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} | 3 | 2014-06-02T09:13:36Z | 2014-06-11T15:10:21Z | 2014-06-11T15:10:21Z | CONTRIBUTOR | null | ```
In [10]: a=TS.DataFrame(np.random.random((10,3)))
In [11]: a
Out[11]:
0 1 2
0 0.293227 0.489837 0.389012
1 0.070556 0.230420 0.259219
2 0.105651 0.026292 0.737140
3 0.859360 0.092215 0.306204
4 0.377702 0.452027 0.409900
5 0.801594 0.123576 0.138083
6 0.810958 0.827973 0.363331
7 0.325093 0.527616 0.226507
8 0.903319 0.611720 0.636183
9 0.951649 0.663440 0.251934
In [12]: a.quantile(0.5,axis=0)
Out[12]:
0 0.589648
1 0.470932
2 0.334767
dtype: float64
In [13]: a.quantile(0.5,axis=1)
Out[13]:
0 0.589648
1 0.470932
2 0.334767
dtype: float64
```
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https://api.github.com/repos/pandas-dev/pandas/issues/7307 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7307/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7307/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7307/events | https://github.com/pandas-dev/pandas/issues/7307 | 34,762,433 | MDU6SXNzdWUzNDc2MjQzMw== | 7,307 | Support or default to less detailed datetime64 | {
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} | 38 | 2014-06-02T10:54:18Z | 2020-04-03T03:52:25Z | null | NONE | null | Hi,
I regularly run into issues where I have dates that fall outside of Pandas's datetime standards. Quite a few data sources have defaults such as "9999-12-31" and stuff like that, leading to issues in pandas.
This is because Pandas defaults to nanoseconds where the time span is quite limited.
See: http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html
Code Meaning Time span (relative) Time span (absolute)
s second +/- 2.9e12 years [ 2.9e9 BC, 2.9e9 AD]
ms millisecond +/- 2.9e9 years [ 2.9e6 BC, 2.9e6 AD]
us microsecond +/- 2.9e6 years [290301 BC, 294241 AD]
ns nanosecond +/- 292 years [ 1678 AD, 2262 AD]
I first thought it was the unit='s' parameter in to_datetime would work (see: http://pandas.pydata.org/pandas-docs/version/0.14.0/generated/pandas.tseries.tools.to_datetime.html), but this is only for translating a different datetime to nano seconds (I think) and the "ns" detail level seems to be rather hard coded.
I cannot imagine the majority of the use cases needing nano seconds; even going to micro seconds extends the date range to something that in my experience should always work. The nanosecond 2262AD is really limited.
Imho, ideally one should be able to choose the detail level. Is this just me or is this a common issue?
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} | 4 | 2014-06-02T13:48:11Z | 2021-04-11T04:41:27Z | null | CONTRIBUTOR | null | ``` python
In [41]: df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]})
In [42]: df['C'] = pd.date_range('2014-01-01', periods=3, freq='m')
In [43]: df
Out[43]:
A B C
0 1 2 2014-01-31
1 2 3 2014-02-28
2 3 4 2014-03-31
In [44]: df[['C']].quantile(.5)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-44-1e5bdc20c0ca> in <module>()
----> 1 df[['C']].quantile(.5)
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas/pandas/core/frame.pyc in quantile(self, q, axis, numeric_only)
4192 quantiles = [[f(vals, x) for x in per]
4193 for (_, vals) in data.iteritems()]
-> 4194 result = DataFrame(quantiles, index=data._info_axis, columns=q).T
4195 if len(is_dt_col) > 0:
4196 result[is_dt_col] = result[is_dt_col].applymap(lib.Timestamp)
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas/pandas/core/frame.pyc in __init__(self, data, index, columns, dtype, copy)
253 else:
254 mgr = self._init_ndarray(data, index, columns, dtype=dtype,
--> 255 copy=copy)
256 elif isinstance(data, collections.Iterator):
257 raise TypeError("data argument can't be an iterator")
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas/pandas/core/frame.pyc in _init_ndarray(self, values, index, columns, dtype, copy)
365 columns = _ensure_index(columns)
366
--> 367 return create_block_manager_from_blocks([values.T], [columns, index])
368
369 @property
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas/pandas/core/internals.pyc in create_block_manager_from_blocks(blocks, axes)
3183 blocks = [getattr(b, 'values', b) for b in blocks]
3184 tot_items = sum(b.shape[0] for b in blocks)
-> 3185 construction_error(tot_items, blocks[0].shape[1:], axes, e)
3186
3187
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas/pandas/core/internals.pyc in construction_error(tot_items, block_shape, axes, e)
3164 raise e
3165 raise ValueError("Shape of passed values is {0}, indices imply {1}".format(
-> 3166 passed,implied))
3167
3168
ValueError: Shape of passed values is (0, 0), indices imply (1, 0)
```
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} | 11 | 2014-06-02T13:49:13Z | 2015-03-31T13:31:25Z | 2015-03-08T14:39:50Z | MEMBER | null | Currently, "rpy2 R interface" documentation has blank section. Added descriptions to cover required basic topics.
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} | 3 | 2014-06-02T14:50:36Z | 2014-06-06T19:34:55Z | 2014-06-06T19:34:55Z | MEMBER | null | Found during #7299.
```
dates = ['2000-01-31', '2000-02-29', '2000-03-31', '2000-04-30']
pd.DatetimeIndex(dates).inferred_freq
# M
pd.DatetimeIndex(dates, tz='US/Eastern').inferred_freq
# M
pd.DatetimeIndex(dates, tz='Mexico/General').inferred_freq
# M
pd.DatetimeIndex(dates, tz='Asia/Tokyo').inferred_freq
# None
pd.DatetimeIndex(dates, tz='Europe/Paris').inferred_freq
# None
pd.DatetimeIndex(dates, tz='Australia/Sydney').inferred_freq
# None
```
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} | 5 | 2014-06-02T14:54:00Z | 2014-06-13T20:29:03Z | 2014-06-11T15:10:21Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/7306
We may want to wait on this till later today while I figure out what will happen in https://github.com/pydata/pandas/issues/7308
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} | 4 | 2014-06-02T15:17:23Z | 2014-06-13T04:48:46Z | 2014-06-02T20:51:23Z | MEMBER | null | Extract currently doesn't preserve group names when a series is returned. This
PR fixes that.
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} | 19 | 2014-06-02T15:58:40Z | 2016-10-12T23:04:53Z | 2014-06-03T00:44:37Z | CONTRIBUTOR | null | I have been looking for a solution for this for a long time. I tried the ideas in the following threads (with the latest Pandas):
1. [Keep finite entries only in Pandas](http://stackoverflow.com/questions/22799208/keep-finite-entries-only-in-pandas)
2. [Row filtering so that we only keep finite entries](http://stackoverflow.com/questions/23921766/row-filtering-so-that-we-only-have-finite-entries-in-a-specific-column?lq=1)
but none of them work. See thread 2 above, and the comments in its only answer to see why.
What is a good way to drop indices (either rows or columns) that meet a specific criteria such as: `"they contain entries that are not finite"`.?
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} | 3 | 2014-06-02T17:47:44Z | 2014-06-24T04:07:16Z | 2014-06-03T00:44:37Z | MEMBER | null | closes #7314
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} | 5 | 2014-06-02T19:11:58Z | 2016-10-12T23:04:53Z | 2014-07-07T16:22:20Z | CONTRIBUTOR | null | related #7308
related #7314
maybe something like this
```
df.get_for_dtypes(types)
```
types could be a list of string/dtypes or a dtype/string
e.g.
```
'float64'
['float']
['float32','float64','object']
['float',np.dtype('bool')]
['numeric']
['datetime','timedelta']
```
are possible values
pretty easy to implement (just translate the types and filter on the blocks, ala `_get_numeric_data`)
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| null | 1 | 2014-06-03T02:35:47Z | 2014-06-26T13:18:58Z | 2014-06-03T10:10:20Z | CONTRIBUTOR | null | {
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} | 2 | 2014-06-03T11:25:36Z | 2014-07-01T15:08:30Z | 2014-06-03T13:16:57Z | MEMBER | null | Closes #7310
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| null | 4 | 2014-06-03T13:10:04Z | 2014-06-04T12:48:19Z | 2014-06-04T12:48:19Z | NONE | null | Dictionary = {"Orange fruit" :1 ,"Cherry fruit" :2,......,"PineApple fruit":10}
MyDataFrame['Number Column'] = MyDataFrame['String Column'].apply(Dictionary.get)
In the figure attached , String Column consists Fruit names
Number Column consists corresponding numbers for fruit names
Only 128 rows are converted to corresponding numbers as assigned in Dictionary.
and then all rows in "Number Column " are not assigned and are empty(NaN)
Note: There are few values in first 125 rows which are not converted to corresponding numbers.
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} | 8 | 2014-06-03T13:38:41Z | 2014-07-12T06:20:06Z | 2014-06-05T16:01:00Z | MEMBER | null | Fix the issue that freq is preserved inappropriately, caused by #7302. Fix preserves freq if edge element is being deleted (same as #7299).
CC: @rosnfeld
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| null | 4 | 2014-06-03T13:57:33Z | 2014-06-03T14:48:13Z | 2014-06-03T14:05:01Z | CONTRIBUTOR | null | I came upon this behavior which seems illogical. With a DataFrame containing columns consisting of both scalar values it is not possible to select a column subset with a list like [col1, col2], where col2 is a tuple. Could this be expected behavior?
Making a pd.Index of the list beforehand works alright.
Using pandas version 0.14.0
``` python
In [69]: n = pd.DataFrame(np.random.randn(3,3), columns=['a','b', ('foo','bar')])
In [70]: n
Out[70]:
a b (foo, bar)
0 -0.050386 0.396910 0.002802
1 0.592425 0.157286 0.410414
2 1.276802 -0.096235 0.663521
In [75]: n[[('foo', 'bar'), 'a']]
Out[75]:
(foo, bar) a
0 0.002802 -0.050386
1 0.410414 0.592425
2 0.663521 1.276802
In [76]: n[pd.Index(['a', ('foo', 'bar')])]
Out[76]:
a (foo, bar)
0 -0.050386 0.002802
1 0.592425 0.410414
2 1.276802 0.663521
In [74]: n[['a',('foo','bar')]]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-74-e8afa5af6389> in <module>()
----> 1 n[['a',('foo','bar')]]
/usr/local/lib/python2.7/dist-packages/pandas/core/frame.pyc in __getitem__(self, key)
1676 if isinstance(key, (Series, np.ndarray, list)):
1677 # either boolean or fancy integer index
-> 1678 return self._getitem_array(key)
1679 elif isinstance(key, DataFrame):
1680 return self._getitem_frame(key)
/usr/local/lib/python2.7/dist-packages/pandas/core/frame.pyc in _getitem_array(self, key)
1703 def _getitem_array(self, key):
1704 # also raises Exception if object array with NA values
-> 1705 if com._is_bool_indexer(key):
1706 # warning here just in case -- previously __setitem__ was
1707 # reindexing but __getitem__ was not; it seems more reasonable to
/usr/local/lib/python2.7/dist-packages/pandas/core/common.pyc in _is_bool_indexer(key)
1819 elif isinstance(key, list):
1820 try:
-> 1821 arr = np.asarray(key)
1822 return arr.dtype == np.bool_ and len(arr) == len(key)
1823 except TypeError: # pragma: no cover
/usr/local/lib/python2.7/dist-packages/numpy/core/numeric.pyc in asarray(a, dtype, order)
458
459 """
--> 460 return array(a, dtype, copy=False, order=order)
461
462 def asanyarray(a, dtype=None, order=None):
ValueError: cannot set an array element with a sequence
```
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} | 15 | 2014-06-03T15:28:12Z | 2014-07-03T20:56:46Z | 2014-07-01T23:59:35Z | CONTRIBUTOR | null | Fixes #2960
Fixes #3490.
Summary of changes:
- for irregular timeseries plots, considers all lines already plotted when calculating xlim
- for plots with secondary_y axis, considers lines on both left and right axes when calculating xlim. There are tests for non-timeseries plots (always worked, but now a test to confirm this), irregular timeseries plots, and regular timeseries plots.
This does not fix #6608, which I now think is a fairly specific/rare case that could always be manually avoided by `x_compat=True`. I will still try and fix that, but it looks a bit uglier and I didn't want to hold back these more important changes.
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} | 7 | 2014-06-03T15:31:29Z | 2014-06-15T10:03:30Z | 2014-06-04T14:03:24Z | MEMBER | null | closes #7220
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} | 3 | 2014-06-03T16:17:02Z | 2014-06-05T21:25:09Z | 2014-06-05T21:25:09Z | NONE | null | ``` python
if 'select' in sql.lower():
```
there's a bug in read_sql line 377 in pandas.io.sql. the problem is that if your query doesn't contain the word 'select', it will not be run. so if you want to exec a stored proc, e.g., it doesn't work.
however, if you override this check, then it executes stored procs successfully.
this used to work in 13.1.
pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 45 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.14.0
nose: 1.3.0
Cython: 0.20.1
numpy: 1.8.0
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 2.0.0
sphinx: 1.2.1
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2013b
bottleneck: None
tables: 3.1.0
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: 1.8.2
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: 0.5.2
lxml: 3.3.1
bs4: 4.3.1
html5lib: None
bq: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
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} | 2 | 2014-06-03T21:52:22Z | 2014-06-04T00:03:36Z | 2014-06-04T00:03:36Z | CONTRIBUTOR | null | Attempting to reduce [this question](http://stackoverflow.com/questions/24024928/division-in-pandas-multiple-columns-by-another-column-of-the-same-dataframe) to a minimal test case produced:
```
>>> df = pd.DataFrame(np.arange(3*2).reshape((3,2)))
>>> df
0 1
0 0 1
1 2 3
2 4 5
>>> df.add(df[0], axis='index')
0 1
0 0 1
1 4 5
2 8 9
>>> df.sub(df[0], axis='index')
0 1
0 0 1
1 0 1
2 0 1
>>> df.mul(df[0], axis='index')
0 1
0 0 0
1 4 6
2 16 20
>>> df.div(df[0], axis='index')
Traceback (most recent call last):
File "<ipython-input-47-5b698f939cc6>", line 1, in <module>
df.div(df[0], axis='index')
File "/usr/local/lib/python2.7/dist-packages/pandas-0.14.0_5_g3b82634-py2.7-linux-x86_64.egg/pandas/core/ops.py", line 763, in f
return self._combine_series(other, na_op, fill_value, axis, level)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.14.0_5_g3b82634-py2.7-linux-x86_64.egg/pandas/core/frame.py", line 2830, in _combine_series
return self._combine_match_index(other, func, level=level, fill_value=fill_value)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.14.0_5_g3b82634-py2.7-linux-x86_64.egg/pandas/core/frame.py", line 2860, in _combine_match_index
return self._constructor(func(left.values.T, right.values).T,
File "/usr/local/lib/python2.7/dist-packages/pandas-0.14.0_5_g3b82634-py2.7-linux-x86_64.egg/pandas/core/ops.py", line 754, in na_op
result = com._fill_zeros(result, x, y, name, fill_zeros)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.14.0_5_g3b82634-py2.7-linux-x86_64.egg/pandas/core/common.py", line 1254, in _fill_zeros
np.putmask(result, mask & ~nans, fill)
ValueError: operands could not be broadcast together with shapes (3,) (6,)
```
For comparison, if we change the types:
```
>>> (df*1.0).div((df*1.0)[0], axis='index')
0 1
0 NaN inf
1 1 1.500000
2 1 1.250000
```
Version info:
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-27-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
pandas: 0.14.0-5-g3b82634
nose: 1.3.3
Cython: 0.20.1post0
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 1.2.1
sphinx: None
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.3
bottleneck: None
tables: None
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: 2.0.3
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.5
lxml: 3.3.3
bs4: 4.3.2
html5lib: 0.999
bq: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 7 | 2014-06-03T22:10:49Z | 2014-08-24T10:28:40Z | 2014-06-05T21:25:09Z | MEMBER | null | Closes #7324.
The `if 'select' in ...'` check was introduced for mysql legacy compatibility, but introduced problem if the query did not contain a select statement (eg a stored procedure). This should solve this, and passes the test suite (locally).
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} | 0 | 2014-06-03T23:39:45Z | 2014-06-16T02:31:33Z | 2014-06-04T00:03:36Z | CONTRIBUTOR | null | closes #7325
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} | 26 | 2014-06-04T01:01:34Z | 2014-07-18T17:45:03Z | 2014-07-01T10:11:23Z | NONE | null | ```
>>> d = pandas.Series({('1ab','2'): 3, ('1ab',3):4}, )
>>> d = pandas.concat([d,d])
>>> d = pandas.concat([d,d], axis=1)
>>> pickle.loads(pickle.dumps(d))
0 1
1ab 3 4 4
2 3 3
3 4 4
2 3 3
>>> pickle.loads(pickle.dumps(d.T))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib64/python2.7/pickle.py", line 1382, in loads
return Unpickler(file).load()
File "/usr/lib64/python2.7/pickle.py", line 858, in load
dispatch[key](self)
File "/usr/lib64/python2.7/pickle.py", line 1217, in load_build
setstate(state)
File "venv/lib/python2.7/site-packages/pandas/core/internals.py", line 2063, in __setstate__
placement=self.axes[0].get_indexer(items))
File "venv/lib/python2.7/site-packages/pandas/core/index.py", line 3200, in get_indexer
raise Exception('Reindexing only valid with uniquely valued Index '
Exception: Reindexing only valid with uniquely valued Index objects
```
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} | 1 | 2014-06-04T02:35:55Z | 2014-06-14T11:02:07Z | 2014-06-14T11:02:07Z | CONTRIBUTOR | null | ```
======================================================================
ERROR: test_integer_col_names (pandas.io.tests.test_sql.TestSQLApi)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/buildslave/nd-bb-slave-sparc-wheezy/pandas-py2_7-wheezy-sparc/build/venv/lib/python2.7/site-packages/pandas-0.14.0_45_g1754bb5-py2.7-linux-sparc64.egg/pandas/io/tests/test_sql.py", line 561, in test_integer_col_names
if_exists='replace')
File "/home/buildslave/nd-bb-slave-sparc-wheezy/pandas-py2_7-wheezy-sparc/build/venv/lib/python2.7/site-packages/pandas-0.14.0_45_g1754bb5-py2.7-linux-sparc64.egg/pandas/io/sql.py", line 440, in to_sql
index_label=index_label)
File "/home/buildslave/nd-bb-slave-sparc-wheezy/pandas-py2_7-wheezy-sparc/build/venv/lib/python2.7/site-packages/pandas-0.14.0_45_g1754bb5-py2.7-linux-sparc64.egg/pandas/io/sql.py", line 815, in to_sql
table.insert()
File "/home/buildslave/nd-bb-slave-sparc-wheezy/pandas-py2_7-wheezy-sparc/build/venv/lib/python2.7/site-packages/pandas-0.14.0_45_g1754bb5-py2.7-linux-sparc64.egg/pandas/io/sql.py", line 584, in insert
self.pd_sql.execute(ins, data_list)
File "/home/buildslave/nd-bb-slave-sparc-wheezy/pandas-py2_7-wheezy-sparc/build/venv/lib/python2.7/site-packages/pandas-0.14.0_45_g1754bb5-py2.7-linux-sparc64.egg/pandas/io/sql.py", line 783, in execute
return self.engine.execute(*args, **kwargs)
File "/usr/lib/python2.7/dist-packages/sqlalchemy/engine/base.py", line 2447, in execute
return connection.execute(statement, *multiparams, **params)
File "/usr/lib/python2.7/dist-packages/sqlalchemy/engine/base.py", line 1449, in execute
params)
File "/usr/lib/python2.7/dist-packages/sqlalchemy/engine/base.py", line 1576, in _execute_clauseelement
inline=len(distilled_params) > 1)
File "/usr/lib/python2.7/dist-packages/sqlalchemy/sql/expression.py", line 1778, in compile
return self._compiler(dialect, bind=bind, **kw)
File "/usr/lib/python2.7/dist-packages/sqlalchemy/sql/expression.py", line 1784, in _compiler
return dialect.statement_compiler(dialect, self, **kw)
File "/usr/lib/python2.7/dist-packages/sqlalchemy/sql/compiler.py", line 277, in __init__
engine.Compiled.__init__(self, dialect, statement, **kwargs)
File "/usr/lib/python2.7/dist-packages/sqlalchemy/engine/base.py", line 705, in __init__
self.string = self.process(self.statement)
File "/usr/lib/python2.7/dist-packages/sqlalchemy/engine/base.py", line 724, in process
return obj._compiler_dispatch(self, **kwargs)
File "/usr/lib/python2.7/dist-packages/sqlalchemy/sql/visitors.py", line 72, in _compiler_dispatch
return getter(visitor)(self, **kw)
File "/usr/lib/python2.7/dist-packages/sqlalchemy/sql/compiler.py", line 1047, in visit_insert
colparams = self._get_colparams(insert_stmt)
File "/usr/lib/python2.7/dist-packages/sqlalchemy/sql/compiler.py", line 1243, in _get_colparams
for key in self.column_keys
File "/usr/lib/python2.7/dist-packages/sqlalchemy/sql/compiler.py", line 1245, in <genexpr>
key not in stmt.parameters)
File "/usr/lib/python2.7/dist-packages/sqlalchemy/sql/expression.py", line 1396, in _column_as_key
return element.key
AttributeError: 'long' object has no attribute 'key'
```
http://nipy.bic.berkeley.edu/builders/pandas-py2.7-wheezy-sparc/builds/193/steps/shell_7/logs/stdio
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.3.final.0
python-bits: 32
OS: Linux
OS-release: 2.6.32-5-sparc64-smp
machine: sparc64
processor:
byteorder: big
LC_ALL: None
LANG: None
pandas: 0.14.0-45-g1754bb5
nose: 1.1.2
Cython: 0.19
numpy: 1.6.2
scipy: 0.10.1
statsmodels: 0.5.0
IPython: 0.13.1
sphinx: 1.1.3
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2012c
bottleneck: None
tables: 2.3.1
numexpr: 2.0.1
matplotlib: 1.1.1rc2
openpyxl: 1.7.0
xlrd: 0.6.1
xlwt: 0.7.4
xlsxwriter: None
lxml: 2.3.2
bs4: 4.1.0
html5lib: 0.95-dev
bq: None
apiclient: None
rpy2: 2.2.6
sqlalchemy: 0.7.8
pymysql: None
psycopg2: None
```
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} | 1 | 2014-06-04T02:56:13Z | 2014-12-01T00:46:44Z | 2014-12-01T00:46:44Z | NONE | null | ```
import pandas as pd
dates = [ 20140101, 20140102, 20140103]
states = [ "CA", "NY", "CA"]
x = pd.DataFrame({ 'dates' : dates, 'states' : states })
#y = pandas.DataFrame({ 'state' : [ 'CA', 'OR' ], 'value' : [ 1, 2]})
y = pd.DataFrame({ 'states' : [ "CA", "NY" ], 'stateid' : [ 1, 2]})
z = pd.merge(x, y, how='left', on='states',)
x=
dates states
0 20140101 CA
1 20140102 NY
2 20140103 CA
y=
stateid states
0 1 CA
1 2 NY
z= dates states stateid
0 20140101 CA 1
1 20140103 CA 1
2 20140102 NY 2
```
---
Note z is always sorted by "states" column whether argument sort=True or False.
This only happens when the x's states column is not unique. If x.states is unique(such as NY, CA, CT), sort=True and False behaves as expected.
This causes inconvenience when x is a time series and merge does not
preserve the time sequence.
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} | 12 | 2014-06-04T05:47:55Z | 2014-06-04T20:30:39Z | 2014-06-04T20:30:39Z | CONTRIBUTOR | null | Found when tracking down what was going on with [this question](http://stackoverflow.com/questions/24028281/pandas-series-operations-very-slow-after-upgrade#comment37041169_24028281) about performance.
First the case that makes sense:
```
>>> s = pd.Series(range(10**3), dtype=np.int32)
>>> s.dtype
dtype('int32')
>>> s.dtype.type
<type 'numpy.int32'>
>>> s.dtype.type in pd.lib._TYPE_MAP
True
>>>
>>> orig_sum_type = (s+s).dtype.type
>>> orig_sum_type
<type 'numpy.int32'>
>>> orig_sum_type in pd.lib._TYPE_MAP
True
```
Now let's increase the length of the series.
```
>>> s = pd.Series(range(10**5), dtype=np.int32)
>>> s.dtype
dtype('int32')
>>> s.dtype.type
<type 'numpy.int32'>
>>> s.dtype.type in pd.lib._TYPE_MAP
True
>>>
>>> new_sum_type = (s+s).dtype.type
>>> new_sum_type
<type 'numpy.int32'>
>>> new_sum_type in pd.lib._TYPE_MAP
False
```
.. wait, what?
```
>>> orig_sum_type, new_sum_type
(<type 'numpy.int32'>, <type 'numpy.int32'>)
>>> orig_sum_type == new_sum_type
False
>>> orig_sum_type is new_sum_type
False
>>> np.int32 is orig_sum_type
True
>>> np.int32 is new_sum_type
False
```
We've now got a new `numpy.int32` type floating around, not equal to the one in `numpy`. The crossover seems to be at 10k:
```
>>> def find_first():
... for i in range(1, 10**5):
... s = pd.Series(range(i), dtype=np.int32)
... if (s+s).dtype.type not in pd.lib._TYPE_MAP:
... return i
...
>>> find_first()
10001
```
ISTM that this lack of recognition of the dtype as in `_TYPE_MAP` prevents the early exit from being taken in `infer_dtype` upon recognition that it's an integer dtype, and that slows things down considerably.
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https://api.github.com/repos/pandas-dev/pandas/issues/7333 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7333/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7333/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7333/events | https://github.com/pandas-dev/pandas/issues/7333 | 34,936,804 | MDU6SXNzdWUzNDkzNjgwNA== | 7,333 | BUG: Series.map(dict) doesn't work when dict keys are tuples of length 1? | {
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} | 2 | 2014-06-04T07:49:00Z | 2014-06-05T17:57:28Z | 2014-06-05T17:57:28Z | CONTRIBUTOR | null | I ran across an odd issue today where some analysis code that was working pre-pandas 0.14.0 started failing. The issue seems to be specific to trying to use `Series.map(some_dict)` when the dict keys (and values in the series) are single item tuples. Code to demonstrate the issue:
``` python
import pandas as pd
pd.version.version
Out[10]: '0.14.0'
df = pd.DataFrame({'a': [(1,), (2,), (3, 4), (5, 6)]})
label_mappings = {
(1,): 'A',
(2,): 'B',
(3, 4): 'A',
(5, 6): 'B'
}
df['labels'] = df['a'].map(label_mappings)
df
Out[7]:
a labels
0 (1,) NaN
1 (2,) NaN
2 (3, 4) A
3 (5, 6) B
```
The expected output here is:
``` python
df
Out[7]:
a labels
0 (1,) A
1 (2,) B
2 (3, 4) A
3 (5, 6) B
```
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} | 18 | 2014-06-04T11:36:56Z | 2015-01-18T21:36:57Z | 2015-01-18T21:36:57Z | CONTRIBUTOR | null | Summary:
- `store.set_attrs(key, **attrs)` to set attributes of a group
- `store.get_attrs(key, attrs, default)` returns namedtuple with values from a groups attributes or single attribute directly if attrs is specified as a single string
- `to_hdf`, `put` and `append` have optional `attrs=dict(...)` argument to update attributes when storing an object
Examples:
```
from pandas.io.pytables import get_attrs
df.to_hdf('h5', 'df', attrs=dict(a=1, b=2))
a,b = get_attrs('h5', 'df', ['a', 'b'])
df = read_hdf('h5', 'df')
```
```
st.put('df', df)
st.set_attrs('df', a=1, b=2)
a,b,c = st.get_attrs('df', 'a b c', default=None)
a = st.get_attrs('df', 'a')
b, = st.get_attrs('df', ['b'])
```
```
st.append_to_mutple({'grp/a': ['A1', 'A2'], 'grp/b': ['B1', 'B2']}, 'grp/a')
st.set_attrs('grp', a=1, b=2)
attrs = st.get_attrs('group', ['a', 'b'])
```
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} | 1 | 2014-06-04T13:05:20Z | 2014-06-08T01:35:01Z | 2014-06-08T01:35:01Z | CONTRIBUTOR | null | https://travis-ci.org/pydata/pandas/jobs/26755159
need a better skipping mechanism
```
======================================================================
ERROR: test_get_call_data_warning (pandas.io.tests.test_data.TestOptionsWarnings)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/virtualenv/python3.2.5/lib/python3.2/site-packages/pandas/util/testing.py", line 1281, in wrapper
return t(*args, **kwargs)
File "/home/travis/virtualenv/python3.2.5/lib/python3.2/site-packages/pandas/io/tests/test_data.py", line 341, in test_get_call_data_warning
self.aapl.get_call_data(month=self.month, year=self.year)
File "/home/travis/virtualenv/python3.2.5/lib/python3.2/site-packages/pandas/io/data.py", line 709, in get_call_data
return self._get_option_data(month, year, expiry, 9, 'calls')
File "/home/travis/virtualenv/python3.2.5/lib/python3.2/site-packages/pandas/io/data.py", line 669, in _get_option_data
" found".format(table_loc, ntables))
IndexError: Table location 9 invalid, 0 tables found
----------------------------------------------------------------------
```
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} | 8 | 2014-06-04T13:22:59Z | 2014-06-16T14:59:08Z | 2014-06-05T17:57:28Z | CONTRIBUTOR | null | closes #7333 seems to be due to the new behaviour of `Series`, which will automatically create a MultiIndex out of the dict's keys when creating a series to map the values. The MultiIndex doesn't match the values for the shorter tuples, so they fail to map.
The fix is fairly simple, just pass the dict keys explicitly as the index.
I've added a test case for this specific issue, but could add some more if others can see related issues that would arise from this. I'll try to finalize the PR (squashing into a single commit, rebasing if necessary) tomorrow if it looks good.
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} | 24 | 2014-06-04T13:55:16Z | 2014-06-09T20:51:41Z | 2014-06-09T20:51:41Z | CONTRIBUTOR | null | cc @dbew
(I am just posting 27-64, but failing on most windows versions). prob the same error.
I think has to do with 32-bit ints (which is the default on windows even for 64-bit versions), being treated as 64-bit.
```
======================================================================
ERROR: test_append_with_timezones_dateutil (pandas.io.tests.test_pytables.TestHDFStore)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\io\tests\test_pytables.py", line 2086, in test_append_with_timezones_dateutil
store.append('df_tz', df, data_columns=['A'])
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\io\pytables.py", line 911, in append
**kwargs)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\io\pytables.py", line 1270, in _write_to_group
s.write(obj=value, append=append, complib=complib, **kwargs)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\io\pytables.py", line 3605, in write
**kwargs)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\io\pytables.py", line 3298, in create_axes
% (b.dtype.name, b_items, str(detail))
Exception: cannot find the correct atom type -> [dtype->object,items->Index([u'A'], dtype='object')] list index out of range
======================================================================
ERROR: test_range_tz_dateutil (pandas.tseries.tests.test_daterange.TestDateRange)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_daterange.py", line 409, in test_range_tz_dateutil
dr = date_range(start=start, end=end)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 1879, in date_range
closed=closed)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 195, in __new__
infer_dst=infer_dst)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 350, in _generate
raise ValueError('Start and end cannot both be tz-aware with '
ValueError: Start and end cannot both be tz-aware with different timezones
======================================================================
ERROR: test_append_concat_tz_explicit_dateutil (pandas.tseries.tests.test_timeseries.TestTimeSeries)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_timeseries.py", line 1814, in test_append_concat_tz_explicit_dateutil
self.assert_(result.index.equals(rng3))
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 1574, in equals
self.tz) == tslib.get_timezone(other.tz)
File "tslib.pyx", line 1013, in pandas.tslib.get_timezone (pandas\tslib.c:16907)
File "tslib.pyx", line 1032, in pandas.tslib._get_zone (pandas\tslib.c:17056)
IndexError: list index out of range
======================================================================
ERROR: test_period_resample_with_local_timezone_dateutil (pandas.tseries.tests.test_timeseries.TestTimeSeries)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_timeseries.py", line 2030, in test_period_resample_with_local_timezone_dateutil
index = pd.date_range(start, end, freq='H')
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 1879, in date_range
closed=closed)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 195, in __new__
infer_dst=infer_dst)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 350, in _generate
raise ValueError('Start and end cannot both be tz-aware with '
ValueError: Start and end cannot both be tz-aware with different timezones
======================================================================
ERROR: test_convert_datetime_list (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_timezones.py", line 694, in test_convert_datetime_list
dr2 = DatetimeIndex(list(dr), name='foo')
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 214, in __new__
data = tools.to_datetime(data, errors='raise')
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tools.py", line 323, in to_datetime
return _convert_listlike(arg, box, format)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tools.py", line 310, in _convert_listlike
values, tz = tslib.datetime_to_datetime64(arg)
File "tslib.pyx", line 1110, in pandas.tslib.datetime_to_datetime64 (pandas\tslib.c:17961)
File "tslib.pyx", line 1032, in pandas.tslib._get_zone (pandas\tslib.c:17056)
IndexError: list index out of range
======================================================================
ERROR: test_convert_tz_aware_datetime_datetime (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_timezones.py", line 587, in test_convert_tz_aware_datetime_datetime
result = to_datetime(dates_aware)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tools.py", line 323, in to_datetime
return _convert_listlike(arg, box, format)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tools.py", line 310, in _convert_listlike
values, tz = tslib.datetime_to_datetime64(arg)
File "tslib.pyx", line 1110, in pandas.tslib.datetime_to_datetime64 (pandas\tslib.c:17961)
File "tslib.pyx", line 1032, in pandas.tslib._get_zone (pandas\tslib.c:17056)
IndexError: list index out of range
======================================================================
ERROR: test_date_range_span_dst_transition (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_timezones.py", line 689, in test_date_range_span_dst_transition
self.assertTrue((dr.hour == 0).all())
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\core\base.py", line 320, in f
return self._ops_compat(name,op_accessor)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\core\base.py", line 349, in _ops_compat
return self._wrap_access_object(getattr(obj,op_accessor))
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 47, in f
values = self._local_timestamps()
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 449, in _local_timestamps
return tslib.tz_convert(self.asi8, utc, self.tz)
File "tslib.pyx", line 1852, in pandas.tslib.tz_convert (pandas\tslib.c:27975)
File "tslib.pyx", line 1032, in pandas.tslib._get_zone (pandas\tslib.c:17056)
IndexError: list index out of range
======================================================================
ERROR: Test different DatetimeIndex constructions with timezone
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_timezones.py", line 756, in test_datetimeindex_tz
self.assertTrue(idx1.equals(other))
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 1574, in equals
self.tz) == tslib.get_timezone(other.tz)
File "tslib.pyx", line 1013, in pandas.tslib.get_timezone (pandas\tslib.c:16907)
File "tslib.pyx", line 1032, in pandas.tslib._get_zone (pandas\tslib.c:17056)
IndexError: list index out of range
======================================================================
ERROR: test_datetimeindex_tz_nat (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_timezones.py", line 759, in test_datetimeindex_tz_nat
idx = to_datetime([Timestamp("2013-1-1", tz=self.tzstr('US/Eastern')), NaT])
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tools.py", line 323, in to_datetime
return _convert_listlike(arg, box, format)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tools.py", line 310, in _convert_listlike
values, tz = tslib.datetime_to_datetime64(arg)
File "tslib.pyx", line 1110, in pandas.tslib.datetime_to_datetime64 (pandas\tslib.c:17961)
File "tslib.pyx", line 1032, in pandas.tslib._get_zone (pandas\tslib.c:17056)
IndexError: list index out of range
======================================================================
ERROR: test_field_access_localize (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_timezones.py", line 382, in test_field_access_localize
self.assertTrue((rng.hour == 0).all())
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\core\base.py", line 320, in f
return self._ops_compat(name,op_accessor)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\core\base.py", line 349, in _ops_compat
return self._wrap_access_object(getattr(obj,op_accessor))
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 47, in f
values = self._local_timestamps()
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 449, in _local_timestamps
return tslib.tz_convert(self.asi8, utc, self.tz)
File "tslib.pyx", line 1852, in pandas.tslib.tz_convert (pandas\tslib.c:27975)
File "tslib.pyx", line 1032, in pandas.tslib._get_zone (pandas\tslib.c:17056)
IndexError: list index out of range
======================================================================
ERROR: test_frame_reset_index (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_timezones.py", line 709, in test_frame_reset_index
roundtripped = df.reset_index().set_index('index')
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\core\frame.py", line 2270, in set_index
index = MultiIndex.from_arrays(arrays, names=names)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\core\index.py", line 2765, in from_arrays
return Index(arrays[0], name=name)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\core\index.py", line 178, in __new__
return DatetimeIndex(subarr, copy=copy, name=name, **kwargs)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 264, in __new__
subarr = tools.to_datetime(data, box=False)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tools.py", line 323, in to_datetime
return _convert_listlike(arg, box, format)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tools.py", line 310, in _convert_listlike
values, tz = tslib.datetime_to_datetime64(arg)
File "tslib.pyx", line 1110, in pandas.tslib.datetime_to_datetime64 (pandas\tslib.c:17961)
File "tslib.pyx", line 1032, in pandas.tslib._get_zone (pandas\tslib.c:17056)
IndexError: list index out of range
======================================================================
ERROR: test_getitem_pydatetime_tz (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_timezones.py", line 731, in test_getitem_pydatetime_tz
ts = Series(index=index, data=index.hour)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\core\base.py", line 320, in f
return self._ops_compat(name,op_accessor)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\core\base.py", line 349, in _ops_compat
return self._wrap_access_object(getattr(obj,op_accessor))
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 47, in f
values = self._local_timestamps()
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 449, in _local_timestamps
return tslib.tz_convert(self.asi8, utc, self.tz)
File "tslib.pyx", line 1852, in pandas.tslib.tz_convert (pandas\tslib.c:27975)
File "tslib.pyx", line 1032, in pandas.tslib._get_zone (pandas\tslib.c:17056)
IndexError: list index out of range
======================================================================
ERROR: test_infer_tz (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_timezones.py", line 483, in test_infer_tz
assert(tools._infer_tzinfo(start, end) is self.localize(eastern, _start).tzinfo)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tools.py", line 53, in _infer_tzinfo
tz = _infer(start, end)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tools.py", line 47, in _infer
if not (tslib.get_timezone(tz) == tslib.get_timezone(b.tzinfo)):
File "tslib.pyx", line 1013, in pandas.tslib.get_timezone (pandas\tslib.c:16907)
File "tslib.pyx", line 1032, in pandas.tslib._get_zone (pandas\tslib.c:17056)
IndexError: list index out of range
======================================================================
ERROR: test_localized_at_time_between_time (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_timezones.py", line 544, in test_localized_at_time_between_time
result = ts_local.at_time(time(10, 0))
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\core\generic.py", line 2769, in at_time
indexer = self.index.indexer_at_time(time, asof=asof)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 1689, in indexer_at_time
time_micros = self._get_time_micros()
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 733, in _get_time_micros
values = self._local_timestamps()
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 449, in _local_timestamps
return tslib.tz_convert(self.asi8, utc, self.tz)
File "tslib.pyx", line 1852, in pandas.tslib.tz_convert (pandas\tslib.c:27975)
File "tslib.pyx", line 1032, in pandas.tslib._get_zone (pandas\tslib.c:17056)
IndexError: list index out of range
======================================================================
ERROR: test_shift_localized (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_timezones.py", line 655, in test_shift_localized
result = dr_tz.shift(1, '10T')
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 872, in shift
result = Index.shift(self, n, freq)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\core\index.py", line 924, in shift
return Index([idx + offset for idx in self], name=self.name)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\core\index.py", line 178, in __new__
return DatetimeIndex(subarr, copy=copy, name=name, **kwargs)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 264, in __new__
subarr = tools.to_datetime(data, box=False)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tools.py", line 323, in to_datetime
return _convert_listlike(arg, box, format)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tools.py", line 310, in _convert_listlike
values, tz = tslib.datetime_to_datetime64(arg)
File "tslib.pyx", line 1110, in pandas.tslib.datetime_to_datetime64 (pandas\tslib.c:17961)
File "tslib.pyx", line 1032, in pandas.tslib._get_zone (pandas\tslib.c:17056)
IndexError: list index out of range
======================================================================
ERROR: test_static_tzinfo (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_timezones.py", line 670, in test_static_tzinfo
index.hour
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\core\base.py", line 320, in f
return self._ops_compat(name,op_accessor)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\core\base.py", line 349, in _ops_compat
return self._wrap_access_object(getattr(obj,op_accessor))
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 47, in f
values = self._local_timestamps()
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 449, in _local_timestamps
return tslib.tz_convert(self.asi8, utc, self.tz)
File "tslib.pyx", line 1852, in pandas.tslib.tz_convert (pandas\tslib.c:27975)
File "tslib.pyx", line 1032, in pandas.tslib._get_zone (pandas\tslib.c:17056)
IndexError: list index out of range
======================================================================
ERROR: test_string_index_alias_tz_aware (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_timezones.py", line 559, in test_string_index_alias_tz_aware
result = ts['1/3/2000']
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\core\series.py", line 479, in __getitem__
result = self.index.get_value(self, key)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 1272, in get_value
loc = self._get_string_slice(key)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 1327, in _get_string_slice
getattr(self, 'inferred_freq', None))
File "properties.pyx", line 34, in pandas.lib.cache_readonly.__get__ (pandas\lib.c:36395)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 1440, in inferred_freq
return infer_freq(self)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\frequencies.py", line 653, in infer_freq
return inferer.get_freq()
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\frequencies.py", line 695, in get_freq
return self._infer_daily_rule()
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\frequencies.py", line 783, in _infer_daily_rule
annual_rule = self._get_annual_rule()
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\frequencies.py", line 825, in _get_annual_rule
pos_check = self.month_position_check()
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\frequencies.py", line 741, in month_position_check
weekdays = self.index.dayofweek
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\core\base.py", line 320, in f
return self._ops_compat(name,op_accessor)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\core\base.py", line 349, in _ops_compat
return self._wrap_access_object(getattr(obj,op_accessor))
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 47, in f
values = self._local_timestamps()
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 449, in _local_timestamps
return tslib.tz_convert(self.asi8, utc, self.tz)
File "tslib.pyx", line 1852, in pandas.tslib.tz_convert (pandas\tslib.c:27975)
File "tslib.pyx", line 1032, in pandas.tslib._get_zone (pandas\tslib.c:17056)
IndexError: list index out of range
======================================================================
ERROR: test_tz_aware_asfreq (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_timezones.py", line 665, in test_tz_aware_asfreq
s.asfreq('T')
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\core\generic.py", line 2754, in asfreq
normalize=normalize)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 434, in asfreq
dti = date_range(obj.index[0], obj.index[-1], freq=freq)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 1879, in date_range
closed=closed)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 195, in __new__
infer_dst=infer_dst)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 350, in _generate
raise ValueError('Start and end cannot both be tz-aware with '
ValueError: Start and end cannot both be tz-aware with different timezones
======================================================================
ERROR: test_tz_convert_unsorted (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_timezones.py", line 647, in test_tz_convert_unsorted
result = dr[::-1].hour
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\core\base.py", line 320, in f
return self._ops_compat(name,op_accessor)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\core\base.py", line 349, in _ops_compat
return self._wrap_access_object(getattr(obj,op_accessor))
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 47, in f
values = self._local_timestamps()
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 453, in _local_timestamps
result = tslib.tz_convert(values.take(indexer), utc, self.tz)
File "tslib.pyx", line 1852, in pandas.tslib.tz_convert (pandas\tslib.c:27975)
File "tslib.pyx", line 1032, in pandas.tslib._get_zone (pandas\tslib.c:17056)
IndexError: list index out of range
======================================================================
ERROR: test_tz_string (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_timezones.py", line 500, in test_tz_string
self.assertTrue(result.equals(expected))
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 1574, in equals
self.tz) == tslib.get_timezone(other.tz)
File "tslib.pyx", line 1013, in pandas.tslib.get_timezone (pandas\tslib.c:16907)
File "tslib.pyx", line 1032, in pandas.tslib._get_zone (pandas\tslib.c:17056)
IndexError: list index out of range
======================================================================
ERROR: test_tzaware_datetime_to_index (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_timezones.py", line 676, in test_tzaware_datetime_to_index
index = DatetimeIndex(d)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\index.py", line 214, in __new__
data = tools.to_datetime(data, errors='raise')
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tools.py", line 323, in to_datetime
return _convert_listlike(arg, box, format)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tseries\tools.py", line 310, in _convert_listlike
values, tz = tslib.datetime_to_datetime64(arg)
File "tslib.pyx", line 1110, in pandas.tslib.datetime_to_datetime64 (pandas\tslib.c:17961)
File "tslib.pyx", line 1032, in pandas.tslib._get_zone (pandas\tslib.c:17056)
IndexError: list index out of range
======================================================================
FAIL: test_pivot_datetime_tz (pandas.tools.tests.test_pivot.TestPivotTable)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\tools\tests\test_pivot.py", line 512, in test_pivot_datetime_tz
tm.assert_frame_equal(result, expected)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\util\testing.py", line 585, in assert_frame_equal
check_exact=check_exact)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\util\testing.py", line 526, in assert_series_equal
assert_attr_equal('dtype', left, right)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\util\testing.py", line 508, in assert_attr_equal
assert_equal(left_attr,right_attr,"attr is not equal [{0}]" .format(attr))
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\util\testing.py", line 491, in assert_equal
assert a == b, "%s: %r != %r" % (msg.format(a,b), a, b)
AssertionError: attr is not equal [dtype]: dtype('int32') != dtype('int64')
----------------------------------------------------------------------
Ran 7308 tests in 459.136s
FAILED (SKIP=133, errors=21, failures=1)
C:\Users\Jeff Reback\Documents\GitHub\pandas> more versions.27-64.log
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.5.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: 0.12.0
nose: 1.3.0
Cython: 0.19.2
numpy: 1.8.0
scipy: 0.13.0
statsmodels: 0.5.0
IPython: 1.1.0
sphinx: None
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2013.8
bottleneck: 0.7.0
tables: 3.0.0
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: 0.5.0
lxml: 3.3.5
bs4: 4.3.2
html5lib: 1.0b3
bq: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.3
pymysql: 0.6.1.None
psycopg2: 2.5.2 (dt dec pq3 ext)
C:\Users\Jeff Reback\Documents\GitHub\pandas>more test.27-64.log
```
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https://api.github.com/repos/pandas-dev/pandas/issues/7338 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7338/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7338/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7338/events | https://github.com/pandas-dev/pandas/pull/7338 | 34,964,246 | MDExOlB1bGxSZXF1ZXN0MTY3MTE2NTU= | 7,338 | add class=„pandas-empty“ to NaN-cells’ HTML | {
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"default": false,
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| closed | false | null | []
| null | 10 | 2014-06-04T14:33:26Z | 2014-06-14T15:47:54Z | 2014-06-05T16:04:24Z | NONE | null | This PR is related to #5330, but is much simpler. There is no new API, this just allows styling empty cells using CSS, e.g. using
``` css
td.pandas-empty { color: #eee }
```
to de-emphasize, or
``` css
td.pandas-empty { background-color: yellow }
```
to let empty cells stand out.
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} | 0 | 2014-06-04T14:42:14Z | 2014-06-04T20:17:29Z | 2014-06-04T20:17:29Z | CONTRIBUTOR | null | Test just needs to use a localized version in the comparison
```
FAIL: test_with_tz (pandas.tseries.tests.test_timezones.TestTimeZoneSupportPytz)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/virtualenv/python3.4.0/lib/python3.4/site-packages/pandas/tseries/tests/test_timezones.py", line 406, in test_with_tz
self.assertIs(central[0].tz, tz)
File "/home/travis/virtualenv/python3.4.0/lib/python3.4/site-packages/pandas/util/testing.py", line 96, in assertIs
assert a is b, "%s: %r is not %r" % (msg.format(a,b), a, b)
AssertionError: : <DstTzInfo 'US/Central' CST-1 day, 18:00:00 STD> is not <DstTzInfo 'US/Central' LMT-1 day, 18:09:00 STD>
----------------------------------------------------------------------
Ran 7104 tests in 558.494s
```
https://travis-ci.org/pydata/pandas/jobs/26755161
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} | 11 | 2014-06-04T16:06:18Z | 2017-11-04T17:27:39Z | 2017-11-04T17:27:13Z | CONTRIBUTOR | null | From a [stackoverflow](http://stackoverflow.com/q/24038946/386279) question, I'm working on a Mac, trying to read a csv generated on Windows that ends with a `'\x1a'` EOF character, and `pd.read_csv` creates a new row at the end, `['\x1a', NaN, NaN, ...]`
```
In [1]: import pandas as pd
In [2]: from StringIO import StringIO
In [3]: s = StringIO('a,b\r\n1,2\r\n3,4\r\n\x1a')
In [4]: df = pd.read_csv(s)
In [5]: df
Out[5]:
a b
0 1 2
1 3 4
2 NaN
```
Now I'm manually checking for that character and a bunch of NaN's in the last row, and dropping it. Would it be worth adding an option to not create this last row (or ignore the EOF automatically)? In python I'm currently doing
```
def strip_eof(df):
"Drop last row if it begins with '\x1a' and ends with NaN's"
lastrow = df.iloc[-1]
if lastrow[0] == '\x1a' and lastrow[1:].isnull().all():
return df.drop([lastrow.name], axis=0)
return df
```
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} | 7 | 2014-06-04T16:46:05Z | 2014-07-12T06:26:14Z | 2014-06-04T19:47:32Z | MEMBER | null | closes #2073
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} | 3 | 2014-06-04T17:02:55Z | 2014-06-21T02:59:17Z | 2014-06-04T20:30:39Z | CONTRIBUTOR | null | closes #7332
for some reason `int64` was not being looked up correctly in the _TYPE_MAP table.
changing to work on name/kind makes this more efficient.
separately better inference of timedelta conversions from i8 were not being done
so that yields a big boost
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
dtype_infer_timedelta64_2 | 3.9814 | 1765.3640 | 0.0023 |
dtype_infer_datetime64 | 4.8450 | 1825.3207 | 0.0027 |
dtype_infer_int64 | 0.7904 | 18.5820 | 0.0425 |
dtype_infer_float64 | 0.8090 | 1.0493 | 0.7710 |
dtype_infer_float32 | 0.3430 | 0.3823 | 0.8971 |
dtype_infer_int32 | 0.3897 | 0.4017 | 0.9701 |
dtype_infer_uint32 | 0.5450 | 0.5583 | 0.9762 |
dtype_infer_timedelta64_1 | 118.2277 | 117.6037 | 1.0053 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [e2f5a50] : PERF: use names/kinds for dtype inference on known types
Base [87660ef] : Merge pull request #6968 from ahlmss/AHLMSS_0.13.1_ahl1
```
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| null | 1 | 2014-06-04T18:58:19Z | 2015-03-08T16:53:46Z | 2015-03-08T16:53:46Z | CONTRIBUTOR | null | http://stackoverflow.com/q/24044734/1240268
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| null | 3 | 2014-06-04T19:51:15Z | 2014-06-04T21:03:31Z | 2014-06-04T20:14:23Z | NONE | null | I am attempting to read CSV files with accented names. Below is an example of the error that gets thrown for a file named: Anzoátegui.csv
``` python
locs = pandas.read_csv(os.path.join(os.getcwd(),os.listdir()[1]),sep=',',skipinitialspace=True)
Traceback (most recent call last):
File "C:\PF2\eclipse-standard-kepler-SR2-win32-x86_64\eclipse\plugins\org.python.pydev_3.3.3.201401272249\pysrc\pydevd_comm.py", line 891, in doIt
result = pydevd_vars.evaluateExpression(self.thread_id, self.frame_id, self.expression, self.doExec)
File "C:\PF2\eclipse-standard-kepler-SR2-win32-x86_64\eclipse\plugins\org.python.pydev_3.3.3.201401272249\pysrc\pydevd_vars.py", line 486, in evaluateExpression
result = eval(compiled, updated_globals, frame.f_locals)
File "<string>", line 1, in <module>
File "C:\Python33\lib\site-packages\pandas\io\parsers.py", line 404, in parser_f
return _read(filepath_or_buffer, kwds)
File "C:\Python33\lib\site-packages\pandas\io\parsers.py", line 205, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "C:\Python33\lib\site-packages\pandas\io\parsers.py", line 486, in __init__
self._make_engine(self.engine)
File "C:\Python33\lib\site-packages\pandas\io\parsers.py", line 594, in _make_engine
self._engine = CParserWrapper(self.f, **self.options)
File "C:\Python33\lib\site-packages\pandas\io\parsers.py", line 952, in __init__
self._reader = _parser.TextReader(src, **kwds)
File "parser.pyx", line 330, in pandas.parser.TextReader.__cinit__ (pandas\parser.c:3040)
File "parser.pyx", line 557, in pandas.parser.TextReader._setup_parser_source (pandas\parser.c:5387)
OSError: File b'C:\\Python code\\DisaggregateExposure\\src\\root\\nested\\Anzo\xc3\xa1tegui.csv' does not exist
```
os.listdir()[1] correctly produces Anzoátegui.csv
The encoding argument in read_csv can sort out accents within the file, but is there a way to force an encoding for the file name aside from the python default utf-8?
Here's what I've got installed:
``` python
INSTALLED VERSIONS
------------------
Python: 3.3.3.final.0
OS: Windows
Release: 7
Processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.13.0
Cython: Not installed
Numpy: 1.7.2
Scipy: 0.13.2
statsmodels: 0.5.0
patsy: 0.2.1
scikits.timeseries: Not installed
dateutil: 2.2
pytz: 2013.9
bottleneck: Not installed
PyTables: Not Installed
numexpr: Not Installed
matplotlib: 1.3.1
openpyxl: Not installed
xlrd: Not installed
xlwt: Not installed
xlsxwriter: Not installed
sqlalchemy: Not installed
lxml: Not installed
bs4: Not installed
html5lib: Not installed
bigquery: Not installed
apiclient: Not installed
```
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| null | 4 | 2014-06-04T20:19:04Z | 2014-07-09T11:44:26Z | 2014-06-04T20:21:00Z | CONTRIBUTOR | null | This change fixes broken tests on windows following `ENH: Support dateutil timezones`
closes #7337
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| null | 2 | 2014-06-05T00:13:20Z | 2014-08-22T07:32:12Z | 2014-08-22T07:32:12Z | NONE | null | When I have to write a frame to the database that has 20,000+ records I get a timeout from MySQL. I've written some code to write the data 20,000 records at a time. I think it would be a useful function to have built into Pandas.
The code works but needs some cleanup. If others agree this is useful enhancement request I can work on this.
```
def write_to_db(engine, frame, table_name, chunk_size):
start_index = 0
end_index = chunk_size if chunk_size < len(frame) else len(frame)
frame = frame.where(pd.notnull(frame), None)
if_exists_param = 'replace'
while start_index != end_index:
print "Writing rows %s through %s" % (start_index, end_index)
frame.iloc[start_index:end_index, :].to_sql(con=engine, name=table_name, if_exists=if_exists_param)
if_exists_param = 'append'
start_index = min(start_index + chunk_size, len(frame))
end_index = min(end_index + chunk_size, len(frame))
engine = sqlalchemy.create_engine('mysql://...') #database details omited
write_to_db(engine, frame, 'retail_pendingcustomers', 20000)
```
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} | 11 | 2014-06-05T08:53:24Z | 2016-03-10T10:37:12Z | 2014-06-05T19:45:05Z | NONE | null | Hi everybody,
lets take the following dates:
`dates = ['04/07/2013 21:40','22/06/2013 02:40']`
if I call `pd.to_datetime` on these two date strings, it produces incorrect results. For the first date '7' is interpreted as day-value while for the second date '22' is interpreted as the day-value.
Please get me right I have no problem with the wrong parsed date value in the first string, but with the missing propagation of the corrent interpretation scheme after reading the second string.
Thanks
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} | 0 | 2014-06-05T11:34:07Z | 2014-06-05T13:30:46Z | 2014-06-05T13:30:46Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/24056277/pandas-use-array-index-all-values)
This is a bug; the first should work like the 2nd (it is incorrectly selecting by position); essentially this is `.ix` fallback type behavior
```
In [10]: df = DataFrame(np.arange(12).reshape(-1,1),index=pd.MultiIndex.from_product([[1,2,3,4],[1,2,3]]))
In [11]: df.loc[[1,2]]
Out[11]:
0
1 2 1
3 2
In [12]: df.loc[([1,2],),:]
Out[12]:
0
1 1 0
2 1
3 2
2 1 3
2 4
3 5
```
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} | 0 | 2014-06-05T12:34:39Z | 2014-06-27T12:37:06Z | 2014-06-05T13:30:46Z | CONTRIBUTOR | null | closes #7349
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} | 4 | 2014-06-05T16:07:55Z | 2014-06-12T22:41:29Z | 2014-06-12T22:41:29Z | CONTRIBUTOR | null | The default value for the `axis` argument for `nanops.nanmedia` is `None`. However, the function raises an exception when using this value:
``` Python
>>> from pandas.core import nanops
>>> import bottleneck
>>> import numpy as np
>>> nanops._USE_BOTTLENECK = False
>>>
>>> val = np.random.randn(13, 11, 7)
>>>
>>> bottleneck.nanmedian(val, axis=None).ndim
0
>>> nanops.nanmedian(val, axis=None)
TypeError: unsupported operand type(s) for +=: 'NoneType' and 'int'
>>> nanops.nanmedian(val)
TypeError: unsupported operand type(s) for +=: 'NoneType' and 'int'
```
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} | 3 | 2014-06-05T16:23:37Z | 2014-06-12T13:47:33Z | 2014-06-12T10:46:48Z | CONTRIBUTOR | null | A number of nanops functions rely on the private internal function `nanops._maybe_null_out`. These include `nansum`, `nanmin`, `nanmax`, and `nanprod`. All of these, in principle, should work with arrays with a complex dtype. However, due to a bug in `_maybe_null_out`, they convert the array to a float under certain situations, resulting in erroneous results. The easiest way to trigger this is to have a complex array where all the imaginary values are nan, and make `axis` argument something other than `None`.
``` Python
>>> from pandas.core import nanops
>>> import bottleneck
>>> import numpy as np
>>> nanops._USE_BOTTLENECK = False
>>>
>>> val = np.tile(1, (11, 7)) + np.tile(np.nan*1j, (11, 7))
>>>
>>> bottleneck.nansum(val, axis=0)
array([ nan+0.j, nan+0.j, nan+0.j, nan+0.j, nan+0.j, nan+0.j, nan+0.j])
>>> nanops.nansum(val, axis=0)
array([ nan, nan, nan, nan, nan, nan, nan])
>>> bottleneck.nansum(val, axis=0).dtype
dtype('complex128')
>>> nanops.nansum(val, axis=0).dtype
dtype('float64')
```
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} | 1 | 2014-06-05T16:30:49Z | 2014-06-12T12:59:31Z | 2014-06-12T12:59:31Z | CONTRIBUTOR | null | Several of the functions in `nanops`, including `nanmean`, `nanmedian`, and anything using `_get_counts` will crash when using a 1-dimensional all-nan array when the `axis` argument is set to 0.
``` Python
>>> from pandas.core import nanops
>>> import bottleneck
>>> import numpy as np
>>> nanops._USE_BOTTLENECK = False
>>>
>>> val = np.tile(np.nan, (5,))
>>>
>>> bottleneck.nanmean(val, axis=0)
nan
>>> nanops.nanmean(val, axis=0)
TypeError: 'numpy.float64' object does not support item assignment
```
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} | 6 | 2014-06-05T16:40:06Z | 2020-10-28T03:35:16Z | 2014-06-06T06:41:34Z | MEMBER | null | From SO: http://stackoverflow.com/questions/24064509/to-sql-pandas-method-changes-the-scheme-of-sqlite-tables
The reason is that `get_table` is not implemented for fallback mode (https://github.com/pydata/pandas/blob/master/pandas/io/sql.py#L1072), so a `create_table_statement` is always called (https://github.com/pydata/pandas/blob/master/pandas/io/sql.py#L525).
Should look at it further to see if this is solvable, or a restriction of sqlite fallback.
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} | 4 | 2014-06-05T16:52:34Z | 2014-06-13T16:43:25Z | 2014-06-13T16:43:25Z | CONTRIBUTOR | null | `nanops._has_infs`, which is used in determining whether to employ bottleneck optimizations, fails on dtypes which should otherwise be supported, including 'f2', complex, and multidimensional 'f4' and 'f8'.
``` Python
>>> from pandas.core import nanops
>>> import numpy as np
>>>
>>> val0 = np.ones(10)*np.inf
>>> val1 = val0.astype('f2')
>>> val2 = val0+val0*1j
>>> val3 = np.tile(val0, (1, 10))
>>>
>>> nanops._has_infs(val0)
True
>>> nanops._has_infs(val1)
False
>>> nanops._has_infs(val2)
False
>>> nanops._has_infs(val3)
ValueError: Buffer has wrong number of dimensions (expected 1, got 2)
```
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} | 9 | 2014-06-05T17:08:03Z | 2014-06-22T15:28:29Z | 2014-06-11T14:33:38Z | CONTRIBUTOR | null | This pull request accomplishes two main things:
1. It adds unit tests for `nanops`
2. It fixes several bugs identified by the unit tests
related #7352
related #7353
related #7354
related #7357
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} | 2 | 2014-06-05T18:21:29Z | 2015-08-04T22:30:32Z | 2015-08-04T22:30:32Z | MEMBER | null | xref: #7356
API like:
``` python
df.nlargest('column_name', 3)
```
impl like:
``` python
def nlargest(self, col, n):
return self.loc[self[col].nlargest(n).index]
```
cc @hayd
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} | 13 | 2014-06-05T18:57:52Z | 2014-06-16T12:51:56Z | 2014-06-16T12:51:56Z | NONE | null | pandas v0.14.0 (May 31 , 2014) seems uncapable of importing Stata 13 datasets although according to this http://pandas.pydata.org/pandas-docs/stable/whatsnew.html, it should. Stata 12 files can be imported without problems.
The output of running this
``` python
import pandas
pandas.show_versions()
dta = pandas.io.stata.read_stata('D:\\Datos\\rferrer\\Desktop\\myauto.dta')
```
follows:
``` python
%run D:/Datos/RFERRER/Desktop/import_stata13.py
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 45 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.14.0
nose: 1.3.0
Cython: 0.19.2
numpy: 1.8.0
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 1.2.1
sphinx: 1.2.2
patsy: 0.2.0
scikits.timeseries: 0.91.3
dateutil: 2.2
pytz: 2013.8
bottleneck: None
tables: 2.4.0
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: 1.8.5
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.2.3
bs4: None
html5lib: 0.95-dev
bq: None
apiclient: None
rpy2: None
sqlalchemy: 0.8.3
pymysql: None
psycopg2: None
C:\Users\rferrer\AppData\Local\Enthought\Canopy\User\lib\site-packages\openpyxl\__init__.py:31: UserWarning: The installed version of lxml is too old to be used with openpyxl
warnings.warn("The installed version of lxml is too old to be used with openpyxl")
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
C:\Users\rferrer\AppData\Local\Enthought\Canopy\App\appdata\canopy-1.4.0.1938.win-x86_64\lib\site-packages\IPython\utils\py3compat.pyc in execfile(fname, glob, loc)
195 else:
196 filename = fname
--> 197 exec compile(scripttext, filename, 'exec') in glob, loc
198 else:
199 def execfile(fname, *where):
D:\Datos\RFERRER\Desktop\import_stata13.py in <module>()
3 pandas.show_versions()
4
----> 5 dta = pandas.io.stata.read_stata('D:\\Datos\\rferrer\\Desktop\\myauto.dta')
C:\Users\rferrer\AppData\Local\Enthought\Canopy\User\lib\site-packages\pandas\io\stata.pyc in read_stata(filepath_or_buffer, convert_dates, convert_categoricals, encoding, index)
45 identifier of column that should be used as index of the DataFrame
46 """
---> 47 reader = StataReader(filepath_or_buffer, encoding)
48
49 return reader.data(convert_dates, convert_categoricals, index)
C:\Users\rferrer\AppData\Local\Enthought\Canopy\User\lib\site-packages\pandas\io\stata.pyc in __init__(self, path_or_buf, encoding)
455 self.path_or_buf = path_or_buf
456
--> 457 self._read_header()
458
459 def _read_header(self):
C:\Users\rferrer\AppData\Local\Enthought\Canopy\User\lib\site-packages\pandas\io\stata.pyc in _read_header(self)
657
658 """Calculate size of a data record."""
--> 659 self.col_sizes = lmap(lambda x: self._calcsize(x), self.typlist)
660
661 def _calcsize(self, fmt):
C:\Users\rferrer\AppData\Local\Enthought\Canopy\User\lib\site-packages\pandas\io\stata.pyc in <lambda>(x)
657
658 """Calculate size of a data record."""
--> 659 self.col_sizes = lmap(lambda x: self._calcsize(x), self.typlist)
660
661 def _calcsize(self, fmt):
C:\Users\rferrer\AppData\Local\Enthought\Canopy\User\lib\site-packages\pandas\io\stata.pyc in _calcsize(self, fmt)
661 def _calcsize(self, fmt):
662 return (type(fmt) is int and fmt
--> 663 or struct.calcsize(self.byteorder + fmt))
664
665 def _col_size(self, k=None):
TypeError: cannot concatenate 'str' and 'NoneType' objects
```
The dataset `myauto.dta` is just the `auto` dataset made available running `sysuse auto` within Stata.
The problem is originally documented here: http://stackoverflow.com/questions/24053652/pandas-and-stata-13-files.
My Python is set up with [Enthough Canopy 1.4.0 (64 bit)](https://www.enthought.com/products/canopy/).
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| null | 6 | 2014-06-05T19:00:16Z | 2014-06-05T21:17:02Z | 2014-06-05T21:17:02Z | CONTRIBUTOR | null | Consider these two datasets: [df.csv](https://www.dropbox.com/meta_dl/eyJzdWJfcGF0aCI6ICIiLCAidGVzdF9saW5rIjogZmFsc2UsICJzZXJ2ZXIiOiAiZGwuZHJvcGJveHVzZXJjb250ZW50LmNvbSIsICJpdGVtX2lkIjogbnVsbCwgImlzX2RpciI6IGZhbHNlLCAidGtleSI6ICJyNzdmajNqOGp6cnNnb2wifQ/AAMY9NezWTa4v1rGZH6U2dENP0_xo8S73JRUY3wLfIX96Q?dl=1) (~110MB) & [common.csv](https://www.dropbox.com/meta_dl/eyJzdWJfcGF0aCI6ICIiLCAidGVzdF9saW5rIjogZmFsc2UsICJzZXJ2ZXIiOiAiZGwuZHJvcGJveHVzZXJjb250ZW50LmNvbSIsICJpdGVtX2lkIjogbnVsbCwgImlzX2RpciI6IGZhbHNlLCAidGtleSI6ICJxaTRyYnBmYnBseGVtdm4ifQ/AAP29aTjNHhpt1LLPyS9-rUV6qoVWn-gXm97cMqI3yVYFw?dl=1) (small)
common.head():
```
key value
0 3559045188 0584e46e2ccefa0c
1 3559045189 df15f61e8d2dbbd4
2 3559045191 bc75d05dae4a1aaf
3 3559045192 41db9e3f3c9996e0
4 3559045398 4933d72213672819
```
df.head():
```
key a datetime
0 968366584 -29.00 2008-01-01 10:10:01
1 968366585 915.83 2008-01-01 10:28:01
2 968366586 -700.00 2008-01-01 22:46:01
3 968366587 2585.85 2008-01-01 22:57:01
4 968366588 500.00 2007-12-31 23:13:01
```
Both are pre-sorted by key (int64). All keys in `common` are also in df.
The following code:
``` python
import pandas as pd
df = pd.read_csv('df.csv')
common = pd.read_csv('common.csv')
df['value']=np.nan
df.loc[df.key.isin(common.key),'value']=common.value
all(pd.isnull(df.value)) # True
```
yields `True` as the `df.value` column contains only `NaN` entries, none of the string entries from `common`.
However, the code:
``` python
df['value']=np.nan
df.loc[df.key.isin(common.key),'value']=common.loc[:,'value'].values
all(pd.isnull(df.value)) # False
```
yields `False` as the common.value entries have been assign to the corresponding keys in df, as expected.
Unsure if this is a bug or my lack of understanding of the mechanics of the syntax (- though it does seem counter-intuitive that the results are not the same in both instances - something to do with indexes perhaps?)
Occurs in Pandas 0.13 and 0.14 (numpy 1.8.0, Win7 64bit)
(for reference, see [stackoverflow question](http://stackoverflow.com/questions/24060890/elegant-and-efficient-way-to-repeatedly-merge-dataframes-into-single-column-of-a) )
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} | 20 | 2014-06-05T21:40:15Z | 2014-07-12T05:50:43Z | 2014-06-09T20:51:41Z | CONTRIBUTOR | null | closes #7337
Use dateutil.tz.tzutc() to construct UTC timezone instead of dateutil.tz.gettz('UTC')
Update docs to reflect this.
Tidy up examples in the timezones section of the docs.
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} | 10 | 2014-06-06T01:13:49Z | 2019-01-07T07:46:28Z | null | NONE | null | When appending a DataFrame to a HDFStore it would be useful if there was a way to resume the current integer index of the existing data (and hence drop the index of new DataFrame).
A work around was posted [here](https://stackoverflow.com/questions/16997048/how-does-one-append-large-amounts-of-data-to-a-pandas-hdfstore-and-get-a-natural), but perhaps it would be better if there was a `ignore_index` flag in `HDFStore.append()`, a la `DataFrame.append()`.
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} | 28 | 2014-06-06T01:39:15Z | 2015-04-17T13:32:33Z | 2015-04-13T21:40:43Z | CONTRIBUTOR | null | Closes #7139, merged via https://github.com/pydata/pandas/commit/c3eeb577b2b06ecc65e96c5f5de69f0fba01c3dd
Fixes an issue where read_sql_table() will throw an error if it is
reading a postgres table with `timestamp with time zone` fields that
contain entries with different time zones (such as for DST).
This also adds a new keyword, convert_dates_to_utc, which optionally
allows the caller to convert `timestamp with time zone` datetimes into
UTC, which allows pandas to store them internally as
`numpy.datetime64`s, allowing for more efficient storage and operation
speed on these columns.
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} | 0 | 2014-06-06T01:48:48Z | 2014-07-16T00:35:09Z | 2014-07-16T00:35:09Z | NONE | null | ``` python
DataFrame(range(10),dtype='uint16').to_stata('test.dta')
```
Mentioned in #7097
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} | 19 | 2014-06-06T02:36:11Z | 2016-10-12T23:04:55Z | 2014-06-06T04:32:03Z | NONE | null | I have a function that builds a DataFrame of summary statistics that I have used routinely for several months, but now breaks due to a change in Pandas over the past few weeks. Specifically, I have the following list of floats:
```
(Pdb) [r[0] for r in ratios]
[1.1200000000000001, 5.0, 0.73999999999999999, 0.35999999999999999,
1.1100000000000001, 1.1699999999999999, 0.92000000000000004, 0.94999999999999996, 1.0600000000000001, 0.77000000000000002,
0.59999999999999998, 2.0099999999999998, 3.2999999999999998, 0.37,
1.6100000000000001, 1.02]
```
Which I use to create a column in the following table:
```
(Pdb) table
oxygen 0 1
male 0.57 0.59
under 2 months 0.06 0.23
2-11 months 0.66 0.59
12-23 months 0.23 0.10
Jordanian 0.90 0.91
Palestinian 0.05 0.06
vitamin D < 20 0.55 0.53
vitamin D < 11 0.40 0.38
prev_cond 0.11 0.11
heart_hx 0.05 0.04
breastfed 0.68 0.56
premature 0.13 0.23
adm_pneumo 0.09 0.25
adm_bronchopneumo 0.52 0.28
adm_sepsis 0.11 0.16
adm_bronchiolitis 0.21 0.21
```
However, this now causes the following:
```
(Pdb) table['foo'] = [r[0] for r in ratios]
*** TypeError: Not implemented for this type
```
Here is a more verbose output:
```
TypeError Traceback (most recent call last)
<ipython-input-49-0723b2a631c0> in <module>()
----> 1 make_table(groupby_o2, table_vars=table_vars, replace_dict={0.0: 'No Oxygen', 1.0: 'Oxygen'})
<ipython-input-47-6f3ebc37c721> in make_table(groupby, table_vars, replace_dict)
3 ratios = [calc_or(groupby, v) for v in table.index]
4 import pdb; pdb.set_trace()
----> 5 table['OR'] = [r[0] for r in ratios]
6 table['Interval'] = [r[1] for r in ratios]
7 table['N'] = [r[2] for r in ratios]
/usr/local/lib/python2.7/site-packages/pandas/core/frame.pyc in __setitem__(self, key, value)
1899 else:
1900 # set column
-> 1901 self._set_item(key, value)
1902
1903 def _setitem_slice(self, key, value):
/usr/local/lib/python2.7/site-packages/pandas/core/frame.pyc in _set_item(self, key, value)
1982 self._ensure_valid_index(value)
1983 value = self._sanitize_column(key, value)
-> 1984 NDFrame._set_item(self, key, value)
1985
1986 # check if we are modifying a copy
/usr/local/lib/python2.7/site-packages/pandas/core/generic.pyc in _set_item(self, key, value)
1137
1138 def _set_item(self, key, value):
-> 1139 self._data.set(key, value)
1140 self._clear_item_cache()
1141
/usr/local/lib/python2.7/site-packages/pandas/core/internals.pyc in set(self, item, value, check)
2637
2638 try:
-> 2639 loc = self.items.get_loc(item)
2640 except KeyError:
2641 # This item wasn't present, just insert at end
/usr/local/lib/python2.7/site-packages/pandas/core/index.pyc in get_loc(self, key)
2055
2056 def get_loc(self, key):
-> 2057 if np.isnan(key):
2058 try:
2059 return self._nan_idxs.item()
TypeError: Not implemented for this type
```
Not exactly sure which change caused it, but this code was working on the same data 3-4 weeks ago.
Currently running 0.13.1-936-g592a537 on OS X 10.9.3, Python 2.7.6 from Homebrew.
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} | 1 | 2014-06-06T03:46:56Z | 2014-06-15T12:57:35Z | 2014-06-06T04:32:03Z | MEMBER | null | closes #7366
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} | 11 | 2014-06-06T04:38:38Z | 2014-06-13T20:33:41Z | 2014-06-13T20:32:59Z | CONTRIBUTOR | null | Opening a Stata dta file with no observations (but having variables) resulted in an error. Example file: https://dl.dropboxusercontent.com/u/6705315/no_obs_v115.dta.
``` python
>>> import pandas as pd
>>> pd.read_stata("no_obs_v115.dta")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Python27\lib\site-packages\pandas\io\stata.py", line 49, in read_stata
return reader.data(convert_dates, convert_categoricals, index)
File "C:\Python27\lib\site-packages\pandas\io\stata.py", line 855, in data
data = DataFrame(data, columns=self.varlist, index=index)
File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 255, in __init__
copy=copy)
File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 367, in _init_ndarray
return create_block_manager_from_blocks([values.T], [columns, index])
File "C:\Python27\lib\site-packages\pandas\core\internals.py", line 3185, in create_block_manager_from_blocks
construction_error(tot_items, blocks[0].shape[1:], axes, e)
File "C:\Python27\lib\site-packages\pandas\core\internals.py", line 3166, in construction_error
passed,implied))
ValueError: Shape of passed values is (0, 0), indices imply (1, 0)
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: AMD64 Family 16 Model 6 Stepping 3, AuthenticAMD
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.14.0
nose: 1.3.3
Cython: 0.20.1
numpy: 1.8.1
scipy: None
statsmodels: None
IPython: None
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.3
bottleneck: 0.8.0
tables: None
numexpr: None
matplotlib: None
openpyxl: 1.8.6
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
bq: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
The PR fixes this issue in stata.py, though maybe the issue should be fixed in DataFrame.
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} | 28 | 2014-06-06T08:13:30Z | 2014-07-01T10:15:36Z | 2014-07-01T10:11:23Z | CONTRIBUTOR | null | Old `BlockManager` pickle format stored items which were ambiguous and not enough for unpickling if non-unique.
After recent BlockManager overhaul, #6745, it's now possible to it's no longer necessary to share items/ref-items between Blocks and their respective managers<del>, so Blocks can now be safely pickled/unpickled on their own</del>, but Blocks still cannot be safely pickled/unpickled on their own, because they a) are part of internal API and are subject to change and b) were never intended to be serialized like that and their setstate is written in non-forward-compatible manner.
So, I've took the freedom to make the format more "extendable" by storing the state as the fourth element in BlockManager state tuple:
```
(axes_array, block_values, block_items,
{
"0.14.1": {
"axes": axes_array,
"blocks": [{"values": blk0.values,
"mgr_locs": blk0.mgr_locs.indexer},
...]
}
})
```
I was aiming for the following design goals:
- use the fact that pickle doesn't store same objects multiple times, i.e. adding an extra element to the tuple should add minimum overhead
- make state as forward compatible as possible: new versions simply add new elements to that dictionary along with the old ones
- make state as backward compatible as possible: no extra rules "if this then parse like that else ...", simply look up a supported version in the dictionary and use it
- don't store classes that are subject to change, I almost got bitten with this one when tried to serialize blocks as a whole, 0.13.1 version, that had different (and incompatible) **getstate** implementation, croaked.
This PR should close #7329.
- [x] io pickle tests: verify 0.14.0 pickles are handled properly
- [X] ensure forward compatibility with 13.1
- [x] generate 14.1 pickles (for which platforms?)
- [x] ensure forward compatibility with 14.0
- [X] <del>ensure forward compatibility with 12.0 ?</del> not necessary
- [x] make sure sparse containers with non-unique items work in those versions too
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} | 6 | 2014-06-06T11:53:48Z | 2014-07-05T04:49:40Z | 2014-06-06T13:00:33Z | MEMBER | null | Found fix #7318 was incorrect. It works for lower frequencies than dates, but not for higher freq than hours.
I couldn't detect as test cases are also incorrect...
This should work for all the freqs with timezones.
NOTE: I couldn't use normal `tslib.tz_convert` which returns incorrect results maybe caused by DST. Applying `tslib.tz_convert_single` can work though.
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| null | 7 | 2014-06-06T12:28:48Z | 2015-01-19T18:13:45Z | 2015-01-19T18:13:45Z | MEMBER | null | Don't know if this is related to the new changes in 0.14 in the display of Dataframes or to #7180. But I find the following behaviour with 0.14 in Spyders IPython qtconsole:
To reproduce:
```
df = pd.DataFrame(np.random.randn(2,20), columns=list('ABCDEFGHIJKLMNOPQRST'))
df['very_long_column_name'] = [1,2]
df['X'] = ['very_long_column_values', 'other_long_string']
```
- This seems not a very usefull behaviour to me?
Should it truncate on width rather than number of columns?
- It is also not truncating (above dataframe has 22 columns, while default `max_columns` is 20 (and I checked the setting)), while it truncated correctly for the text repr.
Other strange thing, I saw this in the Spyder IPython console (which is an embedded qtconsole I think). However, when I tried this in a qtconsole, it gave me first the text repr instead of the html repr (although `pd.options.display.notebook_html_repr` was set to True), so I used `HTML(df.to_html())` for the above picture (but the result is the same as in Spyder).
@bjonen
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} | 5 | 2014-06-06T15:12:56Z | 2014-06-14T15:34:24Z | 2014-06-14T14:00:51Z | MEMBER | null | Closes #7227. `resample(how=count)` should work now.
There are some aggregations which doesn't work with `TimeGrouper` yet, I'll open separate issue.
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} | 2 | 2014-06-06T15:45:41Z | 2014-06-06T17:18:40Z | 2014-06-06T17:18:40Z | CONTRIBUTOR | null | Originally reported https://buildd.debian.org/status/fetch.php?pkg=pandas&arch=kfreebsd-amd64&ver=0.14.0%2Bgit17-g3849d5d-1%2Bb1&stamp=1401974000
I have tried to reproduce on (an interactive) kfreebsd-amd64 porter box, but there tests completed fine.
```
======================================================================
ERROR: test_read_clipboard_infer_excel (pandas.io.tests.test_clipboard.TestClipboard)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/«BUILDDIR»/pandas-0.14.0+git17-g3849d5d/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_clipboard.py", line 81, in test_read_clipboard_infer_excel
df = pd.read_clipboard()
File "/«BUILDDIR»/pandas-0.14.0+git17-g3849d5d/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/clipboard.py", line 51, in read_clipboard
return read_table(StringIO(text), **kwargs)
File "/«BUILDDIR»/pandas-0.14.0+git17-g3849d5d/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 443, in parser_f
return _read(filepath_or_buffer, kwds)
File "/«BUILDDIR»/pandas-0.14.0+git17-g3849d5d/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 228, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/«BUILDDIR»/pandas-0.14.0+git17-g3849d5d/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 533, in __init__
self._make_engine(self.engine)
File "/«BUILDDIR»/pandas-0.14.0+git17-g3849d5d/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 670, in _make_engine
self._engine = CParserWrapper(self.f, **self.options)
File "/«BUILDDIR»/pandas-0.14.0+git17-g3849d5d/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 1032, in __init__
self._reader = _parser.TextReader(src, **kwds)
File "parser.pyx", line 483, in pandas.parser.TextReader.__cinit__ (pandas/parser.c:4626)
File "parser.pyx", line 605, in pandas.parser.TextReader._get_header (pandas/parser.c:6089)
CParserError: Passed header=0 but only 0 lines in file
======================================================================
FAIL: test_round_trip_frame (pandas.io.tests.test_clipboard.TestClipboard)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/«BUILDDIR»/pandas-0.14.0+git17-g3849d5d/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_clipboard.py", line 69, in test_round_trip_frame
self.check_round_trip_frame(dt)
File "/«BUILDDIR»/pandas-0.14.0+git17-g3849d5d/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_clipboard.py", line 57, in check_round_trip_frame
tm.assert_frame_equal(data, result, check_dtype=False)
File "/«BUILDDIR»/pandas-0.14.0+git17-g3849d5d/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 563, in assert_frame_equal
assert_index_equal(left.columns, right.columns)
File "/«BUILDDIR»/pandas-0.14.0+git17-g3849d5d/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 501, in assert_index_equal
right.dtype))
AssertionError: [index] left [object Index([u'LpXeYtA8n5', u'5Y0fhC0WyE', u'tTHQGDhUd3'], dtype='object')], right [Index([u'xKjebdbwV9', u'QQD0rTKXvy', u'MdjTlE5CWY'], dtype='object') object]
======================================================================
FAIL: test_round_trip_frame_sep (pandas.io.tests.test_clipboard.TestClipboard)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/«BUILDDIR»/pandas-0.14.0+git17-g3849d5d/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_clipboard.py", line 61, in test_round_trip_frame_sep
self.check_round_trip_frame(dt,sep=',')
File "/«BUILDDIR»/pandas-0.14.0+git17-g3849d5d/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_clipboard.py", line 57, in check_round_trip_frame
tm.assert_frame_equal(data, result, check_dtype=False)
File "/«BUILDDIR»/pandas-0.14.0+git17-g3849d5d/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 563, in assert_frame_equal
assert_index_equal(left.columns, right.columns)
File "/«BUILDDIR»/pandas-0.14.0+git17-g3849d5d/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 501, in assert_index_equal
right.dtype))
AssertionError: [index] left [object Index([u'9da8bYrdhD', u'h4CraEsPjW', u'N0bmM7oPk7'], dtype='object')], right [Index([u'a', u'b', u'c'], dtype='object') object]
======================================================================
FAIL: test_round_trip_frame_string (pandas.io.tests.test_clipboard.TestClipboard)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/«BUILDDIR»/pandas-0.14.0+git17-g3849d5d/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_clipboard.py", line 65, in test_round_trip_frame_string
self.check_round_trip_frame(dt,excel=False)
File "/«BUILDDIR»/pandas-0.14.0+git17-g3849d5d/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_clipboard.py", line 57, in check_round_trip_frame
tm.assert_frame_equal(data, result, check_dtype=False)
File "/«BUILDDIR»/pandas-0.14.0+git17-g3849d5d/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 563, in assert_frame_equal
assert_index_equal(left.columns, right.columns)
File "/«BUILDDIR»/pandas-0.14.0+git17-g3849d5d/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 501, in assert_index_equal
right.dtype))
AssertionError: [index] left [object Index([u'9da8bYrdhD', u'h4CraEsPjW', u'N0bmM7oPk7'], dtype='object')], right [Index([u'a', u'b', u'c'], dtype='object') object]
----------------------------------------------------------------------
Ran 7157 tests in 763.157s
FAILED (SKIP=247, errors=1, failures=3)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/7375 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7375/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7375/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7375/events | https://github.com/pandas-dev/pandas/pull/7375 | 35,159,182 | MDExOlB1bGxSZXF1ZXN0MTY4Mjg1ODE= | 7,375 | ENH/BUG: Offset.apply dont preserve time | {
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} | 23 | 2014-06-06T16:08:58Z | 2014-07-09T15:34:02Z | 2014-06-11T14:08:31Z | MEMBER | null | Closes #7156.
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} | 8 | 2014-06-06T18:07:38Z | 2016-10-12T23:04:55Z | 2014-06-06T23:28:58Z | CONTRIBUTOR | null | I think this is distinct from #5541 though I agree that an API change here might be helpful. AFAICT, I am using replace as 'expected', but all of the keys aren't being matched. Any idea what's going on?
```
d = {u'fname' :
{'out_augmented_AUG_2011.json' : pd.Period(year=2011, month=8, freq='M'),
'out_augmented_JAN_2011.json' : pd.Period(year=2011, month=1, freq='M'),
'out_augmented_MAY_2012.json' : pd.Period(year=2012, month=5, freq='M'),
'out_augmented_SUBSIDY_WEEK.json' : pd.Period(year=2011, month=4, freq='M'),
'out_augmented_AUG_2012.json' : pd.Period(year=2012, month=8, freq='M'),
'out_augmented_MAY_2011.json' : pd.Period(year=2011, month=5, freq='M'),
'out_augmented_SEP_2013.json' : pd.Period(year=2013, month=9, freq='M'),
}}
df = pd.DataFrame(['out_augmented_AUG_2012.json',
'out_augmented_SEP_2013.json',
'out_augmented_SUBSIDY_WEEK.json',
'out_augmented_MAY_2012.json',
'out_augmented_MAY_2011.json',
'out_augmented_AUG_2011.json',
'out_augmented_JAN_2011.json'], columns=['fname'])
df.replace(d)
```
On
```
[31]: pd.version.version
[31]: '0.13.1-753-g4614ac8'
```
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} | 3 | 2014-06-06T19:18:26Z | 2014-06-06T19:27:11Z | 2014-06-06T19:26:05Z | CONTRIBUTOR | null | #7371
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} | 1 | 2014-06-06T19:39:16Z | 2014-06-11T12:48:06Z | 2014-06-11T12:48:06Z | CONTRIBUTOR | null | https://github.com/pydata/pandas/pull/7299
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} | 8 | 2014-06-06T20:11:06Z | 2014-06-08T17:06:26Z | 2014-06-08T17:05:57Z | NONE | null | Related SO question: http://stackoverflow.com/questions/24096330/sawarning-could-not-instantiate-type-class-sqlalchemy-sql-sqltypes-integer
When I run the code below
```
import pandas as pd
import sqlalchemy
dbname = "sqlite:////Users/leda/home/Magnetic_stars/SQLite/Magnetic_stars.sqlite"
engine = sqlalchemy.create_engine(dbname)
res = pd.io.sql.read_sql("SELECT * FROM Resolved;", engine)
```
I get
```
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sqlalchemy/dialects/sqlite/base.py:860: SAWarning: Could not instantiate type <class 'sqlalchemy.sql.sqltypes.INTEGER'> with reflected arguments [u'4']; using no arguments.
coltype = self._resolve_type_affinity(type_)
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sqlalchemy/dialects/sqlite/base.py:860: SAWarning: Could not instantiate type <class 'sqlalchemy.sql.sqltypes.INTEGER'> with reflected arguments [u'2']; using no arguments.
coltype = self._resolve_type_affinity(type_)
```
```
In [3]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Darwin
OS-release: 13.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: ru_RU.UTF-8
pandas: 0.14.0
nose: 1.3.1
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.11.0
statsmodels: None
IPython: 0.13.2
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.4
bottleneck: None
tables: None
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
bq: None
apiclient: None
rpy2: 2.2.1
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
```
The schema of my SQLite table:
```
sqlite> .schema Resolved
CREATE TABLE `Resolved` (
`Name` TEXT NOT NULL COLLATE NOCASE,
`Count` INTEGER NOT NULL,
`Obs_Date` TEXT NOT NULL,
`Bessel_year` REAL,
`Filter` TEXT NOT NULL,
`Rho_mas` INTEGER,
`Sigma_rho` INTEGER,
`Theta` REAL,
`Sigma_theta` REAL,
`Dm` REAL,
`Sigma_dm` REAL,
`Orbit` INTEGER,
`Ref_star` TEXT COLLATE NOCASE,
`Comments` TEXT COLLATE NOCASE
);
```
P.S. Sorry if something is wrong. This is my first issue.
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| null | 5 | 2014-06-06T21:34:33Z | 2014-06-07T06:13:38Z | 2014-06-07T05:40:47Z | NONE | null | Here's an example of the problem:
``` python
In [16]: pd.to_datetime("12/01/13", format="%m/%d/%y")
Out[16]: Timestamp('2013-12-01 00:00:00', tz=None)
In [17]: pd.to_datetime("12/01/13 3:15", format="%m/%d/%y %I:%M")
Out[17]: Timestamp('2013-12-01 03:15:00', tz=None)
In [18]: pd.to_datetime("12/01/13 3:15pm", format="%m/%d/%y %I:%M%p")
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-18-a00cce7186a4> in <module>()
----> 1 pd.to_datetime("12/01/13 3:15pm", format="%m/%d/%y %I:%M%p")
/home/ely/anaconda/lib/python2.7/site-packages/pandas/tseries/tools.pyc in to_datetime(arg, errors, dayfirst, utc, box, format, coerce, unit)
139 return _convert_listlike(arg, box=box)
140
--> 141 return _convert_listlike(np.array([ arg ]), box=box)[0]
142
143 class DateParseError(ValueError):
/home/ely/anaconda/lib/python2.7/site-packages/pandas/tseries/tools.pyc in _convert_listlike(arg, box)
113 # fallback
114 if result is None:
--> 115 result = tslib.array_strptime(arg, format, coerce=coerce)
116 else:
117 result = tslib.array_to_datetime(arg, raise_=errors == 'raise',
/home/ely/anaconda/lib/python2.7/site-packages/pandas/tslib.so in pandas.tslib.array_strptime (pandas/tslib.c:20989)()
KeyError: 'p'
```
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} | 16 | 2014-06-06T21:38:57Z | 2014-06-09T17:06:16Z | 2014-06-09T16:46:30Z | NONE | null | I'm doing this in an iPython Notebook using 0.13.1 installed by Anaconda on Windows:
``` python
import pandas as pd
import matplotlib.pyplot as plt
from csv import excel
%pylab inline
omob = pd.read_csv("LoadTimes.csv", names=["Timing"], dialect=excel).Timing
sliced = omob[(omob > 0) & (omob < 120000)]
wtf = pd.Series(sorted(sliced))
idk = pd.Series(sorted(omob.clip(0, 120000)))
pd.stats.moments.rolling_mean(wtf, 100).plot(logy=True,color='g')
pd.stats.moments.rolling_mean(idk, 100).plot(logy=True,color='b')
srt = pd.Series(sliced)
odr = sliced.order()
srt.sort()
pd.stats.moments.rolling_mean(srt, 100).plot(logy=True,color='r')
pd.stats.moments.rolling_mean(odr, 100).plot(logy=True,color='black')
```
https://www.dropbox.com/s/ozko9yj6rgzjske/LoadTimes.csv for the data - it's only about half a MB.
Only one of those plots is actually sorted and it's the first one. The second clearly got close and stopped for some reason. The last two look like seismograph readings:
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} | 3 | 2014-06-06T21:50:02Z | 2014-06-16T12:52:18Z | 2014-06-16T12:52:18Z | NONE | null | I have a dataframe where I'm computing some summary items for groups and it's convenient to reconsolidate merge those back into the parent dataframe. Transform seems the most intuitive way to do this but I'm seeing much worse performance than doing a manual aggregation and merge.
Starting from issue #2121 marked as closed:
``` python
import numpy as np
import pandas as pd
from pandas import Index, MultiIndex, DataFrame
n_dates = 20000
n_securities = 50
n_columns = 2
share_na = 0.1
dates = pd.date_range('1997-12-31', periods=n_dates, freq='B')
dates = Index(map(lambda x: x.year * 10000 + x.month * 100 + x.day, dates))
secid_min = int('10000000', 16)
secid_max = int('F0000000', 16)
step = (secid_max - secid_min) // (n_securities - 1)
security_ids = map(lambda x: hex(x)[2:10].upper(), range(secid_min, secid_max + 1, step))
data_index = MultiIndex(levels=[dates.values],
labels=[[i for i in xrange(n_dates) for _ in xrange(n_securities)]],
names=['date'])
n_data = len(data_index)
columns = Index(['factor{}'.format(i) for i in xrange(1, n_columns + 1)])
data = DataFrame(np.random.randn(n_data, n_columns), index=data_index, columns=columns)
grouped = data.groupby(level='date')
```
which gives us a (1000000, 2) dataframe of floats
``` python
%timeit grouped.transform(max)
```
1 loops, best of 3: 8.16 s per loop
my alternative is to write something like this:
``` python
def mergeTransform(grouped, originalDF):
item = grouped.max()
colnames = item.columns
item = originalDF.merge(item, how='outer', left_index=True, right_index=True).ix[:,-item.shape[1]:]
item.columns = colnames
```
which seems to run much faster despite all the indexing going on:
``` python
%%timeit
mergeTransform(grouped, data)
```
10 loops, best of 3: 81.1 **ms** per loop
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} | 0 | 2014-06-06T22:20:59Z | 2021-04-11T04:41:49Z | null | MEMBER | null | Derived from #7299. `numpy.insert` can accept multiple elements / indices as of v1.8.
http://docs.scipy.org/doc/numpy/reference/generated/numpy.insert.html
Allow `Index` to accept multiple elements / indices. Currently, it raises `IndexError`.
```
import pandas as pd
idx1 = pd.Index([1, 2, 3, 4, 5])
idx1.insert([1, 2], 0)
# IndexError: invalid slice
```
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} | 18 | 2014-06-07T14:08:11Z | 2015-11-07T03:43:08Z | 2015-03-08T14:39:07Z | MEMBER | null | Derived from #7309. Create a wrapper for `robjects.r` in `pandas.rpy.common` to perform automatic pandas `DataFrame` and `Series` conversion. `Series` will be converted to R data.frame to preserve rownames (index).
If looks OK, I'll modify the doc (#7309) based on following API.
```
import pandas as pd
import pandas.rpy.common as com
iris = com.load_data('iris')
com.r.assign('iris', iris)
returned = com.r['iris']
type(returned)
# <class 'pandas.core.frame.DataFrame'>
df = pd.DataFrame(np.random.randn(20, 5),
index=pd.date_range(start='2011/01/01', freq='D', periods=20))
com.r.assign('df', df)
returned = com.r['df']
type(returned)
# <class 'pandas.core.frame.DataFrame'>
s = pd.Series(np.random.randn(20), name='test')
com.r.assign('s', s)
returned = com.r['s']
type(returned)
# <class 'pandas.core.frame.DataFrame'>
```
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| null | 3 | 2014-06-07T15:44:49Z | 2014-06-15T03:13:46Z | 2014-06-11T12:48:06Z | MEMBER | null | Closes #7378. The previous error was unrelated to `insert` logic.
It seems to be caused by DST related to `tslib` or `pytz`, and became obvious by the previous test case.
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| null | 5 | 2014-06-07T16:57:58Z | 2021-04-11T04:42:54Z | 2021-04-11T04:42:54Z | MEMBER | null | As pointed out by @dsm054, there are multiple lurking split/partition API requests. Here are the issues and a short summary of what they would do (there are some duplicates here, I've checked off those issues/PRs that have been closed in favor of a related issue):
- [x] #414: (i think) original issue for these ideas going back 3 years
- [x] #936: windowing with time-length windows like `pd.rolling_mean(ts, window='30min')` and possibly even arbitrary windows using another column
- [x] #3066: `split` method on pandas objects, playing around with ideas
- [x] #3101: a closed PR by @y-p to use the args of lambda to group a frame into views of a sliding window
- [x] #3685: resampling using the first `n` samples of a bin.
- [ ] #4059: `np.array_split` style API where you can split a pandas object into a list of `k` groups of possibly unequal size (could be a thin wrapper around `np.array_split`, or more integrated into the pandas DSL). IMO, this issue provides the best starting point for an API. [SO usage](http://stackoverflow.com/questions/24461906/how-can-i-divide-up-a-pandas-dataframe/24462529#24462529)
- [x] #5494: an API for to allow pandas' `groupby` to have `itertools.groupby` semantics (i.e., preserve the order of duplicated group keys), i.e., `'aabbaa'` would yield groups `['aa', 'bb', 'aa']` rather than `['aaaa', 'bb']`. There'd have to be some changes to the use of `dict` in the groupby backend as noted by @y-p here https://github.com/pydata/pandas/issues/4059#issuecomment-29061036.
- [ ] #6675: Ability to select ranged groups via another column, like "select all rows between the values X and Y from column C", e.g., an "events" column where you have a start and end markers and you want to get the data in between the markers. There are a couple of ways you can do this, but it would be nice to have an API for this. This is very similar to #936.
The [`toolz`](https://github.com/pytoolz/toolz) library has a `partitionby` function that provides a nice way to do some of the splitting on sequences and might provide us with some insight on how to approach the API.
cc @jreback @jorisvandenbossche @hayd @danielballan
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} | 3 | 2014-06-07T20:37:53Z | 2014-06-14T06:24:14Z | 2014-06-07T22:29:30Z | MEMBER | null | - now installs any optional dep not relying on numpy for better local test coverage
- you can now pass arguments that will go to nosetests (e.g., for running network tests)
- removed tox_prll.ini because you can `pip install detox` and it will automatically build run and test across the configuration in tox.ini
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} | 5 | 2014-06-07T21:29:45Z | 2014-07-09T18:20:25Z | 2014-06-09T12:54:56Z | MEMBER | null | closes #7150
it seems like there's a better way to do this ....
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} | 18 | 2014-06-07T21:45:50Z | 2014-07-09T12:39:17Z | 2014-07-06T14:09:51Z | MEMBER | null | There is an inconsistency `DataFrame.plot`, `hist` and `boxplot` when subplot enabled (`subplots=True` or use `by` kw).
### Current behaviour
- `plot` and `hist`:
When `ax` kw is passed, plot will be drawn on the figure which the passed ax belongs. The figure will be once cleared even if the required number of subplot is 1. Thus, any artists contained in the passed `ax` will be flushed.
- `box`:
When `ax` kw is passed and a required number of subplots is 1, use the passed `ax` without clearing the existing plot. If more than 1 axes is required for the subplots, raises `ValueError`.
### Fix
- When `ax` kw is passed and a required number of subplots is 1, use the passed `ax` without clearing the existing plot.
- If more than 1 axes is required for the subplots, subplots will be output on the figure which the passed ax belongs. The figure will be once cleared if the number of subplots is more than 1.
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} | 7 | 2014-06-07T22:09:31Z | 2014-06-17T16:37:06Z | 2014-06-08T01:35:01Z | CONTRIBUTOR | null | Tests were failing if the scraper got the webpage but there weren't any tables in it. Added condition to raise RemoteDataError if no tables were found. Still IndexError if it can't find the correct table.
fixes #7335
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| null | 1 | 2014-06-08T01:29:44Z | 2014-07-09T18:20:25Z | 2014-06-08T03:02:20Z | MEMBER | null | closes #6346
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} | 5 | 2014-06-08T04:26:27Z | 2014-07-02T16:46:36Z | 2014-07-01T15:29:22Z | MEMBER | null | Grouped `hist` and `scatter` plots use `figsize` default which is different from pandas current and mpl.
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| null | 1 | 2014-06-08T15:56:53Z | 2015-10-20T22:29:36Z | 2015-10-20T22:29:36Z | MEMBER | null | `unstack/stack` does insert the new level as the inner-most level (last level). Would it be useful to have a keyword to specify where to insert the new level. E.g to have it as the first level you now have to do a `reorder_levels` and `sort_index`:
```
In [47]: df = pd.DataFrame({'A': np.zeros(5), 'B': np.zeros(5)}, index=pd.date_range('2012-01-01', periods=5, freq='D'))
...: df = pd.concat([df]*3, keys=['x1', 'x2', 'x3'])
In [48]: df
Out[48]:
A B
x1 2012-01-01 0 0
2012-01-02 0 0
2012-01-03 0 0
2012-01-04 0 0
2012-01-05 0 0
x2 2012-01-01 0 0
2012-01-02 0 0
2012-01-03 0 0
2012-01-04 0 0
2012-01-05 0 0
x3 2012-01-01 0 0
2012-01-02 0 0
2012-01-03 0 0
2012-01-04 0 0
2012-01-05 0 0
In [49]: df.unstack(0)
Out[49]:
A B
x1 x2 x3 x1 x2 x3
2012-01-01 0 0 0 0 0 0
2012-01-02 0 0 0 0 0 0
2012-01-03 0 0 0 0 0 0
2012-01-04 0 0 0 0 0 0
2012-01-05 0 0 0 0 0 0
In [50]: df.unstack(0).reorder_levels([1,0], axis=1).sort_index(axis=1)
Out[50]:
x1 x2 x3
A B A B A B
2012-01-01 0 0 0 0 0 0
2012-01-02 0 0 0 0 0 0
2012-01-03 0 0 0 0 0 0
2012-01-04 0 0 0 0 0 0
2012-01-05 0 0 0 0 0 0
```
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} | 3 | 2014-06-08T16:44:55Z | 2014-07-05T13:49:29Z | 2014-07-05T13:49:29Z | MEMBER | null | Related: comment of @balancap: https://github.com/pydata/pandas/issues/6416#issuecomment-45343694 and issue #7380 where you get a warning about a not understood type (probably in another table).
**Situation now**: in the creation of a `PandasSQLAlchemy` object (https://github.com/pydata/pandas/blob/v0.14.0/pandas/io/sql.py#L777), a full `MetaData` object is created and reflected (this means: all tables in the database are inspected and the schema's are stored as sqlalchemy `Table` objects). This is done each time `read_sql/read_sql_query/read_sql_table` is called.
**Consequence**:
- this can be costly when having a very large database or having a distant server (https://github.com/pydata/pandas/issues/6416#issuecomment-45343694).
- this can trigger warnings that does not have to do anything with your current query, or the current table you want to read, when eg one of the types in other tables is not known to sqlalchemy (eg a postgis geometry column).
**Possible solution**:
- I think the `read_sql` functions never should inspect all tables, but only the specified table (and `read_sql_query` even not that table, as with a query this information is not used, only for `read_sql_table`)
- This can maybe be achieved with using the `only` keyword in `meta.reflect(engine, only=...)`
- For the OO API interface, we can discuss what should be the default (inspect all tables or not)
@mangecoeur @danielballan @hayd
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} | 4 | 2014-06-09T01:54:45Z | 2014-07-17T03:05:28Z | 2014-07-17T03:05:18Z | NONE | null | Simple changes to io/stata.py to write unsigned integers to Stata files. Sorry about the poor commit messages--I'm still new to git and wasn't able to correct it cleanly.
(See issue #7365)
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} | 27 | 2014-06-09T03:25:04Z | 2015-07-12T15:00:43Z | 2015-07-12T15:00:43Z | NONE | null | 
I've included some documentation on existence-type associations as requested by Jeff in [this question](http://stackoverflow.com/questions/23945493/a-faster-alternative-to-pandas-isin-function/) over at Stack Overflow.
The content includes a couple of plots generated by the script bench/bench_existence.py but I was not able to find any automation that executed those bench/bench_*.py scripts, so I may be missing a hook somewhere. That script also takes a couple of minutes to run.
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https://api.github.com/repos/pandas-dev/pandas/issues/7399 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7399/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7399/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7399/events | https://github.com/pandas-dev/pandas/issues/7399 | 35,261,299 | MDU6SXNzdWUzNTI2MTI5OQ== | 7,399 | BUG: .loc on MultiIndex with list of indexers including an incomplete slice raises KeyError | {
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} | 1 | 2014-06-09T05:36:59Z | 2014-06-09T15:47:17Z | 2014-06-09T15:47:17Z | MEMBER | null | Tested on latest version
``` python
import pandas as pd
midx = pd.MultiIndex.from_product([range(5), ['a', 'b', 'c']])
s = pd.Series(range(15), midx)
s.loc[:, 'a':'c'] # works
s.loc[0:4, 'a':'c'] # works
s.loc[:4, 'a':'c'] # raises KeyError
```
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-5-4865919a19c8> in <module>()
----> 1 s.loc[:4, 'a':'c'] # raises KeyError
/Users/shoyer/dev/pandas/pandas/core/indexing.pyc in __getitem__(self, key)
1125 def __getitem__(self, key):
1126 if type(key) is tuple:
-> 1127 return self._getitem_tuple(key)
1128 else:
1129 return self._getitem_axis(key, axis=0)
/Users/shoyer/dev/pandas/pandas/core/indexing.pyc in _getitem_tuple(self, tup)
643 def _getitem_tuple(self, tup):
644 try:
--> 645 return self._getitem_lowerdim(tup)
646 except IndexingError:
647 pass
/Users/shoyer/dev/pandas/pandas/core/indexing.pyc in _getitem_lowerdim(self, tup)
751 # we may have a nested tuples indexer here
752 if self._is_nested_tuple_indexer(tup):
--> 753 return self._getitem_nested_tuple(tup)
754
755 # we maybe be using a tuple to represent multiple dimensions here
/Users/shoyer/dev/pandas/pandas/core/indexing.pyc in _getitem_nested_tuple(self, tup)
810
811 # this is a series with a multi-index specified a tuple of selectors
--> 812 return self._getitem_axis(tup, axis=0, validate_iterable=True)
813
814 # handle the multi-axis by taking sections and reducing
/Users/shoyer/dev/pandas/pandas/core/indexing.pyc in _getitem_axis(self, key, axis, validate_iterable)
1267 # nested tuple slicing
1268 if _is_nested_tuple(key, labels):
-> 1269 locs = labels.get_locs(key)
1270 indexer = [ slice(None) ] * self.ndim
1271 indexer[axis] = locs
/Users/shoyer/dev/pandas/pandas/core/index.pyc in get_locs(self, tup)
3602 elif isinstance(k,slice):
3603 # a slice, include BOTH of the labels
-> 3604 ranges.append(self._get_level_indexer(k,level=i))
3605 else:
3606 # a single label
/Users/shoyer/dev/pandas/pandas/core/index.pyc in _get_level_indexer(self, key, level)
3527 # otherwise a boolean indexer
3528
-> 3529 start = level_index.get_loc(key.start)
3530 stop = level_index.get_loc(key.stop)
3531 step = key.step
/Users/shoyer/dev/pandas/pandas/core/index.pyc in get_loc(self, key)
1167 loc : int if unique index, possibly slice or mask if not
1168 """
-> 1169 return self._engine.get_loc(_values_from_object(key))
1170
1171 def get_value(self, series, key):
/Users/shoyer/dev/pandas/pandas/index.so in pandas.index.IndexEngine.get_loc (pandas/index.c:3651)()
/Users/shoyer/dev/pandas/pandas/index.so in pandas.index.IndexEngine.get_loc (pandas/index.c:3578)()
KeyError: None
```
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} | 0 | 2014-06-09T05:40:24Z | 2014-06-09T15:47:17Z | 2014-06-09T15:47:17Z | MEMBER | null | Setup (shared with #7399):
``` python
import pandas as pd
midx = pd.MultiIndex.from_product([range(5), ['a', 'b', 'c']])
s = pd.Series(range(15), midx)
print(s.loc[2:4:2, 'a':'c'])
```
What I get:
```
2 a 6
c 8
3 b 10
4 a 12
c 14
dtype: int64
```
What I expected (the step should only apply to the first level):
```
2 a 6
b 7
c 8
4 a 12
b 13
c 14
dtype: int64
```
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