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} | 20 | 2014-04-17T12:30:35Z | 2016-10-12T23:04:47Z | 2014-04-17T23:49:29Z | NONE | null | If your index is a string that contains a `'` or `"` there is potentially no consistent way to do an HDF query from disk. The following is real-ish data from the Wikipedia pagecounts dataset. Worked in 0.12, SyntaxError in 0.13.
Easy to reproduce:
This is `test.csv`
```
title,hits
Al Lawson',30
'Blind' Willie McTell,20
```
```
df = pd.read_csv('test.csv', index_col=0)
store = pd.HDFStore('test.h5')
store.append('df', df)
# bug, SyntaxError
title = """Al Lawson'"""
result = store.select('df', pd.Term('index', '=', title))
result = store.select('df', 'index == {}'.format(title))
# fancy escaping helps, but it isn't a cure all
result = store.select('df', 'index == """{}"""'.format(title))
# SyntaxError
result = store.select('df', "index == '''{}'''".format(title))
```
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} | 9 | 2014-04-17T14:07:00Z | 2014-08-21T07:57:05Z | 2014-04-22T14:33:16Z | CONTRIBUTOR | null | Closes #6796
If any columns names written `to_sql` via a legacy connection _would_ have been modified by previous versions of pandas, a warning is issued, both on table creation and row insertion. But no names are modified.
There is a stray warning message in nosetests. Otherwise good, I think. Does this reflect our consensus?
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| null | 4 | 2014-04-17T16:06:19Z | 2014-05-05T13:23:36Z | 2014-05-05T13:23:36Z | NONE | null | The Series docstring does not include any description for the 'fastpath' parameter.
Couldn't find any example on the website that used this parameter, not sure which needs updating: the init definition or the parameters section.
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} | 3 | 2014-04-17T17:43:19Z | 2014-04-21T14:15:49Z | 2014-04-21T14:15:49Z | NONE | null | ```
File "z:\code\m.py", line 420, in get_panel
self.source_panel.pct_change()
File "c:\anaconda\lib\site-packages\pandas\core\generic.py", line 3418, in pct_change
rs = data / data.shift(periods=periods, freq=freq, **kwds) - 1
TypeError: shift() got an unexpected keyword argument 'periods'
```
> pandas.version.version
> Out[5]: '0.13.1'
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} | 13 | 2014-04-17T22:05:28Z | 2014-07-16T09:02:14Z | 2014-04-17T23:49:30Z | MEMBER | null | closes #6901
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} | 3 | 2014-04-17T23:53:04Z | 2020-05-16T20:11:25Z | null | MEMBER | null | example from #6901:
`test.csv`
```
title,hits
Al Lawson',30
'Blind' Willie McTell,20
```
``` python
df = pd.read_csv('test.csv', index_col=0)
store = pd.HDFStore('test.h5')
store.append('df', df)
```
should querying work the same as `query`/`eval` for `select` queries?
``` python
title = "Al Lawson'"
result = store.select('df', 'index == title')
```
or
``` python
result = store.select('df', 'index == @title')
```
the latter currently doesn't work
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| null | 10 | 2014-04-18T04:55:48Z | 2014-04-18T20:01:38Z | 2014-04-18T15:58:38Z | NONE | null | I only just upgraded to 0.13 so noticing this now. The function below was run on the setup below twice, once with pandas 0.13.1 and another time with 0.12. There is large speed difference as shown in the output. Note that I know that the function as written can be simplified as pd.rolling_std(df1,trail_num, trail_num)*df2, but the below is an example and the actual function I use requires the below approach.
``` python
import pandas as pd
import numpy as np
def main_func2(df1, df2, trail_num):
df3 = pd.DataFrame(index=df1.index, columns=df2.columns)
for inum in range(trail_num, len(df1)):
columns_subset = df2.iloc[inum].valid().index
dfslice = df1.filter(columns_subset)[inum - trail_num: inum]
slicestd = dfslice.std()
df3.iloc[inum] = slicestd
return df3
np.random.seed(1234)
r2 = pd.DataFrame(np.random.randn(1000,100))
l2 = pd.DataFrame(np.random.randn(1000,100))
print pd.__version__
%timeit main_func2(r2,l2,3)
Output 1:
0.13.1
1 loops, best of 3: 6.44 s per loop
Output 2:
0.12.0
1 loops, best of 3: 1.53 s per loop
```
In [3]: pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Windows
OS-release: 2012Server
machine: AMD64
processor: Intel64 Family 6 Model 45 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.13.1
Cython: None
numpy: 1.8.0
scipy: None
statsmodels: None
IPython: 1.1.0
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: None
bottleneck: 0.7.0
tables: None
numexpr: 2.2.2
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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} | 5 | 2014-04-18T12:34:42Z | 2014-06-21T21:47:54Z | 2014-04-19T12:47:08Z | MEMBER | null | closes #6764
`TimeGrouper` may output incorrect results depending on the target column order. The problem seems to be caused by 2 parts.
- `TimeGrouper._get_time_bins` and related methods expects sorted values input.
- `BinGrouper.get_iterator` expects sorted data input.
```
>>> df = pd.DataFrame({'Branch' : 'A A A A A A A B'.split(),
'Buyer': 'Carl Mark Carl Carl Joe Joe Joe Carl'.split(),
'Quantity': [1,3,5,1,8,1,9,3],
'Date' : [
datetime(2013,1,1,13,0), datetime(2013,1,1,13,5),
datetime(2013,10,1,20,0), datetime(2013,10,2,10,0),
datetime(2013,10,1,20,0), datetime(2013,10,2,10,0),
datetime(2013,12,2,12,0), datetime(2013,12,2,14,0),]})
# correct
>>> df.groupby([pd.Grouper(freq='1M',key='Date'),'Buyer']).sum()
Quantity
Date Buyer
2013-01-31 Carl 1
Mark 3
2013-10-31 Carl 6
Joe 9
2013-12-31 Carl 3
Joe 9
[6 rows x 1 columns]
>>> df_sorted = df.sort('Quantity') # change "Date" column unsorted
# incorrect
>>> df_sorted.groupby([pd.Grouper(freq='1M',key='Date'),'Buyer']).sum()
Quantity
Date Buyer
2013-01-31 Carl 1
2013-10-31 Carl 1
Joe 1
Mark 3
2013-12-31 Carl 8
Joe 17
[6 rows x 1 columns]
>>> df_sorted.groupby([pd.Grouper(freq='1M',key='Date', sort=True),'Buyer']).sum()
# same incorrect result
```
```
# correct
>>> df.groupby([pd.Grouper(freq='6M',key='Date'),'Buyer']).sum()
Quantity
Date Buyer
2013-01-31 Carl 1
Mark 3
2014-01-31 Carl 9
Joe 18
[4 rows x 1 columns]
# incorrect
>>> df_sorted.groupby([pd.Grouper(freq='6M',key='Date'),'Buyer']).sum()
Quantity
Date Buyer
2013-01-31 Carl 1
2014-01-31 Carl 9
Joe 18
Mark 3
[4 rows x 1 columns]
```
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} | 9 | 2014-04-18T13:10:39Z | 2017-04-05T02:08:52Z | 2014-04-21T14:15:49Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/6904
There's just a bit of extra index handling that needs to be done before moving on to `generic.pct_change()`. I had to adjust that to use the `.div` and `.sub` ops instead of `/` and `-` to work with panels.
I wasn't sure why axis wasn't included as an actual names keyword arg. `generic` just looks for it in **kwargs. I did the same in panel.
A related issue was the `Panel.shift()` has a different argument signature than `generic.shift()`. I can make those consistent and put in a deprecation warning in this issue or in a new one.
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https://api.github.com/repos/pandas-dev/pandas/issues/6910 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6910/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6910/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6910/events | https://github.com/pandas-dev/pandas/issues/6910 | 31,803,872 | MDU6SXNzdWUzMTgwMzg3Mg== | 6,910 | API: Change Panel.shift arg signature to match generic's | {
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} | 0 | 2014-04-18T15:06:20Z | 2014-04-22T18:30:43Z | 2014-04-22T18:29:10Z | CONTRIBUTOR | null | This surfaced in the Panel `pct_change` implementation at https://github.com/pydata/pandas/pull/6909
`generic`: `def shift(self, periods=1, freq=None, axis=0, **kwds):`
`Panel`: `def shift(self, lags, freq=None, axis='major'):`
I had to adjust the call to `shift` in `generic.pct_change` to not use a kwarg for `lag`/`period`:
Before: `rs = data / data.shift(periods=periods, freq=freq, **kwds) - 1`
After: `rs = data.div(data.shift(periods, freq=freq, **kwds)) - 1`
We'll need to keep the `.div` change, but we can change back the kwarg once the signatures are aligned.
- [x] Add kwarg deprecation decorator
- [x] change `pct_change` back to kwarg
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} | 7 | 2014-04-18T17:37:01Z | 2014-04-18T19:34:45Z | 2014-04-18T19:34:45Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/23158447/convert-float-to-string-in-pandas
currently this just converts to object dtype but not actually stringify
could even handle fixed format dtypes: S10 And such as well
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https://api.github.com/repos/pandas-dev/pandas/issues/6912 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6912/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6912/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6912/events | https://github.com/pandas-dev/pandas/pull/6912 | 31,847,742 | MDExOlB1bGxSZXF1ZXN0MTQ5NDQzMDQ= | 6,912 | TST: restructure sql tests | {
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} | 0 | 2014-04-19T14:34:29Z | 2014-06-25T08:40:32Z | 2014-04-21T19:50:30Z | MEMBER | null | - add some explanation of structure of tests
- general clean-up (some pep8, docstrings, etc)
- move some methods to _TestSQLAlchemy
- use standard quoting for sqlite
I didn't change any tests, so nothing controversial I think. Wanted to do this first, and then in a subsequent PR try to increase the test coverage.
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} | 5 | 2014-04-19T17:12:53Z | 2014-06-13T01:24:57Z | 2014-04-27T13:28:37Z | MEMBER | null | `pivot_table` can accept `Grouper` by `index` and `columns` kw.
```
>>> df = pd.DataFrame({
'Branch' : 'A A A A A A A B'.split(),
'Buyer': 'Carl Mark Carl Carl Joe Joe Joe Carl'.split(),
'Quantity': [1,3,5,1,8,1,9,3],
'Date' : [datetime(2013,11,1,13,0), datetime(2013,9,1,13,5),
datetime(2013,10,1,20,0), datetime(2013,10,2,10,0),
datetime(2013,11,1,20,0), datetime(2013,10,2,10,0),
datetime(2013,10,2,12,0), datetime(2013,12,5,14,0)],
'PayDay' : [datetime(2013,10,4,0,0), datetime(2013,10,15,13,5),
datetime(2013,9,5,20,0), datetime(2013,11,2,10,0),
datetime(2013,10,7,20,0), datetime(2013,9,5,10,0),
datetime(2013,12,30,12,0), datetime(2013,11,20,14,0),]})
>>> pd.pivot_table(df, index=pd.Grouper(freq='M', key='Date'),
columns=pd.Grouper(freq='M', key='PayDay'),
values='Quantity', aggfunc=np.sum)
PayDay 2013-09-30 2013-10-31 2013-11-30 2013-12-31
Date
2013-09-30 NaN 3 NaN NaN
2013-10-31 6 NaN 1 9
2013-11-30 NaN 9 NaN NaN
2013-12-31 NaN NaN 3 NaN
```
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} | 5 | 2014-04-19T18:23:39Z | 2014-06-14T08:31:02Z | 2014-04-28T14:07:40Z | MEMBER | null | `get_group` raises `AttributeError` when the group is created by `TimeGrouper`.
```
>>> df = pd.DataFrame({'Branch' : 'A A A A A A A B'.split(),
'Buyer': 'Carl Mark Carl Carl Joe Joe Joe Carl'.split(),
'Quantity': [1,3,5,1,8,1,9,3],
'Date' : [
datetime(2013,1,1,13,0), datetime(2013,1,1,13,5),
datetime(2013,10,1,20,0), datetime(2013,10,2,10,0),
datetime(2013,10,1,20,0), datetime(2013,10,2,10,0),
datetime(2013,12,2,12,0), datetime(2013,12,2,14,0),]})
>>> grouped = df.groupby(pd.Grouper(freq='1M',key='Date'))
>>> grouped.get_group(pd.Timestamp('2013-12-31'))
AttributeError: 'DataFrameGroupBy' object has no attribute 'indices'
```
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} | 45 | 2014-04-20T00:34:32Z | 2014-04-24T15:16:31Z | 2014-04-23T20:08:32Z | NONE | null | In some cases, the mean is computed incorrectly. Numpy however does the correct calculation. There is no problem with the standard deviation calculation. The following is an example.
```
In [11]: np.array(stateemp.area.tolist()).mean()
Out[11]: 23785.447812211703
In [12]: stateemp.area.mean()
Out[12]: 58.927762478114879
In [13]: np.array(stateemp.area.tolist()).std()
Out[13]: 22883.862745218048
In [14]: stateemp.area.std()
Out[14]: 22883.864924811925
In [15]: pd.__version__
Out[15]: '0.13.1'
In [16]: np.__version__
Out[16]: '1.8.1'
```
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| null | 5 | 2014-04-20T16:21:06Z | 2014-06-12T04:44:39Z | 2014-04-21T05:43:53Z | CONTRIBUTOR | null | Addresses issue https://github.com/pydata/pandas/issues/6186
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} | 4 | 2014-04-21T21:56:08Z | 2021-04-11T04:29:42Z | 2021-04-11T04:29:41Z | CONTRIBUTOR | null | In-line examples when not too long are nice (as well as the link)
for example: https://github.com/pydata/pandas/commit/6fe9539b1c9c5f097d64d72af2c2f26e85dfc0b1
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} | 10 | 2014-04-22T00:21:38Z | 2014-06-14T14:25:16Z | 2014-04-22T13:09:16Z | CONTRIBUTOR | null | closes #6894
Deprecate passing keyword `copy` to:
- `DataFrame/Series/Panel/.xs`
- `Panel.minor_xs`
- `Panel.major_xs`
`.xs` returns a view (as does `.loc`) **IF POSSIBLE**
MultiIndexing with Slicers eliminates the need for this (and `.xs` for the too, but for
some cases its 'simpler').
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} | 2 | 2014-04-22T12:02:33Z | 2015-03-05T23:30:27Z | 2015-03-05T23:30:27Z | CONTRIBUTOR | null | We have this nice comment if you bring up the API page itself by the indexers, but searching and clicking on say pandas.DataFrame.loc just give you an empty page
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} | 16 | 2014-04-22T13:23:43Z | 2014-09-06T09:40:27Z | 2014-05-11T09:55:01Z | CONTRIBUTOR | null | Maybe we can work around this. At least it should raise informatively.
```
In [24]: df = pd.to_timedelta(Series(['00:00:01', '00:00:03'], name='foo')).to_frame()
In [25]: df
Out[25]:
foo
0 00:00:01
1 00:00:03
[2 rows x 1 columns]
In [26]: df.dtypes
Out[26]:
foo timedelta64[ns]
dtype: object
In [27]: df.to_sql('bar', engine)
(...)
StatementError: unsupported operand type(s) for +: 'datetime.datetime' and 'long' (original cause: TypeError: unsupported operand type(s) for +: 'datetime.datetime' and 'long') 'INSERT INTO bar (foo) VALUES (?)' [{'foo': 1000000000L}, {'foo': 3000000000L}]
```
The full traceback is [in this gist](https://gist.github.com/danielballan/11178744).
Using a `sqlite3.Connection` (legacy-style) allows you to write and read.
```
In [32]: conn = sqlite3.connect(':memory:')
In [33]: df.to_sql('bar', conn)
In [37]: pd.read_sql('SELECT * FROM bar', conn, flavor='sqlite')
Out[37]:
index foo
0 0 1000000000
1 1 3000000000
```
The data comes back as `int64` type. There is no clean way around this, no obvious way to tell that these sqlite3 integers are actually timedeltas. We could store timedeltas as strings, like datetimes. But I'm not necessarily in favor of that approach.
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} | 1 | 2014-04-22T13:28:33Z | 2018-07-06T20:21:03Z | 2017-07-10T21:09:50Z | CONTRIBUTOR | null | related #6826, #6909
Just putting this up there if anyone has any ideas. The filling scales with the length of the axis so this is unavoidable (well it _could_ be done in cython but would need a 3d version of certain functions).
can think about changing the default to `fill_method=None` from `fill_method='pad'`
```
In [8]: index = pd.date_range(start="2000", freq="D", periods=1000)
In [9]: panel = pd.Panel(np.random.randn(100, len(index), 1000))
In [10]: %timeit panel.pct_change(1, axis="major", fill_method='pad')
1 loops, best of 3: 3.62 s per loop
In [11]: %timeit panel.pct_change(1, axis="major", fill_method=None)
1 loops, best of 3: 1.46 s per loop
```
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} | 5 | 2014-04-22T13:39:38Z | 2014-04-27T13:34:16Z | 2014-04-22T14:21:46Z | CONTRIBUTOR | null | I got some help from Jeff on [stackoverflow](http://stackoverflow.com/questions/23200524/propagate-pandas-series-metadata-through-joins), but either I'm misunderstanding the way `__finalized__` works, or there's a bug in how it's called. My intent was to preserve series metadata after 2 dataframes being merged, and I believe `__finalize__` should be able to handle this.
I define a couple dataframes, and assign metadata values to all the series:
```
import numpy as np
import pandas as pd
np.random.seed(10)
df1 = pd.DataFrame(np.random.randint(0, 4, (3, 2)), columns=['a', 'b'])
df2 = pd.DataFrame(np.random.randint(0, 4, (3, 2)), columns=['c', 'd'])
df1
a b
0 1 1
1 0 3
2 0 1
df2
c d
0 3 0
1 1 1
2 0 1
```
Then I assign metadata field `filename` to series
```
pd.Series._metadata = ['name', 'filename']
for c1 in df1:
df1[c1].filename = 'fname1.csv'
for c2 in df2:
df2[c2].filename = 'fname2.csv'
```
Now, I'm defining `__finalize__` for series, which I understand is able to propagate metadata from one series to the other, for example when I want to merge. But when I define a `__finalize__` that prints off the metadata that I've already assigned, it looks like by the time it calls `__finalize__`, it no longer has the metadata.
```
def finalize_ser(self, other, method=None, **kwargs):
print 'Self meta: {}'.format(getattr(self, 'filename', None))
print 'Other meta: {}'.format(getattr(other, 'filename', None))
for name in self._metadata:
object.__setattr__(self, name, getattr(other, name, ''))
return self
pd.Series.__finalize__ = finalize_ser
```
When I call `merge`, I never see the correct metadata printed off
```
df1.merge(df2, left_on=['a'], right_on=['c'], how='inner')
Self meta: None
Other meta: None
Self meta: None
Other meta: None
Self meta: None
Other meta: None
Self meta: None
Other meta: None
Out[5]:
a b c d
0 1 1 1 1
1 0 3 0 1
2 0 1 0 1
```
It appears the metadata is lost before it gets to the `__finalize__` call, though it's still in the original series
```
df1.a.filename # => 'fname1.csv'
mgd.a.filename # => AttributeError
```
Is this expected or is there a bug?
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| null | 2 | 2014-04-22T15:25:00Z | 2014-04-22T20:37:57Z | 2014-04-22T20:37:57Z | NONE | null | Hi-
when trying to write a dataframe with non-string named columns via current to_sql the function fails because function _safe_col_name attempts to call the strip method.
NB I do not have master installed I just copied the SQL module and call the v14 functions from it.
``` python
df = pd.DataFrame(randn(2,4))
con = sqlalchemy.create_engine("mssql+pyodbc://server/db?driver=SQL Server Native Client 11.0")
import sql
sql.to_sql(df, "TBL_TEMP", con, if_exists="replace")
```
yields the following error:
```
Traceback (most recent call last):
File "<ipython-input-40-36a592728500>", line 4, in <module>
sql.to_sql(df, "TBL_TEMP", con, if_exists="replace")
File "N:\Python\sql.py", line 399, in to_sql
index_label=index_label)
File "N:\Python\sql.py", line 773, in to_sql
index_label=index_label)
File "N:\Python\sql.py", line 479, in __init__
self.table = self._create_table_statement()
File "N:\Python\sql.py", line 601, in _create_table_statement
for name, typ in zip(safe_columns, column_types)]
File "N:\Python\sql.py", line 600, in <listcomp>
columns = [Column(name, typ)
File "N:\Python\sql.py", line 50, in _safe_col_name
return col_name.strip().replace(' ', '_')
AttributeError: 'numpy.int64' object has no attribute 'strip'
```
However if i replace the first line with
``` python
df = pd.DataFrame(randn(2,4), columns=list("abcd"))
```
there is no error.
showversions info below
## INSTALLED VERSIONS
commit: None
python: 3.3.3.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 44 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_GB
pandas: 0.13.1
Cython: None
numpy: 1.8.0
scipy: 0.13.2
statsmodels: 0.5.0
IPython: 1.1.0
sphinx: 1.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2013.8
bottleneck: None
tables: 3.0.0
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: 1.6.1
xlrd: 0.9.2
xlwt: None
xlsxwriter: 0.5.2
sqlalchemy: 0.9.0
lxml: None
bs4: None
html5lib: None
bq: None
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} | 5 | 2014-04-22T15:49:31Z | 2016-01-27T22:34:10Z | 2014-04-22T20:54:18Z | CONTRIBUTOR | null | ``` python
def factorize(values, sort=False, order=None, na_sentinel=-1):
"""
Encode input values as an enumerated type or categorical variable
Parameters
----------
values : ndarray (1-d)
Sequence
sort : boolean, default False
Sort by values
order :
na_sentinel: int, default -1
Value to mark "not found"
Returns
-------
labels : the indexer to the original array
uniques : the unique values
note: an array of Periods will ignore sort as it returns an always sorted PeriodIndex
"""
```
But it isn't used anywhere. Technically this is an API change, if people aren't using kwargs. So I'll add it to the deprecation list.
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} | 2 | 2014-04-22T16:35:45Z | 2014-04-27T13:34:55Z | 2014-04-22T19:44:47Z | CONTRIBUTOR | null | When I assign metadata to a df
```
import numpy as np
import pandas as pd
np.random.seed(10)
pd.DataFrame._metadata = ['filename']
df1 = pd.DataFrame(np.random.randint(0, 4, (3, 2)), columns=list('ab'))
df1.filenames = {'a': 'f1', 'b': 'f2'}
df1
a b
0 1 1
1 0 3
2 0 1
```
and define a `__finalize__` that prints when it's called
```
def finalize_df(self, other, method=None, **kwargs):
print 'finalize called'
for name in self._metadata:
object.__setattr__(self, name, getattr(other, name, None))
return self
pd.DataFrame.__finalize__ = finalize_df
```
nothing is preserved when `pd.concat` is called:
```
stacked = pd.concat([df1, df1]) # Nothing printed
stacked
a b
0 1 1
1 0 3
2 0 1
0 1 1
1 0 3
2 0 1
stacked.finalize # => AttributeError
```
For this specific case it seems reasonable that `__finalize__` should be used since all of the elements are from the same dataframe, though I'm not sure about the general use since `concat` can also take types other than a DataFrame. But should we/do we have some method to stack dataframes that preserves metadata?
Similar to #6923.
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| null | 2 | 2014-04-22T17:03:48Z | 2017-04-05T02:08:53Z | 2014-04-22T18:29:10Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/6910
Also switches the shift implementation back to using keyword arguments.
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} | 3 | 2014-04-22T18:16:47Z | 2020-11-09T21:20:05Z | 2020-11-09T21:20:05Z | CONTRIBUTOR | null | The numerical stability of the current implementation of `rolling_kurt` and `rolling_skew` is very poor:
```
In [14]: x = np.random.rand(10)
In [15]: pd.Series(x).rolling(4).skew()
Out[15]:
0 NaN
1 NaN
2 NaN
3 -0.971467
4 0.596391
5 -1.163135
6 -0.611469
7 -1.624713
8 -0.021832
9 0.754653
dtype: float64
In [16]: pd.Series(x + 5000).rolling(4).skew()
Out[16]:
0 NaN
1 NaN
2 NaN
3 -0.968964
4 0.608522
5 -1.171751
6 -0.619354
7 -1.628942
8 -0.033949
9 0.741304
dtype: float64
In [17]: pd.Series(x).rolling(4).kurt()
Out[17]:
0 NaN
1 NaN
2 NaN
3 2.030185
4 -2.647975
5 1.183004
6 -2.178587
7 2.658058
8 -4.258505
9 0.939264
dtype: float64
In [18]: pd.Series(x + 100).rolling(4).kurt()
Out[18]:
0 NaN
1 NaN
2 NaN
3 2.030230
4 -2.648801
5 1.182382
6 -2.178574
7 2.657934
8 -4.258813
9 0.938661
dtype: float64
```
This can be solved using an updating algorithm, similar to what is done in #6817 for `rolling_var`.
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} | 1 | 2014-04-22T18:33:03Z | 2017-04-05T02:08:49Z | 2014-04-22T20:54:18Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/6926
`order` wasn't being used at all.
I couldn't use the deprecate_kwarg decorator since the kwarg is being removed, not changed.
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} | 0 | 2014-04-22T19:21:40Z | 2014-07-16T09:02:47Z | 2014-04-22T19:44:47Z | CONTRIBUTOR | null | closes #6927
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} | 7 | 2014-04-22T19:35:46Z | 2014-08-28T18:03:38Z | 2014-08-28T07:03:43Z | NONE | null | Hi-
the following commands throw a DataError --
``` python
con = sqlalchemy.create_engine("mssql+pyodbc://server?driver=SQL Server Native Client 11.0")
df = pd.DataFrame([datetime.time(7,10), datetime.time(7,20)], columns="a")
sql.to_sql(df, "TBL_TEMP", con, index=False)
```
throws the following error:
``` python
Traceback (most recent call last):
File "<ipython-input-275-80a6d739629c>", line 1, in <module>
sql.to_sql(df, "TBL_TEMP3", con, index=False)
File "N:\Python\sql.py", line 399, in to_sql
index_label=index_label)
File "N:\Python\sql.py", line 774, in to_sql
table.insert()
File "N:\Python\sql.py", line 538, in insert
self.pd_sql.execute(ins, data_list)
File "N:\Python\sql.py", line 734, in execute
return self.engine.execute(*args, **kwargs)
File "C:\WinPython3.3.3.2\python-3.3.3.amd64\lib\site-packages\sqlalchemy\engine\base.py", line 1598, in execute
return connection.execute(statement, *multiparams, **params)
File "C:\WinPython3.3.3.2\python-3.3.3.amd64\lib\site-packages\sqlalchemy\engine\base.py", line 664, in execute
return meth(self, multiparams, params)
File "C:\WinPython3.3.3.2\python-3.3.3.amd64\lib\site-packages\sqlalchemy\sql\elements.py", line 282, in _execute_on_connection
return connection._execute_clauseelement(self, multiparams, params)
File "C:\WinPython3.3.3.2\python-3.3.3.amd64\lib\site-packages\sqlalchemy\engine\base.py", line 761, in _execute_clauseelement
compiled_sql, distilled_params
File "C:\WinPython3.3.3.2\python-3.3.3.amd64\lib\site-packages\sqlalchemy\engine\base.py", line 874, in _execute_context
context)
File "C:\WinPython3.3.3.2\python-3.3.3.amd64\lib\site-packages\sqlalchemy\engine\base.py", line 1023, in _handle_dbapi_exception
exc_info
File "C:\WinPython3.3.3.2\python-3.3.3.amd64\lib\site-packages\sqlalchemy\util\compat.py", line 174, in raise_from_cause
reraise(type(exception), exception, tb=exc_tb, cause=exc_value)
File "C:\WinPython3.3.3.2\python-3.3.3.amd64\lib\site-packages\sqlalchemy\util\compat.py", line 167, in reraise
raise value.with_traceback(tb)
File "C:\WinPython3.3.3.2\python-3.3.3.amd64\lib\site-packages\sqlalchemy\engine\base.py", line 856, in _execute_context
context)
File "C:\WinPython3.3.3.2\python-3.3.3.amd64\lib\site-packages\sqlalchemy\engine\default.py", line 385, in do_executemany
cursor.executemany(statement, parameters)
DataError: (DataError) ('22018', '[22018] [Microsoft][SQL Server Native Client 11.0][SQL Server]Operand type clash: time is incompatible with text (206) (SQLExecDirectW)') 'INSERT INTO [TBL_TEMP3] (a) VALUES (?)' ((datetime.time(7, 10),), (datetime.time(7, 20),))
```
I have two columns, one with datetime.date and one with datetime.time, which both exhibited this problem. I force-converted the datetime.date column via pd.to_datetime into a datetimeindex, which to_sql/sqlalchemy correctly formats into an SQL-acceptable date format. However, to_datetime does not work on datetime.date, leaving the pandas datatype as "object" instead of datetime64ns.
Thanks,
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} | 3 | 2014-04-22T20:02:27Z | 2014-05-05T13:27:58Z | 2014-05-05T13:27:58Z | CONTRIBUTOR | null | Been hit by this when optimizing index-oblivious Blocks. In my case,
```
is_deleted = np.zeros(len(index), dtype=np.bool_)
is_deleted[deleted_loc] = True
index = index[~is_deleted]
```
was still a lot faster then than `index.delete(deleted_loc)`.
It appears, that delete doesn't add `fastpath=True` to ctor and that triggers type inference for string (object) indices. There seems to be plenty of methods that do it the same way and hence are slow too, can we do something about that?
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} | 9 | 2014-04-23T00:34:18Z | 2014-06-18T06:59:55Z | 2014-04-24T01:24:01Z | CONTRIBUTOR | null | Fix to allow decimal seconds to be read from Excel dates and times
into datetime objects. #5945.
This required a fix to the `xlrd` module to return milliseconds from Excel dates and times. That fix was recently released to PyPI in xlrd version 0.9.3.
Tests, version updates and release note included.
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| null | 0 | 2014-04-23T02:43:49Z | 2017-04-05T02:08:48Z | 2014-04-23T12:44:59Z | CONTRIBUTOR | null | Came up in the comments at https://github.com/pydata/pandas/pull/6850#issuecomment-41119013
Missed passing the `bins` kwarg to a call to matplotlib's hist.
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} | 12 | 2014-04-23T03:09:28Z | 2021-04-11T04:30:05Z | null | NONE | null | See below. Dates from the last week of December 2013 are interpreted as being from the first week of the year. Is this due to the fact December 30th was a Monday?
``` python
import pandas as pd
dt = '2013-12-30'
pd.to_datetime(dt, coerce=True).year, pd.to_datetime(dt, coerce=True).weekofyear
# I expected: (2013, 53) since this is day 364 and the start of the 53rd week
# Instead it returned: (2013, 1)
```
My current work-around is:
``` python
import pandas as pd
import numpy as np
dt = '2013-12-30'
doy = pd.to_datetime(dt, coerce=True).dayofyear
(np.floor(doy / 7) + 1).astype('int')
# Returns 53
```
``` python
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Linux
OS-release: 3.11.0-15-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.1
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.12.0
statsmodels: 0.4.2
IPython: 2.0.0
sphinx: 1.2.2
patsy: None
scikits.timeseries: None
dateutil: 1.5
pytz: 2012c
bottleneck: None
tables: 2.4.0
numexpr: 2.0.1
matplotlib: 1.2.1
openpyxl: 1.5.8
xlrd: 0.9.2
xlwt: 0.7.4
xlsxwriter: None
sqlalchemy: None
lxml: 3.2.0
bs4: 4.2.0
html5lib: 0.95-dev
bq: None
apiclient: None
```
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} | 16 | 2014-04-23T03:24:11Z | 2014-07-08T13:11:14Z | 2014-06-30T19:30:22Z | CONTRIBUTOR | null | closes #5840 (as new interface makes it obsolete)
closes #6096
@jreback : We still have some documentation to work on, but we would like your initial thoughts on what we have so far. The key change for this version is the removal of bq.py as a dependency (except as a setup method for a test case). Instead, we rely entirely on the BigQuery python API. We also simplified to_gbq() significantly. Though it cost a few features, the code is much more manageable and less error prone (thanks Google!). Test cases are much more granular and run significantly faster. To use the test cases fully, a BigQuery project_id is still required, though there are some unittests offline.
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| null | 1 | 2014-04-23T11:20:50Z | 2014-04-23T12:33:15Z | 2014-04-23T11:24:06Z | CONTRIBUTOR | null | How about adding a `tidy_repr` similar to the one available for Series (https://github.com/pydata/pandas/blob/master/pandas/core/series.py#L845) to the possible ways of displaying a DataFrame. I find this view very convenient for displaying long series. Searching through past issues I couldn't find a discussion on why this representation is not used for DataFrames.
I know the default view has recently changed to `truncate` (https://github.com/pydata/pandas/issues/4886). What I find missing with the truncate view is the possibility to see where the index/columns end. However, I know this is a very subjective topic, so I'd simply like to propose to add this view as an option.
The code for the representation could look similar to this (ignoring the column representation for the moment).
...
```
df = pd.DataFrame(columns=['a','b','c'],
index=pd.DatetimeIndex(start='19900101',end='20000101',freq='BM'))
print df.iloc[:10].__repr__() + '\n...\n' + df.iloc[-10:].__repr__()
```
...
This proposal is related to https://github.com/pydata/pandas/pull/6890.
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} | 25 | 2014-04-23T13:35:07Z | 2014-05-14T19:59:17Z | 2014-05-14T19:57:32Z | MEMBER | null | Update:
- [ ] `max_info_columns` should not behave the same as `max_info_rows`. If `max_info_columns` is exceeded, it should flip to short summary (as `verbose=False`)
- [ ] 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)
---
When you have more columns than specified in `max_info_columns`, `df.info()` will now still show all columns, but just without the information about the number of null values:
```
In [1]: pd.__version__
Out[1]: '0.13.1-656-gf30278e'
In [4]: df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 5 entries, 0 to 4
Data columns (total 5 columns):
0 5 non-null float64
1 5 non-null float64
2 5 non-null float64
3 5 non-null float64
4 5 non-null float64
dtypes: float64(5)
In [5]: df.info(verbose=False)
<class 'pandas.core.frame.DataFrame'>
Int64Index: 5 entries, 0 to 4
Columns: 5 entries, 0 to 4
dtypes: float64(5)
In [6]: pd.options.display.max_info_columns = 4
In [7]: df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 5 entries, 0 to 4
Data columns (total 5 columns):
0 float64
1 float64
2 float64
3 float64
4 float64
dtypes: float64(5)
```
while previously in 0.13 this gave the same behaviour as for `info(verbose=False)`:
```
In [15]: pd.__version__
Out[15]: '0.13.0'
In [16]: pd.options.display.max_info_columns = 4
In [17]: df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 5 entries, 0 to 4
Columns: 5 entries, 0 to 4
dtypes: float64(5)
```
which seems much more logical to me.
I suppose this is related to #5682, which added the behaviour to also show the dtype per column.
Update: it was deliberately added in #5974
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} | 7 | 2014-04-23T15:50:01Z | 2014-04-23T19:50:18Z | 2014-04-23T15:51:47Z | NONE | null | ```
>>>df.info(verbose=True)
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 9063396 entries, (2011-10-19 00:00:00, 000312) to (2014-03-31 00:00:00, Y8565N10)
Data columns (total 5 columns):
weight_benchmark float64
weight_portfolio float64
weight_active float64
weight_msci_acwi float64
investable bool
dtypes: bool(1), float64(4)
>>>df.iloc[:1000].info(verbose=True)
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 1000 entries, (2011-10-19 00:00:00, 000312) to (2011-10-19 00:00:00, 17275510)
Data columns (total 5 columns):
weight_benchmark 1000 non-null float64
weight_portfolio 1000 non-null float64
weight_active 1000 non-null float64
weight_msci_acwi 1000 non-null float64
investable 1000 non-null bool
dtypes: bool(1), float64(4)
```
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} | 0 | 2014-04-23T16:00:53Z | 2014-06-17T14:27:30Z | 2014-04-23T16:26:27Z | CONTRIBUTOR | null | closes #6583
- API: change AssertionError to TypeError for invalid types passed to concat
- REGR: TextFileReader in concat, which was affecting a common user idiom
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} | 6 | 2014-04-23T16:43:15Z | 2021-05-08T19:49:07Z | null | CONTRIBUTOR | null | xref https://github.com/pandas-dev/pandas/issues/14179
xref #12499
xref #18027
http://stackoverflow.com/questions/23250333/in-pandas-dataframe-after-i-set-an-entire-column-i-cant-update-another-column
but `.loc` works
```
In [50]: df = pd.DataFrame(index=pd.date_range(start,periods=1), columns=['timenow','Live'])
In [51]: df.at[start,'timenow'] = datetime.today() # initial value
In [52]: df
Out[52]:
timenow Live
2014-04-01 2014-04-23 12:42:38.883082 NaN
[1 rows x 2 columns]
In [53]: df._data
Out[53]:
BlockManager
Items: Index([u'timenow', u'Live'], dtype='object')
Axis 1: <class 'pandas.tseries.index.DatetimeIndex'>
[2014-04-01]
Length: 1, Freq: D, Timezone: None
ObjectBlock: [timenow, Live], 2 x 1, dtype: object
In [54]: df = pd.DataFrame(index=pd.date_range(start,periods=1), columns=['timenow','Live'])
In [55]: df.loc[start,'timenow'] = datetime.today() # initial value
In [56]: df
Out[56]:
timenow Live
2014-04-01 2014-04-23 12:43:00.554915 NaT
[1 rows x 2 columns]
In [57]: df._data
Out[57]:
BlockManager
Items: Index([u'timenow', u'Live'], dtype='object')
Axis 1: <class 'pandas.tseries.index.DatetimeIndex'>
[2014-04-01]
Length: 1, Freq: D, Timezone: None
DatetimeBlock: [timenow, Live], 2 x 1, dtype: datetime64[ns]
```
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| null | 1 | 2014-04-23T21:20:10Z | 2014-04-23T21:24:01Z | 2014-04-23T21:24:01Z | CONTRIBUTOR | null | I'm going to do https://github.com/pydata/pandas/issues/4196 (multiple quantiles)
But this came up:
you get a `result` from _quantile. Then it checks this:
``` python
if not np.isscalar and com.is_timedelta64_dtype(result):
```
Should `np.isscalar` be called on result?
Currently that `if` block is never entered since `not np.isscaller` is False
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} | 4 | 2014-04-23T21:20:42Z | 2018-01-19T22:04:51Z | null | MEMBER | null | An overview of the reference doc on groupby is given here: http://pandas.pydata.org/pandas-docs/dev/api.html#groupby (apart from the extensive user guide: http://pandas.pydata.org/pandas-docs/dev/groupby.html)
There are some things that could use some improvement:
- [x] add some missing functions to the overview in api.rst (#8231)
- GroupBy.filter
- `first`/`last`/`nth`
- `count`, `cumcount`, ..
- `name`: not sure what the purpose of this is
- [ ] add the `GroupBy` object itself to the api docs (and so automatically all its methods) (#19302)
- [x] put all relevant docstrings in the GroupBy class, and not only in the subclasses DataFrameGroupBy, SeriesGroupBy (eg now the `aggregate` and `transform` docstrings of GroupBy are empty, but are more elaborate in the subclasses) (#8231)
- [ ] general clean-up of all the docstrings
- especially the `apply` docstring is not very clear to me
- [ ] expand DataFrame/Series.groupby() docstring:
- clearly list all possibilities for the `by` arg (and provide some short examples in the 'Examples' section)
- [x] document the whitelisted methods: (#8231)
- this could eg be done by injecting it in the docstring automatically based on `_apply_whitelist`
- or alternatively by ensuring they appear in the methods list of the GroupBy class (which is not the case at the moment, only in instantiated objects) (see also discussion in #2644)
- [x] More clearly document the DataFrameGroupBy and SeriesGroupBy classes (#8231)
- at least mention them in the docs
- one idea is to have a DataFrameGroupBy api pages that just redirects to the general GroupBy page
- [x] Add docstrings to the wrapped whitelisted functions. Eg at present `g = df.groupby(...); g.count?` is returning `<no docstring>` (see https://github.com/pydata/pandas/issues/4500#issuecomment-41220138 for explanation how)
- [ ] Make a clear distinction, about what to expect for the return values of a grouped-apply, e.g. `head/tail/nth` are basically `filter` type of functions, `fillna/shift` are transformers, while almost everything else is a reducer (e.g. `sum/mean/describe`), while `apply/agg` can be any of the above. hmm. maybe needs a separate section for this. (and of course `as_index` just makes this crazy)
If someone wants to tackle this (or parts of this), go ahead!
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} | 3 | 2014-04-23T22:12:02Z | 2017-12-06T11:29:14Z | 2017-12-06T11:29:14Z | CONTRIBUTOR | null | The `rank()` function converts `np.nan` to `np.inf` or `-np.inf`, and therefore can't properly rank existing `np.inf`. See here: https://github.com/pydata/pandas/pull/6886#issuecomment-40773964.
Test for this issue here: https://github.com/pydata/pandas/blob/master/pandas/tests/test_series.py#L4071
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| null | 1 | 2014-04-23T23:47:25Z | 2014-07-16T09:02:57Z | 2014-04-24T00:12:51Z | CONTRIBUTOR | null | Adding a space
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} | 3 | 2014-04-24T00:55:13Z | 2015-06-20T16:20:18Z | 2015-06-20T16:20:18Z | CONTRIBUTOR | null | apparently we are using the regular `xlwt` on the 3.3 build, which doesn't (it skips everything), so need
still in dev on xlwt master branch
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| null | 1 | 2014-04-24T10:26:57Z | 2014-04-24T10:47:52Z | 2014-04-24T10:47:52Z | NONE | null | I think line 567 in tools/rplot.py "ax.contour(Z, extent=[x_min, x_max, y_min, y_max])" should be changed to "ax.contour(Z.T, extent=[x_min, x_max, y_min, y_max])"
When I plot a density map, the graph is rotated by 90 degrees.
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} | 1 | 2014-04-24T10:50:41Z | 2016-04-05T21:50:43Z | 2016-04-05T21:50:43Z | CONTRIBUTOR | null | ``` python
In [1]: pd.__version__
Out[1]: '0.13.1'
In [2]: pd.DataFrame({'a': pd.SparseArray([1,2,3, np.nan, np.nan]), 'b': [1,2,3,np.nan,np.nan]})
Out[2]:
a b
0 1 1
1 2 2
2 3 3
3 NaN NaN
4 NaN NaN
[5 rows x 2 columns]
In [3]: _2.reindex(_2.index[1:], fill_value=10.)
Out[3]:
a b
1 2 2
2 3 3
3 10 NaN
4 10 NaN
[4 rows x 2 columns]
```
Also, this looks weird, I'd expect sparse array to preserve nans:
``` python
In [3]: pd.SparseArray([np.nan, np.nan], fill_value=0.0).to_dense()
Out[3]: array([ 0., 0.])
```
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} | 8 | 2014-04-24T11:53:52Z | 2016-11-16T09:06:06Z | 2016-11-16T09:06:06Z | MEMBER | null | Related to #6913.
`DataFrame.pivot` function uses separate logic and has less keyword options than `pandas.tools.pivot_table`. How about making `DataFrame.pivot` to call `pivot_table` internally to support same keywords as `pivot_table`?
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} | 10 | 2014-04-24T13:02:12Z | 2014-06-13T14:50:07Z | 2014-04-29T16:29:19Z | MEMBER | null | Found 2 minor issues related to plotting with `subplots=True`.
1. If `DataFrame` only has single column, plot raises `TypeError`. It is because `_subplots` returns `Axes` instance, not array.
2. If `kind=scatter` or `kind=hexbin` is specified with `subplots=True`, additional blank axes is drawn.
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} | 6 | 2014-04-24T14:11:52Z | 2017-04-05T02:08:47Z | 2014-04-24T16:10:54Z | CONTRIBUTOR | null | Closes #6952
I wasn't sure what the best way to test this was. I went with calling `_apply_empty_result` directly and checking for a SystemExit to get around the try: except Exception block in `_apply_empty_result`.
I still don't think I've 100% ensured that `_apply_empty_result` will always be _called with_ _args and *_kwargs (if there are any), but if they are passed things will be handled correctly.
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} | 0 | 2014-04-24T14:49:48Z | 2014-07-16T09:03:00Z | 2014-04-24T22:43:18Z | CONTRIBUTOR | null | closes #6915
overflow when doing sum (and mean) using bottleneck/numpy on 32-bit platforms with 32-bit dtypes
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} | 12 | 2014-04-24T15:13:23Z | 2014-07-16T09:03:02Z | 2014-04-25T11:55:06Z | CONTRIBUTOR | null | Doesn't quite finish #4196, but it should be easy not that `quantile` takes arrays.
I did this on top of #6953, so that should go in first.
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} | 11 | 2014-04-24T15:52:32Z | 2014-06-21T08:53:32Z | 2014-05-05T23:12:09Z | CONTRIBUTOR | null | parallel_coordinates
- fix reordering of class column (from set) causing possible color/class
mismatch
- deprecated use of argument colors in favor of color
radviz
- fix reordering of class column (from set) causing possible color/class
mismatch
- added explicit color keyword argument (avoids multiple values 'color' being
passed to plotting method)
andrews_curves
- added explicit color keyword argument (avoids multiple values 'color' being
passed to plotting method)
To recreate the bug:
``` python
import pandas as pd
from pandas.tools.plotting import parallel_coordinates, radviz, andrews_curves
x = pd.DataFrame([[1,2,3,'b'],
[2,2,1,'g'],
[3,3,1,'r']], columns=[0,1,2,'name'])
parallel_coordinates(x, 'name', colors=['b','g','r'])
# TypeError from scatter
radviz(x, 'name', color=['b','g','r'])
# TypeError from plot
andrews_curves(x, 'name', color=['b','g','r'])
```
parallel_coordinates before:
parallel_coordinates after:
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| null | 3 | 2014-04-24T16:12:05Z | 2016-08-05T10:00:20Z | 2014-04-24T16:21:58Z | CONTRIBUTOR | null | ## INPUT
## Reproduce the Error
``` python
import pandas as pd
pd.show_versions()
print "\nCASE 1 : Working with a dataframe with mixed object types ************"
dfa = pd.DataFrame(data=zip(["Silly","Thing","To","Concatenate"],[0.,1.,2.,3.1]),columns=['A','B'])
print " FAILS : sums the number, but returns it as a string, EXPECTED : <type 'numpy.float64'>"
print dfa.sum()['B'] #Here we're summing the entire df, then accessing one column's result.
print type(dfa.sum()['B'])
```
## Work Arounds and Other Notes
```
print " WORKS : Sums the numbers, returns a number"
print dfa['B'].sum() #Note the difference, here we're accessing the sum of a Series.
print type(dfa['B'].sum())
print " NOTE : Multiple dtypes"
print dfa.dtypes
print "\nCASE 2 : Working with a dataframe with numbers only. *****************"
dfb = pd.DataFrame(data=zip([0.,1.,2.,3.1],[0.,1.,2.,3.1]),columns=['A','B'])
print " WORKS : Sums the number, returns a number"
print dfb.sum()['B']
print type(dfb.sum()['B']) #Expected <type 'numpy.float64'>
print " WORKS : Sums the numbers, returns a number"
print dfb['B'].sum()
print type(dfb['B'].sum())
print " NOTE : Identical dtypes"
print dfb.dtypes
print " NOTE : A series of multiple types is allowed, so why would sum, not return a Series of multiple types?"
s = pd.Series([1.0,2,"a string"])
print type(s[1])
print type(s[2])
```
## OUTPUT
note: formatted as ruby, not actually ruby code. This is python console output.
``` ruby
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US
pandas: 0.13.1
Cython: 0.20
numpy: 1.8.0
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 1.1.0
sphinx: 1.2.1
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2013.9
bottleneck: None
tables: 3.1.0
numexpr: 2.3
matplotlib: 1.3.1
openpyxl: None
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
sqlalchemy: 0.9.2
lxml: None
bs4: 4.3.2
html5lib: None
bq: None
apiclient: None
CASE 1 : Working with a dataframe with mixed object types ************
FAILS : sums the number, but returns it as a string, EXPECTED : <type 'numpy.float64'>
6.1
<type 'str'>
WORKS : Sums the numbers, returns a number
6.1
<type 'numpy.float64'>
NOTE : Multiple dtypes
A object
B float64
dtype: object
CASE 2 : Working with a dataframe with numbers only. *****************
WORKS : Sums the number, returns a number
6.1
<type 'numpy.float64'>
WORKS : Sums the numbers, returns a number
6.1
<type 'numpy.float64'>
NOTE : Identical dtypes
A float64
B float64
dtype: object
NOTE : A series of multiple types is allowed, so why would sum, not return a Series of multiple types?
<type 'int'>
<type 'str'>
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6958 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6958/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6958/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6958/events | https://github.com/pandas-dev/pandas/pull/6958 | 32,171,659 | MDExOlB1bGxSZXF1ZXN0MTUxMzMzNzQ= | 6,958 | MAINT: Numpy deprecated boolean unary '-' operator | {
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} | 10 | 2014-04-24T18:06:43Z | 2014-06-18T02:07:24Z | 2014-04-25T00:46:38Z | CONTRIBUTOR | null | I'm sure there are more of these, but I just caught this one when I was raising on all warnings.
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} | 0 | 2014-04-25T00:28:46Z | 2014-04-28T23:21:23Z | 2014-04-28T23:21:23Z | CONTRIBUTOR | null | Because the 13.1 shift uses `.values`, it will upcast mixed dtypes to object.
``` python
import pandas as pd
import numpy as np
import pandas.util.testing as tm
data = [('item '+ch, tm.makeMixedDataFrame()) for ch in list('abcde')]
data = dict(data)
mixed_panel = pd.Panel.from_dict(data, orient='minor')
shifted = mixed_panel.shift(1)
tm.assert_series_equal(mixed_panel.dtypes, shifted.dtypes) #fails
```
Have PR for this.
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} | 1 | 2014-04-25T00:43:53Z | 2014-06-17T08:26:48Z | 2014-04-27T17:47:19Z | CONTRIBUTOR | null | COMPAT: change neg of boolean to inv (deprecation in numpy 1.9)
COMPAT: remove deprecation warnings on **new** (in computation/pytables)
COMPAT: fix array_to_datetime on invalid comparison to NaT
related #6958
add ability to set an environmental variable to actually show the DeprecationWarnings
just
`setenv PANDAS_TESTING_MODE 'numpy_deprecate'`
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} | 11 | 2014-04-25T02:25:25Z | 2014-10-30T11:19:43Z | 2014-10-29T11:39:26Z | CONTRIBUTOR | null | I ran this on master current as of 2014-04-20 or so and was surprised as to the result:
``` python
In [17]: pd.date_range('2014', '2015', freq='M')
Out[17]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2014-04-30, ..., 2015-03-31]
Length: 12, Freq: M, Timezone: None
```
I would have expected that to give me the month-ends in 2014, not a year's worth of month-ends starting from today.
Seems to be related to #3944?
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} | 10 | 2014-04-25T05:00:09Z | 2017-10-04T15:51:59Z | 2017-10-04T15:51:59Z | CONTRIBUTOR | null | [Bokeh](http://bokeh.pydata.org) specializes on large data, and is using pandas in some of their examples (e.g. [here](https://github.com/ContinuumIO/bokeh/blob/master/examples/plotting/file/glucose.py)).
As pandas is built to work with large data-sets as well, I believe they would make an excellent couple!
However, I have tried to use the built-in dataframe plotting in the [glucose notebook](https://github.com/ContinuumIO/bokeh/blob/master/examples/plotting/notebook/glucose.ipynb) but without success, receiving either TypeErrors or KeyErrors, depending on how I initiate the plot:
``` python
data.head()
isig glucose inrange
datetime
2010-03-24 09:51:00 22.59 258 False
2010-03-24 09:56:00 22.52 260 False
2010-03-24 10:01:00 22.23 258 False
2010-03-24 10:06:00 21.56 254 False
2010-03-24 10:11:00 20.79 246 False
```
``` python
data.isig.plot()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-23-14244d9a86ef> in <module>()
----> 1 data.isig.plot()
/Users/maye/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas-0.13.1_648_gad1f47d-py2.7-macosx-10.6-x86_64.egg/pandas/tools/plotting.pyc in plot_series(series, label, kind, use_index, rot, xticks, yticks, xlim, ylim, ax, style, grid, legend, logx, logy, secondary_y, **kwds)
2114 secondary_y=secondary_y, **kwds)
2115
-> 2116 plot_obj.generate()
2117 plot_obj.draw()
2118
/Users/maye/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas-0.13.1_648_gad1f47d-py2.7-macosx-10.6-x86_64.egg/pandas/tools/plotting.pyc in generate(self)
916 def generate(self):
917 self._args_adjust()
--> 918 self._compute_plot_data()
919 self._setup_subplots()
920 self._make_plot()
/Users/maye/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas-0.13.1_648_gad1f47d-py2.7-macosx-10.6-x86_64.egg/pandas/tools/plotting.pyc in _compute_plot_data(self)
1003 if is_empty:
1004 raise TypeError('Empty {0!r}: no numeric data to '
-> 1005 'plot'.format(numeric_data.__class__.__name__))
1006
1007 self.data = numeric_data
TypeError: Empty 'Series': no numeric data to plot
```
``` python
data.plot(data.index.to_series(), data.isig)
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-24-e46a01c88c5e> in <module>()
----> 1 data.plot(data.index.to_series(), data.isig)
/Users/maye/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas-0.13.1_648_gad1f47d-py2.7-macosx-10.6-x86_64.egg/pandas/tools/plotting.pyc in plot_frame(frame, x, y, subplots, sharex, sharey, use_index, figsize, grid, legend, rot, ax, style, title, xlim, ylim, logx, logy, xticks, yticks, kind, sort_columns, fontsize, secondary_y, **kwds)
1985 label = x if x is not None else frame.index.name
1986 label = kwds.pop('label', label)
-> 1987 ser = frame[y]
1988 ser.index.name = label
1989
/Users/maye/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas-0.13.1_648_gad1f47d-py2.7-macosx-10.6-x86_64.egg/pandas/core/frame.pyc in __getitem__(self, key)
1646 if isinstance(key, (Series, np.ndarray, list)):
1647 # either boolean or fancy integer index
-> 1648 return self._getitem_array(key)
1649 elif isinstance(key, DataFrame):
1650 return self._getitem_frame(key)
/Users/maye/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas-0.13.1_648_gad1f47d-py2.7-macosx-10.6-x86_64.egg/pandas/core/frame.pyc in _getitem_array(self, key)
1690 return self.take(indexer, axis=0, convert=False)
1691 else:
-> 1692 indexer = self.ix._convert_to_indexer(key, axis=1)
1693 return self.take(indexer, axis=1, convert=True)
1694
/Users/maye/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas-0.13.1_648_gad1f47d-py2.7-macosx-10.6-x86_64.egg/pandas/core/indexing.pyc in _convert_to_indexer(self, obj, axis, is_setter)
1065 if isinstance(obj, tuple) and is_setter:
1066 return {'key': obj}
-> 1067 raise KeyError('%s not in index' % objarr[mask])
1068
1069 return indexer
KeyError: "['22.59' '22.52' '22.23' ..., '29.06' '29.3' '30.8'] not in index"
```
I am guessing that we need to teach the DataFrame to check if the Bokeh framework is active and then point the plotter to the slightly different syntax for Bokeh plotting, which we would need to implement of course. Can somebody shed a light on what is going wrong in general in above examples?
Considering the possibility of having active zoomable plots over usually massively too long time-series with lots of data points in them I am very intrigued by this feature and would love to see it implemented. If further discussion in this issue can guide me on how to do the recognition part of the active plotting framework (or if somebody would take that on would be great), I am volunteering in wrapping the different Bokeh plotting syntaxes, so that e.g. the standard df.colname.plot() will work as well with Bokeh underneath.
Sounds good?
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} | 16 | 2014-04-25T11:30:38Z | 2020-12-24T15:08:03Z | 2020-12-24T15:08:03Z | MEMBER | null | When concatting two dataframes where there are a) there are duplicate columns in one of the dataframes, and b) there are non-overlapping column names in both, then you get a IndexError:
```
In [9]: df1 = pd.DataFrame(np.random.randn(3,3), columns=['A', 'A', 'B1'])
...: df2 = pd.DataFrame(np.random.randn(3,3), columns=['A', 'A', 'B2'])
In [10]: pd.concat([df1, df2])
Traceback (most recent call last):
File "<ipython-input-10-f61a1ab4009e>", line 1, in <module>
pd.concat([df1, df2])
...
File "c:\users\vdbosscj\scipy\pandas-joris\pandas\core\index.py", line 765, in take
taken = self.view(np.ndarray).take(indexer)
IndexError: index 3 is out of bounds for axis 0 with size 3
```
I don't know if it should work (although I suppose it should, as with only the duplicate columns it does work), but at least the error message is not really helpfull.
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| null | 35 | 2014-04-25T11:31:16Z | 2014-04-29T13:47:59Z | 2014-04-29T12:23:07Z | NONE | null | Right now, as per the gotchas section, pandas raises a value error on an empty frame
http://pandas.pydata.org/pandas-docs/dev/gotchas.html#gotchas-truth
While documentation describes ambiguity, in the python world there is none.
PEP8 (http://legacy.python.org/dev/peps/pep-0008/) recommends: "For sequences, (strings, lists, tuples), use the fact that empty sequences are false."
The check returns False for None and empty values for all iterables. Further, there are any (https://docs.python.org/2/library/functions.html#any) and all(https://docs.python.org/2/library/functions.html#all) functions for the proposed ambiguity checks.
As currently it's just raising a ValueError, fixing the problem to correctly evaluate representing DataFrame.empty would be backwards compatible.
The check becomes important when working in a mixed environment with pandas and other structures - a simple `if foo` can become as convoluted as
`empty = foo.empty() if (foo is not None and isinstance(foo, pd.DataFrame)) else not foo`
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} | 2 | 2014-04-25T11:57:01Z | 2014-05-10T21:51:19Z | 2014-05-10T21:51:19Z | CONTRIBUTOR | null | ``` python
In [2]: df = DataFrame({'a': pd.to_datetime(['2010', '2011']), 'b': [0, 5]})
In [3]: df.quantile(.5, numeric_only=False)
Out[3]:
a 1.278072e+18
b 1.278072e+18
dtype: float64
```
Series handles things fine.
We need to do a `view('i8')` on the date time array somewhere, but I had trouble just sticking it inside of the `f` that gets passed to `data.apply(f, ...)`
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} | 3 | 2014-04-25T14:15:57Z | 2015-04-28T11:56:04Z | 2015-04-28T11:56:04Z | CONTRIBUTOR | null | Here's what I mean:
``` python
In [4]: pd.Series(np.arange(7))
Out[4]:
0 0
1 1
2 2
3 3
4 4
5 5
6 6
dtype: int64
# Let's put some limit on weekends
In [5]: _4.clip_upper([3,3,3,3,3, 4,4])
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-3268c7bee292> in <module>()
----> 1 _4.clip_upper([3,3,3,3,3, 4,4])
/home/dshpektorov/sources/pandas/pandas/core/generic.pyc in clip_upper(self, threshold)
2636 clipped : same type as input
2637 """
-> 2638 if isnull(threshold):
2639 raise ValueError("Cannot use an NA value as a clip threshold")
2640
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
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} | 1 | 2014-04-25T14:37:40Z | 2014-04-28T00:39:31Z | 2014-04-28T00:39:31Z | CONTRIBUTOR | null | https://travis-ci.org/jreback/pandas/jobs/23758981
#6960 got most of the inv/neg ones (e.g. you can no longer user `-` to invert a boolean mask but must use `~`), starting in 1.9
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} | 46 | 2014-04-25T14:49:32Z | 2014-06-12T18:09:13Z | 2014-06-04T12:46:42Z | CONTRIBUTOR | null | closes #4688
This PR should provide support for dateutil timezones. It was discussed quite a bit in https://github.com/pydata/pandas/pull/4689.
The discussion there ended two months back with "we should incorporate this if it can be made seamless". I think I've managed that now so it would be great to get some feedback on the changes in this PR.
Everything that works with pytz timezones should work with dateutil timezones and you shouldn't notice any difference in behaviour when you change between them. There are two exceptions which I'm looking at now: saving data to ujson and pytables.
The changes I've made just allow pandas to treat dateutil timezones exactly the same as pytz timezones (from the user perspective) - just extending conversion logic to deal with both where appropriate. This has made a few methods a bit more complicated but relatively few changes were required.
All of the significant changes are in tslib.pyx. Almost all of the other changes are adding test cases for the dateutil timezones. The test suites pass locally.
Let me know if you have any questions or if there's anything that needs doing before the PR can be accepted.
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} | 1 | 2014-04-25T19:29:00Z | 2014-04-25T19:36:00Z | 2014-04-25T19:36:00Z | MEMBER | null | http://stackoverflow.com/q/23301072/564538
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} | 2 | 2014-04-25T19:32:29Z | 2014-08-19T17:09:14Z | 2014-08-19T17:09:14Z | NONE | null | from SO: http://stackoverflow.com/q/23301072/564538
I'm trying to print three pandas boxplots next to each other in iPython Notebook.
The dataframes each look basically like this:
``` python
sub cond accuracy
s1 A 0.814868
s2 A 0.504574
s3 A 0.438314
s4 A 0.956235
s5 A 0.370771
s1 B 0.228724
s2 B 0.691374
s3 B 0.237314
s4 B 0.32633
s5 B 0.859961
```
When I plot accuracy without subsetting by condition, this works great:
``` python
fig, axes = plt.subplots(nrows=1, ncols=3)
for i, df in enumerate([df1, df2, df3]):
df.boxplot('accuracy',ax=axes[i])
```
However, when I try to plot accuracy by condition for each dataframe using instead:
``` python
df.boxplot('accuracy',by='cond',ax=axes[i])
```
only the last plot displays (and not where it should be according to its axes assignment).
This seems to be an issue ing the `by=X` argument.
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} | 12 | 2014-04-26T12:29:18Z | 2016-07-26T22:44:17Z | 2016-07-26T22:44:16Z | NONE | null | I ran into a bug in the read_csv importer when trying to read in a file with
a European style decimal encoding (e.g. 8.1 -> 8,2). Setting the decimal-parameter
appropriately should make this easy but in my case pandas refused to accepts any different data type than a simple object.
After a few attempts with various files and snipplets of code I nailed down the problem to the skipfooter parameter. As far as I can judge skipfooter causes the decimal parameter to be ignored. Take the following example:
In [44]:
data = 'a;b;c\n1,1;2,2;3,3\n4;5;6\n7;8;9'
data
Out[44]:
'a;b;c\n1,1;2,2;3,3\n4;5;6\n7;8;9'
In [45]:
df = pd.read_csv(io.StringIO(data), sep=";",decimal=",",dtype=np.float64)
df
Out[45]:
a b c
0 1.1 2.2 3.3
1 4.0 5.0 6.0
2 7.0 8.0 9.0
3 rows × 3 columns
In [46]:
df.dtypes
Out[46]:
a float64
b float64
c float64
dtype: object
Perfect - the behaviour I expected. Now let’s add as single line a an arbitrary footer and ignore this line in the import.
In [47]:
data = data+'\nFooter'
data
Out[47]:
'a;b;c\n1,1;2,2;3,3\n4;5;6\n7;8;9\nFooter'
In [48]:
df = pd.read_csv(io.StringIO(data), sep=";",decimal=",",dtype=np.float64,skipfooter=1)
df
Out[48]:
a b c
0 1,1 2,2 3,3
1 4 5 6
2 7 8 9
3 rows × 3 columns
In [49]:
df.dtypes
Out[49]:
a object
b object
c object
dtype: object
Now all data type information is lost supposingly because the conversion from the comma-separated to the dot-separated values failed. Adding an additional converter to the import (converters={'Rate': lambda x: float(x.replace('.','').replace(',','.'))}) fixes the problem and makes it more likely that the skipfooter routine is faulty.
System: iPython 2.0.0, Python 3.3.5, pandas 0.13.0
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} | 2 | 2014-04-26T13:34:43Z | 2014-06-13T16:46:14Z | 2014-04-27T13:58:57Z | CONTRIBUTOR | null | As suggested by Jeff on this [StackOverflow answer](http://stackoverflow.com/a/23233196/1222578), I'm adding an example of a boxplot that uses quartiles of a second variable as the `by` variable.
Jeff also suggested adding the example in-line, rather than just providing a link to StackOverflow, so that's what I've done for now. The resulting plot currently looks like this, I'm happy to tweak it to better get the idea across:
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| null | 6 | 2014-04-26T14:32:16Z | 2014-04-27T13:57:16Z | 2014-04-27T13:56:06Z | MEMBER | null | Assume that I want to take the row-wise maximum of two Series **A** and **B**. As far as I understand, I have two choices:
1)
```
result = pandas.DataFrame({'A' : A, 'B' : B}).max(axis=1)
```
2)
```
result = pandas.Series(numpy.max((A, B), axis=0), index=A.index)
```
(assuming A and B have identical, and sorted, indices).
Ideally, I would just do
3)
```
result = pandas.max((A,B), axis=1)
```
(and have something equivalent to 1) being done behind the curtains).
(Even more ideally, I would like "A or B" to do an equivalent of Python's "a[i] or b[i]" for each row i, and hence the maximum if the dtypes are numeric, but this would break consistency with numpy... which after all does have numpy.max)
I would guess the same enhancement would be nice for "min", and possibly "idxmax", "idxmin", and maybe even "sum" (although there is the "+" operator for the basic case)... ?
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} | 4 | 2014-04-26T19:50:48Z | 2014-06-18T12:23:39Z | 2014-04-28T23:21:23Z | CONTRIBUTOR | null | Reverts #6605 closes #6959 #6826
## vs 13.1
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
panel_shift | 0.0773 | 0.0990 | 0.7809 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [3d0099a] : BUG: Change Panel.shift to use slice_shift #6605 #6826
Base [d10a658] : RLS: set released to True. v0.13.1
```
## vs master
Note that the `pct_change is slower due to the deferred alignment.
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
panel_shift | 0.1650 | 522.0180 | 0.0003 |
panel_pct_change_major | 7615.9450 | 6213.1660 | 1.2258 |
panel_pct_change_minor | 7864.9763 | 5877.1353 | 1.3382 |
panel_pct_change_items | 8646.0753 | 5884.1163 | 1.4694 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [3ec426f] : BUG: Change Panel.shift to use slice_shift #6605 #6826
Base [0d2966f] : COMPAT: 32-bit platform compat for Panel.count
```
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Please be gentle - this is the first time I've tried to contribute either to someone else's python project or another project on github, so if my python or git foo is lacking, I apologise in advance!
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If data includes `NaN`, it will be automatically filled by 0. If data contains negative value, `ValueError` will be raised.
```
import pandas as pd
import numpy as np
series = pd.Series(3 * np.random.rand(4), index=['a', 'b', 'c', 'd'], name='series')
series.plot(kind='pie')
```
### Plotting with DataFrame
Pie plot with `DataFrame` requires either to specify target column by `y` argument or `subplots=True`. When `y` is specified, pie plot of selected column will be drawn. If `subplots=True` is specified, pie plots for each columns are drawn as subplots. Legend will be drawn in each pie plots by default, specify `legend=False` to hide it.
```
df = pd.DataFrame(3 * np.random.rand(4, 2), index=['a', 'b', 'c', 'd'], columns=['x', 'y'])
df.plot(kind='pie', subplots=True)
```
### Plotting with Options
You can use `labels` and `colors` keywords to specify labels and colors of each wedges (Cannot use `label` and `color`, because of matplotlib's specification). If you want to hide wedge labels, specify `labels=None`. If `fontsize` is specified, the value will be applied to wedge labels. Also, other keywords supported by `matplotlib.pyplot.pie` can be used.
```
series.plot(kind='pie', labels=['AA', 'BB', 'CC', 'DD'], colors=['r', 'g', 'b', 'c'],
autopct='%.2f', fontsize=20)
```
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} | 8 | 2014-04-27T02:18:18Z | 2014-07-16T09:03:26Z | 2014-04-27T23:39:52Z | CONTRIBUTOR | null | This is a fix for #5357, and adds a more helpful error message when trying to use an iterator as the `data` argument for a `DataFrame`. I've added the type check for iterators in the final else clause where all the valid options have already been exhausted, so it shouldn't interfere with any valid cases like passing a list of iterators.
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| null | 7 | 2014-04-27T06:04:11Z | 2020-01-29T12:37:48Z | 2014-04-27T08:18:17Z | NONE | null | The mode() function is missing from SeriesGroupBy and DataFrameGroupBy. It should be implemented with the same semanthics as as mean(), median(), max(), etc.
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| null | 6 | 2014-04-27T06:18:12Z | 2014-04-27T08:19:07Z | 2014-04-27T07:12:54Z | NONE | null | Consider the following code:
```
pd.Series([1]).mode()
=> Series([], dtype: int64)
```
Expected value is the same as for:
```
pd.Series([1,1]).mode()
=> 0 1
=> dtype: int64
```
The same problem happens whenever the most common element only occurs once:
```
pd.Series(range(100)).mode()
Series([], dtype: int64)
```
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} | 1 | 2014-04-27T13:44:51Z | 2014-09-19T20:20:24Z | 2014-09-19T20:20:24Z | CONTRIBUTOR | null | PyTables 3.0.0 deprecations every function as they weren't PEP compitable. So need to change all call references.
Deprecation warnings are currnetly turned off by this commit otherwise output is very long (when `PANDAS_TESTING_MODE='deprecate'` is turned on (in numpy-1.9/3.4 builds)
https://github.com/pydata/pandas/commit/8ed820600b2509d09327677e3a600f4779953363
I think this will break PyTables < 3.0.0 IIRC.
https://github.com/PyTables/PyTables/pull/224
Branch that does this (but breaks < 3.0.0)
https://github.com/jreback/pandas/commit/6bb41980052a780d75e45eb768e65be0ab542085
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} | 11 | 2014-04-27T16:12:57Z | 2014-05-02T17:13:04Z | 2014-04-27T22:16:48Z | CONTRIBUTOR | null | ```
======================================================================
FAIL: test_sum (pandas.tests.test_frame.TestDataFrame)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/virtualenv/python2.6_with_system_site_packages/lib/python2.6/site-packages/pandas/tests/test_frame.py", line 10590, in test_sum
has_numeric_only=True, check_dtype=False, check_less_precise=True)
File "/home/travis/virtualenv/python2.6_with_system_site_packages/lib/python2.6/site-packages/pandas/tests/test_frame.py", line 10780, in _check_stat_op
check_less_precise=check_less_precise) # HACK: win32
File "/home/travis/virtualenv/python2.6_with_system_site_packages/lib/python2.6/site-packages/pandas/util/testing.py", line 513, 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:2554)
File "testing.pyx", line 93, in pandas._testing.assert_almost_equal (pandas/src/testing.c:1796)
File "testing.pyx", line 140, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2387)
AssertionError: expected 0.00144 but got 0.00144
```
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} | 1 | 2014-04-27T16:31:13Z | 2014-06-30T18:09:34Z | 2014-04-27T22:15:44Z | CONTRIBUTOR | null | closes #6899
though seems that cPickle can on a small file/dataset be slower
so waiting for results on #6899 tests
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if deprecations mode is turned on #6960 then these become apparant
I think the tests simply need to be change and/or asserted that these are actually producing deprecation warnings
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} | 0 | 2014-04-27T19:02:09Z | 2014-07-16T09:03:32Z | 2014-04-27T22:16:48Z | CONTRIBUTOR | null | closes #6982
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} | 1 | 2014-04-27T20:26:29Z | 2014-07-16T09:03:34Z | 2014-04-27T21:38:28Z | CONTRIBUTOR | null | closes #6984
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} | 20 | 2014-04-27T21:53:08Z | 2014-06-12T12:52:48Z | 2014-05-08T12:26:22Z | MEMBER | null | Some first fixes based on running the old sql tests (from 0.13.1 tag)
- fixed `get_schema` (and adapted is signature back to how it was for 0.13.1). For this I added back the old function, as this functionality was not possible with the refactored class based approach (for those you always have to provide a connection object or engine, this was not necessary for `get_schema`)
- `table_exists` seems to be renamed to `has_table`? I don't know why (and we should also decide if we expose this as a public function), but added it back for now as an alias.
All tests from the old test suite are now running with the new code (for sqlite, not yet tested for mysql), except for the following issues:
- `tquery` had a `retry` and `cur` arguments (which were at least used in the tests), but not that simple to put back with the new implementation
- OperationalError is changed to DatabaseError
@jreback I also changed the DeprecationWarnings to FutureWarnings (I am right this is the canonical way to raise deprecation warnings in pandas?) and catched them in the tests. I don't know if this solves #6984 for the sql warnings?
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} | 7 | 2014-04-28T09:24:28Z | 2014-06-25T22:25:34Z | 2014-04-28T11:20:38Z | CONTRIBUTOR | null | I've implemented the former while refactoring BlockManager (#6745) because I didn't find the latter in lib module. This PR will remove the duplication (and also add missing changelog entry).
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} | 0 | 2014-04-28T11:59:07Z | 2014-07-16T09:03:47Z | 2014-04-28T12:44:00Z | CONTRIBUTOR | null | really close #6982
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} | 11 | 2014-04-28T12:52:51Z | 2014-06-25T17:29:12Z | 2014-05-08T13:21:04Z | CONTRIBUTOR | null | Alright, I think I have a fix for issue #3578. In `plotting._grouped_plot_by_column`, there was no check being done for whether `ax` was `None`, which was the main source of the issue. When the boxplot is of multiple columns, I don't think there's anything sensible that can be done with the `ax` argument, so that
now raises a `ValueError`.
I've written some tests, but I'm not sure if they really get to the heart of the problem, so any insight on how to improve them would be appreciated.
Testing code adapted from the original bug report to demonstrate the new behaviour:
``` python
import matplotlib.pyplot as plt
import pandas
from pandas import DataFrame, Series
data = {'day': Series([1, 1, 1, 2, 2, 2, 3, 3, 3]),
'group2': Series([2, 2, 2, 2, 2, 1, 1, 1, 1]),
'val': Series([3, 4, 5, 6, 7, 8, 9, 10, 11]),
'val2': Series([8, 9, 10, 11, 12, 13, 14, 15])}
df = pandas.DataFrame(data)
# Single-column using existing axis: should create a single plot
plt.figure()
plt.subplot(2, 2, 1)
plt.plot([1, 2, 3])
plt.subplot(2, 2, 4)
ax = plt.gca()
fig = ax.get_figure()
axes = df.boxplot('val', 'day', ax=ax)
print("Testing identity of returned axes: (should be True)")
print(id(axes) == id(ax.get_axes()))
plt.show()
# Multiple column, not using existing axis: should create two plots
plt.figure()
plt.subplot(2, 2, 1)
plt.plot([1, 2, 3])
plt.subplot(2, 2, 4)
ax = plt.gca()
axes = df.boxplot(['val', 'val2'], 'day')
print("Testing identity of returned axes: (should be False)")
print(id(axes) == id(ax.get_axes()))
plt.show()
# Multiple column using existing axis: should raise an exception,
# since it's hard to know what to do: we need an axis for each
# column and ax is just a single axis object
plt.figure()
plt.subplot(2, 2, 1)
plt.plot([1, 2, 3])
plt.subplot(2, 2, 4)
ax = plt.gca()
try:
df.boxplot(['val', 'val2'], 'day', ax=ax)
except ValueError:
print("Raising exception as expected")
plt.show()
```
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} | 6 | 2014-04-28T12:59:16Z | 2015-06-02T19:25:19Z | 2015-05-30T13:05:57Z | MEMBER | null | xref #9236
There seems to be an inconsistency in some GroupBy methods when `NaT` is included in the group key.
- `GroupBy.groups` includes `NaT` as a key.
- `GroupBy.ngroups` doesn't count `NaT`.
- `GroupBy.__iter__` doesn't return `NaT` group.
- `GroupBy.get_group` fails when `NaT` is specified.
I understand `NaT` should be included in the group key according to other function's behaviour, such as `dropna`. Is it OK to fix it to include `NaT`?
```
import pandas as pd
import numpy as np
>>> df = pd.DataFrame({'values': np.random.randn(8),
'dt': [np.nan, pd.Timestamp('2013-01-01'), np.nan, pd.Timestamp('2013-02-01'),
np.nan, pd.Timestamp('2013-02-01'), np.nan, pd.Timestamp('2013-01-01')]})
>>> grouped = df.groupby('dt')
>>> grouped.groups
{numpy.datetime64('NaT'): [0, 2, 4, 6], numpy.datetime64('2013-01-01T09:00:00.000000000+0900'): [1, 7], numpy.datetime64('2013-02-01T09:00:00.000000000+0900'): [3, 5]}
>>> grouped.ngroups
2
>>> grouped.indices
# ValueError: DatetimeIndex with NaT cannot be converted to object
>>> grouped.get_group(pd.NaT)
ValueError: DatetimeIndex with NaT cannot be converted to object
```
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} | 2 | 2014-04-28T14:12:23Z | 2014-04-28T14:40:08Z | 2014-04-28T14:40:08Z | CONTRIBUTOR | null | after this is merged, can prob add in the Notes section of plotting function sto see matplotlib
https://github.com/pydata/pandas/pull/6976
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} | 5 | 2014-04-28T14:16:27Z | 2014-05-05T19:52:08Z | 2014-05-05T19:52:08Z | CONTRIBUTOR | null | With the recent additions of hexbin, area, table, and pie charts (I may be forgetting some), the visualization docs are getting a bit long. I'll reorganize things to hopefully be a bit clearer.
I'll also mention things like seaborn, ggplot, mpld3, vincent, bearcart, and maybe remove the note that we plan to add more integration with maptlotlib.
- [x] link to matplotlib functions
- [x] all calls using random should be seeded so people can reproduce
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} | 0 | 2014-04-28T23:24:08Z | 2018-07-06T22:39:32Z | 2018-07-06T22:39:32Z | CONTRIBUTOR | null | see #6974
for Panel
- like to remove `NDFrame.slice_shift` and roll it into the Block itself as a bit cleaner
- see if can fix `pct_change` perf as its doing a `isnull` on by slice; maybe can just do that once then slice the mask
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} | 35 | 2014-04-28T23:44:28Z | 2015-06-02T19:26:59Z | 2015-05-30T13:05:57Z | MEMBER | null | Closes #6992. Made `GroupBy.get_group` works even if the key contains `NaT`.
NOTE: One issue is that `GroupBy.groups` returns incorrect key in numpy 1.6. This seems to be caused by `_convert_grouper` uses `grouper.reindex(axis).values` to return value. This looks doesn't affect to main functionalities, but is there any expected result?
```
import pandas as pd
import numpy as np
>>> np.__version__
1.6.2
>>> df = pd.DataFrame({'values': np.random.randn(8),
'dt': [np.nan, pd.Timestamp('2013-01-01'), np.nan, pd.Timestamp('2013-02-01'),
np.nan, pd.Timestamp('2013-02-01'), np.nan, pd.Timestamp('2013-01-01')]})
>>> grouped = df.groupby('dt')
>>> grouped.groups
{1970-01-16 48:00:00: [1, 7], 1970-01-16 24:00:00: [3, 5]}
```
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} | 1 | 2014-04-29T04:18:28Z | 2014-04-30T00:21:58Z | 2014-04-30T00:21:58Z | CONTRIBUTOR | null | All of the timestamp/datetimeindex accessors are not in the timeseries documentation (they currently are only referenced in the api documentation). It would be great to have a section on these accessors:
.year
.month
.day
.hour
.minute
.second
.microsecond
.nanosecond
.date
.time
.dayofyear
.weekofyear
.week
.dayofweek
.weekday
.quarter
.is_month_start
.is_month_end
.is_quarter_start
.is_quarter_end
.is_year_start
.is_year_end
Additionally, some of these accessors (e.g. weekofyear) are not frequency aware and users should be alerted to that fact.
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} | 3 | 2014-04-29T14:52:17Z | 2014-06-25T22:25:38Z | 2014-04-29T21:04:04Z | CONTRIBUTOR | null | This patch implements the str.wrap function within core/strings.
Example:
```
>>> s = pd.Series(['line to be wrapped', 'another line to be wrapped'])
>>> s.str.wrap(12)
0 line to be\nwrapped
1 another line\nto be\nwrapped
```
This is a cleaned branch; the original is at #6705.
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