NumPy Library in Data Science

We use the NumPy library in data science with Python because it makes working with numbers and large datasets much faster and easier.


Why NumPy is important in data science:

  1. NumPy stores data as arrays that uses less memory

  2. Applies Math Operations directly on all values in the array at once.

  3. We can work with 1D (vectors), 2D (matrices), and even higher-dimensional data.

  4. Libraries like Pandas, Scikit-learn, TensorFlow, and Matplotlib depend on NumPy for handling numeric data.

  5. NumPy has built-in functions for matrix multiplication, eigenvalues, random number generation, and statistical calculations.

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