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4 Built-in Data Structures in Python: Dictionaries, Lists, Sets, Tuples
In this article, we’ll be focusing on the data structures in Python and help you in understanding the topic clearly. You’ll find out what they are and how they function. We’ve also shared numerous examples to ensure you don’t have any doubts regarding any topics we’ve shared here.
So, without further ado, let’s get started.
What are Data Structures?
Built-in Data Structures in Python
Conclusion
#data science #built in data structures in python #data structures #python
What is GEEK
Buddha Community
1620576960
4 Built-in Data Structures in Python: Dictionaries, Lists, Sets, Tuples
In this article, we’ll be focusing on the data structures in Python and help you in understanding the topic clearly. You’ll find out what they are and how they function. We’ve also shared numerous examples to ensure you don’t have any doubts regarding any topics we’ve shared here.
So, without further ado, let’s get started.
What are Data Structures?
Built-in Data Structures in Python
Conclusion
#data science #built in data structures in python #data structures #python
1619518440
top 30 Python Tips and Tricks for Beginners
Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.
1) swap two numbers.
2) Reversing a string in Python.
3) Create a single string from all the elements in list.
4) Chaining Of Comparison Operators.
5) Print The File Path Of Imported Modules.
6) Return Multiple Values From Functions.
7) Find The Most Frequent Value In A List.
8) Check The Memory Usage Of An Object.
…
#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners
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Types of Data Structures in Python: List, Tuple, Sets & Dictionary
Python is an all-time favourite language for all Data Science enthusiasts. The versatile nature and easy-to-understand approach help developers to focus more on understanding the trends in the data and deriving meaningful insights rather than spending time to fix a minor semicolon bug or closing the overhead bracket. Python being the most popular language among beginners is adapted quickly, so it becomes important to hold a good grasp of this language.
Data Structures is an essential concept in any programming language. It defines how the variables and data can be stored and retrieved from the memory in the best possible way, depending upon the data type. It also defines the relationship between variables, which helps in deciding the operations and functions that should be performed over them. Let’s understand how Python manages data.
Types of Data Structure in Python
#data science #data structures #data structures in python #python
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Basic Data Types in Python | Python Web Development For Beginners
At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.
In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.
Table of Contents hide
III Built-in data types in Python
Mutable objects
The Size and declared value and its sequence of the object can able to be modified called mutable objects.
Mutable Data Types are list, dict, set, byte array
Immutable objects
The Size and declared value and its sequence of the object can able to be modified.
Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.
id() and type() is used to know the Identity and data type of the object
a**=25+**85j
type**(a)**
output**:<class’complex’>**
b**={1:10,2:“Pinky”****}**
id**(b)**
output**:**238989244168
Built-in data types in Python
a**=str(“Hello python world”)****#str**
b**=int(18)****#int**
c**=float(20482.5)****#float**
d**=complex(5+85j)****#complex**
e**=list((“python”,“fast”,“growing”,“in”,2018))****#list**
f**=tuple((“python”,“easy”,“learning”))****#tuple**
g**=range(10)****#range**
h**=dict(name=“Vidu”,age=36)****#dict**
i**=set((“python”,“fast”,“growing”,“in”,2018))****#set**
j**=frozenset((“python”,“fast”,“growing”,“in”,2018))****#frozenset**
k**=bool(18)****#bool**
l**=bytes(8)****#bytes**
m**=bytearray(8)****#bytearray**
n**=memoryview(bytes(18))****#memoryview**
Numbers (int,Float,Complex)
Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.
#signed interger
age**=**18
print**(age)**
Output**:**18
Python supports 3 types of numeric data.
int (signed integers like 20, 2, 225, etc.)
float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)
complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)
A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).
String
The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.
# String Handling
‘Hello Python’
#single (') Quoted String
“Hello Python”
# Double (") Quoted String
“”“Hello Python”“”
‘’‘Hello Python’‘’
# triple (‘’') (“”") Quoted String
In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.
The operator “+” is used to concatenate strings and “*” is used to repeat the string.
“Hello”+“python”
output**:****‘Hello python’**
"python "*****2
'Output : Python python ’
#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type
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Your Data Architecture: Simple Best Practices for Your Data Strategy
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition