{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Other built-in types\n",
    "\n",
    " Python has a lot of objects built-in. Here we are going to have a quick look at those you are most likely to use at the start.\n",
    "\n",
    " In this notebook, we'll see:\n",
    "  - lists\n",
    "  - tuples\n",
    "  - dictionaries\n",
    "  - copy vs. pointers\n",
    "  - some interesting methods of these builtin types (including strings) as exercises."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ---\n",
    " # Lists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Lists look like arrays but are not arrays! Lists can contain elements of different types and are always 1 dimension.\n",
    "\n",
    " A list can probably have any object as an element: other lists, strings, numbers, arrays, functions etc. A list simply allows you to create a collection of objects. It doesn't inform you in any way how those objects are related to each other (or if indeed they are related)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First list length:  2\n",
      "I'm still 2 elements long:  2\n",
      "Easy to add elements to lists:  [['a', 'b'], 3, 5]\n"
     ]
    }
   ],
   "source": [
    "li = [] # Empty list\n",
    "li = ['a',1]  # List of 2 elements\n",
    "print(\"First list length: \",len(li))\n",
    "li = [['a','b'],3]  # A list can have another list as an element. But li is still a 2 elements list.\n",
    "print(\"I'm still 2 elements long: \", len(li))\n",
    "li.append(5)\n",
    "print(\"Easy to add elements to lists: \", li)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Lists are indexable. Python also says \"subscriptable\".\n",
    " \n",
    " Lists are iterable. They can be used to build loops on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 'b']\n",
      "3\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "for n in li:\n",
    "    print(n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List elements can be changed, i.e lists are **mutable** objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 'b']\n",
      "new\n",
      "['new', 3, 5]\n"
     ]
    }
   ],
   "source": [
    "print(li[0])\n",
    "li[0]='new'\n",
    "print(li[0])\n",
    "print(li)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Strings are **immutable** objects. Its elements cannot be changed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'str' 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-4-9f46651a75b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Claire\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Z\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "a=\"Claire\"\n",
    "a[0]=\"Z\"\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " If you have a subscriptable object as a list element, how do you access this element's subscripts? For example, you have the word \"new\" in the first element of a list and you want to know the last character:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'w'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "li[0][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'int' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-a48fcdf0b0ca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Returns an error with non-subscriptable objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mli\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'int' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "# Returns an error with non-subscriptable objects\n",
    "li[1][-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Convert to list\n",
    " You can convert an iterable object to a list with the builtin function: `list()`.\n",
    " Each element of the initial object will become an element in the list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C', 'l', 'a', 'i', 'r', 'e']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=\"Claire\"\n",
    "li=list(a)\n",
    "li"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Claire']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "li=[a]\n",
    "li"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ---\n",
    " # Tuples\n",
    "\n",
    " Tuples are like lists but are immutable. That means elements of a tuple can't be changed, added, removed. It can be useful for a collection of objects you don't want to inadvertently change in your code. For example, a collection of names of experiments, paths, models etc. Although tuples are rarely used and people tend to simply use lists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = ()  # Empty tuple\n",
    "t = (1,2,3)\n",
    "t = 4,5,6  # Parentheses are optional!\n",
    "t = 1,  # Don't forget the comma to define a 1-element tuple!\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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-10-7b1c8be80904>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# As said earlier, they are immutable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\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;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\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": [
    "# As said earlier, they are immutable\n",
    "t=1,2,3\n",
    "t[0]=3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Convert to tuple\n",
    " Same as lists, you can convert iterable objects to tuples with the builtin function: `tuple()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3, 4)\n",
      "([1, 3, 4],)\n"
     ]
    }
   ],
   "source": [
    "a = [1,3,4]\n",
    "b = tuple(a)\n",
    "print(b)\n",
    "# Here again if you want to keep the list as an element of the tuple:\n",
    "c = (a,)\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ---\n",
    " # Copy or not?\n",
    " There is another consequence to all this talk about mutable or immutable. It has to do with how Python manages memory.\n",
    " To reduce the memory footprint of a program, Python will try to make pointers instead of copying the same values at different places in memory.\n",
    " That means if 2 variables are the same mutable object both will change if an element of one is changed.\n",
    " If one of the object is assigned to something else, then only this object will change.\n",
    " Let's see what this means with an example for a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3] [2, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "# If we change the first element of one list, the other one changes too.\n",
    "li = [1,2,3]\n",
    "li2 = list(li)\n",
    "li2[0]=2\n",
    "print(li, li2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 2, 3] [1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "# If we reassign li to a different list (even if it's the same as initially), only li changes.\n",
    "li = [1,2,3]\n",
    "print(li2,li)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 2, 3]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "li=[1,2,3]\n",
    "li[0]=2\n",
    "li"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The easiest way to make a copy of a mutable object is with the `list()` and `dict()` functions (see below for `dict()`).\n",
    " These will only do a shallow copy of the object. It is usually enough.\n",
    " There are ways to make deep copies of mutable objects. That is rarely needed, we'll see it at the end, depending on time.\n",
    "\n",
    " Since it is impossible to change only 1 element of an immutable object, any change made on those isn't reflected to other objects initially pointing to the same memory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ---\n",
    " # Dictionaries\n",
    "\n",
    " Dictionaries allow you to label values. For example, if you'd like to keep track of a grid description (rectilinear), you might need a grid name, first lon/lat, the resolution, last lon/lat. A dictionary allows you to keep all those values together in one object and allows you to refer to each by name instead of position for example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'my_grid',\n",
       " 'first_lon': -180.0,\n",
       " 'first_lat': -90.0,\n",
       " 'last_lon': 180.0,\n",
       " 'last_lat': 90.0,\n",
       " 'res': 0.5}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d={\n",
    "    \"name\":\"my_grid\",\n",
    "    \"first_lon\":-180.,\n",
    "    \"first_lat\":-90.,\n",
    "    \"last_lon\":180.,\n",
    "    \"last_lat\":90.,\n",
    "    \"res\":0.5\n",
    "}\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'my_grid'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[\"name\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The values associated to each key can be very complicated objects. For example, we could have only \"first_point\" and \"last_point\" keys, each pointing to a dictionary with lat and lon as keys:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'lon': -180.0, 'lat': -90.0} {'lon': 180.0, 'lat': 90.0}\n"
     ]
    }
   ],
   "source": [
    "point0={\"lon\":-180., \"lat\":-90.}\n",
    "point1=dict(point0)\n",
    "point1[\"lon\"]=180.\n",
    "point1[\"lat\"]=90.\n",
    "print(point0, point1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'lon': -180.0, 'lat': -90.0}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d={'name':\"my_grid\", \"first_point\":point0, \"last_point\":point1, \"res\":0.5}\n",
    "d[\"first_point\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-180.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[\"first_point\"][\"lon\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Get the keys and values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name\n",
      "first_point\n",
      "last_point\n",
      "res\n",
      "End keys \n",
      "\n",
      "my_grid\n",
      "{'lon': -180.0, 'lat': -90.0}\n",
      "{'lon': 180.0, 'lat': 90.0}\n",
      "0.5\n",
      "End values \n",
      "\n",
      "name my_grid\n",
      "first_point {'lon': -180.0, 'lat': -90.0}\n",
      "last_point {'lon': 180.0, 'lat': 90.0}\n",
      "res 0.5\n",
      "End pairs \n",
      "\n",
      "('name', 'my_grid')\n",
      "('first_point', {'lon': -180.0, 'lat': -90.0})\n",
      "('last_point', {'lon': 180.0, 'lat': 90.0})\n",
      "('res', 0.5)\n"
     ]
    }
   ],
   "source": [
    "# Get the keys in a dictionary:\n",
    "for k in d.keys():\n",
    "    print(k)\n",
    "print(\"End keys \\n\")\n",
    "\n",
    "# Get the values:\n",
    "for v in d.values():\n",
    "    print(v)\n",
    "print(\"End values \\n\")\n",
    "\n",
    "# Get the pairs of keys and values:\n",
    "for k,v in d.items():\n",
    "    print(k,v)\n",
    "print(\"End pairs \\n\")\n",
    "\n",
    "# As you can see here, loops in Python can have several loop variables!\n",
    "# If we give only 1 loop variable:\n",
    "for k in d.items():\n",
    "    print(k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Add a (key,value) pair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'my_grid',\n",
       " 'first_point': {'lon': -180.0, 'lat': -90.0},\n",
       " 'last_point': {'lon': 180.0, 'lat': 90.0},\n",
       " 'res': 0.5,\n",
       " 'projection': 'cartesian'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['projection'] = \"cartesian\"\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " If you have several grids you want to keep track of, each key could have a list of values (or tuples). Or you could create a list of dictionaries. So you have 1 dictionary per grid and you keep them all together in a list (or tuple). The second option might be better as it allows you to create a dictionary for a new grid more easily by simply appending its definition dictionary to the list."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ---\n",
    " # Exercises\n",
    " Below are a few exercises or examples for you to go through.\n",
    "\n",
    " Don't forget to use either the inline help (?var or `tab`) or Google and Stack Overflow."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  ## String formatting with dictionaries\n",
    " We saw earlier that f-strings are the most readable way to format a string.\n",
    "\n",
    " With dictionaries, `str.format()` can be quite powerful and useful:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'my_grid', 'first_point': [-180.0, -90.0], 'last_point': [180.0, 90.0], 'res': 0.5}\n",
      "My grid my_grid has 0.5 degrees resolution, starts at [-180.0, -90.0], ends at [180.0, 90.0]\n",
      "My grid my_grid has 0.5 degrees resolution, starts at [-180.0, -90.0], ends at [180.0, 90.0]\n"
     ]
    }
   ],
   "source": [
    "# Let's define a dictionary:\n",
    "point0=[-180., -90.]\n",
    "point1=[180.,90.]\n",
    "d={'name':\"my_grid\", \"first_point\":point0, \"last_point\":point1, \"res\":0.5}\n",
    "print(d)\n",
    "# Let's print all this information nicely with f-string and then `str.format()`\n",
    "print(f\"My grid {d['name']} has {d['res']} degrees resolution, starts at {d['first_point']}, ends at {d['last_point']}\")\n",
    "print(\"My grid {name} has {res} degrees resolution, starts at {first_point}, ends at {last_point}\".format(**d))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `**` operator allows us to take a dictionary of key-value pairs and unpack it into keyword arguments in a function call. It can be quite useful in other functions. For example to define plot characteristics instead of having a long line of arguments. Or to easily use the same arguments for different functions.\n",
    "\n",
    "There is also a `*` operator.\n",
    "See this page for details: https://treyhunner.com/2018/10/asterisks-in-python-what-they-are-and-how-to-use-them/\n",
    "This page is using a few things we haven't seen but it's very thorough."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ---\n",
    " ## Split and join strings\n",
    " If reading tabulated data or a filename with fields separated by a separator ('\\_', space, etc.), it's common one might want to split the different fields into separate strings or values.\n",
    " Inversely, you might want to create files that have fields separated by '\\_'. You can use `+` to add the different fields, but it gets long pretty quickly. Python provides a better way.\n",
    " \n",
    " #### Split\n",
    " Split the following string along the \"_\" separator. Hint: check the string's methods. \n",
    " \n",
    " What is the type of the result?\n",
    "<div class=\"accordion\" id=\"accordion\">\n",
    "  <div class=\"card\">\n",
    "    <div class=\"card-header\" id=\"headingOne\">\n",
    "      <h5 class=\"mb-0\">\n",
    "        <button class=\"btn btn-link\" type=\"button\" data-toggle=\"collapse\" data-target=\"#falsepy\" aria-expanded=\"true\" aria-controls=\"falsepy\">\n",
    "          Answer\n",
    "        </button>\n",
    "      </h5>\n",
    "    </div>\n",
    "  <div>\n",
    "    <div id=\"falsepy\" class=\"collapse\" aria-labelledby=\"headingOne\" data-parent=\"#accordion\">\n",
    "      <div class=\"card-body\">\n",
    "<pre><code>\n",
    "b=a.split(sep=\"_\")\n",
    "</code></pre>\n",
    "      </div>\n",
    "    </div>\n",
    "  </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tas', 'day', 'MRI-ESM1', 'historical', 'r1i1p1', '18510101-18601231.nc']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the following string along the \"_\" separator. Hint: check the string's methods. What is the type of the result?\n",
    "a=\"tas_day_MRI-ESM1_historical_r1i1p1_18510101-18601231.nc\"\n",
    "a.split(sep=\"_\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Join\n",
    "Join together the elements of the following list to form a filename with the same format as above.\n",
    "\n",
    "Again check the string's methods. Be careful how you specify the separator and the strings to join!\n",
    "<div class=\"accordion\" id=\"accordion\">\n",
    "  <div class=\"card\">\n",
    "    <div class=\"card-header\" id=\"headingOne\">\n",
    "      <h5 class=\"mb-0\">\n",
    "        <button class=\"btn btn-link\" type=\"button\" data-toggle=\"collapse\" data-target=\"#falsepy\" aria-expanded=\"true\" aria-controls=\"falsepy\">\n",
    "          Answer\n",
    "        </button>\n",
    "      </h5>\n",
    "    </div>\n",
    "  <div>\n",
    "    <div id=\"falsepy\" class=\"collapse\" aria-labelledby=\"headingOne\" data-parent=\"#accordion\">\n",
    "      <div class=\"card-body\">\n",
    "<pre><code>\n",
    "c='.'.join(l[-2:])\n",
    "d = '-'.join([l[-3],c])\n",
    "f = l[:-3]\n",
    "f.append(d)\n",
    "\"_\".join(f)\n",
    "</code></pre>\n",
    "      </div>\n",
    "    </div>\n",
    "  </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = [\"ta\", \"day\", \"ACCESS\", \"historical\",\"r1i1p1\", \"18510101\", \"19001231\",\"nc\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'18510101-19001231.nc'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c='.'.join(l[-2:])\n",
    "d = '-'.join([l[-3],c])\n",
    "'-'.join([l[-3],'.'.join(l[-2:])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ta', 'day', 'ACCESS', 'historical', 'r1i1p1', '18510101-19001231.nc']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = l[:-3]\n",
    "f.append(d)\n",
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ta_day_ACCESS_historical_r1i1p1_18510101-19001231.nc'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c='.'.join(l[-2:])\n",
    "d = '-'.join([l[-3],c])\n",
    "f = l[:-3]\n",
    "f.append(d)\n",
    "\"_\".join(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ---\n",
    " ## Zip function\n",
    " It allows you to zip together 2 iterables. So that the first elements of the 2 original iterables become together the 1st element of the result, etc.\n",
    "\n",
    " Obviously, it's much easier to see examples.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('C', 'C'), ('l', 'a'), ('a', 'r'), ('i', 'o'), ('r', 'u'), ('e', 'g')]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=\"Claire\"\n",
    "b=\"Carouge\"\n",
    "c=list(zip(a,b))\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'John', 'age': 32, 'place': 'Paris'}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Given the 2 lists below, create a dictionary where the keys come from the list \"keys\" and values from \"values\"\n",
    "keys=['name', 'age', 'place']\n",
    "values=['John', 32, 'Paris']\n",
    "d = dict(zip(keys,values))\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ---\n",
    " These are all the concepts we wanted to introduce.\n",
    " As a summary we've seen:\n",
    "  - strings, lists, tuples, dictionaries\n",
    "  - how to format strings with `str.format()` and f-strings\n",
    "  - Builtin functions:\n",
    "     - print()\n",
    "     - len()\n",
    "     - any()\n",
    "     - all()\n",
    "     - list()\n",
    "     - tuple()\n",
    "     - dict()\n",
    "  - For loops\n",
    "  - If constructs\n",
    "  - A few useful methods and other builtin functions in the exercises.\n",
    "    For additional builtin functions: https://docs.python.org/3/library/functions.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, ['a', 'b']]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l=[1,2]\n",
    "a=['a','b']\n",
    "l.append(a)\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 'a', 'b']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l=[1,2]\n",
    "a=['a','b']\n",
    "l.extend(a)\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ta_day_ACCESS_historical_r1i1p1_18510101-19001231.nc'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [\"ta\", \"day\", \"ACCESS\", \"historical\",\"r1i1p1\", \"18510101\", \"19001231\",\"nc\"]\n",
    "c='.'.join(l[-2:])\n",
    "d = '-'.join([l[-3],c])\n",
    "\"_\".join([*l[:-3],d])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ta', 'day', 'ACCESS', 'historical', 'r1i1p1']\n",
      "ta day ACCESS historical r1i1p1\n",
      "ta day\n"
     ]
    }
   ],
   "source": [
    "print(l[:-3])\n",
    "print(*l[:-3])\n",
    "print(l[0],l[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "file_extension": ".py",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3
 },
 "nbformat": 4,
 "nbformat_minor": 2
}