![]() Python is versatile and can represent mostly anything, but Python data structures (lists, dictionaries, tuples, etc.) are very slow and can’t be used. But some decisions must be made for other types, such as strings, dates and times, and categories. When loading data into memory, it must decide how it will be stored in memory.įor simple data like integers of floats, this is standard and straightforward. To understand what is new and better with the latest version, let’s briefly discuss how Pandas works.īefore doing anything with Pandas, the general idea is to load data in-memory into a Pandas DataFrame, usually using functions like read_csv, read_sql, read_parquet, etc.
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