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} | 6 | 2014-05-11T21:31:01Z | 2014-05-13T16:27:19Z | 2014-05-13T16:27:19Z | CONTRIBUTOR | null | Taking the simple example of MultiIndex Series from the docs http://pandas.pydata.org/pandas-docs/stable/indexing.html#hierarchical-indexing-multiindexc
Constraining max_rows results in an unexpected output.
The behavior of pd.DataFrame is as expected in 0.13.1:
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| null | 1 | 2014-05-12T15:35:31Z | 2014-07-16T09:05:40Z | 2014-05-12T15:42:55Z | CONTRIBUTOR | null | Confusing doc typo was momentarily confusing.
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} | 5 | 2014-05-13T10:26:47Z | 2014-09-13T21:48:56Z | 2014-09-13T21:48:56Z | MEMBER | null | See the tests I added for datetime, NaT and NaN values in PRs #7082 and #7100.
**Postrgresql** (psycopg2)
- writing and reading works
**SQLite**
- writing and reading works with sqlalchemy
- reading works with `read_sql_table`, and you get strings with `read_sql_query` (sqlite has no native datetime format -> nothing to do about that)
- writing fails with fallback mode 'Unsupported type' (#7567)
**MySQL** (pymysql / MySQLdb / mysql.connector)
- Reading works with all drivers
- Writing datetime64 columns depends on driver:
- `pymysql`: fails with "KeyError: < class 'pandas.tslib.Timestamp' >"
```
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'>
```
- `MySQLdb`: works
- `mysql.connector`: fails with "ProgrammingError: Failed processing pyformat-parameters; Python 'timestamp' cannot be converted to a MySQL type" or with " 'MySQLConverter' object has no attribute '_timestamp_to_mysql' " (see #7936 for more details)
The data are feeded to the database using `df.itertuples()` and then `np.asscalar()`, and this gives data of the `Timestamp` class. And for some reason most drivers can handle this (because it is a subclass of datetime.datetime?), but pymysql and mysql.connector not.
For now, all the tests are skipped for MySQL / pymysql.
**Oracle / SQL Server** ?
---
There are also some issues with NaT handling (see the tests I added for datetime, NaT and NaN values in PRs #7082 and #7100), but to track this there is also an issue specific on NaN handling (#2754)
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} | 4 | 2014-05-13T11:35:57Z | 2016-11-11T10:22:53Z | 2016-11-11T10:22:49Z | NONE | null | Browsing through 'Python for Data Analysts' I expected pandas used YQL to get finance data, but going though it more deeply it seems not be a case. I think it might be nice to have YQL as additional 'reader' in the io sub-module. There is documentation here: https://developer.yahoo.com/yql/
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} | 1 | 2014-05-13T11:38:23Z | 2014-05-13T21:39:52Z | 2014-05-13T21:39:52Z | CONTRIBUTOR | null | This is only for the info repr. It's not a problem with `df.info()`.
http://nbviewer.ipython.org/gist/TomAugspurger/4c83a646ffb93d5ee978
From a console attached to the same session:
``` python
In [1]: df
Out[5]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 2 entries, 0 to 1
Data columns (total 2 columns):
0 2 non-null float64
1 2 non-null float64
dtypes: float64(2)
```
I think the `<class ... >` is being interpreted as an HTML tag. We'll need to escape it. Our test checks whether `<class>` is _in_ the repr, which it is, it's just not visible.
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} | 0 | 2014-05-13T11:57:08Z | 2014-05-13T17:47:22Z | 2014-05-13T12:54:42Z | CONTRIBUTOR | null | This is actually not required, and so should work I think
(requires lexsorting only)
```
In [70]: df = DataFrame(dict(A = ['foo','foo','foo','foo'], B = ['a','a','a','a'], C = [1,2,1,3], D = [1,2,3,4])).set_index(['A','B','C']).sortlevel()
In [71]: df
Out[71]:
D
A B C
foo a 1 1
1 3
2 2
3 4
[4 rows x 1 columns]
In [72]: df.xs(1,level=2)
Out[72]:
D
A B
foo a 1
a 3
[2 rows x 1 columns]
In [73]: df.loc[(slice(None),slice(None),1),:]
ValueError: MultiIndex Slicing requires a unique index
```
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} | 0 | 2014-05-13T12:20:40Z | 2014-06-30T19:48:12Z | 2014-05-13T12:54:42Z | CONTRIBUTOR | null | enables slicing of non-unique multi-indexes with slicers, closes #7106
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} | 9 | 2014-05-13T13:20:41Z | 2014-06-13T10:12:07Z | 2014-05-14T00:47:58Z | CONTRIBUTOR | null | closes #6547
add the option 'trunctate'.
Should this be the new default?????
```
dfd = pd.DataFrame(np.arange(25).reshape(-1,5), index=[0,1,2,3,4], columns=[0,1,2,3,4])
# show dimensions only if truncated
with pd.option_context('display.max_rows', 2, 'display.max_columns', 2, 'display.show_dimensions', 'truncate'):
print(dfd)
```
```
0 1
0 0 1 ...
1 5 6 ...
.. ..
[5 rows x 5 columns]
```
```
# show dimensions only if truncated
with pd.option_context('display.max_rows', 10, 'display.max_columns', 40, 'display.show_dimensions', 'truncate'):
print(dfd)
```
```
0 1 2 3 4
0 0 1 2 3 4
1 5 6 7 8 9
2 10 11 12 13 14
3 15 16 17 18 19
4 20 21 22 23 24
```
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} | 16 | 2014-05-13T13:39:47Z | 2017-07-09T12:04:03Z | 2017-07-09T12:03:55Z | CONTRIBUTOR | null | I noticed xlsx files I was exporting from Pandas with to_excel weren't opening in Numbers and Preview on my Mac. They did, however, open in Excel and Google Drive.
I created a new virtualenv and installed Pandas and openpyxl with pip:
```
numpy==1.8.1
openpyxl==1.8.6
pandas==0.13.1
python-dateutil==2.2
pytz==2014.2
six==1.6.1
wsgiref==0.1.2
```
Running this code creates a xlsx file that seems to be ok (opens in Numbers anyway):
```
>>> import pandas as pd
>>> df = pd.DataFrame.from_records([[1,2],[3,4]], columns=["a","b"])
>>> df.to_excel("without_lxml.xlsx")
```
However, this same code was creating ill-formed xlsx files in my project. I narrowed the culprit down to lxml. I installed lxml in this fresh venv so `pip freeze` gives:
```
lxml==3.3.5
numpy==1.8.1
openpyxl==1.8.6
pandas==0.13.1
python-dateutil==2.2
pytz==2014.2
six==1.6.1
wsgiref==0.1.2
```
At that point, I ran the same code and the new file (with_lxml.xlsx) doesn't open in Numbers or Preview.
```
>>> import pandas as pd
>>> df = pd.DataFrame.from_records([[1,2],[3,4]], columns=["a","b"])
>>> df.to_excel("with_lxml.xlsx")
```
You can see the Excel files created:
- http://stiglerimages.s3.amazonaws.com/dropzone/without_lxml.xlsx
- http://stiglerimages.s3.amazonaws.com/dropzone/with_lxml.xlsx
These files can be unzipped and inspected for possible explanations.
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} | 4 | 2014-05-13T14:05:27Z | 2014-07-05T22:50:19Z | 2014-05-13T21:40:26Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/7105
Before: http://nbviewer.ipython.org/gist/TomAugspurger/4c83a646ffb93d5ee978
After: http://nbviewer.ipython.org/gist/TomAugspurger/81bf15086864ca090bb6
After conosle:
```
In [28]: df
Out[5]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 2 entries, 0 to 1
Data columns (total 2 columns):
0 2 non-null float64
1 2 non-null float64
dtypes: float64(2)
```
FYI, I don't know HTML, but I think this is mostly right.
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} | 17 | 2014-05-13T14:23:37Z | 2014-06-17T12:02:49Z | 2014-06-17T12:02:49Z | NONE | null | The following call to create boxplots of a `DataFrameGroupBy` object used to plot as expected:
```
language_dataset.groupby('bimodal').boxplot(column='age')
```
However, after updating to the current master, it fails with an `AttributeError`:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-11-369e94355e02> in <module>()
----> 1 language_dataset.groupby('bimodal').boxplot(column='age');
/Library/Python/2.7/site-packages/pandas-0.13.1_213_gc174c3d-py2.7-macosx-10.9-intel.egg/pandas/tools/plotting.pyc in boxplot_frame_groupby(grouped, subplots, column, fontsize, rot, grid, figsize, **kwds)
2313 for (key, group), ax in zip(grouped, axes):
2314 d = group.boxplot(ax=ax, column=column, fontsize=fontsize,
-> 2315 rot=rot, grid=grid, figsize=figsize, **kwds)
2316 ax.set_title(com.pprint_thing(key))
2317 ret[key] = d
/Library/Python/2.7/site-packages/pandas-0.13.1_213_gc174c3d-py2.7-macosx-10.9-intel.egg/pandas/core/frame.pyc in boxplot(self, column, by, ax, fontsize, rot, grid, **kwds)
4841 import matplotlib.pyplot as plt
4842 ax = plots.boxplot(self, column=column, by=by, ax=ax,
-> 4843 fontsize=fontsize, grid=grid, rot=rot, **kwds)
4844 plt.draw_if_interactive()
4845 return ax
/Library/Python/2.7/site-packages/pandas-0.13.1_213_gc174c3d-py2.7-macosx-10.9-intel.egg/pandas/tools/plotting.pyc in boxplot(data, column, by, ax, fontsize, rot, grid, figsize, **kwds)
2000 if ax is None:
2001 ax = _gca()
-> 2002 fig = ax.get_figure()
2003 data = data._get_numeric_data()
2004 if columns:
AttributeError: 'numpy.ndarray' object has no attribute 'get_figure'
```
Running Python 2.7.5 on OS X 10.9.2. Numpy version 1.9.0.
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https://api.github.com/repos/pandas-dev/pandas/issues/7112 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7112/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7112/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7112/events | https://github.com/pandas-dev/pandas/pull/7112 | 33,402,970 | MDExOlB1bGxSZXF1ZXN0MTU4MjQ3Nzg= | 7,112 | BUG: tzinfo lost when concatenating multiindex arrays | {
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} | 2 | 2014-05-13T14:25:54Z | 2014-06-14T22:31:57Z | 2014-06-04T00:12:40Z | MEMBER | null | Closes #6606.
The problem is caused by `MultiIndex.append`. Based on current master, `MultiIndex.append` works as below. The fix covers case1 and case2 which the result will be `MultiIndex`.
The fix is applied to `concat`, and also `pivot_table` work as expected.
```
import pandas as pd
idx1 = pd.Index([1.1, 1.2, 1.3])
idx2 = pd.date_range('2011-01-01', freq='D', periods=3, tz='Asia/Tokyo')
idx3 = pd.Index(['A', 'B', 'C'])
midx_lv2 = pd.MultiIndex.from_arrays([idx1, idx2])
midx_lv3 = pd.MultiIndex.from_arrays([idx1, idx2, idx3])
#1 results in MultiIndex, which nlevels is 2
midx_lv2.append(midx_lv2)
#2 results in MultiIndex, which nlevels is 2, not 3. 3rd line will be disappeared.
midx_lv2.append(midx_lv3)
#3 results in tupled Index.
midx_lv2.append(idx1)
#4 results in tupled Index.
result = midx_lv3.append(midx_lv2)
```
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} | 21 | 2014-05-13T14:29:25Z | 2014-07-22T18:13:46Z | 2014-05-14T21:21:48Z | MEMBER | null | closes #3960
xref: #5534
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} | 0 | 2014-05-13T15:56:29Z | 2014-10-30T11:19:43Z | 2014-10-28T00:04:25Z | CONTRIBUTOR | null | This shouldn't raise
from here originally (not the bug, the data/question):
http://stackoverflow.com/questions/23635647/how-to-force-pandas-dataframe-apply-on-a-grouped-dataframe
```
In [30]: df = pd.DataFrame({'a':range(5), 'b': range(5, 10)})
In [31]: df
Out[31]:
a b
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
[5 rows x 2 columns]
In [32]: df.groupby(df.a<2).max()
Out[32]:
a b
a
False 4 9
True 1 6
[2 rows x 2 columns]
In [33]: df.groupby(df.a<2,as_index=False).max()
ValueError: cannot insert a, already exists
```
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} | 4 | 2014-05-13T17:12:33Z | 2014-05-15T14:31:34Z | 2014-05-13T22:20:39Z | CONTRIBUTOR | null | xref: #7043
This fails on numpy master for some reason, can you investigate: https://travis-ci.org/pydata/pandas/jobs/25073475
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} | 8 | 2014-05-13T17:20:11Z | 2017-04-18T08:34:37Z | 2017-04-18T08:34:37Z | CONTRIBUTOR | null | rather than `display.max_rows` directly
for considency with the new 'truncate' option
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} | 0 | 2014-05-13T17:47:22Z | 2014-06-28T11:19:11Z | 2014-05-13T22:20:39Z | CONTRIBUTOR | null | closes #7116
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} | 9 | 2014-05-13T19:52:48Z | 2016-09-11T17:55:04Z | 2016-09-11T17:52:55Z | CONTRIBUTOR | null | I can't get `na_values` to work with a dictionary.
```
from StringIO import StringIO
import pandas as pd
dta = pd.read_csv(StringIO("""var1,var2,var3,var4
1,2,3,MISSING
2,3,4,4.5
"""), na_values={3 : 'MISSING'})
dta = pd.read_csv(StringIO("""var1,var2,var3,var4
1,2,3,MISSING
2,3,4,4.5
"""), na_values={'var4' : 'MISSING'})
dta = pd.read_csv(StringIO("""var1,var2,var3,var4
1,2,3,MISSING
2,3,4,4.5
"""), na_values=['MISSING'])
```
On
```
[~/]
[10]: pd.version.version
[10]: '0.13.1-753-g4614ac8'
```
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} | 10 | 2014-05-13T20:39:16Z | 2014-06-18T01:56:14Z | 2014-05-17T00:04:46Z | MEMBER | null | This should (finally) close #6292.
WIP for now, but comments welcome. Have to add to release.rst.
@jreback Should I mark the new sql as experimental? (and if so, where should I do this?)
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| null | 2 | 2014-05-13T20:46:58Z | 2014-09-11T00:49:44Z | 2014-05-13T22:39:26Z | CONTRIBUTOR | null | **Q1**: For a multi-index dataframe, what is the difference between `sortlevel()` and `sort_index()`?
As far as I can tell, for a dataframe like:
```
df = pd.DataFrame({'A' : [np.random.random_integers(10) for x in xrange(500)],
'B' : [np.random.random_integers(10) for x in xrange(500)],
'C' : [np.random.random_integers(10) for x in xrange(500)],
'data' : randn(500) })
```
the result is the same.
**Q2**: Why do these methods not use the same [naming convention](http://en.wikipedia.org/wiki/Naming_convention_%28programming%29#Multiple-word_identifiers)? `sort_index` uses underscore to separate words, while `sortlevel` is just one word.
What is the official convention in Pandas? Isn't the [Python convention to use underscore splitting?](http://stackoverflow.com/a/159745/1732769)
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} | 0 | 2014-05-14T01:02:39Z | 2014-06-25T17:33:33Z | 2014-05-14T01:34:28Z | CONTRIBUTOR | null | xref: #7108
xref: #6547
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} | 5 | 2014-05-14T02:03:45Z | 2014-06-14T13:16:41Z | 2014-06-14T13:16:41Z | CONTRIBUTOR | null | Starting with the 2nd commit the following test:
`tests/test_groupby/test_series_groupby_plotting_nomially_works`
started failing (works on the first, fails on the 2nd)
on 3.4/windows
https://github.com/pydata/pandas/commit/cd422c2f256ef5bd2a8315e1c177c9e1d8947d4a
https://github.com/pydata/pandas/commit/4c30610f1b6494c48699621755961e98c8fc7805
Its killing python on the 2nd `tm.close()`
I stepped thru it and the first fignum is ok, but there are 2 and the 2nd one crashes it
what changed?
running matplotlib 1.3.1
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} | 2 | 2014-05-14T08:00:13Z | 2015-10-09T13:21:38Z | 2015-10-09T13:21:38Z | NONE | null | When a `DataFrame` with column names is converted to latex, and `header=False` is passed, the `\midrule` that normally follows the headers should be removed.
```
>>> df = pd.DataFrame(np.arange(6).reshape(3,-1), columns=['A', 'B'])
>>> print df.to_latex(header=False)
\begin{tabular}{lrr}
\toprule
0 & 0 & 1 \\
\midrule
1 & 2 & 3 \\
2 & 4 & 5 \\
\bottomrule
\end{tabular}
```
```
>>> pd.show_versions()
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 | 1 | 2014-05-14T09:17:59Z | 2014-07-09T23:34:46Z | 2014-05-14T12:50:22Z | CONTRIBUTOR | null | Updated my blog location where I had kept these entries.
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} | 2 | 2014-05-14T15:00:40Z | 2014-06-10T21:52:01Z | 2014-06-10T21:52:01Z | MEMBER | null | SO xref: http://stackoverflow.com/q/23658092/564538
```
In [45]: paste
>>> s1 = Series([1, True, 3, 5], index=['a', 'b', 'c', 'e'])
## -- End pasted text --
In [46]: s1
Out[46]:
a 1
b True
c 3
e 5
dtype: object
In [47]: s1.convert_objects(convert_numeric=True)
Out[47]:
a 1
b NaN
c 3
e 5
dtype: float64
```
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} | 3 | 2014-05-14T16:08:17Z | 2015-07-16T00:08:25Z | 2015-07-16T00:08:25Z | CONTRIBUTOR | null | seems that we could piggy back on this for testing on OSX, not sure how difficult this is
https://groups.google.com/forum/#!topic/pydata/Urr55F8b-eU
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} | 0 | 2014-05-14T17:20:37Z | 2015-01-19T13:21:48Z | 2015-01-19T13:21:48Z | MEMBER | null | ``` python
s = Series(np.random.choice(list('abcde'), size=(1000)))
ax = s.value_counts().plot(kind='bar')
ax.set_ylim(-10, 250)
plt.gcf().savefig('asdf.png')
```
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} | 2 | 2014-05-14T17:28:38Z | 2014-06-30T00:26:32Z | 2014-05-14T18:22:41Z | CONTRIBUTOR | null | use value_counts instead of `Counter` and make the tests unambiguous.
Came up in https://github.com/pydata/pandas/pull/7088#issuecomment-43034004
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} | 6 | 2014-05-14T18:41:11Z | 2014-06-27T12:23:34Z | 2014-05-14T19:57:32Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/6939
I think this is what we agreed on. The tests are split up. In `test_format` we do more of the following global settings tests. The rest are in `test_frame` which directly call `df.info()` and I'm basically testing that the kwargs there override the global settings.
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} | 4 | 2014-05-14T19:32:05Z | 2014-07-01T17:52:42Z | 2014-07-01T17:52:25Z | CONTRIBUTOR | null | - [x] file open with mode of `'rU'`: https://docs.python.org/3/library/fileinput.html (in test_sql.py), #7625
closed by #7204
- [x] to_csv writing with floating point chunksize
- [x] plotting with floating point axes slices
closed by #7162, #7164, #7165, #7166, #7172, #7182
- [x] `assert_` to `assertTrue`
- [x] `assertEquals` to `assertEqual`
- [x] `assertAlmostEquals` to `assertAlmostEqual`
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} | 0 | 2014-05-14T19:59:17Z | 2021-04-11T04:35:13Z | null | CONTRIBUTOR | null | From https://github.com/pydata/pandas/issues/6939, @jorisvandenbossche made the suggestion that
> we could add a keyword argument like show_counts (or another name) to specify if you want to show the non-null counts (to be able to override the max_info_rows option for a specific info call)
scheduling for .14.1 unless someone gets to it before then.
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} | 22 | 2014-05-15T13:54:42Z | 2014-06-13T13:17:11Z | 2014-06-05T10:22:55Z | CONTRIBUTOR | null | closes #6897
As discussed in issue #6897, here is a pull request implemented "standard error of the mean" (sem) calculations in pandas. This is an extremely common but very simple statistical test. It is used pretty universally in scientific data analysis.
sem is implemented in nanops, generic, and groupby. Unit tests are included and the docs have been updated.
I have also included an improved .gitignore, based on matplotlib's but including everything from the old .gitignore (hopefully better organized now, though).
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| null | 5 | 2014-05-15T14:19:46Z | 2015-11-14T05:26:39Z | 2014-05-15T17:39:14Z | MEMBER | null | I sometimes want cumulative density plot for distribution comparison. Added `cumulative` to KDE plot, like `matplotlib.pyplot.hist`.
```
import pandas as pd
import numpy as np
s = pd.Series(np.random.randn(100))
s.plot(kind='kde', cumulative=True)
```
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| null | 1 | 2014-05-15T15:51:36Z | 2014-07-11T03:19:57Z | 2014-05-15T15:53:34Z | NONE | null | The link to EPD is deprecated and may be removed in the future.
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} | 1 | 2014-05-15T16:39:58Z | 2014-08-29T18:48:45Z | 2014-08-29T15:03:11Z | CONTRIBUTOR | null | - [ ] boxplot https://github.com/pydata/pandas/pull/7096
- [ ] change default of `return_type` from None to `'axes'`
- [ ] update return_type section in visualization.rst
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} | 0 | 2014-05-15T17:03:14Z | 2014-05-16T14:03:21Z | 2014-05-16T14:03:21Z | CONTRIBUTOR | null | only shows on the numpy 1.9-dev build as this is the only one to pull a current version of pytz
Bug report for pytz
https://bugs.launchpad.net/pytz/+bug/1319939
```
INSTALLED VERSIONS
------------------
commit: 592a5379dea49f6e88e736a651bf82a00ea4fcc8
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-5-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: None
nose: 1.3.3
Cython: 0.20.1
numpy: 1.9.0.dev-13b32e9
scipy: None
statsmodels: None
IPython: None
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.3
bottleneck: 0.9.0dev
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
bq: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
```
======================================================================
FAIL: test_getitem_setitem_datetime_tz (pandas.tests.test_series.TestSeries)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/mnt/home/jreback/venv/py2.7_bottleneck/pandas/pandas/tests/test_series.py", line 4594, in test_getitem_setitem_datetime_tz
assert_series_equal(result, ts)
File "/mnt/home/jreback/venv/py2.7_bottleneck/pandas/pandas/util/testing.py", line 530, in assert_series_equal
assert_almost_equal(left.values, right.values, check_less_precise)
File "testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2536)
File "testing.pyx", line 82, in pandas._testing.assert_almost_equal (pandas/src/testing.c:1710)
AssertionError: Length of two iterators not the same: 51 != 50
----------------------------------------------------------------------
Ran 311 tests in 2.951s
FAILED (SKIP=6, failures=1)
(py2.7_bottleneck)jreback@sheep:~/venv/py2.7_bottleneck/pandas$ nosetests pandas//tseries/tests/test_timezones.py
.................E.....F.......................F..................
======================================================================
ERROR: test_getitem_pydatetime_tz (pandas.tseries.tests.test_timezones.TestTimeZoneSupport)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/mnt/home/jreback/venv/py2.7_bottleneck/pandas/pandas/tseries/tests/test_timezones.py", line 648, in test_getitem_pydatetime_tz
self.assertEqual(ts[time_pandas], ts[time_datetime])
File "/mnt/home/jreback/venv/py2.7_bottleneck/pandas/pandas/core/series.py", line 475, in __getitem__
result = self.index.get_value(self, key)
File "/mnt/home/jreback/venv/py2.7_bottleneck/pandas/pandas/tseries/index.py", line 1263, in get_value
return self.get_value_maybe_box(series, key)
File "/mnt/home/jreback/venv/py2.7_bottleneck/pandas/pandas/tseries/index.py", line 1289, in get_value_maybe_box
values = self._engine.get_value(_values_from_object(series), key)
File "index.pyx", line 97, in pandas.index.IndexEngine.get_value (pandas/index.c:2987)
File "index.pyx", line 105, in pandas.index.IndexEngine.get_value (pandas/index.c:2802)
File "index.pyx", line 515, in pandas.index.DatetimeEngine.get_loc (pandas/index.c:9256)
KeyError: Timestamp('2012-12-24 17:07:00+0100', tz='Europe/Berlin')
======================================================================
FAIL: test_infer_tz (pandas.tseries.tests.test_timezones.TestTimeZoneSupport)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/mnt/home/jreback/venv/py2.7_bottleneck/pandas/pandas/tseries/tests/test_timezones.py", line 396, in test_infer_tz
assert(tools._infer_tzinfo(start, end) is eastern)
AssertionError
======================================================================
FAIL: test_with_tz (pandas.tseries.tests.test_timezones.TestTimeZoneSupport)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/mnt/home/jreback/venv/py2.7_bottleneck/pandas/pandas/tseries/tests/test_timezones.py", line 321, in test_with_tz
self.assertIs(central[0].tz, tz)
File "/mnt/home/jreback/venv/py2.7_bottleneck/pandas/pandas/util/testing.py", line 94, 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 66 tests in 0.488s
FAILED (errors=1, failures=2)
(py2.7_bottleneck)jreback@sheep:~/venv/py2.7_bottleneck/pandas$
```
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} | 8 | 2014-05-15T19:47:36Z | 2021-01-14T16:22:48Z | 2021-01-14T16:22:48Z | CONTRIBUTOR | null | `df.iloc[[0,1],[0,1]]` means return the square 0-1 rows and 0-1 columns
might be nice to have a way to mean return the _coordinates_ `(0,0),(0,1)`
related #7522
http://stackoverflow.com/questions/23686561/slice-a-pandas-dataframe-by-an-array-of-indices-and-column-names/23686855#23686855
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} | 12 | 2014-05-15T20:44:48Z | 2015-04-13T21:41:24Z | 2015-04-13T21:41:20Z | MEMBER | null | Left over issue from #6292
I have a postgresql table with a column with type `timestamp with time zone`. Just reading this table (`sql.read_sql_table("everyaware_by_passage", engine)`) did not work, because of a datetime column: `ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True`.
I can import it with read_sql, and there the imported datetime column is of type datetime.datetime with `tzinfo=psycopg2.tz.FixedOffsetTimezone(offset=120, name=None)`
Full error traceback (and it seems an issue in pandas itself):
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-9-13ac3c111575> in <module>()
----> 1 sql.read_sql_table("everyaware_by_passage", engine)
c:\users\vdbosscj\scipy\pandas-joris\pandas\io\sql.pyc in read_sql_table(table_name, con, meta, index_col, coerce_float, parse_dates, columns)
268 table = pandas_sql.read_table(
269 table_name, index_col=index_col, coerce_float=coerce_float,
--> 270 parse_dates=parse_dates, columns=columns)
271
272 if table is not None:
c:\users\vdbosscj\scipy\pandas-joris\pandas\io\sql.pyc in read_table(self, table_name, index_col, coerce_float, parse_dates, columns)
795 table = PandasSQLTable(table_name, self, index=index_col)
796 return table.read(coerce_float=coerce_float,
--> 797 parse_dates=parse_dates, columns=columns)
798
799 def read_sql(self, sql, index_col=None, coerce_float=True,
c:\users\vdbosscj\scipy\pandas-joris\pandas\io\sql.pyc in read(self, coerce_float, parse_dates, columns)
609 data, columns=column_names, coerce_float=coerce_float)
610
--> 611 self._harmonize_columns(parse_dates=parse_dates)
612
613 if self.index is not None:
c:\users\vdbosscj\scipy\pandas-joris\pandas\io\sql.pyc in _harmonize_columns(self, parse_dates)
691 if col_type is datetime or col_type is date:
692 if not issubclass(df_col.dtype.type, np.datetime64):
--> 693 self.frame[col_name] = _handle_date_column(df_col)
694
695 elif col_type is float:
c:\users\vdbosscj\scipy\pandas-joris\pandas\io\sql.pyc in _handle_date_column(col, format)
52 return to_datetime(col, coerce=True, unit=format)
53 else:
---> 54 return to_datetime(col, coerce=True, format=format)
55
56
c:\users\vdbosscj\scipy\pandas-joris\pandas\tseries\tools.pyc in to_datetime(arg, errors, dayfirst, utc, box, format, coerce, unit, infer_datetime_format)
313 return arg
314 elif isinstance(arg, Series):
--> 315 values = _convert_listlike(arg.values, False, format)
316 return Series(values, index=arg.index, name=arg.name)
317 elif com.is_list_like(arg):
c:\users\vdbosscj\scipy\pandas-joris\pandas\tseries\tools.pyc in _convert_listlike(arg, box, format)
306 return DatetimeIndex._simple_new(values, None, tz=tz)
307 except (ValueError, TypeError):
--> 308 raise e
309
310 if arg is None:
ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True
```
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} | 8 | 2014-05-16T10:09:13Z | 2016-10-12T23:04:51Z | 2014-06-02T14:43:50Z | MEMBER | null | I don't know if it is the same bug than #7126 (looks similar, but I didn't check the implementation), but
```
In [1]: from pandas import DataFrame
In [2]: import numpy as np
In [3]: df = DataFrame(index=range(2))
In [4]: df['a'] = True
In [5]: df.a
Out[5]:
0 True
1 True
Name: a, dtype: bool
In [6]: df.replace([np.inf, -np.inf], np.nan).a
Out[6]:
0 NaN
1 NaN
Name: a, dtype: float64
```
Notice the operation of doing such a replace on boolean variables may seem stupid... but it is less so if you consider that "replace" (differently from i.e. dropna) does not support a "subset" argument.
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} | 13 | 2014-05-16T10:44:50Z | 2016-11-30T15:11:05Z | 2016-11-30T15:11:05Z | NONE | null | The documentation implies that you can supply a `dtype` dict to `read_fwf`, but in reality this option is silently dropped as it looks like it's only supported by the c parser. My specific use case is loading Triple-S files which are fairly prolific in the market research world. The Triple-S standard is basically an XML file which describes a fixed width file. It's fairly trivial to get this to work in Pandas in a few lines of code which is great.
The problem arises when these files become really large. I tried using chunked conversion to HDF5 using `append_to_multiple` but ran into a baffling problem of certain chunks failing on append. Stepping through the code, it looked like the underlying block layouts where different per chunk. And this in turn was caused by the fact that column inference is applied per chunk and dtypes are ignored. I suspect this is caused by data being missing in some chunks and not in others.
The lowest hanging fruit is to update that docs and I'm happy to do a PR for this. But it would be awesome if the c parser could be tweaked to allow reading fixed width files as this issue would go away and we'd get a huge speed boost. This initially looked hard, but then started to look like adding a simpler statemachine might be possible if colspecs could be passed in. I could possibly do this as a PR, but would probably need some PR hand holding. Finally, if you could point out a simple place to apply the dtype argument in the read_fwf parser, I can give it a go as a second best case PR.
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} | 4 | 2014-05-16T12:03:32Z | 2017-07-06T22:42:32Z | 2017-07-06T22:42:32Z | NONE | null | When using the pivot_table command, the data type of a float64 column changes to object when I include a boolean column in the pivot table. This is unrelated to issue #713.
I'm using
```
python: 2.7.3.final.0
pandas: 0.13.1
```
For example, I've created the table below with two float64 columns a,b and one boolean column c
``` python
import pandas as pd
import numpy as np
np.random.seed(2)
x = pd.DataFrame(np.random.random((10,2)), columns=list('ab'))
x['pivot_column'] = x.a > 0.5
x['c'] = x.b > 0.5
```
I wish to create a pivot table, splitting on the 'pivot_column'. When using the columns a and b (both float64), the resulting table is as expected:
``` python
p2 = x.reset_index().pivot_table(rows='index',
cols='pivot_column',
values=list('ab'))
p2.info()
```
with output
```
Int64Index: 10 entries, 0 to 9
Data columns (total 4 columns):
(a, False) 6 non-null float64
(a, True) 4 non-null float64
(b, False) 6 non-null float64
(b, True) 4 non-null float64
dtypes: float64(4)<class 'pandas.core.frame.DataFrame'>
```
But, when I use the columns a,b,c the data type of a and b changes from float64 to object
``` python
p1 = x.reset_index().pivot_table(rows='index',
cols='pivot_column',
values=list('abc'))
p1.info()
```
with output
```
Int64Index: 10 entries, 0 to 9
Data columns (total 6 columns):
(a, False) 6 non-null object
(a, True) 4 non-null object
(b, False) 6 non-null object
(b, True) 4 non-null object
(c, False) 6 non-null object
(c, True) 4 non-null object
dtypes: object(6)<class 'pandas.core.frame.DataFrame'>
```
Is this a bug or is there some internal logic I don't know about (or both)?
Using unstack works as expected.
P.s. Pandas is great. Thanks for creating it! D.s.
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} | 6 | 2014-05-16T13:13:37Z | 2016-10-12T23:04:51Z | 2014-06-01T22:08:10Z | MEMBER | null | Not certain if this is a bug or defined behaviour (but then the error message is not clear in any case).
In 0.13.1:
```
In [28]: df = pd.DataFrame(np.random.randn(9).reshape(3,3), index=[0.1,0.2,0.2],
columns=['a','b','c'])
In [29]: df
Out[29]:
a b c
0.1 1.711117 1.218853 -1.322363
0.2 0.956266 0.230374 -1.005935
0.2 -0.137729 -0.993931 -0.902793
In [30]: df.ix[0.2,'a']
Out[30]: array([ 0.95626607, -0.13772877])
In [31]: df.ix[0.2]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
...
ValueError: Length mismatch: Expected axis has 0 elements, new values have 2 ele
ments
```
In master, both (`df.loc[0.2]` and `df.loc[0.2, 'a']`) give this error message. Wile for integer index, this works.
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} | 0 | 2014-05-16T13:30:01Z | 2014-07-16T09:06:18Z | 2014-05-16T14:03:21Z | CONTRIBUTOR | null | closes #7137
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| null | 3 | 2014-05-16T14:41:10Z | 2014-05-16T15:16:03Z | 2014-05-16T14:47:08Z | NONE | null | In pandas 0.13.1 assigning to a DataFrame using a mask causes strange truncation of Timestamp.max values.
Haven't quite managed to get to the bottom of this but here is a standalone repro of the problem.
```
In [1]: from datetime import datetime, timedelta
In [2]: import pandas as pd
In [3]: pd
Out[3]: <module 'pandas' from 'C:\VirtualEnvs\esprit.dev\lib\site-packages\pandas-0.13.1-py2.6-win32.egg\pandas\__init__.pyc'>
In [4]: df = pd.DataFrame(data=[datetime(2013, 1, 2), datetime(2013, 1, 3)], columns=["start_date"])
In [5]: df
Out[5]:
start_date
0 2013-01-02
1 2013-01-03
[2 rows x 1 columns]
In [6]: pd.Timestamp.max
Out[6]: Timestamp('2262-04-11 23:47:16.854775807', tz=None)
In [7]: df["start_date"][df["start_date"] > datetime(2013, 1, 2)] = pd.Timestamp.max
In [8]: df
Out[8]:
start_date
0 2013-01-02 00:00:00
1 2262-04-11 23:47:16.854775
[2 rows x 1 columns]
In [9]: df["start_date"] == pd.Timestamp.max
Out[9]:
0 False
1 False
Name: start_date, dtype: bool
In [10]: df.loc[df["start_date"] > datetime(2013, 1, 2), "start_date"] = pd.Timestamp.max
In [11]: df
Out[11]:
start_date
0 2013-01-02 00:00:00
1 2262-04-11 23:47:16.854775807
[2 rows x 1 columns]
In [12]: df["start_date"] == pd.Timestamp.max
Out[12]:
0 False
1 True
Name: start_date, dtype: bool
```
So the masked assignment causes truncation, but if I use `loc` no truncation occurs.
Interestingly, I ran this same code against pandas 0.12 and observed the exact opposite -- using `loc` caused the truncation, but the straight assignment was fine.
What is going on here?!
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| null | 1 | 2014-05-17T01:12:58Z | 2015-01-18T21:35:52Z | 2015-01-18T21:35:52Z | CONTRIBUTOR | null | This is a WIP and discuss PR :construction: for #3186
Adding small Writer class to have short-lived self.writer in CSVFomatter just for the header line. It is closed after the header was written.
Adding new lib.pyx function write_csv_rows_fast that manages file open/close on its own with self.path_or_buf from CSVFormmatter.
Current speed-up is factor 2.5 compared to before. Possibly the fprintf for each item is inefficient, but I thought as fprintf is buffered (in contrast to write) it can't be too bad. Any suggestions what I'm doing horribly wrong?
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} | 8 | 2014-05-17T01:16:39Z | 2014-06-13T22:10:33Z | 2014-05-17T11:42:19Z | CONTRIBUTOR | null | ```
======================================================================
ERROR: test_pie_series (pandas.tests.test_graphics.TestSeriesPlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "X:\Python34\lib\site-packages\pandas\tests\test_graphics.py", line 484, in test_pie_series
expected_texts = list(it.next() for it in itertools.cycle(iters))
File "X:\Python34\lib\site-packages\pandas\tests\test_graphics.py", line 484, in <genexpr>
expected_texts = list(it.next() for it in itertools.cycle(iters))
AttributeError: 'iterator' object has no attribute 'next'
----------------------------------------------------------------------
```
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} | 3 | 2014-05-17T04:10:48Z | 2014-06-17T05:57:46Z | 2014-06-01T22:08:10Z | MEMBER | null | closes #7143
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} | 4 | 2014-05-17T11:55:33Z | 2014-05-17T12:52:17Z | 2014-05-17T12:52:17Z | CONTRIBUTOR | null | These tests are all marked slow, so not sure if they are being tested on travis. I just enabled them on windows, bunch fail (also some in tests/test_graphics.py), same reason
need to do an additional check not only skip if we don't have scipy, but must have a recent version (not exactly sure when gaussian_kde was added).
using scipy 0.10.1
```
======================================================================
ERROR: test_secondary_kde (pandas.tseries.tests.test_plotting.TestTSPlot)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.6\pandas\tseries\tests\test_plotting.py", line 573, in test_secondary_kde
ax = ser.plot(secondary_y=True, kind='density').right_ax
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.6\pandas\tools\plotting.py", line 2256, in plot_series
plot_obj.generate()
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.6\pandas\tools\plotting.py", line 902, in generate
self._make_plot()
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.6\pandas\tools\plotting.py", line 1346, in _make_plot
from scipy.stats import gaussian_kde
File "C:\python26-32\lib\site-packages\scipy\stats\__init__.py", line 322, in <module>
from stats import *
File "C:\python26-32\lib\site-packages\scipy\stats\stats.py", line 198, in <module>
import distributions
File "C:\python26-32\lib\site-packages\scipy\stats\distributions.py", line 14, in <module>
from scipy import optimize
File "C:\python26-32\lib\site-packages\scipy\optimize\__init__.py", line 135, in <module>
from nonlin import *
File "C:\python26-32\lib\site-packages\scipy\optimize\nonlin.py", line 118, in <module>
import scipy.sparse.linalg
File "C:\python26-32\lib\site-packages\scipy\sparse\__init__.py", line 182, in <module>
from csgraph import *
File "C:\python26-32\lib\site-packages\scipy\sparse\csgraph\__init__.py", line 148, in <module>
from ._shortest_path import shortest_path, floyd_warshall, dijkstra,\
ImportError: cannot import name shortest_path
```
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} | 0 | 2014-05-17T11:58:54Z | 2016-10-12T23:04:51Z | 2014-05-17T13:23:54Z | CONTRIBUTOR | null | when using slow enabled on 2.6-32 bits on windows
```
======================================================================
ERROR: test_ts_plot_format_coord (pandas.tseries.tests.test_plotting.TestTSPlot)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.6\pandas\tseries\tests\test_plotting.py", line 143, in test_ts_plot_format_coord
check_format_of_first_point(annual.plot(), 't = 2014 y = 1.000000')
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.6\pandas\tseries\tests\test_plotting.py", line 140, in check_format_of_first_point
self.assertEqual(expected_string, ax.format_coord(first_x, first_y))
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.6\pandas\tseries\plotting.py", line 86, in <lambda>
ax.format_coord = lambda t, y: "t = {} y = {:8f}".format(Period(ordinal=int(t), freq=ax.freq), y)
ValueError: zero length field name in format
```
I think this test just needs to be changed as this is not supported on 2.6
```
======================================================================
ERROR: test_boxplot (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.6\pandas\tests\test_graphics.py", line 1341, in test_boxplot
self.assertRaisesRegexp(
AttributeError: 'TestDataFramePlots' object has no attribute 'assertRaisesRegexp'
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.6.6.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: x86
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: None
nose: 1.3.0
Cython: 0.19.2
numpy: 1.6.2
scipy: 0.10.1
statsmodels: 0.5.0
IPython: 1.1.0
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 1.5
pytz: 2012c
bottleneck: 0.7.0
tables: None
numexpr: 2.0.1
matplotlib: 1.1.1
openpyxl: None
xlrd: 0.9.2
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
bq: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 5 | 2014-05-17T12:57:36Z | 2014-11-12T12:18:37Z | 2014-11-12T12:18:37Z | CONTRIBUTOR | null | https://github.com/blog/1828-wikis-now-with-more-love
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} | 0 | 2014-05-17T14:41:20Z | 2021-04-11T04:35:31Z | null | CONTRIBUTOR | null | http://stackoverflow.com/questions/23709811/best-way-to-sum-group-value-counts-in-pandas/23712433?noredirect=1#comment36441762_23712433
any of these look palatable?
`df.groupby('c',as_index='exclude').apply(....)`
`df.groupby('c')['~c'].apply(...)`
`df.groupby('c',filter='c')`
`df.groupby('c',filter=True)`
rather than doing a negated selection (`df.columns-['c']`)
```
df = pd.DataFrame.from_dict({'b1':['aa','ac','ac','ad'],\
'b2':['bb','bc','ad','cd'],\
'b3':['cc','cd','cc','ae'],\
'c' :['a','b','a','b']})
df.groupby('c')[df.columns - ['c']].apply(lambda x: x.unstack().value_counts())
Out[92]:
c
a cc 2
bb 1
ad 1
ac 1
aa 1
b cd 2
ad 1
ae 1
ac 1
bc 1
dtype: int64
```
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} | 3 | 2014-05-17T15:03:36Z | 2014-06-11T14:08:31Z | 2014-06-11T14:08:31Z | MEMBER | null | There seems to be inconsistencies regarding time (hours, minutes, etc) handling of `Offset.apply`. Maybe all these should preserve hours/minutes like `DayOffset`? Or any other rules?
Followings are results offset(n=1) applied to `datetime(2011, 1, 1, 9, 0)`.
```
# Offsets which preserves time
(<Day>, datetime.datetime(2011, 1, 2, 9, 0))
(<BusinessDay>, Timestamp('2011-01-03 09:00:00'))
(<CustomBusinessDay>, Timestamp('2011-01-03 09:00:00'))
(<MonthBegin>, Timestamp('2011-02-01 09:00:00'))
(<CustomBusinessMonthBegin>, Timestamp('2011-01-03 09:00:00'))
(<YearBegin: month=1>, Timestamp('2012-01-01 09:00:00'))
(<YearEnd: month=12>, Timestamp('2011-12-31 09:00:00'))
(<BusinessYearEnd: month=12>, Timestamp('2011-12-30 09:00:00'))
(<QuarterBegin: startingMonth=3>, Timestamp('2011-03-01 09:00:00'))
(<BusinessQuarterBegin: startingMonth=3>, Timestamp('2011-03-01 09:00:00'))
(<QuarterEnd: startingMonth=3>, Timestamp('2011-03-31 09:00:00'))
(<BusinessQuarterEnd: startingMonth=3>, Timestamp('2011-03-31 09:00:00'))
(<Week: weekday=None>, Timestamp('2011-01-08 09:00:00'))
# Offsets which don't preserves time
(<BusinessMonthBegin>, Timestamp('2011-01-03 00:00:00'))
(<MonthEnd>, Timestamp('2011-01-31 00:00:00'))
(<BusinessMonthEnd>, Timestamp('2011-01-31 00:00:00'))
(<CustomBusinessMonthEnd>, Timestamp('2011-01-31 00:00:00'))
(<BusinessYearBegin: month=1>, Timestamp('2011-01-03 00:00:00'))
(<LastWeekOfMonth: weekday=5>, Timestamp('2011-01-29 00:00:00'))
(<FY5253Quarter: kwds={'startingMonth': 1, 'weekday': 1, 'variation': 'last'}, qtr_with_extra_week=1>, Timestamp('2011-01-25 00:00:00'))
(<FY5253: kwds={'qtr_with_extra_week': 1}, startingMonth=1, variation='last', weekday=1>, Timestamp('2011-01-25 00:00:00'))
(<LastWeekOfMonth: weekday=5>, Timestamp('2011-01-29 00:00:00'))
(<Easter>, datetime.datetime(2011, 4, 24, 0, 0))
```
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https://api.github.com/repos/pandas-dev/pandas/issues/7157 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7157/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7157/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7157/events | https://github.com/pandas-dev/pandas/pull/7157 | 33,732,616 | MDExOlB1bGxSZXF1ZXN0MTYwMjEyMzE= | 7,157 | BUG: hashtable memory error causes test_factorize_nan crash | {
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} | 6 | 2014-05-17T15:23:33Z | 2014-06-16T02:52:03Z | 2014-05-18T19:57:35Z | CONTRIBUTOR | null | ObjectVector class resizes its array without reseting its capacity count, so subsequent appends are invalid.
Mac OS 10.9, Python 2.7.6, numpy 1.9.0.dev-ee49411.
```
==57654== Invalid write of size 8
==57654== at 0x137F856: __pyx_f_6pandas_9hashtable_12ObjectVector_append (in /Users/mtk/Projects/pandas/pandas/hashtable.so)
==57654== by 0x139B16F: __pyx_pw_6pandas_9hashtable_17PyObjectHashTable_25get_labels (in /Users/mtk/Projects/pandas/pandas/hashtable.so)
==57654== by 0x138CA9E: __pyx_pw_6pandas_9hashtable_10Factorizer_5factorize (in /Users/mtk/Projects/pandas/pandas/hashtable.so)
==57654== by 0xD227E: PyEval_EvalFrameEx (in /usr/local/anaconda/lib/libpython2.7.dylib)
==57654== Address 0x10095efd0 is 16 bytes inside a block of size 256 free'd
==57654== at 0x7858: realloc (in /usr/local/Cellar/valgrind/3.9.0/lib/valgrind/vgpreload_memcheck-amd64-darwin.so)
==57654== by 0x13C0F55: PyDataMem_RENEW (in /usr/local/anaconda/lib/python2.7/site-packages/numpy/core/multiarray.so)
==57654== by 0x1488DE7: PyArray_Resize (in /usr/local/anaconda/lib/python2.7/site-packages/numpy/core/multiarray.so)
==57654== by 0x14647A0: array_resize (in /usr/local/anaconda/lib/python2.7/site-packages/numpy/core/multiarray.so)
==57654== by 0x1396A12: __pyx_pw_6pandas_9hashtable_12ObjectVector_5to_array (in /Users/mtk/Projects/pandas/pandas/hashtable.so)
==57654== by 0x138CFEE: __pyx_pw_6pandas_9hashtable_10Factorizer_5factorize (in /Users/mtk/Projects/pandas/pandas/hashtable.so)
==57654== by 0xD227E: PyEval_EvalFrameEx (in /usr/local/anaconda/lib/libpython2.7.dylib)
```
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} | 5 | 2014-05-17T16:00:42Z | 2014-05-30T22:21:48Z | 2014-05-30T22:21:48Z | MEMBER | null | ```
n = 5
df = DataFrame({'a': randint(3, size=n),
'b': randn(n)})
df2 = DataFrame({'a': randint(4, size=n),
'b': df.b + (rand(n) > 0.5).astype(float)})
df.isin(df2)
```
yields
<table class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>a</th>
<th>b</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td> False</td>
<td> True</td>
</tr>
<tr>
<th>1</th>
<td> False</td>
<td> True</td>
</tr>
<tr>
<th>2</th>
<td> False</td>
<td> False</td>
</tr>
<tr>
<th>3</th>
<td> False</td>
<td> False</td>
</tr>
<tr>
<th>4</th>
<td> False</td>
<td> True</td>
</tr>
</tbody>
</table>
I might expect it to go column by column calling `isin` on the intersection of the columns. I'm not sure if this was ever defined when `DataFrame.isin()` was implemented.
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} | 10 | 2014-05-18T04:06:33Z | 2016-05-23T21:43:04Z | 2016-05-23T21:43:04Z | NONE | null | Tested on 0.13.0, 0.13.1 and 0.14.0rc1:
``` python
from StringIO import StringIO
import pandas as pd
# this is correct
print(pd.DataFrame([[0, 1, 2], [3, 4, 5]], columns=["a", "b", "a"]))
# and this is fine as well
# (although, changing the column names to a,b,a.1)
print(pd.read_csv(StringIO("a,b,a\n0,1,2\n3,4,5")))
# but this is not correct
print(pd.read_csv(StringIO("0,1,2\n3,4,5"), names=["a", "b", "a"]))
```
The last one returns:
```
Out[5]:
a b a
0 2 1 2
1 5 4 5
```
I would expect all 3 methods to return the same DataFrame. I noticed this when I wanted to read csv file that had a separate file with a header (and a duplicated column in it). BTW is there a better way to do it than to read the header file first and pass the output into 'names' parameter of read_csv?
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| null | 2 | 2014-05-18T18:21:07Z | 2014-05-18T22:54:00Z | 2014-05-18T19:45:48Z | NONE | null | Here is the text data I would like to read as a data frame:
data3="Date Open High Low Close Volume\nMay 16, 2014 2.92 2.93 2.82 2.85 25,715,748\nMay 15, 2014 3.02 3.05 2.88 2.92 28,311,224\nAug 8, 2013 1.97 1.98 1.94 1.95 20,100,154\nAug 7, 2013 1.89 1.97 1.87 1.95 29,206,564\nAug 6, 2013 1.95 2.01 1.88 1.90 52,198,842\nAug 5, 2013 1.88 1.94 1.88 1.92 29,041,924\nAug 2, 2013 1.86 1.88 1.83 1.86 22,169,076"
It's from an Html table of 6 columns: Date Open High Low Close Volume.
When I use wether the wether read_csv or read_table, I have a weird output.
Here is my script:
in [0] :
import StringIO as s
import pandas as pd
data3="Date Open High Low Close Volume\nMay 16, 2014 2.92 2.93 2.82 2.85 25,715,748\nMay 15, 2014 3.02 3.05 2.88 2.92 28,311,224\nAug 8, 2013 1.97 1.98 1.94 1.95 20,100,154\nAug 7, 2013 1.89 1.97 1.87 1.95 29,206,564\nAug 6, 2013 1.95 2.01 1.88 1.90 52,198,842\nAug 5, 2013 1.88 1.94 1.88 1.92 29,041,924\nAug 2, 2013 1.86 1.88 1.83 1.86 22,169,076"
pd.read_table(s.StringIO(data3),
header=0,
thousands=',',
sep=" ",
parse_dates=False)
out[0] :
And I would like to have:
And if I set the parameter parse_dates to TRUE, I got this:
Even weirder !!
Im not sure if it is a bug or I did something wrong but if someone can give us a clue.....it would be welcome.
Thanks
Rgds,
Mickael
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} | 19 | 2014-05-18T21:51:53Z | 2015-01-18T21:36:12Z | 2015-01-18T21:36:12Z | CONTRIBUTOR | null | Changes the truncation symbol from '...' to the math characters horizontal, vertical and diagnol ellipsis as proposed in https://github.com/pydata/pandas/pull/7086#issuecomment-42862717.
In the Ipython Notebook, the result looks like this
http://nbviewer.ipython.org/gist/bjonen/492fea9559fd73edf579
To do:
- Do we need to fall back to simple strings in certain environments (https://github.com/pydata/pandas/pull/7086#issuecomment-42999103)?
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} | 3 | 2014-05-19T04:06:39Z | 2014-07-01T15:50:58Z | 2014-07-01T15:50:52Z | NONE | null | pandas version: 0.13.1, Python 2.7 32-bit. Windows.
When attempting to calculate rolling sums for a dataframe with a DateTimeIndex in 1964, and naming the column '64' (as a string), I get a ValueError: could not broadcast input array from shape (x) into shape (x, 1). If I change one of the 64s, the error does not occur.
``` python
import pandas as pd
incr = range(150)
datetimes = pd.date_range('1964/01/01 00:00:00', periods=150)
df = pd.DataFrame(data=incr, index=datetimes)
s = pd.Series(data=incr, index=datetimes)
df.columns = ['increments']
df['64'] = pd.rolling_sum(df['increments'], 5)
```
Traceback:
```
Traceback (most recent call last):
File "C:\Jobs\29075\2014\Software\stormy\pdbug.py", line 9, in <module>
df['64'] = pd.rolling_sum(df['increments'], 5)
File "C:\Python27_w32\lib\site-packages\pandas\core\frame.py", line 1879, in __setitem__
return self._setitem_slice(indexer, value)
File "C:\Python27_w32\lib\site-packages\pandas\core\frame.py", line 1891, in _setitem_slice
self.ix._setitem_with_indexer(key, value)
File "C:\Python27_w32\lib\site-packages\pandas\core\indexing.py", line 422, in _setitem_with_indexer
self.obj._data = self.obj._data.setitem(indexer, value)
File "C:\Python27_w32\lib\site-packages\pandas\core\internals.py", line 2396, in setitem
return self.apply('setitem', *args, **kwargs)
File "C:\Python27_w32\lib\site-packages\pandas\core\internals.py", line 2376, in apply
applied = getattr(blk, f)(*args, **kwargs)
File "C:\Python27_w32\lib\site-packages\pandas\core\internals.py", line 636, in setitem
values[indexer] = value
ValueError: could not broadcast input array from shape (150) into shape (150,1)
```
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} | 22 | 2014-05-19T07:36:12Z | 2014-11-13T11:07:47Z | 2014-05-24T13:05:08Z | NONE | null | - [ ] 3.4 build is now using current version of `openpyxl` (2.0.2), fix for this, see here: https://travis-ci.org/pydata/pandas/jobs/25510797
- [ ] change ci/requirements-2.7.txt to use last version (1.8.6); will need same import fixes
When I try to use `DataFrame` `to_excel`, I was given the following error. I am using `Pandas 0.13.1`, `openpyxl 2.0.2`
```
from openpyxl.style import Style
ImportError: No module named style
```
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} | 1 | 2014-05-19T12:45:06Z | 2015-06-20T16:20:11Z | 2015-06-20T16:20:11Z | CONTRIBUTOR | null | head to head for all the writer engines
`openpyxl`, `xlsxwriter`
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} | 2 | 2014-05-19T12:45:50Z | 2014-06-25T08:58:07Z | 2014-05-19T13:22:55Z | CONTRIBUTOR | null | #7131: Run s/self.assert_(/self.assertTrue( on remaining instances I can find. These were the multi-line expressions, and I tried to retain the formatting of the expressions
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} | 5 | 2014-05-19T13:38:06Z | 2014-09-21T14:51:19Z | 2014-09-10T17:45:32Z | NONE | null | Hi-
interpolate works fine on most of my datasets but I've just encountered for the first time a new error when attempting to run this method.
```
IndexError: arrays used as indices must be of integer (or boolean) type
```
I'm not sure how/if its sensible to paste the dataset I used somehow on here (along with the datetimeindex) so you can attempt to replicate this error -- happy to do so if you tell me how.
This error only occurs when I place a restriction via the limit keyword. Eg.
```
temp1df.interpolate(method='time') works fine.
temp1df.interpolate(method='time', limit=4) yields above error.
```
temp1df is a dataframe with a datetimeindex with a 208 count, and a datetimeindex of 576 length.
I am running 0.13.1 on 3.3.3 on a windows machine.
Full traceback below:
```
Traceback (most recent call last):
File "<ipython-input-88-394bcc7f44e2>", line 1, in <module>
temp1df.between_time('08:00', '18:00').interpolate(limit=4)
File "C:\WinPython3.3.3.2\python-3.3.3.amd64\lib\site-packages\pandas\core\generic.py", line 2532, in interpolate
**kwargs)
File "C:\WinPython3.3.3.2\python-3.3.3.amd64\lib\site-packages\pandas\core\internals.py", line 2404, in interpolate
return self.apply('interpolate', *args, **kwargs)
File "C:\WinPython3.3.3.2\python-3.3.3.amd64\lib\site-packages\pandas\core\internals.py", line 2375, in apply
applied = getattr(blk, f)(*args, **kwargs)
File "C:\WinPython3.3.3.2\python-3.3.3.amd64\lib\site-packages\pandas\core\internals.py", line 821, in interpolate
**kwargs)
File "C:\WinPython3.3.3.2\python-3.3.3.amd64\lib\site-packages\pandas\core\internals.py", line 882, in _interpolate
interp_values = np.apply_along_axis(func, axis, data)
File "C:\WinPython3.3.3.2\python-3.3.3.amd64\lib\site-packages\numpy\lib\shape_base.py", line 79, in apply_along_axis
res = func1d(arr[tuple(i.tolist())],*args)
File "C:\WinPython3.3.3.2\python-3.3.3.amd64\lib\site-packages\pandas\core\internals.py", line 879, in func
bounds_error=False, **kwargs)
File "C:\WinPython3.3.3.2\python-3.3.3.amd64\lib\site-packages\pandas\core\common.py", line 1380, in interpolate_1d
violate_limit = _interp_limit(invalid, limit)
File "C:\WinPython3.3.3.2\python-3.3.3.amd64\lib\site-packages\pandas\core\common.py", line 1374, in _interp_limit
return all_nans[violate] + limit
IndexError: arrays used as indices must be of integer (or boolean) type
```
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} | 17 | 2014-05-19T15:35:12Z | 2014-05-25T16:06:59Z | 2014-05-25T16:06:44Z | MEMBER | null | See this comment: https://github.com/pydata/pandas/issues/7146#issuecomment-43518940
notebook http://goo.gl/qLUUx8
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} | 21 | 2014-05-19T15:45:07Z | 2017-08-07T11:05:59Z | 2017-08-07T11:05:59Z | CONTRIBUTOR | null | I ran into a nasty gotcha I have not seen mentioned in the documentation. In general, you can refer to DataFrame columns like a dictionary key or like an object attribute. But the behavior is inconsistent. In particular, you can modify an existing column using dot notation, but you can't add a new column.
The following example demonstrates.
```
d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),
'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
df = pd.DataFrame(d)
# this adds a column to df
df['three'] = df.two + 1
# this successfully modifies an existing column
df.one += 1
# this seems to work but does not add a column to df
df.four = df.two + 2
print df.three
print df.four
print df
```
Is this behavior unavoidable?
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} | 6 | 2014-05-19T15:53:35Z | 2014-06-17T13:25:53Z | 2014-05-20T00:55:35Z | MEMBER | null | If this looks good, it would be nice to get it in 0.14, too.
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} | 10 | 2014-05-19T18:55:16Z | 2014-09-20T08:53:31Z | 2014-09-20T08:53:31Z | CONTRIBUTOR | null | [OpenPyXL](https://pypi.python.org/pypi/openpyxl) v2.0 was released on May 13th, breaking backwards compatibility in some parts of its API, including those upon which pandas depends. Notably, v2 changes [the style API](https://openpyxl.readthedocs.org/en/2.0.2/styles.html) and makes `Style` objects immutable. This incompatibility is evident at 0.14.0-rc1 in [`pandas.io.excel._OpenpyxlWriter._convert_to_style`](https://github.com/pydata/pandas/blob/v0.14.0rc1/pandas/io/excel.py#L600) and the associated test case, [`pandas.io.tests.test_excel.OpenyxlTests.test_to_excel_styleconverter`](https://github.com/pydata/pandas/blob/v0.14.0rc1/pandas/io/tests/test_excel.py#L1131).
In addition to teaching `pandas` how to use the new API, the project may wish to retain support for the OpenPyXL v1 API, necessitating the design and development of a compatibility shim.
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} | 5 | 2014-05-19T19:20:57Z | 2014-05-20T21:10:17Z | 2014-05-20T21:10:17Z | CONTRIBUTOR | null | In `df.eval()` or `df.query()`, is there any way to run comparisons against `np.Inf`?
When I do:
`temp_df.eval('my_col < Inf')` or
`temp_df.eval('my_col < np.Inf')` or
I get `UndefinedVariableError`
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} | 1 | 2014-05-19T20:16:26Z | 2014-05-23T16:43:09Z | 2014-05-23T16:43:09Z | NONE | null | I'm running into an issue where pandas (.13.1) reindex_like with pad will fill periods where it should not (or previously would not). Looked through the .13 change logs, didn't see anything that pointed out to this decision.
``` python
import datetime
import pandas as pd
print pd.__version__
day1 = datetime.date(2013,3,5)
day2 = datetime.date(2013,5,5)
day3 = datetime.date(2014,3,5)
series1 = pd.Series([5, None, None],[day1, day2, day3])
series2 = pd.Series([None, None], [day1, day3])
series1.reindex_like(series2, method='pad')
```
For .12.0
``` python
## -- End pasted text --
0.12.0
Out[1]:
2013-03-05 5
2014-03-05 NaN
dtype: float64
```
But for version .13.1
``` python
0.13.1
Out[2]:
2013-03-05 5
2014-03-05 5
dtype: float64
```
The behavior of .12 seems correct, in the sense that 'pad' would get the most relevant number, in this case - not look back more than one index prior.
I've used reindex_like frequently with the understanding that this was a big difference between fillna() - where fillna() will look back as far as possible to get a relevant number. But I am not sure if this change to reindex_like was intended.
Thanks!
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} | 20 | 2014-05-19T20:43:23Z | 2014-09-18T13:55:48Z | 2014-09-18T13:55:48Z | CONTRIBUTOR | null | 
According to the description of `max_columns`, should switch to "smaller" (at the beginning to info, nowadays to truncate) view when set to zero instead of wrapping around.
```
display.max_columns: [default: 20] [currently: 0]
: int
max_rows and max_columns are used in __repr__() methods to decide if
to_string() or info() is used to render an object to a string. In case
python/IPython is running in a terminal this can be set to 0 and pandas
will correctly auto-detect the width the terminal and swap to a smaller
format in case all columns would not fit vertically. The IPython notebook,
IPython qtconsole, or IDLE do not run in a terminal and hence it is not
possible to do correct auto-detection.
'None' value means unlimited.
```
Originally introduced here https://github.com/pydata/pandas/pull/453#issuecomment-3047570
Fixed after broken here: https://github.com/pydata/pandas/pull/2881
Behavior change (broken again) here https://github.com/pydata/pandas/pull/5550
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| null | 2 | 2014-05-20T01:07:49Z | 2014-05-20T01:11:10Z | 2014-05-20T01:10:12Z | NONE | null | from pandas.util.print_versions import show_versions
show_versions()
## 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: en_US.UTF-8
pandas: 0.13.1
Cython: None
numpy: 1.6.2
scipy: 0.11.0
statsmodels: None
IPython: 2.0.0
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 1.5
pytz: 2012d
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.1.1
openpyxl: 2.0.2
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
sqlalchemy: 0.9.4
lxml: None
bs4: None
html5lib: None
bq: None
apiclient: None
## THE CODE
import pandas as pd
import numpy as np
dates = pd.date_range('20130101',periods=6)
df = pd.DataFrame(np.random.randn(6,4),index = dates, columns=list('ABCD'))
df.to_excel("foo.xlsx")
## ERROR MESSAGE
Traceback (most recent call last):
File "test.py", line 5, in <module>
df.to_excel("foo.xlsx")
File "/Library/Python/2.7/site-packages/pandas/core/frame.py", line 1204, in to_excel
startrow=startrow, startcol=startcol)
File "/Library/Python/2.7/site-packages/pandas/io/excel.py", line 530, in write_cells
style = self._convert_to_style(cell.style)
File "/Library/Python/2.7/site-packages/pandas/io/excel.py", line 577, in _convert_to_style
from openpyxl.style import Style
ImportError: No module named style
## ANALYSIS
Went into my site packages --> openpyxl
Saw that the folder for style has been renamed to styles.
Did a reinstall of openpyxl with the same results.
Manually renamed to openpyxl.styles, but engendered more bugs (something about borders).
My hypothesis: openpyxl has changed their naming convention that broke the the Pandas.to_excel function.
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} | 1 | 2014-05-20T12:52:48Z | 2018-07-06T20:21:04Z | 2017-07-11T09:58:13Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/23756711/pandas-panel-copy-versus-view
[55] is a view, so `is_copy` should be set (and is not)
```
In [53]: p = tm.makePanel()
In [54]: df = p.iloc[0]
In [55]: df[:] = 0
In [56]: (df.values.ravel()==0).all()
Out[56]: True
In [57]: (p.iloc[0].values.ravel()==0).all()
Out[57]: True
```
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| null | 11 | 2014-05-20T15:14:53Z | 2019-03-31T03:03:18Z | 2014-05-20T15:48:41Z | CONTRIBUTOR | null | consider the following example
```
a = rand(100)
b = np.floor(rand(100)*100)
df = pd.DataFrame({'a' : a , 'b' : b})
grp = df.groupby(df.b)
```
I have grouped the values in `a` by `b`.
Now, if I want to plot the trend over the groups with mean and std I can do
```
grp.a.agg([np.mean, lambda x : np.mean(x) + np.std(x) , lambda x : np.mean(x) - np.std(x) ]).plot()
```
which gives me
```
SpecificationError: Function names must be unique, found multiple named <lambda>
```
while
```
grp.a.agg([np.mean, lambda x : np.mean(x) + np.std(x) ]).plot()
```
which has just one lambda works ok.
Is this a bug?
In order to make the thing work I had to define real functions (i.e. in terms of `def`), to be put in `agg`.
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} | 5 | 2014-05-20T15:31:20Z | 2017-01-20T13:56:52Z | 2017-01-19T22:10:50Z | CONTRIBUTOR | null | ``` Python
In []: df = pd.DataFrame(index=range(10),columns=['a','b',1])
In []: df
Out[]:
a b 1
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
4 NaN NaN NaN
5 NaN NaN NaN
6 NaN NaN NaN
7 NaN NaN NaN
8 NaN NaN NaN
9 NaN NaN NaN
In []: df.ix[:,[0]]
Out[]:
1
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
5 NaN
6 NaN
7 NaN
8 NaN
9 NaN
In []: df.ix[:,[0,1,2]]
Out[]:
1 1 1
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
4 NaN NaN NaN
5 NaN NaN NaN
6 NaN NaN NaN
7 NaN NaN NaN
8 NaN NaN NaN
9 NaN NaN NaN
```
I know in this case one should use iloc, It's not clear what ix should do in this case, but the df.ix[:,[0]] case is certainly not what is expected.
How about either
- issuing a warning or
- raising an Exception
when asked to ix an index like this.
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| null | 3 | 2014-05-20T18:55:37Z | 2014-06-17T00:36:39Z | 2014-05-20T21:28:53Z | CONTRIBUTOR | null | Adding a warning, and example, about using attribute access to create a new column.
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} | 0 | 2014-05-20T23:34:19Z | 2014-06-13T22:52:24Z | 2014-05-21T00:22:35Z | CONTRIBUTOR | null | reported on ML: https://groups.google.com/forum/#!topic/pydata/OscVmQlNdTc
```
In [1]: p = Panel(np.random.rand(4,3,2), items=['A','B','C','D'], major_axis=['U','V','W'], minor_axis=['X','Y'])
In [2]: p
Out[2]:
<class 'pandas.core.panel.Panel'>
Dimensions: 4 (items) x 3 (major_axis) x 2 (minor_axis)
Items axis: A to D
Major_axis axis: U to W
Minor_axis axis: X to Y
In [3]: p['A']
Out[3]:
X Y
U 0.541463 0.546778
V 0.079211 0.229914
W 0.621594 0.973252
In [4]: p.iloc[0,:,:]
Out[4]:
X Y
U 0.541463 0.546778
V 0.079211 0.229914
W 0.621594 0.973252
In [5]: p.iloc[0,[True,True,True],:]
Out[5]:
X Y
U 0.541463 0.546778
V 0.079211 0.229914
W 0.621594 0.973252
In [6]: p.iloc[0,[True,True,True],[0,1]]
Out[6]:
X Y
U 0.541463 0.546778
V 0.079211 0.229914
W 0.621594 0.973252
In [7]: p.iloc[0,[True,True,True],[0,1,2]]
IndexError: positional indexers are out-of-bounds
In [8]: p.iloc[0,[True,True,True],[2]]
IndexError: positional indexers are out-of-bounds
```
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} | 5 | 2014-05-21T15:08:28Z | 2014-05-21T23:26:20Z | 2014-05-21T23:26:20Z | CONTRIBUTOR | null | these cases should give the same results. Somehow the ops order is being reversed or something with numexpr.
numpy 1.8.1, numexpr 2.4, pandas 0.14rc1
Can be worked around like:
```
In [39]: df = DataFrame(dict(A=np.random.randn(25000)))
In [40]: df.iloc[0:5] = np.nan
In [41]: (1-np.isnan(df)).iloc[0:25]
Out[41]:
A
0 0
1 0
2 0
3 0
4 0
5 -1
6 -1
7 -1
8 -1
9 -1
10 -1
11 -1
12 -1
13 -1
14 -1
15 -1
16 -1
17 -1
18 -1
19 -1
20 -1
21 -1
22 -1
23 -1
24 -1
25 -1
```
```
In [43]: df = DataFrame(dict(A=np.random.randn(25)))
In [44]: df.iloc[0:5] = np.nan
In [45]: (1-np.isnan(df)).iloc[0:25]
Out[45]:
A
0 0
1 0
2 0
3 0
4 0
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
```
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| null | 4 | 2014-05-21T15:13:41Z | 2014-07-07T15:48:10Z | 2014-07-07T15:48:10Z | CONTRIBUTOR | null | It looks to me like there's some subtle bug in the nan-handling for groupby. Unfortunately, I can't replicate it. Trying to replicate it, it drops the groups that have nan-keys, as the docs indicate [1].
1) Is it not possible to just compare NaNs in the same way as is handled elsewhere? If not, maybe it's desirable to raise a warning here. This is not what I expected, and I've been working with incorrect results without knowing it.
2) Here's the very odd bug that also led me to have incorrect results. Only evidence that I'm not crazy since I can't replicate. Maybe there will be some hint as to what's going on. Several of the static_variable columns here have NaNs.
```
dta_period = all_dta.groupby(("cityname", "period") +
tuple(static_variables))
```
Let's look at one key that definitely has a NaN in it and wasn't dropped.
```
keys = [i for i in dta_period.groups.keys() if i[0] == 'AACHEN']
```
The last category holds a NaN below. It's not dropped and they're not aggregated together into one group as I expected before knowing these were supposed to be dropped.
```
[20]: for i in keys:
....: print i
....:
```
(u'AACHEN', 56, 3.0, u'Aachen', 50.783000946, 6.08300018311, 50.7755, 6.0815, 50.4700012207, 6.0500001907300005, u'Small States of the Holy Empire of the German Nation', nan)
(u'AACHEN', 56, 3.0, u'Aachen', 50.783000946, 6.08300018311, 50.7755, 6.0815, 50.4700012207, 6.0500001907300005, u'Small States of the Holy Empire of the German Nation', nan)
(u'AACHEN', 56, 3.0, u'Aachen', 50.783000946, 6.08300018311, 50.7755, 6.0815, 50.4700012207, 6.0500001907300005, u'Small States of the Holy Empire of the German Nation', nan)
Now let's aggregate and see what happens to Aachen.
```
agg = dict(zip(variables + ['books'], [np.sum]*(len(variables)+1)))
df = dta_period.aggregate(agg)
[24]: df.index[0]
[24]:
(u'AACHEN',
56,
3.0,
u'Aachen',
50.783000946000001,
6.0830001831100002,
50.775500000000001,
6.0815000000000001,
50.470001220699999,
6.0500001907300005,
u'Small States of the Holy Empire of the German Nation',
1065.0)
```
The NaN category in the group now somehow has data in it. Yikes.
[1] https://pandas-docs.github.io/pandas-docs-travis/groupby.html#na-group-handling
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} | 18 | 2014-05-21T17:34:24Z | 2015-08-02T21:46:34Z | 2015-08-02T21:46:24Z | NONE | null | perf discussion: http://stackoverflow.com/questions/24870953/does-iterrows-have-performance-issues/24871316#24871316
```
import pandas as pd
df = pd.DataFrame({'a':range(5),'b':range(5)})
print df
for _, row in df.iterrows():
row.a += 1
print df
df['c'] = 'what'
for _, row in df.iterrows():
row.b += 1
print df
a b
0 0 0
1 1 1
2 2 2
3 3 3
4 4 4
[5 rows x 2 columns]
a b
0 1 0
1 2 1
2 3 2
3 4 3
4 5 4
[5 rows x 2 columns]
a b c
0 1 0 what
1 2 1 what
2 3 2 what
3 4 3 what
4 5 4 what
[5 rows x 3 columns]
```
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} | 3 | 2014-05-21T20:29:34Z | 2017-12-19T21:17:26Z | 2014-06-03T23:42:06Z | MEMBER | null | When an offset is negative, Easter skips the date just before and returns one before.
```
(- pd.offsets.Easter()).apply(pd.Timestamp('2011-01-01 09:00:00'))
#2009-04-12 00:00:00
(- pd.offsets.Easter()).apply(pd.Timestamp('2012-01-01 09:00:00'))
# returns 2010-04-04 00:00:00
(- pd.offsets.Easter()).apply(pd.Timestamp('2010-04-05 09:00:00'))
#2009-04-12 00:00:00
```
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https://api.github.com/repos/pandas-dev/pandas/issues/7196 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7196/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7196/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7196/events | https://github.com/pandas-dev/pandas/pull/7196 | 34,024,720 | MDExOlB1bGxSZXF1ZXN0MTYxODg1NDA= | 7,196 | BUG: CustomBusinessDay apply raises NameError when np.datetime64 is pass... | {
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} | 2 | 2014-05-21T21:10:50Z | 2014-06-27T07:41:02Z | 2014-06-04T00:06:46Z | MEMBER | null | `CustomBusinessDay` raises `NameError` when `np.datetime64` is passed. It works if `normalize=True` is specified.
```
d = pd.offsets.CustomBusinessDay().apply(np.datetime64('2011-01-01 09:00:00'))
# NameError: global name 'np_day_incr' is not defined
d = pd.offsets.CustomBusinessDay(normalize=True).apply(np.datetime64('2011-01-01 09:00:00'))
#2011-01-03 00:00:00
```
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} | 0 | 2014-05-21T21:29:56Z | 2014-07-16T09:07:04Z | 2014-05-22T00:26:01Z | MEMBER | null | removing some unused imports and added `axis` parameter doc for `filter` method
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} | 2 | 2014-05-21T21:49:50Z | 2014-05-21T23:26:20Z | 2014-05-21T23:26:20Z | CONTRIBUTOR | null | worked in 0.13.1
works for n = 4, but not 4000
```
for n in [ 4, 4000 ]:
df = DataFrame(1,index=range(n),columns=list('abcd'))
df.iloc[0] = 2
expected = DataFrame(np.tile(df.mean().values,n).reshape(n,-1),
columns=list('abcd'))/df
result = df.mean()/df
assert_frame_equal(result,expected)
```
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} | 4 | 2014-05-21T21:51:43Z | 2014-05-22T04:27:46Z | 2014-05-22T00:06:23Z | CONTRIBUTOR | null | I just posted this at https://groups.google.com/forum/#!topic/pydata/wmUjOttpcpI.
All of [6], [7], [8], and [9] below should have the same dimensions, but [8] is incorrect. Similarly, [12] should work and look like [14]. The only difference between wd1 and wd2 is that the former has minor_axis=multi_index, whereas the latter has minor_axis=simple_index.
```
Python 3.4.0 (v3.4.0:04f714765c13, Mar 16 2014, 19:25:23) [MSC v.1600 64 bit (AMD64)]
Type "copyright", "credits" or "license" for more information.
IPython 2.0.0 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
In [1]: import pandas as pd
In [2]: multi_index = pd.MultiIndex.from_tuples([('ONE', 'one'),
('TWO', 'two'),
('THREE', 'three')],
names=['UPPER', 'lower'])
In [3]: simple_index = [x[0] for x in multi_index]
In [4]: wd1 = pd.Panel(items=['First', 'Second'],
major_axis=['a', 'b', 'c', 'd'],
minor_axis=multi_index)
In [5]: wd2 = pd.Panel(items=['First', 'Second'],
major_axis=['a', 'b', 'c', 'd'],
minor_axis=simple_index)
In [6]: wd1['First'].iloc[[True, True, True, False], [0, 2]]
Out[6]:
UPPER ONE THREE
lower one three
a NaN NaN
b NaN NaN
c NaN NaN
In [7]: wd2['First'].iloc[[True, True, True, False], [0, 2]]
Out[7]:
ONE THREE
a NaN NaN
b NaN NaN
c NaN NaN
In [8]: wd1.iloc[0, [True, True, True, False], [0, 2]] # WRONG
Out[8]:
UPPER ONE TWO THREE
lower one two three
a NaN NaN NaN
c NaN NaN NaN
In [9]: wd2.iloc[0, [True, True, True, False], [0, 2]]
Out[9]:
ONE THREE
a NaN NaN
b NaN NaN
c NaN NaN
In [10]: pd.__version__
Out[10]: '0.14.0rc1-39-g9d01fe1'
In [11]: wd1.iloc[0,0,[0,1,2]]
Out[11]:
UPPER lower
ONE one NaN
TWO two NaN
THREE three NaN
Name: a, dtype: float64
In [12]: wd1.iloc[0,[0],[0,1,2]] # should be similar to [14] below
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-12-b14471499110> in <module>()
----> 1 wd1.iloc[0,[0],[0,1,2]]
C:\Python34\lib\site-packages\pandas\core\indexing.py in __getitem__(self, key)
1122 def __getitem__(self, key):
1123 if type(key) is tuple:
-> 1124 return self._getitem_tuple(key)
1125 else:
1126 return self._getitem_axis(key, axis=0)
C:\Python34\lib\site-packages\pandas\core\indexing.py in _getitem_tuple(self, tup)
1329 continue
1330
-> 1331 retval = getattr(retval, self.name)._getitem_axis(key, axis=axis)
1332
1333 # if the dim was reduced, then pass a lower-dim the next time
C:\Python34\lib\site-packages\pandas\core\indexing.py in _getitem_axis(self, key, axis, validate_iterable)
1368
1369 # validate list bounds
-> 1370 self._is_valid_list_like(key, axis)
1371
1372 # force an actual list
C:\Python34\lib\site-packages\pandas\core\indexing.py in _is_valid_list_like(self, key, axis)
1307 l = len(ax)
1308 if len(arr) and (arr.max() >= l or arr.min() <= -l):
-> 1309 raise IndexError("positional indexers are out-of-bounds")
1310
1311 return True
IndexError: positional indexers are out-of-bounds
In [13]: wd2.iloc[0,0,[0,1,2]]
Out[13]:
ONE NaN
TWO NaN
THREE NaN
Name: a, dtype: float64
In [14]: wd2.iloc[0,[0],[0,1,2]]
Out[14]:
ONE TWO THREE
a NaN NaN NaN
```
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| null | 4 | 2014-05-21T21:58:45Z | 2014-05-21T22:26:30Z | 2014-05-21T22:20:33Z | NONE | null | To see the issue, consider the following dataframe
<code>
In [66]: dat = pandas.DataFrame(['a','b','c','d','e','f','g','h'],columns=['letters'])
</code>
<code>
In [67]: dat['numbers'] = pandas.Series([1,2,3,4,5,6,7,8])
</code>
<code>
In [68]: dat['names'] = pandas.Series(['jim','jan','jerry','george','mary','mary','sue','sue'])
</code>
<code>
In [69]: dat
</code>
<code>
Out[69]:
letters numbers names
0 a 1 jim
1 b 2 jan
2 c 3 jerry
3 d 4 george
4 e 5 mary
5 f 6 mary
6 g 7 sue
7 h 8 sue
[8 rows x 3 columns]
</code>
Group by names
<code>
In [78]: dat = dat.groupby(['names'])[['letters']]
</code>
Now my attempt to concat letters produces a funny result:
<code>
In [80]: dat.apply(lambda x: '|'.join(set(x)))
</code>
<code>
Out[80]:
names
george letters|numbers|names
jan letters|numbers|names
jerry letters|numbers|names
jim letters|numbers|names
mary letters|numbers|names
sue letters|numbers|names
dtype: object
</code>
Also strange is this behavior:
<code>
In [79]: dat.apply(lambda x: '|'.join([str(elm) for elm in set(x)]))
</code>
<code>
Out[79]:
names
george letters|numbers|names
jan letters|numbers|names
jerry letters|numbers|names
jim letters|numbers|names
mary letters|numbers|names
sue letters|numbers|names
dtype: object
</code>
The following hack seems to work
<code>
In [81]: In [72]: dat.apply(lambda x: '|'.join([str(elm) for elm in set(x['letters'])]))
Out[81]:
names
george d
jan b
jerry c
jim a
mary e|f
sue h|g
dtype: object
</code>
## 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: en_US.UTF-8
pandas: 0.13.1
Cython: 0.20.1
numpy: 1.6.2
scipy: 0.11.0
statsmodels: 0.5.0
IPython: 2.0.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2012d
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.1.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
sqlalchemy: None
lxml: 3.3.5
bs4: 4.3.2
html5lib: None
bq: None
apiclient: None
`
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