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} | 10 | 2014-09-17T20:41:41Z | 2014-09-19T17:04:47Z | 2014-09-19T17:04:46Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/8150
``` python
In [1]: s = pd.Series([1,2,3])
In [2]: s.plot()
Out[2]: <matplotlib.axes._subplots.AxesSubplot at 0x10da02d68>
```
No release notes since this is just changing it back to how it was for the last release.
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} | 7 | 2014-09-17T21:23:14Z | 2014-11-11T14:32:45Z | 2014-11-11T14:32:45Z | CONTRIBUTOR | null | It appears that Series' `s.any` and `s.all` methods miss level kwargs, unlike their statistical counterparts like `s.sum`:
``` python
In [4]: s = pd.Series([0,1,2], index=[0,0,1])
In [5]: s.sum(level=0)
Out[5]:
0 1
1 2
dtype: int64
In [6]: s.prod(level=0)
Out[6]:
0 0
1 2
dtype: int64
In [7]: s.any(level=0)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-7-1d8c43752bc9> in <module>()
----> 1 s.any(level=0)
/home/immerrr/sources/pandas/pandas/core/series.pyc in f(self, *args, **kwargs)
74 @Appender(func.__doc__)
75 def f(self, *args, **kwargs):
---> 76 result = func(self.values, *args, **kwargs)
77 if isinstance(result, (pa.Array, Series)) and result.ndim == 0:
78 # return NumPy type
TypeError: _any() got an unexpected keyword argument 'level'
In [8]: s.all(level=0)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-8-bca0491001a6> in <module>()
----> 1 s.all(level=0)
/home/immerrr/sources/pandas/pandas/core/series.pyc in f(self, *args, **kwargs)
74 @Appender(func.__doc__)
75 def f(self, *args, **kwargs):
---> 76 result = func(self.values, *args, **kwargs)
77 if isinstance(result, (pa.Array, Series)) and result.ndim == 0:
78 # return NumPy type
TypeError: _all() got an unexpected keyword argument 'level'
```
Frames have those and I think so should series. Maybe, there are more reduction methods that I know not of that also miss those...
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} | 13 | 2014-09-17T21:47:42Z | 2014-09-26T14:35:18Z | 2014-09-26T14:35:18Z | NONE | null | If you give a non-existent key to the subset argument it will default to the last column. For example:
```
In [3]: d = pd.DataFrame({'a':[1],'b':[2],'c':[np.nan]})
In [4]: len(d.dropna(subset=['x']))
Out[4]: 0
```
It seems the problem is in the line where dropna takes the subset:
```
agg_obj = self.take(ax.get_indexer_for(subset),axis=agg_axis)
```
Here, `ax.get_indexer_for(subset)` will return `-1` as a sentinel value for any keys that were not found in `subset`, and `self.take` interprets the `-1` as a request for the last column.
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| null | 2 | 2014-09-17T22:19:13Z | 2015-10-20T22:41:57Z | 2015-10-20T22:41:57Z | CONTRIBUTOR | null | Exception in the following code locks clipboard system-wide until I exit my interactive Python session:
``` python
import pandas as pd
df = pd.DataFrame.from_dict({1: u' \u2014'}, 'index')
df.to_clipboard()
---------------------------------------------------------------------------
UnicodeEncodeError Traceback (most recent call last)
<ipython-input-58-1f8b11f0ff98> in <module>()
----> 1 df.to_clipboard()
C:\Python27\lib\site-packages\pandas\core\generic.pyc in to_clipboard(self, excel, sep, **kwargs)
1000 from pandas.io import clipboard
-> 1001 clipboard.to_clipboard(self, excel=excel, sep=sep, **kwargs)
1002
1003 #----------------------------------------------------------------------
C:\Python27\lib\site-packages\pandas\io\clipboard.pyc in to_clipboard(obj, excel, sep, **kwargs)
95 else:
96 objstr = str(obj)
---> 97 clipboard_set(objstr)
C:\Python27\lib\site-packages\pandas\util\clipboard.pyc in winSetClipboard(text)
63 try:
64 # works on Python 2 (bytes() only takes one argument)
---> 65 hCd = ctypes.windll.kernel32.GlobalAlloc(GMEM_DDESHARE, len(bytes(text))+1)
66 except TypeError:
67 # works on Python 3 (bytes() requires an encoding)
UnicodeEncodeError: 'ascii' codec can't encode character u'\u2014' in position 10: ordinal not in range(128)
```
pandas 0.14.1
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} | 9 | 2014-09-17T22:41:20Z | 2014-09-23T14:40:59Z | 2014-09-23T14:40:59Z | CONTRIBUTOR | null | I hope this is not intended as I can't find the point in this behavior:
``` python
import pandas as pd
df = pd.DataFrame.from_dict({1: ''.join(['1234567890'] * 6)}, 'index')
df.to_clipboard(excel=False)
```
result copied to clipboard:
```
0
1 1234567890123456789012345678901234567890123456...
```
Another issue - trying to omit index:
``` python
df.to_clipboard(excel=False, index=False)
```
result copied to clipboard, just the same as previous (with index):
```
0
1 1234567890123456789012345678901234567890123456...
```
This doesn't happen if I leave `excel` parameter to default value.
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} | 13 | 2014-09-18T11:35:40Z | 2018-06-18T22:34:30Z | 2018-06-18T22:34:30Z | CONTRIBUTOR | null | xref #6552
```
idx = pd.date_range(..., tz='UTC')
idx2 = idx.reindex(pd.date_range(..., tz='US/Pacific')
```
- [ ] subtraction of datetime like
- [ ] intersection / union / difference
- [ ] reindex
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} | 7 | 2014-09-18T13:14:37Z | 2014-09-25T18:41:36Z | 2014-09-25T16:55:01Z | CONTRIBUTOR | null | Closes: https://github.com/pydata/pandas/issues/8198
The idea is to hide the labels around the pie plot so they don't overlap w/ nonzero labels. The NaN/zero labels should still be visible in the legend to indicate that there's missing data. There's a picture of the problem in the linked issue.
NaNs in the top row of plots:
```
In [1]: df = pd.DataFrame(np.random.rand(4, 4))
In [2]: df2 = df.copy()
In [3]: for i in range(len(df)):
...: df.iloc[i, i] = np.nan
...:
In [4]: fig, axes = plt.subplots(figsize=(16, 8), nrows=2, ncols=4)
In [5]: df.plot(kind='pie', subplots=True, ax=axes.ravel()[:4])
In [6]: df2.plot(kind='pie', subplots=True, ax=axes.ravel()[4:])
```
Right now, for **DataFrames only** the NaN/zero labels aren't being put in the legend (or they aren't showing up). Still need to figure out why. It works fine for Series:
```
In [8]: df[0].plot(kind='pie', figsize=(4,4), legend=True)
```
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} | 2 | 2014-09-18T14:28:07Z | 2014-10-04T15:19:50Z | 2014-10-04T15:19:50Z | CONTRIBUTOR | null | They look funny because the aspect ratio is off: http://pandas-docs.github.io/pandas-docs-travis/visualization.html#pie-plot
May also want to note that in the text also.
This would be great for a first pull request if anyone is interested. Just need to set the figsize to something like (8, 8) for each plot (or (16, 8) for the subplots example). Everything is in `doc/source/visualization.rst`
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} | 2 | 2014-09-18T15:29:30Z | 2018-07-06T22:14:58Z | 2018-07-06T22:14:17Z | NONE | null | xref #7751
``` python
import pandas as pd
pd.cut([0,0,0,0], 10)
```
result:
```
ValueError: Bin edges must be unique: array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
```
On the other hand, pandas.cut works on non-unique inputs other than zero. The culprit are the following [lines of code in tile.py](https://github.com/pydata/pandas/blob/v0.14.1/pandas/tools/tile.py#L89-L90):
``` python
if mn == mx: # adjust end points before binning
mn -= .001 * mn
mx += .001 * mx
bins = np.linspace(mn, mx, bins + 1, endpoint=True)
```
When `mn` and `mx` are zero, the adjustment isn't made.
---
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-300.3.1.el6uek.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.1
nose: 1.3.1
Cython: None
numpy: 1.9.0
scipy: 0.14.0
statsmodels: None
IPython: 2.0.0
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.4
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: 2.4.2
sqlalchemy: None
pymysql: None
psycopg2: None
```
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| null | 1 | 2014-09-18T17:47:54Z | 2014-09-18T17:53:18Z | 2014-09-18T17:52:48Z | NONE | null | I'm getting a weird error in 0.14.1 that i didn't have in 0.10.1
``` python
>>> import numpy as np
>>> import pandas
>>> np.__version__
Out[8]: '1.8.2'
>>> pandas.__version__
Out[9]: '0.14.1'
>>> a = np.arange(10)
>>> b = pandas.Series([1,2,3])
>>> a[b]
Traceback (most recent call last):
File "C:\Python27\lib\site-packages\IPython\core\interactiveshell.py", line 2746, in run_code
exec code_obj in self.user_global_ns, self.user_ns
File "<ipython-input-17-a686c4bc0a37>", line 1, in <module>
a[b]
IndexError: unsupported iterator index
```
I would have expected to get something like array([1,2,3]), note this works if i use `.values`
``` python
>>> a[b.values]
Out[20]: array([1, 2, 3])
```
This would print out as expected in 0.10.1:
``` python
In [28]: numpy.__version__
Out[28]: '1.6.2'
In [29]: pandas.__version__
Out[29]: '0.10.1'
In [30]: a = numpy.arange(10)
In [31]: b = pandas.Series([1,2,3])
In [32]: a[b]
Out[32]: array([1, 2, 3])
```
Any ideas?
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| null | 7 | 2014-09-18T19:02:22Z | 2015-10-20T23:12:24Z | 2015-10-20T23:12:24Z | CONTRIBUTOR | null | ``` python
In [1]: import pandas as pd
In [2]: import rpy2
In [7]: import numpy as np
In [4]: arr = rpy2.robjects.IntVector(range(10))
In [5]: arr
Out[5]:
<IntVector - Python:0x102ed3908 / R:0x105a7c178>
[ 0, 1, 2, ..., 7, 8, 9]
In [6]: pd.Series(arr) # wrong behavior
Out[6]:
0 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
dtype: object
In [9]: np.array(arr)
Out[9]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int32)
In [10]: pd.Series(np.array(arr)) # correct behavior
Out[10]:
0 0
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
dtype: int32
```
I guess this is a regression due to Series no longer being a subclass of np.ndarray. Not sure if `numpy` exposes a catchall `isarray` that does the checks.
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} | 7 | 2014-09-18T19:10:38Z | 2014-10-04T18:53:43Z | 2014-10-04T18:01:29Z | MEMBER | null | This is of course taste, but I personally find some of the plots on the docs not very pleasing to the eyes.
Some different things:
- [x] Too dense minor gridlines (solved by using mpl 1.4):
- Eg the first plot on the page: http://pandas-docs.github.io/pandas-docs-travis/visualization.html#basic-plotting-plot
- It becomes worse when the plots become smaller: http://pandas-docs.github.io/pandas-docs-travis/visualization.html#using-layout-and-targetting-multiple-axes
- This could be solved using the new matplotlib styles or adapting our own `options.display.mpl_default` style
- [x] Overlapping axis labels (see also comment from here: https://github.com/pydata/pandas/pull/8301#issuecomment-56084391) (https://github.com/pydata/pandas/pull/8391):
- See the examples with multiple axes: http://pandas-docs.github.io/pandas-docs-travis/visualization.html#using-layout-and-targetting-multiple-axes -> a lot of overlapping axis labels
- [x] Use square pie plots (https://github.com/pydata/pandas/pull/8391)
- ...
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} | 10 | 2014-09-18T19:38:59Z | 2016-11-13T17:51:05Z | 2016-11-13T17:51:05Z | CONTRIBUTOR | null | I could be missing an easier way, but a lot of times I want to apply a rolling function to something like a groupby. This isn't the easiest thing in the world since the rolling functions take 2 arguments and `groupby.apply` is expecting a function with one argument. Maybe there's some way with passing in `args` to `groupby.apply`, but I can never remember what exactly get passed where, and what needs to be in tuples.
The most intuitive way (for me) is to make the function being applied, `f`, a function of one argument (the grouped values). This is difficult/impossible with the rolling funcs since there are 2 positional arguments and we want to `partial` the second. You have go through hoops with flipping function arguments, `partial`ing, and flipping back. meh...
I've got a patch that would allow
``` python
In [1]: df = pd.DataFrame(np.random.randn(100, 4))
In [2]: df['g'] = np.random.randint(0, 3, size=(100))
In [3]: from functools import partial
In [4]: f = partial(pd.rolling_mean, window=4)
In [5]: df.groupby('g').apply(f)
```
This is fully backwards compatible since it just raises if window is None. Any reason not to?
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} | 2 | 2014-09-19T00:03:36Z | 2014-12-04T08:27:53Z | 2014-12-04T08:27:53Z | NONE | null | If I start python with a -O flag, I get an error as soon as I import pandas
``` python
python -O
Python 2.7.6 (default, Mar 22 2014, 22:59:56)
[GCC 4.8.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/username/.virtualenvs/thm/local/lib/python2.7/site-packages/pandas/__init__.py", line 45, in <module>
from pandas.io.api import *
File "/home/username/.virtualenvs/thm/local/lib/python2.7/site-packages/pandas/io/api.py", line 11, in <module>
from pandas.io.sql import read_sql, read_sql_table, read_sql_query
File "/home/username/.virtualenvs/thm/local/lib/python2.7/site-packages/pandas/io/sql.py", line 1243, in <module>
read_frame.__doc__ += read_sql.__doc__
TypeError: unsupported operand type(s) for +=: 'NoneType' and 'NoneType'
```
The lines causing the issue are:
``` python
# Append wrapped function docstrings
read_frame.__doc__ += read_sql.__doc__
frame_query.__doc__ += read_sql.__doc__
```
It appears as if python is stripping the docstrings, which then causes issues. Commenting the lines out fixes the problem.
I'm on Ubuntu 14.04.1 LTS, my version info is:
``` python
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-29-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.1
nose: 1.3.0
Cython: None
numpy: 1.8.0
scipy: 0.9.0
statsmodels: None
IPython: None
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: 2013.9
bottleneck: 0.8.0
tables: None
numexpr: 2.4
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
```
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| null | 1 | 2014-09-19T00:13:00Z | 2014-09-19T09:21:07Z | 2014-09-19T09:21:07Z | CONTRIBUTOR | null | Right now, if inserting an empty table (no rows) using `to_sql`, the following exception is raised:
```
...
File "/Users/artemy/dev/pandas/pandas/io/sql.py", line 1220, in to_sql
table.insert(chunksize)
File "/Users/artemy/dev/pandas/pandas/io/sql.py", line 659, in insert
chunks = int(nrows / chunksize) + 1
ZeroDivisionError: division by zero
```
This fixes this bug and adds tests for it.
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} | 2 | 2014-09-19T02:33:28Z | 2015-12-30T15:19:43Z | 2015-12-30T15:19:43Z | CONTRIBUTOR | null | Per @jorisvandenbossche (https://github.com/pydata/pandas/pull/8286#issuecomment-56043336), we need to discuss a generalized way of enforcing PEP8 on the entire pandas code base.
Matplotlib has something similar here: https://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/tests/test_coding_standards.py, and here's the PR discussion: https://github.com/matplotlib/matplotlib/pull/1945
Some discussion is probably needed on the PEP8 rules we're alright with ignoring as well as whether or not we want to use 80 or 100 lines for max-line-length (http://legacy.python.org/dev/peps/pep-0008/#maximum-line-length).
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| null | 2 | 2014-09-19T04:23:34Z | 2014-09-19T16:44:58Z | 2014-09-19T12:01:11Z | CONTRIBUTOR | null | Consider:
df1 = DataFrame(np.arange(9.).reshape((3, 3)), columns=list('bcd'),
index=['a', 'a', 'c'])
Then,
df1.loc[['a','c'],['b','c']] results in a key error
while,
df1.loc[['a','c'],:].loc[:,['b','c']] produces the intended result.
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} | 1 | 2014-09-19T14:17:47Z | 2020-04-10T02:36:09Z | null | CONTRIBUTOR | null | ```
store.append(df,'df',write_index=True)
```
makes sense (`index` is already taken as its passed thru to determine whether to create an actual on in the table)
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} | 0 | 2014-09-19T15:04:14Z | 2014-09-19T15:40:59Z | 2014-09-19T15:40:59Z | CONTRIBUTOR | null | closes #7814
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| null | 15 | 2014-09-19T16:32:09Z | 2015-05-09T15:58:57Z | 2015-05-09T15:58:57Z | CONTRIBUTOR | null | WIP
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} | 41 | 2014-09-19T19:26:33Z | 2014-12-01T00:55:51Z | 2014-12-01T00:55:51Z | CONTRIBUTOR | null | Contributing Guidlines / Help:
https://github.com/pydata/pandas/wiki
Dev Docs
http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html
Docs:
- Doc Strings examples/links: https://github.com/pydata/pandas/issues/3439, https://github.com/pydata/pandas/issues/2916, https://github.com/pydata/pandas/issues/3324
- ~~Docs on ipython startup files: #5748~~
- Links to API docs in the tutorials: https://github.com/https://github.com/pydata/pandas/issues/3705, #1967
- better docs on DataFrame.apply (examples): https://github.com/pydata/pandas/issues/5299
- ~~GA docs: https://github.com/pydata/pandas/issues/3508~~
- doc groupby NA group work-around: https://github.com/pydata/pandas/issues/5456
- consistent imports in all documentation: #1967
- DOC: improve groupby reference docs #6944
- documenting cython class (eg Timestamp): "cyfunction is not a python function" #5218
- Redesigning/reorganising the documentation website
Perf:
- ~~vbench on different group sizes: https://github.com/pydata/pandas/issues/6787~~
- use seed in vbenches: https://github.com/pydata/pandas/issues/8144
- pandas + [airspeed velocity](https://spacetelescope.github.io/asv/index.html) #8361
Tests:
- matplotlib lib to check plots: https://github.com/pydata/pandas/issues/5379
- harmonize testing namespace: https://github.com/pydata/pandas/issues/8023
- verify timedelta algos: https://github.com/pydata/pandas/issues/5986
- non_unique storage tests for HDFStore: https://github.com/pydata/pandas/issues/7813
Bugs:
- Make changes in numpy API for 1.7: https://github.com/pydata/pandas/issues/8329
- accept scalar in Panel construction: https://github.com/pydata/pandas/issues/8285
- fillna bug: https://github.com/pydata/pandas/issues/8251
- HDFStore modifying columns when passed: https://github.com/pydata/pandas/issues/7212
- Plot label = None instead of provided label in line plot #8905
- plot with kind=scatter fails when providing an array for the size #8852
- Grouper(key='A') gives AttributeError when applying function #8795
- ValueError exception with pd.resample #8683
- to_csv issue with chunksize when large number of columns #8621
- EASY: pd.option_context without 'with' changes option values #8514
Enhancements:
- ENH: support TimedeltaIndex plotting #8711
- as suggested below, an enhancement to `read_csv` (or maybe `read_repr/string` to allow round-triping of the repr (can also serve as a basis for `read_clipboard`)
- raise on invalid compression options in HDFStore: https://github.com/pydata/pandas/issues/4582
- ~~accept Period in DatetimeIndex for start/end: https://github.com/pydata/pandas/issues/6780~~
- dont use bare Exceptions: https://github.com/pydata/pandas/issues/7948
- add more Series/Index ops: https://github.com/pydata/pandas/issues/6382
- ~~to_dict orient parm: https://github.com/pydata/pandas/issues/7840~~
- sort_index to generic.py: https://github.com/pydata/pandas/issues/8283
- get to take an axis argument: https://github.com/pydata/pandas/issues/6703
- axis argument to append: https://github.com/pydata/pandas/issues/8295
- better error on invalid input to cut: https://github.com/pydata/pandas/issues/7751
- ~~level kw to any/all: https://github.com/pydata/pandas/issues/8302~~
- astype accepting a dict: https://github.com/pydata/pandas/issues/7271
- ~~clean up code by removing core/array.py: https://github.com/pydata/pandas/issues/8359~~
IO:
- to_clipboard bug/improvements: https://github.com/pydata/pandas/issues/8304
- to_html to actually create links: https://github.com/pydata/pandas/issues/2679, https://github.com/pydata/pandas/issues/6488, https://github.com/pydata/pandas/issues/4987
- max_colwidth with groupby: https://github.com/pydata/pandas/issues/7856
- date_formatting in to_csv not being passed thru: https://github.com/pydata/pandas/issues/7791
- generate gbq schema: https://github.com/pydata/pandas/issues/8325
- make `to_latex` work with multi-index: https://github.com/pydata/pandas/issues/8336
- `Series.to_html` not working so well: https://github.com/pydata/pandas/issues/5563
- to_csv issue with chunksize when large number of columns #8621
Excel Oriented:
- More customization of Excel input/output could be great, i.e. making it easier to specify per-column colors/formatting, float formats, etc. The code base isn't too complicated there (just a mixture of the formatter and the ExcelWriter stuff) and you could make rapid progress because it's really easy to test and create samples. I think the result would be very immediately rewarding (better looking things, easier to make reports, etc.). Plus for #4679 and #8272 you'd get a better sense of pandas internals too.
- dtypes per column in read in (#8212 and #8272)
- Treat multiple rows/columns as MultiIndex/hierarchical columns #4679
- More flexible output formatting for Excel (mentioned in #8191 , but I'm going to put up an issue about having something like per-column styles, also #1663).
- Allow writing multiple tables to same sheet and/or setting starting position for a particular sheet (mentioned by Wes in an older issue but I can't find it right now).
- extend excel writers to write to open document format #2311
- allow ExcelWriter to automatically convert lists and dict to string #8188
- Performance benchmarks for Excel writers and readers #7171
- Support BytesIO output in ExcelWriter #7074
SQL:
- ENH: specify dtype in `to_sql` per column: #8778
- BUG: to_sql fails with datetime.time values with sqlite fallback mode #8341
More advanced:
- add write_index option to HDFStore: https://github.com/pydata/pandas/issues/8319
- start/stop on HDFstore fixed: https://github.com/pydata/pandas/issues/8287
- block splitting in HDFStore: https://github.com/pydata/pandas/issues/8284
- tz handling using HDFStore / fixed: https://github.com/pydata/pandas/issues/8165, https://github.com/pydata/pandas/issues/7775
- PeriodIndex in HDFStore: https://github.com/pydata/pandas/issues/7796
- Write meta data as a CArray: https://github.com/pydata/pandas/issues/6245
- Modify methods for HDFStore: https://github.com/pydata/pandas/issues/6857
- Integrate multi-index reindexing a bit more: https://github.com/pydata/pandas/issues/7895
- Timedelta support in groupby: #5724
- support numeric index ops: https://github.com/pydata/pandas/issues/8226
- allow index to referenced like a column: https://github.com/pydata/pandas/issues/8162
Collaborative Efforts:
- Missing data support in numpy: https://github.com/pydata/pandas/issues/8350
@jorisvandenbossche @cpcloud @TomAugspurger @hayd
cc @shoyer
cc @immerrr
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} | 2 | 2014-09-19T20:51:24Z | 2014-09-21T17:22:49Z | 2014-09-21T17:22:49Z | CONTRIBUTOR | null | cc @amras1
xref #8044
```
C:\Users\Jeff Reback\Documents\GitHub\pandas>c:\Python26-64\Scripts\nosetests.exe build\lib.win-amd64-2.6\pandas\io\tests\test_parsers.py --pdb --pdb-failure
RuntimeError: module compiled against API version 9 but this version of numpy is 7
..........................................................................................................S.............................................................................................
...> c:\python26-64\lib\unittest.py(325)failUnless()
-> if not expr: raise self.failureException, msg
(Pdb) u
> c:\users\jeff reback\documents\github\pandas\build\lib.win-amd64-2.6\pandas\io\tests\test_parsers.py(3205)test_precise_conversion()
-> self.assertTrue(max(precise_errors) < max(normal_errors))
(Pdb) l
3200 return abs(Decimal('{0:.100}'.format(val)) - actual_val)
3201 normal_errors.append(error(normal_val))
3202 precise_errors.append(error(precise_val))
3203 self.assertEqual(roundtrip_val, float(text[2:])) # round-trip should match float()
3204 self.assertTrue(sum(precise_errors) < sum(normal_errors))
3205 -> self.assertTrue(max(precise_errors) < max(normal_errors))
3206
3207 def test_pass_dtype(self):
3208 data = """\
3209 one,two
3210 1,2.5
(Pdb) p precise_errors
[Decimal('0.0'), Decimal('0E-15'), Decimal('2E-16'), Decimal('0E-16'), Decimal('2E-16'), Decimal('0E-16'), Decimal('0E-16'), Decimal('0E-16'), Decimal('2E-16'), Decimal('0E-16'), Decimal('2E-16'), Dec
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al('2E-16'), Decimal('0E-16'), Decimal('0E-16'), Decimal('0E-16'), Decimal('2E-16'), Decimal('0E-16'), Decimal('2E-16'), Decimal('0E-16'), Decimal('0E-15'), Decimal('0E-16'), Decimal('2E-16'), Decimal
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-16'), Decimal('0E-16'), Decimal('4E-16'), Decimal('5E-16'), Decimal('3E-16'), Decimal('2E-16'), Decimal('2E-16'), Decimal('2E-16'), Decimal('0E-16'), Decimal('2E-16'), Decimal('3E-16'), Decimal('0E-1
6'), Decimal('2E-16'), Decimal('0E-16'), Decimal('0E-15'), Decimal('0E-14'), Decimal('2E-16'), Decimal('0E-16'), Decimal('2E-16'), Decimal('3E-16'), Decimal('0E-16'), Decimal('2E-16'), Decimal('2E-16'
), Decimal('0E-16'), Decimal('2E-16'), Decimal('0E-16'), Decimal('0E-16'), Decimal('0E-16'), Decimal('2E-16'), Decimal('0E-16'), Decimal('2E-16'), Decimal('0E-15'), Decimal('0E-16'), Decimal('0E-16'),
Decimal('0E-16'), Decimal('2E-16'), Decimal('2E-16'), Decimal('2E-16'), Decimal('2E-16'), Decimal('4E-16'), Decimal('3E-16'), Decimal('2E-16'), Decimal('5E-16'), Decimal('2E-16'), Decimal('0.0')]
(Pdb) p max(precise_errors)
Decimal('5E-16')
(Pdb) p max(normal_errors)
Decimal('5E-16')
(Pdb)
```
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} | 5 | 2014-09-19T21:04:05Z | 2015-09-13T20:20:52Z | 2015-09-13T20:20:52Z | CONTRIBUTOR | null | Small extension on top the `to_gbq` so that you can actually create new tables given only an existing dataframe. Given an arbitrary `DataFrame` with a _non_ hierarchical-index, create a schema from it. For now, we'd likely assume that `object` dtype columns are string and maybe allow for specifying some or all columns for the schema so that int columns with nulls come out correctly (otherwise, they'd be coerced to float columns b/c of nan stuff).
E.g.:
``` python
In [6]: import pandas as pd
In [7]: import pandas.util.testing as testing
In [8]: df = testing.makeMixedDataFrame()
In [9]: df
Out[9]:
A B C D
0 0 0 foo1 2009-01-01
1 1 1 foo2 2009-01-02
2 2 0 foo3 2009-01-05
3 3 1 foo4 2009-01-06
4 4 0 foo5 2009-01-07
In [10]: df.dtypes
Out[10]:
A float64
B float64
C object
D datetime64[ns]
dtype: object
```
Then you could do something like:
``` python
In [11]: generate_bq_schema(df)
Out[11]:
{'fields': [{'name': 'A', 'type': 'FLOAT'},
{'name': 'B', 'type': 'FLOAT'},
{'name': 'C', 'type': 'STRING'},
{'name': 'D', 'type': 'TIMESTAMP'}]}
```
and with a named index, that could be added to the schema as well. For now, we could stick to requiring non-hierarchical/MultiIndex, but maybe we could use record types for an index that's MultiIndex in the future?
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} | 14 | 2014-09-19T22:09:37Z | 2015-05-01T16:45:35Z | 2015-05-01T16:45:35Z | CONTRIBUTOR | null | Right now, when you use GbqConnector, you get the following warning:
> WARNING:root:This function, oauth2client.tools.run(), and the use of the gflags library are deprecated and will be removed in a future version of the library.
I'm not sure how quickly this deprecation will happen or if there are alternatives to keep things the way that they are; but at least worthwhile to have an issue here to address it (in the future).
As an aside, I've been glancing at the source for the `bq` client that Google provides as part of Google Cloud SDK and I'm wondering how public the methods are (i.e., if we could adapt some of the code or functions that `bq.py` uses to authenticate and be able to get credentials set up from `gcloud auth login` rather than requiring a separate oauth flow). Not sure though.
Again, cc @jacobschaer , I'd be interested in your opinion on whether this is worth investigating at all. I've been finding `read_gbq` and `to_gbq` quite useful - thanks for putting it together!
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} | 3 | 2014-09-19T22:39:36Z | 2014-09-23T14:40:59Z | 2014-09-23T14:40:47Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/8305
@jreback I made the simple patch as you suggested for fixing truncated columns, although problem seems to be https://github.com/pydata/pandas/blob/master/pandas/core/format.py#L2118 called from https://github.com/pydata/pandas/blob/master/pandas/core/format.py#L411
Perhaps `conf_max` should respect if caller passes `minimum` argument so it wont truncate?
Second issue, where `index=False` was ignored (or just any other argument when `excel=False`) was because arguments were not passed to underlying function `obj.to_string()` and that was easy fix.
I added test for checking if column width greater then `max_colwidth` passes clipboard test.
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} | 22 | 2014-09-20T07:40:02Z | 2018-07-13T02:44:29Z | 2018-07-13T00:36:14Z | CONTRIBUTOR | null | Now that numpy-1.6 support is dropped we could proceed to use np-1.7 API.
This involves fixing a macro or two deep down in pandas internals, but otherwise should be pretty straightforward. As a bonus we can get rid of these annoying build warnings:
```
gcc -pthread -fno-strict-aliasing -g -O2 -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -Ipandas/src/klib -Ipandas/src -I/home/immerrr/.conda/envs/pandas/lib/python2.7/site-packages/numpy/core/include -I/home/immerrr/.conda/envs/pandas/include/python2.7 -c pandas/index.c -o build/temp.linux-x86_64-2.7/pandas/index.o
In file included from /home/immerrr/.conda/envs/pandas/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1761:0,
from /home/immerrr/.conda/envs/pandas/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
from /home/immerrr/.conda/envs/pandas/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/index.c:244:
/home/immerrr/.conda/envs/pandas/lib/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
```
xref https://github.com/pydata/numexpr/pull/228/files
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} | 3 | 2014-09-20T15:23:47Z | 2014-10-07T00:15:24Z | 2014-10-07T00:15:24Z | MEMBER | null | Closes #2908
Should probably add this also to `read_sql_table`. And try to reduce some repetitions (`_wrap_result` can maybe be a top-level (in the file) helper function).
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} | 0 | 2014-09-20T16:00:41Z | 2014-09-20T16:50:06Z | 2014-09-20T16:50:06Z | CONTRIBUTOR | null | closes #8252
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} | 2 | 2014-09-20T20:12:05Z | 2020-02-11T23:11:38Z | 2018-07-06T22:10:45Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/25952790/convert-pandas-series-from-dtype-object-to-float-and-errors-to-nans/25952844#25952844
maybe remove`raise_on_error` and rename to `coerce` (more inline with the rest of pandas API)
```
In [30]: pd.Series([1,2,3,4,'.']).convert_objects(convert_numeric=True)
Out[30]:
0 1
1 2
2 3
3 4
4 NaN
dtype: float64
```
I suspect this might make a nice API as well
```
s = pd.Series([1,2,3,4,'.'])
s.astype('float64', coerce=True)
```
maybe `coerce=True|None|'raise'` make sense
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} | 6 | 2014-09-20T20:12:18Z | 2017-09-18T13:10:38Z | 2017-09-18T10:57:28Z | NONE | null | The to_json() I/O command current provides the following output:
```
split dict like {index -> [index], columns -> [columns], data -> [values]}
records list like [{column -> value}, ... , {column -> value}]
index dict like {index -> {column -> value}}
columns dict like {column -> {index -> value}}
values just the values array
```
It would be handy if the output could also be provided in the format:
```
mdarray [ [index1,value1a,value1b,...] , [index2,value2a,value2b,...], ... ]
```
This format is, for instance, used by the DyGraphs JS library for plotting data.
The following command provides the output I am looking for:
```
np.column_stack((df.index,df.as_matrix())).tolist()
```
(where `df` is a DataFrame)
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| null | 3 | 2014-09-20T21:06:09Z | 2014-10-02T12:22:52Z | 2014-10-02T12:22:52Z | NONE | null | I've needed this feature 3 or 4 times recently.
The way I've typically done it is by making a new dataframe, concatenating the many dataframes after renaming their columns to identify to which dataframe they originally belonged, dropping all NA from the concatenated dataframe, and then splitting up this dataframe and assigning it to the individual dataframes from before.
What is a better, less hacky way of doing this?
Can it already be done easily with an existing function in Pandas?
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| null | 5 | 2014-09-20T22:06:35Z | 2014-09-21T17:24:56Z | 2014-09-21T17:23:11Z | CONTRIBUTOR | null | This should hopefully fix #8324.
@jreback
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} | 3 | 2014-09-20T22:12:29Z | 2017-04-16T08:39:48Z | 2017-04-16T08:39:40Z | CONTRIBUTOR | null | I introduced/uncovered a bug here with #8219.
If you pass in DataFrame with MI index and col names it doesn't output correctly (and never did). Should be able to fix pretty easily, since it's my fault am assigning myself!
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} | 5 | 2014-09-21T01:46:08Z | 2015-05-09T16:07:26Z | 2015-05-09T16:07:26Z | CONTRIBUTOR | null | closes #8295
- [x] Extract `append` method in `frame.py` into `generic.py`
- I left the original code just commented.
- [x] Extract test cases in `test_frame.py` into `test_generic.py`
- [x] Extract test cases in `test_series.py` into `test_generic.py`
- [x] Specific test case for testing `axis=1` in `test_generic.py`
- [x] Generic method `append` in `generic.py`
- [x] `NotImeplementedError` for `Panel`
- [ ] Docstring for generic `append`
- [ ] Release note
- [ ] Documentation
- [ ] CI pass
One more thing, could `Series.append` use `axis=1` as well ?
``` python
pd.Series([1,2,3]).append(pd.Series(['a','b','c']), axis=1)
0 1
0 1 a
1 2 b
2 3 c
# just like below
# pd.concat((pd.Series([1,2,3]), pd.Series(['a','b','c'])), axis=1)
```
It would be awesome if I could get your guidance to close this issue under the project's convention. Thank you !
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} | 3 | 2014-09-21T14:47:59Z | 2014-09-21T16:10:43Z | 2014-09-21T16:10:43Z | NONE | null | Hi,
Pandas' `interpolate()` method crashes if the `limit` parameter is passed and the underlying data frame or series does not contain NaN values, i.e., if there is nothing to interpolate. I think this is a bug.
The following code was tested with Pandas 0.14.1 (NumPy 1.9.0):
```
import pandas as pd
import numpy as np
x = pd.Series(np.arange(4))
x[2] = np.nan
# x looks like this:
#0 0
#1 1
#2 NaN
#3 3
xx = x.interpolate(method='linear', limit=1)
# calling interpolate() a second time...
xx.interpolate(method='linear', limit=1)
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
[...]
/.../python2.7/site-packages/pandas/core/common.pyc in _interp_limit(invalid, limit)
1437 violate = [invalid[x:x + limit + 1] for x in all_nans]
1438 violate = np.array([x.all() & (x.size > limit) for x in violate])
-> 1439 return all_nans[violate] + limit
1440
1441 xvalues = getattr(xvalues, 'values', xvalues)
IndexError: arrays used as indices must be of integer (or boolean) type
```
It seems that – since `all_nans` is empty – the dtype of `violate` is set to float (NumPy default). And float arrays cannot be used for indexing. It's probably safe to replace line `1438` with the following:
```
violate = np.array([x.all() & (x.size > limit) for x in violate], dtype=np.int)
```
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| null | 5 | 2014-09-21T15:40:14Z | 2014-09-21T20:43:59Z | 2014-09-21T16:09:55Z | NONE | null | I have run across some really weird situation where I get segmentation fault only when multiple threads access the same code simultaneously (problems with re-entrancy?). Also this only occurs with certain datasets and I couldn't reproduce it with toydata.
The data.p file required can be downloaded from here:
https://copy.com/WMQ6UP1tHjdE
This is the sample code:
```
import pickle
import concurrent.futures
import pandas as pd
import numpy as np
data = pickle.load(open('data.p', 'rb'))
# cannot reproduce the bug with toy data!
# data_l = pd.Series(np.random.randn(100))
# data_l.index = pd.tseries.index.DatetimeIndex(start='1901-01-01', periods=100, freq='D')
# data_r = pd.Series(np.random.randn(1000))
# data_r.index = pd.tseries.index.DatetimeIndex(start='1900-01-01', periods=1000, freq='D')
# data_r['1900-01-01':'1900-06-30'] = np.nan
def myfun():
return data[0]['AdjClose(GBP)'] * data[1]
# return data_l * data_r
# if using only one thread (ThreadPoolExecutor(1)) everything works...
with concurrent.futures.ThreadPoolExecutor(25) as executor:
futures = []
for _ in range(100000):
futures.append(executor.submit(myfun))
for fut in concurrent.futures.as_completed(futures):
if fut.exception():
print(fut.exception())
else:
print("Success!")
```
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} | 8 | 2014-09-21T19:14:23Z | 2016-02-24T12:55:26Z | 2016-02-24T12:55:26Z | MEMBER | null | Columns of `datetime.time` were not yet supported in sqlite fallback mode (as noted in #7567, and do work for sqlalchemy), but it now seems to fail silently with master (instead of raising an error).
Using:
```
import sqlite3
con = sqlite3.connect(':memory:')
dt = pd.date_range('2014-01-01', periods=10, freq='10h')
df = pd.DataFrame({'date':dt.date, 'time':dt.time})
df.to_sql('test_datetime', con, index=False, if_exists='replace')
```
gives:
```
In [37]: pd.read_sql_query('select * from test_datetime', con)
Out[37]:
Empty DataFrame
Columns: [date, time]
Index: []
```
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} | 31 | 2014-09-21T19:33:47Z | 2015-09-21T15:35:47Z | 2015-09-21T15:35:47Z | CONTRIBUTOR | null | Builds are failing on Travis due to some issue with Excel (weirdly passed on merge but then started failing a few commits later). Currently investigating. C.f. https://travis-ci.org/pydata/pandas/jobs/35873580
@neirbowj - any idea why these tests stopped passing?
It's all some variation on the following:
```
======================================================================
ERROR: test_tsframe (pandas.io.tests.test_excel.OpenpyxlTests_NoMerge)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/virtualenv/python3.4.1/lib/python3.4/site-packages/pandas/io/tests/test_excel.py", line 562, in test_tsframe
df.to_excel(path, 'test1')
File "/home/travis/virtualenv/python3.4.1/lib/python3.4/site-packages/pandas/util/decorators.py", line 88, in wrapper
return func(*args, **kwargs)
File "/home/travis/virtualenv/python3.4.1/lib/python3.4/site-packages/pandas/core/frame.py", line 1228, in to_excel
startrow=startrow, startcol=startcol)
File "/home/travis/virtualenv/python3.4.1/lib/python3.4/site-packages/pandas/io/excel.py", line 687, in write_cells
'number_format':{'format_code': self.datetime_format}}))
File "/home/travis/virtualenv/python3.4.1/lib/python3.4/site-packages/pandas/io/excel.py", line 757, in _convert_to_style_kwargs
new_v = _conv_to_x(v)
File "/home/travis/virtualenv/python3.4.1/lib/python3.4/site-packages/pandas/io/excel.py", line 1043, in _convert_to_number_format
from openpyxl.styles import NumberFormat
ImportError: cannot import name 'NumberFormat'
----------------------------------------------------------------------
```
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} | 3 | 2014-09-21T19:41:06Z | 2018-07-06T22:08:27Z | 2018-07-06T22:08:15Z | CONTRIBUTOR | null | Now that `Timedelta` is a full fledged type. It is quite trivial to convert a `datetime.time` to a `Timedelta`.
pros:
- highly performant
- very similar output
- no need to create a `TimeBlock`
cons:
- no longer a `datetime.time` instance (instead its a sub-class of `datetime.timedelta`)
- doesn't support `tz`(anyone actually use this 'feature')?
- negative times would be allowed (eg. I think this is not allowed for `datetime.time`).
So either we go full-fledged and create a `Time` scalar and `TimeBlock` (though `TimeIndex` could work w/o these.). Its possible to also create a `TimeBlock` and NOT create a `Time` scalar.
That said creating a `Time` scalar is very easy and much simpler than `Timedelta` and shares a lot of code.
```
In [28]: s = Series([datetime.time(12,0,0),datetime.time(14,1,2),datetime.time(2,0,1)])
In [29]: s
Out[29]:
0 12:00:00
1 14:01:02
2 02:00:01
dtype: object
In [30]: pd.lib.infer_dtype(s)
Out [30]: 'time'
In [31]: s.iloc[0]
Out[31]: datetime.time(12, 0)
In [32]: s.apply(lambda x: pd.Timedelta(seconds=x.hour*3600+x.minute*60+x.second,microseconds=x.microsecond))
Out[32]:
0 12:00:00
1 14:01:02
2 02:00:01
dtype: timedelta64[ns]
In [32]: s.apply(lambda x: pd.Timedelta(seconds=x.hour*3600+x.minute*60+x.second,microseconds=x.microsecond)).iloc[0]
Out[32]: Timedelta('0 days 12:00:00')
```
thoughts?
Is `datetime.time` really THAT different from `datetime.timedelta` that it SHOULD be a full-fledged type?
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} | 2 | 2014-09-21T20:35:22Z | 2018-07-06T20:21:05Z | 2017-07-11T09:56:32Z | CONTRIBUTOR | null | Let's take the following example:
``` python
d = {'Item1' : pandas.DataFrame(numpy.random.randn(4, 3)),
'Item2' : pandas.DataFrame(numpy.random.randn(4, 2))}
p = pandas.Panel(d)
p.clip(0,1)
```
If I run the script with pandas 0.14.1 on both python 2.7.8 and 3.4.1 I'm getting the following error:
```
Traceback (most recent call last):
File "Untitled.py", line 11, in <module>
p.clip(0,1)
File "/usr/local/lib/python2.7/site-packages/pandas/core/generic.py", line 2684, in clip
result = result.clip_lower(lower)
File "/usr/local/lib/python2.7/site-packages/pandas/core/generic.py", line 2722, in clip_lower
return self.where((self >= threshold) | isnull(self), threshold)
File "/usr/local/lib/python2.7/site-packages/pandas/core/ops.py", line 934, in f
self._constructor.__name__)
ValueError: Simple arithmetic with Panel can only be done with scalar values
```
Digging a little bit deeper showed that the following line (the | operator to be precise) raises the error:
https://github.com/pydata/pandas/blob/1d65bc89d64c71f8d36f3ca92dd57db2efad7fdb/pandas/core/generic.py#L2796
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} | 0 | 2014-09-21T22:28:34Z | 2014-09-22T19:34:15Z | 2014-09-22T19:29:24Z | CONTRIBUTOR | null | - [x] docs
```
In [4]: pd.set_option('max_rows',10)
In [5]: df = DataFrame({'A' : np.arange(1480)},index=pd.to_timedelta(np.arange(1480),unit='T'))
In [6]: df
Out[6]:
A
0 days 00:00:00 0
0 days 00:01:00 1
0 days 00:02:00 2
0 days 00:03:00 3
0 days 00:04:00 4
... ...
1 days 00:35:00 1475
1 days 00:36:00 1476
1 days 00:37:00 1477
1 days 00:38:00 1478
1 days 00:39:00 1479
[1480 rows x 1 columns]
In [7]: df.resample('30T',how='sum')
Out[7]:
A
0 days 00:00:00 435
0 days 00:30:00 1335
0 days 01:00:00 2235
0 days 01:30:00 3135
0 days 02:00:00 4035
... ...
0 days 22:30:00 40935
0 days 23:00:00 41835
0 days 23:30:00 42735
1 days 00:00:00 43635
1 days 00:30:00 14745
[50 rows x 1 columns]
```
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} | 28 | 2014-09-21T22:46:47Z | 2014-10-21T11:42:23Z | 2014-10-19T21:39:41Z | CONTRIBUTOR | null | Going to push the schedule at least a week (from end of sept).
Let's try for end of 1st week of oct for RC, and end of 2nd week for final.
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} | 2 | 2014-09-21T23:17:17Z | 2014-10-01T09:27:36Z | 2014-09-23T14:32:45Z | CONTRIBUTOR | null | I created this wiki page: https://github.com/pydata/pandas/wiki/Specialities
feel free to add/update/change if you think you should (or not) be tagged in a particular category.
This is really for the core-devs reference as to know whom to cc when an issue comes up.
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} | 5 | 2014-09-22T01:45:34Z | 2014-10-15T17:55:49Z | 2014-10-15T17:55:49Z | CONTRIBUTOR | null | Thanks to @jtratner for catching this.
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} | 8 | 2014-09-22T07:01:38Z | 2014-09-30T16:05:42Z | 2014-09-30T16:05:42Z | CONTRIBUTOR | null | I think there is a bug on pandas 0.14.1 pivot_table using dictionary aggfunc and margins.
Test code:
<code>df=pandas.DataFrame([
{'JOB':'Worker','NAME':'Bob' ,'YEAR':2013,'MONTH':12,'DAYS': 3,'SALARY': 17},
{'JOB':'Employ','NAME':'Mary','YEAR':2013,'MONTH':12,'DAYS': 5,'SALARY': 23},
{'JOB':'Worker','NAME':'Bob' ,'YEAR':2014,'MONTH': 1,'DAYS':10,'SALARY':100},
{'JOB':'Worker','NAME':'Bob' ,'YEAR':2014,'MONTH': 1,'DAYS':11,'SALARY':110},
{'JOB':'Employ','NAME':'Mary','YEAR':2014,'MONTH': 1,'DAYS':15,'SALARY':200},
{'JOB':'Worker','NAME':'Bob' ,'YEAR':2014,'MONTH': 2,'DAYS': 8,'SALARY': 80},
{'JOB':'Employ','NAME':'Mary','YEAR':2014,'MONTH': 2,'DAYS': 5,'SALARY':190}
])</code>
<code>df=df.set_index(['JOB','NAME','YEAR','MONTH'],drop=False,append=False)</code>
<code>df=df.pivot_table(index=['JOB','NAME'],columns=['YEAR','MONTH'],values=['DAYS','SALARY'],aggfunc={'DAYS':'mean','SALARY':'sum'})</code>
All works fine but raise error using margins:
<code>df=df.pivot_table(index=['JOB','NAME'],columns=['YEAR','MONTH'],values=['DAYS','SALARY'],aggfunc={'DAYS':'mean','SALARY':'sum'},margins=True)
df=df.pivot_table(index=['JOB','NAME'],columns=['YEAR','MONTH'],values=['DAYS','SALARY'],aggfunc={'DAYS':'mean','SALARY':'sum'},margins=True)
File "/usr/local/lib/python2.7/site-packages/pandas-0.14.1-py2.7-linux-x86_64.egg/pandas/util/decorators.py", line 60, in wrapper
return func(_args, *_kwargs)
File "/usr/local/lib/python2.7/site-packages/pandas-0.14.1-py2.7-linux-x86_64.egg/pandas/util/decorators.py", line 60, in wrapper
return func(_args, *_kwargs)
File "/usr/local/lib/python2.7/site-packages/pandas-0.14.1-py2.7-linux-x86_64.egg/pandas/tools/pivot.py", line 147, in pivot_table
cols=columns, aggfunc=aggfunc)
File "/usr/local/lib/python2.7/site-packages/pandas-0.14.1-py2.7-linux-x86_64.egg/pandas/tools/pivot.py", line 191, in _add_margins
row_margin[k] = grand_margin[k[0]]
KeyError: 'SALARY'
</code>
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| null | 17 | 2014-09-22T07:26:30Z | 2015-10-20T22:42:35Z | 2015-10-20T22:42:35Z | MEMBER | null | I was thinking particularly of cases like `datetime64`, `Categorical` and `GeoSeries` (from geopandas).
I recently posted on the numpy discussion mailing to try to get a sense of what solutions exist for writing custom dtypes without writing C. Unfortunately, it appears there's not much hope!
http://mail.scipy.org/pipermail/numpy-discussion/2014-September/071231.html
@njsmith suggested that I really should ask pandas developers to chime in to find out why they choose to work around numpy's limitations rather than enhance it. I would love it if someone who understands the "why" for the choices pandas made could add their perspective to that thread.
@jreback @JanSchulz any thoughts to add? or did a sum it up well enough with "nobody wants to write C"?
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} | 7 | 2014-09-22T13:14:35Z | 2014-09-29T13:06:34Z | 2014-09-22T21:46:12Z | CONTRIBUTOR | null | As this is not really a fleshed out section, kind of odd to have it as a top-level
@TomAugspurger
@jorisvandenbossche
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} | 4 | 2014-09-22T14:43:41Z | 2014-09-23T12:54:55Z | 2014-09-22T15:55:24Z | CONTRIBUTOR | null | See #8342. This patch should allow all tests to pass with any supported openpyxl v1, and with openpyxl 2.1.0. Consider this an interim measure, not a complete fix.
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} | 4 | 2014-09-22T14:57:08Z | 2017-05-15T21:16:01Z | 2014-09-22T21:46:12Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/8351
Just moved it and changed a few heading levels. I could add a note about considering seaborn. I've never used rplot.
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} | 9 | 2014-09-22T15:19:37Z | 2014-09-30T16:05:42Z | 2014-09-30T16:05:15Z | CONTRIBUTOR | null | closes #8349
bug on pandas 0.14.1 pivot_table using dictionary aggfunc and margins.
Test code:
<pre>
<code>
df=pandas.DataFrame([
{'JOB':'Worker','NAME':'Bob' ,'YEAR':2013,'MONTH':12,'DAYS': 3,'SALARY': 17},
{'JOB':'Employ','NAME':'Mary','YEAR':2013,'MONTH':12,'DAYS': 5,'SALARY': 23},
{'JOB':'Worker','NAME':'Bob' ,'YEAR':2014,'MONTH': 1,'DAYS':10,'SALARY':100},
{'JOB':'Worker','NAME':'Bob' ,'YEAR':2014,'MONTH': 1,'DAYS':11,'SALARY':110},
{'JOB':'Employ','NAME':'Mary','YEAR':2014,'MONTH': 1,'DAYS':15,'SALARY':200},
{'JOB':'Worker','NAME':'Bob' ,'YEAR':2014,'MONTH': 2,'DAYS': 8,'SALARY': 80},
{'JOB':'Employ','NAME':'Mary','YEAR':2014,'MONTH': 2,'DAYS': 5,'SALARY':190} ])
df=df.set_index(['JOB','NAME','YEAR','MONTH'],drop=False,append=False)
df=df.pivot_table(index=['JOB','NAME'],
columns=['YEAR','MONTH'],
values=['DAYS','SALARY'],
aggfunc={'DAYS':'mean','SALARY':'sum'})
</code>
</pre>
All works fine but raise error using margins:
<pre>
<code>
df=df.pivot_table(index=['JOB','NAME'],
columns=['YEAR','MONTH'],
values=['DAYS','SALARY'],
aggfunc={'DAYS':'mean','SALARY':'sum'},
margins=True)
</code>
</pre>
df=df.pivot_table(index=['JOB','NAME'],columns=['YEAR','MONTH'],values=['DAYS','SALARY'],aggfunc={'DAYS':'mean','SALARY':'sum'},margins=True)
File "/usr/local/lib/python2.7/site-packages/pandas-0.14.1-py2.7-linux-x86_64.egg/pandas/util/decorators.py", line 60, in wrapper
return func(_args, *_kwargs)
File "/usr/local/lib/python2.7/site-packages/pandas-0.14.1-py2.7-linux-x86_64.egg/pandas/util/decorators.py", line 60, in wrapper
return func(_args, *_kwargs)
File "/usr/local/lib/python2.7/site-packages/pandas-0.14.1-py2.7-linux-x86_64.egg/pandas/tools/pivot.py", line 147, in pivot_table
cols=columns, aggfunc=aggfunc)
File "/usr/local/lib/python2.7/site-packages/pandas-0.14.1-py2.7-linux-x86_64.egg/pandas/tools/pivot.py", line 191, in _add_margins
row_margin[k] = grand_margin[k[0]]
KeyError: 'SALARY'
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} | 0 | 2014-09-22T15:54:57Z | 2014-09-22T16:39:26Z | 2014-09-22T16:39:26Z | CONTRIBUTOR | null | xref #8342
compat for openpyxl: 2.0.0 thru 2.1.0, but a bit hacky
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} | 8 | 2014-09-22T16:08:17Z | 2014-09-28T16:24:39Z | 2014-09-27T01:48:08Z | CONTRIBUTOR | null | As @mdickinson noted in #8044.
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| null | 5 | 2014-09-22T16:16:24Z | 2014-09-22T16:39:15Z | 2014-09-22T16:19:20Z | CONTRIBUTOR | null | Curiously one can index a DatetimeIndex by the string representation of a Period, but not by an actual Period object. Is this intentional?
```
Python 3.4.1 (v3.4.1:c0e311e010fc, May 18 2014, 10:45:13) [MSC v.1600 64 bit (AMD64)]
Type "copyright", "credits" or "license" for more information.
IPython 2.2.0 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
In [1]: from pandas import Series, date_range, Period
In [2]: s = Series(range(365), index=date_range('2001-01-01', periods=365))
In [3]: s[str(Period('2001-03', 'M'))]
Out[3]:
2001-03-01 59
2001-03-02 60
2001-03-03 61
2001-03-04 62
2001-03-05 63
2001-03-06 64
2001-03-07 65
2001-03-08 66
2001-03-09 67
2001-03-10 68
2001-03-11 69
2001-03-12 70
2001-03-13 71
2001-03-14 72
2001-03-15 73
2001-03-16 74
2001-03-17 75
2001-03-18 76
2001-03-19 77
2001-03-20 78
2001-03-21 79
2001-03-22 80
2001-03-23 81
2001-03-24 82
2001-03-25 83
2001-03-26 84
2001-03-27 85
2001-03-28 86
2001-03-29 87
2001-03-30 88
2001-03-31 89
Freq: D, dtype: int32
In [4]: s[Period('2001-03', 'M')]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-4-64d0668f17a9> in <module>()
----> 1 s[Period('2001-03', 'M')]
C:\Python34\lib\site-packages\pandas\core\series.py in __getitem__(self, key)
482 def __getitem__(self, key):
483 try:
--> 484 result = self.index.get_value(self, key)
485
486 if not np.isscalar(result):
C:\Python34\lib\site-packages\pandas\tseries\index.py in get_value(self, series, key)
1256 return self.get_value_maybe_box(series, key)
1257 except (TypeError, ValueError, KeyError):
-> 1258 raise KeyError(key)
1259
1260 def get_value_maybe_box(self, series, key):
KeyError: Period('2001-03', 'M')
```
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} | 0 | 2014-09-22T18:25:17Z | 2014-09-29T13:21:50Z | 2014-09-29T13:21:50Z | CONTRIBUTOR | null | https://github.com/pydata/pandas/blob/master/pandas/core/array.py
not necessary and clutters the code ATM.
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} | 5 | 2014-09-22T19:03:23Z | 2014-09-23T14:38:37Z | 2014-09-23T14:24:16Z | CONTRIBUTOR | null | Added statement to raise ValueError when OLS is run with clustering and
Newey-West adjustments. Closes #5884. This is a re-attempt at
GH8170.
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} | 1 | 2014-09-22T20:54:05Z | 2016-07-25T09:43:06Z | 2015-08-19T00:32:16Z | MEMBER | null | Since `vbench` was (at least in part) an inspiration for [Airspeed Velocity](https://github.com/spacetelescope/asv/) (`asv`), I think getting something like this set up for `pandas` would be useful and extremely helpful for us and for `asv`. We have a _ton_ of `vbench` code and we'd probably be a great project for pushing the boundaries of and finding edge cases in `asv`. This would also be a great for someone to get to know the scale of datasets for which pandas was designed.
Here's a nice demo of `astropy` using `asv`: http://mdboom.github.io/astropy-benchmark
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} | 6 | 2014-09-22T21:40:52Z | 2014-09-29T14:28:06Z | 2014-09-29T14:28:06Z | CONTRIBUTOR | null | I ran into an odd behaviour when taking the intersection of two Indexes. Specifically,
``` python
left = pd.Index(['A','B','A','C'])
right = pd.Index(['B','D'])
left.intersection(right)
```
returns
``` python
Index(['B', 'C'], dtype='object')
```
However, I would expect this to return
``` python
Index(['B'], dtype='object')
```
If `Index(['B', 'C'], dtype='object')` is the intended behaviour, can someone explain the rationale behind it?
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} | 1 | 2014-09-22T21:56:11Z | 2014-09-23T01:05:32Z | 2014-09-23T01:05:32Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/25963622/column-operations-with-pandas-dataframe-objects-with-non-unique-indices/25964177?noredirect=1#comment40684868_25964177)
```
In [3]: df = DataFrame({'A' : np.arange(5), 'B' : np.arange(1,6)},index=[2,2,3,3,4])
In [4]: df.B-df.A
Out[4]:
2 1
2 1
3 1
3 1
4 1
dtype: int64
In [5]: df = DataFrame({'A' : date_range('20130101',periods=5), 'B' : date_range('20130101 09:00:00', periods=5)},index=[2,2,3,3,4])
In [6]: df.B-df.A
Out[6]:
2 0 days 09:00:00
2 -1 days +09:00:00
2 1 days 09:00:00
2 0 days 09:00:00
3 0 days 09:00:00
3 -1 days +09:00:00
3 1 days 09:00:00
3 0 days 09:00:00
4 0 days 09:00:00
dtype: timedelta64[ns]
```
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} | 0 | 2014-09-22T22:07:36Z | 2014-09-23T01:05:32Z | 2014-09-23T01:05:32Z | CONTRIBUTOR | null | closes #8363
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} | 2 | 2014-09-22T23:15:24Z | 2015-03-03T01:15:35Z | 2015-03-03T01:15:35Z | NONE | null | There appears to be an issue with floats that are close together in series.rank(), pandas version 0.14. For reference this test worked in pandas 0.12.0.
### Current Behavior
```
>>> series = pd.Series([1000.000669 , 1000.000041 , 1000.000059 , 1000.000063 , 1000.000121 , 1000.000104 , 1000.000040 , 1000.000062 , 1000.000095 , 1000.000091 , 1000.000050 , 1000.000074 , 1000.000063 , 1000.000076 , 1000.000083 , 1000.000061 , 1000.000030 , 1000.000069 , 1000.000090 , 1000.000116 , 1000.000058 , 1000.000074 , 1000.000035 , 1000.000084 , 1000.000067 , 1000.000072 , 1000.000105 , 1000.000091 , 1000.000077 , 1000.000040 , 1000.000108 , 1000.000117 , 1000.000114 , 1000.000117 , 1000.000099 , 1000.000039 , 1000.000046 , 1000.000105 , 1000.000057])
>>> series.rank()
0 39.0
1 19.5
2 19.5
3 19.5
4 19.5
5 19.5
6 19.5
7 19.5
8 19.5
9 19.5
10 19.5
11 19.5
12 19.5
13 19.5
14 19.5
15 19.5
16 19.5
17 19.5
18 19.5
19 19.5
20 19.5
21 19.5
22 19.5
23 19.5
24 19.5
25 19.5
26 19.5
27 19.5
28 19.5
29 19.5
30 19.5
31 19.5
32 19.5
33 19.5
34 19.5
35 19.5
36 19.5
37 19.5
38 19.5
dtype: float64
```
### Expected Behavior
```
>>> from scipy import stats
>>> stats.rankdata(series)
array([ 39. , 6. , 11. , 14.5, 38. , 30. , 4.5, 13. , 28. ,
26.5, 8. , 19.5, 14.5, 21. , 23. , 12. , 1. , 17. ,
25. , 35. , 10. , 19.5, 2. , 24. , 16. , 18. , 31.5,
26.5, 22. , 4.5, 33. , 36.5, 34. , 36.5, 29. , 3. ,
7. , 31.5, 9. ])
```
### System Information
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Darwin
OS-release: 13.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.1
nose: 1.3.4
Cython: 0.21
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: None
sphinx: None
patsy: 0.3.0
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.7
bottleneck: 0.8.0
tables: 3.0.0
numexpr: 2.4
matplotlib: None
openpyxl: 2.1.0
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.8.0
pymysql: None
psycopg2: 2.5.4 (dt dec pq3 ext)
```
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} | 3 | 2014-09-23T01:44:39Z | 2014-09-25T00:38:15Z | 2014-09-25T00:05:35Z | CONTRIBUTOR | null | Bug fix for #8303. Now raise KeyError if any non-existent columns are passed in the subset argument.
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} | 8 | 2014-09-23T07:23:20Z | 2014-09-23T15:12:58Z | 2014-09-23T14:58:45Z | NONE | null | ``` python
>>> c.head(3)
end_time
location_id sensor_id view_id start_time
54 1000305 0 2014-07-21 07:00:00+00:00 2014-07-21 07:15:00+00:00
2014-07-21 07:15:00+00:00 2014-07-21 07:30:00+00:00
2014-07-21 07:30:00+00:00 2014-07-21 07:45:00+00:00
>>> c.to_pickle(r"C:\temp\fff")
>>> pd.read_pickle(r"C:\temp\fff").head(3)
end_time
location_id sensor_id view_id start_time
54 1000305 0 2014-07-21 07:00:00 2014-07-21 07:15:00+00:00
2014-07-21 07:15:00 2014-07-21 07:30:00+00:00
2014-07-21 07:30:00 2014-07-21 07:45:00+00:00
```
Please note that the timezone is lost from `start_time`.
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https://api.github.com/repos/pandas-dev/pandas/issues/8368 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8368/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8368/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8368/events | https://github.com/pandas-dev/pandas/issues/8368 | 43,600,448 | MDU6SXNzdWU0MzYwMDQ0OA== | 8,368 | DatetimeIndex.to_series loses timezone information | {
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| null | 2 | 2014-09-23T07:56:45Z | 2014-09-23T11:39:58Z | 2014-09-23T11:39:50Z | NONE | null | ``` python
>> f
<class 'pandas.tseries.index.DatetimeIndex'>
[2014-07-25 07:00:37+00:00, ..., 2014-07-26 07:50:17+00:00]
Length: 53304, Freq: None, Timezone: UTC
>> f.to_series()
datetime
2014-07-25 07:00:37+00:00 2014-07-25 07:00:37
2014-07-25 07:11:36+00:00 2014-07-25 07:11:36
2014-07-25 08:01:48+00:00 2014-07-25 08:01:48
2014-07-23 07:00:31+00:00 2014-07-23 07:00:31
2014-07-23 07:00:40+00:00 2014-07-23 07:00:40
2014-07-23 17:27:36+00:00 2014-07-23 17:27:36
2014-07-23 17:33:00+00:00 2014-07-23 17:33:00
2014-07-23 19:44:10+00:00 2014-07-23 19:44:10
2014-07-23 20:10:50+00:00 2014-07-23 20:10:50
2014-07-21 07:10:18+00:00 2014-07-21 07:10:18
2014-07-21 07:49:06+00:00 2014-07-21 07:49:06
2014-07-21 21:54:48+00:00 2014-07-21 21:54:48
2014-07-25 07:00:23+00:00 2014-07-25 07:00:23
2014-07-25 07:00:23+00:00 2014-07-25 07:00:23
2014-07-25 08:06:16+00:00 2014-07-25 08:06:16
...
2014-07-27 19:49:12+00:00 2014-07-27 19:49:12
2014-07-27 19:49:28+00:00 2014-07-27 19:49:28
2014-07-27 19:50:10+00:00 2014-07-27 19:50:10
2014-07-27 19:51:51+00:00 2014-07-27 19:51:51
2014-07-27 19:51:52+00:00 2014-07-27 19:51:52
2014-07-27 19:52:52+00:00 2014-07-27 19:52:52
2014-07-26 12:52:46+00:00 2014-07-26 12:52:46
2014-07-26 12:52:46+00:00 2014-07-26 12:52:46
2014-07-26 12:52:46+00:00 2014-07-26 12:52:46
2014-07-23 16:04:22+00:00 2014-07-23 16:04:22
2014-07-23 16:04:22+00:00 2014-07-23 16:04:22
2014-07-23 16:04:22+00:00 2014-07-23 16:04:22
2014-07-26 07:50:07+00:00 2014-07-26 07:50:07
2014-07-26 07:50:16+00:00 2014-07-26 07:50:16
2014-07-26 07:50:17+00:00 2014-07-26 07:50:17
Name: datetime, Length: 53304
>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.7.final.0
python-bits: 64
OS: Windows
OS-release: 8
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.14.0
nose: 1.3.3
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.1.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2014.3
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.5
lxml: 3.3.5
bs4: 4.3.1
html5lib: None
bq: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
```
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| null | 1 | 2014-09-23T08:46:11Z | 2014-09-23T11:37:18Z | 2014-09-23T11:37:08Z | NONE | null | In [97]:df=pd.DataFrame({'x':['A','B','C'],'y':['0','0','0']})
```
x y
```
0 A 0
1 B 0
2 C 0
In [99]:df.set_value(index=df.x.str.startswith('A'),col='y',value='1')
---
TypeError Traceback (most recent call last)
<ipython-input-99-08195440f2b8> in <module>()
----> 1 df.set_value(index=df.x.str.startswith('A'),col='y',value='1')
D:\winpython\WinPython-32bit-2.7.6.4\python-2.7.6\lib\site-packages\pandas\core\frame.pyc in set_value(self, index, col, value, takeable)
1572 series = self._get_item_cache(col)
1573 engine = self.index._engine
-> 1574 engine.set_value(series.values, index, value)
1575 return self
1576 except KeyError:
D:\winpython\WinPython-32bit-2.7.6.4\python-2.7.6\lib\site-packages\pandas\index.pyd in pandas.index.IndexEngine.set_value (pandas\index.c:3231)()
D:\winpython\WinPython-32bit-2.7.6.4\python-2.7.6\lib\site-packages\pandas\index.pyd in pandas.index.IndexEngine.set_value (pandas\index.c:3064)()
D:\winpython\WinPython-32bit-2.7.6.4\python-2.7.6\lib\site-packages\pandas\index.pyd in pandas.index.IndexEngine.get_loc (pandas\index.c:3324)()
TypeError:
In [100]:df.set_value(index=df.x.str.startswith('A'),col='z',value='1')
```
x y z
```
0 A 0 1
1 B 0 NaN
2 C 0 NaN
why the parameter 'col' must set as a new column will be ok?
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.14.0
nose: 1.3.1
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 2.0.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2013.9
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: 1.6.1
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.3.5
bs4: 4.3.2
html5lib: None
bq: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
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} | 0 | 2014-09-23T14:17:10Z | 2014-09-23T14:58:45Z | 2014-09-23T14:58:45Z | CONTRIBUTOR | null | closes #8367
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} | 1 | 2014-09-23T16:22:27Z | 2014-09-26T15:57:37Z | 2014-09-26T15:57:37Z | NONE | null | Hi,
I've found this issue when I tried to resample at different frequencies. When the frequency is a prime number, the first timestamp is not rounded and it takes the first timestamp in the file. Do I need an extra parameter to round timestamps?
``` python
csv = r"""Date,Time,Ask,Bid,AskVolume,BidVolume
11-08-2014,00:00:01.093,1.34046,1.34042,1.25,1.69
11-08-2014,00:00:02.159,1.34047,1.34043,4.69,1
11-08-2014,00:00:02.667,1.34046,1.34042,1.32,2.44
11-08-2014,00:00:03.175,1.34046,1.34043,1.32,1
11-08-2014,00:00:07.058,1.34046,1.34043,3.75,1.69
11-08-2014,00:00:07.362,1.34043,1.34041,2.25,1
11-08-2014,00:00:08.324,1.34043,1.34042,1.5,1
11-08-2014,00:00:08.830,1.34045,1.34043,2.44,1
11-08-2014,00:00:08.982,1.34043,1.34041,3,1.69
11-08-2014,00:00:09.815,1.34042,1.34041,1,1
11-08-2014,00:00:10.540,1.34043,1.34041,2.63,1
11-08-2014,00:00:11.061,1.34043,1.3404,7.99,2.25
11-08-2014,00:00:11.617,1.34042,1.34041,1.13,1
11-08-2014,00:00:13.607,1.34043,1.3404,7.5,3.19
11-08-2014,00:00:14.535,1.34043,1.34041,5.25,1
11-08-2014,00:00:15.525,1.34043,1.34041,3.38,1.25
11-08-2014,00:00:17.960,1.34043,1.34041,4.5,1.25
11-08-2014,00:00:20.674,1.34042,1.3404,1.13,2.07
11-08-2014,00:00:21.191,1.34041,1.3404,1,1"""
filename = 'sample.csv'
f = open(filename, 'w')
f.write(csv)
f.close()
df = pd.read_csv(filename, parse_dates={'Timestamp': ['Date', 'Time']}, index_col='Timestamp')
print df.resample('6s', fill_method='bfill')
print df.resample('7s', fill_method='bfill')
print df.resample('11s', fill_method='bfill')
print df.resample('13s', fill_method='bfill')
print df.resample('17s', fill_method='bfill')
```
Thanks,
Paola
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} | 0 | 2014-09-23T18:02:55Z | 2014-09-26T15:57:37Z | 2014-09-26T15:57:37Z | CONTRIBUTOR | null | closes #8371
```
In [1]: data = """date,time,value
...: 11-08-2014,00:00:01.093,1
...: 11-08-2014,00:00:02.159,1
...: 11-08-2014,00:00:02.667,1
...: 11-08-2014,00:00:03.175,1
...: 11-08-2014,00:00:07.058,1
...: 11-08-2014,00:00:07.362,1
...: 11-08-2014,00:00:08.324,1
...: 11-08-2014,00:00:08.830,1
...: 11-08-2014,00:00:08.982,1
...: 11-08-2014,00:00:09.815,1
...: 11-08-2014,00:00:10.540,1
...: 11-08-2014,00:00:11.061,1
...: 11-08-2014,00:00:11.617,1
...: 11-08-2014,00:00:13.607,1
...: 11-08-2014,00:00:14.535,1
...: 11-08-2014,00:00:15.525,1
...: 11-08-2014,00:00:17.960,1
...: 11-08-2014,00:00:20.674,1
...: 11-08-2014,00:00:21.191,1"""
In [2]: df = pd.read_csv(StringIO(data), parse_dates={'timestamp': ['date', 'time']}, index_col='timestamp')
In [3]: df
Out[3]:
value
timestamp
2014-11-08 00:00:01.093000 1
2014-11-08 00:00:02.159000 1
2014-11-08 00:00:02.667000 1
2014-11-08 00:00:03.175000 1
2014-11-08 00:00:07.058000 1
2014-11-08 00:00:07.362000 1
2014-11-08 00:00:08.324000 1
2014-11-08 00:00:08.830000 1
2014-11-08 00:00:08.982000 1
2014-11-08 00:00:09.815000 1
2014-11-08 00:00:10.540000 1
2014-11-08 00:00:11.061000 1
2014-11-08 00:00:11.617000 1
2014-11-08 00:00:13.607000 1
2014-11-08 00:00:14.535000 1
2014-11-08 00:00:15.525000 1
2014-11-08 00:00:17.960000 1
2014-11-08 00:00:20.674000 1
2014-11-08 00:00:21.191000 1
In [4]: df.resample('6s', how='sum')
Out[4]:
value
timestamp
2014-11-08 00:00:00 4
2014-11-08 00:00:06 9
2014-11-08 00:00:12 4
2014-11-08 00:00:18 2
In [5]: df.resample('7s', how='sum')
Out[5]:
value
timestamp
2014-11-08 00:00:00 4
2014-11-08 00:00:07 10
2014-11-08 00:00:14 4
2014-11-08 00:00:21 1
In [6]: df.resample('11s', how='sum')
Out[6]:
value
timestamp
2014-11-08 00:00:00 11
2014-11-08 00:00:11 8
In [7]: df.resample('13s', how='sum')
Out[7]:
value
timestamp
2014-11-08 00:00:00 13
2014-11-08 00:00:13 6
In [8]: df.resample('17s', how='sum')
Out[8]:
value
timestamp
2014-11-08 00:00:00 16
2014-11-08 00:00:17 3
```
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} | 8 | 2014-09-23T19:00:22Z | 2014-09-26T22:06:29Z | 2014-09-26T22:06:18Z | CONTRIBUTOR | null | closes #7715
Added simple check for file path existence. It might be more advisable to deprecate this function and separate file buffer and file path read functionality, but the current form matches the rest of the API.
I wasn't sure if there was any tests I needed to add, but if there is, let me know.
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} | 5 | 2014-09-23T19:01:12Z | 2014-09-29T14:28:06Z | 2014-09-29T14:27:54Z | CONTRIBUTOR | null | Fixes intersection bug in the case of non-monotonic non-unique indexes.
closes #8362
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} | 12 | 2014-09-23T21:44:14Z | 2018-07-06T22:06:48Z | 2018-07-06T22:06:37Z | CONTRIBUTOR | null | - [ ] check that plot does the right thing, i.e. ordering of the categories
- [ ] Make a utils.testing.makeCategorical() `Series(np.array(list('abcdefghij')).take(np.random.randint(0,10,size=len(df)))).astype('category')` + NaN + type + size + unique kword?
- [ ] check for `self.values.xxx` in `Series` and check that the methods using such call work when values is a `Categorical`. See https://github.com/pydata/pandas/pull/7447
- [ ] Apply and Categoricals: https://github.com/pydata/pandas/issues/8065
- [ ] Categorical.searchsorted() #8420
- [x] docs: "[...] number of possible values (categories; -categories-+levels+ in R). " https://github.com/pydata/pandas/pull/8561
- [x] move assert_categorical_equal to testing.py and fix "categories" failure msg
- [x] check whatsnew: In the note change "levels" to "categories" and `s/Schultz/Schulz/`
- [x] docstring in #8077 (comment)
- [x] add a link to "comparison with R" section from http://pandas-docs.github.io/pandas-docs-travis/comparison_with_r.html
- [x] check that the memory info is doing the right thing for categorical (https://github.com/pydata/pandas/pull/7619)
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} | 6 | 2014-09-23T21:54:53Z | 2016-07-15T10:30:26Z | 2016-07-15T10:28:35Z | CONTRIBUTOR | null | `levels` was renamed to `categories`. Take out the deprecated property, the proerty in the Series.cat accessor, and the attribute in the constructor
Look for `TODO: Remove after deprecation period in 2017/ after 0.18` and `TODO: remove levels after the deprecation period`.
Should be added to #6581
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} | 0 | 2014-09-23T22:56:51Z | 2014-09-26T15:19:39Z | 2014-09-26T15:19:39Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/26002564/pandas-fillna-method-with-multiindex-nans-are-filled-with-wrong-columns)
`df2.fillna(df1)` should be implemented by:
`df2.where(df2.notnull(),df2,df1)`
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} | 0 | 2014-09-23T23:24:28Z | 2014-09-24T11:59:42Z | 2014-09-24T11:59:42Z | CONTRIBUTOR | null | xref #8377
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} | 37 | 2014-09-24T01:40:16Z | 2015-03-05T12:47:34Z | 2015-03-03T01:15:35Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/8365
using tolerance in ranking floats can result in inconsistent behavior; currently on master:
```
>>> pd.Series([1001, 1001.0002]).rank()
0 1
1 2
dtype: float64
>>> pd.Series([1001, 1001.0001, 1001.0002]).rank()
0 2
1 2
2 2
dtype: float64
```
so, in effect `1001 == 1001.0002` if one takes small steps in between.
on branch:
```
>>> pd.Series([1001, 1001.0002]).rank()
0 1
1 2
dtype: float64
>>> pd.Series([1001, 1001.0001, 1001.0002]).rank()
0 1
1 2
2 3
dtype: float64
```
original issue(https://github.com/pydata/pandas/issues/8365):
```
>>> from scipy import stats
>>> ts = pd.Series([1000.000669 , 1000.000041 , 1000.000059 , 1000.000063 , 1000.000121 , 1000.000104 , 1000.000040 , 1000.000062 , 1000.000095 , 1000.000091 , 1000.000050 , 1000.000074 , 1000.000063 , 1000.000076 , 1000.000083 , 1000.000061 , 1000.000030 , 1000.000069 , 1000.000090 , 1000.000116 , 1000.000058 , 1000.000074 , 1000.000035 , 1000.000084 , 1000.000067 , 1000.000072 , 1000.000105 , 1000.000091 , 1000.000077 , 1000.000040 , 1000.000108 , 1000.000117 , 1000.000114 , 1000.000117 , 1000.000099 , 1000.000039 , 1000.000046 , 1000.000105 , 1000.000057])
>>> np.all(ts.rank() == stats.rankdata(ts))
True
```
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| null | 2 | 2014-09-24T07:00:46Z | 2015-10-20T23:11:12Z | 2015-10-20T23:11:12Z | NONE | null | I'm trying to open part of a csv file using pandas read_csv. The section I am opening has a header on line 746, and goes to line 1120.
``` python
df = read_csv(inputfile,header=746,nrows=374,index_col=False)
```
I then get an error
CParserError: Error tokenizing data. C error: Expected 9 fields in line 1121, saw 17
The error makes sense, because in line 1121 of the file, the data changes from 9 fields to 17. What doesn't make sense is why it is trying to read line 1121, as the nrows and header should only open lines up to 1120.
I can get it to work by decreasing the number of rows to below 232. This still works even if I increase the header number so it starts further down the file (eg increase it to 800).
There doesn't seem to be anything special about the last line it will read, and it will read lines further in the file if I increase the header number. Other similar files work fine.
The file is available at http://filedropper.com/lowerove
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} | 16 | 2014-09-24T14:19:15Z | 2015-09-30T11:37:42Z | 2015-09-30T11:26:27Z | NONE | null | Why doesn't the pandas.Series.count() method work as a valid aggregation with groupby when as_index=False?
``` python
df=pd.DataFrame([['foo','foo','bar','bar','bar','oats'],[1.0,2.0,3.0,4.0,4.0,5.0],[2.0,3.0,4.0,5.0,1.0,5.0]]).T
df.columns=['mycat','var1','var2']
df.var1=df.var1.astype('int64')
df.var2=df.var2.astype('int64')
df
```
Now, if I try to do a group by
``` python
df.groupby('mycat', as_index=False).var1.count()
```
Here is the error I get:
```
ValueError Traceback (most recent call last)
<ipython-input-383-27b244bccb8b> in <module>()
----> 1 df.groupby('mycat', as_index=False).var1.count()
/usr/local/lib/python2.7/dist-packages/pandas/core/groupby.pyc in count(self, axis)
740
741 def count(self, axis=0):
--> 742 return self._count().astype('int64')
743
744 def ohlc(self):
/usr/local/lib/python2.7/dist-packages/pandas/core/generic.pyc in astype(self, dtype, copy, raise_on_error)
2096
2097 mgr = self._data.astype(
-> 2098 dtype=dtype, copy=copy, raise_on_error=raise_on_error)
2099 return self._constructor(mgr).__finalize__(self)
2100
/usr/local/lib/python2.7/dist-packages/pandas/core/internals.pyc in astype(self, dtype, **kwargs)
2235
2236 def astype(self, dtype, **kwargs):
-> 2237 return self.apply('astype', dtype=dtype, **kwargs)
2238
2239 def convert(self, **kwargs):
/usr/local/lib/python2.7/dist-packages/pandas/core/internals.pyc 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):
/usr/local/lib/python2.7/dist-packages/pandas/core/internals.pyc in astype(self, dtype, copy, raise_on_error, values)
319 def astype(self, dtype, copy=False, raise_on_error=True, values=None):
320 return self._astype(dtype, copy=copy, raise_on_error=raise_on_error,
--> 321 values=values)
322
323 def _astype(self, dtype, copy=False, raise_on_error=True, values=None,
/usr/local/lib/python2.7/dist-packages/pandas/core/internals.pyc in _astype(self, dtype, copy, raise_on_error, values, klass)
337 if values is None:
338 # _astype_nansafe works fine with 1-d only
--> 339 values = com._astype_nansafe(self.values.ravel(), dtype, copy=True)
340 values = values.reshape(self.values.shape)
341 newb = make_block(values,
/usr/local/lib/python2.7/dist-packages/pandas/core/common.pyc in _astype_nansafe(arr, dtype, copy)
2410 elif arr.dtype == np.object_ and np.issubdtype(dtype.type, np.integer):
2411 # work around NumPy brokenness, #1987
-> 2412 return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape)
2413 elif issubclass(dtype.type, compat.string_types):
2414 return lib.astype_str(arr.ravel()).reshape(arr.shape)
/usr/local/lib/python2.7/dist-packages/pandas/lib.so in pandas.lib.astype_intsafe (pandas/lib.c:13456)()
/usr/local/lib/python2.7/dist-packages/pandas/lib.so in util.set_value_at (pandas/lib.c:55994)()
ValueError: invalid literal for long() with base 10: 'bar'
```
When i set as_index=True, I get
``` python
df.groupby('mycat', as_index=True).var1.count()
```
When I change the agg function and set_index=False, I get a weird result tooL
``` python
df.groupby('mycat', as_index=False).var1.agg(np.count_nonzero)
```
UPDATE: Realized my last result was not counting correctly and am now thoroughly confused.
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} | 11 | 2014-09-24T19:54:04Z | 2014-09-27T13:08:26Z | 2014-09-27T13:08:26Z | CONTRIBUTOR | null | These passed on numpy 1.8/matplotlib 1.3.1 on 34-64 windows
just updated and now failing
```
======================================================================
ERROR: test_boxplot_empty_column (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-3.4\pandas\tests\test_graphics.py", line 1980, in test_boxplot_empty_column
_check_plot_works(df.boxplot, return_type='axes')
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-3.4\pandas\tests\test_graphics.py", line 3349, in _check_plot_works
ret = f(*args, **kwargs)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-3.4\pandas\core\frame.py", line 4921, in boxplot
**kwds)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-3.4\pandas\tools\plotting.py", line 2634, in boxplot
result = plot_group(columns, data.values.T, ax)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-3.4\pandas\tools\plotting.py", line 2585, in plot_group
bp = ax.boxplot(values, **kwds)
File "C:\Python34-64\lib\site-packages\matplotlib\axes\_axes.py", line 3045, in boxplot
labels=labels)
File "C:\Python34-64\lib\site-packages\matplotlib\cbook.py", line 1965, in boxplot_stats
q1, med, q3 = np.percentile(x, [25, 50, 75])
File "C:\Python34-64\lib\site-packages\numpy\lib\function_base.py", line 3042, in percentile
interpolation=interpolation)
File "C:\Python34-64\lib\site-packages\numpy\lib\function_base.py", line 2791, in _ureduce
r = func(a, **kwargs)
File "C:\Python34-64\lib\site-packages\numpy\lib\function_base.py", line 3131, in _percentile
x1 = take(ap, indices_below, axis=axis) * weights_below
File "C:\Python34-64\lib\site-packages\numpy\core\fromnumeric.py", line 121, in take
return take(indices, axis, out, mode)
IndexError: cannot do a non-empty take from an empty axes.
======================================================================
FAIL: test_boxplot (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-3.4\pandas\tests\test_graphics.py", line 1794, in test_boxplot
self.assertEqual(len(ax.lines), 8 * len(numeric_cols))
AssertionError: 21 != 24
======================================================================
FAIL: test_boxplot_vertical (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-3.4\pandas\tests\test_graphics.py", line 1828, in test_boxplot_vertical
self.assertEqual(len(ax.lines), 8 * len(numeric_cols))
AssertionError: 21 != 24
----------------------------------------------------------------------
Ran 7882 tests in 724.270s
FAILED (SKIP=267, errors=1, failures=2)
```
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} | 25 | 2014-09-24T20:01:52Z | 2018-02-19T13:50:57Z | 2018-02-19T13:50:57Z | NONE | null | Using NumPy's nanmin on a pandas Series worked as expected, now however the Series is returned with each element set to the minimum, rather than just getting the minimum. Not sure if this is a Numpy of Pandas issue.
```
np.nanmin(pds.Series([1,2,3,4]))
Out[14]:
0 1
1 1
2 1
3 1
dtype: int64
np.min(pds.Series([1,2,3,4]))
Out[15]: 1
```
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} | 1 | 2014-09-25T00:31:10Z | 2014-09-26T14:35:25Z | 2014-09-26T14:35:18Z | CONTRIBUTOR | null | closes #8303.
Now instead of interpreting a bad column name as the 'last column' of the `DataFrame`, `dropna` will raise a `KeyError` instead. unit test and release note now included.
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} | 10 | 2014-09-25T08:49:11Z | 2015-05-14T15:47:33Z | 2015-05-09T16:09:38Z | NONE | null | this is usefull because you cant reach the same effect by just using
dropna().to_dict() also see
http://stackoverflow.com/questions/26033301/make-pandas-dataframe-to-a-dict-and-dropna/26033302#26033302
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} | 4 | 2014-09-25T10:45:54Z | 2020-03-10T05:19:05Z | 2020-03-10T05:19:05Z | NONE | null | ``` python
>>> from pandas.tseries.frequencies import to_offset as _to_offset
>>> _to_offset('D') < _to_offset('H')
False
>>> _to_offset('D') < _to_offset('W')
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-14-77799b586280> in <module>()
----> 1 _to_offset('D') < _to_offset('W')
C:\Anaconda\lib\site-packages\pandas\tseries\offsets.pyc in f(self, other)
1873 def _tick_comp(op):
1874 def f(self, other):
-> 1875 return op(self.delta, other.delta)
1876
1877 return f
AttributeError: 'Week' object has no attribute 'delta'
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.7.final.0
python-bits: 64
OS: Windows
OS-release: 8
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.14.0
nose: 1.3.3
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.1.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2014.3
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.5
lxml: 3.3.5
bs4: 4.3.1
html5lib: None
bq: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
```
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} | 1 | 2014-09-25T11:06:51Z | 2014-09-29T15:19:49Z | 2014-09-29T15:19:49Z | NONE | null | Consider the following example:
``` python
import pandas
foo = pandas.Series( [ 0, 1, 2, 0 ] )
w = foo > 0
bar = foo[ w ].map( str )
print bar
foo[w] = bar
print foo
foo[w] = bar
print foo
```
gives the following output
```
1 1
2 2
dtype: object
0 0
1 NaN
2 1
3 0
dtype: object
0 0
1 1
2 2
3 0
dtype: object
```
It looks as if in the first assignemend `foo[w] = bar` the matching index of both sides is ignored.
Is this a bug or documented behavior?
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} | 1 | 2014-09-25T13:47:28Z | 2014-09-26T19:11:04Z | 2014-09-26T15:19:39Z | CONTRIBUTOR | null | Related: [SO](http://stackoverflow.com/q/26002564/190597)
Fixes #8377
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} | 18 | 2014-09-25T16:16:15Z | 2018-07-06T20:21:05Z | 2017-07-11T09:56:19Z | CONTRIBUTOR | null | Looks like I can `append()` to an `HDFStore` object a `DataFrame` with a `MultiIndex`, but not a `Panel` with a `MultiIndex`. Similarly `put`ing the `Panel` with `format='fixed'` works, but with `format='table'` doesn't. So the problem appears to be with the table format.
```
Python 3.4.1 (v3.4.1:c0e311e010fc, May 18 2014, 10:45:13) [MSC v.1600 64 bit (AMD64)]
Type "copyright", "credits" or "license" for more information.
IPython 2.2.0 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
In [1]: from pandas import MultiIndex, DataFrame, Panel, HDFStore, show_versions
In [2]: mi = MultiIndex.from_tuples([('A','a'), ('A','b')])
In [3]: df = DataFrame([0., 1.], index=mi, columns=['X'])
In [4]: p = Panel([[[0, 1]]], items=['X'], major_axis=['Y'], minor_axis=mi)
In [5]: store = HDFStore('foo.h5')
In [6]: store.append('df', df)
In [7]: store.append('p', p)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-7-46ccb1da2f9f> in <module>()
----> 1 store.append('p', p)
C:\Python34\lib\site-packages\pandas\io\pytables.py in append(self, key, value, format, append, columns, dropna, **kwargs)
909 kwargs = self._validate_format(format, kwargs)
910 self._write_to_group(key, value, append=append, dropna=dropna,
--> 911 **kwargs)
912
913 def append_to_multiple(self, d, value, selector, data_columns=None,
C:\Python34\lib\site-packages\pandas\io\pytables.py in _write_to_group(self, key, value, format, index, append, complib, encoding, **kwargs)
1268
1269 # write the object
-> 1270 s.write(obj=value, append=append, complib=complib, **kwargs)
1271
1272 if s.is_table and index:
C:\Python34\lib\site-packages\pandas\io\pytables.py in write(self, obj, axes, append, complib, complevel, fletcher32, min_itemsize, chunksize, expectedrows, dropna, **kwargs)
3603 self.create_axes(axes=axes, obj=obj, validate=append,
3604 min_itemsize=min_itemsize,
-> 3605 **kwargs)
3606
3607 if not self.is_exists:
C:\Python34\lib\site-packages\pandas\io\pytables.py in create_axes(self, axes, obj, validate, nan_rep, data_columns, min_itemsize, **kwargs)
3168 index_axes_map[i] = _convert_index(
3169 a, self.encoding, self.format_type
-> 3170 ).set_name(name).set_axis(i)
3171 else:
3172
C:\Python34\lib\site-packages\pandas\io\pytables.py in _convert_index(index, encoding, format_type)
4082
4083 if isinstance(index, MultiIndex):
-> 4084 raise TypeError('MultiIndex not supported here!')
4085
4086 inferred_type = lib.infer_dtype(index)
TypeError: MultiIndex not supported here!
In [13]: store.put('f', p, format='fixed')
In [14]: store.put('t', p, format='table')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-14-f6e096e264c1> in <module>()
----> 1 store.put('t', p, format='table')
C:\Python34\lib\site-packages\pandas\io\pytables.py in put(self, key, value, format, append, **kwargs)
816 format = get_option("io.hdf.default_format") or 'fixed'
817 kwargs = self._validate_format(format, kwargs)
--> 818 self._write_to_group(key, value, append=append, **kwargs)
819
820 def remove(self, key, where=None, start=None, stop=None):
C:\Python34\lib\site-packages\pandas\io\pytables.py in _write_to_group(self, key, value, format, index, append, complib, encoding, **kwargs)
1268
1269 # write the object
-> 1270 s.write(obj=value, append=append, complib=complib, **kwargs)
1271
1272 if s.is_table and index:
C:\Python34\lib\site-packages\pandas\io\pytables.py in write(self, obj, axes, append, complib, complevel, fletcher32, min_itemsize, chunksize, expectedrows, dropna, **kwargs)
3603 self.create_axes(axes=axes, obj=obj, validate=append,
3604 min_itemsize=min_itemsize,
-> 3605 **kwargs)
3606
3607 if not self.is_exists:
C:\Python34\lib\site-packages\pandas\io\pytables.py in create_axes(self, axes, obj, validate, nan_rep, data_columns, min_itemsize, **kwargs)
3168 index_axes_map[i] = _convert_index(
3169 a, self.encoding, self.format_type
-> 3170 ).set_name(name).set_axis(i)
3171 else:
3172
C:\Python34\lib\site-packages\pandas\io\pytables.py in _convert_index(index, encoding, format_type)
4082
4083 if isinstance(index, MultiIndex):
-> 4084 raise TypeError('MultiIndex not supported here!')
4085
4086 inferred_type = lib.infer_dtype(index)
TypeError: MultiIndex not supported here!
In [9]: show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.14.1
nose: 1.3.4
Cython: 0.21
numpy: 1.9.0
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.2.0
sphinx: 1.2.3
patsy: 0.3.0
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.7
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
```
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} | 1 | 2014-09-25T16:54:34Z | 2018-07-06T22:04:19Z | 2018-07-06T22:04:10Z | CONTRIBUTOR | null | Followup to https://github.com/pydata/pandas/pull/8307
```
In [1]: df = pd.DataFrame(np.random.rand(4, 4))
In [2]: df2 = df.copy()
In [3]: for i in range(len(df)):
...: df.iloc[i, i] = np.nan
...:
In [4]: fig, axes = plt.subplots(figsize=(16, 8), nrows=2, ncols=4)
In [5]: df.plot(kind='pie', subplots=True, ax=axes.ravel()[:4])
In [6]: df2.plot(kind='pie', subplots=True, ax=axes.ravel()[4:])
```
Right now, when `subplots=True`, the NaN/zero labels aren't being put in the legend (or they aren't showing up).
```
In [8]: df[0].plot(kind='pie', figsize=(4,4), legend=True)
```
Not a huge priority, we'll see if anyone notices and wants to implement a fix. I think it would involve refactoring all our legend and ticklabels code to be subplot aware.
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} | 2 | 2014-09-25T17:06:53Z | 2015-08-18T12:44:59Z | 2014-10-04T18:01:29Z | CONTRIBUTOR | null | First commit closes https://github.com/pydata/pandas/issues/8308
second closes https://github.com/pydata/pandas/issues/8312 aside from using mpl 1.4 to build the docs.
I can post any examples if you want.
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} | 6 | 2014-09-25T18:08:51Z | 2014-09-26T18:54:21Z | null | CONTRIBUTOR | null | For DataFrames, `HDFStore.append()` works only when "appending" along the `index` direction, i.e. it expects the `columns` to be the same. See sample below. This doesn't appear to be documented. If can only append along a single axis, would be nice to be able to specify which.
Similarly for Panels, it appears to work only along the `major_axis`. I haven't checked with Panel4D.
```
Python 3.4.1 (v3.4.1:c0e311e010fc, May 18 2014, 10:45:13) [MSC v.1600 64 bit (AMD64)]
Type "copyright", "credits" or "license" for more information.
IPython 2.2.0 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
In [1]: from pandas import DataFrame, HDFStore
In [2]: df1 = DataFrame(1., index=range(2), columns=['A','B'])
In [3]: df2 = DataFrame(2., index=range(2,4), columns=['A','B'])
In [4]: store = HDFStore('bar.h5')
In [5]: store.append('df', df1)
In [6]: store.append('df', df2)
In [7]: store.append('df_transpose', df1.transpose())
In [8]: store.append('df_transpose', df2.transpose())
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-8-1d42b87002f5> in <module>()
----> 1 store.append('df_transpose', df2.transpose())
C:\Python34\lib\site-packages\pandas\io\pytables.py in append(self, key, value, format, append, columns, dropna, **kwargs)
909 kwargs = self._validate_format(format, kwargs)
910 self._write_to_group(key, value, append=append, dropna=dropna,
--> 911 **kwargs)
912
913 def append_to_multiple(self, d, value, selector, data_columns=None,
C:\Python34\lib\site-packages\pandas\io\pytables.py in _write_to_group(self, key, value, format, index, append, complib, encoding, **kwargs)
1268
1269 # write the object
-> 1270 s.write(obj=value, append=append, complib=complib, **kwargs)
1271
1272 if s.is_table and index:
C:\Python34\lib\site-packages\pandas\io\pytables.py in write(self, obj, axes, append, complib, complevel, fletcher32, min_itemsize, chunksize, expectedrows, dropna, **kwargs)
3603 self.create_axes(axes=axes, obj=obj, validate=append,
3604 min_itemsize=min_itemsize,
-> 3605 **kwargs)
3606
3607 if not self.is_exists:
C:\Python34\lib\site-packages\pandas\io\pytables.py in create_axes(self, axes, obj, validate, nan_rep, data_columns, min_itemsize, **kwargs)
3246 "cannot match existing table structure for [%s] on "
3247 "appending data" % ','.join(com.pprint_thing(item) for
-> 3248 item in items))
3249 blocks = new_blocks
3250 blk_items = new_blk_items
ValueError: cannot match existing table structure for [0,1] on appending data
In [9]: from pandas import show_versions
In [10]: show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.14.1
nose: 1.3.4
Cython: 0.21
numpy: 1.9.0
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.2.0
sphinx: 1.2.3
patsy: 0.3.0
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.7
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
```
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| null | 0 | 2014-09-25T18:41:16Z | 2017-04-05T02:05:56Z | 2014-09-25T19:46:38Z | CONTRIBUTOR | null | Fixes the build failures from merging https://github.com/pydata/pandas/pull/8307
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} | 5 | 2014-09-26T03:27:22Z | 2014-10-01T00:32:59Z | 2014-09-29T15:19:28Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/8387
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} | 18 | 2014-09-26T05:26:38Z | 2015-10-26T22:40:03Z | 2015-07-12T14:58:11Z | CONTRIBUTOR | null | This fixes #8251. It may need some more comprehensive tests but before I go too much farther I want to make sure the axis numbering scheme makes sense . The default behavior of `fillna` is to fill along `axis=0`. In the case of a DataFrame this fills along the columns. However, in the case of a Panel, filling along `axis=0` means you're filling along items. I'm not sure if that's the behavior that most users would think is the default. You might note that I also removed the references to what the behavior of each axis value is in the docstring since it gets a bit arbitrary when you consider both DataFrames and Panels.
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| null | 8 | 2014-09-26T14:05:10Z | 2014-09-26T18:32:01Z | 2014-09-26T14:44:05Z | CONTRIBUTOR | null | should fix current travis fails on master branch.
i cannot add tests right now because `test_remove_categories` in `test_categorical.py` does not fail on my machine. it also passes on a number of travis builds, and fails only on a couple.
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```
.../Users/tom/Envs/py3/lib/python3.4/site-packages/pandas/pandas/tools/plotting.py:3229: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared
"is being cleared", UserWarning)
../Users/tom/Envs/py3/lib/python3.4/site-packages/pandas/pandas/tools/plotting.py:3212: UserWarning: When passing multiple axes, sharex and sharey are ignored.These settings must be specified when creating axes
"These settings must be specified when creating axes", UserWarning)
............................S..S.../Users/tom/Envs/py3/lib/python3.4/site-packages/matplotlib-1.5.x-py3.4-macosx-10.9-x86_64.egg/matplotlib/axes/_axes.py:475: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.
warnings.warn("No labelled objects found. "
.S.............................................../Users/tom/Envs/py3/lib/python3.4/site-packages/pandas/pandas/tools/plotting.py:3209: UserWarning: When passing multiple axes, layout keyword is ignored
warnings.warn("When passing multiple axes, layout keyword is ignored", UserWarning)
```
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} | 1 | 2014-09-26T15:00:41Z | 2017-04-05T02:05:56Z | 2014-09-27T13:08:26Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/8382
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} | 0 | 2014-09-26T23:36:27Z | 2014-09-26T23:36:40Z | 2014-09-26T23:36:40Z | CONTRIBUTOR | null | closes #7763
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} | 6 | 2014-09-26T23:47:03Z | 2014-09-29T21:42:52Z | 2014-09-29T21:42:44Z | NONE | null | Numpy changed the names of their data types in 1.8
http://docs.scipy.org/doc/numpy-dev/user/basics.types.html
specifically, bool, int, float, and complex are now bool_, int_, float_ and complex_
this requires changing the VECTOR_TYPES and NA_TYPES dictionaries used by convert_to_r_dataframe in pandas/rpy/common.py
numpy 1.7 isn't feasible to include as a project dependency (too many packages require 1.8)
Unfortunately, bool_ etc don't exist in numpy < 1.8, so you'll have to try/except that bit. I can change it and submit a pull request if you like
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