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.

Returns

vDataFrame : self.parent

Example

In [40]:
from verticapy.datasets import load_titanic
titanic = load_titanic()
titanic
Out[40]:
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)
110female2.012113781151.55C22 C26S[null][null]Montreal, PQ / Chesterville, ON
210male30.012113781151.55C22 C26S[null]135Montreal, PQ / Chesterville, ON
310female25.012113781151.55C22 C26S[null][null]Montreal, PQ / Chesterville, ON
410male39.0001120500.0A36S[null][null]Belfast, NI
510male71.000PC 1760949.5042[null]C[null]22Montevideo, Uruguay
610male47.010PC 17757227.525C62 C64C[null]124New York, NY
710male[null]00PC 1731825.925[null]S[null][null]New York, NY
810male24.001PC 17558247.5208B58 B60C[null][null]Montreal, PQ
910male36.0001305075.2417C6CA[null]Winnipeg, MN
1010male25.0001390526.0[null]C[null]148San Francisco, CA
1110male45.00011378435.5TS[null][null]Trenton, NJ
1210male42.00011048926.55D22S[null][null]London / Winnipeg, MB
1310male41.00011305430.5A21S[null][null]Pomeroy, WA
1410male48.000PC 1759150.4958B10C[null]208Omaha, NE
1510male[null]0011237939.6[null]C[null][null]Philadelphia, PA
1610male45.00011305026.55B38S[null][null]Washington, DC
1710male[null]0011379831.0[null]S[null][null][null]
1810male33.0006955.0B51 B53 B55S[null][null]New York, NY
1910male28.00011305947.1[null]S[null][null]Montevideo, Uruguay
2010male17.00011305947.1[null]S[null][null]Montevideo, Uruguay
2110male49.0001992426.0[null]S[null][null]Ascot, Berkshire / Rochester, NY
2210male36.0101987778.85C46S[null]172Little Onn Hall, Staffs
2310male46.010W.E.P. 573461.175E31S[null][null]Amenia, ND
2410male[null]001120510.0[null]S[null][null]Liverpool, England / Belfast
2510male27.01013508136.7792C89C[null][null]Los Angeles, CA
2610male[null]0011046552.0A14S[null][null]Stoughton, MA
2710male47.000572725.5875E58S[null][null]Victoria, BC
2810male37.011PC 1775683.1583E52C[null][null]Lakewood, NJ
2910male[null]0011379126.55[null]S[null][null]Roachdale, IN
3010male70.011WE/P 573571.0B22S[null]269Milwaukee, WI
3110male39.010PC 1759971.2833C85C[null][null]New York, NY
3210male31.010F.C. 1275052.0B71S[null][null]Montreal, PQ
3310male50.010PC 17761106.425C86C[null]62Deephaven, MN / Cedar Rapids, IA
3410male39.000PC 1758029.7A18C[null]133Philadelphia, PA
3510female36.000PC 1753131.6792A29C[null][null]New York, NY
3610male[null]00PC 17483221.7792C95S[null][null][null]
3710male30.00011305127.75C111C[null][null]New York, NY
3810male19.03219950263.0C23 C25 C27S[null][null]Winnipeg, MB
3910male64.01419950263.0C23 C25 C27S[null][null]Winnipeg, MB
4010male[null]0011377826.55D34S[null][null]Westcliff-on-Sea, Essex
4110male[null]001120580.0B102S[null][null][null]
4210male37.01011380353.1C123S[null][null]Scituate, MA
4310male47.00011132038.5E63S[null]275St Anne's-on-Sea, Lancashire
4410male24.000PC 1759379.2B86C[null][null][null]
4510male71.000PC 1775434.6542A5C[null][null]New York, NY
4610male38.001PC 17582153.4625C91S[null]147Winnipeg, MB
4710male46.000PC 1759379.2B82 B84C[null][null]New York, NY
4810male[null]0011379642.4[null]S[null][null][null]
4910male45.0103697383.475C83S[null][null]New York, NY
5010male40.0001120590.0B94S[null]110[null]
5110male55.0111274993.5B69S[null]307Montreal, PQ
5210male42.00011303842.5B11S[null][null]London / Middlesex
5310male[null]001746351.8625E46S[null][null]Brighton, MA
5410male55.00068050.0C39S[null][null]London / Birmingham
5510male42.01011378952.0[null]S[null]38New York, NY
5610male[null]00PC 1760030.6958[null]C14[null]New York, NY
5710female50.000PC 1759528.7125C49C[null][null]Paris, France New York, NY
5810male46.00069426.0[null]S[null]80Bennington, VT
5910male50.00011304426.0E60S[null][null]London
6010male32.500113503211.5C132C[null]45[null]
6110male58.0001177129.7B37C[null]258Buffalo, NY
6210male41.0101746451.8625D21S[null][null]Southington / Noank, CT
6310male[null]0011302826.55C124S[null][null]Portland, OR
6410male[null]00PC 1761227.7208[null]C[null][null]Chicago, IL
6510male29.00011350130.0D6S[null]126Springfield, MA
6610male30.00011380145.5[null]S[null][null]London / New York, NY
6710male30.00011046926.0C106S[null][null]Brockton, MA
6810male19.01011377353.1D30S[null][null]New York, NY
6910male46.0001305075.2417C6C[null]292Vancouver, BC
7010male54.0001746351.8625E46S[null]175Dorchester, MA
7110male28.010PC 1760482.1708[null]C[null][null]New York, NY
7210male65.0001350926.55E38S[null]249East Bridgewater, MA
7310male44.0201992890.0C78Q[null]230Fond du Lac, WI
7410male55.00011378730.5C30S[null][null]Montreal, PQ
7510male47.00011379642.4[null]S[null][null]Washington, DC
7610male37.001PC 1759629.7C118C[null][null]Brooklyn, NY
7710male58.00235273113.275D48C[null]122Lexington, MA
7810male64.00069326.0[null]S[null]263Isle of Wight, England
7910male65.00111350961.9792B30C[null]234Providence, RI
8010male28.500PC 1756227.7208D43C[null]189?Havana, Cuba
8110male[null]001120520.0[null]S[null][null]Belfast
8210male45.50011304328.5C124S[null]166Surbiton Hill, Surrey
8310male23.0001274993.5B24S[null][null]Montreal, PQ
8410male29.01011377666.6C2S[null][null]Isleworth, England
8510male18.010PC 17758108.9C65C[null][null]Madrid, Spain
8610male47.00011046552.0C110S[null]207Worcester, MA
8710male38.000199720.0[null]S[null][null]Rotterdam, Netherlands
8810male22.000PC 17760135.6333[null]C[null]232[null]
8910male[null]00PC 17757227.525[null]C[null][null][null]
9010male31.000PC 1759050.4958A24S[null][null]Trenton, NJ
9110male[null]0011376750.0A32S[null][null]Seattle, WA
9210male36.0001304940.125A10C[null][null]Winnipeg, MB
9310male55.010PC 1760359.4[null]C[null][null]New York, NY
9410male33.00011379026.55[null]S[null]109London
9510male61.013PC 17608262.375B57 B59 B63 B66C[null][null]Haverford, PA / Cooperstown, NY
9610male50.0101350755.9E44S[null][null]Duluth, MN
9710male56.00011379226.55[null]S[null][null]New York, NY
9810male56.0001776430.6958A7C[null][null]St James, Long Island, NY
9910male24.0101369560.0C31S[null][null]Huntington, WV
10010male[null]0011305626.0A19S[null][null]Streatham, Surrey
Rows: 1-100 | Columns: 14
In [38]:
titanic["age"].cut([0, 15, 80])
display(titanic["age"])
titanic["age"].hist()
Abc
age
Varchar(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
fare
Varchar(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
parch
Varchar(5)
1small
2small
3small
4[null]
5[null]
6[null]
7[null]
8small
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]
28small
29[null]
30small
31[null]
32[null]
33[null]
34[null]
35[null]
36[null]
37[null]
38small
39small
40[null]
41[null]
42[null]
43[null]
44[null]
45[null]
46small
47[null]
48[null]
49[null]
50[null]
51small
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]
76small
77small
78[null]
79small
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]
95small
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.