numpy for Numerical and Scientific Computing
As you approach the end of this introduction to constructing arrays in numpy, read this section to familiarize yourself with various numpy methods used throughout the course. Specifically, the need for methods such as shape, size, linspace, reshape, eye, and zeros often arise when manipulating arrays.
Conclusions moving forward
It's important to understand numpy and arrays since most data sets we encounter are rectangular. The notations and operations we saw in numpy will translate to data, except for the fact that data is typically heterogeneous, i.e., of different types. The problem with using numpy for modern data analysis is that if you have mixed data types, it will all be coerced to strings, and then you can't actually do any data analysis.
The solution to this issue (which is also present in Matlab) came about with the pandas package, which is the main workhorse of data science in Python
reticulate::use_condaenv('ds', required=T)