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} | 8 | 2014-09-07T09:41:37Z | 2014-09-07T19:13:15Z | 2014-09-07T16:47:56Z | CONTRIBUTOR | null | See #7269 for more. (doesn't completely resolve the speed difference, but it gets a 10-15% improvement).
Timing improvement is there, but it's small.
```
In [20]: %timeit test.str.match(pattern2)
10 loops, best of 3: 170 ms per loop
In [21]: %timeit test.str.extract(pattern2)
10 loops, best of 3: 317 ms per loop
In [22]: %timeit -n try_except_str_extract2(test, pattern2)
10 loops, best of 3: 265 ms per loop
```
@jreback - this is very minor, but if you think this needs a release note,
where should I put it?
This passed locally for me, but if Travis fails I'll fix it up.
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} | 0 | 2014-09-07T10:01:23Z | 2014-09-07T12:52:01Z | 2014-09-07T12:52:01Z | CONTRIBUTOR | null | We want to support below 2.0 (non-inclusive), but including 1.6.1. Check was
doing the opposite. Not worth merging if we accept #7565 , but just wanted this
here as a ping if we don't so we're internally consistent.
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} | 7 | 2014-09-07T15:43:08Z | 2014-09-12T13:45:03Z | 2014-09-12T13:45:03Z | CONTRIBUTOR | null | DOC: created advanced indexing section
DOC: add internals section (GH4082)
Mostly just splitting up the indexing docs, but I reorded / revised the Multiindex sections
a bit to avoid duplication / make more clear.
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} | 4 | 2014-09-07T20:15:03Z | 2014-09-09T07:56:45Z | 2014-09-09T07:52:53Z | MEMBER | null | Superceded by #8208 (converting to object dtype also converts the datetime64 values to datetime.datetime, so it is no longer needed that sqlalchemy can work with pandas' Timestamp type.
---
Closes #7103, #7936
This adds a `pandas.io.sql.Timestamp` class to handle `Timestamp`s in `to_sql`. This basically converts it to a `datetime.datetime` before writing to the database.
Problem is that this makes it slower (I tested it with sqlite, and it's for a dataframe with only a datetime64 column about 20% slower), while it did already work before for some drivers (like psycopg2 and MySQLdb).
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} | 4 | 2014-09-07T20:29:51Z | 2016-07-29T07:56:46Z | 2016-07-29T07:56:20Z | MEMBER | null | ```
In [3]: df = pd.DataFrame({'a': ['3.4', '34.400'], 'b': ['5,5', '10,200']})
In [4]: df
Out[4]:
a b
0 3.4 5,5
1 34.400 10,200
In [5]: df.to_csv(lineterminator='-')
Out[5]: ',a,b\n0,3.4,"5,5"\n1,34.400,"10,200"\n'
```
I would've expected
```
Out[5]: ',a,b-0,3.4,"5.5"-1,34.400,"10,200"-'
```
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} | 1 | 2014-09-07T20:46:49Z | 2014-10-04T14:32:22Z | 2014-10-04T14:32:22Z | CONTRIBUTOR | null | - [ ] numpy 1.9.0
- [ ] matplotlib 1.4.0
maybe 3.4 build
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} | 15 | 2014-09-07T21:16:07Z | 2014-09-21T19:05:04Z | 2014-09-13T21:48:56Z | MEMBER | null | Closes #2754
Closes #7103, #7936 (supercedes PR #8205 for this)
Closes #7567
Database drivers expect `None` to translate this to `NULL`.
Therefore, dataframe is cast to object dtype to convert `NaN` to `None`.
To check: performance impact. In principle should also not needed for integer colums (can't have NaNs)
Kind of API change: it did already work for postgresql (psycopg2), but it was translated to postgresql's `NaN` and now to `NULL` which is something different (but I think this is actually an improvement)
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} | 11 | 2014-09-08T00:56:58Z | 2014-09-13T22:59:30Z | 2014-09-13T22:59:30Z | NONE | null | In the code below, I expected that the assignment didn't fail, since everything seems "right" . However, AttributeError is being raised.
A bug? Am I doing something wrong?
``` python
In [106]: import pandas as pd
In [107]: import datetime as dt
In [108]: s = pd.Series([])
In [109]: s
Out[109]: Series([], dtype: float64)
In [110]: s.loc['B'] = dt.timedelta(1)
In [111]: s
Out[111]:
B 1 days
dtype: timedelta64[ns]
In [112]: s = s.reindex(s.index.insert(0, 'A'))
In [113]: s
Out[113]:
A NaT
B 1 days
dtype: timedelta64[ns]
In [114]: s.loc['A'] = dt.timedelta(1)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-114-f662acce72ae> in <module>()
----> 1 s.loc['A'] = dt.timedelta(1)
/home/flaviovs/.virtualenv/hm/local/lib/python2.7/site-packages/pandas-0.14.1_354_g12248ff-py2.7-linux-x86_64.egg/pandas/core/indexing.pyc in __setitem__(self, key, value)
/home/flaviovs/.virtualenv/hm/local/lib/python2.7/site-packages/pandas-0.14.1_354_g12248ff-py2.7-linux-x86_64.egg/pandas/core/indexing.pyc in _setitem_with_indexer(self, indexer, value)
/home/flaviovs/.virtualenv/hm/local/lib/python2.7/site-packages/pandas-0.14.1_354_g12248ff-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in setitem(self, **kwargs)
/home/flaviovs/.virtualenv/hm/local/lib/python2.7/site-packages/pandas-0.14.1_354_g12248ff-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in apply(self, f, axes, filter, do_integrity_check, **kwargs)
/home/flaviovs/.virtualenv/hm/local/lib/python2.7/site-packages/pandas-0.14.1_354_g12248ff-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in setitem(self, indexer, value)
/home/flaviovs/.virtualenv/hm/local/lib/python2.7/site-packages/pandas-0.14.1_354_g12248ff-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in _try_coerce_args(self, values, other)
/home/flaviovs/.virtualenv/hm/local/lib/python2.7/site-packages/pandas-0.14.1_354_g12248ff-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in masker(v)
AttributeError: 'datetime.timedelta' object has no attribute 'view'
```
Tried with 0.14.1 and latest on GH (v0.15pre) , to no avail.
I'm so sorry I'm not able to provide a patch right now (still wrapping my head around pandas internals -- any directions about how to fix this is issue will be appreciated, though).
Versions:
``` python
In [125]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.60
machine: x86_64
processor:
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.14.1-354-g12248ff
nose: None
Cython: 0.20.1
numpy: 1.8.1
scipy: None
statsmodels: None
IPython: 2.1.0
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.3
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.1.1rc2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 10 | 2014-09-08T02:41:35Z | 2014-09-14T14:30:10Z | 2014-09-10T19:50:50Z | CONTRIBUTOR | null | closes #7511
Though having duplicated column names in a dataframe is never a good idea, it may happen, and that shouldn't confuse groupby() with a meaningless message. Currently
``` python
>>> obj=pd.DataFrame(columns=["A","B","A", "C"], data=[range(4), range(2,6), range(0, 8, 2)])
>>> obj.groupby("A").mean()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/core/generic.py", line 2846, in groupby
sort=sort, group_keys=group_keys, squeeze=squeeze)
File "pandas/core/groupby.py", line 1173, in groupby
return klass(obj, by, **kwds)
File "pandas/core/groupby.py", line 390, in __init__
level=level, sort=sort)
File "pandas/core/groupby.py", line 2086, in _get_grouper
ping = Grouping(group_axis, gpr, obj=obj, name=name, level=level, sort=sort)
File "pandas/core/groupby.py", line 1925, in __init__
self.grouper = self.index.map(self.grouper)
File "pandas/core/index.py", line 1524, in map
return self._arrmap(self.values, mapper)
File "generated.pyx", line 2203, in pandas.algos.arrmap_int64 (pandas/algos.c:72274)
TypeError: 'DataFrame' object is not callable
```
This patch fixes that.
``` python
>>> obj.groupby("A").mean()
B C
A A
0 2 1 3
4 2 6
2 4 3 5
```
Thanks.
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"color": "005... | closed | false | null | [] | null | 4 | 2014-09-08T05:34:48Z | 2014-09-08T14:44:56Z | 2014-09-08T11:07:41Z | CONTRIBUTOR | null | When constructing a Series from a dictionary, if the dictionary contains floats, all integer values in the dictionary are coerced to `float` **unless** the dictionary contains an integer value > `sys.maxint`.
Minimal repro case:
```
import pandas as pd
import sys
if __name__ == '__main__':
pd.show_versions()
# Works as expected.
t0 = {'A': 1,
'B': 10}
# B gets coerced to float.
t1 = {'A': 1.5,
'B': 10}
# B gets coerced to float.
t2 = {'A': 1.5,
'B': 10,
'C': sys.maxint}
# B is not coerced if the dictionary contains a value > maxint.
t3 = {'A': 1.5,
'B': 10,
'C': sys.maxint + 1}
test_0 = pd.Series(t0)
test_1 = pd.Series(t1)
test_2 = pd.Series(t2)
test_3 = pd.Series(t3)
for test in test_0, test_1, test_2, test_3:
print "Type at index B is %s" % format(type(test.B))
```
Output:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Darwin
OS-release: 13.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.1
nose: 1.3.3
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.13.2
statsmodels: 0.5.0
IPython: 2.2.0
sphinx: 1.2.1
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.4
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.8
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
Type at index B is <type 'numpy.int64'>
Type at index B is <type 'numpy.float64'>
Type at index B is <type 'numpy.float64'>
Type at index B is <type 'int'>
```
Stepping through the code, the coercion looks like it's happening in the call to `lib.fast_multiget` at https://github.com/pydata/pandas/blob/v0.14.1/pandas/core/series.py#L191.
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} | 8 | 2014-09-08T07:14:30Z | 2016-12-03T12:37:32Z | 2016-12-03T12:37:32Z | NONE | null | Dear all,
Here from the mailing list: https://groups.google.com/forum/#!topic/pydata/jKiPOvYUQ1c
I have an excel table about family ages like this
| Family | People | Mean size [cm] |
| --- | --- | --- |
| Foo | 5 | 173.0 |
| Bar | 3 | 189.0 |
and I would like to use read_excel to parse it into Python. I would like "People" to be read as an integer, "Mean size [cm]" as a float. (And "Family" as a string, but that might be a different issue.) Now:
- if I set convert_float=True, the last column reads as int
- if I set convert_float=False, the second column reads as float
Neither one is correct, for a stupid reason: there happen to be those .0 in all sizes! So I would like to specify something like:
``` python
convert_float = ['People']
```
so only that column gets converted. An even better solution would be to be explicit about types of some columns, letting pandas perform the automagic for the others, such as:
``` python
read_excel('foo.xlsx', types={'People': np.uint8, 'Family': 'S3'})
```
but this changes the signature of the function more significantly.
Are you folks in favour of any of this? If yes, I can get a look and try to code it in.
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] | closed | false | null | [] | null | 3 | 2014-09-08T15:32:54Z | 2014-09-09T13:32:45Z | 2014-09-09T13:32:45Z | NONE | null | Me and several of my colleagues typically create output for variables that have greek letters. Inside pandas, we then call them "epsilon", "sBar" or similar. Right now we have to run a string replace on the output of to_latex() and then manually save it to a file.
It'd be nice to be able to give to_latex() a template such as ['epsilon':'$\epsilon$', 'sBar':'$\bar s$'] and then to_latex() would replace these in the output accordingly.
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} | 0 | 2014-09-08T15:39:20Z | 2014-09-13T22:59:30Z | 2014-09-13T22:59:30Z | CONTRIBUTOR | null | https://github.com/pydata/pandas/pull/8184
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https://api.github.com/repos/pandas-dev/pandas/issues/8215 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8215/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8215/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8215/events | https://github.com/pandas-dev/pandas/issues/8215 | 42,210,797 | MDU6SXNzdWU0MjIxMDc5Nw== | 8,215 | Series/Dataframe to_csv inconsistency when path=None [0.14.1] | {
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} | 2 | 2014-09-08T15:44:51Z | 2014-09-09T16:58:14Z | 2014-09-09T16:58:14Z | CONTRIBUTOR | null | It looks like `Series.to_csv` and `DataFrame.to_csv` differ in their handling of `path=None`:
``` python
In [1]: import pandas as pd
In [2]: print pd.Series([1, 1, 1], index=[1, 2, 3]).to_csv(None)
None
In [3]: print pd.DataFrame([1, 1, 1], index=[1, 2, 3]).to_csv(None)
,0
1,1
2,1
3,1
```
I haven't gone digging yet but I expect these two methods share enough code that this is a trivial fix.
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https://api.github.com/repos/pandas-dev/pandas/issues/8216 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8216/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8216/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8216/events | https://github.com/pandas-dev/pandas/issues/8216 | 42,227,469 | MDU6SXNzdWU0MjIyNzQ2OQ== | 8,216 | BUG: data frame plot hangs when logx True and some x are zero | {
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} | [] | closed | false | null | [] | null | 1 | 2014-09-08T18:28:40Z | 2015-10-20T22:41:20Z | 2015-10-20T22:41:19Z | CONTRIBUTOR | null | ```
In [1]: import pandas as pd
In [2]: import matplotlib.pyplot as plt
In [3]: import numpy as np
In [8]: pandas.version.version
Out[8]: '0.14.1'
In [9]: x = [1,0]
In [10]: y= [2,3]
In [11]: df = pd.DataFrame()
In [12]: df['x'] = x
In [13]: df['y'] = y
In [14]: df
Out[14]:
x y
0 1 2
1 0 3
In [15]: df.plot('x', 'y', kind = 'scatter')
Out[15]: <matplotlib.axes.AxesSubplot at 0x10d366590>
In [16]: plt.show() # this works
In [17]: df.plot('x', 'y', logx = True, kind = 'scatter')
Out[17]: <matplotlib.axes.AxesSubplot at 0x10d39bf10>
In [18]: plt.show() # hangs forever
```
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} | 1 | 2014-09-08T19:52:55Z | 2014-09-08T23:40:54Z | 2014-09-08T23:40:44Z | CONTRIBUTOR | null | ``` python
In [1]: import pandas as pd, StringIO
In [2]: csv = StringIO.StringIO('time,data\n0,10\n1,11\n2,12\n4,14\n5,15\n3,13')
In [3]: df = pd.read_csv(csv, index_col='time')
In [4]: df
Out[4]:
data
time
0 10
1 11
2 12
4 14
5 15
3 13
In [5]: df.sort(inplace=True)
In [6]: df
Out[6]:
data
time
0 10
1 11
2 12
3 13
4 14
5 15
```
Specifying `squeeze=True` returns a view instead of first-class object? Is this by design and if so, why?
As of 0.14.1, `inplace=True` is the default (related #5190) for series sorts so the following error will occur by default if `squeeze=True` was specified.
``` python
In [1]: import pandas as pd, StringIO
In [2]: csv = StringIO.StringIO('time,data\n0,10\n1,11\n2,12\n4,14\n5,15\n3,13')
In [3]: df = pd.read_csv(csv, index_col='time', squeeze=True)
In [4]: df
Out[4]:
data
time
0 10
1 11
2 12
4 14
5 15
3 13
In [5]: df.sort(inplace=True)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-850f333de498> in <module>()
----> 1 df.sort(inplace=True)
C:\WinPython-64bit-2.7.5.3\python-2.7.5.amd64\lib\site-packages\pandas\core\series.pyc in sort(self, axis, ascending, kind, na_position, inplace)
1651 kind=kind,
1652 na_position=na_position,
-> 1653 inplace=inplace)
1654
1655 def order(self, na_last=None, ascending=True, kind='quicksort', na_position='last', inplace=False):
C:\WinPython-64bit-2.7.5.3\python-2.7.5.amd64\lib\site-packages\pandas\core\series.pyc in order(self, na_last, ascending, kind, na_position, inplace)
1684 # GH 5856/5853
1685 if inplace and self._is_cached:
-> 1686 raise ValueError("This Series is a view of some other array, to "
1687 "sort in-place you must create a copy")
1688
ValueError: This Series is a view of some other array, to sort in-place you must create a copy
```
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} | 0 | 2014-09-08T23:08:34Z | 2014-09-08T23:40:44Z | 2014-09-08T23:40:44Z | CONTRIBUTOR | null | closes #8217
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} | 2 | 2014-09-09T05:11:14Z | 2014-09-20T22:12:29Z | 2014-09-14T14:13:51Z | CONTRIBUTOR | null | follow up of #7982
_One question I had was the use of `{ }` (is that a convention? I removed it and it seems to compile ok.)_
Note: should test MI cols and MI index. Ah cripes... the names of the column Index don't print, not did they before...
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} | 3 | 2014-09-09T09:08:06Z | 2014-09-09T11:29:36Z | 2014-09-09T11:29:21Z | CONTRIBUTOR | null | Addresses #8215. Currently, `Series.to_csv(path=None)` returns `None`. The `DataFrame` method returns a string, and already had this behaviour mentioned in the docstring, so it should be OK to make the `Series` method match this.
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} | 5 | 2014-09-09T14:30:18Z | 2016-12-24T20:53:51Z | 2016-12-24T20:53:51Z | CONTRIBUTOR | null | I'm unsure about a corner case I encountered:
``` python
>>> pd.DataFrame([]).applymap(round)
Series([], dtype: float64)
```
full example
```
In [1]: df = pandas.DataFrame({ 'x' : [], 'y' : [], 'z' : []})
In [2]: df
Out[2]:
Empty DataFrame
Columns: [x, y, z]
Index: []
In [3]: df.applymap(lambda x: x)
Out[3]:
x NaN
y NaN
z NaN
dtype: float64
In [4]: df = pandas.DataFrame({ 'x' : [1], 'y' : [1], 'z' : [1]})
In [5]: df.applymap(lambda x: x)
Out[5]:
x y z
0 1 1 1
```
I would have expected to get the empty DataFrame back.
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Before:
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https://api.github.com/repos/pandas-dev/pandas/issues/8224 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8224/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8224/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8224/events | https://github.com/pandas-dev/pandas/issues/8224 | 42,315,313 | MDU6SXNzdWU0MjMxNTMxMw== | 8,224 | saving Panel with PeriodIndex to HDF in table format loses Period information | {
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... | closed | false | null | [] | null | 2 | 2014-09-09T14:57:03Z | 2014-09-09T15:02:58Z | 2014-09-09T15:02:12Z | CONTRIBUTOR | null | ```
(Pdb) print pd.__version__
0.14.0-403-ge131df1
(Pdb) print data
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 35 (major_axis) x 21 (minor_axis)
Items axis: Constant Prices to Current Prices
Major_axis axis: Andaman & Nicobar to West Bengal
Minor_axis axis: 1994 to 2014
(Pdb) print data.minor_axis
<class 'pandas.tseries.period.PeriodIndex'>
[1994, ..., 2014]
Length: 21, Freq: A-MAR
(Pdb) data.to_hdf("test.h5", "test_no_table")
(Pdb) data.to_hdf("test.h5", "test_table", format="table")
(Pdb) pd.read_hdf("test.h5", "/test_no_table").minor_axis
<class 'pandas.tseries.period.PeriodIndex'>
[1994, ..., 2014]
Length: 21, Freq: A-MAR
(Pdb) pd.read_hdf("test.h5", "/test_table").minor_axis
Int64Index([35, 36, 37, 38, 39, 40, 41, 42, 43, 44], dtype='int64')
```
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} | 21 | 2014-09-09T18:29:09Z | 2015-10-30T14:58:28Z | 2015-10-30T14:58:28Z | CONTRIBUTOR | null | Original post, edited to correct ISO 8601 formats. The originally titled bug does not exist. There is, however, some unexpected behavior:
> It looks like short-form ISO 8601 parsing uses the right sign after all:
>
> ``` python
> In [1]: import pandas as pd
>
> In [2]: t = pd.Timestamp('2013-11-03 1:30:00', tz='America/Havana')
>
> In [3]: t.strftime('%Y-%m-%d %H:%M:%S %Z %z')
> Out[3]: '2013-11-03 01:30:00 CST -0500'
>
> In [4]: t.strftime('%Y%m%dT%H%M%S%z') # ISO 8601 short format
> Out[4]: '20131103T013000-0500'
>
> In [5]: pd.Timestamp(t.strftime('%Y%m%dT%H%M%S%z'))
> Out[5]: Timestamp('2013-11-03 01:30:00-0500', tz='tzoffset(None, -18000)')
>
> In [6]: t == _
> Out[6]: True # Not a bug after all!
>
> In [7]: t.strftime('%Y-%m-%dT%H:%M:%S%z') # ISO 8601 long format
> Out[7]: '2013-11-03T01:30:00-0500'
>
> In [8]: pd.Timestamp(t.strftime('%Y-%m-%dT%H:%M:%S%z'))
> Out[8]: Timestamp('2013-11-03 01:30:00-0500', tz='pytz.FixedOffset(-300)') # why pytz?
>
> In [9]: t == _
> Out[9]: True
>
> In [10]: t.strftime('%Y%m%dT%H%M%SZ%z') # Not a real ISO 8601 format (note the 'Z')
> Out[10]: '20131103T013000Z-0500'
>
> In [11]: pd.Timestamp(t.strftime('%Y%m%dT%H%M%SZ%z'))
> Out[11]: Timestamp('2013-11-03 01:30:00+0500', tz='tzoffset(None, 18000)')
> # This is the behavior that inspired the bug report. I don't know if this is a valid parse
> # or not, but it sure is unexpected.
> ```
>
> It would be awfully embarrassing if I filed a bug report because I couldn't read ISO 8601...
However, in the process of investigating this issue, I encountered the following, which is definitely a bug:
``` python
In [12]: ts = ['2013-11-%s3 %s1:30:00' % (x, y) for x in ['', '0'] for y in ['', '0']]
In [13]: ts
Out[13]:
['2013-11-3 1:30:00',
'2013-11-3 01:30:00',
'2013-11-03 1:30:00',
'2013-11-03 01:30:00'] # only this one is ISO 8601
In [14]: tzs = ['America/%s' % s for s in ['Chicago', 'New_York', 'Havana']]
In [15]: [[pd.Timestamp(t, tz=tz) for t in ts] for tz in tzs]
Out[15]:
[[Timestamp('2013-11-03 01:30:00-0600', tz='America/Chicago'),
Timestamp('2013-11-03 01:30:00-0600', tz='America/Chicago'),
Timestamp('2013-11-03 01:30:00-0600', tz='America/Chicago'),
Timestamp('2013-11-03 01:30:00-0500', tz='America/Chicago')], # DST
[Timestamp('2013-11-03 01:30:00-0500', tz='America/New_York'),
Timestamp('2013-11-03 01:30:00-0500', tz='America/New_York'),
Timestamp('2013-11-03 01:30:00-0500', tz='America/New_York'),
Timestamp('2013-11-03 01:30:00-0400', tz='America/New_York')], # DST
[Timestamp('2013-11-03 01:30:00-0500', tz='America/Havana'),
Timestamp('2013-11-03 01:30:00-0500', tz='America/Havana'),
Timestamp('2013-11-03 01:30:00-0500', tz='America/Havana'),
Timestamp('2013-11-03 00:30:00-0500', tz='America/Havana')]] # Just plain wrong
```
I'm not sure what's going on here.
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} | 25 | 2014-09-09T19:39:55Z | 2014-09-20T13:42:31Z | 2014-09-11T15:44:57Z | CONTRIBUTOR | null | see comments at the end of #8184
so this type of syntax
`index1 + index2`
would be replaced by
`index1.union(index2)`
and
`index1 - index2`
by
`index1.diff(index2)`
Their is some internal code to fix but not a lot.
This would go a long ways toward simplifying the user API for index ops. I think.
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} | 7 | 2014-09-09T22:31:37Z | 2014-10-06T14:28:34Z | 2014-09-11T15:44:57Z | CONTRIBUTOR | null | closes #8226
- ToDo
- [x] currently a raise `TypeError` (need a better message), possibly could deprecate and just warn
- [x] API example
Combining indexes via `+` now requires `.union()`
Diffing indexes via `-` now requires `.diff()`
These make the Indexes use setops (`&`,`|`,`^`) ONLY
numeric ops will work on appropriate Index ONLY (see #8184) (e.g. `DatetimeIndex` +/- `Timedelta` or `TimedeltaIndex` +/- `TImedeltaIndex`)
Also `Int64/Float64Index` would gain ability to `+/-` as well
This will promote consistency that `+` means add (and not union), and `-` means subtract (and not difference), which IIRC has come up in a few issues
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"description... | closed | false | null | [] | null | 3 | 2014-09-10T00:11:53Z | 2015-10-20T22:41:31Z | 2015-10-20T22:41:31Z | NONE | null | Bonjour,
The pd.read_csv() function crash when common latin1 characters (ex: éàèç) occurs in the file path. We can use the following workaround in python 2.7 (Note: I use pandas 0.14 and from **future** import unicode_literals):
> > > import locale
> > > os_codec = locale.getdefaultlocale()[1] # this is 'cp1252' on winXP
> > > data = pd.read_csv(myfilepath.encode(os_codec))
> > > However in python 3.4 I can't figure how to do it. We get the following error message:
> > > IOError: Expected file path name or file-like object, got <class 'byte'> type.
It would be nice if the getdefaultlocale() could be managed inside pandas instead of at the user level?
Thanks
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} | 1 | 2014-09-10T01:00:38Z | 2018-07-06T22:31:32Z | 2018-07-06T22:31:27Z | NONE | null | From this discussion: https://groups.google.com/d/msg/pydata/AExc0ITFm94/Q5k5hcwZQFMJ
It appears that groupby object cannot be unpickled. The following code reproduces the problem for me:
```
import pandas as pd
import pickle
df = pd.DataFrame([[1,2,3], [4,5,6], [7,8,9]])
gr = df.groupby(0)
b = pickle.dumps(gr)
gr2 = pickle.loads(b) # Fails with RuntimeError
<snip 50 repeat lines...>
File "C:\Miniconda3\lib\site-packages\pandas\core\groupby.py", line 482, in __getattr__
if attr in self.obj:
File "C:\Miniconda3\lib\site-packages\pandas\core\groupby.py", line 482, in __getattr__
if attr in self.obj:
RuntimeError: maximum recursion depth exceeded while calling a Python object
```
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} | 1 | 2014-09-10T01:03:51Z | 2014-09-10T18:09:15Z | 2014-09-10T18:09:15Z | NONE | null | ```
import pandas as pd
df = pd.DataFrame([[0,1,2]], index=['Sum'], columns=['A','B','C'])
print(df.to_string(col_space=20))
print(df.to_string(col_space=20, header=False))
```
Output:
```
A B C
Sum 0 1 2
Sum 0 1 2
```
(a minor irritation...)
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"id": 233160... | closed | false | null | [] | null | 10 | 2014-09-10T02:17:00Z | 2014-09-15T03:47:10Z | 2014-09-14T16:50:10Z | CONTRIBUTOR | null | Addresses #6944, by defining whitelisted methods in the `DataFrameGroupBy` and `SeriesGroupBy` class definitions. After this change, users of the whitelisted methods now get useful results from `help`, argument name completion, and auto-generated api docs. These `DataFrameGroupBy` and `SeriesGroupBy` methods are added to the api doc. Existing unit tests are extended to check for the presence of the whitelisted methods in the class definitions, and a new unit test verifies that old and new paths produce the same results for methods supporting level, axis, skipna arguments. (Existing methods already explicitly defined in `GroupBy` or derived classes are not overwritten.)
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} | 14 | 2014-09-10T04:16:17Z | 2014-09-15T21:50:33Z | 2014-09-15T21:43:01Z | CONTRIBUTOR | null | Closes #8097
Reorganized a lot of the SQL schema creation code. Now database table is not dropped / create when `PandasSQLTable` object is instantiated, but instead when `insert` method is called on it. This allow us to `get_schema` to generate `CREATE TABLE` code without actually creating the table.
(Includes updates from #8083, so will have to be rebased when that is merged)
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} | 2 | 2014-09-10T09:37:14Z | 2014-09-10T18:09:15Z | 2014-09-10T18:09:05Z | CONTRIBUTOR | null | Fixes #8230. I think this is a pretty easy fix, `col_space` wasn't being used at all when `header` was `False` so just had to add the minimum width.
Results:
``` python
import pandas as pd
df = pd.DataFrame([[0,1,2]], index=['Sum'], columns=['A','B','C'])
print(df.to_string(col_space=20))
# Output:
A B C
Sum 0 1 2
print(df.to_string(col_space=20, header=False))
# Output:
Sum 0 1 2
```
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} | 6 | 2014-09-10T12:10:57Z | 2015-02-12T14:22:59Z | 2015-02-12T14:22:59Z | MEMBER | null | Opening an issue to not forget this, see https://github.com/pydata/pandas/pull/7736#issuecomment-53970872
@sinhrks
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https://api.github.com/repos/pandas-dev/pandas/issues/8235 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8235/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8235/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8235/events | https://github.com/pandas-dev/pandas/pull/8235 | 42,424,680 | MDExOlB1bGxSZXF1ZXN0MjEwMjgxNDI= | 8,235 | BUG: interpolate with no nans and limit | {
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} | 1 | 2014-09-10T15:21:35Z | 2014-09-10T17:45:37Z | 2014-09-10T17:45:32Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/7173
I was assuming that there would be at least one NaN when I was adjusting for the limits. Pretty simple fix.
Here's the intended behavior:
``` python
In [1]: s = pd.Series([1., 2, 3])
In [2]: s.interpolate()
Out[2]:
0 1
1 2
2 3
dtype: float64
In [3]: s.interpolate(limit=1)
Out[3]:
0 1
1 2
2 3
dtype: float64
```
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} | 1 | 2014-09-10T16:13:30Z | 2014-10-04T13:37:55Z | 2014-10-04T13:37:55Z | CONTRIBUTOR | null | For rolling back or for moving forward more than one step, `DateOffset`s create a new instance setting `n` [accordingly](https://github.com/bjonen/pandas/blob/master/pandas/tseries/offsets.py#L312). The `CustomBusinessDay` constructor can be very slow, however, when a large list of `holidays` is passed. The holidays are [processed](https://github.com/bjonen/pandas/blob/master/pandas/tseries/offsets.py#L633) to datetime64.
As a result speed deteriorates by two magnitudes.
```
import pandas as pd
import datetime as dt
pd.__version__
Out[6]:
'0.14.1'
In [7]:
cbday = pd.offsets.CustomBusinessDay()
date + cbday
%timeit date + cbday
100000 loops, best of 3: 14.6 µs per loop
In [8]:
hdays = [dt.datetime(2013,1,1) for ele in range(1000)]
cbdayh = pd.offsets.CustomBusinessDay(holidays=hdays)
date + cbdayh
%timeit date + cbdayh
%timeit date + 2*cbdayh
%timeit date - 1*cbdayh
%timeit date - 10*cbday
100000 loops, best of 3: 14.6 µs per loop
100 loops, best of 3: 3.4 ms per loop
100 loops, best of 3: 6.76 ms per loop
10000 loops, best of 3: 27.8 µs per loop
```
I think it makes sense to have the user call `to_dt64` before initializing `CustomBusinessDay` and to rely on the holidays to be passed in the correct format. Any other ideas how to handle this problem? Shall I create a PR for this?
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} | 0 | 2014-09-10T16:14:58Z | 2014-09-10T18:09:28Z | 2014-09-10T18:09:27Z | CONTRIBUTOR | null | closes #7932
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} | 44 | 2014-09-10T21:53:49Z | 2014-09-23T14:34:58Z | 2014-09-23T13:04:18Z | CONTRIBUTOR | null | This closes #7618 and removes the dependency on scikits-timeseries for testing rolling_window functions. Scikits-timeseries has not been maintained since 2009, and I had difficulty simply installing it on my system. Instead of using scikits-timeseries to calculate the various rolling moments on randomly generated data, the tests use pre-calculated data.
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} | 9 | 2014-09-11T00:49:44Z | 2015-08-02T23:01:30Z | 2015-08-02T23:01:29Z | CONTRIBUTOR | null | originally #5190
xref #9816
xref #3942
This issue is for creating a unified API to Series & DataFrame sorting methods. Panels are not addressed (yet) but a unified API should be easy to extend to them. Related are #2094, #5190, #6847, #7121, #2615. As discussion proceeds, this post will be edited.
For reference, the 0.14.1 signatures are:
``` python
Series.sort(axis=0, ascending=True, kind='quicksort', na_position='last', inplace=True)
Series.sort_index(ascending=True)
Series.sortlevel(level=0, ascending=True, sort_remaining=True)
DataFrame.sort(columns=None, axis=0, ascending=True, inplace=False, kind='quicksort',
na_position='last')
DataFrame.sort_index(axis=0, by=None, ascending=True, inplace=False, kind='quicksort',
na_position='last')
DataFrame.sortlevel(level=0, axis=0, ascending=True, inplace=False, sort_remaining=True)
```
Proposed unified signature for `Series.sort` and `DataFrame.sort` (except Series version retains current inplace=True):
``` python
def sort(self, by=None, axis=0, level=None, ascending=True, inplace=False,
kind='quicksort', na_position='last', sort_remaining=True):
"""Sort by labels (along either axis), by the values in column(s) or both.
If both, labels take precedence over columns. If neither is specified,
behavior is object-dependent: series = on values, dataframe = on index.
Parameters
----------
by : column name or list of column names
if not None, sort on values in specified column name; perform nested
sort if list of column names specified. this argument ignored by series
axis : {0, 1}
sort index/rows (0) or columns (1); for Series, only 0 can be specified
level : int or level name or list of ints or list of column names
if not None, sort on values in specified index level(s)
ascending : bool or list of bool
Sort ascending vs. descending. Specify list for multiple sort orders.
inplace : bool
if True, perform operation in-place (without creating new instance)
kind : {‘quicksort’, ‘mergesort’, ‘heapsort’}
Choice of sorting algorithm. See np.sort for more information.
‘mergesort’ is the only stable algorithm. For data frames, this option is
only applied when sorting on a single column or label.
na_position : {'first', 'last'}
‘first’ puts NaNs at the beginning, ‘last’ puts NaNs at the end
sort_remaining : bool
if true and sorting by level and index is multilevel, sort by other levels
too (in order) after sorting by specified level
"""
```
The `sort_index` signatures change too and `sort_columns` is created:
``` python
Series.sort_index(level=0, ascending=True, inplace=False, kind='quicksort',
na_position='last', sort_remaining=True)
DataFrame.sort_index(level=0, axis=0, by=None, ascending=True, inplace=False,
kind='quicksort', na_position='last', sort_remaining=True)
# by is DEPRECATED, see change 7
DataFrame.sort_columns(by=None, level=0, ascending=True, inplace=False,
kind='quicksort', na_position='last', sort_remaining=True)
# or maybe level=None
```
Proposed changes:
1. ~~make `inplace=False` default (changes `Series.sort`)~~ _maybe, possibly in 1.0_
2. new `by` argument to accept column-name/list-of-column-names in first position
- deprecate `columns` keyword of `DataFrame.sort`, replaced with `by` (df.sort signature would need to retain columns keyword until finally removed but it's not shown in proposal)
- don't allow tuples to access levels of multi-index (`columns` arg of `DataFrame.sort` allows tuples); use new `level` argument instead
- don't swap order of `by`/`axis` in `DataFrame.sort_index` (see change 7)
- this argument is ignored by series but `axis` is too so for the sake of working with dataframes, it gets first position
3. new `level` argument to accept integer/level-name/list-of-ints/list-of-level-names for sorting (multi)index by particular level(s)
- replaces tuple behavior of `columns` arg of `DataFrame.sort`
- add `level` argument to `sort_index` in first position so level(s) of multilevel index can be specified; this makes `sort_index`==`sortlevel` (see change 8)
- also adds `sort_remaining` arg to handle multi-level indexes
4. new method `DataFrame.sort_columns`==`sort(axis=1)` (see syntax below)
5. deprecate `Series.order` since change 1 makes `Series.sort` equivalent (?)
6. add `inplace`, `kind`, and `na_position` arguments to `Series.sort_index` (to match `DataFrame.sort_index`); `by` and `axis` args are not added since they don't make sense for series
7. deprecate and eventually remove `by` argument from `DataFrame.sort_index` since it makes `sort_index` equivalent to `sort`
8. deprecate `sortlevel` since change 3b makes `sort_index` equivalent
Notes:
- default behavior of `sort` is still object-dependent: for series, sorts by values and for data frames, sorts by index
- new `level` arg makes `sort_index` and `sortlevel` equivalent. if sortlevel is retained:
- should rename `sortlevel` to `sort_level` for naming conventions
- `Series.sortlevel` should have `inplace` argument added
- maybe don't add `level` and `sort_remaining` args to `sort_index` so it's not equivalent to `sort_level` (intentionally limiting sort_index seems like a bad idea though)
- it's unclear if default should be `level=None` for `sort_columns`. probably not since level=None falls back to level=0 anyway
- both `by` and `axis` arguments should be ignored by `Series.sort`
Syntax:
- dataframes
- `sort()` == `sort(level=0)` == `sort_index()` == `sortlevel()`
- without columns or level specified, defaults to current behavior of sort on index
- `sort(['A','B'])`
- since columns are specified, default index sort should not occur; sorting only happens using columns 'A' and 'B'
- `sort(level='spam')` == `sort_index('spam')` == `sortlevel('spam')`
- sort occurs on row index named 'spam' or level of multi-index named 'spam'
- `sort(['A','B'], level='spam')`
- `level` controls here even though columns are specified so sort happens along row index named 'spam' first, then nested sort occurs using columns 'A' and 'B'
- `sort(axis=1)` == `sort(axis=1, level=0)` == `sort_columns()`
- since data frames default to sort on index, leaving level=None is the same as level=0
- `sort(['A','B'], axis=1)` == `sort_columns(['A','B'])`
- as with preceding example, level=None becomes level=0 in sort_columns
- `sort(['A','B'], axis=1, level='spam')` == `sort_columns(['A','B'], level='spam')`
- `axis` controls `level` so sort will be on columns named 'A' and 'B' in column index named 'spam'
- series:
- `sort()` == `order()` -- sorts on values
- with `level` specified, sorts on index/named index/level of multi-index:
- `sort(level=0)` == `sort_index()` == `sortlevel()`
- `sort(level='spam')` == `sort_index('spam')` == `sortlevel('spam')`
Comments welcome.
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} | 5 | 2014-09-11T02:50:00Z | 2014-09-27T03:17:58Z | 2014-09-17T19:32:26Z | CONTRIBUTOR | null | Fixes #8181. Currently the boxplot fails when trying to compute quantiles on an empty array, which numpy can't deal with. Works okay if we use an array with a single `np.nan` instead, the empty column is just left out of the plot as expected.
I guess that might be a bit of a hack, relying on how numpy deals with a single `np.nan` in an array versus an empty array? If people think it's too risky to depend on that behaviour I'll try to rethink.
New output:
``` python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.DataFrame(np.random.randn(100, 4))
df.loc[:, 0] = np.nan
df.plot(kind='box')
plt.show()
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8241 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8241/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8241/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8241/events | https://github.com/pandas-dev/pandas/issues/8241 | 42,496,620 | MDU6SXNzdWU0MjQ5NjYyMA== | 8,241 | BUG: reindex on level of multi-index does not respect order of new index ? | {
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"description": "Concat, Merge/Join, Stac... | closed | false | null | [] | null | 1 | 2014-09-11T07:31:31Z | 2014-09-11T11:37:35Z | 2014-09-11T11:37:35Z | MEMBER | null | When having a MultiIndex, and reindexing on a level of it, `reindex` does not respect the order of the new index. Is this a bug? Or a limitation of the multi-index reindexing (as in one level you don't have unique elements?) which should be documented?
```
In [69]: s = pd.Series([0,1,2,3,4,5,6,7], index=pd.MultiIndex.from_product([[1,2,3,4],[7,8]]))
In [70]: s
Out[70]:
1 7 0
8 1
2 7 2
8 3
3 7 4
8 5
4 7 6
8 7
dtype: int64
In [71]: s.reindex([3,2], level=0)
Out[71]:
2 7 2
8 3
3 7 4
8 5
dtype: int64
```
Als with a dataframe multi-indexed columns:
```
In [72]: df = pd.DataFrame(np.random.randn(5,6), columns=pd.MultiIndex.from_prod
uct([list('abc'), list('ef')]))
In [73]: df
Out[73]:
a b c
e f e f e f
0 -0.968421 0.343670 0.353754 0.211625 1.964620 0.696301
1 -1.082284 0.532646 0.099657 -1.355934 -0.748171 -0.766453
2 0.674002 -0.440444 -0.296365 0.225170 0.592352 -0.691878
3 0.880808 0.470677 0.330382 0.937828 -0.225192 -1.059934
4 -0.616588 0.501665 -0.232722 1.300403 0.235369 2.318810
In [74]: df.reindex(columns=['c', 'b'], level=0)
Out[74]:
b c
e f e f
0 0.353754 0.211625 1.964620 0.696301
1 0.099657 -1.355934 -0.748171 -0.766453
2 -0.296365 0.225170 0.592352 -0.691878
3 0.330382 0.937828 -0.225192 -1.059934
4 -0.232722 1.300403 0.235369 2.318810
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8242 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8242/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8242/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8242/events | https://github.com/pandas-dev/pandas/issues/8242 | 42,513,987 | MDU6SXNzdWU0MjUxMzk4Nw== | 8,242 | Multiple calls to .plot(): colors not correctly cycled | {
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} | [] | closed | false | null | [] | null | 4 | 2014-09-11T11:27:48Z | 2014-09-12T17:19:58Z | 2014-09-12T17:19:35Z | CONTRIBUTOR | null | Versions:
Pandas 0.14.1
Matplotlib: 1.4.0
Reproduce:
Run:
``` python
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
for i in range(20):
pd.Series(np.random.rand(20)).plot(label=i)
plt.legend()
```
Output:
You can see that the colors are cycled once and then only blue (i.e. the first color) is being used.
Expected output:
You can see that colors are being cycled correctly.
The expected output was produced using matplotlib's `plot()` command. But note that if `Series.plot()` was run once in the interpreter, matplotlib's `plot()` command produces the same errenous behavior (only plotting blue after the first cycle). It thus appears that `Series.plot()` sets some global matplotlib state that causes this.
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} | 8 | 2014-09-11T12:16:26Z | 2016-12-11T14:02:01Z | 2016-12-10T23:29:31Z | NONE | null | numpy version 1.8.2
pandas version 0.14.1
In pandas/stats/ols.py:821, we have:
mask = -np.isnan(betas).any(axis=1)
This causes problem with newer numpy which deprecated negative unary operator (-) on boolean arrays. I got this following error when running into this line:
DeprecationWarning: numpy boolean negative (the unary `-` operator) is deprecated, use the bitwise_xor (the `^` operator) or the logical_xor function instead.
When I change "-" to "~", the problem goes away. It's minor, but hope we can incorporate this into next pandas update. Thanks.
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} | 8 | 2014-09-11T15:53:59Z | 2018-07-06T22:30:32Z | 2018-07-06T22:30:27Z | CONTRIBUTOR | null | You can already kind of do this by passing in the numpy array
```
In [83]: df = pd.DataFrame(np.random.randn(100, 2))
In [84]: df['z'] = np.random.uniform(size=(100))
In [85]: df.plot(kind='scatter', x=0, y=1, s=df.z.values * 1000)
Out[85]: <matplotlib.axes._subplots.AxesSubplot at 0x11a81df60>
```
But when I merge https://github.com/pydata/pandas/pull/7780/ (coloring by column) it would be natural (and awesome) to do `df.plot(kind='scatter', x='x', y='y', c='color', s='size')`
Shouldn't be too hard if we're willing.
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} | 5 | 2014-09-11T16:02:17Z | 2014-09-11T23:38:47Z | 2014-09-11T23:38:47Z | NONE | null | An example is best:
``` python
2 Inn: df = P.DataFrame(index=P.date_range('2014-9-6 1:30', '2014-9-9', freq='6H'))
3 Inn: df
3 Out:
2014-09-06 01:30:00
2014-09-06 07:30:00
2014-09-06 13:30:00
2014-09-06 19:30:00
2014-09-07 01:30:00
2014-09-07 07:30:00
2014-09-07 13:30:00
2014-09-07 19:30:00
2014-09-08 01:30:00
2014-09-08 07:30:00
2014-09-08 13:30:00
2014-09-08 19:30:00
2014-09-09 01:30:00
4 Inn: df.index.asof('2014-9-8') # wrong
4 Out: Timestamp('2014-09-08 00:00:00')
5 Inn: df.index.asof(P.to_datetime('2014-9-8')) # right
5 Out: Timestamp('2014-09-07 19:30:00', offset='6H')
7 Inn: P.version.version
7 Out: '0.14.1'
```
df.index.asof() appears to know how to handle datetime strings, but it cant
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https://api.github.com/repos/pandas-dev/pandas/issues/8246 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8246/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8246/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8246/events | https://github.com/pandas-dev/pandas/pull/8246 | 42,549,871 | MDExOlB1bGxSZXF1ZXN0MjExMDM3NTg= | 8,246 | BUG: DatetimeIndex.asof matches partial dates | {
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} | [] | closed | false | null | [] | null | 1 | 2014-09-11T17:30:33Z | 2014-09-11T23:38:48Z | 2014-09-11T23:38:48Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/8245
current master:
``` python
In [6]: idx.asof('2010-02')
Out[6]: Timestamp('2010-02-01 00:00:00')
```
fixed:
``` python
In [3]: idx.asof('2010-02')
Out[3]: Timestamp('2010-01-31 00:00:00', offset='M')
```
Will try to do a test_perf on this later. I've been working in python 3, but I don't think vbench is ported for that.
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} | 7 | 2014-09-11T19:44:14Z | 2014-09-12T14:54:49Z | 2014-09-12T14:52:16Z | CONTRIBUTOR | null | The function returns indices, not a boolean array. The description now
matches `numpy.nonzero`.
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} | [] | closed | false | null | [] | null | 3 | 2014-09-11T22:17:31Z | 2014-09-11T22:23:33Z | 2014-09-11T22:23:33Z | CONTRIBUTOR | null | This issue was uncovered in this [Stackoverflow question](http://stackoverflow.com/q/25797245/190597):
Using Pandas 0.14.0: `pd.to_timedelta` seems to ignore the units when `unit` is `h` or `m`:
```
In [263]: pd.to_timedelta(16, unit='h')
Out[263]: numpy.timedelta64(16,'ns')
In [264]: pd.to_timedelta(16, unit='m')
Out[264]: numpy.timedelta64(16,'ns')
```
Other units seem to work as expected:
```
In [269]: pd.to_timedelta(16, unit='D')
Out[269]: numpy.timedelta64(1382400000000000,'ns')
In [270]: pd.to_timedelta(16, unit='s')
Out[270]: numpy.timedelta64(16000000000,'ns')
In [271]: pd.to_timedelta(16, unit='ms')
Out[271]: numpy.timedelta64(16000000,'ns')
In [272]: pd.to_timedelta(16, unit='us')
Out[272]: numpy.timedelta64(16000,'ns')
In [273]: pd.to_timedelta(16, unit='ns')
Out[273]: numpy.timedelta64(16,'ns')
```
I think the behavior comes from `tslib.convert_to_timedelta`:
```
In [279]: import pandas.tslib
In [280]: pandas.tslib.convert_to_timedelta(16, 'h')
Out[280]: numpy.timedelta64(16,'ns')
In [281]: pandas.tslib.convert_to_timedelta(16, 'D')
Out[281]: numpy.timedelta64(1382400000000000,'ns')
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8249 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8249/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8249/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8249/events | https://github.com/pandas-dev/pandas/pull/8249 | 42,585,133 | MDExOlB1bGxSZXF1ZXN0MjExMjY1NDQ= | 8,249 | BUG: plot methods modified rcParams | {
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} | [] | closed | false | null | [] | null | 0 | 2014-09-12T00:22:20Z | 2014-09-12T17:19:35Z | 2014-09-12T17:19:35Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/8242
Just made a copy of the rcParams when we get it.
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] | closed | false | null | [] | null | 1 | 2014-09-12T01:17:53Z | 2014-09-12T12:00:55Z | 2014-09-12T12:00:55Z | NONE | null | I have written the code
import pandas as pd
import numpy as np
from scipy.optimize import minimize
pd.set_option('display.mpl_style', 'default')
"""Read the input file into the dataframe""" """T1 file contains the decision variables, corresponding investments and their upper and lower bounds"""
df = pd.DataFrame.from_csv('C:\Users\prashant.mudgal\Downloads\T1.csv')
"""T2 file contains the parameters for the decision variables, note that there can be 2 or more entries for the same decison variable""" """ Our objective func will be prep from T2 file , it will be of the form sum (all DV in params file) (a_(b + c \* DV_name)_\* power) """
df2 = pd.DataFrame.from_csv('C:\Users\prashant.mudgal\Downloads\T2.csv')
"""subset of DV"""
decisionVars= df2['DV']
"""subset for coeff """
coeff = df2['coef']
"""subset for power"""
power = df2['p']
x0 = df['Hist_Inv']
bnds = df[['LB','UB']].values
def objFunc(decisionVars,sign=1.0) :
return sign_(sum(coeff.values[0:] *(df2['weight_f'].values[0:] + df2['weight_v'].values[0:] \* decisionVars[0:])_*power.values[0:]))
"""bounds for the decision variables have been hardcoded """ """ The bounds have been hardcoded and it will be cumbersome if there are thousands of Decision vars """ """ The bounds have been specified in T1.csv file, we want to ream them via the file """
""" Though we are not using constraints as of now, but is it correct form""" """ How to form the constraints for say x + y < 5 ? """ cons = ({'type': 'ineq', 'fun': lambda x: decisionVars[1] - 2 \* decisionVars[1] + 2}, {'type': 'ineq', 'fun': lambda x: -decisionVars[2] - 2 \* decisionVars[1] + 6}, {'type': 'ineq', 'fun': lambda x: -decisionVars[0] + 2 \* decisionVars[1] + 2})
""" the second parameter is the initial values that needs to be optimized""" """ Now, the number of distinct decision vars in the files are 3 in numbers while the total number of decision vars are 12 in number . if we specify x0 as the dataframe of investment (3 in number in the T1 file) , then it gives error that ::: operands could not be broadcast together with shapes (12,) (3,)"""
res = minimize(objFunc, x0,args=(-1.0,),method='SLSQP',bounds = bnds,
options={'disp': True})
"""Print the results"""
print (res)
T1.csv is like DV LB UB Hist_Inv X1 0.7 1.3 28462739.43 X2 0.7 1.3 177407.18 X3 0.7 1.3 1423271.12
T2.csv is
Count DV weight_f weight_v p coef
1 X1 2.310281831 3.661156016 0.5 1828.105881
2 X1 0.693084549 2.20503016 0.5 1460.686147
3 X1 0.207925365 2.030522789 0.5 1436.277144
4 X1 0 5.248353307 0.8 1050.493355
5 X1 0 1.591805116 0.8 983.9964128
6 X1 0 1.933056056 0.8 459.9371809
7 X2 7.322516444 138 0.5 387.4659072
8 X2 3.661258222 139 0.5 606.8684771
9 X2 1.830629111 176.5 0.5 358.8902965
10 X3 164294.4758 77024 0.2 282.0477107
11 X3 98576.68545 122261.4 0.2 345.9217482
12 X3 59146.01127 166242.84 0.2 364.9587162
I create my obj function using vars in T2, there are a lot of vars which are repeated in T2.csv on running optimization I am getting the error operands could not be broadcast together with shapes (12,) (3,) because it is taking all the variables in T2 as different. How should I avoid this issue? X1, X2 AND X3 are the only three vars here.
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} | 4 | 2014-09-12T03:56:24Z | 2018-07-06T20:21:04Z | 2017-07-11T09:57:59Z | NONE | null | This is also a documentation error, as the docs say the default axis is axis = 0
```
import pandas as pd
from pandas.util.testing import makePanel
#
# Demo A: We want to fill along axis 0
#
p = makePanel().transpose(1,0,2)
p.iloc[10:20,0,0] = np.nan
# Demonstrate we have a gap
p.iloc[:,0,0].plot()
# Demonstrate that nothing seems to fill the gap
p.fillna(method='ffill').iloc[:,0,0].plot()
p.fillna(method='ffill', axis=0).iloc[:,0,0].plot()
p.fillna(method='ffill', axis=1).iloc[:,0,0].plot()
p.fillna(method='ffill', axis=2).iloc[:,0,0].plot()
# At least one of the above should be the same as:
p.apply(lambda series: series.fillna(method='ffill'), axis=0).iloc[:,0,0].plot()
#
# Demo B: We want to fill along axis 1
#
p = makePanel()
p.iloc[0, 10:20,0] = np.nan
# Demonstrate we have a gap
p.iloc[0,:,0].plot()
# Demonstrate the gap is filled regardless of the axis
p.fillna(method='ffill').iloc[0,:,0].plot()
p.fillna(method='ffill', axis=0).iloc[0,:,0].plot()
p.fillna(method='ffill', axis=1).iloc[0,:,0].plot()
p.fillna(method='ffill', axis=2).iloc[0,:,0].plot()
```
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} | 3 | 2014-09-12T08:10:40Z | 2014-09-21T19:37:05Z | 2014-09-20T16:50:06Z | NONE | null | I have 240GB of RAM. Nothing else running on the machine. I'm trying to create 1.5E9 rows, which I think should create a data frame of around 100GB, but getting this MemoryError. This works fine with 1E9 but not 1.5E9. I could understand a limit at about 2^31 (2E9) or 2^32 (4E9) but all 240GB seems exhausted (according to htop) at somewhere between 1E9 and 1.5E9 rows. Any ideas? Thanks.
``` python
$ python3
Python 3.4.0 (default, Apr 11 2014, 13:05:11)
[GCC 4.8.2] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> import numpy as np
>>> import timeit
>>> pd.__version__
'0.14.1'
>>> def randChar(f, numGrp, N) :
... things = [f%x for x in range(numGrp)]
... return [things[x] for x in np.random.choice(numGrp, N)]
...
>>> def randFloat(numGrp, N) :
... things = [round(100*np.random.random(),4) for x in range(numGrp)]
... return [things[x] for x in np.random.choice(numGrp, N)]
...
>>> N=int(1.5e9) # N=int(1e9) works fine
>>> K=100
>>> DF = pd.DataFrame({
... 'id1' : randChar("id%03d", K, N), # large groups (char)
... 'id2' : randChar("id%03d", K, N), # large groups (char)
... 'id3' : randChar("id%010d", N//K, N), # small groups (char)
... 'id4' : np.random.choice(K, N), # large groups (int)
... 'id5' : np.random.choice(K, N), # large groups (int)
... 'id6' : np.random.choice(N//K, N), # small groups (int)
... 'v1' : np.random.choice(5, N), # int in range [1,5]
... 'v2' : np.random.choice(5, N), # int in range [1,5]
... 'v3' : randFloat(100,N) # numeric e.g. 23.5749
... })
Traceback (most recent call last):
File "<stdin>", line 10, in <module>
File "/usr/lib/python3/dist-packages/pandas/core/frame.py", line 203, in __init__
mgr = self._init_dict(data, index, columns, dtype=dtype)
File "/usr/lib/python3/dist-packages/pandas/core/frame.py", line 327, in _init_dict
dtype=dtype)
File "/usr/lib/python3/dist-packages/pandas/core/frame.py", line 4630, in _arrays_to_mgr
return create_block_manager_from_arrays(arrays, arr_names, axes)
File "/usr/lib/python3/dist-packages/pandas/core/internals.py", line 3235, in create_block_manager_from_arrays
blocks = form_blocks(arrays, names, axes)
File "/usr/lib/python3/dist-packages/pandas/core/internals.py", line 3322, in form_blocks
object_items, np.object_)
File "/usr/lib/python3/dist-packages/pandas/core/internals.py", line 3346, in _simple_blockify
values, placement = _stack_arrays(tuples, dtype)
File "/usr/lib/python3/dist-packages/pandas/core/internals.py", line 3410, in _stack_arrays
stacked = np.empty(shape, dtype=dtype)
MemoryError
```
``` bash
$ lscpu
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 32
On-line CPU(s) list: 0-31
Thread(s) per core: 2
Core(s) per socket: 8
Socket(s): 2
NUMA node(s): 2
Vendor ID: GenuineIntel
CPU family: 6
Model: 62
Stepping: 4
CPU MHz: 2494.070
BogoMIPS: 5054.21
Hypervisor vendor: Xen
Virtualization type: full
L1d cache: 32K
L1i cache: 32K
L2 cache: 256K
L3 cache: 25600K
NUMA node0 CPU(s): 0-7,16-23
NUMA node1 CPU(s): 8-15,24-31
$ free -h
total used free shared buffers cached
Mem: 240G 2.3G 237G 364K 66M 632M
-/+ buffers/cache: 1.6G 238G
Swap: 0B 0B 0B
$
```
An earlier question on S.O. is here : http://stackoverflow.com/questions/25631076/is-this-the-fastest-way-to-group-in-pandas
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`CustomBusinessDay` is now fast with holiday calendar and incrementing in different ways.
import pandas as pd
import numpy as np
import datetime as dt
date = pd.Timestamp('20120101')
cbday = pd.offsets.CustomBusinessDay()
date + cbday
%timeit date + cbday
10000 loops, best of 3: 17.2 µs per loop
hdays = [dt.datetime(2013,1,1) for ele in range(1000)]
cbdayh = pd.offsets.CustomBusinessDay.from_various(holidays=hdays)
%timeit date + cbdayh
%timeit date + 2 \* cbdayh
%timeit date - 1 \* cbdayh
%timeit date - 10 \* cbday
100000 loops, best of 3: 14.6 µs per loop
100000 loops, best of 3: 19.7 µs per loop
10000 loops, best of 3: 22.7 µs per loop
10000 loops, best of 3: 22.3 µs per loop
Please check whether you agree with the way I initialize custom offsets now (using classmethod).
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} | 17 | 2014-09-12T13:32:57Z | 2016-09-01T08:01:28Z | 2016-08-29T12:42:43Z | CONTRIBUTOR | null | see https://github.com/pydata/pandas/pull/8184
related #8185
`Index.to_datetime` currently exists but really no need for this as `pd.to_datetime` subsumes this functionaility (and is more general). and just pollutes the interface.
so remove:
- [x] `Timestamp.to_datetime`
- [x] `DatetimeIndex.to_datetime` #14096
- [x] `Index.to_datetime` #14096
- [x] `NaT.to_datetime`
- [x] `PeriodIndex.to_datetime`#14113
(and fix the parser to call directly try the conversion / use a helper function).
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] | closed | false | null | [] | null | 1 | 2014-09-12T18:14:53Z | 2015-03-08T14:40:09Z | 2015-03-08T14:40:09Z | NONE | null | When using pandas.rpy.common.convert_to_r_dataframe to convert pandas DataFrame --> R data.frame, columns get assigned two classes: AsIs, some data type
ggplot2 sees AsIs and refuses to deal with the data type properly. It would be nice to have the AsIs automatically stripped like here:
http://stackoverflow.com/questions/25175530/can-rpy2-code-be-run-in-parallel
I put the hack in my code like this:
```
from rpy2.robjects import r as R
hack_pandas = R("""
function(df) {
data.frame(lapply(df, function(X) {
if ("AsIs" %in% class(X))
class(X) <- class(X)[-match("AsIs", class(X))]
X
}))
}
""")
df = hack_pandas(com.convert_to_r_dataframe(data))
```
It would be nice to have this done on-the-fly, perhaps optionally (strip_asis=True?).
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} | 7 | 2014-09-12T19:00:45Z | 2014-09-15T14:52:26Z | 2014-09-15T14:52:07Z | NONE | null | Example:
import pandas as pd
pd.Series(randn(1000)).hist(by=randint(0, 6, 1000), sharex=True, sharey=True)
pd.Series(randn(1000)).hist(by=randint(0, 5, 1000), sharex=True, sharey=True)
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} | 5 | 2014-09-12T19:58:50Z | 2016-11-11T10:33:49Z | 2016-11-11T10:33:42Z | NONE | null | It seems that when comparing two Series objects, index is ignored, here is an trivial example
```
In [177]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.7.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-0.bpo.4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US
pandas: 0.14.0
nose: 1.3.0
Cython: 0.19.2
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.0.0
sphinx: 1.1.3
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2014.3
bottleneck: None
tables: 3.0.0
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: 1.6.2
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.2.3
bs4: 4.3.1
html5lib: None
bq: None
apiclient: None
rpy2: None
sqlalchemy: 0.8.3
pymysql: None
psycopg2: None
In [158]: a = pd.Series([100, 101, 102, 103], index=[1,2,3,4])
In [168]: b = pd.Series([102, 103, 104, 105], index=[3, 4, 5, 6])
In [170]: a
Out[170]:
1 100
2 101
3 102
4 103
dtype: int64
In [171]: b
Out[171]:
3 102
4 103
5 104
6 105
dtype: int64
In [172]: a > b
Out[172]:
1 False
2 False
3 False
4 False
dtype: bool
In [173]: a < b
Out[173]:
1 True
2 True
3 True
4 True
dtype: bool
In [174]: a == b
Out[174]:
1 False
2 False
3 False
4 False
dtype: bool
In [175]: a - b
Out[175]:
1 NaN
2 NaN
3 0
4 0
5 NaN
6 NaN
dtype: float64
```
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} | 0 | 2014-09-12T20:17:25Z | 2014-10-04T17:19:50Z | 2014-10-04T17:19:50Z | NONE | null | Originally posted at stackexchange:
http://stackoverflow.com/questions/25811529/setting-values-on-a-subset-of-rows-indexing-boolean-setting/25812825#25812825
Jeff suggested posting here also.
``` python
df = DataFrame({'L': ['left', 'right', 'left', 'right'],
'R': ['right', 'left', 'right', 'left'],
'idx': [False, True, False, True],
'num': np.arange(4) })
df1 = df.copy()
df2 = df.copy()
df3 = df.copy()
df4 = df.copy()
#1 nothing happens (expected result)
df1.loc[df1.idx,['L','R']] = df1.loc[df1.idx,['R','L']]
#2 something weird happens
df2.loc[df2.idx,['L','R']] = df2[['R','L']]
```
Results below:
``` python
In [572]: df
Out[572]:
L R idx num
0 left right False 0
1 right left True 1
2 left right False 2
3 right left True 3
In [573]: df1
Out[573]:
L R idx num
0 left right False 0
1 right left True 1
2 left right False 2
3 right left True 3
In [574]: df2
Out[574]:
L R idx num
0 left right False 0
1 left right True 1
2 left right False 2
3 right left True 3
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.7.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 26 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.14.1.dev
nose: 1.3.3
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.1.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2014.3
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.5
lxml: 3.3.5
bs4: 4.3.1
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
```
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} | [] | closed | false | null | [] | null | 9 | 2014-09-12T22:47:52Z | 2014-09-12T23:47:04Z | 2014-09-12T22:52:08Z | NONE | null | This bug also happens if you use the .convert_objects() method of DataFrame or Series.
To reproduce:
```
In [83]: import pytz
In [85]: d = datetime.datetime(2014, 9, 3, 2, 40, 16, tzinfo=pytz.utc)
In [88]: pd.to_datetime(pd.Series([d]))[0]
Out[88]: Timestamp('2014-09-03 02:40:16', tz=None)
In [89]: pd.to_datetime([d])[0]
Out[89]: Timestamp('2014-09-03 02:40:16+0000', tz='UTC')
In [91]: pd.__version__
Out[91]: '0.13.1'
```
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} | 3 | 2014-09-12T23:43:59Z | 2015-09-05T16:17:23Z | 2015-09-05T16:17:23Z | CONTRIBUTOR | null | to allow real support / conversions of timezone aware Series.
xref #9560; `DatetimeIndex.to_series()` can be changed to preserve the tz always
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a = df.groupby('actor')
Then I create a new DataFrame as follows:
b = a.get_group(a.groups.keys()[0])
Now, b.ix[0,0] gives an error:
KeyError: 0L
I can still use .ix to slice by index and column, but not by position.
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} | 1 | 2014-09-13T14:13:18Z | 2015-01-17T09:59:00Z | 2014-09-13T22:42:12Z | CONTRIBUTOR | null | Made clear that .ix does not support positional indexing
in the case where the corresponding dimension has an
integer index.
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} | 17 | 2014-09-13T18:36:33Z | 2018-07-06T22:28:25Z | 2018-07-06T22:28:21Z | CONTRIBUTOR | null | import pandas as PD
s1=PD.Series((1,4,9,16), name=dict(a=1,b=2))
s2=s1*2
s2.name=dict(c=5,d=7)
s1
0 1
1 4
2 9
3 16
Name: {'a': 1, 'b': 2}, dtype: int32
df = PD.concat((s1,s2), axis=1) # this should raise ValueError as the resulting df is broken
df.shape # ok, this works...
(4, 2)
df # But this doesn't.
<repr(<pandas.core.frame.DataFrame at 0x35af770>) failed: TypeError: unhashable type: 'dict'>
df.columns # This is not a proper index...
Index([{'a': 1, 'b': 2}, {'c': 5, 'd': 7}], dtype='object')
Comments:
1. There may be other ways aside from concat that lead to broken df's if a Series.name attribute is unhashable.
2. If Series.name is a dict, one could try to convert it into a namedtuple. That's hashable.
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} | 0 | 2014-09-13T23:18:33Z | 2014-09-14T01:09:42Z | 2014-09-14T01:09:42Z | CONTRIBUTOR | null | closes #7888
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... | closed | false | null | [] | null | 14 | 2014-09-14T02:27:16Z | 2015-04-18T19:00:14Z | 2015-04-18T19:00:14Z | CONTRIBUTOR | null | I generated a multindexed DataFrame and wrote it to hdf5 using to_hdf. It uses zlib level 5 compression. The file was written all at once. The file is located here: https://www.dropbox.com/s/122q55g5ubcf4fl/indexIssue.h5?dl=0
The below methods should be identical but the former select with a where clause has 2892 records but getting all values and subselecting on the path returns 2972 (values are missing for path 6 between 3-5-2015 20:00 to 3-6-2015 9:00). I tried using reindex on the able but that didn't fix anything. I don't really know what's going on.
```
store = HDFStore(path_to_file, mode='r')
p1 = store.select('ts', where=Term('Path', '=', 6), auto_close=False)
print(len(p1))
p2 = store.select('ts', auto_close=False)
p2s = p2[p2.index.get_level_values('Path') == 6]
print(len(p2s))
```
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] | closed | false | null | [] | null | 3 | 2014-09-14T05:49:51Z | 2014-10-08T05:31:22Z | 2014-09-16T06:14:26Z | CONTRIBUTOR | null | This is my first PR. It's just an update to two doc strings.
Closes #7573
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} | 0 | 2014-09-14T14:19:16Z | 2014-09-14T15:48:40Z | 2014-09-14T15:48:40Z | CONTRIBUTOR | null | xref #7661
just tests for coverage
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} | 14 | 2014-09-14T16:43:48Z | 2014-09-17T13:07:02Z | 2014-09-17T13:07:02Z | CONTRIBUTOR | null | I noticed while using rolling moment functions with `center=True` and with `min_periods` < `window` that the values at the end of the series were showing up as `NaN` when they should have be finite floating point values. The reason for this behavior is that `_rolling_moment()` concats extra `NaN` values to the end of the input series when `center=True` to allow the `roll_generic()` function to continue on past the end of the original series and compute the values that will "come within view" when the result is centered. However, adding those `NaN` values into the window can bork the calculation and change a result that should be finite into a `NaN`.
This example illustrates the behavior:
```
n = 12
s = Series(np.random.randn(n))
s.plot(color='b')
win=7
minp = 5
pd.rolling_mean(s, win, min_periods=minp, center=False).plot(color='g')
pd.rolling_mean(s, win, min_periods=minp, center=True).plot(color='r')
ticks = plt.xticks(np.arange(0, n, 1.0))
```
I was able to mitigate this behavior, for one calculation at least, by modifying the cython function to essentially strip the trailing `NaN` values for the calculations made towards the end of the series. This requires passing in the `offset` calculated in `_rolling_moment()`.
Fixing this is going to touch a lot of functions that depend on `_rolling_moment()`. Before I go much further with this, I thought I'd raise the issue here and see if anyone else has insight or better ideas about how to fix this.
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} | 20 | 2014-09-14T20:13:46Z | 2014-09-30T03:26:27Z | 2014-09-29T12:57:56Z | CONTRIBUTOR | null | adds _some_ stability to rolling `skew` and `kurt` calculation by using the `z-scores` instead of original values; related: https://github.com/pydata/pandas/issues/6929
on master:
```
In [3]: np.random.seed(2718281)
In [4]: xs = np.random.rand(10)
In [5]: pd.stats.moments.rolling_skew(xs, 4) - pd.stats.moments.rolling_skew(xs + 5000, 4)
Out[5]:
array([ nan, nan, nan,
-1.64816426e-03, 1.10392004e-03, 1.09544324e-03,
7.99253200e-03, 8.66733795e-05, -4.48450842e-03,
-6.03475754e-03])
In [6]: pd.stats.moments.rolling_kurt(xs, 4) - pd.stats.moments.rolling_kurt(xs + 100, 4)
Out[6]:
array([ nan, nan, nan,
-1.39880824e-04, -2.91491317e-04, -2.35463343e-04,
-7.36046530e-04, -3.99141811e-04, -8.61177376e-05,
9.26058376e-06])
```
on branch:
```
In [7]: pd.stats.moments.rolling_skew(xs, 4) - pd.stats.moments.rolling_skew(xs + 5000, 4)
Out[7]:
array([ nan, nan, nan,
-1.03450581e-12, 7.36521955e-13, 2.42605935e-12,
-2.43799425e-12, -1.85962357e-12, -1.06048503e-12,
1.66638925e-12])
In [8]: pd.stats.moments.rolling_kurt(xs, 4) - pd.stats.moments.rolling_kurt(xs + 100, 4)
Out[8]:
array([ nan, nan, nan,
-1.24344979e-13, -7.99360578e-14, 6.75015599e-14,
-9.05941988e-14, -5.15143483e-14, -1.11910481e-13,
-5.15143483e-14])
```
This does _not_ handle all situations that causes instability, but I think it is generally more stable to work with z-scores; In particular, affine transformations generate same results.
Also, `input` is a python 3 built-in. I suggest renaming all instances of `input` in `algo.pyx` file to `inp` or something else.
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https://api.github.com/repos/pandas-dev/pandas/issues/8271 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8271/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8271/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8271/events | https://github.com/pandas-dev/pandas/pull/8271 | 42,744,410 | MDExOlB1bGxSZXF1ZXN0MjEyMTUxNDc= | 8,271 | ENH: Ensure that rolling_var of identical values is exactly zero | {
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} | 30 | 2014-09-15T06:23:09Z | 2014-09-19T23:43:07Z | 2014-09-19T23:33:11Z | CONTRIBUTOR | null | Add a check to rolling_var for repeated observations, in order to
produce an exactly zero value of the variance when all entries are
identical. Related to the discussion in #7900
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https://api.github.com/repos/pandas-dev/pandas/issues/8272 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8272/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8272/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8272/events | https://github.com/pandas-dev/pandas/issues/8272 | 42,757,855 | MDU6SXNzdWU0Mjc1Nzg1NQ== | 8,272 | when a column contains alpha numeric ending with 'e', pandas converts these to float64 | {
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"color": "bfe... | closed | false | null | [] | null | 9 | 2014-09-15T10:14:40Z | 2015-10-20T23:11:53Z | 2015-10-20T23:11:53Z | NONE | null | I'm reading some excel spreadsheets (xlsx format) into pandas using read_excel, which generally works great. The problem I have is that when a column contains alpha numeric ending with 'e', pandas converts these to float64 type without 'e' in its value, and I would like them to be treated as strings. After reading them in, I can convert the column to str:
dataset = read_excel(excel_file,sheet_name,na_values=[" "])
while reading this excel file to dataset it shows loan id as a number without 'e'
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https://api.github.com/repos/pandas-dev/pandas/issues/8273 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8273/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8273/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8273/events | https://github.com/pandas-dev/pandas/issues/8273 | 42,770,788 | MDU6SXNzdWU0Mjc3MDc4OA== | 8,273 | to_html function fail when max_rows is specified | {
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"color": "006b7... | closed | false | null | [] | null | 4 | 2014-09-15T13:15:38Z | 2015-10-20T22:41:46Z | 2015-10-20T22:41:46Z | NONE | null | The error is:
list index out of range
Exception Location: /home/pietro/lod4statenv/local/lib/python2.7/site-packages/pandas/core/format.py in _write_body, line 923
In order to fix it I have replaced line 922 from:
for i in range(len(self.frame)):
to:
for i in range(min(len(self.frame), self.max_rows)):
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https://api.github.com/repos/pandas-dev/pandas/issues/8274 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8274/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8274/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8274/events | https://github.com/pandas-dev/pandas/issues/8274 | 42,785,484 | MDU6SXNzdWU0Mjc4NTQ4NA== | 8,274 | to_hdf:[unicode] is not implemented as a table column | {
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} | [] | closed | false | null | [] | null | 2 | 2014-09-15T15:36:08Z | 2014-09-16T00:06:41Z | 2014-09-16T00:05:12Z | NONE | null | First,sorry for my bad english:)
I want to store data use DataFrame.to_hdf() function.
when i use df600000.to_hdf(path_or_buf='H:\store.h5',key='SH600000',format='f',mode='r+'),it works good,since i change format='t' will raise TypeError,why?
detail as followed:
## In [55]:df600000.to_hdf(path_or_buf='H:\store.h5',key='SH600000',format='t',mode='r+')
TypeError Traceback (most recent call last)
<ipython-input-55-ce1cae8be680> in <module>()
----> 1 df600000.to_hdf(path_or_buf='H:\store.h5',key='SH600000',format='t',mode='r+')
D:\winpython\WinPython-32bit-2.7.6.4\python-2.7.6\lib\site-packages\pandas\core\generic.pyc in to_hdf(self, path_or_buf, key, *_kwargs)
882
883 from pandas.io import pytables
--> 884 return pytables.to_hdf(path_or_buf, key, self, *_kwargs)
885
886 def to_msgpack(self, path_or_buf=None, **kwargs):
D:\winpython\WinPython-32bit-2.7.6.4\python-2.7.6\lib\site-packages\pandas\io\pytables.pyc in to_hdf(path_or_buf, key, value, mode, complevel, complib, append, **kwargs)
277 with get_store(path_or_buf, mode=mode, complevel=complevel,
278 complib=complib) as store:
--> 279 f(store)
280 else:
281 f(path_or_buf)
D:\winpython\WinPython-32bit-2.7.6.4\python-2.7.6\lib\site-packages\pandas\io\pytables.pyc in <lambda>(store)
272 f = lambda store: store.append(key, value, *_kwargs)
273 else:
--> 274 f = lambda store: store.put(key, value, *_kwargs)
275
276 if isinstance(path_or_buf, string_types):
D:\winpython\WinPython-32bit-2.7.6.4\python-2.7.6\lib\site-packages\pandas\io\pytables.pyc in put(self, key, value, format, append, *_kwargs)
819 format = get_option("io.hdf.default_format") or 'fixed'
820 kwargs = self._validate_format(format, kwargs)
--> 821 self._write_to_group(key, value, append=append, *_kwargs)
822
823 def remove(self, key, where=None, start=None, stop=None):
D:\winpython\WinPython-32bit-2.7.6.4\python-2.7.6\lib\site-packages\pandas\io\pytables.pyc in _write_to_group(self, key, value, format, index, append, complib, encoding, *_kwargs)
1271
1272 # write the object
-> 1273 s.write(obj=value, append=append, complib=complib, *_kwargs)
1274
1275 if s.is_table and index:
D:\winpython\WinPython-32bit-2.7.6.4\python-2.7.6\lib\site-packages\pandas\io\pytables.pyc in write(self, obj, axes, append, complib, complevel, fletcher32, min_itemsize, chunksize, expectedrows, dropna, *_kwargs)
3576 self.create_axes(axes=axes, obj=obj, validate=append,
3577 min_itemsize=min_itemsize,
-> 3578 *_kwargs)
3579
3580 if not self.is_exists:
D:\winpython\WinPython-32bit-2.7.6.4\python-2.7.6\lib\site-packages\pandas\io\pytables.pyc in create_axes(self, axes, obj, validate, nan_rep, data_columns, min_itemsize, **kwargs)
3271 self.values_axes.append(col)
3272 except (NotImplementedError, ValueError, TypeError) as e:
-> 3273 raise e
3274 except Exception as detail:
3275 raise Exception(
TypeError: [unicode] is not implemented as a table column
In [5]:df600000.dtypes
Out[5]:
trad_date object
open float64
high float64
low float64
close float64
vol int64
val float64
dtype: object
In [51]: df600000[:5]
Out[51]:
trad_date open high low close vol val
0 2000-01-04 3.43 3.54 3.40 3.51 4496000 113946784
1 2000-01-05 3.51 3.56 3.45 3.47 5252800 134465424
2 2000-01-06 3.45 3.61 3.44 3.57 6229600 160059792
3 2000-01-07 3.61 3.77 3.58 3.69 21355300 575751104
4 2000-01-10 3.70 3.82 3.66 3.74 16539700 450453504
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.13.1
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 2.0.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2013.9
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: None
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: None
sqlalchemy: 0.9.4
lxml: 3.3.5
bs4: 4.3.2
html5lib: None
bq: None
apiclient: None
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} | 11 | 2014-09-15T18:25:33Z | 2014-09-17T13:24:32Z | 2014-09-17T13:07:02Z | CONTRIBUTOR | null | Closes #8269.
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} | 1 | 2014-09-15T19:25:56Z | 2016-08-31T07:57:34Z | 2016-08-31T07:57:34Z | NONE | null | pandas version 0.14.0:
SparseDataFrame.isnull method raises an error
```
>>> import pandas as pd
>>> from numpy.random import rand
>>> a = pd.SparseDataFrame(rand(5,5))
>>> a.isnull()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/isofer/anaconda/lib/python2.7/site-packages/pandas/core/generic.py", line 2606, in isnull
return isnull(self).__finalize__(self)
File "/Users/isofer/anaconda/lib/python2.7/site-packages/pandas/core/common.py", line 129, in isnull
return _isnull(obj)
File "/Users/isofer/anaconda/lib/python2.7/site-packages/pandas/core/common.py", line 141, in _isnull_new
return obj._constructor(obj._data.isnull(func=isnull))
File "/Users/isofer/anaconda/lib/python2.7/site-packages/pandas/core/internals.py", line 2179, in isnull
return self.apply('apply', **kwargs)
File "/Users/isofer/anaconda/lib/python2.7/site-packages/pandas/core/internals.py", line 2164, in apply
applied = getattr(b, f)(**kwargs)
File "/Users/isofer/anaconda/lib/python2.7/site-packages/pandas/core/internals.py", line 234, in apply
result = make_block(values=result, placement=self.mgr_locs,)
File "/Users/isofer/anaconda/lib/python2.7/site-packages/pandas/core/internals.py", line 1837, in make_block
placement=placement)
File "/Users/isofer/anaconda/lib/python2.7/site-packages/pandas/core/internals.py", line 74, in __init__
len(self.values), len(self.mgr_locs)))
ValueError: Wrong number of items passed 5, placement implies 1
>>> a = pd.DataFrame(rand(5,5))
>>> a.isnull()
0 1 2 3 4
0 False False False False False
1 False False False False False
2 False False False False False
3 False False False False False
4 False False False False False
>>>
```
I was also able to reproduce it also in new env with pandas 0.14.1.
(conda create pandas -n test)
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https://api.github.com/repos/pandas-dev/pandas/issues/8277 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8277/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8277/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8277/events | https://github.com/pandas-dev/pandas/issues/8277 | 42,822,946 | MDU6SXNzdWU0MjgyMjk0Ng== | 8,277 | SQL: use of connection context manager not supported by mysql.connector | {
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} | 1 | 2014-09-15T21:49:48Z | 2014-09-17T09:06:27Z | 2014-09-17T09:06:27Z | MEMBER | null | Coming from discussion in #8232, current master breaks with `mysql.connector` as the MySQL driver in the fallback modus (deprecated) due to the use of context managers.
This was introduced in #8062, to ensure that the different chunks would be inserted in one transaction (when using the `chunksize` argument).
The connection context manager is however nog DBAPI specified behaviour, and differs among drivers:
- sqlite3 returns a connection from the context manager, but has a connection.executemany, so that of course works (otherwise the tests would not pass at all)
- pymysql returns a cursor, but does not close that cursor automatically (I don't know if this is a problem)
- MySQLdb does now support such a context manager (from 1.2.5, so only the latest version), and does the same as pymysql
- mysql.connector does not support it, so the current implementation fails with that.
So problem is that with the current code in master, we break the fallback mode when using `mysql.connector`.
Solution: I think remove the context managers for now (which will make the code a bit more complex), until mysql support is fully dropped
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} | 10 | 2014-09-16T02:04:25Z | 2014-09-17T09:06:48Z | 2014-09-17T09:06:27Z | CONTRIBUTOR | null | To close #8277
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} | 1 | 2014-09-16T03:01:14Z | 2014-09-17T18:03:54Z | 2014-09-17T18:03:54Z | CONTRIBUTOR | null | I think `expanding/rolling_cov()` should support a `ddof` argument, the way `expanding/rolling_std/var()` do (though the latter is not documented; see #8064). So long as `ddof` defaults to `1`, it will be backwards compatible, so I don't see any downsides. This is very easy/straightforward.
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closes #8064
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} | 1 | 2014-09-16T08:24:32Z | 2014-10-02T02:35:07Z | 2014-09-16T14:43:54Z | CONTRIBUTOR | null | better error message for groupy on duplicated columns name. (Followup for branch GH8210 (Bug GH7511)). E.g.
```
ValueError: Grouper for 'A' not 1-dimensional
ValueError: Grouper for '<class '__main__.gr'>' not 1-dimensional
```
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closes #8017
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} | 2 | 2014-09-16T15:31:42Z | 2020-09-15T02:11:48Z | 2020-09-15T02:11:48Z | CONTRIBUTOR | null | xref #8282
xref #8239 (this issue won't touch the API per se)
expand extend: `core/generic.py/NDFrame.sort_index`: https://github.com/pydata/pandas/blob/master/pandas/core/generic.py#L1606
with the frame version.
https://github.com/pydata/pandas/blob/master/pandas/core/frame.py#L2689
needs generic docs (e.g. look how reindex is done)
as prob best to start with the frame version that handles ndim=2. Need tests for added capabilities to Series/Panel, e.g. mainly supporting sort_index on a multi-level index.
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} | 9 | 2014-09-16T17:42:07Z | 2019-11-08T01:06:32Z | 2018-07-06T22:22:06Z | NONE | null | When attempting to store data in an HDF5 table, I found a problem where an error is raised if there are multiple object columns containing different data.
``` python
import pandas as pd
data = {'ints':pd.Series([1,2,3], index=index), 'Timestamps': pd.Series([pd.Timestamp('2014-1-1 12:00', tz='UTC'), pd.Timestamp('2014-1-2 12:00', tz='UTC'), pd.Timestamp('2014-1-3 12:00', tz='UTC')], index=index), 'strings': pd.Series(['r','g','b'], index=index)}
df = pd.DataFrame(data)
df.to_hdf('test.h5', 'data', format='table')
```
This leads to an exception: TypeError: Cannot serialize the column [Timestamps] because
its data contents are [datetime] object dtype
However, if I remove the string column:
``` python
del df['strings']
df.to_hdf('test.h5', 'data', format='table')
```
Now it works fine - so it isn't a problem with using the pd.Timestamp type.
Digging a little deeper, it appears the problem is that pandas.io.pytables.Table.create_axes groups the columns by data type, with all columns of type object being grouped into one set of data. Then when set_atom is called, it does this:
``` python
rvalues = block.values.ravel()
inferred_type = lib.infer_dtype(rvalues)
```
This leads to an inferred type of 'mixed' since there are multiple types of objects present, and this isn't handled and throws the exception.
As a fix, it seems that each object column should be handled separately, or at least grouped by the inferred type. I haven't committed to pandas before, or dug this deeply into this section of code, so I'm not sure of the best way to fix this and what other implications there may be, but I'd be happy to help however I can.
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} | 1 | 2014-09-16T22:57:04Z | 2015-03-14T01:31:43Z | 2015-03-14T01:31:43Z | CONTRIBUTOR | null | Is it accidental that the Series and DataFrame constructors accept a single number for `data`, but the Panel and Panel4D constructors do not?
```
In [182]: from pandas import Series, DataFrame, Panel, Panel4D
In [183]: Series(1., index=range(2))
Out[183]:
0 1
1 1
dtype: float64
In [184]: DataFrame(1., index=range(2), columns=range(2))
Out[184]:
0 1
0 1 1
1 1 1
In [185]: Panel(1., items=range(2), major_axis=range(2), minor_axis=range(2))
---------------------------------------------------------------------------
PandasError Traceback (most recent call last)
<ipython-input-185-89f6618b848f> in <module>()
----> 1 Panel(1., items=range(2), major_axis=range(2), minor_axis=range(2))
C:\Python34\lib\site-packages\pandas\core\panel.py in __init__(self, data, items, major_axis, minor_axis, copy, dtype)
133 copy=False, dtype=None):
134 self._init_data(data=data, items=items, major_axis=major_axis,
--> 135 minor_axis=minor_axis, copy=copy, dtype=dtype)
136
137 def _init_data(self, data, copy, dtype, **kwargs):
C:\Python34\lib\site-packages\pandas\core\panel.py in _init_data(self, data, copy, dtype, **kwargs)
161 dtype = None
162 else: # pragma: no cover
--> 163 raise PandasError('Panel constructor not properly called!')
164
165 NDFrame.__init__(self, mgr, axes=axes, copy=copy, dtype=dtype)
PandasError: Panel constructor not properly called!
In [186]: Panel4D(1., labels=range(2), items=range(2), major_axis=range(2), minor_axis=range(2))
---------------------------------------------------------------------------
PandasError Traceback (most recent call last)
<ipython-input-187-8f22d9107a3e> in <module>()
----> 1 Panel4D(1., labels=range(2), items=range(2), major_axis=range(2), minor_axis=range(2))
C:\Python34\lib\site-packages\pandas\core\panel4d.py in panel4d_init(self, data, labels, items, major_axis, minor_axis, copy, dtype)
37 self._init_data(data=data, labels=labels, items=items,
38 major_axis=major_axis, minor_axis=minor_axis,
---> 39 copy=copy, dtype=dtype)
40
41 Panel4D.__init__ = panel4d_init
C:\Python34\lib\site-packages\pandas\core\panel.py in _init_data(self, data, copy, dtype, **kwargs)
161 dtype = None
162 else: # pragma: no cover
--> 163 raise PandasError('Panel constructor not properly called!')
164
165 NDFrame.__init__(self, mgr, axes=axes, copy=copy, dtype=dtype)
PandasError: Panel constructor not properly called!
```
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} | 8 | 2014-09-17T01:41:42Z | 2015-01-25T11:43:56Z | 2014-09-19T12:12:03Z | CONTRIBUTOR | null | While looking at making a separate PR in format.py, I decided to first clean up the file and make it PEP8 compliant. I used 99 maximum-line-length per http://legacy.python.org/dev/peps/pep-0008/#maximum-line-length.
I also noticed that line 965 (in master) has a list comprehension that iterates through a non-instantiated variable (**row**):
``` python
for i in range(nrows):
if truncate_v and i == (self.fmt.tr_row_num):
str_sep_row = [ '...' for ele in row ]
self.write_tr(str_sep_row, indent, self.indent_delta, tags=None, nindex_levels=1)
row = []
row.append(index_values[i])
row.extend(fmt_values[j][i] for j in range(ncols))
```
By the time it has gone through the loop once, **row** is set, but it really should be instantiated ahead of the loop to be safe.
Additionally, I thought it might be useful to add a PEP8 test in test_format, just to make sure that future commits don't break PEP8.
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} | 4 | 2014-09-17T02:39:48Z | 2016-05-24T15:28:17Z | 2016-05-24T15:28:17Z | CONTRIBUTOR | null | Or I'm doing this wrong
```
In [1]: df = pd.DataFrame(np.random.randn(10, 4))
In [3]: df.to_hdf('test.h5', 'df')
In [4]: with pd.get_store('test.h5') as store:
...: df_ = store.select('df', start=0, stop=2)
...:
In [5]: df_
Out[5]:
0 1 2 3
0 1.504814 0.209394 -0.360703 -0.339684
1 0.272718 0.768927 -1.061029 0.919742
2 0.315669 0.337206 -1.358443 1.223715
3 -0.414095 0.015512 -0.320634 0.703489
4 0.889063 -0.194524 1.342604 1.078340
5 -1.636940 -0.725176 -0.096803 0.346356
6 -1.359400 -0.588007 0.651720 -0.127604
7 -1.613468 1.131560 -0.074385 -0.830540
8 0.176463 -1.298918 -0.215537 0.984911
9 -0.372920 0.898528 -0.869616 1.178704
```
Docstring says:
```
start : integer (defaults to None), row number to start selection
stop : integer (defaults to None), row number to stop selection
```
I can look tomorrow (catching up on pandas stuff all day tomorrow hopefully)
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] | closed | false | null | [] | null | 21 | 2014-09-17T03:21:11Z | 2014-10-11T02:34:58Z | 2014-10-10T23:09:30Z | CONTRIBUTOR | null | As per Joris's guidance, this PR is only partially completed. I fully intend to add more. It might be a week or two, possibly more, until i'm finished.
I'll squash it all into one commit when I think I'm done and ready for final review. Feel free to make suggestions as I go, but consider it all a draft until I squash.
Addresses #6918
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} | 1 | 2014-09-17T11:19:57Z | 2015-05-09T16:00:51Z | 2015-05-09T16:00:51Z | CONTRIBUTOR | null | This addresses #8273, but it doesn't actually fully fix the issue, since the same bug still occurs when we have a MultiIndex. I've got a fix for the single-index case, and I'm not sure if I have time to decipher what's happening with hierarchical indexes right now. I'll open a new issue for the MultiIndex case if that's OK, having a MultiIndexed dataframe and then not printing the index seems like a bit of an edge case to me anyway.
``` python
import pandas as pd
import numpy as np
df = pd.DataFrame({'a': np.arange(0, 10), 'b': np.arange(9, -1, -1)})
df.to_html(max_rows=3, index=False)
```
now produces:
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>a</th>
<th>b</th>
</tr>
</thead>
<tbody>
<tr>
<td> 0</td>
<td> 9</td>
</tr>
<tr>
<td>...</td>
<td>...</td>
</tr>
<tr>
<td> 9</td>
<td> 0</td>
</tr>
</tbody>
</table>
as expected.
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} | 2 | 2014-09-17T12:05:30Z | 2014-09-17T14:48:32Z | 2014-09-17T14:48:25Z | CONTRIBUTOR | null | This should fix #8131.
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} | 1 | 2014-09-17T12:20:02Z | 2016-08-31T07:57:34Z | 2016-08-31T07:57:34Z | CONTRIBUTOR | null | This looks weird:
``` python
In [1]: pd.__version__
Out[1]: '0.14.1-388-g54678dd'
In [2]: sp_series = pd.Series([0,0,1]).to_sparse(fill_value=0)
In [3]: sp_series.dtype
Out[3]: dtype('int64')
In [4]: sp_series[::-1].dtype
Out[4]: dtype('float64')
In [5]: sp_series[::2].dtype
Out[5]: dtype('float64')
In [6]: sp_series[:-1].dtype
Out[6]: dtype('float64')
```
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} | 27 | 2014-09-17T13:16:48Z | 2014-10-04T13:38:16Z | 2014-10-04T13:37:55Z | CONTRIBUTOR | null | New attempt to close #8236
CustomBusinessDay is now fast with holiday calendar and incrementing in different ways. `busdaycalendar` is cached in self.kwds.
```
In [15]:
date = pd.Timestamp('20120101')
cbday = pd.offsets.CustomBusinessDay()
date + cbday
%timeit date + cbday
100000 loops, best of 3: 14.9 µs per loop
In [16]:
hdays = [dt.datetime(2013,1,1) for ele in range(1000)]
cbdayh = pd.offsets.CustomBusinessDay(holidays=hdays)
cbme = pd.offsets.CustomBusinessMonthEnd(holidays=hdays)
cbmb = pd.offsets.CustomBusinessMonthBegin(holidays=hdays)
%timeit date + cbdayh
%timeit date + 2 * cbdayh
%timeit date - 1 * cbdayh
%timeit date - 10 * cbday
%timeit date + cbmb
%timeit date + 10 * cbmb
%timeit date - 10 * cbmb
%timeit date + cbme
%timeit date + 10 * cbme
%timeit date - 10 * cbme
```
100000 loops, best of 3: 14.8 µs per loop
10000 loops, best of 3: 20.1 µs per loop
10000 loops, best of 3: 24 µs per loop
10000 loops, best of 3: 23.7 µs per loop
10000 loops, best of 3: 108 µs per loop
10000 loops, best of 3: 104 µs per loop
10000 loops, best of 3: 117 µs per loop
10000 loops, best of 3: 118 µs per loop
10000 loops, best of 3: 188 µs per loop
1000 loops, best of 3: 147 µs per loop
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} | 7 | 2014-09-17T16:22:35Z | 2014-09-17T20:41:33Z | 2014-09-17T20:40:28Z | CONTRIBUTOR | null | Currently, `DataFrame.from_records` raises a misleading exception when the shape of the input data do not match the shape implied by the axes:
``` python
In [1]: import pandas as pd
In [2]: pd.DataFrame.from_records([(0, 1), (0, 1), (0, 1)], index=[0, 1],
...: columns=['A', 'B'])
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-b8c920ac0f12> in <module>()
1 pd.DataFrame.from_records([(0, 1), (0, 1), (0, 1)], index=[0, 1],
----> 2 columns=['A', 'B'])
/Users/afni/homebrew/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/frame.pyc in from_records(cls, data, index, exclude, columns, coerce_float, nrows)
833
834 mgr = _arrays_to_mgr(arrays, arr_columns, result_index,
--> 835 columns)
836
837 return cls(mgr)
/Users/afni/homebrew/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/frame.pyc in _arrays_to_mgr(arrays, arr_names, index, columns, dtype)
4628 axes = [_ensure_index(columns), _ensure_index(index)]
4629
-> 4630 return create_block_manager_from_arrays(arrays, arr_names, axes)
4631
4632
/Users/afni/homebrew/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/internals.pyc in create_block_manager_from_arrays(arrays, names, axes)
3238 return mgr
3239 except (ValueError) as e:
-> 3240 construction_error(len(arrays), arrays[0].shape[1:], axes, e)
3241
3242
/Users/afni/homebrew/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/internals.pyc in construction_error(tot_items, block_shape, axes, e)
3209 raise e
3210 raise ValueError("Shape of passed values is {0}, indices imply {1}".format(
-> 3211 passed,implied))
3212
3213
ValueError: Shape of passed values is (2,), indices imply (2, 2)
```
This error message leads the user to believe that the error is internal (after all, the input data were _definitely_ 2-dimensional) and hides the true error, which is that the shape of the passed values is actually `(2, 3)`.
For some reason, this behavior was explicitly specified by a test.
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} | 5 | 2014-09-17T17:14:11Z | 2018-07-06T22:18:21Z | 2018-07-06T22:18:15Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/25896241/creating-columns-dynamically-assigning-them-a-constant-row-vector/25896504#25896504
e.g.
Instead of
`pd.concat([df,DataFrame([[1,2,3,4]],columns=list('ABCD'),index=df.index)],axis=1)`
allow as a convience feature
`df.append(DataFrame([[1,2,3,4]],columns=list('ABCD'),index=df.index),axis=1)`
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} | 2 | 2014-09-17T18:52:15Z | 2014-10-04T13:56:49Z | 2014-10-04T13:56:26Z | CONTRIBUTOR | null | These are failing for me on master
```
nosetests test_graphics:TestDataFramePlots.test_boxplot test_graphics:TestDataFramePlots.test_boxplot_vertical
FF
======================================================================
FAIL: test_boxplot (test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/tom/Envs/py3/lib/python3.4/site-packages/pandas/pandas/tests/test_graphics.py", line 1736, in test_boxplot
self.assertEqual(len(ax.lines), 8 * len(numeric_cols))
AssertionError: 21 != 24
======================================================================
FAIL: test_boxplot_vertical (test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/tom/Envs/py3/lib/python3.4/site-packages/pandas/pandas/tests/test_graphics.py", line 1770, in test_boxplot_vertical
self.assertEqual(len(ax.lines), 8 * len(numeric_cols))
AssertionError: 21 != 24
----------------------------------------------------------------------
Ran 2 tests in 0.659s
FAILED (failures=2)
```
``` python
In [1]: pd.__version__
Out[1]: '0.14.1-445-g83ff5b1'
```
Will look later.
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} | 6 | 2014-09-17T18:59:06Z | 2014-09-18T18:16:57Z | 2014-09-18T17:16:32Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/8071#issuecomment-55906171
``` python
In [1]: df = pd.DataFrame(np.random.randn(20, 3))
In [2]: df.plot(subplots=True, layout=(-1, 2))
Out[2]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x116b134e0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x117819240>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x11786b6a0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x1178a5ba8>]], dtype=object)
```
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} | 0 | 2014-09-17T19:14:57Z | 2014-09-17T20:46:01Z | 2014-09-17T20:46:01Z | CONTRIBUTOR | null | CLN/COMPAT: cleanup timedelta64[ns] series inferrence
closes #7729
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} | 10 | 2014-09-17T19:42:01Z | 2014-09-23T15:39:55Z | 2014-09-23T15:35:39Z | CONTRIBUTOR | null | This PR suppresses expected warnings in test_moments.py, which are otherwise distracting.
Note that the changes are not nearly as extensive as they look. I simply indented blocks of code under newly added `with warnings.catch_warnings():`.
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} | 11 | 2014-09-17T19:49:11Z | 2019-10-21T19:34:16Z | null | MEMBER | null | On master, with integer index, with more than 10 columns, the numbers of one char are misaligned:
```
In [37]: df = pd.DataFrame(np.random.randn(10,20))
In [38]: pd.options.display.max_columns = 4
In [39]: df
Out[39]:
0 1 ... 18 19
0 -0.112066 -0.315377 ... 1.579197 -0.718384
1 -1.007079 0.234835 ... -0.485022 -0.317984
2 -0.718872 1.094609 ... -2.083531 0.559202
3 -0.165058 -0.127335 ... 0.296100 1.071882
4 0.874132 0.880632 ... -1.648739 0.903484
5 -0.443316 0.673899 ... 0.823925 0.365219
6 0.215746 1.092637 ... 0.645429 -1.164019
7 1.853194 -1.682354 ... 1.071287 -0.366133
8 -0.568050 -1.205619 ... -1.644478 -2.206907
9 2.136091 -0.253217 ... 1.148933 1.390127
[10 rows x 20 columns]
In [40]: df = pd.DataFrame(np.random.randn(10,10))
In [41]: df
Out[41]:
0 1 ... 8 9
0 0.337429 -0.627859 ... 0.174635 1.378869
1 -1.679982 1.498135 ... 0.554562 1.804903
2 0.682877 -0.539606 ... -0.065115 0.871922
3 0.517217 -0.850968 ... 0.998076 -1.141827
4 0.131862 1.702232 ... -1.048214 -1.424597
5 0.926904 -0.120110 ... 0.852535 1.052194
6 -0.474197 -0.984774 ... 1.206264 -0.710416
7 0.532609 -0.797821 ... -1.082022 -0.137153
8 -0.075104 -0.524546 ... 0.177842 -0.030325
9 1.568465 1.206357 ... 0.410863 1.575516
[10 rows x 10 columns]
```
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