Set Operations in Python

Set Operations in Python

In this blog, we will learn set operations. We will see add(), remove(), update(), union(), intersection(), difference() and many more functions with examples.

What is set?

A set object is an unordered collection of distinct hashable objects. Common uses include membership testing, removing duplicates from a sequence, and computing mathematical operations such as intersection, union, difference, and symmetric difference.

Like other collections, sets support x in set, len(set), and for x in set. Being an unordered collection, sets do not record element position or order of insertion. Accordingly, sets do not support indexing, slicing, or other sequence-like behavior. Set items can appear in a different order every time you use them, and cannot be referred to by index or key.

There are currently two built-in set types, set and frozenset.

The set type is mutable — the contents can be changed using methods like add() and remove(). Since it is mutable, it has no hash value and cannot be used as either a dictionary key or as an element of another set.

The frozenset type is immutable and hashable — its contents cannot be altered after it is created; it can therefore be used as a dictionary key or as an element of another set.

The constructors for both classes work the same:

class set([iterable]) class frozenset([iterable])

Return a new set or frozenset object whose elements are taken from iterable.

Instances of set and frozenset provide the following operations:

len(s)

x in s

x not in s

isdisjoint(other)

issubset(other)

issuperset(other)

union(*others)

intersection(*others)

difference(*others)

symmetric_difference(other)

copy()

Create sets

In [1]:
A = {1, 2, 3, 4, 5}
B = {10, 3, 5, 9, 8}
In [2]:
A
Out[2]:
{1, 2, 3, 4, 5}
In [3]:
B
Out[3]:
{3, 5, 8, 9, 10}

Check the len of set A

In [4]:
len(A)
Out[4]:
5

Test x for membership in set.

In [5]:
2 in A
Out[5]:
True
In [6]:
2 in B
Out[6]:
False

Test x for non-membership in set.

In [7]:
20 not in A
Out[7]:
True
In [8]:
5 not in B
Out[8]:
False

isdisjoint

isdisjoint(other)

Return True if the set has no elements in common with other. Sets are disjoint if and only if their intersection is the empty set. Means returns whether two sets have a intersection or not
In [9]:
A.isdisjoint(B)
Out[9]:
False

issubset(other)

set <= other

Test whether every element in the set is in other.
In [10]:
A.issubset(B)
Out[10]:
False

issuperset(other)

set >= other

Test whether every element in other is in the set.

set > other

Test whether the set is a proper superset of other, that is, set >= other and set != other.
In [11]:
A.issuperset(B)
Out[11]:
False

Add item in set A

In [12]:
A = {1, 2, 3, 4, 5}
B = {10, 3, 5, 9, 8}
print(A)
print(B)

{1, 2, 3, 4, 5}
{3, 5, 8, 9, 10}
In [13]:
A.add(12)
In [14]:
A
Out[14]:
{1, 2, 3, 4, 5, 12}
In [15]:
B
Out[15]:
{3, 5, 8, 9, 10}

remove(elem)

Remove element elem from the set. Raises KeyError if elem is not contained in the set.
In [16]:
A.remove(5)
In [17]:
A
Out[17]:
{1, 2, 3, 4, 12}

pop()

Remove and return an arbitrary element from the set. Raises KeyError if the set is empty.
In [18]:
A.pop()
Out[18]:
1
In [19]:
A
Out[19]:
{2, 3, 4, 12}

discard(elem)

Remove element elem from the set if it is present.
In [20]:
A.discard(4)
In [21]:
A
Out[21]:
{2, 3, 12}

Set Union

union(s): Union of A and B is a set of all elements from both sets. Using the operator '|' between 2 existing sets is the same as writing My_Set1.union(My_Set2)..

In [22]:
A|B
Out[22]:
{2, 3, 5, 8, 9, 10, 12}
In [23]:
A.union(B)
Out[23]:
{2, 3, 5, 8, 9, 10, 12}

use union function on B

In [24]:
B.union(A)
Out[24]:
{2, 3, 5, 8, 9, 10, 12}

Set Intersection

intersection(*others)

set & other & ...

Return a new set with elements common to the set and all others.

Intersection of A and B is a set of elements that are common in both the sets.

In [25]:
A & B
Out[25]:
{3}
In [26]:
A.intersection(B)
Out[26]:
{3}
In [27]:
B.intersection(A)
Out[27]:
{3}

Set Difference

difference(*others)

set - other - ...

Return a new set with elements in the set that are not in the others.
In [28]:
A
Out[28]:
{2, 3, 12}
In [29]:
B
Out[29]:
{3, 5, 8, 9, 10}

Difference of the set B from set A(A - B) is a set of elements that are only in A but not in B.

In [30]:
A - B
Out[30]:
{2, 12}
In [31]:
A.difference(B)
Out[31]:
{2, 12}

B - A is a set of elements in B but not in A.

In [32]:
B - A
Out[32]:
{5, 8, 9, 10}
In [33]:
B.difference(A)
Out[33]:
{5, 8, 9, 10}

Symmetric Difference

set ^ other

Return a new set with elements in either the set or other but not both.

The symmetric difference between two sets is the set of all the elements that are either in the first set or the second set but not in both.

In [34]:
A ^ B
Out[34]:
{2, 5, 8, 9, 10, 12}
In [35]:
A.symmetric_difference(B)
Out[35]:
{2, 5, 8, 9, 10, 12}
In [36]:
B.symmetric_difference(A)
Out[36]:
{2, 5, 8, 9, 10, 12}

update(*others)

set |= other | ...

Update the set, adding elements from all others.
In [37]:
A.update(B)
In [38]:
A
Out[38]:
{2, 3, 5, 8, 9, 10, 12}

intersection_update(*others)

set &= other & ...

Update the set, keeping only elements found in it and all others.
In [39]:
A
Out[39]:
{2, 3, 5, 8, 9, 10, 12}
In [40]:
B
Out[40]:
{3, 5, 8, 9, 10}
In [41]:
A.intersection_update(B)
In [42]:
A
Out[42]:
{3, 5, 8, 9, 10}

difference_update(*others)

set -= other | ...

Update the set, removing elements found in others.
In [43]:
A.difference_update(B)
In [44]:
A
Out[44]:
set()

clear()

Remove all elements from the set.
In [45]:
A = {1, 2, 3, 4, 5}
A
Out[45]:
{1, 2, 3, 4, 5}
In [46]:
A.clear()
In [47]:
A
Out[47]:
set()

copy()

Return a shallow copy of the set.
In [48]:
C = B.copy()
In [49]:
C
Out[49]:
{3, 5, 8, 9, 10}

Create another example

In this example, we will create two sets of papa's and mummy's family. We will see which are common members in both the family. If new member comes in one family, how we will add new member. If one member is not there, how we will remove that member. How to get difference between both the family.

In [50]:
papa_family = {'dada', 'dadi', 'papa', 'mummy', 'john', 'priyanka', 'uncle', 'aunty', 'sara'}
mummy_family = {'nana', 'nani', 'mama', 'mami', 'papa', 'mummy', 'john', 'priyanka', 'ratna'}
In [51]:
print(papa_family)
print(mummy_family)

{'dadi', 'john', 'mummy', 'priyanka', 'uncle', 'aunty', 'sara', 'dada', 'papa'}
{'nana', 'john', 'mama', 'mami', 'mummy', 'priyanka', 'nani', 'papa', 'ratna'}

All members in papa's and mummy's family

In [52]:
full_family_members = papa_family | mummy_family
print(full_family_members)

{'nana', 'dadi', 'john', 'mama', 'mummy', 'mami', 'priyanka', 'uncle', 'aunty', 'sara', 'dada', 'nani', 'papa', 'ratna'}

Common members in papa and mammy's family

In [53]:
common_members = papa_family & mummy_family
print(common_members)

{'papa', 'john', 'mummy', 'priyanka'}

Aunty got a new baby boy 'Aarav'. Add new member in papa's family.

In [54]:
new_member = 'aarav'
papa_family.add(new_member)
In [55]:
print(papa_family)

{'dadi', 'john', 'aarav', 'mummy', 'priyanka', 'uncle', 'aunty', 'sara', 'dada', 'papa'}

My nana was suffering from chronic disease. He is no more now.

In [56]:
mummy_family.remove('nana')

After removing nana from mummy's family.

In [57]:
print(mummy_family)

{'john', 'mama', 'mami', 'mummy', 'priyanka', 'nani', 'papa', 'ratna'}

What is difference in papa's family

We have remove commom items,which is present in mummy's family

In [58]:
#papa_family - mummy_family
#or
papa_family.difference(mummy_family)
Out[58]:
{'aarav', 'aunty', 'dada', 'dadi', 'sara', 'uncle'}

Difference in mummy's family

In [59]:
#mummy_family - papa_family
#or
mummy_family.difference(papa_family)
Out[59]:
{'mama', 'mami', 'nani', 'ratna'}

All the members that are either in the papa's family or in the mummy's family but not in both.

In [60]:
#papa_family ^ mummy_family
#or
papa_family.symmetric_difference(mummy_family)
Out[60]:
{'aarav',
 'aunty',
 'dada',
 'dadi',
 'mama',
 'mami',
 'nani',
 'ratna',
 'sara',
 'uncle'}

That's it, in this blog. Thanks for reading.

In [ ]:

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  3. Learn Words

Python for kids

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  2. Plotting points
  3. Learning shapes, size and color
  4. Learning Coordinate Geometry
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Linear Equations

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  3. Cylinder geometry
  4. Disk geometry
  5. Open top box geometry
  6. Rectangle geometry
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  9. Sphere geometry

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Vectors

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  3. Vector with numpy and sympy

Differential equations

  1. First order differential equations

Functions

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  2. Function plotting in 2D

Jacobian

  1. Jacobian

Lagrangian

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  2. Lagrange example

Waves

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Electromagnetism

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  3. Fields
  4. Charges on Equilateral Triangle

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Quantum mechanics concepts

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  3. Electron spin
  4. Position momentum and heisenberg
  5. The EPR paradox explanation
  6. Energy operators the hamiltonian and schrodinger
  7. The harmonic oscillator
  8. Schrodinger equation a simple derivation

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  2. Gamma factor
  3. Spacetime reference frames equations

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  1. Circular motion

Thermodynamics

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  2. Introductory chemical engineering thermodynamics

Formulae

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  1. Classical mechanics

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  2. ChemistryFormulaManager
  3. ElementGroupManager
  4. ElementsManager
  5. EnvironmentManager
  6. MolarManager
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  8. PeriodicTableManager
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  3. Grammar Manager
  4. Tense Manager
  5. Vocabulary Manager
  6. Learn Language Manager
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  2. Plot World, Continent and Cities

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  2. Animate Stars in Different Directions Kids
  3. Move Box in HTML

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  1. Matplotlib Introduction
  2. Single Line Plots
  3. Bar Plots
  4. Histograms Plots
  5. Types of Plotting
  6. Seaborn Introduction
  7. Seaborn Scatterplot
  8. Seaborn Barplot
  9. Seaborn Boxplot
  10. Seaborn Pairplot
  11. Seaborn Heatmap
  12. Seaborn Seaborn Pandas
  13. Seaborn Plot a Simple Linear Relationship Between Two Variables
  14. Seaborn Displot
  15. Seaborn Palette
  16. Three-Dimensional Plotting in Matplotlib
  17. Interactive Visualizations With Bokeh Part 1
  18. Data Visualization With Seaborn - Part 1

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  2. SVG Shapes Advanced
  3. Animate Circle With SVG
  4. SVG Animation
  5. SVG Text
  6. Can We Write Alphabets With Help of SVG?

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  2. Working With MongoDB & Python Using PyMongo and Pandas
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  6. Sentiment Analysis for Hotel Reviews With NLTK and Keras
  7. Simple Sequence Prediction With LSTM
  8. Image Classification With ResNet50 Model
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  10. Predict Diabetes With Machine Learning Algorithms
  11. TensorFlow Build Custom Convolutional Neural Network With MNIST Dataset
  12. Deal Banking Marketing Campaign Dataset With Machine Learning

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  2. Role of StringIndexer and Pipelines in PySpark ML Feature - Part 1
  3. Role of OneHotEncoder and Pipelines in PySpark ML Feature - Part 2
  4. Feature Transformer VectorAssembler in PySpark ML Feature - Part 3
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PyTorch

  1. Build the Neural Network with PyTorch
  2. Image Classification with PyTorch
  3. Twitter Sentiment Classification In PyTorch
  4. Training an Image Classifier in Pytorch

Natural Language Processing

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  2. Handling Text For Machine Learning
  3. Extracting Text From PDF File in Python Using PyPDF2
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  5. Converting Text to Features in Natural Language Processing
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