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} | 1 | 2014-06-28T12:10:47Z | 2014-06-29T20:15:53Z | 2014-06-29T20:15:53Z | MEMBER | null | `DatetimeIndex` and `PeriodIndex` have inconsistent representations. Should `PeriodIndex` behaves as the same as `DatetimeIndex`, or `DatetimeIndex` should have one-liner representation in some methods?
```
import pandas and pd
# DatetimeIndex ---------------------------------------------------------------
idx = pd.DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'], freq='D')
print(idx)
# <class 'pandas.tseries.index.DatetimeIndex'>
# [2011-01-01, ..., 2011-01-03]
# Length: 3, Freq: D, Timezone: None
idx.__repr__()
# <class 'pandas.tseries.index.DatetimeIndex'>
# [2011-01-01, ..., 2011-01-03]
# Length: 3, Freq: D, Timezone: None
idx.__unicode__()
# <class 'pandas.tseries.index.DatetimeIndex'>
# [2011-01-01, ..., 2011-01-03]
# Length: 3, Freq: D, Timezone: None
idx.__str__()
# <class 'pandas.tseries.index.DatetimeIndex'>
# [2011-01-01, ..., 2011-01-03]
# Length: 3, Freq: D, Timezone: None
idx.__bytes__()
# <class 'pandas.tseries.index.DatetimeIndex'>
# [2011-01-01, ..., 2011-01-03]
# Length: 3, Freq: D, Timezone: None
# PeriodIndex ---------------------------------------------------------------
idx = pd.PeriodIndex(['2011-01-01', '2011-01-02', '2011-01-03'], freq='D')
print(idx)
# PeriodIndex([u'2011-01-01', u'2011-01-02', u'2011-01-03'], freq='D')
idx.__repr__()
# <class 'pandas.tseries.period.PeriodIndex'>
# freq: D
# [2011-01-01, ..., 2011-01-03]
# length: 3
idx.__unicode__()
# PeriodIndex([u'2011-01-01', u'2011-01-02', u'2011-01-03'], freq='D')
idx.__str__()
# PeriodIndex([u'2011-01-01', u'2011-01-02', u'2011-01-03'], freq='D')
idx.__bytes__()
# PeriodIndex([u'2011-01-01', u'2011-01-02', u'2011-01-03'], freq='D')
```
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} | 6 | 2014-06-28T14:48:14Z | 2014-09-02T13:21:29Z | 2014-06-29T20:15:53Z | MEMBER | null | Closes #7601. Made `PeriodIndex` to have dummy timezone representation to be consistent with `DatetimeIndex`, but this should be ommited?
### Output after the fix
```
# DatetimeIndex (No change)
pd.date_range(start='2011-01-01', periods=10, freq='M')
# <class 'pandas.tseries.index.DatetimeIndex'>
# [2011-01-31, ..., 2011-10-31]
# Length: 10, Freq: M, Timezone: None
# PeriodIndex
pd.period_range(start='2011-01-01', periods=10, freq='M')
# <class 'pandas.tseries.period.PeriodIndex'>
# [2011-01, ..., 2011-10]
# Length: 10, Freq: M
```
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} | 19 | 2014-06-28T17:21:56Z | 2014-09-10T00:12:55Z | 2014-07-24T17:07:18Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/7543.
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} | 3 | 2014-06-29T00:20:04Z | 2014-09-10T00:12:47Z | 2014-07-01T10:14:18Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/7512.
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| null | 7 | 2014-06-29T00:25:48Z | 2014-06-29T01:58:05Z | 2014-06-29T01:53:38Z | NONE | null | here's the code:
```
records.to_hdf(
args.output, 'records',
mode='w', format='fixed', append=False,
complib='zlib', complevel=7, fletcher32=True)
r2 = pd.read_hdf(
path_or_buf=args.output, key='records',
encoding='utf-8', start=None, stop=None)
from pandas.util.testing import assert_frame_equal
assert_frame_equal(records, r2, check_exact=True)
```
and the traceback:
```
/Users/pball/miniconda3/lib/python3.3/site-packages/pandas/io/pytables.py:2441: PerformanceWarning:
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->mixed,key->block1_values] [items->['dataset', 'record_id', 'DOD', 'CC', 'sex', 'name', 'loc', 'manner_of_death', 'eth', 'social_group', 'occ', 'clean_loc', 'month_of_death', 'year_of_death', 'name_sorted']]
warnings.warn(ws, PerformanceWarning)
Traceback (most recent call last):
File "src/import.py", line 59, in <module>
tools.epilog(args, records, logger)
File "/Users/pball/git/CO/match/import/src/lib/import_tools.py", line 46, in epilog
assert_frame_equal(records, r2, check_exact=True)
File "/Users/pball/miniconda3/lib/python3.3/site-packages/pandas/util/testing.py", line 585, in assert_frame_equal
check_exact=check_exact)
File "/Users/pball/miniconda3/lib/python3.3/site-packages/pandas/util/testing.py", line 530, in assert_series_equal
right.values))
AssertionError: [nan nan nan ..., 'c2681113' 'c12266508' 'c2680757'] is not equal to [nan nan nan ..., 'c2681113' 'c12266508' 'c2680757'].
make: *** [output/input-records.h5] Error 1
```
I've been trying to figure out why upstream fixes didn't seem to appear downstream. I finally came here: apparently to_hdf is writing a file that's different when it's read back. As I've been re-running this over the last hour or so, different fields have come up in the AssertionError.
Here are a few things that do not eliminate the error: with or without compression; format table or fixed. However, changing these arguments does change which field is identified by assert_frame_equal as unequal.
I have no idea how to reproduce this without my entire dataset, which is unfortunately confidential. I'll fall back to csv for now, and I hope that I'm just doing something horribly dumb that we can fix.
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} | 1 | 2014-06-29T06:37:38Z | 2014-06-30T10:58:09Z | 2014-06-29T11:12:57Z | MEMBER | null | `DatetimeIndex.freqstr` raises `AttributeError` if `freq/offset` is `None`, even though docstring says "return the frequency object as a string if its set, otherwise None"
```
idx = pd.DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03', '2011-01-04'])
idx.freqstr
# AttributeError: 'NoneType' object has no attribute 'freqstr'
```
Also, `DataFrame/Series.to_period` has a logic to use `inferred_freq` when the freq is not passed, but it doesn't work actually because of the bug. Moved the logic to `DatetimeIndex.to_period` for consistency and made it works.
The fix will simplify #7602 a little.
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} | 3 | 2014-06-29T10:16:42Z | 2014-07-05T04:48:06Z | 2014-07-03T21:31:22Z | MEMBER | null | Found and fixed `DatetimeIndex.delete` (#7302) results in `ValueError` when it has `tz` and `freq` is less than daily..
```
idx = pd.date_range(start='2011-01-01 09:00', periods=40, freq='H', tz='Asia/Tokyo')
idx.delete(0)
# ValueError: Inferred frequency None from passed dates does notconform to passed frequency H
```
`insert` (#7299) and `take` don't have problems, but changed test case to use hourly frequencies to detect this kind of problem.
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https://api.github.com/repos/pandas-dev/pandas/issues/7609 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7609/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7609/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7609/events | https://github.com/pandas-dev/pandas/pull/7609 | 36,743,303 | MDExOlB1bGxSZXF1ZXN0MTc3NDU4OTc= | 7,609 | BUG: PeriodIndex.min/max returns int | {
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} | 3 | 2014-06-29T10:26:56Z | 2014-06-30T10:58:00Z | 2014-06-29T12:43:00Z | MEMBER | null | Related to #7279. `PeriodIndex.min/max` should return `Period` ignoring `NaT`.
```
pidx = pd.PeriodIndex(['2011-01', 'NaT'], freq='M')
pidx.min()
# -9223372036854775808
pidx.max()
#492
```
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} | 2 | 2014-06-29T14:14:44Z | 2014-07-02T16:45:22Z | 2014-06-30T13:29:09Z | MEMBER | null | When `Timestamp.__new__` accepts a timestamp with nanosecond, nanosecond is not preserved properly.
```
# create Timestamp with ns (OK)
t = pd.Timestamp('2011-01-01') + pd.offsets.Nano(5)
t, t.value
# (Timestamp('2011-01-01 00:00:00.000000005'), 1293840000000000005)
# If it is passed to Timestamp.__init__, ns is not displayed even though internal value includes it. (NG)
t = pd.Timestamp(t)
t, t.value
# (Timestamp('2011-01-01 00:00:00'), 1293840000000000005)
# If offset is added to above result, ns is displayed properly (OK)
t = t + pd.offsets.Nano(5)
t, t.value
# (Timestamp('2011-01-01 00:00:00.000000010'), 1293840000000000010)
```
NOTE: Unrelated to this issue, test_tslib had `test_timedelta_ns_arithmetic` method duplicatelly. Thus renamed.
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} | 12 | 2014-06-29T14:38:05Z | 2014-09-05T19:17:55Z | 2014-09-05T10:24:18Z | CONTRIBUTOR | null | 'h' and 'm' units are not correctly recognized in to_timedelta:
```
print pd.to_timedelta(1,unit='D')
print pd.to_timedelta(1,unit='h')
print pd.to_timedelta(1,unit='m')
print pd.to_timedelta(1,unit='s')
print pd.to_timedelta(1,unit='ms')
print pd.to_timedelta(1,unit='us')
print pd.to_timedelta(1,unit='ns')
```
results in
```
86400000000000 nanoseconds
1 nanoseconds
1 nanoseconds
1000000000 nanoseconds
1000000 nanoseconds
1000 nanoseconds
1 nanoseconds
```
version: 0.14.0
Most likely related to Issue #6423
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} | 7 | 2014-06-29T15:09:37Z | 2019-04-30T12:59:53Z | 2019-04-30T12:59:53Z | CONTRIBUTOR | null | As pointed out here (https://stackoverflow.com/questions/24468333/set-xlim-for-pandas-matplotlib-where-index-is-string/24477138#24477138) if the index of a data frame is objects (that is not, the index obviously convertible to scalars and the data is being plotted against `range(len(df))`), the ticklabels are set to be string representations of the objects (I presume via `set_xticklabels`) but the locator is still a `AutoLocator` which means if you change the xlimits or pan/zoom the axes, the ticklabels become de-coupled from the data.
A solution is to also use a fixed locator which will pin the ticks to only be on the index locations.
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
df = pd.DataFrame({'Foo': pd.Series([2,3,4], index=['2002', '2003', '2004'])})
fig, ax = plt.subplots()
df.plot(ax=ax)
ax.xaxis.set_major_locator(mticker.FixedLocator(np.arange(len(df))))
ax.xaxis.set_major_formatter(mticker.FixedFormatter(df.index))
```
I can make changes to the code if someone points me to where the relevant section is.
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https://api.github.com/repos/pandas-dev/pandas/issues/7613 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7613/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7613/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7613/events | https://github.com/pandas-dev/pandas/pull/7613 | 36,781,055 | MDExOlB1bGxSZXF1ZXN0MTc3NjI4Nzc= | 7,613 | Fix typo in documentation | {
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| null | 1 | 2014-06-30T09:16:50Z | 2014-06-30T09:41:37Z | 2014-06-30T09:41:27Z | CONTRIBUTOR | null | null | {
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| null | 4 | 2014-06-30T11:50:33Z | 2015-10-20T22:27:27Z | 2015-10-20T22:27:27Z | NONE | null | Perhaps this is me misunderstanding the way Pandas handles indices for related objects behind the scenes, but I found the following behavior very unintuitive:
``` python
import pandas as pd
import numpy as np
np.random.seed(0)
idx = pd.MultiIndex.from_product([['John', 'Josh', 'Alex'], list('abcde')],
names=['Person', 'Letter'])
large = pd.DataFrame(data=np.random.randn(15, 2),
index=idx,
columns=['one', 'two'])
small = large.loc[['Jo'==d[0:2] for d in large.index.get_level_values('Person')]]
print small.index.levels[0]
print large.index.levels[0]
```
This returns:
``` python
Index([u'Alex', u'John', u'Josh'], dtype='object')
Index([u'Alex', u'John', u'Josh'], dtype='object')
```
rather than the expected
``` python
Index([u'John', u'Josh'], dtype='object')
Index([u'Alex', u'John', u'Josh'], dtype='object')
```
I could get the results I expected by running
``` python
small.index.get_level_values('Person').unique()
large.index.get_level_values('Person').unique()
```
but could someone explain why the behavior I'm seeing above with `DataFrame.index.levels` is an appropriate result rather than a bug?
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} | 15 | 2014-06-30T12:49:46Z | 2016-10-18T14:46:48Z | 2015-10-12T15:49:37Z | NONE | null | the DataFrame.to_csv method seems to accept a "compression" named parameter:
```
import numpy as np,pandas as pd
data=np.arange(10).reshape(5,2)
df=pd.DataFrame(data,columns=['a','b'])
df.to_csv('test.csv.gz',compression='gzip')
```
However, the file it creates is not compressed at all:
francesco@i3 ~/Desktop $ cat test.csv.gz
,a,b
0,0,1
1,2,3
2,4,5
3,6,7
4,8,9
How about either (i) actually implementing compression, or at least (ii) raise an error? The current behavior is confusing...
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} | 0 | 2014-06-30T13:32:38Z | 2014-09-05T03:18:03Z | 2014-06-30T14:35:18Z | CONTRIBUTOR | null | closes #7611
closes #6423
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} | 12 | 2014-06-30T15:08:40Z | 2014-09-23T13:04:18Z | 2014-09-23T13:04:18Z | CONTRIBUTOR | null | Here's the example in the documentation, modified to have non-zero mean:
```
n = 100
ser = pandas.Series(randn(n)+10, index=pandas.date_range('1/1/2000', periods=n))
pandas.rolling_window(ser, 5, 'triang').plot()
pandas.rolling_window(ser, 5, 'boxcar').plot()
```
The rolling boxcar window is centered around 10, as expected.
The triang window is centered around 6. That suggests that the weights in the window don't add up to 1.
Either that or my expectation of how it should work is wrong?
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} | 61 | 2014-06-30T18:44:51Z | 2014-10-05T02:35:24Z | 2014-10-05T02:31:10Z | CONTRIBUTOR | null | Closes [#6852](https://github.com/pydata/pandas/issues/6852)
For a sample DataFrame:
``` python
df = pd.DataFrame({ 'float' : np.random.randn(10000000), 'int' : np.random.randint(0,5,size=10000000), 'date' : Timestamp('20130101'), 'string' : 'foo', 'smallint' : np.random.randint(0,5,size=10000000).astype('int16') })
```
The memory usage of the elements of each column (in units of bytes) are returned by accessing the `meminfo` method. This returns a Series:
``` python
>>> df.meminfo()
date 80000000
float 80000000
int 80000000
smallint 20000000
string 80000000
dtype: int64
```
Also, the total memory usage (in a human readable format) can be found via the `info(memory_usage=True)` method:
``` python
>>> df.info(memory_usage=True)
<class 'pandas.core.frame.DataFrame'>
Int64Index: 10000000 entries, 0 to 9999999
Data columns (total 5 columns):
date datetime64[ns]
float float64
int int64
smallint int16
string object
dtypes: datetime64[ns](1), float64(1), int16(1), int64(1), object(1)
memory usage: 324.2 MB
```
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} | 9 | 2014-06-30T20:33:28Z | 2014-07-06T14:45:14Z | 2014-07-06T14:45:14Z | CONTRIBUTOR | null | I have HDF5 files created in 0.12 which were localized to a timezone ('US/Eastern'). When the dataframe is updated, it is done by calling combine_first which ultimately calls tseries.index.join. Now when I try to update in 0.14, the HDF5 representation changes. This is because of the different versions of pytz and timezone representation (see below), both sets of data will be converted to UTC and ultimately saved in UTC rather than US/Eastern. This certainly surprised me. This is using master branch as of this morning.
```
tz
Out[10]: <DstTzInfo 'US/Eastern' LMT-1 day, 19:04:00 STD> #New data
data.index.tz
Out[11]: <DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD> #Saved data
data.index.tz == tz
Out[12]: False
```
Also, in ipython typing "data.index.tz" gets stuck at "data.index." for about 20 seconds and my CPU spikes to 100% before completing. I don't know if this is simply related to the versions or something else.
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} | 5 | 2014-06-30T21:19:09Z | 2018-07-06T22:31:40Z | 2018-07-06T22:31:36Z | CONTRIBUTOR | null | Currently df which include a categorical can be written to a hdf store and other on disc storage formats. For formats which include factors/categoricals, also ensure that such data is read into a categorical.
- [ ] CSV #7217, #7444 (just testing, not a completely fungible format)
- [x] HDF5 #7217, #7444 / #8793
- [x] msgpack #8632 (closed by #12573)
- [x] stata #8633 / #8767
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| null | 4 | 2014-06-30T21:48:08Z | 2014-07-18T16:42:38Z | 2014-06-30T22:16:09Z | NONE | null | Hi all,
I've encountered a problem with DataFrames, groupby and timezones.
import pandas as pd
import numpy as np
dt_rng = pd.date_range(start='2014-01-01 00:00', periods = 1000, freq='1s', tz='Europe/Berlin')
df = pd.DataFrame({'a':np.random.randn(1000), 'b': np.random.randn(1000)},index = dt_rng)
df['b'] = df['b'].round()
df.to_csv()
--> Timezones are shown in the csv output, for example 2014-01-01 00:00:00+01:00
Now with resampling:
dt_rng = pd.date_range(start='2014-01-01 00:00', periods = 1000, freq='1s', tz='Europe/Berlin')
df = pd.DataFrame({'a':np.random.randn(1000), 'b': np.random.randn(1000)},index = dt_rng)
df['b'] = df['b'].round()
df.groupby(df['b']).resample('1min').to_csv()
--> 2013-12-31 23:01:00 no timezone info, not even UTC.
However:
dt_rng = pd.date_range(start='2014-01-01 00:00', periods = 1000, freq='1s', tz='Europe/Berlin')
df = pd.DataFrame({'a':np.random.randn(1000), 'b': np.random.randn(1000)},index = dt_rng)
df['b'] = df['b'].round()
df.groupby(df['b']).resample('1min').index.levels[1]
shows: Timezone: Europe/Berlin
So the info seems to be there, but is not exported - even if it was exported before without resampling...
Any ideas?
Thanks and best regards
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} | 7 | 2014-06-30T22:10:22Z | 2014-07-02T14:19:28Z | 2014-07-02T14:19:28Z | CONTRIBUTOR | null | This test is failing on all versions of windows (but not linux).
related to #7582, #7591
cc @amras1
cc @mcwitt
not sure what is failing this
```
FAIL: test_concat_invalid_first_argument (pandas.tools.tests.test_merge.TestConcatenate)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tools\tests\test_merge.py", line 2107, in test_concat_invalid_first_argument
assert_frame_equal(result,expected)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\util\testing.py", line 641, in assert_frame_equal
check_exact=check_exact)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\util\testing.py", line 588, in assert_series_equal
assert_almost_equal(left.values, right.values, check_less_precise)
File "testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas\src\testing.c:2465)
File "testing.pyx", line 93, in pandas._testing.assert_almost_equal (pandas\src\testing.c:1793)
File "testing.pyx", line 69, in pandas._testing.assert_almost_equal (pandas\src\testing.c:1489)
AssertionError: nan != 'bar'
```
can you guys take a look...thxs
Here's some debug output
```
C:\Users\Jeff Reback\Documents\GitHub\pandas>c:\python27-64\Scripts\nosetests.exe build\lib.win-amd64-2.7\pandas\tools\tests\test_merge.py --pdb --pdb-failure
................> c:\users\jeff reback\documents\github\pandas\testing.pyx(69)pandas._testing.assert_almost_equal (pandas\src\testing.c:1489)()
(Pdb) u
> c:\users\jeff reback\documents\github\pandas\testing.pyx(93)pandas._testing.assert_almost_equal (pandas\src\testing.c:1793)()
(Pdb) u
> c:\users\jeff reback\documents\github\pandas\testing.pyx(58)pandas._testing.assert_almost_equal (pandas\src\testing.c:2465)()
(Pdb) u
> c:\users\jeff reback\documents\github\pandas\build\lib.win-amd64-2.7\pandas\util\testing.py(588)assert_series_equal()
-> assert_almost_equal(left.values, right.values, check_less_precise)
(Pdb) u
> c:\users\jeff reback\documents\github\pandas\build\lib.win-amd64-2.7\pandas\util\testing.py(641)assert_frame_equal()
-> check_exact=check_exact)
(Pdb) u
> c:\users\jeff reback\documents\github\pandas\build\lib.win-amd64-2.7\pandas\tools\tests\test_merge.py(2107)test_concat_invalid_first_argument()
-> assert_frame_equal(result,expected)
(Pdb) l
2102 """
2103
2104 reader = read_csv(StringIO(data), chunksize=1)
2105 result = concat(reader, ignore_index=True)
2106 expected = read_csv(StringIO(data))
2107 -> assert_frame_equal(result,expected)
2108
2109 class TestOrderedMerge(tm.TestCase):
2110
2111 def setUp(self):
2112 self.left = DataFrame({'key': ['a', 'c', 'e'],
(Pdb) p result
index A B C D
0 foo 2 3 4 5
1 NaN ?¶ n NaN NaN
2 baz 12 13 14 15
3 qux 12 13 14 15
4 foo2 12 13 14 15
5 bar2 12 13 14 15
(Pdb) p data
'index,A,B,C,D\nfoo,2,3,4,5\nbar,7,8,9,10\nbaz,12,13,14,15\nqux,12,13,14,15\nfoo2,12,13,14,15\nbar2,12,13,14,15\n'
(Pdb) p expected
index A B C D
0 foo 2 3 4 5
1 bar 7 8 9 10
2 baz 12 13 14 15
3 qux 12 13 14 15
4 foo2 12 13 14 15
5 bar2 12 13 14 15
(Pdb) !reader = read_csv(StringIO(data),chunksize=1)
(Pdb) p list(reader)
[ index A B C D
0 foo 2 3 4 5, index A B C D
0 NaN ?¶ n NaN NaN, index A B C D
0 baz 12 13 14 15, index A B C D
0 qux 12 13 14 15, index A B C D
0 foo2 12 13 14 15, index A B C D
0 bar2 12 13 14 15]
```
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} | 1 | 2014-07-01T02:28:22Z | 2014-07-02T11:46:11Z | 2014-07-01T17:52:25Z | CONTRIBUTOR | null | closes #7131
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} | 18 | 2014-07-01T02:55:23Z | 2016-12-06T01:48:05Z | 2016-12-06T01:48:05Z | NONE | null | Reading Australian electricity market data files, read_csv reads past the nrows limit for certain nrows values and consequently fails.
These market data files are 4 csv files combined into a single csv file and so the file has multiple headers and variable field size across the rows.
The first set of data is from rows 1-1442.
Intent was to extract first set of data with nrows = 1442.
Testing several arbitrary CSV files from this data source shows well formed CSV - 120 fields between rows 1 to 1442 (with a 10 field at row 0)
```
lines = [len(line.strip().split(',')) for i,line in enumerate(csvFile) if i < 1442]
s = pd.Series(lines)
print (s.value_counts())
```
returns
120 1441
10 1
dtype: int64
Other python examples of reading the market data using csv module [work fine](https://github.com/hsenot/aemo-json/blob/master/script/extract-historic-public-prices.py)
In the reproducible example below, code works for nrows< 824, but fails on any value above it.
Testing on arbitrary files suggests the 824 limit is variable - sometimes a few more rows, sometimes a few less rows.
```
import requests, io, zipfile
import pandas as pd
url = 'http://www.nemweb.com.au/Reports/CURRENT/Public_Prices/PUBLIC_PRICES_201406290000_20140630040528.zip'
# get the zip-archive
request = requests.get(url)
# make the archive available as a byte-stream
zipdata = io.BytesIO()
zipdata.write(request.content)
thezipfile = zipfile.ZipFile(zipdata, mode='r')
# there is only one csv file per arhive - read it into a Pandas DataFrame
fname = thezipfile.namelist()[0]
with thezipfile.open(fname) as csvFile:
#works for nrows < = 823
df1 = pd.read_csv(csvFile, header=1, index_col=4, parse_dates=True, nrows=823)
print(df1.head())
#fails for n> 823
df1 = pd.read_csv(csvFile, header=1, index_col=4, parse_dates=True, nrows=824)
print(df1.head())
```
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} | 11 | 2014-07-01T05:45:02Z | 2014-07-01T17:42:19Z | 2014-07-01T11:36:24Z | CONTRIBUTOR | null | This PR speeds up MultiIndex.from_product employing the fact that operating on categorical codes is faster than on the values themselves.
This yields about 2x improvement in the benchmark
``` python
In [1]: import pandas.util.testing as tm
In [2]: data = [tm.makeStringIndex(10000), tm.makeFloatIndex(20)]
In [3]: %timeit pd.MultiIndex.from_product(data)
100 loops, best of 3: 10.6 ms per loop
In [4]: %timeit pd.MultiIndex.from_arrays(pd.tools.util.cartesian_product(data))
10 loops, best of 3: 23.4 ms per loop
```
It's only marginally slower in small size cases:
``` python
In [1]: data = [np.arange(20).astype(object), np.arange(20)]
In [2]: %timeit pd.MultiIndex.from_product(data)
1000 loops, best of 3: 317 µs per loop
In [3]: %timeit pd.MultiIndex.from_arrays(pd.tools.util.cartesian_product(data))
1000 loops, best of 3: 308 µs per loop
In [4]: data_int = [np.arange(20), np.arange(20)]
In [5]: %timeit pd.MultiIndex.from_product(data_int)
1000 loops, best of 3: 285 µs per loop
In [6]: %timeit pd.MultiIndex.from_arrays(pd.tools.util.cartesian_product(data_int))
1000 loops, best of 3: 269 µs per loop
```
And this case came as a surprise because the cartesian product is blazingly fast both in old and new versions, but profiling showed that factorization is a lot faster when done on a smaller array:
``` python
In [7]: data_large = [np.arange(10000), np.arange(20)]
In [8]: %timeit pd.MultiIndex.from_arrays(pd.tools.util.cartesian_product(data_large))
100 loops, best of 3: 9.88 ms per loop
In [9]: %timeit pd.MultiIndex.from_product(data_large)
100 loops, best of 3: 2.74 ms per loop
```
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https://api.github.com/repos/pandas-dev/pandas/issues/7628 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7628/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7628/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7628/events | https://github.com/pandas-dev/pandas/issues/7628 | 36,867,599 | MDU6SXNzdWUzNjg2NzU5OQ== | 7,628 | BUG: nth on series groupby with dropna=True gives TypeError | {
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} | 1 | 2014-07-01T07:12:20Z | 2014-07-01T12:50:25Z | 2014-07-01T12:50:25Z | MEMBER | null | From the docs: http://pandas-docs.github.io/pandas-docs-travis/groupby.html#taking-the-nth-row-of-each-group
```
In [1]: df = DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B'])
In [2]: g = df.groupby('A')
In [3]: g.B.nth(0, dropna=True)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-bcb9dcbefdf5> in <module>()
----> 1 g.B.nth(0, dropna=True)
C:\Users\vdbosscj\Scipy\pandas-joris\pandas\core\groupby.py in nth(self, n, drop
na)
831 # get a new grouper for our dropped obj
832 grouper, exclusions, obj = _get_grouper(dropped, key=self.keys,
axis=self.axis,
--> 833 level=self.level, sort=s
elf.sort)
834
835 sizes = obj.groupby(grouper).size()
C:\Users\vdbosscj\Scipy\pandas-joris\pandas\core\groupby.py in _get_grouper(obj,
key, axis, level, sort)
2035 raise AssertionError(errmsg)
2036
-> 2037 ping = Grouping(group_axis, gpr, obj=obj, name=name, level=level
, sort=sort)
2038 groupings.append(ping)
2039
C:\Users\vdbosscj\Scipy\pandas-joris\pandas\core\groupby.py in __init__(self, in
dex, grouper, obj, name, level, sort)
1874 # no level passed
1875 if not isinstance(self.grouper, (Series, np.ndarray)):
-> 1876 self.grouper = self.index.map(self.grouper)
1877 if not (hasattr(self.grouper, "__len__") and
1878 len(self.grouper) == len(self.index)):
C:\Users\vdbosscj\Scipy\pandas-joris\pandas\core\index.py in map(self, mapper)
1332
1333 def map(self, mapper):
-> 1334 return self._arrmap(self.values, mapper)
1335
1336 def isin(self, values):
C:\Users\vdbosscj\Scipy\pandas-joris\pandas\algos.pyd in pandas.algos.arrmap_int
64 (pandas\algos.c:72351)()
TypeError: 'NoneType' object is not callable
```
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} | 9 | 2014-07-01T10:28:34Z | 2015-04-20T11:19:57Z | 2015-04-20T11:19:57Z | CONTRIBUTOR | null | When #7217 becomes available, it would be nice to also have a 'CategoricalIndex' so that the sorting behaviour based on levels will be preserved when a categorical becomes a index:
``` python
cats = pd.Categorical([1,2,3,4], levels=[4,2,3,1])
strings = ["a","b","c","d"]
values = [4,2,3,1]
df = pd.DataFrame({"strings":strings, "values":values}, index=cats)
df.index
# This should sort by levels but does not as there is no CategoricalIndex!
df.sort_index()
```
CC: @shoyer
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} | 3 | 2014-07-01T11:33:16Z | 2016-08-12T15:44:54Z | 2016-08-12T15:44:54Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/24502838/pandas-tzinfo-lost-by-combine-first
```
In [10]: df1 = pd.DataFrame(columns=['UTCdatetime','abc'],
....: data=pd.to_datetime('20100101 01:01').tz_localize('UTC'),
....: index=pd.date_range('20140627', periods=1))
In [11]: print(df1)
UTCdatetime abc
2014-06-27 2010-01-01 01:01:00+00:00 2010-01-01 01:01:00+00:00
In [12]: df2 = pd.DataFrame(columns=['UTCdatetime','xyz'],
....: data=pd.to_datetime('20121212 12:12').tz_localize('UTC'),
....: index=pd.date_range('20140628', periods=1))
In [13]:
In [13]: df1
Out[13]:
UTCdatetime abc
2014-06-27 2010-01-01 01:01:00+00:00 2010-01-01 01:01:00+00:00
In [14]: df2
Out[14]:
UTCdatetime xyz
2014-06-28 2012-12-12 12:12:00+00:00 2012-12-12 12:12:00+00:00
In [15]: df1.dtypes
Out[15]:
UTCdatetime object
abc object
dtype: object
In [16]: df2.dtypes
Out[16]:
UTCdatetime object
xyz object
dtype: object
In [17]: df2[['UTCdatetime']].combine_first(df1)
Out[17]:
UTCdatetime abc
2014-06-27 2010-01-01 01:01:00 2010-01-01 01:01:00+00:00
2014-06-28 2012-12-12 12:12:00 NaN
In [18]: df2[['UTCdatetime']].combine_first(df1).dtypes
Out[18]:
UTCdatetime datetime64[ns]
abc object
dtype: object
```
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} | 0 | 2014-07-01T12:10:14Z | 2014-07-01T12:50:25Z | 2014-07-01T12:50:25Z | CONTRIBUTOR | null | closes #7628
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} | 7 | 2014-07-01T12:32:27Z | 2019-04-09T16:36:32Z | 2018-07-06T22:30:45Z | NONE | null | The following occurs when using pandas.merge for an (left) outer join with the left_index=True and right_on="something" options. The index of the resulting DataFrame contains duplicate values and the "something" column contains new (compared to before) values that look suspiciously like what the index should be.
Is this expected behavior, or could this be a bug?
A minimal illustration is:
``` python
In [2]: pandas.__version__
Out[2]: '0.14.0'
In [3]: df1 = pandas.DataFrame(zeros(8).reshape(4,2), columns=['A', 'B'])
In [4]: df2 = pandas.DataFrame(arange(4).reshape(2,2), columns=['C', 'D'])
In [5]: df1
Out[5]:
A B
0 0 0
1 0 0
2 0 0
3 0 0
In [6]: df2
Out[6]:
C D
0 0 1
1 2 3
In [7]: pandas.merge(df1, df2, left_index=True, right_on='C', how='left')
Out[7]:
A B C D
0 0 0 0 1
1 0 0 1 NaN
1 0 0 2 3
1 0 0 3 NaN
```
What I would expect as the result is the following and can be constructed by adding an extra column carrying the left index, doing the join with left_on instead of left_index, and dropping the extra column again:
``` python
In [8]: df1['i'] = pandas.Series(df1.index)
In [9]: pandas.merge(df1, df2, left_on='i', right_on='C', how='left').drop(['i'],axis=1)
Out[9]:
A B C D
0 0 0 0 1
1 0 0 NaN NaN
2 0 0 2 3
3 0 0 NaN NaN
```
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} | 24 | 2014-07-01T12:45:12Z | 2014-07-18T23:24:46Z | 2014-07-07T19:24:50Z | CONTRIBUTOR | null | We have many + perf fixes in 0.14.1 yeh!
but need to look at these (resamples could be spurious)
```
dataframe_resample_mean_string | 4.0483 | 2.4170 | 1.6749 |
dataframe_resample_max_numpy | 3.3394 | 1.7230 | 1.9381 |
dataframe_resample_max_string | 3.3154 | 1.7047 | 1.9448 |
dataframe_resample_min_numpy | 3.3020 | 1.6843 | 1.9604 |
dataframe_resample_min_string | 3.2830 | 1.6587 | 1.9793 |
timeseries_timestamp_downsample_mean | 11.4803 | 4.0847 | 2.8106 |
timeseries_period_downsample_mean | 29.7631 | 10.3510 | 2.8754 |
datetimeindex_normalize | 77.5570 | 3.0633 | 25.3183 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [272dae5] : BUG: doc example in groupby.rst (GH7559 / GH7628)
Base [da0f7ae] : RLS: 0.14.0 final
```
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} | 10 | 2014-07-01T12:59:02Z | 2014-07-06T14:52:45Z | 2014-07-05T13:48:39Z | MEMBER | null | Closes #7433
Replace the sqlalchemy `Integer` type with `BigInteger` as the default integer type in pandas is int64.
Probably we could be more specific in the type conversion (check for the exact integer type).
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} | 8 | 2014-07-01T13:36:21Z | 2018-07-06T22:26:45Z | 2018-07-06T22:26:26Z | NONE | null | Would it make sense to add functionality for coloring bar charts according to a categorical value in a column? It could then by be passed as an argument such as:
``` python
df.plot(kind='bar', colors=['r', 'g', 'b'], color_by=['B'])
```
which would lead to ['r', 'g', 'b'] being cycled throughout the values in column 'B'
Something along the lines of:
``` python
def color_by_category(series, colormap):
from itertools import cycle
unique_categories = set(series)
color_cycle = cycle(colormap)
repeat_colors = [color_cycle.next() for i in range(len(unique_categories))]
category_color = {i:j for i,j in zip(unique_categories, repeat_colors)}
color_array = [category_color[k] for k in series]
return color_array
```
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| null | 2 | 2014-07-01T14:40:49Z | 2014-08-15T12:49:14Z | 2014-07-07T15:40:28Z | CONTRIBUTOR | null | Suppose I have a Series with a duplicate indices:
``` python
In [109]: s = pd.Series([1, 2, 3, 4, 5], index=['a', 'a', 'a', 'a', 'b'])
In [110]: s
Out[110]:
a 1
a 2
a 3
a 4
b 5
dtype: int64
```
If I access the duplicated index, I get a Series:
``` python
In [111]: s.xs('a')
Out[111]:
a 1
a 2
a 3
a 4
dtype: int64
```
But if I access a unique index, I get a scalar:
``` python
In [112]: s.xs('b')
Out[112]: 5
```
Thus, I get different return types. This led to a really hard bug to track down.
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} | 0 | 2014-07-01T15:39:11Z | 2014-07-01T16:02:12Z | 2014-07-01T16:02:12Z | CONTRIBUTOR | null | closes #7407
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} | 0 | 2014-07-01T15:57:47Z | 2014-07-01T16:33:07Z | 2014-07-01T16:33:07Z | CONTRIBUTOR | null | closes #5521
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} | 20 | 2014-07-01T21:18:41Z | 2018-05-08T00:06:51Z | 2017-04-14T13:32:37Z | MEMBER | null | ### Design
The idea is to have a natural representation of the grids that ubiquitously appear in simulations and measurements of physical systems. Instead of referencing a single value, a grid cell references a _range_ of values, based on the chosen discretization. Typically, cells boundaries would be specified by floating point numbers. In one dimension, a grid cell corresponds to an _interval_, the name we use here.
The key feature of `IntervalIndex` is that looking up an indexer should return all intervals in which the indexer's values fall. `FloatIndex` is a poor substitute, because of [floating point precision issues](https://groups.google.com/d/topic/pydata/8aTLVKrPJfs/discussion), and because I don't want to label values by a single point.
A `IntervalIndex` is uniquely identified by its `intervals` and `closed` (`'left'` or `'right'`) properties, an ndarray of shape `(len(idx), 2)`, indicating each interval. Other useful properties for `IntervalIndex` would include `left`, `right` and `mid`, which should return arrays (indexes?) corresponding to the left, right or mid-points of each interval.
The constructor should allow the optional keyword argument `breaks` (an array of length `len(idx) + 1`) to specified instead of `intervals`.
It's not entirely obvious what `idx.values` should be (`idx.mid`? strings like `'(0, 1]'`? an array of tuples or `Interval` objects?). I think the most useful choice for cross compatibility would probably be to an ndarray like `idx.mid`.
`IntervalIndex` _should_ support mathematical operations (e.g., `idx + 1`), which are calculated by vectorizing the operation over the breaks.
### Examples
An example already in pandas that should be a `IntervalIndex` is the `levels` property of categorical returned by `cut`, which is currently an object array of strings:
```
>>> pd.cut([], [0, 5, 10]).levels
Index([u'(0, 5]', u'(5, 10]'], dtype='object')
```
Example usage:
``` python
>>> # should be equivalent to pd.cut([], [0, 1, 2]).levels
>>> idx = IntervalIndex(intervals=[(0, 1), (1, 2)])
>>> idx2 = IntervalIndex(breaks=[0, 1, 2]) # equivalent
>>> idx
IntervalIndex([(0, 1), (1, 2)], closed='right')
>>> idx.left
np.array([0, 1])
>>> idx.right
np.array([1, 2])
>>> idx.mid
np.array([0.5, 1.5])
>>> s = pd.Series([1, 2], idx)
(0, 1] 1
(1, 2] 2
dtype: int64
>>> s.loc[1]
1
>>> s.loc[0.5]
1
>>> s.loc[0]
KeyError
```
### Implementation
A `IntervalIndex` would be a monotonic and non-overlapping one-dimensional array of intervals. It is _not_ required to be contiguous. A scalar `Interval` would correspond to a contiguous interval between start and stop values (e.g., given by integers, floating point numbers or datetimes).
For index lookups, I propose to do a binary search (`np.searchsorted`) on `idx.left`. If we add the constraint that all intervals must have a fixed width, we could calculate the bin using a formula in constant time, but I'm not sure the loss in flexibility would be worth the speedup.
`IntervalIndex` should play nicely when used as the levels for `Categorical` variable (#7217), but it is _not_ the same as a `CategoricalIndex` (#7629). For example, a `IntervalIndex` should not allow for redundant values. To represent redundant or non-continuous intervals, you would need to make in a `Categorical` or `CategoricalIndex` which uses a `IntervalIndex` for the levels. Calling `df.reset_index()` on an `DataFrame` with an `IntervalIndex` would create a new `Categorical` column.
---
Note: I'm not entirely sure if this design doc belongs here or on mailing list (I'm happy to post it there if requested).
Here is the comment where I brought this up previously: https://github.com/pydata/pandas/issues/5460#issuecomment-44474502
CC @hugadams -- I expect `IntervalIndex` would be very handy for your [pyuvvis](https://github.com/hugadams/pyuvvis).
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| null | 5 | 2014-07-01T23:32:05Z | 2015-10-20T22:28:32Z | 2015-10-20T22:28:32Z | NONE | null | So I have a 1.07 GB json file that I am trying to open. However, the kernel crashes every time I try to run these three lines:
import pandas as pd
f = open("large json file", "r")
pd.read_json(f)
Anyone have any ideas on this? Is it a defect?
Python 2.7.3
[GCC 4.7.2] on linux2
**\* glibc detected **\* python: double free or corruption (out): 0x00007fff0d86a480 ***
======= Backtrace: =========
/lib/x86_64-linux-gnu/libc.so.6(+0x76d76)[0x7fd1b46b2d76]
/lib/x86_64-linux-gnu/libc.so.6(cfree+0x6c)[0x7fd1b46b7aac]
/usr/local/lib/python2.7/dist-packages/pandas/json.so(JSON_DecodeObject+0x149)[0x7fd1afc35909]
/usr/local/lib/python2.7/dist-packages/pandas/json.so(JSONToObj+0x248)[0x7fd1afc33078]
python(PyEval_EvalFrameEx+0x69f0)[0x4b2ce0]
python(PyEval_EvalFrameEx+0xaf0)[0x4acde0]
======= Memory map: ========
00400000-00658000 r-xp 00000000 08:03 2269915 /usr/bin/python2.7
00857000-00858000 r--p 00257000 08:03 2269915 /usr/bin/python2.7
00858000-008c1000 rw-p 00258000 08:03 2269915 /usr/bin/python2.7
008c1000-008d3000 rw-p 00000000 00:00 0
02075000-036ca000 rw-p 00000000 00:00 0 [heap]
7fd164000000-7fd164021000 rw-p 00000000 00:00 0
7fd164021000-7fd168000000 ---p 00000000 00:00 0
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7fd1aeaf2000-7fd1aeafb000 r-xp 00000000 08:03 2370198 /usr/local/lib/python2.7/dist-packages/pandas/_testing.so
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| null | 2 | 2014-07-02T00:31:57Z | 2014-07-21T11:20:49Z | 2014-07-02T00:35:36Z | CONTRIBUTOR | null | Just installed 0.14.0 (on Windows 7) and received this warning:
> Installed openpyxl is not supported at this time. Use >=1.6.1 and <2.0.0.
I did not have any version of openpyxl installed though. Perhaps the change made to `pandas/io/excel.py` in #7214 should have been more like:
``` diff
-register_writer(_OpenpyxlWriter)
+
+openpyxl_check = openpyxl_compat.is_compat()
+if openpyxl_check is None:
+ warn('Could not import openpyxl -- not using.')
+elif openpyxl_check:
+ register_writer(_OpenpyxlWriter)
+else:
+ warn('Installed openpyxl is not supported at this time. Use >={} and <{}.'
+ .format(openpyxl_compat.start_ver, openpyxl_compat.stop_ver))
```
---
As a side note, I received the same warning after installing openpyxl 1.9.0 but it was because I didn't have `lxml` (<-- I may have misspelled this module name).
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| null | 1 | 2014-07-02T01:39:32Z | 2015-10-20T22:30:01Z | 2015-10-20T22:30:01Z | CONTRIBUTOR | null | This seems to be specific to Pandas as opposed to matplotlib. Worked as desired as of a few months ago when I last updated from master.
This is the desired behavior in matplotlib:
``` python
barh([0,1], [.3, .5], xerr=[[.1,.2], [.3,.4]],
color = ['red','green'],
ecolor='black')
```
In the Pandas call, it seems to ignore the second error bar argument, both the upper and lower bounds of the error bar are the same:
``` python
s = pd.Series([.3,.5])
s.plot(kind='barh', xerr=[[.1,.2], [.3,.4]],
color = ['red','green'],
ecolor='black')
```
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| null | 5 | 2014-07-02T04:02:24Z | 2014-10-02T12:29:19Z | 2014-10-02T12:29:19Z | NONE | null | While running OS X 10.9.3 and Python 2.7.6 I get a seg fault when trying to add a new column to an existing DataFrame where the new column is initialized to an empty string (""). When initializing to np.NaN, the fault does not occur. An example is below.
mydataframe['new_column'] = "" <- this results in 'Segmentation Fault 11'
mydataframe['new_column'] = np.nan <- this works
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} | 6 | 2014-07-02T08:38:16Z | 2018-07-06T22:15:43Z | 2018-07-06T22:15:43Z | CONTRIBUTOR | null | ``` python
df = pd.DataFrame({'grp': [0, 0, 1, 1], 'returns': [0, 0.1, -.1, .1]})
df.groupby('grp').returns.compound()
df.groupby('grp').returns.apply('compound')
```
Doesn't seem to work:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-193-cb3ce32c6c2e> in <module>()
1 df = pd.DataFrame({'grp': [0, 0, 1, 1], 'returns': [0, 0.1, -.1, .1]})
----> 2 df.groupby('grp').returns.compound()
3 df.groupby('grp').returns.apply('compound')
/home/wiecki/envs/zipline_p14/lib/python2.7/site-packages/pandas/core/groupby.py in __getattr__(self, attr)
481 return self[attr]
482 if hasattr(self.obj, attr):
--> 483 return self._make_wrapper(attr)
484
485 raise AttributeError("%r object has no attribute %r" %
/home/wiecki/envs/zipline_p14/lib/python2.7/site-packages/pandas/core/groupby.py in _make_wrapper(self, name)
496 "using the 'apply' method".format(kind, name,
497 type(self).__name__))
--> 498 raise AttributeError(msg)
499
500 # need to setup the selection
AttributeError: Cannot access callable attribute 'compound' of 'SeriesGroupBy' objects, try using the 'apply' method
```
apply also doesn't work.
This is with pandas 0.14.
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| null | 9 | 2014-07-02T09:56:04Z | 2014-08-28T18:00:27Z | 2014-07-07T15:39:14Z | NONE | null | I recently upgraded most of my python packages via pip. After the upgrade
```
df.to_hdf('store.h5', 'data', format='table', append=True)
```
gives
```
/home/jussitii/py3env/lib/python3.2/site-packages/pandas/core/generic.py in to_hdf(self, path_or_buf, key, **kwargs)
894
895 from pandas.io import pytables
--> 896 return pytables.to_hdf(path_or_buf, key, self, **kwargs)
897
898 def to_msgpack(self, path_or_buf=None, **kwargs):
/home/jussitii/py3env/lib/python3.2/site-packages/pandas/io/pytables.py in to_hdf(path_or_buf, key, value, mode, complevel, complib, append, **kwargs)
284 f(store)
285 else:
--> 286 f(path_or_buf)
287
288
/home/jussitii/py3env/lib/python3.2/site-packages/pandas/io/pytables.py in <lambda>(store)
275 """ store this object, close it if we opened it """
276 if append:
--> 277 f = lambda store: store.append(key, value, **kwargs)
278 else:
279 f = lambda store: store.put(key, value, **kwargs)
TypeError: append() takes no keyword arguments
```
```
In [13]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.2.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-64-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.14.0
nose: 1.3.3
Cython: 0.20.2
numpy: 1.8.1
scipy: 0.14.0
statsmodels: None
IPython: 1.2.1
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.4
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: 1.8.6
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
bq: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: 0.6.2.None
psycopg2: None
```
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} | 2 | 2014-07-02T12:26:08Z | 2014-09-04T00:21:50Z | 2014-09-04T00:21:50Z | NONE | null | ```
import pandas as pd
import numpy as np
ser = pd.Series(['a', 'b', np.nan])
print ser
ser.loc[ser.isnull()] = None
print ser
ser[ser.isnull()] = None
print ser
```
Output
```
0 a
1 b
2 NaN
dtype: object
0 a
1 b
2 None
dtype: object
0 a
1 b
2 NaN
dtype: object
```
I am not sure whether this behaviour is intentional. Using .loc[], None was correctly assigned but when using [], NaN was assigned instead. Thanks
I am using version 0.14.0-169-g7684b6e
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} | 0 | 2014-07-02T13:14:39Z | 2014-07-07T11:09:41Z | 2014-07-07T11:09:41Z | CONTRIBUTOR | null | cc @dstephens99
prob just need to protect these a bit
here as well: https://travis-ci.org/jreback/pandas/jobs/28958102
```
======================================================================
ERROR: test_get_all_data (pandas.io.tests.test_data.TestYahooOptions)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.7\pandas\util\testing.py", line 1337, in wrapper
return t(*args, **kwargs)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.7\pandas\io\tests\test_data.py", line 307, in test_get_all_data
data = self.aapl.get_all_data(put=True)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.7\pandas\io\data.py", line 1095, in get_all_data
months = self._get_expiry_months()
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.7\pandas\io\data.py", line 1130, in _get_expiry_months
links = root.xpath('.//*[@id="yfncsumtab"]')[0].xpath('.//a')
IndexError: list index out of range
----------------------------------------------------------------------
Ran 6897 tests in 353.413s
FAILED (SKIP=315, errors=1)
C:\Users\Jeff Reback\Documents\GitHub\pandas>
```
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| null | 4 | 2014-07-02T14:45:31Z | 2014-07-02T16:04:21Z | 2014-07-02T15:44:11Z | CONTRIBUTOR | null | Possibly related to issue #7574:
Suppose I have a DataFrame:
``` python
In [61]: df
Out[61]:
ref localtime size
0 45361866 2014-06-25 14:11:11.753597 100
1 45361866 2014-06-25 14:11:11.753769 100
2 45361866 2014-06-25 14:11:11.754350 200
3 45361866 2014-06-25 14:11:11.756413 200
4 45361866 2014-06-25 14:18:59.442972 200
.. ... ... ...
29 204286294 2014-06-25 19:43:27.770083 100
30 204286294 2014-06-25 19:43:27.771266 7036
31 216308339 2014-06-25 19:57:32.468547 100
32 216308339 2014-06-25 19:57:37.534973 200
33 216308339 2014-06-25 19:57:37.535035 300
[34 rows x 3 columns]
```
If I aggregate over an integer, the resulting types are as expected:
``` python
In [62]: df.groupby('ref').first().reset_index().dtypes
Out[62]:
ref int64
localtime datetime64[ns]
size int64
dtype: object
```
However, if I aggregate on an empty DataFrame, then my timestamp's type is converted from datetime64 to float64:
``` python
In [63]: df.query('size>10000').groupby('ref').first().reset_index().dtypes
Out[63]:
index int64
localtime float64
size float64
dtype: object
```
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} | 0 | 2014-07-02T17:17:38Z | 2018-07-06T22:14:47Z | 2018-07-06T22:14:47Z | CONTRIBUTOR | null | https://travis-ci.org/jreback/pandas/jobs/28982207
@cpcloud
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} | 6 | 2014-07-02T17:25:47Z | 2018-07-06T23:08:57Z | 2018-07-06T22:14:27Z | CONTRIBUTOR | null | As the next step of separation-of-concerns plan (#6744) I'd like to
propose adding a method (or several, actually) to `Index` class that
would encapsulate the details of `foo.loc[l1,l2,...]` lookup.
### Implementation Idea
Roughly, the idea is to make `loc`'s getitem as simple as
``` python
def __getitem__(self, indexer):
axes = self.obj.axes
return self.obj.iloc[axes[0].lookup_labels_nd(indexer, axes[1:], typ='loc')]
```
Not quite, but hopefully you get the point. The default `lookup_labels_nd` implementation would then look something like this:
``` python
def lookup(self, indexer, other_axes, typ=None):
if not isinstance(indexer, tuple):
return self.lookup_labels(indexer, typ=typ)
else:
# ndim mismatch error handling is omitted intentionally
return (self.lookup_labels(indexer[0]),) + \
tuple(ax.lookup_labels(ix, typ=typ)
for ax, ix in zip(other_axes, indexer))
```
The result should be an object that could be fed to an underlying
BlockManager to perform the requested operation. To support adding
new rows with "setitem", it is only needed to agree that `lookup_labels_nd` will
never return negative indices unless they reference newly appended
items along that axis.
This would allow to hide Index-subclass-specific lookup peculiarities
in their respective overrides of `lookup_labels_nd` and `lookup_labels` (proposals for
better names are welcome), e.g.:
- looking up str in DatetimeIndex/PeriodIndex
- looking up int in FloatIndex
- looking up per-level slices in MultiIndex
### Benefits
- no more confusing errors due to `try .. catch` block carpet-catching a
logic error, because corner cases will be handled precisely where
they are needed and nowhere else
- no more relying on isinstance checks and exceptions to seek for
alternative lookup scenarios, meaning more performance
- the API will provide a contract that is simple to grasp, test, benchmark and,
eventually, cythonize (as a side effect of this point I'd like to try putting
up a wiki page with indexing API reference)
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} | 1 | 2014-07-02T18:02:53Z | 2014-07-02T18:38:23Z | 2014-07-02T18:38:23Z | CONTRIBUTOR | null | ```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
datetimeindex_normalize | 3.3297 | 83.0923 | 0.0401 |
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [fc88541] : PERF: allow slice indexers to be computed faster
Base [160419e] : TST: fixes for 2.6 comparisons
```
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| null | 9 | 2014-07-02T20:47:36Z | 2014-07-16T23:51:25Z | 2014-07-03T19:40:30Z | NONE | null | I got the following error trying to do
```
store.append(output_name, data_frame)
```
for a DataFrame with 2 rows and 3202 columns:
```
File "C:\Python27\lib\site-packages\pandas\io\pytables.py", line 914, in append
**kwargs)
File "C:\Python27\lib\site-packages\pandas\io\pytables.py", line 1273, in _write_to_group
s.write(obj=value, append=append, complib=complib, **kwargs)
File "C:\Python27\lib\site-packages\pandas\io\pytables.py", line 3589, in write
self.set_attrs()
File "C:\Python27\lib\site-packages\pandas\io\pytables.py", line 2887, in set_attrs
self.attrs.non_index_axes = self.non_index_axes
File "C:\Python27\lib\site-packages\tables\attributeset.py", line 455, in __setattr__
self._g__setattr(name, value)
File "C:\Python27\lib\site-packages\tables\attributeset.py", line 397, in _g__setattr
self._g_setattr(self._v_node, name, stvalue)
File "hdf5extension.pyx", line 704, in tables.hdf5extension.AttributeSet._g_setattr (tables\hdf5extension.c:6832)
tables.exceptions.HDF5ExtError: HDF5 error back trace
File "..\..\hdf5-1.8.11\src\H5A.c", line 254, in H5Acreate2
unable to create attribute
File "..\..\hdf5-1.8.11\src\H5A.c", line 503, in H5A_create
unable to create attribute in object header
File "..\..\hdf5-1.8.11\src\H5Oattribute.c", line 347, in H5O_attr_create
unable to create new attribute in header
File "..\..\hdf5-1.8.11\src\H5Omessage.c", line 224, in H5O_msg_append_real
unable to create new message
File "..\..\hdf5-1.8.11\src\H5Omessage.c", line 1945, in H5O_msg_alloc
unable to allocate space for message
File "..\..\hdf5-1.8.11\src\H5Oalloc.c", line 1142, in H5O_alloc
object header message is too large
End of HDF5 error back trace
Can't set attribute 'non_index_axes' in node:
/alpha_regression_results/liquid/alpha_adv_deciles/factors (Group) u''.
Process finished with exit code 1
```
Frames with 1402 columns saved just fine. Are there any limitations for the number of columns?
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| null | 3 | 2014-07-03T11:37:03Z | 2014-07-03T12:47:18Z | 2014-07-03T12:47:18Z | NONE | null | Is this intentional?
``` python
In [4]: pd.Series([datetime.now()]).dtype
Out[4]: dtype('<M8[ns]')
In [5]: pd.Series([pytz.UTC.localize(datetime.now())]).dtype
Out[5]: dtype('O')
```
pandas 0.13.1 and 0.14.0
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} | 0 | 2014-07-03T12:59:47Z | 2014-10-06T13:03:02Z | 2014-10-06T13:03:02Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/24553921/pandas-matching-on-level-of-hierarchical-index/24554350#24554350
This should align on the columns axis. Not clear how to specify the alignment level though (e.g. broadcasting would be tricky here).
```
import numpy as np
import pandas as pd
arrays = [np.hstack([ ['one']*3, ['two']*3]), ['Dog', 'Bird', 'Cat']*2]
columns = pd.MultiIndex.from_arrays(arrays, names=['foo', 'bar'])
df = pd.DataFrame(np.zeros((3,6)),columns=columns,
index=pd.date_range('20000103',periods=3))
df['one'] = pd.DataFrame({'Bird' : np.ones(3)*2,
'Dog' : np.ones(3),
'Cat' : np.ones(3)*3},
index= pd.date_range('20000103',periods=3))
df['two'] = pd.DataFrame({'Dog' : np.ones(3)*4,
'Bird' : np.ones(3)*5,
'Cat' : np.ones(3)*6,},
index= pd.date_range('20000103',periods=3))
```
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} | 6 | 2014-07-03T14:25:22Z | 2014-07-03T16:26:12Z | 2014-07-03T16:26:12Z | CONTRIBUTOR | null | related #4667, #7647
According to the documentation, `inplace` should replace values appropriately with `other`, but this does not seem to work as expected. This worked in 0.12.
```
In [6]: df = pandas.DataFrame([{'A': 1, 'B': np.nan, 'C': 'Test'}, {'A': np.nan, 'B': 'Test', 'C': np.nan}])
In [7]: df
Out[7]:
A B C
0 1 NaN Test
1 NaN Test NaN
In [8]: df1 = df.where(~pandas.isnull(df), None)
In [9]: df1
Out[9]:
A B C
0 1 None Test
1 None Test None
In [10]: df.where(~pandas.isnull(df), None, inplace=True)
In [11]: df
Out[11]:
A B C
0 1 NaN Test
1 NaN Test NaN
```
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} | 0 | 2014-07-03T15:40:07Z | 2014-07-03T16:26:12Z | 2014-07-03T16:26:12Z | CONTRIBUTOR | null | closes #7656
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} | 8 | 2014-07-03T18:06:36Z | 2014-07-26T00:28:52Z | 2014-07-26T00:28:44Z | NONE | null | The code I ran looked something like the below
df.loc[:, 'A'] = df['A'] \* (df['B'] / df['C'])
and I received a warning from within Pandas
...\pandas\core\indexing.py:385: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_index,col_indexer] = value instead
self.obj[item] = s
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| null | 3 | 2014-07-03T18:11:59Z | 2014-07-03T18:54:12Z | 2014-07-03T18:54:12Z | NONE | null | Hi all,
I noticed there is a typo in the documentation of [pandas.DataFrame.to_excel](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_excel.html):
The parameter startow should be startrow.
**startow**:
upper left cell row to dump data frame
should be:
**startrow**
upper left cell row to dump data frame
I'm not sure how to make this change myself; I've noticed it is correct in the [source code](https://github.com/pydata/pandas/blob/master/pandas/core/frame.py)
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} | 0 | 2014-07-03T19:39:08Z | 2014-07-21T11:42:51Z | 2014-07-21T11:42:51Z | CONTRIBUTOR | null | from #7653
cc @bluefir
```
df = DataFrame(np.random.randn(1000, 27),
columns=MultiIndex.from_tuples(list(
itertools.product(xrange(3), repeat=3))))
df.stack(level=[1, 2])
```
should be equiv to:
`df.stack(level=1).stack(level=1)`
but is raising
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} | 10 | 2014-07-03T21:36:46Z | 2014-09-14T14:20:10Z | 2014-09-14T14:20:10Z | CONTRIBUTOR | null | Currently, coercion of scalar results from `float` to `timedelta64[ns]` passes through `int`, which raises when attempting to coerce `NaN` to `NaT`.
To reproduce:
``` python
import pandas as pd
import numpy as np
pd.Series([np.timedelta64('NaT')]).sum()
# TypeError: reduction operation 'sum' not allowed for this dtype
```
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} | 19 | 2014-07-03T22:06:24Z | 2020-09-09T11:20:23Z | 2016-10-24T22:08:22Z | CONTRIBUTOR | null | related #7615
Bit of a UI problem here (although it's behaving as the docstring says it does, so it doesn't quite qualify as a bug):
```
>>> df = pd.DataFrame({"A": [1,2,3], "B": [4,5,6]})
>>> df.to_csv("tmp.csv", sep=";")
>>> !cat tmp.csv
;A;B
0;1;4
1;2;5
2;3;6
>>> df.to_csv("tmp.csv", delimiter=";")
>>> !cat tmp.csv
,A,B
0,1,4
1,2,5
2,3,6
```
`read_csv` accepts both `sep` and `delimiter` but `to_csv` silently ignores `delimiter`. Someone was recently tripped up by this on SO. I'm fine with either teaching `to_csv` to behave the same way `read_csv` does or, alternatively, raising if `delimiter` is found as a keyword.
That is, I'm less bothered by the inconsistency than the silent unexpected behaviour.
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} | 11 | 2014-07-03T23:26:29Z | 2015-01-17T09:58:20Z | 2015-01-17T09:57:14Z | NONE | null | According to the documentation, ix should accept the all the input to iloc. In the following the .ix raises an KeyError while .iloc work as expected. I suspect the fall back mechanism is broken in this case with integer index.
``` python
import pandas as pd
import numpy as np
df = pd.Series(np.random.randn(9))
df.iloc[-1]
df.ix[-1] # raise KeyError
```
version:
pandas: 0.13.1
python: 2.7.6.final.0
EDIT: Changed title from "df.ix[-1] broken" to "DOC: clarify that .ix[] is always label based with integer index"
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} | 7 | 2014-07-04T02:11:47Z | 2014-07-07T16:00:22Z | 2014-07-07T16:00:22Z | CONTRIBUTOR | null | Only with ubuntu 12.04 i386:
```
======================================================================
ERROR: test_ts_plot_format_coord (pandas.tseries.tests.test_plotting.TestTSPlot)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.14.0+git345-g8cd3dd6/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tseries/tests/test_plotting.py", line 138, in test_ts_plot_format_coord
check_format_of_first_point(annual.plot(), 't = 2014 y = 1.000000')
File "/tmp/buildd/pandas-0.14.0+git345-g8cd3dd6/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tseries/tests/test_plotting.py", line 135, in check_format_of_first_point
self.assertEqual(expected_string, ax.format_coord(first_x, first_y))
File "/tmp/buildd/pandas-0.14.0+git345-g8cd3dd6/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tseries/plotting.py", line 90, in <lambda>
y))
ValueError: Unknown format code 'f' for object of type 'str'
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.3.final.0
python-bits: 32
OS: Linux
OS-release: 3.2.0-4-amd64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: C
pandas: 0.14.0.dev
nose: 1.1.2
Cython: 0.15.1
numpy: 1.6.1
scipy: 0.9.0
statsmodels: 0.5.0
IPython: None
sphinx: 1.1.3
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2012c
bottleneck: None
tables: 2.3.1
numexpr: 1.4.2
matplotlib: 1.1.1rc
openpyxl: 1.7.0
xlrd: 0.6.1
xlwt: 0.7.2
xlsxwriter: None
lxml: None
bs4: 4.0.2
html5lib: 0.90
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 5 | 2014-07-04T04:58:46Z | 2014-07-07T11:09:46Z | 2014-07-07T11:09:41Z | CONTRIBUTOR | null | Removed most references to month and year (passing expiry date around instead).
Also protected with RemoteDataError checking for the expiry month links.
Fixes #7648
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} | 1 | 2014-07-04T06:42:48Z | 2014-07-04T14:57:00Z | 2014-07-04T14:57:00Z | NONE | null | I have a DataFrame that looks like the following format:
``` python
df = pd.DataFrame({'foo': [1, 2, 2], 'bar': [True, False, False]})
```
I want group this by foo and count the number of True values in the bar column. Counting the True values can be achieved with the sum command.
``` python
In [7]: bar = [True, False, True, False, False]
In [8]: sum(bar)
Out[8]: 2
In [9]: sum(df['bar'])
Out[9]: 1
```
To group and count this:
``` python
In [16]: df.groupby('foo').aggregate(sum)
Out[16]:
bar
foo
1 True
2 False
```
This output is erroneous. Expected output is:
``` python
bar
foo
1 1
2 0
```
It works in the following case (changed so that not all cases for foo:2 are false).
``` python
In [18]: df = pd.DataFrame({'foo': [1, 2, 2, 2, 2], 'bar': [True, True, True, False, False]})
In [18]: df.groupby('foo').aggregate(sum)
Out[18]:
bar
foo
1 1
2 2
```
Here are my installed versions:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.7.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.14.0
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
bq: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
```
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} | 26 | 2014-07-05T13:08:45Z | 2017-03-20T13:48:34Z | 2017-03-20T13:48:33Z | MEMBER | null | Must be revisited after #7717.
#6608 seems to be solved by following 4 fixes.
- [ ] `PeriodIndex` should support the same freqs as `DatetimeIndex` (Related to #7222, maybe solved by #5148)
- [ ] Better logic to find common divisor frequency (try to use `tsplot` as much)
- [x] When plotting with `x_compat` to the `ax` which already holds `tsplot` lines, `tsplot` lines must be redrawn on `x_compat` coordinates.
- [ ] Check whether `to_timestamp(how='e')` always revert `PeriodIndex` back to original `DatetimeIndex`
- [x] If target ax already holds `x_compat` lines, continuous plot should be drawn on `x_compat` coordinates (current version already have a logic, but cannot work if `ax` once have `freq` property).
Other refactoring:
- [x] Do not re-plot every row in `tsplot`
- [x] Simplify `LinePlot` flow (separated as #7717)
- [x] Store plot_func for line and area mixed time-series plot (separated as #7733)
Result using current PR (modified #6608 a little to show legend).
```
s1 = pd.Series([1, 2, 3], index=[datetime.datetime(1995, 12, 31),
datetime.datetime(2000, 12, 31),
datetime.datetime(2005, 12, 31)], name='idx1')
s2 = pd.Series([1, 2, 3], index=[datetime.datetime(1997, 12, 31),
datetime.datetime(2003, 12, 31),
datetime.datetime(2008, 12, 31)], name='idx2')
ax = s1.plot(legend=True)
ax = s2.plot(legend=True)
s1.plot(ax=ax, legend=True)
```
One question is whether `tsplot` is categorized as public function? If so, I'll prepare separate func.
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} | 8 | 2014-07-05T14:17:52Z | 2015-06-02T19:26:59Z | 2015-05-15T08:10:13Z | MEMBER | null | As a Series with a DatetimeIndex is no longer presented as a `TimeSeries`, I think we also should not longer mention it in the docs as a 'seperate object', so removed the last few mentions.
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} | 6 | 2014-07-05T21:27:39Z | 2014-07-06T19:02:51Z | 2014-07-06T14:45:15Z | CONTRIBUTOR | null | closes #7620
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} | 5 | 2014-07-05T23:49:20Z | 2014-07-09T12:37:40Z | 2014-07-07T13:12:20Z | MEMBER | null | Related to #7633. It gets better than the result attached #7633, but still slower more than 1.2 times compared to 1.4.0
Modified:
- Avoid every time module `import` in `Index.max/min`
- Avoid duplicated `max` call from `resample/_get_time_bins` and `_get_range_edges`.
- Optimize `lib/generate_bins_dt64` and `tslib/period_asfreq_arr`.
Remaining bottlenecks are `NaT` masking performed in `lib/generate_bins_dt64` and `tslib/period_asfreq_arr`. Is there any better way to do that?
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
dataframe_resample_mean_numpy | 4.9963 | 3.7940 | 1.3169 |
dataframe_resample_mean_string | 5.0424 | 3.8280 | 1.3172 |
dataframe_resample_max_numpy | 4.1796 | 3.0069 | 1.3900 |
dataframe_resample_min_numpy | 4.2127 | 2.9987 | 1.4049 |
dataframe_resample_min_string | 4.1687 | 2.9490 | 1.4136 |
dataframe_resample_max_string | 4.3443 | 2.9283 | 1.4835 |
timeseries_timestamp_downsample_mean | 16.1959 | 8.6366 | 1.8753 |
timeseries_period_downsample_mean | 47.6096 | 19.7030 | 2.4164 |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [54fb875] : PERF: Improve index.min and max perf
Base [da0f7ae] : RLS: 0.14.0 final
```
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} | 0 | 2014-07-06T14:29:18Z | 2014-07-07T07:10:04Z | 2014-07-07T07:09:55Z | MEMBER | null | There are already 4 spaces in the description strings in config_init.py, so no need to add some more.
This caused the descriptions to be longer than 79 characters, and so line wrapping in the terminal. Closes #6838.
Plus, moved the default and current values to the last line of the description as proposed by @jseabold
Example output now is:
```
display.max_colwidth : int
The maximum width in characters of a column in the repr of
a pandas data structure. When the column overflows, a "..."
placeholder is embedded in the output.
[default: 50] [currently: 50]
display.max_info_columns : int
max_info_columns is used in DataFrame.info method to decide if
per column information will be printed.
[default: 100] [currently: 100]
```
Previous in 0.14 this was:
```
display.max_colwidth : [default: 50] [currently: 50]: int
The maximum width in characters of a column in the repr of
a pandas data structure. When the column overflows, a "..."
placeholder is embedded in the output.
display.max_info_columns : [default: 100] [currently: 100]: int
max_info_columns is used in DataFrame.info method to decide if
per column information will be printed.
```
Before 0.14 it was worse (as reported in #6838), but I already improved it a bit some time ago.
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closes #7664
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} | 16 | 2014-07-07T09:08:09Z | 2015-10-19T20:52:48Z | 2015-10-19T20:52:40Z | CONTRIBUTOR | null | In the following scenario:
```
import pandas as pd
import numpy as np
a = np.random.rand(1000)
df = pd.DataFrame({'a' : a })
```
This call doesn't work (while it is supposed to, I guess)
```
df.eval('sqrt(a)')
NotImplementedError: 'Call' nodes are not implemented
```
while this one does
```
df.eval('(a)**(.5)')
```
I guess this is a bug.
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} | 12 | 2014-07-07T09:47:10Z | 2014-07-07T20:07:52Z | 2014-07-07T17:46:41Z | CONTRIBUTOR | null | In the original code, `com.is_bool(arr_or_dtype)` checks whether `arr_or_dtype` is a boolean value instead of a boolean dtype.
A new function `is_bool_dtype` is added to `pandas.core.common` to fix this bug.
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} | 8 | 2014-07-07T12:25:44Z | 2014-09-10T15:25:29Z | 2014-07-07T13:30:46Z | NONE | null | When I try to interpolate time series data it fails with an index error: arrays used as indices must be of integer (or boolean) type.
``` python
from pandas import read_csv
from StringIO import StringIO
data = '''2013-09-28,4018.02
2013-10-05,5806.78
2013-10-26,6334.4
2013-11-02,6199.6
2013-11-09,6213.07
2013-11-16,7099.39
2013-11-23,7332.98
2013-11-30,6157.37
2013-12-07,7427.65
2014-01-04,5856.38
2014-01-11,6208.99
2014-01-18,7440.29
2014-01-25,7295.97
2014-02-08,7654.42
'''
df = read_csv(StringIO(data))
df.interpolate(limit=1)
```
Removing the `limit=1` argument works as expected.
Python 2.7.6, pandas 0.14.0, numpy 1.8.1, on OS X.
I'll try it with a Python 3 environment when I get chance later this week.
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| null | 5 | 2014-07-07T12:57:42Z | 2014-07-07T13:42:43Z | 2014-07-07T13:22:05Z | CONTRIBUTOR | null | ```
pandas.Index([('a',), ('a', 'b')]).values
```
pandas < 0.14 returns:
```
array([('a',), ('a', 'b')], dtype=object)
```
pandas 0.14 returns:
```
array([('a', nan), ('a', 'b')], dtype=object)
```
possibly related to https://github.com/pydata/pandas/issues/7548 ?
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} | 6 | 2014-07-07T15:03:08Z | 2018-07-06T22:04:14Z | 2018-07-06T22:04:06Z | CONTRIBUTOR | null | If multiple credentials exits, many users have profiles in their ~/.boto as such-
``` python
[profile Data-Robot]
aws_access_key_id = a key
aws_secret_access_key = a secret
[Credentials]
aws_access_key_id = default key
aws_secret_access_key = default secret
```
you can pass
``````
In [70]: con = boto.connect_s3(profile_name="Data-Robot")
``` to load from a profile. However, you can not do this in pandas.
``````
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} | 10 | 2014-07-07T18:21:52Z | 2014-07-16T00:29:51Z | 2014-07-16T00:29:51Z | CONTRIBUTOR | null | When using df.iterrows on large data frame, it takes a long time to run and consumes huge amount of memory.
The name of the function implies that it is an iterator and should not take much to run. ~~However~~, ~~in the method it uses builtin method 'zip'~~, ~~which can sometimes generate huge temporary list of tuples if optimisation is not done correctly~~.
Below is the code which can reproduce the issue on a box with 16GB memory.
``` python
s1 = range(30000000)
s2 = np.random.randn(30000000)
ts = pd.date_range('20140101', freq='S', periods=30000000)
df = pd.DataFrame({'s1': s1, 's2': s2}, index=ts)
for r in df.iterrows():
break # expected to return immediately, yet it takes more than 2 minutes and uses 4G memory
```
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} | 0 | 2014-07-07T18:43:34Z | 2014-07-07T19:24:50Z | 2014-07-07T19:24:50Z | CONTRIBUTOR | null | closes #7633
close to what it was in 0.14.0
key was to not keep recomputing whether an index `hasnans` every time we need it (it is now cached).
further `min/max` are optimized if the index is monotonic
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
timeseries_timestamp_downsample_mean | 4.5697 | 7.8034 | 0.5856 |
dataframe_resample_min_string | 1.8380 | 2.5294 | 0.7266 |
dataframe_resample_min_numpy | 1.8580 | 2.5463 | 0.7297 |
dataframe_resample_max_numpy | 1.8887 | 2.5803 | 0.7320 |
dataframe_resample_max_string | 1.9130 | 2.5553 | 0.7486 |
dataframe_resample_mean_numpy | 2.6687 | 3.3340 | 0.8004 |
dataframe_resample_mean_string | 2.7773 | 3.3080 | 0.8396 |
timeseries_period_downsample_mean | 12.2183 | 11.6010 | 1.0532 |
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [d2d30c7] : PERF: better perf on min/max on indices not containing NaT for DatetimeIndex/PeriodIndex
Base [e060616] : DOC: minor corrections in v0.14.1
```
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} | 1 | 2014-07-07T19:24:15Z | 2014-07-08T23:33:47Z | 2014-07-08T23:33:47Z | CONTRIBUTOR | null | have seen this a couple of times: https://travis-ci.org/pydata/pandas/jobs/29340410
slightly different error: https://travis-ci.org/pydata/pandas/jobs/29348881
cc @dstephens99 as you seem to be the expert :)
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} | 3 | 2014-07-07T19:52:11Z | 2014-07-08T08:21:52Z | 2014-07-07T23:58:09Z | CONTRIBUTOR | null | https://travis-ci.org/pydata/pandas/jobs/29348880
started with this merge: https://github.com/pydata/pandas/pull/7678
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} | 3 | 2014-07-07T20:12:21Z | 2018-09-04T11:09:18Z | 2018-09-04T11:09:18Z | NONE | null | From mailing-list: [https://groups.google.com/forum/#!topic/pydata/CDnF9cNR2ho]
When doing a re-sample, loffset has no effect if a numpy.timedelta64 is passed in:
``` python
In [1]: rng = pd.date_range('1/1/2012', periods=4, freq='S')
In [2]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
In [3]: ts.resample('2S')
Out[3]:
2012-01-01 00:00:00 396.0
2012-01-01 00:00:02 214.5
Freq: 2S, dtype: float64
In [4]: import datetime
In [5]: ts.resample('2S',loffset=datetime.timedelta(seconds=10))
Out[5]:
2012-01-01 00:00:10 396.0
2012-01-01 00:00:12 214.5
dtype: float64
In [6]: ts.resample('2S',loffset=np.timedelta64(10,'s'))
Out[6]:
2012-01-01 00:00:00 396.0
2012-01-01 00:00:02 214.5
Freq: 2S, dtype: float64
```
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} | 3 | 2014-07-08T05:31:10Z | 2014-07-09T04:38:52Z | 2014-07-08T23:33:47Z | CONTRIBUTOR | null | Refactor and regression test.
Fixes #7685
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| null | 20 | 2014-07-08T06:13:46Z | 2021-11-17T19:42:32Z | 2014-07-08T19:03:16Z | CONTRIBUTOR | null | Hello,
I'm trying to build a RESTful api with Flask-RESTful which will return Pandas DataFrame as JSON data.
```
from flask import Flask
from flask.ext import restful
from flask.ext.restful import Resource, Api
import pandas as pd
import click
import logging
app = Flask(__name__)
api = restful.Api(app)
class DataWebservice(Resource):
def get(self, size):
logging.info("get %d" % size)
# get DB conn
# df = pd.read_sql(...) # USE LIMIT
df = pd.DataFrame({"col1": [1]*size, "col2": [2]*size})
#return(df.to_json())
return(df)
api.add_resource(DataWebservice, '/api/v1/solar/df/get/<int:size>')
@click.command()
@click.option('--host', default='127.0.0.1', \
help="host (127.0.0.1 or 0.0.0.0 to accept all ip)")
@click.option('--debug/--no-debug', default=False, help="debug mode")
def main(debug, host):
app.run(host=host, debug=debug)
if __name__ == '__main__':
main()
```
I run server using
$ python server.py --debug
I run client using
$ curl http://127.0.0.1:5000/api/v1/solar/df/get/10
but I get the following error
```
TypeError: col1 col2
0 1 2
1 1 2
...
8 1 2
9 1 2
[10 rows x 2 columns] is not JSON serializable
```
So it seems that Pandas DataFrame are not JSON serializable.
I try this using IPython
```
size = 10
df = pd.DataFrame({"col1": [1]*size, "col2": [2]*size})
json.dumps(df)
```
It raises same error.
I'm aware that DataFrame have method named to_json()
but it doesn't help me much as my server will return escaped strings such as
```
"{\"col1\":{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":1,\"6\":1,\"7\":1,\"8\":1,\"9\":1},\"col2\":{\"0\":2,\"1\":2,\"2\":2,\"3\":2,\"4\":2,\"5\":2,\"6\":2,\"7\":2,\"8\":2,\"9\":2}}"
```
see https://github.com/twilio/flask-restful/issues/269
Kind regards
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| null | 4 | 2014-07-08T08:14:11Z | 2014-07-08T15:38:37Z | 2014-07-08T11:43:03Z | NONE | null | https://stackoverflow.com/questions/24536734/rowwise-subset-of-a-dataframe-based-on-index
This should act like an inner join but it adds NULL rows to the table being operated on. At a very least it should raise an error when the index is not found.
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} | 34 | 2014-07-08T08:48:52Z | 2014-09-18T15:36:47Z | 2014-09-18T13:55:33Z | CONTRIBUTOR | null | This PR closes #7180
In the terminal:
If maxcols == 0 then auto detect columns
if maxrows == 0 auto detect rows
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} | 3 | 2014-07-08T12:58:19Z | 2015-09-21T15:32:29Z | 2015-09-21T15:32:29Z | CONTRIBUTOR | null | This works fine
http://stackoverflow.com/questions/24631991/division-of-a-panel-by-a-series/24632381#24632381
```
[8]: p = Panel(np.arange(3*4*5).reshape(3,4,5),items=['ItemA','ItemB','ItemC'],major_axis=date_range('20130101',periods=4),minor_axis=list('ABCDE'))
In [9]: p
Out[9]:
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 4 (major_axis) x 5 (minor_axis)
Items axis: ItemA to ItemC
Major_axis axis: 2013-01-01 00:00:00 to 2013-01-04 00:00:00
Minor_axis axis: A to E
In [10]: d = p.sum(axis=1).ix[0]
In [11]: d
Out[11]:
ItemA 30
ItemB 110
ItemC 190
Name: A, dtype: int64
In [12]: p.iloc[0]
Out[12]:
A B C D E
2013-01-01 0 1 2 3 4
2013-01-02 5 6 7 8 9
2013-01-03 10 11 12 13 14
2013-01-04 15 16 17 18 19
In [13]: p.apply(lambda x: x/d,axis=0).iloc[0]
Out[13]:
A B C D E
2013-01-01 0.000000 0.033333 0.066667 0.100000 0.133333
2013-01-02 0.166667 0.200000 0.233333 0.266667 0.300000
2013-01-03 0.333333 0.366667 0.400000 0.433333 0.466667
2013-01-04 0.500000 0.533333 0.566667 0.600000 0.633333
```
`p.div(d,axis=0)` should raise `NotImplementedError` instead of returning `None`.
https://github.com/pydata/pandas/blob/master/pandas/core/panel.py#L655
needs an `else` that raises.
Of course this can/should be implemented.
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} | 1 | 2014-07-08T13:36:43Z | 2014-07-08T14:15:35Z | 2014-07-08T14:15:35Z | CONTRIBUTOR | null | goes like this
```
======================================================================
FAIL: test_select_dtypes_exclude_include (pandas.tests.test_frame.TestDataFrame)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.14.0+git393-g959e3e4/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_frame.py", line 13039, in test_select_dtypes_exclude_include
tm.assert_frame_equal(r, e)
File "/tmp/buildd/pandas-0.14.0+git393-g959e3e4/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 619, in assert_frame_equal
assert_index_equal(left.columns, right.columns)
File "/tmp/buildd/pandas-0.14.0+git393-g959e3e4/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 557, in assert_index_equal
right.dtype))
AssertionError: [index] left [object Index([u'e'], dtype='object')], right [Index([u'b', u'e'], dtype='object') object]
```
seems everywhere on 32bit
```
pandas_0.14.0+git393-g959e3e4-1~nd12.04+1_i386.build:FAIL: test_select_dtypes_exclude_include (pandas.tests.test_frame.TestDataFrame)
pandas_0.14.0+git393-g959e3e4-1~nd12.10+1_i386.build:FAIL: test_select_dtypes_exclude_include (pandas.tests.test_frame.TestDataFrame)
pandas_0.14.0+git393-g959e3e4-1~nd13.04+1_i386.build:FAIL: test_select_dtypes_exclude_include (pandas.tests.test_frame.TestDataFrame)
pandas_0.14.0+git393-g959e3e4-1~nd13.10+1_i386.build:FAIL: test_select_dtypes_exclude_include (pandas.tests.test_frame.TestDataFrame)
pandas_0.14.0+git393-g959e3e4-1~nd14.04+1_i386.build:FAIL: test_select_dtypes_exclude_include (pandas.tests.test_frame.TestDataFrame)
pandas_0.14.0+git393-g959e3e4-1~nd+1_i386.build:FAIL: test_select_dtypes_exclude_include (pandas.tests.test_frame.TestDataFrame)
pandas_0.14.0+git393-g959e3e4-1~nd70+1_i386.build:FAIL: test_select_dtypes_exclude_include (pandas.tests.test_frame.TestDataFrame)
pandas_0.14.0+git393-g959e3e4-1~nd80+1_i386.build:FAIL: test_select_dtypes_exclude_include (pandas.tests.test_frame.TestDataFrame)
```
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} | 0 | 2014-07-08T13:40:29Z | 2014-07-08T15:40:09Z | 2014-07-08T15:40:09Z | CONTRIBUTOR | null | 0.14.0+git393-g959e3e4 building on not so recent ones (32bit builds are failing on that another one while testing for python2, so never see this one on python3)
```
======================================================================
ERROR: test_select_dtypes_not_an_attr_but_still_valid_dtype (pandas.tests.test_frame.TestDataFrame)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.14.0+git393-g959e3e4/debian/tmp/usr/lib/python3/dist-packages/pandas/tests/test_frame.py", line 13054, in test_select_dtypes_not_an_attr_but_still_valid_dtype
r = df.select_dtypes(include=['i8', 'O', 'timedelta64[ns]'])
File "/tmp/buildd/pandas-0.14.0+git393-g959e3e4/debian/tmp/usr/lib/python3/dist-packages/pandas/core/frame.py", line 1946, in select_dtypes
selection)
File "/tmp/buildd/pandas-0.14.0+git393-g959e3e4/debian/tmp/usr/lib/python3/dist-packages/pandas/core/frame.py", line 1945, in <lambda>
frozenset(map(com._get_dtype_from_object, x)),
File "/tmp/buildd/pandas-0.14.0+git393-g959e3e4/debian/tmp/usr/lib/python3/dist-packages/pandas/core/common.py", line 1650, in _get_dtype_from_object
return _get_dtype_from_object(np.dtype(dtype))
File "/tmp/buildd/pandas-0.14.0+git393-g959e3e4/debian/tmp/usr/lib/python3/dist-packages/pandas/core/common.py", line 1635, in _get_dtype_from_object
_validate_date_like_dtype(dtype)
File "/tmp/buildd/pandas-0.14.0+git393-g959e3e4/debian/tmp/usr/lib/python3/dist-packages/pandas/core/common.py", line 1613, in _validate_date_like_dtype
% (dtype.name, dtype.type.__name__))
ValueError: 'timedelta64[ns]' is too specific of a frequency, try passing 'timedelta64'
```
```
pandas_0.14.0+git393-g959e3e4-1~nd12.04+1_amd64.build:ERROR: test_select_dtypes_not_an_attr_but_still_valid_dtype (pandas.tests.test_frame.TestDataFrame)
pandas_0.14.0+git393-g959e3e4-1~nd12.10+1_amd64.build:ERROR: test_select_dtypes_not_an_attr_but_still_valid_dtype (pandas.tests.test_frame.TestDataFrame)
pandas_0.14.0+git393-g959e3e4-1~nd70+1_amd64.build:ERROR: test_select_dtypes_not_an_attr_but_still_valid_dtype (pandas.tests.test_frame.TestDataFrame)
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.2.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: C
LANG: C
pandas: 0.14.0.dev
nose: 1.1.2
Cython: None
numpy: 1.6.2
scipy: 0.10.1
statsmodels: None
IPython: None
sphinx: 1.1.3
patsy: None
scikits.timeseries: None
dateutil: 2.0
pytz: 2012c
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.1.0
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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| null | 2 | 2014-07-08T14:14:50Z | 2014-07-08T14:46:25Z | 2014-07-08T14:46:25Z | CONTRIBUTOR | null | I'm on pandas 0.14.0 / matplotlib 1.3.1.
Given the DataFrame
```
ipdb> print(df)
Coal Diesel Gas Hydro Nuclear
2003-01-31 0 0.893509 0 0.106491 0
2005-03-31 0 1.218053 0 0.000000 0
2006-03-31 0 1.218053 0 0.106491 0
2007-01-31 0 1.218053 0 0.106491 0
2008-01-31 0 1.218053 0 0.000000 0
2009-03-31 0 1.218053 0 0.000000 0
2010-01-31 0 1.218053 0 0.000000 0
2010-12-31 0 1.218053 0 0.000000 0
2011-11-30 0 1.218053 0 0.000000 0
2013-01-31 0 1.218053 0 0.000000 0
ipdb> df.index
<class 'pandas.tseries.index.DatetimeIndex'>
[2003-01-31, ..., 2013-01-31]
Length: 10, Freq: None, Timezone: None
```
Plotting using `kind='area'` doesn't work:
```
df.plot(ax=ax, grid=False, ylim=(0., 5.),
legend=False, stacked=True,
kind="area")
/home2/hilboll/prj/2014_no2trends-india/code/plots.py in plot_econ_panel_relative(fn_vault, fn_plot)
81
82 axs[3, 0].set_ylabel("Installed capacity (31 Jan 2003 = 1.0)")
---> 83 plt.savefig(fn_plot, bbox_inches="tight")
84 return states, electr
85
/home2/hilboll/prj/2014_no2trends-india/pyenv/lib/python2.7/site-packages/matplotlib/pyplot.pyc in savefig(*args, **kwargs)
559 def savefig(*args, **kwargs):
560 fig = gcf()
--> 561 return fig.savefig(*args, **kwargs)
562
563
/home2/hilboll/prj/2014_no2trends-india/pyenv/lib/python2.7/site-packages/matplotlib/figure.pyc in savefig(self, *args, **kwargs)
1419 self.set_frameon(frameon)
1420
-> 1421 self.canvas.print_figure(*args, **kwargs)
1422
1423 if frameon:
/home2/hilboll/prj/2014_no2trends-india/pyenv/lib/python2.7/site-packages/matplotlib/backends/backend_qt4agg.pyc in print_figure(self, *args, **kwargs)
165
166 def print_figure(self, *args, **kwargs):
--> 167 FigureCanvasAgg.print_figure(self, *args, **kwargs)
168 self.draw()
/home2/hilboll/prj/2014_no2trends-india/pyenv/lib/python2.7/site-packages/matplotlib/backend_bases.pyc in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)
2165 orientation=orientation,
2166 dryrun=True,
-> 2167 **kwargs)
2168 renderer = self.figure._cachedRenderer
2169 bbox_inches = self.figure.get_tightbbox(renderer)
/home2/hilboll/prj/2014_no2trends-india/pyenv/lib/python2.7/site-packages/matplotlib/backend_bases.pyc in print_pdf(self, *args, **kwargs)
1950 from backends.backend_pdf import FigureCanvasPdf # lazy import
1951 pdf = self.switch_backends(FigureCanvasPdf)
-> 1952 return pdf.print_pdf(*args, **kwargs)
1953
1954 def print_pgf(self, *args, **kwargs):
/home2/hilboll/prj/2014_no2trends-india/pyenv/lib/python2.7/site-packages/matplotlib/backends/backend_pdf.pyc in print_pdf(self, filename, **kwargs)
2350 width, height, image_dpi, RendererPdf(file, image_dpi),
2351 bbox_inches_restore=_bbox_inches_restore)
-> 2352 self.figure.draw(renderer)
2353 renderer.finalize()
2354 finally:
/home2/hilboll/prj/2014_no2trends-india/pyenv/lib/python2.7/site-packages/matplotlib/artist.pyc in draw_wrapper(artist, renderer, *args, **kwargs)
53 def draw_wrapper(artist, renderer, *args, **kwargs):
54 before(artist, renderer)
---> 55 draw(artist, renderer, *args, **kwargs)
56 after(artist, renderer)
57
/home2/hilboll/prj/2014_no2trends-india/pyenv/lib/python2.7/site-packages/matplotlib/figure.pyc in draw(self, renderer)
1032 dsu.sort(key=itemgetter(0))
1033 for zorder, a, func, args in dsu:
-> 1034 func(*args)
1035
1036 renderer.close_group('figure')
/home2/hilboll/prj/2014_no2trends-india/pyenv/lib/python2.7/site-packages/matplotlib/artist.pyc in draw_wrapper(artist, renderer, *args, **kwargs)
53 def draw_wrapper(artist, renderer, *args, **kwargs):
54 before(artist, renderer)
---> 55 draw(artist, renderer, *args, **kwargs)
56 after(artist, renderer)
57
/home2/hilboll/prj/2014_no2trends-india/pyenv/lib/python2.7/site-packages/matplotlib/axes.pyc in draw(self, renderer, inframe)
2084
2085 for zorder, a in dsu:
-> 2086 a.draw(renderer)
2087
2088 renderer.close_group('axes')
/home2/hilboll/prj/2014_no2trends-india/pyenv/lib/python2.7/site-packages/matplotlib/artist.pyc in draw_wrapper(artist, renderer, *args, **kwargs)
53 def draw_wrapper(artist, renderer, *args, **kwargs):
54 before(artist, renderer)
---> 55 draw(artist, renderer, *args, **kwargs)
56 after(artist, renderer)
57
/home2/hilboll/prj/2014_no2trends-india/pyenv/lib/python2.7/site-packages/matplotlib/axis.pyc in draw(self, renderer, *args, **kwargs)
1089 renderer.open_group(__name__)
1090
-> 1091 ticks_to_draw = self._update_ticks(renderer)
1092 ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,
1093 renderer)
/home2/hilboll/prj/2014_no2trends-india/pyenv/lib/python2.7/site-packages/matplotlib/axis.pyc in _update_ticks(self, renderer)
943
944 interval = self.get_view_interval()
--> 945 tick_tups = [t for t in self.iter_ticks()]
946 if self._smart_bounds:
947 # handle inverted limits
/home2/hilboll/prj/2014_no2trends-india/pyenv/lib/python2.7/site-packages/matplotlib/axis.pyc in iter_ticks(self)
887 Iterate through all of the major and minor ticks.
888 """
--> 889 majorLocs = self.major.locator()
890 majorTicks = self.get_major_ticks(len(majorLocs))
891 self.major.formatter.set_locs(majorLocs)
/home2/hilboll/prj/2014_no2trends-india/pyenv/lib/python2.7/site-packages/matplotlib/dates.pyc in __call__(self)
800 def __call__(self):
801 'Return the locations of the ticks'
--> 802 self.refresh()
803 return self._locator()
804
/home2/hilboll/prj/2014_no2trends-india/pyenv/lib/python2.7/site-packages/matplotlib/dates.pyc in refresh(self)
817 def refresh(self):
818 'Refresh internal information based on current limits.'
--> 819 dmin, dmax = self.viewlim_to_dt()
820 self._locator = self.get_locator(dmin, dmax)
821
/home2/hilboll/prj/2014_no2trends-india/pyenv/lib/python2.7/site-packages/matplotlib/dates.pyc in viewlim_to_dt(self)
562 def viewlim_to_dt(self):
563 vmin, vmax = self.axis.get_view_interval()
--> 564 return num2date(vmin, self.tz), num2date(vmax, self.tz)
565
566 def _get_unit(self):
/home2/hilboll/prj/2014_no2trends-india/pyenv/lib/python2.7/site-packages/matplotlib/dates.pyc in num2date(x, tz)
309 tz = _get_rc_timezone()
310 if not cbook.iterable(x):
--> 311 return _from_ordinalf(x, tz)
312 else:
313 return [_from_ordinalf(val, tz) for val in x]
/home2/hilboll/prj/2014_no2trends-india/pyenv/lib/python2.7/site-packages/matplotlib/dates.pyc in _from_ordinalf(x, tz)
212 tz = _get_rc_timezone()
213 ix = int(x)
--> 214 dt = datetime.datetime.fromordinal(ix)
215 remainder = float(x) - ix
216 hour, remainder = divmod(24 * remainder, 1)
ValueError: ordinal must be >= 1
```
The same works just fine when I create the plot replacing `kind='area'` with `kind='bar'`.
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} | 0 | 2014-07-08T14:15:02Z | 2014-07-08T15:40:09Z | 2014-07-08T15:40:09Z | CONTRIBUTOR | null | TST: skip on older numpy for (GH7694)
closes #7694
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} | 15 | 2014-07-08T15:55:25Z | 2014-07-25T20:42:15Z | 2014-07-25T15:10:47Z | MEMBER | null | Main Fix is to preserve nanosecond info which can lost during `offset.apply`, but it also includes:
- Support dateutil timezone
- Little performance improvement. Even though v0.14.1 should take longer than v0.14.0 because perf test in v0.14 doesn't perform timestamp conversion which was fixed in #7502.
NOTE: This caches `Tick.delta` because it was calculated 3 times repeatedly, but does it cause any side effect?
### Before
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
timeseries_year_incr | 0.0164 | 0.0103 | 1.5846 |
timeseries_year_apply | 0.0153 | 0.0094 | 1.6356 |
timeseries_day_incr | 0.0187 | 0.0053 | 3.5075 |
timeseries_day_apply | 0.0164 | 0.0033 | 4.9048 |
Target [d0076db] : PERF: Improve index.min and max perf
Base [da0f7ae] : RLS: 0.14.0 final
```
### After the fix
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
timeseries_year_incr | 0.0150 | 0.0087 | 1.7339 |
timeseries_year_apply | 0.0126 | 0.0073 | 1.7283 |
timeseries_day_incr | 0.0130 | 0.0053 | 2.4478 |
timeseries_day_apply | 0.0107 | 0.0033 | 3.2143 |
Target [64dd021] : BUG: offsets.apply doesnt preserve nanosecond
Base [da0f7ae] : RLS: 0.14.0 final
```
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} | 5 | 2014-07-08T21:01:42Z | 2015-04-25T23:33:31Z | 2014-07-09T06:37:28Z | CONTRIBUTOR | null | ...ame.plot()
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https://api.github.com/repos/pandas-dev/pandas/issues/7699 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7699/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7699/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7699/events | https://github.com/pandas-dev/pandas/issues/7699 | 37,430,669 | MDU6SXNzdWUzNzQzMDY2OQ== | 7,699 | Bug in quantile? | {
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| null | 2 | 2014-07-09T03:14:21Z | 2014-07-09T07:00:00Z | 2014-07-09T07:00:00Z | NONE | null | Hi folks,
I'm using Pandas 0.14.0 with Python3 on a 64bit Linux Mint. It looks like the Pandas `quantile` function is not working properly with the `axis=1` keyword. I'm getting the same output as `axis=0`, as you can see below.
```
In [23]: import numpy as np
In [24]: import numpy as np
In [25]: df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
columns=['a', 'b'])
In [26]: df
Out[26]:
a b
0 1 1
1 2 10
2 3 100
3 4 100
In [27]: df.quantile(0.5, axis=1)
Out[27]:
a 2.5
b 55.0
dtype: float64
In [28]: df.quantile(0.5)
Out[28]:
a 2.5
b 55.0
dtype: float64
```
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| null | 3 | 2014-07-09T06:59:15Z | 2014-07-09T21:03:55Z | 2014-07-09T21:03:23Z | NONE | null | Hi folks,
It appears that in Pandas 0.14.0, resampling on data frames with multi-index columns erases the multi-index names. You can see an example of this at http://stackoverflow.com/questions/24442832/how-to-resample-a-pandas-multi-index-data-frame-via-methods-depending-on-the-col/24443544?noredirect=1#comment38000583_24443544.
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