2. Attributes & Functions.ipynb

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   "source": [
    "# NumPy Basics By Phalguni Rathod\n",
    "## Attributes & Functions Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hi, Here we all we do and learn Numpy with hands-on implementaion. This tutorial have have 5 parts as follows:\n",
    "1. Generation\n",
    "2. Attributes & Functions\n",
    "3. Slicing\n",
    "4. Conditional Selection\n",
    "5. Operations"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup - Generating array to work with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    "collapsed": true
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   "outputs": [],
   "source": [
    "arr = np.arange(25)\n",
    "ranarr = np.random.randint(0,50,10)"
   ]
  },
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   "execution_count": 3,
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    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24])"
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    "arr"
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   "cell_type": "code",
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    {
     "data": {
      "text/plain": [
       "array([26, 40, 48,  9, 12,  9, 20, 16, 25, 36])"
      ]
     },
     "execution_count": 4,
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    "ranarr"
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   "cell_type": "markdown",
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   "source": [
    "### max,min,argmax,argmin\n",
    "\n",
    "These are useful methods for finding max or min values. Or to find their index locations using argmin or argmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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    {
     "data": {
      "text/plain": [
       "48"
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     "execution_count": 6,
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   "source": [
    "ranarr.max()"
   ]
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   "cell_type": "code",
   "execution_count": 7,
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    {
     "data": {
      "text/plain": [
       "9"
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     "execution_count": 7,
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    "ranarr.min()"
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       "2"
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     "execution_count": 8,
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    "ranarr.argmax()"
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       "3"
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     "execution_count": 9,
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   "source": [
    "ranarr.argmin()"
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   "cell_type": "code",
   "execution_count": 11,
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    {
     "data": {
      "text/plain": [
       "(25,)"
      ]
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     "execution_count": 11,
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   "source": [
    "arr.shape"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## Reshape\n",
    "Returns an array containing the same data with a new shape."
   ]
  },
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   "execution_count": 12,
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     "data": {
      "text/plain": [
       "(10,)"
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     "execution_count": 12,
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    "ranarr.shape"
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   "execution_count": 13,
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     "data": {
      "text/plain": [
       "array([[26, 40, 48,  9, 12],\n",
       "       [ 9, 20, 16, 25, 36]])"
      ]
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     "execution_count": 13,
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     "output_type": "execute_result"
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   "source": [
    "ranarr.reshape((2,5))"
   ]
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   "cell_type": "code",
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     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24]])"
      ]
     },
     "execution_count": 20,
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     "output_type": "execute_result"
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   "source": [
    "arr = arr.reshape((5,5))\n",
    "arr"
   ]
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   "cell_type": "code",
   "execution_count": 24,
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     "data": {
      "text/plain": [
       "(5, 5)"
      ]
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     "execution_count": 24,
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    "arr.shape"
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   "cell_type": "markdown",
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   "source": [
    "### dtype\n",
    "\n",
    "You can also grab the data type of the object in the array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranarr.dtype"
   ]
  }
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