So much useful data has a date or time component. Often, data has a timestamp to represent when the data was acquired, or when an event will take place, or as an identifying attribute like an expiration date. For this reason, understanding how to work with dates and times effectively can be a very useful …
Use pandas DateOffsets for easy date manipulation Read More »
Most pandas users encounter a situation where choosing to append rows to a pandas DataFrame seems like a good idea. A quick search of the API (or your favorite search engine) reveals that pandas has an append method in DataFrame. You may be tempted to use it. In this article I’ll show you why you …
If you’ve used a Jupyter notebook, you’ve used a kernel. A kernel is a process that executes code from a front-end process. Usually, if you are working in Python, the kernel you use is the IPython kernel. The IPython kernel usually matches the Python version and contains the same libraries as the process running the …
Some sources of financial data can be expensive or difficult to find. For example, some is only available from exchanges or vendors who charge a hefty fee for access. However, the financial industry is also heavily regulated, and one of its main regulators provides free access to its data. The (U.S. Securities and Exchange Commission)[https://www.sec.gov] …
An introduction to accessing financial data in EDGAR, using Python Read More »
Argparse doesn’t support date arguments by default, but it can be easily extended to parse and validate dates in your Python scripts.
Sometimes pandas.read_html doesn’t work for scraping website data, but you can try using requests and BeautifulSoup to do it yourself.
There are big differences between overnight and intraday stock returns. This article shows how to use pandas to understand one big reason.
Stock returns can be heavily impacted by certain events. Sometimes these events are unexpected or a surprise (natural disasters, global pandemics, terrorism) and other times they are scheduled (presidential elections, earnings announcements, financial data releases). We can use pandas to obtain financial data and see the impacts of events the returns of stocks. In my …
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.