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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 Varchar(100)
1	1	0		male	71.0	0	0	PC 17609	49.5042	[null]	C	[null]	22
2	1	0		male	45.0	0	0	113784	35.5	T	S	[null]	[null]
3	1	0		male	[null]	0	0	113798	31.0	[null]	S	[null]	[null]
4	1	0		male	17.0	0	0	113059	47.1	[null]	S	[null]	[null]
5	1	0		male	27.0	1	0	13508	136.7792	C89	C	[null]	[null]
6	1	0		male	37.0	1	1	PC 17756	83.1583	E52	C	[null]	[null]
7	1	0		male	31.0	1	0	F.C. 12750	52.0	B71	S	[null]	[null]
8	1	0		male	50.0	1	0	PC 17761	106.425	C86	C	[null]	62
9	1	0		female	36.0	0	0	PC 17531	31.6792	A29	C	[null]	[null]
10	1	0		male	37.0	1	0	113803	53.1	C123	S	[null]	[null]
11	1	0		male	24.0	0	0	PC 17593	79.2	B86	C	[null]	[null]
12	1	0		male	45.0	1	0	36973	83.475	C83	S	[null]	[null]
13	1	0		male	40.0	0	0	112059	0.0	B94	S	[null]	110
14	1	0		male	42.0	0	0	113038	42.5	B11	S	[null]	[null]
15	1	0		male	[null]	0	0	17463	51.8625	E46	S	[null]	[null]
16	1	0		male	42.0	1	0	113789	52.0	[null]	S	[null]	38
17	1	0		male	[null]	0	0	PC 17600	30.6958	[null]	C	14	[null]
18	1	0		male	29.0	0	0	113501	30.0	D6	S	[null]	126
19	1	0		male	46.0	0	0	13050	75.2417	C6	C	[null]	292
20	1	0		male	54.0	0	0	17463	51.8625	E46	S	[null]	175
21	1	0		male	47.0	0	0	113796	42.4	[null]	S	[null]	[null]
22	1	0		male	58.0	0	2	35273	113.275	D48	C	[null]	122
23	1	0		male	45.5	0	0	113043	28.5	C124	S	[null]	166
24	1	0		male	29.0	1	0	113776	66.6	C2	S	[null]	[null]
25	1	0		male	47.0	0	0	110465	52.0	C110	S	[null]	207
26	1	0		male	38.0	0	0	19972	0.0	[null]	S	[null]	[null]
27	1	0		male	22.0	0	0	PC 17760	135.6333	[null]	C	[null]	232
28	1	0		male	31.0	0	0	PC 17590	50.4958	A24	S	[null]	[null]
29	1	0		male	50.0	1	0	13507	55.9	E44	S	[null]	[null]
30	1	0		male	56.0	0	0	17764	30.6958	A7	C	[null]	[null]
31	1	0		male	57.0	1	0	PC 17569	146.5208	B78	C	[null]	[null]
32	1	0		female	63.0	1	0	PC 17483	221.7792	C55 C57	S	[null]	[null]
33	1	0		male	61.0	0	0	36963	32.3208	D50	S	[null]	46
34	1	0		male	21.0	0	1	35281	77.2875	D26	S	[null]	169
35	1	0		male	51.0	0	1	PC 17597	61.3792	[null]	C	[null]	[null]
36	1	1		female	63.0	1	0	13502	77.9583	D7	S	10	[null]
37	1	1		female	32.0	0	0	11813	76.2917	D15	C	8	[null]
38	1	1		female	58.0	0	0	113783	26.55	C103	S	8	[null]
39	1	1		female	44.0	0	0	PC 17610	27.7208	B4	C	6	[null]
40	1	1		female	41.0	0	0	16966	134.5	E40	C	3	[null]
41	1	1		female	53.0	0	0	PC 17606	27.4458	[null]	C	6	[null]
42	1	1		male	36.0	0	1	PC 17755	512.3292	B51 B53 B55	C	3	[null]
43	1	1		female	58.0	0	1	PC 17755	512.3292	B51 B53 B55	C	3	[null]
44	1	1		male	11.0	1	2	113760	120.0	B96 B98	S	4	[null]
45	1	1		female	76.0	1	0	19877	78.85	C46	S	6	[null]
46	1	1		female	[null]	0	1	113505	55.0	E33	S	6	[null]
47	1	1		female	39.0	1	1	PC 17756	83.1583	E49	C	14	[null]
48	1	1		female	27.0	1	2	F.C. 12750	52.0	B71	S	3	[null]
49	1	1		female	[null]	0	0	17421	110.8833	[null]	C	4	[null]
50	1	1		female	35.0	0	0	113503	211.5	C130	C	4	[null]
51	1	1		female	22.0	0	1	112378	59.4	[null]	C	7	[null]
52	1	1		female	25.0	1	0	11765	55.4417	E50	C	5	[null]
53	1	1		male	48.0	1	0	PC 17572	76.7292	D33	C	3	[null]
54	1	1		female	35.0	1	0	36973	83.475	C83	S	D	[null]
55	1	1		male	27.0	0	0	PC 17572	76.7292	D49	C	3	[null]
56	1	1		female	24.0	0	0	11767	83.1583	C54	C	7	[null]
57	1	1		female	52.0	1	1	12749	93.5	B69	S	3	[null]
58	1	1		female	44.0	0	1	111361	57.9792	B18	C	4	[null]
59	1	1		female	15.0	0	1	24160	211.3375	B5	S	2	[null]
60	1	1		male	30.0	1	0	13236	57.75	C78	C	11	[null]
61	1	1		female	31.0	1	0	35273	113.275	D36	C	6	[null]
62	1	1		female	39.0	0	0	PC 17758	108.9	C105	C	8	[null]
63	1	1		female	22.0	0	1	113509	61.9792	B36	C	5	[null]
64	1	1		male	52.0	0	0	113786	30.5	C104	S	6	[null]
65	1	1		female	43.0	0	1	24160	211.3375	B3	S	2	[null]
66	1	1		female	33.0	0	0	110152	86.5	B77	S	8	[null]
67	1	1		male	45.0	1	1	16966	134.5	E34	C	3	[null]
68	1	1		female	40.0	1	1	16966	134.5	E34	C	3	[null]
69	1	1		male	48.0	1	0	19996	52.0	C126	S	5 7	[null]
70	1	1		female	[null]	0	0	PC 17585	79.2	[null]	C	D	[null]
71	1	1		female	35.0	0	0	PC 17755	512.3292	[null]	C	3	[null]
72	1	1		female	60.0	1	0	110813	75.25	D37	C	5	[null]
73	1	1		male	21.0	0	1	PC 17597	61.3792	[null]	C	A	[null]
74	2	0		male	23.0	0	0	C.A. 31030	10.5	[null]	S	[null]	[null]
75	2	0		male	28.0	0	0	244358	26.0	[null]	S	[null]	[null]
76	2	0		male	60.0	1	1	29750	39.0	[null]	S	[null]	[null]
77	2	0		female	44.0	1	0	244252	26.0	[null]	S	[null]	[null]
78	2	0		male	29.0	1	0	2003	26.0	[null]	S	[null]	[null]
79	2	0		male	18.0	0	0	S.O.C. 14879	73.5	[null]	S	[null]	[null]
80	2	0		male	18.0	0	0	S.O.C. 14879	73.5	[null]	S	[null]	[null]
81	2	0		male	54.0	0	0	28403	26.0	[null]	S	[null]	[null]
82	2	0		male	18.0	0	0	236171	13.0	[null]	S	[null]	[null]
83	2	0		male	36.0	0	0	229236	13.0	[null]	S	[null]	236
84	2	0		male	34.0	1	0	28664	21.0	[null]	S	[null]	[null]
85	2	0		male	21.0	1	0	28133	11.5	[null]	S	[null]	[null]
86	2	0		male	21.0	1	0	28134	11.5	[null]	S	[null]	[null]
87	2	0		male	24.0	0	0	233866	13.0	[null]	S	[null]	155
88	2	0		male	34.0	0	0	12233	13.0	[null]	S	[null]	[null]
89	2	0		male	30.0	0	0	250653	13.0	[null]	S	[null]	75
90	2	0		male	44.0	0	0	248746	13.0	[null]	S	[null]	35
91	2	0		male	49.0	1	2	220845	65.0	[null]	S	[null]	[null]
92	2	0		male	21.0	2	0	S.O.C. 14879	73.5	[null]	S	[null]	[null]
93	2	0		male	21.0	0	0	S.O.C. 14879	73.5	[null]	S	[null]	[null]
94	2	0		female	60.0	1	0	24065	26.0	[null]	S	[null]	[null]
95	2	0		male	24.0	2	0	C.A. 31029	31.5	[null]	S	[null]	[null]
96	2	0		male	22.0	2	0	C.A. 31029	31.5	[null]	S	[null]	[null]
97	2	0		male	35.0	0	0	233734	12.35	[null]	Q	[null]	[null]
98	2	0		male	31.0	0	0	C.A. 18723	10.5	[null]	S	[null]	165
99	2	0		male	36.0	0	0	SC/Paris 2163	12.875	D	C	[null]	[null]
100	2	0		male	[null]	0	0	SC/A.3 2861	15.5792	[null]	C	[null]	[null]

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",
)

	123 boat Integer	123 body Integer	Abc cabin Varchar(20)	123 age Numeric(10)	Abc home.dest Varchar(64)	Abc embarked Varchar(20)	Abc sex Varchar(20)	123 parch Integer	123 pclass Integer	123 fare Numeric(13)	Abc name Varchar(64)	123 sibsp Integer	123 survived Integer	Abc ticket Varchar(20)
1	[null]	[null]	E46	[null]	Brighton, MA	S	male	0	1	51.8625	Hilliard, Mr. Herbert Henry	0	0	17463
2	[null]	[null]	[null]	17.0	Montevideo, Uruguay	S	male	0	1	47.1	Carrau, Mr. Jose Pedro	0	0	113059
3	[null]	62	C86	50.0	Deephaven, MN / Cedar Rapids, IA	C	male	0	1	106.425	Douglas, Mr. Walter Donald	1	0	PC 17761
4	[null]	[null]	B11	42.0	London / Middlesex	S	male	0	1	42.5	Head, Mr. Christopher	0	0	113038
5	[null]	[null]	[null]	[null]	[null]	S	male	0	1	31.0	Cairns, Mr. Alexander	0	0	113798
6	[null]	22	[null]	71.0	Montevideo, Uruguay	C	male	0	1	49.5042	Artagaveytia, Mr. Ramon	0	0	PC 17609
7	[null]	292	C6	46.0	Vancouver, BC	C	male	0	1	75.2417	McCaffry, Mr. Thomas Francis	0	0	13050
8	[null]	[null]	A29	36.0	New York, NY	C	female	0	1	31.6792	Evans, Miss. Edith Corse	0	0	PC 17531
9	[null]	[null]	C83	45.0	New York, NY	S	male	0	1	83.475	Harris, Mr. Henry Birkhardt	1	0	36973
10	[null]	[null]	E52	37.0	Lakewood, NJ	C	male	1	1	83.1583	Compton, Mr. Alexander Taylor Jr	1	0	PC 17756
11	[null]	[null]	T	45.0	Trenton, NJ	S	male	0	1	35.5	Blackwell, Mr. Stephen Weart	0	0	113784
12	[null]	110	B94	40.0	[null]	S	male	0	1	0.0	Harrison, Mr. William	0	0	112059
13	[null]	[null]	B71	31.0	Montreal, PQ	S	male	0	1	52.0	Davidson, Mr. Thornton	1	0	F.C. 12750
14	[null]	[null]	B86	24.0	[null]	C	male	0	1	79.2	Giglio, Mr. Victor	0	0	PC 17593
15	[null]	[null]	C123	37.0	Scituate, MA	S	male	0	1	53.1	Futrelle, Mr. Jacques Heath	1	0	113803
16	[null]	[null]	C89	27.0	Los Angeles, CA	C	male	0	1	136.7792	Clark, Mr. Walter Miller	1	0	13508
17	[null]	38	[null]	42.0	New York, NY	S	male	0	1	52.0	Holverson, Mr. Alexander Oskar	1	0	113789
18	[null]	126	D6	29.0	Springfield, MA	S	male	0	1	30.0	Long, Mr. Milton Clyde	0	0	113501
19	[null]	175	E46	54.0	Dorchester, MA	S	male	0	1	51.8625	McCarthy, Mr. Timothy J	0	0	17463
20	14	[null]	[null]	[null]	New York, NY	C	male	0	1	30.6958	Hoyt, Mr. William Fisher	0	0	PC 17600

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",
    },
)

	123 boat Integer	123 body Integer	Abc cabin Varchar(20)	123 age Numeric(10)	Abc home.dest Varchar(64)	Abc embarked Varchar(20)	Abc sex Varchar(20)	123 parch Integer	123 pclass Integer	123 fare Numeric(13)	Abc name Varchar(64)	123 sibsp Integer	123 survived Integer	Abc ticket Varchar(20)
1	[null]	[null]	E46	[null]	Brighton, MA	S	male	0	1	51.8625	Hilliard, Mr. Herbert Henry	0	0	17463
2	[null]	[null]	[null]	17.0	Montevideo, Uruguay	S	male	0	1	47.1	Carrau, Mr. Jose Pedro	0	0	113059
3	[null]	62	C86	50.0	Deephaven, MN / Cedar Rapids, IA	C	male	0	1	106.425	Douglas, Mr. Walter Donald	1	0	PC 17761
4	[null]	[null]	B11	42.0	London / Middlesex	S	male	0	1	42.5	Head, Mr. Christopher	0	0	113038
5	[null]	[null]	[null]	[null]	[null]	S	male	0	1	31.0	Cairns, Mr. Alexander	0	0	113798
6	[null]	22	[null]	71.0	Montevideo, Uruguay	C	male	0	1	49.5042	Artagaveytia, Mr. Ramon	0	0	PC 17609
7	[null]	292	C6	46.0	Vancouver, BC	C	male	0	1	75.2417	McCaffry, Mr. Thomas Francis	0	0	13050
8	[null]	[null]	A29	36.0	New York, NY	C	female	0	1	31.6792	Evans, Miss. Edith Corse	0	0	PC 17531
9	[null]	[null]	C83	45.0	New York, NY	S	male	0	1	83.475	Harris, Mr. Henry Birkhardt	1	0	36973
10	[null]	[null]	E52	37.0	Lakewood, NJ	C	male	1	1	83.1583	Compton, Mr. Alexander Taylor Jr	1	0	PC 17756
11	[null]	[null]	T	45.0	Trenton, NJ	S	male	0	1	35.5	Blackwell, Mr. Stephen Weart	0	0	113784
12	[null]	110	B94	40.0	[null]	S	male	0	1	0.0	Harrison, Mr. William	0	0	112059
13	[null]	[null]	B71	31.0	Montreal, PQ	S	male	0	1	52.0	Davidson, Mr. Thornton	1	0	F.C. 12750
14	[null]	[null]	B86	24.0	[null]	C	male	0	1	79.2	Giglio, Mr. Victor	0	0	PC 17593
15	[null]	[null]	C123	37.0	Scituate, MA	S	male	0	1	53.1	Futrelle, Mr. Jacques Heath	1	0	113803
16	[null]	[null]	C89	27.0	Los Angeles, CA	C	male	0	1	136.7792	Clark, Mr. Walter Miller	1	0	13508
17	[null]	38	[null]	42.0	New York, NY	S	male	0	1	52.0	Holverson, Mr. Alexander Oskar	1	0	113789
18	[null]	126	D6	29.0	Springfield, MA	S	male	0	1	30.0	Long, Mr. Milton Clyde	0	0	113501
19	[null]	175	E46	54.0	Dorchester, MA	S	male	0	1	51.8625	McCarthy, Mr. Timothy J	0	0	17463
20	14	[null]	[null]	[null]	New York, NY	C	male	0	1	30.6958	Hoyt, Mr. William Fisher	0	0	PC 17600

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",
    ],
)

	123 boat Integer	123 body Integer	Abc cabin Varchar(20)	123 age Numeric(10)	Abc home.dest Varchar(64)	Abc embarked Varchar(20)	Abc sex Varchar(20)	123 parch Integer	123 pclass Integer	123 fare Numeric(13)	Abc name Varchar(64)	123 sibsp Integer	123 survived Integer	Abc ticket Varchar(20)
1	[null]	[null]	E46	[null]	Brighton, MA	S	male	0	1	51.8625	Hilliard, Mr. Herbert Henry	0	0	17463
2	[null]	[null]	[null]	17.0	Montevideo, Uruguay	S	male	0	1	47.1	Carrau, Mr. Jose Pedro	0	0	113059
3	[null]	62	C86	50.0	Deephaven, MN / Cedar Rapids, IA	C	male	0	1	106.425	Douglas, Mr. Walter Donald	1	0	PC 17761
4	[null]	[null]	B11	42.0	London / Middlesex	S	male	0	1	42.5	Head, Mr. Christopher	0	0	113038
5	[null]	[null]	[null]	[null]	[null]	S	male	0	1	31.0	Cairns, Mr. Alexander	0	0	113798
6	[null]	22	[null]	71.0	Montevideo, Uruguay	C	male	0	1	49.5042	Artagaveytia, Mr. Ramon	0	0	PC 17609
7	[null]	292	C6	46.0	Vancouver, BC	C	male	0	1	75.2417	McCaffry, Mr. Thomas Francis	0	0	13050
8	[null]	[null]	A29	36.0	New York, NY	C	female	0	1	31.6792	Evans, Miss. Edith Corse	0	0	PC 17531
9	[null]	[null]	C83	45.0	New York, NY	S	male	0	1	83.475	Harris, Mr. Henry Birkhardt	1	0	36973
10	[null]	[null]	E52	37.0	Lakewood, NJ	C	male	1	1	83.1583	Compton, Mr. Alexander Taylor Jr	1	0	PC 17756
11	[null]	[null]	T	45.0	Trenton, NJ	S	male	0	1	35.5	Blackwell, Mr. Stephen Weart	0	0	113784
12	[null]	110	B94	40.0	[null]	S	male	0	1	0.0	Harrison, Mr. William	0	0	112059
13	[null]	[null]	B71	31.0	Montreal, PQ	S	male	0	1	52.0	Davidson, Mr. Thornton	1	0	F.C. 12750
14	[null]	[null]	B86	24.0	[null]	C	male	0	1	79.2	Giglio, Mr. Victor	0	0	PC 17593
15	[null]	[null]	C123	37.0	Scituate, MA	S	male	0	1	53.1	Futrelle, Mr. Jacques Heath	1	0	113803
16	[null]	[null]	C89	27.0	Los Angeles, CA	C	male	0	1	136.7792	Clark, Mr. Walter Miller	1	0	13508
17	[null]	38	[null]	42.0	New York, NY	S	male	0	1	52.0	Holverson, Mr. Alexander Oskar	1	0	113789
18	[null]	126	D6	29.0	Springfield, MA	S	male	0	1	30.0	Long, Mr. Milton Clyde	0	0	113501
19	[null]	175	E46	54.0	Dorchester, MA	S	male	0	1	51.8625	McCarthy, Mr. Timothy J	0	0	17463
20	14	[null]	[null]	[null]	New York, NY	C	male	0	1	30.6958	Hoyt, Mr. William Fisher	0	0	PC 17600

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.