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?)
4 ways to run Jupyter notebooks
Jupyter notebooks are a popular way to share data and code, and there are multiple ways to run and edit notebooks.
How to use ipywidgets to make your Jupyter notebook interactive
Jupyter widgets can make notebooks be more interactive and make data exploration much easier, especially for end users who are not coders.
Profiling Python code with memory_profiler
When your Python program uses more memory than expected, you can use memory_profiler to find out where memory is allocated.
How to view all your variables in a Jupyter notebook
Jupyter notebooks can easily have hidden state. Use these methods to see all the variables that exist in your Jupyter notebooks.
Using autoreload to speed up IPython and Jupyter work
Using the %autoreload magic in IPython or Jupyter can help you continue working without restarting your session after making local changes.
Unit testing Python code in Jupyter notebooks
It is possible to unit test Python code that lives in a Jupyter notebook. This article looks at three reasonable choices.
Profiling Python code with py-spy
You can use py-spy to profile already running Python code without restarting your process or modifying the source code.
How to remove a column from a DataFrame, with some extra detail
Removing one or more columns from a pandas DataFrame is a pretty common task, but it turns out there are a number of possible ways to perform this task. I found that this StackOverflow question, along with solutions and discussion in it raised a number of interesting topics. It is worth digging in a little bit to the … Continue reading How to remove a column from a DataFrame, with some extra detail
Profiling Python code with line_profiler
Once we have debugged, working, readable (and hopefully testable) code, it may become important to examine it more closely and try to improve the code's performance. Before we can make any progress in determining if our changes are an improvement, we need to measure the current performance and see where it is spending its time. … Continue reading Profiling Python code with line_profiler