What is NumPy?

NumPy stands for Numerical Python.

It is a powerful Python library used for working with arrays (especially large, multi-dimensional arrays) and performing high-level mathematical operations efficiently.



Why NumPy is Faster than Python Lists?



Difference Between NumPy Array and Python List


Feature Python List NumPy Array (ndarray)
Data Type Can store mixed types (int, str, etc.) Stores only same-type data (homogeneous)
Performance Slower in numerical operations Much faster due to internal C implementation
Operations Element-wise ops require loops Supports vectorized operations (no loops)
Memory Usage Consumes more memory More memory-efficient
Functionality Basic operations (append, pop, etc.) Advanced math/statistical functions built-in
Multidimensional Difficult to implement cleanly Easily supports 1D, 2D, 3D, etc.
Use Cases General-purpose programming Data science, machine learning, simulations


Why Use NumPy?

NumPy is used because it makes numerical and scientific computing faster, easier, and more efficient compared to using regular Python lists.



Key Reasons to Use NumPy :

1. Speed :

NumPy arrays are much faster than Python lists due to optimized C-based backend.


2. Less Memory Usage :

NumPy uses fixed-type arrays, which are more memory efficient than lists.


3. Powerful Array Operations :

You can perform mathematical operations (like add, multiply, divide) directly on arrays without using loops.


4. Multi-dimensional Arrays :

Easily handle 2D, 3D, and higher-dimensional data using arrays and matrices.


5. Broadcasting :

Automatically handles operations between arrays of different shapes (e.g., add scalar to array).


6. Built-in Math Functions :

NumPy provides functions like mean( ), sum( ), sqrt( ), sin( ), etc., for quick mathematical and statistical analysis.


7. Foundation for Data Science :

Libraries like Pandas, TensorFlow, Scikit-learn, and Matplotlib are built on or use NumPy internally.



Which Language is NumPy Written In?

NumPy is primarily written in Python, but its core performance-critical parts are implemented in C and Cython.


Component Language Used
High-level API Python
Core performance C
Optimization layer Cython


Why this combination?



Installation of NumPy

Installing NumPy is quick and easy! Just follow the steps below based on your environment.



1. Using pip (Most Common)

pip install numpy

This will download and install the latest version of NumPy.



2. For Jupyter Notebook Users

If you're using Jupyter Notebook, run this in a code cell :

!pip install numpy


3. Using conda (Anaconda Users)

If you're using the Anaconda distribution, run :

conda install numpy

Conda automatically handles dependencies and is great for data science workflows.



4. To Check Installation

After installation, verify it using Python :

import numpy as np
print(np.__version__)


Having Issues?

Try upgrading pip :

pip install --upgrade pip

Or reinstall NumPy :

pip uninstall numpy
pip install numpy


Importing NumPy in Python

To start using NumPy in your Python program, you need to import it first.



Basic Import

import numpy

Now you can use NumPy functions like :

arr = numpy.array([1, 2, 3])
print(arr)


NumPy Alias (as np)

This part creates an alias or a shorter, more convenient name for the numpy library.
Instead of typing numpy.array( ) or numpy.mean( ), you can now use np.array( ) or np.mean( ).



Why np?



Example

import numpy as np
numbers = np.array([10, 20, 30])
print("Array:", numbers)
print("Sum:", np.sum(numbers))

#Output - 
Array: [10 20 30]
Sum: 60


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