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| null | 3 | 2014-03-11T17:54:10Z | 2014-03-14T14:06:53Z | 2014-03-11T18:50:06Z | CONTRIBUTOR | null | Did something change in iloc...? I could have sworn this worked and we talked about it during the PR. I was told _not_ to use values but to use iloc instead IIRC.
```
[~/]
[18]: pd.version.version
[18]: '0.13.1-254-g150f323'
from pandas.util.testing import _create_missing_idx
df = pd.util.testing.makeDataFrame()
density = .9
random_state = None
i, j = _create_missing_idx(*df.shape, density=density,
random_state=random_state)
df.iloc[i, j] = np.nan
```
This must be a copy because there's no assignment.
This still works
```
df.values[i, j] = np.nan
```
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} | 7 | 2014-03-11T19:35:54Z | 2015-01-25T22:57:04Z | 2015-01-25T22:57:04Z | CONTRIBUTOR | null | Scratch an itch I had. Use case
```
df = DataFrame([[np.nan, 'A'],
[np.nan, 'A'],
[np.nan, 'A'],
[1.5, 'B'],
[2.2, 'C'],
[3.1, 'C'],
[1.2,' B']], columns=['number', 'name'])
df2 = pd.DataFrame([[3.5, 'A']], columns=['number', 'name'])
df.update(df2, on='name')
```
The tests fail though because when you reset the index, the column order is not preserved and `self = self[col_order]` doesn't seem to do what I'd expect it to do. Maybe there's a better way to go about doing all of this?
Also, I dropped reindex_like because you were just iterating through columns of NaNs in `other`.
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} | 12 | 2014-03-11T20:10:41Z | 2014-06-29T18:16:04Z | 2014-04-01T20:55:05Z | CONTRIBUTOR | null | closes #4867
This brings `Panel.shift` in line with `DataFrame.shift`. The shifted data retains the same dimensions/indexes and doesn't drop the empty rows. So I have two concerns.
1. This is an API change and could conceivably break existing code.
2. The perf regresses quite a bit.
Here is the vbench.
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
panel_shift | 578.2867 | 0.0773 | 7478.4491 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
```
The previous implementation did not create a copy and thus had a constant time. The general shift scales linearly.
I think the current implementation just defers work done in later alignments, but I can only speak to my own workflow.
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} | 4 | 2014-03-11T20:25:44Z | 2014-06-24T14:50:40Z | 2014-06-04T00:12:39Z | NONE | null | This may be related to https://github.com/pydata/pandas/issues/3950 though my symptoms are a bit different. In that issue, the tzinfo was lost during the creation of a MultiIndex array. In my case, I'm able to create a MultiIndex array with intact tzinfo, but tzinfo gets lost when I concatenate one or more such arrays.
Here's an example. Note the lack of tzinfo in the final line:
``` python
In [47]: import pandas
In [48]: import datetime
In [49]: import pytz
In [50]: array = pandas.DataFrame({'a':[datetime.datetime(2014,1,1,tzinfo=pytz.UTC), datetime.datetime(2014,1,2,tzinfo=pytz.UTC)], 'b':[1, 2], 'c':[3, 4]})
In [51]: a2 = array.set_index('a')
In [52]: a3 = array.set_index(['a', 'b'])
In [53]: a2_concatenated = pandas.concat([a2])
In [54]: a3_concatenated = pandas.concat([a3])
In [55]: array.iloc[0,0]
Out[55]: datetime.datetime(2014, 1, 1, 0, 0, tzinfo=<UTC>)
In [56]: a2.index[0]
Out[56]: Timestamp('2014-01-01 00:00:00+0000', tz='UTC')
In [57]: a3.index[0]
Out[57]: (Timestamp('2014-01-01 00:00:00+0000', tz='UTC'), 1)
In [58]: a2_concatenated.index[0]
Out[58]: Timestamp('2014-01-01 00:00:00+0000', tz='UTC')
In [59]: a3_concatenated.index[0]
Out[59]: (Timestamp('2014-01-01 00:00:00', tz=None), 1)
```
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} | 26 | 2014-03-11T22:19:14Z | 2014-04-23T22:23:11Z | 2014-04-23T22:23:11Z | CONTRIBUTOR | null | related #4363
closes #3374
Here is a minimal example:
``` ipython
In [21]: df = pd.DataFrame({'A': range(5), 'B': rand(5)})
In [22]: df
Out[22]:
A B
0 0 0.402616
1 1 0.880696
2 2 0.184491
3 3 0.832732
4 4 0.393917
[5 rows x 2 columns]
In [23]: df.to_csv('test.csv', sep=' ')
In [24]: pd.read_csv('test.csv', sep=' ', dtype={'A': np.float64}).dtypes
Out[24]:
Unnamed: 0 int64
A float64
B float64
dtype: object
```
Here the dtype argument behaves as expected, and column A has type float. However with sep='\s' the dtype argument appears to be ignored:
``` ipython
In [25]: pd.read_csv('test.csv', sep='\s', dtype={'A': np.float64}).dtypes
Out[25]:
A int64
B float64
dtype: object
```
## Version information
``` ipython
In [27]: show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Darwin
OS-release: 10.8.0
machine: i386
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.1-413-ga71ede3
Cython: 0.20.1
numpy: 1.8.0
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 2.0.0-dev
sphinx: 1.2.1
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2013b
bottleneck: None
tables: 3.1.0
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: 1.8.2
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: 0.5.2
lxml: 3.3.1
bs4: 4.3.1
html5lib: None
bq: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.2
pymysql: None
psycopg2: None
```
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} | 29 | 2014-03-11T23:10:06Z | 2017-05-24T12:12:12Z | 2017-05-24T12:12:12Z | CONTRIBUTOR | null | After some discussion below, here's a simple repro case:
``` python
s1 = pd.Series([1, 2, 3], index=[datetime.datetime(1995, 12, 31), datetime.datetime(2000, 12, 31), datetime.datetime(2005, 12, 31)])
s2 = pd.Series([1, 2, 3], index=[datetime.datetime(1997, 12, 31), datetime.datetime(2003, 12, 31), datetime.datetime(2008, 12, 31)])
# plot first series, then add the second series to those axes, then try adding the first series again
ax = s1.plot()
s2.plot(ax=ax)
s1.plot(ax=ax)
```
causes
```
Traceback (most recent call last):
File "simple_repro.py", line 10, in <module>
s1.plot(ax=ax)
File "/home/andrew/git/pandas-rosnfeld/pandas/tools/plotting.py", line 2116, in plot_series
plot_obj.generate()
File "/home/andrew/git/pandas-rosnfeld/pandas/tools/plotting.py", line 920, in generate
self._make_plot()
File "/home/andrew/git/pandas-rosnfeld/pandas/tools/plotting.py", line 1482, in _make_plot
self._make_ts_plot(data)
File "/home/andrew/git/pandas-rosnfeld/pandas/tools/plotting.py", line 1577, in _make_ts_plot
_plot(data, 0, ax, label, self.style, **kwds)
File "/home/andrew/git/pandas-rosnfeld/pandas/tools/plotting.py", line 1553, in _plot
style=style, **kwds)
File "/home/andrew/git/pandas-rosnfeld/pandas/tseries/plotting.py", line 82, in tsplot
left, right = _get_xlim(ax.get_lines())
File "/home/andrew/git/pandas-rosnfeld/pandas/tseries/plotting.py", line 226, in _get_xlim
left = min(x[0].ordinal, left)
AttributeError: 'datetime.datetime' object has no attribute 'ordinal'
```
-- ORIGINAL MESSAGE --
Here's a small dataset:
```
date,region,value
1996-12-31,BRA,4.5
2003-12-31,BRA,3.7
2007-12-31,BRA,2.2
1995-12-31,COL,6.3
2000-12-31,COL,4.9
2005-12-31,COL,5.1
2010-12-31,COL,3.4
1997-12-31,PAN,6.3
2003-12-31,PAN,5.1
2008-12-31,PAN,3.9
1990-12-31,VEN,6.7
1991-12-31,VEN,5.4
1992-12-31,VEN,4.5
1993-12-31,VEN,4
1994-12-31,VEN,3.9
1995-12-31,VEN,4.1
1996-12-31,VEN,4.4
1997-12-31,VEN,4.5
1998-12-31,VEN,4.6
1999-12-31,VEN,4.1
2000-12-31,VEN,3.9
2007-12-31,VEN,3.7
```
If I read this in using
``` python
data = pd.read_csv('./data.csv', parse_dates='date', index_col='date')
```
and then try and plot it using
``` python
data.groupby('region').value.plot(legend=True)
```
I get more or less what I expect (perhaps the xlim doesn't go up to 2010-12-31, but otherwise fine).
If I delete out the BRA rows and try this again, I get:
```
Traceback (most recent call last):
File "repro.py", line 6, in <module>
data.groupby('region').value.plot()
File "/home/andrew/git/pandas-rosnfeld/pandas/core/groupby.py", line 342, in wrapper
return self.apply(curried)
File "/home/andrew/git/pandas-rosnfeld/pandas/core/groupby.py", line 428, in apply
return self._python_apply_general(f)
File "/home/andrew/git/pandas-rosnfeld/pandas/core/groupby.py", line 432, in _python_apply_general
self.axis)
File "/home/andrew/git/pandas-rosnfeld/pandas/core/groupby.py", line 958, in apply
res = f(group)
File "/home/andrew/git/pandas-rosnfeld/pandas/core/groupby.py", line 426, in f
return func(g, *args, **kwargs)
File "/home/andrew/git/pandas-rosnfeld/pandas/core/groupby.py", line 333, in curried
return f(x, *args, **kwargs)
File "/home/andrew/git/pandas-rosnfeld/pandas/tools/plotting.py", line 1921, in plot_series
plot_obj.generate()
File "/home/andrew/git/pandas-rosnfeld/pandas/tools/plotting.py", line 912, in generate
self._make_plot()
File "/home/andrew/git/pandas-rosnfeld/pandas/tools/plotting.py", line 1379, in _make_plot
self._make_ts_plot(data, **self.kwds)
File "/home/andrew/git/pandas-rosnfeld/pandas/tools/plotting.py", line 1450, in _make_ts_plot
_plot(data, 0, ax, label, self.style, **kwds)
File "/home/andrew/git/pandas-rosnfeld/pandas/tools/plotting.py", line 1434, in _plot
style=style, **kwds)
File "/home/andrew/git/pandas-rosnfeld/pandas/tseries/plotting.py", line 82, in tsplot
left, right = _get_xlim(ax.get_lines())
File "/home/andrew/git/pandas-rosnfeld/pandas/tseries/plotting.py", line 226, in _get_xlim
left = min(x[0].ordinal, left)
AttributeError: 'datetime.datetime' object has no attribute 'ordinal'
```
If I delete out both BRA and VEN rows, then there is no exception raised but I only see one series plotted and the x-axis is not formatted as a date.
One could also approach this whole exercise via something like
``` python
data = pd.read_csv('./data.csv', parse_dates='date')
data.pivot('date', 'region', 'value').plot()
```
but this works even worse, I just get a truncated VEN series and nothing else.
This is with current master pandas (but also happens in 0.13.1) and matplotlib 1.3.1.
Are there known issues with plotting sparse-yet-overlapping timeseries?
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} | 0 | 2014-03-12T04:16:55Z | 2014-03-14T06:18:09Z | 2014-03-14T06:18:09Z | CONTRIBUTOR | null | Or just not respecting na flag.
```
In [44]: s = pd.Series(['a', 0, np.nan])
In [45]: s.str.contains('a', na=False)
Out[45]:
0 True
1 False
2 False
dtype: bool
In [46]: s.str.match('a', na=False)
Out[46]:
0 True
1 NaN
2 NaN
dtype: object
```
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| null | 1 | 2014-03-12T04:46:08Z | 2014-03-12T08:48:56Z | 2014-03-12T08:48:56Z | NONE | null | If I assign a subset of a DataFrame column of type int64 an array of values of float64, printing the DataFrame's values will show the result of the assignment but dumping it to csv will not.
Minimal Reproducible Example on my system (Ubuntu 13.04, python 2.7.4, pandas 0.13.1, numpy 1.8.0):
``` python
import pandas as pd
df = pd.DataFrame({'target':np.zeros(10)}, dtype='int64')
df['target'][0] = 3333.0
df['target'][0:2] = [9999.0, 9999.0]
print df['target']
df.to_csv('bug.csv', index=False)
```
Printing df:
```
0 9999
1 9999
Name: target, dtype: int64
```
But the csv file is:
```
target
3333
0
```
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} | 10 | 2014-03-12T06:11:39Z | 2014-06-20T20:48:38Z | 2014-03-14T06:18:09Z | CONTRIBUTOR | null | fixes #6609
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} | 8 | 2014-03-12T06:48:59Z | 2014-03-12T18:33:43Z | 2014-03-12T18:33:43Z | CONTRIBUTOR | null | I think there might be an issue with `reindex(..., takeable=True)` introduced in a recent release and you might want to consider deprecating/dropping it while it hasn't crept to user code too much.
The main reason is that _reindex takeable_ is conceptually different from _reindex_: the latter can introduce new rows with values that must be filled while the former cannot. Essentially, _reindex takeable_ is no more than a fancy indexing operation of numpy which is already available via `iloc` indexer. If you consider _reindex_ parameters, you can see there' no additional value in _reindex takeable_:
- `method`, `limit` & `fill_value` parameters are useless since no empty cells can appear
- `level` parameter should be available to user via fancy multiindex slicing added recently
- `copy` is also achievable via `.copy()` method
So not only it adds no value, but it also increases complexity:
- externally, by adding yet another way to do fancy indexing
- internally, by adding more code paths to follow when implementing & executing reindex
The last point has already caused a bug:
``` python
In [1]: s = pd.Series(list('abc'), index=np.arange(3)[::-1])
In [2]: s
Out[2]:
2 a
1 b
0 c
dtype: object
In [3]: s.take([2,1,0])
Out[3]:
0 c
1 b
2 a
dtype: object
In [4]: s.reindex([2,1,0], takeable=True)
Out[4]:
2 a
1 b
0 c
dtype: object
In [5]: s.iloc[[2,1,0]]
Out[5]:
2 a
1 b
0 c
dtype: object
```
Of course, this can be fixed by inserting workarounds [here](https://github.com/pydata/pandas/blob/master/pandas/core/generic.py#L1579) and [here](https://github.com/pydata/pandas/blob/master/pandas/core/index.py#L1329) but this will increase complexity even more and won't address the conceptual issue.
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} | 18 | 2014-03-12T09:23:14Z | 2014-07-07T14:23:04Z | 2014-03-12T13:52:25Z | MEMBER | null | PR to fix and enable the MySQL tests for the new sql functionality.
- floats (decimal.Decimal) were not converted due to a small bug in the `coerce_float` arg. This is now OK.
- MySQL has no real BOOL type (it is just an alias for tiny int). So I changed the tests for that (so a bool column is converted to int64 (or float if there are NA's)
We should maybe add somewhere in the sql docs an overview of the limitations of the type conversion.
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} | 4 | 2014-03-12T11:09:22Z | 2014-07-16T08:58:04Z | 2014-03-12T18:33:43Z | CONTRIBUTOR | null | closes #6612
As discussed in #6612, `reindex(..., takeable=True)` as released in 0.13.0 didn't do anything that's not achieved by `.take`, `.iloc` and `.copy` attrs/methods, but also contained a bug which propagated to `iloc` when the latter was changed to use _reindex/takeable_ internally.
The proposed solution is to remove _reindex/takeable_ functionality altogether. According to @jreback, it was never supposed to be a part of public API, so probably no deprecation procedure is necessary.
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fix https://github.com/pydata/pandas/issues/6589
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} | 10 | 2014-03-12T16:22:58Z | 2020-01-13T12:46:31Z | 2016-09-02T11:19:42Z | CONTRIBUTOR | null | This seems strange to me, but I don't often use a MultiIndex so I might be missing something obvious.
```
>>> pd.__version__
'0.13.1-420-g6899ed6'
>>> df2 = pd.DataFrame([1], columns=pd.MultiIndex.from_arrays([[1],[2]]))
>>> df2
1
2
0 1
[1 rows x 1 columns]
>>> df2.columns
MultiIndex(levels=[[1], [2]],
labels=[[0], [0]])
>>> print df2.to_csv()
,1
,2
,
0,1
```
Is there supposed to be that empty line at the end of the header? Compare
```
>>> print df2.to_csv(header=False)
0,1
```
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} | 3 | 2014-03-12T18:18:02Z | 2014-04-30T15:48:21Z | 2014-04-30T15:48:21Z | CONTRIBUTOR | null | wheels are set, and travis I think is almost there
https://github.com/travis-ci/travis-ci/issues/1989
jreback/pandas@5989bbe
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} | 1 | 2014-03-12T19:11:53Z | 2015-02-14T03:10:28Z | 2015-02-14T03:10:28Z | NONE | null | dupe of #3707
groupby often casts int64 data to floats before applying functions to the grouped data, and then casts the result back to an int64. This produces incorrect results.
Here is a simple example:
``` python
import pandas as pd
import numpy as np
label = np.array([1, 2, 2, 3],dtype=np.int)
data = np.array([1, 2, 3,4], dtype=np.int64) + 24650000000000000
z = pd.DataFrame({'a':label,'data':data})
f = z.groupby('a').first()
In [40]: print z
a data
0 1 24650000000000001
1 2 24650000000000002
2 2 24650000000000003
3 3 24650000000000004
In [41]: print f
data
a
1 24650000000000000
2 24650000000000000
3 24650000000000004
```
The result is clearly incorrect and should be
``` python
data
a
1 24650000000000001
2 24650000000000002
3 24650000000000004
```
z.data and f.data are both numpy.int64, which masks the fact that z.data was converted to a float with a loss of precision during the groupby operation.
Tests of software using groupby on int64 will only show a problem if the tests include integers large enough to cause this loss of precision. Otherwise the tests will pass and the software will just quietly produce incorrect results in production.
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} | 10 | 2014-03-12T21:20:11Z | 2014-05-10T19:49:51Z | 2014-05-10T19:49:51Z | CONTRIBUTOR | null | regression in groupby nth, it raises an index error on MI in master. This is my fault (need to add tests, should also test for axis!=0).
http://stackoverflow.com/questions/22361385/integer-position-based-indexing-of-multi-index-dataframes
reason this raise is as I use obj.loc[[[]] :s
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} | 14 | 2014-03-13T00:37:24Z | 2014-07-16T08:58:09Z | 2014-03-13T22:19:16Z | CONTRIBUTOR | null | closes #4558
to_stata does not work correctly when used with non-string names. Since
Stata requires string names, the proposed fix attempts to rename columns using
the string representation of the column name used. A warning is raised if
the column name is changed.
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} | 2 | 2014-03-13T03:12:00Z | 2014-06-12T21:04:04Z | 2014-04-30T03:54:36Z | CONTRIBUTOR | null | In pandas 0.13.1 this code produces a plot that, when "moused over", indicates incorrect dates on the 't' (x) axis, despite having the correct axis label.
``` python
pd.Series([1, 2, 3], index=pd.date_range('2014-01-01', periods=3, freq='D')).plot()
```
(matplotlib version is 1.3.1)
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} | 5 | 2014-03-13T03:21:26Z | 2021-04-11T04:14:44Z | 2021-04-11T04:14:44Z | NONE | null | i'm using pandas to read and join the 2 dataframe, but the data are not aligned on the column i chose for the join. [Here](http://nbviewer.ipython.org/gist/epifanio/9126057) is reproducible example.
_Note at index 8 and 16, the values of SVSPP['_left'] and SVSPP do not match.
with my real data the mismatch is true for all the values and is always equal to 1._
You can see this issue clearly with the much smaller sub frame below:
```
df1 = pd.DataFrame({'SVSPP': {13: 14, 14: 15, 15: 16}, 'COMNAME': {13: 'CHAIN DOGFISH', 14: 'SPINY DOGFISH', 15: 'ATLANTIC ANGEL SHARK'}, 'SCINAME': {13: 'SCYLIORHINUS RETIFER', 14: 'SQUALUS ACANTHIAS', 15: 'SQUATINA DUMERIL'}})
df2 = pd.DataFrame({'ABUNDANCE': {8: 2, 7: 228}, 'CRUISE6': {8: 201105, 7: 201105}, 'SEASON': {8: 'FALL', 7: 'FALL'}, 'SVSPP': {8: 15, 7: 4}, 'BIOMASS': {8: 0.26200000000000001, 7: 5.7850000000000001}, 'LONGITUDE': {8: -74.630740000000003, 7: -74.630740000000003}, 'STATION': {8: 6, 7: 6}, 'LATITUDE': {8: 36.977443000000001, 7: 36.977443000000001}})
In [13]: df2.join(df1, on='SVSPP', lsuffix='_left')
Out[13]:
SEASON CRUISE6 STATION SVSPP_left LATITUDE LONGITUDE ABUNDANCE BIOMASS SVSPP COMNAME SCINAME
7 FALL 201105 6 4 36.977443 -74.63074 228 5.785 NaN NaN NaN
8 FALL 201105 6 15 36.977443 -74.63074 2 0.262 16 ATLANTIC ANGEL SHARK SQUATINA DUMERIL
In [14]: df2.merge(df1, on='SVSPP')
Out[14]:
SEASON CRUISE6 STATION SVSPP LATITUDE LONGITUDE ABUNDANCE BIOMASS COMNAME SCINAME
0 FALL 201105 6 15 36.977443 -74.63074 2 0.262 SPINY DOGFISH SQUALUS ACANTHIAS
```
See how SPINY DOGFISH appears in the second whilst ATLANTIC ANGEL SHARK is incorrectly in the first.
```
In [15]: df2.merge(df1, on='SVSPP')['COMNAME']
Out[15]:
0 SPINY DOGFISH
Name: COMNAME, dtype: object
In [16]: df2.join(df1, on='SVSPP', lsuffix='_left')['COMNAME']
Out[16]:
7 NaN
8 ATLANTIC ANGEL SHARK
Name: COMNAME, dtype: object
In [17]: df2.merge(df1, on='SVSPP', how='left')['COMNAME']
Out[17]:
0 NaN
1 SPINY DOGFISH
Name: COMNAME, dtype: object
```
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} | 3 | 2014-03-13T08:03:26Z | 2016-02-27T15:33:58Z | 2016-02-27T15:33:36Z | CONTRIBUTOR | null | Seems the boolean series is considered as numeric data
```
In [27]: import pandas as pd
In [28]: s = pd.Series([True, True, False])
In [29]: s.describe()
Out[29]:
count 3
mean 0.6666667
std 0.5773503
min False
25% 0.5
50% 1
75% 1
max True
dtype: object
```
Should we deem it as non-numeric data? Says, output as followings:
```
In [31]: s.describe()
Out[31]:
count 3
unique 2
top True
freq 2
dtype: object
```
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} | 1 | 2014-03-13T12:21:31Z | 2018-07-06T21:21:00Z | 2018-07-06T21:21:00Z | CONTRIBUTOR | null | see #6516
after #6516 it is possible to add a `CategoricalGrouper` subclassing `Grouper`, like how `TimeGrouper` does. It won't be user visible, but will 'clean' up things a bit.
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} | 14 | 2014-03-13T12:44:31Z | 2015-03-06T09:11:56Z | 2015-03-06T09:11:14Z | MEMBER | null | Further work for cleaning up the sql code (#6292).
- the `meta` attribute is not updated automatically, with the consequence that eg when you delete a table from sql directly, the `has_table` function does not work anymore:
- I added a test for that
- I converted the `meta` attribute to one which is always updated when called. @mangecoeur looks OK?
- now the tests are skipped when no connection could be made. @jreback, I just did a `raise nose.SkipTest` inside the setup function of the test class. -> moved and merged this in #6651
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} | 2 | 2014-03-13T13:32:40Z | 2014-06-25T08:40:04Z | 2014-03-13T14:37:40Z | CONTRIBUTOR | null | from SO: http://stackoverflow.com/questions/22379343/change-first-occurrence-of-nan-in-a-specific-dataframe-row-to-a-new-value/22380225#22380225
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| null | 2 | 2014-03-13T14:33:59Z | 2014-03-22T21:14:03Z | 2014-03-22T21:13:46Z | CONTRIBUTOR | null | As reported here: [Integer-position-based indexing of Multi-Index DataFrames](http://stackoverflow.com/questions/22361385/integer-position-based-indexing-of-multi-index-dataframes), currently, the only way to do integer-position based indexing in a Multi-Index DataFrame is:
`df.xs(df.index.levels[N][K], level=N)`
or with:
`df.groupby(level=N).nth(K)`
where in both cases, `N` refers to the level, and `K` to the position within that level.
I wonder why this can't be done with `iloc` since technically we are indexing the dataframe using **integer positions** on the DataFrame's `Index`.
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} | 5 | 2014-03-13T17:22:27Z | 2020-04-25T14:00:28Z | null | NONE | null | I have the following `DataFrame` that I want to create a boxplot for:
```
estimates.head()
Out[162]:
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 \
DATE
2009-04-28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2009-04-29 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
2009-04-30 10 10 10 10 10 9 9 9 9 9 9 9 9 9 9 9
2009-05-13 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
2009-05-14 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
16 17 18 19 20 21 22 23 24 25 26 27 28 29
DATE
2009-04-28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...
2009-04-29 6 6 6 6 6 6 6 6 6 6 6 6 6 6 ...
2009-04-30 9 9 9 9 9 9 9 9 9 9 9 9 9 9 ...
2009-05-13 9 9 9 9 9 9 9 9 9 9 9 9 9 9 ...
2009-05-14 11 11 11 11 11 11 11 11 11 11 12 12 12 12 ...
```
along with a `Series` that I want to plot on the same axes:
```
counts.head()
Out[165]:
DATE
2009-04-28 0
2009-04-29 2
2009-04-30 4
2009-05-13 2
2009-05-14 5
Name: OBS1_NMANATEES, dtype: float64
```
They are both the same length, and have the same index (in fact, to ensure the latter, I assigned the index `estimates` to `counts`). However, when I plot them on the same set of axes, the plot for the `counts` is off by one on the x-axis:
```
fig, axes = plt.subplots(figsize=(18,6))
estimates.T.boxplot(ax=axes, grid=False, rot=90)
axes.plot(counts)
plt.tight_layout()
plt.xlabel('Survey')
plt.ylabel('Population estimate')
```
Notice in the plot the line graph does not begin with zero at 2009-04-28 as is clearly shown in the data, and you can see the entire line is shifted. This looks like an alignment error, no?
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} | 2 | 2014-03-13T21:10:09Z | 2014-05-12T13:34:58Z | 2014-05-12T13:34:58Z | CONTRIBUTOR | null | ```
[14]: pd.version.version
[14]: '0.13.1-426-ge19b2eb'
```
Noticed these. This one's minor. `set_index` seems to drop frequency information for DatetimeIndex. I'm not sure what other metadata is ignored.
```
import statsmodels.api as sm
import pandas as pd
index = pd.PeriodIndex(start='1959Q1', end='2009Q3')
dta = sm.datasets.macrodata.load_pandas().data
assert dta.set_index(index.to_timestamp()).index.freq == index.to_timestamp().freq
```
This one is less minor. `set_index` and `reindex` are broken for a PeriodIndex. Treats it as regular integer index I guess. Not sure if it ever worked, though I don't recall running into this before.
```
dta.set_index(index).head()
dta.reindex(index).head()
```
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} | 12 | 2014-03-13T22:50:51Z | 2015-01-26T01:23:15Z | 2015-01-25T22:57:34Z | CONTRIBUTOR | null | Often I find myself wanting to throw away values which aren't in a given set when doing `value_counts`...
This basically just does this but inside value_counts (before possibly sorting):
```
pd.value_counts(x).reindex(base, fill_value=0) # same as Series method
```
_I previously thought this was trickier to implement, so feel a bit stupid, but anyway could be useful....?_
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| null | 2 | 2014-03-13T23:06:27Z | 2014-03-14T11:45:01Z | 2014-03-14T11:44:51Z | CONTRIBUTOR | null | Sorry to do my daily issue bombardment, but I live on the edge for better or worse. I thought this should work. It does if it's a single column DataFrame ie., `y.to_frame().plot()`
```
[~/]
[121]: y = pd.util.testing.makeStringSeries()
[~/]
[122]: y.reset_index(drop=True, inplace=True)
[~/]
[123]: y.plot()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-123-2d08882c171f> in <module>()
----> 1 y.plot()
/home/skipper/src/pandas-skipper/pandas/tools/plotting.pyc in plot_series(series, label, kind, use_index, rot, xticks, yticks, xlim, ylim, ax, style, grid, legend, logx, logy, secondary_y, **kwds)
1919 secondary_y=secondary_y, **kwds)
1920
-> 1921 plot_obj.generate()
1922 plot_obj.draw()
1923
/home/skipper/src/pandas-skipper/pandas/tools/plotting.pyc in generate(self)
910 self._compute_plot_data()
911 self._setup_subplots()
--> 912 self._make_plot()
913 self._post_plot_logic()
914 self._adorn_subplots()
/home/skipper/src/pandas-skipper/pandas/tools/plotting.pyc in _make_plot(self)
1377 if not self.x_compat and self.use_index and self._use_dynamic_x():
1378 data = self._maybe_convert_index(self.data)
-> 1379 self._make_ts_plot(data, **self.kwds)
1380 else:
1381 lines = []
/home/skipper/src/pandas-skipper/pandas/tools/plotting.pyc in _make_ts_plot(self, data, **kwargs)
1448 self._maybe_add_color(colors, kwds, style, 0)
1449
-> 1450 _plot(data, 0, ax, label, self.style, **kwds)
1451 else:
1452 for i, col in enumerate(data.columns):
/home/skipper/src/pandas-skipper/pandas/tools/plotting.pyc in _plot(data, col_num, ax, label, style, **kwds)
1432 def _plot(data, col_num, ax, label, style, **kwds):
1433 newlines = tsplot(data, plotf, ax=ax, label=label,
-> 1434 style=style, **kwds)
1435 ax.grid(self.grid)
1436 lines.append(newlines[0])
/home/skipper/src/pandas-skipper/pandas/tseries/plotting.pyc in tsplot(series, plotf, **kwargs)
80 # set date formatter, locators and rescale limits
81 format_dateaxis(ax, ax.freq)
---> 82 left, right = _get_xlim(ax.get_lines())
83 ax.set_xlim(left, right)
84
/home/skipper/src/pandas-skipper/pandas/tseries/plotting.pyc in _get_xlim(lines)
224 for l in lines:
225 x = l.get_xdata()
--> 226 left = min(x[0].ordinal, left)
227 right = max(x[-1].ordinal, right)
228 return left, right
AttributeError: 'numpy.int64' object has no attribute 'ordinal'
[~/]
[124]: pd.version.version
[124]: '0.13.1-426-ge19b2eb'
```
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} | 11 | 2014-03-14T06:21:17Z | 2014-04-30T16:29:24Z | 2014-04-30T16:29:24Z | CONTRIBUTOR | null | I wrote this so my fault. This probably means some stuff with break when passing stuff with unicode.
Will add example.
The reason is to include integers as strings before getting dummies, maybe should just drop that functionality?
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| null | 3 | 2014-03-14T08:12:02Z | 2014-03-17T18:38:53Z | 2014-03-14T11:35:35Z | NONE | null | ```
>>> from_time = dateutil.parser.parse('2014-02-12 18:30:00')
>>> to_time = dateutil.parser.parse('2014-03-14 18:29:59')
>>> pandas.date_range(from_time, to_time, freq='MS', tz='Asia/Kolkata')
<class 'pandas.tseries.index.DatetimeIndex'>
Length: 0, Freq: MS, Timezone: Asia/Kolkata
```
Why are there no dates being generated even though `2014-03-01 00:00:00+05:30` is in the range?
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"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/24"
} | 11 | 2014-03-14T18:05:35Z | 2014-05-25T20:02:46Z | 2014-03-17T19:48:28Z | CONTRIBUTOR | null | I've recently started using pandas (impressed so far!) and found that plotting large data (from around 100k) samples is quite slow. I traced the bottleneck to the _dt_to_float_ordinal helper function called by DatetimeConverter.(https://github.com/pydata/pandas/blob/master/pandas/tseries/converter.py#L144).
More specifically, this function uses matplotlib's date2num, which converts arrays and iterables using a slow list comprehension. Since pandas seem to natively store datetimes as epoch+nanoseconds in an int64 array, it would be much faster to use matplotlib's vectorized epoch2num instead. In a testcase with 1 million points, using epoch2num is about 100 times faster than date2num:
``` python
from pandas import date_range, DataFrame
from numpy import int64, arange
from matplotlib import pyplot, dates
import time
n = 1e6
df = DataFrame(arange(n), index = date_range('20130101', periods=n, freq='S'))
start = time.time()
pyplot.plot(df.index, df)
print('date2num took {0:g}s'.format(time.time() - start))
pyplot.show()
# monkey patch
import pandas.tseries.converter
def _my_dt_to_float_ordinal(dt):
try:
base = dates.epoch2num(dt.astype(int64) / 1.0E9)
except AttributeError:
base = dates.date2num(dt)
return base
pandas.tseries.converter._dt_to_float_ordinal = _my_dt_to_float_ordinal
start = time.time()
pyplot.plot(df.index, df)
print('epoch2num took {0:g}s'.format(time.time() - start))
pyplot.show()
```
Unfortunately, I am not familiar enough with pandas to know whether date2num is used intentionally or to implement a proper patch myself that works in all cases.
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| null | 5 | 2014-03-14T18:21:27Z | 2014-07-04T19:23:46Z | 2014-03-14T19:37:45Z | CONTRIBUTOR | null | I'm on the fence about this. Having a property with side effects doesn't quite sit right to me. But I think it kinda makes sense here. If it didn't set `freq`, then it really doesn't make sense for `freq` to exist. I'd just use `inferred_freq` all of the time.
Now calling `freq` always tries to infer the frequency. Why not? Seems sensible to me. `inferred_freq` is kind of redundant now, but I left it anyway. I was never really sure of the rationale for its existence.
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} | 5 | 2014-03-14T21:26:36Z | 2021-04-12T16:18:19Z | 2021-04-11T04:21:06Z | CONTRIBUTOR | null | I'm just trying to get my head around what these tools actually do and what they're used for. I can't tell if I'm misunderstanding or this is all just unfinished.
```
from pandas.tseries import frequencies
frequencies._is_annual('AS') # oversight?
# can I go from quarterly to annual?
frequencies.is_subsample('Q', 'A') # yes
frequencies.is_subsample('Q', 'AS') # none?
frequencies.is_subperiod(frequencies.to_offset('Q'), frequencies.to_offset('AS')) # none
# what about weekly to annual?
frequencies.is_subperiod('W', 'A') # false?
```
It looks like a combination of _is_annual and friends not also checking for `AS-`, etc. and the omission of frequency strings in the checks in `is_subperiod`. Easy fix, just want some sanity checking.
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} | 3 | 2014-03-14T21:38:21Z | 2014-06-24T17:52:10Z | 2014-03-15T14:34:37Z | CONTRIBUTOR | null | closes #4370
```
In [3]: df = DataFrame({'one':[2,1,1,1],'two':[1,3,2,4],'three':[5,4,3,2]})
In [7]: df.columns = MultiIndex.from_tuples([('a','one'),('a','two'),('b','three')])
In [8]: df
Out[8]:
a b
one two three
0 2 5 1
1 1 4 3
2 1 3 2
3 1 2 4
[4 rows x 3 columns]
```
These are now equivalent. Used to be able to do only the 2nd.
```
In [9]: df.sort_index(by=('a','two'))
Out[9]:
a b
one two three
3 1 2 4
2 1 3 2
1 1 4 3
0 2 5 1
[4 rows x 3 columns]
In [10]: df.sort_index(by=[('a','two')])
Out[10]:
a b
one two three
3 1 2 4
2 1 3 2
1 1 4 3
0 2 5 1
[4 rows x 3 columns]
```
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} | 1 | 2014-03-14T22:31:13Z | 2014-03-14T22:31:50Z | 2014-03-14T22:31:50Z | CONTRIBUTOR | null | fair warning in #5505
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} | 6 | 2014-03-14T22:49:39Z | 2014-04-08T22:37:50Z | 2014-04-08T22:37:50Z | CONTRIBUTOR | null | need to have open issues so remember to remove these deprecations indciated by FutureWarnings
ideally also link the issues back to the original deprecation. Off the top of my head.
their are more though.
Here is what I found (line numbers as of commit 70de129):
- [x] Deprecate load and save in favor of pickle and to_pickle (#3787)
.\pandas\core\common56.py:{2740, 2756}
.\pandas\core\generic.py:{960, 967}
- [x] Deprecate DateRange in favor of DatetimeIndex (no issue number, commit 6fe2db57)
.\pandas\core\daterange.py:{24}
- [x] Deprecate timeRule and offset in favor of freq (#4853, #4864)
.\pandas\core\datetools.py:{55}
- [x] Deprecate force_unicode (#2224, #2225)
.\pandas\core\format.py:{369, 442}
.\pandas\core\frame.py:{1283,1342,1387}
- [x] Deprecate precision in favor of accuracy (#395)
.\pandas\core\format.py:{2143}
- [x] Deprecate nanRep in favor of na_rep (#275)
.\pandas\core\frame.py:{1133,1285}
.\pandas\core\series.py:{910,1770}
- [x] Deprecate colSpace in favor of col_space (no issue, commit 4a5a677c)
.\pandas\core\frame.py:{1292,1346,1392}
- [x] Deprecate broadcasting TimeSeries along DataFrame index (#2304)
.\pandas\core\frame.py"{2837}
- [x] Deprecate na_last in favor of na_position (#5231, #3917)
.\pandas\core\series.py:{1770}
- [x] Deprecate 'name' in favor of 'symbols' (no issue, commit b921d1a2)
.\pandas\io\data.py:{344}
- [x] Deprecate year/month parameters in finance option signatures (#3822, #3817)
.\pandas\io\data.py:{588,811,848}
- [x] Remove kind from read_excel (#4713)
.\pandas\io\excel.py:{103}
- [x] Deprecate infer_types to have no effect (#4770)
.\pandas\io\html.py:{830}
- [x] Deprecate unique in HDFStore (#3256)
.\pandas\io\pytables.py:{691}
- [x] Deprecate table keyword in HDFStore (#4645)
.\pandas\io\pytables.py:{1124}
- [x] Deprecate rolling_corr_pairwise and expanding_corr_pairwise (#4950)
.\pandas\stats\moments.py:{301,957}
- [x] Deprecate time_rule favor freq (#1042)
.\pandas\stats\moments.py:{554}
- [x] Deprecate inferTimeRule in favor of infer_freq (#391)
.\pandas\tseries\frequencies.py:{247}
- [ ] Deprecate offset in favor of freq (no issue, commit 31363905c20)
.\pandas\tseries\index.py:{180}
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} | 11 | 2014-03-14T23:01:01Z | 2014-06-30T13:49:37Z | 2014-03-28T20:40:51Z | MEMBER | null | Further work on #6292. While looking at possible multi-index support, I thought of first adding this:
- added ability to specify the used column name for the index column in `to_sql` (analoguous to `to_csv`). Good idea?
- I only did it for the new sqlalchemy function, not the legacy one. Only problem is that it starts from the same function call, so all keyword arguments have also to be added to the legacy `to_sql` (https://github.com/jorisvandenbossche/pandas/compare/sql-multiindex?expand=1#diff-b41f9fd042c423682f8e4c4d808dbe64R891) without using it. Is there a better approach? Should I warn that this is ignored if the user specifies this?
- added tests for it
to do:
- [x] should also change this in generic.py
- [ ] check for name conflicts (warn and suggest to use index_label?)
@mangecoeur
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} | 4 | 2014-03-15T03:51:14Z | 2014-06-17T21:54:57Z | 2014-06-17T21:54:57Z | CONTRIBUTOR | null | Many files:
```
['./bench/bench_join_panel.py',
'./pandas/sandbox/qtpandas.py',
'./pandas/tests/test_common.py',
'./pandas/tests/test_frame.py',
'./pandas/tests/test_generic.py',
'./pandas/core/series.py',
'./pandas/core/format.py',
'./pandas/core/internals.py',
'./pandas/core/indexing.py',
'./pandas/io/tests/test_sql.py',
'./pandas/tseries/converter.py',
'./setup.py',
'./doc/make.py',
'./doc/sphinxext/numpydoc/docscrape.py',
'./doc/sphinxext/ipython_sphinxext/ipython_directive.py']
```
have indentation which isn't four-space. Sometimes it's consistently eight spaces:
```
def create_panels_append(cls, panels):
""" return an append list of panels """
panels = [a for a in panels if a is not None]
# corner cases
```
and sometimes it's more inconsistent (5 here, but not elsewhere):
```
with ensure_clean(pname) as path:
self.frame['A'][:5] = nan
```
so that pylint spits out complaints like
```
W:5577,0: Bad indentation. Found 23 spaces, expected 20
```
There's also at least one rogue tab in `testing.py`:
```
def assert_numpy_array_equal(self, np_array, assert_equal):
if np.array_equal(np_array, assert_equal):
return
raise AssertionError('{0} is not equal to {1}.'.format(np_array, assert$
```
Easily fixed, although I wonder if it should be hooked to avoid it happening again.
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} | 18 | 2014-03-15T14:23:05Z | 2014-06-21T02:16:26Z | 2014-03-19T14:04:33Z | MEMBER | null | Following keywords are now acceptable for `dataframe.plot(kind='bar')` and `dataframe.plot(kind='barh')`.
`width`: Specify the bar width. In previous versions, static value 0.5 was passed to matplotlib and it cannot be overwritten.
`pos`: Specify relative alignments for bar plot layout. From 0 (left/bottom-end) to 1(right/top-end). Default is 0.5 (center).
_Examples:_
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} | 5 | 2014-03-15T14:37:40Z | 2014-03-25T11:48:31Z | 2014-03-25T11:48:31Z | CONTRIBUTOR | null | related is #6581
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closes #6643
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#6544
```
In [1]: s = pd.Series(pd.date_range('2012-1-1', periods=3, freq='D'))
In [2]: m = s.max()
In [3]: x = m.to_datetime()
In [4]: x - s
Out[4]:
0 2 days
1 1 days
2 0 days
dtype: timedelta64[ns]
In [5]: m - s
TypeError: ufunc 'negative' did not contain a loop with signature matching types dtype('<M8[ns]') dtype('<M8[ns]')
```
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} | 15 | 2014-03-15T21:17:46Z | 2014-06-19T14:26:36Z | 2014-03-17T19:48:28Z | CONTRIBUTOR | null | closes #6636
This fixes the performance bottleneck described in #6636 by using the vectorized epoch2num for DataIndex arrays. The included test ensures that the DatetimeConverter produces the same results as the Matplotlib's date2num for individual Timestamp objects as well as DatetimeIndex array objects.
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| null | 0 | 2014-03-16T11:11:01Z | 2014-07-03T07:26:40Z | 2014-03-16T11:12:29Z | MEMBER | null | Further work on #6292, non-controversial part of #6627.
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| null | 2 | 2014-03-16T21:32:51Z | 2014-06-21T20:57:48Z | 2014-03-17T14:18:12Z | MEMBER | null | closes #5198
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| null | 0 | 2014-03-16T23:14:57Z | 2014-07-16T08:58:36Z | 2014-03-16T23:15:45Z | MEMBER | null | - some sphinx format tweaks in release.rst
- use new api in 10min.rst
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| null | 2 | 2014-03-17T08:32:34Z | 2014-03-22T21:06:03Z | 2014-03-22T21:06:03Z | NONE | null | Like the title said，we use pandas to handle data from mysql，after some groupby and apply operation，we than send them back to mysql.
The problem is time used to run the same script increased a lot after we update pandas to the newest version.
Could you give any suggestion to the possible reasion for this?
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} | 1 | 2014-03-17T13:23:26Z | 2014-07-08T16:11:44Z | 2014-03-17T13:43:57Z | CONTRIBUTOR | null | address comments from #6516
docs and api
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} | 16 | 2014-03-17T13:38:17Z | 2014-06-12T05:20:06Z | 2014-05-01T15:14:42Z | MEMBER | null | Area plot is added to plotting method. The AreaPlot class is created as a subclass of LinePlot, thus it works also in time series.
By default, area plot is being stacked. When area plot is not stacked (`stacked=False`), alpha value is set to 0.5 to show overlapped area if not configured specifically. As a side benefit, line plot also can be stacked by specifying `stacked=True` (disabled by default). Different from stacked bar plot, I don't know good visualization for positive/negative mixed data. Thus, input must be all positive or all negative when `stacked=True`. I'll try to implement it if there is a good way. Also, area plot doesn't support logy and loglog plot because filling area starts from 0.
Note: Area plot's legend is implemented based on the answer described in:
http://stackoverflow.com/questions/14534130/legend-not-showing-up-in-matplotlib-stacked-area-plot
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} | 3 | 2014-03-17T23:10:30Z | 2014-07-16T08:58:46Z | 2014-03-18T10:09:59Z | CONTRIBUTOR | null | closes #6648.
Admittedly the conditional in `__sub__` is a little gross now.
I didn't add release notes as this is really just patching up a yet-to-be released item, but please let me know if I should.
Also, the test only runs on numpy >= 1.7, is that okay?
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} | 4 | 2014-03-18T03:50:47Z | 2014-03-18T14:39:47Z | 2014-03-18T14:39:47Z | CONTRIBUTOR | null | `test_graphics.py` seems to be failing for me:
```
test_boxplot (__main__.TestDataFramePlots) ... > /usr/local/lib/python2.7/dist-packages/pandas-0.13.1_436_g4216178-py2.7-linux-i686.egg/pandas/core/internals.py(64)__init__()
-> '%d' % (len(values), len(items)))
(Pdb) bt
/usr/lib/python2.7/unittest/case.py(331)run()
-> testMethod()
/home/dsm/sys/pandas/pandas/tests/test_graphics.py(633)test_boxplot()
-> _check_plot_works(df.boxplot, column=['one', 'two'], by='indic')
/home/dsm/sys/pandas/pandas/tests/test_graphics.py(1226)_check_plot_works()
-> ret = f(*args, **kwargs)
/usr/local/lib/python2.7/dist-packages/pandas-0.13.1_436_g4216178-py2.7-linux-i686.egg/pandas/core/frame.py(4865)boxplot()
-> fontsize=fontsize, grid=grid, rot=rot, **kwds)
/usr/local/lib/python2.7/dist-packages/pandas-0.13.1_436_g4216178-py2.7-linux-i686.egg/pandas/tools/plotting.py(1995)boxplot()
-> ax=ax)
/usr/local/lib/python2.7/dist-packages/pandas-0.13.1_436_g4216178-py2.7-linux-i686.egg/pandas/tools/plotting.py(2409)_grouped_plot_by_column()
-> plotf(gp_col, ax, **kwargs)
/usr/local/lib/python2.7/dist-packages/pandas-0.13.1_436_g4216178-py2.7-linux-i686.egg/pandas/tools/plotting.py(1973)plot_group()
-> bp = ax.boxplot(values, **kwds)
/usr/local/lib/python2.7/dist-packages/matplotlib-1.4.x-py2.7-linux-i686.egg/matplotlib/axes/_axes.py(3003)boxplot()
-> labels=labels)
/usr/local/lib/python2.7/dist-packages/matplotlib-1.4.x-py2.7-linux-i686.egg/matplotlib/cbook.py(2029)boxplot_stats()
-> np.compress(x < stats['whislo'], x),
/usr/local/lib/python2.7/dist-packages/numpy/core/fromnumeric.py(1560)compress()
-> return _wrapit(a, 'compress', condition, axis, out)
/usr/local/lib/python2.7/dist-packages/numpy/core/fromnumeric.py(47)_wrapit()
-> result = wrap(result)
/usr/local/lib/python2.7/dist-packages/pandas-0.13.1_436_g4216178-py2.7-linux-i686.egg/pandas/core/series.py(374)__array_wrap__()
-> copy=False).__finalize__(self)
/usr/local/lib/python2.7/dist-packages/pandas-0.13.1_436_g4216178-py2.7-linux-i686.egg/pandas/core/series.py(232)__init__()
-> data = SingleBlockManager(data, index, fastpath=True)
/usr/local/lib/python2.7/dist-packages/pandas-0.13.1_436_g4216178-py2.7-linux-i686.egg/pandas/core/internals.py(3670)__init__()
-> block = make_block(block, axis, axis, ndim=1, fastpath=True)
/usr/local/lib/python2.7/dist-packages/pandas-0.13.1_436_g4216178-py2.7-linux-i686.egg/pandas/core/internals.py(2058)make_block()
-> placement=placement)
> /usr/local/lib/python2.7/dist-packages/pandas-0.13.1_436_g4216178-py2.7-linux-i686.egg/pandas/core/internals.py(64)__init__()
-> '%d' % (len(values), len(items)))
(Pdb) print values
[]
(Pdb) print items
Index([u'b', u'd', u'f'], dtype='object')
(Pdb) print ref_items
Index([u'b', u'd', u'f'], dtype='object')
(Pdb) print ndim
1
(Pdb) print fastpath
True
(Pdb) print placement
None
```
Standard TMI dump:
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.5.final.0
python-bits: 32
OS: Linux
OS-release: 3.11.0-18-generic
machine: i686
processor: i686
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
pandas: 0.13.1-436-g4216178
Cython: 0.20.1
numpy: 1.9.0.dev-e792e15
scipy: 0.15.0.dev-4aa534f
statsmodels: 0.6.0.dev-f3f5467
IPython: 1.2.1
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.1
bottleneck: 0.8.0
tables: None
numexpr: 2.3.1
matplotlib: 1.4.x
openpyxl: 1.8.4
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.3.3
bs4: 4.3.2
html5lib: 1.0b3
bq: None
apiclient: 1.2
rpy2: None
sqlalchemy: 0.9.3
pymysql: None
psycopg2: None
```
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} | 0 | 2014-03-18T12:18:47Z | 2014-06-21T09:06:49Z | 2014-03-18T14:39:47Z | CONTRIBUTOR | null | closes #6658
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} | 11 | 2014-03-18T13:38:31Z | 2014-07-05T02:53:02Z | 2014-03-24T11:03:56Z | CONTRIBUTOR | null | The visualization documentation only mentions plotting using pandas' interface. This patch adds another section mentions that it is also possible to plot directly with MPL and that date and time formatters are automatically available through all plots in MPL. An example shows matplotlib's errorbar function where points and errors on the x axis are respectively timestamps and offsets.
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} | 14 | 2014-03-18T14:42:00Z | 2014-07-08T21:46:11Z | 2014-04-07T15:41:42Z | MEMBER | null | closes #4803
Related to #4803, I prepared some codes to add a `matplotlib.Table` to `DataFrame` and `Series` plot using `table` keyword. `table` can take followings as an input:
- `False`: Do nothing (default).
- `True`: Draw a table using the data called `plot` method. Data will be transposed to meet the matplotlib's default layout.
- `DataFrame` or `Series`: Draw matplotlib.table using the passed data. The data will be drawn as displayed in print method (not transposed automatically).
Also, helper function `pandas.tools.plotting.table` is added to create a table from `DataFrame` and `Series`, and add it to the `matplotlib.Axes`.
_Example:_
```
fig, axes = plt.subplots(1, 3, figsize=(14, 4))
plt.subplots_adjust(top=0.97, bottom=0.2, left=0.05, right=0.97, hspace=0.2)
df.plot(ax=axes[0], table=True, legend=False)
axes[0].get_xaxis().set_visible(False)
df.plot(ax=axes[1], table=np.round(df.T, 2), legend=False)
axes[1].get_xaxis().set_visible(False)
df.plot(ax=axes[2], legend=False)
import pandas.tools.plotting as plotting
plotting.table(axes[2], np.round(df.describe(), 2),
loc='upper right', colWidths=[0.2, 0.2, 0.2])
plt.show()
```
_Outputs:_
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| null | 4 | 2014-03-18T16:11:24Z | 2014-03-18T17:20:01Z | 2014-03-18T16:14:39Z | MEMBER | null | If I have some numeric columns over which I want to compute the mean and I have at least one string column, it takes much too long to compute. I can't even hit Ctrl-C to interrupt the process (if the frame is large enough). Interestingly, the string columns _are_ discarded in the final result. The perf difference is about a factor of 800 when the frame has 10000 elements.
```
In [18]: n = 10000
In [19]: df = DataFrame(randn(n, 2), columns=list('ab'))
In [20]: df['c'] = [pd.util.testing.rands(5) for _ in xrange(n)]
In [21]: df.head(10)
Out[21]:
a b c
0 1.0393 0.5719 AVi6V
1 0.6642 0.7441 mtqXk
2 -1.1552 0.1583 euUoo
3 0.7759 0.7647 cAAk2
4 -0.4958 0.4079 TYRRj
5 -0.7168 -1.1523 YT34i
6 1.5557 -1.7054 vXtgM
7 0.2898 -0.4858 2Rs1P
8 0.3752 0.2802 4UUz1
9 -0.2449 -2.3170 Bbue3
[10 rows x 3 columns]
In [22]: timeit df.mean()
10 loops, best of 3: 48.6 ms per loop
In [23]: dfnum = df[['a', 'b']]
In [24]: timeit dfnum.mean()
10000 loops, best of 3: 61.6 µs per loop
In [25]: 48.6 * 1000 / 61.6
Out[25]: 788.961038961039 # this is huge
```
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| null | 5 | 2014-03-18T16:48:18Z | 2014-03-18T17:16:57Z | 2014-03-18T17:16:52Z | CONTRIBUTOR | null | Let's say I have a DataFrame of stock returns. It would be great to be able to sort easily the DataFrame by the `abs` value of the returns. Currently I do this by inserting a new column and then sorting on the new column and than slicing on the original columns. This is ungainly:
```
ret["returns_abs"] = ret.returns.abs()
ret.sort("returns_abs", ascending=False)[["returns", "key"]]
```
It would be great if I could either pass a Series or a function to the sort method:
```
ret.sort(ret.returns.abs(), ascending=False)
```
or
```
ret.sort(lambda x: abs(x.ix["returns"], ascending=False)
```
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| null | 3 | 2014-03-18T17:05:09Z | 2014-03-21T19:36:31Z | 2014-03-21T19:36:23Z | CONTRIBUTOR | null | If I plot the entire DataFrame, the x-axis has too many labels (there are only 89 rows):
Even plotting 80/89 rows works fine:
I am using the following data:
```
csv_text ="""Date,99%
2013-10-31,1
2013-11-01,1
2013-11-05,1
2013-11-06,1
2013-11-07,1
2013-11-08,1
2013-11-12,1
2013-11-13,1
2013-11-14,1
2013-11-15,1
2013-11-18,1
2013-11-19,1
2013-11-20,1
2013-11-21,1
2013-11-22,1
2013-11-25,1
2013-11-26,1
2013-11-27,1
2013-11-29,1
2013-12-02,1
2013-12-03,1
2013-12-04,1
2013-12-05,1
2013-12-06,1
2013-12-09,1
2013-12-10,1
2013-12-11,1
2013-12-12,1
2013-12-13,1
2013-12-16,1
2013-12-17,1
2013-12-18,1
2013-12-19,1
2013-12-20,1
2013-12-23,1
2013-12-24,1
2013-12-26,1
2013-12-27,1
2013-12-30,1
2013-12-31,1
2014-01-02,1
2014-01-03,1
2014-01-06,1
2014-01-07,1
2014-01-08,1
2014-01-10,1
2014-01-13,1
2014-01-14,1
2014-01-15,1
2014-01-16,1
2014-01-17,1
2014-01-21,1
2014-01-22,1
2014-01-23,1
2014-01-24,1
2014-01-27,1
2014-01-28,1
2014-01-29,1
2014-01-30,1
2014-01-31,1
2014-02-04,1
2014-02-05,1
2014-02-06,1
2014-02-07,1
2014-02-10,1
2014-02-11,1
2014-02-12,1
2014-02-13,1
2014-02-14,1
2014-02-18,1
2014-02-19,1
2014-02-20,1
2014-02-21,1
2014-02-24,1
2014-02-25,1
2014-02-26,1
2014-02-27,1
2014-02-28,1
2014-03-03,1
2014-03-04,1
2014-03-05,1
2014-03-06,1
2014-03-07,1
2014-03-10,1
2014-03-11,1
2014-03-12,1
2014-03-13,1
2014-03-14,1
2014-03-17,1"""
df = pd.read_csv(StringIO(csv_text), parse_dates="Date", index_col=["Date"])
```
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} | 0 | 2014-03-19T01:00:18Z | 2021-04-11T04:21:23Z | null | CONTRIBUTOR | null | maybe some more args similar to read_csv would be good too?
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If none provided, falls back to default behavior.
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} | 33 | 2014-03-19T10:49:48Z | 2014-06-16T02:44:13Z | 2014-04-21T17:42:45Z | CONTRIBUTOR | null | closes #6472
The new keyword prevents the conversion from escaping column names
Currently, a sequence like $e^x$ gets properly formatted when you display a dataframe in an ipython notebook, but gets escaped when converted to latex.
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} | 8 | 2014-03-19T14:33:03Z | 2015-03-08T16:41:48Z | 2015-03-08T16:41:48Z | NONE | null | Hi all
Suppose I have a timeseries indexed by a PeriodIndex at day resolution, and I want to numerically integrate it over a range whose start- and end-point are known to a higher time resolution (say, to the exact second).
As the first step towards this, it'd help to take a slice of my timeseries in which the values at the start and end are computed "pro rata" according to the proportion of that calendar day Period which is included in the range.
I was looking for a way to do this with `reindex`, but it's not really a reindexing, more a slicing kind of operation with special-casing of the endpoints. Is there a way to do this in pandas? Seems like it could be a useful addition if not, I imagine it could be useful for various accounting applications too.
(Even better would be a way to numerically integrate a timeseries directly while specifying endpoints at higher time resolution than the index. Unfortunately this doesn't seem possible with the sample-based integrators in `scipy.integrate`.)
Cheers!
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| null | 5 | 2014-03-19T14:40:29Z | 2017-05-15T21:15:52Z | 2014-03-19T16:59:43Z | CONTRIBUTOR | null | Came up upon merging another PR. See comment https://github.com/pydata/pandas/pull/6644#issuecomment-38057195
The test relies on drawing at least 1 draw out of 10 uniform draw to be larger than 0.5; even if this didn't cause the failure originally, it should still be fixed. I set a seed that's known to produce the desired values.
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} | 8 | 2014-03-19T21:20:40Z | 2019-05-06T23:29:56Z | 2019-05-06T23:29:56Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/22517167/slicing-pandas-dataframe-by-column-numbers-i-dont-want/22517737?noredirect=1#comment34263807_22517737
```
ic = lambda x: pd.Int64Index(np.arange(x))
In [11]: df1.iloc[:,~ic(len(df1.columns)).isin(np.r_[0,3:5])]
```
could borrow from `query` and support parsing I think
```
df1.iloc[:,'~np.r_[0,3:5]`]
```
can't parse this normally as `~` makes the indexer negative which means you index off the rhs of frame, which is not right
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} | 5 | 2014-03-20T04:05:54Z | 2014-08-10T13:36:10Z | 2014-03-20T20:04:35Z | CONTRIBUTOR | null | closes #5609
## Test code
```
import pandas as pd
import numpy as np
import timeit
xdim = 10000
ydim = 500
repeats = 10
df = pd.DataFrame(np.random.rand(xdim,ydim))
# axis 0 shift
s = timeit.default_timer()
for i in range(repeats):
df1 = df.shift(1,axis=0)
e = timeit.default_timer()
print "Axis 0 shift",e-s
# axis 1 shift
s = timeit.default_timer()
for i in range(repeats):
df1 = df.shift(1,axis=1)
e = timeit.default_timer()
print "Axis 1 shift",e-s
```
## Results
```
V0.13.1
Axis 0 shift 0.958936203491
Axis 1 shift: IndexError: index 500 is out of bounds for size 500
----------------------------------------------------------------------------------
V0.13.1-iss5609 branch
Axis 0 shift 0.1622
Axis 1 shift 0.3466
```
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} | 5 | 2014-03-20T09:20:04Z | 2018-07-31T13:13:28Z | 2018-07-31T13:13:28Z | CONTRIBUTOR | null | #5637 has some examples as well
When the end date in `date_range` is not a valid business day it appears that `date_range` first sets it to a valid date _before_ deciding whether to return the last date depending on whether the interval is closed or open on the right. This results in the date range being incorrectly truncated when the interval is open-right - e.g.
``` python
In [22]: for d in pd.date_range('20-Mar-2014', '23-Mar-2014', freq='D'):
...: print d.strftime('%a %d-%b-%Y')
...:
Thu 20-Mar-2014
Fri 21-Mar-2014
Sat 22-Mar-2014
Sun 23-Mar-2014
In [23]: pd.date_range('20-Mar-2014', '23-Mar-2014', freq='B')
Out[23]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2014-03-20, 2014-03-21]
Length: 2, Freq: B, Timezone: None
In [24]: # should return all valid business days <= '23-Mar-2014'
...: pd.date_range('20-Mar-2014', '23-Mar-2014', freq='B', closed='left')
<class 'pandas.tseries.index.DatetimeIndex'>
[2014-03-20]
Length: 1, Freq: B, Timezone: None
```
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| null | 2 | 2014-03-20T10:12:57Z | 2014-03-20T13:25:25Z | 2014-03-20T12:25:07Z | NONE | null | Upon profiling a process which needed to be optimized I found that renaming columns NOT inplace improves performance (execution time) by x120.
Profiling indicates this is related to garbage collection (see below).
Furthermore, the expected performance is recovered by avoiding the dropna method.
The following short example demonstrates a factor x12:
``` python
import pandas as pd
import numpy as np
```
### inplace=True
``` python
%%timeit
np.random.seed(0)
r,c = (7,3)
t = np.random.rand(r)
df1 = pd.DataFrame(np.random.rand(r,c), columns=range(c), index=t)
indx = np.random.choice(range(r),r/3, replace=False)
t[indx] = np.random.rand(len(indx))
df2 = pd.DataFrame(np.random.rand(r,c), columns=range(c), index=t)
df = (df1-df2).dropna()
## inplace rename:
df.rename(columns={col:'d{}'.format(col) for col in df.columns}, inplace=True)
```
> 100 loops, best of 3: 15.6 ms per loop
first output line of `%%prun`:
> ncalls tottime percall cumtime percall filename:lineno(function)
> 1 0.018 0.018 0.018 0.018 {gc.collect}
### inplace=False
``` python
%%timeit
np.random.seed(0)
r,c = (7,3)
t = np.random.rand(r)
df1 = pd.DataFrame(np.random.rand(r,c), columns=range(c), index=t)
indx = np.random.choice(range(r),r/3, replace=False)
t[indx] = np.random.rand(len(indx))
df2 = pd.DataFrame(np.random.rand(r,c), columns=range(c), index=t)
df = (df1-df2).dropna()
## avoid inplace:
df = df.rename(columns={col:'d{}'.format(col) for col in df.columns})
```
> 1000 loops, best of 3: 1.24 ms per loop
### avoiding dropna
The expected performance is recovered by avoiding the `dropna` method:
``` python
%%timeit
np.random.seed(0)
r,c = (7,3)
t = np.random.rand(r)
df1 = pd.DataFrame(np.random.rand(r,c), columns=range(c), index=t)
indx = np.random.choice(range(r),r/3, replace=False)
t[indx] = np.random.rand(len(indx))
df2 = pd.DataFrame(np.random.rand(r,c), columns=range(c), index=t)
#no dropna:
df = (df1-df2)#.dropna()
## inplace rename:
df.rename(columns={col:'d{}'.format(col) for col in df.columns}, inplace=True)
```
> 1000 loops, best of 3: 865 µs per loop
``` python
%%timeit
np.random.seed(0)
r,c = (7,3)
t = np.random.rand(r)
df1 = pd.DataFrame(np.random.rand(r,c), columns=range(c), index=t)
indx = np.random.choice(range(r),r/3, replace=False)
t[indx] = np.random.rand(len(indx))
df2 = pd.DataFrame(np.random.rand(r,c), columns=range(c), index=t)
## no dropna
df = (df1-df2)#.dropna()
## avoid inplace:
df = df.rename(columns={col:'d{}'.format(col) for col in df.columns})
```
> 1000 loops, best of 3: 902 µs per loop
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} | 5 | 2014-03-20T20:37:45Z | 2021-04-11T04:21:52Z | null | MEMBER | null | I often find myself writing code that looks like this:
``` python
start_group = df[df.str_col == 'start'].index.values + 1
end_group = df[df.str_col == 'end'].index.values - 1
for s, e in zip(start_group, end_group):
some_function(df[s:e])
```
to get frame subsets between repeated start and end indicators from a particular column.
Possible API:
```
df.grange(marker_col='str_col', start='start', end='end')
```
which would return an iterator over the the subsets.
I'm not sure if this is general enough to include in library code. I'll happily close the issue if it's not. There also might be a way to do this already.
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} | 1 | 2014-03-21T00:06:53Z | 2014-03-21T12:46:08Z | 2014-03-21T12:46:08Z | MEMBER | null | Current behavior is to use the index over the columns. I think it makes more sense to prefer the columns.
Current behavior:
```
In [7]: n = 10
In [8]: df = DataFrame(randint(40, 50, size=(n, 2)), columns=list('ab'))
In [9]: df.index.name = 'a'
In [10]: df
Out[10]:
a b
a
0 43 40
1 43 46
2 41 42
3 40 45
4 41 41
5 40 45
6 47 44
7 49 41
8 40 40
9 47 40
[10 rows x 2 columns]
In [11]: df.query('a > 5')
Out[11]:
a b
a
6 47 44
7 49 41
8 40 40
9 47 40
[4 rows x 2 columns]
```
New behavior:
```
In [10]: df
Out[10]:
a b
a
0 45 47
1 44 41
2 44 48
3 47 40
4 48 48
5 47 40
6 41 47
7 44 44
8 41 41
9 40 47
[10 rows x 2 columns]
In [11]: df.query('a > 43')
Out[11]:
a b
a
0 45 47
1 44 41
2 44 48
3 47 40
4 48 48
5 47 40
7 44 44
[7 rows x 2 columns]
```
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} | 1 | 2014-03-21T00:09:32Z | 2014-06-24T20:05:14Z | 2014-03-21T12:46:08Z | MEMBER | null | closes #6676
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} | 15 | 2014-03-21T07:30:24Z | 2014-06-12T19:37:46Z | 2014-04-22T13:10:31Z | MEMBER | null | There seems to be some inconsistencies related to `DataFrame.plot` and `Series.plot` legend behaviors.
_Problems:_
- When `DataFrame.plot` or `Series.plot` plots data on the same axes repeatedly:
- If the target axes already has a legend, line plot always appends its legend to existing one ignoring `legend` kw and existing legend will be drawn as line artist regardless of actual artist type. Also, legend cannot be `reverse`ed if the axes already has a legend.
- Bar/BarH plot deletes the existing legend and overwrites with latest one.
- KDE plot appends its legend to the existing one, and will apply `reverse` to all artists including the existing one.
- When `subplots` is enabled, line plot draws legends on each axes but bar plot doesn't.
- Scatter plot does not draw legend even if `label` keyword is passed.
_Fix:_
I've prepared a fix based on following concept, except `hexbin` which doesn't use legend.
- Legend should be drawn by the order of the artists drawn.
- Each legend should be drawn according to the passed `legend` value.
- If df1 plots with `legend=True` and df2 with `legend=False`, only df1's legend should appear.
- If df2 plots with `legend='reverse'`d, only df2's legend should be reversed.
- When `subplots=True` and `legend=True`, each subplot axes should have its own legend (standardize current line plot behavior).
_Example Code_
```
df1 = DataFrame(randn(6, 3), index=range(6), columns=['a', 'b', 'c'])
df2 = DataFrame(randn(6, 3), index=range(6), columns=['d', 'e', 'f'])
df3 = DataFrame(randn(6, 3), index=range(6), columns=['x', 'y', 'z'])
fig, axes = plt.subplots(1, 5, figsize=(14, 3))
for i, (legend, df) in enumerate(zip([True, False, 'reverse'], [df1, df2, df3])):
df.plot(ax=axes[0], legend=legend, title='line')
df.plot(kind='bar', ax=axes[1], legend=legend, title='bar')
df.plot(kind='barh', ax=axes[2], legend=legend, title='barh')
df.plot(kind='kde', ax=axes[3], legend=legend, title='kde')
df.plot(kind='scatter', ax=axes[4], x=df.columns[0], y=df.columns[1], label=i,
legend=legend, title='scatter')
plt.show()
```
_Output using current repository_
- For line, bar, kde plot, expected legend is `a, b, c, z, y x`. Because df2 was plot by `legend=False`, and df3 was plot by `legend='reverse'`.
- For scatter plot, `0, 2` is expected because df2 was plot by `legend=False`.
_Output after fix_
If there is anything should be considered, please let me know. Thanks.
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} | 5 | 2014-03-21T13:57:55Z | 2014-06-26T08:01:41Z | 2014-03-22T21:02:43Z | CONTRIBUTOR | null | The following code:
```
df=DataFrame(data=[1,2,True,4.5], columns=["A"])
df["B"]=[1,1,1,2]
print df.groupby(df["B"]).sum()
print df.groupby(df["B"]).max()
```
gives as ouput
```
B
B
1 3
2 2
[2 rows x 1 columns]
A B
B
1 2.0 1
2 4.5 2
[2 rows x 2 columns]
```
As we can see, the sum() operation has removed the column A (probably because the dtype is object/mixed). Is it expected ? The max() operation has no trouble with the column A.
Moreover
```
df["A"].sum()
```
gives
```
8.5
```
which shows sum() is capable of handling mixed dtype.
Using aggregate(numpy.sum) gives the same result than sum().
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} | 4 | 2014-03-21T14:56:43Z | 2014-04-24T12:01:25Z | 2014-03-25T11:49:45Z | CONTRIBUTOR | null | for consistency with `dropna` (and also to not use `cols` which is being deprecated)
related #6645
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https://api.github.com/repos/pandas-dev/pandas/issues/6681 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6681/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6681/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6681/events | https://github.com/pandas-dev/pandas/issues/6681 | 29,918,780 | MDU6SXNzdWUyOTkxODc4MA== | 6,681 | BUG: align on level for binary ops | {
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} | 0 | 2014-03-21T17:26:56Z | 2014-03-21T20:10:13Z | 2014-03-21T20:10:13Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/22559172/multiply-all-columns-of-a-multi-indexed-dataframe-by-appropriate-values-in-a-ser
related to #5645 as well
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https://api.github.com/repos/pandas-dev/pandas/issues/6682 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6682/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6682/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6682/events | https://github.com/pandas-dev/pandas/pull/6682 | 29,919,095 | MDExOlB1bGxSZXF1ZXN0MTM4NDY5MjQ= | 6,682 | BUG: Bug in binary operations with a rhs of a Series not aligning (GH6681) | {
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} | 0 | 2014-03-21T17:30:43Z | 2014-06-23T20:21:08Z | 2014-03-21T20:10:13Z | CONTRIBUTOR | null | closes #6681
```
In [11]: index=MultiIndex.from_product([list('abc'),
['one','two','three'],
[1,2,3]],
names=['first','second','third'])
In [12]: df = DataFrame(np.arange(27*3).reshape(27,3),
index=index,
columns=['value1','value2','value3']).sortlevel()
In [13]: df
Out[13]:
value1 value2 value3
first second third
a one 1 0 1 2
2 3 4 5
3 6 7 8
three 1 18 19 20
2 21 22 23
3 24 25 26
two 1 9 10 11
2 12 13 14
3 15 16 17
b one 1 27 28 29
2 30 31 32
3 33 34 35
three 1 45 46 47
2 48 49 50
3 51 52 53
two 1 36 37 38
2 39 40 41
3 42 43 44
c one 1 54 55 56
2 57 58 59
3 60 61 62
three 1 72 73 74
2 75 76 77
3 78 79 80
two 1 63 64 65
2 66 67 68
3 69 70 71
[27 rows x 3 columns]
In [15]: x = Series([ 1.0, 10.0], ['two','three'])
In [16]: x
Out[16]:
two 1
three 10
dtype: float64
```
Passing a level now aligns by that level
```
In [14]: df.mul(x,level='second',axis=0)
Out[14]:
value1 value2 value3
first second third
a one 1 NaN NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
three 1 180 190 200
2 210 220 230
3 240 250 260
two 1 9 10 11
2 12 13 14
3 15 16 17
b one 1 NaN NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
three 1 450 460 470
2 480 490 500
3 510 520 530
two 1 36 37 38
2 39 40 41
3 42 43 44
c one 1 NaN NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
three 1 720 730 740
2 750 760 770
3 780 790 800
two 1 63 64 65
2 66 67 68
3 69 70 71
[27 rows x 3 columns]
```
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| null | 23 | 2014-03-21T17:51:15Z | 2021-04-10T16:28:37Z | 2014-03-21T18:24:30Z | NONE | null | When using indices, we are encouraged to use .loc instead of .ix. Also the "SettingWithCopyWarning:" recommends us to use .loc instead. But it seems the performance of .loc and .iloc is 20-30 times slower than .ix (I am using Pandas 0.13.1)
.ix takes 4.54897093773 sec
.iloc takes 111.531260967 sec
.loc takes 92.8014230728 sec
The costs for .loc and .iloc seems too high. Thanks!
-- test code ---
# !/usr/bin/env python
import pandas as pd
import numpy as np
import time
t=pd.DataFrame(data=np.zeros([100000,6]), columns=['A','B','C','D','E','F'])
start=time.time()
for i in t.index:
for j in ['A','B','C','D','E']:
t.ix[i,j]
t1=time.time()
print t1-start
for i in xrange(100000):
for j in xrange(6):
t.iloc[i,j]
t2=time.time()
print t2-t1
for i in t.index:
for j in ['A','B','C','D','E']:
t.loc[i,j]
t3=time.time()
print t3-t2
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} | 9 | 2014-03-21T18:30:20Z | 2014-03-23T13:41:24Z | 2014-03-23T13:41:24Z | NONE | null | Hi,
I noticed that when exporting data to stata the `NaN` values are not always converted to Stata missing values but instead left blank. This somehow confuses Stata which does not allow using the `destring` command to solve the problem nor using `replace value=. if value==.`.
As an Example I downloaded the [World Development Indicators](http://databank.worldbank.org/data/download/WDI_excel.zip) and used the following commands to export National Savings to an the excel and csv file:
```
import pandas as pd
import os
dfwdi=pd.read_excel('WDI.xlsx','Data')
dfwdi.columns
dfout=dfwdi.ix[dfwdi['Indicator Code']=='NY.GDS.TOTL.ZS']
dfout
cols=['savyr'+str(i) for i in xrange(1960,dfwdi.columns.values[-1]+1)]
dfout.reset_index(inplace=True, drop=True)
dfout.to_csv('sav.csv', index=False)
dfout.to_stata('sav.dta', write_index=False)
```
If you import the data into Stata (I am using v.13) and run the following commands, things fail.
```
use "sav.dta", clear
* Correct number of missing values
summ savyr2000
reg savyr2010 savyr 2000
* Correct countries identified as missing
tab code if savyr==.
* replace missing values to "."
* One cannot replace the missing not presented as "."
replace savyr2010==. if savyr==""
* Use "." to identify
replace savyr2010==. if savyr==.
* Perform analysis again
summ savyr2000
reg savyr2010 savyr 2000
* Still fails
```
As you can see Stata does not perform the analysis, even though it correctly recognizes the missing values. But not all of them are presented as ".". If one imports the the csv version into Stata and runs the same initial commands it works fine.
```
import delimited "sav.csv"
* Correct number of missing values
summ savyr2000
reg savyr2010 savyr 2000
```
Furthermore, for some reason the index is still present in the stata file, even though I had used the `write_index=False` option.
I am using Enthought's Canopy distribution on OSX Mavericks with Pandas '0.13.1'. Haven't tried on other Python dists.
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Stata does not correctly handle NaNs, and so these must be replaced with Stata
missing values (. by default). The fix checks floating point columns for nan
and replaces these with the Stata numeric code for (.).
The write_index option was also being ignored by omission. This has been fixed and
numerous tests which were not correct have been fixed.
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I'm running a pretty recent build of Pandas ('0.13.1-213-gc174c3d') on Python 2.7.5 on OS X 0.9.2.
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} | 0 | 2014-03-22T23:25:25Z | 2014-06-27T12:22:23Z | 2014-03-23T13:49:21Z | CONTRIBUTOR | null | closes #4076
this only happened on the cythonized methods and not the python agg ones (this is why count worked, but sum did not)
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} | 7 | 2014-03-23T00:01:35Z | 2014-06-17T15:37:54Z | 2014-03-26T20:50:44Z | MEMBER | null | Closes #4525.
I modified the `BarPlot` internal alignment to meet line coordinates. Previously, `BarPlot` doesn't pass `align` keyword to matplotlib (thus matplotlib uses `align='edge'`), but it adjusts bar locations to looks like `align='center'`.
Now `BarPlot` pass `align=center` to matplotlib by default and ticks are being locates on integer value starts from 0 (0.0, 1.0 ...). Drawing bar and line on the same axes looks like below.
_Output using current master:_
_Output after fix_
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| null | 2 | 2014-03-23T05:10:06Z | 2014-05-05T13:25:59Z | 2014-05-05T13:25:55Z | NONE | null | Wondering if there is a way to insert a pandas DF into Cassandra?
Here is the info of the python driver:
https://github.com/datastax/python-driver
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} | 11 | 2014-03-23T18:42:34Z | 2014-06-17T07:59:48Z | 2014-03-25T11:49:20Z | CONTRIBUTOR | null | closes #6680 -- Deprecate `cols` in `drop_duplicates()` and `duplicated()` (rename to `subset`)
closes #6645 -- Deprecate `cols` in `to_csv()` and `to_excel()` (rename to `columns`)
releated to #6686
Related #5505 -- Deprecate `cols` and `rows` in `pivot_table()` and `crosstab()` (rename to `columns` and `index`)
Related #6581 -- Reminder to come back in 0.16, remove deprecation warnings, and use new arguments
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| null | 7 | 2014-03-23T19:16:40Z | 2014-03-23T20:28:25Z | 2014-03-23T19:36:04Z | CONTRIBUTOR | null | Is this our fault or a pandas bug?
```
======================================================================
ERROR: test suite for <class 'statsmodels.tsa.tests.test_expsmoothing.TestExpSmoothing'>
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python3/dist-packages/nose/suite.py", line 209, in run
self.setUp()
File "/usr/lib/python3/dist-packages/nose/suite.py", line 292, in setUp
self.setupContext(ancestor)
File "/usr/lib/python3/dist-packages/nose/suite.py", line 315, in setupContext
try_run(context, names)
File "/usr/lib/python3/dist-packages/nose/util.py", line 478, in try_run
return func()
File "/usr/local/lib/python3.2/dist-packages/statsmodels/tsa/tests/test_expsmoothing.py", line 13, in setupClass
dta = co2.load_pandas().data
File "/usr/local/lib/python3.2/dist-packages/statsmodels/datasets/co2/data.py", line 62, in load_pandas
index = pd.to_datetime(data.data['date'], format='%Y%m%d')
File "/usr/lib/python3/dist-packages/pandas/tseries/tools.py", line 128, in to_datetime
return _convert_listlike(arg, box=box)
File "/usr/lib/python3/dist-packages/pandas/tseries/tools.py", line 104, in _convert_listlike
result = tslib.array_strptime(arg, format)
File "tslib.pyx", line 1112, in pandas.tslib.array_strptime (pandas/tslib.c:18285)
TypeError: can't use a string pattern on a bytes-like object
```
https://travis-ci.org/statsmodels/statsmodels/jobs/21129388
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| null | 3 | 2014-03-24T09:08:20Z | 2014-03-24T11:09:36Z | 2014-03-24T11:09:36Z | NONE | null | Hello,
I have a strange performance issue that I want to share with you...
When I have a TimeSeries (let's call it "ts"), I discovered that it is faster to apply it on ts.values than on ts (see example 1). Is that logical ?
This have a large impact on performance when running for example the "apply function after a groupby (see example 2 below).
I do not understand exactly the reason but from now, I'm usung a wrapper around the apply function that first checks if a could simply use the "ts.values" version of the function...
Hope this can help to improve this great tool !
Patrick
Example 1
```
import pandas as pd
ts=pd.TimeSeries(index=range(0,10000),data=np.random.normal(scale=100,size=10000))
%timeit np.mean(ts)
10000 loops, best of 3: 42.7 µs per loop
%timeit np.mean(ts.values)
10000 loops, best of 3: 28.9 µs per loop
```
Example 2
```
import pandas as pd
ts=pd.TimeSeries(index=range(0,10000),data=np.random.normal(scale=100,size=10000))
grouped=ts.groupby(by=lambda x:np.round(x/100)*100)
def fct(x):
med = x.median()
threshold = np.percentile(abs(x-med),[90])
return x[abs(x-med)<threshold].mean()
def fct2(x):
x=x.values
med = np.median(x)
threshold = np.percentile(abs(x-med),[90])
return x[abs(x-med)<threshold].mean()
%timeit grouped.apply(fct)
10 loops, best of 3: 76.4 ms per loop
%timeit grouped.apply(fct2)
100 loops, best of 3: 17.9 ms per loop
%timeit np.mean(ts)
10000 loops, best of 3: 42.7 µs per loop
%timeit np.mean(ts.values)
10000 loops, best of 3: 28.9 µs per loop
```
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| null | 1 | 2014-03-24T10:38:30Z | 2014-03-24T11:10:41Z | 2014-03-24T11:10:41Z | NONE | null | I am having trouble using partial with groupby and apply in Pandas. Perhaps I am not using this right?
data = {'a':[1,1,2,2],'b':['Y','Y','N','Y'], 'c':['Y','Y','N','Y']}
df = pandas.DataFrame(data)
def countY(columnName, group): return len(group[group[columnName] == 'Y'])
df.groupby('a').apply(partial(countY, 'b'))
AttributeError: 'functools.partial' object has no attribute '_module_'
welcome@welcome-ThinkCentre-Edge72:~$ python
Python 2.7.3 (default, Feb 27 2014, 19:58:35)
[GCC 4.6.3] on linux2
Type "help", "copyright", "credits" or "license" for more information.
> > > import pandas
> > > from functools import partial
> > > data = {'a':[1,1,2,2],'b':['Y','Y','N','Y'], 'c':['Y','Y','N','Y']}
> > > df = pandas.DataFrame(data)
> > > def countY(columnName, group): return len(group[group[columnName] == 'Y'])
> > > ...
> > > df.groupby('a').apply(partial(countY, 'b'))
> > > Traceback (most recent call last):
> > > File "<stdin>", line 1, in <module>
> > > File "/usr/local/lib/python2.7/dist-packages/pandas/core/groupby.py", line 420, in apply
> > > @wraps(func)
> > > File "/usr/lib/python2.7/functools.py", line 33, in update_wrapper
> > > setattr(wrapper, attr, getattr(wrapped, attr))
> > > AttributeError: 'functools.partial' object has no attribute '**module**'
> > > pandas.**version**
> > > '0.13.1'
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} | 34 | 2014-03-24T11:06:01Z | 2021-10-02T08:10:51Z | null | CONTRIBUTOR | null | I've code which run fine about ~10 month ago and now fails due to running out of memory. The code basically does this:
```
groups = [[1,2,3][222,333,444, 555], ...] # from a 1.4MB json file
# about 30k such groups, results in ~70k replacements
for replace_list in groups:
replacement = sorted(replace_list)[0]
for replace_id in replace_list:
if replace_id == replacement:
continue
replace_dict[replace_id] = replacement
# len(_col) == 974757
_col = df[column].astype("int64")
_col.replace(replace_dict, inplace=True)
```
I've now split the replacement_dict into 2k chunks and and this works (takes about 20 seconds for each chunk).
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| null | 4 | 2014-03-24T15:05:37Z | 2014-03-24T18:02:54Z | 2014-03-24T18:02:54Z | CONTRIBUTOR | null | This code was working in 0.11 but not anymore in newer versions:
```
>>> import pandas as pd
>>> import numpy as np
>>> print(pd.__version__)
0.11.0
>>> df = pd.DataFrame(np.random.rand(20,4), columns=["a","b","c","d"])
>>> df["group"] = ['a', 'b', 'c', 'd'] * 5
>>> def _top10cites(group):
... m = group['a'].quantile(q=0.9)
... return (group['a'] >= m).astype(int)
...
>>> df[["a","c","group"]].groupby("group").apply(_top10cites)
0 0
1 0
2 0
3 0
4 0
5 1
6 1
7 0
8 0
9 0
10 0
11 0
12 1
13 0
14 0
15 0
16 0
17 0
18 0
19 1
Name: a, dtype: int32
```
Newer versions output:
```
0.13.1-489-g7ffa655
Out[39]:
a 0 4 8 12 16
group
a 0 0 0 1 0
b 0 1 0 0 0
c 0 0 0 0 1
d 0 0 1 0 0
[4 rows x 5 columns]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6699 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6699/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6699/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6699/events | https://github.com/pandas-dev/pandas/issues/6699 | 30,067,412 | MDU6SXNzdWUzMDA2NzQxMg== | 6,699 | new row not created upon assignment to nonexistent row label in DataFrame with MultiIndex | {
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} | 3 | 2014-03-24T19:31:57Z | 2017-02-22T18:00:30Z | 2017-02-22T18:00:30Z | NONE | null | According to the [documentation](http://pandas.pydata.org/pandas-docs/stable/indexing.html#setting-with-enlargement) for pandas 0.13+, assigning to a non-existent key via the .ix or .loc operations should create a new row in the DataFrame. Using pandas 0.13.1 with Python 2.7.5, I noticed that this doesn't seem to be the case when the DataFrame has a MultiIndex. To illustrate, consider a DataFrame created as follows:
```
import itertools
import numpy as np
import pandas
rows = 2
cols = 3
a = np.random.randint(0, 2, rows*cols).reshape((rows, cols))
b = a*np.random.rand(rows, cols)
idx = pandas.MultiIndex.from_tuples([(i, j) for i, j in \
itertools.product(xrange(rows), xrange(cols))],
names=['rows','cols'])
df = pandas.DataFrame({'a': a.flatten(),
'b': b.flatten()}, index=idx)
```
If `df` is
```
a b
rows cols
0 0 0 0.000000
1 0 0.000000
2 1 0.315458
1 0 0 0.000000
1 0 0.000000
2 1 0.061142
```
then running `df.loc[(0, 1), :] = (10, 20.0)` modifies `df` as follows:
```
a b
rows cols
0 0 0 0.000000
1 10 20.000000
2 1 0.315458
1 0 0 0.000000
1 0 0.000000
2 1 0.061142
```
However, if one attempts to run `df.loc[(0,3), :] = (10, 20.0)` on the original `df`, one obtains
```
a b
rows cols
0 0 10 20.000000
1 0 0.000000
2 1 0.315458
1 0 10 20.000000
1 0 0.000000
2 1 0.061142
```
Is this behavior expected?
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"title": "0.14.0",
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} | 12 | 2014-03-25T00:06:37Z | 2014-04-06T15:47:41Z | 2014-04-06T15:47:14Z | CONTRIBUTOR | null | itertools.islice would be faster than n*comparisons here:
https://github.com/pydata/pandas/blob/88c4c5526b38194f00a00c207ee5388dfd508092/pandas/core/frame.py#L759
Not sure whether to amortize an `import itertoosl` or not.
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