pydata/pandas — pull request #2373 by y-p (2012-11-28 03:03:09+00:00)
Title: Incorporate a vbench run into Travis-CI

A seperate 2.7 env is spawned, which is allowed to fail without
failing the entire build. The results are posted to a public gist as
a json file, and the gist url is printed in the log.

A helper function in vb_suite/perf_HEAD.py accepts a travis
build number, It then locates the VBENCH job within the build, fetches the log
through the Travis API, extract the gist raw_url from the log, fetches the results
as json and converts it into a pandas dataframe.

It remains to be seen whether the results are consistent
enough between builds over time to actually make this useful.

Here's how to gather the results from a few consecutive test builds I ran:

```python
%cd vb_suite

import pandas as pd
from perf_HEAD import get_build_results
builds=[3393087,3393105,3393122,3393125,3393130]
dfs=[get_build_results(x) for x in builds]
dfs2=[x[['timing']] for x in dfs]
for df,b in zip(dfs2,builds):
df.columns=[str(b)]
df = dfs2[0]
for other in dfs2[1:]:
df=df.join(other,how='outer')
df[:20]

3393087 3393105 3393122 3393125 3393130
name
append_frame_single_homogenous 0.641988 0.340924 0.524198 0.400884 0.373756
append_frame_single_mixed 2.159144 1.354964 1.639278 1.421034 1.301402
concat_series_axis1 131.367922 56.542015 92.097044 75.879002 71.159506
ctor_index_array_string 0.009129 0.004975 0.005761 0.006048 0.007652
dataframe_get_value 0.001885 0.001201 0.001403 0.001305 0.001331
dataframe_getitem_scalar 0.005543 0.003445 0.003394 0.003432 0.003307
dataframe_reindex_columns 0.494599 0.233001 0.370013 0.334528 0.316710
dataframe_reindex_daterange 0.683607 0.329319 0.552313 0.506124 0.439592
datamatrix_getitem_scalar 0.004519 0.003790 0.003355 0.003436 0.003317
datetimeindex_add_offset 0.439327 0.227953 0.327404 0.410341 0.312527
datetimeindex_normalize 73.160005 51.629400 60.980701 59.092712 58.182502
frame_boolean_row_select 0.769411 0.596314 0.723754 0.564437 0.653430
frame_constructor_ndarray 0.064187 0.028872 0.033200 0.033250 0.042343
frame_ctor_list_of_dict 140.382051 86.547899 114.851952 127.656937 114.904165
frame_ctor_nested_dict 124.281883 73.726416 97.840071 106.689930 111.264944
frame_ctor_nested_dict_int64 229.266882 130.098104 143.503904 183.116913 152.652025
frame_fancy_lookup 2.752171 2.173202 2.223320 1.820600 2.148161
frame_fancy_lookup_all 135.908127 113.823605 117.978096 108.165979 113.741159
frame_fillna_inplace 25.430799 16.801000 20.938993 24.916101 21.809912
frame_fillna_many_columns_pad 23.508501 16.926599 20.899606 19.706178 17.867088
```