chore: sync content to repo

roadmaps/python-data-analysis/content/environment-setup@8Z20apU8ugXwdH8rRZAcr.md

@@ -4,5 +4,5 @@ Setting up a proper Python environment for data analysis involves choosing a pac
 
 Visit the following resources to learn more:
 
-- [@video@Setting Up A Python Environment for Data Analysis and Machine Learning](https://www.youtube.com/watch?v=NDFMa5FSQuI)
-- [@article@How to Set Environment Variables in Python (Step-by-Step)](https://roadmap.sh/python/set-environment-variable)
+- [@article@How to Set Environment Variables in Python (Step-by-Step)](https://roadmap.sh/python/set-environment-variable)
+- [@video@Setting Up A Python Environment for Data Analysis and Machine Learning](https://www.youtube.com/watch?v=NDFMa5FSQuI)

roadmaps/python-data-analysis/content/indexing--slicing@mAOljEWt_0Hb9jLBKuc-H.md

@@ -1,6 +1,6 @@
-# Array Operations
+# Indexing & Slicing
 
-NumPy supports a wide range of array operations: element-wise arithmetic, aggregation functions (`sum`, `mean`, `std`, `min`, `max`), reshaping, stacking, and splitting. These operations are vectorized, meaning they apply to the entire array at once without explicit loops, making them highly efficient.
+Indexing and slicing are techniques used to access, extract, or modify specific elements within a NumPy array. Indexing refers to selecting a single item based on its position, typically using square brackets and integer coordinates, while slicing allows you to retrieve a contiguous subset of the array by defining a range of indices using a colon separator. These methods provide a flexible way to work with parts of a dataset, such as selecting individual rows, specific columns, or smaller multi-dimensional segments without needing to iterate through the entire structure.
 
 Visit the following resources to learn more: