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Open Source software projects, resources and information

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Seaborn (Python Library)

Seaborn Logo

https://seaborn.pydata.org

For data scientists using Python, libraries such as NumPy and Pandas are useful for reading and manipulating data on the back-end. How can we visualize the data in Python? The answer is by using a data visualization library called Seaborn. Seaborn allows data in a Python session to be visualized and is built upon another library, MatPlotLib. Seaborn is great for visualizing/graphing statistical data and works especially well with data processed through Pandas. To read a little more about Seaborn, click here to be taken to the Seaborn site.

Uses: Seaborn is a very versatile graphing library. Seaborn can graph relationships between variables, compare different distributions, and even automatically estimate and plot linear regression models for given data. Seaborn also allows for high-level abstraction code that makes graphing complex data easier. In terms of appearance, Seaborn provides many themes and color templates, reshaping and sizing and customization of how the data is displayed. For instance, any data above a threshold can be displayed one color while any data below it can be displayed another.

Documentation: Click Here 

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Pandas (Python Library)

Pandas

https://pandas.pydata.org

Pandas is a Python extension library that provides definitions for operations that manipulate data sets and structures. One of the most practical uses of Pandas is the ability to import data from external files, like CSV, JSON, SQL and Excel files. After importing, Pandas can convert the raw data into a usable data frame. Pandas is a very useful tool for programmers using Python to work with data analytics. Pandas makes importing, manipulating, merging, cleaning and re-exporting data easy to do in a virtual Python environment.  Click here to learn more.

Uses: Pandas is an essential tool for data analysis in Python. Because most data comes in CSV and Excel formats, these data files must be converted into native Python in order to be readable. Pandas performs this task with ease and simplicity, creating data frames that logically and numerically organize data into rows and columns. Pandas also allows for the cleaning of data, such as the removal of unwanted columns or rows, and the merging of data, such as combining data from two different files. 

Documentation: Click Here .

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NumPy

NumPy

https://numpy.org

NumPy is one of the most useful and popular Python tool-kits available for computer scientists, programmers, and data analysts. It is an open-sourced programming extension library that enables numerical computing in Python, i.e. arithmetic functions with arrays and matrices, statistics functions like finding means and medians, and linear algebra functions like finding the determinant of matrices and finding their dot and inner products.  It is developed and improved upon in Github and overseen by its “Steering Council.” Click here to learn more.

Uses: NumPy is a widely used tool by data scientists. Use of the library allows for the use of arrays, vectors and matrices, and their respective functions and attributes (as listed above). This implementation allows for data to be collected and stored in manipulable dimensional spaces. Conceptually, NumPy bridges Python with linear algebra, allowing for the application of formulas and theorems in a Python virtual environment. These concepts allow data scientists to collect, store, manipulate and predict data in Python. These ideas are used in machine learning, artificial intelligence and countless other computer science fields. 

 Documentation: Click here. 

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Python

https://www.python.org

What is Python? Python is an interpreted object-oriented and high-level programming language. Python is a very popular and easy to use language, a favorite among many programmers. One of the greatest benefits of the Python language compared to others is that Python is open-sourced.  This means that anybody can contribute their work to the main Python libraries, packages of pre-made code that can be imported and used in Python. An example of this benefit would be a very efficient arrangement of code that sorts data faster than some other common method of sorting. 

How it works: In general, Python is manually installed onto the path of someone’s computer. One can download the latest version of Python from this website. A quick installation guide can be found here for Windows and here for Mac users. To make the most out of Python, integrated development environments (IDEs) are used to help format, troubleshoot and run Python code. Most IDEs are like more advanced versions of the command prompt/line, they are more visually intuitive and provide the user with troubleshooting and formatting tools. You can use some IDEs to build computer/web applications that run on Python code. 

For parents/privacy: Python can be installed onto your computer from the official Python website. Python is a very popular and secure programming language that’s great for beginners to learn on. Here is the Python code of conduct, and here are Python’s legal notices.

Resources: Interested in Python? Check out these resources to learn more and see some real-world applications!

 

 

Watch Reza Tasooji explain the basics of the Python language and some basic programming terms.

Watch this clip showing some of 2018’s best Python projects (some of them are crazy!)

Also watch Reza give an in depth tutorial on how to install Python onto your Windows operating system and explain why it’s important to create virtual python environments.
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Google Colab

Colab Icon

colab.research.google.com

Google’s Colaboratory, or “Colab” for short, is a free online resource used for sharing code, documentation, and visualized data. Google Colab runs code using Jupyter Notebook, an open-source web app used to create and share documents that can run live code and visualizations. Colab itself runs on a virtual linux operating system. The programming language that can be run live in Google Colab is Python, while Markdown code can be used to format and organize the Colab documents. Google Colab is a very engaging and interactive way to run code, examine data and share ideas on the web.

What you’ll do there:  With Colab you can write and execute Python in your browser. Students in STEM+ Data Analytics course will learn to use Colab as a part of their assignments. One of Colab’s best features is its ability to run live code, which makes teaching and learning Python a lot more engaging and intuitive. In addition to learning Python, students will also learn how to format their documents by using the Markdown language. With these two tools, students will be able to use Python to visualize and analyze data in conjunction with Markdown to clearly format and organize it. 

For parents/privacy:  Students will need a Google account to use Google Colab, as Colab is an app made by Google. Google accounts are free to create and require some basic information, such as first and last name, a username and a password. Here is Google’s privacy policy.

Additional resources: There is a lot to learn about Google Colab, Markdown, and Python – most of which is not covered in the STEM+ Data Analytics course. Here are a few safe additional resources for your convenience: