The Virtual DataFrame¶

The Virtual DataFrame is the main object and star of the library and acts as the perfect transition between small and Big Data. The principle is quite simple: As Vertica is a powerful columnar massive parallel processing (MPP) database with many built-in functions, we want it to do as much of the computation work as possible.

Indeed, columnar orientation allows for high compression, and its structure inherently avoids unncessary parsing when retrieving data. MPP allows to parallelize our computations accross the different nodes.

The best way to take advantage of your data is by simply keeping it in your Vertica database, rather than within the limitations of working memory. VerticaPy pushes all computation to your Vertica database before aggregating the final result, so you can get the best of both worlds: Vertica's power and Python's flexibility.

With Python, it's easy to add abstractions, and the vDataFrame acts as the primary abstraction layer. Simple but powerful, it'll help any user through the data science life cycle.

Creating the vDataFrame¶

There are two main ways to create a vDataFrame.

The first is to create one directly using an existing relation.

In [30]:

from verticapy import vDataFrame
from verticapy import vertica_conn

cur = vertica_conn("VerticaDSN").cursor()

vDataFrame("public.titanic", cur)

	123 fare Numeric(10,5)	123 survived Int	Abc sex Varchar(20)	Abc boat Varchar(100)	123 pclass Int	123 age Numeric(6,3)	Abc ticket Varchar(36)	Abc Varchar(164)	Abc embarked Varchar(20)	Abc cabin Varchar(30)	123 body Int	123 parch Int	Abc home.dest Varchar(100)	123 sibsp Int
1	151.55000	0	female	[null]	1	2.000	113781		S	C22 C26	[null]	2	Montreal, PQ / Chesterville, ON	1
2	151.55000	0	male	[null]	1	30.000	113781		S	C22 C26	135	2	Montreal, PQ / Chesterville, ON	1
3	151.55000	0	female	[null]	1	25.000	113781		S	C22 C26	[null]	2	Montreal, PQ / Chesterville, ON	1
4	0.00000	0	male	[null]	1	39.000	112050		S	A36	[null]	0	Belfast, NI	0
5	49.50420	0	male	[null]	1	71.000	PC 17609		C	[null]	22	0	Montevideo, Uruguay	0
6	227.52500	0	male	[null]	1	47.000	PC 17757		C	C62 C64	124	0	New York, NY	1
7	25.92500	0	male	[null]	1	[null]	PC 17318		S	[null]	[null]	0	New York, NY	0
8	247.52080	0	male	[null]	1	24.000	PC 17558		C	B58 B60	[null]	1	Montreal, PQ	0
9	75.24170	0	male	A	1	36.000	13050		C	C6	[null]	0	Winnipeg, MN	0
10	26.00000	0	male	[null]	1	25.000	13905		C	[null]	148	0	San Francisco, CA	0
11	35.50000	0	male	[null]	1	45.000	113784		S	T	[null]	0	Trenton, NJ	0
12	26.55000	0	male	[null]	1	42.000	110489		S	D22	[null]	0	London / Winnipeg, MB	0
13	30.50000	0	male	[null]	1	41.000	113054		S	A21	[null]	0	Pomeroy, WA	0
14	50.49580	0	male	[null]	1	48.000	PC 17591		C	B10	208	0	Omaha, NE	0
15	39.60000	0	male	[null]	1	[null]	112379		C	[null]	[null]	0	Philadelphia, PA	0
16	26.55000	0	male	[null]	1	45.000	113050		S	B38	[null]	0	Washington, DC	0
17	31.00000	0	male	[null]	1	[null]	113798		S	[null]	[null]	0	[null]	0
18	5.00000	0	male	[null]	1	33.000	695		S	B51 B53 B55	[null]	0	New York, NY	0
19	47.10000	0	male	[null]	1	28.000	113059		S	[null]	[null]	0	Montevideo, Uruguay	0
20	47.10000	0	male	[null]	1	17.000	113059		S	[null]	[null]	0	Montevideo, Uruguay	0
21	26.00000	0	male	[null]	1	49.000	19924		S	[null]	[null]	0	Ascot, Berkshire / Rochester, NY	0
22	78.85000	0	male	[null]	1	36.000	19877		S	C46	172	0	Little Onn Hall, Staffs	1
23	61.17500	0	male	[null]	1	46.000	W.E.P. 5734		S	E31	[null]	0	Amenia, ND	1
24	0.00000	0	male	[null]	1	[null]	112051		S	[null]	[null]	0	Liverpool, England / Belfast	0
25	136.77920	0	male	[null]	1	27.000	13508		C	C89	[null]	0	Los Angeles, CA	1
26	52.00000	0	male	[null]	1	[null]	110465		S	A14	[null]	0	Stoughton, MA	0
27	25.58750	0	male	[null]	1	47.000	5727		S	E58	[null]	0	Victoria, BC	0
28	83.15830	0	male	[null]	1	37.000	PC 17756		C	E52	[null]	1	Lakewood, NJ	1
29	26.55000	0	male	[null]	1	[null]	113791		S	[null]	[null]	0	Roachdale, IN	0
30	71.00000	0	male	[null]	1	70.000	WE/P 5735		S	B22	269	1	Milwaukee, WI	1
31	71.28330	0	male	[null]	1	39.000	PC 17599		C	C85	[null]	0	New York, NY	1
32	52.00000	0	male	[null]	1	31.000	F.C. 12750		S	B71	[null]	0	Montreal, PQ	1
33	106.42500	0	male	[null]	1	50.000	PC 17761		C	C86	62	0	Deephaven, MN / Cedar Rapids, IA	1
34	29.70000	0	male	[null]	1	39.000	PC 17580		C	A18	133	0	Philadelphia, PA	0
35	31.67920	0	female	[null]	1	36.000	PC 17531		C	A29	[null]	0	New York, NY	0
36	221.77920	0	male	[null]	1	[null]	PC 17483		S	C95	[null]	0	[null]	0
37	27.75000	0	male	[null]	1	30.000	113051		C	C111	[null]	0	New York, NY	0
38	263.00000	0	male	[null]	1	19.000	19950		S	C23 C25 C27	[null]	2	Winnipeg, MB	3
39	263.00000	0	male	[null]	1	64.000	19950		S	C23 C25 C27	[null]	4	Winnipeg, MB	1
40	26.55000	0	male	[null]	1	[null]	113778		S	D34	[null]	0	Westcliff-on-Sea, Essex	0
41	0.00000	0	male	[null]	1	[null]	112058		S	B102	[null]	0	[null]	0
42	53.10000	0	male	[null]	1	37.000	113803		S	C123	[null]	0	Scituate, MA	1
43	38.50000	0	male	[null]	1	47.000	111320		S	E63	275	0	St Anne's-on-Sea, Lancashire	0
44	79.20000	0	male	[null]	1	24.000	PC 17593		C	B86	[null]	0	[null]	0
45	34.65420	0	male	[null]	1	71.000	PC 17754		C	A5	[null]	0	New York, NY	0
46	153.46250	0	male	[null]	1	38.000	PC 17582		S	C91	147	1	Winnipeg, MB	0
47	79.20000	0	male	[null]	1	46.000	PC 17593		C	B82 B84	[null]	0	New York, NY	0
48	42.40000	0	male	[null]	1	[null]	113796		S	[null]	[null]	0	[null]	0
49	83.47500	0	male	[null]	1	45.000	36973		S	C83	[null]	0	New York, NY	1
50	0.00000	0	male	[null]	1	40.000	112059		S	B94	110	0	[null]	0
51	93.50000	0	male	[null]	1	55.000	12749		S	B69	307	1	Montreal, PQ	1
52	42.50000	0	male	[null]	1	42.000	113038		S	B11	[null]	0	London / Middlesex	0
53	51.86250	0	male	[null]	1	[null]	17463		S	E46	[null]	0	Brighton, MA	0
54	50.00000	0	male	[null]	1	55.000	680		S	C39	[null]	0	London / Birmingham	0
55	52.00000	0	male	[null]	1	42.000	113789		S	[null]	38	0	New York, NY	1
56	30.69580	0	male	14	1	[null]	PC 17600		C	[null]	[null]	0	New York, NY	0
57	28.71250	0	female	[null]	1	50.000	PC 17595		C	C49	[null]	0	Paris, France New York, NY	0
58	26.00000	0	male	[null]	1	46.000	694		S	[null]	80	0	Bennington, VT	0
59	26.00000	0	male	[null]	1	50.000	113044		S	E60	[null]	0	London	0
60	211.50000	0	male	[null]	1	32.500	113503		C	C132	45	0	[null]	0
61	29.70000	0	male	[null]	1	58.000	11771		C	B37	258	0	Buffalo, NY	0
62	51.86250	0	male	[null]	1	41.000	17464		S	D21	[null]	0	Southington / Noank, CT	1
63	26.55000	0	male	[null]	1	[null]	113028		S	C124	[null]	0	Portland, OR	0
64	27.72080	0	male	[null]	1	[null]	PC 17612		C	[null]	[null]	0	Chicago, IL	0
65	30.00000	0	male	[null]	1	29.000	113501		S	D6	126	0	Springfield, MA	0
66	45.50000	0	male	[null]	1	30.000	113801		S	[null]	[null]	0	London / New York, NY	0
67	26.00000	0	male	[null]	1	30.000	110469		S	C106	[null]	0	Brockton, MA	0
68	53.10000	0	male	[null]	1	19.000	113773		S	D30	[null]	0	New York, NY	1
69	75.24170	0	male	[null]	1	46.000	13050		C	C6	292	0	Vancouver, BC	0
70	51.86250	0	male	[null]	1	54.000	17463		S	E46	175	0	Dorchester, MA	0
71	82.17080	0	male	[null]	1	28.000	PC 17604		C	[null]	[null]	0	New York, NY	1
72	26.55000	0	male	[null]	1	65.000	13509		S	E38	249	0	East Bridgewater, MA	0
73	90.00000	0	male	[null]	1	44.000	19928		Q	C78	230	0	Fond du Lac, WI	2
74	30.50000	0	male	[null]	1	55.000	113787		S	C30	[null]	0	Montreal, PQ	0
75	42.40000	0	male	[null]	1	47.000	113796		S	[null]	[null]	0	Washington, DC	0
76	29.70000	0	male	[null]	1	37.000	PC 17596		C	C118	[null]	1	Brooklyn, NY	0
77	113.27500	0	male	[null]	1	58.000	35273		C	D48	122	2	Lexington, MA	0
78	26.00000	0	male	[null]	1	64.000	693		S	[null]	263	0	Isle of Wight, England	0
79	61.97920	0	male	[null]	1	65.000	113509		C	B30	234	1	Providence, RI	0
80	27.72080	0	male	[null]	1	28.500	PC 17562		C	D43	189	0	?Havana, Cuba	0
81	0.00000	0	male	[null]	1	[null]	112052		S	[null]	[null]	0	Belfast	0
82	28.50000	0	male	[null]	1	45.500	113043		S	C124	166	0	Surbiton Hill, Surrey	0
83	93.50000	0	male	[null]	1	23.000	12749		S	B24	[null]	0	Montreal, PQ	0
84	66.60000	0	male	[null]	1	29.000	113776		S	C2	[null]	0	Isleworth, England	1
85	108.90000	0	male	[null]	1	18.000	PC 17758		C	C65	[null]	0	Madrid, Spain	1
86	52.00000	0	male	[null]	1	47.000	110465		S	C110	207	0	Worcester, MA	0
87	0.00000	0	male	[null]	1	38.000	19972		S	[null]	[null]	0	Rotterdam, Netherlands	0
88	135.63330	0	male	[null]	1	22.000	PC 17760		C	[null]	232	0	[null]	0
89	227.52500	0	male	[null]	1	[null]	PC 17757		C	[null]	[null]	0	[null]	0
90	50.49580	0	male	[null]	1	31.000	PC 17590		S	A24	[null]	0	Trenton, NJ	0
91	50.00000	0	male	[null]	1	[null]	113767		S	A32	[null]	0	Seattle, WA	0
92	40.12500	0	male	[null]	1	36.000	13049		C	A10	[null]	0	Winnipeg, MB	0
93	59.40000	0	male	[null]	1	55.000	PC 17603		C	[null]	[null]	0	New York, NY	1
94	26.55000	0	male	[null]	1	33.000	113790		S	[null]	109	0	London	0
95	262.37500	0	male	[null]	1	61.000	PC 17608		C	B57 B59 B63 B66	[null]	3	Haverford, PA / Cooperstown, NY	1
96	55.90000	0	male	[null]	1	50.000	13507		S	E44	[null]	0	Duluth, MN	1
97	26.55000	0	male	[null]	1	56.000	113792		S	[null]	[null]	0	New York, NY	0
98	30.69580	0	male	[null]	1	56.000	17764		C	A7	[null]	0	St James, Long Island, NY	0
99	60.00000	0	male	[null]	1	24.000	13695		S	C31	[null]	0	Huntington, WV	1
100	26.00000	0	male	[null]	1	[null]	113056		S	A19	[null]	0	Streatham, Surrey	0

Out[30]:

Rows: 1-100 of 1234 | Columns: 14

We can also create one using a customized relation.

In [31]:

from verticapy import vdf_from_relation
vdf_from_relation("(SELECT pclass, AVG(survived) AS survived FROM titanic GROUP BY 1) x", 
                  cursor = cur)

	123 pclass Int	123 survived Float
1	1	0.612179487179487
2	2	0.416988416988417
3	3	0.227752639517345

Out[31]:

Rows: 3 | Columns: 2

In-memory vs. In-database Loading and Processing¶

To understand the main difference between loading data into memory and loading data into a Vertica database, let's create a vDataFrame using an existing relation.

In [32]:

import time

start_time = time.time()
expedia = vDataFrame("public.expedia", cur)
display(expedia)
print("elapsed time = {}".format(time.time() - start_time))

	123 is_mobile Int	123 srch_adults_cnt Int	123 cnt Int	123 user_location_city Int	123 user_location_country Int	123 srch_destination_type_id Int	123 is_package Int	123 srch_rm_cnt Int	123 posa_continent Int	📅 srch_ci Date	📅 date_time Timestamp	123 hotel_market Int	123 user_id Int	123 hotel_country Int	📅 srch_co Date	123 srch_children_cnt Int	123 channel Int	123 is_booking Int	123 orig_destination_distance Numeric(11,5)	123 srch_destination_id Int	123 hotel_continent Int	123 site_name Int	123 user_location_region Int	123 hotel_cluster Int
1	0	2	2	47997	66	3	0	1	3	2013-02-15	2013-02-12 18:36:51	111	691901	8	2013-02-16	0	0	0	1595.39320	8825	4	2	348	52
2	0	2	1	4868	66	1	0	1	3	2013-02-21	2013-02-12 18:36:52	674	829692	50	2013-02-23	0	9	0	198.74790	14846	2	2	314	68
3	0	3	1	56136	0	6	0	2	2	2013-03-05	2013-02-12 18:36:52	4	967703	144	2013-03-09	1	5	0	3908.87200	8407	6	22	317	29
4	0	2	1	4699	3	1	0	1	2	2013-03-16	2013-02-12 18:36:52	232	1173446	126	2013-03-20	0	9	0	[null]	8282	3	24	60	36
5	0	4	1	30797	66	1	0	1	3	2013-05-10	2013-02-12 18:36:53	411	513707	50	2013-05-12	0	9	0	1253.90690	8266	2	2	189	25
6	0	2	1	50761	205	1	0	1	3	2013-05-06	2013-02-12 18:36:54	29	123782	105	2013-05-09	0	3	0	5018.33370	8746	6	11	411	81
7	1	3	1	51464	66	6	0	1	3	2013-03-27	2013-02-12 18:36:55	1854	576033	105	2013-04-01	1	2	0	4893.21680	20255	6	2	331	22
8	0	2	3	40354	66	3	0	1	3	2013-03-13	2013-02-12 18:36:56	1122	853883	50	2013-03-16	0	9	0	1324.82620	1081	2	2	226	91
9	1	2	4	21259	205	5	0	1	3	2013-03-02	2013-02-12 18:36:58	682	826612	50	2013-03-04	0	9	0	1085.94160	12654	2	2	354	77
10	1	1	1	48774	66	1	1	1	3	2013-04-12	2013-02-12 18:37:01	663	1063495	50	2013-04-17	0	9	0	[null]	8281	2	2	189	70
11	0	3	2	2889	66	1	0	1	3	2013-03-22	2013-02-12 18:37:02	100	722285	146	2013-03-29	0	0	0	[null]	22407	4	2	174	61
12	0	2	2	2428	66	6	1	1	3	2013-03-03	2013-02-12 18:37:04	1509	533703	51	2013-03-07	0	9	0	1562.49710	12257	4	2	196	26
13	0	2	5	48127	66	6	0	1	3	2013-02-23	2013-02-12 18:37:04	110	857205	8	2013-03-01	0	3	0	1644.52560	21059	4	2	404	17
14	0	3	3	25315	205	1	1	1	3	2013-09-12	2013-02-12 18:37:04	59	248706	140	2013-09-18	0	9	0	6166.90510	11917	0	34	354	92
15	0	2	1	31206	66	6	0	1	3	2013-02-13	2013-02-12 18:37:08	1230	615724	50	2013-02-14	0	1	0	23.13970	12268	2	2	174	70
16	0	2	5	33125	66	6	0	1	3	2013-05-04	2013-02-12 18:37:10	697	634299	50	2013-05-05	0	0	0	[null]	25819	2	2	363	50
17	0	2	1	19677	66	1	0	1	3	2013-02-16	2013-02-12 18:37:16	428	978113	50	2013-02-17	0	9	0	173.65790	43	2	2	226	15
18	1	2	1	5444	66	6	0	1	3	2013-03-13	2013-02-12 18:37:18	350	166780	50	2013-03-14	1	3	1	422.00270	12178	2	2	220	46
19	0	2	1	38883	1	1	1	1	1	2013-03-28	2013-02-12 18:37:18	19	239117	70	2013-04-04	0	0	0	1220.58810	8253	6	23	189	17
20	0	2	1	11740	66	1	0	1	3	2013-02-14	2013-02-12 18:37:22	592	850320	50	2013-02-15	0	9	1	991.83190	18811	2	2	184	91
21	0	4	5	23659	66	1	1	2	3	2013-05-18	2013-02-12 18:37:23	110	873625	8	2013-05-23	0	0	0	1223.87040	8791	4	2	322	65
22	0	2	1	14566	70	1	0	1	2	2013-03-19	2013-02-12 18:37:23	1033	219481	99	2013-03-28	0	9	0	[null]	268	3	35	47	76
23	0	4	2	16835	66	1	0	2	3	2013-02-17	2013-02-12 18:37:25	699	720088	50	2013-02-18	1	3	0	[null]	11353	2	2	392	72
24	0	2	1	50681	91	1	1	1	1	2013-05-02	2013-02-12 18:37:25	675	1188445	50	2013-05-08	0	1	0	[null]	8267	2	28	45	70
25	0	2	1	4079	3	3	0	1	2	2013-09-14	2013-02-12 18:37:29	212	1166859	50	2013-09-15	0	9	0	[null]	669	2	24	49	97
26	0	2	2	31206	66	6	0	1	3	2013-02-13	2013-02-12 18:37:30	1230	615724	50	2013-02-14	0	1	0	23.27920	12268	2	2	174	98
27	0	4	5	7373	3	6	0	2	2	2013-03-06	2013-02-12 18:37:30	365	1179245	50	2013-03-13	0	9	0	[null]	12216	2	24	79	14
28	0	2	3	34849	66	1	0	2	3	2013-03-01	2013-02-12 18:37:31	27	600881	204	2013-03-10	0	9	0	3750.29920	8745	6	2	363	75
29	0	2	1	20442	3	1	0	1	2	2013-02-23	2013-02-12 18:37:31	255	1162013	106	2013-02-24	0	1	1	[null]	41886	3	24	50	36
30	0	2	1	30958	66	1	0	1	3	2013-04-15	2013-02-12 18:37:37	405	395432	174	2013-04-22	0	9	0	1688.21870	11634	4	2	348	44
31	0	4	1	2086	66	6	0	2	3	2013-03-22	2013-02-12 18:37:39	1457	1137395	50	2013-03-26	2	9	0	130.31530	28514	2	2	220	70
32	0	2	3	34849	66	1	0	2	3	2013-03-01	2013-02-12 18:37:39	27	600881	204	2013-03-10	0	9	0	3751.94450	8745	6	2	363	22
33	0	2	1	8806	205	1	0	1	3	2013-02-13	2013-02-12 18:37:40	397	171068	198	2013-02-15	0	9	0	38.96120	8287	2	11	354	41
34	1	2	1	5444	66	6	0	1	3	2013-03-13	2013-02-12 18:37:43	350	166780	50	2013-03-14	1	3	0	422.00270	12178	2	2	220	46
35	0	2	1	44470	66	1	1	1	3	2013-03-30	2013-02-12 18:37:44	682	96496	50	2013-04-05	2	1	0	[null]	8268	2	2	314	85
36	0	1	1	37449	66	6	0	1	3	2013-02-21	2013-02-12 18:37:45	420	1126467	50	2013-02-22	2	0	1	143.20390	24628	2	2	348	91
37	0	2	6	25443	66	6	0	1	3	2013-07-09	2013-02-12 18:37:46	278	421539	135	2013-07-10	1	9	0	3646.57850	8337	6	2	348	40
38	0	2	2	3169	3	1	0	1	3	2013-05-29	2013-02-12 18:37:47	675	1034717	50	2013-06-01	0	9	0	[null]	8267	2	2	64	3
39	0	2	1	24926	69	1	1	1	1	2013-02-18	2013-02-12 18:37:47	1528	115747	194	2013-02-22	0	1	1	[null]	33651	5	37	567	67
40	0	2	1	27268	66	1	0	1	3	2013-09-25	2013-02-12 18:37:49	35	413063	105	2013-09-28	0	9	0	4197.14050	8785	6	2	337	64
41	0	2	5	14656	66	1	0	1	3	2013-05-01	2013-02-12 18:37:50	1465	907171	204	2013-05-02	0	9	0	4737.55600	18910	6	2	220	62
42	0	2	1	38801	66	1	0	1	3	2013-02-16	2013-02-12 18:37:50	1056	1170201	50	2013-02-17	0	9	0	148.02900	54920	2	2	260	16
43	1	2	1	9821	66	1	1	1	3	2013-04-04	2013-02-12 18:37:53	671	153848	50	2013-04-08	0	0	0	756.74780	8843	2	2	174	92
44	0	2	2	38140	154	3	0	1	3	2013-02-13	2013-02-12 18:37:56	2057	826224	119	2013-02-14	0	9	0	[null]	11711	4	2	196	82
45	0	1	1	25315	205	6	0	1	3	2013-02-20	2013-02-12 18:37:56	637	339033	50	2013-02-23	0	0	0	435.33720	12191	2	11	354	72
46	0	3	1	38140	154	1	1	1	3	2013-02-26	2013-02-12 18:37:58	232	827111	126	2013-03-02	0	0	1	[null]	8282	3	2	196	59
47	0	2	4	3279	66	1	1	1	3	2013-05-31	2013-02-12 18:38:00	191	346460	50	2013-06-07	1	3	0	890.85620	8291	2	2	322	68
48	1	3	1	51464	66	6	0	1	3	2013-03-27	2013-02-12 18:38:04	1854	576033	105	2013-04-01	1	2	0	4892.85670	20255	6	2	331	58
49	1	2	5	26337	66	1	1	1	3	2013-06-13	2013-02-12 18:38:08	110	85336	8	2013-06-17	2	9	0	1005.77010	8791	4	2	442	52
50	0	2	2	2086	66	6	0	1	3	2013-03-22	2013-02-12 18:38:09	1457	1137395	50	2013-03-26	2	9	0	130.31530	28514	2	2	220	70
51	0	1	3	36988	66	1	0	1	3	2013-02-21	2013-02-12 18:38:09	449	85222	50	2013-02-22	0	9	0	315.35740	24492	2	2	447	94
52	0	1	1	4699	3	1	0	1	2	2013-02-12	2013-02-12 18:38:13	232	1159486	126	2013-02-13	0	0	1	[null]	8282	3	24	60	36
53	0	1	3	56153	46	4	0	1	1	2013-02-16	2013-02-12 18:38:14	627	993963	50	2013-02-23	0	9	0	5730.00880	28703	2	37	172	6
54	0	2	1	47357	66	1	0	1	3	2013-02-15	2013-02-12 18:38:15	643	432297	50	2013-02-17	0	0	0	179.40540	8244	2	2	258	94
55	0	4	1	30797	66	1	0	1	3	2013-05-10	2013-02-12 18:38:17	411	513707	50	2013-05-12	0	9	1	1253.90690	8266	2	2	189	25
56	0	3	1	2086	66	6	0	1	3	2013-03-22	2013-02-12 18:38:17	1457	1137395	50	2013-03-26	2	9	0	130.31530	28514	2	2	220	70
57	0	2	1	39058	66	1	0	1	3	2013-04-02	2013-02-12 18:38:18	1500	173960	0	2013-04-06	1	3	1	5984.06390	8247	3	2	351	64
58	0	2	2	19677	66	1	0	1	3	2013-02-16	2013-02-12 18:38:20	428	978113	50	2013-02-17	0	9	0	173.53650	43	2	2	226	7
59	0	2	3	3517	66	1	0	1	3	2013-02-16	2013-02-12 18:38:21	1328	66021	50	2013-02-17	0	0	0	14.69440	221	2	2	254	91
60	0	2	1	23846	66	3	0	1	3	2013-03-29	2013-02-12 18:38:23	191	675848	50	2013-03-31	0	9	0	212.81590	4572	2	2	331	91
61	0	2	1	13320	205	4	0	1	3	2013-04-14	2013-02-12 18:38:25	397	499163	198	2013-04-15	0	9	0	247.89380	30991	2	34	354	59
62	0	2	1	38801	66	1	0	1	3	2013-02-16	2013-02-12 18:38:26	1056	1170201	50	2013-02-17	0	9	0	147.91390	54920	2	2	260	42
63	0	2	1	18982	66	1	1	1	3	2013-03-13	2013-02-12 18:38:29	628	823404	50	2013-03-16	0	0	0	1748.93090	8250	2	2	226	1
64	0	2	11	17974	66	1	1	1	3	2013-08-01	2013-02-12 18:38:29	628	769230	50	2013-08-04	0	3	0	2245.45580	8250	2	2	348	79
65	0	3	1	11409	66	6	0	1	3	2013-03-01	2013-02-12 18:38:33	648	157089	50	2013-03-03	1	1	0	359.79180	23792	2	2	184	59
66	0	2	4	4079	3	3	0	1	2	2013-09-14	2013-02-12 18:38:33	212	1166859	50	2013-09-15	0	9	0	[null]	669	2	24	49	92
67	0	3	5	25315	205	1	1	1	3	2013-09-19	2013-02-12 18:38:36	59	248706	140	2013-09-25	0	9	0	6165.92510	11917	0	34	354	92
68	0	2	2	48862	66	1	1	1	3	2013-05-24	2013-02-12 18:38:41	1508	27717	47	2013-05-31	0	9	0	1560.01500	8864	4	2	348	52
69	0	2	3	27268	66	1	0	1	3	2013-09-25	2013-02-12 18:38:42	35	413063	105	2013-09-28	0	9	0	4196.84770	8785	6	2	337	86
70	0	4	1	42328	194	1	0	1	2	2013-03-17	2013-02-12 18:38:43	663	693891	50	2013-03-20	1	0	0	[null]	8281	2	33	38	18
71	1	2	1	27936	1	3	1	1	1	2013-11-18	2013-02-12 18:38:48	73	442713	152	2013-11-27	0	9	0	4822.72100	1060	3	23	151	11
72	0	1	1	15627	66	6	0	1	3	2013-02-13	2013-02-12 18:38:50	703	711733	50	2013-02-14	0	9	1	[null]	28585	2	2	220	91
73	0	2	4	31674	66	1	0	1	3	2013-03-06	2013-02-12 18:38:54	682	684330	50	2013-03-14	0	9	0	853.66610	8268	2	2	442	54
74	0	2	1	47357	66	1	0	1	3	2013-02-15	2013-02-12 18:38:56	643	432297	50	2013-02-17	0	0	0