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verticapy.read_json

verticapy.read_json(path: str, schema: str | None = None, table_name: str | None = None, usecols: list | None = None, new_name: dict | None = None, insert: bool = False, start_point: str = None, record_terminator: str = None, suppress_nonalphanumeric_key_chars: bool = False, reject_on_materialized_type_error: bool = False, reject_on_duplicate: bool = False, reject_on_empty_key: bool = False, flatten_maps: bool = True, flatten_arrays: bool = False, temporary_table: bool = False, temporary_local_table: bool = True, gen_tmp_table_name: bool = True, ingest_local: bool = True, genSQL: bool = False, materialize: bool = True, use_complex_dt: bool = False, is_avro: bool = False) → vDataFrame

Ingests a JSON file using flex tables.

Parameters

path: str

Absolute path where the JSON file is located.

schema: str, optional

Schema where the JSON file will be ingested.

table_name: str, optional

Final relation name.

usecols: list, optional

list of the JSON parameters to ingest. The other parameters will be ignored. If empty, all the JSON parameters will be ingested.

new_name: dict, optional

Dictionary of the new column names. If the JSON file is nested, it is recommended to change the final names because special characters will be included in the new column names. For example, {"param": {"age": 3, "name": Badr}, "date": 1993-03-11} will create 3 columns: “param.age”, “param.name” and “date”. You can rename these columns using the new_name parameter with the following dictionary: {"param.age": "age", "param.name": "name"}

insert: bool, optional

If set to True, the data is ingested into the input relation. The JSON parameters must be the same as the input relation otherwise they will not be ingested. If set to True, table_name cannot be empty.

start_point: str, optional

str, name of a key in the JSON load data at which to begin parsing. The parser ignores all data before the start_point value. The value is loaded for each object in the file. The parser processes data after the first instance, and up to the second, ignoring any remaining data.

record_terminator: str, optional

When set, any invalid JSON records are skipped and parsing continues with the next record. Records must be terminated uniformly. For example, if your input file has JSON records terminated by newline characters, set this parameter to \n. If any invalid JSON records exist, parsing continues after the next record_terminator. Even if the data does not contain invalid records, specifying an explicit record terminator can improve load performance by allowing cooperative parse and apportioned load to operate more efficiently. When you omit this parameter, parsing ends at the first invalid JSON record.

suppress_nonalphanumeric_key_chars: bool, optional

boolean, whether to suppress non-alphanumeric characters in JSON key values. The parser replaces these characters with an underscore (_) when this parameter is True.

reject_on_materialized_type_error: bool, optional

boolean, whether to reject a data row that contains a materialized column value that cannot be coerced into a compatible data type. If the value is False and the type cannot be coerced, the parser sets the value in that column to None. If the column is a strongly-typed complex type, as opposed to a flexible complex type, then a type mismatch anywhere in the complex type causes the entire column to be treated as a mismatch. The parser does not partially load complex types.

reject_on_duplicate: bool, optional

boolean, whether to ignore duplicate records (False), or to reject duplicates (True). In either case, the load continues.

reject_on_empty_key: bool, optional

boolean, whether to reject any row containing a field key without a value.

flatten_maps: bool, optional

boolean, whether to flatten sub-maps within the JSON data, separating map levels with a period (.). This value affects all data in the load, including nested maps.

flatten_arrays: bool, optional

boolean, whether to convert lists to sub-maps with integer keys. When lists are flattened, key names are concatenated in the same way as maps. lists are not flattened by default. This value affects all data in the load, including nested lists.

temporary_table: bool, optional

If set to True, a temporary table will be created.

temporary_local_table: bool, optional

If set to True, a temporary local table will be created. The parameter schema must be empty, otherwise this parameter is ignored.

gen_tmp_table_name: bool, optional

Sets the name of the temporary table. This parameter is only used when the parameter temporary_local_table is set to True and if the parameters table_name and schema are unspecified.

ingest_local: bool, optional

If set to True, the file will be ingested from the local machine.

genSQL: bool, optional

If set to True, the SQL code for creating the final table is generated but not executed. This is a good way to change the final relation types or to customize the data ingestion.

materialize: bool, optional

If set to True, the flex table is materialized into a table. Otherwise, it will remain a flex table. Flex tables simplify the data ingestion but have worse performace compared to regular tables.

use_complex_dt: bool, optional

boolean, whether the input data file has complex structure. If set to True, most of the other parameters are ignored.

Returns

vDataFrame

The vDataFrame of the relation.

Examples

In this example, we will first create a JSON file using vDataFrame.to_json() and ingest it into Vertica database.

We import verticapy:

import verticapy as vp

Hint

By assigning an alias to verticapy, we mitigate the risk of code collisions with other libraries. This precaution is necessary because verticapy uses commonly known function names like “average” and “median”, which can potentially lead to naming conflicts. The use of an alias ensures that the functions from verticapy are used as intended without interfering with functions from other libraries.

We will use the Titanic dataset.

import verticapy.datasets as vpd

data = vpd.load_titanic()

	123 pclass Integer	123 survived Integer	Abc Varchar(164)	Abc sex Varchar(20)	123 age Numeric(8)	123 sibsp Integer	123 parch Integer	Abc ticket Varchar(36)	123 fare Numeric(12)	Abc cabin Varchar(30)	Abc embarked Varchar(20)	Abc boat Varchar(100)	123 body Integer	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

Note

VerticaPy offers a wide range of sample datasets that are ideal for training and testing purposes. You can explore the full list of available datasets in the Datasets, which provides detailed information on each dataset and how to use them effectively. These datasets are invaluable resources for honing your data analysis and machine learning skills within the VerticaPy environment.

Let’s convert the vDataFrame to a JSON file.

data[0:20].to_json(
    path = "titanic_subset.json",
)

Let’s ingest the json file into the Vertica database.

from verticapy.core.parsers.json import read_json

read_json(
    path = "titanic_subset.json",
    table_name = "titanic_subset",
    schema = "public",
)

	Abc boat Varchar(20)	123 body Integer	Abc cabin Varchar(22)	123 age Numeric(10)	Abc home.dest Varchar(62)	Abc embarked Varchar(20)	123 fare Numeric(13)	Abc Varchar(94)	123 parch Integer	123 pclass Integer	Abc sex Varchar(20)	123 sibsp Integer	123 survived Integer	Abc ticket Varchar(20)
1	A	[null]	C6	36.0	Winnipeg, MN	C	75.2417		0	1	male	0	0	13050
2	[null]	[null]	A21	41.0	Pomeroy, WA	S	30.5		0	1	male	0	0	113054
3	[null]	[null]	A36	39.0	Belfast, NI	S	0.0		0	1	male	0	0	112050
4	[null]	[null]	B38	45.0	Washington, DC	S	26.55		0	1	male	0	0	113050
5	[null]	[null]	B51 B53 B55	33.0	New York, NY	S	5.0		0	1	male	0	0	695
6	[null]	[null]	B58 B60	24.0	Montreal, PQ	C	247.5208		1	1	male	0	0	PC 17558
7	[null]	[null]	C22 C26	2.0	Montreal, PQ / Chesterville, ON	S	151.55		2	1	female	1	0	113781
8	[null]	[null]	C22 C26	25.0	Montreal, PQ / Chesterville, ON	S	151.55		2	1	female	1	0	113781
9	[null]	[null]	D22	42.0	London / Winnipeg, MB	S	26.55		0	1	male	0	0	110489
10	[null]	[null]	T	45.0	Trenton, NJ	S	35.5		0	1	male	0	0	113784
11	[null]	[null]	[null]	[null]	New York, NY	S	25.925		0	1	male	0	0	PC 17318
12	[null]	[null]	[null]	[null]	Philadelphia, PA	C	39.6		0	1	male	0	0	112379
13	[null]	[null]	[null]	[null]	[null]	S	31.0		0	1	male	0	0	113798
14	[null]	[null]	[null]	17.0	Montevideo, Uruguay	S	47.1		0	1	male	0	0	113059
15	[null]	[null]	[null]	28.0	Montevideo, Uruguay	S	47.1		0	1	male	0	0	113059
16	[null]	22	[null]	71.0	Montevideo, Uruguay	C	49.5042		0	1	male	0	0	PC 17609
17	[null]	124	C62 C64	47.0	New York, NY	C	227.525		0	1	male	1	0	PC 17757
18	[null]	135	C22 C26	30.0	Montreal, PQ / Chesterville, ON	S	151.55		2	1	male	1	0	113781
19	[null]	148	[null]	25.0	San Francisco, CA	C	26.0		0	1	male	0	0	13905
20	[null]	208	B10	48.0	Omaha, NE	C	50.4958		0	1	male	0	0	PC 17591

Rows: 1-20 | Columns: 14

Let’s ingest the json and rename some columns.

read_json(
    path = "titanic_subset.json",
    table_name = "titanic_sub_newnames",
    schema = "public",
    new_name = {
        "fields.fare": "fare",
        "fields.sex": "sex",
    },
)

	Abc boat Varchar(20)	123 body Integer	Abc cabin Varchar(22)	123 age Numeric(10)	Abc home.dest Varchar(62)	Abc embarked Varchar(20)	123 fare Numeric(13)	Abc Varchar(94)	123 parch Integer	123 pclass Integer	Abc sex Varchar(20)	123 sibsp Integer	123 survived Integer	Abc ticket Varchar(20)
1	A	[null]	C6	36.0	Winnipeg, MN	C	75.2417		0	1	male	0	0	13050
2	[null]	[null]	A21	41.0	Pomeroy, WA	S	30.5		0	1	male	0	0	113054
3	[null]	[null]	A36	39.0	Belfast, NI	S	0.0		0	1	male	0	0	112050
4	[null]	[null]	B38	45.0	Washington, DC	S	26.55		0	1	male	0	0	113050
5	[null]	[null]	B51 B53 B55	33.0	New York, NY	S	5.0		0	1	male	0	0	695
6	[null]	[null]	B58 B60	24.0	Montreal, PQ	C	247.5208		1	1	male	0	0	PC 17558
7	[null]	[null]	C22 C26	2.0	Montreal, PQ / Chesterville, ON	S	151.55		2	1	female	1	0	113781
8	[null]	[null]	C22 C26	25.0	Montreal, PQ / Chesterville, ON	S	151.55		2	1	female	1	0	113781
9	[null]	[null]	D22	42.0	London / Winnipeg, MB	S	26.55		0	1	male	0	0	110489
10	[null]	[null]	T	45.0	Trenton, NJ	S	35.5		0	1	male	0	0	113784
11	[null]	[null]	[null]	[null]	New York, NY	S	25.925		0	1	male	0	0	PC 17318
12	[null]	[null]	[null]	[null]	Philadelphia, PA	C	39.6		0	1	male	0	0	112379
13	[null]	[null]	[null]	[null]	[null]	S	31.0		0	1	male	0	0	113798
14	[null]	[null]	[null]	17.0	Montevideo, Uruguay	S	47.1		0	1	male	0	0	113059
15	[null]	[null]	[null]	28.0	Montevideo, Uruguay	S	47.1		0	1	male	0	0	113059
16	[null]	22	[null]	71.0	Montevideo, Uruguay	C	49.5042		0	1	male	0	0	PC 17609
17	[null]	124	C62 C64	47.0	New York, NY	C	227.525		0	1	male	1	0	PC 17757
18	[null]	135	C22 C26	30.0	Montreal, PQ / Chesterville, ON	S	151.55		2	1	male	1	0	113781
19	[null]	148	[null]	25.0	San Francisco, CA	C	26.0		0	1	male	0	0	13905
20	[null]	208	B10	48.0	Omaha, NE	C	50.4958		0	1	male	0	0	PC 17591

Rows: 1-20 | Columns: 14

Let’s ingest only two columns from the json.

read_json(
    path = "titanic_subset.json",
    table_name = "titanic_sub_usecols",
    schema = "public",
    usecols  = [
        "fields.fare",
        "fields.sex",
    ],
)

	Abc boat Varchar(20)	123 body Integer	Abc cabin Varchar(22)	123 age Numeric(10)	Abc home.dest Varchar(62)	Abc embarked Varchar(20)	123 fare Numeric(13)	Abc Varchar(94)	123 parch Integer	123 pclass Integer	Abc sex Varchar(20)	123 sibsp Integer	123 survived Integer	Abc ticket Varchar(20)
1	A	[null]	C6	36.0	Winnipeg, MN	C	75.2417		0	1	male	0	0	13050
2	[null]	[null]	A21	41.0	Pomeroy, WA	S	30.5		0	1	male	0	0	113054
3	[null]	[null]	A36	39.0	Belfast, NI	S	0.0		0	1	male	0	0	112050
4	[null]	[null]	B38	45.0	Washington, DC	S	26.55		0	1	male	0	0	113050
5	[null]	[null]	B51 B53 B55	33.0	New York, NY	S	5.0		0	1	male	0	0	695
6	[null]	[null]	B58 B60	24.0	Montreal, PQ	C	247.5208		1	1	male	0	0	PC 17558
7	[null]	[null]	C22 C26	2.0	Montreal, PQ / Chesterville, ON	S	151.55		2	1	female	1	0	113781
8	[null]	[null]	C22 C26	25.0	Montreal, PQ / Chesterville, ON	S	151.55		2	1	female	1	0	113781
9	[null]	[null]	D22	42.0	London / Winnipeg, MB	S	26.55		0	1	male	0	0	110489
10	[null]	[null]	T	45.0	Trenton, NJ	S	35.5		0	1	male	0	0	113784
11	[null]	[null]	[null]	[null]	New York, NY	S	25.925		0	1	male	0	0	PC 17318
12	[null]	[null]	[null]	[null]	Philadelphia, PA	C	39.6		0	1	male	0	0	112379
13	[null]	[null]	[null]	[null]	[null]	S	31.0		0	1	male	0	0	113798
14	[null]	[null]	[null]	17.0	Montevideo, Uruguay	S	47.1		0	1	male	0	0	113059
15	[null]	[null]	[null]	28.0	Montevideo, Uruguay	S	47.1		0	1	male	0	0	113059
16	[null]	22	[null]	71.0	Montevideo, Uruguay	C	49.5042		0	1	male	0	0	PC 17609
17	[null]	124	C62 C64	47.0	New York, NY	C	227.525		0	1	male	1	0	PC 17757
18	[null]	135	C22 C26	30.0	Montreal, PQ / Chesterville, ON	S	151.55		2	1	male	1	0	113781
19	[null]	148	[null]	25.0	San Francisco, CA	C	26.0		0	1	male	0	0	13905
20	[null]	208	B10	48.0	Omaha, NE	C	50.4958		0	1	male	0	0	PC 17591

Rows: 1-20 | Columns: 14

Note

You can ingest multiple JSON files into the Vertica database by using the following syntax.

read_json(
    path = "*.json",
    table_name = "titanic_multi_files",
    schema = "public",
)

See also

read_file() : Ingests an input file into the Vertica DB.

read_avro() : Ingests a AVRO file into the Vertica DB.

read_csv() : Ingests a CSV file into the Vertica DB.

read_pandas() : Ingests the pandas.DataFrame into the Vertica DB.