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https://api.github.com/repos/pandas-dev/pandas/issues/3701 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3701/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3701/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3701/events | https://github.com/pandas-dev/pandas/pull/3701 | 14,820,628 | MDExOlB1bGxSZXF1ZXN0NTk1NTA4Nw== | 3,701 | BUG: DataFrame.to_csv fails silently if nanRep not None | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3702 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3702/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3702/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3702/events | https://github.com/pandas-dev/pandas/pull/3702 | 14,851,407 | MDExOlB1bGxSZXF1ZXN0NTk2OTUwNA== | 3,702 | ENH: allow to_html and to_latex to take a path for their first argument | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3703 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3703/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3703/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3703/events | https://github.com/pandas-dev/pandas/issues/3703 | 14,852,427 | MDU6SXNzdWUxNDg1MjQyNw== | 3,703 | BUG: broadcasting subtraction | {
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} | [] | closed | false | null | [] | null | 9 | 2013-05-28T17:55:34Z | 2013-06-04T17:46:10Z | 2013-05-28T18:16:03Z | NONE | null | This bug applies to version `0.11.0`. I observe correct behavior in version `0.12.0.dev-84fb0e0`, so the issue seems to have been fixed. Still, I thought someone might find it useful to see this report.
---
EDIT: Replaced a larger and more complicated example with a minimal example.
```
In [84]: df = pd.DataFrame({'A':['a1','a2','a3'],'B':[4,5,6]})
In [85]: df
Out[85]:
A B
0 a1 4
1 a2 5
2 a3 6
In [86]: df.ix[[0,1], 'B'] -= 10
In [87]: df
Out[87]:
A B
0 a1 -6
1 a2 -6
2 a3 6
```
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} | 6 | 2013-05-28T22:30:28Z | 2017-03-05T15:14:17Z | 2016-09-29T22:39:31Z | MEMBER | null | Probably good as a first PR. I'm _not_ saying we need links to `DataFrame` every time it's mentioned, but the io docs could use some TLC in this direction.
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} | 4 | 2013-05-29T12:26:28Z | 2013-05-30T05:13:37Z | 2013-05-30T05:11:10Z | CONTRIBUTOR | null | Is this correct? (on current master)
```
In [1]: df = pandas.DataFrame(np.random.randn(10,10000))
In [2]: %timeit repr(df)
1 loops, best of 3: 9.85 s per loop
n [4]: pandas.__version__
Out[4]: '0.12.0.dev-a62ea81'
```
```
%prun repr(df)
ncalls tottime percall cumtime percall filename:lineno(function)
1920528 1.310 0.000 1.710 0.000 re.py:226(_compile)
1119307 1.253 0.000 1.724 0.000 StringIO.py:208(write)
1920528 1.092 0.000 1.092 0.000 {method 'search' of '_sre.SRE_Pattern' objects}
1920528 1.069 0.000 3.870 0.000 re.py:139(search)
210010 1.026 0.000 1.763 0.000 format.py:1537(just)
80022 0.884 0.000 5.224 0.000 config.py:455(_select_options)
60003 0.782 0.000 0.782 0.000 {method 'reduce' of 'numpy.ufunc' objects}
1940531 0.403 0.000 0.403 0.000 {method 'get' of 'dict' objects}
80022 0.402 0.000 0.402 0.000 {sorted}
530020 0.345 0.000 0.345 0.000 {_codecs.utf_8_decode}
```
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} | 20 | 2013-05-29T13:37:42Z | 2017-06-12T01:16:23Z | 2017-01-09T20:10:40Z | NONE | null | To summarize: should raise when loss of precision when casting from ints to floats for computations (everywhere!)
The wrong result is returned in the following case because of unnecessary casting of ints to floats.
```
max_int = np.iinfo(np.int64).max
min_int = np.iinfo(np.int64).min
assert max_int + min_int == -1
assert DataFrame([max_int,min_int], index=[datetime(2013,1,1),datetime(2013,1,1)]).resample('M', how=np.sum)[0][0] == -1
```
This is the offending code in `groupby.py` that does the casting:
```
class NDFrameGroupBy(GroupBy):
def _cython_agg_blocks(self, how, numeric_only=True):
.....
if is_numeric:
values = com.ensure_float(values)
```
and even worse, sneakily casts the floats back to integers hiding the problem!
```
# see if we can cast the block back to the original dtype
result = block._try_cast_result(result)
```
It should be possible to perform this calculation without any casting. For example, the `sum()` of a DataFrame returns the correct result:
```
assert DataFrame([max_int, min_int]).sum()[0] == -1
```
I am working with financial data that needs to stay as int to protect its precision. In my opinion, any operation that casts values to floats do a calculation, but then returns results as ints is _very wrong_. At the very least, the results should be returned as floats to show that a cast has been done. Even better would be to perform the calculations with ints whenever possible.
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} | 3 | 2013-05-29T14:23:51Z | 2014-07-06T13:17:47Z | 2013-05-30T00:46:35Z | MEMBER | null | closes #3682.
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} | 14 | 2013-05-29T23:06:10Z | 2014-06-24T12:36:06Z | 2013-09-30T10:27:38Z | NONE | null | First, I would like to thank the developers for this excellent work.
My patch is about the possibility of specify the workbook encoding when making output to excel xls files. By default, pandas hasn't it, so say-so non ascii language speakers weren't able to save files with _special_ characters, receiving an error like, for instance:
UnicodeDecodeError: 'ascii' codec can't decode byte 0xc3 in position 2: ordinal not in range(128)
My change makes possible to specify both in ExcelWriter and DataFrame.to_excel the parameter encoding which is passed by both the ExcelWriter **init** and DataFrame.to_excel functions to the appropiate xlwt function to make the trick.
I'm sorry my explanations aren't very proffesional, but I think they are enough for this purpose.
Also I have to apologize in the sense that I've run the tests with nosetests and I lack of knowledge about tests interpretation, but I have checked the log and no error comes from the modules/files I've worked with.
Best regards,
Jorge Tornero
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} | 39 | 2013-05-30T00:09:25Z | 2015-07-25T19:02:29Z | 2015-07-24T20:09:24Z | NONE | null | Trying to contribute but get the following error when trying to run nosetests with "nosetests pandas". I get the same error on both OSX 10.8.3 and Linux Ubuntu:
#
## ERROR: Failure: ImportError (No module named dateutil.parser)
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/loader.py", line 390, in loadTestsFromName
addr.filename, addr.module)
File "/usr/lib/python2.7/dist-packages/nose/importer.py", line 39, in importFromPath
return self.importFromDir(dir_path, fqname)
File "/usr/lib/python2.7/dist-packages/nose/importer.py", line 86, in importFromDir
mod = load_module(part_fqname, fh, filename, desc)
File "/home/reedrosenbluth/pandas/pandas/**init**.py", line 6, in <module>
from . import hashtable, tslib, lib
File "tslib.pyx", line 31, in init pandas.tslib (pandas/tslib.c:48137)
ImportError: No module named dateutil.parser
---
Ran 1 test in 0.001s
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} | 5 | 2013-05-30T00:11:09Z | 2014-07-16T08:10:43Z | 2013-05-30T16:41:40Z | MEMBER | null | @jreback i forgot to do this as per our conversation
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} | 2 | 2013-05-30T05:09:21Z | 2014-07-16T08:10:45Z | 2013-05-30T05:10:56Z | NONE | null | #3706
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} | 14 | 2013-05-30T12:21:08Z | 2013-08-11T20:01:03Z | 2013-08-11T20:01:03Z | NONE | null | Bug: Assigning levels/labels to a multiindex (or really any fields in Index) should raise (if done externally)
This is (for me) one of the weirdest things I have found so far (0.10.1 still)
tl;dr
it seems like df.copy() creates a shallow copy of the MultiIndex w.r.t levels
Also, the setting of the index.levels does not seem to have effect on the index itself
Running the same code twice results in different results during the second time
although I do not alter the original object in any place.
also note the line "print data.index.levels[1]" which changes after changing data2
my original goal (concatenating) always contains the original date in all rows
Code
```
import pandas as pd
from datetime import date, timedelta
import datetime
ind = pd.MultiIndex.from_tuples([(pd.Timestamp('2013-04-30 00:00:00'),
datetime.date(2013, 5, 30)), (pd.Timestamp('2013-05-01 00:00:00'),datetime.date(2013, 5, 30))])
data = pd.DataFrame({'orders_ga': {0: 10.0,
1: 15.0},
'revenues_ga': {0: 5.0,
1: 7.0}})
data.index = ind
data2 = data.copy()
print 'original index'
print data.index
print 'copied index levels'
print data2.index.levels[1]
data2.index.levels[1] = pd.Index([date.today() + timedelta(1)])
print 'index levels of original dataframe after assinging new index to the copied df'
print data.index.levels[1]
print 'new dfs index levels after assigning new levels'
print data2.index.levels[1]
#print ##################
#print data.index
#data2['recorded_at'] = date.today() + timedelta(1)
print '################################################## output of concat'
print pd.concat([data, data2])
print '##########################################################################'
print 'one more time'
data2 = data.copy()
print 'original index'
print data.index
print 'copied index levels'
print data2.index.levels[1]
data2.index.levels[1] = pd.Index([date.today() + timedelta(1)])
print 'index levels of original dataframe after assinging new index to the copied df'
print data.index.levels[1]
print 'new dfs index levels after assigning new levels'
print data2.index.levels[1]
#print ##################
#print data.index
#data2['recorded_at'] = date.today() + timedelta(1)
print '################################################## output of concat'
print pd.concat([data, data2])
```
Output:
```
original index
MultiIndex
[(2013-04-30 00:00:00, 2013-05-30), (2013-05-01 00:00:00, 2013-05-30)]
copied index levels
Index([2013-05-30], dtype=object)
index levels of original dataframe after assinging new index to the copied df
Index([2013-05-31], dtype=object)
new dfs index levels after assigning new levels
Index([2013-05-31], dtype=object)
################################################## output of concat
orders_ga revenues_ga
2013-04-30 2013-05-30 10 5
2013-05-01 2013-05-30 15 7
2013-04-30 2013-05-30 10 5
2013-05-01 2013-05-30 15 7
##########################################################################
one more time
original index
MultiIndex
[(2013-04-30 00:00:00, 2013-05-30), (2013-05-01 00:00:00, 2013-05-30)]
copied index levels
Index([2013-05-31], dtype=object)
index levels of original dataframe after assinging new index to the copied df
Index([2013-05-31], dtype=object)
new dfs index levels after assigning new levels
Index([2013-05-31], dtype=object)
################################################## output of concat
orders_ga revenues_ga
2013-04-30 2013-05-30 10 5
2013-05-01 2013-05-30 15 7
2013-04-30 2013-05-30 10 5
2013-05-01 2013-05-30 15 7
```
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} | 3 | 2013-05-30T12:52:37Z | 2017-01-21T22:50:21Z | 2017-01-21T22:50:21Z | NONE | null | see also #3707
Add a `fill_value` option to `resample()` so that it is possible to resample a `TimeSeries` without creating `NaN` values. If the series is an int dtype and an int is passed to `fill_value`, it should be possible to resample the series without casting the values to floats.
```
>>> dates = (datetime(2013, 1, 1), datetime(2013,1,2), datetime(2013,3,1))
>>> s = Series([1,2,4],index=dates)
>>> s
2013-01-01 1
2013-01-02 2
2013-03-01 4
dtype: int64
>>> s.resample('M', how='sum')
2013-01-31 3
2013-02-28 NaN
2013-03-31 4
Freq: M, dtype: float64
>>> s.resample('M', how='sum', fill_value=0)
2013-01-31 3
2013-02-28 0
2013-03-31 4
Freq: M, dtype: int64
```
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} | 0 | 2013-05-30T14:54:58Z | 2013-09-27T19:55:18Z | 2013-09-27T19:55:18Z | CONTRIBUTOR | null | fair warning in 0.11.1 that in 0.12 this is going to change
top-level imports of `ExcelFile/ExcelObject` are unchanged
this only affects directly importing from the parsers module
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https://api.github.com/repos/pandas-dev/pandas/issues/3720 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3720/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3720/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3720/events | https://github.com/pandas-dev/pandas/pull/3720 | 14,948,619 | MDExOlB1bGxSZXF1ZXN0NjAyMjMwNQ== | 3,720 | DOC: clean up io docs and fix up links | {
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} | [] | closed | false | null | [] | null | 1 | 2013-05-30T22:36:56Z | 2013-05-30T23:56:42Z | 2013-05-30T23:56:42Z | MEMBER | null | carrying over from #3720
these sphinx bugs **WILL** be squashed!!
- [x] `lookup`
- [x] `to_csv` (there's a newline character here that's causing the issue. that i'm trying to figure out how to escape it in sphinx) duh use raw strings
- [x] `ExcelFile.parse`
- [x] `read_excel`
- [x] `docscrape.py: Unknown Section Example`
- [x] `docscrape.py: Unknown Section Optional Parameters`
moral of the story: when writing docstrings please follow the [sphinx rules](http://sphinx-doc.org/rest.html) _to a tee_!
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] | closed | false | null | [] | null | 22 | 2013-05-30T23:51:05Z | 2013-06-02T01:29:55Z | 2013-05-31T00:27:51Z | CONTRIBUTOR | null | docs say 4.0.2 on 32-bit. i tried building on 64-bit and it did not work with 4.2.0 but worked with 4.0.2.
I'm updating the docs now.
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} | 6 | 2013-05-31T01:18:48Z | 2013-06-06T02:10:51Z | 2013-06-06T02:10:51Z | MEMBER | null | obvs something like `diff` is fine, but i think sum/prod and most ufuncs should raise a `TypeError` maybe try the ops on the `values` attribute and if it doesn't raise continue
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https://api.github.com/repos/pandas-dev/pandas/issues/3727 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3727/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3727/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3727/events | https://github.com/pandas-dev/pandas/issues/3727 | 14,989,952 | MDU6SXNzdWUxNDk4OTk1Mg== | 3,727 | df.reset_index introduces wrong elements with NaN index values | {
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] | closed | false | null | [] | null | 2 | 2013-05-31T11:52:34Z | 2013-06-01T14:13:57Z | 2013-06-01T14:13:57Z | NONE | null | When there are NaN values in the index, then reset_index introduces incorrect values. That is the case even if the reset_index operation occurs on a different index than the one containing the NaN values:
``` python
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({
...: 'col1' : [1,2,3,4,5,6,7,8],
...: 'col2' : [8,7,6,5,4,3,2,1],
...: })
In [4]: arrays = [
...: ['a','a','a','a','b','b','b','b'],
...: ['c',None,'d',None,'e','e',None,'f']
...: ]
In [6]: idx = pd.MultiIndex.from_tuples(zip(*arrays),
...: names=['first', 'second'])
In [7]: df.index = idx
In [8]: df
Out[8]:
col1 col2
first second
a c 1 8
NaN 2 7
d 3 6
NaN 4 5
b e 5 4
e 6 3
NaN 7 2
f 8 1
In [9]: df.reset_index()
Out[9]:
first second col1 col2
0 a c 1 8
1 a f 2 7
2 a d 3 6
3 a f 4 5
4 b e 5 4
5 b e 6 3
6 b f 7 2
7 b f 8 1
In [10]: df.reset_index('second')
Out[10]:
second col1 col2
first
a c 1 8
a f 2 7
a d 3 6
a f 4 5
b e 5 4
b e 6 3
b f 7 2
b f 8 1
In [11]: df.reset_index('first')
Out[11]:
first col1 col2
second
c a 1 8
f a 2 7
d a 3 6
f a 4 5
e b 5 4
e b 6 3
f b 7 2
f b 8 1
```
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] | closed | false | null | [] | null | 2 | 2013-05-31T13:47:08Z | 2013-12-04T00:56:51Z | 2013-07-18T09:06:02Z | CONTRIBUTOR | null | I don't have time to dig into this at the moment, but I didn't want to forget about it. As you can see in the screenshot below, the points are being plotted in reverse x order (the blue line should be lower on the left). If I call matplotlib's plot (`plot(lc.index, lc.A_mean)`) it works correctly.
pandas 0.10.1, mpl 1.2.1, python 3.3
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} | 52 | 2013-05-31T15:07:17Z | 2021-07-11T14:31:09Z | 2020-05-09T20:10:36Z | CONTRIBUTOR | null | ENH: maybe for now just provide a warning if dropping the nan rows when pivotting...
rom ml
http://stackoverflow.com/questions/16860172/python-pandas-pivot-table-silently-drops-indices-with-nans
This is effectivly trying to groupby on a NaN, currently not allowed
```
In [13]: a = [['a', 'b', 12, 12, 12], ['a', nan, 12.3, 233., 12], ['b', 'a', 123.23, 123, 1], ['a', 'b', 1, 1, 1.]]
In [14]: df = DataFrame(a, columns=['a', 'b', 'c', 'd', 'e'])
In [15]: df.groupby(['a','b']).sum()
Out[15]:
c d e
a b
a b 13.00 13 13
b a 123.23 123 1
```
Workaround to fill the index with a dummy, pivot, and replace
```
In [31]: df2 = df.copy()
In [32]: df2['dummy'] = np.nan
In [33]: df2['b'] = df2['b'].fillna('dummy')
In [34]: df2
Out[34]:
a b c d e dummy
0 a b 12.00 12 12 NaN
1 a dummy 12.30 233 12 NaN
2 b a 123.23 123 1 NaN
3 a b 1.00 1 1 NaN
In [35]: df2.pivot_table(rows=['a', 'b'], values=['c', 'd', 'e'], aggfunc=sum)
Out[35]:
a b c d e
0 a b 13.00 13 13
1 a dummy 12.30 233 12
2 b a 123.23 123 1
In [36]: df2.pivot_table(rows=['a', 'b'], values=['c', 'd', 'e'], aggfunc=sum).replace('dummy',np.nan)
Out[36]:
a b c d e
0 a b 13.00 13 13
1 a NaN 12.30 233 12
2 b a 123.23 123 1
```
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} | 43 | 2013-05-31T16:42:37Z | 2014-06-26T21:36:32Z | 2013-09-28T20:44:42Z | MEMBER | null | continuation of #3519 because of a branch rename.
### UPDATE: The exception parsing script has been moved to [a Gist](https://gist.github.com/cpcloud/6745173)
This PR partially addresses #3024.
- all `AssertionError` exceptions now have an informative error message in them
- some `AssertionErrors` have been converted to different `Exception` subclasses, where it makes sense, and there's a corresponding test wherever these were changed.
- all `nose.SkipTest` exceptions now have an informative message
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} | 56 | 2013-05-31T20:10:10Z | 2014-06-22T11:11:14Z | 2013-06-06T02:10:51Z | MEMBER | null | closes #3726.
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} | 19 | 2013-06-01T07:29:33Z | 2013-06-03T02:30:45Z | 2013-06-02T11:58:40Z | CONTRIBUTOR | null | ``` python
data = np.random.randn(10000, 500)
df = pd.DataFrame(data)
df = df.where(df > 0) # create nans
bools = df > 0
reg_mask = bools.mask(pd.isnull(df)) # walltime 10s
float_mask = bools.astype(float).mask(pd.isnull(df)) # walltime 200ms
tm.assert_frame_equal(reg_mask, float_mask) # success
```
I found that while masking boolean DataFrames, converting to float speeds up the process considerably. When you're masking at least one row in each column, the outputs are equivalent.
Note, the mask is slow even when you're only masking one column
``` python
data = np.random.randn(10000, 500)
df = pd.DataFrame(data)
df.ix[:, 0] = np.nan
bools = df > 0
reg_mask = bools.mask(pd.isnull(df)) # walltime 10s
# dtypes: bool(499), float64(1)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3734 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3734/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3734/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3734/events | https://github.com/pandas-dev/pandas/pull/3734 | 15,031,701 | MDExOlB1bGxSZXF1ZXN0NjA2MjEzMg== | 3,734 | BLD: test_perf.py, add --base-pickle --target-pickle options to test_perf | {
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} | 0 | 2013-06-01T19:49:33Z | 2014-06-24T15:20:16Z | 2013-06-01T19:50:22Z | NONE | null | Until now test_perf either generated a single commit report for HEAD,
or fell back to vbench to compare two commits. with this change, can now
compare results between saved results of test_perf -H invocations.
Flow:
- Use build_cache (cdev from #3156) to jump to desired commit using build_cache
- checkout current vb_suite from upstream/master
- use `test_perf -H -d <commit>-foo.pickle` to save timings to file, for
target and base commits
- Use test_perf options `--base-pickle`, `--target-pickle` to generate comparison report.
cc @jreback
```
λ ./test_perf.sh --base-pickle 31ecaa9-0.10.1.pickle --target-pickle f9eea30-0.11.0.pickle
This script compares the performance of two commits.
Make sure the python 'vbench' library is installed.
Setting the BUILD_CACHE_DIR env var to a temp directory will
potentially speed up subsequent runs.
base_commit not specified, Assuming base_pickle is named <commit>-foo.*
target_commit not specified, Assuming target_pickle is named <commit>-foo.*
f9eea30
Verifying specified commits exist in repo...
31ecaa9: RLS: set released to true
f9eea30: RLS: Version 0.11
***
Invoked with :
--ncalls: 3
--repeats: 3
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
frame_reindex_columns | 0.3260 | 0.2900 | 1.1241 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [f9eea30] : RLS: Version 0.11
Base [31ecaa9] : RLS: set released to true
```
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} | 0 | 2013-06-01T20:37:47Z | 2014-07-16T08:11:20Z | 2013-06-02T11:58:40Z | CONTRIBUTOR | null | close #3733
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} | 5 | 2013-06-02T09:15:07Z | 2014-07-16T08:11:22Z | 2013-06-03T17:11:19Z | CONTRIBUTOR | null | Most tests (in pandas' test suite and in general) are of form
`assert_almost_equal(result, expected)`. Verbose error message was
treating its first argument as expected, this is now fixed.
Previous message was:
``` python
err_msg = lambda a, b: 'expected %.5f but got %.5f' % (a, b)
```
But a, the first argument, is actually `actual`, not expected.
Everywhere else, it makes sense to display `"%s != %s" % (a,b)`
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} | 2 | 2013-06-02T10:27:23Z | 2014-02-13T23:12:32Z | 2014-02-13T23:12:32Z | NONE | null | Bug:
`df.loc['bar'] = df['bar'] * 2`; assignment with rhs having a multi-index
Related to http://stackoverflow.com/questions/16833842/assign-new-values-to-slice-from-multiindex-dataframe
``` python
In [246]: arrays = [np.array(['bar', 'bar', 'baz', 'qux', 'qux', 'bar']),
np.array(['one', 'two', 'one', 'one', 'two', 'one']),
np.arange(0, 6, 1)]
In [247]: df = pd.DataFrame(randn(6, 3),
.....: index=arrays,
.....: columns=['A', 'B', 'C']).sort_index()
In [248]: df
Out[248]:
A B C
bar one 0 2.186362 -1.749287 -0.776363
5 0.953196 -0.717784 -0.528991
two 1 0.692802 -0.166100 1.403452
baz one 2 -0.065485 0.642380 -1.154436
qux one 3 1.515987 1.138678 -0.690564
two 4 0.261254 -0.066171 -1.352841
In [249]: df.loc['bar']
Out[249]:
A B C
one 0 2.186362 -1.749287 -0.776363
5 0.953196 -0.717784 -0.528991
two 1 0.692802 -0.166100 1.403452
In [250]: df.loc['bar'] = df.loc['bar'] * 2
In [251]: df
Out[251]:
A B C
bar one 0 NaN NaN NaN
5 NaN NaN NaN
two 1 NaN NaN NaN
baz one 2 -0.065485 0.642380 -1.154436
qux one 3 1.515987 1.138678 -0.690564
two 4 0.261254 -0.066171 -1.352841
```
This however works:
``` python
In [270]: df
Out[270]:
A B C
bar one 0 1.506060 -2.921602 1.057659
5 -1.264088 6.483417 3.259962
two 1 0.580368 -2.877875 2.916771
baz one 2 -0.003460 -1.779877 0.197661
qux one 3 0.463015 0.193559 -0.705382
two 4 0.074625 -0.258451 0.389310
In [271]: idx = [x for x in df.index if x[0] == 'bar']
In [272]: df.loc[idx] = df.loc[idx] * 2
In [273]: df
Out[273]:
A B C
bar one 0 3.012121 -5.843204 2.115318
5 -2.528176 12.966833 6.519925
two 1 1.160736 -5.755750 5.833541
baz one 2 -0.003460 -1.779877 0.197661
qux one 3 0.463015 0.193559 -0.705382
two 4 0.074625 -0.258451 0.389310
```
I was also wondering: if I want to assign to a selection via MultiIndex, what is the best way to achieve that? Examples:
``` python
df.xs('blah', level=2) = df.xs('blah', level=2) * 2
crit = [x for x in df.index where x[2] == 'blah']
df.ix[crit] = df.ix[crit] * 2
df.loc[crit] = df.loc[crit] * 2
df.reset_index(level=2); df[df.level_2 == 'blah'] = df[df.level_2 == 'blah'] * 2
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3739 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3739/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3739/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3739/events | https://github.com/pandas-dev/pandas/pull/3739 | 15,042,412 | MDExOlB1bGxSZXF1ZXN0NjA2NjAzOQ== | 3,739 | Make div use truediv instead of div | {
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} | [] | closed | false | null | [] | null | 13 | 2013-06-02T17:17:07Z | 2014-07-03T04:46:53Z | 2013-06-02T19:28:32Z | CONTRIBUTOR | null | This changes the `div()` method in pandas on Python 2.x to always do
`truediv` rather than integer division. Currently, pandas doesn't
respect `from __future__ import division` (it's defined in a file that
doesn't start with the import, so it always does integer division).
`__div__`, `__rdiv__`, and `__idiv__` remain the same, so that the `/`
operator still works as expected. This PR also adds `truediv()` and
`floordiv()` methods to series, panel and frame, plus a test utility
to generate integer series.
This change makes `div()` behave the same way in Python 2 and in
Python 3. It also limits the surprise that comes from using the
future import with pandas in Python 2.
You can see the difference between Python 2 and 3 with the following
snippet:
```
from __future__ import division
import pandas
from pandas.util.testing import assert_frame_equal
df = pandas.DataFrame({"A": range(4)})
result = df.div(4)
expected = df / 4
assert_frame_equal(result, expected)
```
It's not easy (or particularly useful) to detect which division setting
is currently active. Plus, if you were to check it, the actual behavior
would depend on [which file imports `pandas`
first](http://stackoverflow.com/questions/16880552/dynamically-detecting-division-future-import).
There might be too many tests associated with this, so if you'd like me
to slim them down, I'd be happy to do so.
Another alternative would to this patch would be to follow the python
`operator` convention and remove `div()` altogether from Python 3, while
leaving `div()` as it previously was in Python 2; however, I think that
will continue to suprise users.
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https://api.github.com/repos/pandas-dev/pandas/issues/3740 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3740/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3740/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3740/events | https://github.com/pandas-dev/pandas/issues/3740 | 15,045,045 | MDU6SXNzdWUxNTA0NTA0NQ== | 3,740 | Unexpected `transform` behavior on grouped dataset | {
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} | 20 | 2013-06-02T20:40:12Z | 2013-06-03T21:44:31Z | 2013-06-03T17:09:29Z | NONE | null | I have a simple longitudinal biomedical dataset that I am grouping according to the patient on which measurements are taken. Here are the first couple of groups:
```
1
patient obs week site id treat age sex twstrs treatment
0 1 1 0 1 1 5000U 65 F 32 1
1 1 2 2 1 1 5000U 65 F 30 1
2 1 3 4 1 1 5000U 65 F 24 1
3 1 4 8 1 1 5000U 65 F 37 1
4 1 5 12 1 1 5000U 65 F 39 1
5 1 6 16 1 1 5000U 65 F 36 1
2
patient obs week site id treat age sex twstrs treatment
6 2 1 0 1 2 10000U 70 F 60 2
7 2 2 2 1 2 10000U 70 F 26 2
8 2 3 4 1 2 10000U 70 F 27 2
9 2 4 8 1 2 10000U 70 F 41 2
10 2 5 12 1 2 10000U 70 F 65 2
11 2 6 16 1 2 10000U 70 F 67 2
```
However, when I try to `transform` these data, say by normalization, I get nonsensical results:
```
normalize = lambda x: (x - x.mean())/x.std()
normed = cdystonia_grouped.transform(normalize)
normed.head(10)
patient obs week site id \
0 -9223372036854775808 -1 -1 -9223372036854775808 -9223372036854775808
1 -9223372036854775808 0 0 -9223372036854775808 -9223372036854775808
2 -9223372036854775808 0 0 -9223372036854775808 -9223372036854775808
3 -9223372036854775808 0 0 -9223372036854775808 -9223372036854775808
4 -9223372036854775808 0 0 -9223372036854775808 -9223372036854775808
age twstrs treatment
0 -9223372036854775808 0 -9223372036854775808
1 -9223372036854775808 0 -9223372036854775808
2 -9223372036854775808 -1 -9223372036854775808
3 -9223372036854775808 0 -9223372036854775808
4 -9223372036854775808 1 -9223372036854775808
```
The `normalize` function is straightforward, and works fine when applied to manually subsetted data:
```
normalize(cdystonia.twstrs[cdystonia.patient==1])
0 -0.181369
1 -0.544107
2 -1.632322
3 0.725476
4 1.088214
5 0.544107
Name: twstrs, dtype: float64
```
Any guidance here much appreciated. I'm hoping its something obvious.
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} | 42 | 2013-06-03T06:13:06Z | 2014-06-12T18:25:57Z | 2013-06-04T16:58:59Z | MEMBER | null | {
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} | 4 | 2013-06-03T13:12:28Z | 2013-07-02T00:07:29Z | 2013-06-27T19:11:37Z | NONE | null | I have raised the issue in [this question](http://stackoverflow.com/questions/15621143/pandas-df-set-value-method-erases-resets-column-names-of-multiindex/15624426#15624426) on Stack Overflow, but I'm not sure it ever made it to the Pandas issue tracker.
I have a MultiIndex'ed DataFrame which I want to expand by using `set_value()`, but doing this destroys the `names` attribute of the index. This does not happen when setting the value of an already existing entry in the DataFrame. An easily reproducible example is to create the dataframe by:
``` python
lev1 = ['hans', 'hans', 'hans', 'grethe', 'grethe', 'grethe']
lev2 = ['1', '2', '3'] * 2
idx = pd.MultiIndex.from_arrays(
[lev1, lev2],
names=['Name', 'Number'])
df = pd.DataFrame(
np.random.randn(6, 4),
columns=['one', 'two', 'three', 'four'],
index=idx)
df = df.sortlevel()
df
```
This shows a neat and nice object, just as I expected, with proper naming of the index columns. If I now run:
``` python
df.set_value(('grethe', '3'), 'one', 99.34)
```
the result is also as expected. But if I run:
``` python
df.set_value(('grethe', '4'), 'one', 99.34)
```
The column names of the index are gone, and the `names` attribute has been set to `[None, None]`.
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} | 47 | 2013-06-03T14:01:05Z | 2014-07-02T10:29:35Z | 2013-06-21T12:39:20Z | CONTRIBUTOR | null | I took a stab at issue #2301 using the busdaycalendar functionality in numpy 1.7.
### Caveats
There are a few issues with this:
- It requires Numpy 1.7 (I've only tested it on numpy 1.7.1).
- The timezone handling in nump 1.7 is broken (according to [this discussion](http://numpy-discussion.10968.n7.nabble.com/timezones-and-datetime64-td33407.html) ) and the datetime64 and timezone API will change in numpy 1.8. So from what I understand the current code as I've written it might move you onto a different day if you're in a UTC-0?00 timezone (I'm in UTC+0200 so it didn't affect me).
That said I didn't want to reinvent the wheel and since the numpy datetime64 api does cater for this usecase I thought it would be good to standardise on that in the long term.
The code does what I need it to do and I'm putting it out there in case it's useful to anyone else. Also, with some feedback maybe we can get it to the point where it could be included in Pandas as an optional DateOffset class for users who have Numpy 1.7.
### Possible Improvements
I can think of:
- Guard in the constructor to raise a meaningful exception when the user doesn't is on Numpy < 1.7.
- Better handling for the timezone issue.
- Unit tests for the CustomBusinessDay behaviour.
### Notes
- I picked the frequency code 'C' because it was available and fit nicely between 'D' and 'B'. I had originally named the class TradingDay and wanted to use 'T' but that's already used for Minutes.
- I have no idea what goes on in frequencies.py and I simply put a 'C' wherever I found a 'B' using the reasoning that as a BusinessDay subclass it should work in all the same places.
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} | 9 | 2013-06-03T15:10:29Z | 2018-04-11T02:29:55Z | 2018-04-11T02:29:55Z | CONTRIBUTOR | null | related to #3714
tz should not be settable
```
df=pd.DataFrame({"events":np.ones(len(dates))},index=motor["timestamps"].astype("datetime64[s]"))
df.describe()
```
| | events |
| --- | --- |
| count | 2393 |
| mean | 1 |
| std | 0 |
| min | 1 |
| 25% | 1 |
| 50% | 1 |
| 75% | 1 |
| max | 1 |
```
df
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2393 entries, 2013-05-30 11:58:36 to 2013-06-03 13:30:43
Data columns (total 1 columns):
events 2393 non-null values
dtypes: float64(1)
df.index.tz='Asia/Kolkata'
df.index
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-05-30 17:28:36, ..., 2013-06-03 19:00:43]
Length: 2393, Freq: None, Timezone: Asia/Kolkata
df2=df[df.index.day==3]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-39-23de8e1402be> in <module>()
----> 1 df2=df[df.index.day==3]
/usr/local/lib/python2.7/dist-packages/pandas/tseries/index.pyc in f(self)
39 utc = _utc()
40 if self.tz is not utc:
---> 41 values = self._local_timestamps()
42 return tslib.get_date_field(values, field)
43 f.__name__ = name
/usr/local/lib/python2.7/dist-packages/pandas/tseries/index.pyc in _local_timestamps(self)
399 values = self.asi8
400 indexer = values.argsort()
--> 401 result = tslib.tz_convert(values.take(indexer), utc, self.tz)
402
403 n = len(indexer)
/usr/local/lib/python2.7/dist-packages/pandas/tslib.so in pandas.tslib.tz_convert (pandas/tslib.c:20949)()
/usr/local/lib/python2.7/dist-packages/pandas/tslib.so in pandas.tslib._get_deltas (pandas/tslib.c:22754)()
/usr/local/lib/python2.7/dist-packages/pandas/tslib.so in pandas.tslib._get_utcoffset (pandas/tslib.c:11327)()
AttributeError: 'str' object has no attribute 'utcoffset'
```
A temporary fix which i did was to add seconds to data manually while creating the index.
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} | 3 | 2013-06-03T16:53:22Z | 2013-06-06T20:20:53Z | 2013-06-06T20:20:53Z | CONTRIBUTOR | null | Was originally reported in http://bugs.debian.org/cgi-bin/bugreport.cgi?bug=700564
it has license header but no copyright information... license without a copyright is ... like a (non-google) car without driver
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} | 16 | 2013-06-03T18:25:22Z | 2013-09-11T19:38:12Z | 2013-06-03T21:06:35Z | NONE | null | Using the example from the docs here: http://pandas.pydata.org/pandas-docs/dev/io.html#storing-multi-index-dataframes but specifying the 'columns' in `[1219]` keyword when selecting:
```
store.select('df_mi', columns=['A', 'B'])
```
leads to the failure below. The original example works, as does selecting columns with a standard index. I'm on a fairly recent master (~1 week) and Python 3.3. Hope this is not expected and I did not miss an issue, I did search for a bit.
```
KeyError Traceback (most recent call last)
<ipython-input-67-678a9c77d998> in <module>()
----> 1 store.select('df_mi', columns=['A', 'B'])
/home/xxx/anaconda/envs/py33/lib/python3.3/site-packages/pandas-0.12.0.dev_2bd1cf8-py3.3-linux-x86_64.egg/pandas/io/pytables.py in select(self, key, where, start, stop, columns, iterator, chunksize, **kwargs)
413 return TableIterator(func, nrows=s.nrows, start=start, stop=stop, chunksize=chunksize)
414
--> 415 return TableIterator(func, nrows=s.nrows, start=start, stop=stop).get_values()
416
417 def select_as_coordinates(self, key, where=None, start=None, stop=None, **kwargs):
/home/xxx/anaconda/envs/py33/lib/python3.3/site-packages/pandas-0.12.0.dev_2bd1cf8-py3.3-linux-x86_64.egg/pandas/io/pytables.py in get_values(self)
931
932 def get_values(self):
--> 933 return self.func(self.start, self.stop)
934
935
/home/xxx/anaconda/envs/py33/lib/python3.3/site-packages/pandas-0.12.0.dev_2bd1cf8-py3.3-linux-x86_64.egg/pandas/io/pytables.py in func(_start, _stop)
408 # what we are actually going to do for a chunk
409 def func(_start, _stop):
--> 410 return s.read(where=where, start=_start, stop=_stop, columns=columns, **kwargs)
411
412 if iterator or chunksize is not None:
/home/xxx/anaconda/envs/py33/lib/python3.3/site-packages/pandas-0.12.0.dev_2bd1cf8-py3.3-linux-x86_64.egg/pandas/io/pytables.py in read(self, *args, **kwargs)
3142 def read(self, *args, **kwargs):
3143 df = super(AppendableMultiFrameTable, self).read(*args, **kwargs)
-> 3144 df.set_index(self.levels, inplace=True)
3145 return df
3146
/home/xxx/anaconda/envs/py33/lib/python3.3/site-packages/pandas-0.12.0.dev_2bd1cf8-py3.3-linux-x86_64.egg/pandas/core/frame.py in set_index(self, keys, drop, append, inplace, verify_integrity)
2795 names.append(None)
2796 else:
-> 2797 level = frame[col].values
2798 names.append(col)
2799 if drop:
/home/xxx/anaconda/envs/py33/lib/python3.3/site-packages/pandas-0.12.0.dev_2bd1cf8-py3.3-linux-x86_64.egg/pandas/core/frame.py in __getitem__(self, key)
1992 else:
1993 # get column
-> 1994 return self._get_item_cache(key)
1995
1996 def _getitem_slice(self, key):
/home/xxx/anaconda/envs/py33/lib/python3.3/site-packages/pandas-0.12.0.dev_2bd1cf8-py3.3-linux-x86_64.egg/pandas/core/generic.py in _get_item_cache(self, item)
572 return cache[item]
573 except Exception:
--> 574 values = self._data.get(item)
575 res = self._box_item_values(item, values)
576 cache[item] = res
/home/xxx/anaconda/envs/py33/lib/python3.3/site-packages/pandas-0.12.0.dev_2bd1cf8-py3.3-linux-x86_64.egg/pandas/core/internals.py in get(self, item)
1646
1647 def get(self, item):
-> 1648 _, block = self._find_block(item)
1649 return block.get(item)
1650
/home/xxx/anaconda/envs/py33/lib/python3.3/site-packages/pandas-0.12.0.dev_2bd1cf8-py3.3-linux-x86_64.egg/pandas/core/internals.py in _find_block(self, item)
1773
1774 def _find_block(self, item):
-> 1775 self._check_have(item)
1776 for i, block in enumerate(self.blocks):
1777 if item in block:
/home/xxx/anaconda/envs/py33/lib/python3.3/site-packages/pandas-0.12.0.dev_2bd1cf8-py3.3-linux-x86_64.egg/pandas/core/internals.py in _check_have(self, item)
1780 def _check_have(self, item):
1781 if item not in self.items:
-> 1782 raise KeyError('no item named %s' % com.pprint_thing(item))
1783
1784 def reindex_axis(self, new_axis, method=None, axis=0, copy=True):
KeyError: 'no item named foo'
```
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} | 0 | 2013-06-03T19:08:40Z | 2014-07-16T08:11:33Z | 2013-06-03T21:06:35Z | CONTRIBUTOR | null | closes #3748
API: raise a `TypeError` on passing `where` or `columns` to select with a Storer
e.g. these are invalid parameters at this time
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} | 3 | 2013-06-03T19:42:15Z | 2013-06-13T15:38:49Z | 2013-06-06T00:14:00Z | CONTRIBUTOR | null | we are not testing this now on travis
fails because pytables 3.0 reads back strings as bytes and
infer_dtype doesn't deal with this
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} | 48 | 2013-06-03T22:24:22Z | 2014-07-16T08:11:35Z | 2013-06-04T20:02:50Z | MEMBER | null | to summarize this mess of deps and check my thoughts here
### if a user insists on using `lxml` (either with or without `bs4`)
- warning about its inability to deal with the modern web
- warning saying that the user should install `html5lib` and `bs4` so that a page will parse even if `lxml` barfs
- test coverage for failing and passing pages (things that would parse "correctly" before will now fail since the parser will be extremely strict) thus only pages validated by the DTD will even try to parse
(to be fair I was really enthusiastic about `lxml` because of how fast it is but now i'm sort of against it)
### what users should really do
- install `bs4`
- install `html5lib`
- happily parse things into `DataFrame`s with a low amount of stress
### anaconda + `lxml` (no `bs4`)
- no problems (modulo the above warnings)
@wesm maybe you could chime in about what (if anything) you did to `libxml2`/`libxslt` i wasn't clear on the details from the mailing list.
### anaconda + `bs4` + `lxml`
- make sure that you're using `bs4==4.2.1`
- make sure that you're using `lxml==3.2.1`
- workout the details of how to do this with `conda` (i did this already, but it was 2 or 3 AM so I'm a little foggy on the details)
### anaconda + `bs4` + `html5lib` (no `lxml`)
- happy parsing of HTML tables
this will be in a gotcha that will be linked to from a warning at the top of the read html section of io.rst
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} | 4 | 2013-06-04T02:23:24Z | 2014-04-29T20:05:59Z | 2014-04-29T20:05:59Z | NONE | null | The issue I am having is that in pandas 0.10.0, a count of boolean column with a single item in it would be 1, while a count of multiple items would of course be the number of items. In 0.11.0, it is now True if it is a single item, and the count otherwise.
Result in 0.10.00
``` console
Single item Groupby
DataFrame
0 bool
0 0 False
Group By
0 bool
0 0 1
Multi item Groupby
DataFrame
0 bool
0 0 False
1 0 True
Group By
0 bool
0 0 2
```
In 0.11.0, this is now the result:
``` console
Single item Groupby
DataFrame
0 bool
0 0 False
Group By
0 bool
0 0 True
Multi item Groupby
DataFrame
0 bool
0 0 False
1 0 True
Group By
0 bool
0 0 2
```
Test Code:
``` python
print('Single item Groupby')
dsd = DataFrame.from_records([(0, False)], columns=['0', 'bool'])
print('DataFrame')
print(dsd)
print('Group By')
print(dsd.groupby(['0'], as_index=False).agg({'bool':Series.count}))
print('Multi item Groupby')
dsd = DataFrame.from_records([(0, False), (0, True)], columns=['0', 'bool'])
print('DataFrame')
print(dsd)
print('Group By')
print(dsd.groupby(['0'], as_index=False).agg({'bool':Series.count}))
``'
```
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} | 7 | 2013-06-04T15:10:42Z | 2015-01-26T01:02:54Z | 2015-01-26T01:02:54Z | NONE | null | The axes drawn by rplot.GeomDensity2D are flipped.
line 565 reads:
`ax.contour(Z, extent=[x_min, x_max, y_min, y_max])`
but should read:
`ax.contour(Z.T, extent=[x_min, x_max, y_min, y_max])`
The following test code gives the results shown below:
``` python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas.tools.rplot as rplot
x = np.arange(1,30)
y = x*x
x = x + np.random.normal(size=x.shape)
y = y + np.random.normal(size=y.shape)
data = data = pd.DataFrame({'x': x, 'y': y})
plot = rplot.RPlot(data, x='x', y='y')
plot.add(rplot.GeomScatter())
plot.add(rplot.GeomDensity2D())
plot.add(rplot.GeomPolyFit(degree=2))
plot.render(plt.gcf())
plt.show()
```
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} | [] | closed | false | null | [] | null | 3 | 2013-06-04T18:44:56Z | 2013-06-04T19:18:13Z | 2013-06-04T19:00:27Z | NONE | null | Seems like the copy docstring in `pandas.Series` is duplicated.
Instead of this:
```
dtype : numpy.dtype or None
If None, dtype will be inferred copy : boolean, default False Copy
input data
copy : boolean, default False
```
Shouldn't it be this?
```
dtype : numpy.dtype or None
If None, dtype will be inferred
copy : boolean, default False
Copy input data
```
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} | 3 | 2013-06-04T22:48:03Z | 2013-09-20T23:36:42Z | 2013-09-20T23:36:42Z | NONE | null | It would be nice to support sparse arrays with `dtype` that is not just `float64`. Specifically, I have some data that is `int8` but sparse, it would be nice to be able to create a `SparseArray` with the `SparseIndex` feature tracking which values are set and which are not set.
Obviously, `np.nan` can't be used to indicate not a value, but maybe a default can be provided (in my case, zero is a nice default).
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] | closed | false | null | [] | null | 16 | 2013-06-05T11:47:24Z | 2013-06-06T15:52:15Z | 2013-06-06T11:57:28Z | CONTRIBUTOR | null | References #3764 , #3540
```
!head 001EC00CC49D_processed
timestamp,frequency,voltage,active_power,energy,cost,current,reactive_power,apparent_power,power_factor,phase_angle
1369210417,49.99,234.187,1.138,0.131,0.000,0.014,3.036,3.242,0.351,69.458
1369210418,49.98,234.276,1.043,0.131,0.000,0.014,3.183,3.350,0.311,71.855
1369210419,49.97,234.306,1.043,0.132,0.000,0.014,3.100,3.271,0.319,71.411
1369210420,49.97,234.288,1.045,0.132,0.000,0.014,3.155,3.324,0.315,71.668
1369210421,49.98,234.330,1.047,0.133,0.000,0.014,3.099,3.271,0.320,71.332
1369210422,49.97,234.228,1.004,0.134,0.000,0.014,3.140,3.296,0.304,72.275
1369210423,49.99,234.260,1.036,0.134,0.000,0.013,2.986,3.161,0.328,70.861
1369210424,49.97,234.292,1.073,0.135,0.000,0.014,3.089,3.270,0.328,70.842
1369210425,49.98,234.259,1.073,0.135,0.000,0.014,3.089,3.270,0.328,70.839
def epoch_to_date(timestamp):
return datetime.datetime.fromtimestamp(int(timestamp))
def epoch_to_date_2(timestamp):
return pd.to_datetime(float(timestamp)*int(1e9))
df=pd.read_csv('001EC00CC49D_processed',parse_dates=[0],date_parser=epoch_to_date,index_col=0,error_bad_lines=False);
df2=pd.read_csv('001EC00CC49D_processed',parse_dates=[0],date_parser=epoch_to_date_2,index_col=0,error_bad_lines=False);
df
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 327818 entries, 2013-05-22 13:43:37 to 2013-06-05 12:22:16
Data columns (total 10 columns):
frequency 327810 non-null values
...
dtypes: float64(9), object(1)
df2
<class 'pandas.core.frame.DataFrame'>
Index: 327818 entries, 1.369210417e+18 to 1.370415136e+18
Data columns (total 10 columns):
frequency 327810 non-null values
....
dtypes: float64(9), object(1)
```
Using pd.to_datetime() took more time and also index is not DateTime
Moreover, since a lot of people tend to record unix timestamps in order to escape different time formats, might be handy to add this functionality inbuilt.
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https://api.github.com/repos/pandas-dev/pandas/issues/3758 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3758/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3758/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3758/events | https://github.com/pandas-dev/pandas/pull/3758 | 15,166,233 | MDExOlB1bGxSZXF1ZXN0NjEzMDAwOA== | 3,758 | BUG: (GH3611) revisited; read_excel not passing thru options to ExcelFile.parse | {
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} | 0 | 2013-06-05T11:56:43Z | 2014-07-16T08:11:38Z | 2013-06-05T14:02:43Z | CONTRIBUTOR | null | recloses #3611
API: add string and numeric versions of na_values when parsing
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https://api.github.com/repos/pandas-dev/pandas/issues/3759 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3759/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3759/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3759/events | https://github.com/pandas-dev/pandas/issues/3759 | 15,167,067 | MDU6SXNzdWUxNTE2NzA2Nw== | 3,759 | TimeSeries integration optimization | {
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] | closed | false | null | [] | null | 11 | 2013-06-05T12:21:03Z | 2013-06-07T13:20:19Z | 2013-06-07T13:20:19Z | CONTRIBUTOR | null | Currently, using scipy.integrate to numerically integrate a pandas TimeSeries incurs quite a large overhead. I sketched up a quick benchmark in [this notebook](http://nbviewer.ipython.org/5512857). This overhead is causing me quite a lot of trouble in my personal work where this integration is the main bottleneck (sometimes lasting minutes on my data, whereas the rest of the analysis executes lightning-fast) and I feel that it shouldn't be that big of a deal since it more-or-less boils down to summing up values.
I already talked about the issue with the scipy folks, to see if it would maybe be feasible to dynamically detect if it's a pandas.TimeSeries and rely on datetime operations, instead of making the user convert the domain to 1 int = 1 second. This would complicate their code a bit, but there would still be only one integration codebase to maintain.
Another option would be to copy the portion of the code regarding sample-based numerical integration (which isn't too complicated - see e.g. [the cumtrapz function](https://github.com/scipy/scipy/blob/master/scipy/integrate/quadrature.py#L245)) into pandas and modify it to work faster.
What is your opinion on the issue I am having? Do you maybe have some idea of how and where to solve it? I would like to hack on the code myself, so if you could point me in the right direction, it would be great.
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https://api.github.com/repos/pandas-dev/pandas/issues/3760 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3760/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3760/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3760/events | https://github.com/pandas-dev/pandas/pull/3760 | 15,168,681 | MDExOlB1bGxSZXF1ZXN0NjEzMTMyMQ== | 3,760 | DataFrame.corr() now computing correlations only once | {
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} | [] | closed | false | null | [] | null | 6 | 2013-06-05T13:05:20Z | 2013-06-09T22:05:06Z | 2013-06-09T22:05:06Z | CONTRIBUTOR | null | DataFrame.corr() for 'spearman' and 'kendall' was computing some correlations twice.
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https://api.github.com/repos/pandas-dev/pandas/issues/3761 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3761/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3761/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3761/events | https://github.com/pandas-dev/pandas/issues/3761 | 15,178,616 | MDU6SXNzdWUxNTE3ODYxNg== | 3,761 | single bad value may cause datatype to become object | {
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} | [] | closed | false | null | [] | null | 9 | 2013-06-05T16:07:45Z | 2013-06-10T12:43:40Z | 2013-06-10T12:43:40Z | CONTRIBUTOR | null | If reading a CSV, where one value got corrupt and doesn't remain a float, the inferred data type from read_csv would be object.
eg.
CSV contents
```
a,b
1.1,3.1
3.4,4,5
5.6,6.2
c3,7.2
In [37]: df=pd.read_csv('test_bad.csv',error_bad_lines=False)
Skipping line 3: expected 2 fields, saw 3
In [38]: df
Out[38]:
a b
0 1.1 3.1
1 5.6 6.2
2 c3 7.2
```
Several ways to look at this:
- read_csv would allow for explicitly giving column types. Those rows which do not adhere are dropped
- after seeing the warning, if the user can omit those rows using a simple method, rather than manually doing the same
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https://api.github.com/repos/pandas-dev/pandas/issues/3762 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3762/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3762/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3762/events | https://github.com/pandas-dev/pandas/pull/3762 | 15,185,162 | MDExOlB1bGxSZXF1ZXN0NjEzOTk3MA== | 3,762 | ENH: support HDFStore in Python3 (via PyTables 3.0.0) | {
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} | 0 | 2013-06-05T18:21:30Z | 2014-07-05T15:12:01Z | 2013-06-06T00:14:00Z | CONTRIBUTOR | null | closes #3750
recently released PyTables 3.0.0 (and numexpr 2.1), which now support python3
were completely broken
These changed all data to be stored as bytes (with to/from encoding/decoding)
This PR supports an encoding argument (if you really want to encode your data),
and provides transparent access for python3 (and backwards compat) to
even python2 written stores
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https://api.github.com/repos/pandas-dev/pandas/issues/3763 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3763/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3763/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3763/events | https://github.com/pandas-dev/pandas/issues/3763 | 15,185,711 | MDU6SXNzdWUxNTE4NTcxMQ== | 3,763 | sql values returned incorrectly. Worked in 0.10.0 doesn't in 0.11.0 | {
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} | [] | closed | false | null | [] | null | 46 | 2013-06-05T18:31:32Z | 2013-06-13T18:15:26Z | 2013-06-13T18:15:26Z | NONE | null | ###### ####### Code
name_q = "SELECT cust_num, name from custaddr"
result = CONNECTION.execute(name_q)
rows = result.fetchall()
COLUMNS = ['cust_num', 'name']
name_df = DataFrame(rows, columns=COLUMNS)
###### ####### Code end
This code worked without fail on 0.10, but started failing as soon as I updated to 0.11.0. It gives me the following error.
---
ValueError Traceback (most recent call last)
<ipython-input-19-7e7e98951b65> in <module>()
5
6 COLUMNS = ['cust_num', 'name']
----> 7 name_df = DataFrame(rows, columns=COLUMNS)
8
9 #name_df[:5]
c:\Python27\lib\site-packages\pandas\core\frame.pyc in **init**(self, data, index, columns, dtype, copy)
426 else:
427 mgr = self._init_ndarray(data, index, columns, dtype=dtype,
--> 428 copy=copy)
429 else:
430 mgr = self._init_ndarray(data, index, columns, dtype=dtype,
c:\Python27\lib\site-packages\pandas\core\frame.pyc in _init_ndarray(self, values, index, columns, dtype, copy)
556 columns = _ensure_index(columns)
557
--> 558 return create_block_manager_from_blocks([ values.T ], [ columns, index ])
559
560 def _wrap_array(self, arr, axes, copy=False):
c:\Python27\lib\site-packages\pandas\core\internals.pyc in create_block_manager_from_blocks(blocks, axes)
1814 blocks = [ getattr(b,'values',b) for b in blocks ]
1815 tot_items = sum(b.shape[0] for b in blocks)
-> 1816 construction_error(tot_items,blocks[0].shape[1:],axes)
1817
1818 def create_block_manager_from_arrays(arrays, names, axes):
c:\Python27\lib\site-packages\pandas\core\internals.pyc in construction_error(tot_items, block_shape, axes)
1797 raise ValueError("Shape of passed values is %s, indices imply %s" % (
1798 tuple(map(int, [tot_items] + list(block_shape))),
-> 1799 tuple(map(int, [len(ax) for ax in axes]))))
1800
1801
ValueError: Shape of passed values is (1, 50178), indices imply (2, 50178)
If I remove the named columns with the following code:
###### ####### Code
name_q = "SELECT cust_num, name from custaddr"
result = CONNECTION.execute(name_q)
rows = result.fetchall()
# COLUMNS = ['cust_num', 'name']
name_df = DataFrame(rows)#, columns=COLUMNS)
name_df[:5]
###### ####### Code end
I get:
0
0 [C001505, @@STOKES GENERAL STORE]
1 [C002815, 3R DISTRIBUTING LLC]
2 [C000032, 4 C'S EXPERT SHARPENING CO]
3 [C000033, 422 SHOES]
4 [C000034, 5 L ENTERPRISES]
in 0.10 this would return two columns of data rather than one column of list.
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} | [] | closed | false | null | [] | null | 40 | 2013-06-05T22:40:00Z | 2014-06-13T05:42:35Z | 2013-06-18T13:01:18Z | CONTRIBUTOR | null | Fixes a number of items relating to accelerated arithmetic in frame.
---
`__truediv__` had been set up to use numexpr, but this happened by passingthe division operator as the string. numexpr's evaluate only checked 2 frames up, which meant that it picked up the division setting from`frame.py` and would do floor/integer division when both inputs were integers.You'd only see that issue with a dataframe large enough to trigger numexpr evaluation (>10000 cells)
This adds test cases to `test_expression.py` that [exhibit this failure under thePython2.7 full deps test](https://travis-ci.org/jtratner/pandas/builds/7822069). The testcases only test `Series` and `DataFrame` (though it looks like neither`Series` nor `Panel` use `numexpr`). It doesn't fail under Python 3 because integer division is totally gone.
Now `evaluate`, `_evaluate_standard` and `_evaluate_numexpr` all accept extra keyword arguments, which are passed to `numexpr.evaluate`.
The test case is currently a separate commit that fails. I wasn't sure whether I should have combined it with the bugfix commit or not. Happyto change it if that's more appropriate.
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} | 18 | 2013-06-05T23:30:38Z | 2014-06-05T12:10:57Z | 2014-06-05T12:10:57Z | CONTRIBUTOR | null | I had learnt a lot from some of the videos like Wes' talk on Pandas for timeseries.
Some of the ones i have viewed and are not found on official docs
- http://pyvideo.org/video/696/pandas-powerful-data-analysis-tools-for-python
- http://pyvideo.org/video/611/data-analysis-in-python-with-pandas
- http://www.youtube.com/watch?v=0unf-C-pBYE
- http://vimeo.com/53065093
I am sure more such stuff is not yet linked
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https://api.github.com/repos/pandas-dev/pandas/issues/3769 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3769/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3769/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3769/events | https://github.com/pandas-dev/pandas/issues/3769 | 15,203,010 | MDU6SXNzdWUxNTIwMzAxMA== | 3,769 | setup.py develop does not perform python 3 conversion | {
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} | [] | closed | false | null | [] | null | 1 | 2013-06-06T02:09:17Z | 2013-06-07T22:52:05Z | 2013-06-07T22:52:05Z | MEMBER | null | just tested this on ubuntu precise 32-bit and `python setup.py install` works correctly
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https://api.github.com/repos/pandas-dev/pandas/issues/3770 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3770/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3770/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3770/events | https://github.com/pandas-dev/pandas/pull/3770 | 15,204,505 | MDExOlB1bGxSZXF1ZXN0NjE1MDMyMg== | 3,770 | TST: let tox run more tests | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3771 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3771/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3771/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3771/events | https://github.com/pandas-dev/pandas/pull/3771 | 15,206,594 | MDExOlB1bGxSZXF1ZXN0NjE1MTM2NQ== | 3,771 | Cookbook epoch handling | {
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} | [] | closed | false | null | [] | null | 0 | 2013-06-06T05:28:32Z | 2014-07-03T09:26:16Z | 2013-06-06T11:57:28Z | CONTRIBUTOR | null | Added link to cookbook explaining how to use `read_csv` to parse files containing epoch timestamps and how to add local timezone information. Closes #3757
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https://api.github.com/repos/pandas-dev/pandas/issues/3772 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3772/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3772/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3772/events | https://github.com/pandas-dev/pandas/pull/3772 | 15,208,295 | MDExOlB1bGxSZXF1ZXN0NjE1MjIyNA== | 3,772 | ENH: Provide a default key name in read_hdf and to_hdf | {
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} | [] | closed | false | null | [] | null | 4 | 2013-06-06T06:53:40Z | 2014-07-02T14:05:41Z | 2013-06-07T13:43:02Z | NONE | null | For convenience, provide a default name ("data") to quickly store and retrieve data from HDF5.
Now for csv:
``` python
df3 = pd.DataFrame(np.random.randn(10, 5), columns=['a', 'b', 'c', 'd', 'e'])
df3.to_csv("df3.csv")
print df3.from_csv("df3.csv")
```
for HDF5:
``` python
df3 = pd.DataFrame(np.random.randn(10, 5), columns=['a', 'b', 'c', 'd', 'e'])
df3.to_hdf("df3.h5", "data")
print pd.read_hdf("df3.h5", "data")
```
I would suggest to provide a default key name that would make it more convenient to read and write to HDF5 when it contains only one dataset.
In this PR:
``` python
df3 = pd.DataFrame(np.random.randn(10, 5), columns=['a', 'b', 'c', 'd', 'e'])
df3.to_hdf("df3.h5")
print pd.read_hdf("df3.h5")
```
Instead I would not provide a default key in `pd.read_hdf` because it would break backward compatibility.
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} | [] | closed | false | null | [] | null | 21 | 2013-06-06T09:21:14Z | 2013-12-04T00:56:39Z | 2013-06-10T16:48:55Z | NONE | null | Dear Developers,
I use the below code to read a netcdf file and get standard deviation of hourly data for each day. Though I get the values they are not matching with the hand computed values. Can any one tell me what I need to do for this to work
with best regards,
Sudheer
``` python
from netCDF4 import Dataset as ncd
from netCDF4 import num2date
from jdcal import *
from pandas import Series,tseries,date_range
import numpy as np
import datetime as dt
from matplotlib import pyplot as plt
ncf=ncd('cur0n90e_10m.cdf','r')
time=ncf.variables['time'][:]
ru=ncf.variables['U_320'][:]
ru[np.where(ru==1e+35)]='NaN'
ru=np.squeeze(ru[:,0,0,0])
torg=ncf.variables['time'].units
dates=num2date(time,torg,calendar='standard')
torg=str(torg[len(torg)-19:len(torg)])
yr=float(torg[0:4])
mn=float(torg[5:7])
dd=float(torg[8:10])
hh=torg[11:13]
mm=torg[14:16]
ss=torg[17:19]
tfrac=(float(hh)+float(mm)/60.0+float(ss)/3600.0)/24.0
tfrac=float("%.2f" % tfrac)
refdate=gcal2jd(yr,mn,dd)
dtref=dt.datetime(int(yr),int(mn),int(dd),int(hh),int(mm),int(ss))
gtime=[]
D1=dates[0]
D2=dates[len(dates)-1]
daily=date_range(D1,D2,freq='D')
#dtrng=date_range(dtref, periods=len(time),freq='10min')
dtrng=date_range(D1,D2,freq='10min')
ts = Series(ru, index=dtrng,dtype='float64')
std=ts.groupby(daily.asof).std()
```
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} | [] | closed | false | null | [] | null | 8 | 2013-06-06T10:20:17Z | 2013-06-07T17:28:04Z | 2013-06-07T17:27:52Z | NONE | null | Dear developers,
Is there a way to add error bar to a ts.plot object? for example I have a time series and I need to plot the standard deviation along with the data how do I do that ?
Thanks
Sudheer
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} | 5 | 2013-06-06T14:05:35Z | 2013-06-06T14:41:32Z | 2013-06-06T14:38:58Z | CONTRIBUTOR | null | I updated pandas and now my data loading scripts are broken.
Something like this works on 0.11.0
```
pandas.concat(pandas.DataFrame(np.random.rand(5,5)) for _ in range(3)).reset_index()
```
Does not work on recent master (6cd4adc).
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} | 23 | 2013-06-06T15:22:33Z | 2018-07-06T20:21:00Z | 2017-07-11T09:58:31Z | CONTRIBUTOR | null | ``` python
In [8]: wp = Panel(randn(2, 5, 4), items=['Item1', 'Item2'], major_axis=date_range('1/1/2000', periods=5), minor_axis=['A', 'B', 'C', 'D'])
In [9]: wp2 = Panel(randn(2, 5, 4), items=['Item1', 'Item2'], major_axis=date_range('1/1/2000', periods=5), minor_axis=['A', 'B', 'C', 'D'])
In [10]: wp.loc[['Item1', 'Item2'], :, ['A', 'B']]
Out[10]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 5 (major_axis) x 2 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: 2000-01-01 00:00:00 to 2000-01-05 00:00:00
Minor_axis axis: A to B
In [11]: wp.loc[['Item1', 'Item2'], :, ['A', 'B']] = wp2.loc[['Item1', 'Item2'], :, ['A', 'B']]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-11-275f8d9be664> in <module>()
----> 1 wp.loc[['Item1', 'Item2'], :, ['A', 'B']] = wp2.loc[['Item1', 'Item2'], :, ['A', 'B']]
/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.pyc in __setitem__(self, key, value)
85 indexer = self._convert_to_indexer(key)
86
---> 87 self._setitem_with_indexer(indexer, value)
88
89 def _has_valid_tuple(self, key):
/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.pyc in _setitem_with_indexer(self, indexer, value)
182 values = self.obj.values
183 if np.prod(values.shape):
--> 184 values[indexer] = value
185
186 def _align_series(self, indexer, ser):
ValueError: array is not broadcastable to correct shape
```
Those should be the same shape so I don't see why this assignment shouldn't work. This is with pandas 0.11.0.
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https://github.com/astropy/astropy/issues/1156
Note that this doesn't fail on DataFrame construction, but on column selection (the last line):
```
import pandas as pd
from astropy.io import fits
data = fits.getdata('table.fits', 1)
table = pd.DataFrame(data)
table = table.set_index(['ROI', 'Solution'])
print(table.head())
#print(table.xs('HESS_J1023m575'))
print(table[['nfev', 'statname']])
```
Is it possible to either make this work directly or to fail on DataFrame construction and tell the user to e.g. fix the issue by setting an option `fix_byte_order=True`?
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} | [] | closed | false | null | [] | null | 8 | 2013-06-06T15:33:33Z | 2013-06-06T15:51:08Z | 2013-06-06T15:40:46Z | NONE | null | I didn't expect using `pandas.Series` to be almost 4 times slower for some common operations:
``` python
In [161]: a = np.arange(100)
In [162]: b = pandas.Series(a)
In [163]: %timeit abs(a).sum()
100000 loops, best of 3: 7.06 us per loop
In [164]: %timeit abs(b).sum()
10000 loops, best of 3: 27.4 us per loop
In [166]: %timeit abs(a)
1000000 loops, best of 3: 1.43 us per loop
In [167]: %timeit abs(b)
100000 loops, best of 3: 4.33 us per loop
```
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} | [] | closed | false | null | [] | null | 12 | 2013-06-06T17:33:11Z | 2014-07-11T02:49:31Z | 2013-06-06T19:00:04Z | MEMBER | null | closes #3778 .
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} | 3 | 2013-06-06T20:21:42Z | 2014-02-18T19:21:48Z | 2014-02-18T19:21:48Z | CONTRIBUTOR | null | Example:
```
In [15]: np.log(Series([1,2,3,4], dtype='object'))
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-15-25deca6462b7> in <module>()
----> 1 np.log(Series([1,2,3,4], dtype='object'))
AttributeError: log
```
This is really a [numpy issue](http://projects.scipy.org/numpy/ticket/1013) (opened by Wes back in 2009, as Andy Hayden pointed out). But since object arrays are especially common in database output, as was the case in this [SO question](http://stackoverflow.com/questions/16968433/error-when-trying-to-apply-log-method-to-pandas-data-frame-column-in-python/1696849), there some places in pandas where we can help avoid this error.
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} | 8 | 2013-06-06T21:01:24Z | 2013-06-15T12:27:36Z | 2013-06-15T12:27:36Z | CONTRIBUTOR | null | Should we change the name of `save` to `to_pickle` inline with all of the other ways to export things. At the moment the docs are pretty light on what it does.
(I guess similarly with `load` to `from_pickle`.)
_... not sure I actually have an opinion, but thought I'd throw it out there._ Thoughts?
(this so question: http://stackoverflow.com/questions/16971803/serialization-of-a-pandas-dataframe)
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} | 2 | 2013-06-06T21:02:13Z | 2013-09-13T22:54:42Z | 2013-09-13T22:54:42Z | CONTRIBUTOR | null | see #3763
to essentially turn into list-of-list (originally np.asaray was doing this,
when we switched to convert_to_platform this changed slightly)
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} | [] | closed | false | null | [] | null | 9 | 2013-06-06T21:57:54Z | 2020-07-30T13:13:38Z | 2013-06-07T17:20:59Z | CONTRIBUTOR | null | This is when running on current master. Might there be some other prereqs missing? Thanks
```
$ cython --version
Cython version 0.17.4
$ python setup.py build
running build
running build_py
copying pandas/version.py -> build/lib.linux-x86_64-2.7/pandas
running build_ext
Traceback (most recent call last):
File "setup.py", line 516, in <module>
**setuptools_kwargs)
File "/usr/lib/python2.7/distutils/core.py", line 152, in setup
dist.run_commands()
File "/usr/lib/python2.7/distutils/dist.py", line 953, in run_commands
self.run_command(cmd)
File "/usr/lib/python2.7/distutils/dist.py", line 972, in run_command
cmd_obj.run()
File "/usr/lib/python2.7/distutils/command/build.py", line 128, in run
self.run_command(cmd_name)
File "/usr/lib/python2.7/distutils/cmd.py", line 326, in run_command
self.distribution.run_command(command)
File "/usr/lib/python2.7/distutils/dist.py", line 972, in run_command
cmd_obj.run()
File "/usr/lib/python2.7/distutils/command/build_ext.py", line 339, in run
self.build_extensions()
File "setup.py", line 328, in build_extensions
self.check_cython_extensions(self.extensions)
File "setup.py", line 325, in check_cython_extensions
""" % src)
Exception: Cython-generated file 'pandas/parser.c' not found.
Cython is required to compile pandas from a development branch.
Please install Cython or download a release package of pandas.
```
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} | 48 | 2013-06-07T01:54:31Z | 2014-06-22T01:54:36Z | 2013-06-15T08:38:56Z | CONTRIBUTOR | null | Add `read_pickle` to top-level and `to_pickle` as instance methods, deprecation warning til 0.12 for save and load everwhere. See [lower down](https://github.com/pydata/pandas/pull/3787#issuecomment-19211203) for how it's working.
Both `read_pickle` and `to_pickle` are in `io.pickle`, save&load remain in `core.common` (but call to_pickle, read_pickle resp).
cc #3782.
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https://api.github.com/repos/pandas-dev/pandas/issues/3788 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3788/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3788/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3788/events | https://github.com/pandas-dev/pandas/issues/3788 | 15,256,033 | MDU6SXNzdWUxNTI1NjAzMw== | 3,788 | Aggregating over arrays | {
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} | 13 | 2013-06-07T03:29:37Z | 2017-08-23T03:46:07Z | 2016-11-22T11:39:11Z | CONTRIBUTOR | null | This _is_ frowned upon behaviour (storing arrays inside DataFrames) but is there a reason for this raise?
Deleting the raising lines seems to only break tests to check that they're raising...
```
df = pd.DataFrame([[1,np.array([10,20,30])],
[1,np.array([40,50,60])],
[2,np.array([20,30,40])],], columns=['category','arraydata'])
g = df.groupby('category')
```
```
g.agg(sum)
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-34-527a2010b455> in <module>()
----> 1 g.agg(sum)
/Users/andy/pandas/pandas/core/groupby.py in agg(self, func, *args, **kwargs)
337 @Appender(_agg_doc)
338 def agg(self, func, *args, **kwargs):
--> 339 return self.aggregate(func, *args, **kwargs)
340
341 def _iterate_slices(self):
/Users/andy/pandas/pandas/core/groupby.py in aggregate(self, arg, *args, **kwargs)
1740 cyfunc = _intercept_cython(arg)
1741 if cyfunc and not args and not kwargs:
-> 1742 return getattr(self, cyfunc)()
1743
1744 if self.grouper.nkeys > 1:
/Users/andy/pandas/pandas/core/groupby.py in f(self)
62 raise SpecificationError(str(e))
63 except Exception:
---> 64 result = self.aggregate(lambda x: npfunc(x, axis=self.axis))
65 if _convert:
66 result = result.convert_objects()
/Users/andy/pandas/pandas/core/groupby.py in aggregate(self, arg, *args, **kwargs)
1745 return self._python_agg_general(arg, *args, **kwargs)
1746 else:
-> 1747 result = self._aggregate_generic(arg, *args, **kwargs)
1748
1749 if not self.as_index:
/Users/andy/pandas/pandas/core/groupby.py in _aggregate_generic(self, func, *args, **kwargs)
1803 result[name] = self._try_cast(func(data, *args, **kwargs),data)
1804 except Exception:
-> 1805 return self._aggregate_item_by_item(func, *args, **kwargs)
1806 else:
1807 for name in self.indices:
/Users/andy/pandas/pandas/core/groupby.py in _aggregate_item_by_item(self, func, *args, **kwargs)
1828 colg = SeriesGroupBy(obj[item], selection=item,
1829 grouper=self.grouper)
-> 1830 result[item] = colg.aggregate(func, *args, **kwargs)
1831 except ValueError:
1832 cannot_agg.append(item)
/Users/andy/pandas/pandas/core/groupby.py in aggregate(self, func_or_funcs, *args, **kwargs)
1425 return self._python_agg_general(func_or_funcs, *args, **kwargs)
1426 except Exception:
-> 1427 result = self._aggregate_named(func_or_funcs, *args, **kwargs)
1428
1429 index = Index(sorted(result), name=self.grouper.names[0])
/Users/andy/pandas/pandas/core/groupby.py in _aggregate_named(self, func, *args, **kwargs)
1509 output = func(group, *args, **kwargs)
1510 if isinstance(output, np.ndarray):
-> 1511 raise Exception('Must produce aggregated value')
1512 result[name] = self._try_cast(output, group)
1513
Exception: Must produce aggregated value
```
http://stackoverflow.com/questions/16975318/pandas-aggregate-when-column-contains-numpy-arrays
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} | [] | closed | false | null | [] | null | 3 | 2013-06-07T03:43:39Z | 2014-07-16T08:12:21Z | 2013-06-07T13:10:00Z | CONTRIBUTOR | null | - Edited link to timezone handling with epochs
- Added link for dealing with bad lines
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} | 20 | 2013-06-07T12:54:41Z | 2021-02-20T08:02:50Z | 2013-06-07T15:40:59Z | NONE | null | BUG: TimeGrouper not too friendly with other groups, e.g.
`df.set_index('Date').groupby([pd.TimeGrouper('6M'),'Branch']).sum()`
should work
---
Hi everybody,
I found two issues with TimeGrouper:
1) TimeGrouper does not work at all:
Let's take the following example:
df = pd.DataFrame({
'Branch' : 'A A A A A B'.split(),
'Buyer': 'Carl Mark Carl Joe Joe Carl'.split(),
'Quantity': [1,3,5,8,9,3],
'Date' : [
DT.datetime(2013,1,1,13,0),
DT.datetime(2013,1,1,13,5),
DT.datetime(2013,10,1,20,0),
DT.datetime(2013,10,3,10,0),
DT.datetime(2013,12,2,12,0),
DT.datetime(2013,12,2,14,0),
]})
gr = df.groupby(pd.TimeGrouper(freq='6M'))
def testgr(df):
print df
gr.apply(testgr)
This will raise the Exception: "Exception: All objects passed were None"
2) With previous Panda's version it was not possible to combine TimeGrouper with another criteria such as "Branch" in my case.
Thank you very much
Andy
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} | [] | closed | false | null | [] | null | 2 | 2013-06-07T13:15:13Z | 2013-06-07T13:21:41Z | 2013-06-07T13:19:19Z | CONTRIBUTOR | null | Am I using this incorrectly? (see [so qustion](http://stackoverflow.com/questions/16982370/pandas-combine-timegrouper-with-another-groupby-argument)).
```
In [1]: df = pd.DataFrame([[1, 2]], pd.date_range('20000101', periods=2))
In [2]: df
Out[2]:
0 1
2000-01-01 1 2
2000-01-02 1 2
In [3]: g = df.groupby(pd.TimeGrouper('20D'))
```
Doesn't have some attributes, like group and friends:
```
In [4]: g.groups
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-4-b207e9b2ead3> in <module>()
----> 1 g.groups
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/groupby.pyc in __getattr__(self, attr)
234
235 raise AttributeError("'%s' object has no attribute '%s'" %
--> 236 (type(self).__name__, attr))
237
238 def __getitem__(self, key):
AttributeError: 'DataFrameGroupBy' object has no attribute 'groups'
In [5]: g.get_group(0)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-5-7ed4a1924eee> in <module>()
----> 1 g.get_group(0)
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/groupby.pyc in get_group(self, name, obj)
270 obj = self.obj
271
--> 272 inds = self.indices[name]
273 return obj.take(inds, axis=self.axis)
274
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/groupby.pyc in __getattr__(self, attr)
234
235 raise AttributeError("'%s' object has no attribute '%s'" %
--> 236 (type(self).__name__, attr))
237
238 def __getitem__(self, key):
AttributeError: 'DataFrameGroupBy' object has no attribute 'indices'
```
Related to #1057 ?
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} | [] | closed | false | null | [] | null | 2 | 2013-06-07T15:30:43Z | 2013-06-07T15:44:34Z | 2013-06-07T15:44:34Z | NONE | null | Hi everybody,
I have the following DataFrame:
df = pd.DataFrame({
'Buyer': 'Carl Mark Carl Joe Joe Carl'.split(),
'Quantity': [1,3,5,8,9,3],
'Date' : [
DT.datetime(2013,9,1,13,0),
DT.datetime(2013,9,1,13,5),
DT.datetime(2013,10,1,20,0),
DT.datetime(2013,10,3,10,0),
DT.datetime(2013,12,2,12,0),
DT.datetime(2013,12,2,14,0),
]})
df = df.set_index('Date', drop=False)
With the following function:
def f(frame):
cum_sum = 0
if (len(frame) > 0):
cum_sum = frame.Quantity.sum()
frame['cum_sum'] = cum_sum
return frame
x1 = df.groupby(pd.TimeGrouper('20M')).apply(f)
I get a wrong output for the first two rows in x1. Instead of 25, 4 is computed as cum_sum. However, if I use 'A' as a yearly quantifier in the TimeGrouper function the cum_sum of the first two rows is computed correctly?
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} | 12 | 2013-06-07T15:51:31Z | 2014-03-15T14:33:32Z | 2014-03-15T14:33:32Z | CONTRIBUTOR | null | see #3791, #2450
The following should work (but raises now)
```
import datetime as DT
df = pd.DataFrame({
'Branch' : 'A A A A A B'.split(),
'Buyer': 'Carl Mark Carl Joe Joe Carl'.split(),
'Quantity': [1,3,5,8,9,3],
'Date' : [
DT.datetime(2013,1,1,13,0),
DT.datetime(2013,1,1,13,5),
DT.datetime(2013,10,1,20,0),
DT.datetime(2013,10,2,10,0),
DT.datetime(2013,12,2,12,0),
DT.datetime(2013,12,2,14,0),
]})
df = df.set_index('Date', drop=False)
df.groupby([TimeGrouper('6M'),'Buyer']).sum()
```
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} | 7 | 2013-06-07T16:50:01Z | 2013-06-07T21:10:07Z | 2013-06-07T21:10:07Z | CONTRIBUTOR | null | OPs code from http://stackoverflow.com/questions/16988526/pandas-reading-csv-as-string-type
Add: auto conversion from a passed `str` or `np.string_` to `np.object` might be ok
```
df
A B
1A 0.209059 0.275554
1B 0.742666 0.721165
df.to_csv(savefile)
df_read = pd.read_csv(savefile, dtype=str, index_col=0)
A B
B ( <
```
Actually I get something different on dev:
```
In [101]: pd.read_csv('a', index_col=0, dtype=str)
Out[101]:
A B
```
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} | 12 | 2013-06-07T17:25:21Z | 2014-03-18T17:42:12Z | 2014-03-18T17:42:12Z | MEMBER | null | quoting @jreback
maybe
s = Series
`s.plot(error_bar = [-1,1])`
equiv to:
```
df = DataFrame(dict(s = s, top = s.std(), bot = -s.std())).plot()
```
bottom seems the right way to do this....
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} | 8 | 2013-06-07T19:06:19Z | 2014-07-16T08:12:26Z | 2013-06-07T21:10:07Z | CONTRIBUTOR | null | ENH: accept 'str' as a dtype in read_csv to provide correct parsing
closes #3795
```
In [1]: df = DataFrame(np.random.rand(5,2),columns=list('AB'),index=['1A','1B','1C','1D','1E'])
In [2]: df
Out[2]:
A B
1A 0.096563 0.761440
1B 0.623102 0.538810
1C 0.498820 0.277789
1D 0.113544 0.723437
1E 0.381104 0.061758
In [3]: df.dtypes
Out[3]:
A float64
B float64
dtype: object
In [4]: path='test.csv'
In [5]: df.to_csv(path)
In [6]: pd.read_csv(path, dtype=str, index_col=0)
Out[6]:
A B
1A 0.09656290409114332 0.761440208545324
1B 0.6231015058315575 0.5388097714651147
1C 0.49881957371373464 0.27778943212477014
1D 0.11354443109778356 0.723437196012621
1E 0.38110436826261596 0.06175758774696094
In [7]: pd.read_csv(path, dtype=str, index_col=0).dtypes
Out[7]:
A object
B object
dtype: object
```
Invalid dtype specifciation, all `TypeError`, slightly different messages depending on what you are doing (e.g. if its completely invalid or really a date spec)
```
In [8]: pd.read_csv(path, index_col=0, dtype={'A' : 'foo'})
TypeError: data type "foo" not understood
In [9]: pd.read_csv(path, index_col=0, dtype={'A' : 'datetime64'})
TypeError: the dtype <M8 is not supported for parsing, pass this column using parse_dates instead
In [10]: pd.read_csv(path, index_col=0, dtype={'A' : 'timedelta64'})
TypeError: the dtype <m8 is not supported for parsing
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3798 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3798/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3798/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3798/events | https://github.com/pandas-dev/pandas/issues/3798 | 15,293,805 | MDU6SXNzdWUxNTI5MzgwNQ== | 3,798 | int handling by ScaleGradient | {
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} | 3 | 2013-06-07T22:05:07Z | 2015-03-04T10:49:21Z | 2015-03-03T01:34:48Z | NONE | null | If the data series passed to `ScaleGradient` in `plot` is integer-valued, it does not generate a correct color gradient. Here is a sequence consisting of integers used in a call to `ScaleGradient`:
And the same after casting to floats:
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https://api.github.com/repos/pandas-dev/pandas/issues/3799 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3799/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3799/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3799/events | https://github.com/pandas-dev/pandas/issues/3799 | 15,294,299 | MDU6SXNzdWUxNTI5NDI5OQ== | 3,799 | add link to tips data set in rplot docs | {
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} | 0 | 2013-06-07T22:18:33Z | 2013-06-08T14:30:35Z | 2013-06-08T14:30:35Z | MEMBER | null | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3800 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3800/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3800/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3800/events | https://github.com/pandas-dev/pandas/issues/3800 | 15,294,569 | MDU6SXNzdWUxNTI5NDU2OQ== | 3,800 | add travis doc build | {
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} | 54 | 2013-06-07T22:27:22Z | 2016-01-24T23:04:08Z | 2016-01-24T23:04:08Z | MEMBER | null | not sure if this is worth the trouble but it may catch version incompatibility issues and other bugs not caught by the test suite.
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