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} | 10 | "2013-02-05T22:48:09Z" | "2013-09-22T15:50:25Z" | "2013-09-22T15:50:17Z" | NONE | null | Using pandas 0.10.
The docs say pandas has vectorized string functions, but they're running far slower for me than plain python (about 6x slower). Here's two nearly identical versions of the code with wildly different performance numbers. Why is the vectorized version so much slower?
I thought the difference might be because the vectorized version has to create an extra temporary Series object that gets thrown away. But then I tried to control for that by creating an extra Series object in my fast version, and that didn't change anything. So why is the vectorized version slow?
``` python
import pandas
strings = [ "hello world" for ii in xrange(10000) ]
p = pandas.DataFrame({ "strings" : strings })
#Use vectorized pandas.
#Takes ~2 min on my computer
for ii in xrange(1000) :
p['isHello'] = p['strings'].str.endswith('world')
#Use non-vectorized list comprehension.
#Takes ~20 seconds on my computer.
for ii in xrange(1000) :
p['isHello'] = [s.endswith('world') for s in p['strings'].values]
#Non-vectorized version, but also create a temp Series object
#Still takes only ~20 seconds
for ii in xrange(1000) :
p['isHello'] = pandas.Series([s.endswith('world') for s in p['strings'].values])
```
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} | 1 | "2013-02-05T23:21:20Z" | "2013-12-04T00:40:16Z" | "2013-03-18T05:33:29Z" | CONTRIBUTOR | null | ```
ts = Series(randn(10))
ts[2:-2] = nan
sts = ts.to_sparse()
print sts
print sts.density
```
results in:
return float(len(self.sp_index)) / len(self.index)
TypeError: object of type 'pandas._sparse.BlockIndex' has no len()
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} | 1 | "2013-02-05T23:54:21Z" | "2014-06-12T14:36:47Z" | "2013-02-10T21:09:24Z" | CONTRIBUTOR | null | using pandas from master:
``` python
import numpy as np
import pandas as pd
pd.DatetimeIndex(np.array([], dtype='datetime64'))
```
I get the following stack trace:
``` python
/data/python/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
229 else:
230 if data.dtype != _NS_DTYPE:
--> 231 subarr = tslib.cast_to_nanoseconds(data)
232 else:
233 subarr = data
/data/python/pandas/pandas/tslib.so in pandas.tslib.cast_to_nanoseconds (pandas/tslib.c:14151)()
IndexError: invalid index
```
This happens because pandas.tslib.cast_to_nanoseconds expects the array to be not empty to get the datetime64 unit of the first element.
One way to fix it would be to check whether `data` is empty before trying the conversion.
It would be nice to have a better error inside `pd.tslib.cast_to_nanoseconds` saying that it expects a non-empty array as well.
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} | 1 | "2013-02-06T16:21:36Z" | "2013-03-14T03:40:33Z" | "2013-03-14T03:40:33Z" | CONTRIBUTOR | null | It seems that the argument `na` doesn't remove the NaN with `str.contains` (see [previous issue](https://github.com/pydata/pandas/issues/1689)).
Using similar to example in [test added for na functionality](https://github.com/pydata/pandas/commit/7a1ea0a3d189b1be7cdd23a7b98f79effd381430):
```
In [1]: values = Series(['om', NA, 'foo_nom', 'nom', 'bar_foo', NA, 'foo'])
In [2]: values.str.contains('foo', na=False)
Out[2]:
0 False
1 NaN
2 True
3 False
4 True
5 NaN
6 True
In [3]: values.str.startswith('foo', na=False)
Out[3]:
0 False
1 False
2 True
3 False
4 False
5 False
6 True
```
_From [Stackoverflow question](http://stackoverflow.com/questions/14731714/value-error-when-slicing-in-pandas/14731958)._
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} | 27 | "2013-02-07T00:28:07Z" | "2014-01-16T15:24:08Z" | "2013-03-12T17:54:32Z" | MEMBER | null | I have DataFrames with about 14 million rows and 16 columns and I have to wait at least 3-4 seconds for it to `repr` in an IPython session. Is there anything that can be done about this?
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} | 7 | "2013-02-07T14:29:32Z" | "2013-09-22T21:18:01Z" | "2013-09-21T23:58:52Z" | CONTRIBUTOR | null | Sometimes I need to "apply" complicated functions that return differently shaped dataframes.
In these situations I often need to handle explicitly the case in which the result of the function is empty and return a properly shaped empty dataframe.
Failing to return a proper df gives normally an exception, but I found a case where the exception is thrown depending on the order of the groups:
``` python
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.10.1'
```
``` python
def f1(x):
y = x[(x.b % 2) == 1]**2
if y.empty:
multiindex = pd.MultiIndex(
levels = [[]]*2,
labels = [[]]*2,
names = ['b', 'c']
)
res = pd.DataFrame(None,
columns=['a'],
index=multiindex)
return res
else:
y = y.set_index(['b','c'])
return y
def f2(x):
y = x[(x.b % 2) == 1]**2
if y.empty:
return pd.DataFrame()
else:
y = y.set_index(['b','c'])
return y
def f3(x):
y = x[(x.b % 2) == 1]**2
if y.empty:
multiindex = pd.MultiIndex(
levels = [[]]*2,
labels = [[]]*2,
names = ['foo', 'bar']
)
res = pd.DataFrame(None,
columns=['a','b'],
index=multiindex)
return res
else:
return y
df = pd.DataFrame({'a':[1,2,2,2],
'b':range(4),
'c':range(5,9)})
df2 = pd.DataFrame({'a':[3,2,2,2],
'b':range(4),
'c':range(5,9)})
```
f1 is the correct function and it works
f2 is wrong because it returns an empty dataframe with a simple index and it fails (as it should)
f3 is wrong but the exception is thrown only with df2
``` python
In [4]: df.groupby('a').apply(f1)
Out[4]:
a
a b c
2 1 36 4
9 64 4
In [5]: df2.groupby('a').apply(f1)
Out[5]:
a
a b c
2 1 36 4
9 64 4
```
``` python
In [6]: df.groupby('a').apply(f3)
Out[6]:
a b c
a
2 1 4 1 36
3 4 9 64
In [7]: df2.groupby('a').apply(f3)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/ldeleo/<ipython console> in <module>()
/home/ldeleo/.local/lib/python2.7/site-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/core/groupby.pyc in apply(self, func, *args, **kwargs)
320 func = _intercept_function(func)
321 f = lambda g: func(g, *args, **kwargs)
--> 322 return self._python_apply_general(f)
323
324 def _python_apply_general(self, f):
/home/ldeleo/.local/lib/python2.7/site-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/core/groupby.pyc in _python_apply_general(self, f)
326
327 return self._wrap_applied_output(keys, values,
--> 328 not_indexed_same=mutated)
329
330 def aggregate(self, func, *args, **kwargs):
/home/ldeleo/.local/lib/python2.7/site-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/core/groupby.pyc in _wrap_applied_output(self, keys, values, not_indexed_same)
1742 if isinstance(values[0], DataFrame):
1743 return self._concat_objects(keys, values,
-> 1744 not_indexed_same=not_indexed_same)
1745 elif hasattr(self.grouper, 'groupings'):
1746 if len(self.grouper.groupings) > 1:
/home/ldeleo/.local/lib/python2.7/site-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/core/groupby.pyc in _concat_objects(self, keys, values, not_indexed_same)
483 group_names = self.grouper.names
484 result = concat(values, axis=self.axis, keys=group_keys,
--> 485 levels=group_levels, names=group_names)
486 else:
487 result = concat(values, axis=self.axis)
/home/ldeleo/.local/lib/python2.7/site-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/tools/merge.pyc in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity)
892 ignore_index=ignore_index, join=join,
893 keys=keys, levels=levels, names=names,
--> 894 verify_integrity=verify_integrity)
895 return op.get_result()
896
/home/ldeleo/.local/lib/python2.7/site-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/tools/merge.pyc in __init__(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity)
962 self.verify_integrity = verify_integrity
963
--> 964 self.new_axes = self._get_new_axes()
965
966 def get_result(self):
/home/ldeleo/.local/lib/python2.7/site-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/tools/merge.pyc in _get_new_axes(self)
1134 concat_axis = None
1135 else:
-> 1136 concat_axis = self._get_concat_axis()
1137
1138 new_axes[self.axis] = concat_axis
/home/ldeleo/.local/lib/python2.7/site-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/tools/merge.pyc in _get_concat_axis(self)
1169 else:
1170 concat_axis = _make_concat_multiindex(indexes, self.keys,
-> 1171 self.levels, self.names)
1172
1173 self._maybe_check_integrity(concat_axis)
/home/ldeleo/.local/lib/python2.7/site-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/tools/merge.pyc in _make_concat_multiindex(indexes, keys, levels, names)
1243 names = names + _get_consensus_names(indexes)
1244
-> 1245 return MultiIndex(levels=levels, labels=label_list, names=names)
1246
1247 new_index = indexes[0]
/home/ldeleo/.local/lib/python2.7/site-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/core/index.pyc in __new__(cls, levels, labels, sortorder, names)
1343 if len(names) != subarr.nlevels:
1344 raise AssertionError(('Length of names must be same as level '
-> 1345 '(%d), got %d') % (subarr.nlevels))
1346
1347 subarr.names = list(names)
TypeError: not enough arguments for format string
```
There are actually two bugs. The first is simply that instead of (subarr.nlevels) on line 1345 there should be (subarr.nlevels, len(names))
The second is more serious and goes back to _get_consensus_names (index.py:2671)
I guess that the task of this function is, given a list of possibly different indexes, to give back the minimal "shape" that can contain all the indexes.
Only that what it does now is to give back the "shape" of the second type encountered along the list.
For this reason, when applied to df, it acts on
[Multiindex[], Int64Index([1,3])]
giving back [[None]]
When applied to df2 which differs only by the order of the groups
[Int64Index([1,3]), Multiindex[]]
it gives back [[None], [None]] and then fails.
I agree that the case I showed is quite abstruse, but the non predictability of the behavior is quite dangerous in my opinion.
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https://api.github.com/repos/pandas-dev/pandas/issues/2809 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2809/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2809/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2809/events | https://github.com/pandas-dev/pandas/issues/2809 | 10,747,976 | MDU6SXNzdWUxMDc0Nzk3Ng== | 2,809 | Series does not upconvert datetimes to datetime64 | {
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} | 4 | "2013-02-07T17:47:21Z" | "2013-02-10T22:34:41Z" | "2013-02-10T22:34:41Z" | CONTRIBUTOR | null | ``` python
import pandas as pd
ind = pd.date_range(start="2000-01-01", freq="D", periods=10)
datetimes = [ts.to_pydatetime() for ts in ind]
datetime_s = pd.Series(datetimes)
datetime_s.dtype
# object
df = pd.DataFrame({'datetime_s':datetime_s})
print df.dtypes
#datetime64
```
Shouldn't Series upconvert to datetime64 since the DataFrame columns do?
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https://api.github.com/repos/pandas-dev/pandas/issues/2810 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2810/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2810/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2810/events | https://github.com/pandas-dev/pandas/issues/2810 | 10,748,069 | MDU6SXNzdWUxMDc0ODA2OQ== | 2,810 | DataFrame constructor doesn't always upconvert datetime | {
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} | 7 | "2013-02-07T17:50:01Z" | "2013-02-10T22:29:03Z" | "2013-02-10T22:29:03Z" | CONTRIBUTOR | null | ```
import pandas as pd
ind = pd.date_range(start="2000-01-01", freq="D", periods=10)
datetimes = [ts.to_pydatetime() for ts in ind]
dates = [ts.date() for ts in ind]
df = pd.DataFrame({'datetimes': datetimes, 'dates':dates})
df.dtypes
# datetimes : object
# dates: object
```
Seems to be an issue with having another object dtype in the constructor. In this case dates.
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} | 1 | "2013-02-07T20:21:56Z" | "2013-03-22T07:11:16Z" | "2013-03-22T07:11:16Z" | MEMBER | null | Reported by user, always does mergesort
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https://api.github.com/repos/pandas-dev/pandas/issues/2812 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2812/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2812/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2812/events | https://github.com/pandas-dev/pandas/pull/2812 | 10,757,330 | MDExOlB1bGxSZXF1ZXN0NDA0NTQ3MA== | 2,812 | Fix typo in performance_doc | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2813 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2813/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2813/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2813/events | https://github.com/pandas-dev/pandas/issues/2813 | 10,759,367 | MDU6SXNzdWUxMDc1OTM2Nw== | 2,813 | Rolling methods should support lists | {
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} | 3 | "2013-02-07T22:26:13Z" | "2017-06-10T18:05:06Z" | "2017-06-10T18:05:06Z" | NONE | null | Looks like the `pandas` rolling methods only support numpy arrays, while numpy and bottleneck methods also support lists for convenience:
```
>>> l = [1,2,3,4]
>>> import numpy as np
>>> np.cumsum(l)
array([ 1, 3, 6, 10])
>>> import bottleneck as bn
>>> bn.move_sum(l, 1)
array([ 1., 2., 3., 4.])
>>> import pandas
>>> pandas.rolling_sum(l, 1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python2.7/site-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/stats/moments.py", line 507, in f
time_rule=time_rule, **kwargs)
File "/usr/lib/python2.7/site-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/stats/moments.py", line 281, in _rolling_moment
return_hook, values = _process_data_structure(arg)
File "/usr/lib/python2.7/site-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/stats/moments.py", line 325, in _process_data_structure
if not issubclass(values.dtype.type, float):
AttributeError: 'list' object has no attribute 'dtype'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2814 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2814/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2814/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2814/events | https://github.com/pandas-dev/pandas/issues/2814 | 10,759,616 | MDU6SXNzdWUxMDc1OTYxNg== | 2,814 | Regression? Using Series for string formatting | {
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} | 1 | "2013-02-07T22:30:18Z" | "2013-03-18T05:43:20Z" | "2013-03-18T05:43:20Z" | MEMBER | null | from mailing list:
Rows used to be able to work as maps for string interpolation, like this:
from pandas import *
speeds = read_csv('results.csv')
row = speeds.ix[3]
print("%(my_column_name)s" % row)
that worked a few months ago, but doesn't seem to work in the latest Pandas. How can I turn a row into a map, or otherwise do simple & concise custom printing of rows?
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} | 1 | "2013-02-08T00:11:57Z" | "2016-10-12T23:03:31Z" | "2013-02-09T20:41:29Z" | CONTRIBUTOR | null | ``` python
import pandas as pd
s = pd.Series()
s.apply(lambda x: x)
```
``` python
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-3-a33a9027c226> in <module>()
----> 1 s.apply(lambda x: x)
/usr/local/lib/python2.7/dist-packages/pandas/core/series.pyc in apply(self, func, convert_dtype, args, **kwds)
2295
2296 mapped = lib.map_infer(values, f, convert=convert_dtype)
-> 2297 if isinstance(mapped[0], Series):
2298 from pandas.core.frame import DataFrame
2299 return DataFrame(mapped.tolist(), index=self.index)
IndexError: index out of bounds
```
This happens since 0.10.0 because of the new feature which upcasts the returned object to a dataframe if the function passed as the `func` argument returns a Series.
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} | 4 | "2013-02-08T00:28:45Z" | "2013-03-29T10:39:09Z" | "2013-03-29T10:39:09Z" | CONTRIBUTOR | null | The fact that it took so long for anyone to realise the behaviour had changed just goes to show that it is a very minor problem. I was pretty sure it used to work at one point so I chased it down. I'd say it is quite useful from time to time when you are working interactively and feeling lazy at the same time.
In case the behaviour change was not intentional I'd be willing to look into it but some pointers would be appreciated to get started.
Here is a snippet:
``` python
import matplotlib.pyplot as plt
import pandas as pd
for i in range(3): pd.util.testing.makeTimeSeries().cumsum().plot()
plt.show()
```
Here is the output with pandas 0.8.0:
Here is the one with pandas 0.8.1:
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} | 10 | "2013-02-08T02:11:43Z" | "2013-03-21T16:02:36Z" | "2013-03-21T16:02:15Z" | NONE | null | The following example works:
df = DataFrame(np.arange(20).reshape(5,4))
df2 = df.set_index(keys=[0,1,2])
df2.ix[(4,5,6)]
for using a MultiIndex
So I created a file sample_data.csv that looks like this:
col1,col2,year,amount
111111,3.5,2012,700
111112,3.5,2011,600
222221,4.0,2012,222
...
I then ran the following:
import numpy as np
import pandas as pd
sd=pd.read_csv('sample_data.csv')
sd2=sd.set_index(keys=['col2','year'])
sd2.ix[(4.0,2012)]
But this produces the following error: IndexError: index out of bounds
Can anyone explain why the former example works, but the latter doesn't ?
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} | 1 | "2013-02-08T03:11:09Z" | "2016-09-29T22:44:19Z" | "2016-09-29T22:17:02Z" | MEMBER | null | http://stackoverflow.com/questions/14590638/pandas-dataframe-resample-at-every-nth-row
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https://api.github.com/repos/pandas-dev/pandas/issues/2819 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2819/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2819/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2819/events | https://github.com/pandas-dev/pandas/pull/2819 | 10,768,731 | MDExOlB1bGxSZXF1ZXN0NDA1MDk3NQ== | 2,819 | ENH: Optimize take_*; improve non-NA fill_value support | {
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} | 29 | "2013-02-08T05:39:04Z" | "2014-06-25T12:22:21Z" | "2013-02-10T20:56:29Z" | CONTRIBUTOR | null | Optimizes/improves `common.take_1d`, `common.take_2d`, `common.take_2d_multi`, and `common.take_fast` in the following ways:
- For common cases, no intermediate buffer is required anymore to convert between two different dtypes (i.e. when upcasting is necessary): the data is taken and upcasted in one step. These cases are optimized in Cython and more can be added if desired.
- Better support for `fill_value` parameters other than NaN: previously, all upcasting was done assuming NaN as the `fill_value`: now, the correct upcast will be chosen based on the type of the `fill_value`. (Ex. integers stay as integers with integer `fill_value` if the value can fit in the existing size without overflow, integers and floats upcast to complex with complex `fill_value`, all numerics upcast to objects with boolean or string `fill_value`, etc.)
- ~~Added option of `fill_value` of `None`, which means that the corresponding entries in the output are left unchanged (only really makes sense when `out` is also provided)~~
~~The last option is used to optimize `internals._merge_blocks` (i.e. the function internally called to do block consolidation): instead of `vstack`-ing the arrays together in an intermediate buffer and then rearranging into the output with a separate operation, the output buffer is allocated uninitialized and directly filled from the inputs using one call to `common.take_fast` per input block, even when they are not contiguous. (However, the special case of contiguous and already properly ordered blocks is still handed with `vstack`, since no other operation is required after that)~~
**EDIT: took out `fill_value` of `None` option and reverted `internals._merge_blocks`: it wasn't actually helping. Upcasting within take functions is definitely faster though.**
The existing vb_suite tests were not really testing upcasting, so I added one for it. Here are the results vs. master:
```
Results:
t_head t_baseline ratio
name
frame_reindex_upcast 26.7874 34.3973 0.7788
```
and against jrebacks/dtypes:
```
Results:
t_head t_baseline ratio
name
frame_reindex_upcast 23.8220 28.0891 0.8481
```
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} | 2 | "2013-02-08T11:02:30Z" | "2018-07-06T21:22:23Z" | "2018-07-06T21:22:16Z" | NONE | null | Closing #2330 , but leaving this here so it'll get picked up one day if needed.
related #2318.
https://groups.google.com/forum/m/#!topic/pydata/ksIPthf2NJI
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https://api.github.com/repos/pandas-dev/pandas/issues/2823 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2823/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2823/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2823/events | https://github.com/pandas-dev/pandas/issues/2823 | 10,804,563 | MDU6SXNzdWUxMDgwNDU2Mw== | 2,823 | When reshaping a Series, the result is unprintable. | {
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} | 3 | "2013-02-09T02:21:42Z" | "2013-02-10T04:47:23Z" | "2013-02-09T20:26:05Z" | NONE | null | A Series is also a NumPy array (in that it inherits from it). As such it inherits the reshape method.
I can reshape, but then the result is unprintable (an exception is thrown). This can confuse people - after all, a Series IS an array.
There's some discussion [here](http://stackoverflow.com/questions/14390224/reshape-of-pandas-series) about this. One option is to disable reshape. I prefer to have it return a NumPy array and not a Series object. My rationale is that people do have the need to reshape data - I did as did the person in the Stack Overflow post. We read data in from a file using read_csv (convenient, allows filtering, etc). Then at some point we need to reshape a given row or something...
In any case, I think it should be handled in some form or other.
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} | 3 | "2013-02-09T17:35:58Z" | "2013-02-10T22:31:07Z" | "2013-02-10T22:31:07Z" | CONTRIBUTOR | null | (This is to be applied after a PR from https://github.com/jreback/pandas/tree/pytables_update7, as it is based on it)
This is my first PR and I am hoping to get right the github worfklow to start with 'real contibutions'. Any hints on how to PR better very welcome.
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} | 10 | "2013-02-09T18:41:00Z" | "2015-08-18T10:48:47Z" | "2015-08-18T10:48:47Z" | MEMBER | null | see e.g. #2763
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} | 2 | "2013-02-09T23:03:51Z" | "2013-02-10T20:40:21Z" | "2013-02-10T20:40:21Z" | CONTRIBUTOR | null | Those are really slowing down test perf
```
join_dataframe_index_single_key_bigger 9.5578 11.1325 0.8585
join_dataframe_integer_2key 4.7822 5.4330 0.8802
panel_from_dict_all_different_indexes 71.4074 81.0831 0.8807
timeseries_add_irregular 21.5136 24.1530 0.8907
panel_from_dict_equiv_indexes 25.8620 28.8970 0.8950
reindex_daterange_pad 0.1732 0.1935 0.8950
read_csv_thou_vb 33.3554 37.2321 0.8959
groupby_frame_singlekey_integer 2.1610 2.4077 0.8976
panel_from_dict_same_index 25.8203 28.3975 0.9092
lib_fast_zip 9.9503 10.7844 0.9227
groupby_multi_different_functions 12.3623 13.2350 0.9341
panel_from_dict_two_different_indexes 49.1490 52.6084 0.9342
frame_fillna_many_columns_pad 14.0718 15.0286 0.9363
stats_rolling_mean 1.2735 1.3558 0.9393
stats_rank_average 25.4836 27.0692 0.9414
series_align_int64_index 37.9021 40.1850 0.9432
frame_drop_duplicates 22.3100 23.5742 0.9464
dataframe_reindex_daterange 0.4227 0.4461 0.9474
reindex_daterange_backfill 0.1947 0.2049 0.9501
index_int64_union 74.8751 78.6099 0.9525
frame_boolean_row_select 0.2366 0.2470 0.9580
dti_reset_index_tz 15.4665 16.1267 0.9591
merge_2intkey_sort 40.6254 42.2835 0.9608
join_dataframe_index_multi 21.5843 22.4350 0.9621
read_csv_comment2 26.2610 27.2638 0.9632
write_store 13.9323 14.4345 0.9652
timeseries_period_downsample_mean 10.2110 10.5601 0.9669
read_csv_vb 27.0146 27.8807 0.9689
write_store_table_mixed 991.7591 1022.1848 0.9702
stat_ops_level_series_sum 2.0037 2.0612 0.9721
groupby_frame_apply_overhead 10.8793 11.1625 0.9746
frame_fillna_inplace 11.9844 12.2915 0.9750
frame_iteritems_cached 0.0742 0.0761 0.9752
read_store_mixed 13.2591 13.5911 0.9756
frame_sort_index_by_columns 42.1859 43.2073 0.9764
stats_rank_average_int 19.7597 20.1886 0.9788
groupby_frame_median 5.5557 5.6732 0.9793
frame_ctor_list_of_dict 72.5023 73.9728 0.9801
index_datetime_union 12.6782 12.9208 0.9812
stat_ops_series_std 0.6514 0.6632 0.9822
groupby_frame_cython_many_columns 2.7002 2.7479 0.9826
period_setitem 843.1060 854.2030 0.9870
query_store_table_wide 15.5194 15.7222 0.9871
stats_rank2d_axis1_average 15.3458 15.5261 0.9884
sort_level_one 3.7356 3.7692 0.9911
stats_rank2d_axis0_average 26.0081 26.2205 0.9919
timeseries_asof 9.7938 9.8685 0.9924
read_store_table_panel 1493.6681 1504.2000 0.9930
stat_ops_level_frame_sum 2.6737 2.6915 0.9934
series_drop_duplicates_string 0.5648 0.5678 0.9948
series_align_left_monotonic 17.5371 17.6139 0.9956
reindex_fillna_pad 0.0782 0.0785 0.9963
frame_reindex_axis0 1.1610 1.1606 1.0003
timeseries_slice_minutely 0.0515 0.0515 1.0010
ctor_index_array_string 0.0148 0.0148 1.0010
timeseries_1min_5min_mean 0.6241 0.6231 1.0017
frame_reindex_axis1 2.7216 2.7157 1.0022
join_dataframe_index_single_key_bigger 17.0331 16.9749 1.0034
frame_fancy_lookup 2.1919 2.1755 1.0075
read_csv_standard 12.7835 12.6814 1.0080
append_frame_single_mixed 1.6413 1.6281 1.0081
frame_drop_dup_inplace 3.0013 2.9758 1.0085
groupby_indices 7.3204 7.2458 1.0103
timeseries_1min_5min_ohlc 0.6520 0.6450 1.0108
concat_series_axis1 62.3090 61.6359 1.0109
timeseries_asof_single 0.0462 0.0456 1.0138
frame_reindex_both_axes 0.2624 0.2584 1.0158
unstack_sparse_keyspace 1.2006 1.1787 1.0186
stat_ops_level_frame_sum_multiple 7.1461 7.0128 1.0190
timeseries_large_lookup_value 0.0254 0.0248 1.0210
series_ctor_from_dict 2.8274 2.7666 1.0220
reindex_frame_level_reindex 0.8442 0.8254 1.0228
reindex_fillna_backfill 0.0973 0.0951 1.0228
sort_level_zero 3.8154 3.7198 1.0257
read_store_table_wide 178.3879 173.5430 1.0279
merge_2intkey_nosort 20.8110 20.2428 1.0281
read_store_table_mixed 32.7878 31.8579 1.0292
dataframe_reindex_columns 0.2559 0.2486 1.0292
append_frame_single_homogenous 0.2881 0.2799 1.0296
datetimeindex_normalize 3.1789 3.0859 1.0301
frame_to_string_floats 52.1017 50.5349 1.0310
index_int64_intersection 26.6096 25.8025 1.0313
series_drop_duplicates_int 0.7330 0.7100 1.0324
series_value_counts_int64 2.1885 2.1093 1.0375
frame_constructor_ndarray 0.0417 0.0402 1.0385
stat_ops_level_series_sum_multiple 6.6431 6.3889 1.0398
dti_reset_index 0.1978 0.1894 1.0443
frame_to_csv 326.8771 312.8140 1.0450
groupby_apply_dict_return 44.8592 42.9204 1.0452
concat_small_frames 13.0044 12.4340 1.0459
write_csv_standard 310.0820 296.3982 1.0462
write_store_table_panel 4754.1928 4540.0400 1.0472
groupby_multi_series_op 15.6579 14.9366 1.0483
melt_dataframe 1.6189 1.5438 1.0486
read_parse_dates_iso8601 1.3405 1.2780 1.0489
write_store_table_wide 908.7529 866.2510 1.0491
groupby_multi_cython 17.1857 16.3653 1.0501
frame_get_numeric_data 0.0600 0.0570 1.0523
datetimeindex_add_offset 0.2049 0.1939 1.0566
write_store_table 441.6649 417.2010 1.0586
read_table_multiple_date 2447.0708 2302.5630 1.0628
indexing_panel_subset 0.5616 0.5265 1.0666
match_strings 0.3960 0.3706 1.0687
groupby_series_simple_cython 5.7912 5.4079 1.0709
frame_ctor_nested_dict_int64 124.0461 115.7539 1.0716
frame_drop_duplicates_na 21.8943 20.4239 1.0720
frame_reindex_both_axes_ix 0.3518 0.3272 1.0755
index_datetime_intersection 12.7059 11.7921 1.0775
groupby_multi_python 62.9150 58.3410 1.0784
replace_fillna 1.6407 1.5212 1.0786
frame_ctor_nested_dict 82.6290 76.4521 1.0808
indexing_dataframe_boolean_rows 0.2257 0.2088 1.0809
read_table_multiple_date_baseline 1158.8562 1071.3720 1.0817
join_dataframe_integer_key 1.8876 1.7371 1.0866
reshape_pivot_time_series 187.2802 172.2631 1.0872
series_align_irregular_string 69.1018 63.4313 1.0894
write_store_mixed 34.8185 31.9361 1.0903
series_value_counts_strings 5.7692 5.2803 1.0926
timeseries_timestamp_tzinfo_cons 0.0206 0.0188 1.0943
reshape_unstack_simple 2.9168 2.6569 1.0978
read_store_table 10.5529 9.6076 1.0984
replace_replacena 1.6949 1.5405 1.1003
series_constructor_ndarray 0.0106 0.0097 1.1009
frame_drop_dup_na_inplace 3.0536 2.7255 1.1204
groupby_multi_different_numpy_functions 14.1280 12.5601 1.1248
reshape_stack_simple 2.3761 2.1098 1.1263
indexing_dataframe_boolean_rows_object 0.4973 0.4413 1.1270
lib_fast_zip_fillna 16.5020 14.6356 1.1275
groupby_first 3.4041 3.0132 1.1297
timeseries_asof_nan 11.6815 10.1902 1.1463
datetimeindex_unique 0.1927 0.1676 1.1494
timeseries_sort_index 24.0719 20.9241 1.1504
groupby_sum_booleans 0.8692 0.7514 1.1567
series_string_vector_slice 330.9171 284.9329 1.1614
groupby_pivot_table 21.4290 18.3129 1.1702
frame_insert_500_columns 98.2690 83.2559 1.1803
timeseries_to_datetime_iso8601 5.6211 4.7594 1.1811
frame_fancy_lookup_all 26.3765 22.2771 1.1840
groupby_simple_compress_timing 40.3659 33.9840 1.1878
query_store_table 6.2538 5.2541 1.1903
frame_iteritems 2.5363 2.1235 1.1944
sparse_series_to_frame 160.5530 131.9690 1.2166
groupbym_frame_apply 67.0725 54.3806 1.2334
reindex_frame_level_align 0.8305 0.6723 1.2353
read_store 7.3415 5.9335 1.2373
join_dataframe_index_single_key_small 11.3160 9.0686 1.2478
reindex_multiindex 1.4660 1.1627 1.2609
groupby_multi_size 35.6657 27.5457 1.2948
sparse_frame_constructor 6.8283 5.1579 1.3239
timeseries_timestamp_downsample_mean 11.0256 8.1208 1.3577
timeseries_infer_freq 10.2630 7.5523 1.3589
groupby_last 4.0580 2.8608 1.4185
```
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} | 1 | "2013-02-10T20:31:41Z" | "2013-02-10T21:01:58Z" | "2013-02-10T21:01:51Z" | CONTRIBUTOR | null | It seems that there is no documentation about the classes parameter in `to_html`.
Current output of documentation in IPython:
```
Docstring:
to_html-specific options
bold_rows : boolean, default True
Make the row labels bold in the output
Render a DataFrame to an html table.
Parameters
----------
frame : DataFrame
object to render
buf : StringIO-like, optional
buffer to write to
columns : sequence, optional
the subset of columns to write; default None writes all columns
col_space : int, optional
the minimum width of each column
header : bool, optional
whether to print column labels, default True
index : bool, optional
whether to print index (row) labels, default True
na_rep : string, optional
string representation of NAN to use, default 'NaN'
formatters : list or dict of one-parameter functions, optional
formatter functions to apply to columns' elements by position or name,
default None, if the result is a string , it must be a unicode
string. List must be of length equal to the number of columns.
float_format : one-parameter function, optional
formatter function to apply to columns' elements if they are floats
default None
sparsify : bool, optional
Set to False for a DataFrame with a hierarchical index to print every
multiindex key at each row, default True
justify : {'left', 'right'}, default None
Left or right-justify the column labels. If None uses the option from
the print configuration (controlled by set_printoptions), 'right' out
of the box.
index_names : bool, optional
Prints the names of the indexes, default True
force_unicode : bool, default False
Always return a unicode result
```
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} | 0 | "2013-02-10T20:37:57Z" | "2013-02-10T20:40:03Z" | "2013-02-10T20:40:03Z" | CONTRIBUTOR | null | - changed rows to test in hdfstore vbench to 25k from 100k
- added data_columns test
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https://api.github.com/repos/pandas-dev/pandas/issues/2835 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2835/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2835/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2835/events | https://github.com/pandas-dev/pandas/pull/2835 | 10,830,859 | MDExOlB1bGxSZXF1ZXN0NDA3NjIyNQ== | 2,835 | DOC: add mention of to_pydatetime method on DatetimeIndex | {
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} | 1 | "2013-02-10T22:45:56Z" | "2014-07-02T07:20:01Z" | "2013-02-11T00:12:27Z" | CONTRIBUTOR | null | Added mention of to_pydatetime instance method under "Time series-related instance methods" section.
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} | 9 | "2013-02-10T23:40:24Z" | "2014-06-26T18:57:31Z" | "2013-03-28T22:17:51Z" | CONTRIBUTOR | null | It should fix #2816 (and #2827 which is a duplicate).
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https://api.github.com/repos/pandas-dev/pandas/issues/2837 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2837/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2837/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2837/events | https://github.com/pandas-dev/pandas/pull/2837 | 10,832,903 | MDExOlB1bGxSZXF1ZXN0NDA3NjYwOA== | 2,837 | TST: fix failing #2752 tests for 32-bit builds | {
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} | 0 | "2013-02-11T00:02:01Z" | "2014-06-12T19:53:14Z" | "2013-02-11T00:10:13Z" | CONTRIBUTOR | null | fix failing 32-bit travis builds after #2708, #2752
still unclear whether the following needs to be fixed on 32-bit:
``` python
In [29]: p.DataFrame([1,2]).dtypes
Out[29]:
0 int32
Dtype: object
In [30]: p.DataFrame({'a': [1,2]}).dtypes
Out[30]:
a int64
Dtype: object
```
so leaving the relevant test hardcoded to int64 for now
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} | 48 | "2013-02-11T02:04:12Z" | "2014-06-27T07:30:14Z" | "2013-02-14T23:23:45Z" | CONTRIBUTOR | null | use int64/float64 defaults in construction when dtype is not specified
this removes a platform dependency issue that caused dataframe and series to
have different dtypes on 32- bit
fixes issues raised in PR #2837
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} | 11 | "2013-02-11T04:03:15Z" | "2019-07-03T08:42:49Z" | "2013-07-29T05:07:06Z" | NONE | null | I am trying to repeat the code in:
http://wesmckinney.com/blog/?p=635
But I can't read in the data. I have more than 4GB memory available (with 8GB memory in total), but still has the following problem:
In [2]: data = pd.read_csv("P00000001-ALL.csv", index_col=False)
Python(9081,0xacb01a28) malloc: **\* mmap(size=24051712) failed (error code=12)
**\* error: can't allocate region
**\* set a breakpoint in malloc_error_break to debug
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} | 4 | "2013-02-11T14:11:55Z" | "2013-03-21T20:16:18Z" | "2013-03-21T20:16:18Z" | NONE | null | Pandas 0.10.1.
It's throwing a FutureWarning "set_index with inplace=True will return None from pandas 0.11 onward" when I call set_index, **_even though I already changed my code to be compliant with that change so the warning is not relevant**_. It prints that warning anytime you call the function with inplace=True even if you are not using the return value. As it stands, the warning is largely useless -- if I call set_index in 20 places and I fixed 19 of them to not use the return code but missed the last one; then the warning is thrown on the first call (which I fixed) but not on the 20th call (which I didn't fix), so it doesn't help anyone find places to fix in their code.
This warning is "harmless" in the sense it doesn't crash my code; but it is harmful in the sense that it clutters my error/warning log and therefore causing false positives in our infrastructure to notify us on job warnings/errors.
Ideally, pandas should only print the warning if its relevant to you (IE, you haven't yet fixed your code) but I realize that's hard. If that's not possible, then can we have some sort of global option to disable that warning?
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} | 5 | "2013-02-11T16:39:54Z" | "2018-07-06T21:55:34Z" | "2018-07-06T21:55:24Z" | NONE | null | From https://github.com/pydata/pandas/issues/1893 (scroll down a long way to the bottom) -- pandas is throwing a warning that we want to disable; but the same conceptual warning is being thrown from many different lines of code, with slightly different messages each time, so there's no way to filter them all out at once.
The python standard warning.filterwarning() method is fragile, cause you have to either
1) specify line number (which will break in the next release when line numbers change)
2) specify regex for the error message (which will break when the error message text changes, plus it's not safe for internationalization)
3) specify the module (which will filter out the warning you want to disable, but also filters out all the OTHER warnings which you want to keep).
So why not give pandas a more robust way of disabling warnings, maybe something like the C++ #pragma warning macro ( http://msdn.microsoft.com/en-us/library/2c8f766e%28v=vs.80%29.aspx ) Something like:
pandas.warnings.disable("InPlaceReturnsObjectIsBeingDeprecated")
pandas.warnings.disable("SomeOtherSpecificWarning")
Or alternatively, instead of a name, maybe that takes a number specifying the warning. Gives maximum flexibility, robustness, and safety when upgrading to the next version.
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} | 1 | "2013-02-11T17:59:49Z" | "2013-03-07T10:58:16Z" | "2013-03-07T10:58:16Z" | NONE | null | ``` python
df1 = pd.DataFrame({"i1" : [0, 1], "i2" : [0, 1]})
df2 = pd.DataFrame({"i1" : [0], "i3" : [0]})
df1.join(df2, on="i1", rsuffix="_")
```
throws:
```
pandas-0.10.0-py2.7-linux-x86_64.egg/pandas/tools/merge.pyc in _maybe_add_join_keys(self, result, left_indexer, right_indexer)
224 continue
225
--> 226 left_na_indexer = left_indexer.take(na_indexer)
227 key_col.put(na_indexer, com.take_1d(self.left_join_keys[i],
228 left_na_indexer))
AttributeError: 'NoneType' object has no attribute 'take'
```
while the following works as expected:
``` python
df1 = pd.DataFrame({"i1" : [0, 1], "i2" : [0.5, 1.5]}).set_index("i1")
df2 = pd.DataFrame({"i1" : [0], "i3" : [0.7]}).set_index("i1")
df1.join(df2, rsuffix="_")
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2844 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2844/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2844/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2844/events | https://github.com/pandas-dev/pandas/issues/2844 | 10,877,573 | MDU6SXNzdWUxMDg3NzU3Mw== | 2,844 | Problem with YearBegin frequency? | {
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} | 1 | "2013-02-11T18:41:43Z" | "2013-04-08T07:18:45Z" | "2013-04-08T07:18:45Z" | CONTRIBUTOR | null | Bug or user/expectation error? Been a while since I looked at this. Shouldn't this work and return days at the start of April?
```
pandas.date_range("1972-04-02", "2007-04-02", freq="AS-APR")
#<class 'pandas.tseries.index.DatetimeIndex'>
#[1973-01-01 00:00:00, ..., 2007-01-01 00:00:00]
#Length: 35, Freq: AS-APR, Timezone: None
pandas.datetools.to_offset("AS-APR")
#<1 YearBegin: kwds={'month': 4}, month=4>
```
This does.
```
pandas.date_range("1972-04-02", "2007-04-02", freq="BAS-APR")
#<class 'pandas.tseries.index.DatetimeIndex'>
#[1972-04-03 00:00:00, ..., 2007-04-02 00:00:00]
#Length: 36, Freq: BAS-APR, Timezone: None
pandas.date_range("1972-04-02", "2007-04-02", freq="A-APR")
#<class 'pandas.tseries.index.DatetimeIndex'>
#[1972-04-30 00:00:00, ..., 2006-04-30 00:00:00]
#Length: 35, Freq: A-APR, Timezone: None
pandas.version.version
# '0.10.1'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2845 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2845/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2845/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2845/events | https://github.com/pandas-dev/pandas/issues/2845 | 10,878,013 | MDU6SXNzdWUxMDg3ODAxMw== | 2,845 | BUG: Various issues with maybe_convert_objects | {
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} | [] | closed | false | null | [] | null | 11 | "2013-02-11T18:54:38Z" | "2013-02-15T00:13:14Z" | "2013-02-15T00:13:14Z" | CONTRIBUTOR | null | Found some issues with `lib.maybe_convert_objects` (which is an internal library function, but the same problems as below occur when calling `DataFrame({'a': [...]})`, for example)
(Also, mostly unrelated to the below, I noticed the inference loop continues for no reason after seeing an object, when it can safely break because the output is a forgone conclusion: added the breaks to make it slightly more efficient.)
##### Fails on python long integer outside `np.int64` range:
``` python
In [4]: arr = np.asarray([2**63], dtype=np.object_)
In [5]: p.lib.maybe_convert_objects(arr)
(exception)
```
Probably should leave the integer unchanged as a long integer object
##### Datetimes and complexes lost completely when mixed with booleans:
It's not even doing a cast: it's reading uninitialized memory as boolean
``` python
In [6]: arr = np.asarray([dt.datetime(2005, 1, 1), True])
In [7]: p.lib.maybe_convert_objects(arr, convert_datetime=1)
Out[7]: array([ True, True], dtype=bool)
In [8]: arr = np.asarray([1.0+2.0j, True], dtype=np.object_)
In [9]: p.lib.maybe_convert_objects(arr)
Out[9]: array([ True, True], dtype=bool)
```
##### `None` converts to `np.nan` with floats, but not complexes:
``` python
In [10]: arr = np.asarray([1.0, None], dtype=np.object_)
In [11]: p.lib.maybe_convert_objects(arr)
Out[11]: array([ 1., nan])
In [12]: arr = np.asarray([1.0+2.0j, None], dtype=np.object_)
In [13]: p.lib.maybe_convert_objects(arr)
Out[13]: array([(1+2j), None], dtype=object)
```
##### `safe` option preserves integer types with `np.nan` but not `None`:
The comment for `safe` says "don't cast int to float, etc." but that is not true with `None`
``` python
In [14]: arr = np.asarray([1, 2.0, np.nan], dtype=np.object_)
In [15]: p.lib.maybe_convert_objects(arr, safe=1)
Out[15]: array([1, 2.0, nan], dtype=object)
In [16]: arr = np.asarray([1, 2.0, None], dtype=np.object_)
In [17]: p.lib.maybe_convert_objects(arr, safe=1)
Out[17]: array([ 1., 2., nan])
```
Not entirely sure if this last one is intended behavior or not; let me know if there is any feedback.
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https://api.github.com/repos/pandas-dev/pandas/issues/2846 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2846/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2846/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2846/events | https://github.com/pandas-dev/pandas/pull/2846 | 10,884,952 | MDExOlB1bGxSZXF1ZXN0NDA5MTk3Mg== | 2,846 | BUG: Various issues with maybe_convert_objects (GH #2845) | {
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} | 0 | "2013-02-11T21:48:45Z" | "2014-06-24T15:33:33Z" | "2013-02-14T22:17:49Z" | CONTRIBUTOR | null | fixes #2845
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https://api.github.com/repos/pandas-dev/pandas/issues/2847 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2847/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2847/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2847/events | https://github.com/pandas-dev/pandas/issues/2847 | 10,892,871 | MDU6SXNzdWUxMDg5Mjg3MQ== | 2,847 | Errors with nosetests-3.3 pandas.io.tests.test_yahoo.TestYahoo pandas, version b0ee363408336 | {
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} | [] | closed | false | null | [] | null | 8 | "2013-02-12T02:20:34Z" | "2013-02-16T20:54:31Z" | "2013-02-16T20:54:31Z" | CONTRIBUTOR | null | I think get_quote_yahoo and get_components_yahoo in data.py need to decode the bytes returned by urllib.request.urlopen(urlStr).readlines() and urllib.urlopen(urlStr).read().strip().strip('"').split('"\r\n"') in python3.3
Also, the expected INTC values in test_get_data() in test_yahoo.py do not agree with the values Yahoo returns. The Adj Close value for 1-18-12 is 24.28 and the calculated Ret_Index values do not agree with the values in the d array.
The tests pass after making these changes. I have not tested the changes in python2.x
##
``` diff
diff --git a/pandas/io/data.py b/pandas/io/data.py
index 5e92fca..dfba151 100644
--- a/pandas/io/data.py
+++ b/pandas/io/data.py
@@ -115,7 +115,7 @@ def get_quote_yahoo(symbols):
return None
for line in lines:
- fields = line.strip().split(',')
+ fields = line.decode().strip().split(',')
for i, field in enumerate(fields):
if field[-2:] == '%"':
data[header[i]].append(float(field.strip('"%')))
@@ -241,7 +241,7 @@ def get_components_yahoo(idx_sym):
#break when no new components are found
while (True in mask):
urlStr = url.format(idx_mod, stats, comp_idx)
- lines = (urllib.urlopen(urlStr).read().strip().
+ lines = (urllib.urlopen(urlStr).read().decode().strip().
strip('"').split('"\r\n"'))
lines = [line.strip().split('","') for line in lines]
diff --git a/pandas/io/tests/test_yahoo.py b/pandas/io/tests/test_yahoo.py
index 1f25e3c..d07c5fd 100644
--- a/pandas/io/tests/test_yahoo.py
+++ b/pandas/io/tests/test_yahoo.py
@@ -92,15 +92,15 @@ class TestYahoo(unittest.TestCase):
pan = web.get_data_yahoo(dfi, 'JAN-01-12', 'JAN-31-12',
adjust_price=True)
- expected = [18.38, 27.45, 24.54]
+ expected = [18.38, 27.45, 24.28]
result = pan.Close.ix['01-18-12'][['GE', 'MSFT', 'INTC']].tolist()
assert result == expected
pan = web.get_data_yahoo(dfi, '2011', ret_index=True)
- d = [[ 1.01757469, 1.01130524, 1.02414183],
- [ 1.00292912, 1.00770812, 1.01735194],
- [ 1.00820152, 1.00462487, 1.01320257],
- [ 1.08025776, 0.99845838, 1.00113165]]
+ d = [[ 1.01757469, 1.01142857, 1.02414183],
+ [ 1.00292912, 1.00779221, 1.01735194],
+ [ 1.00820152, 1.00519481, 1.01320257],
+ [ 1.08025776, 0.99896104, 1.00113165]]
expected = pd.DataFrame(d)
result = pan.Ret_Index.ix['01-18-11':'01-21-11'][['GE', 'INTC', 'MSFT']]
```
##
original errors
nosetests-3.3 pandas.io.tests.test_yahoo.TestYahoo pandas
# .EEE
## ERROR: test_get_quote (pandas.io.tests.test_yahoo.TestYahoo)
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas-0.11.0.dev_b0ee363-py3.3-macosx-10.6-intel.egg/pandas/io/tests/test_yahoo.py", line 49, in test_get_quote
df = web.get_quote_yahoo(pd.Series(['GOOG', 'AAPL', 'GOOG']))
File "/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas-0.11.0.dev_b0ee363-py3.3-macosx-10.6-intel.egg/pandas/io/data.py", line 118, in get_quote_yahoo
fields = line.strip().split(',')
TypeError: Type str doesn't support the buffer API
#
## ERROR: test_get_components (pandas.io.tests.test_yahoo.TestYahoo)
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas-0.11.0.dev_b0ee363-py3.3-macosx-10.6-intel.egg/pandas/io/tests/test_yahoo.py", line 57, in test_get_components
df = web.get_components_yahoo('^DJI') #Dow Jones
File "/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas-0.11.0.dev_b0ee363-py3.3-macosx-10.6-intel.egg/pandas/io/data.py", line 245, in get_components_yahoo
strip('"').split('"\r\n"'))
TypeError: Type str doesn't support the buffer API
#
## ERROR: test_get_data (pandas.io.tests.test_yahoo.TestYahoo)
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas-0.11.0.dev_b0ee363-py3.3-macosx-10.6-intel.egg/pandas/io/tests/test_yahoo.py", line 87, in test_get_data
dfi = web.get_components_yahoo('^DJI')
File "/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas-0.11.0.dev_b0ee363-py3.3-macosx-10.6-intel.egg/pandas/io/data.py", line 245, in get_components_yahoo
strip('"').split('"\r\n"'))
TypeError: Type str doesn't support the buffer API
---
Ran 4 tests in 0.962s
FAILED (errors=3)
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https://api.github.com/repos/pandas-dev/pandas/issues/2848 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2848/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2848/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2848/events | https://github.com/pandas-dev/pandas/pull/2848 | 10,902,176 | MDExOlB1bGxSZXF1ZXN0NDEwMDM2MQ== | 2,848 | Typo fix: propgate -> propagate. | {
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} | [] | closed | false | null | [] | null | 1 | "2013-02-12T10:37:21Z" | "2013-02-12T20:49:40Z" | "2013-02-12T20:49:40Z" | CONTRIBUTOR | null | Few tiny typo fixes.
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https://api.github.com/repos/pandas-dev/pandas/issues/2849 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2849/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2849/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2849/events | https://github.com/pandas-dev/pandas/pull/2849 | 10,906,429 | MDExOlB1bGxSZXF1ZXN0NDEwMjQyOA== | 2,849 | BUG: HDFStore - missing implementation of bool columns in selection | {
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} | [] | closed | false | null | [] | null | 0 | "2013-02-12T13:13:37Z" | "2014-06-18T09:32:12Z" | "2013-02-12T13:38:22Z" | CONTRIBUTOR | null | - added implementation of bool columns in selection (was missing)
- moved dtype like tests to single method
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https://api.github.com/repos/pandas-dev/pandas/issues/2850 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2850/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2850/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2850/events | https://github.com/pandas-dev/pandas/issues/2850 | 10,910,900 | MDU6SXNzdWUxMDkxMDkwMA== | 2,850 | BUG: Set_index fills in some NaN | {
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} | 6 | "2013-02-12T15:25:43Z" | "2013-12-17T07:31:45Z" | "2013-03-14T02:54:57Z" | CONTRIBUTOR | null | In this example when setting the index of a DataFrame to columns with NaN this values is filled:
```
In [11]: df = pd.DataFrame({'id1': {0: '1a3', 1: '9h4'}, 'id2': {0: nan, 1: 'd67'},
'id3': {0: '78d', 1: '79d'}, 'value': {0: 123, 1: 64}})
In [12]: df
Out[12]:
id1 id2 id3 value
0 1a3 NaN 78d 123
1 9h4 d67 79d 64
In [13]: df.set_index(['id1', 'id2', 'id3'])
Out[13]:
value
id1 id2 id3
1a3 d67 78d 123
9h4 d67 79d 64
```
_I see this on `'0.10.1.dev-f73128e'`, apologies if this has been mentioned before._
Note: This example is essentially from [this SO question](http://stackoverflow.com/questions/14835152/difference-between-two-pandas-dataframes-with-multiple-incomplete-indexes).
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https://api.github.com/repos/pandas-dev/pandas/issues/2851 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2851/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2851/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2851/events | https://github.com/pandas-dev/pandas/pull/2851 | 10,917,157 | MDExOlB1bGxSZXF1ZXN0NDEwNzgyOA== | 2,851 | DOC: prepend missing >>> to docstring Examples in set_index. | {
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} | 19 | "2013-02-12T18:29:20Z" | "2013-09-14T21:43:28Z" | "2013-05-08T21:48:02Z" | NONE | null | Create a dataframe with a date (not datetime) column as the index. Save that dataframe to the HDFStore. Now open that same file on a computer in a time zone that's behind you. (IE, write the file in New York and then read the file from Dallas). Result: all the dates are off by one.
Here is a code snippet to demonstrate the problem, and also a proposed fix to the pandas code. (But someone more qualified than me can review this fix).
To repro the problem:
# write a file in New York time zone
import pandas
import datetime
def writeToFile(fileName) :
dates = [ datetime.date.today() , datetime.date.today(), datetime.date.today() ]
numbers = [ 1, 2, 3 ]
p = pandas.DataFrame({ "date" : dates, "number": numbers}, columns = ['date', 'number'])
p.set_index('date', inplace=True)
store = pandas.HDFStore(fileName)
store['obj1'] = p
store.close()
# Then go to a dallas time zone and read that file:
def readFromFile(fileName) :
store = pandas.HDFStore(fileName)
return store['obj1']
# Notice that the dates on the dataframe now show YESTERDAY when they ought to show today.
Proposed fix in pandas.io.pytables.py:
def _convert_index(index):
...
elif inferred_type == 'date':
#OLD LINE: CHANGE THIS LINE
#converted = np.array([time.mktime(v.timetuple()) for v in values],
# dtype=np.int32)
#NEW LINE:
converted = np.array([v.toordinal() for v in values], dtype=np.int32)
and then also change:
def _unconvert_index(data, kind):
...
elif kind == 'date':
# here we'll try from ordinal first. If the date was saved with the old
# mktime mechanism it'll throw an exception as it'll be out of bounds.
# in those cases we'll convert using the old method (with the bug!)
try:
index = np.array([date.fromordinal(v) for v in data], dtype=object)
except ValueError:
index = np.array([date.fromtimestamp(v) for v in data], dtype=object)
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"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/21"
} | 1 | "2013-02-12T20:19:14Z" | "2013-02-15T19:15:22Z" | "2013-02-15T19:15:18Z" | CONTRIBUTOR | null | ``` python
FAIL: test_get_data (pandas.io.tests.test_yahoo.TestYahoo)
----------------------------------------------------------------------
Traceback (most recent call last):
File "<...>/pandas/io/tests/test_yahoo.py", line 97, in test_get_data
assert result == expected
AssertionError
```
``` python
(pandas)[lodagro@ubuntu][1174][i] nosetests --pdb-failure pandas.io.tests.test_yahoo
<...>/pandas/io/tests/test_yahoo.py(97)test_get_data()
-> assert result == expected
(Pdb) p result
[18.38, 27.45, 24.28]
(Pdb) p expected
[18.38, 27.45, 24.54]
(Pdb) p pd.__version__
'0.11.0.dev-dad367e'
```
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======================================================================
FAIL: test_unsorted_index (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "<...>/pandas/tests/test_graphics.py", line 312, in test_unsorted_index
tm.assert_series_equal(rs, df.y)
File "<...>/pandas/util/testing.py", line 166, in assert_series_equal
assert(left.dtype == right.dtype)
AssertionError
...
(Pdb) p left.dtype
dtype('int32')
(Pdb) p right.dtype
dtype('int64')
(Pdb) p pd.__version__
'0.11.0.dev-dad367e'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2855 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2855/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2855/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2855/events | https://github.com/pandas-dev/pandas/pull/2855 | 10,924,266 | MDExOlB1bGxSZXF1ZXN0NDExMTE4NA== | 2,855 | BUG: proper conversion of string to bool HDFStore selection | {
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} | 5 | "2013-02-12T21:23:42Z" | "2013-04-12T13:42:29Z" | "2013-04-12T13:21:06Z" | CONTRIBUTOR | null | ``` python
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: pd.set_option('line_width', 250)
In [4]: pd.set_option('max_columns', 0)
In [5]: pd.describe_option('max_columns')
display.max_columns:
: int
max_rows and max_columns are used in __repr__() methods to decide if
to_string() or info() is used to render an object to a string.
Either one, or both can be set to 0 (experimental). Pandas will figure
out how big the terminal is and will not display more rows or/and
columns that can fit on it.
In [6]: pd.util.terminal.get_terminal_size()
Out[6]: (138, 43)
In [7]: df = pd.DataFrame(np.random.randn(2,50))
```
df wraps it columns, but no switch over to summary view
```
In [9]: pd.__version__
Out[9]: '0.11.0.dev-dad367e'
```
see also [mailing list](https://groups.google.com/forum/?fromgroups=#!topic/pydata/FNcnHz-PskI)
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https://api.github.com/repos/pandas-dev/pandas/issues/2858 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2858/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2858/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2858/events | https://github.com/pandas-dev/pandas/issues/2858 | 10,927,933 | MDU6SXNzdWUxMDkyNzkzMw== | 2,858 | Pandas .fillna() should handle "inf" | {
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} | [] | closed | false | null | [] | null | 5 | "2013-02-12T21:39:19Z" | "2020-03-29T10:18:39Z" | "2013-02-12T22:44:56Z" | NONE | null | Division by 0 in pandas will give the value "inf". But the .fillna() method doesn't recognize that. We should make .fillna() handle "inf" the same way it handles "NaN'. (for reference, the numpy.isfinite() method treats NaN and Inf interchangably -- pandas should do the same).
import pandas
vals = [ 1.1, 1.1, 1.1]
p = pandas.DataFrame( { 'first' : vals }, columns=['first'])
p['first'] = p['first'] / 0 #Creates a bunch of "inf" values
p['first'].fillna(0, inplace=True) #Has no effect
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} | 7 | "2013-02-12T23:22:18Z" | "2017-03-03T13:33:48Z" | "2014-05-29T06:20:11Z" | NONE | null | Hi,
I am running on the git cloned version of `pandas`, and there seems to be quite a few issues with user defined classes extending `DataFrame`.
It seems that `DataFrame` class constructor is hardcoded in a lot of places, where `self.__class__` or `cls` constructors should be used instead. This causes some weird behaviour.
Allow me to illustrate, let's import pandas and define some class that would extend `DataFrame`
``` python
In[2]: import pandas as pd
In [3]: pd.__version__
Out[3]: '0.10.1'
In [4]: class ClassExtendingDataFrame(pd.DataFrame):
...: pass
...:
```
Note that `ClassExtendingDataFrame` does not override anything and is essentially the same `DataFrame`, just renamed.
Now one would expect a new instance of `ClassExtendingDataFrame` to be created by the following code:
``` python
In [5]: dict = {'a' : [1,2,3], 'b': [2,3,4]}
In [6]: x = ClassExtendingDataFrame.from_dict(dict)
```
Unfortunately:
``` python
In [10]: assert(isinstance(x, ClassExtendingDataFrame))
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-10-3f1ceeb1b90f> in <module>()
----> 1 assert(isinstance(x, ClassExtendingDataFrame))
AssertionError:
In [11]: type(x)
Out[11]: pandas.core.frame.DataFrame
```
This is due to `DataFrame` being hardcoded in `from_dict`: https://github.com/pydata/pandas/blob/master/pandas/core/frame.py#L905 .
`cls` variable should be used here.
Note that `ClassExtendingDataFrame` is initialised using constructor, rather than `from_dict` method, correct object is created:
``` python
In [12]: a = ClassExtendingDataFrame(dict)
In [13]: isinstance(a, ClassExtendingDataFrame)
Out[13]: True
```
However, operations as simple as slicing break this:
``` python
In [14]: isinstance(a[:5], ClassExtendingDataFrame)
Out[14]: False
```
These are just the two examples I have noticed myself, but I am sure there could be more.
A thorough review of Hardcoded `DataFrame` constructors is needed to check if they could be replaced by `self.__class__` or `cls` instead.
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} | 5 | "2013-02-13T00:13:49Z" | "2014-06-18T17:25:34Z" | "2013-03-15T20:38:22Z" | NONE | null | #2439
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https://api.github.com/repos/pandas-dev/pandas/issues/2861 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2861/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2861/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2861/events | https://github.com/pandas-dev/pandas/issues/2861 | 10,938,799 | MDU6SXNzdWUxMDkzODc5OQ== | 2,861 | [Docs] parameter 'kind' not documented in timeseries.resample() | {
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} | [] | closed | false | null | [] | null | 0 | "2013-02-13T07:44:07Z" | "2013-06-28T02:51:55Z" | "2013-02-26T06:00:25Z" | NONE | null | > > > df3 = DataFrame({'a':[1]})
> > > df4 = DataFrame({'a':[1,2]})
> > > df3
> > > a
> > > 0 1
> > > df4
> > > a
> > > 0 1
> > > 1 2
> > > df4.index = [1,1]
> > > df4.reindex(df3.index)
> > > Traceback (most recent call last):
> > > File "<stdin>", line 1, in <module>
> > > File "/hostname/home/chik/.pythonbrew/pythons/Python-2.7.3/lib/python2.7/site-packages/pandas/core/frame.py", line 2347, in reindex
> > > 'column labels')
> > > File "/hostname/home/chik/.pythonbrew/pythons/Python-2.7.3/lib/python2.7/site-packages/pandas/core/frame.py", line 2423, in _reindex_index
> > > clidx, cridx = None, None
> > > File "/hostname/home/chik/.pythonbrew/pythons/Python-2.7.3/lib/python2.7/site-packages/pandas/core/index.py", line 828, in reindex
> > > return com._ensure_platform_int(indexer)
> > > File "/hostname/home/chik/.pythonbrew/pythons/Python-2.7.3/lib/python2.7/site-packages/pandas/core/index.py", line 749, in get_indexer
> > > def set_value(self, arr, key, value):
> > > Exception: Reindexing only valid with uniquely valued Index objects
Why is this not possible?
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} | 1 | "2013-02-13T08:16:31Z" | "2013-02-23T18:22:17Z" | "2013-02-23T18:22:17Z" | CONTRIBUTOR | null | the method call docstring for df.plot() has:
``` python
df.plot(...., grid=False, ....)
```
while later in the docstring it states:
``` python
grid : boolean, default True
Axis grid lines
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2865 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2865/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2865/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2865/events | https://github.com/pandas-dev/pandas/pull/2865 | 10,955,849 | MDExOlB1bGxSZXF1ZXN0NDEyNDMzOA== | 2,865 | BUG: Make DataFrame not hardcode it's own constructor in the code. Issue #2859 | {
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} | 1 | "2013-02-13T14:36:53Z" | "2014-07-03T14:05:55Z" | "2013-03-28T05:07:09Z" | NONE | null | BUG: Make DataFrame not hardcode it's own constructor in the code. Issue
Changed all occurrences of `return DataFrame` to `return
self._constructor` and changed the latter to return `self.__class__`
rather than `DataFrame` by default.
Also made static methods like `DataFrame.from_dict` work on the
`cls` passed to them rather than on hard-coded `DataFrame`.
This is a minimal quick win on the cases described in Issue #2859.
A formal approach to make classes friendly to subclassing might still be
needed, see discussion in the case.
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https://api.github.com/repos/pandas-dev/pandas/issues/2866 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2866/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2866/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2866/events | https://github.com/pandas-dev/pandas/issues/2866 | 10,956,544 | MDU6SXNzdWUxMDk1NjU0NA== | 2,866 | Multi-level .ix works on Series but not on DataFrame | {
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] | closed | false | null | [] | null | 7 | "2013-02-13T14:56:31Z" | "2013-03-20T01:08:39Z" | "2013-03-20T01:07:59Z" | NONE | null | Using pandas 0.10.1. The .ix on a multi-index Series seems very inconsistent with the .ix on a multi-index DataFrame.
# From page 147 of Wes's book:
data = Series(np.random.randn(10), index = [['a','a','a','b','b','b', 'c','c','d','d'], [1,2,3,1,2,3,1,2,2,3]])
data.ix['a'] #works as you'd expect, pulls all the 'a'
data.ix[ : , 2 ] #works as you'd expect, pulls all the '2'
# Now the exact same thing except use DataFrame instead of Series.
data = DataFrame(np.random.randn(10), index = [['a','a','a','b','b','b', 'c','c','d','d'], [1,2,3,1,2,3,1,2,2,3]])
data.ix['a'] #works as you'd expect, pulls all the 'a'
data.ix[ : , 2 ] #ERROR! But why? It works for series.
# Also there is...
data.ix[ 'a' , 2 ] #works on both DataFrame and Series
data.ix[ 'a', : ] #works on the DataFrame but ERROR on the series. (Though easy workaround is just .ix['a'])
I'd sure expect the .ix[] to work the same way on DataFrame and Series. Is there any reason they don't? Can we change it so the dataframe has the same functionality as series?
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https://api.github.com/repos/pandas-dev/pandas/issues/2867 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2867/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2867/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2867/events | https://github.com/pandas-dev/pandas/pull/2867 | 10,963,403 | MDExOlB1bGxSZXF1ZXN0NDEyODI1NA== | 2,867 | ENH: Consolidation and further optimization of take functions in common | {
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} | [] | closed | false | null | [] | null | 18 | "2013-02-13T17:36:19Z" | "2014-06-24T10:12:51Z" | "2013-02-14T23:55:50Z" | CONTRIBUTOR | null | I've consolidated `take_nd`, `ndtake`, and `take_fast` in `common` into a single signature `take_nd` which has cleaner semantics (which is documented in a docstring), at least preserves the existing performance properties in all cases, and improves it (sometimes significantly) in some. (In particular, computation of intermediate arrays like boolean masks are still being short-circuited in the same way they were before, in the appropriate situations.) The operation that used to be `take_fast` was also broken for non-NA `fill_value`: this is fixed too.
In addition, I've optimized the Cython implementations of 2-D takes to use row-wise or column-wise ~~memcpys~~ `memmove`s automatically in places where it is appropriate (same type input and output, non-object type, both c-contiguous if slice axis is 0, both f-contiguous if slice axis is 1...). In theory Cython and/or the C compiler could do this automatically, but apparently it doesn't because the performance does improve when this is triggered, at least with gcc on 32-bit linux.
Tests with measurably improved performance (this is on top of the performance improvements in #2819)
```
Results:
t_head t_baseline ratio
name
frame_reindex_axis0 0.6845 3.2839 0.2084
frame_reindex_axis1 4.0088 5.4203 0.7396
frame_boolean_row_select 0.2932 0.3939 0.7443
```
All other ratios are apparently just statistical noise within the +- 10% range that vary from run to run. (Was hoping it would help more, but I guess this is OK)
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https://api.github.com/repos/pandas-dev/pandas/issues/2868 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2868/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2868/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2868/events | https://github.com/pandas-dev/pandas/issues/2868 | 10,973,365 | MDU6SXNzdWUxMDk3MzM2NQ== | 2,868 | Add converters= argument to ExcelFile.parse | {
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] | closed | false | null | [] | null | 1 | "2013-02-13T21:38:51Z" | "2014-12-04T10:10:47Z" | "2014-12-04T10:10:47Z" | NONE | null | I use the `converters=` frequently in `read_csv`, and it would be great to have it in `ExcelFile.parse`
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https://api.github.com/repos/pandas-dev/pandas/issues/2869 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2869/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2869/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2869/events | https://github.com/pandas-dev/pandas/pull/2869 | 10,977,786 | MDExOlB1bGxSZXF1ZXN0NDEzNTMwMA== | 2,869 | ENH: Added notes about changes in Options class to what's new for 0.11 | {
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} | [] | closed | false | null | [] | null | 2 | "2013-02-13T23:31:15Z" | "2013-02-16T21:48:56Z" | "2013-02-16T21:48:56Z" | CONTRIBUTOR | null | BUG: Also fixed a bug in the puts section for Options.get_forward_data.
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https://api.github.com/repos/pandas-dev/pandas/issues/2870 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2870/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2870/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2870/events | https://github.com/pandas-dev/pandas/issues/2870 | 10,978,668 | MDU6SXNzdWUxMDk3ODY2OA== | 2,870 | Cannot np.delete from DataFrame? | {
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} | [] | closed | false | null | [] | null | 2 | "2013-02-13T23:59:03Z" | "2013-02-14T00:01:26Z" | "2013-02-14T00:01:26Z" | NONE | null | I expected to be able to delete columns from a DataFrame using numpy, perhaps this is not a supported feature?
``` python
>>> import pandas
>>> import numpy as np
>>> a = np.random.randn(10, 5)
>>> print a
[[-0.91594461 -0.53053436 -0.85001444 -0.85449801 -0.43819574]
[ 0.95329084 -0.20825167 0.04241911 0.2584316 -0.94726073]
[ 0.77250977 0.43216287 -1.89856666 -0.81366952 1.28053613]
[-0.3429547 -1.48068496 -0.03506405 1.21071247 -1.20183875]
[-0.0370853 -0.31445352 0.06883009 -1.84456128 1.71605349]
[ 1.17911771 0.39696818 0.07772257 -0.32094673 0.31172983]
[-0.66329584 1.00713056 -2.08220188 0.5176764 0.44289719]
[-0.95182981 -0.69766579 -0.82955209 -0.69828292 2.00791278]
[ 0.99209185 0.13148193 -0.13022876 0.74503304 0.33384755]
[-1.12040205 -1.45429834 1.12394894 -1.1043654 1.07285193]]
>>> np.delete(a, [1,2], axis=1)
array([[-0.91594461, -0.85449801, -0.43819574],
[ 0.95329084, 0.2584316 , -0.94726073],
[ 0.77250977, -0.81366952, 1.28053613],
[-0.3429547 , 1.21071247, -1.20183875],
[-0.0370853 , -1.84456128, 1.71605349],
[ 1.17911771, -0.32094673, 0.31172983],
[-0.66329584, 0.5176764 , 0.44289719],
[-0.95182981, -0.69828292, 2.00791278],
[ 0.99209185, 0.74503304, 0.33384755],
[-1.12040205, -1.1043654 , 1.07285193]])
>>> df = pandas.DataFrame(a)
>>> print df
0 1 2 3 4
0 -0.915945 -0.530534 -0.850014 -0.854498 -0.438196
1 0.953291 -0.208252 0.042419 0.258432 -0.947261
2 0.772510 0.432163 -1.898567 -0.813670 1.280536
3 -0.342955 -1.480685 -0.035064 1.210712 -1.201839
4 -0.037085 -0.314454 0.068830 -1.844561 1.716053
5 1.179118 0.396968 0.077723 -0.320947 0.311730
6 -0.663296 1.007131 -2.082202 0.517676 0.442897
7 -0.951830 -0.697666 -0.829552 -0.698283 2.007913
8 0.992092 0.131482 -0.130229 0.745033 0.333848
9 -1.120402 -1.454298 1.123949 -1.104365 1.072852
>>> np.delete(df, [1,2], axis=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python2.7/site-packages/numpy-1.7.0-py2.7-linux-x86_64.egg/numpy/lib/function_base.py", line 3505, in delete
return wrap(new)
File "/usr/lib/python2.7/site-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/core/frame.py", line 1800, in __array_wrap__
columns=self.columns, copy=False)
File "/usr/lib/python2.7/site-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/core/frame.py", line 404, in __init__
copy=copy)
File "/usr/lib/python2.7/site-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/core/frame.py", line 550, in _init_ndarray
block = make_block(values.T, columns, columns)
File "/usr/lib/python2.7/site-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/core/internals.py", line 483, in make_block
return klass(values, items, ref_items, ndim=values.ndim)
File "/usr/lib/python2.7/site-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/core/internals.py", line 33, in __init__
% (len(items), len(values)))
AssertionError: Wrong number of items passed (5 vs 3)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2871 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2871/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2871/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2871/events | https://github.com/pandas-dev/pandas/pull/2871 | 10,982,930 | MDExOlB1bGxSZXF1ZXN0NDEzODAxMw== | 2,871 | ENH: implement Block splitting to avoid upcasts where possible (GH #2794) | {
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- bugfix in internals.Block.putmask; was upcasting always on an int; and upcasting to float, (now float64)
```
In [6]: df = pd.DataFrame(1,columns=['a','b'],index=range(3))
In [7]: df.ix[1,'b'] = 0
In [8]: df
Out[8]:
a b
0 1 1
1 1 0
2 1 1
In [9]: df.dtypes
Out[9]:
a int64
b int64
Dtype: object
In [10]: df[df>0].dtypes
Out[10]:
a int64
b float64
Dtype: object
In [11]: df[df>0]
Out[11]:
a b
0 1 1
1 1 NaN
2 1 1
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2872 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2872/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2872/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2872/events | https://github.com/pandas-dev/pandas/issues/2872 | 10,990,542 | MDU6SXNzdWUxMDk5MDU0Mg== | 2,872 | unique() broken for datetime64[ns] columns | {
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} | 6 | "2013-02-14T10:37:22Z" | "2013-04-19T00:55:56Z" | "2013-03-12T14:59:09Z" | NONE | null | I have a data frame containing a column with timestamps using the numpy datetime data type. The unique() function on that column returns obviously invalid data:
``` python
cues[(cues.component == '/usr/bin/naoqi-bin') & (cues.thread_id == 0)].start_time
Out[50]:
time
2013-02-14 10:11:13.480284 2013-02-14 10:09:34.640000
2013-02-14 10:11:13.728758 2013-02-14 10:09:34.640000
2013-02-14 10:11:13.979455 2013-02-14 10:09:34.640000
2013-02-14 10:11:14.240253 2013-02-14 10:09:34.640000
2013-02-14 10:11:14.484151 2013-02-14 10:09:34.640000
2013-02-14 10:11:14.718189 2013-02-14 10:09:34.640000
2013-02-14 10:11:14.969820 2013-02-14 10:09:34.640000
2013-02-14 10:11:15.237294 2013-02-14 10:09:34.640000
2013-02-14 10:11:15.471394 2013-02-14 10:09:34.640000
2013-02-14 10:11:15.729639 2013-02-14 10:09:34.640000
2013-02-14 10:11:15.980261 2013-02-14 10:09:34.640000
2013-02-14 10:11:16.241234 2013-02-14 10:09:34.640000
2013-02-14 10:11:16.497418 2013-02-14 10:09:34.640000
2013-02-14 10:11:16.738275 2013-02-14 10:09:34.640000
2013-02-14 10:11:16.981937 2013-02-14 10:09:34.640000
...
2013-02-14 10:12:53.737133 2013-02-14 10:09:34.640000
2013-02-14 10:12:53.984177 2013-02-14 10:09:34.640000
2013-02-14 10:12:54.262773 2013-02-14 10:09:34.640000
2013-02-14 10:12:54.505545 2013-02-14 10:09:34.640000
2013-02-14 10:12:54.726044 2013-02-14 10:09:34.640000
2013-02-14 10:12:55.010031 2013-02-14 10:09:34.640000
2013-02-14 10:12:55.245294 2013-02-14 10:09:34.640000
2013-02-14 10:12:55.488452 2013-02-14 10:09:34.640000
2013-02-14 10:12:55.737416 2013-02-14 10:09:34.640000
2013-02-14 10:12:55.980422 2013-02-14 10:09:34.640000
2013-02-14 10:12:56.234256 2013-02-14 10:09:34.640000
2013-02-14 10:12:56.471297 2013-02-14 10:09:34.640000
2013-02-14 10:12:56.721042 2013-02-14 10:09:34.640000
2013-02-14 10:12:56.982471 2013-02-14 10:09:34.640000
2013-02-14 10:12:57.218042 2013-02-14 10:09:34.640000
Name: start_time, Length: 416
cues[(cues.component == '/usr/bin/naoqi-bin') & (cues.thread_id == 0)].start_time.dtype
Out[51]: dtype('datetime64[ns]')
cues[(cues.component == '/usr/bin/naoqi-bin') & (cues.thread_id == 0)].start_time.unique()
Out[52]: array([1970-01-16 90:09:34.640000], dtype=datetime64[ns])
```
My version of pandas is 0.10.1.
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} | 7 | "2013-02-14T16:23:36Z" | "2014-02-28T23:57:54Z" | "2013-09-20T23:01:08Z" | CONTRIBUTOR | null | dup of #667
I use pandas version 0.10.1
When trying to build a DataFrame with columns that can equally be of the type int/float/str
and be sparse or not I ran into trouble.
```
from pandas import DataFrame
x = DataFrame(randn(10000, 2), columns = ['a', 'b'])
y = DataFrame(randn(10000, 2), columns = ['c', 'd'])
import random
import string
z = DataFrame( [ random.choice(string.letters) for i in range(10000)], columns = ['e'])
x.ix[:9998] = 0
x = x.to_sparse(fill_value=0)
print x.density
print y.__class__
df = concat([x, y])
```
works fine. But the following doesn't:
```
df2 = concat([x, y, z])
```
Also, the following example doesn't yield to a SparseDataFrame:
```
df = DataFrame(randn(10000, 4))
df.ix[:9998] = 0
df1 = df.to_sparse(fill_value=0)
print df1.density
df[0] = df[0].to_sparse(fill_value=0)
df[1] = df[1].to_sparse(fill_value=0)
print df[1].__class__
print df.__class__
print SparseDataFrame(df[0]).density
```
but this might be a feature since modifying df[0] should not modify df.
Finally the following doesn't yield to a SparseDataFrame
```
x = Series(randn(10000), name='a')
x = x.to_sparse(fill_value=0)
print x.__class__
df = SparseDataFrame(x)
print df.__class__
```
I would be very happy to use the power of pandas to deal with sparse structures.
So my last item is a question: Is the development of sparse objects a priority of the pandas project team ?
Thank you for your for providing such a nice piece of software
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} | 2 | "2013-02-14T18:56:59Z" | "2013-03-20T01:28:17Z" | "2013-03-20T01:28:17Z" | NONE | null | I would expect the sum of [] to be giving me 0 as numpy would:
```
In [18]: import numpy as np
In [19]: np.sum([])
Out[19]: 0.0
```
but it appears apply cannot handle a lambda with a sum within if its logical selection evaluates to a empty series.
```
In [15]: a = pd.Series([])
In [16]: a.apply(lambda x: sum(a == x))
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-16-a2ddbb49d4e4> in <module>()
----> 1 a.apply(lambda x: sum(a == x))
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site- packages/pandas/core/series.pyc in apply(self, func, convert_dtype, args, **kwds)
2295
2296 mapped = lib.map_infer(values, f, convert=convert_dtype)
-> 2297 if isinstance(mapped[0], Series):
2298 from pandas.core.frame import DataFrame
2299 return DataFrame(mapped.tolist(), index=self.index)
IndexError: index 0 is out of bounds for axis 0 with size 0
```
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} | 2 | "2013-02-15T17:35:14Z" | "2013-02-22T06:22:34Z" | "2013-02-22T06:22:34Z" | NONE | null | For some reason, calling `print df` is really slow:
```
In [1]: import pandas
In [2]: import numpy as np
In [3]: df = pandas.DataFrame(np.random.randn(1000,100000))
In [4]: %timeit print df
1 loops, best of 3: 1.93 s per loop
In [5]: %timeit print df.values
1000 loops, best of 3: 1.25 ms per loop
```
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} | 3 | "2013-02-15T19:55:15Z" | "2013-03-15T20:51:59Z" | "2013-03-15T20:51:59Z" | CONTRIBUTOR | null | Pandas version 0.10.1:
```
import pandas as pd
index = pd.date_range('1/1/2011', periods=2, freq='H', tz='Europe/Brussels')
ts = pd.Series([188.5, 328.25], index=index)
ts.plot()
```
yields the following exception:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Python27\lib\site-packages\pandas\tools\plotting.py", line 1534, in plot_series
plot_obj.generate()
File "C:\Python27\lib\site-packages\pandas\tools\plotting.py", line 735, in generate
self._make_plot()
File "C:\Python27\lib\site-packages\pandas\tools\plotting.py", line 1076, in _make_plot
if not self.x_compat and self.use_index and self._use_dynamic_x():
File "C:\Python27\lib\site-packages\pandas\tools\plotting.py", line 1057, in _use_dynamic_x
return (freq is not None) and self._is_dynamic_freq(freq)
File "C:\Python27\lib\site-packages\pandas\tools\plotting.py", line 1030, in _is_dynamic_freq
return freq is not None and self._no_base(freq)
File "C:\Python27\lib\site-packages\pandas\tools\plotting.py", line 1043, in _no_base
return Period(x[0], freq).to_timestamp() == x[0]
File "tslib.pyx", line 434, in pandas.tslib._Timestamp.__richcmp__ (pandas\tslib.c:7930)
File "tslib.pyx", line 484, in pandas.tslib._Timestamp._assert_tzawareness_compat (pandas\tslib.c:8528)
Exception: Cannot compare tz-naive and tz-aware timestamps
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2878 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2878/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2878/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2878/events | https://github.com/pandas-dev/pandas/pull/2878 | 11,052,138 | MDExOlB1bGxSZXF1ZXN0NDE3MTczNg== | 2,878 | BUG/TST: Yahoo Finance (a foundational resolve of GH: #2847, #2853) | {
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} | 7 | "2013-02-15T20:54:53Z" | "2013-02-17T00:36:44Z" | "2013-02-16T20:54:48Z" | CONTRIBUTOR | null | Resolve issues in `pandas.io.data.py` and `test_yahoo.py`.
- BUG: Fixed decode issue in `get_data_yahoo()` for python 3.3 (resolves #2847)
- ENH: Updated `_calc_return_index()` function to properly handle groups of multiple stocks that do not share the first valid price on the same date (i.e., stocks whose IPO's dates exist within the query date range). Ensures that all stock's first values in `ret_index` are set to 1, regardless of actual initial starting date.
- TST: Modify test suite for yahoo finance functions in `test_yahoo.py` to accurately test queries to yahoo finance (resolves #2853/#2847)
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``` python
In [1]: from numpy.ma import MaskedArray as ma
In [2]: from pandas import Series, DataFrame
In [3]: from datetime import datetime as dt
In [4]: mask = [True, False]
In [5]: Series(ma([2, 0], mask=mask, dtype=int))
(exception)
In [6]: Series(ma([True, False], mask=mask, dtype=bool))
Out[6]:
0 True
1 False
In [7]: Series(ma([dt(2006, 1, 1), dt(2007, 1, 1)],
...: mask=mask, dtype='M8[ns]'))
(exception)
```
After fix:
``` python
In [1]: from numpy.ma import MaskedArray as ma
In [2]: from pandas import Series, DataFrame
In [3]: from datetime import datetime as dt
In [4]: mask = [True, False]
In [5]: Series(ma([2, 0], mask=mask, dtype=int))
Out[5]:
0 NaN
1 0
Dtype: float64
In [6]: Series(ma([True, False], mask=mask, dtype=bool))
Out[6]:
0 NaN
1 False
Dtype: object
In [7]: Series(ma([dt(2006, 1, 1), dt(2007, 1, 1)],
...: mask=mask, dtype='M8[ns]'))
Out[7]:
0 NaT
1 2007-01-01 00:00:00
Dtype: datetime64[ns]
```
The upcasting will only be done if necessary (i.e. if the mask has any `True` elements):
``` python
In [8]: Series(ma([2, 3], mask=[False, False], dtype=int))
Out[8]:
0 2
1 3
Dtype: int32
In [9]: DataFrame(ma([2, 3], mask=[False, False], dtype=int))
Out[9]:
0
0 2
1 3
```
This latter is a minor change to the previous behavior of the DataFrame constructor, which was upcasting correctly but was doing it even if the mask was all `False`: I've changed it to only upcast when necessary because this is more consistent with how masks are treated elsewhere.
Also made some PEP8 line length fixes
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} | 1 | "2013-02-16T19:00:05Z" | "2013-03-14T05:44:16Z" | "2013-03-14T05:44:16Z" | MEMBER | null | SciPy function was rewritten in Cython
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} | 4 | "2013-02-16T20:24:03Z" | "2014-07-08T08:39:35Z" | "2013-04-08T07:18:48Z" | CONTRIBUTOR | null | This should close #2844. Good but unsettling news is it wasn't an implementation bug. The logic for handling months in the YearBegin offset just wasn't written. It was hard-coded to always be Year, 1, 1.
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} | 1 | "2013-02-16T21:47:06Z" | "2014-07-19T05:51:46Z" | "2013-02-17T01:43:18Z" | CONTRIBUTOR | null | BUG: Also correcting a bug in Options.get_forward_data for puts.
Signed-off-by: Spencer Lyon [email protected]
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} | 1 | "2013-02-16T22:09:52Z" | "2020-03-31T03:37:24Z" | "2020-03-31T03:37:24Z" | CONTRIBUTOR | null | related #4804
I don't know if it's worth filing an issue or this is just how it is now, but I find it odd that "Q" is not a default offset like `QuarterEnd` or `quarterEnd`. Makes things a little tricky for statsmodels since I've been treating "Q" as `quarterEnd`.
```
pandas.datetools.to_offset("Q")
#<1 QuarterEnd: startingMonth=12, kwds={'startingMonth': 12}, offset=<3 MonthEnds>>
pandas.datetools.quarterEnd
#<1 QuarterEnd: startingMonth=3, offset=<3 MonthEnds>>
pandas.datetools.QuarterEnd(month=12)
#<1 QuarterEnd: startingMonth=3, kwds={'month': 12}, offset=<3 MonthEnds>>
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2886 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2886/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2886/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2886/events | https://github.com/pandas-dev/pandas/issues/2886 | 11,082,057 | MDU6SXNzdWUxMTA4MjA1Nw== | 2,886 | read_csv() & extra trailing comma(s) cause parsing issues. | {
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} | 12 | "2013-02-17T02:15:20Z" | "2018-09-21T08:46:56Z" | "2013-03-22T20:46:15Z" | NONE | null | I have run into a few opportunities to further improve the wonderful read_csv() function. I'm using the latest x64 0.10.1 build from 10-Feb-2013 16:52.
Symptoms:
1. With extra trailing commas and setting index_col=False read_csv() fails with: IndexError: list index out of range
2. When one or more CSV rows has additional trailing commas (compared to previous rows) read_csv() fails with: CParserError: Error tokenizing data. C error: Expected 5 fields in line 3, saw 6
I believe the opportunity to fix these exceptions would require simply ignoring any extra trailing commas. This is how many CSV readers work such as opening CSVs in excel. In my case I regularly work with 100K line CSVs that occasionally have extra trailing columns causing read_csv() to fail. Perhaps its possible to have an option to ignore trailing commas, or even better an option to ignore/skip any malformed rows without raising a terminal exception. :)
-Gagi
If a CSV has 'n' matched extra trailing columns and you do not specify any index_col then the parser will correctly assume that the first 'n' columns are the index , if you set index_col=False it fails with: IndexError: list index out of range
``` python
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: import StringIO
In [4]: pd.__version__
Out[4]: '0.10.1'
In [5]: data = 'a,b,c\n4,apple,bat,,\n8,orange,cow,,' <-- Matched extra commas
In [6]: data2 = 'a,b,c\n4,apple,bat,,\n8,orange,cow,,,' <-- Miss-matched extra commas
In [7]: print data
a,b,c
4,apple,bat,,
8,orange,cow,,
In [8]: print data2
a,b,c
4,apple,bat,,
8,orange,cow,,,
In [9]: df = pd.read_csv(StringIO.StringIO(data))
In [10]: df
Out[10]: a b c
4 apple bat NaN NaN
8 orange cow NaN NaN
In [11]: df.index
Out[11]: MultiIndex
[(4, apple), (8, orange)]
In [12]: df2 = pd.read_csv(StringIO.StringIO(data), index_col=False)
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-12-4d7ece45eef3> in <module>()
----> 1 df2 = pd.read_csv(StringIO.StringIO(data), index_col=False)
C:\Python27\lib\site-packages\pandas\io\parsers.pyc in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, nrows, iterator, chunksize, verbose, encoding, squeeze)
397 buffer_lines=buffer_lines)
398
--> 399 return _read(filepath_or_buffer, kwds)
400
401 parser_f.__name__ = name
C:\Python27\lib\site-packages\pandas\io\parsers.pyc in _read(filepath_or_buffer, kwds)
213 return parser
214
--> 215 return parser.read()
216
217 _parser_defaults = {
C:\Python27\lib\site-packages\pandas\io\parsers.pyc in read(self, nrows)
629 # self._engine.set_error_bad_lines(False)
630
--> 631 ret = self._engine.read(nrows)
632
633 if self.options.get('as_recarray'):
C:\Python27\lib\site-packages\pandas\io\parsers.pyc in read(self, nrows)
952
953 try:
--> 954 data = self._reader.read(nrows)
955 except StopIteration:
956 if nrows is None:
C:\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser.TextReader.read (pandas\src\parser.c:5915)()
C:\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser.TextReader._read_low_memory (pandas\src\parser.c:6132)()
C:\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser.TextReader._read_rows (pandas\src\parser.c:6946)()
C:\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser.TextReader._convert_column_data (pandas\src\parser.c:7670)()
C:\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser.TextReader._get_column_name (pandas\src\parser.c:10545)()
IndexError: list index out of range
In [13]: df3 = pd.read_csv(StringIO.StringIO(data2), index_col=False)
---------------------------------------------------------------------------
CParserError Traceback (most recent call last)
<ipython-input-13-441bfd4aff6e> in <module>()
----> 1 df3 = pd.read_csv(StringIO.StringIO(data2), index_col=False)
C:\Python27\lib\site-packages\pandas\io\parsers.pyc in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, nrows, iterator, chunksize, verbose, encoding, squeeze)
397 buffer_lines=buffer_lines)
398
--> 399 return _read(filepath_or_buffer, kwds)
400
401 parser_f.__name__ = name
C:\Python27\lib\site-packages\pandas\io\parsers.pyc in _read(filepath_or_buffer, kwds)
213 return parser
214
--> 215 return parser.read()
216
217 _parser_defaults = {
C:\Python27\lib\site-packages\pandas\io\parsers.pyc in read(self, nrows)
629 # self._engine.set_error_bad_lines(False)
630
--> 631 ret = self._engine.read(nrows)
632
633 if self.options.get('as_recarray'):
C:\Python27\lib\site-packages\pandas\io\parsers.pyc in read(self, nrows)
952
953 try:
--> 954 data = self._reader.read(nrows)
955 except StopIteration:
956 if nrows is None:
C:\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser.TextReader.read (pandas\src\parser.c:5915)()
C:\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser.TextReader._read_low_memory (pandas\src\parser.c:6132)()
C:\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser.TextReader._read_rows (pandas\src\parser.c:6734)()
C:\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser.TextReader._tokenize_rows (pandas\src\parser.c:6619)()
C:\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser.raise_parser_error (pandas\src\parser.c:17023)()
CParserError: Error tokenizing data. C error: Expected 5 fields in line 3, saw 6
- show quoted text -
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2887 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2887/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2887/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2887/events | https://github.com/pandas-dev/pandas/issues/2887 | 11,082,576 | MDU6SXNzdWUxMTA4MjU3Ng== | 2,887 | ambiguous col/index name | {
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} | 2 | "2013-02-17T03:25:01Z" | "2014-05-29T06:08:18Z" | "2014-05-29T06:08:18Z" | CONTRIBUTOR | null | This feels vaguely familiar but the DataFrame display is wrong after the stack/reset_index operations below (xref: http://stackoverflow.com/questions/14916358/reshaping-dataframes-in-pandas-based-on-column-labels/14917572#14917572):
```
In [58]: df
Out[58]:
sample s1 s2
variable x y x y
0 0.298171 -1.336739 1.117346 2.291124
1 -1.802796 -0.154719 0.532947 -0.338009
2 0.086519 1.641511 0.001948 -0.575835
3 -1.638318 -0.206623 0.238649 -0.145120
4 0.368954 0.654914 1.529368 0.300910
5 1.609236 -0.418993 -1.088934 0.912224
6 0.437332 2.668343 0.751687 0.289443
7 0.627601 0.057554 0.085994 2.038858
8 0.785895 1.695244 0.085235 1.017542
9 0.998866 1.001776 -0.662358 0.280511
In [59]: df.stack(0).reset_index(1)
Out[59]:
variable sample x y
0 s1 0.298171 -1.336739
0 s2 1.117346 2.291124
1 s1 -1.802796 -0.154719
1 s2 0.532947 -0.338009
2 s1 0.086519 1.641511
2 s2 0.001948 -0.575835
3 s1 -1.638318 -0.206623
3 s2 0.238649 -0.145120
4 s1 0.368954 0.654914
4 s2 1.529368 0.300910
5 s1 1.609236 -0.418993
5 s2 -1.088934 0.912224
6 s1 0.437332 2.668343
6 s2 0.751687 0.289443
7 s1 0.627601 0.057554
7 s2 0.085994 2.038858
8 s1 0.785895 1.695244
8 s2 0.085235 1.017542
9 s1 0.998866 1.001776
9 s2 -0.662358 0.280511
```
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} | [] | closed | false | null | [] | null | 10 | "2013-02-17T15:59:55Z" | "2013-02-23T20:12:44Z" | "2013-02-23T20:12:44Z" | NONE | null | Hi,
I ran the following commands on "pandas.Series" object named "S":
> > > S.std()
> > > 39192.660667875185
> > > S.values.std()
> > > 17570.025893232512
> > > numpy.std(S)
> > > 39192.500822771792
> > > numpy.std(S.values)
> > > 17570.025893232512
Correct standard deviation is 17570.02589
I should have got the same value in all 4 cases.
This seems to be a bug in pandas.Series
Pickle file for "S" object is here:
https://gist.github.com/yashoteja/4971970
Thank you,
Yashoteja
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} | [] | closed | false | null | [] | null | 3 | "2013-02-17T20:22:28Z" | "2014-06-24T16:44:51Z" | "2013-02-23T20:12:43Z" | CONTRIBUTOR | null | fixes #2888
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} | 3 | "2013-02-18T14:33:25Z" | "2018-09-04T22:11:24Z" | "2018-09-04T22:11:23Z" | MEMBER | null | http://www.openbsd.org/cgi-bin/cvsweb/src/lib/libc/time/strptime.c?rev=1.15;content-type=text%2Fplain
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} | 1 | "2013-02-18T16:50:41Z" | "2013-03-30T21:06:16Z" | "2013-03-30T21:06:07Z" | CONTRIBUTOR | null | Running the following snippet on pandas version 0.10.1 leads the traceback at the bottom:
``` python
import numpy as np
import pandas as pd
from pandas import *
prng = period_range('1/1/2011', '1/1/2012', freq='M')
ts = Series(np.random.randn(len(prng)), prng)
ts.save('pd_save')
new_ts = pd.load('pd_save')
print new_ts
ValueError Traceback (most recent call last)
C:\Python27\lib\site-packages\IPython\utils\py3compat.pyc in execfile(fname, glob, loc)
169 else:
170 filename = fname
--> 171 exec compile(scripttext, filename, 'exec') in glob, loc
172 else:
173 def execfile(fname, *where):
C:\development\eclipse-workspace_23\slquant-dev\src\pd_bug.py in <module>()
8 ts.save('pd_save')
9 new_ts = pd.load('pd_save')
---> 10 print new_ts
C:\Python27\lib\site-packages\pandas\core\series.pyc in __str__(self)
968 if py3compat.PY3:
969 return self.__unicode__()
--> 970 return self.__bytes__()
971
972 def __bytes__(self):
C:\Python27\lib\site-packages\pandas\core\series.pyc in __bytes__(self)
978 """
979 encoding = com.get_option("display.encoding")
--> 980 return self.__unicode__().encode(encoding, 'replace')
981
982 def __unicode__(self):
C:\Python27\lib\site-packages\pandas\core\series.pyc in __unicode__(self)
994 result = self._get_repr(print_header=True,
995 length=len(self) > 50,
--> 996 name=True)
997 else:
998 result = com.pprint_thing(self)
C:\Python27\lib\site-packages\pandas\core\series.pyc in _get_repr(self, name, print_header, length, na_rep, float_format)
1080 length=length, na_rep=na_rep,
1081 float_format=float_format)
-> 1082 result = formatter.to_string()
1083 assert type(result) == unicode
1084 return result
C:\Python27\lib\site-packages\pandas\core\format.pyc in to_string(self)
124 return u''
125
--> 126 fmt_index, have_header = self._get_formatted_index()
127 fmt_values = self._get_formatted_values()
128
C:\Python27\lib\site-packages\pandas\core\format.pyc in _get_formatted_index(self)
110 else:
111 have_header = index.name is not None
--> 112 fmt_index = index.format(name=True)
113 return fmt_index, have_header
114
C:\Python27\lib\site-packages\pandas\tseries\period.pyc in format(self, name, formatter)
1006 header.append(str(self.name) if self.name is not None else '')
1007
-> 1008 return header + ['%s' % Period(x, freq=self.freq) for x in self]
1009
1010 def __array_finalize__(self, obj):
C:\Python27\lib\site-packages\pandas\tseries\period.pyc in __iter__(self)
683 def __iter__(self):
684 for val in self.values:
--> 685 yield Period(ordinal=val, freq=self.freq)
686
687 @property
C:\Python27\lib\site-packages\pandas\tseries\period.pyc in __init__(self, value, freq, ordinal, year, month, quarter, day, hour, minute, second)
79 raise ValueError("Ordinal must be an integer")
80 if freq is None:
---> 81 raise ValueError('Must supply freq for ordinal value')
82 self.ordinal = ordinal
83
ValueError: Must supply freq for ordinal value
```
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} | 40 | "2013-02-18T17:53:53Z" | "2013-03-13T00:22:32Z" | "2013-03-13T00:22:32Z" | CONTRIBUTOR | null | Was playing around and managed to get a segfault, in several versions. It's a little intermittent, and it may have to do with how things are left after an error is encountered. The same code without the the faulty take line doesn't crash for me.
``` python
>>> import pandas as pd, sys
>>> sys.version
'2.7.3 (default, Aug 1 2012, 05:16:07) \n[GCC 4.6.3]'
>>> pd.__version__
'0.11.0.dev-14a04dd'
>>> df = pd.DataFrame({'A': {1.0: 0.0, 2.0: 6.0, 3.0: 12.0}, 'C': {1.0: 2.0, 2.0: 8.0, 3.0: 14.0}, 'B': {1.0: 1.0, 2.0: 7.0, 3.0: 13.0}})
>>> df
A B C
1 0 1 2
2 6 7 8
3 12 13 14
>>>
>>> df.take(df.unstack())
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/dist-packages/pandas-0.11.0.dev_14a04dd-py2.7-linux-i686.egg/pandas/core/frame.py", line 2951, in take
new_index = self.index.take(indices)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.11.0.dev_14a04dd-py2.7-linux-i686.egg/pandas/core/index.py", line 416, in take
taken = self.view(np.ndarray).take(indexer)
IndexError: index out of range for array
>>> df.unstack()
A 2 0
2 6
3 12
B 2 1
2 7
3 13
C 2 2
2 8
3 14
Dtype: float64
>>> df.take(df.unstack())
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/dist-packages/pandas-0.11.0.dev_14a04dd-py2.7-linux-i686.egg/pandas/core/frame.py", line 2951, in take
new_index = self.index.take(indices)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.11.0.dev_14a04dd-py2.7-linux-i686.egg/pandas/core/index.py", line 416, in take
taken = self.view(np.ndarray).take(indexer)
IndexError: index out of range for array
>>> df.unstack()
Segmentation fault (core dumped)
```
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} | 2 | "2013-02-18T19:14:58Z" | "2013-05-14T11:54:01Z" | "2013-04-08T16:41:15Z" | NONE | null | I am using groupby().apply() to compute new columns in a dataframe, for example like this:
``` python
df1 = DataFrame([{"val1": 1, "val2" : 20}, {"val1":1, "val2": 19}, {"val1":2, "val2": 27}, {"val1":2, "val2": 12}])
def func(dataf):
return dataf["val2"] - dataf["val2"].mean()
print type(df1.groupby("val1").apply(func)) # this is a Series
df1["centered"] = df1.groupby("val1").apply(func)
print df1
```
However, if the set of values of the grouped by column ("val1") is unique, the groupby above returns a dataframe as opposed to a Serie, in which case the assignment of the result to a column fails:
``` python
df2 = DataFrame([{"val1": 1, "val2" : 20}, {"val1":1, "val2": 19}, {"val1":1, "val2": 27}, {"val1":1, "val2": 12}])
def func(dataf):
return dataf["val2"] - dataf["val2"].mean()
print type(df2.groupby("val1").apply(func)) # this is a DataFrame
df2["centered"] = df2.groupby("val1").apply(func) # this fails: cannot assign a DataFrame to a column
print df2
```
As a result, my code is littered by check of uniqueness of grouped by parameter:
``` python
if len(dataframe["val1"].unique()) == 1:
df2["centered"] = func(df2)
else:
df2["centered"] = df2.groupby("val1").apply(func)
```
Am I taking the wrong approach there? If not, would it be possible to have a consistent returned type?
Thanks!
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} | 5 | "2013-02-18T22:08:37Z" | "2016-09-29T22:44:19Z" | "2016-09-29T22:22:34Z" | NONE | null | I'm seeing Valgrind errors when importing Pandas. This Python is built with --with-valgrind, which disables PyMalloc when running under valgrind. The Pandas version was pulled from Github just now, but this issue happens with 0.10 also.
```
~$ valgrind python -c 'import sys'
==2832== Memcheck, a memory error detector
==2832== Copyright (C) 2002-2012, and GNU GPL'd, by Julian Seward et al.
==2832== Using Valgrind-3.8.1 and LibVEX; rerun with -h for copyright info
==2832== Command: python -c import\ sys
==2832==
==2832==
==2832== HEAP SUMMARY:
==2832== in use at exit: 864,491 bytes in 5,240 blocks
==2832== total heap usage: 42,287 allocs, 37,047 frees, 9,519,928 bytes allocated
==2832==
==2832== LEAK SUMMARY:
==2832== definitely lost: 0 bytes in 0 blocks
==2832== indirectly lost: 0 bytes in 0 blocks
==2832== possibly lost: 281,296 bytes in 1,031 blocks
==2832== still reachable: 583,195 bytes in 4,209 blocks
==2832== suppressed: 0 bytes in 0 blocks
==2832== Rerun with --leak-check=full to see details of leaked memory
==2832==
==2832== For counts of detected and suppressed errors, rerun with: -v
==2832== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 7 from 7)
~$ valgrind python -c 'import pandas'
==2981== Memcheck, a memory error detector
==2981== Copyright (C) 2002-2012, and GNU GPL'd, by Julian Seward et al.
==2981== Using Valgrind-3.8.1 and LibVEX; rerun with -h for copyright info
==2981== Command: python -c import\ pandas
==2981==
==2981== Invalid read of size 4
==2981== at 0x442ECF: long_new (longobject.c:3998)
==2981== by 0x466262: type_call (typeobject.c:721)
==2981== by 0x4189BC: PyObject_Call (abstract.c:2529)
==2981== by 0x49715C: PyEval_EvalFrameEx (ceval.c:4239)
==2981== by 0x49DD7A: PyEval_EvalCodeEx (ceval.c:3253)
==2981== by 0x49B701: PyEval_EvalFrameEx (ceval.c:4117)
==2981== by 0x49DD7A: PyEval_EvalCodeEx (ceval.c:3253)
==2981== by 0x4FCA3E: function_call (funcobject.c:526)
==2981== by 0x4189BC: PyObject_Call (abstract.c:2529)
==2981== by 0x41FECE: instancemethod_call (classobject.c:2578)
==2981== by 0x4189BC: PyObject_Call (abstract.c:2529)
==2981== by 0x46CFF7: slot_tp_init (typeobject.c:5663)
==2981== Address 0xc8f1764 is 68 bytes inside a block of size 69 alloc'd
==2981== at 0x4C238B8: malloc (vg_replace_malloc.c:270)
==2981== by 0x4501D3: PyObject_Malloc (obmalloc.c:943)
==2981== by 0x457CA5: PyString_FromStringAndSize (stringobject.c:88)
==2981== by 0xF2E6457: binascii_hexlify (binascii.c:1075)
==2981== by 0x49C8C1: PyEval_EvalFrameEx (ceval.c:4021)
==2981== by 0x49DD7A: PyEval_EvalCodeEx (ceval.c:3253)
==2981== by 0x49B701: PyEval_EvalFrameEx (ceval.c:4117)
==2981== by 0x49DD7A: PyEval_EvalCodeEx (ceval.c:3253)
==2981== by 0x4FCA3E: function_call (funcobject.c:526)
==2981== by 0x4189BC: PyObject_Call (abstract.c:2529)
==2981== by 0x41FECE: instancemethod_call (classobject.c:2578)
==2981== by 0x4189BC: PyObject_Call (abstract.c:2529)
==2981==
==2981==
==2981== HEAP SUMMARY:
==2981== in use at exit: 10,613,864 bytes in 73,317 blocks
==2981== total heap usage: 586,122 allocs, 512,805 frees, 90,175,227 bytes allocated
==2981==
==2981== LEAK SUMMARY:
==2981== definitely lost: 245 bytes in 3 blocks
==2981== indirectly lost: 0 bytes in 0 blocks
==2981== possibly lost: 3,140,920 bytes in 24,857 blocks
==2981== still reachable: 7,472,699 bytes in 48,457 blocks
==2981== suppressed: 0 bytes in 0 blocks
==2981== Rerun with --leak-check=full to see details of leaked memory
==2981==
==2981== For counts of detected and suppressed errors, rerun with: -v
==2981== ERROR SUMMARY: 2 errors from 1 contexts (suppressed: 202 from 10)
~$ python -c 'import pandas; print pandas.version.version'
0.11.0.dev-14a04dd
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2895 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2895/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2895/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2895/events | https://github.com/pandas-dev/pandas/issues/2895 | 11,140,280 | MDU6SXNzdWUxMTE0MDI4MA== | 2,895 | TEST #notebook #test-tag | {
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|
https://api.github.com/repos/pandas-dev/pandas/issues/2896 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2896/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2896/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2896/events | https://github.com/pandas-dev/pandas/issues/2896 | 11,153,956 | MDU6SXNzdWUxMTE1Mzk1Ng== | 2,896 | Creating a MultiIndex with quantities fails. | {
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] | closed | false | null | [] | null | 6 | "2013-02-19T13:55:24Z" | "2013-03-15T12:16:03Z" | "2013-03-15T12:16:03Z" | NONE | null | Using pandas MultiIndex with indices having quantities fails in some cases. An example is shown below.
```
import quantities as pq
import pandas as pd
i = np.arange(10) * pq.J
j = np.array([1 for _ in xrange(10)]) * pq.K
pd.MultiIndex.from_tuples(zip(i, j), names=['Energy', 'Temperature'])
```
This fails with the following traceback
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-86-c2d09517b80e> in <module>()
5 j = np.array([1 for _ in xrange(10)]) * pq.K
6
----> 7 pd.MultiIndex.from_tuples(zip(i, j), names=['Energy', 'Temperature'])
C:\Python27\lib\site-packages\pandas\core\index.pyc in from_tuples(cls, tuples, sortorder, names)
1685
1686 return MultiIndex.from_arrays(arrays, sortorder=sortorder,
-> 1687 names=names)
1688
1689 @property
C:\Python27\lib\site-packages\pandas\core\index.pyc in from_arrays(cls, arrays, sortorder, names)
1646 return Index(arrays[0], name=name)
1647
-> 1648 cats = [Categorical.from_array(arr) for arr in arrays]
1649 levels = [c.levels for c in cats]
1650 labels = [c.labels for c in cats]
C:\Python27\lib\site-packages\pandas\core\categorical.pyc in from_array(cls, data)
59
60 return Categorical(labels, levels,
---> 61 name=getattr(data, 'name', None))
62
63 _levels = None
C:\Python27\lib\site-packages\pandas\core\categorical.pyc in __init__(self, labels, levels, name)
45 def __init__(self, labels, levels, name=None):
46 self.labels = labels
---> 47 self.levels = levels
48 self.name = name
49
C:\Python27\lib\site-packages\pandas\core\categorical.pyc in _set_levels(self, levels)
68 levels = _ensure_index(levels)
69 if not levels.is_unique:
---> 70 raise ValueError('Categorical levels must be unique')
71 self._levels = levels
72
ValueError: Categorical levels must be unique
```
If I remove the units, it works just fine.
```
i = np.arange(10)
j = np.array([1 for _ in xrange(10)])
pd.MultiIndex.from_tuples(zip(i, j), names=['Energy', 'Temperature'])
```
If I keep the units, but use a unique item for j, it works as well.
```
i = np.arange(10) * pq.J
j = np.arange(10) * pq.K
pd.MultiIndex.from_tuples(zip(i, j), names=['Energy', 'Temperature'])
```
This is of course no option since the indices come from a measurement. I'd really like to keep the units, but since I'm not familiar with pandas internals I don't know how to fix this.
## Versions
I'm using pandas version 0.10.1 and quantities 0.10.1 in python 2.7.
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https://api.github.com/repos/pandas-dev/pandas/issues/2897 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2897/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2897/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2897/events | https://github.com/pandas-dev/pandas/issues/2897 | 11,154,720 | MDU6SXNzdWUxMTE1NDcyMA== | 2,897 | Wrong indexing with time zone support | {
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} | 7 | "2013-02-19T14:18:16Z" | "2013-03-12T19:55:27Z" | "2013-03-12T19:55:27Z" | NONE | null | There is something really wrong with ix method while using time support. Simple example:
```
def test_tz_error(self):
import pandas, pytz
from datetime import datetime
from datetime import timedelta as td
tz = pytz.timezone('Europe/Warsaw')
ts_from = tz.localize(datetime(2012, 1, 1, 0, 0))
ts_to = tz.localize(datetime(2012, 1, 1, 23, 0))
ttt = pandas.DataFrame(index=pandas.date_range(ts_from, ts_to, freq='H'),
columns=['values'])
ttt['value'] = range(1,25)
ts = ts_from
while ts <= ts_to:
print ttt.ix[ts]['value']
ts += td(hours=1)
```
Should print out number 1,2,3,...,24. However i get:
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 24
Why is it from 2, not 1? And why the last value is repeated twice with value 24??
I am using latest release 0.10.1
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https://api.github.com/repos/pandas-dev/pandas/issues/2898 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2898/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2898/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2898/events | https://github.com/pandas-dev/pandas/issues/2898 | 11,156,641 | MDU6SXNzdWUxMTE1NjY0MQ== | 2,898 | BUG: Series subtraction with Timestamp on rhs | {
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} | [] | closed | false | null | [] | null | 1 | "2013-02-19T15:08:04Z" | "2013-02-19T15:52:25Z" | "2013-02-19T15:52:25Z" | CONTRIBUTOR | null | migrated
http://stackoverflow.com/questions/14948838/pandas-timestamp-subtraction
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https://api.github.com/repos/pandas-dev/pandas/issues/2899 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2899/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2899/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2899/events | https://github.com/pandas-dev/pandas/pull/2899 | 11,156,819 | MDExOlB1bGxSZXF1ZXN0NDIxMDYwOQ== | 2,899 | BUG: Series ops with a rhs of a Timestamp raising exception (#2898) | {
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] | closed | false | null | [] | null | 9 | "2013-02-19T15:11:51Z" | "2014-06-12T07:39:59Z" | "2013-02-23T20:30:30Z" | CONTRIBUTOR | null | - Series ops with a rhs of a Timestamp was throwing an exception (#2898)
- fixed issue in _index.convert_scalar where the rhs was a non-scalar, lhs was dtype of M8[ns], and was trying
to convert to a scalar (but didn't need conversion)
- added some utilities in tslib.pyx, inference.pyx to detect and convert timedelta/timedelta64
- added timedelta64 to checknull routines, representing timedelta64 as NaT
- aded setitem support to set NaT via np.nan (analagously to datetime64)
- added ability for Series to detect and set a `timedelta64[ns]` dtype (if all passed objects are timedelta like)
- added correct printing of timedelta64[ns](looks like py3.3 changed the default for str%28x%29, so rolled a new one)
```
In [149]: from datetime import datetime, timedelta
In [150]: s = Series(date_range('2012-1-1', periods=3, freq='D'))
In [151]: td = Series([ timedelta(days=i) for i in range(3) ])
In [152]: df = DataFrame(dict(A = s, B = td))
In [153]: df
Out[153]:
A B
0 2012-01-01 00:00:00 0:00:00
1 2012-01-02 00:00:00 1 day, 0:00:00
2 2012-01-03 00:00:00 2 days, 0:00:00
In [154]: df['C'] = df['A'] + df['B']
In [155]: df
Out[155]:
A B C
0 2012-01-01 00:00:00 0:00:00 2012-01-01 00:00:00
1 2012-01-02 00:00:00 1 day, 0:00:00 2012-01-03 00:00:00
2 2012-01-03 00:00:00 2 days, 0:00:00 2012-01-05 00:00:00
In [156]: df.dtypes
Out[156]:
A datetime64[ns]
B timedelta64[ns]
C datetime64[ns]
Dtype: object
In [60]: s - s.max()
Out[60]:
0 -2 days, 0:00:00
1 -1 day, 0:00:00
2 0:00:00
Dtype: timedelta64[ns]
In [61]: s - datetime(2011,1,1,3,5)
Out[61]:
0 364 days, 20:55:00
1 365 days, 20:55:00
2 366 days, 20:55:00
Dtype: timedelta64[ns]
In [62]: s + timedelta(minutes=5)
Out[62]:
0 2012-01-01 00:05:00
1 2012-01-02 00:05:00
2 2012-01-03 00:05:00
Dtype: datetime64[ns]
In [160]: y = s - s.shift()
In [161]: y
Out[161]:
0 NaT
1 1 day, 0:00:00
2 1 day, 0:00:00
Dtype: timedelta64[ns]
The can be set to NaT using np.nan analagously to datetimes
In [162]: y[1] = np.nan
In [163]: y
Out[163]:
0 NaT
1 NaT
2 1 day, 0:00:00
Dtype: timedelta64[ns]
# works on lhs too
In [64]: s.max() - s
Out[64]:
0 2 days, 0:00:00
1 1 day, 0:00:00
2 0:00:00
Dtype: timedelta64[ns]
In [65]: datetime(2011,1,1,3,5) - s
Out[65]:
0 -365 days, 3:05:00
1 -366 days, 3:05:00
2 -367 days, 3:05:00
Dtype: timedelta64[ns]
In [66]: timedelta(minutes=5) + s
Out[66]:
0 2012-01-01 00:05:00
1 2012-01-02 00:05:00
2 2012-01-03 00:05:00
Dtype: datetime64[ns]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2900 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2900/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2900/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2900/events | https://github.com/pandas-dev/pandas/issues/2900 | 11,160,902 | MDU6SXNzdWUxMTE2MDkwMg== | 2,900 | Consistency between indexing by labels and "exporting" data | {
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} | [] | closed | false | null | [] | null | 21 | "2013-02-19T16:45:21Z" | "2013-03-19T23:53:09Z" | "2013-03-19T23:53:09Z" | NONE | null | I think this falls under the Zen of Python, "explicit is better than implicit".
Documentation says "In pandas, our general viewpoint is that labels matter more than integer locations". But I've been caught a few times when trying to carry over this viewpoint to things that access a DataFrame via .as_matrix(). as_matrix returns the data but without the index labels, which requires that the integer locations be taken into account if you want to relate future results based on that matrix form back to the original DataFrame. The all-important index labels are treated implicitly by as_matrix.
The "as_" prefix suggests to me that I should get a complete view of my data, just in a different format. Since labels matter more than locations, it feels out of place for labels to be left out. This makes it easy for beginners to forget about the labels and accidentally try to apply location-based slices via .ix[] based on location indices calculated from the matrix form. This is quite easy to do if you never cared about your index labels in the first place. Since the index is created by default if not specified, DataFrames act just like they are location sliced until you remove a row or shuffle things around.
It might be worth changing as_matrix to return the index labels somehow (with a flag perhaps to not do so). either as a tuple (df.index ,data) or by pretending that the indices are the first column. This way, users would be required to consider the index labels as an important aspect of their data. It isn't just something Pandas does when printing out rows of the DataFrame, but an actual part of your data.
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