Over the last weeks we got a few nice visualisations out of Matplotlib. But what can we do when we only have temporary data that should not be shared? Is there a way to make it clear that this plot is a stand-in and not the final product?
The default style of Matplotlib is not that visually pleasing. Before we switch to a different library, we explore the various styles that Matplotlib offers.
Matplotlib gives its best to create a plot for us. But sometimes that result can be cleaned-up and improved to better represent the interesting parts of our data. Let us find out how we can do that.
Different types of data need different forms for a graphical representation. In this post we explore the most often used types of diagrams in Matplotlib.
Creating plots in Matplotlib is fun. But when we only can use the visualisations inside our Jupyter notebook we cannot fully benefit from our work. Let us change that and export our plots into various formats.
One of the main concepts you need to understand early on with Matplotlib is the idea of figures and axes. Today we explore these terms and find out how we can create them in code.
The more complex the data, the more helpful a good visual representation is. Python offers us such a wide range of tools to visualise our data that it is difficult to make a choice. Therefore, we will look at a few different approaches to data visualisation in the coming months to use the right tool for our use cases.
After we created our analysis, it is time to share our Jupyter notebooks. Let us look at the options we have.
JupyterLab is a great tool for working with data. As with all tools, work goes much faster if you know their quirks and idiosyncrasies. Today we look at some small but important things that can help you in your daily work with JupyterLab.
Jupyter Notebooks offer us a great flexibility on how we can work with code and documentation. Unfortunately, not every attempt will give us a maintainable notebook. Let us look at ways to organise our code and the data we need.