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   "source": [
    "# Exercises with Numpy arrays\n",
    "\n",
    "## Try me\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ffraile/computer_science_tutorials/blob/main/source/Applied%20Mathematics/exercises/Numpy%20arrays.ipynb)[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/ffraile/computer_science_tutorials/main?labpath=source%2FApplied%20Mathematics%2Fexercises%2FNumpy%20arrays.ipynb)\n",
    "\n",
    "\n",
    "2. Use array programming to calculate the total sales revenues given the vector of sold units:\n",
    "```python sold_units = [15, 13, 23]``` and a the vector of prices per unit ```python prices = [10, 8, 9]```. Both vectors contain the sales and prices of the products in the same ordering by product type. "
   ],
   "metadata": {
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  },
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   "cell_type": "markdown",
   "source": [
    "3. Reshape the following array ```an_array = [0,1,2,3,4,5,6,7]``` into a matrix of rank 3 and equal number of elements in each dimension"
   ],
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
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   "cell_type": "markdown",
   "source": [
    "4. Check the Numpy library documentation and write a Python script to generate a multidimensional array of shape (10,10,1000) where the elements are integer numbers drawn from a discrete uniform distribution in the range [0, 100]."
   ],
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "5. Write a script to compute the average values of the matrix above along each dimension, using the function numpy.mean."
   ],
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "6. Write a Python script that a) defines two arrays of shape (1000,1000) and b) defines a third array that contains the element-wise maxima of the elements in both arrays. Check the numpy.maximum documentation."
   ],
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  }
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