CSVSQL(1) csvkit CSVSQL(1)

csvsql - csvsql Documentation

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] [-L LOCALE]
              [-S] [--blanks] [--null-value NULL_VALUES [NULL_VALUES ...]]
              [--date-format DATE_FORMAT] [--datetime-format DATETIME_FORMAT]
              [-H] [-K SKIP_LINES] [-v] [-l] [--zero] [-V]
              [-i {firebird,mssql,mysql,oracle,postgresql,sqlite,sybase}]
              [--db CONNECTION_STRING] [--query QUERIES] [--insert]
              [--prefix PREFIX] [--before-insert BEFORE_INSERT]
              [--after-insert AFTER_INSERT] [--tables TABLE_NAMES]
              [--no-constraints] [--unique-constraint UNIQUE_CONSTRAINT]
              [--no-create] [--create-if-not-exists] [--overwrite]
              [--db-schema DB_SCHEMA] [-y SNIFF_LIMIT] [-I]
              [--chunk-size CHUNK_SIZE]
              [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 as piped data via STDIN.
optional arguments:
  -h, --help            show this help message and exit
  -i {firebird,mssql,mysql,oracle,postgresql,sqlite,sybase,crate}, --dialect {firebird,mssql,mysql,oracle,postgresql,sqlite,sybase,crate}
                        Dialect of SQL to generate. Cannot be used with --db.
  --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. QUERY may
                        be a filename.
  --insert              Insert the data into the table. Requires --db.
  --prefix PREFIX       Add an expression following the INSERT keyword, like
                        OR IGNORE or OR REPLACE.
  --before-insert BEFORE_INSERT
                        Execute SQL before the INSERT command. Requires
                        --insert.
  --after-insert AFTER_INSERT
                        Execute SQL after the INSERT command. Requires
                        --insert.
  --tables TABLE_NAMES  A comma-separated list of names of 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.
  --unique-constraint UNIQUE_CONSTRAINT
                        A column-separated list of names of columns to include
                        in a UNIQUE constraint.
  --no-create           Skip creating the table. Requires --insert.
  --create-if-not-exists
                        Create the table if it does not exist, otherwise keep
                        going. Requires --insert.
  --overwrite           Drop the table if it already exists. Requires
                        --insert. Cannot be used with --no-create.
  --db-schema DB_SCHEMA
                        Optional name of database schema to create table(s)
                        in.
  -y SNIFF_LIMIT, --snifflimit SNIFF_LIMIT
                        Limit CSV dialect sniffing to the specified number of
                        bytes. Specify "0" to disable sniffing.
  -I, --no-inference    Disable type inference when parsing the input.
  --chunk-size CHUNK_SIZE
                        Chunk size for batch insert into the table. Requires
                        --insert.
  --min-col-len MIN_COL_LEN
                        The minimum length of text columns.
  --col-len-multiplier COL_LEN_MULTIPLIER
                        Multiply the maximum column length by this multiplier
                        to accomodate larger values in later runs.

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.

NOTE:

Using the --query option may cause rounding (in Python 2) or introduce Python floating point issues (in Python 3).

NOTE:

If the CSV file was created from a JSON file using in2csv, remember to quote SQL columns properly. For example: 
echo '{"a":{"b":"c"},"d":"e"}' | in2csv -f ndjson | csvsql --query 'SELECT "a/b" FROM stdin'

NOTE:

Alternatives to csvsql are q and textql.

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 --tables 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 --tables fy09

Create tables for an entire directory 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

You can use csvsql to "directly" query one or more CSV files. Please note that this will create an in-memory SQLite 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

Group rows by one column:

csvsql --query "SELECT * FROM 'dummy3' GROUP BY a" examples/dummy3.csv

Concatenate two columns:

csvsql --query "SELECT a || b FROM 'dummy3'" --no-inference examples/dummy3.csv

If a column contains null values, you must COALESCE the column:

csvsql --query "SELECT a || COALESCE(b, '') FROM 'sort_ints_nulls'" --no-inference examples/sort_ints_nulls.csv

The UPDATE SQL statement produces no output. Remember to SELECT the columns and rows you want:

csvsql --query "UPDATE 'dummy3' SET a = 'foo'; SELECT * FROM 'dummy3'" examples/dummy3.csv

Christopher Groskopf and contributors

2016, Christopher Groskopf and James McKinney

July 12, 2024 2.0.1