16_numpy_exercises.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "1. Write a NumPy program to create a 5x5 array with random values and find the minimum and maximum values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# answer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Write a NumPy program to create a random 10x4 array and extract the first five rows of the array and store them into a variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# answer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Write a NumPy program to create random vector of size 15 and replace the maximum value by -1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# answer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. normalize an array so the values range exactly between 0 and 1?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = np.random.randint(0, 1000, (1, 100))\n",
    "# answer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. convert an nd array of arrays into a flat 1d array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# answer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. from 2010 to 2018 group weekdays and weekends in two lists\n",
    "\n",
    "notes: you are allowed to just use datetime or numpy datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# answer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. Considering a four dimensions array, how to get sum over the last two axis at once?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.47657278, 7.49573118, 9.16275377, 9.23959706, 9.8356955 ],\n",
       "       [8.24146317, 7.40076952, 7.3916324 , 8.3175067 , 9.21488704],\n",
       "       [9.5442995 , 7.05275928, 6.09437271, 7.44193338, 6.31116679],\n",
       "       [8.23126564, 8.33224097, 8.96275701, 9.02782458, 7.45008281]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "array = np.random.random((4, 5, 8, 2))\n",
    "# answer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8. Consider 2 points p0, p1 describing a line (2d) and a point p, how to compute distance from p to this line?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distance(p0, p1, p):\n",
    "    # answer\n",
    "\n",
    "p0 = np.random.uniform(-10, 10, (1, 2))\n",
    "p1 = np.random.uniform(-10, 10, (1, 2))\n",
    "p  = np.random.uniform(-10, 10, (1, 2))\n",
    "print(distance(p0, p1, p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more exercises check the following link:\n",
    "https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*:)*"
   ]
  }
 ],
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