numpy_where.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boolean array and Boolean indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numpy version = 1.14.5\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "print(f'numpy version = {np.__version__}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ex 1: 1-dim array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9, 15, 64, 28, 89, 93, 29,  8, 73,  0, 40, 36, 16, 11, 54, 88, 62,\n",
       "       33, 72, 78])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(10)\n",
    "a=np.random.randint(0, 100, 20)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False,  True, False,  True, False,  True,  True, False,\n",
       "        True, False, False,  True,  True,  True, False, False, False,\n",
       "       False, False])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a % 2 == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8, 92, 82, 20, 22, 96,  0, 12, 18])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[a % 2 == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True, False,  True, False, False, False, False,\n",
       "        True, False, False, False, False, False, False, False, False,\n",
       "       False, False])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(a % 2 == 0) & (a > 50) # & (and)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  2,  4,  6,  7,  9, 12, 13, 14]),)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(a % 2 == 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8, 92, 82, 20, 22, 96,  0, 12, 18])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[np.where(a % 2 == 0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ex 3: 2-dim array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['HUA', 'SAM', 'SIL', 'LUM', 'KHO', 'SIR', 'SUK', 'PET', 'RAM',\n",
       "       'CUL', 'HUI', 'SUT', 'RAT', 'LAT', 'PHA', 'CHA', 'KAM', 'BAN'],\n",
       "      dtype='<U3')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d=np.loadtxt('station.txt', delimiter='\\n', dtype='U')\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d == 'SIL'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['SIL'], dtype='<U3')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[np.where(d == 'SIL')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['HUA', 'SIR', 'CUL', 'HUA', 'SUK', 'HUA', 'SIL', 'LUM', 'LUM',\n",
       "       'LAT', 'SUT', 'KAM', 'LAT', 'SUT', 'BAN', 'SIR', 'SUT', 'SAM',\n",
       "       'SUT', 'HUA'], dtype='<U3')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1=np.random.choice(d, size=20, replace=True) #.resize(10, 2)\n",
    "t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['SIR', 'CUL', 'RAM', 'SIR', 'SUT', 'SUT', 'PET', 'RAM', 'RAM',\n",
       "       'LAT', 'PHA', 'BAN', 'LAT', 'CUL', 'SAM', 'KHO', 'PET', 'PET',\n",
       "       'CUL', 'PET'], dtype='<U3')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2=np.random.choice(d, size=20, replace=True) #.resize(10, 2)\n",
    "t2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['HUA', 'SIR'],\n",
       "       ['SIR', 'CUL'],\n",
       "       ['CUL', 'RAM'],\n",
       "       ['HUA', 'SIR'],\n",
       "       ['SUK', 'SUT'],\n",
       "       ['HUA', 'SUT'],\n",
       "       ['SIL', 'PET'],\n",
       "       ['LUM', 'RAM'],\n",
       "       ['LUM', 'RAM'],\n",
       "       ['LAT', 'LAT'],\n",
       "       ['SUT', 'PHA'],\n",
       "       ['KAM', 'BAN'],\n",
       "       ['LAT', 'LAT'],\n",
       "       ['SUT', 'CUL'],\n",
       "       ['BAN', 'SAM'],\n",
       "       ['SIR', 'KHO'],\n",
       "       ['SUT', 'PET'],\n",
       "       ['SAM', 'PET'],\n",
       "       ['SUT', 'CUL'],\n",
       "       ['HUA', 'PET']], dtype='<U3')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t=np.vstack((t1, t2)).T\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['SAM'], dtype='<U3')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[np.where(d=='SAM')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1]),)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i=np.where(d=='SAM')\n",
    "i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(i[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([6]),)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j=np.where(d=='SUK')\n",
    "j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.abs((i[0]-j[0])[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(d=='SAM')[0] - np.where(d=='SUK')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['SAM'], dtype='<U3')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(t1==t1)[0] - np.where(t2==t2)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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