1. Preface
In this chapter we will learn how to create an array from an existing array.
two.
Similar , but there are only three parameters, two fewer than .
(a, dtype = None, order = None)
Parameter description:
| parameter | describe |
|---|---|
| a | Input parameters of any form can be, list, list tuple, tuple, tuple, tuple list, tuple, multidimensional array |
| dtype | Data type, optional |
| order | Optional, there are two options "C" and "F", which represent the order of storage elements in computer memory, respectively, row priority and column priority. |
Example
Convert the list to ndarray:
import numpy as np x = [1,2,3] a = (x) print (a)
The output result is:
[1 2 3]
Convert tuples to ndarray:
import numpy as np x = (1,2,3) a = (x) print (a)
The output result is:
[1 2 3]
Convert a list of tuples to ndarray:
import numpy as np x = [(1,2,3),(4,5)] a = (x) print (a)
The output result is:
[(1, 2, 3) (4, 5)]
The dtype parameter is set:
import numpy as np x = [1,2,3] a = (x, dtype = float) print (a)
The output result is:
[ 1. 2. 3.]
three.
Used to implement dynamic arrays.
Accept the buffer input parameter and read it in the form of a stream to convert it into an ndarray object.
(buffer, dtype = float, count = -1, offset = 0)
Notice:When buffer is a string, Python3 defaults to str is Unicode type, so it needs to be converted to bytestring and add b before the original str.
Parameter description:
| parameter | describe |
|---|---|
| buffer | It can be any object and will be read in as a stream. |
| dtype | Returns the data type of the array, optional |
| count | The number of data read, default is -1, and all data is read. |
| offset | The start position of read, default is 0. |
import numpy as np s = b'Hello World' a = (s, dtype = 'S1') print (a)
The output result is:
[b'H' b'e' b'l' b'l' b'o' b' ' b'W' b'o' b'r' b'l' b'd']
import numpy as np s = 'Hello World' a = (s, dtype = 'S1') print (a)
The output result is:
['H' 'e' 'l' 'l' 'o' ' ' 'W' 'o' 'r' 'l' 'd']
Four.
The method creates an ndarray object from an iterable object and returns a one-dimensional array.
(iterable, dtype, count=-1)
| parameter | describe |
|---|---|
| iterable | Iterable object |
| dtype | Returns the data type of the array |
| count | The number of data to be read, default is -1, and all data is read |
import numpy as np # Create list objects using range functionlist=range(5) it=iter(list) # Create ndarray using iteratorx=(it, dtype=float) print(x)
The output result is:
[0. 1. 2. 3. 4.]
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