NumPy in Python

What is NumPy?

NumPy (Numerical Python) is a powerful Python library used for numerical computing. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.

Why Use NumPy?

• High Performance: NumPy operations are faster than traditional Python lists.
• Memory Efficient: Uses less memory compared to Python lists.
• Convenient: Provides many built-in mathematical functions.
• Supports Multidimensional Arrays: Enables handling of complex data structures.
• Used in Data Science & AI: Core library for machine learning and scientific computing.

NumPy Array Creation Example

import numpy as np
arr = np.array([1, 2, 3, 4])
print(arr)

Data Types in NumPy

NumPy supports various data types (dtypes) which define the type of elements in an array:

• int_ – Integer
• float_ – Floating point
• complex_ – Complex numbers
• bool_ – Boolean (True/False)
• str_ – String
• object_ – Python objects

Example:

arr = np.array([1, 2, 3], dtype='float32')
print(arr.dtype)

Common Inbuilt Methods in NumPy

Array Creation Functions

• np.array()
• np.zeros()
• np.ones()
• np.arange()
• np.linspace()

Mathematical Functions

• np.sum()
• np.mean()
• np.min()
• np.max()
• np.std()

Array Operations

• np.reshape()
• np.transpose()
• np.concatenate()
• np.split()

Statistical Functions

• np.median()
• np.var()
• np.corrcoef()

Example of NumPy Operations

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

print(a + b)  # Addition
print(a * b)  # Multiplication
print(np.mean(a))

Conclusion

NumPy is an essential library for anyone working with data in Python. Its speed, efficiency, and powerful functionalities make it a foundation for data science, machine learning, and scientific computing.