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https://api.github.com/repos/pandas-dev/pandas/issues/12704 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12704/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12704/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12704/events | https://github.com/pandas-dev/pandas/issues/12704 | 143,039,815 | MDU6SXNzdWUxNDMwMzk4MTU= | 12,704 | TypeError from to_html on a mixed type column | {
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} | [] | closed | false | null | [] | null | 7 | 2016-03-23T18:16:59Z | 2016-03-23T21:45:46Z | 2016-03-23T18:51:15Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
I'm getting an error when running:
``` python
pd.DataFrame.from_dict({'a': {0: 1., 2: "A"}}).to_html()
```
The traceback is:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-114-53996628e131> in <module>()
----> 1 pd.DataFrame.from_dict({'a': {0: 1., 2: "A"}}).to_html()
/Users/tdhopper/miniconda/envs/envname/lib/python2.7/site-packages/pandas/core/frame.pyc in to_html(self, buf, columns, col_space, colSpace, header, index, na_rep, formatters, float_format, sparsify, index_names, justify, bold_rows, classes, escape, max_rows, max_cols, show_dimensions, notebook, decimal)
1539 decimal=decimal)
1540 # TODO: a generic formatter wld b in DataFrameFormatter
-> 1541 formatter.to_html(classes=classes, notebook=notebook)
1542
1543 if buf is None:
/Users/tdhopper/miniconda/envs/envname/lib/python2.7/site-packages/pandas/core/format.pyc in to_html(self, classes, notebook)
667 notebook=notebook)
668 if hasattr(self.buf, 'write'):
--> 669 html_renderer.write_result(self.buf)
670 elif isinstance(self.buf, compat.string_types):
671 with open(self.buf, 'w') as f:
/Users/tdhopper/miniconda/envs/envname/lib/python2.7/site-packages/pandas/core/format.pyc in write_result(self, buf)
987 indent += self.indent_delta
988 indent = self._write_header(indent)
--> 989 indent = self._write_body(indent)
990
991 self.write('</table>', indent)
/Users/tdhopper/miniconda/envs/envname/lib/python2.7/site-packages/pandas/core/format.pyc in _write_body(self, indent)
1139 fmt_values = {}
1140 for i in range(min(len(self.columns), self.max_cols)):
-> 1141 fmt_values[i] = self.fmt._format_col(i)
1142
1143 # write values
/Users/tdhopper/miniconda/envs/envname/lib/python2.7/site-packages/pandas/core/format.pyc in _format_col(self, i)
650 return format_array(frame.iloc[:, i]._values, formatter,
651 float_format=self.float_format, na_rep=self.na_rep,
--> 652 space=self.col_space, decimal=self.decimal)
653
654 def to_html(self, classes=None, notebook=False):
/Users/tdhopper/miniconda/envs/envname/lib/python2.7/site-packages/pandas/core/format.pyc in format_array(values, formatter, float_format, na_rep, digits, space, justify, decimal)
2004 space=space, justify=justify, decimal=decimal)
2005
-> 2006 return fmt_obj.get_result()
2007
2008
/Users/tdhopper/miniconda/envs/envname/lib/python2.7/site-packages/pandas/core/format.pyc in get_result(self)
2023
2024 def get_result(self):
-> 2025 fmt_values = self._format_strings()
2026 return _make_fixed_width(fmt_values, self.justify)
2027
/Users/tdhopper/miniconda/envs/envname/lib/python2.7/site-packages/pandas/core/format.pyc in _format_strings(self)
2064 fmt_values.append(' %s' % _format(v))
2065 elif is_float[i]:
-> 2066 fmt_values.append(float_format(v))
2067 else:
2068 fmt_values.append(' %s' % _format(v))
TypeError: 'str' object is not callable
```
#### Expected Output
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: None
pip: 8.1.1
setuptools: 20.3
Cython: None
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: None
patsy: None
dateutil: 2.5.0
pytz: 2016.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
In [89]:
```
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"descript... | closed | false | null | [] | null | 4 | 2016-03-23T20:01:54Z | 2016-03-23T22:17:14Z | 2016-03-23T20:48:03Z | NONE | null | #### Code Executed:
```
#0.000016 second less than 24 hours, acrossing 2 calendar day.
print pandas.date_range(start='2016-03-22 12:19:49.053501', end='2016-03-23 12:19:49.053485')
```
#### Output of different versions.
- **Prior to 0.17**<br>
Prior to 0.17 versions, I tested 0.16 and 0.13, they both return two days.<br>
`DatetimeIndex(['2016-03-22 12:19:49.053501', '2016-03-23 12:19:49.053501'], dtype='datetime64[ns]', freq='D', tz=None)`
- **V0.17 & 0.18**<br>
Returns one day only<br>
`DatetimeIndex(['2016-03-22 12:19:49.053501'], dtype='datetime64[ns]', freq='D')`
On the [documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.date_range.html?highlight=date_range#pandas.date_range), the **freq** is set to be **calendar daily** by default, the test above covers two calendar days, does it make more sense to return two days like the earlier versions do?
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} | 3 | 2016-03-23T21:44:52Z | 2016-03-26T00:32:57Z | 2016-03-26T00:32:57Z | CONTRIBUTOR | null | ERROR: type should be string, got "https://github.com/pydata/pandas/issues/12704#issuecomment-200554983\n\n```\nIn [1]: pd.describe_option('float_format')\ndisplay.float_format : callable\n The callable should accept a floating point number and return\n a string with the desired format of the number. This is used\n in some places like SeriesFormatter.\n See core.format.EngFormatter for an example.\n [default: None] [currently: None]\n```\n\nThis should raise\n\n```\nIn [2]: pd.set_option('float_format','%.1f')\n```\n" | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12708 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12708/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12708/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12708/events | https://github.com/pandas-dev/pandas/issues/12708 | 143,207,194 | MDU6SXNzdWUxNDMyMDcxOTQ= | 12,708 | pd.rolling_mean for window = 1 does not exactly match input time series | {
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"de... | closed | false | null | [] | null | 1 | 2016-03-24T10:14:47Z | 2016-03-24T14:55:38Z | 2016-03-24T14:55:13Z | NONE | null | In order to have a more generic notation in my code, I want to express my original time series as a moving average over 1 period.
Quite unexpectedly, using pandas pd.rolling_mean function, the two are not exactly the same:
```
import pandas as pd
import numpy as np
np.random.seed(1)
ts = pd.Series(np.random.rand(1000))
mavg = pd.rolling_mean(ts, 1)
(ts - mavg).describe()
Out[120]:
count 1.000000e+03
mean 6.284973e-16
std 3.877250e-16
min -3.330669e-16
25% 3.330669e-16
50% 5.551115e-16
75% 8.881784e-16
max 1.554312e-15
dtype: float64
any((ts - mavg).dropna()>0)
Out[121]: True
```
Clearly the difference is very small and due to float rounding. Still I wonder if the function should not simply default to return the input when window = 1
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https://api.github.com/repos/pandas-dev/pandas/issues/12709 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12709/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12709/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12709/events | https://github.com/pandas-dev/pandas/issues/12709 | 143,265,854 | MDU6SXNzdWUxNDMyNjU4NTQ= | 12,709 | Performance degradation with new pandas version | {
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I noticed that there is some serious degradation in the performance with groupby and aggregate:
For instance, if I run this code:
import pandas as pd, numpy as np,time
nsim=10000
ncol=3000
np.random.seed(42)
index=np.random.randint(0,100,nsim)
values=np.random.normal(size=(nsim, ncol))
df=pd.DataFrame(index=index,data=values)
t=time.time()
df.groupby(lambda x:x,sort=False).agg(np.argmax)
time.time()-t
with the current version (0.18.0) I get:
> > 34.31084680557251
> > with the previous version (0.17.1):
> > 20.637927055358887
For my code performance is very important, so I would be glad if that could be solved.
Thank you very much,
Vincenzo Ferrazzano
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: it_IT.UTF-8
pandas: 0.18.0 ( other version: 0.17.1)
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
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} | 2 | 2016-03-24T16:11:31Z | 2021-04-23T02:47:23Z | null | NONE | null | The regular output of a dataframe with NaN values displays them as "NaN",
when using the HTML styler, they are displayed as "nan"
I'm not sure if this breaks anything but it is an inconsistency.
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} | 19 | 2016-03-24T16:12:47Z | 2016-03-26T11:49:39Z | 2016-03-26T00:32:57Z | CONTRIBUTOR | null | - [x] closes #12706
- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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bbc.to_pickle(r'/home/hsong01/work/dataMart/pcbasel/data/bbc_ifrs9')
#### Expected Output
---
SystemError Traceback (most recent call last)
<ipython-input-28-6d93f9c191d3> in <module>()
----> 1 bbc.to_pickle(r'/home/hsong01/work/dataMart/pcbasel/data/bbc_ifrs9')
/home/hsong01/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc in to_pickle(self, path)
992 """
993 from pandas.io.pickle import to_pickle
--> 994 return to_pickle(self, path)
995
996 def save(self, path): # TODO remove in 0.14
/home/hsong01/anaconda/lib/python2.7/site-packages/pandas/io/pickle.pyc in to_pickle(obj, path)
12 """
13 with open(path, 'wb') as f:
---> 14 pkl.dump(obj, f, protocol=pkl.HIGHEST_PROTOCOL)
15
16
SystemError: error return without exception set
#### output of `pd.show_versions()`
In [29]: pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-573.8.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.2
nose: 1.3.7
Cython: 0.22.1
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 3.2.0
sphinx: 1.3.1
patsy: 0.3.0
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https://api.github.com/repos/pandas-dev/pandas/issues/12714 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12714/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12714/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12714/events | https://github.com/pandas-dev/pandas/pull/12714 | 143,395,392 | MDExOlB1bGxSZXF1ZXN0NjQxMTg2MTI= | 12,714 | Allow float window when using .rolling | {
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"de... | closed | false | null | [] | null | 4 | 2016-03-25T00:37:09Z | 2016-04-10T17:31:05Z | 2016-04-10T17:31:05Z | CONTRIBUTOR | null | - [ ] closes #12669
- [ ] tests added
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
https://github.com/pydata/pandas/issues/12669
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https://api.github.com/repos/pandas-dev/pandas/issues/12715 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12715/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12715/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12715/events | https://github.com/pandas-dev/pandas/issues/12715 | 143,468,159 | MDU6SXNzdWUxNDM0NjgxNTk= | 12,715 | shouldn't pandas.Index.tolist convert from numpy datatypes to native Python datatypes? | {
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"color": "009... | closed | false | null | [] | null | 11 | 2016-03-25T09:57:32Z | 2016-03-25T15:10:20Z | 2016-03-25T13:02:05Z | CONTRIBUTOR | null | I wonder if it wouldn't be better/less surprising if Index.tolist did that conversion. FWIW, I was bitten by it via xlwings which tries to send an index values to Excel via COM by using index.tolist() and since the COM layer only handles basic Python types, it breaks. The patch seems trivial (I can submit a PR if you like), but I don't know whether you'd accept that, nor if it would have any implications.
``` diff
- return list(self.values)
+ return self.values.tolist()
```
FWIW, it seems to be related to #10904.
#### Code Sample, a copy-pastable example if possible
``` python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.zeros((3, 4)), index=np.arange(3))
l = df.index.tolist()
print(type(l[0]))
# <class 'numpy.int64'>
```
#### Expected Output
`<class 'int'>`
#### output of `pd.show_versions()`
python: 3.5.1
pandas: 0.18.0
numpy: 1.10.4
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} | 3 | 2016-03-25T18:48:36Z | 2016-04-06T20:40:14Z | 2016-04-06T20:40:14Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
df = pd.DataFrame([{'ts': pd.Timestamp('2016-01-01', tz='America/Los_Angeles'), 'a': 1}])
df.groupby('a').first()
```
The output is
```
ts
a
1 2016-01-01 08:00:00-08:00
```
#### Expected Output
```
ts
a
1 2016-01-01 00:00:00-08:00
```
Note that the issue is with `first()` and not `groupby`:
```
for _, group in df.groupby('a'):
print(group.ix[0])
```
gives the correct output of
```
a 1
ts 2016-01-01 00:00:00-08:00
Name: 0, dtype: object
```
#### output of `pd.show_versions()`
```
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-60-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.1
pip: 1.5.4
setuptools: 3.3
Cython: None
numpy: 1.10.4
scipy: 0.13.3
statsmodels: 0.5.0
xarray: None
IPython: 4.0.0
sphinx: None
patsy: 0.2.1
dateutil: 2.5.1
pytz: 2016.3
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.3.3
bs4: 4.2.1
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
```
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} | 5 | 2016-03-26T11:35:05Z | 2016-04-01T13:03:09Z | 2016-04-01T13:02:56Z | CONTRIBUTOR | null | - [x] closes #12655
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Fairly simple fix overall, since `get_filepath_or_buffer` is already set up to just convert the path objs to string, we can do that first and then just let the existing logic handle it.
Since the libraries that contain the path objects aren't hard dependencies, I'm not sure how to typecheck against them without first using the `_IS_LIBRARY_INSTALLED` checks and then importing if they're installed. Let me know if this should be done differently.
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- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
Also correct some badge links
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"description": "to_sql, read_sql, read_s... | open | false | null | [] | null | 2 | 2016-03-28T08:22:43Z | 2016-03-29T01:33:26Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import sqlite3
import pandas as pd
conn = sqlite3.connect('tmp')
df = pd.DataFrame({'a': [0,1,2]})
df.to_sql('df', conn)
pd.read_sql('SELECT * FROM df WHERE a = ?', conn, params=[1]).shape # (1,2) as expected
pd.read_sql('SELECT * FROM df WHERE a = ?', conn, params=[np.int64(1)]).shape # (0, 2) - bug
```
#### Expected Output
(1,2)
Actually, it works only with int/float/np.float64, but not with np.int64/np.int32/np.float128/np.float32.
This of course is a problem when the values are obtained from pandas dataframes, where they are often np.int64.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.0.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-77-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 18.2
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.16.1
statsmodels: 0.6.1
xarray: None
IPython: 4.0.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: 1.0.0
tables: None
numexpr: 2.4.6
matplotlib: 1.5.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
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} | 3 | 2016-03-28T15:58:32Z | 2016-04-17T14:13:08Z | 2016-04-17T14:13:08Z | CONTRIBUTOR | null | From https://github.com/dask/dask/issues/1063. `fill_value` doesn't seem to apply if the argument to a binary operator is a constant, but works fine for other arguments:
``` python
In [1]: import pandas as pd
In [2]: df = pd.DataFrame(range(10), columns=['foo'])
In [3]: df2 = df.copy()
In [4]: df2.iloc[0] = None
In [5]: df2.add(2, fill_value=0)
Out[5]:
foo
0 NaN
1 3.0
2 4.0
3 5.0
4 6.0
5 7.0
6 8.0
7 9.0
8 10.0
9 11.0
In [6]: df2.add(df, fill_value=0)
Out[6]:
foo
0 0.0
1 2.0
2 4.0
3 6.0
4 8.0
5 10.0
6 12.0
7 14.0
8 16.0
9 18.0
In [7]: pd.__version__
Out[7]: u'0.18.0rc2+2.g19e40a0'
```
From the docstring I'd expect it to be equivalent to `df2.fillna(0).add(2)`. If this is intended behavior, then the docstring should be updated to clarify this.
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} | 11 | 2016-03-28T19:50:20Z | 2019-12-20T11:56:25Z | 2019-12-20T11:56:25Z | NONE | null | After the holidays refactor in pandas 0.17.0+, I'm seeing errors when calling the AbstractHolidayCalendar.holidays() method with a date range that is before the declared Holiday rule. Consider the example below, I'm declaring a custom MLK holiday for CME equities market with a specific start date of 1998-01-01, with a non-vectorised DateOffset (third monday of January).
If I call calendar.holidays(start='2015-01-01', end='2015-12-31'), it blows up with an exception:
```
Traceback (most recent call last):
File "/tests/test_pandas_features.py", line 35, in test_no_holidays_generated_if_not_in_range
calendar.holidays(start='1995-01-01', end='1995-12-31'))
File "/python3/lib/python3.5/site-packages/pandas/tseries/holiday.py", line 377, in holidays
rule_holidays = rule.dates(start, end, return_name=True)
File "/python3/lib/python3.5/site-packages/pandas/tseries/holiday.py", line 209, in dates
holiday_dates = self._apply_rule(dates)
File "/python3/lib/python3.5/site-packages/pandas/tseries/holiday.py", line 278, in _apply_rule
dates += offset
File "/python3/lib/python3.5/site-packages/pandas/tseries/base.py", line 412, in __add__
return self._add_delta(other)
File "/python3/lib/python3.5/site-packages/pandas/tseries/index.py", line 736, in _add_delta
result = DatetimeIndex(new_values, tz=tz, name=name, freq='infer')
File "/python3/lib/python3.5/site-packages/pandas/util/decorators.py", line 89, in wrapper
return func(*args, **kwargs)
File "/python3/lib/python3.5/site-packages/pandas/tseries/index.py", line 231, in __new__
raise ValueError("Must provide freq argument if no data is "
ValueError: Must provide freq argument if no data is supplied
```
After some digging, it appears that if the supplied date range is a year before the declared holiday rule start date, the DatetimeIndex constructed in tseries/holiday.py is an empty index (a reasonable optimization), but when a non-vectorized DateOffset is being applied to an empty DatetimeIndex, the _add_offset method in tseries/index.py returns a plain empty Index, rather than DatetimeIndex, on which .asi8 is subsequently called and returns None instead of an empty numpy i8 array.
So the underlying problem is that an empty DatetimeIndex doesn't like a non-vectorized DateOffset applied to it.
Observe the testcase below, which reproduces the issue.
#### Test case
``` python
import unittest
import pandas as pd
import pandas.util.testing as pdt
from dateutil.relativedelta import MO
from pandas.tseries.holiday import AbstractHolidayCalendar, Holiday
from pandas.tseries.offsets import DateOffset
CME1998StartUSMartinLutherKingJr = Holiday('Dr. Martin Luther King Jr.', month=1, day=1, offset=DateOffset(weekday=MO(3)), start_date='1998-01-01')
class CustomHolidayCalendar(AbstractHolidayCalendar):
rules = [CME1998StartUSMartinLutherKingJr]
class TestPandasIndexFeatures(unittest.TestCase):
def test_empty_datetime_index_added_to_non_vectorized_date_offset(self):
empty_ts_index = pd.DatetimeIndex([], freq='infer')
# This blows up
new_ts_index = empty_ts_index + DateOffset(weekday=MO(3))
self.assertEqual(0, len(new_ts_index))
def test_no_holidays_generated_if_not_in_range(self):
calendar = CustomHolidayCalendar()
# This blows up
pdt.assert_index_equal(
pd.DatetimeIndex([]),
calendar.holidays(start='1995-01-01', end='1995-12-31'))
def test_holidays_generated_if_in_range(self):
calendar = CustomHolidayCalendar()
pdt.assert_index_equal(
pd.DatetimeIndex(['2015-01-19']),
calendar.holidays(start='2015-01-01', end='2015-12-31'))
```
#### Output
The following tests fail:
- test_empty_datetime_index_added_to_non_vectorized_date_offset
- test_no_holidays_generated_if_not_in_range
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: 5870731f32ae569e01e3c0a8972cdd2c6e0301f8
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.18.0+44.g5870731.dirty
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3.1
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: None
patsy: 0.4.1
dateutil: 2.5.1
pytz: 2016.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12725 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12725/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12725/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12725/events | https://github.com/pandas-dev/pandas/issues/12725 | 144,098,969 | MDU6SXNzdWUxNDQwOTg5Njk= | 12,725 | Loffset doesn't work when resampling with count() | {
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} | 1 | 2016-03-28T22:28:00Z | 2016-03-31T13:15:40Z | 2016-03-31T13:15:40Z | NONE | null | The label offset `loffset` has no effect when resampling a Series using `.count()`:
```
>>> import pandas as pd
>>> import numpy as np
>>> rng = pd.date_range('1/1/2012', periods=100, freq='S')
>>> ts = pd.Series(np.ones(len(rng)), index=rng)
>>> ts.resample('10S', loffset='1s').mean() #Works as expected, labels are offset by 1s
2012-01-01 00:00:01 1.0
2012-01-01 00:00:11 1.0
2012-01-01 00:00:21 1.0
2012-01-01 00:00:31 1.0
2012-01-01 00:00:41 1.0
2012-01-01 00:00:51 1.0
2012-01-01 00:01:01 1.0
2012-01-01 00:01:11 1.0
2012-01-01 00:01:21 1.0
2012-01-01 00:01:31 1.0
Freq: 10S, dtype: float64
>>> ts.resample('10S', loffset='1s').count() #loffset has no effect
2012-01-01 00:00:00 10
2012-01-01 00:00:10 10
2012-01-01 00:00:20 10
2012-01-01 00:00:30 10
2012-01-01 00:00:40 10
2012-01-01 00:00:50 10
2012-01-01 00:01:00 10
2012-01-01 00:01:10 10
2012-01-01 00:01:20 10
2012-01-01 00:01:30 10
Freq: 10S, dtype: int64
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12726 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12726/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12726/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12726/events | https://github.com/pandas-dev/pandas/issues/12726 | 144,113,437 | MDU6SXNzdWUxNDQxMTM0Mzc= | 12,726 | UnboundLocalError: local variable 'mask' referenced before assignment | {
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"name": "Bug",
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{
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"default": false,
"description": "Related to indexing on s... | closed | false | null | [] | null | 1 | 2016-03-28T23:50:52Z | 2016-03-29T01:05:36Z | 2016-03-29T01:05:36Z | NONE | null | #### Code Sample, a copy-pastable example if possible
pd.to_datetime(pd.Series(['Jul 31, 2009', '2010-01-10', None])).to_frame().replace('', np.nan)
```
File "/var/www/envs/fring_internal/local/lib/python2.7/site-packages/pandas/core/generic.py", line 3110, in replace
inplace=inplace, regex=regex)
File "/var/www/envs/fring_internal/local/lib/python2.7/site-packages/pandas/core/internals.py", line 2870, in replace
return self.apply('replace', **kwargs)
File "/var/www/envs/fring_internal/local/lib/python2.7/site-packages/pandas/core/internals.py", line 2823, in apply
applied = getattr(b, f)(**kwargs)
File "/var/www/envs/fring_internal/local/lib/python2.7/site-packages/pandas/core/internals.py", line 607, in replace
if not mask.any():
UnboundLocalError: local variable 'mask' referenced before assignment
```
#### Expected Output
Not sure, raise a TypeError("datetime64[ns] is not coercible to string") probably.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Darwin
OS-release: 14.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 7.0.3
setuptools: 17.0
Cython: 0.23.4
numpy: 1.8.2
scipy: 0.16.0
statsmodels: None
IPython: 3.2.0
sphinx: 1.3.1
patsy: None
dateutil: 1.5
pytz: 2013.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.3.1
openpyxl: 2.3.0
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.8
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
Jinja2: None
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https://api.github.com/repos/pandas-dev/pandas/issues/12727 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12727/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12727/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12727/events | https://github.com/pandas-dev/pandas/issues/12727 | 144,151,923 | MDU6SXNzdWUxNDQxNTE5MjM= | 12,727 | pd.timedelta has smaller than expected range | {
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} | 4 | 2016-03-29T04:14:23Z | 2016-03-31T13:24:07Z | 2016-03-31T13:24:07Z | CONTRIBUTOR | null | The pandas timedelta class has less than expected minimum and maximum timedeltas. The range is `[-99999, 99999]` days instead of [`-999999999, 999999999]` days
#### Code Sample
``` python
expected_max_days = 999999999 # 9 nines
max_days = 99999 # 5 nines
print("1. Expected minimum and maximum")
print(pd.Timedelta.min, '\t', pd.Timedelta.max)
print(datetime.timedelta.min, '\t', datetime.timedelta.max)
print("\n2. Expected min and max as pd.Timedelta objects")
print(pd.Timedelta(pd.Timedelta.min), '\t', pd.Timedelta(pd.Timedelta.max)) # overflow, no error
print("\n3. True min and max (in days) and comparing with numpy")
print(np.timedelta64(-max_days, 'D'), '\t\t', np.timedelta64(max_days, 'D'))
print(pd.Timedelta(-max_days, 'D'), '\t', pd.Timedelta(max_days, 'D'))
print("\n4. Expected min and max (in days) and comparing with numpy")
print(np.timedelta64(-expected_max_days, 'D'), '\t', np.timedelta64(expected_max_days, 'D'))
print(pd.Timedelta(-expected_max_days, 'D'), '\t', pd.Timedelta(expected_max_days, 'D')) # overflowError
```
#### Expected Output
```
1. Expected minimum and maximum
-999999999 days, 0:00:00 999999999 days, 23:59:59.999999
-999999999 days, 0:00:00 999999999 days, 23:59:59.999999
2. Expected min and max as pd.Timedelta objects
52654 days 06:07:35.539769 -52654 days +17:52:24.460229
3. True min and max (in days) and comparing with numpy
-99999 days 99999 days
-99999 days +00:00:00 99999 days 00:00:00
4. Expected min and max (in days) and comparing with numpy
-999999999 days 999999999 days
---------------------------------------------------------------------------
OverflowError Traceback (most recent call last)
<ipython-input-104-9f01918ed468> in <module>()
15 print("\n4. Expected min and max (in days) and comparing with numpy")
16 print(np.timedelta64(-expected_max_days, 'D'), '\t', np.timedelta64(expected_max_days, 'D'))
---> 17 print(pd.Timedelta(-expected_max_days, 'D'), '\t', pd.Timedelta(expected_max_days, 'D')) # overflowError
pandas/tslib.pyx in pandas.tslib.Timedelta.__new__ (pandas/tslib.c:43558)()
pandas/tslib.pyx in pandas.tslib.convert_to_timedelta64 (pandas/tslib.c:53419)()
pandas/tslib.pyx in pandas.tslib.cast_from_unit (pandas/tslib.c:58740)()
OverflowError: Python int too large to convert to C long
```
The problem is illustrated at points `2` (silent overflow) and `4`(overflow error)
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.1.1-gentoo-r1
machine: x86_64
processor: Intel(R) Core(TM)2 Quad CPU Q6600 @ 2.40GHz
byteorder: little
LC_ALL: None
LANG: en_US.utf8
pandas: 0.18.0
nose: None
pip: 8.1.1
setuptools: 20.3
Cython: None
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.6
patsy: 0.4.1
dateutil: 2.5.0
pytz: 2016.1
blosc: None
bottleneck: None
tables: None
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
```
I get similar results on python `3.5.1`.
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} | 3 | 2016-03-29T07:28:44Z | 2016-03-31T13:24:43Z | 2016-03-31T13:24:07Z | CONTRIBUTOR | null | - [x] closes #12727
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
_Problem_
Pandas Timedelta derives from `datetime.timedelta` and increase
the resolution of the timedeltas. As such the Pandas.Timedelta
object can only have a smaller range of values.
_Solution_
This change modifies the properties that report
the range and resolution to reflect Pandas capabilities.
**Reference**
https://github.com/python/cpython/blob/8d1d7e6816753248768e4cc1c0370221814e9cf1/Lib/datetime.py#L651-L654
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https://api.github.com/repos/pandas-dev/pandas/issues/12729 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12729/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12729/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12729/events | https://github.com/pandas-dev/pandas/issues/12729 | 144,208,820 | MDU6SXNzdWUxNDQyMDg4MjA= | 12,729 | Test errors for numpy 1.11 / pandas 0.18.0 | {
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} | 4 | 2016-03-29T09:30:07Z | 2016-04-05T13:37:52Z | 2016-04-05T13:37:52Z | CONTRIBUTOR | null | See: https://travis-ci.org/matthew-brett/manylinux-testing/jobs/119188668#L3214
```
======================================================================
ERROR: test_range_slice_day (pandas.tseries.tests.test_period.TestPeriodIndex)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/matthew-brett/manylinux-testing/venv/lib/python2.7/site-packages/pandas/tseries/tests/test_period.py", line 2603, in test_range_slice_day
idx[v:]
File "/home/travis/build/matthew-brett/manylinux-testing/venv/lib/python2.7/site-packages/pandas/tseries/base.py", line 175, in __getitem__
result = getitem(key)
IndexError: failed to coerce slice entry of type str to integer
======================================================================
ERROR: test_range_slice_seconds (pandas.tseries.tests.test_period.TestPeriodIndex)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/matthew-brett/manylinux-testing/venv/lib/python2.7/site-packages/pandas/tseries/tests/test_period.py", line 2660, in test_range_slice_seconds
idx[v:]
File "/home/travis/build/matthew-brett/manylinux-testing/venv/lib/python2.7/site-packages/pandas/tseries/base.py", line 175, in __getitem__
result = getitem(key)
IndexError: failed to coerce slice entry of type str to integer
```
No such error testing against numpy 1.10.4 : https://travis-ci.org/matthew-brett/manylinux-testing/jobs/119039450#L523
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https://api.github.com/repos/pandas-dev/pandas/issues/12730 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12730/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12730/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12730/events | https://github.com/pandas-dev/pandas/issues/12730 | 144,277,146 | MDU6SXNzdWUxNDQyNzcxNDY= | 12,730 | COMPAT: dateutil 2.5.2 broke sometests | {
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} | 0 | 2016-03-29T14:13:56Z | 2016-03-29T17:29:48Z | 2016-03-29T17:29:48Z | CONTRIBUTOR | null | https://github.com/dateutil/dateutil/issues/217 was fixed in 2.5.2
similar to fixes in #12613
so need to adjust our tests based on version >= 2.5.2 to handle the new behavior.
the OSX build uses pip to pick up the new version so currently is failing
the 3.5 build uses latest conda, so its still using 2.5.1
```
======================================================================
ERROR: test_parsers_dayfirst_yearfirst (pandas.tseries.tests.test_tslib.TestDatetimeParsingWrappers)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/jreback/pandas/pandas/tseries/tests/test_tslib.py", line 608, in test_parsers_dayfirst_yearfirst
yearfirst=yearfirst)
File "/Users/jreback/pandas/pandas/tseries/tools.py", line 523, in parse_time_string
yearfirst=yearfirst)
File "pandas/tslib.pyx", line 1765, in pandas.tslib.parse_datetime_string_with_reso (pandas/tslib.c:32262)
raise DateParseError(e)
DateParseError: month must be in 1..12
```
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} | 1 | 2016-03-29T14:32:03Z | 2016-03-29T17:29:48Z | 2016-03-29T17:29:48Z | CONTRIBUTOR | null | closes #12730
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} | 5 | 2016-03-29T16:51:05Z | 2021-04-23T02:53:33Z | null | CONTRIBUTOR | null | So combine with blocks==1 does unecessary work, right [here](https://github.com/pydata/pandas/blob/master/pandas/core/internals.py#L3002)
can be seen vs 0.17.1 from [SO](http://stackoverflow.com/questions/36274447/pd-rolling-mean-becoming-deprecated-alternatives-for-ndarrays/36274775#36274775)
```
import pandas as pd
import numpy as np
s = pd.Series(np.random.normal(size=100000))
s.rolling(window=2).mean()
```
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} | 10 | 2016-03-29T17:05:23Z | 2016-03-30T00:57:56Z | 2016-03-29T18:25:59Z | NONE | null | I have been using a "hack" for sometime that has broken in 0.18.0. I have a use case where the aggfunc should return an object (so that I can iterate over the DataFrame and grab properties of the object). I am not interested in a singe numerical value. I might be abusing Pandas a bit here but I have seen others use the same hack.
This code worked prior to 1.18.0:
`df.pivot_table('Object', index=['Index1', 'Index2'], columns=['Col1'], aggfunc=lambda x: x)`
This now raises the "Function does not reduce":
```
File "<path>/views.py" in get_context_data
290. pt = df.pivot_table('Object', index=['Index1', 'Index2'], columns=['Col1'], aggfunc=lambda x: x)
File "<path>/lib/python2.7/site-packages/pandas/tools/pivot.py" in pivot_table
121. agged = grouped.agg(aggfunc)
File "<path>/lib/python2.7/site-packages/pandas/core/groupby.py" in aggregate
3586. return super(DataFrameGroupBy, self).aggregate(arg, *args, **kwargs)
File "<path>/lib/python2.7/site-packages/pandas/core/groupby.py" in aggregate
3111. return self._python_agg_general(arg, *args, **kwargs)
File "<path>/lib/python2.7/site-packages/pandas/core/groupby.py" in _python_agg_general
764. result, counts = self.grouper.agg_series(obj, f)
File "<path>/lib/python2.7/site-packages/pandas/core/groupby.py" in agg_series
1863. return self._aggregate_series_pure_python(obj, func)
File "<path>/lib/python2.7/site-packages/pandas/core/groupby.py" in _aggregate_series_pure_python
1896. raise ValueError('Function does not reduce')
```
The root of the issue is obfuscated a bit by the exception handling. The actual issue is that BaseGrouper has no _is_builtin_func method which is called here:
https://github.com/pydata/pandas/blob/master/pandas/core/groupby.py#L1865
Typically self is an object which gets it's _is_builtin_func method from SelectionMixin however when aggfunc=lambda x: x self is a BaseGrouper which only inherits from object and does not provide _is_builtin_func.
I have monkey patched _is_builtin_func method (just returns the func passed in) onto BaseGrouper as a temporary fix. Not sure the best way to fix this. Two naive ways of fixing this would be:
1) add `_is_builtin_func(self, func): return func` to BaseGrouper
2) check `hasattr(self, '_is_builtin_func')` before calling it, if not just use func that was passed in.
These both smell - I am sure there is a better solution.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 15.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: None
pip: 8.1.1
setuptools: 20.2.2
Cython: None
numpy: 1.11.0
scipy: None
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: None
patsy: None
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: 2.3.4
xlrd: 0.9.4
xlwt: None
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
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} | 1 | 2016-03-29T17:31:24Z | 2016-04-26T21:52:46Z | 2016-04-26T21:52:46Z | CONTRIBUTOR | null | xref #12731
since `dayfirst=True, yearfirst` are now doing the correct thing, have to fix / document what we are doing and any changes (#12731 just fixed the tests)
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} | 1 | 2016-03-29T17:53:29Z | 2016-03-29T18:27:10Z | 2016-03-29T18:26:55Z | NONE | null | When doing some data analysis with pandas, the python shell breaks and I get "Segmentation fault (core dumped)".
A little bit hard to reproduce since this only happens when the work i'm doing is a little bit heavy.
But basically, I'm just reading dataframes and using stuff like groupby(), apply(), .loc[x, y], itertuples() and regular slicing. I'm not writing to or updating a dataframe, just reading and transforming.
Also, I did some memory profiling and there appears to be no memory leak (memory peak stable at ~166MB)
#### output of `pd.show_versions()`
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-56-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.0
nose: 1.3.4
pip: 6.0.6
setuptools: 20.3.1
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.16.1
statsmodels: 0.6.1
IPython: 4.1.2
sphinx: None
patsy: 0.4.1
dateutil: 2.4.0
pytz: 2014.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.0
openpyxl: 2.3.1
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.4.0
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: 2.5.4 (dt dec pq3 ext)
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} | 1 | 2016-03-29T18:17:16Z | 2016-04-05T13:37:52Z | 2016-04-05T13:37:52Z | CONTRIBUTOR | null | was a revert of # https://github.com/numpy/numpy/pull/6271
closes #12729
closes #12792
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} | 2 | 2016-03-29T19:46:54Z | 2016-05-18T13:19:39Z | 2016-05-18T13:19:39Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/36286803/how-to-speed-up-the-replacement-of-missing-values-for-each-groupby-group-in-pand)
```
import pandas as pd
import numpy as np
df = pd.DataFrame({'group': np.repeat(np.arange(1000), 10),
'B': np.nan,
'C': np.nan})
df.ix[4::10, 'B':'C'] = 5 # every 4th row of a group is non-null
df.groupby('group').transform('first')
```
This is then iterating over groups. Last I can see this was changed is: [here](https://github.com/pydata/pandas/pull/9994). My recollection is that this was ONLY supposed to hit in a special case, and the general case is simply a repeat based on the indices.
This seems to be hitting in all cases makes transform back to super SLOW.
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} | 1 | 2016-03-29T23:21:04Z | 2016-04-26T15:03:02Z | 2016-04-26T15:03:02Z | NONE | null | Right now when I want to have expanding operate at the level of a group I have to apply a convenience function like so:
``` python
def expanding_sum(s):
return s.expanding().sum()
expanded_sum = df.groupby('mygroup')['mynum'].apply(expanding_sum)
```
Would be nice to be able to call a method directly on a groupby object:
``` python
expanded_sum = df.groupby('mygroup')['mynum'].expanding().sum()
```
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} | 24 | 2016-03-29T23:53:49Z | 2016-05-13T23:37:22Z | 2016-05-13T23:37:18Z | NONE | null | - [x] ./test_fast.sh works fine
Ran 8463 tests in 127.338
OK (SKIP=592)
- [x] passes `git diff upstream/master | flake8 --diff`
A port of ujson 1.35 feature: object can define `__json__` attribute for custom serialization. See
https://github.com/esnme/ultrajson/commit/a8f0f0f1010956b27bf0c2cb5e52d85bb84e273a
```
class ujson_as_is(object):
def __init__(self, value):
self.value = value
def __json__(self):
return self.value
df = pd.DataFrame([{"foo": ujson_as_is('{"parrot": 42.0}')}])
df.to_json(orient = 'records')
```
result `[{"foo":{"parrot": 42.0}}]`
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} | 1 | 2016-03-29T23:58:21Z | 2016-03-30T12:46:54Z | 2016-03-30T12:46:54Z | MEMBER | null | - [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 0 | 2016-03-30T01:49:08Z | 2016-04-03T17:03:18Z | 2016-04-03T17:03:18Z | MEMBER | null | Based on the discussion in #12532. Move boxing logic to `Block/BlockManager` from `common.py`.
Add either `.asobject` property (compat with `Index`) or `.get_object_values()` to `BlockManager/Block`.
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} | 1 | 2016-03-30T01:54:22Z | 2016-03-30T12:40:56Z | 2016-03-30T12:39:02Z | MEMBER | null | - [x] closes #10452
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Based on existing tests / text search, no other metohd depends on current `SparseSeries.shape` (buggy) behavior.
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} | 28 | 2016-03-30T03:38:55Z | 2017-08-11T11:41:05Z | 2016-04-26T15:03:02Z | CONTRIBUTOR | null | closes #12738
closes #12486
closes #12363
- [x] more tests (other methods)
~~\- [ ] doc section in groupby~~ will do later
```
In [3]: pd.options.display.max_rows=10
In [4]: df = pd.DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8,
'B': np.arange(40)})
In [5]: df
Out[5]:
A B
0 1 0
1 1 1
2 1 2
3 1 3
4 1 4
.. .. ..
35 3 35
36 3 36
37 3 37
38 3 38
39 3 39
[40 rows x 2 columns]
In [6]: df.groupby('A').apply(lambda x: x.rolling(4).B.mean())
Out[6]:
A
1 0 NaN
1 NaN
2 NaN
3 1.5
4 2.5
...
3 35 33.5
36 34.5
37 35.5
38 36.5
39 37.5
Name: B, dtype: float64
In [7]: df.groupby('A').rolling(4).B.mean()
Out[7]:
A
1 0 NaN
1 NaN
2 NaN
3 1.5
4 2.5
...
3 35 33.5
36 34.5
37 35.5
38 36.5
39 37.5
Name: B, dtype: float64
In [9]: df.index = pd.date_range('20130101',freq='s',periods=40)
In [10]: df
Out[10]:
A B
2013-01-01 00:00:00 1 0
2013-01-01 00:00:01 1 1
2013-01-01 00:00:02 1 2
2013-01-01 00:00:03 1 3
2013-01-01 00:00:04 1 4
... .. ..
2013-01-01 00:00:35 3 35
2013-01-01 00:00:36 3 36
2013-01-01 00:00:37 3 37
2013-01-01 00:00:38 3 38
2013-01-01 00:00:39 3 39
[40 rows x 2 columns]
In [11]: df.groupby('A').apply(lambda x: x.resample('4s').mean())
Out[11]:
A B
A
1 2013-01-01 00:00:00 1.0 1.5
2013-01-01 00:00:04 1.0 5.5
2013-01-01 00:00:08 1.0 9.5
2013-01-01 00:00:12 1.0 13.5
2013-01-01 00:00:16 1.0 17.5
2 2013-01-01 00:00:20 2.0 21.5
2013-01-01 00:00:24 2.0 25.5
2013-01-01 00:00:28 2.0 29.5
3 2013-01-01 00:00:32 3.0 33.5
2013-01-01 00:00:36 3.0 37.5
In [12]: df.groupby('A').resample('4s').mean()
Out[12]:
A B
A
1 2013-01-01 00:00:00 1.0 1.5
2013-01-01 00:00:04 1.0 5.5
2013-01-01 00:00:08 1.0 9.5
2013-01-01 00:00:12 1.0 13.5
2013-01-01 00:00:16 1.0 17.5
2 2013-01-01 00:00:20 2.0 21.5
2013-01-01 00:00:24 2.0 25.5
2013-01-01 00:00:28 2.0 29.5
3 2013-01-01 00:00:32 3.0 33.5
2013-01-01 00:00:36 3.0 37.5
```
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} | [] | closed | false | null | [] | null | 1 | 2016-03-30T09:04:10Z | 2016-03-30T13:49:18Z | 2016-03-30T13:48:40Z | NONE | null | The code below randomly generates a seg fault. I'm just doing the exact same thing 10000 times. The segfault happens randomly, even if the first iterations have succeeded. Sometimes the whole code will run with no problem.
The bug appears more often when using ipython. It also appears when using a regular python sheel but it's difficult to reproduce.
```
import pandas as pd
pool_data = pd.read_csv('repro2.csv')
for i in range(10000):
print i
b = ((pool_data['day_x'] >= -5) & (pool_data['day_x'] <= -10) & (pool_data['cabine'] == 1))
adj = pool_data.loc[b]
```
data:
https://drive.google.com/file/d/0B1VHOF9GpzXOdVBRSktyelJsM2s/view?usp=sharing
I did some tests. On ipython:
Run n°1: segfault at iteration 2666
Run n°2: no segfault
Run n°3: segfault at iteration 5157
Run n°4: segfault at iteration 3191
Run n°5: segfault at iteration 3422
On regular python shell:
Run n°1: no segfault
Run n°2: segfault at iteration 2593
Run n°3: no segfault
Run n°4: no segfault
Run n°5: segfault at iteration 6220
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-56-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.4
pip: 6.0.6
setuptools: 20.3.1
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.16.1
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: None
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.0
openpyxl: 2.3.1
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.4.0
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: 2.5.4 (dt dec pq3 ext)
jinja2: 2.8
boto: None
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} | 8 | 2016-03-30T10:13:47Z | 2016-11-30T11:46:48Z | 2016-11-30T11:46:27Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
When `.replace` is called with `dict`, replacements are done per value. Current impl try to soft convert the dtype in every replacement, but it is enough to be done in the final replacement.
#### Bench
```
- 712.83ms 355.93ms 0.50 replace.replace_convert.time_replace_series_timestamp
- 1.50s 698.21ms 0.46 replace.replace_convert.time_replace_frame_timestamp
- 3.12s 690.48ms 0.22 replace.replace_convert.time_replace_frame_timedelta
- 1.69s 354.83ms 0.21 replace.replace_convert.time_replace_series_timedelta
```
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"de... | closed | false | null | [] | null | 2 | 2016-03-30T12:08:17Z | 2016-03-30T17:26:37Z | 2016-03-30T12:09:24Z | NONE | null | I have an empty DataFrame which can be empty in some cases, when i cast applymap() and return the value it is a Series object.
**v 0.18 and v 0.17.1**
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} | 4 | 2016-03-30T14:54:41Z | 2017-03-20T16:25:53Z | 2017-03-20T16:25:53Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
##### 1. replace bool with int (#12780)
NG, should be int Series
```
pd.Series([True]).replace({True: 3})
#0 True
# dtype: bool
```
##### 2. replace datetime/timedelta with int (#12780)
NG, should be int Series
```
pd.Series([pd.Timestamp('2011-01-01')]).replace({pd.Timestamp('2011-01-01'): 3})
#0 1970-01-01 00:00:00.000000003
# dtype: datetime64[ns]
pd.Series([pd.Timedelta('1 days')]).replace({pd.Timedelta('1 days'): 3})
#0 00:00:00.000000
# dtype: timedelta64[ns]
```
##### 3. replace datetime/timedelta with object (#12780)
NG, coerced to internal repr unexpectedly.
```
rep = {pd.Timestamp('2011-01-01'): 'a', pd.Timestamp('2011-01-02'): 'b'}
pd.Series([pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02')]).replace(rep)
#0 1293840000000000000
#1 b
# dtype: object
```
##### 4. replace with datetimetz (#12780)
NG, coerced to GMT
```
pd.Series(['a']).replace({'a': pd.Timestamp('2011-01-01', tz='US/Eastern')})
#0 2011-01-01 05:00:00
# dtype: datetime64[ns]
```
##### 5. replacement of string (open, see #15743)
```
In [37]: pd.Series([1,2,3]).replace('1', np.nan)
Out[37]:
0 NaN
1 2.0
2 3.0
dtype: float64
```
#### output of `pd.show_versions()`
Latest master.
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} | 1 | 2016-03-30T15:26:03Z | 2016-04-01T13:16:59Z | 2016-04-01T13:16:59Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- xref #12549
- added more `Period` related tests in preparation for period dtype.
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} | 10 | 2016-03-30T15:59:37Z | 2016-04-11T23:57:05Z | 2016-04-11T23:57:05Z | NONE | null | cc @mrocklin
Hi,
Context:
Right now, pandas treats numexpr as an optional dependency. I'm a packager for archlinux, and recently, I got a feedback on the dask package saying that numexpr was missing as a dependency of this package. https://aur.archlinux.org/packages/python-dask/
However, dask does not explicitly on numexpr. The reason is detailed below.
Analysis:
In `pandas.computations`, `eval()` takes an optional argument `engine='numexpr'`.
If numexpr is not install, then any call with default arguments will raise an exception importError from the function `_check_engine` in pandas/computations/eval.py.
`eval()` is called (at least) from query, that's why we are in trouble if we run dask's test without numexpr.
RFC:
Here is the question: is numexpr really an optional dependency since it's the default argument?
I would say no, but comments are open :)
From the dask devs point of view, they do not have to mark numexpr as a dependency because they do not use it explicitly. To me, they can expect that default arguments from pandas work out of the box.
From the pandas packager (not me), pandas says that numexpr is optional, treated as optional. No problem here too, he followed the guidelines.
I see two options:
- pandas changes the default backend
- or pandas adds numexpr as a true dependency.
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"color": "02d7e1"... | closed | false | null | [] | null | 20 | 2016-03-30T16:57:48Z | 2017-08-18T00:58:04Z | 2017-08-18T00:58:04Z | CONTRIBUTOR | null | Initial implementation for https://github.com/pydata/pandas/issues/11167.
Needs some additional tests - for dataframes, other axis values etc, but want to get this out there and get feedback.
Note that the implemenation is different than that for `min_periods`. This uses a single function to mask all rolling calculations. `min_periods` is implemented separately for each function. I imagine the latter is a bit faster, although also more complicated.
- [x] closes #11167
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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"description": "Unexpect... | closed | false | null | [] | null | 2 | 2016-03-30T19:21:16Z | 2016-03-30T22:58:10Z | 2016-03-30T22:53:39Z | NONE | null | It would be nice to be able to specify `dtype` of the various columns to a `DataFrame` in the `DataFrame.from_records()` constructor. Mostly because this is the constructor used by Spark in its [toPandas()](https://github.com/apache/spark/blob/master/python/pyspark/sql/dataframe.py#L1366) method.
Something like this would make it easier to either preserve type information from a Spark dataframe, or take advantage of pandas types without having to load dates, categoricals, etc as objects first.
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} | 4 | 2016-03-30T22:48:53Z | 2016-04-03T17:04:29Z | 2016-04-03T17:03:18Z | MEMBER | null | - [x] closes #12741 closes #12772 closes #12469
- [x] tests added / passed
- [x] `map` related boxing
- [x] `quantile`
- [x] passes `git diff upstream/master | flake8 --diff`
- added `Series.asobject` to return boxed `object` values
- remove `common/i8_boxer()`, `Series._maybe_box()` (only used by `.quantile()`)
If base direction looks OK, going to add some tests based on the spec.
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} | [] | closed | false | null | [] | null | 2 | 2016-03-30T23:33:50Z | 2016-03-31T12:14:41Z | 2016-03-31T12:14:35Z | NONE | null | Hi everyone,
I'm a pandas user and just wanted to contribute but it was very complicated to commit as a developer...
BTW, I added Manhattan plot which is very popular in genetics and biology
Here is the R equivalent
https://cran.r-project.org/web/packages/qqman/qqman.pdf
## Cheers,
ARYA
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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} | 3 | 2016-03-31T07:18:55Z | 2020-01-04T17:25:15Z | 2020-01-04T17:25:15Z | NONE | null | _NOTE: This issue has been already [submitted to SO](http://stackoverflow.com/questions/36221441/why-large-pandas-dataframe-shows-only-nan-values-after-i-drop-selected-rows/36223039) where the only person replying did replicate the behaviour in pandas 18.0._
Using the pandas library v. 17.1, I am trying to remove the rows from a large (882504 rows) dataframe named productDataNat where parName=='rt', but then all the other rows become NaN:
#### Code Sample, a copy-pastable example if possible
Importing and showing the multiaxis dataframe..
```
productDataNat = pd.read_csv('https://lobianco.org/temp/productData_P0-Mi-Ei.csv',sep=';', dtype={'value': np.float64})
productDataNat = productDataNat.drop(['Unnamed: 8'],axis=1)
productDataNat.set_index(['scen','country','region','prod','freeDim','year','parName'], inplace=True)
productDataNat.head()
```
Here I drop by same values in an internal axis.. all the remaning values are NaN!:
`productDataNat.drop('rt', level='parName', axis=0)`
#### Expected Output
When instead I play with an example dataframe it works as expected:
```
midx = pd.MultiIndex(levels=[['one', 'two'], ['x','y']], labels=[[1,1,1,0],[1,0,1,0]])
dfmix = pd.DataFrame({'A' : [1, 2, 3, 4], 'B': [5, 6, 7, 8]}, index=midx)
dfmix
```
`dfmix.drop('x',level=1,axis=0)`
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-83-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.1
pip: 1.5.4
setuptools: 3.3
Cython: None
numpy: 1.10.4
scipy: 0.13.3
statsmodels: None
IPython: 4.0.0
sphinx: 1.2.2
patsy: None
dateutil: 2.5.0
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: None
numexpr: 2.5
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: None
```
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} | 5 | 2016-03-31T07:43:35Z | 2020-04-17T22:53:45Z | null | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```
In [2]: a = pd.Series(1, index=range(3))
In [3]: b = pd.Series(2, index=range(4))
In [4]: a.dtype
Out[4]: dtype('int64')
In [5]: a += b
In [6]: a.dtype
Out[6]: dtype('float64')
In [7]: a.index == b.index
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-8-2fae764c9047> in <module>()
----> 1 a.index == b.index
/home/nobackup/repo/pandas/pandas/indexes/base.pyc in _evaluate_compare(self, other)
3026 if isinstance(other, (np.ndarray, Index, ABCSeries)):
3027 if other.ndim > 0 and len(self) != len(other):
-> 3028 raise ValueError('Lengths must match to compare')
3029 func = getattr(self.values, op)
3030 result = func(np.asarray(other))
ValueError: Lengths must match to compare
```
#### Expected Output
My preference would be
```
In [6]: a.dtype
Out[6]: dtype('int64')
```
... but if one prioritises coherence with `a = a + b`, then the expected behaviour would probably be
```
In [6]: a.dtype
Out[6]: dtype('float64')
In [7]: a.index == b.index
Out[7]: array([ True, True, True, True], dtype=bool)
```
(outer join). Instead I think the current behaviour is hard to rationalise (i.e. it is dictated by implementation).
#### output of `pd.show_versions()`
```
In [7]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: 22cf50bbef7f28c63941328ea47502f1b202bda2
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-1-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
pandas: 0.18.0+48.g22cf50b.dirty
nose: 1.3.6
pip: 1.5.6
setuptools: 18.4
Cython: 0.23.2
numpy: 1.10.4
scipy: 0.16.0
statsmodels: 0.8.0.dev0+755fa81
xarray: None
IPython: 2.4.1
sphinx: 1.3.1
patsy: 0.3.0-dev
dateutil: 2.2
pytz: 2012c
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.3
matplotlib: 1.5.0rc2
openpyxl: 2.3.0
xlrd: 0.9.4
xlwt: 0.7.5
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.4.0
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: 2.6.1 (dt dec mx pq3 ext lo64)
jinja2: 2.8
boto: 2.38.0
pandas_datareader: None
```
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} | 3 | 2016-03-31T08:47:00Z | 2017-11-25T14:24:16Z | 2017-11-25T14:24:16Z | NONE | null | A DataFrame index has the map function, which expects a mapper. However, unlike mapping a Series, it only accepts callables, and won't work when mapping through dictionaries or series.
#### Code Sample:
``` python
df = pd.DataFrame({'col1': range(5)})
d = {0: 'zero', 1: 'one', 2: 'two', 3: 'three', 4: 'four'}
df.col1.map(d) # perfectly valid
df.index.map(d) # TypeError: 'dict' object is not callable
s = pd.Series(d)
df.col1.map(s) # perfectly valid
df.index.map(s) # TypeError: 'Series' object is not callable
```
#### Expected Output:
I would expect the same output for `df.col1.map(s)` and `df.index.map(s)`, or at least that it returns an nparray with the mapped values, as it does when we do pass a callable, but not that it breaks like it does.
#### output of `pd.show_versions()`:
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-67-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_DE.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.6.6
Cython: 0.23.5
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.4
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: None
numexpr: 2.5.1
matplotlib: 1.5.1
openpyxl: 2.3.4
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: 3.6.0
bs4: None
html5lib: 0.9999999
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.39.0
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} | 1 | 2016-03-31T13:08:17Z | 2016-03-31T13:15:40Z | 2016-03-31T13:15:40Z | CONTRIBUTOR | null | - [x] closes #12725
- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
The code to do this already existed in the `_downsample` method, which is called when using functions like `mean`. `max`, etc., but there was nothing in the `_groupby_and_aggregate` method used for `count` and `size`. If we pull out the offset code from the `_downsample` method into a separate method, we can reuse it without duplicating it.
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} | 4 | 2016-03-31T13:39:50Z | 2017-03-29T12:11:12Z | 2017-03-29T12:11:12Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
Some inconsistencies there.
```
# OK: specifying int dtype with float value coerces to int (same as numpy)
pd.Series([1, 2, 3.5], dtype=int)
#0 1
#1 2
#2 3
# dtype: int64
# OK
pd.Index(np.array([1, 2, 3.5], dtype=int))
# Int64Index([1, 2, 3], dtype='int64')
# NG: If list is given, dtype is ignored
pd.Index([1, 2, 3.5], dtype=int)
# Float64Index([1.0, 2.0, 3.5], dtype='float64')
```
#### Expected Output
- All list-like input should be handled by a consistent logic.
- If `dtype` is specified, the result must be the specified `dtype`. Otherwise raise.
- To do this, merge base `Index` creation and `Series` sanitizing logic to use same `ndarray` `dtype` handling.
#### output of `pd.show_versions()`
Current master.
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} | 1 | 2016-03-31T14:30:22Z | 2016-07-15T00:30:34Z | 2016-07-15T00:30:34Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
`Timedelta` returns `NaT` when input can be regarded as `NaT`. Otherwise `Period` returns its special representation (This was done in #7485 prior to `Timedelta`).
```
pd.Timedelta('NaT')
# NaT
pd.Period('NaT', freq='M')
# Period('NaT', 'M')
```
#### Expected Output
```
pd.Period('NaT', freq='M')
# NaT
```
The fix should affects:
- [ ] `Period` and `PeriodIndex`, `Series` ops
- [ ] add, sub, comp
- [x] `PeriodIndex` creation from list-like which contains `Period` and `NaT` (#13430)
- [ ] `PeriodIndex` boxing
- [ ] `.to_period`
- [ ] `__contains__` any NaT-like(`pd.NaT, None, float('nan'), np.nan`) (#13582)
#### output of `pd.show_versions()`
Current master.
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https://github.com/pydata/pandas/blob/master/pandas/io/sql.py#L745
The SQLite version _does_ use `executemany`: https://github.com/pydata/pandas/blob/master/pandas/io/sql.py#L1441
Does anyone know why this uses `execute`? Or whether my assumptions on the speed difference are incorrect?
CC @zzzeek
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https://api.github.com/repos/pandas-dev/pandas/issues/12764 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12764/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12764/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12764/events | https://github.com/pandas-dev/pandas/issues/12764 | 145,181,612 | MDU6SXNzdWUxNDUxODE2MTI= | 12,764 | CLN: internal array detection | {
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"color": "207de5... | closed | false | null | [] | null | 3 | 2016-04-01T13:32:44Z | 2021-04-23T02:59:39Z | 2021-04-23T02:59:39Z | CONTRIBUTOR | null | so need to add some functions to replace lots of instance checking for compat in `core/common.py`
```
def is_array_like(arr):
return isinstance(arr, np.ndarray)
```
analagously to `is_list_like` functions.
These will isolate code changes later.
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https://api.github.com/repos/pandas-dev/pandas/issues/12765 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12765/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12765/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12765/events | https://github.com/pandas-dev/pandas/issues/12765 | 145,203,446 | MDU6SXNzdWUxNDUyMDM0NDY= | 12,765 | rolling with custom weights does not work | {
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"de... | closed | false | null | [] | null | 3 | 2016-04-01T14:58:33Z | 2016-04-01T16:13:43Z | 2016-04-01T15:57:49Z | NONE | null | #### Code Sample, a copy-pastable example if possible
import pandas as pd
In [1]: df = pd.DataFrame([1,1,1,1])
In [2]: pd.rolling_window(df, window=[1,1], mean=False)
C:\Users\login\AppData\Local\Continuum\Anaconda3\lib\site-packages\spyderlib\widgets\externalshell\start_ipython_kernel.py:1: FutureWarning: pd.rolling_sum is deprecated for DataFrame and will be removed in a future version, replace with
DataFrame.rolling(window=[1, 1],center=False,axis=0).sum()
# -_\- coding: utf-8 -_-
Traceback (most recent call last):
File "<ipython-input-41-0804fe3086b4>", line 1, in <module>
pd.rolling_window(pd.DataFrame([1,1,1,1]), window=[1,1], mean=False)
File "C:\Users\login\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\stats\moments.py", line 657, in rolling_window
**kwargs)
File "C:\Users\login\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\stats\moments.py", line 240, in ensure_compat
result = getattr(r, name)(_args, *_kwds)
File "C:\Users\login\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\window.py", line 789, in sum
return super(Rolling, self).sum(**kwargs)
File "C:\Users\login\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\window.py", line 552, in sum
return self._apply('roll_sum', **kwargs)
File "C:\Users\login\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\window.py", line 487, in _apply
result = np.apply_along_axis(calc, self.axis, values)
File "C:\Users\login\AppData\Local\Continuum\Anaconda3\lib\site-packages\numpy\lib\shape_base.py", line 91, in apply_along_axis
res = func1d(arr[tuple(i.tolist())], _args, *_kwargs)
File "C:\Users\login\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\window.py", line 484, in calc
return func(x, window, min_periods=self.min_periods)
File "C:\Users\login\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\window.py", line 471, in func
window, minp, **kwargs)
File "pandas\algos.pyx", line 901, in pandas.algos.roll_sum (pandas\algos.c:28213)
TypeError: an integer is required
In [3]: df.rolling(window=[1, 1],center=False,axis=0).sum()
Traceback (most recent call last):
File "<ipython-input-46-9c6d4166134a>", line 1, in <module>
df.rolling(window=[1, 1],center=False,axis=0).sum()
File "C:\Users\login\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\window.py", line 789, in sum
return super(Rolling, self).sum(**kwargs)
File "C:\Users\login\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\window.py", line 552, in sum
return self._apply('roll_sum', **kwargs)
File "C:\Users\login\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\window.py", line 487, in _apply
result = np.apply_along_axis(calc, self.axis, values)
File "C:\Users\login\AppData\Local\Continuum\Anaconda3\lib\site-packages\numpy\lib\shape_base.py", line 91, in apply_along_axis
res = func1d(arr[tuple(i.tolist())], _args, *_kwargs)
File "C:\Users\login\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\window.py", line 484, in calc
return func(x, window, min_periods=self.min_periods)
File "C:\Users\login\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\window.py", line 471, in func
window, minp, **kwargs)
File "pandas\algos.pyx", line 901, in pandas.algos.roll_sum (pandas\algos.c:28213)
TypeError: an integer is required
#### Expected Output
[NA, 2, 2, 2]
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 26 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
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} | 4 | 2016-04-01T15:45:32Z | 2016-12-16T23:26:00Z | 2016-12-16T23:26:00Z | NONE | null | ``` python
df2=pd.DataFrame([[0,1],[2,3]],columns=['c1','c2'],index=['i1','i2'])
df2.index.name='index'
df2.to_html()
```
``` html
<table border="1" class="dataframe">\n <thead>\n <tr style="text-align: right;">\n <th></th>\n <th>c1</th>\n <th>c2</th>\n </tr>\n <tr>\n <th>index</th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>i1</th>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>i2</th>\n <td>2</td>\n <td>3</td>\n </tr>\n </tbody>\n</table>
```
``` python
df2.index = df2.index.map(lambda x: x.upper())
df2.to_html()
```
``` html
<table border="1" class="dataframe">\n <thead>\n <tr style="text-align: right;">\n <th></th>\n <th>c1</th>\n <th>c2</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>I1</th>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>I2</th>\n <td>2</td>\n <td>3</td>\n </tr>\n </tbody>\n</table>
```
I think `map` should accept `inplace`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 18.2
Cython: None
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: None
patsy: None
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: None
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
```
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} | 2 | 2016-04-01T15:53:52Z | 2016-04-03T14:34:12Z | 2016-04-03T14:33:08Z | NONE | null | For a DataFrame I want to preserve rows that belong to groups that fulfil specific condition and replace other rows with NaN. I have used a combination of 'groupby' and 'filter' (with dropna=False). In a special case when there are no groups fulfilling the condition an exception occured.
``` python
AttributeError Traceback (most recent call last)
<ipython-input-11-ffb9adbc134a> in <module>()
----> 1 pd.DataFrame({'a': [1,1,2], 'b':[1,2,0]}).groupby('a').filter(lambda x: x['b'].sum() > 5, dropna=False)
....../local/lib/python2.7/site-packages/pandas/core/groupby.py in filter(self, func, dropna, *args, **kwargs)
3570 type(res).__name__)
3571
-> 3572 return self._apply_filter(indices, dropna)
3573
3574
....../local/lib/python2.7/site-packages/pandas/core/groupby.py in _apply_filter(self, indices, dropna)
831 mask = np.empty(len(self._selected_obj.index), dtype=bool)
832 mask.fill(False)
--> 833 mask[indices.astype(int)] = True
834 # mask fails to broadcast when passed to where; broadcast manually.
835 mask = np.tile(mask, list(self._selected_obj.shape[1:]) + [1]).T
AttributeError: 'list' object has no attribute 'astype'
```
The problem I have identified is in the _apply_filter method of _GroupBy class (core/groupby.py) -- line with "mask[indices.astype(int)] = True" throws because in my case indices is equal to []; shouldn't it be "indices = np.array([])" instead of "indices = []" in the case when len(indices) == 0
``` python
def _apply_filter(self, indices, dropna):
if len(indices) == 0:
indices = []
else:
indices = np.sort(np.concatenate(indices))
if dropna:
filtered = self._selected_obj.take(indices, axis=self.axis)
else:
mask = np.empty(len(self._selected_obj.index), dtype=bool)
mask.fill(False)
mask[indices.astype(int)] = True
# mask fails to broadcast when passed to where; broadcast manually.
mask = np.tile(mask, list(self._selected_obj.shape[1:]) + [1]).T
filtered = self._selected_obj.where(mask) # Fill with NaNs.
return filtered
```
#### Code Sample, a copy-pastable example if possible
``` python
>>> import pandas as pd
>>> pd.DataFrame({'a': [1,1,2], 'b': [1,2,0]}).groupby('a').filter(lambda x: x['b'].sum() > 5, dropna=False)
```
#### Expected Output
``` python
a b
0 NaN NaN
1 NaN NaN
2 NaN NaN
```
#### output of `pd.show_versions()`
``` python
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-56-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 1.5.6
setuptools: 12.2
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.16.1
statsmodels: None
xarray: None
IPython: 4.0.3
sphinx: None
patsy: 0.4.0
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.7.6
lxml: None
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9
apiclient: None
sqlalchemy: None
pymysql: 0.6.6.None
psycopg2: None
jinja2: 2.8
boto: None
```
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} | 1 | 2016-04-01T22:49:34Z | 2016-04-03T20:35:52Z | 2016-04-03T20:35:52Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
``` python
In [17]: series=pd.Series(range(10), pd.period_range(start='2000', periods=10, name='date', freq='M'))
In [18]: series
Out[18]:
date
2000-01 0
2000-02 1
2000-03 2
2000-04 3
2000-05 4
2000-06 5
2000-07 6
2000-08 7
2000-09 8
2000-10 9
Freq: M, dtype: int64
In [19]: series.index
Out[19]:
PeriodIndex(['2000-01', '2000-02', '2000-03', '2000-04', '2000-05', '2000-06',
'2000-07', '2000-08', '2000-09', '2000-10'],
dtype='int64', name='date', freq='M')
# Note name='date'
In [20]: series.resample('D').pad().index
Out[20]:
PeriodIndex(['2000-01-01', '2000-01-02', '2000-01-03', '2000-01-04',
'2000-01-05', '2000-01-06', '2000-01-07', '2000-01-08',
'2000-01-09', '2000-01-10',
...
'2000-10-22', '2000-10-23', '2000-10-24', '2000-10-25',
'2000-10-26', '2000-10-27', '2000-10-28', '2000-10-29',
'2000-10-30', '2000-10-31'],
dtype='int64', length=305, freq='D')
# now no name
```
#### Expected Output
``` python
Out[20]:
PeriodIndex(['2000-01-01', '2000-01-02', '2000-01-03', '2000-01-04',
'2000-01-05', '2000-01-06', '2000-01-07', '2000-01-08',
'2000-01-09', '2000-01-10',
...
'2000-10-22', '2000-10-23', '2000-10-24', '2000-10-25',
'2000-10-26', '2000-10-27', '2000-10-28', '2000-10-29',
'2000-10-30', '2000-10-31'],
dtype='int64', length=305, freq='D', name='date')
```
#### output of `pd.show_versions()`
`Out[21]: '0.18.0'`
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} | 3 | 2016-04-01T23:07:50Z | 2016-04-13T01:12:56Z | 2016-04-13T01:12:56Z | CONTRIBUTOR | null | Not a big bug, but not ideal behavior:
``` python
In [26]: series=pd.Series(range(10), pd.period_range(start='2000', periods=10, name='date', freq='M'))
In [25]: series
Out[25]:
date
2000-01 0
2000-02 1
2000-03 2
2000-04 3
2000-05 4
2000-06 5
2000-07 6
2000-08 7
2000-09 8
2000-10 9
Freq: M, dtype: int64
In [30]: series.resample('M').first()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-30-4637af8f5d6f> in <module>()
----> 1 series.resample('M').first()
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/tseries/resample.py in f(self, _method)
465
466 def f(self, _method=method):
--> 467 return self._downsample(_method)
468 f.__doc__ = getattr(GroupBy, method).__doc__
469 setattr(Resampler, method, f)
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/tseries/resample.py in _downsample(self, how, **kwargs)
709 'resampled to {freq}'.format(
710 axfreq=ax.freq,
--> 711 freq=self.freq))
712
713 def _upsample(self, method, limit=None):
ValueError: Frequency <MonthEnd> cannot be resampled to <MonthEnd>
# --> which is fair, even if I could imagine it returning itself
In [31]: series.resample('M').pad()
---------------------------------------------------------------------------
RecursionError Traceback (most recent call last)
<ipython-input-24-9df8c7209780> in <module>()
----> 1 series.resample('M').pad()
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/tseries/resample.py in pad(self, limit)
393 DataFrame.fillna
394 """
--> 395 return self._upsample('pad', limit=limit)
396 ffill = pad
397
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/tseries/resample.py in _upsample(self, method, limit)
736
737 if not is_superperiod(ax.freq, self.freq):
--> 738 return self.asfreq()
739
740 # Start vs. end of period
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/tseries/resample.py in asfreq(self)
435 essentially a reindex with (no filling)
436 """
--> 437 return self._upsample(None)
438
439 def std(self, ddof=1):
# ...............
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/tseries/period.py in _generate_range(cls, start, end, periods, freq, fields)
200 raise ValueError('Can either instantiate from fields '
201 'or endpoints, but not both')
--> 202 subarr, freq = _get_ordinal_range(start, end, periods, freq)
203 elif field_count > 0:
204 subarr, freq = _range_from_fields(freq=freq, **fields)
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/tseries/period.py in _get_ordinal_range(start, end, periods, freq, mult)
995
996 if start is not None:
--> 997 start = Period(start, freq)
998 if end is not None:
999 end = Period(end, freq)
pandas/src/period.pyx in pandas._period.Period.__init__ (pandas/src/period.c:10867)()
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/importlib/_bootstrap.py in _find_and_load(name, import_)
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/importlib/_bootstrap.py in __enter__(self)
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/importlib/_bootstrap.py in _get_module_lock(name)
RecursionError: maximum recursion depth exceeded
```
#### Expected Output
Raise on the initial resample? Return itself? Deliberately raise on `pad`?
#### output of `pd.show_versions()`
18.0
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} | 9 | 2016-04-02T00:08:01Z | 2016-04-03T20:37:39Z | 2016-04-03T20:35:52Z | CONTRIBUTOR | null | - [x] closes #12769
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 0 | 2016-04-02T02:56:04Z | 2016-04-03T17:03:18Z | 2016-04-03T17:03:18Z | MEMBER | null | Found some issues when working on #12572. Both being fixed.
#### Code Sample, a copy-pastable example if possible
##### 1. DataFrame may coerce to float even if interpolation
```
# OK, same as numpy
pd.Series([1, 2, 3]).quantile([0.5], interpolation='lower').dtypes
# dtype('int64')
# NG, it must be int
pd.DataFrame({'A': [1, 2, 3]}).quantile([0.5], interpolation='lower').dtypes
# A float64
# dtype: object
```
##### 2. May return scalar even if its input is a list-like
```
# NG, it must be a Series
pd.Series([]).quantile([0.2, 0.3])
# nan
```
#### output of `pd.show_versions()`
Current master.
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} | 4 | 2016-04-02T03:12:46Z | 2016-04-06T04:23:14Z | 2016-04-06T01:42:12Z | CONTRIBUTOR | null | - [X] DOC: ecosystem.rst
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} | 2 | 2016-04-02T05:03:08Z | 2016-04-13T01:12:56Z | 2016-04-13T01:12:56Z | CONTRIBUTOR | null | ``` python
In [38]: pd.Series(1, index=pd.period_range(start='2000', periods=100)).resample('M').count()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-38-295afa97781f> in <module>()
----> 1 pd.Series(1, index=pd.period_range(start='2000', periods=100)).resample('M').count()
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/tseries/resample.py in f(self, _method)
473
474 def f(self, _method=method):
--> 475 return self._groupby_and_aggregate(None, _method)
476 f.__doc__ = getattr(GroupBy, method).__doc__
477 setattr(Resampler, method, f)
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/tseries/resample.py in _groupby_and_aggregate(self, grouper, how, *args, **kwargs)
353
354 if grouper is None:
--> 355 self._set_binner()
356 grouper = self.grouper
357
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/tseries/resample.py in _set_binner(self)
202
203 if self.binner is None:
--> 204 self.binner, self.grouper = self._get_binner()
205
206 def _get_binner(self):
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/tseries/resample.py in _get_binner(self)
210 """
211
--> 212 binner, bins, binlabels = self._get_binner_for_time()
213 bin_grouper = BinGrouper(bins, binlabels)
214 return binner, bin_grouper
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/tseries/resample.py in _get_binner_for_time(self)
538 if self.kind == 'period':
539 return self.groupby._get_time_period_bins(self.ax)
--> 540 return self.groupby._get_time_bins(self.ax)
541
542 def _downsample(self, how, **kwargs):
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/tseries/resample.py in _get_time_bins(self, ax)
905 if not isinstance(ax, DatetimeIndex):
906 raise TypeError('axis must be a DatetimeIndex, but got '
--> 907 'an instance of %r' % type(ax).__name__)
908
909 if len(ax) == 0:
TypeError: axis must be a DatetimeIndex, but got an instance of 'PeriodIndex'
```
Expected:
```
Out[39]:
2000-01 31
2000-02 29
2000-03 31
2000-04 9
Freq: M, dtype: int64
```
On pandas 0.18
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} | 5 | 2016-04-02T07:46:34Z | 2016-04-22T15:20:13Z | 2016-04-22T15:20:13Z | NONE | null | #### Code Sample, a copy-pastable example if possible
``` python
>>> import pandas as pd
>>> from StringIO import StringIO
>>> data = """id,text,num_lines
1,"line 11
line 12",2
2,"line 21
line 22",2
3,"line 31",1"""
>>> pd.read_csv(StringIO(data))
Out[2]:
id text num_lines
0 1 'line 11\nline 12' 2
1 2 'line 21\nline 22' 2
2 3 'line 31' 1
>>> pd.read_csv(StringIO(data), skiprows=[1])
Out[3]:
id text num_lines
0 'line 12"' 2 NaN
1 2 'line 21\nline 22' 2.0
2 3 'line 31' 1.0
...
```
#### Expected Output
``` python
>>> pd.read_csv(StringIO(data), skiprows=[1])
Out[3]:
id text num_lines
0 2 'line 21\nline 22' 2
1 3 'line 31' 1
...
```
It should skip '1,"line 11\nline 12",2' instead skip '1,"line 11'
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.3-300.fc23.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 18.0.1
Cython: None
numpy: 1.11.0
scipy: 0.14.1
statsmodels: 0.6.1
xarray: None
IPython: 3.2.1
sphinx: 1.2.3
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: 0.6.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
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} | 2 | 2016-04-02T17:22:59Z | 2016-04-03T14:34:00Z | 2016-04-03T14:33:08Z | CONTRIBUTOR | null | - [x] closes #12768
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Implemented the fix for #12768 which was proposed in the bug report.
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} | 8 | 2016-04-02T17:54:56Z | 2016-04-29T17:20:57Z | 2016-04-29T17:20:57Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Skip `object` conversion if input is numeric already.
```
- 146.41ms 26.45μs 0.00 miscellaneous.to_numeric.time_from_float
```
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} | 3 | 2016-04-03T07:53:56Z | 2016-04-03T15:35:10Z | 2016-04-03T14:20:30Z | MEMBER | null | - [x] closes #10560
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Fixes following issues.
```
# NG, should be SparseArray (categorized as API change)
a = pd.SparseArray([1, np.nan, np.nan, 3, np.nan])
type(a.take([1, 2]))
# numpy.ndarray
# NG
s = pd.Series([1, np.nan, np.nan, 3, np.nan]).to_sparse()
s.loc[[1, 3]]
# TypeError: reindex() got an unexpected keyword argument 'level'
# NG
s.iloc[2]
# IndexError: index out of bounds
# NG, must be SparseSeries (root cause of 10560)
type(s.iloc[2:])
# pandas.sparse.array.SparseArray
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12780 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12780/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12780/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12780/events | https://github.com/pandas-dev/pandas/pull/12780 | 145,469,694 | MDExOlB1bGxSZXF1ZXN0NjUwNjIzOTc= | 12,780 | BUG: replace coerces incorrect dtype | {
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} | 8 | 2016-04-03T08:20:04Z | 2017-03-20T14:22:53Z | 2017-03-20T14:22:53Z | MEMBER | null | - [x] closes #12747
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 3 | 2016-04-03T13:27:16Z | 2016-04-03T15:41:14Z | 2016-04-03T15:41:14Z | CONTRIBUTOR | null | - [x] closes #10208
- [ ] tests added / passed -- only docs, not needed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry -- neccessary for docs?
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} | 1 | 2016-04-03T14:36:13Z | 2016-04-03T17:01:17Z | 2016-04-03T17:00:21Z | NONE | null | #### Code Sample
`df.groupby(level=['first_name','last_name']).rolling(window=5)`
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-35-1ad06da0e074> in <module>()
----> 1 df.groupby(level=['first_name','last_name']).rolling(window=5)#.corr(df['px_last'])
/home/code/anaconda2/lib/python2.7/site-packages/pandas/core/groupby.pyc in __getattr__(self, attr)
482 return self[attr]
483 if hasattr(self.obj, attr):
--> 484 return self._make_wrapper(attr)
485
486 raise AttributeError("%r object has no attribute %r" %
/home/code/anaconda2/lib/python2.7/site-packages/pandas/core/groupby.pyc in _make_wrapper(self, name)
496 "using the 'apply' method".format(kind, name,
497 type(self).__name__))
--> 498 raise AttributeError(msg)
499
500 # need to setup the selection
AttributeError: Cannot access callable attribute 'rolling' of 'DataFrameGroupBy' objects, try using the 'apply' method
```
#### Copy and paste runnable code sample
```
import pandas as pd
import numpy as np
dates = pd.date_range('20130101',periods=6).append(pd.date_range('20130101',periods=6))
df = pd.DataFrame(np.random.randn(12,2),index=dates,columns=list('AB'))
df.groupby(df.index).rolling(window=5)
```
#### Expected Output
The code should result in a `pandas.core.window.Rolling object`.
`df.groupby(level=['first_name','last_name'])['column'].apply(pd.rolling_mean, 5)` works however according to the [documentation](http://pandas.pydata.org/pandas-docs/stable/whatsnew.html?highlight=rolling_mean#deprecations) this functionality has been deprecated?
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-56-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_AU.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.5
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.3
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.1
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.8
boto: 2.39.0
```
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} | 3 | 2016-04-03T15:05:02Z | 2016-04-03T17:52:49Z | 2016-04-03T17:52:22Z | CONTRIBUTOR | null | - [x] closes #12701
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Closes #12701
Follows the suggested fix in the comments to the bug report.
Also added a line in whatsnew and a test.
Regarding the tests it passes the one I added. But I just noticed that plenty others fail and I have yet to find out whether it's caused by this (who knows, it might)
Update: It's the same tests failing on master branch as on this one (phew..). I guess the errors deserve bug reports but that's for another time.
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https://api.github.com/repos/pandas-dev/pandas/issues/12784 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12784/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12784/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12784/events | https://github.com/pandas-dev/pandas/issues/12784 | 145,511,817 | MDU6SXNzdWUxNDU1MTE4MTc= | 12,784 | COMPAT: sparse issue on compat build | {
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} | 3 | 2016-04-03T15:38:21Z | 2016-04-03T18:16:36Z | 2016-04-03T18:15:01Z | CONTRIBUTOR | null | xref #12779
https://travis-ci.org/pydata/pandas/jobs/120431675
I suspect because of `numpy=1.7.1`
```
======================================================================
ERROR: test_take (pandas.sparse.tests.test_array.TestSparseArray)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/pydata/pandas/pandas/sparse/tests/test_array.py", line 40, in test_take
self.assertEqual(self.arr.take(2), np.take(self.arr_data, 2))
File "/home/travis/miniconda/envs/pandas/lib/python2.7/site-packages/numpy/core/fromnumeric.py", line 104, in take
return take(indices, axis, out, mode)
ValueError: object of too small depth for desired array
```
on win64, python 3.5, numpy ==1.10.4
This is prob a take issue as well, I think these should _always_ be `int32` (the block locations).
```
======================================================================
FAIL: test_sparse_max_row (pandas.tests.test_format.TestSeriesFormatting)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\conda\Documents\pandas3.5\pandas\tests\test_format.py", line 3721, in test_sparse_max_row
self.assertEqual(result, exp)
AssertionError: '0 [98 chars]0, 3])\nBlock lengths: array([1, 1])' != '0 [98 chars]0, 3], dtype=int32)\nBlock length
s: array([1, 1], dtype=int32)'
0 1.0
1 NaN
2 NaN
3 3.0
4 NaN
dtype: float64
BlockIndex
- Block locations: array([0, 3])
+ Block locations: array([0, 3], dtype=int32)
? +++++++++++++
- Block lengths: array([1, 1])+ Block lengths: array([1, 1], dtype=int32)? +++++++++++++
```
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} | 26 | 2016-04-03T16:47:24Z | 2016-04-14T01:17:55Z | 2016-04-14T01:17:55Z | MEMBER | null | In `categorical.py`, we enforce the fact that `self` must be ordered when calling `min` or `max`. However, `self` can be unordered when calling `sort_values`. This doesn't make sense in my mind, for if you can sort the values for unordered `self`, then I can then find a minimum value of `self`. The same comment applies to `argsort` as well.
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- [x] passes `git diff upstream/master | flake8 --diff`
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} | 2 | 2016-04-03T19:29:38Z | 2016-04-04T17:53:42Z | 2016-04-04T17:53:40Z | MEMBER | null | - [x] related to #4400 (not close yet)
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Found a below bug in `SparseDataFrame` indexing.
```
# NG, must be SparseSeries
df = pd.DataFrame([[1, 2], [np.nan, 4]]).to_sparse()
type(df.loc[0])
# pandas.core.series.Series
```
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"default"... | closed | false | null | [] | null | 4 | 2016-04-03T20:03:06Z | 2016-05-31T18:07:27Z | 2016-05-31T18:07:27Z | NONE | null | #### Code Sample, a copy-pastable example if possible
Running the following with a huge DataFrame with lots of variance within "huge_col" can often cause massive memory overuse:
`pd.get_dummies(huge_df["huge_col"]).values.argmax(1) #times out`
The point here, of course, is to be able to convert the elements of "huge_col" into a column of integers rather than the one-hot coded vectors that `pd.get_dummies()` returns. However, this method of doing so is inefficient since it expands out the one-hot coded vectors in memory. Adding an option to `get_dummies` to allow for integer return values or introducing a different function seems like an ideal solution to this issue. If there is an alternative, straightforward solution that's recommended, please do suggest!
Thanks.
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- [x] passes ``git diff upstream/master | flake8 --diff`
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"color": "207d... | closed | false | null | [] | null | 16 | 2016-04-03T22:54:34Z | 2016-11-17T23:51:58Z | 2016-11-16T22:15:16Z | NONE | null | - [x] closes #12660
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.loc was fixed to always raise a KeyError with a helpful error message when called on an unsorted MultiIndex DataFrame
Tests ran fine the last time I checked, but if I run them with the latest upstream now I get a totally unrelated ImportError error - I assume it is not related to my changes. Btw this is my first real contribution to a large open source project, I tried to pay attention to everything but let me know if anything needs to be improved!
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} | 6 | 2016-04-04T05:28:47Z | 2016-04-17T14:13:11Z | 2016-04-17T14:13:08Z | CONTRIBUTOR | null | - [ ] closes #xxxx
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closes #12723
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} | 0 | 2016-04-04T15:15:55Z | 2016-04-05T13:37:52Z | 2016-04-05T13:37:52Z | CONTRIBUTOR | null | we are not fully testing with numpy 1.11 with all of our dependencies as they are not quite available in conda yet. But you can update an existing env and it will work. Some warnings in current master.
```
/Users/jreback/miniconda/lib/python2.7/site-packages/nose/importer.py:94: FutureWarning: The pandas.rpy module is deprecated and will be removed in a future version. We refer to external packages like rpy2.
See here for a guide on how to port your code to rpy2: http://pandas.pydata.org/pandas-docs/stable/r_interface.html
mod = load_module(part_fqname, fh, filename, desc)
......................................................................................S...........S.......S.......S.......S...............................................................................................................................................................................................F.......S.........................S...SS...S...........SSS..........S........S......SS................................SS.....................S.........................................................S..................................S..................................S...................SS..............................................................SS............................................................SS.............SSSSSSSSSSSSSSSSSSS........................................................................................................................................................................................................................................................................................................................................./Users/jreback/pandas/pandas/io/pytables.py:1479: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
values = values[self.cname]
...........................S...................../Users/jreback/pandas/pandas/io/pytables.py:2005: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
values = values[self.cname]
............................../Users/jreback/pandas/pandas/io/tests/test_pytables.py:3005: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
n=1, p=.01, size=(1e3, 10))).to_sparse(fill_value=0)
/Users/jreback/pandas/pandas/io/tests/test_pytables.py:3018: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
df[0] = np.zeros(1e3)
....................................SSS.........................................................................S................................................................................S....................................S.........................S....................................................S............................................................S........................................................................./Users/jreback/miniconda/lib/python2.7/site-packages/numpy/lib/function_base.py:3823: RuntimeWarning: Invalid value encountered in percentile
RuntimeWarning)
................................................................................................S..............................................................................................................S.................................................................................................................................................................................................................................................................................................................................................................................................................................................S..S.................................................................................................................................................................................S..................................................S...S................................................................................................................................E.E...................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................S.................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................S......................................SS.....S...SS.......S.S.......................SS.....S...SS.......S.S........................................................................................................................................S.....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................S.......................................................................................................................S..................................S.........S................................................................................................................/Users/jreback/pandas/pandas/tseries/base.py:156: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
val = getitem(key)
...............................................................................................................................................................................................S...............................................................................................................................................................................................................................................................................................................................................................................................................................SSS................................................................................................................................................................................S...............S.....S......................................................................................................................................................................................................................................................................................................................................................................................................S.............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
.....SS.....SSSS.....................................................................................................................................................................................................................................................................................................................................................................S....................................................................................................................................................................................................................................................................................................................................................................................................
======================================================================
ERROR: test_range_slice_day (pandas.tseries.tests.test_period.TestPeriodIndex)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/jreback/pandas/pandas/tseries/tests/test_period.py", line 2619, in test_range_slice_day
idx[v:]
File "/Users/jreback/pandas/pandas/tseries/base.py", line 176, in __getitem__
result = getitem(key)
IndexError: failed to coerce slice entry of type str to integer
======================================================================
ERROR: test_range_slice_seconds (pandas.tseries.tests.test_period.TestPeriodIndex)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/jreback/pandas/pandas/tseries/tests/test_period.py", line 2676, in test_range_slice_seconds
idx[v:]
File "/Users/jreback/pandas/pandas/tseries/base.py", line 176, in __getitem__
result = getitem(key)
IndexError: failed to coerce slice entry of type str to integer
```
The errors are fixed in #12736
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} | 4 | 2016-04-04T15:39:07Z | 2016-04-04T15:47:42Z | 2016-04-04T15:41:20Z | NONE | null | It seems like Series.dt timezone properties to not interact well with series.apply(lambda x : x.strftime("..")) leading to some odd behaviour. Here is a script demonstrating the problem:
```
import pandas as pd
import logging
import pytz
from pytz import timezone
date_format_string = "%a %b %d %Y %H:%M"
def convert_datetime(x) :
return x.strftime(date_format_string)
def longer_convert(x, tz = timezone('Europe/London')) :
return convert_datetime(pytz.utc.localize(x).astimezone(tz))
if __name__=="__main__" :
rng = pd.date_range('6/6/2011', periods=6, freq='H')
rng2 = pd.date_range('6/6/2011', periods=6, freq='D')
series = pd.Series(data=rng, index=rng2)
series.name = "Original"
converted_series = series.dt.tz_localize('UTC').dt.tz_convert('Europe/London')
converted_series.name = "Converted"
formatted_series = converted_series.apply(convert_datetime)
formatted_series.name = "Formatted"
longer_converted = series.apply(longer_convert)
longer_converted.name = "Using pytz"
df=pd.concat([series,converted_series,formatted_series, longer_converted], axis=1)
print df
```
Which yields the output
```
Original Converted
2011-06-06 2011-06-06 00:00:00 2011-06-06 01:00:00+01:00
2011-06-07 2011-06-06 01:00:00 2011-06-06 02:00:00+01:00
2011-06-08 2011-06-06 02:00:00 2011-06-06 03:00:00+01:00
2011-06-09 2011-06-06 03:00:00 2011-06-06 04:00:00+01:00
2011-06-10 2011-06-06 04:00:00 2011-06-06 05:00:00+01:00
2011-06-11 2011-06-06 05:00:00 2011-06-06 06:00:00+01:00
Formatted Using pytz
2011-06-06 Mon Jun 06 2011 00:00 Mon Jun 06 2011 01:00
2011-06-07 Mon Jun 06 2011 01:00 Mon Jun 06 2011 02:00
2011-06-08 Mon Jun 06 2011 02:00 Mon Jun 06 2011 03:00
2011-06-09 Mon Jun 06 2011 03:00 Mon Jun 06 2011 04:00
2011-06-10 Mon Jun 06 2011 04:00 Mon Jun 06 2011 05:00
2011-06-11 Mon Jun 06 2011 05:00 Mon Jun 06 2011 06:00
```
In which the application of the format string using apply caused the times to revert to UTC (Compare the hours in Formatted vs Converted), whereas I expected to get output similar to the "Using pytz" column, where I have displayed the time in local time with no timezone adjustment.
Is this expected behaviour? Can I format to a string directly through Series.dt?
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} | 13 | 2016-04-04T15:45:27Z | 2016-04-28T14:04:31Z | 2016-04-28T14:04:31Z | MEMBER | null | 1) `SparseArray` inherits from `np.ndarray`, but it doesn't seem like the instance itself can take on more than one dimension? Documentation doesn't make that super clear IMO.
2) If having 1-D sparse arrays is the intention, then I think there should be stronger checks against multi-dimensional `data` inputs. For example, if I pass in a multi-dimensional `ndarray` into `SparseArray`, I get a not so helpful error message that it does not have an `sp_index`.
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} | 4 | 2016-04-04T18:41:56Z | 2016-04-04T19:28:01Z | 2016-04-04T19:12:01Z | NONE | null | Hello,
Please see here http://stackoverflow.com/questions/36409183/pandas-test-fails-after-fresh-anaconda-install?noredirect=1#comment60435297_36409183.
I have downloaded a fresh anaconda installer, and ran pandas.test() afterwards. My computer cannot connect to the internet but I am getting failures for tests that do not seem to need connectivity. Is this worrisome or my installation is OK?
Many thanks!
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} | 2 | 2016-04-04T21:27:28Z | 2016-04-07T14:37:05Z | 2016-04-07T14:36:57Z | MEMBER | null | - [x] related to #4400 (not close yet as `SparseDataFrame` indexing test is not sufficient)
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Added more tests for sparse indexing. Fixed followings:
- `SparseArray.take` has optimized logic to omit dense `np.ndarray` creation.
- `SparseSeires.iloc` can work with negative indices. Made `SparseArray.take` to handle negative indices as the same rule as `Index` (#12676)
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} | 5 | 2016-04-04T22:10:28Z | 2016-04-09T14:44:21Z | 2016-04-09T14:44:21Z | MEMBER | null | Sparse looks to handle `missing (NaN)` and `fill_value` confusingly. Based on the doc, I understand `fill_value` is a user-specified value to be omitted in the sparse internal repr. `fill_value` may be different from missing (NaN).
#### Code Sample, a copy-pastable example if possible
```
# NG, 2nd and last element must be NaN
pd.SparseArray([1, np.nan, 0, 3, np.nan], fill_value=0).to_dense()
# array([ 1., 0., 0., 3., 0.])
# NG, 2nd element must be NaN
orig = pd.Series([1, np.nan, 0, 3, np.nan], index=list('ABCDE'))
sparse = orig.to_sparse(fill_value=0)
sparse.reindex(['A', 'B', 'C'])
# A 1.0
# B 0.0
# C 0.0
# dtype: float64
# BlockIndex
# Block locations: array([0], dtype=int32)
# Block lengths: array([1], dtype=int32)
```
#### Expected Output
```
pd.SparseArray([1, np.nan, 0, 3, np.nan], fill_value=0).to_dense()
# array([ 1., np.nan, 0., 3., np.nan])
sparse = orig.to_sparse(fill_value=0)
sparse.reindex(['A', 'B', 'C'])
# A 1.0
# B NaN
# C 0.0
# dtype: float64
# BlockIndex
# Block locations: array([0], dtype=int32)
# Block lengths: array([1], dtype=int32)
```
#### output of `pd.show_versions()`
Current master.
The fix itself looks straightforward, but it breaks some tests use dubious comparison.
- https://github.com/pydata/pandas/blob/master/pandas/sparse/tests/test_sparse.py#L1730
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} | 27 | 2016-04-05T01:36:20Z | 2016-12-16T23:26:23Z | 2016-11-07T20:54:33Z | NONE | null | Continued in #14506
- [x] closes #12766
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 10 | 2016-04-05T07:55:51Z | 2019-04-03T12:29:11Z | 2016-04-05T12:07:01Z | NONE | null | `df = pd.DataFrame([[1, 'a'],[2, 'b'],[3, 'c'],[4, 'd']], columns=['_id', 'id'])
for row in df.itertuples():
print row.id
print row._id
`
Expected output:
a 1 ......
Actual output
**\* AttributeError: 'Pandas' object has no attribute '_id'
because the _id column gets renamed to _1 in the row object
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} | 3 | 2016-04-05T12:36:26Z | 2016-04-11T12:59:58Z | 2016-04-11T12:59:58Z | NONE | null | ```
idx = pd.DataFrame().last_valid_index() #throws index error
idx = pd.Series().last_valid_index() #returns None
```
if you call last_valid_index() on df it throws an IndexError, if you call it on series it returns None. It seems like it would be very little effort to make these two cases consistent. Moreover, I believe they should be consistent with the behavior of
```
series.get_value(index_value)
```
where index value is not in the index (i.e. a KeyError).
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 45 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
nose: 1.3.4
pip: 8.1.1
setuptools: 20.2.2
Cython: None
numpy: 1.9.2
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 2.3.0
sphinx: None
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.2
openpyxl: 2.3.1
xlrd: 0.9.4
xlwt: None
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.7.3
boto: None
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"color": "009... | closed | false | null | [] | null | 1 | 2016-04-05T14:57:01Z | 2016-04-05T15:07:35Z | 2016-04-05T15:03:28Z | NONE | null | Currently `pd.DataFrame.astype` only allows for one type.
I would like it to support a `dict` mapping column names to dtypes.
Like for example the `dtype` argument of `pd.read_json`.
``` python
df = pd.DataFrame({'a': [1, 2, 3], 'b': [1, 2, 3]})
df = df.astype({'a': 'int16', 'b': 'float32'})
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12802 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12802/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12802/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12802/events | https://github.com/pandas-dev/pandas/pull/12802 | 146,054,168 | MDExOlB1bGxSZXF1ZXN0NjUzNDIxMTY= | 12,802 | BUG: fix json segfaults | {
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} | 26 | 2016-04-05T17:21:19Z | 2016-04-26T18:55:09Z | 2016-04-26T18:54:47Z | CONTRIBUTOR | null | closes #11473
closes #10778
closes #11299
- [x] tests added / passed
- [x] vbench / asv ok
- [x] windows tests pass
- [x] valgrind clean
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
This fixes several potential segfaults in the json code:
- time objects were freed and potentially garbage collected before their data had been read #11473. Was also segfaulting if an exception was raised during conversion (e.g. when using dateutil timezones).
- 0d arrays were not being handled corectly (needed `goto INVALID`) #11299
- all blocks were assumed to be ndarrays #10778
Fixing #10778 means non-ndarray blocks are now supported (although I think at present `category` is the only one?).
Not tested on windows yet. Seeing some unrelated travis failures on my fork (msgpack), is that normal?
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} | 11 | 2016-04-05T18:41:59Z | 2016-04-26T13:32:32Z | 2016-04-26T13:30:42Z | NONE | null | closes #11128
replaces #11813
- [x] tests added / passed
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- [x] whatsnew entry
Completed merge of Bahrunnur's PR 'weekday-name'
Moved 'enhancements' to v0.18.1.txt
Added tests to pandas/tests/series/test_datetime_values.py
Added weekday_name property producing 'NaT' to NaTType class
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