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Python Data Science using List and Iterators
Data science is the process of organizing, processing, and analyzing vast amounts of data in order to extract knowledge and insights from them. It involves a number of different fields, including statistical and mathematical modelling, data extraction from its source, and methods for data visualization. Working with big data technology to gather both structured and unstructured data is commonly required. In the parts that follow, we'll examine several applications of data science and how python might be useful there.
Python is a widely used high-level, general-purpose, object-oriented, and interpreted language. To utilize Python for a task, one only needs to supply the logic because Python itself handles the majority of the coding work. Python has a highly comprehensive library that contains pre-defined code for practically every use.
Introduction to Data Science using Python
Python is a high-level language that we use in various domains, including programming and application development. And as we discussed above data science is a field of study in which we work with various data from numerous fields to derive useful business insights. Here, we talked about using Python, a high-level language, for data science.
A very flexible language that is easy to program in and can perform extremely complex mathematical processing is needed for data science programming. Given that it has previously established itself as a language for both general and scientific computing, Python is best suited for such requirements. Additionally, it is constantly improved with fresh additions to its assortment of libraries tailored to different programming requirements.
What is a List in Python?
Using lists, you can keep several elements in a single variable. The list is one of four built-in data types in Python for storing data collections; the others are tuple, set, and dictionary, each of which serves a particular function. Data structures are the containers that are used to store the elements in a proper way such that the elements can be arranged and operated with maximum efficiency of time and space or memory.
One of the main advantages of List in Python − The fact that a list comprehension in Python is a single tool that can be applied to a variety of circumstances is one of its key advantages. List comprehensions can be used for filtering and mapping in addition to creating regular lists. For each situation, you don't have to take a distinct method of action. List stores all types of data types variables in it, including lists itself and it is very flexible to work with.
To get an element of the list once can use the iterator or by using the indexing method we can get the elements from the list.
What is an Iterator?
A collection of elements or items, such as a List or Set, can be iterated through or looped through using an iterator object.
Iterating, a technical term for looping the elements, gave rise to the name "iterator."
We have many data structures in each programming languages, some of the data structures shares the contiguous memory and iterators are not really needed there, but when a data structure don’t have the contiguous memory then we can use the concept of the iterator to iterate over them. Iterator are objects which moves overs the data structure based on the defined algorithm to insert, delete, or get an element which reduces the time complexity of these operations.
What is an Iterator in Python?
In Python, an object called an iterator is used to loop through objects that can be iterated, such as lists, tuples, dicts, and sets. The iter() method is used to initialize the iterator object. And we can iterates over the data structures or the containers using the next() method.
__iter__() − To initialise an iterator, the iter() method is used. An iterator object is the result of this. Let’s see its syntax
myiterator = iter()
Here ‘myiterator’ is the name of the iterator and iter() is the method to initialize the iterator object.
__next__() − The iterable's next value is returned by the __next__() method. A for loop internally employs the iter() method to obtain an iterator object, which then uses the next() method to iterate over any iterable object that is being traversed. To indicate the end of the iteration, this method raises a StopIteration.
item = next(myiterator)
Here ‘myiterator’ is the name of the iterator and next() is the method to move to the next position in the given data structure and it will return an iterator which will be stored in the item variable.
Iterator vs. Iterable
The iterable objects in Python are the List, Tuple, Set, and Dict. Iterable is basically an object on which a user can iterator by using an iterator. The iter() function on these iterable objects can be used to obtain an iterator. Additionally, it regarded the Python string object as an iterable object.
tupleObj = ("Black", "Yellow") myiterator = iter(tupleObj) print(next(myiterator))
In the above syntax, we have tupleObj is a tuple object which contains two values. In the second line, we have myiterator object which is an iterator as it is storing the return value of the function iter() over the tuple object. At last, we can see the use of the next() function which is just used to take the iterator one memory address next.
In this article, we have learned that Data science is the process of organizing, processing, and analyzing vast amounts of data in order to extract knowledge and insights from them. Python is a high-level language that we use in various domains, including programming and application development. The list is one of four built-in data types in Python for storing data collections; the others are tuple, set, and dictionary, each of which serves a particular function. The iterable objects in Python are the List, Tuple, Set, and Dict. An Iterable is basically an object on which a user can iterator by using an iterator.
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