{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "![](http://www.upm.es/sfs/Rectorado/Gabinete%20del%20Rector/Logos/UPM/EscPolitecnica/EscUpmPolit_p.gif \"UPM\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Course Notes for Learning Intelligent Systems" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2015 Carlos A. Iglesias" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Built-in Types: Sets and Mappings" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* *Sets* are unordered bags of values\n", "* *Dictionaries* are unordered bags of key-values pairs\n", "\n", "A set object is an unordered collection of distinct objects. There are two built-in set types: **set** (mutable) and **frozenset** (inmutable).\n", "\n", "A mapping object maps hashable values to arbitrary objects. Mappings are mutable objects. There is only one bultin mapping type: **dictionary**." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## 1. Sets" ] }, { "cell_type": "code", "execution_count": 413, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "set()" ] }, "execution_count": 413, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_set = set() #create a set\n", "my_set" ] }, { "cell_type": "code", "execution_count": 414, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{1}" ] }, "execution_count": 414, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_set.add(1) # add an element\n", "my_set" ] }, { "cell_type": "code", "execution_count": 415, "metadata": { "collapsed": false }, "outputs": [], "source": [ "my_set.add(2) # add another element" ] }, { "cell_type": "code", "execution_count": 416, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{1, 2}" ] }, "execution_count": 416, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_set" ] }, { "cell_type": "code", "execution_count": 417, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{1, 2, 3}" ] }, "execution_count": 417, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_set.add(3) # add another one\n", "my_set" ] }, { "cell_type": "code", "execution_count": 418, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{1, 2, 3}" ] }, "execution_count": 418, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_set.add(1) #try to add a repeated element\n", "my_set" ] }, { "cell_type": "code", "execution_count": 419, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}" ] }, "execution_count": 419, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s2 = set(range(10)) # we can create a set from a range\n", "s2" ] }, { "cell_type": "code", "execution_count": 420, "metadata": { "collapsed": false }, "outputs": [], "source": [ "l = ['a', 'a', 'b', 'c', 'c', 'c']" ] }, { "cell_type": "code", "execution_count": 421, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'a', 'b', 'c'}" ] }, "execution_count": 421, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s3 = set(l) # if we create a set from a list, elements are not repeated\n", "s3" ] }, { "cell_type": "code", "execution_count": 422, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 422, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(s3) " ] }, { "cell_type": "code", "execution_count": 423, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{0, 1, 2, 3, 4, 5, 'c', 6, 7, 8, 9, 'a', 'b'}" ] }, "execution_count": 423, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s3.union(s2) # we can use set methods: union(), intersection(), difference(), ..." ] }, { "cell_type": "code", "execution_count": 424, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 424, "metadata": {}, "output_type": "execute_result" } ], "source": [ "3 in my_set #check membership" ] }, { "cell_type": "code", "execution_count": 425, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "set" ] }, "execution_count": 425, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(s3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Dictionaries" ] }, { "cell_type": "code", "execution_count": 426, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'key1': 1, 'key2': 2, 'key3': 3}" ] }, "execution_count": 426, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_dictionary = {'key1': 1, 'key2': 2, 'key3': 3} # pairs of key-value mappings\n", "my_dictionary" ] }, { "cell_type": "code", "execution_count": 427, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 427, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_dictionary['key1'] #retrieve a value given a key" ] }, { "cell_type": "code", "execution_count": 428, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'key1': 1, 'key2': 2, 'key3': 3}" ] }, "execution_count": 428, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_dict = dict()\n", "my_dict['key1'] = 1\n", "my_dict['key2'] = 2\n", "my_dict['key3'] = 3\n", "my_dict # alternative way to create a dictionary " ] }, { "cell_type": "code", "execution_count": 429, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 429, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_dict == my_dictionary # check if both dictionaries are equal" ] }, { "cell_type": "code", "execution_count": 430, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'one': {'two': {'three': 'Nested dict'}}}" ] }, "execution_count": 430, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_dict2 = {'one': {'two': {'three': 'Nested dict'}}} #nested dictionary\n", "my_dict2" ] }, { "cell_type": "code", "execution_count": 431, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'Nested dict'" ] }, "execution_count": 431, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_dict2['one']['two']['three'] #access the value" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Dictionaries have different methods, check them with Tab." ] }, { "cell_type": "code", "execution_count": 473, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "dict_keys(['key2', 'key3', 'key1'])" ] }, "execution_count": 473, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_dict.keys() # in Python3 we get a View object that changes when the dictionary changes" ] }, { "cell_type": "code", "execution_count": 478, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['key2', 'key3', 'key1']" ] }, "execution_count": 478, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(my_dict.keys()) # we can convert it to a list, we see dicionaries are unordered" ] }, { "cell_type": "code", "execution_count": 474, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "dict_values([2, 3, 1])" ] }, "execution_count": 474, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_dict.values()" ] }, { "cell_type": "code", "execution_count": 476, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[2, 3, 1]" ] }, "execution_count": 476, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(my_dict.values())" ] }, { "cell_type": "code", "execution_count": 479, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "dict" ] }, "execution_count": 479, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(my_dict)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Licence" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n", "\n", "© 2015 Carlos A. Iglesias, Universidad Politécnica de Madrid." ] } ], "metadata": { "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": "3.4.3+" } }, "nbformat": 4, "nbformat_minor": 0 }