From c660c6b0a23269907051cd4b90d6bbe42c950815 Mon Sep 17 00:00:00 2001 From: Scott Henderson Date: Sun, 7 Jul 2024 22:08:55 -0700 Subject: [PATCH] Apply suggestions from code review Co-authored-by: Jessica Scheick --- fundamentals/01_data_structures.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/fundamentals/01_data_structures.md b/fundamentals/01_data_structures.md index 04b1b907..e4cc74a0 100644 --- a/fundamentals/01_data_structures.md +++ b/fundamentals/01_data_structures.md @@ -9,14 +9,14 @@ However, real-world datasets are usually more than just raw numbers; they have labels which encode information about how the array values map to locations in space, time, etc. -The N-dimensional nature of xarray’s data structures makes it suitable for +The N-dimensional nature of Xarray’s data structures makes it suitable for dealing with multi-dimensional scientific data, and its use of dimension names instead of axis labels (`dim='time'` instead of `axis=0`) makes such arrays much -more manageable than the raw numpy ndarray: with xarray, you don’t need to keep +more manageable than the raw NumPy ndarray: with Xarray, you don’t need to keep track of the order of an array’s dimensions or insert dummy dimensions of size 1 to align arrays (e.g., using np.newaxis). -The immediate payoff of using xarray is that you’ll write less code. The +The immediate payoff of using Xarray is that you’ll write less code. The long-term payoff is that you’ll understand what you were thinking when you come back to look at it weeks or months later.