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```
pd.DataFrame([True]).groupby(lambda x: 0).sum()
0
0 True
pd.DataFrame([True,True]).groupby(lambda x: 0).sum()
0
0 2
pd.DataFrame([False]).groupby(lambda x: 0).sum()
0
0 False
pd.DataFrame([False,False]).groupby(lambda x: 0).sum()
0
0 False
pd.DataFrame([False,False,True]).groupby(lambda x: 0).sum()
0
0 True
pd.DataFrame([False,False,True,True]).groupby(lambda x: 0).sum()
0
0 2
pd.DataFrame([False,False]).sum()
0 0
dtype: int64
```
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} | 6 | 2014-04-29T20:26:56Z | 2020-06-14T22:20:51Z | 2020-06-14T22:20:51Z | CONTRIBUTOR | null | related https://github.com/pydata/pandas/pull/7000
```
In [1]: df = DataFrame({'foo1' : ['one', 'two', 'two', 'three', 'one', 'two'],
'foo2' : np.random.randn(6)})
In [2]: df
Out[2]:
foo1 foo2
0 one 1.006666
1 two 0.002063
2 two 1.507785
3 three 1.865921
4 one 0.141202
5 two -1.079792
[6 rows x 2 columns]
```
```
In [3]: df.groupby('foo1').mean()
Out[3]:
foo2
foo1
one 0.573934
three 1.865921
two 0.143352
[3 rows x 1 columns]
In [4]: df.groupby('foo1').apply(lambda x: x.mean())
Out[4]:
foo2
foo1
one 0.573934
three 1.865921
two 0.143352
[3 rows x 1 columns]
```
This should return the foo1 column as well
```
[6]: df.groupby('foo1',as_index=False).apply(lambda x: x.mean())
Out[6]:
foo2
0 0.573934
1 1.865921
2 0.143352
[3 rows x 1 columns]
In [7]: df.groupby('foo1',as_index=False).mean()
Out[7]:
foo1 foo2
0 one 0.573934
1 three 1.865921
2 two 0.143352
[3 rows x 2 columns]
```
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Additionally, I fixed a small pep8 issue in head()
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} | 7 | 2014-04-30T04:27:11Z | 2014-06-12T21:07:21Z | 2014-05-01T17:50:35Z | CONTRIBUTOR | null | Fixes #5453
I am not sure how to test a mouse-over, and the original commit 922c6102eebb7347cea587fffc4795a3ca3e73b2 introducing this functionality did not have tests either, which is sadly probably how this got broken. @willfurnass do you happen to have any ideas for how to test this functionality, maybe from when you first looked at this?
The new code doesn't display timezone information like the old code once did (before it was broken) as Periods don't seem to have timezone info, and all `tseries/plotting.py` plots are Period-based. One could perhaps do some minor acrobatics to convert the tz-naive start time of the Period into a tz-aware Timestamp, I can research that if people feel strongly about it.
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| null | 2 | 2014-04-30T09:34:09Z | 2014-06-24T04:26:08Z | 2014-04-30T12:48:54Z | CONTRIBUTOR | null | There were about 30 vbench tests that failed due to multiple recent deprecations and copy-pasting.
This PR fixes all but 8 of those that correspond to constructing frame with "exotic" time offsets (`WeekOfYear`, `LastWeekOfYear`, `FY5253` and `FY5253Quarter`) that apparently require some kwargs for construction.
This also fixes the "different name/same test" situation with `eval_frame_and` benchmarks where for some reason single-thread and multi-thread cases share setup code.
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} | 2 | 2014-04-30T13:00:43Z | 2014-07-16T09:03:59Z | 2014-04-30T15:48:21Z | CONTRIBUTOR | null | closes #6619
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} | 7 | 2014-04-30T16:09:42Z | 2014-05-05T08:30:11Z | 2014-05-05T08:30:11Z | CONTRIBUTOR | null | I am unable to run `read_sql` against a PostgreSQL database with the current master branch. This error only happens with master. If I switch back to pandas `0.13.1` (stable) everything works again.
For reference, I am using `psycopg2 2.5.2` from `pip install`.
Below is some example code:
``` python
# Connect to the database
host = 'some.remote.host.net'
database = 'some_remote_database'
port = '54321'
username = 'josh'
my_query = 'select item_uid from db.items as item\nwhere item.end_date > (current_date + 1);'
connection = pg.connect(host=host, dbname=database, port=port, user=username)
my_dataframe = psql.read_sql(my_query, connection)
```
The error that I get is:
```
DatabaseError: Execution failed on sql: SELECT name FROM sqlite_master WHERE type='table'
AND name='select item_uid from db.items as item\nwhere item.end_date > (current_date + 1);'
```
I get the same error with `read_frame`
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} | 3 | 2014-04-30T18:02:32Z | 2016-10-12T23:04:49Z | 2014-05-14T05:00:40Z | NONE | null | In the current master, I am no longer able to add or subtract `bool` scalars from Series. For example:
```
In [63]: (covariates.isnull().sum(axis=1).astype(bool))
Out[63]:
case_id
A0001 False
A0002 False
A0003 False
A0004 False
A0005 False
A0006 False
A0007 False
A0008 False
A0009 False
A0010 False
A0011 False
A0012 False
A0013 False
A0014 False
A0015 False
...
D3760 True
D3761 False
D3762 False
D3763 False
D3764 False
D3765 False
D3766 False
D3767 False
D3768 False
D3769 False
D3770 False
D3771 False
D3772 False
D3773 False
D3774 False
Length: 3169, dtype: bool
In [65]: (covariates.isnull().sum(axis=1).astype(bool)).value_counts()
Out[65]:
False 2677
True 492
dtype: int64
```
When I subtract `True` to flip the trues and falses, I get:
```
In [66]: (covariates.isnull().sum(axis=1).astype(bool)) - True
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-66-6db12b7f95bf> in <module>()
----> 1 (covariates.isnull().sum(axis=1).astype(bool)) - True
/usr/local/Cellar/python/2.7.6/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.13.1_780_gdc098d8-py2.7-macosx-10.9-x86_64.egg/pandas/core/ops.pyc in wrapper(left, right, name)
501 if hasattr(lvalues, 'values'):
502 lvalues = lvalues.values
--> 503 return left._constructor(wrap_results(na_op(lvalues, rvalues)),
504 index=left.index, name=left.name,
505 dtype=dtype)
/usr/local/Cellar/python/2.7.6/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.13.1_780_gdc098d8-py2.7-macosx-10.9-x86_64.egg/pandas/core/ops.pyc in na_op(x, y)
441 try:
442 result = expressions.evaluate(op, str_rep, x, y,
--> 443 raise_on_error=True, **eval_kwargs)
444 except TypeError:
445 if isinstance(y, (pa.Array, pd.Series)):
/usr/local/Cellar/python/2.7.6/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.13.1_780_gdc098d8-py2.7-macosx-10.9-x86_64.egg/pandas/computation/expressions.pyc in evaluate(op, op_str, a, b, raise_on_error, use_numexpr, **eval_kwargs)
185 use_numexpr : whether to try to use numexpr (default True)
186 """
--> 187 _bool_arith_check(op_str, a, b)
188 if use_numexpr:
189 return _evaluate(op, op_str, a, b, raise_on_error=raise_on_error,
/usr/local/Cellar/python/2.7.6/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.13.1_780_gdc098d8-py2.7-macosx-10.9-x86_64.egg/pandas/computation/expressions.pyc in _bool_arith_check(op_str, a, b, not_allowed)
164 def _bool_arith_check(op_str, a, b, not_allowed=frozenset(('+', '*', '-', '/',
165 '//', '**'))):
--> 166 if op_str in not_allowed and _has_bool_dtype(a) and _has_bool_dtype(b):
167 raise NotImplementedError("operator %r not implemented for bool "
168 "dtypes" % op_str)
/usr/local/Cellar/python/2.7.6/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.13.1_780_gdc098d8-py2.7-macosx-10.9-x86_64.egg/pandas/computation/expressions.pyc in _has_bool_dtype(x)
159 return x.dtype == bool
160 except AttributeError:
--> 161 return 'bool' in x.blocks
162
163
AttributeError: 'bool' object has no attribute 'blocks'
```
Running current master on Python 2.7.5 (OS X 10.9.2).
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} | 1 | 2014-04-30T18:15:27Z | 2014-06-30T11:25:11Z | 2014-04-30T18:56:37Z | CONTRIBUTOR | null | I added a (for me) very helpful Q&A from Stackoverflow on groupby/minimization while retaining the original DataFrame columns. Additionally, I cleaned up some links in the same cookbok section.
(Disclaimer: this is my first pull request.)
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} | 11 | 2014-04-30T18:22:04Z | 2014-04-30T19:25:02Z | 2014-04-30T18:56:19Z | MEMBER | null | http://pandas-docs.github.io/pandas-docs-travis/ gives a `404 page not found` error.
So also http://pandas.pydata.org/pandas-docs/dev/ does not work (as this is redirected to the former).
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} | 6 | 2014-04-30T19:28:11Z | 2017-04-14T15:37:04Z | 2017-04-14T15:37:04Z | NONE | null | Would come in handy for a number of a different applications from basic statistics, to understanding one's data, to estimating machine learning algorithmic load.
dupe of #2749
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} | 35 | 2014-04-30T22:12:47Z | 2014-06-13T01:26:49Z | 2014-05-05T22:18:47Z | MEMBER | null | closes #7003
- [x] vbench
- ~~axis parameter~~ (this only works in non-cython land)
- [x] tests for `object` dtype if there are none
- [x] datetime64/timedelta64 count
vbench results:
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
groupby_multi_count | 7.3980 | 6814.2579 | 0.0011 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
```
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| null | 1 | 2014-05-01T11:35:32Z | 2014-05-01T13:52:50Z | 2014-05-01T13:52:50Z | NONE | null | The following throws a very confusing error (pandas 0.13.1):
pd.DataFrame([[1,"abc", 1]], columns=["a", "b", "a"]).describe()
File "C:\Program Files\Python 3.3.3\lib\site-packages\pandas\core\frame.py", line 3790, in describe
numdata = self._get_numeric_data()
File "C:\Program Files\Python 3.3.3\lib\site-packages\pandas\core\generic.py", line 1894, in _get_numeric_data
self._data.get_numeric_data()).__finalize__(self)
File "C:\Program Files\Python 3.3.3\lib\site-packages\pandas\core\internals.py", line 2596, in get_numeric_data
return self.get_data(**kwargs)
File "C:\Program Files\Python 3.3.3\lib\site-packages\pandas\core\internals.py", line 2610, in get_data
return self.combine(blocks)
File "C:\Program Files\Python 3.3.3\lib\site-packages\pandas\core\internals.py", line 2624, in combine
return self.**class**(new_blocks, new_axes, do_integrity_check=False)
File "C:\Program Files\Python 3.3.3\lib\site-packages\pandas\core\internals.py", line 2037, in **init**
self._set_ref_locs(do_refs=True)
File "C:\Program Files\Python 3.3.3\lib\site-packages\pandas\core\internals.py", line 2189, in _set_ref_locs
rl[loc] = (block, i)
IndexError: list assignment index out of range
pd.DataFrame([[1,"abc", 1]], columns=["a", "b", "a"]).info()
File "C:\Program Files\Python 3.3.3\lib\site-packages\pandas\core\frame.py", line 1443, in info
counts = self.count()
File "C:\Program Files\Python 3.3.3\lib\site-packages\pandas\core\frame.py", line 3862, in count
result = notnull(frame).sum(axis=axis)
File "C:\Program Files\Python 3.3.3\lib\site-packages\pandas\core\common.py", line 273, in notnull
res = isnull(obj)
File "C:\Program Files\Python 3.3.3\lib\site-packages\pandas\core\common.py", line 125, in isnull
return _isnull(obj)
File "C:\Program Files\Python 3.3.3\lib\site-packages\pandas\core\common.py", line 137, in _isnull_new
return obj._constructor(obj._data.apply(lambda x: isnull(x.values)))
File "C:\Program Files\Python 3.3.3\lib\site-packages\pandas\core\internals.py", line 2384, in apply
do_integrity_check=do_integrity_check)
File "C:\Program Files\Python 3.3.3\lib\site-packages\pandas\core\internals.py", line 2037, in __init__
self._set_ref_locs(do_refs=True)
File "C:\Program Files\Python 3.3.3\lib\site-packages\pandas\core\internals.py", line 2177, in _set_ref_locs
'have _ref_locs set' % (block, labels))
AssertionError: Cannot create BlockManager._ref_locs because block [BoolBlock: [a, a], 2 x 1, dtype: bool] with duplicate items [Index(['a', 'b', 'a'], dtype='object')] does not have _ref_locs set
Note that there are multiple ways to avoid the error:
- not having duplicate column names
- changing the type of column "b" or deleting it!
In my program I had duplicate names only accidently, but still this behaviour and dependence on the non-duplicate column looks like a bug to me.
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} | 1 | 2014-05-01T14:03:25Z | 2021-04-11T04:32:05Z | null | CONTRIBUTOR | null | When lifting limits on display options, for some of them one can use `None`:
```
display.max_columns = None
display.display.max_rows = None
display.width = None
```
but for other options, Pandas complains if you use `None` returning `ValueError: Value must have type '<type 'int'>'`, so you have to use 0
```
display.max_col_width = 0
```
Is this intentional? If so, what is the rationale behind it?
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} | 6 | 2014-05-01T20:48:31Z | 2017-12-07T11:44:00Z | null | NONE | null | With this code the style spec `'o'` is ignored when plotting error bars (#5638):
``` python
In [25]: from numpy.random import randn
In [26]: import pandas as pd
In [27]: df = pd.DataFrame(randn(8,3), columns = "A B yerr".split())
In [28]: df.plot(x='A', y='B',style='o' ,yerr='yerr')
Out[28]: <matplotlib.axes.AxesSubplot at 0x5259cd0>
In [29]: pd.__version__
Out[29]: '0.13.1-791-g07f6d46'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/7024 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7024/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7024/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7024/events | https://github.com/pandas-dev/pandas/issues/7024 | 32,651,166 | MDU6SXNzdWUzMjY1MTE2Ng== | 7,024 | pd.concat(objs) drops type of the inputs if all inputs are empty | {
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| null | 3 | 2014-05-01T21:50:57Z | 2014-05-01T22:22:51Z | 2014-05-01T22:22:08Z | NONE | null | When concatenating several pd.Series of the same type, I would expect the output Series to have the same type. However, this does not hold if all input Series are empty:
```
In [0]: x = pd.Series([], dtype=int)
In [1]: pd.concat([x, x])
Series([], dtype: object)
```
How I would expect it to work:
_pd.concat([x, x]) should return Series([], dtype=int)_
Just as an example, this works fine:
```
In [2]: y = pd.Series([1], dtype=int)
In [3]: pd.concat([y, y]) # This works fine:
0 1
0 1
dtype: int64
```
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https://api.github.com/repos/pandas-dev/pandas/issues/7025 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7025/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7025/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7025/events | https://github.com/pandas-dev/pandas/issues/7025 | 32,688,171 | MDU6SXNzdWUzMjY4ODE3MQ== | 7,025 | GroupBy with Float Index not Plotting | {
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} | 3 | 2014-05-02T12:31:04Z | 2014-05-02T21:37:21Z | 2014-05-02T21:37:21Z | NONE | null | I have the following problem: I have a dataset with a float index that I want to group and plot. If I'm using integer indices it works, with floats it doesn't. Here is the minimal example that once works and once not. I'm testing with the current master. Am I doing something wrong?
This code snippet fails:
```
import pandas as pd
import numpy as np
test = pd.DataFrame({'def': [1,1,1,2,2,2,3,3,3], 'val': np.random.randn(9)}, index=[1.0,2.0,3.0,1.0,2.0,3.0,1.0,2.0,3.0])
test.groupby('def')['val'].plot()
```
While this works:
```
# This Works
test = pd.DataFrame({'def': [1,1,1,2,2,2,3,3,3], 'val': np.random.randn(9)}, index= [1,2,3,1,2,3,1,2,3])
test.groupby('def')['val'].plot()
```
The error I get is:
```
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-83-e50f1a9b07c6> in <module>()
----> 1 test.groupby('def')['val'].plot()
/Library/Python/2.7/site-packages/pandas-0.13.1_758_g4a67608-py2.7-macosx-10.9-intel.egg/pandas/core/groupby.pyc in __getattr__(self, attr)
452 if attr in self._internal_names_set:
453 return object.__getattribute__(self, attr)
--> 454 if attr in self.obj:
455 return self[attr]
456
/Library/Python/2.7/site-packages/pandas-0.13.1_758_g4a67608-py2.7-macosx-10.9-intel.egg/pandas/core/series.pyc in __contains__(self, key)
379
380 def __contains__(self, key):
--> 381 return key in self.index
382
383 # complex
/Library/Python/2.7/site-packages/pandas-0.13.1_758_g4a67608-py2.7-macosx-10.9-intel.egg/pandas/core/index.pyc in __contains__(self, other)
2042 try:
2043 # if other is a sequence this throws a ValueError
-> 2044 return np.isnan(other) and self._hasnans
2045 except ValueError:
2046 try:
NotImplementedError: Not implemented for this type
```
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} | 0 | 2014-05-02T12:44:11Z | 2014-07-05T02:58:59Z | 2014-05-02T21:37:21Z | CONTRIBUTOR | null | closes #7025
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} | 16 | 2014-05-02T18:07:46Z | 2017-04-05T02:07:52Z | 2014-05-05T19:52:08Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/6994
I still need to do one more read through. Happy to hear additional suggestions.
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https://api.github.com/repos/pandas-dev/pandas/issues/7029 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7029/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7029/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7029/events | https://github.com/pandas-dev/pandas/pull/7029 | 32,753,996 | MDExOlB1bGxSZXF1ZXN0MTU0NjU2ODE= | 7,029 | BUG: fix reading multi-index data in python parser | {
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} | 7 | 2014-05-03T20:04:50Z | 2014-06-23T23:28:38Z | 2014-05-06T18:02:22Z | CONTRIBUTOR | null | partial fix for #6893
The python parser has a problem reading data with a multi-index specified in the row following the header, for example
``` python
In [3]: text = """ A B C D E
one two three four
a b 10.0032 5 -0.5109 -2.3358 -0.4645 0.05076 0.3640
a q 20 4 0.4473 1.4152 0.2834 1.00661 0.1744
x q 30 3 -0.6662 -0.5243 -0.3580 0.89145 2.5838"""
In [4]: pd.read_table(StringIO(text), sep='\s+', engine='python')
Out[4]:
E
one two three four
a b 10.0032 5 0.3640
q 20.0000 4 0.1744
x q 30.0000 3 2.5838
[3 rows x 1 columns]
```
(the C parser doesn't make it this far, see #6893)
This PR fixes the bug in the python parser:
``` python
In [4]: pd.read_table(StringIO(text), sep='\s+', engine='python')
Out[4]:
A B C D E
one two three four
a b 10.0032 5 -0.5109 -2.3358 -0.4645 0.05076 0.3640
q 20.0000 4 0.4473 1.4152 0.2834 1.00661 0.1744
x q 30.0000 3 -0.6662 -0.5243 -0.3580 0.89145 2.5838
[3 rows x 5 columns]
```
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} | 5 | 2014-05-03T20:28:08Z | 2019-12-22T16:55:36Z | 2019-12-22T16:55:35Z | CONTRIBUTOR | null | As an example, I would like to be able to read a dataset like this one:
http://www.cdc.gov/nchs/nsfg/nsfg_cycle6.htm
The data files themselves are ASCII with fixed-width fields. The variables names, types, and indices are in a separate data description file, available for SAS, SPSS and STATA. I would like to add a parser that reads at least one of these description files and then parses the data file. Since two files are used, it might require changes in the Parser API.
I am happy to write a parser that reads the dictionary file and then the data file. I could use help with either setting up the new parser ahead of time or (after the fact) integrating my code with the existing structure.
Also, is there a preference for the SAS, SPSS, or Stata format?
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} | 6 | 2014-05-04T07:37:49Z | 2017-06-08T11:14:01Z | 2017-06-08T11:13:39Z | NONE | null | related #6322
Hi,
I've found this accidentally that after the subtraction between `df1` and `df2`, the `nan` in the multi index has turned to a `string` rather than the original `float` type. And it's also weird that this issue only happens with the `parse_dates` set to `False`.
The following is the code to reproduce:
``` python
import StringIO
from pandas import DataFrame
index = ['ID', 'Index']
input1 = '''"value","ID","Index"
"0.0","B9",""
"0.0","B",""
'''
input2 = '''"value","ID","Index"
"1.5375969","M","Blah"
'''
df1 = DataFrame.from_csv(StringIO.StringIO(input1), index_col=index, parse_dates=False)
df2 = DataFrame.from_csv(StringIO.StringIO(input2), index_col=index, parse_dates=False)
## The following two lines can work as expected
#df1 = DataFrame.from_csv(file1, index_col=index, )
#df2 = DataFrame.from_csv(file2, index_col=index, )
diff = df2['value'] - df1['value']
print diff.index[0], diff.index[1] # => ('B', 'nan') ('B9', 'nan')
print df1.index[0], df1.index[1] # => ('B9', nan) ('B', nan)
```
versions of pandas and it's dependencies:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Darwin
OS-release: 13.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: zh_HK.UTF-8
pandas: 0.13.1
Cython: None
numpy: 1.8.1
scipy: None
statsmodels: None
IPython: 1.1.0
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.2
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
sqlalchemy: None
lxml: None
bs4: None
html5lib: None
bq: None
apiclient: None
```
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} | 10 | 2014-05-04T20:38:00Z | 2014-07-28T13:24:39Z | 2014-07-28T13:24:39Z | NONE | null | As can be seen in the below code, column 3, 8, 9, and 10 were misinterpreted as datetime objects. Columns 1, 6 and 7 should be integer. How do I force the columns to be interpreted as the proper type? Only 2, 4, 5 and 11 appear to have been read properly. I can pass 'infer_types=False' I suppose and do manual conversion afterwards, but since infer_types is going away, this won't work.
```
In [63]: import pandas as pd
In [64]: path = r"http://en.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_population"
In [65]: tables = pd.read_html(path)
In [66]: df = tables[1]
In [67]: df.head()
Out[67]:
1 2 3 4 5 6 7 8 \
1 !000001 California NaT 37253956 33871648 !000053 !000055 NaT
2 !000002 Texas NaT 25145561 20851820 !000036 !000038 NaT
3 !000003 New York 1965-11-27 19378102 18976457 !000027 !000029 NaT
4 !000004 Florida NaT 18801310 15982378 !000027 !000029 NaT
5 !000005 Illinois NaT 12830632 12419293 !000018 !000020 NaT
9 10 11
1 NaT NaT 11.91%
2 NaT NaT 8.04%
3 NaT NaT 6.19%
4 NaT NaT 6.01%
5 NaT NaT 4.10%
[5 rows x 11 columns]
dtype: object
In [68]: df.dtypes
Out[68]:
1 object
2 object
3 datetime64[ns]
4 object
5 object
6 object
7 object
8 datetime64[ns]
9 datetime64[ns]
10 datetime64[ns]
11 object
dtype: object
```
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} | 13 | 2014-05-04T21:46:30Z | 2017-04-05T02:08:43Z | 2015-05-09T16:08:38Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/5677
As a reminder, with a data frame like:
``` python
from itertools import cycle, islice
np.random.seed(0)
df = pd.DataFrame(np.random.randn(20, 2))
df.columns = ["foo","bar"]
df['g0'] = list(islice(cycle('ab'), 20))
df['g1'] = ['a'] * 10 + ['b'] * 10
df['g2'] = ['c'] * 5 + ['d'] * 5 + ['c'] * 5 + ['d'] * 5
df = df.set_index(['g1', 'g2'])
```
Before if you wanted to groupby a a column and index level, you'd have to
``` python
g = df.reset_index().groupby(['g0', 'g1'])
```
Now you can just do `df.groupby(['g0', 'g1'])`. In ambiguous cases where a there's a key in `by` that's in both the index names and columns we warn and proceed with grouping by the columns (I haven't tested this part yet).
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} | 3 | 2014-05-04T23:08:12Z | 2014-06-16T05:08:36Z | 2014-05-05T10:00:59Z | CONTRIBUTOR | null | Prevents tests from failing if data isn't able to be downloaded from Yahoo Finance.
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https://api.github.com/repos/pandas-dev/pandas/issues/7035 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7035/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7035/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7035/events | https://github.com/pandas-dev/pandas/pull/7035 | 32,780,042 | MDExOlB1bGxSZXF1ZXN0MTU0NzY2MDI= | 7,035 | ENH/BUG: boxplot now supports layout | {
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} | 15 | 2014-05-04T23:16:27Z | 2014-06-14T11:21:40Z | 2014-05-10T16:16:08Z | MEMBER | null | Closes #6769. Added `layout` kw to `boxplot`. I think tests can be improved if boxplot can return ndarray with the same shape as layout, as the same manner as hist (maybe after #4472).
## Bug fix
It includes the fix to hide unnecessary axes than required in boxplot and hist. For example, `layout=(2, 2)` is specified for 3 subplots. I've listed affected cases as below.
```
import pandas as pd
import numpy as np
df = pd.DataFrame({'3g': 'A A A B B B C C C'.split(),
'2g': [1, 2, 1, 2, 1, 2, 1, 2, 2],
'values': np.random.rand(9),
'values2': np.random.rand(9),
'values3': np.random.rand(9)})
# BoxPlot
# specify 3 columns with by keyword -> should be 3 axes in (2, 2) layout
df.boxplot(column=['values', 'values2', 'values3'], by='2g')
# groupby results in 3 groups -> should be 3 axes
df.groupby('3g').boxplot()
# groupby results in 3 groups, with column kw -> should be 3 axes in (2, 2) layout
df.groupby('3g').boxplot(column=['values', 'values2', 'values3'])
# Histogram
# specify 3 columns without by kw -> should be 3 axes in (2, 2) layout
df.hist(column=['values', 'values2', 'values3'])
# groupby results 3 groups results of by kw -> should be 3 axes in (2, 2) layout
df.hist(column=['values'], by='3g')
# This results KeyError: '3g' in current master, and the error has been fixed.
# groupby results in 3 groups -> should be 3 axes in (2, 2) layout
df.hist(by='3g')
# layout contains more size than groups -> should be 2 axes in specified layout
df.hist(by='2g', layout=(2, 2))
```
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} | 4 | 2014-05-05T03:44:37Z | 2014-05-05T13:50:55Z | 2014-05-05T13:50:55Z | MEMBER | null | xref: #7032
really just remove the parameter's effect (no signature change) ... but give a _full_ removal warning for 0.15
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} | 8 | 2014-05-05T04:12:11Z | 2014-07-16T09:04:30Z | 2014-05-10T11:32:16Z | MEMBER | null | Closes #5917.
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} | 0 | 2014-05-05T10:02:54Z | 2014-05-12T12:37:23Z | 2014-05-12T12:37:23Z | CONTRIBUTOR | null | https://github.com/pydata/pandas/pull/7034
should have closed all the io/data get options data failures..
if you see other ones pls report here!
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} | 3 | 2014-05-05T11:18:41Z | 2014-06-16T01:51:43Z | 2014-05-05T13:27:58Z | CONTRIBUTOR | null | This should close #6933.
The downside of this patch is that we'll lose type inference that might change index type in rare occasions when index items have different dtypes. This is the same issue that occurred in #6440, and it was ruled out as infrequent and worth dropping for extra performance.
Note though, that the benchmark results are not exactly what I've expected:
``` python
In [39]: idx = tm.makeStringIndex(100000)
In [40]: timeit idx.delete(-1)
1000 loops, best of 3: 1.46 ms per loop
In [41]: timeit np.delete(idx, -1)
1000 loops, best of 3: 1.05 ms per loop
```
The result is faster, but not as fast as I'd expect. This is probably because of object refcounting overhead, because MultiIndex fares a lot better with its Categorial-like implementation:
``` python
In [42]: midx = pd.MultiIndex.from_product([[0], idx])
# <..snip..>
In [45]: timeit midx.delete(-1)
1000 loops, best of 3: 225 µs per loop
```
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Also changed `Categorical` and `PeriodIndex.factorize` to make index sorted like other indexes do.
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} | 7 | 2014-05-05T12:43:30Z | 2014-07-16T09:04:40Z | 2014-05-12T12:41:17Z | CONTRIBUTOR | null | closes #6732
`nth` sets the index appropriately and the same as first/last.
this becomes less like head/tail in that `as_index` determines when you have an index
here's the revised behavior:
```
In [1]: df = DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B'])
In [3]: df
Out[3]:
A B
0 1 NaN
1 1 4
2 5 6
[3 rows x 2 columns]
In [2]: g = df.groupby('A')
In [9]: gni = df.groupby('A',as_index=False)
```
```
In [5]: g.first()
Out[5]:
B
A
1 4
5 6
[2 rows x 1 columns]
# this was a regression from 0.13.1 (in that before this PR, this was returning like ``as_index=False``)
In [6]: g.nth(0)
Out[6]:
B
A
1 NaN
5 6
[2 rows x 1 columns]
In [7]: g.nth(0,dropna='all')
Out[7]:
B
A
1 4
5 6
[2 rows x 1 columns]
```
```
In [10]: gni.nth(0)
Out[10]:
A B
0 1 NaN
2 5 6
[2 rows x 2 columns]
In [11]: gni.nth(0,dropna='all')
Out[11]:
A B
0 1 4
1 5 6
[2 rows x 2 columns]
```
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} | 2 | 2014-05-05T16:20:06Z | 2021-04-11T04:32:37Z | null | CONTRIBUTOR | null | So the plot method is quite... flexible (bloated). This does give great flexibility for quickly plotting stuff, but not all args make sense for every kind of plot. Some plots (for example PiePlot) will ignore arguments like `logx` since it doesn't make any sense. This case is obviously nonsense, so it probably won't happen, but it would be nice to decorate the `_args_adjust` methods to warn when arguments are being ignored / adjusted if the user has explicitly changed them from the defaults. I should be able to do this one.
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| null | 1 | 2014-05-05T19:54:13Z | 2014-06-28T15:35:13Z | 2014-05-05T21:04:26Z | CONTRIBUTOR | null | I just kinda guessed what `precision` does.
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| null | 1 | 2014-05-05T20:29:06Z | 2014-07-16T09:04:45Z | 2014-05-05T21:07:18Z | CONTRIBUTOR | null | {
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} | 3 | 2014-05-05T21:13:02Z | 2016-01-07T19:56:31Z | 2016-01-07T19:56:31Z | NONE | null | I was loading some relatively large CSV files (roughly three hundred by one million), and noticed that the layout of those CSVs is actually critical to Pandas' ability to read them to memory. A "wide" format uses large amounts of memory and takes an extremely long time to run (killed the process after a half hour). The exact same data, but presented in a "tall" format (ie 300 columns and one million rows) reads within about sixty seconds. Given there is no sort of "warning" message regarding the number of columns when reading the CSV file, I could easily see this confusing a new user and leading them to believe that Pandas is simply unable to handle that volume of data, when in fact it can, given that the data is formatted correctly. Perhaps some broader documentation on wide vs tall inputs would be appropriate.
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} | 20 | 2014-05-05T22:54:07Z | 2021-04-11T04:33:43Z | null | NONE | null | There are a number of offset aliases listed here:
http://pandas.pydata.org/pandas-docs/dev/timeseries.html#offset-aliases
I'm currently trying to resample a timeseries where the buckets are return with the beginning and/or end of the week. There are a number of Start Month/End Month frequency aliases. Am I missing something or should we include Week End/Week Start resampling aliases as well?
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} | 6 | 2014-05-05T23:16:21Z | 2014-10-30T04:10:53Z | 2014-10-30T04:10:53Z | NONE | null | A TypeError is raised when attempting to subtract a Timestamp from a DatetimeIndex when the result of the subtraction is zero.
```
dr = pd.date_range('20140101', freq='S', periods=11)
zero = dr[5]
diff = dr - zero
```
raises a TypeError, however,
```
diff = [i - zero for i in dr]
```
produces the desired list of timedelta objects.
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Darwin
OS-release: 12.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.1
Cython: 0.20.1
numpy: 1.8.0
scipy: 0.13.3
statsmodels: None
IPython: 2.0.0
sphinx: 1.2.2
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: 2013.9
bottleneck: 0.6.0
tables: 3.1.1
numexpr: 2.1
matplotlib: 1.3.1
openpyxl: None
xlrd: 0.9.2
xlwt: None
xlsxwriter: None
sqlalchemy: None
lxml: None
bs4: None
html5lib: None
bq: None
apiclient: None
```
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| null | 11 | 2014-05-06T04:27:42Z | 2014-05-10T00:27:22Z | 2014-05-06T12:02:24Z | CONTRIBUTOR | null | I have a performance issue that I discovered by noticing that 173 MB sized CSV files take quite a while to write out (I have to write many of them). The io profiler I use is `iotop` that indicated that my write rate was a meager 9 MB/s while the raid I am writing to supports up to 20 times that, as I have confirmed with a pure `open(fname,'w').write(longstring)`.
Here's info on the dataframe:
``` python
In [17]: df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 590604 entries, 0 to 590603
Data columns (total 36 columns):
jdate 590604 non-null float64
orbit 590604 non-null int64
sundist 590604 non-null float64
sunlat 590604 non-null float64
sunlon 590604 non-null float64
sclk 590604 non-null float64
sclat 590604 non-null float64
sclon 590604 non-null float64
scrad 590604 non-null float64
scalt 590604 non-null float64
el_cmd 590604 non-null float64
az_cmd 590604 non-null float64
af 590604 non-null float64
orientlat 590604 non-null float64
orientlon 590604 non-null float64
det 590604 non-null int64
vlookx 590604 non-null float64
vlooky 590604 non-null float64
qge 590604 non-null float64
clat 183036 non-null float64
csunzen 183036 non-null float64
qca 590604 non-null float64
vlookz 590604 non-null float64
clon 183036 non-null float64
cloctime 183036 non-null float64
csunazi 183036 non-null float64
cemis 183036 non-null float64
tb 590604 non-null float64
radiance 590604 non-null float64
hour 590604 non-null int64
minute 590604 non-null int64
second 590604 non-null float64
year 590604 non-null int64
month 590604 non-null int64
date 590604 non-null int64
c 590604 non-null int64
dtypes: float64(28), int64(8)
```
and here's the output of a profiling run:
``` bash
In [16]: %prun df.to_csv()
2969486 function calls in 19.916 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
213 16.523 0.078 18.138 0.085 {pandas.lib.write_csv_rows}
590605 1.101 0.000 1.616 0.000 StringIO.py:208(write)
427 0.980 0.002 0.980 0.002 {pandas.lib.isnullobj}
593811 0.279 0.000 0.284 0.000 {isinstance}
854 0.221 0.000 0.221 0.000 {numpy.core.multiarray.array}
640 0.180 0.000 0.180 0.000 {method 'tolist' of 'numpy.ndarray' objects}
213 0.168 0.001 19.784 0.093 format.py:1283(_save_chunk)
590606 0.084 0.000 0.084 0.000 StringIO.py:38(_complain_ifclosed)
590605 0.082 0.000 0.082 0.000 {method 'append' of 'list' objects}
1 0.082 0.082 0.082 0.082 {method 'join' of 'str' objects}
590821 0.074 0.000 0.074 0.000 {len}
213 0.048 0.000 1.198 0.006 internals.py:1041(to_native_types)
1 0.043 0.043 0.124 0.124 StringIO.py:258(getvalue)
640 0.013 0.000 1.001 0.002 common.py:202(_isnull_ndarraylike)
213 0.009 0.000 0.250 0.001 internals.py:420(to_native_types)
1 0.004 0.004 19.788 19.788 format.py:1265(_save)
2134 0.004 0.000 0.005 0.000 {getattr}
640 0.003 0.000 1.011 0.002 common.py:132(_isnull_new)
1 0.002 0.002 19.916 19.916 decorators.py:47(wrapper)
214 0.002 0.000 0.023 0.000 index.py:821(_format_native_types)
213 0.002 0.000 0.004 0.000 index.py:694(__getitem__)
853 0.002 0.000 0.003 0.000 numeric.py:1810(isscalar)
214 0.002 0.000 0.029 0.000 index.py:814(to_native_types)
1280 0.002 0.000 0.004 0.000 common.py:58(_check)
427 0.002 0.000 0.002 0.000 {numpy.core.multiarray.empty}
640 0.001 0.000 0.004 0.000 {pandas.lib.isscalar}
427 0.001 0.000 0.001 0.000 {method 'reshape' of 'numpy.ndarray' objects}
213 0.001 0.000 0.001 0.000 index.py:245(__array_finalize__)
640 0.000 0.000 1.012 0.002 common.py:111(isnull)
1 0.000 0.000 19.916 19.916 <string>:1(<module>)
427 0.000 0.000 0.000 0.000 {method 'ravel' of 'numpy.ndarray' objects}
213 0.000 0.000 0.000 0.000 index.py:207(_reset_identity)
426 0.000 0.000 0.000 0.000 internals.py:93(mgr_locs)
214 0.000 0.000 0.001 0.000 numeric.py:392(asarray)
213 0.000 0.000 0.000 0.000 {min}
214 0.000 0.000 0.001 0.000 index.py:432(values)
1 0.000 0.000 0.000 0.000 format.py:948(__init__)
1 0.000 0.000 19.913 19.913 frame.py:1069(to_csv)
1 0.000 0.000 19.788 19.788 format.py:1149(save)
1 0.000 0.000 0.000 0.000 format.py:1187(_save_header)
1 0.000 0.000 0.000 0.000 {method 'writerow' of '_csv.writer' objects}
3 0.000 0.000 0.000 0.000 format.py:1027(<genexpr>)
1 0.000 0.000 0.000 0.000 {_csv.writer}
1 0.000 0.000 0.000 0.000 StringIO.py:54(__init__)
1 0.000 0.000 0.000 0.000 {sum}
2 0.000 0.000 0.000 0.000 internals.py:171(shape)
1 0.000 0.000 0.000 0.000 {hasattr}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
1 0.000 0.000 0.000 0.000 {method 'get' of 'dict' objects}
1 0.000 0.000 0.000 0.000 index.py:345(nlevels)
1 0.000 0.000 0.000 0.000 {method 'pop' of 'dict' objects}
```
Note that above time of about 20 seconds is only the conversion to a string, which seems to be the longest part.
For completeness, here's the profile when writing the file:
``` bash
In [19]: %prun df.to_csv('/raid1/maye/rdr_out/test_write_speed.csv')
16458 function calls in 18.126 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
213 16.447 0.077 16.447 0.077 {pandas.lib.write_csv_rows}
427 0.979 0.002 0.979 0.002 {pandas.lib.isnullobj}
854 0.182 0.000 0.182 0.000 {numpy.core.multiarray.array}
213 0.167 0.001 18.029 0.085 format.py:1283(_save_chunk)
640 0.159 0.000 0.159 0.000 {method 'tolist' of 'numpy.ndarray' objects}
1 0.059 0.059 0.059 0.059 {method 'close' of 'file' objects}
213 0.048 0.000 1.137 0.005 internals.py:1041(to_native_types)
1 0.032 0.032 0.032 0.032 {open}
640 0.013 0.000 1.000 0.002 common.py:202(_isnull_ndarraylike)
213 0.009 0.000 0.249 0.001 internals.py:420(to_native_types)
1 0.004 0.004 18.033 18.033 format.py:1265(_save)
3205 0.004 0.000 0.008 0.000 {isinstance}
2134 0.004 0.000 0.005 0.000 {getattr}
640 0.003 0.000 1.009 0.002 common.py:132(_isnull_new)
214 0.002 0.000 0.023 0.000 index.py:821(_format_native_types)
213 0.002 0.000 0.004 0.000 index.py:694(__getitem__)
214 0.002 0.000 0.029 0.000 index.py:814(to_native_types)
853 0.002 0.000 0.003 0.000 numeric.py:1810(isscalar)
1280 0.002 0.000 0.004 0.000 common.py:58(_check)
427 0.001 0.000 0.001 0.000 {numpy.core.multiarray.empty}
640 0.001 0.000 0.003 0.000 {pandas.lib.isscalar}
427 0.001 0.000 0.001 0.000 {method 'reshape' of 'numpy.ndarray' objects}
1 0.001 0.001 18.126 18.126 decorators.py:47(wrapper)
213 0.001 0.000 0.001 0.000 index.py:245(__array_finalize__)
640 0.000 0.000 1.009 0.002 common.py:111(isnull)
427 0.000 0.000 0.000 0.000 {method 'ravel' of 'numpy.ndarray' objects}
213 0.000 0.000 0.000 0.000 index.py:207(_reset_identity)
426 0.000 0.000 0.000 0.000 internals.py:93(mgr_locs)
214 0.000 0.000 0.001 0.000 numeric.py:392(asarray)
213 0.000 0.000 0.000 0.000 {min}
214 0.000 0.000 0.001 0.000 index.py:432(values)
215 0.000 0.000 0.000 0.000 {len}
1 0.000 0.000 18.125 18.125 format.py:1149(save)
1 0.000 0.000 0.000 0.000 format.py:948(__init__)
1 0.000 0.000 18.125 18.125 frame.py:1069(to_csv)
1 0.000 0.000 0.000 0.000 {method 'writerow' of '_csv.writer' objects}
1 0.000 0.000 0.000 0.000 format.py:1187(_save_header)
1 0.000 0.000 0.000 0.000 {_csv.writer}
3 0.000 0.000 0.000 0.000 format.py:1027(<genexpr>)
1 0.000 0.000 18.126 18.126 <string>:1(<module>)
1 0.000 0.000 0.032 0.032 common.py:2336(_get_handle)
1 0.000 0.000 0.000 0.000 {sum}
2 0.000 0.000 0.000 0.000 internals.py:171(shape)
1 0.000 0.000 0.000 0.000 {hasattr}
1 0.000 0.000 0.000 0.000 index.py:345(nlevels)
1 0.000 0.000 0.000 0.000 {method 'pop' of 'dict' objects}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
1 0.000 0.000 0.000 0.000 {method 'get' of 'dict' objects}
```
Pandas version: ['0.13.1-769-gf26cf9e'](f26cf9e) , running on a 64-bit CentOS6.
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} | 1 | 2014-05-06T08:59:35Z | 2014-05-06T10:14:52Z | 2014-05-06T10:14:52Z | NONE | null | related #6322
dupe of #7031
I'm on 0.13.1, don't know if this is already fixed on master, but haven't found any issues about this. If I you have `NaN` value in MultiIndex, reindexing will fill the first value from reindexed series to all `NaN` indices. Perhaps an example will clarify it
Code:
``` python
import pandas as pd
import numpy as np
ix = pd.MultiIndex.from_tuples([('a',np.nan)], names=['col1', 'col2'])
pd.Series([1], index=[1]).reindex(ix, level=1)
```
Output:
```
col1 col2
a NaN 1
```
Desired output:
```
col1 col2
a NaN NaN
```
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} | 2 | 2014-05-06T12:04:58Z | 2016-10-12T23:04:49Z | 2014-06-09T03:00:56Z | CONTRIBUTOR | null | see #5534
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} | 3 | 2014-05-06T13:39:00Z | 2017-04-05T02:08:36Z | 2014-05-06T14:22:31Z | CONTRIBUTOR | null | DOC: Give KDE a section header
DOC: remove extra slice in hexbin example
DOC: move notes to look nicer
A few changes that came up in https://github.com/pydata/pandas/pull/7027
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} | 12 | 2014-05-06T14:02:58Z | 2014-07-16T09:04:47Z | 2014-05-08T17:55:21Z | MEMBER | null | - use size instead of cythonized count for integer case since we cannot have
`nan` in that case
- not faster perf wise
- compilation time is shorter because count has no int templates except for
dates and times
- any precision issues (don't think there were any) with other integer types are gone, since only `int64` is compared with dates, whereas lower precision integers use `size`.
Anywho, here's the vbench results for this PR vs master:
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
groupby_int_count | 4.0247 | 4.0337 | 0.9978 |
groupby_multi_count | 7.5080 | 7.4813 | 1.0036 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
```
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} | 17 | 2014-05-06T14:03:16Z | 2019-06-29T20:54:28Z | 2019-06-29T20:54:28Z | NONE | null | Hi
Something is wrong when trying to save a dataframe with tz-aware timestamps to xlsx, using pd.Dataframe.to_excel:
``` Python
df = pd.DataFrame([1], index=[pd.Timestamp('2014-05-02', tz='CET')])
df.to_excel('test.xlsx')
```
yields an exception on my system:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-2-4e18a4be2a71> in <module>()
----> 1 df.to_excel('test.xlsx')
/home/silvio/prod34/lib/python3.4/site-packages/pandas/core/frame.py in to_excel(self, excel_writer, sheet_name, na_rep, float_format, cols, header, index, index_label, startrow, startcol, engine, merge_cells)
1202 formatted_cells = formatter.get_formatted_cells()
1203 excel_writer.write_cells(formatted_cells, sheet_name,
-> 1204 startrow=startrow, startcol=startcol)
1205 if need_save:
1206 excel_writer.save()
/home/silvio/prod34/lib/python3.4/site-packages/pandas/io/excel.py in write_cells(self, cells, sheet_name, startrow, startcol)
771 wks.write(startrow + cell.row,
772 startcol + cell.col,
--> 773 cell.val, style)
774
775 def _convert_to_style(self, style_dict, num_format_str=None):
/home/silvio/prod34/lib/python3.4/site-packages/xlsxwriter/worksheet.py in cell_wrapper(self, *args, **kwargs)
55 if len(args):
56 int(args[0])
---> 57 return method(self, *args, **kwargs)
58 except ValueError:
59 # First arg isn't an int, convert to A1 notation.
/home/silvio/prod34/lib/python3.4/site-packages/xlsxwriter/worksheet.py in write(self, row, col, *args)
374 # Write datetime objects.
375 if isinstance(token, date_types):
--> 376 return self.write_datetime(row, col, *args)
377
378 # Write number types.
/home/silvio/prod34/lib/python3.4/site-packages/xlsxwriter/worksheet.py in cell_wrapper(self, *args, **kwargs)
55 if len(args):
56 int(args[0])
---> 57 return method(self, *args, **kwargs)
58 except ValueError:
59 # First arg isn't an int, convert to A1 notation.
/home/silvio/prod34/lib/python3.4/site-packages/xlsxwriter/worksheet.py in write_datetime(self, row, col, date, cell_format)
666
667 # Convert datetime to an Excel date.
--> 668 number = self._convert_date_time(date)
669
670 # Add the default date format.
/home/silvio/prod34/lib/python3.4/site-packages/xlsxwriter/worksheet.py in _convert_date_time(self, dt_obj)
3265 def _convert_date_time(self, dt_obj):
3266 # Convert a datetime object to an Excel serial date and time.
-> 3267 return datetime_to_excel_datetime(dt_obj, self.date_1904)
3268
3269 def _options_changed(self):
/home/silvio/prod34/lib/python3.4/site-packages/xlsxwriter/utility.py in datetime_to_excel_datetime(dt_obj, date_1904)
577
578 # Convert a Python datetime.datetime value to an Excel date number.
--> 579 delta = dt_obj - epoch
580 excel_time = (delta.days
581 + (float(delta.seconds)
/home/silvio/prod34/lib/python3.4/site-packages/pandas/tslib.cpython-34m.so in pandas.tslib._Timestamp.__sub__ (pandas/tslib.c:11918)()
TypeError: can't subtract offset-naive and offset-aware datetimes
```
Using Python 3.4.0 on Arch Linux, list of installed packages:
```
~% pip freeze
Cython==0.20.1
Jinja2==2.7.2
MarkupSafe==0.21
Pygments==1.6
SQLAlchemy==0.9.4
XlsxWriter==0.5.3
ecdsa==0.11
ipdb==0.8
ipython==2.0.0
ipython-sql==0.3.1
lxml==3.3.5
matplotlib==1.3.1
mysql-connector-python==1.1.6
nose==1.3.1
numpy==1.8.1
openpyxl==1.8.5
pandas==0.13.1
paramiko==1.13.0
patsy==0.2.1
prettytable==0.7.2
psycopg2==2.5.2
pycrypto==2.6.1
pyodbc==3.0.7
pyparsing==2.0.2
python-dateutil==2.2
pytz==2014.2
requests==2.2.1
scipy==0.13.3
six==1.6.1
sqlparse==0.1.11
statsmodels==0.6.0
tornado==3.2
xlrd==0.9.3
```
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| null | 7 | 2014-05-06T15:16:25Z | 2014-05-16T17:48:39Z | 2014-05-16T17:48:39Z | NONE | null | I have a problem where I need to use the value of the column by which a grouped dataframe is transformed.
The code is a complicated case of the following, where column are labelled by instances of a Python class:
```
# group by a value in the column instance
per_column = results.groupby(lambda x : x.value, 1)
for k, v in per_column:
v = v.apply(function(k, v))
```
Basically, a transformation for a group needs to consider both the value of the index by which the frame is grouped and the dataframe itself.
This works, but the grouped object's values aren't actually changed, so I have to load them into a new dictionary.
Is this possible creating a new dictionary (that I then can't really converted into a grouped object)?
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| null | 8 | 2014-05-06T16:23:50Z | 2014-05-06T19:51:23Z | 2014-05-06T19:51:23Z | NONE | null | Ipython 64, pandas installed with pip, 16 gb ram with sizable swap.
The following seems to replicate the issue I have with my data:
``` python
import pandas as pd
import numpy as np
pdf = pd.DataFrame(np.random.rand(600000,25), columns = list('abcdefghijklmnopqrstuvwxy'))
sample=np.random.rand(900)
for i in range(100):
pdf['a'][np.random.randint(0,600000)]=np.random.choice(sample)
pdf=pdf.set_index(['a','b'])
p=pdf.to_panel()
```
throws:
``` python
MemoryError Traceback (most recent call last)
<ipython-input-120-f97286b2b93a> in <module>()
----> 1 p=pdf.to_panel()
/home/user1/.pyenv/versions/theano/lib/python2.7/site-packages/pandas/core/frame.pyc in to_panel(self)
1051 newb = block2d_to_blocknd(block.values.T, block.items, shape,
1052 [major_labels, minor_labels],
-> 1053 ref_items=selfsorted.columns)
1054 new_blocks.append(newb)
1055
/home/user1/.pyenv/versions/theano/lib/python2.7/site-packages/pandas/core/reshape.pyc in block2d_to_blocknd(values, items, shape, labels, ref_items)
1034 # labels, for converting to panel format.
1035 selector = factor_indexer(shape[1:], labels)
-> 1036 mask = np.zeros(np.prod(shape), dtype=bool)
1037 mask.put(selector, True)
1038
MemoryError:
```
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} | 3 | 2014-05-06T18:16:36Z | 2015-03-06T00:42:13Z | null | CONTRIBUTOR | null | https://github.com/statsmodels/statsmodels/issues/1584
So should do _something_ with the headers (maybe do a '....')
```
In [2]: df = DataFrame(1,index=range(2),columns=['short','really long'])
In [3]: df
Out[3]:
short really long
0 1 1
1 1 1
[2 rows x 2 columns]
In [6]: with pd.option_context('display.max_colwidth',5):
...: print(df)
...:
short really long
0 1 1
1 1 1
[2 rows x 2 columns]
```
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| null | 3 | 2014-05-06T21:34:21Z | 2014-08-22T12:01:02Z | 2014-08-22T12:00:52Z | NONE | null | fixes #7056
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} | 10 | 2014-05-06T23:00:35Z | 2016-01-15T16:24:52Z | 2016-01-15T16:24:52Z | CONTRIBUTOR | null | I can't seem to get this one to work and `to_latex` doesn't allow a user-specified encoding. I think this might need a look.
I have it in unicode, so try that way.
```
pd.DataFrame([[u'au\xdfgangen']]).to_latex('test.tex')
```
Nope. Ok, so let's encode it as a utf-8 string
```
pd.DataFrame([[u'au\xdfgangen']]).apply(lambda x : x.str.encode('utf-8')).to_latex('test.tex')
```
Nope. It looks like it's getting coerced back to unicode in formatter._to_str_columns() then tries to write it as ASCII...
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| null | 1 | 2014-05-07T12:44:27Z | 2014-07-16T09:04:50Z | 2014-05-07T13:14:48Z | CONTRIBUTOR | null | Followup to https://github.com/pydata/pandas/pull/7054
DOC: fix formatting on plot formattting
DOC: add KDE to other plots
more bits of formatting
Thanks for catching these
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directly to the internal _write_to_group() method, but it was not
explicitly documented (as it is for append).
Just added 2 lines to the put() method docstring to mention it explicitly.
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directly to the internal _write_to_group() method, but it was not
explicitly documented (as it is for append). Also settable via option.
Just added 2 lines to the put() method docstring to mention it explicitly - copied from append()
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} | 5 | 2014-05-07T20:32:34Z | 2014-10-30T11:19:43Z | 2014-10-10T01:17:56Z | CONTRIBUTOR | null | related:
#8509
https://github.com/pydata/pandas/pull/5283
https://github.com/numpy/numpy/issues/4685
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} | 6 | 2014-05-07T21:00:13Z | 2016-10-12T23:04:50Z | 2014-05-08T12:39:13Z | CONTRIBUTOR | null | This looks like a regression to me, the following code used to print `[ True]`, which I think is the correct behaviour, but prints `[False]` in recent development versions (0.13.1.dev).
```
import numpy as np
import pandas as pd
i = pd.Index([np.nan])
print i.isin({0}) # prints [ True], should be [False]
```
I do not think the following is relevant but just in case:
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-24-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: es_ES.UTF-8
pandas: 0.13.1.dev
nose: 1.3.1
Cython: None
numpy: 1.8.1
scipy: 0.13.3
statsmodels: 0.6.0.dev-Unknown
IPython: 3.0.0-dev
sphinx: None
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2012c
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: 0.5.2
lxml: 3.3.3
bs4: 4.2.1
html5lib: 0.999
bq: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: 2.4.5 (dt dec mx pq3 ext)
```
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| null | 3 | 2014-05-07T21:35:28Z | 2014-05-07T21:45:30Z | 2014-05-07T21:44:06Z | NONE | null | In [40]: bnds.date[1]
Out[40]: Timestamp('2000-02-17 00:00:00', tz=None)
In [41]: tmp = bnds.date.values[1]
In [42]: tmp
Out[42]: numpy.datetime64('2000-02-16T19:00:00.000000000-0500')
In [43]: tmp.astype('datetime64[D]')
Out[43]: numpy.datetime64('2000-02-17')
Anyone know why Out[42] is February 16? I'm quite confused and the documentation on numpy site doesn't seem to explain this issue.
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} | 2 | 2014-05-07T22:53:11Z | 2014-08-19T17:09:15Z | 2014-08-19T17:09:15Z | CONTRIBUTOR | null | As discussed in #6991. At the moment with `ax` accepting a single axis you cannot map a multi-axis boxplot onto existing axes, but we could allow `ax` to be an array of axes. Would require:
- Checking that the number of axes passed is equal to the number of axes the boxplot
wants to produce.
- Some documentation to make clear whether the axes get used in row or column order
(or maybe it could support two-dimensional arrays of axes?)
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} | 39 | 2014-05-08T00:10:29Z | 2018-02-09T11:56:21Z | 2018-02-09T11:56:20Z | CONTRIBUTOR | null | Using new code in #6719
See: http://www.nyx.com/holidays-and-hours/nyse
and with some guidlines: http://www.investmentcontrarians.com/nyse-2013-stock-market-holidays/
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| null | 6 | 2014-05-08T06:10:59Z | 2018-07-05T19:49:21Z | 2014-05-09T13:27:29Z | NONE | null | I think it would be nice if regex were compatible with `pandas.DataFrame.isin`.
Lets say I am inputting the way snakes attack in a dataframe df I would tagging a column `df['Attack method']` using a list `poison` and a list `squeeze` to check if there are matches in `df[Genus]`
```
df['Attack method'] = (df.Genus.isin(poison).map({True:'Poison',False:''}) +
df.Genus.isin(squeeze).map({True:'Squeeze',False:''}))
```
If I was using Regex I would have to somehow get rid of the user warning
```
UserWarning: This pattern has match groups. To actually get the groups, use str.extract. " groups, use str.extract.", UserWarning
```
and create for loops like this:
```
for i in range(len(poison)):
df.loc[df.Genus.str.contains(poison[i]) & (df.Attack method == ''),'Attack method'] = 'Poison'
for i in range(len(squeeze)):
df.loc[df.Genus.str.contains(squeeze[i]) & (df.Attack method == ''),'Attack method'] = 'Squeeze'
```
unless there are other ways I missed out?
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} | 5 | 2014-05-08T06:37:07Z | 2019-12-31T15:04:45Z | 2019-12-31T15:04:45Z | NONE | null | hi, thank you very much for the awesome pandas tool. I would like to report an issue i noticed and seek guidance on remedial steps. When i store a long integer into a file using pd.to_csv, it stores the data fine - but when i read it back using pd.read_csv, it messes with the last 3 digits. When i try to save it back again using to_csv (without any edits), the numbers in resulting CSV file is different from the original CSV file. I've illustrated the problem below (notice how 4321113141090630389 becomes 4321113141090630400 and 4321583677327450765 becomes 4321583677327450880):
original CSV file created by pd.to_csv:
> > grep -e 321583677327450 -e 321113141090630 orig.piece
> > orig.piece:1,1;0;0;0;1;1;3844;3844;3844;1;1;1;1;1;1;0;0;1;1;0;0,,,4321583677327450765
> > orig.piece:5,1;0;0;0;1;1;843;843;843;1;1;1;1;1;1;0;0;1;1;0;0,64.0,;,4321113141090630389
> >
> > > import pandas as pd
> > > import numpy as np
> > >
> > > orig = pd.read_csv('orig.piece')
> > > orig.dtypes
> > > Unnamed: 0 int64
> > > aa object
> > > act float64
> > > ...
> > > ...
> > > s_act float64
> > > dtype: object
> > > orig['s_act'].head(6)
> > > 0 NaN
> > > 1 4.321584e+18
> > > 2 4.321974e+18
> > > 3 4.321494e+18
> > > 4 4.321283e+18
> > > 5 4.321113e+18
> > > Name: s_act, dtype: float64
> > >
> > > orig['s_act'].fillna(0).astype(int).head(6)
> > > 0 0
> > > 1 4321583677327450880
> > > 2 4321973950881710336
> > > 3 4321493786516159488
> > > 4 4321282586859217408
> > > 5 4321113141090630400
> > >
> > > orig.to_csv('convert.piece')
> >
> > grep -e 321583677327450 -e 321113141090630 orig.piece convert.piece
> > orig.piece:1,1;0;0;0;1;1;3844;3844;3844;1;1;1;1;1;1;0;0;1;1;0;0,,,4321583677327450765
> > orig.piece:5,1;0;0;0;1;1;843;843;843;1;1;1;1;1;1;0;0;1;1;0;0,64.0,;,4321113141090630389
> > convert.piece:1,1;0;0;0;1;1;3844;3844;3844;1;1;1;1;1;1;0;0;1;1;0;0,,,4.321583677327451e+18
> > convert.piece:5,1;0;0;0;1;1;843;843;843;1;1;1;1;1;1;0;0;1;1;0;0,64.0,;,4.3211131410906304e+18
could you please help me understand why read_csv jumbles the last three digits? Its not even a rounding issue, the digits are totally different (like 4321583677327450765 becomes 4321583677327450880 above) Is it because of the scientific notation that comes in the way - how can we disable it and let pandas treat this data as jus object/string or plan integer/float?
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| null | 3 | 2014-05-08T06:56:58Z | 2014-07-07T15:49:10Z | 2014-07-07T15:49:10Z | NONE | null | ``` python
data = data.sortlevel()
print(data.loc[("STW AU Equity", "2014-04-29"), "split"])
data.loc[("STW AU Equity", "2014-04-29"), "split"] = 1
print(data.loc[("STW AU Equity", "2014-04-29"), "split"])
# result
1.0
symbol date
STW AU Equity 2014-04-29 1
2014-04-29 1
```
Is there something I'm doing wrong, or is Pandas duplicating the value instead of updating it? If there's nothing obvious - I'll break out into a self-contained example.
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} | 1 | 2014-05-08T07:47:28Z | 2015-06-20T16:20:11Z | 2015-06-20T16:20:11Z | CONTRIBUTOR | null | At least XlsxWriter supports writing to buffers but the `ExcelWriter` constructors try to read the extension of the supplied `path`, so it fails when you initialise it as `ExcelWriter(buf, engine="xlsxwriter")`.
Currently you can hack that into pandas using
```
b = BytesIO()
w = ExcelWriter("data.xlsx", engine="xlsxwriter")
w.book = xlsxwriter.Workbook(b)
df.to_excel(w)
w.save()
b.getvalue()
```
but it would of course be nice if this just worked out-of-the-box. I'll try to build a PR for that.
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} | 6 | 2014-05-08T08:57:46Z | 2015-01-06T14:44:42Z | 2014-05-08T13:38:37Z | CONTRIBUTOR | null | In the `XlsxWriter` and `XlwtWriter` implementations there is a minor bug resulting in the `datetime_format` being discarded (i.e. https://github.com/pydata/pandas/blob/master/pandas/io/excel.py#L662):
```
if isinstance(cell.val, datetime.datetime):
num_format_str = self.datetime_format
if isinstance(cell.val, datetime.date):
num_format_str = self.date_format
```
Since `datetime.datetime` derives from `datetime.date` the second `if`-clause in the will always trigger, leading to the `num_format_str` being overwritten by the `date_format`.
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} | 7 | 2014-05-08T14:43:56Z | 2014-09-06T09:40:27Z | 2014-05-11T09:55:01Z | MEMBER | null | Closes #6921
Converting to an int was actually also how it was now with sqlite DBAPI fallback (as timedelta64 is subclass of integer), so this solution is also backwards compatible and consistent between sqlalchemy and fallback mode.
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} | 3 | 2014-05-08T14:45:34Z | 2016-07-11T12:50:56Z | 2014-05-11T09:54:15Z | MEMBER | null | - removed warning for not using sqlalchemy (as sqlite DBAPI connection is fully supported, no warning needed)
- added deprecation warning for 'mysql' flavor
- removed necessity of providing flavor kwarg (no warning if not provided, assumed to be sqlite3)
- removed `flavor` kwarg from execute and read_sql_query (because a) are not database specific functions + b) would only be needed for mysql, but this will is deprecated so no need to introduce it)
- updated the tests to reflect this
- updated docs and docstrings to reflect that sqlite3 is the only but fully supported DBAPI connection (no 'legacy')
Closes #6900.
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https://api.github.com/repos/pandas-dev/pandas/issues/7078 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7078/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7078/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7078/events | https://github.com/pandas-dev/pandas/pull/7078 | 33,112,405 | MDExOlB1bGxSZXF1ZXN0MTU2NjY5MTk= | 7,078 | DOC: Remove newline in @verbatim code-block. | {
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| null | 2 | 2014-05-08T18:55:44Z | 2014-07-16T09:05:01Z | 2014-05-08T19:42:27Z | CONTRIBUTOR | null | This makes the tab-completed code show up in the rendered documentation.
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https://api.github.com/repos/pandas-dev/pandas/issues/7079 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7079/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7079/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7079/events | https://github.com/pandas-dev/pandas/issues/7079 | 33,113,636 | MDU6SXNzdWUzMzExMzYzNg== | 7,079 | BUG: None should be a valid colspec? | {
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} | 10 | 2014-05-08T19:08:43Z | 2014-05-09T00:30:13Z | 2014-05-09T00:30:13Z | CONTRIBUTOR | null | In https://github.com/pydata/pandas/blob/master/pandas/io/parsers.py, the **init** method for FixedWidthReaders checks whether the colspecs are valid. Since None is a value slice index, should it be allowed as a colspec?
This would help me because I don't know the width of the last column until I start reading the file.
```
class FixedWidthReader(object):
"""
A reader of fixed-width lines.
"""
def __init__(self, f, colspecs, delimiter, comment):
self.f = f
self.buffer = None
self.delimiter = '\r\n' + delimiter if delimiter else '\n\r\t '
self.comment = comment
if colspecs == 'infer':
self.colspecs = self.detect_colspecs()
else:
self.colspecs = colspecs
if not isinstance(self.colspecs, (tuple, list)):
raise TypeError("column specifications must be a list or tuple, "
"input was a %r" % type(colspecs).__name__)
for colspec in self.colspecs:
if not (isinstance(colspec, (tuple, list)) and
len(colspec) == 2 and
isinstance(colspec[0], (int, np.integer)) and
isinstance(colspec[1], (int, np.integer))):
raise TypeError('Each column specification must be '
'2 element tuple or list of integers')
```
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| null | 1 | 2014-05-08T20:46:03Z | 2014-05-08T23:10:20Z | 2014-05-08T23:10:20Z | NONE | null | I noticed the following in tracking down a problem a user of ours encountered. Not sure this is a bug per se and with the caveat that I'm not a Python expert, but I thought I would mention it.
This is occurring with pandas 0.12.0 but given the nature of the issue is probably present in more recent versions.
If a user calls numpy's amin() [or amax()] on a pandas DataFrame, then within amin() the following lines of code will often be called
```
amin = a.min
amin(axis = axis, out = out)
```
The problem is that if the first argument to amin() is a pandas dataframe then the min method for 'a' inside amin() is the pandas dataframe min method, which does not take 'out' as an argument. This is for numpy 1.8.0.
The above issue was disguised somewhat in earlier numpy versions (e.g. 1.6.1) because 'out' was not a named argument, instead the line above was
amin(axis,out)
so in many cases the call to amin() would return a result.
Perhaps users shouldn't be calling amin() or amax() on pandas dataframes but nothing prevents them from doing so, resulting in what is a fairly subtle bug in their code and (at least in later numpy versions) an error message that requires a bit of investigation of the code in amin() to understand.
-Chris
---
Chris Paciorek
Statistical Computing Consultant
Statistical Computing Facility and Econometrics Laboratory
Office: 495 Evans Hall Email: [email protected]
Mailing Address: Voice: 510-842-6670
Department of Statistics Fax: 510-642-7892
367 Evans Hall Skype: cjpaciorek
University of California, Berkeley WWW: www.stat.berkeley.edu/~paciorek
Berkeley, CA 94720 USA Permanent forward: [email protected]
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https://api.github.com/repos/pandas-dev/pandas/issues/7081 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7081/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7081/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7081/events | https://github.com/pandas-dev/pandas/pull/7081 | 33,124,133 | MDExOlB1bGxSZXF1ZXN0MTU2NzQwMDc= | 7,081 | BUG: read_fwf colspec should treat None like slice | {
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} | 3 | 2014-05-08T21:08:46Z | 2014-07-16T09:05:02Z | 2014-05-09T00:30:13Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/7079
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} | 4 | 2014-05-08T21:40:56Z | 2014-06-17T10:50:08Z | 2014-05-11T10:52:47Z | MEMBER | null | There were apparantly not yet tests for writing datetime data. So added a test case for both query/table.
For MySQL using the pymysql driver, writing a datetime column is not working ... It gives following error:
```
In [65]: import pymysql
In [66]: engine_mysql = sqlalchemy.create_engine('mysql+pymysql://root@localhost/pandas_nosetest')
In [67]: df = pd.DataFrame({'A': pd.date_range('2013-01-01 09:00:00', periods=3),
...: 'B': np.arange(3.0)})
In [68]: df.to_sql('test_datetime', engine_mysql, if_exists='replace')
Traceback (most recent call last):
File "<ipython-input-68-95961c7ce232>", line 1, in <module>
df.to_sql('test_datetime', engine_mysql, if_exists='replace')
...
File "C:\Anaconda\envs\devel\lib\site-packages\pymysql\converters.py", line 24, in escape_item
encoder = encoders[type(val)]
KeyError: <class 'pandas.tslib.Timestamp'>
```
The data are feeded to the database using `df.itertuples()` and then `np.asscalar()`, and apparantly this gives data of the `Timestamp` class. And for some reason the other drivers can handle this (because it is a subclass of datetime.datetime?), and pymysql not. Which is not good...
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} | 8 | 2014-05-08T22:52:22Z | 2021-04-11T04:34:51Z | null | NONE | null | Here is the code:
```
import pandas
import matplotlib.pyplot as plt
from pandas.tools.plotting import parallel_coordinates
import numpy as np
from pandas import DataFrame
mypos = np.random.randint(10, size=(100, 2))
mydata = DataFrame(mypos, columns=['x', 'y'])
myres = np.random.rand(100, 1)
myclass = np.random.randint(3, size=(100, 1))
mydata['res'] = myres
mydata['class'] = myclass
parallel_coordinates(mydata, 'class')
```
Clearly, putting all dependent variables on the same scale is inappropriate here.
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} | 7 | 2014-05-09T00:50:09Z | 2014-05-09T18:21:49Z | 2014-05-09T18:21:49Z | NONE | null | I am initializing a DataFrame with 0 and then update it by iteratively indexing into indvidual columns. The behavior of my code has changed with pandas 0.13.0 such that resulting DataFrame out[['A']] remains 0 but series out['A'] has the correct values:
```
>>> print out[['A']]
A
2014-05-07 0
2014-05-08 0
2014-05-09 0
>>> print out['A']
2014-05-07 600
2014-05-08 600
2014-05-09 600
```
Is this a bug?
``` python
import pandas
#initialize a DataFrame with 0 values
out = pandas.DataFrame({'A': [0, 0, 0]})
out.index = pandas.date_range('5/7/2014', '5/9/2014')
#DataFrame to update out with
df = pandas.DataFrame({'C': ['A', 'A', 'A'], 'D': [100, 200, 300]})
#loop through df to update out
for ix, row in df.iterrows():
six = pandas.Timestamp('5/7/2014')
eix = pandas.Timestamp('5/9/2014')
out[row['C']][six:eix] = out[row['C']][six:eix] + row['D']
print out
print out[['A']]
print out['A']
```
```
INSTALLED VERSIONS
------------------
Python: 2.7.6.final.0
OS: Windows
Release: 7
Processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US
pandas: 0.13.0
Cython: 0.20
Numpy: 1.7.1
Scipy: 0.12.0
statsmodels: 0.5.0
patsy: 0.2.1
scikits.timeseries: Not installed
dateutil: 1.5
pytz: 2013.9
bottleneck: 0.7.0
PyTables: 3.1.0rc2
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: 1.8.2
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: Not installed
sqlalchemy: Not installed
lxml: Not installed
bs4: 4.3.2
html5lib: Not installed
bigquery: Not installed
apiclient: Not installed
```
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} | 37 | 2014-05-09T01:24:13Z | 2014-06-24T13:16:58Z | 2014-06-24T13:16:52Z | CONTRIBUTOR | null | ..., as the user intention most likely is to get a TextFileReader, when using the chunksize option.
Fixes #6774
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} | 59 | 2014-05-09T13:06:30Z | 2014-06-17T06:37:31Z | 2014-05-16T19:44:16Z | CONTRIBUTOR | null | This PR closes #5603
Dataframes are now truncated centrally (similar to pd.Series).
Before:
After:
Ipython notebook:
Simple Index:
MultiLevel Index:
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} | 0 | 2014-05-09T13:25:30Z | 2014-06-26T19:40:48Z | 2014-05-09T18:21:49Z | CONTRIBUTOR | null | closes #7084
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} | 17 | 2014-05-09T18:17:16Z | 2014-06-17T22:26:35Z | 2014-05-14T00:45:22Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/4196
This is for frames. I'm going to refactor this into generic since to cover series / frames.
A couple questions:
- API wise, I added a new kwarg `percentiles`. For backwards compat, we keep the `percentile_width` kwarg. I changed the default `percentile_width` from 50 to `None` (but the default output is the same) Cases:
1. You specify `percentile_width` and `percentiles` -> `ValueError`
2. You specify neither `percentile_width` nor `percentiles` -> `percentile_width` set to 50 and same as before
3. You specify one of those, everything goes as expected.
- I'm accepting either decimals (e.g. [0.25, .5, .75]) or percentages (e.g. [25, 50, 75]). Those two are equivalent in output. Should I move this logic to the `.quantile` to be more consistent?
- I'm choosing to not sort the provided percentiles. It's easy for the user to sort, but hard for them to unsort if for some reason they want it in a specific order. I'd rather not add another kwarg.
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} | 4 | 2014-05-10T07:10:27Z | 2014-06-17T15:25:56Z | 2014-05-10T14:12:03Z | MEMBER | null | As pointed in #7041, I prepared a PR to add `factorize` to `IndexOpsMixin`.
As a side benefit, `Multiindex.from_arrays` can preserve original `DatetimeIndex.freq` and `tz`. (Related to #3950 and #6606. These issues are not solved yet because these use different methods to create `MultiIndex`).
I would like to confirm following points before adding more tests.
- What `Index.factorize` and `Series.factorize` should return as unique values, `ndarray` or `Index`? I think it should return `Index` to preserve `DatetimeIndex` attributes (`freq` and `tz`).
- Is this should be added to `api.rst`?
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} | 5 | 2014-05-10T14:26:09Z | 2018-07-06T21:58:15Z | 2018-07-06T21:57:39Z | CONTRIBUTOR | null | So I think that the SQL test classes should inherit from:
https://github.com/pydata/pandas/blob/master/pandas/util/testing.py#L65
then you can add
```
class BaseSQLTestCase(tm.TestCase):
@classmethod
def setUpClass(cls):
super(BaseSQLTestCase, cls).setUpClass()
# create the ``pandas_nosetest`` db here
@classmethod
def tearDownClass(cls):
super(BaseSQLTestCase, cls).tearDownClass()
# drop the ``pandas_nosetest`` db here
```
and remove this:
https://github.com/pydata/pandas/blob/master/.travis.yml#L100
This doesn't affect travis, but testing on a static platform (e.g. windows)
where the database in theory could have something in it (if a test was stopped in the middle) and then the next time it runs their is stuff there
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} | 2 | 2014-05-10T15:28:00Z | 2014-06-13T19:01:07Z | 2014-05-12T13:34:58Z | MEMBER | null | Closes #6631. Closes #5878. Regarding #3950, original problem can be fixed by this, but `groupby` problem isn't.
Also, this includes the fix for `MultiIndex.get_level_values` doesn't retain `tz` and `freq`, as the method is used in `set_index`.
```
# current master
>>> import pandas as pd
>>> didx = pd.DatetimeIndex(start='2013/01/01', freq='H', periods=4, tz='Asia/Tokyo')
>>> pidx = pd.PeriodIndex(start='2013/01/01', freq='H', periods=4)
>>> midx = pd.MultiIndex.from_arrays([didx, pidx])
>>> midx.get_level_values(0)
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-12-31 15:00:00, ..., 2012-12-31 18:00:00]
Length: 4, Freq: None, Timezone: None
>>> midx.get_level_values(1)
Int64Index([376944, 376945, 376946, 376947], dtype='int64')
```
```
# after fix
>>> midx.get_level_values(0)
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-01 00:00:00+09:00, ..., 2013-01-01 03:00:00+09:00]
Length: 4, Freq: H, Timezone: Asia/Tokyo
>>> midx.get_level_values(1)
PeriodIndex([u'2013-01-01 00:00', u'2013-01-01 01:00', u'2013-01-01 02:00', u'2013-01-01 03:00'], freq='H')
```
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} | 4 | 2014-05-10T17:20:15Z | 2014-07-16T09:05:32Z | 2014-05-10T21:51:19Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/6965
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} | 0 | 2014-05-10T19:31:34Z | 2014-07-16T09:05:33Z | 2014-05-10T19:49:51Z | CONTRIBUTOR | null | closes #6621
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} | 1 | 2014-05-10T21:28:22Z | 2015-09-20T18:52:13Z | 2015-09-20T18:52:13Z | CONTRIBUTOR | null | from SO: https://groups.google.com/forum/#!topic/pydata/C8VoV4do7io
need to catch errors on the putmask and skip that block (returning it unchanged)
```
In [9]: df = DataFrame(dict(A = np.random.randn(3),B=date_range('20130101',periods=3),C=['foo','bar','bah'],D=['foo2','bar2','bah2']),index=date_range('20130110',periods=3))
In [10]: df
Out[10]:
A B C D
2013-01-10 -2.002643 2013-01-01 foo foo2
2013-01-11 1.897382 2013-01-02 bar bar2
2013-01-12 0.038903 2013-01-03 bah bah2
[3 rows x 4 columns]
In [11]: df.iloc[2,[0,2,3]] = np.nan
In [12]: df
Out[12]:
A B C D
2013-01-10 -2.002643 2013-01-01 foo foo2
2013-01-11 1.897382 2013-01-02 bar bar2
2013-01-12 NaN 2013-01-03 NaN NaN
[3 rows x 4 columns]
In [13]: df.fillna(-1)
Out[13]:
A B C D
2013-01-10 -2.002643 2013-01-01 foo foo2
2013-01-11 1.897382 2013-01-02 bar bar2
2013-01-12 -1.000000 2013-01-03 -1 -1
[3 rows x 4 columns]
In [14]: df.fillna('?')
ValueError: Error parsing datetime string "?" at position 0
```
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} | 42 | 2014-05-10T23:44:48Z | 2015-08-23T13:18:51Z | 2014-06-05T01:13:23Z | CONTRIBUTOR | null | Closes: https://github.com/pydata/pandas/issues/4264
Will go in place of https://github.com/pydata/pandas/pull/4472
In https://github.com/pydata/pandas/issues/985 we intentionally decided to have boxplot return a dict with the Lines of the boxplot.
I've added a kwarg `return_dict`. By default, `return_dict` is False. In this case only a `matplotlib.axes` is returned. When `return_dict` is True, we should either
- return the `matplotlib.axes` **and** the dict in a namedtuple
- return just the dict
Both of these are API breaking. Right now I've got both in the named tuple, but I may change that to just return just the dict. If we just return the dict, the only change people will have to make to their code is to add `return_dict` to True in their boxplot calls. Thoughts?
cc @jseabold, @fonnesbeck
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} | 7 | 2014-05-11T00:04:39Z | 2014-07-16T00:35:09Z | 2014-07-16T00:35:09Z | NONE | null | I'd expected this to generate a row of 1's and 0's in the dta file...
```
DataFrame([True, False]).to_stata('test.dta')
```
But it generates the following error:
sbox/lib/python2.7/site-packages/pandas/io/stata.pyc in _dtype_to_stata_type(dtype)
884 else: # pragma : no cover
885 raise ValueError("Data type %s not currently understood. "
--> 886 "Please report an error to the developers." % dtype)
887
888
ValueError: Data type bool not currently understood. Please report an error to the developers.
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} | 2 | 2014-05-11T14:01:33Z | 2014-06-20T17:01:29Z | 2014-05-14T13:08:10Z | MEMBER | null | When `DatetimeIndex` is created by passing `freq` and `check_integrity=True`, passed `freq` and `inferred_freq` must be identical. But this comparison can fail if length of passed data doesn't have enough length to infer freq.
Added additional logic to determine whether passed values are on the passed `freq`.
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} | 2 | 2014-05-11T15:03:14Z | 2014-06-13T19:00:49Z | 2014-05-13T12:13:26Z | MEMBER | null | Closes #3950. This is a fix for `groupby` issue.
(#7092 is for original report)
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} | 8 | 2014-05-11T17:03:28Z | 2014-07-08T03:52:05Z | 2014-05-13T10:29:47Z | MEMBER | null | This PR just adds some tests, not fixes to get them all running.
Summary:
- NaN writing not working for MySQL (or pymysql), but does work for sqlite and postgresql
- NaN reading does work for float columns. But not for string columns (converted to None or u'NaN') or for sqlite full float columns
- NaT in datetime columns only works for sqlite for writing. Writing postgresql gives an error on inserting the date `0001-255-255T00:00:00`.
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