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Python for Data Science - Numpy Cheat Sheet (DRAFT) by

This is a draft cheat sheet. It is a work in progress and is not finished yet.

Getting started

import numpy as np
Import Numpy using 
np
as alias
 
np.__v­ers­ion__
Numpy version
 
np.<Ta­b>
Display all contents of the numpy namespace
 
np?
Display Numpy built-in docume­ntation

A Python integer is more than just an integer

When we define an integer in Python, such as x = 1, x is not just a "­raw­" integer. It's actually a pointer to a compound C structure, which contains several values 
ob_refcnt
(a reference count that helps Python handle memory alloca­tion), 
ob_type
(the type of the variable), 
ob_size
(the size of the following data members), 
ob_digit
(the actual integer value the Python variable to repres­ent). Here 
PyObje­ct_HEAD
is the part of the structure containing the info mentioned above.

A Python list more than just a list

L = list(r­ang­e(10))
to create a list of integers. 
L2 = [True, "­2", 3.0, 4]
to create a hetero­geneous lists

But to allow these flexible types, each item in the list must contain its own type info, reference count, and other inform­ati­on–that is, each item is a complete Python object. In the special case that all variables are of the same type, much of this inform­ation is redundant: it can be much more efficient to store data in a fixed-type array (Numpy)