Profitable Python Podcast - Show Notes

Posted by Jack McKew on Fri 10 July 2020 in Software • Tagged with software, datascience • 4 min read

I was recently a guest on the Profitable Python podcast with host Ben McNeill, the episode can be found at: https://anchor.fm/profitablepythonfm/episodes/Pandas-Alive--Jack-McKew-efui92/a-a2idber. This blog post serves as the show notes, if I've missed anything, please drop a comment below!

Projects Mentioned

A project where the …


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Geopandas and Pandas Alive

Posted by Jack McKew on Fri 12 June 2020 in Python • Tagged with python, visualisation • 6 min read

Geopandas and Pandas_Alive

Following on from a previous post on making animated charts with pandas_alive, let's go into generating animated charts specifically for geospatial data with geopandas. Support for geopandas was introduced into pandas_alive in version 0.2.0, along with functionality to interface with contextily for enabling basemaps. The …


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Translating Text in Python

Posted by Jack McKew on Fri 29 May 2020 in Data Science • Tagged with datascience, python • 9 min read

Working with data in a connected digital world, means you will possibly encounter data in a language outside your own. In this post we'll go into ways to translate this data in Python.

First off we need some sample text, and what is better to read about then pizza!

In …

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Creating Animated Plots with Pandas_Alive

Posted by Jack McKew on Thu 21 May 2020 in Data Science • Tagged with datascience, python, data-viz, datavisualisation • 5 min read

In this tutorial we'll learn how to create a series of animations using Pandas_Alive. This post is rendered in the style of a Jupyter Notebook. Find the source here: https://github.com/JackMcKew/jackmckew.dev/tree/master/content/2020/pandas_alive/notebooks/pandas_alive_demo.ipynb.

Pandas_Alive was created by me! I set …


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COVID-19 Confirmed Cases NSW Australia - Animated Statistics over Time

Posted by Jack McKew on Thu 14 May 2020 in Data Science • Tagged with datascience, python, data-viz, datavisualiation • 1 min read

Recently, I had wanted to build a visualisation of the confirmed cases of COVID-19 in my home state NSW. This post is to cover the release of the visualisation on YouTube, and there is hopes to write future post(s) about building this visualisation & developing Pandas_Alive. Would love to hear …


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3D Gradient Descent in Python

Posted by Jack McKew on Wed 26 February 2020 in Python • Tagged with python, visualisation, generative • 40 min read

Visualising gradient descent in 3 dimensions

Building upon our terrain generator from the blog post: https://jackmckew.dev/3d-terrain-in-python.html, today we will implement a demonstration of how gradient descent behaves in 3 dimensions and produce an interactive visualisation similar to the terrain visualisation. Note that my understanding of gradient …


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3D Terrain in Python

Posted by Jack McKew on Fri 24 January 2020 in Python • Tagged with python, visualisation, generative • 21 min read

Generating & Visualising 3D Terrains in Python

Today, let's put together a 3D visualisation of randomly generated 'terrain' with Python. Data visualisation is an absolutely key skill in any developers pocket, as communicating both data, analysis and more is thoroughly simplified through the use of graphs. While a picture tells a …


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Intro To GeoPandas

Posted by Jack McKew on Wed 15 January 2020 in Python • Tagged with python, visualisation • 6 min read

Pandas for geospatial data

Personally whenever I am faced with a problem that involves analysing geospatial data, GeoPandas is the first tool/package I reach for. Extending on the Pandas dataframe data structure, GeoPandas brings functionality for working with points, polygons and more out of the box. This post is …


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Linear Regression: Under the Hood with the Normal Equation

Posted by Jack McKew on Sun 25 August 2019 in Data Science • Tagged with python • 3 min read

Let's dive deeper into how linear regression works.

Linear regression follows a general formula:

$$ \hat{y} = \theta_0 + \theta_1x_1 + \theta_2x_2 + \cdots + \theta_nx_n $$

Where \(\hat{y}\) is the predicted value, \(n\) is the number of features, \(x_i\) is the \(i^{th}\) feature value and \(\theta_n\) is the \(n^{th}\) model parameter. This …


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Looking for Patterns in City Names & Interactive Plotting

Posted by Jack McKew on Fri 16 August 2019 in Python • Tagged with python, datascience • 5 min read

Recently, I was traveling around New Zealand, and noticed in the Maori language they use letters back to back a lot like in the original Maori name for Stratford ("whakaahurangi"). So as any normal person does, I thought, well what town has the most repeated letters, and the idea for …


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