Optimize getitem operations by checking for same indexes

[kozlov-alexey]

Some performance results (from laptop):

with fix:

				median	min	max	compile	boxing
name	nthreads	type	size
DataFrame.getitem_filter_by_value	1	Python	10000000	0.328003	0.318	0.357003	NaN	NaN
		SDC	10000000	0.34	0.287	0.404	0.715081	1.126521
	2	SDC	10000000	0.205	0.194	0.243	0.666005	0.958931
	4	SDC	10000000	0.154	0.128	0.176	0.616491	0.935159

without fix (on master):

				median	min	max	compile	boxing
name	nthreads	type	size
DataFrame.getitem_filter_by_value	1	Python	10000000	0.314004	0.311996	0.318	NaN	NaN
		SDC	10000000	3.748	3.618	4.133	0.731427	0.853143
	2	SDC	10000000	3.158	3.113	3.632	0.78739	0.859766
	4	SDC	10000000	3.06	3.007	3.454	0.813918	0.958313

For some reason on nnlmlp01 (but not on ansatclx1004 and my laptop) SDC is slower than python by 2 times on single thread (on the same test). This needs to be investigated further.
with fix on nnlmlp01:

				median	min	max	compile	boxing
name	nthreads	type	size
DataFrame.getitem_filter_by_value	1	Python	10000000	0.268062	0.267576	0.270783	NaN	NaN
		SDC	10000000	0.361877	0.361485	0.362171	0.612621	1.002072
	2	SDC	10000000	0.213841	0.212518	0.215923	0.672137	1.019482
	4	SDC	10000000	0.120625	0.11833	0.125019	0.622494	1.026524
	8	SDC	10000000	0.075155	0.074595	0.075756	0.624533	1.027116
	16	SDC	10000000	0.059031	0.058086	0.075269	0.612236	1.051869
	28	SDC	10000000	0.054463	0.051837	0.056121	0.608134	1.074058
	56	SDC	10000000	0.060003	0.056724	0.062026	0.644352	1.094665

[AlexanderKalistratov]

Looks great

[AlexanderKalistratov]

Looks good.

We would need to implement such method for each new series type (e.g. categorical, datetime, etc.)