🙌🏻Simple combo for data analyst: 3 frameworks joining Python + spreadsheets
In practice, any data analyst works with datasets not only in Jupyter Notebook or Google Colab. Sometimes you have to open spreadsheet files Excel and Google Spreadsheets. Therefore, there is a need to combine Python scripts with built-in spreadsheet tools. The following frameworks come in handy for this:
• XLWings is a Python package that is actually preinstalled on Anaconda and is most often used to automate Excel processes. It is similar to Openpyxl, but more reliable and user-friendly. For example, you can write your own UDF in Python to parse web pages, machine learning, or solve NLP problems on data in a spreadsheet. https://www.xlwings.org/tutorials/
• Mito is a spreadsheet interface for Python, a spreadsheet within Jupyter that generates code. Mito supports basic Python functions like: merge, join, pivot, filtering, sorting, visualization, adding columns, using spreadsheet formulas, etc. https://docs.trymito.io/
• Openpyxl is a set of Python packages for reading from and writing to Excel. For example, you can connect to a local Excel file and access a specific cell or group of cells by fetching data into a DataFrame. And after processing, you can send the data back to the Excel file. In practice, this package is most often used in the financial sector, since processing large datasets in Excel is too slow. https://foss.heptapod.net/openpyxl/openpyxl