📊NumPy Practice !

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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:

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