Skip to main content

Convert CSV files into a SQLite database

Project description

csvs-to-sqlite

PyPI Changelog Tests License

Convert CSV files into a SQLite database. Browse and publish that SQLite database with Datasette.

Basic usage:

csvs-to-sqlite myfile.csv mydatabase.db

This will create a new SQLite database called mydatabase.db containing a single table, myfile, containing the CSV content.

You can provide multiple CSV files:

csvs-to-sqlite one.csv two.csv bundle.db

The bundle.db database will contain two tables, one and two.

This means you can use wildcards:

csvs-to-sqlite ~/Downloads/*.csv my-downloads.db

If you pass a path to one or more directories, the script will recursively search those directories for CSV files and create tables for each one.

csvs-to-sqlite ~/path/to/directory all-my-csvs.db

Handling TSV (tab-separated values)

You can use the -s option to specify a different delimiter. If you want to use a tab character you'll need to apply shell escaping like so:

csvs-to-sqlite my-file.tsv my-file.db -s $'\t'

Refactoring columns into separate lookup tables

Let's say you have a CSV file that looks like this:

county,precinct,office,district,party,candidate,votes
Clark,1,President,,REP,John R. Kasich,5
Clark,2,President,,REP,John R. Kasich,0
Clark,3,President,,REP,John R. Kasich,7

(Real example taken from the Open Elections project)

You can now convert selected columns into separate lookup tables using the new --extract-column option (shortname: -c) - for example:

csvs-to-sqlite openelections-data-*/*.csv \
    -c county:County:name \
    -c precinct:Precinct:name \
    -c office -c district -c party -c candidate \
    openelections.db

The format is as follows:

column_name:optional_table_name:optional_table_value_column_name

If you just specify the column name e.g. -c office, the following table will be created:

CREATE TABLE "office" (
    "id" INTEGER PRIMARY KEY,
    "value" TEXT
);

If you specify all three options, e.g. -c precinct:Precinct:name the table will look like this:

CREATE TABLE "Precinct" (
    "id" INTEGER PRIMARY KEY,
    "name" TEXT
);

The original tables will be created like this:

CREATE TABLE "ca__primary__san_francisco__precinct" (
    "county" INTEGER,
    "precinct" INTEGER,
    "office" INTEGER,
    "district" INTEGER,
    "party" INTEGER,
    "candidate" INTEGER,
    "votes" INTEGER,
    FOREIGN KEY (county) REFERENCES County(id),
    FOREIGN KEY (party) REFERENCES party(id),
    FOREIGN KEY (precinct) REFERENCES Precinct(id),
    FOREIGN KEY (office) REFERENCES office(id),
    FOREIGN KEY (candidate) REFERENCES candidate(id)
);

They will be populated with IDs that reference the new derived tables.

Installation

$ pip install csvs-to-sqlite

csvs-to-sqlite now requires Python 3. If you are running Python 2 you can install the last version to support Python 2:

$ pip install csvs-to-sqlite==0.9.2

csvs-to-sqlite --help

Usage: csvs-to-sqlite [OPTIONS] PATHS... DBNAME

  PATHS: paths to individual .csv files or to directories containing .csvs

  DBNAME: name of the SQLite database file to create

Options:
  -s, --separator TEXT            Field separator in input .csv
  -q, --quoting INTEGER           Control field quoting behavior per csv.QUOTE_*
                                  constants. Use one of QUOTE_MINIMAL (0),
                                  QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or
                                  QUOTE_NONE (3).

  --skip-errors                   Skip lines with too many fields instead of
                                  stopping the import

  --replace-tables                Replace tables if they already exist
  -t, --table TEXT                Table to use (instead of using CSV filename)
  -c, --extract-column TEXT       One or more columns to 'extract' into a
                                  separate lookup table. If you pass a simple
                                  column name that column will be replaced with
                                  integer foreign key references to a new table
                                  of that name. You can customize the name of
                                  the table like so:     state:States:state_name
                                  
                                  This will pull unique values from the 'state'
                                  column and use them to populate a new 'States'
                                  table, with an id column primary key and a
                                  state_name column containing the strings from
                                  the original column.

  -d, --date TEXT                 One or more columns to parse into ISO
                                  formatted dates

  -dt, --datetime TEXT            One or more columns to parse into ISO
                                  formatted datetimes

  -df, --datetime-format TEXT     One or more custom date format strings to try
                                  when parsing dates/datetimes

  -pk, --primary-key TEXT         One or more columns to use as the primary key
  -f, --fts TEXT                  One or more columns to use to populate a full-
                                  text index

  -i, --index TEXT                Add index on this column (or a compound index
                                  with -i col1,col2)

  --shape TEXT                    Custom shape for the DB table - format is
                                  csvcol:dbcol(TYPE),...

  --filename-column TEXT          Add a column with this name and populate with
                                  CSV file name

  --fixed-column <TEXT TEXT>...   Populate column with a fixed string
  --fixed-column-int <TEXT INTEGER>...
                                  Populate column with a fixed integer
  --fixed-column-float <TEXT FLOAT>...
                                  Populate column with a fixed float
  --no-index-fks                  Skip adding index to foreign key columns
                                  created using --extract-column (default is to
                                  add them)

  --no-fulltext-fks               Skip adding full-text index on values
                                  extracted using --extract-column (default is
                                  to add them)

  --just-strings                  Import all columns as text strings by default
                                  (and, if specified, still obey --shape,
                                  --date/datetime, and --datetime-format)

  --version                       Show the version and exit.
  --help                          Show this message and exit.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

csvs_to_sqlite-1.3.tar.gz (17.8 kB view details)

Uploaded Source

Built Distribution

csvs_to_sqlite-1.3-py2.py3-none-any.whl (16.7 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file csvs_to_sqlite-1.3.tar.gz.

File metadata

  • Download URL: csvs_to_sqlite-1.3.tar.gz
  • Upload date:
  • Size: 17.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.8

File hashes

Hashes for csvs_to_sqlite-1.3.tar.gz
Algorithm Hash digest
SHA256 638623ff5462e60123da07860a096f9cff0d7aa8f036e68cc98001e006adea59
MD5 242aba42f4ab2be64c14941b95ace7e9
BLAKE2b-256 c1485ddd047d3e76fbee30b2aaece88568e4fa42bef0e6011f61f0b364eeb1b2

See more details on using hashes here.

File details

Details for the file csvs_to_sqlite-1.3-py2.py3-none-any.whl.

File metadata

  • Download URL: csvs_to_sqlite-1.3-py2.py3-none-any.whl
  • Upload date:
  • Size: 16.7 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.8

File hashes

Hashes for csvs_to_sqlite-1.3-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 a1d94624d4d501f2c9661f648e537cd2acb1ac3451cbd78384cf8abae00ec8c7
MD5 65309c26466f87933048490cf27cfc32
BLAKE2b-256 24aa64b113c2f0af61ab85de26f0ba4204dbeff8507ff160a66635ed08751b17

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page