From de0e71c5b2e042f0c61f163c7db953eea125d17b Mon Sep 17 00:00:00 2001 From: cswever Date: Thu, 21 Sep 2017 17:23:08 -0700 Subject: [PATCH] all notebooks at python3 --- .../lesson01-checkpoint.ipynb | 194 ++--- Lesson01_Variables/MilestonesSOLUTION.py | 8 +- Lesson01_Variables/lesson01.ipynb | 144 +--- .../Conditional Execution-checkpoint.ipynb | 583 +------------ .../CarRecommenderSOLUTION.py | 2 +- .../Conditional Execution.ipynb | 583 +------------ Lesson03_Functions/Functions.ipynb | 84 +- Lesson03_Functions/yahtzeeSOLUTION.py | 20 +- Lesson04_Iteration/Iterations.ipynb | 104 +-- Lesson04_Iteration/wandererSOLUTION.py | 4 +- .../NumpyMatplotlib_part1.ipynb | 757 +---------------- Lesson05_Strings/DNAExtravaganzaSOLUTION.py | 12 +- Lesson05_Strings/Strings.ipynb | 724 +++-------------- Lesson06_Files/Files.ipynb | 729 ++--------------- .../Lists and Tuples-checkpoint.ipynb | 255 +++--- .../Lists and Tuples - after class.ipynb | 56 +- .../Lists and Tuples.ipynb | 87 +- .../Party Budget SOLUTION.ipynb | 12 +- .../Dictionary-checkpoint.ipynb | 140 ++-- Lesson08_Dictionaries/Dictionary.ipynb | 26 +- .../acrosticChallengeSOLUTION.py | 2 +- Lesson08_Dictionaries/acrosticSOLUTION.py | 2 +- .../MF Promotions Solution.ipynb | 161 +--- .../MF Promotions-Template.ipynb | 46 +- .../PasswordHacker/Passwords.ipynb | 39 +- .../PasswordHacker/PasswordsSOLUTION.ipynb | 34 +- .../PasswordHacker/password_check.py | 8 +- Lesson10_Regexs/DocClerkSOLUTION.py | 2 +- Lesson10_Regexs/RegularExpressions.ipynb | 116 +-- Lesson11_JSONandAPIs/JSONandAPIs.ipynb | 64 +- Lesson12_TabularData/Tabular Data.ipynb | 76 +- Lesson13_GUIs/GUI.ipynb | 62 +- Lesson14_NumpyAndMatplotlib/matplotlib.ipynb | 765 +----------------- Lesson14_NumpyAndMatplotlib/numpy.ipynb | 673 +-------------- 34 files changed, 818 insertions(+), 5756 deletions(-) diff --git a/Lesson01_Variables/.ipynb_checkpoints/lesson01-checkpoint.ipynb b/Lesson01_Variables/.ipynb_checkpoints/lesson01-checkpoint.ipynb index ff35c32..e2ede24 100644 --- a/Lesson01_Variables/.ipynb_checkpoints/lesson01-checkpoint.ipynb +++ b/Lesson01_Variables/.ipynb_checkpoints/lesson01-checkpoint.ipynb @@ -23,9 +23,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "type('I am amazing!')" @@ -34,9 +32,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "type(145)" @@ -45,9 +41,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "type(2.5)" @@ -57,20 +51,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To print a value to the screen, we use the command 'print'\n", + "To print a value to the screen, we use the function 'print()'\n", "\n", - "e.g. print 1" + "e.g. print(1)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "print \"Hello World\"" + "print(\"Hello World\")" ] }, { @@ -84,9 +76,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -122,18 +112,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Notice that when you ran that, nothing printed out. To print a variable, you use the same statement you would use to print the value. e.g. print WHALE" + "Notice that when you ran that, nothing printed out. To print a variable, you use the same statement you would use to print the value. e.g. print(WHALE)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "print number_of_whales" + "print(number_of_whales)" ] }, { @@ -147,9 +135,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -183,9 +169,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "1 + 2" @@ -194,33 +178,27 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "apples = 15\n", - "apples_left = 15 - 3\n", - "print apples_left" + "apples_left = apples - 3\n", + "print(apples_left)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "print 3 * 2.1" + "print((3 * 2.1))" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "number_of_whales ** 2" @@ -229,32 +207,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print 5 / 2" - ] - }, - { - "cell_type": "markdown", "metadata": {}, - "source": [ - "Wait, what happened with the division? You have to be careful with division of integers in python. It always rounds down. If you want the un-rounded answer. You need to make sure that at least one of the operands is a float.\n", - "\n", - "ex. 5/2.0" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, "outputs": [], "source": [ - "5/2.0" + "print((5 / 2))" ] }, { @@ -267,14 +223,12 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "number_of_whales = 8\n", "number_of_whales = number_of_whales + 2 \n", - "print number_of_whales" + "print(number_of_whales)" ] }, { @@ -288,9 +242,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -306,9 +258,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "2 * 3 + 4 / 2" @@ -317,9 +267,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "(2 * (3 + 4)) / 2" @@ -337,9 +285,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "5 % 2" @@ -356,9 +302,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -373,23 +317,19 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "print 'Hello ' + 'Coding Circle'" + "print(('Hello ' + 'Coding Circle'))" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "print \"The \" + WHALE + \" lives in the sea.\"" + "print((\"The \" + WHALE + \" lives in the sea.\"))" ] }, { @@ -402,12 +342,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "print \"My name is\" + \"Charlotte\"" + "print((\"My name is\" + \"Charlotte\"))" ] }, { @@ -421,9 +359,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -433,7 +369,7 @@ "source": [ "## Asking the user for input\n", "\n", - "To get an input for the user we use the built-in function raw_input() and assign it to a variable.\n", + "To get an input for the user we use the built-in function input() and assign it to a variable.\n", "\n", "NOTE: The result is always a string." ] @@ -441,22 +377,20 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "my_name = raw_input()\n", - "print my_name" + "my_name = eval(input())\n", + "print(my_name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You can pass a string to the raw_input() function to prompt the user for what you are looking for.\n", + "You can pass a string to the input() function to prompt the user for what you are looking for.\n", "\n", - "e.g. raw_input('How are you feeling?')\n", + "e.g. input('How are you feeling?')\n", "\n", "Hint, add a new line character \"\\n\" to the end of the prompt to make the user enter it on a new line." ] @@ -464,13 +398,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "favorite_ocean_animal = raw_input(\"What is your favorite sea creature?\\n\")\n", - "print \"The \" + favorite_ocean_animal + \" is so cool!\"" + "favorite_ocean_animal = eval(input(\"What is your favorite sea creature?\\n\"))\n", + "print((\"The \" + favorite_ocean_animal + \" is so cool!\"))" ] }, { @@ -487,14 +419,12 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "number_of_apples = raw_input(\"How many apples do you want?\\n\")\n", + "number_of_apples = eval(input(\"How many apples do you want?\\n\"))\n", "number_of_apples_int = int(number_of_apples)\n", - "print int(number_of_apples_int) * 1.05" + "print((number_of_apples_int * 1.05))" ] }, { @@ -508,9 +438,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -527,15 +455,13 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Calculate the price of apples that a user wants\n", - "number_of_apples = raw_input(\"How many apples do you want?\\n\") # Ask user for quantity of apples\n", + "number_of_apples = eval(input(\"How many apples do you want?\\n\")) # Ask user for quantity of apples\n", "number_of_apples_int = int(number_of_apples) # raw_input returns string, so convert to integer\n", - "print number_of_apples_int * 1.05 # multiply by price of apples" + "print((number_of_apples_int * 1.05)) # multiply by price of apples" ] }, { @@ -549,9 +475,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -573,9 +497,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "\n", @@ -585,23 +507,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Lesson01_Variables/MilestonesSOLUTION.py b/Lesson01_Variables/MilestonesSOLUTION.py index 7c4b4b2..6c1d8b2 100644 --- a/Lesson01_Variables/MilestonesSOLUTION.py +++ b/Lesson01_Variables/MilestonesSOLUTION.py @@ -1,4 +1,4 @@ -birth_year = input("What is the birth year for the milestones?") +birth_year = eval(input("What is the birth year for the milestones?")) # raw_input always gives strings, so convert to int birth_year = int(birth_year) @@ -7,6 +7,6 @@ drinking_year = birth_year + 21 president_year = birth_year + 35 -print("You are able to drive in " + str(driving_year)) -print("You are able to drink in " + str(drinking_year)) -print("You are able to run for president in " + str(president_year)) \ No newline at end of file +print(("You are able to drive in " + str(driving_year))) +print(("You are able to drink in " + str(drinking_year))) +print(("You are able to run for president in " + str(president_year))) \ No newline at end of file diff --git a/Lesson01_Variables/lesson01.ipynb b/Lesson01_Variables/lesson01.ipynb index 83c7d41..e2ede24 100644 --- a/Lesson01_Variables/lesson01.ipynb +++ b/Lesson01_Variables/lesson01.ipynb @@ -23,9 +23,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "type('I am amazing!')" @@ -34,9 +32,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "type(145)" @@ -45,9 +41,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "type(2.5)" @@ -65,9 +59,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "print(\"Hello World\")" @@ -84,9 +76,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -128,9 +118,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "print(number_of_whales)" @@ -147,9 +135,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -183,9 +169,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "1 + 2" @@ -194,9 +178,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "apples = 15\n", @@ -207,20 +189,16 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "print(3 * 2.1)" + "print((3 * 2.1))" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "number_of_whales ** 2" @@ -229,12 +207,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "print(5 / 2)" + "print((5 / 2))" ] }, { @@ -247,9 +223,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "number_of_whales = 8\n", @@ -268,9 +242,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -286,9 +258,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "2 * 3 + 4 / 2" @@ -297,9 +267,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "(2 * (3 + 4)) / 2" @@ -317,9 +285,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "5 % 2" @@ -336,9 +302,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -353,23 +317,19 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "print('Hello ' + 'Coding Circle')" + "print(('Hello ' + 'Coding Circle'))" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "print(\"The \" + WHALE + \" lives in the sea.\")" + "print((\"The \" + WHALE + \" lives in the sea.\"))" ] }, { @@ -382,12 +342,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "print(\"My name is\" + \"Charlotte\")" + "print((\"My name is\" + \"Charlotte\"))" ] }, { @@ -401,9 +359,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -421,12 +377,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "my_name = input()\n", + "my_name = eval(input())\n", "print(my_name)" ] }, @@ -444,13 +398,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "favorite_ocean_animal = input(\"What is your favorite sea creature?\\n\")\n", - "print(\"The \" + favorite_ocean_animal + \" is so cool!\")" + "favorite_ocean_animal = eval(input(\"What is your favorite sea creature?\\n\"))\n", + "print((\"The \" + favorite_ocean_animal + \" is so cool!\"))" ] }, { @@ -467,14 +419,12 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "number_of_apples = input(\"How many apples do you want?\\n\")\n", + "number_of_apples = eval(input(\"How many apples do you want?\\n\"))\n", "number_of_apples_int = int(number_of_apples)\n", - "print(number_of_apples_int * 1.05)" + "print((number_of_apples_int * 1.05))" ] }, { @@ -488,9 +438,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -507,15 +455,13 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Calculate the price of apples that a user wants\n", - "number_of_apples = input(\"How many apples do you want?\\n\") # Ask user for quantity of apples\n", + "number_of_apples = eval(input(\"How many apples do you want?\\n\")) # Ask user for quantity of apples\n", "number_of_apples_int = int(number_of_apples) # raw_input returns string, so convert to integer\n", - "print(number_of_apples_int * 1.05) # multiply by price of apples" + "print((number_of_apples_int * 1.05)) # multiply by price of apples" ] }, { @@ -529,9 +475,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -553,9 +497,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "\n", @@ -579,9 +521,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Lesson02_Conditionals/.ipynb_checkpoints/Conditional Execution-checkpoint.ipynb b/Lesson02_Conditionals/.ipynb_checkpoints/Conditional Execution-checkpoint.ipynb index e2a1fdd..29451f9 100644 --- a/Lesson02_Conditionals/.ipynb_checkpoints/Conditional Execution-checkpoint.ipynb +++ b/Lesson02_Conditionals/.ipynb_checkpoints/Conditional Execution-checkpoint.ipynb @@ -1,582 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Conditional Execution\n", - "\n", - "## Boolean Expressions\n", - "We introduce a new type, the boolean. A boolean can have one of two values: True or False\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "cleaned_room = True\n", - "took_out_trash = False\n", - "\n", - "print cleaned_room\n", - "print type(took_out_trash)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can compare values together and get a boolean result\n", - "\n", - "Operator Meaning\n", - "\n", - "**x == y ** x equal to y\n", - "\n", - "**x != y** x not equal to y\n", - "\n", - "**x \\> y ** x greater than y \n", - "\n", - "**x < y ** x less than y\n", - "\n", - "**x \\>= y** x greater than or equal to y\n", - "\n", - "**x <= y** x less than or equal to y\n", - "\n", - "**x is y** x is the same as y\n", - "\n", - "**x is not y** x is not the same as y\n", - "\n", - "By using the operators in an expression the result evaluates to a boolean. x and y can be any type of value\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print 5 == 6" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print \"Hello\" != \"Goodbye\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# You can compare to variables too\n", - "x = 5\n", - "\n", - "print 5 >= x" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print x is True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TRY IT\n", - "See if 5.0000001 is greater than 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conditional Execution\n", - "\n", - "We can write programs that change their behavior depending on the conditions.\n", - "\n", - "We use an if statement to run a block of code if a condition is true. It won't run if the condition is false.\n", - "\n", - " if (condition):\n", - " code_to_execute # if condition is true\n", - " \n", - "In python indentation matters. The code to execute must be indented (4 spaces is best, though I like tabs) more than the if condition." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# cleaned_room is true\n", - "if cleaned_room:\n", - " print \"Good girl! You can watch TV.\"\n", - " \n", - "# took_out_trash if false\n", - "if took_out_trash:\n", - " print \"Thank you!\"\n", - " \n", - "print took_out_trash" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can include more than one statement in the block of code in the if statement. You can tell python that this code should be part of the if statement by indenting it. This is called a 'block' of code\n", - "\n", - " if (condition):\n", - " statement1\n", - " statement2\n", - " statement3\n", - " \n", - "You can tell python that the statement is not part of the if block by dedenting it to the original level\n", - "\n", - " if (condition):\n", - " statement1\n", - " statement2\n", - " statement3 # statement3 will run even if condition is false" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# cleaned_room is true\n", - "if cleaned_room:\n", - " print \"Good job! You can watch TV.\"\n", - " print \"Or play outside\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# took_out_trash is false\n", - "if took_out_trash:\n", - " print \"Thank you!\"\n", - " print \"You are a good helper\"\n", - "print \"It is time for lunch\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In alternative execution, there are two possibilities. One that happens if the condition is true, and one that happens if it is false. It is not possible to have both execute.\n", - "\n", - "You use if/else syntax\n", - "\n", - " if (condition):\n", - " code_runs_if_true\n", - " else:\n", - " code_runs_if_false\n", - " \n", - "Again, note the colons and spacing. These are necessary in python." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "candies_taken = 4\n", - "\n", - "if (candies_taken < 3):\n", - " print 'Enjoy!'\n", - "else:\n", - " print 'Put some back'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Chained conditionals allow you to check several conditions. Only one block of code will ever run, though.\n", - "\n", - "To run a chained conditional, you use if/elif/else syntax. You can use as many elifs as you want.\n", - "\n", - " if (condition1):\n", - " run_this_code1\n", - " elif (condition2):\n", - " run_this_code2\n", - " elif (condition3):\n", - " run_this_code3\n", - " else:\n", - " run_this_code4\n", - " \n", - "You are not required to have an else block.\n", - " \n", - " if (condition1):\n", - " run_this_code1\n", - " elif (condition2):\n", - " run_this_code2\n", - " \n", - "Each condition is checked in order. If the first is false, the next is checked, and so on. If one of them is true, the corresponding branch executes, and the statement ends. Even if more than one condition is true, only the first true branch executes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "did_homework = True\n", - "took_out_trash = True\n", - "cleaned_room = False\n", - "allowance = 0\n", - "if (cleaned_room):\n", - " allowance = 10\n", - "elif (took_out_trash):\n", - " allowance = 5\n", - "elif (did_homework):\n", - " allowance = 4\n", - "else:\n", - " allowance = 2\n", - "print allowance" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TRY IT\n", - "Check if did_homework is true, if so, print out \"You can play a video game\", otherwise print out \"Go get your backpack\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Logical Operators\n", - "\n", - "Logical operators allow you to combine two or more booleans. They are **and**, **or**, **not**" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "and Truth table (only true if both values are true)\n", - "val1 | val2 | val1 and val2\n", - "true | true | true\n", - "true | false | false\n", - "false | true | false\n", - "false | false | false\n", - "\n", - "or Truth table (true if at least 1 value is true)\n", - "val1 | val2 | val1 or val2\n", - "true | true | true\n", - "true | false | true\n", - "false | true | true\n", - "false | false | false\n", - "\n", - "not Truth table (the opposite of the value)\n", - "val1 | not val1\n", - "true | false\n", - "false | true" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print True and True\n", - "\n", - "print False or True\n", - "\n", - "print not False" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can use the logical operators in if statements" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "cleaned_room = True\n", - "took_out_trash = False\n", - "if (cleaned_room and took_out_trash):\n", - " print \"Let's go to Chuck-E-Cheese's.\"\n", - "else:\n", - " print \"Get to work!\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "if (not did_homework):\n", - " print \"You're going to get a bad grade.\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TRY IT\n", - "Check if the room is clean or the trash is taken out and if so print \"Here is your allowance\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Nested Conditionals\n", - "You can nest conditional branches inside another. You just indent each level more.\n", - "\n", - " if (condition):\n", - " run_this\n", - " else:\n", - " if (condition2):\n", - " run_this2\n", - " else:\n", - " run_this3\n", - " \n", - "Avoid nesting too deep, it becomes difficult to read." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "allowance = 1\n", - "\n", - "if (allowance > 2):\n", - " if (allowance >= 8):\n", - " print \"Buy toys!\"\n", - " else:\n", - " print \"Buy candy!\"\n", - "else:\n", - " print \"Save it until I have enough to buy something good.\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Catching exceptions using try and except\n", - "\n", - "You can put code into a try/except block. If the code has an error in the try block, it will stop running and go to the except block. If there is no error, the try block completes and the except block never runs.\n", - "\n", - " try:\n", - " code\n", - " except:\n", - " code_runs_if_error" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "try:\n", - " print \"Before\"\n", - " y = 5/0\n", - " print \"After\"\n", - "except:\n", - " print \"I'm sorry, the universe doesn't work that way...\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This can be useful when evaluating a user's input, to make sure it is what you expected." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "inp = raw_input('Enter Fahrenheit Temperature:')\n", - "try:\n", - " fahr = float(inp)\n", - " cel = (fahr - 32.0) * 5.0 / 9.0\n", - " print cel\n", - "except:\n", - " print 'Please enter a number'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TRY IT\n", - "Try converting the string 'hi' into an integer. If there is an error, print \"What did you think would happen?\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Short-circuit evaluation of logical expressions\n", - "Python (and most other languages) are very lazy about logical expressions. As soon as it knows the value of the whole expression, it stops evaluating the expression.\n", - "\n", - " if (condition1 and condition2):\n", - " run_code\n", - " \n", - "In the above example, if condition1 is false then condition2 is never evaluated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "if ((1 < 2) or (5/0)):\n", - " print \"How did we do that?\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# PROJECT: Car selector\n", - "We are going to build an application that recommends a car based on the user's budget.\n", - "\n", - "1. Ask the user what their car buying budget is and store in a variable called budget.\n", - "2. Try to convert the budget into an integer and store back in the variable called budget.\n", - "3. If step 2 fails print out \"Please be realistic, you can't buy a car on rainbows and love.\"\n", - "4. If their budget is greater than 75000, tell them to buy a Tesla.\n", - "5. If their budget is less than 500 tell them they are better off riding the bus (it will be way more reliable than a $500 car)\n", - "6. Otherwise, tell them to buy a Toyota Corolla or something.\n", - "7. Regardless of what their budget is let them know they can get all their car shopping done at \"\\[your name here\\] Auto Depot.\"\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} +{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Conditional Execution\n", "\n", "## Boolean Expressions\n", "We introduce a new type, the boolean. A boolean can have one of two values: True or False\n"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["cleaned_room = True\n", "took_out_trash = False\n", "\n", "print(cleaned_room)\n", "print(type(took_out_trash))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["You can compare values together and get a boolean result\n", "\n", "Operator Meaning\n", "\n", "**x == y ** x equal to y\n", "\n", "**x != y** x not equal to y\n", "\n", "**x \\> y ** x greater than y \n", "\n", "**x < y ** x less than y\n", "\n", "**x \\>= y** x greater than or equal to y\n", "\n", "**x <= y** x less than or equal to y\n", "\n", "**x is y** x is the same as y\n", "\n", "**x is not y** x is not the same as y\n", "\n", "By using the operators in an expression the result evaluates to a boolean. x and y can be any type of value\n"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["print(5 == 6)"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["print(\"Hello\" != \"Goodbye\")"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["# You can compare to variables too\n", "x = 5\n", "\n", "print(5 >= x)"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["print(x is True)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### TRY IT\n", "See if 5.0000001 is greater than 5"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## Conditional Execution\n", "\n", "We can write programs that change their behavior depending on the conditions.\n", "\n", "We use an if statement to run a block of code if a condition is true. It won't run if the condition is false.\n", "\n", " if (condition):\n", " code_to_execute # if condition is true\n", " \n", "In python indentation matters. The code to execute must be indented (4 spaces is best, though I like tabs) more than the if condition."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["# cleaned_room is true\n", "if cleaned_room:\n", " print(\"Good girl! You can watch TV.\")\n", " \n", "# took_out_trash if false\n", "if took_out_trash:\n", " print(\"Thank you!\")\n", " \n", "print(took_out_trash)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["You can include more than one statement in the block of code in the if statement. You can tell python that this code should be part of the if statement by indenting it. This is called a 'block' of code\n", "\n", " if (condition):\n", " statement1\n", " statement2\n", " statement3\n", " \n", "You can tell python that the statement is not part of the if block by dedenting it to the original level\n", "\n", " if (condition):\n", " statement1\n", " statement2\n", " statement3 # statement3 will run even if condition is false"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["# cleaned_room is true\n", "if cleaned_room:\n", " print(\"Good job! You can watch TV.\")\n", " print(\"Or play outside\")"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["# took_out_trash is false\n", "if took_out_trash:\n", " print(\"Thank you!\")\n", " print(\"You are a good helper\")\n", "print(\"It is time for lunch\")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["In alternative execution, there are two possibilities. One that happens if the condition is true, and one that happens if it is false. It is not possible to have both execute.\n", "\n", "You use if/else syntax\n", "\n", " if (condition):\n", " code_runs_if_true\n", " else:\n", " code_runs_if_false\n", " \n", "Again, note the colons and spacing. These are necessary in python."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["candies_taken = 4\n", "\n", "if (candies_taken < 3):\n", " print('Enjoy!')\n", "else:\n", " print('Put some back')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Chained conditionals allow you to check several conditions. Only one block of code will ever run, though.\n", "\n", "To run a chained conditional, you use if/elif/else syntax. You can use as many elifs as you want.\n", "\n", " if (condition1):\n", " run_this_code1\n", " elif (condition2):\n", " run_this_code2\n", " elif (condition3):\n", " run_this_code3\n", " else:\n", " run_this_code4\n", " \n", "You are not required to have an else block.\n", " \n", " if (condition1):\n", " run_this_code1\n", " elif (condition2):\n", " run_this_code2\n", " \n", "Each condition is checked in order. If the first is false, the next is checked, and so on. If one of them is true, the corresponding branch executes, and the statement ends. Even if more than one condition is true, only the first true branch executes."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["did_homework = True\n", "took_out_trash = True\n", "cleaned_room = False\n", "allowance = 0\n", "if (cleaned_room):\n", " allowance = 10\n", "elif (took_out_trash):\n", " allowance = 5\n", "elif (did_homework):\n", " allowance = 4\n", "else:\n", " allowance = 2\n", "print(allowance)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### TRY IT\n", "Check if did_homework is true, if so, print out \"You can play a video game\", otherwise print out \"Go get your backpack\""]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## Logical Operators\n", "\n", "Logical operators allow you to combine two or more booleans. They are **and**, **or**, **not**"]}, {"cell_type": "raw", "metadata": {}, "source": ["and Truth table (only true if both values are true)\n", "val1 | val2 | val1 and val2\n", "true | true | true\n", "true | false | false\n", "false | true | false\n", "false | false | false\n", "\n", "or Truth table (true if at least 1 value is true)\n", "val1 | val2 | val1 or val2\n", "true | true | true\n", "true | false | true\n", "false | true | true\n", "false | false | false\n", "\n", "not Truth table (the opposite of the value)\n", "val1 | not val1\n", "true | false\n", "false | true"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["print(True and True)\n", "\n", "print(False or True)\n", "\n", "print(not False)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["You can use the logical operators in if statements"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["cleaned_room = True\n", "took_out_trash = False\n", "if (cleaned_room and took_out_trash):\n", " print(\"Let's go to Chuck-E-Cheese's.\")\n", "else:\n", " print(\"Get to work!\")"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": true}, "outputs": [], "source": ["if (not did_homework):\n", " print(\"You're going to get a bad grade.\")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### TRY IT\n", "Check if the room is clean or the trash is taken out and if so print \"Here is your allowance\""]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": true}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## Nested Conditionals\n", "You can nest conditional branches inside another. You just indent each level more.\n", "\n", " if (condition):\n", " run_this\n", " else:\n", " if (condition2):\n", " run_this2\n", " else:\n", " run_this3\n", " \n", "Avoid nesting too deep, it becomes difficult to read."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["allowance = 1\n", "\n", "if (allowance > 2):\n", " if (allowance >= 8):\n", " print(\"Buy toys!\")\n", " else:\n", " print(\"Buy candy!\")\n", "else:\n", " print(\"Save it until I have enough to buy something good.\")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Catching exceptions using try and except\n", "\n", "You can put code into a try/except block. If the code has an error in the try block, it will stop running and go to the except block. If there is no error, the try block completes and the except block never runs.\n", "\n", " try:\n", " code\n", " except:\n", " code_runs_if_error"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["try:\n", " print(\"Before\")\n", " y = 5/0\n", " print(\"After\")\n", "except:\n", " print(\"I'm sorry, the universe doesn't work that way...\")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["This can be useful when evaluating a user's input, to make sure it is what you expected."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["inp = input('Enter Fahrenheit Temperature:')\n", "try:\n", " fahr = float(inp)\n", " cel = (fahr - 32.0) * 5.0 / 9.0\n", " print(cel)\n", "except:\n", " print('Please enter a number')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### TRY IT\n", "Try converting the string 'hi' into an integer. If there is an error, print \"What did you think would happen?\""]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## Short-circuit evaluation of logical expressions\n", "Python (and most other languages) are very lazy about logical expressions. As soon as it knows the value of the whole expression, it stops evaluating the expression.\n", "\n", " if (condition1 and condition2):\n", " run_code\n", " \n", "In the above example, if condition1 is false then condition2 is never evaluated."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["if ((1 < 2) or (5/0)):\n", " print(\"How did we do that?\")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["# PROJECT: Car selector\n", "We are going to build an application that recommends a car based on the user's budget.\n", "\n", "1. Ask the user what their car buying budget is and store in a variable called budget.\n", "2. Try to convert the budget into an integer and store back in the variable called budget.\n", "3. If step 2 fails print out \"Please be realistic, you can't buy a car on rainbows and love.\"\n", "4. If their budget is greater than 75000, tell them to buy a Tesla.\n", "5. If their budget is less than 500 tell them they are better off riding the bus (it will be way more reliable than a $500 car)\n", "6. Otherwise, tell them to buy a Toyota Corolla or something.\n", "7. Regardless of what their budget is let them know they can get all their car shopping done at \"\\[your name here\\] Auto Depot.\"\n", "\n"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": true}, "outputs": [], "source": []}], "metadata": {"kernelspec": {"display_name": "Python 2", "language": "python", "name": "python2"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 2}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10"}}, "nbformat": 4, "nbformat_minor": 0} \ No newline at end of file diff --git a/Lesson02_Conditionals/CarRecommenderSOLUTION.py b/Lesson02_Conditionals/CarRecommenderSOLUTION.py index 883dcee..8d9826e 100644 --- a/Lesson02_Conditionals/CarRecommenderSOLUTION.py +++ b/Lesson02_Conditionals/CarRecommenderSOLUTION.py @@ -1,5 +1,5 @@ # Ask get budget for car from user -budget = input("What is your budget for a car?\n") +budget = eval(input("What is your budget for a car?\n")) try: # input returns a string and we need a number so convert to int budget = int(budget) diff --git a/Lesson02_Conditionals/Conditional Execution.ipynb b/Lesson02_Conditionals/Conditional Execution.ipynb index 6a71b74..03e838a 100644 --- a/Lesson02_Conditionals/Conditional Execution.ipynb +++ b/Lesson02_Conditionals/Conditional Execution.ipynb @@ -1,582 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Conditional Execution\n", - "\n", - "## Boolean Expressions\n", - "We introduce a new type, the boolean. A boolean can have one of two values: True or False\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "cleaned_room = True\n", - "took_out_trash = False\n", - "\n", - "print(cleaned_room)\n", - "print(type(took_out_trash))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can compare values together and get a boolean result\n", - "\n", - "Operator Meaning\n", - "\n", - "**x == y ** x equal to y\n", - "\n", - "**x != y** x not equal to y\n", - "\n", - "**x \\> y ** x greater than y \n", - "\n", - "**x < y ** x less than y\n", - "\n", - "**x \\>= y** x greater than or equal to y\n", - "\n", - "**x <= y** x less than or equal to y\n", - "\n", - "**x is y** x is the same as y\n", - "\n", - "**x is not y** x is not the same as y\n", - "\n", - "By using the operators in an expression the result evaluates to a boolean. x and y can be any type of value\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print(5 == 6)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print(\"Hello\" != \"Goodbye\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# You can compare to variables too\n", - "x = 5\n", - "\n", - "print(5 >= x)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print(x is True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TRY IT\n", - "See if 5.0000001 is greater than 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conditional Execution\n", - "\n", - "We can write programs that change their behavior depending on the conditions.\n", - "\n", - "We use an if statement to run a block of code if a condition is true. It won't run if the condition is false.\n", - "\n", - " if (condition):\n", - " code_to_execute # if condition is true\n", - " \n", - "In python indentation matters. The code to execute must be indented (4 spaces is best, though I like tabs) more than the if condition." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# cleaned_room is true\n", - "if cleaned_room:\n", - " print(\"Good girl! You can watch TV.\")\n", - " \n", - "# took_out_trash if false\n", - "if took_out_trash:\n", - " print(\"Thank you!\")\n", - " \n", - "print(took_out_trash)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can include more than one statement in the block of code in the if statement. You can tell python that this code should be part of the if statement by indenting it. This is called a 'block' of code\n", - "\n", - " if (condition):\n", - " statement1\n", - " statement2\n", - " statement3\n", - " \n", - "You can tell python that the statement is not part of the if block by dedenting it to the original level\n", - "\n", - " if (condition):\n", - " statement1\n", - " statement2\n", - " statement3 # statement3 will run even if condition is false" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# cleaned_room is true\n", - "if cleaned_room:\n", - " print(\"Good job! You can watch TV.\")\n", - " print(\"Or play outside\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# took_out_trash is false\n", - "if took_out_trash:\n", - " print(\"Thank you!\")\n", - " print(\"You are a good helper\")\n", - "print(\"It is time for lunch\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In alternative execution, there are two possibilities. One that happens if the condition is true, and one that happens if it is false. It is not possible to have both execute.\n", - "\n", - "You use if/else syntax\n", - "\n", - " if (condition):\n", - " code_runs_if_true\n", - " else:\n", - " code_runs_if_false\n", - " \n", - "Again, note the colons and spacing. These are necessary in python." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "candies_taken = 4\n", - "\n", - "if (candies_taken < 3):\n", - " print('Enjoy!')\n", - "else:\n", - " print('Put some back')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Chained conditionals allow you to check several conditions. Only one block of code will ever run, though.\n", - "\n", - "To run a chained conditional, you use if/elif/else syntax. You can use as many elifs as you want.\n", - "\n", - " if (condition1):\n", - " run_this_code1\n", - " elif (condition2):\n", - " run_this_code2\n", - " elif (condition3):\n", - " run_this_code3\n", - " else:\n", - " run_this_code4\n", - " \n", - "You are not required to have an else block.\n", - " \n", - " if (condition1):\n", - " run_this_code1\n", - " elif (condition2):\n", - " run_this_code2\n", - " \n", - "Each condition is checked in order. If the first is false, the next is checked, and so on. If one of them is true, the corresponding branch executes, and the statement ends. Even if more than one condition is true, only the first true branch executes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "did_homework = True\n", - "took_out_trash = True\n", - "cleaned_room = False\n", - "allowance = 0\n", - "if (cleaned_room):\n", - " allowance = 10\n", - "elif (took_out_trash):\n", - " allowance = 5\n", - "elif (did_homework):\n", - " allowance = 4\n", - "else:\n", - " allowance = 2\n", - "print(allowance)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TRY IT\n", - "Check if did_homework is true, if so, print out \"You can play a video game\", otherwise print out \"Go get your backpack\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Logical Operators\n", - "\n", - "Logical operators allow you to combine two or more booleans. They are **and**, **or**, **not**" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "and Truth table (only true if both values are true)\n", - "val1 | val2 | val1 and val2\n", - "true | true | true\n", - "true | false | false\n", - "false | true | false\n", - "false | false | false\n", - "\n", - "or Truth table (true if at least 1 value is true)\n", - "val1 | val2 | val1 or val2\n", - "true | true | true\n", - "true | false | true\n", - "false | true | true\n", - "false | false | false\n", - "\n", - "not Truth table (the opposite of the value)\n", - "val1 | not val1\n", - "true | false\n", - "false | true" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print(True and True)\n", - "\n", - "print(False or True)\n", - "\n", - "print(not False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can use the logical operators in if statements" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "cleaned_room = True\n", - "took_out_trash = False\n", - "if (cleaned_room and took_out_trash):\n", - " print(\"Let's go to Chuck-E-Cheese's.\")\n", - "else:\n", - " print(\"Get to work!\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "if (not did_homework):\n", - " print(\"You're going to get a bad grade.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TRY IT\n", - "Check if the room is clean or the trash is taken out and if so print \"Here is your allowance\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Nested Conditionals\n", - "You can nest conditional branches inside another. You just indent each level more.\n", - "\n", - " if (condition):\n", - " run_this\n", - " else:\n", - " if (condition2):\n", - " run_this2\n", - " else:\n", - " run_this3\n", - " \n", - "Avoid nesting too deep, it becomes difficult to read." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "allowance = 1\n", - "\n", - "if (allowance > 2):\n", - " if (allowance >= 8):\n", - " print(\"Buy toys!\")\n", - " else:\n", - " print(\"Buy candy!\")\n", - "else:\n", - " print(\"Save it until I have enough to buy something good.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Catching exceptions using try and except\n", - "\n", - "You can put code into a try/except block. If the code has an error in the try block, it will stop running and go to the except block. If there is no error, the try block completes and the except block never runs.\n", - "\n", - " try:\n", - " code\n", - " except:\n", - " code_runs_if_error" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "try:\n", - " print(\"Before\")\n", - " y = 5/0\n", - " print(\"After\")\n", - "except:\n", - " print(\"I'm sorry, the universe doesn't work that way...\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This can be useful when evaluating a user's input, to make sure it is what you expected." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "inp = input('Enter Fahrenheit Temperature:')\n", - "try:\n", - " fahr = float(inp)\n", - " cel = (fahr - 32.0) * 5.0 / 9.0\n", - " print(cel)\n", - "except:\n", - " print('Please enter a number')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TRY IT\n", - "Try converting the string 'hi' into an integer. If there is an error, print \"What did you think would happen?\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Short-circuit evaluation of logical expressions\n", - "Python (and most other languages) are very lazy about logical expressions. As soon as it knows the value of the whole expression, it stops evaluating the expression.\n", - "\n", - " if (condition1 and condition2):\n", - " run_code\n", - " \n", - "In the above example, if condition1 is false then condition2 is never evaluated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "if ((1 < 2) or (5/0)):\n", - " print(\"How did we do that?\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# PROJECT: Car selector\n", - "We are going to build an application that recommends a car based on the user's budget.\n", - "\n", - "1. Ask the user what their car buying budget is and store in a variable called budget.\n", - "2. Try to convert the budget into an integer and store back in the variable called budget.\n", - "3. If step 2 fails print out \"Please be realistic, you can't buy a car on rainbows and love.\"\n", - "4. If their budget is greater than 75000, tell them to buy a Tesla.\n", - "5. If their budget is less than 500 tell them they are better off riding the bus (it will be way more reliable than a $500 car)\n", - "6. Otherwise, tell them to buy a Toyota Corolla or something.\n", - "7. Regardless of what their budget is let them know they can get all their car shopping done at \"\\[your name here\\] Auto Depot.\"\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "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": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} +{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Conditional Execution\n", "\n", "## Boolean Expressions\n", "We introduce a new type, the boolean. A boolean can have one of two values: True or False\n"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["cleaned_room = True\n", "took_out_trash = False\n", "\n", "print(cleaned_room)\n", "print((type(took_out_trash)))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["You can compare values together and get a boolean result\n", "\n", "Operator Meaning\n", "\n", "**x == y ** x equal to y\n", "\n", "**x != y** x not equal to y\n", "\n", "**x \\> y ** x greater than y \n", "\n", "**x < y ** x less than y\n", "\n", "**x \\>= y** x greater than or equal to y\n", "\n", "**x <= y** x less than or equal to y\n", "\n", "**x is y** x is the same as y\n", "\n", "**x is not y** x is not the same as y\n", "\n", "By using the operators in an expression the result evaluates to a boolean. x and y can be any type of value\n"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["print((5 == 6))"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["print((\"Hello\" != \"Goodbye\"))"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["# You can compare to variables too\n", "x = 5\n", "\n", "print((5 >= x))"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["print((x is True))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### TRY IT\n", "See if 5.0000001 is greater than 5"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## Conditional Execution\n", "\n", "We can write programs that change their behavior depending on the conditions.\n", "\n", "We use an if statement to run a block of code if a condition is true. It won't run if the condition is false.\n", "\n", " if (condition):\n", " code_to_execute # if condition is true\n", " \n", "In python indentation matters. The code to execute must be indented (4 spaces is best, though I like tabs) more than the if condition."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["# cleaned_room is true\n", "if cleaned_room:\n", " print(\"Good girl! You can watch TV.\")\n", " \n", "# took_out_trash if false\n", "if took_out_trash:\n", " print(\"Thank you!\")\n", " \n", "print(took_out_trash)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["You can include more than one statement in the block of code in the if statement. You can tell python that this code should be part of the if statement by indenting it. This is called a 'block' of code\n", "\n", " if (condition):\n", " statement1\n", " statement2\n", " statement3\n", " \n", "You can tell python that the statement is not part of the if block by dedenting it to the original level\n", "\n", " if (condition):\n", " statement1\n", " statement2\n", " statement3 # statement3 will run even if condition is false"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["# cleaned_room is true\n", "if cleaned_room:\n", " print(\"Good job! You can watch TV.\")\n", " print(\"Or play outside\")"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["# took_out_trash is false\n", "if took_out_trash:\n", " print(\"Thank you!\")\n", " print(\"You are a good helper\")\n", "print(\"It is time for lunch\")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["In alternative execution, there are two possibilities. One that happens if the condition is true, and one that happens if it is false. It is not possible to have both execute.\n", "\n", "You use if/else syntax\n", "\n", " if (condition):\n", " code_runs_if_true\n", " else:\n", " code_runs_if_false\n", " \n", "Again, note the colons and spacing. These are necessary in python."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["candies_taken = 4\n", "\n", "if (candies_taken < 3):\n", " print('Enjoy!')\n", "else:\n", " print('Put some back')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Chained conditionals allow you to check several conditions. Only one block of code will ever run, though.\n", "\n", "To run a chained conditional, you use if/elif/else syntax. You can use as many elifs as you want.\n", "\n", " if (condition1):\n", " run_this_code1\n", " elif (condition2):\n", " run_this_code2\n", " elif (condition3):\n", " run_this_code3\n", " else:\n", " run_this_code4\n", " \n", "You are not required to have an else block.\n", " \n", " if (condition1):\n", " run_this_code1\n", " elif (condition2):\n", " run_this_code2\n", " \n", "Each condition is checked in order. If the first is false, the next is checked, and so on. If one of them is true, the corresponding branch executes, and the statement ends. Even if more than one condition is true, only the first true branch executes."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["did_homework = True\n", "took_out_trash = True\n", "cleaned_room = False\n", "allowance = 0\n", "if (cleaned_room):\n", " allowance = 10\n", "elif (took_out_trash):\n", " allowance = 5\n", "elif (did_homework):\n", " allowance = 4\n", "else:\n", " allowance = 2\n", "print(allowance)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### TRY IT\n", "Check if did_homework is true, if so, print out \"You can play a video game\", otherwise print out \"Go get your backpack\""]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## Logical Operators\n", "\n", "Logical operators allow you to combine two or more booleans. They are **and**, **or**, **not**"]}, {"cell_type": "raw", "metadata": {}, "source": ["and Truth table (only true if both values are true)\n", "val1 | val2 | val1 and val2\n", "true | true | true\n", "true | false | false\n", "false | true | false\n", "false | false | false\n", "\n", "or Truth table (true if at least 1 value is true)\n", "val1 | val2 | val1 or val2\n", "true | true | true\n", "true | false | true\n", "false | true | true\n", "false | false | false\n", "\n", "not Truth table (the opposite of the value)\n", "val1 | not val1\n", "true | false\n", "false | true"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["print((True and True))\n", "\n", "print((False or True))\n", "\n", "print((not False))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["You can use the logical operators in if statements"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["cleaned_room = True\n", "took_out_trash = False\n", "if (cleaned_room and took_out_trash):\n", " print(\"Let's go to Chuck-E-Cheese's.\")\n", "else:\n", " print(\"Get to work!\")"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": true}, "outputs": [], "source": ["if (not did_homework):\n", " print(\"You're going to get a bad grade.\")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### TRY IT\n", "Check if the room is clean or the trash is taken out and if so print \"Here is your allowance\""]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": true}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## Nested Conditionals\n", "You can nest conditional branches inside another. You just indent each level more.\n", "\n", " if (condition):\n", " run_this\n", " else:\n", " if (condition2):\n", " run_this2\n", " else:\n", " run_this3\n", " \n", "Avoid nesting too deep, it becomes difficult to read."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["allowance = 1\n", "\n", "if (allowance > 2):\n", " if (allowance >= 8):\n", " print(\"Buy toys!\")\n", " else:\n", " print(\"Buy candy!\")\n", "else:\n", " print(\"Save it until I have enough to buy something good.\")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Catching exceptions using try and except\n", "\n", "You can put code into a try/except block. If the code has an error in the try block, it will stop running and go to the except block. If there is no error, the try block completes and the except block never runs.\n", "\n", " try:\n", " code\n", " except:\n", " code_runs_if_error"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["try:\n", " print(\"Before\")\n", " y = 5/0\n", " print(\"After\")\n", "except:\n", " print(\"I'm sorry, the universe doesn't work that way...\")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["This can be useful when evaluating a user's input, to make sure it is what you expected."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["inp = eval(input('Enter Fahrenheit Temperature:'))\n", "try:\n", " fahr = float(inp)\n", " cel = (fahr - 32.0) * 5.0 / 9.0\n", " print(cel)\n", "except:\n", " print('Please enter a number')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### TRY IT\n", "Try converting the string 'hi' into an integer. If there is an error, print \"What did you think would happen?\""]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## Short-circuit evaluation of logical expressions\n", "Python (and most other languages) are very lazy about logical expressions. As soon as it knows the value of the whole expression, it stops evaluating the expression.\n", "\n", " if (condition1 and condition2):\n", " run_code\n", " \n", "In the above example, if condition1 is false then condition2 is never evaluated."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["if ((1 < 2) or (5/0)):\n", " print(\"How did we do that?\")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["# PROJECT: Car selector\n", "We are going to build an application that recommends a car based on the user's budget.\n", "\n", "1. Ask the user what their car buying budget is and store in a variable called budget.\n", "2. Try to convert the budget into an integer and store back in the variable called budget.\n", "3. If step 2 fails print out \"Please be realistic, you can't buy a car on rainbows and love.\"\n", "4. If their budget is greater than 75000, tell them to buy a Tesla.\n", "5. If their budget is less than 500 tell them they are better off riding the bus (it will be way more reliable than a $500 car)\n", "6. Otherwise, tell them to buy a Toyota Corolla or something.\n", "7. Regardless of what their budget is let them know they can get all their car shopping done at \"\\[your name here\\] Auto Depot.\"\n", "\n"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": true}, "outputs": [], "source": []}], "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": "3.5.2"}}, "nbformat": 4, "nbformat_minor": 0} \ No newline at end of file diff --git a/Lesson03_Functions/Functions.ipynb b/Lesson03_Functions/Functions.ipynb index 477a7b6..7990c00 100644 --- a/Lesson03_Functions/Functions.ipynb +++ b/Lesson03_Functions/Functions.ipynb @@ -24,16 +24,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "my_string = \"Howdy, welcome to my ranch?\"\n", "\n", - "print((len(my_string)))\n", - "print((max(my_string)))\n", - "print((min(my_string)))" + "print(len(my_string))\n", + "print(max(my_string))\n", + "print(min(my_string))" ] }, { @@ -49,9 +47,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -73,18 +69,16 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "print((int('5')))\n", - "print((float(5)))\n", - "print((str(5)))\n", - "print((bool(1)))\n", + "print(int('5'))\n", + "print(float(5))\n", + "print(str(5))\n", + "print(bool(1))\n", "\n", "# Uh-Oh\n", - "print((int('five')))" + "print(int('five'))" ] }, { @@ -98,9 +92,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -123,9 +115,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -177,9 +167,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -207,9 +195,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -245,9 +231,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -283,9 +267,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -323,9 +305,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# lets call our new function\n", @@ -342,9 +322,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def cowgirl():\n", @@ -367,9 +345,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -393,9 +369,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def feed_animal(type_of_feed):\n", @@ -423,9 +397,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -451,9 +423,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def cost_of_feed(type_of_feed):\n", @@ -492,9 +462,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -551,9 +519,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Lesson03_Functions/yahtzeeSOLUTION.py b/Lesson03_Functions/yahtzeeSOLUTION.py index 9df8865..edb3282 100644 --- a/Lesson03_Functions/yahtzeeSOLUTION.py +++ b/Lesson03_Functions/yahtzeeSOLUTION.py @@ -18,14 +18,14 @@ def yahtzee(): # Calls yahtzee 10 times -print(yahtzee()) -print(yahtzee()) -print(yahtzee()) -print(yahtzee()) -print(yahtzee()) -print(yahtzee()) -print(yahtzee()) -print(yahtzee()) -print(yahtzee()) -print(yahtzee()) +print((yahtzee())) +print((yahtzee())) +print((yahtzee())) +print((yahtzee())) +print((yahtzee())) +print((yahtzee())) +print((yahtzee())) +print((yahtzee())) +print((yahtzee())) +print((yahtzee())) diff --git a/Lesson04_Iteration/Iterations.ipynb b/Lesson04_Iteration/Iterations.ipynb index b037b18..3bbd079 100644 --- a/Lesson04_Iteration/Iterations.ipynb +++ b/Lesson04_Iteration/Iterations.ipynb @@ -24,9 +24,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "count = 5\n", @@ -51,9 +49,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "print(count)\n", @@ -74,9 +70,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "x = 5\n", @@ -102,9 +96,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "feet_of_snow = 0\n", @@ -125,9 +117,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -144,9 +134,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "snowflakes = 0\n", @@ -160,9 +148,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# The other common infinite loop is forgetting to update to a counting variable\n", @@ -202,9 +188,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "weathers = ['rain', 'sun', 'snow']\n", @@ -225,9 +209,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "print(weathers[0])\n", @@ -246,9 +228,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "print(len(tens))" @@ -265,9 +245,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -286,13 +264,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "while True:\n", - " user_message = input('> ')\n", + " user_message = eval(input('> '))\n", " # Leave when the user talks about snow.\n", " if user_message == 'snow':\n", " print(\"I don't want to hear about that.\")\n", @@ -303,9 +279,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "stock_up = ['eggs', 'milk', 'bread', 'puppies', 'cereal', 'toilet paper']\n", @@ -336,9 +310,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Let's rewrite the stock up loop as a for loop (we'll add the puppies in later)\n", @@ -352,9 +324,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Let's add the puppies condition just to show that break and continue work with for loops too\n", @@ -378,9 +348,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "hourly_snow_fall = [4, 3, 2, 4, 1, 4, 3, 2, 1, 1, 1, 0]\n", @@ -396,9 +364,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Finding the average\n", @@ -419,9 +385,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Finding the max\n", @@ -443,9 +407,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -461,22 +423,22 @@ "metadata": {}, "source": [ "## Range\n", - "Python has a built in function called `range()` that generates a list of integers in order\n", + "Python has a built in function called `range()` that returns a generator of integers in order\n", "\n", " # If you only include 1 parameter it starts at zero and goes to that number - 1\n", " range(5) #-> [0, 1, 2, 3, 4]\n", " # If you include 2 parameters it goes from [start, stop)\n", " range(2,5) #-> [2,3,4]\n", " # If you include 3 parameters it goes from [start, stop) and skips by the third number\n", - " range(1,10, 2) #-> [1,3,5,7,9]" + " range(1,10, 2) #-> [1,3,5,7,9]\n", + " \n", + "What is a generator? Well it is a list that only stores one item at a time. We can just make it a list by using type casting `list(generator)`. You can use generators without casting them to lists when you are in a loop." ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "cumulative_snowfall = list(range(1, 15))\n", @@ -496,9 +458,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "for inches in range(1,7):\n", @@ -521,9 +481,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -559,9 +517,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] } @@ -582,9 +538,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Lesson04_Iteration/wandererSOLUTION.py b/Lesson04_Iteration/wandererSOLUTION.py index f860007..a303d70 100644 --- a/Lesson04_Iteration/wandererSOLUTION.py +++ b/Lesson04_Iteration/wandererSOLUTION.py @@ -4,7 +4,7 @@ treasure_x = 5 treasure_y = 1 while True: - direction = input("Which direction would you like to travel in? (N, S, E, or W. done to quit)") + direction = eval(input("Which direction would you like to travel in? (N, S, E, or W. done to quit)")) if direction == 'E': wanderer_x += 1 elif direction == 'W': @@ -18,4 +18,4 @@ if (wanderer_y == treasure_y and wanderer_x == treasure_x): print("Congrats you found a gold necklace!") break -print("You walked to x: " + str(wanderer_x) + " y: " + str(wanderer_y)) +print(("You walked to x: " + str(wanderer_x) + " y: " + str(wanderer_y))) diff --git a/Lesson05_NumpyAndMatplotlib_part1/NumpyMatplotlib_part1.ipynb b/Lesson05_NumpyAndMatplotlib_part1/NumpyMatplotlib_part1.ipynb index 15f89d5..cf0a540 100644 --- a/Lesson05_NumpyAndMatplotlib_part1/NumpyMatplotlib_part1.ipynb +++ b/Lesson05_NumpyAndMatplotlib_part1/NumpyMatplotlib_part1.ipynb @@ -1,756 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introducing NumPy and plotting with matplotlib – part 1\n", - "\n", - "Adapted from Scientific Python: Part 1 (lessons/thw-numpy/numpy.ipynb) and Lesson14." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "## 1. Introducing NumPy\n", - "NumPy is a Python package implementing efficient collections of specific types of data (generally numerical), similar to the standard array module (but with many more features). NumPy arrays differ from lists and tuples in that the data is contiguous in memory. A Python list, [0, 1, 2], in contrast, is actually an array of pointers to Python objects representing each number. This allows NumPy arrays to be considerably faster for numerical operations than Python lists/tuples." - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# by convention, we typically import numpy as the alias np\n", - "import numpy as np " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's see what numpy can do." - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "np?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Type \"np.\" and hit \"tab\".\n", - "You can learn more about a specific function by adding the question mark." - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [], - "source": [ - "np.tan?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can try out some of those constants and functions:" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.41421356237\n", - "3.141592653589793\n", - "-1.0\n" - ] - } - ], - "source": [ - "print(np.sqrt(2))\n", - "print(np.pi) # a constant\n", - "print(np.cos(np.pi))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TRY IT\n", - "Find the square root of pi using numpy functions and constants" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.77245385091\n" - ] - } - ], - "source": [ - "print(np.sqrt(np.pi))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating Arrays (part 1)\n", - "There are many other ways to create NumPy arrays, such as np.identity, np.zeros, np.zeros_like, np.ones, np.ones_like\n", - "\n", - "This topic will be covered in more depth in a later lesson." - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2 rows, 3 columns of zeros:\n", - " [[ 0. 0. 0.]\n", - " [ 0. 0. 0.]]\n", - "4x4 identity matrix:\n", - " [[ 1. 0. 0. 0.]\n", - " [ 0. 1. 0. 0.]\n", - " [ 0. 0. 1. 0.]\n", - " [ 0. 0. 0. 1.]]\n", - "[0, 1, 4, 9, 16]\n", - "a:\n", - " [ 0 1 4 9 16]\n", - "b:\n", - " [0 0 0 0 0]\n" - ] - } - ], - "source": [ - "print('2 rows, 3 columns of zeros:\\n', np.zeros((2,3))) \n", - "print('4x4 identity matrix:\\n', np.identity(4))\n", - "squared = []\n", - "for x in range(5):\n", - " squared.append(x**2)\n", - "print(squared)\n", - "a = np.array(squared)\n", - "b = np.zeros_like(a)\n", - "\n", - "print('a:\\n', a)\n", - "print('b:\\n', b)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These arrays have attributes, like `.ndim` and `.shape` that tell us about the number and length of the dimensions.\n", - "\n", - "The dimension of an array is the number of indices needed to select an element. Thus, if the array is seen as a function on a set of possible index combinations, it is the dimension of the space of which its domain is a discrete subset. Thus a one-dimensional array is a list of data, a two-dimensional array a rectangle of data, a three-dimensional array a block of data, etc.\n", - "\n", - "The shape is the number of elements in each dimension of data" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "number of dimensions of c: 2\n", - "length of c in each dimension: (15, 30)\n", - "number of dimensions of x: 3\n", - "length of x in each dimension: (2, 3, 3)\n" - ] - } - ], - "source": [ - "c = np.ones((15, 30))\n", - "print('number of dimensions of c:', c.ndim) \n", - "print('length of c in each dimension:', c.shape)\n", - "\n", - "x = np.array([[[1,2,3],[4,5,6],[7,8,9]] , [[0,0,0],[0,0,0],[0,0,0]]])\n", - "print('number of dimensions of x:', x.ndim) \n", - "print('length of x in each dimension:', x.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "NumPy has its own `range()` function, `np.arange()` (stands for array-range), that is more efficient for building larger arrays. It functions in much the same way as `range()`.\n", - "\n", - "NumPy also has `linspace()` and `logspace()`, that can generate equally spaced samples between a start-point and an end-point. Find out more with `np.linspace?`." - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Arange\n", - "[0 1 2 3 4 5 6 7 8 9]\n", - "Linspace\n", - "[ 5. 5.5 6. 6.5 7. 7.5 8. 8.5 9. 9.5 10. ]\n", - "Logspace\n", - "[ 1. 1.77827941 3.16227766 5.62341325 10. ]\n", - "[ 1. 1.18920712 1.41421356 1.68179283 2. ]\n" - ] - } - ], - "source": [ - "print(\"Arange\")\n", - "print(np.arange(10))\n", - "\n", - "# Args: start, stop, number of elements\n", - "print(\"Linspace\")\n", - "print(np.linspace(5, 10, 11))\n", - "\n", - "# logspace can also take a base argument, by default it is 10\n", - "print(\"Logspace\")\n", - "print(np.logspace(0, 1, 5))\n", - "print(np.logspace(0, 1, 5, base=2))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Plotting with matplotlib\n", - "### 2.1. Getting Started\n", - "#### What is matplotlib?\n", - "Matplotlib is the most popular and mature library for plotting data using Python. It has all of the functionality you would expect, including the ability to control the formatting of plots and figures at a very fine level.\n", - "\n", - "The official matplotlib documentation is at http://matplotlib.org/ \n", - "The matplotlib gallery is at http://matplotlib.org/gallery.html\n", - "\n", - "#### Importing matplotlib\n", - "Matplotlib is often used through 'pyplot', which provides a high-level interface forplotting." - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In IPython or the IPython notebook, it's easiest to use the pylab magic, which imports matplotlib, numpy, and scipy.\n", - "\n", - "The matplotlib notebook flag means that plots will be shown interactively in the notebooks, rather than in pop-up windows." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "%matplotlib notebook" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you don't want to use the matplotlib notebook magic, it is still useful to use the inline magic, which makes sure that matplotlib plots are shown inside the notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2. Creating Figures\n", - "There are two major challenges with creating figures. First is understanding the syntax to actually make the basic plot appear. Second is formatting the basic plot to look exactly how you would like it to look. In general, the formatting will probably take you longer...\n", - "\n", - "Within pyplot (currently imported as 'plt'), there are two basic ways to go about making plots - using the Matlab-like clone, and using the object-oriented approach. The latter provides better control over plot features, while only requiring slightly more typing. It's easy to quickly outgrow the Matlab clone, so we'll go right to the object-oriented syntax." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### A first plot\n", - "In simple matplotlib plotting, there are two concepts to distinguish:\n", - "- __Figure__ - the entire figure, like what you might see in a journal, including all subplots, axes, lines, labels, etc. The whole enchilada. \n", - " \n", - "- __Subplot/Axes__ - one of the sub-sections of the figure, labeled (a), (b), etc. in articles. Each subplot will contain one Axes object, which is the container where all of the useful stuff, such as actual lines, legends, labels, etc., are actually housed.\n", - "\n", - "For example, here's how to make one figure with two subplots, the second of which contains two lines." - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# First we make some data to plot\n", - "numPts = 100\n", - "t = np.linspace(-2*np.pi, 2*np.pi,numPts)\n", - "y1 = np.sin(t)\n", - "y2 = np.cos(t)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, create an empty figure with 2 subplots using the subplots method\n", - "\n", - "`figure, axes = plt.subplots(rows, columns)`\n", - " \n", - "- The arguments (1, 2) indicate 1 row and 2 cols\n", - "- The function plt.subplots returns an object for the figure and for each axes\n", - "- There are multiple ways to accomplish this same goal, but this is probably the simplest - notice that each subplot is associated with one of the axes objects.\n", - "\n", - "Now let's actually plot the data using the plot method on an axis\n", - "\n", - "`axis.plot(x, y)`" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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fK6tLKMrPrGlWxgovJZX5taMiIhKbRNSobQeOufsJdx8CngDuG1fmPuDvPew1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\nGSsTJNyuXbsumNnpOE9TBVxIRDwJkmr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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "myfirstfig, axs = plt.subplots(1,2, figsize=(10,4))\n", - "\n", - "# We plot one line on the first axis\n", - "axs[0].plot(t, y1);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can plot multiple lines on an axis" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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AWxH1vkIayos4mU1ymsETgDQ8ZQ5EG2UrePXaFPXeQtvCXSZ37agklkjSPRS2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\nkmLsXdZEG2WaD7A4NZbrl6bxy9I6xvxo8Dsw4uLpI03cvCqD+CgdFG4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N7XSYzfQXaB0G\nzO/ty98BOwBEZCkQgYUNUJVSJ5RSC5VS+UqpfIyLYUOwFdpUiMgtGO7kO5RSgxaJcQhYIiKLRSQC\nuBd4xiJZABDjzvNT4IxS6jtWygKglPqaUirbe+3cC7xmsTLDe+02iMgy71s7gdMWihRq2EaHaf01\nJbbSYXbTX6B1mI+Q9pRNwSPAIyJyEhgF7rdwFWVn/gOIBF7xroAPKKX+1EwBlFIuEfki8BLgBB5R\nSp0yU4Zx2AZ8CjghIke97/2tUup5C2WyI18CHvPeiKqBz1osTyihddjUWK6/wJY6TOuv6WOqDtMV\n/TUajUaj0WhswHzevtRoNBqNRqOxDdoo02g0Go1Go7EB2ijTaDQajUajsQHaKNNoNBqNRqOxAdoo\n02g0Go1Go7EB2ijTaDQajUajsQHaKNNoNBqNRqOxAdoo02g0Go1Go7EB/x/p08GJmH7MRAAAAABJ\nRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "execution_count": 106, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# plot both lines on the second axis\n", - "axs[1].plot(t, y1)\n", - "axs[1].plot(t, y2)\n", - "\n", - "myfig" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also iterate through the axes:" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "enumerate?" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Q/CNdR5+erELEDgp11D0dZ9a8PerVUsN3MnVACLDRt8J52Zqr7UfWKsoOkHWQ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WXweHlBYat9Xl7MUK/ks0nXw6fsT1JIE7Uuj4pNcqHL7FP08BmNWCZGHgbWZ3R+ph4wZfC6LodP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\n40bY2L8kMETAiOEjAKJNx2BSJauFlXlaD44FaD188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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "coeffs = [3, 5, 10]\n", - "\n", - "numPts = 1000\n", - "t = np.linspace(-2*np.pi, 2*np.pi,numPts)\n", - "\n", - "myfig, axs = plt.subplots(1,3, figsize=(15,4))\n", - "\n", - "for i, a in enumerate(coeffs):\n", - " axs[i].plot(t, np.sin(a*t))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.3. Formatting figures\n", - "\n", - "The formatting of figures often takes longer than actually setting them up and adding data. There are many different approaches to formatting figures in matplotlib (many goals can be accomplished in different ways, using different commands), and you will come across many of these as you learn more. The tips below give a few simple ways to get started.\n", - "\n", - "#### Line formatting\n", - "The plot method has several available keyword arguments that you can use to change the line formatting.\n", - "- color - Chages color of line. examples: 'red', 'blue', 'r', 'k', 0.5, '#ffaa00', (0,0.5,0.75)\n", - "- linewidth - Weight of line. Takes float value in points (like font)\n", - "- linestyle - Solid, dashed, or other. examples: -, --, -." - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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zJTbcZplzKJthhUg6nU7TGmH7/f5ebQf+8Ic/8M4772Q8b1+SEwgEaGpqynhe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\nv//97yxZsiSjeaF/zC0tLWzatGnQ8yp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2h/E47ZoNzhWUZonwdSbShI2QY+X4WWmducKc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\ntBUBTB2V+rWaNlpeQ08t3EBFfSv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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mycol = ['steelblue','grey','tomato']\n", - "myline = ['-','-.','--']\n", - "\n", - "myfig, axs = plt.subplots(1,3, figsize=(15,4))\n", - "\n", - "for ind, a in enumerate(coeffs):\n", - " axs[ind].plot(t, np.sin(a*t), color = mycol[ind], linestyle = myline[ind])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Figure layout\n", - "Many of the basic formatting problems you have will be solved by the magic of tight_layout. Before you start tweaking how you figure looks, try it out.\n", - "\n", - "`plt.tight_layout()`\n", - "\n", - "#### Axis labels and limits\n", - "You can also change the axis limits, add axis labels and add legends. These operate on axis objects." - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "plt.legend?" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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uogTUrhLdggguLREjDn9b1VhyvK6JkSHWNj8jfgSMlrUN7GSr2yvrHVAY8Cld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\n3hq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1g5v8VJBD2fGTQ8kms+K/bt1tGfE0DkmsWLGCFStWRH3c5XKZQlS9//77/Pvf/476\n+IQJE5g5c2bUx2WhmWtGUyT079+fGTNmmKaMiLbDbxaxYLVadY1JzSJKcnNzOfrooykoiLxj1tTU\nxHfffcfBgwcTmsfovAUCAf71r3/x/fffJzSPNlcyWkicTqehcawZ88yfP59nn3223edJo53hyAwV\n+jNvhT88HNt4j1tOkh5eSL32JPzJcP88bEwTZ548hmKHwuyvt+qPqT4A38wX8n/Z4j1csbBhNcx5\nCbweXl+yA5c3wEVjIqRxdSqD86+Czt3liZLqKvjNBUKN0kx6GK2tlemlDCxWYfw56Ej5tWntLC6X\naAlZvUScQyM4MoXKw2IVbScnnim/zkBAXD+yvisu8btcvvV9dh5s4pO1BoaunbsJo0uAkePkiIUv\n34eX/yluuwS5EgioPLdwGyO6F7T0QQlfm3ae16+ELRIxpeNPg6v/IG5rLSRGmP8W/OZ8cc5k1S5h\nJKChSahJSHkLSRotUZBlZ2dVbG7a1anayY7TDLE6RQqMgmw76yskPijDoJ3b1BXZsRMu3YoNjKTa\nARo5pKqqdOHXTLwl+brNstuwKEoc162HwhQQb2mYjxkzZuhGY7rdblNMAY2MDp1OZ9S4y1jgcDia\nTS8TTZgwmgfaX4FRW1vLN998w4gRIygvb5v2axZREggEqKqqIjMzM+LrXVNTw7x58zjvvPOiqkFk\nYLVasVqtUc+b3++nqKgo4d8HxDnRU2CcddZZplxzl1xyie7jXq+XnJzE/xaNHDlStz3EDPPTNJKA\nUcdClxjjsj+fC6//Gx54UX73OLyQcksWrWPGw4hx4MzEabFwybE+HvlkA5sq6+lTEuUa3r0dnnkQ\nfn+/KEBtGcaxkeEExuZ18M4LeCefzTPfbGVMr6KWiRMa8gpg8lnidklnGD3e2M8iPIGjmVgwKELD\nlR6bf4APX4dzrhRz6iHgFwVrwC88N2SUNY5MQazE0nYy+ngoNVhLa/zjTyKB5ee/D84rc/1kCb8N\nVWVS5RK6dB7Fswu2ctKgSKnzQVz6q9Dtsy6XW9u2DbB0gYgSDpIrX2yoZMv+Bh65IIrnjyMrdM5e\neVIYtP7qz/rzdO0Zah2aeYscQaWRXa6m+N53zz0MPyyHe58zHpcA0gqMDob8bEdcLSTZDht2W3J7\n85tVAjH4NLi9fpo8/tR4YATbc2Lpm9aK7GT7i2Q5bFiUeMghD/kpaiHxBVQa3fKpAZpiI9nkkKIo\n5GZmxEUOaXG8aRza0CMvQBTiZhRFRj4By5cv11WCyMLI9PKjjz7SVYLIorS0lGOPPTZqAWwWseDx\neFixYgXV1ZEJZ7OIEr/fz+OPPx5V+WCW+SnoR9BmZGRw/vnnM3DgwHadB6Bnz54RSSGzYZYyolu3\nbgwYMEB3njSBcQigz8CQnP/VWaFECT0oiELX7YKefeGIocZjtELd7QruOEtcGxl2kSRiscD2TVxS\n7sZutfDMNzpRneFKD1eTscIBQkWexyV+L6uVuWv2s6u6iauOj+zBQ8APFdtFSwgIksSIwAhXU7gk\nFRhWK5x8DvTuL9pwlnwtV+zeeCG8+iT06At/e174gBjB4Qy9riC3w3/uz0VLUeUeuOc3sE7CWNnr\nhdqDIi2m72ChZjGCpnLwerCpfi7t6mPBpgOsrqgxHgvi9ZKBMzOkjgmSBE9/vYXSXAenDo5C1Did\nor3D55NviTmwD5Z+I0gSZ2az54rh2iBoNitJYBR2Eq+9NdhSZWt/b7s0gdHBkJ9lp7bRSyDGIjsV\nioZspw2F2FQCqWrJALHT7w+o1LtiK7JznBlkWJP7VrEoCjnOjJjIIZfHh9vrpyAFRXYoAlj+Wqhu\n8KCQfBNPIEhgyJ9bVVWDyqG0AuPHgBUrVvDBBx9EfdztdpvmE+Dz+QhEkVQuXrzYlIQLIwKjpKSE\n7t1j3P2M8jwTJ06MGvs6cuRIrrzyyogGn7GgU6dO3HzzzfTvH9kB3iyiRGtTMVKUmFEgG3lTmAW9\neQKBAGvWrNH1lJDF999/z5w5c6I+bhaBUVtby5YtW6K+h8x6r6bRzqivE5J3n1ekKcjI38NJgrMu\nF4oAI+TmQVlXUAPyxdeBffD6U4IomPMSJf99nGkjuvDfJTupitZ27Ai1nTDlQvjd34znKS4Vcv7e\nA8DVhOrI5MmvtnB4aQ4TjiiNPMblgj/OhIWfClJBazvQgyOsAFVVYcZp1DqgKIIkGDhSviUGBEEU\na9StI1P8Xs3zxGD82VArYkuNEl8gSBK4hCrk5gdg1PHGY3r2E+axwd/p4h5Wcp02Hv9sY/Qxn74L\n9/1WxKfOPF0u7cSRKUiFgB/OvIRlPY7iqw37+dkxvbDbotQbk6bBw68Lsskj6bWxbhn8806oqYIP\n/wuLvjAeo3msuBsFMVHezXhM38Fw031Q1kW879o5gQTSBEaHQ36WnYAq4iVlkarCyhLcya6NqWgN\n7rqnsMiuicGrQZBDqflylJcZmx9KdSrJoeCc2usrg+oGN3lZdqxGuwntgLxMOzUxEG9NHj8eXyAl\n6pY0zMeuXbt0TTzNkqUbtVyYNY9RaseIESM46aSTEp5HVVUaGhqizpOTk8Nhhx0W8TEzYZYCQzO9\nNPL0MEuBEe28VVRUcP/997Nli86OryRGjx7N6NGjIz7mdrt5/fXXWb9+fcLz1NTUsG9f9FSI0tLS\nhGNhAVavXs3zzz/f7u+hNNoZyxbA3TcI74h4ZOmyGDAC7p4tZPOyvhkNtfDhG7B3Z3CMg5nH98bt\nCzDry81R1ham9Cgo1k/40ODMEkV0UQm4mvjc2Zu1u2uZeXxvLJYorbea2q2pQZgpLvjYeB6LRczV\n1AgnnA7/eFMoTIzgahRRr7EoI7R5Vi2Gh2+X87M46gQYMlr4WXTuLhJPjPDE3fCX6+QVJRBqVYkF\np5wLl9/YPC4vx8llR/fk/VV72LA3irFrbXUwHrdR3Jf5e9GscnDBCafz2BYLBVkZXHp0D50xWZCT\nK8gmWT8LjYxwNYlEkZUShqnhCozLb4Qrfms8Jhyy7+8EkSYwOhji3clOlblgQbajuc1CBs2pHqlo\ncwiqVGIiBVLk1wFBY8yYCIHUJKZAqA0ktnObmjhdEIRLLIauzcqhdAvJjwJ6ppeqquL1ek0rWkHf\n9NLMeaIVeYnGjWqorq7mgQceYM2aNREf37RpU9THYoGqqrz++uusXLky4uNutxtFUUzx+9AjFsxU\nYOi1drhcLhobGw1bm2QwcOBABg8eHHUNV199ddTHY8EJJ5zAlVdG3xG/5JJLGDduXMLzaK+x3muU\nVmAcAtCKYY9bFIcyxVc4gfGnq4UfRiwYfzocM9n4uBbtII3gzOLw0lzOHNaF5xZsZX99hPdt+NoW\nfylUJTJYsxQqthNwNfGAZRiHFWYybXjX6MdbrEHFQqN8qwrAtBkwNDKRGRV//Y3wL4jFmyIzW6xt\nX4UgMWQs0E4+ByZMES1Bd86CwyIkbrSG1Sbm0VI4pIr3oCHn1vVw+y+EckMWtgwRlVvahSuO7YXT\nZo2uwnBkCnVIXU3ovhGy8yC/CDwuVi9by8dr93HFMb3Icej4qFRshzeeDpKAki0krf0sZM5b2WEw\n7TJBtMlibwXc+jNY9m2awPiporkQjKVwbUxN8gSIQvBgHEV2KggXbc5YCJdUkkOF2Q4ORvrDGQU1\nKfKUgHB1S2yKkVSpWwqyHVTFcG5DZq7pL8o/BmgFaSSDTbOLVohOYJhVfPXo0YM77riDHj0i797M\nmjWL1157LeF5srOzOfXUU+naNfIX7sWLF/Pll18mPI+iKGzYsIHdu3dHfLyoqIj+/fubkhSj13Jh\npgJDRulhxjXX2NgYVRlhsVgoKyszxVwzWdDOfTQz3LQC4xCBVkjV1wkSQ0YRUFQKx54EOXnCl0Em\n3aCqEv72O1i9VJAXRlGgECq2XE0idjW4tl9N7Ivb5+fJLza1HZOdC398TDz/R2/CZ+8ZzwPw+F/g\n6w/5YNzlrKaA30zqF71lQENmUOUgW7SCaDcYOFLsuj//iNwYbR67XfhFyPgYZGYLdUgsqo1AII62\nk6A3RTAhRIpc6TMQjjxWRMju2SF3/Xz2Hvz2QvH6Xn0b9BtCUbadS4/uwTvLK1i7O0IcrXZt1x4M\nrdUIx0yGB18Gi5W/P/cxuTaVy8b11B9zYC+8/5r4/7f3hTxl9NCsFAoSGDLnrahEtEV1KoeH/iDU\nSUaw2aBytzgHY06AkccYj0kQaQKjgyFWBYY/oFKbwkKwMNsRI4GRwiI7O/YiuyaV5zbHwcEYyRZI\nTZHdTGDEtN7UqVsKsx3UNnrwS8Y9hc5t+ovyjwF6xIKZxaTePKqqmlZ8WSwWw1QVm5FDvgTsdjtj\nxoyhpCTyzszUqVO5+OKLE55HmytawT98+HDOO+88U+bRU0aYSWZlZWVF9QYxqyUG4JtvvmHWrFkR\nH6upqWHRokXU1UWRQseAdevW8fTTT+NytS1Eamtrefzxx/nhBwmfAwPoKTBUVU0rMA4VaG0CBytF\ngZQXObq4BTp3ExL2ks6i+JIhPSwWETFZuRv27BTFtREyg8/b1CD+BdfapySHaSO68vzCbeyoamw5\nxmqF7ocLciUWZYQzC19TIw9+spHDS8XzS63P1SiKdxmCAITnwf49QnWwRjKe2Zkl5jnpbPkEiTHj\nYdxkUSArllDSih6efxhu/zks/ATuu0mOzNDaQZyZ0OsIubaTsSfCpdeHnl/mNQr4oeZgG4+Na0/o\nQ54zg3veX9d2jEYKVB+QnyeIhT/s4WNHb67pm0F+pgFh5AhrO+k3GEq7GE+graWuRhCHmRLnLRAQ\nvjCN9eK9VCfRFpQdfN6mBjhpOhx/qvGYBJEmMDoYCmIssuuaPATU1BVWhTkOqutjIwQyrBYy7clN\nTIEwckiScPEHAkFyKHVFdr3Li9cfY5GdAsWII8OKM8Mae+tTCskhFfn3maZuSVXLSxrmQitIIxVf\nFouFYcOGRS3S45knUoHs8/lQVdWU4tjtdvPee++xeXPkfm0zi7x9+/ZRUxPZkd3pdJKbKxGjJwGj\nOFCzYKTAsNlsprR2nHXWWVFbLswkMIYMGcL06dMjtg3t3buX999/n9raCLuIMaKpqYkdO3ZEfA8p\nikJpaalpEcEQXcU0Y8YMhg2TSD5II7UILzj/9oJo75CBZtzY+jmM5qmrFkWyTNqJwxlqUZh5i1Av\nBPG7k4/AoijcNTdCa9zCj0XbhLtJnlhwZvLC3kw2VTZwUw8X1mjeF+E46zI4YYpoz8iWVE89/QDM\nulcU4rKqDU1NEQvGTYITzxTKDadT+DMYQSNK9u4Svh4yrYCaIefgUXDbI8J3RAaBgPD1ALnrRyv4\nF34MvzoHdu8AxPfq6088nC/XV/LF+sqWYwpLhIllbj6cfLac6WXFNgKP/JG752+iq7+WK4bEkA5S\ntU+s7+B+4zElnUXUb4/DxX2Z68fVBDfPEGoUn1eOOHRkCgKroV4oXmTTWBJA4lsyaZiKvBh3slNZ\ntIIoshs9PlxeP84MY1JCK1rNkP7GCrvNSpbdJl201jZ6UUld20BhjvjiVt3gpiTP+I9jTaNbEAn2\n1Lyt87Pt0q1PXn+Aepc3ZZ4ShcHX9GC9m6Ic4z/uqfQXScN86BVFOTk5TJs2rc3P40FBQQHjx4+n\nsLCwzWNmFq2KorB27VrKy8vp3bttHJ+ZSQ3PPPMMQ4YM4bTTTmvz2MKFCyksLIyaHhIL9IiF119/\nHZ/Px4UXXpjwPA6Hg4aGyF/ak7W7b6bqp7y8PGpMqpnzaOREJAIjNzeXc889N+E5wueJ9F5VFIVe\nvST659NIPYpK4Bc3w+ExRAV7PXDdWTB2orgvU4DaHaL1oXKP/BhFgcffitgy0Tk/k+tOPJz7P/yB\nrzZUclzfMGL7vf8IFUZTo/Su+z5HIQ8dLOM49nBy1QHgZONBmhxfJqJUgzMLqvaL3ylbklTWWkj+\nO1sUrhdcbTzG6xEEQVYO9OgnN092rpin9qAgTSwSG5qHD4TJ00UkqkXyM/l/n8Hs++G4U0PzGkFT\n4+ytEAqEsNf10qN78PzCbfx5zmrm/eq4UM3Tf1jotRkwQm5tHg8vbWhiVQ480rgQZ57Ea6utZdtG\n+Pw9uOFOkRKiB4cT+g0Rt5+cK9J5jKARXlVBoiZL4rwpiiA6qg/Ar8+Ds6+AU81RSUZDWoHRwZBh\ntZDjlC+yq1PoewBhRbakn0B1Y+raBiBYZMue2xQmpkCIOJH1wUilogHEeZInh1LX7gKh61a2Rae6\n0UOW3YZDgqRLo+NDrygyy/ASRCF3wgknUFzcdrfIzGLSbrfzu9/9jlGjRrV5LBAI4PP5TPMJ0CMW\nFi5caErbgDZPtF33bt26mRILq80T7fcZP348M2bMMGWetWvX8uqrr0a8vjweD4qimNLm09DQwKZN\nmyL+TslqjzITemqppqYmVq5caUpLTBrtDIcTjpogEhsevqN5Z1sXGXZR3HrccPREYS4og+wc4RMA\noYLUCLYMscO/6IvQ2CB+flwvehZnccfbq2hw+8LmyRXS/MZ60UpiAFVV+bNvMG7Vwl/qv0TJk9h1\nB2GQuTnGz9XMLKGmqK+RWhsg/CJOOx82rIJdW+XGfPhfuOliOP1CuOleuTEakbCvQn5tg46E82fC\ney/Dg7fKjXFmCQWG3Q6DR8spUXKCr8m+XS3XCjhsVu6aNpjNlQ08+smGtmNdTeKcS3yH2BVwcG/W\nOI7LOMiZ7vVy5Ip2TGXQG0qGnANY9LloJbJa5XxNrEHj2P3aPJLvoRHjQsoY2TEJIE1gdEDkZzmk\n2xxS6XsAQoEBSPtgpDJ5AoRSRdbEM5WxpABFOTGe28bUnluRmhIjOZTC9hyIhRxypyNUf0TQK77W\nrVvHXXfdxd69e9s8FitUVaWuro6mprZRbmb6K+jBzKJVex69RIhkzDN27FiOOcYck7ChQ4cyfvz4\niI/l5uZSVlZmyjyNjY1UVVXh8/naPKYpZMxQJm7bto0XX3yR6uq2fcvtQWBEIhbWrFnDfffdx4ED\nBxKeR49srKqq4s0334xq9ppGB8OG1bBiEaz6TuykyyArRxSeV/4Oeh8hN6b3AKHEAHkCY/5b8J9/\nwax7RN9/GBw2K/eePZRtVY3cNTcsySIrVxQ7IrRIAAAgAElEQVSsD7wEE6caTvHm0l3M9ZTwqyOL\n6eXaGyqWjfDef+Bfd4oo0cVfyY3R2kGycoQZowwGjxK/R32t/Nq0mM5YWk+05967S57AUFVBMO3a\nKtc6ASJyFISZ6a/vlGtvKS6B0ePFfHZH6DoK4vh+JZx75GE8+eVmvt8eNO2srxUpJ3deB9efLdQr\nOvAHVG75ZBcBReGvPWpQZtxgrKQA8Vo+/rYg80C+nejlf8KrT8IL/wipKoyQVwB1taI1pqhUbszl\nvxERuSDntZEg0gRGB0QsyR41qS4EYyyya1JcZIvYV/miFUhhwovWQiLp09DgTlkrEYi2jFiILOgI\nCoxD47pNw1w4HA4URYlYTBYVFTF27FhTkhpUVeWhhx7i22+/bfNYSUkJv/nNbyK2fMSDOXPmsGBB\n2yg/M1tVtOeJplwxs+VCT4Gh+YeYgT59+jB8+PCIj61evdo0RcmRRx7JNddcEzH61UziRzv/kc6d\ny+UyLX5Wj1hwuVy4XC7TYm6152yNsrIyrr322qjpO2l0MDz5V5jzkrgtu3ucnSMUDrHgl3+EyWfF\nNs/a7+HrD8XtCMXX2N7FXHV8H/6zaDvvrahoubaCYsNCfHNlPX98ZxVjehZxzYSgH4Fs8Z5XIIr2\nxV+FWmOMkJMvCv4b74HzfiE3xusRypjqqlDxbwStiL73RqGOkEG33jDlIuHL0FOy7WTbBvjlNBHT\nKdsSoxEl9ZE9myKiuAyuulWksESZ5/bTB9I538kvX1rKgXq3aO3Ys0MQMnaHoZHpo59s4KtNVdzh\nWki3PLswvJRpQVIUQeZp7weZ1g6A3ALYvA6+mCteY6kx+eL3v/mB2Nq+GoNEliy5kgDSHhgdEMW5\nTjbvkTPZqm7wYFEg18i9tp2gFaAyRbaqqlTVuZqVBalAca6D1TuqpI5NeZEdo0qgqt7N4eWSrHk7\noDjHSXWDG39ANTSmSnV7TmawHUSWwKiqc9OlKKudV5VGspCfn88dd9wRcce7rKzMtF13i8XCGWec\nEdGTwGq1kpcn+QVWAtEMFZOlwPB6vaaZkoJ+a8ff//53Bg0aFNGHI1Y0NTVRXV1NWVlZG7POBQsW\nkJWVxRFHSO78xomysrKoCSWxwihhRyPvzJon0jWn/cyMa8Fms3HWWWfRuXPniI+ZYbabRpKg9chr\nt2WQmQNLv4FrzoR7npE3byw7DC6+DsokUj6gJdERhfS4cXI/vttaxW9fW06XgkxGZucKOf+7L4pI\nyyi76FUNHn727Hc4M6w8NKkz1s+CxqKyBEb475wrOWboGMgvkjtWw8rv4J93Btcm+V2yMPj+27tL\ntAfJoHM3mBZja1522O8tS65o53f2A7D8fyIWVRZ9BorCPwLyszJ44pIjOftfC7j2paU8d8UYnJp/\niAG5Mm/lbh79dANnjzyMCzf7RFrM9o3CS0UGH74Bc/8jbsuSc7n5oInUZN93J58jWkliwfOPwJfv\ni9tpBcZPE0U5Dqpki9YGN/lZDiwpMMWEkEpAZr21TV58AZWi3MSdyeNFUY6T2iYvHp+xQ+7Bejc2\ni0KOMzXkkCNDmI7KFNn+gEp1gzul5FBRrpOAKpfyUtWsHEqtYkTWu+VAfWqJtzTMhaIoUYs4t9tN\nU1OTaTv8I0eOpEuXtnFne/bs4YsvvojYXhIPoikWzFZgRIsdNXue7OzsqM/ldrtN2d0HWLFiBbNm\nzYpYiM+YMYPp06ebMs/OnTuZPXs2+/bta/PY2LFjmTJliinzaOcsEvljptJDT4Gh/cysa2Ho0KER\niYo9e/awcOHCdvfhSMMkaEWx3SGf2nHUCaKA93rkC/45LwnzxglTIL+tgXLktYU9d5TC326zMOvS\nIynPd3Lls9+x/Mgz4cJrBIHRENmHpbLOzUVPfcvuGhezZozisIq1ogi973nhGSCDcAJDlljo1hsG\nDIeHb4d1yyXnCSNgZNtOwkmbWNpBag7GpqwJJy2kTUmzYcIZ4nYEtWVU3HK5IBXOviLqIYO75vO3\nc4ayaGsV17y4BFd2oeHa5q/Zyw2vfM/I7oXcNW0wyi0PCo+Xx/4iv7ZV34nzfPs/QNYzKTd4zSiK\nPLFw5LHiNbr9F20iZaNCIzzOvEQoWNoZaQKjA6I410mjx0eTx/gNV1Xnojg3dYVVhtVCbmaGXNFa\nJ74gFqdYgQFyqoYD9S6Kcp0pI4dAtDrIrLW6wU1AJaXkkPa6ypBZVXVunBlWshypE4EV5jiaiRQ9\neHx+6pq8FKfw3KZhPubNm8fy5W2/2H399dfcf//9ps1TWVlJZWXbvtOKigo+//xzvF79fllZRCMW\nMjMzGTlyZMQklHjnSYbSY+LEiVx//fVtfu73+/H7/abN07dvX84///yIxbbD4SAzU7LQMoDP52Pn\nzp1RE0/MgowCwwzYbDasVmvUa8EspQeI90okn4vt27fz0UcfRWwF+7FDUZRzFUVZrShKQFGUtu69\noeNOURTlB0VRNiqKcksy19gGWrHbe4CcHwGIiM6hY0QRJmNACMKDYP1K2BE5Vlp3bQAF0ZULxTkO\nXrjiKHKcNi58aRVv7bGgQqhIDMOyHdWc/a8FbD3QwFMzRnFkj8LQc9dUgWw8cwsCQ5Ik8Png+wXi\nPHgkCb6i4Dm45Ho4ZrLcmMJO4jWCiOcgIrwe+O2FIqb0m/lyY5xZode/R1+5MRYLXPxLQSrIElkg\niIFKYw+sqcO78tezhvDZD5Wca5nEDkteRBWOP6DyxBebuOqFxQzonMfTl48m0x4s9g9WyvlfaMgt\nEARQT8lzAKIFCcR1JEt61NfCNx+JyFbZGF7t9zj5nNCc7YiUExiKohQpijJfUZQNwf/bXGWKohyh\nKMqysH+1iqL8OvjY/ymKsivsscQ1pSlGcyFYJ1EI1rtTXlgVZssV2Vphm9IiOzj3ARkCo86dUrIF\n5P1QtHObyvVqr+uBurZfaFtDu25TEaeroTDbQXW9ceuTdm3/VBQYP5XP5B07dlBV1badTNulNuva\nfPfdd/nwww/b/HzkyJHccccd5OZK7iYZIBqBUVxczBlnnEGnTjF8SdJBZmambtuAtjPfXtDmMYtY\nKCoqon///hETQD7++GO2bNliyjx6ioUnn3yS9957z5R59AgMl8tlqmls586dI77eZhIlAHPnzuXT\nTz9t83OzVT+HGFYB04Evox2gKIoVeBw4FRgIXKgoSgwN7SajU5nYpb0hhh1ngDXfQ14MBWhxcPf3\nzutiGFMqCuSf/dawYOtenMUbV49jYGkWv1lp4eK8aXy0w8WBeje1Lm9zm8nZ/1qAP6Dy8i/GMr5f\nUEGkkRH/+JNUWgUAXXrA2BNFQV7StpUqIjwueOUJcbu0rQIwIvIKQLHIGz2CUNMceWxwHsl2nXBj\nTNnPCUWB0s4wfKyUYWoz6muFOkb2vIEwrVz1nVDWGODCMd3594xRbFFymVh4CXfmHM+KndXUu33s\nqXHx1vc7OeMfX3Pv++s4ZXA5r8wcS77W8v/l+0IdUyxpkqmtrXI3rFosP+bU86HvoNjm+e5L4Zuh\nKPJko/a+275Rfp4E0BE8MG4BPlFV9d4gO3wLcHP4Aaqq/gAMh+YP5F3AW2GH/F1V1QeStN52R3Mh\nWO+ia7F+v9KBOjf9urQ/06WHWIvslLY5NJNDMkW2i8OK27+PSw+F2Q627zeW2WmkQVEK1TiaukVG\ngXEgxcohEO1Pq3ccNDwudN3+ZBQYP4nP5Kuuuiriz10ul6lFeDTFAtDGcyHReSIVrX6/H4vFYhoh\n43Q68Xq9+P3+Fr4NZhMY27dv5+uvv2bKlCktvELMnsftdrN9+3Y6d+7cwrjV5/PxzTff4HA46NWr\nV8LzaOuN1DLUv39/CgrM+Tuu500xbdo0/H7j9klZXHnllRF/bmarCsDpp58esWXI7XZjsVhMiZ89\n1KCq6lrA6H09Btioqurm4LGvAFOBNe2+wEgYNxmGjBGyeVlsXgf794gIVln0GSD+7zdEfszwcfDP\nd6RVEaV5Tl69bDjP3Xo3T2SNYuaL37d43Jlh4fJxPfnVxL6hghWgvJv4v65GvjDMyoaf/178k0V4\n+ops8W6xCoJp3isw9VJ5DwSbDbr3EW0rsijrKnwzekiaeAIcPTk25QHAi/8Q/8uahQKUdYE1S6V/\nn0kDy5j/+4nc9/46nluxm9mPfdPi8R7FWTx+0UhOG1Le8v2qBoJri+F30pJ4NqwSqTEyKCqBmx+U\nN/AMn0d2DoBewTFvPQe/N0/FGg0d4VN/KnBC8PZzwOe0+rLcChOBTaqqbmvfZaUO2i660U62zx9I\nue8BiMJu/W5j854DHaKFJDYFxtAekoZR7YTCHAfLthpH0XWEIrsw24GCnALjQL2Lfp1TS7wV5Tio\nbfTgDwSw6nxpab5uU0y4JBE/6c/k9iAwamrauqAvW7aMyspKJk+WlOoaIJoHxrfffsvHH3/Mrbfe\naspOdb9+/SImtJhNLPh8Purq6tp4OWgEgFnzHDx4kJdffpnzzjuPAQMGNP/c7N9He55IxEK0GNd4\nYLFYopJm+fnJMXk2W+kRyUMGxLXgdKZWydfB0RXYEXZ/J3BUtIMVRZkJzATo3v3/27vz6LjqK8Hj\n31ulXaVdsizLsrxhG2Mwi00bDCEYE9Zm6aQJNEkgDIdJk3XiMElDT8/0nE4g6Qxpuic9TRYymQE6\nC4ExGSAMJu4EO2BjIAngBbzKlqzdUkkllUpS/eaPV1Wukqok2XpP7xW6n3N8rHoq6Xfr6emqfvf9\nlgX2R1NbP/VFNeMWLYcv/R0sPWvqX1O/EDY9CA1TXBgRpj60Pom/pJS7vvhJPlExh109Pva19TE8\nGmVBZTEXL62iNN36aXn5sOmhU5s2cDpE4IFHIBo9tcUYH3jEGrFwKl+zZCX8zXdPLb4vP2jdqa+Z\n4lobANf8+am1AfCpL8HqdbD8nKl/zcfuhsUrYHXGX5Vx6soK+Ydbz+Nv/vQstu3vpKVnkKI8P6vq\nyzh3fjm+dAvbX3o1lFXBqgumHtu5F8EX/uupvZ64SXZHSbFgKXzlm9bon6maMw/u+9ap/46fJi8U\nMGqNMfHJja3AZCt/3Ar865hjnxeRTwG7gE3GmLS3VR1PzjaJj8CY7E72idAQBlyfQlJZkk/Xe0MY\nYyZ8I9HdP0RRfg4Fee5ddqVFefh9Mmkne2h4lP6w++seVJUU0B8eZmh4lPzczH9Q4iNKKlwsDuX4\nfZQV50163Rpj6OobonKZuwWBqpICDNZ1WVOaeTi6F4pDM2xGcrLb+fjll18mHA5z3XXXpRy3u4CR\nqbBw8OBBjh07ZlsBo6CggKGhIaLRaMrIjoaGBi699FLbFr2cO3du2l1VVqxYwaZNm2yb2rF48eK0\no2TsLizE4x3b4be7nUwjI5K3n7VzlEy6AsbOnTupra21bdvRF198kXA4zI03pg7pHhoasvV3qKWl\nha6uLs4+O/WOejgctu168yIR2QKk6+E9YIzZbHd7xpjvAd8DWLNmjT2rGE+XyKndBY478zz7Y0nn\njFXkARdXw8VLp1iUODP9ts22i98RPxWnMopiOqrmnNqUhtNVVAwXXXFqX5NfABdtPK3mKovzuGH1\nFKfs+PzWlJhT4fNZa8I4TQRWrD71rzudwsppmpE1MERki4i8k+Zfyl89Yy37njFpikgecAPw86TD\n/wNYjDWc+Tjw3zJ9vTHme8aYNcaYNV7eeqs4P4f8HN+kneyuPvenZADUlBTEOvwTL6TV7YGdHHwi\n1uKNk3SyE2tKuHzXvTpWQOmc7FroH6KsKI9cv7vL2lQFCia9bgeGRhgaHqXK5YJA4twGJ463u38I\nnwhlLu6YYjcv5GS383FnZydNTU3jjg8ODtraKYoXFsayu1ASj3lsWwsWLGDDhg22dY4jkQjNzc3j\nOsh+v59AIGDbdqCZzNTICLvbEZG0hYVQKMRDDz3Erl2nMKd5Etdddx0XXjj+Te5LL73Ee++9Z1s7\nubm5aQtjCxcutGXaTdwf//jHtGuE2P075DXGmI3GmFVp/k21eNEMNCQ9nh87ppRSWW1GboUbYzKW\nskSkTUTqjDHHRaQOGL/H2EnXAG8aYxLLwyZ/LCLfB+xZCctFIkJlScHknezE0HaXO4Kxu9edwUFK\nCjPf5evqc3/BUbA62ZOtgXFyuou78daUnuxk11dmXg+lu9/9qURgjcaZ7Lr1ypSM6tKpFTC6+qzC\nm5u70dhNc3LmNSPsHv6en59PJBIZN0ItPvzdLoFAgLKyMiKRSEoBpr+/H5/PR1FRkS3ttLW18dhj\nj3H77bezdOnJIdr79u2jtbXVtukQoVCIJ598kvXr17Ny5cl1B+0uLMRHPoxdm8KJRUnTFTCcaOeM\nM9LPqb7vvvtsawNgw4YNaY9v3Hh6dy8zKSgoIBKJjBtd9EEvYNjgdeAMEVmEVbi4FfgLd0NSSqnp\nc30XEuBZ4I7Yx3cAE1WWb2PMUOXYG+y4m7FWZs56lYH8yUdgeGWUQOnURgl4YQQGxM/tVDvZbheH\n4p3sifdh7u4Lu7q7S9xURmCcHN3ijXPbMYV4vXDdzqBZkZMzDbO3e1h6pl0h7O58rVq1ii996Uvj\n1jnYvHkzTzzxhG3t1NTUcNttt1FXl7ow3KFDh3j99ddtaycnJ4eWlhZ6elLXV6qpqWHNmjW2/YxE\nJO2aEdlcwGhvb+fgwfFbSObl5c3Ijh1mqrsrTFGm3yG7R0tlExG5WUSOARcBz4nIi7Hj80TkeQBj\nzAjwOeBFYA/wM2PMu27FrJRSdvFCAeMh4EoReR/YGHuckoRjj4uBK4Gnx3z9t0TkbRH5I3A58B9m\nJmxnVZUUTLqNandf2BraXuSNaQ4dE9zJjsbWPXC70wpWwae7f2rFITd39YCpnVuwikdub/kK1vnq\nCQ0xGs38BtYro1tKCnLJz/FNWhzq8khxaAbNipxcWFjI0NBQyq4Mo6OjDA8POzK1Y2BgIOX4TN09\ntrudgoICli1bRnFx6oiwq6++mk2bNtnWTnxkxNgOf2NjI9ddd52tO0+km+bjRGGhtrZ23La5TrTz\n6quvsnlzat2xr6+PX/3qV7S3TzSg6tTs2LGDhx9+OKVgMTIywte//nVee+0129rJtAXtbB6BYYx5\nxhgz3xiTb4ypNcZcFTveYoy5Nul5zxtjlhljlhhjvu5exEopZR/XF/E0xnRhrWI/9ngLkJyEQ8C4\nLSGMMZ90NECXVAbyeX2STnZnbGi7P93qtjPIGl4/8VD8ntAQw6NR5pS5f7ekMlBAcHCYyMgoeTnp\n52t39YXJ9fsoSbeK9AwqyMshUJA74eiWyMgo3f1D1Hrk3EaN9fPOVKzq7PNGcUhEqC4tnHQKSVvv\noOu70cyk2ZKTkxdvjHfGjTF8+MMfZuHChba1E5+6kW6Kgp13j4PBIM8++ywXX3wxixefXIgtHA6n\nbEM6XcYY3nvvPcrLy6mtTV3f1c7dIDKNjIhEIvj9flvX2pipkRE33XTTuGNOtHPJJZewbl3q4nA9\nPT3s2LGDpUuXMmeOPYvnjY6OJnaKiY+SiEajrFu3Lu1Cr6cr0wiMe+65x/E1V5RSSnmPF0ZgqDRq\nSgsZjFg7YWTS3jvoiYJAjt9HRSCfzr7Md7Lbe603aV7oZNeUTb72QVvPILVlhZ7Ynq2mtGDCERjx\n1zGn3APntnTyESPtvQOUFuZS6OJuNHHVpQUTFof6w8MMDI144vdM2StePEguLOTk5HDZZZfZuitK\nXV0dN910E+XlJ7cNHhkZYWRkxNZOq9/vJxwOMzKSupiy3WttiAhPPfUUf/jDH1KO//rXv2bHjh22\ntQPpR0Y8++yz/PM//7Ot7RQWFqYtYPj9fltHeqTjRAGjqqpqXHEpfp3bWTRL9zuUl5fHxo0bbS0C\nZlpotaysLO2WvkoppT7YtIDhUbWxzmhbT+aiQJtHChgA1SUT38lu77VehxfirS2z7oi29U50bgcS\nPwO3VZcWTDjNIXFuJ9gKdKbMLY+d256BjM/x1nVbMGkhC7xx3Sp7pZvaMTw8TDAYTJlWMl0lJSWs\nXr06ZcqFE53W4uJi7r77bpYtW5Zy3Ilh9ulGLOzevZujR4863s6qVatYv3694+1s2LCBTZs22VrE\n3rlzJz/4wQ9SjsXbtXPh2K6uLnbt2kUkEkkcc6KAER9dlPw7NDIyQjgctnUdjHTtDA4O8tvf/paO\njg7b2lFKKZUdtIDhUYmOYG/6juBo1NDeO+ipTvZEd93jr8MLHcGTxaEJOtk9HutkTzBKoM1DxaF4\nDBMWh3oGqS23Z0eE6YqPwIhmeLPdEfTOuVX2Sje14/Dhw3znO9+hpaXFtnai0ShHjx7lxIkTiWND\nQ0P4fD7H5++PjIwwOjo6IwUMu3dvAauzPXbqzYoVKzj//PNtbWf9+vVce+21Kcd8Pp/tC0Tm5+cT\nCARSOvcDAwPk5OSk3Y70dB07doznnnuO/v7+lHbA+QLG+++/zze/+U3a2toyfZkt7QSDQbZu3aoF\nDKWUmoW0gOFR8akWmUZgdPeHGY2aRKHDbdY0h8GMd13aewcpyrfWc3BbTWkBPpGM5zY8PErvQMRD\nnexCekIRhobT3xVu7x1EOLmrhpuK8nMoLczNWBwyxtDW453RLTWlBYxGDScybP0aL8R4YeqTsldR\nUREVFRUpd9jnzJnD9ddfT1WVvWuePPbYYylTLqqqqvjrv/5rVq1aZWs7jz/+OFu2bEk8duKue/z7\nJRcwjDEMDg7atlVrXFFREaFQKOVYZ2fnuKLGdNXX19PY2Jhy7NVXX+XNN9+0tZ3Vq1dz6623plxz\nAwMDFBUV2TrSI93UjsHBQUTE1mJWppERyTE41U5tbS0PPPAAy5cvt60dpZRS2UELGB5VUphLYZ6f\n1gwdQa8Nba+rKGIwYnX802nvDXumE+j3+agpLch4bttjx70S77wK681bxnh7B6kI5GdckHSm1ZYX\n0ZqhONQ7EGFoJOqZc1tXYQ3rbzmR+dzm+n2UFTu/9aCaWeXl5XzhC19ImXJRVlbGBRdcYGtH3Ofz\n8YlPfIJzzz035biI2L7GTjAYpLu7O/E43vkfu2PIdI0dgREOh4lGo7a3U1RUNG73lu9///v85je/\nsbWd3t5e3n333ZT1Q/bu3cuBAwdsbSedJUuWsHbtWlu/Z/znkFz8iW85auc1l24UkxMFjJycHO66\n6y7OO++8ccd1EU+llJp9tIDhUSJCbVlRxlECbR7rZNfFOtkTdQS9UmwBaxpJpmkOibvuHhklED+3\nxyc4t165DsC6JjONwDg5osEbo1tOnttQ2s/HpxL5PLCYq3JeV1cXra2ttn/fJUuWpCziuX//fjZv\n3jxugcrpKiwsTOnwO1XAGDu1I96O3SMw5syZQ21tbWJk3/DwMJFIxPbXc+jQIZ566imCwWDi2Kc/\n/Wk+9rGP2dpOc3Mz3/72tzl8+HDi2Nlnn80ll1xiazsTFTDsVFBQgIikXHMDAwP4/X5bp8QANDQ0\npCzYefDgQV544YWUdT6UUkrNDlrA8LCpdLK9UhSYF7+T3Z2+I9jeO+CZWMEaJZCxOOSxTva8yolH\nCXhpUUw4ed2mm07ktZFDtbHiREt3dhTelL1+9rOf8corryQe/+53v+Pxxx+3vZ3Dhw+zf//+xONg\nMMiBAwdsv3scCARSOq0VFRVcccUVVFZW2tpOfGpH/HfcqULJmjVruPPOOxOjBuIdZbsLJcuWLeMz\nn/nMuO1m7R4hk5eXRygUSlmbIhQK2bpoLJw8P04XMERkXNHMiZEeYK2tsWfPnsTjpqYmdu7cqSMw\nlFJqFtIChofVlk98J7uiOJ/8XG/88a4tL0RIP0ogOBChPzzimfU6AOaWFdLVFyYyMv6NY1vPIDk+\nobLE3gXpTldpYS5F+Tlpi0Mjo1Haewc9dW5ry4uIjETpCY2/M5YYOeSR0S05fh+15YUZR7ccPxFi\nrkdiVfbz+/34fCf/DIZCIds74QDbtm1j69aticfnn38+X/7yl23forO4uDilc1xZWckll1xi+1aT\ngUAgMRoCThYWnDh3yZwqlBQVFVFbW5v4eYTDYX76059y8OBBW9tJNzLikUceSVm3xA55eXnk5uY6\nXsAAWL58ecqaMU61s3PnTrZt25Z4HAqFKCws1AKGUkrNQs5ucK6mZW55EaGhEYIDEUqLUufgt3SH\nEsPfvSAvx09NWWHaTvax2LH5Vc6+uT0VteVFGKxiRUN16pv75u4QcyuKPDNtQESYV1GUtpPd2jPA\naNQwv9o75zbe4W85EaIikFoEau4OUVqY64nFXOPqKorSXrfBgQjBwWHmV9nb+VPe8dGPfjTlcSgU\nsr2zD1bHtbOz0/bvO1YgECAcDjMyMkJOTg7BYBBjDGVlZba3A9Df309+fr5jhYXW1lZ+8YtfcP31\n19PY2OhYoWR4eJi33nqLhoYG6urq6OvrY+/evaxcudLWduIjE+LnyxjDlVdeSW1tra3tgHWOkkdG\n3H333baP9AC44YYbUh47VQS88cYbUwp+TrWjlFLK+3QEhoc1xDpOR7v6x32uqbOfBdXe6lhl6mQf\ni8Xf4KGOYLxoke7cHvXgua2rKM5wbuPFIe/EG/85x2NLdrQrNK5g5LZ5FUVpp+d4sfCmnOVUp6ik\npIS+vr7ElIvnnnuOl156yfZ2xt7h37p1Kz/84Q9tb2fJkiXccccdlJSUACQKJnZP7SgoKKCmpibR\ncXWqUALwwgsvJKb5OFUoEZGUnVVEhLVr17JgwQJb2wEr9uQRGD6fz/Z1KdLp7+93pAgYCARSdlDR\nAoZSSs1eWsDwsHgn+khHaic7OBihJxTx1F13iN3JTtcR7AyR4xPmVnhnKH5D7Nwd7Uw9tyOjUVq6\nQ54qtoDVyW7rGWA0Gk05Hi/AeKmTXVteRK7fx5GOvnGfO9rZ77kCRl1FMf3hYYKDqVNevFh4U/b6\n3e9+xyOPPJKyloNTBYxoNJroGDc1NblofgIAABhsSURBVKXsFmKX5JERYK0hcc011zjSzsKFC8nL\ns0YGrlu3jvvvv9/24fzl5eXccsst1NfXA84tFpqbm5tYn8LJdiB1ZMTQ0BCtra0MDw870k78dYTD\nYX75y19y7Ngx29t5+eWX+cd//EfAGlHiVAGjpaWFLVu2JHaKcWq0lFJKKe/TAoaHzSkvJD/HN66T\nHb+z7bWOVUN1gN6BCD2h1JX1j8Wmu/h93rncivNzqS4poGnMuW3tGWAkajzXyW6oDjASNTSPWWyy\nuStEWVEepYXe2ebT7xPmVxWPu26DAxF6ByIevG6tDmtTx/jfM7/HCm/Kfj09PQwNDTm2wwWQGKnQ\n12cV9ZwqlMQ7dPGOa319PWeeeabt7USjUd55552UHVvsXrQxWbzANDAwgM/nIz/f/vWJkgsLTo70\nSC4sNDU18eijjzqy881VV13FLbfcAlivZ9++fSm7rNilrq6O5cuXY4zBGMP69etZunSp7e20t7ez\nffv2xGvo7+93pMCklFLK+7zTo1Tj+ESYXxUY18mOdwy9Ns1hca21gvvBttQ770fa+zwXK1hFgbHn\nNv7YawWMxbVWB+hgW+ob0MMdfZ6LFaxrc9x12+X16zb13B5p76O+sthThTdlr/iuE319fY52WpML\nGPGRGE50vkpKSmhsbExMFThw4AA9PT22twPw9NNPs3v3bgBefPFFtm/f7kg7jz76KM8++yxgnb+S\nkhJHiiWBQCBRYAoGg4iIY0Wm5HaAcbuf2KGyspKKigoAqqqq+MpXvmL7mh4AK1eu5KqrrkJE8Pl8\nXHbZZSxZssT2duLnKBgMMjIywtDQkI7AUEqpWUrfmXtcY02AQ+2pHav9rb0U5Pqp9dDOE5C+Ixga\nGuZYd4ildfYuImeHxpoARzr6U6ZlHGgN4hPrc17SUB3A75OUczsajXKwNcjSufa/+Z2uxpoS2noG\nCYVPDo3e32rFvnBOiVthpVVdUkCgIHdcAeP91l7O8OB1q+yTXFjo7e0FnOlMJne+4mth2L2wZryd\nO++8k0WLFhGNRnniiSd48803bW/H5/Nx7733sn79esAaxeLE3X2wpnfEizDnnHMOl19+uSPtlJWV\nJdoJBoOUlpam7FBjl9LSUoLBINFolN7eXkQkcR3aqbu7m+3bt6cs5OmU4eFhRkZGCIfDKWu92Gns\n7xDgyHlTSinlfVrA8Ljl9eV09Q3RERxMHNvX3MuyeWX4fd7YJSOurCiPqpL8lI7ggVin1YsdweXz\nyhkaHuVw+8mRAvtaelhQXUJhnrc26MnL8bOgOsChpHN7tDPE0EjUm+e2vhwDvHe8N3FsX3MPlYF8\nakoLMn+hC0SExbUlKSOHuvvDdPUNebLwpuwT7wAFg8F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- "text/plain": [ - "" - ] - }, - "execution_count": 114, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "for i in range(3):\n", - " axs[i].set_xlabel('angle t [rad]', fontsize=13)\n", - " axs[i].set_ylabel('sin('+str(coeffs[i])+'*t)', fontsize=13)\n", - "\n", - "axs[2].set_ylim(-1.5, 1.5)\n", - "\n", - "axs[2].plot(t, np.cos(t))\n", - "axs[2].legend(['my legend', 'no line to match this'])\n", - "\n", - "myfig.tight_layout() # finally automatically format the whole figure\n", - "\n", - "myfig # necessary to show the figure below this cell" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Saving a figure\n", - "To save your figure you can use the savefig command:\n", - "\n", - "`fig.savefig('fileanme', format='png')`\n", - "\n", - "Format options include png, pdf, ps, eps and svg" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/Users/haberkernh/Documents/GitHub/CodingCirclePython/Lesson05_NumpyAndMatplotlib_part1'" - ] - }, - "execution_count": 121, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pwd" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "myfig.savefig('first_plot.jpg');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TRY IT\n", - "Create a line graph plotting the function f(x) = x^3 for values of x 0-10. Save it as a pdf." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.4. Other plot types\n", - "\n", - "So far we have only looked at line plots. Here are a few other plot types that can easily be created with matplotlib.\n", - "- Bar graph: ax.bar(x, y)\n", - "- Scatter plot: ax.scatter(x,y)\n", - "- Horizontal bar plot: ax.barh(x,y)\n", - "- Boxplot: ax.boxplot(x)\n", - "- Log-log plot: ax.loglog(x,y)\n", - "- Semilog plot: ax.semilogx(x,y), ax.semilogy(x,y)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [], - "source": [ - "# Some data\n", - "numPts = 100\n", - "t = np.linspace(-2*np.pi, 2*np.pi,numPts)\n", - "y1 = np.sin(t) + np.random.rand(1, numPts)\n", - "y2 = np.cos(t) + np.random.rand(1, numPts)" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "myscatter, scatterax = plt.subplots(1,1)\n", - "scatterax.scatter(t, y1, s=10, marker='o', alpha=0.5, color='grey');" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scatterax.scatter(t, y1, s=(y2 + 5), c=y2, cmap='rainbow')\n", - "myscatter" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "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": "3.6.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["## Introducing NumPy and plotting with matplotlib \u2013 part 1\n", "\n", "Adapted from Scientific Python: Part 1 (lessons/thw-numpy/numpy.ipynb) and Lesson14."]}, {"cell_type": "markdown", "metadata": {"collapsed": true}, "source": ["## 1. Introducing NumPy\n", "NumPy is a Python package implementing efficient collections of specific types of data (generally numerical), similar to the standard array module (but with many more features). NumPy arrays differ from lists and tuples in that the data is contiguous in memory. A Python list, [0, 1, 2], in contrast, is actually an array of pointers to Python objects representing each number. This allows NumPy arrays to be considerably faster for numerical operations than Python lists/tuples."]}, {"cell_type": "code", "execution_count": 86, "metadata": {"collapsed": true}, "outputs": [], "source": ["# by convention, we typically import numpy as the alias np\n", "import numpy as np "]}, {"cell_type": "markdown", "metadata": {}, "source": ["Let's see what numpy can do."]}, {"cell_type": "code", "execution_count": 87, "metadata": {"collapsed": true}, "outputs": [], "source": ["np?"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Type \"np.\" and hit \"tab\".\n", "You can learn more about a specific function by adding the question mark."]}, {"cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [], "source": ["np.tan?"]}, {"cell_type": "markdown", "metadata": {}, "source": ["We can try out some of those constants and functions:"]}, {"cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["1.41421356237\n", "3.141592653589793\n", "-1.0\n"]}], "source": ["print((np.sqrt(2)))\n", "print((np.pi)) # a constant\n", "print((np.cos(np.pi)))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### TRY IT\n", "Find the square root of pi using numpy functions and constants"]}, {"cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["1.77245385091\n"]}], "source": ["print((np.sqrt(np.pi)))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Creating Arrays (part 1)\n", "There are many other ways to create NumPy arrays, such as np.identity, np.zeros, np.zeros_like, np.ones, np.ones_like\n", "\n", "This topic will be covered in more depth in a later lesson."]}, {"cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["2 rows, 3 columns of zeros:\n", " [[ 0. 0. 0.]\n", " [ 0. 0. 0.]]\n", "4x4 identity matrix:\n", " [[ 1. 0. 0. 0.]\n", " [ 0. 1. 0. 0.]\n", " [ 0. 0. 1. 0.]\n", " [ 0. 0. 0. 1.]]\n", "[0, 1, 4, 9, 16]\n", "a:\n", " [ 0 1 4 9 16]\n", "b:\n", " [0 0 0 0 0]\n"]}], "source": ["print(('2 rows, 3 columns of zeros:\\n', np.zeros((2,3)))) \n", "print(('4x4 identity matrix:\\n', np.identity(4)))\n", "squared = []\n", "for x in range(5):\n", " squared.append(x**2)\n", "print(squared)\n", "a = np.array(squared)\n", "b = np.zeros_like(a)\n", "\n", "print(('a:\\n', a))\n", "print(('b:\\n', b))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["These arrays have attributes, like `.ndim` and `.shape` that tell us about the number and length of the dimensions.\n", "\n", "The dimension of an array is the number of indices needed to select an element. Thus, if the array is seen as a function on a set of possible index combinations, it is the dimension of the space of which its domain is a discrete subset. Thus a one-dimensional array is a list of data, a two-dimensional array a rectangle of data, a three-dimensional array a block of data, etc.\n", "\n", "The shape is the number of elements in each dimension of data"]}, {"cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["number of dimensions of c: 2\n", "length of c in each dimension: (15, 30)\n", "number of dimensions of x: 3\n", "length of x in each dimension: (2, 3, 3)\n"]}], "source": ["c = np.ones((15, 30))\n", "print(('number of dimensions of c:', c.ndim)) \n", "print(('length of c in each dimension:', c.shape))\n", "\n", "x = np.array([[[1,2,3],[4,5,6],[7,8,9]] , [[0,0,0],[0,0,0],[0,0,0]]])\n", "print(('number of dimensions of x:', x.ndim)) \n", "print(('length of x in each dimension:', x.shape))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["NumPy has its own `range()` function, `np.arange()` (stands for array-range), that is more efficient for building larger arrays. It functions in much the same way as `range()`.\n", "\n", "NumPy also has `linspace()` and `logspace()`, that can generate equally spaced samples between a start-point and an end-point. Find out more with `np.linspace?`."]}, {"cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Arange\n", "[0 1 2 3 4 5 6 7 8 9]\n", "Linspace\n", "[ 5. 5.5 6. 6.5 7. 7.5 8. 8.5 9. 9.5 10. ]\n", "Logspace\n", "[ 1. 1.77827941 3.16227766 5.62341325 10. ]\n", "[ 1. 1.18920712 1.41421356 1.68179283 2. ]\n"]}], "source": ["print(\"Arange\")\n", "print((np.arange(10)))\n", "\n", "# Args: start, stop, number of elements\n", "print(\"Linspace\")\n", "print((np.linspace(5, 10, 11)))\n", "\n", "# logspace can also take a base argument, by default it is 10\n", "print(\"Logspace\")\n", "print((np.logspace(0, 1, 5)))\n", "print((np.logspace(0, 1, 5, base=2)))"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": true}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## 2. Plotting with matplotlib\n", "### 2.1. Getting Started\n", "#### What is matplotlib?\n", "Matplotlib is the most popular and mature library for plotting data using Python. It has all of the functionality you would expect, including the ability to control the formatting of plots and figures at a very fine level.\n", "\n", "The official matplotlib documentation is at http://matplotlib.org/ \n", "The matplotlib gallery is at http://matplotlib.org/gallery.html\n", "\n", "#### Importing matplotlib\n", "Matplotlib is often used through 'pyplot', which provides a high-level interface forplotting."]}, {"cell_type": "code", "execution_count": 100, "metadata": {"collapsed": true}, "outputs": [], "source": ["import matplotlib.pyplot as plt"]}, {"cell_type": "markdown", "metadata": {}, "source": ["In IPython or the IPython notebook, it's easiest to use the pylab magic, which imports matplotlib, numpy, and scipy.\n", "\n", "The matplotlib notebook flag means that plots will be shown interactively in the notebooks, rather than in pop-up windows."]}, {"cell_type": "code", "execution_count": 3, "metadata": {"collapsed": true}, "outputs": [], "source": ["%matplotlib notebook"]}, {"cell_type": "markdown", "metadata": {}, "source": ["If you don't want to use the matplotlib notebook magic, it is still useful to use the inline magic, which makes sure that matplotlib plots are shown inside the notebook"]}, {"cell_type": "code", "execution_count": 101, "metadata": {"collapsed": true}, "outputs": [], "source": ["%matplotlib inline"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### 2.2. Creating Figures\n", "There are two major challenges with creating figures. First is understanding the syntax to actually make the basic plot appear. Second is formatting the basic plot to look exactly how you would like it to look. In general, the formatting will probably take you longer...\n", "\n", "Within pyplot (currently imported as 'plt'), there are two basic ways to go about making plots - using the Matlab-like clone, and using the object-oriented approach. The latter provides better control over plot features, while only requiring slightly more typing. It's easy to quickly outgrow the Matlab clone, so we'll go right to the object-oriented syntax."]}, {"cell_type": "markdown", "metadata": {}, "source": ["#### A first plot\n", "In simple matplotlib plotting, there are two concepts to distinguish:\n", "- __Figure__ - the entire figure, like what you might see in a journal, including all subplots, axes, lines, labels, etc. The whole enchilada. \n", " \n", "- __Subplot/Axes__ - one of the sub-sections of the figure, labeled (a), (b), etc. in articles. Each subplot will contain one Axes object, which is the container where all of the useful stuff, such as actual lines, legends, labels, etc., are actually housed.\n", "\n", "For example, here's how to make one figure with two subplots, the second of which contains two lines."]}, {"cell_type": "code", "execution_count": 117, "metadata": {"collapsed": true}, "outputs": [], "source": ["# First we make some data to plot\n", "numPts = 100\n", "t = np.linspace(-2*np.pi, 2*np.pi,numPts)\n", "y1 = np.sin(t)\n", "y2 = np.cos(t)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["First, create an empty figure with 2 subplots using the subplots method\n", "\n", "`figure, axes = plt.subplots(rows, columns)`\n", " \n", "- The arguments (1, 2) indicate 1 row and 2 cols\n", "- The function plt.subplots returns an object for the figure and for each axes\n", "- There are multiple ways to accomplish this same goal, but this is probably the simplest - notice that each subplot is associated with one of the axes objects.\n", "\n", "Now let's actually plot the data using the plot method on an axis\n", "\n", "`axis.plot(x, y)`"]}, {"cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [{"data": {"image/png": 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fK6tLKMrPrGlWxgovJZX5taMiIhKbRNSobQeOufsJdx8CngDuG1fmPuDvPew1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\nIiIikqL+f7HZZ8i9CE/wAAAAAElFTkSuQmCC\n", "text/plain": [""]}, "metadata": {}, "output_type": "display_data"}], "source": ["myfirstfig, axs = plt.subplots(1,2, figsize=(10,4))\n", "\n", "# We plot one line on the first axis\n", "axs[0].plot(t, y1);"]}, {"cell_type": "markdown", "metadata": {}, "source": ["You can plot multiple lines on an axis"]}, {"cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [{"data": {"image/png": 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AWxH1vkIayos4mU1ymsETgDQ8ZQ5EG2UrePXaFPXeQtvCXSZ37agklkjSPRS2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\nRU5ErkJggg==\n", "text/plain": [""]}, "execution_count": 106, "metadata": {}, "output_type": "execute_result"}], "source": ["# plot both lines on the second axis\n", "axs[1].plot(t, y1)\n", "axs[1].plot(t, y2)\n", "\n", "myfig"]}, {"cell_type": "markdown", "metadata": {}, "source": ["You can also iterate through the axes:"]}, {"cell_type": "code", "execution_count": 109, "metadata": {"collapsed": true}, "outputs": [], "source": ["enumerate?"]}, {"cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [{"data": {"image/png": 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Q/CNdR5+erELEDgp11D0dZ9a8PerVUsN3MnVACLDRt8J52Zqr7UfWKsoOkHWQ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WXweHlBYat9Xl7MUK/ks0nXw6fsT1JIE7Uuj4pNcqHL7FP08BmNWCZGHgbWZ3R+ph4wZfC6LodP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\n40bY2L8kMETAiOEjAKJNx2BSJauFlXlaD44FaD188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"text/plain": [""]}, "metadata": {}, "output_type": "display_data"}], "source": ["coeffs = [3, 5, 10]\n", "\n", "numPts = 1000\n", "t = np.linspace(-2*np.pi, 2*np.pi,numPts)\n", "\n", "myfig, axs = plt.subplots(1,3, figsize=(15,4))\n", "\n", "for i, a in enumerate(coeffs):\n", " axs[i].plot(t, np.sin(a*t))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### 2.3. Formatting figures\n", "\n", "The formatting of figures often takes longer than actually setting them up and adding data. There are many different approaches to formatting figures in matplotlib (many goals can be accomplished in different ways, using different commands), and you will come across many of these as you learn more. The tips below give a few simple ways to get started.\n", "\n", "#### Line formatting\n", "The plot method has several available keyword arguments that you can use to change the line formatting.\n", "- color - Chages color of line. examples: 'red', 'blue', 'r', 'k', 0.5, '#ffaa00', (0,0.5,0.75)\n", "- linewidth - Weight of line. Takes float value in points (like font)\n", "- linestyle - Solid, dashed, or other. examples: -, --, -."]}, {"cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [{"data": {"image/png": 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zJTbcZplzKJthhUg6nU7TGmH7/f5ebQf+8Ic/8M4772Q8b1+SEwgEaGpqynhe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\nv//97yxZsiSjeaF/zC0tLWzatGnQ8yp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2h/E47ZoNzhWUZonwdSbShI2QY+X4WWmducKc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\n", "text/plain": [""]}, "metadata": {}, "output_type": "display_data"}], "source": ["mycol = ['steelblue','grey','tomato']\n", "myline = ['-','-.','--']\n", "\n", "myfig, axs = plt.subplots(1,3, figsize=(15,4))\n", "\n", "for ind, a in enumerate(coeffs):\n", " axs[ind].plot(t, np.sin(a*t), color = mycol[ind], linestyle = myline[ind])"]}, {"cell_type": "markdown", "metadata": {}, "source": ["#### Figure layout\n", "Many of the basic formatting problems you have will be solved by the magic of tight_layout. Before you start tweaking how you figure looks, try it out.\n", "\n", "`plt.tight_layout()`\n", "\n", "#### Axis labels and limits\n", "You can also change the axis limits, add axis labels and add legends. These operate on axis objects."]}, {"cell_type": "code", "execution_count": 115, "metadata": {"collapsed": true}, "outputs": [], "source": ["plt.legend?"]}, {"cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [{"data": {"image/png": 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uogTUrhLdggguLREjDn9b1VhyvK6JkSHWNj8jfgSMlrUN7GSr2yvrHVAY8Cld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\n3hq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1g5v8VJBD2fGTQ8kms+K/bt1tGfE0DkmsWLGCFStWRH3c5XKZQlS9//77/Pvf/476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\nakoLMn+hC0SExbUlKSOHuvvDdPUNebLwpuwT7wAFg8F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"text/plain": [""]}, "execution_count": 114, "metadata": {}, "output_type": "execute_result"}], "source": ["for i in range(3):\n", " axs[i].set_xlabel('angle t [rad]', fontsize=13)\n", " axs[i].set_ylabel('sin('+str(coeffs[i])+'*t)', fontsize=13)\n", "\n", "axs[2].set_ylim(-1.5, 1.5)\n", "\n", "axs[2].plot(t, np.cos(t))\n", "axs[2].legend(['my legend', 'no line to match this'])\n", "\n", "myfig.tight_layout() # finally automatically format the whole figure\n", "\n", "myfig # necessary to show the figure below this cell"]}, {"cell_type": "markdown", "metadata": {}, "source": ["#### Saving a figure\n", "To save your figure you can use the savefig command:\n", "\n", "`fig.savefig('fileanme', format='png')`\n", "\n", "Format options include png, pdf, ps, eps and svg"]}, {"cell_type": "code", "execution_count": 121, "metadata": {}, "outputs": [{"data": {"text/plain": ["'/Users/haberkernh/Documents/GitHub/CodingCirclePython/Lesson05_NumpyAndMatplotlib_part1'"]}, "execution_count": 121, "metadata": {}, "output_type": "execute_result"}], "source": ["pwd"]}, {"cell_type": "code", "execution_count": 127, "metadata": {"collapsed": true}, "outputs": [], "source": ["myfig.savefig('first_plot.jpg');"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### TRY IT\n", "Create a line graph plotting the function f(x) = x^3 for values of x 0-10. Save it as a pdf."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": true}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["### 2.4. Other plot types\n", "\n", "So far we have only looked at line plots. Here are a few other plot types that can easily be created with matplotlib.\n", "- Bar graph: ax.bar(x, y)\n", "- Scatter plot: ax.scatter(x,y)\n", "- Horizontal bar plot: ax.barh(x,y)\n", "- Boxplot: ax.boxplot(x)\n", "- Log-log plot: ax.loglog(x,y)\n", "- Semilog plot: ax.semilogx(x,y), ax.semilogy(x,y)"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": []}, {"cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": ["# Some data\n", "numPts = 100\n", "t = np.linspace(-2*np.pi, 2*np.pi,numPts)\n", "y1 = np.sin(t) + np.random.rand(1, numPts)\n", "y2 = np.cos(t) + np.random.rand(1, numPts)"]}, {"cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 83, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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44KM5Usvkhh3vKQYrd8/Naep0OjE/P1/1MFCA1HoAqIeqjpu8+63yeLd9KCI6WbZNbhUQ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\nNgAAAABJRU5ErkJggg==\n", "text/plain": [""]}, "metadata": {}, "output_type": "display_data"}], "source": ["myscatter, scatterax = plt.subplots(1,1)\n", "scatterax.scatter(t, y1, s=10, marker='o', alpha=0.5, color='grey');"]}, {"cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [{"data": {"image/png": 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3hPdCRYldivveNzGj+mOGHcbed99yOpp9YpEAdgHFrR4Pjj7X2X1UCvvGshpc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\nKZWmNAEopVSa0gSglFJp6v8D4ra/12E2ByUAAAAASUVORK5CYII=\n", "text/plain": [""]}, "execution_count": 85, "metadata": {}, "output_type": "execute_result"}], "source": ["scatterax.scatter(t, y1, s=(y2 + 5), c=y2, cmap='rainbow')\n", "myscatter"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": true}, "outputs": [], "source": []}], "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": "3.6.1"}}, "nbformat": 4, "nbformat_minor": 2} \ No newline at end of file diff --git a/Lesson05_Strings/DNAExtravaganzaSOLUTION.py b/Lesson05_Strings/DNAExtravaganzaSOLUTION.py index 97ec9f4..1065da3 100644 --- a/Lesson05_Strings/DNAExtravaganzaSOLUTION.py +++ b/Lesson05_Strings/DNAExtravaganzaSOLUTION.py @@ -86,9 +86,9 @@ def transcription(seq): coding_dna = codingDNA(dna) coding_rna = transcription(coding_dna) -print("DNA: {}".format(dna)) -print("CODONS: {}".format(codons)) -print("START: {}".format(start)) -print("STOP: {}".format(stop)) -print("CODING DNA: {}".format(coding_dna)) -print("TRANSCRIBED RNA: {}".format(coding_rna)) +print(("DNA: {}".format(dna))) +print(("CODONS: {}".format(codons))) +print(("START: {}".format(start))) +print(("STOP: {}".format(stop))) +print(("CODING DNA: {}".format(coding_dna))) +print(("TRANSCRIBED RNA: {}".format(coding_rna))) diff --git a/Lesson05_Strings/Strings.ipynb b/Lesson05_Strings/Strings.ipynb index 1a29f09..db5d7bd 100644 --- a/Lesson05_Strings/Strings.ipynb +++ b/Lesson05_Strings/Strings.ipynb @@ -16,20 +16,9 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "R\n", - "a\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "chinese_zodiac = \"Rat Ox Tiger Rabbit Dragon Snake Horse Goat Monkey Rooster Dog Pig\"\n", "print(chinese_zodiac[0])\n", @@ -46,23 +35,10 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "d\n" - ] - } - ], - "source": [ - "elements = \"wood fire earth metal water\"\n", - "print(elements[3])" - ] + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -75,19 +51,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "66\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(len(chinese_zodiac))" ] @@ -103,23 +69,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "ename": "IndexError", - "evalue": "string index out of range", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mzlen\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchinese_zodiac\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m# WRONG\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchinese_zodiac\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mzlen\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;31m# RIGHT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchinese_zodiac\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mzlen\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[0;31mIndexError\u001b[0m: string index out of range" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "zlen = len(chinese_zodiac)\n", "# WRONG\n", @@ -139,19 +91,9 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "g\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(chinese_zodiac[-1])" ] @@ -166,34 +108,10 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "e\n" - ] - }, - { - "data": { - "text/plain": [ - "'e'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "name = \"Charlotte\"\n", - "print(name[len(name) - 1])\n", - "name[-1]\n" - ] + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -207,19 +125,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ox\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "second_animal = chinese_zodiac[4:6]\n", "print(second_animal)" @@ -234,20 +142,9 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Rat Ox Tiger Rabbit Dragon Snake\n", - "Horse Goat Monkey Rooster Dog Pig\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "first_six = chinese_zodiac[:32]\n", "print(first_six)\n", @@ -266,25 +163,10 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'Rat Ox Tiger Rabbit Dragon Snake Horse Goat Monkey Rooster Dog Pig'" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "chinese_zodiac[:]" - ] + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -297,23 +179,9 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "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\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# This will fail\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mchinese_zodiac\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'A'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# This will fail\n", "chinese_zodiac[0] = 'A'" @@ -321,21 +189,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Rat Ox Tiger Rabbit Dragon Snake Horse Goat Monkey Rooster Dog Pig\n", - "at Ox Tiger Rabbit Dragon Snake Horse Goat Monkey Rooster Dog Pig\n", - "Cat Ox Tiger Rabbit Dragon Snake Horse Goat Monkey Rooster Dog Pig\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# This is better\n", "print(chinese_zodiac)\n", @@ -361,22 +217,9 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cat in zodiac:\n", - "False\n", - "Dragon in zodiac:\n", - "True\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print('Cat in zodiac:') \n", "print('Cat' in chinese_zodiac)\n", @@ -399,21 +242,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "R\n", - "a\n", - "t\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "for character in 'Rat':\n", " print(character)" @@ -421,24 +252,9 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "A\n", - "E\n", - "I\n", - "O\n", - "U\n", - "Y\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Print only vowels\n", "for letter in 'ABCDEFGHIJKLMNOPQRSTUVWXYZ':\n", @@ -456,54 +272,10 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "R\n", - "A\n", - "T\n", - "O\n", - "T\n", - "E\n", - "R\n", - "R\n", - "A\n", - "T\n", - "R\n", - "A\n", - "O\n", - "A\n", - "E\n", - "H\n", - "O\n", - "R\n", - "E\n", - "O\n", - "A\n", - "T\n", - "O\n", - "E\n", - "R\n", - "O\n", - "O\n", - "T\n", - "E\n", - "R\n", - "O\n" - ] - } - ], - "source": [ - "for letter in chinese_zodiac.upper():\n", - " if letter in \"CHARLOTE\":\n", - " print(letter)" - ] + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -518,22 +290,9 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "False\n", - "True\n", - "False\n", - "False\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print('A' > 'a')\n", "print('A' < 'a')\n", @@ -543,24 +302,13 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "False\n", - "True\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Case matters in equality\n", - "print('cat' == 'Cat')\n", - "print('cat' == 'cat')" + "print(('cat' == 'Cat'))\n", + "print(('cat' == 'cat'))" ] }, { @@ -575,97 +323,9 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['__add__',\n", - " '__class__',\n", - " '__contains__',\n", - " '__delattr__',\n", - " '__dir__',\n", - " '__doc__',\n", - " '__eq__',\n", - " '__format__',\n", - " '__ge__',\n", - " '__getattribute__',\n", - " '__getitem__',\n", - " '__getnewargs__',\n", - " '__gt__',\n", - " '__hash__',\n", - " '__init__',\n", - " '__iter__',\n", - " '__le__',\n", - " '__len__',\n", - " '__lt__',\n", - " '__mod__',\n", - " '__mul__',\n", - " '__ne__',\n", - " '__new__',\n", - " '__reduce__',\n", - " '__reduce_ex__',\n", - " '__repr__',\n", - " '__rmod__',\n", - " '__rmul__',\n", - " '__setattr__',\n", - " '__sizeof__',\n", - " '__str__',\n", - " '__subclasshook__',\n", - " 'capitalize',\n", - " 'casefold',\n", - " 'center',\n", - " 'count',\n", - " 'encode',\n", - " 'endswith',\n", - " 'expandtabs',\n", - " 'find',\n", - " 'format',\n", - " 'format_map',\n", - " 'index',\n", - " 'isalnum',\n", - " 'isalpha',\n", - " 'isdecimal',\n", - " 'isdigit',\n", - " 'isidentifier',\n", - " 'islower',\n", - " 'isnumeric',\n", - " 'isprintable',\n", - " 'isspace',\n", - " 'istitle',\n", - " 'isupper',\n", - " 'join',\n", - " 'ljust',\n", - " 'lower',\n", - " 'lstrip',\n", - " 'maketrans',\n", - " 'partition',\n", - " 'replace',\n", - " 'rfind',\n", - " 'rindex',\n", - " 'rjust',\n", - " 'rpartition',\n", - " 'rsplit',\n", - " 'rstrip',\n", - " 'split',\n", - " 'splitlines',\n", - " 'startswith',\n", - " 'strip',\n", - " 'swapcase',\n", - " 'title',\n", - " 'translate',\n", - " 'upper',\n", - " 'zfill']" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "dir(chinese_zodiac)" ] @@ -681,20 +341,9 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rat ox tiger rabbit dragon snake horse goat monkey rooster dog pig\n", - "RAT OX TIGER RABBIT DRAGON SNAKE HORSE GOAT MONKEY ROOSTER DOG PIG\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(chinese_zodiac.lower())\n", "print(chinese_zodiac.upper())" @@ -717,20 +366,9 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Rat', 'Ox', 'Tiger', 'Rabbit', 'Dragon', 'Snake', 'Horse', 'Goat', 'Monkey', 'Rooster', 'Dog', 'Pig']\n", - "Rat, Ox, Tiger, Rabbit, Dragon, Snake, Horse, Goat, Monkey, Rooster, Dog, Pig\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "cz_list = chinese_zodiac.split(' ')\n", "print(cz_list)\n", @@ -751,21 +389,9 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "27\n", - "2\n", - "True\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(chinese_zodiac.find('Snake'))\n", "\n", @@ -783,23 +409,11 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "print(''.join(cz_list)\n", - " .lower()\n", - " .startswith('ra'))" + "print(''.join(cz_list).lower().startswith('ra'))" ] }, { @@ -814,37 +428,10 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['WOOD', 'FIRE', 'EARTH', 'METAL', 'WATER']\n" - ] - }, - { - "data": { - "text/plain": [ - "['WOOD', 'FIRE', 'EARTH', 'METAL', 'WATER']" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "upper_el = elements.upper()\n", - "\n", - "el_list = upper_el.split(\" \")\n", - "print(el_list)\n", - "\n", - "elements.upper().split(\" \")" - ] + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -857,19 +444,9 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ox Tiger Rabbit Dragon Snake Horse Goat \n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Lets find the zodiac animals between Ox and Monkey\n", "ox_idx = chinese_zodiac.find('Ox')\n", @@ -880,19 +457,9 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tiger Rabbit Dragon Snake Horse Goat \n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Wait, I wanted to exclude Ox\n", "ox_end = ox_idx + len('Ox ')\n", @@ -909,30 +476,11 @@ }, { "cell_type": "code", - "execution_count": 40, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "gmail\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "email = 'my.name@gmail.com'\n", - "at_idx = email.find('@')\n", - "# host_site = email[at_idx+1:]\n", - "# print(host_site)\n", - "# dot_idx = host_site.find('.')\n", - "# print(host_site[:dot_idx])\n", - "dot_idx = email.find('.', at_idx)\n", - "\n", - "\n", - "print(email[at_idx + 1:dot_idx])" + "email = 'my.name@gmail.com'" ] }, { @@ -952,20 +500,9 @@ }, { "cell_type": "code", - "execution_count": 41, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The ox has 4 toes per limb and thus is considered yin\n", - "The tiger has 5 toes per limb and thus is considered yang\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print('The {0} has {1} toes per limb and thus is considered {2}'.format('ox', 4, 'yin'))\n", "print('The {0} has {1} toes per limb and thus is considered {2}'.format('tiger', 5, 'yang'))\n", @@ -982,20 +519,9 @@ }, { "cell_type": "code", - "execution_count": 42, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The 4 has ox toes per limb and thus is considered yin\n", - "The yang has yang toes per limb and thus is considered yang\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print('The {1} has {0} toes per limb and thus is considered {2}'.format('ox', 4, 'yin'))\n", "print('The {2} has {2} toes per limb and thus is considered {2}'.format('tiger', 5, 'yang'))" @@ -1010,22 +536,9 @@ }, { "cell_type": "code", - "execution_count": 43, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "\"The snake's attribute is flexibility\"" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "\"The {animal}'s attribute is {attribute}\".format(animal='snake', attribute='flexibility')" ] @@ -1041,21 +554,9 @@ }, { "cell_type": "code", - "execution_count": 44, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "left aligned \n", - " right aligned\n", - "3.14; -3.1400000\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print('{:<30}'.format('left aligned'))\n", "print('{:>30}'.format('right aligned'))\n", @@ -1072,25 +573,10 @@ }, { "cell_type": "code", - "execution_count": 45, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'Charlotte finds string formatting alright'" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\"{0} finds string formatting {1}\".format(\"Charlotte\", \"alright\")" - ] + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -1174,9 +660,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Lesson06_Files/Files.ipynb b/Lesson06_Files/Files.ipynb index 4a6b94b..594073e 100644 --- a/Lesson06_Files/Files.ipynb +++ b/Lesson06_Files/Files.ipynb @@ -20,22 +20,9 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "('theyellowwallpaper.txt', )" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Run these to get some of the files we will be using today. \n", "# These are the salaries of public workers in California from the website transparentcalifornia\n", @@ -49,45 +36,21 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "<_io.TextIOWrapper name='san-francisco-2014.csv' mode='r' encoding='UTF-8'>\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Opening a file\n", "fh = open('san-francisco-2014.csv')\n", "print(fh)\n", - "fh.close()\n" + "fh.close()" ] }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: 'i_dont_exist.txt'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Opening a non-existent file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'i_dont_exist.txt'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mfh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'i_dont_exist.txt'" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Opening a non-existent file\n", "fh = open('i_dont_exist.txt')\n", @@ -105,15 +68,10 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], - "source": [ - "fh = open(\"san-francisco-2013.csv\")\n", - "fh.close()" - ] + "source": [] }, { "cell_type": "markdown", @@ -128,21 +86,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Golden\n", - "Gate\n", - "Bridge\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(\"Golden\\nGate\\nBridge\")" ] @@ -157,23 +103,10 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Charlotte\n", - "Weaver\n" - ] - } - ], - "source": [ - "print(\"Charlotte\\nWeaver\")" - ] + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -196,53 +129,21 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Alcatraz\n", - "Golden Gate Bridge\n", - "Golden Gate Park\n", - "The Exploratorium\n", - "Pier 39\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fh = open('thingstodo.txt')\n", "for line in fh:\n", " print(line.rstrip())\n", - "fh.close()\n" + "fh.close()" ] }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Alcatraz\n", - "Golden Gate Bridge\n", - "Golden Gate Park\n", - "The Exploratorium\n", - "Pier 39\n", - "\n", - "\n", - "['Alcatraz\\n', 'Golden Gate Bridge\\n', 'Golden Gate Park\\n', 'The Exploratorium\\n', 'Pier 39\\n']\n", - "\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fh = open('thingstodo.txt')\n", "contents = fh.read()\n", @@ -269,111 +170,10 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "employee_name,job_title,base_pay,overtime_pay,other_pay,total_benefits,total_pay,total_pay_benefits,year,notes,jurisdiction_name\n", - "\n" - ] - } - ], - "source": [ - "fh = open('san-francisco-2013.csv')\n", - "\n", - "for line in fh:\n", - " print(line)\n", - " break\n", - " \n", - "fh.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "employee_name,job_title,base_pay,overtime_pay,other_pay,total_benefits,total_pay,total_pay_benefits,year,notes,jurisdiction_name\n", - "\n" - ] - } - ], - "source": [ - "fh = open('san-francisco-2013.csv')\n", - "\n", - "\n", - "lines = fh.readlines()\n", - "print(lines[0])\n", - "# for line in fh:\n", - "# print(line)\n", - "# break\n", - " \n", - "fh.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "employee_name,job_title,base_pay,overtime_pay,other_pay,total_benefits,total_pay,total_pay_benefits,year,notes,jurisdiction_name\n" - ] - } - ], - "source": [ - "fh = open('san-francisco-2013.csv')\n", - "\n", - "\n", - "lines = fh.read()\n", - "end_of_first = lines.find(\"\\n\")\n", - "print(lines[:end_of_first])\n", - "# for line in fh:\n", - "# print(line)\n", - "# break\n", - " \n", - "fh.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Project Gutenberg's The Yellow Wallpaper, by Charlotte Perkins Gilman\n", - "\n" - ] - } - ], - "source": [ - "fh = open('theyellowwallpaper.txt')\n", - "for line in fh:\n", - " print(line)\n", - " break\n", - "fh.close()" - ] + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -388,38 +188,9 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Charlotte L Jaques,\"AprntcStatnry Eng,WtrTreatPlnt\",86679.08,8924.69,5713.36,34054.46,101317.13,135371.59,2014,,San Francisco,FT\n", - "\n", - "Charlotte R Kuo,Nurse Practitioner,83539.45,0.00,250.00,26455.08,83789.45,110244.53,2014,,San Francisco,PT\n", - "\n", - "Charlotte C Wu,Senior Management Assistant,74163.11,0.00,0.00,30828.83,74163.11,104991.94,2014,,San Francisco,FT\n", - "\n", - "Charlotte Coloyan Dela Cruz,Legal Secretary 1,66492.03,0.00,0.00,29062.63,66492.03,95554.66,2014,,San Francisco,FT\n", - "\n", - "Charlotte E Grimes-Brown,Health Worker 2,59388.65,3880.56,2482.62,27328.09,65751.83,93079.92,2014,,San Francisco,FT\n", - "\n", - "Charlotte B Coquia,Personnel Trainee,56456.54,0.00,1061.50,27700.77,57518.04,85218.81,2014,,San Francisco,FT\n", - "\n", - "Charlotte R Sanders,Librarian 1,49527.35,0.00,943.69,19430.78,50471.04,69901.82,2014,,San Francisco,PT\n", - "\n", - "Charlotte L Leung,HSA Sr Eligibility Worker,21401.54,0.00,14556.04,8669.72,35957.58,44627.30,2014,,San Francisco,PT\n", - "\n", - "Charlotte L Vance,Public Svc Aide-Public Works,5392.91,989.32,53.28,4078.86,6435.51,10514.37,2014,,San Francisco,PT\n", - "\n", - "Charlotte A Holper,Public Service Trainee,2625.93,0.00,2.58,26.28,2628.51,2654.79,2014,,San Francisco,PT\n", - "\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Looking for a line that starts with something\n", "\n", @@ -433,92 +204,9 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "John L Martin,Dept Head V,311298.55,0.00,0.00,89772.32,311298.55,401070.87,2014,,San Francisco,FT\n", - "\n", - "Barbara A Garcia,Dept Head V,279839.22,0.00,2164.54,82884.27,282003.76,364888.03,2014,,San Francisco,FT\n", - "\n", - "Naomi M Kelly,Dept Head V,267914.01,0.00,0.00,80361.22,267914.01,348275.23,2014,,San Francisco,FT\n", - "\n", - "Trent E Rhorer,Dept Head V,267914.00,0.00,0.00,79799.88,267914.00,347713.88,2014,,San Francisco,FT\n", - "\n", - "Jay P Huish,Dept Head V,267914.00,0.00,0.00,79799.88,267914.00,347713.88,2014,,San Francisco,FT\n", - "\n", - "John S Rahaim,Dept Head IV,232489.33,0.00,0.00,72233.72,232489.33,304723.05,2014,,San Francisco,FT\n", - "\n", - "Luis Herrera,Dept Head IV,226832.02,0.00,0.00,71586.74,226832.02,298418.76,2014,,San Francisco,FT\n", - "\n", - "Mohammed C Nuru,Dept Head IV,219184.68,0.00,0.00,70095.24,219184.68,289279.92,2014,,San Francisco,FT\n", - "\n", - "Anne M Kronenberg,Dept Head IV,219212.28,0.00,0.00,69397.69,219212.28,288609.97,2014,,San Francisco,FT\n", - "\n", - "Tom C Hui,Dept Head III,202290.14,0.00,14958.35,68955.03,217248.49,286203.52,2014,,San Francisco,FT\n", - "\n", - "Philip A Ginsburg,Dept Head IV,219184.67,0.00,0.00,59225.09,219184.67,278409.76,2014,,San Francisco,FT\n", - "\n", - "Karen M Roye,Dept Head III,201294.51,0.00,0.00,65570.63,201294.51,266865.14,2014,,San Francisco,FT\n", - "\n", - "Catherine J Dodd,Dept Head III,195227.21,0.00,0.00,64274.88,195227.21,259502.09,2014,,San Francisco,FT\n", - "\n", - "Rebecca Katz,Dept Head II,133351.99,0.00,77843.50,45143.14,211195.49,256338.63,2014,,San Francisco,PT\n", - "\n", - "Colin B Bailey,Dept Head III,187571.00,0.00,0.00,64515.27,187571.00,252086.27,2014,,San Francisco,FT\n", - "\n", - "Jay J Xu,Dept Head III,187571.00,0.00,0.00,63200.90,187571.00,250771.90,2014,,San Francisco,FT\n", - "\n", - "Anne E Hinton,Dept Head III,187571.00,0.00,0.00,62639.56,187571.00,250210.56,2014,,San Francisco,FT\n", - "\n", - "Todd Rufo,Dept Head III,187546.19,0.00,0.00,62634.12,187546.19,250180.31,2014,,San Francisco,FT\n", - "\n", - "Angela C Calvillo,Dept Head III,187544.95,0.00,0.00,62633.86,187544.95,250178.81,2014,,San Francisco,FT\n", - "\n", - "Joyce M Hicks,Dept Head I,187210.93,0.00,0.00,62562.66,187210.93,249773.59,2014,,San Francisco,FT\n", - "\n", - "John Arntz,Dept Head II,185161.72,0.00,0.00,62115.63,185161.72,247277.35,2014,,San Francisco,FT\n", - "\n", - "Elizabeth Murray,Dept Head II,179041.55,0.00,0.00,60817.76,179041.55,239859.31,2014,,San Francisco,FT\n", - "\n", - "John T Noguchi,Dept Head II,176484.05,0.00,0.00,60832.58,176484.05,237316.63,2014,,San Francisco,FT\n", - "\n", - "Maria Su,Dept Head II,176470.18,0.00,0.00,60829.71,176470.18,237299.89,2014,,San Francisco,FT\n", - "\n", - "Theresa L Sparks,Dept Head II,170015.41,0.00,0.00,58342.45,170015.41,228357.86,2014,,San Francisco,FT\n", - "\n", - "Delene S Wolf,Dept Head I,157766.89,0.00,0.00,56834.97,157766.89,214601.86,2014,,San Francisco,FT\n", - "\n", - "Thomas E Decaigny Ii,Dept Head I,153432.44,0.00,0.00,56882.16,153432.44,210314.60,2014,,San Francisco,FT\n", - "\n", - "Cynthia G Goldstein,Dept Head I,148579.76,0.00,0.00,54872.74,148579.76,203452.50,2014,,San Francisco,FT\n", - "\n", - "John St. Croix,Dept Head I,148426.18,0.00,0.00,54278.51,148426.18,202704.69,2014,,San Francisco,FT\n", - "\n", - "Susannah G Robbins,Dept Head I,144514.52,0.00,0.00,54888.12,144514.52,199402.64,2014,,San Francisco,FT\n", - "\n", - "Emily Murase,Dept Head I,142364.06,0.00,0.00,53545.01,142364.06,195909.07,2014,,San Francisco,FT\n", - "\n", - "Laurel Kloomok,Dept Head I,142499.55,0.00,0.00,52985.69,142499.55,195485.24,2014,,San Francisco,FT\n", - "\n", - "Jocelyn M Kane,Dept Head I,133520.35,0.00,0.00,51668.07,133520.35,185188.42,2014,,San Francisco,FT\n", - "\n", - "Regina M Dick-Endrizzi,Dept Head I,132774.29,0.00,0.00,51496.71,132774.29,184271.00,2014,,San Francisco,FT\n", - "\n", - "Marc Y Touitou,Dept Head IV,137488.57,0.00,104.37,44969.73,137592.94,182562.67,2014,,San Francisco,PT\n", - "\n", - "Deborah O Raphael,Dept Head II,94612.51,0.00,0.00,34632.55,94612.51,129245.06,2014,,San Francisco,PT\n", - "\n", - "Melanie L Nutter,Dept Head II,30870.89,0.00,23270.47,10937.59,54141.36,65078.95,2014,,San Francisco,PT\n", - "\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Looking for lines that contain a specific string\n", "fh = open('san-francisco-2014.csv')\n", @@ -532,19 +220,9 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "There are 518 trainees\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Counting lines that match criteria\n", "fh = open('san-francisco-2014.csv')\n", @@ -559,186 +237,9 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Emily Lee', 'Senior Physician Specialist', '178950.03', '0.00', '0.00', '52859.83', '178950.03', '231809.86', '2014', '', 'San Francisco', 'FT\\n']\n", - "Senior Physician Specialist 178950.03 FT\n", - "\n", - "['Emily Goldman', 'Attorney (Civil/Criminal)', '172150.70', '0.00', '1312.50', '50927.47', '173463.20', '224390.67', '2014', '', 'San Francisco', 'FT\\n']\n", - "Attorney (Civil/Criminal) 172150.70 FT\n", - "\n", - "['Emily Prescott', '\"Manager', 'Employee Relations Div\"', '152325.27', '0.00', '0.00', '56584.45', '152325.27', '208909.72', '2014', '', 'San Francisco', 'FT\\n']\n", - "\"Manager Employee Relations Div\" FT\n", - "\n", - "['Emily Murase', 'Dept Head I', '142364.06', '0.00', '0.00', '53545.01', '142364.06', '195909.07', '2014', '', 'San Francisco', 'FT\\n']\n", - "Dept Head I 142364.06 FT\n", - "\n", - "['Emily M Morrison', 'Manager III', '132690.02', '0.00', '0.00', '50917.46', '132690.02', '183607.48', '2014', '', 'San Francisco', 'FT\\n']\n", - "Manager III 132690.02 FT\n", - "\n", - "['Emily L Dahm', 'Attorney (Civil/Criminal)', '125830.00', '0.00', '5041.50', '41764.64', '130871.50', '172636.14', '2014', '', 'San Francisco', 'FT\\n']\n", - "Attorney (Civil/Criminal) 125830.00 FT\n", - "\n", - "['Emily R Luck', 'Diagnostic Imaging Tech IV', '113566.58', '6618.71', '10067.18', '39672.40', '130252.47', '169924.87', '2014', '', 'San Francisco', 'FT\\n']\n", - "Diagnostic Imaging Tech IV 113566.58 FT\n", - "\n", - "['Emily B Gerber', 'Manager I', '112649.00', '0.00', '0.00', '45432.00', '112649.00', '158081.00', '2014', '', 'San Francisco', 'FT\\n']\n", - "Manager I 112649.00 FT\n", - "\n", - "[\"Emily S O'Rourke\", 'Firefighter', '96940.13', '6000.31', '6809.44', '38479.04', '109749.88', '148228.92', '2014', '', 'San Francisco', 'FT\\n']\n", - "Firefighter 96940.13 FT\n", - "\n", - "['Emily R Watters', 'Senior Physician Specialist', '101753.60', '0.00', '5087.68', '31519.53', '106841.28', '138360.81', '2014', '', 'San Francisco', 'PT\\n']\n", - "Senior Physician Specialist 101753.60 PT\n", - "\n", - "['Emily K Anderson', 'EMT/Paramedic/Firefighter', '80594.00', '9794.44', '10526.38', '34697.35', '100914.82', '135612.17', '2014', '', 'San Francisco', 'FT\\n']\n", - "EMT/Paramedic/Firefighter 80594.00 FT\n", - "\n", - "['Emily Yee', 'Senior Administrative Analyst', '98753.05', '0.00', '0.00', '35445.10', '98753.05', '134198.15', '2014', '', 'San Francisco', 'FT\\n']\n", - "Senior Administrative Analyst 98753.05 FT\n", - "\n", - "['Emily R Lesk', 'Senior Administrative Analyst', '94229.51', '0.00', '0.00', '35399.77', '94229.51', '129629.28', '2014', '', 'San Francisco', 'FT\\n']\n", - "Senior Administrative Analyst 94229.51 FT\n", - "\n", - "['Emily Ho', 'Medical Social Worker', '90185.02', '0.00', '1664.00', '33936.79', '91849.02', '125785.81', '2014', '', 'San Francisco', 'FT\\n']\n", - "Medical Social Worker 90185.02 FT\n", - "\n", - "['Emily J Gerth', 'Senior Administrative Analyst', '89586.46', '0.00', '0.00', '34284.41', '89586.46', '123870.87', '2014', '', 'San Francisco', 'FT\\n']\n", - "Senior Administrative Analyst 89586.46 FT\n", - "\n", - "['Emily B Williams', '\"Manager II', ' MTA\"', '92283.45', '0.00', '0.00', '28840.50', '92283.45', '121123.95', '2014', '', 'San Francisco', 'FT\\n']\n", - "\"Manager II MTA\" FT\n", - "\n", - "['Emily Lau Sing', 'Assoc Engineer', '86868.14', '0.00', '2387.67', '30869.19', '89255.81', '120125.00', '2014', '', 'San Francisco', 'PT\\n']\n", - "Assoc Engineer 86868.14 PT\n", - "\n", - "['Emily Wang', 'Senior Administrative Analyst', '82825.42', '0.00', '0.00', '32801.48', '82825.42', '115626.90', '2014', '', 'San Francisco', 'FT\\n']\n", - "Senior Administrative Analyst 82825.42 FT\n", - "\n", - "['Emily S Chang', 'Program Specialist', '82589.08', '0.00', '200.00', '31958.49', '82789.08', '114747.57', '2014', '', 'San Francisco', 'FT\\n']\n", - "Program Specialist 82589.08 FT\n", - "\n", - "['Emily A Roberts', 'Estate Investigator', '82052.03', '0.00', '0.00', '31795.22', '82052.03', '113847.25', '2014', '', 'San Francisco', 'FT\\n']\n", - "Estate Investigator 82052.03 FT\n", - "\n", - "['Emily Chea', 'Employment & Training Spec 3', '81005.01', '0.00', '0.00', '32376.58', '81005.01', '113381.59', '2014', '', 'San Francisco', 'FT\\n']\n", - "Employment & Training Spec 3 81005.01 FT\n", - "\n", - "['Emily H Chau', 'Secretary 2', '65854.06', '9309.23', '0.00', '28255.50', '75163.29', '103418.79', '2014', '', 'San Francisco', 'FT\\n']\n", - "Secretary 2 65854.06 FT\n", - "\n", - "['Emily G Espino', 'Payroll Clerk', '66995.00', '4182.45', '624.00', '28641.14', '71801.45', '100442.59', '2014', '', 'San Francisco', 'FT\\n']\n", - "Payroll Clerk 66995.00 FT\n", - "\n", - "['Emily C Davis', 'Executive Secretary 1', '67037.39', '0.00', '0.00', '29094.78', '67037.39', '96132.17', '2014', '', 'San Francisco', 'FT\\n']\n", - "Executive Secretary 1 67037.39 FT\n", - "\n", - "['Emily P Wong', 'Health Worker 3', '62780.22', '1405.80', '1284.11', '28522.94', '65470.13', '93993.07', '2014', '', 'San Francisco', 'FT\\n']\n", - "Health Worker 3 62780.22 FT\n", - "\n", - "['Emily R Uphoff', 'Special Nurse', '77970.70', '1557.68', '12711.93', '922.40', '92240.31', '93162.71', '2014', '', 'San Francisco', 'PT\\n']\n", - "Special Nurse 77970.70 PT\n", - "\n", - "['Emily L Schwartz', 'Fingerprint Technician 1', '60141.11', '5604.30', '0.00', '26996.43', '65745.41', '92741.84', '2014', '', 'San Francisco', 'FT\\n']\n", - "Fingerprint Technician 1 60141.11 FT\n", - "\n", - "['Emily Hunter', 'HSA Sr Eligibility Worker', '62095.45', '952.18', '1861.77', '25348.38', '64909.40', '90257.78', '2014', '', 'San Francisco', 'PT\\n']\n", - "HSA Sr Eligibility Worker 62095.45 PT\n", - "\n", - "['Emily Lee', 'IS Business Analyst-Senior', '71136.77', '0.00', '0.00', '17764.46', '71136.77', '88901.23', '2014', '', 'San Francisco', 'PT\\n']\n", - "IS Business Analyst-Senior 71136.77 PT\n", - "\n", - "['Emily M Vasquez', 'Health Worker 3', '58880.61', '1241.90', '62.38', '27344.95', '60184.89', '87529.84', '2014', '', 'San Francisco', 'FT\\n']\n", - "Health Worker 3 58880.61 FT\n", - "\n", - "['Emily S Chesley', 'Accountant II', '60320.29', '0.00', '0.00', '25362.82', '60320.29', '85683.11', '2014', '', 'San Francisco', 'PT\\n']\n", - "Accountant II 60320.29 PT\n", - "\n", - "['Emily M Manuel', 'Nutritionist', '61297.53', '0.00', '0.00', '23599.04', '61297.53', '84896.57', '2014', '', 'San Francisco', 'PT\\n']\n", - "Nutritionist 61297.53 PT\n", - "\n", - "['Emily M Volberding', 'Mayoral Staff IV', '46688.03', '0.00', '0.00', '34851.10', '46688.03', '81539.13', '2014', '', 'San Francisco', 'FT\\n']\n", - "Mayoral Staff IV 46688.03 FT\n", - "\n", - "['Emily V Callorina', 'Senior Personnel Clerk', '52013.71', '0.00', '484.00', '27727.14', '52497.71', '80224.85', '2014', '', 'San Francisco', 'FT\\n']\n", - "Senior Personnel Clerk 52013.71 FT\n", - "\n", - "['Emily A Palmer', 'Special Nurse', '65238.61', '465.22', '5282.11', '709.84', '70985.94', '71695.78', '2014', '', 'San Francisco', 'PT\\n']\n", - "Special Nurse 65238.61 PT\n", - "\n", - "['Emily Hazel - Geran', 'Parking Control Officer', '44264.02', '0.00', '1171.86', '20213.34', '45435.88', '65649.22', '2014', '', 'San Francisco', 'PT\\n']\n", - "Parking Control Officer 44264.02 PT\n", - "\n", - "['Emily F Shannon', 'Invstgtor Ofc Citizen Cmplnts', '50030.32', '0.00', '0.00', '12297.31', '50030.32', '62327.63', '2014', '', 'San Francisco', 'PT\\n']\n", - "Invstgtor Ofc Citizen Cmplnts 50030.32 PT\n", - "\n", - "['Emily A Meneses', 'Psychiatric Social Worker', '42850.31', '0.00', '0.00', '9031.16', '42850.31', '51881.47', '2014', '', 'San Francisco', 'PT\\n']\n", - "Psychiatric Social Worker 42850.31 PT\n", - "\n", - "['Emily K Taplin', 'Psychiatric Social Worker', '36304.18', '0.00', '0.00', '7103.11', '36304.18', '43407.29', '2014', '', 'San Francisco', 'PT\\n']\n", - "Psychiatric Social Worker 36304.18 PT\n", - "\n", - "['Emily C Stout', 'Special Nurse', '37032.11', '1341.30', '2449.72', '408.23', '40823.13', '41231.36', '2014', '', 'San Francisco', 'PT\\n']\n", - "Special Nurse 37032.11 PT\n", - "\n", - "['Emily E Read', 'Watershed Keeper', '28713.70', '0.00', '155.20', '12073.45', '28868.90', '40942.35', '2014', '', 'San Francisco', 'PT\\n']\n", - "Watershed Keeper 28713.70 PT\n", - "\n", - "['Emily J Agosta', 'Senior Clerk', '19109.00', '0.00', '0.00', '10986.47', '19109.00', '30095.47', '2014', '', 'San Francisco', 'PT\\n']\n", - "Senior Clerk 19109.00 PT\n", - "\n", - "['Emily Y Liang', 'Senior Account Clerk', '20276.42', '0.00', '0.00', '8387.11', '20276.42', '28663.53', '2014', '', 'San Francisco', 'PT\\n']\n", - "Senior Account Clerk 20276.42 PT\n", - "\n", - "['Emily Cun', 'Food Service Worker', '17864.61', '0.00', '24.11', '178.88', '17888.72', '18067.60', '2014', '', 'San Francisco', 'PT\\n']\n", - "Food Service Worker 17864.61 PT\n", - "\n", - "['Emily A Hanna', 'Licensed Vocational Nurse', '15790.50', '0.00', '1263.26', '170.52', '17053.76', '17224.28', '2014', '', 'San Francisco', 'PT\\n']\n", - "Licensed Vocational Nurse 15790.50 PT\n", - "\n", - "['Emily C Alvarez', '\"StdntDsgnTrain1', ' Arch/Eng/Plng\"', '16419.46', '0.00', '0.00', '164.22', '16419.46', '16583.68', '2014', '', 'San Francisco', 'PT\\n']\n", - "\"StdntDsgnTrain1 Arch/Eng/Plng\" PT\n", - "\n", - "['Emily J Alt', 'PS Aide to Prof', '13907.67', '0.00', '0.00', '139.10', '13907.67', '14046.77', '2014', '', 'San Francisco', 'PT\\n']\n", - "PS Aide to Prof 13907.67 PT\n", - "\n", - "['Emily C Claymore', 'PS Aide Health Services', '9763.69', '0.00', '0.00', '97.64', '9763.69', '9861.33', '2014', '', 'San Francisco', 'PT\\n']\n", - "PS Aide Health Services 9763.69 PT\n", - "\n", - "['Emily K Shaw', 'Social Worker', '6534.00', '0.00', '0.00', '3130.55', '6534.00', '9664.55', '2014', '', 'San Francisco', 'PT\\n']\n", - "Social Worker 6534.00 PT\n", - "\n", - "['Emily A Smith', 'Public Svc Aide-Public Works', '8515.57', '0.00', '85.36', '86.00', '8600.93', '8686.93', '2014', '', 'San Francisco', 'PT\\n']\n", - "Public Svc Aide-Public Works 8515.57 PT\n", - "\n", - "['Emily Yu', 'Public Service Trainee', '5431.02', '0.00', '0.00', '54.30', '5431.02', '5485.32', '2014', '', 'San Francisco', 'PT\\n']\n", - "Public Service Trainee 5431.02 PT\n", - "\n", - "['Emily Lee', 'Physician Specialist', '4092.51', '0.00', '0.00', '914.80', '4092.51', '5007.31', '2014', '', 'San Francisco', 'PT\\n']\n", - "Physician Specialist 4092.51 PT\n", - "\n", - "['Emily E Woo', 'Pool Lifeguard', '4758.23', '0.00', '103.63', '48.62', '4861.86', '4910.48', '2014', '', 'San Francisco', 'PT\\n']\n", - "Pool Lifeguard 4758.23 PT\n", - "\n", - "['Emily K Riggs', 'Health Care Analyst', '0.00', '0.00', '0.00', '4645.56', '0.00', '4645.56', '2014', '', 'San Francisco', 'PT\\n']\n", - "Health Care Analyst 0.00 PT\n", - "\n", - "['Emily E Niznik-Salvaterra', 'Public Service Aide-Admin', '4149.30', '0.00', '0.00', '350.66', '4149.30', '4499.96', '2014', '', 'San Francisco', 'PT\\n']\n", - "Public Service Aide-Admin 4149.30 PT\n", - "\n", - "['Emily Gong', 'Camp Assistant', '1932.48', '0.00', '0.00', '19.32', '1932.48', '1951.80', '2014', '', 'San Francisco', 'PT\\n']\n", - "Camp Assistant 1932.48 PT\n", - "\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Splitting lines, this is great for excel like data (tsv, csv)\n", "# I want to see salary data of women with my name\n", @@ -761,24 +262,9 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Alcatraz\n", - "\n", - "The Exploratorium\n", - "\n", - "Pier 39\n", - "\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Skipping lines\n", "fh = open('thingstodo.txt')\n", @@ -809,19 +295,9 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File does not exist\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Opening a non-existent file\n", "try:\n", @@ -854,19 +330,9 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['0\\n', '1\\n', '2\\n', '3\\n', '4\\n', '5\\n', '6\\n', '7\\n', '8\\n', '9\\n']\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fh = open('numbers.txt', 'w')\n", "for i in range(10):\n", @@ -892,16 +358,10 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], - "source": [ - "fh = open(\"my_favorite_cities.txt\", 'w')\n", - "fh.write(\"Charlotte, NC\\nAtlanta, GA\\nBangkok, Thailand\")\n", - "fh.close()" - ] + "source": [] }, { "cell_type": "markdown", @@ -920,27 +380,13 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Alcatraz\n", - "Golden Gate Bridge\n", - "Golden Gate Park\n", - "The Exploratorium\n", - "Pier 39\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "with open('thingstodo.txt') as fh:\n", " for line in fh:\n", - " print(line.rstrip())" + " print((line.rstrip()))" ] }, { @@ -952,30 +398,16 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "1\n", - "2\n", - "3\n", - "4\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "with open('numbers2.txt', 'w') as fh:\n", " for i in range(5):\n", " fh.write(str(i) + '\\n')\n", "with open('numbers2.txt') as fh:\n", " for line in fh:\n", - " print(line.rstrip())" + " print((line.rstrip()))" ] }, { @@ -998,29 +430,10 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "There are 518 trainees\n" - ] - } - ], - "source": [ - "with open('san-francisco-2014.csv') as fh:\n", - " num_trainees = 0\n", - " for line in fh:\n", - " # Remember if find doesn't find the string, it returns -1\n", - " if line.find('Trainee') != -1:\n", - " num_trainees += 1\n", - "\n", - "print(\"There are {0} trainees\".format(num_trainees))" - ] + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -1043,9 +456,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] } @@ -1066,9 +477,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Lesson07_ListsandTuples/.ipynb_checkpoints/Lists and Tuples-checkpoint.ipynb b/Lesson07_ListsandTuples/.ipynb_checkpoints/Lists and Tuples-checkpoint.ipynb index 7a36acd..209bef0 100644 --- a/Lesson07_ListsandTuples/.ipynb_checkpoints/Lists and Tuples-checkpoint.ipynb +++ b/Lesson07_ListsandTuples/.ipynb_checkpoints/Lists and Tuples-checkpoint.ipynb @@ -16,9 +16,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "sushi_order = ['unagi', 'hamachi', 'otoro']\n", @@ -39,9 +37,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "print(sushi_order[0])\n", @@ -60,9 +56,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "print(len(sushi_order))" @@ -78,12 +72,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "print(sushi_order[-1])" + "print(sushi_order[-3])" ] }, { @@ -92,15 +84,14 @@ "source": [ "## Nested lists\n", "\n", - "lists can contain other lists as elements" + "Lists can contain other lists as elements.\n", + "This is a convenient alternative to a matrix. You can arrange lists of varying lengths (and contents) in a specific order and you can iterate over the elements (see below)." ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "everyones_order = [['california roll'], ['unagi', 'dragon roll'], sushi_order]\n", @@ -111,44 +102,59 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To access an element in a nested list, first index to the inner list, then index to the item\n", + "To access an element in a nested list, first index to the inner list, then index to the item.\n", "\n", + "Example:\n", + "```\n", " list_of_lists = [[1,2], [3,4], []]\n", - " # first index to the inner list\n", + "```\n", + "Acess the first index to the inner list and index to the item\n", + "```python\n", " inner_list = list_of_lists[1] # [3,4]\n", - " # then index to the item\n", - " print inner_list[0] # 3 \n", - " # even quicker\n", + " print inner_list[0] # 3\n", + "``` \n", + " \n", + "Or even quicker:\n", + "```python\n", " list_of_lists[1][0] # 3\n", - " \n" + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get dragon roll from the sushi order, first we get the second element (index 1) then we get the the second item (index 1)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "# To get dragon roll from the sushi order, first we get the second element (index 1) \n", - "# then we get the the second item (index 1)\n", - "print(everyones_order[1][1])" + "everyones_order[1][1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### TRY IT\n", - "Print california roll from the list `everyones_order`" + "### TRY IT" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the california roll from the list `everyones_order`. As a challenge print all the items from the second person's order." ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [] @@ -170,9 +176,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "sushi_order[0] = 'caterpillar roll'\n", @@ -191,7 +195,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [] @@ -208,13 +212,27 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, + "outputs": [], + "source": [ + "sushi_order" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('hamachi' in sushi_order)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ - "print('unagi' in sushi_order)\n", - "\n", "if 'otoro' in sushi_order:\n", " print(\"Big spender!\")" ] @@ -233,9 +251,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "print(sushi_order * 3)" @@ -244,14 +260,29 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "print(prices + sushi_order)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: You can only concatenate lists with lists! If you want to add a \"non-list\" element you can use the append() function that we will learn about in the next section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# WRONG\n", + "prices + 22" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -263,11 +294,11 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "scrolled": true }, "outputs": [], "source": [ - "inexpensive = sushi_order[:2]\n", + "inexpensive = sushi_order[:2] #takes only the first two elements from list\n", "print(inexpensive)" ] }, @@ -281,16 +312,25 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "for item in sushi_order:\n", " print(\"I'd like to order the {}.\".format(item))\n", + " \n", "print(\"And hold the wasabi!\")" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for idx, item in enumerate(sushi_order):\n", + " print(\"I'd like to order the {0} for {1}.\".format(item, prices[idx]))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -303,7 +343,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [] @@ -314,7 +354,7 @@ "source": [ "## Adding and deleting elements \n", "\n", - "To add an element to a list, you have a few of options\n", + "To add an element to a list, you have a few options\n", "\n", "1. the append method adds an element or elements to the end of a list, if you pass it a list, the next element with be a list (making a list of lists)\n", "\n", @@ -326,9 +366,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "my_sushis = ['maguro', 'rock n roll']\n", @@ -341,9 +379,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "my_sushis = ['maguro', 'rock n roll']\n", @@ -369,9 +405,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "print(my_sushis)\n", @@ -382,9 +416,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "my_sushis.remove('maguro')\n", @@ -394,9 +426,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "del my_sushis[1:]\n", @@ -438,9 +468,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "numbers = [1, 1, 2, 3, 5, 8]\n", @@ -448,8 +476,7 @@ "print(max(numbers))\n", "print(min(numbers))\n", "print(sum(numbers))\n", - "print(len(numbers))\n", - "\n" + "print(len(numbers))" ] }, { @@ -457,7 +484,9 @@ "metadata": {}, "source": [ "### TRY IT\n", - "Find the average of `numbers` using list functions (and not a loop!)" + "Find the average of `numbers` using list functions (and not a loop!)\n", + "\n", + "(And if you are feeling self-loathing, look back at lesson 4 and see how many lines of code it took to do this without aggregation functions)" ] }, { @@ -481,9 +510,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "cooked_rolls = ['unagi roll', 'shrimp tempura roll']\n", @@ -503,9 +530,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "print(my_order is cooked_rolls)" @@ -517,15 +542,15 @@ "source": [ "To fix this, you can make a copy of the list using the list function\n", "\n", - "`list` takes a sequence and turns it into a list" + "`list` takes a sequence and turns it into a list.\n", + "\n", + "Alternatively you can use the `copy()` method: `my_order = cooked_rolls.copy()`" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "cooked_rolls = ['unagi roll', 'shrimp tempura roll']\n", @@ -541,7 +566,7 @@ "source": [ "## Tuples\n", "\n", - "Tuples are very similar to lists. The major difference is that tuples are immutable meaning that you can add, remove, or assign new values to a tuple.\n", + "Tuples are very similar to lists. The major difference is that tuples are immutable meaning that you can not add, remove, or assign new values to a tuple.\n", "\n", "The creator of a tuple is the comma `,` but by convention people usually surround tuples with parenthesis." ] @@ -550,7 +575,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [], @@ -569,9 +593,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "sushi_tuple = tuple(my_order)\n", @@ -591,9 +613,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "single_element_tuple = (1,)\n", @@ -613,9 +633,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "print(noodles[0])\n", @@ -625,9 +643,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# This should throw an error\n", @@ -644,9 +660,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "print(sushi_tuple)\n", @@ -664,9 +678,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "for noodle in noodles:\n", @@ -685,7 +697,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [] @@ -696,17 +708,17 @@ "source": [ "## Zip\n", "\n", - "the zip function takes any number of lists of the same length and returns a list of tuples where the tuples will contain the i-th element from each of the lists.\n", + "the zip function takes any number of lists of the same length and returns a generator for lists of tuples where the tuples will contain the i-th element from each of the lists.\n", + "\n", + "This is really useful when combining lists that are related (especially for looping)\n", "\n", - "This is really useful when combining lists that are related (especially for looping)" + "** remider ** to print a generator, just cast the results as a list (you can cast them as a tuple instead, actually)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "print(list(zip([1,2,3], [4,5,6])))" @@ -715,9 +727,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "sushi = ['salmon', 'tuna', 'sea urchin']\n", @@ -725,6 +735,15 @@ "\n", "sushi_and_prices = list(zip(sushi, prices))\n", "\n", + "sushi_and_prices" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "for sushi, price in sushi_and_prices:\n", " print(\"The {0} costs ${1}\".format(sushi, price))" ] @@ -744,9 +763,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "exotic_sushi = ['tako', 'toro', 'uni', 'hirame']\n", @@ -812,9 +829,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Lesson07_ListsandTuples/Lists and Tuples - after class.ipynb b/Lesson07_ListsandTuples/Lists and Tuples - after class.ipynb index afd439a..a7685c2 100644 --- a/Lesson07_ListsandTuples/Lists and Tuples - after class.ipynb +++ b/Lesson07_ListsandTuples/Lists and Tuples - after class.ipynb @@ -192,9 +192,7 @@ "output_type": "execute_result" } ], - "source": [ - "everyones_order[1][1]" - ] + "source": [] }, { "cell_type": "markdown", @@ -219,9 +217,7 @@ "output_type": "execute_result" } ], - "source": [ - "everyones_order[0][0]" - ] + "source": [] }, { "cell_type": "markdown", @@ -246,9 +242,7 @@ "output_type": "execute_result" } ], - "source": [ - "everyones_order[1][:]" - ] + "source": [] }, { "cell_type": "markdown", @@ -351,7 +345,7 @@ } ], "source": [ - "print('hamachi' in sushi_order)" + "print(('hamachi' in sushi_order))" ] }, { @@ -397,7 +391,7 @@ } ], "source": [ - "print(sushi_order * 3)" + "print((sushi_order * 3))" ] }, { @@ -435,7 +429,7 @@ } ], "source": [ - "print(prices + sushi_order)" + "print((prices + sushi_order))" ] }, { @@ -536,7 +530,7 @@ ], "source": [ "for item in sushi_order:\n", - " print(\"I'd like to order the {}.\".format(item))\n", + " print((\"I'd like to order the {}.\".format(item)))\n", " \n", "print(\"And hold the wasabi!\")" ] @@ -558,7 +552,7 @@ ], "source": [ "for ind, item in enumerate(sushi_order):\n", - " print(\"I'd like to order the {0} for {1}.\".format(item, prices[ind]))" + " print((\"I'd like to order the {0} for {1}.\".format(item, prices[ind])))" ] }, { @@ -725,7 +719,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [] }, @@ -763,10 +759,10 @@ "source": [ "numbers = [1, 1, 2, 3, 5, 8]\n", "\n", - "print(max(numbers))\n", - "print(min(numbers))\n", - "print(sum(numbers))\n", - "print(len(numbers))\n", + "print((max(numbers)))\n", + "print((min(numbers)))\n", + "print((sum(numbers)))\n", + "print((len(numbers)))\n", "\n" ] }, @@ -850,7 +846,7 @@ } ], "source": [ - "print(my_order is cooked_rolls)" + "print((my_order is cooked_rolls))" ] }, { @@ -914,7 +910,7 @@ ], "source": [ "noodles = ('soba', 'udon', 'ramen', 'lo mein', 'somen', 'rice noodle')\n", - "print(type(noodles))" + "print((type(noodles)))" ] }, { @@ -970,7 +966,7 @@ "source": [ "single_element_tuple = (1,)\n", "print(single_element_tuple)\n", - "print(type(single_element_tuple))" + "print((type(single_element_tuple)))" ] }, { @@ -997,8 +993,8 @@ } ], "source": [ - "print(noodles[0])\n", - "print(noodles[4:])" + "print((noodles[0]))\n", + "print((noodles[4:]))" ] }, { @@ -1077,7 +1073,7 @@ ], "source": [ "for noodle in noodles:\n", - " print(\"Yummy, yummy {0} and {1}\".format(noodle, 'sushi'))" + " print((\"Yummy, yummy {0} and {1}\".format(noodle, 'sushi')))" ] }, { @@ -1091,7 +1087,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [] }, @@ -1120,7 +1118,7 @@ } ], "source": [ - "print(list(zip([1,2,3], [4,5,6])))" + "print((list(zip([1,2,3], [4,5,6]))))" ] }, { @@ -1165,7 +1163,7 @@ ], "source": [ "for sushi, price in sushi_and_prices:\n", - " print(\"The {0} costs ${1}\".format(sushi, price))" + " print((\"The {0} costs ${1}\".format(sushi, price)))" ] }, { @@ -1199,7 +1197,7 @@ "source": [ "exotic_sushi = ['tako', 'toro', 'uni', 'hirame']\n", "for index, item in enumerate(exotic_sushi):\n", - " print(index, item)" + " print((index, item))" ] }, { diff --git a/Lesson07_ListsandTuples/Lists and Tuples.ipynb b/Lesson07_ListsandTuples/Lists and Tuples.ipynb index 9f80104..209bef0 100644 --- a/Lesson07_ListsandTuples/Lists and Tuples.ipynb +++ b/Lesson07_ListsandTuples/Lists and Tuples.ipynb @@ -124,14 +124,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### TRY IT" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1) To get dragon roll from the sushi order, first we get the second element (index 1) then we get the the second item (index 1)" + "To get dragon roll from the sushi order, first we get the second element (index 1) then we get the the second item (index 1)" ] }, { @@ -139,33 +132,30 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "everyones_order[1][1]" + ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "2) Print california roll from the list `everyones_order`:" + "### TRY IT" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "3) Print all items from the second person's order" + "Get the california roll from the list `everyones_order`. As a challenge print all the items from the second person's order." ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [] }, @@ -204,12 +194,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], - "source": [ - "prices[-1] = 21.00\n", - "print(prices)" - ] + "source": [] }, { "cell_type": "markdown", @@ -268,16 +257,6 @@ "print(sushi_order * 3)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "exprep = ['rep'+str(i) for i in range(5)]\n", - "exprep" - ] - }, { "cell_type": "code", "execution_count": null, @@ -291,18 +270,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note: You can only concatenate lists with lists! If you want to add a \"non-list\" element you can use the append() function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "newprices = prices.copy()\n", - "newprices.append(22)\n", - "print(newprices)" + "Note: You can only concatenate lists with lists! If you want to add a \"non-list\" element you can use the append() function that we will learn about in the next section." ] }, { @@ -311,7 +279,8 @@ "metadata": {}, "outputs": [], "source": [ - "prices" + "# WRONG\n", + "prices + 22" ] }, { @@ -358,8 +327,8 @@ "metadata": {}, "outputs": [], "source": [ - "for ind, item in enumerate(sushi_order):\n", - " print(\"I'd like to order the {0} for {1}.\".format(item, prices[ind]))" + "for idx, item in enumerate(sushi_order):\n", + " print(\"I'd like to order the {0} for {1}.\".format(item, prices[idx]))" ] }, { @@ -373,7 +342,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [] }, @@ -513,13 +484,17 @@ "metadata": {}, "source": [ "### TRY IT\n", - "Find the average of `numbers` using list functions (and not a loop!)" + "Find the average of `numbers` using list functions (and not a loop!)\n", + "\n", + "(And if you are feeling self-loathing, look back at lesson 4 and see how many lines of code it took to do this without aggregation functions)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [] }, @@ -707,7 +682,7 @@ "outputs": [], "source": [ "for noodle in noodles:\n", - " print(\"Yummy, yummy {0} and {1}\".format(noodle, 'sushi'))" + " print(\"Yummy, yummy {0}\".format(noodle))" ] }, { @@ -733,9 +708,11 @@ "source": [ "## Zip\n", "\n", - "the zip function takes any number of lists of the same length and returns a list of tuples where the tuples will contain the i-th element from each of the lists.\n", + "the zip function takes any number of lists of the same length and returns a generator for lists of tuples where the tuples will contain the i-th element from each of the lists.\n", + "\n", + "This is really useful when combining lists that are related (especially for looping)\n", "\n", - "This is really useful when combining lists that are related (especially for looping)" + "** remider ** to print a generator, just cast the results as a list (you can cast them as a tuple instead, actually)" ] }, { diff --git a/Lesson07_ListsandTuples/Party Budget SOLUTION.ipynb b/Lesson07_ListsandTuples/Party Budget SOLUTION.ipynb index 5507d1e..0b2db66 100644 --- a/Lesson07_ListsandTuples/Party Budget SOLUTION.ipynb +++ b/Lesson07_ListsandTuples/Party Budget SOLUTION.ipynb @@ -3,9 +3,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Part 1\n", @@ -29,9 +27,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Part 2\n", @@ -77,9 +73,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Lesson08_Dictionaries/.ipynb_checkpoints/Dictionary-checkpoint.ipynb b/Lesson08_Dictionaries/.ipynb_checkpoints/Dictionary-checkpoint.ipynb index 5e832a2..3704818 100644 --- a/Lesson08_Dictionaries/.ipynb_checkpoints/Dictionary-checkpoint.ipynb +++ b/Lesson08_Dictionaries/.ipynb_checkpoints/Dictionary-checkpoint.ipynb @@ -22,9 +22,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "fruit_season = {\n", @@ -34,8 +32,8 @@ " 'grape' : 'August'\n", "} \n", "\n", - "print type(fruit_season)\n", - "print fruit_season" + "print(type(fruit_season))\n", + "print(fruit_season)" ] }, { @@ -50,13 +48,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "raspberry_season = fruit_season['raspberry']\n", - "print raspberry_season" + "print(raspberry_season)" ] }, { @@ -69,12 +65,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "print fruit_season['mangos']" + "print(fruit_season['mangos'])" ] }, { @@ -89,13 +83,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "fruit_season['strawberry'] = 'May'\n", - "print fruit_season" + "print(fruit_season)" ] }, { @@ -110,13 +102,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "del fruit_season['strawberry']\n", - "print fruit_season" + "print(fruit_season)" ] }, { @@ -132,24 +122,20 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "duplicate_fruit_season = {\n", " 'raspberry': 'May',\n", " 'raspberry': 'June',\n", "} \n", - "print duplicate_fruit_season" + "print(duplicate_fruit_season)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "mutable_key = {\n", @@ -183,7 +169,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [] @@ -202,13 +188,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "print 'raspberry' in fruit_season\n", - "print 'mangos' in fruit_season" + "print('raspberry' in fruit_season)\n", + "print('mangos' in fruit_season)" ] }, { @@ -221,15 +205,13 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "if 'pineapple' in fruit_season:\n", - " print 'Lets eat tropical fruit'\n", + " print('Lets eat tropical fruit')\n", "else:\n", - " print \"Temperate fruit it is.\"" + " print(\"Temperate fruit it is.\")" ] }, { @@ -264,13 +246,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "for fruit in fruit_season:\n", - " print \"{0} is best in {1} (at least in Virginia)\".format(fruit.title(), fruit_season[fruit])" + " print(\"{0} is best in {1} (at least in Virginia)\".format(fruit.title(), fruit_season[fruit]))" ] }, { @@ -287,37 +267,31 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "print fruit_season.keys()\n", - "print fruit_season.values()\n", - "print fruit_season.items()" + "print(list(fruit_season.keys()))\n", + "print(list(fruit_season.values()))\n", + "print(list(fruit_season.items()))" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "for key, value in fruit_season.items():\n", - " print \"In {0} eat a {1}\".format(value, key)" + "for key, value in list(fruit_season.items()):\n", + " print(\"In {0} eat a {1}\".format(value, key))" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "print sorted(fruit_season.keys())" + "print(sorted(fruit_season.keys()))" ] }, { @@ -332,7 +306,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [] @@ -352,9 +326,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "my_complicated_dictionary = {\n", @@ -368,7 +340,7 @@ " },\n", " 9: [3, 3]\n", "}\n", - "print my_complicated_dictionary" + "print(my_complicated_dictionary)" ] }, { @@ -381,9 +353,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "true_fruit_season = {\n", @@ -393,24 +363,22 @@ " 'grape': ['August', 'September', 'October']\n", "} \n", "\n", - "print true_fruit_season" + "print(true_fruit_season)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "months = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']\n", "\n", "for month in months:\n", - " print 'It is {0}'.format(month)\n", - " for fruit, season in true_fruit_season.items():\n", + " print(('It is {0}'.format(month)))\n", + " for fruit, season in list(true_fruit_season.items()):\n", " if month in season:\n", - " print \"\\tEat {0}\".format(fruit)" + " print((\"\\tEat {0}\".format(fruit)))" ] }, { @@ -425,7 +393,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [] @@ -471,9 +439,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# If you have a list of adjectives\n", @@ -488,15 +454,13 @@ " first_char = i[0]\n", " if first_char in my_dict:\n", " my_dict[first_char].append(i)\n", - "print my_dict" + "print(my_dict)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Generating from a file\n", @@ -513,7 +477,7 @@ " if first_char in my_dict:\n", " my_dict[first_char].append(word)\n", " \n", - "print my_dict['A']" + "print(my_dict['A'])" ] }, { @@ -528,23 +492,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Lesson08_Dictionaries/Dictionary.ipynb b/Lesson08_Dictionaries/Dictionary.ipynb index 55c3408..3704818 100644 --- a/Lesson08_Dictionaries/Dictionary.ipynb +++ b/Lesson08_Dictionaries/Dictionary.ipynb @@ -168,7 +168,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [] }, @@ -268,9 +270,9 @@ "metadata": {}, "outputs": [], "source": [ - "print(fruit_season.keys())\n", - "print(fruit_season.values())\n", - "print(fruit_season.items())" + "print(list(fruit_season.keys()))\n", + "print(list(fruit_season.values()))\n", + "print(list(fruit_season.items()))" ] }, { @@ -279,7 +281,7 @@ "metadata": {}, "outputs": [], "source": [ - "for key, value in fruit_season.items():\n", + "for key, value in list(fruit_season.items()):\n", " print(\"In {0} eat a {1}\".format(value, key))" ] }, @@ -303,7 +305,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [] }, @@ -371,10 +375,10 @@ "months = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']\n", "\n", "for month in months:\n", - " print('It is {0}'.format(month))\n", - " for fruit, season in true_fruit_season.items():\n", + " print(('It is {0}'.format(month)))\n", + " for fruit, season in list(true_fruit_season.items()):\n", " if month in season:\n", - " print(\"\\tEat {0}\".format(fruit))" + " print((\"\\tEat {0}\".format(fruit)))" ] }, { @@ -388,7 +392,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [] }, diff --git a/Lesson08_Dictionaries/acrosticChallengeSOLUTION.py b/Lesson08_Dictionaries/acrosticChallengeSOLUTION.py index fa91c1d..0bb1fda 100644 --- a/Lesson08_Dictionaries/acrosticChallengeSOLUTION.py +++ b/Lesson08_Dictionaries/acrosticChallengeSOLUTION.py @@ -35,7 +35,7 @@ def acrostic(name): for letter in name: rand_idx = random.randint(0, len(adjectives[letter]) - 1) current_adj = adjectives[letter][rand_idx] - print "{0}-{1}".format(letter, current_adj) + print(("{0}-{1}".format(letter, current_adj))) acrostic('Charlotte') diff --git a/Lesson08_Dictionaries/acrosticSOLUTION.py b/Lesson08_Dictionaries/acrosticSOLUTION.py index 0fb8184..24893f9 100644 --- a/Lesson08_Dictionaries/acrosticSOLUTION.py +++ b/Lesson08_Dictionaries/acrosticSOLUTION.py @@ -32,7 +32,7 @@ def acrostic(name): name = name.upper() for letter in name: current_adj = adjectives[letter] - print "{0}-{1}".format(letter, current_adj) + print(("{0}-{1}".format(letter, current_adj))) acrostic('Charlotte') diff --git a/Lesson09_Project/MF Promotions/MF Promotions Solution.ipynb b/Lesson09_Project/MF Promotions/MF Promotions Solution.ipynb index ae0d8e7..c92c0b7 100644 --- a/Lesson09_Project/MF Promotions/MF Promotions Solution.ipynb +++ b/Lesson09_Project/MF Promotions/MF Promotions Solution.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": true }, @@ -13,19 +13,9 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'new': True, 'level': 0, 'score': 61.72882823379742, 'sex': 'woman'}\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "def generateRandomPerson(level=0, new=True):\n", " score = 0\n", @@ -40,15 +30,13 @@ " 'level': level,\n", " 'new': new\n", " }\n", - "print generateRandomPerson()" + "print(generateRandomPerson())" ] }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "def generateStaff(levels):\n", @@ -60,10 +48,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "def allAreNew(staff):\n", @@ -76,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "collapsed": true }, @@ -93,10 +79,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "def pickBest(level):\n", @@ -111,14 +95,12 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "def promote(staff):\n", - " for level in reversed(range(len(staff))):\n", + " for level in reversed(list(range(len(staff)))):\n", " newlevel = []\n", " for idx, employee in enumerate(staff[level]):\n", " if employee is None:\n", @@ -138,10 +120,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "def mfratio(level):\n", @@ -157,26 +137,9 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(235, 265, 0.47, 0.53)\n", - "(176, 174, 0.5028571428571429, 0.49714285714285716)\n", - "(108, 92, 0.54, 0.46)\n", - "(70, 80, 0.4666666666666667, 0.5333333333333333)\n", - "(59, 41, 0.59, 0.41)\n", - "(37, 38, 0.49333333333333335, 0.5066666666666667)\n", - "(23, 17, 0.575, 0.425)\n", - "(6, 4, 0.6, 0.4)\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "levels = [500, 350, 200, 150, 100, 75, 40, 10]\n", "attrition = 0.15\n", @@ -187,84 +150,18 @@ " staff = promote(staff)\n", "# print results\n", "for level in staff:\n", - " print mfratio(level)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[{'new': True, 'level': 7, 'score': 74.43470909236358, 'sex': 'man'}, {'new': True, 'level': 7, 'score': 75.17777956219028, 'sex': 'woman'}, {'new': True, 'level': 7, 'score': 76.13682393415695, 'sex': 'man'}, {'new': True, 'level': 7, 'score': 82.32275173464656, 'sex': 'man'}, {'new': True, 'level': 7, 'score': 75.64152362961048, 'sex': 'man'}, {'new': True, 'level': 7, 'score': 74.13941485421459, 'sex': 'man'}, {'new': True, 'level': 7, 'score': 69.46617940781046, 'sex': 'woman'}, {'new': True, 'level': 7, 'score': 73.58898026601628, 'sex': 'woman'}, {'new': True, 'level': 7, 'score': 70.8767501187351, 'sex': 'man'}, {'new': True, 'level': 7, 'score': 69.10711788124188, 'sex': 'man'}]\n" - ] - } - ], - "source": [ - "print staff[-1]" + " print(mfratio(level))" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ - "\n" + "print(staff[-1])" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -277,23 +174,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Lesson09_Project/MF Promotions/MF Promotions-Template.ipynb b/Lesson09_Project/MF Promotions/MF Promotions-Template.ipynb index ac278b5..6b27c79 100644 --- a/Lesson09_Project/MF Promotions/MF Promotions-Template.ipynb +++ b/Lesson09_Project/MF Promotions/MF Promotions-Template.ipynb @@ -2,9 +2,7 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "We are going to replacte the results in this paper: http://www.ruf.rice.edu/~lane/papers/male_female.pdf Take a minute to read the paper. They found that small biases in evalutations of men and women in the workplace can result in a significan underrepresntation of women in top roles.\n", "\n", @@ -36,9 +34,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def generateRandomPerson(level=0, new=True):\n", @@ -60,15 +56,13 @@ " 'level': level,\n", " 'new': new\n", " }\n", - "print generateRandomPerson()" + "print(generateRandomPerson())" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def generateStaff(levels):\n", @@ -85,9 +79,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def allAreNew(staff):\n", @@ -115,9 +107,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def pickBest(staff_level):\n", @@ -131,9 +121,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def promote(staff):\n", @@ -149,9 +137,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def mfratio(staff_level):\n", @@ -164,9 +150,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Main program\n", @@ -183,23 +167,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Lesson09_Project/PasswordHacker/Passwords.ipynb b/Lesson09_Project/PasswordHacker/Passwords.ipynb index 37092d8..454ebe3 100644 --- a/Lesson09_Project/PasswordHacker/Passwords.ipynb +++ b/Lesson09_Project/PasswordHacker/Passwords.ipynb @@ -70,9 +70,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def check_passwords_plus_number(user):\n", @@ -107,9 +105,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def check_passwords_plus_num_and_special_char(user):\n", @@ -127,9 +123,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def common_replacements(user):\n", @@ -147,9 +141,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Get a list of all passwords from the password file\n", @@ -161,38 +153,31 @@ " check_passwords_plus_special_char(u) or check_passwords_plus_num_and_special_char(u) or \\\n", " common_replacements(u)\n", " if password:\n", - " print u, password\n", + " print(u, password)\n", " else:\n", - " print \"Oops\"\n" + " print(\"Oops\")\n" ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Lesson09_Project/PasswordHacker/PasswordsSOLUTION.ipynb b/Lesson09_Project/PasswordHacker/PasswordsSOLUTION.ipynb index aa0a017..64e498a 100644 --- a/Lesson09_Project/PasswordHacker/PasswordsSOLUTION.ipynb +++ b/Lesson09_Project/PasswordHacker/PasswordsSOLUTION.ipynb @@ -59,9 +59,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def check_passwords_plus_number(user):\n", @@ -118,9 +116,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def check_passwords_plus_num_and_special_char(user):\n", @@ -151,9 +147,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def common_replacements(user):\n", @@ -171,7 +165,7 @@ " 'o': '0',\n", " }\n", " for p in passwords:\n", - " for letter, replacement in letters_to_numbers.items():\n", + " for letter, replacement in list(letters_to_numbers.items()):\n", " translated_password = p.replace(letter, replacement)\n", " success = check_user_pass(u, translated_password)\n", " if success:\n", @@ -183,9 +177,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "passwords = getallpasswords()\n", @@ -194,9 +186,9 @@ " check_passwords_plus_special_char(u) or check_passwords_plus_num_and_special_char(u) or \\\n", " common_replacements(u)\n", " if password:\n", - " print u, password\n", + " print(u, password)\n", " else:\n", - " print \"Oops\"" + " print(\"Oops\")" ] }, { @@ -211,23 +203,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Lesson09_Project/PasswordHacker/password_check.py b/Lesson09_Project/PasswordHacker/password_check.py index e49efa0..f036207 100644 --- a/Lesson09_Project/PasswordHacker/password_check.py +++ b/Lesson09_Project/PasswordHacker/password_check.py @@ -1,8 +1,8 @@ def check_user_pass(user, password): - """ - Checks to see if the user and password are a match - Returns: True if match, False if not - """ + """ + Checks to see if the user and password are a match + Returns: True if match, False if not + """ user_pass = { 'avery': 'rosebud', 'bruce': 'harley', diff --git a/Lesson10_Regexs/DocClerkSOLUTION.py b/Lesson10_Regexs/DocClerkSOLUTION.py index bce0a14..1596425 100644 --- a/Lesson10_Regexs/DocClerkSOLUTION.py +++ b/Lesson10_Regexs/DocClerkSOLUTION.py @@ -13,7 +13,7 @@ # print the paragraph if we found a match to the regex if line == '\n': if found_match: - print ''.join(paragraph) + print((''.join(paragraph))) # Reset found_match and paragraph for the next paragraph found_match = False paragraph = [] diff --git a/Lesson10_Regexs/RegularExpressions.ipynb b/Lesson10_Regexs/RegularExpressions.ipynb index f7f5ca4..773a0ad 100644 --- a/Lesson10_Regexs/RegularExpressions.ipynb +++ b/Lesson10_Regexs/RegularExpressions.ipynb @@ -112,49 +112,41 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "print re.match('a', 'abcde')\n", - "print re.match('c', 'abcde')" + "print(re.match('a', 'abcde'))\n", + "print(re.match('c', 'abcde'))" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "print re.search('a', 'abcde')\n", - "print re.search('c', 'abcde')" + "print(re.search('a', 'abcde'))\n", + "print(re.search('c', 'abcde'))" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "print re.match('version', django_logs)\n", - "print re.search('version', django_logs)" + "print(re.match('version', django_logs))\n", + "print(re.search('version', django_logs))" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "if re.search('commit', django_logs):\n", - " print \"Someone has been doing work.\"" + " print(\"Someone has been doing work.\")" ] }, { @@ -168,9 +160,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -207,45 +197,39 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Start simple, match any character 2 times\n", - "print re.search('..', django_logs)\n", + "print(re.search('..', django_logs))\n", "\n", "# just to prove it works\n", - "print re.search('..', 'aa')\n", - "print re.search('..', 'a')\n", - "print re.search('..', '^%')" + "print(re.search('..', 'aa'))\n", + "print(re.search('..', 'a'))\n", + "print(re.search('..', '^%'))" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# to match a commit hash (numbers and letters a-f repeated) we can use a regex\n", "commit_pattern = '[0-9a-f]+'\n", - "print re.search(commit_pattern, django_logs)\n" + "print(re.search(commit_pattern, django_logs))\n" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Let's match the time syntax\n", "time_pattern = '\\d\\d:\\d\\d:\\d\\d'\n", "time_pattern = '\\d{2}:\\d{2}:\\d{2}'\n", - "print re.search(time_pattern, django_logs)" + "print(re.search(time_pattern, django_logs))" ] }, { @@ -259,9 +243,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -279,13 +261,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "print re.search('markus holtermann', django_logs)\n", - "print re.search('markus holtermann', django_logs, re.IGNORECASE)" + "print(re.search('markus holtermann', django_logs))\n", + "print(re.search('markus holtermann', django_logs, re.IGNORECASE))" ] }, { @@ -299,9 +279,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -321,15 +299,13 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Let's match the time syntax\n", "time_pattern = '\\d\\d:\\d\\d:\\d\\d'\n", "m = re.search(time_pattern, django_logs)\n", - "print m.group(0)" + "print(m.group(0))" ] }, { @@ -344,13 +320,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "time_pattern = '\\d\\d:\\d\\d:\\d\\d'\n", - "print re.findall(time_pattern, django_logs)" + "print(re.findall(time_pattern, django_logs))" ] }, { @@ -366,32 +340,28 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "time_pattern = '(\\d\\d):\\d\\d:\\d\\d'\n", "hours = re.findall(time_pattern, django_logs)\n", - "print sorted(hours)" + "print(sorted(hours))" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# you can capture more than one match\n", "time_pattern = '(\\d\\d):(\\d\\d):\\d\\d'\n", "times = re.findall(time_pattern, django_logs)\n", - "print times\n", + "print(times)\n", "\n", "# Unpacking the tuple in the first line\n", "for hours, mins in times:\n", - " print \"{} hr {} min\".format(hours, mins)" + " print(\"{} hr {} min\".format(hours, mins))" ] }, { @@ -405,9 +375,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -469,23 +437,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Lesson11_JSONandAPIs/JSONandAPIs.ipynb b/Lesson11_JSONandAPIs/JSONandAPIs.ipynb index 7061bfa..cfde599 100644 --- a/Lesson11_JSONandAPIs/JSONandAPIs.ipynb +++ b/Lesson11_JSONandAPIs/JSONandAPIs.ipynb @@ -64,29 +64,25 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Load from file\n", "with open('shapes.json') as fh:\n", " shapes = json.load(fh)\n", - "print shapes" + "print(shapes)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Load from string\n", "complex_shapes_string = '[\"pentagon\", \"spiral\", \"double helix\"]'\n", "complex_shapes = json.loads(complex_shapes_string)\n", - "print complex_shapes" + "print(complex_shapes)" ] }, { @@ -117,15 +113,13 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "for shape in shapes:\n", " title_shape = shape.title()\n", " area_formula = shapes[shape]['area']\n", - " print \"{}'s area can be calculated using {}\".format(title_shape, area_formula)" + " print(\"{}'s area can be calculated using {}\".format(title_shape, area_formula))" ] }, { @@ -161,23 +155,19 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Dumping to string\n", "favorite_shapes = ['hexagon', 'heart']\n", "fav_shapes_json = json.dumps(favorite_shapes)\n", - "print fav_shapes_json" + "print(fav_shapes_json)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Dumping to a file\n", @@ -422,19 +412,17 @@ }, "outputs": [], "source": [ - "import urllib2" + "import urllib.request, urllib.error, urllib.parse" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "game_id = str(251990)\n", - "connection = urllib2.urlopen('http://store.steampowered.com/api/appdetails?appids=' + game_id)\n", + "connection = urllib.request.urlopen('http://store.steampowered.com/api/appdetails?appids=' + game_id)\n", "data = connection.read()\n", "type(data)" ] @@ -449,13 +437,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "game_data = json.loads(data)\n", - "print type(game_data)" + "print(type(game_data))" ] }, { @@ -468,14 +454,12 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "print game_data[game_id]['data']['name']\n", - "print game_data[game_id]['data']['about_the_game']\n", - "print game_data[game_id]['data']['price_overview']['final']\n" + "print(game_data[game_id]['data']['name'])\n", + "print(game_data[game_id]['data']['about_the_game'])\n", + "print(game_data[game_id]['data']['price_overview']['final'])\n" ] }, { @@ -525,23 +509,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Lesson12_TabularData/Tabular Data.ipynb b/Lesson12_TabularData/Tabular Data.ipynb index 533fc69..37d94d6 100644 --- a/Lesson12_TabularData/Tabular Data.ipynb +++ b/Lesson12_TabularData/Tabular Data.ipynb @@ -40,9 +40,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "with open('walks.csv', 'r') as fh:\n", @@ -68,15 +66,13 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "with open('walks.csv', 'r') as fh:\n", " reader = csv.reader(fh, delimiter=',')\n", " for row in reader:\n", - " print row " + " print(row) " ] }, { @@ -117,17 +113,15 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "with open('walks.csv', 'r') as fh:\n", " reader = csv.reader(fh, delimiter=',')\n", - " header = reader.next()\n", + " header = next(reader)\n", " for row in reader:\n", - " print row \n", - " print \"Header\", header" + " print(row) \n", + " print(\"Header\", header)" ] }, { @@ -142,17 +136,15 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "with open('walks.csv', 'r') as fh:\n", " reader = csv.reader(fh, delimiter=',')\n", - " header = reader.next()\n", + " header = next(reader)\n", " for row in reader:\n", " float_row = [float(row[0]), float(row[1])]\n", - " print float_row " + " print(float_row) " ] }, { @@ -184,16 +176,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Let's find the average distance for all walks. \n", "\n", "with open('walks.csv', 'r') as fh:\n", " reader = csv.reader(fh, delimiter=',')\n", - " header = reader.next()\n", + " header = next(reader)\n", " # Empty list for storing all distances\n", " walks = []\n", " for row in reader:\n", @@ -206,21 +196,19 @@ " \n", " # Use list aggregation methods to get average distance\n", " ave_dist = sum(walks) / len(walks)\n", - " print \"Average distance walked: {0:.1f}\".format(ave_dist)" + " print(\"Average distance walked: {0:.1f}\".format(ave_dist))" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Let's see our pace for each walk\n", "with open('walks.csv', 'r') as fh:\n", " reader = csv.reader(fh, delimiter=',')\n", - " header = reader.next()\n", + " header = next(reader)\n", " for row in reader:\n", " #distance is in the first column\n", " dist = row[0]\n", @@ -232,7 +220,7 @@ " time_minutes = float(time_minutes)\n", " # calculate pace as minutes / kilometer\n", " pace = time_minutes /dist\n", - " print \"Pace: {0:.1f} min/km\".format(pace)\n", + " print(\"Pace: {0:.1f} min/km\".format(pace))\n", " \n", "# If you want a challenge, try to make this seconds/mile" ] @@ -240,9 +228,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# We can filter data. Let's get the ave pace only for walks longer than\n", @@ -251,7 +237,7 @@ "# Let's see our pace for each walk\n", "with open('walks.csv', 'r') as fh:\n", " reader = csv.reader(fh, delimiter=',')\n", - " header = reader.next()\n", + " header = next(reader)\n", " paces = []\n", " for row in reader:\n", " #distance is in the first column\n", @@ -268,7 +254,7 @@ " paces.append(pace)\n", " \n", "ave_pace = sum(paces) / len(paces)\n", - "print \"Average walking pace: {0:.1f} min/km\".format(ave_pace)" + "print(\"Average walking pace: {0:.1f} min/km\".format(ave_pace))" ] }, { @@ -281,15 +267,13 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Lets see our pace for each walk\n", "with open('walks.csv', 'r') as fh:\n", " reader = csv.reader(fh, delimiter=',')\n", - " header = reader.next()\n", + " header = next(reader)\n", " # This is the dictionary we will put our data from the csv into\n", " # The key's are the column headers and the values is a list of\n", " # all the data in that column (transformed into floats)\n", @@ -307,7 +291,7 @@ " # append data to dictionary's list for that column\n", " data[column].append(data_point)\n", " # look at that beautiful data. You can do anything with that!\n", - " print data\n", + " print(data)\n", " " ] }, @@ -349,9 +333,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import random\n", @@ -427,23 +409,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Lesson13_GUIs/GUI.ipynb b/Lesson13_GUIs/GUI.ipynb index 4f75900..fb70555 100644 --- a/Lesson13_GUIs/GUI.ipynb +++ b/Lesson13_GUIs/GUI.ipynb @@ -25,12 +25,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "import Tkinter" + "import tkinter" ] }, { @@ -47,9 +45,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "root = Tkinter.Tk()" @@ -93,9 +89,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "root.destroy()" @@ -112,9 +106,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -147,9 +139,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# A basic button\n", @@ -165,14 +155,12 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# a button with a callback\n", "def my_callback():\n", - " print \"Here i am\"\n", + " print(\"Here i am\")\n", "\n", "root = Tkinter.Tk()\n", "# Widgets go here\n", @@ -196,9 +184,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# A basic label\n", @@ -265,9 +251,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# A basic entry\n", @@ -280,14 +264,12 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# An entry where we get the text entered.\n", "def what_entered():\n", - " print e.get()\n", + " print(e.get())\n", "\n", "root = Tkinter.Tk()\n", "\n", @@ -377,9 +359,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# A basic button\n", @@ -407,9 +387,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# A basic button\n", @@ -560,23 +538,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/Lesson14_NumpyAndMatplotlib/matplotlib.ipynb b/Lesson14_NumpyAndMatplotlib/matplotlib.ipynb index dddbc62..03556f0 100644 --- a/Lesson14_NumpyAndMatplotlib/matplotlib.ipynb +++ b/Lesson14_NumpyAndMatplotlib/matplotlib.ipynb @@ -1,764 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plotting with matplotlib" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Getting Started" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.1 What is matplotlib?\n", - "\n", - "Matplotlib is the most popular and mature library for plotting data using\n", - "Python. It has all of the functionality you would expect, including the ability to control\n", - "the formatting of plots and figures at a very fine level.\n", - "\n", - "The official matplotlib documentation is at http://matplotlib.org/ \n", - "The matplotlib gallery is at http://matplotlib.org/gallery.html" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.2 Importing matplotlib\n", - "\n", - "Matplotlib is often used through 'pyplot', which provides a high-level interface for\n", - "plotting." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# In IPython or the IPython notebook, it's easiest to use the pylab magic, which\n", - "# imports matplotlib, numpy, and scipy.\n", - "\n", - "# The matplotlib notebook flag means that plots will be shown interactively in the\n", - "# notebooks, rather than in pop-up windows.\n", - "\n", - "%matplotlib notebook\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Creating Figures\n", - "\n", - "There are two major challenges with creating figures. First is understanding the\n", - "syntax to actually make the basic plot appear. Second is formatting the basic plot to look\n", - "exactly how you would like it to look. In general, the formatting will probably take you\n", - "longer...\n", - "\n", - "Within pyplot (currently imported as 'plt'), there are two basic ways to go about making\n", - "plots - using the Matlab-like clone, and using the object-oriented approach. The latter\n", - "provides better control over plot features, while only requiring slightly more typing. It's\n", - "easy to quickly outgrow the Matlab clone, so we'll go right to the object-oriented syntax." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.1 A first plot\n", - "\n", - "In simple matplotlib plotting, there are two concepts to distinguish:\n", - "\n", - "- __Figure__ - the entire figure, like what you might see in a journal, including all\n", - "subplots, axes, lines, labels, etc. The whole enchilada. \n", - " \n", - "- __Subplot/Axes__ - one of the sub-sections of the figure, labeled (a), (b), etc. in\n", - "articles. Each subplot will contain one Axes object, which is the container where all of the\n", - "useful stuff, such as actual lines, legends, labels, etc., are actually housed.\n", - "\n", - "For example, here's how to make one figure with two subplots, the second of which contains\n", - "two lines." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# First we make some data to plot\n", - "x = np.linspace(-2*np.pi, 2*np.pi)\n", - "y1 = np.sin(x)\n", - "y2 = np.cos(x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, create an empty figure with 2 subplots\n", - "using the subplots method\n", - " \n", - " figure, axes = plt.subplots(rows, columns)\n", - "\n", - "- The arguments (1, 2) indicate 1 row and 2 cols\n", - "- The function plt.subplots returns an object for the figure and for each axes\n", - "- There are multiple ways to accomplish this same goal, but this is probably the simplest - notice that each subplot is associated with one of the axes objects." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1,2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's actually plot the data using the plot method on an axis\n", - "\n", - " axis.plot(x, y)\n", - " \n", - "You can plot multiple lines on an axis " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "fig, axes = plt.subplots(1,2)\n", - "\n", - "# We plot one line on the first axis\n", - "axes[0].plot(x, y1)\n", - "# and both lines on the second axis\n", - "axes[1].plot(x, y1)\n", - "axes[1].plot(x, y2);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Many of the basic formatting problems you have will be solved by the magic of `tight_layou`t. Before you start tweaking how you figure looks, try it out.\n", - "\n", - " plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1,2)\n", - "axes[0].plot(x, y1)\n", - "axes[1].plot(x, y1)\n", - "axes[1].plot(x, y2)\n", - "\n", - "plt.tight_layout();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To save your figure you can use the `savefig` command:\n", - "\n", - " fig.savefig('fileanme', format='png')\n", - " \n", - "Format options include png, pdf, ps, eps and svg" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1,2)\n", - "axes[0].plot(x, y1)\n", - "axes[1].plot(x, y1)\n", - "axes[1].plot(x, y2)\n", - "\n", - "fig.savefig('first_plot.png', format='png');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TRY IT\n", - "Create a line graph plotting the function f(x) = x^3 for values of x 0-10." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Formatting Figures\n", - "\n", - "The formatting of figures often takes longer than actually setting them up and adding data. There are many different approaches to formatting figures in matplotlib (many goals can be accomplished in different ways, using different commands), and you will come across many of these as you learn more. The tips below give a few simple ways to get started.\n", - "\n", - "### 3.1 Line formatting\n", - "\n", - "The plot method has several available keyword arguments that you can use to change the line formatting.\n", - "\n", - "* color - Chages color of line. examples: 'red', 'blue', 'r', 'k', 0.5, '#ffaa00', (0,0.5,0.75)\n", - "* linewidth - Weight of line. Takes float value in points (like font)\n", - "* linestyle - Solid, dashed, or other. examples: -, --, -." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "x = np.linspace(-2*np.pi, 2*np.pi)\n", - "y1 = np.sin(x)\n", - "y2 = np.cos(x)\n", - "\n", - "fig, axes = plt.subplots(1,2)\n", - "axes[0].plot(x, y1, color='r', linewidth=5)\n", - "axes[1].plot(x, y1, color='#ffaa00', linewidth=0.5, linestyle='--')\n", - "axes[1].plot(x, y2, color='green', linestyle='-.');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.2 Tick marks\n", - "\n", - "You can set the values where the ticks are located using the `xticks` and `yticks` methods.\n", - "\n", - "They take a list of values\n", - "\n", - " plt.xticks([val1, val2, val3])\n", - " plt.yticks([yval1, yval2, yval3])\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [], - "source": [ - "x = np.linspace(-2*np.pi, 2*np.pi)\n", - "y1 = np.sin(x)\n", - "y2 = np.cos(x)\n", - "\n", - "fig, axes = plt.subplots(1,2)\n", - "axes[0].plot(x, y1, color='r', linewidth=5)\n", - "axes[1].plot(x, y1, color='#ffaa00', linewidth=0.5, linestyle='--')\n", - "axes[1].plot(x, y2, color='green', linestyle='-.')\n", - "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n", - "plt.yticks([-1, 0, 1]);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Oh no! That changed it for the last plot but not for the first plot. \n", - "\n", - "To set each plot individually, you need to set the current axis to the subplot you are interested in using the method `sca`. Then you can use `xticks` and `yticks`.\n", - "\n", - " plt.sca(axis)\n", - " plt.xticks([val1, val2, val3])\n", - " plt.yticks([yval1, yval2, yval3])\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "x = np.linspace(-2*np.pi, 2*np.pi)\n", - "y1 = np.sin(x)\n", - "y2 = np.cos(x)\n", - "\n", - "fig, axes = plt.subplots(1,2)\n", - "axes[0].plot(x, y1, color='r', linewidth=5)\n", - "axes[1].plot(x, y1, color='#ffaa00', linewidth=0.5, linestyle='--')\n", - "axes[1].plot(x, y2, color='green', linestyle='-.')\n", - "\n", - "# Set the current axis to the first subplot\n", - "fig.sca(axes[0])\n", - "# set x and y ticks\n", - "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n", - "plt.yticks([-1, 0, 1])\n", - "\n", - "# Set the current axis to the second subplot\n", - "fig.sca(axes[1])\n", - "# set x and y ticks\n", - "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n", - "plt.yticks([-1, 0, 1]);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.3 Axis limits\n", - "\n", - "Setting the limits of the axes is very similar to setting the ticks. The command to set the limits are `xlim` and `ylim`. Remember if you have more than one subplot, you will need to set the current axis before you set that axis' limits.\n", - "\n", - " plt.sca(axis)\n", - " plt.xlim(xmin, xmax)\n", - " plt.ylim(ymin, ymax)\n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "x = np.linspace(-2*np.pi, 2*np.pi)\n", - "y1 = np.sin(x)\n", - "y2 = np.cos(x)\n", - "\n", - "fig, axes = plt.subplots(1,2)\n", - "axes[0].plot(x, y1, color='r', linewidth=5)\n", - "axes[1].plot(x, y1, color='#ffaa00', linewidth=0.5, linestyle='--')\n", - "axes[1].plot(x, y2, color='green', linestyle='-.')\n", - "\n", - "# Set the current axis to the first subplot\n", - "fig.sca(axes[0])\n", - "# set x and y ticks\n", - "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n", - "plt.yticks([-1, 0, 1])\n", - "\n", - "\n", - "# set x and y limits\n", - "plt.xlim(-np.pi, np.pi)\n", - "plt.ylim(-1, 1)\n", - "\n", - "# Set the current axis to the second subplot\n", - "fig.sca(axes[1])\n", - "# set x and y ticks\n", - "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n", - "plt.yticks([-1, 0, 1])\n", - "\n", - "\n", - "# set x and y limits\n", - "plt.xlim(-np.pi, np.pi)\n", - "plt.ylim(-1, 1);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.4 Setting tick labels\n", - "\n", - "To set the tick labels, you pass a second parameter to the `xticks` and `yticks` methods with a list of labels for that axis. \n", - "\n", - " plt.sca(axis)\n", - " plt.xticks([tickvalues], [ticklabels])\n", - " plt.yticks([tickvalues], [ticklabels])\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "x = np.linspace(-2*np.pi, 2*np.pi)\n", - "y1 = np.sin(x)\n", - "y2 = np.cos(x)\n", - "\n", - "fig, axes = plt.subplots(1,2)\n", - "axes[0].plot(x, y1, color='r', linewidth=5)\n", - "axes[1].plot(x, y1, color='#ffaa00', linewidth=0.5, linestyle='--')\n", - "axes[1].plot(x, y2, color='green', linestyle='-.')\n", - "\n", - "# Set the current axis to the first subplot\n", - "fig.sca(axes[0])\n", - "# set x and y ticks\n", - "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], ['-pi', '-pi/2', '0', 'pi/2', 'pi'])\n", - "# You probably don't want to set the labels when you just want the exact numbers.\n", - "plt.yticks([-1, 0, 1], ['-1', '0', '1'])\n", - "\n", - "\n", - "# set x and y limits\n", - "plt.xlim(-np.pi, np.pi)\n", - "plt.ylim(-1, 1)\n", - "\n", - "# Set the current axis to the second subplot\n", - "fig.sca(axes[1])\n", - "# set x and y ticks\n", - "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], ['-pi', '-pi/2', '0', 'pi/2', 'pi'])\n", - "plt.yticks([-1, 0, 1], ['-1', '0', '1'])\n", - "\n", - "\n", - "# set x and y limits\n", - "plt.xlim(-np.pi, np.pi)\n", - "plt.ylim(-1, 1);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.5 Legend\n", - "\n", - "When you create a line on a plot, you can pass it a keyword argument `label` and then create a legend that will use that label using the `legend` method. The `legend` method takes an optional parameter of `loc` for the location of the legend. You can see the values allowed here: http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.legend This is another one of those commands that you need to set the current axis if you have more than one subplot.\n", - "\n", - " plt.plot(x,y, label='my_label')\n", - " plt.legend(loc='best')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "x = np.linspace(-2*np.pi, 2*np.pi)\n", - "y1 = np.sin(x)\n", - "y2 = np.cos(x)\n", - "\n", - "fig, axes = plt.subplots(1,2)\n", - "# Let's set labels here\n", - "axes[0].plot(x, y1, color='r', linewidth=5, label='sin(x)')\n", - "axes[1].plot(x, y1, color='#ffaa00', linewidth=0.5, linestyle='--', label='sin(x)')\n", - "axes[1].plot(x, y2, color='green', linestyle='-.', label='cos(x)')\n", - "\n", - "# Set the current axis to the first subplot\n", - "fig.sca(axes[0])\n", - "# set x and y ticks\n", - "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], ['-pi', '-pi/2', '0', 'pi/2', 'pi'])\n", - "# You probably don't want to set the labels when you just want the exact numbers.\n", - "plt.yticks([-1, 0, 1], ['-1', '0', '1'])\n", - "plt.legend(loc='best')\n", - "\n", - "# set x and y limits\n", - "plt.xlim(-np.pi, np.pi)\n", - "plt.ylim(-1, 1)\n", - "\n", - "# Set the current axis to the second subplot\n", - "fig.sca(axes[1])\n", - "# set x and y ticks\n", - "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], ['-pi', '-pi/2', '0', 'pi/2', 'pi'])\n", - "plt.yticks([-1, 0, 1], ['-1', '0', '1'])\n", - "plt.legend(loc='upper right')\n", - "\n", - "# set x and y limits\n", - "plt.xlim(-np.pi, np.pi)\n", - "plt.ylim(-1, 1);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TRY IT\n", - "Go back to your plot of f(x) = x^3 for values of x 0-10. Try out some of the formatting options you just learned to make your plot look \"just right\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Other types of plots\n", - "\n", - "Matplotlib is more than just line plots, let's see what else it can do.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.1 Other plots\n", - "\n", - "In the examples above, we used the plot method to make line plots. There are also methods to\n", - "make scatter plots, barplots, histograms, loglog plots, semilog plots, etc.\n", - "\n", - " # Bar graph\n", - " ax.bar(x, y)\n", - " \n", - " # Scatter plot\n", - " ax.scatter(x,y)\n", - " \n", - " # Horizontal bar plot\n", - " ax.barh(x,y)\n", - " \n", - " # Boxplot\n", - " ax.boxplot(x)\n", - " \n", - " # Log-log plot\n", - " ax.loglog(x,y)\n", - " \n", - " # Semilog plot\n", - " ax.semilogx(x,y)\n", - " ax.semilogy(x,y)\n", - " \n", - "Plots too squished? Check out plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Make some data to plot\n", - "x = np.arange(0, 100)\n", - "y = np.random.rand(100) # 100 random numbers\n", - "\n", - "# Make a figure with 6 subplots and axes\n", - "# Notice that we are doing some arguement unpacking to get six subplots. You can use indexing instead if you prefer\n", - "fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(3, 2)\n", - "\n", - "# Add data to each axis. Optional arguments to each method will customize each plot.\n", - "ax1.bar(x,y)\n", - "ax2.scatter(x,y)\n", - "ax3.barh(x,y)\n", - "ax4.boxplot(x)\n", - "ax5.loglog(x,y)\n", - "ax6.semilogx(x,y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Many of the same formatting options as the line plot are available for these additional plots. There are also some other options. The gallery (section 5) is the best place to find all the options. \n", - "\n", - "Let's try changing the marker on the scatter plot: http://matplotlib.org/exmples/lines_bars_and_markers/marker_reference.html\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(1,1)\n", - "ax.scatter(x, y, marker='x')\n", - "ax.scatter(x, y + 2, marker='>', color='#00aaff')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.2 Plotting images\n", - "\n", - "Matplotlib also makes it easy to plot images. For this, you can use the plot method `imshow`\n", - "(syntax borrowed from Matlab).\n", - "\n", - "To load in an image we use the `imread` function. This takes a file path and reads the file into a numpy ndarray\n", - "\n", - "To plot an image, you can use `imshow` function giving it an array. \n", - "\n", - "A 1D array will be rendered as grayscale and a 3D or (4D with transparency) array will be a full color image.\n", - "\n", - "To set the colormap of a grayscale image, you can use the optional `cmap` key word argument to `imshow`. Options available are listed here: http://matplotlib.org/examples/color/colormaps_reference.html" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Read image from file and display it\n", - "img1 = plt.imread('astronaut.png')\n", - "# Uncomment following line to prove it still works without the alpha channel\n", - "# img1 = img1[:,:, 0:3]\n", - "fig, ax = plt.subplots(1,1)\n", - "ax.imshow(img1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# We can plot random noise in the viridis colormap.\n", - "img2 = np.random.rand(128, 128)\n", - "\n", - "fig, ax = plt.subplots(1,1)\n", - "ax = ax.imshow(img2, cmap='viridis')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TRY IT\n", - "Plot the cubes from 1-10 in a vertical bar chart and in a scatter plot (2 separate subplots). Change the colors of the bars to green. Change the marker of the scatter plot to plus signs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. The matplotlib gallery\n", - "\n", - "It can be very intimidating to try to craft exactly the figure that you want, especially if\n", - "you are used to being able to adjust things visually using a program like Excel.\n", - "\n", - "If you get stuck and don't know where to start, or just want to learn more about what\n", - "matplotlib can do, a great option is to have a look at the matplotlib gallery, which can be\n", - "found at http://matplotlib.org/gallery.html. A good way to get started is to find a figure\n", - "here that sort of looks like what you want, copy the code, and modify it for your own needs." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5.1 Exploring the matplotlib gallery\n", - "\n", - "Have a look at the matplotlib gallery. If find a cool looking figure you can copy the code into a code line. You can of course do this manually but you can also use IPython \"load magic\" Type %loadpy and then the URL of the py file containing the code, and it will automatically copy it into a cell below. Run the cell with the code to see the\n", - "figure. Now you can make small (or large) tweaks to get your perfect figure.\n", - "\n", - "\n", - "Note that some of the examples might require packages that are not installed on your machine (in particular those that make maps) - if this is the case, pick another example for the purposes of this exercise.\n", - "\n", - "**Hint** to get the raw python url right click on the source code link towards the top and pick copy link." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# %load http://matplotlib.org/mpl_examples/pylab_examples/contour_demo.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TRY IT\n", - "Find an example from the gallery and run it here." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} +{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Plotting with matplotlib"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## 1. Getting Started"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### 1.1 What is matplotlib?\n", "\n", "Matplotlib is the most popular and mature library for plotting data using\n", "Python. It has all of the functionality you would expect, including the ability to control\n", "the formatting of plots and figures at a very fine level.\n", "\n", "The official matplotlib documentation is at http://matplotlib.org/ \n", "The matplotlib gallery is at http://matplotlib.org/gallery.html"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### 1.2 Importing matplotlib\n", "\n", "Matplotlib is often used through 'pyplot', which provides a high-level interface for\n", "plotting."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["# In IPython or the IPython notebook, it's easiest to use the pylab magic, which\n", "# imports matplotlib, numpy, and scipy.\n", "\n", "# The matplotlib notebook flag means that plots will be shown interactively in the\n", "# notebooks, rather than in pop-up windows.\n", "\n", "%matplotlib notebook\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## 2. Creating Figures\n", "\n", "There are two major challenges with creating figures. First is understanding the\n", "syntax to actually make the basic plot appear. Second is formatting the basic plot to look\n", "exactly how you would like it to look. In general, the formatting will probably take you\n", "longer...\n", "\n", "Within pyplot (currently imported as 'plt'), there are two basic ways to go about making\n", "plots - using the Matlab-like clone, and using the object-oriented approach. The latter\n", "provides better control over plot features, while only requiring slightly more typing. It's\n", "easy to quickly outgrow the Matlab clone, so we'll go right to the object-oriented syntax."]}, {"cell_type": "markdown", "metadata": {}, "source": ["### 2.1 A first plot\n", "\n", "In simple matplotlib plotting, there are two concepts to distinguish:\n", "\n", "- __Figure__ - the entire figure, like what you might see in a journal, including all\n", "subplots, axes, lines, labels, etc. The whole enchilada. \n", " \n", "- __Subplot/Axes__ - one of the sub-sections of the figure, labeled (a), (b), etc. in\n", "articles. Each subplot will contain one Axes object, which is the container where all of the\n", "useful stuff, such as actual lines, legends, labels, etc., are actually housed.\n", "\n", "For example, here's how to make one figure with two subplots, the second of which contains\n", "two lines."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": true}, "outputs": [], "source": ["# First we make some data to plot\n", "x = np.linspace(-2*np.pi, 2*np.pi)\n", "y1 = np.sin(x)\n", "y2 = np.cos(x)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["First, create an empty figure with 2 subplots\n", "using the subplots method\n", " \n", " figure, axes = plt.subplots(rows, columns)\n", "\n", "- The arguments (1, 2) indicate 1 row and 2 cols\n", "- The function plt.subplots returns an object for the figure and for each axes\n", "- There are multiple ways to accomplish this same goal, but this is probably the simplest - notice that each subplot is associated with one of the axes objects."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["fig, axes = plt.subplots(1,2)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Now let's actually plot the data using the plot method on an axis\n", "\n", " axis.plot(x, y)\n", " \n", "You can plot multiple lines on an axis "]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["\n", "fig, axes = plt.subplots(1,2)\n", "\n", "# We plot one line on the first axis\n", "axes[0].plot(x, y1)\n", "# and both lines on the second axis\n", "axes[1].plot(x, y1)\n", "axes[1].plot(x, y2);"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Many of the basic formatting problems you have will be solved by the magic of `tight_layou`t. Before you start tweaking how you figure looks, try it out.\n", "\n", " plt.tight_layout()"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["fig, axes = plt.subplots(1,2)\n", "axes[0].plot(x, y1)\n", "axes[1].plot(x, y1)\n", "axes[1].plot(x, y2)\n", "\n", "plt.tight_layout();"]}, {"cell_type": "markdown", "metadata": {}, "source": ["To save your figure you can use the `savefig` command:\n", "\n", " fig.savefig('fileanme', format='png')\n", " \n", "Format options include png, pdf, ps, eps and svg"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["fig, axes = plt.subplots(1,2)\n", "axes[0].plot(x, y1)\n", "axes[1].plot(x, y1)\n", "axes[1].plot(x, y2)\n", "\n", "fig.savefig('first_plot.png', format='png');"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### TRY IT\n", "Create a line graph plotting the function f(x) = x^3 for values of x 0-10."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## 3. Formatting Figures\n", "\n", "The formatting of figures often takes longer than actually setting them up and adding data. There are many different approaches to formatting figures in matplotlib (many goals can be accomplished in different ways, using different commands), and you will come across many of these as you learn more. The tips below give a few simple ways to get started.\n", "\n", "### 3.1 Line formatting\n", "\n", "The plot method has several available keyword arguments that you can use to change the line formatting.\n", "\n", "* color - Chages color of line. examples: 'red', 'blue', 'r', 'k', 0.5, '#ffaa00', (0,0.5,0.75)\n", "* linewidth - Weight of line. Takes float value in points (like font)\n", "* linestyle - Solid, dashed, or other. examples: -, --, -."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["x = np.linspace(-2*np.pi, 2*np.pi)\n", "y1 = np.sin(x)\n", "y2 = np.cos(x)\n", "\n", "fig, axes = plt.subplots(1,2)\n", "axes[0].plot(x, y1, color='r', linewidth=5)\n", "axes[1].plot(x, y1, color='#ffaa00', linewidth=0.5, linestyle='--')\n", "axes[1].plot(x, y2, color='green', linestyle='-.');"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### 3.2 Tick marks\n", "\n", "You can set the values where the ticks are located using the `xticks` and `yticks` methods.\n", "\n", "They take a list of values\n", "\n", " plt.xticks([val1, val2, val3])\n", " plt.yticks([yval1, yval2, yval3])\n", " "]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false, "scrolled": true}, "outputs": [], "source": ["x = np.linspace(-2*np.pi, 2*np.pi)\n", "y1 = np.sin(x)\n", "y2 = np.cos(x)\n", "\n", "fig, axes = plt.subplots(1,2)\n", "axes[0].plot(x, y1, color='r', linewidth=5)\n", "axes[1].plot(x, y1, color='#ffaa00', linewidth=0.5, linestyle='--')\n", "axes[1].plot(x, y2, color='green', linestyle='-.')\n", "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n", "plt.yticks([-1, 0, 1]);"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Oh no! That changed it for the last plot but not for the first plot. \n", "\n", "To set each plot individually, you need to set the current axis to the subplot you are interested in using the method `sca`. Then you can use `xticks` and `yticks`.\n", "\n", " plt.sca(axis)\n", " plt.xticks([val1, val2, val3])\n", " plt.yticks([yval1, yval2, yval3])\n", " "]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["x = np.linspace(-2*np.pi, 2*np.pi)\n", "y1 = np.sin(x)\n", "y2 = np.cos(x)\n", "\n", "fig, axes = plt.subplots(1,2)\n", "axes[0].plot(x, y1, color='r', linewidth=5)\n", "axes[1].plot(x, y1, color='#ffaa00', linewidth=0.5, linestyle='--')\n", "axes[1].plot(x, y2, color='green', linestyle='-.')\n", "\n", "# Set the current axis to the first subplot\n", "fig.sca(axes[0])\n", "# set x and y ticks\n", "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n", "plt.yticks([-1, 0, 1])\n", "\n", "# Set the current axis to the second subplot\n", "fig.sca(axes[1])\n", "# set x and y ticks\n", "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n", "plt.yticks([-1, 0, 1]);"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### 3.3 Axis limits\n", "\n", "Setting the limits of the axes is very similar to setting the ticks. The command to set the limits are `xlim` and `ylim`. Remember if you have more than one subplot, you will need to set the current axis before you set that axis' limits.\n", "\n", " plt.sca(axis)\n", " plt.xlim(xmin, xmax)\n", " plt.ylim(ymin, ymax)\n", " \n", " "]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["x = np.linspace(-2*np.pi, 2*np.pi)\n", "y1 = np.sin(x)\n", "y2 = np.cos(x)\n", "\n", "fig, axes = plt.subplots(1,2)\n", "axes[0].plot(x, y1, color='r', linewidth=5)\n", "axes[1].plot(x, y1, color='#ffaa00', linewidth=0.5, linestyle='--')\n", "axes[1].plot(x, y2, color='green', linestyle='-.')\n", "\n", "# Set the current axis to the first subplot\n", "fig.sca(axes[0])\n", "# set x and y ticks\n", "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n", "plt.yticks([-1, 0, 1])\n", "\n", "\n", "# set x and y limits\n", "plt.xlim(-np.pi, np.pi)\n", "plt.ylim(-1, 1)\n", "\n", "# Set the current axis to the second subplot\n", "fig.sca(axes[1])\n", "# set x and y ticks\n", "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n", "plt.yticks([-1, 0, 1])\n", "\n", "\n", "# set x and y limits\n", "plt.xlim(-np.pi, np.pi)\n", "plt.ylim(-1, 1);"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### 3.4 Setting tick labels\n", "\n", "To set the tick labels, you pass a second parameter to the `xticks` and `yticks` methods with a list of labels for that axis. \n", "\n", " plt.sca(axis)\n", " plt.xticks([tickvalues], [ticklabels])\n", " plt.yticks([tickvalues], [ticklabels])\n", " "]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["x = np.linspace(-2*np.pi, 2*np.pi)\n", "y1 = np.sin(x)\n", "y2 = np.cos(x)\n", "\n", "fig, axes = plt.subplots(1,2)\n", "axes[0].plot(x, y1, color='r', linewidth=5)\n", "axes[1].plot(x, y1, color='#ffaa00', linewidth=0.5, linestyle='--')\n", "axes[1].plot(x, y2, color='green', linestyle='-.')\n", "\n", "# Set the current axis to the first subplot\n", "fig.sca(axes[0])\n", "# set x and y ticks\n", "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], ['-pi', '-pi/2', '0', 'pi/2', 'pi'])\n", "# You probably don't want to set the labels when you just want the exact numbers.\n", "plt.yticks([-1, 0, 1], ['-1', '0', '1'])\n", "\n", "\n", "# set x and y limits\n", "plt.xlim(-np.pi, np.pi)\n", "plt.ylim(-1, 1)\n", "\n", "# Set the current axis to the second subplot\n", "fig.sca(axes[1])\n", "# set x and y ticks\n", "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], ['-pi', '-pi/2', '0', 'pi/2', 'pi'])\n", "plt.yticks([-1, 0, 1], ['-1', '0', '1'])\n", "\n", "\n", "# set x and y limits\n", "plt.xlim(-np.pi, np.pi)\n", "plt.ylim(-1, 1);"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### 3.5 Legend\n", "\n", "When you create a line on a plot, you can pass it a keyword argument `label` and then create a legend that will use that label using the `legend` method. The `legend` method takes an optional parameter of `loc` for the location of the legend. You can see the values allowed here: http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.legend This is another one of those commands that you need to set the current axis if you have more than one subplot.\n", "\n", " plt.plot(x,y, label='my_label')\n", " plt.legend(loc='best')"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["x = np.linspace(-2*np.pi, 2*np.pi)\n", "y1 = np.sin(x)\n", "y2 = np.cos(x)\n", "\n", "fig, axes = plt.subplots(1,2)\n", "# Let's set labels here\n", "axes[0].plot(x, y1, color='r', linewidth=5, label='sin(x)')\n", "axes[1].plot(x, y1, color='#ffaa00', linewidth=0.5, linestyle='--', label='sin(x)')\n", "axes[1].plot(x, y2, color='green', linestyle='-.', label='cos(x)')\n", "\n", "# Set the current axis to the first subplot\n", "fig.sca(axes[0])\n", "# set x and y ticks\n", "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], ['-pi', '-pi/2', '0', 'pi/2', 'pi'])\n", "# You probably don't want to set the labels when you just want the exact numbers.\n", "plt.yticks([-1, 0, 1], ['-1', '0', '1'])\n", "plt.legend(loc='best')\n", "\n", "# set x and y limits\n", "plt.xlim(-np.pi, np.pi)\n", "plt.ylim(-1, 1)\n", "\n", "# Set the current axis to the second subplot\n", "fig.sca(axes[1])\n", "# set x and y ticks\n", "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], ['-pi', '-pi/2', '0', 'pi/2', 'pi'])\n", "plt.yticks([-1, 0, 1], ['-1', '0', '1'])\n", "plt.legend(loc='upper right')\n", "\n", "# set x and y limits\n", "plt.xlim(-np.pi, np.pi)\n", "plt.ylim(-1, 1);"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### TRY IT\n", "Go back to your plot of f(x) = x^3 for values of x 0-10. Try out some of the formatting options you just learned to make your plot look \"just right\""]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": true}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## 4. Other types of plots\n", "\n", "Matplotlib is more than just line plots, let's see what else it can do.\n"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### 1.1 Other plots\n", "\n", "In the examples above, we used the plot method to make line plots. There are also methods to\n", "make scatter plots, barplots, histograms, loglog plots, semilog plots, etc.\n", "\n", " # Bar graph\n", " ax.bar(x, y)\n", " \n", " # Scatter plot\n", " ax.scatter(x,y)\n", " \n", " # Horizontal bar plot\n", " ax.barh(x,y)\n", " \n", " # Boxplot\n", " ax.boxplot(x)\n", " \n", " # Log-log plot\n", " ax.loglog(x,y)\n", " \n", " # Semilog plot\n", " ax.semilogx(x,y)\n", " ax.semilogy(x,y)\n", " \n", "Plots too squished? Check out plt.tight_layout()"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["# Make some data to plot\n", "x = np.arange(0, 100)\n", "y = np.random.rand(100) # 100 random numbers\n", "\n", "# Make a figure with 6 subplots and axes\n", "# Notice that we are doing some arguement unpacking to get six subplots. You can use indexing instead if you prefer\n", "fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(3, 2)\n", "\n", "# Add data to each axis. Optional arguments to each method will customize each plot.\n", "ax1.bar(x,y)\n", "ax2.scatter(x,y)\n", "ax3.barh(x,y)\n", "ax4.boxplot(x)\n", "ax5.loglog(x,y)\n", "ax6.semilogx(x,y)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Many of the same formatting options as the line plot are available for these additional plots. There are also some other options. The gallery (section 5) is the best place to find all the options. \n", "\n", "Let's try changing the marker on the scatter plot: http://matplotlib.org/exmples/lines_bars_and_markers/marker_reference.html\n", "\n"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["fig, ax = plt.subplots(1,1)\n", "ax.scatter(x, y, marker='x')\n", "ax.scatter(x, y + 2, marker='>', color='#00aaff')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### 4.2 Plotting images\n", "\n", "Matplotlib also makes it easy to plot images. For this, you can use the plot method `imshow`\n", "(syntax borrowed from Matlab).\n", "\n", "To load in an image we use the `imread` function. This takes a file path and reads the file into a numpy ndarray\n", "\n", "To plot an image, you can use `imshow` function giving it an array. \n", "\n", "A 1D array will be rendered as grayscale and a 3D or (4D with transparency) array will be a full color image.\n", "\n", "To set the colormap of a grayscale image, you can use the optional `cmap` key word argument to `imshow`. Options available are listed here: http://matplotlib.org/examples/color/colormaps_reference.html"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["# Read image from file and display it\n", "img1 = plt.imread('astronaut.png')\n", "# Uncomment following line to prove it still works without the alpha channel\n", "# img1 = img1[:,:, 0:3]\n", "fig, ax = plt.subplots(1,1)\n", "ax.imshow(img1)"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["# We can plot random noise in the viridis colormap.\n", "img2 = np.random.rand(128, 128)\n", "\n", "fig, ax = plt.subplots(1,1)\n", "ax = ax.imshow(img2, cmap='viridis')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### TRY IT\n", "Plot the cubes from 1-10 in a vertical bar chart and in a scatter plot (2 separate subplots). Change the colors of the bars to green. Change the marker of the scatter plot to plus signs."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## 5. The matplotlib gallery\n", "\n", "It can be very intimidating to try to craft exactly the figure that you want, especially if\n", "you are used to being able to adjust things visually using a program like Excel.\n", "\n", "If you get stuck and don't know where to start, or just want to learn more about what\n", "matplotlib can do, a great option is to have a look at the matplotlib gallery, which can be\n", "found at http://matplotlib.org/gallery.html. A good way to get started is to find a figure\n", "here that sort of looks like what you want, copy the code, and modify it for your own needs."]}, {"cell_type": "markdown", "metadata": {}, "source": ["### 5.1 Exploring the matplotlib gallery\n", "\n", "Have a look at the matplotlib gallery. If find a cool looking figure you can copy the code into a code line. You can of course do this manually but you can also use IPython \"load magic\" Type %loadpy and then the URL of the py file containing the code, and it will automatically copy it into a cell below. Run the cell with the code to see the\n", "figure. Now you can make small (or large) tweaks to get your perfect figure.\n", "\n", "\n", "Note that some of the examples might require packages that are not installed on your machine (in particular those that make maps) - if this is the case, pick another example for the purposes of this exercise.\n", "\n", "**Hint** to get the raw python url right click on the source code link towards the top and pick copy link."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["# %load http://matplotlib.org/mpl_examples/pylab_examples/contour_demo.py"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### TRY IT\n", "Find an example from the gallery and run it here."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": true}, "outputs": [], "source": []}], "metadata": {"kernelspec": {"display_name": "Python 2", "language": "python", "name": "python2"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 2}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11"}}, "nbformat": 4, "nbformat_minor": 0} \ No newline at end of file diff --git a/Lesson14_NumpyAndMatplotlib/numpy.ipynb b/Lesson14_NumpyAndMatplotlib/numpy.ipynb index f1ad504..b8a7ef0 100644 --- a/Lesson14_NumpyAndMatplotlib/numpy.ipynb +++ b/Lesson14_NumpyAndMatplotlib/numpy.ipynb @@ -1,672 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Adapted from Scientific Python: Part 1 (lessons/thw-numpy/numpy.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introducing NumPy\n", - "\n", - "NumPy is a Python package implementing efficient collections of specific types of data (generally numerical), similar to the standard array\n", - "module (but with many more features). NumPy arrays differ from lists and tuples in that the data is contiguous in memory. A Python **list**, \n", - "```[0, 1, 2]```, in contrast, is actually an array of pointers to Python objects representing each number. This allows NumPy arrays to be\n", - "considerably faster for numerical operations than Python lists/tuples." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# by convention, we typically import numpy as the alias np\n", - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's see what numpy can do." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "#np?\n", - "#np." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can try out some of those constants and functions:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print(np.sqrt(4))\n", - "print(np.pi) # a constant\n", - "print(np.sin(np.pi))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"That's great,\" you're thinking. \"`math` already has all of those functions and constants.\" But that's not the real beauty of NumPy." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TRY IT\n", - "Find the square root of pi using numpy functions and constants" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Numpy arrays (ndarrays)\n", - "\n", - "Creating a NumPy array is as simple as passing a sequence to numpy.array:\n", - " \n", - "Numpy arrays are collections of things, **all of which must be the same type**, that work\n", - "similarly to lists (as we've described them so far). The most important are:\n", - "\n", - "1. You can easily perform elementwise operations (and matrix algebra) on arrays\n", - "1. Arrays can be n-dimensional\n", - "1. Arrays must be pre-allocated (ie, there is no equivalent to append)\n", - "\n", - "Arrays can be created from existing collections such as lists, or instantiated \"from scratch\" in a \n", - "few useful ways." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "arr1 = np.array([1, 2.3, 4]) \n", - "# Type of a numpy array\n", - "print(type(arr1))\n", - "# Type of the data inside a numpy array dtype=data type\n", - "print(arr1.dtype) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TRY IT\n", - "Create an array from the list [0,1,2] and print out it's dtype" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Datatype options\n", - "*Choose your datatype based on how large the largest values could be, and how much memory you expect to use*\n", - "\n", - "* **bool_**\t- Boolean (True or False) stored as a byte\n", - "* **int_** - Default integer type (same as C long; normally either int64 or int32)\n", - "* **int8** - Byte (-128 to 127)\n", - "* **int16** - Integer (-32768 to 32767)\n", - "* **int32** - Integer (-2147483648 to 2147483647)\n", - "* **int64** - Integer (-9223372036854775808 to 9223372036854775807)\n", - "* **uint8** - Unsigned integer (0 to 255)\n", - "* **uint16** - Unsigned integer (0 to 65535)\n", - "* **uint32** - Unsigned integer (0 to 4294967295)\n", - "* **uint64** - Unsigned integer (0 to 18446744073709551615)\n", - "* **float_** - Shorthand for float64.\n", - "* **float16** - Half precision float: sign bit, 5 bits exponent, 10 bits mantissa\n", - "* **float32** - Single precision float: sign bit, 8 bits exponent, 23 bits mantissa\n", - "* **float64** - Double precision float: sign bit, 11 bits exponent, 52 bits mantissa\n", - "* **complex_** - Shorthand for complex128.\n", - "* **complex64** - Complex number, represented by two 32-bit floats (real and imaginary components)\n", - "* **complex128** - Complex number, represented by two 64-bit floats (real and imaginary components)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating Arrays\n", - "There are many other ways to create NumPy arrays, such as `np.identity`, `np.zeros`, `np.zeros_like`, `np.ones`, `np.ones_like`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print('2 rows, 3 columns of zeros:\\n', np.zeros((2,3))) \n", - "print('4x4 identity matrix:\\n', np.identity(4))\n", - "squared = []\n", - "for x in range(5):\n", - " squared.append(x**2)\n", - "print(squared)\n", - "a = np.array(squared)\n", - "b = np.zeros_like(a)\n", - "\n", - "print('a:\\n', a)\n", - "print('b:\\n', b)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These arrays have attributes, like `.ndim` and `.shape` that tell us about the number and length of the dimensions.\n", - "\n", - "The dimension of an array is the number of indices needed to select an element. Thus, if the array is seen as a function on a set of possible index combinations, it is the dimension of the space of which its domain is a discrete subset. Thus a one-dimensional array is a list of data, a two-dimensional array a rectangle of data, a three-dimensional array a block of data, etc.\n", - "\n", - "The shape is the number of elements in each dimension of data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "c = np.ones((15, 30))\n", - "print('number of dimensions of c:', c.ndim) \n", - "print('length of c in each dimension:', c.shape)\n", - "\n", - "x = np.array([[[1,2,3],[4,5,6],[7,8,9]] , [[0,0,0],[0,0,0],[0,0,0]]])\n", - "print('number of dimensions of x:', x.ndim) \n", - "print('length of x in each dimension:', x.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "NumPy has its own `range()` function, `np.arange()` (stands for array-range), that is more efficient for building larger arrays. It functions in much the same way as `range()`.\n", - "\n", - "NumPy also has `linspace()` and `logspace()`, that can generate equally spaced samples between a start-point and an end-point. Find out more with `np.linspace?`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print(\"Arange\")\n", - "print(np.arange(5))\n", - "\n", - "# Args: start, stop, number of elements\n", - "print(\"Linspace\")\n", - "print(np.linspace(5, 10, 5))\n", - "\n", - "# logspace can also take a base argument, by default it is 10\n", - "print(\"Logspace\")\n", - "print(np.logspace(0, 1, 5))\n", - "print(np.logspace(0, 1, 5, base=2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TRY IT\n", - "Create a numpy array with 8 rows and 50 columns of 0's" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating numpy arrays from text files\n", - "You can use loadtxt to load data from a text file (csv or tab-delimited data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "np.loadtxt?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The simplest way to use it is to just give it a file name. By default, your data will be loaded as floats with whitespace being the delimiter\n", - "\n", - "my_arr = np.loadtxt('myfile.txt')\n", - "\n", - "More likely you will need to use some of the keyword arguments. like `dtype`, `delimiter`, `skiprows`, or `usecols` Docs available here: http://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html\n", - "\n", - "my_array = loadtxt('myfile.csv', usecols=[1,2,3,4,5,6,7,8,9,10,11,12], delimiter=',')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "np.loadtxt('simple.csv', delimiter=',')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TRY IT\n", - "Load the file 'example.tsv' a tab delimited file. Once you have that working, only load the odd numbered columns (1,3,5)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Arithmetic with ndarrays\n", - "\n", - "Standard arithmetic operators perform element-wise operations on arrays of the same size." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "A = np.arange(5)\n", - "B = np.arange(5, 10)\n", - "\n", - "print('A', A)\n", - "print('B', B)\n", - "\n", - "print('A+B', A+B)\n", - "print('B-A', B-A)\n", - "print('A*B', A*B)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In addition, if one of the arguments is a scalar, that value will be applied to all the elements of the array.\n", - "\n", - "**scalar** - a quantity possessing only magnitude. (In this case we mean a single number either an int or a float)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "A = np.arange(5)\n", - "print('A', A)\n", - "print('A+10', A+10)\n", - "print('2 * A', 2*A)\n", - "print('A ** 2', A**2) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Linear algebra with arrays\n", - "\n", - "You can use arrays as vectors and matrices in linear algebra operations\n", - "\n", - "Specifically, you can perform matrix/vector multiplication between arrays, by using the .dot method, or the np.dot function\n", - "\n", - "**dot product** - the dot product between two vectors is based on the projection of one vector onto another." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print(A.dot(B))\n", - "print(np.dot(A, B))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you are planning on doing serious linear algebra, you might be better off using the np.matrix object instead of np.array." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Numpy 'gotchas'\n", - "**Multiplication and Addition**\n", - "\n", - "As you may have noticed above, since NumPy arrays are modeled more closely after vectors and matrices, multiplying by a scalar will multiply each element of the array, whereas multiplying a list by a scalar will repeat that list N times." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Numpy arrays\n", - "A = np.arange(5)*2\n", - "print(A)\n", - "# Lists\n", - "B = list(range(5))*2\n", - "print(B)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly, when adding two numpy arrays together, we get the vector sum back, whereas when adding two lists together, we get the concatenation back." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Numpy arrays\n", - "A = np.arange(5) + np.arange(5)\n", - "print(A)\n", - "# Lists\n", - "B = list(range(5)) + list(range(5))\n", - "print(B)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Boolean operators work on arrays too, and they return boolean arrays\n", - "Much like the basic arithmetic operations we discussed above, comparison operations are performed element-wise. That is, rather than returning a\n", - "single boolean, comparison operators compare each element in both arrays pairwise, and return an `array` of booleans (if the sizes of the input\n", - "arrays are incompatible, the comparison will simply return False). For example:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "arr1 = np.array([1, 2, 3, 4, 5])\n", - "arr2 = np.array([1, 1, 3, 3, 5])\n", - "print((arr1 == arr2))\n", - "c = (arr1 == arr2)\n", - "print(type(c))\n", - "print(c.dtype)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can get a portion of an array by using a boolean array as the index. It will return an array where only true values are returned" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print(arr1)\n", - "print(c)\n", - "print(arr1[c])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note: You can use the methods `.any()` and `.all()` or the functions `np.any` and `np.all` to return a single boolean indicating whether any or all values in the array are `True`, respectively." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print(np.all(c))\n", - "print(c.all())\n", - "print(c.any())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TRY IT\n", - "Create a boolean array for arr1 for where values are >= 3" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Views vs. Copies\n", - "\n", - "In order to be as efficient as possible, numpy uses \"views\" instead of copies wherever possible. That is, numpy arrays derived from another base array generally refer to the ''exact same data'' as the base array. The consequence of this is that modification of these derived arrays will also modify the base array. The result of an array indexed by an array of indices is a ''copy'', but an array indexed by an array of booleans is a ''view''. \n", - "\n", - "Specifically, slices of arrays are always views, unlike slices of lists or tuples, which are always copies." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "A = np.arange(5)\n", - "B = A[0:1]\n", - "B[0] = 42\n", - "print(A)\n", - "\n", - "A = list(range(5))\n", - "B = A[0:1]\n", - "B[0] = 42\n", - "print(A)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Indexing arrays\n", - "\n", - "In addition to the usual methods of indexing lists with an integer (or with a series of colon-separated integers for a slice), numpy allows you\n", - "to index arrays in a wide variety of different ways for more advanced operations.\n", - "\n", - "First, the simple way:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "a = np.array([1,2,3])\n", - "print(a[0:2])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "How can we index if the array has more than one dimension? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "c = np.random.rand(3,3)\n", - "print(c)\n", - "print((c[1:3,0:2]))\n", - "print(a)\n", - "c[0,:] = a\n", - "print(c)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "### TRY IT\n", - "Create a random 4x4 array, print out the second row, second column." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} +{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["Adapted from Scientific Python: Part 1 (lessons/thw-numpy/numpy.ipynb)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Introducing NumPy\n", "\n", "NumPy is a Python package implementing efficient collections of specific types of data (generally numerical), similar to the standard array\n", "module (but with many more features). NumPy arrays differ from lists and tuples in that the data is contiguous in memory. A Python **list**, \n", "```[0, 1, 2]```, in contrast, is actually an array of pointers to Python objects representing each number. This allows NumPy arrays to be\n", "considerably faster for numerical operations than Python lists/tuples."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["# by convention, we typically import numpy as the alias np\n", "import numpy as np"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Let's see what numpy can do."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": true}, "outputs": [], "source": ["#np?\n", "#np."]}, {"cell_type": "markdown", "metadata": {}, "source": ["We can try out some of those constants and functions:"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["print((np.sqrt(4)))\n", "print((np.pi)) # a constant\n", "print((np.sin(np.pi)))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["\"That's great,\" you're thinking. \"`math` already has all of those functions and constants.\" But that's not the real beauty of NumPy."]}, {"cell_type": "markdown", "metadata": {}, "source": ["### TRY IT\n", "Find the square root of pi using numpy functions and constants"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": true}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["### Numpy arrays (ndarrays)\n", "\n", "Creating a NumPy array is as simple as passing a sequence to numpy.array:\n", " \n", "Numpy arrays are collections of things, **all of which must be the same type**, that work\n", "similarly to lists (as we've described them so far). The most important are:\n", "\n", "1. You can easily perform elementwise operations (and matrix algebra) on arrays\n", "1. Arrays can be n-dimensional\n", "1. Arrays must be pre-allocated (ie, there is no equivalent to append)\n", "\n", "Arrays can be created from existing collections such as lists, or instantiated \"from scratch\" in a \n", "few useful ways."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["arr1 = np.array([1, 2.3, 4]) \n", "# Type of a numpy array\n", "print((type(arr1)))\n", "# Type of the data inside a numpy array dtype=data type\n", "print((arr1.dtype)) "]}, {"cell_type": "markdown", "metadata": {}, "source": ["### TRY IT\n", "Create an array from the list [0,1,2] and print out it's dtype"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": true}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["### Datatype options\n", "*Choose your datatype based on how large the largest values could be, and how much memory you expect to use*\n", "\n", "* **bool_**\t- Boolean (True or False) stored as a byte\n", "* **int_** - Default integer type (same as C long; normally either int64 or int32)\n", "* **int8** - Byte (-128 to 127)\n", "* **int16** - Integer (-32768 to 32767)\n", "* **int32** - Integer (-2147483648 to 2147483647)\n", "* **int64** - Integer (-9223372036854775808 to 9223372036854775807)\n", "* **uint8** - Unsigned integer (0 to 255)\n", "* **uint16** - Unsigned integer (0 to 65535)\n", "* **uint32** - Unsigned integer (0 to 4294967295)\n", "* **uint64** - Unsigned integer (0 to 18446744073709551615)\n", "* **float_** - Shorthand for float64.\n", "* **float16** - Half precision float: sign bit, 5 bits exponent, 10 bits mantissa\n", "* **float32** - Single precision float: sign bit, 8 bits exponent, 23 bits mantissa\n", "* **float64** - Double precision float: sign bit, 11 bits exponent, 52 bits mantissa\n", "* **complex_** - Shorthand for complex128.\n", "* **complex64** - Complex number, represented by two 32-bit floats (real and imaginary components)\n", "* **complex128** - Complex number, represented by two 64-bit floats (real and imaginary components)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Creating Arrays\n", "There are many other ways to create NumPy arrays, such as `np.identity`, `np.zeros`, `np.zeros_like`, `np.ones`, `np.ones_like`"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["print(('2 rows, 3 columns of zeros:\\n', np.zeros((2,3)))) \n", "print(('4x4 identity matrix:\\n', np.identity(4)))\n", "squared = []\n", "for x in range(5):\n", " squared.append(x**2)\n", "print(squared)\n", "a = np.array(squared)\n", "b = np.zeros_like(a)\n", "\n", "print(('a:\\n', a))\n", "print(('b:\\n', b))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["These arrays have attributes, like `.ndim` and `.shape` that tell us about the number and length of the dimensions.\n", "\n", "The dimension of an array is the number of indices needed to select an element. Thus, if the array is seen as a function on a set of possible index combinations, it is the dimension of the space of which its domain is a discrete subset. Thus a one-dimensional array is a list of data, a two-dimensional array a rectangle of data, a three-dimensional array a block of data, etc.\n", "\n", "The shape is the number of elements in each dimension of data"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["c = np.ones((15, 30))\n", "print(('number of dimensions of c:', c.ndim)) \n", "print(('length of c in each dimension:', c.shape))\n", "\n", "x = np.array([[[1,2,3],[4,5,6],[7,8,9]] , [[0,0,0],[0,0,0],[0,0,0]]])\n", "print(('number of dimensions of x:', x.ndim)) \n", "print(('length of x in each dimension:', x.shape))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["NumPy has its own `range()` function, `np.arange()` (stands for array-range), that is more efficient for building larger arrays. It functions in much the same way as `range()`.\n", "\n", "NumPy also has `linspace()` and `logspace()`, that can generate equally spaced samples between a start-point and an end-point. Find out more with `np.linspace?`."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["print(\"Arange\")\n", "print((np.arange(5)))\n", "\n", "# Args: start, stop, number of elements\n", "print(\"Linspace\")\n", "print((np.linspace(5, 10, 5)))\n", "\n", "# logspace can also take a base argument, by default it is 10\n", "print(\"Logspace\")\n", "print((np.logspace(0, 1, 5)))\n", "print((np.logspace(0, 1, 5, base=2)))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### TRY IT\n", "Create a numpy array with 8 rows and 50 columns of 0's"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["### Creating numpy arrays from text files\n", "You can use loadtxt to load data from a text file (csv or tab-delimited data)"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["np.loadtxt?"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The simplest way to use it is to just give it a file name. By default, your data will be loaded as floats with whitespace being the delimiter\n", "\n", "my_arr = np.loadtxt('myfile.txt')\n", "\n", "More likely you will need to use some of the keyword arguments. like `dtype`, `delimiter`, `skiprows`, or `usecols` Docs available here: http://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html\n", "\n", "my_array = loadtxt('myfile.csv', usecols=[1,2,3,4,5,6,7,8,9,10,11,12], delimiter=',')"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["np.loadtxt('simple.csv', delimiter=',')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### TRY IT\n", "Load the file 'example.tsv' a tab delimited file. Once you have that working, only load the odd numbered columns (1,3,5)."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## Arithmetic with ndarrays\n", "\n", "Standard arithmetic operators perform element-wise operations on arrays of the same size."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["A = np.arange(5)\n", "B = np.arange(5, 10)\n", "\n", "print(('A', A))\n", "print(('B', B))\n", "\n", "print(('A+B', A+B))\n", "print(('B-A', B-A))\n", "print(('A*B', A*B))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["In addition, if one of the arguments is a scalar, that value will be applied to all the elements of the array.\n", "\n", "**scalar** - a quantity possessing only magnitude. (In this case we mean a single number either an int or a float)"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["A = np.arange(5)\n", "print(('A', A))\n", "print(('A+10', A+10))\n", "print(('2 * A', 2*A))\n", "print(('A ** 2', A**2)) "]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Linear algebra with arrays\n", "\n", "You can use arrays as vectors and matrices in linear algebra operations\n", "\n", "Specifically, you can perform matrix/vector multiplication between arrays, by using the .dot method, or the np.dot function\n", "\n", "**dot product** - the dot product between two vectors is based on the projection of one vector onto another."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["print((A.dot(B)))\n", "print((np.dot(A, B)))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["If you are planning on doing serious linear algebra, you might be better off using the np.matrix object instead of np.array."]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Numpy 'gotchas'\n", "**Multiplication and Addition**\n", "\n", "As you may have noticed above, since NumPy arrays are modeled more closely after vectors and matrices, multiplying by a scalar will multiply each element of the array, whereas multiplying a list by a scalar will repeat that list N times."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["# Numpy arrays\n", "A = np.arange(5)*2\n", "print(A)\n", "# Lists\n", "B = list(range(5))*2\n", "print(B)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Similarly, when adding two numpy arrays together, we get the vector sum back, whereas when adding two lists together, we get the concatenation back."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["# Numpy arrays\n", "A = np.arange(5) + np.arange(5)\n", "print(A)\n", "# Lists\n", "B = list(range(5)) + list(range(5))\n", "print(B)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Boolean operators work on arrays too, and they return boolean arrays\n", "Much like the basic arithmetic operations we discussed above, comparison operations are performed element-wise. That is, rather than returning a\n", "single boolean, comparison operators compare each element in both arrays pairwise, and return an `array` of booleans (if the sizes of the input\n", "arrays are incompatible, the comparison will simply return False). For example:"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["arr1 = np.array([1, 2, 3, 4, 5])\n", "arr2 = np.array([1, 1, 3, 3, 5])\n", "print((arr1 == arr2))\n", "c = (arr1 == arr2)\n", "print((type(c)))\n", "print((c.dtype))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["You can get a portion of an array by using a boolean array as the index. It will return an array where only true values are returned"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["print(arr1)\n", "print(c)\n", "print((arr1[c]))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Note: You can use the methods `.any()` and `.all()` or the functions `np.any` and `np.all` to return a single boolean indicating whether any or all values in the array are `True`, respectively."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["print((np.all(c)))\n", "print((c.all()))\n", "print((c.any()))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### TRY IT\n", "Create a boolean array for arr1 for where values are >= 3"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": true}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["### Views vs. Copies\n", "\n", "In order to be as efficient as possible, numpy uses \"views\" instead of copies wherever possible. That is, numpy arrays derived from another base array generally refer to the ''exact same data'' as the base array. The consequence of this is that modification of these derived arrays will also modify the base array. The result of an array indexed by an array of indices is a ''copy'', but an array indexed by an array of booleans is a ''view''. \n", "\n", "Specifically, slices of arrays are always views, unlike slices of lists or tuples, which are always copies."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["A = np.arange(5)\n", "B = A[0:1]\n", "B[0] = 42\n", "print(A)\n", "\n", "A = list(range(5))\n", "B = A[0:1]\n", "B[0] = 42\n", "print(A)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Indexing arrays\n", "\n", "In addition to the usual methods of indexing lists with an integer (or with a series of colon-separated integers for a slice), numpy allows you\n", "to index arrays in a wide variety of different ways for more advanced operations.\n", "\n", "First, the simple way:"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["a = np.array([1,2,3])\n", "print((a[0:2]))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["How can we index if the array has more than one dimension? "]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["c = np.random.rand(3,3)\n", "print(c)\n", "print((c[1:3,0:2]))\n", "print(a)\n", "c[0,:] = a\n", "print(c)"]}, {"cell_type": "markdown", "metadata": {"collapsed": true}, "source": ["### TRY IT\n", "Create a random 4x4 array, print out the second row, second column."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": true}, "outputs": [], "source": []}], "metadata": {"kernelspec": {"display_name": "Python 2", "language": "python", "name": "python2"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 2}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11"}}, "nbformat": 4, "nbformat_minor": 0} \ No newline at end of file