https://github.com/yashuv/numpy-for-data-science

A well structured practical deep dive into functional programming in NumPy.
https://github.com/yashuv/numpy-for-data-science

ipynb-notebook numpy python3

Last synced: about 2 months ago
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A well structured practical deep dive into functional programming in NumPy.

• Host: GitHub
• URL: https://github.com/yashuv/numpy-for-data-science
• Owner: yashuv
• Created: 2022-05-25T06:37:20.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-05-28T07:23:26.000Z (over 2 years ago)
• Last Synced: 2024-11-13T08:42:05.002Z (about 2 months ago)
• Topics: ipynb-notebook, numpy, python3
• Language: Jupyter Notebook
• Homepage:
• Size: 347 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# NumPy-for-Data-Science 🔥

What I learnt? 👨‍💻📙🐍 


  🔸Basics of NumPy              🔸Vectorization

  🔸Ndarray                      🔸Apply Functions

  🔸Data Types                   🔸Interpolation 

  🔸Import and Export Data       🔸Universal Functions (uFuncs)

  🔸Handling Missing Data        🔸Fitting Polynomials

  🔸Random Numbers               🔸Matrix Opertions for Linear Algebra 

  🔸Statistical Summaries        🔸Solving Linear Equations

  🔸Data Manipulation





To run the code in your local machine, follow some steps below:


1. Download the root folder (Numpy for Data Science) in your machine. Set this folder as the current working directory.

2. You can run the Jupyter Notebooks in the corresponding folder. All files are in .ipynb format beacause you can visualize the code line by line for clear understanding of concepts.

3. It is required to have installed some python packages as mentioned in 'requirements.txt' file. Command for this: "pip install -r requirements.txt" OR You can do it one by one on your own.

4. Open Juputer notebook and load the folder on it so that you can run code file of your choice. 

To get rid of all these steps, you can run those files in the Google Colab (https://colab.research.google.com/) without having overhead.

If problems still exist, you can mail me on [email protected]

Thank You! HappY Coding..