connect_sqlite3(name=':memory:', time_formats=None, conn_id='default')¶
Creates a new SQLite3 database connection.
SQLite3 is a popular in-memory database. It is faster than distributed databases, like PostgreSQL, but less stable under massive parallel access, consumes more memory and requires all contained data sets to be loaded into memory, which might fill up too much of your RAM, especially for large data sets.
- name (str, optional):
Name of the sqlite3 file. If the file does not exist, it will be created. Set to “:memory:” for a purely in-memory SQLite3 database.
- 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
DataFramecontaining all its data. Alternatively you can use the
read_query()methods to replace the content of the current
DataFrameinstance or append further rows based on either a table or a specific query.
You can also write your results back into the SQLite3 database. By passing the name for the destination table to
getml.Pipeline.transform(), the features generated from your raw data will be written back. Passing them into
getml.Pipeline.predict(), instead, makes predictions of the target variables to new, unseen data and stores the result into the corresponding table.