#163: Share Your Jupyter Notebooks
After we created our analysis, it is time to share our Jupyter notebooks. Let us look at the options we have.
analytics
#162: Tips & Tricks to Work Effectively With JupyterLab
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.
#161: Organise Data and Code in Jupyter Notebooks
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.
#160: Jupyter Notebooks for Your Data Analysis
While working through a Python data analytics course, I started to like Jupyter Notebooks a lot. Not only do we get much faster feedback when we plot our data, this format offers an effortless way to keep your documentation next to your code. Let us explore this fascinating runtime environment for our code.
#109: Set Operations on Lists With NumPy
Whenever I need to find values that are part of one list but not another one, I like to work with set operations. I find them more elegant than looping through the lists. Let’s look what Python to solve this problem.
#104: Creating a Pivot Table in Pandas
Last week we looked at how to import data into pandas. With this problem solved, we can now move on to the more interesting task of converting our data into a pivot table.
#103: Working With CSV Files in Pandas
Until now I use Excel to make a Pivot out of my data stored in CSV files. While this works, I need a lot of manual steps that are cumbersome and time-consuming. Pandas offers an automatable way to do this task, but before I can create the pivot, I need to import my data. Let us look how we can do this part with pandas.