numpy rubber duck intro

Where I mainly cut and paste to build muscle memory.

see: https://docs.scipy.org/doc/numpy-dev/user/quickstart.html

From the docs: NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of positive integers. In NumPy dimensions are called axes. The number of axes is rank.



In [1]:

    
# Seems like everyone aliases numpy
import numpy as np



In [2]:

    
# Hello world for numpy
a = np.arange(15).reshape(3, 5)



In [3]:

    
a









    Out[3]:





array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14]])



In [4]:

    
a.shape









    Out[4]:





(3, 5)



In [5]:

    
a.ndim









    Out[5]:





2



In [6]:

    
a.dtype.name









    Out[6]:





'int64'



In [7]:

    
a.itemsize









    Out[7]:





8



In [8]:

    
a.size









    Out[8]:





15



In [9]:

    
type(a)









    Out[9]:





numpy.ndarray



In [10]:

    
b = np.array([6, 7, 8])



In [11]:

    
b









    Out[11]:





array([6, 7, 8])



In [12]:

    
type(b)









    Out[12]:





numpy.ndarray



In [13]:

    
coord1 = np.array((1, 1), dtype=int)



In [14]:

    
coord2 = np.array((1, 2), dtype=int)



In [15]:

    
# Element addition
coord1 + coord2









    Out[15]:





array([2, 3])



In [16]:

    
# Element multiplication
coord1 * coord2









    Out[16]:





array([1, 2])



In [17]:

    
# Dot product via function
coord1.dot(coord2)









    Out[17]:





3



In [18]:

    
# apply operation from module
np.cross(coord1, coord2)









    Out[18]:





array(1)



In [ ]: