DataScience Classroomnotes 14/Mar/2022

Python’s Main Tools for Statistics

  • Tools can be broken into three tool-centric components
    • Representation and Engineering
    • Analysis and Computation
    • Visualizations
  • Examples:
    • pandas
    • NumPy
    • Matplotlib
    • Seaborn


  • This is main workhorse of many scientific computing projects in Python.
  • Creating a virtual environment and installing numpy
  • Creating virtual environments in pip and conda
# Traditional Python Way
mkdir my-ds-learning
cd my-ds-learning
python -m venv venv  

# Anaconda
conda create --name my-ds-learning

# To install packages
pip install numpy

conda install numpy

# To create requirements.txt
pip freeze > requirements.txt
conda list --export requirements.txt

# To use requirements.txt to create a new environment
# create  a new virtual environment 
pip install -r requirements.txt

conda create --name my-new-env --file requirements.txt
  • Installing the packages in jupyter notebooks Refer Here

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