November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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Python
、
NumPy
、
matplotlib
和
pandas
｜
347
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]])
In [89]: b = np.array(b, dtype=np.float)
In [90]: b
Out[90]: array([[ 0., 1., 2., 3., 4., 5.],
[ 6., 7., 8., 9., 10., 11.],
[12., 13., 14., 15., 16., 17.],
[18., 19., 20., 21., 22., 23.]])
按照惯例将
NumPy
导入为
np
。
使用
range
对象实例化一个
ndarray
对象，也可以使用
np.arange
。
打印数值。
将对象重塑为二维的……
……并打印出结果。
将对象重塑为三维的……
……并打印出结果。
这会将对象的
dtype
更改为
np.float
……
……显示一组新的（现在是浮点数）数字。
许多
Python
数据结构被设计得非常通用。其中一个例子是可变列表对象，
它可以通过多种方式轻松操作（添加和删除元素、存储其他复杂数据结
构等）。
NumPy
与常规
ndarray
对象的策略是提供一种更专业的数据结构，
其中所有元素都具有相同的原子类型，从而允许在内存中连续存储。这
使得
ndarray
对象在解决某些情况下的问题时要好得多，例如在处理更 ...
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