top of page
learn_data_science.jpg

Data Scientist Program

 

Free Online Data Science Training for Complete Beginners.
 


No prior coding knowledge required!

Python Concepts for Data Science : Vectors

Vectors

In this tutorial, we will learn about various vector creation methods in Python.

Python vectors are one-dimensional arrays that are the most common NumPy data structure.

🛑 Do not confuse NumPy vectors with mathematical vectors.


Let’s see how they’re created:


Creation

1-D arrays can be created in many ways, and we can create them based on our needs. Here are some ways to create 1-D arrays:


Method 1

By entering the individual elements of an array, we can create an array. Here is an example:


import numpy as np
​
x = np.array([1, 3, 5, 7, 9])
print(x)

By using the np.array() function with its input argument being a Python list, we are actually converting a Python list into a vector in the above code

Method 2

The np.ones(size) function creates an array of the specified size filled with 1. Similarly, np.zeros(size) creates an array of the value 0.


v1 = np.ones(5)
v0 = np.zeros(5)
print(v1)
print(v0)

Note: Data type of values inside the vectors generated from ones() and zeros() functions are floating points.

Method 3 The arange() function can be used to initialize an array. It can take up to three arguments. np.arange(start, end, step) The first argument indicates the start point, the second argument indicates the endpoint, and the third argument indicates the step size.


print(np.arange(1, 7)) # Takes default steps of 1 and doesn't include 7
print(np.arange(5)) # Starts at 0 by defualt and ends at 4, giving 5 numbers
print(np.arange(1, 10, 3))   # Starts at 1 and ends at less than 10, with a step size of 3
                                

Method 4 By using the linspace() function, we can also define an array of equally spaced elements containing both endpoints. Run the code below to see the implementation of linspace():

print(np.linspace(1, 12, 12)) 
print(np.linspace(1, 12, 5))
print(np.linspace(1, 12, 3))

The juypter notebook related to this post can be found here


 
 
 

Comentarios


COURSES, PROGRAMS & CERTIFICATIONS

 

Advanced Business Analytics Specialization

Applied Data Science with Python (University of Michigan)

Data Analyst Professional Certificate (IBM)

Data Science Professional Certificate (IBM)

Data Science Specialization (John Hopkins University)

Data Science with Python Certification Training 

Data Scientist Career Path

Data Scientist Nano Degree Program

Data Scientist Program

Deep Learning Specialization

Machine Learning Course (Andrew Ng @ Stanford)

Machine Learning, Data Science and Deep Learning

Machine Learning Specialization (University of Washington)

Master Python for Data Science

Mathematics for Machine Learning (Imperial College London)

Programming with Python

Python for Everybody Specialization (University of Michigan)

Python Machine Learning Certification Training

Reinforcement Learning Specialization (University of Alberta)

Join our mailing list

Data Insight participates in affiliate programs and may sometimes get a commission through purchases made through our links without any additional cost to our visitors.

bottom of page