#176: Visualise Data in Pandas With Hist() & Boxplot()
Plotting with Pandas works without much effort. Today we look at the two dedicated methods hist() and boxplot() directly on the data frame.
data-visualisation
#175: Visualise Data in Pandas With Plot()
We are back on our journey to data visualisation. Matplotlib offered us a lot of features, but especially the combination of multiple plots into one graphic is painful. With Pandas we get an abstraction of Matplotlib that works on the whole data frame. Let us explore the plotting capabilities we get in Pandas.
#174: Untangle Project Dependencies With NetworkX
Last week we explored the basic features for creating graphs with NetworkX. Today we put that knowledge to practice and untangle dependencies between projects.
#173: Create Graphs with NetworkX
Today we make a small deviation from the plots of Matplotlib and work with networks and graphs (as in computer science). Having a structure that allows us to define objects and their relation to each other is not only interesting for theoretical problems. In this post we look at the basics and next week we put that knowledge into practical use.
#172: Combine Multiple Plots in Matplotlib
Matplotlib allows us to draw our annotations on top of a plot. Today we look what happens if we draw a plot on top of another plot.
#171: Annotate Your Matplotlib Plots
Matplotlib can create a wide range of plots for us. But sometimes we need to point out an important aspect that only we know about. Let us figure out how we can do that.
#170: XKCD Styled Plots for Matplotlib
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?
#169: Style Your Plots in Matplotlib
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.
#168: Customise Your Plots in Matplotlib
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.
#167: Often used Diagrams for Matplotlib
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.