Spreadsheets share many features with databases, but they are not quite the same. A table extracted from an SQL query from a database can somewhat resemble a spreadsheet. Not surprisingly, spreadsheets can be used to import large amounts of data into a database and a table can be exported from the database in the form of a spreadsheet. But the similarities end there.
Database tables are often the result of an SQL query, which is a series of SQL language statements that allow you to extract, select, and transform the data contained within a database, modifying its format and its original structure to get a data table. Therefore, while the spreadsheets introduce excellent calculation tools applicable to the various areas of the data table, the SQL tables are the result of a complex series of manipulations carried out on original data, based on selections of data of interest from a vast amount of data.
The figure below shows how the SQL Tables started from the characteristics present in the Paper Worksheets, to adapt to relational databases, showing new and powerful properties:
The idea of being able to treat data in memory as if it were an SQL table is incredibly captivating. Likewise, selectively interacting on data selections by applying formulas and calculations as is done with spreadsheets can make the tabular data management powerful.
Well, the pandas library introduced DataFrames as structured data, which makes all this possible. These objects that are the basis of data analysis with Pandas, have an internal tabular structure and are the evolution of the two previous technologies. By combining the calculation features of spreadsheets and the way of working of SQL languages, they provide a new and powerful tool for data analysis.
The figure below shows how many fundamental properties of Spreadsheets and SQL Tables can be found in the DataFrame, the main data structures of the Pandas library:
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