pcsv

In [ ]:
pcsv(path: str,
     sep: str = ",",
     header: bool = True,
     header_names: list = [],
     na_rep: str = "",
     quotechar: str = '"',
     escape: str = "\027",
     record_terminator: str = "\n",
     trim: bool = True,
     omit_empty_keys: bool = False,
     reject_on_duplicate: bool = False,
     reject_on_empty_key: bool = False,
     reject_on_materialized_type_error: bool = False,
     ingest_local: bool = True,
     flex_name: str = "",
     genSQL: bool = False,)

Parses a CSV file using flex tables. It will identify the columns and their respective types.

Parameters

Name Type Optional Description
path
str
❌
 Absolute path where the CSV file is located.
sep
str
✓
 Column separator.
header
bool
✓
 If set to False, the parameter 'header_names' will be to use to name the different columns.
header_names
list
✓
 List of the columns names.
na_rep
str
✓
 Missing values representation.
quotechar
str
✓
 Char which is enclosing the str values.
escape
str
✓
 Separator between each record.
record_terminator
str
✓
 A single-character value used to specify the end of a record.
trim
bool
✓
 Boolean, specifies whether to trim white space from header names and key values.
omit_empty_keys
bool
✓
 Boolean, specifies how the parser handles header keys without values. If true, keys with an empty value in the header row are not loaded.
reject_on_duplicate
bool
✓
 Boolean, specifies whether to ignore duplicate records (False), or to reject duplicates (True). In either case, the load continues.
reject_on_empty_key
bool
✓
 Boolean, specifies whether to reject any row containing a key without a value.
reject_on_materialized_type_error
bool
✓
 Boolean, specifies whether to reject any materialized column value that the parser cannot coerce into a compatible data type.
ingest_local
bool
✓
 If set to True, the file will be ingested from the local machine.
flex_name
str
✓
 Flex table name.
genSQL
bool
✓
 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.

Returns

dict : dictionary containing for each column its type.

Example

In [32]:
from verticapy.utilities import *
# Parses the CSV file 
pcsv("titanic.csv", 
     sep = ",",
     na_rep = "")
Out[32]:
{'age': 'Numeric(6,3)',
 'boat': 'Varchar(100)',
 'body': 'Integer',
 'cabin': 'Varchar(30)',
 'embarked': 'Varchar(20)',
 'fare': 'Numeric(10,5)',
 'home.dest': 'Varchar(100)',
 'name': 'Varchar(164)',
 'parch': 'Integer',
 'pclass': 'Integer',
 'sex': 'Varchar(20)',
 'sibsp': 'Integer',
 'survived': 'Integer',
 'ticket': 'Varchar(36)'}
In [35]:
# You can also rename the columns or name them if it has 
# no header by using the parameter 'header_names'
pcsv("titanic.csv", 
     sep = ",",
     na_rep = "",
     header = True,
     header_names = ["new_name1", "new_name2"])
Out[35]:
{'new_name1': 'Integer',
 'new_name2': 'Integer',
 'ucol10': 'Varchar(20)',
 'ucol11': 'Varchar(100)',
 'ucol12': 'Integer',
 'ucol13': 'Varchar(100)',
 'ucol2': 'Varchar(164)',
 'ucol3': 'Varchar(20)',
 'ucol4': 'Numeric(6,3)',
 'ucol5': 'Integer',
 'ucol6': 'Integer',
 'ucol7': 'Varchar(36)',
 'ucol8': 'Numeric(10,5)',
 'ucol9': 'Varchar(30)'}

See Also

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