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vDataFrame[].cut¶
In [ ]:
vDataFrame[].cut(breaks: list,
labels: list = [],
include_lowest: bool = True,
right: bool = True)
Discretizes the vColumn using the input list.
Parameters¶
Name | Type | Optional | Description |
---|---|---|---|
breaks | list | ❌ | List of values used to cut the vColumn. |
labels | list | ✓ | Labels used to name the new categories. If empty, names will be generated. |
include_lowest | bool | ✓ | If set to True, the lowest element of the list will be included. |
right | bool | ✓ | How the intervals should be closed. If set to True, the intervals will be closed on the right. |
In [40]:
from verticapy.datasets import load_titanic
titanic = load_titanic()
titanic
Out[40]:
123 pclassInt | 123 survivedInt | Abc Varchar(164) | Abc sexVarchar(20) | 123 ageNumeric(6,3) | 123 sibspInt | 123 parchInt | Abc ticketVarchar(36) | 123 fareNumeric(10,5) | Abc cabinVarchar(30) | Abc embarkedVarchar(20) | Abc boatVarchar(100) | 123 bodyInt | Abc home.destVarchar(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
In [38]:
titanic["age"].cut([0, 15, 80])
display(titanic["age"])
titanic["age"].hist()
Abc ageVarchar(7) | |
1 | [0;15] |
2 | ]15;80] |
3 | ]15;80] |
4 | ]15;80] |
5 | ]15;80] |
6 | ]15;80] |
7 | [null] |
8 | ]15;80] |
9 | ]15;80] |
10 | ]15;80] |
11 | ]15;80] |
12 | ]15;80] |
13 | ]15;80] |
14 | ]15;80] |
15 | [null] |
16 | ]15;80] |
17 | [null] |
18 | ]15;80] |
19 | ]15;80] |
20 | ]15;80] |
21 | ]15;80] |
22 | ]15;80] |
23 | ]15;80] |
24 | [null] |
25 | ]15;80] |
26 | [null] |
27 | ]15;80] |
28 | ]15;80] |
29 | [null] |
30 | ]15;80] |
31 | ]15;80] |
32 | ]15;80] |
33 | ]15;80] |
34 | ]15;80] |
35 | ]15;80] |
36 | [null] |
37 | ]15;80] |
38 | ]15;80] |
39 | ]15;80] |
40 | [null] |
41 | [null] |
42 | ]15;80] |
43 | ]15;80] |
44 | ]15;80] |
45 | ]15;80] |
46 | ]15;80] |
47 | ]15;80] |
48 | [null] |
49 | ]15;80] |
50 | ]15;80] |
51 | ]15;80] |
52 | ]15;80] |
53 | [null] |
54 | ]15;80] |
55 | ]15;80] |
56 | [null] |
57 | ]15;80] |
58 | ]15;80] |
59 | ]15;80] |
60 | ]15;80] |
61 | ]15;80] |
62 | ]15;80] |
63 | [null] |
64 | [null] |
65 | ]15;80] |
66 | ]15;80] |
67 | ]15;80] |
68 | ]15;80] |
69 | ]15;80] |
70 | ]15;80] |
71 | ]15;80] |
72 | ]15;80] |
73 | ]15;80] |
74 | ]15;80] |
75 | ]15;80] |
76 | ]15;80] |
77 | ]15;80] |
78 | ]15;80] |
79 | ]15;80] |
80 | ]15;80] |
81 | [null] |
82 | ]15;80] |
83 | ]15;80] |
84 | ]15;80] |
85 | ]15;80] |
86 | ]15;80] |
87 | ]15;80] |
88 | ]15;80] |
89 | [null] |
90 | ]15;80] |
91 | [null] |
92 | ]15;80] |
93 | ]15;80] |
94 | ]15;80] |
95 | ]15;80] |
96 | ]15;80] |
97 | ]15;80] |
98 | ]15;80] |
99 | ]15;80] |
100 | [null] |
Rows: 1-100 of 1234 | Column: age | Type: Varchar(7)
Out[38]:
<AxesSubplot:xlabel='"age"', ylabel='Density'>
In [39]:
titanic["fare"].cut([0, 15, 800], right=False, include_lowest=False,)
display(titanic["fare"])
titanic["fare"].hist()
Abc fareVarchar(8) | |
1 | [15;800[ |
2 | [15;800[ |
3 | [15;800[ |
4 | [null] |
5 | [15;800[ |
6 | [15;800[ |
7 | [15;800[ |
8 | [15;800[ |
9 | [15;800[ |
10 | [15;800[ |
11 | [15;800[ |
12 | [15;800[ |
13 | [15;800[ |
14 | [15;800[ |
15 | [15;800[ |
16 | [15;800[ |
17 | [15;800[ |
18 | ]0;15[ |
19 | [15;800[ |
20 | [15;800[ |
21 | [15;800[ |
22 | [15;800[ |
23 | [15;800[ |
24 | [null] |
25 | [15;800[ |
26 | [15;800[ |
27 | [15;800[ |
28 | [15;800[ |
29 | [15;800[ |
30 | [15;800[ |
31 | [15;800[ |
32 | [15;800[ |
33 | [15;800[ |
34 | [15;800[ |
35 | [15;800[ |
36 | [15;800[ |
37 | [15;800[ |
38 | [15;800[ |
39 | [15;800[ |
40 | [15;800[ |
41 | [null] |
42 | [15;800[ |
43 | [15;800[ |
44 | [15;800[ |
45 | [15;800[ |
46 | [15;800[ |
47 | [15;800[ |
48 | [15;800[ |
49 | [15;800[ |
50 | [null] |
51 | [15;800[ |
52 | [15;800[ |
53 | [15;800[ |
54 | [15;800[ |
55 | [15;800[ |
56 | [15;800[ |
57 | [15;800[ |
58 | [15;800[ |
59 | [15;800[ |
60 | [15;800[ |
61 | [15;800[ |
62 | [15;800[ |
63 | [15;800[ |
64 | [15;800[ |
65 | [15;800[ |
66 | [15;800[ |
67 | [15;800[ |
68 | [15;800[ |
69 | [15;800[ |
70 | [15;800[ |
71 | [15;800[ |
72 | [15;800[ |
73 | [15;800[ |
74 | [15;800[ |
75 | [15;800[ |
76 | [15;800[ |
77 | [15;800[ |
78 | [15;800[ |
79 | [15;800[ |
80 | [15;800[ |
81 | [null] |
82 | [15;800[ |
83 | [15;800[ |
84 | [15;800[ |
85 | [15;800[ |
86 | [15;800[ |
87 | [null] |
88 | [15;800[ |
89 | [15;800[ |
90 | [15;800[ |
91 | [15;800[ |
92 | [15;800[ |
93 | [15;800[ |
94 | [15;800[ |
95 | [15;800[ |
96 | [15;800[ |
97 | [15;800[ |
98 | [15;800[ |
99 | [15;800[ |
100 | [15;800[ |
Rows: 1-100 of 1234 | Column: fare | Type: Varchar(8)
Out[39]:
<AxesSubplot:xlabel='"fare"', ylabel='Density'>
In [41]:
titanic["parch"].cut([0, 5, 10], right=False, include_lowest=False, labels=["small", "big"],)
display(titanic["parch"])
titanic["parch"].hist()
Abc parchVarchar(5) | |
1 | small |
2 | small |
3 | small |
4 | [null] |
5 | [null] |
6 | [null] |
7 | [null] |
8 | small |
9 | [null] |
10 | [null] |
11 | [null] |
12 | [null] |
13 | [null] |
14 | [null] |
15 | [null] |
16 | [null] |
17 | [null] |
18 | [null] |
19 | [null] |
20 | [null] |
21 | [null] |
22 | [null] |
23 | [null] |
24 | [null] |
25 | [null] |
26 | [null] |
27 | [null] |
28 | small |
29 | [null] |
30 | small |
31 | [null] |
32 | [null] |
33 | [null] |
34 | [null] |
35 | [null] |
36 | [null] |
37 | [null] |
38 | small |
39 | small |
40 | [null] |
41 | [null] |
42 | [null] |
43 | [null] |
44 | [null] |
45 | [null] |
46 | small |
47 | [null] |
48 | [null] |
49 | [null] |
50 | [null] |
51 | small |
52 | [null] |
53 | [null] |
54 | [null] |
55 | [null] |
56 | [null] |
57 | [null] |
58 | [null] |
59 | [null] |
60 | [null] |
61 | [null] |
62 | [null] |
63 | [null] |
64 | [null] |
65 | [null] |
66 | [null] |
67 | [null] |
68 | [null] |
69 | [null] |
70 | [null] |
71 | [null] |
72 | [null] |
73 | [null] |
74 | [null] |
75 | [null] |
76 | small |
77 | small |
78 | [null] |
79 | small |
80 | [null] |
81 | [null] |
82 | [null] |
83 | [null] |
84 | [null] |
85 | [null] |
86 | [null] |
87 | [null] |
88 | [null] |
89 | [null] |
90 | [null] |
91 | [null] |
92 | [null] |
93 | [null] |
94 | [null] |
95 | small |
96 | [null] |
97 | [null] |
98 | [null] |
99 | [null] |
100 | [null] |
Rows: 1-100 of 1234 | Column: parch | Type: Varchar(5)
Out[41]:
<AxesSubplot:xlabel='"parch"', ylabel='Density'>
See Also¶
vDataFrame[].apply | Applies a function to the input vColumn. |
(c) Copyright [2020-2022] Vertica