I mostly write about programming in Python (and sometimes, Julia) for Data Visualization and Data Science but occasionally I stray into other areas of science and technology.
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The SQLite database is a built-in feature of Python and a very useful one, at that. It is not a complete implementation of SQL but it has all the features that you need for a personal database or even a backend for a data-driven web site.
Using it with Pandas is simple and really useful. You can permanently store your dataframes in a table and read them directly into a new dataframe as you need them.
But it isn’t just the storage aspect that is so useful. …
Why would you want to write HTML in Python? It baffles me. But that is what you need to do if you want to write a Dash app.
While I understand that there is a certain amount of satisfaction in having all your code in a single file, it seems to me that if you know enough about HTML to code it in a different language then you certainly know enough to write it in HTML!
I’d love for Dash to be the solution to writing web visualization apps but I’m not convinced.
Consider the following:
Flask is a web application micro-framework which means it is a very lightweight library for developing web apps. Creating your first Flask app is easy but first you will need to install it with pip or conda, for example:
pip3 install flask
Next, open a new directory and create a file app.py with the following code:
from flask import Flaskapp = Flask(__name__)@app.route(‘/’)
The code is pretty straightforward. The first line imports the Flask library; the second creates a Flask object (that will be run); and the rest of it defines a route. A route…
When Buzz Aldrin guided himself, and Neil Armstrong, to the surface of the moon on July, 20th, 1969, he was aided by a computer that was the first such device built with integrated circuits and, unsurprisingly, was the state of the art at the time.
That was over 50 years ago, so of course, you would expect computers to have got better. But how much? Ten times more powerful, 100 times, a 1000 times, perhaps. Maybe you’d be surprised to know that the smartphone in your pocket is 100,000 times more powerful than Aldrin’s computer.
If you believe the AI that created male and female images from pictures of their heads, men typically wear suits, while women prefer low-cut tops and bikinis.
It’s been reported in the press that this is evidence that the future of AI is sexist. The argument being that because the internet is awash with pictures of scantily-clad females, AI will assume that this is normal.
A fair point? Not necessarily — the whole point of this research was to demonstrate the danger of inheriting biases from data that has been collected from human activity.
The experiment used the ImageNet library…
Web sites, publications and news sources all have their own styles. Take a look at the financial data published by, say, the BBC or The NewYork Times. Or polling data on Nate Silver’s FiveThirtyEight web site. They each have a clear and consistent look.
We are going to look at how we can achieve something similar with our Pandas charts and plots. First, by using the built-in styles that are available to us and then by looking at how we can create our own customizations.
The default style renders a line graph like the image, below. It’s a clean looking…
I hate throwing things away — especially if they still work. There are ways to dispose of old technology in an environmentally friendly way, but giving it a new lease on life is more fun.
Chances are that you have at least one old laptop or PC lying around, unused, in a cupboard or drawer. Maybe they used to run Windows Vista or even XP, and they don’t have the oomph for a modern operating system like Windows 10 or Unbuntu. They probably even struggle with running the original software they came packaged with.
So why don’t we fix them…
Google Charts have been around for more years than I care to remember, so I think we can safely assume that it is a mature and stable technology.
It can also be used to create attractive charts and graphs.
Sentiment Analysis, or Opinion Mining, is often used by marketing departments to monitor customer satisfaction with a service, product or brand when a large volume of feedback is obtained through social media.
Gone are the days of reading individual letters sent by post. Today’s customers produce vast numbers of comments on Twitter or other social media.
Such a large amount of data cannot be reasonably analysed individually, so what is produced electronically has to be analysed electronically.
There are two fundamental Sentiment Analysis solutions: first, there are rule based systems that use a lexicon of words and rules to classify…