Python Numpy的常用操作
今天想给大家带来的是Numpy的常用操作；因为此前我用这个都是现查现用，但是发现常见的也就那么几个，如果你不背下来，那么查来查去也是很耽误写代码的时间的，尤其是在数据挖掘竞赛中，我们应该尽可能缩短写代码的时间，更多的时间应该放在建模和特征工程上，这就很需要我们熟练操作Numpy和Pandas的能力了。对于科学计算的工作者对Numpy的操作自然也已经了然于心，因此一定要好好掌握和背诵这个包的功能。相信看完今天的内容，你很快就能掌握常见的操作。记得学习之后一定要动手实践噢！
首先importnumpy as np肯定要的，我用notebook给大家展示一下，基本看注释就可以理解了：
一、基本
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二、索引和维度变换
也重点看一下铺平的操作。
转置可以有三种方法**.transpose()；.T； resize()；.reshape()**。下面会一一给大家演示。 Python学习交流群：1004391443
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下图的...是省略多个维度
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也重点看一下铺平的操作。
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三、组合(vstack,hstack, dstack)
一会我们专门b1和b2进行操作，先记住一下它们长什么样子。
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1.水平组合有三种方法。
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2.垂直组合有同样有三种方式。
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3. 深度组合，深度组合做图像处理的时候会经常用到。这里我解释一下，一般图像数据的维度是（长，宽，高（颜色深度）），这里改变的就是数组的这个高。
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看一下了发生什么变化（shape会隐藏为1的维度）。
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np.vsplit(),np.hsplit(), np.dsplit()分别是逆操作，内容太长就不截图了，直接复制输入和输出，比如可以：
输入：
d,v = np.dsplit(c,2)
print(d,"\n\n",v)。
输出：
[[[0]
[1]
[2]]
[[3]
[4]
[5]]
[[6]
[7]
[8]]]
[[[ 0]
[ 2]
[ 4]]
[[ 6]
[ 8]
[10]]
[[12]
[14]
[16]]]
也可以用np.split()替换三个函数，np.split()有三个参数，第一个是操作对象，第二个是返回的数组个数，第三个是操作的维度。
输入：
print(np.split(c,3,axis= 0))
print(np.split(c,3,axis= 1))
print(np.split(c,2,axis= 2))
输出：
[array([[[0, 0],
[1, 2],
[2, 4]]]), array([[[ 3, 6],
[ 4, 8],
[ 5, 10]]]), array([[[ 6, 12],
[ 7, 14],
[ 8, 16]]])]
[array([[[ 0, 0]],
[[ 3, 6]],
[[ 6, 12]]]), array([[[ 1, 2]],
[[ 4, 8]],
[[ 7, 14]]]), array([[[ 2, 4]],
[[ 5, 10]],
[[ 8, 16]]])]
[array([[[0],
[1],
[2]],
[[3],
[4],
[5]],
[[6],
[7],
[8]]]), array([[[ 0],
[ 2],
[ 4]],
[[ 6],
[ 8],
[10]],
[[12],
[14],
[16]]])]
是不是很简单，快动手试试吧！