Data Science Practicals

A simple NPM CLI package containing 27 Data Science and Python practical programs.

This package provides quick reference access to practical programs covering Pandas, NumPy, Matplotlib, Seaborn, and Scikit-Learn.

Important: This package only displays the practical source code. It does not execute or compile the Python programs.

Installation & Usage

You can run it directly without installing using npx:

npx ds-practicals 1

Or install it globally:

npm install -g ds-practicals

Then run:

ds-practicals 1

Running without a practical number displays the list of all available practicals and usage instructions:

npx ds-practicals

The package displays ONLY the selected practical.

Examples

Run any practical number from 1 to 27:

npx ds-practicals 1
npx ds-practicals 5
npx ds-practicals 10
npx ds-practicals 18
npx ds-practicals 25
npx ds-practicals 27

List of Practicals

| # | Title | |---|---| | 1 | Slip 1 — Student Dataset: One-Hot and Label Encoding | | 2 | Slip 2 — Student Performance Pie and Bar Charts | | 3 | Slip 3 — Random Array Visualization | | 4 | Slip 4 — Salary Quartiles, Percentiles and Outliers | | 5 | Slip 5 — Student Performance Pie and Bar Charts | | 6 | Slip 6 — Employee DataFrame Operations | | 7 | Slip 7 — Employee Age Histogram with Custom Bins | | 8 | Slip 8 — Student Dataset Missing Values and Encoding | | 9 | Slip 9 — Random Array Visualization | | 10 | Slip 10 — Employee DataFrame Operations | | 11 | Slip 11 — Iris Species Bar Plot and Histogram | | 12 | Slip 12 — Student Data Missing Values and Encoding | | 13 | Slip 13 — Country One-Hot and Purchased Label Encoding | | 14 | Slip 14 — Employee Salary Descriptive Statistics | | 15 | Slip 15 — CSV DataFrame Exploration | | 16 | Slip 16 — Weighted Arithmetic, Harmonic and Geometric Means | | 17 | Slip 17 — UCI Dataset Exploration | | 18 | Slip 18 — Iris Dataset Sampling and Summary | | 19 | Slip 19 — Weighted Means of Experimental Data | | 20 | Slip 20 — Outlier Detection and Effect Analysis | | 21 | Slip 21 — Random Array with Outliers Visualization | | 22 | Slip 22 — Employee CSV DataFrame Exploration | | 23 | Slip 23 — L1 Normalization | | 24 | Slip 24 — Diabetes Data Binarization | | 25 | Slip 25 — Equal-Width Price Binning | | 26 | Slip 26 — Iris Species Box Plot | | 27 | Slip 27 — Iris Sepal Length and Width Joint Plot |

Publishing to NPM

1. Log in to your NPM account

npm login

2. Verify the package contents (Dry Run)

npm pack --dry-run

3. Publish to NPM

npm publish

4. Run after publishing

npx ds-practicals 1

5. Update the package later

npm version patch
npm publish

License

MIT © Rohit