{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Lists, tuples, dictionaries\n\nLists\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "list1 = [\"physics\", \"chemistry\", 1997, 2000]\nlist2 = [1, 2, 3, 4, 5]\nlist3 = [\"a\", \"b\", \"c\", \"d\"]\n\nprint(\"list1[0]: \", list1[0])\nprint(\"list2[1:5]: \", list2[1:5])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Updating lists\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "list1[2] = 2001\nprint(\"New value available at index 2 : \", list1[2])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Delete list item\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "del list1[2]\nprint(\"After deleting value at index 2 : \", list1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "List operations\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "len(list1)  # length of list\nlist1 + list2  # concatenate list\n[\"ho\"] * 4  # repetition\n2000 in list1  # membership\nfor obj in list1:  # iterations\n    print(obj)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "List functions, methods\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "list1.append(\"newVal\")  # append value\nlist1.append(2000)\nlist1.count(2000)  # count how many times object occurs in list\nlist1.index(2000)  # lowest index in list object appears\nlist1.insert(3, 2022)  # insert object into list at index#\nlist1.pop()  # remove last object from list\nlist1.remove(2022)  # remove object from list\nlist1.reverse()  # reverse objects in list\nlist2.sort()  # sorts objects in list. Works only if list contains object of same dtype"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Tuples\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "tup1 = (\"physics\", \"chemistry\", 1997, 2000)\ntup2 = (1, 2, 3, 4, 5, 6, 7)\nprint(\"tup1[0]: \", tup1[0])\nprint(\"tup2[1:5]: \", tup2[1:5])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Updating tuples\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "tup3 = tup1 + tup2\nprint(tup3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Tuples operations\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "len(tup1)  # length of list\ntup1 + tup2  # concatenate list\n(\"ho\") * 4  # repetition\n2000 in tup1  # membership\nfor obj in tup1:  # iterations\n    print(obj)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Dictionary\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "dict1 = {\"Name\": \"Zara\", \"Age\": 7, \"Class\": \"First\"}\nprint(\"dict1['Name']: \", dict1[\"Name\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Updating dictionary\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "dict1[\"Age\"] = 8  # update existing entry\ndict1[\"School\"] = \"DPS School\"  # Add new entry\n\nprint(\"dict1['Age']: \", dict1[\"Age\"])\nprint(\"dict1['School']: \", dict1[\"School\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Delete elements\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "del dict1[\"Name\"]  # remove entry with key 'Name'\nprint(\"dict1['Age']: \", dict1[\"Age\"])\nprint(\"dict1['School']: \", dict1[\"School\"])\ndict1.clear()  # remove all entries in dict\ndel dict1  # delete entire dictionary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Dictionary functions, methods\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "dict1 = {\"Name\": \"Zara\", \"Age\": 7, \"Class\": \"First\"}\ndict1.copy()  # returns shallow copy of dict\ndict.fromkeys(dict1)  # new dict with keys from sequence and values set to value\ndict1.items()  # returns list of dict (key, value) tuple pairs\ndict1.keys()  # return list of keys\nfor key, value in dict1.items():  # iterations\n    print(key, value)"
      ]
    }
  ],
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      "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": "3.8.10"
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  },
  "nbformat": 4,
  "nbformat_minor": 0
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