{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Python Refresher \n",
"\n",
"This document summarizes what you need to know about Python for doing CS homeworks in Jupyter Notebooks. \n",
"\n",
"There are three sections:\n",
"\n",
"- **Basic Python Programming** shows you all the basic features you need to get started\n",
"- **Advanced Python** explains a number of advanced features which everyone should know about after the first week\n",
"- **Appendices** present a number of special topics which may or may not be useful, depending on your project\n",
"\n",
"For a brief introduction to using Jupyter Notebooks, \n",
"\n",
"I also refer you to the tutorial notebooks on Latex, Markdown, Numpy, and Pandas. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic Python Programming"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### First Lesson in Python: Variables and assignment statements\n",
"\n",
"Python is an **interpreted** language, which means is mostly acts like a calculator,\n",
"accepting *definitions* of variables and functions, and *expressions* which are evaluated\n",
"in the context of the global definitions. \n",
"\n",
"Defining variables is done using the equals sign. You must line up the beginning of each statement,\n",
"and you don't need a semicolon. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4\n",
"6\n",
"[4, 5]\n"
]
}
],
"source": [
"x = 4\n",
"y = x + 2\n",
"a = [4,5]\n",
"\n",
"print(x)\n",
"print(y)\n",
"print(a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Types and variables\n",
"\n",
"Python, like many interpreted languages, is \"weakly-typed,\" that is, values have types, but\n",
"variables are NOT declared with a type, and in general, you can assign any type of value to\n",
"any variable. \n",
"\n",
"Here are some examples:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4\n",
"2.3\n",
"True\n",
"hi there\n"
]
}
],
"source": [
"x = 4\n",
"\n",
"print(x)\n",
"\n",
"x = 2.3\n",
"\n",
"print(x)\n",
"\n",
"x = True\n",
"\n",
"print(x)\n",
"\n",
"x = \"hi there\"\n",
"\n",
"print(x)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Types are checked when variables are used, and may generate errors at execution time:"
]
},
{
"attachments": {
"Screen%20Shot%202023-01-10%20at%2010.08.02%20AM.png": {
"image/png": "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"
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"![Screen%20Shot%202023-01-10%20at%2010.08.02%20AM.png](attachment:Screen%20Shot%202023-01-10%20at%2010.08.02%20AM.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Second lesson in Python: IF statements and how to indicate scope and nested context\n",
"\n",
"The most distinctive feature of Python is\n",
"that it does not use an \"end of line\" character, and uses indentation to indicate nested contexts--what in other languages might be indicated with curly braces `{....}`:\n",
"\n",
"Java:\n",
"\n",
" if(count < 20) {\n",
" final_count = count;\n",
" System.out.println(\"count =\", count);\n",
" }\n",
" \n",
"Python:\n",
"\n",
" if(count < 20):\n",
" final_count = count\n",
" print(\"count =\"count)\n",
" \n",
"What is other languages is indicated using opening and closing parentheses (or curly braces)\n",
"is indicated in Python by a tab-indented line. The nesting level of a nested statement is\n",
"indicated by its alignment on the page, NOT by the presence of opening and closing characters such\n",
"as `{....}`. \n",
"\n",
"Therefore you will need to pay attention to where statements line up rather than what\n",
"characters surround it. \n",
"\n",
"The other difference is that at the end of the conditional expression, a colon (:) will introduce\n",
"the nested code block. \n",
"\n",
"Here are some examples of conditional statements. \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x1 is greater than 2\n",
"x2 is NOT greater than 2\n",
"5 <= x3 < 8 \n"
]
}
],
"source": [
"x1 = 5\n",
"\n",
"if(x1 >2):\n",
" print(\"x1 is greater than 2\")\n",
"\n",
"x2 = 1\n",
"if(x2 >2):\n",
" print(\"x2 is greater than 2\")\n",
"else:\n",
" print(\"x2 is NOT greater than 2\")\n",
" \n",
"x3 = 7\n",
"if(x3 < 2):\n",
" print(\"x3 < 2\")\n",
"elif(x3 < 5):\n",
" print(\"2 <= x3 < 5\")\n",
"elif(x3 < 8):\n",
" print(\"5 <= x3 < 8 \")\n",
"else:\n",
" print(\"x3 >= 8\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Third lesson in Python: lists as the primary data structure\n",
"\n",
"Python, like most interpreted languages, makes heavy use of lists rather than arrays. What's the difference? Mostly, that lists can change shape, and (because of weak typing) can hold any kind of data:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"A = [1,2,3,4]\n",
"B = [\"hi\", \"there\", \"Paul\"]\n",
"C = [a,'hi', True,[2,'b']]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 3, 4]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['hi', 'there', 'Paul']\n"
]
}
],
"source": [
"print(B)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[4, 5], 'hi', True, [2, 'b']]\n"
]
}
],
"source": [
"print(C)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Paul\n",
"Paul\n"
]
}
],
"source": [
"# Pulling stuff out of lists\n",
"\n",
"print(B[2])\n",
"\n",
"# Using negative indices (going right to left)\n",
"\n",
"print(B[-1]) # last element of the list"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['hi', 'there', 'Wayne']\n"
]
}
],
"source": [
"# Replacing elements of lists\n",
"\n",
"B[2] = 'Wayne'\n",
"print(B)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can use ranges of indices to extract parts of lists. Just remember that the second number in\n",
"the range is always 1 more than the last index you want. "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['there', 'Wayne']"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"B[1:]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"['hi', 'there']"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"B[:2]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['there', 'Wayne']"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"B[1:3]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['hi', 'there', 'Wayne']"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"B[:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### The two most useful operations on lists\n",
"\n",
"Here are two ways you will manipulate lists most often. For additional functions, see the section **Useful List Functions** in **Advanced Python** below. "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['hi', 'there', 'Wayne', 'how', 'are', 'you']\n"
]
}
],
"source": [
"# append two lists using +\n",
"\n",
"D = B + ['how','are','you']\n",
"print( D )"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hi is in the list\n"
]
}
],
"source": [
"# Check if an object is a member of a list using in\n",
"\n",
"if( 'hi' in D ):\n",
" print('hi is in the list')\n",
"else:\n",
" print('hi is not in the list')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Strings can be manipulated as lists\n",
"\n",
"Strings have their own set of associated functions, but basically they are just lists of characters, and\n",
"you can call many (but not all) list functions on them. "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"s = \"Hi there Wayne\""
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(s)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'y'"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max(s)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"' ther'"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[2:7]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Addendum to Third Lesson: Tuples \n",
"\n",
"An alternative to lists is *tuples*, which are essentially immutable lists, i.e., once created, they can not be modified; this turns out to be necessary in some cases. Mostly, you can just treat them like lists, \n",
"as shown here:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"D = (\"hi\", \"there\", \"Paul\")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('there', 'Paul')"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"D[1:]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('hi', 'there')"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"D[:2]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('there', 'Paul')"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"D[1:3]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(D)"
]
},
{
"attachments": {
"Screen%20Shot%202023-01-10%20at%2010.29.50%20AM.png": {
"image/png": "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"
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"But if you try to change a tuple's contents, you will get an error:\n",
"\n",
"![Screen%20Shot%202023-01-10%20at%2010.29.50%20AM.png](attachment:Screen%20Shot%202023-01-10%20at%2010.29.50%20AM.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fourth Lesson: Loops: while and for\n",
"\n",
"The while statement should cause no problems, just remember to indent and\n",
"use a colon (:) at the end of the condition, which introduces the intended\n",
"code block:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4\n",
"5\n",
"6\n",
"7\n",
"8\n",
"9\n"
]
}
],
"source": [
"x = 4\n",
"while(x < 10):\n",
" print(x)\n",
" x += 1 # Python does not allow \"++x\" so you'll have to do this to increment a variable\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### For loops\n",
"\n",
"for loops iterate over the elements of a list, which may be created by the `range(...)` function. Just remember\n",
"that the upper bound in a range is always 1 more than the last number you want to produce:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"2\n",
"3\n",
"5\n"
]
}
],
"source": [
"for k in [1,2,3,5]:\n",
" print(k)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(range(10)) # a single number is assumed to be an upper bound with a starting value of 0"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[2, 3, 4, 5, 6, 7, 8, 9]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(range(2,10))"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[2, 5, 8]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(range(2,10,3)) # a third argument is the skip"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(range(10))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fifth Lesson: Defining functions\n",
"\n",
"Functions are defined using the keyword `def` and using the colon and indented block syntax\n",
"we have seen above. Functions which return a value must use `return`:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5 5 5\n"
]
}
],
"source": [
"def f(x):\n",
" print(x,x,x)\n",
" \n",
"f(5)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def is_even(x):\n",
" if(x % 2 == 0):\n",
" return True\n",
" else:\n",
" return False\n",
" \n",
"is_even(5)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"here is a definition\n"
]
},
{
"data": {
"text/plain": [
"'Hi there, Paul'"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"here is a definition\")\n",
"\n",
"def sayHi(name):\n",
" return \"Hi there, \" + name\n",
"\n",
"sayHi('Paul')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Functions introduce a local scope for variables which are not available outside the function, just\n",
"as in Java and other languages. You may refer to variables outside the scope, but if you want\n",
"to assign to them, you have to declare them as **global**. If you don't do this, you will get an error!\n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"7\n"
]
}
],
"source": [
"N = 5\n",
"\n",
"def add2N(x):\n",
" global N\n",
" N = N + x\n",
" \n",
"add2N(2)\n",
"\n",
"print(N)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Advanced Features of Python"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Useful List Functions\n",
"\n",
"Here are some examples of useful functions that work on lists. There are two important things to remember\n",
"about calling a function on a list, the first having to do with the syntax and the second having to\n",
"do with the effect of the function:\n",
"\n",
"1. Functions will be called in one of two ways, either as a function:\n",
"\n",
" n = max(C)\n",
" \n",
"or using the dot notation familiar from object-oriented languages such as Java:\n",
"\n",
" k = C.index(3)\n",
" \n",
"2. Functions may return a value (perhaps another list), leaving the original list unchanged:\n",
"\n",
" C1 = C.copy()\n",
" \n",
"or they may modify the list \"in place\" and return None. In this case, you use them\n",
"as \"imperative\"statements that modify the list:\n",
"\n",
" C.append(6)\n",
" \n",
"**Note that functions which modify the original list will always use the \"dot notation.\"**\n",
" \n",
"Here are examples of the most useful functions which return values. "
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"7\n",
"2\n",
"3\n",
"5\n",
"1\n",
"[1, 2, 3, 3, 4, 4, 5]\n",
"[5, 4, 4, 3, 3, 2, 1]\n",
"[1, 3, 4, 2, 3, 4, 5]\n",
"[1, 3, 4, 2, 3, 4, 5]\n"
]
}
],
"source": [
"C = [1,3,4,2,3,4,5]\n",
"\n",
"# Return the length of the list\n",
"\n",
"print(len(C))\n",
"\n",
"# Return a count of how many times a given element occurs in the list\n",
"\n",
"print(C.count(4))\n",
"\n",
"# Return the index of the first occurrence of an element (error if not found)\n",
"\n",
"print(C.index(2))\n",
"\n",
"# Return the largest/smallest element in the list\n",
"\n",
"print(max(C))\n",
"print(min(C))\n",
"\n",
"# Return a sorted copy of the list, in ascending order by default\n",
"\n",
"print(sorted(C))\n",
"print(sorted(C,reverse=True))\n",
"\n",
"# Return a copy of the list (two different ways)\n",
"\n",
"print(C.copy())\n",
"print(C[:])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here are two useful functions which change the list in place and return None. "
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 3, 4, 2, 3, 4, 5, 12]\n",
"[1, 2, 3, 3, 4, 4, 5, 12]\n",
"[12, 5, 4, 4, 3, 3, 2, 1]\n"
]
}
],
"source": [
"# Add a new element to the end of the list\n",
"\n",
"C.append(12)\n",
"print(C)\n",
"\n",
"# Sort the list in place\n",
"\n",
"C.sort()\n",
"print(C)\n",
"\n",
"C.sort(reverse=True)\n",
"print(C)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### List Comprehensions\n",
"\n",
"A list comprehension is a great way to create lists with a minimum of fuss and bugs. \n",
"The basic idea is that instead of creating a list by specifying every step, say like this:"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a list of the first 10 squares\n",
"\n",
"L = [0] * 10 # create a list of 10 zeros\n",
"\n",
"for k in range(len(L)):\n",
" L[k] = (k+1)**2\n",
"L"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"you can do it all in one line:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]\n"
]
}
],
"source": [
"L1 = [ (k+1)**2 for k in range(len(L)) ]\n",
"print(L1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The idea is to collect together all instances of the expression at the beginning, for all values of k produces\n",
"by the for. Some examples may clarify. "
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.5070673275206681, 0.3273805686543323, 0.8412274449804783, 0.835724182196044, 0.35999478038816213, 0.17099486665971741, 0.7854551215753977, 0.8151163509924048, 0.07280493543428213, 0.4540995910610309]\n"
]
}
],
"source": [
"from random import random # The function random() returns a random double in the range [0..1)\n",
"\n",
"L2 = [ random() for k in range(10) ]\n",
"print(L2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also use multiple for loops:"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['0A', '0B', '0C', '0D', '0E', '1A', '1B', '1C', '1D', '1E', '2A', '2B', '2C', '2D', '2E', '3A', '3B', '3C', '3D', '3E', '4A', '4B', '4C', '4D', '4E']\n"
]
}
],
"source": [
"D = ['0','1','2','3','4']\n",
"L = ['A','B','C','D','E']\n",
" \n",
"X = [ d + el for d in D for el in L ]\n",
"print(X)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, watch out, because the order of the **for**'s must be the same as if you were writing a loop:"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 3, 4, 5, 6, 7, 8, 9]"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"T = [ [ 1,2,3] , [4,5,6], [7,8,9]]\n",
"\n",
"Tall = [ x for lst in T for x in lst ]\n",
"\n",
"Tall"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"List comprehensions can do a lot, especiallly if you use conditions in the \"loop\" part:"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[63, 241, 7, 43, 99, 132, 6, -3, 71, 235, 24, 66]\n",
"[7, 43, 6, -3, 24]\n",
"[63, 241, 99, 132, 71, 235, 66]\n"
]
}
],
"source": [
"L3 = [63,241,7,43,99,132,6,-3,71,235,24,66] \n",
"\n",
"# let's pick 63 as the \"pivot\" for quicksort and partition the list into those\n",
"# numbers less than 63 and those greater or equal:\n",
"\n",
"left = [ x for x in L3 if x < 63 ]\n",
"right = [ x for x in L3 if x >= 63 ]\n",
"\n",
"print(L3)\n",
"print(left)\n",
"print(right)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[-3, 6, 7, 24, 43, 63, 66, 71, 99, 132, 235, 241]"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# List comprehensions can be used to write very complicated algorithms with very few lines of code!\n",
"\n",
"def quicksort(L):\n",
" if(L == []):\n",
" return []\n",
" else:\n",
" pivot = L[0]\n",
" left = [ x for x in L[1:] if x < pivot ] # partition list around pivot\n",
" right = [ x for x in L[1:] if x >= pivot ] \n",
" return ( quicksort(left) + [pivot] + quicksort(right) )\n",
"\n",
"quicksort(L3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sets\n",
"\n",
"A set in mathematics is a collection of elements in which there are no duplicates and order does not matter.\n",
"In Python, we can create sets, which are implemented by hash tables, and are much more efficient than lists, if\n",
"a set is really what you want. \n"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{2, 3, 4, 5}\n",
"{3, 4}\n",
"\n",
"True\n",
"True\n",
"\n",
"{2, 3, 4, 5, 7}\n",
"{2, 4, 5, 7}\n",
"\n",
"A = {'d', 'c', 'a'}\n",
"B = {'d', 2, 'c'}\n",
"C = {1, 2, 3}\n",
"\n",
"A U B = {'d', 2, 'a', 'c'}\n",
"B U C = {'d', 2, 1, 3, 'c'}\n",
"A U B U C = {'d', 2, 1, 3, 'a', 'c'}\n",
"A.union() = {'d', 'c', 'a'}\n",
"\n",
"A n B = {'d', 'c'}\n",
"B n C = {2}\n",
"A n C = set() <- this is how empty set can be represented\n",
"C n A n B = set()\n",
"\n",
"A - B = {'a'}\n",
"B - A = {2}\n",
"A - C = {'d', 'a', 'c'}\n"
]
}
],
"source": [
"# create a set\n",
"S= {2,3,4,5}\n",
"print(S)\n",
"\n",
"# duplicates are ignored\n",
"T = { 3, 4,3 }\n",
"print(T)\n",
"print()\n",
"\n",
"# membership test\n",
"print( (3 in S) )\n",
"\n",
"# subset test\n",
"print( (T.issubset(S)) )\n",
"print()\n",
"\n",
"# add an element to a set\n",
"S.add(7)\n",
"print(S)\n",
"\n",
"# remove an element from a set\n",
"S.remove(3)\n",
"print(S)\n",
"print()\n",
"\n",
"# create a new set using set operations union, intersection, and set difference\n",
"\n",
"A = {'a', 'c', 'd'}\n",
"B = {'c', 'd', 2 }\n",
"C = {1, 2, 3}\n",
"\n",
"print('A =',A)\n",
"print('B =',B)\n",
"print('C =',C)\n",
"print()\n",
"\n",
"print('A U B =', A.union(B))\n",
"print('B U C =', B.union(C))\n",
"print('A U B U C =', A.union(B, C))\n",
"print('A.union() =', A.union()) # just make a copy\n",
"print()\n",
"\n",
"# return a new set which is the intersecction of others\n",
"print('A n B =',B.intersection(A))\n",
"print('B n C =',B.intersection(C))\n",
"print('A n C =',A.intersection(C),' <- this is how empty set can be represented')\n",
"print('C n A n B =',C.intersection(A, B))\n",
"print()\n",
"\n",
"# return a new set which is the set difference with another\n",
"print('A - B =',A.difference(B))\n",
"print('B - A =',B.difference(A))\n",
"print('A - C =',A.difference(C))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dictionaries\n",
"\n",
"A dictionary is a data structures which stores (key,value) pairs (typically implemented as a hash table).\n",
"This is a great data structure for storing information about objects without having to muck around with lists.\n"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'a': 2, 'c': 8, 'b': 1}\n",
"{}\n"
]
}
],
"source": [
"D = { 'a' : 2, 'c' : 8, 'b' : 1} # Dictionary storing, say, how many times a letter appears in a string\n",
"print(D)\n",
"E = {} # empty dictionary\n",
"print(E)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Basic Dictionary Operations\n",
"\n",
"Most of the manipulation of dictionaries looks like array or list manipulation\n",
"but using the keys instead of the position of the elements in the list. \n"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Find the value associated with a particular key\n",
"D['c']"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'a': 4, 'c': 8, 'b': 1, 'z': 23}"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Insert a key-value pair \n",
"D['a'] = 4\n",
"D['z'] = 23\n",
"D"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'a': 4, 'c': 8, 'b': 1, 'z': 25}"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# update a value associated with a key by doing both\n",
"D['z'] = D['z'] + 2\n",
"D"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dict_keys(['a', 'c', 'b', 'z'])\n",
"['a', 'c', 'b', 'z']\n",
"[4, 8, 1, 25]\n"
]
}
],
"source": [
"### Miscellaneous functions\n",
"\n",
"# get all keys\n",
"print( D.keys() )\n",
"print( list(D.keys()) ) # to just get a list\n",
"\n",
"# get all values\n",
"print( list(D.values()) )"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n"
]
}
],
"source": [
"# Default values for dictionaries\n",
"\n",
"# simplest: use the function get(...)\n",
"x = D.get('q',0) # first argument to get is the key, the second is the default\n",
" # value if the key is not in the dictionary\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5\n",
"0\n"
]
}
],
"source": [
"# The second way is to use a different kind of dictionary, which you have to import,\n",
"# and then define a function to return the default value\n",
"\n",
"from collections import defaultdict\n",
"\n",
"def get_default():\n",
" return 0\n",
"\n",
"A = defaultdict(get_default)\n",
"\n",
"A['a'] = 5\n",
"print( A['a'] )\n",
"print( A['b'] )"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Counter creates a dictionary giving the frequency counts of each element in the list.\n",
"Counter({3: 5, 4: 5, 2: 2, 5: 2, 8: 1})\n",
"The list has 5 instances of the number 3.\n"
]
}
],
"source": [
"### Calculating frequency counts using a dictionary\n",
"\n",
"from collections import Counter \n",
"\n",
"F = Counter([3,4,2,3,4,5,4,3,2,3,4,5,4,3,8])\n",
"\n",
"print(\"Counter creates a dictionary giving the frequency counts of each element in the list.\")\n",
"print(F)\n",
"\n",
"n = 3\n",
"print(\"The list has\",F[n], 'instances of the number '+ str(n) + '.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plotting and Graphing\n",
"\n",
"The scatter(...) function is used to display points from a list of x values and the associated y values. "
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"This is the list of points: [(1, 2), (2, 3), (3, 6), (4, 8)]\n",
"They must be input to the function as separate lists:\n",
"\tX = [1, 2, 3, 4]\n",
"\tY = [2, 3, 6, 8] \n",
"\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"