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} | 2 | 2015-02-17T03:31:11Z | 2021-04-12T05:19:04Z | null | NONE | null | This is a repost of #9359 with some new fancy code that handles lots of different kinds of edge cases. Please take a look at the code:
https://gist.github.com/cloudformdesign/13278001b1a0b0cde647
Basically this allows automatic loading of nested dictionaries, whether those dictionaries are a "list of dictionaries" or a "dictionary of lists", it handles them as you would expect them to be handled by automatically creating a MultiIndex.
My hope is that the `dataframe_dict` function be used instead of the standard `DataFrame.from_dict` method.
This is not an edge case -- this is a use case that arrives frequently for people who use mongo or who store data in nested dictionaries.
If you agree that it is functionality worth adding, I can make the changes to pandas and make a pull request.
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} | 2 | 2015-02-17T04:10:15Z | 2017-02-17T13:50:19Z | 2017-02-17T13:50:19Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/28541302/pandas-to-csv-with-quoting-3-quote-nonnumeric-doesnt-work
remove the actual numbers and show the usage of the csv module constants
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} | 10 | 2015-02-17T16:38:03Z | 2015-03-01T21:18:33Z | 2015-03-01T21:18:33Z | CONTRIBUTOR | null | Each commit passes `test_fast.sh`, so performance can be measured between commits.
I can break any of these commits into separate PRs if needed.
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} | 2 | 2015-02-17T17:25:34Z | 2015-02-18T00:22:49Z | 2015-02-18T00:22:49Z | NONE | null | See repro below:
``` python
import pandas as pd
df_with_ts = pd.DataFrame(data=[['BL', pd.Timestamp('2015-02-01')], ['TH', pd.Timestamp('2015-02-02')]], columns=['item', 'date'])
res = df_with_ts.groupby(['date']).apply(lambda x: pd.Series(x['item'].unique()[0]))
In []: res
Out[]:
0
date
2015-02-01 NaT
2015-02-02 2015-02-17
In []: res.dtypes
Out[]:
0 datetime64[ns]
dtype: object
```
It looks like this is happening because pd.Timestamp('TH') is valid and results in no exceptions. If you replace the string 'TH' with something else then the resulting column's dtype is object, as expected. The order of columns in the above example does not matter.
For comparison, this case works as expected:
``` python
df = pd.DataFrame(data=[['BL', '2015-02-01'], ['TH', '2015-02-02']], columns=['item', 'date'])
res = df.groupby(['date']).apply(lambda x: pd.Series(x['item'].unique()[0]))
In []: res
Out[]:
0
date
2015-02-01 BL
2015-02-02 TH
In []: res.dtypes
Out[]:
0 object
dtype: object
```
output of show_versions() below (using Miniconda):
``` python
In []: from pandas.util.print_versions import show_versions
In []: show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.15.2
nose: 1.3.4
Cython: 0.21.2
numpy: 1.8.2
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.3.0
sphinx: 1.2.3
patsy: 0.2.1
dateutil: 2.2
pytz: 2014.9
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.0
openpyxl: 2.0.2
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.6.6
lxml: 3.4.1
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
```
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} | 1 | 2015-02-17T18:29:12Z | 2019-12-23T04:38:16Z | null | NONE | null | When using apply on a series, pandas is returning a series of int64 even though the target values were uint64.
``` python
import numpy as np
import pandas as pd
uintDF = pd.DataFrame(np.uint64([1,2,3,4,5]),columns=['Numbers'])
indexDF = pd.DataFrame([2,3,2,1,2],columns=['Indices'])
def retrieve(targetRow,targetDF):
val = targetDF['Numbers'].iloc[targetRow]
return val
resultDF = indexDF['Indices'].apply(retrieve,args=(uintDF,))
```
``` python
print uintDF.dtypes
```
Numbers uint64
dtype: object
``` python
print resultDF.dtypes
```
dtype('int64')
I'm aware this specific functionality is possible through iloc(columnVector) but my real retrieve function is more complex.
Thank you.
## INSTALLED VERSIONS
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Linux
OS-release: 3.0.38-0.5-default
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2
nose: 1.2.1
Cython: 0.21.1
numpy: 1.7.1
scipy: 0.12.0
statsmodels: 0.6.1
IPython: 2.3.1
sphinx: 1.1.3
patsy: 0.3.0
dateutil: 2.3
pytz: 2012j
bottleneck: 0.6.0
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.2.0
openpyxl: 1.6.2
xlrd: 0.9.0
xlwt: 0.7.4
xlsxwriter: None
lxml: 3.1.0
bs4: 4.2.1
html5lib: None
httplib2: None
apiclient: None
rpy2: 2.3.3
sqlalchemy: 0.7.8
pymysql: None
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| null | 1 | 2015-02-17T20:02:38Z | 2015-02-17T21:29:15Z | 2015-02-17T21:29:15Z | CONTRIBUTOR | null | `period_helper.c` should be added to `_clean_exclude` in `setup.py`
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| null | 2 | 2015-02-17T20:47:51Z | 2015-02-17T23:26:53Z | 2015-02-17T21:29:04Z | CONTRIBUTOR | null | I missed this when I was rebasing.
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} | 4 | 2015-02-17T22:38:10Z | 2016-11-11T11:43:16Z | 2016-11-11T11:43:13Z | NONE | null | ``` python
import pandas as pd
from datetime import datetime
pd.datetools.thisYearBegin(datetime(2015,2,1))
```
This returns Timestamp('2016-01-01 00:00:00'), but should return 201**5**-01-01. Should thisYearBegin() just be removed? I don't think it's documented.
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} | 1 | 2015-02-18T03:24:55Z | 2021-04-12T05:21:10Z | null | MEMBER | null | Recently, I've been working on adding a 'nearest' method to reindexing: https://github.com/pydata/pandas/pull/9258
It occurs to me that we could easily extend reindexing/get_indexer methods to work with unordered indexes if we were willing to do a sort operation on the index if necessary. This would probably entail saving the sorted result on the parent index, similarly to how get_indexer is currently supported on MultiIndex by creating a tuple index internally.
I think this would be a nice usability gain over the current implementation, and not be _too_ surprising. Sorting indexes (once) is pretty fast, for anything up to millions of rows.
Thoughts?
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| null | 0 | 2015-02-18T16:49:00Z | 2017-07-08T22:17:23Z | 2017-07-08T22:17:23Z | CONTRIBUTOR | null | Old style Cython debugging ([link](https://github.com/cython/cython/wiki/DebuggingTechniques)) probably works. I haven't tried it.
Cython 0.14+ comes with `cygdb`, but `setup.py` must use `cythonize` on extensions in order to use it. I will implement this if I have a need for it, but PRs welcome :grin:.
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| null | 4 | 2015-02-18T18:47:30Z | 2015-02-20T00:04:45Z | 2015-02-20T00:04:45Z | CONTRIBUTOR | null | This was fun to debug.
``` python
In [1]: import pandas as pd
In [2]: 0 in pd.Int64Index([0, 0, 1])
Out[2]: True
In [3]: 0 in pd.Int64Index([0, 1, 0])
Out[3]: True
In [4]: 0 in pd.Int64Index([0, 0, -1])
Out[4]: True
In [5]: pd.Timestamp(0) in pd.DatetimeIndex([0, 1, -1])
Out[5]: True
In [6]: pd.Timestamp(0) in pd.DatetimeIndex([0, 1, 0])
Out[6]: False # BAD
In [7]: pd.Timestamp(0) in pd.DatetimeIndex([0, 0, 1])
Out[7]: True
In [8]: pd.Timestamp(0) in pd.DatetimeIndex([0, 0, -1])
Out[8]: False # BAD
```
TimedeltaIndex is also broken.
The problem is in [`DatetimeIndexOpsMixin.__contains__`](https://github.com/pydata/pandas/blob/v0.15.2/pandas/tseries/base.py#L68), which checks the type of `idx.get_loc(key)` to determine whether the key was found in the index. If the index contains duplicate entries and is not monotonic increasing (for some reason, monotonic decreasing doesn't cut it), `get_loc` eventually falls back to [`Int64Engine._maybe_get_bool_indexer`](https://github.com/pydata/pandas/blob/v0.15.2/pandas/index.pyx#L376), which returns an ndarray of bools if the key is duplicated. Since the original `__contains__` method is looking for scalars or slices, it reports that the duplicated entry is not present.
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} | 12 | 2015-02-18T20:19:01Z | 2015-07-17T17:21:03Z | 2015-07-17T17:21:03Z | CONTRIBUTOR | null | Hello,
`pd.NaT.date()` returns `datetime.date(1, 255, 255)`
that's an odd results
I was expecting `None` (or something else) because that's really strange to have
date of year "1" in an Excel file
Kind regards
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} | 6 | 2015-02-18T22:06:57Z | 2020-07-19T18:56:44Z | null | CONTRIBUTOR | null | In 0.15.2 (and I believe this remains the case), the docstring for `DataFrame.unstack()` states `The level involved will automatically get sorted.`. This is not necessarily the case when `level` is a list of levels.
```
In [40]: df = pd.DataFrame(np.arange(8).reshape((4, 2)),
index=pd.MultiIndex.from_tuples([(100, 'A', 'y'), (100, 'A', 'x'),
(100, 'B', 'x'), (200, 'B', 'y')],
names=['Nums', 'Upper','Lower']))
In [41]: df
Out[41]:
0 1
Nums Upper Lower
100 A y 0 1
x 2 3
B x 4 5
200 B y 6 7
In [42]: df.unstack([1, 2])
Out[42]:
0 1
Upper A B A B
Lower y x x y y x x y
Nums
100 0 2 4 NaN 1 3 5 NaN
200 NaN NaN NaN 6 NaN NaN NaN 7
```
Note that the pivoted tuples are ordered as `[(A, y), (A, x), (B, x), (B, y)]`, which is not sorted.
I would expect the result to be the same as the following:
```
In [43]: df.T.stack([1, 2]).T
Out[43]:
0 1
Upper A B A B
Lower x y x y x y x y
Nums
100 2 0 4 NaN 3 1 5 NaN
200 NaN NaN NaN 6 NaN NaN NaN 7
```
In fact, there seems to be a problem even when `level` is a list containing just a single level. Compare the following:
```
In [47]: df.unstack(2)
Out[47]:
0 1
Lower x y x y
Nums Upper
100 A 2 0 3 1
B 4 NaN 5 NaN
200 B NaN 6 NaN 7
In [48]: df.unstack([2])
Out[48]:
0 1
Lower y x y x
Nums Upper
100 A 0 2 1 3
B NaN 4 NaN 5
200 B 6 NaN 7 NaN
In [50]: df.T.stack(2).T
Out[50]:
0 1
Lower x y x y
Nums Upper
100 A 2 0 3 1
B 4 NaN 5 NaN
200 B NaN 6 NaN 7
In [51]: df.T.stack([2]).T
Out[51]:
0 1
Lower x y x y
Nums Upper
100 A 2 0 3 1
B 4 NaN 5 NaN
200 B NaN 6 NaN 7
```
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} | 2 | 2015-02-18T22:10:59Z | 2015-02-20T16:11:04Z | 2015-02-20T00:04:45Z | CONTRIBUTOR | null | `DatetimeIndex.__contains__` and `TimedeltaIndex.__contains__` were failing to see duplicated elements in some circumstances.
Fixes #9512
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} | 3 | 2015-02-18T22:55:23Z | 2015-02-20T00:25:06Z | 2015-02-19T13:19:45Z | CONTRIBUTOR | null | This is in Pandas `0.15.2` (but I also tried it on '0.15.2-148-g484f668'):
``` python
df = pd.DataFrame()
df.loc[np.datetime64(datetime.datetime.now()),'one'] = 100
df.loc[np.datetime64(datetime.datetime.now()),'one'] = 100
```
This is the output:
``` text
one
2015-02-18 14:50:05.606510 100
1970-01-17 11:37:51.007748755 100
```
It seems the the first usage of `loc` correctly works around limitations of `datetime64` but I cannot explain the second one.
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} | 1 | 2015-02-19T02:50:53Z | 2015-03-06T02:52:19Z | 2015-03-05T23:25:34Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/9497
```
>>> mi
jim joe
1st 2nd 3rd
1 2014-02-01 -1 days 100 -20.87
2 NaT NaT 101 5.76
1 2014-02-03 1 days 102 -4.94
2 NaT 2 days 103 -0.79
1 2014-02-05 NaT 104 -12.51
2 2014-02-06 4 days 105 -9.89
>>> mi.unstack(['2nd', '3rd']).fillna('.')
jim joe
2nd 2014-02-01 NaT 2014-02-03 NaT 2014-02-05 2014-02-06 2014-02-01 NaT 2014-02-03 NaT 2014-02-05 2014-02-06
3rd -1 days NaT 1 days 2 days NaT 4 days -1 days NaT 1 days 2 days NaT 4 days
1st
1 100 . 102 . 104 . -20.87 . -4.94 . -12.51 .
2 . 101 . 103 . 105 . 5.76 . -0.79 . -9.89
```
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} | 30 | 2015-02-19T05:08:27Z | 2015-07-16T00:08:25Z | 2015-07-16T00:08:25Z | MEMBER | null | Doing an inplace install with recent versions of pip (e.g., with `pip install -e .`) currently produces this warning:
`UserWarning: The version specified ('0.15.2-224-geadfd92') is an invalid version, this may not work as expected with newer versions of setuptools, pip, and PyPI. Please see PEP 440 for more details.`
We should update setup.py to produce PEP 440 compliant versions such as `'0.15.2+224.geadfd92'` instead. For an example of how to convert between, see https://github.com/warner/python-versioneer/pull/45#issuecomment-74085795
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} | 10 | 2015-02-19T07:28:54Z | 2021-04-12T05:25:06Z | null | MEMBER | null | I already mentioned this in https://github.com/pydata/pandas/issues/9466 but I think it deserves its own bug report:
```
In [2]: s = pd.Series([1, 2, 3], index=(1,1,2))
In [3]: s
Out[3]:
1 1
1 2
2 3
dtype: int64
In [4]: s.loc[1]
Out[4]:
1 1
1 2
dtype: int64
In [5]: type(s.loc[1])
Out[5]: pandas.core.series.Series
In [6]: s.loc[2]
Out[6]: 3
In [7]: type(s.loc[2])
Out[7]: numpy.int64
```
Quoting https://github.com/pydata/pandas/issues/5678 , _"You are selecting out of a duplicated index Series. You could argue that you should get back another Series"_
I really think life would be easier if s.loc[2] returned a Series of length one (and DataFrames and Panels behaved similarly). One is assumed to know (and can check in O(1)) if an index is unique, but maybe not if a _given label_ is unique.
With higher dimensions structures it's even more messy because if e.g. .loc[lab_a, lab_b, lab_c] yields a lower dimension structure, but still a pandas structure, you have to find out which dimensions have been lost/kept (i.e. which of the labels were duplicates).
I don't think I have the skills to propose a PR, but I would volunteer to fix the broken tests.
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| null | 0 | 2015-02-19T09:57:27Z | 2015-02-19T17:17:00Z | 2015-02-19T17:17:00Z | CONTRIBUTOR | null | Fix https://github.com/pydata/pandas/issues/9513
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} | 1 | 2015-02-19T12:12:12Z | 2015-02-19T12:13:14Z | 2015-02-19T12:13:14Z | CONTRIBUTOR | null | So this can be fixed by inferring after the set. We need to do this because we first set the value to a null-type (nan/NaT), then set the value. This works fine for datetime/timedelta/floats/strings, but not for integers which get set as `float`.
However, this _can_ be an expensive operation as potentially the entire column needs to be scaned for nulls.
```
In [4]: df = pd.DataFrame()
In [5]: df.loc[1,'foo'] = 2
In [6]: df
Out[6]:
foo
1 2
In [7]: df.dtypes
Out[7]:
foo float64
dtype: object
```
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} | 0 | 2015-02-19T12:24:15Z | 2015-02-19T13:19:46Z | 2015-02-19T13:19:45Z | CONTRIBUTOR | null | closes #9516
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} | 2 | 2015-02-19T17:16:47Z | 2015-05-09T16:01:43Z | 2015-05-09T16:01:43Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/9513
pd.NaT.date() was returning datetime.date(1, 255, 255)
it raises now a ValueError
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} | 5 | 2015-02-19T17:36:57Z | 2015-08-16T13:50:46Z | 2015-08-16T13:50:01Z | NONE | null | The two DataFrames are from 2014, but from the figure you can see that pandas throws one of them to the year 2050!
I am using pandas 0.15.2 and the notebook below describes the problem and show a few workarounds that might help debug this:
http://nbviewer.ipython.org/gist/ocefpaf/e7e085c84fd3ea7dbc24
Here is a quick SSCCE to generate the figure above:
``` python
import numpy as np
import pandas as pd
index = pd.date_range(start='2014-06-01 00:00:00', end='2014-09-01 00:00:00', freq='H')
data = np.random.rand(len(index))
df0 = pd.DataFrame(data=data, index=index, columns=['DF0'])
index = pd.date_range(start='2014-04-07 10:57:00', end='2014-10-29 09:32:00', freq='D')
data = np.random.rand(len(index))
df1 = pd.DataFrame(data=data, index=index, columns=['DF1'])
ax = df0.plot(legend=True, figsize=(12, 4))
ax = df1.plot(ax=ax)
```
Thanks!
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} | 2 | 2015-02-19T20:58:07Z | 2015-02-20T00:04:47Z | 2015-02-20T00:04:45Z | MEMBER | null | Fixes #9512
Closes #9515
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| null | 3 | 2015-02-19T21:35:11Z | 2015-05-09T16:00:27Z | 2015-05-09T16:00:27Z | MEMBER | null | ... unless I'm really missing something trivial...
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| null | 2 | 2015-02-19T21:41:41Z | 2015-02-19T22:50:53Z | 2015-02-19T22:16:02Z | NONE | null | Pandas resample bugs when trying to resample a time serie with same size splits :
I have a time serie of **size 10**:
```
rng = pd.date_range('20130101',periods=10,freq='T')
ts=pd.Series(np.random.randn(len(rng)), index=rng)
```
`print(ts)`
```
2013-01-01 00:00:00 -1.811999
2013-01-01 00:01:00 -0.890837
2013-01-01 00:02:00 -0.363520
2013-01-01 00:03:00 -0.026245
2013-01-01 00:04:00 1.515072
2013-01-01 00:05:00 0.920129
2013-01-01 00:06:00 -0.125954
2013-01-01 00:07:00 0.588933
2013-01-01 00:08:00 -1.278408
2013-01-01 00:09:00 -0.172525
Freq: T, dtype: float64
```
When trying to resample in **8 equal parts it works fine:**
```
length = 8
print(ts)
timeSpan = (ts.index[-1]-ts.index[0]+timedelta(minutes=1))
rule = int(timeSpan.total_seconds()/length)
tsNew=ts.resample(str(rule)+"S")
```
`print(tsNew)`
```
2013-01-01 00:00:00 0.124147
2013-01-01 00:01:15 0.558947
2013-01-01 00:02:30 0.076321
2013-01-01 00:03:45 0.178429
2013-01-01 00:05:00 -1.357948
2013-01-01 00:06:15 0.931305
2013-01-01 00:07:30 0.984052
2013-01-01 00:08:45 -1.758608
Freq: 75S, dtype: float64
```
But when tying to split into **9 parts** there are nan arriving :
```
length = 9
print(ts)
timeSpan = (ts.index[-1]-ts.index[0]+timedelta(minutes=1))
rule = int(timeSpan.total_seconds()/length)
tsNew=ts.resample(str(rule)+"S")
```
`print(tsNew)`
```
2013-01-01 00:00:00 -0.264389
2013-01-01 00:01:06 NaN
2013-01-01 00:02:12 NaN
2013-01-01 00:03:18 NaN
2013-01-01 00:04:24 NaN
2013-01-01 00:05:30 NaN
2013-01-01 00:06:36 NaN
2013-01-01 00:07:42 NaN
2013-01-01 00:08:48 NaN
2013-01-01 00:09:54 NaN
Freq: 66S, dtype: float64
```
Do you have an idea how to solve this issue ?
PS: with length > 10 it doesn't works neither...
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| null | 6 | 2015-02-19T22:57:28Z | 2021-04-12T05:33:19Z | null | NONE | null | Pandas resample bugs when upsampling a time serie with same size splits :
For instance, I have a time serie of **size 10**:
```
rng = pd.date_range('20130101',periods=10,freq='T')
ts=pd.Series(np.random.randn(len(rng)), index=rng)
```
`print(ts)`
```
2013-01-01 00:00:00 -1.811999
2013-01-01 00:01:00 -0.890837
2013-01-01 00:02:00 -0.363520
2013-01-01 00:03:00 -0.026245
2013-01-01 00:04:00 1.515072
2013-01-01 00:05:00 0.920129
2013-01-01 00:06:00 -0.125954
2013-01-01 00:07:00 0.588933
2013-01-01 00:08:00 -1.278408
2013-01-01 00:09:00 -0.172525
Freq: T, dtype: float64
```
When trying to resample in **N > 10** parts it doesn't work:
```
from datetime import timedelta
length = 11
timeSpan = (ts.index[-1]-ts.index[0]+timedelta(minutes=1))
rule = int(timeSpan.total_seconds()/length)
tsNew=ts.resample(str(rule)+"S").mean()
```
`print(tsNew)`
```
2013-01-01 00:00:00 1.845181
2013-01-01 00:00:54 NaN
2013-01-01 00:01:48 NaN
2013-01-01 00:02:42 NaN
2013-01-01 00:03:36 NaN
2013-01-01 00:04:30 NaN
2013-01-01 00:05:24 NaN
2013-01-01 00:06:18 NaN
2013-01-01 00:07:12 NaN
2013-01-01 00:08:06 NaN
2013-01-01 00:09:00 -0.997419
Freq: 54S, dtype: float64
```
Note: here is my versions:
`pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.2.final.0
python-bits: 64
OS: Darwin
OS-release: 14.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2
nose: 1.3.4
Cython: 0.21
numpy: 1.9.1
scipy: 0.15.1
statsmodels: 0.5.0
IPython: 2.3.1
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.1
pytz: 2014.9
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.0
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.7
pymysql: 0.6.3.None
psycopg2: None
```
Thank you for your help
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} | 9 | 2015-02-20T02:57:32Z | 2015-05-09T16:02:27Z | 2015-05-09T16:02:27Z | NONE | null | Fixes #9502
See the [cloudtb tests/test_pandas.py](https://github.com/cloudformdesign/cloudtb/blob/f/pandas/tests/test_pandas.py) for more examples of use.
Once it is approved to go forward, I will add tests to show more about how
it can be used and make sure that every edge case if functional.
Has no effect on unit tests, although on my machine two were failing
before change, and several were skipped (and still fail after change).
Please re-run unit tests.
Should have a negligible effect on performance, as it first checks whether
it should be used. If performance does end up being an issue, I
recommend adding a kwarg `flat=False` to the `DataFrame.from_dict`
method for speedups when the user knows their dictionary is flat
(i.e. is not nested)
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| null | 3 | 2015-02-20T05:21:40Z | 2021-04-12T05:34:49Z | null | MEMBER | null | The "exact match" requirement for index alignment doesn't work very well when using Float64Index if the index values may result from computations.
Here's an example of how the current behavior results in a poor user experience:
```
In [22]: s1 = pd.Series(range(3), np.arange(3.0))
In [23]: s2 = pd.Series(range(3), s1.index + 0.15 - 0.15)
In [24]: s1 + s2
Out[24]:
0 0
1 NaN
1 NaN
2 4
dtype: float64
In [25]: s1.index
Out[25]: Float64Index([0.0, 1.0, 2.0], dtype='float64')
In [26]: s2.index
Out[26]: Float64Index([0.0, 1.0, 2.0], dtype='float64')
In [27]: s1.index[1]
Out[27]: 1.0
In [28]: s2.index[1]
Out[28]: 0.99999999999999989
```
reindex/joins with `method='nearest'` will help (https://github.com/pydata/pandas/pull/9258), but automatic alignment will still remain broken. Ideally, we should figure out some way for array alignment to ignore very small differences that are almost certainly due to the precision limits of floating point arithmetic.
I'm not entirely sure what the right solution here looks like, but I wanted to open this to discussion.
CC @hugadams who I'm guessing has almost certainly encountered these sort of issues when doing computations on indexes for [scikit-spectra](https://github.com/hugadams/scikit-spectra).
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| null | 1 | 2015-02-20T06:59:43Z | 2015-02-20T13:06:03Z | 2015-02-20T13:06:03Z | NONE | null | The float_format string works for single values i.e. float_format='%.1f', but it does not appear to work for a formatted string of columns. For example, If I had four columns with increasing significant digits, separated with tabs:
```
fmt_string='%.1f\t%.2f\t%.3f\t%.4f'
```
Then for some DataFrame X, I should be able to use:
```
X.to_csv('./out.txt',float_format=fmt_string,index=False)
```
But it unfortunately balks at me:
```
TypeError: not enough arguments for format string
```
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| null | 9 | 2015-02-20T12:32:22Z | 2017-09-07T11:58:18Z | 2017-09-07T11:58:18Z | NONE | null | I couldn't find any official statement about PyPy support.
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} | 0 | 2015-02-22T20:03:28Z | 2016-08-11T22:31:37Z | null | CONTRIBUTOR | null | Add `dropna` and `sequentially` parameters to `DataFrame.unstack()`, to match `DataFrame.stack()`. For backwards compatibility, these should default to `dropna=False` and `sequentially=False` -- which are different from the defaults for `DataFrame.stack()`.
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} | 2 | 2015-02-23T07:27:33Z | 2016-12-06T21:34:18Z | 2016-12-06T19:10:40Z | NONE | null | Here is the test code, that return the right table:
```
import pandas as pd
data = {"C1":["A", "B", "C", "C"], "C2":["a", "a", "b", "b"], "V":[1, 2, 3, 4]}
df = pd.DataFrame(data)
df.pivot_table("V", index="C1", columns="C2", aggfunc="count")
```
when convert column to category, ValueError is raised:
```
df2 = df.copy()
df2["C1"] = df2["C1"].astype("category")
df2.pivot_table("V", index="C1", columns="C2", aggfunc="count")
```
groupby also raise the same error:
```
df2.groupby(["C1", "C2"]).count()
```
add `dropna()` to `count()` in `groupby.py` fix this problem:
```
def count(self, axis=0):
return self._count().dropna().astype('int64')
```
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} | 4 | 2015-02-23T14:52:46Z | 2015-08-15T22:22:57Z | 2015-08-15T22:22:57Z | NONE | null | The problem occurs when using read_csv with nrows specified on a file that only contains a row of column names, but no newlines after that. An empty DataFrame would be expected but a StopIteration error is raised instead.
Code:
`import pandas`
`pandas.read_csv(one_line_file, nrows=0)`
## INSTALLED VERSIONS
commit: None
python: 3.4.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-28-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.1
nose: 1.3.4
Cython: None
numpy: 1.8.2
scipy: 0.14.0
statsmodels: None
IPython: None
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 2.0
pytz: 2012c
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.3.6
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
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} | 4 | 2015-02-23T19:47:50Z | 2020-07-17T16:26:47Z | 2020-07-17T16:26:47Z | NONE | null | According to [the documentation](http://pandas-docs.github.io/pandas-docs-travis/visualization.html#plotting-with-error-bars), asymmetric error bars should be able to be passed in to `plot` as an Mx2 array for a `Series` object or a Mx2xN array for a `DataFrame` object.
However, the code only seems to parse this correctly for a `DataFrame`: https://github.com/pydata/pandas/blob/master/pandas/tools/plotting.py#L1338
When passing raw error values as `yerr`, it seems that the first values are used as two-sided error differences, instead of being plotted as raw upper and lower values. Note the symmetric error bars below:
Using the underlying matplotlib function directly yields the expected results, but without the pandas-generated decorations:
This was originally noted in [this StackOverflow question](http://stackoverflow.com/q/26793758/586086) and I corroborated the other user's experience.
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| null | 2 | 2015-02-23T21:37:39Z | 2015-02-24T14:57:52Z | 2015-02-24T14:57:52Z | CONTRIBUTOR | null | For a df I'd like to handle nan's with the query functionality. `df.query('~A.isnull() & ...')` doesn't work because eval doesn't support function calls (`"NotImplementedError: 'Call' nodes are not implemented"`), and `df('A != @nan')` fails in the way you'd expect it to with nan comparison (that is, not filtering anything). Should we (or do we already) have a way to filter nulls with `query()`?
Also, I can open a separate issue if it deserves one, but this related error message seemed a bit funny
```
df.query('A != @np.nan') # => NotImplementedError: N-dimensional objects, where N > 2, are not supported with eval
```
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} | 2 | 2015-02-23T22:53:21Z | 2015-03-02T19:07:55Z | 2015-03-02T19:07:55Z | NONE | null | I am using python 2.6.2 64 bit with windows 8.1. I am very new to python and I have downloaded some libraries such as pandas. My main focus is not python, rather it is on the integration of some simulation software that I use with python. This integration requires downloading some libraries (pandas, numpy, .. etc). So, I was trying to run the **init**.py associated with pandas open source library. Every time I run it I receive the following message:
Attempted relative import in non-package
Traceback (most recent call last):
File "c:\Python26\Lib\site-packages\pandas__init__.py", line 7, in <module>
from . import hashtable, tslib, lib
value error: Attempted relative import in non-package
the hashtable, tslib, and lib files are .pyd files that are located in the same folder as the **init**.py file.
Any idea what is wrong here and what needs to be done to overcome that problem?
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} | 2 | 2015-02-24T11:16:51Z | 2020-04-17T18:08:36Z | 2020-04-17T18:08:36Z | CONTRIBUTOR | null | Backstory - I created some H5 files with h5py containing large 3D arrays (not python dataframes). Then I forgot this and tried to load with Pandas. Since they didn't contain real dataframes the load understandably failed. However the error thrown was:
`TypeError: cannot create a storer if the object is not existing nor a value are passed`
(line 1148 in pandas.io.pytables) which apart from being awkward grammar (rather `cannot create a storer if the object does not exist nor a value given`) doesn't suggest anything about the h5 file not being supported.
Since passing around h5 files is fairly common and sometimes they contain tables and sometimes not, it would be nice if Pandas warned you clearly that the object you are trying to load isn't a pandas-compatible table.
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| null | 4 | 2015-02-24T15:52:37Z | 2021-04-12T05:35:42Z | null | NONE | null | Wanted to produce grouped histogram such that the heights of the bars add up to 1. The following code results in **ValueError: weights should have the same shape as x**
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(123)
n = 100
df = pd.DataFrame(np.random.randn(n), columns=['a'])
by = np.random.randint(1,5,n)
df.hist(by=by) # works
plt.show()
weights = np.repeat(1/len(df), len(df))
df.hist(weights = weights) # works
plt.show()
df.hist(by = by, weights = weights) # does not work
plt.show()
```
In [15]: pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 3.4.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.18.6-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2
nose: 1.3.4
Cython: None
numpy: 1.9.1
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 2.4.1
sphinx: None
patsy: 0.3.0
dateutil: 2.4.0
pytz: 2014.10
bottleneck: 1.0.0
tables: None
numexpr: 2.4
matplotlib: 1.4.3
openpyxl: 1.8.6
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: 2.5.6
sqlalchemy: None
pymysql: None
psycopg2: None
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| null | 7 | 2015-02-24T16:10:57Z | 2015-02-24T23:27:54Z | 2015-02-24T21:29:10Z | CONTRIBUTOR | null | Something like this is super useful and not clear (to me). Maybe there's a more idiomatic way? If so, I didn't see it in the docs.
http://stackoverflow.com/questions/18215317/extracting-days-from-a-numpy-timedelta64-value
(Possible enhancement, adding accessors to datetime-typed columns for things like `day`, `year`, etc. similar to `.str.` or `.cat.`)
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} | 22 | 2015-02-24T16:54:05Z | 2021-10-18T17:54:25Z | 2015-03-31T11:55:16Z | NONE | null | The following code plots two lines. The column names appear in the legend.
``` python
x=np.linspace(-10,10,201)
y,z=np.sin(x),np.cos(x)
x,y,z=pd.Series(x),pd.Series(y),pd.Series(z)
df=pd.concat([x,y,z],axis=1)
df.columns=['x','sin(x)','cos(x)']
df=df.set_index('x')
df.plot()
plt.show()
plt.clf();plt.close()
```
However, the following equivalent code shows **None** as legend, even though the labels are explicitly set.
``` python
ax=df.plot(y='sin(x)',label='sin(x)')
df.plot(y='cos(x)',label='cos(x)',ax=ax)
plt.show()
```
Of course there are alternative ways to make this work, but it appears to me that passing the labels should suffice. In particular, I don't think their values should be replaced with None, or printed as x-label.
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} | 4 | 2015-02-24T17:28:51Z | 2015-06-07T22:54:49Z | 2015-06-07T22:54:49Z | CONTRIBUTOR | null | Hi all,
I don't know if this could be an enhancement but: why do not use string values such as `'index'` or `'columns'` in the axis parameter for `DataFrame.quantile()` function? Such as in `DataFrame.div()`..
I think that it would be more straightforward to use.
Thanks
Francesco
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} | 9 | 2015-02-24T21:12:07Z | 2015-06-07T22:54:38Z | 2015-06-07T22:54:38Z | CONTRIBUTOR | null | closes #9543
I added the values "index" and "columns" for the axis parameter of the method pandas.DataFrame.quantile().
Also, there was no check on the value inserted for axis. E.g. if 2 or "foo" was inserted, no ValueError() was raised.
I added a basic check.
Thanks
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| null | 13 | 2015-02-24T21:47:41Z | 2021-10-24T00:59:05Z | null | NONE | null | groupby-apply workflows are important pandas idioms. Here's a brief example grouping on a named DataFrame column:
```
>>> df = pd.DataFrame({'key': [1, 1, 1, 2, 2, 2, 3, 3, 3], 'value': range(9)})
>>> result = df.groupby('key').apply(lambda x: x['key'])
>>> result
key
1 0 1
1 1
2 1
2 3 2
4 2
5 2
3 6 3
7 3
8 3
Name: key, dtype: int64
```
An important highlight of this example is the ability to reference the grouped value -- eg, `x['key']` -- inside the applied function.
pandas also supports grouping on arbitrary mapping functions, iterables, and lots of other objects. In these cases, the grouped value is not represented as a named column in the DataFrame. Thus, when using apply(...), there is no apparent way to access the group key value. The only alternative is to use a (slow) for-loop solution as in:
```
foo = lambda _k, _g: ...
grouped = df.groupby(grouper)
result_iter = (foo(key, group) for key, group in grouped)
key_iter = (key for key, group in grouped)
pd.DataFrame.from_records(result_iter, index=key_iter)
```
IMHO, the ability to access the grouped value in an idiomatic way from within the applied function is ergonomically important; the groupby-apply idiom is at best partially realized without it.
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} | 5 | 2015-02-24T23:02:34Z | 2017-07-08T22:21:18Z | 2017-07-08T22:21:14Z | NONE | null | I am using Python 3, it might be a source of the problems.
I have a dataframe with columns ['genotype_cont', 0, 2.5, 5]. Trying to call attribute `boxplot` I am getting following problem:
# Code position: index.py, line 1344:
Original line:
theDiff = sorted(set(self) - set(other))
fix:
```
def keyfunction(x):
if isinstance(x, int):
v = '0%d' % x
else:
v = x[0]
return v
theDiff = sorted(set(self) - set(other), key=keyfunction)
```
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| null | 8 | 2015-02-24T23:56:35Z | 2015-02-26T15:39:14Z | 2015-02-26T15:39:14Z | NONE | null | Hi guys,
For certain values of start dates, the method `resample` seems to bug.
I explain with an example :
```
rng = pd.date_range('20110105 12:20:00',periods=5*60,freq='min')
tsMin=pd.Series(range(len(rng)), index=rng)
tsMin
2011-01-05 12:20:00 0
2011-01-05 12:21:00 1
2011-01-05 12:22:00 2
2011-01-05 12:23:00 3
...
2011-01-05 17:18:00 298
2011-01-05 17:19:00 299
Freq: T, Length: 300
```
When trying to resample in `300 minutes` it comes:
```
tsMin.resample("300min")
2011-01-05 10:00:00 79.5
2011-01-05 15:00:00 229.5
Freq: 300T, dtype: float64
```
The result is not what expected...
What is wired is that it works when I replace `2011-01-05 12:20:00` by `2011-01-05 00:00:00`
Thank you for your help
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} | 4 | 2015-02-25T05:29:01Z | 2018-07-06T20:21:07Z | 2017-07-10T21:07:01Z | NONE | null | apologies if this is a manifest of another issue, though I couldn't find one after some searching.
the following behavior can be seen in 0.14.1 and 0.15.2:
```
pt = pd.Panel({'a': {'b': {'c': 1, 'd': 1}, 'e': {'c': 2, 'd': 2}}})
pt2 = pd.Panel({'a': {'b': {'c': 3, 'd': 5}, 'e': {'c': 4, 'd': 6}}})
pt.a, pt2.a
mask = (pt.a > 1) & (pt2.a > 4)
pt.a[mask] = pt2.a[mask]
pt.loc[['a'],:,'e']
pt.loc['a',:,'e']
```
the pt.loc[['a'],:,'e'] shows the original 2 while pt.loc['a',:,'e'] shows the updated 6 from pt2.a
is there a better way to do this, basically i'd like to set the values of pt.a to that of pt2.a based on some mask. any direction would be much appreciated, thanks!
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} | 18 | 2015-02-25T10:54:56Z | 2017-01-12T22:57:06Z | 2017-01-12T22:57:06Z | CONTRIBUTOR | null | So I have a lot of csv's which are clean till say 7 columns and have no missing values but have strings at random places starting after column 7.
I know it is clean till only 7. So, when I say usecols and list the 7 columns, I want it to ignore the other columns, probably truncate the remaining parts in the row when reading too. Shouldn't that be the functionality?
I don't want to skip over bad lines.
Is there a way in which I can force pandas to only read 7 columns and expect 7 rows while reading and hence not raise an exception?
Another method is to use names = range(35), an arbitrarily large number. But then I lose the real headers in my file and can't say what they are talking about. These columns are not fixed.
edit: It's my first issue report in a huge python package. Please bear if I didn't follow any protocol.
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| null | 3 | 2015-02-25T12:25:32Z | 2015-02-25T14:09:59Z | 2015-02-25T14:01:09Z | CONTRIBUTOR | null | `HDFStore.select()` becomes extremely slow in case when condition is not met in the HDF5 file.
For example:
```
### Preparing data
NUM = 5000000
COLS = 100
IDX = np.random.random_integers(0, 100000, size=NUM)*10
df = pd.DataFrame(np.random.randn(NUM, COLS), index=IDX)
### Save: ~4GB uncompressed
st = pd.HDFStore('tmp.h5')
st.append('data', df)
st.close()
del df
### Reading
st = pd.HDFStore('tmp.h5')
### First call is usually slower, perhaps the indices are being loaded in memory, so - skip it
df = st.select('data', where='index==%d' % (IDX[1000]))
%time df = st.select('data', where='index==%d' % (IDX[1000]))
print len(df)
%time df = st.select('data', where='index==55')
print len(df)
%time df = st.select('data', where='index==-1')
print len(df)
st.close()
```
It gives 8.7s and 1.1s for empty results versus 11.2ms for existing data:
```
CPU times: user 2.6 ms, sys: 8.58 ms, total: 11.2 ms
Wall time: 11.2 ms
50
CPU times: user 1.15 s, sys: 1.82 s, total: 2.97 s
Wall time: 8.73 s
0
CPU times: user 433 ms, sys: 649 ms, total: 1.08 s
Wall time: 1.11 s
0
```
In case when the file is relatively big(~10s of GB, compressed), the call of `select` with a "wrong query" can take up to a minute in comparison with several tens of millisecond. These are some numbers for my real data:
```
>>> %time df = st.select('data', 'aid=18')
CPU times: user 61.7 ms, sys: 23.2 ms, total: 84.8 ms
Wall time: 84.4 ms
>>>print len(df)
4281
>>> %time df = st.select('data', 'aid=-1')
CPU times: user 35.7 s, sys: 8.55 s, total: 44.3 s
Wall time: 42.2 s
>>> print len(df)
0
```
My versions:
pandas: 0.15.2
Cython: 0.21.2
numpy: 1.9.1
tables: 3.1.1
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} | 13 | 2015-02-25T12:37:31Z | 2015-03-23T10:54:50Z | 2015-03-05T23:24:41Z | CONTRIBUTOR | null | Currently the `HDFStore` in `read_hdf` is opened in `rw` mode by default, leading to permission
errors when reading a non-writeable file.
This patch changes the default mode to `r`.
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} | 2 | 2015-02-25T17:29:33Z | 2015-04-14T16:40:34Z | 2015-04-14T16:40:34Z | CONTRIBUTOR | null | By caching at the _class_ level it prevents instances from defining different rules - e.g.
``` python
import numpy as np
from pandas.tseries.holiday import AbstractHolidayCalendar, Holiday
class HolidayCalendar(AbstractHolidayCalendar):
def __init__(self, rules):
self.rules = list(np.atleast_1d(rules))
jan1 = HolidayCalendar(Holiday('jan1', year=2015, month=1, day=1))
jan2 = HolidayCalendar(Holiday('jan2', year=2015, month=1, day=2))
jan1.holidays()
Out[2]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2015-01-01]
Length: 1, Freq: None, Timezone: None
jan2.holidays()
Out[3]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2015-01-01] <--------------------------- uses cached result from *other* instance!
Length: 1, Freq: None, Timezone: None
```
``` python
import numpy as np
from pandas.tseries.holiday import AbstractHolidayCalendar, Holiday
class HolidayCalendar(AbstractHolidayCalendar):
def __init__(self, rules):
self.rules = list(np.atleast_1d(rules))
jan1 = HolidayCalendar(Holiday('jan1', year=2015, month=1, day=1))
jan2 = HolidayCalendar(Holiday('jan2', year=2015, month=1, day=2))
jan2.holidays()
Out[2]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2015-01-02]
Length: 1, Freq: None, Timezone: None
jan1.holidays()
Out[3]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2015-01-02] <--------------------------- uses cached result from *other* instance!
Length: 1, Freq: None, Timezone: None
```
https://github.com/pydata/pandas/blob/a72d95163b4d268012709255d7a52bbe5c1a7eb6/pandas/tseries/holiday.py#L351-L357
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} | 1 | 2015-02-25T20:07:30Z | 2015-02-25T23:54:44Z | 2015-02-25T23:54:44Z | NONE | null | Pandas isnull failing for NaT period index
> s= pd.PeriodIndex([pd.NaT,'2014-01-01'],freq='M')
> pd.isnull(s)
>
> array([False, False], dtype=bool)
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} | 1 | 2015-02-25T23:12:59Z | 2015-02-25T23:46:23Z | 2015-02-25T23:46:14Z | CONTRIBUTOR | null | Consider
```
import pandas as pd
df = pd.DataFrame(dict(A=np.array(range(0, 200)),
B=pd.Categorical(['a']*200, categories=['a', 'b'])))
# Setting a categorical with boolean indexing works fine for small indices
print df.loc[df.A == 5]
df.loc[df.A == 5, 'B'] = 'b'
print df.loc[df.A == 5]
print ""
# ... but fails for large indices
print df.loc[df.A == 150]
df.loc[df.A == 150, 'B'] = 'b'
print df.loc[df.A == 150]
```
outputs at my pandas 0.15.1.dev installation (note that the value is correctly updated for A==5, but not for A==150):
```
A B
5 5 a
A B
5 5 b
A B
150 150 a
A B
150 150 a
```
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} | 7 | 2015-02-26T09:11:20Z | 2015-04-14T17:19:27Z | 2015-04-14T16:40:34Z | CONTRIBUTOR | null | Closes #9552
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} | 6 | 2015-02-26T17:33:42Z | 2015-08-21T07:33:31Z | 2015-08-21T07:33:31Z | NONE | null | Took me a while to find this (in the end by stackoverflow), but I ran into the problem while trying to parse a csv file. Panda kept parsing the first column as the index column instead of generating a new index. I could not find a way to disable this after reading the docs here:
http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.from_csv.html
As it turns out, due to the line of code below, if the argument is set to False, then it has the behavior I desired. This is completely undocumented and would be very helpful to have. I haven't contributed before to Pandas, so if it is more than a quick change a maintainer can make, I could put the work in for a PR, even if its just documentation changes.
https://github.com/pydata/pandas/blob/master/pandas/io/parsers.py#L1064
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} | 9 | 2015-02-26T18:24:02Z | 2015-11-14T19:35:17Z | 2015-02-26T22:50:20Z | NONE | null | Hi all, I am using python 2.7.9 on linux centos which I just installed
When I execute my python script to produce an excel report,
I get a traceback error as:
Traceback (most recent call last):
File "./bin/si_under_development_iml_orders_last_120_days.py", line 285, in <module>
main()
File "./bin/si_under_development_iml_orders_last_120_days.py", line 274, in main
con=run_report(con,date_pattern)
File "./bin/si_under_development_iml_orders_last_120_days.py", line 236, in run_report
output_worksheet(out_filename,current_date,imlL,last_n_days_ago)
File "./bin/si_under_development_iml_orders_last_120_days.py", line 59, in output_worksheet
from openpyxl.style import Color, Fill
ImportError: No module named style
I installed the openpyxl module.
How can I resolve this?
Thanks,
Ron
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} | 5 | 2015-02-26T19:10:15Z | 2015-10-28T03:37:05Z | 2015-10-28T03:37:05Z | CONTRIBUTOR | null | I want to use `where` to set values across a DataFrame where a Series meets a condition.
For example:
``` python
df=pd.DataFrame({'a':[1,2,3], 'b':[4,5,6]})
```
But this
``` python
df.where(df['a']==2,0)
```
returns an unedited DataFrame.
Whereas this returns just the middle row, as you'd expect:
``` python
df[df['a']==2]
```
Supplying `axis=0` or `axis=1` doesn't help. Am I doing something wrong? Or this unintended behavior?
> Versions
> python: 2.7.6.final.0
> pandas: 0.14.1
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} | 4 | 2015-02-26T20:06:13Z | 2018-02-15T09:01:16Z | 2018-02-13T12:00:33Z | NONE | null | When I set the error_bad_lines=False, then the conversion of some data (most likely in bad lines) to float in csv is not performed.
pandas version '0.15.2'
numpy version '1.9.1'
Python 2.7.9 (default, Dec 10 2014, 12:24:55) [MSC v.1500 32 bit (Intel)] on win32
## INSTALLED VERSIONS
commit: None
python: 2.7.9.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: None
pandas: 0.15.2
nose: 1.3.4
Cython: 0.21.2
numpy: 1.9.1
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 2.3.1
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 1.5
pytz: 2014.9
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
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| null | 1 | 2015-02-26T23:30:36Z | 2015-02-27T11:54:15Z | 2015-02-27T11:51:04Z | NONE | null | Hi, I am seeing a difference in behavior on this groupby between Pandas 0.13.1 and 0.15.2. Specifically, it's like 0.15.2 is doing a cross join while 0.13.1 isn't.
```
print pandas.DataFrame([
{'a': 1, 'b': 2, 'c': 3},
{'a': 4, 'b': 5, 'c': 6}, ]).set_index(
list('ab')).groupby(level=list('ab')).mean()
```
0.13.1 produces:
```
c
a b
1 2 3
4 5 6
[2 rows x 1 columns]
```
while 0.15.2 produces
```
c
a b
1 2 3
5 NaN
4 2 NaN
5 6
```
basically, the same matrix, but with extra cross NaN entries.
We're wondering if this behavior is intentional, or a bug. It wasn't entirely clear from the set of release notes that the groupby behavior changed so much.
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} | 4 | 2015-02-27T02:29:26Z | 2017-04-05T02:06:14Z | 2015-03-06T03:20:52Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/9452
Needed to pass along encoding parameter.
No tests... Kind of hard since it's only s3 only.
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} | 6 | 2015-02-27T11:01:34Z | 2015-02-27T12:03:31Z | 2015-02-27T11:59:57Z | MEMBER | null | The docstring of `to_series` says for `keep_tz=True`: _"If the timezone is not set or is UTC, the resulting Series will have a datetime64[ns] dtype. Otherwise the Series will have an object dtype."_
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DatetimeIndex.to_series.html
But is seems that also for UTC, `keep_tz=True` does the conversion to object/Timestamp?
```
In [1]: idx = pd.date_range('1/1/2000', periods=20, freq='1H', tz='UTC')
In [2]: idx
Out[2]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2000-01-01 00:00:00+00:00, ..., 2000-01-01 19:00:00+00:00]
Length: 20, Freq: H, Timezone: UTC
In [3]: idx.to_series()[0]
Out[3]: Timestamp('2000-01-01 00:00:00')
In [4]: idx.to_series(keep_tz=True)[0]
Out[4]: Timestamp('2000-01-01 00:00:00+0000', tz='UTC', offset='H')
```
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| null | 1 | 2015-02-27T12:35:24Z | 2015-12-13T00:29:45Z | 2015-12-13T00:29:45Z | CONTRIBUTOR | null | I am trying to build a development version of pandas off master, which I have done successfully in the past. One change is that I did try to build a development version of scipy which is apparently quite difficult (I could not get it to work) and I may have messed up my build system in the process. I have spent many hours trying to figure it out and I cannot. I don't suspect this is a bug as these errors are not new lines of code but maybe I am wrong. Any ideas/suggestions?
Current system is Mac OS X 10.8.5 (notice the 10.6 version below but this is carryover from when I upgraded my os). I tried gcc-4.2, llvm-gcc-4.2, clang and all have similar issues with compiling parser.
```
cythoning pandas/parser.pyx to pandas/parser.c
Error compiling Cython file:
------------------------------------------------------------
...
self.compact_ints = compact_ints
self.use_unsigned = use_unsigned
self.verbose = verbose
self.low_memory = low_memory
self.parser.converter = xstrtod
^
------------------------------------------------------------
pandas/parser.pyx:437:39: Cannot assign type 'double (char const *, char **, char, char, char, int)' to 'double (*)(char const , char **, char, char, char, int)'
Error compiling Cython file:
------------------------------------------------------------
...
self.verbose = verbose
self.low_memory = low_memory
self.parser.converter = xstrtod
if float_precision == 'high':
self.parser.converter = precise_xstrtod
^
------------------------------------------------------------
pandas/parser.pyx:439:51: Cannot assign type 'double (char const *, char **, char, char, char, int)' to 'double (*)(char const , char **, char, char, char, int)'
Error compiling Cython file:
------------------------------------------------------------
...
self.low_memory = low_memory
self.parser.converter = xstrtod
if float_precision == 'high':
self.parser.converter = precise_xstrtod
elif float_precision == 'round_trip':
self.parser.converter = round_trip
^
------------------------------------------------------------
pandas/parser.pyx:441:46: Cannot assign type 'double (char const *, char **, char, char, char, int)' to 'double (*)(char const , char **, char, char, char, int)'
Error compiling Cython file:
------------------------------------------------------------
...
# in the hash table
if k != na_hashset.n_buckets:
na_count += 1
data[0] = NA
else:
data[0] = parser.converter(word, &p_end, parser.decimal, parser.sci,
^
------------------------------------------------------------
pandas/parser.pyx:1496:47: Cannot assign type 'char *' to 'char const '
Error compiling Cython file:
------------------------------------------------------------
...
data[0] = NA
data += 1
else:
for i in range(lines):
word = COLITER_NEXT(it)
data[0] = parser.converter(word, &p_end, parser.decimal, parser.sci,
^
------------------------------------------------------------
pandas/parser.pyx:1513:43: Cannot assign type 'char *' to 'char const '
building 'pandas.parser' extension
creating build/temp.macosx-10.6-intel-3.4/pandas/src/parser
gcc-4.2 -fno-strict-aliasing -Werror=declaration-after-statement -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -arch i386 -arch x86_64 -isysroot /Developer/SDKs/MacOSX10.6.sdk -g -Ipandas/src/klib -Ipandas/src -I/Users/temp/Projects/PYTHON3.4/lib/python3.4/site-packages/numpy/core/include -I/Library/Frameworks/Python.framework/Versions/3.4/include/python3.4m -c pandas/parser.c -o build/temp.macosx-10.6-intel-3.4/pandas/parser.o
```
gcc version:
```
gcc-4.2 --version
i686-apple-darwin10-gcc-4.2.1 (GCC) 4.2.1 (Apple Inc. build 5666) (dot 3)
Copyright (C) 2007 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
```
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} | 4 | 2015-02-27T14:08:07Z | 2015-03-01T11:07:21Z | 2015-02-28T14:57:43Z | CONTRIBUTOR | null | Hi,
I was just wondering if all the licenses are up to date? I only ask because I notice a dependency is python-dateutil (which is BSD licensed) but no copy of its license is in the licenses folder.
Nothing major, I have just been considering how to license my own code, that uses Pandas as a library, and my impression was I would have to restate your licence along with my own.
Am I wrong?
Mat
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} | 1 | 2015-02-27T19:28:28Z | 2015-06-09T10:49:31Z | 2015-06-09T10:49:31Z | NONE | null | In the code snippet below, I expect that both the values in the c1 and c2 column both are 4.5.
When the Python parser engine is used, this gives me 4 iso 4.5. When using the C parser engine, I get 4.5.
Is this expected behaviour?
``` python
import StringIO
import pandas
csv = """
c1,c2
45e-1,45.0e-1
"""
stream = StringIO.StringIO(csv)
pandas.read_csv(stream, engine='python')
```
I used the following versions: (pandas.show_versions)
## INSTALLED VERSIONS
commit: None
python: 2.7.5.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: None
pandas: 0.15.2
nose: None
Cython: None
numpy: 1.9.1
scipy: 0.14.0
statsmodels: None
IPython: 2.3.1
sphinx: None
patsy: None
dateutil: 2.1
pytz: 2014.7
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.3.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
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} | 21 | 2015-02-28T21:01:15Z | 2015-03-13T17:47:35Z | 2015-03-04T20:46:25Z | CONTRIBUTOR | null | closes #8613
```
In [1]: df = DataFrame(np.random.randn(5,4), columns=list('ABCD'), index=date_range('20130101',periods=5))
In [2]: df
Out[2]:
A B C D
2013-01-01 -1.380065 1.221596 0.279703 -0.119258
2013-01-02 1.684202 -0.202251 -0.961145 -1.595015
2013-01-03 -0.623634 -2.377577 -3.024554 -0.298809
2013-01-04 -1.251699 0.456356 1.257002 1.465878
2013-01-05 0.687818 -2.125079 1.454911 1.914207
In [5]: s = Series(range(5),[-2,-1,1,2,3])
In [6]: s
Out[6]:
-2 0
-1 1
1 2
2 3
3 4
dtype: int64
In [7]: df.loc['2013-01-02':'2013-01-10']
Out[7]:
A B C D
2013-01-02 1.684202 -0.202251 -0.961145 -1.595015
2013-01-03 -0.623634 -2.377577 -3.024554 -0.298809
2013-01-04 -1.251699 0.456356 1.257002 1.465878
2013-01-05 0.687818 -2.125079 1.454911 1.914207
In [9]: s.loc[-10:3]
Out[9]:
-2 0
-1 1
1 2
2 3
3 4
dtype: int64
```
```
# this used to be a KeyError
In [15]: df.loc[2:3]
TypeError: Cannot index datetime64 with <type 'int'> keys
```
Slicing with float indexers; now works for ix (loc worked before)
```
In [3]: s.loc[-1.0:2]
Out[3]:
-1 1
1 2
2 3
dtype: int64
In [4]: s.ix[-1.0:2]
Out[4]:
-1 1
1 2
2 3
dtype: int64
# [] raises
In [5]: s[-1.0:2]
TypeError: cannot do slice start value indexing on <class 'pandas.core.index.Int64Index'> with these indexers [-1.0] of <type 'float'>
```
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| null | 4 | 2015-02-28T23:58:04Z | 2015-03-01T21:53:43Z | 2015-03-01T21:36:24Z | NONE | null | When plotting large datasets, pandas.plot() is much slower than the direct use of Matplotlib:
``` python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame(data=np.random.normal(0, 1, (1, 1000)))
%timeit df.plot()
1 loops, best of 3: 11.2 s per loop
%timeit plt.plot(df.index, df.values)
1 loops, best of 3: 2.1 s per loop
```
## INSTALLED VERSIONS
commit: None
python: 3.4.1.final.0
python-bits: 32
OS: Windows
OS-release: 8
machine: x86
processor: x86 Family 6 Model 55 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.15.0
nose: 1.3.4
Cython: 0.21
numpy: 1.9.1
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 2.4.1
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.1
pytz: 2014.9
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.2
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
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} | 12 | 2015-03-01T12:33:52Z | 2021-04-12T05:41:09Z | null | NONE | null | method naming consistency issue #577
- [ ] `to_csv` uses line_terminator and `read_csv` uses lineterminator
- [x] `compression` kw #7615 (#11219)
- [x] `sep/delimiter` #7662
- [x] `from_csv` differs from `read_csv` #9556 (#10163)
- [x] `decimal` #8448 (https://github.com/pandas-dev/pandas/commit/671c4b3c52a02ee3736752b2645d08762c0fedaf)
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| null | 1 | 2015-03-01T20:29:19Z | 2015-03-01T20:40:31Z | 2015-03-01T20:40:31Z | CONTRIBUTOR | null | .head() and .tail() are great tools for quick data interrogations, but when data is sorted they are often far from representative. It would be great if there was a simple command to pull an arbitrary number of random rows and display them for a more representative way to spotcheck data.
It would behave something like:
```
def rand_rows(df, num_rows = 5):
from numpy import random as rm
subset = rm.choice(df.index.values, size = num_rows)
return df.loc[subset]
a_data_frame = pd.DataFrame({'col1':range(10,20), 'col2':range(20,30)})
rand_rows(a_data_frame)
rand_rows(a_data_frame, 6)
```
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} | 7 | 2015-03-02T17:26:37Z | 2015-04-13T16:35:08Z | 2015-04-13T16:35:08Z | NONE | null | `Timedelta('00:00:00')` works fine whereas `Timedelta('0:00:00')` raises and error. Unsure whether to call this a bug, but under some circumstances the `datetime` module in pure python will produce time delta strings without the leading 0.
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} | 1 | 2015-03-02T20:21:43Z | 2015-04-07T09:05:25Z | 2015-04-07T09:04:18Z | NONE | null | Hi,
I am not sure if this issue is related to Pandas or Yahoo Finance however whenever you try to load a stock data with anything different than a 3 at the end of the ticker it returns the wrong number of points.
For instance, if one fixes the start_date and end_date variables and set s="GOLL4.SA" it will return a different number of points than s="EMBR3.SA" for the same time span. Also, the first point is shifted 1 day.
Below is a print of the issue:
``` shell
$Loading from the web stock EMBR3.SA data...
Open High Low Close Volume Adj Close
Date
2013-01-01 14.45 14.45 14.45 14.45 0 14.03
2013-01-02 14.57 14.82 14.36 14.73 2206900 14.30
2013-01-03 14.72 14.82 14.55 14.56 2323400 14.14
... ... ... ... ... ... ...
2015-02-27 26.10 26.17 24.82 24.90 5060700 24.90
[562 rows x 6 columns]
$Loading from the web stock SUZB5.SA data...
Open High Low Close Volume Adj Close
Date
2013-01-02 7.15 7.44 7.15 7.40 6356000 7.19
2013-01-03 7.44 7.48 7.26 7.34 4037300 7.13
2013-01-04 7.35 7.41 7.14 7.19 7911800 6.98
... ... ... ... ... ... ...
2015-02-27 12.05 12.26 11.93 12.06 6909200 12.06
[532 rows x 6 columns]
```
I have tested with other tickers that follow this pattern and all of them have the same problem. By the way, I am looking into brazilian stocks, so the rule for the symbols is "XXXX[3 or 4 or 5].SA" where XXXX are letters.
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} | 0 | 2015-03-03T00:32:13Z | 2015-03-06T23:09:16Z | 2015-03-06T23:09:16Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/28819470/numbers-of-day-in-month?nah=1#28821925
xref #4640
This should be exposed as a vectorized method in tslib (e.g. take in an ndarray of i8 and return an ndarray)
```
pd.tslib.monthrange is an unadvertised / undocumented function that handles the days_in_month calculation (adjusting for leap years). This could/should prob be added as a property to Timestamp/DatetimeIndex (and to ``.dt``)
In [34]: df = DataFrame({'date' : pd.date_range('20120101',periods=15,freq='M') })
In [35]: df['year'] = df['date'].dt.year
In [36]: df['month'] = df['date'].dt.month
In [37]: df['days_in_month'] = df.apply(lambda x: pd.tslib.monthrange(x['year'],x['month'])[1], axis=1)
In [38]: df
Out[38]:
date year month days_in_month
0 2012-01-31 2012 1 31
1 2012-02-29 2012 2 29
2 2012-03-31 2012 3 31
3 2012-04-30 2012 4 30
4 2012-05-31 2012 5 31
5 2012-06-30 2012 6 30
6 2012-07-31 2012 7 31
7 2012-08-31 2012 8 31
8 2012-09-30 2012 9 30
9 2012-10-31 2012 10 31
10 2012-11-30 2012 11 30
11 2012-12-31 2012 12 31
12 2013-01-31 2013 1 31
13 2013-02-28 2013 2 28
14 2013-03-31 2013 3 31
```
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} | 3 | 2015-03-03T00:53:08Z | 2015-06-15T10:44:29Z | 2015-06-15T10:44:29Z | NONE | null | ```
import pandas as pd
df = pd.DataFrame({"c0":["A","A","B","B"], "c1":["C","C","D","D"]})
df.apply(lambda s:s.astype("category"))
```
the resut is a series with series as element, not a dataframe:
```
c0 [A, A, B, B]
Categories (2, object): [A < B]
c1 [C, C, D, D]
Categories (2, object): [C < D]
dtype: object
```
```
Here is the output of `show_vershons()`:
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: x86
processor: x86 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.15.2.dev
nose: 1.3.4
Cython: 0.21.2
numpy: 1.9.1
scipy: 0.15.0
statsmodels: 0.6.1
IPython: 3.0.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.3
pytz: 2014.10
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.2
openpyxl: None
xlrd: 0.9.3
xlwt: None
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html5lib: None
httplib2: None
apiclient: None
rpy2: 2.5.4
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
```
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} | 23 | 2015-03-03T03:39:15Z | 2015-05-01T16:06:11Z | 2015-03-31T01:29:08Z | NONE | null | Closes https://github.com/pydata/pandas/issues/9542, #8905
The following behavior has been tested:
- `df.plot(y='sin(x)')` -> gives a legend with label 'None' -> this should give no legend instead (as it plots one series, and then we don't automatically add a legend, see behaviour of df['sin(x)'].plot())
- `df.plot(y='sin(x)', legend=True)` -> gives a legend with label 'None' -> this should of course give a legend with label 'sin(x)' (behaviour as df['sin(x)'].plot(legend=True))
- `df.plot(y='sin(x)', label='something else', legend=True)` -> gives a legend with label 'None' -> should be a legend with label 'something else', as we want that the label kwarg overwrites the column name.
based on following data:
`x=np.linspace(-10,10,201)`
`y,z=np.sin(x),np.cos(x)`
`x,y,z=pd.Series(x),pd.Series(y),pd.Series(z)`
`df=pd.concat([x,y,z],axis=1)`
`df.columns=['x','sin(x)','cos(x)']`
`df=df.set_index('x')`
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} | 1 | 2015-03-03T04:06:38Z | 2017-04-05T02:06:18Z | 2015-03-03T21:15:19Z | CONTRIBUTOR | null | Should fix the image being too big: http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#new-features
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} | 1 | 2015-03-03T05:50:07Z | 2015-08-31T12:13:52Z | 2015-08-31T12:13:52Z | CONTRIBUTOR | null | Pandas json serializer Segfault when trying to serialize a 0d `numpy.array`. Ideally, it should able to serialize this structure as a scalar or at least raise a `TypeError`.
``` python
import numpy as np
import pandas as pd
pd.json.dumps(np.array(1)) # Segfault
pd.Series([np.array(x) for x in range(5)]).to_json() # Segfault
```
pandas version
(also tested with python2.7/pandas0.15.2/gentoo and python3.3/pandas0.15.2/gentoo and python3.4/pandas0.15.2/Ubuntu/anaconda)
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-gentoo
machine: x86_64
processor: Intel(R) Core(TM) i7-3632QM CPU @ 2.20GHz
byteorder: little
LC_ALL: None
LANG: fr_CA.utf8
pandas: 0.15.2
nose: 1.3.4
Cython: 0.22
numpy: 1.9.1
scipy: 0.15.0
statsmodels: 0.6.1
IPython: 2.4.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.0
pytz: 2014.10
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.3
openpyxl: 2.1.0
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.5.6
lxml: 3.4.2
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
rpy2: 2.5.6
sqlalchemy: 0.9.8
pymysql: 0.6.3.None
psycopg2: 2.6 (dt dec pq3 ext lo64)
```
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| null | 4 | 2015-03-03T15:30:40Z | 2015-05-09T16:10:18Z | 2015-05-09T16:10:18Z | NONE | null | closes #9471
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} | 23 | 2015-03-03T16:13:23Z | 2015-08-31T12:13:27Z | 2015-08-31T12:13:27Z | CONTRIBUTOR | null | Previously pandas ujson Segfault. see #9576
This is an initial fix. I'll try to make sens out of ujson and see if 0d array can be handle in a better way. However, At the moment raising an exception is still better than a Segfault.
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| null | 23 | 2015-03-03T17:07:06Z | 2015-11-10T01:30:23Z | 2015-11-10T01:30:23Z | NONE | null | I've created a [gist](https://gist.github.com/d1manson/f2589d4e4b44ff3ba85b) to explain in more detail, and with a screenshot.
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} | 10 | 2015-03-03T17:08:56Z | 2017-02-17T13:56:41Z | 2017-02-17T13:56:41Z | CONTRIBUTOR | null | Say I make a category-type Series:
``` python
s = pd.Series(["a", "b", "c"], dtype="category")
```
I want to pass this to a function that expects a numpy array, so I use the `values` property. According to the documentation:
``` python
s.values?
```
```
Type: property
String form: <property object at 0x106d84050>
Docstring:
Return Series as ndarray
Returns
-------
arr : numpy.ndarray
```
However:
``` python
s.values
```
```
[a, b, c]
Categories (3, object): [a < b < c]
```
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| null | 26 | 2015-03-03T19:57:28Z | 2020-12-05T09:26:48Z | null | CONTRIBUTOR | null | ``` python
In[14]: import pandas as pd
Backend Qt4Agg is interactive backend. Turning interactive mode on.
In[15]: import numpy as np
In[16]: s = pd.Series([1,2,3,1,2,3]).astype("category")
In[17]: s
Out[17]:
0 1
1 2
2 3
3 1
4 2
5 3
dtype: category
Categories (3, int64): [1 < 2 < 3]
In[18]: np.issubdtype(s.dtype, np.bool_)
Traceback (most recent call last):
File "c:\data\external\ipython\IPython\core\interactiveshell.py", line 3032, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-18-607a91e2a828>", line 1, in <module>
np.issubdtype(s.dtype, np.bool_)
File "C:\portabel\miniconda\envs\ipython\lib\site-packages\numpy\core\numerictypes.py", line 763, in issubdtype
return issubclass(dtype(arg1).type, arg2)
TypeError: data type not understood
```
This is a problem in https://github.com/pydata/patsy/pull/47
Not sure if there is an easy way to get numpy to understand this (I've absolutely no numpy foo :-/ ). If not this means that every patsy/statsmodels method which does dtype magic has to guard against the category dtype :-/
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} | 3 | 2015-03-03T23:34:30Z | 2015-03-04T20:50:38Z | 2015-03-04T20:50:28Z | CONTRIBUTOR | null | These are the deprecations that are to be removed in 0.16, per #6581, except for the deprecations around the boxplot function.
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} | 2 | 2015-03-03T23:44:05Z | 2015-03-07T12:43:55Z | 2015-03-05T23:21:12Z | CONTRIBUTOR | null | ...w/col_levels is 1.
The docs for sparse to_coo methods failed to build. There was some case (row_levels len 1) that failed in python 2.7 only that I failed to test (and I have been building docs in python 3). Have added test and patched. Also needed to expand the interator (list(map(... ) in the "# squish" line as there was some tupleizing differences between python 3 and 2.
Perhaps there is a better way to avoid these issues? Waiting for Travis.
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} | 4 | 2015-03-04T06:34:54Z | 2015-09-04T09:33:57Z | 2015-09-04T09:33:57Z | NONE | null | Running my test suite, I see:
```
/home/njs/.user-python2.7-64bit-3/local/lib/python2.7/site-packages/pandas/core/categorical.py:443: FutureWarning: Accessing 'levels' is deprecated, use 'categories'
warn("Accessing 'levels' is deprecated, use 'categories'", FutureWarning)
/home/njs/.user-python2.7-64bit-3/local/lib/python2.7/site-packages/pandas/core/categorical.py:291: RuntimeWarning: Values and categories have different dtypes. Did you mean to use
'Categorical.from_codes(codes, categories)'?
"'Categorical.from_codes(codes, categories)'?", RuntimeWarning)
/home/njs/.user-python2.7-64bit-3/local/lib/python2.7/site-packages/pandas/core/categorical.py:295: RuntimeWarning: None of the categories were found in values. Did you mean to use
'Categorical.from_codes(codes, categories)'?
"'Categorical.from_codes(codes, categories)'?", RuntimeWarning)
./home/njs/.user-python2.7-64bit-3/local/lib/python2.7/site-packages/pandas/core/categorical.py:395: FutureWarning: 'labels' is deprecated. Use 'codes' instead
warnings.warn("'labels' is deprecated. Use 'codes' instead", FutureWarning)
```
Okay, fair enough. But the problem is that all of the listed lines of code are inside pandas, so I have no idea which parts of my code are actually doing the wrong thing. `warnings.warn` provides a `stacklevel` argument, where the idea is that you should pass a non-zero value so that warnings are attributed to the code that actually needs to be changed. E.g., `stacklevel=1` means that the code which is doing something wrong is the code which called the function containing the call to `warnings.warn`.
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| null | 1 | 2015-03-04T09:24:38Z | 2021-04-12T05:42:58Z | null | MEMBER | null | See http://stackoverflow.com/questions/28833074/aggregate-group-with-multi-level-columns
So we want to have some built-in functionality for this?
The example:
```
import itertools
import pandas as pd
lev1 = ['foo', 'bar', 'baz']
lev2 = list('abc')
n = 6
df = pd.DataFrame({k: np.random.randn(n) for k in itertools.product(lev1,lev2)},
index=pd.DatetimeIndex(start='2015-01-01', periods=n, freq='11D'))
```
```
bar baz foo
a b c a b c a b c
2015-01-01 -1.11 2.12 -1.00 0.18 0.14 1.24 0.73 0.06 3.66
2015-01-12 -1.43 0.75 0.38 0.04 -0.33 -0.42 1.00 -1.63 -1.35
2015-01-23 0.01 -1.70 -1.39 0.59 -1.10 -1.17 -1.51 -0.54 -1.11
2015-02-03 0.93 0.70 -0.12 1.07 -0.97 -0.45 -0.19 0.11 -0.79
2015-02-14 0.30 0.49 0.60 -0.28 -0.38 1.11 0.15 0.78 -0.58
2015-02-25 -0.26 0.51 0.82 0.05 -1.45 0.14 0.53 -0.33 -1.35
```
The question is here if it should be possible in `groupby().aggregate()` to specify that you want to apply a function to all columns of a certain level label.
E.g. `df.groupby(pd.TimeGrouper('MS')).aggregate({'bar': np.sum, 'baz': np.mean, 'foo': np.min})` does not work at the moment.
Or does this lead to far?
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| null | 2 | 2015-03-04T09:52:47Z | 2021-04-12T05:44:11Z | null | MEMBER | null | xref #2665
xref #5440
Resample appears to be use an inconsistent label convention depending on whether the target frequency is sub-daily/daily or super-daily:
- For sub-daily/daily frequencies, `label='left'` makes labels at the timestamp corresponding to the start of each frequency bin, and `label='right'` that makes labels at that timestamp plus the frequency (at the timestamp dividing exactly dividing bins).
- For super-daily frequencies, both labels appears to shifted minus one day to the left, so the timestamps no longer cleanly divide the frequencies. Moreover, the default label shifts from `'left'` to `'right'`! My guess is that the default was changed here because users were confused by `label='left'` no longer falling inside the expected interval. (I guess I could check `git blame` for the details.)
I found this behavior quite surprising and confusing. Is it intentional? I would like to rationalize this if possible, because this strikes me as very poor design. The behavior also couples in a weird way with the `closed` argument (see the linked issues).
From my perspective (as someone who uses monthly and yearly data), the sub-daily/daily behavior makes sense and the super-daily behavior is a bug: there's no particular reason why it makes sense to use 1 day as an offset for frequencies with super-daily resolution.
CC @Cd48 @kdebrab
---
Here's my test script:
``` python
for orig_freq, target_freq in [('20s', '1min'), ('20min', '1H'), ('10H', '1D'),
('3D', '10D'), ('10D', '1M'), ('1M', 'Q'), ('3M', 'A')]:
print '%s -> %s:' % (orig_freq, target_freq)
ind = pd.date_range('2000-01-01', freq=orig_freq, periods=10)
s = pd.Series(np.arange(10), ind)
print 'default', s.resample(target_freq, how='first').index[0]
print 'left', s.resample(target_freq, label='left', how='first').index[0]
print 'right', s.resample(target_freq, label='right', how='first').index[0]
```
```
20s -> 1min:
default 2000-01-01 00:00:00
left 2000-01-01 00:00:00
right 2000-01-01 00:01:00
20min -> 1H:
default 2000-01-01 00:00:00
left 2000-01-01 00:00:00
right 2000-01-01 01:00:00
10H -> 1D:
default 2000-01-01 00:00:00
left 2000-01-01 00:00:00
right 2000-01-02 00:00:00
3D -> 10D:
default 2000-01-01 00:00:00
left 2000-01-01 00:00:00
right 2000-01-11 00:00:00
10D -> 1M:
default 2000-01-31 00:00:00
left 1999-12-31 00:00:00
right 2000-01-31 00:00:00
1M -> Q:
default 2000-03-31 00:00:00
left 1999-12-31 00:00:00
right 2000-03-31 00:00:00
3M -> A:
default 2000-12-31 00:00:00
left 1999-12-31 00:00:00
right 2000-12-31 00:00:00
```
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| null | 2 | 2015-03-04T15:04:37Z | 2015-03-04T17:56:07Z | 2015-03-04T17:41:58Z | CONTRIBUTOR | null | I was using pandas 0.13.1 in a project. Later, I tried the same code in a different machine, which had the newer 0.15.2. I found that I had a bug in my code, which was only made clear by some API change that occurred somewhere between these two versions.
Let me show an example.
**In pandas 0.13.1**
``` python
>>> import pandas as pd
>>> import numpy as np
>>> my_data = pd.DataFrame(np.arange(12).reshape(3, 4))
>>> my_data
0 1 2 3
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
[3 rows x 4 columns]
>>> temp = my_data
>>> temp += 2
>>> temp
0 1 2 3
0 2 3 4 5
1 6 7 8 9
2 10 11 12 13
[3 rows x 4 columns]
>>> my_data
0 1 2 3
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
[3 rows x 4 columns]
>>> id(my_data)
140175467916176
>>> id(temp)
140175467916432
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-25-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: pt_PT.UTF-8
pandas: 0.13.1
Cython: None
numpy: 1.9.2
scipy: 0.15.1
statsmodels: None
IPython: None
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 2.4.0
pytz: 2014.10
bottleneck: None
tables: None
numexpr: 2.4
matplotlib: None
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
sqlalchemy: None
lxml: None
bs4: None
html5lib: None
bq: None
apiclient: None
>>>
```
**Now with pandas 0.15.2**
``` python
>>> my_data = pd.DataFrame(np.arange(12).reshape(3, 4))
>>> my_data
0 1 2 3
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
>>> temp = my_data
>>> temp += 2
>>> temp
0 1 2 3
0 2 3 4 5
1 6 7 8 9
2 10 11 12 13
>>> my_data
0 1 2 3
0 2 3 4 5
1 6 7 8 9
2 10 11 12 13
>>> id(my_data)
139722601609360
>>> id(temp)
139722601609360
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-25-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: pt_PT.UTF-8
pandas: 0.15.2
nose: None
Cython: None
numpy: 1.8.2
scipy: 0.13.3
statsmodels: None
IPython: 1.2.1
sphinx: 1.2.3
patsy: None
dateutil: 2.4.0
pytz: 2014.10
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: None
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.8
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
If I understand correctly, in the first case, the assignment created a new object, while in the latter, the assignment created a shallow copy.
I am sorry if this was previously asked/documented, but I couldn't find such reference. I `CTRL+F`'ed the [What's New](pandas.pydata.org/pandas-docs/version/0.15.2/whatsnew.html) page for some keywords (shallow, deep, copy, assign), and couldn't find something related.
Is this a bug in 0.15.2, or was it a bug in 0.13.1? If so, where was it corrected?
Thank you.
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} | 0 | 2015-03-04T15:46:25Z | 2015-03-05T09:20:43Z | 2015-03-05T09:20:43Z | MEMBER | null | This was apparantly already changed in 0.7, but the docs were still wrong about it. The current example did not make much sense: http://pandas.pydata.org/pandas-docs/stable/io.html#automatically-sniffing-the-delimiter
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} | 32 | 2015-03-04T20:40:53Z | 2015-07-14T12:47:25Z | 2015-07-14T12:47:25Z | CONTRIBUTOR | null | ```
s=pd.Series(['a','a','a'])
s.convert_objects(convert_numeric=True)
Out[78]:
0 a
1 a
2 a
dtype: object
s[0]=1.0
s
Out[80]:
0 1
1 a
2 a
dtype: object
s.convert_objects(convert_numeric=True)
Out[81]:
0 1
1 NaN
2 NaN
dtype: float64
```
Having a single number changes behavior. Makes `convert_objects` unreliable when the data type must be numeric.
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} | 12 | 2015-03-04T20:55:36Z | 2019-10-21T20:06:54Z | null | NONE | null | I wrote a small class that will allow users to utilize HDF tables with significantly more columns than the ~2000 limit currently imposed. I work with mass spec data, and have matrices of ~20,000 x 20,000 x sample_number to deal with so I need things to be written on disk, and compressed.
I'm asking if this is something that would be appreciated to be cleaned up and put in as a PR. It's currently just addressing my needs as they arise, but I could extend and optimize it if there's interest. Alternatively, if I'm reinventing a wheel that already exists in pandas, I'd love to know that as well.
Here's the code for the interested
``` python
class WideTable(object):
"""
Breaks up a massive table into individual chunks and stores these chunks as separate tables.
Extends the select and append method to be pandas-like and handle this indexing to mimic a single
DataFrame
"""
def __init__(self, store, chunk_size=None, min_itemsize=255):
self.store = store
self.index_name = '_store_index'
self.chunk_size_name = '_chunk_size'
self.index_itemsize = min_itemsize
try:
self.store_index = self.store.select(self.index_name)
except KeyError:
self.store_index = pd.DataFrame(columns=['chunk'])
self.store.put(self.index_name, self.store_index)
finally:
self.tables = [i[0] for i in self.store.iteritems()]
try:
# stored as a series
self.chunk_size = self.store.select(self.chunk_size_name)
except KeyError:
self.chunk_size = pd.Series(chunk_size if chunk_size is not None else 100)
self.store.append(self.chunk_size_name, self.chunk_size)
self.chunk_size = self.chunk_size[0]
def get_chunk_name(self, name, chunk):
return '_{0}_{1}'.format(name, chunk)
def append(self, name, data, column=0):
# get the existing chunks from the data and insert them to those blocks
chunks = self.store_index['chunk']
existing_map = chunks[data.columns]
new = existing_map[existing_map.isnull()] # Series of new columns
existing = existing_map.dropna().to_dict() # dict of {'column': 'chunk'}, we want {'chunk': columns set}
existing_rev = {}
for key, value in existing.iteritems():
try:
existing_rev[value].append(key)
except KeyError:
existing_rev[value] = [key]
# update existing entries
columns_added = []
for chunk_index, chunk_columns in existing_rev.iteritems():
print self.get_chunk_name(name, chunk_index), chunk_columns
self.store.append(self.get_chunk_name(name, chunk_index), data.loc[:,chunk_columns])
columns_added.extend(chunk_columns)
new_cols = list(set(data.columns)-set(columns_added))
if new_cols:
data2 = data[new_cols]
current_chunk = chunks.astype('int').max()
if pd.isnull(current_chunk):
current_chunk = 0
space_taken = len(chunks.where(chunks == str(current_chunk)).dropna())
if space_taken >= self.chunk_size:
current_chunk += 1
end = self.chunk_size
else:
end = self.chunk_size-space_taken
if end > len(new_cols):
end = len(new_cols)
start = 0
while start < len(new_cols):
chunk_name = self.get_chunk_name(name, current_chunk)
td = data2.iloc[:,start:end]
self.store.append(chunk_name, td)
for col_name in td.columns:
self.store_index.loc[col_name,'chunk'] = str(current_chunk)
self.tables.append(chunk_name)
start = end
end += self.chunk_size
if end > len(new_cols):
end = len(new_cols)
current_chunk += 1
self.store.put(self.index_name, self.store_index)
def get_matching_tables(self, name):
return [i for i in self.tables if i.startswith('/_{0}_'.format(name))]
def select(self, name, **kwargs):
t = pd.DataFrame()
for i in self.get_matching_tables(name):
values = self.store.select(i,**kwargs)
if values.empty:
continue
t = t.join(values, how='outer')
return t
```
Basic usage:
``` python
import pandas as pd
import numpy as np
# chunk size is how many columns you want per sub-table and is only required for new tables
wt = WideTable(pd.HDFStore('wide_table.h5', complevel=9, complib='blosc'), chunk_size=3)
df = pd.DataFrame(np.random.randn(8, 10), index=range(8), columns=[chr(i) for i in xrange(70,70+10)])
wt.append('test_table', df)
# show that chunk size is optional
wt = WideTable(pd.HDFStore('wide_table.h5', complevel=9, complib='blosc'))
print wt.chunk_size
# select data
print wt.select('test_table', where='columns==J')
# across tables
print wt.select('test_table', where='columns==[N,I,H]')
print wt.select('test_table', where='columns==[F,J,O]')
```
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} | 8 | 2015-03-04T22:15:55Z | 2016-12-09T14:08:15Z | 2016-12-09T11:53:59Z | NONE | null | I've just tried to build pandas0.15.2 and hit the problem below:
```
pandas/parser.pyx:164:39: Expected ')', found '*'
building 'pandas.parser' extension
creating build/temp.linux-x86_64-2.7/pandas/src/parser
gcc -pthread -fno-strict-aliasing -I/usr/local/uvcdat/2015-02-10/Externals/include -L/usr/local/uvcdat/2015-02-10/Externals/lib -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -Ipandas/src/klib -Ipandas/src -I/usr/local/uvcdat/latest/lib/python2.7/site-packages/numpy/core/include -I/usr/local/uvcdat/latest/include/python2.7 -c pandas/parser.c -o build/temp.linux-x86_64-2.7/pandas/parser.o
pandas/parser.c:1:2: error: #error Do not use this file, it is the result of a failed Cython compilation.
error: command 'gcc' failed with exit status 1
[durack1@oceanonly pandas0.15.2]$ which python
/usr/local/uvcdat/2015-02-25/bin/python
[durack1@oceanonly pandas0.15.2]$ python
Python 2.7.9 (default, Feb 25 2015, 21:24:47)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-11)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import cython
>>> cython.__version__
'0.21.2'
```
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} | 0 | 2015-03-04T22:39:47Z | 2015-03-06T00:33:23Z | null | NONE | null | When plotting time series after importing pandas, the default locator/formatter do not support microsecond resolution handling although it is available in matplotlib 1.3.0+. Besides backwards compatibility reasons, there might not be any reason anymore to keep around the PandasAutoDateLocator/PandasAutoDateFormatter classes, which were introduced in [this commit](https://github.com/changhiskhan/pandas/commit/d0d15b0fa69a0d464a939b757a104d57fc7b4d58).
See also [my comment here](https://github.com/pydata/pandas/issues/1599) and [this related issue](https://github.com/pydata/pandas/issues/2579). An example of this is shown in [this notebook](http://nbviewer.ipython.org/github/azjps/matplotlib-tick-formatters/blob/master/notebooks/microsecond_precision.ipynb).
```
pd.show_versions():
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.17.7-el5.ia32e.lime.0
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.utf8
pandas: 0.14.0
nose: 1.3.4
Cython: 0.21
numpy: 1.8.2
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.3.0
sphinx: 1.1.3
patsy: 0.3.0
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.4
bottleneck: None
tables: None
numexpr: 2.4
matplotlib: 1.4.0
openpyxl: 1.8.6
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
lxml: 3.3.6
bs4: None
html5lib: None
bq: None
apiclient: None
rpy2: 2.4.3
sqlalchemy: None
pymysql: 0.6.2.None
psycopg2: None
```
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} | 2 | 2015-03-04T23:03:24Z | 2016-06-23T09:38:24Z | 2016-06-23T09:38:22Z | NONE | null | ```
>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame(index=range(10),data=np.random.randint(0,3,10),columns=['y'])
```
Gives,
```
>>> df.y
0 2
1 0
2 2
3 1
4 0
5 0
6 0
7 1
8 2
9 1
Name: y, dtype: int32
>>> df.groupby('y').count()
Empty DataFrame
Columns: []
Index: [0, 1, 2]
```
But I'm trying to get the count of every item in 'y'. Something like,
```
>>> from collections import Counter
>>> Counter(df.y)
Counter({0: 4, 1: 3, 2: 3})
```
Am I doing something wrong here?
Thanks!
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| null | 6 | 2015-03-05T02:05:50Z | 2016-03-19T02:46:05Z | 2015-03-05T17:24:36Z | NONE | null | Hi,
I noticed that datetime parsing for large sql/csv tables is really slow. Would it be acceptable to use a technique where repeated calculations are cached? For example, instead of:
``` python
def parse_date(date_str) :
return datetime.datetime.strptime(date_str,FMT)
def parse_date_col(str_col) :
return [parse_date(date_str) for date_str in str_col]
```
use
``` python
def parse_date(date_str) :
return datetime.datetime.strptime(date_str,FMT)
def parse_date_col(str_col) :
cache = dict()
for date_str in str_col :
if date_str not in cache :
cache[date_str] = parse_date(date_str)
return [cache[date_str] for date_str in str_col]
```
The reason this works is that string hashing / comparison / dictionary insertion is much much faster than strptime.
For tables where dates are repeated many times this can result in orders of magnitude speedup.
Thanks
Charles
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| null | 20 | 2015-03-05T12:58:01Z | 2021-04-12T05:45:08Z | null | MEMBER | null | some examples (on Series only) in #12890
I started making an overview of the indexing semantics with http://nbviewer.ipython.org/gist/jorisvandenbossche/7889b389a21b41bc1063 (only for series/frame, not for panel)
Conclusion: it is mess :-)
---
#### Summary for slicing
- Slicing with integer labels is:
- always _integer location based_
- except for a _float indexer_ where it is label based
- Slicing with other types of labels is always label based if it is of appropriate type for the indexer.
So, you can say that the behaviour is equivalent to `.ix`, except that the behaviour for integer labels is different for integer indexers (swapped). (For `.ix`, when having an integer axis, it is always label based and no fallback to integer location based).
#### Summary for single label
- Indexing with a single label is **always label based**
- But, there is fallback to integer location based, except for integer and float indexers
#### Summary for indexing with list of labels
- It is primarily _label based_, but:
- There is fallback to integer location based apart from int/float integer axis
- It is a pure reindex, also if no label of the list is found, you just get an all NaN series (which contrasts with loc, where at least one label should be found)
- String parsing for a datetime index does not seem to work
This mainly follows `ix`, apart from points 2 and 3
#### Summary for boolean indexing
- This is simple, it just works as expected
#### Summary for DataFrames
- It uses the 'information' axis (axis 1) for:
- single labels
- list of labels
- It uses the rows (axis 0) for:
- slicing
- boolean indexing
This is as documented (only the boolean case is not explicitely documented I think).
For the rest (on the choses axis), it follows the same semantics as `[]` on a series, **but**:
- for a list of labels, now all labels must be present (no pure reindex as with series)
- for single labels: no fallback to integer location based for non-numeric index (but this _does_ fallback for a list of labels ...)
---
Questions are here:
- Are there things we _can_ change? (that would not be too disruptive .. maybe not?) And _want_ change?
- How do we document this best?
- Now you have the "basics" section (http://pandas.pydata.org/pandas-docs/stable/indexing.html#basics) and the slicing section (http://pandas.pydata.org/pandas-docs/stable/indexing.html#slicing-ranges), but this does not cover all cases at all.
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} | 9 | 2015-03-05T15:47:10Z | 2015-04-20T09:13:32Z | 2015-04-18T20:42:41Z | NONE | null | ``` python
df = pd.DataFrame({'a': ['1', '2', '3'],
'b': ['11', '22', '33'],
'c': ['111', '222', '333']})
df.loc[df.b.isnull(), 'a'] = df.b
```
The filtering condition is always false (b is never null) and the above code produces this error:
`Array is not broadcastable to correct shape`.
If, however, I change the type for column 'c' - which is not even mentioned in the assignment expression (!) - from str to int,
``` python
df = pd.DataFrame({'a': ['1', '2', '3'],
'b': ['11', '22', '33'],
'c': [111, 222, 333]}) # Type changed to int
df.loc[df.b.isnull(), 'a'] = df.b
```
No errors are produced.
Would be nice to have consistent behaviour and not have any errors - could not find anything in the documentation saying that applying .loc on empty filter is considered illegal?
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} | 0 | 2015-03-06T00:32:55Z | 2015-03-06T03:18:11Z | 2015-03-06T03:18:10Z | CONTRIBUTOR | null | closes #9426
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} | 24 | 2015-03-06T01:33:28Z | 2015-03-23T15:51:27Z | 2015-03-23T10:48:53Z | CONTRIBUTOR | null | planning on a release on March 8/9. If any objections. pls comment.
@jorisvandenbossche @shoyer @TomAugspurger
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} | 2 | 2015-03-06T09:30:10Z | 2015-05-11T12:47:47Z | 2015-05-11T12:47:47Z | MEMBER | null | See my comment here: https://github.com/pydata/pandas/pull/9322#issuecomment-71385126
For the methods on `dt` and `str` I fixed the api docs (https://github.com/pydata/pandas/commit/9b5a5ea18aceddecc3e033bc21f93c12e58bd313), but not yet for `dt` and `str`. Ideally, this needs to be done for the docs for final 0.16
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| null | 1 | 2015-03-06T09:42:51Z | 2015-03-06T12:52:33Z | 2015-03-06T12:52:33Z | NONE | null | ```
dff = DataFrame({'A': np.arange(8), 'B': list('aabbbbcc')})
dff['C'] = np.arange(8)
dff.groupby('B').filter(lambda x: x['C'] == 6)
```
the result is as below:
```
A,B,C
6,6,c,6,
7,7,c,7
2 rows × 3 columns
```
the column C with the row `7,7,c,7` is not 6 but it was selected!
```
for name, group in dff.groupby('B'):
print '###########'
print group['C']==6
```
the result is as below:
```
###########
0 False
1 False
Name: C, dtype: bool
###########
2 False
3 False
4 False
5 False
Name: C, dtype: bool
###########
6 True
7 False
Name: C, dtype: bool
```
So I guess the filter '==' may only test the on the first element, not on the whole group!
The same things also occured when we use the following commands:
```
dff.groupby('B').filter(lambda x: x['C'] <= 6)
dff.groupby('B').filter(lambda x: x['C'] >= 6)
```
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} | 2 | 2015-03-06T12:35:44Z | 2015-11-12T23:44:11Z | 2015-03-06T23:01:56Z | CONTRIBUTOR | null | Related to #9493
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