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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)