numpy_learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5102c106",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "755b9663",
   "metadata": {},
   "source": [
    "# numpy的基本使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2ca28eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1,2,3])\n",
    "b = np.array([[1,2,3],[4,5,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "94c489d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "#获取维度\n",
    "print(a.ndim)\n",
    "print(b.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2d93648a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3,)\n",
      "(2, 3)\n"
     ]
    }
   ],
   "source": [
    "#获取形状\n",
    "print(a.shape)\n",
    "print(b.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "10a0401a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int32\n",
      "int32\n",
      "int16\n",
      "int32\n"
     ]
    }
   ],
   "source": [
    "#获取类型\n",
    "print(a.dtype)\n",
    "print(b.dtype)\n",
    "#默认是int32,但因为我们设置的2个数组的数字大小比较小，我们可以指定为int16\n",
    "a = np.array([1,2,3],dtype='int16')\n",
    "print(a.dtype)\n",
    "print(b.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d07eb496",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1,2,3])\n",
    "b = np.array([[1,2,3],[4,5,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "497bb07e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "#获取数据的大小，int32是4bytes,int16是2bytes\n",
    "print(a.itemsize)\n",
    "a = np.array([1,2,3],dtype='int16')\n",
    "print(a.itemsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b8a559f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1,2,3])\n",
    "b = np.array([[1,2,3],[4,5,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "00fb5856",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "print(a.size)\n",
    "print(b.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2638b5a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n",
      "24\n"
     ]
    }
   ],
   "source": [
    "#获取字节大小\n",
    "print(a.nbytes)\n",
    "print(b.nbytes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "681aeadb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "#获取特定位置的元素\n",
    "print(b[1,2])\n",
    "print(b[-1,-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c002f71e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n",
      "[1 2 3]\n",
      "[1 4]\n"
     ]
    }
   ],
   "source": [
    "#获取特定的行\n",
    "print(b[0,:])\n",
    "print(b[0])\n",
    "#获取特定的列\n",
    "print(b[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7e6b4171",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 4 6 8]\n"
     ]
    }
   ],
   "source": [
    "#以特定的步长获取多个元素2,4,6,8\n",
    "a=np.array([[1,2,3,4,5,6,7,8,9],[11,22,33,44,55,66,77,88,99]])\n",
    "print(a[0,1:8:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f81c784e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  2  3  4  5  6  7  8  9]\n",
      " [11 22 33 44 55 66 77 88 99]]\n"
     ]
    }
   ],
   "source": [
    "#改变特定位置的元素\n",
    "a=np.array([[1,2,3,4,5,6,7,8,9],[11,22,33,44,55,66,77,88,99]])\n",
    "a[0,0]=0\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c9bb7ab2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    }
   ],
   "source": [
    "#三维数组中获取特定位置的元素6\n",
    "b=np.array([[[1,2],[3,4]],[[5,6],[7,8]]])\n",
    "print(b[1,0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "649b45dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 1. 1.]\n",
      "[[1. 1. 1.]\n",
      " [1. 1. 1.]]\n",
      "[[[1. 1.]\n",
      "  [1. 1.]]\n",
      "\n",
      " [[1. 1.]\n",
      "  [1. 1.]]]\n"
     ]
    }
   ],
   "source": [
    "#初始化全1矩阵\n",
    "a=np.ones(3)\n",
    "b=np.ones((2,3))\n",
    "c=np.ones((2,2,2))\n",
    "print(a)\n",
    "print(b)\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a89a2e65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9 9]\n",
      " [9 9]]\n"
     ]
    }
   ],
   "source": [
    "#初始化全x矩阵\n",
    "x=9\n",
    "a=np.full((2,2),x)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "18d6eab4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.32719944, 0.60202487],\n",
       "       [0.1377539 , 0.7480258 ],\n",
       "       [0.55890584, 0.94970265],\n",
       "       [0.93089125, 0.07948315]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#生成随机数\n",
    "np.random.rand(4,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "17fc36ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0],\n",
       "       [1, 0],\n",
       "       [1, 1],\n",
       "       [1, 0]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(2,size=(4,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e6de107b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-8,  4],\n",
       "       [ 3,  4],\n",
       "       [-5, -1],\n",
       "       [-5,  1]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(-9,5,size=(4,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "de476430",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0., 0.],\n",
       "       [0., 0., 0., 0., 1., 0.],\n",
       "       [0., 0., 0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#生成单位矩阵\n",
    "np.identity(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "91a93e8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [1 2 3]\n",
      " [1 2 3]\n",
      " [1 2 3]]\n",
      "[[1 1 1 1 2 2 2 2 3 3 3 3]]\n"
     ]
    }
   ],
   "source": [
    "#复制数组\n",
    "arr=np.array([[1,2,3]])\n",
    "arr2 = np.repeat(arr,4,axis=0)\n",
    "print(arr2)\n",
    "arr3 = np.repeat(arr,4,axis=1)\n",
    "print(arr3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2de2fbad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n",
      "[1 2 3]\n",
      "[99  2  3]\n",
      "[99  2  3]\n"
     ]
    }
   ],
   "source": [
    "#numpy的拷贝\n",
    "#浅拷贝，只是拷贝了值，地址还是共用的\n",
    "a=np.array([1,2,3])\n",
    "b=a\n",
    "print(a)\n",
    "print(b)\n",
    "b[0]=99\n",
    "print(a)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a2f0d9d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n",
      "[1 2 3]\n",
      "[1 2 3]\n",
      "[99  2  3]\n"
     ]
    }
   ],
   "source": [
    "#深拷贝，拷贝了值，还自己创建了一个地址空间\n",
    "a=np.array([1,2,3])\n",
    "b=a.copy()\n",
    "print(a)\n",
    "print(b)\n",
    "b[0]=99\n",
    "print(a)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52cbaaf7",
   "metadata": {},
   "source": [
    "# 1.numpy的数学运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3d442c3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "a= np.array([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "9703b734",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 6])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4ff6ea41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  0,  1])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "16216864",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4, 5])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a+2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1631088f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5, 1. , 1.5])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c8fd3f19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1], dtype=int32)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a%2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c933d41e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 9], dtype=int32)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9867dc35",
   "metadata": {},
   "outputs": [],
   "source": [
    "a= np.array([1,2,3])\n",
    "b=np.array([4,5,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "40c2e47d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 7, 9])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "476d0858",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4, 10, 18])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a*b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "7a2adc57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.84147098, 0.90929743, 0.14112001])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#取sin值\n",
    "a=np.array([1, 2,3])\n",
    "np.sin(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "10c5e47c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.54030231, -0.41614684, -0.9899925 ])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cos(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1d012a4",
   "metadata": {},
   "source": [
    "# 统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "5ef5f6bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "stats = np.array([[1,2,3],[4,5,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "296ffa55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "7f3ebea3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "ae96209d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79418cc4",
   "metadata": {},
   "source": [
    "# 重组数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "d9fd823b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]]\n",
      "[[[1]\n",
      "  [2]\n",
      "  [3]]\n",
      "\n",
      " [[4]\n",
      "  [5]\n",
      "  [6]]]\n"
     ]
    }
   ],
   "source": [
    "before = np.array([[1,2,3],[4,5,6]])\n",
    "print(before)\n",
    "after= before.reshape((2,3,1))\n",
    "print(after)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa49b650",
   "metadata": {},
   "source": [
    "# 堆叠数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "da961c22",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array([1,2,3])\n",
    "arr2 = np.array([4,5,6])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "fd28a76a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3 = np.vstack((arr,arr2))\n",
    "arr3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "78ab4398",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr4=np.hstack((arr,arr2))\n",
    "arr4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "81a9d186",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1,  13,  21,  11, 196,  75,   4,   3,  34,   6,   7,   8,   0,\n",
       "          1,   2,   3,   4,   5],\n",
       "       [  3,  42,  12,  33, 766,  75,   4,  55,   6,   4,   3,   4,   5,\n",
       "          6,   7,   0,  11,  12],\n",
       "       [  1,  22,  33,  11, 999,  11,   2,   1,  78,   0,   1,   2,   9,\n",
       "          8,   7,   1,  76,  88]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filedata=np.genfromtxt('NumPy-master/data.txt',delimiter=',')\n",
    "filedata=filedata.astype('int32')\n",
    "filedata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "56c30175",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, False,  True,  True, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False],\n",
       "       [False, False, False, False,  True,  True, False,  True, False,\n",
       "        False, False, False, False, False, False, False, False, False],\n",
       "       [False, False, False, False,  True, False, False, False,  True,\n",
       "        False, False, False, False, False, False, False,  True,  True]])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filedata >50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "feef9479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([196,  75, 766,  75,  55, 999,  78,  76,  88])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filedata[filedata>50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "2f5c123f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1,2,3,4,5,6,7,8,9])\n",
    "a[[1,2,3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "ca3015bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1,  13,  21,  11, 196,  75,   4,   3,  34,   6,   7,   8,   0,\n",
       "          1,   2,   3,   4,   5],\n",
       "       [  3,  42,  12,  33, 766,  75,   4,  55,   6,   4,   3,   4,   5,\n",
       "          6,   7,   0,  11,  12],\n",
       "       [  1,  22,  33,  11, 999,  11,   2,   1,  78,   0,   1,   2,   9,\n",
       "          8,   7,   1,  76,  88]])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filedata=np.genfromtxt('NumPy-master/data.txt',delimiter=',')\n",
    "filedata=filedata.astype('int32')\n",
    "filedata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "fe2ec5bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False,  True,  True, False,  True,  True,\n",
       "       False, False, False, False, False, False, False,  True,  True])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.any(filedata>50,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "3440b1b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False,  True, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(filedata>50,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "7b5d4880",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.any(filedata>50,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "dec2f173",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(filedata>50,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "5942de3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, False, False,  True, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False],\n",
       "       [False, False, False, False, False,  True, False,  True, False,\n",
       "        False, False, False, False, False, False, False, False, False],\n",
       "       [False, False, False, False, False, False, False, False,  True,\n",
       "        False, False, False, False, False, False, False,  True,  True]])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((filedata>50)&(filedata<100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "cd554ce2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True,  True,  True,  True,  True, False,  True,  True,  True,\n",
       "         True,  True,  True,  True,  True,  True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True,  True, False,  True, False,  True,\n",
       "         True,  True,  True,  True,  True,  True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True,  True,  True,  True,  True, False,\n",
       "         True,  True,  True,  True,  True,  True,  True, False, False]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "~((filedata>50)&(filedata<100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ad36be5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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