I created wrighters.io as a platform to share thoughts on software and data, mostly about Python and some of the many interesting libraries in the Python ecosystem. My Python usage has centered around financial applications, specifically in the proprietary trading industry. I have been using Python as one of my main programming languages since 2010, with other work usually done in C++, bash shell scripts, or R. . Although much of my more recent work in Python has been focused around finance, I’m interested in more general use of the language, including applications in data science. I’ve found Python to be a mostly enjoyable and productive language to work with. I’ve spent the last decade working in the proprietary trading industry, mostly trading equities and futures over a wide variety of timescales. I’ve worked on almost every part of the trading process, from infrastructure to risk management to trading strategies to execution. Previously, I worked for Lehman Brothers in Asset Management, with previous experience in telecom and manufacturing.

Indexing in pandas can be so confusing

There are so many ways to do the same thing! What is the difference between .loc, .iloc, .ix, and []?  You can read the official documentation but there's so much of it and it seems so confusing. You can ask a question on Stack Overflow, but you're just as likely to get too many different and confusing answers as no answer at all. And existing answers don't fit your scenario.

You just need to get started with the basics.

What if you could quickly learn the basics of indexing and selecting data in pandas with clear examples and instructions on why and when you should use each one? What if the examples were all consistent, used realistic data, and included extra relevant background information?

Master the basics of pandas indexing with my free ebook. You'll learn what you need to get comfortable with pandas indexing. Covered topics include:

  • what an index is and why it is needed
  • how to select data in both a Series and DataFrame.
  • the difference between .loc, .iloc, .ix, and [] and when (and if) you should use them.
  • slicing, and how pandas slicing compares to regular Python slicing
  • boolean indexing
  • selecting via callable
  • how to use where and mask.
  • how to use query, and how it can help performance
  • time series indexing

Because it's highly focused, you'll learn the basics of indexing and be able to fall back on this knowledge time and again as you use other features in pandas.

Just give me your email and you'll get the free 57 page e-book, along with helpful articles about Python, pandas, and related technologies once or twice a month. Unsubscribe at any time.

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