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Welcome to Python Cheatsheet! Anyone can forget how to make character classes for a regex, slice a list or do a for loop.This cheat sheet tries to provide a basic reference for beginner and advanced developers, lower the entry barrier for newcomers and help veterans refresh the old tricks. The functionalities discussed have very speci c applications and act like a SHORTCUT or a CHEAT-SHEET in competitive coding. Having these useful tricks up your sleeve might just give someone the COMPETITIVE EDGE that they were looking for!! Java and Python for Competitive Programming Set 2 Article Tags: Competitive Programming Python 7. Python: Python is commonly used in scientific computing, data mining, and machine learning. It's the fastest-growing programming language, and is currently No. 3 on the TIOBE Index.
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Python is one of the preferred languages among coders for most of the competitive programming challenges. Most of the problems are easily computed in a reasonable time frame using python.
For some of the complex problem, writing fast-enough python code is often a challenge. Below are some of the pythonic code constructs that help to improve the performance of your code in competitive coding −
1. Strings concatenation: Do not use the below construct.
Above method gives huge time overhead.Instead, try to use this (join method) −
2. The Map function
Generally, you have an input in competitive coding, something like −
To get them as a list of numbers simply
Always use the input() function irrespective of the type of input and then convert it using the map function.
The map function is one of the beautiful in-built function of python, which comes handy many times. Worth knowing.
3. Collections module
In case we want to remove duplicates from a list. While in other languages like Java you may have to use HashMap or any other freaky way, however, in pytho it's simply
Also, be careful to use extend() and append() in lists, while merging two or more lists.
4. Language constructs
It's better to write your code within functions, although the procedural code is supported in Python.
is much better than
It is faster to store local variables than globals because of the underlying Cpython implementation.
5. Use the standard library:
It’s better to use built-in functions and standard library package as much as possible. There, instead of −
Use this −
Likewise, try to use the itertools(standard library), as they are much faster for a common task. For example, you can have something like permutation for a loop with a few lines of code.
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Generators are excellent constructs to reduce both, the memory footprint and the average time complexity of the code you’ve written.
This post updates a previous very popular post 50+ Data Science, Machine Learning Cheat Sheets by Bhavya Geethika. If we missed some popular cheat sheets, add them in the comments below.
Cheatsheets on Python, R and Numpy, Scipy, Pandas
Data science is a multi-disciplinary field. Thus, there are thousands of packages and hundreds of programming functions out there in the data science world! An aspiring data enthusiast need not know all. A cheat sheet or reference card is a compilation of mostly used commands to help you learn that language’s syntax at a faster rate. Here are the most important ones that have been brainstormed and captured in a few compact pages.
Mastering Data science involves understanding of statistics, mathematics, programming knowledge especially in R, Python & SQL and then deploying a combination of all these to derive insights using the business understanding & a human instinct—that drives decisions.
Here are the cheat sheets by category:
Cheat sheets for Python:
Python is a popular choice for beginners, yet still powerful enough to back some of the world’s most popular products and applications. It's design makes the programming experience feel almost as natural as writing in English. Python basics or Python Debugger cheat sheets for beginners covers important syntax to get started. Community-provided libraries such as numpy, scipy, sci-kit and pandas are highly relied on and the NumPy/SciPy/Pandas Cheat Sheet provides a quick refresher to these.
- Python Cheat Sheet by DaveChild via cheatography.com
- Python Basics Reference sheet via cogsci.rpi.edu
- OverAPI.com Python cheatsheet
- Python 3 Cheat Sheet by Laurent Pointal
Cheat sheets for R:
The R's ecosystem has been expanding so much that a lot of referencing is needed. The R Reference Card covers most of the R world in few pages. The Rstudio has also published a series of cheat sheets to make it easier for the R community. The data visualization with ggplot2 seems to be a favorite as it helps when you are working on creating graphs of your results.
- R markdown cheatsheet, part 2
- DataCamp’s Data Analysis the data.table way
Cheat sheets for MySQL & SQL:
For a data scientist basics of SQL are as important as any other language as well. Both PIG and Hive Query Language are closely associated with SQL- the original Structured Query Language. SQL cheatsheets provide a 5 minute quick guide to learning it and then you may explore Hive & MySQL!
- SQL for dummies cheat sheet
Cheat sheets for Spark, Scala, Java:
Apache Spark is an engine for large-scale data processing. For certain applications, such as iterative machine learning, Spark can be up to 100x faster than Hadoop (using MapReduce). The essentials of Apache Spark cheatsheet explains its place in the big data ecosystem, walks through setup and creation of a basic Spark application, and explains commonly used actions and operations.
- Dzone.com’s Apache Spark reference card
- DZone.com’s Scala reference card
- Openkd.info’s Scala on Spark cheat sheet
- Java cheat sheet at MIT.edu
- Cheat Sheets for Java at Princeton.edu
Cheat sheets for Hadoop & Hive:
Hadoop emerged as an untraditional tool to solve what was thought to be unsolvable by providing an open source software framework for the parallel processing of massive amounts of data. Explore the Hadoop cheatsheets to find out Useful commands when using Hadoop on the command line. A combination of SQL & Hive functions is another one to check out.
Cheat sheets for web application framework Django:
Django is a free and open source web application framework, written in Python. If you are new to Django, you can go over these cheatsheets and brainstorm quick concepts and dive in each one to a deeper level.
- Django cheat sheet part 1, part 2, part 3, part 4
Cheat sheets for Machine learning:
We often find ourselves spending time thinking which algorithm is best? And then go back to our big books for reference! These cheat sheets gives an idea about both the nature of your data and the problem you're working to address, and then suggests an algorithm for you to try.
- Machine Learning cheat sheet at scikit-learn.org
- Scikit-Learn Cheat Sheet: Python Machine Learning from yhat (added by GP)
- Patterns for Predictive Learning cheat sheet at Dzone.com
- Equations and tricks Machine Learning cheat sheet at Github.com
- Supervised learning superstitions cheatsheet at Github.com
Cheat sheets for Matlab/Octave
MATLAB (MATrix LABoratory) was developed by MathWorks in 1984. Matlab d has been the most popular language for numeric computation used in academia. It is suitable for tackling basically every possible science and engineering task with several highly optimized toolboxes. MATLAB is not an open-sourced tool however there is an alternative free GNU Octave re-implementation that follows the same syntactic rules so that most of coding is compatible to MATLAB.
Cheat sheets for Cross Reference between languages
Python Programming Language Cheat Sheet