Numpy Array Practice via introduction

Numpy.json

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   "source": [
    "# Author: Mohd. Azhar Khan"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# importing numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
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   "source": [
    "import numpy as np"
   ]
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   "execution_count": 24,
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   "source": [
    "height = [1,2,3,4,5]"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# numpy arrays can be created from python lists\n",
    "# numpy array can only contain one type of data"
   ]
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   "cell_type": "code",
   "execution_count": 25,
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   "outputs": [],
   "source": [
    "weight = [30,40,50,60,55]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
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   "outputs": [],
   "source": [
    "height=np.array(height)"
   ]
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   "cell_type": "code",
   "execution_count": 27,
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   "outputs": [],
   "source": [
    "weight=np.array(weight)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# numpy does element calculation on array, so be careful while playing with them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "bmi=weight/height**2"
   ]
  },
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   "cell_type": "code",
   "execution_count": 29,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 30.        ,  10.        ,   5.55555556,   3.75      ,   2.2       ])"
      ]
     },
     "execution_count": 29,
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    "bmi"
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     "text": [
      "[ 30.          10.           5.55555556   3.75         2.2       ]\n"
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   "source": [
    "print(bmi)"
   ]
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   "execution_count": 31,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False, False, False, False], dtype=bool)"
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     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "bmi>10"
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   "execution_count": 32,
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    {
     "data": {
      "text/plain": [
       "array([ 30.])"
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     "execution_count": 32,
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   "source": [
    "bmi[bmi>10]"
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   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(bmi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    " np2d=np.array([[1.73,1.68,1.71,1.89,1.79],[65.4,59.2,63.6,88.4,68.7]])"
   ]
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   "cell_type": "code",
   "execution_count": 35,
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     "data": {
      "text/plain": [
       "array([[  1.73,   1.68,   1.71,   1.89,   1.79],\n",
       "       [ 65.4 ,  59.2 ,  63.6 ,  88.4 ,  68.7 ]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "np2d"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# numpy supports multi dimentional arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 5)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "np2d.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Subsetting in Numpy Arrays"
   ]
  },
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   "cell_type": "code",
   "execution_count": 37,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.73,  1.68,  1.71,  1.89,  1.79])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np2d[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.71"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "np2d[0][2]"
   ]
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   "cell_type": "code",
   "execution_count": 39,
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    {
     "data": {
      "text/plain": [
       "1.71"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "np2d[0,2]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.68,   1.71],\n",
       "       [ 59.2 ,  63.6 ]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np2d[:,1:3]"
   ]
  },
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   "cell_type": "code",
   "execution_count": 20,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 65.4,  59.2,  63.6,  88.4,  68.7])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np2d[1,:]"
   ]
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