How to utilize both sides of Matplotlib to create complex data visualizations.
Matplotlib is the definitive tool for creating data visualizations of all types in Python. Whether its a simple bar chart or a complex interactive scatterplot, Matplotlib has a diverse range of functionality.
Inspired by MATLAB, it takes a lot of its key functions and naming schemes from the software. If you are comfortable with using MATLAB, then you’ll be natural at using Matplotlib’s interface PyPlot.
Pandas is an open-source data analysis and manipulation tool for Python.
The name? It comes from the econometrics term “panel data”, which is multi-dimensional data with measurements over time. It’s also pretty cute so that’s a bonus!
At its core, it allows us to easily use spreadsheet-like data. From there, you can clean the data, preform any additional modifications, and analyse it to gain some insight into your data.
NumPy is an open-source Python library which is at the core of several of the science, engineering, and technology efforts you’ve heard of.
Don’t believe me?
It’s a powerful thing. But what does it actually do?
NumPy allows you to create multidimensional homogeneous arrays in Python, and do a whole collection of different mathematical operations with them.
An array is essentially just a list, and usually in our case, of numbers. “Multidimensional” in this sense means that…
When I first began programming, I thought you had to do everything by scratch.
Do you want to scrap some text off a website? You’ve gotta build it. Do you want to create a table? You’ve gotta build it. Do you want to do machine learning? Well, I guess you have to learn how to build that too.
But then I learned of the wonderful world of open-source programming, and the beauty of the word import. However, there was a whole slew of equally wonderful jargon I was met with; modules, packages, libraries, and dependencies.
I hope to disambiguate these…
Nowadays it feels like there is a whole laundry list of benefits to having a personal website. So, I decided to join the fray.
This project began as a convenient way to learn the ins and outs of the “full-stack” of web development. Recently, I applied to be a part of a “hackathon”, where the main premise was to create a final web application. The goal was to create something that is practical and productive for society.
Quite boldly, I sent off my application without even the slightest clue of how to accomplish something even vaguely functioning. But, armed…
A Chemical Engineering student who is incredibly interested in Data Science