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} | 2 | "2013-03-28T14:12:53Z" | "2013-04-10T06:21:15Z" | "2013-04-10T06:21:15Z" | CONTRIBUTOR | null | Two examples where line_width display option requirement is not met:
``` python
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.11.0.dev-250c8e0'
In [3]: len(repr(pd.DataFrame(123456, range(5), range(15))).split('\n')[1])
Out[3]: 92
In [4]: pd.options.display.max_columns = 100
In [5]: len(repr(pd.DataFrame(0, range(5), range(35))).split('\n')[1])
Out[5]: 104
```
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} | 32 | "2013-03-28T16:17:53Z" | "2016-09-15T22:18:15Z" | "2016-09-15T21:46:15Z" | CONTRIBUTOR | null | Conceptually create a pipeline processor that performs out-of-core computation.
This is easily parallelizable (multi-core or machines), in theory cython / ipython / joblib / hadoop could operate with this
## requirements
the data set must support chunking, and the function must operate only on that chunk
Useful in cases of a large number of rows, or a problem that you want to parrallelize.
## input
a chunking iterator that reads from disk (could take chunksize parameters,
a handle and just call the iterators as well)
- `read_csv`
- `HDFStore`
## function
take an iterated chunk, and an axis and return another pandas object
(could be a reduction, transformation, whatever)
## output
an output mechanism to take the function application, must support appending
- `to_csv` (with appending)
- `HDFStore` (table)
- another pipeline
- in memory
Map-reduce is an example of this type of pipelining.
Interesting Library
https://github.com/enthought/distarray
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} | 1 | "2013-03-28T17:07:05Z" | "2014-07-16T06:25:18Z" | "2013-04-14T14:12:39Z" | NONE | null | This is the logical followup to the cython cache hack.
Using both, I put together a server on Redhat Openshift to serve as
network cache storage, and a travis build now takes 1.5 minutes bare,
and up to 4 minutes for a FULL_DEPS build.
The traffic used up by each build is significant, so the travis bit can't
be put on master. the 2to3 cache will shave another couple of minutes
off local iterations, though.
https://travis-ci.org/y-p/pandas/builds/5873760
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BUG: fix in spare_frame for issue with running apply on a SparseFrame didn't
allow the setting of the columns with a rename to work properly
something broke this......
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https://api.github.com/repos/pandas-dev/pandas/issues/3206 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3206/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3206/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3206/events | https://github.com/pandas-dev/pandas/issues/3206 | 12,581,858 | MDU6SXNzdWUxMjU4MTg1OA== | 3,206 | using BDay for offsets I see a wrong result | {
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} | [] | closed | false | null | [] | null | 1 | "2013-03-28T20:06:11Z" | "2013-04-09T00:19:02Z" | "2013-04-09T00:19:02Z" | NONE | null | I can't tell if the below is a bug or if I'm misusing pandas somehow. but it seems like if I take a date and calculate the prior business day I get different results using the 2 different methods below.
```
import datetime
import pandas
from pandas.tseries.offsets import *
X = datetime.datetime(2013, 3, 22, 0, 0)
print 'Date = ' + str(X)
offset = BDay()
print 'Rollback 1 bday (method 1): ' + str(offset.rollback(X))
print 'Rollback 1 bday (method 2): ' + str(X - BDay())
>>>
Date = 2013-03-22 00:00:00
Rollback 1 bday (method 1): 2013-03-22 00:00:00
Rollback 1 bday (method 2): 2013-03-21 00:00:00
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3207 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3207/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3207/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3207/events | https://github.com/pandas-dev/pandas/pull/3207 | 12,586,583 | MDExOlB1bGxSZXF1ZXN0NDg3NTg1MQ== | 3,207 | Gh2836 resolve merge conflict | {
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} | [] | closed | false | null | [] | null | 0 | "2013-03-28T21:52:41Z" | "2013-03-28T22:17:44Z" | "2013-03-28T22:17:44Z" | CONTRIBUTOR | null | Rebased and resolved merge conflict of #2836.
(Is the correct thing to do here to set up a new branch? I wasn't brave enough to try and force directly to the pr...)
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https://api.github.com/repos/pandas-dev/pandas/issues/3208 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3208/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3208/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3208/events | https://github.com/pandas-dev/pandas/pull/3208 | 12,586,724 | MDExOlB1bGxSZXF1ZXN0NDg3NTkyNw== | 3,208 | BUG: take into account adjoin width, closes #3201 | {
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} | [] | closed | false | null | [] | null | 4 | "2013-03-29T00:11:43Z" | "2016-01-08T04:37:40Z" | "2013-04-02T19:49:45Z" | CONTRIBUTOR | null | I have a CSV with several columns. The first of which is a field called `id` with entries of the type `0001`, `0002`, etc.
When loading this file, the following works:
``` python
pd.read_csv(my_path, dtype={'id' : np.int})
```
but the following doesn't:
``` python
pd.read_csv(my_path, dtype={'id' : np.str})
```
nor does this either:
``` python
pd.read_csv(my_path, dtype={'id' : str})
```
I get: `Datatype not understood`
This is with `pandas-0.10.1`
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} | 1 | "2013-03-29T01:33:40Z" | "2013-04-08T04:46:30Z" | "2013-04-08T04:46:30Z" | MEMBER | null | Migrated from pydata mailing list
Let me begin by saying -- I have worked out a way around the problem using .shift(). But I still thought I would mention what I ran into and see if it's worthy of attention/fixing.
I create a DatetimeIndex with a timezone:
```
In [29]: dates = pd.DatetimeIndex(start='2012-11-01',periods = 10, freq = 'D', tz='US/Pacific')
In [30]: dates
Out[30]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-11-01 00:00:00, ..., 2012-11-10 00:00:00]
Length: 10, Freq: D, Timezone: US/Pacific
```
Great! Now I want to have all of my timestamps be at 5am. So I try:
```
dates + pd.offsets.Hour(5)
```
And get:
```
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-11-01 12:00:00, ..., 2012-11-10 13:00:00]
Length: 10, Freq: None, Timezone: US/Pacific
```
Hmm. Suddenly its 12 noon -- not 5am. It looks like pandas is trying to do some UTC to US/Pacific conversion -- even though I've already specified my timezone.
This just surprised me and has the feeling of a bug.
Again, I solved it when I remembered I could do .shift()
```
In [32]: dates.shift(5,freq='H')
Out[32]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-11-01 05:00:00, ..., 2012-11-10 05:00:00]
Length: 10, Freq: None, Timezone: US/Pacific
```
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] | closed | false | null | [] | null | 4 | "2013-03-29T01:35:12Z" | "2013-04-01T20:54:48Z" | "2013-04-01T20:54:48Z" | CONTRIBUTOR | null | ``` python
import pandas as pd
import numpy as np
print pd.__version__
#0.11.0.dev-184f2db
lev1 = np.arange(10)
lev2 = np.linspace(0, 9, 10)
mi = pd.MultiIndex.from_arrays([lev1, lev2])
# ints work fine
assert lev1.dtype == mi.levels[0].dtype
# failure
assert lev2.dtype == mi.levels[1].dtype
print lev2.dtype, mi.levels[1].dtype
# float64 object
```
Ran into this when using `np.isclose` on the level values. Numpy fails due to casting rules and object dtype.
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} | 0 | "2013-03-29T14:56:00Z" | "2013-03-31T00:12:53Z" | "2013-03-31T00:12:53Z" | CONTRIBUTOR | null | http://stackoverflow.com/questions/15704274/adding-new-column-to-pandas-dataframe-with-values-for-particular-items
```
In [18]: df = pd.DataFrame([{"a": 1}, {"a": 3, "b": 2}])
In [19]: df['c'] = np.nan
In [21]: df.ix[0,'c'] = 'foo'
---------------------------------------------------------------------------
ValueError: could not convert string to float: f
```
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} | 1 | "2013-03-29T16:20:41Z" | "2013-08-16T19:27:53Z" | "2013-08-16T19:27:53Z" | CONTRIBUTOR | null | see also #3216 , #3386
test in tests/test_series.py (test_update)
but cannot be fixed until Series is not longer a ndarray sub-class
as numpy cannot handle the float64 to object _in-place_ dtype conversion
(which is why the sub-class issue comes up), if we have a single block
then can just swap out the block rather than attempting numpy magic here
```
In [18]: df = pd.DataFrame([{"a": 1}, {"a": 3, "b": 2}])
In [19]: df['c'] = np.nan
In [20]: df['c'].update(pd.Series(['foo'],index=[0]))
TypeError: Cannot change data-type for object array.
```
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} | 6 | "2013-03-29T17:47:58Z" | "2014-06-20T15:07:41Z" | "2013-03-31T00:12:48Z" | CONTRIBUTOR | null | closes #3216
BUG: GH3216 Upcast when needed to DataFrame when setitem with indexer
The following would raise previously
```
In [8]: df = pd.DataFrame([{"a": 1}, {"a": 3, "b": 2}])
In [9]: df['c'] = np.nan
In [10]: df.ix[0,'c'] = 'foo'
In [11]: df
Out[11]:
a b c
0 1 NaN foo
1 3 2 NaN
In [12]: df.dtypes
Out[12]:
a int64
b float64
c object
dtype: object
```
ENH: On a mixed DataFrame, allow setting with indexers with
ndarray/DataFrame on rhs (this was disallowed in the code previously)
```
In [8]: df = DataFrame([[1,2,'foo'],[3,4,'bar']],columns=['A','B','C'])
In [9]: df2 = df.copy()
In [10]: df2.ix[:,['A','B']] = df.ix[:,['A','B']]+0.5
In [11]: df2
Out[11]:
A B C
0 1.5 2.5 foo
1 3.5 4.5 bar
In [12]: df2.dtypes
Out[12]:
A float64
B float64
C object
dtype: object
In [13]: df.dtypes
Out[13]:
A int64
B int64
C object
dtype: object
```
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} | 2 | "2013-03-29T21:54:34Z" | "2015-05-21T11:11:42Z" | "2013-09-21T23:55:03Z" | NONE | null | Editing csv in some editors appends an extra extra carriage return at the end of the file, which in turn is converted to a row of NaNs. What is the best way to prevent read_csv from converting the last carriage return into a row?
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} | 2 | "2013-03-29T23:40:50Z" | "2018-07-06T22:48:09Z" | "2018-07-06T22:48:05Z" | CONTRIBUTOR | null | would be useful (mainly for out-of-core) to be able to specify the start and stop rows and quickly skip to them to get a specific chunk out of a file.
these rows refer to data rows / alternately this could just return the nth chunk
(but skip parsing the n-1 chunks for efficiency)
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} | 1 | "2013-03-30T15:04:12Z" | "2013-04-02T19:09:37Z" | "2013-04-02T19:09:37Z" | CONTRIBUTOR | null | add in HDFStore, import in core.api
provide same API as append/select
use get_store to open/close files
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} | 1 | "2013-03-30T19:19:15Z" | "2014-07-05T02:54:12Z" | "2013-03-30T21:05:23Z" | NONE | null | #2891
Only the usual fear and trembling associated with touching the pickle code.
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Subtract list of tuples from MultiIndexs (without it dropping the names), and also no name dropping with subtracting arrays from Indexs.
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} | 6 | "2013-03-31T23:04:51Z" | "2014-08-18T15:27:13Z" | "2013-03-31T23:44:28Z" | CONTRIBUTOR | null | should close #3069
will print additional output if this somehow fails, which I have never seen (except on travis)
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} | 0 | "2013-04-01T12:25:15Z" | "2014-07-04T06:31:36Z" | "2013-04-02T12:24:50Z" | CONTRIBUTOR | null | closes #2808
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} | 9 | "2013-04-01T13:17:14Z" | "2013-04-01T18:27:35Z" | "2013-04-01T17:25:01Z" | NONE | null | Could be related to #2923. The following code produces two different plots for me in pandas 10.1.
```
import pandas
from matplotlib import pyplot
from numpy.random import randn
data = pandas.DataFrame(randn(6, 2))
pyplot.plot(data[0], data[1])
data.plot(0, 1)
```
One can actually implement a test for this by comparing the collections produced by the plot.
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} | 2 | "2013-04-01T15:23:10Z" | "2013-05-02T14:54:40Z" | "2013-05-02T14:54:40Z" | NONE | null | #2786 , #3102, #3092
In 0.11, this raises a ValueError Exception, previously returned
wrong data.
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} | 6 | "2013-04-01T15:38:34Z" | "2013-07-02T01:43:20Z" | "2013-07-02T01:43:20Z" | NONE | null | `generic.PandasObject` is not it since it applies to data containers, not `Period` or `TimeStamp` for example.
Should leverage subclasse's `__unicode__` to do the right thing
on py2/py3 when it comes to repr,str,bytes #3183
I suggest putting in a mandatory `self.random_example()` (name?) method
that generates a random instance of the object. Convenient for testing, and for code snippets
in test cases/issues. Complements the more general `makeX` generator functions in util.testing.
Side benefit, can more easily add one-size-fits-all sanity check for a pickle roundrip,
which would have caught #2891.
More ideas?
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} | 10 | "2013-04-01T21:26:55Z" | "2019-03-26T03:46:15Z" | "2019-03-26T00:25:02Z" | CONTRIBUTOR | null | Hey all,
I have a `DataFrame` (df) that stores live sensor data that is captured at a specific frequency. New raw data from sensor is updated at a set interval (an attempt at bandwidth conservation), which is parsed into a new df object.
These new update dataframes are of the same frequency, and contain data that is 'continuous' in time (i.e., they pick up right where the last timestamp left off), and ultimately I would like to append this new data to the existing dataframe while preserving the main dataframe frequency attribute. I tried by using a `concat` of old and new dataframes, however, it seems that `concat` doesn't check this case for continuous time series, and loses its frequency attribute. This can be reproduced in code below:
``` python
import pandas as pd
import numpy as np
dr = pd.date_range('01-Jan-2013', periods=100, freq='50L', tz='UTC')
df = pd.DataFrame(np.random.randn(100, 2), index=dr)
df.index
```
``` python
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-01 00:00:00, ..., 2013-01-01 00:00:04.950000]
Length: 100, Freq: 50L, Timezone: UTC
```
These guys look good:
``` python
#Preserves frequency
print df[:50].index
print df[50:].index
```
``` python
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-01 00:00:00, ..., 2013-01-01 00:00:02.450000]
Length: 50, Freq: 50L, Timezone: UTC
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-01 00:00:02.500000, ..., 2013-01-01 00:00:04.950000]
Length: 50, Freq: 50L, Timezone: UTC
```
However, these guys, together, forget where they came from:
``` python
#Loses frequency
pd.concat([df[:50], df[50:]]).index
```
``` python
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-01 00:00:00, ..., 2013-01-01 00:00:04.950000]
Length: 100, Freq: None, Timezone: UTC
```
I currently get around this with a `resample` of the resulting df to set frequency, which isn't that big of a deal, however, thought I'd mention it so that a more elegant behavior could be implemented. I'll try and take a look when I have time, but I know that all you here are so much more familiar with pandas internals. Any pointers?
And, as always, thank you! :)
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} | 1 | "2013-04-02T02:57:15Z" | "2013-04-03T15:06:45Z" | "2013-04-03T15:05:25Z" | NONE | null | ``` python
df=mkdf(100,2,data_gen_f=lambda r,c: r+ rand(1)*5,r_idx_type='i')
df.plot()
# vs
df.C_l0_g0.plot()
```
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} | 5 | "2013-04-02T07:16:26Z" | "2013-04-03T11:31:51Z" | "2013-04-03T11:31:51Z" | CONTRIBUTOR | null | GitHub does not fork automatically in the online editor.
Here's my change:
```
`How to rearrange a python pandas dataframe?
<http://stackoverflow.com/questions/15432659/how-to-rearrange-a-python-pandas-dataframe>`__ -- Turn a matrix with horus in columns and days in rows in a continous row sequence in form of a time series.
```
the full file:
```
.. _cookbook:
.. currentmodule:: pandas
.. ipython:: python
:suppress:
import numpy as np
import random
import os
np.random.seed(123456)
from pandas import *
import pandas as pd
randn = np.random.randn
randint = np.random.randint
np.set_printoptions(precision=4, suppress=True)
********
Cookbook
********
This is a respository for *short and sweet* examples and links for useful pandas recipes.
We encourage users to add to this documentation.
This is a great *First Pull Request* (to add interesting links and/or put short code inline
for existing links)
.. _cookbook.selection:
Selection
---------
The :ref:`indexing <indexing>` docs.
`Boolean Rows Indexing
<http://stackoverflow.com/questions/14725068/pandas-using-row-labels-in-boolean-indexing>`__
`Using loc and iloc in selections
<https://github.com/pydata/pandas/issues/2904>`__
`Extending a panel along the minor axis
<http://stackoverflow.com/questions/15364050/extending-a-pandas-panel-frame-along-the-minor-axis>`__
`Boolean masking in a panel
<http://stackoverflow.com/questions/14650341/boolean-mask-in-pandas-panel>`__
`Selecting via the complement
<http://stackoverflow.com/questions/14986510/picking-out-elements-based-on-complement-of-indices-in-python-pandas>`__
.. _cookbook.multi_index:
MultiIndexing
-------------
The :ref:`multindexing <indexing.hierarchical>` docs.
`Creating a multi-index from a labeled frame
<http://stackoverflow.com/questions/14916358/reshaping-dataframes-in-pandas-based-on-column-labels>`__
Slicing
```
`Slicing a multi-index with xs
<http://stackoverflow.com/questions/12590131/how-to-slice-multindex-columns-in-pandas-dataframes>`__
`Slicing a multi-index with xs #2
<http://stackoverflow.com/questions/14964493/multiindex-based-indexing-in-pandas>`__
Sorting
```
`Multi-index sorting
<http://stackoverflow.com/questions/14733871/mutli-index-sorting-in-pandas>`__
`Partial Selection, the need for sortedness
<https://github.com/pydata/pandas/issues/2995>`__
Levels
```
`Prepending a level to a multiindex
<http://stackoverflow.com/questions/14744068/prepend-a-level-to-a-pandas-multiindex>`__
`Flatten Hierarchical columns
<http://stackoverflow.com/questions/14507794/python-pandas-how-to-flatten-a-hierarchical-index-in-columns>`__
.. _cookbook.grouping:
## Grouping
The :ref:`grouping <groupby>` docs.
`Basic grouping with apply
<http://stackoverflow.com/questions/15322632/python-pandas-df-groupy-agg-column-reference-in-agg>`__
`Using get_group
<http://stackoverflow.com/questions/14734533/how-to-access-pandas-groupby-dataframe-by-key>`__
`Apply to different items in a group
<http://stackoverflow.com/questions/15262134/apply-different-functions-to-different-items-in-group-object-python-pandas>`__
`Expanding Apply
<http://stackoverflow.com/questions/14542145/reductions-down-a-column-in-pandas>`__
`Replacing values with groupby means
<http://stackoverflow.com/questions/14760757/replacing-values-with-groupby-means>`__
`Sort by group with aggregation
<http://stackoverflow.com/questions/14941366/pandas-sort-by-group-aggregate-and-column>`__
`Create multiple aggregated columns
<http://stackoverflow.com/questions/14897100/create-multiple-columns-in-pandas-aggregation-function>`__
Expanding Data
```
`Alignment and to-date
<http://stackoverflow.com/questions/15489011/python-time-series-alignment-and-to-date-functions>`__
`Rolling Computation window based on values instead of counts
<http://stackoverflow.com/questions/14300768/pandas-rolling-computation-with-window-based-on-values-instead-of-counts>`__
Splitting
```
`Splitting a frame
<http://stackoverflow.com/questions/13353233/best-way-to-split-a-dataframe-given-an-edge/15449992#15449992>`__
.. _cookbook.pivot:
Pivot
```
The :ref:`Pivot <reshaping.pivot>` docs.
`Partial sums and subtotals
<http://stackoverflow.com/questions/15570099/pandas-pivot-tables-row-subtotals/15574875#15574875>`__
`Frequency table like plyr in R
<http://stackoverflow.com/questions/15589354/frequency-tables-in-pandas-like-plyr-in-r>`__
Timeseries
----------
`Between times
<http://stackoverflow.com/questions/14539992/pandas-drop-rows-outside-of-time-range>`__
`Vectorized Lookup
<http://stackoverflow.com/questions/13893227/vectorized-look-up-of-values-in-pandas-dataframe>`__
.. _cookbook.resample:
Resampling
```
The :ref:`Resample <timeseries.resampling>` docs.
`TimeGrouping of values grouped across time
<http://stackoverflow.com/questions/15297053/how-can-i-divide-single-values-of-a-dataframe-by-monthly-averages>`__
`TimeGrouping #2
<http://stackoverflow.com/questions/14569223/timegrouper-pandas>`__
`Resampling with custom periods
<http://stackoverflow.com/questions/15408156/resampling-with-custom-periods>`__
`Resample intraday frame without adding new days
<http://stackoverflow.com/questions/14898574/resample-intrday-pandas-dataframe-without-add-new-days>`__
`Resample minute data
<http://stackoverflow.com/questions/14861023/resampling-minute-data>`__
.. _cookbook.merge:
## Merge
The :ref:`Concat <merging.concatenation>` docs. The :ref:`Join <merging.join>` docs.
`emulate R rbind
<http://stackoverflow.com/questions/14988480/pandas-version-of-rbind>`__
`Self Join
<https://github.com/pydata/pandas/issues/2996>`__
`How to set the index and join
<http://stackoverflow.com/questions/14341805/pandas-merge-pd-merge-how-to-set-the-index-and-join>`__
`KDB like asof join
<http://stackoverflow.com/questions/12322289/kdb-like-asof-join-for-timeseries-data-in-pandas/12336039#12336039>`__
`Join with a criteria based on the values
<http://stackoverflow.com/questions/15581829/how-to-perform-an-inner-or-outer-join-of-dataframes-with-pandas-on-non-simplisti>`__
.. _cookbook.plotting:
## Plotting
The :ref:`Plotting <visualization>` docs.
`Make Matplotlib look like R
<http://stackoverflow.com/questions/14349055/making-matplotlib-graphs-look-like-r-by-default>`__
`Setting x-axis major and minor labels
<http://stackoverflow.com/questions/12945971/pandas-timeseries-plot-setting-x-axis-major-and-minor-ticks-and-labels>`__
## Data In/Out
.. _cookbook.csv:
CSV
```
The :ref:`CSV <io.read_csv_table>` docs
`read_csv in action
<http://wesmckinney.com/blog/?p=635>`__
`Reading a csv chunk-by-chunk
<http://stackoverflow.com/questions/11622652/large-persistent-dataframe-in-pandas/12193309#12193309>`__
`Reading the first few lines of a frame
<http://stackoverflow.com/questions/15008970/way-to-read-first-few-lines-for-pandas-dataframe>`__
`Inferring dtypes from a file
<http://stackoverflow.com/questions/15555005/get-inferred-dataframe-types-iteratively-using-chunksize>`__
`Dealing with bad lines
<https://github.com/pydata/pandas/issues/2886>`__
.. _cookbook.sql:
SQL
```
The :ref:`SQL <io.sql>` docs
`Reading from databases with SQL
<http://stackoverflow.com/questions/10065051/python-pandas-and-databases-like-mysql>`__
.. _cookbook.excel:
Excel
```
The :ref:`Excel <io.excel>` docs
`Reading from a filelike handle
<http://stackoverflow.com/questions/15588713/sheets-of-excel-workbook-from-a-url-into-a-pandas-dataframe>`__
.. _cookbook.hdf:
HDFStore
```
The :ref:`HDFStores <io.hdf5>` docs
`Simple Queries with a Timestamp Index
<http://stackoverflow.com/questions/13926089/selecting-columns-from-pandas-hdfstore-table>`__
`Managing heteregenous data using a linked multiple table hierarchy
<https://github.com/pydata/pandas/issues/3032>`__
`Merging on-disk tables with millions of rows
<http://stackoverflow.com/questions/14614512/merging-two-tables-with-millions-of-rows-in-python/14617925#14617925>`__
`Large Data work flows
<http://stackoverflow.com/questions/14262433/large-data-work-flows-using-pandas>`__
`Troubleshoot HDFStore exceptions
<http://stackoverflow.com/questions/15488809/how-to-trouble-shoot-hdfstore-exception-cannot-find-the-correct-atom-type>`__
Storing Attributes to a group node
.. ipython:: python
```
df = DataFrame(np.random.randn(8,3))
store = HDFStore('test.h5')
store.put('df',df)
# you can store an arbitrary python object via pickle
store.get_storer('df').attrs.my_attribute = dict(A = 10)
store.get_storer('df').attrs.my_attribute
```
.. ipython:: python
:suppress:
```
store.close()
os.remove('test.h5')
```
## Miscellaneous
The :ref:`Timedeltas <timeseries.timedeltas>` docs.
`Operating with timedeltas
<https://github.com/pydata/pandas/pull/2899>`__
`Create timedeltas with date differences
<http://stackoverflow.com/questions/15683588/iterating-through-a-pandas-dataframe>`__
## Aliasing Axis Names
To globally provide aliases for axis names, one can define these 2 functions:
.. ipython:: python
def set_axis_alias(cls, axis, alias):
if axis not in cls._AXIS_NUMBERS:
raise Exception("invalid axis [%s] for alias [%s]" % (axis, alias))
cls._AXIS_ALIASES[alias] = axis
def clear_axis_alias(cls, axis, alias):
if axis not in cls._AXIS_NUMBERS:
raise Exception("invalid axis [%s] for alias [%s]" % (axis, alias))
cls._AXIS_ALIASES.pop(alias,None)
set_axis_alias(DataFrame,'columns', 'myaxis2')
df2 = DataFrame(randn(3,2),columns=['c1','c2'],index=['i1','i2','i3'])
df2.sum(axis='myaxis2')
clear_axis_alias(DataFrame,'columns', 'myaxis2')
```
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3235 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3235/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3235/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3235/events | https://github.com/pandas-dev/pandas/issues/3235 | 12,699,369 | MDU6SXNzdWUxMjY5OTM2OQ== | 3,235 | assigning list to series with boolean indexing | {
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} | 6 | "2013-04-02T10:36:33Z" | "2013-04-02T15:39:58Z" | "2013-04-02T12:42:58Z" | NONE | null | Whenever a boolean array is used to assign some list or array to another series, it returns "ValueError: Length of replacements must equal series length". This was working fine in pandas 10.0, but broke with 10.1
``` python
import pandas as pd
import numpy as np
x = pd.Series(np.arange(10))
sel = x < 5
len(x[sel])
x[sel] = range(5) # doesn't work anymore
x[0:5] = range(5) # works
```
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} | 0 | "2013-04-02T12:18:13Z" | "2014-07-28T09:58:44Z" | "2013-04-02T12:42:28Z" | CONTRIBUTOR | null | closes #3235, related to #2745
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https://api.github.com/repos/pandas-dev/pandas/issues/3237 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3237/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3237/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3237/events | https://github.com/pandas-dev/pandas/issues/3237 | 12,703,997 | MDU6SXNzdWUxMjcwMzk5Nw== | 3,237 | Feature request: df that only return dfs when indexing. | {
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} | [] | closed | false | null | [] | null | 7 | "2013-04-02T13:08:03Z" | "2013-05-13T16:54:59Z" | "2013-05-13T16:54:59Z" | CONTRIBUTOR | null | With more complex dataframes, I often stumble over this:
``` python
In [1]: x = pandas.DataFrame({'a': [1,2,3], 'b': [4,5,6]})
In [2]: x.ix[0].a
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-8-8358f19a1e70> in <module>()
----> 1 x.ix[0].a
AttributeError: 'Series' object has no attribute 'a'
In [3]: x.a.ix[0]
Out[3]: 1
```
This is just an example, at other times I end up converting the `Series` back to a `DataFrame` because that's what the rest of the code expects.
I know that there have been attempts to solve this issue by adding attribute lookup to `Series` (e.g. #1904) but they seem to come with a performance penalty.
Often, however, I care more about expressiveness than performance. I thus propose the addition of an option like:
``` python
x = DataFrame(data, slicing_returns_df=True)
```
Which will cause x.ix[0] or x.a to return again a `DataFrame` rather than a `Series` and make the above work. The default would be False so that there are no backward compat issues. Alternatively there could be a new DataFrame class that inherits from DataFrame and has the desired behavior.
I'm happy to gives this a crack, however, I wanted to first make sure that it's not only me who thinks that'd be a good idea or that this can't work for obvious reason X.
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https://api.github.com/repos/pandas-dev/pandas/issues/3238 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3238/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3238/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3238/events | https://github.com/pandas-dev/pandas/issues/3238 | 12,709,767 | MDU6SXNzdWUxMjcwOTc2Nw== | 3,238 | agg() swallows exceptions. | {
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} | 12 | "2013-04-02T15:16:34Z" | "2013-04-08T07:12:45Z" | "2013-04-08T07:12:45Z" | CONTRIBUTOR | null | agg() appears to swallow exceptions.
This was the source of a hard-to-find bug today.
Here's a program that replicates the problem.
```
#!/usr/bin/env python
from pandas import *
from numpy.random import randint
df = DataFrame(randint(10, size=(20, 10)))
def raiseException(df):
print '----------------------------------------'
print df.to_string()
raise TypeError
df.groupby(0).agg(raiseException)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3239 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3239/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3239/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3239/events | https://github.com/pandas-dev/pandas/pull/3239 | 12,709,890 | MDExOlB1bGxSZXF1ZXN0NDkzMjA0Mg== | 3,239 | BUG: GH2893 Produce a series on apply if we have a singular series and have a simple index | {
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} | 2 | "2013-04-02T15:18:34Z" | "2014-07-03T05:21:15Z" | "2013-04-08T16:40:31Z" | CONTRIBUTOR | null | closes #2893
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} | 9 | "2013-04-02T17:21:06Z" | "2018-07-06T22:44:48Z" | "2018-07-06T22:44:42Z" | CONTRIBUTOR | null | http://stackoverflow.com/questions/15769706/converting-a-list-in-a-dict-to-a-series/15770083?noredirect=1#comment22416395_15770083
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} | 2 | "2013-04-02T17:40:12Z" | "2015-01-26T00:48:04Z" | "2015-01-26T00:48:04Z" | NONE | null | `apply` looks at the returned values from the user-supplied transformer function
and does convenient things based on the type returned.
However, there's no opt-out if you just want to transform groups
into something and get it back as-is:
``` python
In [16]: df=mkdf(10,2,data_gen_f=lambda x,y: randint(1,10))
...: df
...:
...:
...:
Out[16]:
C0 C_l0_g0 C_l0_g1
R0
R_l0_g0 9 1
R_l0_g1 3 7
R_l0_g2 8 1
R_l0_g3 4 3
R_l0_g4 5 3
R_l0_g5 7 2
R_l0_g6 4 1
R_l0_g7 5 4
R_l0_g8 9 7
R_l0_g9 4 8
In [17]: def f1(g):
...: return g.sort('C_l0_g0')
...: # group on the suffix of the running index
...: g=df.groupby(lambda key: int(key.split("g")[-1]) >= 5)
...: r=g.apply(f1)
...:
# we want to get a bunch of sorted dataframes, but get concat-ed against our will
In [18]: r
Out[18]:
C0 C_l0_g0 C_l0_g1
R0
False R_l0_g1 3 7
R_l0_g3 4 3
R_l0_g4 5 3
R_l0_g2 8 1
R_l0_g0 9 1
True R_l0_g6 4 1
R_l0_g9 4 8
R_l0_g7 5 4
R_l0_g5 7 2
R_l0_g8 9 7
```
More failed guessing of what the user wants: #5908.
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} | 11 | "2013-04-02T17:42:34Z" | "2014-06-18T13:12:19Z" | "2014-01-26T21:32:08Z" | NONE | null | #3241
Right now:
``` python
In [16]: df=mkdf(10,2,data_gen_f=lambda x,y: randint(1,10))
...: df
...:
...:
...:
Out[16]:
C0 C_l0_g0 C_l0_g1
R0
R_l0_g0 9 1
R_l0_g1 3 7
R_l0_g2 8 1
R_l0_g3 4 3
R_l0_g4 5 3
R_l0_g5 7 2
R_l0_g6 4 1
R_l0_g7 5 4
R_l0_g8 9 7
R_l0_g9 4 8
In [17]: def f1(g):
...: return g.sort('C_l0_g0')
...: # group on the suffix of the running index
...: g=df.groupby(lambda key: int(key.split("g")[-1]) >= 5)
...: r=g.apply(f1)
...:
# we want to return each group dataframe sorted, but we get concatted against our will
In [18]: r
Out[18]:
C0 C_l0_g0 C_l0_g1
R0
False R_l0_g1 3 7
R_l0_g3 4 3
R_l0_g4 5 3
R_l0_g2 8 1
R_l0_g0 9 1
True R_l0_g6 4 1
R_l0_g9 4 8
R_l0_g7 5 4
R_l0_g5 7 2
R_l0_g8 9 7
# what we want really, is a couple of sorted dataframes:
In [20]: map(lambda r: r[1].sort('C_l0_g0'),g)
Out[20]:
[C0 C_l0_g0 C_l0_g1
R0
R_l0_g1 3 7
R_l0_g3 4 3
R_l0_g4 5 3
R_l0_g2 8 1
R_l0_g0 9 1,
C0 C_l0_g0 C_l0_g1
R0
R_l0_g6 4 1
R_l0_g9 4 8
R_l0_g7 5 4
R_l0_g5 7 2
R_l0_g8 9 7]
```
#### With this PR:
``` python
In [21]: def f1(g): # same f1 as above
...: return g.sort('C_l0_g0')
...: def f2(g,raw=None):
...: return g.sort('C_l0_g0')
...: def f3(g,**kwds):
...: return g.sort('C_l0_g0')
...: # the `raw` keyword is the new bit
...: r1=g.apply(f1,raw=True)
...: r2=g.apply(f2,raw=True)
...: r3=g.apply(f2,raw=True)
...:
# a bunch of sorted frames
In [22]: print r1
[(False, C0 C_l0_g0 C_l0_g1
R0
R_l0_g1 3 7
R_l0_g3 4 3
R_l0_g4 5 3
R_l0_g2 8 1
R_l0_g0 9 1), (True, C0 C_l0_g0 C_l0_g1
R0
R_l0_g6 4 1
R_l0_g9 4 8
R_l0_g7 5 4
R_l0_g5 7 2
R_l0_g8 9 7)]
# but not if the transformer function signature uses **kwds, or 'raw' already
In [23]: print r2
C0 C_l0_g0 C_l0_g1
R0
False R_l0_g1 3 7
R_l0_g3 4 3
R_l0_g4 5 3
R_l0_g2 8 1
R_l0_g0 9 1
True R_l0_g6 4 1
R_l0_g9 4 8
R_l0_g7 5 4
R_l0_g5 7 2
R_l0_g8 9 7
In [24]: print r3
C0 C_l0_g0 C_l0_g1
R0
False R_l0_g1 3 7
R_l0_g3 4 3
R_l0_g4 5 3
R_l0_g2 8 1
R_l0_g0 9 1
True R_l0_g6 4 1
R_l0_g9 4 8
R_l0_g7 5 4
R_l0_g5 7 2
R_l0_g8 9 7
```
```
```
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} | 1 | "2013-04-02T19:04:41Z" | "2013-04-03T14:35:52Z" | "2013-04-03T14:35:52Z" | NONE | null | ``` python
pd.DataFrame([DataFrame([])])
/home/user1/src/pandas/pandas/core/frame.pyc in __getattr__(self, name)
1965 return self[name]
1966 raise AttributeError("'%s' object has no attribute '%s'" %
-> 1967 (type(self).__name__, name))
1968
1969 def __setattr__(self, name, value):
AttributeError: 'DataFrame' object has no attribute 'dtype'
```
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} | 2 | "2013-04-02T20:35:16Z" | "2013-04-03T14:35:43Z" | "2013-04-03T14:35:43Z" | CONTRIBUTOR | null | closes #3243
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In [4]: df = DataFrame(np.arange(9).reshape((3, 3)),
index=["A", "B", "C"], columns=["A", "B", "C"])
In [5]: df.iloc[[0,1],[1,2]] += 100
In [6]: df.iloc[[0,1],[1,2]]
Out[6]:
B C
A 101 102
B 104 105
In [7]: df
Out[7]:
A B C
A 0 101 102
B 3 104 105
C 6 7 8
```
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} | 2 | "2013-04-03T02:43:06Z" | "2013-04-03T20:04:18Z" | "2013-04-03T20:04:18Z" | NONE | null | When I try use log scales on a bar plot in pandas, the area of the bar disappears. Example in [this ipynb](http://nbviewer.ipython.org/urls/gist.github.com/gjx/5298007/raw/d1a0bd774a253fa1fc1f12a005dd70a697e8f360/pandas_bar_log)
I think the answer is to pass `log=True` to `bar`, as in [this SO answer](http://stackoverflow.com/questions/14047068/python-pyplot-bar-plot-bars-disapear-when-using-log-scale), but when I add `log=True` as a kwarg to `Series.plot()`, it doesn't fix the problem.
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I went along. Could you look this over and merge if these changes
look ok to you?
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] | closed | false | null | [] | null | 1 | "2013-04-03T14:20:55Z" | "2013-12-04T00:57:23Z" | "2013-04-03T15:01:24Z" | NONE | null | Hi,
I don't know if this is a bug or a feature, or even if you guys noticed it already and it's not an issue, but this caused me some problems so I thought I should report it.
When you use the read_csv command to read a csv, you can pass the columns you want to set as indexes. If those columns contain duplicated entries, the index will have duplicated entries.
To reproduce this, read a csv file with this contents:
```
indexcol, anothercol, yetanothercol
1, 1, 1
1, 2, 3
2, 1, 2
```
with the command:
```
In [1]: import pandas
In [2]: pandas.read_csv('bar.csv', index_col = 'indexcol')
Out[2]:
anothercol yetanothercol
indexcol
1 1 1
1 2 3
2 1 2
```
Is this what should happen? Is there a way to prevent it (like, for example, just read the first ocurrence of each duplicated index an drop the rest)? What I did to get rid of the duplicated entries was:
```
In [3]: df = pandas.read_csv('bar.csv', index_col = 'indexcol')
In [4]: df.groupby(lambda x:x).first()
Out[4]:
anothercol yetanothercol
1 1 1
2 1 2
```
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} | 0 | "2013-04-03T15:15:24Z" | "2013-04-03T15:15:46Z" | "2013-04-03T15:15:46Z" | NONE | null | ;)
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} | 2 | "2013-04-03T18:50:42Z" | "2014-01-03T20:53:08Z" | "2014-01-03T20:53:08Z" | CONTRIBUTOR | null | Because pprint_thing checks isinstance(thing, dict), it also catches objects that inherit from dict and forces them to print in the {'a':1, ... } format regardless of the object's __unicode__, __str__, or __repr__ definitions. On my machine, I've changed the _pprint_dict function to just:
```
return unicode(seq)
```
which seems to give the behavior I'm looking for. I see pprint_thing warns against unicode(object) in general due to nested sequences, but since _pprint_dict doesn't do anything with _nest_lvl anyway, I figured it was probably safe.
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} | 6 | "2013-04-03T19:58:04Z" | "2014-07-04T22:23:57Z" | "2013-04-23T02:12:54Z" | NONE | null | #3251
**before**
``` python
pd.options.display.max_seq_items= 5
print pd.core.common.pprint_thing(dict(foo=range(100)))
foo = range(5)
foo[0] = foo
print pd.core.common.pprint_thing(dict(foo=foo))
{'foo': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]}
{'foo': [[...], 1, 2, 3, 4]}
```
**After**:
``` python
In [16]: pd.options.display.max_seq_items= 5
...: pd.core.common.pprint_thing(dict(foo=range(100)))
...:
Out[16]: u'{foo: [0, 1, 2, 3, 4, ...]}'
In [17]: foo = range(5)
...: foo[0] = foo
...: pd.core.common.pprint_thing(dict(foo=foo))
...:
Out[17]: u'{foo: [[[[...], 1, 2, 3, 4], 1, 2, 3, 4], 1, 2, 3, 4]}'
```
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Have to consider corner cases before merging, rebased on top of #3252
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xref:https://github.com/matplotlib/matplotlib/issues/1882
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} | 3 | "2013-04-03T23:39:07Z" | "2013-09-22T00:04:36Z" | "2013-09-22T00:04:36Z" | MEMBER | null | This one is a real doozy
http://support.picloud.com/entries/23466073-Help-please-an-UnpicklingError-coming-from-pandas-index-with-freq-A-
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https://api.github.com/repos/pandas-dev/pandas/issues/3257 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3257/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3257/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3257/events | https://github.com/pandas-dev/pandas/issues/3257 | 12,796,754 | MDU6SXNzdWUxMjc5Njc1NA== | 3,257 | diff(0) causes ValueError on Series and TimeSeries | {
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} | 2 | "2013-04-04T11:58:29Z" | "2013-04-07T14:28:48Z" | "2013-04-07T14:28:48Z" | NONE | null | import pandas as pd
import scipy as sp
df = pd.DataFrame(sp.randn(5,2))
ts = df[0]
### If you run: `df.diff(0)`
Out[43]:
0 1
0 0 0
1 0 0
2 0 0
3 0 0
4 0 0
### This works as expeted but if you run `ts.diff(0)` :
---
ValueError Traceback (most recent call last)
/home/jzunigavu/workspace/hf_vol_data/predictor_curves/<ipython console> in <module>()
/home/jzunigavu/.local/lib/python2.7/site-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/core/series.pyc in diff(self, periods)
1736 diffed : Series
1737 """
-> 1738 result = com.diff(self.values, periods)
1739 return Series(result, self.index, name=self.name)
1740
/home/jzunigavu/.local/lib/python2.7/site-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/core/common.pyc in diff(arr, n, axis)
489 lag_indexer = tuple(lag_indexer)
490
--> 491 out_arr[res_indexer] = arr[res_indexer] - arr[lag_indexer]
492
493 return out_arr
ValueError: operands could not be broadcast together with shapes (5) (0)
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https://api.github.com/repos/pandas-dev/pandas/issues/3258 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3258/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3258/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3258/events | https://github.com/pandas-dev/pandas/issues/3258 | 12,819,311 | MDU6SXNzdWUxMjgxOTMxMQ== | 3,258 | read_csv, read_table in version 0.9.0 are parsing integers as double but reporting type as int64 | {
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```
>>> pandas.read_csv("file.log")
produces the following output:
Numbers
0 17007000002000192
1 17007000002000192
2 17007000002000192
3 17007000002000192
4 17007000002000192
5 17007000002000192
6 17007000002000192
7 17007000002000192
8 17007000002000192
9 17007000002000194
>> numpy.spacing(17007000002000192)
```
is 2.0 for this range of numbers
but the type reported for the value is int64 not double/float64
file.log contains:
```
Numbers
17007000002000191
17007000002000191
17007000002000191
17007000002000191
17007000002000192
17007000002000192
17007000002000192
17007000002000192
17007000002000192
17007000002000194
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3259 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3259/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3259/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3259/events | https://github.com/pandas-dev/pandas/issues/3259 | 12,823,845 | MDU6SXNzdWUxMjgyMzg0NQ== | 3,259 | PERF: concat with empty frame and timeseries indexed frames | {
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"closed_at": "2014-05-30T12:17:57Z",
"closed_issues": 755,
"created_at": "2014-01-03T20:19:04Z",
"creator": null,
"description": "2014-02-3: previous version released",
"due_on": "2014-05-31T07:00:00Z",
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} | 1 | "2013-04-04T22:27:05Z" | "2014-02-21T22:44:05Z" | "2014-02-21T22:44:05Z" | CONTRIBUTOR | null | http://stackoverflow.com/questions/15819050/pandas-dataframe-concat-vs-append/15822811#15822811
Very curious why this would be
```
In [17]: df1 = pd.DataFrame(dict(A = range(10000)),index=pd.date_range('20130101',periods=10000,freq='s'))
In [18]: df1
Out[18]:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 10000 entries, 2013-01-01 00:00:00 to 2013-01-01 02:46:39
Freq: S
Data columns (total 1 columns):
A 10000 non-null values
dtypes: int64(1)
In [19]: df4 = pd.DataFrame()
The concat
In [20]: %timeit pd.concat([df1,df2,df3])
1000 loops, best of 3: 270 us per loop
This is equavalent of your append
In [21]: %timeit pd.concat([df4,df1,df2,df3])
10 loops, best of
3: 56.8 ms per loop
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3260 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3260/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3260/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3260/events | https://github.com/pandas-dev/pandas/issues/3260 | 12,829,166 | MDU6SXNzdWUxMjgyOTE2Ng== | 3,260 | BUG: concat if datetime index with tz | {
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} | 2 | "2013-04-05T01:37:19Z" | "2013-10-02T21:04:31Z" | "2013-09-21T23:55:44Z" | CONTRIBUTOR | null | http://stackoverflow.com/questions/15819050/pandas-dataframe-concat-vs-append/15822811?noredirect=1#comment22510428_15822811
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} | 7 | "2013-04-06T02:10:07Z" | "2013-04-14T08:30:29Z" | "2013-04-14T08:30:22Z" | NONE | null | So, now that vbsuite is more stable, and collecting that data is much faster
due to the new build cache and perf tweaks to test_perf, I've set up a job to
gradually sample the commit history perf profile.
Even with the faster iterations, It'll still take a few days to collect the data
(I'm going back to 0.9.0), but here are some results from 300 uniform sample
points. (This is all ad-hoc and might contain errors, I'm working on some new
tools to make this easier).
Jeff, this one should be familiar :) :
``` python
In [304]: pd.options.display.mpl_style='default'
```
There are also several regression,again, it'll take a few days to collect
the full dta and then bisect to the changepoint commit.
Here are are all vbenches at least 20% slower today, compared to
0.9.0:
```
reindex_fillna_backfill 1.208748
write_store_table 1.209075
frame_drop_dup_na_inplace 1.209770
replace_fillna 1.221003
replace_replacena 1.244413
stat_ops_level_frame_sum_multiple 1.248258
misc_cache_readonly 1.250000
stat_ops_level_series_sum_multiple 1.269106
write_store_table_panel 1.298524
groupby_apply_dict_return 1.304015
read_store_table_mixed 1.348121
series_constructor_ndarray 1.359551
append_frame_single_mixed 1.370398
frame_multi_and_no_ne 1.372070
write_store_table_wide 1.461807
reindex_frame_level_reindex 1.532964
reindex_frame_level_align 1.537642
stat_ops_series_std 1.566043
series_getitem_scalar 1.800000
ctor_index_array_string 1.873016
write_store_table_dc 8.468982
```
some graphs:
The spikes are probably due to git log having strange commit ordering when merges are involved,
working on it.
collect.sh
```
#!/bin/bash
function gho
{
git show-ref --heads --abbrev=8 $1 | head -n 1
}
function gch {
gho $(git rev-parse --abbrev-ref HEAD) | cut -d ' ' -f 1 # show curerent branch head
}
git fetch upstream
git reset --hard upstream/master
gch > .head;
./hash_stab.py | while read N; do
git reset --hard $N
FNAME="vbench_results/$N.pickle"
if [ -e $FNAME ]; then
echo skipping $N
else
./sane.sh # copy over the setup.py and vb_suite/* from upstream/HEAD
python ./setup.py develop;
time ./test_perf.sh -H -c 1 -d $FNAME;
fi;
done
```
hash_stab.py
```
#!/usr/bin/env python
import sh
r=sh.git('log --format="%h" v0.9.0.. --'.split(" "),_tty_out=False).replace('"',"").split("\n")[:-1]
print("%d commits" % len(r))
l = [r[0],r[-1]]
segsize = len(r)
while len(l) < len(r):
cands = range(0+segsize/2,len(r),segsize)
cands = [r[i] for i in cands if r[i] and r[i] not in l]
segsize/=2
if len(cands) == 0:
l.extend([x for x in r if x not in l])
break
l.extend(cands)
for h in l:
print h
```
analyze results:
``` python
h=sh.git('log --date-order --format="%h###%ci" --no-merges'.split(" ")).replace('"',"").split("\n")[:-1]
h=map(lambda x:x.split("###"),h)
from dateutil import parser
h=dict(map(lambda x: (x[0],parser.parse(x[1])),h))
fs = [x for x in os.listdir('.') if x.split('.')[0] in h]
fs.sort(key=lambda x: h[x.split('.')[0]])
results = list()
for o in fs:
s = Series(pd.load(o).icol(0),name=o.split(".")[0])
results.append(s)
df=pd.DataFrame(results).T
f=df.icol(-1)/df.icol(0)
f=f[f> 1.2]
def show(n):
if isinstance(n,basestring):
s=df.ix[n]
else:
s=df.ix[f.index[n]]
name = s.name
s=mom.rolling_mean(s,5,center=True).dropna()
s.name=name
plt.title(s.name)
plt.ylim(0,s.max())
s.plot()
s=s.dropna()
return name
show(3)
# plot here
show('dti_reset_index_tz')
# plot here
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3262 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3262/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3262/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3262/events | https://github.com/pandas-dev/pandas/pull/3262 | 12,872,529 | MDExOlB1bGxSZXF1ZXN0NTAxNDgyNQ== | 3,262 | DOC: update scipy.py #2916 | {
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} | [] | closed | false | null | [] | null | 1 | "2013-04-06T05:46:04Z" | "2013-04-06T06:03:03Z" | "2013-04-06T06:02:40Z" | CONTRIBUTOR | null | renamed interpolation -> interpolation_method.
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https://api.github.com/repos/pandas-dev/pandas/issues/3263 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3263/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3263/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3263/events | https://github.com/pandas-dev/pandas/issues/3263 | 12,879,698 | MDU6SXNzdWUxMjg3OTY5OA== | 3,263 | to_csv no longer supports file mode | {
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} | 1 | "2013-04-06T16:15:02Z" | "2013-04-06T17:54:16Z" | "2013-04-06T17:54:16Z" | NONE | null | The `mode` param passed to `DataFrame.to_csv` is being ignored.
```
In [20]: f = pd.DataFrame({'a':[1,2,2,3,4], 'b':[4,3,2,1,4]})
In [21]: f.to_csv('bar.csv', mode='w')
In [22]: !cat bar.csv
,a,b
0,1,4
1,2,3
2,2,2
3,3,1
4,4,4
In [23]: f.to_csv('bar.csv', mode='a')
In [24]: !cat bar.csv
,a,b
0,1,4
1,2,3
2,2,2
3,3,1
4,4,4
```
The flag should have be passed to the CSVFormatter in this commit:
https://github.com/pydata/pandas/commit/099520871736ad741904d9f65c255831e526e9db#pandas/core/frame.py
I don't have a full dev env setup to run all the tests, but changing `frame.py:1354` to `index_label=index_label,mode=mode,` fixes it.
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} | 1 | "2013-04-07T13:34:50Z" | "2014-06-21T21:02:26Z" | "2013-04-08T23:54:20Z" | CONTRIBUTOR | null | Just a small doc string fix, hopefully trivial to merge.
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} | 0 | "2013-04-07T17:15:20Z" | "2013-06-03T01:16:15Z" | "2013-06-03T01:16:15Z" | CONTRIBUTOR | null | If diagonal='kde', then the keyword arguments are passed to both plot and scatter. They don't have the same call signatures, so you can't do this. I guess you need separate keyword arguments. I would vote for removing **kwds from the diagonal plots entirely and just fixing those axes ex-post if you need something changed. For example,
```
import numpy as np
import pandas
import matplotlib.pyplot as plt
url = ("http://archive.ics.uci.edu/ml/machine-learning-databases"
"/pima-indians-diabetes/pima-indians-diabetes.data")
names = ["num_pregnant", "plasma_glucose", "blood_press",
"skin_fold", "insulin", "bmi", "pedigree", "age",
"class"]
pima_data = pandas.read_csv(url, header=None, names=names)
outcome = pima_data.pop("class")
```
Say I want to differentiate the features by color across the class. I can pass color keyword to scatter.
```
class_color = list(outcome.apply(lambda x : "red" if x else "blue"))
features = [names[1], names[4], names[5], names[7]]
X = pima_data[features]
pandas.scatter_matrix(X, color=class_color)
```
but as soon as you try to also use 'kde' this breaks.
```
pandas.scatter_matrix(X, color=class_color, diagonal='kde')
```
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} | 30 | "2013-04-07T18:43:42Z" | "2019-01-01T17:30:09Z" | "2019-01-01T17:30:08Z" | NONE | null | Have you ever written code that looks like this:
```
if isinstance(d.index, MultiIndex):
results = []
for l in d.index.levels:
for x in baz(l):
results.append(foo)
elif isinstance(d.index, Index):
for x in d.index:
foo
```
I've had to special case the handling of index vs. multindex several times in the past.
Conceptually, I should be able to treat index as a private case of MultIndex
with nlevels =1, and supporting that in the API would make things nicer.
---
**Edit by @cpcloud:**
Tasks :
### API Unification
Method unification is relatively simple:
- [ ] `Index.from_tuples` and `Index.from_arrays` are just `MultiIndex.from_tuples` and `MultiIndex.from_arrays` moved to classmethods of `Index`.
- [ ] `droplevel`, ` just raises on Index (right? what would it mean?): #21115
- [ ] `has_duplicates` is straightforward
- [ ] `truncate` should be equivalent to slicing
- [ ] `reorder_levels` raises if not level=0 or name of index
- [ ] `equal_levels` - straightforward
- [ ] `levshape` - (len(ind),)
- [ ] `sortorder` - None
- [ ] `get_loc_level` - I think meaningless with tuple, raises whatever if not 0 or index name
- [ ] `is_lexsorted` - doesn't need to change
- [ ] `is_lexosrted_tuple` - doesn't need to change
- [ ] `is_monotonic_*`
- [ ] `lexsort_depth` - doesn't need to be changed at all
- [ ] `searchsorted`
- [ ] `repeat`
- [ ] `levels` and `labels` property for Index - question on whether it should be sorted.
- [ ] change to `rename` behavior: `Index` will accept either string or single-element list; MI continues to handle only list
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} | 4 | "2013-04-07T19:10:20Z" | "2020-01-29T12:59:36Z" | "2016-12-29T11:12:55Z" | NONE | null | In shell grep you can do stuff like:
```
λ seq 1000 | grep -C 3 500
497
498
499
**500**
501
502
503
```
```
λ seq 1000 | grep -B 1 -A 3 500
499
**500**
501
502
503
```
I'd be glad to have something like
``` python
s.grep_index(pat_or_pred,A=1,B=3) # -> list of series/df , each of len(A+B) or C
s.grep_index(pat_or_pred,C=5)
also
s.grep_index(label_list,C=5)
```
in core.
An example use-case:
looking for point of change in a timeseries, where you want to
grab a window around a point of change:
``` python
s = some_timeseries
# pickout index label where a big change occured
cps = s[abs(s.diff()- threshold) > threshold]
# cps is a list of labels for "interest points"
list_of_s = s.grep_index(cps,C=5)
s[0]
< 5 rows from s, with the middle one being the first "intrest point" in cps >
```
the "return list of series" is really a sort of variant on groupby,
and could concievably be implemented on top of something like
#3101, after de-warting.
Would also love to have this working with #2460 (if it ever makes it in)
for the same (but slow) on plain,unindexed data columns.
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} | 71 | "2013-04-07T19:57:24Z" | "2014-06-13T17:39:52Z" | "2013-05-16T11:31:47Z" | CONTRIBUTOR | null | This pull request aims at fixing ticket #1512 and contains both a reader and a writer for Stata .dta files.
The code basically comes from th statsmodels project, however, I adapted it to the needs of pandas and implemented support for reading out stata value labels. The writer does not write those labels back.
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} | 0 | "2013-04-07T20:20:33Z" | "2013-04-08T05:06:12Z" | "2013-04-08T05:06:11Z" | NONE | null | any sentimental value there?
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} | 18 | "2013-04-07T23:01:04Z" | "2014-01-20T18:02:42Z" | "2013-04-08T21:23:19Z" | NONE | null | @wesm, any objection to changing over to ["nature"](http://sphinx-doc.org/latest/theming.html#builtin-themes)?
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https://api.github.com/repos/pandas-dev/pandas/issues/3274 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3274/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3274/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3274/events | https://github.com/pandas-dev/pandas/pull/3274 | 12,905,602 | MDExOlB1bGxSZXF1ZXN0NTAyNDU3NA== | 3,274 | ENH: an example of a sandbox feature | {
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} | 3 | "2013-04-08T01:37:45Z" | "2013-04-08T09:41:25Z" | "2013-04-08T09:41:07Z" | NONE | null | Earlier today I suggested allowing "sandbox" features into releases,
to make it easier to get some more testing and feedback before
commiting something into core proper.
Here's an example of how I imaging this to work:
```
In [1]: pd.describe_option("sandbox")
sandbox.experimental_feature: [default: False] [currently: False]
: boolean
Enables a snazzy, but not yet stable feature that could use
some feedback before going into core.
In [1]: pd.options.sandbox.experimental_feature=True
This is an experimental feature being considered for inclusion in pandas core.
We'd appreciate your feedback on it in the appropriate Github issue page:
http://github.com/pydata/pandas/issues/3274
If you find this feature useful, lacking in major functionality or buggy please
take a moment to let us know, so we can make pandas (even) better.
Thank you,
The Pandas dev team
P.S.
love the hair.
In [2]: df=mkdf(3,2)
...: df.foo()
Hi, I'm a new experimental method. grrrr!
In [3]: pd.options.sandbox.experimental_feature=False
In [4]: df=mkdf(3,2)
...: df.foo()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-4-66a4f195a54c> in <module>()
1 df=mkdf(3,2)
----> 2 df.foo()
/home/user1/src/pandas/pandas/core/frame.pyc in __getattr__(self, name)
1965 return self[name]
1966 raise AttributeError("'%s' object has no attribute '%s'" %
-> 1967 (type(self).__name__, name))
1968
1969 def __setattr__(self, name, value):
AttributeError: 'DataFrame' object has no attribute 'foo'
```
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```
I have a sort of philosophical question about the use of
indexes (especially MultiIndexes) versus just keeping data in
columns. When using groupby, you tend to get a lot of results
with MultiIndexes, and the indexes are convenient for simple
accessing of items. However, I've found that index objects lack
key features of ordinary columns. I often find myself swapping a
particular dimension back and forth from index to column, either
because I need one or other, or because Pandas gives me one when
I want the other. What I'm wondering is if I'm using pandas in a
nonidiomatic way, or if there's some way to get around these
difficulties I'm having, or what.
The three main things I've noticed right now to be irritating
about Index objects are:
A) Extracting and using the level values is awkward. When I have
a column, I can get the values just with df.SomeCol or
df['SomeCol']. For indexes, I have to do
df.index.get_level_values('IndexLevel'), and even then I just get
another Index instance, which I may have to convert to a series
for other things, because. . .
B) Indexes do not support the convenient convenient operations on
Series, in particular Series.map. This means that, although I
can easily do df1.ix[df.SomeCol.map(someThingElse)], I cannot do
this when SomeCol is an index instead of just a column in the
data. I have to extract the index level values as above and then
convert to a series before I can map them.
C) There doesn't appear to be a way to group a DataFrame by a
combination of columns and index levels. groupby allows a "by"
argument for columns and a "level" argument for index levels, but
using both gives an error. Even if I could do this, it's not
clear how I would specify the order of the grouping.
The solutions that come to mind for these problems are: A) give
MultiIndex objects a simple means of accessing the level values
as a Series. Something like df.index.levels.Level or df
index.levels['Level']. Basically make MultiIndexes indexable in
somewhat the same way that DataFrames already are. B) Give
Indexes a map-like operator, and maybe some of the other useful
stuff from Series. C) Provide some way of grouping using both
columns and index levels. Maybe some sort of "IndexGroup" class
that would wrap a level name, so you could do groupby(["Column",
IG("IndexLevel"), "OtherColumn"]) to insert an index level in the
grouping order.
Pandas provides a lot of functionality for slicing and dicing the
data in the different ways, but I feel like sometimes I'm forced
to slice it and dice it back and forth by converting indexes to
columns and vice versa instead of being able to directly access
what I want. I'd be interested to hear how/whether other people
deal with these issues. Are there ways of doing these things
that I'm missing?
```
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} | 2 | "2013-04-08T05:25:49Z" | "2014-07-22T00:40:26Z" | "2013-07-22T20:05:13Z" | NONE | null | #2460
partly fulfilles #3269, should be extended to an equivalent method on
index labels? (vectorized? numexpr?). Just experimenting with the API,
performance later (as much as possible with unindexed data).
- [x] need to add option to interpret multiple columns as a predicate expecting
a list of values corresponding to the row values on the specified columns,
to allow for boolean expressions across columns, rather then single value at a time.
- [x] tests
- [ ] make lazy like groupby?
- [ ] need to add `axis` argument, per jeff's suggestion.
- [ ] documentation.
``` python
In [7]: pd.options.sandbox.dgrep=True
This is an experimental feature being considered for inclusion in pandas core.
We'd appreciate your feedback on it in the Github issue page:
http://github.com/pydata/pandas/issues/2460
If you find this useful, lacking in major functionality or buggy please
take a moment to let us know, so we can make pandas (even) better.
Thank you,
The Pandas dev team
P.S.
Series/DataFrame now have a .dgrep method.
See the docstring for usage examples.
In [8]: df=mkdf(30,4,r_idx_nlevels=3)
...: df.index=range(30)
...: df.iloc[5,0] = "supercool"
...: df.iloc[6,0] = "supercool"
...: df.iloc[29,0] = "supercool"
...: df.iloc[15,1] = "supercool"
...: # accepts colname and regex string
...: print "\n" + str(df.dgrep(".cool$","C_l0_g0"))
...: # accepts lists of cols,
...: print "\n" + str(df.dgrep(".cool$",["C_l0_g0",'C_l0_g1']))
...: # specifying C=2 (or A/B=) does a grep context , providing
...: # context lines around the hit
...: # NB overlapping context lines do not cause line duplication (*)
...: print "\n" + str(df.dgrep(".cool$",["C_l0_g0"],C=2))
...: # also accepts lambda
...: # NB, last match is at end, so only previous line of context displayed
...: print "\n" + str(df.dgrep(lambda x: bool(re.search(".cool$",x)),["C_l0_g0"],C=3))
...: # split=True returns a series of (index_label_matched, dataframe)
...: # pairs, similar to groupby
...: # NB some lines appear in more then one group in this case (*)
...: print "\n" + "\n".join(map(str,df.dgrep(".cool$",["C_l0_g0"],split=True,C=3)))
...:
...: # works on series too
...: print "\n" + str(df.C_l0_g0.dgrep(".cool$",C=3))
C0 C_l0_g0 C_l0_g1 C_l0_g2 C_l0_g3
5 supercool R5C1 R5C2 R5C3
6 supercool R6C1 R6C2 R6C3
29 supercool R29C1 R29C2 R29C3
C0 C_l0_g0 C_l0_g1 C_l0_g2 C_l0_g3
5 supercool R5C1 R5C2 R5C3
6 supercool R6C1 R6C2 R6C3
15 R15C0 supercool R15C2 R15C3
29 supercool R29C1 R29C2 R29C3
C0 C_l0_g0 C_l0_g1 C_l0_g2 C_l0_g3
4 R4C0 R4C1 R4C2 R4C3
5 supercool R5C1 R5C2 R5C3
6 supercool R6C1 R6C2 R6C3
28 R28C0 R28C1 R28C2 R28C3
29 supercool R29C1 R29C2 R29C3
C0 C_l0_g0 C_l0_g1 C_l0_g2 C_l0_g3
4 R4C0 R4C1 R4C2 R4C3
5 supercool R5C1 R5C2 R5C3
6 supercool R6C1 R6C2 R6C3
7 R7C0 R7C1 R7C2 R7C3
28 R28C0 R28C1 R28C2 R28C3
29 supercool R29C1 R29C2 R29C3
(5, C0 C_l0_g0 C_l0_g1 C_l0_g2 C_l0_g3
4 R4C0 R4C1 R4C2 R4C3
5 supercool R5C1 R5C2 R5C3
6 supercool R6C1 R6C2 R6C3)
(6, C0 C_l0_g0 C_l0_g1 C_l0_g2 C_l0_g3
5 supercool R5C1 R5C2 R5C3
6 supercool R6C1 R6C2 R6C3
7 R7C0 R7C1 R7C2 R7C3)
(29, C0 C_l0_g0 C_l0_g1 C_l0_g2 C_l0_g3
28 R28C0 R28C1 R28C2 R28C3
29 supercool R29C1 R29C2 R29C3)
4 R4C0
5 supercool
6 supercool
7 R7C0
28 R28C0
29 supercool
Name: C_l0_g0, dtype: object
# can also get the values "applied" onto the function
df.dgrep(lambda c1,c2: "cool" in c1 or "cool" in c2,df.columns[:2])
# which also works with *args
df.dgrep(lambda *args: "supercool" in args,df.columns[:3])
```
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} | 0 | "2013-04-08T06:51:51Z" | "2014-08-12T07:30:23Z" | "2013-04-08T07:12:24Z" | MEMBER | null | Fixes #3238
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} | 5 | "2013-04-08T11:29:06Z" | "2014-06-21T14:58:26Z" | "2013-04-09T20:05:09Z" | NONE | null | #3275
``` python
In [10]: df=mkdf(10,5,r_idx_nlevels=2)
In [11]: df.index
Out[11]:
MultiIndex
[R_l0_g0 R_l1_g0, R_l0_g1 R_l1_g1, R_l0_g2 R_l1_g2, R_l0_g3 R_l1_g3, R_l0_g4 R_l1_g4, R_l0_g5 R_l1_g5, R_l0_g6 R_l1_g6, R_l0_g7 R_l1_g7, R_l0_g8 R_l1_g8, R_l0_g9 R_l1_g9]
In [12]: # new method
...: df.index.to_series()
Out[12]:
R0 R1
R_l0_g0 R_l1_g0 (R_l0_g0, R_l1_g0)
R_l0_g1 R_l1_g1 (R_l0_g1, R_l1_g1)
R_l0_g2 R_l1_g2 (R_l0_g2, R_l1_g2)
R_l0_g3 R_l1_g3 (R_l0_g3, R_l1_g3)
R_l0_g4 R_l1_g4 (R_l0_g4, R_l1_g4)
R_l0_g5 R_l1_g5 (R_l0_g5, R_l1_g5)
R_l0_g6 R_l1_g6 (R_l0_g6, R_l1_g6)
R_l0_g7 R_l1_g7 (R_l0_g7, R_l1_g7)
R_l0_g8 R_l1_g8 (R_l0_g8, R_l1_g8)
R_l0_g9 R_l1_g9 (R_l0_g9, R_l1_g9)
dtype: object
In [13]: # so now this is possible
...: df[df.index.to_series().map(lambda x: "4" in x[0])]
Out[13]:
C0 C_l0_g0 C_l0_g1 C_l0_g2 C_l0_g3 C_l0_g4
R0 R1
R_l0_g4 R_l1_g4 R4C0 R4C1 R4C2 R4C3 R4C4
```
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} | 2 | "2013-04-08T11:49:42Z" | "2013-09-28T20:54:56Z" | "2013-09-28T20:54:28Z" | NONE | null | If their install process ever becomes less ridiculous,
and the processing time reasonable it might be interesting
to make pdf tables -> pandas seamless. I know some civic
hacking people this would make happy.
https://github.com/jazzido/tabula
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} | 2 | "2013-04-08T13:05:25Z" | "2013-04-08T15:10:35Z" | "2013-04-08T15:10:35Z" | NONE | null | When executing this example code, the graph produced has the columns resorted such that they read `["col1", "col10", "col11", "col2", ...]` instead of the correctly sorted `["col1", "col2", "col3", "col4", ...]`
```
from collections import OrderedDict
import pandas, random
data = OrderedDict([('col%s' % i, random.random()) for i in range(12)])
series = pandas.Series(data)
series.plot(kind='bar', color='gray')
```
Of course, the problem is solved by doing:
```
series = pandas.Series(data.values(), index=data.keys())
```
Just thought you would want to fix that though.
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} | 1 | "2013-04-08T13:11:49Z" | "2013-12-04T00:57:21Z" | "2013-04-08T15:33:05Z" | NONE | null | I am having issues with pivot table creation making columns into floats when they don't need to be.
```
In [313]: f = pd.DataFrame({'a' : ['cat', 'bat', 'cat', 'bat'], 'v' : [1,2,3,4], 'i' : ['a','b','a','b']})
In [314]: z = pd.pivot_table(f, values='v', rows=['a'], cols=['i'], fill_value=0, aggfunc=np.sum)
In [315]: z
Out[315]:
i a b
a
bat 0 6
cat 4 0
In [316]: z.b.dtype
Out[316]: dtype('float64')
In [317]: f.v.dtype
Out[317]: dtype('int64')
In [318]: np.sum(f.v)
Out[318]: 10
In [319]: type(np.sum(f.v))
Out[319]: numpy.int64
```
This is making stuff ugly when I to_csv it.
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] | closed | false | null | [] | null | 2 | "2013-04-08T13:38:27Z" | "2013-04-08T13:56:00Z" | "2013-04-08T13:56:00Z" | NONE | null | I can't seem to get the arguments on the apply function to work when there is only one argument. The following code reproduces my error in Pandas 0.91. It doesn't seem to recognize a single argument as a one element tupple.
``` python
data = {'state':['Ohio','Ohio','Ohio','Nevada','Nevada'], 'year':[2000,2001,2002,2001,2002],'pop':[1.5,1.7,3.6,2.4,2.9]}
frame = pd.DataFrame(data)
def testfunc(x, *args):
summand = args[0]
y = x + summand
return y
#this works okay
frame['year'].apply(testfunc, args = (7,7))
#this does not
frame['year'].apply(testfunc, args = (7))
#TypeError: testfunc() argument after * must be a sequence, not int
```
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} | 1 | "2013-04-08T14:38:43Z" | "2013-04-08T16:54:29Z" | "2013-04-08T16:54:21Z" | CONTRIBUTOR | null | Added params argument to read_frame that is passed directly to the
execute method. This allows users to use a parameterized query
rather than having to pre-format the SQL themselves.
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} | 0 | "2013-04-08T14:43:28Z" | "2014-06-20T01:23:28Z" | "2013-04-08T15:32:30Z" | CONTRIBUTOR | null | closes #3283
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} | 5 | "2013-04-08T16:16:53Z" | "2014-07-02T01:57:24Z" | "2013-04-12T04:14:13Z" | CONTRIBUTOR | null | ...#3108
suppressing AttributeError here so you can plot the DataFrame and automatically exclude non-numeric columns. @y-p @wesm @jreback any thoughts on 1) whether it should raise instead of exclude and 2) whether catching the AttributeError here is too dangerously broad?
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} | 4 | "2013-04-08T16:21:19Z" | "2013-04-09T02:47:03Z" | "2013-04-09T02:07:17Z" | CONTRIBUTOR | null | in test_graphics.py
```
@slow
def test_time_series_plot_color_with_empty_kwargs(self):
import matplotlib.pyplot as plt
plt.close('all')
for i in range(3):
ax = Series(np.arange(12) + 1, index=date_range(
'1/1/2000', periods=12)).plot()
line_colors = [ l.get_color() for l in ax.get_lines() ]
self.assert_(line_colors == ['b', 'g', 'r'])
```
fails because line_colors are returned as hex (MPL 1.1.0)
Can anyone confirm?
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} | 0 | "2013-04-09T00:11:31Z" | "2013-09-21T01:26:36Z" | "2013-09-21T01:26:36Z" | CONTRIBUTOR | null | see #3219
we now allow setting of mixed type frames with a ndarray/frame that can be aligned, docs mention the old behavior, need example maybe
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} | 1 | "2013-04-09T01:15:53Z" | "2013-05-18T15:13:52Z" | "2013-04-09T14:40:02Z" | MEMBER | null | @jreback this is a test case that shows why assigning to `dtype` is unsafe, namely that the dtype item sizes have to be compatible obviously =P Could you take a look at this and fix? This came up when I booted the windows box back up because Windows uses int32 by default.
There is a secondary bug here that the test case in question probably _should not_ cause the dtype of the resulting Series to change from int64 to float64, agreed?
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} | 1 | "2013-04-09T01:45:21Z" | "2013-04-09T02:17:35Z" | "2013-04-09T02:17:14Z" | MEMBER | null | ```
In [3]: (5,)
Out[3]: (5,)
In [4]: Series([(4,), (5,), (6,)])
Out[4]:
0 (4)
1 (5)
2 (6)
dtype: object
```
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} | 5 | "2013-04-09T02:06:06Z" | "2013-04-09T19:44:07Z" | "2013-04-09T17:18:25Z" | CONTRIBUTOR | null | fixes assignment to smaller itemsizes on dtype upcasting
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} | 2 | "2013-04-09T02:22:30Z" | "2013-07-14T14:40:50Z" | "2013-07-14T14:40:50Z" | NONE | null | probably applies to ipython sphinx ext as well, I did always
find them somewhat grainy:
http://mail.scipy.org/pipermail/ipython-user/2011-October/008699.html
investigate.
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} | 2 | "2013-04-09T13:03:08Z" | "2014-03-14T12:41:12Z" | "2014-03-14T12:41:12Z" | CONTRIBUTOR | null | Both `DataFrame.hist` and `DataFrame.boxplot` support `by` to create grouped plots, however, `DataFrame.hist` requires that any column in `by` is also included in `column` while `DataFrame.boxplot` its vice-versa.
Here is a minimal example::
```
>>> df = pd.DataFrame({'a': [1, 1, 2, 2, 2, 1], 'b': list('AAABBB')})
>>> df.hist(column=['a', 'b'], by=['b'])
>>> df.boxplot(column=['a'], by=['b'])
>>> df.hist(column=['a'], by=['b']) # raises an error
KeyError: u'no item named b'
```
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} | 1 | "2013-04-09T14:09:40Z" | "2013-04-10T07:01:13Z" | "2013-04-10T07:01:13Z" | CONTRIBUTOR | null | `DataFrame.hist` allows arbitrary keyword arguments which (according to the docstring) are passed to `matplotlib.hist`, however, it ignores keyword arguments in `pandas.tools.plotting.grouped_hist` (does not pass them to `hist`).
This would be useful in order to create normalized and/or cumulative histograms.
It requires just a minor fix - if you want I can submit a PR.
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} | 22 | "2013-04-09T18:24:51Z" | "2014-06-24T19:43:15Z" | "2013-05-18T15:05:08Z" | NONE | null | #2485, with much reduced scope.
While collecting vbench data recently, it became painfully
obvious how useful it would be to be able to attach metadata
tags to serialized frames/series/panel.
This is a rough draft for something I'd like to see happen in 0.12.
It creates yet another version of pickle files, so much testing
needs to be done there.
There's a plan (#686, timeline unclear) for implementing a binary serialization
format for pandas, which will need to replicate this functionality if this makes it in.
@jreback, if you have something planned in this direction, I'm glad to
withdraw this PR, it's just a statement of intent to prod us into getting something
working during the 0.12 release cycle.
The design choices I'm going for right now:
- **no propagation** semantics through operations, the metadata tags are restored
if present, and are guranteed on load, only.
Example use case : store measurements as dataframes with data,
location, etc'. Then, load a mess of them back up and (generally) you may
use the metadata either as column/index labels, or just pick out a subset
based on them, sort based on them, etc'. **edit:** Document this very clearly.
- **only JSON-able** data allowed in dicts. Allowing anything else would bring
us back to the pickle problem, might create issues if #686 gets implemented
(difficulty in serializing arbitrary objects in metadata without using pickle), and is generally
an unknown quantity. JSON can cover a lot of mileage.
letting objects define their own way to be serialized for metadata purposes
is reinventing pickle, so no. (JSONable not yet enforced in the code).
- TODO: consider the binary blob case.
- TODO: if there's no propogation, should the data live on the object at all? ( what about `o, meta = pd.load()`?)
- reserved keywords/namespace for pandas' use?
- establish naming convention (for reserved kwds or more broadly?)
- make it minimal, no setdefault(), save/load -time hooks etc, users should roll their own there.
- currently, it's just a dict `.meta` that gets pickled and unpickled with the object.
- versioning/schema to be baked in as reserved keywords? the pickle
code taught me that much.
- The usual "I want tab completion/attr access/call it _foo" discussion to follow.
``` python
In [8]: df=mkdf(10,5)
...: df.meta['Im']="persistent"
...: df.save('/tmp/1.pickle')
...: dfrt =pd.load('/tmp/1.pickle')
...:
In [9]: dfrt.meta
Out[9]: {'Im': 'persistent'}
```
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} | 0 | "2013-04-09T19:08:54Z" | "2013-04-09T20:39:52Z" | "2013-04-09T20:39:52Z" | NONE | null | https://github.com/matplotlib/matplotlib/issues/1892
fix shortly
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} | 2 | "2013-04-09T19:15:11Z" | "2013-04-10T07:01:03Z" | "2013-04-10T07:01:03Z" | CONTRIBUTOR | null | This is a fix for https://github.com/pydata/pandas/issues/3296 .
Now keyword arguments to `plotting.grouped_hist` are passed to `matplotlib.Axes.hist`. This allows you to create e.g. normalized, cumulative, or log scaled histograms::
```
df = pd.DataFrame(np.random.randn(500, 2), columns=['A', 'B'])
df['C'] = np.random.randint(0, 4, 500)
df.hist(column=['A'], by=df.C, normed=True)
```
PS: the guideline says I should enable Travis-CI on my fork - is there a tutorial how to do that? thx!
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https://api.github.com/repos/pandas-dev/pandas/issues/3300 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3300/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3300/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3300/events | https://github.com/pandas-dev/pandas/pull/3300 | 12,988,209 | MDExOlB1bGxSZXF1ZXN0NTA2NjI0OA== | 3,300 | Work around mpl 1.2.1 regression re bar log plot GH3298, GH3254 | {
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} | 0 | "2013-04-09T19:34:39Z" | "2013-04-09T20:39:25Z" | "2013-04-09T20:39:25Z" | NONE | null | #3298, #3254
added tests and Tested visually on 1.2.0 and 1.2.1.
y-p's 2nd rule of github - if you see an area in the codebase that you never ever want
to touch, you'll end up maintaining it.
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