记录贴。
参考：
NumPy是使用Python进行科学计算的基础软件包。包括：1)功能强大的N维数组对象。2)精密广播功能函数。3)集成C/C+和Fortran代码的工具。4)强大的线性代数、傅立叶变换和随机数功能。
- NumPy 最重要的一个特点是其 N 维数组对象 ndarray，它是一系列同类型数据的集合，以 0 下标为开始进行集合中元素的索引。ndarray 对象是用于存放同类型元素的多维数组。ndarray 中的每个元素在内存中都有相同存储大小的区域。
- ndarray对象的内容可以通过索引或切片来访问和修改，与 Python 中 list 的切片操作一样。ndarray 数组可以基于 0 - n 的下标进行索引，切片对象可以通过内置的 slice 函数，并设置 start, stop 及 step 参数进行，从原数组中切割出一个新数组。
import numpy as np
数据拼接
非系统性总结，持续更新……
np.column_stack(tup)
Stack 1-D arrays as columns into a 2-D array.
Take a sequence of 1-D arrays and stack them as columns to make a single 2-D array. 2-D arrays are stacked as-is, just like with hstack. 1-D arrays are turned into 2-D columns first.
- [Parameters]:
tup, sequence of 1-D or 2-D arrays. Arrays to stack. All of them must have the same first dimension.- [Returns]:
stacked, 2-D array. The array formed by stacking the given arrays.
np.hstack(tup)
Stack arrays in sequence horizontally (column wise).
This is equivalent to concatenation along the second axis, except for 1-D arrays where it concatenates along the first axis.
This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis).
Similarly,
np.vstack(tup): Stack arrays in sequence vertically (row wise).
np.concatenate()
np.concatenate((a1, a2, ...), axis=0, out=None, dtype=None, casting="same_kind"), Join a sequence of arrays along an existing axis.
Tips
UserWarning: loaded more than 1 DLL from .libs: 【参考：numpy版本问题】