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] [-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.
--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 In addition to creating the table, also insert the
data into the table. Only valid when --db is
specified.
--prefix PREFIX Add an expression following the INSERT keyword, like
IGNORE or REPLACE.
--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
specified.
--overwrite Drop the table before creating.
--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.
-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.
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 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 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