verticapy.pandas_to_vertica#
- verticapy.pandas_to_vertica(df: DataFrame, name: str | None = None, schema: str | None = None, dtype: dict | None = None, parse_nrows: int = 10000, temp_path: str | None = None, insert: bool = False, abort_on_error: bool = False) vDataFrame #
Ingests a
pandas.DataFrame
into the Vertica database by creating a CSV file and then using flex tables 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. If empty, a temporary schema is used. To modify the temporary schema, use the
set_option()
function.- 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, VerticaPy uses the specified input types instead.
- parse_nrows: int, optional
If this parameter is greater than zero, VerticaPy 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 thepandas.DataFrame
must match.- abort_on_error: bool, optional
If set to
True
, any parser error that would reject a row will cause the copy statement to fail and rollback.
Returns#
- vDataFrame
vDataFrame
of the new relation.
Examples#
In this example, we will first create a
pandas.DataFrame
usingvDataFrame.
to_pandas()
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 fromverticapy
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()
123pclassInteger123survivedIntegerAbcVarchar(164)AbcsexVarchar(20)123ageNumeric(8)123sibspInteger123parchIntegerAbcticketVarchar(36)123fareNumeric(12)AbccabinVarchar(30)AbcembarkedVarchar(20)AbcboatVarchar(100)123bodyIntegerAbchome.destVarchar(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: 14Note
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 apandas.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 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 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 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 Now, we will ingest the
pandas.DataFrame
into the Vertica database.from verticapy.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 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
Let’s specify data types using “dtypes” parameter.
read_pandas( df = pandas_df, name = "titanic_pandas_dtypes", 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)", }, )
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
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
.See also
read_avro()
: Ingests a AVRO file into the Vertica DB.read_csv()
: Ingests a CSV file into the Vertica DB.read_file()
: Ingests an input file into the Vertica DB.read_json()
: Ingests a JSON file into the Vertica DB.