How it WorksΒΆ
The msdss_base_database.core.Database class wraps around sqlalchemy for managing databases and pandas for convenient data processing.
sqlalchemy is used for creating connections, building query statements, and executing queries, while pandas is used for bulk reading and writing data from and to databases.
![digraph methods {
rankdir=TB;
sqlalchemy[label="SQLAlchemy" URL="https://www.sqlalchemy.org/" style=filled];
select[label=".select()" shape=rect style=rounded URL="https://docs.sqlalchemy.org/en/14/core/metadata.html?highlight=select#sqlalchemy.schema.Table.select"];
insert[label=".insert()" shape=rect style=rounded URL="https://docs.sqlalchemy.org/en/14/core/metadata.html?highlight=insert#sqlalchemy.schema.Table.insert"];
update[label=".update()" shape=rect style=rounded URL="https://docs.sqlalchemy.org/en/14/core/metadata.html?highlight=update#sqlalchemy.schema.Table.update"];
drop[label=".drop()" shape=rect style=rounded URL="https://docs.sqlalchemy.org/en/14/core/metadata.html?highlight=drop#sqlalchemy.schema.Table.drop"];
delete[label=".delete()" shape=rect style=rounded URL="https://docs.sqlalchemy.org/en/14/core/metadata.html?highlight=delete#sqlalchemy.schema.Table.delete"];
hastable[label=".has_table()" shape=rect style=rounded URL="https://docs.sqlalchemy.org/en/14/core/internals.html?highlight=has_table#sqlalchemy.engine.default.DefaultDialect.has_table"];
execute[label=".execute()" shape=rect style=rounded URL="https://docs.sqlalchemy.org/en/14/core/connections.html?highlight=execute#sqlalchemy.engine.Connection.execute"];
engine[label=".Engine" shape=rect URL="https://docs.sqlalchemy.org/en/14/core/connections.html?highlight=engine#sqlalchemy.engine.Engine"];
inspector[label=".Inspector" shape=rect URL="https://docs.sqlalchemy.org/en/14/core/reflection.html?highlight=inspector#sqlalchemy.engine.reflection.Inspector"];
connection[label=".Connection" shape=rect URL="https://docs.sqlalchemy.org/en/14/core/connections.html?highlight=connection#sqlalchemy.engine.Connection"];
table[label=".Table" shape=rect URL="https://docs.sqlalchemy.org/en/14/core/metadata.html?highlight=table#sqlalchemy.schema.Table"];
pandas[label="pandas" URL="https://pandas.pydata.org/" style=filled];
readsql[label=".read_sql()" shape=rect style=rounded URL="https://pandas.pydata.org/docs/reference/api/pandas.read_sql.html"];
tosql[label=".to_sql()" shape=rect style=rounded URL="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_sql.html?highlight=to_sql"];
dataframe[label=".DataFrame" shape=rect URL="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html?highlight=dataframe#pandas.DataFrame"];
subgraph cluster {
label=< <B>msdss_base_database.core.Database</B> >;
style=rounded;
{rank=min; sqlalchemy -> pandas;}
engine -> sqlalchemy[arrowhead=none];
engine -> table -> {select;insert;update;drop;delete} -> connection -> execute;
engine -> inspector -> hastable;
dataframe -> pandas[arrowhead=none];
dataframe -> {readsql;tosql};
}
}](_images/graphviz-e7c6687149abc8c886d4e0ed45a0c3f8026318f7.png)