https://github.com/ahnaf19/clean_bankingdata

Here I tried to practice simple ETL tasks. I know how to perform these tasks in SQL, here just explored my way around using pandas as well.
https://github.com/ahnaf19/clean_bankingdata

data-analysis data-cleaning pandas python

Last synced: 2 months ago
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Here I tried to practice simple ETL tasks. I know how to perform these tasks in SQL, here just explored my way around using pandas as well.

• Host: GitHub
• URL: https://github.com/ahnaf19/clean_bankingdata
• Owner: Ahnaf19
• Created: 2024-08-23T13:38:05.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-08-23T14:06:49.000Z (almost 2 years ago)
• Last Synced: 2025-03-28T05:25:15.504Z (about 1 year ago)
• Topics: data-analysis, data-cleaning, pandas, python
• Language: Jupyter Notebook
• Homepage:
• Size: 1.33 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# clean_bankingData

Here I tried to practice simple ETL tasks. I know how to perform these tasks in `postgreSQL`, here just explored my way around using `pandas` as well. In future projects, I would try to work with such a dataset where data cleaning would include handling missing data as well. I would also try to come up with more transformation tasks such as feature engineering and would data analytics pondering over those.

Have a look at my this repo where I went from data cleaning to data analysis with visualization.

## Task

- Extract the given csv file and divide them into three csv files where the data are transformed accordingly

- Transformation instruction  can be found at the beginning of the notebook