In this blogpost I will share some tips for working with Jupyter Notebooks. Those tips greatly improved my productivity when working with Jupyter Notebooks and I wish someone would have told me earlier. The two main topics of this post are extensions and magic commands.
Jupyter Extensions Have you ever missed a feature in your Jupyter Notebook that IDEs have? E.g. you were hoping for autocompletion or automatically formatting code? Then there might be a Jupyter Notebook extension for you.
Seaborn is a python library for creating plots. It is based on matplotlib and provides a high-level interface for drawing statistical graphics.
Seaborn integrates nicely with pandas: It operates on DataFrames and arrays and does aggregations and semantic mapping automatically, which makes it a quick, convenient option for data visualization in your data projects. One you understand the basic concepts, you can create plots really easily without using stack overflow too much.
table { width:80% !important;} When I started working with pandas I noticed that there were so many ways how to subset, filter and join data with pandas. But I was lacking a systematic overview. How do the different approaches differ and when to use which?
In this blogpost we’ll look at different ways for subsetting, filtering and combining DataFrames.
Subsetting Data: Selecting subsets of rows and columns by labels and positions .
