28 Jupyter Notebook Tips, Tricks, and Shortcuts for Data Science. Jupyter Notebook Jupyter notebook, formerly known as the IPython notebook, is a flexible tool that helps you create readable analyses, as you can keep code, images, comments, formulae and plots together. In this post, we’ve collected some of the top Jupyter notebook tips to quickly turn you into a Jupyter power user!
(This post is based on a post that originally appeared on Alex Rogozhnikov’s blog, ‘Brilliantly Wrong’. We have expanded the post and will continue to do so over time — if you have a suggestion please let us know. Thanks to Alex for graciously letting us republish his work here.) Jupyter is quite extensible, supports many programming languages and is easily hosted on your computer or on almost any server — you only need to have ssh or http access. Best of all, it’s completely free. The Jupyter interface. When working with Python in Jupyter, the IPython kernel is used, which gives us some handy access to IPython features from within our Jupyter notebooks (more on that later!) ? ! Exploring Magic Methods in Python 3 is Vital for Programmers. 4. Map, Filter and Reduce — Python Tips 0.1 documentation.
These are three functions which facilitate a functional approach to programming. We will discuss them one by one and understand their use cases. 4.1. Map¶ Map applies a function to all the items in an input_list. Here is the blueprint: Blueprint map(function_to_apply, list_of_inputs) Most of the times we want to pass all the list elements to a function one-by-one and then collect the output. Items = [1, 2, 3, 4, 5]squared = []for i in items: squared.append(i**2) Map allows us to implement this in a much simpler and nicer way. Items = [1, 2, 3, 4, 5]squared = list(map(lambda x: x**2, items)) Most of the times we use lambdas with map so I did the same. Def multiply(x): return (x*x)def add(x): return (x+x) funcs = [multiply, add]for i in range(5): value = list(map(lambda x: x(i), funcs)) print(value) # Output:# [0, 0]# [1, 2]# [4, 4]# [9, 6]# [16, 8] 4.2. As the name suggests, filter creates a list of elements for which a function returns true. 4.3.
Now let’s try it with reduce: Python in Visual Studio Code. Working with Python in Visual Studio Code, using the Microsoft Python extension, is simple, fun, and productive. The extension makes VS Code an excellent Python editor, and works on any operating system with a variety of Python interpreters. It leverages all of VS Code's power to provide auto complete and IntelliSense, linting, debugging, and unit testing, along with the ability to easily switch between Python environments, including virtual and conda environments. This article provides only an overview of the different capabilities of the Python extension for VS Code. For a walkthrough of editing, running, and debugging code, use the button below.
Python Hello World Tutorial Install Python and the Python extension# The tutorial guides you through installing Python and using the extension. Once you have a version of Python installed, activate it using the Python: Select Interpreter command. You can configure the Python extension through settings. Insiders program# Run Python code# Linting# Top 10 Python Web Frameworks to Learn in 2018. Frameworks make developers’ lives easier by offering them a structure for application development. They automate the implementation of common solutions, cutting development time and allowing developers to focus more on application logic instead of routine elements. In this article, we share our own list of the top ten Python web frameworks that we believe will be useful on your way to becoming a professional backend developer and improving your existing skill set. Keep in mind that these frameworks are listed in no particular order: we’ve focused on showing what you’re able to do as opposed to telling what you should do.
Some things to consider When deciding which framework to use, look at the size and complexity of your project. You can find information about the type and focus of some frameworks here. However, frameworks can also stand in the way of development. Full-stack frameworks Django Official Website | GitHub | PyPI | Awesome GitHub Stars: 31,592 | GitHub Forks: 13,361 Pyramid Web2py. Python 3 Regular Expressions. Advertisements A regular expression is a special sequence of characters that helps you match or find other strings or sets of strings, using a specialized syntax held in a pattern.
Regular expressions are widely used in UNIX world. The module re provides full support for Perl-like regular expressions in Python. The re module raises the exception re.error if an error occurs while compiling or using a regular expression. We would cover two important functions, which would be used to handle regular expressions. Nevertheless, a small thing first: There are various characters, which would have special meaning when they are used in regular expression. Basic patterns that match single chars Compilation flags Compilation flags let you modify some aspects of how regular expressions work.
The match Function This function attempts to match RE pattern to string with optional flags. Here is the syntax for this function − re.match(pattern, string, flags = 0) Here is the description of the parameters − Example.