Getting Started with Numpy

1. Numpy basics

1.1. numpy.ndarray Basics

1.1.1. numpy.ndarray attribute element

numpy.ndarray attribute element Acquisition contents Example 1
np.array([[1,0,0],[0,1,2]])
ndim Number of dimensions 2
shape Array shape (2,3)
size Number of array elements 6
dtype Array element data type int32
T Transpose array np.array([[1 0]
[0 1]
[0 2]])
flags Memory layout
flat One-dimensional (flattened) array generation
Array definition example: np.array(Array variables.flat)
imag Imaginary value array of array elements
real Real part array of array elements
itemsize Array element size (bytes)
Example: int32-> 32/8 =4 bytes
4
nbytes Array size(Part-Time Job) 32
strides Misalignment of adjacent array elements (bytes) (12,4)
Vertical: 12 bytes
->4 bytes3 elements(Lateral direction)
Lateral direction:4バイト
->4 bytes
1 element
ctypes Used in ctypes module
base Referenced array None

Example_1.py


### Library definition
import numpy as np

### Function definition
def print_attribute(input):
    print("")
    for key, value in input.items():
        print(">>> " + str(key))
        print("IN: print("+str(key)+")")
        print("OUT: "+str(value))
        print("")

### Array definition
array = np.array( [[1,0,0],[0,1,2]])

### numpy.ndarray element definition

attribute ={}
attribute['array.ndim'] = array.ndim
attribute['array.shape'] = array.shape
attribute['array.size'] = array.size
attribute['array.dtype'] = array.dtype
attribute['array.T'] = array.T
attribute['array.flags'] = array.flags
attribute['array.flat'] = array.flat
attribute['np.array(array.flat)'] = np.array(array.flat)
attribute['array.imag'] = array.imag
attribute['array.real'] = array.real
attribute['array.itemsize'] = array.itemsize
attribute['array.nbytes'] = array.nbytes
attribute['array.strides'] = array.strides
attribute['array.ctypes'] = array.ctypes
attribute['array.base'] = array.base

### numpy.ndarray element acquisition example
print_attribute(attribute)
"""

>>> array.ndim
IN: print(array.ndim)
OUT: 2

>>> array.shape
IN: print(array.shape)
OUT: (2, 3)

>>> array.size
IN: print(array.size)
OUT: 6

>>> array.dtype
IN: print(array.dtype)
OUT: int32

>>> array.T
IN: print(array.T)
OUT: [[1 0]
 [0 1]
 [0 2]]

>>> array.flags
IN: print(array.flags)
OUT:   C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False


>>> array.flat
IN: print(array.flat)
OUT: <numpy.flatiter object at 0x000001E00DB70A00>

>>> np.array(array.flat)
IN: print(np.array(array.flat))
OUT: [1 0 0 0 1 2]

>>> array.imag
IN: print(array.imag)
OUT: [[0 0 0]
 [0 0 0]]

>>> array.real
IN: print(array.real)
OUT: [[1 0 0]
 [0 1 2]]

>>> array.itemsize
IN: print(array.itemsize)
OUT: 4

>>> array.nbytes
IN: print(array.nbytes)
OUT: 24

>>> array.strides
IN: print(array.strides)
OUT: (12, 4)

>>> array.ctypes
IN: print(array.ctypes)
OUT: <numpy.core._internal._ctypes object at 0x000001E00D84BC50>

>>> array.base
IN: print(array.base)
OUT: None

"""

References

  1. Numpy Official User Guide (Version: 1.18)
  2. Numpy Official Reference (numpy.ndarray)

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