Summary (Beginning -> keyboard shortcuts in the ipython shell)

1. ipython, jupyter notebook
2. shortcut for jupyter notebook: control enter, option enter, command enter, shift enter
   (command mode and edit mode, by esc and return)
3. python 2 or 3, depends
4. use help function (help(function)), or function? or L.insert?
   (really useful)
5. remember to put description at the first line of your function
   ("""description""")
6. tab completion (very useful -> like the search list), but not work on notebook, probably need to check why
   wild search: * (indicates other string)

Notes

1. IPython and Jupyter: these packages provide the computational environment in which many Python-using data scientists work.
2. NumPy: this library provides the ndarray for efficient storage and manipulation of dense data arrays in Python.
3. Pandas: this library provides the DataFrame for efficient storage and manipulation of labeled/columnar data in Python.
4. Matplotlib: this library provides capabilities for a flexible range of data visualizations in Python.
5. Scikit-Learn: this library provides efficient & clean Python implementations of the most important and established machine learning algorithms.