{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"toc": true
},
"source": [
"<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
"<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#数组的合并\" data-toc-modified-id=\"数组的合并-1\"><span class=\"toc-item-num\">1 </span>数组的合并</a></span><ul class=\"toc-item\"><li><span><a href=\"#np.vstack-沿纵轴拼接\" data-toc-modified-id=\"np.vstack-沿纵轴拼接-1.1\"><span class=\"toc-item-num\">1.1 </span>np.vstack 沿纵轴拼接</a></span></li><li><span><a href=\"#np.hstack-沿横轴拼接\" data-toc-modified-id=\"np.hstack-沿横轴拼接-1.2\"><span class=\"toc-item-num\">1.2 </span>np.hstack 沿横轴拼接</a></span></li><li><span><a href=\"#指定拼接方向的np.concatenate()\" data-toc-modified-id=\"指定拼接方向的np.concatenate()-1.3\"><span class=\"toc-item-num\">1.3 </span>指定拼接方向的np.concatenate()</a></span></li></ul></li><li><span><a href=\"#数组的分割\" data-toc-modified-id=\"数组的分割-2\"><span class=\"toc-item-num\">2 </span>数组的分割</a></span><ul class=\"toc-item\"><li><span><a href=\"#np.hsplit-横向进行分割\" data-toc-modified-id=\"np.hsplit-横向进行分割-2.1\"><span class=\"toc-item-num\">2.1 </span>np.hsplit 横向进行分割</a></span></li><li><span><a href=\"#np.vsplit()横向进行分割\" data-toc-modified-id=\"np.vsplit()横向进行分割-2.2\"><span class=\"toc-item-num\">2.2 </span>np.vsplit()横向进行分割</a></span></li><li><span><a href=\"#可指定方向的np.array_split()\" data-toc-modified-id=\"可指定方向的np.array_split()-2.3\"><span class=\"toc-item-num\">2.3 </span>可指定方向的np.array_split()</a></span></li></ul></li></ul></div>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
" import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#全部行都能输出\n",
"from IPython.core.interactiveshell import InteractiveShell\n",
"InteractiveShell.ast_node_interactivity = \"all\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 数组的合并"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"a = np.array([[1, 2,3], [7, 8, 9]])\n",
"b = np.array([[100, 101, 102], [103, 104, 105]])\n",
"c = np.array([[0,0], [0, 0]])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 2, 3],\n",
" [7, 8, 9]])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"array([[100, 101, 102],\n",
" [103, 104, 105]])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"array([[0, 0],\n",
" [0, 0]])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a\n",
"b\n",
"c"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## np.vstack 沿纵轴拼接"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**在纵向拼接， 增加的是行，列不变**"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1, 2, 3],\n",
" [ 7, 8, 9],\n",
" [100, 101, 102],\n",
" [103, 104, 105]])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.vstack((a,b))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "all the input array dimensions except for the concatenation axis must match exactly",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-6-abd99944b1f4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#维度必须匹配\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\numpy\\core\\shape_base.py\u001b[0m in \u001b[0;36mvstack\u001b[1;34m(tup)\u001b[0m\n\u001b[0;32m 281\u001b[0m \"\"\"\n\u001b[0;32m 282\u001b[0m \u001b[0m_warn_for_nonsequence\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 283\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_nx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0matleast_2d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_m\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0m_m\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtup\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 284\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 285\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: all the input array dimensions except for the concatenation axis must match exactly"
]
}
],
"source": [
"np.vstack((a,c)) #维度必须匹配"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## np.hstack 沿横轴拼接"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**在横向拼接， 增加的是列，行不变**"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"a = np.array([[1, 2,3], [7, 8, 9]])\n",
"b = np.array([[100, 101, 102], [103, 104, 105]])\n",
"c = np.array([[0,0], [0, 0]])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 2, 3],\n",
" [7, 8, 9]])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"array([[100, 101, 102],\n",
" [103, 104, 105]])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"array([[0, 0],\n",
" [0, 0]])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a\n",
"b\n",
"c"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1, 2, 3, 100, 101, 102],\n",
" [ 7, 8, 9, 103, 104, 105]])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.hstack((a,b))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 2, 3, 0, 0],\n",
" [7, 8, 9, 0, 0]])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.hstack((a,c))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 指定拼接方向的np.concatenate()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**参数axis=0默认在纵轴上拼接，axis=1横向拼接**"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"a = np.array([[1, 2,3], [7, 8, 9]])\n",
"b = np.array([[100, 101, 102], [103, 104, 105]])\n",
"c = np.array([[0,0], [0, 0]])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 2, 3],\n",
" [7, 8, 9]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"array([[100, 101, 102],\n",
" [103, 104, 105]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"array([[0, 0],\n",
" [0, 0]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a\n",
"b\n",
"c"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1, 2, 3],\n",
" [ 7, 8, 9],\n",
" [100, 101, 102],\n",
" [103, 104, 105]])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.concatenate((a,b), axis=0) #axis=0默认在纵轴上拼接"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1, 2, 3, 100, 101, 102],\n",
" [ 7, 8, 9, 103, 104, 105]])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.concatenate((a,b), axis=1) #axis=1在横轴上拼接"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 数组的分割"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
" [10, 11, 12, 13, 14, 15, 16, 17, 18],\n",
" [19, 20, 21, 22, 23, 24, 25, 26, 27],\n",
" [28, 29, 30, 31, 32, 33, 34, 35, 36]])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.arange(1,37).reshape(4,9)\n",
"a"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## np.hsplit 横向进行分割"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[array([[ 1, 2, 3],\n",
" [10, 11, 12],\n",
" [19, 20, 21],\n",
" [28, 29, 30]]), array([[ 4, 5, 6],\n",
" [13, 14, 15],\n",
" [22, 23, 24],\n",
" [31, 32, 33]]), array([[ 7, 8, 9],\n",
" [16, 17, 18],\n",
" [25, 26, 27],\n",
" [34, 35, 36]])]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b = np.hsplit(a,3) # 第二个参数只写一个整数时，会在横向进行平均分割\n",
"b"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 1 2 3]\n",
" [10 11 12]\n",
" [19 20 21]\n",
" [28 29 30]]\n",
"******************************\n",
"[[ 4 5 6]\n",
" [13 14 15]\n",
" [22 23 24]\n",
" [31 32 33]]\n",
"******************************\n",
"[[ 7 8 9]\n",
" [16 17 18]\n",
" [25 26 27]\n",
" [34 35 36]]\n",
"******************************\n"
]
}
],
"source": [
"for i in b:\n",
" print(i)\n",
" print('*'*30)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
" [10, 11, 12, 13, 14, 15, 16, 17, 18],\n",
" [19, 20, 21, 22, 23, 24, 25, 26, 27],\n",
" [28, 29, 30, 31, 32, 33, 34, 35, 36]])"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[array([[ 1, 2, 3],\n",
" [10, 11, 12],\n",
" [19, 20, 21],\n",
" [28, 29, 30]]), array([[ 4, 5],\n",
" [13, 14],\n",
" [22, 23],\n",
" [31, 32]]), array([[ 6, 7, 8, 9],\n",
" [15, 16, 17, 18],\n",
" [24, 25, 26, 27],\n",
" [33, 34, 35, 36]])]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.hsplit(a,(3,5)) #在第三列和第五列的后面划一刀"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[array([[ 1, 2, 3],\n",
" [10, 11, 12],\n",
" [19, 20, 21],\n",
" [28, 29, 30]]), array([[ 4, 5],\n",
" [13, 14],\n",
" [22, 23],\n",
" [31, 32]]), array([[ 6],\n",
" [15],\n",
" [24],\n",
" [33]]), array([[ 7],\n",
" [16],\n",
" [25],\n",
" [34]]), array([[ 8, 9],\n",
" [17, 18],\n",
" [26, 27],\n",
" [35, 36]])]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.hsplit(a,(3,5,6,7)) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## np.vsplit()横向进行分割"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
" [10, 11, 12, 13, 14, 15, 16, 17, 18],\n",
" [19, 20, 21, 22, 23, 24, 25, 26, 27],\n",
" [28, 29, 30, 31, 32, 33, 34, 35, 36]])"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[array([[1, 2, 3, 4, 5, 6, 7, 8, 9]]),\n",
" array([[10, 11, 12, 13, 14, 15, 16, 17, 18]]),\n",
" array([[19, 20, 21, 22, 23, 24, 25, 26, 27]]),\n",
" array([[28, 29, 30, 31, 32, 33, 34, 35, 36]])]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.vsplit(a,4)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[array([[1, 2, 3, 4, 5, 6, 7, 8, 9]]),\n",
" array([[10, 11, 12, 13, 14, 15, 16, 17, 18],\n",
" [19, 20, 21, 22, 23, 24, 25, 26, 27]]),\n",
" array([[28, 29, 30, 31, 32, 33, 34, 35, 36]])]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.vsplit(a,(1,3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 可指定方向的np.array_split()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
" [10, 11, 12, 13, 14, 15, 16, 17, 18],\n",
" [19, 20, 21, 22, 23, 24, 25, 26, 27],\n",
" [28, 29, 30, 31, 32, 33, 34, 35, 36]])"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[array([[1, 2, 3, 4, 5, 6, 7, 8, 9]]),\n",
" array([[10, 11, 12, 13, 14, 15, 16, 17, 18],\n",
" [19, 20, 21, 22, 23, 24, 25, 26, 27]]),\n",
" array([[28, 29, 30, 31, 32, 33, 34, 35, 36]])]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array_split(a,(1,3),axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[array([[ 1],\n",
" [10],\n",
" [19],\n",
" [28]]), array([[ 2, 3],\n",
" [11, 12],\n",
" [20, 21],\n",
" [29, 30]]), array([[ 4, 5, 6, 7, 8, 9],\n",
" [13, 14, 15, 16, 17, 18],\n",
" [22, 23, 24, 25, 26, 27],\n",
" [31, 32, 33, 34, 35, 36]])]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array_split(a,(1,3),axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"toc": {
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"nav_menu": {},
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"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": true,
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"left": "0px",
"top": "110.284px",
"width": "269.813px"
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