The Plotly graphing library has a wide array of visualizations for datasets. And it has native support for Pandas DataFrame, which makes it convenient for datasets coming from a wide range of sources. One visualization that I find particularly useful for hierarchical data is the TreeMap, which can group on data lineage, uses area as … Python: TreeMap visualization of hierarchical Pandas DataFrame
If the dataset you want to analyze with pandas is coming from a normalized relational database, then you can use ‘pandas.read_sql‘ to pull the data directly. In this article, we will deploy a small MariaDB instance with Docker and show how we can create DataFrame directly from a single table or from a join between … Python: constructing a DataFrame from a relational database with pandas
If your data is coming from CSV dumps of relational database tables, then you probably need to flatten the data before starting the training or analysis. It requires an understanding of the relational schema so you can specify the foreign key lookups, but from that pandas can reconstruct the relationships and produce a single flattened … Python: flattening exported csv relational data with pandas