updated the whole page like Scipy

Author: Musharraffaijaz

content/en/install.md

@@ -2,207 +2,127 @@
 title: Installing NumPy
 sidebar: false
 ---
-
+{{< admonition tip >}}
+This page assumes you are comfortable using a terminal and are familiar with package managers. 
 The only prerequisite for installing NumPy is Python itself. If you don't have
 Python yet and want the simplest way to get started, we recommend you use the
 [Anaconda Distribution](https://www.anaconda.com/download) - it includes
 Python, NumPy, and many other commonly used packages for scientific computing
 and data science.
+{{< /admonition >}}
 
-NumPy can be installed with `conda`, with `pip`, with a package manager on
-macOS and Linux, or [from source](https://numpy.org/devdocs/building).
-For more detailed instructions, consult our [Python and NumPy
-installation guide](#python-numpy-install-guide) below.
-
-**CONDA**
+The recommended method of installing NumPy depends on your preferred workflow. Below, we break down the installation methods into the following categories:
 
-If you use `conda`, you can install NumPy from the `defaults` or `conda-forge`
-channels:
-
-```bash
-# Best practice, use an environment rather than install in the base env
-conda create -n my-env
-conda activate my-env
-# If you want to install from conda-forge
-conda config --env --add channels conda-forge
-# The actual install command
-conda install numpy
-```
-
-**PIP**
-
-If you use `pip`, you can install NumPy with:
-
-```bash
-pip install numpy
-```
-Also when using pip, it's good practice to use a virtual environment -
-see  [Reproducible Installs](#reproducible-installs) below for why, and
-[this guide](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto)
-for details on using virtual environments.
+- **Project-based** (e.g., uv, pixi) *(recommended for new users)*
+- **Environment-based** (e.g., pip, conda) *(the traditional workflow)*
+- **System package managers** *(not recommended for most users)*
+- **Building from source** *(for advanced users and development purposes)*
 
+Choose the method that best suits your needs. If you're unsure, start with the **Environment-based** method using `conda` or `pip`.
 
-<a name="python-numpy-install-guide"></a>
-# Python and NumPy installation guide
 
-Installing and managing packages in Python is complicated, there are a
-number of alternative solutions for most tasks. This guide tries to give the
-reader a sense of the best (or most popular) solutions, and give clear
-recommendations. It focuses on users of Python, NumPy, and the PyData (or
-numerical computing) stack on common operating systems and hardware.
+NumPy can be installed with a `package manager` on
+macOS and Linux, or [from source](https://numpy.org/devdocs/building).
+For more detailed instructions, consult our **Python and NumPy
+installation** guide below.
 
+Below are the different methods for **installing NumPy**. Click on the tabs to explore each method:
 {{< tabs >}}
 
 [[tab]]
-name = 'Recommended Method'
+name = 'Project Based'
 content = '''
 
-We'll start with recommendations based on the user's experience level and
-operating system of interest. If you're in between "beginning" and "advanced",
-please go with "beginning" if you want to keep things simple, and with
-"advanced" if you want to work according to best practices that go a longer way
-in the future.
-
-### Beginning users
-
-On all of Windows, macOS, and Linux:
-
-- Install [Anaconda](https://www.anaconda.com/download) (it installs all
-  packages you need and all other tools mentioned below).
-- For writing and executing code, use notebooks in
-  [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) for
-  exploratory and interactive computing, and
-  [Spyder](https://www.spyder-ide.org/) or [Visual Studio Code](https://code.visualstudio.com/)
-  for writing scripts and packages.
-- Use [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) to
-  manage your packages and start JupyterLab, Spyder, or Visual Studio Code.
-
+Recommended for new users who want a streamlined workflow.
 
-### Advanced users
+- **uv:** A modern Python package manager designed for speed and simplicity.
+  ```bash
+  uv pip install numpy
+  ```
 
-#### Conda
-
-- Install [Miniforge](https://github.com/conda-forge/miniforge).
-- Keep the `base` conda environment minimal, and use one or more
-  [conda environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)
-  to install the package you need for the task or project you're working on.
-
-#### Alternative if you prefer pip/PyPI
-
-For users who know, from personal preference or reading about the main
-differences between conda and pip below, they prefer a pip/PyPI-based solution,
-we recommend:
-- Install Python from [python.org](https://www.python.org/downloads/),
-  [Homebrew](https://brew.sh/), or your Linux package manager.
-- Use [Poetry](https://python-poetry.org/) as the most well-maintained tool
-  that provides a dependency resolver and environment management capabilities
-  in a similar fashion as conda does.
+- **pixi:** A cross-platform package manager for Python and other languages.
+  ```bash
+  pixi add numpy
+  ```
 
 '''
 
 [[tab]]
-name = 'Python Package Management'
+name = 'Environment Based'
 content = '''
 
-Managing packages is a challenging problem, and, as a result, there are lots of
-tools. For web and general purpose Python development there's a whole
-[host of tools](https://packaging.python.org/guides/tool-recommendations/)
-complementary with pip. For high-performance computing (HPC),
-[Spack](https://github.com/spack/spack) is worth considering. For most NumPy
-users though, [conda](https://conda.io/en/latest/) and
-[pip](https://pip.pypa.io/en/stable/) are the two most popular tools.
-
-
-### Pip & conda
-
-The two main tools that install Python packages are `pip` and `conda`. Their
-functionality partially overlaps (e.g. both can install `numpy`), however, they
-can also work together. We'll discuss the major differences between pip and
-conda here - this is important to understand if you want to manage packages
-effectively.
-
-The first difference is that conda is cross-language and it can install Python,
-while pip is installed for a particular Python on your system and installs other
-packages to that same Python install only. This also means conda can install
-non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while
-pip can't.
-
-The second difference is that pip installs from the Python Packaging Index
-(PyPI), while conda installs from its own channels (typically "defaults" or
-"conda-forge"). PyPI is the largest collection of packages by far, however, all
-popular packages are available for conda as well.
-
-The third difference is that conda is an integrated solution for managing
-packages, dependencies and environments, while with pip you may need another
-tool (there are many!) for dealing with environments or complex dependencies.
-
-<a name="reproducible-installs"></a>
-
-### Reproducible installs
-
-As libraries get updated, results from running your code can change, or your
-code can break completely. It's important to be able to reconstruct the set
-of packages and versions you're using. Best practice is to:
+The two main tools that install Python packages are `pip` and `conda`. Their functionality partially overlaps (e.g. both can install `numpy`), however, they can also work together. We’ll discuss the major differences between pip and conda here - this is important to understand if you want to manage packages effectively.
+
+The first difference is that conda is cross-language and it can install Python, while pip is installed for a particular Python on your system and installs other packages to that same Python install only. This also means conda can install non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while pip can’t.
+
+The second difference is that pip installs from the Python Packaging Index (PyPI), while conda installs from its own channels (typically “defaults” or “conda-forge”). PyPI is the largest collection of packages by far, however, all popular packages are available for conda as well.
+
+The third difference is that conda is an integrated solution for managing packages, dependencies and environments, while with pip you may need another tool (there are many!) for dealing with environments or complex dependencies.
+
+- **Conda:** If you use conda, you can install NumPy from the defaults or conda-forge channels:
+  ```bash
+  conda create -n my-env
+  conda activate my-env
+  conda install numpy
+  ```
+- **Pip:**
+  ```bash
+  pip install numpy
+  ```
+{{< admonition tip >}}
+**Tip:** Use a virtual environment for better dependency management
+{{< /admonition >}}
+
+  ```bash
+  python -m venv my-env
+  source my-env/bin/activate  # macOS/Linux
+  my-env\Scripts\activate     # Windows
+  pip install numpy
+  ```
+'''
 
-1. use a different environment per project you're working on,
-2. record package names and versions using your package installer;
-   each has its own metadata format for this:
-   - Conda: [conda environments and environment.yml](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)
-   - Pip: [virtual environments](https://docs.python.org/3/tutorial/venv.html) and
-  [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files)
-   - Poetry: [virtual environments and pyproject.toml](https://python-poetry.org/docs/basic-usage/)
+[[tab]]
+name = 'System Package Managers'
+content = '''
+Not recommended for most users, but available for convenience.
 
+**macOS (Homebrew):**
+```bash
+brew install numpy
+```
+**Linux (APT):**
+```bash
+sudo apt install python3-numpy
+```
+**Windows (Chocolatey):**
+```bash
+choco install numpy
+```
 
 '''
 
 [[tab]]
-name = 'NumPy packages & Libraries'
+name = 'Building from Source'
 content = '''
+For advanced users and developers who want to customize or debug **NumPy**.
+
+A word of warning: building Numpy from source can be a nontrivial exercise. 
+We recommend using binaries instead if those are available for your platform via one of the above methods. 
+For details on how to build from source, see [the building from source guide in the Numpy docs](https://numpy.org/devdocs/building/).
 
-NumPy doesn't depend on any other Python packages, however, it does depend on an
-accelerated linear algebra library - typically
-[Intel MKL](https://software.intel.com/en-us/mkl) or
-[OpenBLAS](https://www.openblas.net/). Users don't have to worry about
-installing those (they're automatically included in all NumPy install methods).
-Power users may still want to know the details, because the used BLAS can
-affect performance, behavior and size on disk:
-
-- The NumPy wheels on PyPI, which is what pip installs, are built with OpenBLAS.
-  The OpenBLAS libraries are included in the wheel. This makes the wheel
-  larger, and if a user installs (for example) SciPy as well, they will now
-  have two copies of OpenBLAS on disk.
-
-- In the conda defaults channel, NumPy is built against Intel MKL. MKL is a
-  separate package that will be installed in the users' environment when they
-  install NumPy.
-
-- In the conda-forge channel, NumPy is built against a dummy "BLAS" package. When
-  a user installs NumPy from conda-forge, that BLAS package then gets installed
-  together with the actual library - this defaults to OpenBLAS, but it can also
-  be MKL (from the defaults channel), or even
-  [BLIS](https://github.com/flame/blis) or reference BLAS.
-
-- The MKL package is a lot larger than OpenBLAS, it's about 700 MB on disk
-  while OpenBLAS is about 30 MB.
-
-- MKL is typically a little faster and more robust than OpenBLAS.
-
-Besides install sizes, performance and robustness, there are two more things to
-consider:
-
-- Intel MKL is not open source. For normal use this is not a problem, but if
-  a user needs to redistribute an application built with NumPy, this could be
-  an issue.
-- Both MKL and OpenBLAS will use multi-threading for function calls like
-  `np.dot`, with the number of threads being determined by both a build-time
-  option and an environment variable. Often all CPU cores will be used. This is
-  sometimes unexpected for users; NumPy itself doesn't auto-parallelize any
-  function calls. It typically yields better performance, but can also be
-  harmful - for example when using another level of parallelization with Dask,
-  scikit-learn or multiprocessing.
 '''
 {{< /tabs >}}
 
+## Verifying the Installation
+
+After installing NumPy, verify the installation by running the following in a Python shell or script:
+```python
+import numpy as np
+print(np.__version__)
+```
+
+This should print the installed version of NumPy without errors.
+
 ## Troubleshooting
 
 If your installation fails with the message below, see [Troubleshooting