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