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} | 3 | 2014-03-25T12:12:44Z | 2019-10-22T04:02:07Z | 2019-10-22T04:02:06Z | CONTRIBUTOR | null | 0.14 Enhancements : #6292
- [x] handle no rows returns from a sql query, #3745
- [ ] option for default_charset to sql.write_frame #5008
- [x] sql to provided NaN/NaT conversions #2754 -> PR #8208
- [x] specify chunksize for sql.read_frame #2908 -> PR #8330
- [ ] SQL through SQLAlchemy - check performance #6416
- [ ] mangle_dup_columns for read_frame (#2738)
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} | 1 | 2014-03-25T14:37:51Z | 2016-07-21T15:45:00Z | 2015-11-13T13:55:15Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/22636487/selection-in-multi-indexed-dataframe-when-key-does-not-exists/22637762#22637762
```
In [23]: df = DataFrame({'a':[1,2,3],'b':[1,2,3],'c':[1,2,3]})
In [24]: x = df.set_index(['a','b'])
In [25]: x
Out[25]:
c
a b
1 1 1
2 2 2
3 3 3
[3 rows x 1 columns]
In [26]: x.T.get((1,1))
Out[26]:
c 1
Name: (1, 1), dtype: int64
In [27]: x.T.get((1,5),default='foo')
Out[27]: 'foo'
```
`x.get((1,5),default='foo',axis=0)` should work (still need the `info_axis` to be the default though)
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| null | 4 | 2014-03-25T15:09:37Z | 2014-03-26T04:10:34Z | 2014-03-26T04:10:34Z | NONE | null | No sure if anybody has brought this up before(may it's not even worth mentioning). But it would be nice & convenient to have an additional 'exclude_columns' option.
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} | 16 | 2014-03-25T19:15:47Z | 2014-07-02T17:34:55Z | 2014-04-29T16:29:53Z | CONTRIBUTOR | null | This patch implements the str_wrap function within core/strings. The implementation follows
the behavior of R's stringr library. When a string is 'wrap'ped, the return value will be a
paragraph with lines of max(word length, width) length.
Example:
``` python
>>> str_wrap(Series([u'line to be wrapped', u'another line to be wrapped']), width=12)
Series([u'line to be\nwrapped', u'another\nline to be\nwrapped'])
```
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} | 3 | 2014-03-25T23:46:02Z | 2019-12-11T22:39:05Z | null | NONE | null | As I understand it, read_csv and read_table both use the C parser, but read_fwf uses a pure Python parser. As such, the latter is significantly slower and there doesn't seem to be any workaround. Is there any plan to add an optimized fwf reader?
See: https://groups.google.com/forum/#!topic/pystatsmodels/0DVBmCsDrVI
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} | 4 | 2014-03-26T00:29:18Z | 2016-11-23T12:22:37Z | 2014-03-26T18:24:42Z | NONE | null | I'm trying to get through the MovieLens 1M Data Set, but am having an issue with the merge step. I checked all of my dataframes and confirmed that I successfully imported the data from the tables, even though the book/git don't have appropriately marked links. On page 28, you are supposed to merge ratings with users, then merge the result with movies. My code looks like this:
data = pd.merge(pd.merge(ratings, users), movies)
the output that results looks like this:
---
MergeError Traceback (most recent call last)
<ipython-input-135-65bf0f0e3564> in <module>()
4 # pandas infers which columns to use as the join keys based on overlapping names
5
----> 6 data = pd.merge(pd.merge(ratings, users), movies)
//anaconda/lib/python2.7/site-packages/pandas/tools/merge.pyc in merge(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy)
37 right_on=right_on, left_index=left_index,
38 right_index=right_index, sort=sort, suffixes=suffixes,
---> 39 copy=copy)
40 return op.get_result()
41 if **debug**:
//anaconda/lib/python2.7/site-packages/pandas/tools/merge.pyc in **init**(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, copy)
181 (self.left_join_keys,
182 self.right_join_keys,
--> 183 self.join_names) = self._get_merge_keys()
184
185 def get_result(self):
//anaconda/lib/python2.7/site-packages/pandas/tools/merge.pyc in _get_merge_keys(self)
299 left_keys, right_keys
300 """
--> 301 self._validate_specification()
302
303 left_keys = []
//anaconda/lib/python2.7/site-packages/pandas/tools/merge.pyc in _validate_specification(self)
398 self.right.columns)
399 if len(common_cols) == 0:
--> 400 raise MergeError('No common columns to perform merge on')
401 self.left_on = self.right_on = common_cols
402 elif self.on is not None:
MergeError: No common columns to perform merge on
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} | 0 | 2014-03-26T02:20:28Z | 2014-03-31T16:49:52Z | 2014-03-31T16:49:52Z | NONE | null | The function [pandas.io.sql._convert_params](https://github.com/pydata/pandas/blob/b77eb1f11bcd65515ac33e1510fe03fd365bc433/pandas/io/sql.py#L30-L35) assume that the parameters are passed as a list. However the [DBAPI2.0](http://legacy.python.org/dev/peps/pep-0249/#id14) specify that the parameters can be a list or a mapping. This is a regression compared to the current stable version 0.13.1.
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} | 13 | 2014-03-26T02:34:29Z | 2014-07-01T14:57:33Z | 2014-03-31T16:49:52Z | NONE | null | According to the DBAPI2.0 the parameters of the execute method can be a list or a mapping. The code in the master branch assume that this parameter is a list which can break working code. That's a regression compared to the pandas 0.13.1
Closes #6708
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} | 9 | 2014-03-26T16:52:59Z | 2017-01-11T07:57:21Z | 2017-01-11T07:57:21Z | NONE | null | xref #4749
xref #8985
This is a bit like issue #4749, but the conditions are different.
I have been able to reproduce it by specifying `usecols`:
``` python
In [98]: mydata='1,2,3\n1,2\n1,2\n'
In [99]: pd.read_csv(StringIO(mydata), names=['a', 'b', 'c'], usecols=['a', 'c'])
Out[99]:
a c
0 1 3
1 1 NaN
2 1 NaN
[3 rows x 2 columns]
In [100]: mydata='1,2\n1,2,3\n4,5,6\n'
In [101]: pd.read_csv(StringIO(mydata), names=['a', 'b', 'c'], usecols=['a', 'c'])
---------------------------------------------------------------------------
CParserError Traceback (most recent call last)
<ipython-input-101-f60127771eeb> in <module>()
----> 1 pd.read_csv(StringIO(mydata), names=['a', 'b', 'c'], usecols=['a', 'c'])
/home/altaurog/venv/p27/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, na_fvalues, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, nrows, iterator, chunksize, verbose, encoding, squeeze, mangle_dupe_cols, tupleize_cols, infer_datetime_format)
418 infer_datetime_format=infer_datetime_format)
419
--> 420 return _read(filepath_or_buffer, kwds)
421
422 parser_f.__name__ = name
/home/altaurog/venv/p27/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in _read(filepath_or_buffer, kwds)
223 return parser
224
--> 225 return parser.read()
226
227 _parser_defaults = {
/home/altaurog/venv/p27/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in read(self, nrows)
624 raise ValueError('skip_footer not supported for iteration')
625
--> 626 ret = self._engine.read(nrows)
627
628 if self.options.get('as_recarray'):
/home/altaurog/venv/p27/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in read(self, nrows)
1068
1069 try:
-> 1070 data = self._reader.read(nrows)
1071 except StopIteration:
1072 if nrows is None:
/home/altaurog/venv/p27/local/lib/python2.7/site-packages/pandas/parser.so in pandas.parser.TextReader.read (pandas/parser.c:6866)()
/home/altaurog/venv/p27/local/lib/python2.7/site-packages/pandas/parser.so in pandas.parser.TextReader._read_low_memory (pandas/parser.c:7086)()
/home/altaurog/venv/p27/local/lib/python2.7/site-packages/pandas/parser.so in pandas.parser.TextReader._read_rows (pandas/parser.c:7691)()
/home/altaurog/venv/p27/local/lib/python2.7/site-packages/pandas/parser.so in pandas.parser.TextReader._tokenize_rows (pandas/parser.c:7575)()
/home/altaurog/venv/p27/local/lib/python2.7/site-packages/pandas/parser.so in pandas.parser.raise_parser_error (pandas/parser.c:19038)()
CParserError: Error tokenizing data. C error: Expected 2 fields in line 2, saw 3
In [102]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.1
Cython: 0.20
numpy: 1.8.0
scipy: None
statsmodels: None
IPython: 1.2.1
sphinx: 1.1.3
patsy: None
scikits.timeseries: None
dateutil: 1.5
pytz: 2012c
bottleneck: 0.8.0
tables: 3.1.0
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: 0.7.4
xlsxwriter: None
sqlalchemy: None
lxml: None
bs4: None
html5lib: 0.999
bq: None
apiclient: None
```
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| null | 5 | 2014-03-26T18:03:53Z | 2014-03-26T19:22:52Z | 2014-03-26T19:20:38Z | NONE | null | I've installed the 0.13.1 version with pip, and it installed successfully.
But when checking in pip freeze I get:
```
pip freeze | grep pandas
pandas==0.12.0
```
It looks like the package is in the right version, ie:
```
In [11]: import pandas as pd; pd.__version__
Out[11]: '0.13.1'
```
and only pip thinks that the version is 0.12.0.
It is extremely annoying since I got pandas in a requirements file with the specific version, and every time I install the requirements, pandas is installed all over again
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} | 2 | 2014-03-26T18:42:19Z | 2016-10-12T23:04:44Z | 2014-10-05T01:02:29Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/22669208/attributeerror-series-object-has-no-attribute-searchsorted-pandas
essentially a pass thru to `np.searchsorted`
- should return the index (rather than indices)
- can better handle `datetime64[ns]` (e.g. coerce input to/fro)
- could check if sorted internally (e.g. `is_monotonic` on the index), other pass the `sorter` argument)
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} | 12 | 2014-03-26T20:17:07Z | 2021-04-11T04:23:22Z | null | CONTRIBUTOR | null | http://stackoverflow.com/questions/22670904/python-pandas-turn-absolute-periods-into-relative-periods/22671904?noredirect=1#comment34537319_22671904
this works, but I think could be more efficient on a whole frame basis
```
In [37]: def f(x):
....: y = x.dropna()
....: return Series(y.values,x.index[len(x)-len(y):])
....:
In [40]: roller = pd.rolling_sum(df,3).reset_index(drop=True)
In [41]: roller
Out[41]:
1 2 3
0 NaN NaN NaN
1 NaN NaN NaN
2 61 56 56
3 9 9 9
4 12 12 12
5 NaN 15 15
6 NaN NaN 11
7 NaN NaN NaN
[8 rows x 3 columns]
In [43]: roller.apply(f).reindex_like(roller)
Out[43]:
1 2 3
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 NaN NaN 56
4 NaN 56 9
5 61 9 12
6 9 12 15
7 12 15 11
[8 rows x 3 columns]
```
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| null | 1 | 2014-03-26T23:31:40Z | 2014-03-27T11:50:12Z | 2014-03-27T11:49:56Z | NONE | null | This works:
pd.Timestamp('2014-01-01 00:00 Z', tz='US/Eastern')
=> Timestamp('2013-12-31 19:00:00-0500', tz='US/Eastern')
and this works:
pd.date_range('2014-01-01 00:00', periods=1, tz='US/Eastern')
=> <class 'pandas.tseries.index.DatetimeIndex'>
[2014-01-01 00:00:00]
Length: 1, Freq: D, Timezone: US/Eastern
but this does not:
pd.date_range('2014-01-01 00:00Z', periods=1, tz='US/Eastern')
=> assertion error
Is there a reason that I would not be able to create a date range with start time in UTC but to be used for another timezone?
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} | 5 | 2014-03-27T09:28:09Z | 2014-03-28T12:34:13Z | 2014-03-28T12:34:13Z | MEMBER | null | I stumbled upon an inconsistent behaviour in (groupby/dataframe) apply when updating some code from 0.12 to 0.13.
Say you have following custom function:
```
def P1(a):
try:
return np.percentile(a.dropna(), q=1)
except:
return np.nan
def P1_withouttry(a):
return np.percentile(a.dropna(), q=1)
```
When you apply this function to a dataframe:
```
In [3]: df = pd.DataFrame({'col1':[1,2,3,4],'col2':[10,25,26,31],
...: 'date':[dt.date(2013,2,10),dt.date(2013,2,10),dt.date(2013,2,11),dt.date(2013,2,11)]})
In [4]: df
Out[4]:
col1 col2 date
0 1 10 2013-02-10
1 2 25 2013-02-10
2 3 26 2013-02-11
3 4 31 2013-02-11
In [136]: df.apply(P1)
Out[136]:
col1 1.03
col2 10.45
date NaN
dtype: float64
In [138]: df.apply(P1_withouttry)
Traceback (most recent call last):
...
TypeError: ("unsupported operand type(s) for *: 'datetime.date' and 'float'", u'occurred at index date')
```
this does work with `P1`, but not with `P1_withouttry`. So I constructed my original function with a try/except to be able to apply this on a dataframe with also non-numeric columns.
However, when applying this on a groupby, it does not work anymore like this:
```
In [6]: g = df.groupby('date')
In [7]: g.apply(P1)
Out[7]:
date
2013-02-10 NaN
2013-02-11 NaN
dtype: float64
In [8]: g.apply(P1_withouttry)
Traceback (most recent call last):
...
TypeError: can't compare datetime.date to long
In [8]: g.agg(P1)
Out[8]:
col1 col2
date
2013-02-10 NaN NaN
2013-02-11 NaN NaN
In [143]: g.agg(P1_withouttry)
Out[143]:
col1 col2
date
2013-02-10 1.01 10.15
2013-02-11 3.01 26.05
```
So, with `apply` it does not work, with `aggregate` it does, but only the `P1_withouttry` that didn't work with `df.apply()`.
When using `g.agg([P1]`) this does work again on master, but not with 0.13.1 (then it gives the same as `g.agg(P1)`), although this did work
in 0.12:
```
In [11]: g.agg([P1])
Out[11]:
col1 col2
P1 P1
date
2013-02-10 1.01 10.15
2013-02-11 3.01 26.05
```
It was this last pattern I was using in my code in 0.12 that does not work anymore in 0.13.1 (I had something like `g.agg([P1, P5, P10, P25, np.median, np.mean])`).
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} | 0 | 2014-03-27T11:23:10Z | 2014-05-13T14:29:35Z | 2014-05-13T14:29:35Z | CONTRIBUTOR | null | partial indexing works with time series, but not period index
```
In [27]: df2 = DataFrame(np.arange(20).reshape(10,2),index=pd.date_range('20130101 09:00',freq='T',periods=10))
In [28]: df = DataFrame(np.arange(20).reshape(10,2),index=pd.period_range('20130101 09:00',freq='T',periods=10))
In [29]: df2['20130101']
Out[29]:
0 1
2013-01-01 09:00:00 0 1
2013-01-01 09:01:00 2 3
2013-01-01 09:02:00 4 5
2013-01-01 09:03:00 6 7
2013-01-01 09:04:00 8 9
2013-01-01 09:05:00 10 11
2013-01-01 09:06:00 12 13
2013-01-01 09:07:00 14 15
2013-01-01 09:08:00 16 17
2013-01-01 09:09:00 18 19
[10 rows x 2 columns]
In [30]: df['20130101']
KeyError: u'no item named 20130101'
In [31]: df2['20130101 09']
Out[31]:
0 1
2013-01-01 09:00:00 0 1
2013-01-01 09:01:00 2 3
2013-01-01 09:02:00 4 5
2013-01-01 09:03:00 6 7
2013-01-01 09:04:00 8 9
2013-01-01 09:05:00 10 11
2013-01-01 09:06:00 12 13
2013-01-01 09:07:00 14 15
2013-01-01 09:08:00 16 17
2013-01-01 09:09:00 18 19
[10 rows x 2 columns]
In [32]: df['20130101 09']
KeyError: u'no item named 20130101 09'
```
Works fine for exact start points
```
In [34]: df2['20130101 09:05':]
Out[34]:
0 1
2013-01-01 09:05:00 10 11
2013-01-01 09:06:00 12 13
2013-01-01 09:07:00 14 15
2013-01-01 09:08:00 16 17
2013-01-01 09:09:00 18 19
[5 rows x 2 columns]
In [35]: df['20130101 09:05':]
Out[35]:
0 1
2013-01-01 09:05 10 11
2013-01-01 09:06 12 13
2013-01-01 09:07 14 15
2013-01-01 09:08 16 17
2013-01-01 09:09 18 19
[5 rows x 2 columns]
```
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} | 1 | 2014-03-27T12:07:42Z | 2014-03-28T15:17:31Z | 2014-03-28T15:17:31Z | CONTRIBUTOR | null | related #5971
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} | 29 | 2014-03-27T14:53:14Z | 2014-06-17T12:11:47Z | 2014-04-07T13:57:46Z | CONTRIBUTOR | null | Implementation of holidays based on DateOffset objects and calendars that are collections of those holidays. These calendars can be passed into CustomBusinessDay. Also, add an Easter offset for use in holiday calendars.
Currently the default dates are hard-coded in the calendar class but this should move somewhere else and be calculated differently. I'm open to suggestions here. Also, the 4-day weekend observance needs to be incorporated (for example, if July 4th is on a Tuesday, perhaps Monday is also a holiday). This would require a little bit of tweaking, but not much.
```
from pandas.tseries.holiday import Holiday, USMemorialDay,\
AbstractHolidayCalendar, Nearest, MO, USFederalHolidayCalendar
from pandas.tseries.offsets import DateOffset, CustomBusinessDay
from datetime import datetime
class ExampleCalendar(AbstractHolidayCalendar):
_rule_table = [
USMemorialDay,
Holiday('July 4th', month=7, day=4, observance=Nearest),
Holiday('Columbus Day', month=10, day=1,
offset=DateOffset(weekday=MO(2))),
]
cal = ExampleCalendar()
cal.holidays()
print(datetime(2012, 5, 25) + CustomBusinessDay(calendar=cal))
print(datetime(2012, 5, 25) + CustomBusinessDay(calendar=USFederalHolidayCalendar()))
```
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| null | 12 | 2014-03-27T15:20:34Z | 2015-01-22T23:36:28Z | 2015-01-22T23:36:28Z | CONTRIBUTOR | null | from SO: http://stackoverflow.com/questions/23537265/inconsistent-system-error-with-pandas-0-13-series-operation/23542455#23542455
When performing some expressions involving Series returned from DataFrame column indexing (Pandas 0.13.1 with Numpy 1.8.0 in Windows 7), a new pythonw.exe console window appears (empty) but the initiating GUI indicates the following error text was returned:
```
OMP: Error #134: Cannot set thread affinity mask.
OMP: System error #87: The parameter is incorrect.
```
It occurs with expressions such as:
`df = df.loc[(df['timediff'] > -20) & (df['timediff'] < 20),:]`
and
`df['GP'] = (df['Price'] - df['Cost'] - (df['Discount'] / df['Qty'])) * df['Qty']`
(this is on moderately small DataFrames < 10 million rows)
As a workaround, disabling numexpr operations seems to avoid it:
```
pandas.computation.expressions.set_use_numexpr(False)
```
as suggested in issue #3434.
Possibly related (at least by error text):
http://software.intel.com/en-us/forums/topic/279406
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https://api.github.com/repos/pandas-dev/pandas/issues/6721 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6721/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6721/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6721/events | https://github.com/pandas-dev/pandas/issues/6721 | 30,322,178 | MDU6SXNzdWUzMDMyMjE3OA== | 6,721 | Regression in DataFrameGroupBy.head() | {
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} | 5 | 2014-03-27T17:18:49Z | 2019-08-23T08:06:04Z | 2019-08-23T08:06:04Z | CONTRIBUTOR | null | For [this SO question](http://stackoverflow.com/questions/22691010/how-to-print-a-groupby-object) I isolated a regression in `DataFrameGroupBy.head()` between 0.12.0 and 0.13.0.
```
In [1]: df = pd.DataFrame({'A': ['one', 'one', 'two', 'three', 'three', 'one'], 'B': range(6)})
```
In 0.12.0:
```
In [2]: df.groupby('A').head()
Out[2]:
A B
A
one 0 one 0
1 one 1
5 one 5
three 3 three 3
4 three 4
two 2 two 2
```
which effectively returns the rows of the underlying `.obj` sorted by group. In 0.13.0:
```
In [2]: df.groupby('A').head()
Out[2]:
A B
A
one 0 one 0
1 one 1
two 2 two 2
three 3 three 3
4 three 4
one 5 one 5
[6 rows x 2 columns]
```
which returns the rows of the underlying `.obj` in their original order. The former is more intuitive.
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} | 4 | 2014-03-27T20:30:06Z | 2014-07-09T08:24:18Z | 2014-03-30T14:42:26Z | MEMBER | null | Changes made in #5638 make `loglog` keyword not worked, and it fixes the problem.
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| null | 1 | 2014-03-27T20:44:44Z | 2014-03-27T20:52:26Z | 2014-03-27T20:52:26Z | NONE | null | I'm trying to get through the baby names example walkthrough and came across an error that I'm not sure how to resolve. I was able to get through the first few steps of the 'measuring the increase in naming diversity' section, but was stumped by the following error message: "AttributeError: 'Series' object has no attribute 'searchsorted'
My full code is included below.
Thanks!
Measuring the increase in naming diversity
In [63]:
# One reason why these names appear to be falling out of favor is that parents are making more diverse choices
# Lets test this by evaluating the proportion of births represented by the top 1000 most common nanmes, aggregated by year + sex
table = top1000.pivot_table('prop', rows='year', cols='sex',
aggfunc=sum)
table.plot(title='Sum of table1000.prop by year and sex',
yticks=np.linspace(0, 1.2, 13), xticks=range(1880, 2020, 10))
Out[63]:
<matplotlib.axes.AxesSubplot at 0x10dab77d0>
Indeed it appears that there is an increase in naming diversity, as the proportion of baby names represented by the top 1000 names seems to decrease over the observed period.
In [64]:
# Another interesting measure is the number of distinct names,
# taken in order of popularity from highest to lowest, in the top 50% of births.
df = boys[boys.year == 2010]
df.info()
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 1000 entries, (2010, M, 1676644) to (2010, M, 1677643)
Data columns (total 5 columns):
name 1000 non-null object
sex 1000 non-null object
births 1000 non-null int64
year 1000 non-null int64
prop 1000 non-null float64
dtypes: float64(1), int64(2), object(2)
In [65]:
# After sorting prop in descending order, we want to know how many of the most popular
# names it takes to represent 50% of the total # of births.
# Taking the cumulative sum <cumsum> of <prop> tgeb cakkubg tge netgid <searchsorted>
# returns the position in the cumulative sum at which 0.5 would need to be inserted to keep it in sorted order:
prop_cumsum = df.sort_index(by='prop', ascending=False).prop.cumsum()
In [66]:
prop_cumsum[:10]
Out[66]:
year sex
2010 M 1676644 0.011523
1676645 0.020934
1676646 0.029959
1676647 0.038930
1676648 0.047817
1676649 0.056579
1676650 0.065155
1676651 0.073414
1676652 0.081528
1676653 0.089621
Name: prop, dtype: float64
In [67]:
## prop_cumsum.searchsorted(0.5)
AttributeError Traceback (most recent call last)
<ipython-input-67-f2e2bb3f5ba0> in <module>()
----> 1 prop_cumsum.searchsorted(0.5)
//anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc in **getattr**(self, name)
1813 return self[name]
1814 raise AttributeError("'%s' object has no attribute '%s'" %
-> 1815 (type(self).**name**, name))
1816
1817 def **setattr**(self, name, value):
AttributeError: 'Series' object has no attribute 'searchsorted'
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| null | 0 | 2014-03-28T01:05:07Z | 2014-03-28T03:39:43Z | 2014-03-28T03:39:43Z | CONTRIBUTOR | null | ```
df.shift(1, axis='columns')
# TypeError: tuple indices must be integers, not str
```
np.roll is being passed the original axis
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| null | 2 | 2014-03-28T01:09:06Z | 2014-07-16T08:59:30Z | 2014-03-28T03:39:43Z | CONTRIBUTOR | null | simple fix for #6724. was blocking the panel general shift PR.
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| null | 2 | 2014-03-28T01:46:28Z | 2014-03-28T02:35:42Z | 2014-03-28T02:35:42Z | CONTRIBUTOR | null | ``` python
df = pd.DataFrame.from_dict({'a':'C C A'.split(),
'b':'C C A'.split(),
'c':'C A A'.split(),
'd':'C A A'.split()})
```
``` python
df.apply(len)
a 3
b 3
c 3
d 3
dtype: int64
```
``` python
df.apply(len, axis=1)
0 4
1 4
2 4
dtype: int64
```
The documentation says:
``` bash
Objects passed to functions are Series objects having index
either the DataFrame's index (axis=0) or the columns (axis=1).
Return type depends on whether passed function aggregates, or the
reduce argument if the DataFrame is empty.
```
That should mean that the above case with axis=0 should have [0,1,2] as index, doesn't it?
Version:
'0.13.1-492-g599156f'
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| null | 3 | 2014-03-28T08:42:44Z | 2014-03-29T00:10:56Z | 2014-03-29T00:10:56Z | NONE | null | I think the `.plot()` should also apply to OLS regression from `pandas.stats.api import ols`
Let say I have some points and I do
`regression = ols(y=df['Y_VALUES'], x=df[['X_VALUES']])`
So if I type
`print (regression)`
it gives me a summary of the data.
What I am suggesting is that if I type something like:
```
regression.plot()
plt.show()
```
It should give me a scatter plot with the best fit line.
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@jreback asked for DataFrame rank to have a percentage option to match consistency with series
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} | 0 | 2014-03-28T19:28:36Z | 2014-04-09T21:07:29Z | 2014-04-09T21:07:29Z | CONTRIBUTOR | null | ``` python
df = pd.DataFrame([1,1], index=pd.MultiIndex.from_tuples([('a', 'a'), ('a', 'b')], names=['i1', 'i1']))
In [5]: df.unstack('i1')
...
KeyError: 'Level i1 not found'
```
I can do a PR later.
Should also probably be a `ValueError` instead of a `KeyError`.
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} | 12 | 2014-03-29T03:50:09Z | 2020-10-04T00:39:33Z | 2014-05-12T12:41:17Z | CONTRIBUTOR | null | I do this
```
>>> df = pd.DataFrame([[1,np.nan,0],[1,1,1],[2,2,2],[2,3,3]], columns=list('abc'))
>>> print(df)
a b c
0 1 NaN 0
1 1 1 1
2 2 2 2
3 2 3 3
[4 rows x 3 columns]
>>> print(df.groupby('a').first())
b c
a
1 1 0
2 2 2
[2 rows x 2 columns]
```
but I expected this
```
>>> print(df.groupby('a').first())
b c
a
1 NaN 0
2 2 2
[2 rows x 2 columns]
```
Is it possible to achieve my expected output? I get the same output in master and 0.13.1.
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} | 1 | 2014-03-29T08:25:42Z | 2014-03-29T14:46:24Z | 2014-03-29T14:46:24Z | NONE | null | In this snippet:
```
import pandas as pd
import numpy as np
print 'numpy', np.__version__
print 'pandas', pd.__version__
df = pd.DataFrame({'x': [],'range': np.arange(0)})
t = df.sort('x').groupby('x').first()
print 'types', df['range'].dtype, t['range'].dtype
```
it is shown that after the groupby, an empty "int" column becomes an empty "float". This is the output on my machine:
```
numpy 1.6.2
pandas 0.13.1
types int32 float64
```
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} | 13 | 2014-03-29T11:28:29Z | 2014-06-20T16:59:37Z | 2014-04-14T12:00:02Z | MEMBER | null | Further work on #6292.
This adds:
- fixes the `columns` argument (wasn't passed) + added test for that
- adds multi-index support in `to_sql`, by just using `reset_index` on the frame (+ tests)
- adds multi-index support in read_table (+ tests)
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Currently `drop` raises `ValueError` when non-existing label is passed. I think it is useful if `drop` has an option to suppress error and drop existing labels only.
For example, I sometimes process lots of files which has slightly different columns, and want to `drop` if data has unnecessary columns. Previously, I have to prepare different `drop` arguments checking columns existing each data.
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} | 6 | 2014-03-29T14:56:10Z | 2017-04-05T02:08:56Z | 2014-04-09T15:38:35Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/6729
We should raise a ValueError when (un)stacking and referring to a non-unique index _name_. You can still stack and unstack by position like normal (no ambiguity there, unless your names are integers and that integer is duplicated, in which case we'll raise a ValueError).
Right now I'm assuming that index names are always hashable, but that's not necessarily true, is it? Any good ways to check the uniqueness of the names? I didn't see anything on MultiIndex that did this.
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} | 7 | 2014-03-30T03:24:24Z | 2014-06-19T06:36:46Z | 2014-04-22T15:31:57Z | CONTRIBUTOR | null | Fixes #5824, with a test to verify the DeprecationWarning is no longer raised.
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} | 11 | 2014-03-30T09:25:39Z | 2016-11-30T15:31:22Z | 2016-05-21T15:33:41Z | NONE | null | xref to #7307
Converting from `datetime64[ns]` to `datetime64[m]` is disallowed, as mentioned in issue #3416, apparently because of a numpy bug. It looks to me like numpy does this correctly, however. It would be nice if this could work without resorting to raw numpy arrays:
``` python
In [89]: s = pd.Series(['20140330','19740821'])
In [90]: pd.to_datetime(s)
Out[90]:
0 2014-03-30
1 1974-08-21
dtype: datetime64[ns]
In [91]: pd.to_datetime(s).values
Out[91]:
array(['2014-03-30T03:00:00.000000000+0300',
'1974-08-21T03:00:00.000000000+0300'], dtype='datetime64[ns]')
In [92]: pd.to_datetime(s).values.astype('datetime64[h]')
Out[92]: array(['2014-03-30T03+0300', '1974-08-21T03+0300'], dtype='datetime64[h]')
```
My other workaround, which is slightly less clumsy, is do just convert manually, since casting to int64 is permitted:
``` python
In [103]: pd.to_datetime(s).astype('int64').div(6e10) # minutes since epoch
```
But it'd be nicer not to have to do all that.
``` python
In [93]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.1
Cython: 0.20
numpy: 1.8.0
scipy: None
statsmodels: None
IPython: 1.2.1
sphinx: 1.1.3
patsy: None
scikits.timeseries: None
dateutil: 1.5
pytz: 2012c
bottleneck: 0.8.0
tables: 3.1.0
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: 0.7.4
xlsxwriter: None
sqlalchemy: None
lxml: None
bs4: None
html5lib: 0.999
bq: None
apiclient: None
```
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} | 4 | 2014-03-30T10:38:06Z | 2014-06-18T00:22:54Z | 2014-03-31T14:36:45Z | MEMBER | null | Closes #4606.
I'm not sure this also be a solution for #5388, but seems to be a workaround.
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} | 0 | 2014-03-30T15:11:07Z | 2014-06-27T23:41:02Z | 2014-03-30T15:23:19Z | CONTRIBUTOR | null | Business/Week frequencies were incorrect
closes #4069
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} | 4 | 2014-03-30T17:52:30Z | 2020-04-15T04:49:19Z | 2020-04-15T04:49:18Z | CONTRIBUTOR | null | This is an attempt to simplify/streamline internal API that has been brewing inside my head for quite a while. It does mean a significant overhaul and may take time, but it may prove worth the while. I'm putting it here for discussion ahead of time to make sure the effort isn't wasted for going in the wrong direction.
### Idea
The idea is simple: "augmented take" operation — with -1 taking from nowhere and creating a new column — is enough to express any reindexing/merging/joining that may happen at Index level. So, lower levels of API that do the heavy lifting may be relieved from the burden of operating on labels and keeping them in sync. This will make them more self-contained with the following benefits:
- simplify implementation
- regularize data-handling operations making them more dependable, no more "oh no, I have a duplicate/timestamp/period/multiindex/etc. label in the index, all slicing operations are now 10x slower", this happened to me more times than I'm proud of.
- simplify contributions from new developers, no more tracking down all the code paths up to the public API to fix one small check.
- make code more test- and benchmark-friendly, no more exponential growth in number of tests/benchmarks for each new feature
- declaring an API will simplify developing and maintaining "unconventional" storages (sparse, categorical, compressed, etc.)
There's a [three-year-old ticket](https://github.com/pydata/pandas/issues/162) ticket that mentions a similar (if not the same) idea. As mentioned there, this _may_ break pickles and other deserialization and thus it will require a separate legacy deserialization compatibility layer.
[Another ticket](https://github.com/pydata/pandas/issues/163) mentions moving Block & BlockManager internals to cython level and dropping axes dependency will definitely facilitate that.
### Goals
The end goal is to have internals layered as follows:
- **Block**: a _proper_ homogenous ndarray
- think numpy.ndarray with all necessary fixes/workarounds
- datatype inference
- support for custom pandas datatypes
- typical (slice, concatenate) and pandas-specific (take-with-insert) operations
- **RemappableBlock**: homogeneous ndarray that supports "remapping" one of its axes
- think _Block_ + _ref_locs_
- _ref_locs_ should be Int64Index (platform-int-index?, also [RangeIndex](https://github.com/pydata/pandas/issues/939) will help)
- _Block_ instances may be shared between _RemappableBlocks_
- optimizations for no-remapping mode of work (think _SingleBlockManager_)
- FIXME: better name?
- **BlockManager**: a _proper_ heterogeneous ndarray
- external interface is similar to that of **Block**
- can share _RemappableBlocks_
- **NDFrame**: labeled heterogeneous ndarray
- more or less equivalent to current _NDFrames_
- merging/joining/reindexing only appears at this level
### Deliverables
- Stage 1 **DONE**
- make ref_locs primary source of information (leaving items/ref_items in place to back it up and avoid breakage)
- port merging/joining internals to loc-based implementation (there's quite a number of hacks a.t.m. that make this non-trivial)
- drop Block `items` & `ref_items` fields (ensure io backward compatibility!)
- fix performance issues & integrate with mainline
- Stage 2: TBA
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} | 48 | 2014-03-30T18:16:10Z | 2020-05-01T19:33:53Z | 2014-04-25T14:22:31Z | CONTRIBUTOR | null | This is a WIP request for Stage 1 deliverable of #6744.
Roadmap:
- [x] make ref_locs primary source of information (leaving items/ref_items in place to back it up and avoid breakage)
- [x] port groupby to loc-based implementation (there's quite a number of hacks a.t.m. that make this non-trivial)
- [x] ditto for merge/join/concat
- [x] ditto for io code
- [x] ditto for reshape
- [x] clean up Block `items`
- [x] clean up Block `ref_items`
- [x] fix performance issues & integrate with mainline
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| null | 1 | 2014-03-31T02:45:19Z | 2014-07-16T08:59:58Z | 2014-03-31T09:41:59Z | CONTRIBUTOR | null | Adjustments to the quick fix PR.
https://gist.github.com/dalejung/9731798#file-vb_suite-log
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
frame_shift_c_order_axis1 | 14.4524 | 52.8037 | 0.2737 |
frame_shift_c_order_axis0 | 16.9516 | 46.5320 | 0.3643 |
frame_shift_axis0 | 15.2656 | 16.3440 | 0.9340 |
frame_shift_axis1 | 22.2600 | 22.0567 | 1.0092 |
```
Note, that the speed up was really in sending a C order array to `np.take`. You'll notice that master is slow for `frame_shift_c_order_` which are tests where the `block.values` is c_contiguous. Thus current master always transposes.
The original example had it's speed up because it `np.roll`ed `df.values` which is C ordered (fast) while the `.blocks` were were (slow).
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} | 0 | 2014-03-31T08:33:07Z | 2014-06-12T21:22:30Z | 2014-03-31T13:33:15Z | CONTRIBUTOR | null | SparseBlocks don't consolidate, so previous implementation silently dropped all but the last blocks for given dtype:
``` python
In [1]: pd.__version__
Out[1]: '0.13.1-527-g73506cb'
In [2]: pd.SparseDataFrame({'a': [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.blocks
Out[3]:
{'float64': b
0 1
1 2
2 3
3 NaN
4 NaN
[5 rows x 1 columns]}
```
when the last output should be:
``` python
In [3]: _2.blocks
Out[3]:
{'float64': a b
0 1 1
1 2 2
2 3 3
3 NaN NaN
4 NaN NaN
[5 rows x 2 columns]}
```
This also drops the `columns` kwarg of as_blocks since it doubles reindex functionality.
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} | 3 | 2014-03-31T13:50:22Z | 2016-04-29T17:17:59Z | 2016-04-29T17:17:59Z | NONE | null | Based on a suggestion from an user at stackoverflow.com, I am reporting a bug / enhancement request for sparse data frames. Please let me know if you need any more information.
Thanks in advance.
I am encountering a TypeError with a pandas sparse data frame when I use the value_counts method. I have listed the versions of the packages that I am using.
Python 2.7.6 |Anaconda 1.9.1 (x86_64)| (default, Jan 10 2014, 11:23:15)
[GCC 4.0.1 (Apple Inc. build 5493)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
> > > import pandas
> > > print pandas.**version**
> > > 0.13.1
> > > import numpy
> > > print numpy.**version**
> > > 1.8.0
> > >
> > > dense_df = pandas.DataFrame(numpy.zeros((10, 10))
> > > ,columns=['x%d' % ix for ix in range(10)])
> > > dense_df['x5'] = [1.0, 0.0, 0.0, 1.0, 2.1, 3.0, 0.0, 0.0, 0.0, 0.0]
> > > print dense_df['x5'].value_counts()
> > > 0.0 6
> > > 1.0 2
> > > 3.0 1
> > > 2.1 1
> > > dtype: int64
> > >
> > > sparse_df = dense_df.to_sparse(fill_value=0) # Tried fill_value=0.0 also
> > > print sparse_df.density
> > > 0.04
> > >
> > > print sparse_df['x5'].value_counts()
> > > Traceback (most recent call last):
> > > File "<stdin>", line 1, in <module>
> > > File "//anaconda/lib/python2.7/site-packages/pandas/core/series.py", line 1156, in value_counts
> > > normalize=normalize, bins=bins)
> > > File "//anaconda/lib/python2.7/site-packages/pandas/core/algorithms.py", line 231, in value_counts
> > > values = com._ensure_object(values)
> > > File "generated.pyx", line 112, in pandas.algos.ensure_object (pandas/algos.c:38788)
> > > File "generated.pyx", line 117, in pandas.algos.ensure_object (pandas/algos.c:38695)
> > > File "//anaconda/lib/python2.7/site-packages/pandas/sparse/array.py", line 377, in astype
> > > raise TypeError('Can only support floating point data for now')
> > > TypeError: Can only support floating point data for now
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| null | 2 | 2014-03-31T14:39:15Z | 2014-03-31T14:45:28Z | 2014-03-31T14:45:13Z | NONE | null | I get the following error
``` python
Traceback (most recent call last):
File "asys.py", line 65, in <module>
compSub()
File "asys.py", line 46, in compSub
plt.hist(data['Total'],linewidth=0, alpha=.7)
File "/home/storm/Documents/lol/local/lib/python2.7/site-packages/matplotlib/pyplot.py", line 2827, in hist
stacked=stacked, **kwargs)
File "/home/storm/Documents/lol/local/lib/python2.7/site-packages/matplotlib/axes.py", line 8249, in hist
if isinstance(x, np.ndarray) or not iterable(x[0]):
File "/home/storm/Documents/lol/local/lib/python2.7/site-packages/pandas/core/series.py", line 491, in __getitem__
result = self.index.get_value(self, key)
File "/home/storm/Documents/lol/local/lib/python2.7/site-packages/pandas/core/index.py", line 1032, in get_value
return self._engine.get_value(s, k)
File "index.pyx", line 97, in pandas.index.IndexEngine.get_value (pandas/index.c:2661)
File "index.pyx", line 105, in pandas.index.IndexEngine.get_value (pandas/index.c:2476)
File "index.pyx", line 149, in pandas.index.IndexEngine.get_loc (pandas/index.c:3215)
File "hashtable.pyx", line 382, in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:6450)
File "hashtable.pyx", line 388, in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:6394)
KeyError: 0L
```
when I execute the following code(using `pandas 0.13.1`)
``` python
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
import statsmodels.api as sm
from numpy.random import randn
import matplotlib as mpl
import seaborn as sns
sns.set_color_palette("deep", desat=.6)
mpl.rc("figure", figsize=(8, 4))
def Compavg():
data=Total()
markMax=[]
markAvg=[]
N = 5
ind = np.arange(N)
width = 0.35
fig = plt.figure()
ax = fig.add_subplot(111)
markMax.extend((data["Total"].max(),data["Total.1"].max(),data["Total.2"].max(),data["Total.3"].max(),data["Total.4"].max()))
markAvg.extend((data["Total"].mean(),data["Total.1"].mean(),data["Total.2"].mean(),data["Total.3"].mean(),data["Total.4"].mean()))
rects1 = ax.bar(ind, markMax, width, color='black')
rects2 = ax.bar(ind+width, markAvg, width, color='green')
ax.set_xlim(-width,len(ind)+width)
ax.set_ylim(0,120)
ax.set_ylabel('Marks')
ax.set_title('Max, Mean and Your Marks')
xTickMarks = ['Subject'+str(i) for i in range(1,6)]
ax.set_xticks(ind+width)
xtickNames = ax.set_xticklabels(xTickMarks)
plt.setp(xtickNames, rotation=10, fontsize=10)
ax.legend( (rects1[0], rects2[0]), ('Max', 'Mean') )
plt.show()
print "finished reading 1"
def compSub():
#max_data = np.r_[data["Total"]].max()
#bins = np.linspace(0, max_data, max_data + 1)
data=Total()
print data['Total']
plt.hist(data['Total'],linewidth=0, alpha=.7)
plt.hist(data['Total.1'],linewidth=0,alpha=.7)
plt.hist(data['Total.2'],linewidth=0,alpha=.7)
plt.hist(data['Total.3'],linewidth=0,alpha=.7)
plt.hist(data['Total.4'],linewidth=0,alpha=.7)
plt.title("Total marks Histogram")
plt.xlabel("Value")
plt.ylabel("Frequency")
plt.show()
print "finished reading 2"
def Total():
print "going to read"
data=pd.read_csv("output11cs.csv")
df3=data[['Total','Total.1','Total.2','Total.3','Total.4','Total.5','Total.6','Total.7']]
data["Main Total"]=df3.sum(axis=1)
data = data.dropna()
data.reset_index(drop=True)
print data
return data
Compavg()
compSub()
```
but it works perfectly with pandas version `0.10.0`
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| null | 12 | 2014-03-31T16:27:12Z | 2014-03-31T18:41:55Z | 2014-03-31T17:34:55Z | NONE | null | Pandas data frame does not respect custom series class of a column.
For example
```
>>> import pandas
>>> class XSeries(pandas.Series): pass
>>> df = pandas.DataFrame({'a': XSeries([1, 2, 3]), 'b': pandas.Series([3, 4, 5])})
>>> type(df.a)
pandas.core.series.Series
```
I would expect type(df.a) to be XSeries in the example above.
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| null | 4 | 2014-04-01T04:13:20Z | 2014-04-01T11:55:04Z | 2014-04-01T11:55:04Z | NONE | null | Part of my workflow involves reading in files with dates that I convert into timestamps. The files are big but not huge, say a million lines with 10 columns.
The operation that takes a bit of time is the following:
```
self.SEEDFILE['tmp_refdate'] = self.SEEDFILE[['year','month','day','seconds']].apply(lambda x: pd.datetime(x['year'], x['month'], x['day'],0,0,x['seconds']), axis=1)
```
As you can see this now takes longer on 0.13.1 as compared to 0.12.0...?
INFO: Numpy version: 1.8.0
INFO: Pandas version: 0.13.1
DEBUG: time for refdate: 61.992943
INFO: Numpy version: 1.8.1
INFO: Pandas version: 0.12.0
DEBUG: time for refdate: 14.616941
Is this expected behavior?
Also, the ability to add a datetime array to a np.timedelta64 objects seems to have died in 0.13.1, this used to work but now I get:
```
TypeError: Argument 'values' has incorrect type (expected numpy.ndarray, got Series)
```
The workaround has been to load the times into a pandas.timestamp using read_csv's time parser options. Thus, I'm not really posting this looking for a solution. Any thoughts/comments appreciated. I had a look in the 0.13.1 docs and I gather there have been other changes to the datetime interface so perhaps my problems are unfortunate side effects..?
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} | 4 | 2014-04-01T09:34:17Z | 2019-08-15T06:44:43Z | 2014-04-01T13:18:29Z | CONTRIBUTOR | null | When calling the apply function of a DataFrame its called twice for the first row. The following code
``` python
df = DataFrame({"a":["x", "y"], "b":[1,2]})
def identity(row):
print tuple(row)
return row
df2=df.apply(identity, axis=1)
```
prints
``` python
('x', 1)
('x', 1)
('y', 2)
```
The result df2 is not affected by this behavior. However, when the function called by apply (identity in the example above) has side-effects, this can lead to very surprising and unexpected effects.
It would be good to have at least a note on this behavior in the documentation of the apply function.
This is related to #2656 and #2936, where this behaviour was reported for calling apply after groupby. Here it appears for a DataFrame.
## INSTALLED VERSIONS
commit: None
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.utf8
pandas: 0.13.1
Cython: 0.15.1
numpy: 1.8.1
scipy: 0.13.3
statsmodels: None
IPython: 1.2.1
sphinx: 1.1.3
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.2
bottleneck: None
tables: 2.3.1
numexpr: 2.0.1
matplotlib: 1.3.1
openpyxl: 1.5.8
xlrd: 0.6.1
xlwt: 0.7.4
xlsxwriter: None
sqlalchemy: None
lxml: None
bs4: None
html5lib: None
bq: None
apiclient: None
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} | 0 | 2014-04-01T12:15:16Z | 2014-06-12T08:05:49Z | 2014-04-01T13:03:04Z | CONTRIBUTOR | null | ```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
timedelta_convert | 0.2253 | 71.0374 | 0.0032 |
timedelta_convert_string_seconds | 186.8856 | 184.1813 | 1.0147 |
timedelta_convert_string | 185.8193 | 181.2983 | 1.0249 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
```
Using shortcuts for integers arrays
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} | 0 | 2014-04-01T12:45:27Z | 2015-06-22T08:34:19Z | 2015-06-22T08:34:19Z | CONTRIBUTOR | null | see #6754
related #3009, #8190
from [SO](http://stackoverflow.com/questions/30894267/defining-a-function-pandas)
convering string like timedeltas, e.g.
'1 days', '3 days, 00:00:03', '3 days 2 hours'
is slow because it requires a regex match for each element. Can put cythonized (like format parsing for datetimes), or since only very limited formats supported can simply test them (in cython) and convert.
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} | 3 | 2014-04-01T12:50:45Z | 2020-05-14T09:34:31Z | 2014-04-01T12:52:59Z | CONTRIBUTOR | null | Related issues are #2656, #2936 and #6753.
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https://api.github.com/repos/pandas-dev/pandas/issues/6757 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6757/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6757/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6757/events | https://github.com/pandas-dev/pandas/issues/6757 | 30,595,814 | MDU6SXNzdWUzMDU5NTgxNA== | 6,757 | Possible spurious SettingWithCopyWarning | {
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| null | 26 | 2014-04-01T12:53:54Z | 2015-10-20T22:42:18Z | 2015-10-20T22:42:18Z | MEMBER | null | Found some `SettingWithCopyWarning` warnings in older code with newer pandas. But it's quite possible that these are just side-effects of the warning ('False positives') and it cannot easily be detected, but I am no expert in that field.
Simplified dummy example:
```
In [1]: df = pd.DataFrame(np.arange(20).reshape(5, 4), columns=list('ABCD'))
In [2]: df
Out[2]:
A B C D
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
3 12 13 14 15
4 16 17 18 19
# reassign a selection to df
In [3]: df = df[(df['A']%5)!=0]
```
Setting a value with `.loc[]` (this output is from master, in 0.13.1 I got even the advice to `Try using .loc[row_index,col_indexer] = value instead` while I was using it ... in master just the warning):
```
In [4]: df.loc[df['B']==17, 'C'] = 1000
C:\Anaconda\envs\devel\Scripts\ipython-script.py:1: SettingWithCopyWarning: A va
lue is trying to be set on a copy of a slice from a DataFrame
#!C:\Anaconda\envs\devel\python.exe
```
And then replacing a value with `replace` (also here the advice to use `.loc` is a little bit strange I think):
```
In [5]: df['D'] = df['D'].replace({7:2000})
C:\Anaconda\envs\devel\Scripts\ipython-script.py:1: SettingWithCopyWarning: A va
lue is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_index,col_indexer] = value instead
#!C:\Anaconda\envs\devel\python.exe
In [6]: df
Out[6]:
A B C D
1 4 5 6 2000
2 8 9 10 11
3 12 13 14 15
4 16 17 1000 19
```
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| null | 3 | 2014-04-01T13:18:01Z | 2014-04-01T14:03:36Z | 2014-04-01T14:03:36Z | CONTRIBUTOR | null | Changing a DataFrame element which is a list, works only when the length of the list is not larger then two.
``` python
df = DataFrame({"a":[[1,2,3], [4,5,6]], "b":[[1,2],[3,4]]})
df.iloc[0,1] = [1,20] #works
df.iloc[0,0] = [1,2,30] #throws exception
ValueError: Must have equal len keys and valuewhen setting with an iterable
```
The behavior is the same for numpy-arrays and tuples.
Is the use of lists as elements of a DataFrame not allowed in pandas?
## INSTALLED VERSIONS
pandas: 0.13.1
Cython: 0.15.1
numpy: 1.8.1
scipy: 0.13.3
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} | 14 | 2014-04-01T15:56:46Z | 2014-04-28T23:35:24Z | 2014-04-28T23:35:24Z | CONTRIBUTOR | null | Wanted to get eyes on this. The original axis=1 2d methods were optimized for Fortran. I changed it to be friendly to C order and transposed the array when F ordered.
These timings are for master. `cdf` is just the consolidated dataframe to switch from C to F.
```
In [3]:
%timeit a = df.take(indexes, axis=0)
10 loops, best of 3: 214 ms per loop
In [4]:
%timeit a = df.take(indexes, axis=1)
1 loops, best of 3: 978 ms per loop
In [8]:
%timeit cdf.take(indexes, axis=0)
1 loops, best of 3: 1.6 s per loop
In [9]:
%timeit cdf.take(indexes, axis=1)
1 loops, best of 3: 216 ms per loop
```
http://nbviewer.ipython.org/gist/dalejung/9914956/pandas%20perf_baseline.ipynb
The PR timings are:
```
In [18]:
%timeit a = df.take(indexes, axis=0)
10 loops, best of 3: 215 ms per loop
In [19]:
%timeit a = df.take(indexes, axis=1)
1 loops, best of 3: 265 ms per loop
In [23]:
%timeit cdf.take(indexes, axis=0)
1 loops, best of 3: 290 ms per loop
In [24]:
%timeit cdf.take(indexes, axis=1)
1 loops, best of 3: 222 ms per loop
```
http://nbviewer.ipython.org/gist/dalejung/9914956/pandas%20perf.ipynb
It's similar behavior to the shift perf update.
For a numpy baseline i wrote:
``` python
# wrap np.take to reshape to C order
def quick_take(values, indexer, axis=0):
f_ordered = values.flags.f_contiguous
if f_ordered:
values = values.T
axis = values.ndim - axis - 1
res = values.take(indexes, axis)
if f_ordered:
res = res.T
return res
%timeit quick_take(df.values, indexes, axis=0)
1 loops, best of 3: 234 ms per loop
In [14]:
%timeit quick_take(df.values, indexes, axis=1)
1 loops, best of 3: 271 ms per loop
```
Which are similar timings.
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| null | 2 | 2014-04-01T17:44:41Z | 2014-04-01T20:36:23Z | 2014-04-01T20:36:23Z | CONTRIBUTOR | null | The docstring indicates that it is supposed to be lazy, but we return `lzip(iter(self.index), iter(self))`. That should be `zip(...)` right? Or is the docstring wrong?
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| null | 3 | 2014-04-01T19:07:10Z | 2014-06-30T14:03:50Z | 2014-04-01T20:36:23Z | CONTRIBUTOR | null | Closes #6760
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} | 24 | 2014-04-01T20:48:21Z | 2014-05-22T19:53:06Z | 2014-04-05T20:18:58Z | NONE | null | I just installed numexpr 2.3.1 with pandas 0.13.1 and got the following error:
File "C:\Python27\lib\site-packages\pandas\core\ops.py", line 496, in wrapper
arr = na_op(lvalues, rvalues)
File "C:\Python27\lib\site-packages\pandas\core\ops.py", line 443, in na_op
raise_on_error=True, *_eval_kwargs)
File "C:\Python27\lib\site-packages\pandas\computation\expressions.py", line 176, in evaluate
*_eval_kwargs)
File "C:\Python27\lib\site-packages\pandas\computation\expressions.py", line 104, in _evaluate_numexpr
*_eval_kwargs)
File "C:\Python27\lib\site-packages\numexpr\necompiler.py", line 738, in evaluate
NumExpr(ex, signature, *_context)
File "C:\Python27\lib\site-packages\numexpr\necompiler.py", line 554, in NumExpr
precompile(ex, signature, context)
File "C:\Python27\lib\site-packages\numexpr\necompiler.py", line 498, in precompile
ast = typeCompileAst(ast)
File "C:\Python27\lib\site-packages\numexpr\necompiler.py", line 163, in typeCompileAst
% (ast.value + '_' + retsig+basesig))
NotImplementedError: couldn't find matching opcode for 'mul_bbb'
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} | 4 | 2014-04-02T11:41:40Z | 2014-04-03T12:42:23Z | 2014-04-03T12:42:23Z | CONTRIBUTOR | null | In the Docstring for the **Merge** method, the example provided:
```
merge(A, B, left_on='lkey', right_on='rkey', how='outer')
```
returns a _sorted_ DataFrame. Thus, we need to either add a **sort=True** _kwarg_ to the above code or replace the purported output with the actual (unsorted) output:
```
lkey value_x rkey value_y
0 foo 1 foo 5
1 foo 4 foo 5
2 bar 2 bar 6
3 bar 2 bar 8
4 baz 3 NaN NaN
5 NaN NaN qux 7
```
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} | 2 | 2014-04-02T12:29:16Z | 2014-04-19T12:47:08Z | 2014-04-19T12:47:08Z | CONTRIBUTOR | null | resampling has been fixed, so this is only with 2 or more groupers (#6516)
```
In [9]:
df = DataFrame({
'date' : pd.to_datetime([
'20121002','20121007','20130130','20130202','20130305','20121002',
'20121207','20130130','20130202','20130305','20130202','20130305']),
'user_id' : [1,1,1,1,1,3,3,3,5,5,5,5],
'whole_cost' : [1790,364,280,259,201,623,90,312,359,301,359,801],
'cost1' : [12,15,10,24,39,1,0,90,45,34,1,12] }).set_index('date')
expected = df.groupby('user_id')['whole_cost'].resample(
'M', how='sum').dropna().reorder_levels(['date','user_id']).sortlevel().astype('int64')
expected.name = 'whole_cost'
In [10]: expected
Out[10]:
date user_id
2012-10-31 1 2154
3 623
2012-12-31 3 90
2013-01-31 1 280
3 312
2013-02-28 1 259
5 718
2013-03-31 1 201
5 1102
Name: whole_cost, dtype: int64
```
These should be equivalent
```
In [11]: df.sort_index().groupby([pd.TimeGrouper(freq='M'), 'user_id'])['whole_cost'].sum()
Out[11]:
date user_id
2012-10-31 1 2154
3 623
2012-12-31 3 90
2013-01-31 1 280
3 312
2013-02-28 1 259
5 718
2013-03-31 1 201
5 1102
Name: whole_cost, dtype: int64
In [13]: df.groupby([pd.TimeGrouper(freq='M'), 'user_id'])['whole_cost'].sum()
ValueError: cannot reindex from a duplicate axis
```
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| null | 1 | 2014-04-02T13:28:31Z | 2017-05-15T21:15:56Z | 2014-04-04T12:34:11Z | CONTRIBUTOR | null | As pointed out at https://github.com/pydata/pandas/pull/6761#issuecomment-39291213, `__getslice__` was deprecated in Py3, so the test was faulty. I switched to checking for `reverse`.
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} | 13 | 2014-04-02T14:41:17Z | 2014-04-30T01:05:17Z | 2014-04-30T01:05:17Z | NONE | null | After a concat of two DataFrames with the same columns. I want to consolidate some data and remove NaNs in some columns by values in other columns. I ended up with a DataFrame that magically had additional columns.
This is the minimum example that I can give to reproduce the faulty behaviour using current master (70de129):
```
df1 = pd.DataFrame([{'A':None, 'B':1},{'A':2, 'B':2}])
df2 = pd.DataFrame([{'A':3, 'B':3},{'A':4, 'B':4}])
df = pd.concat([df1, df2], axis=1)
```
```
>>> df1
A B
0 NaN 1
1 2 2
[2 rows x 2 columns]
>>> df2
A B
0 3 3
1 4 4
[2 rows x 2 columns]
>>> df
A B A B
0 NaN 1 3 3
1 2 2 4 4
[2 rows x 4 columns]
```
Now replacing NaNs in the 0 column with (corresponding) values in the 2 column ('A'), I expected to simply write a 3 into NaN (which it did), but it actually added a column '0' at the end of the DataFrame even though iloc is not supposed to enlarge the dataset. Clearly a bug.
```
inds = np.isnan(df.iloc[:, 0])
df.iloc[:, 0][inds] = df.iloc[:, 2][inds]
```
```
>>> df
A B A B 0
0 3 1 3 3 3
1 2 2 4 4 2
[2 rows x 5 columns]
```
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} | 4 | 2014-04-02T14:58:15Z | 2015-11-14T05:27:03Z | 2014-04-05T15:58:05Z | MEMBER | null | Same background as #6736.
Currently,
- When a function is passed, any error caused by the function results in `rename` error.
- When a dict is passed, label which is not included in the axis will be silently skipped. Even though `drop` raises `ValueError` in such a case.
```
>>> df = pd.DataFrame({1: [1, 2], 'B': pd.to_datetime(['2010-01-01', np.nan])})
>>> renamed_func = df.rename(columns=lambda x: x + 1)
TypeError: cannot concatenate 'str' and 'int' objects
>>> renamed_dict = df.rename(columns={'B':'C', 'D':'E'})
Index([1, u'C'], dtype='object')
```
I think it is nice if `rename` also has `errors` keyword to:
- Suppress error raised by the function and `rename` only non-problematic labels (`errors='ignore'`), or raise error whatever derived from the function (`errors='raise'`).
- Suppress error if label is not found in the target axis, and `rename` only non-problematic labels (`errors='ignore'`), or raise error if any of label is not included in the target axis (`errors='raise'`).
I feel the default should be `errors='raise'` in the future version based on the other functions behavior. This doesn't affect to the current behavior when a function is passed, but affects to when a dict is passed.
In this version, `rename` raise `FutureWarning` for future precaution if it is called with non-existing label. And it is possible to force `rename` to raise `ValueError` in such a case by specifying `errors='raise'`.
```
>>> renamed_func = df.rename(columns=lambda x: x + 1, errors='ignore')
Index([2, u'B'], dtype='object')
>>> renamed_dict = df.rename(columns={'B':'C', 'D':'E', 'F':'G'}, errors='ignore')
Index([1, u'C'], dtype='object')
>>> renamed_func = df.rename(columns=lambda x: x + 1, errors='raise')
TypeError: cannot concatenate 'str' and 'int' objects
>>> renamed_dict = df.rename(columns={'B':'C', 'D':'E', 'F':'G'}, errors='raise')
ValueError: labels ['D' 'F'] not contained in axis
```
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| null | 11 | 2014-04-02T15:37:14Z | 2014-04-24T13:33:43Z | 2014-04-03T17:37:38Z | NONE | null | After "upgrading" to Xcode 5.1, I can no longer build Pandas on OS X 10.9.2. I'm wondering if anyone has successfully done so. I get the following when running build_ext:
```
python setup.py build_ext --inplace
running build_ext
skipping 'pandas/index.c' Cython extension (up-to-date)
building 'pandas.index' extension
creating build
creating build/temp.macosx-10.9-intel-2.7
creating build/temp.macosx-10.9-intel-2.7/pandas
creating build/temp.macosx-10.9-intel-2.7/pandas/src
creating build/temp.macosx-10.9-intel-2.7/pandas/src/datetime
cc -fno-strict-aliasing -fno-common -dynamic -arch x86_64 -arch i386 -g -Os -pipe -fno-common -fno-strict-aliasing -fwrapv -mno-fused-madd -DENABLE_DTRACE -DMACOSX -DNDEBUG -Wall -Wstrict-prototypes -Wshorten-64-to-32 -DNDEBUG -g -fwrapv -Os -Wall -Wstrict-prototypes -DENABLE_DTRACE -arch x86_64 -arch i386 -pipe -Ipandas/src/klib -Ipandas/src -I/Library/Python/2.7/site-packages/numpy-1.9.0.dev_297f54b-py2.7-macosx-10.9-intel.egg/numpy/core/include -I/System/Library/Frameworks/Python.framework/Versions/2.7/include/python2.7 -c pandas/index.c -o build/temp.macosx-10.9-intel-2.7/pandas/index.o
clang: error: unknown argument: '-mno-fused-madd' [-Wunused-command-line-argument-hard-error-in-future]
clang: note: this will be a hard error (cannot be downgraded to a warning) in the future
error: command 'cc' failed with exit status 1
make: *** [tseries] Error 1
```
After a little digging around, I noticed some folks got rid of some clang problems with `export ARCHFLAGS="-Wno-error=unused-command-line-argument-hard-error-in-future"`, so I did that. I got a little further, but it [still failed](http://d.pr/f/cPOy).
Any suggestions for a fix are most welcome!
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} | 5 | 2014-04-02T16:27:48Z | 2014-05-16T20:15:53Z | 2014-05-16T20:15:53Z | NONE | null | When calling `boxplot` on a `DataFrameGroupBy` object, it does not seem to be able to properly infer the number of subplots required to account for the number of groups. For example, the following:
```
lsl_dr[lsl_dr.test_type==t].groupby('type').boxplot(column='score', grid=False)
```
generates this plot:
Notice that it generates a 2x2 grid, even though there are only three groups. I thought I could deal with this manually by calling `pyplot.subplots(1,3)`, but the `boxplot` method does not appear to be able to accept an axis argument like the `DataFrame.boxplot` method does. Is there a work-around for this?
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} | 2 | 2014-04-02T16:30:29Z | 2014-09-10T13:15:28Z | 2014-09-10T13:14:50Z | CONTRIBUTOR | null | When I tried 'DataFrame.read_csv' for the file named with local-language in Windows (the filesystem encoding is 'cp932'), it has failed with the error 'File does not exist'.
I find the line 538 in 'pandas / parser.pyx' that set filename encoding into utf-8 regardless its environment,
```
if isinstance(source, basestring):
if not isinstance(source, bytes):
source = source.encode('utf-8')
```
I suggest to modify it like,
```
source = source.encode(sys.getfilesystemencoding())
```
In my environment, WIndows8(x64) with Japanese language, this modification work.
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} | 4 | 2014-04-02T18:34:46Z | 2021-04-11T04:24:38Z | null | CONTRIBUTOR | null | xref #8466
Can't it just return the `freq` for you instead of raising an error? Helps with duck typing.
```
[~/]
[1]: pd.version.version
[1]: '0.13.1-434-gdcbbc59'
[2]: pd.tseries.frequencies.infer_freq(pd.PeriodIndex(['2000-01-01'], freq='D'))
```
Error message
```
File "/home/skipper/src/pandas-skipper/pandas/tseries/frequencies.py", line 656, in infer_freq
raise TypeError("PeriodIndex given. Check the `freq` attribute "
TypeError: PeriodIndex given. Check the `freq` attribute instead of using infer_freq.
```
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} | 6 | 2014-04-02T21:45:13Z | 2020-03-11T16:23:47Z | 2019-12-28T05:11:56Z | CONTRIBUTOR | null | https://groups.google.com/forum/#!msg/pydata/LLPnzMfDlSg/OpK-mHJzJwYJ
http://stackoverflow.com/questions/22820292/how-to-use-pandas-rolling-functions-on-a-forward-looking-basis
Applying to reverse Series and reversing could work on all (?) rolling functions, I think sometimes can just do on values array, a kwarg would be nice.
```
pd.rolling_(s[::-1])[::-1]
pd.Series(pd.rolling_(s.values[::-1])[::-1), s.index, name=s.name) # faster?
```
(OP says that this doesn't actually work in some cases: http://stackoverflow.com/questions/22820292/how-to-use-pandas-rolling-functions-on-a-forward-looking-basis/22820689?noredirect=1#comment34813442_22820689... ?)
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| null | 8 | 2014-04-03T01:48:44Z | 2014-04-03T22:14:08Z | 2014-04-03T22:05:19Z | CONTRIBUTOR | null | I'm no scientist, but I think the intuitive behavior when adding or subtracting values is this: **if no explicit addition/subtraction/mathematical-operation is demanded and specified (on some values), do not change the values you already have**.
Instead, what pandas does is, with lack of explicit instructions, convert your nice data into useless NaNs. Here's an example:
``` python
>>> data = pd.Series(np.zeros(3))
>>> data
0 0
1 0
2 0
dtype: float64
>>> additional_data = pd.Series([3], index=[1]) # only partly defined
>>> data.add(additional_data)
0 NaN
1 3
2 NaN
dtype: float64
```
IMHO it would be much more intuitive to **assume no change** with **`fill_value=0` as default**.
``` python
>>> data.add(additional_data, fill_value=0)
0 0
1 3
2 0
dtype: float64
```
The philosophical shift is this: when the addend is omitted, instead of adding something that is not-a-number, we just don't add anything at all.
When one wants to botch their already-existing, nice data, they can always pass `fill_value=np.nan` explicitly. :-)
But I'm no scientist.
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} | 4 | 2014-04-03T05:14:21Z | 2017-09-29T15:45:08Z | 2014-06-24T13:16:52Z | CONTRIBUTOR | null | If nrows are determined, the use of chunksize option does not create a TextFileReader object.
``` python
reader = pd.read_table(fname, sep=',',chunksize=4, na_values=['null'],
nrows=20)
type(reader)
```
``` bash
pandas.core.frame.DataFrame
```
My suggestion:
- Either put into docs that they are not to be used concurrently
- or it's a feature request that they can.
I would find it useful to get chunks of x size, but only for the first n row of a huge file.
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| null | 2 | 2014-04-03T05:24:21Z | 2014-04-03T11:38:19Z | 2014-04-03T07:46:53Z | CONTRIBUTOR | null | Hi,
I noticed that after slicing, i couldn't have a clean repr() of my DataFrame due to an altered dtype. E.g.
``` python
import pandas as pd
print pd.__version__
pl = pd.DataFrame([range(10,15), range(11,16)])
_ = pl.loc[:,1]
print pl.dtypes
_ = pl.loc[:,1:3]
print pl.dtypes
print pl[3].dtype
```
The output being:
``` python
>>> import pandas as pd
>>> print pd.__version__
0.13.1
>>> pl = pd.DataFrame([range(10,15), range(11,16)])
>>> _ = pl.loc[:,1]
>>> print pl.dtypes
0 int64
1 int64
2 int64
3 int64
4 int64
dtype: object
>>> _ = pl.loc[:,1:3]
>>> print pl.dtypes
0 int64
1 int64
2 int64
3 None
4 int64
dtype: object
>>> print pl[3].dtype
int64
```
The "dtypes" list seems to be inconsitent. And, as a consequence the to_string() methods fail.
This happen on version 0.13.1, yet worked correctly on 0.12.0
Thanks
## INSTALLED VERSIONS
commit: None
python: 2.7.3.final.0
python-bits: 32
OS: Linux
OS-release: 3.2.0-58-generic-pae
machine: i686
processor: i686
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.1
Cython: 0.19.2
numpy: 1.8.1
scipy: 0.12.0
statsmodels: 0.5.0.dev-1bbd4ca
IPython: 2.0.0
sphinx: 1.1.3
patsy: 0.1.0
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.2
bottleneck: None
tables: 2.3.1
numexpr: 1.4.2
matplotlib: 1.3.0
openpyxl: 1.6.1
xlrd: 0.6.1
xlwt: 0.7.2
xlsxwriter: None
sqlalchemy: None
lxml: None
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bq: None
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} | 3 | 2014-04-03T12:23:54Z | 2014-07-16T09:00:13Z | 2014-04-03T12:42:23Z | CONTRIBUTOR | null | Corrected the example provided in the MERGE function docstring.
Closes #6763
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} | 2 | 2014-04-03T12:57:06Z | 2016-10-12T23:04:45Z | 2014-04-04T20:56:16Z | NONE | null | Dear developers,
I think that it is confusing how DataFrame.replace interprets the to_replace values as a pure string or a regexp.
When you pass a dictionary of from and to values, these are interpreted as pure string literals and work as expected. If you pass a nested dictionary with the same values, it is interpreted as a regex even if regex = False is specifically added. This causes a problem if you want to replace values that have special characters in them.
Let's see an example:
``` python
@buddha[J:T26]|19> df = pd.DataFrame({'a' : ['()', 'something else']})
@buddha[J:T26]|20> df
<20>
a
0 ()
1 something else
[2 rows x 1 columns]
@buddha[J:T26]|21> df.replace({'()' : 'parantheses'})
<21>
a
0 parantheses
1 something else
[2 rows x 1 columns]
@buddha[J:T26]|22> df.replace({'a' : {'()' : 'parantheses'}})
<22>
a
0 parantheses(parantheses)parantheses
1 paranthesessparanthesesoparanthesesmparanthese...
[2 rows x 1 columns]
```
As you can see, in the first case the () got replaced as expected. In the second case, even though the only thing I changed was to specify the column in which the replace should occur, the parentheses got treated as a regex. The same happens if I add regex = False to the options.
The current workaround for me is to make sure that the from/to values are regexes.
Cheers,
Adam
Ps.: Pandas is awesome, thanks a lot for all the effort!
@buddha[J:T26]|24> pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: x86
processor: x86 Family 6 Model 23 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.13.1
Cython: None
numpy: 1.8.0
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 1.2.0
sphinx: 1.2.1
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2013.9
bottleneck: 0.8.0
tables: 3.1.0
numexpr: 2.3
matplotlib: 1.3.1
openpyxl: None
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
sqlalchemy: 0.8.4
lxml: 3.3.1
bs4: 4.3.2
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bq: None
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} | 6 | 2014-04-03T14:55:05Z | 2014-07-16T09:00:14Z | 2014-04-04T20:56:16Z | MEMBER | null | closes #6777
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} | 4 | 2014-04-03T15:35:40Z | 2017-11-24T10:50:44Z | 2017-11-24T10:50:08Z | CONTRIBUTOR | null | This works
```
from pandas.tseries.frequencies import to_offset
pd.Timestamp('1990-9-30') + 2*to_offset('Q')
```
This doesn't. Shouldn't it? Workaround is to convert with `to_timestamp`.
```
pd.Period('1990q1') + 2*to_offset('Q')
```
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} | 7 | 2014-04-03T15:41:25Z | 2014-09-29T22:07:51Z | 2014-09-29T22:07:51Z | CONTRIBUTOR | null | This doesn't work. Looks like `start` and `end` should try a bit harder to convert to timestamp?
```
from datetime import datetime
pd.DatetimeIndex(start=pd.Period('1990q1'), end=datetime(1992, 12, 31), freq='Q')
```
This does.
```
pd.DatetimeIndex(start=pd.Period('1990q1').to_timestamp(), end=datetime(1992, 12, 31), freq='Q')
```
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} | 6 | 2014-04-03T17:05:50Z | 2014-10-10T15:07:00Z | 2014-10-10T15:07:00Z | NONE | null | When invoking `DataFrame.convert_objects()` with `copy=False`, it does not affect affect the original object.
It should be:
1. either mentioned in the documentation (as #3436 suggest),
2. enforce it, or
3. drop the kw-arg.
``` python
>>> import pandas as pd
>>> df = pd.DataFrame([['1',2], [3,'4']])
>>> df.dtypes
Out[57]:
0 object
1 object
dtype: object
>>> df.convert_objects(convert_numeric = True, copy = False)
>>> df.dtypes
Out[59]:
0 object
1 object
dtype: object
```
```
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 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
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: None
xlsxwriter: None
sqlalchemy: 0.9.2
lxml: None
bs4: None
html5lib: None
bq: None
apiclient: None
```
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} | 8 | 2014-04-03T17:17:05Z | 2016-10-12T23:04:45Z | 2014-04-05T18:06:13Z | NONE | null | this works for openpyxl but it fails for xlsxwriter
TypeError("NAN/INF not supported in write_number()")
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} | 5 | 2014-04-03T17:28:14Z | 2019-12-31T15:04:45Z | 2019-12-31T15:04:45Z | MEMBER | null | ```
In [24]: x = Series(np.around(randn(10) * 100).astype('timedelta64[ns]'))
In [25]: x
Out[25]:
0 -00:00:00.000000
1 00:00:00.000000
2 -00:00:00.000000
3 -00:00:00.000000
4 00:00:00.000000
5 -00:00:00.000000
6 -00:00:00.000000
7 -00:00:00.000000
8 -00:00:00.000000
9 -00:00:00.000000
dtype: timedelta64[ns]
In [26]: x.values
Out[26]: array([-259, 31, -49, -41, 141, -14, -111, -117, -20, -35], dtype='timedelta64[ns]')
In [27]: buf = StringIO()
In [28]: x.to_csv(buf)
In [29]: print(buf.getvalue())
0,-00:00:00.000000
1,00:00:00.000000
2,-00:00:00.000000
3,-00:00:00.000000
4,00:00:00.000000
5,-00:00:00.000000
6,-00:00:00.000000
7,-00:00:00.000000
8,-00:00:00.000000
9,-00:00:00.000000
```
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} | 4 | 2014-04-03T19:03:24Z | 2014-04-03T21:07:22Z | 2014-04-03T21:07:11Z | NONE | null | Starting with a DataFrame like this:
```
df = pandas.DataFrame([{'ts':datetime(2014, 4, 1, tzinfo=pytz.utc), 'foo':1}])
```
This works fine:
```
df = df.set_index('ts')
print(df)
```
```
foo
ts
2014-04-01 00:00:00+00:00 1
```
This yields an AttributeError:
```
df.index = df['ts']
print(df)
```
```
In [75]: print(df)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-75-1152f648e0af> in <module>()
----> 1 print(df)
/home/sucram/.virtualenvs/ehubdata/local/lib/python2.7/site-packages/pandas/core/base.pyc in __str__(self)
33 if compat.PY3:
34 return self.__unicode__()
---> 35 return self.__bytes__()
36
37 def __bytes__(self):
/home/sucram/.virtualenvs/ehubdata/local/lib/python2.7/site-packages/pandas/core/base.pyc in __bytes__(self)
45
46 encoding = get_option("display.encoding")
---> 47 return self.__unicode__().encode(encoding, 'replace')
48
49 def __repr__(self):
/home/sucram/.virtualenvs/ehubdata/local/lib/python2.7/site-packages/pandas/core/frame.pyc in __unicode__(self)
458 width = None
459 self.to_string(buf=buf, max_rows=max_rows, max_cols=max_cols,
--> 460 line_width=width, show_dimensions=show_dimensions)
461
462 return buf.getvalue()
/home/sucram/.virtualenvs/ehubdata/local/lib/python2.7/site-packages/pandas/core/frame.pyc in to_string(self, buf, columns, col_space, colSpace, header, index, na_rep, formatters, float_format, sparsify, nanRep, index_names, justify, force_unicode, line_width, max_rows, max_cols, show_dimensions)
1297 max_cols=max_cols,
1298 show_dimensions=show_dimensions)
-> 1299 formatter.to_string()
1300
1301 if buf is None:
/home/sucram/.virtualenvs/ehubdata/local/lib/python2.7/site-packages/pandas/core/format.pyc in to_string(self, force_unicode)
378 text = info_line
379 else:
--> 380 strcols = self._to_str_columns()
381 if self.line_width is None:
382 text = adjoin(1, *strcols)
/home/sucram/.virtualenvs/ehubdata/local/lib/python2.7/site-packages/pandas/core/format.pyc in _to_str_columns(self)
310
311 # may include levels names also
--> 312 str_index = self._get_formatted_index()
313 str_columns = self._get_formatted_column_labels()
314
/home/sucram/.virtualenvs/ehubdata/local/lib/python2.7/site-packages/pandas/core/format.pyc in _get_formatted_index(self)
583 formatter=fmt)
584 else:
--> 585 fmt_index = [index.format(name=show_index_names, formatter=fmt)]
586
587 adjoined = adjoin(1, *fmt_index).split('\n')
/home/sucram/.virtualenvs/ehubdata/local/lib/python2.7/site-packages/pandas/core/generic.pyc in __getattr__(self, name)
1813 return self[name]
1814 raise AttributeError("'%s' object has no attribute '%s'" %
-> 1815 (type(self).__name__, name))
1816
1817 def __setattr__(self, name, value):
AttributeError: 'Series' object has no attribute 'format'
```
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} | 0 | 2014-04-03T20:17:23Z | 2014-06-30T14:03:57Z | 2014-04-03T21:07:11Z | CONTRIBUTOR | null | closes #6785
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} | 0 | 2014-04-03T20:45:48Z | 2014-09-30T15:53:52Z | 2014-09-30T15:53:52Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/22845856/performance-issues-with-groupbys-last-in-pandas/22846215#22846215
want to run all built-in functions (e.g. `first,last,nth,sum,min`...etc with
large number of groups, say 10000 to verify perf.
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} | 10 | 2014-04-03T20:55:05Z | 2014-04-03T22:18:18Z | 2014-04-03T22:11:49Z | NONE | null | The following code errors out. Seems like it should just return the original dataframe.
pandas: 0.13.1-552-g8120a59
``` python
import pandas as pd
import numpy as np
from itertools import product
attributes = ['Attribute' + str(i) for i in range(1)]
attribute_values = ['Value' + str(i) for i in range(1000)]
index = pd.MultiIndex.from_tuples(list(product(attributes, attribute_values)))
df = 0.1 * np.random.randn(10, 1 * 1000) + 0.5
df = pd.DataFrame(df, columns=index)
df[attributes]
```
Stacktrace
```
(pandas)pandas git:master ❯ python example_failure.py ✭
Traceback (most recent call last):
File "example_failure.py", line 12, in <module>
df[attributes]
File "PATH/pandas/pandas/core/frame.py", line 1672, in __getitem__
return self._getitem_array(key)
File "PATH/pandas/pandas/core/frame.py", line 1717, in _getitem_array
return self.take(indexer, axis=1, convert=True)
File "PATH/pandas/pandas/core/generic.py", line 1224, in take
indices, len(self._get_axis(axis)))
File "PATH/pandas/pandas/core/indexing.py", line 1564, in _maybe_convert_indices
if mask.any():
AttributeError: 'bool' object has no attribute 'any'
```
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| null | 9 | 2014-04-03T21:02:12Z | 2014-04-04T16:44:02Z | 2014-04-03T22:56:09Z | NONE | null | Even a single numeric-compatible field is sufficient to convert the whole column to numeric, rendering all other non-numeric fields (in that column) NaN.
The following code snippets demonstrates the problem.
``` python
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.13.1'
In [3]: df = pd.DataFrame(["x","y","10","z"])
In [4]: df[0]
Out[4]:
0 x
1 y
2 10
3 z
Name: 0, dtype: object
In [5]: df = df.convert_objects(convert_numeric=True)
In [6]: df[0]
Out[6]:
0 NaN
1 NaN
2 10
3 NaN
Name: 0, dtype: float64
```
The expected result is that this column should _not_ be converted to numeric, and should remain of type object. Just because that one element happens to be numeric-compatible doesn't mean that the entire column should be converted.
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} | 6 | 2014-04-04T00:59:52Z | 2021-04-11T04:25:28Z | null | CONTRIBUTOR | null | atm we're checking dict-like by doing isinstance(x, dict), I think we should be using collections.MutableMapping, slightly more general.
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| null | 10 | 2014-04-04T01:08:04Z | 2014-04-06T01:10:58Z | 2014-04-04T01:10:01Z | CONTRIBUTOR | null | For example in `test_get_data_single_symbol`:
```
In [4]: df = web.get_data_yahoo('GOOG')
In [5]: df
Out[5]:
Open High Low Close Volume Adj Close
Date
2014-03-27 568.00 568.00 552.92 558.46 13100 558.46
2014-03-28 561.20 566.43 558.67 559.99 41100 559.99
2014-03-31 566.89 567.00 556.93 556.97 10800 556.97
2014-04-01 558.71 568.45 558.71 567.16 7900 567.16
2014-04-02 599.99 604.83 562.19 567.00 146700 567.00
2014-04-03 569.85 587.28 564.13 569.74 5085200 569.74
[6 rows x 6 columns]
```
So this line fails:
```
self.assertEqual(df.Volume.ix['OCT-08-2010'], 2859200)
```
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| null | 4 | 2014-04-04T01:47:11Z | 2014-05-16T17:47:54Z | 2014-05-16T17:47:54Z | NONE | null | is it hard to add sheets to an existing excel file on the disk?
```
import pandas as pd
import numpy as np
a=pd.DataFrame(np.random.random((3,1)))
excel_writer=pd.ExcelWriter('c:\excel.xlsx')
a.to_excel(excel_writer, 'a1')
excel_writer.save()
excel_writer=pd.ExcelWriter('c:\excel.xlsx')
a.to_excel(excel_writer, 'a2')
excel_writer.save()
```
here only sheet 'a2" is save, but I like to save both 'a1' and 'a2'.
I know it is possible to add sheets to an existing workbook.
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} | 4 | 2014-04-04T04:08:09Z | 2019-12-12T21:05:08Z | 2019-12-12T21:05:08Z | NONE | null | rolling_max with center=True and min_periods=1 returns NaN's
```
Python 3.3.5 (default, Mar 10 2014, 03:21:31)
[GCC 4.8.2 20140206 (prerelease)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> df=pd.DataFrame([0,1,2,1,0], columns=['a']
### This is OK
>>> pd.rolling_max(df['a'], window=3, center=True)
0 NaN
1 2
2 2
3 2
4 NaN
dtype: float64
### This should return [1, 2, 2, 2, 1]
>>> pd.rolling_max(df['a'], window=3, center=True, min_periods=1)
0 1
1 2
2 2
3 2
4 NaN
dtype: float64
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.3.5.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.6-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.1
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 1.2.1
sphinx: 1.2.2
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.1
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
sqlalchemy: None
lxml: None
bs4: None
html5lib: None
bq: None
apiclient: None
```
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} | 11 | 2014-04-04T08:27:52Z | 2014-04-22T14:33:16Z | 2014-04-22T14:33:16Z | MEMBER | null | At the moment, with the new sql code, there is a bug in the conversion of invalid column names (trough the function `_safe_col_name`). Eg:
```
In [1]: import sqlalchemy
In [2]: engine = sqlalchemy.create_engine('sqlite:///:memory:')
In [4]: df = pd.DataFrame({'safe_col':[1,2], 'invalid col':[3,4]})
In [7]: pd.io.sql.to_sql(df, 'test', engine)
In [9]: pd.io.sql.read_table('test', engine)
Out[9]:
pandas_index invalid_col safe_col
0 0 None 1
1 1 None 2
```
I know the reason, and it is easily fixed (the table statement uses the adapted column, but the inserting still the old one, so the data are not inserted). But I was wondering if this is actually necessary?
Because, databases can handle column names with spaces (at least sqlite, mysql and postgresql that I know a little bit), the names are then just quoted and sqlalchemy ensures this is done properly for us.
Often, it is advised to not use spaces (or capital letters) in column names, as it is just tedious to always have to quote them and it is more robust to not have to do that. But it does work, so I would think it is up to the user to provide the column names he/she wants.
So proposal:
- we don't change the column names and leave them as the user provides them
- we could provide a kwarg to do this if the users wants to, but I don't think this is priority at the moment (and I think we should then also convert to lower case, as there is as much reason to do that as to changes spaces to underscores)
@mangecoeur @hayd
PS: the fact there are `None`s in the example above and not `NaN`s is still another issue I think.
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} | 2 | 2014-04-04T08:31:43Z | 2015-11-08T19:46:21Z | 2015-11-08T19:45:26Z | NONE | null | Using pandas 0.13.1 ...
When df is a 2-level datetime indexed dataframe, to_csv prints 00:00 following the dates, even when you specify date_format = '%Y-%m-%d'.
df.to_csv(date_format = '%Y-%m-%d')
To get around this, I'm currently having to use reset_index; then to_csv works as expected.
```
In [4]: df = DataFrame(np.random.randn(6,2),index=MultiIndex.from_product([['A','B'],date_range('20130101',periods=3)]))
In [5]: df
Out[5]:
0 1
A 2013-01-01 -0.180284 -0.632280
2013-01-02 2.986923 -0.422540
2013-01-03 -1.435938 1.700168
B 2013-01-01 -0.163560 -1.047000
2013-01-02 1.852092 -0.552963
2013-01-03 1.375277 0.737837
[6 rows x 2 columns]
In [6]: df.to_csv('test.csv',mode='w')
In [7]: !cat test.csv
,,0,1
A,2013-01-01 00:00:00,-0.1802842406155066,-0.6322803999738553
A,2013-01-02 00:00:00,2.9869225314020604,-0.42253974812704775
A,2013-01-03 00:00:00,-1.435937967237287,1.700167741194332
B,2013-01-01 00:00:00,-0.1635604685556706,-1.0469998404335645
B,2013-01-02 00:00:00,1.8520922142464138,-0.5529633735794578
B,2013-01-03 00:00:00,1.3752768989190265,0.7378371099828236
In [8]: df.to_csv('test.csv',mode='w',date_format='%Y-%m-%d')
In [9]: !cat test.csv
,,0,1
A,2013-01-01 00:00:00,-0.1802842406155066,-0.6322803999738553
A,2013-01-02 00:00:00,2.9869225314020604,-0.42253974812704775
A,2013-01-03 00:00:00,-1.435937967237287,1.700167741194332
B,2013-01-01 00:00:00,-0.1635604685556706,-1.0469998404335645
B,2013-01-02 00:00:00,1.8520922142464138,-0.5529633735794578
B,2013-01-03 00:00:00,1.3752768989190265,0.7378371099828236
In [10]: df.reset_index().to_csv('test.csv',mode='w',date_format='%Y-%m-%d')
In [11]: !cat test.csv
,level_0,level_1,0,1
0,A,2013-01-01,-0.1802842406155066,-0.6322803999738553
1,A,2013-01-02,2.9869225314020604,-0.42253974812704775
2,A,2013-01-03,-1.435937967237287,1.700167741194332
3,B,2013-01-01,-0.1635604685556706,-1.0469998404335645
4,B,2013-01-02,1.8520922142464138,-0.5529633735794578
5,B,2013-01-03,1.3752768989190265,0.7378371099828236
```
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} | 17 | 2014-04-04T13:02:45Z | 2021-05-10T16:24:32Z | 2014-07-07T16:55:21Z | NONE | null | The `pandas.io.sql` module has a convenient `read_frame` function which has been of great use to me. The function has an argument `coerce_float`. I propose to make that more general akin to the `pandas.DataFrame` initialization method which has an argument `dtype`. This would allow passing a `dict` with column names as keys and the desired data type as values. Columns not in the `dict` would be inferred normally.
If this is not planned but desired I can look into patching that myself.
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} | 0 | 2014-04-04T14:03:09Z | 2014-06-16T05:11:38Z | 2014-04-04T14:42:33Z | CONTRIBUTOR | null | fix the getitem issues for iloc on #6766
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| null | 2 | 2014-04-04T16:22:25Z | 2014-07-07T15:49:51Z | 2014-07-07T15:49:51Z | NONE | null | Using pandas 0.13.1
I have a dataframe with three columns. The first two contain datetimes and the third floats (the entire data frame originates from df = pandas.io.sql.frame_query(...,...) ).
I need to remove rows for which dates in the first two columns are duplicated. So something like the following would seem to be appropriate:
```
df = df.groupby(['col1','col2']).first()
df.reset_index(inplace = True)
```
However, this doesn't work: it executes without error, but returns df unmodified.
I can get the functionality I need by first converting the datetimes to strings:
```
df['col1'] = [date.strftime('%Y-%m-%d') for date in df['col1']]
df['col2'] = [date.strftime('%Y-%m-%d') for date in df['col2']]
```
Only after this, it seems, do the original commands work:
```
df = df.groupby(['col1','col2']).first()
df.reset_index(inplace = True)
```
Then to get back to the dataframe format I need, I have to revert back to datetimes:
```
df['col1'] = to_datetime(df['col1'])
df['col2'] = to_datetime(df['col2'])
```
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