pydata/pandas — pull request #2349 by y-p (2012-11-24 21:51:27+00:00)
Title: BUG: del df[k] with non-unique key

This touches some delicate functions to be messing with, so I've split off the
underlying logic into a function in common and added an exhaustive test.
Would welcome review none the less.

Also, I tried to test for nagative perf. There must something
wrong with the test since comparing the commit after just adding a test to the baseline,
shows 50% degredation in a bunch of things. as I said, I don't trust this, ideas?

```
timeseries_asof_nan 23.4131 15.7471 1.4868
datetimeindex_normalize 1153.1918 774.8010 1.4884
timeseries_asof 22.3342 14.9964 1.4893
reshape_unstack_simple 4.8106 3.2289 1.4899
read_table_multiple_date_baseline 980.0920 657.3641 1.4909
timeseries_1min_5min_mean 0.8087 0.5422 1.4915
timeseries_timestamp_tzinfo_cons 0.0210 0.0141 1.4922
match_strings 0.5718 0.3817 1.4982
timeseries_large_lookup_value 0.0301 0.0201 1.4998
reindex_fillna_pad 0.1828 0.1217 1.5018
timeseries_to_datetime_iso8601 5.6048 3.7270 1.5038
read_table_multiple_date 2169.5440 1441.5460 1.5050
reindex_daterange_pad 0.2608 0.1731 1.5066
timeseries_1min_5min_ohlc 0.8046 0.5331 1.5092
reindex_daterange_backfill 0.2466 0.1634 1.5094
period_setitem 1166.9910 772.4509 1.5108
reindex_fillna_backfill 0.1790 0.1183 1.5130
timeseries_asof_single 0.0656 0.0434 1.5136
append_frame_single_mixed 2.0747 1.3697 1.5147
timeseries_slice_minutely 0.0762 0.0501 1.5208

Columns: test_name | target_duration [ms] | baseline_duration [ms] | ratio

(01bc3e0 against 81169f9)

- a Ratio of 1.30 means the target commit is 30% slower then the baseline.
```