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A list is a container that stores items of different data types (ints, floats, Boolean, strings, etc.) in an ordered sequence. It is an important data structure that is in-built in Python. The data is written inside square brackets ([]), and the values are separated by comma(,).

The items inside the list are indexed with the first element starting at index 0. You can make changes in the created list by adding new items or by updating, deleting the existing ones. It can also have duplicate items and a nested list.

There are many methods available on a list, and of the important one is the index().

In this tutorial, you will learn:

Python List index()

The list index() method helps you to find the first lowest index of the given element. If there are duplicate elements inside the list, the first index of the element is returned. This is the easiest and straightforward way to get the index.

Besides the built-in list index() method, you can also use other ways to get the index like looping through the list, using list comprehensions, enumerate(), filter methods.

The list index() method returns the first lowest index of the given element.

Syntax list.index(element, start, end) Parameters

Parameters Description

element The element that you want to get the index.

start This parameter is optional. You can define the start: index to search for the element. If not given, the default value is 0.

end This parameter is optional. You can specify the end index for the element to be searched. If not given, it is considered until the end of the list.

Return Value

The list index() method returns the index of the given element. If the element is not present in the list, the index() method will throw an error, for example, ValueError: ‘Element’ is not in the list.

Example: To find the index of the given element.

In the list my_list = [‘A’, ‘B’, ‘C’, ‘D’, ‘E’, ‘F’] , we would like to know the index for element C and F.

The example below shows how to get the index.

my_list = ['A', 'B', 'C', 'D', 'E', 'F'] print("The index of element C is ", my_list.index('C')) print("The index of element F is ", my_list.index('F'))

Output:

The index of element C is 2 The index of element F is 5 Example: Using start and end in index()

In this example will try to limit searching for index in a list using start and end index.

my_list = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'] print("The index of element C is ", my_list.index('C', 1, 5)) print("The index of element F is ", my_list.index('F', 3, 7)) #using just the startindex print("The index of element D is ", my_list.index('D', 1))

Output:

The index of element C is 2 The index of element F is 5 The index of element D is 3 Example: To test index() method with an element that is not present.

When you try to search for index in the list for element that is not present ,you will get an error as shown below:

my_list = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'] print("The index of element C is ", my_list.index('Z'))

Output:

Traceback (most recent call last): print("The index of element C is ", my_list.index('Z')) ValueError: 'Z' is not in list Using for-loop to get the index of an element in a list

With the list.index() method, we have seen that it gives the index of the element that is passed as an argument.

Now consider the list as : my_list = [‘Guru’, ‘Siya’, ‘Tiya’, ‘Guru’, ‘Daksh’, ‘Riya’, ‘Guru’] . The name ‘Guru’ is present 3 times in the index, and I want all the indexes with the name ‘Guru’.

Using for-loop we should be able to get the multiple indexes as shown in the example below.

my_list = ['Guru', 'Siya', 'Tiya', 'Guru', 'Daksh', 'Riya', 'Guru'] all_indexes = [] for i in range(0, len(my_list)) : if my_list[i] == 'Guru' : all_indexes.append(i) print("Originallist ", my_list) print("Indexes for element Guru : ", all_indexes)

Output:

Originallist ['Guru', 'Siya', 'Tiya', 'Guru', 'Daksh', 'Riya', 'Guru'] Indexes for element Guru : [0, 3, 6] Using while-loop and list.index()

Using a while-loop will loop through the list given to get all the indexes of the given element.

In the list : my_list = [‘Guru’, ‘Siya’, ‘Tiya’, ‘Guru’, ‘Daksh’, ‘Riya’, ‘Guru’], we need the all the indexes of element ‘Guru’.

Below given is an example shows how to get all the indexes using while-loop

my_list = ['Guru', 'Siya', 'Tiya', 'Guru', 'Daksh', 'Riya', 'Guru'] result = [] elementindex = -1 while True: try: elementindex = my_list.index('Guru', elementindex+1) result.append(elementindex) except ValueError: break print("OriginalList is ", my_list) print("The index for element Guru is ", result)

Output:

OriginalList is ['Guru', 'Siya', 'Tiya', 'Guru', 'Daksh', 'Riya', 'Guru'] The index for element Guru is [0, 3, 6] Using list comprehension to get the index of element in a list

To get all the indexes, a fast and straightforward way is to make use of list comprehension on the list.

List comprehensions are Python functions that are used for creating new sequences (such as lists, dictionaries, etc.) i.e., using sequences that have already been created.

They help to reduce longer loops and make your code easier to read and maintain.

Following example shows how to do it:

my_list = ['Guru', 'Siya', 'Tiya', 'Guru', 'Daksh', 'Riya', 'Guru'] print("Originallist ", my_list) all_indexes = [a for a in range(len(my_list)) if my_list[a] == 'Guru'] print("Indexes for element Guru : ", all_indexes)

Output:

Originallist ['Guru', 'Siya', 'Tiya', 'Guru', 'Daksh', 'Riya', 'Guru'] Indexes for element Guru : [0, 3, 6] Using Enumerate to get the index of an element in a list

Enumerate() function is a built-in function available with python. You can make use of enumerate to get all the indexes of the element in a list. It takes input as an iterable object (i.e., an object that can be looped), and the output is an object with a counter to each item.

Following example shows how to make use of enumerate on a list to get the all the indexes for given element.

my_list = ['Guru', 'Siya', 'Tiya', 'Guru', 'Daksh', 'Riya', 'Guru'] print("Originallist ", my_list) print("Indexes for element Guru : ", [i for i, e in enumerate(my_list) if e == 'Guru'])

Output:

Originallist ['Guru', 'Siya', 'Tiya', 'Guru', 'Daksh', 'Riya', 'Guru'] Indexes for element Guru : [0, 3, 6] Using filter to get the index of an element in a list

The filter() method filters the given list based on the function given. Each element of the list will be passed to the function, and the elements required will be filtered based on the condition given in the function.

Let us use the filter() method to get the indexes for the given element in the list.

Following example shows how to make use of filter on a list.

my_list = ['Guru', 'Siya', 'Tiya', 'Guru', 'Daksh', 'Riya', 'Guru'] print("Originallist ", my_list) all_indexes = list(filter(lambda i: my_list[i] == 'Guru', range(len(my_list)))) print("Indexes for element Guru : ", all_indexes)

Output:

Originallist ['Guru', 'Siya', 'Tiya', 'Guru', 'Daksh', 'Riya', 'Guru'] Indexes for element Guru : [0, 3, 6] Using NumPy to get the index of an element in a list

NumPy library is specially used for arrays. So here will make use of NumPy to get the index of the element we need from the list given.

To make use of NumPy, we have to install it and import it.

Here are the steps for same:

Step 1) Install NumPy

pip install numpy

Step 2)Import the NumPy Module.

import numpy as np

Step 3)Make use of np.array to convert list to an array

my_list = ['Guru', 'Siya', 'Tiya', 'Guru', 'Daksh', 'Riya', 'Guru'] np_array = np.array(my_list)

Step 4)Get the index of the element you want, usingnp.where()

item_index = np.where(np_array == 'Guru')[0]

The final working code with output is as follows:

import numpy as np my_list = ['Guru', 'Siya', 'Tiya', 'Guru', 'Daksh', 'Riya', 'Guru'] np_array = np.array(my_list) item_index = np.where(np_array == 'Guru')[0] print("Originallist ", my_list) print("Indexes for element Guru :", item_index)

Output:

Originallist['Guru', 'Siya', 'Tiya', 'Guru', 'Daksh', 'Riya', 'Guru'] Indexes for element Guru : [0 3 6] Using more_itertools.locate() to get the index of an element in a list

The more_itertools.locate() helps to find the indexes of the element in the chúng tôi module will work with python version 3.5+. The package more_itertools has to be installed first to make use of it.

Following are the steps to install and make use of more_itertools

Step1)Install more_itertools using pip (python package manager). The command is

pip install more_itertools

Step 2) Once the installation is done, import the locate module as shown below

from more_itertools import locate

Now you can make use of locate module on a list as shown below in the example:

from more_itertools import locate my_list = ['Guru', 'Siya', 'Tiya', 'Guru', 'Daksh', 'Riya', 'Guru'] print("Originallist : ", my_list) print("Indexes for element Guru :", list(locate(my_list, lambda x: x == 'Guru')))

Output:

Originallist : ['Guru', 'Siya', 'Tiya', 'Guru', 'Daksh', 'Riya', 'Guru'] Indexes for element Guru : [0, 3, 6] Summary:

The list index() method helps you to find the index of the given element. This is the easiest and straightforward way to get the index.

The list index() method returns the index of the given element.

If the element is not present in the list, the index() method will throw an error, for example, ValueError: ‘Element’ is not in list.

Besides the built-in list method, you can also make use of other ways to get the index like looping through the list, using list comprehensions, using enumerate(), using a filter, etc.

Using for-loop and while-loop to get the multiple indexes of a given element.

To get all the indexes, a fast and straightforward way is to make use of list comprehension on the list.

List comprehensions are Python functions that are used for creating new sequences.

They help to reduce longer loops and make your code easier to read and maintain.

You can make use of enumerate to get all the indexes of the element in a list.

Enumerate() function is a built-in function available with python. It takes input as an iterable object (i.e., an object that can be looped), and the output is an object with a counter to each item.

The filter() method filters the given list based on the function given.

Numpy library is specially used for arrays. You can make use of NumPy to get the index of the element given in the list.

The more_itertools.locate() is yet another python library that helps to find the indexes of the list given.

You're reading Python List Index() With Example

Std::list In C++ With Example

What is an std::list?

In C++, the std::list refers to a storage container. The std:list allows you to insert and remove items from anywhere. The std::list is implemented as a doubly-linked list. This means list data can be accessed bi-directionally and sequentially.

The Standard Template Library list doesn’t support fast random access, but it supports sequential access from all directions.

You can scatter list elements in different memory chunks. The information needed for sequential access to data is stored in a container. The std::list can expand and shrink from both ends as needed during runtime. An internal allocator automatically fulfills the storage requirements.

In this C++ tutorial, you will learn:

Why use std::list?

Here, are reason of using std::List :

The std::list does better compare to other sequence containers like array and vector.

They have a better performance in inserting, moving, and extracting elements from any position.

The std::list also does better with algorithms that perform such operations intensively.

List Syntax

Here is a description of the above parameters:

T – Defines the type of element chúng tôi can substitute T by any data type, even user-defined types.

Alloc – Defines the type of the allocator chúng tôi uses the allocator class template by default. It’s value-dependent and uses a simple memory allocation model.

Examples 1:

int main() {

for (int x : my_list) { std::cout << x << ‘n’; } }

Output:

Here is a screenshot of the code:

Code Explanation:

Include the algorithm header file to use its functions.

Include the iostream header file to use its functions.

Include the list header file to use its functions.

Call the main() function. The program logic should be added within the body of this function.

Create a list named my_list with a set of 4 integers.

Use a for loop to create a loop variable x. This variable will be used to iterate over the list elements.

Print out the values of the list on the console.

End of the body of the for a loop.

End of the body of the main() function.

C++ List Functions

Here are the common std::list functions:

Function Description

insert() This function inserts a new item before the position the iterator points.

push_back() This functions add a new item at the list’s end.

push_front() It adds a new item at the list’s front.

pop_front() It deletes the list’s first item.

size() This function determines the number of list elements.

front() To determines the list’s first items.

back() To determines the list’s last item.

reverse() It reverses the list items.

merge() It merges two sorted lists.

Default constructor std::list::list()- It creates an empty list, that, with zero elements.

Fill constructor std::list::list()- It creates a list with n elements and assigns a value of zero (0) to each element.

Range constructor std::list::list()- creates a list with many elements in the range of first to last.

Copy constructor std::list::list()- It creates a list with a copy of each element contained in the existing list.

Move constructor std::list::list()- creates a list with the elements of another list using move semantics.

Initializer list constructor std::list::list()-It creates a list with the elements of another list using move semantics.

Example 2:

using namespace std; int main(void) { cout << “Size of list l: ” << l.size() << endl; cout << “List l2 contents: ” << endl; for (auto it = l2.begin(); it != l2.end(); ++it) cout << *it << endl; cout << “List l3 contents: ” << endl; for (auto it = l3.begin(); it != l3.end(); ++it) cout << *it << endl; return 0; }

Output:

Here is a screenshot of the code:

Code Explanation:

Include the iostream header file to use its functions.

Include the list header file to use its functions.

Include the std namespace in the code to use its classes without calling it.

Call the main() function. The program logic should be added within the body of this function.

Create an empty list named l.

Create a list named l1 with a set of 3 integers.

Create a list named l2 with all elements in the list named l1, from the beginning to the end.

Create a list named l3 using move semantics. The list l3 will have same contents as the list l2.

Print the size of the list named l on the console alongside other text.

Print some text on the console.

Create an iterator named it and use it to iterate over the elements of the list named l2.

Print the elements of the list named l2 on the console.

Print some text on the console.

Create an iterator named it and use it to iterate over the elements of the list named l3.

Print the elements of the list named l3 on the console.

The program must return value upon successful completion.

End of the body of the main() function.

Container properties

Here is the list of container properties:

Property Description

Sequence Sequence containers order their elements in a strict linear sequence. Elements are accessed by their position in the sequence.

Doubly-linked list Every element has information on how to locate previous and next elements. This allows for constant time for insertion and deletion operations.

Allocator-aware An allocator object is used for modifying the storage size dynamically.

Inserting into a List

There are different functions that we can use to insert values into a list. Let’s demonstrate this:

Example 3:

int main() { my_list.push_front(11); my_list.push_back(18); auto it = std::find(my_list.begin(), my_list.end(), 10); if (it != my_list.end()) { my_list.insert(it, 21); } for (int x : my_list) { std::cout << x << ‘n’; } }

Output:

Here is a screenshot of the code:

Code Explanation:

Include the algorithm header file to use its functions.

Include the iostream header file to use its functions.

Include the list header file to use its functions.

Call the main() function. The program logic should be added within the body of this function.

Create a list named my_list with a set of 4 integers.

Insert the element 11 to the front of the list named my_list.

Insert element 18 to the end of the list named my_list.

Create an iterator it and using it to find the element 10 from the list my_list.

Use an if statement to determine if the above element was found or not.

Insert element 21 before the above element if it was found.

End of the body of the if statement.

Use a for loop to create a loop variable x. This variable will be used to iterate over the list elements.

Print out the values of the list on the console.

End of the body of the for a loop.

End of the body of the main() function.

Deleting from a List

It’s possible to delete items from a list. The erase() function allows you to delete an item or a range of items from a list.

To delete a single item, you simply pass one integer position. The item will be deleted.

To delete a range, you pass the starting and the ending iterators. Let’s demonstrate this.

Example 4:

using namespace std; int main() { cout << “List elements before deletion: “; for (int x : my_list) { std::cout << x << ‘n’; } my_list.erase(i); cout << “nList elements after deletion: “; for (int x : my_list) { std::cout << x << ‘n’; } return 0; }

Output:

Here is screenshot of the code:

Code Explanation:

Include the algorithm header file to use its functions.

Include the iostream header file to use its functions.

Include the list header file to use its functions.

Include the std namespace in our program to use its classes without calling it.

Call the main() function. The program logic should be added within the body of this function.

Create a list named my_list with a set of 4 integers.

Print some text on the console.

Use a for loop to create a loop variable x. This variable will be used to iterate over the list elements.

Print out the values of the list on the console.

End of the body of the for loop.

Create an iterator i that points to the first element of the list.

Use the erase() function pointed by the iterator i.

Print some text on the console.

Use a for loop to create a loop variable x. This variable will be used to iterate over the list elements.

Print out the values of the list on the console. This comes after deletion.

End of the body of the for loop.

The program must return a value upon successful completion.

End of the body of the main() function.

Summary:

The std::list is a storage container.

It allows the insertion and deletion of items from anywhere at constant time.

It is implemented as a doubly-link

The std::list data can be accessed bi-directionally and sequentially.

std::list doesn’t support fast random access. However, it supports sequential access from all directions.

You can scatter list elements of std::list in different memory chunks.

You can shrink or expand std::list from both ends as needed during runtime.

To insert items into std::list, we use the insert() function.

To delete items from the std::list, we use the erase() function.

Python Program To Insert Multiple Elements Into The Array From The Specified Index

An array is a collection of homogeneous data elements stored in an organized way. And each data element in the array is identified by an index value.

Arrays in python

Python does not have a native array data structure. So that, we can use the list data structure as an alternative to the arrays.

[10, 4, 11, 76, 99]

Also we can Python Numpy module to work with arrays.

An array defined by the numpy module is −

array([1, 2, 3, 4])

The indexing in python starts from 0, so that the above array elements are accessed using their respective index values like 0, 1, 2, upto n-1.

In the article below, we see different ways to insert multiple elements into the array at the specified index.

Input Output Scenarios

Assume we have an array A with 4 integer values. And the resultant array will have inserted multiple elements at a specified index position.

Input array: [9, 3, 7, 1] Output array: [9, 3, 6, 2, 10, 7, 1]

The elements 6, 2, 10 are inserted at the index position 2 and the element count is increased to 7.

Input arrays: [2 4 6 8 1 3 9] Output array: [1 1 1 2 4 6 8 1 3 9]

Here the elements 1 1 1 are inserted at the 0th index position.

Using List slicing

To insert multiple elements at a specified index, we can use list slice.

Example

In this example, we will use the list slicing.

l = [2, 3, 1, 4, 7, 5] # print initial array print("Original array:", l) specified_index = 1 multiple_elements = 10, 11, 12 # insert element l[specified_index:specified_index] = multiple_elements print("Array after inserting multiple elements:", l) Output Original array: [2, 3, 1, 4, 7, 5] Array after inserting multiple elements: [2, 10, 11, 12, 3, 1, 4, 7, 5] Using List concatenation

Using list slicing and list concatenation we will create a function to insert multiple elements at a specified position. The Python list does not have any method to insert multiple elements at a specified position.

Example

Here we will define a function to insert multiple elements at a given index.

def insert_elements(array, index, elements): return array[:index] + elements + array[index:] l = [1, 2, 3, 4, 5, 6] # print initial array print("Original array: ", l) specified_index = 2 multiple_elements = list(range(1, 4)) # insert element result = insert_elements(l, specified_index, multiple_elements) print("Array after inserting multiple elements: ", result) Output Original array: [1, 2, 3, 4, 5, 6] Array after inserting multiple elements: [1, 2, 1, 2, 3, 3, 4, 5, 6]

The insert_elements function inserted the elements ranging from 1 to 4 at the 2nd index position.

Using numpy.insert() method

In this example, we will use the numpy.insert() method to insert multiple values at the given indices. Following is the syntax −

numpy.insert(arr, obj, values, axis=None)

The method returns a copy of the input array with inserted values. But it does not update the original array.

Example

In this example, we will use the numpy.insert() method to insert 3 elements at 2nd index position.

import numpy as np arr = np.array([2, 4, 6, 8, 1, 3, 9]) # print initial array print("Original array: ", arr) specified_index = 2 multiple_elements = 1, 1, 1 # insert element result = np.insert(arr, specified_index, multiple_elements) print("Array {} after inserting multiple elements at the index {} ".format(result,specified_index)) Output Original array: [2 4 6 8 1 3 9] Array [2 4 1 1 1 6 8 1 3 9] after inserting multiple elements at the index 2

The 3 elements 1, 1, 1 are inserted into the array arr at position 2 successfully.

Example

In this example, we will use the numpy array with all string elements.

import numpy as np arr = np.array(['a','b', 'c', 'd']) # print initial array print("Original array: ", arr) specified_index = 0 multiple_elements = list('ijk') # insert element result = np.insert(arr, specified_index, multiple_elements) print("Array {} after inserting multiple elements at the index {} ".format(result,specified_index)) Output Original array: ['a' 'b' 'c' 'd'] Array ['i' 'j' 'k' 'a' 'b' 'c' 'd'] after inserting multiple elements at the index 0

The elements ‘i’ ‘j’ ‘k’ are inserted at the 0th index position.

Postgresql Create View With Example

What is PostgreSQL View?

In PostgreSQL, a view is a pseudo-table. This means that a view is not a real table. However, we can SELECT it as an ordinary table. A view can have all or some of the table columns. A view can also be a representation of more than one table.

The tables are referred to as base tables. When creating a view, you just need to create a query then give it a name, making it a useful tool for wrapping complex and commonly used queries.

In this PostgreSQL Tutorial, you will learn the following:

Creating PostgreSQL Views

To create a PostgreSQL view, we use the CREATE VIEW statement. Here is the syntax for this statement:

CREATE [OR REPLACE] VIEW view-name AS SELECT column(s) FROM table(s) [WHERE condition(s)];

The OR REPLACE parameter will replace the view if it already exists. If omitted and the view already exists, an error will be returned.

The view-name parameter is the name of the view that you need to create.

The WHERE condition(s) are options, and they must be satisfied for any record to be added to the view.

Consider the Price table given below:

Price:

Let us create a view from the above table:

CREATE VIEW Price_View AS SELECT id, price FROM Price

The above command will create a view based on the SELECT statement. Only the records where the price is greater than 200 will be added to the view. The view has been given the name Price_View. Let us query it to see its contents:

SELECT * FROM Price_View;

This returns the following:

Even though the base table has 4 records, only 2 were added to the view.

Here, we can add only one column to the view. Let us create a view that included only one column of the Price table:

CREATE VIEW Price_View2 AS SELECT price FROM Price

The view has been given the name Price_View2 and includes only the price column of the Price table. Let us query the view to see its contents:

SELECT * FROM Price_View2;

This returns the following:

Changing PostgreSQL Views

The definition of a view can be changed without having to drop it. This is done using the CREATE OR REPLACE VIEW statement.

Let us demonstrate this by updating the view named Price_View2.

Price_View2:

The Book table is as follows:

Book:

The Price table is as follows:

Price:

The following query will help us update the view Price_View2:

CREATE or REPLACE VIEW Price_View2 AS SELECT price, name FROM Book INNER JOIN Price ON chúng tôi = Price.id

Let us now query the view to see its contents:

The view has been changed, and now we have two columns from two different tables. This has been achieved using a JOIN statement.

Deleting PostgreSQL Views

Anytime you need to delete a PostgreSQL view. You can use the DROP VIEW statement. Here is the syntax for the statement:

DROP VIEW [IF EXISTS] view-name;

The parameter view-name is the name of the view that is to be deleted.

In this syntax, IF EXISTS is optional. It is only required. If you don’t specify it and attempt to delete a view that does not exist, you will get an error.

For example, to drop the view named Price_View2, we can run the following statement:

DROP VIEW Price_View2;

The view will be deleted.

Using pgAdmin

Now let’s see how these actions can be performed using pgAdmin.

Creating PostgreSQL Views

To accomplish the same through pgAdmin, do this:

Step 1) Login to your pgAdmin account.

Step 2)

Step 3) Type the query in the query editor:

CREATE VIEW Price_View AS SELECT id, price FROM Price

Step 5) To view the contents of the view, do the following:

Type the following command in the query editor:

SELECT * FROM Price_View;

This will return the following:

To create the view Price_View2, do the following:

Step 1) Type the following query in the query editor:

CREATE VIEW Price_View2 AS SELECT price FROM Price

Step 3) To see the contents of the view, do the following:

Type the following query in the query editor:

SELECT * FROM Price_View2;

This will return the following:

Changing PostgreSQL Views

To accomplish the same through pgAdmin, do this:

Step 1) Login to your pgAdmin account.

Step 2)

Step 3) Type the query in the query editor:

CREATE or REPLACE VIEW Price_View2 AS SELECT price, name FROM Book INNER JOIN Price ON chúng tôi = Price.id

Step 5) Type the following query in the query editor:

SELECT * FROM Price_View2;

This will return the following:

Deleting PostgreSQL Views

To accomplish the same through pgAdmin, do this:

Step 1) Login to your pgAdmin account.

Step 2)

Step 3) Type the query in the query editor:

DROP VIEW Price_View2;

The view will be deleted.

Summary:

A PostgreSQL view is a pseudo-table, meaning that it is not a real table.

A view can be create from one or more tables.

The tables from which a view is created are known as base tables.

To create a view, we use the CREATE OR REPLACE VIEW statement.

To change the definition of a view, we use the CREATE OR REPLACE VIEW statement.

To delete a view, we use the DROP VIEW statement.

Download the Database used in this Tutorial

Understanding Text Classification In Nlp With Movie Review Example Example

This article was published as a part of the Data Science Blogathon.

Introduction

Artificial intelligence has been improved tremendously without needing to change the underlying hardware infrastructure. Users can run an Artificial intelligence program in an old computer system. On the other hand, the beneficiary effect of machine learning is unlimited. Natural Language Processing is one of the branches of AI that gives the machines the ability to read, understand, and deliver meaning. NLP has been very successful in healthcare, media, finance, and human resource.

The most common form of unstructured data is texts and speeches. It’s plenty but hard to extract useful information. If not, it would take a long time to mine the information. Written text and speech contain rich information. It’s because we, as intelligent beings, use writing and speaking as the primary form of communication. NLP can analyze these data for us and do the task like sentiment analysis, cognitive assistant, span filtering, identifying fake news, and real-time language translation.

Topic List

Understand what NLP is?

What does NLP use for?

Words and Sequences

Text classification

Vector Semantic and Word embedding

Probabilistic Language Models

Sequence labeling

Parsers

Semantics

Performing Semantic Analysis on IMDB movie review data project

NLP has widely used in cars, smartphones, speakers, computers, websites, etc. Google Translator usage machine translator which is the NLP system. Google Translator wrote and spoken natural language to desire language users want to translate. NLP helps google translator to understand the word in context, remove extra noises, and build CNN to understand native voice.

NLP is also popular in chatbots. Chatbots is very useful because it reduces the human work of asking what customer needs. NLP chatbot cans ask sequential questions like what the user problem is and where to find the solution. Apple and AMAZON have a robust chatbot in their system. When the user asks some questions, the chatbot converts them into understandable phrases in the internal system.

It’s call toke. Then token goes into NLP to get the idea of what users are asking. NLP is used in information retrieval (IR). IR is a software program that deals with large storage, evaluation of information from large text documents from repositories. It will retrieve only relevant information. For example, it is used in google voice detection to trim unnecessary words.

Application of NLP

Machine Translation i.e. Google Translator

Information retrieval

Question Answering i.e. ChatBot

Summarization

Sentiment Analysis

Social Media Analysis

Mining large data

Words and Sequences

NLP system needs to understand text, sign, and semantic properly. Many methods help the NLP system to understand text and symbols. They are text classification, vector semantic, word embedding, probabilistic language model, sequence labeling, and speech reorganization.

Text classification

Text clarification is the process of categorizing the text into a group of words. By using NLP, text classification can automatically analyze text and then assign a set of predefined tags or categories based on its context. NLP is used for sentiment analysis, topic detection, and language detection. There is mainly three text classification approach-

Rule-based System,

Machine System

Hybrid System.

In the rule-based approach, texts are separated into an organized group using a set of handicraft linguistic rules. Those handicraft linguistic rules contain users to define a list of words that are characterized by groups. For example, words like Donald Trump and Boris Johnson would be categorized into politics. People like LeBron James and Ronaldo would be categorized into sports.

Machine-based classifier learns to make a classification based on past observation from the data sets. User data is prelabeled as tarin and test data. It collects the classification strategy from the previous inputs and learns continuously. Machine-based classifier usage a bag of a word for feature extension.

Source

In a bag of words, a vector represents the frequency of words in a predefined dictionary of a word list. We can perform NLP using the following machine learning algorithms: Naïve Bayer, SVM, and Deep Learning.

The third approach to text classification is the Hybrid Approach. Hybrid approach usage combines a rule-based and machine Based approach. Hybrid based approach usage of the rule-based system to create a tag and use machine learning to train the system and create a rule. Then the machine-based rule list is compared with the rule-based rule list. If something does not match on the tags, humans improve the list manually. It is the best method to implement text classification

Vector Semantic

Vector Semantic is another way of word and sequence analysis. Vector semantic defines semantic and interprets words meaning to explain features such as similar words and opposite words. The main idea behind vector semantic is two words are alike if they have used in a similar context. Vector semantic divide the words in a multi-dimensional vector space. Vector semantic is useful in sentiment analysis.

Source

Word Embedding

Word2vec

Doc2Vec.

Word2Vec is a statistical method for effectively learning a standalone word embedding from a text corpus.

Source

Doc2Vec is similar to Doc2Vec, but it analyzes a group of text like pages.

Probabilistic Language Model

Another approach to word and sequence analysis is the probabilistic language model. The goal of the probabilistic language model is to calculate the probability of a sentence of a sequence of words. For example, the probability of the word “a” occurring in a given word “to” is 0.00013131 percent.

Source

Sequence Labeling

Sequence labeling is a typical NLP task that assigns a class or label to each token in a given input sequence. If someone says “play the movie by tom hanks”. In sequence, labeling will be [play, movie, tom hanks]. Play determines an action. Movies are an instance of action. Tom Hanks goes for a search entity. It divides the input into multiple tokens and uses LSTM to analyze it. There are two forms of sequence labeling. They are token labeling and span labeling.

Parsing is a phase of NLP where the parser determines the syntactic structure of a text by analyzing its constituent words based on an underlying grammar. For example, “tom ate an apple” will be divided into proper noun  tom, verb  ate, determiner  , noun  apple. The best example is Amazon Alexa.

Source

This project covers text mining techniques like Text Embedding, Bags of Words, word context, and other things. We will also cover the introduction of a bidirectional LSTM sentiment classifier. We will also look at how to import a labeled dataset from TensorFlow automatically. This project also covers steps like data cleaning, text processing, data balance through sampling, and train and test a deep learning model to classify text.

Parsing

Parser determines the syntactic structure of a text by analyzing its constituent words based on an underlying grammar. It divides group words into component parts and separates words.

For more details about parsing, check this article.

Semantic

Text is at the heart of how we communicate. What is really difficult is understanding what is being said in written or spoken conversation? Understanding lengthy articles and books are even more difficult.  Semantic is a process that seeks to understand linguistic meaning by constructing a model of the principle that the speaker uses to convey meaning. It’s has been used in customer feedback analysis, article analysis, fake news detection, Semantic analysis, etc.

Example Application

Here is the code Sample:

Importing necessary library 

# For example, here’s several helpful packages to load import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) # Input data files are available in the read-only “../input/” directory import os for dirname, _, filenames in os.walk(‘/kaggle/input’): for filename in filenames: print(os.path.join(dirname, filename)) # You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using “Save & Run All” # You can also write temporary files to /kaggle/temp/, but they won’t be saved outside of the current session #Importing require Libraries import os import matplotlib.pyplot as plt import nltk from tkinter import * import seaborn as sns import matplotlib.pyplot as plt sns.set() import scipy import tensorflow as tf import tensorflow_hub as hub import tensorflow_datasets as tfds from tensorflow.python import keras from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Embedding, LSTM from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report

Downloading necessary file # this cells takes time, please run once # Split the training set into 60% and 40%, so we'll end up with 15,000 examples # for training, 10,000 examples for validation and 25,000 examples for testing. original_train_data, original_validation_data, original_test_data = tfds.load( name="imdb_reviews", split=('train[:60%]', 'train[60%:]', 'test'), as_supervised=True)

Getting word index from Keras datasets #tokanizing by tensorflow word_index = tf.keras.datasets.imdb.get_word_index( path='imdb_word_index.json'

)

In [8]:

{k:v for (k,v) in word_index.items() if v < 20}

Out[8]:

{'with': 16, 'i': 10, 'as': 14, 'it': 9, 'is': 6, 'in': 8, 'but': 18, 'of': 4, 'this': 11, 'a': 3, 'for': 15, 'br': 7, 'the': 1, 'was': 13, 'and': 2, 'to': 5, 'film': 19, 'movie': 17, 'that': 12} Positive and Negative Review Comparision Creating Train, Test Data Model and Model Summary Splitting data and fitting the model Model effect Overview Confusion Matrix and Correlation Report

Note: Data Source and Data for this model is publicly available and can be accessed by using Tensorflow.

For the complete code and details, please follow this GitHub Repository.

In conclusion, NLP is a field full of opportunities. NLP has a tremendous effect on how to analyze text and speeches. NLP is doing better and better every day. Knowledge extraction from the large data set was impossible five years ago. The rise of the NLP technique made it possible and easy. There are still many opportunities to discover in NLP.

Related

How Does Numpy.mean() Work With Example

Introduction to numpy.mean()

Numpy.mean() is function in Python language which is responsible for calculating the arithmetic mean for the all the elements present in the array entered by the user. Simply put the functions takes the sum of all the individual elements present along the provided axis and divides the summation by the number of individual calculated elements. The axis along which the calculation is made has to be prespecified or else the default value for axes will be taken.

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Syntax and Parameters

The following is the syntax that displays how to implement numpy.mean().

The syntax entered by the user is sent in terms of float * 64 intermediate and there by returns the value for the associated integers corresponding for the mean value.

The parameter used in the Syntax for using numpy.mean()

a *: *array *_ *like *

The array is being entered by the user or prompted to be entered. In case the array entered is not of an integer data type, then the conversion of the form is tried on the data entered.

axis : None *, *  *int *, *  *tuple * (optional parameter)

The computation of the axis along the elements of the specified array entered by the user is done. By default, the mean of the pre-flattened array is computed. In case the array entered is a tuple, in such a case the mean is computed over various axes of the array.

 dtype * *: * *data *– *type *, (parameter is optional)

For the computation of the mean the parameter type is utilized. By default, the float 64data type is used for arrays with integer data sets. In case the data being input is floating it remains the same as the dtype entered.

out : ndarray, (parameter is optional)

keepdims: bool, (parameter is optional)

If the parameter specified is True, the axis or axes which are deduced are kept in the expected result as the dimensions having size one. The option enables the result to be broadcasted correctly in response to the array which has been entered. In case value by default, a parameter is passed then the keepdims parameter would not be passed on to the method-specific for mean with respect to the array and its sub-classes. However, it must be noted that for non-default values passed the keepdims parameter would be applicable to raising exceptions if any.

m : ndarray

If the parameter out=None, then in such a case a new array is returned which contains the mean values. Else, in such cases, the reference values with respect to the elements if retuned.

Example to Implementation NumPy.mean()

Below are the examples mentioned:

Code:

import numpy as n1 a1 = n1.array([[10,20,30],[30,40,50],[40,50,60]]) print 'The new array entered by the user is:' print a1 print 'Application of the Numpy.mean() function on the array entered:' print n1.mean(a1) print 'Application of the mean() function alongside the axis - 0:' print n1.mean(a1, axis = 0) print ' Application of the mean() function alongside the axis - 1:' print n1.mean(a1, axis = 1)

The following output would be produced for the code specified above:

How Does the numpy.mean() Work?

The function scans through the values which are specified in the array which is provided by the user. It firstly tries to flatten the resultant array before the computation of the arithmetic mean on the same. The below diagrammatic systemic representation shows the function actually executes the calculation:

We can use the NumPy mean function to compute the mean value:

As the function for mean travels through various axis or axes provided by the user, it scan through and tries to integrate the arithmetic mean functionality for all integral values, Where the elements do not match up to be integral data type, it tries to convert such numbers.

Here you can see for a single dimensional array with six specified elements, the functions scans each of the elements and then divides the total summation of the elements by the total number of elements present in the array (here 6).

This way for arrays with multiple dimensions all or specified axis is mentioned along which the mean is calculated which is displayed in an array form for more than one-dimensional arrays.

Conclusion 

The function mean() in NumPy is very useful for calculating the arithmetic average of elements especially in terms of data given in array subsets. This being calculated through manual code impacts the verbosity of the code and thus impacts on the computation time for long codes with large data sets.

Recommended Articles

This is a guide to numpy.mean(). Here we discuss the introduction and working of numpy.mean() along with different examples and its code implementation. You may also look at the following articles to learn more –

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