Category: Python

Python tutorials

Python

Python Practice Problems: Parsing CSV Files

Day,MxT,MnT,AvT,AvDP,1HrP TPcpn,PDir,AvSp,Dir,MxS,SkyC,MxR,Mn,R AvSLP 1,88,59,74,53.8,0,280,9.6,270,17,1.6,93,23,1004.5 2,79,63,71,46.5,0,330,8.7,340,23,3.3,70,28,1004.5 3,77,55,66,39.6,0,350,5,350,9,2.8,59,24,1016.8 4,77,59,68,51.1,0,110,9.1,130,12,8.6,62,40,1021.1 5,90,66,78,68.3,0,220,8.3,260,12,6.9,84,55,1014.4 6,81,61,71,63.7,0,30,6.2,30,13,9.7,93,60,1012.7 7,73,57,65,53,0,50,9.5,50,17,5.3,90,48,1021.8 8,75,54,65,50,0,160,4.2,150,10,2.6,93,41,1026.3 9,86,32,59,61.5,0,240,7.6,220,12,6,78,46,1018.6 10,84,64,74,57.5,0,210,6.6,50,9,3.4,84,40,1019 11,91,59,75,66.3,0,250,7.1,230,12,2.5,93,45,1012.6 12,88,73,81,68.7,0,250,8.1,270,21,7.9,94,51,1007 13,70,59,65,55,0,150,3,150,8,10,83,59,1012.6 14,61,59,60,55.9,0,60,6.7,80,9,10,93,87,1008.6 15,64,55,60,54.9,0,40,4.3,200,7,9.6,96,70,1006.1 16,79,59,69,56.7,0,250,7.6,240,21,7.8,87,44,1007 17,81,57,69,51.7,0,260,9.1,270,29,5.2,90,34,1012.5 18,82,52,67,52.6,0,230,4,190,12,5,93,34,1021.3 19,81,61,71,58.9,0,250,5.2,230,12,5.3,87,44,1028.5 20,84,57,71,58.9,0,150,6.3,160,13,3.6,90,43,1032.5 21,86,59,73,57.7,0,240,6.1,250,12,1,87,35,1030.7 22,90,64,77,61.1,0,250,6.4,230,9,0.2,78,38,1026.4 23,90,68,79,63.1,0,240,8.3,230,12,0.2,68,42,1021.3 24,90,77,84,67.5,0,350,8.5,10,14,6.9,74,48,1018.2 25,90,72,81,61.3,0,190,4.9,230,9,5.6,81,29,1019.6 26,97,64,81,70.4,0,50,5.1,200,12,4,107,45,1014.9 27,91,72,82,69.7,0,250,12.1,230,17,7.1,90,47,1009 28,84,68,76,65.6,0,280,7.6,340,16,7,100,51,1011 29,88,66,77,59.7,0,40,5.4,20,9,5.3,84,33,1020.6 30,90,45,68,63.6,0,240,6,220,17,4.8,200,41,1022.7    

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Advanced, Listicle, NLP, Python, Text, Unstructured Data

Must Known Techniques for text preprocessing in NLP

This article was published as a part of the Data Science Blogathon In any Machine learning task, cleaning or preprocessing the data is as important as model building. Text data is one of the most unstructured forms of available data and when comes to deal with Human language then it’s too complex. Have you ever wondered how Alexa, Siri, Google assistant can understand, process, and respond in Human language. NLP is a technology that works behind it where before any response […]

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Advanced, Data Science, NLP, Project, Python, PyTorch, Unstructured Data

Language Translation with Transformer In Python!

This article was published as a part of the Data Science Blogathon Introduction Natural Language Processing (NLP) is a field at the convergence of artificial intelligence, and linguistics. The aim is to make the computers understand real-world language or natural language so that they can perform tasks like Question Answering, Language Translation, and many more. NLP has lots of applications in different fields. 1. NLP enables the recognition and prediction of diseases based on electronic health records. 2. It is used […]

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Advanced, NLP, Project, Python, Structured Data

Develop a Customer Review Analysis Platform from scratch

This article was published as a part of the Data Science Blogathon Introduction When we go to buy anything, what is the one factor that helps us choosing one thing over another? Isn’t it the reviews of that product or service, which represent the brand value? In the era of digital advancement and e-commence, almost every product or service has an indirect or direct digital presence. Consumers of these products and services leave feedback on these over various mediums which creates […]

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Python

Python’s filter(): Extract Values From Iterables

Python’s filter() is a built-in function that allows you to process an iterable and extract those items that satisfy a given condition. This process is commonly known as a filtering operation. With filter(), you can apply a filtering function to an iterable and produce a new iterable with the items that satisfy the condition at hand. In Python, filter() is one of the tools you can use for functional programming. In this tutorial, you’ll learn how to: Use Python’s filter() […]

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Python

Python Basics: Setting Up Python

Setting up Python is the first step to becoming a Python programmer. In this course, you’ll learn how to download and install Python for Windows, macOS, and Ubuntu Linux and how to open Python’s Integrated Development and Learning Environment, IDLE. There are many ways to install Python. You can download official Python distributions from Python.org, install from a package manager, and even install specialized distributions for scientific computing, Internet of Things, and embedded systems. This course focuses on official distributions, […]

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Beginner, Data Science, Machine Learning, NLP, Python, Text

Text Analytics of Resume Dataset with NLP!

This article was published as a part of the Data Science Blogathon Introduction We all have made our resumes at some point in time. In a resume, we try to include important facts about ourselves like our education, work experience, skills, etc. Let us work on a resume dataset today.  The text we put in our resume speaks a lot about us. For example, our education, skills, work experience, and other random information about us are all present in a resume. […]

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Python

Python Community Interview With Sebastián Ramírez

Today, I’m joined by Sebastián Ramírez, a software developer at Explosion AI. He is also the creator of the popular frameworks FastAPI and Typer. In this interview, we discuss typing in Python, his motivations for creating FastAPI and the future of the framework, and much more. Without further ado, let’s get into it. Ricky: Thanks for joining me, Sebastián. I’d like to start with the same questions I do with all my guests: how did you get into programming, and […]

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Python

Context Managers and Python’s with Statement

The with statement in Python is a quite useful tool for properly managing external resources in your programs. It allows you to take advantage of existing context managers to automatically handle the setup and teardown phases whenever you’re dealing with external resources or with operations that require those phases. Besides, the context management protocol allows you to create your own context managers so you can customize the way you deal with system resources. So, what’s the with statement good for? […]

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Python

Explore Your Dataset With Pandas

Do you have a large dataset that’s full of interesting insights, but you’re not sure where to start exploring it? Has your boss asked you to generate some statistics from it, but they’re not so easy to extract? These are precisely the use cases where Pandas and Python can help you! With these tools, you’ll be able to slice a large dataset down into manageable parts and glean insight from that information. In this course, you’ll learn how to: Calculate […]

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