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| null | 11 | 2016-04-15T16:26:04Z | 2017-02-01T20:52:39Z | 2017-02-01T20:52:39Z | NONE | null | Support for creating secondary indexes in `to_sql` via the parameter `indexes`.
The code also tries to avoid creating duplicate indexes when `keys` is specified in `SQLTable`.
(Once the PR for passing the `keys` this would be useful)
This PR introduces a new method in `SQLTable` called `_is_column_indexed` so that subclasses can easily override or change the logic.
Interesting future work will be to also support creating compound indexes.
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} | 22 | 2016-04-15T16:45:07Z | 2019-12-11T13:29:52Z | 2019-12-11T13:29:52Z | NONE | null | Hi guys,
I am happy to help you improve `msgpack.`
I tried to export my massive dataframe this morning using `msgpack`, and I got this error.
`ValueError: EXT data is too large`
What does that mean? Is there a size limit?
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} | 2 | 2016-04-15T23:00:05Z | 2016-04-17T13:54:55Z | 2016-04-17T13:54:53Z | MEMBER | null | - [x] closes #11559
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 3 | 2016-04-16T11:48:55Z | 2016-05-27T00:15:29Z | 2016-05-27T00:15:29Z | NONE | null | read_csv has support for declaring the decimal and thousands separator.
read_html is missing the 'decimal' parameter. it'd be useful and more consistent to accept it too.
Example:
```
pd.read_html(html, thousands='.', decimal=',')
```
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} | 1 | 2016-04-16T12:11:50Z | 2016-04-20T01:07:53Z | 2016-04-20T01:07:37Z | MEMBER | null | - [x] no existing issue
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Fixed following bugs on current master. Also, moved `TestSparseArrayIndex` to `test_libsparse`
#### 1. shift
```
pd.SparseSeries([1, 2, 3], fill_value=0).shift(1)
# NameError: global name 'kwds' is not defined
pd.SparseSeries([1, 2, 3]).shift(1)
# TypeError: %d format: a number is required, not float
```
#### 2. dtype
```
from pandas.sparse.array import IntIndex
arr = pd.SparseArray([1, 2], sparse_index=IntIndex(4, [1, 2]), dtype=None)
arr.dtype
# dtype('int64')
arr.values
# array([-9223372036854775808, 1, 2,
# -9223372036854775808])
# Expected outputs
arr.dtype
# dtype('float64')
arr.values
# array([ nan, 1., 2., nan])
```
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} | 20 | 2016-04-16T22:08:18Z | 2016-04-26T20:06:16Z | 2016-04-26T19:21:19Z | CONTRIBUTOR | null | closes #12903
closes #11831
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/12910 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12910/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12910/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12910/events | https://github.com/pandas-dev/pandas/pull/12910 | 148,919,538 | MDExOlB1bGxSZXF1ZXN0NjY3NTg2NzI= | 12,910 | BUG: SparseArray numeric ops misc fixes | {
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} | 3 | 2016-04-17T05:10:55Z | 2016-04-18T17:13:21Z | 2016-04-18T17:13:21Z | MEMBER | null | - [x] no existing issue
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Fixed following 3 issues occurred on the current master.
### 1. addition ignores rhs `fill_value`
```
pd.SparseArray([1., 1.]) + pd.SparseArray([1., 0.], fill_value=0.)
# [2.0, nan]
# Fill: nan
# IntIndex
# Indices: array([0], dtype=int32)
Expected:
# [2.0, 1.0]
```
### 2. mod raises `AttributeError`
```
pd.SparseArray([1, 1]) % pd.SparseArray([1, np.nan])
# AttributeError: 'module' object has no attribute 'sparse_nanmod'
```
### 3. pow outputs incorrect result wiht `1.0 ** np.nan`
```
pd.SparseArray([1., 1.]) ** pd.SparseArray([1., np.nan])
# [1.0, nan]
# Fill: nan
# IntIndex
# Indices: array([0], dtype=int32)
Expected:
# [1.0, 1.0]
# NumPy result
np.array([1., 1.]) ** np.array([1, np.nan])
# array([ 1., 1.])
```
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} | 4 | 2016-04-17T14:17:59Z | 2016-04-18T15:26:14Z | 2016-04-18T15:26:14Z | CONTRIBUTOR | null | Someone pointed this out on twitter (sorry, forgot who), but it you should be `y_{it} =` not `y_it` as it looks like it might be now. The `t` should also be subscript.
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https://api.github.com/repos/pandas-dev/pandas/issues/12912 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12912/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12912/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12912/events | https://github.com/pandas-dev/pandas/issues/12912 | 148,957,490 | MDU6SXNzdWUxNDg5NTc0OTA= | 12,912 | DOC, BUG: Clarify and Standardize Whitespace Delimiter Behaviour with Custom Line Terminator | {
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} | 13 | 2016-04-17T14:35:20Z | 2016-04-21T21:10:15Z | 2016-04-21T21:10:15Z | MEMBER | null | ``` python
>>> from pandas import read_csv
>>> from pandas.compat import StringIO
>>> data = """a b c~1 2 3~4 5 6~7 8 9"""
>>> df = read_csv(StringIO(data), lineterminator='~', delim_whitespace=True)
>>> df
Empty DataFrame
Columns: [a, b, c~1, 2, 3~4, 5, 6~7, 8, 9]
Index: []
```
expected:
``` python
>>> df
a b c
0 1 2 3
1 4 5 6
2 7 8 9
```
Note that this bug is only for the C engine, as the Python engine does not yet support `delim_whitespace`.
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} | 3 | 2016-04-17T14:38:45Z | 2016-04-17T20:37:34Z | 2016-04-17T20:37:34Z | CONTRIBUTOR | null | - [x] closes #9915
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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} | 4 | 2016-04-17T16:03:27Z | 2016-04-18T15:51:44Z | 2016-04-18T15:51:26Z | MEMBER | null | Add test to verify that #7773 is no longer an issue. Closes #7773.
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} | 2 | 2016-04-17T19:22:42Z | 2016-04-19T18:36:05Z | 2016-04-19T18:36:05Z | MEMBER | null | Title is self-explanatory. Closes #5636.
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} | 8 | 2016-04-18T03:07:51Z | 2020-06-02T22:42:51Z | 2020-06-02T22:42:50Z | NONE | null | It looks like `df.ffill(axis=0)` has the same behavior as `test_df.interpolate(method='ffill', axis=1)`.
```
from pandas.util.testing import assert_frame_equal
import numpy as np
import pandas as pd
n = np.nan
test_df = pd.DataFrame([[0, 2, n, n],
[1, n, 4, 6],
[n, 3, 5, n]])
assert_frame_equal(
test_df.interpolate(method='ffill', axis=1),
test_df.ffill(axis=0),
)
```
Is this the desired behavior?
```
In [2]: pandas.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: 1.3.7
pip: 8.1.1
setuptools: 20.3.1
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.4.3
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 lo64)
jinja2: 2.7.3
boto: 2.39.0
```
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} | 1 | 2016-04-18T15:17:58Z | 2016-04-25T10:46:37Z | 2016-04-18T15:26:14Z | CONTRIBUTOR | null | - [ ] partially closes #12911
- [x] tests added / passed (N/A)
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} | 2 | 2016-04-18T23:32:56Z | 2016-07-27T21:31:55Z | 2016-07-27T21:31:55Z | NONE | null | #### Code Sample, a copy-pastable example if possible
``` python
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.18.0'
In [3]: di = pd.date_range('2006-10-29 00:00:00', periods=3, freq='H', tz='US/Pacific')
In [4]: di
Out[4]:
DatetimeIndex(['2006-10-29 00:00:00-07:00', '2006-10-29 01:00:00-07:00',
'2006-10-29 01:00:00-08:00'],
dtype='datetime64[ns, US/Pacific]', freq='H')
In [5]: df = pd.DataFrame(data={'a': [0,1,2], 'b': [3,4,5]}, index=di).reset_index()
In [6]: df
Out[6]:
index a b
0 2006-10-29 00:00:00-07:00 0 3
1 2006-10-29 01:00:00-07:00 1 4
2 2006-10-29 01:00:00-08:00 2 5
In [7]: df.set_index('index')
Out[7]:
a b
index
2006-10-29 00:00:00-07:00 0 3
2006-10-29 01:00:00-07:00 1 4
2006-10-29 01:00:00-08:00 2 5
In [8]: df.set_index(['index', 'a'])
---------------------------------------------------------------------------
AmbiguousTimeError Traceback (most recent call last)
<ipython-input-7-ecc11858b82b> in <module>()
----> 1 df.reset_index().set_index(['index', 'a'])
/usr/local/lib/python3.5/site-packages/pandas/core/frame.py in set_index(self, keys, drop, append, inplace, verify_integrity)
2835 arrays.append(level)
2836
-> 2837 index = MultiIndex.from_arrays(arrays, names=names)
2838
2839 if verify_integrity and not index.is_unique:
/usr/local/lib/python3.5/site-packages/pandas/indexes/multi.py in from_arrays(cls, arrays, sortorder, names)
835 return Index(arrays[0], name=name)
836
--> 837 cats = [Categorical.from_array(arr, ordered=True) for arr in arrays]
838 levels = [c.categories for c in cats]
839 labels = [c.codes for c in cats]
/usr/local/lib/python3.5/site-packages/pandas/indexes/multi.py in <listcomp>(.0)
835 return Index(arrays[0], name=name)
836
--> 837 cats = [Categorical.from_array(arr, ordered=True) for arr in arrays]
838 levels = [c.categories for c in cats]
839 labels = [c.codes for c in cats]
/usr/local/lib/python3.5/site-packages/pandas/core/categorical.py in from_array(cls, data, **kwargs)
377 the unique values of `data`.
378 """
--> 379 return Categorical(data, **kwargs)
380
381 @classmethod
/usr/local/lib/python3.5/site-packages/pandas/core/categorical.py in __init__(self, values, categories, ordered, name, fastpath, levels)
281 if categories is None:
282 try:
--> 283 codes, categories = factorize(values, sort=True)
284 except TypeError:
285 codes, categories = factorize(values, sort=False)
/usr/local/lib/python3.5/site-packages/pandas/core/algorithms.py in factorize(values, sort, order, na_sentinel, size_hint)
230 # reset tz
231 uniques = DatetimeIndex(uniques.astype('M8[ns]')).tz_localize(
--> 232 values.tz)
233 elif is_datetime:
234 uniques = uniques.astype('M8[ns]')
/usr/local/lib/python3.5/site-packages/pandas/util/decorators.py in wrapper(*args, **kwargs)
89 else:
90 kwargs[new_arg_name] = new_arg_value
---> 91 return func(*args, **kwargs)
92 return wrapper
93 return _deprecate_kwarg
/usr/local/lib/python3.5/site-packages/pandas/tseries/index.py in tz_localize(self, tz, ambiguous)
1841
1842 new_dates = tslib.tz_localize_to_utc(self.asi8, tz,
-> 1843 ambiguous=ambiguous)
1844 new_dates = new_dates.view(_NS_DTYPE)
1845 return self._shallow_copy(new_dates, tz=tz)
pandas/tslib.pyx in pandas.tslib.tz_localize_to_utc (pandas/tslib.c:67354)()
AmbiguousTimeError: Cannot infer dst time from Timestamp('2006-10-29 01:00:00'), try using the 'ambiguous' argument
```
#### Expected Output
``` python
In [8]: df.set_index(['index', 'a'])
Out[8]:
b
index a
2006-10-29 00:00:00-07:00 0 3
2006-10-29 01:00:00-07:00 1 4
2006-10-29 01:00:00-08:00 2 5
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
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: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.9.0
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: 0.7.0
IPython: 4.1.2
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12921 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12921/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12921/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12921/events | https://github.com/pandas-dev/pandas/pull/12921 | 149,318,462 | MDExOlB1bGxSZXF1ZXN0NjY5NDY5ODQ= | 12,921 | Correct out-of-bounds error with large indeces | {
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} | 4 | 2016-04-19T01:17:34Z | 2016-04-20T00:05:32Z | 2016-04-20T00:04:18Z | CONTRIBUTOR | null | - [x] closes #12527
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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} | 4 | 2016-04-19T07:10:14Z | 2016-09-09T21:14:52Z | 2016-06-16T12:32:24Z | CONTRIBUTOR | null | #### Failing test
```
def test_pandas():
import tempfile
import csv
import pandas as pd
import numpy as np
df = pd.DataFrame.from_dict({'column': [1.0, 2.0]})
assert df['column'].dtype == np.dtype('float')
with tempfile.TemporaryFile() as f:
df.to_csv(f, quoting=csv.QUOTE_NONNUMERIC, index=False)
f.seek(0)
lines = f.read().splitlines()
assert lines[0] == '"column"'
assert not lines[1].startswith('"') # <--- THIS FAILS
assert [1, 2] == map(float, lines[1:])
```
The issue is that the floats are being output wrapped with quotes, even though I requested QUOTE_NONNUMERIC.
The problem is that `pandas.core.internals.FloatBlock.to_native_types` (and by extension `pandas.formats.format.FloatArrayFormatter.get_result_as_array`) unconditionally formats the float array to a str array, which is then passed unchanged to the `csv` module and hence will be wrapped in quotes by that code.
I'm not 100% sure but the fix may be to have `FloatBlock.to_native_types` check if quoting is set, and if so to skip using the `FloatArrayFormatter`? I say this because `pandas.indexes.base.Index._format_native_types` already has a special case along these lines. This does seem a bit dirty though!
Here is an awful monkeypatch that works around the problem:
```
orig_to_native_types = pd.core.internals.FloatBlock.to_native_types
def to_native_types(self, *args, **kwargs):
if kwargs.get('quoting'):
values = self.values
slicer = kwargs.get('slicer')
if slicer is not None:
values = values[:, slicer]
return values
res = orig_to_native_types(self, *args, **kwargs)
print 'FloatBlock.to_native_types', args, kwargs, '=', res
return res
pd.core.internals.FloatBlock.to_native_types = to_native_types
```
#### output of `pd.show_versions()`
```
commit: None
python: 2.7.9.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: None
pip: 8.1.1
setuptools: 7.0
Cython: 0.20.1
numpy: 1.11.0
scipy: 0.13.3
statsmodels: None
xarray: None
IPython: 3.2.1
sphinx: None
patsy: 0.3.0
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: 1.0.0
tables: None
numexpr: None
matplotlib: 1.3.1
openpyxl: 2.0.4
xlrd: 0.9.2
xlwt: None
xlsxwriter: None
lxml: 3.3.2
bs4: 4.2.0
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
jinja2: 2.7.2
boto: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12923 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12923/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12923/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12923/events | https://github.com/pandas-dev/pandas/issues/12923 | 149,381,866 | MDU6SXNzdWUxNDkzODE4NjY= | 12,923 | Lots of unexpected behavior using resample after groupby | {
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| null | 5 | 2016-04-19T08:13:19Z | 2016-04-19T13:53:28Z | 2016-04-19T12:08:27Z | NONE | null | #### Code Sample, a copy-pastable example if possible
# PANDAS 0.18 code :
```
df=pd.DataFrame(np.ones((150,4)),columns=['A','B','C','D'],
index=pd.date_range('2014-01-01',freq='D',periods=150))
df2=pd.DataFrame(np.zeros((150,4)),columns=['A','B','C','D'],
index=pd.date_range('2014-01-01',freq='D',periods=150))
df=pd.concat([df,df2])
print df.groupby('B').mean()
print df.groupby('B').resample('MS').mean().head()
print 'shape : ',df.groupby('B').resample('MS').mean().shape
print df.groupby('B').apply(lambda x:x.resample('MS').mean()).head()
print 'shape : ',df.groupby('B').apply(lambda x:x.resample('MS').mean()).shape
print df.groupby('B').mean()
print df.groupby('B').resample('H').mean().head()
print 'shape : ',df.groupby('B').resample('H').mean().shape
print df.groupby('B').apply(lambda x:x.resample('H').mean()).head()
print 'shape : ',df.groupby('B').apply(lambda x:x.resample('H').mean()).shape
print 'pd version', pd.__version__
```
# PANDAS 0.17 equivalent code:
```
df=pd.DataFrame(np.ones((150,4)),columns=['A','B','C','D'],index=pd.date_range('2014-01-01',freq='D',periods=150))
df2=pd.DataFrame(np.zeros((150,4)),columns=['A','B','C','D'],index=pd.date_range('2014-01-01',freq='D',periods=150))
df=pd.concat([df,df2])
print df.groupby('B').mean()
print df.groupby('B').resample('MS').head()
print 'shape : ',df.groupby('B').resample('MS').shape
print df.groupby('B').apply(lambda x:x.resample('MS')).head()
print 'shape : ',df.groupby('B').apply(lambda x:x.resample('MS')).shape
print df.groupby('B').mean()
print df.groupby('B').resample('H').head()
print 'shape : ',df.groupby('B').resample('H').shape
print df.groupby('B').apply(lambda x:x.resample('H')).head()
print 'shape : ',df.groupby('B').apply(lambda x:x.resample('H')).shape
print 'pd version', pd.__version__
```
#### Expected Output
# Pandas 0.18 code Output :
```
A C D
```
B
0.0 0.0 0.0 0.0
1.0 1.0 1.0 1.0
A B C D
B
0.0 2014-01-01 0.0 0.0 0.0 0.0
2014-02-01 0.0 0.0 0.0 0.0
2014-03-01 0.0 0.0 0.0 0.0
2014-04-01 0.0 0.0 0.0 0.0
2014-05-01 0.0 0.0 0.0 0.0
shape : (10, 4)
A B C D
B
0.0 2014-01-01 0.0 0.0 0.0 0.0
2014-02-01 0.0 0.0 0.0 0.0
2014-03-01 0.0 0.0 0.0 0.0
2014-04-01 0.0 0.0 0.0 0.0
2014-05-01 0.0 0.0 0.0 0.0
shape : (10, 4)
A C D
B
0.0 0.0 0.0 0.0
1.0 1.0 1.0 1.0
A B C D
B
0.0 2014-01-01 0.0 0.0 0.0 0.0
2014-01-02 0.0 0.0 0.0 0.0
2014-01-03 0.0 0.0 0.0 0.0
2014-01-04 0.0 0.0 0.0 0.0
2014-01-05 0.0 0.0 0.0 0.0
shape : (300, 4)
A B C D
B
0.0 2014-01-01 00:00:00 0.0 0.0 0.0 0.0
2014-01-01 01:00:00 NaN NaN NaN NaN
2014-01-01 02:00:00 NaN NaN NaN NaN
2014-01-01 03:00:00 NaN NaN NaN NaN
2014-01-01 04:00:00 NaN NaN NaN NaN
shape : (7154, 4)
pd version 0.18.0
# Pandas 0.17 equivalent code Output :
A C D
B
0 0 0 0
1 1 1 1
A C D
B
0 2014-01-01 0 0 0
2014-02-01 0 0 0
2014-03-01 0 0 0
2014-04-01 0 0 0
2014-05-01 0 0 0
shape : (10, 3)
A B C D
B
0 2014-01-01 0 0 0 0
2014-02-01 0 0 0 0
2014-03-01 0 0 0 0
2014-04-01 0 0 0 0
2014-05-01 0 0 0 0
shape : (10, 4)
A C D
B
0 0 0 0
1 1 1 1
A C D
B
0 2014-01-01 00:00:00 0 0 0
2014-01-01 01:00:00 NaN NaN NaN
2014-01-01 02:00:00 NaN NaN NaN
2014-01-01 03:00:00 NaN NaN NaN
2014-01-01 04:00:00 NaN NaN NaN
shape : (7154, 3)
A B C D
B
0 2014-01-01 00:00:00 0 0 0 0
2014-01-01 01:00:00 NaN NaN NaN NaN
2014-01-01 02:00:00 NaN NaN NaN NaN
2014-01-01 03:00:00 NaN NaN NaN NaN
2014-01-01 04:00:00 NaN NaN NaN NaN
shape : (7154, 4)
pd version 0.17.1
# ISSUES :
in pandas 0.18.0 the column B is not dropped when applying resample afterwards (it should be dropped and put in index like with the simple example using .mean() after groupby).
in pandas 0.18.0 the behavior is correct when downsampling (example with 'MS') but is wrong when upsampling (example with 'H') The dataframe is not upsampled in that case and stays at freq='D'
A workaround is to use df.groupby('B').apply(lambda x: x.resample.mean()) but it's inelegant to say the least and does not solve the issue of B being not dropped in columns.
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https://api.github.com/repos/pandas-dev/pandas/issues/12924 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12924/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12924/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12924/events | https://github.com/pandas-dev/pandas/pull/12924 | 149,449,367 | MDExOlB1bGxSZXF1ZXN0NjcwMTA5Nzk= | 12,924 | BUG: Fix argument order in call to super | {
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} | 6 | 2016-04-19T12:40:36Z | 2016-05-19T14:53:00Z | 2016-05-19T14:52:39Z | CONTRIBUTOR | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
`super` should have arguments (type, object), not (object, type).
I've run `test_fast.sh` using Python 2.7 and it gives [2 failures](https://github.com/pydata/pandas/files/225866/testfail.txt) relating to language locales (I'm using en_GB). Pretty sure these are unrelated to the commit.
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https://api.github.com/repos/pandas-dev/pandas/issues/12925 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12925/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12925/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12925/events | https://github.com/pandas-dev/pandas/issues/12925 | 149,449,545 | MDU6SXNzdWUxNDk0NDk1NDU= | 12,925 | ENH: resample().interpolate() | {
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} | 5 | 2016-04-19T12:41:26Z | 2016-04-26T21:37:31Z | 2016-04-26T21:37:31Z | MEMBER | null | From https://github.com/pydata/pandas/pull/12449#issuecomment-211899951
When downsampling on a Resampler object, you now have different `fillna` methods to fill the NaNs (or `asfreq` for a plain reindex like operation without NaN filling). I can possibly make sense to also have `interpolate` available to fill the missing values directly (instead of first calling `mean`/`asfreq`)
```
In [20]: df = pd.DataFrame(data=[1,3], index=[dt.timedelta(), dt.timedelta(minut
es=3)])
In [21]: df
Out[21]:
0
00:00:00 1
00:03:00 3
In [22]: df.resample('1T').mean().interpolate('linear')
Out[22]:
0
00:00:00 1.000000
00:01:00 1.666667
00:02:00 2.333333
00:03:00 3.000000
```
Should `df.resample('1T').interpolate('linear')` give the same result?
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https://api.github.com/repos/pandas-dev/pandas/issues/12926 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12926/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12926/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12926/events | https://github.com/pandas-dev/pandas/issues/12926 | 149,452,813 | MDU6SXNzdWUxNDk0NTI4MTM= | 12,926 | BUG: resample().asfreq() looses end period with TimedeltaIndex | {
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} | 2 | 2016-04-19T12:52:21Z | 2016-04-20T01:03:18Z | 2016-04-20T01:03:18Z | MEMBER | null | ```
In [16]: df=pd.DataFrame(data=[1,3], index=[dt.timedelta(), dt.timedelta(minutes=3)])
In [17]: df
Out[17]:
0
00:00:00 1
00:03:00 3
In [18]: df.resample('1T').mean()
Out[18]:
0
00:00:00 1.0
00:01:00 NaN
00:02:00 NaN
00:03:00 3.0
In [19]: df.resample('1T').asfreq()
Out[19]:
0
00:00:00 1.0
00:01:00 NaN
00:02:00 NaN
```
The two examples should give the same result.
Tried it with a DatetimeIndex, and there I don't see this problem.
cc @jreback
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} | 5 | 2016-04-19T18:29:20Z | 2016-04-30T23:17:03Z | 2016-04-30T23:15:55Z | CONTRIBUTOR | null | While building a fresh snapshot (6c692ae) for debians ran into the following consistent one on debian stable and testing (built fine on 64bit, but failed in 32bit userspace):
```
======================================================================
ERROR: test_searchsorted_sorter (pandas.tests.series.test_analytics.TestSeriesAnalytics)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/build/pandas-0.18.0+git114-g6c692ae/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/series/test_analytics.py", line 1331, in test_searchsorted_sorter
r = s.searchsorted([0, 3], sorter=np.argsort(s))
File "/build/pandas-0.18.0+git114-g6c692ae/debian/tmp/usr/lib/python2.7/dist-packages/pandas/core/series.py", line 1488, in searchsorted
side=side, sorter=sorter)
ValueError: could not parse sorter argument
======================================================================
FAIL: test_sparse_max_row (pandas.tests.formats.test_format.TestSeriesFormatting)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/build/pandas-0.18.0+git114-g6c692ae/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/formats/test_format.py", line 3724, in test_sparse_max_row
self.assertEqual(result, exp)
AssertionError: '0 1.0\n1 NaN\n2 NaN\n3 3.0\n4 NaN\ndtype: float64\nBlockIndex\nBlock locations: array([0, 3])\nBlock lengths: array([1, 1])' != '0 1.0\n1 NaN\n2 NaN\n3 3.0\n4 NaN\ndtype: float64\nBlockIndex\nBlock locations: array([0, 3], dtype=int32)\nBlock lengths: array([1, 1], dtype=int32)'
```
I will replicate in interactive env atm, but thought that may be you have dealt with it already or smth so I wouldn't ;-)
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} | 3 | 2016-04-19T19:12:54Z | 2016-04-20T01:03:18Z | 2016-04-20T01:03:18Z | CONTRIBUTOR | null | closes #12926
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| null | 13 | 2016-04-19T20:43:30Z | 2016-08-27T12:48:52Z | 2016-08-26T20:24:31Z | CONTRIBUTOR | null | closes #12423
```
In [1]: pd.options.display.width=80
In [2]: pd.options.display.max_seq_items=10
In [3]: pd.options.display.max_seq_items=100
In [4]: mi = pd.MultiIndex.from_tuples([('A', 1), ('A', 2),
('B', 3), ('B', 4)])
In [5]: mi
Out[5]:
MultiIndex(levels=[[u'A', u'B'],
[1, 2, 3, 4]],
labels=[[0, 0, 1, 1],
[0, 1, 2, 3]])
In [6]: mi = MultiIndex.from_product([list('abcdefg'),
range(10),
pd.date_range('20130101', periods=10)],
names=['first', 'second', 'third'])
In [7]: mi
Out[7]:
MultiIndex(levels=[[u'a', u'b', u'c', u'd', u'e', u'f', u'g'],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06', '2013-01-07', '2013-01-08', '2013-01-09', '2013-01-10']],
labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
...
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2,
...
7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4,
...
5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],
names=[u'first', u'second', u'third'])
```
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} | 9 | 2016-04-20T10:33:55Z | 2018-09-16T17:02:25Z | 2016-05-16T12:09:51Z | CONTRIBUTOR | null | - [*] closes #xxxx
- [*] tests added / passed
- [*] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
Closes #11773
Travis-ci test JOB_NAME=27_slow_nnet_LOCALE consistently fails due to locale issue... zh_CN.GB18030??
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| null | 0 | 2016-04-20T11:30:16Z | 2016-04-20T13:12:24Z | 2016-04-20T13:12:24Z | CONTRIBUTOR | null | - [ ] closes #xxxx
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} | 16 | 2016-04-20T13:11:53Z | 2016-04-28T17:29:42Z | 2016-04-28T17:29:10Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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- Top N rows with offset
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- UPDATE
- DELETE
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} | 0 | 2016-04-20T13:36:53Z | 2016-05-22T20:02:38Z | 2016-05-22T20:02:38Z | CONTRIBUTOR | null | ```
In [5]: pd.read_csv(StringIO('1,2;2,3'),decimal=',',sep=';',header=None,engine='c')
Out[5]:
0 1
0 1.2 2.3
In [6]: pd.read_csv(StringIO('1,2;2,3'),decimal=',',sep=';',header=None,engine='python')
ValueError: The 'decimal' option is not supported with the 'python' engine
```
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} | 1 | 2016-04-20T14:43:49Z | 2016-04-22T17:04:17Z | 2016-04-22T17:03:29Z | CONTRIBUTOR | null | I will be happy to write up a pull request, but first wanted to gauge the sanity of my suggestion:
I think that `swaplevel()` deserves default values for its parameters, just like its friends like `stack()` and `unstack()` and `sortlevel()` that also all take an initial `level` argument. I suggest:
```
def swaplevel(self, i=-2, j=-1, axis=0):
```
This provides the greatest symmetry with the other methods that operate on levels: they all, if no level is specified, operate on the innermost level as their default.
In the very common case where there are only two levels to the multi-index anyway, this would reduce this frequent operation to simply `.swaplevel()` or `.swaplevel(axis='columns')` without, I don't think, any more loss of readability than when `stack()` or `unstack()` fail to specify the level upon which they are operating.
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} | 44 | 2016-04-20T16:14:12Z | 2017-06-22T03:19:02Z | 2016-05-23T21:43:04Z | MEMBER | null | Introduces `mappings` and `reverse_map` attributes to the parser in `pandas.io.parsers` that allow it to differentiate between duplicate columns that may be present in a file.
Closes #7160.
Closes #9424.
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} | 1 | 2016-04-20T21:04:04Z | 2016-04-20T21:35:21Z | 2016-04-20T21:33:57Z | CONTRIBUTOR | null | makes creation w/o specifying a dtype choose `np.int64/float64` regardless of the platform. This is similar to how Series works. This only is exposed on windows (as the default ndarray creation is np.int32) rather than np.int64
maybe _should_ use `pandas.core.series._sanitize_array` (but needs some tweeks I think).
cc @sinhrks
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} | 1 | 2016-04-20T21:58:37Z | 2016-06-24T21:57:28Z | 2016-04-20T23:02:53Z | NONE | null | ``` python
In [195]: df = pd.DataFrame(np.random.randn(14), index=np.arange(138.915, 140.0, 0.08),
columns=['noise'])
In [206]: df
Out[206]:
noise
138.915 -1.474675
138.995 0.768455
139.075 0.154067
139.155 1.201179
139.235 -0.554067
139.315 -0.713285
139.395 0.643849
139.475 0.601655
139.555 1.331853
139.635 -0.900771
139.715 0.823289
139.795 -0.604716
139.875 -0.625836
139.955 0.604149
In [196]: df.index
Out[196]:
Float64Index([138.915, 138.995, 139.075, 139.155, 139.235, 139.315, 139.395,
139.475, 139.555, 139.635, 139.715, 139.795, 139.875, 139.955],
dtype='float64')
```
If I have the above data frame, label based slicing only work for the first two index.
``` python
In [204]: df.loc[138.915]
Out[204]:
noise -1.474675
Name: 138.915, dtype: float64
In [202]: df.loc[138.995]
Out[202]:
noise 0.768455
Name: 138.995, dtype: float64
In [205]: df.loc[139.075]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/data2/piyanat/src/miniconda3/lib/python3.5/site-packages/pandas/core/indexing.py in _has_valid_type(self, key, axis)
1382 if key not in ax:
-> 1383 error()
1384 except TypeError as e:
/data2/piyanat/src/miniconda3/lib/python3.5/site-packages/pandas/core/indexing.py in error()
1377 raise KeyError("the label [%s] is not in the [%s]" %
-> 1378 (key, self.obj._get_axis_name(axis)))
1379
KeyError: 'the label [139.075] is not in the [index]'
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
<ipython-input-205-069cc26fcd45> in <module>()
----> 1 df.loc[139.075]
/data2/piyanat/src/miniconda3/lib/python3.5/site-packages/pandas/core/indexing.py in __getitem__(self, key)
1284 return self._getitem_tuple(key)
1285 else:
-> 1286 return self._getitem_axis(key, axis=0)
1287
1288 def _getitem_axis(self, key, axis=0):
/data2/piyanat/src/miniconda3/lib/python3.5/site-packages/pandas/core/indexing.py in _getitem_axis(self, key, axis)
1427
1428 # fall thru to straight lookup
-> 1429 self._has_valid_type(key, axis)
1430 return self._get_label(key, axis=axis)
1431
/data2/piyanat/src/miniconda3/lib/python3.5/site-packages/pandas/core/indexing.py in _has_valid_type(self, key, axis)
1389 raise
1390 except:
-> 1391 error()
1392
1393 return True
/data2/piyanat/src/miniconda3/lib/python3.5/site-packages/pandas/core/indexing.py in error()
1376 "key")
1377 raise KeyError("the label [%s] is not in the [%s]" %
-> 1378 (key, self.obj._get_axis_name(axis)))
1379
1380 try:
KeyError: 'the label [139.075] is not in the [index]'
```
But range slicing works.
``` python
In [207]: df.loc[139.3:139.8]
Out[207]:
noise
139.315 -0.713285
139.395 0.643849
139.475 0.601655
139.555 1.331853
139.635 -0.900771
139.715 0.823289
139.795 -0.604716
```
#### output of `pd.show_versions()`
``` python
In [209]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 3.5.0-41-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: en_US.UTF-8
pandas: 0.18.0
nose: None
pip: 8.1.1
setuptools: 20.6.7
Cython: None
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: None
patsy: None
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.2
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: None
boto: None
```
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} | 6 | 2016-04-20T22:40:55Z | 2017-03-28T17:12:27Z | 2017-03-28T12:44:10Z | NONE | null | Hi, using different indexers on a dataframes with timezone-aware timestamps may return naive timestamps. (A brief search attempt didn't turn up any related issues.)
#### Code Sample, a copy-pastable example if possible
``` python
>>> import pandas as pd
>>> df = pd.Series([pd.tslib.Timestamp('2016-03-30 14:35:25.506088-04:00')]).to_frame()
>>> df = pd.concat([df, df]).reset_index(drop=True)
>>> df[0][0]
Timestamp('2016-03-30 14:35:25.506088-0400', tz='pytz.FixedOffset(-240)')
>>> df.iloc[0,0]
Timestamp('2016-03-30 14:35:25.506088-0400', tz='pytz.FixedOffset(-240)')
>>> df.loc[0,0]
Timestamp('2016-03-30 14:35:25.506088-0400', tz='pytz.FixedOffset(-240)')
>>> df.iat[0,0]
Timestamp('2016-03-30 14:35:25.506088-0400', tz='pytz.FixedOffset(-240)')
>>> df.at[0,0]
Timestamp('2016-03-30 18:35:25.506088')
>>> df.ix[0,0]
Timestamp('2016-03-30 18:35:25.506088')
>>> df.ix[0]
Timestamp('2016-03-30 14:35:25.506088-0400', tz='pytz.FixedOffset(-240)')
>>> df[0].at[0]
Timestamp('2016-03-30 14:35:25.506088-0400', tz='pytz.FixedOffset(-240)')
>>> df[0].ix[0]
Timestamp('2016-03-30 14:35:25.506088-0400', tz='pytz.FixedOffset(-240)')
```
#### Expected Output
I'd expect `df.at[0,0]` and `df.ix[0,0]` to show the same timezone-aware timestamps as the other indexing methods.
#### output of `pd.show_versions()`
``` python
>>> import pandas as pd
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.18.12-el6.ia32e.lime.0
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: None
pip: 7.1.0
setuptools: 19.4
Cython: None
numpy: 1.11.0
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
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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https://api.github.com/repos/pandas-dev/pandas/issues/12939 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12939/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12939/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12939/events | https://github.com/pandas-dev/pandas/pull/12939 | 149,914,371 | MDExOlB1bGxSZXF1ZXN0NjcyNzUxOTg= | 12,939 | BUG, DOC: Allow custom line terminator with delim_whitespace=True | {
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} | 17 | 2016-04-20T23:32:11Z | 2016-04-21T21:11:51Z | 2016-04-21T21:10:15Z | MEMBER | null | Title is self-explanatory. Closes #12912.
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| null | 5 | 2016-04-21T05:54:20Z | 2016-04-21T17:45:38Z | 2016-04-21T09:47:59Z | NONE | null | **I want to be able to create `n-dimensional` dataframes.** I've heard of a method for 3D dataframes using `panels` in `pandas` but, if possible, I would like to extend the dimensions past 3 dims by combining different datasets into a _super dataframe_
I made a random test dataset with arbitrary axis data trying to mimic a real situation; there are 3 axis (i.e. patients, years, and samples). I tried adding a bunch of dataframes to a list and then making a dataframe with that but it didn't work :( I even tried a `panel` as in the 2nd link above but I couldn't get it to work either.
**It would be really helpful to have a method to create a N-dimensional pandas dataframe w/ labels**
The first way I tried:
```
#Reproducibility
np.random.seed(1618033)
#Set 3 axis labels/dims
axis_1 = np.arange(2000,2010) #Years
axis_2 = np.arange(0,20) #Samples
axis_3 = np.array(["patient_%d" % i for i in range(0,3)]) #Patients
#Create random 3D array to simulate data from dims above
A_3D = np.random.random((years.size, samples.size, len(patients))) #(10, 20, 3)
#Create empty list to store 2D dataframes (axis_2=rows, axis_3=columns) along axis_1
list_of_dataframes=[]
#Iterate through all of the year indices
for i in range(axis_1.size):
#Create dataframe of (samples, patients)
DF_slice = pd.DataFrame(A_3D[i,:,:],index=axis_2,columns=axis_3)
list_of_dataframes.append(DF_slice)
# print(DF_slice) #preview of the 2D dataframes "slice" of the 3D array
# patient_0 patient_1 patient_2
# 0 0.727753 0.154701 0.205916
# 1 0.796355 0.597207 0.897153
# 2 0.603955 0.469707 0.580368
# 3 0.365432 0.852758 0.293725
# 4 0.906906 0.355509 0.994513
# 5 0.576911 0.336848 0.265967
# ...
# 19 0.583495 0.400417 0.020099
# DF_3D = pd.DataFrame(list_of_dataframes,index=axis_2, columns=axis_1)
# Error
# Shape of passed values is (1, 10), indices imply (10, 20)
```
I could do something like this I guess, but I really like `pandas` and would rather use one of their methods if one exists:
```
#Set data for query
query_year = 2007
query_sample = 15
query_patient = "patient_1"
#Index based on query
A_3D[
(axis_1 == query_year).argmax(),
(axis_2 == query_sample).argmax(),
(axis_3 == query_patient).argmax()
]
#0.1231212416981845
```
It would be awesome to access the data in this way:
```
DF_3D[query_year][query_sample][query_patient]
#Where DF_3D[query_year] would give a list of 2D arrays (row=sample, col=patient)
# DF_3D[query_year][query_sample] would give a 1D vector/list of patient data for a particular year, of a particular sample.
# and DF_3D[query_year][query_sample][query_patient] would be a particular sample of a particular patient of a particular year
```
My apologies if this isn't the right place for feature requests.
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} | 3 | 2016-04-21T09:06:24Z | 2016-08-06T22:57:12Z | 2016-08-06T22:57:12Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
df = pd.DataFrame({'t1': pd.NaT, 't2': pd.NaT}, index=[1])
df.min(axis=1)
df.max(axis=1)
df.sum(axis=1)
```
#### Expected Output
All results above return a `float64` series instead of a `datetime64[ns]` series.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
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: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.18.0
nose: None
pip: 8.1.1
setuptools: 20.9.0
Cython: None
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.3
blosc: None
bottleneck: None
tables: None
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: 0.7.2.None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
```
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| null | 2 | 2016-04-21T15:23:33Z | 2016-04-21T15:32:03Z | 2016-04-21T15:31:41Z | NONE | null | #### Code Sample, a copy-pastable example if possible
`
pd.isnull(np.array([np.nan, 1, 2, "s"]))
Out[120]: array([False, False, False, False], dtype=bool)
`
`
pd.isnull(pd.Series([np.nan, 1, 2, "s"]))
Out[116]:
0 True
1 False
2 False
3 False
`
#### Expected Output
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.10.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: 0.16.2
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
dateutil: 2.4.2
pytz: 2015.4
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.5
pymysql: None
psycopg2: None
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} | 1 | 2016-04-21T15:42:49Z | 2016-04-26T21:51:34Z | 2016-04-26T21:51:34Z | CONTRIBUTOR | null | so this parade of dateutil changes causes havoc in the testsuite as we adjust.
previous fixes: #12731, #12613
revert this: https://github.com/pydata/pandas/commit/161724470784da042f8fd5294cfb39acad6dc5c2
Let's see what the correct fixes are and maybe need to ban certain buggy versions.
```
======================================================================
FAIL: 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 631, in test_parsers_dayfirst_yearfirst
self.assertRaises(tslib.DateParseError, f, date_str)
File "/Users/jreback/pandas/pandas/util/testing.py", line 2162, in assertRaises
_callable(*args, **kwargs)
File "/Users/jreback/pandas/pandas/util/testing.py", line 2232, in __exit__
raise AssertionError("{0} not raised.".format(name))
AssertionError: DateParseError not raised.
```
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| null | 2 | 2016-04-21T15:50:47Z | 2020-06-17T09:39:28Z | null | CONTRIBUTOR | null | It would be nice to be able to write ternary operator into expressions submitted to `eval`.
#### Code Sample
``` python
df = pd.DataFrame({'a': range(10), 'b': range(10,20)})
df.eval('x = a if a < 5 else b', inplace = True)
df
Traceback (most recent call last):
File "<ipython-input-27-89e215206825>", line 1, in <module>
df.eval('x = a if a < 5 else b')
File "/home/benjello/.local/lib/python2.7/site-packages/pandas/core/frame.py", line 2209, in eval
return _eval(expr, inplace=inplace, **kwargs)
File "/home/benjello/.local/lib/python2.7/site-packages/pandas/computation/eval.py", line 245, in eval
truediv=truediv)
File "/home/benjello/.local/lib/python2.7/site-packages/pandas/computation/expr.py", line 709, in __init__
self.terms = self.parse()
File "/home/benjello/.local/lib/python2.7/site-packages/pandas/computation/expr.py", line 726, in parse
return self._visitor.visit(self.expr)
File "/home/benjello/.local/lib/python2.7/site-packages/pandas/computation/expr.py", line 310, in visit
return visitor(node, **kwargs)
File "/home/benjello/.local/lib/python2.7/site-packages/pandas/computation/expr.py", line 316, in visit_Module
return self.visit(expr, **kwargs)
File "/home/benjello/.local/lib/python2.7/site-packages/pandas/computation/expr.py", line 310, in visit
return visitor(node, **kwargs)
File "/home/benjello/.local/lib/python2.7/site-packages/pandas/computation/expr.py", line 494, in visit_Assign
return self.visit(node.value, **kwargs)
File "/home/benjello/.local/lib/python2.7/site-packages/pandas/computation/expr.py", line 310, in visit
return visitor(node, **kwargs)
File "/home/benjello/.local/lib/python2.7/site-packages/pandas/computation/expr.py", line 201, in f
"implemented".format(node_name))
NotImplementedError: 'IfExp' nodes are not implemented
```
#### Expected Output
```
a b x
0 0 10 0
1 1 11 1
2 2 12 2
3 3 13 3
4 4 14 4
5 5 15 15
6 6 16 16
7 7 17 17
8 8 18 18
9 9 19 19
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-1-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 1.5.6
setuptools: 18.8
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.0.3
sphinx: 1.3.6
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.0
xlrd: 0.9.4
xlwt: 0.7.5
xlsxwriter: 0.7.3
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
```
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} | 47 | 2016-04-21T16:13:19Z | 2016-06-24T01:34:38Z | 2016-06-24T00:01:47Z | CONTRIBUTOR | null | - [ ] closes #10598
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
Travis is not always detecting sudo use and is running forks using container-based infrastructure. `JOB_NAME=27_slow_nnet_LOCALE` fails when using containers because of setlocale. This PR adds `sudo: required` to make sure travis does not use containers while sudo is needed in the travis set up scripts.
Example:
- https://travis-ci.org/nparley/pandas/jobs/124695152 (container)
- https://travis-ci.org/nparley/pandas/jobs/124778492 (non container)
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} | 4 | 2016-04-21T16:49:02Z | 2016-05-07T14:51:35Z | 2016-05-07T14:51:35Z | NONE | null | ``` python
df = pandas.DataFrame([[1,2,3,4,5]], columns=["A", "B", "C", "D", "E"])
# Suppose you want to set df.B to df.C when df.A ==1
# then the following both work:
df.loc[df.A== 1, "B"] = df.loc[df.A == 1, "C"]
df.ix[df.A == 1, "B"] = df.ix[df.A == 1, "C"]
# you can even mix and match them with ix on one side and loc on the other
# but maybe you have two or more columns you want to set... Its natural to think that:
df.loc[df.A== 1, ["B", "C"]] = df.loc[df.A == 1, ["D", "E"]]
df.ix[df.A == 1, ["B", "C"]] = df.ix[df.A == 1, ["D", "E"]]
# but they actually just NaN out df.B and df.C (it isn't an issue of a silent copy losing updates)
# in fact the application of NaN even happens if you have singletons
df.loc[df.A== 1, ["D"]] = df.loc[df.A == 1, ["E"]]
# presumably because
type(df.ix[df.A == 1, "B"])
# is pandas.core.series.Series but
type(df.ix[df.A == 1, ["B"]])
# is pandas.core.frame.DataFrame
# but when printed they look really similar...
#0 3
# Name: B, dtype: int64
# versus
# B
#0 3
# so it is easy to get confused
# If this can't be made to work in the natural fashion it would be a lot nicer if it could just throw an error
# the same way the following does:
df.ix[df.A == 1, ["B", "C"]] = df[df.A == 1]["D", "E"]
```
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.29.2.el6.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: 20.4
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: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: None
boto: None
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https://api.github.com/repos/pandas-dev/pandas/issues/12948 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12948/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12948/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12948/events | https://github.com/pandas-dev/pandas/issues/12948 | 150,133,662 | MDU6SXNzdWUxNTAxMzM2NjI= | 12,948 | BUG: bug in construction using a tuple indexer with embedded None | {
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"description": "Changes that would be nice to have in the next release. These issues are not blocking. They will be pushed to the next release if no one has time to fix them.",
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} | 5 | 2016-04-21T17:02:17Z | 2020-01-21T01:06:10Z | 2020-01-21T01:06:10Z | NONE | null | In the following example, I was surprised that the `None` in the index somehow converts the value 1.0 to be stored as a `nan` in the values. Feel free to close if this is obviously user error :)
#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
d = {(1, 1, None):-1.0, (1, 1, 1):-2.0, (1, 1, 2):-3.0}
x = pd.Series(d)
x
#### Naive Expected Output
1 1 None -1.0
1 -2.0
2 -3.0
dtype: float64
#### Actual observed output:
1 1 NaN NaN
1 -2.0
2 -3.0
dtype: float64
#### output of ``pd.show_versions()``
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.0.3
setuptools: 20.1.1
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.1
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
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: 3.5.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12949 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12949/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12949/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12949/events | https://github.com/pandas-dev/pandas/pull/12949 | 150,141,527 | MDExOlB1bGxSZXF1ZXN0NjczOTMwOTY= | 12,949 | Fix Scatter plot datetime and Axis | {
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| null | 27 | 2016-04-21T17:34:39Z | 2017-08-01T22:55:52Z | 2017-08-01T22:55:52Z | CONTRIBUTOR | null | closes #10611
closes #8113
closes #10678
- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
This is a first go at trying to fix the above problems. I have created two before and after notebooks here: https://gist.github.com/nparley/08c81672b0e3d7d85b00694a671f57ad
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| null | 2 | 2016-04-21T18:14:59Z | 2016-04-21T20:52:07Z | 2016-04-21T20:50:41Z | CONTRIBUTOR | null | `.apply(...)` on window-based groupbys receive an `ndarray` rather than a Pandas object.
Is this intentional? Is it a performance issue?
It means that functions that work on `Series` can't necessarily be used on groupbys, which limits abstraction.
```
In [7]: pd.Series(range(10)).expanding().apply(lambda x: x.pow(2).sum())
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-7-970afaf538ff> in <module>()
----> 1 pd.Series(range(10)).expanding().apply(lambda x: x.pow(2).sum())
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/window.py in apply(self, func, args, kwargs)
929 @Appender(_shared_docs['apply'])
930 def apply(self, func, args=(), kwargs={}):
--> 931 return super(Expanding, self).apply(func, args=args, kwargs=kwargs)
932
933 @Substitution(name='expanding')
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/window.py in apply(self, func, args, kwargs)
547 kwargs)
548
--> 549 return self._apply(f, center=False)
550
551 def sum(self, **kwargs):
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/window.py in _apply(self, func, window, center, check_minp, how, **kwargs)
487 result = np.apply_along_axis(calc, self.axis, values)
488 else:
--> 489 result = calc(values)
490
491 if center:
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/window.py in calc(x)
482
483 def calc(x):
--> 484 return func(x, window, min_periods=self.min_periods)
485
486 if values.ndim > 1:
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/window.py in f(arg, window, min_periods)
545 minp = _use_window(min_periods, window)
546 return algos.roll_generic(arg, window, minp, offset, func, args,
--> 547 kwargs)
548
549 return self._apply(f, center=False)
pandas/algos.pyx in pandas.algos.roll_generic (pandas/algos.c:40613)()
<ipython-input-7-970afaf538ff> in <lambda>(x)
----> 1 pd.Series(range(10)).expanding().apply(lambda x: x.pow(2).sum())
AttributeError: 'numpy.ndarray' object has no attribute 'pow'
```
###### Expected (without the coercion):
``` python
In [9]: pd.Series(range(10)).expanding().apply(lambda x: pd.Series(x).pow(2).sum())
Out[9]:
0 0.0
1 1.0
2 5.0
3 14.0
4 30.0
5 55.0
6 91.0
7 140.0
8 204.0
9 285.0
dtype: float64
```
Pandas 0.18
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https://api.github.com/repos/pandas-dev/pandas/issues/12951 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12951/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12951/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12951/events | https://github.com/pandas-dev/pandas/pull/12951 | 150,186,761 | MDExOlB1bGxSZXF1ZXN0Njc0MTk2MDY= | 12,951 | ENH/COMPAT: update tests for dateutil 2.5.3 | {
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} | 9 | 2016-04-21T20:46:46Z | 2016-04-26T23:17:37Z | 2016-04-26T21:51:34Z | CONTRIBUTOR | null | closes #12944
so this fixes the compat with all dateutils in the test suite
add the `dayfirst` and `yearfirst` args to `Timestamp`. This make testing and compat a lot
easier. xref discussion in #7599 where we didn't implement it, though the feeling was sort of +0.
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} | 2 | 2016-04-22T00:49:51Z | 2019-10-21T20:03:53Z | null | CONTRIBUTOR | null | #### Code
``` python
store = pd.HDFStore('/tmp/tmp.h5')
dfq = pd.DataFrame(np.random.randn(10,4), columns=list('ABCD'), index=pd.date_range('20130101',periods=10))
store.append('dfq', dfq, format='table')
store.select('dfq', where="columns=['A']", chunksize=3) # or iterator=True
```
#### Error
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-202-a39cd8f01057> in <module>()
----> 1 store.select('dfq', where="columns=['A']", chunksize=3)
/usr/lib/python3.5/site-packages/pandas/io/pytables.py in select(self, key, where, start, stop, columns, iterator, chunksize, a
uto_close, **kwargs)
678 chunksize=chunksize, auto_close=auto_close)
679
--> 680 return it.get_result()
681
682 def select_as_coordinates(
/usr/lib/python3.5/site-packages/pandas/io/pytables.py in get_result(self, coordinates)
1351 "can only use an iterator or chunksize on a table")
1352
-> 1353 self.coordinates = self.s.read_coordinates(where=self.where)
1354
1355 return self
/usr/lib/python3.5/site-packages/pandas/io/pytables.py in read_coordinates(self, where, start, stop, **kwargs)
3581 for field, op, filt in self.selection.filter.format():
3582 data = self.read_column(
-> 3583 field, start=coords.min(), stop=coords.max() + 1)
3584 coords = coords[
3585 op(data.iloc[coords - coords.min()], filt).values]
/usr/lib/python3.5/site-packages/pandas/io/pytables.py in read_column(self, column, where, start, stop, **kwargs)
3621 a.tz, True), name=column)
3622
-> 3623 raise KeyError("column [%s] not found in the table" % column)
3624
3625
KeyError: 'column [columns] not found in the table'
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.5.0-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: None
pip: 8.1.1
setuptools: 20.9.0
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
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: 2.39.0
```
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| null | 1 | 2016-04-22T06:08:43Z | 2016-04-22T10:26:41Z | 2016-04-22T10:26:41Z | NONE | null | #### Code Sample, a copy-pastable example if possible
df = pd.DataFrame({'a': [1, 2]}, index={'two', 'one'})
df
df = pd.DataFrame({'a': [1, 2]}, index={'one', 'two'})
df
got the same result :
a
two 1
one 2
#### Expected Output
```
a
```
two 1
one 2
```
a
```
one 1
two 2
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 32
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: 16.0
Cython: None
numpy: 1.11.0
scipy: 0.16.1
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: 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
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| null | 2 | 2016-04-22T06:31:32Z | 2016-04-22T10:34:16Z | 2016-04-22T10:30:40Z | NONE | null | #### Code Sample, a copy-pastable example if possible
import numpy as np
import pandas as pd
df = pd.DataFrame(np.arange(0, 15).reshape(5, 3),
index=['a', 'b', 'c', 'd', 'e'],
columns=['c1', 'c2', 'c3'])
How to change all the places where c1==0 to c1=5 for example? (In this example only the [a][c1] is changed, but could be multiple places if there are more 0 in the c1 column.)
Thinking of something like df[df.c1==0].c1=5.
(This is similar to fill na with a pecified value)
#### Expected Output
#### output of `pd.show_versions()`
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| null | 2 | 2016-04-22T08:06:01Z | 2016-04-22T14:22:58Z | 2016-04-22T14:22:58Z | NONE | null | ```
#### Code Sample, a copy-pastable example if possible
df = pd.DataFrame({'Label': list('AABCCC'),
'Values': [1, 2, 3, 4, 6, 8]})
ranges = [6, 12, 18, 35, 50]
agebins = pd.cut(df['Values'],ranges)
agebins.describe()
```
Not knowing if there is an alternative approach than a covert to numpy array
Usually data isloaded from a csv file, it would be convinient to do some discritization and statistics like
that can be done with a numpython array. for example:
eg:
```
np.random.seed(123456)
ages = np.random.randint(6, 45, 50)
ranges = [6, 12, 18, 35, 50]
agebins = pd.cut(ages, ranges)
agebins.describe()
Output:
counts freqs
categories
(6, 12] 8 0.16
(12, 18] 9 0.18
(18, 35] 21 0.42
(35, 50] 12 0.24
#### Expected Output
counts freqs
categories
(6, 12]
(12, 18]
(18, 35]
(35, 50] 12 0.24
#### output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 32
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: 16.0
Cython: None
numpy: 1.11.0
scipy: 0.16.1
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: 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
```
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| null | 5 | 2016-04-22T12:37:28Z | 2016-04-22T13:19:29Z | 2016-04-22T13:03:21Z | NONE | null | - In system env, I am not able to import pandas after updating to 0.18.0. Installed/updated by conda on Win 8.1.
- running
1. `conda remove pandas`
2. `conda install pandas`
does not help
- after installing into fresh new virtual env, import works.
Probably it is bad state of the environment, could you please advise me how to fix it?
#### ImportError on importing
``` bash
C:\WINDOWS\system32>python
Python 2.7.11 |Anaconda 2.3.0 (64-bit)| (default, Feb 16 2016, 09:58:36) [MSC v.
1500 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://anaconda.org
>>> import pandas
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Program Files\Anaconda\lib\site-packages\pandas\__init__.py", line 44
, in <module>
from pandas.core.api import *
File "C:\Program Files\Anaconda\lib\site-packages\pandas\core\api.py", line 10
, in <module>
from pandas.core.groupby import Grouper
File "C:\Program Files\Anaconda\lib\site-packages\pandas\core\groupby.py", lin
e 18, in <module>
from pandas.core.frame import DataFrame
File "C:\Program Files\Anaconda\lib\site-packages\pandas\core\frame.py", line
5230, in <module>
_EMPTY_SERIES = Series([])
File "C:\Program Files\Anaconda\lib\site-packages\pandas\core\series.py", line
216, in __init__
index = _default_index(len(data))
File "C:\Program Files\Anaconda\lib\site-packages\pandas\core\common.py", line
1867, in _default_index
from pandas.core.index import RangeIndex
ImportError: cannot import name RangeIndex
```
#### conda information
C:\WINDOWS\system32>conda list pandas
# packages in environment at C:\Program Files\Anaconda:
#
pandas 0.18.0 np110py27_0
#### output of `pd.show_versions()`
Not available.
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| null | 3 | 2016-04-22T15:43:53Z | 2016-04-22T23:09:14Z | 2016-04-22T23:09:14Z | NONE | null | Let's say we have a file with 2 json encoded rows that we turn into a DataFrame:
```
{"first_name":"Manuel", "last_name":"Garrido Peña","experience":[{"name":"Peña Industries", "start":"May 2014"}]}
{"first_name":"Ana", "last_name":"Cañón", "experience":[{"name":"Cañón technologies","start":"April 2016"}]}
```
If one of the fields in a dataframe is a python object ( e.g. a dictionary) with unicode strings on it, the
output of `df.to_csv(NAME, encoding='utf8', index=False) is different with Python2 than with Python3.
```
#python2 output
experience,first_name,last_name
"[{u'start': u'May 2014', u'name': u'Peña Industries'}]",Manuel,Garrido Peña
"[{u'start': u'April 2016', u'name': u'Cañón technologies'}]",Ana,Cañón
#python3 output (expected)
experience,first_name,last_name
"[{'name': 'Peña Industries', 'start': 'May 2014'}]",Manuel,Garrido Peña
"[{'name': 'Cañón technologies', 'start': 'April 2016'}]",Ana,Cañón
```
Basically, it would make sense to be able to read back the `experience` field on that csv (maybe with a safe_eval?), and the unicode flags make it very hard to parse.
The files (and versions) for this example can be found on [this gist](https://gist.github.com/manugarri/4c0d68f9c2c8434654402e6053620b68)
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} | 1 | 2016-04-22T16:01:19Z | 2021-04-23T05:07:58Z | null | CONTRIBUTOR | null | xref #12943
seems we have lots of implementations of the same routine with slightly different API.
`pd.DataFrame.swaplevel(self, i, j, axis=0, inplace=False)` (this is before the default args).
I would remove the `copy` arg from `Series` and use something like this.
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https://api.github.com/repos/pandas-dev/pandas/issues/12961 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12961/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12961/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12961/events | https://github.com/pandas-dev/pandas/pull/12961 | 150,428,278 | MDExOlB1bGxSZXF1ZXN0Njc1NDYzODQ= | 12,961 | PERF: some more perf/clean in saslib.pyx | {
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} | 9 | 2016-04-22T17:33:01Z | 2016-04-27T13:42:10Z | 2016-04-27T13:41:30Z | CONTRIBUTOR | null | more perf
cc @kshedden
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} | 3 | 2016-04-22T20:21:44Z | 2017-09-25T11:12:27Z | 2017-09-25T11:12:27Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
mylist = [pd.Series([1,2,3])]
for m in mylist:
m /= 2.0
print '***printing m***'
print m
print
print '***mylist[0] - should be same as m, but isnt***'
print mylist[0]
```
#### observed output:
```
***printing m***
0 0.5
1 1.0
2 1.5
dtype: float64
***mylist[0] - should be same as m, but isnt***
0 1
1 2
2 3
dtype: int64
```
#### Expected Output
```
***printing m***
0 1
1 2
2 3
dtype: float64
***mylist[0] - should be same as m, but isnt***
0 1
1 2
2 3
dtype: int64
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
nose: None
pip: 8.1.1
setuptools: 20.6.7
Cython: None
numpy: 1.11.0
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
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: None
boto: None
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https://api.github.com/repos/pandas-dev/pandas/issues/12963 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12963/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12963/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12963/events | https://github.com/pandas-dev/pandas/issues/12963 | 150,471,288 | MDU6SXNzdWUxNTA0NzEyODg= | 12,963 | drop_duplicates slow for data frame of boolean columns | {
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} | 1 | 2016-04-22T21:05:36Z | 2017-03-20T22:47:22Z | 2017-03-20T22:47:22Z | CONTRIBUTOR | null | In trying to find out the fast way to count unique rows in a data frame, I [stumbled upon an issue](https://gist.github.com/jreback/9758917f02d5c2140b82c9c368b02d3b) where dropping duplicates from a data frame of booleans is quite slow. @jreback asked me to open a ticket.
```
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: N = 100000
In [4]: np.random.seed(1234)
In [5]: df = pd.DataFrame({str(i) : [bool(x) for x in np.random.randint(0,2,size=N)] for i in range(500)})
In [6]: %timeit df.drop_duplicates()
1 loop, best of 3: 3.01 s per loop
In [7]: %timeit df.astype(int).drop_duplicates()
1 loop, best of 3: 1.4 s per loop
```
```
INSTALLED VERSIONS
------------------
commit: None
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_US.UTF-8
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: 1.3.5
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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} | 34 | 2016-04-23T01:59:54Z | 2016-04-30T20:20:33Z | 2016-04-30T14:59:20Z | MEMBER | null | Refactored tests in `test_parsers.py` to increase coverage of the different types of parsers and remove nearly duplicate testing in some cases.
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} | 5 | 2016-04-23T13:57:08Z | 2016-04-25T13:42:24Z | 2016-04-25T13:42:20Z | MEMBER | null | - [x] closes #9765
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
This also added tests related to #12174 and fixed `concat(axis=1)` issue.
```
# on current master
dense1 = pd.DataFrame({'A': [1, 2, 3, np.nan],
'B': [0, 0, 0, 0],
'C': [np.nan, np.nan, np.nan, np.nan],
'D': [1, 2, 3, 4]})
dense2 = pd.DataFrame({'E': [1, 2, 3, np.nan],
'F': [0, 0, 0, 0],
'G': [np.nan, np.nan, np.nan, np.nan],
'H': [1, 2, 3, 4]})
sparse1 = dense1.to_sparse()
sparse2 = dense2.to_sparse()
pd.concat([sparse2, sparse1], axis=1)
# AttributeError: 'int' object has no attribute 'ravel'
```
One point to be discussed is the logic for return type. Currently, `SparseDataFrame` is returned only when all blocks are all sparse. Because `SparseDataFrame` can't work properly if dense block is contained.
Thus, dense and sparse `concat` with axis=0 resunts in `SparseDataFrame`, and axis=1 results in normal `DataFrame`.
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} | 14 | 2016-04-23T14:38:45Z | 2016-04-26T15:35:26Z | 2016-04-26T15:17:03Z | CONTRIBUTOR | null | closes #8158
See the references SO questions in the issue. but allows highly performant construction of datetime series from specified DataFrame columns with a minimal syntax
```
In [12]: pd.options.display.max_rows=10
In [13]: year = np.arange(2010, 2020)
In [14]: months = np.arange(1, 13)
In [15]: days = np.arange(1, 29)
In [16]: y, m, d = map(np.ravel, np.broadcast_arrays(*np.ix_(year, months, days)))
In [17]: df = DataFrame({'year' : y, 'month' : m, 'day' : d})
In [18]: df
Out[18]:
day month year
0 1 1 2010
1 2 1 2010
2 3 1 2010
3 4 1 2010
4 5 1 2010
... ... ... ...
3355 24 12 2019
3356 25 12 2019
3357 26 12 2019
3358 27 12 2019
3359 28 12 2019
[3360 rows x 3 columns]
In [19]: pd.to_datetime(df, unit={ c:c for c in df.columns })
Out[19]:
0 2010-01-01
1 2010-01-02
2 2010-01-03
3 2010-01-04
4 2010-01-05
...
3355 2019-12-24
3356 2019-12-25
3357 2019-12-26
3358 2019-12-27
3359 2019-12-28
dtype: datetime64[ns]
In [20]: %timeit pd.to_datetime(df, unit={ c:c for c in df.columns })
100 loops, best of 3: 2.33 ms per loop
# we are passing a dict of mapping from the df columns to their units.
# obviously also includes hours, min, seconds, ms, etc. as well as aliases for
# these (e.g. H for 'hours'). I wrote them out to avoid confusion of ``M``, is this Month or Minute.
# could also accept ``%Y`` for the strptime mappings.
In [21]: { c:c for c in df.columns }
Out[21]: {'day': 'day', 'month': 'month', 'year': 'year'}
```
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} | 1 | 2016-04-23T15:31:31Z | 2016-04-25T13:36:32Z | 2016-04-25T13:36:31Z | MEMBER | null | - [x] tests added / passed
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} | 1 | 2016-04-23T15:49:50Z | 2016-04-25T13:30:26Z | 2016-04-25T13:30:26Z | CONTRIBUTOR | null | cc @sinhrks
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| null | 4 | 2016-04-23T19:03:29Z | 2016-04-24T04:41:13Z | 2016-04-23T19:06:23Z | NONE | null | ```
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-34-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_IN
pandas: 0.18.0
nose: None
pip: 8.1.1
setuptools: 20.3.1
Cython: 0.23.5
numpy: 1.11.0
scipy: 0.17.0
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: 1.0.0
tables: None
numexpr: 2.5.1
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 0.999
httplib2: 0.9
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
```
```
from nsepy import get_history
from datetime import date
import pandas as pd
import requests
from io import BytesIO
import certifi
from dateutil.relativedelta import relativedelta
#import numpy as np
#import matplotlib.pyplot as plt
import datetime
import numpy as np
import matplotlib.colors as colors
import matplotlib.finance as finance
import matplotlib.dates as mdates
import matplotlib.ticker as mticker
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import matplotlib.font_manager as font_manager
import requests
from nsepy.archives import html_to_rows
from bs4 import BeautifulSoup
import pandas as pd
import numpy as np
import matplotlib as plt
'''
import sys
reload(sys)
sys.setdefaultencoding('Cp1252')
'''
url1 = 'http://www.nseindia.com/live_market/dynaContent/live_watch/option_chain/optionKeys.jsp?segmentLink=17&instrument=OPTIDX&symbol=NIFTY&date=26MAY2016'
def dataFromHtmlInPy(url):
headers = { 'Accept' : '*/*',
'User-Agent' : 'Mozilla/5.0',
'Refers' : 'http://www.nseindia.com',
'Connection' : 'keep-alive'
}
getContents = requests.get(url,headers=headers).text
#dataFromHtm=pd.read_html(BytesIO(getContents))
dataFromHtm = BeautifulSoup(getContents, "lxml")
return dataFromHtm
data=pd.DataFrame(dataFromHtmlInPy(url=url1))
print(data.head())
```
I am new to panda and python.however I need dataframe as the same in the url1.
few info i need to scrapped from there. i need to calculate daily PCR.
i encounter an error like:
```
Traceback (most recent call last):
File "dataFromNsePage.py", line 46, in <module>
data=pd.DataFrame(dataFromHtmlInPy(url=url1))
File "/usr/local/lib/python2.7/dist-packages/pandas/core/frame.py", line 282, in __init__
raise TypeError("data argument can't be an iterator")
TypeError: data argument can't be an iterator
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12971 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12971/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12971/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12971/events | https://github.com/pandas-dev/pandas/pull/12971 | 150,597,949 | MDExOlB1bGxSZXF1ZXN0Njc2MTkwNDA= | 12,971 | ENH/BUG: Sparse now supports comparison op | {
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} | 2 | 2016-04-23T21:23:08Z | 2016-04-25T22:00:30Z | 2016-04-25T22:00:23Z | MEMBER | null | - [x] no open issue
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
## on current master
```
pd.SparseArray([1, 2, np.nan]) > 0
# [True, True, nan]
# Fill: nan
# IntIndex
# Indices: array([0, 1], dtype=int32)
# Expected
# [True, True, False]
```
```
a = pd.SparseArray([1, 2, 0])
b = pd.SparseArray([0, 1, np.nan])
a > b
# ValueError: operands could not be broadcast together with shapes (3,) (2,)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12972 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12972/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12972/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12972/events | https://github.com/pandas-dev/pandas/issues/12972 | 150,654,840 | MDU6SXNzdWUxNTA2NTQ4NDA= | 12,972 | having trouble for calculating dataframe for put call ratios | {
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| null | 2 | 2016-04-24T11:52:36Z | 2016-04-24T12:11:19Z | 2016-04-24T11:55:53Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
from nsepy import get_history
from datetime import date
import pandas as pd
import requests
from io import BytesIO
import certifi
from scipy import stats
from dateutil.relativedelta import relativedelta
import numpy as np
#import matplotlib.pyplot as plt
import datetime
import numpy as np
import matplotlib.colors as colors
import matplotlib.finance as finance
import matplotlib.dates as mdates
import matplotlib.ticker as mticker
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import matplotlib.font_manager as font_manager
import talib as ta
from talib import MA_Type
import statsmodels as sm
nf_calls=[]
nf_puts=[]
wPCR=[]
#nf_calls[['VolumeCalls']]=np.nan
#nf_puts[['VolumeCalls']]=np.nan
i=min_avalable_strikes=4850
max_avalable_strike=9400
while i in range(min_avalable_strikes,max_avalable_strike):
nf_opt_CE = get_history(symbol="NIFTY",
start=date(2016,4,1),
end=date(2016,4,22),
index=True,
option_type="CE",
strike_price=i,
expiry_date=date(2016,4,28))
#print(nf_opt_CE.head())
#if nf_opt_CE['Number of Contracts'].values >0 :
'''if nf_opt_CE.empty :
nf_opt_CE.append(0)
'''
nf_opt_PE = get_history(symbol="NIFTY",
start=date(2016,1,1),
end=date(2016,4,18),
index=True,
option_type="PE",
strike_price=i,
expiry_date=date(2016,4,28))
print(nf_opt_PE.head())
#print(nf_opt_PE.head())
#print(i)
#if nf_opt_PE['Number of Contracts'].values>0 :
'''if nf_opt_CE.empty :
nf_opt_PE.append(0)
'''
i=i+50
#print(wPCR)
'''def PCRForSym():
return NULL
'''
if (all(nf_opt_PE['Number of Contracts'] > 0)) or ( all(nf_opt_PE['Number of Contracts'] > 0)):
nf_calls=nf_opt_PE['Number of Contracts']* nf_opt_PE['Close']
nf_puts= nf_opt_CE['Number of Contracts']*nf_opt_CE['Close']
print(nf_calls.head())
#wPCR=nf_puts
print(nf_opt_PE.head())
```
#### Expected Output
some what of a ratio between 0 to 2
i need to calculate summation of nf_puts/summation of nf_calls for same day And need to plot the PCR.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-34-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_IN
pandas: 0.18.0
nose: None
pip: 8.1.1
setuptools: 20.3.1
Cython: 0.23.5
numpy: 1.11.0
scipy: 0.17.0
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: 1.0.0
tables: None
numexpr: 2.5.1
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.8.5
lxml: None
bs4: 4.4.1
html5lib: 0.999
httplib2: 0.9
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
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| null | 5 | 2016-04-24T15:31:06Z | 2017-02-01T20:52:16Z | 2017-02-01T20:52:16Z | NONE | null | closes #9084
- new parameters are added to the end of the definition to remain backwards compatible.
- `index` defaults to `False` (not `True` as in `to_sql`) to remain backwards compatible.
- added tests
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} | 5 | 2016-04-24T16:23:47Z | 2016-04-27T07:19:52Z | 2016-04-26T21:37:31Z | CONTRIBUTOR | null | - [x] closes #12925
- [X] tests added / passed in pandas/tseries/tests/test_resample.py: Base.test_resample_interpolate()
These tests simply check that df.resample(...).mean().interpolate() gives the same result than df.resample(...).interpolate()
- [X] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
-> Edit: 24/04/2016 20:25
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} | 7 | 2016-04-24T20:17:15Z | 2016-04-25T21:57:21Z | 2016-04-25T21:57:20Z | CONTRIBUTOR | null | - [x] closes [#9178](https://github.com/pydata/pandas/issues/9178)
- [x] The test is added and passing (while failing before the fix).
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Fix as had been proposed in [PR 9194](https://github.com/pydata/pandas/pull/9194#issuecomment-209849563), but this PR was closed because of tests missing. They are added now.
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} | 5 | 2016-04-24T22:08:54Z | 2017-05-06T22:47:52Z | 2017-05-06T22:47:52Z | CONTRIBUTOR | null | ``` python
In [127]: isinstance(pd.Timestamp(np.nan), pd.tslib.Timestamp)
Out[127]: False
In [128]: isinstance(pd.Timestamp('2015'), pd.tslib.Timestamp)
Out[128]: True
In [129]: isinstance(pd.Timestamp(np.nan), datetime.datetime)
Out[129]: True
In [130]: isinstance(pd.Timestamp('2015'), datetime.datetime)
Out[130]: True
```
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} | 5 | 2016-04-25T07:36:38Z | 2016-05-20T14:09:46Z | 2016-05-20T14:09:29Z | CONTRIBUTOR | null | - [x] closes #12824
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
In `_wrap_applied_output` for `NDFrameGroupBy`, if the first result of `apply` is `None`, find the first non-`None` result to determine behavior
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| null | 1 | 2016-04-25T08:17:38Z | 2016-04-25T12:52:20Z | 2016-04-25T12:52:09Z | NONE | null | The `ExcelWriter` class in line 550 of [pandas/pandas/io/excel.py](https://github.com/pydata/pandas/blob/6c692aee6d411d67997c4fadd023a1d4b7f91c12/pandas/io/excel.py#L550) takes a `datetime_format` parameter to format a datetime column.
Wouldn't it be useful to include a `font_size` parameter as well? Otherwise I don't see how I can set the font size apart from the hard way through `xlsxwriter`. Correct me if I am wrong.
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} | 3 | 2016-04-25T11:39:10Z | 2016-04-29T16:09:14Z | 2016-04-29T16:09:14Z | CONTRIBUTOR | null | Two minor issues with the width optional argument of plot.bar() in Pandas 0.18.0:
1) Using width=1 (integer) causes misalignment of the x-axis
``` python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.random((6, 5)) * 10, index=list('abcdef'), columns=list('ABCDE'))
df.plot.bar(stacked=True, width=1)
```
Compare this with the correct behaviour when defining width as a float:
``` python
df.plot.bar(stacked=True, width=1.)
```
2) Using width=None causes a TypeError, where I would expect the default behaviour.
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| null | 2 | 2016-04-25T12:07:46Z | 2016-04-25T12:43:23Z | 2016-04-25T12:42:56Z | NONE | null | #### Code Sample, a copy-pastable example if possible
Worked in prior versions...
```
import pandas as pd
raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'],
'company': ['infantry', 'infantry', 'cavalry', 'cavalry', 'infantry', 'infantry', 'cavalry', 'cavalry','infantry', 'infantry', 'cavalry', 'cavalry'],
'experience': ['veteran', 'rookie', 'veteran', 'rookie', 'veteran', 'rookie', 'veteran', 'rookie','veteran', 'rookie', 'veteran', 'rookie'],
'name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze', 'Jacon', 'Ryaner', 'Sone', 'Sloan', 'Piger', 'Riani', 'Ali'],
'preTestScore': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],
'postTestScore': [25, 94, 57, 62, 70, 25, 94, 57, 62, 70, 62, 70]}
df = pd.DataFrame(raw_data, columns = ['regiment', 'company', 'experience', 'name', 'preTestScore', 'postTestScore'])
```
```
import numpy as np
np.round(df.regiment,1)
```
#### Expected Output
a number
ERROR !!!! not a numpy bug..
TypeError Traceback (most recent call last)
<ipython-input-7-71f0b6b75303> in <module>()
1
2 import numpy as np
----> 3 np.round(df.regiment,1)
/Library/Python/2.7/site-packages/numpy/core/fromnumeric.pyc in round_(a, decimals, out)
2791 except AttributeError:
2792 return _wrapit(a, 'round', decimals, out)
-> 2793 return round(decimals, out)
2794
2795
TypeError: round() takes at most 2 arguments (3 given)
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Darwin
OS-release: 13.4.0
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: 20.9.0
Cython: None
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
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: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
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} | 3 | 2016-04-25T14:31:11Z | 2016-04-26T13:22:55Z | 2016-04-26T13:22:55Z | CONTRIBUTOR | null | Starting from pandas 0.17, certain assignments to DataFrames cause offset-aware datetime columns to be converted to offset-naive columns. Specifically, it seems that if any data realignment is required when assigning the RHS to a a slice of the DataFrame, then timezone info is lost. Here's an example:
``` python
from __future__ import print_function
import pandas
print("Pandas version:", pandas.__version__)
start = pandas.Timestamp('2015-01-01', tz='utc')
df = pandas.DataFrame({'dates': pandas.date_range(start, periods=3)})
print("Before assignment")
print(df['dates'])
# Shuffle column and reassign, causing RHS to need to be realigned on assignment
df['dates'] = df['dates'][[1,0,2]]
print("\nAfter assignment")
print(df['dates'])
```
The output I'd expect, which is what I get from pandas 0.16.2, is:
```
Pandas version: 0.16.2
Before assignment
0 2015-01-01 00:00:00+00:00
1 2015-01-02 00:00:00+00:00
2 2015-01-03 00:00:00+00:00
Name: dates, dtype: object
After assignment
0 2015-01-01 00:00:00+00:00
1 2015-01-02 00:00:00+00:00
2 2015-01-03 00:00:00+00:00
Name: dates, dtype: object
```
However when I run this with pandas 0.18.0, after the assignment the timezone info is lost:
```
Pandas version: 0.18.0
Before assignment
0 2015-01-01 00:00:00+00:00
1 2015-01-02 00:00:00+00:00
2 2015-01-03 00:00:00+00:00
Name: dates, dtype: datetime64[ns, UTC]
After assignment
0 2015-01-01
1 2015-01-02
2 2015-01-03
Name: dates, dtype: datetime64[ns]
```
It seems the custom timezone-aware dtype that pandas started using for timezone-aware time series in 0.17.x doesn't get correctly propagated in this operation.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.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: 1.3.7
pip: 8.1.1
setuptools: 20.9.0
Cython: None
numpy: 1.11.0
scipy: 0.15.1
statsmodels: None
xarray: None
IPython: 3.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.3
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.4.2
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: None
boto: None
```
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} | 3 | 2016-04-25T16:11:22Z | 2016-04-26T13:23:00Z | 2016-04-26T13:22:55Z | CONTRIBUTOR | null | - [x] closes #12981
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
A fix for #12981. When doing a slice assign to a DataFrame where the
right-hand-side was timezone-aware datetime Series which required
realignment to perform the assignment, the timezone would be stripped
from the RHS on assignment.
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} | 2 | 2016-04-25T16:50:36Z | 2016-04-30T14:56:43Z | 2016-04-30T14:56:43Z | NONE | null | ```
# my_dataframe.py
import pandas as pd
class MySeries(pd.Series):
@property
def _constructor(self):
return MySeries
@property
def _constructor_expanddim(self):
return MyDataFrame
class MyDataFrame(pd.DataFrame):
def __init__(self, data=None, index=None, columns=None, dtype=None, copy=False, **kwargs):
super(MyDataFrame, self).__init__(data=data, index=index, columns=columns, dtype=dtype, copy=copy, **kwargs)
@property
def _constructor(self):
return MyDataFrame
@property
def _constructor_sliced(self):
return MySeries
@property
def _constructor_expanddim(self):
return MyPanel
class MyPanel(pd.Panel):
def __init__(self, data=None, items=None, major_axis=None, minor_axis=None, copy=False, dtype=None, **kwargs):
super(MyPanel, self).__init__(data=data, items=items, major_axis=major_axis, minor_axis=minor_axis, copy=copy,
dtype=dtype, **kwargs)
@property
def _constructor(self):
return MyPanel
@property
def _constructor_sliced(self):
return MyDataFrame
def f():
df1 = MyDataFrame(pd.DataFrame([1, 2, 3, 4, 5], columns=['A']))
df2 = MyDataFrame(pd.DataFrame([1, 2, 3, 4, 5], columns=['A']))
print type(df1.align(df2, axis=0)[0]) # Note, if aligning with frame, it works fine, returns MyDataFrame
print type(df1.align(df2.A, axis=0)[0]) # But if aligning with series, it does not, returns DataFrame
# import my_dataframe
my_dataframe.f()
<class 'com.utils.my_dataframe.MyDataFrame'> # Correct
<class 'pandas.core.frame.DataFrame'> # Does not invoke _constructor()
```
When aligning with a series, the code seems to explicitly create the DataFrame using DataFrame() constructor.
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| null | 4 | 2016-04-25T17:59:41Z | 2016-04-25T22:20:36Z | 2016-04-25T22:20:36Z | CONTRIBUTOR | null | Floating an idea: `pd.PeriodIndex.from_range` & `pd.DatetimeIndex.from_range` as `classmethod`s to mirror `pd.period_range` & `pd.date_range` respectively. This encapsulates constructors on the class.
Also could have `pd.DatetimeIndex.from_dataframe` for https://github.com/pydata/pandas/pull/12967
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} | 12 | 2016-04-25T21:31:31Z | 2018-04-24T09:42:28Z | 2017-11-25T15:52:41Z | CONTRIBUTOR | null | This is something that works in pandas 0.16.2 but fails in pandas 0.18. If you append to an empty DataFrame, and the DataFrame you are appending contains a tz-aware datetime column, the append fails.
``` python
import pandas
df = pandas.DataFrame(columns=['dates'])
df_to_append = pandas.DataFrame({'dates': pandas.date_range('2015', periods=3, tz='utc')})
# Fails here
df.append(df_to_append)
```
Here is the failure I get:
```
Traceback (most recent call last):
File "/Users/ajenkins/Library/Preferences/PyCharm50/scratches/test.py", line 8, in <module>
df.append(df_to_append)
File "/Users/ajenkins/dev/pandas/pandas/core/frame.py", line 4334, in append
verify_integrity=verify_integrity)
File "/Users/ajenkins/dev/pandas/pandas/tools/merge.py", line 846, in concat
return op.get_result()
File "/Users/ajenkins/dev/pandas/pandas/tools/merge.py", line 1038, in get_result
copy=self.copy)
File "/Users/ajenkins/dev/pandas/pandas/core/internals.py", line 4545, in concatenate_block_managers
for placement, join_units in concat_plan]
File "/Users/ajenkins/dev/pandas/pandas/core/internals.py", line 4642, in concatenate_join_units
for ju in join_units]
File "/Users/ajenkins/dev/pandas/pandas/core/internals.py", line 4915, in get_reindexed_values
missing_arr = np.empty(self.shape, dtype=empty_dtype)
TypeError: data type not understood
```
The error is because pandas is passing an instance of DatetimeTZDtype to numpy, and numpy doesn't recognize it as a valid dtype.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: 13a07055dedb0f1d06abf30b185a289220ce5c85
python: 2.7.9.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+154.g13a0705.dirty
nose: 1.3.7
pip: 8.1.1
setuptools: 20.9.0
Cython: 0.24
numpy: 1.11.0
scipy: 0.15.1
statsmodels: None
xarray: None
IPython: 3.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.2
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: None
boto: None
pandas_datareader: None
```
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} | 2 | 2016-04-25T22:51:14Z | 2018-07-06T20:21:10Z | 2017-07-10T21:08:21Z | CONTRIBUTOR | null | In pandas 0.18, if you create a Panel where at least one of the items consists of all tz-aware timestamps, then trying to access any elements of that item raises an error.
``` python
import pandas, numpy
data = numpy.array(list(pandas.date_range('2015', periods=24, tz='utc'))).reshape(2,3,4)
panel = pandas.Panel(data)
panel[0] # this fails
panel.loc[0,0,0] # fails with the same error
panel.to_frame() # same error
```
The error raised is:
```
Traceback (most recent call last):
File "/Users/ajenkins/Library/Preferences/PyCharm50/scratches/test.py", line 6, in <module>
panel[0] # this fails
File "/Users/ajenkins/dev/pandas/pandas/core/panel.py", line 274, in __getitem__
return super(Panel, self).__getitem__(key)
File "/Users/ajenkins/dev/pandas/pandas/core/generic.py", line 1344, in __getitem__
return self._get_item_cache(item)
File "/Users/ajenkins/dev/pandas/pandas/core/generic.py", line 1352, in _get_item_cache
res = self._box_item_values(item, values)
File "/Users/ajenkins/dev/pandas/pandas/core/panel.py", line 567, in _box_item_values
return self._constructor_sliced(values, **d)
File "/Users/ajenkins/dev/pandas/pandas/core/frame.py", line 255, in __init__
copy=copy)
File "/Users/ajenkins/dev/pandas/pandas/core/frame.py", line 432, in _init_ndarray
return create_block_manager_from_blocks([values], [columns, index])
File "/Users/ajenkins/dev/pandas/pandas/core/internals.py", line 3986, in create_block_manager_from_blocks
mgr = BlockManager(blocks, axes)
File "/Users/ajenkins/dev/pandas/pandas/core/internals.py", line 2591, in __init__
(block.ndim, self.ndim))
AssertionError: Number of Block dimensions (1) must equal number of axes (2)
```
The error seems to be caused by a change in `pandas.core.common._possibly_infer_to_datetimelike` to make it use a tz-aware DatetimeIndex where possible. In the case where the `values` parameter is an ndarray of tz-aware timestamps, it returns a tz-aware DatetimeIndex, which loses the original shape of the input. This causes an error later.
Perhaps the fix should be to make this function only perform the conversion to DatetimeIndex when the input is 1D.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: 856c3bde62fa7b753a25cfbacc544d1fa415a676
python: 2.7.9.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+150.g856c3bd
nose: 1.3.7
pip: 8.1.1
setuptools: 20.9.0
Cython: 0.24
numpy: 1.11.0
scipy: 0.15.1
statsmodels: None
xarray: None
IPython: 3.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.2
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: None
boto: None
pandas_datareader: None
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https://api.github.com/repos/pandas-dev/pandas/issues/12987 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12987/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12987/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12987/events | https://github.com/pandas-dev/pandas/issues/12987 | 151,010,722 | MDU6SXNzdWUxNTEwMTA3MjI= | 12,987 | to_panel error when DataFrame contains tz-awared datetime column | {
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} | 3 | 2016-04-26T00:07:10Z | 2018-07-06T20:21:10Z | 2017-07-11T09:55:27Z | CONTRIBUTOR | null | This code fails with an exception in pandas 0.18
``` python
import pandas
df = pandas.DataFrame({'dates': pandas.date_range('2015', periods=6, tz='utc'), 'nums': range(6)},
index=pandas.MultiIndex.from_product([[1, 2], ['a', 'b', 'c']]))
panel = df.to_panel() # this fails
```
The error is:
```
Traceback (most recent call last):
File "/Users/ajenkins/Library/Preferences/PyCharm50/scratches/test.py", line 6, in <module>
panel = df.to_panel()
File "/Users/ajenkins/dev/pandas/pandas/core/frame.py", line 1257, in to_panel
ref_items=selfsorted.columns)
File "/Users/ajenkins/dev/pandas/pandas/core/internals.py", line 2994, in reshape_nd
return self.apply('reshape_nd', axes=axes, **kwargs)
File "/Users/ajenkins/dev/pandas/pandas/core/internals.py", line 2890, in apply
applied = getattr(b, f)(**kwargs)
File "/Users/ajenkins/dev/pandas/pandas/core/internals.py", line 242, in reshape_nd
labels=labels, ref_items=ref_items)
File "/Users/ajenkins/dev/pandas/pandas/core/internals.py", line 4386, in _block2d_to_blocknd
pvalues = np.empty(panel_shape, dtype=values.dtype)
TypeError: data type not understood
```
This is another error where code is passing a DatetimeTZDtype object to numpy.empty, and numpy is complaining because it doesn't recognize that dtype.
I tried fixing that problem with this patch:
```
diff --git a/pandas/core/internals.py b/pandas/core/internals.py
index abfc5c9..108981f 100644
--- a/pandas/core/internals.py
+++ b/pandas/core/internals.py
@@ -4383,7 +4383,10 @@ def _block2d_to_blocknd(values, placement, shape, labels, ref_items):
mask.put(selector, True)
if mask.all():
- pvalues = np.empty(panel_shape, dtype=values.dtype)
+ dtype = values.dtype
+ if is_extension_type(dtype):
+ dtype = dtype.base
+ pvalues = np.empty(panel_shape, dtype=dtype)
else:
dtype, fill_value = _maybe_promote(values.dtype)
pvalues = np.empty(panel_shape, dtype=dtype)
```
but then I get another error which isn't as obvious to me how to fix:
```
Traceback (most recent call last):
File "/Users/ajenkins/Library/Preferences/PyCharm50/scratches/test.py", line 7, in <module>
panel = df.to_panel() # this fails
File "/Users/ajenkins/dev/pandas/pandas/core/frame.py", line 1257, in to_panel
ref_items=selfsorted.columns)
File "/Users/ajenkins/dev/pandas/pandas/core/internals.py", line 2994, in reshape_nd
return self.apply('reshape_nd', axes=axes, **kwargs)
File "/Users/ajenkins/dev/pandas/pandas/core/internals.py", line 2890, in apply
applied = getattr(b, f)(**kwargs)
File "/Users/ajenkins/dev/pandas/pandas/core/internals.py", line 242, in reshape_nd
labels=labels, ref_items=ref_items)
File "/Users/ajenkins/dev/pandas/pandas/core/internals.py", line 4397, in _block2d_to_blocknd
pvalues[i].flat[mask] = values[:, i]
File "/Users/ajenkins/dev/pandas/pandas/tseries/base.py", line 210, in __getitem__
result = getitem(key)
IndexError: too many indices for array
```
This error seems to occur because the code is assuming that the `values` property of an ND `Block` object will itself be an ND array. However for tz-aware datetime blocks, the `values` property is a DatetimeIndex object, so when the code tries to index into it with 2 indices it fails.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: 856c3bde62fa7b753a25cfbacc544d1fa415a676
python: 2.7.9.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+150.g856c3bd.dirty
nose: 1.3.7
pip: 8.1.1
setuptools: 20.9.0
Cython: 0.24
numpy: 1.11.0
scipy: 0.15.1
statsmodels: None
xarray: None
IPython: 3.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.2
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: None
boto: None
pandas_datareader: None
```
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} | 5 | 2016-04-26T00:15:25Z | 2016-05-01T15:29:52Z | 2016-05-01T15:29:52Z | NONE | null | When trying to use `Series.isin(values)` for Timestamps, an exception is raised if `values` is a set.
```
d = {'Dates':[pd.Timestamp('2013-01-02'),
pd.Timestamp('2013-01-03'),
pd.Timestamp('2013-01-04')],
'Num1':[1,2,3],
'Num2':[-1,-2,-3]}
df = pd.DataFrame(data=d)
>>> df['Dates'].isin({pd.Timestamp('2013-01-04')})
ValueError: Buffer has wrong number of dimensions (expected 1, got 0)
>>> df['Dates'].isin([pd.Timestamp('2013-01-04')])
0 False
1 False
2 True
Name: Dates, dtype: bool
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.1.13-100.fc21.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_AU.utf8
pandas: 0.18.0
nose: None
pip: 8.1.1
setuptools: 20.2.2
Cython: None
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: 0.7.2
IPython: 4.1.2
sphinx: 1.3.5
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
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
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12989 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12989/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12989/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12989/events | https://github.com/pandas-dev/pandas/pull/12989 | 151,057,590 | MDExOlB1bGxSZXF1ZXN0Njc4MzYwMzY= | 12,989 | PERF: Improved performance for StringMethods.encode/decode | {
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| null | 4 | 2016-04-26T07:03:38Z | 2016-04-27T11:09:58Z | 2016-04-27T11:09:58Z | CONTRIBUTOR | null | Reusing encoding/decoding function for a specified encoding doubles speed.
```
import pandas as pd
s1 = pd.Series(["Dzień добрый"]*100000)
s2 = s1.str.encode('cp1251', 'ignore')
%timeit s1.str.encode('cp1251', 'ignore')
%timeit s2.str.decode('cp1251')
```
10 loops, best of 3: 157 ms per loop --> 80.9 ms per loop
10 loops, best of 3: 179 ms per loop --> 84.1 ms per loop
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| closed | false | null | []
| null | 2 | 2016-04-26T10:57:11Z | 2016-04-26T13:08:48Z | 2016-04-26T13:07:26Z | NONE | null | ## sample input file
[ml_col_idx_read.xlsx](https://github.com/pydata/pandas/files/236380/ml_col_idx_read.xlsx)
## Code Sample 1
```
ddf = pd.read_excel('ml_col_idx_read.xlsx', header=(0,1))
ddf.info()
ddf.reset_index()
```
## Output 1
```
<class 'pandas.core.frame.DataFrame'>
Int64Index: 4 entries, 1 to 4
Data columns (total 2 columns):
(A2, bar) 4 non-null object
(A3, baz) 4 non-null int64
dtypes: int64(1), object(1)
memory usage: 96.0+ bytes
```
```
A1 index A2 A3
foo bar baz
0 1 yes 15
1 2 no 10
2 3 no 4
3 4 yes 1
```
### expected output:
```
A1 A2 A3
foo bar baz
0 1 yes 15
1 2 no 10
2 3 no 4
3 4 yes 1
```
## Code Sample 2
- `index_col=None` should be default anyways...
```
# ddf = pd.read_excel('ml_col_idx_read.xlsx', header=(0,1),)
# OR
ddf = pd.read_excel('ml_col_idx_read.xlsx', header=(0,1), index_col=None)
ddf.info()
pint(ddf)
```
## Output 2
- **non-respected default** behavior, **non-respected explicit** behavior
- column 0 is still used for index
```
<class 'pandas.core.frame.DataFrame'>
Int64Index: 4 entries, 1 to 4
Data columns (total 2 columns):
(A2, bar) 4 non-null object
(A3, baz) 4 non-null int64
dtypes: int64(1), object(1)
memory usage: 96.0+ bytes
```
```
A1 A2 A3
foo bar baz
1 yes 15
2 no 10
3 no 4
4 yes 1
```
### expected output:
```
A1 A2 A3
foo bar baz
0 1 yes 15
1 2 no 10
2 3 no 4
3 4 yes 1
```
## `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-43-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: de_AT.UTF-8
pandas: 0.18.0+git114-g6c692ae
nose: 1.3.6
pip: None
setuptools: 18.4
Cython: 0.23.3
numpy: 1.8.2
scipy: 0.14.1
statsmodels: None
xarray: None
IPython: 2.3.0
sphinx: None
patsy: 0.4.1
dateutil: 2.2
pytz: 2014.10
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.3
matplotlib: 1.4.2
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: 0.7.3
lxml: None
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9
apiclient: None
sqlalchemy: 1.0.8
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
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} | 10 | 2016-04-26T12:32:23Z | 2020-05-31T23:19:46Z | null | NONE | null | Creating a DataFrame with two columns given duplicate names, changing data via indexer changes both columns:
``` python
a = pd.DataFrame(index=['a', 'b', 'c'], columns=['d', 'e', 'd']).fillna(0)
a.iloc[:, 0]['a'] = 3
```
Gives:
d e d
a 3 0 3
b 0 0 0
c 0 0 0
Instead, it should only edit first column:
d e d
a 3 0 0
b 0 0 0
c 0 0 0
## INSTALLED VERSIONS
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-68-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
pandas: 0.17.1
nose: 1.3.6
pip: 1.5.4
setuptools: 3.3
Cython: None
numpy: 1.10.1
scipy: 0.13.3
statsmodels: None
IPython: 3.1.0
sphinx: 1.3.1
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: None
xlrd: 0.9.3
xlwt: 0.8.0
xlsxwriter: 0.7.7
lxml: 3.3.3
bs4: 4.2.1
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: 1.0.4
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
Jinja2: None
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closes #12941
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```
In [1]: s = Series(pd.date_range('20130101',periods=10))
In [2]: s.dt.weekday
Out[2]:
0 1
1 2
2 3
3 4
4 5
5 6
6 0
7 1
8 2
9 3
dtype: int64
In [3]: s.dt.weekday_name
Out[3]:
0 Tuesday
1 Wednesday
2 Thursday
3 Friday
4 Saturday
5 Sunday
6 Monday
7 Tuesday
8 Wednesday
9 Thursday
dtype: object
```
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} | 2 | 2016-04-26T14:46:47Z | 2016-04-26T15:21:57Z | 2016-04-26T15:21:57Z | CONTRIBUTOR | null | mostly in cython, but in tests as well. This is as per PEP. not to mention that it screws up syntax highliting.
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} | 1 | 2016-04-26T14:50:13Z | 2016-09-06T10:18:08Z | 2016-09-06T10:18:08Z | CONTRIBUTOR | null | we don't by default check the .pyx files, we should. So this issue will cover fixing & updating the `setup.cfg`, need to add: `filename: *.py, *.pyx`
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} | 4 | 2016-04-26T15:46:33Z | 2016-04-27T00:39:41Z | 2016-04-26T18:57:56Z | CONTRIBUTOR | null | xref #12967
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} | 11 | 2016-04-26T15:53:21Z | 2021-05-26T15:56:21Z | null | CONTRIBUTOR | null | #### Code Sample
```
In [99]: ts = Timestamp('2013-01-10 00:00:00-0500', tz='US/Eastern')
In [100]: pd.io.json.dumps(ts, iso_dates=True)
Out[100]: '"2013-01-10T05:00:00.000Z"'
```
#### Expected Output
```
In [100]: pd.io.json.dumps(ts, iso_dates=True)
Out[100]: '"2013-01-10T00:00:00.000-05:00"'
```
cf
```
In [109]: ts.isoformat()
Out[109]: '2013-01-10T00:00:00-05:00'
```
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} | 2 | 2016-04-26T16:30:27Z | 2016-04-26T16:35:28Z | 2016-04-26T16:34:20Z | NONE | null | I am not 100% sure if this is a bug, but it generates very unexpected behaviour!
Essentially, if columns are of type Categorical, selecting a single one using df[column_name] a DataFrame is returned, unlike for columns of other types, for which a Series would be returned.
#### Code Sample, a copy-pastable example if possible
```
print pd.DataFrame([[1,2]], columns=['a','b'])['a'].__class__
print pd.DataFrame([[1,2]], columns=pd.Categorical(['a','b']))['a'].__class__
```
produces
```
<class 'pandas.core.series.Series'> <--- Ok....
<class 'pandas.core.frame.DataFrame'> <----- What?!
```
#### Expected Output
```
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
```
#### output of `pd.show_versions()`
pandas: 0.18.0
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} | 1 | 2016-04-26T17:09:57Z | 2016-04-26T17:23:24Z | 2016-04-26T17:23:24Z | NONE | null | Hi, I'm not sure if this is intended but when using the **index_col** parameter in **read_csv** it ignore the input format specified in dtype. It's reproducible as following using pandas 0.18.0 and numpy 1.11.0.
```
>>> from StringIO import StringIO
import pandas as pd
df_csv = """request_hour,request_date,size
03,2016-04-26,2580954.0
04,2016-04-26,12003662.0
05,2016-04-26,13042624.0
06,2016-04-26,2899309.0
07,2016-04-26,-1.0"""
>>> pd.read_csv(StringIO(df_csv), dtype={'request_hour': 'string'}).set_index('request_hour')
request_date size
request_hour
03 2016-04-26 2580954.0
04 2016-04-26 12003662.0
05 2016-04-26 13042624.0
06 2016-04-26 2899309.0
07 2016-04-26 -1.0
# This is what I would expected as output
>>> pd.read_csv(StringIO(df_csv), dtype={'request_hour': 'string'}, index_col=0)
request_date size
request_hour
3 2016-04-26 2580954.0
4 2016-04-26 12003662.0
5 2016-04-26 13042624.0
6 2016-04-26 2899309.0
7 2016-04-26 -1.0
# I'm surprise that the index has been converted to int
```
I couldn't find any specs on this anywhere, so I wonder if something with the **read_csv** or I'm doing something wrong.
Thanks!
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} | 6 | 2016-04-26T17:25:07Z | 2019-12-12T21:05:09Z | 2019-12-12T21:05:09Z | NONE | null | When generating a multi-indexed dataframe, if the default index is renamed and the dataframe is longer than the `max_rows` display option. This cropped up after updating from 0.17 to 0.18.
#### Code Sample, a copy-pastable example if possible
```
>>> df = pd.DataFrame({'Vals':range(100)})
>>> df = pd.concat([df, ], keys=['Sweep'], names=['Sweep', 'Index'])
>>> df
Traceback (most recent call last):
File "C:\Users\User\Anaconda3\lib\site-packages\pandas\indexes\range.py", line 124, in _simple_new
return RangeIndex(start, stop, step, name=name, **kwargs)
File "C:\Users\User\Anaconda3\lib\site-packages\pandas\indexes\range.py", line 73, in __new__
start = _ensure_int(start, 'start')
File "C:\Users\User\Anaconda3\lib\site-packages\pandas\indexes\range.py", line 59, in _ensure_int
new_value = int(value)
TypeError: only length-1 arrays can be converted to Python scalars
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\User\Anaconda3\lib\site-packages\pandas\core\base.py", line 63, in __repr__
return str(self)
File "C:\Users\User\Anaconda3\lib\site-packages\pandas\core\base.py", line 42, in __str__
return self.__unicode__()
File "C:\Users\User\Anaconda3\lib\site-packages\pandas\core\frame.py", line 534, in __unicode__
line_width=width, show_dimensions=show_dimensions)
File "C:\Users\User\Anaconda3\lib\site-packages\pandas\core\frame.py", line 1486, in to_string
show_dimensions=show_dimensions)
File "C:\Users\User\Anaconda3\lib\site-packages\pandas\core\format.py", line 378, in __init__
self._chk_truncate()
File "C:\Users\User\Anaconda3\lib\site-packages\pandas\core\format.py", line 450, in _chk_truncate
frame.iloc[-row_num:, :]))
File "C:\Users\User\Anaconda3\lib\site-packages\pandas\tools\merge.py", line 834, in concat
copy=copy)
File "C:\Users\User\Anaconda3\lib\site-packages\pandas\tools\merge.py", line 972, in __init__
self.new_axes = self._get_new_axes()
File "C:\Users\User\Anaconda3\lib\site-packages\pandas\tools\merge.py", line 1059, in _get_new_axes
new_axes[self.axis] = self._get_concat_axis()
File "C:\Users\User\Anaconda3\lib\site-packages\pandas\tools\merge.py", line 1111, in _get_concat_axis
concat_axis = _concat_indexes(indexes)
File "C:\Users\User\Anaconda3\lib\site-packages\pandas\tools\merge.py", line 1129, in _concat_indexes
return indexes[0].append(indexes[1:])
File "C:\Users\User\Anaconda3\lib\site-packages\pandas\indexes\multi.py", line 1029, in append
label = self.get_level_values(i)
File "C:\Users\User\Anaconda3\lib\site-packages\pandas\indexes\multi.py", line 666, in get_level_values
tz=getattr(unique, 'tz', None))
File "C:\Users\User\Anaconda3\lib\site-packages\pandas\indexes\range.py", line 126, in _simple_new
return Index(start, stop, step, name=name, **kwargs)
File "C:\Users\User\Anaconda3\lib\site-packages\pandas\indexes\base.py", line 214, in __new__
return Int64Index(data, copy=copy, dtype=dtype, name=name)
File "C:\Users\User\Anaconda3\lib\site-packages\pandas\indexes\numeric.py", line 107, in __new__
not issubclass(np.dtype(dtype).type, np.integer)):
TypeError: data type "Index" not understood
```
#### Expected Output
The expected outcome is that the dataframe should print out without producing a traceback, as occurs when the length is less than that set by `max_rows`. I.e.
```
>>> df = pd.DataFrame({'Vals':range(10)})
>>> df = pd.concat([df, ], keys=['Sweep'], names=['Sweep', 'Index'])
>>> df
Vals
Sweep Index
Sweep 0 0
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
```
The issue only seems to be with changing the default index name, as you can change the other index name without issue:
```
>>> df = pd.DataFrame({'Vals':range(100)})
>>> df = pd.concat([df, ], keys=['Sweep'], names=['Sweep',])
>>> df
Vals
Sweep
Sweep 0 0
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
... ...
70 70
71 71
72 72
73 73
74 74
75 75
76 76
77 77
78 78
79 79
80 80
81 81
82 82
83 83
84 84
85 85
86 86
87 87
88 88
89 89
90 90
91 91
92 92
93 93
94 94
95 95
96 96
97 97
98 98
99 99
[100 rows x 1 columns]
```
#### output of `pd.show_versions()`
```
>>> 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 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.0
setuptools: 20.2.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.0.3
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
```
Discussed in more detail [here](http://stackoverflow.com/questions/36869745/pandas-0-18-setting-multi-index-name-on-df-from-read-csv-resulting-in-typee?noredirect=1#comment61309343_36869745)
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https://api.github.com/repos/pandas-dev/pandas/issues/13001 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13001/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13001/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13001/events | https://github.com/pandas-dev/pandas/issues/13001 | 151,201,371 | MDU6SXNzdWUxNTEyMDEzNzE= | 13,001 | _combine_const() in pandas.sparse.frame does not have uniform method signature with pandas.core.frame | {
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} | 8 | 2016-04-26T18:07:33Z | 2016-08-31T07:58:23Z | 2016-08-31T07:57:34Z | NONE | null | #### Code Sample
`>>> import pandas as pd`
`>>> df = pd.DataFrame(np.zeros((4,5),dtype=int))`
`>>> sparse = df.to_sparse()`
`>>> sparse.loc[[1]] != 0`
#### Expected Output
`0 1 2 3 4`
`1 False False False False False`
### Observed Output
`TypeError: _combine_const() got an unexpected keyword argument 'raise_on_error'`
#### output of `pd.show_versions()`
INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-35-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: None
pip: 8.1.1
setuptools: 20.9.0
Cython: None
numpy: 1.10.4
scipy: 0.16.1
statsmodels: None
xarray: None
IPython: None
sphinx: 1.3.1
patsy: None
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: 1.0.0
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
### Reason
`pandas.core.frame` implements `_combine_const(self, other, func, raise_on_error=True)`
while `pandas.sparse.frame` implements `_combine_const(self, other, func)`
and the missing argument in the signature is the cause of the error when using sparse frames. This is raised due to the call `res = self._combine_const(other, func, raise_on_error=False)` from `_comp_method_FRAME` at `pandas.core.ops`
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} | 0 | 2016-04-26T22:54:33Z | 2016-04-26T23:40:38Z | 2016-04-26T23:40:38Z | CONTRIBUTOR | null | closes #12669
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"description": "after 0.18.0 of course!",
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} | 4 | 2016-04-26T22:58:14Z | 2016-05-12T15:32:04Z | 2016-05-05T00:34:29Z | CONTRIBUTOR | null | if we can finish up remaining issues, I'd like to release this next sunday/monday (may 1 or 2nd).
Planning on an 0.18.2, so if something is not in then we can address then.
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