getml.database.connect_odbc(server_name, user='', password='', escape_chars='"', double_precision='DOUBLE PRECISION', integer='INTEGER', text='TEXT', time_formats=None, conn_id='default')[source]

Creates a new ODBC database connection.

ODBC is standardized format that can be used to connect to almost any database.

Before you use the ODBC connector, make sure the database is up and running and that the appropriate ODBC drivers are installed.


server_name (str): The server name, as referenced in your .obdc.ini file.

user (str, optional): User name with which to log into the database.

You do not need to pass this, if it is already contained in your .odbc.ini.

password (str, optional): Password with which to log into the database.

You do not need to pass this, if it is already contained in your .odbc.ini.

escape_chars (str, optional): ODBC drivers are supposed to support

escaping table and column names using ‘”’ characters irrespective of the syntax in the target database. Unfortunately, not ODBC drivers follow this standard. This is why some tuning might be necessary.

The escape_chars value behaves as follows:

  • If you pass an empty string, schema, table and column names will not be escaped at all. This is not a problem unless some table or column names are identical to SQL keywords.

  • If you pass a single character, schema, table and column names will be enveloped in that character: “TABLE_NAME”.”COLUMN_NAME” (standard SQL) or TABLE_NAME.`COLUMN_NAME` (MySQL/MariaDB style).

  • If you pass two characters, table, column and schema names will be enveloped between these to characters. For instance, if you pass “[]”, the produced queries look as follows: [TABLE_NAME].[COLUMN_NAME] (MS SQL Server style).

  • If you pass more than two characters, the engine will throw an exception.

double_precision (str, optional): The keyword used for double precision columns.

integer (str, optional): The keyword used for integer columns.

text (str, optional): The keyword used for text columns.

time_formats (List[str], optional):

The list of formats tried when parsing time stamps.

The formats are allowed to contain the following special characters:

  • %w - abbreviated weekday (Mon, Tue, …)

  • %W - full weekday (Monday, Tuesday, …)

  • %b - abbreviated month (Jan, Feb, …)

  • %B - full month (January, February, …)

  • %d - zero-padded day of month (01 .. 31)

  • %e - day of month (1 .. 31)

  • %f - space-padded day of month ( 1 .. 31)

  • %m - zero-padded month (01 .. 12)

  • %n - month (1 .. 12)

  • %o - space-padded month ( 1 .. 12)

  • %y - year without century (70)

  • %Y - year with century (1970)

  • %H - hour (00 .. 23)

  • %h - hour (00 .. 12)

  • %a - am/pm

  • %A - AM/PM

  • %M - minute (00 .. 59)

  • %S - second (00 .. 59)

  • %s - seconds and microseconds (equivalent to %S.%F)

  • %i - millisecond (000 .. 999)

  • %c - centisecond (0 .. 9)

  • %F - fractional seconds/microseconds (000000 - 999999)

  • %z - time zone differential in ISO 8601 format (Z or +NN.NN)

  • %Z - time zone differential in RFC format (GMT or +NNNN)

  • %% - percent sign

conn_id (str, optional): The name to be used to reference the connection.

If you do not pass anything, this will create a new default connection.


By selecting an existing table of your database in from_db() function, you can create a new DataFrame containing all its data. Alternatively you can use the read_db() and read_query() methods to replace the content of the current DataFrame instance or append further rows based on either a table or a specific query.

You can also write your results back into the database. By passing the name for the destination table to getml.pipeline.Pipeline.transform(), the features generated from your raw data will be written back. Passing them into getml.pipeline.Pipeline.predict(), instead, makes predictions of the target variables to new, unseen data and stores the result into the corresponding table.