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} | 1 | 2014-12-18T03:09:51Z | 2014-12-20T20:31:55Z | 2014-12-20T20:31:45Z | CONTRIBUTOR | null | closes #9079
Fix the read_csv bug introduced in 6bf83c5d when using skiprows on a file with CR line terminators
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} | 2 | 2014-12-18T09:17:04Z | 2019-12-11T13:31:14Z | 2019-12-11T13:31:14Z | CONTRIBUTOR | null | The following call fails with a `PackValueError: recursion limit exceeded.`, spamming the console with references to `pandas.msgpack.Packer._pack` lines `3295` and `2391`:
```
df = pd.DataFrame([0], index=[datetime.time(1, 2, 3)])
df.to_msgpack()
```
This also happens for other unsupported data like a custom object `class A: pass; A()` instead of `datetime.time`. In both cases I'd expect the function to immediately stop processing when it encounters an unsupported object (or try to pickle like `to_hdf` does). Also, an index of `datetime.time` can be produced directly from a `pd.DateTimeIndex`, so it would be good to at least special-case this.
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} | 0 | 2014-12-18T11:28:40Z | 2014-12-19T21:03:11Z | 2014-12-19T21:03:11Z | CONTRIBUTOR | null | closes #9100
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} | 3 | 2014-12-18T14:04:11Z | 2014-12-20T20:06:40Z | 2014-12-20T20:05:52Z | NONE | null | closes #9099
This is a somewhat brute-force fix for supporting copy and dtype arguments in DataFrame constructor. Some data might be copied twice if both arguments are set, but if I did the operations in a different order, one other unit test would fail.
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- [ ] might need a test to show the `SettingWithCopyWarning` on this example
``` python
data = pd.DataFrame.from_items([(0, ['a', 'b', 'c']), (0, ['1', '2', '3'])])
data.index=['<3', 'u', 'i']
index_order = ['i', '<3', 'u']
index_dict = dict([(index, order) for order, index in enumerate(index_order)])
data['index_rank'] = list(data.index)
data['index_rank'].replace(index_dict, inplace=True)
print(data)
```
```
0 0 index_rank
<3 a 1 <3
u b 2 u
i c 3 i
```
``` python
data['index_rank'] = data['index_rank'].replace(index_dict, inplace=False)
print(data)
```
```
0 0 index_rank
<3 a 1 1
u b 2 2
i c 3 0
```
Assuring unique column names yields the expected behavior.
``` python
data = pd.DataFrame.from_items([(0, ['a', 'b', 'c']), (1, ['1', '2', '3'])])
data.index=['<3', 'u', 'i']
index_order = ['i', '<3', 'u']
index_dict = dict([(index, order) for order, index in enumerate(index_order)])
data['index_rank'] = list(data.index)
data['index_rank'].replace(index_dict, inplace=True)
print(data)
```
```
0 1 index_rank
<3 a 1 1
u b 2 2
i c 3 0
```
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} | 4 | 2014-12-18T23:25:59Z | 2019-09-12T12:50:29Z | 2019-09-12T12:50:29Z | CONTRIBUTOR | null | When use `coerce=True` in `pandas.to_datetime` old dates (before 1677-09-22) return NaT. The same dates without `coerce` are parsed correctly:
```
print pd.to_datetime('13000101', format='%Y%m%d', coerce=True)
print pd.to_datetime('13000101', format='%Y%m%d')
```
returns
```
NaT
1300-01-01 00:00:00
```
pandas: 0.15.2
numpy: 1.9.1
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} | 2 | 2014-12-19T15:39:55Z | 2014-12-20T22:05:18Z | 2014-12-20T22:05:18Z | MEMBER | null | Added a `SetUpClass` to skip the full class when no connection can be made to the database, and made some of the other methods class methods, to be able to use them in `SetUpClass`
Eg if you are testing locally and don't have one of the databases available, then it will test the connection and skip each individual test, which can take some time.
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} | 6 | 2014-12-20T02:03:31Z | 2015-06-09T12:24:19Z | 2015-06-09T00:39:23Z | MEMBER | null | Should `StringMethods` be compatible with standard `str`, or have only useful methods? Sometimes I have to adjust text formats before converting other types, and I've chosen additionals which makes formatting / adjustments easier. Any ideas?
- [x] `str.find` (#9386)
- [x] `str.rfind` (#9386)
- [x] `str.isalnum` (#9282)
- [x] `str.isalpha` (#9282)
- [x] `str.isdigit` (#9282)
- [x] `str.isspace` (#9282)
- [x] `str.islower` (#9282)
- [x] `str.isupper` (#9282)
- [x] `str.istitle` (#9282)
- [x] `str.ljust` (#9352)
- [x] `str.rjust` (#9352): `StringMethods.pad` works as the same as `ljust`, `rjust` and `center` by switching `side` option. And `StringMethods.center` exists, `ljust` and `rjust` don't.
- [x] `str.rsplit`: `split` also supports regex, and there seems no straightforward way to `rsplit` using regex. (#10303)
- [x] `str.zfill` (#9387)
- [x] `StringMethods.center` and `StringMethods.pad` should have 'fillchar` kw to specify padding character. (#9352)
- [x] `str.capitalize` (#9766)
- [x] `str.swapcase` (#9766)
- [x] `str.translate` (#10052)
- [x] `str.index` (#10045)
- [x] `str.rindex` (#10045)
- [x] `str.partition` (#9773)
- [x] `str.rpartition` (#9773)
- [x] `unicode.isnumeric` (#9439)
- [x] `unicode.isdecimal` (#9439)
- [x] `unicodedata.normalize` (#10031)
**NOTE** For python 2.x, once convert target to unicode internally then call the func.
## Which may not be very userful:
- `expandtabs`
- `splitlines`
I think there is an option to implement these to say "support the same methods as standard `str`".
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https://api.github.com/repos/pandas-dev/pandas/issues/9112 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9112/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9112/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9112/events | https://github.com/pandas-dev/pandas/pull/9112 | 52,548,801 | MDExOlB1bGxSZXF1ZXN0MjY0MTA5MDI= | 9,112 | API: Index.duplicated should return np.array | {
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} | 1 | 2014-12-20T02:31:55Z | 2015-02-15T03:26:28Z | 2014-12-20T20:10:58Z | MEMBER | null | For 2nd issue of #8875.
- Change `Index.duplicated` to return `np.array`.
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} | 2 | 2014-12-20T05:23:50Z | 2019-11-03T01:42:18Z | 2019-11-03T01:42:18Z | MEMBER | null | ```
# NG
s = pd.Series([1, 2, 3])
s.replace(2, True)
#0 1
#1 1
#2 3
# dtype: int64
```
If target has `object` dtype, it works.
```
s = pd.Series(['x', 2, 3])
s.replace(2, True)
#0 x
#1 True
#2 3
# dtype: object
```
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} | 1 | 2014-12-20T08:56:33Z | 2016-07-24T13:54:58Z | 2016-07-24T13:54:50Z | MEMBER | null | ```
In [11]: t = pd.to_datetime(['2000-01-01T00:00', 'NaT']).values.astype('datetime64[us]')
In [12]: pd.DatetimeIndex(t)
OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 294247-01-09 19:59:05
In [13]: pd.to_datetime(t)
Out[13]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2000-01-01, NaT]
Length: 2, Freq: None, Timezone: None
```
The second line should give the same result as the last one.
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} | 1 | 2014-12-20T19:46:39Z | 2014-12-23T03:13:20Z | 2014-12-23T03:13:20Z | CONTRIBUTOR | null | cc @jnmclarty
can you take a look
```
FSS.S
======================================================================
FAIL: test_wdi_download (pandas.io.tests.test_wb.TestWB)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/jreback/pandas/pandas/util/testing.py", line 1441, in wrapper
return t(*args, **kwargs)
File "/Users/jreback/pandas/pandas/io/tests/test_wb.py", line 48, in test_wdi_download
assert_frame_equal(result, pandas.DataFrame(expected))
File "/Users/jreback/pandas/pandas/util/testing.py", line 730, in assert_frame_equal
check_exact=check_exact)
File "/Users/jreback/pandas/pandas/util/testing.py", line 674, in assert_series_equal
assert_almost_equal(left.values, right.values, check_less_precise)
File "das/src/testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2740)
File "das/src/testing.pyx", line 93, in pandas._testing.assert_almost_equal (pandas/src/testing.c:1825)
File "das/src/testing.pyx", line 140, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2572)
AssertionError: expected 39682.47225 but got 39677.30176, with decimal 5
----------------------------------------------------------------------
Ran 5 tests in 7.469s
```
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} | 6 | 2014-12-20T21:26:08Z | 2015-01-17T03:34:52Z | 2015-01-05T23:48:24Z | MEMBER | null | Related to #6469. Made all datetime-like indexes `summary` should output the same format.
#### Before
```
didx = pd.date_range('2014-12-20', freq='M', periods=3)
pidx = pd.period_range('2014-12-20', freq='M', periods=3)
tdidx = pd.timedelta_range(start='1 days', freq='D', periods=3)
didx.summary()
# DatetimeIndex: 3 entries, 2014-12-31 00:00:00 to 2015-02-28 00:00:00
# Freq: M
pidx.summary()
# PeriodIndex: 3 entries, 2014-12 to 2015-02
tdidx.summary()
# TimedeltaIndex: 3 entries, '1 days' to '3 days'
# Freq: <Day>
```
#### After
- `DatetimeIndex` and `PeriodIndex` use `_format_func`. This made `DatetimeIndex` not to output hour/minutes.. when its frequency is more than a day.
- `PeriodIndex` outputs its `freq`
- `TimedeltaIndex` has no changes.
```
# DatetimeIndex: 3 entries, 2014-12-31 to 2015-02-28
# Freq: M
# PeriodIndex: 3 entries, 2014-12 to 2015-02
# Freq: M
# TimedeltaIndex: 3 entries, '1 days' to '3 days'
# Freq: <Day>
```
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} | 12 | 2014-12-20T21:55:54Z | 2015-11-14T05:26:47Z | 2015-05-11T14:55:18Z | MEMBER | null | Closes #8028. It is still under work, but want to clarify a point. Currently it works as:
```
s = pd.Series(['A', 'B', 'A', 'C'])
s
#0 A
#1 B
#2 A
#3 C
# dtype: object
pd.get_dummies(s)
# A B C
#0 1 0 0
#1 0 1 0
#2 1 0 0
#3 0 0 1
pd.get_dummies(s, value='YES', na_value='NO')
# A B C
#0 YES NO NO
#1 NO YES NO
#2 YES NO NO
#3 NO NO YES
pd.get_dummies(s, value={'A': 'X', 'B': 'Y', 'C': 'Z'})
# A B C
#0 X 0 0
#1 0 Y 0
#2 X 0 0
#3 0 0 Z
```
And what I'm concerning is cases for list-likes.
```
# This is natural and compat with replace, because the number of the hole will be the same number as list (one hole in each column).
pd.get_dummies(s, value=['A', 'B', 'A', 'C'])
# A B C
#0 A 0 0
#1 0 B 0
#2 A 0 0
#3 0 0 C
# When we specify na_value and make it compat with replace, behavior should be:
pd.get_dummies(s, na_value=['A', 'B', 'A', 'C'])
# A B C
0 1 A B
1 A 1 C
2 1 A B
3 A C 1
# Or it needs different broadcasting rule?
pd.get_dummies(s, na_value=['A', 'B', 'A', 'C'])
# A B C
#0 1 A A
#1 B 1 B
#2 1 A A
#3 C C 1
```
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} | 2 | 2014-12-20T22:09:48Z | 2015-06-22T08:46:37Z | 2015-06-22T08:46:37Z | CONTRIBUTOR | null | someone want to sample the scientific stack and see who still supoorts 3.2?
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} | 3 | 2014-12-20T23:50:47Z | 2018-10-03T15:25:45Z | 2018-10-03T15:25:45Z | CONTRIBUTOR | null | ``` python
In [1]: import pandas as pd
In [2]: rng = pd.date_range("2013-04-01", "2013-05-01", tz='Europe/London', freq='H')
In [4]: series = pd.Series(index=rng)
In [6]: series.resample('W')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-6-906374246edd> in <module>()
----> 1 series.resample('W')
/usr/local/lib/python2.7/dist-packages/pandas/core/generic.pyc in resample(self, rule, how, axis, fill_method, closed, label, convention, kind, loffset, limit, base)
3003 fill_method=fill_method, convention=convention,
3004 limit=limit, base=base)
-> 3005 return sampler.resample(self).__finalize__(self)
3006
3007 def first(self, offset):
/usr/local/lib/python2.7/dist-packages/pandas/tseries/resample.pyc in resample(self, obj)
83
84 if isinstance(ax, DatetimeIndex):
---> 85 rs = self._resample_timestamps()
86 elif isinstance(ax, PeriodIndex):
87 offset = to_offset(self.freq)
/usr/local/lib/python2.7/dist-packages/pandas/tseries/resample.pyc in _resample_timestamps(self, kind)
273 axlabels = self.ax
274
--> 275 self._get_binner_for_resample(kind=kind)
276 grouper = self.grouper
277 binner = self.binner
/usr/local/lib/python2.7/dist-packages/pandas/tseries/resample.pyc in _get_binner_for_resample(self, kind)
121 kind = self.kind
122 if kind is None or kind == 'timestamp':
--> 123 self.binner, bins, binlabels = self._get_time_bins(ax)
124 elif kind == 'timedelta':
125 self.binner, bins, binlabels = self._get_time_delta_bins(ax)
/usr/local/lib/python2.7/dist-packages/pandas/tseries/resample.pyc in _get_time_bins(self, ax)
182
183 # general version, knowing nothing about relative frequencies
--> 184 bins = lib.generate_bins_dt64(ax_values, bin_edges, self.closed, hasnans=ax.hasnans)
185
186 if self.closed == 'right':
/usr/local/lib/python2.7/dist-packages/pandas/lib.so in pandas.lib.generate_bins_dt64 (pandas/lib.c:17825)()
ValueError: Values falls before first bin
```
Resampling to weekly works fine if we first remove the timezone:
``` python
In [7]: series.tz_localize(None).resample('W')
Out[7]:
2013-04-07 NaN
2013-04-14 NaN
2013-04-21 NaN
2013-04-28 NaN
2013-05-05 NaN
Freq: W-SUN, dtype: float64
```
And resampling the TZ-aware Series to daily works fine:
``` python
In [8]: series.resample('D')
Out[8]:
2013-04-01 00:00:00+01:00 NaN
2013-04-02 00:00:00+01:00 NaN
2013-04-03 00:00:00+01:00 NaN
2013-04-04 00:00:00+01:00 NaN
2013-04-05 00:00:00+01:00 NaN
2013-04-06 00:00:00+01:00 NaN
2013-04-07 00:00:00+01:00 NaN
2013-04-08 00:00:00+01:00 NaN
2013-04-09 00:00:00+01:00 NaN
2013-04-10 00:00:00+01:00 NaN
2013-04-11 00:00:00+01:00 NaN
2013-04-12 00:00:00+01:00 NaN
2013-04-13 00:00:00+01:00 NaN
2013-04-14 00:00:00+01:00 NaN
2013-04-15 00:00:00+01:00 NaN
2013-04-16 00:00:00+01:00 NaN
2013-04-17 00:00:00+01:00 NaN
2013-04-18 00:00:00+01:00 NaN
2013-04-19 00:00:00+01:00 NaN
2013-04-20 00:00:00+01:00 NaN
2013-04-21 00:00:00+01:00 NaN
2013-04-22 00:00:00+01:00 NaN
2013-04-23 00:00:00+01:00 NaN
2013-04-24 00:00:00+01:00 NaN
2013-04-25 00:00:00+01:00 NaN
2013-04-26 00:00:00+01:00 NaN
2013-04-27 00:00:00+01:00 NaN
2013-04-28 00:00:00+01:00 NaN
2013-04-29 00:00:00+01:00 NaN
2013-04-30 00:00:00+01:00 NaN
2013-05-01 00:00:00+01:00 NaN
Freq: D, dtype: float64
```
This issue is similar (but not identical) to #1459 and #8941
My install versions:
``` python
In [2]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-28-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.15.2
nose: 1.3.4
Cython: 0.21.1
numpy: 1.9.1
scipy: 0.14.0
statsmodels: 0.6.1
IPython: 2.3.1
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.3
pytz: 2014.10
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.2
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.3.6
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9
apiclient: None
rpy2: 2.3.8
sqlalchemy: None
pymysql: None
psycopg2: 2.5.3 (dt dec pq3 ext)
```
(as always, I must applaud the Pandas dev team. It's an awesome animal!)
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| null | 1 | 2014-12-21T01:03:19Z | 2014-12-21T07:26:53Z | 2014-12-21T07:26:53Z | CONTRIBUTOR | null | some escape characters snuck in there that cause the URL to be invalid
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| null | 2 | 2014-12-21T10:49:37Z | 2014-12-21T12:46:32Z | 2014-12-21T12:46:32Z | CONTRIBUTOR | null | Hello,
I want to use [Sourcegraph code search and code review](https://sourcegraph.com) with pandas. A project maintainer needs to enable it to set up a webhook so the code is up-to-date there.
Could you please enable pandas on @Sourcegraph by going to https://sourcegraph.com/github.com/pydata/pandas and clicking on Settings? (It should only take 15 seconds.)
Thank you!
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} | 4 | 2014-12-21T15:44:10Z | 2017-04-05T02:06:12Z | 2015-01-19T13:21:48Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/9088
Closes https://github.com/pydata/pandas/issues/7128
Votes on whether we should
1. Remove the line entirely
2. Add a parameter to `.plot` control whether the line should be added.
I vote for 1, but I can go either way.
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} | 14 | 2014-12-21T18:45:24Z | 2015-06-02T19:26:59Z | 2015-05-15T20:26:37Z | CONTRIBUTOR | null | zoneinfo.gettz() seems to have problems (1 & 2) on system which do not install
the zoninfo tarball (e.g. Debian, Gentoo and Fedora) but rely on the system
zoneinfo files. This results in test failures (3 & 4)
tz.gettz() doesn't suffer from this problem.
1 https://github.com/dateutil/dateutil/issues/8
2 https://github.com/dateutil/dateutil/issues/11
closes https://github.com/pydata/pandas/issues/9059
closes https://github.com/pydata/pandas/issues/8639
closes #10121
Signed-off-by: Justin Lecher [email protected]
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} | 17 | 2014-12-21T20:59:30Z | 2015-09-11T04:42:33Z | 2015-09-11T04:42:33Z | MEMBER | null | Currently, we use the `plot` for every possible kind of plot, and switch between them using the `kind` keyword argument.
From an API design and discoverability perspective, this seems less than ideal. If we had separate methods, standard introspection tools like IPython and autocomplete could help users figure out which arguments are applicable for which type of plot with less reliance on the documentation.
Another option is to make a `.plot` also an accessor, so we could have `df.plot.scatter` and so forth. But I suspect `.plot_scatter` would be a bit more discoverable.
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} | 9 | 2014-12-21T23:47:56Z | 2014-12-23T12:59:59Z | 2014-12-23T10:57:56Z | CONTRIBUTOR | null | ```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
multiindex_duplicated | 139.2883 | 469.6666 | 0.2966 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [2b99b80] : use labels to identify duplicates in multi-index
Base [0fe43a6] : Merge pull request #9120 from minrk/nbviewer-link
fix an nbviewer link in tutorials
```
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} | 1 | 2014-12-22T06:14:12Z | 2014-12-23T01:24:17Z | 2014-12-23T01:24:17Z | NONE | null | import pandas as pd
s=pd.Series(xrange(5000000))
%timeit s.loc[[0]] # You need pandas 0.15.1 or newer for it to be that slow
1 loops, best of 3: 445 ms per loop
Also see the question and the answer here: http://stackoverflow.com/questions/27596832/why-is-dataframe-loc1-1-800x-slower-than-df-ix-1-and-3-500x-than-df-loc
Since .loc[] started to raise KeyError in pandas 0.15.1, the calls via `.loc[ [1] ]` (when passed a list) have slowed down enormously, because, I think, an exhaustive search is done to determine if a KeyError has to be raised or not, even if the list has only one element that can be easily located.
Profiling:
File: .../anaconda/lib/python2.7/site-packages/pandas/core/indexing.py,
```
1278 # require at least 1 element in the index
1279 1 241 241.0 0.1 idx = _ensure_index(key)
1280 1 391040 391040.0 99.9 if len(idx) and not idx.isin(ax).any():
1281
1282 raise KeyError("None of [%s] are in the [%s]" %
```
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} | 10 | 2014-12-22T07:49:35Z | 2014-12-24T01:57:20Z | 2014-12-23T01:24:17Z | MEMBER | null | Fixes #9126
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
series_loc_list_like | 0.2840 | 125.0146 | 0.0023 |
series_loc_array | 0.9947 | 125.5813 | 0.0079 |
series_loc_scalar | 0.0430 | 0.0424 | 1.0150 |
series_loc_slice | 0.0647 | 0.0606 | 1.0668 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
```
Whee!
I also wrote this fix for `IntervalIndex` (#8707); this change pulls it out separately. I believe it changes the complexity of the lookup check from O(n*m) for length n index and length m key to O(n+m).
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} | 7 | 2014-12-22T11:57:36Z | 2014-12-25T07:33:06Z | 2014-12-24T19:11:56Z | CONTRIBUTOR | null | closes #9066
First time contributor, tried to follow all rules as good as possible.
If something is missing please let me know.
As specified in task 9066 tilde in a pathname get resolved into a absolute path.
In case the object is a buffer the buffer will be returned.
I did not add additional tests, since I could not find any module explicitly testing pandas/io/common.py
if that is necessary please let me know how to do that. The current tests are passing on my system and seem to cover the method below.
Best Regards,
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} | 2 | 2014-12-22T13:29:35Z | 2014-12-23T03:22:57Z | 2014-12-23T03:22:57Z | CONTRIBUTOR | null | ```
In [158]: x = pd.to_datetime([np.nan, '2000-1-1'])
In [159]: y = x.to_period(freq='m')
In [160]: y
Out[160]:
<class 'pandas.tseries.period.PeriodIndex'>
[NaT, 2000-01]
Length: 2, Freq: M
In [161]: pd.isnull(x)
Out[161]: array([ True, False], dtype=bool)
In [162]: pd.isnull(y)
Out[162]: array([False, False], dtype=bool)
```
I think `pd.isnull(y)` should be returning `array([True, False], dtype=bool)`.
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} | 19 | 2014-12-22T16:13:48Z | 2014-12-24T18:27:30Z | 2014-12-24T15:53:17Z | CONTRIBUTOR | null | This adds block support to the JSON serialiser, as per #9037. I also added code to directly cast and serialise numpy data, which replaces the previous use of intermediate Python objects.
Large performance improvement (~25x) for mixed frames containing datetimes / timedeltas.
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
packers_write_json_mixed_delta_int_tstamp | 97.1374 | 2633.7843 | 0.0369 |
packers_write_json_mixed_float_int_T | 68.7390 | 86.4150 | 0.7955 |
packers_write_json_date_index | 68.9886 | 83.2930 | 0.8283 |
packers_write_json_T | 61.1283 | 72.9477 | 0.8380 |
packers_write_json | 60.7293 | 71.3053 | 0.8517 |
packers_read_json_date_index | 157.4903 | 161.6703 | 0.9741 |
packers_read_json | 157.4220 | 157.8477 | 0.9973 |
packers_write_json_mixed_float_int | 98.1897 | 98.1696 | 1.0002 |
packers_write_json_mixed_float_int_str | 84.0390 | 83.6937 | 1.0041 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
```
Some questions, any comments appreciated:
1. there's a little overhead to dealing with blocks so I'm avoiding using them and using `values` instead if the frame is 'simple'. I'm using the BlockManager methods `_is_single_block` and `is_mixed_dtype` for this to check for a simple frame.
2. I'm using the DataFrame `_data` attr and `mgr_locs` to get access to the block data and the block-to-column mapping. Are there any caveats to this? I know the DataFrame does some caching, but I'm not familiar enough with the details.
Tested locally on Python 2.7 for 32 & 64 bit linux and 3.3. on 64 bit linux. JSON tests run through valgrind. Would appreciate if someone could give it a bash on Windows before merging.
@cpcloud I also fixed a ref leak and added support for `date_unit` in the #9028 code.
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| null | 5 | 2014-12-22T17:38:25Z | 2014-12-23T10:49:50Z | 2014-12-23T10:49:50Z | CONTRIBUTOR | null | Hello,
I noticed a strange behavior when a Numpy array is given to a Pandas DataFrame constructor.
I don't know really if it should be considered as an issue (or a feature)...
but anyway a tip to fix this will be nice.
```
import pandas as pd
import numpy as np
lst = [{'a':1, 'b':2}, {'a':3, 'b':2, 'c':3}]
In []: pd.DataFrame(lst)
Out[]:
a b c
0 1 2 NaN
1 3 2 3
```
but with Numpy array
```
In []: arr=np.array(lst)
In []: arr
Out[]: array([{'a': 1, 'b': 2}, {'a': 3, 'c': 3, 'b': 2}], dtype=object)
In []: pd.DataFrame(arr)
Out[]:
0
0 {u'a': 1, u'b': 2}
1 {u'a': 3, u'c': 3, u'b': 2}
```
I was expecting same results. I was expecting a DataFrame with columns named 'a', 'b', 'c' like
when I feed DataFrame with a standard list.
I can "fix" this using
```
In []: pd.DataFrame(list(arr))
Out[]:
a b c
0 1 2 NaN
1 3 2 3
```
I don't think that `pd.DataFrame(list(arr))` is a nice idea... (with a big array it will be probably very long)
Any idea ?
Kind regards
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| null | 6 | 2014-12-22T19:24:19Z | 2014-12-23T14:19:49Z | 2014-12-22T19:36:50Z | CONTRIBUTOR | null | There is an inconsistency apparently in the way dtypes are parsed when DataFrames are created from Series:
```
>>> import pandas as pd
>>> df = pd.DataFrame({'f':pd.Series([1.5,], dtype='float32'), 'i':pd.Series([1,], dtype='int32')})
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1 entries, 0 to 0
Data columns (total 2 columns):
f 1 non-null float32
i 1 non-null int32
dtypes: float32(1), int32(1)
memory usage: 16.0 bytes
>>> df2 = pd.DataFrame([pd.Series([1.5,], dtype='float32'), pd.Series([1,], dtype='int32')], columns=['f','i'])
>>> df2.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 2 entries, 0 to 1
Data columns (total 2 columns):
f 0 non-null float64
i 0 non-null float64
dtypes: float64(2)
memory usage: 48.0 bytes
```
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| null | 2 | 2014-12-22T19:26:32Z | 2014-12-22T19:38:42Z | 2014-12-22T19:37:48Z | CONTRIBUTOR | null | The following seems like very natural syntax to specify the dtypes of various columns passed into the DataFrame constructor:
```
df = pd.DataFrame({'a':[1,2,3], 'b':[2.5,3.5,6.7]}, dtype={'a':'int32','b':'float32'})
```
However, right now it raises an error. Perhaps something worth implementing? See also http://stackoverflow.com/questions/21197774/assign-pandas-dataframe-column-dtypes
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} | 9 | 2014-12-22T21:00:13Z | 2014-12-23T03:23:23Z | 2014-12-23T03:22:57Z | CONTRIBUTOR | null | closes #9129
Currently, `Series.nunique` [(source)](https://github.com/pydata/pandas/blob/master/pandas/core/base.py#L429) calls `Series.value_counts` [(source)](https://github.com/pydata/pandas/blob/master/pandas/core/base.py#L371), which by default, sorts the values.
Counting unique values certainly doesn't require sorting, so we could fix this
by passing `sort=False` to `value_counts`.
But `nunique` can also be calculated by calling `Series.unique` instead of
`value_counts`, and using `com.isnull` to handle the `dropna` parameter.
This PR attempts to implement this. Here is a vbench perf test which seems to show an improvement for tests using `nunique`.
```
/usr/bin/time -v ./test_perf.sh -b master -t nunique-unique -r groupby
```
Here are the best and worst ratios:
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
groupby_ngroups_10000_nunique | 900.9844 | 3768.5087 | 0.2391 |
groupby_ngroups_100_nunique | 9.6850 | 38.9043 | 0.2489 |
groupby_ngroups_100_sem | 0.7834 | 1.4904 | 0.5256 |
groupby_ngroups_100_size | 0.4920 | 0.8560 | 0.5748 |
groupby_ngroups_10000_max | 2.5337 | 4.1683 | 0.6078 |
groupby_frame_nth_none | 2.3570 | 3.2880 | 0.7168 |
groupby_ngroups_10000_var | 2.4277 | 3.3390 | 0.7271 |
groupby_ngroups_10000_sem | 3.5107 | 4.6523 | 0.7546 |
...
groupby_transform_multi_key3 | 630.2720 | 599.7047 | 1.0510 |
groupby_nth_datetimes_none | 424.5253 | 403.6326 | 1.0518 |
groupby_transform_series2 | 109.7130 | 104.1580 | 1.0533 |
groupby_transform_multi_key1 | 58.9686 | 55.7120 | 1.0585 |
groupby_nth_datetimes_any | 1206.7020 | 1132.4236 | 1.0656 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
```
While working on this PR I ran into issue GH 9129.
This PR includes Jeff's suggested fix.
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} | 0 | 2014-12-22T22:02:36Z | 2014-12-22T23:49:43Z | 2014-12-22T23:49:43Z | CONTRIBUTOR | null | fixes w.r.t. comment in #9101
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} | 11 | 2014-12-22T23:05:39Z | 2014-12-24T15:55:26Z | 2014-12-24T15:55:19Z | CONTRIBUTOR | null | @jreback I noticed a codepath asymmetry in core.frame.count that leads to a substantial difference in dropna() performance depending on the axis. Using the path `df.dropna(axis=0)` takes yields a 2.5-5x improvement.
```
$ python vb_suite/test_perf.py -b upstream/master -t HEAD -r "dropna" -S -n 30
Invoked with :
--ncalls: 3
--repeats: 30
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
frame_dropna_axis1_any | 18.5493 | 101.6117 | 0.1826 |
frame_dropna_axis1_all | 48.0193 | 128.8197 | 0.3728 |
frame_dropna_axis0_any | 17.0240 | 17.3127 | 0.9833 |
frame_dropna_axis0_all | 43.1127 | 43.3304 | 0.9950 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [84ad341] : Remove codepath asymmetry in dataframe count()
Base [099a02c] : Merge pull request #9061 from behzadnouri/nan-pivot
pivot & unstack with nan in the index
count mean std min 25% 50% 75% max
frame_dropna_axis1_any 3 40.114509 54.044075 0.182551 9.365917 18.549283 60.080489 101.611694
frame_dropna_axis1_all 3 59.070599 64.932673 0.372764 24.196047 48.019330 88.419517 128.819704
frame_dropna_axis0_any 3 11.773351 9.345549 0.983328 9.003684 17.024040 17.168363 17.312686
frame_dropna_axis0_all 3 29.146001 24.379745 0.994976 22.053826 43.112675 43.221513 43.330352
```
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} | 3 | 2014-12-23T01:41:05Z | 2015-02-09T00:37:14Z | 2014-12-23T03:13:20Z | CONTRIBUTOR | null | Closes #9115
I just round the expected and result data for the failing test. I have no idea why the WB would be revising 2003 and 2004 data, but they did.
I would have redesigned the test, but spending that time has minimal benefit.
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https://api.github.com/repos/pandas-dev/pandas/issues/9138 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9138/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9138/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9138/events | https://github.com/pandas-dev/pandas/pull/9138 | 52,729,587 | MDExOlB1bGxSZXF1ZXN0MjY1MDM4MDk= | 9,138 | FIX: to_sql dtype argument accepting SQLAlchemy type instance (GH9083) | {
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| null | 2 | 2014-12-23T10:27:51Z | 2014-12-24T10:36:06Z | 2014-12-24T10:36:06Z | MEMBER | null | Closes #9083
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} | 17 | 2014-12-23T17:18:39Z | 2015-02-17T14:03:47Z | 2015-01-16T16:54:46Z | CONTRIBUTOR | null | Hello,
I try this:
```
import requests
from pandas.io.json import json_normalize
import json
import pandas as pd
response = requests.get("http://api.openweathermap.org/data/2.5/history/station?start=1356220800&end=1356307200&type=hour&id=5530")
df = json_normalize(json.loads(response.text)['list'])
df['dt'] = pd.to_datetime(df['dt'], unit='s')
#df = df.set_index('dt')
print(df)
df.to_excel("out.xls")
```
It displays:
```
dt humidity.c ... pressure.v temp.c temp.ma \
0 2012-12-23 00:00:00 2 ... 1017.0 2 286.15
1 2012-12-23 01:00:00 2 ... 1017.0 2 286.15
2 2012-12-23 02:00:00 2 ... 1017.0 2 285.15
3 2012-12-23 03:00:00 2 ... 1017.0 2 284.15
4 2012-12-23 04:00:00 2 ... 1017.0 2 283.15
...
19 2012-12-23 19:00:00 1 ... 1016.0 1 282.15
20 2012-12-23 20:00:00 1 ... 1015.0 1 281.15
21 2012-12-23 21:00:00 1 ... 1015.0 1 282.15
22 2012-12-23 22:00:00 1 ... 1015.0 1 281.15
23 2012-12-23 23:00:00 1 ... 1005.0 1 286.15
24 2012-12-24 00:00:00 2 ... 1013.5 2 280.15
```
but if we look at Excel file `dt` column looks like
```
dt
2012-12-23 00:00:00
2012-12-23 00:00:00
2012-12-23 00:00:00
2012-12-23 00:00:00
2012-12-23 00:00:00
...
2012-12-23 00:00:00
2012-12-23 00:00:00
2012-12-23 00:00:00
2012-12-23 00:00:00
2012-12-24 00:00:00
```
`to_csv` method is ok
```
In [1]: pd.__version__
Out[1]: '0.15.2'
```
Kind regards
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} | 7 | 2014-12-23T18:09:11Z | 2014-12-24T15:50:18Z | 2014-12-24T15:50:18Z | NONE | null | The snippet of code works fine in 0.15.0. Not sure about 0.15.1 as I did not try it on that version. However on 0.15.2 it causes an infinite loop
```
import pandas
import numpy
class TestObject:
def __str__(self):
return "TestObject"
obj = TestObject()
wp = pandas.Panel(numpy.random.randn(1,5,4), items=[obj],
major_axis = pandas.date_range('1/1/2000', periods=5),
minor_axis=['A', 'B', 'C', 'D'])
print(wp)
print(wp[obj])
```
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} | 8 | 2014-12-23T19:46:31Z | 2015-01-18T21:09:18Z | 2015-01-18T21:09:11Z | CONTRIBUTOR | null | xref #8728
jobComplete can be False in query_reply. Simply checking for the existence of the field isn't enough, as a False value will cause a KeyError when checking for query_reply['totalRows']
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| null | 1 | 2014-12-24T00:17:50Z | 2014-12-24T01:18:19Z | 2014-12-24T01:18:19Z | CONTRIBUTOR | null | I have objects in the items axis of a Panel, selecting by an object gets me an infinite recursion.
``` python
import pandas as pd
import numpy as np
class Key:
def __init__(self, name, id):
self.name = name
self.id = id
def __repr__(self):
return "Key(name={name}, id={id})".format(**self.__dict__)
names = ['dale', 'bob', 'frank']
N = len(names)
items = [Key(name, id) for name, id in zip(names, range(1, N+1))]
panel = pd.Panel(np.random.randn(N, 10, 10), items=items)
panel.ix[items[0]] # infinite recursion
super(pd.Panel, panel).__getitem__(items[0]) # works
```
It appears the issue is that the `Key` object isn't be counted as scalar in the `Panel.__getitem__`
``` python
253 def __getitem__(self, key):
254 if isinstance(self._info_axis, MultiIndex):
255 return self._getitem_multilevel(key)
256 if lib.isscalar(key):
257 return super(Panel, self).__getitem__(key)
258 return self.ix[key]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/9143 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9143/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9143/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9143/events | https://github.com/pandas-dev/pandas/pull/9143 | 52,790,202 | MDExOlB1bGxSZXF1ZXN0MjY1NDE3Nzk= | 9,143 | BUG: Bug in Panel indexing with an object-like (GH9140) | {
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} | 0 | 2014-12-24T02:04:20Z | 2014-12-24T15:50:19Z | 2014-12-24T15:50:18Z | CONTRIBUTOR | null | closes #9140
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} | 2 | 2014-12-24T03:57:14Z | 2015-02-16T12:35:24Z | 2015-02-16T12:35:24Z | NONE | null | ```
In [3]: s = pd.Series([1.0, 2.0])
In [4]: 0 / s
Out[4]:
0 inf
1 inf
dtype: float64
In [5]: 0.0 / s
Out[5]:
0 0
1 0
dtype: float64
```
This is seems quite wired. Should not 0/s be 0 as well?
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} | 6 | 2014-12-24T08:18:37Z | 2020-05-07T22:14:01Z | 2020-05-07T22:14:01Z | CONTRIBUTOR | null | Hello,
try
```python
import requests
from pandas.io.json import json_normalize
import json
import pandas as pd
response = requests.get("http://api.openweathermap.org/data/2.5/history/station?start=1356220800&end=1356307200&type=hour&id=5530")
df = json_normalize(json.loads(response.text)['list'])
df['dt'] = pd.to_datetime(df['dt'], unit='s')
#df = df.set_index('dt')
df = df[['dt', 'main.temp.ma']]
print(df)
engine = 'openpyxl'
filename = "out_%s.xlsx" % engine
writer = pd.ExcelWriter(filename, engine=engine)
#df.to_excel("out.xls")
df.to_excel(writer)
engine = 'xlsxwriter'
filename = "out_%s.xlsx" % engine
writer = pd.ExcelWriter(filename, engine=engine)
#df.to_excel("out.xls")
df.to_excel(writer)
engine = 'xlwt'
filename = "out_%s.xls" % engine
writer = pd.ExcelWriter(filename, engine=engine)
#df.to_excel("out.xls")
df.to_excel(writer)
```
Only `out_xlsxwriter.xlsx` is create
I was expecting `out_openpyxl.xlsx` and `out_xlwt.xls` to be created.
See also https://github.com/pydata/pandas/issues/9139
Kind regards
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} | 3 | 2014-12-24T18:19:01Z | 2018-01-06T17:33:07Z | 2018-01-06T17:33:07Z | CONTRIBUTOR | null | Add a round-trippable json orient (maybe "roundtrip") to provide enough metadata to reconstruct a frame or series with 100% fidelity. As discussed in #9130
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} | 2 | 2014-12-24T18:26:46Z | 2019-08-27T18:45:52Z | 2019-08-27T18:45:52Z | CONTRIBUTOR | null | To explicitly encourage / support serialisation of other objects (e.g. numpy arrays) and better support custom JSON serialisation of Pandas types. As discussed in #9130.
Would welcome more thoughts / ideas on this. Not 100% sold myself.
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} | 23 | 2014-12-24T19:18:31Z | 2015-03-11T12:11:49Z | 2015-03-11T12:11:49Z | CONTRIBUTOR | null | I was looking at supporting Categorical objects in statsmodels [1] recently and again now in seaborn [2]. I think it would be great to support both ordered and unordered Categorical variables (via a flag or something). IIRC, there was some discussion about this. Is it possible? From what I could tell all Categorical types are assumed to have some order.
[1] https://github.com/statsmodels/statsmodels/issues/2133
[2] https://github.com/mwaskom/seaborn/issues/361
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} | 12 | 2014-12-24T19:41:44Z | 2020-01-03T23:23:44Z | 2020-01-03T23:23:44Z | NONE | null | As the subject says, if I try to call .agg() with a dictionary with a column that has all np.nan's it falls back to python agg functions instead of numpy.
To reproduce: (my dataset is 60 cols, 100000 rows)
I imported a csv and one column was all null (np.nan). The column dtype was set to object. (that's one issue, why the large upcast container to store np.nan?)
```
sq = pd.read_table(sqFile, sep='\t', skiprows = 1, nrows=None, header=0)
sq_g=sq.groupby(all_key_cols, as_index=False, sort=False)
```
`sq_g.agg(sum)`
Without specifying a dictionary and using sum over the entire dataframe it correctly uses the cython optimized numpy.sum:
`10 loops, best of 3: 48.3 ms per loop`
`sq_g.agg(collections.OrderedDict({'ColumnRef53':'sum'}))`
Specifying a dictionary and a column that is of dtype object that is entirely rows of np.nan falls back to python (bad):
`1 loops, best of 3: 7.26 s per loop`
For reference (ColumnRef20 has floats, ColumnRef53 has entirely np.nan's):
```
sq.dtypes
# Row float64
Rowdesc.iphost object
...
ColumnRef20 float64
ColumnRef53 object
...
dtype: object
```
My workaround is to downcast these np.nan filled columns back to float64, then the dictionary aggregation correctly uses the numpy optimized functions and not python:
```
# workaround for numpy groupby issue:
# downcast columns with all NaN from object to float64 so agg() doesn't fallback to python.
# first find all columns with all np.nan rows
data_cols = [x for x in sq_concat.columns.tolist() if x.startswith('Column')]
all_nan = pd.isnull(sq_concat[data_cols]).all()
all_nan_cols = all_nan[all_nan == True].index.values.tolist()
# only need to downcast if type is object
obj_downcast = sq_concat[all_nan_cols].dtypes == object
obj_downcast_cols = obj_downcast[obj_downcast == True].index.values.tolist()
# downcast object to np.float64
for nan_col in obj_downcast_cols:
sq_concat[nan_col] = sq_concat[nan_col].apply(np.float64)
```
Then the dictionary .agg() works as expected:
`sq_g.agg(collections.OrderedDict({'ColumnRef53':'sum'}))`
`100 loops, best of 3: 6.2 ms per loop`
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https://api.github.com/repos/pandas-dev/pandas/issues/9150 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9150/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9150/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9150/events | https://github.com/pandas-dev/pandas/issues/9150 | 52,835,373 | MDU6SXNzdWU1MjgzNTM3Mw== | 9,150 | Runtime terminate it in an unusual way | {
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| null | 5 | 2014-12-24T20:20:55Z | 2016-08-04T15:51:34Z | 2015-01-02T17:16:18Z | NONE | null | Hello Pandas developers.
Running any python script with the pandas produce 'Runtime to terminate it in an unusual way'
To reproduce the error use:
- Pandas version 0.15.2, C Python
- Python 2.7.6 (default, Nov 10 2013, 19:24:18) [MSC v.1500 32 bit (Intel)] on win32
- SO windows 7
create a python script (myfile.py) with the line of code
import pandas as pd
run the code with
c:\my_path>python myfile.py
This application has requested the Runtime to terminate it in an unusual way.
Please contact the application's support team for more information.
Kind regards
Adrian
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} | 4 | 2014-12-24T23:50:09Z | 2014-12-29T15:43:56Z | 2014-12-28T23:43:38Z | CONTRIBUTOR | null | In join operations, current master switches to [a less efficient path](https://github.com/pydata/pandas/blob/def58c9bcabd5bda63696a30f25d4012ab1160ac/pandas/tools/merge.py#L471) if the key space exceeds `int64` bounds. This commit improves performance and memory usage:
on master:
```
In [1]: np.random.seed(2718281)
In [2]: left = DataFrame(np.random.randint(-1 << 10, 1 << 10, (1 << 20, 8)),
...: columns=list('ABCDEFG') + ['left'])
In [3]: i = np.random.permutation(len(left))
In [4]: right = left.iloc[i].copy()
In [5]: right.columns = right.columns[:-1].tolist() + ['right']
In [6]: %timeit pd.merge(left, right, how='outer')
1 loops, best of 3: 13.8 s per loop
In [7]: %memit pd.merge(left, right, how='outer')
peak memory: 1064.16 MiB, increment: 820.65 MiB
```
on branch:
```
In [6]: %timeit pd.merge(left, right, how='outer')
1 loops, best of 3: 1.42 s per loop
In [7]: %memit pd.merge(left, right, how='outer')
peak memory: 440.72 MiB, increment: 199.89 MiB
```
`join|merge` benchmarks:
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
i8merge | 1510.8590 | 13001.0207 | 0.1162 |
merge_2intkey_nosort | 20.9847 | 27.0010 | 0.7772 |
join_dataframe_index_single_key_small | 16.4944 | 19.0903 | 0.8640 |
join_dataframe_integer_2key | 7.3093 | 8.0273 | 0.9106 |
merge_2intkey_sort | 60.6734 | 61.8456 | 0.9810 |
left_outer_join_index | 3165.2357 | 3214.8040 | 0.9846 |
join_dataframe_index_multi | 36.0967 | 36.5300 | 0.9881 |
join_dataframe_index_single_key_bigger_sort | 24.7893 | 25.0640 | 0.9890 |
join_non_unique_equal | 0.9350 | 0.9403 | 0.9943 |
join_dataframe_index_single_key_bigger | 24.8820 | 24.9500 | 0.9973 |
strings_join_split | 57.1507 | 56.9630 | 1.0033 |
join_dataframe_integer_key | 2.9247 | 2.8093 | 1.0411 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [f2d9e17] : improves merge performance when key space exceeds i8 bounds
Base [def58c9] : Merge pull request #9128 from hsperr/expanduser
ENH: Expanduser in to_file methods GH9066
```
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} | 1 | 2014-12-25T07:20:17Z | 2016-06-23T09:36:03Z | 2016-06-23T09:36:03Z | NONE | null | I get a NameError exception when I try to filter DataFrame by selected index values (inside IPython session). You can see that [`valid`](http://www.datafilehost.com/d/a5c78493) is `numpy.array` while [`lab`](http://www.datafilehost.com/d/cf3704a0) is a `pandas.DataFrame` object. Both of them are initialized and accessible. However I cannot put them together. Here is the error:
``` python
In [51]: valid
Out[51]:
array([38661, 44593, 38705, 38918, 38727, 38757, 38751, 38777, 38787,
...,
45328, 45337, 43645, 43694, 43701])
In [52]: lab
Out[52]:
0
39333 -1
39173 -1
42756 -1
39633 -1
38661 -1
44801 81
... ...
39379 -1
39742 -1
44765 108
44279 -1
40584 -1
41047 -1
41833 98
[3299 rows x 1 columns]
In [53]: lab[lab.index.map(lambda x: x in valid)]
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
/home/vitaly/progs/vnii_gochs/venv/lib/python2.7/site-packages/django/core/management/commands/shell.pyc in <module>()
----> 1 lab[lab.index.map(lambda x: x in valid)]
/home/vitaly/progs/vnii_gochs/venv/lib/python2.7/site-packages/pandas/core/index.pyc in map(self, mapper)
1558
1559 def map(self, mapper):
-> 1560 return self._arrmap(self.values, mapper)
1561
1562 def isin(self, values, level=None):
/home/vitaly/progs/vnii_gochs/venv/lib/python2.7/site-packages/pandas/algos.so in pandas.algos.arrmap_int64 (pandas/algos.c:78469)()
/home/vitaly/progs/vnii_gochs/venv/lib/python2.7/site-packages/django/core/management/commands/shell.pyc in <lambda>(x)
----> 1 lab[lab.index.map(lambda x: x in valid)]
NameError: global name 'valid' is not defined
```
What's wrong with this code? I use pandas 0.15.1.
(This issue is a duplicate of SO's question http://stackoverflow.com/questions/27637825/variable-visibility-issue-with-pandas-and-ipython/27643446#27643446)
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| null | 5 | 2014-12-25T10:00:02Z | 2015-05-13T15:08:06Z | 2015-05-13T15:07:34Z | NONE | null | Hello, such code
``` python
import pandas
import matplotlib.pyplot as plt
plt.plot([1, 2, 3])
plt.show()
```
freezes matplotlib file save dialog (the same issue with wxpython FilePickerCtrl).
What is the reason of such issue? How to avoid it?
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} | 12 | 2014-12-26T09:40:57Z | 2017-09-16T19:21:41Z | 2017-09-15T14:17:26Z | CONTRIBUTOR | null | Hello,
I did
```python
import numpy as np
import pandas as pd
from matplotlib.pyplot as plt
idx = pd.date_range('20140101', '20140201')
df = pd.DataFrame(index=idx)
df['col0'] = np.random.randn(len(idx))
s_idx = pd.Series(idx, index=idx) # need to do this because we can't shift index
diff_idx = (s_idx-s_idx.shift(1)).fillna(pd.Timedelta(0))
df['diff_dt'] = diff_idx
df['diff_dt'].plot()
```
but it raises `Empty 'Series': no numeric data to plot`
```python
In [78]: df.dtypes
Out[78]:
col0 float64
diff_dt timedelta64[ns]
dtype: object
In [79]: type(df['diff_dt'][1])
Out[79]: pandas.tslib.Timedelta
```
I don't understand if data inside `diff_dt` columns are `numpy.timedelta64` or `pd.Timedelta`
```python
df['diff_dt'].map(lambda x: x.value)
```
raises `AttributeError: 'numpy.timedelta64' object has no attribute 'value'`
but it seems that I can get `value`for a given data (let's say row 10)
```python
In [97]: df['diff_dt'][10].value
Out[97]: 86400000000000
```
I don't understand why...
But I also don't understand how I could plot diff_dt column without doing an uggly:
```
df['diff_dt'].map(lambda x: x/np.timedelta64(1, 'ns')).plot()
```
But I don't know how I could automatically get this `np.timedelta64(1, 'ns')`
Maybe Pandas could plot Timedelta (or `np.timedelta64`) out of the box ?
because that's interesting to know for example if sampling period is constant.
Kind regards
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} | 4 | 2014-12-26T10:07:40Z | 2018-01-27T16:56:34Z | 2018-01-27T16:56:34Z | CONTRIBUTOR | null | Hello,
`to_excel` method with `engine='xlwt'` doesn't support `pandas.tslib.Timedelta`
```
import numpy as np
import pandas as pd
idx = pd.date_range('20140101', '20140201')
df = pd.DataFrame(index=idx)
df['col0'] = np.random.randn(len(idx))
s_idx = pd.Series(idx, index=idx)
diff_idx = (s_idx-s_idx.shift(1)).fillna(pd.Timedelta(0))
df['diff_dt'] = diff_idx
df.to_excel("out.xls", engine='xlwt')
```
it works fine with xlsx files (engine `openpyxl` or `xlsxwriter`).
Kind regards
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| null | 13 | 2014-12-26T20:31:30Z | 2014-12-28T04:04:26Z | 2014-12-27T17:55:50Z | CONTRIBUTOR | null | This works fine:
```
In [24]: np.asarray(np.array([np.asarray([10, 20])]), np.float32)
Out[24]:
array([[ 10., 20.]], dtype=float32)
```
but this does not work:
```
In [25]: np.asarray(pd.Series([np.asarray([10, 20])]), np.float32)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-25-afb8b06bdf8c> in <module>()
----> 1 np.asarray(pd.Series([np.asarray([10, 20])]), np.float32)
/usr/local/lib/python2.7/dist-packages/numpy/core/numeric.pyc in asarray(a, dtype, order)
460
461 """
--> 462 return array(a, dtype, copy=False, order=order)
463
464 def asanyarray(a, dtype=None, order=None):
ValueError: setting an array element with a sequence.
```
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} | 9 | 2014-12-27T09:12:06Z | 2019-12-12T23:13:35Z | 2015-08-15T17:05:31Z | CONTRIBUTOR | null | I just saw following problem on stackoverflow: http://stackoverflow.com/questions/27665560/concatenating-files-with-python-pandas
It is not possible to concatenate an empty sequence of data frames.
This tiny code fragment
```
import pandas as pd
dfs = []
pd.concat(dfs)
```
Throws following error:
```
/Users/ch/miniconda/envs/sci33/lib/python3.3/site-packages/pandas/tools/merge.py in __init__(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy)
765
766 if len(objs) == 0:
--> 767 raise ValueError('All objects passed were None')
768
769 # consolidate data & figure out what our result ndim is going to be
ValueError: All objects passed were None
```
I would suggest catching this special case in `pd.concat` before trying to merge and to raise an error with a clearer error description. For this special case, the current error message is not very helpful.
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} | 1 | 2014-12-27T13:38:05Z | 2015-04-20T18:41:21Z | 2015-04-20T18:41:21Z | NONE | null | This worked on 0.15.1, but not anymore on 0.15.2:
``` python
import pandas as pd
d = {'A' : [1., 2., 3., 4.], 'B' : [4., 3., 2., 1.], 'C': [5, 1, 3, 4]}
df = pd.DataFrame(d, index=pd.date_range('2014 10 11', '2014 10 14'))
axes = df[['A','B']].plot(subplots=True)
df['C'].plot(ax=axes[0], secondary_y=True) # x tick labels dissappear when this line is executed
```
No x tick labels are shown when using secondary_y on a subplots within a figure containing multiple subplots.
secondary_y works well when only using one subplot (subplots=False) or when layouting the subplots in columns instead of rows.
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.15.2
nose: 1.3.4
Cython: 0.21.1
numpy: 1.9.1
scipy: 0.14.0
statsmodels: 0.6.1
IPython: 2.3.1
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.3
pytz: 2014.10
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.2
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.6.4
lxml: 3.4.1
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: 2.5.2
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
```
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| null | 1 | 2014-12-27T15:35:55Z | 2014-12-27T17:58:58Z | 2014-12-27T17:58:50Z | CONTRIBUTOR | null | Ideally some utility method that works to shuffle both dataframes, series and numpy arrays.
For example to shuffle a Dataframe: df.ix[shuffle(df.index)].
See https://github.com/scikit-learn/scikit-learn/issues/4008 for discussion.
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| null | 2 | 2014-12-27T15:37:59Z | 2014-12-27T18:32:14Z | 2014-12-27T18:32:14Z | NONE | null | I rarely use Pandas without calling some_dataframe.plot() at the end of some processing.
Saving a plot to disk is a very common requirement.
Having a handy df.plot(save_as="filename") would feel very natural.
By the way, the figsize keyword requires a tuple in inches. It would be nice to support sizes in pixels as well.
Thanks!
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} | 9 | 2014-12-28T00:11:19Z | 2016-08-10T12:25:06Z | 2016-08-10T12:25:06Z | NONE | null | I'm using the latest matplotlib and pandas. If you run the following code, do the legends look right to you? The unit tests don't like them.
``` python
df = DataFrame(
np.random.rand(10, 3),
index=list(string.ascii_letters[:10]))
df.plot(kind='area', subplots=True, sharex=True, legend=True)
```
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} | 1 | 2014-12-28T02:50:28Z | 2020-01-01T16:02:30Z | 2020-01-01T15:54:42Z | CONTRIBUTOR | null | I had mentioned this at one of the pydata talks. Just to put a couple of ideas down.
Currently the internal structure of a `NDFrame` is a collection of `Block` objects, managed by a `BlockManager`. Each of these is a nd-dim array of a single dtype (generally these are the same dim as the parent object, eg. a DataFrame will have 2-d objects). Certain types are currently treated slightly differently, e.g. `Categorical` and `Sparse` in they are always unconsolidated (meaning you can have multiple ones).
I had 2 thoughts:
- allow these to be numpy `mmap` objects
- allow these to be a `bcolz` `carray`(or possible the entire structure as a `ctable`)
In theory this should allow transparent seemless serialization/deserialization to disk. Hence you could have a virtual DataFrame larger that actual memory.
I am not sure how feasible this is in practice. But might be worth trying out.
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} | 5 | 2014-12-28T09:07:36Z | 2014-12-29T03:05:54Z | 2014-12-29T00:08:52Z | CONTRIBUTOR | null | @jreback Same underlying cause as #9136 but the solution is a bit more involved here.
Basically, we're transposing a potentially mixed-type frame before calling `notnull(frame.values)`; the same result can be obtained by deferring the transpose until after `notnull` gives us a non-mixed frame.
Here are the vbench results:
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
frame_count_level_axis1_mixed_dtypes_multi | 82.2484 | 1489.9830 | 0.0552 |
frame_count_level_axis0_mixed_dtypes_multi | 101.2537 | 1737.6347 | 0.0583 |
frame_count_level_axis0_multi | 51.0643 | 51.3713 | 0.9940 |
frame_count_level_axis1_multi | 98.5887 | 82.6767 | 1.1925 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [04ec4c6] : PERF: Utilize mixed dtypes in df.count() with MultiIndexes
Base [def58c9] : Merge pull request #9128 from hsperr/expanduser
```
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} | 8 | 2014-12-28T14:28:29Z | 2015-10-31T02:39:15Z | 2015-04-20T12:12:47Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/9158
``` python
import pandas as pd
d = {'A' : [1., 2., 3., 4.], 'B' : [4., 3., 2., 1.], 'C': [5, 1, 3, 4]}
df = pd.DataFrame(d, index=pd.date_range('2014 10 11', '2014 10 14'))
axes = df[['A','B']].plot(subplots=True)
df['C'].plot(ax=axes[0], secondary_y=True) # x tick labels dissappear when this line is executed
```
Introduced in the subplot refactoring in https://github.com/pydata/pandas/pull/7457
cc @sinhrks
The fix is pretty simple, but I'm worried I may have broken other things. No tests failed though, so hopefully we're good.
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} | 2 | 2014-12-28T20:12:12Z | 2015-08-06T11:23:55Z | 2014-12-29T14:06:30Z | CONTRIBUTOR | null | might be nice to adapt this scipy script: https://github.com/scipy/scipy/blob/master/tools/refguide_check.py
- To find all things that are not documented
- make an issue so they can get fixed.
- add this procedure to the wiki
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} | 7 | 2014-12-29T13:25:15Z | 2019-12-22T16:56:34Z | 2019-12-22T16:56:33Z | NONE | null | n.b. I'm looking at how to store a `DataFrame` in BSON format (see #4329) in MongoDB. This is my attempt to gather all the relevant information before adding yet another *SON serializer. Please don't treat this as a change request - I'm just trying to get my head around all the relevant bits. Maybe skip to the summary and start from there ...
## Refactoring *SON to use common schema
There are a number of *SON formats which are similar: e.g. JSON, Msgpack (in pandas), BSON (not in pandas) recently announced [JBSON](http://www.phoronix.com/scan.php?page=news_item&px=MTY0MTU). They have some common components:
1. Conversion of python object graph into Document(s) (think dict) made up only of JSON/Msgpack/BSON primitives. You can think of this in terms of a _schema_, as discussed in #3525, #3297, #2485. In an ideal world, this schema would be shared by all formats.
2. Serialization into a binary format (Msgpack/BSON only) or text (JSON). This is generally done by an external library separately for each format.
Currently, I don't think any of the code between JSON and Msgpack is shared. The schema used by Msgpack is entirely different from JSON. I'm guessing this is by design; JSON has been kept as human-readable as possible, whereas Msgpack is more focused on a serialization format. To illustrate this, two examples. First a simple Dataframe:
```
bid offer
2014-12-15 10:07:00 0.562804 0.398798
2014-12-15 10:07:01 0.399399 0.896809
2014-12-15 10:07:02 0.747191 0.098048
```
produces the following document before serialization in msgpack:
``` python
{'axes': [{'data': ['bid', 'offer'],
'dtype': 17,
'klass': 'Index',
'name': None,
'typ': 'index'},
{'data': '...',
'dtype': 21,
'freq': 'S',
'klass': 'DatetimeIndex',
'name': None,
'typ': 'datetime_index',
'tz': None}],
'blocks': [{'compress': 'blosc',
'dtype': 12,
'items': {'data': ['bid', 'offer'],
'dtype': 17,
'klass': 'Index',
'name': None,
'typ': 'index'},
'klass': 'FloatBlock',
'shape': (2, 3),
'values': '...'}],
'klass': 'DataFrame',
'typ': 'block_manager'}
```
I produced this by calling `encode` recursively in `packing.py`, replicating what `pack` does. There have been plenty of discussions regarding storage of metadata in #3525, #3297. Now the result of calling `to_json()`:
``` python
'{"bid":{"1418638020000":0.5628044127,"1418638021000":0.3993987818,"1418638022000":0.7471914537},"offer":{"1418638020000":0.398797779,"1418638021000":0.8968090851,"1418638022000":0.0980482752}}'
```
Both appear to use blocking (see #9130 ). Also, as described in #3525, dates are stored as integers (in strings) for performance reasons.
The second example, which has a multi-index:
```
bid ask \
95.0 95.5 96.0 96.5 97.0 97.5 98.0 98.5
2014-12-15 10:07:00 25030 16800 42580 58560 75110 10400 89240 3990
2014-12-15 10:07:01 42620 57860 80010 81500 98880 99610 65770 83930
2014-12-15 10:07:02 94170 98040 74840 41690 48960 76510 88530 48770
2014-12-15 10:07:03 65090 23700 16390 45700 500 29290 32370 68350
```
This kind of works for `to_msgpack()` but not for `to_json()` (throws an exception):
```
{'axes': [{'data': [('bid', 95.0),
('bid', 95.5),
('bid', 96.0),
...
('ask', 99.5)],
'dtype': 17,
'klass': 'MultiIndex',
'names': FrozenList([None, None]),
'typ': 'multi_index'},
{'data': '...',
'dtype': 21,
'freq': 'S',
'klass': 'DatetimeIndex',
'name': None,
'typ': 'datetime_index',
'tz': None}],
'blocks': [{'compress': 'blosc',
'dtype': 12,
'items': {'data': [('bid', 95.0),
('bid', 95.5),
('bid', 96.0),
...
('ask', 99.5)],
'dtype': 17,
'klass': 'MultiIndex',
'names': FrozenList([None, None]),
'typ': 'multi_index'},
'klass': 'FloatBlock',
'shape': (10, 4),
'values': '...'}],
'klass': 'DataFrame',
'typ': 'block_manager'}
```
It would be nice to expose the API of the 'intermediate' representation (i.e. at the end of step 1) for the advanced user, in order to store the dataframe in multiple 'Documents'. If storing to a file, this doesn't make any sense - you want to have a single document with many embedded documents; This is the JSON paradigm. But if you're storing into a database, you would want flexibility in order to store each nested doc/dict in a different collection/table. There are reasons for this e.g. a technical reason is the MongoDB maximum record/document size; however, it would generally be driven by a use-case.
What would this intermediate document look like? A dictionary, with only python (and numpy - see below) primitives? What are the primitives that are different for *SON formats (e.g. dates)?
## Serialization of vectorized data
A separate issue, that is somewhat at odds with the above, is the storage of vectorized data. i.e. the underlying numpy array in `to_msgpack()` is encoded into a string and stored in binary format. While this makes sense for performance reasons (see https://github.com/pydata/pandas/pull/3525#issuecomment-17442698), it's giving up one of the advantages of using msgpack (or BSON going forward), in my opinion: portability. If you go to http://msgpack.org, you'll see they have APIs for every conceivable language (likewise for MongoDB). Wouldn't it be nice if you could use one of these APIs and load up your pandas data in e.g. Java, Haskell or C? At present, this is possible with the exception of the 'values' fields, which you would have to decode by hand. Sure you could argue to use JSON for portability, but then you're trading off performance.
This is at odds with adding compression - which we still want. A compromise could be to use the native *SON list type when `compress=None`, when we assume that speed isn't important, and the current solution (encode as string) when compression is active, and speed is important (and compressed data is not handled by the abovementioned APIs)?
Note also, as described in links in https://github.com/pydata/pandas/issues/4329#issuecomment-27095723, BSON/MongoDB appears to have some support for vectorised lists that avoids having to convert to a list and then serialize each element separately.
## Summary
In adding yet another *SON serializer, it would not make sense to have a third codebase that handles the issues discussed above in a new way:
- Would it make sense to combine existing msgpack/JSON 'packing' code, to have a standard intermediate 'schema', shared by all *SON? cons: breaks backward compatibility, makes JSON document much more complex, pros: support more complex pandas structures (DF, panel, sparse df etc.).
- Could code for storing vector data be shared? Could we allow native storage of vector data (e.g. supported by BSON), for example, when not using the `compress` argument in `to_msgpack`, to allow portability? Ideally also using compress? could we somehow replicate what _monary_ is doing for BSON in the msgpack serializer, i.e. altogether avoiding conversion to lists and memory copies?
Again, I want to stress I'm just trying to create some discussion here rather than opening a specific change req... Sorry if raising an issue wasn't the correct procedure.
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} | 0 | 2014-12-29T16:36:50Z | 2015-01-06T00:12:48Z | 2015-01-06T00:12:48Z | CONTRIBUTOR | null | The `xlsxwriter` engine (which I helped implement) erroneously adds a default `General` format to cells that don't require it.
This doesn't affect spreadsheets created with `to_excel()`. However, it does prevent the user from overwriting the cell format with a column format.
For example consider the following program:
``` python
import pandas as pd
from pandas import ExcelWriter
df = pd.DataFrame([[123456, 123456],
[987654, 987654]],
columns=['First', 'Second'])
writer = ExcelWriter('test.xlsx', engine='xlsxwriter')
df.to_excel(writer)
workbook = writer.book
worksheet = writer.book.worksheets()[0]
# Add a column format.
myformat = workbook.add_format({'num_format': '#,##0'})
worksheet.set_column(1, 1, None, myformat)
writer.save()
```
The gives the following output:
However, it should look like this (note the format in the B column cells):
I'll submit a PR to fix this as soon as I get a working test.
Versions:
```
INSTALLED VERSIONS
------------------
commit: 4aa0e0a674a0823b5a81944f087e744f27c4e21d
python: 2.7.2.final.0
python-bits: 64
OS: Darwin
OS-release: 14.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_IE.UTF-8
pandas: 0.15.2-50-g4aa0e0a
nose: 1.3.0
Cython: 0.19.1
numpy: 1.7.1
scipy: None
statsmodels: None
IPython: 1.1.0
sphinx: 1.2b3
patsy: None
dateutil: 2.2
pytz: 2013b
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.x
openpyxl: 2.0.2
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.6.4
lxml: 3.2.3
bs4: None
html5lib: 1.0b3
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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| null | 4 | 2014-12-29T22:26:12Z | 2021-04-12T04:43:08Z | null | CONTRIBUTOR | null | Consider a time-series, e.g.:
```
some_quantity
timestamp
2014-10-06 18:00:40.063000-04:00 0.568000
2014-10-06 18:00:41.361000-04:00 3.014770
2014-10-06 18:00:42.896000-04:00 0.878154
2014-10-06 18:00:43.040000-04:00 0.723077
2014-10-06 18:00:44.791000-04:00 0.723077
2014-10-06 18:00:45.496000-04:00 0.309539
2014-10-06 18:00:45.799000-04:00 3.032000
2014-10-06 18:00:47.470000-04:00 3.014770
2014-10-06 18:00:48.092000-04:00 1.584616
```
and an independent set of timestamps, e.g.:
```
start_time = datetime.datetime(year = 2014,
month = 10,
day = 6,
hour = 18,
tzinfo = pytz.timezone('US/Eastern')
# Finish 400 seconds later
end_time = start_time + datetime.timedelta(seconds=400)
new_timestamps = pd.date_range(start = start_time,
end = end_time,
freq = '2.5s')
```
Is there any way to sample the original time-series so that, for each of the new timestamps, we get the sum of values of the original time-series between the new time-stamps?
Another way to put it, is there any way to call `resample` by passing it a series of timestamps on which we want to do the sampling? e.g. something like `my_series.resample(how='sum', new_timestamps)`
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} | 1 | 2014-12-30T03:43:01Z | 2015-01-10T20:19:44Z | 2015-01-02T15:41:25Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/5873
code changes were done in https://github.com/pydata/pandas/pull/9101 and https://github.com/pydata/pandas/pull/9061. just adding tests to confirm bugs reported there are handled.
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| null | 1 | 2014-12-30T03:55:22Z | 2015-01-02T15:43:23Z | 2015-01-02T15:43:15Z | CONTRIBUTOR | null | integer index with float data types:
```
>>> i
Int64Index([1, 2], dtype='int64')
>>> i._shallow_copy(i.values.astype('f8'))
Int64Index([1.0, 2.0], dtype='float64')
```
another issue: an index which is not equal to its copy:
```
>>> i
Float64Index([1.0, 2.0], dtype='float64')
>>> j = i._shallow_copy(i.values.astype('O'))
>>> j
Float64Index([1.0, 2.0], dtype='object')
>>> j.equals(i)
False
>>> j.equals(j.copy())
False
```
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https://api.github.com/repos/pandas-dev/pandas/issues/9171 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9171/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9171/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9171/events | https://github.com/pandas-dev/pandas/pull/9171 | 53,092,855 | MDExOlB1bGxSZXF1ZXN0MjY2NzM0Mjc= | 9,171 | BUG: Fix for extraneous default cell format in xlsxwriter files. | {
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} | 5 | 2014-12-30T04:40:18Z | 2015-01-06T00:13:06Z | 2015-01-06T00:13:06Z | CONTRIBUTOR | null | Fix for issue in the xlsxwriter engine where is adds a default
'General' format to cells if no other format is applied. This
isn't a bug, per se, but it prevents other row or column formatting.
closes #9167
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} | 16 | 2014-12-30T13:35:46Z | 2015-08-16T08:17:52Z | 2015-08-15T17:05:08Z | CONTRIBUTOR | null | closes #9157
This is a first attempt to improve the error message when trying to concat an empty list/set of objects:
`pd.concat([])` currently raises following error:
```
/Users/ch/miniconda/envs/sci33/lib/python3.3/site-packages/pandas/tools/merge.py in __init__(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy)
765
766 if len(objs) == 0:
--> 767 raise ValueError('All objects passed were None')
768
769 # consolidate data & figure out what our result ndim is going to be
ValueError: All objects passed were None
```
After the tiny fix it is:
```
/Users/ch/repo/pandas/pandas/tools/merge.py in __init__(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy)
767
768 if len(objs) == 0:
--> 769 raise ValueError('No objects passed')
770
771 if isinstance(objs, dict):
ValueError: No objects passed
```
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} | 3 | 2014-12-30T14:24:52Z | 2015-03-11T12:10:42Z | 2015-03-11T12:10:42Z | NONE | null | I just upgraded to pandas 0.15.2 and now hit this when I do resample. It worked fine in 0.15.1 (I rolled back to 0.15.1 to confirm). As far as I can tell it occurs when the first date in the index is during daylight savings and the last is not, or vice versa.
For example, with a date just before and another just after the DST change date of April 6:
```
df = pd.DataFrame(index=pd.DatetimeIndex(['2014-04-04 10:00:00', '2014-04-07 10:00:00']), data={'open':0.5}).tz_localize('Australia/Sydney')
print(df.resample('1D', how={'open':'first'}))
```
Traceback (most recent call last):
File "D:/Documents/Programming/Projects/IB/play.py", line 1332, in <module>
main()
File "D:/Documents/Programming/Projects/IB/play.py", line 48, in main
print(df.resample('1D', how={'open':'first'}))
File "C:\Users\Tom\Anaconda\envs\py2test\lib\site-packages\pandas\core\generic.py", line 3005, in resample
return sampler.resample(self).**finalize**(self)
File "C:\Users\Tom\Anaconda\envs\py2test\lib\site-packages\pandas\tseries\resample.py", line 85, in resample
rs = self._resample_timestamps()
File "C:\Users\Tom\Anaconda\envs\py2test\lib\site-packages\pandas\tseries\resample.py", line 275, in _resample_timestamps
self._get_binner_for_resample(kind=kind)
File "C:\Users\Tom\Anaconda\envs\py2test\lib\site-packages\pandas\tseries\resample.py", line 123, in _get_binner_for_resample
self.binner, bins, binlabels = self._get_time_bins(ax)
File "C:\Users\Tom\Anaconda\envs\py2test\lib\site-packages\pandas\tseries\resample.py", line 184, in _get_time_bins
bins = lib.generate_bins_dt64(ax_values, bin_edges, self.closed, hasnans=ax.hasnans)
File "pandas\lib.pyx", line 1064, in pandas.lib.generate_bins_dt64 (pandas\lib.c:17790)
ValueError: Values falls after last bin
On the other hand if I roll back to 0.15.1 I get what I expect:
open
2014-04-04 00:00:00+11:00 0.5
2014-04-05 00:00:00+11:00 NaN
2014-04-06 00:00:00+11:00 NaN
2014-04-07 00:00:00+10:00 0.5
Alternatively in 0.15.2 if I add another date in the previous year, such that the first and last dates are either both in daylight savings (even in a different year) or both not in daylight savings, it also seems to work fine.
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| null | 2 | 2014-12-30T17:07:34Z | 2014-12-30T20:03:29Z | 2014-12-30T20:03:29Z | NONE | null | I just posted this, slightly modified, on [SO](http://stackoverflow.com/questions/27709433/why-does-a-pandas-series-of-dataframe-mean-fail-but-sum-does-not-and-how-to-ma). I'm getting a `TypeError` when I try to take the mean of a series of pandas DataFrames, as shown below:
``` python
import pandas as pd
import numpy as np
df1 = pd.DataFrame([[1, 0], [1, 2], [2, 0]], columns=['a', 'b'])
df2 = df1.copy()
df3 = df1.copy()
idx = pd.date_range("2010-01-01", freq='H', periods=3)
s = pd.Series([df1, df2, df3], index=idx)
# This causes an error
s.mean()
```
I won't post the whole traceback, but the main error message is interesting:
```
TypeError: Could not convert melt T_s
0 6 12
1 0 6
2 6 10 to numeric
```
It looks like the dataframe was successfully summed, but not divided by the length of the series.
However, we _can_ take the sum of the dataframes in the series:
``` python
s.sum()
```
Returns
```
a b
0 6 12
1 0 6
2 6 10
```
Why wouldn't mean work when sum does? Is this a bug or a missing feature? This does work:
``` python
(df1 + df2 + df3)/3.0
```
But this of course is not ideal. Is this a missing feature or a bug?
Here's my version/dependencies info:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Darwin
OS-release: 14.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.1
nose: 1.3.4
Cython: 0.21
numpy: 1.9.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.2.0
sphinx: 1.2.3
patsy: 0.3.0
scikits.timeseries: None
dateutil: 1.5
pytz: 2014.9
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.0
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
```
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} | 5 | 2014-12-31T03:04:58Z | 2015-01-15T02:35:28Z | 2015-01-15T02:35:28Z | CONTRIBUTOR | null | Fix for writing Timestamp objects using the xlwt and xlsxwriter
engines. Both modules write Excel dates and times using
datetime.timedelta which differs from pandas.Timedelta. This
fix coerces Timestamp objects to datetime objects.
fixes #9139.
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} | 3 | 2014-12-31T05:32:02Z | 2015-01-10T17:33:28Z | 2015-01-10T17:33:28Z | CONTRIBUTOR | null | Fixes #9082
My access to the internet is spotty for the next few days. This fixes 9082 and does not break any of the existing tests so hopefully it won't break anyone's existing code. Happy New Year!
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} | 21 | 2014-12-31T16:16:37Z | 2015-02-11T01:49:04Z | 2015-02-10T14:52:04Z | NONE | null | closes #9049.
closes #9344
_self._was_factor_ is not appropriate to judge whether grouper is Categorical or not, because it can be "True" when we groupby indices (not columns). So, I added another flag _self._is_categorical_ to judge Categorical state.
Also, I added a GroupBy test for MultiIndexed data, which was failed before this fix.
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} | 5 | 2014-12-31T19:39:48Z | 2016-04-25T21:57:21Z | 2016-04-25T21:57:21Z | NONE | null | I was trying to read a package tracking table from finnish post office's website and I got
```
Traceback (most recent call last):
File "./posti.py", line 69, in <module>
dfs = read_html(html)
File "/usr/lib/python3.4/site-packages/pandas/io/html.py", line 851, in read_html
parse_dates, tupleize_cols, thousands, attrs, encoding)
File "/usr/lib/python3.4/site-packages/pandas/io/html.py", line 721, in _parse
infer_types, parse_dates, tupleize_cols, thousands))
File "/usr/lib/python3.4/site-packages/pandas/io/html.py", line 609, in _data_to_frame
_expand_elements(body)
File "/usr/lib/python3.4/site-packages/pandas/io/html.py", line 586, in _expand_elements
lens = Series(lmap(len, body))
File "/usr/lib/python3.4/site-packages/pandas/compat/__init__.py", line 87, in lmap
return list(map(*args, **kwargs))
TypeError: len() of unsized object
```
I isolated the offending table into this script:
https://gist.github.com/boarpig/de4044f4188fac700c68
The problem seems to be related to [`parse_raw_thead`](https://github.com/pydata/pandas/blob/master/pandas/io/html.py#L344) function
```
def _parse_raw_thead(self, table):
thead = self._parse_thead(table)
res = []
if thead:
res = lmap(self._text_getter, self._parse_th(thead[0]))
return np.array(res).squeeze() if res and len(res) == 1 else res
```
Where `res` contains `['Tapahtumat']` which comes out of numpy array creation as
`array('Tapahtumat', dtype='<U10')`
which then produces previously mentioned error because you cannot take a len from that.
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| null | 10 | 2015-01-01T17:36:22Z | 2015-03-29T15:40:01Z | 2015-03-29T15:40:01Z | NONE | null | R has a very rudimentary function (utils::View()) to display data interactively. On X11 it's so broken I basically never used it, yet, it would be very nice if pandas had something similar.
With python, there's tabview: https://github.com/firecat53/tabview/
which can be used as a module as well:
```
import tabview.tabview as t
t.view([matrix data])
```
This runs a full-screen curses interfaces with a simple spreadsheet like display, which is actually _much_ nicer than what R offers.
I've started to use and customize the cli wrapper a lot (display X,Y coordinates, etc), to the point that I would like direct integration with DataFrame.
Would such a patch be accepted?
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} | 22 | 2015-01-01T23:46:25Z | 2021-07-20T10:15:44Z | 2016-07-24T14:14:06Z | NONE | null | Hey all,
I'm a developer on [dat project](https://github.com/maxogden/dat) (git for data) and we are building a python library to interact with the data store.
Everything in dat is streaming, and we use newline delimited json as the official transfer format between processes.
[Take a look at the specification for newline delimited json here](https://github.com/ndjson/ndjson-spec/)
Does pandas support this yet, and if not, would you consider adding a `to_ndjson` function to the existing output formats?
For example, the following table:
```
> df
a b key version
0 True False ci4diwru70000x6xmmn19nba1 1
1 False True ci4diww6j0001x6xmbyp5o2f0 1
```
Would be converted to
``` python
> df.to_ndjson()
'{ "a": True, "b": False, "key": ci4diwru70000x6xmmn19nba1, "version": 1}\n{ "a": False, "b": True, "key": ci4diww6j0001x6xmbyp5o2f0, "version": 1}'
```
For general streaming use cases, it might be nice to also consider other ways of supporting this format, like a generator function that outputs ndjson-able objects
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| null | 5 | 2015-01-02T01:23:19Z | 2015-05-09T16:17:11Z | 2015-05-09T16:17:11Z | CONTRIBUTOR | null | closes #8188
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} | 28 | 2015-01-02T03:08:18Z | 2015-03-17T00:19:21Z | 2015-03-17T00:18:39Z | CONTRIBUTOR | null | This PR harmonizes the way DataFrame and Series are printed.
closes #8532
closes #7508
Before
```
In [1]: import pandas as pd
In [2]: pd.options.display.max_rows = 10
In [3]: s = pd.Series([1,1,1,1,1,1,1,1,1,1,0.9999,1,1]*10)
In [4]: s
Out[4]:
0 1
1 1
2 1
...
127 0.9999
128 1.0000
129 1.0000
Length: 130, dtype: float64
```
Now
```
0 1.0000
1 1.0000
2 1.0000
3 1.0000
4 1.0000
...
125 1.0000
126 1.0000
127 0.9999
128 1.0000
129 1.0000
dtype: float64
```
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| null | 3 | 2015-01-02T07:55:50Z | 2015-01-02T17:48:23Z | 2015-01-02T17:48:23Z | NONE | null | I am using Pandas 0.15.2. I am trying to read the following space-delimited file, which needs first three lines to be skipped:
```
from pandas import *
tumor_df = read_csv("http://www.stat.columbia.edu/~gelman/book/data/rats.asc", index_col=False, skiprows=3, delim_whitespace=True)
```
However, it results in an IndexError.
```
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-9-b72cfe7f87e1> in <module>()
3 #tumor_df = read_csv("http://www.stat.columbia.edu/~gelman/book/data/rats.asc", index_col=False, skiprows=3, sep=' ')
4 #tumor_df = read_csv("rat_tumor.txt", index_col=False, delim_whitespace=True)
----> 5 tumor_df = read_csv("http://www.stat.columbia.edu/~gelman/book/data/rats.asc", index_col=False, skiprows=3, delim_whitespace=True)
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/io/parsers.pyc in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, na_fvalues, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, float_precision, nrows, iterator, chunksize, verbose, encoding, squeeze, mangle_dupe_cols, tupleize_cols, infer_datetime_format, skip_blank_lines)
463 skip_blank_lines=skip_blank_lines)
464
--> 465 return _read(filepath_or_buffer, kwds)
466
467 parser_f.__name__ = name
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/io/parsers.pyc in _read(filepath_or_buffer, kwds)
249 return parser
250
--> 251 return parser.read()
252
253 _parser_defaults = {
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/io/parsers.pyc in read(self, nrows)
708 raise ValueError('skip_footer not supported for iteration')
709
--> 710 ret = self._engine.read(nrows)
711
712 if self.options.get('as_recarray'):
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/io/parsers.pyc in read(self, nrows)
1157
1158 try:
-> 1159 data = self._reader.read(nrows)
1160 except StopIteration:
1161 if nrows is None:
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/parser.so in pandas.parser.TextReader.read (pandas/parser.c:7396)()
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/parser.so in pandas.parser.TextReader._read_low_memory (pandas/parser.c:7636)()
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/parser.so in pandas.parser.TextReader._read_rows (pandas/parser.c:8474)()
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/parser.so in pandas.parser.TextReader._convert_column_data (pandas/parser.c:9485)()
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/parser.so in pandas.parser.TextReader._get_column_name (pandas/parser.c:13179)()
IndexError: list index out of range
```
When I removed first three lines and did not use skiprows argument, read_csv() worked fine.
This is my first time submitting a bug report, so I apologize for your inconvenience if this deviates from the convention.
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} | 12 | 2015-01-02T14:22:34Z | 2015-01-25T20:06:58Z | 2015-01-25T20:06:58Z | NONE | null | ```
import pandas as pd
df = pd.DataFrame({'a': ['x', 'y', 'z'], 'b': [np.nan, np.nan, np.nan]})
```
Applying to string column - produces correct result:
```
df['a'].str.contains('c', na=False)
0 False
1 False
2 False
Name: a, dtype: bool
```
Applying to float column - returns **zeroes** instead of **bools** and return type is **float64**:
```
df['b'].str.contains('c', na=False)
0 0
1 0
2 0
Name: b, dtype: float64
```
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} | 6 | 2015-01-02T16:41:51Z | 2015-01-16T16:54:45Z | 2015-01-16T16:54:45Z | MEMBER | null | With the new `Timedelta` type (see https://github.com/pydata/pandas/pull/8184), we introduced behavior that breaks compatibility with the superclass `timedelta`: `td.seconds` refers to seconds since the last minute, rather than seconds since the last day (as in the superclass).
I think breaking compatibility with the superclass here was a mistake. User code clearly _does_ rely on the old behavior, e.g., as manifested in bugs for exporting datetime columns to Excel: #9139. Moreover, it's likely to remain an issue causing silent bugs, because external libraries are going to continue to use `isinstance` checks to verify that something is a `datetime`/`timedelta` object. We chose to subclass `timedelta` here, so we really are stuck with full API compatibility.
So I think we should make this change in 0.16. It's a breaking change, but 0.15 was already a breaking change in this respect, and in any case I think we should give ourselves some allowance for API changes for new types.
CC @jorisvandenbossche @jreback
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} | 2 | 2015-01-02T16:50:11Z | 2020-01-03T23:23:44Z | 2020-01-03T23:23:44Z | NONE | null | This is a bit of an edge case, but in Pandas 0.15.2 when you try to pivot on an empty column you should get back an empty dataframe. This is the behaviour when the default aggregation function is used, but if you specify an aggfunc argument it fails.
Setup dataframe with empty column:
```
>>> df = pd.DataFrame({'A': [2,2,3,3,2], 'id': [5,6,7,8,9], 'C':['p', 'q', 'q', 'p', 'q'], 'D':[None, None, None, None, None]})
>>> df
A C D id
0 2 p None 5
1 2 q None 6
2 3 q None 7
3 3 p None 8
4 2 q None 9
```
Expected behaviour when pivoting on the empty column:
```
>>> df.pivot_table(index='A', columns='D', values='id')
Empty DataFrame
Columns: []
Index: []
```
But if you specify an aggfunc it blows up:
```
>>> df.pivot_table(index='A', columns='D', values='id', aggfunc=np.size)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/data/virtualenv/default/lib/python2.7/site-packages/pandas/util/decorators.py", line 88, in wrapper
return func(*args, **kwargs)
File "/data/virtualenv/default/lib/python2.7/site-packages/pandas/util/decorators.py", line 88, in wrapper
return func(*args, **kwargs)
File "/data/virtualenv/default/lib/python2.7/site-packages/pandas/tools/pivot.py", line 151, in pivot_table
table = table[values[0]]
File "/data/virtualenv/default/lib/python2.7/site-packages/pandas/core/frame.py", line 1780, in __getitem__
return self._getitem_column(key)
File "/data/virtualenv/default/lib/python2.7/site-packages/pandas/core/frame.py", line 1787, in _getitem_column
return self._get_item_cache(key)
File "/data/virtualenv/default/lib/python2.7/site-packages/pandas/core/generic.py", line 1068, in _get_item_cache
values = self._data.get(item)
File "/data/virtualenv/default/lib/python2.7/site-packages/pandas/core/internals.py", line 2849, in get
loc = self.items.get_loc(item)
File "/data/virtualenv/default/lib/python2.7/site-packages/pandas/core/index.py", line 1402, in get_loc
return self._engine.get_loc(_values_from_object(key))
File "pandas/index.pyx", line 134, in pandas.index.IndexEngine.get_loc (pandas/index.c:3812)
File "pandas/index.pyx", line 154, in pandas.index.IndexEngine.get_loc (pandas/index.c:3692)
File "pandas/hashtable.pyx", line 696, in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12299)
File "pandas/hashtable.pyx", line 704, in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12250)
KeyError: 'id'
```
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} | 3 | 2015-01-02T20:20:20Z | 2021-05-10T21:45:31Z | 2021-05-10T21:45:31Z | CONTRIBUTOR | null | xref #9149
The resultant has all missing and when concat with an empty frame should result in the same dtype (and not promotion)
```
In [1]: df_1 = pd.DataFrame({"Row":[0,1,1], "EmptyCol":np.nan, "NumberCol":[1,2,3]})
In [2]: df_2 = pd.DataFrame(columns = df_1.columns)
In [3]: df_concat = pd.concat([df_1, df_2], axis=0)
In [4]: df_1.dtypes
Out[4]:
EmptyCol float64
NumberCol int64
Row int64
dtype: object
In [6]: df_2.dtypes
Out[6]:
EmptyCol object
NumberCol object
Row object
dtype: object
In [7]: df_concat.dtypes
Out[7]:
EmptyCol object
NumberCol float64
Row float64
dtype: object
```
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} | 3 | 2015-01-02T21:38:28Z | 2015-01-06T11:17:47Z | 2015-01-04T22:36:13Z | CONTRIBUTOR | null | Following the issue "to_datetime returns NaT for old dates with coerce=True".
Do I understand correctly that online documentation is build automatically from docstrings? I.e. are there any other files to change?
Also it would be good to make a link to [this section of "Caveats and Gotchas"](http://pandas.pydata.org/pandas-docs/version/0.15.0/gotchas.html#timestamp-limitations), but I'm not sure if the docstring is a good place to do this.
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} | 12 | 2015-01-02T22:30:24Z | 2015-03-11T12:11:49Z | 2015-03-11T12:11:49Z | CONTRIBUTOR | null | I was thinking about trying to add a `to_unordered` method, then I thought maybe a `reset_order` (or `reorder`?) with an optional `drop` keyword à la `reset_index` makes more sense. I didn't see if this was possible yet, so this is also a question. Is this possible via some other syntactic sugar? I might see if I can hack this together at some point unless someone beats me to it. My motivation for this is that I'm getting all ordered or unordered factors using `read_stata`. This could be "fixed" there by taking a list in addition to the boolean convert to ordered or whatever, but I think a method like this would be generally useful, plus I never peak at data before reading it.
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| null | 13 | 2015-01-02T22:37:18Z | 2015-03-29T17:15:11Z | 2015-03-29T17:15:11Z | NONE | null | This is a first attempt at addressing #9179
We add a DataFrame.view method to visualize the contents of a dataframe interactively.
The viewing itself is performed using the "tabview" module from https://github.com/firecat53/tabview
A few initial notes:
- tabview requires python3 currently, so please test using python3. There are no significant changes to make tabview work on python 2.7 as well but I'd like some feedback first.
- tabview tries to sys.exit() on quit even when loaded as a module. Also needs a fix. ipython complains, but prevents the module to exit.
- I have no idea where it would be more appropriate to put the dependency/suggestion to install tabview.
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| null | 19 | 2015-01-03T15:50:34Z | 2015-05-14T15:48:28Z | 2015-05-09T15:59:50Z | CONTRIBUTOR | null | addresses part of issue #8263.
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closes #9178
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} | 9 | 2015-01-03T22:12:54Z | 2015-01-06T13:27:59Z | 2015-01-06T13:14:19Z | CONTRIBUTOR | null | Following an old issue #5630 (Documentation / Remote Data Access - 3rd pary libraries).
Quandl was already described in Ecosystem / API section, so I've added link to pydatastream that targets Thomson Reuters Datastream.
closes #5630.
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| null | 8 | 2015-01-03T22:49:35Z | 2015-01-05T09:19:55Z | 2015-01-05T00:54:07Z | NONE | null | Why is this hardcoded to 63?
https://github.com/pydata/pandas/blob/master/pandas/io/sql.py#L1212
It's causing my data to be truncated. Should I submit a PR to change it to text?
-Hanley
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https://api.github.com/repos/pandas-dev/pandas/issues/9197 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9197/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9197/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9197/events | https://github.com/pandas-dev/pandas/pull/9197 | 53,327,492 | MDExOlB1bGxSZXF1ZXN0MjY3OTE1OTc= | 9,197 | fixes division by zero error for kurt() | {
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| null | 7 | 2015-01-04T06:33:03Z | 2015-04-29T15:32:55Z | 2015-01-05T06:37:52Z | CONTRIBUTOR | null | Currently if `kurt()` is called on a `Series` with equal values, it throws a `ZeroDivisionError`
```
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: s = pd.Series(np.ones(5))
In [4]: s.kurt()
ZeroDivisionError: float division by zero
```
This is not consistent with the case when `kurt()` is called on a `DataFrame` of equal values
```
In [5]: df = pd.DataFrame(np.ones((5, 5)))
In [6]: df.kurt()
Out[6]:
0 0
1 0
2 0
3 0
4 0
dtype: float64
```
with this patch `s.kurt()` will return `0` instead of throwing.
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https://api.github.com/repos/pandas-dev/pandas/issues/9198 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9198/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9198/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9198/events | https://github.com/pandas-dev/pandas/pull/9198 | 53,362,180 | MDExOlB1bGxSZXF1ZXN0MjY4MDUzODI= | 9,198 | BUG: fix for GH9010 | {
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} | 7 | 2015-01-05T03:58:04Z | 2015-03-04T10:41:26Z | 2015-03-04T10:40:47Z | NONE | null | Superseded by #9358
---
This is an update of PR #9024.
closes #9010
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https://api.github.com/repos/pandas-dev/pandas/issues/9199 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9199/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9199/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9199/events | https://github.com/pandas-dev/pandas/issues/9199 | 53,382,148 | MDU6SXNzdWU1MzM4MjE0OA== | 9,199 | pandas 0.15.2 is not deployable via cx_freeze with python | {
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| null | 6 | 2015-01-05T10:23:17Z | 2019-06-07T19:06:28Z | 2019-06-07T19:06:28Z | NONE | null | I am deploying an application using cx_freeze on Python 2.7.8.final.0 and pandas 0.15.2.
I get the following error with pandas 0.15.2 but no error with 0.15.1. [SO: http://stackoverflow.com/questions/27705163/error-3-on-dateutil-zoneinfo-using-cx-freeze-and-pandas]
I have include the dateutil/zoneinfo files with the include_files function but the problem still occurs.
**hello.py :**
```
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
print 'hello world'
df_dates = pd.date_range(start='01/01/2013', freq='H', periods=100)
df = pd.DataFrame(range(len(df_dates)), index=df_dates)
print df
print df.info()
df.plot()
plt.savefig('test.png')
plt.show(block=False)
```
**setup.py :**
```
includes = ['dateutil.zoneinfo', 'pandas'] # nommer les modules non trouves par cx_freeze
excludes = []
packages = ['os', 'matplotlib.backends.backend_qt4agg', 'dateutil', 'dateutil.zoneinfo', 'pandas']
def include_files():
path_base = "C:\\Anaconda\\Lib\\site-packages\\dateutil\\zoneinfo\\"
skip_count = len(path_base)
zip_includes = [(path_base, "dateutil/zoneinfo/")]
for root, sub_folders, files in os.walk(path_base):
for file_in_root in files:
zip_includes.append(
("{}".format(os.path.join(root, file_in_root)),
"{}".format(os.path.join("dateutil/zoneinfo", root[skip_count:], file_in_root))
)
)
return zip_includes
includefiles = include_files()
zip_includes = include_files()
optimize = 1
silent = True
options = { #'path': path,
'includes': includes,
'excludes': excludes,
'packages': packages,
'include_files': includefiles,
'zip_includes': zip_includes,
#'bin_path_includes': binpathincludes,
'create_shared_zip': True, # <= ne pas generer de fichier zip
'include_in_shared_zip': True, # <= ne pas generer de fichier zip
'compressed': True, # <= ne pas generer de fichier zip
'optimize': optimize,
'silent': silent
}
base = None
cible_1 = Executable('hello.py', targetName="hello.exe")
#base=base,
#compress=True, # <= ne pas generer de fichier zip
#copyDependentFiles=True,
#appendScriptToExe=True,
#appendScriptToLibrary=True, # <= ne pas generer de fichier zip
#icon=icone
#)
setup(
name = "salut",
author = 'XXX',
version = "0.1",
description = "Ce programme vous dit bonjour",
options={"build_exe": options},
executables = [cible_1],
)
```
**The error is :**
```
[Error 3] Le chemin dÆaccÞs spÚcifiÚ est introuvable: 'C:\\Users\\Utilisateur\\D
esktop\\compilation\\build\\exe.win-amd64-2.7\\library.zip\\dateutil\\zoneinfo/*
.*'
Traceback (most recent call last):
File "C:\Anaconda\lib\site-packages\cx_Freeze\initscripts\Console.py", line 27
, in <module>
exec(code, m.__dict__)
File "hello.py", line 2, in <module>
File "C:\Anaconda\lib\site-packages\pandas\__init__.py", line 7, in <module>
from . import hashtable, tslib, lib
File "ExtensionLoader_pandas_tslib.py", line 22, in <module>
File "ExtensionLoader_pandas_tslib.py", line 14, in __bootstrap__
File "pandas\tslib.pyx", line 44, in init pandas.tslib (pandas\tslib.c:79879)
File "C:\Anaconda\lib\site-packages\dateutil\zoneinfo\__init__.py", line 32, i
n <module>
ZONEINFOFILE = getzoneinfofile()
File "C:\Anaconda\lib\site-packages\dateutil\zoneinfo\__init__.py", line 24, i
n getzoneinfofile
filenames = os.listdir(os.path.join(os.path.dirname(__file__)))
WindowsError: [Error 3] Le chemin dÆaccÞs spÚcifiÚ est introuvable: 'C:\\Users\\
Utilisateur\\Desktop\\compilation\\build\\exe.win-amd64-2.7\\library.zip\\dateut
il\\zoneinfo/*.*'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/9200 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9200/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9200/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9200/events | https://github.com/pandas-dev/pandas/issues/9200 | 53,474,027 | MDU6SXNzdWU1MzQ3NDAyNw== | 9,200 | DataFrame assignment bug - columns getting jumbled (0.13.1) | {
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| null | 6 | 2015-01-06T02:32:32Z | 2015-01-07T02:22:15Z | 2015-01-07T02:22:15Z | NONE | null | I am trying to write Python Pandas code to merge the data in two DataFrames, with the new DataFrame's data replacing the old DataFrame's data if the index and columns are identical. There seems to be a bug in Pandas that sometimes causes the column names to be mixed up.
Here is an example. First, create the two DataFrames:
```
In [1]: df1 = DataFrame([[1, 2, 3, 4]]*3, columns=["A1", "B2", "C3", "D4"], index=[0, 1, 2])
In [2]: df2 = DataFrame([[30, 10, 40, 20]]*3, columns=["C3", "A1", "D4", "B2"], index=[1, 2, 3])
In [3]: df1
Out[3]:
A1 B2 C3 D4
0 1 2 3 4
1 1 2 3 4
2 1 2 3 4
[3 rows x 4 columns]
In [4]: df2
Out[4]:
C3 A1 D4 B2
1 30 10 40 20
2 30 10 40 20
3 30 10 40 20
[3 rows x 4 columns]
```
Notice df1 and df2 have the same columns but the index is offset by 1. The data in df2 is 10 times df1.
Now try merging the data:
```
In [5]: merge_df = DataFrame(index=df1.index.union(df2.index), columns=df1.columns.union(df2.columns))
In [6]: merge_df.loc[df1.index, df1.columns] = df1
In [7]: merge_df.loc[df2.index, df2.columns] = df2
In [8]: merge_df
Out[8]:
A1 B2 C3 D4
0 1 2 3 4
1 10 20 30 40
2 10 20 30 40
3 10 20 30 40
[4 rows x 4 columns]
```
That worked correctly.
Now change df2 so that it has the same index values and repeat the same merge procedure.
```
In [9]: df2 = DataFrame([[30, 10, 40, 20]]*3, columns=["C3", "A1", "D4", "B2"], index=[0, 1, 2])
In [10]: df2
Out[10]:
C3 A1 D4 B2
0 30 10 40 20
1 30 10 40 20
2 30 10 40 20
[3 rows x 4 columns]
In [11]: merge_df = DataFrame(index=df1.index.union(df2.index), columns=df1.columns.union(df2.columns))
In [12]: merge_df.loc[df1.index, df1.columns] = df1
In [13]: merge_df.loc[df2.index, df2.columns] = df2
In [14]: merge_df
Out[14]:
A1 B2 C3 D4
0 30 10 40 20
1 30 10 40 20
2 30 10 40 20
[3 rows x 4 columns]
```
The column names are in the same order as df1 but the data is as it is in df2! How did that happen?
This is a bug I discovered on my work (Windows) computer. I am using Anaconda Python (Python 2.7.6 |Anaconda 1.9.2 (64-bit)| (default, Nov 11 2013, 10:49:15) [MSC v.1500 64 bit (AMD64)]) with numpy version 1.8.0 and pandas version 0.13.1.
Interestingly I cannot reproduce the problem on my home computer. This (Ubuntu) computer has the same version of Python and Pandas but numpy 1.8.2. I installed 1.8.2 on my work computer but that did not fix the problem.
I tried posting this on stackoverflow but nobody was able to help me.
http://stackoverflow.com/questions/27712500/pandas-dataframe-assignment-issue-possible-bug
This is an important bug for my team as it caused critical analytical errors. I wrote a workaround for the place where I found it but we fear we are making the same mistake elsewhere. We would like a real fix. I imagine we could all upgrade to the 0.15.1 of Pandas and that would probably make it go away, but our team can't handle the consequences of jumping two versions right now. Does anyone know where this bug comes from? Am I confused and am doing something wrong? What do I need to do to fix this?
Thank you.
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} | 4 | 2015-01-06T03:25:17Z | 2015-03-12T12:08:53Z | 2015-03-12T12:08:53Z | CONTRIBUTOR | null | The docstring shows a `numeric_only` option for `DataFrame.std()` but it does not seem to actually be implemented. I'm happy to take a crack at fixing it but I'm not sure whether it's the doc or the implementation that needs fixing.
To see this consider a mixed-type `DataFrame` where I'm setting one entry to be a `str` of `'100'` while all other entries are `float`. For `std()` It does not matter whether `numeric_only` is `True` or `False`, but for `max()` it clearly makes a difference.
```
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: df = pd.DataFrame(np.random.randn(5, 2), columns=['foo', 'bar'])
In [4]: df.ix[0, 'foo'] = '100'
In [5]: df
Out[5]:
foo bar
0 100 -1.958036
1 0.221049 0.309971
2 1.200093 -0.103244
3 -2.475388 -2.279483
4 0.1623936 -1.185682
In [6]: df.std(numeric_only=True)
Out[6]:
foo 44.841828
bar 1.129182
dtype: float64
In [7]: df.std(numeric_only=False)
Out[7]:
foo 44.841828
bar 1.129182
dtype: float64
In [8]: df.max(numeric_only=False)
Out[8]:
foo 100.000000
bar 0.309971
dtype: float64
In [9]: df.max(numeric_only=True)
Out[9]:
bar 0.309971
dtype: float64
```
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