numpy库中的一些函数简介、使用方法
1、np.concatenate()
1.1、函数案例
import numpy as np
a=np.array([1,2,3])
b=np.array([11,22,33])
c=np.array([44,55,66])
d=np.concatenate((a,b,c),axis=0) # 默认情况下，axis=0可以不写
print(d) #输出array([ 1, 2, 3, 11, 22, 33, 44, 55, 66])，对于一维数组拼接，axis的值不影响最后的结果
1.2、函数用法
concatenate Found at: numpy.core.multiarray
concatenate((a1, a2, ...), axis=0, out=None)
Join a sequence of arrays along an existing axis.
Parameters
----------
a1, a2, ... : sequence of array_like. The arrays must have the same shape, except in the dimension corresponding to `axis` (the first, by default).
axis : int, optional. The axis along which the arrays will be joined. Default is 0.
out : ndarray, optional. If provided, the destination to place the result. The shape must be correct, matching that of what concatenate would have returned if no out argument were specified.
Returns
-------
res : ndarray
The concatenated array.
在:numpy.core.multiarray找到连接
连接((a1, a2，…)，axis=0, out=None)
沿着现有的轴连接数组序列。
参数
----------
a1, a2,…:数组类型的序列。数组必须具有相同的形状，除了与“axis”对应的维度(默认情况下为第一个维度)。
axis: int，可选。数组连接的轴线。默认值为0。
out : ndarray，可选。如果提供，放置结果的目的地。形状必须正确，如果没有指定out参数，则匹配concatenate将返回的形状。
返回
-------
res: ndarray
连接后的字符串数组。
See Also
--------
ma.concatenate : Concatenate function that preserves input masks.
array_split : Split an array into multiple sub-arrays of equal or
near-equal size.
split : Split array into a list of multiple sub-arrays of equal size.
hsplit : Split array into multiple sub-arrays horizontally (column wise)
vsplit : Split array into multiple sub-arrays vertically (row wise)
dsplit : Split array into multiple sub-arrays along the 3rd axis (depth).
stack : Stack a sequence of arrays along a new axis.
hstack : Stack arrays in sequence horizontally (column wise)
vstack : Stack arrays in sequence vertically (row wise)
dstack : Stack arrays in sequence depth wise (along third dimension)
Notes
-----
When one or more of the arrays to be concatenated is a MaskedArray, this function will return a MaskedArray object instead of an ndarray, but the input masks are *not* preserved. In cases where a MaskedArray is expected as input, use the ma.concatenate function from the masked array module instead. 另请参阅
--------
马。保存输入掩码的连接函数。
array_split:将一个数组分割成多个相等或的子数组
与大小。
分割:将数组分割成相同大小的多个子数组。
hsplit:水平(按列)将数组分割为多个子数组
垂直(按行)将数组分割为多个子数组
dsplit:沿着第三轴(深度)将数组分割成多个子数组。
堆栈:将数组序列沿着一个新的轴进行堆栈。
hstack:水平排列(按列排列)
垂直(行向)按顺序堆叠数组。
dstack:按深度顺序排列的堆栈数组(沿三维方向)
笔记
-----
当一个或多个要连接的数组是一个MaskedArray时，这个函数将返回一个MaskedArray对象而不是ndarray，但是输入掩码*不*保留。在需要MaskedArray作为输入的情况下，使用ma。连接函数从掩码数组模块代替。
Examples
--------
>>> a = np.array([[1, 2], [3, 4]])
>>> b = np.array([[5, 6]])
>>> np.concatenate((a, b), axis=0)
array([[1, 2],
[3, 4],
[5, 6]])
>>> np.concatenate((a, b.T), axis=1)
array([[1, 2, 5],
[3, 4, 6]])
This function will not preserve masking of MaskedArray
inputs.
>>> a = np.ma.arange(3)
>>> a[1] = np.ma.masked
>>> b = np.arange(2, 5)
>>> a
masked_array(data = [0 -- 2],
mask = [False True False],
fill_value = 999999)
>>> b
array([2, 3, 4])
>>> np.concatenate([a, b])
masked_array(data = [0 1 2 2 3 4],
mask = False,
fill_value = 999999)
>>> np.ma.concatenate([a, b])
masked_array(data = [0 -- 2 2 3 4],
mask = [False True False False False False],
fill_value = 999999)
