https://github.com/18alantom/the_genius_batch

In Mumbai University, we are all geniuses!
https://github.com/18alantom/the_genius_batch

data-visualization seaborn

Last synced: 6 days ago
JSON representation

In Mumbai University, we are all geniuses!

• Host: GitHub
• URL: https://github.com/18alantom/the_genius_batch
• Owner: 18alantom
• License: mit
• Created: 2021-05-22T15:26:02.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-06-21T17:57:03.000Z (over 3 years ago)
• Last Synced: 2024-11-08T05:40:31.345Z (about 2 months ago)
• Topics: data-visualization, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 6.56 MB
• Stars: 3
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# the_genius_batch

![2019 and 2020 histograms](png/BannerPlot.png)

Mumbai University results across a few branches for the years 2019 (pre COVID era), and 2020 (COVID era).

Article: [Mumbai University, Everyone Cheated](https://18alan.medium.com/mumbai-university-everyone-cheated-83320b8c351a)

Source: http://mumresults.in  (2nd April, 2021)

Some points:

- Results for engineering courses have the most data, even individual subject marks have been cleaned into `.csv` files.

- Results for non engineering courses mostly have only *Total Marks* or *GPA* or both.

- Each `.csv` file has an accompanying `.json` file that has some metadata.

---

Some convenience code has been written to interface with the csv data and clean it.

```

from mapper import D

d = D()

```

The above line takes about a minute to run.

The `D` object has access to all 32 `.csv` files in the form of `pd.DataFrame` objects. 

To access a data frame this: `d.y[YY].[course].df` can be used.

The `__repr__` methods of `D`'s attributes gives some meaningful info.

---

*Check the `Plotting It.ipynb` notebook to see how the data is accessed.*