📊NumPy Practice !

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

📌 Matrix Multiplication

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

📌 Conditional Selection

np.where(condition, value_if_true, value_if_false)

📌 Sorting & Unique Values

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

📌 Loading CSV Files

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

📌 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

Source: DEV Community.