Update 2025-02-14-Why-are-there-so-many-Python-package-managers?.md

_posts/2025-02-14-Why-are-there-so-many-Python-package-managers?.md

@@ -43,17 +43,13 @@ Eventually, what started as a simple attempt to set up a new project
 and practice some Python left me feeling completely out to sea.
 After days of wrestling with pip, poetry, conda, pipenv, and virtualenv,
 I was left scratching my head about Python’s package management
-landscape. While choice is generally good, the sheer number of package
-managers in the Python ecosystem feels overwhelming, especially for
-newcomers. I can’t help but wonder: what the hell happened?
-
-Coming from R’s relatively straightforward package management ecosystem
-with just {renv} handling most of my needs, I was overwhelmed by the
-sheer number of Python package management tools. Each seems to solve a
-different piece of the puzzle, and every blog post or tutorial I read
-seems to recommend a different approach. After spending weeks exploring
-various options and dealing with environment conflicts, I needed to make
-a choice that would work well for my data science workflow.
+landscape. I can’t help but wonder: what the hell happened?
+
+Coming from R’s relatively straightforward package management ecosystem, with just {renv} handling most of my needs, I was overwhelmed by the
+sheer number of Python package management tools. While choice is generally good, each one seemed to solve a
+different piece of the puzzle, and every blog post or tutorial I read recommended a different approach. After spending what felt like weeks exploring
+various options and dealing with environment conflicts, I eventually decided I just needed to make
+a choice and pick something that would work well for my data science workflow.
 
 I mean just look at the table I made summarizing my notes!
 
@@ -193,12 +189,12 @@ And I’m certain I missed more than a few.
 
 After exploring these numerous options, I settled on
 [pixi](https://github.com/prefix-dev/pixi) for my data science workflow,
-and here’s why: As someone coming from R, I needed a tool that could
+and here’s why: I needed a tool that could
 handle both Python packages and the scientific computing dependencies
 that often come with data science work. What really sealed the deal was
-discovering that pixi can handle R packages, via conda-forge, alongside
-Python ones - which could be a game-changer for hybrid workflows where I
-might need both languages.
+discovering that pixi can handle R packages alongside Python ones - 
+which could be a game-changer for hybrid workflows where I might 
+need both languages.
 
 While conda is the traditional choice here, pixi offers a more modern
 and streamlined approach. Its `.toml` and `.lock` files ensure