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A03_Numpy_Cheat_Sheet.md

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Numpy Cheat Sheet

Table of contents

Introduction to Numpy

Why is Numpy important?

  • How many decimal numbers we can store with n bits ?
    • n bits is equal to 3 positions to store 0 & 1.
    • Formula: 2^(n) = 8 decimal numbers
  • Numpy allow you to specify more precisely number of memory you need for storing the data
#Python costs 28 bytes to store x = 5 since it is Integer Object
import sys
x = 5
sys.getsizeof(x) #return 28 - means variable x = 5 costs 28 bytes of memory

#Numpy : allow you to specify more precisely number of bits (memory) you need for storing the data
np.int8 #8-bit
  • Numpy is Array Processing
    • Built-in DS in Python List NOT optimized for High-Level Processing as List in Python is Object and they will not store elements in separate position in Memory
    • In constrast, Numpy will store Array Elements in Continuous Positions in memory

Numpy is more efficient for storing and manipulating data

  • Numpy array : essentially contains a single pointer to one contiguous block of data
  • Python list : contains a pointer to a block of pointers, each of which in turn points to a full Python object

Numpy Data Types and Attributes

  • Main Numpy Data Type is ndarray
  • Attributes: shape, ndim, size, dtype