@@ -51,7 +51,11 @@ reader a sense of the best (or most popular) solutions, and give clear
5151recommendations. It focuses on users of Python, NumPy, and the PyData (or
5252numerical computing) stack on common operating systems and hardware.
5353
54- ## Recommendations
54+ {{< tabs >}}
55+
56+ [[ tab]]
57+ name = 'Recommended Method'
58+ content = '''
5559
5660We'll start with recommendations based on the user's experience level and
5761operating system of interest. If you're in between "beginning" and "advanced",
@@ -94,8 +98,11 @@ we recommend:
9498 that provides a dependency resolver and environment management capabilities
9599 in a similar fashion as conda does.
96100
101+ '''
97102
98- ## Python package management
103+ [[ tab]]
104+ name = 'Python Package Management'
105+ content = '''
99106
100107Managing packages is a challenging problem, and, as a result, there are lots of
101108tools. For web and general purpose Python development there's a whole
@@ -146,8 +153,11 @@ of packages and versions you're using. Best practice is to:
146153 - Poetry: [ virtual environments and pyproject.toml] ( https://python-poetry.org/docs/basic-usage/ )
147154
148155
156+ '''
149157
150- ## NumPy packages & accelerated linear algebra libraries
158+ [[ tab]]
159+ name = 'NumPy packages & Libraries'
160+ content = '''
151161
152162NumPy doesn't depend on any other Python packages, however, it does depend on an
153163accelerated linear algebra library - typically
@@ -190,7 +200,8 @@ consider:
190200 function calls. It typically yields better performance, but can also be
191201 harmful - for example when using another level of parallelization with Dask,
192202 scikit-learn or multiprocessing.
193-
203+ '''
204+ {{< /tabs >}}
194205
195206## Troubleshooting
196207
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