Numpy Tutorial — Part 1

2 min readJan 3, 2022

PYTHON· DATA SCIENCE / DATA ANALYST
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Numpy was created in 2005 by Travis Oliphant. It is open source python package and free to use. Numpy stands for Numerical Python. It is the core library for scientific computing in python. It is written in partially in Python and mainly in C/ C++.

Numpy array can be used as a Multi-Dimensional container. It is up to 50X faster than lists. It Occupies less space and convenient to use. The Array object is called ndarray.

Install Numpy

Go to Command prompt(Start>run>cmd )
Type

>>pip install numpy

Once installation is complete, go to your IDE(pycharm, jupyter etc. ) and import Numpy library it by typing

import numpy as np

Version of numpy

import numpy as np
print( np.__version__)

numpy Array

The key difference between List and numpy array is numpy array are designed to handle vectorized operations.

A Numpy array is a grid of values, all the same type, and is indexed by a tuple of non- negative integers. The number of dimensions is the rank of the array, the shape of an array is a tuple of integers giving the size of the array along each dimension.

Using array() function we can create Numpy ndarray.

Zero Dimensional

import numpy as np
arr1 = np.array(100)
print(arr1)

One Dimensional

import numpy as np
arr1 = np.array([1,2,3,4])
print(arr1)

N- Dimensional

import numpy as np
arr1 = np.array([[1,2],[3,4]])
print(arr1)
arr2 = np.array([[[1,2,3],[4,5,6]],[[5,6,7],[6,7,8]]])
print(arr2)

Type

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

Numpy attributes

· ndim : returns an integer value that tells us how many dimension the array have

import numpy as np
a = np.array([[[1,2,3],[4,5,6]],[[7,8,9],[10,11,12]]])
print(a.ndim)

· itemsize : returns the size of each element

import numpy as np
a = np.array([1,2,3,4])
print(a.itemsize)
b =  np.array(['a','b'])
print(b.itemsize)
c=  np.array([1.1,2.,3.1,4.1])
print(c.itemsize)

Output:
4
4
8

· nbytes : number of bytes used to strore data

print(a.nbytes)

· size : returns total number of elements in the array

print(a.size)

· shape : returns a tuple that specifies the number of elements for each dimension of the array.

print(a.shape)

· dtypes : returns the datatype of elements stored in array

print(a.dtype)

Indexing / accession array element

You can access an array element by using its index number. Positive Index starts with Zero.

We can use negative indexing to access elements from the array.

Array →
[11,12,13,14,15]
Index ->
0, 1, 2, 3, 4
-5, -4, -3, -2, -1

One Dimension

import numpy as np
arr= np.array([11,12,13,14,15])
print(arr[0])
print(arr[-5])

Two Dimension

import numpy as np
arr = np.array([[1,2,3],[4,5,6]])
print(arr[1,1])
print(arr[-1,-2])

Download → GITHUB Link — https://github.com/keycomputer/Python/blob/main/Numpy%20Part%201.ipynb