pandas indexing can be so confusing!
You have a lot of data and really want to better understand it. You also have heard that pandas is a great library with lots of features, but it's hard to get started with even the basics of working with your data. 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.
Since pandas comes highly recommended and has the features you need to answer all your questions about your data, 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 e-book. You'll learn what you need to get comfortable with pandas indexing. Covered topics include:
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. No spam, I promise. Unsubscribe at any time.
Since pandas comes highly recommended and has the features you need to answer all your questions about your data, 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 e-book. 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
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. No spam, I promise. Unsubscribe at any time.