0_python_basics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basics of the Python syntax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Author:** Ties de Kok ([Personal Website](http://www.tiesdekok.com))  \n",
    "**Last updated:** 7 Oct 2017  \n",
    "**Python version:** Python 3.5  \n",
    "**License:** MIT License  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Some features (like the ToC) will only work if you run it locally, use Binder, or use nbviewer by clicking this link: \n",
    "https://nbviewer.jupyter.org/github/TiesdeKok/LearnPythonforResearch/blob/master/0_python_basics.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Introduction*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook contains an overview of basic Python functionality that you might come across using Python for Social Science Research.   \n",
    "\n",
    "**Note:** this notebook is purposely not 100% comprehensive, it only discusses the basic things you need to get started."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Table of Contents* <a id='toc'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [Variables](#variables)   \n",
    "* [Displaying something](#display)\n",
    "* [Numerical operations](#num-operations)   \n",
    "* [String operations](#string-operations)   \n",
    "* [Data structures](#data-structures)   \n",
    "* [Slicing](#slicing)   \n",
    "* [Functions](#functions)   \n",
    "* [Whitespaces](#whitespace)   \n",
    "* [Conditionals](#conditionals)   \n",
    "* [Looping](#looping)   \n",
    "* [Comprehensions](#comprehensions)   \n",
    "* [Catching exceptions](#catching-exceptions)   \n",
    "* [Importing libraries](#importing)   \n",
    "* [OS operations](#os-operations)   \n",
    "* [File Input / Output](#files)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"text-decoration: underline;\">Variables</span><a id='variables'></a> [(to top)](#toc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic numeric types in Python are `int` for integers and `float` for floating point numbers.  \n",
    "Strings are represented by `str`, in Python 3.x this implies a sequence of Unicode characters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = 5\n",
    "b = 3.5\n",
    "c = 'A string'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(int, float, str)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a), type(b), type(c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting types:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, '5')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(3.6), str(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking types:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(int, float, str)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a), type(b), type(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isinstance(a, float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"text-decoration: underline;\">Displaying something</span><a id='display'></a> [(to top)](#toc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello\n"
     ]
    }
   ],
   "source": [
    "print('Hello')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note:* `print 'Hello'` does not work in Python 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World\n"
     ]
    }
   ],
   "source": [
    "print('Hello ' + 'World')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have 2 apples\n"
     ]
    }
   ],
   "source": [
    "apples = 'apples'\n",
    "print('I have', 2, apples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"text-decoration: underline;\">Numerical operations</span><a id='num-operations'></a> [(to top)](#toc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2+2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 / 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note:* if you use Python 2 you have to do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 / float(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"text-decoration: underline;\">String operations</span><a id='string-operations'></a> [(to top)](#toc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define strings with single, double or triple quotes (for multi-line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hello = 'world'\n",
    "saying = \"hello world\"\n",
    "paragraph = \"\"\" This is\n",
    "a paragraph\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variables in strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'20'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'%d' % 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'20.000 0.33'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'%.3f %.2f' % (20, 1/3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A more clean alternative is to use `.format()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'20 0.3333333333333333'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'{} {}'.format(20, 1/3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.3333333333333333 20'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'{1} {0}'.format(20, 1/3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'20.000 0.33'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'{:.3f} {:.2f}'.format(20, 1/3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"text-decoration: underline;\">Data structures</span><a id='data-structures'></a> [(to top)](#toc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 4 basic data structures: lists (`list`), tuples (`tuple`), dictionaries (`dict`), and sets (`set`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lists are enclosed in brackets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dogs', 'cat', 'bird', 'lizard']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pets = ['dogs', 'cat', 'bird'] \n",
    "pets.append('lizard')\n",
    "pets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tuples are enclosed in parentheses  \n",
    "*Note:* You cannot add or remove elements from a tuple but they are faster and consume less memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('dogs', 'cat', 'bird')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pets = ('dogs', 'cat', 'bird')\n",
    "pets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dictionaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dictionaries are build using curly brackets  \n",
    "*Note:* Dictionaries are unordered but have key, value pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fred 29\n"
     ]
    }
   ],
   "source": [
    "person = {'name': 'fred', 'age': 29}\n",
    "print(person['name'], person['age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'money': 50, 'name': 'fred'}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person['money'] = 50\n",
    "del person['age']\n",
    "person"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A set is like a list but it can only hold unique values.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dogs', 'horse', 'zebra'}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pets_1 = set(['dogs', 'cat', 'bird'])\n",
    "pets_2 = set(['dogs', 'horse', 'zebra', 'zebra'])\n",
    "pets_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many useful operations that you can perform using sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bird', 'cat', 'dogs', 'horse', 'zebra'}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pets_1.union(pets_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dogs'}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pets_1.intersection(pets_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bird', 'cat'}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pets_1.difference(pets_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combinations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data structures can hold any Python object!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('apple', 'orange')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combo = ('apple', 'orange')\n",
    "mix = {'fruit' : [combo, ('banana', 'pear')]}\n",
    "mix['fruit'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"text-decoration: underline;\">Slicing</span><a id='slicing'></a> [(to top)](#toc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If an object is ordered (such as a list or tuple) you can select on index  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pets = ['dogs', 'cat', 'bird', 'lizzard']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dogs'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "favorite_pet = pets[0]\n",
    "favorite_pet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'lizzard'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reptile = pets[-1]\n",
    "reptile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cat', 'bird']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pets[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dogs', 'cat']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pets[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note:* this also works on strings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ba'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruit = 'banana'\n",
    "fruit[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"text-decoration: underline;\">Functions</span><a id='functions'></a> [(to top)](#toc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Python function takes arguments as input and defines logic to process these inputs (and possibly returns something)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def add_5(number):\n",
    "    return number + 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The action of defining a function does not execute the code! It will only execute once you call the function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_5(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also add arguments with default values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def add(number, add=5):\n",
    "    return number + add"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add(10, add=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"text-decoration: underline;\">Whitespace (blocks)</span><a id='whitespace'></a> [(to top)](#toc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indentations are required by Python to sub-set blocks of code.  \n",
    "*Note:* these subsets have their own local scope, notice variable `a`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def example():\n",
    "    a = 'Layer 1'\n",
    "    print(a)\n",
    "    \n",
    "    def layer_2():\n",
    "        a = 'Layer 2'\n",
    "        print(a)\n",
    "        \n",
    "    layer_2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layer 1\n",
      "Layer 2\n"
     ]
    }
   ],
   "source": [
    "example()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"text-decoration: underline;\">Conditionals</span><a id='conditionals'></a> [(to top)](#toc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C\n"
     ]
    }
   ],
   "source": [
    "grade = 95\n",
    "if grade == 90:\n",
    "    print('A')\n",
    "elif grade < 90:\n",
    "    print('B')\n",
    "elif grade >= 80:\n",
    "    print('C')\n",
    "else:\n",
    "    print('D')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"text-decoration: underline;\">Looping</span><a id='looping'></a> [(to top)](#toc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "2\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for num in range(0, 6, 2):\n",
    "    print(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Apple\n",
      "Banana\n",
      "Orange\n"
     ]
    }
   ],
   "source": [
    "list_fruit = ['Apple', 'Banana', 'Orange']\n",
    "for fruit in list_fruit:\n",
    "    print(fruit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "for num in range(100):\n",
    "    print(num)\n",
    "    if num == 2:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also (infinitely) loop using `while`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "count = 0\n",
    "while count < 4:\n",
    "    print(count)\n",
    "    count += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looping over a tuple in a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "tuple_in_list = [(1, 2), (3, 4)]\n",
    "for a, b in tuple_in_list:\n",
    "    print(a + b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looping over a dictionary:  \n",
    "*Note:* if using Python 2.7 you need to use `.iteritems()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "three 13\n",
      "one 11\n",
      "two 12\n"
     ]
    }
   ],
   "source": [
    "dictionary = {'one' : 1, 'two' : 2, 'three' : 3}\n",
    "for k, v in dictionary.items():\n",
    "    print(k, v + 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"text-decoration: underline;\">Comprehensions</span><a id='comprehensions'></a> [(to top)](#toc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A comprehension makes it easier to generate a list or dictionary using a loop.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**List comprehension:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 6, 7, 8, 9, 10]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_list = [x + 5 for x in range(0,6)]\n",
    "new_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Traditional way to achieve the same:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 6, 7, 8, 9, 10]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_list = []\n",
    "for x in range(0,6):\n",
    "    new_list.append(x + 5)\n",
    "new_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dictionary comprehension:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'num_0': 5, 'num_1': 6, 'num_2': 7, 'num_3': 8, 'num_4': 9, 'num_5': 10}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dict = {'num_{}'.format(x) : x + 5 for x in range(0,6)}\n",
    "new_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Traditional way to achieve the same:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'num_0': 5, 'num_1': 6, 'num_2': 7, 'num_3': 8, 'num_4': 9, 'num_5': 10}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dict = {}\n",
    "for x in range(0,6):\n",
    "    new_dict['num_{}'.format(x)] = x + 5\n",
    "new_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"text-decoration: underline;\">Catching Exceptions</span><a id='catching-exceptions'></a> [(to top)](#toc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Python exception looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "list.remove(x): x not in list",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-57-1fc12122611b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mnum_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mnum_list\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list"
     ]
    }
   ],
   "source": [
    "num_list = [1, 2, 3]\n",
    "num_list.remove(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can catch exceptions using `try` and `except`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR!\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    num_list.remove(4)\n",
    "except:\n",
    "    print('ERROR!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is usually best practice to specify the error type to except:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number not in the list\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    num_list.remove(4)\n",
    "except ValueError as e:\n",
    "    print('Number not in the list')\n",
    "except Exception as e:\n",
    "    print ('Generic error')\n",
    "finally:\n",
    "    print('Done')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"text-decoration: underline;\">Importing Libraries</span><a id='importing'></a> [(to top)](#toc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8414709848078965"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "math.sin(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8414709848078965"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math as math_lib\n",
    "math_lib.sin(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8414709848078965"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from math import sin\n",
    "sin(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"text-decoration: underline;\">OS operations</span><a id='os-operations'></a> [(to top)](#toc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get current working directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Stack\\\\Work\\\\Programming\\\\Active\\\\PythonAccountingResearch'"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List files/folders in directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.git',\n",
       " '.ipynb_checkpoints',\n",
       " '0_Python_Basics.ipynb',\n",
       " '0_Python_Basics.py',\n",
       " 'Cheatsheet_Printable']"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change working directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "os.chdir(r'C:\\Stack\\Work\\Programming\\Active\\PythonAccountingResearch')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note:* `r'path'` indicates a raw string  \n",
    "A raw string does not see `\\` as a special character"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"text-decoration: underline;\">File Input/Output</span><a id='files'></a> [(to top)](#toc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can open a file with different file modes:  \n",
    "`w` -> write only  \n",
    "`r` -> read only  \n",
    "`w+` -> read and write + completely overwrite file   \n",
    "`a+` -> read and write + append at the bottom\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('new_file.txt', 'w') as file:\n",
    "    file.write('Content of new file. \\nHi there!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('new_file.txt', 'r') as file:\n",
    "    file_content = file.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Content of new file. \\nHi there!'"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Content of new file. \n",
      "Hi there!\n"
     ]
    }
   ],
   "source": [
    "print(file_content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('new_file.txt', 'a+') as file:\n",
    "    file.write('\\n' + 'New line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Content of new file. \n",
      "Hi there!\n",
      "New line\n"
     ]
    }
   ],
   "source": [
    "with open('new_file.txt', 'r') as file:\n",
    "    print(file.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note:* using `with` is best as it automatically closes the file"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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