# `sqlite3` — Interface DB-API 2.0 pour bases de données SQLite

Code source : Lib/sqlite3/

SQLite est une bibliothèque C qui fournit une base de données légère sur disque ne nécessitant pas de processus serveur et qui utilise une variante (non standard) du langage de requête SQL pour accéder aux données. Certaines applications peuvent utiliser SQLite pour le stockage de données internes. Il est également possible de créer une application prototype utilisant SQLite, puis de modifier le code pour utiliser une base de données plus robuste telle que PostgreSQL ou Oracle.

The `sqlite3` module was written by Gerhard Häring. It provides an SQL interface compliant with the DB-API 2.0 specification described by PEP 249, and requires SQLite 3.7.15 or newer.

Ce document inclus 4 sections principales :

- tutoriel sqlite3 explique comment utiliser le module `sqlite3`.

- référence sqlite3 décrit les classes et les fonctions que ce module définit.

- guide sqlite3 détaille comment gérer des tâches spécifiques.

- explications sqlite3 propose un contexte détaillé du contrôle de transaction.

Voir aussi

https://www.sqlite.org

  Dans la page Web de SQLite, la documentation décrit la syntaxe et les types de données disponibles qui sont pris en charge par cette variante SQL.

https://www.w3schools.com/sql/

  Tutoriel, référence et exemples pour apprendre la syntaxe SQL.

PEP 249 — Spécifications de l'API 2.0 pour la base de données

  PEP écrite par Marc-André Lemburg.

## Tutoriel

Dans ce tutoriel, vous allez créer une base de données des films des Monty Python en utilisant les fonctionnalités de base de `sqlite3`. Cela nécessite une compréhension élémentaire des concepts des bases de données, notamment les curseurs et les transactions.

First, we need to create a new database and open a database connection to allow `sqlite3` to work with it. Call `sqlite3.connect()` to create a connection to the database `tutorial.db` in the current working directory, implicitly creating it if it does not exist:

    import sqlite3
    con = sqlite3.connect("tutorial.db")

L’objet `Connection` renvoyé — `con` — représente la connexion à la base de données sur disque.

Afin d’exécuter les instructions SQL et de récupérer les résultats des requêtes SQL, nous devrons utiliser un curseur de base de données. Appelez `con.cursor()` pour créer la `Cursor` :

    cur = con.cursor()

Maintenant que nous avons une connexion à la base de données et un curseur, nous pouvons créer une table `movie` avec des colonnes pour le titre, l’année de sortie et la note de la critique. Pour plus de simplicité, nous pouvons simplement utiliser les noms des colonnes dans la déclaration de la table — grâce à la fonctionnalité de typage flexible de SQLite, spécifier les types de données est facultatif. Exécutez l’instruction `CREATE TABLE` en appelant `cur.execute(…)` :

    cur.execute("CREATE TABLE movie(title, year, score)")

Nous pouvons vérifier que la nouvelle table a été créée en interrogeant la table `sqlite_master` intégrée à SQLite, qui devrait maintenant contenir une entrée pour la définition de la table `movie` (voir le schéma Table pour plus de détails). Exécutez cette requête en appelant `cur.execute(…)`, affectez le résultat à `res`, et appelez `res.fetchone()` pour récupérer la ligne résultante :

    >>> res = cur.execute("SELECT name FROM sqlite_master")
    >>> res.fetchone()
    ('movie',)

Nous pouvons voir que la table a été créée, puisque la requête retourne un `tuple` contenant le nom de la table. Si nous interrogeons `sqlite_master` pour une table `spam` inexistante, `res.fetchone()`()` retournera `None` :

    >>> res = cur.execute("SELECT name FROM sqlite_master WHERE name='spam'")
    >>> res.fetchone() is None
    True

Maintenant, ajoutez deux lignes de données en tant que littéraux SQL en exécutant une instruction `INSERT`, une fois encore en appelant `cur.execute(…)` :

    cur.execute("""
        INSERT INTO movie VALUES
            ('Monty Python and the Holy Grail', 1975, 8.2),
            ('And Now for Something Completely Different', 1971, 7.5)
    """)

L’instruction `INSERT` ouvre implicitement une transaction, qui doit être validée avant que les modifications ne soient enregistrées dans la base de données (voir contrôle des transactions SQL pour plus de détails). Appelez `con.commit()` sur l’objet de connexion pour valider la transaction :

    con.commit()

Nous pouvons vérifier que les données ont été insérées correctement en exécutant une requête `SELECT`. Utilisez la désormais familière `cur.execute(…)` pour affecter le résultat à `res`, et appelez `res.fetchall()` pour retourner toutes les lignes résultantes :

    >>> res = cur.execute("SELECT score FROM movie")
    >>> res.fetchall()
    [(8.2,), (7.5,)]

Le résultat est une `liste` de deux `tuple`s, une par ligne, chacun contenant la valeur `score` de cette ligne.

Maintenant, insérez trois lignes supplémentaires en appelant `cur.executemany(…)` :

    data = [
        ("Monty Python Live at the Hollywood Bowl", 1982, 7.9),
        ("Monty Python's The Meaning of Life", 1983, 7.5),
        ("Monty Python's Life of Brian", 1979, 8.0),
    ]
    cur.executemany("INSERT INTO movie VALUES(?, ?, ?)", data)
    con.commit()  # Remember to commit the transaction after executing INSERT.

Remarquez que les marqueurs `?` sont utilisés pour lier les `data` à la requête. Utilisez toujours les marqueurs au lieu d’expressions formatées pour lier les valeurs Python aux instructions SQL, afin d’éviter les injections SQL (voir placeholder SQL pour plus de détails).

Nous pouvons vérifier que les nouvelles lignes ont été insérées en exécutant une requête `SELECT`, cette fois-ci en itérant sur les résultats de la requête :

    >>> for row in cur.execute("SELECT year, title FROM movie ORDER BY year"):
    ...     print(row)
    (1971, 'And Now for Something Completely Different')
    (1975, 'Monty Python and the Holy Grail')
    (1979, "Monty Python's Life of Brian")
    (1982, 'Monty Python Live at the Hollywood Bowl')
    (1983, "Monty Python's The Meaning of Life")

Chaque ligne est un `tuple` de deux éléments `(année, titre)`, correspondant aux colonnes sélectionnées dans la requête.

Enfin, vérifiez que la base de données a été écrite sur le disque en appelant `con.close()` pour fermer la connexion existante, en ouvrir une nouvelle, créer un nouveau curseur, puis interroger la base de données :

    >>> con.close()
    >>> new_con = sqlite3.connect("tutorial.db")
    >>> new_cur = new_con.cursor()
    >>> res = new_cur.execute("SELECT title, year FROM movie ORDER BY score DESC")
    >>> title, year = res.fetchone()
    >>> print(f'The highest scoring Monty Python movie is {title!r}, released in {year}')
    The highest scoring Monty Python movie is 'Monty Python and the Holy Grail', released in 1975

Vous avez maintenant créé une base de données SQLite à l’aide du module `sqlite3`, inséré des données et récupéré des valeurs de plusieurs façons.

Voir aussi

- guide sqlite3 pour une lecture plus poussée:

  > - placeholders sqlite3

  > - adaptateurs sqlite3

  > - convertisseurs sqlite3

  > - gestionnaire de contexte de connexion sqlite3

  > - guide de fabrique de ligne sqlite3

- explications sqlite3 pour un contexte détaillé du contrôle de transaction.

## Référence

### Fonctions du module

`sqlite3.``connect`(database, timeout=5.0, detect_types=0, isolation_level='DEFERRED', check_same_thread=True, factory=sqlite3.Connection, cached_statements=100, uri=False)

  Ouvrez une connexion à une base de données SQLite.

  Paramètres

    - database (path-like object) -- Le chemin d’accès au fichier de la base de données à ouvrir. Passez `":memory:"` pour ouvrir une connexion à une base de données qui est dans la RAM plutôt que sur le disque.

    - timeout (float) -- How many seconds the connection should wait before raising an `OperationalError` when a table is locked. If another connection opens a transaction to modify a table, that table will be locked until the transaction is committed. Default five seconds.

    - detect_types (int) -- Contrôle si et comment les types de données non nativement pris en charge par SQLite sont recherchés pour être convertis en types Python, en utilisant les convertisseurs enregistrés avec `register_converter()`. Définissez-le à n’importe quelle combinaison (en utilisant `|`, opérateurs bit-à-bit OR) de `PARSE_DECLTYPES` et `PARSE_COLNAMES` pour activer ceci. Les noms de colonnes ont la priorité sur les types déclarés si les deux drapeaux sont activés. Les types ne peuvent pas être détectés pour les champs générés (par exemple `max(data)`), même si le paramètre detect_types est activé ; `str` sera retourné à la place. Par défaut (`0`), la détection des types est désactivée.

    - isolation_level (str | None) -- L’attribut `isolation_level` de la connexion, contrôlant si et comment les transactions sont ouvertes implicitement. Peut être `"DEFERRED"` (par défaut), `"EXCLUSIVE"` ou `"IMMEDIATE"` ; ou `None` pour désactiver l’ouverture implicite des transactions. Voir contrôle des transactions sqlite3 pour en savoir plus.

    - check_same_thread (bool) -- Si `True` (par défaut), `ProgrammingError` sera levée si la connexion à la base de données est utilisée par un thread autre que celui qui l’a créée. Si `False`, la connexion peut être utilisée par plusieurs threads ; les opérations d’écriture devront peut-être être sérialisées par l’utilisateur pour éviter la corruption des données. Voir `sécurité des threads` pour plus d’informations.

    - factory (Connection) -- Une sous-classe personnalisée de `Connection` pour créer la connexion, si ce n’est pas la classe par défaut `Connection`.

    - cached_statements (int) -- The number of statements that `sqlite3` should internally cache for this connection, to avoid parsing overhead. By default, 100 statements.

    - uri (bool) -- Si elle a pour valeur `True`, la base de données est interprétée comme un URI avec un chemin d’accès au fichier et une chaîne de requête facultative. La partie schéma doit être `"file:"`, et le chemin peut être relatif ou absolu. La chaîne d’interrogation permet de passer des paramètres à SQLite, ce qui permet d’activer diverses astuces d’URI sqlite3.

  Type renvoyé

    Connection

  Raises an auditing event `sqlite3.connect` with argument `database`.

  Raises an auditing event `sqlite3.connect/handle` with argument `connection_handle`.

  Nouveau dans la version 3.4: The uri parameter.

  Modifié dans la version 3.7: database peut maintenant aussi être un objet de type chemin, et pas seulement une chaîne de caractères.

  Nouveau dans la version 3.10: The `sqlite3.connect/handle` auditing event.

`sqlite3.``complete_statement`(statement)

  Renvoie `True` si la déclaration de la chaîne semble contenir une ou plusieurs déclarations SQL complètes. Aucune vérification syntaxique ou analyse syntaxique d’aucune sorte n’est effectuée, si ce n’est la vérification qu’il n’y a pas de chaîne littérale non fermée et que l’instruction se termine par un point-virgule.

  Par exemple :

      >>> sqlite3.complete_statement("SELECT foo FROM bar;")
      True
      >>> sqlite3.complete_statement("SELECT foo")
      False

  Cette fonction peut être utile pendant la saisie en ligne de commande pour déterminer si le texte saisi semble former une instruction SQL complète, ou si une saisie supplémentaire est nécessaire avant d’appeler `execute()`.

`sqlite3.``enable_callback_tracebacks`(flag, /)

  Enable or disable callback tracebacks. By default you will not get any tracebacks in user-defined functions, aggregates, converters, authorizer callbacks etc. If you want to debug them, you can call this function with flag set to `True`. Afterwards, you will get tracebacks from callbacks on `sys.stderr`. Use `False` to disable the feature again.

`sqlite3.``register_adapter`(type, adapter, /)

  Enregistre un adaptateur appelable pour adapter le type Python type en un type SQLite. L’adaptateur est appelé avec un objet Python de type type comme unique argument, et doit retourner une valeur d’un type que SQLite comprend nativement.

`sqlite3.``register_converter`(typename, converter, /)

  Enregistre le convertisseur appelable pour convertir les objets SQLite de type typename en un objet Python d’un type spécifique. Le convertisseur est invoqué pour toutes les valeurs SQLite de type typename ; on lui passe un objet `bytes` et il doit retourner un objet du type Python désiré. Consultez le paramètre detect_types de `connect()` pour des informations sur le fonctionnement de la détection des types.

  Remarque : typename et le nom du type dans votre requête sont comparés sans tenir compte de la casse.

### Module constants

`sqlite3.``PARSE_COLNAMES`

  Pass this flag value to the detect_types parameter of `connect()` to look up a converter function by using the type name, parsed from the query column name, as the converter dictionary key. The type name must be wrapped in square brackets (`[]`).

      SELECT p as "p [point]" FROM test;  ! will look up converter "point"

  This flag may be combined with `PARSE_DECLTYPES` using the `|` (bitwise or) operator.

`sqlite3.``PARSE_DECLTYPES`

  Pass this flag value to the detect_types parameter of `connect()` to look up a converter function using the declared types for each column. The types are declared when the database table is created. `sqlite3` will look up a converter function using the first word of the declared type as the converter dictionary key. For example:

      CREATE TABLE test(
         i integer primary key,  ! will look up a converter named "integer"
         p point,                ! will look up a converter named "point"
         n number(10)            ! will look up a converter named "number"
       )

  This flag may be combined with `PARSE_COLNAMES` using the `|` (bitwise or) operator.

`sqlite3.``SQLITE_OK`
`sqlite3.``SQLITE_DENY`
`sqlite3.``SQLITE_IGNORE`

  Flags that should be returned by the authorizer_callback callable passed to `Connection.set_authorizer()`, to indicate whether:

  - Access is allowed (`SQLITE_OK`),

  - The SQL statement should be aborted with an error (`SQLITE_DENY`)

  - The column should be treated as a `NULL` value (`SQLITE_IGNORE`)

`sqlite3.``apilevel`

  String constant stating the supported DB-API level. Required by the DB-API. Hard-coded to `"2.0"`.

`sqlite3.``paramstyle`

  String constant stating the type of parameter marker formatting expected by the `sqlite3` module. Required by the DB-API. Hard-coded to `"qmark"`.

  Note

  The `named` DB-API parameter style is also supported.

`sqlite3.``sqlite_version`

  Version number of the runtime SQLite library as a `string`.

`sqlite3.``sqlite_version_info`

  Version number of the runtime SQLite library as a `tuple` of `integers`.

`sqlite3.``threadsafety`

  Integer constant required by the DB-API, stating the level of thread safety the `sqlite3` module supports. Currently hard-coded to `1`, meaning "Threads may share the module, but not connections." However, this may not always be true. You can check the underlying SQLite library's compile-time threaded mode using the following query:

      import sqlite3
      con = sqlite3.connect(":memory:")
      con.execute("""
          select * from pragma_compile_options
          where compile_options like 'THREADSAFE=%'
      """).fetchall()

  Note that the SQLITE_THREADSAFE levels do not match the DB-API 2.0 `threadsafety` levels.

`sqlite3.``version`

  Version number of this module as a `string`. This is not the version of the SQLite library.

`sqlite3.``version_info`

  Version number of this module as a `tuple` of `integers`. This is not the version of the SQLite library.

### Connection objects

class `sqlite3.``Connection`

  Each open SQLite database is represented by a `Connection` object, which is created using `sqlite3.connect()`. Their main purpose is creating `Cursor` objects, and Transaction control.

  Voir aussi

  - How to use connection shortcut methods

  - gestionnaire de contexte de connexion sqlite3

  An SQLite database connection has the following attributes and methods:

  `cursor`(factory=Cursor)

    Create and return a `Cursor` object. The cursor method accepts a single optional parameter factory. If supplied, this must be a callable returning an instance of `Cursor` or its subclasses.

  `commit`()

    Commit any pending transaction to the database. If there is no open transaction, this method is a no-op.

  `rollback`()

    Roll back to the start of any pending transaction. If there is no open transaction, this method is a no-op.

  `close`()

    Close the database connection. Any pending transaction is not committed implicitly; make sure to `commit()` before closing to avoid losing pending changes.

  `execute`(sql, parameters=(), /)

    Create a new `Cursor` object and call `execute()` on it with the given sql and parameters. Return the new cursor object.

  `executemany`(sql, parameters, /)

    Create a new `Cursor` object and call `executemany()` on it with the given sql and parameters. Return the new cursor object.

  `executescript`(sql_script, /)

    Create a new `Cursor` object and call `executescript()` on it with the given sql_script. Return the new cursor object.

  `create_function`(name, narg, func, *, deterministic=False)

    Create or remove a user-defined SQL function.

    Paramètres

      - name (str) -- The name of the SQL function.

      - narg (int) -- The number of arguments the SQL function can accept. If `-1`, it may take any number of arguments.

      - func (callback | None) -- A callable that is called when the SQL function is invoked. The callable must return a type natively supported by SQLite. Set to `None` to remove an existing SQL function.

      - deterministic (bool) -- If `True`, the created SQL function is marked as deterministic, which allows SQLite to perform additional optimizations.

    Lève

      NotSupportedError -- If deterministic is used with SQLite versions older than 3.8.3.

    Nouveau dans la version 3.8: The deterministic parameter.

    Exemple :

        >>> import hashlib
        >>> def md5sum(t):
        ...     return hashlib.md5(t).hexdigest()
        >>> con = sqlite3.connect(":memory:")
        >>> con.create_function("md5", 1, md5sum)
        >>> for row in con.execute("SELECT md5(?)", (b"foo",)):
        ...     print(row)
        ('acbd18db4cc2f85cedef654fccc4a4d8',)

  `create_aggregate`(name, /, n_arg, aggregate_class)

    Create or remove a user-defined SQL aggregate function.

    Paramètres

      - name (str) -- The name of the SQL aggregate function.

      - n_arg (int) -- The number of arguments the SQL aggregate function can accept. If `-1`, it may take any number of arguments.

      - aggregate_class (class | None) --

        A class must implement the following methods:

        - `step()`: Add a row to the aggregate.

        - `finalize()`: Return the final result of the aggregate as a type natively supported by SQLite.

        The number of arguments that the `step()` method must accept is controlled by n_arg.

        Set to `None` to remove an existing SQL aggregate function.

    Exemple :

        class MySum:
            def __init__(self):
                self.count = 0

            def step(self, value):
                self.count += value

            def finalize(self):
                return self.count

        con = sqlite3.connect(":memory:")
        con.create_aggregate("mysum", 1, MySum)
        cur = con.execute("CREATE TABLE test(i)")
        cur.execute("INSERT INTO test(i) VALUES(1)")
        cur.execute("INSERT INTO test(i) VALUES(2)")
        cur.execute("SELECT mysum(i) FROM test")
        print(cur.fetchone()[0])

        con.close()

  `create_collation`(name, callable)

    Create a collation named name using the collating function callable. callable is passed two `string` arguments, and it should return an `integer`:

    - `1` if the first is ordered higher than the second

    - `-1` if the first is ordered lower than the second

    - `0` if they are ordered equal

    The following example shows a reverse sorting collation:

        def collate_reverse(string1, string2):
            if string1 == string2:
                return 0
            elif string1 < string2:
                return 1
            else:
                return -1

        con = sqlite3.connect(":memory:")
        con.create_collation("reverse", collate_reverse)

        cur = con.execute("CREATE TABLE test(x)")
        cur.executemany("INSERT INTO test(x) VALUES(?)", [("a",), ("b",)])
        cur.execute("SELECT x FROM test ORDER BY x COLLATE reverse")
        for row in cur:
            print(row)
        con.close()

    Remove a collation function by setting callable to `None`.

  `interrupt`()

    Call this method from a different thread to abort any queries that might be executing on the connection. Aborted queries will raise an `OperationalError`.

  `set_authorizer`(authorizer_callback)

    Register callable authorizer_callback to be invoked for each attempt to access a column of a table in the database. The callback should return one of `SQLITE_OK`, `SQLITE_DENY`, or `SQLITE_IGNORE` to signal how access to the column should be handled by the underlying SQLite library.

    The first argument to the callback signifies what kind of operation is to be authorized. The second and third argument will be arguments or `None` depending on the first argument. The 4th argument is the name of the database ("main", "temp", etc.) if applicable. The 5th argument is the name of the inner-most trigger or view that is responsible for the access attempt or `None` if this access attempt is directly from input SQL code.

    Please consult the SQLite documentation about the possible values for the first argument and the meaning of the second and third argument depending on the first one. All necessary constants are available in the `sqlite3` module.

  `set_progress_handler`(progress_handler, n)

    Register callable progress_handler to be invoked for every n instructions of the SQLite virtual machine. This is useful if you want to get called from SQLite during long-running operations, for example to update a GUI.

    If you want to clear any previously installed progress handler, call the method with `None` for progress_handler.

    Returning a non-zero value from the handler function will terminate the currently executing query and cause it to raise an `OperationalError` exception.

  `set_trace_callback`(trace_callback)

    Register callable trace_callback to be invoked for each SQL statement that is actually executed by the SQLite backend.

    The only argument passed to the callback is the statement (as `str`) that is being executed. The return value of the callback is ignored. Note that the backend does not only run statements passed to the `Cursor.execute()` methods. Other sources include the transaction management of the `sqlite3` module and the execution of triggers defined in the current database.

    Passing `None` as trace_callback will disable the trace callback.

    Note

    Exceptions raised in the trace callback are not propagated. As a development and debugging aid, use `enable_callback_tracebacks()` to enable printing tracebacks from exceptions raised in the trace callback.

    Nouveau dans la version 3.3.

  `enable_load_extension`(enabled, /)

    Enable the SQLite engine to load SQLite extensions from shared libraries if enabled is `True`; else, disallow loading SQLite extensions. SQLite extensions can define new functions, aggregates or whole new virtual table implementations. One well-known extension is the fulltext-search extension distributed with SQLite.

    Note

    The `sqlite3` module is not built with loadable extension support by default, because some platforms (notably macOS) have SQLite libraries which are compiled without this feature. To get loadable extension support, you must pass the `--enable-loadable-sqlite-extensions` option to configure.

    Raises an auditing event `sqlite3.enable_load_extension` with arguments `connection`, `enabled`.

    Nouveau dans la version 3.2.

    Modifié dans la version 3.10: Added the `sqlite3.enable_load_extension` auditing event.

        con.enable_load_extension(True)

        # Load the fulltext search extension
        con.execute("select load_extension('./fts3.so')")

        # alternatively you can load the extension using an API call:
        # con.load_extension("./fts3.so")

        # disable extension loading again
        con.enable_load_extension(False)

        # example from SQLite wiki
        con.execute("CREATE VIRTUAL TABLE recipe USING fts3(name, ingredients)")
        con.executescript("""
            INSERT INTO recipe (name, ingredients) VALUES('broccoli stew', 'broccoli peppers cheese tomatoes');
            INSERT INTO recipe (name, ingredients) VALUES('pumpkin stew', 'pumpkin onions garlic celery');
            INSERT INTO recipe (name, ingredients) VALUES('broccoli pie', 'broccoli cheese onions flour');
            INSERT INTO recipe (name, ingredients) VALUES('pumpkin pie', 'pumpkin sugar flour butter');
            """)
        for row in con.execute("SELECT rowid, name, ingredients FROM recipe WHERE name MATCH 'pie'"):
            print(row)

        con.close()

  `load_extension`(path, /)

    Load an SQLite extension from a shared library located at path. Enable extension loading with `enable_load_extension()` before calling this method.

    Raises an auditing event `sqlite3.load_extension` with arguments `connection`, `path`.

    Nouveau dans la version 3.2.

    Modifié dans la version 3.10: Added the `sqlite3.load_extension` auditing event.

  `iterdump`()

    Return an iterator to dump the database as SQL source code. Useful when saving an in-memory database for later restoration. Similar to the `.dump` command in the sqlite3 shell.

    Exemple :

        # Convert file example.db to SQL dump file dump.sql
        con = sqlite3.connect('example.db')
        with open('dump.sql', 'w') as f:
            for line in con.iterdump():
                f.write('%s\n' % line)
        con.close()

  `backup`(target, *, pages=- 1, progress=None, name='main', sleep=0.250)

    Create a backup of an SQLite database.

    Works even if the database is being accessed by other clients or concurrently by the same connection.

    Paramètres

      - target (Connection) -- The database connection to save the backup to.

      - pages (int) -- The number of pages to copy at a time. If equal to or less than `0`, the entire database is copied in a single step. Defaults to `-1`.

      - progress (callback | None) -- If set to a callable, it is invoked with three integer arguments for every backup iteration: the status of the last iteration, the remaining number of pages still to be copied, and the total number of pages. Defaults to `None`.

      - name (str) -- The name of the database to back up. Either `"main"` (the default) for the main database, `"temp"` for the temporary database, or the name of a custom database as attached using the `ATTACH DATABASE` SQL statement.

      - sleep (float) -- The number of seconds to sleep between successive attempts to back up remaining pages.

    Example 1, copy an existing database into another:

        def progress(status, remaining, total):
            print(f'Copied {total-remaining} of {total} pages...')

        src = sqlite3.connect('example.db')
        dst = sqlite3.connect('backup.db')
        with dst:
            src.backup(dst, pages=1, progress=progress)
        dst.close()
        src.close()

    Example 2, copy an existing database into a transient copy:

        src = sqlite3.connect('example.db')
        dst = sqlite3.connect(':memory:')
        src.backup(dst)

    Nouveau dans la version 3.7.

  `in_transaction`

    This read-only attribute corresponds to the low-level SQLite autocommit mode.

    `True` if a transaction is active (there are uncommitted changes), `False` otherwise.

    Nouveau dans la version 3.2.

  `isolation_level`

    This attribute controls the transaction handling performed by `sqlite3`. If set to `None`, transactions are never implicitly opened. If set to one of `"DEFERRED"`, `"IMMEDIATE"`, or `"EXCLUSIVE"`, corresponding to the underlying SQLite transaction behaviour, implicit transaction management is performed.

    If not overridden by the isolation_level parameter of `connect()`, the default is `""`, which is an alias for `"DEFERRED"`.

  `row_factory`

    The initial `row_factory` for `Cursor` objects created from this connection. Assigning to this attribute does not affect the `row_factory` of existing cursors belonging to this connection, only new ones. Is `None` by default, meaning each row is returned as a `tuple`.

    See How to create and use row factories for more details.

  `text_factory`

    A callable that accepts a `bytes` parameter and returns a text representation of it. The callable is invoked for SQLite values with the `TEXT` data type. By default, this attribute is set to `str`. If you want to return `bytes` instead, set text_factory to `bytes`.

    Exemple :

        con = sqlite3.connect(":memory:")
        cur = con.cursor()

        AUSTRIA = "Österreich"

        # by default, rows are returned as str
        cur.execute("SELECT ?", (AUSTRIA,))
        row = cur.fetchone()
        assert row[0] == AUSTRIA

        # but we can make sqlite3 always return bytestrings ...
        con.text_factory = bytes
        cur.execute("SELECT ?", (AUSTRIA,))
        row = cur.fetchone()
        assert type(row[0]) is bytes
        # the bytestrings will be encoded in UTF-8, unless you stored garbage in the
        # database ...
        assert row[0] == AUSTRIA.encode("utf-8")

        # we can also implement a custom text_factory ...
        # here we implement one that appends "foo" to all strings
        con.text_factory = lambda x: x.decode("utf-8") + "foo"
        cur.execute("SELECT ?", ("bar",))
        row = cur.fetchone()
        assert row[0] == "barfoo"

        con.close()

  `total_changes`

    Return the total number of database rows that have been modified, inserted, or deleted since the database connection was opened.

### Cursor objects

> A `Cursor` object represents a database cursor which is used to execute SQL statements, and manage the context of a fetch operation. Cursors are created using `Connection.cursor()`, or by using any of the connection shortcut methods.

> Cursor objects are iterators, meaning that if you `execute()` a `SELECT` query, you can simply iterate over the cursor to fetch the resulting rows:

>     for row in cur.execute("SELECT t FROM data"):
>         print(row)

class `sqlite3.``Cursor`

  A `Cursor` instance has the following attributes and methods.

  `execute`(sql, parameters=(), /)

    Execute SQL a single SQL statement, optionally binding Python values using placeholders.

    Paramètres

      - sql (str) -- A single SQL statement.

      - parameters (`dict` | sequence) -- Python values to bind to placeholders in sql. A `dict` if named placeholders are used. A sequence if unnamed placeholders are used. See How to use placeholders to bind values in SQL queries.

    Lève

      Warning -- If sql contains more than one SQL statement.

    If `isolation_level` is not `None`, sql is an `INSERT`, `UPDATE`, `DELETE`, or `REPLACE` statement, and there is no open transaction, a transaction is implicitly opened before executing sql.

    Use `executescript()` to execute multiple SQL statements.

  `executemany`(sql, parameters, /)

    For every item in parameters, repeatedly execute the parameterized SQL statement sql.

    Uses the same implicit transaction handling as `execute()`.

    Paramètres

      - sql (str) -- A single SQL DML statement.

      - parameters (iterable) -- An iterable of parameters to bind with the placeholders in sql. See How to use placeholders to bind values in SQL queries.

    Lève

      - ProgrammingError -- If sql is not a DML statment.

      - Warning -- If sql contains more than one SQL statement.

    Exemple :

        rows = [
            ("row1",),
            ("row2",),
        ]
        # cur is an sqlite3.Cursor object
        cur.executemany("INSERT INTO data VALUES(?)", rows)

  `executescript`(sql_script, /)

    Execute the SQL statements in sql_script. If there is a pending transaction, an implicit `COMMIT` statement is executed first. No other implicit transaction control is performed; any transaction control must be added to sql_script.

    sql_script must be a `string`.

    Exemple :

        # cur is an sqlite3.Cursor object
        cur.executescript("""
            BEGIN;
            CREATE TABLE person(firstname, lastname, age);
            CREATE TABLE book(title, author, published);
            CREATE TABLE publisher(name, address);
            COMMIT;
        """)

  `fetchone`()

    If `row_factory` is `None`, return the next row query result set as a `tuple`. Else, pass it to the row factory and return its result. Return `None` if no more data is available.

  `fetchmany`(size=cursor.arraysize)

    Return the next set of rows of a query result as a `list`. Return an empty list if no more rows are available.

    The number of rows to fetch per call is specified by the size parameter. If size is not given, `arraysize` determines the number of rows to be fetched. If fewer than size rows are available, as many rows as are available are returned.

    Note there are performance considerations involved with the size parameter. For optimal performance, it is usually best to use the arraysize attribute. If the size parameter is used, then it is best for it to retain the same value from one `fetchmany()` call to the next.

  `fetchall`()

    Return all (remaining) rows of a query result as a `list`. Return an empty list if no rows are available. Note that the `arraysize` attribute can affect the performance of this operation.

  `close`()

    Close the cursor now (rather than whenever `__del__` is called).

    The cursor will be unusable from this point forward; a `ProgrammingError` exception will be raised if any operation is attempted with the cursor.

  `setinputsizes`(sizes, /)

    Required by the DB-API. Does nothing in `sqlite3`.

  `setoutputsize`(size, column=None, /)

    Required by the DB-API. Does nothing in `sqlite3`.

  `arraysize`

    Read/write attribute that controls the number of rows returned by `fetchmany()`. The default value is 1 which means a single row would be fetched per call.

  `connection`

    Read-only attribute that provides the SQLite database `Connection` belonging to the cursor. A `Cursor` object created by calling `con.cursor()` will have a `connection` attribute that refers to con:

        >>> con = sqlite3.connect(":memory:")
        >>> cur = con.cursor()
        >>> cur.connection == con
        True

  `description`

    Read-only attribute that provides the column names of the last query. To remain compatible with the Python DB API, it returns a 7-tuple for each column where the last six items of each tuple are `None`.

    It is set for `SELECT` statements without any matching rows as well.

  `lastrowid`

    Read-only attribute that provides the row id of the last inserted row. It is only updated after successful `INSERT` or `REPLACE` statements using the `execute()` method. For other statements, after `executemany()` or `executescript()`, or if the insertion failed, the value of `lastrowid` is left unchanged. The initial value of `lastrowid` is `None`.

    Note

    Inserts into `WITHOUT ROWID` tables are not recorded.

    Modifié dans la version 3.6: Added support for the `REPLACE` statement.

  `rowcount`

    Read-only attribute that provides the number of modified rows for `INSERT`, `UPDATE`, `DELETE`, and `REPLACE` statements; is `-1` for other statements, including CTE queries. It is only updated by the `execute()` and `executemany()` methods.

  `row_factory`

    Control how a row fetched from this `Cursor` is represented. If `None`, a row is represented as a `tuple`. Can be set to the included `sqlite3.Row`; or a callable that accepts two arguments, a `Cursor` object and the `tuple` of row values, and returns a custom object representing an SQLite row.

    Defaults to what `Connection.row_factory` was set to when the `Cursor` was created. Assigning to this attribute does not affect `Connection.row_factory` of the parent connection.

    See How to create and use row factories for more details.

### Row objects

class `sqlite3.``Row`

  A `Row` instance serves as a highly optimized `row_factory` for `Connection` objects. It supports iteration, equality testing, `len()`, and mapping access by column name and index.

  Two `Row` objects compare equal if they have identical column names and values.

  See How to create and use row factories for more details.

  `keys`()

    Return a `list` of column names as `strings`. Immediately after a query, it is the first member of each tuple in `Cursor.description`.

  Modifié dans la version 3.5: Added support of slicing.

### PrepareProtocol objects

class `sqlite3.``PrepareProtocol`

  The PrepareProtocol type's single purpose is to act as a PEP 246 style adaption protocol for objects that can adapt themselves to native SQLite types.

### Exceptions

The exception hierarchy is defined by the DB-API 2.0 (PEP 249).

exception `sqlite3.``Warning`

  This exception is raised by `sqlite3` if an SQL query is not a `string`, or if multiple statements are passed to `execute()` or `executemany()`. `Warning` is a subclass of `Exception`.

exception `sqlite3.``Error`

  The base class of the other exceptions in this module. Use this to catch all errors with one single `except` statement. `Error` is a subclass of `Exception`.

exception `sqlite3.``InterfaceError`

  This exception is raised by `sqlite3` for fetch across rollback, or if `sqlite3` is unable to bind parameters. `InterfaceError` is a subclass of `Error`.

exception `sqlite3.``DatabaseError`

  Exception raised for errors that are related to the database. This serves as the base exception for several types of database errors. It is only raised implicitly through the specialised subclasses. `DatabaseError` is a subclass of `Error`.

exception `sqlite3.``DataError`

  Exception raised for errors caused by problems with the processed data, like numeric values out of range, and strings which are too long. `DataError` is a subclass of `DatabaseError`.

exception `sqlite3.``OperationalError`

  Exception raised for errors that are related to the database's operation, and not necessarily under the control of the programmer. For example, the database path is not found, or a transaction could not be processed. `OperationalError` is a subclass of `DatabaseError`.

exception `sqlite3.``IntegrityError`

  Exception raised when the relational integrity of the database is affected, e.g. a foreign key check fails. It is a subclass of `DatabaseError`.

exception `sqlite3.``InternalError`

  Exception raised when SQLite encounters an internal error. If this is raised, it may indicate that there is a problem with the runtime SQLite library. `InternalError` is a subclass of `DatabaseError`.

exception `sqlite3.``ProgrammingError`

  Exception raised for `sqlite3` API programming errors, for example trying to operate on a closed `Connection`, or trying to execute non-DML statements with `executemany()`. `ProgrammingError` is a subclass of `DatabaseError`.

exception `sqlite3.``NotSupportedError`

  Exception raised in case a method or database API is not supported by the underlying SQLite library. For example, setting deterministic to `True` in `create_function()`, if the underlying SQLite library does not support deterministic functions. `NotSupportedError` is a subclass of `DatabaseError`.

### SQLite and Python types

SQLite natively supports the following types: `NULL`, `INTEGER`, `REAL`, `TEXT`, `BLOB`.

The following Python types can thus be sent to SQLite without any problem:

Type Python | SQLite type
--- | ---
`None` | `NULL`
`int` | `INTEGER`
`float` | `REAL`
`str` | `TEXT`
`bytes` | `BLOB`

This is how SQLite types are converted to Python types by default:

SQLite type | Type Python
--- | ---
`NULL` | `None`
`INTEGER` | `int`
`REAL` | `float`
`TEXT` | depends on `text_factory`, `str` by default
`BLOB` | `bytes`

The type system of the `sqlite3` module is extensible in two ways: you can store additional Python types in an SQLite database via object adapters, and you can let the `sqlite3` module convert SQLite types to Python types via converters.

### Default adapters and converters

There are default adapters for the date and datetime types in the datetime module. They will be sent as ISO dates/ISO timestamps to SQLite.

The default converters are registered under the name "date" for `datetime.date` and under the name "timestamp" for `datetime.datetime`.

This way, you can use date/timestamps from Python without any additional fiddling in most cases. The format of the adapters is also compatible with the experimental SQLite date/time functions.

The following example demonstrates this.

    import sqlite3
    import datetime

    con = sqlite3.connect(":memory:", detect_types=sqlite3.PARSE_DECLTYPES|sqlite3.PARSE_COLNAMES)
    cur = con.cursor()
    cur.execute("create table test(d date, ts timestamp)")

    today = datetime.date.today()
    now = datetime.datetime.now()

    cur.execute("insert into test(d, ts) values (?, ?)", (today, now))
    cur.execute("select d, ts from test")
    row = cur.fetchone()
    print(today, "=>", row[0], type(row[0]))
    print(now, "=>", row[1], type(row[1]))

    cur.execute('select current_date as "d [date]", current_timestamp as "ts [timestamp]"')
    row = cur.fetchone()
    print("current_date", row[0], type(row[0]))
    print("current_timestamp", row[1], type(row[1]))

    con.close()

If a timestamp stored in SQLite has a fractional part longer than 6 numbers, its value will be truncated to microsecond precision by the timestamp converter.

Note

The default "timestamp" converter ignores UTC offsets in the database and always returns a naive `datetime.datetime` object. To preserve UTC offsets in timestamps, either leave converters disabled, or register an offset-aware converter with `register_converter()`.

## How-to guides

### How to use placeholders to bind values in SQL queries

SQL operations usually need to use values from Python variables. However, beware of using Python's string operations to assemble queries, as they are vulnerable to SQL injection attacks. For example, an attacker can simply close the single quote and inject `OR TRUE` to select all rows:

    >>> # Never do this -- insecure!
    >>> symbol = input()
    ' OR TRUE; --
    >>> sql = "SELECT * FROM stocks WHERE symbol = '%s'" % symbol
    >>> print(sql)
    SELECT * FROM stocks WHERE symbol = '' OR TRUE; --'
    >>> cur.execute(sql)

Instead, use the DB-API's parameter substitution. To insert a variable into a query string, use a placeholder in the string, and substitute the actual values into the query by providing them as a `tuple` of values to the second argument of the cursor's `execute()` method.

An SQL statement may use one of two kinds of placeholders: question marks (qmark style) or named placeholders (named style). For the qmark style, parameters must be a sequence whose length must match the number of placeholders, or a `ProgrammingError` is raised. For the named style, parameters should be an instance of a `dict` (or a subclass), which must contain keys for all named parameters; any extra items are ignored. Here's an example of both styles:

    con = sqlite3.connect(":memory:")
    cur = con.execute("CREATE TABLE lang(name, first_appeared)")

    # This is the named style used with executemany():
    data = (
        {"name": "C", "year": 1972},
        {"name": "Fortran", "year": 1957},
        {"name": "Python", "year": 1991},
        {"name": "Go", "year": 2009},
    )
    cur.executemany("INSERT INTO lang VALUES(:name, :year)", data)

    # This is the qmark style used in a SELECT query:
    params = (1972,)
    cur.execute("SELECT * FROM lang WHERE first_appeared = ?", params)
    print(cur.fetchall())

Note

PEP 249 numeric placeholders are not supported. If used, they will be interpreted as named placeholders.

### How to adapt custom Python types to SQLite values

SQLite supports only a limited set of data types natively. To store custom Python types in SQLite databases, adapt them to one of the Python types SQLite natively understands.

There are two ways to adapt Python objects to SQLite types: letting your object adapt itself, or using an adapter callable. The latter will take precedence above the former. For a library that exports a custom type, it may make sense to enable that type to adapt itself. As an application developer, it may make more sense to take direct control by registering custom adapter functions.

#### How to write adaptable objects

Suppose we have a `Point` class that represents a pair of coordinates, `x` and `y`, in a Cartesian coordinate system. The coordinate pair will be stored as a text string in the database, using a semicolon to separate the coordinates. This can be implemented by adding a `__conform__(self, protocol)` method which returns the adapted value. The object passed to protocol will be of type `PrepareProtocol`.

    class Point:
        def __init__(self, x, y):
            self.x, self.y = x, y

        def __conform__(self, protocol):
            if protocol is sqlite3.PrepareProtocol:
                return f"{self.x};{self.y}"

    con = sqlite3.connect(":memory:")
    cur = con.cursor()

    cur.execute("SELECT ?", (Point(4.0, -3.2),))
    print(cur.fetchone()[0])

#### How to register adapter callables

The other possibility is to create a function that converts the Python object to an SQLite-compatible type. This function can then be registered using `register_adapter()`.

    class Point:
        def __init__(self, x, y):
            self.x, self.y = x, y

    def adapt_point(point):
        return f"{point.x};{point.y}"

    sqlite3.register_adapter(Point, adapt_point)

    con = sqlite3.connect(":memory:")
    cur = con.cursor()

    cur.execute("SELECT ?", (Point(1.0, 2.5),))
    print(cur.fetchone()[0])

### How to convert SQLite values to custom Python types

Writing an adapter lets you convert from custom Python types to SQLite values. To be able to convert from SQLite values to custom Python types, we use converters.

Let's go back to the `Point` class. We stored the x and y coordinates separated via semicolons as strings in SQLite.

First, we'll define a converter function that accepts the string as a parameter and constructs a `Point` object from it.

Note

Converter functions are always passed a `bytes` object, no matter the underlying SQLite data type.

    def convert_point(s):
        x, y = map(float, s.split(b";"))
        return Point(x, y)

We now need to tell `sqlite3` when it should convert a given SQLite value. This is done when connecting to a database, using the detect_types parameter of `connect()`. There are three options:

- Implicit: set detect_types to `PARSE_DECLTYPES`

- Explicit: set detect_types to `PARSE_COLNAMES`

- Both: set detect_types to `sqlite3.PARSE_DECLTYPES | sqlite3.PARSE_COLNAMES`. Column names take precedence over declared types.

The following example illustrates the implicit and explicit approaches:

    class Point:
        def __init__(self, x, y):
            self.x, self.y = x, y

        def __repr__(self):
            return f"Point({self.x}, {self.y})"

    def adapt_point(point):
        return f"{point.x};{point.y}"

    def convert_point(s):
        x, y = list(map(float, s.split(b";")))
        return Point(x, y)

    # Register the adapter and converter
    sqlite3.register_adapter(Point, adapt_point)
    sqlite3.register_converter("point", convert_point)

    # 1) Parse using declared types
    p = Point(4.0, -3.2)
    con = sqlite3.connect(":memory:", detect_types=sqlite3.PARSE_DECLTYPES)
    cur = con.execute("CREATE TABLE test(p point)")

    cur.execute("INSERT INTO test(p) VALUES(?)", (p,))
    cur.execute("SELECT p FROM test")
    print("with declared types:", cur.fetchone()[0])
    cur.close()
    con.close()

    # 2) Parse using column names
    con = sqlite3.connect(":memory:", detect_types=sqlite3.PARSE_COLNAMES)
    cur = con.execute("CREATE TABLE test(p)")

    cur.execute("INSERT INTO test(p) VALUES(?)", (p,))
    cur.execute('SELECT p AS "p [point]" FROM test')
    print("with column names:", cur.fetchone()[0])

### Adapter and converter recipes

This section shows recipes for common adapters and converters.

    import datetime
    import sqlite3

    def adapt_date_iso(val):
        """Adapt datetime.date to ISO 8601 date."""
        return val.isoformat()

    def adapt_datetime_iso(val):
        """Adapt datetime.datetime to timezone-naive ISO 8601 date."""
        return val.isoformat()

    def adapt_datetime_epoch(val):
        """Adapt datetime.datetime to Unix timestamp."""
        return int(val.timestamp())

    sqlite3.register_adapter(datetime.date, adapt_date_iso)
    sqlite3.register_adapter(datetime.datetime, adapt_datetime_iso)
    sqlite3.register_adapter(datetime.datetime, adapt_datetime_epoch)

    def convert_date(val):
        """Convert ISO 8601 date to datetime.date object."""
        return datetime.date.fromisoformat(val.decode())

    def convert_datetime(val):
        """Convert ISO 8601 datetime to datetime.datetime object."""
        return datetime.datetime.fromisoformat(val.decode())

    def convert_timestamp(val):
        """Convert Unix epoch timestamp to datetime.datetime object."""
        return datetime.datetime.fromtimestamp(int(val))

    sqlite3.register_converter("date", convert_date)
    sqlite3.register_converter("datetime", convert_datetime)
    sqlite3.register_converter("timestamp", convert_timestamp)

### How to use connection shortcut methods

Using the `execute()`, `executemany()`, and `executescript()` methods of the `Connection` class, your code can be written more concisely because you don't have to create the (often superfluous) `Cursor` objects explicitly. Instead, the `Cursor` objects are created implicitly and these shortcut methods return the cursor objects. This way, you can execute a `SELECT` statement and iterate over it directly using only a single call on the `Connection` object.

    # Create and fill the table.
    con = sqlite3.connect(":memory:")
    con.execute("CREATE TABLE lang(name, first_appeared)")
    data = [
        ("C++", 1985),
        ("Objective-C", 1984),
    ]
    con.executemany("INSERT INTO lang(name, first_appeared) VALUES(?, ?)", data)

    # Print the table contents
    for row in con.execute("SELECT name, first_appeared FROM lang"):
        print(row)

    print("I just deleted", con.execute("DELETE FROM lang").rowcount, "rows")

    # close() is not a shortcut method and it's not called automatically;
    # the connection object should be closed manually
    con.close()

### How to use the connection context manager

A `Connection` object can be used as a context manager that automatically commits or rolls back open transactions when leaving the body of the context manager. If the body of the `with` statement finishes without exceptions, the transaction is committed. If this commit fails, or if the body of the `with` statement raises an uncaught exception, the transaction is rolled back.

If there is no open transaction upon leaving the body of the `with` statement, the context manager is a no-op.

Note

The context manager neither implicitly opens a new transaction nor closes the connection.

    con = sqlite3.connect(":memory:")
    con.execute("CREATE TABLE lang(id INTEGER PRIMARY KEY, name VARCHAR UNIQUE)")

    # Successful, con.commit() is called automatically afterwards
    with con:
        con.execute("INSERT INTO lang(name) VALUES(?)", ("Python",))

    # con.rollback() is called after the with block finishes with an exception,
    # the exception is still raised and must be caught
    try:
        with con:
            con.execute("INSERT INTO lang(name) VALUES(?)", ("Python",))
    except sqlite3.IntegrityError:
        print("couldn't add Python twice")

    # Connection object used as context manager only commits or rollbacks transactions,
    # so the connection object should be closed manually
    con.close()

### How to work with SQLite URIs

Some useful URI tricks include:

- Open a database in read-only mode:

    >>> con = sqlite3.connect("file:tutorial.db?mode=ro", uri=True)
    >>> con.execute("CREATE TABLE readonly(data)")
    Traceback (most recent call last):
    OperationalError: attempt to write a readonly database

- Do not implicitly create a new database file if it does not already exist; will raise `OperationalError` if unable to create a new file:

    >>> con = sqlite3.connect("file:nosuchdb.db?mode=rw", uri=True)
    Traceback (most recent call last):
    OperationalError: unable to open database file

- Create a shared named in-memory database:

    db = "file:mem1?mode=memory&cache=shared"
    con1 = sqlite3.connect(db, uri=True)
    con2 = sqlite3.connect(db, uri=True)
    with con1:
        con1.execute("CREATE TABLE shared(data)")
        con1.execute("INSERT INTO shared VALUES(28)")
    res = con2.execute("SELECT data FROM shared")
    assert res.fetchone() == (28,)

More information about this feature, including a list of parameters, can be found in the SQLite URI documentation.

### How to create and use row factories

By default, `sqlite3` represents each row as a `tuple`. If a `tuple` does not suit your needs, you can use the `sqlite3.Row` class or a custom `row_factory`.

While `row_factory` exists as an attribute both on the `Cursor` and the `Connection`, it is recommended to set `Connection.row_factory`, so all cursors created from the connection will use the same row factory.

`Row` provides indexed and case-insensitive named access to columns, with minimal memory overhead and performance impact over a `tuple`. To use `Row` as a row factory, assign it to the `row_factory` attribute:

    >>> con = sqlite3.connect(":memory:")
    >>> con.row_factory = sqlite3.Row

Queries now return `Row` objects:

    >>> res = con.execute("SELECT 'Earth' AS name, 6378 AS radius")
    >>> row = res.fetchone()
    >>> row.keys()
    ['name', 'radius']
    >>> row[0]         # Access by index.
    'Earth'
    >>> row["name"]    # Access by name.
    'Earth'
    >>> row["RADIUS"]  # Column names are case-insensitive.
    6378

You can create a custom `row_factory` that returns each row as a `dict`, with column names mapped to values:

    def dict_factory(cursor, row):
        fields = [column[0] for column in cursor.description]
        return {key: value for key, value in zip(fields, row)}

Using it, queries now return a `dict` instead of a `tuple`:

    >>> con = sqlite3.connect(":memory:")
    >>> con.row_factory = dict_factory
    >>> for row in con.execute("SELECT 1 AS a, 2 AS b"):
    ...     print(row)
    {'a': 1, 'b': 2}

The following row factory returns a named tuple:

    from collections import namedtuple

    def namedtuple_factory(cursor, row):
        fields = [column[0] for column in cursor.description]
        cls = namedtuple("Row", fields)
        return cls._make(row)

`namedtuple_factory()` can be used as follows:

    >>> con = sqlite3.connect(":memory:")
    >>> con.row_factory = namedtuple_factory
    >>> cur = con.execute("SELECT 1 AS a, 2 AS b")
    >>> row = cur.fetchone()
    >>> row
    Row(a=1, b=2)
    >>> row[0]  # Indexed access.
    1
    >>> row.b   # Attribute access.
    2

With some adjustments, the above recipe can be adapted to use a `dataclass`, or any other custom class, instead of a `namedtuple`.

## Explication

### Transaction control

The `sqlite3` module does not adhere to the transaction handling recommended by PEP 249.

If the connection attribute `isolation_level` is not `None`, new transactions are implicitly opened before `execute()` and `executemany()` executes `INSERT`, `UPDATE`, `DELETE`, or `REPLACE` statements; for other statements, no implicit transaction handling is performed. Use the `commit()` and `rollback()` methods to respectively commit and roll back pending transactions. You can choose the underlying SQLite transaction behaviour — that is, whether and what type of `BEGIN` statements `sqlite3` implicitly executes – via the `isolation_level` attribute.

If `isolation_level` is set to `None`, no transactions are implicitly opened at all. This leaves the underlying SQLite library in autocommit mode, but also allows the user to perform their own transaction handling using explicit SQL statements. The underlying SQLite library autocommit mode can be queried using the `in_transaction` attribute.

The `executescript()` method implicitly commits any pending transaction before execution of the given SQL script, regardless of the value of `isolation_level`.

Modifié dans la version 3.6: `sqlite3` used to implicitly commit an open transaction before DDL statements. This is no longer the case.