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100 измененных файлов с 2042 добавлено и 0 удалено
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+ 813 - 0
20200404127王志鹏/Day2 20200404127王志鹏/Jupyter_test.ipynb

@@ -0,0 +1,813 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "你好 Python\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('你好 Python')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('test')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(10):\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = 3.14"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 一级标题\n",
+    "## 二级标题\n",
+    "### 三级标题"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 魔法命令"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## %run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "大家好,很高兴认识你\n"
+     ]
+    }
+   ],
+   "source": [
+    "%run ./My_Test.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## %load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %load ./My_Test.py\n",
+    "print('大家好,很高兴认识你')\n",
+    "\n",
+    "def my_print(x):\n",
+    "\tprint(x*2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python好,大家好\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_print('Python好,大家好')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "大家好,很高兴认识你\n",
+      "xixi\n"
+     ]
+    }
+   ],
+   "source": [
+    "from My_Test import my_print\n",
+    "my_print('xixi')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "大家好,很高兴认识你\n"
+     ]
+    }
+   ],
+   "source": [
+    "# notebook对同一个文件只会导入一次\n",
+    "%run ./My_Test.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python好,大家好Python好,大家好\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_print('Python好,大家好')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# %timeit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "415 µs ± 16.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit li = [i**2 for i in range(1000)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "469 ms ± 21.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit li = [i**2 for i in range(1000000)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5.44 µs ± 421 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit li = [i**2 for i in range(10)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %timeit 后面只跟一句代码\n",
+    "# 测试代码块 用%%timit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "742 µs ± 71.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "li = []\n",
+    "for i in range(1000):\n",
+    "    li.append(i**2)\n",
+    "# 在python中使用列表生成式比for高效    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## %time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %time 只会测量一次代码的执行时间"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 662 µs, sys: 2 µs, total: 664 µs\n",
+      "Wall time: 678 µs\n"
+     ]
+    }
+   ],
+   "source": [
+    "%time li = [i**2 for i in range(1000)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 953 µs, sys: 60 µs, total: 1.01 ms\n",
+      "Wall time: 1.23 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "li = []\n",
+    "for i in range(1000):\n",
+    "    li.append(i**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.18 ms ± 403 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import random\n",
+    "li = [random.random() for i in range(100000)]\n",
+    "%timeit li.sort()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 27.1 ms, sys: 2.68 ms, total: 29.8 ms\n",
+      "Wall time: 31 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "li = [random.random() for i in range(100000)]\n",
+    "%time li.sort()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 3.61 ms, sys: 133 µs, total: 3.74 ms\n",
+      "Wall time: 4.09 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%time li.sort()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 3.3 ms, sys: 14 µs, total: 3.31 ms\n",
+      "Wall time: 3.4 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%time li.sort()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 4.06 ms, sys: 137 µs, total: 4.19 ms\n",
+      "Wall time: 4.28 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%time li.sort()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## %%html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div class='mytest' style='color:red'>html content</div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%html\n",
+    "<div class='mytest' style='color:red'>html content</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## %%js"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "document.querySelector('.mytest').innerHTML='我成功了'"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%js\n",
+    "document.querySelector('.mytest').innerHTML='我成功了'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## %%writefile "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing haha.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile haha.py\n",
+    "import random\n",
+    "li = [random.random() for i in range(100000)]\n",
+    "%timeit li.sort()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([1, 2, 3])\n",
+    "type(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3,)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = np.array([[1,2,3],\n",
+    "              [4,5,6]]) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 3)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.zeros((5,5,4)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]]])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1,  2,  3,  4],\n",
+       "       [ 5,  6,  7,  8],\n",
+       "       [ 9, 10, 11, 12]])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = a[:2, 1:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2, 3],\n",
+       "       [6, 7]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array([[1,2],[3,4]], dtype=np.float64)\n",
+    "y = np.array([[5,6],[7,8]], dtype=np.float64)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 6.,  8.],\n",
+       "       [10., 12.]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x + y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 6.,  8.],\n",
+       "       [10., 12.]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.add(x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

+ 631 - 0
20200404127王志鹏/Day3 20200404127王志鹏/6.14作业.ipynb

@@ -0,0 +1,631 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "303095f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([37.2, 37.6, 36.8])"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "X = np.array([[1.2, 1.5, 1.8],\n",
+    "               [1.3, 1.4, 1.9],\n",
+    "               [1.1, 1.6, 1.7]])\n",
+    "y = np.array([5, 10, 9]).T\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "X.dot(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9670b1f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 3)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b1ab79e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "797 ns ± 37.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit X.dot(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4d088725",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "17.9 µs ± 257 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "total = []\n",
+    "for i in range(X.shape[0]):\n",
+    "    each_price = 0\n",
+    "    for j in range(X.shape[1]):\n",
+    "        each_price += X[i,j] * y[j]\n",
+    "#         each_price = each_price + X[i,j] * y[j]\n",
+    "    total.append(round(each_price, 1))\n",
+    "total  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df3dfeec",
+   "metadata": {},
+   "source": [
+    "# #第二题"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "81dc806e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([6, 9, 6, 1, 1, 2, 8, 7, 3, 5, 6, 3, 5, 3, 5, 8, 8, 2, 8, 1, 7, 8,\n",
+       "       7, 2, 1, 2, 9, 9, 4, 9])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.seed(1)\n",
+    "X = np.random.randint(1, 10, size=30)\n",
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "adb7722a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[6, 9, 6],\n",
+       "       [1, 1, 2],\n",
+       "       [8, 7, 3],\n",
+       "       [5, 6, 3],\n",
+       "       [5, 3, 5],\n",
+       "       [8, 8, 2],\n",
+       "       [8, 1, 7],\n",
+       "       [8, 7, 2],\n",
+       "       [1, 2, 9],\n",
+       "       [9, 4, 9]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.reshape(10,-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6d0d6242",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[6, 9, 6],\n",
+       "       [1, 1, 2],\n",
+       "       [8, 7, 3],\n",
+       "       [5, 6, 3],\n",
+       "       [5, 3, 5],\n",
+       "       [8, 8, 2],\n",
+       "       [8, 1, 7],\n",
+       "       [8, 7, 2],\n",
+       "       [1, 2, 9],\n",
+       "       [9, 4, 9]])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X1 = X.reshape(-1 ,3)\n",
+    "X1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a5854aeb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([6, 2, 3, 3, 5, 2, 7, 2, 9, 9])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "last1 = X1[:, 2]\n",
+    "last1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3c001e71",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False,  True,  True,  True, False,  True, False,  True, False,\n",
+       "       False])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "last1 <= 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "35b66010",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 3, 3, 2, 2])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp1 = last1[last1 <= 3]\n",
+    "temp1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "b41674c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([1, 2, 3, 5, 7], dtype=int64),)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.where(last1 <= 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "42b271c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([6, 2, 3, 3, 5, 2, 7, 2, 9, 9])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "last1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ce487a19",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([6, 5])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp2 =  last1[(last1 > 3) & (last1 <= 6)]\n",
+    "temp2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "82b9f3ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([7, 9, 9])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp3 =  last1[last1 > 6]\n",
+    "temp3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "f28879be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "last1[last1 <= 3] = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "4dc020af",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([6, 0, 0, 0, 5, 0, 7, 0, 9, 9])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "last1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e475fd7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "last1[(last1 > 3) & (last1 <= 6)] = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "cfeff15c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0, 0, 0, 1, 0, 7, 0, 9, 9])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "last1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dda79074",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "last1[last1 > 6] = 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "9ee04243",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0, 0, 0, 1, 0, 2, 0, 2, 2])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "last1 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "255d8986",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = X1[:, 0:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "6587e601",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[6, 9],\n",
+       "       [1, 1],\n",
+       "       [8, 7],\n",
+       "       [5, 6],\n",
+       "       [5, 3],\n",
+       "       [8, 8],\n",
+       "       [8, 1],\n",
+       "       [8, 7],\n",
+       "       [1, 2],\n",
+       "       [9, 4]])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "78de1d84",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = last1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "06103c3e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0, 0, 0, 1, 0, 2, 0, 2, 2])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "b01a5a6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 1],\n",
+       "       [8, 7],\n",
+       "       [5, 6],\n",
+       "       [8, 8],\n",
+       "       [8, 7]])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train[y_train == 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "54c10551",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False,  True,  True,  True, False,  True, False,  True, False,\n",
+       "       False])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train == 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "e1eb67be",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[6, 9],\n",
+       "       [5, 3]])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train[y_train == 1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "da29cec8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[8, 1],\n",
+       "       [1, 2],\n",
+       "       [9, 4]])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train[y_train == 2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27d8f581",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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+ 424 - 0
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