Update data_analytics_with_python.md. Still two TODOs left, due to software installation

doc.zih.tu-dresden.de/docs/software/data_analytics_with_python.md

 # Python for Data Analytics
 
-Python is a high-level interpreted language widely used in research and science. Using ZIH system 
+Python is a high-level interpreted language widely used in research and science. Using ZIH system
 allows you to work with python quicker and more effective. Here the general introduction to working
-with python on ZIH system is given. For specific machine learning frameworks see respective 
-documentation in [machine learning](machine_learning.md) section.   
+with python on ZIH system is given. For specific machine learning frameworks see respective
+documentation in [machine learning](machine_learning.md) section.
 
 ## Python Virtual Environments
 
-Often it is useful to create an isolated development environment, which can be shared among 
-a research group and/or teaching class. For this purpose python virtual environments can be used. 
+Often it is useful to create an isolated development environment, which can be shared among
+a research group and/or teaching class. For this purpose python virtual environments can be used.
 For more details see [here](python_virtual_environments.md).
 
 ## Jupyter Notebooks
@@ -17,201 +17,138 @@ Jupyter notebooks are a great way for interactive computing in a web
 browser. They allow working with data cleaning and transformation,
 numerical simulation, statistical modelling, data visualization and machine learning.
 
-On ZIH system a [JupyterHub](../access/jupyterhub.md) is available, which can be used to run 
+On ZIH system a [JupyterHub](../access/jupyterhub.md) is available, which can be used to run
 a Jupyter notebook on an HPC node, as well using a GPU when needed.  
 
-## Dask
+## Parallel Computing with Python
 
-**Dask** is an open-source library for parallel computing. Dask is a flexible library for parallel
-computing in Python.
+### Dask
 
-Dask natively scales Python. It provides advanced parallelism for analytics, enabling performance at
+[Dask](https://dask.org/) is a flexible and open-source library for parallel computing in Python.
+It natively scales Python and provides advanced parallelism for analytics, enabling performance at
 scale for some of the popular tools. For instance: Dask arrays scale Numpy workflows, Dask
 dataframes scale Pandas workflows, Dask-ML scales machine learning APIs like Scikit-Learn and
 XGBoost.
 
 Dask is composed of two parts:
 
-- Dynamic task scheduling optimized for computation and interactive
-  computational workloads.
-- Big Data collections like parallel arrays, data frames, and lists
-  that extend common interfaces like NumPy, Pandas, or Python
-  iterators to larger-than-memory or distributed environments. These
-  parallel collections run on top of dynamic task schedulers.
+- Dynamic task scheduling optimized for computation and interactive computational workloads.
+- Big Data collections like parallel arrays, data frames, and lists that extend common interfaces
+  like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments.
+  These parallel collections run on top of dynamic task schedulers.
 
 Dask supports several user interfaces:
 
-High-Level:
-
-- Arrays: Parallel NumPy
-- Bags: Parallel lists
-- DataFrames: Parallel Pandas
-- Machine Learning : Parallel Scikit-Learn
-- Others from external projects, like XArray
-
-Low-Level:
-
-- Delayed: Parallel function evaluation
-- Futures: Real-time parallel function evaluation
-
-### Installation
-
-### Installation Using Conda
-
-Dask is installed by default in [Anaconda](https://www.anaconda.com/download/). To install/update
-Dask on a Taurus with using the [conda](https://www.anaconda.com/download/) follow the example:
-
-```Bash
-# Job submission in ml nodes with allocating: 1 node, 1 gpu per node, 4 hours
-srun -p ml -N 1 -n 1 --mem-per-cpu=5772 --gres=gpu:1 --time=04:00:00 --pty bash
+- High-Level
+    - Arrays: Parallel NumPy
+    - Bags: Parallel lists
+    - DataFrames: Parallel Pandas
+    - Machine Learning: Parallel Scikit-Learn
+    - Others from external projects, like XArray
+- Low-Level
+    - Delayed: Parallel function evaluation
+    - Futures: Real-time parallel function evaluation
+
+#### Dask Installation
+
+!!! hint
+    This step might be obsolete, since the library may be already available as a module.
+    Check it with
+    ```console
+    marie@compute$ module spider dask
+    ```
+
+The installation of Dask is very easy and can be done by a user using a [virtual environment](python_virtual_environments.md)
+
+```console
+marie@compute$ module load SciPy-bundle/2020.11-fosscuda-2020b Pillow/8.0.1-GCCcore-10.2.0
+marie@compute$ virtualenv --system-site-packages dask-test
+created virtual environment CPython3.8.6.final.0-64 in 10325ms
+  creator CPython3Posix(dest=~/dask-test, clear=False, global=True)
+  seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=~/.local/share/virtualenv)
+    added seed packages: pip==21.1.3, setuptools==57.4.0, wheel==0.36.2
+  activators BashActivator,CShellActivator,FishActivator,PowerShellActivator,PythonActivator,XonshActivator
+marie@compute$ source dask-test/bin/activate
+(dask-test) marie@compute$ pip install dask dask-jobqueue
+[...]
+marie@compute$ python -c "import dask; print(dask.__version__)"
+2021.08.1
 ```
 
-Create a conda virtual environment. We would recommend using a workspace. See the example (use
-`--prefix` flag to specify the directory).
-
-**Note:** You could work with simple examples in your home directory (where you are loading by
-default). However, in accordance with the
-[HPC storage concept](../data_lifecycle/hpc_storage_concept2019.md) please use a
-[workspaces](../data_lifecycle/workspaces.md) for your study and work projects.
-
-```Bash
-conda create --prefix /scratch/ws/0/aabc1234-Workproject/conda-virtual-environment/dask-test python=3.6
-```
-
-By default, conda will locate the environment in your home directory:
-
-```Bash
-conda create -n dask-test python=3.6
-```
-
-Activate the virtual environment, install Dask and verify the installation:
-
-```Bash
-ml modenv/ml
-ml PythonAnaconda/3.6
-conda activate /scratch/ws/0/aabc1234-Workproject/conda-virtual-environment/dask-test python=3.6
-which python
-which conda
-conda install dask
-python
-
-from dask.distributed import Client, progress
-client = Client(n_workers=4, threads_per_worker=1)
-client
-```
-
-### Installation Using Pip
-
-You can install everything required for most common uses of Dask (arrays, dataframes, etc)
-
-```Bash
-srun -p ml -N 1 -n 1 --mem-per-cpu=5772 --gres=gpu:1 --time=04:00:00 --pty bash
-
-cd /scratch/ws/0/aabc1234-Workproject/python-virtual-environment/dask-test
-
-ml modenv/ml
-module load PythonAnaconda/3.6
-which python
+The preferred and simplest way to run Dask on HPC system is using
+[dask-jobqueue](https://jobqueue.dask.org/).
 
-python3 -m venv --system-site-packages dask-test
-source dask-test/bin/activate
-python -m pip install "dask[complete]"
+**TODO** create better example with jobqueue
 
-python
+```python
 from dask.distributed import Client, progress
 client = Client(n_workers=4, threads_per_worker=1)
 client
 ```
 
-Distributed scheduler
-
-?
-
-### Run Dask on Taurus
-
-The preferred and simplest way to run Dask on HPC systems today both for new, experienced users or
-administrator is to use [dask-jobqueue](https://jobqueue.dask.org/).
-
-You can install dask-jobqueue with `pip` or `conda`
-
-Installation with Pip
-
-```Bash
-srun -p haswell -N 1 -n 1 -c 4 --mem-per-cpu=2583 --time=01:00:00 --pty bash
-cd
-/scratch/ws/0/aabc1234-Workproject/python-virtual-environment/dask-test
-ml modenv/ml module load PythonAnaconda/3.6 which python
-
-source dask-test/bin/activate pip
-install dask-jobqueue --upgrade # Install everything from last released version
-```
-
-Installation with Conda
-
-```Bash
-srun -p haswell -N 1 -n 1 -c 4 --mem-per-cpu=2583 --time=01:00:00 --pty bash
-
-ml modenv/ml module load PythonAnaconda/3.6 source
-dask-test/bin/activate
-
-conda install dask-jobqueue -c conda-forge\</verbatim>
-```
-
-## MPI for Python
+### mpi4py -  MPI for Python
 
 Message Passing Interface (MPI) is a standardized and portable
 message-passing standard designed to function on a wide variety of
 parallel computing architectures. The Message Passing Interface (MPI) is
 a library specification that allows HPC to pass information between its
-various nodes and clusters. MPI designed to provide access to advanced
+various nodes and clusters. MPI is designed to provide access to advanced
 parallel hardware for end-users, library writers and tool developers.
 
-### Why use MPI?
-
-MPI provides a powerful, efficient and portable way to express parallel
-programs.
-Among many parallel computational models, message-passing has proven to be an effective one.
-
-### Parallel Python with mpi4py
-
 Mpi4py(MPI for Python) package provides bindings of the MPI standard for
 the python programming language, allowing any Python program to exploit
 multiple processors.
 
-#### Why use mpi4py?
-
 Mpi4py based on MPI-2 C++ bindings. It supports almost all MPI calls.
 This implementation is popular on Linux clusters and in the SciPy
 community. Operations are primarily methods of communicator objects. It
 supports communication of pickleable Python objects. Mpi4py provides
 optimized communication of NumPy arrays.
 
-Mpi4py is included as an extension of the SciPy-bundle modules on
-taurus.
-
-Please check the SoftwareModulesList for the modules availability. The availability of the mpi4py
-in the module you can check by
-the `module whatis <name_of_the module>` command. The `module whatis`
-command displays a short information and included extensions of the
-module.
-
-Moreover, it is possible to install mpi4py in your local conda
-environment:
-
-```Bash
-srun -p ml --time=04:00:00 -n 1 --pty --mem-per-cpu=8000 bash                            #allocate recources
-module load modenv/ml
-module load PythonAnaconda/3.6                                                           #load module to use conda
-conda create --prefix=<location_for_your_environment> python=3.6 anaconda                #create conda virtual environment
-
-conda activate <location_for_your_environment>                                          #activate your virtual environment
+Mpi4py is included as an extension of the SciPy-bundle modules on an HPC system
+
+```console
+marie@compute$ module load SciPy-bundle/2020.11-foss-2020b
+Module SciPy-bundle/2020.11-foss-2020b and 28 dependencies loaded.
+marie@compute$ pip list
+Package                       Version
+----------------------------- ----------
+[...]
+mpi4py                        3.0.3
+[...]
+```
 
-conda install -c conda-forge mpi4py                                                      #install mpi4py
+Other versions of the package can be found with
+
+```console
+marie@compute$ module spider mpi4py
+-----------------------------------------------------------------------------------------------------------------------------------------
+  mpi4py:
+-----------------------------------------------------------------------------------------------------------------------------------------
+     Versions:
+        mpi4py/1.3.1
+        mpi4py/2.0.0-impi
+        mpi4py/3.0.0 (E)
+        mpi4py/3.0.2 (E)
+        mpi4py/3.0.3 (E)
+
+Names marked by a trailing (E) are extensions provided by another module.
+
+-----------------------------------------------------------------------------------------------------------------------------------------
+  For detailed information about a specific "mpi4py" package (including how to load the modules) use the module's full name.
+  Note that names that have a trailing (E) are extensions provided by other modules.
+  For example:
+
+     $ module spider mpi4py/3.0.3
+-----------------------------------------------------------------------------------------------------------------------------------------
+```
 
-python                                                                                   #start python
+Check if mpi4py is running correctly
 
-from mpi4py import MPI                                                                   #verify your mpi4py
+```python
+from mpi4py import MPI
 comm = MPI.COMM_WORLD
 print("%d of %d" % (comm.Get_rank(), comm.Get_size()))
 ```
 
+**TODO** verify mpi4py installation