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 needed Saved a copy of the notebook and maybe …
Parameterizing and automating Jupyter notebooks with papermill Read More »
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 …
How to iterate over DataFrame rows (and should you?) Read More »
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.
It is possible to unit test Python code that lives in a Jupyter notebook. This article looks at three reasonable choices.
You can use py-spy to profile already running Python code without restarting your process or modifying the source code.