7.3 array Attributes

  • attributes enable you to discover information about its structure and contents
In [1]:
import numpy as np
In [2]:
integers = np.array([[1, 2, 3], [4, 5, 6]])
In [3]:
integers
Out[3]:
array([[1, 2, 3],
       [4, 5, 6]])
In [4]:
floats = np.array([0.0, 0.1, 0.2, 0.3, 0.4])
In [5]:
floats
Out[5]:
array([0. , 0.1, 0.2, 0.3, 0.4])
  • NumPy does not display trailing 0s

Determining an array’s Element Type

In [6]:
integers.dtype
Out[6]:
dtype('int64')
In [7]:
floats.dtype
Out[7]:
dtype('float64')
  • For performance reasons, NumPy is written in the C programming language and uses C’s data types
  • Other NumPy types

Determining an array’s Dimensions

  • ndim contains an array’s number of dimensions
  • shape contains a tuple specifying an array’s dimensions
In [8]:
integers.ndim
Out[8]:
2
In [9]:
floats.ndim
Out[9]:
1
In [10]:
integers.shape
Out[10]:
(2, 3)
In [11]:
floats.shape
Out[11]:
(5,)

Determining an array’s Number of Elements and Element Size

  • view an array’s total number of elements with size
  • view number of bytes required to store each element with itemsize
In [12]:
integers.size
Out[12]:
6
In [13]:
integers.itemsize
Out[13]:
8
In [14]:
floats.size
Out[14]:
5
In [15]:
floats.itemsize
Out[15]:
8

Iterating through a Multidimensional array’s Elements

In [16]:
for row in integers:
    for column in row:
        print(column, end='  ')
    print() 

1  2  3  
4  5  6
  • Iterate through a multidimensional array as if it were one-dimensional by using flat
In [17]:
for i in integers.flat:
    print(i, end='  ')

1  2  3  4  5  6

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