pandas_to_vertica¶

In [ ]:

pandas_to_vertica(df, 
                  name: str = "", 
                  schema: str = "", 
                  dtype: dict = {}, 
                  parse_nrows: int = 10000, 
                  temp_path: str = "", 
                  insert: bool = False,)

Ingests a pandas DataFrame into the Vertica database by creating a CSV file and then using flex tables.

Parameters¶

Name	Type	Optional	Description
df	pandas.DataFrame	❌	The pandas.DataFrame to ingest.
name	str	✓	Name of the new relation, or an existing relation, in which to insert the data. If unspecified, the temporary schema will be used. To change the default relation, use the set_option function.
schema	str	✓	Schema of the new relation. If unspecified, a temporary table is created.
dtype	dict	✓	Dictionary of input types. Providing a dictionary can increase ingestion speed and precision. If specified, VerticaPy uses the designated input types instead of parsing the intermediate CSV and inferring the input types.
parse_nrows	int	✓	If this parameter is greater than 0, VerticaPy creates and ingests a temporary file containing 'parse_nrows' lines to determine the input data types. VerticaPy then ingests the remainder of the intermediate CSV file containing the rest of the data. This method of data type identification is less accurate, but much faster for large datasets.
temp_path	str	✓	The path to which the intermediate CSV file is written. This is useful in cases where the user does not have write permissions on the current directory.
insert	bool	✓	If set to True, the data is ingested into the input relation. The column names of your table and the pandas.DataFrame must match.

Returns¶

vDataFrame : vDataFrame of the new relation.

Example¶

In [19]:

from verticapy.datasets import load_titanic
titanic = load_titanic()
df = titanic.to_pandas()
df

Out[19]:

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest
0	1	0	Allison, Miss. Helen Loraine	female	2.000	1	2	113781	151.55000	C22 C26	S	None	NaN	Montreal, PQ / Chesterville, ON
1	1	0	Allison, Mr. Hudson Joshua Creighton	male	30.000	1	2	113781	151.55000	C22 C26	S	None	135.0	Montreal, PQ / Chesterville, ON
2	1	0	Allison, Mrs. Hudson J C (Bessie Waldo Daniels)	female	25.000	1	2	113781	151.55000	C22 C26	S	None	NaN	Montreal, PQ / Chesterville, ON
3	1	0	Andrews, Mr. Thomas Jr	male	39.000	0	0	112050	0.00000	A36	S	None	NaN	Belfast, NI
4	1	0	Artagaveytia, Mr. Ramon	male	71.000	0	0	PC 17609	49.50420	None	C	None	22.0	Montevideo, Uruguay
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1229	3	1	Wilkes, Mrs. James (Ellen Needs)	female	47.000	1	0	363272	7.00000	None	S	None	NaN	None
1230	3	1	Yasbeck, Mrs. Antoni (Selini Alexander)	female	15.000	1	0	2659	14.45420	None	C	None	NaN	None
1231	3	1	de Messemaeker, Mr. Guillaume Joseph	male	36.500	1	0	345572	17.40000	None	S	15	NaN	Tampico, MT
1232	3	1	de Messemaeker, Mrs. Guillaume Joseph (Emma)	female	36.000	1	0	345572	17.40000	None	S	13	NaN	Tampico, MT
1233	3	1	de Mulder, Mr. Theodore	male	30.000	0	0	345774	9.50000	None	S	11	NaN	Belgium Detroit, MI

1234 rows × 14 columns

In [3]:

from verticapy.utilities import *
pandas_to_vertica(df = df, name = "titanic_pandas", schema = "public")

	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[3]:

Rows: 1-100 of 1234 | Columns: 14

In [20]:

# using the dtype parameter
pandas_to_vertica(df = df, 
                  name = "titanic_pandas", 
                  schema = "public",
                  dtype = {"pclass": "Integer", 
                           "survived": "Integer", 
                           "name": "Varchar(164)", 
                           "sex": "Varchar(20)", 
                           "age": "Numeric(6,3)", 
                           "sibsp": "Integer", 
                           "parch": "Integer", 
                           "ticket": "Varchar(36)", 
                           "fare": "Numeric(10,5)", 
                           "cabin": "Varchar(30)", 
                           "embarked": "Varchar(20)", 
                           "boat": "Varchar(100)", 
                           "body": "Integer", 
                           "home.dest": "Varchar(100)"})

The table "public"."titanic_pandas" has been successfully created.

Out[20]:

	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)
1	1	0		female	2.0	1	2	113781	151.55	C22 C26	S	[null]	[null]
2	1	0		female	25.0	1	2	113781	151.55	C22 C26	S	[null]	[null]
3	1	0		male	39.0	0	0	112050	0.0	A36	S	[null]	[null]
4	1	0		male	[null]	0	0	PC 17318	25.925	[null]	S	[null]	[null]
5	1	0		male	24.0	0	1	PC 17558	247.5208	B58 B60	C	[null]	[null]
6	1	0		male	36.0	0	0	13050	75.2417	C6	C	A	[null]
7	1	0		male	45.0	0	0	113784	35.5	T	S	[null]	[null]
8	1	0		male	42.0	0	0	110489	26.55	D22	S	[null]	[null]
9	1	0		male	41.0	0	0	113054	30.5	A21	S	[null]	[null]
10	1	0		male	[null]	0	0	112379	39.6	[null]	C	[null]	[null]
11	1	0		male	45.0	0	0	113050	26.55	B38	S	[null]	[null]
12	1	0		male	[null]	0	0	113798	31.0	[null]	S	[null]	[null]
13	1	0		male	33.0	0	0	695	5.0	B51 B53 B55	S	[null]	[null]
14	1	0		male	28.0	0	0	113059	47.1	[null]	S	[null]	[null]
15	1	0		male	17.0	0	0	113059	47.1	[null]	S	[null]	[null]
16	1	0		male	49.0	0	0	19924	26.0	[null]	S	[null]	[null]
17	1	0		male	46.0	1	0	W.E.P. 5734	61.175	E31	S	[null]	[null]
18	1	0		male	[null]	0	0	112051	0.0	[null]	S	[null]	[null]
19	1	0		male	27.0	1	0	13508	136.7792	C89	C	[null]	[null]
20	1	0		male	[null]	0	0	110465	52.0	A14	S	[null]	[null]
21	1	0		male	47.0	0	0	5727	25.5875	E58	S	[null]	[null]
22	1	0		male	37.0	1	1	PC 17756	83.1583	E52	C	[null]	[null]
23	1	0		male	[null]	0	0	113791	26.55	[null]	S	[null]	[null]
24	1	0		male	39.0	1	0	PC 17599	71.2833	C85	C	[null]	[null]
25	1	0		male	31.0	1	0	F.C. 12750	52.0	B71	S	[null]	[null]
26	1	0		female	36.0	0	0	PC 17531	31.6792	A29	C	[null]	[null]
27	1	0		male	[null]	0	0	PC 17483	221.7792	C95	S	[null]	[null]
28	1	0		male	30.0	0	0	113051	27.75	C111	C	[null]	[null]
29	1	0		male	19.0	3	2	19950	263.0	C23 C25 C27	S	[null]	[null]
30	1	0		male	64.0	1	4	19950	263.0	C23 C25 C27	S	[null]	[null]
31	1	0		male	[null]	0	0	113778	26.55	D34	S	[null]	[null]
32	1	0		male	[null]	0	0	112058	0.0	B102	S	[null]	[null]
33	1	0		male	37.0	1	0	113803	53.1	C123	S	[null]	[null]
34	1	0		male	24.0	0	0	PC 17593	79.2	B86	C	[null]	[null]
35	1	0		male	71.0	0	0	PC 17754	34.6542	A5	C	[null]	[null]
36	1	0		male	46.0	0	0	PC 17593	79.2	B82 B84	C	[null]	[null]
37	1	0		male	[null]	0	0	113796	42.4	[null]	S	[null]	[null]
38	1	0		male	45.0	1	0	36973	83.475	C83	S	[null]	[null]
39	1	0		male	42.0	0	0	113038	42.5	B11	S	[null]	[null]
40	1	0		male	[null]	0	0	17463	51.8625	E46	S	[null]	[null]
41	1	0		male	55.0	0	0	680	50.0	C39	S	[null]	[null]
42	1	0		male	[null]	0	0	PC 17600	30.6958	[null]	C	14	[null]
43	1	0		female	50.0	0	0	PC 17595	28.7125	C49	C	[null]	[null]
44	1	0		male	50.0	0	0	113044	26.0	E60	S	[null]	[null]
45	1	0		male	41.0	1	0	17464	51.8625	D21	S	[null]	[null]
46	1	0		male	[null]	0	0	113028	26.55	C124	S	[null]	[null]
47	1	0		male	[null]	0	0	PC 17612	27.7208	[null]	C	[null]	[null]
48	1	0		male	30.0	0	0	113801	45.5	[null]	S	[null]	[null]
49	1	0		male	30.0	0	0	110469	26.0	C106	S	[null]	[null]
50	1	0		male	19.0	1	0	113773	53.1	D30	S	[null]	[null]
51	1	0		male	28.0	1	0	PC 17604	82.1708	[null]	C	[null]	[null]
52	1	0		male	55.0	0	0	113787	30.5	C30	S	[null]	[null]
53	1	0		male	47.0	0	0	113796	42.4	[null]	S	[null]	[null]
54	1	0		male	37.0	0	1	PC 17596	29.7	C118	C	[null]	[null]
55	1	0		male	[null]	0	0	112052	0.0	[null]	S	[null]	[null]
56	1	0		male	23.0	0	0	12749	93.5	B24	S	[null]	[null]
57	1	0		male	29.0	1	0	113776	66.6	C2	S	[null]	[null]
58	1	0		male	18.0	1	0	PC 17758	108.9	C65	C	[null]	[null]
59	1	0		male	38.0	0	0	19972	0.0	[null]	S	[null]	[null]
60	1	0		male	[null]	0	0	PC 17757	227.525	[null]	C	[null]	[null]
61	1	0		male	31.0	0	0	PC 17590	50.4958	A24	S	[null]	[null]
62	1	0		male	[null]	0	0	113767	50.0	A32	S	[null]	[null]
63	1	0		male	36.0	0	0	13049	40.125	A10	C	[null]	[null]
64	1	0		male	55.0	1	0	PC 17603	59.4	[null]	C	[null]	[null]
65	1	0		male	61.0	1	3	PC 17608	262.375	B57 B59 B63 B66	C	[null]	[null]
66	1	0		male	50.0	1	0	13507	55.9	E44	S	[null]	[null]
67	1	0		male	56.0	0	0	113792	26.55	[null]	S	[null]	[null]
68	1	0		male	56.0	0	0	17764	30.6958	A7	C	[null]	[null]
69	1	0		male	24.0	1	0	13695	60.0	C31	S	[null]	[null]
70	1	0		male	[null]	0	0	113056	26.0	A19	S	[null]	[null]
71	1	0		male	57.0	1	0	PC 17569	146.5208	B78	C	[null]	[null]
72	1	0		male	62.0	0	0	113514	26.55	C87	S	[null]	[null]
73	1	0		male	[null]	0	0	PC 17605	27.7208	[null]	C	[null]	[null]
74	1	0		female	63.0	1	0	PC 17483	221.7792	C55 C57	S	[null]	[null]
75	1	0		male	52.0	1	1	110413	79.65	E67	S	[null]	[null]
76	1	0		male	49.0	1	1	17421	110.8833	C68	C	[null]	[null]
77	1	0		male	40.0	0	0	PC 17601	27.7208	[null]	C	[null]	[null]
78	1	0		male	47.0	0	0	36967	34.0208	D46	S	[null]	[null]
79	1	0		male	64.0	1	0	110813	75.25	D37	C	[null]	[null]
80	1	0		male	60.0	0	0	113800	26.55	[null]	S	[null]	[null]
81	1	0		male	54.0	0	1	35281	77.2875	D26	S	[null]	[null]
82	1	0		male	57.0	1	1	36928	164.8667	[null]	S	[null]	[null]
83	1	0		male	50.0	1	1	113503	211.5	C80	C	[null]	[null]
84	1	0		male	27.0	0	2	113503	211.5	C82	C	[null]	[null]
85	1	0		male	51.0	0	1	PC 17597	61.3792	[null]	C	[null]	[null]
86	1	0		male	[null]	0	0	113510	35.0	C128	S	[null]	[null]
87	1	0		male	62.0	0	0	113807	26.55	[null]	S	[null]	[null]
88	1	1		female	29.0	0	0	24160	211.3375	B5	S	2	[null]
89	1	1		male	0.92	1	2	113781	151.55	C22 C26	S	11	[null]
90	1	1		male	48.0	0	0	19952	26.55	E12	S	3	[null]
91	1	1		female	63.0	1	0	13502	77.9583	D7	S	10	[null]
92	1	1		female	53.0	2	0	11769	51.4792	C101	S	D	[null]
93	1	1		female	18.0	1	0	PC 17757	227.525	C62 C64	C	4	[null]
94	1	1		female	24.0	0	0	PC 17477	69.3	B35	C	9	[null]
95	1	1		male	80.0	0	0	27042	30.0	A23	S	B	[null]
96	1	1		female	50.0	0	1	PC 17558	247.5208	B58 B60	C	6	[null]
97	1	1		female	32.0	0	0	11813	76.2917	D15	C	8	[null]
98	1	1		male	37.0	1	1	11751	52.5542	D35	S	5	[null]
99	1	1		female	47.0	1	1	11751	52.5542	D35	S	5	[null]
100	1	1		male	26.0	0	0	111369	30.0	C148	C	5	[null]

Rows: 1-100 | Columns: 14

read_csv	Ingests a CSV file into the Vertica DB.
read_json	Ingests a JSON file into the Vertica DB.

pandas_to_vertica¶

Parameters¶

Returns¶

Example¶

See Also¶