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 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 {mssql,mysql,oracle,postgresql,sqlite,duckdb,crate,ingres}, --dialect {mssql,mysql,oracle,postgresql,sqlite,duckdb,crate,ingres}
 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.
 --engine-option ENGINE_OPTION ENGINE_OPTION
 A keyword argument to SQLAlchemy's create_engine(), as
 a space-separated pair. This option can be specified
 multiple times. For example: thick_mode True
 --query QUERIES Execute one or more SQL queries delimited by --sql-
 delimiter, and output the result of the last query as
 CSV. QUERY may be a filename. --query may be specified
 multiple times.
 --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
 Before the INSERT command, execute one or more SQL
 queries delimited by --sql-delimiter. Requires
 --insert.
 --after-insert AFTER_INSERT
 After the INSERT command, execute one or more SQL
 queries delimited by --sql-delimiter. Requires
 --insert.
 --sql-delimiter SQL_DELIMITER
 Delimiter separating SQL queries in --query, --before-
 insert, and --after-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 entirely, or
 "-1" to sniff the entire file.
 -I, --no-inference Disable type inference (and --locale, --date-format,
 --datetime-format, --no-leading-zeroes) 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-fndjson|csvsql--query'SELECT "a/b" FROM stdin'

Note

Alternatives to csvsql are q and textql.

Examples

Generate SQL statements

Generate a statement in the PostgreSQL dialect:

csvsql-ipostgresqlexamples/realdata/FY09_EDU_Recipients_by_State.csv

Interact with a SQL database

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

createdbtest
csvsql--dbpostgresql:///test--tablesfy09--insertexamples/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-n20examples/realdata/FY09_EDU_Recipients_by_State.csv|csvsql--no-constraints--tablesfy09

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

createdbtest
csvsql--dbpostgresql:///test--insertexamples/*_converted.csv

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

createdbtest
csvstackexamples/dummy?.csv|csvsql--dbpostgresql:///test--insert

Query and output CSV files using SQL

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.csvexamples/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-inferenceexamples/dummy3.csv

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

csvsql--query"SELECT a || COALESCE(b, '') FROM 'sort_ints_nulls'"--no-inferenceexamples/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