#190: Interactive Plots With Plotly
We came a long way with data visualisation over the last few months. But so far, all plots we created were static. Now it is time to change this and look at interactive plotting libraries.
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#189: Visualize Text With a Word Cloud
Creating graphs with numerical and categorical data is something we got comfortable with over the last months. But how can we visualize a text to spot the common words and get a hint of the topic? Let us figure out how we can tackle such a challenge.
#188: Working With the NDC Talks Data
Last week we formulated our thesis and went on to capture the data to (dis-) prove our assumption. Today we clean the data file, turn it into a graph and check the merit of our thesis.
#187: Extracting the NDC Talks Data From YouTube
It is time for a hands-on walkthrough to collect data and turn it into useful plots. For this exercise we are going to collect metadata from YouTube and run it through Seaborn to figure out if my hunch is correct or not.
#186: Filter Data in Pandas
Sometimes we only want to work with part of the data in a DataFrame. In this post, we explore the different ways that Pandas gives us to filter the data we want.
#185: Creating DataFrames in Pandas
While working on my upcoming blog post on filtering data in Pandas, I noticed a little gap in my knowledge: How can we create a DataFrame without the help of a CSV file? Let us find out what options we have.
#184: Performance-Related Magic Commands in Jupyter
When we work with larger datasets in Jupyter, we will notice a slowdown in the execution time. Let us look at 4 magic commands that Jupyter offers us to check the performance of our statements.
#183: Advanced Tips & Tricks for JupyterLab
JupyterLab offers us many little tricks to work more effectively. In this post we explore a few helpful tips I would no longer want to miss.
#182: Style Your Plots in Seaborn
Seaborn is a powerful library that we can use to explore data. Once we have figured out what we want to show, it is time to adapt the plots to our requirements. In this post we explore the options we have for styling our plots.
#181: Making Sense of Your Data With Seaborn
Making sense of the data is an important first step before we can visualise the data. In this post we continue with Seaborn and explore the various ways it can help us to better understand what is going on.