PythonIntro_ListsTuplesDictionaries.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A *data structure* is a term used in computer science to describe a type of \"data container\".  They are used to store multiples of (usually) the same type of data and provide certain *accessor* functions to extract the data from the container.  Different data structures have varying properties that trade off ease of use, speed of access, memory footprint, etc.\n",
    "\n",
    "Below we cover a few data structures in the Python language.  There are a few more not covered here, but these are by far the most common."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## List\n",
    "\n",
    "Python lists are used to store a collection of data. For example, a collection of integers:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "popout"
    ]
   },
   "source": [
    "Notice Python lists use square brackets `[]` and separate the entries with commas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 6, 4]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = [1, 2, 6, 4]\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or a collection of floating point numbers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.1, 3.0]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = [1.1, 3.0]\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or even heterogeneous data, like an integer and a floating point number and a string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1.3, 'string']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = [1, 1.3, 'string']\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can even have list of lists, and those can be heterogeneous as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['a list', 'of strings'], [1, 2, 3]]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = [['a list', 'of strings'],[1,2,3]]\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lists support *indexing* to extract the data stored in them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "popout"
    ]
   },
   "source": [
    "The `[0]` takes the first entry in `x` which is itself a list containing `['a list', 'of strings']`.  The second `[1]` takes the `1`-index entry in that list `'of strings'`.  In Python, counting starts from 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'of strings'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0][1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also perform operations on lists, e.g. appending values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = [1,2,3]\n",
    "x.append(4)\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or inserting into the middle of them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "popout"
    ]
   },
   "source": [
    "The `2` indicates the index location where the insertion should occur.  The `10` is the value to insert."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 10, 3, 4]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.insert(2, 10)\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or sorting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "popout"
    ]
   },
   "source": [
    "Notice the sorting occurs *in place*, i.e. the order of `x` is permanently changed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 10]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sort()\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a shorthand syntax for appending lists to other lists.  Just as we can add numbers to other numbers and strings to other strings, we can add two lists together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [['a list', 'of strings'],[1,2,3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['a list', 'of strings'], [1, 2, 3], [1.1, 2.2]]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x + [[1.1, 2.2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also use the indexing syntax for reassignment.  For example, to replace the first position of the list `x`, you can use the `O`th index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['another list'], [1, 2, 3]]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0] = ['another list']\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lists are mutable!  Meaning they can be changed, extended, appended, inserted into, sorted, etc.  In some languages, functions that change a data structure are called *mutator* or *setter* functions, and functions that extract data from a data structure are called *accessor* or *getter* functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuple\n",
    "\n",
    "Tuples are kind of like *constant* list.  If you have data that you want to store in a structure, but you know it will not be changed, tuples can be more efficient.  The syntax for tuples is given in the example below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "popout"
    ]
   },
   "source": [
    "Notice the surrounding parentheses `()` on a tuple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = (1, 2, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is an efficient \"unpacking\" syntax for tuples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "popout"
    ]
   },
   "source": [
    "Here the first entry in the tuple gets assigned to `a`, the second to `b`, and the third to `c`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "a, b, c = x\n",
    "print(a)\n",
    "print(b)\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You cannot reassign values in a tuple.  They are immutable!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": [
     "full_width"
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   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-4fb93c654a12>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "x[0] = 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dictionary\n",
    "\n",
    "Dictionaries are a way to efficiently store and retrieve `keyword: value` type data.  For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_dict = {'numerical' : 1, 'alphabetical': 'abc'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can look up the value of `'numerical'` quickly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict['numerical']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or `'alphabetical'`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'abc'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict['alphabetical']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just likes lists, you can have a dictionary-of-dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_dict = {'numerical' : {'linear solver' : 'gmres'}, \n",
    "           'alphabetical': 'abc'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To return the value of `'linear solver'` directly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'gmres'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict['numerical']['linear solver']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to access parts of a dictionary is the with `get` function, e.g."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'abc'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict.get('alphabetical')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One advantage the `get` function has over the bracket `[]` syntax is that it will not error if the keyword does not exist in the dictionary, e.g."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": [
     "full_width"
    ]
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'name'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-6a9c34cc7f98>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmy_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'name'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: 'name'"
     ]
    }
   ],
   "source": [
    "my_dict['name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_dict.get('name')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, it does not return a value if `'name'` is not defined in the dictionary, but the `get()` function allows for setting a default value in the event the keyword is undefined\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Romeo'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict.get('name', 'Romeo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further reading\n",
    "\n",
    "Further reading on data structures can be found in the [Python documentation]( https://docs.python.org/3.8/tutorial/datastructures.html)."
   ]
  }
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