csvsql¶

Description¶

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 MAXFIELDSIZE] [-e ENCODING] [-H] [-v]
              [-y SNIFFLIMIT]
              [-i {access,sybase,sqlite,informix,firebird,mysql,oracle,maxdb,postgresql,mssql}]
              [--db CONNECTION_STRING] [--insert]
              [FILE]

Generate SQL statements for a CSV file or create execute those statements
directly on a database.

Generate a SQL CREATE TABLE statement for a CSV file.

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
  -y SNIFFLIMIT, --snifflimit SNIFFLIMIT
                        Limit CSV dialect sniffing to the specified number of
                        bytes. Specify "0" to disable sniffing entirely.
  -i {access,sybase,sqlite,informix,firebird,mysql,oracle,maxdb,postgresql,mssql}, --dialect {access,sybase,sqlite,informix,firebird,mysql,oracle,maxdb,postgresql,mssql}
                        Dialect of SQL to generate. Only valid when --db is
                        not specified.
  --db CONNECTION_STRING
                        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
                        specified.
  --table TABLE_NAME    Specify a name for the table to be created. If
                        omitted, the filename (minus extension) will be used.
  --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
                        specified.
  --blanks              Do not coerce empty strings to NULL values.
  --no-inference        Disable type inference when parsing the input.
  --db-schema           Optional name of database schema to create table(s)
                        in.

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

Note

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).

Examples¶

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 Postgres:

$ 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 Postgres:

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