vDataFrame[].fillna¶

In [ ]:

vDataFrame[].fillna(val = None,
                    method: str = "auto",
                    expr: str = "",
                    by: list = [],
                    order_by: list = [])

Fills the vcolumn missing elements using specific rules.

Parameters¶

Name	Type	Optional	Description
val	int / float / str	✓	Value to use to impute the vcolumn.
method	dict	✓	Method to use to impute the missing values. auto : Mean for the numerical and Mode for the categorical vcolumns. bfill : Back Propagation of the next element (Constant Interpolation). ffill : Propagation of the first element (Constant Interpolation). mean : Average. median : Median. mode : Mode (most occurent element). 0ifnull : 0 when the vcolumn is null, 1 otherwise.
expr	str	✓	SQL expression.
by	list	✓	vcolumns used in the partition.
order_by	list	✓	List of the vcolumns to use to sort the data when using TS methods.

Returns¶

vDataFrame : self.parent

Example¶

In [72]:

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

	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

Rows: 1-100 of 1234 | Columns: 14

In [73]:

display(titanic["age"])
# Imputation using the avg over partition by pclass and sex
titanic["age"].fillna(method = "avg", by = ["pclass", "sex"])

	123 age Numeric(6,3)
1	2.000
2	30.000
3	25.000
4	39.000
5	71.000
6	47.000
7	[null]
8	24.000
9	36.000
10	25.000
11	45.000
12	42.000
13	41.000
14	48.000
15	[null]
16	45.000
17	[null]
18	33.000
19	28.000
20	17.000
21	49.000
22	36.000
23	46.000
24	[null]
25	27.000
26	[null]
27	47.000
28	37.000
29	[null]
30	70.000
31	39.000
32	31.000
33	50.000
34	39.000
35	36.000
36	[null]
37	30.000
38	19.000
39	64.000
40	[null]
41	[null]
42	37.000
43	47.000
44	24.000
45	71.000
46	38.000
47	46.000
48	[null]
49	45.000
50	40.000
51	55.000
52	42.000
53	[null]
54	55.000
55	42.000
56	[null]
57	50.000
58	46.000
59	50.000
60	32.500
61	58.000
62	41.000
63	[null]
64	[null]
65	29.000
66	30.000
67	30.000
68	19.000
69	46.000
70	54.000
71	28.000
72	65.000
73	44.000
74	55.000
75	47.000
76	37.000
77	58.000
78	64.000
79	65.000
80	28.500
81	[null]
82	45.500
83	23.000
84	29.000
85	18.000
86	47.000
87	38.000
88	22.000
89	[null]
90	31.000
91	[null]
92	36.000
93	55.000
94	33.000
95	61.000
96	50.000
97	56.000
98	56.000
99	24.000
100	[null]

Rows: 1-100 of 1234 | Column: age | Type: numeric(6,3)
237 element(s) was/were filled

	123 age Numeric(6,3)
1	36.0
2	31.0
3	21.0
4	50.0
5	45.0
6	31.0
7	55.0
8	60.0
9	35.0
10	37.2635658914729
11	39.0
12	37.2635658914729
13	39.0
14	18.0
15	48.0
16	62.0
17	52.0
18	43.0
19	37.2635658914729
20	40.0
21	23.0
22	18.0
23	39.0
24	40.0
25	30.0
26	35.0
27	24.0
28	48.0
29	18.0
30	54.0
31	33.0
32	33.0
33	43.0
34	56.0
35	30.0
36	17.0
37	22.0
38	22.0
39	39.0
40	19.0
41	23.0
42	31.0
43	37.0
44	33.0
45	37.2635658914729
46	18.0
47	16.0
48	15.0
49	58.0
50	21.0
51	51.0
52	16.0
53	55.0
54	49.0
55	30.0
56	39.0
57	45.0
58	37.2635658914729
59	38.0
60	35.0
61	35.0
62	51.0
63	44.0
64	16.0
65	52.0
66	24.0
67	35.0
68	49.0
69	25.0
70	45.0
71	58.0
72	19.0
73	37.2635658914729
74	45.0
75	22.0
76	35.0
77	35.0
78	48.0
79	22.0
80	37.2635658914729
81	30.0
82	60.0
83	23.0
84	28.0
85	24.0
86	37.2635658914729
87	37.2635658914729
88	54.0
89	38.0
90	23.0
91	48.0
92	27.0
93	54.0
94	17.0
95	27.0
96	33.0
97	38.0
98	64.0
99	36.0
100	55.0

Out[73]:

Rows: 1-100 of 1234 | Column: age | Type: numeric(6,3)

In [74]:

display(titanic["age"])
# Imputation using the median over partition by pclass and sex
titanic["age"].fillna(method = "median", by = ["pclass", "sex"])

	123 age Numeric(6,3)
1	36.0
2	31.0
3	21.0
4	50.0
5	45.0
6	31.0
7	55.0
8	60.0
9	35.0
10	37.2635658914729
11	39.0
12	37.2635658914729
13	39.0
14	18.0
15	48.0
16	62.0
17	52.0
18	43.0
19	37.2635658914729
20	40.0
21	23.0
22	18.0
23	39.0
24	40.0
25	30.0
26	35.0
27	24.0
28	48.0
29	18.0
30	54.0
31	33.0
32	33.0
33	43.0
34	56.0
35	30.0
36	17.0
37	22.0
38	22.0
39	39.0
40	19.0
41	23.0
42	31.0
43	37.0
44	33.0
45	37.2635658914729
46	18.0
47	16.0
48	15.0
49	58.0
50	21.0
51	51.0
52	16.0
53	55.0
54	49.0
55	30.0
56	39.0
57	45.0
58	37.2635658914729
59	38.0
60	35.0
61	35.0
62	51.0
63	44.0
64	16.0
65	52.0
66	24.0
67	35.0
68	49.0
69	25.0
70	45.0
71	58.0
72	19.0
73	37.2635658914729
74	45.0
75	22.0
76	35.0
77	35.0
78	48.0
79	22.0
80	37.2635658914729
81	30.0
82	60.0
83	23.0
84	28.0
85	24.0
86	37.2635658914729
87	37.2635658914729
88	54.0
89	38.0
90	23.0
91	48.0
92	27.0
93	54.0
94	17.0
95	27.0
96	33.0
97	38.0
98	64.0
99	36.0
100	55.0

Rows: 1-100 of 1234 | Column: age | Type: numeric(6,3)
Nothing was filled

	123 age Numeric(6,3)
1	36.0
2	31.0
3	21.0
4	50.0
5	45.0
6	31.0
7	55.0
8	60.0
9	35.0
10	37.2635658914729
11	39.0
12	37.2635658914729
13	39.0
14	18.0
15	48.0
16	62.0
17	52.0
18	43.0
19	37.2635658914729
20	40.0
21	23.0
22	18.0
23	39.0
24	40.0
25	30.0
26	35.0
27	24.0
28	48.0
29	18.0
30	54.0
31	33.0
32	33.0
33	43.0
34	56.0
35	30.0
36	17.0
37	22.0
38	22.0
39	39.0
40	19.0
41	23.0
42	31.0
43	37.0
44	33.0
45	37.2635658914729
46	18.0
47	16.0
48	15.0
49	58.0
50	21.0
51	51.0
52	16.0
53	55.0
54	49.0
55	30.0
56	39.0
57	45.0
58	37.2635658914729
59	38.0
60	35.0
61	35.0
62	51.0
63	44.0
64	16.0
65	52.0
66	24.0
67	35.0
68	49.0
69	25.0
70	45.0
71	58.0
72	19.0
73	37.2635658914729
74	45.0
75	22.0
76	35.0
77	35.0
78	48.0
79	22.0
80	37.2635658914729
81	30.0
82	60.0
83	23.0
84	28.0
85	24.0
86	37.2635658914729
87	37.2635658914729
88	54.0
89	38.0
90	23.0
91	48.0
92	27.0
93	54.0
94	17.0
95	27.0
96	33.0
97	38.0
98	64.0
99	36.0
100	55.0

Out[74]:

Rows: 1-100 of 1234 | Column: age | Type: numeric(6,3)

In [75]:

display(titanic["embarked"])
# Imputation using the mode
titanic["embarked"].fillna(method = "mode")

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

Rows: 1-100 of 1234 | Column: embarked | Type: varchar(20)
2 element(s) was/were filled

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

Out[75]:

Rows: 1-100 of 1234 | Column: embarked | Type: varchar(20)

In [76]:

display(titanic["boat"])
# As the missing values are the most occurent categories, an idea
# could be to merge all the non-null category (category 1) and to
# impute the missing values par 0. '0ifnull' method is following this
# process
titanic["boat"].fillna(method = "0ifnull")

	Abc boat Varchar(100)
1	[null]
2	[null]
3	[null]
4	[null]
5	[null]
6	[null]
7	[null]
8	[null]
9	A
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	[null]
29	[null]
30	[null]
31	[null]
32	[null]
33	[null]
34	[null]
35	[null]
36	[null]
37	[null]
38	[null]
39	[null]
40	[null]
41	[null]
42	[null]
43	[null]
44	[null]
45	[null]
46	[null]
47	[null]
48	[null]
49	[null]
50	[null]
51	[null]
52	[null]
53	[null]
54	[null]
55	[null]
56	14
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	[null]
77	[null]
78	[null]
79	[null]
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	[null]
96	[null]
97	[null]
98	[null]
99	[null]
100	[null]

Rows: 1-100 of 1234 | Column: boat | Type: varchar(100)
795 element(s) was/were filled

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

Out[76]:

Rows: 1-100 of 1234 | Column: boat | Type: bool

vDataFrame[].fillna¶

Parameters¶

Returns¶

Example¶

See Also¶