- Python Data Structures And Algorithms Cheat Sheet
- Python Cheat Sheet
- Python Machine Learning Algorithms Cheat Sheet
- Python Language Cheat Sheet
- Python Cheat Sheet Pdf
- Python Algorithm Cheat Sheet
This article is for you. We will show you how to accomplish the most common data analysis tasks with Python, from the features of Python itself to using modules like Pandas to a simple machine learning example with TensorFlow. A Note About Python Versions. All examples in this cheat sheet use Python 3. Python makes using algorithms easier because it comes with a lot of built-in and extended support (through the use of packages, datasets, and other resources). This Cheat Sheet helps you access the most commonly needed tips for making your use of algorithms fast and easy. Locating the Algorithm You Need. Big O Cheat Sheet for Common Data Structures and Algorithms 3 min read on August 29, 2019 When measuring the efficiency of an algorithm, we usually take into account the time and space complexity. Overview It makes a huge difference if you present your data science work with clean code, amazing graphs and Markdowns(WoW)!!! We often get so indulge in getting things done that we forget about the representation. Markdowns in Jupyter notebooks beautify them and also improves the readability of the code. Remember to add graphs and Continue reading 'Markdown in Jupyter/Ipython notebook. Python Cheat Sheet for Scikit-learn Scikit-learn is an open source Python library used for machine learning, preprocessing, cross-validation and visualization algorithms. It provides a range of supervised and unsupervised learning algorithms in Python.
A Cheatsheet to Find the Best-Fit Python Library
Over 137,000 libraries exist in Python’s repository. So, how do you choose the right one for your machine learning project? A cheat sheet on proven uses can help.
Nothing beats Python in finding solutions to complex mathematical and computational problems. It is a versatile language that can be easily used across domains and is easier to debug too.
45% of tech organizations use Python for their machine learning and AI projects. – Builtwith.com
Python libraries are a work in progress and their use cases and toolkits are continuously advancing. Therefore, AI engineers need to keep a constant tab on the latest developments, more so if they intend to use Python for their machine learning projects.
Python can be used by beginners and experienced AI Engineers, which makes it a popular choice across levels.
Before we begin with the cheat sheet, please note that Python libraries can be multi-purpose and can be placed in multiple categories. Also, the use of libraries is not constrained to the highlighted tasks.
This cheat sheet aims to help you dig out the best fit.
Top Python Libraries for Deep Learning
We’ve weighed the pros and cons and arrived at these four top picks.
TensorFlow
A plug-and-play library with an extensive resource of commonly-used machine learning models and algorithms, TensorFlow comes with facial recognition capabilities. It is one of the biggest open-source Python libraries and a must-have for beginners.
Level: Good for beginners
Keras
It runs on top of libraries like CNTK, Theano, and TensorFlow, and offers specific support to deep learning applications. Easier prototyping, modularity, and a user-friendly interface make it an excellent choice for beginners.
Level: Excellent choice for beginners
Caffe
Convolutional Architecture for Fast Feature Embedding (Caffe) reiterates versions of an algorithm to get the best solution in time. Mostly used for image classification and segmentation, it is popular in academic research and industrial applications.
Level: Steep learning curve for beginners
PyTorch
With an option to create an n-dimensional array, it gives more control over a dataset. PyTorch functions well with statistical operations and is popular among researchers.
Level: Good for beginners
Python Data Structures And Algorithms Cheat Sheet
Top Python Libraries for Computer Vision
If you want to preprocess images and videos for machine learning, check out these three libraries.
OpenCV
One of the oldest libraries for computer vision applications, OpenCV supports PyTorch, TensorFlow, and Caffe. Its features include:
Object identification
Facial recognition
Object extraction
Action classification
Tracking
Reverse image search
Its image modification features can remove red eye, detect scenes, do AR overlays, and correct eye movement.
Level: Steep learning curve for beginners
MATLAB
Generally used for research purposes, this library is a great choice for fast prototyping. One of the main advantages of MATLAB is that it can handle errors before execution.
Level: Steep learning curve for beginners
CUDA
It uses the power of GPU to deliver impressive video, image, and signal functions.
TensorFlow and Keras can also be used for computer vision.
Top Python Libraries for Visualization
Python is an unmatched language for data manipulation and visualization in deep learning. Visualization helps machine learning engineers present their results in an understandable way.
Use these libraries to get the best outputs.
Matplotlib
This visualizer can create plots like histograms, line charts, scatter plots, and bar charts to find patterns in data. Its figures are interactive and the designer has full control over its properties. The final output can be extended to different formats.
Level: Knowledge of MATLAB helps
Seaborn
Based on Matplotlib, Seaborn is an excellent choice to create a high-level interface. If you are looking to create pretty charts, get this one.
Bokeh
A library for modern web browsers, Bokeh can be the right choice if you are looking for high- performance, interactive visualizations in large streaming datasets.
Top Libraries for Data Manipulation
These three libraries are excellent for data preprocessing and wrangling.
Pandas
Pandas is an excellent choice for statistical functions and data analysis. It makes preprocessing and data wrangling easy with its easy-to-use structures. It can perform complex functions. Its tools include:
Data indexing
Data manipulation
Reshaping data sets
Merging and joining data set
Filtering data
Time series functionality
Level: Previous knowledge of NumPy helps
NumPy
A must-have library for efficiently handling data and numbers, NumPy offers a transformative function for your data. It changes the inbuilt list data structure of Python and creates an n-dimensional array. It can also make your data adaptable. Mostly used for scientific and research applications, NumPy is also used to create generic data.
Python Cheat Sheet
It is a great library to handle heterogeneous data and gives greater freedom with data. NumPy finds its best use in solving random numbers, linear algebra, and transformations.
Level: Suitable for beginners
Scikit-Learn
If data analysis is what you are looking for, then Scikit-Learn is the best library. It can help you with data classification and structuring. Its uses extend to clustering, regression, model selection dimensionality reduction, and data preprocessing, and it has gradient boosting, random forests, and support vector machines algorithms.
Scikit-Learn is the most popular machine learning library and 40% of projects on GitHub use Scikit-Learn.
Level: Excellent choice for beginners
Top Python Libraries for Web Scraping
These are our top picks for web scraping.
Scrapy
It systematically crawls the web and scrapes data for you, and can be used for mining, monitoring, automating, and testing data.Beautiful Soup
It can extract information from HTML and XML files. Its simplicity and ease of use make it ideal for beginners. It is an excellent option for scraping unstructured information for small projects.
Top Python Libraries for Natural Language Processing (NLP)
NLTK
With its roots in educational purposes, NLTK has produced major breakthroughs. It is used for:
Classification
Stemming
Tagging
Parsing
Semantic reasoning
Tokenization
Level: Steep learning curve
The applications of Python are extensive, and this list must not be taken as the final benchmark. Having a working knowledge of these libraries can help you efficiently solve problems and improve your understanding of machine learning.
So, which is your favorite library? Do you want to add your favorite library here? Write to us.
Follow us for our next update on top Python libraries for 3D machine learning.
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Are you looking for examples of using Python for data analysis? This article is for you. We will show you how to accomplish the most common data analysis tasks with Python, from the features of Python itself to using modules like Pandas to a simple machine learning example with TensorFlow. Let’s dive in.
A Note About Python Versions
All examples in this cheat sheet use Python 3. We recommend using the latest stable version of Python, for example, Python 3.8. You can check which version you have installed on your machine by running the following command in the system shell:
Sometimes, a development machine will have Python 2 and Python 3 installed side by side. Having two Python versions available is common on macOS. If that is the case for you, you can use the python3 command to run Python 3 even if Python 2 is the default in your environment:
If you don’t have Python 3 installed yet, visit the Python Downloads page for instructions on installing it.
Launch a Python interpreter by running the python3 command in your shell:
Libraries and Imports
The easiest way to install Python modules that are needed for data analysis is to use pip. Installing NumPy and Pandas takes only a few seconds:
Once you’ve installed the modules, use the import statement to make the modules available in your program:
Getting Help With Python Data Analysis Functions
If you get stuck, the built-in Python docs are a great place to check for tips and ways to solve the problem. The Python help() function displays the help article for a method or a class:
The help function uses the system text pagination program, also known as the pager, to display the documentation. Many systems use less as the default text pager, just in case you aren’t familiar with the Vi shortcuts here are the basics:
- j and k navigate up and down line by line.
- / searches for content in a documentation page.
- After pressing / type in the search query, press Enter to go to the first occurrence.
- Press n and N to go forward and back through the search results.
- Ctrl+d and Ctrl+u move the cursor one page down and one page up, respectively.
Another useful place to check out for help articles is the online documentation for Python data analysis modules like Pandas and NumPy. For example, the Pandas user guides cover all the Pandas functionality with explanations and examples.
Basic language features
A quick tour through the Python basics:
There are many more useful string methods in Python, find out more about them in the Python string docs.
Working with data sources
Pandas provides a number of easy-to-use data import methods, including CSV and TSV import, copying from the system clipboard, and reading and writing JSON files. This is sufficient for most Python data analysis tasks:
Find all other Pandas data import functions in the Pandas docs.
Working with Pandas Data Frames
Pandas data frames are a great way to explore, clean, tweak, and filter your data sets while doing data analysis in Python. This section covers a few of the things you can do with your Pandas data frames.
Exploring data
Here are a few functions that allow you to easily know more about the data set you are working on:
Statistical operations
All standard statistical operations like minimums, maximums, and custom quantiles are present in Pandas:
Cleaning the Data
Python Machine Learning Algorithms Cheat Sheet
It is quite common to have not-a-number (NaN) values in your data set. To be able to operate on a data set with statistical methods, you’ll first need to clean up the data. The fillna and dropna Pandas functions are a convenient way to replace the NaN values with something more representative for your data set, for example, a zero, or to remove the rows with NaN values from the data frame.
Filtering and sorting
Here are some basic commands for filtering and sorting the data in your data frames.
Machine Learning
While machine learning algorithms can be incredibly complex, Python’s popular modules make creating a machine learning program straightforward. Below is an example of a simple ML algorithm that uses Python and its data analysis and machine learning modules, namely NumPy, TensorFlow, Keras, and SciKit-Learn.
In this program, we generate a sample data set with pizza diameters and their respective prices, train the model on this data set, and then use the model to predict the price of a pizza of a diameter that we choose.
Python Language Cheat Sheet
Once the model is set up we can use it to predict a result:
Python Cheat Sheet Pdf
Summary
In this article, we’ve taken a look at the basics of using Python for data analysis. For more details on the functionality available in Pandas, visit the Pandas user guides. For more powerful math with NumPy (it can be used together with Pandas), check out the NumPy getting started guide.
Python Algorithm Cheat Sheet
To learn more about Python for data analysis, enroll in our Data Analysis Nanodegree program today.