Provided by: csvkit_2.0.1-3_all 
NAME
csvsql - csvsql Documentation
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 {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.
EXAMPLES
Generate SQL statements
Generate a statement in the PostgreSQL dialect:
csvsql -i postgresql examples/realdata/FY09_EDU_Recipients_by_State.csv
Interact with a SQL database
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
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.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
AUTHOR
Christopher Groskopf and contributors
COPYRIGHT
2016, Christopher Groskopf and James McKinney