The id column is of integer type, and the data column is of BLOB type, used to store arbitrary file data. Suppose we have a table named files with two columns: id and data. ![]() Finally, we use the mit function to commit the transaction and close the connection. Note that we use SQLite’s Binary function to convert the image data into a binary format that SQLite can recognize. Next, we use the conn.execute function to insert the image data into the images table. Then, we use Python’s built-in open function to read the image file and store it in the image_data variable. The SQL to create this field is: name TEXT NOT NULL. ![]() To prevent a record from being created without data in a specified field, you can add the NOT NULL directive. In the above code, we first open a connection to the SQLite database. For my example table, Im creating a name column to hold TEXT data. execute ( 'INSERT INTO images (data) VALUES (?)', ) # Commit the transaction conn. read () # Insert the image data into the database conn. connect ( 'mydatabase.db' ) # Read the image file with open ( 'image.png', 'rb' ) as file : image_data = file. SQL Standard and Multiple Vendor “UPPERCASE” Types.Import sqlite3 # Open a connection to the database conn = sqlite3. Reference for the general set of “UPPERCASE” datatypes is below at SQL types that typically expect to be available on at least two backends The “UPPERCASE” datatypes that are part of sqlalchemy.types are common INTEGER, and TIMESTAMP, which inherit directlyįrom the previously mentioned “CamelCase” types Of UPPERCASE types include VARCHAR, NUMERIC, Of “UPPERCASE” types in a SQLAlchemy application indicates that specificĭatatypes are required, which then implies that the application would normally,īe limited to those backends which use the type exactly as given. Whether or not the current backend supports it. The name of the type is always rendered exactly as given, without regard for Theseĭatatypes are always inherited from a particular “CamelCase” datatype, andĪlways represent an exact datatype. If you do it this way, you dont have to worry about headers in csv file being imported as data. In contrast to the “CamelCase” types are the “UPPERCASE” datatypes. read alterTable.sql sqlite> drop table employeesold At this point, the imported employee data should have the correct data types instead of the default text field. Reference for the general set of “CamelCase” datatypes is below at “CamelCase” types in the general case, as they will generally provide the bestīasic behavior and be automatically portable to all backends. The typical SQLAlchemy application will likely wish to use primarily Interpreting Python numeric or boolean values. As data is sent and receivedįrom the database using this type, based on the dialect in use it may be The BLOB data type in SQLite represents binary data, such as images, audio files, and video files. May render BOOLEAN on a backend such as PostgreSQL, BIT on the Or BIT values 0 and 1, some have boolean literal constants true andįalse while others dont. Not every backend has a real “boolean” datatype some make use of integers Which represents a string datatype that all databases have, ![]() If arguments are needed, such as the lengthĪrgument of 60 in the "email_address" column above, the type may beĪnother “CamelCase” datatype that expresses more backend-specific behavior Table definition or in any SQL expression overall, if noĪrguments are required it may be passed as the class itself, that is, without When using a particular TypeEngine class in a _processor()įrom sqlalchemy import MetaData from sqlalchemy import Table, Column, Integer, String metadata_obj = MetaData () user = Table ( "user", metadata_obj, Column ( "user_name", String, primary_key = True ), Column ( "email_address", String ( 60 )), ).SQL Standard and Multiple Vendor “UPPERCASE” Types.Using “UPPERCASE” and Backend-specific types for multiple backends.
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