how to update mass data using Python Pandas смотреть онлайн

Part 14/20

## Effortlessly Updating Mass Columns in Multiple Files
## with Custom Functions using Pandas, PyArrow and Polars
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Notebook link
https://github.com/AmitXShukla/RPA/blob/main/notebooks/Basic%20calculator.ipynb

YouTube Playlist
https://www.youtube.com/playlist?list=PLp0TENYyY8lEVXWsuaQR5nebML55QBcwN

GitHub Repo
https://github.com/AmitXShukla/RPA

This tutorial will guide you through the process of writing code to update mass columns across CSV or data files.

While this initial notebook focuses on simple calculations,

the Automation series will gradually introduce more complex RPA projects that rely on similar code snippets for mass updates.

By learning to code a basic calculator, you'll have a foundation for more advanced techniques.

Throughout the notebook, you'll define functions for simple arithmetic calculations and algebraic operations passed as parameters.

These concepts will later be applied to perform mass calculations across multiple files and directories.

Keep this notebook handy as a reference.

- define functions for simple arithmetic calculations
- define functions to pass arithmetic/algebraic operations as parameters
- performing mass calculations across files and directories

Imagine yourself as an employee of a bank, tasked with the responsibility of recalculating or verifying the calculations in the aforementioned spreadsheet.

Although it may appear straightforward initially, the task becomes incredibly challenging when you consider that you need to carry it out for

- a total of 350 banks
- each with a minimum of 1+ million daily transactions
- over a period of 365 days
- and for the past 5 years

Here are few options
- using pandas to update columns with calculation using functions
- using pyarrow to update columns with calculation using functions
- using polars to update columns with calculation using functions
- using polars and transform columns inline (with out calling functions)

## mass calculations
- Step 1: OOPs - classes, data structures, custom transformation methods
- Step 2: data transformation using Pandas
- Step 3: data transformation using PyArrow
- Step 4: data transformation using Polars