learn Python By Example – How to Find All Combinations For A List Of Objects

All Combinations For A List Of Objects


/* Import combinations with replacements from itertools */
from itertools import combinations_with_replacement

Create a list of objects

/* Create a list of objects to combine */
list_of_objects = ['warplanes', 'armor', 'infantry']

Find all combinations (with replacement) for the list

/* Create an empty list object to hold the results of the loop */
combinations = []

/* Create a loop for every item in the length of list_of_objects, that, */
for i in list(range(len(list_of_objects))):
    /* Finds every combination (with replacement) for each object in the list */
    combinations.append(combinations_with_replacement(list_of_objects, i+1))
[<itertools.combinations_with_replacement at 0x7f85d03c6408>,
 <itertools.combinations_with_replacement at 0x7f85d03c6458>,
 <itertools.combinations_with_replacement at 0x7f85d03c64a8>]

/* Flatten the list of iterators into a single list */
combinations = [i for row in combinations for i in row]

/* View the results */
 ('warplanes', 'warplanes'),
 ('warplanes', 'armor'),
 ('warplanes', 'infantry'),
 ('armor', 'armor'),
 ('armor', 'infantry'),
 ('infantry', 'infantry'),
 ('warplanes', 'warplanes', 'warplanes'),
 ('warplanes', 'warplanes', 'armor'),
 ('warplanes', 'warplanes', 'infantry'),
 ('warplanes', 'armor', 'armor'),
 ('warplanes', 'armor', 'infantry'),
 ('warplanes', 'infantry', 'infantry'),
 ('armor', 'armor', 'armor'),
 ('armor', 'armor', 'infantry'),
 ('armor', 'infantry', 'infantry'),
 ('infantry', 'infantry', 'infantry')]



Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes

Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!

Latest end-to-end Learn by Coding Recipes in Project-Based Learning:

Applied Statistics with R for Beginners and Business Professionals

Data Science and Machine Learning Projects in Python: Tabular Data Analytics

Data Science and Machine Learning Projects in R: Tabular Data Analytics

Python Machine Learning & Data Science Recipes: Learn by Coding

R Machine Learning & Data Science Recipes: Learn by Coding

Comparing Different Machine Learning Algorithms in Python for Classification (FREE)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.  

Google –> SETScholars