Python, Miniconda, Jupyterlab & Python Packages

Python, Miniconda, Jupyterlab & Python Packages

Basic install

Open Ports Anaconda

In, in tab: external access, add the following rules:

Rule & Description

ports might change, easy to check with a local computer: open cmd, ping <url>
  1. Open conda prompt in admin mode
  2. conda install -c conda-forge <package>

Install Miniconda

  1. download from:
  2. Install as admin

Install Jupyterlab

  1. Open conda prompt in admin mode
  2. conda install -c conda-forge jupyterlab

Set default location to z: (optional)

This allows any user to quickly start Jupyter Labs from the desktop. All Jupyter Lab Notebooks are automatically created (and thus backuped as well) on the Z:-drive.  
  1. Create a folder on the Z:\Jupyter Labs
This is at the cost of a bit of performance for the Z-drive is not as fast ast the C-drive. Alternatively create a folder on the C:-drive and change after putting the shortcut on the desktop in the properties of the shortcut the target and start-in
  1. Download and upload it to the Workspace
  2. Extract the zip
  3. Put the shortcut in: c:\users\public\desktop  (just type desktop behind public)
  4. Put the icon in %ALLUSERSPROFILE%\Miniconda3\Menu\

Useful commands / packages

  1. Open conda prompt in admin mode


  1. conda update -c conda-forge jupyterlab
  2. conda update -c conda-forge --all
  3. conda update -c conda-forge python

Interesting packages

  1. Tabulate: conda install -c conda-forge tabulate
  2. OpenPyXL: conda install -c conda-forge openpyxl
  3. numpy: conda install -c conda-forge numpy
  4. pandas: conda install -c conda-forge pandas
  5. matplotlib: conda install -c conda-forge matplotlib
  6. seaborn: conda install -c conda-forge seaborn
  7. Castor: conda install -c conda-forge castorapi
** you can install multiple packages in one go like: conda install -c conda-forge tabulate seaborn castorapi

Jupyter Lab and multiprocessing

Cell 1
  1. %%writefile
  2. def your_function(f):
  3.     return f

  4. def process_frame(f):
  5.     return f, study_check_v3(f)
  1. from tqdm import tqdm
  2. from multiprocessing import Pool
  3. from magic_functions import process_frame

  4. frames_list = [x + batch*y for x in range(0, batch)]
  5. with Pool() as p:
  6.     pool_outputs = list(
  7.         tqdm(
  8.             p.imap(process_frame, frames_list),
  9.             total=len(frames_list)))

  10. print(pool_outputs)
  11. new_dict = dict(pool_outputs)
  12. print("dict: ", new_dict)

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