Pandas is a great tool for time series analysis of financial market data. Because pandas DataFrames and Series work well with a date/time based index, they can be used effectively to analyze historical data. By financial market data, I mean data like historical price information on a publicly traded financial instrument. However, any sort of …
Jupytext is a Jupyter plugin that allows you to convert Jupyter notebooks to Markdown or Python script files and back again.
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 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.