đź“ŠNumPy Practice !

🔢 Building Strong Data Analytics Foundations 🚀

Today I focused on improving my Python skills by practicing NumPy, one of the most powerful libraries used in Data Analytics and Machine Learning.
NumPy makes numerical operations faster, cleane…


This content originally appeared on DEV Community and was authored by Ramya .C

🔢 Building Strong Data Analytics Foundations 🚀

Today I focused on improving my Python skills by practicing NumPy, one of the most powerful libraries used in Data Analytics and Machine Learning.
NumPy makes numerical operations faster, cleaner, and more efficient—especially when working with large datasets.

🔍 What I Practiced

✔️ Basic Array Operations

  • Creating arrays: array(), arange(), linspace()
  • Indexing & slicing (1D, 2D, 3D)
  • Mathematical & statistical operations
  • Broadcasting
  • Reshaping arrays with reshape()

✔️ Intermediate Operations

  • Horizontal & vertical stacking
  • Boolean filtering
  • Using the random module: 
  np.random.rand()
  np.random.randn()
  np.random.randint()
  • Vectorization for fast computations

🔥 Additional Advanced Practice

đź“Ś Matrix Multiplication 

np.dot(a, b)
np.matmul(a, b)

đź“Ś Conditional Selection 

np.where(condition, value_if_true, value_if_false)

đź“Ś Sorting & Unique Values 

np.sort(arr)
np.unique(arr)

đź“Ś Loading CSV Files 

np.genfromtxt("data.csv", delimiter=",")

đź“Ś Performance Practice

  • Compared memory usage of Python lists vs NumPy arrays
  • Used %timeit in Jupyter Notebook for speed comparison

🧠 Why NumPy Matters in Data Analytics?

  • Extremely fast numerical operations
  • Clean, simple, and vectorized code
  • Foundation for Pandas, Matplotlib, Scikit-Learn
  • Widely used in data preprocessing and ML models

đź”— GitHub Repository

You can check my NumPy practice code here:
👉 GitHub: https://github.com/ramyacse21/numpy_workspace


This content originally appeared on DEV Community and was authored by Ramya .C


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