Python_Course_MeInBio_Numpy.ipynb

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   "source": [
    "<img src=\"IMAGES_PYTHON_COURSE_2018/LOGO_MEINBIO_TRANSPARENT.png\" width=\"200\" height=\"200\" />\n",
    "# <center>Python course 2018\n",
    "### <center>MeInBio Training Group\n",
    "#### <center> by Florian Heyl & Francesco Ferrari \n",
    "Welcome to this introduction to Python. \n"
   ]
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    "# PART : NUMPY\n",
    "\n",
    "## Exercise 1: Introduction\n",
    "\n",
    "Numpy is one of the most important packages for python. Packages are collections of classes and functions that can be imported into a python script. Only when you import certain packages, you are allowed to use specific functions. the keyword **import** can be used to import a package. "
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     "text": [
      "[2 3 4]\n"
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   "source": [
    "import numpy\n",
    "\n",
    "a = numpy.array([2,3,4])\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see we defined a new array **a** which is equivalent to a list in python. However, in order to use an numpy array we specifically have to say where the class **array** comes from. The package (or scope) is our package numpy. So what happens if we don't specifiy the scope."
   ]
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    {
     "ename": "NameError",
     "evalue": "name 'array' is not defined",
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     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-7e2b40d54410>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'array' is not defined"
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    "a = array([2,3,4])"
   ]
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   "source": [
    "You see, that python doesn't know the class **array** since it is not defined in the basic class package.\n",
    "\n",
    "Ok, actually writing allways **numpy.** for every class that is definined in numpy might be really laborious. However, we can redifine the package name with:"
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      "[2 3 4]\n"
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   "source": [
    "import numpy as np\n",
    "\n",
    "a = np.array([2,3,4])\n",
    "print(a)"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, this is way faster. The only thing is, that you have to remember what **np** stands for."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 2: Why do we have to use numpy?\n",
    "\n",
    "Numpy is a very powerful package that make arithmatically calculation easier and more handy. Lets give you an example:\n",
    "\n",
    "### TASK\n",
    "\n",
    "Add the two vectors together in the best way you can think of **without using numpy**: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [1,2,3]\n",
    "b = [4,5,6]\n",
    "\n",
    "### put your code here"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "You may have realised that certain things are not as intutive in python as it seems, especially when it comes to arithmetically stuff. Intuitively, you would think that a simple summation like **a+b** would do it, but this just concatenates the two lists together. Therefore, numpy is our savior. \n",
    "\n",
    "### TASK\n",
    "\n",
    "Define the two vectors above as **numpy.array**(s) and simply do a summation (**a+b**)."
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
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   "source": [
    "### put your code here"
   ]
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   "source": [
    "Again, numpy is our savior when it comes to mathematical stuff. Lets do another task this time with a higher dimension. Lets say we want to substract two matrices, we first have to learn how to define a matrix:"
   ]
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      "1\n",
      "4\n",
      "2\n",
      "[1 2 3]\n",
      "[2 5 8]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1,2,3],\n",
    "              [4,5,6],\n",
    "              [7,8,9]])\n",
    "print(a[0,0])\n",
    "print(a[1,0])\n",
    "print(a[0,1])\n",
    "\n",
    "print(a[0,:])\n",
    "print(a[:,1])"
   ]
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   "cell_type": "markdown",
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   "source": [
    "A matrix is just a two dimensional object with row (i) and columns (j). As you can see, **a** holds a matrix with three rows and three columns. Getting one specific cell out of the matrix, then I have to use the snythax **a[i,j]**. The last to lines then gives me the first row and the seconds column. The symbol **:** indicates that I go over ever element. "
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK\n",
    "Lets return to our task and simply substract the two defined matrices from each other:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([[1,2,3],\n",
    "              [4,5,6],\n",
    "              [7,8,9]])\n",
    "b = np.array([[1,2,3],\n",
    "              [1,2,3],\n",
    "              [1,2,3]])\n",
    "\n",
    "### put your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 3: Other things you can do in numpy?\n",
    "\n",
    "Python has certain other usefull operations you can only do with numpy. For example:\n",
    "\n",
    "### TASK\n",
    "\n",
    "Write a loop that finds the maximum of the given list **a** and print the index of this elemnts **without numpy**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [6,10,2,22,305,103,1,0,56,110,102,255,10,310,3,1,5,7,309,206]\n",
    "\n",
    "### put your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With numpy this is a very easy task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-da59453e3873>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
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   ],
   "source": [
    "i = np.argmax(a)\n",
    "print(a[i])\n",
    "print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK\n",
    "\n",
    "a) Write a functions that calculates you the mean of the list **a** wihout numpy. \n",
    "b) Find an equivalent function for numpy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "### put your code here for (a)\n",
    "\n",
    "\n",
    "### put your code here for (b)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK\n",
    "\n",
    "Find a function in numpy that calculates you the standard deviation of the list **a**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "### put your code here"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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