Jupyter notebooks are hard to diff and merge since they contain both code and output, but tools and practices can make version control easier.
The scrapbook library allows you to save state inside the notebook file itself, making it easier to develop workflows using Jupyter notebooks.
Have you ever created a Jupyter notebook and wished you could generate the notebook with a different set of parameters? If so, you've probably done at least one of the following: Edited the variables in a cell and reran the notebook, saving off a copy as neededSaved a copy of the notebook and maybe hacked … Continue reading Parameterizing and automating Jupyter notebooks with papermill
Indexing time series data in pandas is similar to other types, but there are a number of convenient functions unique to time series.
One of the most searched for (and discussed) questions about pandas is how to iterate over rows in a DataFrame. Often this question comes up right away for new users who have loaded some data into a DataFrame and now want to do something useful with it. The natural way for most programmers to think … Continue reading How to iterate over DataFrame rows (and should you?)
Jupyter notebooks are a popular way to share data and code, and there are multiple ways to run and edit notebooks.
Jupyter widgets can make notebooks be more interactive and make data exploration much easier, especially for end users who are not coders.
When your Python program uses more memory than expected, you can use memory_profiler to find out where memory is allocated.
Jupyter notebooks can easily have hidden state. Use these methods to see all the variables that exist in your Jupyter notebooks.
Using the %autoreload magic in IPython or Jupyter can help you continue working without restarting your session after making local changes.