Generate SQL statements for a CSV file or execute those statements directly on a database. In the latter case supports both creating tables and inserting data:

usage: csvsql [-h] [-d DELIMITER] [-t] [-q QUOTECHAR] [-u {0,1,2,3}] [-b]
              [-p ESCAPECHAR] [-z FIELD_SIZE_LIMIT] [-e ENCODING] [-S] [-H]
              [-v] [--zero] [-V]
              [-i {firebird,mssql,mysql,oracle,postgresql,sqlite,sybase}]
              [--db CONNECTION_STRING] [--query QUERY] [--insert]
              [--tables TABLE_NAMES] [--no-constraints] [--no-create]
              [--blanks] [--db-schema DB_SCHEMA] [-y SNIFF_LIMIT] [-I]
              [FILE [FILE ...]]

Generate SQL statements for one or more CSV files, or execute those statements
directly on a database, and execute one or more SQL queries.

positional arguments:
  FILE                  The CSV file(s) to operate on. If omitted, will accept
                        input on STDIN.

optional arguments:
  -h, --help            show this help message and exit
  -i {firebird,mssql,mysql,oracle,postgresql,sqlite,sybase}, --dialect {firebird,mssql,mysql,oracle,postgresql,sqlite,sybase}
                        Dialect of SQL to generate. Only valid when --db is
                        not specified.
                        If present, a sqlalchemy connection string to use to
                        directly execute generated SQL on a database.
  --query QUERY         Execute one or more SQL queries delimited by ";" and
                        output the result of the last query as CSV.
  --insert              In addition to creating the table, also insert the
                        data into the table. Only valid when --db is
  --tables TABLE_NAMES  Specify the names of the tables to be created. By
                        default, the tables will be named after the filenames
                        without extensions or "stdin".
  --no-constraints      Generate a schema without length limits or null
                        checks. Useful when sampling big tables.
  --no-create           Skip creating a table. Only valid when --insert is
  --blanks              Do not coerce empty strings to NULL values.
  --db-schema DB_SCHEMA
                        Optional name of database schema to create table(s)
  -y SNIFF_LIMIT, --snifflimit SNIFF_LIMIT
                        Limit CSV dialect sniffing to the specified number of
                        bytes. Specify "0" to disable sniffing entirely.
  -I, --no-inference    Disable type inference when parsing the input.

See also: Arguments common to all tools.

For information on connection strings and supported dialects refer to the SQLAlchemy documentation.

If you prefer not to enter your password in the connection string, store the password securely in a PostgreSQL Password File, a MySQL Options File or similar files for other systems.


Using the --query option may cause rounding (in Python 2) or introduce [Python floating point issues](https://docs.python.org/3.4/tutorial/floatingpoint.html) (in Python 3).


Generate a statement in the PostgreSQL dialect:

csvsql -i postgresql examples/realdata/FY09_EDU_Recipients_by_State.csv

Create a table and import data from the CSV directly into PostgreSQL:

createdb test
csvsql --db postgresql:///test --table fy09 --insert examples/realdata/FY09_EDU_Recipients_by_State.csv

For large tables it may not be practical to process the entire table. One solution to this is to analyze a sample of the table. In this case it can be useful to turn off length limits and null checks with the no-constraints option:

head -n 20 examples/realdata/FY09_EDU_Recipients_by_State.csv | csvsql --no-constraints --table fy09

Create tables for an entire folder of CSVs and import data from those files directly into PostgreSQL:

createdb test
csvsql --db postgresql:///test --insert examples/*_converted.csv

If those CSVs have identical headers, you can import them into the same table by using csvstack first:

createdb test
csvstack examples/dummy?.csv | csvsql --db postgresql:///test --insert

Group rows by one column:

csvsql --query "select * from 'dummy3' group by a" examples/dummy3.csv

You can also use CSVSQL to “directly” query one or more CSV files. Please note that this will create an in-memory SQL database, so it won’t be very fast:

csvsql --query  "select m.usda_id, avg(i.sepal_length) as mean_sepal_length from iris as i join irismeta as m on (i.species = m.species) group by m.species" examples/iris.csv examples/irismeta.csv