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https://api.github.com/repos/pandas-dev/pandas/issues/9202 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9202/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9202/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9202/events | https://github.com/pandas-dev/pandas/issues/9202 | 53,543,111 | MDU6SXNzdWU1MzU0MzExMQ== | 9,202 | Pandas Dataframe apply() method row object vanishes | {
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| null | 4 | 2015-01-06T17:59:51Z | 2015-01-07T10:49:12Z | 2015-01-07T10:49:12Z | NONE | null | See the following code. Does it mean I have to go via route `.iterrows`?
``` python
df = pd.DataFrame({'x': range(5)})
df['y'] = df['x'].astype(str)
b = df.apply(lambda row: [row], axis=1)
0 [[nan, nan]]
1 [[nan, nan]]
2 [[nan, nan]]
3 [[nan, nan]]
4 [[nan, nan]]
```
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} | 2 | 2015-01-06T19:52:04Z | 2015-01-07T16:25:03Z | 2015-01-07T16:25:03Z | CONTRIBUTOR | null | ..._last variable in doc string. Original definition only made sense for drop_duplicates().
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} | 3 | 2015-01-06T21:51:56Z | 2015-01-18T20:59:16Z | 2015-01-18T20:59:16Z | CONTRIBUTOR | null | This is not a serious issue, but the list of stubnames passed into `wide_to_long` is modified by the function call.
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-43-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.1
nose: 1.3.1
Cython: None
numpy: 1.9.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 1.2.1
sphinx: 1.2.2
patsy: 0.2.1
dateutil: 2.3
pytz: 2014.10
bottleneck: None
tables: None
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.3.3
bs4: 4.2.1
html5lib: 0.999
httplib2: 0.8
apiclient: None
rpy2: 2.5.2
sqlalchemy: None
pymysql: None
psycopg2: None
``` python
>>> import pandas as pd
>>> df = pd.DataFrame([[0,1,2,3,8],[4,5,6,7,9]])
>>> df.columns = ['id', 'inc1', 'inc2', 'edu1', 'edu2']
>>> df
id inc1 inc2 edu1 edu2
0 0 1 2 3 8
1 4 5 6 7 9
>>> stubs = ['inc', 'edu']
>>> df_long = pd.wide_to_long(df, stubs, i='id', j='age')
>>> df_long
inc edu
id age
0 1 1 3
4 1 5 7
0 2 2 8
4 2 6 9
>>> stubs
['edu']
```
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} | 20 | 2015-01-06T22:52:45Z | 2015-02-05T11:35:06Z | 2015-02-05T11:35:06Z | CONTRIBUTOR | null | Doing this simple thing can kill an IPython 2 notebook kernel (version:GH master):
``` python
url = 'http://nssdc.gsfc.nasa.gov/planetary/factsheet/index.html'
pd.read_table(url)
```
Note that I know this will fail, I just don't expect it to kill a notebook kernel?
Version:
pandas: 128ce857f1cd
IPython: 13facaf0206240a7301e045666143d68305d0119
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} | 3 | 2015-01-07T16:16:56Z | 2015-01-07T16:33:34Z | 2015-01-07T16:25:16Z | CONTRIBUTOR | null | Redefined `take_last` variable in doc string. Original definition only
made sense for drop_duplicates()
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https://api.github.com/repos/pandas-dev/pandas/issues/9207 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9207/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9207/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9207/events | https://github.com/pandas-dev/pandas/issues/9207 | 53,651,896 | MDU6SXNzdWU1MzY1MTg5Ng== | 9,207 | Shift Inplace in DF | {
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| null | 6 | 2015-01-07T16:36:26Z | 2018-02-22T19:58:37Z | 2015-01-09T02:39:02Z | NONE | null | Is it possible to add in place shifting
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https://api.github.com/repos/pandas-dev/pandas/issues/9208 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9208/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9208/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9208/events | https://github.com/pandas-dev/pandas/issues/9208 | 53,684,841 | MDU6SXNzdWU1MzY4NDg0MQ== | 9,208 | read_excel drops columns when reading a one row file with a MultiIndex | {
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} | 2 | 2015-01-07T21:32:12Z | 2018-11-14T19:14:49Z | 2018-11-13T17:32:52Z | CONTRIBUTOR | null | I'm seeing the weirdest error.
When I read an .xlsx excel file with only one row (example below)...
... and try to set the `index_col`, then pandas incorrectly reads the columns:
I.e. for a file with only one row, column headers A, B, C, D, E, F
``` python
pandas.read_excel(filename, 'Sheet1', index_col=['A', 'B', 'C'])
```
Yields
(I get an index with columns A, B, C, and a dataframe with columns B, D, F - what happened to E?)
Note that if I don't use `index_col`, I get a dataframe with columns A-F as expected. Likewise, if I add a row of actual data, the dataframe loads as expected.
Relevant versions:
`pandas version 0.15.1 running on 2.7.7 |Anaconda 2.0.1 (64-bit)| (default, Jun 11 2014, 10:40:02) [MSC v.1500 64 bit (AMD64)]`
`xlrd.__VERSION__`=0.9.3
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} | 10 | 2015-01-07T23:36:46Z | 2015-04-29T15:31:04Z | 2015-03-12T12:08:53Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/9201, the `numeric_only` option is missing for `DataFrame.std()` (and also `DataFrame.var()` and `DataFrame.sem()`), this is a fix for it
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} | 3 | 2015-01-08T01:36:53Z | 2015-01-10T20:17:03Z | 2015-01-10T18:07:10Z | CONTRIBUTOR | null | on master:
```
In [8]: left
Out[8]:
1st 2nd 3rd
0 c c 13
1 b b 79
2 a a 27
3 b b 27
4 c a 86
In [9]: right
Out[9]:
4th
1st 2nd
c a -86
b b -79
c c -13
b b -27
a a -27
```
`sort=True` is ignored, and the result is not sorted by the join key:
```
In [10]: left.join(right, on=['1st', '2nd'], how='left', sort=True)
Out[10]:
1st 2nd 3rd 4th
0 c c 13 -13
1 b b 79 -79
1 b b 79 -27
2 a a 27 -27
3 b b 27 -79
3 b b 27 -27
4 c a 86 -86
```
in addition:
```
In [44]: left
Out[44]:
1st 2nd 3rd
0 NaN a 14
1 a NaN 10
2 a b 19
3 NaN NaN 62
4 a c 90
In [45]: right
Out[45]:
4th
1st 2nd
NaN a -14
a c -90
NaN -10
b -19
NaN NaN -62
```
this works:
```
In [46]: merge(left, right.reset_index(), on=['1st', '2nd'], how='left')
Out[46]:
1st 2nd 3rd 4th
0 NaN a 14 -14
1 a NaN 10 -10
2 a b 19 -19
3 NaN NaN 62 -62
4 a c 90 -90
```
but this does not:
```
In [47]: left.join(right, on=['1st', '2nd'], how='left')
Out[47]:
1st 2nd 3rd 4th
0 NaN a 14 NaN
1 a NaN 10 NaN
2 a b 19 -19
3 NaN NaN 62 NaN
4 a c 90 -90
```
also, [`get_group_index`](https://github.com/pydata/pandas/blob/b62754d4de0b60fdbfd67e0d0216ad7cd51d3c5f/pandas/core/groupby.py#L3493) called in [these lines](https://github.com/pydata/pandas/blob/b62754d4de0b60fdbfd67e0d0216ad7cd51d3c5f/pandas/tools/merge.py#L536) is subject to overflow, and should be avoided.
`r 'join|merge'` benchmarks:
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
join_dataframe_index_multi | 35.4387 | 36.5883 | 0.9686 |
join_dataframe_index_single_key_bigger_sort | 24.7660 | 24.8604 | 0.9962 |
strings_join_split | 57.6473 | 57.6183 | 1.0005 |
join_dataframe_index_single_key_small | 16.6840 | 16.6461 | 1.0023 |
merge_2intkey_sort | 61.2427 | 60.5460 | 1.0115 |
join_non_unique_equal | 0.9513 | 0.9391 | 1.0130 |
left_outer_join_index | 2887.7623 | 2839.2557 | 1.0171 |
i8merge | 1534.6023 | 1506.8540 | 1.0184 |
join_dataframe_index_single_key_bigger | 25.3410 | 24.8287 | 1.0206 |
merge_2intkey_nosort | 21.6643 | 21.2137 | 1.0212 |
join_dataframe_integer_key | 3.0307 | 2.9414 | 1.0304 |
join_dataframe_integer_2key | 7.7363 | 7.4220 | 1.0423 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [f02ef89] : bug in left join on multi-index with sort=True or nulls
Base [b62754d] : Merge pull request #9206 from robertdavidwest/9203_resubmitted_in_single_commit
9203 SQUASHED - DOCS: doc string edited pandas/core/frame.duplicated()
```
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| null | 5 | 2015-01-08T06:43:25Z | 2020-05-13T02:10:10Z | null | NONE | null | I'm using 0.15.0 version, and I find when I apply drop_duplicates() on "PostCode" column, it will occupy all the CPU forever, and I have to kill the process to stop it.
dataset = pd.read_csv(dataset_path, encoding = 'GB2312')
dataset['PostCode'].drop_duplicates()
"PostCode" column contains a list of integers but with some "NULL" string, I'm not sure if it's related to column dtype, which for "PostCode" is float64...but the interesting thing is there is another column "AreaCode" which is similar with "PostCode", containing a list of integers and some "NULL", and it works well with drop_duplicates()
Here is my test file download link:
https://drive.google.com/file/d/0B501Knu4O8vZT1RyampvU2RNTms/view?usp=sharing
Which contains more than 300k rows data
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} | 28 | 2015-01-08T09:28:20Z | 2016-07-20T20:22:00Z | 2016-06-12T16:50:05Z | NONE | null | I feel like I've encountered a bug. In the following scenario, the first `sort_index` call behaves as expected, but the second does not. Does someone know what the difference is here?
```
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.15.2'
In [3]: tuples = [(' foo', 'bar'), ('foo', 'bar'), (' foo ()', 'bar')]
In [4]: cols = pd.MultiIndex.from_tuples(tuples)
In [5]: df = pd.DataFrame(index=cols, data={'baz': [0, 1, 2]})
In [6]: df
Out[6]:
baz
foo bar 0
foo bar 1
foo () bar 2
In [7]: df.sort_index()
Out[7]:
baz
foo bar 0
foo () bar 2
foo bar 1
In [8]: tuples = [(' foo', 'bar'), ('foo', 'bar')]
In [9]: cols = pd.MultiIndex.from_tuples(tuples)
In [10]: df = pd.DataFrame(index=cols, data={'baz': [0, 1]})
In [11]: df
Out[11]:
baz
foo bar 0
foo bar 1
In [12]: df.ix[(' foo ()', 'bar'), 'baz'] = 2
In [13]: df
Out[13]:
baz
foo bar 0
foo bar 1
foo () bar 2
In [14]: df.sort_index()
Out[14]:
baz
foo bar 0
foo bar 1
foo () bar 2
```
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| null | 10 | 2015-01-08T12:14:10Z | 2021-04-12T04:49:54Z | null | CONTRIBUTOR | null | Depending on the runtime value used in `[` indexing on a series, the semantics may be array-indexing, or slicing based on a MultiIndex. This is extremely error-prone behavior in a very basic operation.
Is the answer that production code should always use `.loc`/`.ix` instead of `[`? If so, I don't think the [docs](http://pandas.pydata.org/pandas-docs/stable/advanced.html#basic-indexing-on-axis-with-multiindex) make that clear.
``` python
>>> s = pandas.Series({(1, 1): 11, (1, 2): 12, (2, 1): 21, (2, 2): 22})
>>> s
1 1 11
2 12
2 1 21
2 22
dtype: int64
#0 isn't recognized as something to do with the index
# so treat it as an array index
>>> s[0]
11
# But this could be asking for a slice,
# so return a slice rather than an array-indexed element.
>>> s[1]
1 11
2 12
dtype: int64
>>> pandas.__version__
'0.15.2'
```
See also #3390
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} | 9 | 2015-01-08T23:02:53Z | 2019-12-11T16:20:58Z | 2019-12-11T16:20:57Z | CONTRIBUTOR | null | More drive-by reports. I'll try to close some of these issues sometime...
``` python
In [8]: s = pd.Series(np.random.randn(10))
In [9]: s.groupby(s > 0).apply(lambda x: x.head(-1)) # works nicely
Out[9]:
False 0 -0.277400
1 -0.875231
3 -0.315087
4 -0.966649
6 -0.474389
7 -0.121225
True 2 0.237861
5 1.311601
dtype: float64
In [10]: s.groupby(s > 0).head(-1) # doesn't.
Out[10]: Series([], dtype: float64)
```
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} | 6 | 2015-01-09T00:25:22Z | 2015-01-18T20:59:05Z | 2015-01-18T20:59:05Z | CONTRIBUTOR | null | Simple fix that prevents modification of the list `stubnames`, which is an argument to `wide_to_long`.
Closes #9204
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} | 59 | 2015-01-09T01:12:00Z | 2021-05-18T22:11:05Z | null | NONE | null | After hours of tearing my hair, I've come to the conclusion that it is impossible to create a mixed dtype DataFrame without copying all of its data in. That is, no matter what you do, if you want to create a mixed dtype DataFrame, you will inevitably create a temporary version of the data (e.g. using np.empty), and the various DataFrame will constructors will always make copies of this temporary. This issue has already been brought up, a year ago: https://github.com/pydata/pandas/issues/5902.
This is especially terrible for interoperability with other programming languages. If you plan to populate the data in the DataFrame from e.g. a call to C, the easiest way to do it by far is to create the DataFrame in python, get pointers to the underlying data, which are np.arrays, and pass these np.arrays along so that they can be populated. In this situation, you simply don't care what data the DataFrame starts off with, the goal is just to allocate the memory so you know what you're copying to.
This is also just generally frustrating because it implies that in principle (depending potentially on the specific situation, and the implementation specifics, etc) it is hard to guarantee that you will not end up using twice the memory you really should.
This has an extremely simple solution that is already grounded in the quantitative python stack: have a method analagous to numpy's empty. This allocates the space, but does not actually waste any time writing or copying anything. Since empty is already taken, I would propose calling the method from_empty. It would accept an index (mandatory, most common use case would be to pass np.arange(N)), columns (mandatory, typically a list of strings), types (list of acceptable types for columns, same length as columns). The list of types should include support for all numpy numeric types (ints, floats), as well as special Pandas columns such as DatetimeIndex and Categorical.
As an added bonus, since the implementation is in a completely separate method, it will not interfere with the existing API at all.
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} | 5 | 2015-01-09T20:05:25Z | 2017-02-14T13:34:23Z | 2017-02-14T13:34:23Z | MEMBER | null | Current behavior:
```
In [28]: s = pd.Series([1, 2, np.nan, np.nan, 5])
In [29]: s
Out[29]:
0 1
1 2
2 NaN
3 NaN
4 5
dtype: float64
In [30]: s.interpolate(limit=0)
Out[30]:
0 1
1 2
2 3
3 4
4 5
dtype: float64
```
But in fact, `limit=0` in contrast to `limit=None` should mean no interpolation.
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} | 10 | 2015-01-09T20:40:56Z | 2018-08-31T18:37:25Z | 2015-09-01T12:04:25Z | MEMBER | null | The current documentation for the limit parameter looks like this:
```
limit : int, default None.
Maximum number of consecutive NaNs to fill.
```
Several features were not immediately obvious to me and could probably be resolved by slightly better docstring or API design:
1. What happens where the max number of consecutive NaNs is reached? pandas simply stops replacing values, e.g., if there is a gap of 3 values and limit=2, pandas replaces the first 2 values. The alternative would be to entirely ignore gaps of more than 2 values. I'm not saying this would be a better alternative, but we should clarify this.
2. The limit refers to forward filling only, even though interpolation is not inherently directional. It would be nice if there was some way to trigger a limit for back filling at the same time, e.g., with `forward_limit` and `backward_limit` arguments:
```
>>> pd.Series([1, np.nan, np.nan, np.nan, 5]).interpolate(forward_limit=1, backward_limit=1)
0 1
1 2
2 NaN
3 4
4 5
dtype: float64
```
Thoughts?
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| null | 7 | 2015-01-09T22:35:44Z | 2017-03-28T12:48:04Z | 2015-01-09T23:16:46Z | CONTRIBUTOR | null | This may be more of a PyTables issue but it affects Pandas users too
``` Python
>>> import pandas as pd
>>> df = pd.DataFrame({'a': [1, 2, 3]})
>>> df.to_hdf('myfile.hdf5', '/data', append=True)
File "/home/mrocklin/Software/anaconda/envs/py34/lib/python3.4/site-packages/tables/attributeset.py", line 381, in _g__setattr
elif name == "FILTERS" and self._v__format_version >= (2, 0):
TypeError: unorderable types: NoneType() >= tuple()
```
It appears that `self._v__format_version is None`. Not sure why.
``` Python
>>> import sys
>>> sys.version
'3.4.2 |Anaconda 2.1.0 (64-bit)| (default, Oct 21 2014, 17:16:37) \n[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]'
>>> pd.__version__
'0.14.1'
>>> import tables
>>> tables.__version__
'3.1.1'
```
This works fine with `append=False`
``` Python
>>> df.to_hdf('myfile.hdf5', '/data', append=False)
>>>
```
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} | 9 | 2015-01-09T23:21:58Z | 2020-11-20T04:44:02Z | null | MEMBER | null | open issues & xref
* (fillna) #19205
* #10382
* #15517
* #2511
* #16686
think this should be a bug. When calling the fill_na method in a pandas Series of type np.float16 it raises a ValueError, because of invalid dtype. May be there is a reason for that I can't guess, but I think it's a bug because it does not detect np.float16 as a subclass of np.floating. I'm using Python 3.4, didn't test if it also fails in Python 2. It fails for both methods, 'pad' and 'bfill'.
For np.float32 and np.float64 it works correctly. This code reproduces the error with np.float16:
```
import numpy as np
import pandas as pd
s = pd.Series(np.random.randn(100), dtype=np.float16)
rets = s.fillna(method='bfill')
```
and this is the ValueError it generates:
---
```
ValueError Traceback (most recent call last)
<ipython-input-133-5610749057ca> in <module>()
3
4 s = pd.Series(np.random.randn(100), dtype=np.float16)
----> 5 rets = s.fillna(method='bfill')
/opt/anaconda3/lib/python3.4/site-packages/pandas/core/generic.py in fillna(self, value, method, axis, inplace, limit, downcast)
2227 inplace=inplace,
2228 coerce=True,
-> 2229 downcast=downcast)
2230 else:
2231 if method is not None:
/opt/anaconda3/lib/python3.4/site-packages/pandas/core/internals.py in interpolate(self, **kwargs)
2223
2224 def interpolate(self, **kwargs):
-> 2225 return self.apply('interpolate', **kwargs)
2226
2227 def shift(self, **kwargs):
/opt/anaconda3/lib/python3.4/site-packages/pandas/core/internals.py in apply(self, f, axes, filter, do_integrity_check, **kwargs)
2190 copy=align_copy)
2191
-> 2192 applied = getattr(b, f)(**kwargs)
2193
2194 if isinstance(applied, list):
/opt/anaconda3/lib/python3.4/site-packages/pandas/core/internals.py in interpolate(self, method, axis, index, values, inplace, limit, fill_value, coerce, downcast, **kwargs)
648 fill_value=fill_value,
649 coerce=coerce,
--> 650 downcast=downcast)
651 # try an interp method
652 try:
/opt/anaconda3/lib/python3.4/site-packages/pandas/core/internals.py in _interpolate_with_fill(self, method, axis, inplace, limit, fill_value, coerce, downcast)
693 limit=limit,
694 fill_value=fill_value,
--> 695 dtype=self.dtype)
696 values = self._try_coerce_result(values)
697
/opt/anaconda3/lib/python3.4/site-packages/pandas/core/common.py in interpolate_2d(values, method, axis, limit, fill_value, dtype)
1576 values = transf(pad_2d(transf(values), limit=limit, mask=mask, dtype=dtype))
1577 else:
-> 1578 values = transf(backfill_2d(transf(values), limit=limit, mask=mask, dtype=dtype))
1579
1580 # reshape back
/opt/anaconda3/lib/python3.4/site-packages/pandas/core/common.py in backfill_2d(values, limit, mask, dtype)
1378
1379 if _method is None:
-> 1380 raise ValueError('Invalid dtype for backfill_2d [%s]' % dtype.name)
1381
1382 if mask is None:
ValueError: Invalid dtype for backfill_2d [float16]
```
And my guess is that the problem is this part of the code, I can debug it once you confirm this is a bug:
https://github.com/pydata/pandas/blob/master/pandas/core/common.py#L2459
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} | 9 | 2015-01-10T14:04:00Z | 2015-01-10T17:23:12Z | 2015-01-10T17:23:12Z | NONE | null | groupby.fillna not working along column axis.
The bug exists for 15.1 and 15.2
Works for 13.1
```
import pandas as pd
import numpy as np
print pd.__version__
test = pd.DataFrame(index=pd.MultiIndex.from_product([['value1','value2'],pd.date_range('2014-01-01','2014-01-06')]),
columns=pd.Index(['1','2'], name='id'))
test['1'] = [np.nan, 1, np.nan, np.nan, 11, np.nan, np.nan, 2, np.nan, np.nan, 22, np.nan]
test['2'] = [np.nan, 3, np.nan, np.nan, 33, np.nan, np.nan, 4, np.nan, np.nan, 44, np.nan]
testT = test.T
print '*' * 30
print test
print '*' * 30
print test.groupby(level=0, axis=0).fillna(method='ffill')
print '*' * 30
print testT
print '*' * 30
print testT.groupby(level=0, axis=1).fillna(method='ffill')
print '*' * 30
```
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} | 0 | 2015-01-10T15:48:22Z | 2015-01-10T17:23:12Z | 2015-01-10T17:23:12Z | CONTRIBUTOR | null | closes #9221
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| null | 1 | 2015-01-10T15:57:11Z | 2015-01-10T16:02:54Z | 2015-01-10T16:02:46Z | CONTRIBUTOR | null | It would be logical to add a `name` field to DataFrame, similar to Series. Then when you construct a Panel it can try to use the `name`, in the same way as when you construct a DataFrame from Series.
It would also be useful when loading/saving DataFrames to files and DBs, for example at the moment to save to HDF you always have to add a key and for SQL a table name, it would be logical to attach these to the DataFrame itself as its name parameter.
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https://api.github.com/repos/pandas-dev/pandas/issues/9224 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9224/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9224/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9224/events | https://github.com/pandas-dev/pandas/issues/9224 | 53,984,291 | MDU6SXNzdWU1Mzk4NDI5MQ== | 9,224 | Syntax error thrown by python at pandas/scripts/windows_builder/check_and_build.py +52 | {
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| null | 2 | 2015-01-11T07:02:39Z | 2015-01-11T17:35:59Z | 2015-01-11T14:09:17Z | CONTRIBUTOR | null | Syntax error on line 52 of pandas/scripts/windows_builder/check_and_build.py?
$ python scripts/windows_builder/check_and_build.py
File "scripts/windows_builder/check_and_build.py", line 52
password=############
^
SyntaxError: invalid syntax
Fix: password='############' ?
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https://api.github.com/repos/pandas-dev/pandas/issues/9225 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9225/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9225/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9225/events | https://github.com/pandas-dev/pandas/pull/9225 | 53,987,031 | MDExOlB1bGxSZXF1ZXN0MjcxNjA3Nzc= | 9,225 | Fix randomly failing test in test_frame.py | {
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} | 9 | 2015-01-11T09:44:43Z | 2015-01-15T03:25:21Z | 2015-01-15T03:25:17Z | CONTRIBUTOR | null | @jreback I noticed a randomly failing test while doing a full suite run a week or two ago. The test is using random integers as part of a `MultiIndex`, which randomly leads to a non-unique index. Attempting to re-index the `DataFrame` therefore randomly fails. The solution is to just sample without replacement.
The following code gives a failure rate of ~1.5%, which is in line with birthday problem estimates (`d=1000` and four iterations each of `n=3` and `n=2`):
```
import subprocess
total = 0
n = 10
for i in range(n):
result = subprocess.call('nosetests pandas/tests/test_frame.py:TestDataFrame.test_reindex_level', shell=True)
if result:
print i
total += 1
print 'Total error rate of {0:.6f}'.format(total/float(n))
```
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} | 22 | 2015-01-12T03:39:28Z | 2015-01-19T02:18:33Z | 2015-01-18T20:53:40Z | CONTRIBUTOR | null | Closes #9064
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} | 10 | 2015-01-12T03:39:43Z | 2015-03-05T23:35:52Z | 2015-03-05T23:35:47Z | CONTRIBUTOR | null | Mostly just stylistic changes. The only substantive one is the test now on line 56 (`has_horizontally_truncated_repr`). I can't figure what the test was supposed to be doing. Suggestions?
Original Test:
``` python
def has_horizontally_truncated_repr(df):
try: # Check header row
fst_line = np.array(repr(df).splitlines()[0].split())
cand_col = np.where(fst_line=='...')[0][0]
except:
return False
# Make sure each row has this ... in the same place
r = repr(df)
for ix,l in enumerate(r.splitlines()):
if not r.split()[cand_col] == '...':
return False
return True
```
New Test:
``` python
def has_horizontally_truncated_repr(df):
try: # Check header row
fst_line = np.array(repr(df).splitlines()[0].split())
cand_col = np.where(fst_line=='...')[0][0]
except:
return False
# Make sure each row has this ... in the same place
return repr(df).split()[cand_col] == "..."
```
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} | 5 | 2015-01-12T13:21:08Z | 2019-10-07T01:09:15Z | 2019-10-07T01:09:14Z | CONTRIBUTOR | null | When stty columns = 0, I guess this test error.
```
======================================================================
FAIL: test_auto_detect (pandas.tests.test_format.TestDataFrameFormatting)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/tests/test_format.py", line 315, in test_auto_detect
self.assertTrue(has_expanded_repr(df))
AssertionError: False is not true
```
To reproduce:
```
stty columns 0
nosetests pandas.tests.test_format
```
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} | 23 | 2015-01-12T14:10:28Z | 2015-03-01T14:51:09Z | 2015-03-01T14:51:09Z | CONTRIBUTOR | null | In my [notebook](http://nbviewer.ipython.org/6e052140eaa5fdb6e8c0) comparing dplyr and pandas, I gained a new level of appreciation for the ability to chain strings of operations together. In my own code, the biggest impediment to this is adding additional columns that are calculations on existing columns. For example
``` R
# R / dplyr
mutate(flights,
gain = arr_delay - dep_delay,
speed = distance / air_time * 60)
# ... calculation involving these
```
vs.
``` python
flights['gain'] = flights.arr_delay - flights.dep_delay
flights['speed'] = flights.distance / flights.air_time * 60
# ... calculation involving these later
```
just doesn't flow as nicely, especially if this `mutate` is in the middle of a chain.
I'd propose a new method (perhaps stealing `mutate`) that's similar to dplyr's.
The function signature could be kwarg only, where the keywords are the new column names. e.g.
``` python
flights.mutate(gain=flights.arr_delay - flights.dep_delay
```
This would return a DataFrame with the new column `gain` in addition to the original columns.
Worked out example
``` python
import pandas as pd
import seaborn as sns
iris = sns.load_dataset('iris')
(iris.query('sepal_length > 4.5')
.mutate(ratio=iris.sepal_length / iris.sepal_width) # new part
.groupby(pd.cut(iris.ratio)).mean()
)
```
Thoughts?
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| null | 3 | 2015-01-12T17:38:48Z | 2015-01-12T22:30:38Z | 2015-01-12T22:30:32Z | CONTRIBUTOR | null | Reading from s3 on Python 3 returns a Bytes object.
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} | 4 | 2015-01-12T17:41:41Z | 2015-01-16T12:48:10Z | 2015-01-16T12:48:10Z | CONTRIBUTOR | null | I'm not entirely sure the root cause yet. It's something to do with the default encoding I think. I'm pretty sure Stata uses `iso-8859-1` not `cp1252`. Changing this inside `pandas.io.maybe_read_encoded_stream` works, but changing the encoding in StataReader breaks something else that I don't have time to debug.
Can someone sanity check me. Does this work on Python 2?
```
dta = pd.read_stata("http://www.stata-press.com/data/r13/fullauto.dta")
```
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} | 7 | 2015-01-12T19:47:39Z | 2019-12-24T14:24:25Z | 2019-12-24T14:24:25Z | NONE | null | DataFrame constructor example:
``` python
In [55]: x = pd.Series(range(5), name=1)
In [56]: y = pd.Series(range(5), name=0)
In [57]: pd.DataFrame(x, columns=[0])
Out[57]:
Empty DataFrame
Columns: [0]
Index: []
In [58]: pd.DataFrame(x, columns=[1])
Out[58]:
1
0 0
1 1
2 2
3 3
4 4
```
and for y, the name and columns don't have to match for it to construct.
```
In [59]: pd.DataFrame(y, columns=[0])
Out[59]:
0
0 0
1 1
2 2
3 3
4 4
In [60]: pd.DataFrame(y, columns=[1])
Out[60]:
1
0 0
1 1
2 2
3 3
4 4
```
---
Using Pandas 0.15.2-1. After renaming a column in a dataframe, a .join operation fails.
``` python
import numpy as np,pandas as pd
df1=pd.DataFrame(range(1,1001)).rename(columns={0:1})
a=np.log(df1[1])
df2=df1.join(pd.DataFrame(a,columns=[0]))
```
The second column contains all NaN, instead of the values from _a_. The same can be accomplished by other means, but I believe the above should work. For example, the following works:
``` python
df1=pd.DataFrame(range(1,1001))
a=np.log(df1[0])
df2=df1.join(pd.DataFrame(a,columns=[2]))
```
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| null | 4 | 2015-01-12T19:54:46Z | 2015-01-13T11:07:51Z | 2015-01-13T11:07:45Z | NONE | null | I entered "pip install pandas" in my terminal window to install it into a local environment but the installation looped infinitely and threw up a bunch of errors. I'm not sure if it is something wrong with pip or my virtual environment but I wanted to see if anybody else was having trouble with this.
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} | 4 | 2015-01-12T21:11:42Z | 2015-01-19T13:58:49Z | 2015-01-19T13:58:48Z | MEMBER | null | I always thought that `append` was for appending rows, not columns. So, unless I am completely missing something, the docstring was saying the wrong thing.
So I reworded it a bit, see below.
Apart from that, there were some other things that are not fully clear to me in the docstring:
- The docstring says that `other` should be a "list of Series/dict-like objects" (or a Dataframe):
- Does it need to be a list? It also works with just a Series. Or what are the differences between both?
- With a simple example, I don't see a difference, although in implementation, there is.
- The docstring says for the `ignore_index` kwarg: "Useful for gluing together record arrays"
- How does this look in practice? If I try to append a record array, I get "TypeError: cannot concatenate a non-NDFrame object" -> so I will remove this
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| null | 4 | 2015-01-12T21:47:19Z | 2015-01-13T11:08:58Z | 2015-01-13T11:08:47Z | CONTRIBUTOR | null | Hi,
I understand this ticket is a borderline usage question, but I think it may signal an example of inconsistency either in the documentation or on the way these methods work.
The original question on SO is [here](http://stackoverflow.com/questions/27517425/apply-vs-transform-on-a-group-object).
Consider the following dataframe:
```
A B C D
0 foo one 0.162003 0.087469
1 bar one -1.156319 -1.526272
2 foo two 0.833892 -1.666304
3 bar three -2.026673 -0.322057
4 foo two 0.411452 -0.954371
5 bar two 0.765878 -0.095968
6 foo one -0.654890 0.678091
7 foo three -1.789842 -1.130922
```
The following commands work:
```
> df.groupby('A').apply(lambda x: (x['C'] - x['D']))
> df.groupby('A').apply(lambda x: (x['C'] - x['D']).mean())
```
but none of the following work:
```
> df.groupby('A').transform(lambda x: (x['C'] - x['D']))
ValueError: could not broadcast input array from shape (5) into shape (5,3)
> df.groupby('A').transform(lambda x: (x['C'] - x['D']).mean())
TypeError: cannot concatenate a non-NDFrame object
```
**Why?** [The example on the documentation](http://pandas.pydata.org/pandas-docs/stable/groupby.html#transformation) seems to suggest that calling `transform` on a group allows one to do row-wise operation processing:
``` python
# Note that the following suggests row-wise operation (x.mean is the column mean)
zscore = lambda x: (x - x.mean()) / x.std()
transformed = ts.groupby(key).transform(zscore)
```
In other words, I thought that transform is essentially a specific type of apply (the one that does not aggregate). Where am I wrong?
For reference, below is the construction of the original dataframe above:
``` python
df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'foo', 'foo'],
'B' : ['one', 'one', 'two', 'three',
'two', 'two', 'one', 'three'],
'C' : randn(8), 'D' : randn(8)})
```
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} | 8 | 2015-01-13T01:26:40Z | 2019-02-19T13:32:42Z | 2019-02-19T13:32:42Z | NONE | null | It seems that the `groupby` operation fails when the row index is a MultiIndex containing NaT values. For example, the following code fails (v0.15.2) with `TypeError: 'numpy.ndarray' object is not callable`:
``` python
midx = pd.MultiIndex(levels=[[pd.NaT, pd.datetime(2012,1,2),
pd.datetime(2012,1,3)], ['a', 'b']],
labels=[[0, 1, 1, 2], [0, 0, 1, 0]], names=['date', None])
df = pd.Series(pd.np.random.rand(4), index=midx)
df.groupby(level=1)
```
However, it seems as though np.nan values are handled properly:
``` python
midx = pd.MultiIndex(levels=[[pd.np.nan, 10, 20], ['a', 'b']],
labels=[[0, 1, 1, 2], [0, 0, 1, 0]], names=['date', None])
df = pd.Series(pd.np.random.rand(4), index=midx)
df.groupby(level=1)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/9237 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9237/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9237/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9237/events | https://github.com/pandas-dev/pandas/pull/9237 | 54,141,701 | MDExOlB1bGxSZXF1ZXN0MjcyNDMwNTM= | 9,237 | BUG: fix DataFrame constructor w named Series | {
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} | 28 | 2015-01-13T02:03:58Z | 2017-04-05T02:06:18Z | 2015-10-19T13:10:55Z | CONTRIBUTOR | null | closes #7893
Closes https://github.com/pydata/pandas/issues/9232
Problem was passing Series w/ a name to DataFrame w/ the `columns` kwarg.
Before:
``` python
In [55]: x = pd.Series(range(5), name=1)
In [56]: y = pd.Series(range(5), name=0)
In [57]: pd.DataFrame(x, columns=[0])
Out[57]:
Empty DataFrame
Columns: [0]
Index: []
In [58]: pd.DataFrame(x, columns=[1])
Out[58]:
1
0 0
1 1
2 2
3 3
4 4
In [59]: pd.DataFrame(y, columns=[0])
Out[59]:
0
0 0
1 1
2 2
3 3
4 4
In [60]: pd.DataFrame(y, columns=[1])
Out[60]:
1
0 0
1 1
2 2
3 3
4 4
```
after
``` python
In [1]: x = pd.Series(range(5), name=1)
In [2]: y = pd.Series(range(5), name=0)
In [4]: pd.DataFrame(x, columns=[0])
Out[4]:
0
0 0
1 1
2 2
3 3
4 4
In [5]: pd.DataFrame(y, columns=[1])
Out[5]:
1
0 0
1 1
2 2
3 3
4 4
```
There were two intertwined problems
1. we checked `if getattr(data, 'name', None):`, which returned False when data.name was `False`ish (like 0). I now compare it directly against None.
2. If `data` has a name and the columns kwarg is specified, the constructor returned an Empty DataFrame w/ the column specified in columns. Now, we do what's [documented](http://pandas.pydata.org/pandas-docs/version/0.15.2/dsintro.html#from-a-series):
> The result will be a DataFrame with the same index as the input Series, and with one column whose name is the original name of the Series (only if no other column name provided).
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https://api.github.com/repos/pandas-dev/pandas/issues/9238 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9238/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9238/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9238/events | https://github.com/pandas-dev/pandas/pull/9238 | 54,145,031 | MDExOlB1bGxSZXF1ZXN0MjcyNDQ4MTI= | 9,238 | DOC: Small syntax change to indexing.rst to address GH8686. | {
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} | 1 | 2015-01-13T02:54:36Z | 2015-01-13T08:09:48Z | 2015-01-13T08:09:37Z | CONTRIBUTOR | null | Brings indexing.rst docs for .loc into line with .iloc. Closes https://github.com/pydata/pandas/issues/8686.
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} | 61 | 2015-01-13T13:32:24Z | 2017-04-05T02:06:15Z | 2015-03-01T14:51:09Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/9229
signature: `DataFrame.transform(**kwargs)`
- the keyword is the name of the new column (existing columns are overwritten if there's a name conflict, as in dplyr)
- the value is either
- called on self if it's callable. The callable should be a function of 1 argument, the DataFrame being called on.
- inserted otherwise
``` python
In [7]: df.head()
Out[7]:
sepal_length sepal_width petal_length petal_width species
0 5.1 3.5 1.4 0.2 setosa
1 4.9 3.0 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
3 4.6 3.1 1.5 0.2 setosa
4 5.0 3.6 1.4 0.2 setosa
In [8]: (df.query('species == "virginica"')
.transform(sepal_ratio=lambda x: x.sepal_length / x.sepal_width)
.head())
Out[8]:
sepal_length sepal_width petal_length petal_width species \
100 6.3 3.3 6.0 2.5 virginica
101 5.8 2.7 5.1 1.9 virginica
102 7.1 3.0 5.9 2.1 virginica
103 6.3 2.9 5.6 1.8 virginica
104 6.5 3.0 5.8 2.2 virginica
sepal_ratio
100 1.909091
101 2.148148
102 2.366667
103 2.172414
104 2.166667
```
My question now is
- How strict should we be on the shape of the `transformed` DataFrame? Should we do any kind of checking on the index or columns?
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| null | 2 | 2015-01-13T15:14:04Z | 2015-01-13T23:47:40Z | 2015-01-13T23:47:35Z | NONE | null | Hi,
I was wondering if I could make a regex query (re.search say) in the query expression.
Dataframe.filter supports it. Is there any particular reason that query does not?
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| null | 6 | 2015-01-13T15:57:17Z | 2015-01-14T14:25:34Z | 2015-01-14T14:15:55Z | CONTRIBUTOR | null | First assignment with logical indexing is 1000 slower than second, third, etc. for pandas.Series:
```
import pandas.io.data as web
f = web.DataReader("MSFT", 'yahoo')['Adj Close']
%time f[f==0] = np.NaN
%time f[f==0.1] = np.NaN
```
It gives
```
CPU times: user 1.52 s, sys: 1.65 ms, total: 1.52 s
Wall time: 1.52 s
CPU times: user 704 µs, sys: 22 µs, total: 726 µs
Wall time: 720 µs
```
But for DataFrames everything is OK:
```
import pandas.io.data as web
f = web.DataReader(["MSFT","GOOG"], 'yahoo')['Adj Close']
%time f[f==0] = np.NaN
%time f[f==0.1] = np.NaN
```
gives
```
CPU times: user 1.28 ms, sys: 1e+03 ns, total: 1.28 ms
Wall time: 1.28 ms
CPU times: user 1.23 ms, sys: 1 µs, total: 1.23 ms
Wall time: 1.24 ms
```
I used here data from Yahoo!Finance with datetimeindex since I was not able to reproduce this behavior in Series with integer index.
My versions:
pandas: 0.15.2
Cython: 0.21.2
numpy: 1.9.1
dateutil: 2.4.0
pytz: 2014.10
numexpr: 2.4
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} | 3 | 2015-01-13T17:59:38Z | 2015-02-27T10:56:10Z | 2015-01-13T23:40:09Z | NONE | null | At the moment, I don't see any way to have performant, timezone aware columns. This seems rather surprising, as generally when you have a column with timestamps, they will all likely be of the same timezone. Right now, as far as I can see you have two options:
1) Columns that are basically just numpy datetime64[ns] types. These seem to be timezone unaware.If you make such a column timezone aware (by e.g., dataframe.time_column.dt.tz_localize('UTC')) it becomes a column of dtype object.
2) A DatetimeIndex, which keeps track of timezone information at seemingly the column level (which is laudable). However, this only seems to really work used as an index. If I assign it to a column, it again gets converted to a dtype object, and things get slow.
Am I missing something? Is there a really good reason why a DatetimeIndex can't just be used, as is, in a column, without the dtype=object conversion?
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} | 6 | 2015-01-13T19:13:32Z | 2018-06-28T10:23:02Z | 2018-06-28T10:23:02Z | NONE | null | Hello
I am getting a core dump when truncating a series with a timezone specified on the index. Please see below:
This works fine:
```
import pandas
import datetime
import numpy as np
rng = pandas.date_range('1/2/2005', '11/1/2010', freq='D')
ts = pandas.Series(np.random.randn(len(rng)), index=rng)
ts.truncate(datetime.datetime(2009,1,1), datetime.datetime(2011,1,1)).resample('D', how='sum')
```
However this core dumps for me:
```
import pandas
import datetime
import numpy as np
rng = pandas.date_range('1/2/2005', '11/1/2010', freq='D', tz='US/Eastern')
ts = pandas.Series(np.random.randn(len(rng)), index=rng)
ts.truncate(datetime.datetime(2009,1,1), datetime.datetime(2011,1,1)).resample('D', how='sum')
```
The only difference is adding the TZ parameter to the date_range to set the index for a particular timezone.
This was working fine in .15, but seems to be broken in .15.2
Thanks
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} | 5 | 2015-01-13T22:09:31Z | 2016-01-14T12:36:43Z | 2016-01-14T12:36:43Z | MEMBER | null | In 0.14:
```
In [1]: pd.__version__
Out[1]: '0.14.1'
In [2]: intidx = pd.Index(range(10))
In [3]: intidx % 2
Out[3]: Int64Index([0, 1, 0, 1, 0, 1, 0, 1, 0, 1], dtype='int64')
```
now:
```
In [1]: pd.__version__
Out[1]: '0.15.2'
In [2]: intidx = pd.Index(range(10))
In [3]: intidx % 2
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-de0ac1282c31> in <module>()
----> 1 intidx % 2
TypeError: unsupported operand type(s) for %: 'Int64Index' and 'int'
```
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} | 6 | 2015-01-14T03:43:56Z | 2015-01-18T22:42:19Z | 2015-01-16T12:48:10Z | CONTRIBUTOR | null | Fix encoding so that StataReader can correctly read URLs
closes #9231
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| null | 14 | 2015-01-14T06:37:43Z | 2021-05-18T13:00:45Z | 2015-01-14T14:06:17Z | CONTRIBUTOR | null | ```
In [1]: import io
In [2]: xx = io.BytesIO()
In [4]: from pandas import DataFrame
In [5]: from numpy.random import randn
In [6]: df = DataFrame(randn(1000000,2),columns=list('AB'))
----> 1 df.to_hdf(xx,'xx')
/root/pyenvs/aft_env/lib/python3.4/site-packages/pandas/core/generic.py in to_hdf(self, path_or_buf, key, **kwargs)
900
901 from pandas.io import pytables
--> 902 return pytables.to_hdf(path_or_buf, key, self, **kwargs)
903
904 def to_msgpack(self, path_or_buf=None, **kwargs):
/root/pyenvs/aft_env/lib/python3.4/site-packages/pandas/io/pytables.py in to_hdf(path_or_buf, key, value, mode, complevel, complib, append, **kwargs)
267 f(store)
268 else:
--> 269 f(path_or_buf)
270
271
/root/pyenvs/aft_env/lib/python3.4/site-packages/pandas/io/pytables.py in <lambda>(store)
260 f = lambda store: store.append(key, value, **kwargs)
261 else:
--> 262 f = lambda store: store.put(key, value, **kwargs)
263
264 if isinstance(path_or_buf, string_types):
AttributeError: '_io.BytesIO' object has no attribute 'put'
```
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} | 1 | 2015-01-14T09:14:13Z | 2015-01-14T14:39:57Z | 2015-01-14T14:39:57Z | MEMBER | null | ```
In [70]: tds = pd.Series(pd.timedelta_range('1 days', periods=5, freq='2 h'), index=list('ABCDE'))
In [71]: tds
Out[71]:
A 1 days 00:00:00
B 1 days 02:00:00
C 1 days 04:00:00
D 1 days 06:00:00
E 1 days 08:00:00
dtype: timedelta64[ns]
In [72]: tds.dt.hours
Out[72]:
A 0
B 2
C 4
D 6
E 8
dtype: int64
In [73]: tds.dt.components.hours
Out[73]:
0 0
1 2
2 4
3 6
4 8
Name: hours, dtype: int64
```
So the direct attributes via `.dt` like `.dt.hours` above does preserve the index, but the `components` not.
@jreback I don't know if this was done on purpose or is an oversight, I can't remember, and I looked briefly to the long discussion in #8184, but didn't find something directly.
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} | 1 | 2015-01-14T13:55:39Z | 2015-01-14T14:39:57Z | 2015-01-14T14:39:57Z | CONTRIBUTOR | null | closes #9247
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} | 6 | 2015-01-14T14:25:22Z | 2016-04-09T14:34:51Z | 2016-04-09T14:34:51Z | CONTRIBUTOR | null | which fills up the /tmp on a test box:
```
/tmp# ls -l *tmp_to_csv* | nl | tail
56 -rw------- 1 buildslave buildslave 1509 Jan 13 08:22 tmpuI3G3o__tmp_to_csv_deprecated_options__
57 -rw------- 1 buildslave buildslave 1514 Jan 13 06:04 tmpVmc8Qa__tmp_to_csv_deprecated_options__
58 -rw------- 1 buildslave buildslave 1513 Jan 6 00:19 tmpwHmsVO__tmp_to_csv_deprecated_options__
59 -rw------- 1 buildslave buildslave 1511 Jan 10 19:19 tmpwmWIUl__tmp_to_csv_deprecated_options__
60 -rw------- 1 buildslave buildslave 1513 Dec 28 19:57 tmpx480b9bk__tmp_to_csv_deprecated_options__
61 -rw------- 1 buildslave buildslave 1523 Jan 4 23:21 tmpYpSmZM__tmp_to_csv_deprecated_options__
62 -rw------- 1 buildslave buildslave 1520 Dec 28 21:47 tmpz55nmj_u__tmp_to_csv_deprecated_options__
63 -rw------- 1 buildslave buildslave 1526 Jan 10 12:56 tmpzczy25oz__tmp_to_csv_deprecated_options__
64 -rw------- 1 buildslave buildslave 1513 Dec 28 20:03 tmpZGClRm__tmp_to_csv_deprecated_options__
65 -rw------- 1 buildslave buildslave 1511 Jan 12 19:56 tmpzktpvkfo__tmp_to_csv_deprecated_options__
```
which seems to come from one of the test_frame tests which is not among failing tests (http://nipy.bic.berkeley.edu/builders/pandas-py2.x-sid-sparc/builds/1141/steps/shell_5/logs/stdio) so should be cleaned up:
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} | 6 | 2015-01-14T15:56:18Z | 2015-01-16T12:51:42Z | 2015-01-16T12:51:42Z | NONE | null | ```
In [1]:
import pandas as pd
import numpy as np
print pd.__version__
a = np.linspace(0, 10, 11)
idx = [('test1', i) for i in range(5)]
idx.extend([('test2', i) for i in range(6)])
idx = pd.MultiIndex.from_tuples(idx)
0.15.2-80-ge69deae
In [2]:
a = pd.Series(a, idx)
a
Out[2]:
test1 0 0
1 1
2 2
3 3
4 4
test2 0 5
1 6
2 7
3 8
4 9
5 10
dtype: float64
In [3]:
a.loc[('test1', 2)] = 13
a
Out[3]:
test1 0 0
1 1
2 13
3 3
4 4
test2 0 5
1 6
2 7
3 8
4 9
5 10
dtype: float64
In [4]:
a.loc[('test', 17)] = 13
---------------------------------------------------------------------------
IndexingError Traceback (most recent call last)
<ipython-input-4-157acf13152f> in <module>()
----> 1 a.loc[('test', 17)] = 13
/home/federico/Python_Libraries/pandas/pandas/core/indexing.pyc in __setitem__(self, key, value)
114 if len(key) > self.ndim:
115 raise IndexingError('only tuples of length <= %d supported' %
--> 116 self.ndim)
117 indexer = self._convert_tuple(key, is_setter=True)
118 else:
IndexingError: only tuples of length <= 1 supported
In [5]:
a.ix[('test', 17)] = 13
---------------------------------------------------------------------------
IndexingError Traceback (most recent call last)
<ipython-input-5-89467846f8af> in <module>()
----> 1 a.ix[('test', 17)] = 13
/home/federico/Python_Libraries/pandas/pandas/core/indexing.pyc in __setitem__(self, key, value)
114 if len(key) > self.ndim:
115 raise IndexingError('only tuples of length <= %d supported' %
--> 116 self.ndim)
117 indexer = self._convert_tuple(key, is_setter=True)
118 else:
IndexingError: only tuples of length <= 1 supported
```
Somehow - the enlarging fails. Any clue how to get it to work? I have also noticed that the 'fast' indexing methods, iat and at fail with DataFrames that are multi indexed.
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} | 1 | 2015-01-14T16:09:50Z | 2021-04-12T04:54:39Z | null | NONE | null | Consider the following:
```
ix = pd.MultiIndex.from_product([range(3), list('abc')])
df = pd.DataFrame(0, index = ix, columns = ['A','B'])
df.loc[[1, 1, 2], 'A']
Out[1896]:
1 a 0
b 0
c 0
2 a 0
b 0
c 0
```
Note that I've passed the index 1 twice (pointless I know). Now if I try to use the same indexing to assign a values or values I get an error:
```
df.loc[[1, 1, 2], 'A'] = 4
NotImplementedError: Index._join_level on non-unique index is not implemented
```
This seems unexpected as there is no problem returning a view but the assignation doesn't work.
I understand it's unusual and bad usage to pass the same index more than once here but in my original use case the indices were generated by some code which didn't ensure to remove duplicates, leading to this esoteric error message.
Is it intended that Pandas will deal with duplicate index entries in this scenario or should the user be more careful?
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} | 2 | 2015-01-14T16:40:27Z | 2015-09-20T21:16:30Z | 2015-09-20T21:16:30Z | CONTRIBUTOR | null | My understanding is that SparsePanel is supposed to have the same API as Panel, so I expected `sparse_pan[:,:,1]` to work:
``` python
In [11]: data = np.zeros((3,3,3))
In [12]: data[:] = np.nan
In [13]: data[1,1,1] = 1
In [14]: pan = pd.Panel(data)
In [15]: pan[:,:,1]
Out[15]:
0 1 2
0 NaN NaN NaN
1 NaN 1 NaN
2 NaN NaN NaN
In [16]: sparse_pan = pan.to_sparse()
In [17]: sparse_pan[:,:,1]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-17-e57ddf7a7092> in <module>()
----> 1 sparse_pan[:,:,1]
/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/panel.py in __getitem__(self, key)
256 if lib.isscalar(key):
257 return super(Panel, self).__getitem__(key)
--> 258 return self.ix[key]
259
260 def _getitem_multilevel(self, key):
/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/indexing.py in __getitem__(self, key)
68 pass
69
---> 70 return self._getitem_tuple(key)
71 else:
72 return self._getitem_axis(key, axis=0)
/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/indexing.py in _getitem_tuple(self, tup)
697 def _getitem_tuple(self, tup):
698 try:
--> 699 return self._getitem_lowerdim(tup)
700 except IndexingError:
701 pass
/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/indexing.py in _getitem_lowerdim(self, tup)
822 for i, key in enumerate(tup):
823 if _is_label_like(key) or isinstance(key, tuple):
--> 824 section = self._getitem_axis(key, axis=i)
825
826 # we have yielded a scalar ?
/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/indexing.py in _getitem_axis(self, key, axis)
926 return self._get_loc(key, axis=axis)
927
--> 928 return self._get_label(key, axis=axis)
929
930 def _getitem_iterable(self, key, axis=0):
/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/indexing.py in _get_label(self, label, axis)
86 raise IndexingError('no slices here, handle elsewhere')
87
---> 88 return self.obj._xs(label, axis=axis)
89
90 def _get_loc(self, key, axis=0):
/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/panel.py in xs(self, key, axis, copy)
787 self._consolidate_inplace()
788 axis_number = self._get_axis_number(axis)
--> 789 new_data = self._data.xs(key, axis=axis_number, copy=False)
790 result = self._construct_return_type(new_data)
791 copy = new_data.is_mixed_type
/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/generic.py in __getattr__(self, name)
1938
1939 if name in self._internal_names_set:
-> 1940 return object.__getattribute__(self, name)
1941 elif name in self._metadata:
1942 return object.__getattribute__(self, name)
AttributeError: 'SparsePanel' object has no attribute '_data'
```
No surprise: same behavior with ix and iloc.
Accessing via `minor_xs` does work
```
In [23]: sparse_pan.minor_xs(1)
Out[23]:
0 1 2
0 NaN NaN NaN
1 NaN 1 NaN
2 NaN NaN NaN
```
``` python
In [3]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.1.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.17.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2
nose: 1.3.4
Cython: 0.20.2
numpy: 1.9.1
scipy: 0.14.1
```
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| null | 2 | 2015-01-14T16:49:12Z | 2021-04-12T04:58:34Z | 2021-04-12T04:58:34Z | NONE | null | Trying to plot the NaT (not a time) value of pandas gives an error, where plotting NaN does not.
Expected (and hereby requested) is that NaT is treated just like NaN - i.e., ignore it.
``` python
from pylab import *
import pandas as pd
plot(pd.NaT,5)
```
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-60addc339c2f> in <module>()
----> 1 plot(pd.NaT,5)
C:\Anaconda\lib\site-packages\matplotlib\pyplot.pyc in plot(*args, **kwargs)
3091 ax.hold(hold)
3092 try:
-> 3093 ret = ax.plot(*args, **kwargs)
3094 draw_if_interactive()
3095 finally:
C:\Anaconda\lib\site-packages\matplotlib\axes\_axes.pyc in plot(self, *args, **kwargs)
1372
1373 for line in self._get_lines(*args, **kwargs):
-> 1374 self.add_line(line)
1375 lines.append(line)
1376
C:\Anaconda\lib\site-packages\matplotlib\axes\_base.pyc in add_line(self, line)
1484 line.set_clip_path(self.patch)
1485
-> 1486 self._update_line_limits(line)
1487 if not line.get_label():
1488 line.set_label('_line%d' % len(self.lines))
C:\Anaconda\lib\site-packages\matplotlib\axes\_base.pyc in _update_line_limits(self, line)
1495 Figures out the data limit of the given line, updating self.dataLim.
1496 """
-> 1497 path = line.get_path()
1498 if path.vertices.size == 0:
1499 return
C:\Anaconda\lib\site-packages\matplotlib\lines.pyc in get_path(self)
863 """
864 if self._invalidy or self._invalidx:
--> 865 self.recache()
866 return self._path
867
C:\Anaconda\lib\site-packages\matplotlib\lines.pyc in recache(self, always)
564 x = ma.asarray(xconv, np.float_)
565 else:
--> 566 x = np.asarray(xconv, np.float_)
567 x = x.ravel()
568 else:
C:\Anaconda\lib\site-packages\numpy\core\numeric.pyc in asarray(a, dtype, order)
460
461 """
--> 462 return array(a, dtype, copy=False, order=order)
463
464 def asanyarray(a, dtype=None, order=None):
TypeError: float() argument must be a string or a number
```
Calling plot with a np.nan results in the point being silently ignored, which I believe should also happen for pd.NaT.
``` python
plot(np.nan,5)
```
This seems related to #8914, but this example gives an error, where the example of #8914 only results in wrong axes.
This issue was posted in the matplotlib issue tracker as matplotlib/matplotlib#4000, but they believe it can only be fixed in pandas.
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| null | 1 | 2015-01-14T19:25:36Z | 2015-01-14T20:00:36Z | 2015-01-14T20:00:36Z | NONE | null | So when I use `groupby` and then set it as a column in my original array, everything looks good if I use bracket indexing:
```
In [11]:otms['LastOrd'] = otms.EVENTTIME.where(otms.EVENT=='Submitted')
otms.LastOrd = otms.groupby(['OPTION','EXCHANGE','BUYSELL', 'TRIGGER',]).LastOrd.shift(1)
otms.LastOrd = otms.groupby(['OPTION','EXCHANGE','BUYSELL', 'TRIGGER',]).LastOrd.ffill()
In [12]: df['LastOrd'].head(7)
Out[12]: 0 NaT
1 09:44:17.918848
2 09:44:17.918848
3 09:44:17.918848
4 NaT
5 NaT
6 09:44:18.049305
Name: LastOrd, dtype: timedelta64[ns]
```
That is a `Int64Index`, like the parent DF. But when I try to use dot indexing, it gives me a `MultiIndex` series:
```
In [13]: df.LastOrd.head(7)
Out[13]: 0 1022 NaT
1024 10:05:08.418368
1026 10:05:08.418368
1 7406 NaT
7407 14:01:59.547820
7408 14:01:59.547820
2 1021 NaT
dtype: timedelta64[ns]
```
This is not expected behavior, correct?
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} | 7 | 2015-01-15T00:47:10Z | 2015-01-15T04:43:16Z | 2015-01-15T04:43:11Z | MEMBER | null | This PR adds a `Timestamp.to_datetime64()` method to complement the `Timedelta.to_timedelta64()` method I added in #8884. It is a continuation of the aborted #8916.
Arguably, there should also be the alias `Timestamp.values` to complement the series property but I haven't added that yet.
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} | 3 | 2015-01-15T01:16:58Z | 2015-01-17T11:33:53Z | 2015-01-16T12:51:42Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/9250
on master:
```
>>> df
3rd
1st 2nd
a b 0
b d 1
>>> df.loc[('b', 'x'), '3rd'] = 2 # this works!
>>> df
3rd
1st 2nd
a b 0
b d 1
x 2
>>> df.loc[('b', 'a'), '3rd'] = -1 # fails! sets everything to -1
>>> df
3rd
1st 2nd
a b -1
b d -1
x -1
>>> df.loc[('b', 'b'), '3rd'] = 3 # erros!
NotImplementedError: Index._join_level on non-unique index is not implemented
```
on branch:
```
>>> df
3rd
1st 2nd
a b 0
b d 1
>>> df.loc[('b', 'x'), '3rd'] = 2
>>> df.loc[('b', 'a'), '3rd'] = -1
>>> df.loc[('b', 'b'), '3rd'] = 3
>>> df
3rd
1st 2nd
a b 0
b d 1
x 2
a -1
b 3
```
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} | 5 | 2015-01-15T02:34:37Z | 2015-01-16T17:01:13Z | 2015-01-16T16:54:46Z | CONTRIBUTOR | null | closes #9185
closes #9139
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} | 40 | 2015-01-15T05:53:32Z | 2015-04-13T22:55:42Z | 2015-02-23T18:30:41Z | MEMBER | null | Fixes #8845
Currently only an index method; I'm open to ideas for how to expose it more
directly -- maybe `df.loc_nearest`?.
In particular, I think this is usually a more useful way to do indexing for
floats than pandas's reindex based `.loc`, because floats are inherently
imprecise.
CC @jreback @jorisvandenbossche @immerrr @hugadams
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} | 3 | 2015-01-15T09:40:59Z | 2021-04-12T04:59:23Z | null | NONE | null | Split off from: #9250
```
In [6]:
# Dataframe now:
cols = [('a_col', chr(i+65)) for i in range(5)]
cols.extend([('b_col', chr(i+65)) for i in range(5, 10)])
idx = [('a_row', chr(i+65)) for i in range(5)]
idx.extend([('b_row', chr(i+65)) for i in range(5, 10)])
df = pd.DataFrame(np.linspace(1, 100, 100).reshape(10, 10),
index = pd.MultiIndex.from_tuples(idx),
columns = pd.MultiIndex.from_tuples(cols))
In [7]:
df
Out[7]:
a_col b_col
A B C D E F G H I J
a_row A 1 2 3 4 5 6 7 8 9 10
B 11 12 13 14 15 16 17 18 19 20
C 21 22 23 24 25 26 27 28 29 30
D 31 32 33 34 35 36 37 38 39 40
E 41 42 43 44 45 46 47 48 49 50
b_row F 51 52 53 54 55 56 57 58 59 60
G 61 62 63 64 65 66 67 68 69 70
H 71 72 73 74 75 76 77 78 79 80
I 81 82 83 84 85 86 87 88 89 90
J 91 92 93 94 95 96 97 98 99 100
In [8]:
df.loc[('a_row', 'A')] # Works
df.loc[:, ('a_col', 'A')] # Works
df.ix[('a_row', 'B')] # Works
Out[8]:
a_col A 11
B 12
C 13
D 14
E 15
b_col F 16
G 17
H 18
I 19
J 20
Name: (a_row, B), dtype: float64
In [9]:
df.at[('a_row', 'A')] # fails
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-9-6b9462dd2fc9> in <module>()
----> 1 df.at[('a_row', 'A')] # fails
/home/federico/Python_Libraries/pandas/pandas/core/indexing.pyc in __getitem__(self, key)
1505
1506 key = self._convert_key(key)
-> 1507 return self.obj.get_value(*key, takeable=self._takeable)
1508
1509 def __setitem__(self, key, value):
/home/federico/Python_Libraries/pandas/pandas/core/frame.pyc in get_value(self, index, col, takeable)
1640 return _maybe_box_datetimelike(series.values[index])
1641
-> 1642 series = self._get_item_cache(col)
1643 engine = self.index._engine
1644 return engine.get_value(series.get_values(), index)
/home/federico/Python_Libraries/pandas/pandas/core/generic.pyc in _get_item_cache(self, item)
1066 res = cache.get(item)
1067 if res is None:
-> 1068 values = self._data.get(item)
1069 res = self._box_item_values(item, values)
1070 cache[item] = res
/home/federico/Python_Libraries/pandas/pandas/core/internals.pyc in get(self, item, fastpath)
2847
2848 if not isnull(item):
-> 2849 loc = self.items.get_loc(item)
2850 else:
2851 indexer = np.arange(len(self.items))[isnull(self.items)]
/home/federico/Python_Libraries/pandas/pandas/core/index.pyc in get_loc(self, key)
4123
4124 if not isinstance(key, tuple):
-> 4125 loc = self._get_level_indexer(key, level=0)
4126 return _maybe_to_slice(loc)
4127
/home/federico/Python_Libraries/pandas/pandas/core/index.pyc in _get_level_indexer(self, key, level)
4359 else:
4360
-> 4361 loc = level_index.get_loc(key)
4362 if level > 0 or self.lexsort_depth == 0:
4363 return np.array(labels == loc,dtype=bool)
/home/federico/Python_Libraries/pandas/pandas/core/index.pyc in get_loc(self, key)
1405 loc : int if unique index, possibly slice or mask if not
1406 """
-> 1407 return self._engine.get_loc(_values_from_object(key))
1408
1409 def get_value(self, series, key):
/home/federico/Python_Libraries/pandas/pandas/index.so in pandas.index.IndexEngine.get_loc (pandas/index.c:3812)()
/home/federico/Python_Libraries/pandas/pandas/index.so in pandas.index.IndexEngine.get_loc (pandas/index.c:3692)()
/home/federico/Python_Libraries/pandas/pandas/hashtable.so in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12318)()
/home/federico/Python_Libraries/pandas/pandas/hashtable.so in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12269)()
KeyError: 'A'
```
It also fails on Series with multi-indexing.
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| null | 4 | 2015-01-15T09:42:01Z | 2015-06-24T21:06:19Z | 2015-01-16T12:38:11Z | NONE | null | If think it was possible to construct a DataFrame from a dict, which gives an error with v0.15
``` python
In [4]: td = {'one': 1, 'two': 2}
In [5]: pd.DataFrame(td)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-71317f07d925> in <module>()
----> 1 pd.DataFrame(td)
.../python2.7/site-packages/pandas/core/frame.pyc in __init__(self, data, index, columns, dtype, copy)
206 dtype=dtype, copy=copy)
207 elif isinstance(data, dict):
--> 208 mgr = self._init_dict(data, index, columns, dtype=dtype)
209 elif isinstance(data, ma.MaskedArray):
210 import numpy.ma.mrecords as mrecords
.../lib/python2.7/site-packages/pandas/core/frame.pyc in _init_dict(self, data, index, columns, dtype)
334
335 return _arrays_to_mgr(arrays, data_names, index, columns,
--> 336 dtype=dtype)
337
338 def _init_ndarray(self, values, index, columns, dtype=None,
.../python2.7/site-packages/pandas/core/frame.pyc in _arrays_to_mgr(arrays, arr_names, index, columns, dtype)
4615 # figure out the index, if necessary
4616 if index is None:
-> 4617 index = extract_index(arrays)
4618 else:
4619 index = _ensure_index(index)
.../python2.7/site-packages/pandas/core/frame.pyc in extract_index(data)
4654
4655 if not indexes and not raw_lengths:
-> 4656 raise ValueError('If using all scalar values, you must pass'
4657 ' an index')
4658
ValueError: If using all scalar values, you must pass an index
```
The same is true if I use `DataFrame.from_dict` to construct the DataFrame.
Not sure if this was changed or if it is a bug.
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| null | 1 | 2015-01-15T09:58:56Z | 2020-05-16T03:38:40Z | null | MEMBER | null | From: http://stackoverflow.com/questions/27908071/pandas-interprets-timestamp-without-timezones-columns-as-different-types/27960779#27960779
Using:
```
df = pd.DataFrame({'col1':[0,1], 'col2':pd.date_range('2012-01-01', periods=2, freq='1h')})
df.to_sql('test_invalid_date', engine_postgres, if_exists='replace', index=False)
engine_postgres.execute("INSERT INTO test_invalid_date VALUES (2, '3012-01-01 00:00:00');")
```
it gives a different handling of the invalid date depending on using `read_sql_table` or `read_sql_query`:
```
In [34]: df1 = pd.read_sql_table('test_invalid_date', engine_postgres)
In [35]: df1
Out[35]:
col1 col2
0 0 2012-01-01 00:00:00
1 1 2012-01-01 01:00:00
2 2 NaT
In [36]: df1.dtypes
Out[36]:
col1 int64
col2 datetime64[ns]
dtype: object
In [37]: df2 = pd.read_sql_query('SELECT * FROM test_invalid_date', engine_postgres)
In [38]: df2
Out[38]:
col1 col2
0 0 2012-01-01 00:00:00
1 1 2012-01-01 01:00:00
2 2 3012-01-01 00:00:00
In [39]: df2.dtypes
Out[39]:
col1 int64
col2 object
dtype: object
In [40]: df2.col2.values
Out[40]:
array([datetime.datetime(2012, 1, 1, 0, 0),
datetime.datetime(2012, 1, 1, 1, 0),
datetime.datetime(3012, 1, 1, 0, 0)], dtype=object)
```
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| null | 3 | 2015-01-15T12:11:24Z | 2015-01-15T13:42:22Z | 2015-01-15T13:38:40Z | NONE | null | An error message about 'incorrect table name' is given by the input below, though the table name is correct. I get the impression that the real 'error' is the length of the sql statement, perhaps in combination with the underscores. The queries sql and sql5 below give an error message about incorrect tables, the other statements do not. All queries give the desired result when directly passed to the database.
``` python
import pandas as pd
from sqlalchemy import create_engine
db_conn = create_engine('mysql://user:pass@host/scheme')
sql = 'SELECT starttime_down, endtime_up_ms, endtime_down FROM recon_netflow042'
sql1 = 'SELECT endtime_up_ms, endtime_down FROM recon_netflow042'
sql2 = 'SELECT starttime_down, endtime_down FROM recon_netflow042'
sql3 = 'SELECT starttime_down, endtime_up_ms FROM recon_netflow042'
sql4 = 'SELECT endtime_up_ms, sip, dip, id, dp, sp FROM recon_netflow042'
sql5 = 'SELECT endtime_up_ms, endtime_down, sip, dip, id, dp, sp FROM recon_netflow042'
df = pd.read_sql(sql, db_conn)
df = pd.read_sql(sql1, db_conn)
df = pd.read_sql(sql2, db_conn)
df = pd.read_sql(sql3, db_conn)
df = pd.read_sql(sql4, db_conn)
df = pd.read_sql(sql5, db_conn)
```
Error message for sql; df=pd.read_sql(sql,cb_conn):
```
---------------------------------------------------------------------------
ProgrammingError Traceback (most recent call last)
<ipython-input-6-152f1c22f712> in <module>()
----> 1 df = pd.read_sql(sql, db_conn)
C:\Anaconda\lib\site-packages\pandas\io\sql.pyc in read_sql(sql, con, index_col, coerce_float, params, parse_dates, columns)
421 coerce_float=coerce_float, parse_dates=parse_dates)
422
--> 423 if pandas_sql.has_table(sql):
424 pandas_sql.meta.reflect(only=[sql])
425 return pandas_sql.read_table(
C:\Anaconda\lib\site-packages\pandas\io\sql.pyc in has_table(self, name)
847
848 def has_table(self, name):
--> 849 return self.engine.has_table(name)
850
851 def get_table(self, table_name):
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in has_table(self, table_name, schema)
1827
1828 """
-> 1829 return self.run_callable(self.dialect.has_table, table_name, schema)
1830
1831 def raw_connection(self):
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in run_callable(self, callable_, *args, **kwargs)
1731 """
1732 with self.contextual_connect() as conn:
-> 1733 return conn.run_callable(callable_, *args, **kwargs)
1734
1735 def execute(self, statement, *multiparams, **params):
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in run_callable(self, callable_, *args, **kwargs)
1240
1241 """
-> 1242 return callable_(self, *args, **kwargs)
1243
1244 def _run_visitor(self, visitorcallable, element, **kwargs):
C:\Anaconda\lib\site-packages\sqlalchemy\dialects\mysql\base.pyc in has_table(self, connection, table_name, schema)
2346 try:
2347 try:
-> 2348 rs = connection.execute(st)
2349 have = rs.fetchone() is not None
2350 rs.close()
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in execute(self, object, *multiparams, **params)
719 """
720 if isinstance(object, util.string_types[0]):
--> 721 return self._execute_text(object, multiparams, params)
722 try:
723 meth = object._execute_on_connection
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in _execute_text(self, statement, multiparams, params)
868 statement,
869 parameters,
--> 870 statement, parameters
871 )
872 if self._has_events or self.engine._has_events:
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in _execute_context(self, dialect, constructor, statement, parameters, *args)
956 parameters,
957 cursor,
--> 958 context)
959
960 if self._has_events or self.engine._has_events:
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in _handle_dbapi_exception(self, e, statement, parameters, cursor, context)
1158 util.raise_from_cause(
1159 sqlalchemy_exception,
-> 1160 exc_info
1161 )
1162 else:
C:\Anaconda\lib\site-packages\sqlalchemy\util\compat.pyc in raise_from_cause(exception, exc_info)
197 exc_info = sys.exc_info()
198 exc_type, exc_value, exc_tb = exc_info
--> 199 reraise(type(exception), exception, tb=exc_tb)
200
201 if py3k:
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in _execute_context(self, dialect, constructor, statement, parameters, *args)
949 statement,
950 parameters,
--> 951 context)
952 except Exception as e:
953 self._handle_dbapi_exception(
C:\Anaconda\lib\site-packages\sqlalchemy\engine\default.pyc in do_execute(self, cursor, statement, parameters, context)
434
435 def do_execute(self, cursor, statement, parameters, context=None):
--> 436 cursor.execute(statement, parameters)
437
438 def do_execute_no_params(self, cursor, statement, context=None):
C:\Anaconda\lib\site-packages\MySQLdb\cursors.pyc in execute(self, query, args)
203 del tb
204 self.messages.append((exc, value))
--> 205 self.errorhandler(self, exc, value)
206 self._executed = query
207 if not self._defer_warnings: self._warning_check()
C:\Anaconda\lib\site-packages\MySQLdb\connections.pyc in defaulterrorhandler(***failed resolving arguments***)
34 del cursor
35 del connection
---> 36 raise errorclass, errorvalue
37
38 re_numeric_part = re.compile(r"^(\d+)")
ProgrammingError: (ProgrammingError) (1103, "Incorrect table name 'SELECT starttime_down, endtime_up_ms, endtime_down FROM recon_netflow042'") 'DESCRIBE `SELECT starttime_down, endtime_up_ms, endtime_down FROM recon_netflow042`' ()
```
Error message for sql5; df=pd.read_sql(sql5,cb_conn):
```
---------------------------------------------------------------------------
ProgrammingError Traceback (most recent call last)
<ipython-input-11-039efdf529da> in <module>()
----> 1 df = pd.read_sql(sql5, db_conn)
C:\Anaconda\lib\site-packages\pandas\io\sql.pyc in read_sql(sql, con, index_col, coerce_float, params, parse_dates, columns)
421 coerce_float=coerce_float, parse_dates=parse_dates)
422
--> 423 if pandas_sql.has_table(sql):
424 pandas_sql.meta.reflect(only=[sql])
425 return pandas_sql.read_table(
C:\Anaconda\lib\site-packages\pandas\io\sql.pyc in has_table(self, name)
847
848 def has_table(self, name):
--> 849 return self.engine.has_table(name)
850
851 def get_table(self, table_name):
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in has_table(self, table_name, schema)
1827
1828 """
-> 1829 return self.run_callable(self.dialect.has_table, table_name, schema)
1830
1831 def raw_connection(self):
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in run_callable(self, callable_, *args, **kwargs)
1731 """
1732 with self.contextual_connect() as conn:
-> 1733 return conn.run_callable(callable_, *args, **kwargs)
1734
1735 def execute(self, statement, *multiparams, **params):
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in run_callable(self, callable_, *args, **kwargs)
1240
1241 """
-> 1242 return callable_(self, *args, **kwargs)
1243
1244 def _run_visitor(self, visitorcallable, element, **kwargs):
C:\Anaconda\lib\site-packages\sqlalchemy\dialects\mysql\base.pyc in has_table(self, connection, table_name, schema)
2346 try:
2347 try:
-> 2348 rs = connection.execute(st)
2349 have = rs.fetchone() is not None
2350 rs.close()
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in execute(self, object, *multiparams, **params)
719 """
720 if isinstance(object, util.string_types[0]):
--> 721 return self._execute_text(object, multiparams, params)
722 try:
723 meth = object._execute_on_connection
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in _execute_text(self, statement, multiparams, params)
868 statement,
869 parameters,
--> 870 statement, parameters
871 )
872 if self._has_events or self.engine._has_events:
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in _execute_context(self, dialect, constructor, statement, parameters, *args)
956 parameters,
957 cursor,
--> 958 context)
959
960 if self._has_events or self.engine._has_events:
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in _handle_dbapi_exception(self, e, statement, parameters, cursor, context)
1158 util.raise_from_cause(
1159 sqlalchemy_exception,
-> 1160 exc_info
1161 )
1162 else:
C:\Anaconda\lib\site-packages\sqlalchemy\util\compat.pyc in raise_from_cause(exception, exc_info)
197 exc_info = sys.exc_info()
198 exc_type, exc_value, exc_tb = exc_info
--> 199 reraise(type(exception), exception, tb=exc_tb)
200
201 if py3k:
C:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in _execute_context(self, dialect, constructor, statement, parameters, *args)
949 statement,
950 parameters,
--> 951 context)
952 except Exception as e:
953 self._handle_dbapi_exception(
C:\Anaconda\lib\site-packages\sqlalchemy\engine\default.pyc in do_execute(self, cursor, statement, parameters, context)
434
435 def do_execute(self, cursor, statement, parameters, context=None):
--> 436 cursor.execute(statement, parameters)
437
438 def do_execute_no_params(self, cursor, statement, context=None):
C:\Anaconda\lib\site-packages\MySQLdb\cursors.pyc in execute(self, query, args)
203 del tb
204 self.messages.append((exc, value))
--> 205 self.errorhandler(self, exc, value)
206 self._executed = query
207 if not self._defer_warnings: self._warning_check()
C:\Anaconda\lib\site-packages\MySQLdb\connections.pyc in defaulterrorhandler(***failed resolving arguments***)
34 del cursor
35 del connection
---> 36 raise errorclass, errorvalue
37
38 re_numeric_part = re.compile(r"^(\d+)")
ProgrammingError: (ProgrammingError) (1103, "Incorrect table name 'SELECT endtime_up_ms, endtime_down, sip, dip, id, dp, sp FROM recon_netflow042'") 'DESCRIBE `SELECT endtime_up_ms, endtime_down, sip, dip, id, dp, sp FROM recon_netflow042`' ()
```
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} | 6 | 2015-01-15T16:33:41Z | 2015-12-05T15:33:54Z | 2015-12-05T15:33:54Z | CONTRIBUTOR | null | Recent versions of pyperclip (`pandas.util.clipboard`) use CF_UNICODETEXT getting data from and putting data to the WIndows Clipboard.
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} | 7 | 2015-01-15T20:14:19Z | 2015-01-18T22:42:19Z | 2015-01-16T15:10:44Z | CONTRIBUTOR | null | Added endianess flat to data type to allow data to be read cross platforms
closes #8688
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} | 3 | 2015-01-16T00:11:05Z | 2021-11-02T02:40:59Z | null | CONTRIBUTOR | null | Recognizing that this is probably not very high on the priority list, I'll document that a user tried to use clip on sparse structures and wished that it worked.
Some ideas for what to do in order of best to worst solution:
1. Make it work in a memory and cpu efficient way.
2. Raise a NotImplementedError (possibly suggesting converting to a dense structure).
3. Make clip first convert to a dense structure, apply the limits, and return a sparse structure.
I am willing to submit PRs for 2 and 3, although I think that 3 is a bad idea. I need to understand the source better before I can consider volunteering to help with 1.
SparseSeries, SparseDataFrame, and SparsePanel all raise different exceptions.
``` python
pd.SparseSeries([0,1,np.nan,3,4]).clip(1,2)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-159-4c91e2de7ae6> in <module>()
----> 1 pd.SparseSeries([0,1,np.nan,3,4]).clip(1,2)
/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/generic.py in clip(self, lower, upper, out)
2805 result = self
2806 if lower is not None:
-> 2807 result = result.clip_lower(lower)
2808 if upper is not None:
2809 result = result.clip_upper(upper)
/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/generic.py in clip_lower(self, threshold)
2843 raise ValueError("Cannot use an NA value as a clip threshold")
2844
-> 2845 return self.where((self >= threshold) | isnull(self), threshold)
2846
2847 def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True,
/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/ops.py in wrapper(self, other)
661 return self._constructor(na_op(self.values, other.values),
662 index=self.index,
--> 663 name=name).fillna(False).astype(bool)
664 elif isinstance(other, pd.DataFrame):
665 return NotImplemented
/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/ops.py in na_op(x, y)
627 def na_op(x, y):
628 try:
--> 629 result = op(x, y)
630 except TypeError:
631 if isinstance(y, list):
ValueError: operands could not be broadcast together with shapes (5,) (0,)
```
``` python
pd.SparseDataFrame([[0,1,np.nan,3,4],[0,1,np.nan,3,4]]).clip(1,2)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-160-b399e57148ac> in <module>()
----> 1 pd.SparseDataFrame([[0,1,np.nan,3,4],[0,1,np.nan,3,4]]).clip(1,2)
/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/generic.py in clip(self, lower, upper, out)
2805 result = self
2806 if lower is not None:
-> 2807 result = result.clip_lower(lower)
2808 if upper is not None:
2809 result = result.clip_upper(upper)
/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/generic.py in clip_lower(self, threshold)
2843 raise ValueError("Cannot use an NA value as a clip threshold")
2844
-> 2845 return self.where((self >= threshold) | isnull(self), threshold)
2846
2847 def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True,
/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/ops.py in f(self, other)
917 # straight boolean comparisions we want to allow all columns
918 # (regardless of dtype to pass thru) See #4537 for discussion.
--> 919 res = self._combine_const(other, func, raise_on_error=False)
920 return res.fillna(True).astype(bool)
921
TypeError: _combine_const() got an unexpected keyword argument 'raise_on_error'
```
``` python
pd.Panel(np.zeros((3,3,3))).to_sparse().clip(1,2)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-171-ec53f9e1dd24> in <module>()
----> 1 pd.Panel(np.zeros((3,3,3))).to_sparse().clip(1,2)
/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/generic.py in clip(self, lower, upper, out)
2805 result = self
2806 if lower is not None:
-> 2807 result = result.clip_lower(lower)
2808 if upper is not None:
2809 result = result.clip_upper(upper)
/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/generic.py in clip_lower(self, threshold)
2843 raise ValueError("Cannot use an NA value as a clip threshold")
2844
-> 2845 return self.where((self >= threshold) | isnull(self), threshold)
2846
2847 def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True,
/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/ops.py in f(self, other)
960 raise ValueError('Simple arithmetic with %s can only be '
961 'done with scalar values' %
--> 962 self._constructor.__name__)
963
964 return self._combine(other, op)
ValueError: Simple arithmetic with SparsePanel can only be done with scalar values
```
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} | 8 | 2015-01-16T05:33:51Z | 2015-07-21T10:52:45Z | 2015-07-21T10:52:45Z | NONE | null | while using read_fwf I had a bug that had this line from parsers.py
https://github.com/pydata/pandas/blob/072e40b0b5beab36c2adda9e0bcbb26755e1928f/pandas/io/parsers.py#L989
in which there is a np.uin8 which I'm pretty sure should be a np.uint8
apologise if this has already been submitted - I did a search on uin8 and only got the offending line in the code.
I just fixed on my arch source code to keep working on my problem (I'm an engineer not a software guy!). I will try and find time for a patch and test (I failed on my last one)
CHeers
Rick
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| null | 1 | 2015-01-16T08:38:18Z | 2015-01-16T12:31:26Z | 2015-01-16T12:31:26Z | NONE | null | Similar as https://github.com/pydata/pandas/issues/5226
Enlargment doesn't work for .ix on empty dataframe.
.loc works fine.
Affected version 0.15.2
```
df = pd.DataFrame(columns = ['a', 'b'])
df.ix[1] =[1, 1]
```
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-320-58384ba7155e> in <module>()
1 df = pd.DataFrame(columns = ['a', 'b'])
----> 2 df.ix[1] =[1, 1]
/usr/local/pr/anaconda/ipyNotebook/lib/python2.7/site-packages/pandas/core/indexing.pyc in __setitem__(self, key, value)
115 indexer = self._convert_tuple(key, is_setter=True)
116 else:
--> 117 indexer = self._convert_to_indexer(key, is_setter=True)
118
119 self._setitem_with_indexer(indexer, value)
/usr/local/pr/anaconda/ipyNotebook/lib/python2.7/site-packages/pandas/core/indexing.pyc in _convert_to_indexer(self, obj, axis, is_setter)
1010 if (obj >= self.obj.shape[axis] and
1011 not isinstance(labels, MultiIndex)):
-> 1012 raise ValueError("cannot set by positional indexing with "
1013 "enlargement")
1014
ValueError: cannot set by positional indexing with enlargement
```
Using .loc first:
```
df = pd.DataFrame(columns = ['a', 'b'])
df.loc[0] = [1, 1]
df.ix[1] =[1, 1]
```
everything is ok.
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} | 7 | 2015-01-16T10:54:43Z | 2016-07-04T14:06:05Z | 2016-07-04T14:05:55Z | NONE | null | This line: div style="max-height:1000px;max-width:1500px;overflow:auto;" is added to the output if the df is too large. This behaviour is handled in ipython anyway so should be removed or optional.
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} | 5 | 2015-01-16T12:30:52Z | 2016-09-30T20:42:23Z | 2016-09-30T20:42:14Z | CONTRIBUTOR | null | xref https://github.com/pydata/pandas/issues/8596
```
>>> df
a
0 1
>>> df.dtypes
a int64
dtype: object
>>> df.loc[1, 'a'] = 2
>>> df
a
0 1
1 2
>>> df.dtypes
a float64
dtype: object
```
in an intermediate step `nan` is inserted, and the data are casted to floats
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} | 3 | 2015-01-16T15:03:21Z | 2015-01-16T15:11:02Z | 2015-01-16T15:08:32Z | NONE | null | I found that when retrieving a DataFrame which had a MultiIndex containing a DatetimeIndex from a HDFStore, the TZ awareness was somehow lost.
Here is a demonstration:
```
import datetime as dt
import pandas as pd
start, end = dt.datetime(2015, 1, 16, 8), dt.datetime(2015, 1, 16, 17)
times = pd.date_range(start, end, freq='60min', tz='US/Eastern')
mix = pd.MultiIndex.from_arrays([2*range(5), times])
df = pd.DataFrame(1, index= mix, columns =range(3))
df
0 1 2
0 2015-01-16 08:00:00-05:00 1 1 1
1 2015-01-16 09:00:00-05:00 1 1 1
2 2015-01-16 10:00:00-05:00 1 1 1
```
So far so good. Now push and pull from HDFStore:
```
store = pd.HDFStore('test')
store['test'] = df.swaplevel(0, 1)
store['test']
0 1 2
2015-01-16 13:00:00 0 1 1 1
2015-01-16 14:00:00 1 1 1 1
2015-01-16 15:00:00 2 1 1 1
store['test'].index.get_level_values(1).tz is None
True
```
The TZ awareness is gone. A minor issue but I thought I would bring it up. Note that this happens regardless of the order of the index levels and that there is no problem in the case of a single index.and
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} | 2 | 2015-01-16T15:10:36Z | 2015-01-18T22:42:19Z | 2015-01-18T20:41:28Z | CONTRIBUTOR | null | Removes three tests that use proprietary data that is not available for testing.
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} | 2 | 2015-01-16T18:39:04Z | 2015-01-29T11:33:24Z | 2015-01-29T11:33:24Z | CONTRIBUTOR | null | Continuing on my recent sparse kick...
I appreciate the fact that `Series()`, `DataFrame()`, and `Panel()` all return empty versions of themselves. I think it's reasonable to expect that their sparse counterparts would behave the same way, but this is only the case for `SparseDataFrame()` (it's even [tested](https://github.com/pydata/pandas/blob/master/pandas/sparse/tests/test_sparse.py#L865)).
``` python
pd.SparseSeries()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-375-46c1881ac0a7> in <module>()
----> 1 pd.SparseSeries()
TypeError: __init__() missing 1 required positional argument: 'data'
```
and similar for `SparsePanel()`.
I can submit a PR to change this behavior.
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} | 4 | 2015-01-16T18:45:17Z | 2015-01-19T03:17:20Z | 2015-01-19T03:17:20Z | CONTRIBUTOR | null | xref #9226
Noticing an inconsistency. pd.Timedelta doesn't allow nanoseconds in constructor but its components list till nanoseconds. Here is what I can reproduce. (used then current master on OS X 10.10.1)
Example:
<pre>
Python 2.7.9 (v2.7.9:648dcafa7e5f, Dec 10 2014, 10:10:46)
[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from pandas.tslib import Timedelta
>>> td = Timedelta(nanoseconds=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/tslib.pyx", line 1723, in pandas.tslib.Timedelta.__new__ (pandas/tslib.c:29743)
raise ValueError("cannot construct a TimeDelta from the passed arguments, allowed keywords
are " [days, seconds, microseconds, milliseconds, minutes, hours, weeks]
>>> td=Timedelta(seconds=1)
>>> td.components._fields
('days', 'hours', 'minutes', 'seconds', 'milliseconds', 'microseconds', 'nanoseconds')
</pre>
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} | 3 | 2015-01-16T20:13:41Z | 2015-03-05T23:50:24Z | null | CONTRIBUTOR | null | Maybe this is a request for enhancement, or documentation of a bug - I don't know.
A tutorial showed that `DataFrame.boxplot()` can take a `by=` keyword to produced plot stratified by the values in one column. I naively assumed I could apply that to other plots as well. `DataFrame.plot()` accepts `by=` but seems to silently ignores it. This seems to be an [undocumented](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html#pandas.DataFrame.plot) keyword for `DataFrame.plot()`.
Is this intentional? Why not include the groupby functionality from `boxplot` into `plot` so it can do something reasonable with all the other plot types as well? If not, then why does `plot` accept the `by=` keyword?
Example code below,
```
import pandas as pd
pd.show_versions()
plt.rcParams.update(pd.tools.plotting.mpl_stylesheet)
# taken from http://pandas.pydata.org/pandas-docs/stable/visualization.html#box-plots
df = pd.DataFrame(rand(10,2), columns=['Col1', 'Col2'] )
df['X'] = pd.Series(['A','A','A','A','A','B','B','B','B','B'])
plt.figure();
print(df)
# boxplot produces two subplots segregated by the values in X
df.boxplot(by='X')
# Other kinds of plots seem to silently ignore 'by='
df.plot(kind='box', by='X')
# Other kinds of plots seem to silently ignore 'by='
df.plot(kind='scatter', x=0, y=1, by='X')
# Other kinds of plots seem to silently ignore 'by='
df.plot(kind='line', by='X')
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-32-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2
nose: 1.3.4
Cython: 0.20.1post0
numpy: 1.9.1
scipy: 0.13.3
statsmodels: 0.6.0
IPython: 2.3.1
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.0
pytz: 2014.10
bottleneck: None
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.4.2
openpyxl: 1.8.6
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.3.3
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9
apiclient: None
rpy2: 2.5.2
sqlalchemy: 0.8.4
pymysql: None
psycopg2: None
Col1 Col2 X
0 0.561433 0.329668 A
1 0.502967 0.111894 A
2 0.607194 0.565945 A
3 0.006764 0.617442 A
4 0.912123 0.790524 A
5 0.992081 0.958802 B
6 0.791964 0.285251 B
7 0.624917 0.478094 B
8 0.195675 0.382317 B
9 0.053874 0.451648 B
```
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} | 2 | 2015-01-16T20:14:17Z | 2019-09-18T14:02:17Z | 2019-09-18T14:02:17Z | CONTRIBUTOR | null | 1. Catalog and document all of the methods that don't currently work on sparse structures.
2. Raise NotImplemented exceptions for these methods.
Would be happy for suggestions for how to do this systematically.
Originally suggested by @jreback in #9265.
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} | 0 | 2015-01-16T20:38:34Z | 2015-02-16T21:53:33Z | 2015-02-16T21:53:33Z | MEMBER | null | xref #8929
xref #9193
Here are [some test results](https://travis-ci.org/shoyer/pandas/builds/47184451) on Travis-CI illustrating the issue and my [commit](https://github.com/shoyer/pandas/commit/f8eb1c687164274a6b9a9640e565de60293ad2c1)
Any ideas for the fix? This is quite likely a numpy bug, but it's one we need to work around. The simplest thing would be revert back to just calling `hash`, but I expect that would cause some other test cases to fail.
CC @aevri
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} | 3 | 2015-01-16T21:34:05Z | 2015-01-29T11:33:13Z | 2015-01-29T11:33:13Z | CONTRIBUTOR | null | Closes #9272.
Note that my solution is to provide a default `data=[]` for SparseSeries and `frames={}` for SparsePanel. This differs from the dense implementations that have `None` defaults that are then changed to the above. Doesn't seem like a big deal to me, but I can do that here if needed.
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} | 4 | 2015-01-17T02:24:35Z | 2015-01-19T14:37:26Z | 2015-01-18T13:24:08Z | MEMBER | null | Related to #8877 and 80a730c93717e7fc01ae2f880109bc752519cecf
Because `legend=True` is default, `DataFrame.plot(kind='box', subplots=True)` shows unnecessary warnings.
```
# OK, no legend / no warnings
df.plot(kind='box')
```
```
# NG: no legend and warnings
df.plot(kind='box', subplots=True)
# UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.
# warnings.warn("No labelled objects found. "
```
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| null | 1 | 2015-01-17T03:31:05Z | 2015-01-21T19:20:27Z | 2015-01-21T19:20:27Z | NONE | null | Currently, pandas makes clever use of `__getitem__` to filter/mask Series and DataFrame. At times, the current abstraction is leaky - for instance, `df[18 < df['age'] < 25]` would raise an exception, even though cascaded compare symbols in that manner are legal Python syntax.
It would be awesome to also be able to pass in a function to the `__getitem__` function that would act as a filter. This would allow users to use all of Python's language features for filtering while still maintaining the brevity intended for these statements.
For example:
``` python
df[lambda row: 18 < row['age'] < 25]
```
This would also allow lengthier filters to be defined outside of the getter square brackets:
``` python
def age_filter(row):
return 18 < row['age'] < 25 if row['country'] == 'USA' else row['age'] > 13
df[age_filter]
```
Functions as viable filters would also allow for chaining of filter functions without creating multiple intermediary DataFrames or Series, making optimization of cascaded filters much easier.
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} | 1 | 2015-01-17T04:41:29Z | 2015-01-18T20:36:40Z | 2015-01-18T20:36:40Z | MEMBER | null | Replacing some numeric values using `where` affects to dtype unrelated to the condition.
```
s = pd.Series(np.random.randn(5))
u = s.where(s<0, 'X', axis=1)
u
#0 -1.01421184508
#1 X
#2 X
#3 -0.228117839063
#4 -0.393903873887
# dtype: object
```
The result looks OK, but internally coerced to all `str`.
```
u.apply(type)
#0 <type 'str'>
#1 <type 'str'>
#2 <type 'str'>
#3 <type 'str'>
#4 <type 'str'>
# dtype: object
```
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| null | 3 | 2015-01-17T06:52:31Z | 2015-01-19T12:14:11Z | 2015-01-19T12:14:11Z | CONTRIBUTOR | null | Not sure if this is reproducible by others in their enviroment/platform but here is what I am seeing on my machine.
---
FAIL: test_now (pandas.tseries.tests.test_tslib.TestTimestamp)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/tvyomkesh/pandas/pandas/tseries/tests/test_tslib.py", line 343, in test_now
self.assertTrue((ts_from_string_tz.tz_localize(None) - ts_from_string) < delta)
AssertionError: False is not true
---
FAIL: test_today (pandas.tseries.tests.test_tslib.TestTimestamp)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/tvyomkesh/pandas/pandas/tseries/tests/test_tslib.py", line 359, in test_today
self.assertTrue((ts_from_string_tz.tz_localize(None) - ts_from_string) < delta)
AssertionError: False is not true
---
Ran 7309 tests in 500.577s
FAILED (SKIP=468, failures=2)
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} | 7 | 2015-01-17T10:07:19Z | 2015-01-29T23:28:37Z | 2015-01-29T11:10:35Z | MEMBER | null | Derived from #9111.
Add following methods to be compat with standard `str`.
- StringMethods.isalnum
- StringMethods.isalpha
- StringMethods.isdigit
- StringMethods.isspace
- StringMethods.islower
- StringMethods.isupper
- StringMethods.istitle
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} | 2 | 2015-01-17T10:35:48Z | 2015-01-19T14:31:31Z | 2015-01-18T20:36:40Z | MEMBER | null | Closes #9280.
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} | 0 | 2015-01-17T14:17:51Z | 2015-01-25T18:41:40Z | 2015-01-25T18:41:40Z | CONTRIBUTOR | null | xref #9226
<pre>
In [11]: from pandas.tseries import offsets
In [12]: t1 = offsets.Nano(100)
In [13]: t2 = offsets.Micro(1)
In [14]: t3 = t1 + t2
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-14-4ee6d99529e1> in <module>()
----> 1 t3 = t1 + t2
/Users/tvyomkesh/pandas/pandas/tseries/offsets.pyc in __add__(self, other)
2025 return type(self)(self.n + other.n)
2026 else:
-> 2027 return _delta_to_tick(self.delta + other.delta)
2028 try:
2029 return self.apply(other)
TypeError: ufunc add cannot use operands with types dtype('<m8[ns]') and dtype('O')
</pre>
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} | 10 | 2015-01-17T16:32:01Z | 2015-03-23T13:55:59Z | 2015-03-06T23:06:34Z | CONTRIBUTOR | null | Closes #4987
This is my first non-doc PR, so please bare with me. Is it worth my while to go through the tests and standarize all of the test HTML to all block-strings, or is the mix of individual lines/blocks ok?
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} | 5 | 2015-01-17T17:54:36Z | 2015-01-17T19:07:01Z | 2015-01-17T18:01:59Z | NONE | null | Pandas returns the wrong result for the operation **0/y** where **y** is a dataframe or series of non-zero values:
``` python
import pandas as pd
y=pd.Series(range(1,10))
print 0/y # wrong
```
However, this operation would work:
``` python
for i in y:print 0/i # correct
```
I'm using Pandas 0.15.2-1.
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| null | 3 | 2015-01-17T23:16:49Z | 2020-09-23T16:15:29Z | 2015-01-17T23:30:29Z | CONTRIBUTOR | null | Apologies if this feature has been suggested before. Many of the IO functions (e.g. `read_csv`) allow use to easily specify the format for each column using a dictionary. As far as I understand, this is **not**
possible with the regular dataframe construction, e.g:
``` python
df = pd.DataFrame(data=data, columns=columns, dtypes={'colname1': str, 'colname2': np.int})
```
**Even better**, it would be great if one could change the `dtypes` for the dataframe columns using a similar contruction, e.g.:
``` python
df.change_types({'colname1': str, 'colname2': np.int})
```
Is anything like this planned for already?
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| null | 1 | 2015-01-18T02:54:44Z | 2015-01-18T20:24:56Z | 2015-01-18T20:24:45Z | NONE | null | I am reading the book "Python for Data Analysis" and try some examples. But I found something strange.
On page 256, it says:
Quote:
Indexing a GroupBy object created from a DataFrame with a column name or array of column names has the effect of selecting those columns for aggregation. This means that:
```
df.groupby('key1')['data1']
df.groupby('key1')[['data2']]
```
are syntactic sugar for:
```
df['data1'].groupby(df['key1'])
df[['data2']].groupby(df['key1'])
```
I think this makes sense, but when I tried a testing code, it didn't work as expected.
```
df.groupby('key1')[['data2']]
```
Here, it still returns the whole DataFrame. It does not perform the same as
```
df[['data2']].groupby(df['key1'])
```
I used "for" loop to iterate the object, maybe this is the reason?
Testing code:
```
df = DataFrame({'key1' : ['a', 'a', 'b', 'b', 'a'],
'key2' : ['one', 'two', 'one', 'two', 'one'],
'data1' : np.random.randn(5),
'data2' : np.random.randn(5)})
df
```
```
Out[5]:
data1 data2 key1 key2
0 0.894564 1.050070 a one
1 0.688548 -0.740128 a two
2 0.196103 0.041584 b one
3 0.413136 2.180567 b two
4 -0.489973 -0.274152 a one
```
```
for key, value in df[['data2']].groupby(df['key1']):
print key
print value
output:
a
data2
0 1.050070
1 -0.740128
4 -0.274152
b
data2
2 0.041584
3 2.180567
```
```
for key, value in df.groupby('key1')[['data2']]:
print key
print value
output:
a
data1 data2 key1 key2
0 0.894564 1.050070 a one
1 0.688548 -0.740128 a two
4 -0.489973 -0.274152 a one
b
data1 data2 key1 key2
2 0.196103 0.041584 b one
3 0.413136 2.180567 b two
```
Environment:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.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: 0.19.2
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.0.0
sphinx: 1.2.2
patsy: 0.2.0
dateutil: 2.2
pytz: 2014.9
bottleneck: None
tables: 2.4.0
numexpr: 2.2.2
matplotlib: 1.4.0
openpyxl: 1.8.5
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.2.3
bs4: 4.3.2
html5lib: 0.95-dev
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.8.3
pymysql: None
psycopg2: None
```
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} | 3 | 2015-01-18T05:56:43Z | 2015-01-19T06:47:47Z | 2015-01-19T03:17:20Z | CONTRIBUTOR | null | closes #9273
This patch adds nanoseconds keyword support in Timedelta constructor.
<pre>
In [1]: from pandas import Timedelta
In [2]: td = Timedelta(nanoseconds=1)
In [3]: td1 = Timedelta(microseconds=1)
In [4]: td.components
Out[4]: Components(days=0, hours=0, minutes=0, seconds=0, milliseconds=0, microseconds=0, nanoseconds=1)
In [5]: (td + td1).components
Out[5]: Components(days=0, hours=0, minutes=0, seconds=0, milliseconds=0, microseconds=1, nanoseconds=1)
</pre>
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| null | 3 | 2015-01-18T10:37:35Z | 2015-01-20T00:25:21Z | 2015-01-20T00:25:21Z | CONTRIBUTOR | null | Sorry for this sketch of a bug report, but I am seeing these errors with Macports Python 2.7 and the current pandas 0.15.2 release:
https://travis-ci.org/MacPython/scipy-stack-osx-testing/jobs/47389975#L757
Here's an example error:
```
======================================================================
ERROR: test_url (pandas.io.tests.test_json.test_pandas.TestPandasContainer)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/util/testing.py", line 1441, in wrapper
return t(*args, **kwargs)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/io/tests/test_json/test_pandas.py", line 599, in test_url
result = read_json(url, convert_dates=True)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/io/json.py", line 174, in read_json
filepath_or_buffer, _ = get_filepath_or_buffer(path_or_buf)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/io/common.py", line 118, in get_filepath_or_buffer
req = _urlopen(str(filepath_or_buffer))
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib2.py", line 154, in urlopen
return opener.open(url, data, timeout)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib2.py", line 431, in open
response = self._open(req, data)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib2.py", line 449, in _open
'_open', req)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib2.py", line 409, in _call_chain
result = func(*args)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib2.py", line 1240, in https_open
context=self._context)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib2.py", line 1197, in do_open
raise URLError(err)
URLError: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:581)>
```
No such errors for Python 3:
https://travis-ci.org/MacPython/scipy-stack-osx-testing/jobs/47389977
Is this a bug in Macports Python or Pandas?
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} | 5 | 2015-01-18T13:34:15Z | 2015-01-31T02:57:19Z | 2015-01-25T18:41:40Z | MEMBER | null | Closes #9284.
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} | 4 | 2015-01-18T23:28:06Z | 2015-02-13T01:59:02Z | 2015-01-26T01:29:07Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/4862
closes https://github.com/pydata/pandas/issues/7401
closes https://github.com/pydata/pandas/issues/7403
closes https://github.com/pydata/pandas/issues/7405
minor code change to https://github.com/pydata/pandas/pull/9061; otherwise only tests. the code change is to avoid https://github.com/pydata/pandas/issues/9170 issue.
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} | 1 | 2015-01-19T04:45:17Z | 2015-01-19T09:33:44Z | 2015-01-19T09:33:27Z | CONTRIBUTOR | null | Added ax docstring.
Address issue generated by `scripts/find_undoc_args.py`:
[+2892 tools/plotting.py boxplot_frame_groupby()](https://github.com/pydata/pandas/blob/master/pandas/tools/plotting.py#L2892): Missing[1/9]=['ax']
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| null | 9 | 2015-01-19T09:08:03Z | 2017-04-26T15:19:31Z | 2015-01-19T12:20:08Z | NONE | null | On this csv file:
```
DecisionM,IntelligentM,freq,total
0, 5, 9, 20
0, 6, 21,33
0, 7, 35,65
0, 8, 35,83
0, 9, 14,41
0, 10, 10,26
1, 5, 11,20
1, 6, 12,33
1, 7, 30,65
1, 8, 48,83
1, 9, 27, 41,,
1, 10, 16, 26
```
`pandas.read_csv()` gives me
```
---------------------------------------------------------------------------
CParserError Traceback (most recent call last)
<ipython-input-9-28b9c031f12d> in <module>()
----> 1 pandas.read_csv("speeddating.csv")
/usr/lib/python3.4/site-packages/pandas/io/parsers.py 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, float_precision, nrows, iterator, chunksize, verbose, encoding, squeeze, mangle_dupe_cols, tupleize_cols, infer_datetime_format, skip_blank_lines)
463 skip_blank_lines=skip_blank_lines)
464
--> 465 return _read(filepath_or_buffer, kwds)
466
467 parser_f.__name__ = name
/usr/lib/python3.4/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
249 return parser
250
--> 251 return parser.read()
252
253 _parser_defaults = {
/usr/lib/python3.4/site-packages/pandas/io/parsers.py in read(self, nrows)
708 raise ValueError('skip_footer not supported for iteration')
709
--> 710 ret = self._engine.read(nrows)
711
712 if self.options.get('as_recarray'):
/usr/lib/python3.4/site-packages/pandas/io/parsers.py in read(self, nrows)
1157
1158 try:
-> 1159 data = self._reader.read(nrows)
1160 except StopIteration:
1161 if nrows is None:
/usr/lib/python3.4/site-packages/pandas/parser.cpython-34m.so in pandas.parser.TextReader.read (pandas/parser.c:7403)()
/usr/lib/python3.4/site-packages/pandas/parser.cpython-34m.so in pandas.parser.TextReader._read_low_memory (pandas/parser.c:7643)()
/usr/lib/python3.4/site-packages/pandas/parser.cpython-34m.so in pandas.parser.TextReader._read_rows (pandas/parser.c:8265)()
/usr/lib/python3.4/site-packages/pandas/parser.cpython-34m.so in pandas.parser.TextReader._tokenize_rows (pandas/parser.c:8139)()
/usr/lib/python3.4/site-packages/pandas/parser.cpython-34m.so in pandas.parser.raise_parser_error (pandas/parser.c:20776)()
CParserError: Error tokenizing data. C error: Expected 4 fields in line 12, saw 6
```
This seems odd to me. There's nothing in those fields to worry about anyway, they can just be dropped. Even if there was data there, if it doesn't have a column to go with it should be dropped too. Lots of csv files are messy like this; that's basically why they're used all the time.
I am on pandas '0.15.2' and Python3
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| null | 2 | 2015-01-19T09:12:01Z | 2015-01-19T12:22:03Z | 2015-01-19T12:21:52Z | NONE | null | This csv file loads fine:
```
DecisionM,IntelligentM,freq,total
0, 5, 9, 20
0, 6, 21,33
0, 7, 35,65
0, 8, 35,83
0, 9, 14,41
0, 10, 10,26
1, 5, 11,20
1, 6, 12,33
1, 7, 30,65
1, 8, 48,83
1, 9, 27, 41
1, 10, 16, 26
```
```
In [11]: pandas.read_csv("speeddating.csv")
Out[11]:
DecisionM IntelligentM freq total
0 0 5 9 20
1 0 6 21 33
2 0 7 35 65
3 0 8 35 83
4 0 9 14 41
5 0 10 10 26
6 1 5 11 20
7 1 6 12 33
8 1 7 30 65
9 1 8 48 83
10 1 9 27 41
11 1 10 16 26
In [12]:
```
A small tweak causes the dataset to be silently corrupted:
```
DecisionM,IntelligentM,freq,total
0, 5, 9, 20,
0, 6, 21,33
0, 7, 35,65
0, 8, 35,83
0, 9, 14,41
0, 10, 10,26
1, 5, 11,20
1, 6, 12,33
1, 7, 30,65
1, 8, 48,83
1, 9, 27, 41
1, 10, 16, 26
```
```
In [10]: pandas.read_csv("speeddating.csv")
Out[10]:
DecisionM IntelligentM freq total
0 5 9 20 NaN
0 6 21 33 NaN
0 7 35 65 NaN
0 8 35 83 NaN
0 9 14 41 NaN
0 10 10 26 NaN
1 5 11 20 NaN
1 6 12 33 NaN
1 7 30 65 NaN
1 8 48 83 NaN
1 9 27 41 NaN
1 10 16 26 NaN
```
Notice how every column is shifted over by one. I'm confused about this in light of #9294: shouldn't it just die?
Putting the extra comma on the second row causes #9294 .
I'm on Pandas '0.15.2' on Python3 on ArchLinux.
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} | 3 | 2015-01-19T09:26:05Z | 2015-01-19T19:32:00Z | 2015-01-19T12:14:11Z | CONTRIBUTOR | null | closes #9281
The last test of both test_now and test_today was passing for anyone with a timezone
of UTC-5 or greater (but failed, for example, in US/Pacific). The test was not
testing what the original author meant it to (which is that the times are very close
together) so I added abs(.) around the Timedeltas and also fixed the errant test.
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} | 9 | 2015-01-19T10:52:09Z | 2016-01-04T02:17:37Z | 2016-01-04T02:17:37Z | MEMBER | null | Building off of #9229, it's generally much better to create new dataframes rather than to modify existing ones. It's both more chainable and less prone to the bugs/errors associated with mutable state.
So it seems to me that a saner API design would be to make assignment operations with eval/query return a new frame rather than modify the existing one, i.e., `df.eval('x = y ** 2')` should be equivalent to `df.assign(x = lambda df: df.y ** 2)` (as currently defined in #9239).
In my experience, something like `df.query('x = 0')` is more likely intended to be equivalent to `df.query('x == 0')` rather than `df['x'] = 0`, which has the unfortunate effect of also modifying the original data. In fact, I have made this exact mistake before -- and apparently others have as well (see #8664).
Thoughts? The `query`/`eval` syntax is marked as experimental, so I think we have the freedom to change this.
CC @TomAugspurger @cpcloud @jreback
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| null | 1 | 2015-01-19T11:27:37Z | 2015-01-19T12:15:36Z | 2015-01-19T12:15:36Z | NONE | null | When you want to initialise a DF to a particular value you can use `data = 0.`. But this does not work in Panel
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} | 1 | 2015-01-19T13:07:17Z | 2021-04-12T05:02:08Z | null | NONE | null | Hello,
I'm using the pandas function "pandas.core.groupby.GroupBy.get_group()" to parallelize my code.
This function raises a "KeyError" exception if the passed index is not contained in the index list of the grouped DataFrame.
A basic example is:
data = DataFrame({'ind':[0,0,2,2]})
gb=a.groupby('ind')
gb.get_group(1)
Which raises "KeyError: 1"
In order to use intensively the "get_group()" function in code parallelization, an option to avoid this exception would be very useful, similar to "error_bad_lines=False" of the "read_csv()" function.
An empty DataFrame could be returned by the "get_group()" function when this option and an invalid index are given.
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| null | 4 | 2015-01-19T13:33:26Z | 2015-01-25T23:26:59Z | 2015-01-25T23:12:23Z | NONE | null | Not that familiar (at all :) with pandas internals, but I don't think this is expected behaviour.
``` python
f3 = DataFrame(
[
[95820843523155097, 1, 'director', 1],
[95820843523155098, 1, 'director', 2],
[95820843523155099, 1, 'director', 3],
[95820843523155100, 2, 'director', 4],
[95820843523155101, 2, 'computer system management (director)', 5],
[95820843523155102, 3, 'company director', 6],
[95820843523155103, 3, 'office manager', 7]
],
columns=['uid', 'cid', 'role', 'idx']
)
f3.dtypes
```
```
uid int64
cid int64
role object
idx int64
dtype: object
```
#### Observed behaviour
``` python
f3.groupby('cid').first()
```
| | uid | role | idx |
| --- | --- | --- | --- |
| cid | | | |
| 1 | 95820843523155104 | director | 1 |
| 2 | 95820843523155104 | director | 4 |
| 3 | 95820843523155104 | company director | 6 |
The `uid` column contains values that are all the same and aren't in the original data. (This isn't always true in larger sets; sometimes there's an overlap.)
#### Expected behaviour
``` python
f3.groupby('cid').apply(lambda g: g[:1])
```
| | | uid | role | idx |
| --- | --- | --- | --- | --- |
| cid | | | | |
| 1 | 0 | 95820843523155097 | director | 1 |
| 2 | 3 | 95820843523155100 | director | 4 |
| 3 | 5 | 95820843523155102 | company director | 6 |
This is what I expected to happen (i.e. the `uid` matches the rest of the row).
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-24-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2
nose: 1.3.4
Cython: 0.21.1
numpy: 1.8.2
scipy: 0.14.0
statsmodels: 0.6.1
IPython: 2.3.1
sphinx: None
patsy: 0.3.0
dateutil: 2.1
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: None
xlsxwriter: 0.6.4
lxml: 3.4.1
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: 2.5.4 (dt dec pq3 ext)
```
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} | 3 | 2015-01-19T15:30:32Z | 2015-05-08T07:45:25Z | 2015-05-08T07:45:25Z | MEMBER | null | I think the doc is little difficult for beginners, because examples use slicing to prepare input which cannot imagine immediately.
- http://pandas.pydata.org/pandas-docs/stable/merging.html
The alternative is using diagrams like below. I've prepared a function to output it using #6661, thus it can be generated during the doc build. If looks useful, I'll work on it.
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