Towards seamless integration
Posted: Mon Jan 27, 2025 4:50 am
From an analyst's point of view, however, an even simpler data transfer would be desirable. Manufacturers of analytical work environments already take such requirements into account and make their notebook systems more convenient in this respect. For example, by automatically saving the result set of an executed SQL query in one or more data frames in a notebook cell. As a data analyst, you then have direct access to intermediate results already obtained in any subsequent notebook cells and can continue working without interruption. The advantages are obvious: general time savings and you can immediately focus on the next analysis step.
Fig. 3 illustrates possible algeria telegram screening options as an example: In a so-called "SQL cell" of a hex notebook, an analyst selects a (previously set up) database connection and executes an SQL query [6] . The result returned by the database could now either be linked to another SQL cell in the same notebook or, as in this case, automatically saved as a data frame query_result_6 . The content of dataframe query_result_6 is then used in a visualization component provided by the Hex Notebook system (bar chart in Fig. 3) and is later used again in a Python cell.
Since Hex uses dataframe objects based on the Pandas software library, analysts can immediately carry out further data manipulation and analysis steps in a Python cell [7] . And the reverse path also works, i.e. the result dataframe of a Python cell can also be referenced in a subsequent SQL cell in the notebook and queried using an SQL statement. In the background, Hex processes dataframe SQL queries using DuckDB , a process-integrated SQL OLAP database [8] . The SQL syntax is very similar to that of PostgreSQL and therefore does not require any special knowledge.
Fig. 3 illustrates possible algeria telegram screening options as an example: In a so-called "SQL cell" of a hex notebook, an analyst selects a (previously set up) database connection and executes an SQL query [6] . The result returned by the database could now either be linked to another SQL cell in the same notebook or, as in this case, automatically saved as a data frame query_result_6 . The content of dataframe query_result_6 is then used in a visualization component provided by the Hex Notebook system (bar chart in Fig. 3) and is later used again in a Python cell.
Since Hex uses dataframe objects based on the Pandas software library, analysts can immediately carry out further data manipulation and analysis steps in a Python cell [7] . And the reverse path also works, i.e. the result dataframe of a Python cell can also be referenced in a subsequent SQL cell in the notebook and queried using an SQL statement. In the background, Hex processes dataframe SQL queries using DuckDB , a process-integrated SQL OLAP database [8] . The SQL syntax is very similar to that of PostgreSQL and therefore does not require any special knowledge.