brief_python_intro.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome to python.  Let's take a quick look at some basic operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'print' function works with strings and string variables as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello\n",
      "world\n",
      "hello world\n",
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "print(\"hello\")\n",
    "string_var = 'world'\n",
    "print(string_var)\n",
    "print(\"hello \"+string_var)\n",
    "print(type(string_var))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For loops do the same operation a number of times.  Everything indented is repeated in the loop (this is known as whitespace in python)."
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1\n",
      "1 2\n",
      "2 4\n",
      "3 7\n",
      "4 11\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "x = 1\n",
    "for i in range(5):\n",
    "    x += i\n",
    "    print(i, x)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python has standard libraries that can be imported and a number of useful functions can then be called.  Numpy.arange produces an array of numbers counting up to the value given in the argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in /anaconda3/lib/python3.6/site-packages (1.16.1)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "x_arr = np.arange(10)\n",
    "\n",
    "print(x_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If/else: statements are logical tests that have different outcomes depending on the conditions that are set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for i in x_arr:\n",
    "    if i > 3:\n",
    "        print(i)\n",
    "    else:\n",
    "        print(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standard array operations are as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2  3  4  5  6  7  8  9 10 11]\n",
      "[ 4  6  8 10 12 14 16 18 20 22]\n"
     ]
    }
   ],
   "source": [
    "x_arr = x_arr + 2\n",
    "print(x_arr)\n",
    "\n",
    "x_arr = x_arr * 2\n",
    "print(x_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definitions can be set as functions to be called at a later stage to perform tasks without duplicating written code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sum_numbers(num1,num2):\n",
    "    total = num1 + num2\n",
    "    return total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13\n"
     ]
    }
   ],
   "source": [
    "z = sum_numbers(5,8)\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These can take arguments that are local variables that will not be able to be called outside of that function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'num1' 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-10-01dfdea16339>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'num1' is not defined"
     ]
    }
   ],
   "source": [
    "print(num1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Variables that can be called in and out of functions are called global variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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