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Import/Export data to/from relational databases using SQL statements with CSV files

Project description


Simple command line tool that can be used to:

  • SELECT data from database and export the result as CSV
  • INSERT data into database from CSV

Note that it works only with Python 3, not 2.


Via PyPI:

$ pip3 install sqlcsv

It does not specify any database drivers as explicit dependencies, so install the one you need:

$ pip3 install mysqlclient

# PostgreSQL
$ pip3 install psycopg2

Basic usage

In the examples below following table schema with MySQL is used:

CREATE TABLE testtable(
  int_col INT,
  float_col FLOAT,
  varchar_col VARCHAR(255)

Database connection

Database connection can be specified using --db-url option in the form of SQLAlchemy URL:

$ sqlcsv --db-url 'mysql://testuser:testpassword@' <subcommand> ...

Also it will be read from SQLCSV_DB_URL environment variable if set:

$ export SQLCSV_DB_URL='mysql://testuser:testpassword@'
$ sqlcsv <subcommand> ...

From here they are omitted from command line examples.


Assume we already have following records on the table:

| id | int_col | float_col | varchar_col |
|  1 |       1 |         1 | aaa         |
|  2 |       2 |         2 | bbb         |
|  3 |    NULL |      NULL | NULL        |

Use select subcommand and give SELECT query using --sql option:

$ sqlcsv select --sql 'SELECT * FROM testtable'

If you want to save the result to file, use --outfile option:

$ sqlcsv select --sql 'SELECT * FROM testtable' --outfile out.csv


Assume we already have following dataset in input.csv:


Use insert subcommand and give INSERT query with placeholders using --sql option, followed by --types option specifying types of each field:

$ sqlcsv insert \
  --sql 'INSERT INTO testtable(int_col, float_col, varchar_col) VALUES (%s, %s, %s)' \
  --infle input.csv --types int,float,str

The resulted records in the table would be:

| id | int_col | float_col | varchar_col |
|  1 |       1 |         1 | aaa         |
|  2 |       2 |         2 | bbb         |

Note that type names in --types are the same as Python primitive type function names. Also it can be short form like --types i,f,s

Currently it supports only int, float and str.


You may have CSV file contains empty cell like:


If you want to treat them as 'NULL' in database, use --nullable option to convert them before insertion:

$ sqlcsv insert
  --sql 'INSERT INTO testtable(int_col, float_col, varchar_col) VALUES (%s, %s, %s)
  --infile input.csv --types int,float,str --nullable false,true,true \

The result would be:

| id | int_col | float_col | varchar_col |
|  1 |       1 |      NULL | aaa         |
|  2 |       2 |         2 | NULL        |

Note that values of --nullable have to be one of true or false, and they can also be written as t or f in a short form.

More options

CSV dialect

If your desired input or output is tab-separated (TSV), use --tab option:

$ sqlcsv --tab select --sql 'SELECT * FROM testtable'
id	int_col	float_col	varchar_col
1	1	1.0	aaa
2	2	2.0	bbb

For other format settings, see sqlcsv --help. Basically it supports the same dialect specification as csv package in Python's standard libraries does.

Read SQL from file

In both select and insert subcommands you can use --sqlfile option intead of --sql in order to read query from a file:

$ sqlcsv select --sqlfile query.sql
$ sqlcsv insert --sqlfile query.sql ...

Pre and post querying

In case you need to execute short query before/after the main query runs, it provides --pre-sql and --post-sql options to satisfy such needs:

$ sqlcsv select --pre-sql 'SET SESSION wait_timeout = 60' --sql ...

Chunked insertion

When you import a large number of records into database, --chunk-size option is helpful to save memory usage by splitting file contents up into different pieces and transfer each of them to the database repeatedly.

$ sqlcsv insert --sql ... --infile ... --types ... --chunk-size 1000

Run in transaction

If you want multiple queries executed in single command call such as ones specified by --pre-sql or --post-sql to be run in the same transaction, use --transaction option as follows:

$ sqlcsv --transaction select --pre-sql ... --post-sql ... --sql ...

It is also a good practice to use this option with --chunk-size in order to execute chunked insersion atomically and to avoid leaving incomplete data on table when the query is cancelled or aborted.

Comparison between other tools


Major RDBMSs usually have built-in instructions to import data from files such as LOAD for MySQL or COPY for PostgreSQL. They are obviously the primary choices you may consider but also have some limitations:

  • Few platform support import/export across network; others only can do from local files
  • Specification for data format or instruction varies for each platform

Sqlcsv works remotely and provides unified interfaces (except SQL dialects).


CSVKit is a popular toolkit for manipulating CSV files. It provides sql2csv and csvsql commands for export/import data from/to SQL databases. Consider using them before choosing sqlcsv if they just satisfy your needs, as they have much more users and contributers. Hoever, there sill might be a few reasons to prefer sqlcsv to them (and this is why it was created):

  • CSVKit depends on several libraries including agate but not all of them are needed for interoperability between SQL databases and CSV files. Sqlcsv uses csv package in Python's standard libraries to interact with CSV files and SQLAlchemy to query SQL databases, which leads to less library dependencies.
  • CSVKit's csvsql command takes just table name for import, which make it easy to use. However, it is sometimes inconvenient in such cases where CSV file includes only a part of columns and others are generated dynamically by SQL expressions. Sqlcsv's insert subcommand, by contrast, takes INSERT statement, which might be verbose but provides more flexibility.


If you do not care about library dependencies, do not need custom INSERT statement to be specified, and even do not need command line interfaces, then just use pandas' DataFrame.to_sql or read_sql. They will help you a lot if used with DataFrame.to_csv or read_csv.


If your dataset is so large that requires optimization such as parallel processing, or you want some sophisticated I/O functionality such as retrying, consider using Embulk. It also provides well-developed plugin ecosystem that enables support of various data stores and data formats.

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