NumPy (Numerical Python) is the fundamental library for numerical and scientific computing in Python. It provides a fast, memory-efficient way to handle large datasets, perform mathematical operations, and work with multidimensional arrays.
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Whether you’re doing data analysis, machine learning, image processing, or simulations—NumPy is the first tool you must learn.
1. What Is NumPy and Why Use It?
NumPy is a Python library that supports:
- Fast mathematical operations
- Multidimensional array objects (called ndarrays)
- Linear algebra, Fourier transforms, random numbers
- Efficient data handling compared to Python lists
Why you need NumPy
- Python lists are slow for numerical operations.
- NumPy arrays are stored in contiguous memory, making computation much faster.
- Many major libraries (Pandas, SciPy, scikit-learn, TensorFlow) are built on top of NumPy.
2. Installation
If you don’t have NumPy installed:
pip install numpyImport it in your script:
import numpy as npThe alias np is the global standard.
3. Creating NumPy Arrays
From a Python list
import numpy as np
arr = np.array([1, 2, 3, 4])
print(arr)Multidimensional array
matrix = np.array([[1, 2], [3, 4]])Using built-in array generators
np.zeros((3, 3)) # 3x3 matrix of zeros
np.ones((2, 4)) # 2x4 matrix of ones
np.arange(0, 10, 2) # array: [0 2 4 6 8]
np.linspace(1, 5, 4) # evenly spaced: [1. 2.33 3.66 5.]4. NumPy Array Attributes
arr = np.array([[1, 2, 3], [4, 5, 6]])
arr.shape # (2, 3)
arr.ndim # 2
arr.size # 6
arr.dtype # int64 (or similar)5. Indexing and Slicing
Works like Python lists, but more powerful.
arr = np.array([10, 20, 30, 40, 50])
arr[0] # 10
arr[-1] # 50
arr[1:4] # [20 30 40]2D indexing
matrix = np.array([[10, 20, 30],
[40, 50, 60]])
matrix[0, 1] # 20
matrix[:, 2] # third column: [30 60]6. Vectorized Operations
NumPy shines here—no loops needed.
arr = np.array([1, 2, 3, 4])
arr + 5 # [6 7 8 9]
arr * 2 # [2 4 6 8]
arr ** 2 # [1 4 9 16]Element-wise operations make NumPy extremely fast.
7. Mathematical Functions
Built-in universal functions (ufuncs):
np.sqrt(arr)
np.log(arr)
np.sin(arr)
np.sum(arr)
np.mean(arr)
np.max(arr)
np.min(arr)8. Reshaping Arrays
arr = np.arange(12)
arr.reshape(3, 4) # 3 rows, 4 columns9. Combining and Splitting Arrays
Stacking
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
np.vstack((a, b)) # vertical stack
np.hstack((a, b)) # horizontal stackSplitting
np.split(np.arange(10), 2)10. Random Numbers in NumPy
np.random.rand(3, 3) # uniform distribution
np.random.randn(3, 3) # normal distribution
np.random.randint(0, 10, 5) # 5 integers between 0–9Conclusion
NumPy is the backbone of scientific computing in Python. Its powerful array system, speed, and mathematical functions make it essential for data analysis, ML, AI, and numerical simulations. Once you master NumPy, advanced libraries like Pandas and TensorFlow become much easier to understand.
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