
1495 # set column
/home/adam/code/pandas/pandas/core/frame.py in _set_item_multiple(self, keys, value)
1516 self[k1] = value[k2]
1517 else:
-> 1518 self.ix[:, keys] = value
1519
1520 def _set_item(self, key, value):
/home/adam/code/pandas/pandas/core/indexing.py in __setitem__(self, key, value)
59 raise IndexingError('only tuples of length <= %d supported',
60 self.ndim)
---> 61 indexer = self._convert_tuple(key)
62 else:
63 indexer = self._convert_to_indexer(key)
/home/adam/code/pandas/pandas/core/indexing.py in _convert_tuple(self, key)
68 keyidx = []
69 for i, k in enumerate(key):
---> 70 idx = self._convert_to_indexer(k, axis=i)
71 keyidx.append(idx)
72 return tuple(keyidx)
/home/adam/code/pandas/pandas/core/indexing.py in _convert_to_indexer(self, obj, axis)
282 elif _is_list_like(obj):
283 if com._is_bool_indexer(obj):
--> 284 objarr = _check_bool_indexer(labels, obj)
285 return objarr
286 else:
/home/adam/code/pandas/pandas/core/indexing.py in _check_bool_indexer(ax, key)
417 mask = com.isnull(result)
418 if mask.any():
--> 419 raise IndexingError('cannot index with vector containing '
420 'NA / NaN values')
421
IndexingError: cannot index with vector containing NA / NaN values
```
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http://en.wikipedia.org/wiki/Exponential_smoothing#The_weighted_moving_average
cf http://stackoverflow.com/questions/9621362/how-do-i-compute-a-weighted-moving-average-using-pandas
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} | 0 | "2012-03-08T20:52:15Z" | "2012-03-11T17:01:00Z" | "2012-03-11T17:00:57Z" | NONE | null | [also, should probably be combine_add]
from pandas import DataFrame
df = DataFrame()
sdf = df.to_sparse()
sdf.combineAdd(DataFrame())
pandas\core\frame.pyc in combineAdd(self, other)
3861 DataFrame
3862 """
-> 3863 return self.add(other, fill_value=0.)
3864
3865 def combineMult(self, other):
pandas\core\frame.pyc in f(self, other, axis, level, fill_value)
173 def f(self, other, axis=default_axis, level=None, fill_value=None):
174 if isinstance(other, DataFrame): # Another DataFrame
--> 175 return self._combine_frame(other, func, fill_value, level)
176 elif isinstance(other, Series):
177 return self._combine_series(other, func, fill_value, axis, level)
pandas\sparse\frame.pyc in _combine_frame(self, other, func, fill_value, level)
414
415 if fill_value is not None or level is not None:
--> 416 raise NotImplementedError
417
418 if not self and not other:
NotImplementedError:
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https://api.github.com/repos/pandas-dev/pandas/issues/888 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/888/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/888/comments | https://api.github.com/repos/pandas-dev/pandas/issues/888/events | https://github.com/pandas-dev/pandas/pull/888 | 3,579,202 | MDExOlB1bGxSZXF1ZXN0OTU1MzA2 | 888 | Bugfix: Couldn't subclass Panel | {
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} | [] | closed | false | null | [] | null | 4 | "2012-03-09T06:46:25Z" | "2014-06-12T13:17:37Z" | "2012-03-14T15:21:09Z" | CONTRIBUTOR | null | I was trying to create a subclass from Panel and came into this snag. I'm not sure if there was a reason Panel was using a hard coded _constructor method, but I changed it to be more generic. I'm new to open source, so constructive criticism is welcome. Am I doing this right?
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} | [] | closed | false | null | [] | null | 1 | "2012-03-09T10:10:53Z" | "2012-03-09T10:38:40Z" | "2012-03-09T10:38:40Z" | NONE | null | assignment based on a condition, something like
df[df['column']==1] = 5
raises an exception, so i have to use
df.ix[df['column']==1,'column'] = 5
or iterate through the whole DataFrame.
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} | 0 | "2012-03-09T14:07:51Z" | "2012-03-11T16:26:59Z" | "2012-03-11T16:26:59Z" | CONTRIBUTOR | null | Reported on [mailing list](https://groups.google.com/forum/?fromgroups#!topic/pystatsmodels/lK1odlK5zjU), columns argument in `DataFrame.to_html()` broken.
``` python
In [31]: df
Out[31]:
a b c d
0 4 -0.548572 -0.333272 NaN
1 1 -0.502314 0.975798 NaN
2 9 0.283227 1.486831 NaN
3 3 1.514658 -0.747403 NaN
4 7 -1.415763 -0.697034 NaN
In [32]: print df.to_html(columns=['a'])
<table border="1">
<thead>
<tr>
<th></th>
<th>a</th>
<th>b</th>
<th>c</th>
<th>d</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>0</strong></td>
<td> 4</td>
<td>-0.548572</td>
<td>-0.333272</td>
<td>NaN</td>
</tr>
<tr>
<td><strong>1</strong></td>
<td> 1</td>
<td>-0.502314</td>
<td> 0.975798</td>
<td>NaN</td>
</tr>
<tr>
<td><strong>2</strong></td>
<td> 9</td>
<td> 0.283227</td>
<td> 1.486831</td>
<td>NaN</td>
</tr>
<tr>
<td><strong>3</strong></td>
<td> 3</td>
<td> 1.514658</td>
<td>-0.747403</td>
<td>NaN</td>
</tr>
<tr>
<td><strong>4</strong></td>
<td> 7</td>
<td>-1.415763</td>
<td>-0.697034</td>
<td>NaN</td>
</tr>
</tbody>
</table>
```
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} | 2 | "2012-03-09T14:15:05Z" | "2012-03-15T21:10:38Z" | "2012-03-15T21:09:53Z" | CONTRIBUTOR | null | see also [mailing list](https://groups.google.com/forum/?fromgroups#!topic/pystatsmodels/e5J1yvCGmmI)
``` python
In [34]: df = pandas.DataFrame({'A': [u'\u03c3']})
In [35]: df
Out[35]:
A
0 σ
In [36]: print df.to_html()
---------------------------------------------------------------------------
UnicodeEncodeError Traceback (most recent call last)
...
UnicodeEncodeError: 'ascii' codec can't encode character u'\u03c3' in position 1: ordinal not in range(128)
In [37]:
```
further more from the thread:
```
... I wanted to spot as well that
force_unicode is in the doc but is not available.
TypeError: to_html() got an unexpected keyword argument 'force_unicode'
```
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} | 4 | "2012-03-09T16:32:02Z" | "2012-03-13T01:29:02Z" | "2012-03-13T01:29:02Z" | NONE | null | Somehow I can't set single NaN entries to be None, but I can set a whole column of NaN entries to a column of None entries. ( x[1].ix[3]=None doesnt work either)
> > > from pandas import *
> > > x = DataFrame(columns=[0,1], index=[3,4])
> > > x
> > > 0 1
> > > 3 NaN NaN
> > > 4 NaN NaN
> > >
> > > x[0]= [None,None] # this works fine
> > > x
> > > 0 1
> > > 3 None NaN
> > > 4 None NaN
> > >
> > > x[1][3]=None # this has no effect and is my issue
> > > x
> > > 0 1
> > > 3 None NaN
> > > 4 None NaN
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} | 4 | "2012-03-09T18:19:30Z" | "2012-03-15T17:19:06Z" | "2012-03-15T16:49:22Z" | NONE | null | I still run frequently into problems with "NaN" and "None".
For example:
I retrieve Data from an SQL DB (postgres in my case) and then I have a list of tuples with "None" (in SQL NULL) values.
I then convert to a DF by using DataFrame.from_records()
Now, to avoid object Series it would be nice to have None automatically mapped to "NaN" in the same way as "from_csv" does.
Right now I would have to do something like DF.fillna(np.nan) and then convert the numeric columns again to their original type which is kind of ugly.
```
time0 = datetime.now()
result_dataframe = DataFrame.from_records(results, columns=columns)
time1 = datetime.now()
for col in result_dataframe:
if result_dataframe.dtypes[col] == object:
result_dataframe[col].fillna(np.nan, inplace=True)
result_dataframe = result_dataframe.from_records(result_dataframe)
print datetime.now() - time1, time1-time0
```
> > 0:00:07.477906 0:00:01.971754
The fillna takes much longer than the construction of the DF itself. Example with 750k rows.
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} | 2 | "2012-03-10T00:22:44Z" | "2012-04-13T01:37:40Z" | "2012-04-13T01:37:40Z" | CONTRIBUTOR | null | would it be possible to shorten:
```
index = DateRange('1/1/2000 00:00', periods=8, offset=datetools.Hour())
```
to
```
index = DateRange('1/1/2000 00:00', periods=8, offset=h)
```
(maybe I am still spoiled by scikits.timeseries)
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} | 1 | "2012-03-10T00:53:13Z" | "2012-03-11T16:41:21Z" | "2012-03-11T16:41:21Z" | CONTRIBUTOR | null | ```
rng = DateRange(start, end, offset=BDay())
```
triggers:
NameError: name 'BDay' is not defined
should be
```
rng = DateRange(start, end, offset=datetools.BDay())
```
http://pandas.pydata.org/pandas-docs/stable/timeseries.html#generating-date-ranges-daterange
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} | 7 | "2012-03-10T01:08:46Z" | "2012-04-21T17:45:17Z" | "2012-04-21T17:45:17Z" | CONTRIBUTOR | null | Target: create a 12 month date range:
base data
```
rng = DateRange('1/1/2000 00:00', periods=8760, offset=datetools.Hour())
ts = Series(randn(len(rng)), index=rng)
```
the range
```
ts.index[0]
Out[95]: datetime.datetime(2000, 1, 1, 0, 0)
ts.index[-1]
Out[96]: datetime.datetime(2000, 12, 30, 23, 0)
mrg = DateRange(start=ts.index[0], end=ts.index[-1], offset=datetools.MonthEnd())
mrg
Out[98]:
<class 'pandas.core.daterange.DateRange'>
offset: <1 MonthEnd>, tzinfo: None
[2000-01-31 00:00:00, ..., 2000-11-30 00:00:00]
length: 11
```
Why do I not get 12 months? Jan-Dec?
And how could I get a range out of 12 months from the ts?
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730 self.fm_date = xlwt.easyxf(num_format_str='YYYY-MM-DD')
731 else:
--> 732 from openpyxl import Workbook
733 self.book = Workbook(optimized_write = True)
734 self.path = path
ImportError: cannot import name Workbook
```
I think should be workbook in low case letter.
But maybe they changed in the new versions?
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https://api.github.com/repos/pandas-dev/pandas/issues/898 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/898/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/898/comments | https://api.github.com/repos/pandas-dev/pandas/issues/898/events | https://github.com/pandas-dev/pandas/issues/898 | 3,594,317 | MDU6SXNzdWUzNTk0MzE3 | 898 | Averaging along row looses timestamp information | {
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} | 5 | "2012-03-10T10:17:33Z" | "2012-04-10T22:40:31Z" | "2012-04-05T03:58:22Z" | CONTRIBUTOR | null | data
```
import numpy as np
import pandas
data1 = np.arange(24)
data2 = 24 - np.arange(24)
dates = pandas.DateRange(start=datetime.datetime(2005,1,1),
periods=24, offset=pandas.datetools.MonthEnd())
DF = pandas.DataFrame({'data1':data1, 'data2':data2}, index=dates)
ann_range = pandas.DateRange(start=datetime.datetime(2005,1,1,0),
end=datetime.datetime(2007,1,1),offset=12*pandas.datetools.MonthEnd())
DF.groupby(clim_range.asof).mean()
```
Now averaging data1 & data2 for each row
```
df.groupby(lambda x: x.month).mean()
```
After this, the timestamp is lost:
An enumerated key (1-12) is returned.
One would need to have now a series with frequency monthly.
Reference:
https://groups.google.com/group/pystatsmodels/browse_thread/thread/9b1b9a3152c3709a/0b01d056cf9491ff?hl=en%EF%BF%BDb01d056cf9491ff
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https://api.github.com/repos/pandas-dev/pandas/issues/899 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/899/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/899/comments | https://api.github.com/repos/pandas-dev/pandas/issues/899/events | https://github.com/pandas-dev/pandas/issues/899 | 3,601,513 | MDU6SXNzdWUzNjAxNTEz | 899 | Skip tests requiring pytz | {
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https://api.github.com/repos/pandas-dev/pandas/issues/900 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/900/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/900/comments | https://api.github.com/repos/pandas-dev/pandas/issues/900/events | https://github.com/pandas-dev/pandas/issues/900 | 3,602,535 | MDU6SXNzdWUzNjAyNTM1 | 900 | timeseries branch, fix labels in plotting | {
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} | 1 | "2012-03-11T18:27:25Z" | "2012-05-01T17:05:56Z" | "2012-05-01T17:05:56Z" | CONTRIBUTOR | null | They come out as ints
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