module 2 week 3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[0], [0], [0], [0], [0]], [[0], [0], [0], [0]], [[0, [0], [0], [0], [0]]]]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "arr = [[[[0],[0],[0],[0],[0]]\n",
    "        ,[[0],[0],[0],[0]],\n",
    "        [[0,[0],[0],\n",
    "          [0],[0]]]]]\n",
    "        \n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 3, 2, 3)\n",
      "[[[[0. 0. 0.]\n",
      "   [0. 0. 0.]]\n",
      "\n",
      "  [[0. 0. 0.]\n",
      "   [0. 0. 0.]]\n",
      "\n",
      "  [[0. 0. 0.]\n",
      "   [0. 0. 0.]]]\n",
      "\n",
      "\n",
      " [[[0. 0. 0.]\n",
      "   [0. 0. 0.]]\n",
      "\n",
      "  [[0. 7. 7.]\n",
      "   [0. 0. 0.]]\n",
      "\n",
      "  [[0. 0. 0.]\n",
      "   [0. 0. 0.]]]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "nda1 = np.zeros([2,3,2,3]) #2 dimension 2 ke ander 3(2 array 3 ke ander) uskay ander phr 3\n",
    "print(nda1.shape)\n",
    "nda1[1,1,0,1:3] =7 #1 array uthao phr 1 walay pr jao phr 0 uthao phr 1:3(3 wala nh hoga 2 tk jaiga)woh sara phr 7 ke barabar hoga\n",
    "\n",
    "print(nda1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.abs? #to find something"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.zeros(4)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = arr + (-1)\n",
    "arr\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.abs(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.where?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.arange(1,11)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.where(arr>5) #dhooond ke laiga jo zyada howa 5 se"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr>5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mean\n",
    "arr.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cumsum   #1,1+2,3+3\n",
    "arr.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cumprod #same as cumsum but multiply karata ha\n",
    "arr.cumprod()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr[arr > 5].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ar2 = arr>5\n",
    "ar2.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "any(arr==5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all(arr==2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all(arr<11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.sort(arr) #sort the values of array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.zeros(5)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.unique(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr4=1,2,3,4,5,6,7\n",
    "np.save('bantoo baba',arr4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.load('bantoo baba.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  3.,  4.,  5., -3.])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "arr9=[1.2,2.4,3.7,4.3,-3]\n",
    "np.ceil(arr9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  3.,  4., -3.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.floor(arr9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.5488135  0.71518937 0.60276338]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.random.seed(0)\n",
    "perm =np.random.rand(3)\n",
    "print(perm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(1,13)\n",
    "arr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  5,  9],\n",
       "       [ 2,  6, 10],\n",
       "       [ 3,  7, 11],\n",
       "       [ 4,  8, 12]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(((4,3)),order='F')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 20, 30, 40, 50, 60, 70, 80, 90])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr10 = np.arange(10,100,10)\n",
    "arr10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "a,b,c = np.split(arr10,[2,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 20])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([30, 40, 50, 60])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([70, 80, 90])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  4  5  6  7  8  9 10]\n",
      "[1 2]\n",
      "[3 4]\n",
      "[5 6]\n",
      "[7 8]\n",
      "[ 9 10]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "arr = np.arange(1,11)\n",
    "print(arr)\n",
    "\n",
    "arr = np.split(arr,[2,4,6,8,10])\n",
    "print(arr[0])\n",
    "print(arr[1])\n",
    "print(arr[2])\n",
    "print(arr[3])\n",
    "print(arr[4])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# image to numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "for i in range(1):\n",
    "    im =plt.imread(\"image ki file\")\n",
    "    print((type)im)\n",
    "    print(m.shape)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#20 photos uthani hain unko resize krni hain 512,512,3 mein phr 1 array mein 20 element banainay aur unko numpy mein convert krna ha"
   ]
  }
 ],
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