01_intro_to_numpy.ipynb

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   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating numpy arrays from list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "[5 7 9] <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1,2,3])\n",
    "b = np.array([4,5,6])\n",
    "c = a + b\n",
    "print(c, type(c))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparing numpy addition with list addition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   "source": [
    "a1 = [1,2,3]\n",
    "b1 = [4,5,6]\n",
    "c1 = a1 + b1\n",
    "c1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numpy arrays cannot contain elements with different types. If you try to build such a list, some of the elements' types are changed to end up with a homogeneous list. This is known as type coercion.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', '1'], dtype='<U21')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2 = np.array([1,'1'])\n",
    "a2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, '1']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a3 = [1, '1']\n",
    "a3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Building a numpy array using some builtin function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.89837112 2.27073555 1.88579698 ... 1.79528718 1.92735475 1.85088875]\n",
      "[1.9  2.27 1.89 ... 1.8  1.93 1.85]\n"
     ]
    }
   ],
   "source": [
    "height = np.random.normal(1.75, 0.20, 5000)\n",
    "print(height)\n",
    "height_r = np.round(height, 2)\n",
    "print(height_r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([53.9, 45.7, 52.2, ..., 67.4, 75.7, 56. ])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight = np.random.normal(60.5, 15, 5000)\n",
    "weight_r = np.round(weight, 1)\n",
    "weight_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.9 , 53.9 ],\n",
       "       [ 2.27, 45.7 ],\n",
       "       [ 1.89, 52.2 ],\n",
       "       ...,\n",
       "       [ 1.8 , 67.4 ],\n",
       "       [ 1.93, 75.7 ],\n",
       "       [ 1.85, 56.  ]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_bmi = np.column_stack((height_r, weight_r))\n",
    "np_bmi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60.92661999999999 61.0 15.017341754638203\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(weight_r), np.median(weight_r), np.std(weight_r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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