The 'Magic' Methods of the vDataFrame¶

VerticaPy 0.3.2 introduces the 'Magic' methods, which offer some additional flexilibility for mathematical operations in the vDataFrame. These methods let you handle many operations in a 'pandas-like' or Pythonic style.

In [11]:

from verticapy.datasets import load_titanic
titanic = load_titanic()
display(titanic)

	123 pclass Int	123 survived Int	Abc Varchar(164)	Abc sex Varchar(20)	123 age Numeric(6,3)	123 sibsp Int	123 parch Int	Abc ticket Varchar(36)	123 fare Numeric(10,5)	Abc cabin Varchar(30)	Abc embarked Varchar(20)	Abc boat Varchar(100)	123 body Int	Abc home.dest Varchar(100)
1	1	0		female	2.0	1	2	113781	151.55	C22 C26	S	[null]	[null]	Montreal, PQ / Chesterville, ON
2	1	0		male	30.0	1	2	113781	151.55	C22 C26	S	[null]	135	Montreal, PQ / Chesterville, ON
3	1	0		female	25.0	1	2	113781	151.55	C22 C26	S	[null]	[null]	Montreal, PQ / Chesterville, ON
4	1	0		male	39.0	0	0	112050	0.0	A36	S	[null]	[null]	Belfast, NI
5	1	0		male	71.0	0	0	PC 17609	49.5042	[null]	C	[null]	22	Montevideo, Uruguay
6	1	0		male	47.0	1	0	PC 17757	227.525	C62 C64	C	[null]	124	New York, NY
7	1	0		male	[null]	0	0	PC 17318	25.925	[null]	S	[null]	[null]	New York, NY
8	1	0		male	24.0	0	1	PC 17558	247.5208	B58 B60	C	[null]	[null]	Montreal, PQ
9	1	0		male	36.0	0	0	13050	75.2417	C6	C	A	[null]	Winnipeg, MN
10	1	0		male	25.0	0	0	13905	26.0	[null]	C	[null]	148	San Francisco, CA
11	1	0		male	45.0	0	0	113784	35.5	T	S	[null]	[null]	Trenton, NJ
12	1	0		male	42.0	0	0	110489	26.55	D22	S	[null]	[null]	London / Winnipeg, MB
13	1	0		male	41.0	0	0	113054	30.5	A21	S	[null]	[null]	Pomeroy, WA
14	1	0		male	48.0	0	0	PC 17591	50.4958	B10	C	[null]	208	Omaha, NE
15	1	0		male	[null]	0	0	112379	39.6	[null]	C	[null]	[null]	Philadelphia, PA
16	1	0		male	45.0	0	0	113050	26.55	B38	S	[null]	[null]	Washington, DC
17	1	0		male	[null]	0	0	113798	31.0	[null]	S	[null]	[null]	[null]
18	1	0		male	33.0	0	0	695	5.0	B51 B53 B55	S	[null]	[null]	New York, NY
19	1	0		male	28.0	0	0	113059	47.1	[null]	S	[null]	[null]	Montevideo, Uruguay
20	1	0		male	17.0	0	0	113059	47.1	[null]	S	[null]	[null]	Montevideo, Uruguay
21	1	0		male	49.0	0	0	19924	26.0	[null]	S	[null]	[null]	Ascot, Berkshire / Rochester, NY
22	1	0		male	36.0	1	0	19877	78.85	C46	S	[null]	172	Little Onn Hall, Staffs
23	1	0		male	46.0	1	0	W.E.P. 5734	61.175	E31	S	[null]	[null]	Amenia, ND
24	1	0		male	[null]	0	0	112051	0.0	[null]	S	[null]	[null]	Liverpool, England / Belfast
25	1	0		male	27.0	1	0	13508	136.7792	C89	C	[null]	[null]	Los Angeles, CA
26	1	0		male	[null]	0	0	110465	52.0	A14	S	[null]	[null]	Stoughton, MA
27	1	0		male	47.0	0	0	5727	25.5875	E58	S	[null]	[null]	Victoria, BC
28	1	0		male	37.0	1	1	PC 17756	83.1583	E52	C	[null]	[null]	Lakewood, NJ
29	1	0		male	[null]	0	0	113791	26.55	[null]	S	[null]	[null]	Roachdale, IN
30	1	0		male	70.0	1	1	WE/P 5735	71.0	B22	S	[null]	269	Milwaukee, WI
31	1	0		male	39.0	1	0	PC 17599	71.2833	C85	C	[null]	[null]	New York, NY
32	1	0		male	31.0	1	0	F.C. 12750	52.0	B71	S	[null]	[null]	Montreal, PQ
33	1	0		male	50.0	1	0	PC 17761	106.425	C86	C	[null]	62	Deephaven, MN / Cedar Rapids, IA
34	1	0		male	39.0	0	0	PC 17580	29.7	A18	C	[null]	133	Philadelphia, PA
35	1	0		female	36.0	0	0	PC 17531	31.6792	A29	C	[null]	[null]	New York, NY
36	1	0		male	[null]	0	0	PC 17483	221.7792	C95	S	[null]	[null]	[null]
37	1	0		male	30.0	0	0	113051	27.75	C111	C	[null]	[null]	New York, NY
38	1	0		male	19.0	3	2	19950	263.0	C23 C25 C27	S	[null]	[null]	Winnipeg, MB
39	1	0		male	64.0	1	4	19950	263.0	C23 C25 C27	S	[null]	[null]	Winnipeg, MB
40	1	0		male	[null]	0	0	113778	26.55	D34	S	[null]	[null]	Westcliff-on-Sea, Essex
41	1	0		male	[null]	0	0	112058	0.0	B102	S	[null]	[null]	[null]
42	1	0		male	37.0	1	0	113803	53.1	C123	S	[null]	[null]	Scituate, MA
43	1	0		male	47.0	0	0	111320	38.5	E63	S	[null]	275	St Anne's-on-Sea, Lancashire
44	1	0		male	24.0	0	0	PC 17593	79.2	B86	C	[null]	[null]	[null]
45	1	0		male	71.0	0	0	PC 17754	34.6542	A5	C	[null]	[null]	New York, NY
46	1	0		male	38.0	0	1	PC 17582	153.4625	C91	S	[null]	147	Winnipeg, MB
47	1	0		male	46.0	0	0	PC 17593	79.2	B82 B84	C	[null]	[null]	New York, NY
48	1	0		male	[null]	0	0	113796	42.4	[null]	S	[null]	[null]	[null]
49	1	0		male	45.0	1	0	36973	83.475	C83	S	[null]	[null]	New York, NY
50	1	0		male	40.0	0	0	112059	0.0	B94	S	[null]	110	[null]
51	1	0		male	55.0	1	1	12749	93.5	B69	S	[null]	307	Montreal, PQ
52	1	0		male	42.0	0	0	113038	42.5	B11	S	[null]	[null]	London / Middlesex
53	1	0		male	[null]	0	0	17463	51.8625	E46	S	[null]	[null]	Brighton, MA
54	1	0		male	55.0	0	0	680	50.0	C39	S	[null]	[null]	London / Birmingham
55	1	0		male	42.0	1	0	113789	52.0	[null]	S	[null]	38	New York, NY
56	1	0		male	[null]	0	0	PC 17600	30.6958	[null]	C	14	[null]	New York, NY
57	1	0		female	50.0	0	0	PC 17595	28.7125	C49	C	[null]	[null]	Paris, France New York, NY
58	1	0		male	46.0	0	0	694	26.0	[null]	S	[null]	80	Bennington, VT
59	1	0		male	50.0	0	0	113044	26.0	E60	S	[null]	[null]	London
60	1	0		male	32.5	0	0	113503	211.5	C132	C	[null]	45	[null]
61	1	0		male	58.0	0	0	11771	29.7	B37	C	[null]	258	Buffalo, NY
62	1	0		male	41.0	1	0	17464	51.8625	D21	S	[null]	[null]	Southington / Noank, CT
63	1	0		male	[null]	0	0	113028	26.55	C124	S	[null]	[null]	Portland, OR
64	1	0		male	[null]	0	0	PC 17612	27.7208	[null]	C	[null]	[null]	Chicago, IL
65	1	0		male	29.0	0	0	113501	30.0	D6	S	[null]	126	Springfield, MA
66	1	0		male	30.0	0	0	113801	45.5	[null]	S	[null]	[null]	London / New York, NY
67	1	0		male	30.0	0	0	110469	26.0	C106	S	[null]	[null]	Brockton, MA
68	1	0		male	19.0	1	0	113773	53.1	D30	S	[null]	[null]	New York, NY
69	1	0		male	46.0	0	0	13050	75.2417	C6	C	[null]	292	Vancouver, BC
70	1	0		male	54.0	0	0	17463	51.8625	E46	S	[null]	175	Dorchester, MA
71	1	0		male	28.0	1	0	PC 17604	82.1708	[null]	C	[null]	[null]	New York, NY
72	1	0		male	65.0	0	0	13509	26.55	E38	S	[null]	249	East Bridgewater, MA
73	1	0		male	44.0	2	0	19928	90.0	C78	Q	[null]	230	Fond du Lac, WI
74	1	0		male	55.0	0	0	113787	30.5	C30	S	[null]	[null]	Montreal, PQ
75	1	0		male	47.0	0	0	113796	42.4	[null]	S	[null]	[null]	Washington, DC
76	1	0		male	37.0	0	1	PC 17596	29.7	C118	C	[null]	[null]	Brooklyn, NY
77	1	0		male	58.0	0	2	35273	113.275	D48	C	[null]	122	Lexington, MA
78	1	0		male	64.0	0	0	693	26.0	[null]	S	[null]	263	Isle of Wight, England
79	1	0		male	65.0	0	1	113509	61.9792	B30	C	[null]	234	Providence, RI
80	1	0		male	28.5	0	0	PC 17562	27.7208	D43	C	[null]	189	?Havana, Cuba
81	1	0		male	[null]	0	0	112052	0.0	[null]	S	[null]	[null]	Belfast
82	1	0		male	45.5	0	0	113043	28.5	C124	S	[null]	166	Surbiton Hill, Surrey
83	1	0		male	23.0	0	0	12749	93.5	B24	S	[null]	[null]	Montreal, PQ
84	1	0		male	29.0	1	0	113776	66.6	C2	S	[null]	[null]	Isleworth, England
85	1	0		male	18.0	1	0	PC 17758	108.9	C65	C	[null]	[null]	Madrid, Spain
86	1	0		male	47.0	0	0	110465	52.0	C110	S	[null]	207	Worcester, MA
87	1	0		male	38.0	0	0	19972	0.0	[null]	S	[null]	[null]	Rotterdam, Netherlands
88	1	0		male	22.0	0	0	PC 17760	135.6333	[null]	C	[null]	232	[null]
89	1	0		male	[null]	0	0	PC 17757	227.525	[null]	C	[null]	[null]	[null]
90	1	0		male	31.0	0	0	PC 17590	50.4958	A24	S	[null]	[null]	Trenton, NJ
91	1	0		male	[null]	0	0	113767	50.0	A32	S	[null]	[null]	Seattle, WA
92	1	0		male	36.0	0	0	13049	40.125	A10	C	[null]	[null]	Winnipeg, MB
93	1	0		male	55.0	1	0	PC 17603	59.4	[null]	C	[null]	[null]	New York, NY
94	1	0		male	33.0	0	0	113790	26.55	[null]	S	[null]	109	London
95	1	0		male	61.0	1	3	PC 17608	262.375	B57 B59 B63 B66	C	[null]	[null]	Haverford, PA / Cooperstown, NY
96	1	0		male	50.0	1	0	13507	55.9	E44	S	[null]	[null]	Duluth, MN
97	1	0		male	56.0	0	0	113792	26.55	[null]	S	[null]	[null]	New York, NY
98	1	0		male	56.0	0	0	17764	30.6958	A7	C	[null]	[null]	St James, Long Island, NY
99	1	0		male	24.0	1	0	13695	60.0	C31	S	[null]	[null]	Huntington, WV
100	1	0		male	[null]	0	0	113056	26.0	A19	S	[null]	[null]	Streatham, Surrey

Rows: 1-100 | Columns: 14

Feature Engineering, 'pandas'-style¶

You can create new features with in a 'pandas' style.

In [12]:

titanic["family_size"] = titanic["parch"] + titanic["sibsp"] + 1
titanic[["sibsp", "parch", "family_size"]]

Out[12]:

	123 sibsp Int	123 parch Int	123 family_size Int
1	1	2	4
2	1	2	4
3	1	2	4
4	0	0	1
5	0	0	1
6	1	0	2
7	0	0	1
8	0	1	2
9	0	0	1
10	0	0	1
11	0	0	1
12	0	0	1
13	0	0	1
14	0	0	1
15	0	0	1
16	0	0	1
17	0	0	1
18	0	0	1
19	0	0	1
20	0	0	1
21	0	0	1
22	1	0	2
23	1	0	2
24	0	0	1
25	1	0	2
26	0	0	1
27	0	0	1
28	1	1	3
29	0	0	1
30	1	1	3
31	1	0	2
32	1	0	2
33	1	0	2
34	0	0	1
35	0	0	1
36	0	0	1
37	0	0	1
38	3	2	6
39	1	4	6
40	0	0	1
41	0	0	1
42	1	0	2
43	0	0	1
44	0	0	1
45	0	0	1
46	0	1	2
47	0	0	1
48	0	0	1
49	1	0	2
50	0	0	1
51	1	1	3
52	0	0	1
53	0	0	1
54	0	0	1
55	1	0	2
56	0	0	1
57	0	0	1
58	0	0	1
59	0	0	1
60	0	0	1
61	0	0	1
62	1	0	2
63	0	0	1
64	0	0	1
65	0	0	1
66	0	0	1
67	0	0	1
68	1	0	2
69	0	0	1
70	0	0	1
71	1	0	2
72	0	0	1
73	2	0	3
74	0	0	1
75	0	0	1
76	0	1	2
77	0	2	3
78	0	0	1
79	0	1	2
80	0	0	1
81	0	0	1
82	0	0	1
83	0	0	1
84	1	0	2
85	1	0	2
86	0	0	1
87	0	0	1
88	0	0	1
89	0	0	1
90	0	0	1
91	0	0	1
92	0	0	1
93	1	0	2
94	0	0	1
95	1	3	5
96	1	0	2
97	0	0	1
98	0	0	1
99	1	0	2
100	0	0	1

Rows: 1-100 | Columns: 3

You can also create features from various mathematical functions.

In [3]:

import verticapy.stats as st
titanic["ln_fare"] = st.ln(titanic["fare"])
titanic[["fare", "ln_fare"]]

Out[3]:

	123 fare Numeric(10,5)	123 ln_fare Float
1	151.55	5.02091560350381
2	151.55	5.02091560350381
3	151.55	5.02091560350381
4	0.0	-inf
5	49.5042	3.90205751446006
6	227.525	5.42726012246342
7	25.925	3.25520775411559
8	247.5208	5.51149461888159
9	75.2417	4.32070559854228
10	26.0	3.25809653802148
11	35.5	3.56953269648137
12	26.55	3.27902974768795
13	30.5	3.41772668361337
14	50.4958	3.92189016450581
15	39.6	3.67882911826043
16	26.55	3.27902974768795
17	31.0	3.43398720448515
18	5.0	1.6094379124341
19	47.1	3.85227300102237
20	47.1	3.85227300102237
21	26.0	3.25809653802148
22	78.85	4.36754731340905
23	61.175	4.11373860932874
24	0.0	-inf
25	136.7792	4.91836794684454
26	52.0	3.95124371858143
27	25.5875	3.24210395098741
28	83.1583	4.42074602026042
29	26.55	3.27902974768795
30	71.0	4.26267987704132
31	71.2833	4.26666207838721
32	52.0	3.95124371858143
33	106.425	4.66744051171422
34	29.7	3.39114704580865
35	31.6792	3.45566031410212
36	221.7792	5.40168229234028
37	27.75	3.32323584019244
38	263.0	5.57215403217776
39	263.0	5.57215403217776
40	26.55	3.27902974768795
41	0.0	-inf
42	53.1	3.97217692824789
43	38.5	3.65065824129374
44	79.2	4.37197629882038
45	34.6542	3.54541893041153
46	153.4625	5.03345623750696
47	79.2	4.37197629882038
48	42.4	3.74714836223791
49	83.475	4.42454718582972
50	0.0	-inf
51	93.5	4.53796143629464
52	42.5	3.74950407593037
53	51.8625	3.94859598565899
54	50.0	3.91202300542815
55	52.0	3.95124371858143
56	30.6958	3.42412583741652
57	28.7125	3.35733256801522
58	26.0	3.25809653802148
59	26.0	3.25809653802148
60	211.5	5.35422499848633
61	29.7	3.39114704580865
62	51.8625	3.94859598565899
63	26.55	3.27902974768795
64	27.7208	3.32218303393412
65	30.0	3.40119738166216
66	45.5	3.8177123259569
67	26.0	3.25809653802148
68	53.1	3.97217692824789
69	75.2417	4.32070559854228
70	51.8625	3.94859598565899
71	82.1708	4.40880000780905
72	26.55	3.27902974768795
73	90.0	4.49980967033027
74	30.5	3.41772668361337
75	42.4	3.74714836223791
76	29.7	3.39114704580865
77	113.275	4.7298184905532
78	26.0	3.25809653802148
79	61.9792	4.12679884488682
80	27.7208	3.32218303393412
81	0.0	-inf
82	28.5	3.3499040872746
83	93.5	4.53796143629464
84	66.6	4.19870457754634
85	108.9	4.69043002993891
86	52.0	3.95124371858143
87	0.0	-inf
88	135.6333	4.90995492057888
89	227.525	5.42726012246342
90	50.4958	3.92189016450581
91	50.0	3.91202300542815
92	40.125	3.69199958145018
93	59.4	4.0842942263686
94	26.55	3.27902974768795
95	262.375	5.5697747781408
96	55.9	4.02356438016105
97	26.55	3.27902974768795
98	30.6958	3.42412583741652
99	60.0	4.0943445622221
100	26.0	3.25809653802148

Rows: 1-100 | Columns: 2

In [4]:

titanic["x"] = 1 - st.exp(-titanic["fare"])
titanic[["fare", "x"]]

Out[4]:

	123 fare Numeric(10,5)	123 x Float
1	151.55	1.0
2	151.55	1.0
3	151.55	1.0
4	0.0	0.0
5	49.5042	1.0
6	227.525	1.0
7	25.925	0.999999999994493
8	247.5208	1.0
9	75.2417	1.0
10	26.0	0.999999999994891
11	35.5	1.0
12	26.55	0.999999999997052
13	30.5	0.999999999999943
14	50.4958	1.0
15	39.6	1.0
16	26.55	0.999999999997052
17	31.0	0.999999999999966
18	5.0	0.993262053000915
19	47.1	1.0
20	47.1	1.0
21	26.0	0.999999999994891
22	78.85	1.0
23	61.175	1.0
24	0.0	0.0
25	136.7792	1.0
26	52.0	1.0
27	25.5875	0.999999999992282
28	83.1583	1.0
29	26.55	0.999999999997052
30	71.0	1.0
31	71.2833	1.0
32	52.0	1.0
33	106.425	1.0
34	29.7	0.999999999999874
35	31.6792	0.999999999999983
36	221.7792	1.0
37	27.75	0.999999999999112
38	263.0	1.0
39	263.0	1.0
40	26.55	0.999999999997052
41	0.0	0.0
42	53.1	1.0
43	38.5	1.0
44	79.2	1.0
45	34.6542	0.999999999999999
46	153.4625	1.0
47	79.2	1.0
48	42.4	1.0
49	83.475	1.0
50	0.0	0.0
51	93.5	1.0
52	42.5	1.0
53	51.8625	1.0
54	50.0	1.0
55	52.0	1.0
56	30.6958	0.999999999999953
57	28.7125	0.999999999999661
58	26.0	0.999999999994891
59	26.0	0.999999999994891
60	211.5	1.0
61	29.7	0.999999999999874
62	51.8625	1.0
63	26.55	0.999999999997052
64	27.7208	0.999999999999086
65	30.0	0.999999999999906
66	45.5	1.0
67	26.0	0.999999999994891
68	53.1	1.0
69	75.2417	1.0
70	51.8625	1.0
71	82.1708	1.0
72	26.55	0.999999999997052
73	90.0	1.0
74	30.5	0.999999999999943
75	42.4	1.0
76	29.7	0.999999999999874
77	113.275	1.0
78	26.0	0.999999999994891
79	61.9792	1.0
80	27.7208	0.999999999999086
81	0.0	0.0
82	28.5	0.999999999999581
83	93.5	1.0
84	66.6	1.0
85	108.9	1.0
86	52.0	1.0
87	0.0	0.0
88	135.6333	1.0
89	227.525	1.0
90	50.4958	1.0
91	50.0	1.0
92	40.125	1.0
93	59.4	1.0
94	26.55	0.999999999997052
95	262.375	1.0
96	55.9	1.0
97	26.55	0.999999999997052
98	30.6958	0.999999999999953
99	60.0	1.0
100	26.0	0.999999999994891

Rows: 1-100 | Columns: 2

Conditional Operators¶

You can now filter your data with conditional operators like and ('&'), or ('|'), equals ('=='), not equals (!=), and more!

Equal Operator (==)

In [13]:

# Identifies the passengers who came alone

single_family = titanic[titanic["family_size"] == 1]
single_family

Out[13]:

	123 pclass Int	123 survived Int	Abc Varchar(164)	Abc sex Varchar(20)	123 age Numeric(6,3)	123 sibsp Int	123 parch Int	Abc ticket Varchar(36)	123 fare Numeric(10,5)	Abc cabin Varchar(30)	Abc embarked Varchar(20)	Abc boat Varchar(100)	123 body Int	Abc Varchar(100)	123 family_size Int
1	1	0		male	39.0	0	0	112050	0.0	A36	S	[null]	[null]		1
2	1	0		male	71.0	0	0	PC 17609	49.5042	[null]	C	[null]	22		1
3	1	0		male	[null]	0	0	PC 17318	25.925	[null]	S	[null]	[null]		1
4	1	0		male	36.0	0	0	13050	75.2417	C6	C	A	[null]		1
5	1	0		male	25.0	0	0	13905	26.0	[null]	C	[null]	148		1
6	1	0		male	45.0	0	0	113784	35.5	T	S	[null]	[null]		1
7	1	0		male	42.0	0	0	110489	26.55	D22	S	[null]	[null]		1
8	1	0		male	41.0	0	0	113054	30.5	A21	S	[null]	[null]		1
9	1	0		male	48.0	0	0	PC 17591	50.4958	B10	C	[null]	208		1
10	1	0		male	[null]	0	0	112379	39.6	[null]	C	[null]	[null]		1
11	1	0		male	45.0	0	0	113050	26.55	B38	S	[null]	[null]		1
12	1	0		male	[null]	0	0	113798	31.0	[null]	S	[null]	[null]		1
13	1	0		male	33.0	0	0	695	5.0	B51 B53 B55	S	[null]	[null]		1
14	1	0		male	28.0	0	0	113059	47.1	[null]	S	[null]	[null]		1
15	1	0		male	17.0	0	0	113059	47.1	[null]	S	[null]	[null]		1
16	1	0		male	49.0	0	0	19924	26.0	[null]	S	[null]	[null]		1
17	1	0		male	[null]	0	0	112051	0.0