Dejah  Reinger

Dejah Reinger

1599786000

Complete Guide On NLP Profiler: Python Tool For Profiling of Textual Dataset

Natural Language Processing is a subfield of Artificial Intelligence that works on making the human language understandable to the machine/computer. NLP has different functionalities that work on the textual data and find out useful insights and information. NLP can practically be used for Speech Recognition, creating voice search engines, etc. NLP can be used to perform a large variety of operations on text data like tokenizing, lamenting, stemming POS tagging, etc.

NLP Profiler is a simple NLP library which works on profiling of textual datasets with one one more text columns. Basically NLP profilers provide us with high-level insights about the data along with the statistical properties of the data. It works the same way as pandas.describe() works for pandas dataframe for statistical properties.

It takes the textual data as input with at least one column with text data and returns a dataframe which contains useful insights about the data like sentiment analysis, the subjectivity of data, etc. NLP profiler is in its early stage and is continuously improving.

In this article, we will explore what are the different functionalities that are there in NLP profiler and implement them in order to gain useful insights from the data.

Implementation:

NLP Profiler can be installed using the git repository where it is hosted. Before Installing it you need to download and install the git version according to your operating system. After git is installed we can install NLP Profiler by running the below-given command in the command prompt.

pip install git+[https://github.com/neomatrix369/nlp_profiler.git@master](https://github.com/neomatrix369/nlp_profiler.git@master)

  1. Importing required libraries

We will load the data using pandas so we will import pandas and for creating the data profile we will import the NLP profiler.

import pandas as pd

from nlp_profiler.core import apply_text_profiling


#developers corner #nlp #pandas profiling #profile #python

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Complete Guide On NLP Profiler: Python Tool For Profiling of Textual Dataset
Dejah  Reinger

Dejah Reinger

1599786000

Complete Guide On NLP Profiler: Python Tool For Profiling of Textual Dataset

Natural Language Processing is a subfield of Artificial Intelligence that works on making the human language understandable to the machine/computer. NLP has different functionalities that work on the textual data and find out useful insights and information. NLP can practically be used for Speech Recognition, creating voice search engines, etc. NLP can be used to perform a large variety of operations on text data like tokenizing, lamenting, stemming POS tagging, etc.

NLP Profiler is a simple NLP library which works on profiling of textual datasets with one one more text columns. Basically NLP profilers provide us with high-level insights about the data along with the statistical properties of the data. It works the same way as pandas.describe() works for pandas dataframe for statistical properties.

It takes the textual data as input with at least one column with text data and returns a dataframe which contains useful insights about the data like sentiment analysis, the subjectivity of data, etc. NLP profiler is in its early stage and is continuously improving.

In this article, we will explore what are the different functionalities that are there in NLP profiler and implement them in order to gain useful insights from the data.

Implementation:

NLP Profiler can be installed using the git repository where it is hosted. Before Installing it you need to download and install the git version according to your operating system. After git is installed we can install NLP Profiler by running the below-given command in the command prompt.

pip install git+[https://github.com/neomatrix369/nlp_profiler.git@master](https://github.com/neomatrix369/nlp_profiler.git@master)

  1. Importing required libraries

We will load the data using pandas so we will import pandas and for creating the data profile we will import the NLP profiler.

import pandas as pd

from nlp_profiler.core import apply_text_profiling


#developers corner #nlp #pandas profiling #profile #python

8 Open-Source Tools To Start Your NLP Journey

Teaching machines to understand human context can be a daunting task. With the current evolving landscape, Natural Language Processing (NLP) has turned out to be an extraordinary breakthrough with its advancements in semantic and linguistic knowledge. NLP is vastly leveraged by businesses to build customised chatbots and voice assistants using its optical character and speed recognition techniques along with text simplification.

To address the current requirements of NLP, there are many open-source NLP tools, which are free and flexible enough for developers to customise it according to their needs. Not only these tools will help businesses analyse the required information from the unstructured text but also help in dealing with text analysis problems like classification, word ambiguity, sentiment analysis etc.

Here are eight NLP toolkits, in no particular order, that can help any enthusiast start their journey with Natural language Processing.


Also Read: Deep Learning-Based Text Analysis Tools NLP Enthusiasts Can Use To Parse Text

1| Natural Language Toolkit (NLTK)

About: Natural Language Toolkit aka NLTK is an open-source platform primarily used for Python programming which analyses human language. The platform has been trained on more than 50 corpora and lexical resources, including multilingual WordNet. Along with that, NLTK also includes many text processing libraries which can be used for text classification tokenisation, parsing, and semantic reasoning, to name a few. The platform is vastly used by students, linguists, educators as well as researchers to analyse text and make meaning out of it.


#developers corner #learning nlp #natural language processing #natural language processing tools #nlp #nlp career #nlp tools #open source nlp tools #opensource nlp tools

Ray  Patel

Ray Patel

1619510796

Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Shardul Bhatt

Shardul Bhatt

1626775355

Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

Complete Guide On NLP Profiler: Python Tool For Profiling of Textual Dataset

Natural Language Processing is a subfield of Artificial Intelligence that works on making the human language understandable to the machine/computer. NLP has different functionalities that work on the textual data and find out useful insights and information. NLP can practically be used for Speech Recognition, creating voice search engines, etc. NLP can be used to perform a large variety of operations on text data like tokenizing, lamenting, stemming POS tagging, etc.

Read more: https://analyticsindiamag.com/complete-guide-on-nlp-profiler-python-tool-for-profiling-of-textual-dataset/

#machine-learning #dataset #nlp #data