DEV Community

Ramya .C
Ramya .C

Posted on

📊NumPy Practice !

#datascience #beginners #learning #python

🔢 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()
Enter fullscreen mode Exit fullscreen mode
  • Vectorization for fast computations

🔥 Additional Advanced Practice

📌 Matrix Multiplication 

np.dot(a, b)
np.matmul(a, b)
Enter fullscreen mode Exit fullscreen mode

📌 Conditional Selection 

np.where(condition, value_if_true, value_if_false)
Enter fullscreen mode Exit fullscreen mode

📌 Sorting & Unique Values 

np.sort(arr)
np.unique(arr)
Enter fullscreen mode Exit fullscreen mode

📌 Loading CSV Files 

np.genfromtxt("data.csv", delimiter=",")
Enter fullscreen mode Exit fullscreen mode

📌 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

🔗 GitHub Repository

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

Top comments (0)