📅 最后修改于: 2020-11-08 07:34:30 🧑 作者: Mango
在本章中，我们将讨论如何从现有数据创建数组。
此函数与numpy.array相似，除了它具有较少的参数外。该例程对于将Python序列转换为ndarray很有用。
numpy.asarray(a, dtype = None, order = None)
构造函数采用以下参数。
| Sr.No. | Parameter & Description |
|---|---|
| 1 |
a Input data in any form such as list, list of tuples, tuples, tuple of tuples or tuple of lists |
| 2 |
dtype By default, the data type of input data is applied to the resultant ndarray |
| 3 |
order C (row major) or F (column major). C is default |
以下示例说明如何使用asarray函数。
# convert list to ndarray
import numpy as np
x = [1,2,3]
a = np.asarray(x)
print a
其输出如下-
[1 2 3]
# dtype is set
import numpy as np
x = [1,2,3]
a = np.asarray(x, dtype = float)
print a
现在，输出将如下所示：
[ 1. 2. 3.]
# ndarray from tuple
import numpy as np
x = (1,2,3)
a = np.asarray(x)
print a
它的输出将是-
[1 2 3]
# ndarray from list of tuples
import numpy as np
x = [(1,2,3),(4,5)]
a = np.asarray(x)
print a
在这里，输出将如下所示：
[(1, 2, 3) (4, 5)]
此函数将缓冲区解释为一维数组。公开缓冲区接口的任何对象都用作返回ndarray的参数。
numpy.frombuffer(buffer, dtype = float, count = -1, offset = 0)
构造函数采用以下参数。
| Sr.No. | Parameter & Description |
|---|---|
| 1 |
buffer Any object that exposes buffer interface |
| 2 |
dtype Data type of returned ndarray. Defaults to float |
| 3 |
count The number of items to read, default -1 means all data |
| 4 |
offset The starting position to read from. Default is 0 |
以下示例演示了frombuffer函数的用法。
import numpy as np
s = 'Hello World'
a = np.frombuffer(s, dtype = 'S1')
print a
这是它的输出-
['H' 'e' 'l' 'l' 'o' ' ' 'W' 'o' 'r' 'l' 'd']
此函数从任何可迭代对象构建ndarray对象。此函数返回一个新的一维数组。
numpy.fromiter(iterable, dtype, count = -1)
在这里，构造函数采用以下参数。
| Sr.No. | Parameter & Description |
|---|---|
| 1 |
iterable Any iterable object |
| 2 |
dtype Data type of resultant array |
| 3 |
count The number of items to be read from iterator. Default is -1 which means all data to be read |
下面的示例演示如何使用内置的range()函数返回列表对象。此列表的迭代器用于形成ndarray对象。
# create list object using range function
import numpy as np
list = range(5)
print list
其输出如下-
[0, 1, 2, 3, 4]
# obtain iterator object from list
import numpy as np
list = range(5)
it = iter(list)
# use iterator to create ndarray
x = np.fromiter(it, dtype = float)
print x
现在，输出将如下所示：
[0. 1. 2. 3. 4.]