vastorbit.read_pandas¶

vastorbit.read_pandas(df: DataFrame, name: str | None = None, schema: str | None = None, catalog: str | None = None, dtype: dict | None = None, parse_nrows: int = 10000, temp_path: str | None = None, insert: bool = False, temporary_table: bool = False) → VastFrame¶

Ingests a pandas.DataFrame into the VAST DataBase via Trino by creating a temporary CSV file and using the CSV parser to load the data.

Parameters:

df (pandas.DataFrame) – The pandas.DataFrame to ingest.
name (str, optional) – Name of the new relation or the relation in which to insert the data. If unspecified, a temporary local table is created. This temporary table is dropped at the end of the local session.
schema (str, optional) – Schema of the new relation. Supports formats: - ‘schema_name’ (uses default catalog from config) - ‘catalog.schema_name’ (catalog and schema) If empty, a temporary schema is used. To modify the temporary schema, use the set_option() function.
catalog (str, optional) – Target catalog (overrides catalog from schema parameter) Examples: ‘hive’, ‘postgresql’, ‘vast’, ‘memory’
dtype (dict, optional) – Dictionary of input types. Providing a dictionary can increase ingestion speed and precision. If specified, rather than parsing the intermediate CSV and guessing the input types, vastorbit uses the specified input types instead.
parse_nrows (int, optional) – If this parameter is greater than zero, vastorbit creates and ingests a temporary file containing parse_nrows number of rows to determine the input data types before ingesting the intermediate CSV file containing the rest of the data. This method of data type identification is less accurate, but is much faster for large datasets.
temp_path (str, optional) – The path to which to write the intermediate CSV file. This is useful in cases where the user does not have write permissions on the current directory.
insert (bool, optional) – If set to True, the data are ingested into the input relation. The column names of your table and the pandas.DataFrame must match.
temporary_table (bool, optional) – If True, create temporary table

Returns:

VastFrame of the new relation.

Return type:

VastFrame

Examples

In this example, we will first create a pandas.DataFrame using VastFrame.to_pandas() and ingest it into VAST DataBase.

We import vastorbit:

import vastorbit as vo

Hint

By assigning an alias to vastorbit, we mitigate the risk of code collisions with other libraries. This precaution is necessary because vastorbit 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 vastorbit are used as intended without interfering with functions from other libraries.

We will use the Titanic dataset.

import vastorbit.datasets as vod

data = vod.load_titanic()

	123 pclass Integer	123 survived Integer	Abc name Varchar(164)	Abc sex Varchar(20)	123 age Double	123 sibsp Integer	123 parch Integer	Abc ticket Varchar(36)	123 fare Double	Abc cabin Varchar(30)	Abc embarked Varchar(20)	Abc boat Varchar(100)	123 body Integer	Abc home.dest Varchar(100)
1	3	1	McCormack, Mr. Thomas Joseph	male	[null]	0	0	367228	7.75	[null]	Q	[null]	[null]	[null]
2	3	1	McCoy, Miss. Agnes	female	[null]	2	0	367226	23.25	[null]	Q	16	[null]	[null]
3	3	1	McCoy, Miss. Alicia	female	[null]	2	0	367226	23.25	[null]	Q	16	[null]	[null]
4	3	1	McCoy, Mr. Bernard	male	[null]	2	0	367226	23.25	[null]	Q	16	[null]	[null]
5	3	1	McDermott, Miss. Brigdet Delia	female	[null]	0	0	330932	7.7875	[null]	Q	13	[null]	[null]
6	3	0	McEvoy, Mr. Michael	male	[null]	0	0	36568	15.5	[null]	Q	[null]	[null]	[null]
7	3	1	McGovern, Miss. Mary	female	[null]	0	0	330931	7.8792	[null]	Q	13	[null]	[null]
8	3	1	McGowan, Miss. Anna "Annie"	female	15.0	0	0	330923	8.0292	[null]	Q	[null]	[null]	[null]
9	3	0	McGowan, Miss. Katherine	female	35.0	0	0	9232	7.75	[null]	Q	[null]	[null]	[null]
10	3	0	McMahon, Mr. Martin	male	[null]	0	0	370372	7.75	[null]	Q	[null]	[null]	[null]
11	3	0	McNamee, Mr. Neal	male	24.0	1	0	376566	16.1	[null]	S	[null]	[null]	[null]
12	3	0	McNamee, Mrs. Neal (Eileen O'Leary)	female	19.0	1	0	376566	16.1	[null]	S	[null]	53	[null]
13	3	0	McNeill, Miss. Bridget	female	[null]	0	0	370368	7.75	[null]	Q	[null]	[null]	[null]
14	3	0	Meanwell, Miss. (Marion Ogden)	female	[null]	0	0	SOTON/O.Q. 392087	8.05	[null]	S	[null]	[null]	[null]
15	3	0	Meek, Mrs. Thomas (Annie Louise Rowley)	female	[null]	0	0	343095	8.05	[null]	S	[null]	[null]	[null]
16	3	0	Meo, Mr. Alfonzo	male	55.5	0	0	A.5. 11206	8.05	[null]	S	[null]	201	[null]
17	3	0	Mernagh, Mr. Robert	male	[null]	0	0	368703	7.75	[null]	Q	[null]	[null]	[null]
18	3	1	Midtsjo, Mr. Karl Albert	male	21.0	0	0	345501	7.775	[null]	S	15	[null]	[null]
19	3	0	Miles, Mr. Frank	male	[null]	0	0	359306	8.05	[null]	S	[null]	[null]	[null]
20	3	0	Mineff, Mr. Ivan	male	24.0	0	0	349233	7.8958	[null]	S	[null]	[null]	[null]
21	3	0	Minkoff, Mr. Lazar	male	21.0	0	0	349211	7.8958	[null]	S	[null]	[null]	[null]
22	3	0	Mionoff, Mr. Stoytcho	male	28.0	0	0	349207	7.8958	[null]	S	[null]	[null]	[null]
23	3	0	Mitkoff, Mr. Mito	male	[null]	0	0	349221	7.8958	[null]	S	[null]	[null]	[null]
24	3	1	Mockler, Miss. Helen Mary "Ellie"	female	[null]	0	0	330980	7.8792	[null]	Q	16	[null]	[null]
25	3	0	Moen, Mr. Sigurd Hansen	male	25.0	0	0	348123	7.65	F G73	S	[null]	309	[null]
26	3	1	Moor, Master. Meier	male	6.0	0	1	392096	12.475	E121	S	14	[null]	[null]
27	3	1	Moor, Mrs. (Beila)	female	27.0	0	1	392096	12.475	E121	S	14	[null]	[null]
28	3	0	Moore, Mr. Leonard Charles	male	[null]	0	0	A4. 54510	8.05	[null]	S	[null]	[null]	[null]
29	3	1	Moran, Miss. Bertha	female	[null]	1	0	371110	24.15	[null]	Q	16	[null]	[null]
30	3	0	Moran, Mr. Daniel J	male	[null]	1	0	371110	24.15	[null]	Q	[null]	[null]	[null]
31	3	0	Moran, Mr. James	male	[null]	0	0	330877	8.4583	[null]	Q	[null]	[null]	[null]
32	3	0	Morley, Mr. William	male	34.0	0	0	364506	8.05	[null]	S	[null]	[null]	[null]
33	3	0	Morrow, Mr. Thomas Rowan	male	[null]	0	0	372622	7.75	[null]	Q	[null]	[null]	[null]
34	3	1	Moss, Mr. Albert Johan	male	[null]	0	0	312991	7.775	[null]	S	B	[null]	[null]
35	3	1	Moubarek, Master. Gerios	male	[null]	1	1	2661	15.2458	[null]	C	C	[null]	[null]
36	3	1	Moubarek, Master. Halim Gonios ("William George")	male	[null]	1	1	2661	15.2458	[null]	C	C	[null]	[null]
37	3	1	Moubarek, Mrs. George (Omine "Amenia" Alexander)	female	[null]	0	2	2661	15.2458	[null]	C	C	[null]	[null]
38	3	1	Moussa, Mrs. (Mantoura Boulos)	female	[null]	0	0	2626	7.2292	[null]	C	[null]	[null]	[null]
39	3	0	Moutal, Mr. Rahamin Haim	male	[null]	0	0	374746	8.05	[null]	S	[null]	[null]	[null]
40	3	1	Mullens, Miss. Katherine "Katie"	female	[null]	0	0	35852	7.7333	[null]	Q	16	[null]	[null]
41	3	1	Mulvihill, Miss. Bertha E	female	24.0	0	0	382653	7.75	[null]	Q	15	[null]	[null]
42	3	0	Murdlin, Mr. Joseph	male	[null]	0	0	A./5. 3235	8.05	[null]	S	[null]	[null]	[null]
43	3	1	Murphy, Miss. Katherine "Kate"	female	[null]	1	0	367230	15.5	[null]	Q	16	[null]	[null]
44	3	1	Murphy, Miss. Margaret Jane	female	[null]	1	0	367230	15.5	[null]	Q	16	[null]	[null]
45	3	1	Murphy, Miss. Nora	female	[null]	0	0	36568	15.5	[null]	Q	16	[null]	[null]
46	3	0	Myhrman, Mr. Pehr Fabian Oliver Malkolm	male	18.0	0	0	347078	7.75	[null]	S	[null]	[null]	[null]
47	3	0	Naidenoff, Mr. Penko	male	22.0	0	0	349206	7.8958	[null]	S	[null]	[null]	[null]
48	3	1	Najib, Miss. Adele Kiamie "Jane"	female	15.0	0	0	2667	7.225	[null]	C	C	[null]	[null]
49	3	1	Nakid, Miss. Maria ("Mary")	female	1.0	0	2	2653	15.7417	[null]	C	C	[null]	[null]
50	3	1	Nakid, Mr. Sahid	male	20.0	1	1	2653	15.7417	[null]	C	C	[null]	[null]
51	3	1	Nakid, Mrs. Said (Waika "Mary" Mowad)	female	19.0	1	1	2653	15.7417	[null]	C	C	[null]	[null]
52	3	0	Nancarrow, Mr. William Henry	male	33.0	0	0	A./5. 3338	8.05	[null]	S	[null]	[null]	[null]
53	3	0	Nankoff, Mr. Minko	male	[null]	0	0	349218	7.8958	[null]	S	[null]	[null]	[null]
54	3	0	Nasr, Mr. Mustafa	male	[null]	0	0	2652	7.2292	[null]	C	[null]	[null]	[null]
55	3	0	Naughton, Miss. Hannah	female	[null]	0	0	365237	7.75	[null]	Q	[null]	[null]	[null]
56	3	0	Nenkoff, Mr. Christo	male	[null]	0	0	349234	7.8958	[null]	S	[null]	[null]	[null]
57	3	1	Nicola-Yarred, Master. Elias	male	12.0	1	0	2651	11.2417	[null]	C	C	[null]	[null]
58	3	1	Nicola-Yarred, Miss. Jamila	female	14.0	1	0	2651	11.2417	[null]	C	C	[null]	[null]
59	3	0	Nieminen, Miss. Manta Josefina	female	29.0	0	0	3101297	7.925	[null]	S	[null]	[null]	[null]
60	3	0	Niklasson, Mr. Samuel	male	28.0	0	0	363611	8.05	[null]	S	[null]	[null]	[null]
61	3	1	Nilsson, Miss. Berta Olivia	female	18.0	0	0	347066	7.775	[null]	S	D	[null]	[null]
62	3	1	Nilsson, Miss. Helmina Josefina	female	26.0	0	0	347470	7.8542	[null]	S	13	[null]	[null]
63	3	0	Nilsson, Mr. August Ferdinand	male	21.0	0	0	350410	7.8542	[null]	S	[null]	[null]	[null]
64	3	0	Nirva, Mr. Iisakki Antino Aijo	male	41.0	0	0	SOTON/O2 3101272	7.125	[null]	S	[null]	[null]	Finland Sudbury, ON
65	3	1	Niskanen, Mr. Juha	male	39.0	0	0	STON/O 2. 3101289	7.925	[null]	S	9	[null]	[null]
66	3	0	Nosworthy, Mr. Richard Cater	male	21.0	0	0	A/4. 39886	7.8	[null]	S	[null]	[null]	[null]
67	3	0	Novel, Mr. Mansouer	male	28.5	0	0	2697	7.2292	[null]	C	[null]	181	[null]
68	3	1	Nysten, Miss. Anna Sofia	female	22.0	0	0	347081	7.75	[null]	S	13	[null]	[null]
69	3	0	Nysveen, Mr. Johan Hansen	male	61.0	0	0	345364	6.2375	[null]	S	[null]	[null]	[null]
70	3	0	O'Brien, Mr. Thomas	male	[null]	1	0	370365	15.5	[null]	Q	[null]	[null]	[null]
71	3	0	O'Brien, Mr. Timothy	male	[null]	0	0	330979	7.8292	[null]	Q	[null]	[null]	[null]
72	3	1	O'Brien, Mrs. Thomas (Johanna "Hannah" Godfrey)	female	[null]	1	0	370365	15.5	[null]	Q	[null]	[null]	[null]
73	3	0	O'Connell, Mr. Patrick D	male	[null]	0	0	334912	7.7333	[null]	Q	[null]	[null]	[null]
74	3	0	O'Connor, Mr. Maurice	male	[null]	0	0	371060	7.75	[null]	Q	[null]	[null]	[null]
75	3	0	O'Connor, Mr. Patrick	male	[null]	0	0	366713	7.75	[null]	Q	[null]	[null]	[null]
76	3	0	Odahl, Mr. Nils Martin	male	23.0	0	0	7267	9.225	[null]	S	[null]	[null]	[null]
77	3	0	O'Donoghue, Ms. Bridget	female	[null]	0	0	364856	7.75	[null]	Q	[null]	[null]	[null]
78	3	1	O'Driscoll, Miss. Bridget	female	[null]	0	0	14311	7.75	[null]	Q	D	[null]	[null]
79	3	1	O'Dwyer, Miss. Ellen "Nellie"	female	[null]	0	0	330959	7.8792	[null]	Q	[null]	[null]	[null]
80	3	1	Ohman, Miss. Velin	female	22.0	0	0	347085	7.775	[null]	S	C	[null]	[null]
81	3	1	O'Keefe, Mr. Patrick	male	[null]	0	0	368402	7.75	[null]	Q	B	[null]	[null]
82	3	1	O'Leary, Miss. Hanora "Norah"	female	[null]	0	0	330919	7.8292	[null]	Q	13	[null]	[null]
83	3	1	Olsen, Master. Artur Karl	male	9.0	0	1	C 17368	3.1708	[null]	S	13	[null]	[null]
84	3	0	Olsen, Mr. Henry Margido	male	28.0	0	0	C 4001	22.525	[null]	S	[null]	173	[null]
85	3	0	Olsen, Mr. Karl Siegwart Andreas	male	42.0	0	1	4579	8.4042	[null]	S	[null]	[null]	[null]
86	3	0	Olsen, Mr. Ole Martin	male	[null]	0	0	Fa 265302	7.3125	[null]	S	[null]	[null]	[null]
87	3	0	Olsson, Miss. Elina	female	31.0	0	0	350407	7.8542	[null]	S	[null]	[null]	[null]
88	3	0	Olsson, Mr. Nils Johan Goransson	male	28.0	0	0	347464	7.8542	[null]	S	[null]	[null]	[null]
89	3	1	Olsson, Mr. Oscar Wilhelm	male	32.0	0	0	347079	7.775	[null]	S	A	[null]	[null]
90	3	0	Olsvigen, Mr. Thor Anderson	male	20.0	0	0	6563	9.225	[null]	S	[null]	89	Oslo, Norway Cameron, WI
91	3	0	Oreskovic, Miss. Jelka	female	23.0	0	0	315085	8.6625	[null]	S	[null]	[null]	[null]
92	3	0	Oreskovic, Miss. Marija	female	20.0	0	0	315096	8.6625	[null]	S	[null]	[null]	[null]
93	3	0	Oreskovic, Mr. Luka	male	20.0	0	0	315094	8.6625	[null]	S	[null]	[null]	[null]
94	3	0	Osen, Mr. Olaf Elon	male	16.0	0	0	7534	9.2167	[null]	S	[null]	[null]	[null]
95	3	1	Osman, Mrs. Mara	female	31.0	0	0	349244	8.6833	[null]	S	[null]	[null]	[null]
96	3	0	O'Sullivan, Miss. Bridget Mary	female	[null]	0	0	330909	7.6292	[null]	Q	[null]	[null]	[null]
97	3	0	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.075	[null]	S	[null]	4	[null]
98	3	0	Palsson, Master. Paul Folke	male	6.0	3	1	349909	21.075	[null]	S	[null]	[null]	[null]
99	3	0	Palsson, Miss. Stina Viola	female	3.0	3	1	349909	21.075	[null]	S	[null]	[null]	[null]
100	3	0	Palsson, Miss. Torborg Danira	female	8.0	3	1	349909	21.075	[null]	S	[null]	[null]	[null]

Rows: 1-100 | Columns: 14

Note

vastorbit 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 vastorbit environment.

Let’s convert the VastFrame to a pandas.DataFrame.

pandas_df = data.to_pandas()
display(pandas_df)

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest
0	3	1	McCormack, Mr. Thomas Joseph	male	NaN	0	0	367228	7.7500	None	Q	None	NaN	None
1	3	1	McCoy, Miss. Agnes	female	NaN	2	0	367226	23.2500	None	Q	16	NaN	None
2	3	1	McCoy, Miss. Alicia	female	NaN	2	0	367226	23.2500	None	Q	16	NaN	None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1306	3	1	Masselmani, Mrs. Fatima	female	NaN	0	0	2649	7.2250	None	C	C	NaN	None
1307	3	0	Matinoff, Mr. Nicola	male	NaN	0	0	349255	7.8958	None	C	None	NaN	None
1308	3	1	McCarthy, Miss. Catherine "Katie"	female	NaN	0	0	383123	7.7500	None	Q	15 16	NaN	None

Now, we will ingest the pandas.DataFrame into the VAST DataBase.

from vastorbit.core.parsers import read_pandas

read_pandas(
    df = pandas_df,
    name = "titanic_pandas",
    schema = "public",
)

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest
0	3	1	McCormack, Mr. Thomas Joseph	male	NaN	0	0	367228	7.7500	None	Q	None	NaN	None
1	3	1	McCoy, Miss. Agnes	female	NaN	2	0	367226	23.2500	None	Q	16	NaN	None
2	3	1	McCoy, Miss. Alicia	female	NaN	2	0	367226	23.2500	None	Q	16	NaN	None
3	3	1	McCoy, Mr. Bernard	male	NaN	2	0	367226	23.2500	None	Q	16	NaN	None
4	3	1	McDermott, Miss. Brigdet Delia	female	NaN	0	0	330932	7.7875	None	Q	13	NaN	None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1304	3	0	Markoff, Mr. Marin	male	35.0	0	0	349213	7.8958	None	C	None	NaN	None
1305	3	0	Markun, Mr. Johann	male	33.0	0	0	349257	7.8958	None	S	None	NaN	None
1306	3	1	Masselmani, Mrs. Fatima	female	NaN	0	0	2649	7.2250	None	C	C	NaN	None
1307	3	0	Matinoff, Mr. Nicola	male	NaN	0	0	349255	7.8958	None	C	None	NaN	None
1308	3	1	McCarthy, Miss. Catherine "Katie"	female	NaN	0	0	383123	7.7500	None	Q	15 16	NaN	None

1309 rows × 14 columns

Let’s specify data types using “dtype” parameter.

read_pandas(
    df = pandas_df,
    name = "titanic_pandas_dtypes",
    schema = "public",
    dtype = {
        "pclass": "INTEGER",
        "survived": "INTEGER",
        "name": "VARCHAR(164)",
        "sex": "VARCHAR(20)",
        "age": "DOUBLE",
        "sibsp": "INTEGER",
        "parch": "INTEGER",
        "ticket": "VARCHAR(36)",
        "fare": "DOUBLE",
        "cabin": "VARCHAR(30)",
        "embarked": "VARCHAR(20)",
        "boat": "VARCHAR(100)",
        "body": "INTEGER",
        "home.dest": "VARCHAR(100)",
    },
)

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest
0	3	1	McCormack, Mr. Thomas Joseph	male	NaN	0	0	367228	7.7500	None	Q	None	NaN	None
1	3	1	McCoy, Miss. Agnes	female	NaN	2	0	367226	23.2500	None	Q	16	NaN	None
2	3	1	McCoy, Miss. Alicia	female	NaN	2	0	367226	23.2500	None	Q	16	NaN	None
3	3	1	McCoy, Mr. Bernard	male	NaN	2	0	367226	23.2500	None	Q	16	NaN	None
4	3	1	McDermott, Miss. Brigdet Delia	female	NaN	0	0	330932	7.7875	None	Q	13	NaN	None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1304	3	0	Markoff, Mr. Marin	male	35.0	0	0	349213	7.8958	None	C	None	NaN	None
1305	3	0	Markun, Mr. Johann	male	33.0	0	0	349257	7.8958	None	S	None	NaN	None
1306	3	1	Masselmani, Mrs. Fatima	female	NaN	0	0	2649	7.2250	None	C	C	NaN	None
1307	3	0	Matinoff, Mr. Nicola	male	NaN	0	0	349255	7.8958	None	C	None	NaN	None
1308	3	1	McCarthy, Miss. Catherine "Katie"	female	NaN	0	0	383123	7.7500	None	Q	15 16	NaN	None

1309 rows × 14 columns

Important

A limited number of rows, determined by the parse_nrows parameter, is ingested. If your dataset is large and you want to ingest the entire dataset, increase its value.

Note

During the ingestion process, an intermediate CSV file is created. You can retrieve its location by using the temp_path parameter.

Note

If you want to ingest into an existing table, set the insert parameter to True.